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Spatial-temporal characteristics of China’s industrial wastewater discharge at different scales

Environmental Science and Pollution Research volume 27, pages 8103–8118 (2020)Cite this article

Abstract

Industrial wastewater is one of the three industrial wastes. If substandard industrial wastewater is discharged into the environment, there will be a serious impact on the environmental quality. Excessive emissions also indicate that water resources utilization is unreasonable. Therefore, it is of great significance to study the changing trends and influencing factors of industrial wastewater discharge in order to effectively conserve resources and improve the environmental quality. In this study, the spatial autocorrelation and the environmental Kuznets curve were used to study the spatial-temporal changes and characteristics of industrial wastewater discharge at the provincial scale and prefectural scale in China in 2004–2015. Then, the Logarithmic Mean Divisia Index was used to analyze the influencing factors of wastewater discharge in this period. China’s total industrial wastewater discharge showed a trend of increasing at the beginning and then decreasing, and more than half provinces or cities show this trend of decoupling from economic development. Moreover, wastewater discharge was higher in the east region and lower in the west region at both the provincial scale and prefectural scale, but the aggregation degree on the prefectural scale is more obvious than that on the provincial scale. The technical effect has a general inhibitory effect on industrial wastewater discharge, but it also promoted the discharge in a few cities; the structure effect on industrial wastewater discharge has generally changed from promotion to inhibition during the study period; and economic effect and population effect were mainly to promote industrial wastewater discharge. Therefore, a few cities should accelerate technology upgrading and industrial restructuring in recent years in order to change the promoting effect, and most cities need to strengthen the implementation of economic measures and improve the residents’ environmental awareness.

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References

  • Abid M (2015) The close relationship between informal economic growth and carbon emissions in Tunisia since 1980: the (ir) relevance of structural breaks. Sustain Cities Soc (15):11–21

  • Ajmi AN, Hammoudeh S, Nguyen DK, Sato JR (2015) On the relationships between CO2 emissions, energy consumption and income: The importance of time variation. Energy Econ 49:629–638

    Article  Google Scholar 

  • Al-Saydeh SA, EL-Naas MH, Zaidi SJ (2017) Copper removal from industrial wastewater: A comprehensive review. J Ind Eng Chem 56: 35–44

  • Ang B (2005) The LMDI approach to decomposition analysis: a practical guide. Energy Policy 33(7):867–871

    Article  Google Scholar 

  • Arrow K, Bolin B, Costanza R, Dasgupta P, Folke C, Holling C, Jansson B, Levin S, Maler K, Perrings C, Pimentel D (1995) Economic growth, carrying capacity and the environment. Science 268:520–521

    Article  CAS  Google Scholar 

  • Aulinas M, Tolchinsky P, Turon C, Poch M, Cortés U (2012) Argumentation-based framework for industrial wastewater discharges management. Eng Appl Artif Intel 25(2):317–325

    Article  Google Scholar 

  • Azam M, Khan AQ (2016) Testing the Environmental Kuznets Curve hypothesis: A comparative empirical study for low, lower middle, upper middle and high income countries. Renew Sust Energ Rev 63:556–567

    Article  CAS  Google Scholar 

  • Azomahou T, Laisney F, Nguyen P (2006) Economic development and CO2 emissions: a nonparametric panel approach. J Public Econ 90(6–7):1347–1363

    Article  Google Scholar 

  • Balado-Naves R, Baños-Pino JF, Mayor M (2018) Do countries influence neighbouring pollution? A spatial analysis of the EKC for CO2 emissions. Energy Policy 123:266–279

    Article  Google Scholar 

  • Bimonte S, Stabile A (2017) Land consumption and income in Italy: a case of inverted EKC. Ecological Econ 131:36–43

    Article  Google Scholar 

  • Branger F, Quirion P (2015) Reaping the carbon rent: abatement and overallocation profits in the European cement industry, insights from an LMDI decomposition analysis. Energy Econ 47:189–205

    Article  Google Scholar 

  • Burkhard R, Deletic A, Craig A (2000) Techniques for water and wastewater management: a review of techniques and their integration in planning. Urban Water 2(3):197–221

