Advertisement

Journal of Geographical Sciences

, Volume 29, Issue 2, pp 231–252 | Cite as

Spatial spillover effect and driving forces of carbon emission intensity at the city level in China

  • Shaojian Wang
  • Yongyuan Huang
  • Yuquan Zhou
Article
  • 47 Downloads

Abstract

In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial spillover effects and driving factors of carbon emission intensity in 283 Chinese cities from 1992 to 2013. The following results were obtained. (1) Nuclear density estimation shows that the overall average carbon intensity of cities in China has decreased, with differences gradually narrowing. (2) The spatial autocorrelation Moran’s I index indicates significant spatial agglomeration of carbon emission intensity is gradually increasing; however, differences between regions have remained stable. (3) Spatial Markov chain analysis shows a Matthew effect in China’s urban carbon emission intensity. In addition, low-intensity and high-intensity cities characteristically maintain their initial state during the transition period. Furthermore, there is a clear “Spatial Spillover” effect in urban carbon emission intensity and there is heterogeneity in the spillover effect in different regional contexts; that is, if a city is near a city with low carbon emission intensity, the carbon emission intensity of the first city has a higher probability of upward transfer, and vice versa. (4) Panel quantile results indicate that in cities with low carbon emission intensity, economic growth, technological progress, and appropriate population density play an important role in reducing emissions. In addition, foreign investment intensity and traffic emissions are the main factors that increase carbon emission intensity. In cities with high carbon intensity, population density is an important emission reduction factor, and technological progress has no significant effect. In contrast, industrial emissions, extensive capital investment, and urban land expansion are the main factors driving the increase in carbon intensity.

