Research on the Evaluation and Spatial Characteristics of China’s Provincial Socioeconomic Development and Pollution Control Based on the Lotka–Volterra Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Sources
2.2. The Proportion of HDI
2.2.1. Provincial HDI Calculation Method
2.2.2. Calculate the Proportion of HDI
2.3. Indicators of Industrial and Domestic Pollution Effectiveness
2.3.1. Calculation Method of Industrial and Domestic Pollution Effectiveness Indicators
2.3.2. Calculation of the Effectiveness Indicators of Industrial and Domestic Pollution
2.4. Model Building
2.4.1. The Lotka–Volterra Model
dH(t)/dt = RH(t)H(t) × {1 − [H(t)/KH(t)] + β(t)[I(t)/KH(t)]}
dH(t)/dt = aHH(t) + bHH(t)2 + cHH(t) × I(t)
2.4.2. Modeling the Threshold Value Function
3. Results
3.1. Force-On Index
3.2. Mutualism Degree Index and Ranking
3.3. Spatial Autocorrelation and Spatial Heterogeneity Analysis of Ranks
3.3.1. The Space Weight Matrix Selection
3.3.2. Global Spatial Autocorrelation Analysis
3.3.3. Local Spatial Autocorrelation Analysis
4. Conclusions and Policy Recommendations
4.1. Conclusions
- (1)
- The proportion of HDI and the overall change trend of industrial and domestic pollution effectiveness indicators in the country from 2011 to 2020 were similar, and the proportion or effectiveness index value of approximately half of the provinces increased. The proportion of HDI in most provinces in the target year range did not change substantially, indicating that the socioeconomic development rate of all provinces remained relatively consistent. The indicators of industrial and domestic pollution effectiveness in some areas with high-level socioeconomic development showed an upward trend, that is, pollution emissions decreased. However, the indicators of industrial and domestic pollution efficacy in some areas with rapid socioeconomic development showed a downward trend.
- (2)
- The degree of coordination between industrial pollution prevention and control and socioeconomic development in various provinces was obviously better than that of domestic pollution. Compared with 2011–2015, the number of provinces with mutual promotion of the HDI and industrial pollution prevention and control effectiveness decreased slightly from 2016 to 2020, while the number of provinces with HDI and domestic pollution prevention and control effectiveness decreased significantly. The overall rate of industrial pollution prevention and control in the country was slightly unbalanced compared with the growth rate of socioeconomic development. The speed of domestic pollution prevention and control did not match the growth rate of socioeconomic development.
- (3)
- Except for Hubei, Shaanxi and Qinghai, which had an advanced degree of mutualism of industry and domestic pollution at the same time, most provinces were not at the same S-level in terms of their industrial and domestic pollution ranks. From 2016 to 2020, 14 provinces were in the advanced mutualism degree of pollution and socioeconomic development, that is, at the S-level. Eleven provinces had industrial pollution ranks at the D-level or E-level, and eight provinces had a low degree of mutualism. In terms of the mutualism degree index and ranks of domestic pollution, from 2016 to 2020, there were eight provinces rated S-level in the advanced mutualism degree of pollution and socioeconomic development. Fifteen provinces had a pollution rank at the D-level or E-level, and ten provinces had a low degree of mutualism.
- (4)
- The spatial distribution of ranks tended to be generally balanced. Most provinces with significant agglomerations had a negative impact on neighboring provinces. There was significant spatial heterogeneity between the four economic regions. From 2016 to 2020, the global Moran’s I of industrial and domestic pollution ranks based on administrative division was randomly distributed, and there were no high–high type provinces with a leading role in the local Moran’s I. The local Moran’s I of domestic pollution rank under the four economic regions in the target year showed that some eastern coastal provinces had a positive spatial autocorrelation of the high–high type, while the provinces in the western region mainly had a negative spatial autocorrelation of the high–low type.
