Abstract

Urbanization is considered a main indicator of regional economic development due to its positive effect on promoting industrial development; however, many regions, especially developing countries, have troubled in its negative effect—the aggravating environmental pollution. Many researchers have addressed that the rapid urbanization stimulated the expansion of the industrial production and increased the industrial pollutant emissions. However, this statement is exposed to a grave drawback in that urbanization not only expands industrial production but also improves labor productivity and changes industrial structure. To make up this drawback, we first decompose the influence of urbanization impacts on the industrial pollutant emissions into the scale effect, the intensive effect, and the structure effect by using the Kaya Identity and the LMDI Method; second, we perform an empirical study of the three effects by applying the spatial panel model on the basis of the data from 282 prefecture-level cities of China from 2003 to 2014. Our results indicate that (1) there are significant reverse U-shapes between China’s urbanization rate and the volume of industrial wastewater discharge, sulfur dioxide emissions and soot (dust) emissions; (2) the relationship between China’s urbanization and the industrial pollutant emissions depends on the scale effect, the intensive effect and the structure effect jointly. Specifically, the scale effect and the structure effect tend to aggravate the industrial wastewater discharge, the sulfur dioxide emissions and the soot (dust) emissions in China’s cities, while the intensive effect results in decreasing the three types of industrial pollutant emissions; (3) there are significant spatial autocorrelations of the industrial pollutant emissions among China’s cities, but the spatial spillover effect is non-existent or non-significant. We attempt to explain this contradiction due to the fact that the vast rural areas around China’s cities serve as sponge belts and absorb the spatial spillover of the industrial pollutant emissions from cities. According to the results, we argue the decomposition of the three effects is necessary and meaningful, it establishes a cornerstone in understanding the definite relationship between urbanization and industrial pollutant emissions, and effectively contributes to the relative policy making.

Highlights

  • The theme of Shanghai World Expo in 2010—Better City, Better Life—exhibits China’s great ambition for urbanization

  • We first decompose the influence of urbanization impacts on the industrial pollutant emissions into the scale effect, the intensive effect and the structure effect by using the Kaya Identity and the LMDI Method; second, we perform an empirical study of the three effects by applying the spatial panel model on the basis of the data from 282 prefecture-level cities in China from 2003 to 2014

  • Our results indicate that (1) there are significant reverse U-shapes between China’s urbanization rate and the volume of industrial wastewater discharge, sulfur dioxide emissions and soot emissions; (2) the relationship between China’s urbanization and the industrial pollutant emissions depends on the scale effect, the intensive effect and the structure effect jointly

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Summary

Introduction

The theme of Shanghai World Expo in 2010—Better City, Better Life—exhibits China’s great ambition for urbanization. We highlight our research in the following aspects: first, we explore the mechanism analysis between urbanization and industrial pollutant emissions and apply the Kaya Identity and the LMDI Method to decompose out three effects (i.e., the scale effect, the intensive effect and the structure effect) of the urbanization impacts on the industrial pollutant emissions; second, we elaborate a description of the relationship between urbanization rate and industrial pollutants emissions of China, and re-examine the reverse U-shapes between them, we perform an empirical study of the three effects on the basis of the data from 282 cities of China from 2003 to 2014; third, as economic developments are strongly related with each other in different regions, and the assumption of no spatial autocorrelations has been questioned by many scholars, we amend the traditional panel model by introducing the spatial panel model to incorporate the spatial spillover effect. The rest of the paper is arranged as follows: Section 2 briefly reviews the previous studies; Section 3 analyses the mechanisms between urbanization and industrial pollutant emissions; Section 4 establishes the spatial panel model and introduces the parameters; Section 5 presents the empirical analysis; and Section 6 presents the study’s conclusions and offers a discussion

Literature Review
Mechanisms Analysis and Hypotheses
Modeling
Parameters
Test of the Spatial Autocorrelations of Industrial Pollutant Emissions
Analyses of the Spatial Spillover Effect
Discussion and Conclusions

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