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Public perceptions of low carbon economy development in smaller cities in China Cheng, Zhaohua 2016

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PUBLIC PERCEPTIONS OF LOW CARBON ECONOMY DEVELOPMENT IN SMALLER CITIES IN CHINA by  Zhaohua Cheng B.Sc., The University of British Columbia, 2013  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Forestry)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  April 2016 © Zhaohua Cheng, 2016 ii  Abstract Low carbon economies have been proposed in many areas of the world as a strategy to mitigate climate change, including in the world’s biggest greenhouse gas emitting country, China. While much effort has been put into developing low carbon economies in major cities in China, less attention has been paid to smaller cities and counties. The goal of this exploratory research is to develop a preliminary understanding of citizens’ perceptions of low carbon economies and their attitude towards low carbon policies in smaller cities (or counties) in China. A questionnaire-based cross-sectional survey was conducted in Fuding City and Zherong County in Fujian Province (southeastern China), with three sub-populations - general public, community residents, and government employees. Results indicated several possible knowledge gaps and inconsistencies in perceptions about climate change and the low carbon economy among citizens. However, citizens did indicate high levels of support for developing a low carbon economy in local areas.  Binary and multinomial logistic regression models developed in this study indicated that citizens’ knowledge of low carbon economies and their level of concern about climate change were significant factors influencing their level of support for a low carbon economy. In general, citizens with more knowledge of low carbon economies and a greater level of concern about climate change showed greater support for developing a low carbon economy. However, greater knowledge and more supportive attitudes did not necessarily lead to behavioural changes. This research discovered an ‘attitude-behaviour’ gap between someone showing greater support for low carbon policies and having less intention to change their behaviour (on average, respondents had tried three to four low carbon activities but were willing only to conduct one more low carbon activity to further lower their carbon footprint). Significant differences were found between the study areas and between different sub-populations, which suggested priorities for further engagement and social learning among the populations of smaller cities in China. Findings call for more effort to be put towards informing and engaging citizens in order to improve their understanding of low carbon economies and more closely align behaviours with attitudes in response to climate change.  iii  Preface This research project was mainly completed by the author, Zhaohua Cheng, and this dissertation is an original, unpublished, independent work of hers. In addition to the author, Dr. Stephen Sheppard, Dr. Guangyu Wang and Dr. John Innes played leading roles in the research design, implementation, data interpretation and writing of this thesis. This project has been approved by the UBC Behavioural Research Ethics Board. The UBC Ethics Certificate number is H13-02277. iv  Table of Contents Abstract ................................................................................................................................... ii Preface .................................................................................................................................... iii Table of Contents ................................................................................................................... iv List of Tables ............................................................................................................................x List of Figures ...................................................................................................................... xiii Acknowledgements ................................................................................................................xv Chapter 1: Introduction ..........................................................................................................1 1.1 Structure of the Thesis .............................................................................................. 5 1.2 Literature Review and Research Backgrounds ......................................................... 5 1.2.1 The Low Carbon Economy ................................................................................... 5 1.2.2 Public Perceptions and Awareness ....................................................................... 8 Chapter 2: Study Areas and Methods .................................................................................17 2.1 Study Areas ............................................................................................................. 17 2.2 Data Acquisition ..................................................................................................... 19 v  2.3 Data Analysis Procedures ....................................................................................... 27 2.3.1 Descriptive Analysis ........................................................................................... 27 2.3.2 Factor Analysis ................................................................................................... 28 2.3.3 Multinomial and Binary Logistic Regression Model Building .......................... 29 Chapter 3: Overall Analysis of Public Perceptions in Smaller Cities in Eastern China .31 3.1 Descriptive Analysis ............................................................................................... 31 3.1.1 Socio-Demographic Description ........................................................................ 31 3.1.2 Perceptions of Climate Change and Low Carbon Economies ............................ 33 3.1.3 Perceptions of Low Carbon Economies ............................................................. 34 3.1.4 Suggestions for Government Actions ................................................................. 35 3.2 Factor Analysis ....................................................................................................... 37 3.3 Multinomial Logistic Regression ........................................................................... 38 3.3.1 Model Comparison and Selection ....................................................................... 39 3.3.2 Parameter Estimates and Interpretation .............................................................. 41 3.4 Discussion ............................................................................................................... 44 vi  Chapter 4: Comparison of Public Perceptions and Behaviours between Fuding City and Zherong County .....................................................................................................................49 4.1 Descriptive Analysis ............................................................................................... 49 4.1.1 Socio-Demographic Information ........................................................................ 49 4.1.2 Perceptions of Climate Change and Low Carbon Economies ............................ 52 4.1.2.1 Significant Differences between Place of Residence ................................. 52 4.1.2.1.1 Perceptions of climate change and the low carbon economy ............... 54 4.1.2.1.2 Number of low carbon activities at present and in the future ............... 56 4.1.2.1.3 Willingness to contribute to low carbon economy development .......... 60 4.1.2.1.4 Opinion of low carbon measures .......................................................... 62 4.1.2.2 Other Differences between Fuding City and Zherong County ................... 63 4.1.2.2.1 Ways to learn about low carbon economies ......................................... 63 4.1.2.2.2 Support for a local low carbon economy .............................................. 64 4.1.2.2.3 Suggestions regarding future direction ................................................. 66 4.2 Multinomial Logistic Regression ........................................................................... 67 4.2.1 Fuding Model ..................................................................................................... 69 vii  4.2.2 Zherong Model ................................................................................................... 71 4.3 Discussion ............................................................................................................... 72 Chapter 5: Comparison of Public Perceptions and Behaviours between the Public, Community Residents, and Government Employees .........................................................78 5.1 Descriptive Analysis ............................................................................................... 78 5.1.1 Socio-Demographic Information ........................................................................ 78 5.1.2 Perceptions of Climate Change and Low Carbon Economies ............................ 81 5.1.2.1 Differences between Sample Groups.......................................................... 81 5.1.2.2 Other Differences between Sample Groups ............................................... 91 5.2 Multinomial/Binary Logistic Regression ............................................................... 96 5.3 Discussion ............................................................................................................... 98 Chapter 6: Conclusions .......................................................................................................104 6.1 Summary of Findings ........................................................................................... 104 6.2 Recommendations for Local Governments .......................................................... 109 6.2.1 Increased Efforts on Education and Engagement Programs............................. 110 6.2.2 More Incentive Mechanisms and Supportive Infrastructure ............................ 114 viii  6.2.3 Collaborate with Third-party Intervenors to Mobilize Public Actions on the Low Carbon Economy .......................................................................................................... 115 6.2.4 Prioritize Decarbonisation Efforts on Industry, Technology, Forestry and Energy …………………….......................................................................................................116 6.3 Limitations and Future Directions ........................................................................ 118 Bibliography .........................................................................................................................122 Appendices ...........................................................................................................................143 Appendix A : Consent Letter ............................................................................................ 143 Appendix B : Questionnaires ............................................................................................ 145 B.1 Questionnaire for the Public Group .................................................................. 145 B.2 Questionnaire for the Community Group ......................................................... 151 B.3 Questionnaire for the Government Group ........................................................ 158 Appendix C : Concern about Climate Change at Four Different Scales .......................... 164 C.1 Overall Analysis of Public Perceptions in Smaller Cities in Eastern China..... 164 C.2 Comparison between Fuding City and Zherong County .................................. 167 ix  C.3 Compare between the Public, Community Residents and Government Employees..................................................................................................................... 172  x  List of Tables Table 1.1 Examples of Previous Studies on Social Aspects of the Low Carbon Economy or Related Topics in China.......................................................................................................... 13 Table 2.1 Quota Matrix for Government Staff Participating in the Survey ........................... 22 Table 2.2 Quota Matrix for Local Communities Participating in the Survey ........................ 23 Table 2.3 List of Key Variables and Measuring Questionnaire Items ................................... 26 Table 3.1  Socio-Demographic Information of the Samples in the Survey ............................ 32 Table 3.2 Mean Level of Concern about Climate Change on Different Scales ..................... 34 Table 3.3  Rotated Factor Pattern (Orthogonal Rotation: Varimax) ...................................... 38 Table 3.4 Model Fitting Information of Model 1 & Model 4 ................................................. 41 Table 3.5 Likelihood Ratio Tests of Independent Variables in Model 1 & Model 4 ............. 41 Table 3.6  Parameter Estimates of Model 1 ............................................................................ 42 Table 4.1 Socio-Demographic Information by Study Area .................................................... 51 Table 4.2 Significant Differences between Fuding City and Zherong County............................. 52 Table 4.3 Mean Concern about Climate Change on Four Scales by Study Area ................... 54 xi  Table 4.4 Frequency Distribution of Responses on Knowledge of Climate Change and the Low Carbon Economy by Study Area .................................................................................... 55 Table 4.5 Number of Low Carbon Activities that Respondents Report Having Done (Current) or Are Willing to Try (Future) by Study Area ........................................................ 57 Table 4.6 Willingness to Contribute (by Paying Money or Spending Time) to Low Carbon Economy Development by Study Area .................................................................................. 61 Table 5.1 Socio-Demographic Information of Each Sample Group ...................................... 80 Table 5.2 Significant Differences between the Public (P), Community Residents (C) and Government Employees (G) ................................................................................................... 81 Table 5.3 Willingness to Contribute (by Paying Money or Spending Time) to Low Carbon Economy Development by Sample Group ............................................................................. 89 Table C.1.1 Frequency Distributions of Variables that Measure the Perception of a Low Carbon Economy…………………………………………………………………………...164 Table C.1.2 Model Fitting Information of Model 2 & Model 3 ........................................... 166 Table C.1.3 Parameter Estimates of Model 4 ....................................................................... 166 Table C.1.4 Prediction Accuracy of Model 1 and Model 4 .................................................. 166 Table C.2.1  Frequency Distributions of Variables that Measure the Perception of a Low Carbon Economy by Study Area .......................................................................................... 167 xii  Table C.2.2  Parameter Estimates of Fuding Model ............................................................. 170 Table C.2.3 Parameter Estimates of Zherong Model ........................................................... 171 Table C.3.1 Frequency Distributions of Variables that Measure the Perception of Low Carbon Economies by Sample Group .................................................................................. 172 Table C.3.2 Mann-Whitney Tests on Concern about Climate Change between Sample Groups................................................................................................................................... 174 Table C.3.3 Number of Low Carbon Activities that Respondents Have Done (Current) and Activities They Are Willing to Try (Future) by Sample Group ........................................... 175 Table C.3.4 Rotated Factor Pattern and Factor Label (Varimax Rotation) by Sample Group .............................................................................................................................................. 175 Table C.3.5 Parameter Estimates for Models of Sample Groups ......................................... 176  xiii  List of Figures Figure 2.1 Industry structure (primary, secondary and tertiary industry) in Fuding City and Zherong County ...................................................................................................................... 18 Figure 2.2 Geographic location of the study areas ................................................................. 19 Figure 3.1 Mean level of concern about climate change ........................................................ 33 Figure 3.2 Sector as the first step to develop a low carbon economy .................................... 36 Figure 3.3 Priorities for low carbon economy development .................................................. 36 Figure 4.1 Mean level of concern about the impacts of climate change by study area .......... 54 Figure 4.2 Mean knowledge score of climate change and the low carbon economy by study area .......................................................................................................................................... 56 Figure 4.3 Mean number of current and future low carbon activities by study area .............. 57 Figure 4.4 Low carbon activities that respondents have done and are willing to try, by study area .......................................................................................................................................... 59 Figure 4.5 Willingness to contribute to low carbon economy development by study area .... 62 Figure 4.6 Average support for low carbon measures by study area...................................... 63 Figure 4.7 Support for a low carbon economy by study area ................................................. 64 xiv  Figure 4.8 Sector as the first step to develop a low carbon economy by study area .............. 67 Figure 4.9 Priority for low carbon economy development by study area .............................. 67 Figure 5.1 Mean level of concern about climate change by sample group ............................ 83 Figure 5.2  Knowledge of low carbon economies by sample group ...................................... 84 Figure 5.3 Number of low carbon activities by sample group ............................................... 85 Figure 5.4 Current low carbon activities by sample group ..................................................... 87 Figure 5.5 Willingness to pay & contribute time between the general public and community residents .................................................................................................................................. 90 Figure 5.6 Average support for low carbon measures by sample group ................................ 91 Figure 5.7 Sector to be the first step in developing a low carbon economy by sample group 95 Figure 5.8 Priorities for low carbon economy development by sample group ....................... 95  xv  Acknowledgements I would like to express my sincere thanks and appreciation to my supervisor, Dr. Stephen Sheppard, who dedicated countless hours to this project. Thanks for his patience, guidance, insights and motivation throughout the whole process. I want to acknowledge Dr. Guangyu Wang for his ongoing support and mentoring since my undergraduate study, which has helped me to develop my research and work skills tremendously. A special thanks to Dr. John Innes for his extensive knowledge and insightful feedback to this project and dissertation. I would like to thank the China Green Carbon Foundation for all their support and for the help they provided throughout this research project. This project would not be possible without them.  I am extremely grateful for all the opportunities I have received and for all the wonderful people I have met during my period of study at UBC. In particular, I would like to thank Dr. Val LeMay and Dr. David Tindall for their invaluable help in my research design and statistical analysis, Brianne Riehl for her excellent editing skills and constructive comments, Dr. Liguo Wang, Ligui Zhao and Sha Tang for their extensive help in conducting the survey and their great support in writing the thesis, and Johnathon Hirschi for his detailed review of the statistical analysis and interpretation of results. There are many other people who have helped and encouraged me during this process and it is a pity that I cannot list them all here. I would like to thank them all for their kind hearts and warm encouragement. ii  Last but not the least, I would like to thank my parents, Baojiao Ye and Shuping Cheng, for their unconditional support. Their love is my biggest motivation to carry on.  1  Chapter 1: Introduction Climate change is happening and accelerating. Scientists have observed a consistent increase in global average temperature since the 1950s, and the magnitude of this trend is unprecedented over at least the past 1400 years (IPCC, 2013a; NASA, 2015a, 2015b). Climate change has imposed various impacts on the earth’s ecosystems and on human society, such as sea level rise (NASA, 2015a), glacial retreat (NASA, 2015a; Piao et al., 2010), altered precipitation and heat distribution (NASA, 2015a ; Piao et al., 2010), and increased risk of cardiovascular and respiratory diseases (Patz, Campbell-Lendrum, Holloway & Foley, 2005). Much evidence has indicated that greenhouse gas (GHG) emissions from human activities such as fossil fuel combustion and industrial processes are the main drivers of climate change (IPCC, 2013a; NASA, 2015d). Among all GHGs, carbon dioxide (CO2) emissions alone account for over 60% of the total radiative forcing1 (RF) (Hofmann et al., 2006; IPCC, 2013a). Therefore, a fundamental step towards mitigating2 climate change is to reduce society’s heavy reliance on fossil fuels and other carbon-intensive products and services.  As the leading GHG emitter and largest developing country in the world, China also considers developing a low carbon economy as a feasible solution to mitigate climate change while maintaining current economic growth and improving people’s living standard (Baeumler, Ijjasz-                                                 1 RF “quantifies the change in energy flux caused by changes in drivers of climate change relative to 1750” (IPCC, 2013a, p.13). 2 As slowing down 2  Vasquez, & Mehndiratta, 2012; Hannon, Liu, Walker, & Wu, 2011; Leggett, 2011; Sustainable Development Research Group of Chinese Academy of Science (CAS), 2009; Wang & Watson, 2007).  After setting its mitigation goals (to reduce its emissions per unit of GDP by 40-45% from 2005 level by 2020) in 2009, the Chinese government has put great effort into developing a low carbon economy as its primary strategy to reduce GHG emissions (CAS, 2009).  Although there are various definitions of the low carbon economy, this study defines “a low carbon economy” as an economic structure that yields higher productivity (e.g. producing more GDP using less materials) and efficiency (e.g. reducing wastes and time) primarily in industry, energy production, forestry, agriculture, business3 and tourism4, more sustainable development planning (to reduce embodied carbon and to avoid locking in high carbon growth patterns) and greater human development (including higher quality of life, greater climate change awareness and low carbon awareness and lower carbon lifestyles), with minimal GHG emissions (Pan, Zhuang, Zheng, Zhu, & Xie, 2011). This definition is discussed in more depth later in this chapter.  A rapid shift towards developing a low carbon economy could introduce dramatic changes to local residents’ daily lives. Therefore, it is important for the government to understand the attitude and reaction of people towards this idea and its possible consequences (both positive and                                                  3 In this study, business refers to institutions or activities that trade and distribute goods and services to consumers, while industry involves extraction of natural resources or conversion of raw materials into intermediate or final products. 4 Tourism is an important source of revenue for local governments in the study areas used for this study 3  negative) in order to smoothly and effectively implement low carbon strategies. In addition, residents play a prominent role in low carbon economy development, as they account for nearly one-fifth of the national emissions in China directly, not considering their indirect emissions such as their share of emissions from manufacturing and transportation of commodities to fulfill  individual’s demands (Zheng et al., 2010). However, little attention has been paid to understanding public awareness, knowledge, and behaviours related to the development of low carbon economies (Chen & Taylor, 2011). Of the studies that have been done in this area, most focus on big cities (e.g. province capital cities or prefecture cities) than smaller cities (i.e. county-level cities), which are likely to become a major GHG emitter in the future. In addition, previous research in this area tends to focus on the public, university students, professionals, corporations or consumers separately (Cai et al., 2009; Chen & Taylor, 2011; Heiskanen, Johnson, Robinson, Vadovics, & Saastamoinen, 2010; Liu et al., 2009). Very few studies have compared different groups of people (e.g. the public, community residents and government employees) in terms of their perceptions of climate change and the low carbon economy. Greater knowledge of the attitudes and expectations that citizens have of their local governments is crucial to winning their support. In the meantime, understanding knowledge gaps and misconceptions about climate change or low carbon economies would allow the government to carry out  programs to bridge these knowledge gaps and engage the public more effectively (CRED & EcoAmerica, 2014). In addition, a comparison between groups can be very helpful to identify distinctive traits and specific issues for different groups (e.g. knowledge gaps, expectations of the government), which is extremely useful for local governments and 4  researchers to tailor the content of public education and engagement programs on climate change and low carbon economies, thus accelerating the low carbon development process. This study is part of a large climate change research program sponsored by the China Green Carbon Foundation and the Asia Pacific Network for Sustainable Forest Management and Rehabilitation (APFNet). As an exploratory study that has been carried out in the study sites for the first time, it aims to fill the research gap and to explore local citizens’ attitudes towards low carbon economies in smaller cities/counties (Fuding City and Zherong County, Fujian Province) in China. The outcomes will help the state and local governments gain a better understanding of local citizens’ knowledge, concerns, and attitudes towards low carbon economies. The research questions are as follows:  1) How do people in smaller cities perceive climate change and low carbon economies? 2) What are their behavioural responses to climate change?  3) What roles do socio-demographic background, perceptions of climate change and perceptions of low carbon economies have in influencing someone’s support for developing a low carbon economy in their hometown?  4) Are there any differences in terms of awareness, knowledge or behaviour related to climate change and low carbon economies between residents of areas with different levels of economic development (e.g. Fuding City representing a more developed area vs. Zherong County representing a more rural area with less development)? 5  5) Are there any differences in terms of awareness, knowledge and behaviour related to climate change and low carbon economies between groups with distinct backgrounds (i.e. the public, community residents and government employees)? 1.1 Structure of the Thesis This thesis includes four parts: Introduction, Methods, Results & Discussion, and Conclusion. In the remainder of this chapter, I will provide a literature review on current low carbon economy practices and on public perceptions and behavioural responses to climate change and the low carbon economy. Chapter 2 contains the research methods, including an introduction of the study sites, sampling design, collection techniques, and operationalization of variables in the questionnaire. Chapters 3 to Chapter 5 contain the main results and discussion. Finally, Chapter 6 are the conclusions, providing a short summary and discussion of the key findings, suggestions for local governments, and implications for future research. 1.2 Literature Review and Research Backgrounds 1.2.1 The Low Carbon Economy The low carbon economy is proposed as a solution to climate change by detaching economic development from the heavy use of fossil fuels (CAS, 2009; Pan, et al., 2011). Although there are various definitions of this concept, all of them cover three aspects: clean energy development, efficient use of resources, and technology innovation (Pan et al., 2010). Many countries have shown great interest in this idea and some have actually started implementing a low carbon economy themselves. Most developed countries, such as Germany, have committed 6  to developing a low carbon economy and reduce their GHG emissions to a certain level (usually measured in % reduction compared to a baseline level). Besides the three main aspects mentioned above, some countries have expanded the concept to include human aspects such as green jobs, green buildings and green lifestyles (2050 Japan Low-Carbon Society scenario team, 2009; Committee on Climate Change of the UK, 2010; European Commission, 2015; Pan et al., 2010).   As the leading GHG emitter and largest developing country in the world, China is facing critical challenges to accomplishing its goals of improving people’s living standards through economic development, protecting the environment and reducing its GHG emissions (Baeumler, Ijjasz-Vasquez, & Mehndiratta, 2012; Hannon, Liu, Walker & Wu, 2011). Developing a low carbon economy is a solution that could help China achieve these goals all at once (Leggett, 2011). In fact, China has achieved major progress in developing a low carbon economy over the last six years. The energy intensity (i.e. energy used per unit of GDP) decreased by 19.1% while the economy kept growing at an average of 11.2% per year during the 11th Five-Year Plan (2006–2010). This is equivalent to mitigating 630 million tonnes of coal or 1.46 billion tonnes of CO2eq (Baeumuler et al., 2012). By investing billions of dollars into developing the renewable energy sector (investing US$89.5 billion in 2014 alone) and introducing favourable policies, China has become the world’s largest producer of hydropower, wind power, and solar-photovoltaic cells, generating 378 gigawatts of renewable energy (including hydro, wind, nuclear, solar and bio-power), more than twice the production of the U.S. (172 gigawatts) (Mathews & Tan, 2014; Shukman, 2014). China has also implemented the world’s largest plantation program, which is intended to absorb large amounts of GHGs through forest carbon sequestration, as well as 7  providing a number of other environmental benefits (Baeumuler et al., 2012; Morgan, 2011; National Development and Reform Commission (NDRC), 2007). Besides, China is an advocate for emissions trading market development. Currently, the Chinese government has established several pilot cap-and-trade markets in large cities, such as Shanghai and Shenzhen. As a support for the market, the central government is developing its own carbon accounting method for the business sector (Wang, 2013). At the municipal level, many low carbon pilot programs and studies have been carried out in major cities in China since 2008, including the Low Carbon City Programme, the Eco-city Programme, and the Eco-garden City Programme (Khanna, Fridley, & Hong, 2013). Using the Low Carbon City Programme as an example, this program was introduced by China’s top planning agency, National Development Reform Commission, in 2010 with the purpose of promoting low carbon development at provincial and municipal levels. Initially, five provinces and eight cities were selected as pilot sites. The state government required each pilot province/city to set up a climate change task force, and to establish mitigation goals and low carbon development plans (Khanna et al., 2013). So far, there are six low carbon pilot provinces and 36 low carbon pilot municipalities (Energy Foundation China, 2014). These provinces and cities have been actively developing projects dedicated to establishing a low carbon economy. For example, Shanghai is building the first carbon-neutral region in Dongtan, Chongming as a demonstration of a low carbon city, while Baoding endeavors to develop clean energy as a supporting industry to reduce emissions (Su, Liang, Chen, Chen & Yang, 2012; Liu et al., 2010).  