Potential of different governance mechanisms for achieving Global Biodiversity Framework goals

Potential of different governance mechanisms for achieving Global Biodiversity Framework 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 Potential of different governance mechanisms for achieving Global Biodiversity Framework goals Pablo Jose Negret, Victor Rincon, Sidney Novoa, Marvin Quispe, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4170734/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 The Kunming-Montreal Global Biodiversity Framework includes a target of 30% of land protected by 2030 and refers to other effective area based conservation measures (OECMs) as complementary to PAs, but robust evaluations of the effectiveness of governance mechanisms that could act as OECMs in preventing forest loss and carbon emissions remain sparse. Here we assessed the impact of PAs and two potential OECMS: Indigenous Lands (ILs), and Non-Timber Forest products Concessions (NTCs) on forest loss and its associated carbon emissions in the Peruvian Amazon from 2000 to 2021. We also assessed two governance mechanisms with a commercial extractive use, Logging (LCs) and Mining Concessions (MCs). We used a robust before–after control intervention study design, with statistical matching, to account for the non-random spatial distribution of deforestation pressure and the governance mechanisms analysed. PAs were the most effective, having avoided 88% of the expected forest loss, followed by NTCs (64%) and ILs (44%). LCs also reduced expected forest loss by 29%, while MCs increased expected forest loss by 24%, showing that extractive governance mechanisms can have marked differences in their impact to forest cover. Our study provides evidence of long-term positive impacts of potential OECMs and other mechanisms at preventing forest loss and reducing carbon emission. This information is key to more effectively achieve targets from the Kunming-Montreal Global Biodiversity Framework and the UN Framework Convention on Climate Change. Earth and environmental sciences/Environmental sciences/Environmental impact Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation Earth and environmental sciences/Ecology/Conservation biology Earth and environmental sciences/Environmental social sciences/Climate-change impacts/Governance Other area based conservation interventions Deforestation Peru Amazon Indigenous land forest concessions mining concessions carbon markets selective logging Figures Figure 1 Figure 2 Figure 3 Introduction Tropical forests are amongst the most biodiverse ecosystems globally 1,2 . They also provide multiple crucial services to humanity at local and global scales 3,4 , including storing and sequestering large amounts of atmospheric carbon, which is key to directly regulate climate 5 . Despite this, the loss and degradation of tropical forest has increased over time 6,7 , and it has been shown to contribute to up to 20% of the world’s greenhouse gas emissions 5,8 . Protected areas (PAs) have always been regarded as a fundamental tool for the protection of forest ecosystems and the ecosystem services they provide. Now more than 15% of global land is under a certain type of protection regime 9 . Despite this, evidence shows that PAs are not always effective at preventing deforestation. One third of PAs globally are under intense human pressure 10 , half of protected forests have low or medium integrity 11 and 3% of the forest inside PAs was lost in the first decade of the 20th century 12 . Additionally, socioeconomic conflicts may arise where PAs are established, due to restrictions in access to natural resources and territory, and those socioeconomic aspects must be taken into account when practical conservation decisions are taken 13 . Furthermore, in some circumstances, strict protection might not be appropriate or feasible, and other conservation actions can be more suitable 14 . Due to the socioeconomic conflicts that might derive from PAs establishment under certain circumstances and that PAs alone are not enough to meet the commitments of the Kunming-Montreal Global Biodiversity Framework 15 , calls have mounted for the identification of other effective area-based conservation measures (OECMs) that can deliver biodiversity outcomes to complement those provided by PAs 9,16 . Indigenous management of land, for example represents a form of governance that has proven to have positive impacts on biodiversity. There is growing evidence showing that indigenous lands (ILs) can be effective at reducing deforestation under certain management conditions 17–19 . However, there is a need for a better understanding of the interactions between deforestation trends and ILs management in different regional and local contexts 18,20 . Forest concessions, a widely used contractual arrangement, in which a government temporarily provides public forested land access, management and exclusion rights to non-government actors, is another governance mechanism that can provide positive outcomes for forest conservation while generating a potential source of income for local communities 21 . There are at least 122 million hectares of Forest Concessions in West and Central Africa, Latin America and Southeast Asia 21 , with different uses ranging from industrial extraction, including timber extraction or logging (logging concessions, LCs) 22–24 , to more sustainable uses, like agroforestry 25 , tourism and conservation 26 , hereafter referred to as Non-Timber Forest Product Concessions (NTCs). Their effectiveness in reducing deforestation varies depending on the type of concession and the political characteristics in the country they are established, but there is prevalent support for positive outcomes under adequate management and supportive political circumstances 22,26,27 . Rigorous evaluations of the effectiveness of potential OECMs and other alternative governance mechanisms in preventing forest loss, particularly relative to PAs, remain relatively sparse 28–30 . For OECMs the main limiting factor is that it has not yet been possible to track their extent systematically 9 , and some countries are still in the process of identifying OECMs within their territory 29 . On the other hand, studies assessing the impact of PAs preventing deforestation, have often failed to account for their non-random location. Some studies, for example, simply compare areas that are protected to all the unprotected area present in the landscape 12,31 , or to neighbouring buffers 32 . This can cause bias in the estimated impact of PAs as they are non-randomly distributed across space tending in general to be located towards land that, if unprotected, is less likely than average to be cleared 14,33,34 . Identifying governance mechanisms that can be potential OECMs is needed for efficient conservation planning 9,29,35 . The Kunming-Montreal Global Biodiversity Framework includes a target of 30% of land protected by 2030 15 . On the other hand, under the UN Framework Convention on Climate Change 36 , countries revise their nationally determined contributions to reduce emissions every five years. Both conventions cover commitments to reduce deforestation and its associated emissions through protection of natural systems. It is then crucial to understand the contribution that PAs and potential OECMs have towards achieving these commitments and how they can complement each other to have the best conservation outcomes 29 . Here we assessed the impact of government-managed PAs and other governance mechanisms on forest loss and carbon emissions using a robust before–after control intervention study design, with statistical matching. We used the Peruvian Amazon as our study area (Fig. 1). The country is megadiverse 37 , has multiple land tenure types 30 and its Amazon region accounts for 60% of the country’s land mass and for 90% of its forest 38,39 . Additionally, the Peruvian Amazon is experiencing high rates of forest loss and degradation 30,31,40 . We used statistical matching as this methodology evaluates the effectiveness of conservation interventions by accounting for the non-random spatial distribution of those interventions and of deforestation risks 18,41–43 , comparing treatment sites to matched control ones (i.e., areas with no intervention with characteristics similar to treatment sites) 33,44,45 . A previous study in the Peruvian Amazon found that between 2006 and 2011 PAs, ILs and Conservation Concessions avoided deforestation and degradation compared to analogous areas in the unprotected landscape 30 , and another study, found a positive association between titling of ILs and the reduction of clearing and forest disturbance between 2002 and 2005 46 . While these studies suggest a positive impact of PAs, Conservation Concessions and ILs on the reduction of forest loss, they did not assess the impact of other governance regimes with conservation potential, like agroforestry, reforestation or tourism concessions, or the effect of forest loss on carbon emissions. Furthermore, the timeframe of their analysis is too short to provide an overview of the longer-term impacts of these governance mechanisms on forest loss. Particularly in this study we assessed the impact of government-managed PAs, and two potential OECMS: Indigenous Lands (ILs), and Non-Timber Forest Products Concessions (NTCs) on forest loss and its associated carbon emissions in the Peruvian Amazon from 2000 to 2021. We also assessed two other governance mechanisms with a mostly commercial extractive use, Logging Concessions (LCs) and Mining Concessions (MCs), as a point of contrast to assess how different governance mechanisms impact forest loss. Mining concessions in particular, have been strongly associated with forest 30,47,48 and biodiversity loss 49 in the tropics. Methods Study Area and governance mechanisms analysed : We used the Peruvian Ministry of Environment delimitation of the Peruvian Amazon 50 , which includes low elevation areas but also Amazon Andes foothills. We used the information on spatial distribution and establishment dates of government-managed PAs defined by the Peruvian Protected Areas’ Service - SERNANP 51 . For the spatial distribution of ILs we made a request of information to the Ministry of Culture 52 . Details on ILs creation dates were obtained from the database of Indigenous and Native Peoples 53 . For the spatial distribution of Forest Concessions we used information from the National Forest and Wildlife Service 54 and from a request of information to the regional governments of Madre de Dios, Loreto and Ucayali. When there were discrepancies with SERFOR, information from regional governments was used, as these entities have a more detailed record of their jurisdiction than national entities. Details on Forest Concessions establishment dates were obtained from the Forestry and Wildlife Resources Supervision Agency 55 . Information on MCs spatial distribution, start and closure dates were obtained from the mining and geological geoportal from the Ministry of Energy and Mines 56 . We divided the Forest Concessions into two groups taking into account the purpose of the concession and the definition of potential OECMs 57 , 58 . We aggregated the concessions that in principle do not use timber products as a source of income, including conservation, ecotourism, reforestation, restoration