Reducing emissions from deforestation and forest degradation (REDD+) has gained international att... more Reducing emissions from deforestation and forest degradation (REDD+) has gained international attention over the past decade, as manifested in both United Nations policy discussions and hundreds of voluntary projects launched to earn carbon-offset credits. There are ongoing discussions about whether and how projects should be integrated into national climate change mitigation efforts under the Paris Agreement. One consideration is whether these projects have generated additional impacts over and above national policies and other measures. To help inform these discussions, we compare the crediting baselines established ex-ante by voluntary REDD+ projects in the Brazilian Amazon to counterfactuals constructed ex-post based on the quasi-experimental synthetic control method. We find that the crediting baselines assume consistently higher deforestation than counterfactual forest loss in synthetic control sites. This gap is partially due to decreased deforestation in the Brazilian Amazon during the early implementation phase of the REDD+ projects considered here. This suggests that forest carbon finance must strike a balance between controlling conservation investment risk and ensuring the environmental integrity of carbon emission offsets. Relatedly, our results point to the need to better align project-and national-level carbon accounting. impact evaluation | synthetic control | payment for environmental services | carbon credit | deforestation C oncerns over global warming have led both the public and private sectors to promote climate change mitigation through the reduction of carbon (CO 2) emissions from deforestation and forest degradation in tropical countries-a concept known as REDD+ (1). This strategy gained international attention after 2005 as a voluntary, performance-based payment mechanism for reduced carbon emissions (2). While the regulations and capacity for national REDD+ programs are still under development in many countries, hundreds of voluntary, subnational REDD+ projects are operational worldwide (3). These projects intend to preserve forests through a variety of activities, e.g., improved monitoring and control, promotion of sustainable land uses, and engagement of local communities (4), either as proof of concept or to profit from the commercialization of "carbon-offset credits" (i.e., Mg CO 2 removed from or not emitted to the atmosphere) in a variety of markets. While these markets do not provide the level of funding originally envisioned for national REDD+ programs, they are substantial: In 2018 alone, the volume of carbon offsets traded totaled 98.4 million Mg CO 2 , with a market value of US$295.7 million; a third of those credits (30.5 million Mg CO 2) were generated by REDD+ projects (5). The Paris Agreement has raised thorny questions about how the carbon emission reductions claimed by these projects relate to nationally determined contributions (NDCs) and national greenhouse gas (GHG) emission inventories reported to the United Nations Framework Convention on Climate Change (6-8). Carbon credits from REDD+ [at both the project and national levels (1)] are issued based on performance, as defined by the comparison of realized forest cover to a baseline scenario constructed by projecting the forest cover expected in the absence of REDD+ (9). These baseline scenarios typically assume a continuation of historical deforestation trends (10), and thus eventually become unrealistic counterfactuals as the regional economic and political context change. Notably, these types of changes were observed in the Brazilian Amazon during 2004-2012, a period of sharply declining rates of forest loss (11), and also during 2019, when deforestation soared again (12) (Fig. 1). Consequently, credits for reduced deforestation (or lack thereof) claimed by voluntary REDD+ projects in the Brazilian Amazon may have been artifacts of external factors rather than REDD+ activities. Furthermore, critics of voluntary REDD+ projects have raised concerns that deforestation baselines might be intentionally inflated by profiteers seeking to financially benefit from the commercialization of superfluous credits, or "hot air" (13-15). In addition to the direct cost of not effectively offsetting GHG emissions, the excess credits generated by these projects impose an indirect cost on legitimate climate change mitigation efforts by undercutting the price of their credits. Early efforts to address these concerns included the establishment of standards and registries for voluntary carbon-offset projects. These standards were designed to ensure the environmental integrity of carbon offsets by requiring projects to use approved carbon-accounting methodologies for establishing deforestation baselines, monitoring, and reporting, all subject to third-party audits. Among those, the verified carbon standard Significance There are efforts to integrate the reduced carbon emissions from avoided deforestation claimed by voluntary REDD+ projects into national greenhouse gas emission inventories. This requires careful consideration of whether and how much of the reduced carbon emissions can be attributed to projects. However , credible evidence on the effectiveness of such voluntary activities is limited. We adopted the quasi-experimental synthetic control method to examine the causal effects of 12 voluntary REDD+ projects in the Brazilian Amazon. We compared these ex-post estimates of impacts with the reductions in forest loss claimed by those projects based on ex-ante baselines. Results suggest that the accepted methodologies for quantifying carbon credits overstate impacts on avoided deforestation and climate change mitigation.
Several payments for environmental services (PES) schemes to promote large-scale afforestation ar... more Several payments for environmental services (PES) schemes to promote large-scale afforestation are currently operational in New Zealand. In addition, the country has the only national greenhouse gas (GHG) Emissions Trading Scheme centred on forest carbon credits in the world. These market-based instruments are paramount in promoting the changes in land use required to achieve New Zealand's GHG-emission targets under the Paris Agreement, as well as to pave the way towards the goal of a low-carbon economy by 2050. We estimated the impacts of PES schemes on the profitability of Pinus radiata forestry to identify low-productivity grasslands where afforestation is likely to financially offset the current land use (i.e., mainly sheep-and-beef farming), under the assumption of constant returns to scale. We find that even without PES, 56% of these agricultural lands present lower economic returns than forestry. When PES are considered, results suggest that, depending on the PES schemes and carbon prices, 77-100% of the low-productivity grassland areas could financially benefit from afforestation. If realized, afforestation of those lands could result in the long-term removal of 1.2-1.5 Pg CO 2 from the atmosphere.