    Article  CAS  Google Scholar 

  • Cansino J, Sánchez-Braza A, Rodríguez-Arévalo M (2015) Driving forces of Spain’s CO2 emissions: a LMDI decomposition approach. Renew Sust Energ Rev 48:749–759

    Article  CAS  Google Scholar 

  • Chen K, Liu X, Ding L, Huang GZ, Li ZG (2016) Spatial characteristics and driving factors of the provincial wastewater discharge in China. Int J Environ Res Public Health 13(12):1221

    Article  Google Scholar 

  • Chen K., Guo Y., Liu X., Zhang Z. Spatial-temporal pattern and driving factors of industrial wastewater discharge in the Yangtze River Economic Zone. Sci Geogr Sin, 2017 ,37(11):1668–1677 (in Chinese)

  • Dixon A, Simon M, Burkitt T (2003) Assessing the environmental impact of two options for small-scale wastewater treatment: comparing a reedbed and an aerated biological filter using a life cycle approach. Ecol Eng 20(4):297–308

    Article  Google Scholar 

  • Domínguez-Mariani E, Carrillo-Chávez A, Ortega A, Orozco-Esquivel MT (2004) Wastewater reuse in valsequillo agricultural area, Mexico:environmental impact on groundwater. Water Air Soil Poll 155:251–267

    Article  Google Scholar 

  • Dong L, Liang H (2014) Spatial analysis on China’s regional air pollutants and CO2 emissions: emission pattern and regional disparity. Atmos Environ 92:280–291

    Article  CAS  Google Scholar 

  • Evans A, Mateo-Sagasta J, Qadir M, Boelee E, Lessio Ippolito A (2019) Agricultural water pollution: key knowledge gaps and research needs. Curr Opin Environ Sustain 36:20–27

    Article  Google Scholar 

  • Fosten J, Morley B, Taylor T (2012) Dynamic misspecification in the environmental Kuznets curve: evidence from CO2 and SO2 emissions in the United Kingdom. Ecol Econ 76:25–33

    Article  Google Scholar 

  • Geng Y, Wang M, Sarkis J, Xue B, Zhang L et al (2014) Spatial-temporal patterns and driving factors for industrial wastewater emission in China. J Clean Prod 76:116–124

    Article  Google Scholar 

  • González P, Landajo M, Presno M (2014) Tracking European Union CO2 emissions through LMDI (logarithmic-mean Divisia index) decomposition. The activity revaluation approach. Energy 73:741–750

    Article  CAS  Google Scholar 

  • Guan D, Hubacek K, Weber C, Peters G, Reiner DM (2008) The drivers of Chinese CO2 emissions from 1980 to 2030. Glob Environ Chang 18(4):626–634

    Article  Google Scholar 

  • He S, Su Y, Wang L, Gallagher L, Cheng H (2018) Taking an ecosystem services approach for a new national park system in China. Resour Conserv Recycl 137:136–144

    Article  Google Scholar 

  • Hoekstra AY (2016) A critique on the water-scarcity weighted water footprint in LCA. Ecol Indic 66:564–574

    Article  Google Scholar 

  • Jeong K, Kim S (2013) LMDI decomposition analysis of greenhouse gas emissions in the Korean manufacturing sector. Energy Policy 62:1245–1253

    Article  CAS  Google Scholar 

  • Jesus P (2019) Effect of generation capacity factors on carbon emission intensity of electricity of Latin America & the Caribbean, a temporal IDA-LMDI analysis. Renew Sust Energ Rev 101:516–526

    Article  Google Scholar 

  • Kaya Y (1989) Impact of carbon dioxide emission on GNP growth: interpretation of proposed scenarios. Presentation in the Energy and Industry Subgroup, Response Strategies Working Group, IPCC, Paris, pp 1–25

    Google Scholar 

  • Kendy E, Zhang Y, Liu C, Wang J, Steenhuis T (2004) Groundwater recharge from irrigated cropland in the North China Plain: case study of Luancheng County, Hebei Province, 1949-2000. Hydrol Process 18(12):2289–2302

    Article  Google Scholar 

  • Kondolf GM, Podolak K (2014) Space and time scales in human-landscape systems. Environ Manag 53(1):76–87

