Re-examining the drive forces of China’s industrial wastewater pollution based on GWR model at provincial level

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Highlights

  • Spatial econometric (OLS) model and GWR model were employed to trace drivers of industrial wastewater discharge.

  • The industrial wastewater discharge showed a downward trend in China from 2004 to 2015.

  • There was a significant positive spatial autocorrelation of industrial wastewater discharge among China’s provinces.

  • The nationalization level of industry, industrial structure and environmental protection measures were major driving forces.

Abstract

Quantitative analysis of the spatiotemporal changes in China’s industrial wastewater and the hidden driving factors can provide important information for the overall process management of industrial wastewater. Taking China’s 31 provincial-level administrative regions as research objects, this paper employed spatial econometric (OLS) model and geographically weighted regression (GWR) model to evaluate the spatial spillover effects and identify the drive forces of wastewater discharge between provinces during 2004–2015 period. The results show that industrial wastewater discharge at the national level showed a trend of first increasing to 24.7 billion tons in 2007 and then decreasing to 19.9 billion tons in 2015. There was a significant positive spatial autocorrelation of industrial wastewater discharge among China’s provinces and the emission hotspots were mainly concentrated in central-western China. Moreover, the nationalization level of industry, industrial structure and environmental protection measures were found to be major driving forces of the spatial changes of industrial wastewater discharge. Our findings indicated that strengthening industrial nationalization as well as encouraging cooperation between neighboring provinces may help to reduce the industrial wastewater discharge, which can pave the way for other developing countries that face similar water pollution problems.

Introduction

Over the past century, the promotion of industrialization and urbanization has led to a constant increase in the consumption of global water resources (Dalin et al., 2012; Hoekstra et al., 2012; Kummu et al., 2016; Hoy, 2017; Larsen et al., 2016), with a six-fold increase in global water usage, or twice the population growth rate (UNESCO, 2012; UNESCO, 2015; Veldkamp et al., 2017; Dalin et al., 2017). Between 1995 and 2025, areas affected by “severe water stress” have expanded and intensified and will continue to expand and intensify, with the global range of 36.4 million km2 expanding to 38.6 million km2 (Alcamo et al., 2000). By 2050, global water usage will have grown by 55% from 2000, and nearly 3.9 billion people will face severe water scarcity (Saritas et al., 2017). Furthermore, water quality has important functions across the water resource portfolio of all countries (Rice et al., 2017; Ludwig et al., 2014; Yue et al., 2017). A rapidly growing economy has changed the hydrological process, leading to severe water scarcity in approximately 400 Chinese cities (Larsen et al., 2016) and different degrees of water pollution in three-quarters of lakes. Moreover, the water scarcity in southern China is largely due to water pollution (Cai et al., 2017). At the end of 2015, China’s industrial wastewater discharge amount to 181.6 billion tons (Ministry of environmental protection of the people’s republic of China, 2018), posing a huge threat to households and the economic development of fisheries, agriculture and other sectors. Resource allocation and environmental pollution-related issues are gradually posing a threat to water resources, and there are increasing challenges in terms of global wastewater discharge (Morris et al., 2017). Water-related risks were identified as the most crucial factor influencing the global economy (Jensen and Wu, 2018), and water pollution was singled out as one of the issues that need to be addressed urgently (Ilyas et al., 2019; Cheng et al., 2016).

Academicians started taking heed of industrial pollution earlier and proposed that the “Kuznets curve” be used to study various types of environmental pollution (Grossman and Krueger, 1994; Grossman and. Krueger, 1995; Stem et al., 2001). Studies on industrial wastewater are mostly centered around the problems of a single sector or plant type, such as the wastewater discharge, industrial wastewater handling (Hashemi et al., 2019; Zakaria et al., 2017) and waste distribution (Qin et al., 2009) of the wine industry (Castex et al., 2015), steel industry (Gu et al., 2015) and certain processing industries (Popat et al., 2019). In such studies, the issue of metal pollution has become a pain point in the process of treating industrial wastewater (Guo et al., 2016; Wang and Yang, 2016). Because industrial wastewater can be reused more easily, governance studies on its characteristics are gradually becoming a focal point. However, due to the influence of various conditions, China’s industrial wastewater are still discharged in a natural manner, and studies tend to revolve around the area-related differences of these discharge (Huang et al., 2019), the relationship between pollution and economic development (Ma et al., 2015; Zhang et al., 2019a), and driving factors (Li et al., 2009, 2013).