8  However, to date there has been little focus on smaller cities (e.g. county-level cities), which are likely to become major sources of GHG emissions in the near future. As of 2013, 268 million people live in smaller cities, including county-level cities and administered towns, comprising approximately 40% of the total urban population in China (China Development Research Foundation, 2013). Unlike big cities, smaller cities are still going through rapid urbanization that requires massive planning and construction. Since the primary energy source in China is still fossil fuels, the rapid development of smaller cities is likely to emit large volumes of GHGs, especially if the current development patterns remain unchanged and no proper guidance is available (Bulkeley, Broto, Hodson & Marvin, 2011). On the other hand, there is also substantial potential for low carbon economy development in smaller cities if integrated with rapid urbanization. Unlike large cities with established facilities and infrastructure, smaller cities have yet to implement a scalable system of infrastructure. This leaves room in smaller cities to develop new urban centers featuring low-carbon energy, transportation, housing (including more wood structured buildings), and infrastructure. In addition, developing new facilities and infrastructure in smaller cities is likely to be less expensive and less difficult than replacing existing ones, not to mention the higher land prices and greater density in larger cities (B. F. Cai, Wang, Yang, Liu, & Cao, 2012).   1.2.2 Public Perceptions and Awareness Besides the government’s efforts, public support and cooperation are crucial to achieve an efficient low carbon transition (Q. Liu, Li, Zuo, Zhang, & Wang, 2009; Semenza et al., 2008). First of all, the GHG emissions from household and individuals account for a significant 9  proportion of the national total. Residents in China account for nearly 20% of the total GHG emissions5, in addition to other indirect emissions from building production and manufacturing to meet household’s demands (Zheng et al., 2010). If citizens are willing to engage in a more sustainable, low carbon lifestyle, a great amount of GHG emissions will be reduced (Semenza et al., 2008). Understanding public perception and behaviour in response to climate change (the “human dimensions of climate change”) has been a growing research interest in North America and Europe since the 1980s (Bord et al., 1998; Dunlap, 2000; Swim et al., 2011, p.242). However, the emerging issues of climate change and environmental impacts were not subjects of heavy research in China. Therefore, most of the studies reviewed here focus on populations in North America and Europe, with results that may differ from the Chinese population. Studies in this field usually focus on people’s awareness, knowledge, concern about potential impacts, and willingness to contribute (e.g. pay money or take actions) in relation to climate change (Bord et al., 1998; Richard et al., 1998).  Improved knowledge of these topics can help government identify the knowledge gaps and any misunderstandings of climate change among citizens, and understand their expectations of government’s policies and actions, which are essential for effective policy development and implementation (Bord et al., 1998).                                                   5 The estimation mainly focused on household energy consumption (including transportation) (Zheng et al., 2010) 10  Previous studies indicate that, overall, the public is aware of climate change and their knowledge of the topic is increasing gradually (Lorenzoni, Nicholson-Cole, & Whitmarsh, 2007; Thornton, 2009). In the U.S., for example, the proportion of respondents who know about climate change increased from 39% in 1986 to 91% in 2006 (Chang, Huang, Li, & Li, 2012; Nisbet & Myers, 2007). A recent survey in China indicate that over 93% of respondents know climate change exists and 92% believe that climate change is happening (Center for China Climate Change Communication & OXFAM Hong Kong, 2012). Despite these improvements, significant evidence of knowledge gaps and public confusion regarding climate change has been discovered since the 90s and continues to be a problem for the development of low carbon economies (Lorenzoni et al., 2007; Wolf & Moser, 2011; Kempton, 1990; Bostrom et al., 1994; Dunlap, 1998; Papadimitriou, 2004; Sheppard, 2012; Swim et al., 2011; Leiserowitz et al., 2014). Common knowledge gaps that have been identified include the causes and impacts of climate change, and the connection between human activities and climate change (Leiserowitz et al., 2011; Lorenzoni et al., 2007) (e.g. Kempton, 1991 and 1993 in the USA; Kempton et al., 1995 in the USA; Dunlap, 1998-1992 in Canada, USA, Mexico, Brazil and Portugal, Lorenzoni 2003 in Italy and UK, as cited in Lorenzoni & Pidgeon, 2006). People tend to confuse climate change with other environmental issues such as ozone layer depletion and air pollution (Kempton, 1990; Bostrom et al., 1994; Dunlap, 1998; Papadimitriou, 2004; Leiserowitz et al., 2011; Leiserowitz et al., 2014), while failing to associate extreme climatic events like flooding with climate change (Meijinders, 1998). Possible reasons for this may include insufficient education/information, selective information uptake due to existing mental models, and a lack of interaction with the natural environment (Wolf & Moser, 2011). 11  In general, the public is concerned about climate change, but they consider it to be an issue of lesser importance compared to other personal and social problems, such as health care and the job market (Blake, 1999; Krosnik et al., 2006; Leiserowitz et al., 2005; Leiserowitz et al., 2014; Lorenzoni & Pidgeon, 2006; Newport, 2014). Even in the context of other environmental issues, the ranking of climate change remains low. In a recent survey in U.S., climate change and global warming ranked as the least important issue compared to six other environmental problems (Newport, 2014). Similar results have been found in other surveys, which indicate that concern about climate change is a low priority, as people tend to care more about personal, visible and local issues (Pew Research Center, 2009; Motel, 2014; Riffkin, 2014). This low priority of climate change indicates that most people view the issue as a non-personal threat associated with ‘other places and other people’. This is possibly due to knowledge gaps, misunderstanding about climate change and associated future impacts, a lack of direct and personal experience, perceived self-inefficacy, denial of climate change, and poor engagement and communication strategies by climate change communicators (CRED & EcoAmerica, 2014; Lorenzoni et al., 2007; Lorenzoni & Pidgeon, 2006b; Scannell & Gifford, 2011; Wolf & Moser, 2011).  In terms of the low carbon economy, most previous studies have focused on theoretical and technical aspects, such as the definition of a low carbon economy and possible pathways to achieve it. There are relatively few studies covering the social aspects of a low carbon economy, such as the public’s awareness and perceptions (see examples in Table 1.1). Among the social studies that have been completed in this field, most indicate that the public is generally aware of the low carbon economy, but their understanding of the topic is very limited. This is possibly due to lack of information, misunderstanding, or confusion with similar terms, such as sustainable 12  development, green economy, circular economy and eco-economy (Chen & Taylor, 2011; Learning and Skills Council, 2010; Y. J. Li, 2015; Song, Su, Jiang, & Su, 2012; Zhao, Zhu, Huang, & Lu, 2013). On the other hand, residents in bigger cities in China show a strong interest in learning more about the low carbon economy and are very supportive of developing a low carbon economy in China (Chen & Taylor, 2011; Jiang, He, & Guo, 2014; Y. J. Li, 2015; Song et al., 2012).  13  Table 1.1 Examples of Previous Studies on Social Aspects of the Low Carbon Economy or Related Topics in China Author & Year Study Areas Sample Study Topics He, Li, & Gai, 2011 National study: 4 cities (Shanghai City – eastern China, Shenzhen City – eastern China, Changsha City – central China, and Chengdu City – western China) 327 residents Urban resident’s attention and sense of responsibility of low carbon consumption and a comparison between eastern, central and western China Y.J. Li, 2015 National study: 20 municipal cities 2000 residents Public low carbon awareness and behaviour in cities in China Cao, 2010 Southern coast: Changzhou City, Jiangsu Province 195 residents Public’s knowledge of low carbon economies and their attitude towards low carbon economy related policies Shi, 2011 Southern coast: Fuzhou City, Fujian Province 279 residents Factors affecting urban resident’s low carbon consumption behaviour Jiang, He, & Guo, 2014 Southern coast: Zhengjiang City, Zhejiang Province 1083 university students University students’ low carbon awareness and behaviour Wang & Mao, 2010 Southern coast: Hangzhou City, Zhejiang Province Residents (didn’t specify sample size) Consumer’s low carbon awareness and behaviour (low carbon lifestyle) Chen & Taylor, 2011 Northern interior: Zhengzhou City, Henan Province 157 households Public awareness and interpretation of a low carbon economy, and their low carbon behaviour Song, Su, Jiang, & Su, 2012 Northern interior: Taiyuan City, Shanxi Province 562 university students University students’ perceptions, attitudes towards low carbon lifestyle and their behavioural responses Zhao, Zhu, Huang, & Lu, 2013 Northern coast: Tianjin City  1894 citizens  Public’s awareness and opinion of low carbon products/consumption, and their low carbon consumption behaviour 14  Despite the public’s general concern about climate change and supportive attitude towards the low carbon economy, numerous studies have discovered that people are reluctant to change their behaviors to help mitigate climate change (the ‘attitude-behaviour’ gap6) ( Blake, 1999; Kollmuss & Agyeman, 2002; Eiser, 1994; Ungar, 1994, as cited in Lorenzoni et al., 2007, p.447). The most prevalent low carbon behaviours are activities that require the lowest cost (i.e. money, energy or time) and sometimes even generate monetary returns, such as conserving energy (Blake, Guppy, & Urmetzer, 1996; Chen & Taylor, 2011; Jiang et al., 2014; Q. Liu et al., 2009; Lorenzoni et al., 2007; Semenza et al., 2008; Song et al., 2012) and recycling (Blake et al., 1996; Chen & Taylor, 2011; Huang et al., 2013; Lorenzoni et al., 2007; Song et al., 2012). However, these activities are often ‘token activities’ that reduce minimal amounts of GHG emissions (Gifford, 2011; Lorenzoni et al., 2007; Wolf & Moser, 2011, p. 561). Most people are very resistant to more costly but more effective mitigation actions, such as taking public transit instead of driving a car, especially those who can afford to live “a consumptive lifestyle” (Harris, 2006, p. 10; Lorenzoni et al., 2007; Wolf & Moser, 2011).  There are many cognitive, social, and institutional barriers to explain this ‘attitude-behaviour’ gap. However, there is no definitive answer to this issue, given the complexity of climate change and the human brain (emotional and logical ways of processing information).                                                  6 “The disparity between public awareness and concern about climate change on the one hand and limited behavioural response on the other” (Lorenzoni et al., 2007, p.447) 15  The following are examples of key barriers identified by previous literature (adapted from Gifford, 2011; Lorenzoni et al., 2007; Sheppard, 2012):  Limited cognition: Lack basic understanding of the causes and impacts of climate change, human contribution to climate change, and potential mitigation and adaptation strategies  Invisibility of carbon, climate change impacts and solutions, which contributes to a lack of awareness & understanding of climate change, and failure to establish a personal connection and take effective actions  Skepticism: Skeptical about whether climate change is happening due to mistrust and confusion  Ignorance: Filter out information about climate change through existing mental models, or get overexposed to information about climate change  Discounting: View climate change as a distant non-personal issue that should be worried about in the future  Perceived self-inefficacy and external blame: Feel that individual actions have very little impact given the scale of climate change, and feel that government or industry should take the responsibility of resolving the problem  Inertia: Maintain the status quo, resist changing habits, choose convenience and comfort over the environment  Perceived risks: Feel that behavioural changes can increase risks of the following types: o functional (e.g. possible battery problem with electrical vehicles) o physical (e.g. biking is less safe than driving) o social (e.g. it is hard to be ‘different’) o financial (e.g. buying a more expensive electrical vehicle)  o temporal (e.g. spending more time bussing home from work)  16   Social comparison: Compare with others and act in accordance with the majority, or obey social norms   Limited behaviour: Prefer token activities over more effective actions, or rebound after making some progress towards being low carbon (e.g. driving more after buying a fuel-efficient vehicle)   Lack of supportive institutions and infrastructure: Fail to change behaviours due to unavailable resources or infrastructure. To conclude, individuals play an important role in climate change mitigation given their significant contribution to GHG emissions from households and personal consumption. To develop a low carbon economy, public ‘buy-in’ and adaptation of individual’s actions are key (Wolf & Moser, 2011). There is a rich volume of literatures on public awareness, understanding, and behaviours related to climate change, but studies covering the social aspects of the low carbon economy are very limited. Literature on both of these topics indicates a gap between people’s values/perceptions/attitudes and their actual behaviours. Many cognitive, social, and institutional barriers have been identified to explain this resistance to change, but so far there is no definitive explanation of the phenomenon. 17  Chapter 2: Study Areas and Methods 2.1 Study Areas Fujian Province is located on the south-eastern coast in China. With the highest forest cover in China, the province provides a great opportunity to mitigate climate change through forest carbon sequestration. On the other hand, Fujian Province is experiencing a dramatic increase in GHG emissions due to a high energy intensity and rapid industrialization. Total emissions in Fujian Province have increased by more than 200% from 1997 to 2007, much higher than the national average (L. C. Liu, Wang, Wu, & Wei, 2010). Carbon emissions per capita increased tenfold over the past decade, from less than 0.5 tonne in 1997 (L. C. Liu et al., 2010) to 4-6 tonnes in 2012 (Z. Liu, 2015), about the same as the global average (5.0 tonne/person). These findings suggest that Fujian Province is in urgent need of developing a low carbon economy to increase its efficiency in consuming resources (especially energy) and reduce its GHG emissions over the short- and long-term.  For this study, I selected two adjacent areas in Fujian Province as study sites, Fuding City and Zherong County. They were chosen because of their rapid urbanization and unique geographic locations (typical of less-developed areas on the eastern coast). Like many other smaller cities/counties, Fuding City and Zherong Country are developing at an unprecedented speed (Zherong Bureau of Statistics, 2012; Fuding Bureau of Statistics, 2013). The total GDP of these two areas has sextupled from ¥5.5 billion (US$ 0.89 billion) to ¥32.80 billion (US$ 5.28 billion) over 11 years (2003 to 2014). The GDP of Fuding City is 18  ¥28.2 billion and the GDP of Zherong County is ¥4.6 billion in 2014. During the same period, the population of these areas increased at 7% per year, reaching 707,198 residents: 597,965 residents in Fuding City and 109,233 residents in Zherong County (Zherong Bureau of Statistics, 2010, 2015; Fuding Bureau of Statistics, 2010, 2015). Both areas have very similar industrial structures, with secondary industries dominating (Industry and manufacturing, contributing around 60% of GDP) and primary industry (agriculture including forestry, animal husbandry, and fishery) being the smallest (14% - 17%) of the three (primary, secondary, tertiary) sectors (Zherong Bureau of Statistics, 2015; Fuding Bureau of Statistics, 2015) (Figure 2.1). In addition, both areas are located near the eastern coastline of the province, and subject to future impacts of climate change, such as more frequent typhoons and heat waves (NDRC, 2007). Mitigating climate change and preparing for future changes are therefore extremely important for local people in these two areas (Figure 2.2).  Figure 2.1 Industry structure (primary, secondary and tertiary industry) in Fuding City and Zherong County 14%60%26%Fuding City17%58%25%Zherong CountyPrimary IndustrySecondary IndustryTertiary Industry19   Figure 2.2 Geographic location of the study areas 2.2 Data Acquisition The research data were collected through a cross-sectional questionnaire that was carried out in November 2013. A cross-sectional questionnaire is conducted once across different sections (or sample groups). This type of questionnaire is easy and relatively cheap to conduct. It is also able to achieve a large sample size within a short period. It is commonly adopted in many areas, especially when measuring public opinion and perceptions. However, this type of questionnaire is not useful for exploring a causal relationship between variables nor for measuring sequential development over time (Babbie & Benaquisto, 2009).  20  The sampling techniques adopted in this study was convenience sampling and non-proportional quota sampling. Convenience sampling is a way to recruit respondents based purely on their availability. This sampling method is subject to potential bias, especially when the researchers try to generalize the sample to the whole population. In my study, the local governments could not provide local residents’ contact information (e.g. name, address and telephone number), as it is treated as confidential and private information. Without a list of the study population, there were no means to select probability samples. As a result, convenience sampling of the general public was the best alternative sampling method, given limited time and access to population information.  The sampling process used was fairly straightforward: 1) Walk through an area to become familiar with it, noting popular places where local people are more likely to be, such as parks, restaurants, bus/train stations, and schools (primary schools and kindergartens); 2) Visit these locations with printed questionnaires; 3) Ask people passing by if they are willing to complete the questionnaires after a brief introduction to the research project; and 4) After achieving their consent to participate, give them a questionnaire to fill in, and collect the questionnaires once they are completed. Researchers’ contact information (telephone numbers and emails) was provided to participants so that they can contact us with further questions and requests. 21  The other sampling technique adopted in this study was non-proportional quota sampling, mainly for local government employees and community residents. For this, samples were selected based on a fixed number quota matrix (i.e. recruiting an equal number of samples from each category). This sampling technique is relatively quick and easy to conduct. It also ensures the inclusion of sufficient samples with desired characteristics (Black, 1999). Criticisms of the technique are that the sampling process can be very subjective and that the sampling bias is hard to estimate (Babbie & Benaquisto, 2009).  To minimize the subjectivity and bias of the sampling process, I first selected 13 government bureaus most closely related to low carbon economy development, such as natural resources, economics, and planning. From each bureau, 10 employees across a range of job positions were chosen. Government employees were recruited in their offices with help from the secretary of the general office. Questionnaires were distributed after receiving consent from the officials, and questionnaires were only provided to people who agreed to participate in the study. In total 2727 government employees were recruited in this study, which is considered an adequate number to generalize the government group’s responses (Table 2.1). For the community sample group, communities located in the city/county center were selected, as the study was primarily focused on urban communities. The quota per community was 100 in Fuding City and 50 in Zherong County. Community residents were                                                  7 I collected 20 more questionnaires completed by employees from the Court and the Bureau of Foreign Trade and Economic Cooperation in Zherong County (10 questionnaires per bureau). But I was only able to survey two people working in the Bureau of Tourism in Zherong County, as other employees were on a business trip. 22  approached in local community centers and their apartment buildings with help from local community staff. As with the government group, questionnaires were distributed only after receiving consent from community residents. In total, I recruited 450 respondents for the quota sampling (Table 2.2). Table 2.1 Quota Matrix for Government Staff Participating in the Survey (Total Sample Size = 2608) Bureau Number Fuding Ctiy Zherong County Agriculture  10 10 Civil Administration 10 10 Transportation 10 10 Urban Development & Planning 10 10 Education 10 10 Finance & Economy 10 10 Tourism 10 10 Industry & Commerce 10 10 Auditing & Supervision 10 10 Forestry 10 10 Public Health 10 10 Public Security 10 10 Justice 10 10                                                      8 In fact I collected 272 samples in the study area, please see footnote 6 for more information. 23  Table 2.2 Quota Matrix for Local Communities Participating in the Survey (Total sample size = 450) Study Areas Community Names Number Fuding City Tongnan 100 Shiting 100 Longshan 100 Zherong County Chengnan 50 Shangcheng 50  Zhayang 50 Three sets of questionnaires were designed for the three sample groups: general public, community residents, and government employees. Each questionnaire was tailored specifically to the characteristics of its targeted group and each was therefore slightly different than the others. For example, the public and community residents were asked about their knowledge of climate change, but government employees were not, as the majority of government employees have already received training and workshops on climate change and relevant policies. As a result, the questionnaire for government employees focused more on local policies such as the biggest barriers to developing a low carbon economy in local areas.  Since the survey was conducted in China, all questionnaires were translated into Chinese. The responses were in Chinese and translated into English for statistical analysis with SPSS. Twelve pre-test questionnaires were conducted prior to the survey (two per sample group in each study area). Based on the feedback and results of the pre-tests, the following modifications were made to the cover letter and questionnaires:   24  Cover letter: 1) Added a brief definition of the low carbon economy as necessary context for this relatively new concept;  2) Deleted unnecessary repetition that “all responses are anonymous”; and 3) Added the questionnaire number (for coding purposes) and the date that the questionnaire was completed in the header. Questionnaire: 1) Added a definition of forest carbon sequestration in all three questionnaires; 2) Added definitions of carbon capture and storage and geoexchange in the questionnaires for community residents and government employees;  3) Added the option “primary school or less”  to the question about the respondent’s educational background in all three questionnaires; 4) Added the option “Don’t know” to questions that asked for the respondent’s opinion on very specific issues (e.g. which practice has the highest potential to reduce GHG emissions in the industry sector in local areas); 5) Added question numbers to sub-questions so respondents could see them more easily; and 6) Changed the table format for clarification and provided descriptions of each score when asking about the respondent’s knowledge of climate change and low carbon economies (e.g. 1 – Never heard of it; 2 – Heard of it but know little about it), instead of providing the score range only (i.e. 1 to 4). 25  The questionnaire design was divided into two main parts: The first part measured the citizen’s awareness and attitudes towards climate change and the low carbon economy (such as people’s knowledge and support for a low carbon economy in local areas); the second part recorded the respondents’ socio-demographic background (such as age, gender, and occupation). There were 31 variables used in the statistical analysis: five variables measuring the respondents’ knowledge and level of concern about climate change at four different scales (global, local community, immediate families and future generations); 10 variables describing respondents’ perceptions of the low carbon economy (such as people’s knowledge of the low carbon economy, whether they support a low carbon economy in local areas etc.); 10 variables measures respondents’ behaviours and lifestyle related to low carbon economies (e.g. number of low carbon activities they have done, whether they are willing to pay a carbon tax); and six variables describing the socio-demographic background of respondents, including gender, age, income, occupation, education and whether they have one or more children. Table 2.3 provides a more detailed description of the variables. In total, 1253 questionnaires were distributed and 1208 of them were returned. The overall response rate was 87.4%9. Of these, 113 samples were excluded from further analysis due to                                                  9 Response rate = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑜𝑚𝑝𝑙𝑒𝑡𝑒𝑑 𝑞𝑢𝑒𝑠𝑡𝑖𝑜𝑛𝑛𝑎𝑖𝑟𝑒𝑠𝑡𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑞𝑢𝑒𝑠𝑡𝑖𝑜𝑛𝑛𝑎𝑖𝑟𝑒𝑠 𝑏𝑒𝑖𝑛𝑔 𝑑𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑒𝑑 × 100% 26  being incomplete10 or having suspicious11 responses, leaving 1095 samples as valid observations.  Table 2.3 List of Key Variables and Measuring Questionnaire Items No. Measured Variables Description 1 Support for Low Carbon Economy (LCE) Whether they support development of a LCE in local areas. This is the Y variable for logistic regression analysis. 2 Knowledge of Climate Change Frequency of exposure to information and knowledge level about climate change  *This variable is measured in the questionnaires for the public and community members 3 Knowledge of Low Carbon Economy Frequency of exposure to information and knowledge level about LCE 4 Concern level of climate change Concern level about the effects of climate change on global scale 5 Concern level about the effects of climate change on local community 6 Concern level about the effects of climate change on immediate family 7 Concern level about the effects of climate change on future generations 8 Ways to learned about LCE Approaches to acquire information on climate change and LCE 9 1st step in developing a LCE Sectors that the government should start with if developing a low carbon economy 10 Priority for developing a LCE Sectors that the government should prioritize over the others when developing a LCE 11 Reasons to support a LCE Reasons why the respondent supports to develop a low carbon economy in local areas 12 Reasons not to support a LCE Reasons why the respondent does not support development of a LCE in local areas 13 Attitude towards local climate policies *These variables are measured in the questionnaires for the public and government employees Introducing carbon tax 14 Providing more subsidies/investment in low carbon projects  15 Introducing loans with preferential interest rates for low carbon projects                                                   10 Over 50% of the questions were not answered  11 Ticked all options given for over 50% of the questions, ticked options that are mutually exclusive (e.g. chose both “female” and “male” for gender), or over 50% of the responses from two or more questionnaires were the same 27  Table 2.3 List of Key Variables and Measuring Questionnaire Items No. Measured Variables Description 16 Low carbon activities/lifestyle *These variables are measured in the questionnaires for the public and community members Number of activities that the local resident has done to reduce their carbon footprint 17 Number of activities that the local resident would like to do in the near future to reduce their carbon footprint 18 Willingness to contribute  money to developing a low carbon economy *These variables are measured in the questionnaires for the public and community residents Willingness to pay a carbon tax 19 The amount of money that respondents are willing to pay for a carbon tax per year 20 Willingness to donate money for a low carbon economy 21 The amount of money that respondents are willing to donate per year 22 Willingness to contribute  time to developing a low carbon economy *These variables are measured in the questionnaires for the public and community residents Willingness to spend time talking with family and friends about low carbon economies 23 Number of hours that respondents are willing to spend per month talking about low carbon economies with family and friends 24 Willingness to spend time volunteering at low carbon events 25 Number of hours that respondents are willing to spend time per month volunteering at low carbon events 26 Age N/A 27 Gender N/A 28 Occupation N/A 29 Education N/A 30 Income N/A 31 Whether have children N/A  2.3 Data Analysis Procedures12 2.3.1 Descriptive Analysis Frequency analysis was used first to show the frequency distribution of each variable, i.e. the number and percent of occurrences of each response category among participants. It also                                                  12 All data analysis was conducted using IBM SPSS Statistics Version 20.0. Unless otherwise specified, the significance level (α) is 0.05 for all statistical analyses.  28  generates simple statistics for some numeric variables, such as mean and skewness (Information Technology Service of California State University, 2013).  2.3.2  Factor Analysis Factor analysis is a useful tool to discover latent variables (i.e. factors) that explain variation in the data, but are not easily measured directly. Every factor analysis first extracts the same number of factors as the number of measured variables of interest from the data pool. Each factor is a linear combination of all selected measured variables, and explains a certain amount of variance within the data for those variables. To reduce the number of variables, researchers usually discard factors that account for the least amount of variance, assuming that they reflect data noise. The second step is to rotate the factor axes to achieve a simple structure – a structure where most data points fall on or are very close to one factor axis. For example, in this survey, I tried to discover what factors influence people’s attitudes towards low carbon economies. If their concern scores of the impacts of climate change at different scales (e.g. impacts on their immediate family and future generations) are closely correlated with the first factor, while their knowledge of climate change and low carbon economies are closely correlated with the second factor, I can easily interpret them: the first factor as people’s concern about climate change, and the second one as people’s knowledge of climate change and low carbon economies. There are various rotation methods that can be divided into two main categories: orthogonal rotation and oblique rotation. Orthogonal rotation always keeps factor axes perpendicular to each other (i.e. staying independent of each other), 29  while oblique rotation may create a structure with correlated factors (Kim & Mueller, 1978; Abdi, 2003).  Factor analysis was conducted for a subset of variables related to people’s perceptions of climate change and low carbon economies, for three reasons. First of all, factor analysis was the preliminary step towards building multinomial/binary logistic regression models. It could help simplify the model building process by reducing the dimensions (i.e. reducing the number of variables) and simplify model interpretation by converting categorical variables into continuous and independent variables. It also allowed me to explore and to analyze the underlying structure of the subset of data. Socio-demographic variables were not included in the factor analysis, as I intended to keep the original variables in the model and see the impact of each variable on people’s support for low carbon economies. 2.3.3 Multinomial and Binary Logistic Regression Model Building Multinomial logistic regression is a type of model where the outcome/dependent variable (Y) is categorical with more than two categories. A multinomial logistic regression model selects one category of Y as the reference category (in this study, “Yes [I support a low carbon economy]”) and predicts the log probability of a subject (i.e. a respondent in a survey) falling into the other categories (e.g. “No [I do not support a low carbon economy]” or “I’m not sure”) relative to the log probability of that subject falling into the reference category respectively (El-Habil, 2012; Field, 2009; Kwak & Clayton-Matthews, 2002). This study developed multinomial logistic regression models to identify the variables that significantly 30  influence respondents’ support for a low carbon economy in the local area. The dependent variable (Y) has three categories: “yes” for supporters, “no” for opponents, and “not sure”. Binary logistic regression models are very similar to multinomial logistic regression models. The only difference is that the outcome variables of binary logistic regression models are dichotomous (i.e. have two levels). 