4.2. Policy Recommendations
- (1)
- Provinces can adjust their socioeconomic development and pollution prevention policies according to their ranks so that the two can develop at a higher level of coordination. S-level provinces should continue to maintain the current level of coordination of socioeconomic development and pollution prevention and control. A-level and C-level provinces should coordinate the speed of industrial and domestic pollution prevention and control based on the level of socioeconomic development. Compared with A-level provinces, C-level provinces should pay more attention to the coordination of the two through the effective combination of the role of the government and the market, rationally allocate all kinds of public and social resources and vigorously promote socioeconomic development, while maintaining the existing speed of pollution prevention and control. B-level and D-level provinces should strengthen the prevention and control of industrial and domestic pollution, and D-level provinces should increase pollution prevention and control. In terms of industrial pollution, provinces at these two levels can promote the use of new technologies and new environmentally friendly energy sources, optimize the industrial structure and promote industrial transformation and upgrades to prevent and control pollution. In terms of domestic pollution, the government can improve the public transportation network, advocate people to develop a green and low-carbon lifestyle and promote the harmlessness and recycling of garbage to reduce pollution emissions in all aspects. E-level provinces should reasonably adopt the methods mentioned above to improve the coordination level of socioeconomic development and pollution prevention and control. These provinces can first adopt similar measures to B-level and D-level provinces to prevent and control pollution, and then coordinate the speed of industrial and domestic pollution prevention and control according to the level of socioeconomic development.
- (2)
- The government should pay attention to the prevention and control of industrial and domestic pollution at the same frequency to achieve the effect of improving both. It is necessary to reasonably formulate domestic pollution control plans and implementation plans, improve management systems and mechanisms and improve the emission standard and normative system. In terms of investment in pollution prevention and control, government departments should refine the amount of investment in domestic pollution. Some major polluting provinces should appropriately increase the amount of investment and simultaneously strengthen the enforcement of illegal domestic pollution. According to the rank results, it can be seen that different provinces have different emphases and effects on the prevention and control of industrial and domestic pollution. There is no domestic pollution source index in the amount of current pollution investment; thus, the statistical department should refine the domestic pollution prevention and control investment index. In addition, the amount of investment in industrial pollution prevention and control varies greatly between provinces and across different years within the same province and is not obviously related to pollution emissions. Major polluting provinces should adjust the amount of annual investment according to the amount of pollution discharge.
- (3)
- Combined with the spatial nature of the ranking, targeted regional linkage policies should be formulated from the perspective of the whole. The eastern and western regions should appropriately tilt resources from high-rank provinces to provinces negatively related to the surrounding space in the point axis mode to promote the coordinated progress of pollution prevention and social development in their neighboring regions to improve the overall development strength and coordination level of the region. The difference in resource allocation between the four economic regions is an important reason for the significant regional heterogeneity in the eastern region. The eastern region should improve the coordination mechanism of socioeconomic development and pollution prevention and control in terms of domestic pollution, amplify regional advantages, continue to deepen the linkage and cooperation with the central and western regions and realize the full docking of their respective advantages and the smooth flow of resources and elements. High-rank provinces in the central and western regions that have not played a leading role in industrial and domestic pollution should implement the strategy of regional cooperation in pollution prevention and control. Provinces with lower ranks should rely on the advantages of the policy tilt, strengthen intraregional linkages and exchange pollution prevention and control experience with high-rated provinces to improve the coordination level of their own socioeconomic development and industrial and domestic pollution prevention and control.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
References
- The Bulletin on the Second National Census of Pollution Sources. Available online: https://www.mee.gov.cn/xxgk2018/xxgk/xxgk01/202006/t20200610_783547.html (accessed on 17 February 2023).
- 14th Five-Year Plan (2021–2025) for National Economic and Social Development and the Long-Range Objectives Through the Year 2035. Available online: http://www.gov.cn/xinwen/2021-03/13/content_5592681.htm (accessed on 17 February 2023).
