Science, Faculty of
Resources, Environment and Sustainability (IRES), Institute for
DSpace
UBCV
Cheng, Yuqing
Master of Science - MSc
University of British Columbia
Coastal flood protection measures are increasingly vital due to climate change and rising sea levels, and their long-term success depends on both technical efficacy and local community support. Coastal green infrastructure (CGI) supplements traditional hard structures (THS) for managing floods, offering both coastal erosion prevention and ecological benefits. Prior studies show that immigrant and non-immigrant groups differ in environmental risk perceptions and landscape preferences; however, little is known about their respective views on climate change-related coastal flooding. This study explores factors that influence public perceptions of coastal flood risk and support for CGI, focusing on how such perceptions and support vary across population subgroups based on immigration status. Three groups were compared: recent immigrants and temporary residents (RITR, defined as those having migrated to Canada within the last 5 years and those on study and work permits), long-term immigrants (LI, defined as those having migrated to Canada over 5 years ago), and those born in Canada (BIC). A questionnaire survey was conducted in Richmond, British Columbia – where immigrants constitute over 60% of the population – between July and September 2023. Results showed high general perception of coastal flooding risks among participants, with LI and BIC groups exhibiting higher risk perceptions than RITR group. These differences were partly due to immigration status moderating the relationships between risk perception and its determining factors: for LI and BIC groups, risk perceptions were primarily driven by hazard awareness; for the RITR group, factors such as awareness of existing flood protections, access to flood-related information, and homeownership also played significant roles. Most participants supported including CGI in flood risk management strategies. More BIC participants favored hybrid solutions, while more LI and RITR participants abstained from an opinion. Immigration status again moderated the relationships between flood risk management preferences and its determining factors: for all groups, knowledge about CGI’s benefits and higher CGI familiarity positively correlated with support for CGI; while for LI and RITR groups, support for CGI was also correlated with environmental values. These findings highlight the importance of targeted communication strategies to enhance flood risk awareness and support for CGI across diverse immigration groups.
IMMIGRATION STATUS, FLOOD RISK PERCEPTION, AND POLICY SUPPORT: LESSONS FROM RICHMOND, CANADA by Yuqing Cheng B.Sc., The University of British Columbia, 2022 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Resources, Environment and Sustainability) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) July 2024 © Yuqing Cheng, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Immigration Status, Flood Risk Perception, and Policy Support: Lessons from Richmond, Canada submitted by Yuqing Cheng in partial fulfilment of the requirements for the degree of Master of Science in Resources, Environment and Sustainability Examining Committee: Dr. Stephanie Chang, Professor, Institute for Resource, Environment and Sustainability, UBC Supervisor Dr. Ethan Raker, Assistant Professor, Department of Sociology, UBC Supervisory Committee Member Dr. Terre Satterfield, Professor, Institute for Resource, Environment and Sustainability, UBC Supervisory Committee Member Dr. Holly Caggiano, Assistant Professor, School of Community and Regional Planning, UBC Additional Examiner iii Abstract Coastal flood protection measures are increasingly vital due to climate change and rising sea levels, and their long-term success depends on both technical efficacy and local community support. Coastal green infrastructure (CGI) supplements traditional hard structures (THS) for managing floods, offering both coastal erosion prevention and ecological benefits. Prior studies show that immigrant and non-immigrant groups differ in environmental risk perceptions and landscape preferences; however, little is known about their respective views on climate change-related coastal flooding. This study explores factors that influence public perceptions of coastal flood risk and support for CGI, focusing on how such perceptions and support vary across population subgroups based on immigration status. Three groups were compared: recent immigrants and temporary residents (RITR, defined as those having migrated to Canada within the last 5 years and those on study and work permits), long-term immigrants (LI, defined as those having migrated to Canada over 5 years ago), and those born in Canada (BIC). A questionnaire survey was conducted in Richmond, British Columbia – where immigrants constitute over 60% of the population – between July and September 2023. Results showed high general perception of coastal flooding risks among participants, with LI and BIC groups exhibiting higher risk perceptions than RITR group. These differences were partly due to immigration status moderating the relationships between risk perception and its determining factors: for LI and BIC groups, risk perceptions were primarily driven by hazard awareness; for the RITR group, factors such as awareness of existing flood protections, access to flood-related information, and homeownership also played significant roles. iv Most participants supported including CGI in flood risk management strategies. More BIC participants favored hybrid solutions, while more LI and RITR participants abstained from an opinion. Immigration status again moderated the relationships between flood risk management preferences and its determining factors: for all groups, knowledge about CGI’s benefits and higher CGI familiarity positively correlated with support for CGI; while for LI and RITR groups, support for CGI was also correlated with environmental values. These findings highlight the importance of targeted communication strategies to enhance flood risk awareness and support for CGI across diverse immigration groups. v Lay Summary Coastal flood protection is crucial due to climate change and rising sea levels. Coastal green infrastructure (CGI), like wetland restoration, offers both flood protection and ecological benefits. This study explored how different immigration groups in Richmond, British Columbia perceive flood risks and support CGI. Richmond, where over 60% of the population are immigrants, provided a diverse setting for this research conducted between July and September 2023. The study found that long-term immigrants and native-born residents had higher flood risk perceptions than recent immigrants and temporary residents. For long-term residents, hazard awareness was key, while recent immigrants also considered homeownership, flood-related information provided, and existing flood protections. Most participants supported CGI, though native-born residents showed the most support. Lower environmental consciousness contributed to lower CGI support among the other two groups. These findings suggest that tailored communication strategies are essential to increase flood risk awareness and CGI support among diverse communities. vi Preface This thesis, encompassing the research design, execution of all research components, and data analysis, represents the original intellectual work of the author, Yuqing Cheng, with advice from the supervisory committee. The study involved residents of Richmond, BC, and received approval from the UBC Behavioral Research Ethics Board. The surveys conducted were authorized under UBC Ethics Certificate number H23-00298. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ..................................................................................................................................x List of Figures .............................................................................................................................. xii List of Abbreviations ................................................................................................................. xiii Acknowledgements .................................................................................................................... xiv Dedication ................................................................................................................................... xvi Chapter 1: Introduction ................................................................................................................1 1.1 Problem and Research Objective .................................................................................... 1 1.1.1 Coastal Flooding and Sea Level Rise ......................................................................... 1 1.1.2 Traditional Hard Structure vs Coastal Green Infrastructure ....................................... 2 1.1.3 Public Attitudes toward Coastal Green Infrastructure ................................................ 4 1.1.4 Flood Risk Perception ................................................................................................. 5 1.1.5 Immigrants and Natural Hazards ................................................................................ 6 1.1.6 Canadian Context and Research Objective ................................................................. 7 1.2 Study Site: City of Richmond, British Columbia ........................................................... 9 1.2.1 Natural Environment and Coastal Flood Risk .......................................................... 10 1.2.2 Current Flood Protection Measures .......................................................................... 11 1.2.3 History of Immigration ............................................................................................. 12 viii 1.3 Literature Review.......................................................................................................... 13 1.3.1 Risk Perception and Immigration ............................................................................. 13 1.3.2 Support for Coastal Green Infrastructure and Immigration ...................................... 16 1.4 Conceptual Framework ................................................................................................. 19 Chapter 2: Methodology..............................................................................................................23 2.1 Research Design ............................................................................................................ 23 2.1.1 Survey Design ........................................................................................................... 23 2.1.2 Data collection .......................................................................................................... 25 2.2 Survey Responses ......................................................................................................... 27 2.3 Data Analysis ................................................................................................................ 35 2.3.1 Quantitative data analysis ......................................................................................... 35 2.3.2 Qualitative data analysis ........................................................................................... 36 Chapter 3: Results........................................................................................................................37 3.1 Risk Perceptions ............................................................................................................ 37 3.1.1 Descriptive Statistics ................................................................................................. 37 3.1.2 Comparative Analysis - Risk Perceptions ................................................................. 40 3.1.3 Comparative Analysis - Potential Mediators ............................................................ 45 3.1.4 Correlation Analysis between Potential Mediators and Risk Perception ................. 47 3.1.5 Mediation Analysis ................................................................................................... 49 3.1.6 Regression Analysis .................................................................................................. 51 3.2 Support for Coastal Green Infrastructure ...................................................................... 54 3.2.1 Descriptive Statistics ................................................................................................. 54 3.2.2 Comparative Analysis – Support for CGI ................................................................. 55 ix 3.2.3 Comparative Analysis – Potential Mediators ........................................................... 57 3.2.4 Influence of Potential Mediators on Funding Preferences ........................................ 60 3.2.5 Mediation Analysis ................................................................................................... 62 3.2.6 Regression Analysis .................................................................................................. 63 3.2.7 Qualitative Analysis .................................................................................................. 67 Chapter 4: Conclusion .................................................................................................................71 4.1 Summary of Results ...................................................................................................... 71 4.1.1 Risk Perception ......................................................................................................... 71 4.1.2 Support for CGI ........................................................................................................ 72 4.2 Key Conclusions and Policy Implications .................................................................... 73 4.3 Contributions and Limitations ...................................................................................... 78 4.4 Future Research ............................................................................................................ 80 References .....................................................................................................................................82 Appendices ..................................................................................................................................107 Appendix A Survey Questionnaire ......................................................................................... 107 Appendix B Regression Analysis with Interaction Terms ...................................................... 117 B.1 Results ..................................................................................................................... 117 B.2 Scatter plots: predictors vs. risk perception by immigration status ........................ 120 x List of Tables Table 2.1 Demographics and socioeconomic characteristics of the sample. ................................ 31 Table 2.2 Immigration status categories used in the study. .......................................................... 35 Table 3.1 Risk perception mean by immigration group. .............................................................. 40 Table 3.2 Kruskal-Wallis Test: Risk Perception Differences by Immigration Group. ................. 41 Table 3.3 Kruskal-Wallis and Chi-square Test: Potential Mediator Differences by Immigration Group. ........................................................................................................................................... 46 Table 3.4 Kendall's Tau Test: Correlation between Mediator Variables and RP4. ...................... 48 Table 3.5 Results from Cronbach’s Alpha test for risk perception predictors. ............................. 51 Table 3.6 Stratified regression for flood risk perception by immigration status. ......................... 53 Table 3.7 Attitudes toward incorporating wetland restoration in flood management, N=283. .... 54 Table 3.8 Coastal flooding funding allocation preferences, N=281 ............................................. 55 Table 3.9 Attitudes toward incorporating wetland restoration in flood management, mean scores by immigration status, N=270. ..................................................................................................... 56 Table 3.10 Coastal flooding funding allocation preferences by immigration status, N=270. ...... 57 Table 3.11 Kruskal-Wallis Test: Potential Mediator Differences by Immigration Group. .......... 59 Table 3.12 Kruskal-Wallis test: potential mediator differences by funding allocation group. ..... 61 Table 3.13 Results from Cronbach’s Alpha test for funding allocation predictors. ..................... 63 Table 3.14 Multinominal logistic regression for flood funding allocation preference. ................ 65 Table 3.15 Stratified regression for flood funding allocation preference by immigration status. 67 Table 3.16 Results from Open-ended question on reasons behind participants’ high or low support for including coastal green infrastructure in flood management. .................................... 69 xi Table B.1 Multiple regression for flood risk perception without and with immigration interaction terms. ........................................................................................................................................... 118 xii List of Figures Figure 1.1 City of Richmond, BC. .................................................................................................. 9 Figure 1.2 Conceptual framework 1 (mediation framework) with immigration being the independent variable. .................................................................................................................... 20 Figure 1.3 Conceptual framework 2 (moderation framework) with immigration being moderator variable. ......................................................................................................................................... 