31  Chapter 3: Overall Analysis of Public Perceptions in Smaller Cities in Eastern China In this chapter, I explore local citizens’ perceptions and attitudes towards low carbon economies in smaller cities in China (considers all samples so as to provide an overview of response patterns), specifically focusing on the following aspects:  1) How do local citizens perceive climate change and low carbon economies? (Research Question 1) 2) What role do socio-demographic background, perceptions of climate change and perceptions of low carbon economies have in influencing someone’s support for developing a low carbon economy in their hometown? (Research Question 3)  3.1 Descriptive Analysis 3.1.1 Socio-Demographic Description  There were six variables measuring the socio-demographic traits of the respondents. The majority of respondents were young parents who had completed secondary education (high school or above) and had low to medium incomes (¥1500 to ¥4500) (Table 3.1). The distribution of occupations in part reflected the sampling approach, notably including a substantial number of government employees. 32  Table 3.1  Socio-Demographic Information of the Samples in the Survey Demographic Variables %13 Age 19 to 30 52.4 31 to 40 29.0 41 to 50 12.4 51 to 60 3.5 61 and higher 2.6 Gender Male 50.2  Female 49.8 Occupation Government employee 36.3  Public-owned company employee 9.3  Student 10.2  Private company employee 14.2  Farmer 2.9  Not presently employed 15.5  Entrepreneur 2.5  Self-employed 13.7  Retired 5.7  Other14 4.8 Education Primary School or Less 7.1     Middle School Diploma 17.0     High School Diploma or Equivalent  33.0     Bachelor’s Degree or College Diploma 41.0     Master’s Degree or above 1.9 Monthly Income                ¥1500 and lower 22.0 ¥1500 to ¥4500 69.5 ¥4501 to ¥9000 5.5 ¥9001 to ¥35000 2.0 ¥35001 to ¥55000 0.3 ¥55001 to ¥80000 - ¥80001 and higher 0.6 Whether have children  Yes 58.7 No 41.3                                                  13 Unless otherwise specified, all percentages presented in this study are valid percentages (i.e. exclude the missing and invalid responses). 14 Include customer service manager (1 respondent), foreign company employee (2), hair stylist (2), housewife (2), soldier/military (2), restaurant service (1), teacher (7), and tourism agent (1). 33  3.1.2 Perceptions of Climate Change and Low Carbon Economies Concern about the impacts of climate change  Respondents were asked to indicate their level of concern about the impacts of climate change, ranging from 1 (not concerned at all) to 4 (very concerned). Based on their scores, mean concern level about climate change was calculated at each scale (see Figure 3.1 and Table 3.2). In general, respondents were “somewhat concerned” about climate change, with a mean concern around 3 points out of 4. There was an increasing level of concern about climate change as the spatial scale narrowed from the global to local community, and to family. Temporally, local citizens showed greater concern over impacts on future generations than on the present (i.e. respondents were the most concerned about the impacts on future generations overall).    Figure 3.1 Mean level of concern about climate change  2.97 3.043.143.251234Global impacts Impacts on localcommunityImpacts onimmediatefamiliesImpacts on futuregenerationsVery ConcernedSomewhat ConcernedSlightly ConcernedNot ConcernedSpatial Future 34  Table 3.2 Mean Level of Concern about Climate Change on Different Scales Concern about Climate Change Mean Mode Std. Dev. Concern about global impacts 2.97 3 0.82 Concern about impacts on local community  3.04 3 0.82 Concern about impacts on immediate families  3.14 3 0.82 Concern about impacts on future generations  3.25 4 0.82   3.1.3 Perceptions of Low Carbon Economies For knowledge of low carbon economies, the majority (90%) of local citizens had heard of the term, but over half (55%) had either never heard or indicated they had heard but knew little about it. Only 7% knew the term very well. In terms of how people knew about low carbon economies, television was the most popular means (cited by 80% of respondents). The second most common approach was through the Internet (cited by 60%). Around 30% of the participants learned through thematic events, advertisements, newspaper and magazines. Public lectures were the least popular way, cited by 14% of respondents. With great concerns about climate change and some knowledge of low carbon economies, the respondents overall were very supportive of low carbon economy development in their hometown: 85% indicated their support, whereas 6% were against the idea, with the rest (9%) undecided. Respondents were also asked to indicate why they do or do not support a low carbon economy based on a number of reasons provided in the survey. The most frequently cited reason for supporting a low carbon economy was to improve the environment (by 75% of respondents). Climate change mitigation and improvement of quality of life were the second 35  and the third most popular reasons, respectively, cited by around 65% of respondents. Efficiency came next, cited by 55% of respondents. Leadership in developing a low carbon economy was the least popular reason (by 39%).  Fewer people answered why they were against a low carbon economy, as the majority of respondents supported the idea. Among 377 respondents (34% of the total) who answered this question, over half (54%) thought cost was the main barrier. People also reported lack of support due to issues of decreasing quality of life (46%) and slowing down current economic growth (36%) as consequences of developing a low carbon economy. A few respondents (18%) thought that local environmental measures were sufficient, and that a low carbon economy was not needed in their hometown. 3.1.4 Suggestions for Government Actions Respondents were asked what the government should do (i.e. the first step and priorities) to develop a low carbon economy (Table 3.3). Most (64%) identified industry as the sector that should take the first step in the low carbon transition (Figure 3.2). This was also the only sector to be cited by over half of survey respondents. Forestry was the second, cited by 48%. Only 31% suggested that business should take the first step. Introducing clean energy and low carbon technologies in industry were the two most frequently cited options of priorities for developing a low carbon economy (by 74% and 64% respectively) (Figure 3.3). Developing forest carbon projects (i.e. increasing forest carbon sequestration) and recycling programs came next, suggested by 59% of respondents. More education/outreach events to increase public awareness and knowledge was suggested by 53% of respondents. The least 36  cited option was to develop green buildings. Even so, around half (49%) of respondents still thought that green building should be a priority for low carbon economy development.    Figure 3.2 Sector as the first step to develop a low carbon economy (which sector do you think should be the first step for the local government to develop the low carbon economy?)  Figure 3.3 Priorities for low carbon economy development (which ways do you think should be the top priority if the local government decides to develop a low carbon economy?)   47.8%38.8%63.6%39.5%30.8%43.1% 42.1%0%50%100%Forestry Agriculture Industry Tourism Business Building & DevelopmentEnergy59.8% 59.3%64.2%56.7% 53.0%48.5%74.1%0%50%100%Fo res t  ca rb o n  p ro j ec t sRecyc l ing Lo w ca rb o n  t echno lo gyP ub l i c  t r ansp o r tP ub l i c  p e r cep t io nGreen  b u i ld ingClean  ene rgy37  3.2 Factor Analysis There were five input variables into this analysis, including people’s concern levels about impacts of climate change on four scales – globe, local community, immediate family and future generations (four variables) – and their knowledge of low carbon economies (one variable).  There are two key steps in factor analysis: determining the number of factors to keep and the rotation method. Three factors were discarded, leaving two factors in the analysis, as the five input variables are naturally grouped into two aspects, concern about impacts of climate change and knowledge of low carbon economies.  The selected factors together explained the majority (84.56%) of variance (the common variance is 4.23 out of 5.00). Varimax (orthogonal) rotation was used to create a simple structure on all factors that was easier to interpret. According to the factor pattern (Table 3.4), variables that measured people’s concern about climate change were only highly correlated with Factor 1, and variables measuring knowledge of low carbon economies were only highly correlated with Factor 2. Factor 1 can therefore be labelled as concern about climate change and Factor 2 as knowledge of low carbon economies. The higher score of Factor 1 represented a greater concern level about climate change. Similarly, a higher score of Factor 2 indicated a higher knowledge of low carbon economies. 38  Table 3.3  Rotated Factor Pattern (Orthogonal Rotation: Varimax) No. * Label Factor 1 Factor 2 2 Knowledge of low carbon economies 0.128 0.990 3 Concern about global impacts of climate change 0.813 0.180 4 Concern about impacts of climate change on local community 0.924 0.093 5 Concern about impacts of climate change on immediate family 0.937 0.074 6 Concern about impacts of climate change on future generations 0.881 0.122 Factor Label Concern about climate change Knowledge of low carbon economies * Refers to the numbering of variables in Table 1.1 3.3 Multinomial Logistic Regression Multinomial logistic regression models were developed to identify which independent variables significantly influence people’s support for a low carbon economy in the local area (dependent variable). The factors derived from the factor analysis (called perception variables hereafter) and socio-demographic variables were independent variables in the model. Four multinomial logistic regression models were built based on different groups of independent variables. Independent variables in the first three models were selected by the forward stepwise method (the α-to-enter/remove significance level was 0.1), while the variables in Model 4 were selected by the backward stepwise method.   Model 1: used perception variables; variables were selected by the forward stepwise method;  Model 2: used socio-demographic variables only; variables were selected by the forward stepwise method; 39   Model 3: included perceptions and socio-demographic variables; variables were selected by the forward stepwise method;  Model 4: similar to Model 3; variables were selected by the backward stepwise method. 3.3.1 Model Comparison and Selection Four multinomial logistic regression models were developed. Models 2 and 3 both encountered unexpected singularities in the Hessian matrix, indicating that some independent variables should be excluded and the models might not be reliable.  After testing different combinations of independent variables in the model, occupation and education level were found to be the causes of the problems. These two variables are possibly combinations of different social or demographic variables, which caused multicollinearity (correlation between independent variables that can lead to the unexpected singularities in the Hessian matrix) in the model. Models 2 and 3 were therefore excluded.  The remaining two models were very similar in terms of their AIC, BIC, -2Log Likelihood and Pseudo R2. The Pseudo R2 of both models were relatively low (Model 1: 0.063; Model 4: 0.071). However, unlike R2 in a linear regression, pseudo R2 cannot be interpreted as the proportion of variance within data explained by the model. Previous studies suggest that pseudo R2 should be interpreted with extra caution and that it should not be expected to be as 40  high as R2 15 (Louviere, 2000). Therefore, pseudo R2 was not the primary selection criteria in this study. The likelihood ratio tests indicated that the performance of both models was significantly improved over the null model (Table 3.5: AIC, BIC and -2LL of both models decreased, and the decreases were very significant).   Both models have a very high prediction accuracy (85% of the predictions were correct) due to a nearly 100% accuracy in predicting supporters. However, this model is fairly poor at predicting opponents and people who are not sure about low carbon economies, with an accuracy of less than 1%  (please see Table C.1.4 in the Appendix C.1 for more details). As noted, the independent variables in Models 1 and 4 were very similar. Model 4 had one more variable (income) than Model 1. Although the likelihood ratio test (Table 3.4) indicated that income was significantly contributing to the model, the p value was 0.049, very close to α=0.05, implying relatively little contribution. In addition, the coefficient estimate of income was smaller than 0.001, possibly due to large numbers in this variable, which made it very difficult to interpret the contribution of income to the model (Table C.1.3 in Appendix C.1). Therefore, income was dropped from the model and Model 1 was selected as the best model for further analysis.                                                   15 Values of pseudo R2 ranging from 0.2 to 0.4 indicate “extremely good model fits”, which is equivalent to a R2 of 0.7 to 0.9 for a linear function (Domeneich & McFadden, 1975; Louviere, 2000, p. 55). 41  Table 3.4 Model Fitting Information of Model 1 & Model 4  Model Model Fitting Criteria Likelihood Ratio Test* AIC BIC -2Log Likelihood Chi-Square Sig. Model 1 Intercept Only 465.407 475.309 461.407 75.087 <.001 Final Model 405.944 435.649 393.944   Model 4 Intercept Only 580.782 590.668 576.782 67.463 <.001 Final 517.695 557.240 501.695   * Likelihood Ratio Test: H0: The final model is not significantly improved from the null model (with intercept only) H1: The final model is significantly improved from the null model (with intercept only)  Table 3.5 Likelihood Ratio Tests of Independent Variables in Model 1 & Model 4 Effect Model Fitting Criteria Likelihood Ratio Tests* AIC of Reduced Model BIC of Reduced Model -2LL of Reduced Model Chi-Square df Sig. Model 1 Intercept 1650.08*** 1669.89 1642.08 1248.14 2 <.001 Concern about Climate Change 411.24*** 431.04 403.24 9.29 2 <.001 Knowledge of Low Carbon Economies 459.73*** 479.54 451.73 57.79 2 <.001 Model 4 Intercept 1085.45*** 1115.10 1073.45 571.75 2 <.001 Concern about Climate Change 523.78** 553.46 511.80 10.10 2 .006 Knowledge of Low Carbon Economies 575.38*** 605.04 563.38 61.68 2 <.001 Income 519.72* 549.38 507.72 6.03 2 .049 * Likelihood Ratio Tests H0: The independent variable is not significantly contributing to the model H1: The independent variable is significantly contributing to the model  3.3.2 Parameter Estimates and Interpretation Model 1 contained perception variables only. It suggested that both perception variables (i.e. concern about the impacts of climate change, and knowledge of low carbon economies) 42  significantly influenced local citizen’s support for low carbon economies. Two estimates of all parameters among three levels (people who said ‘yes’, ‘no’ and ‘don't know’) were computed for the multinomial logistic model. Each model was compared with the reference level – people who answered ‘yes’ in the survey. Therefore, all the parameter estimates and the logit of the dependent variable (Table 3.6) were relative to the reference group (Institute for Digital Research and Education (IDRE) University of California Los Angeles (UCLA), 2007). Table 3.6  Parameter Estimates of Model 1 Model 1 B Std. Error Wald’s (df =1) P Odds Ratio Hesitant People vs. Supporters      Intercept -2.593*** 0.138 351.128 < .001  Concern about climate change  -0.317** 0.103 9.444 .002 0.728 Knowledge of low carbon economies  -0.808*** 0.127 40.823 < .001 0.446 Opponents vs. Supporter       Intercept -2.706*** 0.139 376.415 < .001  Concern about climate change  -0.098 0.126 0.610 .435 0.907 Knowledge of low carbon economies  -0.546*** 0.137 15.947 < .001 0.579 Note: Pseudo R2 =.063 (Cox & Snell), .097 (Nagelkerke), 0.071 (McFadden). Model 𝜒2 (4) =67.463, p=.000. *p<.05, **p<.01, ***p<.001. * For the Wald’s test: H0: The coefficient (B) of each independent variable equals zero H1: The coefficient (B) of each independent variable does not equal zero B stands for the estimated multinomial logistic regression coefficients for each independent variable in the model.   The coefficient in Table 3.6 represents the estimated change in the multinomial log-odds of a person falling into one comparison group (i.e. being uncertain or opposing a low carbon economy) relative to being supportive of a low carbon economy due to one unit change in value of an independent variable. 43  The odds ratio is the estimated change in the odds of a person falling into the comparison category (in this case, ‘uncertain people’ or ‘opponent’) relative to the odds of a person falling into the reference group (i.e. being supportive of a low carbon economy) caused by one unit change in an independent variable while holding the other independent variables constant. It is the exponentiation of the coefficients. Coefficients and odd ratios have very similar meanings and implications. Therefore, I have only interpreted coefficients in this study.  Hesitant people (Who Said ‘Don’t Know’) vs. Supporters (Who Said ‘Yes’)  Concern about climate change was very significant in distinguishing people who were unsure about low carbon economies from the supporters. If a person’s concern score were to increase one point, the multinomial log odds of him/her being uncertain (relative to being supportive) would be expected to decrease by 0.317. In other words, people with greater concern about climate change were less likely to hesitate about whether to support a low carbon economy instead of showing support.  Knowledge of low carbon economies was also very significant in differentiating people who were unsure about low carbon economies from the supporters. If a person’s knowledge score were to increase one point, the multinomial log odds of this person being uncertain (relative to being supportive) would be expected to decrease by 0.808. In other words, people who knew less about low carbon economies were more likely to be uncertain about whether to support a low carbon economy rather than to be supportive.  44  Opponents (Who Said ‘No’) vs. Supporters (Who Said ‘Yes’)   Concern about climate change was no longer significant when comparing opponents with supporters. In other words, opponents’ and supporters’ concern about climate change were not significantly different.   Knowledge of low carbon economies was very significant in differentiating low carbon economy opponents and supporters. If a person’s knowledge score were to increase one point, the multinomial log odds of this person opposing relative to supporting a low carbon economy would be expected to decrease by 0.546. In other words, people with lower knowledge of low carbon economies were more likely to oppose a low carbon economy than support it. 3.4 Discussion  In this study, I sought to understand how concerned people are about climate change and how they see the low carbon economy as a solution. The people who responded to the questionnaires generally reported high levels of concern but were more concerned about direct and local impacts of climate change (i.e. impacts on immediate families and future generations) than those that seemed more distant to them. A possible reason for this is that people tend to discount negative impacts or costs that are not imposed directly on them in the short term. As a result, they care less about ‘distant’ impacts happening at a global scale (Leiserowitz, 2007a; CRED, & EcoAmerica, 2014; Swim et al., 2011). In addition, most people showed the greatest concern for future generations. This is perhaps not surprising, due to the so-called “little emperor syndrome” resulting from the one-child policy and the 45  Chinese tradition of putting the child or grandchild as the top priority in the family (Hesketh & Zhu, 1997). Moreover, with a limited understanding of climate change and its impacts, respondents likely don’t see it as an immediate crisis but a future threat to their descendants. Despite their great concern about climate change, the majority of respondents indicated a limited understanding of climate change and the low carbon economy. However, they were very supportive of developing a low carbon economy in local areas. Protecting the environment, mitigating climate change, and improving the quality of life were the most frequently cited reasons. On the other hand, the cost of developing a low carbon economy was identified as the biggest barrier. Local citizens were also worried about its impacts on their own lives and local economic growth (i.e. decreasing quality of life and slowing down economic development), which is in line with previous studies (e.g. Chen & Taylor, 2010; Q. Liu et al., 2009). Quality of life was cited as a reason to support or oppose a low carbon economy at the same time (sometimes even by the same person), indicating that local citizens subdivide the impacts of low carbon economy development on their quality of life into two parts: positive and negative impacts. Although it was not tested in this study, some possible examples of positive impacts on the quality of life could be physical and psychological benefits of living in a greener city (e.g. less pollution), more resilient and sustainable communities, and more job opportunities due to the development of low-carbon technology and renewable energy sources. On the other hand, local citizens might worry about major lifestyle changes due to punitive measures (e.g. tax on the use of cars), less job opportunities in profitable heavy industries, and less government investment in infrastructure 46  due to a slowdown in the local economy during the process of developing a low carbon economy. Results also suggest that local citizens rely heavily on mass media to learn about climate change and the low carbon economy. Television and the Internet were the most frequently cited ways to gather relevant information, whereas public lectures were the least popular approach. A similar pattern has been found in other studies of public perception in China (Chen & Taylor, 2011; Wang & Mao, 2010). There are 400 million households that own a television and 649 million people using the Internet in China (China Internet Network Information Center (CCNIC), 2015; Yan, 2013). On average, a Chinese person spends 2.6 hours watching television and 3.6 hours on the Internet every day (including people who watch television and surf the Internet simultaneously) (Statista, 2015a, 2015b). It is therefore not a surprise to find that local citizens learn about climate change and the low carbon economy primarily through television and the internet.  With regard to suggestions for government’s actions, most respondents believed that the government should start reforming the industry, forestry and energy sectors, giving them higher priority in the process of developing a low carbon economy. This reflected the local citizens’ perceptions of a low carbon economy (e.g. low-carbon industry, carbon sequestration in forests, and clean energy sources) and their expectations of local governments. It may also have indicated that they saw more room for improvements in these sectors than others. Building and development was the third most frequently voted as the sector where the government should start, while developing green buildings was the least 47  popular option as a priority in low carbon economy development, indicating possible confusion about green buildings among local citizens. Other identified priorities in a low carbon economy included introducing low carbon technologies into industrial processes, developing waste recycling programs, and increasing the citizens’ understanding of the low carbon economy (supporting the earlier findings of a low general level of understanding).  The multinomial models revealed that: 1) people who knew more about low carbon economies were more likely to show support for a local low carbon economy; 2) people with greater concern about climate change were more likely to have a clear mindset on the question of supporting a local low carbon economy rather than being uncertain about it, but were not necessarily more supportive.  Knowledge of low carbon economies was the most significant variable in the model explaining participants’ responses. Knowledge has been well studied as an important factor that influences people’s environmental awareness and attitude to environmental policies (Lorenzoni et al., 2007; O’Connor, Bord, & Fisher, 1999; Zahran, Brody, Grover, & Vedlitz, 2006). In fact, many studies have shown that people who did not worry about climate change or were ‘climate change non-believers’ usually lacked a proper understanding of the issue (CRED, 2009; Leiserowitz, 2007a; Sheppard, 2012). With an adequate knowledge of climate change and low carbon economies, people may realize the causes of climate change and understand the costs of not taking immediate actions (i.e. connecting the dots) (Sheppard, 2012). However, there is much research (e.g. Kollmuss & Agyeman, 2002; Moser & Dilling, 2007; Wolf & Moser, 2011) that has demonstrated that conventional information and 48  knowledge were often insufficient to motivate actions, and more compelling and meaningful forms of interaction with people are required. For example, Sheppard (2012) reported that community members on west coast of Canada were more willing to support mitigation policies such as low carbon economies when engaged with visualizations of local climate change scenarios.  Concern about climate change was another significant variable in the model, which was consistent with previous studies. Concern about direct impacts of climate change is one of the main drivers of attitudinal and behavioural changes in people (Bord et al., 1998; Ditz et al., 2007; Semenza et al., 2008). The more a person worries about the impacts of climate change, the more likely that person is to support mitigation policies such as developing a low carbon economy. However, contrary to the reports of previous studies, I did not find a significant difference regarding concern about climate change between supporters and opponents of the low carbon economy. A possible reason for this is that both sides (supporters or opponents) showed great concerns about the impacts of climate change. Therefore, this variable did not help distinguish between supporters and opponents. Although many other variables have been found to be important in predicting people’s attitude towards climate policies in other countries, such as gender (Black et al., 1996; Dietz, Dan, & Shwom, 2007; Wolf & Moser, 2011; Zahran et al., 2006), age ( Berenguer, Corraliza, & Martín, 2005; O'Connor et al., 1999), education (Harris, 2006; Q. Liu et al., 2009) and income (O’Connor et al., 1999; Savage, 1993), they were not significant in this analysis.   49 Chapter 4: Comparison of Public Perceptions and Behaviours between Fuding City and Zherong County This chapter is focused on comparison between the two study areas, Fuding City and Zherong County, to answer three of the five research questions: 1) What are local citizens’ behavioural responses to climate change in each study area? (Research Question 2) 2) Are there any differences in terms of awareness, knowledge, and behaviour related to climate change and low carbon economies between residents of areas with different levels of economic development (e.g. Fuding City representing a more developed area vs. Zherong County representing a more rural area with less development)? (Research Question 4) 3) What roles do socio-demographic background, perception of climate change and perception of low carbon economies have in influencing someone’s support for developing a low carbon economy in their hometown? (Research Question 3) 4.1 Descriptive Analysis 4.1.1 Socio-Demographic Information As indicated in Table 4.1, most respondents in this study were under 41 years old with at least a secondary education (high school diploma or higher) and a low-middle income (¥1500 to ¥4500). There were some variations between Fuding City and Zherong County. A much larger percentage of respondents from Zherong County work in the government   50 and have a low-medium monthly income (under ¥4500) compared to respondents from Fuding City. The distribution of occupations in part reflects the sampling approach, including a notably substantial number of government employees.   51 Table 4.1 Socio-Demographic Information by Study Area Demographic Variables Fuding City Zherong County % % Age  19 to 30 53.8 49.6 31 to 40 27.2 32.7 41 to 50 11.5 14.2 51 to 60 4.0 2.5 61 and higher 3.4 1.1 Gender Male 51.2 48.2    Female 48.8 51.8 Occupation  Government employee 26.7 55.9  Public company employee 8.6 4.1  Student 10.9 1.4  Private company employee 13.0 6.3  Farmer 2.2 2.2  Not presently employed 17.4 11.8  Entrepreneur 2.6 .5  Self-employed 8.8 13.7  Retired 6.0 .8  Other16 3.8 3.3 Education  Primary School or Less 8.8 3.8     Middle School Diploma 19.3 12.3     High School Diploma or Equivalent  30.7 37.5     Bachelor’s Degree or Equivalent 38.8 45.2     Master’s Degree or above 2.3 1.1 Monthly Income                     ¥1500 and lower 27.6 19.0 ¥1500 to ¥4500 60.6 74.6 ¥4501 to ¥9000 7.4 3.9 ¥9001 to ¥35000 3.0 1.7 ¥35001 to ¥55000 .2 .9 ¥55001 to ¥80000 - - ¥80001 and higher 1.2 - Have children  Yes 58.3 59.4 No 41.7 40.6                                                   16 Includes one customer service manager, two foreign company employees, two army soldiers, one restaurant server, four teachers, one stay-home mom, one travel agent and 17 respondents who did not specify their occupation in Fuding City; In Zherong County, the other category includes two hair stylists, one stay-home mom, three teachers and six respondents who did not specify their occupation.     52 4.1.2 Perceptions of Climate Change and Low Carbon Economies 4.1.2.1 Significant Differences between Place of Residence Mann-Whitney tests were computed to determine any differences between residents from Fuding City and Zherong County on 14 variables, including their level of concern about the impacts of climate change, knowledge of climate change and low carbon economies, and attitude towards low carbon economies and relevant policies. Results showing significant differences between locations are shown in Table 4.2, and then discussed together with broad findings for both locations. Table 4.2 Significant Differences between Fuding City and Zherong County Tested Variables Fuding City (FD) vs.  Zherong County (ZR) Knowledge of climate change - Concern about the globe impacts of climate change *  FD < ZR Concern about impacts of climate change on local communities - Concern about impacts of climate change on immediate family *** FD < ZR Concern about impacts of climate change on future generations *** FD < ZR Knowledge of low carbon economies - Number of current low carbon behaviours - Number of future low carbon behaviours - Willingness to pay (¥ per year) - Willingness to contribute time (hours per month) - Support for a low carbon economy in the local areas - Support for a carbon tax - Support for government investment in low carbon projects - Support for preferential loans for low carbon projects *  FD > ZR        Significant differences: * 0.01 ≤  𝑝 ≤  0.05      ** 0.001 ≤  𝑝 < 0.01    ***  𝑝 <  0.001 -         Not significant    53 Concern about the impacts of climate change  In the questionnaire, participants were asked to rate their level of concern about the impacts of climate change on four different scales: 1) the world; 2) your local community; 3) you and your immediate family; and 4) future generations of your family. On a scale from 1 (not concerned at all) to 4 (very concerned), most respondents were ‘somewhat’ concerned about climate change, with a mean concern score around 3.00 (Table 4.3). The mean score of residents from both places increased when the spatial scale narrowed from the world to immediate family. The majority of local residents were more concerned about the impacts on future generations (with the highest means, 3.20 for Fuding residents and 3.34 for Zherong residents) than present impacts (Figure 4.1).  When comparing concern scores between the two places, respondents in Zherong County overall scored (0.1-0.15) higher at each scale than respondents in Fuding City. Respondents from Fuding City were significantly less concerned about the impacts of climate change on the global scale (Mann-Whitney, 𝑝 = .004), on immediate families (Mann-Whitney, 𝑝 =.003), and on future generations (Mann-Whitney, 𝑝 = .001) (Table 4.3). On the other hand, no significant differences were found in their levels of concern over the impacts on local communities. Please see Table C.2.1 in the Appendix C.2 for more details on respondents’ concern scores.    54 Table 4.3 Mean Concern about Climate Change on Four Scales by Study Area Concern of Climate Change Fuding City Zherong County FD vs. ZR* Mean Std. Dev. Mode Mean Std. Dev. Mode U z r The globe 2.93 0.80 3 3.06 0.85 3 116322 -2.91 -0.09 Local community 3.02 0.79 3 3.08 0.88 3 122109 -1.48 -0.05 Immediate family 3.10 0.79 3 3.21 0.87 4 113876 -3.00 -0.09 Future generations 3.20 0.81 3 3.34 0.84 4 112039 -3.44 -0.11 *Mann-Whitney test results  Figure 4.1 Mean level of concern about the impacts of climate change by study area  4.1.2.1.1 Perceptions of climate change and the low carbon economy Respondents were asked to rate their knowledge of climate change and the low carbon economy on a scale from 1 (never heard of it) to 4 (know it very well). Overall, regardless of where they were from, respondents indicated very limited understanding of both topics (around 2.5 out of 4) (Table 4.4 and Figure 4.2). Respondents from Zherong County 2.933.023.103.203.06 3.083.213.341234Global impacts Impacts on localcommunityImpacts onimmediate familiesImpacts on futuregenerationsFuding City Zherong CountyVery ConcernedSomewhat ConcernedSlightly ConcernedNot ConcernedSpatial Future   55 seemed to know both topics better than respondents from Fuding City, but the differences were minimal (0.07 point out of 4.00) and not statistically significant.  