- Miah, M.H.; Chand, D.S.; Malhi, G.S. Selected river pollution in Bangladesh based on industrial growth and economic perspective: A review. Environ. Monit. Assess. 2023, 195, 98. [Google Scholar] [CrossRef]
- Sakti, A.D.; Anggraini, T.S.; Ihsan, K.T.N.; Misra, P.; Trang, N.T.Q.; Pradhan, B.; Wenten, I.G.; Hadi, P.O.; Wikantika, K. Multi-air pollution risk assessment in Southeast Asia region using integrated remote sensing and socio-economic data products. Sci. Total Environ. 2023, 854, 158825. [Google Scholar] [CrossRef]
- Mele, M.; Nieddu, L.; Abbafati, C.; Quarto, A. An ANN experiment on the Indian economy: Can the change in pollution generate an increase or decrease in GDP acceleration? Environ. Sci. Pollut. Res. 2021, 28, 35777–35789. [Google Scholar] [CrossRef]
- Halkos, G.E.; Gkampoura, E.-C. Examining the Linkages among Carbon Dioxide Emissions, Electricity Production and Economic Growth in Different Income Levels. Energies 2021, 14, 1682. [Google Scholar] [CrossRef]
- Zhao, Q.; Yuan, C.-H. Did Haze Pollution Harm the Quality of Economic Development?—An Empirical Study Based on China’s PM2.5 Concentrations. Sustainability 2020, 12, 1607. [Google Scholar] [CrossRef] [Green Version]
- Cheng, K.; Sheng, B.; Zhao, Y.; Guo, W.; Guo, J. An Urban Water Pollution Model for Wuhu City. Water 2022, 14, 386. [Google Scholar] [CrossRef]
- Fang, D.; Yu, B. Driving mechanism and decoupling effect of PM2.5 emissions: Empirical evidence from China’s industrial sector. Energy Policy 2021, 149, 112017. [Google Scholar] [CrossRef]
- Jiang, S.; Tan, X.; Hu, P.; Wang, Y.; Shi, L.; Ma, Z.; Lu, G. Air pollution and economic growth under local government competition: Evidence from China, 2007–2016. J. Clean. Prod. 2022, 334, 130231. [Google Scholar] [CrossRef]
- Liu, Y.; Yang, L.; Jiang, W. Qualitative and quantitative analysis of the relationship between water pollution and economic growth: A case study in Nansi Lake catchment, China. Environ. Sci. Pollut. Res. 2020, 27, 4008–4020. [Google Scholar] [CrossRef]
- Li, X.; Xu, Y.; Yao, X. Effects of industrial agglomeration on haze pollution: A Chinese city-level study. Energy Policy 2021, 148, 111928. [Google Scholar] [CrossRef]
- Liang, Y.; Zhang, J.; Zhou, K. Study on Driving Factors and Spatial Effects of Environmental Pollution in the Pearl River-Xijiang River Economic Belt, China. Int. J. Environ. Res. Public Health 2022, 19, 6833. [Google Scholar] [CrossRef] [PubMed]
- Dang, Y.; Song, Y.; Mohiuddin, M.; Sheng, D. Towards Cleaner Production Ecosystem: An Analysis of Embodied Industrial Pollution in International Trade of China’s Processing versus Normal Exports. Int. J. Environ. Res. Public Health 2022, 19, 9900. [Google Scholar] [CrossRef]
- Liu, Y.; Zhang, Z.; Zhang, F. Challenges for Water Security and Sustainable Socioeconomic Development: A Case Study of Industrial, Domestic Water Use and Pollution Management in Shandong, China. Water 2019, 11, 1630. [Google Scholar] [CrossRef] [Green Version]
