Abstract
This study estimates the environmental Kuznets curve (EKC) relationship at the province level in China. We apply empirical methods to test three industrial pollutants—SO2 emission, wastewater discharge, and solid waste production—in 29 Chinese provinces in 1994–2010. We use the geographically weighted regression (GWR) approach, wherein the model can be fitted at each spatial location in the data, weighting all observations by a function of distance from the regression point. Hence, considering spatial heterogeneity, the EKC relationship can be analyzed region-specifically through this approach, rather than describing the average relationship over the entire area examined. We also investigate the spatial stratified heterogeneity to verify and compare risk factors that affect regional pollution with statistical models. This study finds that the GWR model, aimed at considering spatial heterogeneity, outperforms the OLS model; it is more effective at explaining the relationships between environmental performance and economic growth in China. The results indicate a significant variation in the existence of the EKC relationship. Such spatial patterns suggest province-specific policymaking to achieve balanced growth in those provinces.
Similar content being viewed by others
Notes
These values are in 2010 constant prices. The rate of exchange in 2010 was approximately US$ 1 = 6.77 yuan. The index of GDP per capita for 2013 is 1,837.5 if using 1978 as the base year.
The “new toxics” scenario argues that new pollutants, for example, CO2, may not show the inverted U-shape curve. The revised EKC scenario claims that technological change may accelerate to achieve the turning point; thus, the EKC graph shifting downward and left. The race to bottom scenario insist the greatest increase of environmental regulations and policies happen from low to middle economic levels (Dasgupta et al. 2002).
Bandwidth defines how each data point is weighted by the distance from the regression point. This is determined by a spatial weighting function that affects the distance between regression and data points (Fotheringham et al. 2002). Therefore, in the adaptive spatial kernels, we can observe larger bandwidths of kernels, where data are scarce, and smaller bandwidths, where data are dense, while all regression points have the same bandwidth in the fixed kernel function.
In this function, if a locally weighted regression parameter is similar to a global OLS model, w ij would be close to 1 regardless of d ij . In other words, a value of w ij close to 0 indicates that the estimated parameter would vary across space. This function allows us to use the bandwidth with the same number of data points with non-zero weights (Fotheringham et al. 2002; Fischer and Getis 2009).
AIC is a model selection technique based on information theory, providing the information loss of models between the goodness-of-fit and degrees of freedom. In this analysis, AIC evaluates an optimal bandwidth between the global OLS and GWR models. The bandwidth with minimized AIC value is utilized in the GWR estimation (Zhen et al. 2013).
Tibet is not included in our analysis, because of data limitations from statistical data sources. Chongqing is also not included, because it was split from Sichuan during the estimation period (in 1997). To maintain data consistency, we merge Chongqing and Sichuan data, and treat them as a single province in this study.
Coastal provinces refer to Beijing, Fujian, Guangdong, Guangxi, Hainan, Hebei, Jiangsu, Liaoning, Shanghai, Shandong, Tianjin, and Zhejiang.
GRP per capita and statistics of three pollutants in 2012 are used to identify the trend of sustainable development after 2010 in Table 7.
To conduct GeogDetector, the numerical dependent variables—GRP per capita and population density—were transformed to the categorical variables based on the ranking among the provinces, since the precondition for this program is “Y is numerical and X MUST be categorical” (Wang et al. 2010).
A positive value of the diff-criterion (AICc, AIC, Bayesian inference criterion/minimum description length, or coefficient of variation) suggests no spatial variability in terms of model selection criteria.
Adjusted GRP per capita by CPI (1993 = 100) of each province in 2012 is as follows: Beijing (1.223), Shanghai (1.112), Liaoning (0.794), Tianjin (0.759), Jiangsu (0.708), Zhejiang (0.706), Guangdong (0.609), Xinjiang (0.566), Shandong (0.553), Heilongjiang (0.543), Fujian (0.530), Inner Mongolia (0.530), Hainan (0.513), Jilin (0.512), Ningxia (0.489), Shaanxi (0.482), Shanxi (0.475), Qinghai (0.457), Hebei (0.457), Hubei (0.443), Hunan (0.439), Sichuan (0.399), Henan (0.385), Yunnan (0.343), Guangxi (0.343), Jiangxi (0.338), Gansu (0.324), Anhui (0.324), and Guizhou (0.298).
