Global forest conservation policies and the Sustainable Development Goals

Global forest conservation policies and the Sustainable Development Goals | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Global forest conservation policies and the Sustainable Development Goals Wolfgang Britz, Rienne Wilts, David Wuepper This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8210376/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Forest conservation is widely heralded as a cost-efficient climate change mitigation strategy. Additional benefits of forest conservation include a range of ecosystem services including biodiversity conservation. But besides costs of governance and conservation payments, also opportunity costs should be considered, such as a potential decline in food production when protected areas are not available for agriculture. We therefore combine recent econometric estimates of impacts of national-level forest conservation policies with a Computable General Equilibrium model to assess existing forest conservation policies. We find that these policies cost a small fraction of gross domestic product and have quite limited effects on food security. As desired, they prevent large amounts of greenhouse gas emissions, mostly in form of prevented carbon stock losses, mitigating around twelve times the green house gases emitted globally in 2023. They also contribute considerably to biodiversity protection, despite increasing the intensity of land management. Given their small economic and social costs and wide-ranging environmental benefits, existing forest conservation policies are even more cost effective than previously thought. Earth and environmental sciences/Environmental social sciences/Climate-change mitigation Earth and environmental sciences/Environmental social sciences/Environmental economics Forest Conservation Policies Sustainable Development Goal Computable General Equilibrium Model Food security Border discontinuity Figures Figure 1 Figure 2 1 Introduction Agricultural and commodity-driven land expansion is a main cause of permanent logging and fire clearance, driven by growing demands for food and biomass due to population and income growth [ 1 , 2 ]. Forest conservation thus competes with food production on agricultural areas. But forests also contribute to food security directly by provision of food and indirectly by yield increasing ecosystem services [ 3 ]. Their conservation can also induce agricultural productivity gains by increasing land scarcity [ 4 , 5 ]. Deforestation comes along with several negative side-effects such as carbon release, reduced precipitation [ 6 ] and biodiversity losses through shrinking natural habitats and fragmentation [ 7 ], linked to several of the UN’s Sustainable Development Goals (SDGs). To what extent socio-economic dynamics drive deforestation depends on different factors, such as expected land rents on cleared spots or costs of deforestation, and prominently the governance of land use change [ 8 ]. Forest conservation policies interact globally since effective protection of a country’s own forests can increase the country’s imports or decrease its exports of biomass, driving up land rents elsewhere. This can imply higher agricultural land expansion where no effective conservation policies exist or can demand their stricter enforcement. Accordingly, understanding the combined effect of existing national conservation policies, including their economic cost, requires a global analysis considering these feedback channels. While a large literature focuses on the effectiveness of current forest conservation policies [ 9 , 10 , 11 , 12 ], their global effect on markets and food prices has found limited attention. To advance here, we feed the empirical estimates of Wuepper et al. [ 12 ] on avoided yearly deforestation by existing policies from 2001 to 2017/2018 into the global Computable General Equilibrium (CGE) model CGEBox [ 13 ]. This enables a what-if analysis to capture market-mediated effects across sectors and regions of existing conservation policies, and to quantify their impacts on various SDG-related indicators. Rooted in micro-economic theory and consistent physical and economic balancing, the CGE model provides a sound methodological framework to incorporate both direct and indirect effects. For example, when forests are protected, this constrains the area available for farming. However, through market mechanisms, this incentivizes farmers to become more productive on the existing agricultural areas, for instance, by increasing yield increasing inputs or investing into technology. Thus, the loss in output can be smaller than under the assumption that farmers do not adapt at all, and it is even possible that output increases [ 14 ]. This is just one example of the many complex dynamics that play out when forest conservation and markets interact [ 15 ]. Existing literature that applied (CGE) models in the context of potential deforestation or climate change mitigation policies such as forest carbon payments or afforestation including REDD scenarios conducted analysis mostly at country level [ 16 , 17 ] partly in single-country models [ 18 ]. Existing global analysis used rather large regional aggregates [ 19 , 20 , 21 , 22 , 23 , 24 ] to quantify, for instance, impacts on land or food prices. These assessments are predominantly based on assumptions about afforested or protected areas, while our approach is based on empirically estimated effects of currently implemented policies. Our analysis provides policy relevant insights into the global market mediated costs and benefits of forest conservation policies; by covering more than 120 individual countries, remaining ones are aggregated to larger model regions as summarized in the Annex Table A.1. This global detail allows to capture the manifold interactions and regional differences of current conservation policies and their effects on multiple SDGs. No equivalent analysis on global economic effects of current forest conservation policies has been conducted so far. The current status of land cover and of the food system reflects many factors, including existing conservation policies. As we assess currently enforced policies, our experiment reduces exogenously in each nation the forest area, according to the estimates of effectively avoided deforestation. This isolates the impact of conservation policies by keeping all other drivers unchanged, such as population size, technology, carbon or trade policies, or dietary preferences. The additionally deforested land will be used for agriculture, and the simulation with the global economic model spells out the consequences on supply and demand, prices, trade and income along global value chains. In addition, it quantifies various indicators, such as Gross Domestic Products (GDP), Greenhouse Gas (GHG) emissions or resource extraction, globally and for each region. The underlying global interlinkages are addressed by simulating bi-lateral trade of products and services, alongside with regional consumption and production decisions. One additional novelty of this approach is the explicit modelling of unmanaged forest as a land use category for land conversion and their associated carbon stocks. 