- Gong, W.; Kong, Y. Nonlinear Influence of Chinese Real Estate Development on Environmental Pollution: New Evidence from Spatial Econometric Model. Int. J. Environ. Res. Public Health 2022, 19, 588. [Google Scholar] [CrossRef]
- Cheng, Z.; Xu, Q.; Sangerson, I.F. China’s economic growth and haze pollution control. Nat. Hazards 2021, 107, 2653–2669. [Google Scholar] [CrossRef]
- Velis, C.A.; Wilson, D.C.; Gavish, Y.; Grimes, S.M.; Whiteman, A. Socio-economic development drives solid waste management performance in cities: A global analysis using machine learning. Sci. Total Environ. 2023, 872, 161913. [Google Scholar] [CrossRef]
- Trifković, M.; Kuburić, M.; Nestorović, Ž.; Jovanović, G.; Kekanović, M. The Attractiveness of Urban Complexes: Economic Aspect and Risks of Environmental Pollution. Sustainability 2021, 13, 8098. [Google Scholar] [CrossRef]
- Wang, S.; Pei, J.; Zhang, K.; Gong, D.; Rokpelnis, K.; Yang, W.; Yu, X. Does Individuals’ Perception of Wastewater Pollution Decrease Their Self-Rated Health? Evidence from China. Int. J. Environ. Res. Public Health 2022, 19, 7291. [Google Scholar] [CrossRef] [PubMed]
- Liang, W.; Yang, M. Urbanization, economic growth and environmental pollution: Evidence from China. Sustain. Comput. Inform. Syst. 2019, 21, 1–9. [Google Scholar] [CrossRef]
- Meng, M.; Pang, T.; Fan, L. Measuring and Fitting the Relationship between Socioeconomic Development and Environmental Pollution: A Case of Beijing–Tianjin–Hebei Region, China. Discret. Dyn. Nat. Soc. 2019, 1570364. [Google Scholar] [CrossRef]
- Yang, M.; Wang, W.; Li, Y.; Du, Y.; Tian, F. Revealing the Impact of Socio-Economic Metrics on the Air Quality on Northeast China Using Multivariate Statistical Analysis. Discret. Dyn. Nat. Soc. 2022, 31, 3373–3385. [Google Scholar] [CrossRef]
- Wang, Q.; Hao, D.; Li, F.; Guan, X.; Chen, P. Development of a new framework to identify pathways from socioeconomic development to environmental pollution. J. Clean. Prod. 2020, 253, 119962. [Google Scholar] [CrossRef]
- Zhang, Y.; Wu, Z. Environmental performance and human development for sustainability: Towards to a new Environmental Human Index. Sci. Total Environ. 2022, 838, 156491. [Google Scholar] [CrossRef]
- Zuo, X.; Hua, H.; Dong, Z.; Hao, C. Environmental Performance Index at the Provincial Level for China 2006–2011. Ecol. Indic. 2017, 75, 48–56. [Google Scholar] [CrossRef]
- Luo, X.; Qin, J.; Wan, Q.; Jin, G. Spatial Human Development Index in China: Measurement and Interpretation Based on Bayesian Estimation. Int. J. Environ. Res. Public Health 2023, 20, 818. [Google Scholar] [CrossRef]
- United Nations Development Programme. Human Development Report 2020: The Next Frontier: Human Development and the Anthropocene; United Nations Development Programme: New York, NY, USA, 2020.
- United Nations Development Programme. China National Human Development Report Special Edition; China Publishing Group Corporation, China Translation & Publishing House: Beijing, China, 2019.
- Accomplishment of the International Comparison Program 2017. Available online: http://www.stats.gov.cn/english./pressrelease/202005/t20200519_1746571.html (accessed on 17 February 2023).