22 Figure 2.1 Table setup at one of the data collection sites: the Steveston Community Center. Photo taken by Yuqing Cheng. ..................................................................................................... 26 Figure 2.2 The map of Richmond geographically segmented into regions labeled A through J, to query participants about their approximate distance from the coastline. ...................................... 30 Figure 3.1 Responses to risk perception statements (N=314). ..................................................... 38 Figure 3.2 Mean scores of risk perception statements by immigration group (N=270). .............. 43 Figure 3.3 Box plots illustrating variations in risk perception statements RP1 (a), RP 2 (b), and RP 3 (c) among three immigration groups. ................................................................................... 44 Figure 3.4 Results of multiple mediation model explaining why long-term immigrants had higher risk perception of potential damage caused by future coastal flooding and sea level rise. .......... 50 Figure 3.5 Results of multiple mediation model explaining why born-in-Canada individuals more likely support allocating most flood protection funding for hybrid solutions. ............................. 62 xiii List of Abbreviations ANOVA Analysis of Variance BC British Columbia BIC Born in Canada CGI Coastal Green Infrastructure LI Long-term Immigrants RITR Recent Immigrants and Temporary Residents SLR Sea Level Rise THS Traditional Hard Structure xiv Acknowledgements First and foremost, I would like to express my deepest gratitude to my supervisor, Stephanie Chang, for her invaluable guidance and continuous support throughout my research. Her insightful feedback and encouragement have been instrumental in the completion of this thesis. I would also like to extend my sincere thanks to the members of my thesis committee, Ethan Raker and Terre Satterfield, for their valuable time, suggestions, and constructive criticism, which greatly contributed to improving the quality of my work. I am deeply grateful to my colleagues and friends at the Institute for Resources, Environment and Sustainability for creating a stimulating and enjoyable environment in which to learn and grow. In particular, I would like to thank Tatiana Chamorro, Charly Phillips, and Gabby Fraser for always being there to discuss ideas and provide moral support. I would also like to acknowledge the financial support from the Pacific Institute for Climate Solutions and the Pei-Huang Tung and Tan-Wen Tung Graduate Fellowship, which made this research possible. Their generosity and belief in my work have been greatly appreciated. I would like to thank the individuals from Richmond, BC, who participated in this study and offered their valuable opinions. xv Finally, I am profoundly grateful to my parents for their love and support. I also feel extremely lucky to have my partner, Jiafan, and my dear friend, Shijing, for always believing in me and encouraging me. xvi Dedication To a better future. 1 Chapter 1: Introduction 1.1 Problem and Research Objective 1.1.1 Coastal Flooding and Sea Level Rise Around one-fourth of the global population lives within 100 km of a shoreline (Zanuttigh et al., 2015). Coastal flooding is a major natural hazard that poses serious threats to coastal communities. Common triggers of coastal flooding include high winds, high tides, storm surge, and tsunami caused by an earthquake or submarine landslides (ICLR, 2021). Due to climate change, sea level rise (SLR) has also become a major concern in coastal flood management. SLR is mainly caused by the melting of land ice, including glaciers and ice sheets, and the thermal expansion of ocean water (Cazenave & Cozannet, 2014). Evidence has shown that the rate of SLR since the early 1990s (3.2-3.4 mm/year) is double the average rate of the twentieth century (1.6-1.8 mm/year) (Dieng et al., 2017). Even though sea-level rise appears slow and gradual, it can increase coastal flood frequency exponentially: based on their SLR projections, Taherkhani et al. (2020) found that the likelihood of extreme coastal flooding along the U.S. coasts will likely double about every five years as we move forward into the future. Vitousek et al. (2017) suggested that a sea-level rise of 10 to 20 cm, anticipated by 2050 at the latest, will more than double the occurrence of extreme coastal flooding events in the Tropics. In addition to the heightened frequency of coastal flooding, SLR will drive a surge in beach erosion, thereby compounding the risk of flooding. This arises from the fact that deteriorated sandy beaches can no longer afford the flood protection they once provided when they were robust and intact (Zhang et al., 2004). An increase of 10 cm in sea level along the shoreline is estimated to cause 15 m of coastline erosion (Leatherman et al., 2000). 2 Flood risk arises not only from the probability and magnitude of the flood event, but also from the exposure and vulnerability of society, which together determine hazard consequences (Klijn et al., 2015; Solín & Skubinčan, 2013). With rapid population growth in the urbanization of coastal cities, the number of people and infrastructure susceptible to future flooding will increase significantly. In a global assessment of coastal population growth, even under the most conservative assumptions, the number of people living in low-elevation coastal zones are estimated to increase by about 50% by 2030 and 122% by 2060, compared to the base year 2000 (Neumann et al., 2015). This surge in exposure presents additional complexities for flood risk management, posing significant challenges in effectively mitigating and addressing the potential impacts of flooding (Neumann et al., 2015). Additionally, certain groups of people are more vulnerable to coastal floods due to their socio-economic status, age, gender, ethnicity, and other relevant factors (Pricope et al., 2019). It is imperative to adopt effective and equitable flood protection strategies to deal with future sea level rise and coastal flooding events. 1.1.2 Traditional Hard Structure vs Coastal Green Infrastructure Various strategies have been adopted to deal with sea level rise and coastal flooding, which scholars have mapped along the grey-green spectrum of coastal protection. Traditional hard infrastructure (THS), positioned at the 'grey infrastructure' end, consists of flood protection structures built with abiotic materials such as cement, steel, or rock (Singhvi et al., 2022). They include most dikes, seawalls, and levees. THS has been studied and used for hundreds of years, resulting in a relatively comprehensive knowledge base concerning their design and effectiveness (Waryszak et al., 2021). However, more and more researchers have realized the shortcomings of THS. For one, the construction of THS can alter the level and velocity of water, reduce water 3 quality, and damage nearby wildlife habitats (Brody et al., 2010; Bulleri & Chapman, 2010; Onuma & Tsuge, 2018). Moreover, if the flooding exceeds the THS’s resistance capacity, the breach of the structure can result in larger flood damages compared to having no structure at all (Schmocker & Hager, 2012; Takeda et al., 2021). Furthermore, THS can create a false sense of security and lead to more construction in flood-prone areas (Logan et al., 2018). Coastal green infrastructure (CGI) is increasingly recognized as a desirable approach to complement THS for flood management (Chávez et al., 2021; Conger & Chang, 2019). CGI, which is also called “nature-based solutions,” refers to natural or semi-natural structures that offer flood protection but also deliver multiple ecosystem services ((Tyler, 2016). Common examples include natural or restored coastal vegetation, dunes, coral reefs, and sand nourishment; it also includes de-engineering, which means the removal of existing coastal structures (Silva et al., 2017). CGI can help prevent coastal erosion and flooding by trapping shore sediments, buffering wave energy, and absorbing excessive water (Morris et al., 2018; Onuma & Tsuge, 2018; Park et al., 2023; Ruangpan et al., 2020). For example, it has been estimated that coral reefs and salt marshes can reduce wave height by 70% and 72% respectively (Narayan et al., 2016). Additionally, CGI is more than physical infrastructure for flood protection – it is an interdisciplinary framework that recognizes coastal regions as socio-ecological systems and aims to maintain human livelihood and ecosystem health simultaneously (Chávez et al., 2021; Smith et al., 2020). Despite being multifunctional and adaptive, the complexity of CGI can also hinder its application. CGI usually requires more space and time to provide protection equivalent to THS. Besides, CGI itself can be susceptible to seasonal change and the negative impacts of climate change and human activities (Conger & Chang, 2019). Other challenges include a lack of standardized design processes and regulations, insufficient data and expertise, difficulties in 4 measuring co-benefits, and low levels of public knowledge about CGI (Linton, 2018; Zuniga-Teran et al., 2020). These factors can lead to path dependency in flood risk management – the resistance to changing defense strategies even when new or better alternatives are available (Matthews et al., 2015; Rawal, 2022). Given the constraints inherent in each form of infrastructure, there is a burgeoning interest in integrating THS and CGI to form hybrid approaches to mitigate the risk of coastal flooding (Singhvi et al., 2022; Waryszak et al., 2021). In hybrid approaches, hard structures (e.g., dikes, temporary seawalls, and flood gates) are built simultaneously with restored coastal vegetation, sediment, or coral reefs (Cheong et al., 2013; Waryszak et al., 2021). The green infrastructure can offer low to medium storm protection and other socio-ecological benefits, while the hard structure can provide additional protection during large storms (Sutton-Grier et al., 2015). Nevertheless, hybrid approaches do not come without trade-offs. Firstly, the built component will still negatively impact the ecological connectivity and species diversity in the region (Bishop et al., 2017). Secondly, hybrid approaches can be expensive and encounter a difficult process of getting construction permits (Nordstrom, 2014). Additionally, there is limited data on system performance and cost-to-benefit ratio of hybrid coastal structures (Waryszak et al., 2021). 1.1.3 Public Attitudes toward Coastal Green Infrastructure Choosing between traditional hard structure, coastal green infrastructure, and hybrid solutions presents a complex challenge (Sutton-Grier et al., 2015; Waryszak et al., 2021). It is not a solely engineering issue but a value-laden task that needs answers to critical questions such as: How should we deal with potential trade-offs between flood protection and the preservation of coastal habitats? What level of uncertainty should be accepted in the planning process? How far 5 into the future should we plan for? To ensure sustainable and equitable flood management, it is essential to include not only the insights of the scientific community but also the diverse perspectives of the general public (Anderson & Renaud, 2021; Gray et al., 2017a; Oliveira & Pinto, 2021). Having long-term public support is critical for the success of governance initiatives aimed at mitigating flood hazards (Anderson & Renaud, 2021; Gray et al., 2017b). 1.1.4 Flood Risk Perception The importance of public risk perception has long been recognized in the field of flood risk management (Armas & Avram, 2009; Brilly & Polic, 2005; Buchecker et al., 2013; Kellens et al., 2013; Rana et al., 2020). For the purposes of this study, flood risk perception is defined as an individual's assessment of the likelihood of flood-related hazards, the potential consequences upon their realization, and the level of concern associated with these factors (Bubeck et al., 2012; Lechowska, 2018). Public risk perceptions play a crucial role for several reasons. Firstly, public risk perception can reveal factors that may be underestimated or overlooked by expert-led risk assessments (Baan & Klijn, 2004; Bradford et al., 2012). Experts typically rely on theoretical models and quantitative estimates – often expressed in terms of fatalities — to assess risks, which have traditionally been viewed as “objective” assessments (Plattner et al., 2006; Skjong & Wentworth, 2001). However, this objectivity has been questioned – every step of the evaluation process, from defining "risk" to selecting timeframes, models, and identifying potential victims, involves inherent assumptions and value judgments (Green et al., 1991; Plattner et al., 2006; Scheuer & Haase, 2012; Slovic & Weber, 2013). Meanwhile, experts’ opinions can be influenced by other constraints, such as budgets, career considerations, and bureaucratic expansion (Baan & Klijn, 2004). Public perceptions of risk, informed by different assumptions and factors, 6 can offer valuable insights into flood risk management, highlighting aspects such as equity considerations, resilience, and threats to future generations (Slovic et al., 1982). Understanding how individuals perceive risks can guide decision-makers in addressing public concerns, building trust in local institutions, effectively disseminating flood-related information, and developing more equitable and effective management strategies (Bradford et al., 2012; Burningham et al., 2008). Another critical aspect of public risk perception is its influence on people’s behaviors regarding disaster preparedness and response (Reynaud et al., 2013; Vinh et al., 2007). While the relationships between risk perception and preparedness behaviors are complex and subject to debate, numerous studies indicate that risk perception is a significant determinant of disaster preparedness (Reynaud et al., 2013; Wachinger et al., 2013; Yong & Lemyre, 2019). 1.1.5 Immigrants and Natural Hazards Immigrants are often marginalized in the context of natural hazards and environmental decision making. Defined as individuals who have settled (semi-)permanently away from their habitual residence (Urquia & Gagnon, 2011), their vulnerability stems from multiple barriers including language, cultural, social, and economic challenges when settling in a new country (Donner & Rodríguez, 2008; Nepal et al., 2012; Yong et al., 2017). Immigrant groups are also likely to be ethnic and cultural minorities, therefore, they may encounter additional discrimination and obstacles in disaster preparedness and response, which can exacerbate their exclusion (Burke et al., 2012; Hanson-Easey et al., 2018; Maldonado et al., 2016). These barriers can further lead to little local knowledge, weak community ties, scarce resources for evacuation and recovery, hesitancy to seek help, and conflicts of values with local institutions (Donner & Rodríguez, 2008; Eisenman et al., 2007; Maldonado et al., 2016). 7 Consequently, immigrants often experience more serious and lasting impacts from environmental disasters (Arunotai, 2008; Pellow & Vazin, 2019; Raker, 2022). For example, during the 2004 Indian Ocean Tsunami, immigrants in Thailand were disproportionately affected (Arunotai, 2008), and similarly, during Hurricane Harvey, immigrants experienced greater health declines than their counterparts (Raker, 2022). Economic desperation post-disaster may also lead them to accept employment in unsafe conditions. For instance, following Hurricanes Katrina and Sandy, Latino immigrants in the United States, employed as reconstruction day laborers, faced increasingly dangerous situations and exposure to hazardous materials, stemming from the incompetence or ignorance of homeowners and contractors who failed to provide safe working conditions (Cordero-Guzman et al., 2013; Delp et al., 2009). Despite their high vulnerability to natural hazards, immigrants are frequently overlooked in disaster preparedness planning (Cuervo et al., 2017; Kyaw, 2021; Yong et al., 2017). There is a lack of culturally sensitive, linguistically diverse disaster education and emergency information offered to immigrants, contributing to their low awareness of local hazards, difficulties in receiving and interpretating warning messages, and limited access to post-disaster relief (Bhandari & Takahashi, 2022; Chen, 2014; Guadagno et al., 2017; Mathew & Kelly, 2008; Nepal et al., 2012). 1.1.6 Canadian Context and Research Objective In Canada, projected local sea level rise is expected to intensify coastal flooding along most of the Pacific and Atlantic coasts, leading to coastline erosion, ecosystem degradation, and infrastructure damage; this puts over seven million coastal residents at risk (Canada’s Changing Climate Report, 2019; Jalili Pirani & Najafi, 2020). Meanwhile, immigrants make up almost a quarter of Canada’s total population (Government of Canada, 2022a). The figure is expected to 8 increase to between 29.1% and 34.0% by 2041 if current trends continue (Statistics Canada, 2022). Moreover, the Red Cross identified new immigrants and cultural minorities as one of the ten higher risk groups in disasters (Canadian Red Cross, 2007). However, there has been scant research on how immigrants perceive the risks associated with sea level rise and coastal flooding in Canada. Furthermore, as coastal green infrastructure projects gain traction in Canada, little is understood about the public’s attitudes toward these initiatives and how they may vary by immigration status. The primary objective of the research is to address the following overarching question: How do risk perceptions of coastal flooding and support for coastal green infrastructure vary based on immigration status? Considering potential interaction between immigration status and length of residency, this study aims specifically to examine the following sub-questions: 1) How do coastal flood risk perceptions differ among native-born residents, long-term immigrants, and recent immigrants? 2) How does support for coastal green infrastructure differ among native-born residents, long-term immigrants, and recent immigrants? 