2 Results Simulating a world without current forest protection policies results globally in a reduction of managed forests by around 1.5% (25.4 Mio ha) and of unmanaged ones by around 1.3% (34.6 Mio ha) (see Fig. 1 .a). These numbers suggest that without these policies, deforestation would have been twelve times higher than the average yearly observed between 2000 and 2020 [ 25 ]. For more than 70% of the countries, the avoided deforestation is smaller than 3%. Values beyond 10% are observed for 1% of the cases, only, predominantly located in Europe and Africa, see in Fig. 1 .a. Given the abundance of natural forest in Russia, Indonesia, and the Democratic Republic Congo, these countries are the top three in terms of avoided absolute deforestation, even if they do not stand out in relative terms. The additional deforestation expands both pastures and crop lands, the resulting relative change for cropland is depicted in Fig. 1 .b. In countries with large forest areas relative to cropland, dropping conservation policies can imply very large relative changes in croplands, such as in Estonia, Rest of Oceania and Finland. Biomass production in sum is not changing much, with the largest expansion observed for paddy rice by 0.55%, followed by sugar cane with 0.4%. As the relative change in agricultural land exceeds these muted relative increases in biomass outputs, crop yields fall, as seen in Fig. 1 .c. The limited output effects reflect that demand for biomass is in sum quite price inelastic, leading to a less intensive use of agricultural land. The related reduction of agro-chemicals per hectare is beneficial for biodiversity. Less intensive production is also observed for pasture land (Annex Figure A.1), where stocking densities decreases uniformly across the countries. However, the net biodiversity effect of the additional deforestation is likely negative, as natural forest and habitats have a higher species richness compared to agricultural lands (land sharing vs. land sparing). The additional biomass produced on the increased agricultural area is absorbed by value chains only at dropping prices. This benefits sectors in biomass related value chains as they (indirectly) face lower costs of biomass-based intermediates, while impacts on other sectors are negligible. These dynamics result in lower food prices (see Fig. 1 .d), with slight positive effects for food security-related SDG indicators, such as nutrient consumption and food expenditure shares (see Annex Figure A.2). The food price decrease is marginal for most countries, with less than 0.5% for about 89% of the countries. The Democratic Republic Congo provides an exception with a 5% drop, driven by considerable cropland expansion, which reflects the large area of forest relative to agricultural land. Existing conservation policies are hence not threatening food security or causing hunger. Other changes related to production, consumption or prices of various goods and services are even smaller than for food. The additional deforestation results in a meagre increase of 0.02% in global real GDP, as a measure for the consumption possibilities of all goods and service. The maximum increases of around 1.2% and 0.9% are found for the Democratic Republic of Congo and Laos, respectively, see Fig. 1 .e. Countries in the global South which spend higher shares of their income on food tend to benefit more from additional agricultural land expansion if conservation policies would be removed. We find a few cases with quite muted GDP losses, not exceeding − 0.02%. These countries hence lose from the additional land expansion, or put otherwise, they actually gain from existing conservation policies. As GDP changes are quite small, economic costs of current forest conservation are low, also reflected by the other economy related SDG indicators (see Figure A.3). Even if the impact on economic output is limited, we find globally a 0.06% increase in CO 2 and non-CO 2 emissions, both along value chains and by final consumption. This reflects that certain agricultural production processes are quite emission intensive, such as paddy rice or ruminant production. Their emissions increase in relative terms more than the rest of the economy under expansion of agricultural areas. Far more relevant for climate change mitigation are however the carbon stock losses from increased deforestation, amounting globally to 1.3*10 10 t of carbon lost. This is equal to about twelve years of current global emissions summarized in CO 2 equivalents [ 26 ]. As deforestation policies preserve carbon stocks in existing forests and do not increase these stocks from year to year, the avoided loss in stocks cannot be directly compared to annual CO 2 and non-CO 2 emissions. As other studies, we therefore depreciate the carbon stock losses over 25 years (see Fig. 1 .f). The related average yearly increase in GHG emission is still considerable, exceeding 10% for most countries. This shows the importance of avoided deforestation of existing conservation policies. Given their limited economic cost (0.02% in terms of GDP globally, less than 1% of GDP in 99% of the countries), conservation policies are hence a highly cost-efficient way to prevent further GHG emissions, besides other positive eco-system services such as maintaining biodiversity. With regard to other impacts, the biosphere-related SDGs (Figure A.1) show the strongest effects, followed by the society-related ones (Figure A.2), while the SDGs associated with economic aspects show the least ones (Figure A.3). 3 Discussion The combination of econometric evidence and global CGE analysis offers new insights in market-mediated impacts of forest conservation policies and allows to quantify related annual economy-wide opportunity costs. Our analysis suggests that these costs are rather limited. For a given global population and state of the economy, bringing additional land into production implies only very modest opportunities to expand global economic output. However, we did not consider long-term dynamics here to isolate the effect of the policies. Massive population and income growth [ 27 ] are expected in the upcoming decades, identified in the past as the main driving forces of deforestation [ 8 ]. Without enhancing land productivity through technical progress, growing land scarcity is expected, increasing the pressure on natural vegetation. The consequences of this mechanism could already be observed in Sub-Saharan Africa over the last decades where high population growth and stagnating crop yields funneled massive expansions in agricultural land [ 28 ]. Baselines generated with the same CGE model as in the underlying assessment show strong land expansion over time due to socio-economic dynamics until 2050 [ 29 ]. These could be reinforced by policies that replace fossil fuels by biomass [ 30 ]. Sustainable intensification of crop production which allows for higher crop yields and reduced meat consumption could hence be key strategies to support forest conservation policies [ 23 ]. CGE models require a large set of input data to parameterize their production, demand and other functions. The related estimates are often taken from literature covering a few sectors or regions, only, or are rather old. The simulated results, included land use changes and related indicators, depend on the model’s parameterization that determines its response in the policy experiment [ 31 ]. Besides parameters, data, including carbon contents and the land cover classification (such as the differentiation between managed and unmanaged forests) shape the outcome. Other studies have shown that these data might vary depending on the underlying assumptions regarding the definition of land use as ‘forest’ or the average per