- Yang, S.; He, L.-Y. Transport pollution in China–Evidence from Beijing. Energy Environ. 2016, 27, 377–388. [Google Scholar] [CrossRef]
- Zhang, Y.; Huang, G. Identifying network structure characteristics and key factors for the co-evolution between high-quality industrial development and ecological environment. Envion. Dev. Sustain. 2022. [Google Scholar] [CrossRef]
- Zhang, Y.; Huang, G. Grey Lotka-Volterra model for the co-evolution of technological innovation, resource consumption, environmental quality, and high-quality industrial development in Shaanxi Province, China. Environ. Sci. Pollut. Res. 2021, 28, 57751–57768. [Google Scholar] [CrossRef]
- Chen, W.; Chen, Y. Pre-Warning Measurement of Water Resources Security in the Yangtze River Basin from the Perspective of Water-Energy-Food Symbiosis. Water 2021, 13, 475. [Google Scholar] [CrossRef]
- Guo, X.; Wu, L.; Wang, M. Application of Grey Lotka-Volterra Model in Water-Economy-Industry-Technology Innovation System in Beijing-Tianjin-Hebei Region. Int. J. Environ. Res. Public Health 2022, 19, 8969. [Google Scholar] [CrossRef]
- Wu, L.; Liu, S.; Wang, Y. Grey Lotka–Volterra model and its application. Technol. Forecast. Soc. Chang. 2012, 79, 1720–1730. [Google Scholar] [CrossRef]
Mutualism Degree | Force-On Indices | Mutualism Degree Index | Ranks |
---|---|---|---|
Advanced mutualism degree | SI(t) > 0, SH(t) > 0 | S1(t) > 1 | S-level |
Higher mutualism degree | SI(t) > 0, SH(t) < 0, |SH(t)| < SI(t) | 0 < S1(t) < 1 | A-level |
SI(t) < 0, SH(t) > 0, |SI(t)| < SH(t) | 0 < S1(t) < 1 | B-level | |
Lower mutualism degree | SI(t) > 0, SH(t) < 0, SI(t)< |SH(t)| | −1 < S1(t) < 0 | C-level |
SI(t) < 0, SH(t) > 0, SH(t) < |SI(t)| | −1 < S1(t) < 0 | D-level | |
Low mutualism degree | SI(t) < 0, SH(t) < 0 | S1(t) < −1 | E-level |
Classification Basis | Explanation of the Classification | 2011–2015 | 2016–2020 |
---|---|---|---|
SI(t) > 0, SH(t) > 0 | Industrial pollution effectiveness and HDI reinforce each other | Tianjin, Hebei, Shanxi, Nei Mongol, Heilongjiang, Jiangsu, Zhejiang, Anhui, Fujian, Henan, Hubei, Guangdong, Chongqing, Guizhou, Xizang, Gansu, Ningxia | Beijing, Hebei, Liaoning, Jilin, Fujian, Jiangxi, Henan, Hubei, Guangdong, Chongqing, Xizang, Shaanxi, Qinghai, Xinjiang |
SI(t) > 0, SH(t) < 0 | HDI reinforces industrial pollution effectiveness while industrial pollution effectiveness suppresses HDI | Shanghai, Hunan, Yunnan, Shaanxi | Heilongjiang, Shandong, Guangxi, Hainan, Ningxia |
SI(t) < 0, SH(t) > 0 | HDI suppresses industrial pollution effectiveness while industrial pollution effectiveness reinforces HDI | Jilin, Jiangxi, Guangxi, Sichuan, Xinjiang | Tianjin, Jiangsu, Zhejiang, Gansu |
SI(t) < 0, SH(t) < 0 | Industrial pollution effectiveness and HDI suppress each other | Beijing, Liaoning, Shandong, Hainan, Qinghai | Shanxi, Nei Mongol, Shanghai, Anhui, Hunan, Sichuan, Guizhou, Yunnan |
Classification Basis | Explanation of the Classification | 2011–2015 | 2016–2020 |
---|---|---|---|
