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Pan, Xiongfeng; Ai, Bowei; Li, Changyu; Pan, Xianyou; Yan, Yaobo
Technological forecasting & social change, 07/2019, Volume: 144Journal Article
Based on the large scale provincial panel data on in China from 2006 to 2015, this paper uses the directed acyclic graph (DAG) and structure vector autoregrression (SVAR) to study the internal dynamic relationship among the environmental regulation, technological innovation and energy efficiency. The results of the DAG analysis confirm the existence of three conduct paths among environmental regulation, technological innovation and energy efficiency. First, the market incentive environmental regulation contributes directly to energy efficiency. Second, the market incentive environmental regulation drives the energy efficiency through technological innovation. Third, the command control environmental regulation contributes directly to energy efficiency. The results of forecast error variance decomposition based on SVAR model corroborate the view that the impacts of the command control environmental regulation and market incentive environmental regulation on energy efficiency have no obvious difference in the short term. In addition, with the extension of the forecast period, the promotion effect of the command control environmental regulation on energy efficiency gradually decreases, whereas the promotion effect of the market incentive environmental regulation on energy efficiency gradually increases. Technological innovation has a significant role in promoting energy efficiency both in the short and the long term. The changes in technological innovation are affected not only by itself, but also by the market incentive environmental regulation, whereas the command control environmental regulation has no obvious impact on technological innovation. •Directed acyclic graph is used to find the conduct paths of the variables.•SVAR model is used to study the Dynamic Relationship of the variables.•The different effects of Market incentive and command control environmental regulation are studied.•Large-dimensional panel data of 30 provinces in China from 2006 to 2015 is used.
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