and agroforestry into a single category of Non-Timber Forest Product Concessions (NTCs). These concessions meet the criteria of the definition of potential OECMs 57 , 58 . Logging concessions (LCs) on the other hand were included in a separate group as their main objective is commercial extraction of wood. This does not align with criterion six required for their identification (Jonas et al 2023) and criterion C for their recognition and reporting as OECMs 57 . These two criteria specify the impossibility of sites that are subject to environmentally damaging industrial-scale activities to be classified as OECMs. Mapping forest loss and carbon emissions : We used the map of Amazon forest cover for 2000 and the one of Amazon forest loss from 2000 to 2021 developed by the Peruvian Ministry of the Environment and the Ministry of Agriculture and Irrigation 59 . These maps have a resolution of 30 by 30 meters where each pixel is either forest or no forest in the map of forest cover for the year 2000 and have a value from 1 to 21 in the map of forest loss. Each value represents the year when the forest cover on that pixel was lost. The classification of a pixel into either forest or no forest is based on different Landsat images of the Peruvian Amazon for each year, which are then processed at a pixel level. The accuracy of these maps was 92.2% for forest loss and 99.5% for no change 40 , 60 . We then quantified the extent and rate of forest loss for each year between 2000 and 2021 and the total cumulative loss between 2000 and 2021. This was done for the whole basin and individually for each governance mechanism assessed. We used the high-resolution carbon per hectare density estimations for Peru, generated by Asner et al. (2014) 61 , to calculate the metric tons (Mg) of carbon by forest pixel. We assumed that if a pixel was deforested, all of its carbon was emitted into the atmosphere. Then we summed the amount and proportion of carbon emissions by year and cumulative between 2000 and 2021 for the whole landscape, and for each governance mechanism as well as for the selected control and treatment pixels after the matching analysis. In this study, “carbon emissions” refers to emissions “committed” at the time of disturbance or clearing, noting that there may be a time lag until they are “realized”. Cofounding factors : To account for the determinants of deforestation pressure and the location of the different governance mechanisms assessed, we used a suite of biophysical and socioeconomic cofounding factors that have been found to be associated with the non-random spatial distribution of these interventions and of the distribution of deforestation risk in the Peruvian Amazon and other tropical landscapes 30 , 42 , 43 , 62 . These cofounding variables were estimated at the pixel level and included in the matching analysis. These included measures of forest accessibility (i.e. distance to roads, rivers and previous deforestation), anthropic pressure (i.e. distance to settlement, travel time to cities, human population density), suitability of conversion to agricultural land (i.e. precipitation, precipitation seasonality, ecoregion, elevation and slope) and the socio-political environment (i.e. administrative region) (Table 1). Spatial information that was at a different spatial resolution was resampled to the forest pixels’ resolution of 30 by 30 meters. Statistical Matching : We used forest loss between two time periods as alternative outcome variables for our matching analysis in order to assess the robustness of our results. Forest loss by pixel between 2000 and 2021 and forest loss by pixel between 2005 and 2021. We excluded governance mechanisms that were established after 2005. Areas of overlap, and other types of governance mechanisms with conservation potential that were region-specific were also excluded. Also, concessions that stopped operating before 2015 were excluded. To avoid the potential spill-over effects, particularly leakage 44 , 63 , we excluded the pixels within a buffer of 1 km (NTCs, LCs, MCs and ILs) and 5 km (PAs) around treatment areas 30 . In order to avoid autocorrelation we generated a set of random samples at least 500 m apart from each other for each governance mechanism and for the potential control units 42 , 64 . Control units were defined as pixels which were not assigned to any of the governance mechanisms assessed in this study. To assess the impacts of the governance mechanisms (treatments) on forest loss we used a before–after control intervention study design, with matching analysis, creating artificial control groups for each treatment unit (pixel) while controlling for differences in observed covariates 43 , 65 . For each pixel, we extracted the values of the cofounding variables associated with deforestation and the governance mechanisms’ location. This was done both for the treatment group (each governance mechanism pixel) and the potential control group (pixels that where not classified under any of the assessed governance mechanism). To determine the best minimum set of cofounding variables, we first ran a binomial logistic regression model using each governance mechanism as the response variable and the cofounding factors as predictor variables. Then we ran a multicollinearity test to the coefficients of the covariates of each governance mechanism model. Through this analysis, we identified groups of variables with a collinearity higher than 0.65. The variable with the lowest Variance Inflation Factor (VIF) for each group was retained in the model 30 , 66 . We then used 'backward' and 'forward' techniques in an iterative stepwise process for model selection, to identify the optimal model. The model that achieved the greatest reduction in the Akaike’s Information Criterion (AIC) was selected 30 (Supplementary Information 1). For this we used the leaps package 67 in R (version 3.5.1) software 68 . The variables from the best model were used in the subsequent analyses and for the matching procedure for each governance mechanism. We then used the MatchIt package 69 in the R statistical software to pair treatment and control pixels with similar values for each of the covariates 68 . We used Propensity Score Matching to create a statistically balanced counterfactual sample to evaluate the effect of each governance mechanism on forest loss. Despite its limitations, Propensity Score Matching remains the most widely used matching approach 30 , 42 , 43 , 65 . We matched each treatment observation to a unique control observation to ensure each governance mechanism pixel was paired with only one control pixel that had not been matched previously. Pixels could be reselected as control samples for each independent governance mechanism analysis. Matched pairs of pixels had to be within 0.25 standard deviation of the propensity scores 43 , 70 . As part of the diagnostic statistics we calculated the index of covariate imbalance for every matching analysis for each governance mechanism to assess the robustness of the matching analysis reducing bias in the control units (Supplementary Information 2a). Absolute scores > 25% are considered an indication of a possible imbalance for that specific variable 43 , 71 . Results Deforestation in the Peruvian Amazon and the different governance mechanisms Annual deforestation over the last 20 years in the Peruvian Amazon almost doubled, from 840 km² in 2001 to more than 1,400 km² in 2021. Annual deforestation has been increasing, especially since 2006. There have been two notorious increases in yearly deforestation, one in 2005 and another in 2020 (Fig. 1 a & b). In relation to the governance mechanism, ILs had the highest absolute yearly forest loss followed by LCs (Fig. 2a). In proportional terms, MCs showed the highest yearly and accumulated loss since 2000 with a total of 21% of their forest cover lost in the last 20 years. LCs experienced the second highest cumulative loss with 4% of the forest lost in the last 20 years. On the other hand, PAs showed the lowest yearly and cumulative loss with 0.6% of the forest lost in the last 20 years (Fig. 2 b & c). Carbon emissions in the Peruvian Amazon and the different governance mechanisms Carbon emissions in the Peruvian Amazon have consistently increased in absolute and in percentage terms since 2000. This aligns with the increase in deforestation observed over the past 20 years (Fig. 1. a-c). Particularly in 2020 there was a high net increase in yearly emissions for the Peruvian Amazon (Fig. 2 a). This increase can also be seen in proportional terms (Fig. 2 b), and is also observed in ILs, LCs and MCs (Fig. 3). The governance mechanisms with the highest yearly net carbon emissions were ILs, followed by LCs (Fig. 3 a). MCs had the highest proportional carbon emissions yearly and cumulative to the stock in 2000, while PAs had the lowest (Fig. 3 b & c). Robustness of the matching analyses Most covariate imbalances between the treatment and control units were reduced through the matching analysis. For ILs, LCs and MCs these reductions meant that all the variables had indices of covariate imbalance below 25% post matching. For PAs, only the variable distance to settlements of more than 10 people in 2017 had an index of imbalance of 49% post matching. However, this was a reduction from 74% pre matching. For NTCs, 16 variables had indices of covariate imbalance below 25% post matching. Three had indices higher than 25% post matching, but for those variables these values implied reductions of 10% or more in covariate imbalance post matching. For two covariates, there was an increase in covariate imbalance post matching (Supplementary Information 2a). Overall, there was notable improvements reducing bias in the control units through the matching analysis (Supplementary Information 2a, 3a & 4a). Consistent results were obtained when using forest loss between 2005 and 2021 for the analysis (Supplementary Information 2b, 3b & 4b). Impact of the different governance mechanisms on forest and carbon loss Our results show that PAs, ILs, NTCs and LCs significantly decrease forest loss compared to the matched control areas. PAs were the most effective in preventing forest loss, avoiding 88% of expected loss, followed by NTCs (64%), ILs (44%), and LCs (29%). On the other hand, MCs showed a 24% increase in expected forest loss, compared to matched control areas, although the increase was not significant (Fig. 2d). Consistent results were found when using forest loss between 2005 and 2021 for the matching analysis (Supplementary Information 5). The median carbon density by pixel was highest for forest in NTCs, followed by LCs. The lowest median carbon density was for MCs. The median carbon density of the pixels without any governance mechanism designation was higher than those of all the governance mechanism except for NTCs and LCs. However, none of these differences in median carbon densities were statistically significant (non-parametric Kruskal-Wallis test, p value; PAs = 0.17, ILs = 0.62, CCs = 0.35, LCs = 0.38, MCs = 0.49). There was a high dispersion in the carbon density values by pixels and an overlap in this dispersion between all of the governance mechanisms (Fig. 2e). Finally, with regards to carbon emissions represented by the loss of forest, PAs, ILs, NTCs and LCs had lower emissions than matched control areas. This decrease was 