Over 92% of respondents from both study areas had heard of climate change. Around half of them (46% from Fuding City and 52% from Zherong County, the largest group) indicated that they had reasonable understanding of this topic (“Heard of it and know about it”). Eleven percent from Fuding City and 12% from Zherong County knew this topic very well. On the other hand, around 40% of respondents knew very little about it, or have never heard of it.   When it comes to the low carbon economy, more than 85% of respondents had heard of this term. Fuding City had a higher percentage of respondents who had heard of this term (92% vs. 85%), but a smaller proportion with reasonable knowledge, including people who knew something (36% vs. 44%) and people who knew it very well (5% vs. 8%). Table 4.4 Frequency Distribution of Responses on Knowledge of Climate Change and the Low Carbon Economy by Study Area Variables Fuding City Zherong County % Mean Std. Dev. % Mean Std. Dev. How much do you know about climate change?  (nFD=596; nZR=233) Never heard of it 5.2 2.63 0.75 7.7 2.69 0.79 Heard of it but know little about it 37.6   28.3   Heard of it and know about it 46.0   51.5   Know it very well 11.2   12.4   How much do you know about low carbon economies? (nFD=718; nZR=366) Never heard of it 8.2 2.38 0.71 15.0 2.45 0.84 Heard of it but know little about it 50.8   33.1   Heard of it and know about it 35.9   44.0   Know it very well 5.0   7.9      56  Figure 4.2 Mean knowledge score of climate change and the low carbon economy by study area  4.1.2.1.2 Number of low carbon activities at present and in the future The survey provided participants with a list of low carbon activities (i.e. activities to reduce their personal carbon footprint) and asked them to pick the ones they had done or hadn’t done but would like to try (please see the full list of activities in Table C.2.1). In general, respondents from both areas had done three to four low carbon activities in their daily lives (averaged at 3.57 for Fuding residents and 4.29 for Zherong residents) and would like to try another one or two activities in the future (1.62 for Fuding residents and 1.82 for Zherong residents). According to Mann-Whitney tests, no significant differences were found in the number of current or future low carbon activities between citizens from the study areas. In other words, citizens from both places had done a nearly equal number of activities to lower 2.63 2.692.382.451234Fuding City Zherong CountyClimate Change Low Carbon EconomyNever heard of itHeard of itKnow little about itHeard of it Know about itKnow it very well  57 their carbon footprints and would like to try a nearly equal number of activities in the future. Table 4.5 Number of Low Carbon Activities that Respondents Report Having Done (Current) or Are Willing to Try (Future) by Study Area # of Low Carbon Activities Fuding City Zherong County n Mean Std. Deviation n Mean Std. Deviation Current 601 3.57 2.621 233 4.29 2.684 Future 608 1.62 1.431 233 1.82 1.963    Figure 4.3 Mean number of current and future low carbon activities by study area Overall, respondents from different study areas were quite similar in terms of the choice of low carbon activities that they participated in. The most popular low carbon activities were shutting down electrical equipment when not in use (cited by over 60% of respondents who answered this question in both areas), consuming rationally/reducing waste (cited by around 57% of respondents), and using energy-saving facilities such as energy efficient 3.571.624.291.82012345Current low carbon activity Future low carbon activityMean number of low carbon activitiesFuding City Zherong County  58 refrigerators (cited by over 50% of respondents). Activities that usually require more time, energy or monetary expense, such as cycling or walking instead of driving and recycling grey water, were less popular. Cycling was the least favourable activity, cited by 20% of respondents from Fuding City and 33% from Zherong County. Seven percent of respondents from Zherong County indicated that they had never tried anything to lower their carbon footprints, while no respondents from Fuding City claimed this. When it comes to the future, classifying and recycling garbage became the most popular activity (ranked as the 4th / 5th most popular activity that participants had done to date), voted for by 52% of respondents from Fuding City and 58% of respondents from Zherong County. Activities such as installing energy-saving facilities, consuming rationally/reducing waste (by buying less), and shutting down facilities when they are not in use remained popular among those who hadn’t tried these activities yet, and were cited by about half of respondents who answered the question. Respondents from both areas, especially those from Fuding City, were less willing to try more time-/energy-/money-consuming activities such as cycling, walking, avoiding use of disposable items and recycling grey water. In fact, recycling grey water was the least favourable activity among Fuding citizens, with 20% of respondents willing to try it, half of the percentage for Zherong County (40%). Another big discrepancy found between the study areas was the percentage of respondents indicating that they would start using shopping bags, voted for by 24% of respondents from Fuding City and 47% of respondents from Zherong County. In addition, 1 % of respondents in Fuding City indicated no intention of trying any other activities to lower their carbon emission, while no one from Zherong County claimed this.    59  Figure 4.4 Low carbon activities that respondents have done and are willing to try, by study area 1.6%7.0%29.5%23.0%33.5%20.1%39.7%29.9%36.5%27.0%34.8%23.8%39.0%29.5%39.7%19.9%41.5%39.4%47.3%24.0%50.5%41.9%42.4%38.0%45.5%45.9%57.6%52.3%45.5%46.7%53.6%42.3%58.0%50.6%50.9%49.1%56.5%57.1%50.4%48.9%62.5%63.4%0% 50% 100%Zherong County - FutureFuding City - FutureZherong County - CurrentFuding City - CurrentShut down electrical equipment when it's not in useConsume rationally (reduce wastes)Use energy-saving facilitiesClassify garbage  Take public transitUse shopping bagsRecycle grey waterWalk instead of drivingAvoid using disposable itemsRide bike instead of drivingNone  60 4.1.2.1.3 Willingness to contribute to low carbon economy development  Respondents were also asked to indicate their willingness to pay money (through a carbon tax or donation) or to spend time (through volunteering or spreading the knowledge among their families/friends) to support low carbon economy development in local areas. Some extreme values17 were excluded in calculating the mean values in order to get more accurate estimates of the amount of money and time that each group was willing to contribute. Overall, 60% of respondents from both areas were not willing to pay a carbon tax (Fuding City: 67%, Zherong County: 60%) or donate (Fuding City: 61%, Zherong County: 62%) for developing a low carbon economy. Nonetheless, Fuding citizens on average reported that they were willing to pay ¥380.53 for a carbon tax or donate ¥162.00 per year, much higher than the amount that Zherong citizens indicated (¥114.39 for a carbon tax and ¥109.20 for donation) (Table 4.6). However, the differences were not significant in Mann-Whitney tests.  When it comes to their willingness to contribute time, over 60% of respondents from both area were not willing to contribute their time (Fuding City: 66% said no to volunteer for low carbon events, 61% for talking with family/friends; Zherong County: 63% for volunteering and 55% for talking with family/friends). Among those who indicated the amount of time that they would contribute (Figure 4.5), Fuding citizens preferred to spend                                                  17 Three respondents in Fuding City indicated that they were willing to pay over ¥10000 per year (the highest amount is ¥25000 per year), while most respondents indicated a much lower value (e.g. ¥100 per year).    61 more time volunteering for the government or environmental organizations (averaged at 19.57 hours per month) than talking about low carbon economies with families and friends (10.36 hours per month), whereas Zherong respondents showed no obvious preference (12 hours for volunteering and 13 hours for talking with family members and friends). No significant differences were found between Fuding City and Zherong County in the number of hours that respondents were willing to contribute to low carbon economy development. Table 4.6 Willingness to Contribute (by Paying Money or Spending Time) to Low Carbon Economy Development by Study Area Willingness to Contribute N Mean Std. Dev. Mode Range Fuding City By Paying Money 102 258.41 583.11 100 1-5000    Carbon Tax 45 380.53 812.28 100 1-5000    Donation 57 162.00 270.92 100 1-1500 By Spending Time 125 15.22 20.00 1 1-120    Volunteering 66 19.57 21.39 24 1-120 Talking with Family/Friends 59 10.36 17.22 1 1-100 Zherong County By Paying Money 48 111.69 165.51 100 1-1000    Carbon Tax 23 114.39 136.77 100 1-500    Donation 25 109.20 191.00 100 10-1000 By Spending Time 46 13.25 22.20 2 1-100    Volunteering 20 12.63 13.79 10 1-60 Talking with Family/Friends 26 13.73 27.24 1.5 1-100    62   Figure 4.5 Willingness to contribute to low carbon economy development by study area 4.1.2.1.4 Opinion of low carbon measures  In the questionnaire, participants were asked for their opinion on three selected low carbon measures: introducing a carbon tax, providing more subsidies to low carbon projects, and introducing preferential loans for low carbon projects, on a scale from 1 (strongly disagree) to 5 (strongly agree).  Overall, respondents from both places were supportive of the listed low carbon measures. They were more supportive of government subsidies (average of 4.15) and preferential loans (average of 4.07) than of a carbon tax (average of 3.30) (please see more details of response distributions in Table C.2.1). Respondents from both study areas shared a very similar opinion on carbon tax and government subsidies for low carbon projects (Figure 8), but their opinion on whether the government should provide preferential loans for low carbon projects varied significantly 380.53114.39162.00109.200100200300400Fuding City Zherong County¥/yearCarbon Tax Donation19.5712.6310.3613.7301020Fuding City Zherong Countyhr/monthVolunteering Talking with Family/Friends  63 between study areas. Respondents from Fuding City were significantly more supportive of a preferential loan policy than Zherong respondents.   Figure 4.6 Average support for low carbon measures by study area 4.1.2.2 Other Differences between Fuding City and Zherong County Some questionnaire items could not be tested by a Mann-Whitney test, but may be of great importance for local governments in future low carbon planning. For these variables, I have mainly focused on their frequency distributions.  4.1.2.2.1 Ways to learn about low carbon economies The survey asked respondents to indicate how they learned about low carbon economies. Respondents from both areas were quite consistent with their preferences. Television was the most popular approach (cited by 82% of respondents in Fuding City and 87% of respondents in Zherong County). The Internet came in second (59% in Fuding City and 67% in Zherong County). Newspaper, magazines and advertisements were the next most 3.253.394.21 4.094.11 4.0412345Fuding City Zherong CountyCarbon tax LC investment LC preferential loanStrongly AgreeAgreeStrongly DisagreeDisagreeNeutral  64 common approaches, cited by one-third of respondents in both places. Public lectures were the least popular approach, and this was the only option that was more popular among Fuding citizens than Zherong citizens (15% vs. 12%). 4.1.2.2.2 Support for a local low carbon economy Despite limited understanding of climate change and low carbon economies, around 85% of respondents in both areas indicated their support in developing a low carbon economy in their hometown. Around 10% of respondents from Fuding City were not sure about this idea, almost twice as many as in Zherong County (6%). The trend was reversed for the proportion of opponents in the study areas: 5% of Fuding respondents opposed a low carbon economy, 3% lower than that in Zherong County (8%).  Figure 4.7 Support for a low carbon economy by study area 84.7% 86.2%5.2%8.0%10.2% 5.8%0%50%100%Fud ing  Ci ty Zhero ng  Co untyYes No Don't Know  65 To gain a better understanding of people’s attitudes and knowledge of the low carbon economy, the survey asked why they supported or opposed a low carbon economy. Responses from both areas were very similar. The majority believed that developing a low carbon economy could improve the environment, mitigate climate change, and improve their quality of life. Improving the environment was the most popular reason, cited by 76% of Fuding citizens and 73% of Zherong citizens. Other reasons voted for by over half of respondents included mitigating climate change (about 66%), improving quality of life (about 64%), and enhancing efficiency of production and resource utilization (51% of Fuding citizens and 62% of Zherong citizens). Being a leader in low carbon economy development was the least convincing reason to respondents in both areas, and this was the only option cited by less than half (33% in Fuding City and 49% in Zherong County) of respondents, though still an important rationale for many citizens.  As for the reasons why respondents opposed a low carbon economy in local areas, respondents from both study areas indicated that cost was the main concern (51% of Fuding citizens and 59% of Zherong citizens who answered this question). Other frequently cited reasons included decreasing the quality of life and limiting local economic growth. Less than 20% of respondents (the smallest group) in both areas indicated that a low carbon economy was not necessary since they believed sufficient environmental measures were already in place.     66 4.1.2.2.3 Suggestions regarding future direction  Respondents were asked to provide suggestions for the direction of the government’s work and their opinion on selected low carbon measures. Respondents from both study areas shared very similar opinions on what the first step towards developing a low carbon economy should be (Figure 4.8). Approximately 63% of respondents suggested that the government should start with the industry sector, followed by the forestry sector (46% of Fuding residents and 51% of Zherong residents). A similar percentage of respondents voted for the building and development sector and the energy sector (the 3rd most popular options). The smallest percentage of respondents believed that the government should start with reforming the business sector. In terms of priorities for developing a low carbon economy (Figure 4.9), introducing clean energy and low-carbon technologies in industry were the most popular options (voted for by more than 63% of respondents in both study areas). Developing forest carbon projects, establishing a garbage collecting and recycling system, and developing/upgrading the public transit system tied for the next most popular option, voted for by about 50% to 60% of respondents. A slightly higher percentage (4%) of respondents from Fuding City voted for public transit as a priority for low carbon economy development than respondents from Zherong County, and this was the only option with more votes from Fuding citizens. The least cited option was to develop green buildings in local areas. However, the differences in votes here were minimal (10% less compared to the others).   67  Figure 4.8 Sector as the first step to develop a low carbon economy by study area   Figure 4.9 Priority for low carbon economy development by study area 4.2 Multinomial Logistic Regression  Two multinomial logistic models were developed for Fuding City and Zherong County (called Fuding Model and Zherong Model) to identify variables that significantly influenced people’s attitudes towards developing a low carbon economy in local areas. Perception variables that represent local citizens’ level of concern about climate change and 46.3%36.1%63.9%39.0%29.8%42.1% 41.1%50.6%44.4%63.5%41.0%33.1%45.5 44.4%0%50%100%Forestry Agriculture Industry Tourism Business Building EnergyFuding City Zherong County58.4% 57.9%63.3% 57.9%51.0%47.1%71.1%62.5% 62.2%66.1%54.2%57.2%51.4%80.3%0%50%100%Forest carbonprojectsRecycling Low carbontechnologyPublictransportPublicperceptionGreenbuildingClean energyFuding City Zherong County  68 knowledge of the low carbon economy (see more details on factor analysis in Chapter 3) and socio-demographic variables were input variables (or independent variables) in both models. Support for a low carbon economy in local areas (with three levels: ‘yes’, ‘no’ and ‘don’t know’) was the dependent variable. Perception variables and socio-demographic variables were selected as independent variables based on their significance in the model (i.e. whether or not their coefficients were significantly different from zero). Only variables that were significantly contributing to the model (i.e. significantly different from zero) were kept. Both final models – Fuding Model and Zherong Model – retained two common independent variables: knowledge of low carbon economies and the number of low carbon activities that respondents had done. The Fuding Model included two more independent variables than the Zherong Model: the number of low carbon activities that people would like to try, and respondent’s age (see Table C.2.2 and Table C.2.3 for parameter estimates of both models).    69 4.2.1 Fuding Model Hesitant people (who said ‘don’t know’) vs. supporters (who said ‘yes’) ● Knowledge of low carbon economies was very significant in the model: 𝑏 = −0.443,𝑊𝑎𝑙𝑑 𝜒2(1) = 7.743, 𝑝 = 0.005. If a person’s knowledge score were to increase one unit, the multinomial logistic odds of this person being uncertain relative to being supportive of a low carbon economy would be expected to decrease by 0.443, given that all other variables are constant. In short, people with greater knowledge of low carbon economies were less likely to be hesitant to support a low carbon economy in local areas. ● Number of current low carbon activities was extremely significant in the model: 𝑏 =−0.248, 𝑊𝑎𝑙𝑑 𝜒2(1) = 14.360, 𝑝 <  0.001. If a person had done one more low carbon activity, the multinomial logistic odds of this person being uncertain relative to being supportive decreased by 0.248. In other words, people who had done more low carbon activities were less likely to be uncertain about whether or not they support a low carbon economy. ● Number of low carbon activities that people would like to try was significantly contributing to the model: 𝑏 = −0.228, 𝑊𝑎𝑙𝑑 𝜒2(1) = 4.571, 𝑝 = 0.033. The negative coefficient indicated that the odds of a person being uncertain (rather than supportive) would decrease if this person would like to try more low carbon activities. In other words, people who were willing to try out more activities to reduce their carbon emissions were more likely to support a low carbon economy instead of being uncertain.   70 ● Age was not significant in this comparison, and therefore, not useful in distinguishing people who were not sure about developing a low carbon economy from people who supported the idea. Opponents (who said ‘no’) vs. supporters (who said ‘yes’)  ● Number of low carbon activities respondents had tried was significant in the model: 𝑏 = −0.273, 𝑊𝑎𝑙𝑑 𝜒2(1) = 8.727, 𝑝 = 0.003. If the number of activities were to increase by one, the multinomial logistic odds of a person opposing a low carbon economy relative to the odds of this person supporting would be expected to decrease by 0.273. In short, people who had tried more low carbon activities were more likely to support rather than opposed a low carbon economy. On the other hand, the number of low carbon activities that people were willing to try was not significant in the model: 𝑏 = −0.071, 𝑊𝑎𝑙𝑑 𝜒2(1) = 0.273, 𝑝 = 0.601. The numbers of activities that opponents and supporters were willing to try were not significantly different (i.e. they were similar). ● Age became very significant: 𝑏 = 0.041, 𝑊𝑎𝑙𝑑 𝜒2(1) = 9.337, 𝑝 = 0.002. If a person were one year older, his/her multinomial logistic odds of opposing a low carbon economy relative to the odds of being supportive would increase by 0.041. In other words, opponents were significantly older than supporters.      71 4.2.2 Zherong Model Hesitant people (who said ‘don’t know’) vs. supporters (who said ‘yes’) ● Knowledge of low carbon economies was very significant in the model: 𝑏 = −0.973,𝑊𝑎𝑙𝑑 𝜒2(1) = 10.073, 𝑝 = 0.002. If a person’s knowledge score were to increase one unit, the multinomial logistic odds of this person being uncertain relative to being supportive of a low carbon economy would be expected to decrease by 0.973, given that all other variables remained constant. In short, people with greater knowledge of low carbon economies were less likely to be hesitant to support a low carbon economy in local areas. ● Number of current low carbon activities was significant in the model: 𝑏 =−0.250, 𝑊𝑎𝑙𝑑 𝜒2(1) = 4.577, 𝑝 = 0.032. If a person had done one more low carbon activity, the multinomial logistic odds of this person being uncertain relative to being supportive would decrease by 0.248. In other words, people who had done more low carbon activities were more likely to be supportive than uncertain about a low carbon economy. Opponents (who said ‘no’) vs. supporters (who said ‘yes’)  ● Knowledge of low carbon economies and number of low carbon activities respondents have tried were no longer significant in the model. In other words, people who opposed a low carbon economy in local areas and those who supported this idea had very similar knowledge of low carbon economies and had tried very similar   72 numbers of low carbon activities. These two variables were not useful in distinguishing opponents and supporters. 4.3 Discussion As a follow up to the previous analysis of people’s perceptions of the low carbon economy in smaller cities in China, this study focused primarily on differences in people’s understanding and support for low carbon economies and relevant policies between two study areas, Fuding City and Zherong County. The results showed that citizens from Fuding City and Zherong County were not quite the same in terms of their level of concern about climate change, lifestyle (i.e. number of low carbon activities they had done), and attitudes towards the low carbon economy. Significant differences were found in their concern levels over the impacts of climate change at the global scale, on their immediate family and on future generations. Although Mann-Whitney tests indicated no significant differences in citizens’ knowledge of the low carbon economy and support for government’s investment in low carbon projects, the small p-value (0.051) indicated big differences between the study areas. In general, Zherong citizens tended to be more knowledgeable about climate change and more supportive of the low carbon economy. They also seemed to participate in more low carbon activities in their daily lives and were more willing to change their lifestyle to lower their carbon footprint, compared to citizens of Fuding City. On the other hand, Fuding citizens favoured more financial aids from the government (e.g. more investment or preferential loans for low carbon projects) and were willing to pay more money to support a low carbon economy.   73 Different directions of development in Fuding City and Zherong County is probably the biggest reason for the differences in responses between these places. Zherong County is an ‘ecological function zone’ that prioritizes environmental protection over economic development, while Fuding City is a key economic development zone, putting more emphasis on economic growth (Zhang & Liu, 2014). With different focuses, the governments of these two places work very differently regarding climate change mitigation and low carbon economy development. For example, major streams and rivers that supply water to surrounding cities and counties, including Fuding City, originate in Zherong County, and there are therefore much stricter regulations on waste treatment to protect the watershed in this location. The county has turned down more than ¥10 million (approximately US$1.6 million) worth of investments from heavy polluting industries (e.g. pulp and paper industry) (Sina News, 2014). In contrast, Fuding City, located downstream, is more welcoming to profitable heavy industries to fuel its economic growth (Zhang & Liu, 2014). As a result, local residents in Zherong County may hear about climate change and low carbon economies more often than residents in Fuding City.  In addition, to secure the public’s support, the government in Zherong County has put great effort into publicizing the importance of protecting the environment and developing a sustainable and low-carbon society, while the Fuding government focuses more on its achievements in economic growth, especially in industry and business (Anonymous, personal communication, 2013; Huang & Pan, 2013). Therefore, residents of Zherong County may have become more concerned about climate change and may be more willing to make personal behavioural changes to mitigate climate change. Residents in Fuding City   74 are more informed about economic development and thus may be more supportive of ideas regarding investment and low-interest loans. Geographic location is another reason for the different directions of development between these two places. Fuding City is located on the coast and is connected to Zhejiang Province (with the 4th highest GDP in China) (Sohu, 2015). It is very easy to access by bus, fast train, ship or even airplane (the airport is a 30-minute drive away), while Zherong County is located in a mountainous area, which can only be accessed by car or bus (Fuding Chamber of Commerce in Beijing, n.d.; Government of Zherong County, 2012). Limited transportation availability is a barrier to the economic development and the improvement of local citizens’ standard of living in Zherong County (Public-Private Infrastructure Advisory Facility (PPIAF), 2015). Compared to Fuding City, Zherong citizens have a lower standard of living (the average annual income in Zherong County is ¥19871 or US$3050/person, ¥5500 lower than Fuding City, ¥25378 or US$3890/person) (Fuding Bureau of Statistics, 2015; Zherong Bureau of Statistics, 2015), and their costs or barriers of participating in low carbon activities are not as high as that for Fuding citizens. Take travel habits as an example: small counties in China like Zherong County are usually very compact (unlike suburban areas in North America), whereas cities such as Fuding City are consisted of “single-use superblocks” with segregated land uses (e.g. residential areas are separated from retail stores and offices) (Balula & Bina, 2015, p. 105). As a result, many residents in Fuding City do not want to give up driving, as it is much larger (and more congested) than Zherong County to travel around. It may take one hour to get to their workplace from home if commuting by bus in Fuding City, whereas most places in Zherong County are within   75 10-minute walking distance (personal communication, 2013). In addition, quitting driving is much easier for residents in Zherong County, as most of them do not own a car (either do not see a need to drive or cannot afford it) (personal communication, 2013). This may explain why Zherong citizens have done more activities to lower their carbon footprints and why they are more willing to commute in a low-carbon way.  In addition, living in a more economically developed area, Fuding citizens have a greater standard of living and a higher average income (Fuding Bureau of Statistics, 2013; Zherong Bureau of Statistics, 2012). It is not surprising that Fuding residents are willing to pay more money to support the development of a low carbon economy.  In fact, six respondents from Fuding City indicated that they would pay more than ¥10000 (one respondent is willing to pay ¥25000, or US$3790) per year, while only one respondent from Zherong County is willing to pay ¥1000 (US$152) or more. On the other hand, Fuding City and Zherong County are adjacent to each other. The governments and residents in these two areas communicate frequently. Therefore, they share many common ideas and perspectives.  For example, most respondents in the survey, regardless of where they came from, were least concerned about the impacts of climate change that seemed to be remote (e.g. impacts at the global scale), and they were much more concerned about their immediate families, as they were able to make a personal connection with smaller scale impacts (CRED, 2009; Sheppard, 2012; Swim et al., 2009). The majority of respondents in both places were most concerned about future generations, which was not a very uncommon finding in previous studies. However, this may be explained in part by the so-called “little emperor syndrome” in China – a phenomenon that   76 children or grandchildren are the top priority in the family due to the one-child policy and social norms in Chinese culture (Hesketh & Zhu, 1997).  In addition, most respondents in both areas were very supportive of a low carbon economy in local areas, and their reasons and concerns about developing a low carbon economy were very similar. Most respondents indicated that a low carbon economy could protect the environment and combat climate change while improving their quality of life. This is very consistent with Chapter 3 and previous studies (e.g. Chen & Taylor, 2010). Cost, decreasing quality of life and limiting local economic growth were identified as the biggest concerns about developing a low carbon economy in local areas, which was also consistent with our previous results and Chen & Taylor’s study (2010).  Results also indicated that local citizens in both areas learned about climate change and the low carbon economy through mass media, such TV, Internet, magazines and newspaper. Similar findings have been cited in previous studies in China where TV and newspapers were identified as the main ways to gather information about climate change and the low carbon economy (Chen & Taylor, 2010; Xue et al., 2010). In terms of willingness to contribute, this study discovered the evidence of ‘single action bias’ among citizens in both study areas, i.e. citizens were willing to try one more low carbon activity in the future (e.g. I use energy-saving facilities, therefore I am low-carbon). However, considering China’s enormous GHG emissions and its ambitious reduction targets, these changes are unlikely to be sufficient to fulfil these targets or develop a low carbon economy.  According to the multinomial logistic regression models, knowledge of climate change and behaviour variables (number of current or future low carbon activities) were the most   77 influential to people’s attitudes towards a low carbon economy. Surprisingly, people’s level of concern about climate change was not as influential as the knowledge and behaviour variables in these models. A possible reason is that most respondents, regardless of their attitude towards a low carbon economy, indicated that they were very concerned about climate change (averaged at 3 out of 4). Therefore, the models were not able to use level of concern to distinguish people with different attitudes. Fuding Model also indicated that people who had tried more low carbon activities were more likely to support a low carbon economy, rather than opposing or hesitating. Fuding citizens who knew more about low carbon economies or were willing to try more low carbon activities were more likely to decide whether or not they support a low carbon economy. Age was the only socio-demographic variable found to be influential in the Fuding Model and younger people were less likely to oppose a low carbon economy. The Zherong Model had fewer significant independent variables, demonstrating only that people with more knowledge about low carbon economies or who had tried more low carbon activities were more likely to set their mind on whether they support a low carbon economy.    78 Chapter 5: Comparison of Public Perceptions and Behaviours between the Public, Community Residents, and Government Employees In this chapter, differences between three groups of people (the public, community residents, and government employees) are explored to answer the following three research questions: 1) What are each sample group’s behavioural responses to climate change? (Research Question 2) 2) Are there any differences in terms of awareness, knowledge and behaviour related to climate change and low carbon economies between groups with distinctive backgrounds (i.e. the public, community residents and government employees)? (Research Question 5) 3) What roles do socio-demographic background, perceptions of climate change and perceptions of low carbon economies have in influencing someone’s support for developing a low carbon economy in their hometown? (Research Question 3) 5.1 Descriptive Analysis 5.1.1 Socio-Demographic Information As indicated in Table 5.1, most respondents in this study were under 41 years of age, had completed secondary education and had a low to middle income (¥1500 to ¥4500).   