Revealing the power of market-based energy policy: Evidence from China's energy quota trading system using machine learning

The effect of market-based climate policy instruments on a just transition cannot be underestimated, especially for developing economies. In this study, we provide rigorous empirical evidence on how China's Energy Quota Trading System (EQTS) can drive green technology innovation and support an equitable, low-carbon transition. Specifically, based on a quasi-experimental modeling framework, we use a Double Debiased Machine Learning method to estimate the casual effect of China's EQTS on energy productivity. Further, we explore the mechanisms of impact and examine heterogeneity effects from regional, resource endowment, and environmental regulation stringency perspectives. The empirical findings show that EQTS significantly improves energy productivity, exhibiting an average marginal effect of 13.2 %. Robustness checks confirm the validity of the results after controlling for potential confounders. Green technology innovation and energy transition function as critical pathways through which the policy enhances energy productivity. This study presents empirical evidence on how effective market-based regulatory mechanism are in the energy sector and offers practical policy recommendations for integrating innovation-driven strategies within national carbon mitigation frameworks.