Abstract
The identification of the driving forces of industrial water pollutant emissions in China is conducive to its effective abatement. It also promotes the coordinated development of China’s economic growth and the environment protection. Utilizing the Kaya equation and China’s provincial panel data from 1999 to 2015, this paper investigates the spatial-dynamic driving forces governing industrial water pollutant emission. We decompose and quantify the heterogeneous effects of different drivers, that is, technology, energy consumption, and economic size distribution. Applying the LMDI decomposition method, this paper also calculates the contribution of the three drivers to the abatement of industrial water pollutant emissions. The analysis indicates that the most important contribution to pollutant abatement is the development of technology, followed by energy consumption, and the least affected is the distribution of economic scale. In the future, the Chinese government should pay more attention to the impact of energy consumption on pollution abatement. This paper suggests that the Chinese government should improve the clean use of fossil fuel, optimize the energy consumption structure, and develop the use of more clean energy.
Highlights
Since the implementation of the policy of reform and opening up, China’s economy has maintained a relatively fast growth trend
Utilizing the Kaya equation and China’s provincial panel data from 1999 to 2015, this paper investigates the spatial-dynamic driving forces governing industrial water pollutant emission
Applying the Logarithmic Mean Divisia Index (LMDI) decomposition method, this paper calculates the contribution of the three drivers to the abatement of industrial water pollutant emissions
Summary
Since the implementation of the policy of reform and opening up, China’s economy has maintained a relatively fast growth trend. It is necessary to explore the driving forces of industrial water pollution emission by decomposing and analyzing its spatial evolution trend. Chen et al (2016) utilized the Exploratory Spatial Data Analysis (ESDA) method to analyze the characteristics of the spatio-temporal distribution of the total wastewater discharge among 31 provinces in China from 2002 to 2013 It discussed about the driving factors affected the wastewater discharge through the Logarithmic Mean Divisia Index (LMDI) method [11]. To explore the driving forces of industrial water pollutant emission from spatial-dynamic perspective in China, this paper applies the provincial panel data from 1999 to 2015 to decompose the industrial COD emissions per unit of GDP (i.e. the intensity of pollution emissions). Rational and effective policy recommendations are drawn in order to achieve high-quality development goals to control water pollution emissions while developing the economy
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