79 However, the three sample groups had somewhat different characteristics, which in part reflected the sampling approach:  The public group (as sampled in streets, restaurants, parks and schools) was found to be much younger in general (70% of the public group were under 31 years old, as opposed to 40% in the other two groups) and more affluent (18% of the public group earned more than ¥4500/month, 15% higher than the community group). The public group also had a higher percentage of respondents who were parents (53%), about 20% higher than the other two groups. In terms of occupation, the public group had more students (13% of the public vs. 2% of community residents), retirees (7% of the public vs. 1% of community residents) and entrepreneurs (3% of the public vs. 1% of community residents).    Community residents who were recruited in various neighbourhoods and community centers were the least educated among all groups (22% with post-secondary education, less than half of the percentage in the other groups), with the highest percentage of seniors (13% over 50 years old vs. 3% of the public and government employees). This group also had more self-employed individuals, compared to the public group (22% of community residents vs. 11% of the public).  Government employees (recruited in their offices) were the most educated among the three groups (63% with post-secondary education vs. 48% of the public group and 22% of the community group), and were less diversified in terms of income (no one receives more than ¥9000 per month, and 85% received a monthly income of ¥1500-¥4500).   80 Table 5.1 Socio-Demographic Information of Each Sample Group Demographic Variables Public  (%) Community (%) Government (%) Age  19 to 30 70.3 39.1 39.1 31 to 40 19.2 32.2 42.1 41 to 50 7.9 15.8 15.7 51 to 60 .9 7.1 3.1 61 and higher 1.7 5.7 - Gender Male 51.1 47.9 51.8    Female 48.9 52.1 48.2 Occupation  Government employee 10.9 30.3 100.0     Urban planning - - 7.7     Justice - - 11.5     Transportation - - 6.5     Public security - - 6.9     Medicine and public health - - 7.7     Finance and economy - - 10.7     Civil administration - - 7.3     Auditing and supervision - - 7.7     Education - - 7.7     Business - - 7.3     Agriculture - - 6.5     Tourism - - 4.6     Forestry - - 7.7     Environment protection   3.8     Other - - .4  Public company employee 10.4 6.5 -  Student 13.4 2.2 -  Private company employee 15.9 10.0 -  Farmer 2.7 3.5 -  Not presently employed 21.0 18.6 -  Entrepreneur 3.2 .9 -  Self-employed 10.7 21.6 -  Retired 7.3 1.3 -  Other 4.7 5.2 - Education  Primary School or Less 2.6 18.1 -     High School Diploma 11.6 33.2 3.8     College or Equivalent Diploma 37.6 26.8 33.3     Bachelor’s Degree 45.6 20.8 60.9     Master’s Degree 2.6 1.1 1.9 Monthly Income                     ¥1500 and lower 24.0 26.6 11.9 ¥1500 to ¥4500 60.0 70.3 85.4 ¥4501 to ¥9000 10.0 1.9 2.7 ¥9001 to ¥35000 4.1 .8 - ¥35001 to ¥55000 .4 .3 - ¥55001 to ¥80000 - - - ¥80001 and higher 1.5 - - Have children  Yes 53.0 30.4 35.8 No 47.0 69.6 64.2   81 5.1.2 Perceptions of Climate Change and Low Carbon Economies 5.1.2.1 Differences between Sample Groups Kruskal-Wallis tests were computed to discover any differences between the three sample groups in terms of their level of concern about climate change, knowledge of low carbon economies, low carbon lifestyles and attitude towards low carbon economies (Table 5.2). Table 5.2 Significant Differences between the Public (P), Community Residents (C) and Government Employees (G) Tested Variables P vs. C P vs. G C vs. G Concern about the globe impacts of climate change       * *** *** Concern about impacts of climate change on local communities *** *** *** Concern about impacts of climate change on immediate family ***         * *** Concern about impacts of climate change on future generations ***         * *** Knowledge of low carbon economies *** *** *** Number of current low carbon behaviours        * N/A N/A Number of future low carbon behaviours - N/A N/A Willingness to pay (¥ per year)        * N/A N/A Willingness to contribute time (hours per month) - N/A N/A Support for a low carbon economy in the local areas - *** *** Support for a carbon tax - - - Support for government investment in low carbon projects - -         * Support for preferential loans for low carbon projects -         * ***        Significant differences: * 0.01 ≤  𝑝 ≤  0.016718      ** 0.001 ≤  𝑝 < 0.01    ***  𝑝 <  0.001 -         Not significant N/A   Not comparable due to questionnaire design (i.e. variable was only measured for one group but not for the other)                                                  18 A Bonferroni correction was applied to post-hoc tests, so all effects are reported at a .0167 level of significance:  𝑝′ =𝛼𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑜𝑠𝑡 ℎ𝑜𝑐 𝑡𝑒𝑠𝑡𝑠=0.053= 0.0167   82 Concern about the impacts of climate change  The questionnaire asked participants to rate their level of concern about the impacts of climate change on four different scales: 1) the world; 2) your local community; 3) you and your immediate family; and 4) future generations of your family. On a scale from 1 (not concerned at all) to 4 (very concerned), most respondents were ‘somewhat’ concerned about climate change, with a mean concern score around 3. The mean concern score of respondents increased as the spatial scale narrowed from the world to local community, and to immediate family. This trend was fairly consistent across groups. In addition, respondents in all sample groups were more concerned about the impacts on future generations than the impacts that were happening at present (i.e. all groups showed the greatest concern towards future generations over any other scale). When comparing between groups, significant differences were found in their level of concern about the impacts on all scales: global impacts (𝐻(2) = 50.57, 𝑝 < 0.001); impacts on the local community (𝐻(2) = 57.23, 𝑝 < 0.001); impacts on immediate families (𝐻(2) =56.46, 𝑝 < 0.001); and impacts on future generations (𝐻(2) = 83.47, 𝑝 < 0.001). Post-hoc (Mann-Whitney) tests showed that all groups were significantly different from each other in regards to their level of concern on all scales (Table C.3.2). Government employees scored the highest on each scale, with all mean scores above 3.20, whereas community members were the least concerned (all mean scores are below 3.00).  Please see Table C.3.1 in the Appendix C.3 for more detailed response distributions of this question.    83  Figure 5.1 Mean level of concern about climate change by sample group Perceptions of the low carbon economy Each sample group was asked to rate their knowledge level of low carbon economies at a scale from 1 (never heard of it) to 4 (know it very well). The majority of respondents (90% of the public respondents, 85% of community residents and 95% of government employees) had heard of the low carbon economy. However, about half of respondents from the public and the community group knew very little about this, especially the community group (59%). Overall, the government group reported the greatest knowledge of low carbon economies: over 60% of this group indicated that they had adequate knowledge of the low carbon economy, 18% higher than the public group and 39% higher than the community group. The government group also had the lowest percentage of respondents who had never heard of this term (5%), 6% lower than the public group and 10% lower than the community group. The differences between groups 2.963.053.183.322.80 2.842.92 2.983.23 3.293.353.491234Global impacts Impacts on local communitiesImpacts on immediate familiesImpacts on future generationsPublic Community GovernmentVery Concerned Somewhat Concerned Slightly Concerned Not Concerned Spatial Future   84 were very significant (𝐻(2) = 82.91, 𝑝 < 0.001). The post-hoc tests showed that all sample groups were significantly different from each other: Public vs. Government: 𝑈 = 49596.5, 𝑟 =0.17, 𝑝 < 0.001; Community vs. Government: 𝑈 = 28337.5, 𝑟 = 0.36, 𝑝 < 0.001; Public vs. Community: 𝑈 = 66095.5, 𝑟 = 0.19, 𝑝 < 0.001. Government employees rated significantly higher than the other two groups on their knowledge of the low carbon economy, with the public coming in second and community residents ranking lowest among all.  Figure 5.2  Knowledge of low carbon economies by sample group Number of low carbon activities at present and in the future (public and community residents only) In questionnaires given to the public and the community groups, participants were asked to select which low carbon activities (i.e. activities that can help a person lower his/her carbon footprint) 10.5%14.8%4.6%41.9%58.9%30.7%41.5%21.5%57.1%6.0% 4.7% 7.7%0%50%100%P ub l i c Co mmuni t y Go vernmentNever heard of it Heard of it but know little about itHeard of it and know about it Know it very well  85 they had done in their daily lives and which ones they would like to try (Figure 5.3 and Table 5.4; please see the full list of activities in Table C.3.1). In general, respondents from both groups had tried three to four low carbon activities (3.92 activities for the public group and 3.42 for the community group), and they would like to try about two more activities (1.73 for the public group and 1.64 for the community group). Post-hoc (Mann-Whitney) tests showed that the number of low carbon activities the public had tried was significantly higher than the number that community residents had (U=75121.000, z=-3.074, p=.002, r=-0.106). No significant difference was found between groups regarding the number of future low carbon activities. More details of Mann-Whitney tests are available in Table C.3.3 in the Appendix C.3.  Figure 5.3 Number of low carbon activities by sample group Figure 5.4 provides more details about the low carbon activities that each group had done and would like to try. Overall, the most popular activities for both groups were “low-effort” activities, such as shutting down electrical equipment when not in use and consuming less/reducing wastes (cited by over 60% of the public group and over 50% of the community 3.923.421.73 1.64012345P ub l i c Co mmuni t yMean number of low carbon activitiesCurrent Low Carbon Activities Future Low Carbon Activities  86 group). These types of activities usually require less time or energy, and sometimes even involve benefits such as monetary returns. On the other hand, activities that were more time/energy-consuming, such as recycling grey water, were less popular. In this survey, cycling (instead of driving) is the least favourable activity, cited by 26% of the public group and 21% of the community group. About 2% of respondents in each group indicated that they had never done anything to lower their carbon footprint.  Besides these consistent trends, I also found some discrepancies between the two groups. Taking public transit and using energy-saving facilities seemed to be much more popular among the public than community residents, cited by over half of the public group (ranked as the 2nd and 3rd most popular activities), about 20% higher than in the community group (ranked 4th and 5th). On the other hand, classifying and recycling garbage was more popular among community residents than the general public. It was the 3rd most popular activity in the community group, as opposed to 6th in the public group.  When it comes to the future, classifying and recycling garbage became the most popular activity that both groups would like to try, voted for by 38% of the public group and 29% of the community group. Activities such as using energy-saving facilities, shutting down electrical equipment when not in use, and taking public transit were cited by around 20% of respondents from both groups. On the other hand, noteworthy discrepancies were found between groups in regards to their preference for means of transport, waste reduction and recycling. The general public seemed to be more willing to reduce waste and recycle (e.g. to recycle grey water and to use energy-saving facilities), while community residents favoured low carbon transportation such as biking and walking much more (27% chose to walk and 24% chose to bike instead of   87 driving, nearly 10% higher than the percentage of the public).  Another substantial difference between groups was their opinion on disposable items. 33% of the public group indicated that they would avoid using disposable items (the 3rd most popular activity), as opposed to 20% of community residents (the 3rd least popular).   Figure 5.4 Current low carbon activities by sample group 1.8%2.1%23.2%16.4%21.1%26.1%19.6%33.1%27.2%31.6%27.1%13.3%24.5%38.9%6.8%18.6%39.8%39.9%29.3%37.8%45.9%46.7%8.6%24.8%40.4%47.8%23.2%23.8%36.1%54.0%24.3%31.0%52.6%60.6%27.5%26.0%41.6%62.1%24.3%26.0%53.2%71.5%0% 50% 100%Community - FuturePublic - FutureCommunity - CurrentPublic - CurrentShut down electrical equipmentwhen it's not in useUse energy-saving facilitiesConsume rationally (reducewastes)Take public transitUse shopping bagsClassify and recycle garbageRecycle grey waterWalk instead of drivingAvoid using disposable itemsRide bike instead of drivingNone  88 Willingness to contribute to the development of a low carbon economy Respondents were also asked to indicate their willingness to contribute to the development of a low carbon economy through paying money (a carbon tax or donation) or spending time (volunteering or spreading knowledge among family/friends) (Table 5.5). Some extreme values19 were excluded in calculating the mean values of the money and time that each group was willing to contribute. In general, more people in both the public and community groups were willing to donate than paying tax. About one-third of the respondents from the public and community groups (32% of the public group and 38% of the community group) would pay a carbon tax, while over half of both groups (56% of the public group and 68% of the community group) preferred donating money instead. However, both groups indicated a much (twice) higher amount of money that they were willing to pay through a carbon tax than a donation, possibly due to the high values (e.g. ¥5000/year for a carbon tax) that some respondents indicated in the survey. On average, the public group was willing to pay ¥348/year through a carbon tax and ¥169/year through donation, two times higher than the amount indicated by community residents (¥118/year for a carbon tax and ¥51/year for donation) (Figure 5.5). However, no significant difference was found between groups regarding their willingness to pay for a carbon tax. I only found one significant difference in the amount of money that respondents were willing to donate (U = 306.500, z = -2.763, p = .006, r = -0.305). In other words, the public was willing to donate a higher amount of money for low carbon economy development than the community group. For their willingness to contribute time, 44% of the public group and 33% of the community group                                                  19 Six respondents from the public group indicated that they would pay or donate more than ¥10000 per year, while most respondents in the group are willing to pay ¥100 per year.   89 indicated that they would spend time volunteering for low carbon outreach events, slightly higher than the percentage of respondents who were willing to talk with their family and friends about the low carbon economy (37% of the public and 32% of community residents). Both groups also indicated that they would spend more time volunteering for outreach events than talking with family and friends, especially the public group (on average 18 hours/month for volunteering and 10 hours/month for talking with family and friends). The difference in time that community residents indicated in the survey was not obvious, 18 hours for volunteering and 17 hours for talking with family and friends per month (Figure 5.5). Mann-Whitney tests showed no significant differences between groups (volunteer: U=408.000, z=-1.126, p=.260, r=-0.12; talking with family/friends: U=623.000, z=-0.508, p=.611, r=-0.055). Table 5.3 Willingness to Contribute (by Paying Money or Spending Time) to Low Carbon Economy Development by Sample Group Willingness to Contribute N Mean Std. Dev. Mode Range Public By Paying Money 117 247.02 550.89 100 1-5000    Carbon Tax 51 348.10 787.42 100 1-5000    Donation 66 168.91 272.68 100 1-1500 By Spending Time 136 14.00 19.54 1 1-120    Volunteering 72 17.88 21.04 10 1-120    Talking with Family/Friends  64 9.65 16.81 1 1-100 Community By Paying Money 33 85.39 119.38 100 1-500    Carbon Tax 17 117.76 157.57 10 1-500    Donation 16 51.00 38.38 50 2-100 By Spending Time 35 17.36 24.30 25 1-100    Volunteering 14 18.36 14.30 24 2-60    Talking with Family/Friends  21 16.69 29.48 1 1-100   90   Figure 5.5 Willingness to pay & contribute time between the general public and community residents  Opinion on low carbon measures  The questionnaire asked the public, community residents and government employees about their opinion on three selected low carbon measures to be introduced by local governments: introducing a carbon tax, providing more subsidies for low carbon projects and introducing preferential loans for low carbon projects. On a scale from 1 (strongly disagree) to 5 (strongly agree), all groups were more supportive of government subsidies and preferential loans for low carbon projects (averaged at 4.1) than a carbon tax (averaged at around 3.3) (Figure 5.6). Overall, the government group had the highest support score for each low carbon measure, whereas the community group had the lowest. Significant differences between groups were found in their scores of government subsidies and preferential loans for low carbon projects, but not in the carbon tax.  Government officials scored low carbon preferential loans significantly higher than both the public and community residents (government vs. public: U=51389.000, z=-348.1117.76168.9151.000100200300400P ub l i c Co mmuni t y¥/yearCarbon Tax Donation17.88 18.369.6516.6901020P ub l i c Co mmuni t yhr/monthVolunteering Talking with Family/Friends  91 3.405, p=.001, r=-0.127; government vs. community: U=11823.000, z=-3.899, p<.001, r=-0.201). However, no significant differences were found between the public and community residents. As for the opinion on government subsidies, both government employees and the public scored significantly higher than community residents (government vs. community: U=12541, z=-3.384, p=.001, r=0.174; public vs. community: U=24553.500, z=-2.244, p=.025, r=-0.093). No significant differences were found between the public and government employees. Figure 5.6 is an illustration of the differences between groups in terms of their opinion on low carbon measures.  Figure 5.6 Average support for low carbon measures by sample group 5.1.2.2 Other Differences between Sample Groups Some variables could not be statistically tested but still provide valuable information for researchers and local governments to help them better understand how different groups perceive 3.253.35 3.394.163.944.264.043.844.2612345P ub l i c Co mmuni t y Go vernmentCarbon tax LC investment LC preferential loanStrongly AgreeAgreeNeutralDisagreeStrongly Disagree  92 low carbon economies and their perspective on how the government should develop a low carbon economy. Ways to learn about the low carbon economy In the questionnaire, respondents were asked how they learned about the low carbon economy. All groups were quite consistent with each other. Television was the most popular approach (cited by around 80% of respondents from each group). The Internet came in second (cited by 79% of government employees, 59% of the general public and 52% of community residents). Newspaper, magazines and advertisement were the third most common approach, more popular within the government staff than in other groups (cited by 49% of government employees, 36% of the public and 31% of community residents). Thematic events and public lectures were relatively less popular, especially public lectures, cited by 21% of the public, 13.1% of government employees and 5.2% of community residents. In addition, all approaches seemed to work better for the public and government employees than community residents. Support for a local low carbon economy Despite having a limited understanding of low carbon economies, more than 80% of respondents, regardless of group, indicated support for developing a low carbon economy in their hometown. Overall, the government group showed the most support (96%), 14% higher than the other groups (support rate: 82%). They also had the lowest against rate (4%) and unsure rate (0%). In the public group, there were slightly fewer opponents of the low carbon economy than people who were uncertain about it (5% vs. 13%), while the percentages of the community group were almost equal (9%).    93 The majority of the public and community residents in the survey supported a low carbon economy as they believed that it could protect the environment, mitigate climate change, and improve their quality of life. Environmental protection is the most popular reason, cited by 86% of the public and 69% of the community residents. However, this did not interest government employees as much: only 63% (the 4th highest percentage among five) chose this option. The majority (93%) of government employees supported a low carbon economy for a more sustainable development pattern featuring a higher efficiency of production and resource utilization. Improving the quality of life (by 82% of the group) and mitigating climate change (by 77%) came as the 2nd and 3rd most popular reasons for the government group. Becoming a leader in low carbon economy development was the least popular reason across the groups, cited by around 30% of the public and community residents, and half of the government group. When asked about their reason for opposing a low carbon economy in local areas, the majority of respondents, especially in the government group, indicated cost as the main barrier (49% of the public, 53% of community residents and 65% of government employees). Respondents were also worried about the adverse impacts of developing a low carbon economy on their quality of life and on the local economy. Unlike the public and government employees, a slightly higher percentage of community residents worried more about their quality of life over cost. Only a few respondents thought that the local government already had sufficient environmental measures and did not need a low carbon economy. This was also the least frequently cited reason across all groups.    94 Suggestions regarding the future direction of low carbon economy development Respondents were asked for their opinion on the direction of government initiatives (i.e. the first step and top priorities for the local government to develop a low carbon economy) (Figure 5.7). Opinions differed dramatically between groups in regards to energy. While 60% of the public (the 2nd largest group) and 39% of government employees (the 4th largest) indicated that the government should start by reforming the energy sector, the majority of community residents did not agree. Only 23% (the smallest group) of the community group chose ‘energy’ as the first step to developing a low carbon economy. Aside from energy, all groups agreed on the level of importance of the other options for developing a low carbon economy. The industry sector was cited by the majority of respondents as the first step to developing a low carbon economy (nearly 70% of the public group and the government group, and 52% of the community group), while business was the least frequently cited sector other than energy (30% of respondents in each group). When it comes to the priority for developing a low carbon economy, the majority of respondents across groups believed that clean energy and low carbon technologies should be the government’s main focus (Figure 5.8). Recycling and developing forest carbon projects came next, cited by around 70% of government officials, 60% of the public and 48% of community residents. Responses to other options were quite similar, aside from public perception. Around 60% of the public group and the government group suggested the government prioritize increasing public perception and understanding of the low carbon economy and low carbon lifestyle, almost doubled the percentage of community residents who chose this option (35%, the smallest percentage).    95 Government employees were asked about barriers hindering the development of a low carbon economy in local areas. Low public awareness was identified as the biggest obstacle, according to 82% of the group. Other main barriers, voted for by over half of the group, include ineffective policy (60%), low priority in government agenda (58%), current extensive development mode (54%), and lack of guidance for local governments (52%). Most government employees did not think lack of cooperation with non-governmental organizations (NGOs) was an obstacle to developing a low carbon economy (73%).  Figure 5.7 Sector to be the first step in developing a low carbon economy by sample group  Figure 5.8 Priorities for low carbon economy development by sample group 54.7%40.0%69.6%42.9%35.7%50.9%59.6%43.6%36.9%52.2%37.2%25.3%38.6%22.8%41.3%39.8%69.5%37.5%30.5%36.3%39.0%0%50%100%Fo res t ry Agr icu l tu r e Ind us t ry T o ur i sm B us iness B ui ld ing EnergyPublic Community Government63.3% 61.8% 64.6% 62.9% 58.1%49.7%75.5%47.8% 47.8%55.5%40.9%35.4%44.2%66.5%70.4%71.2%75.9%67.7% 68.9%52.5%82.5%0%50%100%F o r e s t  c a r b o n  p r o j e c t sR e c yc l i n g Lo w c a r b o n  t e c h n o l o g yP u b l i c  t r a n s p o r tP u b l i c  p e r c e p t i o nGr e e n  b u i l d i n gC l e a n  e n e r g yPublic Community Government  96  5.2 Multinomial/Binary Logistic Regression  Since the three sample groups were significantly different in terms of their understandings and attitudes towards low carbon economies, factor analyses followed by logistic regression models were completed for each sample group.  For each sample group, eight variables that measure local citizen’s level of concern about climate change (four variables), knowledge of low carbon economies (one variable), and support for selected low carbon policies (three variables) were input for factor analysis. Three factors were identified for each sample group: Factor 1 was concern about climate change, Factor 2 was support for low carbon policies/measures, and Factor 3 was knowledge about low carbon economies. A higher score of Factor 1 represented a greater level of concern about the impacts of climate change. Similarly, a higher score of Factor 2 or Factor 3 indicated a bigger support for low carbon measures or a greater knowledge of low carbon economies, respectively. These factors explained around 76% to 84% of the variance in the sample data. Please see Table C.3.4 in the Appendix C.3 for the factor pattern of each group after varimax rotation and refer to Chapter 2 for more details on factor analysis and varimax rotation. These factors, together with socio-demographic variables, then became the dependent variables for the multinomial/binary logistic regression models. Three models were built (one for each sample group) to identify variables that significantly influenced participants’ attitudes towards a low carbon economy in local areas: Two multinomial logistic regression models for the public group and the community group respectively and a binary logistic regression model for the   97 government group (as all respondents in the government group were either supporters or opponents of low carbon economies).  For most groups, perception variables such as knowledge of the low carbon economy were most significant in the models (please see more details on parameter estimates in Table C.3.5 in the Appendix C.3). Despite this common trend, each group had different combinations of variables that influenced their attitudes towards low carbon economies:  Public group: The model indicated that respondents in the public group who were not sure about whether or not they support a low carbon economy were significantly different from those who supported a low carbon economy in the survey in terms of their concern about the impacts of climate change (𝑏 = −0.293, 𝑊𝑎𝑙𝑑 𝜒2(1) = 4.467, 𝑝 = 0.035), their support for low carbon policies (𝑏 = −0.402, 𝑊𝑎𝑙𝑑 𝜒2(1) = 7.726, 𝑝 = 0.005) and their knowledge of low carbon economies (𝑏 = −0.656, 𝑊𝑎𝑙𝑑 𝜒2(1) = 18.121, 𝑝 <0.001). In the public group, people who were more concerned about climate change, showed a greater support for low carbon measures, and knew more about low carbon economies were more likely to support a low carbon economy in local areas rather than being uncertain about it. On the other hand, no significant differences were found between supporters and opponents of the low carbon economy in the public group (i.e. both opponents and supporters had very similar levels of concern about climate change, support for low carbon measures and knowledge of low carbon economies).  Community Group:  Knowledge of low carbon economies was the only significant variable in this model. Community residents with a greater knowledge of the low carbon economy were more likely to support low carbon economy development in their   98 hometown than to be uncertain about it (𝑏 = −1.359, 𝑊𝑎𝑙𝑑 𝜒2(1) = 6.845, 𝑝 = 0.009). No significant difference was found between supporters and opponents of low carbon economies in this model.   Government Group: Government employees who supported a low carbon economy in local areas, compared with the opponents, were significantly more concerned about the impacts of climate change (𝑏 = 0.896, 𝑊𝑎𝑙𝑑 𝜒2(1) = 6.771, 𝑝 = 0.009), more supportive of low carbon measures (𝑏 = 1.240, 𝑊𝑎𝑙𝑑 𝜒2(1) = 13.003, 𝑝 < 0.001), and more knowledgeable about low carbon economies (𝑏 = 1.185, 𝑊𝑎𝑙𝑑 𝜒2(1) =8.170, 𝑝 = 0.004). No respondents from the government group were uncertain about their attitudes towards low carbon economy development.   5.3 Discussion This study, as a follow up to the analysis of public perceptions of climate change and low carbon economies in smaller cities in China, was focused primarily on the differences between three different groups of citizens: the general public, residents of local communities and local government employees. Some significant differences were found between groups. Generally speaking, local government employees indicated the greatest level of concern about the impacts of climate change, the best knowledge of low carbon economies, and the highest support for government aids for low carbon projects and developing a low carbon economy in local areas. On the other hand, the community group knew the least about climate change and low carbon economies, showed the lowest level of concern about climate change, and had the highest percentage of opponents against a low carbon economy. Inconsistent responses by community residents regarding energy (as the first step or a priority for low carbon economy development)   99 may imply a knowledge gap or confusion about the role of clean energy in low carbon economy development (i.e. the lowest percentage of community residents thought energy should be the first step to develop a low carbon economy, while the majority believed developing clean energy should be a priority for low carbon economy development). Although few studies have compared government employees and the public in terms of their understanding and attitudes towards a low carbon economy, it was not surprising to see that government employees had a much better reported understanding of climate change and the low carbon economy than the other groups. Environmental protection has become increasingly important in China since the first environmental protection law was drafted in 1979 (Lo & Leung, 2000). After decades of development, the environmental protection and regulation system in China is now well established at all levels of government. In addition, current leaders of China are strong advocates for the low carbon economy. Developing a low carbon economy was written in the 12th Five-Year Plan by the state government as one of China’s climate change mitigation strategies (Calderon & Steer, 2014). To facilitate work on climate change mitigation, all levels of government provide substantial workshops and training on climate change and low carbon economies (National Development Reform Commission of China (NDRC), 2007). Therefore, government officials have more opportunities and resources available to learn about this topic compared to the general public and community residents.  Even with the same opportunities and resources, citizens working in non-government institutes or companies may not put as much time and effort as government employees into learning about climate change and low carbon economies, especially when they have limited understanding of the topic and fail to make a personal connection to the causes and impacts of climate change (CRED & EcoAmerica, 2014; Gifford, 2011).    