Over three-fourths of Brazil’s greenhouse gas (GHG) emissions originate from land-use/cover chang... more Over three-fourths of Brazil’s greenhouse gas (GHG) emissions originate from land-use/cover change (LUCC). Alarming rates of forest loss in the Amazon region gained international attention in the past, but deforestation declined substantially since 2004 (although the rate has trended upwards since 2012). This result conforms to the GHG emission reduction targets set by the National Climate Change Plan, originally divided into three phases: 2006–2009; 2010–2013; and 2014–2017. According to the plan, emission reductions are measured against a deforestation baseline, initially corresponding to the average forest loss observed from 1996 to 2005 (reference period). We used an artificial neural network (ANN) model to spatialize the plan’s original three-phase baseline and compare (i) the simulated carbon emissions from the baseline scenario to (ii) the emissions from observed deforestation during 2006–2017. Baseline spatialization was performed at the state level and informed by the 2000–2004 deforestation patterns in each state, resulting in nine state-specific calibrated ANNs. Simulated (baseline) and observed deforestation were compared to five biomass-density maps in order to estimate Brazil’s GHG emission reductions. Our results indicate that forest loss during the study period was 62,321 km2 lower than the baseline and associated with 1.5 ± 0.4 Pg of avoided CO2 emissions to the atmosphere. Virtually all emission reductions (93%) occurred in the states of Mato Grosso (74%) and Rondônia (19%). In contrast, Roraima, Amazonas, and Amapá states increased GHG emissions by 44.8, 36.7, and 14.7 Tg CO2, respectively. Lastly, we discuss the issue of attribution of deforestation reductions for results-based payments (REDD+) and the emission reduction certificates issued by the Amazon Fund.
In 2018, New Zealand announced an ambitious effort to plant one billion trees by 2028 as part of ... more In 2018, New Zealand announced an ambitious effort to plant one billion trees by 2028 as part of its climate change mitigation plan and Paris Agreement targets for 2030 and 2050. Afforestation will be promoted through a series of incentives, including payments for carbon sequestration linked to New Zealand's Emission Trading Scheme. Given that afforestation will be pursued voluntarily, the identification of future afforestation areas is paramount to assist policy formulation and avoid unintended land-use outcomes. We identified spatially-explicit drivers of forest gain and the locations most likely to experience afforestation in the country using two distinct spatial modelling frameworks: logistic regressions and artificial neural networks (ANN). Five of the eight most significant drivers of forest gain that have influenced past afforestation patterns according to our logistic regressions (i.e., erosion potential, distance from exotic forests, woody grasslands, grassland productivity, and slope) were also ranked among the ten most influential drivers by the ANN model. These results indicate an overall agreement between the two modelling approaches, despite substantial methodological differences. In the absence of changes in current incentives for afforestation in the country, simulations suggest that the largest afforestation areas would be in the northeast region of the North Island. These findings can assist policymakers and landscape planners to achieve desirable land-use objectives for New Zealand.
Deforestation driven by agricultural expansion is a major threat to the biodiversity of the Amazo... more Deforestation driven by agricultural expansion is a major threat to the biodiversity of the Amazon Basin. Modelling how deforestation responds to environmental policy implementation has thus become a policy relevant scientific undertaking. However, empirical parameterization of land-use/cover change (LUCC) models is challenging due to the high complexity and uncertainty of land-use decisions. Bayesian Network (BN) modelling provides an effective framework to integrate various data sources including expert knowledge. In this study, we integrate remote sensing products with data from farm-household surveys and a decision game to model LUCC at the BR-163, in Brazil. Our 'business as usual' scenario indicates cumulative forest cover loss in the study region of 8,000 km 2 between 2014 and 2030, whereas 'intensified law-enforcement' would reduce cumulative deforestation to 1,600 km 2 over the same time interval. Our findings underline the importance of conservation law enforcement in modulating the impact of agricultural market incentives on land cover change.
Land-use change results from the decisions of diverse actors in response to economic and politica... more Land-use change results from the decisions of diverse actors in response to economic and political contexts. Identification of underlying decision-making processes is key to understanding land-use patterns, anticipating trends, and designing effective environmental governance mechanisms. Here, we use a scenario-based decision game to examine hypothetical land-use decisions among four groups of rural producers in the municipalities of Sinop, Guarantã do Norte and Novo Progresso in the Brazilian Amazon. We simulate changes in agricultural prices, production cost, and frequency of environmental monitoring (in situ inspections) to understand how land-use decisions are made and change with shifts in economic and governance incentives. Hypothetical land-use decisions vary across scenarios, but not across study sites; environmental law enforcement influence land decisions, but not to the extent of dominating market factors and not for all types of producers. Decision games cannot substitute approaches to explain behavioral responses from observational data. However, they can provide immediate feedback on behavioral hypotheses before comprehensive observational data becomes available and support the development of models for land-use policy planning at local and regional scales.
This study adapts the Faustmann model (1849) to include the effects of carbon-based payments for ... more This study adapts the Faustmann model (1849) to include the effects of carbon-based payments for environmental services on the optimal rotation length of forest plantations. We assume payments occur at the end of the harvesting cycle and are based on the additional average carbon stock over the rotation length relative to an optimal management scenario without carbon incentives. We present numerical applications of our model based on the four most planted tree species in the world: Eucalyptus sp., Acacia sp., Pinus sp. and Tectona sp. Simulations were performed to quantify the impact of different payment levels (USD Mg CO 2 −1) on optimal rotation lengths and carbon stocks. We find diminishing sequestration returns to increasing carbon payments. Overall, our results suggest that targeting plantations with longer rotations, associated with slow-growing species, is a more cost-effective strategy to retain additional forest carbon stored (USD Mg CO 2 −1). However, such a strategy results in lower proportional increases in carbon stocks (Mg CO 2 ha −1) compared to faster-growing species, which benefit the most from the carbon incentives. As a result, increasing payments for additional carbon stored could change the choice of which tree species to produce and ironically promote plantations with lower total carbon stocks.
