Conference Agenda

This paper analyses the influence of income inequality on the adoption of mitigation policies. Despite considerable attention in the literature to the connection between inequality and environmental outcomes, the role of policies remains implicit and has not been singled out. Our study focuses on the national mitigation policies adopted in the G20 countries between 1997 and 2021 as measured by the Climate Policy Database. We examine the impact of average income and inequality on policy adoption by using a Poisson fixed-effects and correlated random-effects regression model. Several measures of inequality are derived from the World Inequality Database to capture various aspects of income distribution. The findings show that the effect of inequality on the application of climate policies depends on the average income level of the country. In richer countries, a decrease in inequality is associated with an increase in the adoption of climate change policies, while in poorer countries a decrease in inequality is associated with a reduction in the adoption of climate change policies. While high levels of inequality have a negative impact on policy implementation in affluent countries, it might have a positive impact in nations with lower income levels. Our findings confirm the existence of a possible trade-off between inequality and environmental protection and provide new insights into its structure.

Teleworking has emerged as a potential strategy to reduce commuting-related greenhouse gas emissions by altering mobility behaviors. Using a nationally representative dataset from metropolitan France covering two survey waves 2020 and 2023, this study examines the extent to which teleworking effectively lowers emissions and whether rebound effects mitigate its environmental benefits. The results indicate that teleworking adoption increased significantly, from 19.6% of workers in 2020 to 32.5% in 2023. This shift has led to a substantial reduction in commuting-related CO2 emissions, with teleworkers averaging a 28.8% decline in emissions compared to non-teleworkers. However, the study also highlights compensatory behaviors: while teleworkers commute less, they tend to engage in more non-work-related travel, leading to private emissions of up to 923 kgCO2eq annually for individuals teleworking four days per week. These findings suggest that teleworking alone is not sufficient to achieve optimal environmental benefits. Public policies should integrate incentives for teleworking with measures that promote low-emission mobility, taxation measures to promote decarbonized transport, and behavioral nudges to reduce rebound effects. While teleworking offers a promising avenue for lowering emissions,

Public investments in subway systems are often partly motivated by improving local air quality and greenhouse gas emissions. Recent studies have investigated the air quality effects of subway investments, reaching differing conclusions across cities and periods. To reconcile these findings, we examine the air quality effects of all 359 subway system expansions in China between 2013 and 2018. The machine learning (ML) method adopted in this paper removes the variation of the high-frequency and seasonal air quality and therefore substantially improves the consistency and precision of the estimates. Based on the ML method, we find that although, on average, subway system expansions did not improve air quality in the short term, there is evidence of air quality improvement in the long term. This helps reconcile the different findings of the studies with different bandwidths. We also find that cities with low incomes or high economic growth experienced statistically significant improvements in air quality, which helps explain the different findings in the literature for cities with different characteristics.

China’s national emissions trading system (ETS) employs an unconventional rate-based design to allocate permits, which inherently creates “winners” and “losers”. This paper evaluates its emissions abatement effects using a unique facility-level dataset and a difference-in-differences approach with continuous treatment intensity. We find that the ETS reduces CO2 emissions of regulated power units by 6.9%, driven entirely by units with deficit allowances (“losers”), while units with surplus allowances (“winners”) show no significant emissions change. We also uncover output substitution between surplus and deficit units within the same regional grids and parent companies, alongside substantial co-benefits by mitigating air pollution.