100 Results also suggested that the public was significantly more concerned about the impacts of climate change, more knowledgeable about low carbon economies, and had tried more low carbon activities to lower their own carbon footprint, compared to the community group. Looking at the socio-demographic profile of these two groups, the public group contained more young parents with a higher education level (i.e. bachelor’s degree or above) than the community group, and the differences were significant (p<0.001). Although it was not tested in this study, variations in the socio-demographic background could help explain the differences between the public and local community residents. Many previous studies have shown that people at a younger age, with a higher education level and who have children tend to know and worry more about climate change (Berenguer et al., 2005; Blake et al., 1996). Differences in sampling approaches could also cause this difference. Respondents in the public group of this study were recruited at a larger spatial scale (i.e. all residents in the study sites, including both urban and rural areas), while the community group primarily focusing on urban communities. As indicated in Chapter 4, respondents from more rural areas (i.e. Zherong County) tended to have a higher level of concern about the impacts of climate change and a greater understanding of the low carbon economy than more urbanized areas (e.g. Fuding City) (please refer to the previous chapter for more details). A mix of urban and rural residents in the public group may be a reason why they are more low carbon conscious than the (urban) community residents. To better understand different groups’ attitudes towards a low carbon economy, this study developed a multinomial logistic regression model for the public and the community groups separately, and a logistic regression model for the government group. Similar to our previous analysis, perception variables were much more significant in the models than socio-demographic variables (all three models only included perception variables instead of socio-demographic   101 variables). In fact, knowledge of the low carbon economy was the only variable that was significant in all three models, indicating its strong influence on people’s attitudes towards the low carbon economy. This finding is also consistent with previous studies on environmental perception (Chen & Taylor, 2010; Dunlap, 1998; Jaeger, Dürrenberger, Kastenholz & Truffer, 1993; Lorenzoni et al., 2007; O’Connor et al., 1999).  However, in the public and community models, perception variables, such as knowledge of the low carbon economy, were only able to distinguish supporters and people who are uncertain about the topic. No significant differences were found between supporters and opponents of the low carbon economy, which is similar to our previous findings in Chapters 3 and 4. A possible explanation is that people reported very similar levels of knowledge in the survey. On the other hand, the government model (a binary logistic regression model) was the only model out of three that could successfully distinguish supporters from opponents. Supporters of the low carbon economy were significantly more knowledgeable about this topic and more supportive of low carbon policies (e.g. carbon tax and government subsidies) than its opponents. Despite these differences, I did discover some common trends across groups. First of all, the majority of respondents were very supportive of a low carbon economy in local areas, as they believed that a low carbon economy could protect the environment and combat climate change, while improving their quality of life. This is very similar to findings of the previous chapters and Chen & Taylor’s study in Zhengzhou City (2010). Most respondents across all groups indicated their concerns about the cost to develop a low carbon economy, and its impact on their quality of life and local economic growth. This is also very consistent with the previous chapters and Chen & Taylor’s study (2010).   102 Another consistent trend was found among people’s concern about climate change. All groups were more concerned about the impacts of climate change that were more direct and closer to themselves than the ‘distant’ impacts on a larger spatial scale, such as global impacts. This finding, as discussed in previous chapters, is consistent with previous studies. In addition, all groups showed the greatest concern for future generations, which is also very consistent with our previous analysis. A possible explanation for this is that most residents in the study areas have a limited understanding of climate change and they see climate change more as a future threat than a present crisis. I also suspect that the Chinese culture (placing the future generations as top priorities) and the previous one-child policy may help explain why respondents worry about the impacts on future generations the most. For daily behaviors, both the public and community groups preferred ‘low-effort’ activities that were less time/energy consuming, such as shutting down electrical equipment when not in use. On the other hand, ‘high-effort’ activities, such as cycling and walking instead of driving, were the least favourable activities, even though these activities might provide the greatest benefits to the environment (Blake et al., 1996; Diekmann & Preisendo, 2003; Scannell & Gifford, 2011). Another possible reason involves the “cool” factor. Cycling and walking compared to driving a car may conflict with personal desires for living a luxurious lifestyle and may not be looked upon favourably by their peers. Most respondents from both groups indicated great interest in garbage classifying and recycling, which had not yet been developed/implemented in either Fuding City or Zherong County. Although garbage classification and recycling often does not help reduce a huge amount of GHGs compared to other mitigation methods, this could be a starting point for local governments to motivate behavioural changes. In addition, the public and community residents showed different preferences for low carbon activities that they would like to try in the   103 future. The greatest discrepancy between groups was in their preference for activities related to recycling and low carbon transportation. The public preferred recycling (e.g. avoiding disposable items, using shopping bags, and recycling grey water), while community residents favouring low carbon transportation (e.g. biking and walking). This finding can be of great importance to the local government in planning for public engagement events/programs. Depending on the group they are targeting, these results can be used to develop different approaches and themes to increase the effectiveness and efficiency of behavioural interventions.  As for respondents’ opinions on government actions, most suggested that the government should start by decarbonizing industry, as opposed to the business sector. When it comes to priorities in developing a low carbon economy, most respondents suggested the government to focus on low carbon technologies and clean energy. Shifting public perceptions of the low carbon economy was also deemed as a priority by the public group and by government employees. In addition, different groups viewed the energy sector very differently. Developing clean energy sources was one of the most frequently voted options as the first step towards developing a low carbon economy by the public group and government employees. However, it was the least popular option in the community group, maybe due to their unfamiliarity with modern energy issues.  More research with a focus on public opinion on clean energy is needed to better understand the discrepancy found here.   104 Chapter 6: Conclusions In this chapter, first a brief summary and discussion of the research background and key findings of this study (please see more detailed discussion at the end of Chapters 3 to 5) are presented, followed by recommendations for governments and 3rd party interveners in China, and discussion of the research methods and future research directions. The main focus of this chapter is to make recommendations to different levels of government in China, especially county and municipal governments in smaller cities, and to 3rd parties such as China Green Carbon Foundation regarding public engagement, social learning, and low carbon economy development.  6.1 Summary of Findings Climate change is the greatest challenge currently faced by human society. Many countries in the world have planned and implemented policies to develop a low carbon economy as a way to combat climate change. China, as the top GHG emitter in the world, has promised to decarbonize its economy and has recently announced ambitious emission reduction targets, to cut emissions per unit of GDP by 60-65% from 2005 levels and to reach its peak emissions by 2030 (Y. Li, 2015). Communities and citizens play an important role in meeting these targets, considering their numbers, their willingness to moderate growth in carbon footprints as lifestyles shift, and their response in implementing government policy. Individuals are the ultimate actors who initiate, develop and implement changes to reduce GHG emissions in the process of developing a low carbon economy (Harris, 2006; Wolf & Moser, 2011). Therefore, understanding individuals’ perceptions of climate change and low carbon economies is crucial for the successful   105 implementation of low carbon policies (Y. P. Cai, Huang, Yang, Sun, & Chen, 2009; Chen & Taylor, 2011).  There is an emerging research interest in public perceptions of climate change and low carbon economies in China. However, most research has been done in big cities (e.g. Beijing, Tianjin). Little attention has been paid to smaller cities (e.g. county-level cities), which are rapidly developing and likely to become major GHG emitters in the near future. This research has helped to fill this gap by evaluating residents’ understanding and attitudes towards climate change and low carbon economies in two smaller city and county jurisdictions (Fuding City and Zherong County in Fujian Province), using a cross-sectional questionnaire. A total of 1295 questionnaires were distributed to three sample groups (the general public interviewed on the streets, community residents polled through local community centers, and local government employees) in the two study areas.   Results of this study indicated a high awareness of climate change among citizens, as well as significant concern regarding the impacts of climate change, particularly local and relatable impacts (e.g. impacts on immediate families and future generations). However, results also indicated a limited understanding of climate change and low carbon economies in terms of respondents' level of familiarity with these terms and their connection to their everyday life. For example, over half of the respondents indicated that they had never heard of or knew little about low carbon economies, while 6% reported that they knew this term “very well” (see more details in Chapter 3). In this study, I identified some particular knowledge gaps among citizens from the study areas, including a weak understanding of the global impacts of climate change and of green buildings as a key component in the future low-carbon economy development (see more details   106 in Chapter 3). Inconsistent patterns were also found in participants’ responses, indicating possible confusion about the meaning of a low carbon economy. For example, while most community residents suggested that the government should start to decarbonize sectors other than energy, such as industry and construction, the majority respondents considered developing clean energy sources as the top priority in low carbon economy development (see more details in Chapter 5). This limited knowledge of what a low carbon economy really means may be due to a combination of reasons, such as lack of relevant information/education (Chen & Taylor, 2011), and limited efforts on public engagement (Xue et al., 2010) (reasons identified in previous studies on public perceptions of climate change or low carbon economies in China). Other reasons derived from previous studies on environmental awareness and perceptions may also be applicable here: lack of interest in searching for information (Lorenzoni et al., 2007), confusion and misunderstanding due to conflicting information from various sources (Center for Research on Environmental Decisions & EcoAmerica, 2014; Dugan, 2014; Lorenzoni et al., 2007; Swim et al., 2011), and confusion about information that conflicts with personal values and experiences (Lorenzoni et al., 2007; Scannell & Gifford, 2011; Wolf & Moser, 2011) (more details on public understanding are discussed in the recommendations section of this chapter).  In general, citizens in the study areas were very supportive of developing a low carbon economy in their local area, with their main reasons for support being to protect the environment, mitigate climate change and improve their quality of life (possible examples of improved quality of life include health benefits of living in a more low-carbon sustainable society and more green job opportunities as a result of developing low-carbon technology and energy sources). On the other   107 hand, cost, negative impacts on the local economy and quality of life (e.g. the assumption that the local economy will be jeopardized, which may directly affect personal jobs and income) were the biggest concerns about developing a low carbon economy. Perception factors, such as knowledge of low carbon economies and concern about climate change, were much more associated with people’s attitudes towards a low carbon economy than socio-demographic variables. Age was the only socio-demographic variable that was found to be significantly linked to attitudes towards low carbon economies in the modelling results for Fuding City, with younger people showing increased support for low carbon economies (rather than opposing it). In general, people with a deeper understanding of low carbon economies and a greater concern about the impacts of climate change were significantly more likely to support the development of a low carbon economy in local areas. People’s knowledge of low carbon economies was much more strongly linked to their level of support than the concern about climate change. Despite the high level of support, most respondents indicated a low intention to change behaviours or contribute (money/time) towards the development of a low carbon economy. This is commonly referred to as the ‘attitude-behaviour’ gap (Kollmuss & Agyeman, 2002; Lorenzoni et al., 2007). Most respondents had taken three to four actions to lower their carbon footprints and were willing to take one more action in the future, implying possible ‘single action biases’. Among those who had done or were willing to try low carbon activities, most citizens showed a strong preference towards ‘low-effort’ activities, which usually required less time/energy/money inputs or could even generate financial dividends (e.g. conserve energy), over ‘high-effort’ activities that were more time/energy consuming and might reduce GHG emissions more effectively (e.g. change driving habits). Although most people in smaller cities are living a relatively low carbon life with a lower-than-global-average carbon footprint, they are quickly   108 adapting western high-carbon lifestyles to fulfill their personal desires for ownership and consumption of goods (e.g. cars and single-family houses). Given China’s GHG emissions and people’s increasing carbon footprints (especially among urban areas), taking four or five “low-effort” actions may not contribute greatly to achieve China’s ambitious mitigation goals. In relation to low carbon policies, citizens in the study areas showed a greater support for reward-based mechanisms (e.g. government subsidies) for low carbon projects than punitive policies (e.g. carbon tax). Approximately 60% were not willing to pay a carbon tax or donate to the development of a low carbon economy. Those who were willing to pay indicated a higher amount of money for a carbon tax than for a donation on average. In addition, the majority (60%) were not willing to contribute their time. Among those who wanted to spend time making a contribution to low carbon economy development, they were willing to spend more time on average volunteering at low carbon economy related events than talking with family and friends about the topic. Compared to previous studies that have focused on larger cities in China (with a population greater than one million), results of this study indicated a lower level of awareness and knowledge about climate change and low carbon economies in smaller cities (Chen & Taylor, 2011; Harris, 2006; Y. J. Li, 2015; Q. Liu et al., 2009; Wang & Mao, 2010). On the other hand, a comparison between the two study areas indicated that people from more rural areas (represented by Zherong residents) tended to be more concerned about climate change but less supportive of reward-based policies than urban residents (represented by Fuding residents). Significant differences were also found between sample groups. Government employees, in general, showed the greatest concern about climate change, the most knowledge about low carbon economies and   109 the highest support for climate policies such as developing a low carbon economy in local areas, whereas community residents showed the lowest level of concern about climate change, the least LCE-related knowledge and the lowest support for climate policies.  6.2 Recommendations for Local Governments The following two key issues were derived from the findings of this study and should be prioritized by local governments for improvement of public engagement and social learning about climate change and low carbon economies. 1) Citizens in the study areas have limited understanding of climate change and low carbon economies. Despite their high awareness of the term “low carbon economy” and general concern about climate change, several knowledge gaps and possible inconsistencies have been identified. According to previous studies, a low carbon economy is defined as an economic form with higher efficiency, productivity, and more advanced human development (e.g. increasing quality of life) but lower GHG emissions. However, citizens in the study areas tend to think of low carbon economies as an approach to protect the environment and mitigate climate change. Some even think that a low carbon economy is a ‘poverty economy’ with higher cost and a lower quality of life (Pan, Zhuang, Zheng, Zhu, & Xie, 2011, p.5). There is an urgent need to clarify with citizens that developing a low carbon economy does not necessarily come with a high cost or a decreased quality of life. Instead, this economic approach can lead to reduced costs while increasing quality of life through enhanced productivity and technological advancements (Pan et al., 2011). 2) There is an ‘attitude-behaviour’ gap in the study areas. Although most respondents associate a low carbon economy with the correct terms, such as technology, clean energy, and   110 forest carbon, there is a discrepancy between great support for developing a low carbon economy and a low intention to change behaviours, particularly travel behaviours. This implies a possible disconnect among citizens between their personal behaviour and the causes of climate change (i.e. GHG emissions), as well as a lack of motivation to change. The following recommendations in the next section were derived from a combination of previous literature and the key results of this study, keeping in mind the two key issues identified above. These recommendations are meant for the governments of Fuding City and Zherong County, as well as 3rd party interveners such as China Green Carbon Foundation, and are in regards to public engagement and the development of a low carbon economy. However, these recommendations are not location-specific and may therefore also be applicable to other cities/counties of similar sizes and to the provincial and state governments of China. 6.2.1 Increased Efforts on Education and Engagement Programs 1) Provide credible information sources or educational programs. Results in this study suggest that people’s knowledge of low carbon economies is one of the most influential variables impacting their attitude towards the low carbon economy. However, citizens in this study generally had poor relevant knowledge and limited behavioural responses to climate change. The first step towards increasing public awareness is to provide the public with credible and easy-to-access sources of information or educational programs on climate change and low carbon economies. This information can provide an introduction to basic relevant science, highlighting local and tangible impacts of climate change, the link between daily behaviours and the causes of climate change, and the benefits of developing a low carbon economy. It is also crucial to develop and portray a clear picture of what a low carbon economy really means and might look   111 like in their own locality.  This information can be made available through mass media such as television programs, government websites and news articles. In addition to providing compelling and easy to understand media, some education on ‘media awareness’ is necessary to help the public distinguish credible information from unreliable sources in the media (Lorenzoni et al., 2007, p. 455, citing Bibbings 2004).  2) Develop public engagement programs through bottom-up and top-down approaches. Providing information can help to overcome a lack of basic knowledge. It is usually not, however, sufficient to initiate behavioural changes (Gifford, 2011; Lorenzoni et al., 2007; Wolf & Moser, 2011). There is a growing trend of ‘rampant consumption and conspicuous consumerism’ in China, especially in the young middle class (Harris, 2006, p.9), which could defeat implementation of a low carbon economy. To curb and reverse this trend, more effort should be put into active public engagement programs about climate change and low carbon lifestyles. This program can be in various forms, such as workshops, conferences, community meetings, or neighbourhood social gatherings, either dedicated to this topic or as a crucial part of routine community/land use planning processes (CRED & EcoAmerica, 2014; Sheppard et al., 2015). The main purpose is to foster interactive communication, shared learning, and collective action within groups. Ideally, the program would involve the public, the government, and other stakeholders to share their views and ideas on climate change solutions and low carbon economy development at the local level. Socially-driven activities with competitions and awards (e.g. holding a ‘lights out’ day to compare the amount of energy saved with friends or neighbours) have the potential to be very effective in attracting youth and new middle class (in a transition towards a higher carbon lifestyle) in China (Galli et al., 2012; Harris, 2006; Senbel, McDaniels, & Dowlatabadi, 2003; Sheppard, Iype, Cote & Salter, 2015). The great success of China’s   112 climate campaigns, such as national tree planting programs, suggests that the top-down approach can yield very promising outcomes in China. On the other hand, a bottom-up approach is also very important to decrease Chinese people’s reliance on top-down governance (e.g. ‘it’s the government’s problem’) and to provide a sense of personal responsibility and policy efficacy (Lo & Leung, 2000; Ross, 1999, as cited in Harris, 2006, p. 11; Sheppard et al., 2015). It can be important for people to see other local people like themselves actively practicing low-carbon lifestyles, as in the use of highly visible blue bin recycling in many North American communities (Sheppard et al., 2015).  The most effective strategy may be to combine both bottom-up and top-down approaches to increase flexibility and transparency of the policy planning process, while reinforcing efficient and effective implementation of decisions from the public engagement program (Sheppard et al., 2015).   The following are more specific suggestions for the presentation and framing of content on climate change and low carbon economies to the public: a) Tailor content and engagement methods to the target group to increase the chances of effective engagement and meaningful communication (CRED & EcoAmerica, 2014; Wolf & Moser, 2011). For example, this study indicates that more government employees learn about low carbon economies from newspapers and magazines. Thus, information on these two types of media can be more targeted towards government employees.  b) Target information in educational programs towards filling knowledge gaps and misunderstandings regarding the low carbon economy (CRED & EcoAmerica, 2014; Y. J. Li, 2015). This study discovered a few knowledge gaps and misunderstandings among   113 the public, including green buildings, the link between personal behaviour and climate change, and the impression that low carbon economy development can decrease quality of life. c) Combine the engagement process with social media to make it more fun. Social media, such as Wechat and Weibo, is popular in China, especially among younger generations. These tools can be integrated into the engagement process in order to increase public participation (e.g. share personal experiences and opinion) and initiate more open-ended communication (Sheppard et al., 2015). d) Use visual learning tools. Visualization of the impacts of climate change under different scenarios and possible solutions (e.g. what has been done and what can be done) is a very powerful way to enhance the public’s understanding and to strengthen emotional connections to climate change (Sheppard, 2012; O'Neal et al., 2013; Sheppard et al., 2015). e) Avoid scientific jargon. It is recommended that scientific findings be Interpreted/translated into a version that is clear and easy to understand for all (CRED & EcoAmerica, 2014; Sheppard, 2012).  f) Prioritize information on local and tangible impacts, and focus on the connection between an individual’s lifestyle and climate change. One of the reasons people aren’t concerned enough about climate change is that they fail to associate climate change with themselves personally (Lorenzoni et al., 2007). Highlighting local and tangible impacts helps people to relate to climate change and establish a personal connection with its causes and   114 impacts, therefore promoting attitudinal and even behavioural changes (CRED & EcoAmerica, 2014; Sheppard, 2012). g) Highlight solutions and benefits, as opposed to impacts and costs, to attract interest and increase engagement. A ‘solutions-first’ strategy can initiate positive emotional responses about climate change, enhance self-efficacy (‘I can do something to help’) and increase interest in public engagement (CRED & EcoAmerica, 2014, p. 23). On the other hand, focusing too much on the problems associated with climate change can create negative emotional responses towards the topic, and may eventually lead to environmental numbness (e.g. feeling hopeless) (CRED & EcoAmerica, 2014; Sheppard et al., 2015). This strategy has been practiced successfully in many locations. For example, workshops organized by the Canadian Centre for Policy Alternatives (CCPA) in British Columbia focuses 75% of its content on climate change solutions and 25% on the associated problems (Sheppard et al., 2015). 6.2.2 More Incentive Mechanisms and Supportive Infrastructure  1) Introduce a garbage recycling system. This study discovered a strong interest in garbage recycling among the public. However, they are currently unable to do so due to lack of recycling infrastructure (e.g. separate garbage bins for recyclable materials). The local government should introduce more supportive infrastructure to enable low carbon activities like garbage recycling.   2) Introduce more incentive mechanisms as motivations for individuals to make changes. The public generally prefers incentive mechanisms to punitive policies. Governments can encourage behavioural changes by giving rewards such as coupons for trying low carbon activities or by providing low carbon alternatives such as free bus tickets. Many cities in China   115 have introduced similar practices to encourage behaviour change. For example, Fuzhou City introduced a bike rental program for citizens to use at a minimal charge to encourage low carbon commuting (Hu & Yang, 2011). This can be very effective in rapidly expanding cities like Fuding City where a car culture is emerging. In addition, social recognition, such as publicly acknowledging individuals’ progress or giving rewards to households with the lowest carbon footprint, can be very helpful in maintaining low carbon behaviours and promoting further progress (CRED & EcoAmerica, 2014).  iii) Develop demonstrations of low carbon technologies, low carbon energy solutions or low carbon buildings. This can help the public to better understand what a low carbon economy looks like (respondents in this study were confused about low carbon energy and low carbon buildings) and how it can be achieved. These demonstrations (especially low carbon buildings) can also show people the possibility of building or living in a low carbon home. 6.2.3 Collaborate with Third-party Intervenors to Mobilize Public Actions on the Low Carbon Economy Third party organizations, such as Foundations, environmental non-governmental organizations (ENGOs), universities, local businesses and other ‘grassroots’ groups can play a key role in stimulating action and supporting local initiatives, by reinforcing what government policies encourage and providing local trusted leaders to help inform and mobilize local groups (Sheppard et al., 2015).  Research in Canada suggests that government support of and coordination with such intervenors, who may play lead roles in new engagement (programs), can be effective in accelerating action on the ground. For example, China Green Carbon Foundation (CGCF) (a non-profit public foundation and the funder of this project) has played a key role in   116 engaging and motivating the public, acting as a support for government climate policies. This foundation has organized various public outreach events, such as tree planting campaigns and public lectures, to increase public awareness and motivate behavioural responses to climate change (CGCF, 2015). Since its establishment in 2010, CGCF has received donations of more than ¥400 million (approximately US$ 61 million) from citizens and corporations to offset their carbon footprints and to support afforestation projects. So far, it has afforested more than 80,000 ha in more than 20 provinces in China through various afforestation projects and public engagement events (CGCF, 2015).  6.2.4 Prioritize Decarbonisation Efforts on Industry, Technology, Forestry and Energy The majority of respondents in this study suggested that the government should prioritize decarbonizing the industry sector and the energy sector, while developing the forest sector for increased forest stock in local areas. Industry is responsible for the highest GHG emissions (about 60% of the total emissions). Energy is another major GHG emission source, with electricity (from the State Grid) contributing about 60% of the total emissions from energy. Low carbon technologies such as carbon capture and storage can be a very effective way to reduce emissions from industry. Developing low carbon energy is another feasible option, such as hydroelectricity and tidal energy in coastal cities like Fuding City, and wind power and biogas in more agriculture-dominated areas with high elevations like Zherong County.  Also, existing schemes in China for encouraging distributed use of renewable energy in residential and industrial neighbourhoods, such as roof-top solar hot water heating systems, could be expanded to provide substantial reductions in emissions (Stremke & Van Den Dobbelsteen, 2012).   117 Previous studies also suggest that government development decisions regarding urban planning (infrastructure building) is extremely important to the future of a city’s GHG emissions. Urban structure, once decided, is virtually irreversible. It will directly impact household and personal behaviours (e.g. how people go to work depends on their proximity to a bus stop or highway) (Zheng et al., 2010). In general, denser and more public transit friendly neighbourhoods with mixed uses (e.g. local shops, schools, and offices) have lower GHG emission and happier residents than more car-oriented cities with lower density and single use (Hurd & Hurd, 2012; Zheng et al., 2010). In smaller cities that need substantial urban planning and new development, such as Fuding City and Zherong County, decision-makers should develop zoning strategies for compact and multifunctional neighbourhoods and avoid segregating the land into blocks with a single use (e.g. solely residential or commercial areas).  In addition, a stronger regulation and tax framework be put in place in smaller cities or on a larger scale is suggested to promote more low carbon strategies in response to climate change. Although many studies suggest that regulations and economic measures are not the most effective way to initiate value and behavioural changes to climate change, China has been very successful in using this means to lead different sectors and the public in a desirable direction (Lorenzoni et al., 2007). The government should consider introducing several measures for cumulative changes. Given the success of the Plastic Bag Ban in China (90% of the public refuses to use a plastic bag), the government should consider similar regulations such as a carbon tax on high-carbon industries and on the consumption of fossil fuels (Chen & Taylor, 2011).     118 6.3 Limitations and Future Directions  There are several limitations within the research design, sampling approach and sample data of this exploratory study. To begin with, all respondents were recruited by convenience sampling and non-proportional quota sampling, as I was unable to acquire contact information of local citizens in the study areas due to regulations on access to contact information. These sampling approaches are non-probability sampling methods, meaning that the samples may not represent the population well and sample errors are unknown, leading to very limited ability to generalize the findings to a larger population. In addition, due to the limited access of information including census data, I was unable to compare respondents’ socio-demographic information with the population as a whole, which is arguably the only way to verify data quality (Statistics Canada, 2013). The survey design is another limitation in the study. All findings in this study were based on self-reported data from citizens of two smaller cities on the southeast coast of China. Surveys in other parts of China may yield different results. It is possible for example that respondents overstated their responses to certain questions, such as rating their concern about climate change, in order to provide the answers they felt they should be giving. In addition, developing a low carbon economy is a complicated and broad topic that covers a variety of sectors and activities in society. Without knowing much about this topic, respondents could have oversimplified assumptions about how a low carbon economy would work in the local areas, and could have underestimated the impacts that a low carbon transition would bring to their lives.  