Global change drivers of land-use/cover change (LUCC) like population dynamics, economic developm... more Global change drivers of land-use/cover change (LUCC) like population dynamics, economic development, and climate change are increasingly important to local sustainability studies, and can only be properly analyzed at fine-scales that capture local biophysical and socio-economic conditions. When sufficiently widespread, local feedback to stresses originating from global drivers can have regional, national, and even global impacts. A multiscale, global-to-local-to-global (GLG) framework is thus needed for comprehensive analyses of LUCC and leakage. The number of GLG-LUCC studies has grown substantially over the past years, but no reviews of this literature and their contributions have been completed so far. In fact, the largest body of literature pertains to global-to-local impacts exclusively, whereas research on local feedback to regional, national, and global spheres remain scarce, and are almost solely undertaken within large modeling institutes. As such, those are rarely readily accessible for modification and extension by outside contributors. This review of the recent GLG-LUCC studies calls for more open-source modeling and availability of data, arguing that the latter is the real constraint to more widespread analyses of GLG-LUCC impacts. Progress in this field will require contributions from hundreds of researchers around the world and from a wide variety of disciplines.
Historically, the expansion of soy plantations has been a major driver of land-use/cover change (... more Historically, the expansion of soy plantations has been a major driver of land-use/cover change (LUCC) in Brazil. While a series of recent public actions and supply-chain commitments reportedly curbed the replacement of forests by soy, the expansion of the agricultural commodity still poses a considerable threat to the Amazonian and Cerrado biomes. Identification of areas under high risk of soy expansion is thus paramount to assist conservation efforts in the region. We mapped the areas suitable for undergoing transition to soy plantations in the Legal Amazon with a machine-learning approach adopted from the ecological modeling literature. Simulated soy expansion for the year 2014 exhibited favorable validation scores compared to other LUCC models. We then used our model to simulate how potential future infrastructure improvements would affect the 2014 probabilities of soy occurrence in the region. In addition to the 2.3 Mha of planted soy in the Legal Amazon in 2014, our model identified another 14.7 Mha with high probability of soy conversion in the region given the infrastructure conditions at that time. Out of those, pastures and forests represented 9.8 and 0.4 Mha, respectively. Under the new infrastructure scenarios simulated, the Legal Amazonian area under high risk of soy conversion increased by up to 2.1 Mha (14.6%). These changes led to up to 11.4 and 51.4% increases in the high-risk of conversion areas of pastures and forests, respectively. If conversion occurs in the identified high-risk areas, at least 4.8 Pg of CO 2 could be released into the atmosphere, a value that represents 10 times the total CO 2 emissions of Brazil in 2014. Our results highlight the importance of targeting conservation policies and enforcement actions, including the Soy Moratorium, to mitigate future forest cover loss associated with infrastructure improvements in the region.
REDD+ was initially conceived of as a multi-level carbon-based payment for environmental services... more REDD+ was initially conceived of as a multi-level carbon-based payment for environmental services (PES). It is still often assumed to be a cost-effective climate change mitigation strategy, but this assumption is mostly based on theoretical studies and static opportunity cost calculations. We used spatial and socioeconomic datasets from an Amazonian deforestation frontier in Brazil to construct a simulation model of REDD+ payments to households that can be used to assess REDD+ interventions. Our SimREDD+ model consists of dynamic optimization and land-use/cover change allocation sub-models built into an agent-based model platform. The model assumes that households maximize profit under perfect market conditions and calculates the optimal household land-use/cover configuration at equilibrium under a given REDD+ PES scenario. These scenarios include PES based on (1) forest area and (2) carbon stocks. Insights gained from simulations under different conditions can assist in the design of more effective, efficient, and equitable REDD+ programs.
We used a hybrid optimization-agent-based model to simulate REDD+ (Reduced Emissions from Defores... more We used a hybrid optimization-agent-based model to simulate REDD+ (Reduced Emissions from Deforestation and forest Degradation in combination with conservation, sustainable forest management, and enhancement of forest carbon stocks) payment scenarios to farm households in the old deforestation frontier of Rondônia, Brazil. Payments varied from $5 to $30 per ton of net CO 2 either not emitted or removed from the atmosphere relative to a baseline scenario. The impacts of REDD+ were assessed as changes in land use/cover, net CO 2 emissions, program costs, community welfare, and agricultural production. Our results suggest that interventions aimed at zero gross deforestation would require unrealistically large annual disbursements. In contrast, zero net carbon emissions can be achieved at approximately two-thirds the cost with reduced impacts on food production. Overall, simulated payments increased inequality among households, which conflicts with desired REDD+ outcomes. Results suggest that REDD+ might be more successful solely as a climate change mitigation mechanism as opposed to a complex multi-objective development program.