88% for PAs, 65% for NTCs, 42% for ILs and 20% for LCs. On the other hand, MCs had a 35% increase in carbon emissions compared to the matched control areas (Fig. 3d). Consistent results were found when using forest loss between 2005 and 2021 for the matching analysis (Supplementary Information 5). Discussion In our study of the Peruvian Amazon we found that 4% of the forest has been lost in the last two decades. Putting this into perspective, until today the Brazilian Amazon has lost 20% of the historical forest cover 72 . However, PAs and other governance mechanisms assessed here have played a significant role in avoiding an even stronger decline of forest cover in the region. PAs experienced nine times less forest loss than similar areas without protection, while NTCs experienced three times less and ILs two times less losses. These three types of governance mechanisms cover 17.3, 1.3 and 20% of the Peruvian Amazon’s area, respectively, and their impact on the region´s deforestation rate is quite remarkable. Identifying potential OECMs that are effective in preventing biodiversity loss, assessing how they perform in comparison to PAs and evaluating in which circumstances they can deliver biodiversity outcomes to complement those provided by PAs is fundamental for advancing the goal of encompassing other approaches to conservation beyond formally designated PAs 9 , 28 , 29 and to achieve Target 3 of the Kunming-Montreal Global Biodiversity Framework 15 . Our results clearly show that ILs and NTCs are governance mechanisms that achieve long term in situ conservation of biodiversity, thus meeting criterion six and seven required for their identification as OECMs 58 and criterion B and C for their recognition and reporting 57 . These criteria specify that potential OECMs are expected to have a type of management that deliver long-term effective in situ conservation of biodiversity. The contribution of these governance mechanisms to regional and global conservation goals should be acknowledged and conservation funding should be channelled to maintain or increase their contributions to conservation as well as to have a better understanding of under which circumstances they can provide the best conservation outcomes. The forest that is retained by any governance mechanism also stores large amounts of atmospheric carbon, which is key to climate regulation 73 . In our study we found that PAs avoided 88% of the carbon emissions expected in the area due to forest loss, while NTCs and ILs avoided 65% and 42% respectively. The estimation of the amount of carbon emissions avoided is a key measure to understand the role of these governance mechanisms in carbon cycles and regional climate regulation 9 , 73 . Additionally, robust estimates of the impact of different governance mechanisms on carbon retention is crucial for the acquisition of funding for carbon payments as it is a requirement for satisfying Monitoring, Evaluation and Reporting (MER) programs to access REDD + and other funding streams (e.g., the Global Environmental Facility and the World Bank) 42 , 74 . Over the last two decades, ILs avoided 54% of the forest loss and 51% of the carbon emissions expected in those areas. These results show that ILs can provide important conservation benefits in terms of preventing forest loss and reducing carbon emissions. This aligns with previous research conducted at the Amazon level 17 , 30 , 75 and globally 18 . One fifth of the Peruvian Amazon is under Indigenous management, the highest proportion of all the governance mechanisms assessed. This in part explains why these territories had the highest absolute loss of forest. Due to the large extent of these territories and the potential to improve their effects on protection of forests, it is key to support Indigenous Communities in the development of financial mechanisms that allow them to receive income from the forest through activities that do not harm biodiversity. It is also important to support Indigenous Communities to further improve the governance of their communal land, especially in reference to activities potentially harmful to biodiversity, such as illegal mining and crops cultivation and deforestation 76 – 78 . As the influence that Indigenous land management has on the regional trends of deforestation and carbon emissions is substantial, it is crucial to support the claims by Indigenous Peoples in the Amazon and globally, who frequently advocate for more recognition on their contributions to conservation and active participation in environmental and forestry policy 18 , 76 . Acknowledging their contribution through stronger participation in environmental policy-making and through the provision of funding to support their manifold approaches to management is necessary. Also, the identification of the specific management practices that are effective to prevent unintended deforestation is crucial so that they can be scaled. We found that LCs avoided 29% of the deforestation and 20% of the expected carbon emissions in the last twenty years. These concessions constitute a considerable proportion (8.1%) of the Peruvian Amazon area. While LCs do not meet all the criteria to be defined as OECMs, our results show that they could contribute to other conservation goals like target 10 of the Kunming-Montreal Global Biodiversity Framework 15 , which aims to ensure the sustainable management of forestry, in particular through the sustainable use of biodiversity. While our results may be counterintuitive, as the main purpose of these concessions is to extract wood, they support findings from previous research showing that these concessions can have a positive impact on reducing forest loss, specially the certified ones 30 , 79 – 81 . The establishment of this type of land use could prevent widespread illegal logging activities due to an incentive to defend forest assets, and timber profits, by excluding other illegal and legal actors interested in extractive activities (i.e., logging, mining, land-grabbing) 80 , 81 . However, it is noteworthy that despite of this benefit, logging also has a negative impact on the local ecosystem, as the extraction of trees degrades the forest 23 , 82 . Logging Concessions (LCs) provide economic benefits from the extraction of wood 79 , 81 . However, in these concessions there can be other economic activities such as agroforestry and tourism, which could increase the economic benefits these landscapes provide to the local population, with a comparatively low impact on the ecosystems than other land uses like large-scale agriculture or mining. By turning LCs into multifunctional Forest Concessions, where the management party develops not only wood extraction but also a range of other economic activities (i.e., tourism, conservation, research), there could be a more sustainable and diversified regional economy. However, verification and monitoring has to be enforced for these schemes to have benefits for people and nature. There is evidence that shows that the effectiveness of LCs in preventing forest loss is higher in areas with higher deforestation pressure 80 . More studies on LCs are needed to understand what factors and which management conditions foster positive impacts on reducing forest loss. Furthermore, we need a better understanding of how the extraction of wood affects the overall biodiversity and carbon density 23 , 79 , 82 . Forest loss in the Peruvian Amazon has been increasing through time, especially from 2006 to 2016. It is crucial to monitor these changes in forest loss along the years as well as the abnormal increases in yearly deforestation, to understand which factors drive them, and to identify how they can be prevented or managed. The 2005 increase in deforestation in the Peruvian Amazon coincides with a particularly dry year, which had three times more fires than the previous year and twice more fires than the posterior one 83 . On the other hand, the 2020 increase overlaps with the implementation of confinement measures to reduce COVID-19 in the country, which reduced the capacity for enforcement and control by the authorities and permitted the appearance of new logging roads as well as increases in illegal logging and mining activities 84 . Our results show that there are certain years, like 2005, with high forest loss that do not show congruent patterns in relation to carbon emissions, on the other hand there are years, like 2019 and 2021, with high carbon emissions in relation to the deforestation observed that year. Factors like the construction of roads may be related to these differences. The construction of the Interoceanic Highway which connects Brazil and Peru, made primary forest more accessible for selective logging and deforestation 85 , 86 . As primary forest is known to be more dense than secondary or disturbed forest 87 , 88 , these road construction events through dense forest can increase disproportionately the carbon emissions. The factors that influence the increase or decrease of carbon emissions, apart from the loss of forest, need to be better studied as these factors can have significant impacts for carbon circles and regional and global climate regulation. The Kunming-Montreal Global Biodiversity Framework includes a target of 30% of land protected by 2030 and refers to OECMs as a complementary conservation approach to PAs 15 . Our study is one of the first to provide robust evidence of long-term positive impacts of multiple types of governance mechanisms for the conservation of biodiversity. Some of them with the potential to be classified as OECMs. While our results show that PAs are the most effective measure preventing forest loss and carbon emissions, we also found that there are several other governance mechanisms that can deliver conservation benefits at different extents and with different management characteristics. To allow nations to more effectively achieve the different targets from the Kunming-Montreal Global Biodiversity Framework and the UN Framework Convention on Climate Change 36 , it will be crucial to (i) better understand how different types of governance mechanisms operate and under which circumstances they can provide the most beneficial conservation outcomes, and (ii) strengthen their management through funding mechanisms specific for each governance mechanism. Declarations Author Contributions: P.J.N., A.V., M.S. and J.G.Z. designed the research. P.J.N., S.N. and M.Q. processed the data. P.J.N. and V.R. performed the analysis. P.J.N. wrote the initial draft. P.J.N, V.R., S.N., M.Q., A.V., G.F., T.A., M.S., J.S. and J.G.Z. reviewed and edited the manuscript. Acknowledgements: We would like to acknowledge the people from the Asociación para la Conservación de la Cuenca Amazónica (ACCA) and from the Land Systems and Sustainability Transformations research team for helpful discussion on the interpretation of the results. This study contributes to the strategy and goals of the Global Land Programme ( www.glp.earth ). References Pillay, R. et al. Tropical forests are home to over half of the world’s vertebrate species. Frontiers in Ecology and the Environment 20 , 10–15 (2022). Lewis, S. L., Edwards, D. P. & Galbraith, D. Increasing human dominance of tropical forests. Science 349 , 827–832 (2015). Mori, A. S., Lertzman, K. P. & Gustafsson, L. Biodiversity and ecosystem services in forest ecosystems: a research agenda for applied forest ecology. Journal of Applied Ecology 54 , 12–27 (2017). Strand, J. et al. 