LAUSR

Abstract This paper combines the dynamic complex network and DID (difference-in-differences) analysis to evaluate the effect of the negative list policy aimed at adjusting the sectoral structure of Shanghai. We construct a causal dynamic complex network based on the high frequency electricity consumption data of industrial and commercial firms to study the sectoral structure change and assess the impact of the negative list policy. Specifically, we use the negative list policy of industrial restructuring first implemented in Shanghai in 2014 to establish the DID model. The results show that the degree centrality, which reflects the importance of a sector in the sectoral structure, of these sectors regulated by the negative list policy is down 13.68 after June 2014, which is a percentage drop of 8.74%, and the other indicators of the complex network for the sectors treated by the policy are mostly decreasing. These results indicate that the policy definitely decreases the importance of sectors with more emissions and energy consumption, and the sectoral structure has been adjusted and upgraded in the direction of less energy consumption and less emissions. After introducing interaction items, assessing the impacts of other policies, changing the parameters of the network construction, and using a network processed by the PMFG (plane maximum filter graph) and other means to check the robustness of results, the negative effects are still statistically significant.