Does Industrial Agglomeration Promote Carbon Efficiency? A Spatial Econometric Analysis and Fractional-Order Grey Forecasting

Zuoren Sun,Yi Liu,Lifeng Wu

doi:10.1155/2021/5242414

Abstract

In theory, the industrial agglomeration is a double-edged sword as there are both positive and negative externalities. China’s cities, with great disparities on degrees of the industrial agglomeration, often face different energy and carbon dioxide emission problems, which raise the question whether the industrial agglomeration promotes or inhibits energy efficiency and carbon dioxide emission. This paper explored the effects of the industrial agglomeration on carbon efficiency in China. Spatial econometric methods were implemented using panel data (2007–2016) of 285 cities above the prefecture level. The results revealed that industrial agglomerations have significant impacts on the urban carbon efficiency with significant spatial spillover effects. The agglomerations of the manufacturing and high-end productive service industries take positive effects on carbon efficiency while the low-end productive and living service industries take negative effects. As a comparison, we found that the agglomeration effects at the level of the megalopolis are greater than those at the national level, especially for the living services industry, in which the higher levels of agglomeration make the effects on carbon efficiency change from negative to positive. The divisions of labor for the central and common cities in the megalopolises are integrated into the industrial agglomeration. Furthermore, the fractional-order grey forecasting model is used in this paper. By the virtue of its advantage in dealing with small sample data which lack statistical rules, this paper makes an out-of-sample prediction of carbon efficiency and industrial agglomeration degree of Chinese cities. By adding the predicted results to the spatial correlation test, new evidence on the spatial correlation of carbon efficiency and spatial division of labor between cities is obtained. Based on the empirical results of the present study, we have proposed some policy recommendations.

Highlights

Since its reform and opening up, China’s rapid urbanization process has brought about a significant increase in energy consumption and CO2 emissions
Compared to the extant research, the present study makes three major contributions: (1) the present study built a spatial Durbin model to study the effects of the heterogeneity of the industrial agglomeration on carbon efficiency at the national level and at the level of the megalopolis; (2) we explored the different impacts from various service industry agglomerations on urban carbon efficiency and identified specific impacts in megalopolises; (3) comparisons between the manufacturing and segmented service industries were made, and we sought the optimal development path of cities for the dual goals of economic development and the reduction of CO2 emissions
As the spatial spillover effect, the change of explanatory variables will cause the change of the carbon efficiency both in the local area and surrounding areas. erefore, the change of carbon efficiency in surrounding areas will eventually cause the change of carbon efficiency in the local area owing to the feedback effect [65]

Summary

Introduction

Since its reform and opening up, China’s rapid urbanization process has brought about a significant increase in energy consumption and CO2 emissions. E International Energy Agency (IEA) reported that China has, since 2007, far exceeded the CO2 emissions of the European Union and the United States [1]. In 2014, the CO2 emissions of the European Union and the United States were only 3.24 and 5.25 billion tons, respectively [2]. E Chinese government has made a list of commitments for reducing carbon emissions and to curb global climate change. At the 2015 Paris Climate Conference, the Chinese government committed to CO2 emissions per unit of GDP being 60–65% lower in 2030 than the level in 2005. China has controlled the expansion of the production capacity of the manufacturing industry because this industry produces high levels of pollution, consumes a large amount of energy, and is responsible for high CO2 emissions. The implementation of restrictions on the manufacturing industry is bound to slow down economic growth

Methods

Results

Conclusion