Skip to main content
SpringerLink
Log in

Drivers of Regional Environmental Pollution Load and Zoning Control: A Case Study of the Yangtze River Economic Belt, China

  • Published:
Chinese Geographical Science Aims and scope Submit manuscript

Abstract

The high environmental pollution load caused by the massive pollutant emissions and the accumulation of endogenous and cross-regional pollution has become an important obstacle to the current ecological civilization construction in the Yangtze River Economic Belt (YREB) in China. Taking the YREB as an example, by using four environmental pollutant emission indicators, including chemical oxygen demand (COD), ammonia nitrogen (NH3-N), sulfur dioxide (SO2), and nitrogen oxides (NOx), this paper established an environmental pollution load index (EPLI) based on the entropy-based measurement. Moreover, the Spatial Durbin Model was used to quantitatively analyze the drivers and spatial effects of environmental pollution load. Finally, specific scientific references were provided for formulating environmental regulations of pollution source control in the YREB. The results showed that: 1) During 2011–2015, the EPLI in the YREB was reduced significantly and the environmental pollution load increased from upstream to downstream. Among them, the pollution load levels in the Upper Mainstream subbasin, Taihu Lake subbasin, and Lower Mainstream subbasin were the most prominent. 2) The environmental pollution load situation in the YREB was generally stable and partially improved. High load level areas were mainly concentrated in the Yangtze River Delta Region and the provincial borders in upstream, midstream, and downstream areas. The high load level areas already formed in Chengdu and Chongqing were also the key regulatory points in the future. 3) The degree of local environmental pollution load was apparently affected by the adjacent cities. The population size, industrialization level, and the fiscal decentralization not only drove the increase of the local environmental pollution load level, but also affected the adjacent areas through the spatial spillover effects. The land development intensity mainly drove the increase in the local EPLI in the YREB. While factors such as economic development level and agricultural economic share could only act on the environmental pollution load process in adjacent cities. 4) According to the differentiation characteristics of drivers of each city, the YREB was divided into seven zones based on k-medoids cluster method, and targeted zoning control policy recommendations for alleviating environmental pollution load in the YREB were proposed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Anselin L, 1988. Spatial Econometrics: Methods and Models. Dordrecht: Springer Science & Business Media. doi: https://doi.org/10.1007/978-94-015-7799-1

    Book  Google Scholar 

  • Baek J, 2016. A new look at the FDI-income-energy-environment nexus: dynamic panel data analysis of ASEAN. Energy Policy, 91: 22–27. doi: https://doi.org/10.1016/j.enpol.2015.12.045

    Article  Google Scholar 

  • Clark T L, 1980. Annual anthropogenic pollutant emissions in the United States and southern Canada east of the Rocky Mountains. Atmospheric Environment, 14(8): 961–970. doi: https://doi.org/10.1016/0004-6981(80)90010-4

    Article  Google Scholar 

  • Cole M A, Neumayer E, 2004. Examining the impact of demographic factors on air pollution. Population and Environment, 26(1): 5–21. doi: https://doi.org/10.1023/B:POEN.0000039950.85422.eb

    Article  Google Scholar 

  • Dietz T, Rosa E A, 1997. Effects of population and affluence on CO2 emissions. Proceedings of the National Academy of Sciences of the United States of America, 94(1): 175–179. doi: https://doi.org/10.1073/pnas.94.1.175

    Article  Google Scholar 

  • Ehrlich P R, Holdren J P, 1971. Impact of population growth. Science, 171(3977): 1212–1217. doi: https://doi.org/10.1126/science.171.3977.1212

    Article  Google Scholar 

  • El Ouardighi F, Benchekroun H, Grass D, 2014. Controlling pollution and environmental absorption capacity. Annals of Operations Research, 220(1): 111–133. doi: https://doi.org/10.1007/s10479-011-0982-4

    Article  Google Scholar 

  • Elhorst J P, 2010. Applied spatial econometrics: raising the bar. Spatial Economic Analysis, 5(1): 9–28. doi: https://doi.org/10.1080/17421770903541772

    Article  Google Scholar 

  • Elhorst J P, 2014. Spatial Econometrics: from Cross-Sectional Data to Spatial Panels. Berlin, Heidelberg: Springer. doi: https://doi.org/10.1007/978-3-642-40340-8

