Can River Chief System Policy Improve Enterprises’ Energy Efficiency? Evidence from China

Abstract

The river chief system (RCS) is an autonomous environmental policy implemented by local governments in China that incorporates environmental responsibilities into the performance evaluation. Although existing literature suggests that RCS can reduce water pollution, the impact of RCS on energy efficiency has not been assessed. Therefore, this paper compiles data on industrial enterprises and industrial pollution in China from 2003 to 2013 and empirically examines the impact of RCS on green total factor energy efficiency (GTFEE) by using a multiple difference-in-difference approach. The results show that RCS significantly enhances firms’ GTFEE, and a series of tests confirm the robustness of the findings. Second, we further explore how RCS affects GTFEE, the mechanism tests conclude that the RCS improves GTFEE mainly through optimizing energy structure and promoting technological innovation. Third, compared with small firms, exporters, and firms in non-heavy polluting industries, the RCS has a greater effect on improving the GTFEE of large firms, non-exporters, and firms in heavily polluting industries. This study provides new and novel ideas for emerging countries to improve environmental policies and achieve sustainable development.

Evaluation Model and Application of the Implementation Effectiveness of the River Chief System (RCS)—Taking Henan Province as an Example

“…From the perspective of RCS research, the majority of the relevant literature focuses on the implementation of the system nationwide [7]. In terms of research content, it primarily includes studies on the current state of implementation [12], analysis of the effects of the RCS [35], the impact of the system on enterprise development [36,37], evaluation of river chief work [5], and the control of agricultural pollution under the RCS [38]. A few scholars have also explored the effectiveness of the implementation of the RCS, and the identified evaluation factors are mainly related to water pollution, water ecology, and organizational supervision.…”

Greening China’s Growth: Assessing the Synergistic Impact of Financial Development and Technological Innovation on Environmental Pollution Reduction—A Spatial STIRPAT Analysis

“…Environmental regulation, as the core means of national machinery to control environmental pollution, helps to consolidate the path of regional green development and make up for the lack of market governance [ 27 ]. For a long time, governments have reduced environmental pollution mainly through administrative control measures such as limiting the amount of pollution discharged, setting discharge standards, and taxing pollution behaviors.…”

The Low-Carbon Policy and Urban Green Total Factor Energy Efficiency: Evidence from a Spatial Difference-in-Difference Method

“…Before constructing the SDID model, this study first needs to measure the GTFEE to be the indicator as the dependent variable. Referring to Gao et al [ 39 ], we utilize the undesirable-SBM model to assess the GTFEE of 277 Chinese cities from 2004 to 2019. More specifically, this study assumes that each of the N decision-making units (DMU) has M inputs, expected outputs, and undesirable outputs, respectively, which can be expressed in the form of matrix , , ; the relaxation vectors of inputs are ; the desirable outputs and undesirable outputs are and , and stands for the weight vector.…”