    Article  Google Scholar 

  • Kuznets S (1955) Economic growth and income inequality. Am Econ Rev 45(1):1–28

    Google Scholar 

  • Lei H, Xia X, Li C, Xi B (2012) Decomposition analysis of wastewater pollutant discharges in industrial sectors of China (2001–2009) using the LMDI I method. Int J Environ Res Public Health 9(6):2226–2240

    Article  CAS  Google Scholar 

  • Li H, Du X, Wang H, Lv C, Wang X, Li F (2008) Research on the spatio-temporal variation of industrial wastewater pollutants in the three provinces of Northeast China. 21(2):168–174 (in Chinese)

  • Li W, Hua T, Zhou Q, Zhang S, Rong W (2011) Toxicity identification and high-efficiency treatment of aging chemical industrial wastewater from the Hangu Reservoir, China. J Environ Qual 2011 40(6):1714–1721

    CAS  Google Scholar 

  • Lin B, Omoju OE, Nwakeze NM, Okonkwo JU, Megbowon ET (2016) Is the environmental Kuznets curve hypothesis a sound basis for environmental policy in Africa? J Clean Prod 133(1):712–724

    Article  Google Scholar 

  • Liu L, Fan Y, Wu G, Wei Y (2007) Using LMDI method to analyze the change of China’s industrial CO2 emissions from final fuel use: an empirical analysis. Energy Policy 35(11):5892–5900

    Article  Google Scholar 

  • Liu Y, Zhou Y, Wu X (2015) Assessing the impact of population, income and technology on energy consumption and industrial pollutant emissions in China. Appl Energy 155:904–917

    Article  CAS  Google Scholar 

  • Long S, Zhao L, Shi T, Li J, Yang J et al (2018) Pollution control and cost analysis of wastewater treatment at industrial parks in Taihu and Haihe water basins, China. Journal of Clearner Production 172:2435–2442

    Article  CAS  Google Scholar 

  • Lu S, Gao X, Li W, Jiang S, Huang L (2018) A study on the spatial and temporal variability of the urban residential water consumption and its influencing factors in the major cities of China. 78:27–40

  • Luo W, Bai H, Jing Q, Liu T, Xu H (2018) Urbanization-induced ecological degradation in Midwestern China: an analysis based on an improved ecological footprint model. Resour Conserv Recycl 137:113–125

    Article  Google Scholar 

  • Ma M, Cai W, Cai W (2018) Carbon abatement in China’s commercial building sector: a bottom-up measurement model based on Kaya-LMDI methods. Energy. 165:350–368

    Article  Google Scholar 

  • Managi S, Kaneko S (2009) Environmental performance and returns to pollution abatement in China. Ecological Economic 68(6):1643–1651

    Article  Google Scholar 

  • Mao J, Yang Q, Zhang N, Zhang W, Zheng Y, Zhang Z (2018) A review on agro-industrial waste (AIW) derived adsorbents for water and wastewater treatment. J Environ Manag 227:395–405

    Article  CAS  Google Scholar 

  • Massei G, Rocchi L, Paolotti L, Greco S, Boggia A (2014) Decision Support Systems for environmental management: A case study on wastewater from agriculture. J Environ Manage 146(15):491–504

    Article  Google Scholar 

  • Ministry of Ecology and Environment of PRC. Bulletin on China’s ecology and environment in 2017. 2018.(in Chinese)

    Google Scholar 

  • Moutinho V, Moreira A, Silva P (2015b) The driving forces of change in energy-related CO2 emissions in Eastern, Western, Northern and Southern Europe: the LMDI approach to decomposition analysis. Renew Sust Energ Rev 50:1485–1499

    Article  CAS  Google Scholar 

  • Orubu C, Omotor D (2011) Environmental quality and economic growth: searching for environmental Kuznets curves for air and water pollutants in Africa. Energy Policy 39(7):4178–4188

    Article  CAS  Google Scholar 

  • Ozturk I, Al-Mulali U, Saboori B (2016) Investigating the environmental Kuznets curve hypothesis: the role of tourism and ecological footprint. Environ Sci Pollut Res 22:1916–1928