hectare soil carbon [ 32 , 19 ] which affect the size of the effect, especially regarding GHG emissions, however not their direction and trends. The country specific policy effect estimates that are the foundation of our analysis are also subject to uncertainties. Our estimates are provided by Wuepper et al. [ 12 ] who rely on a spatial difference in discontinuities design [ 33 ]. It is well-known that this research design can have limited external validity, as causal identification is achieved very locally. For our modelling here, we extrapolate the causal estimates from national borders to the rest of each country. Wuepper et al. [ 12 ] provide evidence that this works globally on average. However, at individual countries, border regions can be quite different from the non-border ones, which then only balances out globally, but can bias individual border estimates upwards or downwards. The avoided loss of carbon stocks of current conservation policy is considerable compared to current global yearly GHG emissions. These global findings reflect not only the estimated percentage of avoided deforestation by country, but also their absolute forest areas and locations. For instance, without conservation policy, deforestation is estimated to increase drastically in absolute terms in the Democratic Republic Congo, amounting to five times the deforestation observed in the last two decades. Given the carbon richness of this native forest, preventing deforestation and land use change in this country is pivotal. Forest conservation in countries with large forest areas does not only matter from the carbon stock perspective, but also for biodiversity. Seven of the ten countries with the highest absolute estimates of avoided deforestation are classified as megadiverse countries, due to their extraordinary species richness which largely depends on native land and in particular on forests. 4 Conclusion We conduct a global analysis of consequences of existing forest conservation policies on selected SDG indicators based on the combination of econometric quantification of their impact on land cover change and assessing these changes with global CGE analysis. Our comparative-static analysis for 2017 suggests that these policies have reduced croplands by 1.96% and pasturelands by 0.96%. Price and income inelastic demand for biomass limits the impact on global biomass production, such that global economic output is hardly affected, the same holds for income. The implicit cost of the policies amount to around 1.7 USD per capita globally. For other SDGs, especially impacts on global GHG emissions are relevant due to avoided carbon stock losses. Overall, the analysis underlines that forest conservation policies have relative limited costs in terms of income foregone and worsening of food security, but are an important part of GHG mitigation and biodiversity policies. 5 Methodology 5.1 CGE model choice and set-up Our model set-up draws in its core on the widely used GTAP Standard Model Version 7 [ 34 ] as implemented in GAMS by van der Mensbrugghe [ 35 ], integrated in the flexible and modular framework for CGE modelling CGEBox [ 13 ]. The GTAP Standard model applies the usual micro-economic assumptions used in CGE analysis, namely competitive markets for products and factors, utility maximizing final demanders and one aggregate cost-minimizing representative firm for each sector, operating under constant-returns-to-scale. Households own the production factors and allocate them to sectors such as to maximize revenues with no forward-looking behavior. Macro identities are added as constraints, such as investments equal savings, closed balances of payments and expenditures being equal to revenues for the different accounts. The model depicts bi-lateral trade based on the Armington assumption such that products are quality differentiated across regions. Combined with endogenous industry-by-industry demand, this captures domestic and international supply chains. Foreign savings are endogenous, driven by a virtual global investor who collects all savings and distributes them to the model regions such as to equilibrate expected returns to capital. This also implies endogenous balances of trade of each region as the second element of the closed balances of payments. Energy demands are measured in the Million Tons of Oil Equivalents (MTOE) and related demand equations employ the volume preserving Constant-Elasticity of Substitution (CES) functional form to balance energy in physical terms. Multi-layer CES functions for intermediate and final demand from GTAP-E/GTAP-Power [ 36 ] depict substitution between energy carriers and capital. Further CES nests model substitution possibilities between different crops in animal feed and the food industry, between a feed aggregate and land for livestock and between chemicals and land for crops. Multiple CES nests are also used for final consumption to better depict cross-price effects. The production factors captured in this configuration of CGEBox are land, labor (differentiated in skilled and unskilled), natural resources and capital. To improve the representation of land in the model, the GTAP-AEZ component is incorporated, drawing on Lee [ 37 ]. It depicts competition between land use sectors at the level of so-called Agro-Ecological Zones (AEZs), to consider that land is spatially immobile, and offers a quantification of physical land use in hectares, including for unmanaged land. Instead of the usual representation with one Constant-Elasticity-of-Transformation (CET) function which distributes a total land stock to all land uses in many CGE models, the GTAP-AEZ component in CGEBox uses a multi-layer CET set-up to better reflect substitution possibilities between different land use categories. Specifically, it employs a so-called Additive CET functional form [ 38 ] which ensures physical balancing in hectares. The available data also include carbon stocks for the different land use categories (i.e., pasture, cropland, forest and native land uses) at a per hectare level, allowing for the assessment of carbon stock changes by land cover changes. In contrast to labor and capital, natural resources (i.e., oil wells, gas fields, mines, fish stocks etc.) are considered as sector specific and thus immobile. 5.2 Data The analysis builds on the latest GTAP-Power Data base 11c [ 39 ] with the base year 2017. The dis-aggregation of the single electricity sector into different power generation sectors and a power distribution and transformation sector allows to consider biomass use in power generation. Due to the focus of the study, most non-transport and non-food related service, and manufacture sectors not related to biomass use are aggregated, such that the model depicts 53 sectors instead of the 76 available in the data base. In order to exploit the detail on regional land use changes, we keep most of the regional detail of the data base which covers 160 single countries and regional aggregates, resulting in an analysis with 124 model regions. Aggregated countries are especially those for which no results are directly available from the econometric analysis, such as for regions with limited tree cover or where the econometric results do not predict in impact of policies on past tree cover change, found, for instance, in Northern Africa, the Arabian Peninsula and Central Asia, or islands with no border to another country. 