In their analyses, the city type of Beijing shows significant improvement between 1990 and 2000, but is categorized with other cities (Wang et al. 2012a).
References
Arsanjani JJ, Helbich M, de Noronha Vaz E (2013) Spatiotemporal simulation of urban growth patterns using agent-based modeling: the case of Tehran. Cities 32:33–42
Aslanidis N, Iranzo S (2009) Environment and development: is there a Kuznets curve for CO2 emissions? Appl Econ 41(6):803–810
Azomahou T, Laisney F, Van PN (2006) Economic development and CO2 emissions: a nonparametric panel approach. J. Public Econ 90(6):1347–1363
Berkowitz P, Gomez G, Gjermano L, Schafer G (2007) Brand China: using the 2008 Olympic games to enhance China’s image. Place Brand Public Dipl 3(2):164–178
Brajer V, Mead RW, Xiao F (2008) Health benefits of tunneling through the Chinese environmental Kuznets curve (EKC). Ecol Econ 66(4):674–686
Brajer V, Mead RW, Xiao F (2011) Searching for an environmental Kuznets curve in China’s air pollution. China Econ Rev 22(3):383–397
Bravo G, Marelli B (2007) Micro-foundations of the environmental Kuznets curve hypothesis: an empirical analysis. Int J Innov Sustain Dev 2(1):36–62
Brundtland GH, Khalid M (1987) Our common future. United Nations, New York
Cavanaugh JE (1997) Unifying the derivations for the Akaike and corrected Akaike information criteria. Stat Probab Lett 33(2):201–208
China Coal Cap Project (2015) China coal consumption cap plan and research report: recommendations for the 13th five-year plan. National Resources Defense Council, Beijing. https://d2ouvy59p0dg6k.cloudfront.net/downloads/china_coal_consumption_cap_plan_and_research_report__recommendations_for_the_13fyp.pdf. Accessed 30 Nov 2017
Cho SH, Bowker JM, Park WM (2006) Measuring the contribution of water and green space amenities to housing values: an application and comparison of spatially weighted Hedonic models. J Agric Resour Econ 31(3):485–507
Chuai X, Huang X, Wang W, Wen J, Chen Q, Peng J (2012) Spatial econometric analysis of carbon emissions from energy consumption in China. J Geogr Sci 22(4):630–642
Chung SS, Lo CWH (2012) Marketisation of public services in China: reforming the provision of solid waste services in Guangzhou’s environmental sector. World Rev Sci Technol Sustain Dev 9(1):34–55
Chung SS, Poon CS (2001) Accounting for the shortage of solid waste disposal facilities in southern China. Environ Conserv 28(2):99–103
Dasgupta S, Laplante B, Wang H, Wheeler D (2002) Confronting the environmental Kuznets curve. J Econ Perspect 16(1):147–168
De Groot HL, Withagen CA, Minliang Z (2004) Dynamics of China’s regional development and pollution: an investigation into the environmental Kuznets curve. Environ Dev Econ 9:507–537
Ediger L, Hwang L (2009) Water quality and environmental health in southern China. BSR forum, May 15, 2009
Ekins P (1997) The Kuznets curve for the environment and economic growth: examining the evidence. Environ Plan A 29(5):805–830
Fei X, Wu J, Liu Q, Ren Y, Lou Z (2016) Spatiotemporal analysis and risk assessment of thyroid cancer in Hangzhou, China. Stoch Environ Res Risk Assess 30(8):2155–2168
Fischer MM, Getis A (2009) Handbook of applied spatial analysis: software tools, methods and applications. Springer, Berlin
Fotheringham AS, Brunsdon C, Charlton M (2002) Geographically weighted regression: the analysis of spatially varying relationships. Wiley, Chichester