    Article  Google Scholar 

  • Papaioannou A, Plageras P, Dovriki E, Minas A, Krikelis V, Nastos PT, Kakavas K, Paliatsos AG (2007) Groundwater quality and location of productive activities in the region of Thessaly (Greece). Desalination 213(1-3):209–217

    Article  CAS  Google Scholar 

  • Park S, Lee Y (2011) Regional model of EKC for air pollution: evidence from the Republic of Korea. Energy Policy 39:5840–5849

    Article  CAS  Google Scholar 

  • Perestrello de Vasconcelos MJ, ZeiglerLee BP, Graham LA (1993) Modeling multi-scale spatial ecological processes under the discrete event systems paradigm. Landsc Ecol 8(4):273–286

    Article  Google Scholar 

  • Pérez-Suárez R, López-Menéndez AJ (2015) Growing green? Forecasting CO2 emissions with environmental Kuznets curves and logistic growth models. Environ Sci Pol 54:428–437

    Article  CAS  Google Scholar 

  • Poumanyvong P, Kaneko S (2010) Does urbanization lead to less energy use and lower CO2 emissions? A cross-country analysis. Ecol Econ 70(2):434–444

    Article  Google Scholar 

  • Raghuvanshi S, Bhakar V, Sowmya C, Sangwan KS (2017) Waste water treatment plant life cycle assessment: treatment process to reuse of water. Procedia CIRP 61:761–766

    Article  Google Scholar 

  • Riti JS, Song D, Shu Y, Kamah M (2017) Decoupling CO2 emission and economic growth in China: is there consistency in estimation results in analyzing environmental Kuznets curve? J Clean Prod 166:1448–1461

    Article  Google Scholar 

  • Román R, Cansino J, Rodasa J (2018) Analysis of the main drivers of CO2 emissions changes in Colombia (1990–2012) and its political implications. Renew Energy 116:402–411

    Article  Google Scholar 

  • Saboori B, Sulaiman J, Mohd S (2012) Economic growth and CO2 emissions in Malaysia: a cointegration analysis of the environmental Kuznets curve. Energy Policy 51:184–191

    Article  Google Scholar 

  • Selden T, Song D (1994) Environmental quality and development: is there a Kuznets curve for air pollution emissions? J Environ Econ Manag 27:147–162

    Article  Google Scholar 

  • Shannon M, Bohn P, Elimelech M, Georgiadis J, Marinas BJ, Mayes AM (2008) Science and technology for water purification in the coming decades. Nature. 452:301–310

    Article  CAS  Google Scholar 

  • Shi X, Fu L, Wu Y, Zhao H, Zhao S, Xu S (2017) Functionalized dithiocarbamate chelating resin for the removal of Co2+ from simulated wastewater. Appl Water Sci 7(8):4352–4360

    Article  CAS  Google Scholar 

  • Su D, Wang T, Liu L, Bai L (2010) Research on the spatio-temporal variation of pollutant discharged from industrial wastewater in the Liaohe River Basin. Ecol Environ Sci 19(12):2953–2959 (in Chinese)

    Google Scholar 

  • Thompson A (2012) Water abundance and an EKC for water pollution. Econ Lett 117(2):423–425

    Article  Google Scholar 

  • Turner MG, Gardner RH (2015) Landscape ecology theory and practice, pattern and process (second edition). Springer, New York

    Google Scholar 

  • Wang S, Zhou C (2018) Examining the determinants and the spatial nexus of city-level CO2 emissions in China: a dynamic spatial panel analysis of China’s cities. J Clean Prod 171:917–926

    Article  CAS  Google Scholar 

  • Wang Z, Bu C, Lia H, Wei W (2019) Seawater environmental Kuznets curve: evidence from seawater quality in China’s coastal waters. J Clean Prod 219(10):925–935

    Article  Google Scholar 

  • Wu J (2004) Effects of changing scale on landscape pattern analysis: scaling relations. Landsc Ecol 19(2):125–138

    Article  Google Scholar 

  • Wu J, Hobbs R (2002) Key issues and research priorities in landscape ecology: an idiosyncratic synthesis. Landsc Ecol 17(4):355–365