3) What are some potential reasons contributing to observed differences and similarities? These questions are investigated through a case study of Richmond, BC. 9 1.2 Study Site: City of Richmond, British Columbia Richmond is a coastal city located on the lower Fraser River estuary in British Columbia, Canada (Figure 1.1). It is an exceptional research site for the study due to its unique combination of geographic location, the advent of coastal green infrastructure, and population composition. The city is situated at an average of 1 meter above sea level, facing high risks of sea level rise and coastal flooding (City of Richmond, 2022a). The city is surrounded by flood protection dikes. According to the 2021 Census, immigrants account for over 60% of Richmond’s population (Government of Canada, 2022b). Between 2016 and 2021, about 13,700 immigrants settled in Figure 1.1 City of Richmond, BC. (Image used with permission from https://www.richmond.ca/culture/discover-richmond/profile.htm) Sturgeon Bank 10 Richmond, and despite the increasing flood risks, the proportion of immigrants is expected to continue rising (City of Richmond, 2022c). 1.2.1 Natural Environment and Coastal Flood Risk As an island city at the mouth of the Fraser River, Richmond historically comprised a series of wetlands and bogs, providing significant aquatic and terrestrial habitats for various species, including invertebrates, salmon, the Steller’s sea lions, the mighty white sturgeon, raptors, and migratory birds along the Pacific Flyway (City of Richmond, 2023b). Over the past decades, Richmond has experienced intensive development, transforming into a dynamic urban center with a population of 230,584 people (City of Richmond, 2023a). Three of the City’s 17 islands are highly developed: Lulu Island, which is the largest island and contains the developing urban section (60%) and agricultural land (40%) within the province’s Agricultural Land Reserve; Sea Island, home to the Vancouver International Airport; and Mitchell Island, which hosts industrial activities (City of Richmond, 2019; Malik & Abdalla, 2016). These developments have led to the damage and loss of local natural habitats, while also increasing the City’s exposure to natural hazard risks. Richmond faces significant flood hazards from sea level rise, coastal storm surges, and extreme weather events due to its extremely low elevation relative to sea level. With sea levels along BC’s coast expected to rise by 1 meter by 2100, Richmond’s land is estimated to subside by 0.2 meters over the same period (City of Richmond, 2022a). One of the environmentally sensitive areas surrounding Richmond is the Sturgeon Bank along the City’s west boundary, characterized by brackish marshes on its foreshore (Figure 1.1). This area, designated as a Wildlife Management Area by the BC Ministry of Forests, Lands, Natural Resource Operations and Rural Development, 11 has lost at least 160 hectares of tidal marshes from 1989 to 2011 (Balke, 2017; McDonald, 2018). This loss is particularly unfortunate as it not only leads to habitat destruction but also diminishes the marshes' crucial role in dissipating wave energy and reducing Richmond’s coastal flood risks (Forysinski, 2019). Moreover, Richmond faces flooding risks from a potential megathrust earthquake and subsequent tsunami originating from the Cascadia subduction zone. While estimates vary, one study suggests there is approximately a 10% probability that a major earthquake will occur in this zone within the next 50 years (Leonard et al., 2012). Despite being partially sheltered by Vancouver Island, Richmond is still expected to experience tsunami run-ups several meters high (Cheff et al., 2016; Ng et al., 1990). Additionally, the area is vulnerable to local landslide tsunamis due to unstable sediments (Cheff et al., 2016). 1.2.2 Current Flood Protection Measures The primary flood protection system of the city is the 49 km Perimeter Dike around Lulu Island, complemented by drainage pipes, culverts, channelized watercourses, drainage pump stations, and flood protection sensors (City of Richmond, 2022a). Richmond’s Flood Protection Management Strategy serves as the guiding document for developing the City’s flood protection over the coming decades (City of Richmond, 2019). A key component of the strategy is the Dike Master Plan, which aims to upgrade the existing dikes in five phases. According to the Plan, the dikes will be raised from 3.5 meters to 4.7 meters in elevation within the next 50 years (City of Richmond, 2022a). While traditional hard structures remain the primary flood risk management system for the City, there have been initiatives to incorporate coastal green infrastructure into the climate change 12 adaptation plans. Led by the Ducks Unlimited Canada (DUC) and other partners, the Sturgeon Bank Sediment Enhancement Pilot Project was launched in October 2022 to restore degraded tidal marshes by pumping dredged sediments from the Fraser River onto the southern Lulu Island foreshore. By mimicking natural deposition processes, the added sediments are anticipated to enable up to 40 hectares of tidal marshes to regrow during the next few years. This restoration effort is expected to benefit species such as steelhead trout, Pacific salmon, and white sturgeon, while also providing coastal flood protection to the city (Maxwell, 2021). 1.2.3 History of Immigration Indigenous Peoples inhabited the land of Richmond for millennia before European settlers first arrived in the 1860s. Drawn by the burgeoning agriculture, fishing, shipping, and boat-building industries, many immigrants moved to the area. Most of these newcomers were from European countries, but Richmond also attracted Japanese fishermen and Chinese contract workers who initially came to build the railway in British Columbia. By 1986, immigrants constituted 31.5% of Richmond's population, with the majority having arrived between 1946 and 1976 from European countries, particularly the UK (Rose, 2001). Between 1991 and 1996, Richmond experienced another significant influx of immigrants, this time primarily from Asian countries, especially China. In 1996, ethnic Chinese made up 33.7% of the population (Rose, 2001). Today, according to the most recent census (2021), 60.3% of Richmond residents were not born in Canada. Among these immigrants, the Chinese community represents the largest ethnicity, accounting for approximately 54% of the total population, followed by the South Asian community at 7.4%. Consequently, Richmond showcases remarkable diversity, with approximately 80% of its population being visible minorities (Government of Canada, 2022b). 13 While the City and non-profit organizations offer various programs to enhance information-sharing and connection-building for immigrants, there is a lack of long-term initiatives to involve newcomers in community decision-making and policy-making (Chia, 2014; Rose, 2007). Major challenges that hinder immigrants' participation in civic engagement include language barriers, confusion about government systems, professional jargon, inadequate representation, and perceptions of consultation processes as intimidating and "white spaces" (Chia, 2014). It is important to improve the City’s consultation processes to ensure that immigrants’ concerns and needs are addressed, particularly in the context of disaster preparedness. 1.3 Literature Review 1.3.1 Risk Perception and Immigration While the literature is limited, a few studies have started to examine the role immigration plays in influencing people’s perceptions of environmental risks. These studies reveal that, in some cases, immigrants exhibit higher levels of risk perception compared to native residents (Adeola, 2007; Maldonado et al., 2016; Olofsson & Öhman, 2015; Olofsson & Rashid, 2011). Potential explanations for this include immigrants experiencing greater alienation, having less control over environmental risks, facing competing life demands, and harboring political distrust (Adeola, 2007; Yong et al., 2017). However, other studies have found no significant difference in the perception of natural disaster risks between immigrants and Canadian-born individuals (Yong et al., 2017). Several gaps exist in the current literature. Firstly, there is a lack of understanding about which factors contribute to the differences in risk perceptions between immigrants and native-born individuals, and which of those factors are particularly important. The perception of natural hazard risks is a complex concept that involves 14 not only the likelihood of the hazard but also the severity of potential consequences. The latter is determined by both the hazard itself and people’s exposure and vulnerability to it (Bradford et al., 2012). Based on a systemic review of 50 empirical studies, Lechowska (2018) categorizes factors determining risk perceptions of natural hazard into six groups: 1) cognitive (e.g., worry, direct experience, 2) behavioral (e.g., preparedness), 3) socio-economical and demographical (e.g., age, incomes, home ownership), 4) geographical (e.g., location, hazard proximity), 5) informational (e.g., awareness, indirect experience), and 6) contextual (e.g., trust in government and protection measures, history and culture). Among these, some factors are identified as highly influential in shaping people's perception of flood risk, including past experiences, hazard awareness, preparedness, place attachment, home ownership, geographical location, trust in decision-makers and protective measures, and length of residency (Kellens et al., 2013; Lechowska, 2018; Wachinger et al., 2013). Being an immigrant can influence all of these factors. Settling in a new country usually comes with a lack of familiarity of local hazards, which can lead to lower risk perceptions, relative to the native-born population (Yong et al., 2017). Meanwhile, immigrants may have lower awareness of existing protective measures, lower preparedness due to competing life demands, and a lack of social capital and resources to cope with natural hazards, which conversely can increase their risk perceptions (Chen, 2014; Liu & Balk, 2020). Furthermore, immigrants tend to have lower trust in public authorities due to factors such as language barriers, unfamiliarity with political systems, and value conflicts, which can also increase their perceived risks (Chia, 2014). Additionally, immigrants, particularly newcomers, often have lower place attachment, defined as “an affective bond or link between people and specific places” (Hidalgo & Hernández, 2001), than their native counterparts (Hernández et al., 2007), which is correlated with natural disaster risk 15 perceptions. However, prior evidence on this relationships is mixed (Bonaiuto et al., 2016; De Dominicis et al., 2015). Immigrants also likely have different past disaster experience than native-born individuals, including direct experiences and passive experience (e.g., media), which can lead to differential risk perceptions (Rana et al., 2020). Lastly, length of residency is often positively associated with flood risk perceptions, as individuals who have lived in a place longer are more likely to be aware of historical flood events or have experienced them personally (Burningham et al., 2008; Kreutzwiser et al., 1994; Pagneux et al., 2011; Thistlethwaite et al., 2018). This association is particularly relevant in the context of immigration. These factors may influence immigrants’ and non-immigrants’ risk perceptions in different ways, however, meaning that the most influential factors in the mechanism may differ between the two groups (Bonaiuto et al., 2016; Wachinger et al., 2013). For example, past experience may be more important for immigrants in influencing risk perceptions as they tend to have fewer information sources than natives (Carter-Pokras et al., 2007; Wachinger et al., 2013). Similarly, the influence of place attachment on risk perception is susceptible to the effects of other factors, including immigration (Bonaiuto et al., 2016). The second research gap is the inadequate representation of immigrants relative to native-born individuals in sampling (Yong et al., 2017). Studies have indicated the challenge of collecting sufficient immigrant samples for surveys and interviews, often resulting in unbalanced proportions of immigrants and natives in the sample and limiting the reliability of the results (Adeola, 2007; Olofsson & Rashid, 2011). Another shortcoming of the existing studies is the tendency to view immigrants as a homogeneous group (Asakawa et al., 2004; Beery, 2018; Macháč et al., 2022). It is crucial to differentiate between sub-groups such as long-term and recent immigrants because their 16 experiences, challenges, and perspectives can vary dramatically. Long-term immigrants, who have had more time to integrate into their new environment, may develop different risk perceptions compared to recent immigrants who are still in the process of adapting (Jasinskaja‐Lahti, 2008). Factors such as language proficiency, social networks, employment status, and familiarity with local customs and laws can influence how these groups perceive and respond to risks (Hagan, 1998; Jia et al., 2002; Stick et al., 2021). Ignoring these differences can lead to oversimplified conclusions that do not accurately reflect the nuances of immigrant experiences. Addressing these gaps requires a more nuanced and representative approach to studying immigrant populations, ensuring that the diverse experiences and perspectives of immigrants are adequately captured and understood. 1.3.2 Support for Coastal Green Infrastructure and Immigration While there is a broad consensus that green infrastructure should be considered in comprehensive coastal flood protection strategies, public attitudes toward coastal green infrastructure remain relatively underexplored (Mallette, 2021). The adoption of CGI still faces enormous resistance due to competing societal interests and limited cooperation between stakeholders (Zuniga-Teran et al., 2020). Developing a comprehensive understanding of public attitudes toward CGI can aid in addressing these competing interests, integrating local knowledge and skills, and fostering cooperative management and monitoring (Anderson & Renaud, 2021). Conversely, negative and biased perceptions of CGI can impede its uptake and slow systemic change (S. Han & Kuhlicke, 2019; Mallette, 2021). This thesis addresses two outstanding limitations that have been identified by previous research. First, scholars have called for more research that examines peoples’ attitudes toward 17 incorporating coastal green infrastructure in coastal adaptation (Anderson et al., 2022; Loos & Rogers, 2016; Singhvi et al., 2022). Second, others have argued for more attention to people who are historically disconnected from environmental planning, including immigrants (Barnhill & Smardon, 2012; Garcia-Cuerva et al., 2018; Miller & Montalto, 2019). Contradictory findings have emerged from different contexts around the world. While numerous studies found a strong preference among participants for CGI and hybrid structures (Akerlof et al., 2019; Anderson et al., 2022; Gray et al., 2017a; Loos & Rogers, 2016; Martinez-Juarez et al., 2019; Oliveira & Pinto, 2021; Wong-Parodi & Klima, 2017), other studies suggest that people tend to prefer hard measures over soft measures (Mallette, 2021; Schmidt et al., 2013; Sherren et al., 2016). Additionally, some studies demonstrated diverse perspectives among participants, suggesting that the measure preferences depend on various factors (Imamura et al., 2016; Liski et al., 2019; Milman et al., 2018; Omori, 2021; Touili et al., 2014) In a review of 99 journal articles, Anderson & Renaud (2021) grouped the influencing factors for public acceptance of flood-related green infrastructure into three categories: measure-related, society-related and individual-related. Common measure-related factors include cost and funding, scale, complexity, uncertainty, and equity considerations of different types of green infrastructure; society-related factors that occur frequently are competing societal interests, social norms, and political support for green infrastructure (Anderson & Renaud, 2021; Moreno et al., 2022; Venkataramanan et al., 2020; Yong & Lemyre, 2019). The last group, individual-related factors (Anderson & Renaud, 2021; S. Han & Kuhlicke, 2019; Venkataramanan et al., 2020), is particularly important when considering how immigrants might differ from their counterparts in attitudes toward CGI. It is important to note that the influence of immigration was not mentioned 18 in the review at all, indicating a significant neglect of this factor in the current literature (Anderson & Renaud, 2021). A widely recognized individual-related factor that influences public acceptance of CGI is risk perception (Anderson & Renaud, 2021). People with higher risk perceptions tend to have lower levels of support for nature-based solutions because the visibility of man-made structures, such as flood protection dikes, tend to provide a higher sense of security (S. Han & Kuhlicke, 2019; Imamura et al., 2016). People’ attitudes toward applying CGI in flood protection can also be influenced by their familiarity and understanding of CGI (Chou, 2016; Schernewski et al., 2018). For example, Schernewski et al. (2018) found that a lack of reliable information and misunderstanding of how CGI functions largely contribute to people’s low support for CGI. Another important factor is awareness of the co-benefits provided by CGI (Everett et al., 2018; Scholte et al., 2016). Results from Scholte et al. (2016) demonstrate that the ecological, cultural, and economic values people assign to the benefits of wetlands are correlated with their attitudes toward wetland restoration. Additionally, environmental values are also a key factor in determining people’s attitudes toward CGI (Kloek, 2013; Miller & Montalto, 2019). Environmental values encompass the intrinsic and instrumental worth individuals and society attribute to nature, influencing behaviors, decision-making, identity, and ethical considerations regarding the environment (Dietz et al., 2005). Buijs et al. (2009) found that immigrants in Netherland had different images of nature compared to native-born individuals: immigrants from Islamic countries had a more anthropocentric view of human–nature relationships and lower preferences for unmanaged and wild landscapes, while native Dutch people endorsed the wildness image of nature and had a higher preference for non-urban landscapes. Similarly, Christianson et al. (2014) found that culture and 19 values influence Indigenous Peoples’ preferences for wildfire mitigation measures. Just as immigration status can affect flood risk perceptions, it may also influence attitudes toward CGI through its effects on these factors. Furthermore, recent studies call for research that examines peoples’ preferences for CGI compared with other alternative coastal adaptation measures, rather than simply asking their attitudes toward CGI without any context (Anderson et al., 2022; Loos & Rogers, 2016; Singhvi et al., 2022). This approach aims to create real-life decision-making scenarios for participants, where they need to consider the trade-offs involved in different adaptation measures. 