In summary, most studies tend to focus on spatiotemporal patterns based on statistical data and overlook the impact of various factors on discharge trends. In terms of analytical methods, the classical statistical methods find only an average or global estimation on the parameters, ignoring the spatially non-stationary characteristics of the parameters (Anselin, 1988; LeSage and Pace, 2008; Zhao et al., 2017). In terms of study scales, most of the existing research focused solely on the industrial wastewater discharge of a single sector in a local area or certain watersheds. Regional or large-scale studies are missing. Such as some scholars study the factors affecting Wastewater Discharge in China based on the LMDI model, from four aspects: resources, technology, economy and population. Such as Chen et al. (2016), conducted research on the discharge of wastewater (including domestic wastewater and industrial wastewater) and conducted research on 31 provincial administrative regions in China, while Geng et al. (2014) conducted research on industrial wastewater discharge, focusing on the analysis of four provinces and cities in Beijing, Jiangsu, Chongqing and Tibet. However, it is more constrained by subjective action, and limited by the model itself. On the one hand, it fails to obtain the degree of impact of specific sub-factors on wastewater discharge in several items after decomposition, on the other hand, the traditional factor decomposition model ignores the characteristics of parameters in space factors, so it may cause the results to have some deviation. Therefore, aiming at filling this gap, OLS and GWR model are used instead of the LMDI model, this paper extends the framework of the traditional model, combines spatial correlation with spatial differences, solves the spatial heterogeneity between the various factors, and satisfies that the relationship between the variables can change with the spatial position, and the calculation results are more in line with objective reality.

Section snippets

Spatial correlation analysis

Based on the first law of geography, correlations exist between the spatial units or attributes that are distributed in a regulated, agglomerated or randomized manner, and there is an inverse relationship between correlation and distance (Tobler, 1970). This phenomenon is known as spatial autocorrelation (Moran, 1948; Geary, 1954). Spatial autocorrelation is the correlation among values on a two-dimensional surface, and it may be used to measure the distribution characteristics of a research

Spatiotemporal distribution of national industrial wastewater discharge

The discharge quantity reflects changes in the national industrial wastewater discharge quantity to a certain degree, and it directly demonstrates the actual changes in quantity. Meanwhile, industrial production, which is one of the key pillars of China’s economy, is also a key contributor to pollution, with 47% of pollutants being emitted from the industrial sector (Zhang et al., 2010). As such, discharge intensity was selected to reflect the impact of industrialization on wastewater

Discussion

Using exploratory methods, this paper analyzed the spatial spillover effects of inter-provincial wastewater discharge and the differences in driving factors of wastewater discharge, which can effectively conserve resources and improve the environmental quality (Zhang et al., 2015a).

Trend change analysis shows that there exists a rather large difference between discharge quantity and discharge intensity trends, which might be because industrial wastewater discharge remained in a state of natural

Conclusion

China has experienced excellent industrialization and urbanization over the past several years. Due to China’s complex national conditions and unbalanced development, significant differences exist in industrial wastewater discharge, discharge intensity and driving factors between provinces.

This study explored the pattern of industrial wastewater discharge, discharge intensity and driving forces between provinces, which contributed to the understanding of significant policy implications. From

CRediT authorship contribution statement

Pengyan Zhang: Conceptualization, Methodology, Software, Investigation, Writing - original draft. Dan Yang: Validation, Formal analysis, Visualization, Software. Yu Zhang: Validation, Formal analysis, Visualization. Yanyan Li: Writing - review & editing. Yu Liu: Resources, Writing - review & editing, Supervision. Yunfeng Cen: Writing - review & editing. Wei Zhang: Resources, Writing - review & editing, Supervision. Wenliang Geng: Writing - review & editing. Tianqi Rong: Writing - review &

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This paper was supported by the National Key Research and Development Program of China (2016YFA0602500). National Natural Science Foundation of China (41601175). Program for Innovative Research Talent in University of Henan Province (20HASTIT017). 2018 Young Backbone Teachers Foundation from Henan Province (2018GGJS019). Key R&D and extension projects in Henan Province in 2019 (agriculture and social development field) (192102310002), and supported by innovation team cultivation projects of the

References (79)

  • W. Jin et al.