5.3 Scenario design and policy effect To quantify the effect of the forest conservation policies at global scale, we benchmark the CGE model on the described data base and then conduct a comparative static analysis, i.e., assume a removal of all conservation policies in the setting of the base year 2017. This configuration enables to disentangle the policy effect from other dynamics and fits the estimation of the input data incorporated from Wuepper et al. [ 12 ]. We differentiate the capital stock into old and new as non-perfect substitutes, considering six years of depreciation, where new capital is linked to investments and thus reflects capital accumulation. The findings from Wuepper et al. [ 12 ] are available for 105 countries, which are all countries that share land-borders with other countries and have enough tree cover near these borders to make the use of the difference in discontinuity design sufficiently reliable [ 32 ]. There are thus no estimates for island states, such as Australia, New Zealand, Philippines or Great Britain. For a few countries, there are small, negative effects estimated for existing policies on forest conservation, which have been set to zero for this analysis. The policy effect underlying this analysis is depicted in Fig. 2 with grey representing missing values. The estimates from Wuepper et al. [ 12 ] quantify the impact of policies on the probability of deforestation in the analyzed border zone at pixel level. They are used as average country-level effects in the CGE assessment. Wuepper et al. [ 12 ] define forest based on a minimum amount of tree cover detected by remote sensing, whereas the GTAP data differentiate between managed and natural forests, drawing on an older global land cover map. Managed forests are mainly estimated using accessibility criteria, such as distance to the road network. There are two other important aspects for the analysis. First, land expansion in the GTAP-AEZ module stems from managed land which includes agriculture and managed forests. And second, land expansion will not only imply loss of natural forests, but also reductions in other types of natural vegetation, such as savannas or shrubland. In many OECD countries, natural forest cover is limited and conservation policies mainly protect managed forests, when measured in protected area. In the GTAP-AEZ module, shocking the unmanaged forest would have small effects in such cases. Moreover, reducing the unmanaged forest exogenously in the GTAP-AEZ module according to the estimates will expand all types of managed land such that part of the lost unmanaged forest would be converted to managed one. The change in tree-covered area would be smaller than what the econometric analysis suggests. We therefore added two equations to our model of which the first forces a decrease in unmanaged forests in percentage terms according to the estimates. The second one superimposes the same relative decrease in managed forest land, by a closure swap with the share parameter in the CET land allocation function. In both cases, we restrict the change to 50% of the total managed respectively non-managed land not under tree cover. 5.4 SDG indicators The assessment draws on an indicator framework developed by Wilts and Britz [ 28 ] that quantifies indicators for 15 of the 17 SDGs based on the CGE model results. The coverage of the SDGs is slightly lower in this study as some relevant features of CGEBox such as the post-model microsimulation, a production factor split for irrigation water and a sectoral disaggregation of the fishery sector are not part of this assessment. This reflects the lower detail of the data base, necessary to cover the large number of countries. Only the SDG indicators closely related to changes in forest and agricultural land are discussed in this paper, while the others can be found in the annex (see Figure A.1-A.3). 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Britz","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-8532-3823","institution":"University of Bonn, Bonn, Germany","correspondingAuthor":true,"prefix":"","firstName":"Wolfgang","middleName":"","lastName":"Britz","suffix":""},{"id":557165164,"identity":"2615b79b-42ca-48fb-bb9e-da7eca51586a","order_by":1,"name":"Rienne Wilts","email":"","orcid":"https://orcid.org/0000-0002-0516-7227","institution":"Institute for Food and Resource Economics, University 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1","display":"","copyAsset":false,"role":"figure","size":7803091,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage changes in \u003cstrong\u003ea\u003c/strong\u003e forest area, \u003cstrong\u003eb\u003c/strong\u003e cropland area, \u003cstrong\u003ec\u003c/strong\u003eland productivity, \u003cstrong\u003ed\u003c/strong\u003e food price index, \u003cstrong\u003ee\u003c/strong\u003e real GDP per capita, and \u003cstrong\u003ef\u003c/strong\u003e GHG emissions as a result of a drop in forest loss protection policies * = the GHG emissions include carbon emissions from land use change that are depreciated over 25 years. Source: Model results.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8210376/v1/d6335bebf9b280e4bdc283f6.png"},{"id":97774608,"identity":"7d424dae-9032-4f9f-a5c1-49c5e1e7a05b","added_by":"auto","created_at":"2025-12-09 08:46:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82879,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of forest conservation policies on avoided deforestation. Source: based on Wuepper et al., [12].\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8210376/v1/b199e12979e5db8b71d1ce04.png"},{"id":97902846,"identity":"585617b4-a9a3-4595-a731-1e56390c118a","added_by":"auto","created_at":"2025-12-10 15:53:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6935681,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8210376/v1/9208855f-8956-450d-8a30-86700c755750.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Global forest conservation policies and the Sustainable Development Goals","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAgricultural and commodity-driven land expansion is a main cause of permanent logging and fire clearance, driven by growing demands for food and biomass due to population and income growth [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Forest conservation thus competes with food production on agricultural areas. But forests also contribute to food security directly by provision of food and indirectly by yield increasing ecosystem services [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Their conservation can also induce agricultural productivity gains by increasing land scarcity [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Deforestation comes along with several negative side-effects such as carbon release, reduced precipitation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and biodiversity losses through shrinking natural habitats and fragmentation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], linked to several of the UN\u0026rsquo;s Sustainable Development Goals (SDGs).\u003c/p\u003e\u003cp\u003eTo what extent socio-economic dynamics drive deforestation depends on different factors, such as expected land rents on cleared spots or costs of deforestation, and prominently the governance of land use change [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Forest conservation policies interact globally since effective protection of a country\u0026rsquo;s own forests can increase the country\u0026rsquo;s imports or decrease its exports of biomass, driving up land rents elsewhere. This can imply higher agricultural land expansion where no effective conservation policies exist or can demand their stricter enforcement. Accordingly, understanding the combined effect of existing national conservation policies, including their economic cost, requires a global analysis considering these feedback channels.