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Scenario analysis using carbon budget modelling for alternative forest management strategies in Turkey… Satir, Enes 2018

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Scenario Analysis Using Carbon Budget Modelling for Alternative Forest Management Strategies in Turkey: the Case Study of Arikaya  by Enes Satir B.S.F, Karadeniz Technical University, 2013   A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF  THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2017   © Enes Satir, 2017  ii   Abstract Increasing carbon stock in forests is fundamental for climate change mitigation. Forest carbon management can also play a critical role in keeping forests healthy, while addressing multiple wildlife and human needs. To fulfill this potential, forest management practices require an improved understanding of annual carbon stocks and carbon dynamics. However, this information is oftentimes not properly accounted for in forest management plans, particularly in the case of developing countries. This thesis focuses on a case study in Turkey to demonstrate the potential to enhance Turkish forest management plans by including carbon stock accounting. The Forest Planning Studios Atlas (FPS-Atlas) and the Carbon Budget Model of the Canadian Forest Service (CBM-CFS3) software programs were used to assess three alternative forest management scenarios in the case study. Carbon stock estimates for each scenario were compared to a baseline based on the current management plan. The first alternative scenario assumed an accelerating harvest rate over time, driven mainly by population growth. The second alternative scenario assumed rehabilitation of non-productive areas, a practice that has been gaining attention in Turkey over the last two decades. The third alternative scenario assumed the rehabilitation practices are combined with a low harvest flow. A carbon price analysis was conducted comparing the baseline with the third alternative scenario. Results showed that accelerating harvest can negatively affect the carbon stocks in a period of one hundred years. Rehabilitation, on the other hand, showed a positive impact on carbon sequestration potential when compared to the baseline after a hundred years. The rehabilitation scenario with low harvest flow showed promising results for international carbon trading. Overall, the methods used in this research proved useful to improve current forest managements strategies in Turkey, particularly in relation to climate change mitigation.   iii   Lay Summary Increasing carbon stock in forests is fundamental for climate change mitigation. Forest carbon management can also play a critical role in keeping forests healthy, while addressing multiple wildlife and human needs. To fulfill this potential, forest management practices require an improved understanding of annual carbon stocks and carbon dynamics. Oftentimes, however, this information is not properly accounted for in forest management plans, particularly in the case of developing countries. This thesis focuses on a case study in Turkey to demonstrate the potential of enhancing Turkish forest management plans by including carbon budget considerations. Results show that accelerating harvest may negatively affect carbon stocks, while rehabilitation can have a positive impact on carbon budget.    iv  Preface This dissertation is an original intellectual product of the author, E. Satir.  v   Table of Contents  Abstract ...................................................................................................................................................... ii Lay Summary ............................................................................................................................................ iii Preface........................................................................................................................................................ iv Table of Contents ........................................................................................................................................ v List of Tables ........................................................................................................................................... vii List of Figures ......................................................................................................................................... viii Acronyms and Abbreviations .................................................................................................................... ix Glossary ...................................................................................................................................................... x Acknowledgements .................................................................................................................................... xi Dedication ................................................................................................................................................ xii 1 Introduction ......................................................................................................................................... 1 2 Thesis Objectives ................................................................................................................................. 3 3 Forest Management in Turkey ............................................................................................................. 4 3.1 Historical Perspective of Forestry and Forestry Laws ................................................................. 4 3.2 Forest Functions ........................................................................................................................... 6 3.2.1 Economic Function ............................................................................................................... 7 3.2.2 Ecological Function .............................................................................................................. 8 3.2.3 Sociocultural Function .......................................................................................................... 9 3.3 Forest Resources Planning ......................................................................................................... 10 3.4 Forest Use and Social Perception ............................................................................................... 12 4 Carbon Market in Turkey .................................................................................................................. 13 5 Case Study Description ..................................................................................................................... 14 vi  6 Methodology ...................................................................................................................................... 19 6.1 Modelling Carbon Sequestration Potential ................................................................................ 19 6.2 Alternative Forest Management Scenarios ................................................................................ 23 6.2.1 Baseline: Current Forest Management in the Region (B1) ................................................. 23 6.2.2 Increased Harvest Linked to Population Growth (B2) ....................................................... 23 6.2.3 Rehabilitation of Non-productive Area (R1) ...................................................................... 24 6.2.4 Rehabilitation with Low Harvest Flow (R2) ...................................................................... 24 7 Results ............................................................................................................................................... 26 8 Discussion .......................................................................................................................................... 34 8.1 Study Implications for Forest Carbon Management in Turkey.................................................. 34 8.2 Study Limitations and Future Improvements ............................................................................. 35 8.3 Recommendations for Further Research .................................................................................... 37 9 Conclusion ......................................................................................................................................... 38 10 References ...................................................................................................................................... 40 Appendices ................................................................................................................................................ 43 APPENDIX A: List of tables included in the forest management plans of Turkey.............................. 43 APPENDIX B: Population (2014) in the towns and villages of Arikaya, Turkey ................................ 45 APPENDIX C: Mitigation scenario included in the Intended Nationally Determined Contribution (INDC) report developed by Turkey ..................................................................................................... 47 APPENDIX D: Stand canopy closure and canopy cover ...................................................................... 48 APPENDIX E: List of analysis units used in the model ....................................................................... 49 APPENDIX F: Python script for assignment of Analysis Units ........................................................... 51 APPENDIX G: Tables of carbon and timber prices calculation ........................................................... 55 APPENDIX H: Distribution of forests in Turkey (2009) ..................................................................... 71  vii   List of Tables Table 1: Symbols and colors used for the forest function map ................................................................... 7 Table 2: Working cycle groups in the Arikaya forest management unit ................................................... 10 Table 3: Distribution of area, growing stock and increment by age class in even-aged forests, Arikaya forest management unit ............................................................................................................................. 11 Table 4: Description of the Arikaya forest management unit ................................................................... 12 Table 5: Description of scenarios simulated in this study. ....................................................................... 24 Table 6: Carbon stock estimates for different forest management scenarios in Arikaya ......................... 26 Table 7: Comparison of carbon and timber revenues estimated for the baseline (B1) and the rehabilitation low harvest (R2) scenario ......................................................................................................................... 28   viii   List of Figures Figure 1: Map of regional directorates of Turkey and location of the Arikaya forest sub-district directorate ................................................................................................................................................. 15 Figure 2: Land use classification in the Arikaya forest management unit ............................................... 16 Figure 3: Spatial cover of main tree species ............................................................................................ 17 Figure 4: Spatial distribution of tree species by age-class....................................................................... 18 Figure 5: Methodology flow chart ............................................................................................................ 21 Figure 6: Annual harvest flow of different forest management strategies in Arikaya.............................. 27 Figure 7: Net change in carbon stock between the baseline (B1) and the rehabilitation with low harvest (R2) scenario ............................................................................................................................................. 29 Figure 8: Spatial cover of tree species age-classes under different scenarios and temporal horizons ... 31 Figure 9: Annual carbon stock estimates for all scenarios over a 20-year period .................................. 32 Figure 10: Annual carbon stock estimates for all scenarios over a 100-year period .............................. 33 ix  Acronyms and Abbreviations  AGB Above Ground Biomass AGC Above Ground Carbon CFDD Catacik Forest District Directorate COP Conference of the Parties CBM-CFS3 Carbon Budget Model of the Canadian Forest Sector  DBH Diameter Breast Height FPS-Atlas Forest Planning Studios Atlas FMP  Forest Management Plan FMPD Forest Management and Planning Department GDF General Directorate of Forestry GIS Geographic information Systems. GPG Good Practice Guidance KP Kyoto Protocol LULUFC Land Use, Land-Use Change, and Forestry IPCC Intergovernmental Panel on Climate Change RD Regional Directorate RS Remote Sensing UNFCCC United Nations Framework Convention on Climate Change x  Glossary Artificial Regeneration: Regeneration of forest areas, where natural regeneration is not possible or replacement with new species is inevitable, by the planting of seedlings or by the direct planting of seeds. Broadleaved forest: Forest where broadleaved species predominate. Coniferous forest: Forest where coniferous species predominate. Coppice Forest: Forests originating mainly from sprouts rather than seeds are managed at short rotation.  