Identifying and analyzing the regional heterogeneity in green innovation effect from China’s pilot carbon emissions trading scheme through a quasi-natural experiment

Abstract

China has initiated a carbon emissions trading scheme (ETS) in some pilot regions to promote green innovation by local industrial firms. Nevertheless, not all pilot ETSs can effectively stimulate local green innovation, considering the differences in regional economic development as well as the design of the ETS. With this in mind, we develop a computerized methodology to empirically identify and analyze the regional heterogeneity in the green innovation effect from the ETS through a quasi-natural experiment. First, we propose a mathematical framework to empirically identify the regional heterogeneous effect of ETS on green innovation based on the synthetic control method. Then we construct a moderating effect model to verify the influencing mechanism of regional external factors on the heterogeneous green innovation effect of the ETS. Finally, we develop a framework for causal inference using a dynamic effect model to estimate the regional heterogeneous green innovation effect from the ETS, which varies over time. We have identified the following regional heterogeneity in the green innovation effect from China’s pilot ETSs: only Hubei and Guangdong ETSs have always effectively promoted local green innovation; Tianjin ETS has a short-term promotion effect before the economic sluggish period, while this positive effect appeared gradually in Shenzhen ETS after several years; however, the pilot ETSs in other regions do not have a positive effect on green innovation with a different evolutionary path. Appropriate industrial coverage and emissions control targets, as well as active trading platforms, have been identified as important external factors that positively moderate the green innovation effect of regional ETSs. Finally, we provide several managerial implications for ETS policymakers and regulators to stimulate green innovation considering regional heterogeneity.