SD(t) > 0, SL(t) > 0 | Domestic pollution effectiveness and HDI reinforce each other | Tianjin, Hebei, Shanxi, Liaoning, Heilongjiang, Shanghai, Zhejiang, Anhui, Fujian, Jiangxi, Hunan, Guangdong, Sichuan, Guizhou, Yunnan, Xizang, Gansu, Ningxia | Tianjin, Nei Mongol, Hubei, Hunan, Sichuan, Shaanxi, Qinghai, Ningxia |
SD(t) > 0, SL(t) < 0 | HDI reinforces domestic pollution effectiveness while domestic pollution effectiveness suppresses HDI | Jilin, Henan, Guangxi, Hainan, Chongqing, Xinjiang | Jiangsu, Anhui, Henan, Guangxi, Chongqing, Xinjiang |
SD(t) < 0, SL(t) > 0 | HDI suppresses domestic pollution effectiveness while domestic pollution effectiveness reinforces HDI | Jiangsu, Shandong | Beijing, Shanxi, Heilongjiang, Zhejiang, Fujian, Shandong, Guangdong, Guizhou, Gansu |
SD(t) < 0, SL(t) < 0 | Domestic pollution effectiveness and HDI suppress each other | Beijing, Nei Mongol, Hubei, Shaanxi, Qinghai | Hebei, Liaoning, Jilin, Shanghai, Jiangxi, Hainan, Yunnan, Xizang |
Rank of Industrial Pollution in 2011–2015 | Rank of Industrial Pollution in 2016–2020 | Rank of Domestic Pollution in 2011–2015 | Rank of Domestic Pollution in 2016–2020 | |
---|---|---|---|---|
Moran’s I of administrative division | −0.199 | −0.001 | −0.222 | 0.104 |
p-value of administrative division | 0.06 | 0.39 | 0.04 | 0.15 |
Moran’s I of four economic regions | −0.103 | −0.054 | −0.09 | 0.068 |
p-value of four economic regions | 0.13 | 0.46 | 0.21 | 0.09 |
Types | Rank of Industrial Pollution in 2011–2015 | Rank of Industrial Pollution in 2016–2020 | Rank of Domestic Pollution in 2011–2015 | Rank of Domestic Pollution in 2016–2020 |
---|---|---|---|---|
High–high | None | None | None | None |
Low–low | Shaanxi | None | None | Chongqing, Shaanxi |
Low–high | Xizang | Guangxi | Heilongjiang, Ningxia | Tianjin |
High–low | Beijing, Shanghai, Jiangxi, Shandong, Hainan | Tianjin | Beijing, Guangxi, Hainan | Gansu |
Types | Rank of Industrial Pollution in 2011–2015 | Rank of Industrial Pollution in 2016–2020 | Rank of Domestic Pollution in 2011–2015 | Rank of Domestic Pollution in 2016–2020 |
---|---|---|---|---|
High–high | None | None | None | Jiangsu, Zhejiang, Fujian |
Low–low | None | None | None | Guizhou, Chongqing, Shaanxi |
Low–high | None | None | None | Tianjin |
High–low | Jiangxi | None | None | Guangxi, Yunnan, Xizang, Gansu, Xinjiang |
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Zhou, X.; Wang, J. Research on the Evaluation and Spatial Characteristics of China’s Provincial Socioeconomic Development and Pollution Control Based on the Lotka–Volterra Model. Int. J. Environ. Res. Public Health 2023, 20, 4561. https://doi.org/10.3390/ijerph20054561
Zhou X, Wang J. Research on the Evaluation and Spatial Characteristics of China’s Provincial Socioeconomic Development and Pollution Control Based on the Lotka–Volterra Model. International Journal of Environmental Research and Public Health. 2023; 20(5):4561. https://doi.org/10.3390/ijerph20054561
Chicago/Turabian StyleZhou, Xue, and Jiapeng Wang. 2023. "Research on the Evaluation and Spatial Characteristics of China’s Provincial Socioeconomic Development and Pollution Control Based on the Lotka–Volterra Model" International Journal of Environmental Research and Public Health 20, no. 5: 4561. https://doi.org/10.3390/ijerph20054561