Deforestation: The conversion of forest to other land use or the long-term reduction of the tree canopy cover below the minimum 10 percent threshold. Degraded forest: Stands with very low crown closure (less than 10%) are considered degraded forest areas (which need to be reforested or rehabilated). Erosion Control: Establishment of forest to prevent erosion through planting and/ or deliberate seeding on land. High Forest: Forest mainly established by seed naturally or by human interference, (Usually species which are expected to have a long maturity age and relatively high are chosen).  Mixed forest: Forests where broad-leaved and coniferous species co-exist Productive Forest: the forestland where tree canopy cover is between 11-100%. Rehabilitation: Plantation and protection actions on degraded forestlands for the recovery of forest structure, ecological functioning, and biodiversity. Reforestation: Natural regeneration or re-establishment of forest through planting and deliberate seeding on land already in forest land use.   xi   Acknowledgements Thank you to my generous supervisors Dr. Gary Bull and Dr. Verena Griess, who shared their positive energy and motivated me constantly. Thank you to Dr. Cosmin Man who helped me build the model. It was such a pleasure. Many thanks to Dr. Tahia Devisscher who helped me a lot during the writing process.  Lastly, special thanks to my friends, family and colleagues who helped me and gave me their best at all times.    xii  Dedication This study is dedicated to my parents who helped me throughout my education.   1  1 Introduction  Forests play a vital role in the carbon cycle (Kurz et al. 2009). The rise of atmospheric greenhouse gases and the potential effects of future climate change have increased global interest in understanding and quantifying the role of forest ecosystems in carbon management (Baskent & Keleş, 2009). Countries wishing to take part in mitigating climate change through forest management require a sound understanding of carbon dynamics. To this end, forest managers, policy-makers, and local governments are looking for ways to improve their understanding of former and current carbon stocks in forest ecosystems, and how future forests and carbon stocks could be affected by different land-use policies.  One approach to assess carbon dynamics and estimate carbon stocks across large forest landscapes is through the development and application of cost-effective modelling tools. Modelling helps gain insight into the future by simulating and changing current conditions. Forest ecosystems are so diverse that field examinations would never be enough to describe them all (Running & Gower, 1991). The use of models is therefore of great help to simulate different field conditions. Simulation would also need to account for landscape-level characteristics. Developing a regional forest carbon model based on stand-level dynamics would be challenging because of differences in geography, tree species, soil types, and climatic conditions (Kim et al. 2015). The climate mitigation role of forests has been acknowledged by the United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol (KP) through the emission and removals from the Land Use, Land-Use Change, and Forestry (LULUCF) sector. In 2011, aforestation and reforestation activities were included in the LULUCF rules (Pilli et al. 2014).  International agreements such as the UNFCCC, KP, and the Montreal Process require countries to monitor and report on forest carbon stocks and stock changes. Guidance is available for the development of monitoring programs through methodology produced by the Intergovernmental Panel on Climate Change (IPCC 2003). The main goal of carbon management under international agreements is to increase the amount of carbon accumulated in forest ecosystems. Measures to this end include decreasing deforestation, wildfire and illegal cutting, and increasing appropriate silvicultural practices, reforestation, and afforestation (Baskent & Keleş 2009; Tolunay 2011).  Turkey signed both the UNFCCC and the KP in 2004 and 2009 respectively. However, recent biomass increases and changes in the country’s carbon inventory methodologies suggest forest carbon stocks in  2  Turkey should be recalculated. To reduce uncertainties in Turkey’s carbon inventory, there is a need to adapt domestic methods to internationally agreed requirements, particularly in relation to stem volume. For instance, currently Turkey produces carbon inventories for the main forest tree species, but carbon stocks for mixed forests and secondary forests are not included. The Carbon Budget Model of the Canadian Forest Service (CBM-CFS3) addresses several of the international requirements for carbon inventories. In addition to Canada, the CBM-CFS3 has been used at national and regional scales in Russia (Zamolodchikov et al. 2013), Mexico, and Italy (Pilli et al. 2013). The model outputs can be used to forecast carbon dynamics – at stand and landscape levels – of aboveground and belowground biomass and dead organic matter, including soil (Kurz et al. 2009).    3  2 Thesis Objectives Forest ecosystems have satisfied human needs for millennia and play a vital role in the global carbon cycle. At present, the forestry sector in Turkey is shifting from a timber-centric approach to manage forests to a more sustainable, multi-purpose approach that combines ecosystem integrity with timber production. The new management plan for the Turkish forestry sector, which is currently under development, includes objectives for timber production, soil and water protection, nature conservation, and recreation. The new forestry plan also includes the valuation of forest carbon, which will require the quantification of carbon stocks in the countries coniferous and broadleaved forests (Baskent et al. 2008).  Using the CBM-CFS3, the first objective of this study is to assess the impact of different forest management scenarios on carbon dynamics and stocks. The second objective is to generate detailed carbon management information required by international carbon agreements. Ultimately, the results generated in this thesis have the potential to inform Turkey’s new forestry plan. The main research question underpinning the research is the following: How can carbon sequestration in Turkey be enhanced with alternative forest management strategies?    4  3 Forest Management in Turkey Forest management in Turkey has changed considerably in the last 100 years due to political, social, and economic developments. Even-aged methods have been used on 96% of all forests (Zengin et al. 2013). However, over the last 40 years, uneven-aged methods such as single-tree selection have been applied in certain areas (Zengin et al. 2013). From 1918 to 1980, forest management and timber production were synonymous. Management plans were revised on a 10-year basis and the annual allowable cut emphasized wood production. Although about 43% of Turkish forests are still managed this way, current forest management practices in Turkey tend to prioritize biological diversity and regenerative capacity to satisfy ecologic, economic, and social desires (Zengin et al. 2013). Current plans by forest enterprises also consider villagers living within planning units, which was previously not the case. In 2008, the latest forest management regulation was introduced, forming the legal basis of multi-functional ecosystem-based forest management. Forest areas were categorized into three main groups according to economic, ecological and sociocultural functions (Forestry, 2009). As the concept of multiple-use forestry became more widely accepted, forest planning followed a more holistic approach. Despite multiple-use forestry being currently accepted as the leading forest management policy in Turkey, there is still a need to develop planning models that adequately incorporate multiple uses of the forest as management objectives (Baskent & Keleş, 2009).  3.1 Historical Perspective of Forestry and Forestry Laws The development of the Turkish Forest Legislation can be broadly divided into two periods. The first period began in 1870 during the Ottoman Empire when citizens used forests for economic purpose. The Empire also used forests to pay its debts, a practice that continued until the Republic of Turkey was founded in 1919. The second period began in 1937 when the Turkish government reformed the national regulatory framework. The new forest law was introduced in 1956, and since then it has been modified several times.  Many regulations in the Turkish law protect forest land and prevent fragmentation. For example, Article 169 of the Constitution of the Republic of Turkey states that permission will not be granted to any operation or harmful action to forests. Article 17 prohibits construction in forested areas on government land, although it may be permitted if public benefits would result from the construction. Because the concept of public benefit can be subjective, it is difficult to regulate this action using objective criteria. In  5  recent years, rapid population growth and industrialization have added pressure on forest land, with some recent articles in the Turkish law complicating matters even more. For example, some forests have been recently degraded due to new tourist activities introduced by the government. Despite the forest law has undergone multiple modifications, it is not yet fully aligned with international agreements, particularly in relation to forest protection and biological diversity conservation. For example, National Parks and protected forests exist alongside productive forests, but they are much fewer in number and smaller in size compared to other countries. Furthermore, sustainability has not yet been considered in the Turkish Forest Act. This concept is only now being incorporated in the new forestry plan.    6  3.2 Forest Functions Forest functions are services forests provide during their lifespan. The determination of functions is dictated by whether the relationship between a function and the human need for this function is positive. If the relationship is neutral, the function may go under noticed. Forests provide many services and those having economic value can usually be measured. However, it is complicated to specify a service that cannot be priced, yet provides many social and environmental benefits. Table 1 shows the three criteria used by the Turkish forest law article 6832/2008 for determining forest function: (1) the need of the community for forest products, (2) the ecosystem characteristics that will be adversely affected by the production of forest products, and (3) the services other than forests products provided by forests which are needed by society (GDF, 2014). Forests managed according to these three basic criteria are separated into three categories: economic, ecological and sociocultural. Table 1 also includes ten general functions under the main economic, ecological and sociocultural functions.  Forest functions are assessed to prepare forest function maps. Forest function maps show the important forest functions or functional groups within a forest planning unit. When these maps are prepared, each function is handled separately. Borders are transposed based on the natural lines and stand boundaries with the help of technical and scientific indicators which change by function features. The forest function maps determine the different silvicultural treatments to be applied. For silvicultural purposes, each of the allocated function area is a separate technical process unit. Establishing and maintaining the forest structure depends on the composition and structure required by the objective of the function. In the function map, management objectives have different colors, as shown in Table 1.   7   Table 1: Symbols and colors used for the forest function map FUNCTIONS GENERAL FOREST FUNCTIONS SYMBOLS COLORS Economic Forest Products Production PR  Ecological Nature Conservation NC  Erosion Control EC  Protection of the Climate PC  Hydrological HL  Sociocultural Public Health PH  Aesthetics ES  Ecotourism and Recreation ER  National Defense ND  Scientific SC   3.2.1 Economic Function  Forests designated for economic function (wood production) are those that provide forest products (i.e., timber, industrial wood, fiber and cellulose, poles and rods, firewood) which have economic value and are managed to meet the demands of the national and international economy. Forest areas are categorized to have “Economic Function”, if: (1) the continuity and health of the forest are not affected adversely by the production of wood and other products; (2) the forest can renew itself; (3) the production does not threaten to harm the ecosystem; (4) the production is carried out for economic benefits; and (5) other issues are not a priority. Forests managed for economic purpose are managed with different working cycles depending on the tree species. Areas where road/transportation facilities do not exist or road construction is not economically feasible should not be assigned to the ‘economic function’ category.  