Conference Agenda

We study the effect of air pollution on human capital accumulation indicators, focusing on school absenteeism and on low-stakes standardised tests.

The empirical analysis takes place in Chile, where air pollution is a severe public-health issue. We use administrative education records and environmental information from satellite and monitoring stations to carry out our analyses.

In order to tackle the identification issues associated with air pollution, we use the height of the planetary boundary layer and thermal inversions as instruments of air pollution, and we exploit the longitudinal nature of administrative data.

Results show that air pollution increases school absenteeism, especially for girls. Air pollution also causes a reduction in standardised test core results for maths and reading, which can be linked to previous research on its detrimental effect on cognitive ability. Our findings show the importance of integrating environmental and educational policies, and they contribute to our understanding of how environmental factors shape individual and societal welfare and potentially their future outlook.

The objective of this study is to investigate the impact of wildfires on the local labor market, using exogenous variation in wildfires attributable to lightning strikes. While existing literature focuses on the negative effects of smoke exposure, this study isolates the impact on areas directly affected by fire. This environmental shock creates heterogeneous economic outcomes across industries. Specifically, the construction sector experiences increased earnings and employment due to reconstruction demand following wildfires. In contrast, the tourism and service sectors suffer severe contractions in employment. These findings reveal a structural shift in the local labor market: while reconstruction mitigates some economic losses, it reallocates labor from the service sector to the construction sector. Consequently, disaster relief policies must move beyond blanket transfers and prioritize targeted support for the disproportionately affected non-tradable sectors to achieve environmental justice.

Innovation in energy technologies is essential for decarbonization and economic growth, yet quantitative evidence on what accelerates technologies from conception to commercialization remains limited. We analyze the drivers of innovation using a dataset on 40 technologies, measured by progression along the Technology Readiness Level (TRL) scale from early research to first commercialization. Using a Cox proportional hazards model with a counting-process formulation, we test 13 hypotheses on the speed of technological development.

We find that innovation timelines are primarily shaped by technology characteristics, sectoral context, and energy price signals, rather than by broad macroeconomic or political conditions. Higher oil prices consistently accelerate technological progression (hazard ratio = 1.60, p = 0.000046). Electricity and industrial technologies advance substantially faster than building technologies. In contrast, democracy, GDP growth, interest rates, and technology cost measures do not exhibit robust effects. Stage-specific results indicate that the drivers of innovation vary across development phases, with implications for innovation and policy design.

The EU Emissions Trading System (EU ETS) is the European Union's flagship instrument for achieving carbon neutrality by 2050. Because the policy applies to a subset of economy-wide emissions, a central question is whether and how low-carbon technological change extends beyond the set of regulated activities. This paper documents a sharp increase in low-carbon patenting among firms that are not directly regulated by the EU ETS and examines whether this pattern is systematically related to these firms' positions in European value chains connected to EU-ETS-regulated production. Using sector-level evidence and mapping innovation to value-chain exposure, we show that the growth in low-carbon patenting among unregulated firms is concentrated in sectors that are upstream or downstream of regulated production networks. Methodologically, we highlight that measuring exposure using the total coefficients matrix---rather than the direct coefficients matrix---can alter qualitative conclusions relative to common approaches in the literature.

Climate change poses significant challenges to labor markets in developing countries, where workers face high exposure to extreme heat and firms have limited adaptive capacity. This paper examines how heat stress affects the labor share—measured as labor compensation relative to firm sales—using a large panel of Indian manufacturing firms. Exploiting within-firm temperature variation over time, we find that heat stress significantly reduces the labor share, with results robust across alternative specifications. This decline is driven primarily by production adjustments, especially capital-labor substitution. We also present suggestive evidence that heat stress may strengthen firms’ market power and interact with labor regulations in shaping firm responses. Combining our estimates with climate projections, future warming is likely to intensify labor share declines across regions. Our findings identify climate change as an important and underexplored driver of distributional change in developing economies.

Accelerating low-carbon technological innovation is central to achieving global climate targets, yet robust evidence on whether national climate policies effectively trigger real-world innovation remains limited. Existing research largely focuses on single technologies or individual policy instruments, limiting its ability to generalize across heterogeneous technological systems. Here, we develop a machine-learning-assisted analytical framework to systematically assess policy-induced innovation dynamics across 34 economies, nine major low-carbon technologies, and more than 4,100 policy interventions over the period 1990~2023. By combining structural break detection with a multidimensional characterization of policy intensity, stability, and policy mix, we identify statistically significant innovation take-offs and trace the policy conditions under which they emerge. We find that only 3~32% of climate policies lead to measurable innovation take-offs, with pronounced heterogeneity across technologies. Mature renewable technologies exhibit low marginal policy sensitivity but generate large innovation surges once triggered, whereas high-cost and long-cycle technologies respond weakly and often with substantial delays of 1.5~2.5 years. Nearly 60% of observed innovation take-offs occur under relatively stable policy environments, highlighting the importance of long-term institutional credibility. Nevertheless, well-timed policy strengthening can still induce breakthrough effects during specific technological opportunity windows. Overall, our findings demonstrate that climate policy does not automatically translate into effective technological innovation; policy effectiveness instead depends on alignment with technological maturity, cost structures, development horizons, and national institutional contexts to enable sustained progress toward net-zero targets.

This study examines the impact of air pollution on the academic publications of individual scholars in China. Using a large dataset of scholars' publications and cellphone information from five major cities, we found that a 1% increase in PM2.5 concentration results in a reduction of approximately 1,053 publications nationwide. This effect is more pronounced during years of heavy pollution, especially among highly productive, and younger scholars. Further analysis of mechanisms reveals that higher pollution levels reduce working hours, increase hospital visits, and hinder collaborations. The disclosure of pollution information can mitigate these negative effects among scholars with medical backgrounds due to heightened health awareness.