    Book  Google Scholar 

  • Epstein D, Jackson R, Braithwaite P, 2011. Delivering London 2012: sustainability strategy. Proceedings of the Institution of Civil Engineers-Civil Engineering, 164(5): 27–33. doi: https://doi.org/10.1680/cien.2011.164.5.27

    Google Scholar 

  • European Commission (EC), 2010. A European green deal. Brussels: EC. Available at: https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en

  • Fong E, Shibuya K, 2020. Migration patterns in East and southeast Asia: causes and consequences. Annual Review of Sociology, 46: 511–531. doi: https://doi.org/10.1146/annurev-soc-121919-054644

    Article  Google Scholar 

  • Gao Wei, Bai Hui, Yan Chang’an et al., 2019. Spatiotemporal evolution of natural and anthropogenic nitrogen inputs to Yangtze River Economic Belt from 1952 to 2016. Acta Scientiae Circumstantiae, 39(9): 3134–3143. (in Chinese)

    Google Scholar 

  • Getis A, Ord J K, 1992. The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3): 189–206. doi: https://doi.org/10.1007/978-3-642-01976-0_10

    Article  Google Scholar 

  • Ord J K, Getis A, 1995. Local spatial autocorrelation statistics: distributional issues and an application. Geographic Analysis, 27(4): 286–306. doi: https://doi.org/10.1111/j.1538-4632.1995.tb00912.x

    Article  Google Scholar 

  • Grossman G M, Krueger A B, 1991. Environmental Impacts of a North American Free Trade Agreement. Cambridge: National Bureau of Economic Research.

    Book  Google Scholar 

  • Grossman G M, Krueger A B, 1995. Economic growth and the environment. The Quarterly Journal of Economics, 110(2): 353–377. doi: https://doi.org/10.2307/2118443

    Article  Google Scholar 

  • Gu Hengyu, Meng Xin, Shen Tiyan et al., 2020. Spatial variation of the determinants of China’s urban floating population’s settlement intention. Acta Geographica Sinica, 75(2): 240–254. (in Chinese)

    Google Scholar 

  • Guo Sidai, Zhang Hua, Guo Jie et al., 2018. Evaluation and impact factors of environmental pollution of China based on spatial metric model. Chinese Journal of Ecology, 37(2): 471–181. (in Chinese)

    Google Scholar 

  • Han W Y, Geng Y, Lu Y S Y et al., 2018. Urban metabolism of megacities: a comparative analysis of Shanghai, Tokyo, London and Paris to inform low carbon and sustainable development pathways. Energy, 155: 887–898. doi: https://doi.org/10.1016/j.energy.2018.05.073

    Article  Google Scholar 

  • Jia Zhuo, Qiang Wenli, Wang Yueju et al., 2020. The spatial characteristics and spatial effect of industrial pollution agglomeration in Lanzhou-Xining urban agglomeration. Economic Geography, 40(1): 68–75: 84. (in Chinese)

    Google Scholar 

  • Jones D W, 1991. How urbanization affects energy-use in developing countries. Energy Policy, 19(7): 621–630. doi: https://doi.org/10.1016/0301-4215(91)90094-5

    Article  Google Scholar 

  • Lazarus R S, Cohen J B, 1997. Human behavior and environment. In: Environmental stress. Boston: Springer.

    Google Scholar 

  • LeSage J, Pace R K, 2009. Introduction to Spatial Econometrics. Boca Raton: Chemical Rubber Company Press.

    Book  Google Scholar 

  • Li H B, Zhou L A, 2005. Political turnover and economic performance: the incentive role of personnel control in China. Journal of Public Economics, 89(9–10): 1743–1762. doi: https://doi.org/10.1016/j.jpubeco.2004.06.009

    Article  Google Scholar 

  • Li Jing, Tan Qingmei, Bai Junhong, 2010. Spatial econometric analysis on region innovation production in China. Management World, (7):43–55: 65. (in Chinese)

  • Li Q, Song J P, Wang E R et al., 2014. Economic growth and pollutant emissions in China: a spatial econometric analysis. Stochastic Environmental Research and Risk Assessment, 28(2): 429–442. doi: https://doi.org/10.1007/S00477-013-0762-6