8  When the aim is to obtain the highest amount of timber in an area designed for economic function, the following criteria and indicators are considered: - The slope of the land should be suitable for mechanical operations (less than 30%) - Site productivity (site index) should be high (areas where soil factors are appropriate and site index class I and II). - The selected fast-growing species should be suitable for the habitat, and improved seeds/seedlings should be used. - Industrial plantation areas are planned only for wood production. - Forests where the slope of the land is more than 60% are not scheduled for clear-cut coppice working cycle. These forests are planned as high forest or conversion of coppice to high forest working cycle. The term ‘high forest’ refers to the management type which maximizes the amount and quality of yield allowed by site conditions, and provides the products needed by the national economy in a variety of sizes and quality. It is deemed important to convert current coppice forests to high forest in order to protect the coppice forest for a longer period of time, while rehabilitating them. Conversion of coppice forest to high forest cannot be considered without replacing first current products obtained from coppice forests, such as firewood, poles, and other wood products. If it is not possible to convert coppice forests to high forest because the products cannot be replaced, existing coppice forests must be managed intensively.  3.2.2 Ecological Function Forest areas are protected when they are valuable due to biological, ecological and cultural features, and & or because of poor site conditions. In functional planning, forest areas having unique features are designated as nature conservation zones. The following criteria and indicators are used to designate nature conservation zones: (1) areas for protection from social pressure located close to settlements such as villages and summer pastures where illegal logging is present, and have been fragmented by human activity; and (2) areas for biodiversity conservation that incorporate many different types of tree species, herbs, and shrubs seen in forest areas. Ecological function and non-wood values assigned to protected forests include nature conservation, erosion control, climate protection and hydrologic function. Forest areas with high ecological function must also be protected because of their natural, scientific, aesthetic characteristics. Some forests do not have good regeneration capacity because of poor site conditions and are called Nature Conservation  9  Forests. In some cases, these forests are genetically diverse because they thrive in different extreme conditions, i.e. highest elevation above sea level, lowest elevation area, being the most northern, southern natural spreading area, etc. For this reason, stands which provide nature conservation functions also ensure the protection of intraspecific genetic diversity in its natural environment (in-situ). Stands used for seed supply (Registered Seed Stands) are also asigned the category of nature conservation forests.  3.2.3 Sociocultural Function Forests can also play an important sociocultural function. For example, forests can minimize the impacts of noise, toxic gases, and dust harmful to human health. Forests that hide the visual impacts of quarries (metal, stone, and marble) and factories that spoil the natural view of the environment. Forests along ridgetops, especially those offering panoramic views, are also contributing to a social function.   Furthermore, ecotourism can promote the sustainability of earth’s natural resources and support economic development in local communities. Ecotourism aims to use traditional architecture and local resources from small facilities. Forests allocated for ecotourism provide recreational opportunities, such as hiking, birdwatching, hunting, and camping, benefiting people’s physical, spiritual, and mental health.     10  3.3 Forest Resources Planning The main task of forestry enterprises is to meet society's demand for forest goods and services. Therefore, demand must be known to determine the management objectives. In other words, forest function and management objectives need to be aligned. The Forest Functions in Table 1 are used to determine forest management objectives under an ecosystem-based planning approach.  The first phase of forest resources planning involves determining the forest management objectives of the planning unit based on the forest function maps introduced in section 3.2 (see Table 1). Forest functions, management objectives, and conservation targets for the planning unit are identified using the draft function map. The second phase of the forest resources planning involves defining working cycles to ensure the fulfillment of management objectives such as biodiversity conservation, development, aesthetics, and recreation. There are six different working cycles in the Arikaya forest management unit (Table 2). Each working cycle has a different rotation length. For instance, the rotation length for working cycle A is 100 years, while for B is 140 years. The difference is mainly driven by the existing species, e.g. scots pine grows slower than black pine in the region.  Table 2: Working cycle groups in the Arikaya forest management unit  Working Cycle Group Name Functions A Black Pine Maximum Industrial Wood Production Working Cycle  B Scots Pine Maximum Industrial Wood Production Working Cycle  C Turkey Oak Maximum Industrial Wood Production Working Cycle  D Turkey Oak + Black Pine Non-timber Forest Production(Honey) Working Cycle  E Turkey Oak + Black Pine + Scots Pine Biodiversity Conservation Working Cycle  F Turkey Oak + Black Pine + Turkish Pine Biodiversity Conservation Working Cycle   11  Multiple-use forests can serve to two or more purposes simultaneously. However, when more than one management objective is determined, one of them should be specified as the main objective and the rest as secondary objectives. All the objectives should support each other. The possible conflicts between function and objective should be solved and the restrictions on the management techniques should be determined. If ecological functions were identified, conservation targets will be given priority in the management objectives. In the Turkish forestry sector, there are 32 tables used to develop a forest management plan (see Appendix A). These tables were used to extract information on distribution of area, growing stock and increment by age classes and generate the yield curves in this study (see example in Table 3). Table 4 shows a summary of the forest management plan for the Arikaya unit, including the different working cycles for the different management objectives and species in the area.   Table 3: Distribution of area, growing stock and increment by age class in even-aged forests, Arikaya forest management unit Working Cycle A Age Classes Area Growing Stock No Limits Real Area(ha) Reductive Area(ha) Volume (m3) Increment (m3) I 1 -- 20 76.6 59.2 968 44 II 21 -- 40 163.2 157.9 14222 617 III 41 -- 60 212.6 206.1 36170 1172 IV 61 -- 80 581.8 556.8 139854 3648 V 81 -- 100 512.4 454.4 111490 2536 Total 1546.6 1434.4 302704 8017     12  Table 4: Description of the Arikaya forest management unit  3.4 Forest Use and Social Perception People residing in or near forest areas of the planning unit depend on agriculture and animal husbandry for their livelihood and do not work directly in forestry activities, i.e. cutting, skidding, transport, etc. However, part of the local population works in the forest administration. Also, some landholders may graze their animals in forest clearings. During the field studies, it was also noticed that local people benefit from illegal forest cutting. In total, 11882 inhabitants live in the planning unit. Information on population of the districts, towns, and villages within the planning unit is shown in Appendix B.  People living within the planning unit area and its vicinity have a negative attitude toward industrial forestry operations, despite the employment opportunities it may bring. In order to improve relations, forest operations are taking precautions. The forest administration must explain to the local population the benefits of forestry activities so that they can understand the importance of forestry to the country’s economy. For instance, the GDF gives seminars to the local community about the general benefits of forestry. Those seminars help communities get a better understanding of forestry practices. Generally, if forest operations intensify (particularly if reforestation is accelerated in degraded areas), more employment for local people is available, fostering the transition from traditional animal husbandry to more intensive husbandry systems.    PROVINCE ESKISEHIR 14 GEORAPHIC REGION CENTRAL ANATOLIA 7 7DISTRICT CATACIK 1404 PROVINCE OF THE PLANNING UNIT ESKISEHIR 26 228PLANNING UNITS ARIKAYA 140402 OWNERSHIP OF THE FOREST STATE 1 77PLAN UNITS SERIES ARIKAYA 14040201 PLANNING TEAM OFFICIAL TEAM 1 7Working CirclesWORKING CIRCLESPlan Period Validity YearsRotation LengthPlan PeriodRegeneration PeriodCycle of TendingExpiration Date of the Harvest PlanRegulation PeriodNumber of Plan RenewalsA Black Pine 2015-2034 100 20 20 10 2034 100 3B Scots Pine 2015-2034 140 20 20 10 2034 140 3C Turkey Oak 2015-2034 100 20   10 2034 100 3D Turkey Oak + Black Pine 2015-2034 200 20   10 2034 200 3E Turkey Oak + Black Pine + Scots Pine 2015-2034 200 20   10 2034 200 3F Turkey Oak + Black Pine + Turkish Pine 2015-2034 200 20   10 2034 200 3G Turkey Oak + Scots Pine + Turkish Pine 2015-2034 180 20   10 2034 180 3Biodiversity ConservationBiodiversity ConservationForest Soil ConservationOTHER FEATURES AND CODE NUMBERS DIVISIONS AND QUANTITYWORKING CIRCLECOMPARTMENTSTAND TYPETREE SPECIESUNIT NAMES AND CODE NUMBERSMANAGEMENT OBJECTIVESMaximum Industrial Wood ProductionMaximum Industrial Wood ProductionMaximum Industrial Wood ProductionNon-timber Forest Production(Honey) 13  4  Carbon Market in Turkey Most of the income generated by Turkey’s forestry sector comes from the commercialization of wood and non-wood forest products. This income is partially used to finance climate change mitigation efforts (Asan, 2010). Forest carbon credits can be acquired and merchandised in voluntary carbon markets through afforestation/reforestation, as well as improved forest management and agroforestry projects (Ufuk et al. 2011). There is a large afforestation potential in Turkey because approximately half of the forestland is degraded (Turker et al. 2001). Degraded land can be rehabilitated and afforested by improved forest management activities.  Increasing forest areas through the conversion of degraded forests can boost Turkey’s carbon sequestration potential, providing opportunities for the Turkish forestry sector, and competitive advantages in international carbon markets (U Demirci & Ozturk, 2015). For example, the National Afforestation and Erosion Control Mobilization Plan (NAAP) coordinated by the national government from 2008 to 2012, afforested and rehabilitated 2.3 million ha. It has been estimated that by 2020, the project activities will account for 181.4 million tons of sequestered forest carbon (GDF, 2010).  Forestry projects have increased in recent decades, but more are needed to meet actual carbon demand. Because Turkey is a developing country, instruments to offset greenhouse emissions must be cost-effective. The voluntary market may be a viable alternative for the forestry sector (Ufuk Demirci et al., 2011). Regarding the Kyoto Protocol’s post-2012 regime, Turkey cannot benefit because it is no longer an Annex I country. Therefore, it needs to reformulate its conditions under UNFCCC, KP and other agreements in the international negotiation process. Turkey participated in the climate change conferences held in Doha in 2012, and Warsaw in 2013. Under the Paris Agreement, Turkey committed to reduce up to 21% in GHG emissions by 2030 compared to the business as usual baseline (see Appendix C, GDF, 2015). If Turkey is to participate effectively in voluntary carbon markets, the country urgently needs a comprehensive methodology to forecast its national carbon sequestration potential (Khan, 2010).    14  5 Case Study Description The study area for this research is the Arikaya forest sub-district directorate located north of the Eskisehir Regional Directorate in northwest Turkey (Figures 1 and 2). The area is bounded by latitudes 39° 51' 47" N and 40° 02' 12" N and longitudes 30° 13’ 31" E and 31° 05' 47"E. According to the 2015 forest management plan, the total management area in the Arikaya forest sub-district is 13355 ha. The productive forestland portion is 8192 ha. The remaining 5152 ha are used for agriculture, livestock production, and water conservation. Detailed area distribution is shown in Figure 3. The General Directorate of Forestry is divided into departments and regional directorates which are working together. There are 28 Regional directorates in Turkey (Figure 1). Under the regional directorates, every directorate has district directorates. Each district directorate has sub-district directorates (see Figure 1 for an example of district and sub-district directorates). Every single sub-district directorate has its own management plan.   15    Figure 1: Map of regional directorates of Turkey and location of the Arikaya forest sub-district directorate  16   Figure 2: Land use classification in the Arikaya forest management unit   17  In the study area, there are five dominant species. Most of these dominant species are softwood tree species, namely Black pine (Pinus nigra), Scots pine (Pinus sylvestris), Turkey oak (Quercus cerris), Turkish pine (Pinus brutia), and common hornbeam (Carpinus betulus). The area occupied by these species is distributed as follows (Figure 4 Pinus nigra 2,139 ha, Pinus brutia 808 ha, Pinus sylvestris 374 ha, Quercus cerris 1059ha, and Juniperus comminus 62 ha. These areas have been allocated different working cycles after management objectives were determined. Each area has also been assigned a function. In the Arikaya forest management unit, the economic function has not been divided from the ecological and sociocultural functions, as it used to be the case previously. It is assumed that this approach will allow the local community to realize greater environmental and social benefits.  