Luis Sarmiento

We investigate the spillover effects of low emission zones on traffic, air pollution, and health, focusing on Madrid Central. Combining satellite and station-level air quality data with nearly 4,000 traffic sensors, we find that while the policy reduced NO2 and PM2.5 inside the restricted area, it displaced traffic by 2.7 percent city-wide, generating heterogeneous spillovers: PM2.5 increased by up-to 0.4 µg/m3 in some districts while decreasing in others. Two spatial channels shape these spillovers: wind transport carries cleaner air from the restriction zone to downwind neighborhoods, while more distant districts bear the full cost of rerouted traffic. These pollution increases fall disproportionately on lower-income peripheral neighborhoods. Comparing districts by the direction of their estimated pollution change, we find suggestive evidence of higher hospitalization and mortality rates in pollution-increase vis-à-vis pollution-decrease districts. These findings suggest that geographically targeted driving restrictions can redistribute pollution in ways that systematically disadvantage peripheral communities.

We present a data-driven microsimulation model analyzing residential photovoltaic adoption in Germany to address uncertainties in future adoption rates. We develop and calibrate a technology adoption model incorporating economic, financial, and behavioral factors to replicate historical adoption patterns from 2007 to 2020 and project future scenarios. The model simulates household adoption decisions based on expected profitability, environmental attitudes, financial constraints, and peer effects, while differentiating across income groups and geographical locations. Combining household survey data with installation records, we demonstrate that our model accurately reproduces historical adoption patterns. Our projections follow the sigmoid curve typical of innovation diffusion theory, with peer effects accelerating adoption as ownership increases. The baseline scenario predicts approximately 47% household saturation by 2050, with maximum adoption growth occurring around 2030, though systemic grid-level constraints may limit penetration well before reaching such levels. Alternative scenarios demonstrate the critical role of technology costs, electricity prices, and feed-in tariffs in driving adoption. We find significant disparities between income groups, with the highest income decile reaching 77% adoption by 2050 compared to only 6% for the lowest decile. This disparity highlights the regressive distribution of feed-in tariff benefits, with wealthier households receiving disproportionately larger subsidies, though historical technology diffusion patterns suggest such inequities may diminish over time. Our findings provide valuable insights for policymakers regarding grid planning, subsidy design, and the distributional implications of renewable energy policies.

This study explores the relationship between air pollution and judicial rulings. Although environmental factors should not affect judicial decisions, realists contend that there is substantial room for external factors to transpire into sentencing and sway human reasoning. We hypothesize that air pollution is one of these factors. Using Poisson panel models and instrumental variable techniques, we show that exposure leads to more convictions. We posit that this effect occurs because the impact of exposure on the central nervous system changes the cognitive performance and empathy of judges. Back-of-the-envelope calculations suggest that decreasing average air pollution in India by one standard deviation would lead to up to 145,000 fewer convictions regarding currently active cases.

This article proposes an inverted U-shape relationship between air pollution and criminal behavior. Exposure increases criminality by raising criminals’ taste for risk and violent behavior while also reducing it by changing the number of felons and crime opportunities in the market through exacerbated morbidity and avoidance behavior. I illustrate both mechanisms with an expected utility model of the decision to delict and a simplified search and matching frictions model between criminals and crime opportunities. Linear, quadratic, and nonparametric Poisson pseudo-maximum likelihood estimator panel models confirm this bell-shaped relationship for Mexico City and New York, suggesting that the linear association between pollution and criminality uncovered by late studies may be better estimated with nonlinear models.

The influence of air pollution on cognitive productivity is a highly relevant and not fully understood economic issue. This study provides evidence of an adverse effect of exposure on the productivity of Mexican judicial workers. The empirical strategy approximates productivity with the length of the hearing and infers causality through instrumental variable panel models. Results show that air pollution decreases the productivity of judicial workers. For instance, increasing particulate matter by 10 units raises the length of hearings by 6.7 percent. Back‐of‐the‐envelope calculations suggest that if particulate matter had been ten units lower, the sample of courthouses would have gained 81,712 minutes or roughly 56 work‐days between 2015 and 2017.