1.4 Conceptual Framework Based on the literature, this study proposes two potential conceptual frameworks to explore the interactions among variables within the context of coastal flooding risk perceptions and attitudes toward coastal green infrastructure: 1) mediation framework and 2) moderation framework. These frameworks are visualized through two interrelated components, distinguished by light and dark yellow boxes in Figure 1.2 and Figure 1.3. The light-yellow box represents the initial phase of the study, focusing on assessing the influence of immigration status on coastal flooding risk perceptions. The dark yellow box delineates the subsequent phase, which investigates residents' support for CGI, the interplay between support level and risk perceptions, and the effect of immigration status. Figure 1.2 outlines the mediation framework. Mediation occurs when an independent variable exerts its influence on a dependent variable through one or more intermediary variables (Baron & Kenny, 1986). In the light-yellow box, the hypothesis is that immigration status affects risk perceptions directly and/or indirectly via mediator variables, such as past experience, 20 awareness, and preparedness. Additionally, homeownership and proximity to the coast are identified as significant predictors of flood risk perception, with empirical evidence suggesting that owning a property and residing closer to the coastline are correlated with heightened perceptions of flood risk (Burningham et al., 2008; Kellens et al., 2013). These factors are accounted for in the data analysis to control for their effects. In the dark-yellow box, it is posited that an individual’s flood risk perceptions, understanding of CGI, environmental values, and familiarity with CGI and THS mediate the impact of immigration status on support for CGI. Figure 1.2 Conceptual framework 1 (mediation framework) with immigration being the independent variable. 21 Figure 1.3 illustrates the moderation framework, which posits immigration as a moderating variable. According to Baron & Kenny (1986), a moderator affects the strength and direction (positive or negative) of the relationship between independent and dependent variables. In the light-yellow box, immigration status is hypothesized to alter how independent variables such as past experience, hazard awareness, and preparedness impact the dependent variable, which is risk perceptions. Similarly, in the dark-yellow box, immigration status is expected to moderate the relationship between the independent variables (risk perceptions, CGI knowledge, environmental values, and familiarity with CGI and THS) and the dependent variable, which is support for CGI. Due to the exploratory nature of the conceptual frameworks, no specific hypotheses were proposed for each independent variable. Given that three groups were compared, predicting differences among them for each potential mediator variable is challenging based on the existing literature. 22 Figure 1.3 Conceptual framework 2 (moderation framework) with immigration being moderator variable. Both conceptual frameworks are supported by the literature and robust rationales. To empirically test and compare them, data were collected from a questionnaire survey administered to the residents of Richmond. 23 Chapter 2: Methodology 2.1 Research Design This section outlines the research design, including the construction of the questionnaire, sampling methodology, and the data collection process, aimed at empirically testing these theoretical models. Questionnaire surveys are appropriate for answering the research questions because they can effectively capture diverse perceptions and attitudes among different groups (Roopa & Rani, 2012). Surveys allow for the collection of direct, standardized responses from native-born residents, long-term immigrants, and recent immigrants, providing quantitative data that can be statistically analyzed. Statistical analysis can then identify correlations, significant differences, and interactions between the contributing factors. Furthermore, surveys can include open-ended questions to obtain qualitative insights, complementing the quantitative results. 2.1.1 Survey Design The survey is structured into three sections, drawing upon established instruments and items from previous research on flood risk perceptions and attitudes towards CGI (Rana et al., 2020; Shoji et al., 2022; Terpstra, 2011; Terpstra et al., 2006). It aims to provide a comprehensive assessment of the residents' risk perceptions, CGI attitudes, and socio-demographic characteristics, with a particular focus on the nuanced aspects of risk perception and the support for CGI in flood risk management. The questionnaire is included as Appendix A. The initial section adopts the psychometric paradigm (Slovic et al., 1982) to quantitatively explore various dimensions of risk perception among the residents. This is achieved by employing a 1-5 Likert scale that measures the extent of respondents' agreement or disagreement with targeted 24 statements. This encompasses the perceived likelihood of a major coastal flooding event occurring within the next 50 years, as well as the anticipated 1.2 m sea level rise by 2100, a projection provided by the City of Richmond. Another dimension examines the perceived consequences of flooding—encompassing both collective and individual impacts—through six statements. Additionally, respondents' emotional response, particularly worry concerning sea level rise (SLR) and coastal flooding, is assessed through a dedicated statement. The survey also investigates factors that may influence these perceptions, including respondents' geographic location, their awareness of hazards and existing protective measures, levels of trust in authorities and preparedness, and any prior experiences with flooding. In the second section, the focus shifts to residents’ attitudes towards wetland restoration and CGI in general, along with the factors that shape these attitudes, including familiarity with CGI and THS, knowledge of the benefits offered by brackish marshes, and environmental values. Attitudes toward CGI are measured in two ways. Attitudinal measures incorporate two Likert scale statements concerning the necessity of wetland restoration and the integration of nature-based solutions into flood risk management. Moreover, respondents are presented with a question about their preferences for the allocation of flood protection funding between traditional hard structures and hybrid solutions. An open-ended question seeks to uncover the rationale behind participants’ levels of support for incorporating CGI into flood protection strategies. Environmental values are assessed using six statements derived from the revised New Ecological Paradigm (NEP) scale, which focuses on environmental beliefs about humanity’s right to dominant the environment, limits to growth, and the balance of nature (Dunlap et al., 2000). Three of the statements chosen reflect the NEP, emphasizing the interconnectedness between humans and the rest of nature and advocating for environmentally responsible behaviors. The other three statements represent the 25 Dominant Social Paradigm (DSP), emphasizing the values of economic growth and human dominance while downplaying the significance of environmental issues (Kilbourne et al., 2002). The final section gathers information on immigration status, demographics, and socioeconomic characteristics of the respondents. None of the questions were mandatory for respondents to answer. 2.1.2 Data collection Recognizing the significant proportion of Chinese-speaking residents in the City, the questionnaire was made available in both English and Chinese to ensure inclusivity and accessibility. The survey was designed to be completed within approximately 10 minutes and was available in both a paper-based format and an online format hosted on Qualtrics. The survey was administered from July to September 2023, employing a multifaceted recruitment strategy that included direct outreach, public postings, and referrals through word of mouth. In-person recruitment took place at eight public sites throughout the Richmond area, including four community centers, three public parks and dike trails, and the Richmond Library. These sites offer several compelling advantages. For one, they typically experience high foot traffic, which increases the likelihood of obtaining a diverse and representative sample of the population. They are also accessible to a wide range of individuals, including various age groups, socioeconomic backgrounds, and ethnicities, ensuring that the survey reaches a broad demographic. Additionally, these locations serve as hubs of community activity and engagement, fostering a sense of involvement and increasing participant willingness to contribute to research that benefits their local area. 26 At these locations, I typically set up a desk and a few chairs in an open, visible space (Figure 2.1). I then politely approach people as they walk by and invite them to participate in the study. This method ensures a level of randomness in the sampling process, as participants are selected based on their presence at the venue rather than any predetermined criteria. Individuals approached in person were given the option to complete the survey either on paper or electronically via their own devices, according to their preference. As a gesture of gratitude, participants were presented with a postcard as a small token of appreciation. Moreover, those interested in further disseminating the survey among the community members were provided with a piece of paper containing the QR code linked to the survey. Figure 2.1 Table setup at one of the data collection sites: the Steveston Community Center. Photo taken by Yuqing Cheng. 27 Additionally, with appropriate permissions obtained, I placed posters featuring QR codes at some community centers to facilitate easy access to the survey. The survey's availability was further broadcasted through social media platforms, including Facebook groups related to Richmond. To encourage participation, the study included two lucky draws, with each offering the possibility of winning a $25 gift card. Participants interested in the draws were instructed to provide their email addresses during the survey. To maintain anonymity, they were directed to a separate survey link at the conclusion of the primary survey to submit their email addresses for the draw. 2.2 Survey Responses A total of 330 responses were collected, of which 285 were fully completed, yielding a completion rate of 86.4%. Among the 45 incomplete responses, 14 were devoid of any answers and were thus excluded from the data analysis. The remaining 31 responses, which completed at least the risk perception section, were incorporated into the descriptive analysis. The survey responses present a fairly representative sample of residents in Richmond (Table 2.1). When comparing the demographics of the sample with the 2021 Canada Census, the sample closely matches the gender proportions and household income distribution. However, it shows a lower representation of long-term immigrants and a higher representation of temporary residents. Additionally, the sample indicates a higher representation of the 35-44 age group and a lower representation of residents over 65 years old. Long-term immigrants, who have resided in Canada for over 5 years, make up 36.7% of the sample. On average, they immigrated to Canada at the age of 19.9 years (std.=14.6 years) and have lived in Richmond for approximately 13.8 years (std.=11.8 years). Native-born Canadians represent 29.0% of the sample, followed by temporary 28 residents at 26.5%, and recent immigrants (those residing in Canada for less than 5 years) at 3.2%. On average, native-born Canadians have lived in Richmond for around 23.3 years (std.=15.2 years). Temporary residents, on average, arrived in Canada at the age of 32.4 years (std.=10.3 years) and have lived in Richmond for around 7.0 years (std.=7.6 years). Recent immigrants typically arrived in Canada at the age of 37.4 (std.=2.6 years) and have been in Richmond for about 3.4 years (std.=1.5 years). The gender distribution among participants is nearly equal, with 48.2% identifying as male and 50.0% as female. Age-wise, the largest cohort falls within the 35-44 years category (32.4%), followed by the 45-54 years group (16.5%). The age groups of 18-24 years, 25-34 years, and 55-64 years also exhibit substantial representation, each accounting for more than 10% of the total respondents. Almost 60% of participants own their homes and 32.7% of them rent. The annual household income distribution reveals a varied economic landscape, with 24.6% of respondents choosing not to disclose their income. Among those who did, the majority fell within the $100,000-$149,999 bracket (17.3%). The cultural background data showcases the cultural diversity of Richmond's population, with East Asian (56.7%) and European (23.8%) backgrounds being predominant, followed by South East Asian (7.1%) and South Asian (5.0%) backgrounds. Place of birth data highlights the international nature of Richmond, with 62.3% born outside Canada and only 8.1% born in Richmond. Reasons for moving to Richmond vary, with family and relationships (39.8%) and quality of life (33.6%) being prominent factors. Around one-fifth of participants also indicated that employment opportunities (21.5%), education (19.5%), and cultural and social environment (19.1%) influenced their decision to move to Richmond. Fluency in English is high, with 33.9% 29 identifying as native speakers, 41.3% as fluent, and only 7.5% indicating little to no fluency. About one-third of participants reported experiencing natural hazards prior to the survey period, with 3.5% suffering severe losses and 30.2% experiencing some losses. In terms of proximity to the coast, the majority of participants reside near the coastline, mirroring the city's population distribution. In the survey, Richmond was geographically segmented into ten regions, labeled A through J, to categorize areas based on their proximity to the coast—from nearest to farthest (Figure 2.2). This classification was employed to query participants about their approximate distance from the coastline. Of the 299 respondents who provided their geographic locations, Region A emerged as the most represented, comprising 34.8% of responses. Regions B and C followed, with 22.1% and 21.7% respectively, while Region D accounted for 11.0% of the survey population. The least represented regions, E through J, saw participation rates ranging from 0.3% to 2.3% 30 Figure 2.2 The map of Richmond geographically segmented into regions labeled A through J, to query participants about their approximate distance from the coastline. (Image used with permission from: https://archives.richmond.ca/archives/maps/map-search.aspx) 31 Table 2.1 Demographics and socioeconomic characteristics of the sample. Information sought Reponses Frequency % Gender Male 137 48.2 Female 142 50.0 Prefer not to answer 5 1.8 Total 284 100.0 Age 18-24 years 34 12.0 25-34 years 41 14.4 35-44 years 92 32.4 45-54 years 47 16.5 55-64 years 41 14.4 65-74 years 19 6.7 75-84 years 6 2.1 Above 84 years 2 0.7 Prefer not to answer 2 0.7 Total 284 100.0 Home ownership Homeowner 164 57.7 Renter 93 32.7 Other 14 4.9 Prefer not to answer 13 4.6 Total 284 100.0 Annual household income Less than $20,000 15 5.3 32 $20,000-$39,999 26 9.2 $40,000-$59,999 36 12.7 $60,000-$79,999 25 8.8 $80,000-$99,999 29 10.2 $100,000-$149,999 49 17.3 $150,000-$199,999 17 6.0 $200,000 or more 17 6.0 Prefer not to answer 70 24.6 Total 284 100.0 Place of birth Richmond 23 8.1 Elsewhere in Metro Vancouver 34 12.0 Elsewhere in Canada 32 11.3 Outside Canada 177 62.3 Prefer not to answer 18 6.3 Total 284 100.0 Reasons for moving to Richmond* Employment opportunities 55 21.5 Education 50 19.5 Quality of life 86 33.6 Family and relationships 102 39.8 Cultural and social environment 49 19.1 Climate and natural environment 40 15.6 Retirement 9 3.5 33 Other 20 7.8 Prefer not to answer 14 5.5 Immigration status Born in Canada 82 29.0 Long-term immigrant (>5 years) 104 36.7 Recent immigrant (<= 5 years) 9 3.2 Temporary resident 75 26.5 Prefer not to answer 13 4.6 Total 283 100.0 Cultural background* African 2 0.7 European 67 23.8 East Asian 160 56.7 South Asian 14 5.0 South East Asian 20 7.1 First Nations or Indigenous 4 1.4 Hispanic or Latinx 5 1.8 Middle Eastern 2 0.7 Other 7 2.5 Prefer not to answer 10 3.5 Fluency in English Native Speaker 96 33.9 Fluent 117 41.3 Some 46 16.3 Little 16 5.7 34 Not at all 5 1.8 Prefer not to answer 3 1.1 Total 283 100.0 Experienced a natural hazard before? Yes 88 31.5 No 184 65.9 Prefer not to answer 7 2.5 Total 279 100.0 The natural hazard(s) had adverse effects? Yes, suffered severe losses 3 3.5 Yes, suffered some losses 26 30.2 Not really 56 65.1 Prefer not to answer 1 1.2 Total 86 100.0 * multiple responses possible Regarding language choice, 240 participants (72.7%) completed the survey in English, while the remaining participants chose to complete it in Chinese. Among those who responded in English, the majority were either born in Canada or were long-term immigrants. In contrast, only one participant who completed the survey in Chinese was born in Canada; most were recent immigrants or temporary residents. 