\u003c/p\u003e\u003cp\u003eWhile a large literature focuses on the effectiveness of current forest conservation policies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], their global effect on markets and food prices has found limited attention. To advance here, we feed the empirical estimates of Wuepper et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] on avoided yearly deforestation by existing policies from 2001 to 2017/2018 into the global Computable General Equilibrium (CGE) model CGEBox [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This enables a what-if analysis to capture market-mediated effects across sectors and regions of existing conservation policies, and to quantify their impacts on various SDG-related indicators. Rooted in micro-economic theory and consistent physical and economic balancing, the CGE model provides a sound methodological framework to incorporate both direct and indirect effects. For example, when forests are protected, this constrains the area available for farming. However, through market mechanisms, this incentivizes farmers to become more productive on the existing agricultural areas, for instance, by increasing yield increasing inputs or investing into technology. Thus, the loss in output can be smaller than under the assumption that farmers do not adapt at all, and it is even possible that output increases [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This is just one example of the many complex dynamics that play out when forest conservation and markets interact [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eExisting literature that applied (CGE) models in the context of potential deforestation or climate change mitigation policies such as forest carbon payments or afforestation including REDD scenarios conducted analysis mostly at country level [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] partly in single-country models [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Existing global analysis used rather large regional aggregates [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] to quantify, for instance, impacts on land or food prices. These assessments are predominantly based on assumptions about afforested or protected areas, while our approach is based on empirically estimated effects of currently implemented policies. Our analysis provides policy relevant insights into the global market mediated costs and benefits of forest conservation policies; by covering more than 120 individual countries, remaining ones are aggregated to larger model regions as summarized in the Annex Table A.1. This global detail allows to capture the manifold interactions and regional differences of current conservation policies and their effects on multiple SDGs. No equivalent analysis on global economic effects of current forest conservation policies has been conducted so far.\u003c/p\u003e\u003cp\u003eThe current status of land cover and of the food system reflects many factors, including existing conservation policies. As we assess currently enforced policies, our experiment reduces exogenously in each nation the forest area, according to the estimates of effectively avoided deforestation. This isolates the impact of conservation policies by keeping all other drivers unchanged, such as population size, technology, carbon or trade policies, or dietary preferences. The additionally deforested land will be used for agriculture, and the simulation with the global economic model spells out the consequences on supply and demand, prices, trade and income along global value chains. In addition, it quantifies various indicators, such as Gross Domestic Products (GDP), Greenhouse Gas (GHG) emissions or resource extraction, globally and for each region. The underlying global interlinkages are addressed by simulating bi-lateral trade of products and services, alongside with regional consumption and production decisions. One additional novelty of this approach is the explicit modelling of unmanaged forest as a land use category for land conversion and their associated carbon stocks.\u003c/p\u003e"},{"header":"2 Results","content":"\u003cp\u003eSimulating a world without current forest protection policies results globally in a reduction of managed forests by around 1.5% (25.4 Mio ha) and of unmanaged ones by around 1.3% (34.6 Mio ha) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.a). These numbers suggest that without these policies, deforestation would have been twelve times higher than the average yearly observed between 2000 and 2020 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. For more than 70% of the countries, the avoided deforestation is smaller than 3%. Values beyond 10% are observed for 1% of the cases, only, predominantly located in Europe and Africa, see in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.a. Given the abundance of natural forest in Russia, Indonesia, and the Democratic Republic Congo, these countries are the top three in terms of avoided absolute deforestation, even if they do not stand out in relative terms.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe additional deforestation expands both pastures and crop lands, the resulting relative change for cropland is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.b. In countries with large forest areas relative to cropland, dropping conservation policies can imply very large relative changes in croplands, such as in Estonia, Rest of Oceania and Finland. Biomass production in sum is not changing much, with the largest expansion observed for paddy rice by 0.55%, followed by sugar cane with 0.4%. As the relative change in agricultural land exceeds these muted relative increases in biomass outputs, crop yields fall, as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.c. The limited output effects reflect that demand for biomass is in sum quite price inelastic, leading to a less intensive use of agricultural land. The related reduction of agro-chemicals per hectare is beneficial for biodiversity. Less intensive production is also observed for pasture land (Annex Figure A.1), where stocking densities decreases uniformly across the countries. However, the net biodiversity effect of the additional deforestation is likely negative, as natural forest and habitats have a higher species richness compared to agricultural lands (land sharing vs. land sparing). The additional biomass produced on the increased agricultural area is absorbed by value chains only at dropping prices. This benefits sectors in biomass related value chains as they (indirectly) face lower costs of biomass-based intermediates, while impacts on other sectors are negligible.\u003c/p\u003e\u003cp\u003eThese dynamics result in lower food prices (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.d), with slight positive effects for food security-related SDG indicators, such as nutrient consumption and food expenditure shares (see Annex Figure A.2). The food price decrease is marginal for most countries, with less than 0.5% for about 89% of the countries. The Democratic Republic Congo provides an exception with a 5% drop, driven by considerable cropland expansion, which reflects the large area of forest relative to agricultural land. Existing conservation policies are hence not threatening food security or causing hunger. Other changes related to production, consumption or prices of various goods and services are even smaller than for food.