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Nature Ecology and Evolution 2 , 599–610 (2018). Tables Table 1 Variables used for the matching analysis. Variable Description Variable Name Year Resolution Source Annual Precipitation Annual_Prec historical 30 m https://www.worldclim.org/ Precipitation Seasonality Prec_Seas historical 30 m https://www.worldclim.org/ Distance to nearest previous deforested area Dis_Def 2000 30 m Ministerio del Ambiente (MINAM) & Ministerio de Agricultura y Riego (MIDAGRI) Distance to nearest navigable river Dis_Rivers 2021 30 m Sistema Nacional de Información de Recursos Hídricos (SNIRH) from the Autoridad Nacional del Agua (ANA) Distance to nearest district road District_R 2018 30 m Ministerio de Transportes y Comunicaciones (MTC) Distance to nearest department road Departme_R 2018 30 m Ministerio de Transportes y Comunicaciones (MTC) Distance to nearest national road National_R 2018 30 m Ministerio de Transportes y Comunicaciones (MTC) Distance to nearest settlement of more than 10 people D7Set10 2007 30 m Instituto Nacional de Estadística e Informática (INEI) Distance to nearest settlement of more than 1000 people D7Set1000 2007 30 m Instituto Nacional de Estadística e Informática (INEI) Distance to nearest settlement of more than 5000 people D7Set5000 2007 30 m Instituto Nacional de Estadística e Informática (INEI) Distance to nearest settlement of more than 10000 people D7Set10000 2007 30 m Instituto Nacional de Estadística e Informática (INEI) Distance to nearest settlement of more than 10 people D17Set10 2017 30 m Instituto Nacional de Estadística e Informática (INEI) Distance to nearest settlement of more than 1000 people D17Set1000 2017 30 m Instituto Nacional de Estadística e Informática (INEI) Distance to nearest settlement of more than 5000 people D17Set5000 2017 30 m Instituto Nacional de Estadística e Informática (INEI) Distance to nearest settlement of more than 10000 people D17Se10000 2017 30 m Instituto Nacional de Estadística e Informática (INEI) Ecoregions Ecoregions - - Ministerio del Ambiente (MINAM) Departments of the Peruvian Amazon Department - - Plataforma Nacional de Datos Abiertos Elevation Elevation - 30 m Nasa shuttle radar topography mission Slope Slope - 30 m Nasa shuttle radar topography mission Population density Pop2000 2000 1 km https://www.worldpop.org/ Population density Pop2020 2020 1 km https://www.worldpop.org/ Travel time to major cities Tra_Time00 2000 1 km https://forobs.jrc.ec.europa.eu/gam Travel time to major cities Tra_Time15 2015 1 km https://www.nature.com/articles/s41597-019-0265-5 Additional Declarations There is NO Competing Interest. 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Centre for Development and Environment, University of Bern, Switzerland; Institute of Geography, University of Bern, Switzerland","correspondingAuthor":false,"prefix":"","firstName":"Julie","middleName":"Gwendolin","lastName":"Zaehringer","suffix":""}],"badges":[],"createdAt":"2024-03-26 14:51:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4170734/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4170734/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54078080,"identity":"083f3e79-17a9-4bd7-ace4-000f03ad0eab","added_by":"auto","created_at":"2024-04-04 09:10:41","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":947874,"visible":true,"origin":"","legend":"\u003cp\u003eMap of (\u003cstrong\u003ea\u003c/strong\u003e) the study area in Peru and (\u003cstrong\u003eb\u003c/strong\u003e) the Peruvian Amazon with the extent of Protected Areas, Indigenous land, Non-Timber Forest Products, Logging and Mining Concessions included in this study. Darker colours demark forested areas in 2000.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4170734/v1/7ac628d0f0d080b3b4ea45b2.jpeg"},{"id":54078081,"identity":"f6477be3-c7f9-468c-92d2-47d5490b86ba","added_by":"auto","created_at":"2024-04-04 09:10:41","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":576394,"visible":true,"origin":"","legend":"\u003cp\u003eYearly \u003cstrong\u003e(a)\u003c/strong\u003eabsolute area, \u003cstrong\u003e(b)\u003c/strong\u003e percentage and \u003cstrong\u003e(c)\u003c/strong\u003e cumulative percentage of forest lost and carbon emissions for the Peruvian Amazon between 2000 and 2021.\u003cstrong\u003e (d) \u003c/strong\u003eEstimated proportional forest loss and carbon emissions inside Protected Areas, Indigenous land, Non-Timber Forest Products, Logging and Mining Concessions in the Peruvian Amazon from 2000 to 2021 against analogous matched areas. black lines, 95% CI; Percentage values on top of each governance mechanism reflect the decrease (green) or increase (red) in deforestation proportional to the matched control pixels. statistically significant difference between control and treatment, *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001). In parenthesis are the number of pixels used for the treatment and control of the matching analysis for each governance mechanism assessed. \u003cstrong\u003e(e)\u003c/strong\u003e Median and dispersion of the carbon density (tonnes of CO₂ per 30 m pixels) in the area base interventions, the wider landscape without any area base intervention and the matched control pixels.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4170734/v1/74c85539fa8084327e122acd.jpeg"},{"id":54078082,"identity":"d32aa987-f969-470e-89a5-bba6cc3a5146","added_by":"auto","created_at":"2024-04-04 09:10:41","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":923536,"visible":true,"origin":"","legend":"\u003cp\u003eYearly \u003cstrong\u003e(a)\u003c/strong\u003e absolute area \u003cstrong\u003e(b)\u003c/strong\u003e percentage and \u003cstrong\u003e(c)\u003c/strong\u003e cumulative percentage from the year 2000 of forest lost and carbon emissions inside Protected Areas, Indigenous Land, Non-Timber Forest Products, Logging and Mining concessions in the study area between 2000 and 2021.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4170734/v1/959490f06a41e14294efa128.jpeg"},{"id":86867707,"identity":"d14d1425-7a49-4926-b078-96c65cd62e12","added_by":"auto","created_at":"2025-07-16 13:43:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3426218,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4170734/v1/abb3b04c-d845-47f5-b223-7d0701ea35ee.pdf"},{"id":54078083,"identity":"bcaa2e75-d2f9-4cc0-85ad-e6fce13c857d","added_by":"auto","created_at":"2024-04-04 09:10:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2671744,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4170734/v1/5e35a693470db9a65630648c.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Potential of different governance mechanisms for achieving Global Biodiversity Framework goals","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTropical forests are amongst the most biodiverse ecosystems globally\u0026nbsp;\u003csup\u003e1,2\u003c/sup\u003e. They also provide multiple crucial services to humanity at local and global scales\u0026nbsp;\u003csup\u003e3,4\u003c/sup\u003e, including storing and sequestering large amounts of atmospheric carbon, which is key to directly regulate climate\u0026nbsp;\u003csup\u003e5\u003c/sup\u003e. Despite this, the loss and degradation of tropical forest has increased over time\u0026nbsp;\u003csup\u003e6,7\u003c/sup\u003e, and it has been shown to contribute to up to 20% of the world\u0026rsquo;s greenhouse gas emissions\u0026nbsp;\u003csup\u003e5,8\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eProtected areas (PAs) have always been regarded as a fundamental tool for the protection of forest ecosystems and the ecosystem services they provide. Now more than 15% of global land is under a certain type of protection regime\u0026nbsp;\u003csup\u003e9\u003c/sup\u003e. Despite this, evidence shows that PAs are not always effective at preventing deforestation. One third of PAs globally are under intense human pressure\u0026nbsp;\u003csup\u003e10\u003c/sup\u003e, half of protected forests have low or medium integrity\u0026nbsp;\u003csup\u003e11\u003c/sup\u003e and 3% of the forest inside PAs was lost in the first decade of the 20th century\u0026nbsp;\u003csup\u003e12\u003c/sup\u003e. Additionally,\u0026nbsp;socioeconomic conflicts may arise where PAs are established, due to restrictions in access to natural resources and territory, and those socioeconomic aspects must be taken into account when practical conservation decisions are taken\u0026nbsp;\u003csup\u003e13\u003c/sup\u003e. Furthermore, in some circumstances, strict protection might not be appropriate or feasible, and other conservation actions can be more suitable\u0026nbsp;\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eDue to the socioeconomic conflicts that might derive from PAs establishment under certain circumstances and that PAs alone are not enough to meet the commitments of the\u0026nbsp;Kunming-Montreal Global Biodiversity Framework\u0026nbsp;\u003csup\u003e15\u003c/sup\u003e, calls have mounted for the identification of other effective area-based conservation measures (OECMs) that can deliver biodiversity outcomes to complement those provided by PAs\u0026nbsp;\u003csup\u003e9,16\u003c/sup\u003e.\u0026nbsp;Indigenous management of land, for example represents a form of governance that has proven to have positive impacts on biodiversity. There is growing evidence showing that indigenous lands (ILs) can be effective at reducing deforestation under certain management conditions\u0026nbsp;\u003csup\u003e17\u0026ndash;19\u003c/sup\u003e. However, there is a need for a better understanding of the interactions between deforestation trends and ILs management in different regional and local\u0026nbsp;contexts\u0026nbsp;\u003csup\u003e18,20\u003c/sup\u003e. Forest concessions, a widely used contractual arrangement, in which a government temporarily provides public forested land access, management and exclusion rights to non-government actors, is another\u0026nbsp;governance mechanism\u0026nbsp;that can provide positive outcomes for forest conservation while generating a potential source of income for local communities\u0026nbsp;\u003csup\u003e21\u003c/sup\u003e. There are at least 122 million hectares of Forest Concessions in West and Central Africa, Latin America and Southeast Asia\u0026nbsp;\u003csup\u003e21\u003c/sup\u003e, with different uses ranging from industrial extraction, including timber extraction or logging (logging concessions, LCs)\u0026nbsp;\u003csup\u003e22\u0026ndash;24\u003c/sup\u003e, to more sustainable uses, like agroforestry\u0026nbsp;\u003csup\u003e25\u003c/sup\u003e, tourism and conservation\u0026nbsp;\u003csup\u003e26\u003c/sup\u003e, hereafter referred to as Non-Timber Forest Product Concessions (NTCs). Their effectiveness in reducing deforestation varies depending on the type of concession and the political characteristics in the country they are established, but there is prevalent support for positive outcomes under adequate management and supportive political circumstances\u0026nbsp;\u003csup\u003e22,26,27\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRigorous evaluations of the effectiveness of potential OECMs and other alternative governance mechanisms in preventing forest loss, particularly relative to PAs, remain relatively sparse\u0026nbsp;\u003csup\u003e28\u0026ndash;30\u003c/sup\u003e.