Conference Agenda

Payments for Ecosystem Services are a tool for reducing carbon emissions from deforestation by paying households to conserve forest. Empirical evaluations have found small-scale subsidy interventions to have a large impact on deforestation. However, little is known about the general equilibrium effect of implementing these policies at a larger scale. I develop a tractable model of smallholders with dynamic incentives for land use to study the general equilibrium impact of at-scale forest subsidies. The quantified model implies that an at-scale intervention has only one-sixth of the impact on the level of forest of an otherwise-identical local intervention. This is because the intervention increases the equilibrium price of wood products, increasing households' incentive to deforest. However, the duration of subsidy payments is a crucial determinant of their cost-effectiveness: Comparing long-term and short-term interventions with the same total cost, long-term interventions can more than double the increase in the level of forest. Next, at scale, equilibrium forces make more households marginal to the subsidy and reduce the cost-effective degree of targeting. Finally, at-scale interventions are more progressive than local interventions because equilibrium price changes favor households with low land productivity.

This paper examines whether pasture recovery can effectively curb deforestation in Brazil, using a structural dynamic model to analyse land-use decisions. The model incorporates both the extensive margin, where improved pasture quality is linked to lower conversion rates, and the intensive margin, where increased productivity incentivizes greater land conversion. By disentangling these effects, I empirically assess their net impact on deforestation and carbon emissions. Employing detailed data on pasture degradation, land-use changes, and livestock systems, I estimate model parameters and evaluate two counterfactual policy scenarios. The first examines a pasture recovery policy, while the second assesses a carbon tax that internalizes the social cost of emissions. The results indicate that while pasture recovery reduces deforestation, a significant portion of its benefits is offset by increased economic incentives for land conversion. In contrast, even modest carbon taxes achieve substantial reductions in deforestation and emissions. These findings underscore the limited effectiveness of pasture recovery as a stand-alone solution and suggest that policy approaches integrating carbon pricing may offer greater potential for mitigating deforestation and achieving climate goals.

Tropical forests are disappearing at an alarming rate, yet large efficiency gains remain untapped in international climate finance. A promising solution is to pay countries directly for forest conservation based on measured deforestation rates. However, existing payment schemes have a critical flaw: they set individual base- lines for each country, often adjusted based on past performance. This creates a "ratchet effect"- countries deliberately underperform today to avoid tougher targets tomorrow, undermining effectiveness. We propose an optimized jurisdictional reward fund (JRF) with a universal, formula-based design that avoids this perverse incentive. We explore the optimal design of the Reversing Deforestation Mechanism (RDM) proposed by Assuncao et al. (2025), a jurisdictional reward fund that would pay jurisdictions based on the net deforestation rate, defined as the emissions from deforestation and degradation minus the carbon captured through forest restoration. In this paper, we develop a dynamic model in which countries choose net deforestation rates in response to reward payments linked to a universal reference level. We show that static, contemporaneous incentives dominate dynamic, stock-based incentives, implying that optimal design can be well approximated using a static framework. We derive closed-form formulas for the optimal reference level, reward rate, and emission reductions as functions of budget, calibrated to empirical deforestation data from 76 tropical forest countries (2002–2024). Using a uniform distribution approximation, we show remarkable tractability with less than 10% error compared to discrete-country models. In the funding game, China, EU emerge as a stable coalition that would each year donate USD 9.6 billion out of collective self-interest, avoiding an annual flow of 213 MtCO2 emissions by saving an annual flow of 0.52 million hectares of tropical forests.

Global agricultural supply chains are associated with numerous environmental and human rights risks, which have been growing in scope and complexity. While regulatory due diligence has become a prominent instrument for monitoring and mitigating these risks, supply chain actors fear negative trade implications and perceive it as a non-tariff barrier to trade. However, empirical evidence regarding the impacts of such policies remains limited. Here, we examine the effects of due diligence regulations on trade for the example of the 2017 French Corporate Duty of Vigilance Law and its implications on global palm oil supply chains. Employing a combined synthetic control and difference-in-differences approach, we find that the French palm oil import volume from Indonesia increases after the implementation of the law. Our complementary gravity model analysis supports this result. Indonesia’s dominant role and competitiveness in the global palm oil trade network as well as its high number of certified palm oil mills can explain these findings. This research contributes to the ongoing debate about the future of human rights and environmental due diligence by indicating that trade implications need not be negative, but a shift to more sustainable supply chains may be fostered.