Figure 3: Spatial cover of main tree species   05001000150020002500Pinus nigra Pinus brutia Pinus sylyvestris Quercus cerris Juniperus comminusArea(hectares)Species 18  In the Arikaya forest management unit, most of the forests stands are young (Figure 5). Age classes are divided into periods of 20 years, starting with 1-20 years for period one, followed by 21-40 years for period two, and so on. Harvest age depends on working cycles as explained in chapter 3.3. According to the Turkish forest management plan, the canopy cover class is defined as the degree of canopy crown closure on the soil surface. Detailed information about canopy cover classes is given in Appendix D.   Figure 4: Spatial distribution of tree species by age-class   05001000150020002500300035001 2 3 4 5 6 7 8 9 10Area(hectares)Period(20years)Age-class Disturbution 19  6 Methodology 6.1 Modelling Carbon Sequestration Potential The FPS ATLAS and CBM-CFS3 are the two computer-based models used for this research. The first step involved running the FPS-ATLAS. The second step entailed running the CBM-CFS3 model using the FPS-ATLAS outputs as inputs. The FPS-ATLAS is a spatially explicit harvest simulation model, which can link to forest polygons and road networks. The F2C is a tool designed to bridge FPS-ATLAS and CBM-CFS3, which has been designed using a Microsoft Office database (.mdb file). The F2C tool was designed to transfer and format FPS-ATLAS inputs and outputs into CBM-CFS3 friendly import tables. The tool has three different object types which are tied together, i.e. tables, queries, and macros. The CBM-CFS3 modelling framework is used for forest planning and is based on forest inventories.  The current version of the CBM-CFS3 provides the required parameters needed to replicate natural and human-induced disturbance events (Kull et al. 2014).  Homogenous groups of trees in the landscape were displayed in the CBM-CFS3 model as an accumulation of spatial units. Spatial referencing provides structure at various scales, from local to landscape levels. For the Arikaya forest management unit, twenty-six different analysis units were created using ArcGIS software (See analysis units in Appendix E and python script in Appendix F). Stands could be defined by size (ha), age and land class (i.e. managed forest or forest converted to crop land), as well as other categories (IPCC 2003). The model user can describe land area in terms of site productivity, crown closure, or leading species as classifiers. Groups of spatial units or individual spatial units would then be linked to all input data and modelling parameters by classifiers (Smiley et al. 2016). The classifiers used in this study were species and stand groups.   The FPS-ATLAS model was run using a MS Access database. It was recommended to use a blank MS database to prevent errors. A blank database was provided by the creator of this tool, Dr. Cosmin Man (Man, 2016b). To run the FPS-ATLAS model for the Arikaya forest management unit, seven tables were populated in the blank database, namely the StandGroup, Curve, StandGroup_Curve, StandGroup_Treatment, Curve_Data, and Polygon tables. The tables needed to be populated in this specific sequence (Man, 2016b). To populate the yield curves for tables StandGroup_Curve and Curve_data, growing stock and increment information was obtained from the original management plan,  20  which is currently implemented by the Turkish Forest Service (see Site Class Distribution of Area, Growing Stock and Increment by Stand Types in Even-Aged Forests, Table 16 in Appendix A). After populating all the tables, the information was linked to the forest polygons using a Geographical System Information (GIS) file (Man, 2016c). The GIS file with forest polygons was provided by the Forest Management and Planning Department in Turkey (FMPD). Combining all the information contained in the tables with the GIS file, I exported a resultant.txt file to run queries and get outputs with the FPS-ATLAS model.  The next step was to populate the tables in the MS database of the F2C tool using the outputs of the FPS-ATLAS model. This produced friendly import tables that could be used in the CBM-CFS3 model to analyze the carbon dynamics of the Arikaya forest management unit. In the F2C tool, there were two main table sections which needed to be populated, called ‘Atlas tables’ and ‘CBM tables’ respectively. Within the ‘Atlas tables’ section there are six different blank tables that needed to be populated, namely ATLAS_SG_Species, ATLAS_StandGroup, ATLAS_StandGroup_Curve, ATLAS_StandGroup_Treatment, ATLAS_Curve_Data, ATLAS_Period_to_years. All Atlas tables were populated using the FPS-ATLAS outputs. When the Atlas tables were populated, the CBM tables were filled out automatically running the macros contained in F2C (Man, 2016b). After running the macros, the MS database file was ready to use in the CBM-CFS3 model. The final step involved running simulations with the CBM-CFS3 model. In the CBM-CFS3 model, only two disturbance types were used: clear cutting and rehabilitation. To create and analyse the results generated by CBM-CFS3, all steps indicated in the User’s guide (Man, 2016a) were followed. These steps are illustrated in Figure 6. 21    Figure 5: Methodology flow chart Forest C dynamics were simulated in CBM-CFS3 by combining process modelling and statistical modelling. Yield information collected from forest inventory data together with allometric equations were used to grow live biomass components on an annual basis, and dead organic matter pool dynamics were simulated using process modelling (Stinson et al., 2011). Specific information (e.g. age, dominant species, productivity level, etc.) for each stand was required to run CBM-CFS3 simulations. Simulations also needed general information on ecological parameters (e.g. decomposition and turnover rates), climate, and disturbance (e.g. location, type, etc.) (Boisvenue, Smiley, White, Kurz, & Wulder, 2016). Parameters such as tree growth, decomposition, and allometry vary by management area. Using the default parameter values, a management area’s spatial units must be referenced to the eligible province or territory and ecozone (Kurz et al., 2009). Because the cold, wet winters and warm, dry summers of the Montane Cordillera Ecozone in Canada are similar to those in the Central Anatolia Region of Turkey (Sensoy, DEMİRCAN, ULUPINAR, & BALTA, 2008), Canadian default parameters for this eco-zone were used in this study (Man, 2016b). Lastly, some limitations in the modelling exercise need to be mentioned. Some model parameters for tree species in the Arikaya forest management unit were not available in the CBM-CFS3 model, and hence defaults for Canada needed to be used instead, with the appropriate precautions. Mapping species using CBM-CFS3 was simple when the species were available in the model, such as Pinus  22  sylvestris and Juniper comminus. However, if the species were not available in CBM-CFS3, I linked similar type of species such as Pinus nigra to Pinus genus type in CBM-CFS3. The suitability of assignment depends on similarity in species between Canada and Turkey, and it was evaluated using specific gravity of the stem wood and tree form. Gravity has a large impact on volume-to-biomass conversion parameters. Tree form affects the proportional distribution of stem wood, branches, and foliage.    23  6.2 Alternative Forest Management Scenarios  6.2.1 Baseline: Current Forest Management in the Region (B1) The baseline scenario for this study assumes the continuation of activities under the current forest management plan developed by the FMPD for the Arikaya forest (See Table 5, detailed information on the principles and processes underpinning the Turkish forest management plans are explained in section 3). The current management plan for the Arikaya forest was established in 2015 and will remain in effect until 2035. Original data (i.e. Turkish version) were translated into English using the Canadian forest terminology, and used as model input in the CBM-CFS3 software program. Where original data were not available, the default ecological parameters for the Montane Cordillera Ecozone in Canada were used (Kull et al. 2014, see section 6.1).  The current management plan for the Arikaya forest management unit has limited information on carbon dynamics. Carbon stocks were only estimated for 2015, the year the plan was introduced. The plan does not consider future change in carbon dynamics due to forest practices. In this study, carbon estimates for the baseline assume that current management practices and yield curves continue into the future without any modifications. 6.2.2 Increased Harvest Linked to Population Growth (B2) This forest management scenario assumes that harvest rate will accelerate driven by population growth and increasing demand for forest resources. Population rate has increased 25% during the last couple of decades in Turkey. Over the past decades, urban population has been increasing rapidly while rural population is decreasing. Although an increasing urban population had positive effects on economic development, it has also added pressure on forest resources for construction, energy generation, etc. This domestic demand for forest resources influenced the forest management plan. For example, the harvest flow introduced in the previous forest management plan was 8,000 m³/year between 1993-2013, which supplied wood to fulfil people’s needs. To supply increased domestic demand for wood, the new management plans established a higher harvest flow of 12,700 m³/year from 2015 to 2034. The assumption of this second alternative scenario is that harvest rate will increase as population (and demand) continues to grow (Table 5). Harvest rate was accelerated over four decades. In the first decade, harvest rate was assumed to be 11,000 m³/year. In the second decade, harvest flow increased by 25%. In the third decade, it further increased by 25%. In the fourth decade, harvest flow was  24  increased by an additional 9% and this rate was then maintained stable for the next decades of the simulation horizon. 6.2.3 Rehabilitation of Non-productive Area (R1) Forest services in Turkey have started to rehabilitate degraded forests. Rehabilitation is one of the ways to convert non-productive forest into productive forest. Other activities include afforestation and erosion control. Different stakeholders are involved in these projects, which are mainly driven by the Forestry Directorates. The General Directorate of Forestry started to implement rehabilitation projects as part of Strategic Plans aimed at converting degraded forests to productive forests in about 300,000 ha per year. Projects under the Strategic Plans have been implemented in tandem with the National Afforestation and Erosion Control Mobilization Plan (NAAP) introduced in 2008 (U Demirci & Ozturk, 2015). Rehabilitation is going to continue in the future. Therefore, it is sensible to assume rehabilitation as part of an alternative forest management scenario. The Arikaya forest management unit has 3,202 ha of degraded forest land. It was assumed that this degraded forest area will be rehabilitated in 40 years. There is no available information on the yield curve in degraded forest land. I assumed that the yield curve in degraded land was 10% of the original yield curves in the surrounding area. For a period of 40 years, 10% of the original yields was used in the modelling exercise, and then yield curves were changed to the original values (Table 5). The only difference in forest management practices between the baseline and this alternative scenario is the conversion of degraded forest land. 6.2.4 Rehabilitation with Low Harvest Flow (R2) This forest management scenario is not constrained by current forest management practices. In addition to rehabilitation of 3,202 ha of degraded forest land, this scenario assumes a low harvest flow in productive areas, stabilized at 1000 m³/year over a hundred year (Table 5). I used a low harvest flow to explore the implications for carbon stock and the potential for carbon trading in the international market. This could be used as a climate mitigation strategy that could contribute to the GHG emissions reduction targets committed by Turkey under the Paris Agreement and its INDC report. In addition, carbon trading could create additional revenue to invest in further carbon offsetting practices based on forest management, such as rehabilitation, plantation and afforestation.  Table 5: Description of scenarios simulated in this study.  25   Productive forest area (ha) Main assumptions Change in variables Baseline 8,192 Current practices are maintained. Initial harvest volume is 12,700 m³/year Rapid Harvest Flow 8,192 Harvest rate increased based on population growth. Harvest rate increased by 25% per decade over the first three decades, 9% for the fourth decade and maintained constant thereafter. Rehabilitation 11,394 Current practices are maintained in harvest area. Additionally, non-productive area is converted to productive area. In the rehabilitated area, 10% of the original yields is maintained for the first four decades, then the yield curves are reverted to original values. Rehabilitation with low harvest flow 11,394 Harvested area is limited. Additionally, non-productive area is converted to productive area. In the harvest area, harvested volume is maintained to 1000m³/year. In the rehabilitated area, 10% of the original yields is maintained for the first four decades, then yield curves are reverted to original values.     