\u0026nbsp;For OECMs the main limiting factor is that it has not yet been possible to track their extent systematically\u0026nbsp;\u003csup\u003e9\u003c/sup\u003e, and some countries are still in the process of identifying OECMs within their territory\u0026nbsp;\u003csup\u003e29\u003c/sup\u003e. On the other hand, \u0026nbsp;studies assessing the impact of PAs preventing deforestation, have often failed to account for their non-random location. Some studies, for example, simply compare areas that are protected to all the unprotected area present in the landscape\u0026nbsp;\u003csup\u003e12,31\u003c/sup\u003e, or to neighbouring buffers\u0026nbsp;\u003csup\u003e32\u003c/sup\u003e. This can cause bias in the estimated impact of PAs as they are non-randomly distributed across space tending in general to be located towards land that, if unprotected, is less likely than average to be cleared\u0026nbsp;\u003csup\u003e14,33,34\u003c/sup\u003e. Identifying governance mechanisms that can be potential OECMs is needed for efficient conservation planning\u0026nbsp;\u003csup\u003e9,29,35\u003c/sup\u003e. The\u0026nbsp;Kunming-Montreal Global Biodiversity Framework includes a target of 30% of land protected by 2030\u0026nbsp;\u003csup\u003e15\u003c/sup\u003e.\u0026nbsp;On the other hand, under the UN Framework Convention on Climate Change\u0026nbsp;\u003csup\u003e36\u003c/sup\u003e, countries revise their nationally determined contributions to reduce emissions every five years. Both conventions cover commitments to reduce deforestation and its associated emissions through protection of natural systems. It is then crucial to understand the contribution that PAs and potential OECMs have towards achieving these commitments and how they can complement each other to have the best conservation outcomes\u0026nbsp;\u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHere we assessed the impact of government-managed PAs and other governance mechanisms on forest loss and carbon emissions using\u0026nbsp;a\u0026nbsp;robust before\u0026ndash;after control intervention study design,\u0026nbsp;with\u0026nbsp;statistical matching.\u0026nbsp;We used the Peruvian Amazon as our study area (Fig. 1). The country is megadiverse\u0026nbsp;\u003csup\u003e37\u003c/sup\u003e,\u0026nbsp;has multiple land tenure types\u0026nbsp;\u003csup\u003e30\u003c/sup\u003e and its Amazon region accounts for 60% of the country\u0026rsquo;s land mass and for 90% of its forest\u0026nbsp;\u003csup\u003e38,39\u003c/sup\u003e. Additionally, the Peruvian Amazon is experiencing high rates of forest loss and degradation\u0026nbsp;\u003csup\u003e30,31,40\u003c/sup\u003e. We used\u0026nbsp;statistical matching as this methodology evaluates the effectiveness of conservation interventions by accounting for the non-random spatial distribution of those interventions and of deforestation risks\u0026nbsp;\u003csup\u003e18,41\u0026ndash;43\u003c/sup\u003e,\u0026nbsp;comparing treatment sites to matched control ones (i.e., areas with no intervention with characteristics similar to treatment sites)\u0026nbsp;\u003csup\u003e33,44,45\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;A previous study in the Peruvian Amazon found that between 2006 and 2011 PAs, ILs and Conservation Concessions avoided deforestation and degradation compared to analogous areas in the unprotected landscape\u0026nbsp;\u003csup\u003e30\u003c/sup\u003e, and another study, found a positive association between titling of ILs and the reduction of clearing and forest disturbance between 2002 and 2005\u0026nbsp;\u003csup\u003e46\u003c/sup\u003e. While these studies suggest a positive impact of PAs, Conservation Concessions and ILs on the reduction of forest loss, they did not assess the impact of other governance regimes with conservation potential, like agroforestry, reforestation or tourism concessions, or the effect of forest loss on carbon emissions. Furthermore, the timeframe of their analysis is too short to provide an overview of the longer-term impacts of these governance mechanisms on forest loss.\u003c/p\u003e\n\u003cp\u003eParticularly in this study we assessed the impact of government-managed PAs, and two potential OECMS: Indigenous Lands (ILs), and Non-Timber Forest Products Concessions (NTCs) on forest loss and its associated carbon emissions in the Peruvian Amazon from 2000 to 2021. We also assessed two other governance mechanisms with a mostly commercial extractive use, Logging Concessions (LCs) and \u0026nbsp;Mining Concessions (MCs), \u0026nbsp;as a point of contrast to assess how different governance mechanisms impact forest loss. Mining concessions in particular, have been strongly associated with forest\u0026nbsp;\u003csup\u003e30,47,48\u003c/sup\u003e and biodiversity loss\u0026nbsp;\u003csup\u003e49\u003c/sup\u003e in the tropics.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Area and governance mechanisms analysed\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eWe used the Peruvian Ministry of Environment delimitation of the Peruvian Amazon \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, which includes low elevation areas but also Amazon Andes foothills. We used the information on spatial distribution and establishment dates of government-managed PAs defined by the Peruvian Protected Areas\u0026rsquo; Service - SERNANP \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. For the spatial distribution of ILs we made a request of information to the Ministry of Culture \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Details on ILs creation dates were obtained from the database of Indigenous and Native Peoples \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. For the spatial distribution of Forest Concessions we used information from the National Forest and Wildlife Service \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e and from a request of information to the regional governments of Madre de Dios, Loreto and Ucayali. When there were discrepancies with SERFOR, information from regional governments was used, as these entities have a more detailed record of their jurisdiction than national entities. Details on Forest Concessions establishment dates were obtained from the Forestry and Wildlife Resources Supervision Agency \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Information on MCs spatial distribution, start and closure dates were obtained from the mining and geological geoportal from the Ministry of Energy and Mines \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe divided the Forest Concessions into two groups taking into account the purpose of the concession and the definition of potential OECMs \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. We aggregated the concessions that in principle do not use timber products as a source of income, including conservation, ecotourism, reforestation, restoration and agroforestry into a single category of Non-Timber Forest Product Concessions (NTCs). These concessions meet the criteria of the definition of potential OECMs \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Logging concessions (LCs) on the other hand were included in a separate group as their main objective is commercial extraction of wood. This does not align with criterion six required for their identification (Jonas et al 2023) and criterion C for their recognition and reporting as OECMs \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. These two criteria specify the impossibility of sites that are subject to environmentally damaging industrial-scale activities to be classified as OECMs.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMapping forest loss and carbon emissions\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eWe used the map of Amazon forest cover for 2000 and the one of Amazon forest loss from 2000 to 2021 developed by the Peruvian Ministry of the Environment and the Ministry of Agriculture and Irrigation \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. These maps have a resolution of 30 by 30 meters where each pixel is either forest or no forest in the map of forest cover for the year 2000 and have a value from 1 to 21 in the map of forest loss. Each value represents the year when the forest cover on that pixel was lost. The classification of a pixel into either forest or no forest is based on different Landsat images of the Peruvian Amazon for each year, which are then processed at a pixel level. The accuracy of these maps was 92.2% for forest loss and 99.5% for no change \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. We then quantified the extent and rate of forest loss for each year between 2000 and 2021 and the total cumulative loss between 2000 and 2021. This was done for the whole basin and individually for each governance mechanism assessed.\u003c/p\u003e\u003cp\u003eWe used the high-resolution carbon per hectare density estimations for Peru, generated by Asner et al. (2014) \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e, to calculate the metric tons (Mg) of carbon by forest pixel. We assumed that if a pixel was deforested, all of its carbon was emitted into the atmosphere. Then we summed the amount and proportion of carbon emissions by year and cumulative between 2000 and 2021 for the whole landscape, and for each governance mechanism as well as for the selected control and treatment pixels after the matching analysis. In this study, \u0026ldquo;carbon emissions\u0026rdquo; refers to emissions \u0026ldquo;committed\u0026rdquo; at the time of disturbance or clearing, noting that there may be a time lag until they are \u0026ldquo;realized\u0026rdquo;.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCofounding factors\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eTo account for the determinants of deforestation pressure and the location of the different governance mechanisms assessed, we used a suite of biophysical and socioeconomic cofounding factors that have been found to be associated with the non-random spatial distribution of these interventions and of the distribution of deforestation risk in the Peruvian Amazon and other tropical landscapes \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. These cofounding variables were estimated at the pixel level and included in the matching analysis. These included measures of forest accessibility (i.e. distance to roads, rivers and previous deforestation), anthropic pressure (i.e. distance to settlement, travel time to cities, human population density), suitability of conversion to agricultural land (i.e. precipitation, precipitation seasonality, ecoregion, elevation and slope) and the socio-political environment (i.e. administrative region) (Table\u0026nbsp;1). Spatial information that was at a different spatial resolution was resampled to the forest pixels\u0026rsquo; resolution of 30 by 30 meters.