    Article  Google Scholar 

  • Li R, Cui L L, Li J L et al., 2017. Spatial and temporal variation of particulate matter and gaseous pollutants in China during 2014–2016. Atmospheric Environment, 161: 235–246. doi: https://doi.org/10.1016/j.atmosenv.2017.05.008

    Article  Google Scholar 

  • Liu H M, Fang C L, Zhang X L et al., 2017. The effect of natural and anthropogenic factors on haze pollution in Chinese cities: a spatial econometrics approach. Journal of Cleaner Production, 165: 323–333. doi: https://doi.org/10.1016/j.jclepro.2017.07.127

    Article  Google Scholar 

  • Liu K, Lin B Q, 2019. Research on influencing factors of environmental pollution in China: a spatial econometric analysis. Journal of Cleaner Production, 206: 356–364. doi: https://doi.org/10.1016/j.jclepro.2018.09.194

    Article  Google Scholar 

  • Liu Q Q, Wang S J, Zhang W Z et al., 2018. Does foreign direct investment affect environmental pollution in China’s cities? A spatial econometric perspective. Science of the Total Environment, 613–614: 521–529. doi: https://doi.org/10.1016/j.scitotenv.2017.09.110

    Article  Google Scholar 

  • Liu Yufeng, Gao Liangmou, 2019. Influence of Chinese provincial FDI on environmental pollution. Economic Geography, 39(5): 47–54. (in Chinese)

    Google Scholar 

  • Lu Dadao, 2018. Conservation of the Yangtza River and sustainable development of the Yangtze River Economic Belt: an understanding of General Secretary Xi Jinping’s important instructions and suggestions for their implementation. Acta Geographica Sinica, 73(10): 1829–1836. (in Chinese)

    Google Scholar 

  • Lu Y L, Song S, Wang R S et al., 2015. Impacts of soil and water pollution on food safety and health risks in China. Environment International, 77: 5–15. doi: https://doi.org/10.1016/j.envint.2014.12.010

    Article  Google Scholar 

  • National Bureau of Statistics of China, 2012–2016a. China City Statistical Yearbook (2012–2016). Beijing: China Statistical Press. (in Chinese)

    Google Scholar 

  • National Bureau of Statistics of China, 2012–2016b. China Environmental Yearbook (2012–2016). Beijing: China Statistical Press. (in Chinese)

    Google Scholar 

  • National Bureau of Statistics of China, 2012–2016c. China Statistical Yearbook for Regional Economy (2012–2016). Beijing: China Statistical Press. (in Chinese)

    Google Scholar 

  • National Bureau of Statistics of China, 2012–2016d. China Statistical Yearbook of the Environment (2012–2016). Beijing: China Statistical Press. (in Chinese)

    Google Scholar 

  • National Development and Reform Commission (NDRC), 2016. The 13th Five-Year Plan for Economic and Social Development of the People’s Republic of China. Beijing: NDRC. https://www.cma.org.cn/attachment/2016322/1458614099605.pdf. (in Chinese)

    Google Scholar 

  • Parikh J, Shukla V, 1995. Urbanization, energy use and greenhouse effects in economic development: results from a cross-national study of developing countries. Global Environmental Change, 5(2): 87–103. doi: https://doi.org/10.1016/0959-3780(95)00015-G

    Article  Google Scholar 

  • Ping Zhiyi, Wu Xuebing, Wu Xuelian, 2019. Analysis of the impact of economic growth on industrial pollution in the Yangtze River Economic Belt based on Spatial Dubin Model of geographic distance matrix. Ecological Economy, 35(7): 161–167. (in Chinese)

    Google Scholar 

  • Sapkota P, Bastola U, 2017. Foreign direct investment, income, and environmental pollution in developing countries: panel data analysis of Latin America. Energy Economics, 64: 206–212. doi: https://doi.org/10.1016/j.eneco.2017.04.001

    Article  Google Scholar 

  • Shen Kunrong, Fu Wenlin, 2005. The relationship between China’s decentralized system in finance and her regional economic growth. Management World, (1): 31–39. (in Chinese)

  • Song C B, Wu L, Xie Y C et al., 2017. Air pollution in China: status and spatiotemporal variations. Environmental Pollution, 227: 334–347. doi: https://doi.org/10.1016/j.envpol.2017.04.075

    Article  Google Scholar 

  • Sun Bowen, Cheng Zhiqiang, 2019. Research on industrial pollution discharge mechanism of market integration: taking the Yangtze River Economic Belt as an example. China Environmental Science, 39(2): 868–878. (in Chinese)

    Google Scholar 

  • Tan Zhixiong, Zhang Yangyang, 2015. An empirical research on the relation between fiscal decentralization and environmental pollution. China Population, Resources and Environment, 25(4): 110–117. (in Chinese)

    Google Scholar 

  • United Nations (UN), 2015. Sustainable development goals. UN, New York. https://sdgs.un.org/.

