Abstract

Atmospheric pollution gradually become a focus of concern all over the world owing to its detrimental influence on human health as well as long range impact on global ecosystem. This paper investigated the relationship among SO2 emissions, GDP, fossil fuel energy consumption, energy consumption intensity, and economic structure of five provinces in China with the highest SO2 emissions spanning from 2002–2015 based on panel data model. Through comparatively analyzing the coefficients in the established panel data model for Hebei, Henan, Inner Mongolia, Shandong, and Shanxi, we can obtain that: (1) fossil fuel energy consumption made the most devotion to SO2 discharge compared with GDP, energy consumption intensity, and economic structure. And the more the fossil fuel energy consumption, the more the devotion made by it to SO2 discharge. (2) GDP devoted less to SO2 emissions than fossil fuel energy consumption, and the larger the scale of the economy, the greater the contribution made by it to SO2 emissions. (3) The higher the proportion of the secondary industry added value accounted in GDP, the more the devotion made by the economic structure and energy consumption intensity to SO2 emissions. Through analyzing the Granger causality examination results, it can be concluded that: (1) there existed a bi-directional causal relationship between fossil fuel energy consumption and SO2 emissions among five selected provinces. (2) There existed uni-directional causal nexus running from GDP to SO2 emissions, from energy consumption intensity to SO2 emissions, and from economic structure to SO2 emissions among five chosen provinces. Based on the empirical analysis, several policy implications were proposed to provide references for policy makers, which were (1) Giving full play to the guiding role of price signals, and improving the price policy for desulfurization. (2) Formulating a new comprehensive evaluation system to measure the regional development level considering economic development and environmental impacts. (3) Exploring renewable and sustainable energy sources to substitute for fossil fuel energy according to regional resources endowment. (4) Developing high value added and low pollution emissions industries and reducing the proportion of secondary industry.

Highlights

  • As the second largest economy in the world, China is facing serious environmental issues owing to rapid development of economy

  • (3) The higher the proportion of the secondary industry added value accounted in gross domestic production (GDP), the more the devotion made by the economic structure and energy consumption intensity to SO2 emissions

  • At the aim of exploring the contributions of various socio-economic factors to SO2 discharge and carrying out policy recommendations to reduce SO2 emissions, this paper investigates the contributions of economic development, fossil fuel energy consumption, energy consuming intensity, and economic structure to SO2 discharge using the data of five provinces with the highest SO2 emissions in China during the period of 2002–2015

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Summary

Introduction

As the second largest economy in the world, China is facing serious environmental issues owing to rapid development of economy. All, considering about the shortcomings of existing literatures on researching the relationship between sulfur emissions and socio-economic driving forces, this paper established a multi-variate panel data model for five provinces with the largest sulfur dioxide (SO2) emissions in China taking economic development, fossil fuel energy consumption, energy consuming intensity, and economic structure into consideration spanning the period of 2002–2015. To the best of our knowledge, this paper is the first study in the field of investigating the relationship between sulfur discharge and socio-economic forces to simultaneously explore the contribution of economic growth, energy consumption, technical progress, and economic structure to SO2 emissions using panel data unit root test and panel co-integration theory.

Test for Cross-Sectional Dependence
Test for Panel Unit Root
Test for Panel Co-Integration
Test for Panel Data Model Form
Test for Causality
Theoretical Framework
Study Area and Data Sources
Pre-Analysis
Results for Cross-Sectional Dependence Examination
Results for Unit Root Examination
Results for Panel Co-Integration Examination
Model Form Determination
LR Test Results
Estimation for Panel Data Model and Provincial Comparative Analysis
Analysis for Granger Causality Relationship
Conclusions and Policy Implications
Full Text
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