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical Matching\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eWe used forest loss between two time periods as alternative outcome variables for our matching analysis in order to assess the robustness of our results. Forest loss by pixel between 2000 and 2021 and forest loss by pixel between 2005 and 2021. We excluded governance mechanisms that were established after 2005. Areas of overlap, and other types of governance mechanisms with conservation potential that were region-specific were also excluded. Also, concessions that stopped operating before 2015 were excluded. To avoid the potential spill-over effects, particularly leakage \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e, we excluded the pixels within a buffer of 1 km (NTCs, LCs, MCs and ILs) and 5 km (PAs) around treatment areas \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In order to avoid autocorrelation we generated a set of random samples at least 500 m apart from each other for each governance mechanism and for the potential control units \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Control units were defined as pixels which were not assigned to any of the governance mechanisms assessed in this study.\u003c/p\u003e\u003cp\u003eTo assess the impacts of the governance mechanisms (treatments) on forest loss we used a before\u0026ndash;after control intervention study design, with matching analysis, creating artificial control groups for each treatment unit (pixel) while controlling for differences in observed covariates \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. For each pixel, we extracted the values of the cofounding variables associated with deforestation and the governance mechanisms\u0026rsquo; location. This was done both for the treatment group (each governance mechanism pixel) and the potential control group (pixels that where not classified under any of the assessed governance mechanism). To determine the best minimum set of cofounding variables, we first ran a binomial logistic regression model using each governance mechanism as the response variable and the cofounding factors as predictor variables. Then we ran a multicollinearity test to the coefficients of the covariates of each governance mechanism model. Through this analysis, we identified groups of variables with a collinearity higher than 0.65. The variable with the lowest Variance Inflation Factor (VIF) for each group was retained in the model \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. We then used 'backward' and 'forward' techniques in an iterative stepwise process for model selection, to identify the optimal model. The model that achieved the greatest reduction in the Akaike\u0026rsquo;s Information Criterion (AIC) was selected \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e (Supplementary Information 1). For this we used the leaps package \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e in R (version 3.5.1) software \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. The variables from the best model were used in the subsequent analyses and for the matching procedure for each governance mechanism.\u003c/p\u003e\u003cp\u003eWe then used the MatchIt package \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e in the R statistical software to pair treatment and control pixels with similar values for each of the covariates \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. We used Propensity Score Matching to create a statistically balanced counterfactual sample to evaluate the effect of each governance mechanism on forest loss. Despite its limitations, Propensity Score Matching remains the most widely used matching approach \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. We matched each treatment observation to a unique control observation to ensure each governance mechanism pixel was paired with only one control pixel that had not been matched previously. Pixels could be reselected as control samples for each independent governance mechanism analysis. Matched pairs of pixels had to be within 0.25 standard deviation of the propensity scores \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. As part of the diagnostic statistics we calculated the index of covariate imbalance for every matching analysis for each governance mechanism to assess the robustness of the matching analysis reducing bias in the control units (Supplementary Information 2a). Absolute scores\u0026thinsp;\u0026gt;\u0026thinsp;25% are considered an indication of a possible imbalance for that specific variable \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eDeforestation in the Peruvian Amazon and the different governance mechanisms\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAnnual deforestation over the last 20 years in the Peruvian Amazon almost doubled, from 840 km\u0026sup2; in 2001 to more than 1,400 km\u0026sup2; in 2021. Annual deforestation has been increasing, especially since 2006. There have been two notorious increases in yearly deforestation, one in 2005 and another in 2020 (Fig.\u0026nbsp;1 a \u0026amp; b). In relation to the governance mechanism, ILs had the highest absolute yearly forest loss followed by LCs (Fig.\u0026nbsp;2a). In proportional terms, MCs showed the highest yearly and accumulated loss since 2000 with a total of 21% of their forest cover lost in the last 20 years. LCs experienced the second highest cumulative loss with 4% of the forest lost in the last 20 years. On the other hand, PAs showed the lowest yearly and cumulative loss with 0.6% of the forest lost in the last 20 years (Fig.\u0026nbsp;2 b \u0026amp; c).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCarbon emissions in the Peruvian Amazon and the different governance mechanisms\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCarbon emissions in the Peruvian Amazon have consistently increased in absolute and in percentage terms since 2000. This aligns with the increase in deforestation observed over the past 20 years (Fig.\u0026nbsp;1. a-c). Particularly in 2020 there was a high net increase in yearly emissions for the Peruvian Amazon (Fig.\u0026nbsp;2 a). This increase can also be seen in proportional terms (Fig.\u0026nbsp;2 b), and is also observed in ILs, LCs and MCs (Fig.\u0026nbsp;3). The governance mechanisms with the highest yearly net carbon emissions were ILs, followed by LCs (Fig.\u0026nbsp;3 a). MCs had the highest proportional carbon emissions yearly and cumulative to the stock in 2000, while PAs had the lowest (Fig.\u0026nbsp;3 b \u0026amp; c).\u003c/p\u003e\u003cp\u003e\u003cb\u003eRobustness of the matching analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMost covariate imbalances between the treatment and control units were reduced through the matching analysis. For ILs, LCs and MCs these reductions meant that all the variables had indices of covariate imbalance below 25% post matching. For PAs, only the variable distance to settlements of more than 10 people in 2017 had an index of imbalance of 49% post matching. However, this was a reduction from 74% pre matching. For NTCs, 16 variables had indices of covariate imbalance below 25% post matching. Three had indices higher than 25% post matching, but for those variables these values implied reductions of 10% or more in covariate imbalance post matching. For two covariates, there was an increase in covariate imbalance post matching (Supplementary Information 2a). Overall, there was notable improvements reducing bias in the control units through the matching analysis (Supplementary Information 2a, 3a \u0026amp; 4a). Consistent results were obtained when using forest loss between 2005 and 2021 for the analysis (Supplementary Information 2b, 3b \u0026amp; 4b).\u003c/p\u003e\u003cp\u003e\u003cb\u003eImpact of the different governance mechanisms on forest and carbon loss\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur results show that PAs, ILs, NTCs and LCs significantly decrease forest loss compared to the matched control areas. PAs were the most effective in preventing forest loss, avoiding 88% of expected loss, followed by NTCs (64%), ILs (44%), and LCs (29%). On the other hand, MCs showed a 24% increase in expected forest loss, compared to matched control areas, although the increase was not significant (Fig.\u0026nbsp;2d). Consistent results were found when using forest loss between 2005 and 2021 for the matching analysis (Supplementary Information 5).\u003c/p\u003e\u003cp\u003eThe median carbon density by pixel was highest for forest in NTCs, followed by LCs. The lowest median carbon density was for MCs. The median carbon density of the pixels without any governance mechanism designation was higher than those of all the governance mechanism except for NTCs and LCs. However, none of these differences in median carbon densities were statistically significant (non-parametric Kruskal-Wallis test, p value; PAs\u0026thinsp;=\u0026thinsp;0.17, ILs\u0026thinsp;=\u0026thinsp;0.62, CCs\u0026thinsp;=\u0026thinsp;0.35, LCs\u0026thinsp;=\u0026thinsp;0.38, MCs\u0026thinsp;=\u0026thinsp;0.49). There was a high dispersion in the carbon density values by pixels and an overlap in this dispersion between all of the governance mechanisms (Fig.\u0026nbsp;2e). Finally, with regards to carbon emissions represented by the loss of forest, PAs, ILs, NTCs and LCs had lower emissions than matched control areas. This decrease was 88% for PAs, 65% for NTCs, 42% for ILs and 20% for LCs. On the other hand, MCs had a 35% increase in carbon emissions compared to the matched control areas (Fig.\u0026nbsp;3d). Consistent results were found when using forest loss between 2005 and 2021 for the matching analysis (Supplementary Information 5).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study of the Peruvian Amazon we found that 4% of the forest has been lost in the last two decades. Putting this into perspective, until today the Brazilian Amazon has lost 20% of the historical forest cover \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. However, PAs and other governance mechanisms assessed here have played a significant role in avoiding an even stronger decline of forest cover in the region. PAs experienced nine times less forest loss than similar areas without protection, while NTCs experienced three times less and ILs two times less losses. These three types of governance mechanisms cover 17.3, 1.3 and 20% of the Peruvian Amazon\u0026rsquo;s area, respectively, and their impact on the region\u0026acute;s deforestation rate is quite remarkable. Identifying potential OECMs that are effective in preventing biodiversity loss, assessing how they perform in comparison to PAs and evaluating in which circumstances they can deliver biodiversity outcomes to complement those provided by PAs is fundamental for advancing the goal of encompassing other approaches to conservation beyond formally designated PAs \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and to achieve Target 3 of the Kunming-Montreal Global Biodiversity Framework \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Our results clearly show that ILs and NTCs are governance mechanisms that achieve long term in situ conservation of biodiversity, thus meeting criterion six and seven required for their identification as OECMs \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e and criterion B and C for their recognition and reporting \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. These criteria specify that potential OECMs are expected to have a type of management that deliver long-term effective \u003cem\u003ein situ\u003c/em\u003e conservation of biodiversity. The contribution of these governance mechanisms to regional and global conservation goals should be acknowledged and conservation funding should be channelled to maintain or increase their contributions to conservation as well as to have a better understanding of under which circumstances they can provide the best conservation outcomes.