Conference Agenda

Historical pollution incidents offer a rare opportunity to examine managerial decision-making under binding social and environmental constraints, even though the available records are often fragmentary.　 This study examines the relationship between pollution and compensation associated with two major copper mines—Besshi and Hitachi—during Japan’s early twentieth-century industrialization, with particular attention to the decision-making options available to mine operators. We reconstructed pollution levels using the atmospheric dispersion simulation model AIST-ADMER and estimated an empirical pollution–compensation relationship based on data from the Besshi mine. The estimated model was then applied to both mines to explore counterfactual outcomes. The results indicate that, despite strong profit incentives to increase production volumes even in the presence of rising compensation payments, mine operators in practice constrained their output under social and political pressure. As an additional analytical extension, we introduced a probabilistic model of technological innovation for pollution elimination. The analysis suggests that in-house development of pollution-control technologies would have been prohibitively costly, whereas the introduction of technologies from abroad represented a more feasible strategy—a pattern consistent with historical evidence. Overall, this study shows that simulation-based reconstruction can recover otherwise unobservable managerial trade-offs and illuminate how firms navigated production incentives, compensation pressures, and technological uncertainty in the governance of industrial pollution.

Judicial institutions are increasingly recognized as central to development, yet their role in environmental governance remains underexplored. This paper investigates the causal impact of environmental court establishment on corporate environmental alliance formation in China, using a staggered difference-in-differences design and panel data on 2,961 publicly listed firms from 2005 to 2022. We find that courts substantially increase the likelihood of forming environmental alliances. The induced alliances follow two distinct logics. Alliances with government entities act as institutional insurance: they substitute for voluntary disclosure and buffer firms against subsidy losses, with stronger effects in heavy‑polluting sectors, but they do not yield measurable environmental improvements. Alliances with academic partners build capability: they increase green invention patenting and environmental investment, resulting in improvements in environmental quality. Such effects are larger where legal capacity is weak, suggesting that courts function as institutional substitutes in low‑capacity settings. We conclude that environmental courts shift firms toward collaboration, but environmental gains depend on alliance type. These findings highlight how judicial innovations can strengthen environmental governance in contexts with limited enforcement capacity, which is essential for sustainable development in emerging economies.

We show that interactions between overlapping environmental regulations—particularly when one involves a threshold-based standard—can substantially attenuate the benefits predicted when policies are evaluated in isolation. This attenuation arises because conventional models and analyses typically assume local emissions are exogenous to upwind regulatory shocks. To study the impacts of this assumption, we develop a model in which threshold-based air-quality standards overlap with regulations targeting major point sources. We then empirically test the model’s predictions and document evidence consistent with regulatory rebound: when upwind power-plant emissions fall (e.g., due to point-source rules), local emissions in NAAQS-constrained areas rise. We find that regulatory rebound offsets 50–70 percent of expected PM₂.₅ improvements. To quantify rebound’s impact, we develop a high-resolution modeling framework that combines advanced particle-trajectory modeling (NOAA’s HYSPLIT) with machine learning to map upwind power-plant emissions to downwind air quality with state-of-the-art accuracy. We use these predictions to quantify how sustained reductions in upwind emissions of PM₂.₅ precursors affect downwind local PM₂.₅ concentrations. The results are consistent with local pollution rebounding in response to upwind emission reductions—particularly in NAAQS-constrained areas. Our findings underscore the importance of accounting for behavioral and regulatory interactions when designing and evaluating environmental policy—critical for efficiency and equity.

This study provides an assessment of individuals’ willingness to pay (WTP) for the electrification of urban bus services, reflecting environmental and service-quality improvements such as reduced emissions, lower noise, and better waiting conditions. We also examine how this WTP changes when electrification is bundled with increases in bus frequency, which deliver travel time and crowding reductions. Beyond estimating the existence and magnitude of these “pure” and “bundled” WTP components, we investigate the underlying determinants of preference heterogeneity, including: (i) utility-based determinants, such as environmental and externality-related motivations, public transport (PT) usage and commuting habits, current satisfaction with PT performance, and awareness and experience with e-buses; (ii) psychological determinants, such as personal norms and perceived social norms toward both transport electrification and public transport. Finally, by leveraging responses to two policy scenarios, we distinguish real service-improvement WTP from pollution/congestion-oriented WTP or potentially symbolic WTP, thus offering clearer interpretation of consumer support for PT electrification policies.