In addition, a brief definition of a low carbon economy was provided in the consent letter to all respondents in the survey (this was done to address a comment from the pre-tests, in order to   119 help people decide whether or not to complete the survey); this could possibly have jeopardized the validity of certain responses, particularly respondents’ self-estimated knowledge level of low carbon economies, as some respondents may have learned about low carbon economies from reading the provided definition first, though the Chinese version made more of a reference to their knowledge before taking the survey than the English version. However, based on respondents’ self-reports on their limited understanding of low carbon economies, this likely had limited influence on responses. Factor analysis was used to derive two factors from five variables that can be easily grouped into two categories (i.e. people’s knowledge of low carbon economies and level of concern about the impacts of climate change at four scales), instead of using all independent variables (including socio-demographic variables) in the regression models. However, most socio-demographic variables were dropped due to their insignificant contribution to the model or due to multicollinearity issues. Using factor analysis to convert them into independent variables could possibly resolve the multicollinearity problem and improve model performance. On the other hand, factor analysis would be strengthened with more input variables and a pre-established framework, which could provide more meaningful outcomes from the raw data, other than simply reducing dimensions and converting variables. The six multinomial/binomial logistic regression models developed in this study have rather low pseudo R2s, possibly implying very poor fit of data. Therefore, the model outcomes should be interpreted and applied with extra cautions. Since people’s perceptions and decision-making (e.g. deciding whether to support a low carbon economy) involve a complicated process that cannot be explained or predicted by a few socio-demographic or perception variables, this study could   120 have incorporated more diverse research methods (such as interviews or focus groups) besides questionnaires, to gather more (especially qualitative) information for a deeper understanding of what influences people’s support for a low carbon economy.  To better understand the public’s attitude towards low carbon economies, more research is needed in this area. Future research should look more deeply into how the public perceives climate change and low carbon economies and what they expect the government to do in terms of climate change mitigation. A broader questionnaire with a larger sample size across more cities of different sizes is suggested for future research. More specific questions to assess people’s knowledge of low carbon economies would be very helpful for researchers to fully understand a participant’s actual level of knowledge and to identify their knowledge gaps. Post surveys could be conducted to assess any differences in awareness, perception and attitude before and after low carbon relevant engagement program (e.g. workshops, block parties, and car free days) to understand the effectiveness of these different engagement strategies. While the majority of data in this study is quantitative, a combination of qualitative and quantitative analyses based on other methods, such as in-depth interviews in a focus group, would help in the understanding of some broad terms in this study, as well as help to explore answers that are not listed in the questionnaire.  It would also be instructive to assess the extent to which routine procedures for community and development planning in smaller cities do or can integrate key aspects of a low carbon economy, and their potential for educating and engaging citizens in planning the transition to a modern low-carbon economy.  For example, new urban plans, development designs and urban forestry strategies may provide critical platforms for local involvement in establishing low carbon   121 lifestyles and economies for their community.  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Zheng, Siqi, Wang, Rui, Glaeser, Edward, … Matthew. (2010). The Greenness of China: Household Carbon Dioxide Emissions and Urban Development (Discussion Paper No. 2010-12). Cambridge, UK. Retrieved November, 11, 2015, from http://heep.hks.harvard.edu/files/heep/files/dp12_zheng-etal.pdf Zherong Bureau of Statistics. (2010, March 8). 宁德市柘荣县 2003年国民经济和社会发展统计公报[Statistics Report of Economic and Social Development in 2003 in Zherong County, Ningde City]. Retrieved from China Statistics Information website: http://www.tjcn.org/tjgb/201003/7494_2.html   142 Zherong Bureau of Statistics. (2011, September 3). 宁德市柘荣县 2010年国民经济和社会发展统计公报[Statistics Report of Economic and Social Development in 2010 in Zherong County, Ningde City]. China Statistics Information. Retrieved from China Statistics Information website: http://www.tjcn.org/tjgb/201109/20441.html Zherong Bureau of Statistics. (2012). 柘荣县 2011统计年鉴 [Zherong Statistics Yearbook 2011]. Zherong County: Zherong Bureau of Statistics Press. Zherong Bureau of Statistics. (2015). 柘荣县 2014统计年鉴 [Zherong Statistics Yearbook 2014]. Zherong County: Zherong Bureau of Statistics Press.    143 Appendices Appendix A  : Consent Letter  Hello! We are a research team from Faculty of Forestry, University of British Columbia (UBC), Canada. Currently, we are conducting a survey as a part of the broader project – “Adaptation of Asia-Pacific Forests to Climate Change”. It is developed by Asia-Pacific Network (APFNet) for Sustainable Forest Management and Rehabilitation in partnership with the China Green Carbon Foundation.  The purpose of this questionnaire is to survey how much you know about climate change and your knowledge and opinions on developing a low carbon economy in your area. This information will help the researchers develop better public outreach and low-carbon development recommendations. We are conducting the survey within three groups: three to five communities/neighbourhoods, local government staff, and the general public from both Fuding City and Zherong County. As part of this project, we would like to invite you to participate in this survey.  Please understand that your participation is voluntary and you may withdraw from this research any time you wish or skip any questions you don’t feel comfortable answering, without penalty. You will NOT be asked to provide any private information and all responses are anonymous. Your participation involves no risk whatsoever. This study is not directly beneficial to any participants. However, your honest response will be of value for this project. . Please note that you must be 19 years or older in order to complete this questionnaire. This questionnaire abides by all commonly acknowledged ethical codes. It is strictly anonymous, and no personal information on participants will be released in any published written data analysis. Only researchers involved in this project (listed below) have access to the survey data.  The questionnaire will take you approximately 20-30 minutes. Please feel free to contact us if you have any questions. If you have questions after the survey, please contact the research team listed in the following: Project Coordinator: Zhaohua Cheng (speaks Mandarin), Graduate student in Master of Science in Forestry Program, University of British Columbia Email:  (Local contact information (e.g. telephone number) will be provided when Zhaohua arrives at the study sites in China) Principal Investigator: Dr. John L. Innes, Dean of the Faculty of Forestry, University of British Columbia Office: XXX-XXX-XXXX               Email:     144 Gayle Kosh, Manager of Graduate Program, Faculty of Forestry, University of British Columbia Office: XXX-XXX-XXXX               Email:   Co-Investigators: Dr. Stephen Sheppard, Professor and Director of the Collaborative for Advanced Landscape Planning (CALP), University of British Columbia  Office: XXX-XXX-XXXX               Email:  Dr. Guangyu Wang (speaks Mandarin), Director of Asia Strategies, the Faculty of Forestry, University of British Columbia Office: XXX-XXX-XXXX               Email:  The study may involve a follow-up survey within approximately two years. Would you be willing to participate in this survey (this is completely optional)?  If yes, please check this box                                                         ☐ If you have any concerns about your rights as a research subject and/or your experiences while participating in this study, you may contact the Research Subject Information Line in the UBC Office of Research Services at 604-822-8598, or if long distance e-mail: RSIL@ors.ubc.ca or call toll free: 1-877-822-8598. I have read and understood the above, and I agree to participate in the study. Please check the following box if you would like to continue with the survey                    ☐ Thank you for your precious time and support!   Note: A Low carbon economy is a form of sustainable economic development that reduces resources utilization and greenhouse gas emissions through multiple ways, such as low-carbon technology innovation, clean energy development, and low-carbon lifestyles.     145 Appendix B  : Questionnaires B.1 Questionnaire for the Public Group Please choose the boxes that best describe your answers to the following questions:  1. How much have you heard about climate change? Never heard of it*                                                                          Heard of it but know little about it Heard of it and know something about it Heard of it and know it very well 1 2 4 5     * If you never heard of this term, please skip question 2  2. Please indicate your concern level about the effects of climate change (tick one box for each category).             Concern    Level   Category  Not concerned at all  Slightly concerned Somewhat concerned Very concerned 1 2 3 4 a) Globally     b) To your local community     c) To you and your immediate family     d) To future generations of your family      3. Do you think carbon is important? ☐ Yes                                                              ☐ No                                                ☐ Don’t know   If yes, please tick all boxes that explain why: ☐ It may cause temperatures to rise                  ☐ It may acidify the ocean  ☐ It may lead to accelerated glacial melting     ☐ It may lead to reduced biodiversity ☐ It may lead to more extreme weather            ☐ Other (please specify) ________________  4. How much have you heard about the “low carbon economy”? Never heard of it*                                                                          Heard of it but know little about it Heard of it and know something about it Heard of it and know it very well 1 2 4 5     * If you never heard of this term, please skip question 5 and question 6     146 5. How did you learn about the “low carbon economy”? (Tick all applicable boxes) ☐ TV news                                                                     ☐ Thematic events ☐ Internet               ☐ Advertisements                                                                   ☐ Newspaper and magazines                                   ☐ Lectures ☐ Other (please specify) _______________________________  6. What might a “low carbon economy” look like? (Tick all applicable boxes) ☐ An economy that uses low-carbon renewable energy as the main power source                                           ☐ An economy with large-scale afforestation/reforestation to increase forest carbon storage ☐ An economy with an established garbage separation and recycling system ☐ An economy that develops green buildings to replace typical concrete buildings ☐ An economy that develops low-carbon energy-efficient technologies (especially in power generation and industrial sectors) ☐ An economy which reduces the amount of greenhouse gas emissions per unit of industrial      production  ☐ An economy that provides an efficient public transit system  ☐ An economy where individuals develop or maintain a low-carbon lifestyle ☐ Other (please specify) _______________________________  7. Have you done anything in your daily life to reduce your personal carbon emissions?  ☐ Yes                                                        ☐ No                       ☐ Don’t know   If yes, please tick all applicable boxes: ☐ Garbage separation and recycling               ☐ Use energy-saving facilities ☐ Consume rationally, reduce waste                 ☐ Shut down electrical equipment if they are not in use  ☐ Recycle grey water  ☐ Use shopping bags instead of plastic bags   ☐ Take public transit as much as possible ☐ Avoid using disposable items such as bottles, tableware etc. ☐ Walk to work/school/shops ☐ Cycle to work/school/shops ☐ Other (please specify) ______________________________ ☐ None of above  8. Are you willing to contribute personally to help develop a “low carbon economy” in your area?  ☐ Yes                                                    ☐ No          ☐ Don’t know     147 If yes, please indicate the actions you would take (tick all applicable boxes): 8.1☐ I’d like to keep or change to a low-carbon lifestyle (circle the three most likely steps you would take)         A. Garbage separation and recycling         B. Use energy-saving facilities       C. Consume rationally, reduce waste         D. Shut down electrical equipment if not in use            E. Walk to work/school/shops                    F. Cycle to work/school/shops        G. Take public transit as much as possible       H. Refuse to use disposable items        I. Other (please specify) ______________________________         8.2☐ I’d be willing to contribute some money______________yuan/year (please specify the amount in ¥) to local low-carbon initiatives through the following method (circle the preferred method):        A. Carbon Tax                                            B. Voluntary donation 8.3☐ I’d contribute my time__________________ hour(s)/month (please specify the hours) by doing the following (circle the preferred activity):        A. Volunteering in low-carbon related events held by the government and environmental NGOs                                                       B. Introducing the idea of low-carbon lifestyle to family, friends and neighbours  9. Do you think it is necessary to develop “low carbon economy” in your community? ☐ Yes, it is (skip question 11)              ☐ No, it is not (skip question 10)             ☐ Don’t know    10. Why do you think it is necessary to develop a “low carbon economy” in your community? (Tick all applicable boxes) ☐ It can reduce the severity of climate change              ☐ It can improve citizen’s quality of life   ☐ It can enhance production efficiency and resource utilization ☐ It can improve the environment ☐ It can promote Fuding/Zherong as leaders in sustainable development ☐ Other (please specify) ______________________________  11. Why do you think it is not necessary to develop a “low carbon economy”? (Tick all applicable boxes) ☐ It can cost a lot of money          ☐ It can reduce the quality of life   ☐ It can limit economic growth   ☐ Current environmental measures are sufficient ☐ Other (please specify) ______________________________      148 12. Which sector in the following sectors do you think would be the first step for the local government to develop a low carbon economy? ☐ Forestry (e.g. afforestation to increase forest carbon storage)      ☐ Agriculture (e.g. use low-carbon fertilizer) (continued) ☐ Business (e.g. invest in low-carbon projects) ☐ Industry (e.g. introduce energy-saving techniques)  ☐ Tourism (e.g. protect natural/scenic areas) ☐ Building and development (e.g. introduce green building) ☐ Energy production (e.g. promote renewable energy) ☐ Other (please specify) ______________________________  13. Do you agree with the following statements? (Tick the appropriate box for each statement) Statement Strongly Agree Agree Neutral Disagree Strongly Disagree 1 2 3 4 5 The local government should collect carbon tax (from those who purchase traditional fuels with very high emissions, such as gasoline)       The local government should subsidize or invest in low-carbon projects, including alternative energy development and forest carbon etc.      The local government should provide low-carbon projects loans with a lower interest rate       14. Which way(s) do you think should be the top priority if the local government decides to establish a low-carbon community as a demonstration zone in the future? (Tick all applicable boxes) ☐ Develop and encourage the use of clean energy sources (e.g. solar energy, wind power)  ☐ Introduce low-carbon technology to reduce greenhouse gas emissions from production and manufacturing processes  ☐ Invest in forest carbon program20, while increasing forest cover (continued)                                                  20 Forest carbon sequestration is a process of forest absorbing carbon from the atmosphere and storing it in forest biomass and soils through photosynthesis. This process helps reduce the concentration of carbon dioxide in the   149 ☐ Promote green building techniques (e.g. build with wood, reduce emissions from buildings and construction) ☐ Develop/upgrade public transit systems and encourage residents to travel in a low-carbon way ☐ Establish a garbage collection and recycling system ☐ Promote public education/outreach events on low carbon economy to improve public awareness  ☐ Other (please specify) ______________________________  15. Your age:  ☐ 19 to 30                                    ☐ 31 to 40                                       ☐ 41 to 50                                                  ☐ 51 to 60                                    ☐ 61 and higher  16. Your gender:   ☐ Male                                               ☐ Female                17. Your occupation:  ☐ Government employee/official                              ☐ Public-owned company employee                                  ☐ Private company employee                                    ☐ Farmer  ☐ Self-employed                                             ☐ Student       ☐ Retiree                                                  ☐ Not presently employed                                                     ☐ Entrepreneur                                                          ☐ Other___________________________  18. Highest Education Earned:  ☐ Middle school or less                                            ☐ High school diploma                                           ☐ College diploma                                                    ☐ Bachelor’s degree  ☐ Master’s degree                                                     ☐ Doctoral degree  ☐ Other__________________  19. Your annual income: ☐ ¥1500 and lower                       ☐ ¥1500 to ¥4500                       ☐ ¥4500 to ¥9000          ☐ ¥9000 to ¥35000                       ☐ ¥35000 to ¥55000                   ☐ ¥55000 to ¥80000 ☐ ¥80000 and higher  20. Do you have kids? ☐ Yes                ☐ No                                                   atmosphere. Forest is the biggest terrestrial carbon sink, playing an important role in reducing greenhouse gases and slowing down climate change.     150 21. Any other comments on the subject of a low carbon economy or this questionnaire? ___________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________  This is the end of the questionnaire. Thank you again!   151 B.2 Questionnaire for the Community Group 1. How much have you heard about climate change? Never heard of it*                                                                          Heard of it but know little about it Heard of it and know something about it Heard of it and know it very well 1 2 4 5     * If you never heard of this term, please skip question 2  2. Please indicate your concern level about the effects of climate change (tick one box for each category).             Concern    Level   Category  Not concerned at all  Slightly concerned Somewhat concerned Very concerned 1 2 3 4 a) Globally     b) To your local community     c) To you and your immediate family     d) To future generations of your family      3. Do you think carbon is important? ☐ Yes                                                       ☐ No                                                 ☐ Don’t know    If yes, please tick all boxes that explain why: ☐ It may cause temperatures to rise                   ☐ It may acidify the ocean  ☐ It may lead to accelerated glacial melting      ☐ It may lead to reduced biodiversity ☐ It may lead to more extreme weather             ☐ Other (please specify) ________________  4. How much have you heard about the “low carbon economy”? Never heard of it*                                                                          Heard of it but know little about it Heard of it and know something about it Heard of it and know it very well 1 2 4 5     * If you never heard of this term, please skip question 5 and question 6  5. How did you learn about the “low carbon economy”? (Tick all applicable boxes) ☐ TV news                                                                  ☐ Thematic events ☐ Internet                   ☐ Advertisements                                                                   ☐ Newspaper and magazines                                      ☐ Lectures ☐ Other (please specify) ______________________________   152 6. What might a “low carbon economy” look like? (Tick all applicable boxes) ☐ An economy that uses low-carbon renewable energy as the main power source                                           ☐ An economy with large-scale afforestation/reforestation to increase forest carbon storage ☐ An economy with an established garbage separation and recycling system ☐ An economy that develops green buildings to replace typical concrete buildings ☐ An economy that develops low-carbon energy-efficient technologies (especially in power generation and industrial sectors) ☐ An economy which reduces the amount of greenhouse gas emissions per unit of industrial      production  ☐ An economy that provides an efficient public transit system  ☐ An economy where individuals develop or maintain a low-carbon lifestyle ☐ Other (please specify) ______________________________  7. Have you done anything in your daily life to reduce your personal carbon emissions?  ☐ Yes                                         ☐ No                 ☐ Don’t know    If yes, please tick all applicable boxes: ☐ Garbage separation and recycling               ☐ Use energy-saving facilities ☐ Consume rationally, reduce waste                 ☐ Shut down electrical equipment if they are not in use (continued) ☐ Recycle grey water  ☐ Use shopping bags instead of plastic bags   ☐ Take public transit as much as possible ☐ Avoid using disposable items such as bottles, tableware etc. ☐ Walk to work/school/shops ☐ Cycle to work/school/shops ☐ Other (please specify) ______________________________ ☐ None of above  8. Are you willing to contribute personally to help develop a “low carbon economy” in your area?  ☐ Yes                                                          ☐ No                  ☐ Don’t know    If yes, please indicate the actions you would take (tick all applicable boxes): 8.1☐ I’d like to keep or change to a low-carbon lifestyle (circle the three most likely steps you would take)         A. Garbage separation and recycling         B. Use energy-saving facilities       C. Consume rationally, reduce waste         D. Shut down electrical equipment if not in use            E. Walk to work/school/shops              F. Cycle to work/school/shops        G. Take public transit as much as possible       H. Refuse to use disposable items        I. Other (please specify) ______________________________           153 8.2☐ I’d be willing to contribute some money______________yuan/year (please specify the amount in ¥) to local low-carbon initiatives through the following method (circle the preferred method):        A. Carbon Tax                                            B. Voluntary donation 8.3☐ I’d contribute my time__________________ hour(s)/month (please specify the hours) by doing the following (circle the preferred activity):        A. Volunteering in low-carbon related events held by the government and environmental NGOs                                                       B. Introducing the idea of low-carbon lifestyle to family, friends and neighbours  9. Do you think it is necessary to develop “low carbon economy” in your community? ☐ Yes, it is (skip question 12)              ☐ No, it is not (skip question 11)             ☐ Don’t know    10. Why do you think it is necessary to develop a “low carbon economy” in your community? (Tick all applicable boxes) ☐ It can reduce the severity of climate change              ☐ It can improve citizen’s quality of life   ☐ It can enhance production efficiency and resource utilization ☐ It can improve the environment ☐ It can promote Fuding/Zherong as leaders in sustainable development ☐ Other (please specify) ______________________________  11. Why do you think it is not necessary to develop a “low carbon economy”? (Tick all applicable boxes) ☐ It can cost a lot of money          ☐ It can reduce the quality of life   ☐ It can limit economic growth   ☐ Current environmental measures are sufficient ☐ Other (please specify) ______________________________  12. What support do you think the local government should provide to develop a low carbon economy in your community? (Tick all applicable boxes) ☐ Financial support to local initiatives                                                    ☐ Free workshop or training  ☐ Media support (to publicize the idea of low carbon economy) ☐ Introduction of a system of incentives for low-carbon activities  ☐ Other (please specify) ______________________________  ☐ No support    154 13. Which way(s) do you think should be the top priority if the local government decides to develop a low carbon economy in the future? (Tick all applicable boxes) ☐ Develop and encourage the use of clean energy sources (e.g. solar energy, wind power)  ☐ Introduce low-carbon technology to reduce greenhouse gas emissions from production and manufacturing processes  ☐ Invest in forest carbon program21, while increasing forest cover ☐ Promote green building techniques (e.g. build with wood, reduce emissions from buildings and construction) ☐ Develop/upgrade public transit systems and encourage residents to travel in a low-carbon way ☐ Establish a garbage collection and recycling system ☐ Promote public education/outreach events on low carbon economy to improve public awareness  ☐ Other (please specify) ______________________________  14. Which approach do you think has the highest potential to help reduce greenhouse gas emissions from the local forestry sector? ☐ Afforestation to increase the area of forest cover             ☐ Improved forest management to increase carbon storage capacity per unit area of forest ☐ Prevent illegal logging              ☐ Conserve and restore existing forest vegetation    ☐ Sequester carbon in wood-frame buildings ☐ Use sustainable biomass as a low-carbon energy source (instead of fossil fuels) ☐ Don’t know  15. Which approach do you think has the highest potential to help reduce greenhouse gas emissions from the local agriculture sector? ☐ Develop organic farming with improved carbon storage in soil  ☐ Encourage local farmers to use low-carbon fertilizer ☐ Encourage intercropping and companion planting               ☐ Develop community gardens in parks ☐ Develop an agricultural waste recycling system                                                    ☐ Use biogas from sewage systems ☐ Don’t know                                                   21 Forest carbon sequestration is a process of forest absorbing carbon from the atmosphere and storing it in forest biomass and soils through photosynthesis. This process helps reduce the concentration of carbon dioxide in the atmosphere. Forest is the biggest terrestrial carbon sink, playing an important role in reducing greenhouse gases and slowing down climate change.   155 16. Which approach do you think has the highest potential to help reduce greenhouse gas emissions from the local industry sector? ☐ Encourage to use raw materials with relatively low greenhouse gas emissions  ☐ Introduce low-carbon technologies such as carbon capture and storage22  ☐ Recycle industrial waste               ☐ Improve the energy efficiency of freight transport ☐ Improve industrial wastewater treatment                                                     ☐ Buy local products/materials (continued) ☐ Capture or reuse waste heat ☐ Introduce renewable energy on site  ☐ Don’t know  17. Which approach do you think has the highest potential to help reduce greenhouse gas emissions from the local tourism sector? ☐ Use energy-saving lighting in tourism sites ☐ Encourage tourists to take low-carbon transit (e.g. bus)              ☐ Introduce the concepts of low carbon economy and low-carbon lifestyle to tourists through pamphlet, events, posters etc. ☐ Take measures to strengthen environmental protection in tourist sites ☐ Encourage tourists to visit sites closer to where they live ☐ Don’t know  18. Which approach do you think has the highest potential to help reduce greenhouse gas emissions from buildings and the local construction sector? ☐ Introduce the technology of thermal insulation sandwich wall to reduce energy (heating/cooling) needs ☐ Introduce roof technologies, such as solar panels, to reduce conventional energy use ☐ Introduce energy-saving lighting technologies              ☐ Introduce energy-efficient windows and doors ☐ Use green building materials, e.g. wood to sequester carbon ☐ Use local materials  ☐ Don’t know                                                   22 Carbon capture and storage is a technique that captures the waste carbon dioxide from major emission sources (mainly industrial processes e.g. electricity generation) and deposits it in a secure storage, so it does not enter the atmosphere.   156 19. Which energy source listed in the following do you think has the highest potential to help reduce greenhouse gas emissions? ☐ Solar power                                      ☐ Wind power ☐ Hydropower                                      ☐ Biogas (e.g. methane) ☐ Geoexchange (for cooling/heating)23                    ☐ Biomass from vegetation ☐ Other (please specify)  __________________      ☐ Don’t know  20. Which option listed in the following do you think has the highest potential to help reduce greenhouse gas emissions from transportation? ☐ Bicycling                                       ☐ Public transit ☐ Carpooling                                             ☐ Traffic efficiency management ☐ Walking           ☐ Other (please specify)____________                                            21. Which sector in the following do you think would be a good start for the local government to develop a low carbon economy? ☐ Forestry (e.g. afforestation to increase forest carbon storage)      ☐ Agriculture (e.g. use low-carbon fertilizer) ☐ Business (e.g. invest in low-carbon projects) ☐ Industry (e.g. introduce energy-saving techniques)  ☐ Tourism (e.g. promote resource conservation at tourist sites) ☐ Building and development (e.g. introduce green buildings) (continued) ☐ Energy production (e.g. promote renewable energy) ☐ Transportation (e.g. increase public transit)  22. Your age:  ☐ 19 to 30                                           ☐ 31 to 40                                       ☐ 41 to 50                                                   ☐ 51 to 60                                           ☐ 61 and higher  23. Your gender:   ☐ Male                                              ☐ Female                24. Your occupation:  ☐ Government employee/official                             ☐ Public-owned company employee                                  ☐ Private company employee                                   ☐ Farmer  ☐ Self-employed                                             ☐ Student (continued)                                                        23 Geoexchange is a technique that uses the ground’s energy (geothermal energy) to heat or cool your house as a way to reduce your consumption of energy from fossil fuels and to increase the energy efficiency of your home.   157 ☐ Retiree                                                 ☐ Not presently employed                                                     ☐ Entrepreneur                                                         ☐ Other___________________________  25. Highest Education Earned:  ☐ Primary school or less                                           ☐ Middle school diploma  ☐ High school diploma                                             ☐ College diploma or Bachelor’s degree  ☐ Master’s degree or above                                      ☐ Other ___________________________  26. Your monthly income: ☐ ¥1500 and lower                     ☐ ¥1500 to ¥4500                     ☐ ¥4500 to ¥9000          ☐ ¥9000 to ¥35000                     ☐ ¥35000 to ¥55000                 ☐ ¥55000 to ¥80000 ☐ ¥80000 and higher  27. Do you have children? ☐ Yes                                                        ☐ No                                    28. Any other comments on the subject of a low carbon economy or this questionnaire? ___________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________  This is the end of the questionnaire. Thank you again!   