26  7 Results The results of this study show that the rehabilitation (R2) scenario with low harvest flow has the highest carbon sequestration potential compared to the baseline and the other alternative scenarios over a 100-year period (Table 6). On the opposite end, the rapid harvest (B2) scenario shows the least carbon sequestration potential with a negative net carbon change compared to the baseline (Table 6). Table 6: Carbon stock estimates for different forest management scenarios in Arikaya  Scenario Productive forest area (ha) Short description Annual carbon stock (20 years) in million metric tons Annual carbon stock (100 years) in million metric tons Net carbon change (compared to B1, 100 years) in million metric tons B1 8192 Current practices are maintained. 3.45  4.0  0  B2 8192 Harvest rate is increased based on population growth. 3.44  3.7  -0.3  R1 11394 Current practices are maintained in harvest area. Additionally, non-productive area is converted to productive area. 3.52  4.5  0.4  R2 11394 Harvest flow is limited. Additionally, non-productive area is converted to productive area. 3.6  4.9  0.9   In the first twenty years, the difference in harvest volume between the baseline (B1) and the rapid harvest (B2) scenario is about 2,500 m³/year (Figure 6). Results are provided for a 20-year time horizon because the current management plan (baseline) runs for 20 years. The harvest of timber  27  accelerates under the rapid harvest scenario in the 20-40-year time horizon. After 40 years, the harvest volume stabilizes. In the 40-100-year time horizon, the difference in harvest volume between the baseline (B1) and the rapid harvest (B2) scenario is roughly 7,500 m³/year (see Figure 6).   Figure 6: Annual harvest flow of different forest management strategies in Arikaya The rehabilitation (R2) scenario with low harvest (1000 m³/year) was created after comparing the baseline with the rehabilitation (R1) scenario, and recognizing that the carbon accumulation differential was not enough to generate carbon credits that could compensate the loss of harvest revenue. Figure 7 shows the carbon accumulation difference between the baseline and the rehabilitation (R2) scenario. In the first 20 years, the change in carbon stock escalated from around 1.5 tCO2e/ha to about 4.3 tCO2e/ha (Figure 7). In the 20-100-year time horizon it is possible to see a fluctuation, but net carbon change with the R2 scenario remains relative high at an average of 4.5 tCO2e/ha. This displays a promising opportunity to trade carbon in the international carbon market. Turkey has been trying to be part of European Union, and thus the European Union Emissions Trading System (ETS). Currently, the ETS carbon tax is in average 6 USD/tCO2e, but the carbon tax in 2017 varied widely depending on the country, with Portugal carbon tax at about 7 USD/tCO2e, Denmark at 25 USD/tCO2e, and Finland at 62 USD/tCO2e. To capture this variation, this study used different carbon taxes, namely 050001000015000200002500010 20 30 40 50 60 70 80 90 100Harvest Volume(m³/year)YearsAnnual HarvestB1 AND R1 B2 R2 28  6 USD/tCO2e, 20 USD/tCO2e, 40 USD/tCO2e, and 60 USD/tCO2e. Before estimating potential carbon revenue, the total amount of carbon was converted to tCO2 eq/ha using a 3.67 ratio. Assuming the net carbon change in the rehabilitation (R2) scenario would be traded in the international carbon market, the forest sector could generate revenues ranging between 5,596,766 USD and 50,837,656 USD after 20 years (Table 7, for details see Appendix G). Assuming some fluctuation in timber price (i.e. 30 to 60 USD/m³), the baseline scenario would generate revenues ranging between 7,144,200 USD and 14,288,400 USD (Table 7).  If the timber price is assumed at 30 USD/m³, the R2 scenario has the potential to generate more revenues than the baseline even if the carbon tax is at 6 USD/tCO2e (Table 7). If the timber price is assumed at 60 USD/m³, the carbon tax would need to be higher (> 20 USD/tCO2e) for the R2 scenario to generate more revenues than the baseline (Table 7). The additional revenues that could potentially be generated with carbon trading under the R2 scenario could be used for forest practices such as plantations, rehabilitation, and afforestation, which could further increase the carbon sequestration potential.  Table 7: Comparison of carbon and timber revenues estimated for the baseline (B1) and the rehabilitation low harvest (R2) scenario     Carbon taxes and timber @ 30 USD/m3 (R2) Timber @ 30 USD/m3 (B1)  6 USD /tCO2e 20 USD /tCO2e 40 USD /tCO2e 60 USD /tCO2e  Year  2 154,538 445,128 860,257 1,275,385 762,144 Year 20 5,596,766 17,325,855 34,511,771 50,837,656 7,144,200 Year 50 17,096,157 53,557,191 105,644,383 157,731,574 17,363,310 Year 100 36,189,905 113,703,015 224,436,031 335,169,046 34,556,280        Carbon taxes and timber @ 60 USD/m3 (R2) Timber @ 60 USD/m3 (B1) 6 USD /tCO2e 20 USD /tCO2e 40 USD /tCO2e 60 USD /tCO2e  Year  2 184,538 475,128 890,257 1,305,385 1,524,288 Year 20 6,166,766 24,062,651 52,547,656 86,059,427 14,288,400 Year 50 18,566,157 55,027,191 107,114,383 159,201,574 34,726,620 Year 100 39,159,905 116,673,015 227,406,031 338,139,046 69,112,560  29    Figure 7: Net change in carbon stock between the baseline (B1) and the rehabilitation with low harvest (R2) scenario At present and under baseline conditions, the immature age class (0-20 years) is the dominant considering all tree species. Area of immature class is roughly 8000 ha (Figure 8). In 10 years, the first and second age class, 0-19 years and 20-39 years respectively, become more dominant. The second age class increased from 700 ha to 3000 ha. In 50 years, the age classes are more even in all scenarios, except for the rehabilitation with low harvest flow (R2) scenario. The third and fourth age classes, 41-60 years and 61-80 years respectively, increased from 500 ha to 3000 ha (Figure 8).  0.01.02.03.04.05.06.00 10 20 30 40 50 60 70 80 90 100CO2 (tons/ha)YearsChange in CO2 stored tons 30    0100020003000400050006000700080009000Age Class Distribution:Year 0Age Class Distribution:Year 10Age Class Distribution:Year 50Area(ha)Age Class Distributions- Baseline0-20 21-40 41-60 61-80 81-1000100020003000400050006000700080009000Age Class Distribution:Year 0Age Class Distribution:Year 10Age Class Distribution:Year 50Area(ha)Age Class Distributions- Rapid Harvest Growth0-20 21-40 41-60 61-80 81-100 31     Figure 8: Spatial cover of tree species age-classes under different scenarios and temporal horizons In the current forest management plan, the annual carbon stock is calculated at 3.39 Mt C. Assuming the current management plan is implemented over the next 20 years, the model estimated an annual carbon stock of 3,45 Mt C. This estimate is very close to the 20-year estimate obtained for the rapid growth (B2) scenario (Figure 9).  0100020003000400050006000700080009000Age Class Distribution:Year 0Age Class Distribution:Year 10Age Class Distribution:Year 50Area(ha)Age Class Distributions- Rehabilitation0-20 21-40 41-60 61-80 81-1000100020003000400050006000700080009000Age Class Distribution:Year 0Age Class Distribution:Year 10Age Class Distribution:Year 50Area(ha)Age Class Distributions- Rehabilitation with Low Harvest Flow0-20 21-40 41-60 61-80 81-100 32   Figure 9: Annual carbon stock estimates for all scenarios over a 20-year period The carbon stock of the rehabilitation scenario with low harvest flow is higher than the baseline, particularly if estimates are calculated for a 100-year time horizon (Figure 10). If rehabilitation practices were applied during the next hundred years, the annual carbon stock generated under the rehabilitation scenario would potentially total 4.9 Mt C, which is 1.0 Mt C above the baseline and 1.4 Mt C higher than the rapid growth harvest scenario (Figure 10). Not surprisingly, the accelerated harvest scenario presents the lowest carbon stock potential among all the scenarios. During a 100-year time horizon, the annual carbon stock under the accelerated harvest scenario had a minimal increase from 3.4 Mt C to 3.6 Mt C. On the other hand, the baseline annual carbon stock showed an increase from 3.4 Mt C to 3.9 Mt C. The annual carbon stock under the rehabilitation scenario with low harvest flow increased from 3.4 Mt C to 4.9 Mt C, which is the highest rise among the all the scenarios. 3.353.43.453.53.553.63.650 2 4 6 8 10 12 14 16 18 20Carbon(Mt)YearsB1 R1 B2 R2 33   Figure 10: Annual carbon stock estimates for all scenarios over a 100-year period   3.33.53.73.94.14.34.54.74.95.15.30 10 20 30 40 50 60 70 80 90 100Carbon(Mt)YearsAnnual Carbon StocksB1 R1 B2 R2 34  8 Discussion This thesis showed that CBM-CFS3 is an appropriate modelling tool to evaluate annual carbon stocks in the Arikaya forest management unit, Turkey. The current forest management plan was used to generate a baseline in this study. The first alternative scenario involved the rehabilitation of non-productive areas to productive areas. The second alternative scenario assumed an acceleration of the harvest rate based on population growth, and increased demand for timber. The third alternative scenario assumed rehabilitation combined with a low harvest flow. All four scenarios were compared to each other in terms of carbon stock levels and spatial cover of tree species categorized by age classes. A carbon pricing analysis was conducted comparing the third scenario with the baseline. I discuss here the implications of the case study findings for forest carbon management in Turkey, and specific steps that could be applied to improve the modelling exercise, and hence the outputs of this study. I conclude with some recommendations for further research. 8.1 Study Implications for Forest Carbon Management in Turkey The findings in this study complement current forest management plans in Turkey, which lack information about carbon dynamics. At present, carbon stocks in national forest management plans are estimated only for the first year of operation.  Future carbon accounting is not included. In this research, I estimated potential future carbon stocks associated to alternative forest management approaches. Understanding the carbon implications of different management practices is important to improve the current system. This integrated approach has generated insights into alternative ways to enhance carbon sequestration and achieve national climate mitigation goals. The results of this study highlight the limitations of using static carbon estimates as it is the case in the current national forestry system of Turkey, and provide a concrete example of how carbon dynamics could be studied at the forest management unit level. This approach could be replicated in other forest management units across the country. Stakeholders such as forest sub-district managers, policy makers, researchers, and non-governmental organizations could benefit from this approach by improving carbon monitoring practices, and thus demonstrating their contribution to national goals and international agreements.  35  8.2 Study Limitations and Future Improvements There are many disturbances that can have an impact on forests with different effects, including a reduction in forest cover. Changing climatic conditions are also indirectly impacting forests. For example, more frequent droughts are exacerbating pest and wildfire outbreaks, which in turn impact forests. Wildfires and insect disturbances are among the top-ranking factors causing forest damage in Turkey (Ertugrul et al. 2017). Wildfires are threating forests particularly in the southern and western parts of the country. The Mediterranean region is sensitive to wildfires because of its climatic conditions, i.e. hot and dry during the summer (Ozturk et al. 2010).  In Turkey, the General Directorate of Forests (GDF) estimated a total of 104,276 wildfires and 1,662,032 ha of burnt forests between 1937 and 2016. Since then, these burnt areas have been afforested by the Turkish forest service (Ayberk et al. 2010, GDF 2016). In 2004, 2009, and 2010, there were several large-scale wildfires in Turkey burning over 10,000 ha of high conservation value forests. This included Calabrian pine, which resulted in a huge loss of habitat for endemic and threatened forest species. About 65% of Turkey’s wildfires affect the 160 km-wide belt of forest spreading across the Mediterranean and Aegean regions (see Appendix H).  It has been estimated that wildfires affecting the Turkish forests in these two regions released about 782,000 tCO2-eq/year. The GDF is taking actions to protect forests and prevent wildfires.  According to the GDF reports and statistics, insect disturbances have affected 532,187 ha of forests in Turkey between 1995 and 2014. This is equivalent to about 26,609 ha affected by insect disturbance every year. Bark beetle (Dendroctonus micans) and the pine processionary caterpillar (Thaumetopoea pityocampa) are the two main invasive insects in Turkey. Damage caused by these invasive species can occur quickly and unexpectedly.  Wildfires and insect disturbances play a key role in the development of Turkish forest ecosystems, and hence influence carbon dynamics. However, in the Arikaya forest management unit, wildfires and insect disturbances were not included in the CBM-CFS3 model for two reasons. Firstly, there was a lack of data on wildfire and insect disturbance for this area. Secondly, the study area is in the Central Anatolian region, which is not as sensitive to fire as the Mediterranean and Aegean regions. Because of those two reasons, CBM-CFS3 simulations did not account for wildfire and pest effects on carbon dynamics. Nevertheless, the results would be more accurate if wildfire and insect disturbance data could be collected and included in the modelling exercise.   