Concerns about the sustainability of tropical forestry motivated this study on post-logging timbe... more Concerns about the sustainability of tropical forestry motivated this study on post-logging timber and carbon dynamics over a 20-year period in Paragominas, Pará, Brazil. Previously unlogged forest was subjected to conventional logging (CL), reduced-impact logging (RIL), or was set aside as an unlogged control. All trees P25 cm DBH and all trees of commercial species P10 cm DBH were monitored in a 24.5 ha plot in each treatment, with a 5.25 ha subplot in each for monitoring all trees P10 cm DBH. Above-ground biomass and bole volumes of merchantable species were tracked based on 10 inventories made between 1993 and 2014. Pre-logging biomass and bole volumes of commercial species were estimated as 237, 231, and 211 Mg ha À1 , and 78, 80, and 70 m 3 ha À1 , in the RIL, CL, and unlogged plots, respectively. One year after logging, biomass was reduced 14% by RIL and 24% by CL with corresponding merchantable species volume reductions of 21% and 31%. By 2014, biomass and bole volumes of commercial species had recovered 95% and 98% of their pre-logging stocks in the RIL plot but only 76% and 72% in the CL plot, respectively ; timber volumes from large trees (P50 cm DBH) were only recovered to 81% in the RIL plot and to 53% in the CL plot. Over the first twenty years after logging, average volume increments from commercial species were substantially higher in the RIL plot (0.72 m 3 ha À1 year À1) than in the CL plot (0.08 m 3 ha À1-year À1). Recovery of both biomass and timber volumes were temporarily reversed between 2009 and 2014 due to a 4-fold increase in annual mortality rates in the RIL plot and a 5.5-fold increase in the CL plot (as well as a 3-fold increase in the control plot), all presumably related to the extreme drought of 2010. Our findings support the claim that use of RIL techniques accelerates rates of biomass and timber stock recovery after selective logging.
While around 20% of the Amazonian forest has been cleared for pastures and agriculture, one fourt... more While around 20% of the Amazonian forest has been cleared for pastures and agriculture, one fourth of the remaining forest is dedicated to wood production [1]. Most of these production forests have been or will be selectively harvested for commercial timber, but recent studies show that even soon after logging, harvested stands retain much of their tree-biomass carbon and biodiversity [2,3]. Comparing species richness of various animal taxa among logged and unlogged forests across the tropics, Burivalova et al.[4] found that despite some variability among taxa, biodiversity loss was generally explained by logging intensity (the number of trees extracted). Here, we use a network of 79 permanent sample plots (376 ha total) located at 10 sites across the Amazon Basin [5] to assess the main drivers of time-to-recovery of post-logging tree carbon (Table S1). Recovery time is of direct relevance to policies governing management practices (i.e., allowable volumes cut and cutting cycle leng...
This supplementary material was developed to address the limitation of our sample design experime... more This supplementary material was developed to address the limitation of our sample design experiment. In particular, our study consisted of one replicate of each treatment. Even though the individual plots were large (24.5 ha with 5.25 ha nested sub-plots) and monitored for 16 years, the observed difference in rates of biomass recovery after conventional logging (CL), reduced-impact logging (RIL), and in the control plot could have been due to pre-existing conditions in the plots. In particular, our finding of much more rapid stand biomass recovery after RIL could have been due to favorable conditions in that plot. We tested this possibility with: (1) a detailed comparison of pre-logging forest characteristics; (2) a geostatistical analyses of pre-logging aboveground biomass of trees ≥25 cm dbh; and (3) a comparison of post-logging growth rates of residual trees of species common to both logged plots. We believe that these results are sufficiently strong to allay concerns about the effects of pre-existing conditions confounding the treatment effect.
Successful efforts of indigenous groups to reduce emissions from deforestation and forest degrada... more Successful efforts of indigenous groups to reduce emissions from deforestation and forest degradation in developing countries (REDD +) will likely vary with how the initiatives are designed and implemented. Whether REDD + initiatives are carried out by national governments or decentralized to sub-national or project-level institutions with a nested approach could be of great consequence. I describe the SuruíForest Carbon Project in Amazonian Brazil, one of the first REDD + pilot projects implemented with indigenous people in the world. I emphasize (1) how enfranchisement of community members in the policy-planning process , fund management, and carbon baseline establishment increased project reliability and equity, and (2) how the project's quality would have likely been diminished if implemented under a centralized REDD + scheme. Policy relevance This article explores a decentralized REDD + intervention established in an indigenous land in Brazil. It expands the theoretical discussions on REDD + governance and highlights how centralized REDD + programmes are likely to be less effective than project-level interventions assisted by NGOs in terms of social benefits and community engagement. Additionally, the case study described can serve as reference for the design of critical social and technical components of REDD + .