158  B.3 Questionnaire for the Government Group 1. Please indicate your concern level about the effects of climate change (tick one box for each category).           Concern    Level   Category  Not concerned at all  Slightly concerned Somewhat concerned Very concerned 1 2 3 4 a) Globally     b) To your local community     c) To you and your immediate family     d) To future generations of your family      2. How much have you heard about the “low carbon economy”? Never heard of it*                                                                          Heard of it but know little about it Heard of it and know something about it Heard of it and know it very well 1 2 4 5     * If you never heard of this term, please skip question 3   3. How did you learn about the “low carbon economy”? (Tick all applicable boxes) ☐ TV news                                                              ☐ Thematic events ☐ Internet               ☐ Advertisements                                                                   ☐ Newspaper and magazines                                  ☐ Lectures ☐ Other (please specify) ______________________________  4. Do you think it is necessary to develop “low carbon economy” in Fuding City/Zherong County? ☐ Yes, it is (skip question 6)                     ☐ No, it is not (skip question 5)                                                    5. Why do you think it is necessary to develop a local “low carbon economy”? (Tick all applicable boxes) ☐ Mitigate the adverse impacts of climate change on local society             ☐ Promote a sustainable development path to balance economic growth and environmental protection by enhancing production efficiency and resource utilization, ☐ Protect the environment by encouraging the use of green energy with less pollutions and greenhouse gas emissions (continued)   159 ☐ Enhance the quality of life for local residents by increasing per capita resource availability  ☐ Promote Fuding/Zherong as leaders in sustainable development ☐ Other (please specify) ______________________________  6. Why do you think it is not necessary to develop a “low carbon economy”? (Tick all applicable boxes) ☐ It requires to introduce advanced green technologies and low-carbon energy, which costs a lot of money          ☐ It is for developed areas, not for developing cities/towns like Fuding or Zherong  ☐ It is a slow development approach that limits the growth of heavy industry with a higher profits ☐ Current environmental measures are sufficient ☐ Other (please specify) __________________  7. What do you think is/are the biggest obstacle to developing a “low carbon economy” locally? (Tick all applicable boxes) ☐ Low public awareness             ☐ Local government has other priorities ☐ Current policies are not effective and hard to change                          ☐ Lack of cooperation between the government and environmental Non-Government Organizations (NGO) ☐ Current economic development approaches are hard to change ☐ Huge initial investment is a heavy burden to the government financially ☐ Lack of appropriate technical guidance for local sectors/industries   ☐ Other (please specify) ______________________________  8. Which way(s) do you think should be the top priority if the local government decides to establish a low-carbon community as a demonstration zone in the future?  ☐ Develop and encourage the use of clean energy sources (e.g. solar energy, wind power)  ☐ Introduce low-carbon technology to reduce greenhouse gas emissions from production and manufacturing processes  ☐ Invest in forest carbon program24, while increasing forest cover ☐ Promote green building techniques (e.g. build with wood, reduce emissions from buildings and construction) (continued)                                                   24 Forest carbon sequestration is a process of forest absorbing carbon from the atmosphere through photosynthesis and sequestering it in forest biomass and soils. This process helps reduce the concentration of carbon dioxide in the atmosphere. Forest is the biggest carbon sink in the terrestrial ecosystem. It plays an important role in reducing greenhouse gases and slowing down climate change.   160 ☐ Develop/upgrade public transit systems and encourage residents to travel in a low-carbon way  ☐ Establish a garbage collection and recycling system (continued) ☐ Promote public education/outreach events on low carbon economy to improve public awareness  ☐ Other (please specify) ______________________________  9. Do you agree with the following statements? (Please tick the box that you agree the most) Statement Strongly Agree Agree Neutral Disagree Strongly Disagree 1 2 3 4 5 The local government should collect carbon tax (from those who purchase traditional fuels with very high emissions, such as gasoline)       The local government should subsidize or invest in low-carbon projects, including alternative energy development and forest carbon etc.      The local government should provide low-carbon projects loans with a lower interest rate       10. Do you support the idea of developing a low-carbon neighbourhood as a demonstration zone in Fuding City/Zherong County? ☐ Yes                 ☐ No   11. Which one do you think has the highest potential to help reduce greenhouse gas emissions from the local forestry sector? ☐ Afforestation to increase the area of forest cover             ☐ Improved forest management to increase carbon storage capacity per unit area of forest ☐ Prevent illegal logging              ☐ Conserve and restore existing forest vegetation    ☐ Sequester carbon in wood-frame buildings  ☐ Use sustainable biomass as a low-carbon energy source (instead of fossil fuels) ☐ Don’t know  12. Which one do you think has the highest potential to help reduce greenhouse gas emissions from the local agriculture sector? ☐ Develop organic farming with improved carbon storage in soil  ☐ Encourage local farmers to use low-carbon fertilizer   161 ☐ Encourage intercropping and companion planting (continued)             ☐ Develop community gardens in parks ☐ Develop an agricultural waste recycling system                                                    ☐ Use biogas from sewage systems ☐ Don’t know  13. Which one do you think has the highest potential to help reduce greenhouse gas emissions from the local industry sector? ☐ Encourage to use raw materials with relatively low greenhouse gas emissions  ☐ Introduce low-carbon technologies such as carbon capture and storage25  ☐ Recycle industrial waste               ☐ Improve the energy efficiency of freight transport ☐ Improve industrial wastewater treatment                                                     ☐ Buy local products/materials ☐ Capture or reuse waste heat ☐ Introduce renewable energy on site  ☐ Don’t know  14. Which one do you think has the highest potential to help reduce greenhouse gas emissions from the local tourism sector? ☐ Use energy-saving lighting in tourism sites ☐ Encourage tourists to take low-carbon transit (e.g. bus)              ☐ Introduce the concepts of low carbon economy and low-carbon lifestyle to tourists through pamphlet, events, posters etc. ☐ Take measures to strengthen environmental protection in tourist sites ☐ Encourage tourists to visit sites closer to where they live ☐ Don’t know  15. Which one do you think has the highest potential to help reduce greenhouse gas emissions from the local construction sector? ☐ Introduce the technology of thermal insulation sandwich wall to reduce energy (heating/cooling) needs ☐ Introduce roof technologies, such as solar panels, to reduce conventional energy use ☐ Introduce energy-saving lighting technologies              ☐ Introduce energy-efficient windows and doors (continued)                                                  25 Carbon capture and storage is a technique that captures the waste carbon dioxide from major emission sources (mainly industrial processes e.g. electricity generation) and deposits it in a secure storage, so it does not enter the atmosphere   162 ☐ Use green building materials, e.g. wood to sequester carbon ☐ Use local materials  ☐ Don’t know  16. Which energy source listed in the following do you think has the highest potential to help reduce greenhouse gas emissions? ☐ Solar power                                      ☐ Wind power ☐ Hydropower                                      ☐ Biogas (e.g. methane) ☐ Geoexchange (for cooling/heating)26                    ☐ Biomass from vegetation ☐ Other (please specify)  __________________      ☐ Don’t know                              17. Which option listed in the following do you think has the highest potential to help reduce greenhouse gas emissions from transportation? ☐ Bicycle                                       ☐ Public transit ☐ Carpooling                                             ☐ Traffic efficiency management ☐ Travel to places close by for vacations                  ☐ Other (please specify)______________                                     18. Which sector in the following do you think would be a good start for the local government to develop a low carbon economy? ☐ Forestry (e.g. afforestation to increase forest carbon storage)      ☐ Agriculture (e.g. use low-carbon fertilizer) ☐ Business (e.g. invest in low-carbon projects) ☐ Industry (e.g. introduce energy-saving techniques)  ☐ Tourism (e.g. promote resource conservation at tourist sites) ☐ Building and development (e.g. introduce green buildings) ☐ Energy production (e.g. promote renewable energy) ☐ Other (please specify)______________________  19. Your age:  ☐ 19 to 30                                    ☐ 31 to 40                                       ☐ 41 to 50                                                  ☐ 51 to 60                                    ☐ 61 and higher  20. Your gender:   ☐ Male                                             ☐ Female                                                                 26 Geoexchange is a technique that uses the ground’s energy (geothermal energy) to heat or cool your house as a way to reduce your consumption of energy from fossil fuels and to increase the energy efficiency of your home.   163 21. Your occupation:  ☐ Urban development planning                  ☐ Justice                                    ☐ Transportation                                       ☐ Public Security                                                                  ☐ Medicine and Public Health                     ☐ Finance and Economy ☐ Civil Administration                                 ☐ Auditing and Supervision                                     ☐ Education                                                ☐ Business                                     ☐ Agriculture                                          ☐ Tourism                                     ☐ Forestry                                       ☐ Other__________________                                      22. Highest Education Earned:  ☐ Primary school or less                                           ☐ Middle school diploma  ☐ High school diploma                                             ☐ College diploma or Bachelor’s degree  ☐ Master’s degree or above                                      ☐ Other ___________________________  23. Your annual income: ☐ ¥1500 and lower                      ☐ ¥1500 to ¥4500                       ☐ ¥4500 to ¥9000          ☐ ¥9000 to ¥35000                      ☐ ¥35000 to ¥55000                   ☐ ¥55000 to ¥80000 ☐ ¥80000 and higher  24. Do you have kid(s)? ☐ Yes                 ☐ No   25. Any other comments on the subject of a low carbon economy or this questionnaire? ____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________   This is the end of the questionnaire. Thank you again!     164  Appendix C  : Concern about Climate Change at Four Different Scales C.1 Overall Analysis of Public Perceptions in Smaller Cities in Eastern China Table C.1.1 Frequency Distributions of Variables that Measure the Perception of a Low Carbon Economy  Variables  n % Please indicate your concern level about the effects of climate change. 1) Global Impacts (n=1079)   Not concerned at all 6327 5.9 Slightly concerned 186 17.2 Somewhat concerned 550 51.0 Very concerned 280 25.9 2) Impacts on Local Communities (n=1077) Not concerned at all 4827 4.5 Slightly concerned 146 13.6 Somewhat concerned 487 45.5 Very concerned 389 36.4 3) Impacts on Immediate Families (n=1070) Not concerned at all 4927 4.6 Slightly concerned 106 9.9 Somewhat concerned 446 41.7 Very concerned 469 43.8 4) Impacts on Future Generations (n = 1068)  Not concerned at all 4727 4.4 Slightly concerned 106 9.9 Somewhat concerned 446 41.8 Very concerned 469 43.9 How much do you know about low carbon economies? (n=1084) Never heard of it 114 10.5 Heard of it but know little about it 486 44.8 Heard of it and know about it 419 38.7 Know it very well 65 6.0 How do you learn about low carbon economies? (n=1015) Television 845 83.3% Thematic events 264 26.0% Internet 626 61.7%                                                  27 Include respondents who skipped this question as they indicated that they had never heard of climate change before.   165 Variables  n % Advertisements 317 31.2% Newspaper and magazines 379 37.3% Public lectures 143 14.1% Do you support a low carbon economy in your hometown? (n=1081) Yes 921 85.2 No 66 6.1 Don't know 94 8.7 Why do you support a low carbon economy in the local area? (n=999) Mitigate CC 661 66.2% Improve quality of life 644 64.5% Enhance efficiency of production and resource utilization 547 54.8% Improve environment 748 74.9% Leadership in developing low carbon economies 385 38.5% Why do you not support a low carbon economy in local areas? (n=377) Cost 203 53.8% Decrease life quality 174 46.2% Limit economic growth 136 36.1% Sufficient measures 69 18.3% Which sector do you think should be the first step for the local government to develop a low carbon economy? (n=1070) Forestry 802 47.8% Agriculture 695 38.8% Business 647 30.8% Industry 525 63.6% Tourism 613 39.5% Building and development  642 43.1% Energy 574 42.1% Which way(s) do you think should be the top priority if the local government decides to develop a low carbon economy? (n=1082) Develop and encourage the use of clean energy sources (e.g. solar energy, wind power) 802 74.1% Introduce low-carbon technology to reduce GHG emissions from production and manufacturing processes 695 64.2% Invest in forest carbon program, while increasing forest cover 647 59.8% Promote green building techniques (e.g. build with wood, reduce emissions from buildings and construction) 525 48.5% Develop/upgrade the public transit system and encourage residents to travel in a low-carbon way 613 56.7% Establish a garbage collection and recycling system 642 59.3% Promote education/outreach events to improve public awareness of climate change and the low carbon economy 574 53.0%    166 Table C.1.2 Model Fitting Information of Model 2 & Model 3  Model Model Fitting Criteria Pseudo R-Square AIC BIC -2Log Likelihood Cox & Snell Nagelkerke McFadden Model 2 Intercept Only 736.716 746.049 732.716 .072 .104 .063 Final Model 737.610 886.953 673.610    Model 3 Intercept Only 867.195 876.459 863.195 .082 .119 .073 Final Model 830.210 904.322 798.210      Table C.1.3 Parameter Estimates of Model 4 For the Wald’s test: H0: The coefficient (B) of each independent variable equals zero H1: The coefficient (B) of each independent variable does not equal zero Model 4 B Std. Error Wald’s (df =1) p Odds Ratio Hesitant People vs. Supporters      Intercept -2.753*** 0.155 314.070 <.001  Concern about climate change  -0.330** 0.104 10.122 .001 0.719 Knowledge of low carbon economies  -0.853*** 0.128 44.141 <.001 0.426 Income 0.000** 0.000 7.328 .007 1.000 Opponents vs. Supporter       Intercept -2.783*** 0.157 316.023 <.001  Concern about climate change  -0.120 0.126 0.913 .339 0.887 Knowledge of low carbon economies  -0.553*** 0.138 16.091 <.001 0.575 Income  0.000 0.000 1.281 .258 1.000 Note: R2 = 0.070 (Cox & Snell), 0.109 (Nagelkerke), 0.071 (McFadden). Model 𝜒2 (6) =75.087, p=.000. *p<.05, **p<.01, ***p<.001.  Table C.1.4 Prediction Accuracy of Model 1 and Model 4                            Predicted  Observed Don’t Know No Yes Percent Correct Model 1 Don’t know 0 0 87 0.0% No 0 0 65 0.0% Yes 0 0 892 100.0% Overall Percentage 0.0% 0.0% 100.0% 85.4% Model 4 Don’t know 1 0 85 1.2% No 0 0 64 0.0% Yes 1 0 885 99.9% Overall Percentage 0.2% 0.0% 99.8% 85.5%    167 C.2 Comparison between Fuding City and Zherong County Table C.2.1  Frequency Distributions of Variables that Measure the Perception of a Low Carbon Economy by Study Area Variables Fuding City Zherong County n % n % Please indicate your concern level about the effects of climate change.  1) Global Impacts (nFD = 720; nZR = 359) Not concerned at all 4027 5.6 23 6.4 Slightly concerned 135 18.8 51 14.2 Somewhat concerned 382 53.1 168 46.8 Very concerned 163 22.6 117 32.6 2) Impacts on Local Communities (nFD = 719; nZR = 358) Not concerned at all 3427 4.8 20 5.6 Slightly concerned 108 15.0 67 18.7 Somewhat concerned 383 53.3 137 38.3 Very concerned 194 27.0 134 37.4 3) Impacts on Immediate Families (nFD = 714; nZR = 356) Not concerned at all 2927 4.1 19 5.3 Slightly concerned 98 13.7 48 13.5 Somewhat concerned 360 50.4 127 35.7 Very concerned 227 31.8 162 45.5 4) Impacts on Future Generations (nFD = 714; nZR = 356) Not concerned at all 3027 4.2 19 5.3 Slightly concerned 79 11.1 27 7.6 Somewhat concerned 322 45.1 124 34.8 Very concerned 283 39.6 186 52.2 How much do you know about climate change?  (nFD=596; nZR=233) Never heard of it 31 5.2 18 7.7 Heard of it but know little about it 224 37.6 66 28.3 Heard of it and know about it 274 46.0 120 51.5 Know it very well 67 11.2 29 12.4 How much do you know about low carbon economies? (nFD=718; nZR=366) Never heard of it 59 8.2 55 15.0 Heard of it but know little about it 365 50.8 121 33.1 Heard of it and know about it 258 35.9 161 44.0 Know it very well 36 5.0 29 7.9 How did you learn about low carbon economies? (nFD=681; nZR=333) Television 555 81.5 289 86.8 Thematic events 171 25.1 93 27.9 Internet 403 59.2 222 66.7 Advertisements 202 29.7 115 34.5 Newspaper and magazines 222 32.6 157 47.1 Public lectures 102 15.0 41 12.3   168 Variables Fuding City Zherong County n % n % Do you support a low carbon economy? (nFD=718; nZR=363) Yes 608 84.7 313 86.2 No 37 5.2 29 8.0 Don't know 73 10.2 21 5.8 Why do you support a low carbon economy in local areas? (nFD=668; nZR=331) Mitigate climate change 437 65.4 224 67.7 Improve quality of life 427 63.9 217 65.6 Enhance efficiency of production and resource utilization 342 51.2 205 61.9 Improve environment 508 76.0 240 72.5 Leadership in low carbon economies 223 33.4 162 48.9 Why do you not support a low carbon economy in local areas? (nFD=246; nZR=130) Cost 126 51.2 76 58.5 Decrease life quality 120 48.8 54 41.5 Limit economic growth 90 36.6 46 35.4 Sufficient measures 43 17.5 26 20.0 Have you done anything in your daily life to reduce your personal carbon emissions? (nFD=508; nZR=200) Garbage separation and recycling 237 46.7 91 45.5 Use energy-saving facility 257 50.6 116 58.0 Consume rationally to reduce wastes 290 57.1 113 56.5 Shut down electrical equipment if they are not in use 322 63.4 125 62.5 Recycle grey water 200 39.4 83 41.5 Use shopping bag 213 41.9 101 50.5 Take public transit 233 45.9 91 45.5 Avoid using disposable items 137 27.0 73 36.5 Walk to work/school/shops 150 29.5 78 39.0 Cycle to work/school/shops 102 20.1 67 33.5 None of above - - 14 7.0 Are you willing to contribute to developing a low carbon economy in your hometown?  1) Change lifestyle (nFD=451; nZR=159) Garbage separation and recycling 147 32.6 57 35.8 Use energy-saving facility 115 25.5 46 28.9 Consume rationally to reduce wastes 123 27.3 45 28.3 Shut down electrical equipment if they are not in use 108 23.9 44 27.7 Recycle grey water 48 10.6 31 19.5 Use shopping bag 64 14.2 40 25.2 Take public transit 95 21.1 47 29.6 Avoid using disposable items 112 24.8 50 31.4 Walk to work/school/shops 85 18.8 34 21.4   169 Variables Fuding City Zherong County n % n % Cycle to work/school/shops 88 19.5 30 18.9 None of above 7 1.6 - - 2) Contribute money or time (nFD=457; nZR=191) Pay carbon tax 197 43.1 92 48.2 Donate money (voluntary) 234 51.2 89 46.6 Volunteer at low carbon related events 206 45.1 86 45.0 Talk about low carbon lifestyle with families and friends 236 51.6 105 55.0 Which sector do you think should be the first step to develop a low carbon economy? (nFD=711; nZR=356)  Forestry 329 46.3 180 50.6  Agriculture 257 36.1 158 44.4  Business 212 29.8 118 33.1  Industry 454 63.9 226 63.5  Tourism 277 39.0 146 41.0  Building and Development 299 42.1 162 45.5  Energy 292 41.1 158 44.4 Which approach do you think should be a priority to develop a low carbon economy? (nFD=722; nZR=360) Develop and encourage the use of clean energy sources (e.g. solar energy, wind power) 513 71.1 289 80.3 Introduce low-carbon technology to reduce GHG emissions from production and manufacturing processes 457 63.3 238 66.1 Invest in a forest carbon program, while increasing forest cover 422 58.4 225 62.5 Promote green building techniques (e.g. build with wood, reduce emissions from buildings and construction) 340 47.1 185 51.4 Develop/upgrade the public transit system and encourage residents to travel in a low-carbon way 418 57.9 195 54.2 Establish a garbage collection and recycling system 418 57.9 224 62.2 Promote education/outreach events to improve public awareness of climate change and the low carbon economy 368 51.0 206 57.2 The local government should collect carbon tax (nFD=249; nZR=361) Strongly disagree 46 9.6 24 6.6   170 Variables Fuding City Zherong County n % n % Disagree 70 14.6 43 11.9 Partly disagree/agree 169 35.3 133 36.8 Agree 108 22.5 90 24.9 Strongly agree 86 18.0 71 19.7 The local government should subsidize or invest in low-carbon projects (nFD=249; nZR=364) Strongly disagree 4 .8 8 2.2 Disagree 7 1.5 2 .5 Partly disagree/agree 71 14.8 69 19.0 Agree 198 41.3 154 42.3 Strongly agree 199 41.5 131 36.0 The local government should provide low-carbon project loans with a lower interest rate (nFD=479; nZR=362) Strongly disagree 5 1.0 9 2.5 Disagree 9 1.9 4 1.1 Partly disagree/agree 97 20.3 80 22.1 Agree 185 38.6 141 39.0 Strongly agree 183 38.2 128 35.4  Table C.2.2  Parameter Estimates of Fuding Model  For the Wald’s test: H0: The coefficient (B) of each independent variable equals zero H1: The coefficient (B) of each independent variable does not equal zero Support for Low Carbon Economies B Std. Error Wald’s (df =1) p Odds Ratio Hesitant People vs. Supporters      Intercept -0.810 0.542 2.230 .135  Knowledge of low carbon economies  -0.443** 0.159 7.743 .005 0.642 Number of current LC activities -0.248*** 0.065 14.360 <.001 0.781 Number of future LC activities -0.228* 0.107 4.571 .033 0.796 Age -0.002 0.012 0.037 .847 0.998 Opponents vs. Supporter       Intercept -3.023*** 0.690 19.213 <.001  Knowledge of low carbon economies  0.036 0.209 0.030 .862 1.037 Number of current LC activities -0.273** 0.093 8.727 .003 0.761 Number of future LC activities -0.071 0.135 0.273 .601 0.932 Age 0.041** 0.013 9.337 .002 1.042 Note: R2 =0.089 (Cox & Snell), 0.127 (Nagelkerke). Model 𝜒2 (8) =53.201, p<.001. *p<.05, **p<.01, ***p<.001.   171  Table C.2.3 Parameter Estimates of Zherong Model  For the Wald’s test: H0: The coefficient (B) of each independent variable equals zero H1: The coefficient (B) of each independent variable does not equal zero Support for Low Carbon Economies  B Std. Error Wald’s (df =1) p Odds Ratio Hesitant People vs. Supporters Intercept -2.223*** 0.504 19.498 <.001  Knowledge of low carbon economies -0.973** 0.307 10.073 .002 0.378 Number of current LC activities -0.250* 0.117 4.577 .032 0.779 Opponents vs. Supporters       Intercept -1.727*** 0.385 20.136 <.001  Knowledge of low carbon economies -0.336 0.234 2.067 .150 0.714 Number of current LC activities -0.179 0.093 3.699 .054 0.836 Note: R2 =0.130 (Cox & Snell), 0.193 (Nagelkerke). Model 𝜒2 (4) =30.567, p<.001. *p<.05, **p<.01, ***p<.001.    172 C.3 Compare between the Public, Community Residents and Government Employees Table C.3.1 Frequency Distributions of Variables that Measure the Perception of Low Carbon Economies by Sample Group Variables Public Community Government n % n % n % How much do you know about low carbon economies? (np=465; nc=358; ng=261) Never heard of it 49 10.5 53 14.8 12 4.6 Heard of it but know little about it 195 41.9 211 58.9 80 30.7 Heard of it and know about it 193 41.5 77 21.5 149 57.1 Know it very well 28 6.0 17 4.7 20 7.7 How do you learn about low carbon economies? (np=434; nc=328; ng=252) Television 346 79.7 284 86.6 214 84.9 Thematic events 117 27.0 80 24.4 67 26.6 Internet 254 58.5 172 52.4 199 79.0 Advertisements 146 33.6 74 22.6 97 38.5 Newspaper and magazines 155 35.7 100 30.5 124 49.2 Public lectures 93 21.4 17 5.2 33 13.1 Do you support a low carbon economy? (np=460; nc=362; ng=259) Yes 377 82.0 295 82.5 249 96.1 No 23 4.9 33 9.1 10 3.9 Don't know 60 12.8 34 9.4 - - Why do you support a low carbon economy in the local area? (np=429; nc=319; ng=251) Mitigate climate change 263 61.3 206 64.6 192 76.5 Improve quality of life 250 58.3 189 59.2 205 81.7 Promote sustainable development 199 46.4 115 36.1 233 92.8 Improve environment 368 85.8 221 69.3 159 63.3 Leadership in low carbon economies 158 36.8 98 30.7 129 51.4 Why do you not support a low carbon economy in local areas? (np=149; nc=152; ng=75) Cost 73 49.0 80 52.6 49 65.3 Decrease life quality 56 37.6 87 57.2 31 41.3 Limit economic growth 56 37.6 47 30.9 33 44.0 Sufficient measures 24 16.1 35 23.0 10 13.3 Have you done anything in your daily life to reduce your carbon emissions? (np=383; nc=327) Garbage separation and recycling 179 46.7 150 45.9 - - Use energy-saving facility 238 62.1 136 41.6 - - Consume rationally to reduce wastes 232 60.6 172 52.6 - - Shut down electrical equipment if they are not in use 274 71.5 174 53.2 - - Recycle grey water 153 39.9 130 39.8 - - Use shopping bag 183 47.8 132 40.4 - - Take public transit 207 54.0 118 36.1 - - Avoid using disposable items 121 31.6 89 27.2 - -   173 Variables Public Community Government n % n % n % Walk to work/school/shops 149 38.9 80 24.5 - - Cycle to work/school/shops 100 26.1 69 21.1 - - None of above 8 2.1 6 1.8 - - Are you willing to contribute to developing a low carbon economy in your hometown?  1) Change lifestyle(np=323; nc=280) Garbage separation and recycling 122 37.8 82 29.3 - - Use energy-saving facility 84 26.0 77 27.5 - - Consume rationally to reduce wastes 100 31.0 68 24.3 - - Shut down electrical equipment if they are not in use 84 26.0 68 24.3 - - Recycle grey water 60 18.6 19 6.8 - - Use shopping bag 80 24.8 24 8.6 - - Take public transit 77 23.8 65 23.2 - - Avoid using disposable items 107 33.1 55 19.6 - - Walk to work/school/shops 43 13.3 76 27.1 - - Cycle to work/school/shops 53 16.4 65 23.2 - - None of above - - - - - - 2) Contribute money or time (np=361; nc=257) Pay carbon tax 150 42.6 139 54.3 - - Donate money (voluntary) 204 58.0 119 46.5 - - Volunteer at low carbon related events 175 48.5 117 45.5 - - Talk about low carbon lifestyle with family and friends 197 54.6 144 56.0 - - Which sector do you think should be the first step to developing a low carbon economy? (np=448; nc=360; ng=259) Forestry 245 54.7 157 43.6 107 41.3 Agriculture 179 40.0 133 36.9 103 39.8 Business 160 35.7 91 25.3 79 30.5 Industry 312 69.6 188 52.2 180 69.5 Tourism 192 42.9 134 37.2 97 37.5 Building and development 228 50.9 139 38.6 94 36.3 Energy 267 59.6 82 22.8 101 39.0 Which sector do you think should be the priority for developing a low carbon economy? (np=461; nc=364; ng=257) Clean energy 348 75.5 242 66.5 212 82.5 Low carbon technology 298 64.6 202 55.5 195 75.9 Forest carbon projects 292 63.3 174 47.8 181 70.4 Green building 229 49.7 161 44.2 135 52.5 Public transport 290 62.9 149 40.9 174 67.7 Recycling 285 61.8 174 47.8 183 71.2 Public perception 268 58.1 129 35.4 177 68.9 What do you think is/are the biggest obstacle to developing a low carbon economy locally?   174 Variables Public Community Government n % n % n % (ng=259) Low public awareness - - - - 214 82.0 Low priority in government agenda - - - - 151 57.9 Ineffective policy - - - - 156 59.8 Lack of cooperation with NGOs - - - - 70 26.8 Hard to change current development pattern  - - - - 142 54.4 Huge financial investment as a burden - - - - 100 38.3 Lack of guidance - - - - 136 52.1 The local government should collect carbon tax (np=464; nc=120;  ng=256) Strongly disagree 39 8.4 7 5.8 24 9.4 Disagree 63 13.6 14 11.7 36 14.1 Partly disagree/agree 179 38.6 51 42.5 72 28.1 Agree 108 23.3 26 21.7 64 25.0 Strongly agree 75 16.2 22 18.3 60 23.4 The local government should subsidize or invest in low carbon projects (np=463; nc=121;  ng=259) Strongly disagree 4 .9 5 4.1 3 1.2 Disagree 5 1.1 27 22.3 4 1.5 Partly disagree/agree 83 17.7 - - 30 11.6 Agree 191 40.8 54 44.6 107 41.3 Strongly agree 180 38.5 35 28.9 115 44.4 The local government should provide loans to low carbon projects with a lower interest rate (np=463; nc=119;  ng=259) Strongly disagree 5 1.1 6 5.0 3 1.2 Disagree 10 2.2 2 1.7 1 .4 Partly disagree/agree 111 24.0 32 26.9 34 13.1 Agree 174 37.6 44 37.0 108 41.7 Strongly agree 163 35.2 35 29.4 113 43.6   Table C.3.2 Mann-Whitney Tests on Concern about Climate Change between Sample Groups Concern of Climate Change P vs. C  P vs. G  C vs. G  U z p r U r p r U r p r The globe 73511 -2.98 0.003 0.10 47457 -4.92 <0.001 0.18 32114 -6.85 <0.001 0.28 Local community 69782 -4.02 <0.001 0.14 48707 -4.31 <0.001 0.16 31011 -7.48 <0.001 0.30 Immediate family 65772 -5.15 <0.001 0.18 50987 -2.92 0.003 0.11 30900 -7.29 <0.001 0.29 Future generations 61407 -6.51 <0.001 0.23 50455 -3.31 0.001 0.12 28208 -8.68 <0.001 0.35    175 Table C.3.3 Number of Low Carbon Activities that Respondents Have Done (Current) and Activities They Are Willing to Try (Future) by Sample Group # of Low Carbon Activities Public  Community  P vs. C n Mean Std. Dev. n Mean Std. Dev. U z p r Current 468 3.92 2.88 366 3.42 2.59 75121 -3.074 0.002 -0.106 Future 468 1.73 1.80 366 1.64 1.29 83718 -0.573 0.567 -0.020  Table C.3.4 Rotated Factor Pattern and Factor Label (Varimax Rotation) by Sample Group Label Factor 1 Factor 2 Factor 3 Public Group Concern about global impacts of climate change .775 .021 .113 Concern about impacts on local community .919 .016 .058 Concern about impacts on immediate family .922 .050 .034 Concern about impacts on future generations .852 .062 .078 Knowledge of low carbon economies .165 .018 .959 Support for carbon tax .052 .539 -.190 Support for low carbon subsidies -.005 .870 .112 Support for low carbon preferential loans .049 .874 .124 Community Group Concern about global impacts of climate change .718 -.002 .199 Concern about impacts on local community .908 -.074 -.071 Concern about impacts on immediate family .928 -.043 -.004 Concern about impacts on future generations .859 -.031 .118 Knowledge of low carbon economies .140 .084 .967 Support to carbon tax -.201 .727 .213 Support to low carbon subsidies .105 .876 .018 Support to low carbon preferential loans -.038 .859 -.068 Government Group Concern about global impacts of climate change .835 .006 .121 Concern about impacts on local community .926 -.109 .019 Concern about impacts on immediate family .947 -.065 .025 Concern about impacts on future generations .908 -.044 .086 Knowledge of low carbon economies .130 .023 .985 Support to carbon tax -.040 .583 .084 Support to low carbon subsidies -.025 .886 -.087 Support to low carbon preferential loans -.069 .877 -.002   176 Label Factor 1 Factor 2 Factor 3 Factor Labels Concern about climate change Support for low carbon measures Knowledge of low carbon economies Extraction Method: Principal Component Analysis Rotation Method: Varimax with Kaiser Normalization Numbers in bold indicate the factor that each variable is the closely related to   Table C.3.5 Parameter Estimates for Models of Sample Groups Model B Std. Error Wald’s (df =1) Odds Ratio (95% CI) Public Group: R2 =.075 (Cox & Snell), .110 (Nagelkerke). Model 𝝌𝟐 (6) =34.564, p=.000 Hesitant People vs. Supporters      Intercept (Constant) -2.062*** 0.169 149.550  Concern about climate change  -0.293* 0.139 4.467 0.746 (0.569-0.979) Support for low carbon policies -0.402** 0.145 7.726 0.669 (0.504-0.888) Knowledge of low carbon economies -0.656*** 0.154 18.121 0.519 (0.384-0.702) Opponents vs. Supporters      Intercept -2.801*** 0.226 153.699  Concern about climate change  -0.006 0.222 .001 0.994 (0.643-1.534) Support for low carbon policies -0.392 0.206 3.630 0.676 (0.452-1.011) Knowledge of low carbon economies -0.253 0.218 1.350 0.776 (0.507-1.190) Community Group: R2 =.091 (Cox & Snell), .141 (Nagelkerke). Model 𝝌𝟐 (2) =10.944, p=.004 Hesitant People vs. Supporters      Intercept -2.843*** 0.518 30.130  Knowledge of low carbon economies -1.359** 0.519 6.845 0.257 (0.093-0.711) Opponents vs. Supporters      Intercept -2.735*** 0.431 40.255  Knowledge of low carbon economies -0.608 0.471 1.668 0.544 (0.216-1.370) Government Group: R2 =.134 (Cox & Snell), .468 (Nagelkerke). Model 𝝌𝟐 (3) =35.661, p=.000 Intercept 4.865*** 0.762 40.781 129.623 Concern about climate change  0.896** 0.344 6.771 2.450 (1.248-4.813) Support for low carbon policies 1.240*** 0.344 13.003 3.456 (1.761-6.781) Knowledge of low carbon economies 1.185** 0.415 8.170 3.271 (1.451-7.374) *p<.05, **p<.01, ***p<.001   

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