\u003c/p\u003e\u003cp\u003eThe additional deforestation results in a meagre increase of 0.02% in global real GDP, as a measure for the consumption possibilities of all goods and service. The maximum increases of around 1.2% and 0.9% are found for the Democratic Republic of Congo and Laos, respectively, see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.e. Countries in the global South which spend higher shares of their income on food tend to benefit more from additional agricultural land expansion if conservation policies would be removed. We find a few cases with quite muted GDP losses, not exceeding \u0026minus;\u0026thinsp;0.02%. These countries hence lose from the additional land expansion, or put otherwise, they actually gain from existing conservation policies. As GDP changes are quite small, economic costs of current forest conservation are low, also reflected by the other economy related SDG indicators (see Figure A.3).\u003c/p\u003e\u003cp\u003eEven if the impact on economic output is limited, we find globally a 0.06% increase in CO\u003csub\u003e2\u003c/sub\u003e and non-CO\u003csub\u003e2\u003c/sub\u003e emissions, both along value chains and by final consumption. This reflects that certain agricultural production processes are quite emission intensive, such as paddy rice or ruminant production. Their emissions increase in relative terms more than the rest of the economy under expansion of agricultural areas. Far more relevant for climate change mitigation are however the carbon stock losses from increased deforestation, amounting globally to 1.3*10\u003csup\u003e10\u003c/sup\u003e t of carbon lost. This is equal to about twelve years of current global emissions summarized in CO\u003csub\u003e2\u003c/sub\u003e equivalents [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. As deforestation policies preserve carbon stocks in existing forests and do not increase these stocks from year to year, the avoided loss in stocks cannot be directly compared to annual CO\u003csub\u003e2\u003c/sub\u003e and non-CO\u003csub\u003e2\u003c/sub\u003e emissions. As other studies, we therefore depreciate the carbon stock losses over 25 years (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.f). The related average yearly increase in GHG emission is still considerable, exceeding 10% for most countries. This shows the importance of avoided deforestation of existing conservation policies. Given their limited economic cost (0.02% in terms of GDP globally, less than 1% of GDP in 99% of the countries), conservation policies are hence a highly cost-efficient way to prevent further GHG emissions, besides other positive eco-system services such as maintaining biodiversity.\u003c/p\u003e\u003cp\u003eWith regard to other impacts, the biosphere-related SDGs (Figure A.1) show the strongest effects, followed by the society-related ones (Figure A.2), while the SDGs associated with economic aspects show the least ones (Figure A.3).\u003c/p\u003e"},{"header":"3 Discussion","content":"\u003cp\u003eThe combination of econometric evidence and global CGE analysis offers new insights in market-mediated impacts of forest conservation policies and allows to quantify related annual economy-wide opportunity costs. Our analysis suggests that these costs are rather limited. For a given global population and state of the economy, bringing additional land into production implies only very modest opportunities to expand global economic output. However, we did not consider long-term dynamics here to isolate the effect of the policies. Massive population and income growth [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] are expected in the upcoming decades, identified in the past as the main driving forces of deforestation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Without enhancing land productivity through technical progress, growing land scarcity is expected, increasing the pressure on natural vegetation. The consequences of this mechanism could already be observed in Sub-Saharan Africa over the last decades where high population growth and stagnating crop yields funneled massive expansions in agricultural land [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Baselines generated with the same CGE model as in the underlying assessment show strong land expansion over time due to socio-economic dynamics until 2050 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These could be reinforced by policies that replace fossil fuels by biomass [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Sustainable intensification of crop production which allows for higher crop yields and reduced meat consumption could hence be key strategies to support forest conservation policies [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCGE models require a large set of input data to parameterize their production, demand and other functions. The related estimates are often taken from literature covering a few sectors or regions, only, or are rather old. The simulated results, included land use changes and related indicators, depend on the model\u0026rsquo;s parameterization that determines its response in the policy experiment [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Besides parameters, data, including carbon contents and the land cover classification (such as the differentiation between managed and unmanaged forests) shape the outcome. Other studies have shown that these data might vary depending on the underlying assumptions regarding the definition of land use as \u0026lsquo;forest\u0026rsquo; or the average per hectare soil carbon [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] which affect the size of the effect, especially regarding GHG emissions, however not their direction and trends.\u003c/p\u003e\u003cp\u003eThe country specific policy effect estimates that are the foundation of our analysis are also subject to uncertainties. Our estimates are provided by Wuepper et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] who rely on a spatial difference in discontinuities design [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. It is well-known that this research design can have limited external validity, as causal identification is achieved very locally. For our modelling here, we extrapolate the causal estimates from national borders to the rest of each country. Wuepper et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] provide evidence that this works globally on average. However, at individual countries, border regions can be quite different from the non-border ones, which then only balances out globally, but can bias individual border estimates upwards or downwards.\u003c/p\u003e\u003cp\u003eThe avoided loss of carbon stocks of current conservation policy is considerable compared to current global yearly GHG emissions. These global findings reflect not only the estimated percentage of avoided deforestation by country, but also their absolute forest areas and locations. For instance, without conservation policy, deforestation is estimated to increase drastically in absolute terms in the Democratic Republic Congo, amounting to five times the deforestation observed in the last two decades. Given the carbon richness of this native forest, preventing deforestation and land use change in this country is pivotal. Forest conservation in countries with large forest areas does not only matter from the carbon stock perspective, but also for biodiversity. Seven of the ten countries with the highest absolute estimates of avoided deforestation are classified as megadiverse countries, due to their extraordinary species richness which largely depends on native land and in particular on forests.