35 2.3 Data Analysis 2.3.1 Quantitative data analysis Quantitative analysis was conducted using SPSS version 29 and STATA version 18. For analyses that involve immigration status, participants in this study are categorized based on their immigration status in Canada into three distinct groups: recent immigrants and temporary residents (RITR, defined as those having migrated to Canada within the last 5 years and those on study and work permits), long-term immigrants (LI, defined as those having migrated to Canada over 5 years ago), and those born in Canada (BIC) (Table 2.2). Table 2.2 Immigration status categories used in the study. N % Born-in-Canada 82 30.4 Long-term immigrants (>5 years) 104 38.5 Recent immigrants or temporary residents 84 31.1 Total 270 100 To examine the mediating effects posited by Conceptual Framework 1, a two-phase analysis is conducted for risk perceptions and attitudes toward CGI respectively. For risk perceptions, the initial phase aims to identify potential mediator variables that could influence the relationship between immigration status and risk perceptions. This involves two steps: 1) identifying a set of potential mediator variables which statistically differ across the three immigration groups and 2) further identifying which variables from the set are correlated with risk perceptions. Bonferroni Correction is used to account for the risk of Type I errors (false positives) 36 due to multiple comparisons. In the second phase, path analysis was conducted to investigate the presence of mediating effects among the identified variables. Furthermore, this two-phase analysis was carried out at the item level, rather than aggregating data into indices, to foster a more detailed exploration of the underlying relationships. Where significant relationships were observed between two or more items pertaining to the same variable, their means were computed to collectively represent the variable, thereby enhancing the depth and precision of our understanding of the mediation effects within the conceptual framework. A similar procedure is applied to the analysis of support for CGI, specifically the assessment of funding allocation preference. To test the moderating effects outlined in Conceptual Framework 2, stratified regression and multinominal logistic regression are utilized for the relationships concerning risk perceptions and support for CGI respectively. 2.3.2 Qualitative data analysis While the study mainly relied on statistical modeling, limited qualitative data analysis was conducted using responses to one open-ended survey question. This study employed thematic analysis, following the methodological framework outlined by Braun & Clarke (2006), to systematically identify and analyze patterns within the qualitative data regarding the reasons behind varying levels of support for integrating CGI in flood risk management. After becoming familiar with the data, I coded the responses manually, and the codes were further developed into themes. 37 Chapter 3: Results 3.1 Risk Perceptions 3.1.1 Descriptive Statistics In general, survey respondents indicated a high level of perception about the likelihood and potential impacts of future sea level rise and coastal flooding, with all statements having at least 55% of participants agreeing (Figure 3.1). However, the results also show that the level of risk perception depends on the type of consequence that is being referred to. The perception of ecological impacts surfaces prominently in the data. RP7 reveals a substantial level of agreement, with 40.8% strongly agreeing and 41.4% somewhat agreeing. This suggests that the majority of participants recognize the broader environmental implications of rising sea levels, indicating a shared concern for local coastal ecosystems. The statement that also received very high overall agreement is RP8, with 45.8% strongly agreeing and 32.7% somewhat agreeing. This suggests a widespread apprehension among participants regarding the incremental consequences of sea level rise and coastal flooding on succeeding generations. 38 0 50 100 150 200 250 300 350RP6: Future sea level rise and coastal flooding will threaten my and my family’s livelihood (e.g., income)RP1: A major coastal flooding event will likely occur in Richmond during the next 50 yearsRP2: The sea level along Richmond’s coast will likely rise by at least 1.2 m by 2100RP5: Future sea level rise and coastal flooding will threaten my home in RichmondRP9: I’m worried about the danger of coastal flooding on the Richmond coastRP4: Future sea level rise and coastal flooding will cause severe damage to Richmond in generalRP3: Future sea level rise and coastal flooding will threaten people’s lives in RichmondRP8: Future sea level rise and coastal flooding will threaten future generationsRP7: Future sea level rise and coastal flooding will destroy local coastal ecosystems and speciesNumber of ResponsesStrongly agree Somewhat agree Neutral Somewhat disagree Strongly disagree Don't knowFigure 3.1 Responses to risk perception statements (N=314). 39 Interestingly, participants demonstrated higher risk perceptions and less ambiguity about the general consequences of future sea level rise and coastal flooding than the personal implications. The four statements that address consequences to Richmond in general all had approximately 80% of participants agreeing, while the two statements regarding personal implications had only 57.5% and 69.0% of participants agreeing. Between the two statements, participants appeared more concerned about potential damages to their homes (RP5) than the impacts on individuals and their families’ livelihood (RP6). Regarding ambiguity, the percentages of participants who chose “Don’t know” appear higher for statements on personal implications than for statements on general impacts. Furthermore, participants expressed higher degrees of uncertainty about specific predictions related to sea level rise and coastal flooding. RP2 elicits diverse opinions, with 27.5% strongly agreeing, 38.4% somewhat agreeing, and 13.8% expressing uncertainty by selecting "Don't know." Similarly, RP1 only had 25.2% of participants strongly agreeing (the lowest among all risk perception statements) and 8.3% selecting “Don’t know.” This suggests a level of ambiguity among participants regarding the precise extent of future sea level rise and the likelihood of occurrence of a severe coastal flooding event. Finally, the majority of participants (71.9%) expressed worry regarding the danger of coastal flooding at Richmond’s coast (RP9). Notably, this apprehension is positioned centrally among all the risk perception statements (Figure 3.1), suggesting that the degree of worry serve as a representative indicator of their comprehensive concerns regarding the anticipated impacts of future coastal flooding and sea level rise. 40 3.1.2 Comparative Analysis - Risk Perceptions To investigate whether there are significant differences in risk perceptions across immigration groups, risk perception statements are coded from (1) for strongly disagree to (5) for strongly agree, and the means of the three immigration groups were calculated respectively for each of the statements and the risk perception index (RP Index) (Table 3.1). RP index was computed by averaging responses across the nine statements. Table 3.1 Risk perception mean by immigration group. Risk Perception Statement/Index BIC (N=82) LI (N=104) RITR (N=84) Total Sample Mean RP7: Future sea level rise and coastal flooding will destroy local coastal ecosystems and species 4.10 4.28 4.17 4.20 RP8: Future sea level rise and coastal flooding will threaten future generations 4.15 4.26 4.09 4.18 RP3: Future sea level rise and coastal flooding will threaten people’s lives in Richmond 3.99 4.26 4.08 4.12 RP4: Future sea level rise and coastal flooding will cause severe damage to Richmond in general 4.08 4.26 3.97 4.12 RP2: The sea level along Richmond’s coast will likely rise by at least 1.2 m by 2100 4.08 4.08 3.76 3.97 RP9: I’m worried about the danger of coastal flooding on the Richmond coast 3.85 3.97 3.94 3.93 RP5: Future sea level rise and coastal flooding will threaten my home in Richmond 3.94 4.10 3.72 3.92 RP1: A major coastal flooding event will likely occur in Richmond during the next 50 years 3.95 4.02 3.58 3.85 RP6: Future sea level rise and coastal flooding will threaten my and my family’s livelihood (e.g., income) 3.42 3.79 3.57 3.63 RP Index (mean across RP1-RP9) 3.95 4.11 3.88 3.99 Note: BIC = Born-in-Canada individuals, LI = Long-term immigrants (>5 years), RITR = Recent immigrants and temporary residents The highest mean for each statement is in bold. In general, the long-term immigrant (LI) group exhibited highest risk perception among the three groups, with the highest mean scores across all risk perception statements (see Figure 41 3.2). Among the nine statements, the born-in-Canada (BIC) group demonstrated higher means for five statements (RP1, RP2, RP4, RP5, and RP8) compared to the recent immigrant and temporary residents (RITR) group. Conversely, the results were reversed for the remaining four statements (RP3, RP6, and RP7). Kruskal-Wallis test was used to investigate if the differences across the three immigration groups are statistically significant. As a Kruskal-Wallis test only examines whether statistically significant differences exist across all three groups considered together, post hoc tests were done to compare groups pairwise. The results are shown below (Table 3.2). The H statistic represents the degree to which the observed data deviate from the null hypothesis, which states that all groups have the same median. A higher H value indicates a greater degree of difference between the groups Table 3.2 Kruskal-Wallis Test: Risk Perception Differences by Immigration Group. Risk perception statement Sample size H Statistic Post hoc test RP1: A major coastal flooding event will likely occur in Richmond during the next 50 years 247 11.33* BIC > RITR* LI > RITR** RP2: The sea level along Richmond’s coast will likely rise by at least 1.2 m by 2100 233 6.51* LI > RITR* RP3: Future sea level rise and coastal flooding will threaten people’s lives in Richmond 260 3.20 RP4: Future sea level rise and coastal flooding will cause severe damage to Richmond in general 258 6.00* LI > RITR* RP5: Future sea level rise and coastal flooding will threaten my home in Richmond 250 5.52 RP6: Future sea level rise and coastal flooding will threaten my and my family’s livelihood (e.g., income) 256 4.30 RP7: Future sea level rise and coastal flooding will destroy local coastal ecosystems and species 262 1.77 RP8: Future sea level rise and coastal flooding will threaten future generations 260 1.90 RP9: I’m worried about the danger of coastal flooding on the Richmond coast 263 0.39 RP Index 263 4.82+ LI > RITR* +p<.10; *p<.05; **p<.01; ***p<.001 (two-tailed t-tests) 42 The immigration groups statistically differ in responses to RP1, RP2, and RP4 (p<.05). Figure 3.3 shows the box plots regarding the three statements. This difference also applies to the RP Index. In all cases where there is a significant difference, long-term immigrants have higher risk perception than recent immigrants and temporary residents. For RP1, born-in-Canada individuals also have higher risk perception than recent immigrants and temporary residents. From the results above, we find that immigration groups significantly differ in their predictions of future sea level rise and coastal flooding as well as in their concern on whether these future events will cause severe damage to Richmond in general. Among these, RP4 (“Future sea level rise and coastal flooding will cause severe damage to Richmond in general”) was chosen for further correlation analysis. This decision was informed by the distinctive focus of RP4 on the perceived consequences of future coastal hazards, which aligns more directly with the mediator variables under investigation. This contrasts with RP1 and RP2, which only address the perceived likelihood of such hazards occurring. The following correlation and mediation analyses were performed to investigate why long-term immigrants perceived the general damage caused by future coastal hazards to Richmond as more severe than the other two groups did. 43 Figure 3.2 Mean scores of risk perception statements by immigration group (N=270). Note: Risk perception statements are coded from (1) for strongly disagree to (5) for strongly agree. The ends of the X-axis were adjusted to make the differences more visible. 3.00 3.50 4.00 4.50RP7: Future sea level rise and coastal flooding will destroy local coastal ecosystemsand speciesRP 8: Future sea level rise and coastal flooding will threaten future generationsRP3: Future sea level rise and coastal flooding will threaten people’s lives in RichmondRP4: Future sea level rise and coastal flooding will cause severe damage toRichmond in generalRP2: The sea level along Richmond’s coast will likely rise by at least 1.2 m by 2100RP9: I’m worried about the danger of coastal flooding on the Richmond coastRP5: Future sea level rise and coastal flooding will threaten my home in RichmondRP1: A major coastal flooding event will likely occur in Richmond during the next 50yearsRP6: Future sea level rise and coastal flooding will threaten my and my family’s livelihood (e.g., income)Mean ScoreRecent immigrants and temporary residents Long-term immigrants (>5 years) Born in Canada44 Figure 3.3 Box plots illustrating variations in risk perception statements RP1 (a), RP 2 (b), and RP 3 (c) among three immigration groups. 45 3.1.3 Comparative Analysis - Potential Mediators Conceptual framework 1 hypothesized that variables including hazard awareness, preparedness, place attachment, trust, awareness of existing measures, and past experience may mediate the influence of immigration on risk perceptions (Figure 1.2 above). This section explores which of the potential mediator variables are correlated with immigration status. For data that are ordinal rather than interval, Kruskal-Wallis test is used as a non-parametric alternative to ANOVA. Kruskal-Wallis tests were conducted for all Likert-scale statements associated with the identified variables. Chi-square tests were conducted for binary variables, including distance to coast (participants who lived in Region A are categorized as coastal, while the rest are categorized as non-coastal), homeownership and past experience. One-way ANOVA was conducted for the number of flood-related information sources, which is a continuous variable. Based on the results, six mediator variables/statements appear to be correlated with immigration status: awareness of potential earthquake and tsunami, preparedness, place attachment, awareness of existing measures, distance to coast, and home ownership (Table 3.3). Informed by the post-hoc tests, being a long-term immigrant significantly influenced all of the six variables/statements except for one, awareness of potential earthquake and tsunami. 46 Table 3.3 Kruskal-Wallis and Chi-square Test: Potential Mediator Differences by Immigration Group. Variable Statement H or χ2 Post-hoc test Hazard Awareness Richmond is susceptible to flooding from a potential earthquake and subsequent tsunami 12.62** BIC > RITR** Preparedness In case of a coastal flood, I can keep myself safe 7.36* BIC > RITR* I have flood insurance that will compensate damages caused by coastal flooding 9.65** BIC > RITR* BIC > LI * I know exactly what I could do to prevent or reduce damages caused by coastal flooding 6.01* RITR > LI* Place Attachment I am very attached to Richmond 6.70* LI > RITR* Awareness of Existing Measures I am aware of the dike surrounding Richmond 44.27*** BIC > LI** BIC > RITR** LI > RITR** I am aware of the drainage pipes and pump stations that transport water out of the city 25.12*** BIC > LI* BIC > RITR** LI > RITR* I am aware of the culverts and tunnels that carry streams and store rainwater 9.19* BIC > LI* BIC > RITR* I am aware of the Dike Master Plan that aims to upgrade the City’s dike 8.98* BIC > LI** Distance to Coast Coastal vs. Non-coastal 8.56* Homeownership Homeowner vs. Renter 12.02** +p<.10; *p<.05; **p<.01; ***p<.001 (two-tailed t-tests) Only statistically significant results are included in the table Chi-square tests are conducted for “Distance to Coast” and “Homeownership,” while Kruskal-Wallis tests are conducted for the rest variables/statements 47 The results indicate significant differences in risk perception of potential earthquakes and tsunamis among the immigration groups, with both the BIC group reporting higher perceptions than the RITR group (p<0.01). Preparedness levels also varied: the BIC group demonstrated significantly greater preparedness, particularly in personal safety measures and flood insurance, compared to the other groups. Out of the 38 RITR homeowners, only 9 (23.7%) indicated that they had flood insurance, compared to long-term immigrants (33.3%) and native-born respondents (61.8%). Conversely, the RITR group expressed greater confidence in their ability to prevent or mitigate flood damage. Regarding place attachment, the LI group had a significantly stronger attachment than the RITR group (p<0.05), with the BIC group’s attachment closely aligning with that of the LI group. Measure awareness, especially concerning traditional hard structures like dikes, drainage pipes, and pump stations, also differed significantly across the groups. The BIC group showed the highest awareness, followed by the LI group, which was significantly more aware than the RITR group. Additionally, differences emerged in residential tendencies, with variations among the groups in coastal living and homeownership. 3.1.4 Correlation Analysis between Potential Mediators and Risk Perception Kendall's tau test was employed as a non-parametric alternative to Pearson’s correlation test to examine the correlations between the five mediator variables and participants’ risk perception score. Because “Distance to Coast” and “Homeownership” are categorical variables, Mann-Whitney U test was used instead to conduct comparative analysis. 