36  Comparative analysis for potential carbon and timber revenues was conducted using only two scenarios, namely the baseline (B1) and the rehabilitation with low harvest (R2) scenario. The analysis did not include other revenue sources, however more detailed economic analyses that include additional revenue sources could help generate more accurate results. For instance, harvested wood products could be included in the economic analysis. In Turkey, firewood usage has been decreasing because of conversion of coppice forest to normal forests. This means that industrial round wood has been prioritized, and harvested wood products should be monitored and included in future carbon calculations (Bouyer & Serengil, 2016). In this study, considerations related to the effects of harvested wood products on carbon sequestration were limited, and could be analyzed in further research. If harvested wood products would be included in this study, the findings would change in different ways. For instance, the R2 scenario may not have the same competitive advantage compared to the baseline. In this regard, results linked to wood revenue in this study may be underestimated.  In addition, the economic analysis could include information on costs. In this study, the economic analysis is limited to only revenue information. Including costs could generate a more complete picture of the economic benefits. Integrating cost and revenue information could also change the competitive advantage of the rehabilitation scenarios, which require additional practices with associated costs. Another analysis that could strengthen this study is a statistical analysis, which could be used to evaluate the significance of net carbon change between the baseline and the scenarios. A statistical analysis could also be integrated in the economic analysis.  Finally, the model application in the Arikaya forest management unit used default (Canadian) ecological parameters. Creating ecological parameters for Turkey would require additional time and collaboration, which were beyond the capacity available for this study. However, developing administrative boundaries and ecological parameters specific to Turkey would help generate more accurate model outputs. As it was mentioned before, the CBM-CFS3 model has been used in several countries, e.g. Italy, Russia, and Mexico. New ecological parameters were created for those countries. Similarly, CBM-CFS3 could be better adapted to suit the Turkish biophysical conditions, and mainstreamed to manage carbon in accordance to internationally-agreed requirements. The CBM-CFS3 has been developed in line with IPCC standards (Kurz et al., 2009). Creating and running the CBM-CFS3 model at the national level would help generate more reliable carbon estimates for Turkey. This approach could therefore ease forest carbon monitoring and reporting at the national level.   37  8.3 Recommendations for Further Research In this research, only one silvicultural activity was applied in each scenario modelled with CBM-CFS3. However, several silvicultural practices have been implemented in Turkey in the past decades, including clear-cuts, rehabilitation, afforestation, and reforestation. Afforestation has been applied by governmental organizations, mainly the GDF, and non-governmental organizations. Further research could use the CBM-CFS3 model to study the implementation of multiple silvicultural activities.  In addition, the effect of human population growth could further be analyzed in the modelling exercise. In the study, only the B2 scenario assumed effects of human population growth on timber demand. This analysis could also be included in the baseline and the other scenarios. Different human population projections could be included in the carbon modelling. Furthermore, some Mediterranean species were not available in the CBM-CFS3 software yet, such as the Turkish Pine (Pinus brutia). Including a larger number of species (and their associated yield curves) into the CBM-CFS3 database would help generate more accurate carbon estimates for Turkey, and other countries in the region. Finally, this research focused only on one forest management unit: The Arikaya forest. More forest management units in Turkey could be studied, particularly in the Mediterranean and Aegean regions which play an important role in the forestry sector for their high productivity. Wildfires and pests have impacted forests in these regions and the GDF has been trying to recover affected areas. If CBM-CFS3 modelling could include wildfire and pest disturbance effects, forest management plans in Turkey would greatly benefit.   38  9 Conclusion To gain a better understanding of possible carbon sequestration enhancement alternatives in Turkey, a case study approach was used to compare scenarios based on different forest management practices. The main idea of this study was to improve forest management plans with emphasis on carbon accounting for climate change mitigation. Currently, forest management plans lack detailed information on forest carbon dynamics. To address my main research question “How can carbon sequestration in Turkey be enhanced with alternative forest management strategies?”, I created three alternative scenarios. On the one hand, some scenarios involved the rehabilitation of degraded forests. On the one hand, a rapid increase of the harvest rate was assumed given population growth. Using the CBM-CFS3 model, I assessed the future impacts of these scenarios in terms of annual and accumulated carbon stock.  The rapid harvest scenario (B2 scenario) showed negative future impacts on carbon stocks compared to the baseline. Results for this scenario raise awareness about unsustainable practices that need to be avoided, even if domestic demand for timber increases in the coming decades. On the contrary, the rehabilitation management approach (R1 scenario) showed a potential increase in annual carbon stock by 0.5 Mt C above the baseline over a period of 100 years. Rehabilitation of non-productive forests represent a great potential for carbon enhancement in Turkey, particularly considering that almost half of the Turkish forests are degraded. Some silvicultural practices have been introduced in the last decades, such as afforestation and reforestation, however there is still a clear need for more rehabilitation activities in the future. Efforts to convert non-productive forests to productive forests should also include monitoring of carbon dynamics.  Rehabilitation projects will yield carbon benefits, particularly if they are combined with a reduction in timber harvest levels, such as the rehabilitation scenario with low harvest flow (R2 scenario). The R2 scenario was further analysed to assess the economic gains from potential carbon trading. Results from this analysis showed that the GDF could benefit from carbon tax revenues, which could then be invested to support forest practices such as rehabilitation, afforestation, and plantations. These practices would in turn enhance carbon sequestration, which could be traded in the carbon market.   39  Importantly, the economic analysis also showed that the carbon price assumptions will have an impact on the viability to manage forests for carbon trading. To deal with carbon market fluctuations and risks associated to this, Turkey may need to create an institution to manage carbon (and risk of carbon loss) based on nationally-agreed forest carbon standards that guide the carbon accounting rules, in line with international carbon agreements. Overall, carbon management could enhance the health of forests in Arikaya, and Turkey in general. This research approach, included the use of CBM-CFS3 software program, proved helpful to monitor carbon dynamics linked to different management practices in Arikaya. The findings can be used to improve current forest managements plans in other regions of Turkey, particularly in relation to climate change mitigation. Forest management plans could be enhanced by assessing alternative scenarios that can help prioritize practices that successfully contribute to climate mitigation over the long term. This approach could also be used in other countries of the region.    40  10 References Asan, Ü. (2010). Developing Turkey’s National Climate Change Action Plan. Report in Turkish. Ayberk, H., Kucukosmanoglu, A., & Cebeci, H. (2010). The structure and importance of fire suppressing organizations in Turkey. Scientific Research and Essays, 5(5), 456–460. Baskent, E. Z., & Keleş, S. (2009). Developing alternative forest management planning strategies incorporating timber, water and carbon values: An examination of their interactions. Environmental Modelling and Assessment, 14(4), 467–480. https://doi.org/10.1007/s10666-008-9148-4 Baskent, E. Z., Keles, S., & Yolasigmaz, H. A. (2008). Comparing multipurpose forest management with timber management, incorporating timber, carbon and oxygen values: A case study. Scandinavian Journal of Forest Research, 23(2), 105–120. https://doi.org/10.1080/02827580701803536 Boisvenue, C., Smiley, B. P., White, J. C., Kurz, W. A., & Wulder, M. A. (2016). Improving carbon monitoring and reporting in forests using spatially-explicit information. Carbon Balance and Management, 11(1), 23. https://doi.org/10.1186/s13021-016-0065-6 Bouyer, O., & Serengil, Y. (2016). Türkiye’de hasat edilmiş orman ürünlerinde tutulan karbon ve 2020 projeksiyonları. İstanbul Üniversitesi Orman Fakültesi Dergisi, 66(1), 295–302. https://doi.org/10.17099/jffiu.48603 Cosslett, C. E., & International, E. S. (2013). Understanding the potential impacts of REDD + on the financing and achievement of sustainable forest management, (March). Demirci, U., & Ozturk, A. (2015). Carbon markets as a financial instrument in the forestry sector in Turkey. Commonwealth Forestry Association, 17(2), 141–152. https://doi.org/10.1505/146554815815500606 Demirci, U., Öztürk, A., & Aydin, I. Z. (2011). The Usability of Voluntary Carbon Markets as a Financial Instrument in Turkish Forestry Sector, (Demirci), 634–640. Ertugrul, M., Varol, T., Toper Kaygin, A. and, & Ozel, H. B. (2017). (2017). The relationship between climate change and forest disturbance in Turkey. Fresen. Environ. Bull., 26, 4064-4074., (July). Forestry, R. of T. M. of E. and F. D. of. (2009). State of Turkey’s Forests. GDF. (2010). Carbon sectors in Forestry, General Directorate of Forestry Report. Report in Turkish. GDF. (2014). Functional forest management plan’s laws and rules in Turkish. GDF. (2015) Intended Nationally Determined Contributuion report http://www4.unfccc.int/submissions/INDC/Published%20Documents/Turkey/1/The_INDC_of_TURKEY_v.15.19.30.pdf GDF. (2016). Forest statistics https://www.ogm.gov.tr/ekutuphane/Sayfalar/Istatistikler.aspx  41  Khan. (2010). Turkey’s Forestry Sector & the Carbon Market. Carbon. Kim, H., Kim, Y.-H., Kim, R., & Park, H. (2015). Reviews of forest carbon dynamics models that use empirical yield curves: CBM-CFS3, CO2FIX, CASMOFOR, EFISCEN. Forest Science and Technology, 11(4), 212–222. https://doi.org/10.1080/21580103.2014.987325 Kull S, Rampley G, Morken S, Metsaranta J, Neilson E, K. W. (2014). Operational Scale Carbon Budget Model of the Canadian Forest S ector ( CBM-CFS3 ) USER’S GUIDE. Kurz, W. A., Dymond, C. C., White, T. M., Stinson, G., Shaw, C. H., Rampley, G. J., … Apps, M. J. (2009). CBM-CFS3: A model of carbon-dynamics in forestry and land-use change implementing IPCC standards. Ecological Modelling, 220(4), 480–504. https://doi.org/10.1016/j.ecolmodel.2008.10.018 Man, C. (2016a). FPS-ATLAS to CBM-CFS3 ( F2C ), 3. Man, C. (2016b). Guide to build a FPS- ATLAS model v1. Retrieved from http://sfmtutorials.sites.olt.ubc.ca/files/2015/01/02_GUIDE_Build_FPS_model_v1_July29_2016.pdf Man, C. (2016c). Guide to build a resultant GIS v1. Ozturk, M., Gucel, S., Kucuk, M., & Sakcali, S. (2010). Forest diversity, climate change and forest fires in the Mediterranean region of Turkey. Journal of Environmental Biology, 31(January), 1–9. Pilli, R., Grassi, G., Kurz, W. a., Smyth, C. E., & Blujdea, V. (2013). Application of the CBM-CFS3 model to estimate Italy’s forest carbon budget, 1995-2020. Ecological Modelling, 266, 144–171. https://doi.org/10.1016/j.ecolmodel.2013.07.007 Pilli, R., Grassi, G., Moris, J., & Kurz, W. (2014). Assessing the carbon sink of afforestation with the Carbon Budget Model at the country level: an example for Italy. iForest - Biogeosciences and Forestry, 59(2), e1–e12. https://doi.org/10.3832/ifor1257-007 Running, S. W., & Gower, S. T. (1991). FOREST-BGC, A general model of forest ecosystem processes for regional applications. II. Dynamic carbon allocation and nitrogen budgets. Tree Physiology, 9, 147–160. https://doi.org/citeulike-article-id:2850231 Sensoy, S., DEMİRCAN, M., ULUPINAR, Y., & BALTA, İ. (2008). Climate of Turkey. Republic of Turkey Ministry of Environment and Forestry Turkish State Meteorological Service. Retrieved December, 10, 2007. Retrieved from http://www.mgm.gov.tr/files/en-US/climateofturkey.pdf Smiley, B. P., Trofymow, J. A., & Niemann, K. O. (2016). Spatially-explicit reconstruction of 100 years of forest land use and disturbance on a coastal British Columbia Douglas-fir-dominated landscape: Implications for future watershed-scale carbon stock recovery. Applied Geography, 74, 109–122. https://doi.org/10.1016/j.apgeog.2016.06.011 Stinson, G., Kurz, W. a., Smyth, C. E., Neilson, E. T., Dymond, C. C., Metsaranta, J. M., … Blain, D. (2011). An inventory-based analysis of Canada’s managed forest carbon dynamics, 1990 to 2008. Global Change Biology, 17(6), 2227–2244. https://doi.org/10.1111/j.1365-2486.2010.02369.x  42  Tolunay, D. (2011). Total carbon stocks and carbon accumulation in living tree biomass in forest ecosystems of Turkey. Turkish Journal of Agriculture and Forestry, 35, 265–279. https://doi.org/10.3906/tar-0909-369 Turker, M, H., Ozturk, A., & Pak, M. (2001). Forest law and