\u003c/p\u003e \u003cp\u003eThe forest that is retained by any governance mechanism also stores large amounts of atmospheric carbon, which is key to climate regulation \u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. In our study we found that PAs avoided 88% of the carbon emissions expected in the area due to forest loss, while NTCs and ILs avoided 65% and 42% respectively. The estimation of the amount of carbon emissions avoided is a key measure to understand the role of these governance mechanisms in carbon cycles and regional climate regulation \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. Additionally, robust estimates of the impact of different governance mechanisms on carbon retention is crucial for the acquisition of funding for carbon payments as it is a requirement for satisfying Monitoring, Evaluation and Reporting (MER) programs to access REDD\u0026thinsp;+\u0026thinsp;and other funding streams (e.g., the Global Environmental Facility and the World Bank) \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOver the last two decades, ILs avoided 54% of the forest loss and 51% of the carbon emissions expected in those areas. These results show that ILs can provide important conservation benefits in terms of preventing forest loss and reducing carbon emissions. This aligns with previous research conducted at the Amazon level \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e and globally \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. One fifth of the Peruvian Amazon is under Indigenous management, the highest proportion of all the governance mechanisms assessed. This in part explains why these territories had the highest absolute loss of forest. Due to the large extent of these territories and the potential to improve their effects on protection of forests, it is key to support Indigenous Communities in the development of financial mechanisms that allow them to receive income from the forest through activities that do not harm biodiversity. It is also important to support Indigenous Communities to further improve the governance of their communal land, especially in reference to activities potentially harmful to biodiversity, such as illegal mining and crops cultivation and deforestation \u003csup\u003e\u003cspan additionalcitationids=\"CR77\" citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. As the influence that Indigenous land management has on the regional trends of deforestation and carbon emissions is substantial, it is crucial to support the claims by Indigenous Peoples in the Amazon and globally, who frequently advocate for more recognition on their contributions to conservation and active participation in environmental and forestry policy \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. Acknowledging their contribution through stronger participation in environmental policy-making and through the provision of funding to support their manifold approaches to management is necessary. Also, the identification of the specific management practices that are effective to prevent unintended deforestation is crucial so that they can be scaled.\u003c/p\u003e \u003cp\u003eWe found that LCs avoided 29% of the deforestation and 20% of the expected carbon emissions in the last twenty years. These concessions constitute a considerable proportion (8.1%) of the Peruvian Amazon area. While LCs do not meet all the criteria to be defined as OECMs, our results show that they could contribute to other conservation goals like target 10 of the Kunming-Montreal Global Biodiversity Framework \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, which aims to ensure the sustainable management of forestry, in particular through the sustainable use of biodiversity. While our results may be counterintuitive, as the main purpose of these concessions is to extract wood, they support findings from previous research showing that these concessions can have a positive impact on reducing forest loss, specially the certified ones \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan additionalcitationids=\"CR80\" citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. The establishment of this type of land use could prevent widespread illegal logging activities due to an incentive to defend forest assets, and timber profits, by excluding other illegal and legal actors interested in extractive activities (i.e., logging, mining, land-grabbing) \u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e,\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. However, it is noteworthy that despite of this benefit, logging also has a negative impact on the local ecosystem, as the extraction of trees degrades the forest \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLogging Concessions (LCs) provide economic benefits from the extraction of wood \u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e,\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. However, in these concessions there can be other economic activities such as agroforestry and tourism, which could increase the economic benefits these landscapes provide to the local population, with a comparatively low impact on the ecosystems than other land uses like large-scale agriculture or mining. By turning LCs into multifunctional Forest Concessions, where the management party develops not only wood extraction but also a range of other economic activities (i.e., tourism, conservation, research), there could be a more sustainable and diversified regional economy. However, verification and monitoring has to be enforced for these schemes to have benefits for people and nature. There is evidence that shows that the effectiveness of LCs in preventing forest loss is higher in areas with higher deforestation pressure \u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. More studies on LCs are needed to understand what factors and which management conditions foster positive impacts on reducing forest loss. Furthermore, we need a better understanding of how the extraction of wood affects the overall biodiversity and carbon density \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e,\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eForest loss in the Peruvian Amazon has been increasing through time, especially from 2006 to 2016. It is crucial to monitor these changes in forest loss along the years as well as the abnormal increases in yearly deforestation, to understand which factors drive them, and to identify how they can be prevented or managed. The 2005 increase in deforestation in the Peruvian Amazon coincides with a particularly dry year, which had three times more fires than the previous year and twice more fires than the posterior one \u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. On the other hand, the 2020 increase overlaps with the implementation of confinement measures to reduce COVID-19 in the country, which reduced the capacity for enforcement and control by the authorities and permitted the appearance of new logging roads as well as increases in illegal logging and mining activities \u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur results show that there are certain years, like 2005, with high forest loss that do not show congruent patterns in relation to carbon emissions, on the other hand there are years, like 2019 and 2021, with high carbon emissions in relation to the deforestation observed that year. Factors like the construction of roads may be related to these differences. The construction of the Interoceanic Highway which connects Brazil and Peru, made primary forest more accessible for selective logging and deforestation \u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e,\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. As primary forest is known to be more dense than secondary or disturbed forest \u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e,\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e, these road construction events through dense forest can increase disproportionately the carbon emissions. The factors that influence the increase or decrease of carbon emissions, apart from the loss of forest, need to be better studied as these factors can have significant impacts for carbon circles and regional and global climate regulation.\u003c/p\u003e \u003cp\u003eThe Kunming-Montreal Global Biodiversity Framework includes a target of 30% of land protected by 2030 and refers to OECMs as a complementary conservation approach to PAs \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Our study is one of the first to provide robust evidence of long-term positive impacts of multiple types of governance mechanisms for the conservation of biodiversity. Some of them with the potential to be classified as OECMs. While our results show that PAs are the most effective measure preventing forest loss and carbon emissions, we also found that there are several other governance mechanisms that can deliver conservation benefits at different extents and with different management characteristics. To allow nations to more effectively achieve the different targets from the Kunming-Montreal Global Biodiversity Framework and the UN Framework Convention on Climate Change \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, it will be crucial to (i) better understand how different types of governance mechanisms operate and under which circumstances they can provide the most beneficial conservation outcomes, and (ii) strengthen their management through funding mechanisms specific for each governance mechanism.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contributions:\u003c/h2\u003e\n\u003cp\u003eP.J.N., A.V., M.S. and J.G.Z. designed the research. P.J.N., S.N. and M.Q. processed the data. P.J.N. and V.R. performed the analysis. P.J.N. wrote the initial draft. P.J.N, V.R., S.N., M.Q., A.V., G.F., T.A., M.S., J.S. and J.G.Z. reviewed and edited the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\n\u003cp\u003eWe would like to acknowledge the people from the Asociaci\u0026oacute;n para la Conservaci\u0026oacute;n de la Cuenca Amaz\u0026oacute;nica (ACCA) and from the Land Systems and Sustainability Transformations research team for helpful discussion on the interpretation of the results. This study contributes to the strategy and goals of the Global Land Programme (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.glp.earth\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePillay, R. \u003cem\u003eet al.\u003c/em\u003e Tropical forests are home to over half of the world\u0026rsquo;s vertebrate species. \u003cem\u003eFrontiers in Ecology and the Environment\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 10\u0026ndash;15 (2022).\u003c/li\u003e\n\u003cli\u003eLewis, S. L., Edwards, D. P. \u0026amp; Galbraith, D. Increasing human dominance of tropical forests. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e349\u003c/strong\u003e, 827\u0026ndash;832 (2015).\u003c/li\u003e\n\u003cli\u003eMori, A. S., Lertzman, K. P. \u0026amp; Gustafsson, L. 