48 The correlation findings are detailed in Table 3.4. Place attachment is positively correlated with agreement to RP4. Contrary to expectations, awareness of infrastructure elements like dikes, drainage pipes, pump stations, and culverts and tunnels also shows a positive correlation with RP4 agreement levels. Furthermore, there is no statistically significant correlation between participants' preparedness and their agreement with RP4. Interestingly, while homeowners tend to agree more with RP4 compared to renters, residing close to the coast does not necessarily enhance RP4 agreement levels. Table 3.4 Kendall's Tau Test: Correlation between Mediator Variables and RP4. Variable Statement τor Z N Preparedness In case of a coastal flood, I can keep myself safe -0.03 288 I have flood insurance that will compensate damages caused by coastal flooding 0.05 287 I know exactly what I could do to prevent or reduce damages caused by coastal flooding -0.02 288 Place Attachment I am very attached to Richmond 0.16** 285 Awareness of Existing Measures I am aware of the dike surrounding Richmond 0.18*** 282 I am aware of the drainage pipes and pump stations that transport water out of the city 0.19*** 283 I am aware of the culverts and tunnels that carry streams and store rainwater 0.12* 281 I am aware of the Dike Master Plan that aims to upgrade the City’s dike 0.07 282 Distance to Coast Non-coastal vs. Coastal -0.01 Homeownership Renter vs. Homeowner -1.80+ *p<.05; **p<.01; ***p<.001; +p<.10 (two-tailed t-tests) RP4: Future sea level rise and coastal flooding will cause severe damage to Richmond in general Mann-Whitney U tests are conducted for “Distance to Coast” and “Homeownership” instead 49 In summary, place attachment, awareness of existing measures and homeownership are likely to be the factors that mediate the influence of being a long-term immigrant on participants’ response to statement RP4. 3.1.5 Mediation Analysis The comparative and correlation analyses informed the development of a proposed multiple mediation model, detailed in Figure 3.4. This model designates LI as the independent variable—coded as 1 for long-term immigrants, and 0 for individuals from other groups (BIC and RITR)—and RP4 as the dependent variable. The mediators in the model include Atta (place attachment), MA3_index (awareness of three existing tidal health and safety measures), and Homeownership (coded as 1 for homeowners and 0 for renters). This model was tested using STATA, employing the classical approach of Baron & Kenny (1986), which involves estimating a series of regression models: 1) regressing the potential mediators on LI, 2) regressing RP4 on LI without considering mediators, and 3) regressing RP4 on both LI and the mediators that are significantly associated with LI from Step 1). Results of the mediation analysis are presented in Figure 3.4. 50 Figure 3.4 Results of multiple mediation model explaining why long-term immigrants had higher risk perception of potential damage caused by future coastal flooding and sea level rise. Note: The solid arrow indicates the direct effect of LI on RP4, and the dashed arrow indicates the total effect. +p<.10; *p<.05; **p<.01; ***p<.001 In the first step of the analysis, LI significantly predicted place attachment but not other potential variables (p = 0.08). In the second step, the regression of RP4 on LI—without including the mediators—was significant at the 10% level (p = 0.06), as indicated by a horizontal dashed line. In the final step, only LI itself significantly predicted RP4 (p = 0.09). Although the coefficient for LI decreased slightly when place attachment was included in the regression model, the coefficient for place attachment was not significant. Therefore, there is insufficient evidence to conclude that place attachment mediates the relationship between LI and RP4. These results do not support the proposed mediation model. 51 3.1.6 Regression Analysis Multiple regression analysis was performed to investigate the predictors of risk perceptions among participants, specifically examining the moderating effects of immigration status as outlined in Conceptual Framework 2 (Figure 1.3 above). To assess various predictors of risk perception, indices were computed by averaging responses across relevant statements for hazard awareness, measure awareness, trust, place attachment, and preparedness. The reliability of these indices was evaluated using Cronbach's 𝛼 to ensure internal consistency, as shown in Table 3.5. All indices, except for preparedness, demonstrated acceptable reliability with alpha coefficients above 0.7, the generally accepted threshold. The Preparedness Index, which showed no significant correlation with the Risk Perception Index (p=0.83), was subsequently excluded from the models. Additionally, direct past experience was operationalized as a binary variable (1 for having experienced a natural disaster and 0 for none), and indirect past experience was quantified by the number of flood-related information sources accessed. Table 3.5 Results from Cronbach’s Alpha test for risk perception predictors. Index Number of Statements Cronbach's α Risk perception 9 0.91 Hazard awareness 5 0.77 Preparedness 3 0.60 Trust 4 0.88 Place Attachment 2 0.74 Measure awareness 5 0.86 52 Furthermore, the conceptual framework guided the inclusion of homeownership and distance to coast as control variables in the models. Distance to coast was defined as a binary variable, with 1 indicating coastal residents (living in Region A) and 0 for non-coastal residents (living in other regions). The models also incorporated demographic controls including gender, age, income, and homeownership to adjust for basic sociodemographic differences among participants. Additionally, the number of risk perception questions answered by each participant was included as a control variable to account for the variability in the amount of data contributed by each participant, as differences in the number of questions answered could affect the responses' variance and other statistical properties. Ideally, the analysis would include only those participants who answered all questions, but given the relatively small sample size, excluding participants could significantly reduce the sample and potentially bias the results. This methodological choice is limited by the assumption that the impact of answering more or fewer questions is linear and consistent across all respondents. Regression models stratified by immigration status were used to analyze the determining factors of risk perceptions within each immigration status group. Stratified models are statistical models used to analyze data by dividing the population into distinct subgroups, based on specific characteristics. Each group is analyzed separately to account for potential differences among the subgroups that might influence the outcome of interest. Due to smaller sample sizes, variables not significant in the full model were omitted, including past disaster experience, trust, and location. The stratified models (Table 3.6) revealed that for both born-in-Canada individuals and long-term immigrants, hazard awareness was the only significant predictor of risk perception. However, its influence was less pronounced among recent immigrants and temporary residents, with coefficients of 0.43 compared to 0.75 and 0.72 for the other groups. For recent immigrants 53 and temporary residents, higher measure awareness, an increased number of flood-related information sources, and homeownership also elevated risk perception concerning coastal flooding and sea level rise. Table 3.6 Stratified regression for flood risk perception by immigration status. Model 1B Model 1L Model 1R (Born in Canada) (Long-term immigrants) (Recent immigrants and temporary residents) Coef. (SE) Coef. (SE) Coef. (SE) Hazard Awareness 0.75*** (0.12) 0.72*** (0.14) 0.43*** (0.14) Place Attachment 0.06 (0.06) -0.02 (0.07) 0.09 (0.06) Measure Awareness 0.05 (0.12) -0.02 (0.07) 0.17* (0.07) N of flood-related information sources 0.02 (0.08) 0.04 (0.03) 0.08* (0.03) Total N of RP responses -0.08 (0.10) 0.01 (0.06) -0.06 (0.08) Female 0.03 (0.19) -0.20 (0.13) -0.03 (0.14) Age -0.14 (0.10) -0.00 (0.05) -0.07 (0.06) Income -0.01 (0.06) -0.03 (0.04) -0.04 (0.04) Homeownership Renter -0.51 (0.45) -0.21 (0.17) -0.37+ (0.19) No response -0.60 (0.39) 0.31 (0.17) 0.22 (0.31) N 58 73 71 Adjusted R2 0.4759 0.5236 0.5385 With robust standard errors in parentheses; +p<.10; *p<.05; **p<.01; ***p<.001 (two-tailed tests) Regression models with interaction terms were also developed to test the moderation effects of immigration status (detailed in Appendix B). This analysis is presented in the appendix rather than the main text to emphasize the stratified models, which yielded more robust results for the moderation conceptual framework. The results from the interaction models suggest that the interactions between immigration status and both hazard awareness and measure awareness were significant at the 10% level, partially supporting the findings from the stratified models. 54 Overall, the results from the mediation and regression analyses provide no evidence supporting the mediation conceptual framework but offer strong evidence for the moderation effects of immigration status on the relationships between predictors and flood risk perceptions. 3.2 Support for Coastal Green Infrastructure 3.2.1 Descriptive Statistics Two statements served as metrics to assess participants’ attitudes toward incorporating wetland restoration in flood protection (Table 3.7). Impressively, over 70% of respondents expressed a favorable stance on both statements, and only less than 5% of participants expressed some level of disagreement with either statement. Table 3.7 Attitudes toward incorporating wetland restoration in flood management (percentage), N=283. Strongly agree Somewhat agree Neutral Somewhat disagree Strongly disagree We should incorporate more wetland restoration and other nature-based designs in flood risk management 39.6% 37.1% 21.2% 0.7% 1.4% Brackish marsh restoration is necessary for flood protection 35.7% 35.0% 26.1% 1.4% 1.8% When participants were queried about the allocation of funds between traditional hard structure and hybrid solutions, a notable preference emerged for hybrid solutions, combining nature-based approaches with traditional hard structures, over allocating funds solely to THS (Table 3.8). 55 Table 3.8 Coastal flooding funding allocation preferences, N=281 % We should allocate most of the funding to hybrid solutions 65.8 We should allocate most of the funding to traditional hard structures 14.6 I have no opinion on how to allocate flood protection funding 19.6 A majority advocated for prioritizing funding towards hybrid approaches. In contrast, a much smaller proportion favored allocating most funding exclusively to THS such as dikes and seawalls. Notably, almost one-fifth of the participants expressed no specific opinion on the matter. It’s also noteworthy that, despite 76.7% of participants displaying positive attitudes toward nature-based solutions in the preceding question, approximately 11% fewer participants supported the idea of the City allocating most of the funding to hybrid solutions. 3.2.2 Comparative Analysis – Support for CGI To determine if there are significant differences in attitudes toward CGI across immigration groups, we calculated group mean scores for the two attitude statements (Table 3.9). The BIC group showed stronger support for integrating wetland restoration and other forms of CGI into flood risk management compared to the LI and RITR groups. However, according to the Kruskal-Wallis test, these differences were not statistically significant. 56 Table 3.9 Attitudes toward incorporating wetland restoration in flood management, mean scores by immigration status, N=270. Born in Canada Long-term immigrants (>5 years) Recent immigrants and temporary residents Brackish marsh restoration is necessary for flood protection 4.16 3.97 4.00 We should incorporate more wetland restoration and other nature-based designs in flood risk management 4.33 4.17 3.96 Note: The highest mean for each statement is in bold. In contrast, distinct patterns were observed in preferences for funding allocation among the groups (Table 3.10). Approximately 80% of the BIC participants supported allocating the majority of funding to hybrid solutions, compared to 64.7% of LI and 61.0% of RITR participants. Notably, only 7.4% of BIC participants had no opinion on this issue, whereas more than 20% of both LI and RITR participants expressed no preference. Support for directing most funding to traditional hard structures (THS) was similarly low among BIC (13.6%) and LI (12.7%) participants, with RITR participants showing slightly more support (18.3%). The Chi-square test revealed significant differences among the groups in terms of their opinions on funding allocation, with a χ2 of 10.00 (df=4, N=265) and a p-value of 0.04. 57 Table 3.10 Coastal flooding funding allocation preferences by immigration status, N=270. Born in Canada Long-term immigrants (> 5 years) Recent immigrants or temporary residents We should allocate most of the funding to traditional hard structures (e.g., dikes, seawalls) for flood protection. 13.6% 12.7% 18.3% We should allocate most of the funding to hybrid solutions that combine traditional hard structures and nature-based solutions for flood protection. 79.0% 64.7% 61.0% I have no opinion on how to allocate flood protection funding 7.4% 22.5% 20.7% Given that attitudes toward the two CGI statements did not vary significantly across immigration groups, yet distinct preferences for funding allocation emerged, the following sections will explore the underlying reasons behind the higher support for funding hybrid solutions among individuals born in Canada. 3.2.3 Comparative Analysis – Potential Mediators Conceptual Framework 1 posits that variables such as risk perceptions, knowledge of CGI benefits, familiarity with both THS and CGI, and environmental values might mediate the impact of immigration status on attitudes towards CGI (Figure 1.2 above). This section examines which of these potential mediators are associated with immigration status. To do this, Kruskal-Wallis tests were conducted on the Likert-scale statements related to these identified variables. The analysis reveals that five mediator variables/statements are significantly correlated with immigration status: 1) risk perception concerning the likelihood and general damage of coastal 58 flooding, 2) knowledge about the importance of brackish marshes in habitat provision and bank stabilization, 3) familiarity with traditional hard structures (THS), 4) familiarity with coastal green infrastructure (CGI), specifically wetland restoration, and 5) agreement with the Dominant Social Paradigm (DSP), as detailed in Table 3.11. Participants from the BIC group displayed significantly higher knowledge levels than those from the RITR group concerning 'habitat provision' (p<0.01) and 'sediment trapping and bank stabilization' (p<0.05). Familiarity with THS also varied significantly among the groups, with the BIC group being the most familiar, followed by the LI group, which was significantly more familiar than the RITR group. Additionally, the BIC group demonstrated greater familiarity with brackish marshes compared to the RITR group. There was also noticeable variation in environmental values, particularly regarding the Dominant Social Paradigm (DSP). The BIC individuals were more likely to disagree with the DSP statements compared to their RITR and LI counterparts, indicating stronger environmental consciousness。 59 Table 3.11 Kruskal-Wallis Test: Potential Mediator Differences by Immigration Group. Variable Statement H Post-hoc test Risk Perception A major coastal flooding event will likely occur in Richmond during the next 50 years 11.33* BIC > RITR* LI > RITR** The sea level along Richmond’s coast will likely rise by at least 1.2 m by 2100 6.51** LI > RITR* Future sea level rise and coastal flooding will cause severe damage to Richmond in general 6.00* LI > RITR* Knowledge about CGI benefits Provide critical habitat for many species including salmon and shorebirds 10.60** BIC > RITR** Trap sediments and stabilize the bank 8.55* BIC > RITR* Familiarity with THS I am familiar with seawalls used for flood protection 31.61*** BIC > LI* BIC > RITR*** LI > RITR** I am familiar with dikes used for flood protection 45.75*** BIC > LI** BIC > RITR*** LI > RITR** I am familiar with breakwaters used for flood protection 14.21*** BIC > LI** BIC > RITR** Familiarity with CGI I am familiar with wetland restoration used for flood protection 10.79** BIC > RITR** Environmental Values: Dominant Social Paradigm Humans have the right to modify the natural environment to suit their needs 10.16** LI > BIC * RITR > BIC * Nature is strong enough to cope with the impacts of modern industrial nations 12.49** RITR > BIC** The so-called ‘ecological crisis’ facing humankind has been greatly exaggerated 8.80* LI > BIC* RITR > BIC* *p<.05; **p<.01; ***p<.001; +p<.10 (two-tailed t-tests) Only statistically significant results are included in the table 60 3.2.4 Influence of Potential Mediators on Funding Preferences To examine whether the five mediator variables/statements identified may have influenced participants’ funding allocation preferences, Kruskal-Wallis tests were employed to compare across the three groups (MTHS, MHS, and NOOP) with different funding allocation preferences. MTHS refers to the group that supports “most funding to traditional hard structure,” MHS represents the group that supports “most funding to hybrid solutions,” and NOOP refers to the group that indicated no opinion. The findings are presented in Table 3.12. Results indicate thatthe "most to HS" group had a significantly higher mean score than the "no opinion" group concerning the risk perception statement (p<0.05). It also had significantly higher familiarity with both THS and CGI compared with the "no opinion" group. Results underscore significant disparities in knowledge levels between the "most to HS" group and the "no opinion" group, particularly concerning wetland' importance in "habitat provision" (p<0.05) and "sediment trapping and bank stabilization" (p<0.05). In addition, the “most to HS” group exhibits a significantly higher level of disagreement with the DSP statements compared to the “most to THS” group (p<0.1 and p<0.05). 