Gangadharan L, Valenzuela MR (2001) Interrelationships between income, health and the environment: extending the environmental Kuznets curve hypothesis. Ecol Econ 36(3):513–531
Grossman GM, Krueger AB (1991) Environmental impacts of a North American free trade agreement (No. w3914). National Bureau of Economic Research
Harris P, Clarke A, Juggins S, Brunsdon C, Charlton M (2014) Geographically weighted methods and their use in network re-designs for environmental monitoring. Stoch Environ Res Risk Assess 28(7):1869–1887
He C, Huang Z, Ye X (2014) Spatial heterogeneity of economic development and industrial pollution in urban China. Stoch Environ Res Risk Assess 28(4):767–781
Helbich M, Brunauer W, Vaz E, Nijkamp P (2013) Spatial heterogeneity in hedonic house price models: the case of Austria. Urban Stud 51(2):390–411
Hurvich CM, Simonoff JS, Tsai CL (1998) Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. J R Stat Soc Ser B (Stat Methodol) 60(2):271–293
Kim Y, Tanaka K, Zhang X (2017) A spatial analysis of the causal factors influencing China's air pollution. Asian J Atmos Environ 11(3):194–201
Kuznets S (1955) Economic growth and income inequality. Am Econ Rev 45(1):1–28
Li X, Xie Y, Wang J, Christakos G, Si J, Zhao H, Ding Y, Li J (2013) Influence of planting patterns on fluoroquinolone residues in the soil of an intensive vegetable cultivation area in northern China. Sci Total Environ 458:63–69
Li Q, Song J, Wang E, Hu H, Zhang J, Wang Y (2014) Economic growth and pollutant emissions in China: a spatial econometric analysis. Stoch Environ Res Risk Assess 28(2):429–442
Li L, Hong X, Tang D, Na M (2016) GHG emissions, economic growth and urbanization: a spatial approach. Sustainability 8(5):462
Liu Y, Xiao H, Zikhali P, Lv Y (2014) Carbon emissions in China: a spatial econometric analysis at the regional level. Sustainability 6(9):6005–6023
Lopez R (1994) The environment as a factor of production: the effects of economic growth and trade liberalization. J Environ Econ Manag 27(2):163–184
Managi S, Kaneko S (2009) Environmental performance and returns to pollution abatement in China. Ecol Econ 68(6):1643–1651
Matsuoka S (2004) nvironmental problems in Asia. In: Kitahara A, Nishizawa A (eds) Asia economy, chapter 6. Minervashobo, Kyoto, pp 141–165 (in Japanese)
Matsuoka S, Matsumoto R, Kochi I (1998) Economic growth and environmental problem in developing countries: the environmental Kuznets curve do exist? Environ Sci 11(4):349–362 (in Japanese)
Montello D, Sutton P (2012) An introduction to scientific research methods in geography and environmental studies. Sage, London
Paruolo P, Murphy B, Janssens-Maenhout G (2015) Do emissions and income have a common trend? A country-specific, time-series, global analysis, 1970–2008. Stoch Environ Res Risk Assess 29(1):93–107
Rupasingha A, Goetz SJ, Debertin DL, Pagoulatos A (2004) The environmental Kuznets curve for US counties: a spatial econometric analysis with extensions. Pap Reg Sci 83(2):407–424
Selden TM, Song D (1994) Environmental quality and development: is there a Kuznets curve for air pollution emissions? J Environ Econ Manag 27:147–162
Shen J (2006) A simultaneous estimation of environmental Kuznets curve: evidence from China. China Econ Rev 17:383–394
Song ML, Zhang W, Wang SH (2013) Inflection point of environmental Kuznets curve in mainland China. Energy Policy 57:14–20
State Statistical Bureau (1995–2013) China statistical yearbook. China Statistical Publishing House, Beijing