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\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAnnual_Prec\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ehistorical\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 m\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldclim.org/\u003c/span\u003e\u003cspan address=\"https://www.worldclim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePrecipitation Seasonality\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePrec_Seas\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ehistorical\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 m\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldclim.org/\u003c/span\u003e\u003cspan address=\"https://www.worldclim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDistance to nearest previous deforested area\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eDis_Def\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2000\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 m\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eMinisterio del Ambiente (MINAM) \u0026amp; Ministerio de Agricultura y Riego (MIDAGRI)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDistance to nearest navigable river\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eDis_Rivers\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2021\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 m\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eSistema Nacional de Informaci\u0026oacute;n de Recursos H\u0026iacute;dricos (SNIRH) from the Autoridad Nacional del Agua (ANA)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDistance to nearest district road\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eDistrict_R\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2018\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 m\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eMinisterio de Transportes y Comunicaciones (MTC)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDistance to nearest department road\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eDepartme_R\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2018\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 m\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eMinisterio de Transportes y Comunicaciones (MTC)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDistance to nearest national road\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNational_R\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2018\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 m\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eMinisterio de Transportes y Comunicaciones (MTC)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDistance to nearest settlement of more than 10 people\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eD7Set10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2007\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 m\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eInstituto Nacional de Estad\u0026iacute;stica e Inform\u0026aacute;tica (INEI)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDistance to nearest settlement of more than 1000 people\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eD7Set1000\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2007\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 m\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eInstituto Nacional de Estad\u0026iacute;stica e Inform\u0026aacute;tica (INEI)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDistance to nearest settlement of more than 5000 people\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eD7Set5000\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2007\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 m\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eInstituto Nacional de Estad\u0026iacute;stica e Inform\u0026aacute;tica (INEI)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDistance to nearest settlement of more than 10000 people\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eD7Set10000\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2007\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 m\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eInstituto Nacional de Estad\u0026iacute;stica e Inform\u0026aacute;tica (INEI)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDistance to nearest settlement of more than 10 people\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eD17Set10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2017\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 m\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eInstituto Nacional de Estad\u0026iacute;stica e Inform\u0026aacute;tica (INEI)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDistance to nearest settlement of more than 1000 people\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eD17Set1000\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2017\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 m\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eInstituto Nacional de Estad\u0026iacute;stica e Inform\u0026aacute;tica (INEI)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDistance to nearest settlement of more than 5000 people\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eD17Set5000\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2017\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 m\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eInstituto Nacional de Estad\u0026iacute;stica e Inform\u0026aacute;tica (INEI)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDistance to nearest settlement of more than 10000 people\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eD17Se10000\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2017\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 m\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eInstituto Nacional de Estad\u0026iacute;stica e Inform\u0026aacute;tica (INEI)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eEcoregions\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eEcoregions\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eMinisterio del Ambiente (MINAM)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDepartments of the Peruvian Amazon\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eDepartment\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003ePlataforma Nacional de Datos Abiertos\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eElevation\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eElevation\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 m\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eNasa shuttle radar topography mission\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSlope\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eSlope\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 m\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eNasa shuttle radar topography mission\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePopulation density\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePop2000\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2000\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1 km\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldpop.org/\u003c/span\u003e\u003cspan address=\"https://www.worldpop.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePopulation density\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePop2020\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2020\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1 km\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldpop.org/\u003c/span\u003e\u003cspan address=\"https://www.worldpop.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTravel time to major cities\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTra_Time00\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2000\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1 km\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://forobs.jrc.ec.europa.eu/gam\u003c/span\u003e\u003cspan address=\"https://forobs.jrc.ec.europa.eu/gam\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTravel time to major cities\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTra_Time15\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2015\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1 km\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nature.com/articles/s41597-019-0265-5\u003c/span\u003e\u003cspan address=\"https://www.nature.com/articles/s41597-019-0265-5\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e 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concessions, carbon markets, selective logging","lastPublishedDoi":"10.21203/rs.3.rs-4170734/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4170734/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Kunming-Montreal Global Biodiversity Framework includes a target of 30% of land protected by 2030 and refers to other effective area based conservation measures (OECMs) as complementary to PAs, but robust evaluations of the effectiveness of governance mechanisms that could act as OECMs in preventing forest loss and carbon emissions remain sparse. Here we assessed the impact of PAs and two potential OECMS: Indigenous Lands (ILs), and Non-Timber Forest products Concessions (NTCs) on forest loss and its associated carbon emissions in the Peruvian Amazon from 2000 to 2021. We also assessed two governance mechanisms with a commercial extractive use, Logging (LCs) and Mining Concessions (MCs). We used a robust before–after control intervention study design, with statistical matching, to account for the non-random spatial distribution of deforestation pressure and the governance mechanisms analysed. PAs were the most effective, having avoided 88% of the expected forest loss, followed by NTCs (64%) and ILs (44%). LCs also reduced expected forest loss by 29%, while MCs increased expected forest loss by 24%, showing that extractive governance mechanisms can have marked differences in their impact to forest cover. Our study provides evidence of long-term positive impacts of potential OECMs and other mechanisms at preventing forest loss and reducing carbon emission. This information is key to more effectively achieve targets from the Kunming-Montreal Global Biodiversity Framework and the UN Framework Convention on Climate Change.\u003c/p\u003e","manuscriptTitle":"Potential of different governance mechanisms for achieving Global Biodiversity Framework goals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-04 09:10:36","doi":"10.21203/rs.3.rs-4170734/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":"6e6f4609-3518-4168-9783-c37dae4a6d2e","owner":[],"postedDate":"April 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":30192556,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":30192557,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation"},{"id":30192558,"name":"Earth and environmental sciences/Ecology/Conservation biology"},{"id":30192559,"name":"Earth and environmental sciences/Environmental social sciences/Climate-change impacts/Governance"}],"tags":[],"updatedAt":"2026-04-21T13:01:53+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-04 09:10:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4170734","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4170734","identity":"rs-4170734","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}