    Technological innovation, environmental regulation, and green total factor efficiency of industrial water resources

    J. Clean. Prod.

    (2019)
  • F. Ludwig et al.

    Climate change adaptation and integrated water resource management in the water sector

    J. Hydrol

    (2014)
  • W. Luo et al.

    Urbanization-induced ecological degradation in Midwestern China: an analysis based on an improved ecological footprint model

    Resour. Conserv. Recycl.

    (2018)
  • L. Morris et al.

    Municipal wastewater effluent licensing: a global perspective and recommendations for best practice

    Sci. Total Environ.

    (2017)
  • A. Popat et al.

    Mixed industrial wastewater treatment by combined electrochemical advanced oxidation and biological processes

    Chemosphere

    (2019)
  • D.I. Stern et al.

    Is there an environmental Kuznets curve for sulfur?

    J. Environ. Econ. Manag.

    (2001)
  • Q. Wang et al.

    Industrial water pollution, water environment treatment, and health risks in China

    Environ. Pollut.

    (2016)
  • Y.N. Wang et al.

    Spatial correlation of factors affecting CO2 emission at provincial level in China: a geographically weighted regression approach

    J. Clean. Prod.

    (2018)
  • L.Y. Wu et al.

    Research on the contribution of structure adjustment on carbon dioxide emissions reduction based on LMDI method

    Procedia Comput. Sci.

    (2013)
  • X. Yang et al.

    Consumption, energy structure, and treatment technology on SO2 emissions: a multi-scale LMDI decomposition analysis in China

    Appl. Energy

    (2016)
  • Y. Zhang et al.

    Socioeconomic factors of PM2.5 concentrations in 152 Chinese cities: decomposition analysis using LMDI

    J. Clean. Prod.

    (2019)
  • Z.J. Zhang et al.

    Distribution and conservation of threatened plants in China

    Biol. Conserv.

    (2015)
  • X.F. Zhao et al.

    Driving forces and the spatial patterns of industrial sulfur dioxide discharge in China

    Sci. Total Environ.

    (2017)
  • L. Zhu et al.

    The impact of foreign direct investment on SO2 emissions in the Beijing-Tianjin-Hebei region: a spatial econometric analysis

    J. Clean. Prod.

    (2017)
  • H. Akaike

    Information Theory and an Extension of the Maximum Likelihood Principle

    (1988)
  • J. Alcamo et al.

    World Water in 2025-Global Modeling Scenarios for the World Commission on Water for the 21st Century; Report A0002

    (2000)
  • L. Anselin

    Spatial Econometrics: Methods and Models

    (1988)
  • C. Brunsdon et al.

    Geographically weighted regression

    J. Roy. Stat. Soc. D-Sta.

    (1998)
  • G.Q. Chen et al.

    Spatial agglomeration and evolution of urban population in China

    Acta Geograph. Sin.

    (2008)
  • K.L. Chen et al.

    Spatial characteristics and driving factors of provincial wastewater discharge in China

    Int. J. Environ. Res. Publ. Health

    (2016)
  • P.F. Cheng et al.

    Coupling of hydrocarbon accumulation and cobalt removal during treatment of cobalt enriched industrial wastewater with botryococcusbraunii biofilm attached cultivation

    Environ. Sci.

    (2016)
  • Y.Q. Cheng et al.

    Spatial econometric analysis of carbon emission intensity and its driving factors from energy consumption in China

    Acta Geograph. Sin.

    (2013)
  • C.B. Cui et al.

    The analysis of spatial variability of influencing factors to county economy in Hebei-based on BGWR

    Econ. Geogr.

    (2012)
  • C. Dalin et al.

    Evolution of the global virtual water trade network

    Proc. Natl. Acad. Sci. U.S.A.

    (2012)
  • C. Dalin et al.

    Groundwater depletion embedded in international food trade

    Nature

    (2017)
  • A.P. Dempster et al.

    A simulation study of alternatives to ordinary least squares

    J. Am. Stat. Assoc.

    (1977)
  • A.S. Fotheringham et al.

    Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis

    Environ. Plann.

    (1998)
  • X.G. Gao

    Spatial heterogeneity effect of the Chinese high technology industry’s innovation efficiency factors

    World Regional Studies

    (2016)
  • R.C. Geary

    The contiguity ratio and statistical mapping

    Inc. Statistician

    (1954)
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