    Article  Google Scholar 

  • Wu L, Zeng W (2013) Research on the contribution of structure adjustment on carbon dioxide emissions reduction based on LMDI method. Procedia Computer Science 17:744–751

    Article  Google Scholar 

  • Wu S, Wallace S, Brix H, Kuschk P, Kirui WK, Masi F, Dong R (2015) Treatment of industrial effluents in constructed wetlands: Challenges, operational strategies and overall performance. Environ Pollut 201:107–120

    Article  CAS  Google Scholar 

  • Xu J, Lu F, Su F, Lu Y (2005) Spatial and temporal scale analysis on the regional economic disparities in China. Geogr Res 1:57–68 (in Chinese)

    Google Scholar 

  • Xu S, He Z, Long R (2014) Factors that influence carbon emissions due to energy consumption in China: decomposition analysis using LMDI. Appl Energy 127:182–193

    Article  CAS  Google Scholar 

  • Yang X, Wang S, Zhang W, Li Z, Zou Y (2016) Consumption, energy structure, and treatment technology on SO2 emissions: a multi-scale LMDI decomposition analysis in China. Appl Energy 184:714–726

    Article  CAS  Google Scholar 

  • Zhang W, Wang M (2018) Spatial-temporal characteristics and determinants of land urbanization quality in China: evidence from 285 prefecture-level cities. Sustain Cities Soc 38:70–79

    Article  Google Scholar 

  • Zhang Z, He J, Li J, Tang Z (2015) Distribution and conservation of threatened plants in China. Biol Conserv 192:454–460

    Article  Google Scholar 

  • Zhang W, Ding L, Luo J, Jaffin M, Tang B (2016a) Membrane fouling in photocatalytic membrane reactors (PMRs) for water and wastewater treatment: a critical review. Chem Eng J 302:446–458

    Article  CAS  Google Scholar 

  • Zhang Y, Hao J, Song J (2016b) The CO2 emission efficiency, reduction potential and spatial clustering in China’s industry: evidence from the regional level. Appl Energy 174:213–223

    Article  Google Scholar 

  • Zhang Y, Shuai C, Bian J, Chen X, Wu Y, Shen L (2019) Socioeconomic factors of PM2.5 concentrations in 152 Chinese cities: decomposition analysis using LMDI. J Clean Prod 218(1):96–107

    Article  Google Scholar 

  • Zhao X, Burnett J, Fletcher J (2014) Spatial analysis of China province-level CO2 emission intensity. Renew Sust Energ Rev 33:1–10

    Article  CAS  Google Scholar 

  • Zhou X, Sun H (2013) The industrial wastewater emissions and economic growth in regional differentiation in China. J Arid Land Resour Environ 27(12):15–19 (in Chinese)

    CAS  Google Scholar 

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Funding

This research was funded by the National Social Science Fund of China (Grant 17BGL256).

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Affiliations

  1. State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Xinjie Kouwai Street, Beijing, 100875, People’s Republic of China

    Bingran Ma & Guangjin Tian

  2. School of Government, Beijing Normal University, Xinjie Kouwai Street, Beijing, 100875, People’s Republic of China

    Guangjin Tian

  3. Center for Human-Environment System Sustainability (CHESS), State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, People’s Republic of China

    Lingqiang Kong

Authors
  1. Bingran Ma
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  2. Guangjin Tian
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Correspondence to Guangjin Tian.

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Highlights

1. Industrial wastewater discharge at the prefectural scale was analyzed.

2. More than half of the cities’ discharge show a trend of decoupling from economic development.

3. Technical effect promotes a few cities’ wastewater discharge.

4. Structural effect changed from promotion to inhibition on wastewater discharge.

Responsible Editor: Philippe Garrigues

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Ma, B., Tian, G. & Kong, L. Spatial-temporal characteristics of China’s industrial wastewater discharge at different scales. Environ Sci Pollut Res 27, 8103–8118 (2020). https://doi.org/10.1007/s11356-019-07488-7

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  • DOI: https://doi.org/10.1007/s11356-019-07488-7

Keywords

  • Industrial wastewater
  • Prefectural scale
  • Spatial autocorrelation
  • Environmental Kuznets curve
  • Logarithmic Mean Divisia Index