economics;ownership,scale size and economic analysis of the state forest enterprises constitute the frame of forestry sector. Proceedings of the 31st Annual Southern Forest Economics Workshop, March 2001, Atlanta, Georgia. Zamolodchikov, D. G., Grabovskii, V. I., Korovin, G. N., Gitarskii, M. L., Blinov, V. G., Dmitriev, V. V., & Kurz, W. a. (2013). Carbon budget of managed forests in the Russian Federation in 1990–2050: Post-evaluation and forecasting. Russian Meteorology and Hydrology, 38(10), 701–714. https://doi.org/10.3103/S1068373913100087 Zengin, H., Yeşil, A., Asan, Ü., Bettinger, P., Cieszewski, C., & Siry, J. P. (2013). Evolution of Modern Forest Management Planning in the Republic of Turkey. Journal of Forestry, 111(4), 239–248. https://doi.org/10.5849/jof.11-103     43  Appendices APPENDIX A: List of tables included in the forest management plans of Turkey Table 1: Summary of Forested, Non-Forested and Total Real Areas by Compartments (Table No.: 1)  Table 2: Distribution of the Forest Area within the Planning Unit by Stand Types (Table No.: 2) Table 3: Summary of Forested and Non-Forested Areas by Working Cycle (Table No.: 3)  Table 4: Distribution of the Planning Unit Area by Tree Species (Table No.: 4)  Table 5: Distribution of Forested Areas by Management Types (Working Cycles) (Table No.: 5)  Table 6: Site Class Distribution of Forest Areas (Table No.: 6)  Table 7: Age Class Distribution of High Forests (Except Degraded) (Table No.: 7)  Table 8: Stand Types Introduction Table (Table No.: 13)  Table 9: Tree Species and Diameter Classes Distribution of Working Cycle Growing Stock by Stand Types (Table No.: 14) Table 10: Diameter Classes Distribution of Growing Stock by Tree Species and Their Rates (Table No.: 15) Table 11: Tree Species and Quality Classes Distribution of Working Cycle Growing Stock by Stand Types (Table No.: 16)  Table 12: Quality Classes Distribution of Growing Stock by Tree Species and Their Rates (Table No.: 17)  Table 13: Table of Errors and Statistical Data for the Growing Stock Inventory of Stand Types (Table No.: 18)  Table 14: Distribution of Area, Growing Stock and Increment by Age Classes in Even-Aged Forests (Table No.: 24)  Table 15: Age Classes Distribution of Area, Growing Stock and Increment by Stand Types in Even-Aged Forests (Table No.: 24/A)  Table 16: Site Classes Distribution of Area, Growing Stock and Increment by Stand Types in Even-Aged Forests (Table No.: 24/B) Table 17: Seral Stages Distribution of Area, Growing Stock, and Increment by Stand Types in Even-Aged Forests (Table No.: 24/C)   44  Table 18: Volume and Increment Introduction Table for Coppice Forests (Table No.: 20)  Table 19: Numerical Display of the Optimal Structure in Even-Aged Forests (Table No.: 25)  Table 20: Comparison of the Actual and Optimal Situations of the Working Cycles for Economic Function (Table 26) Table 21: Final Yield Harvest Plan Table in Even-Aged High Forests (Table No.: 28)  Table 22: Table of Intermediate Yield Harvest Plan for High Forests (Table No.: 23-1)  Table 23: Stand Introduction and Harvest Plan for Coppice Forests (Table No.: 32)  Table 24: Areas Related to Afforestation, Improvement and Erosion Control (Table No.: 22)  Table 25: Table of Protection Areas (Areas with Very Low Coverage and Treeless Forest Areas) (Table No.: 22/A)  Table 26: Epilogue Table for Ecosystem-Based Functional Forest Management Plan for the Management Unit (Epilogue Table No.: 1)  Table 27: Distribution of Areas by Working Cycles (Epilogue Table No.: 2)  Table 28: Distribution of Growing Stock and Increment by Working Cycles (Epilogue Table No.: 3)  Table 29: Distribution of Regeneration Areas and Growing Stock by Working Cycles (Epilogue Table No.: 4)  Table 30: Distribution of Allowable Cut by Tree Species (Epilogue Table No.: 5)  Table 31: Distribution of Growing Stock and Increment by Tree Species (Epilogue Table No.: 6)  Table 32: Calculation of Carbon Sequestration in the Planning Unit (Epilogue Table No.: 8    45  APPENDIX B: Population (2014) in the towns and villages of Arikaya, Turkey Settlement Area  Population Fevzi Pasa 1792 Bozan 1575 Yunusemre 1515 Osmaniye 1006 Fatih 999 Kemalpasa 652 Agachisar 561 Karakamis 424 Bahcecik 352 Sakarikaracaoren 345 Bugduz 229 Karacaoren 213 Ozdenk 209 Arikaya 206 Uyuzhamamkoyu 170 Sogutcuk 161 Kosmat 153 Derekoy 143  46  Mamure 129 Sarikavak 125 Basoren 124 Esence 111 Fevziye 88 Alapinar 76 Belkese 67 Cukurhisar 64 Aktepe 58 Gokceoglu 57 Yesildon 55 Isikoren 50 Guroluk 44 Gokcekaya 37 Cardakbasi 32 Total 11882     47  APPENDIX C: Mitigation scenario included in the Intended Nationally Determined Contribution (INDC) report developed by Turkey    48  APPENDIX D: Stand canopy closure and canopy cover Code Canopy Closure Canopy Cover (%) Degraded Degraded stands canopy cover < 10% 1 Open forest 11% < canopy cover < 40%  2 Mid covered 41% < canopy cover < 70%  3 Other Full canopy Other than forest  71% < canopy cover      49   APPENDIX E: List of analysis units used in the model Species Priority  Black Pine Crown closure, site quality and forest types (pure and mix) Scots Pine Crown closure, forest types Turkish Pine Crown closure, forest types Oak Forest types Juniperus Forest types Species Priority  Black pine site quality 3 and crown closure 3 site quality 4 and crown closure 3 site quality 3 and crown closure 2 site quality 4 and crown closure 2 site quality 4 and crown closure 1 site quality 5 and crown closure 3 site quality 5 and crown closure 1 site quality 5 and crown closure 2 oak and even age oak and uneven age  50  juniperus and oak scots pine Scots pine crown closure 3 crown closure 2 crown closure 1 Turkish pine crown closure 3 crown closure 2 crown closure 1 oak and black pine Oak pure black pine juniperus Juniperus pure oak     51  APPENDIX F: Python script for assignment of Analysis Units # Enes Satir # This code assigns analysis units to the feature class with stand data for purpose of forest modelling # June 2017 import arcpy import time Start = time.time() print 'Start script' arcpy.env.workspace = r"F:\Documents\Thesis\ENES\BOLMECIK.gdb" fc = "BOLMECIK" try:     arcpy.AddField_management(fc, "AU", "LONG") except:     pass ## short code to enable the use of field names flist = arcpy.ListFields(fc) fdic = {} fl = [] print 'Creating flist' for f in flist:     fdic[f.name] = flist.index(f)     fl.append(f.name) with arcpy.da.UpdateCursor(fc, fl) as cursor:     for row in cursor:         row[fdic["AU"]]=999         if row[fdic["Species"]] == "Black Pine":             if row[fdic["BONITET"]] == 3:  52                  if row[fdic["KAPALILIK"]] == 3:                     row[fdic["AU"]]= 1         if row[fdic["Species"]] == "Black Pine":             if row[fdic["BONITET"]] == 4:                 if row[fdic["KAPALILIK"]] == 3:                     row[fdic["AU"]]= 2         if row[fdic["Species"]] == "Black Pine":             if row[fdic["BONITET"]] ==3:                 if row[fdic["KAPALILIK"]] == 2:                     row[fdic["AU"]]=3         if row[fdic["Species"]] == "Black Pine":             if row[fdic["BONITET"]] ==4:                 if row[fdic["KAPALILIK"]] == 2:                     row[fdic["AU"]]=4         if row[fdic["Species"]] == "Black Pine":             if row[fdic["BONITET"]] ==4:                 if row[fdic["KAPALILIK"]] == 1:                     row[fdic["AU"]]=5         if row[fdic["Species"]] == "Black Pine":             if row[fdic["BONITET"]] ==5:                 if row[fdic["KAPALILIK"]] ==3:                     row[fdic["AU"]]=6          if row[fdic["Species"]] == "Black Pine":             if row[fdic["BONITET"]] ==5:                 if row[fdic["KAPALILIK"]] ==2:                     row[fdic["AU"]]=7         if row[fdic["Species"]] == "Black Pine":  53              if row[fdic["BONITET"]] ==5:                 if row[fdic["KAPALILIK"]] ==1:                     row[fdic["AU"]]=8           if row[fdic["Species"]] == "Black Pine-Oak":             if row[fdic["Even_uneven"]] == "Even":                 row[fdic["AU"]]=9         if row[fdic["Species"]] == "Black Pine-Oak":             if row[fdic["Even_uneven"]] == "uneven":                 row[fdic["AU"]]=10         if row[fdic["Species"]] == "Black Pine-Juniperus-Oak":             row[fdic["AU"]]=11         if row[fdic["Species"]] == "Black Pine-Scots Pine":             row[fdic["AU"]]=12         if row[fdic["Species"]] == "Scots Pine":             if row[fdic["KAPALILIK"]] == 3:                 row[fdic["AU"]]=13         if row[fdic["Species"]] == "Scots Pine":             if row[fdic["KAPALILIK"]] == 2:                 row[fdic["AU"]]=14         if row[fdic["Species"]] == "Scots Pine":             if row[fdic["KAPALILIK"]] == 1:                 row[fdic["AU"]]=15         if row[fdic["Species"]] == "Scots Pine-Black Pine":             row[fdic["AU"]]=16         if row[fdic["Species"]] == "Scots Pine-Black Pine-Oak":             row[fdic["AU"]]=17         if row[fdic["Species"]] == "Turkish Pine":  54              if row[fdic["KAPALILIK"]] == 3:                 row[fdic["AU"]]=18         if row[fdic["Species"]] == "Turkish Pine":             if row[fdic["KAPALILIK"]] == 2:                 row[fdic["AU"]]=19         if row[fdic["Species"]] == "Turkish Pine":             if row[fdic["KAPALILIK"]] == 1:                 row[fdic["AU"]]=20         if row[fdic["Species"]] in ["Turkish Pine-Black Pine" , "Turkish Pine-Oak"]:             row[fdic["AU"]]=21          if row[fdic["Species"]] == "Oak":             row[fdic["AU"]]=22         if row[fdic["Species"]] == "Oak-Black Pine":             row[fdic["AU"]]=23          if row[fdic["Species"]] == "Oak-Juniperus":             row[fdic["AU"]]=24         if row[fdic["Species"]] == "Juniperus":             row[fdic["AU"]]=25         if row[fdic["Species"]] == "Juniperus-Oak":             row[fdic["AU"]]=26         if row[fdic["Landbase"]] == "Non_productive":             row[fdic["AU"]]= 999         cursor.updateRow(row) print ('It took ', round((time.time()-Start)/60,1), " minutes to run this script.")    55  APPENDIX G: Tables of carbon and timber prices calculation   Carbon and Timber @ 30 USD/m3 (R2) Years Change in carbon stored tons/ha Carbon Price  (6 USD /tCO2e) Carbon Price (20 USD /tCO2e) Carbon Price   (40 USD /tCO2e) Carbon Price   (60 USD /tCO2e) 2 1.6  154,538   445,128   860,257   1,275,385  3 1.7  163,657   475,524   921,047   1,366,571  4 1.9  180,392   531,306   1,032,611   1,533,917  5 1.9  185,940   549,798   1,069,597   1,589,395  6 2.3  211,678   635,593   1,241,185   1,846,778  7 2.6  235,974   716,579   1,403,159   2,089,738  8 2.9  260,894   799,647   1,569,293   2,338,940  9 3.2  283,098   873,659   1,717,317   2,560,976  10 3.4  304,593   945,310   1,860,620   2,775,930  11 3.7  325,812   1,016,041   2,002,082   2,988,123  12 3.9  342,202   1,070,674   2,111,347   3,152,021  13 4.0  353,653   1,108,844   2,187,688   3,266,533  14 4.2  362,698   1,138,994   2,247,987   3,356,981  15 4.2  366,984   1,153,280   2,276,559   3,399,839  16 4.2  370,157   1,163,857   2,297,714   3,431,571  17 4.3  371,818   1,169,392   2,308,784   3,448,175  18 4.3  372,823   1,172,743   2,315,486   3,458,229  19 4.3  374,204   1,177,347   2,324,693   3,472,040  20 4.3  375,652   1,182,172   2,334,343   3,486,515  21 4.1  358,392   1,124,640   2,219,281   3,313,921  22 4.1  357,162   1,120,541   2,211,081   3,301,622  23 4.1  356,558   1,118,526   2,207,052   3,295,578  24 4.1  356,042   1,116,807   2,203,615   3,290,422  25 4.1  358,506   1,125,021   2,220,042   3,315,063  26 4.1  361,444   1,134,812   2,239,625   3,344,437  27 4.2  364,467   1,144,891   2,259,781   3,374,672   56   Carbon and Timber @ 30 USD/m3 (R2) Years Change in carbon stored tons/ha Carbon Price  (6 USD /tCO2e) Carbon Price (20 USD /tCO2e) Carbon Price   (40 USD /tCO2e) Carbon Price   (60 USD /tCO2e) 28 4.2  367,327   1,154,423   2,278,846   3,403,269  29 4.3  370,429   1,164,765   2,299,530   3,434,295  30 4.3  373,538   1,175,125   2,320,251   3,465,376  31 4.4  382,937   1,206,457   2,382,913   3,559,370  32 4.4  385,497   1,214,988   2,399,977   3,584,965  33 4.5  387,939   1,223,129   2,416,257   3,609,386  34 4.5  390,393   1,231,310   2,432,620   3,633,931  35 4.5  392,744   1,239,147   2,448,294   3,657,440  36 4.6  394,992   1,246,641   2,463,281   3,679,922  37 4.6  397,188   1,253,961   2,477,921   3,701,882  38 4.6  399,346   1,261,153   2,492,306   3,723,458  39 4.7  402,600   1,272,000   2,514,000   3,756,000  40 4.7  408,169   1,290,565   2,551,130   3,811,694  41 4.5  386,945   1,219,816   2,409,632   3,599,448  42 4.5  388,421   1,224,735   2,419,471   3,614,206  43 4.5  390,069   1,230,229   2,430,458   3,630,687  44 4.5  391,596   1,235,321   2,440,642   3,645,963  45 4.5  392,972   1,239,908   2,449,815   3,659,723  46 4.5  394,317   1,244,391   2,458,782   3,673,174  47 4.6  395,575   1,248,582   2,467,164   3,685,746  48 4.6  396,775   1,252,584   2,475,167   3,697,751  49 4.6  397,928   1,256,425   2,482,851   3,709,276  50 4.6  399,124   1,260,414   2,490,827   3,721,241  51 4.7  402,662   1,272,206   2,514,411   3,756,617  52 4.7  403,992   1,276,640   2,523,281   3,769,921  53 4.7  405,303   1,281,009   2,532,018   3,783,028  54 4.7  406,580   1,285,265   2,540,531   3,795,796   57   Carbon and Timber @ 30 USD/m3 (R2) Years Change in carbon stored tons/ha Carbon Price  (6 USD /tCO2e) Carbon Price (20 USD /tCO2e) Carbon Price   (40 USD /tCO2e) Carbon Price   (60 USD /tCO2e) 55 4.7  407,830   1,289,434   2,548,868   3,808,302  56 4.7  409,057   1,293,524   2,557,047   3,820,571  57 4.7  410,264   1,297,547   2,565,093   3,832,640  58 4.8  411,455   1,301,518   2,573,035   3,844,553  59 4.8  412,806   1,306,020   2,582,041   3,858,061  60 4.8  415,387   1,314,623   2,599,245   3,883,868  