The LEEP Seminar Series brings together scholars from around the world to share their research in environmental, resource and energy economics, through a friendly and collaborative platform.

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Programme Autumn 2024

2nd Oct 2024: The Greener Gender: Women Politicians and Deforestation by Kathryn Baragwanath, University of Melbourne

Abstract: Women are often heralded as environmental leaders, yet the reasons for their presumed environmental commitment remain unclear. This paper examines the impact of women’s political representation on deforestation rates in Brazil. We argue that women, when elected to office, are more likely to drive improved environmental outcomes due to their reduced access to corrupt networks that influence the enforcement of environmental laws. We exploit close election regression discontinuity design in order to establish the causal effects of electing a woman on deforestation. Consistent with our theory, we find that electing a woman as mayor leads to significantly lower rates of deforestation during the woman’s time in office. We show that women are significantly less corrupt than men and are less likely to have connections to or receive campaign funding from the agricultural sector, which has vested interests in deforestation. This, in turn, drives the observed effects on deforestation. Additionally, we show suggestive evidence that these effects are not driven by differential preferences or ability (educational levels) between men and women. Altogether, our findings demonstrate that women’s political representation significantly reduces deforestation rates in the Brazil. This reduction is driven by women’s lower propensity for corruption and their decreased likelihood of being influenced by vested interests in the agricultural sector, the main driver of Brazilian deforestation.

16th Oct 2024: Beyond the Canopy: Sources of Satellite Data and Deforestation Policy Evaluation in Brazil by Nilesh Shinde, University of Massachusetts, Amherst

Abstract: Satellite data is essential for monitoring deforestation and informing environmental policy, but the resolution of these data can significantly influence both policy implementation and evaluation. This study examines the impact of Brazil’s 2008 Blacklisting policy in the Amazon, using multiple satellite datasets. We find that the dataset traditionally used for both monitoring and evaluation systematically overestimates the policy’s effectiveness due to its inability to detect small deforestation patches. We argue that its dual role as both the monitoring tool for enforcement and the source of data for policy assessment, together with its coarse resolution, leads to additional issues caused by strategic adaptation. By analyzing over 500GB of data, equivalent to over 180 billion Landsat pixels, we identify significant discrepancies in deforestation behaviors captured by varying resolutions of satellite data. Our analysis reveals that deforesting agents strategically adapt their behavior in response to stricter monitoring, creating smaller patches that evade detection by coarser datasets. These findings highlight the importance of high-resolution data for evaluating the true effects of deforestation policies and addressing strategic adaptations that challenge enforcement efforts.

23rd Oct 2024: Paula Pereda, University of Sao Paulo

Abstract: Human economic activities have transformed over half of the Earth’s surface, particularly through large-scale conversion of forested land for agricultural production. Access to credit can enable producers to expand the land area or increase yield per unit of land. Still, these effects’ relative dominance and spatial variation remain underexplored. To address this, we propose a typology of forest contexts based on factors that can shape how credit affects cattle and crop production and, thus, also deforestation. Using this topology, we analyze the heterogeneous impacts of credit by applying it to a 452-municipality panel dataset from the Brazilian Amazon. Employing a shift-share instrument for credit supply, we estimate deforestation impacts in each context. We find distinct impacts for expected contexts, which differ for cattle versus for crops. In each sector, credit drives deforestation only under specific local conditions.

6th Nov 2024: Elisabeth Gsottbauer, London School of Economics

Abstract: This study investigates the influence of dual-process decision-making on consumer food choices. Utilizing a representative UK sample, we designed a randomized experiment where participants selected dinner items from a simulated food delivery platform under varying conditions of time pressure and nudge interventions including re-ordering and carbon labels. We explore the effects of these interventions on both fast, intuitive selections and more deliberate (slow) choices. Moreover, we measure consumer welfare by using an incentive compatible multiple price list to determine willingness to pay for being nudged, i.e. the amount of money individuals are willing to spend to have their choices guided or influenced by behavioral interventions. Results show that the repositioning intervention is most effective in reducing high carbon meal choices, particularly within the initial decision-making window. This finding indicate that order effects are particularly strong when decisions are made quickly.

20th Nov 2024: Young Kim, University of Oxford

Abstract: Payments for ecosystem services (PES) programs can enhance resilience to extreme weather events by establishing natural infrastructure.  I investigate the effectiveness of the Conservation Reserve Enhancement Program (CREP) in the United States in mitigating flooded crop losses through the restoration of riparian buffers and wetlands.  By leveraging variation in the timing of the program’s introduction across counties, I find that CREP reduced the number of flooded crop acres by 39 percent and the extent of damage on those acres by 26 percent during the initial 11 years of program implementation.  The flood mitigation benefits of CREP also generated financial spillover effects on the federal crop insurance program, saving $94 million in indemnity payouts that would have otherwise been paid to insured farmers.  Two-thirds of these benefits resulted from reduced flood damage on cropland in production, while the remaining benefits were attributed to the removal of at-risk cropland from production.  The magnitude of benefits varied spatially and temporally depending on the duration of program availability, the extent of program participation, and the adoption of alternative risk management strategies.  Overall, these findings underscore the critical role of PES programs in facilitating nature-based solutions for climate change adaptation.

11th Dec 2024: Matt Cole, University of Birmingham **CANCELLED DUE TO ILLNESS**

Centralising the Enforcement of Environmental Regulations: Using Machine Learning to Aid Policy Evaluation in China

 Abstract: To overcome key challenges in environmental policy evaluation we use machine learning based weather normalisation techniques to strip out the effect of weather on air pollution estimates. Combined with Augmented Synthetic Control Methods (ASCM) we provide a causal estimate of the impact of China’s decision to centralise environmental policy enforcement. We find that the recently introduced Central Environmental Inspection Policy led to a short-term reduction in PM2.5 and SO2 immediately after the inspection. However, within 3 months of the inspection team leaving, pollution levels had returned to previous levels. Comparisons with Difference-in-Difference estimations show the importance of both weather normalising and using an ASCM approach, particularly in the absence of parallel pre-trends.