61 Table 3.12 Kruskal-Wallis test: potential mediator differences by funding allocation group. Variable Statement H Post-hoc test Risk Perception A major coastal flooding event will likely occur in Richmond during the next 50 years 8.67* MTHS > NOOP* MHS > NOOP+ Knowledge about CGI benefits Provide critical habitat for many species including salmon and shorebirds 6.00* MHS > NOOP* Trap sediments and stabilize the bank 8.40* MHS > NOOP* Familiarity with THS I am familiar with seawalls used for flood protection 10.87** MTHS > NOOP** MHS > NOOP* I am familiar with dikes used for flood protection 14.19*** MTHS > NOOP*** MHS > NOOP* I am familiar with breakwaters used for flood protection 28.94*** MTHS > NOOP*** MHS > NOOP*** Familiarity with CGI I am familiar with wetland restoration used for flood protection 26.90** MTHS > NOOP*** MHS > NOOP*** Environmental Values: Dominant Social Paradigm Humans have the right to modify the natural environment to suit their needs 5.40+ MTHS > MHS+ The so-called ‘ecological crisis’ facing humankind has been greatly exaggerated 8.66* MTHS > MHS* *p<.05; **p<.01; ***p<.001; +p<.10 (two-tailed t-tests) MTHS = most funding to traditional hard structure, MHS = most funding to hybrid solutions, NOOP = no opinion Only statistically significant results are included in the table In summary, the ‘most to HS’ group significantly differed from the other two funding allocation groups across all five variables/statements identified in the comparative analysis section above, except for two of the risk perception statements and one of the environmental value statements. Thus, these five variables/statements are likely to mediate the influence of being born in Canada on the preference for funding hybrid solutions. 62 3.2.5 Mediation Analysis Comparative analysis led to the development of a proposed mediation model. This model features BIC as the independent variable and Hybrid as the dependent variable (where 1 indicates support for allocating most funding to hybrid solutions and 0 otherwise). Proposed mediators include RP1 (perceived likelihood of a major coastal flooding event), Knowl2_index (knowledge of salt marsh benefits in habitat provision and bank stabilization), Fam_THS (familiarity with traditional hard structures), Fam_wetland (familiarity with wetland restoration), and DSP2_index (average of two Dominant Social Paradigm statements) as the mediator. The model was tested using the classical approach by Baron & Kenny (1986) in three steps, with results displayed in Figure 3.5. Figure 3.5 Results of multiple mediation model explaining why born-in-Canada individuals more likely support allocating most flood protection funding for hybrid solutions. Note: The solid arrow indicates the direct effect of BIC on Hybrid, and the dashed arrow indicates the total effect. *p<.05; **p<.01; ***p<.001; +p<.10 63 In the first step of the analysis, BIC significantly predicted all of the mediators, except for RP1. However, in the second step, the regression of Hybrid on BIC—without including the mediators—was not significant (p = 0.38), as indicated by a horizontal dashed arrow. In the final step, Knowl2_index, Fam_THS, and DSP2_index significantly predicted RP4. These results do not strongly support the proposed mediation model. 3.2.6 Regression Analysis A multinomial logistic regression was conducted to explore the impact of various predictors on funding allocation preference, with a focus on the moderation effects of immigration status as outlined in the Conceptual Framework 2 (Figure 1.3). Predictors included indices for risk perception, knowledge of CGI benefits, agreement with the Dominant Social Paradigm (DSP), and familiarity with both CGI and THS, calculated as the mean across relevant statements. The reliability of these indices was assessed using Cronbach's alpha, with all indices showing acceptable internal consistency (α≥ 0.7, Table 3.13). Table 3.13 Results from Cronbach’s Alpha test for funding allocation predictors. Index Number of Statements Cronbach's α CGI Knowledge 5 0.80 Agreement with DSP 3 0.70 Familiarity with CGI 2 0.83 Familiarity with THS 3 0.86 64 The initial model (Model 3A) incorporated all aforementioned variables and demonstrated a good model fit, as indicated by a Chi-square test result of 45.15 (p < 0.001), indicating that the model significantly improves fit over an intercept-only model. Further testing of interaction terms identified that including the interaction between immigration status and agreement with DSP enhanced the model's fit, leading to Model 3B, which had a Chi-square statistic of 53.24 (p < 0.001). Results are detailed in Table 3.14. 65 Table 3.14 Multinominal logistic regression for flood funding allocation preference. Model 3A Model 3B Coef. (SE) Coef. (SE) Most to Traditional Hard Structure Immigration Status Long-term immigrants -0.05 (0.51) -2.31 (1.59) Recent immigrants and temporary residents 0.54 (0.47) -3.72 (5.15) Risk Perception 0.28 (0.32) 0.18 (0.33) CGI Knowledge -0.51+ (0.29) -0.54+ (0.31) Agreement with DSP 0.59** (0.22) -0.27 (0.40) Immigrant Status # Agreement with DSP Long-term immigrants 0.95* (0.56) Recent immigrants and temporary residents 1.50** (0.58) Familiarity with CGI -0.01 (0.22) 0.01 (0.24) Familiarity with THS 0.61* (0.30) 0.57+ (0.33) Female 0.06 (0.41) -0.08 (0.42) Age 0.03 (0.13) -0.02 (0.13) Most to Hybrid Solutions (base outcome) No Opinion Immigration Status Long-term immigrants 1.54* (0.61) 0.52 (1.22) Recent immigrants and temporary residents 1.26+ (0.71) 3.02* (1.31) Risk Perception -0.05 (0.23) 0.02 (0.23) CGI Knowledge -0.40 (0.24) -0.42+ (0.24) Agreement with DSP 0.17 (0.19) 0.24 (0.29) Immigrant Status # Agreement with DSP Long-term immigrants 0.35 (0.40) Recent immigrants and temporary residents -0.67 (0.45) Familiarity with CGI -0.46* (0.25) -0.43+ (0.25) Familiarity with THS 0.09 (0.25) 0.09 (0.24) Female 0.06 (0.38) 0.15 (0.39) Age 0.03 (0.13) 0.03 (0.14) N 249 249 χ2 45.15 53.24 Robust standard errors displayed in parentheses; +p<.10; *p<.05; **p<.01; ***p<.001 (two-tailed tests) # means interaction terms 66 Analyzing the effects, for each additional unit on the CGI Knowledge Index, the log odds of choosing 'most funding to THS' over 'most funding to hybrid solutions' decreased by 0.54 (p < 0.1). Within the LI and RITR groups, each unit increase in agreement with DSP corresponded to increases in the log odds of choosing 'most funding to THS' over 'most funding to hybrid solutions' by 0.95 (p < 0.05) and 1.50 (p < 0.01), respectively. Moreover, each unit increase in familiarity with THS was associated with an increase in the log odds of the same choice by 0.57 (p < 0.05). Participants of the RITR group were notably more likely to choose 'no opinion' over 'most funding to hybrid solutions,' as indicated by a coefficient of 3.02 (p<0.05). Additionally, each additional point on the CGI Knowledge Index and CGI Familiarity Index decreased the log odds of choosing 'no opinion' by 0.42 (p<0.1) and 0.43 (p<0.1), respectively. To further investigate these patterns, a stratified logistic regression was conducted across immigration groups. Because only the model of the RITR group had a Chi-square statistic that was significant (χ2 = 30.45, p < 0.01), only results of this model is presented in Table 3.15. Contrary to findings from the full model, within the RITR group, increased familiarity with THS did not enhance the likelihood of selecting 'most funding to THS' over 'most funding to hybrid solutions.' Furthermore, greater knowledge about CGI benefits did not reduce the tendency of RITR participants to select 'no opinion,' suggesting that their indifference towards flood funding allocation might be influenced by other factors not explored in this study. 67 Table 3.15 Stratified regression for flood funding allocation preference by immigration status. Model 3 (Recent immigrants and temporary residents) Coef. (SE) Most to Traditional Hard Structure Risk Perception 0.71 (0.65) CGI Knowledge -0.84+ (0.51) Agreement with DSP 1.30** (0.46) Familiarity with CGI 0.96 (0.70) Familiarity with THS -0.40 (0.66) Female -0.19 (0.73) Age -0.12 (0.32) Most to Hybrid Solutions (base outcome) No Opinion Risk Perception -0.02 (0.97) CGI Knowledge -0.26 (0.39) Agreement with DSP -0.35 (0.38) Familiarity with CGI -0.60 (0.44) Familiarity with THS 0.30 (0.40) Female -0.62 (0.67) Age 0.17 (0.26) N 80 χ2 30.45 With robust standard errors in parentheses; +p<.10; *p<.05; **p<.01; ***p<.001 (two-tailed tests) 3.2.7 Qualitative Analysis A total of 158 participants responded to an open-ended question exploring the reasons for their varying levels of support for integrating coastal green infrastructure into flood management. After filtering out irrelevant responses, 130 were thematically analyzed, with the results summarized in Table 3.16. The demographic breakdown showed that 43.8% of participants were long-term immigrants (LI), followed by 40.8% who were born in Canada (BIC), and 13.8% recent immigrants or temporary residents (RITR). More than half of the respondents (53.8%) provided 68 reasons for supporting CGI, while 31.5% explained their hesitations. Approximately 37% of the respondents endorsed CGI for its co-benefits, with 18 from BIC, 22 from LI, and 8 from RITR. Four types of co-benefits mentioned: ecosystem protection and restoration, conservation of natural habitats and biodiversity, climate benefits, and aesthetic improvements. Many stressed the importance of balancing effective flood management with nature protection. The second most common reason, cited by 19.2% of participants, was CGI's perceived effectiveness in flood protection, with contributions from 8 BIC, 14 LI, and 2 RITR participants. Additionally, 10% of respondents, specifically from the BIC and LI groups, praised CGI's sustainability and adaptability as attributes that support better long-term outcomes. Other noted reasons included an attachment to the brackish marshes, mentioned by one participant each from BIC and LI, and a belief that traditional hard structures do more harm than good in terms of flood protection. 69 Table 3.16 Results from Open-ended question on reasons behind participants’ high or low support for including coastal green infrastructure in flood management. Theme Sub Theme Frequency Percentage Reasons for High Support Co-benefits (48) Ecosystem protection and restoration 30 36.9% Conservation of natural habitats and biodiversity 5 Climate benefits 5 Aesthetic improvements 1 Effectiveness in flood protection 25 19.2% Sustainability and adaptability 13 10.0% Attachment to brackish marshes 2 1.5% Concerns about THS 2 1.5% Reasons for Low Support/Hesitation Lack of CGI Knowledge 23 17.7% Concerns about effectiveness in flood protection 13 10.0% Concerns about required time 4 3.1% Concerns about funding and implementation 2 1.5% Concerns about maintenance costs 1 0.8% Conversely, the most common reason for low support or hesitation towards CGI was a lack of knowledge about how it works, with close to 18% of participants expressing a desire for more information (11 from BIC, 8 from LI, and 4 from RITR). Ten percentage of participants doubted CGI’s effectiveness in flood protection, favoring THS instead; this group included 6 participants each from BIC and LI, and 1 from RITR. Four respondents were concerned about the time it might 70 take for CGI to become effective. Additionally, one participant worried about the likelihood of CGI receiving adequate funding and another cited concerns over maintenance costs. Finally, over 12% of the participants advocated for hybrid solutions, emphasizing their necessity for optimal flood protection in Richmond. 71 Chapter 4: Conclusion 4.1 Summary of Results The main objective of this study is to examine how coastal flood risk perceptions and support for coastal green infrastructure vary according to immigration status and to explore the reasons behind these differences. This research focuses on Richmond, BC, an immigrant coastal city in Canada that faces significant risks from sea level rise and flooding. A questionnaire survey was conducted with Richmond residents, both in-person and online, from July to September 2023. A total of 330 responses were collected, with 285 fully completed surveys. 4.1.1 Risk Perception While general awareness of the coastal flood risks is high, risk perceptions differ among born-in-Canada residents (BIC), long-term immigrants (LI), and recent immigrants and temporary residents (RITR). LI exhibited the highest risk perceptions, followed by BIC, with significant differences noted between RITR and the other two groups. A closer look into the nine risk perception statements (RP1-RP9) reveals that the differences mainly lie in their predictions of the likelihood of future sea level rise and coastal flooding as well as in their concern on whether these future events will cause severe damage to Richmond in general (RP1, RP2, and RP4). Two approaches were taken to investigate what factors contributed to the observed differences: mediation analysis and stratified regression analysis. The proposed mediation model was not supported by the results. Regression analysis stratified by immigration status revealed that while hazard awareness primarily influenced BIC and LI's risk perceptions, RITR's perceptions 72 were also shaped by measure awareness, the number of flood-related information sources, and homeownership, providing strong evidence for the moderating effects of immigration status. Combining the results from the comparative and correlation analyses, the main finding is that individuals born in Canada and long-term immigrants had higher perceptions of coastal flooding risk due to their heightened awareness of potential earthquakes and subsequent tsunamis in the region. This hazard awareness significantly influenced their risk perceptions. In contrast, recent immigrants and temporary residents were less aware of the potential earthquake, and this awareness had a lesser impact on their risk perceptions. Additionally, being a renter significantly decreases RITR’s risk perceptions. Moreover, awareness of existing flood protection measures and access to more flood-related information sources also contribute to increased risk perceptions for RITR, though to a lesser extent. 4.1.2 Support for CGI Most participants advocated for the inclusion of wetland restoration and other nature-based designs in flood risk management. Nevertheless, about 11% fewer participants supported allocating the majority of funding to hybrid solutions that integrate nature-based and traditional hard structures. Chi-square tests highlighted significant differences among immigrant groups in their funding allocation preferences, with a higher percentage of BIC participants favoring hybrid solutions, a higher percentage of RITR participants favoring traditional hard structure, and more LI and RITR participants abstaining from an opinion. Two approaches were taken to investigate what factors contributed to the observed differences: mediation analysis and multinomial regression analysis. The proposed mediation model was not supported by the results. Multinomial regression analysis indicated that while lower 73 CGI knowledge and higher familiarity with THS tended to increase support for traditional hard structure across all groups, LI and RITR's preferences were also highly shaped by their agreement with the DSP statements. Additionally, being RITR, having less CGI knowledge, and lower familiarity with CGI were associated with a higher likelihood of expressing no opinion on funding allocation. Contrary to expectation, risk perception did not significantly affect funding preferences. Integrating the results from the comparative analyses, the main finding is that individuals born in Canada show greater support for allocating most of the funding to hybrid solutions compared to the other groups, due to their increased knowledge about CGI benefits and higher familiarity with CGI. In contrast, recent immigrants and temporary residents prefer traditional hard structure more than the other two groups, due to their agreement with the DSP, indicating lower environmental consciousness. This alignment with DSP significantly influences their funding allocation preferences. Both RITR and LI are more likely to express no opinion on funding allocation due to lower levels of CGI knowledge and familiarity. Additionally, RITR are particularly prone to indicating no opinion, suggesting the influence of other factors not examined in this study. Qualitative results further revealed that recognition of CGI’s effectiveness in flood protection, along with its sustainability and adaptability, were other primary reasons participants supported integrating CGI into flood management. Conversely, lack of knowledge and doubts about CGI’s effectiveness were the main reasons for hesitation. 