\u003c/p\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eWe conduct a global analysis of consequences of existing forest conservation policies on selected SDG indicators based on the combination of econometric quantification of their impact on land cover change and assessing these changes with global CGE analysis. Our comparative-static analysis for 2017 suggests that these policies have reduced croplands by 1.96% and pasturelands by 0.96%. Price and income inelastic demand for biomass limits the impact on global biomass production, such that global economic output is hardly affected, the same holds for income. The implicit cost of the policies amount to around 1.7 USD per capita globally. For other SDGs, especially impacts on global GHG emissions are relevant due to avoided carbon stock losses. Overall, the analysis underlines that forest conservation policies have relative limited costs in terms of income foregone and worsening of food security, but are an important part of GHG mitigation and biodiversity policies.\u003c/p\u003e"},{"header":"5 Methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e5.1 CGE model choice and set-up\u003c/h2\u003e\u003cp\u003eOur model set-up draws in its core on the widely used GTAP Standard Model Version 7 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] as implemented in GAMS by van der Mensbrugghe [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], integrated in the flexible and modular framework for CGE modelling CGEBox [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The GTAP Standard model applies the usual micro-economic assumptions used in CGE analysis, namely competitive markets for products and factors, utility maximizing final demanders and one aggregate cost-minimizing representative firm for each sector, operating under constant-returns-to-scale. Households own the production factors and allocate them to sectors such as to maximize revenues with no forward-looking behavior. Macro identities are added as constraints, such as investments equal savings, closed balances of payments and expenditures being equal to revenues for the different accounts.\u003c/p\u003e\u003cp\u003eThe model depicts bi-lateral trade based on the Armington assumption such that products are quality differentiated across regions. Combined with endogenous industry-by-industry demand, this captures domestic and international supply chains. Foreign savings are endogenous, driven by a virtual global investor who collects all savings and distributes them to the model regions such as to equilibrate expected returns to capital. This also implies endogenous balances of trade of each region as the second element of the closed balances of payments.\u003c/p\u003e\u003cp\u003eEnergy demands are measured in the Million Tons of Oil Equivalents (MTOE) and related demand equations employ the volume preserving Constant-Elasticity of Substitution (CES) functional form to balance energy in physical terms. Multi-layer CES functions for intermediate and final demand from GTAP-E/GTAP-Power [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] depict substitution between energy carriers and capital. Further CES nests model substitution possibilities between different crops in animal feed and the food industry, between a feed aggregate and land for livestock and between chemicals and land for crops. Multiple CES nests are also used for final consumption to better depict cross-price effects.\u003c/p\u003e\u003cp\u003eThe production factors captured in this configuration of CGEBox are land, labor (differentiated in skilled and unskilled), natural resources and capital. To improve the representation of land in the model, the GTAP-AEZ component is incorporated, drawing on Lee [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. It depicts competition between land use sectors at the level of so-called Agro-Ecological Zones (AEZs), to consider that land is spatially immobile, and offers a quantification of physical land use in hectares, including for unmanaged land. Instead of the usual representation with one Constant-Elasticity-of-Transformation (CET) function which distributes a total land stock to all land uses in many CGE models, the GTAP-AEZ component in CGEBox uses a multi-layer CET set-up to better reflect substitution possibilities between different land use categories. Specifically, it employs a so-called Additive CET functional form [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] which ensures physical balancing in hectares. The available data also include carbon stocks for the different land use categories (i.e., pasture, cropland, forest and native land uses) at a per hectare level, allowing for the assessment of carbon stock changes by land cover changes. In contrast to labor and capital, natural resources (i.e., oil wells, gas fields, mines, fish stocks etc.) are considered as sector specific and thus immobile.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Data\u003c/h2\u003e\u003cp\u003eThe analysis builds on the latest GTAP-Power Data base 11c [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] with the base year 2017. The dis-aggregation of the single electricity sector into different power generation sectors and a power distribution and transformation sector allows to consider biomass use in power generation. Due to the focus of the study, most non-transport and non-food related service, and manufacture sectors not related to biomass use are aggregated, such that the model depicts 53 sectors instead of the 76 available in the data base. In order to exploit the detail on regional land use changes, we keep most of the regional detail of the data base which covers 160 single countries and regional aggregates, resulting in an analysis with 124 model regions. Aggregated countries are especially those for which no results are directly available from the econometric analysis, such as for regions with limited tree cover or where the econometric results do not predict in impact of policies on past tree cover change, found, for instance, in Northern Africa, the Arabian Peninsula and Central Asia, or islands with no border to another country.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Scenario design and policy effect\u003c/h2\u003e\u003cp\u003eTo quantify the effect of the forest conservation policies at global scale, we benchmark the CGE model on the described data base and then conduct a comparative static analysis, i.e., assume a removal of all conservation policies in the setting of the base year 2017. This configuration enables to disentangle the policy effect from other dynamics and fits the estimation of the input data incorporated from Wuepper et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. We differentiate the capital stock into old and new as non-perfect substitutes, considering six years of depreciation, where new capital is linked to investments and thus reflects capital accumulation. The findings from Wuepper et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] are available for 105 countries, which are all countries that share land-borders with other countries and have enough tree cover near these borders to make the use of the difference in discontinuity design sufficiently reliable [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThere are thus no estimates for island states, such as Australia, New Zealand, Philippines or Great Britain. For a few countries, there are small, negative effects estimated for existing policies on forest conservation, which have been set to zero for this analysis. The policy effect underlying this analysis is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e with grey representing missing values.