Reducing emissions from deforestation and forest degradation (REDD+) has gained international att... more Reducing emissions from deforestation and forest degradation (REDD+) has gained international attention over the past decade, as manifested in both United Nations policy discussions and hundreds of voluntary projects launched to earn carbon-offset credits. There are ongoing discussions about whether and how projects should be integrated into national climate change mitigation efforts under the Paris Agreement. One consideration is whether these projects have generated additional impacts over and above national policies and other measures. To help inform these discussions, we compare the crediting baselines established ex-ante by voluntary REDD+ projects in the Brazilian Amazon to counterfactuals constructed ex-post based on the quasi-experimental synthetic control method. We find that the crediting baselines assume consistently higher deforestation than counterfactual forest loss in synthetic control sites. This gap is partially due to decreased deforestation in the Brazilian Amazon during the early implementation phase of the REDD+ projects considered here. This suggests that forest carbon finance must strike a balance between controlling conservation investment risk and ensuring the environmental integrity of carbon emission offsets. Relatedly, our results point to the need to better align project-and national-level carbon accounting. impact evaluation | synthetic control | payment for environmental services | carbon credit | deforestation C oncerns over global warming have led both the public and private sectors to promote climate change mitigation through the reduction of carbon (CO 2) emissions from deforestation and forest degradation in tropical countries-a concept known as REDD+ (1). This strategy gained international attention after 2005 as a voluntary, performance-based payment mechanism for reduced carbon emissions (2). While the regulations and capacity for national REDD+ programs are still under development in many countries, hundreds of voluntary, subnational REDD+ projects are operational worldwide (3). These projects intend to preserve forests through a variety of activities, e.g., improved monitoring and control, promotion of sustainable land uses, and engagement of local communities (4), either as proof of concept or to profit from the commercialization of "carbon-offset credits" (i.e., Mg CO 2 removed from or not emitted to the atmosphere) in a variety of markets. While these markets do not provide the level of funding originally envisioned for national REDD+ programs, they are substantial: In 2018 alone, the volume of carbon offsets traded totaled 98.4 million Mg CO 2 , with a market value of US$295.7 million; a third of those credits (30.5 million Mg CO 2) were generated by REDD+ projects (5). The Paris Agreement has raised thorny questions about how the carbon emission reductions claimed by these projects relate to nationally determined contributions (NDCs) and national greenhouse gas (GHG) emission inventories reported to the United Nations Framework Convention on Climate Change (6-8). Carbon credits from REDD+ [at both the project and national levels (1)] are issued based on performance, as defined by the comparison of realized forest cover to a baseline scenario constructed by projecting the forest cover expected in the absence of REDD+ (9). These baseline scenarios typically assume a continuation of historical deforestation trends (10), and thus eventually become unrealistic counterfactuals as the regional economic and political context change. Notably, these types of changes were observed in the Brazilian Amazon during 2004-2012, a period of sharply declining rates of forest loss (11), and also during 2019, when deforestation soared again (12) (Fig. 1). Consequently, credits for reduced deforestation (or lack thereof) claimed by voluntary REDD+ projects in the Brazilian Amazon may have been artifacts of external factors rather than REDD+ activities. Furthermore, critics of voluntary REDD+ projects have raised concerns that deforestation baselines might be intentionally inflated by profiteers seeking to financially benefit from the commercialization of superfluous credits, or "hot air" (13-15). In addition to the direct cost of not effectively offsetting GHG emissions, the excess credits generated by these projects impose an indirect cost on legitimate climate change mitigation efforts by undercutting the price of their credits. Early efforts to address these concerns included the establishment of standards and registries for voluntary carbon-offset projects. These standards were designed to ensure the environmental integrity of carbon offsets by requiring projects to use approved carbon-accounting methodologies for establishing deforestation baselines, monitoring, and reporting, all subject to third-party audits. Among those, the verified carbon standard Significance There are efforts to integrate the reduced carbon emissions from avoided deforestation claimed by voluntary REDD+ projects into national greenhouse gas emission inventories. This requires careful consideration of whether and how much of the reduced carbon emissions can be attributed to projects. However , credible evidence on the effectiveness of such voluntary activities is limited. We adopted the quasi-experimental synthetic control method to examine the causal effects of 12 voluntary REDD+ projects in the Brazilian Amazon. We compared these ex-post estimates of impacts with the reductions in forest loss claimed by those projects based on ex-ante baselines. Results suggest that the accepted methodologies for quantifying carbon credits overstate impacts on avoided deforestation and climate change mitigation.
Several payments for environmental services (PES) schemes to promote large-scale afforestation ar... more Several payments for environmental services (PES) schemes to promote large-scale afforestation are currently operational in New Zealand. In addition, the country has the only national greenhouse gas (GHG) Emissions Trading Scheme centred on forest carbon credits in the world. These market-based instruments are paramount in promoting the changes in land use required to achieve New Zealand's GHG-emission targets under the Paris Agreement, as well as to pave the way towards the goal of a low-carbon economy by 2050. We estimated the impacts of PES schemes on the profitability of Pinus radiata forestry to identify low-productivity grasslands where afforestation is likely to financially offset the current land use (i.e., mainly sheep-and-beef farming), under the assumption of constant returns to scale. We find that even without PES, 56% of these agricultural lands present lower economic returns than forestry. When PES are considered, results suggest that, depending on the PES schemes and carbon prices, 77-100% of the low-productivity grassland areas could financially benefit from afforestation. If realized, afforestation of those lands could result in the long-term removal of 1.2-1.5 Pg CO 2 from the atmosphere.
Over three-fourths of Brazil’s greenhouse gas (GHG) emissions originate from land-use/cover chang... more Over three-fourths of Brazil’s greenhouse gas (GHG) emissions originate from land-use/cover change (LUCC). Alarming rates of forest loss in the Amazon region gained international attention in the past, but deforestation declined substantially since 2004 (although the rate has trended upwards since 2012). This result conforms to the GHG emission reduction targets set by the National Climate Change Plan, originally divided into three phases: 2006–2009; 2010–2013; and 2014–2017. According to the plan, emission reductions are measured against a deforestation baseline, initially corresponding to the average forest loss observed from 1996 to 2005 (reference period). We used an artificial neural network (ANN) model to spatialize the plan’s original three-phase baseline and compare (i) the simulated carbon emissions from the baseline scenario to (ii) the emissions from observed deforestation during 2006–2017. Baseline spatialization was performed at the state level and informed by the 2000–2004 deforestation patterns in each state, resulting in nine state-specific calibrated ANNs. Simulated (baseline) and observed deforestation were compared to five biomass-density maps in order to estimate Brazil’s GHG emission reductions. Our results indicate that forest loss during the study period was 62,321 km2 lower than the baseline and associated with 1.5 ± 0.4 Pg of avoided CO2 emissions to the atmosphere. Virtually all emission reductions (93%) occurred in the states of Mato Grosso (74%) and Rondônia (19%). In contrast, Roraima, Amazonas, and Amapá states increased GHG emissions by 44.8, 36.7, and 14.7 Tg CO2, respectively. Lastly, we discuss the issue of attribution of deforestation reductions for results-based payments (REDD+) and the emission reduction certificates issued by the Amazon Fund.