Stern DI (1998) Non-Interpretive mechanisms in psychoanalytic therapy: the ‘something more’ than interpretation. Int J Psychoanal 79(5):903
Stern DI (2004) The rise and fall of the environmental Kuznets curve. World Dev 32(8):1419–1439
Stern DI, Common MS (2001) Is there an environmental Kuznets curve for sulfur? J Environ Econ Manag 41(2):162–178
Stern DI, Common MS, Barbier EB (1996) Economic growth and environmental degradation: the environmental Kuznets curve and sustainable development. World Dev 24(7):1151–1160
Tao S, Zheng T, Lianjun T (2008) An empirical test of the environmental Kuznets curve in China: a panel cointegration approach. China Econ Rev 19(3):381–392
Todorova Y, Lincheva S, Yotinov I, Topalova Y (2016) Contamination and ecological risk assessment of long-term polluted sediments with heavy metals in small hydropower cascade. Water Res Manag 30(12):4171–4184
Wang JF, Li XH, Christakos G, Liao YL, Zhang T, Gu X, Zheng XY (2010) Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China. Int J Geogr Inf Sci 24(1):107–127
Wang JF, Liu XH, Peng L, Chen HY, Driskell L, Zheng XY (2012a) Cities evolution tree and applications to predicting urban growth. Popul Environ 33(2–3):186–201
Wang Q, Yuan X, Lai Y, Ma C, Ren W (2012b) Research on interactive coupling mechanism and regularity between urbanization and atmospheric environment: a case study in Shandong Province, China. Stoch Environ Res Risk Assess 26(7):887–898
Wang S, Ma H, Zhao Y (2014) Exploring the relationship between urbanization and the eco-environment—a case study of Beijing–Tianjin–Hebei region. Ecol Indic 45:171–183
Wang JF, Zhang TL, Fu BJ (2016) A measure of spatial stratified heterogeneity. Ecol Indic 67(2016):250–256
Wang C, Du S, Wen J, Zhang M, Gu H, Shi Y, Xu H (2017) Analyzing explanatory factors of urban pluvial floods in Shanghai using geographically weighted regression. Stoch Environ Res Risk Assess 31(7):1777–1790
Wheeler DC (2014) Geographically weighted regression. In: Fischer MM, Nijkamp P (eds) Handbook of regional science. Springer, Berlin, pp 1435–1459
World Bank (1992) World development report, 1992. Oxford University Press, New York
Wu Y (2010) Regional environmental performance and its determinants in China. China World Econ 18(3):73–89
Zhang K, Dearing JA, Tong SL, Hughes TP (2016) China’s degraded environment enters a new normal. Trends Ecol Evol 31(3):175–177
Zhen Z, Li F, Liu Z, Liu C, Zhao Y, Ma Z, Zhang L (2013) Geographically local modeling of occurrence, count, and volume of downwood in northeast China. Appl Geogr 37:114–126
Zhong LJ, Louie PKK, Zheng J, Wai KM, Ho JWK, Yuan Z, Lau AKH, Yue D, Zhou Y (2013) The Pearl River Delta regional air quality monitoring network-regional collaborative efforts on joint air quality management. Aerosol Air Qual Res 13(5):1582–1597
Acknowledgements
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2013S1A5B8A01054955).
Author information
Authors and Affiliations
Corresponding author
Appendix: Distributions of the GRP per capita and the pollutants in 1994 and 2010
Appendix: Distributions of the GRP per capita and the pollutants in 1994 and 2010
Rights and permissions
About this article
Cite this article
Kim, Y., Tanaka, K. & Ge, C. Estimating the provincial environmental Kuznets curve in China: a geographically weighted regression approach. Stoch Environ Res Risk Assess 32, 2147–2163 (2018). https://doi.org/10.1007/s00477-017-1503-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00477-017-1503-z