An analysis of the driving forces behind pollutant emission reduction in Chinese industry

Yao, Liang; Liu, Jingru; Tao, Zhou; Wang, Rusong

doi:10.1016/j.jclepro.2015.02.078

Cited by 26 publications

(8 citation statements)

References 21 publications

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“…During the outbreak of COVID-19, emission reduction of industry and traffic mobility were the two major reasons reducing air pollution [ 19 ]. It has been confirmed that the emission of the industry has a close relationship with the secondary industrial output values (SIOV, i.e., the gross product of secondary industry of each city) [ 22 , 34 , 35 ]. Therefore, for indicating the decrease of PM concentration caused by the reduction of industrial operation, decline rates of the secondary industrial output values (drSIOV) in the first quarter in 2020 (compared with the first quarter in 2019) of prefectural cities were obtained from the socio-economic operation bulletins.…”

Section: Methodsmentioning

confidence: 99%

How Did Distribution Patterns of Particulate Matter Air Pollution (PM2.5 and PM10) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level

Fan

Zhan

Yang

et al. 2020

IJERPH

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Due to the suspension of traffic mobility and industrial activities during the COVID-19, particulate matter (PM) pollution has decreased in China. However, rarely have research studies discussed the spatiotemporal pattern of this change and related influencing factors at city-scale across the nation. In this research, the clustering patterns of the decline rates of PM2.5 and PM10 during the period from 20 January to 8 April in 2020, compared with the same period of 2019, were investigated using spatial autocorrelation analysis. Four meteorological factors and two socioeconomic factors, i.e., the decline of intra-city mobility intensity (dIMI) representing the effect of traffic mobility and the decline rates of the secondary industrial output values (drSIOV), were adopted in the regression analysis. Then, multi-scale geographically weighted regression (MGWR), a model allowing the particular processing scale for each independent variable, was applied for investigating the relationship between PM pollution reductions and influencing factors. For comparison, ordinary least square (OLS) regression and the classic geographically weighted regression (GWR) were also performed. The research found that there were 16% and 20% reduction of PM2.5 and PM10 concentration across China and significant PM pollution mitigation in central, east, and south regions of China. As for the regression analysis results, MGWR outperformed the other two models, with R2 of 0.711 and 0.732 for PM2.5 and PM10, respectively. The results of MGWR revealed that the two socioeconomic factors had more significant impacts than meteorological factors. It showed that the reduction of traffic mobility caused more relative declines of PM2.5 in east China (e.g., cities in Jiangsu), while it caused more relative declines of PM10 in central China (e.g., cities in Henan). The reduction of industrial operation had a strong relationship with the PM10 drop in northeast China. The results are crucial for understanding how the decline pattern of PM pollution varied spatially during the COVID-19 outbreak, and it also provides a good reference for air pollution control in the future.

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Section: Methodsmentioning

confidence: 99%

How Did Distribution Patterns of Particulate Matter Air Pollution (PM2.5 and PM10) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level

Fan

Zhan

Yang

et al. 2020

IJERPH

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“…The studies of Dasgupta et al (2002), Stern (2004), Dinda (2004), Brock and Taylor (2005), Carson (2010), Chowdhury and Moran (2012) and de Ribeiro and Kruglianskas, 2015 among others, provide extensive review surveys of the literature which tested the economic growth-environmental pollution nexus and the EKC hypothesis. There is an increasing progress of researches on air or water pollution, deforestation, diversity and indicators of environmental amenities up until for some recent findings around the world (e. g. Kaika and Zervas, 2013;Katz, 2015;Wang et al, 2015Wang et al, , 2016a, also in China (e. g. Xu and Lin, 2015;Yao et al, 2016;Hao and Liu, 2016;Wang et al, 2016b). However, researches on the relationship between agriculture-related pollution and economic growth are relatively scanty (Shortle and Abler, 2001;Aftab et al, 2010), few of which were related to Environmental Kuznets Curve.…”

Section: Environmental Kuznets Curvementioning

confidence: 99%

Waste from livestock and poultry breeding and its potential assessment of biogas energy in rural China

