Abstract

This paper uses both fiscal expenditure policy and fiscal revenue policy as input indicators and selects environmental pollution control results reflecting different forms and sources of pollution as output indicators. The efficiency of fiscal policies for environmental pollution control (EFPE) of 30 provincial-level administrative divisions in China from 2007 to 2017 is measured by adopting the data envelopment analysis (DEA) method. Then, the spatial effect of fiscal decentralization on EFPE is empirically analyzed by using the spatial lag model (SLM). The results show that EFPE values in China have been greatly improved overall since 2014. The change in technical efficiency (TE) is caused mainly by the change in pure technical efficiency (PTE). EFPE values have regional heterogeneity and convergence. The eastern region has clearly higher EFPE values than other regions. The growth rate of the low efficient region is greater than that of the high efficient region. Fiscal expenditure decentralization has a direct negative effect and spatial spillover effect on EFPE values, while fiscal revenue decentralization has a non-significant effect. Based on these results, this paper proposes the following policy implications: increasing the level of fiscal expenditure of environmental pollution control and improving the central transfer payment system for environmental protection; reforming the government performance assessment system and innovating the conditions of government expenditure on environmental pollution control; and promoting horizontal fiscal cooperation in cross-regional environmental governance.

Highlights

  • Performance evaluation was first applied to government management in the performance budget system of the United States in the 1950s

  • With the establishment of the concept of fiscal expenditure efficiency in China [26], many scholars have carried out studies to measure the efficiency of fiscal policies for environmental pollution control based on effect analysis

  • This paper proposes the question that needs to be solved urgently in China, that is, what is the efficiency of fiscal policies for environmental pollution control in China? Does fiscal decentralization affect the efficiency of fiscal policies for environmental pollution control? Is there a spatial effect of fiscal decentralization? What policies and suggestions could be proposed to improve the efficiency of fiscal policies for environmental pollution control in China? It innovates the indicators of input and output in the efficiency measurement of fiscal policies for environmental pollution control, considers the spatial effect in the analysis of influencing factors, and subdivides the fiscal decentralization into variables with Chinese characteristics

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Summary

Introduction

Performance evaluation was first applied to government management in the performance budget system of the United States in the 1950s. With the establishment of the concept of fiscal expenditure efficiency in China [26], many scholars have carried out studies to measure the efficiency of fiscal policies for environmental pollution control based on effect analysis. To fill in the gaps in existing research, the spatial lag model (SLM) is used to empirically test the direct and spatial spillover effects of fiscal expenditure decentralization and fiscal revenue decentralization on EFPE in this paper On this basis, this paper proposes the question that needs to be solved urgently in China, that is, what is the efficiency of fiscal policies for environmental pollution control in China? It innovates the indicators of input and output in the efficiency measurement of fiscal policies for environmental pollution control, considers the spatial effect in the analysis of influencing factors, and subdivides the fiscal decentralization into variables with Chinese characteristics.

DEA Approach
Indicator Selection and Data Description
Research Data
Efficiency of Fiscal Policies for Environmental Pollution Control
The which which proves proves that that changes changes in in TE
Malmquist
Convergence
Index Selection and Data Description
Spatial Autocorrelation
Conclusions and Suggestions
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