In 2018, New Zealand announced an ambitious effort to plant one billion trees by 2028 as part of ... more In 2018, New Zealand announced an ambitious effort to plant one billion trees by 2028 as part of its climate change mitigation plan and Paris Agreement targets for 2030 and 2050. Afforestation will be promoted through a series of incentives, including payments for carbon sequestration linked to New Zealand's Emission Trading Scheme. Given that afforestation will be pursued voluntarily, the identification of future afforestation areas is paramount to assist policy formulation and avoid unintended land-use outcomes. We identified spatially-explicit drivers of forest gain and the locations most likely to experience afforestation in the country using two distinct spatial modelling frameworks: logistic regressions and artificial neural networks (ANN). Five of the eight most significant drivers of forest gain that have influenced past afforestation patterns according to our logistic regressions (i.e., erosion potential, distance from exotic forests, woody grasslands, grassland productivity, and slope) were also ranked among the ten most influential drivers by the ANN model. These results indicate an overall agreement between the two modelling approaches, despite substantial methodological differences. In the absence of changes in current incentives for afforestation in the country, simulations suggest that the largest afforestation areas would be in the northeast region of the North Island. These findings can assist policymakers and landscape planners to achieve desirable land-use objectives for New Zealand.
Deforestation driven by agricultural expansion is a major threat to the biodiversity of the Amazo... more Deforestation driven by agricultural expansion is a major threat to the biodiversity of the Amazon Basin. Modelling how deforestation responds to environmental policy implementation has thus become a policy relevant scientific undertaking. However, empirical parameterization of land-use/cover change (LUCC) models is challenging due to the high complexity and uncertainty of land-use decisions. Bayesian Network (BN) modelling provides an effective framework to integrate various data sources including expert knowledge. In this study, we integrate remote sensing products with data from farm-household surveys and a decision game to model LUCC at the BR-163, in Brazil. Our 'business as usual' scenario indicates cumulative forest cover loss in the study region of 8,000 km 2 between 2014 and 2030, whereas 'intensified law-enforcement' would reduce cumulative deforestation to 1,600 km 2 over the same time interval. Our findings underline the importance of conservation law enforcement in modulating the impact of agricultural market incentives on land cover change.
Land-use change results from the decisions of diverse actors in response to economic and politica... more Land-use change results from the decisions of diverse actors in response to economic and political contexts. Identification of underlying decision-making processes is key to understanding land-use patterns, anticipating trends, and designing effective environmental governance mechanisms. Here, we use a scenario-based decision game to examine hypothetical land-use decisions among four groups of rural producers in the municipalities of Sinop, Guarantã do Norte and Novo Progresso in the Brazilian Amazon. We simulate changes in agricultural prices, production cost, and frequency of environmental monitoring (in situ inspections) to understand how land-use decisions are made and change with shifts in economic and governance incentives. Hypothetical land-use decisions vary across scenarios, but not across study sites; environmental law enforcement influence land decisions, but not to the extent of dominating market factors and not for all types of producers. Decision games cannot substitute approaches to explain behavioral responses from observational data. However, they can provide immediate feedback on behavioral hypotheses before comprehensive observational data becomes available and support the development of models for land-use policy planning at local and regional scales.
This study adapts the Faustmann model (1849) to include the effects of carbon-based payments for ... more This study adapts the Faustmann model (1849) to include the effects of carbon-based payments for environmental services on the optimal rotation length of forest plantations. We assume payments occur at the end of the harvesting cycle and are based on the additional average carbon stock over the rotation length relative to an optimal management scenario without carbon incentives. We present numerical applications of our model based on the four most planted tree species in the world: Eucalyptus sp., Acacia sp., Pinus sp. and Tectona sp. Simulations were performed to quantify the impact of different payment levels (USD Mg CO 2 −1) on optimal rotation lengths and carbon stocks. We find diminishing sequestration returns to increasing carbon payments. Overall, our results suggest that targeting plantations with longer rotations, associated with slow-growing species, is a more cost-effective strategy to retain additional forest carbon stored (USD Mg CO 2 −1). However, such a strategy results in lower proportional increases in carbon stocks (Mg CO 2 ha −1) compared to faster-growing species, which benefit the most from the carbon incentives. As a result, increasing payments for additional carbon stored could change the choice of which tree species to produce and ironically promote plantations with lower total carbon stocks.
Global change drivers of land-use/cover change (LUCC) like population dynamics, economic developm... more Global change drivers of land-use/cover change (LUCC) like population dynamics, economic development, and climate change are increasingly important to local sustainability studies, and can only be properly analyzed at fine-scales that capture local biophysical and socio-economic conditions. When sufficiently widespread, local feedback to stresses originating from global drivers can have regional, national, and even global impacts. A multiscale, global-to-local-to-global (GLG) framework is thus needed for comprehensive analyses of LUCC and leakage. The number of GLG-LUCC studies has grown substantially over the past years, but no reviews of this literature and their contributions have been completed so far. In fact, the largest body of literature pertains to global-to-local impacts exclusively, whereas research on local feedback to regional, national, and global spheres remain scarce, and are almost solely undertaken within large modeling institutes. As such, those are rarely readily accessible for modification and extension by outside contributors. This review of the recent GLG-LUCC studies calls for more open-source modeling and availability of data, arguing that the latter is the real constraint to more widespread analyses of GLG-LUCC impacts. Progress in this field will require contributions from hundreds of researchers around the world and from a wide variety of disciplines.