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe estimates from Wuepper et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] quantify the impact of policies on the probability of deforestation in the analyzed border zone at pixel level. They are used as average country-level effects in the CGE assessment. Wuepper et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] define forest based on a minimum amount of tree cover detected by remote sensing, whereas the GTAP data differentiate between managed and natural forests, drawing on an older global land cover map. Managed forests are mainly estimated using accessibility criteria, such as distance to the road network. There are two other important aspects for the analysis. First, land expansion in the GTAP-AEZ module stems from managed land which includes agriculture and managed forests. And second, land expansion will not only imply loss of natural forests, but also reductions in other types of natural vegetation, such as savannas or shrubland.\u003c/p\u003e\u003cp\u003eIn many OECD countries, natural forest cover is limited and conservation policies mainly protect managed forests, when measured in protected area. In the GTAP-AEZ module, shocking the unmanaged forest would have small effects in such cases. Moreover, reducing the unmanaged forest exogenously in the GTAP-AEZ module according to the estimates will expand all types of managed land such that part of the lost unmanaged forest would be converted to managed one. The change in tree-covered area would be smaller than what the econometric analysis suggests. We therefore added two equations to our model of which the first forces a decrease in unmanaged forests in percentage terms according to the estimates. The second one superimposes the same relative decrease in managed forest land, by a closure swap with the share parameter in the CET land allocation function. In both cases, we restrict the change to 50% of the total managed respectively non-managed land not under tree cover.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e5.4 SDG indicators\u003c/h2\u003e\u003cp\u003eThe assessment draws on an indicator framework developed by Wilts and Britz [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] that quantifies indicators for 15 of the 17 SDGs based on the CGE model results. The coverage of the SDGs is slightly lower in this study as some relevant features of CGEBox such as the post-model microsimulation, a production factor split for irrigation water and a sectoral disaggregation of the fishery sector are not part of this assessment. This reflects the lower detail of the data base, necessary to cover the large number of countries. Only the SDG indicators closely related to changes in forest and agricultural land are discussed in this paper, while the others can be found in the annex (see Figure A.1-A.3). A challenge provides the comparison between yearly GHG emissions from combustion of fossil fuels and from non-CO\u003csub\u003e2\u003c/sub\u003e process emissions with the one-time effect of the carbon stock changes resulting from the simulated changes in land cover. We address this by depreciating the carbon stock changes over 25 years when aggregating it with yearly GHG emissions, such as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef, following the logic discussed by Searchinger et al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eWuepper acknowledges funding by European Research Council (ERC), Grant No. 101075824 (ERC Starting Grant LAND-POLICY), and the German Research Foundation (DFG), under Germany\u0026apos;s Excellence Strategy EXC 2070, Grant No. 390732324 \u0026ndash; PhenoRob.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArmenteras D, Espelta JM, Rodr\u0026iacute;guez N, Retana J (2017) Deforestation dynamics and drivers in different forest types in Latin America: Three decades of studies (1980\u0026ndash;2010). Glob Environ Change 46:139\u0026ndash;147\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCurtis PG, Slay CM, Harris NL, Tyukavina A, Hansen MC (2018) Classifying drivers of global forest loss. 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Use of US croplands for biofuels increases greenhouse gases through emissions from land-use change. \u003cem\u003eScience\u003c/em\u003e, 319(5867), 1238\u0026ndash;1240\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Forest Conservation Policies, Sustainable Development Goal, Computable General Equilibrium Model, Food security, Border discontinuity","lastPublishedDoi":"10.21203/rs.3.rs-8210376/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8210376/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eForest conservation is widely heralded as a cost-efficient climate change mitigation strategy. Additional benefits of forest conservation include a range of ecosystem services including biodiversity conservation. But besides costs of governance and conservation payments, also opportunity costs should be considered, such as a potential decline in food production when protected areas are not available for agriculture. We therefore combine recent econometric estimates of impacts of national-level forest conservation policies with a Computable General Equilibrium model to assess existing forest conservation policies. We find that these policies cost a small fraction of gross domestic product and have quite limited effects on food security. As desired, they prevent large amounts of greenhouse gas emissions, mostly in form of prevented carbon stock losses, mitigating around twelve times the green house gases emitted globally in 2023. They also contribute considerably to biodiversity protection, despite increasing the intensity of land management. Given their small economic and social costs and wide-ranging environmental benefits, existing forest conservation policies are even more cost effective than previously thought.\u003c/p\u003e","manuscriptTitle":"Global forest conservation policies and the Sustainable Development Goals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-09 08:46:25","doi":"10.21203/rs.3.rs-8210376/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b02c676b-9309-489d-a13b-6d9fff2389a7","owner":[],"postedDate":"December 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59299036,"name":"Earth and environmental sciences/Environmental social sciences/Climate-change mitigation"},{"id":59299037,"name":"Earth and environmental sciences/Environmental social sciences/Environmental economics"}],"tags":[],"updatedAt":"2025-12-09T08:46:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-09 08:46:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8210376","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8210376","identity":"rs-8210376","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}