61 4.4  383,529   1,208,430   2,386,860   3,565,290  62 4.4  383,891   1,209,638   2,389,275   3,568,913  63 4.4  384,280   1,210,932   2,391,865   3,572,797  64 4.4  384,692   1,212,306   2,394,611   3,576,917  65 4.4  385,126   1,213,753   2,397,505   3,581,258  66 4.4  385,575   1,215,250   2,400,499   3,585,749  67 4.4  386,022   1,216,741   2,403,482   3,590,223  68 4.5  386,465   1,218,217   2,406,435   3,594,652  69 4.5  386,924   1,219,748   2,409,496   3,599,244  70 4.5  387,380   1,221,267   2,412,534   3,603,801  71 4.4  383,383   1,207,944   2,385,888   3,563,833  72 4.4  383,538   1,208,461   2,386,922   3,565,383  73 4.4  384,162   1,210,538   2,391,077   3,571,615  74 4.4  384,373   1,211,244   2,392,489   3,573,733  75 4.4  384,604   1,212,014   2,394,027   3,576,041  76 4.4  384,846   1,212,820   2,395,641   3,578,461  77 4.4  385,095   1,213,652   2,397,303   3,580,955  78 4.4  385,405   1,214,683   2,399,366   3,584,050  79 4.5  386,678   1,218,926   2,407,851   3,596,777  80 4.5  388,147   1,223,822   2,417,644   3,611,465  81 4.2  363,408   1,141,361   2,252,722   3,364,084   58   Carbon and Timber @ 30 USD/m3 (R2) Years Change in carbon stored tons/ha Carbon Price  (6 USD /tCO2e) Carbon Price (20 USD /tCO2e) Carbon Price   (40 USD /tCO2e) Carbon Price   (60 USD /tCO2e) 82 4.2  363,470   1,141,565   2,253,130   3,364,695  83 4.2  363,573   1,141,912   2,253,823   3,365,735  84 4.2  363,728   1,142,427   2,254,854   3,367,281  85 4.2  363,910   1,143,033   2,256,065   3,369,098  86 4.2  364,124   1,143,745   2,257,490   3,371,235  87 4.2  364,337   1,144,458   2,258,915   3,373,373  88 4.2  364,574   1,145,245   2,260,490   3,375,736  89 4.2  364,817   1,146,058   2,262,115   3,378,173  90 4.2  365,145   1,147,149   2,264,298   3,381,447  91 4.2  365,324   1,147,748   2,265,495   3,383,243  92 4.2  365,491   1,148,303   2,266,607   3,384,910  93 4.2  366,016   1,150,052   2,270,105   3,390,157  94 4.2  366,102   1,150,340   2,270,680   3,391,020  95 4.2  366,190   1,150,632   2,271,264   3,391,896  96 4.2  366,272   1,150,907   2,271,814   3,392,722  97 4.2  366,348   1,151,159   2,272,317   3,393,476  98 4.2  366,466   1,151,553   2,273,106   3,394,659  99 4.2  367,440   1,154,798   2,279,597   3,404,395  100 4.2  367,563   1,155,209   2,280,417   3,405,626    Carbon and Timber @ 60 USD/m3 (R2) Years Change in carbon stored tons/ha Carbon Price     (6 USD/tCO2e) Carbon Price (20 USD /tCO2e) Carbon Price   (40 USD /tCO2e) Carbon Price   (60 USD /tCO2e) 2 1.6  184,538   445,128   860,257   1,275,385  3 1.7  193,657   475,524   921,047   1,366,571  4 1.9  210,392   475,128   890,257   1,305,385   59   Carbon and Timber @ 60 USD/m3 (R2) Years Change in carbon stored tons/ha Carbon Price     (6 USD/tCO2e) Carbon Price (20 USD /tCO2e) Carbon Price   (40 USD /tCO2e) Carbon Price   (60 USD /tCO2e) 5 1.9  215,940   505,524   951,047   1,396,571  6 2.3  241,678   561,306   1,062,611   1,563,917  7 2.6  265,974   579,798   1,099,597   1,619,395  8 2.9  290,894   665,593   1,271,185   1,876,778  9 3.2  313,098   746,579   1,433,159   2,119,738  10 3.4  334,593   829,647   1,599,293   2,368,940  11 3.7  355,812   903,659   1,747,317   2,590,976  12 3.9  372,202   975,310   1,890,620   2,805,930  13 4.0  383,653   1,046,041   2,032,082   3,018,123  14 4.2  392,698   1,100,674   2,141,347   3,182,021  15 4.2  396,984   1,138,844   2,217,688   3,296,533  16 4.2  400,157   1,168,994   2,277,987   3,386,981  17 4.3  401,818   1,183,280   2,306,559   3,429,839  18 4.3  402,823   1,193,857   2,327,714   3,461,571  19 4.3  404,204   1,199,392   2,338,784   3,478,175  20 4.3  405,652   1,202,743   2,345,486   3,488,229  21 4.1  388,392   1,207,347   2,354,693   3,502,040  22 4.1  387,162   1,212,172   2,364,343   3,516,515  23 4.1  386,558   1,154,640   2,249,281   3,343,921  24 4.1  386,042   1,150,541   2,241,081   3,331,622  25 4.1  388,506   1,148,526   2,237,052   3,325,578  26 4.1  391,444   1,146,807   2,233,615   3,320,422  27 4.2  394,467   1,155,021   2,250,042   3,345,063  28 4.2  397,327   1,164,812   2,269,625   3,374,437  29 4.3  400,429   1,174,891   2,289,781   3,404,672  30 4.3  403,538   1,184,423   2,308,846   3,433,269  31 4.4  412,937   1,194,765   2,329,530   3,464,295   60   Carbon and Timber @ 60 USD/m3 (R2) Years Change in carbon stored tons/ha Carbon Price     (6 USD/tCO2e) Carbon Price (20 USD /tCO2e) Carbon Price   (40 USD /tCO2e) Carbon Price   (60 USD /tCO2e) 32 4.4  415,497   1,205,125   2,350,251   3,495,376  33 4.5  417,939   1,236,457   2,412,913   3,589,370  34 4.5  420,393   1,244,988   2,429,977   3,614,965  35 4.5  422,744   1,253,129   2,446,257   3,639,386  36 4.6  424,992   1,261,310   2,462,620   3,663,931  37 4.6  427,188   1,269,147   2,478,294   3,687,440  38 4.6  429,346   1,276,641   2,493,281   3,709,922  39 4.7  432,600   1,283,961   2,507,921   3,731,882  40 4.7  438,169   1,291,153   2,522,306   3,753,458  41 4.5  416,945   1,302,000   2,544,000   3,786,000  42 4.5  418,421   1,320,565   2,581,130   3,841,694  43 4.5  420,069   1,249,816   2,439,632   3,629,448  44 4.5  421,596   1,254,735   2,449,471   3,644,206  45 4.5  422,972   1,260,229   2,460,458   3,660,687  46 4.5  424,317   1,265,321   2,470,642   3,675,963  47 4.6  425,575   1,269,908   2,479,815   3,689,723  48 4.6  426,775   1,274,391   2,488,782   3,703,174  49 4.6  427,928   1,278,582   2,497,164   3,715,746  50 4.6  429,124   1,282,584   2,505,167   3,727,751  51 4.7  432,662   1,286,425   2,512,851   3,739,276  52 4.7  433,992   1,290,414   2,520,827   3,751,241  53 4.7  435,303   1,302,206   2,544,411   3,786,617  54 4.7  436,580   1,306,640   2,553,281   3,799,921  55 4.7  437,830   1,311,009   2,562,018   3,813,028  56 4.7  439,057   1,315,265   2,570,531   3,825,796  57 4.7  440,264   1,319,434   2,578,868   3,838,302  58 4.8  441,455   1,323,524   2,587,047   3,850,571   61   Carbon and Timber @ 60 USD/m3 (R2) Years Change in carbon stored tons/ha Carbon Price     (6 USD/tCO2e) Carbon Price (20 USD /tCO2e) Carbon Price   (40 USD /tCO2e) Carbon Price   (60 USD /tCO2e) 59 4.8  442,806   1,327,547   2,595,093   3,862,640  60 4.8  445,387   1,331,518   2,603,035   3,874,553  61 4.4  413,529   1,336,020   2,612,041   3,888,061  62 4.4  413,891   1,344,623   2,629,245   3,913,868  63 4.4  414,280   1,238,430   2,416,860   3,595,290  64 4.4  414,692   1,239,638   2,419,275   3,598,913  65 4.4  415,126   1,240,932   2,421,865   3,602,797  66 4.4  415,575   1,242,306   2,424,611   3,606,917  67 4.4  416,022   1,243,753   2,427,505   3,611,258  68 4.5  416,465   1,245,250   2,430,499   3,615,749  69 4.5  416,924   1,246,741   2,433,482   3,620,223  70 4.5  417,380   1,248,217   2,436,435   3,624,652  71 4.4  413,383   1,249,748   2,439,496   3,629,244  72 4.4  413,538   1,251,267   2,442,534   3,633,801  73 4.4  414,162   1,237,944   2,415,888   3,593,833  74 4.4  414,373   1,238,461   2,416,922   3,595,383  75 4.4  414,604   1,240,538   2,421,077   3,601,615  76 4.4  414,846   1,241,244   2,422,489   3,603,733  77 4.4  415,095   1,242,014   2,424,027   3,606,041  78 4.4  415,405   1,242,820   2,425,641   3,608,461  79 4.5  416,678   1,243,652   2,427,303   3,610,955  80 4.5  418,147   1,244,683   2,429,366   3,614,050  81 4.2  393,408   1,248,926   2,437,851   3,626,777  82 4.2  393,470   1,253,822   2,447,644   3,641,465  83 4.2  393,573   1,171,361   2,282,722   3,394,084  84 4.2  393,728   1,171,565   2,283,130   3,394,695  85 4.2  393,910   1,171,912   2,283,823   3,395,735   62   Carbon and Timber @ 60 USD/m3 (R2) Years Change in carbon stored tons/ha Carbon Price     (6 USD/tCO2e) Carbon Price (20 USD /tCO2e) Carbon Price   (40 USD /tCO2e) Carbon Price   (60 USD /tCO2e) 86 4.2  394,124   1,172,427   2,284,854   3,397,281  87 4.2  394,337   1,173,033   2,286,065   3,399,098  88 4.2  394,574   1,173,745   2,287,490   3,401,235  89 4.2  394,817   1,174,458   2,288,915   3,403,373  90 4.2  395,145   1,175,245   2,290,490   3,405,736  91 4.2  395,324   1,176,058   2,292,115   3,408,173  92 4.2  395,491   1,177,149   2,294,298   3,411,447  93 4.2  396,016   1,177,748   2,295,495   3,413,243  94 4.2  396,102   1,178,303   2,296,607   3,414,910  95 4.2  396,190   1,180,052   2,300,105   3,420,157  96 4.2  396,272   1,180,340   2,300,680   3,421,020  97 4.2  396,348   1,180,632   2,301,264   3,421,896  98 4.2  396,466   1,180,907   2,301,814   3,422,722  99 4.2  397,440   1,181,159   2,302,317   3,423,476  100 4.2  397,563   1,181,553   2,303,106   3,424,659          Baseline Years Harvest Level (m³/yr) Carbon Accumulation (tons/ha) tCO2e (tons/ha) 1 12,702 254.4 934 2 12,702 254.5 934 3 12,702 254.6 934 4 12,702 254.6 934 5 12,702 254.7 935 6 12,702 254.7 935  63   Baseline Years Harvest Level (m³/yr) Carbon Accumulation (tons/ha) tCO2e (tons/ha) 7 12,702 254.8 935 8 12,702 254.9 936 9 12,702 255.1 936 10 12,702 255.3 937 11 11,112 255.5 938 12 11,112 255.8 939 13 11,112 256.1 940 14 11,112 256.4 941 15 11,112 256.7 942 16 11,112 257.1 944 17 11,112 257.5 945 18 11,112 257.9 946 19 11,112 258.3 948 20 11,112 258.7 949 21 11,433 259.0 950 22 11,433 259.3 952 23 11,433 259.7 953 24 11,433 260.0 954 25 11,433 260.3 955 26 11,433 260.7 957 27 11,433 261.0 958 28 11,433 261.4 959 29 11,433 261.7 961 30 11,433 262.1 962 31 11,433 262.6 964 32 11,433 263.0 965 33 11,433 263.5 967  64   Baseline Years Harvest Level (m³/yr) Carbon Accumulation (tons/ha) tCO2e (tons/ha) 34 11,433 264.0 969 35 11,433 264.5 971 36 11,433 265.0 972 37 11,433 265.5 974 38 11,433 266.0 976 39 11,433 266.5 978 40 11,433 267.0 980 41 11,198 267.6 982 42 11,198 268.2 984 43 11,198 268.7 986 44 11,198 269.2 988 45 11,198 269.8 990 46 11,198 270.3 992 47 11,198 270.8 994 48 11,198 271.3 996 49 11,198 271.8 997 50 11,198 272.3 999 51 12,043 272.8 1,001 52 12,043 273.3 1,003 53 12,043 273.8 1,005 54 12,043 274.3 1,007 55 12,043 274.8 1,009 56 12,043 275.3 1,011 57 12,043 275.9 1,012 58 12,043 276.4 1,014 59 12,043 276.9 1,016 60 12,043 277.4 1,018  65   Baseline Years Harvest Level (m³/yr) Carbon Accumulation (tons/ha) tCO2e (tons/ha) 61 11,403 277.9 1,020 62 11,403 278.3 1,021 63 11,403 278.8 1,023 64 11,403 279.3 1,025 65 11,403 279.8 1,027 66 11,403 280.3 1,029 67 11,403 280.8 1,030 68 11,403 281.6 1,033 69 11,403 282.1 1,035 70 11,403 282.7 1,037 71 11,201 283.2 1,039 72 11,201 283.6 1,041 73 11,201 284.1 1,043 74 11,201 284.6 1,044 75 11,201 285.0 1,046 76 11,201 285.5 1,048 77 11,201 285.9 1,049 78 11,201 286.4 1,051 79 11,201 286.8 1,053 80 11,201 287.3 1,054 81 11,485 287.7 1,056 82 11,485 288.1 1,057 83 11,485 288.5 1,059 84 11,485 289.0 1,060 85 11,485 289.4 1,062 86 11,485 289.8 1,064 87 11,485 290.3 1,065  66   Baseline Years Harvest Level (m³/yr) Carbon Accumulation (tons/ha) tCO2e (tons/ha) 88 11,485 290.7 1,067 89 11,485 291.1 1,068 90 11,485 291.6 1,070 91 11,178 292.1 1,072 92 11,178 292.6 1,074 93 11,178 293.1 1,076 94 11,178 293.6 1,077 95 11,178 294.1 1,079 96 11,178 294.6 1,081 97 11,178 295.1 1,083 98 11,178 295.6 1,085 99 11,178 296.1 1,087 100 11,178 296.6 1,089   Rehabilitation with 1000 m³/year harvest flow (R2) scenario Years Harvest Level (m³/yr) Carbon Accumulation (tons/ha) tCO2e (tons/ha) 1 1000 252.5 927 2 1000 252.9 928 3 1000 253.4 930 4 1000 253.9 932 5 1000 254.4 934 6 1000 255.0 936 7 1000 255.7 939 8 1000 256.5 941 9 1000 257.4 945  67   Rehabilitation with 1000 m³/year harvest flow (R2) scenario Years Harvest Level (m³/yr) Carbon Accumulation (tons/ha) tCO2e (tons/ha) 10 1000 258.3 948 11 1000 259.3 952 12 1000 260.4 956 13 1000 261.5 960 14 1000 262.6 964 15 1000 263.8 968 16 1000 264.9 972 17 1000 266.1 977 18 1000 267.3 981 19 1000 268.4 985 20 1000 269.6 989 21 1000 270.7 994 22 1000 271.8 998 23 1000 273.0 1,002 24 1000 274.1 1,006 25 1000 275.2 1,010 26 1000 276.3 1,014 27 1000 277.4 1,018 28 1000 278.6 1,022 29 1000 279.8 1,027 30 1000 280.9 1,031 31 1000 282.1 1,035 32 1000 283.3 1,040 33 1000 284.6 1,044 34 1000 285.8 1,049 35 1000 287.0 1,053  68   Rehabilitation with 1000 m³/year harvest flow (R2) scenario Years Harvest Level (m³/yr) Carbon Accumulation (tons/ha) tCO2e (tons/ha) 36 1000 288.3 1,058 37 1000 289.5 1,062 38 1000 290.8 1,067 39 1000 292.0 1,072 40 1000 293.3 1,076 41 1000 294.5 1,081 42 1000 295.7 1,085 43 1000 297.0 1,090 44 1000 298.2 1,094 45 1000 299.4 1,099 46 1000 300.7 1,103 47 1000 301.9 1,108 48 1000 303.2 1,113 49 1000 304.4 1,117 50 1000 305.7 1,122 51 1000 306.9 1,127 52 1000 308.2 1,131 53 1000 309.5 1,136 54 1000 310.8 1,141 55 1000 312.1 1,145 56 1000 313.4 1,150 57 1000 314.7 1,155 58 1000 315.9 1,160 59 1000 317.3 1,164 60 1000 318.6 1,169 61 1000 319.8 1,174  69   Rehabilitation with 1000 m³/year harvest flow (R2) scenario Years Harvest Level (m³/yr) Carbon Accumulation (tons/ha) tCO2e (tons/ha) 62 1000 321.0 1,178 63 1000 322.2 1,182 64 1000 323.4 1,187 65 1000 324.6 1,191 66 1000 325.8 1,196 67 1000 327.0 1,200 68 1000 328.2 1,205 69 1000 329.4 1,209 70 1000 330.7 1,214 71 1000 331.9 1,218 72 1000 333.1 1,222 73 1000 334.3 1,227 74 1000 335.5 1,231 75 1000 336.7 1,236 76 1000 337.9 1,240 77 1000 339.1 1,244 78 1000 340.3 1,249 79 1000 341.5 1,253 80 1000 342.7 1,258 81 1000 343.9 1,262 82 1000 345.0 1,266 83 1000 346.1 1,270 84 1000 347.3 1,275 85 1000 348.4 1,279 86 1000 349.6 1,283 87 1000 350.7 1,287  70   Rehabilitation with 1000 m³/year harvest flow (R2) scenario Years Harvest Level (m³/yr) Carbon Accumulation (tons/ha) tCO2e (tons/ha) 88 1000 351.8 1,291 89 1000 353.0 1,295 90 1000 354.1 1,300 91 1000 355.3 1,304 92 1000 356.4 1,308 93 1000 357.5 1,312 94 1000 358.7 1,316 95 1000 359.8 1,321 96 1000 361.0 1,325 97 1000 362.1 1,329 98 1000 363.3 1,333 99 1000 364.4 1,337 100 1000 365.6 1,342    71  APPENDIX H: Distribution of forests in Turkey (2009)  

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