Historically, the expansion of soy plantations has been a major driver of land-use/cover change (... more Historically, the expansion of soy plantations has been a major driver of land-use/cover change (LUCC) in Brazil. While a series of recent public actions and supply-chain commitments reportedly curbed the replacement of forests by soy, the expansion of the agricultural commodity still poses a considerable threat to the Amazonian and Cerrado biomes. Identification of areas under high risk of soy expansion is thus paramount to assist conservation efforts in the region. We mapped the areas suitable for undergoing transition to soy plantations in the Legal Amazon with a machine-learning approach adopted from the ecological modeling literature. Simulated soy expansion for the year 2014 exhibited favorable validation scores compared to other LUCC models. We then used our model to simulate how potential future infrastructure improvements would affect the 2014 probabilities of soy occurrence in the region. In addition to the 2.3 Mha of planted soy in the Legal Amazon in 2014, our model identified another 14.7 Mha with high probability of soy conversion in the region given the infrastructure conditions at that time. Out of those, pastures and forests represented 9.8 and 0.4 Mha, respectively. Under the new infrastructure scenarios simulated, the Legal Amazonian area under high risk of soy conversion increased by up to 2.1 Mha (14.6%). These changes led to up to 11.4 and 51.4% increases in the high-risk of conversion areas of pastures and forests, respectively. If conversion occurs in the identified high-risk areas, at least 4.8 Pg of CO 2 could be released into the atmosphere, a value that represents 10 times the total CO 2 emissions of Brazil in 2014. Our results highlight the importance of targeting conservation policies and enforcement actions, including the Soy Moratorium, to mitigate future forest cover loss associated with infrastructure improvements in the region.
REDD+ was initially conceived of as a multi-level carbon-based payment for environmental services... more REDD+ was initially conceived of as a multi-level carbon-based payment for environmental services (PES). It is still often assumed to be a cost-effective climate change mitigation strategy, but this assumption is mostly based on theoretical studies and static opportunity cost calculations. We used spatial and socioeconomic datasets from an Amazonian deforestation frontier in Brazil to construct a simulation model of REDD+ payments to households that can be used to assess REDD+ interventions. Our SimREDD+ model consists of dynamic optimization and land-use/cover change allocation sub-models built into an agent-based model platform. The model assumes that households maximize profit under perfect market conditions and calculates the optimal household land-use/cover configuration at equilibrium under a given REDD+ PES scenario. These scenarios include PES based on (1) forest area and (2) carbon stocks. Insights gained from simulations under different conditions can assist in the design of more effective, efficient, and equitable REDD+ programs.
We used a hybrid optimization-agent-based model to simulate REDD+ (Reduced Emissions from Defores... more We used a hybrid optimization-agent-based model to simulate REDD+ (Reduced Emissions from Deforestation and forest Degradation in combination with conservation, sustainable forest management, and enhancement of forest carbon stocks) payment scenarios to farm households in the old deforestation frontier of Rondônia, Brazil. Payments varied from $5 to $30 per ton of net CO 2 either not emitted or removed from the atmosphere relative to a baseline scenario. The impacts of REDD+ were assessed as changes in land use/cover, net CO 2 emissions, program costs, community welfare, and agricultural production. Our results suggest that interventions aimed at zero gross deforestation would require unrealistically large annual disbursements. In contrast, zero net carbon emissions can be achieved at approximately two-thirds the cost with reduced impacts on food production. Overall, simulated payments increased inequality among households, which conflicts with desired REDD+ outcomes. Results suggest that REDD+ might be more successful solely as a climate change mitigation mechanism as opposed to a complex multi-objective development program.
Concerns about the sustainability of tropical forestry motivated this study on post-logging timbe... more Concerns about the sustainability of tropical forestry motivated this study on post-logging timber and carbon dynamics over a 20-year period in Paragominas, Pará, Brazil. Previously unlogged forest was subjected to conventional logging (CL), reduced-impact logging (RIL), or was set aside as an unlogged control. All trees P25 cm DBH and all trees of commercial species P10 cm DBH were monitored in a 24.5 ha plot in each treatment, with a 5.25 ha subplot in each for monitoring all trees P10 cm DBH. Above-ground biomass and bole volumes of merchantable species were tracked based on 10 inventories made between 1993 and 2014. Pre-logging biomass and bole volumes of commercial species were estimated as 237, 231, and 211 Mg ha À1 , and 78, 80, and 70 m 3 ha À1 , in the RIL, CL, and unlogged plots, respectively. One year after logging, biomass was reduced 14% by RIL and 24% by CL with corresponding merchantable species volume reductions of 21% and 31%. By 2014, biomass and bole volumes of commercial species had recovered 95% and 98% of their pre-logging stocks in the RIL plot but only 76% and 72% in the CL plot, respectively ; timber volumes from large trees (P50 cm DBH) were only recovered to 81% in the RIL plot and to 53% in the CL plot. Over the first twenty years after logging, average volume increments from commercial species were substantially higher in the RIL plot (0.72 m 3 ha À1 year À1) than in the CL plot (0.08 m 3 ha À1-year À1). Recovery of both biomass and timber volumes were temporarily reversed between 2009 and 2014 due to a 4-fold increase in annual mortality rates in the RIL plot and a 5.5-fold increase in the CL plot (as well as a 3-fold increase in the control plot), all presumably related to the extreme drought of 2010. Our findings support the claim that use of RIL techniques accelerates rates of biomass and timber stock recovery after selective logging.