    Google Scholar 

  • Walter I, Ugelow J L, 1979. Environmental policies in developing countries. AMBIO, 8(2–3): 102–109.

    Google Scholar 

  • Wang Yuxin, Yu Xinghou, Xiong Xing, 2019. Study on spatial difference and influencing factors of pollutant emission intensity in the Yangtze River Economic Belt. West Forum, 29(3): 104–114. (in Chinese)

    Google Scholar 

  • Wu Jianxiong, Zhou Kan, Liu Hanchu, 2021. Driving effects and spatial interaction of urbanization on ammonia nitrogen emissions: a case study of the Yangtze River Delta. Acta Scientiae Circumstantiae, 41(10): 3893–3904. (in Chinese)

    Google Scholar 

  • Xia Jun, Zuo Qiting, 2018. The utilization and protection of water resource in China (1978–2018). Urban and Environmental Studies, (2): 18–32. (in Chinese)

  • Xu Hui, Yang Ye, Nie Du, 2017. Influencing path of fiscal decentralization on environmental pollution in China’s top 10 urban agglomerations. Urban Problems, (6): 14–24. (in Chinese)

  • Zhang Hua, Feng Chao, Liu Guanchun, 2017. Chinese-style environmental federalism: a study on the effect of environmental decentralization on carbon emissions. Journal of Finance and Economic, 43(9): 33–49. (in Chinese)

    Google Scholar 

  • Zhou Jiewen, Jiang Zhengyun, Li Feng, 2018. Research on the development and influencing factors of the green economy in the Yangtze River Economic Belt. Ecological Economy, 34(12): 47–53: 69. (in Chinese)

    Google Scholar 

  • Zhou Kan, Fan Jie, Liu Hanchu, 2017. Spatiotemporal patterns and driving forces of water pollutant discharge in the Bohai Rim Region. Progress in Geography, 36(2): 171–181. (in Chinese). doi: https://doi.org/10.18306/dlkxjz.2017.02.004

    Google Scholar 

  • Zhou K, Wu J X, Liu H C, 2021. Spatiotemporal variations and determinants of water pollutant discharge in the Yangtze River Economic Belt, China: a spatial econometric analysis. Environmental Pollution, 271: 116320. doi: https://doi.org/10.1016/j.envpol.2020.116320

    Article  Google Scholar 

  • Zhu Xiangdong, He Canfei, Li Qian et al., 2018. Influence of local government competition and environmental regulations on Chinese urban air quality. China Population, Resources and Environment, 28(6): 103–110. (in Chinese)

    Google Scholar 

  • Zou Zhihong, Sun Jingnan, Ren Guangping, 2005. Study and application on the entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. Acta Scientiae Circumstantiae, 25(4): 552–556. (in Chinese)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

    Kan Zhou, Jianxiong Wu & Jie Fan

  2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China

    Kan Zhou, Jianxiong Wu & Jie Fan

  3. Institute of Science and Development, Chinese Academy of Sciences, Beijing, 100190, China

    Jie Fan & Hanchu Liu

Authors
  1. Kan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianxiong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jie Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hanchu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jie Fan.

Additional information

Foundation item: Under the auspices of National Natural Science Foundation of China (No.41971164, 41530634), Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA23020101), Second Tibetan Plateau Scientific Expedition and Research Program (No. 2019QZKK0406)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, K., Wu, J., Fan, J. et al. Drivers of Regional Environmental Pollution Load and Zoning Control: A Case Study of the Yangtze River Economic Belt, China. Chin. Geogr. Sci. 32, 31–48 (2022). https://doi.org/10.1007/s11769-022-1257-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11769-022-1257-5

Keywords

Search

Navigation