Cheng

huilu

et al. 2016

Journal of Cleaner Production

143

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a b s t r a c tGlobal livestock and poultry industry is growing continuously, with large amounts of excreta produced. These wastes could be either important anaerobic fermentation materials of biogas energy or, if discharged into the environment without appropriate processing, serious pollution sources to soil and water systems. In developing countries, rural energy poverty is currently a major threat to sustainable development and livelihoods. Hence, the availability of clean, affordable and reliable energy is of significant concern in both policy and scholarly circles. Moreover, pollutants related with animal husbandry have been identified as one of the dominant contributors to contamination of water systems, such as surface water eutrophication and groundwater nitrate enrichment. Consequently, assessing waste from livestock and poultry breeding and the associated biogas systems is of critical essence in rural energy and environmental decision-making. The present study concentrates on a Chinese context and attempts to explicitly investigate Environmental Kuznets Curve (EKC) applying heterogeneous panel cointegration methods, combining with distribution and characteristics of waste from livestock and poultry breeding for China's 31 provincial economies from 1991 to 2013. And, potentiality of biogas energy and its CO 2 emission reduction based on the utilization of waste is assessed. The empirical results indicate an inverted U-shaped Environmental Kuznets Curve in N indicator resulting from livestock and poultry breeding, where value of the turning point is approximately CNY 51,800. With economic growth and consumptive change, problems arising from animal husbandry mode can be increasingly great. There will be many major challenges to address these special environmental issues in rural area, especially in Central China and West China. Biogas production by animal excreta could be one of the most important rural energies and waste treatment patterns, which could reach 63.65 billion m 3 in 2013, equivalent to approximately 20% of natural gas used, and might offer a GHG reduction of more than 220 million tons of CO 2 eq. China should assign greater importance to special agricultural pollution and energy options in rural communities, in addition to 'beautiful countryside' propaganda.

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“…The census assessed the basic situation of enterprises, main pollutants generated, and those discharged after end‐of‐pipe treatment for different kinds of pollution sources. Data on emission levels, main pollutants, and operating conditions of pollution control facilities were comprehensively investigated [ Yao et al ., ]. In this study, we choose the chemical oxygen demand (COD) and ammoniacal nitrogen (NH 3 ‐N) as proxies of water quality.…”

Section: Datamentioning

confidence: 99%

Burden shifting of water quantity and quality stress from megacity Shanghai

Zhao

Liu

Yang

et al. 2016

Water Resources Research

100

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Much attention has been paid to burden shifting of CO2 emissions from developed regions to developing regions through trade. However, less discussed is that trade also acts as a mechanism enabling wealthy consumers to shift water quantity and quality stress to their trading partners. In this study, we investigate how Shanghai, the largest megacity in China, draws water resources from all over China and outsources its pollution through virtual quantity and quality water flows associated with trade. The results show that Shanghai's consumption of goods and services in 2007 led to 11.6 billion m3 of freshwater consumption, 796 thousand tons of COD, and 16.2 thousand tons of NH3‐N in discharged wastewater. Of this, 79% of freshwater consumption, 82.9% of COD and 82.5% of NH3‐N occurred in other Chinese Provinces which provide goods and services to Shanghai. Thirteen Provinces with severe and extreme water quantity stress accounted for 60% of net virtual water import to Shanghai, while 19 Provinces experiencing water quality stress endured 79% of net COD outsourcing and 75.5% of net NH3‐N outsourcing from Shanghai. In accordance with the three “redlines” recently put forward by the Chinese central government to control water pollution and cap total water use in all provinces, we suggest that Shanghai should share its responsibility for reducing water quantity and quality stress in its trading partners through taking measures at provincial, industrial, and consumer levels. In the meantime, Shanghai needs to enhance demand side management by promoting low water intensity consumption.

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An analysis of the driving forces behind pollutant emission reduction in Chinese industry

Cited by 26 publications

References 21 publications

How Did Distribution Patterns of Particulate Matter Air Pollution (PM2.5 and PM10) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level

How Did Distribution Patterns of Particulate Matter Air Pollution (PM2.5 and PM10) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level

Waste from livestock and poultry breeding and its potential assessment of biogas energy in rural China

Burden shifting of water quantity and quality stress from megacity Shanghai

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