While around 20% of the Amazonian forest has been cleared for pastures and agriculture, one fourt... more While around 20% of the Amazonian forest has been cleared for pastures and agriculture, one fourth of the remaining forest is dedicated to wood production [1]. Most of these production forests have been or will be selectively harvested for commercial timber, but recent studies show that even soon after logging, harvested stands retain much of their tree-biomass carbon and biodiversity [2,3]. Comparing species richness of various animal taxa among logged and unlogged forests across the tropics, Burivalova et al.[4] found that despite some variability among taxa, biodiversity loss was generally explained by logging intensity (the number of trees extracted). Here, we use a network of 79 permanent sample plots (376 ha total) located at 10 sites across the Amazon Basin [5] to assess the main drivers of time-to-recovery of post-logging tree carbon (Table S1). Recovery time is of direct relevance to policies governing management practices (i.e., allowable volumes cut and cutting cycle leng...
This supplementary material was developed to address the limitation of our sample design experime... more This supplementary material was developed to address the limitation of our sample design experiment. In particular, our study consisted of one replicate of each treatment. Even though the individual plots were large (24.5 ha with 5.25 ha nested sub-plots) and monitored for 16 years, the observed difference in rates of biomass recovery after conventional logging (CL), reduced-impact logging (RIL), and in the control plot could have been due to pre-existing conditions in the plots. In particular, our finding of much more rapid stand biomass recovery after RIL could have been due to favorable conditions in that plot. We tested this possibility with: (1) a detailed comparison of pre-logging forest characteristics; (2) a geostatistical analyses of pre-logging aboveground biomass of trees ≥25 cm dbh; and (3) a comparison of post-logging growth rates of residual trees of species common to both logged plots. We believe that these results are sufficiently strong to allay concerns about the effects of pre-existing conditions confounding the treatment effect.
Successful efforts of indigenous groups to reduce emissions from deforestation and forest degrada... more Successful efforts of indigenous groups to reduce emissions from deforestation and forest degradation in developing countries (REDD +) will likely vary with how the initiatives are designed and implemented. Whether REDD + initiatives are carried out by national governments or decentralized to sub-national or project-level institutions with a nested approach could be of great consequence. I describe the SuruíForest Carbon Project in Amazonian Brazil, one of the first REDD + pilot projects implemented with indigenous people in the world. I emphasize (1) how enfranchisement of community members in the policy-planning process , fund management, and carbon baseline establishment increased project reliability and equity, and (2) how the project's quality would have likely been diminished if implemented under a centralized REDD + scheme. Policy relevance This article explores a decentralized REDD + intervention established in an indigenous land in Brazil. It expands the theoretical discussions on REDD + governance and highlights how centralized REDD + programmes are likely to be less effective than project-level interventions assisted by NGOs in terms of social benefits and community engagement. Additionally, the case study described can serve as reference for the design of critical social and technical components of REDD + .
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deforestation declined substantially since 2004 (although the rate has trended upwards since 2012). This result conforms to the GHG
emission reduction targets set by the National Climate Change Plan, originally divided into three phases: 2006–2009; 2010–2013; and 2014–2017. According to the plan, emission reductions are measured against a deforestation baseline, initially corresponding to the average forest loss observed from 1996 to 2005 (reference period). We used an
artificial neural network (ANN) model to spatialize the plan’s original three-phase baseline and compare (i) the simulated carbon emissions from the baseline scenario to (ii) the emissions from observed deforestation during 2006–2017. Baseline spatialization was
performed at the state level and informed by the 2000–2004 deforestation patterns in each state, resulting in nine state-specific calibrated ANNs. Simulated (baseline) and observed deforestation were compared to five biomass-density maps in order to estimate
Brazil’s GHG emission reductions. Our results indicate that forest loss during the study period was 62,321 km2 lower than the baseline and associated with 1.5 ± 0.4 Pg of avoided CO2 emissions to the atmosphere. Virtually all emission reductions (93%)
occurred in the states of Mato Grosso (74%) and Rondônia (19%). In contrast, Roraima, Amazonas, and Amapá states increased GHG emissions by 44.8, 36.7, and 14.7 Tg CO2, respectively. Lastly, we discuss the issue of attribution of deforestation reductions
for results-based payments (REDD+) and the emission reduction certificates issued by the Amazon Fund.
deforestation declined substantially since 2004 (although the rate has trended upwards since 2012). This result conforms to the GHG
emission reduction targets set by the National Climate Change Plan, originally divided into three phases: 2006–2009; 2010–2013; and 2014–2017. According to the plan, emission reductions are measured against a deforestation baseline, initially corresponding to the average forest loss observed from 1996 to 2005 (reference period). We used an
artificial neural network (ANN) model to spatialize the plan’s original three-phase baseline and compare (i) the simulated carbon emissions from the baseline scenario to (ii) the emissions from observed deforestation during 2006–2017. Baseline spatialization was
performed at the state level and informed by the 2000–2004 deforestation patterns in each state, resulting in nine state-specific calibrated ANNs. Simulated (baseline) and observed deforestation were compared to five biomass-density maps in order to estimate
Brazil’s GHG emission reductions. Our results indicate that forest loss during the study period was 62,321 km2 lower than the baseline and associated with 1.5 ± 0.4 Pg of avoided CO2 emissions to the atmosphere. Virtually all emission reductions (93%)
occurred in the states of Mato Grosso (74%) and Rondônia (19%). In contrast, Roraima, Amazonas, and Amapá states increased GHG emissions by 44.8, 36.7, and 14.7 Tg CO2, respectively. Lastly, we discuss the issue of attribution of deforestation reductions
for results-based payments (REDD+) and the emission reduction certificates issued by the Amazon Fund.