Is the energy quota trading policy a solution to carbon inequality in China? Evidence from double machine learning

Implementing China's energy quota trading policy, as a typical market-based environmental regulation, thoroughly deepens the reform of energy market allocation. While the inhibitory effect of energy quota trading on carbon emissions is evident, its impact on carbon inequality remains largely unexplored. Thus, we investigate the nexus between energy quota trading and carbon inequality by employing double machine learning and causal forest approach, using panel data from 279 cities in China during 2011-2021. We find that carbon inequality in pilot cities decreased by 6.79 % compared to non-pilot cities. The main conclusions still hold after a various robustness checks. We also find that energy quota trading has a dual green effect in reducing carbon inequality within and between cities. Moreover, the mitigating effects are more pronounced in inland regions, urban clusters, and cities with energy affluence. Based on the Coase theorem, industrial structure, energy transition, and environmental awareness are three channels that link energy quota trading and carbon inequality. Furthermore, energy quota trading has generated additional environmental dividends without causing significant social welfare losses. These findings offer novel insights into the green effects of market-based environmental regulation.