4.2 Key Conclusions and Policy Implications Native-born residents and long-term immigrants exhibited higher risk perceptions than recent immigrants and temporary residents, particularly regarding the likelihood of future coastal flooding events and potential general damage to Richmond. The findings suggest that high levels 74 of hazard awareness, particularly the recognition that Richmond is susceptible to flooding caused by potential earthquakes and subsequent tsunamis, dominate the risk assessments of native-born residents and long-term immigrants. This heightened awareness significantly influences their perception of sea level rise and coastal flooding risks, overshadowing other factors. Previous studies have similarly highlighted the significant positive impact of hazard awareness and knowledge on risk perception in both natural and technological hazards (Houston et al., 2019; Simpson, 1988). However, the overshadowing of other factors is not commonly observed in other studies, which might be attributed to the tendency of individuals to focus more on rare, catastrophic events than on frequent, milder incidents (Pagneux et al., 2011; Slovic et al., 2000). This heightened focus on major earthquake-induced tsunamis could potentially lead to the underestimation of risks from less catastrophic events, such as king tide flooding and storm surges. Therefore, risk communication strategies must ensure that these less dramatic but still significant risks are also emphasized to the public. Recent immigrants and temporary residents exhibit different patterns in their flood risk perceptions. These groups are less aware of the potential for earthquake-induced tsunamis, and their risk perceptions are also influenced by factors such as homeownership, awareness of existing measures, and access to flood-related information. Their lack of hazard awareness is likely due to shorter residency durations and limited access to local knowledge (Pagneux et al., 2011; Thistlethwaite et al., 2018). Homeownership typically correlates with higher risk perceptions, especially when homeowners lack flood insurance, as it increases the stakes involved (Grothmann & Reusswig, 2006; Thistlethwaite et al., 2018). Canadian homeowners currently bear 75% of the approximately $800 million in uninsured losses caused by flooding each year (Swiss Re, 2016). In this study, only 23.7% of immigrant homeowners had flood insurance compared to 33.3% of 75 long-term immigrants and 61.8% of native-born respondents, indicating that immigrant homeowners are less prepared for flood-induced losses. Efforts should be directed toward protecting immigrant homeowners by increasing their awareness of flood risks and access to flood insurance. Interestingly, the awareness of existing public preventive measures increased recent immigrants' and temporary residents' (RITR) risk perceptions, contrary to the "levee effect" identified in previous studies where the presence of flood defense structures created a false sense of security and decreased risk perceptions (Logan et al., 2018; Ludy & Kondolf, 2012). This might be explained by their perception that existing measures are insufficient to handle future coastal flooding, based on their observations of the current infrastructure's performance (Houston et al., 2019). Additionally, the positive association between access to more flood-related information and higher risk perceptions aligns with previous findings (Bustillos Ardaya et al., 2017; Działek et al., 2014; Shen, 2009). This underscores the importance of creating more information channels for recent immigrants and temporary residents to help them become more aware of flooding risks. Several predictors identified in other studies did not significantly influence risk perceptions in this research, such as distance to the coast, trust, place attachment, past direct experience, and other demographic factors. There were no significant differences in risk perceptions between coastal and inland residents. Richmond's unique geographical characteristics, including its location at the Fraser River estuary and low elevation, might explain this anomaly. Similar findings were observed in Pagneux et al. (2011), possibly due to the small size of the town studied. Trust in institutions, which has been negatively associated with risk perception in some studies (Houston et al., 2019; Terpstra, 2011), did not significantly influence risk perceptions in this study. This might be because trust in stakeholders is more critical for technical risks and less so for natural 76 disasters, which involve more complex interactions and cultural factors like fatalism (Cisternas et al., 2024; Z. Han et al., 2022). Past direct experience was also not associated with higher flood risk perceptions among respondents. Two possible reasons include the fact that nearly two-thirds of participants who had experienced natural hazards did not suffer losses, reducing the impact of these experiences on their risk perceptions (Baan & Klijn, 2004; Bichard & Kazmierczak, 2012). Additionally, previous studies often considered experiences of natural hazards at the same location (Kellens et al., 2011; Thistlethwaite et al., 2018), whereas immigrant experiences in this study may have occurred outside Canada, thereby diminishing their impact. Demographic factors such as gender, age, and income were also not strongly associated with coastal flooding risk perceptions. In Richmond, there is substantial public support for incorporating wetland restoration and other types of Coastal Green Infrastructure (CGI) in flood risk management, indicating a high receptivity to future CGI projects. However, funding allocation preferences vary across the immigration groups, with native-born individuals showing higher support for CGI than other groups. Knowledge about CGI benefits and its perceived effectiveness in flood protection are significant factors influencing respondents' support for funding CGI. Many respondents expressed hesitation about advocating for CGI due to low familiarity and lack of knowledge about how CGI works in flood protection, consistent with findings from Goeldner-Gianella et al. (2015). Therefore, more education and communication programs about CGI’s functionality and co-benefits can increase public support for its adoption. For immigrant groups, low levels of environmental consciousness and underestimation of current environmental issues contributed to their preference for traditional hard structures over CGI. Education about nature degradation and the climate crisis could potentially shift their preferences toward CGI. 77 Contrary to some previous findings where higher risk perception decreased acceptance of more nature-based measures (Goeldner-Gianella et al., 2015; Kim & Petrolia, 2013), flood risk perceptions did not significantly affect participants’ support for CGI in this study. The lack of significant relationship between risk perception and CGI acceptance is also observed in Schernewski et al. (2018). This supports Anderson & Renaud (2021) conclusion that the relationship between risk perception and acceptance of risk management measures is complex and likely mediated by other factors, such as perceived effectiveness and knowledge about co-benefits. Thus, the finding suggests that as long as people understand the effectiveness and co-benefits of CGI, high levels of risk perception will not hinder their support for CGI. Building on these conclusions, several key policy implications are proposed to enhance flood risk management and community resilience in Richmond: Enhanced Risk Communication: It is crucial to develop comprehensive risk communication strategies that emphasize more frequent and less dramatic risks like king tides and storm surges. This will help ensure a well-rounded understanding of all potential risks from sea level rise and coastal flooding among residents. Support for Immigrant Homeowners: Policies should focus on increasing flood insurance coverage among immigrant homeowners. This can be achieved through targeted outreach and education campaigns about the importance of flood insurance and the availability of affordable options. Information Accessibility: Creating more accessible and diverse information channels is vital for recent immigrants and temporary residents to improve their awareness of flood risks. This can include multilingual resources and community-based information sessions. 78 Public Education on CGI: To increase support for CGI projects, it is essential to implement public education programs that explain how CGI works and its benefits. This is particularly important for immigrant communities who may be less familiar with these concepts. Promoting Environmental Consciousness: Efforts to raise environmental awareness among immigrant groups can help shift their preferences toward nature-based flood protection measures. Educational initiatives about the climate crisis and environmental degradation can play a significant role in this. 4.3 Contributions and Limitations This study makes several key contributions. Firstly, it recognizes the heterogeneity within the immigrant group by examining the interaction between length of residency and immigration status. The results reveal significant differences between these subgroups. This addresses the common misconception of immigrants as a homogeneous group and highlights the need for targeted risk communication strategies for each subgroup, which is particularly relevant given the increasing number of immigrants in Richmond and other coastal cities. Moreover, the use of both English and Chinese versions of the survey helped achieve a balanced representation of immigrants in this study, addressing the underrepresentation of immigrants noted as a limitation in some previous studies (Olofsson & Rashid, 2011; Yong et al., 2017) Secondly, the study dissects various dimensions of flood risk perception. By using nine statements in the survey to measure flood risk perception, encompassing likelihood, different consequences, and emotional responses, the study reveals that perceived risks differ depending on not only the dimension but also the type of consequence referred to, such as personal versus general impacts. This helps explain contradictory findings in the literature, as studies often use different 79 indicators or statements to measure risk perception (Guo et al., 2020; Kellens et al., 2011; McGuire, 2015). The results underscore the importance of comparing measurement methods of risk perception when comparing results across different studies. Thirdly, this study provides actionable insights for policymakers to develop tailored communication strategies and support systems for different immigrant subgroups, enhancing the overall effectiveness of flood risk management. By involving diverse demographic groups, the study identifies specific vulnerabilities and fosters greater community awareness and engagement, potentially leading to more resilient and informed communities. However, the study has several limitations. Firstly, due to the small sample size of recent immigrants, they were grouped together with temporary residents, who are study and work permit holders. While these groups share similarities, such as shorter residency durations, there may be important differences. For example, their access to social and legal resources might differ, and temporary residents might be uncertain about their future residency, potentially influencing their risk perceptions and policy support. Secondly, due to privacy concerns, respondents were not asked about their citizenship. This could influence their opinions on flood funding allocation, as those without voting rights might be more inclined to express no opinion. Another limitation is the timing of the survey, conducted from July to September 2023, a particularly dry summer in Richmond. This timing might have given residents a false impression of flooding risks, leading to underestimations or reduced engagement in the research. Additionally, as storm surges typically occur in winter, respondents might have found it difficult to recall such experiences. The use of surveys might introduce bias due to question framing and respondents’ misunderstanding of the questions and technical terms, which can be mitigated by incorporating complementary data collection methods like interviews and observations. Additionally, the 80 findings are specific to Richmond and may not be generalizable to other regions with different geographic, socio-economic, and demographic contexts. Nevertheless, the emphasis on the heterogeneity of immigrants and the interaction between immigration status and duration of residence provides valuable insights for future studies. 4.4 Future Research An unanswered question is why long-term immigrants exhibited the highest risk perceptions among the three groups. One possible explanation is that while long-term immigrants are as aware of local hazards as native-born individuals, they may have lower disaster preparedness compared to their native-born counterparts. However, in this study, although the two groups significantly differed in preparedness, this difference did not significantly influence their risk perception. Another possibility is that native-born individuals have become accustomed to higher levels of risk due to their prolonged residency (Houston et al., 2019). Future studies could employ other research methods, such as in-depth interviews and focus groups, to explore whether these hypotheses are accurate or whether other factors uniquely heighten the risk perceptions of long-term immigrants. Future studies should also consider employing larger sample sizes to explore the nuances within immigrant subgroups more comprehensively. This will help to further delineate the specific needs and perceptions of recent immigrants versus temporary residents, and how their unique experiences and resources influence their risk perceptions and policy preferences. To make coastal green infrastructure concepts more accessible for participants, this study primarily used wetland restoration as an example. Future studies can focus on other types of coastal green infrastructure or emerging hybrid structures tailored to local contexts, thereby further testing the findings of this study. Given that CGI is unfamiliar to most of the public, future studies could 81 utilize in-depth interviews and focus groups to provide participants with more time to understand the concept and to examine in greater depth how the public evaluates trade-offs when deciding between different flood protection measures. Additionally, duplicating the research methodology in another coastal immigrant city or expanding research to include multiple geographic regions with varied socio-economic and demographic contexts can enhance the generalizability of the findings. As mentioned earlier in the thesis, the majority of Richmond’s immigrants are from East Asia, especially China. 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Journal of Environmental Planning and Management, 63(4), 710–732. https://doi.org/10.1080/09640568.2019.1605890 107 Appendices Appendix A Survey Questionnaire 108 109 110 111 112 113 114 115 116 117 Appendix B Regression Analysis with Interaction Terms B.1 Results Analysis of scatter plots revealed potential interactions between immigration status and variables such as hazard awareness, place attachment, measure awareness, and the number of flood-related information sources in predicting risk perception, with varying slopes among the three immigration groups (detailed in Appendix B.2). Model 4A did not include any interaction terms, which explained around 44.0% of the variance in risk perception, with an F-statistic of 10.07 (df = 17, 184), significant at p < 0.001 (Table B.1) In testing the model with and without each interaction term, the interaction involving hazard awareness and measure awareness significantly contributed to the model, leading to their inclusion in Model 4B. This model achieved an adjusted R² of 0.4448, with an F-statistic of 9.62 (df = 21, 180), significant at p < 0.001. 118 Table B.1 Multiple regression for flood risk perception without and with immigration interaction terms. Model 4A Model 4B Immigration Status (Ref=RITR) Coef. (SE) Coef. (SE) Born in Canada -0.14 (0.12) -0.99 (0.76) Long-term immigrants 0.12 (0.10) 0.06 (0.62) Hazard Awareness 0.63*** (0.07) 0.49*** (0.12) Immigrant Status # Hazard Awareness Born in Canada 0.32+ (0.17) Long-term immigrants 0.16 (0.18) Measure Awareness 0.11* (0.05) 0.19** (0.07) Immigrant Status # Measure Awareness Born in Canada -0.11 (0.11) Long-term immigrants -0.17+ (0.10) Place Attachment 0.04+ (0.02) 0.05 (0.04) N of flood-related information sources 0.04+ (0.02) 0.05* (0.02) Past disaster experience Yes -0.04 (0.10) -0.03 (0.10) No response 0.07 (0.21) 0.05 (0.23) Trust -0.06 (0.05) -0.05 (0.05) Location (Ref=non-coastal) Coastal -0.03 (0.10) -0.01 (0.10) No response 0.11 (0.15) 0.15 (0.16) Total N of RP responses -0.06 (0.05) -0.06 (0.04) Female -0.09 (0.09) -0.09 (0.09) Age -0.06 (0.04) -0.07 (0.04) Income -0.02 (0.02) -0.01 (0.02) Homeownership (Ref=homeowner) Renter -0.30* (0.14) -0.32* (0.14) No response -0.15 (0.19) -0.07 (0.19) N 202 202 Adjusted R2 0.4398 0.4448 Note: Robust standard errors displayed in parentheses; +p<.10; *p<.05; **p<.01; ***p<.001 (two-tailed tests) # means interaction term The results highlighted that hazard awareness was significantly and strongly associated with risk perception among recent immigrants and temporary residents (RITR, the reference category) (p < 0.001). The interaction term for the BIC group is significant at the 10% level, 119 suggesting that hazard awareness has a greater, positive association with risk perceptions for individuals born in Canada, relative to RITRs. Similarly, measure awareness significantly affected risk perception among RITRs (p < 0.01). The interaction term for the long-term immigrant group is significant at the 10% level, suggesting that while increased awareness enhances flood risk perception among recent immigrants and temporary residents, it does not substantially influence the flood risk perceptions of long-term immigrants, with the coefficient being close to zero. Additionally, having a greater number of flood-related information sources was significantly, albeit weakly, associated with risk perception (p < 0.05), and being a renter was associated with a decrease in risk perception (p < 0.05). Interestingly, place attachment was significant in the full model but became insignificant when its interaction term was removed. Other variables such as past disaster experience, trust, and proximity to the coast were not significant predictors of risk perception 120 B.2 Scatter plots: predictors vs. risk perception by immigration status 121
Thesis/Dissertation
eng
Resources, Environment and Sustainability
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Chang, S. E. (Stephanie Ei-Ling)
Immigration status, flood risk perception, and policy support : lessons from Richmond, Canada
Text