Keywords

Chinese cities kernel density estimation spatial autocorrelation spatial spillover effect spatial Markov chain quantile regression panel model 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Cai B F, Cao D, Liu L C et al., 2011. China transport CO2 emission study. Advances in Climate Change Research, 7(3): 197–203. (in Chinese)Google Scholar
  2. Cai Y Y, Sun B D, 2013. Spatial dispersion of population and economic growth: Based on empirical analysis of megacities. Urban Insight, 27(5): 94–101. (in Chinese)Google Scholar
  3. Chai Z X, 2013. Density effects, development level and China’s urban carbon dioxide emission. On Economic Problems, (3): 25–31. (in Chinese)Google Scholar
  4. Chen L W, Yang K Z, 2007. Productivity, urban scale and economic density: An empirical study on the economic effect of urban agglomeration. Social Sciences in Guizhou, (2): 113–119. (in Chinese)Google Scholar
  5. Chen P Y, Zhu X G, 2013. Regional convergence at county level in China. Scientia Geographica Sinica, 33(11): 1302–1308. (in Chinese)Google Scholar
  6. Chen Q, 2010. Advanced Econometrics and Stata Application. Beijing: Higher Education Press. (in Chinese)Google Scholar
  7. Cheng Y Q, Zhang S Z, Ye X Y et al., 2013. Spatial econometric analysis of CEI and its driving factors from energy consumption in China. Acta Geographica Sinica, 68(10): 1418–1431. (in Chinese)Google Scholar
  8. Cong J H, Liu X M, Zhao X R, 2014. Demarcation problems and the corresponding measurement methods of the urban carbon accounting. China Population, Resources and Environment, 24(4): 19–26. (in Chinese)Google Scholar
  9. Dong F, Yu B L, Hadachin T et al., 2018. Drivers of CEI change in China. Resources, Conservation and Recycling, 129: 187–201.Google Scholar
  10. Gallo J L, 2004. Space-time analysis of GDP disparities among European regions: A Markov chain approach. International Regional Science Review, 27(2): 138–163.Google Scholar
  11. Glaeser E L, Kahn M E, 2010. The greenness of cities: Carbon dioxide emissions and urban development. Journal of Urban Economics, 67(3): 404–418.Google Scholar
  12. Gu C L, Tan Z B, Liu W et al., 2009. A study on climate change, carbon emissions and low-carbon city planning. Urban Planning Forum, (3): 38–45. (in Chinese)Google Scholar
  13. Guo C X, 2010. An analysis of the increase of CO2 emission in China: Based on SDA technique. China Industrial Economics, (12): 47–56. (in Chinese)Google Scholar
  14. International Energy Agency (IEA), 2010. World Energy Outlook 2009 Factsheet.Google Scholar
  15. Jiao W X, Chen X P, 2012. Environmental impact analysis of Gansu province based on the stirpat model. Re sources and Environment in the Yangtze Basin, 21(1): 105–110. (in Chinese)Google Scholar
  16. Jotzo F, Pezzey J C V, 2007. Optimal intensity targets for greenhouse gas emissions trading under uncertainty. Environmental & Resource Economics, 38(2): 259–284.Google Scholar
  17. Koenker R, 2004. Quantile regression for longitudinal data. Journal Multivariate Analysis, 91(1): 74–89.Google Scholar
  18. Koenker R, Bassett G, 1978. Regression quantiles. Econometrica, 46(1): 33–50.Google Scholar
  19. Li G Z, Wang S, 2008. Regional factor decompositions in China’s energy intensity change: Base on LMDI technique. Journal of Finance and Economics, 34(8): 52–62. (in Chinese)Google Scholar
  20. Li J X, Chen Y N, Li Z et al., 2018. Quantitative analysis of the impact factors of conventional energy carbon emissions in Kazakhstan based on LMDI decomposition and STIRPAT model. Journal of Geographical Sciences, 28(7): 1001–1019.Google Scholar
  21. Li K J, Qu R X, 2012. Impact of technological change on carbon dioxide emission: An empirical analysis based on provincial dynamic panel data model. Journal of Beijing Normal University (Social Sciences), (5): 129–139. (in Chinese)Google Scholar
  22. Li L S, Zhou Y, 2006. Can technological progress improve energy efficiency? Empirical test based on China’s industrial sector. Management World, (10): 82–89. (in Chinese)Google Scholar
  23. Li S T, Hou Y Z, Liu Y Z et al., 2004. The analysis on survey of local protection in China domestic market. Economic Research Journal, (11): 78–84. (in Chinese)Google Scholar
  24. Li Y M, Zhang L, Cheng X L, 2010. A decomposition model and reduction approaches for carbon dioxide emissions in China. Resources Science, 32(2): 218–222. (in Chinese)Google Scholar
  25. Li Z H, Liu H H, 2011. FDI, technology progress and emission of CO2: Evidence from Chinese provincial data. Studies in Science of Science, 29(10): 1495–1503. (in Chinese)Google Scholar
  26. Lin B Q, Huang X G, 2011. Evolution trend of China’s regional carbon emission under the gradient development model—Based on the perspective of spatial analysis. Journal of Financial Research, (12): 35–46. (in Chinese)Google Scholar
  27. Liu Y H, Gao C C, Lu Y Y, 2017. The impact of urbanization on GHG emissions in China: The role of population density. Journal of Clean Production, 157: 299–309.Google Scholar
  28. Liu Y H, Ge Q S, He F N et al., 2008. Countermeasures against international pressure of reducing CO2 emissions and analysis on China’s potential of CO2 emission reduction. Acta Geographica Sinica, 63(7): 675–682. (in Chinese)Google Scholar
  29. Long G Y, 2003. Understanding China’s recent growth experience: A spatial econometric perspective. Annals of Regional Science, 37(4): 613–628.Google Scholar
  30. Luo Y X, Tian M Z, 2010. Quantile regression for panel data and its simulation study. Statistical Research, 27(10): 81–87. (in Chinese)Google Scholar
  31. Ma D L, Chen Z C, Wang L, 2015. Spatial econometrics research on inter-provincial carbon emissions efficiency in China. China Population, Resources and Environment, 25(1): 67–77. (in Chinese)Google Scholar
  32. Pan M, Lv B, Zhang C et al., 2010. Thinking of model system construction of building energy efficiency and green building. Urban Studies, (7): 6–11. (in Chinese)Google Scholar
  33. Pan W Q, 2012. Regional linkage and the spatial spillover effects on regional economic growth in China. Economic Research Journal, (1): 54–65. (in Chinese)Google Scholar
  34. Park J, 2014. The Effects of Compact City Form on Transportation Energy Consumption and Air Pollution. Beijing: Tsinghua University Press. (in Chinese)Google Scholar
  35. Pettersson F, Maddison D, Acar S et al., 2014. Convergence of carbon dioxide emissions: A review of the literature. International Review of Environmental & Resource Economics, 7(2): 141–178.Google Scholar
  36. She Q N, Jia W X, Pan C et al., 2015. Spatial and temporal variation characteristics of urban forms' impact on regional carbon emissions in the Yangtze River Delta. China Population, Resources and Environment, 25(11): 44–51. (in Chinese)Google Scholar
  37. Su W S, Liu Y Y, Wang S J et al., 2018. Regional inequality, spatial spillover effects, and the factors influencing city-level energy-related carbon emissions in China. Journal of Geographical Sciences, 28(4): 495–513.Google Scholar
  38. Sun Y H, Zhong W Z, Qing D R, 2012. Analysis on differences of CEI of each province in China based on Theil index. Finance and Trade Research, 23(3): 1–7. (in Chinese)Google Scholar
  39. Tian L, 2011. Urbanization of land in urbanization progress of China: Boon or bane? City Planning Review, 35(2): 11–12. (in Chinese)Google Scholar
  40. Wang S J, Fang C L, Guan X L et al., 2014a. Urbanization, energy consumption, and CO2 emissions in China: A panel data analysis of China’s province. Applied Energy, 136: 738–749.Google Scholar
  41. Wang S J, Fang C L, Ma H T et al., 2014b. Spatial differences and multi-mechanism of carbon footprint based on GWR model in provincial China. Journal of Geographical Sciences, 24(4): 804–822.Google Scholar
  42. Wang S J, Fang C L, Wang Y, 2016a. Spatiotemporal variations of energy-related CO2 emissions in China and its influencing factors: An empirical analysis based on provincial panel data. Renewable & Sustainable Energy Reviews, 55: 505–515.Google Scholar
  43. Wang S J, Fang C L, Wang Y et al., 2015. Quantifying the relationship between urban development intensity and carbon dioxide emissions using a panel data analysis. Ecological Indicators, 49: 121–131.Google Scholar
  44. Wang S J, Li Q Y, Fang C L et al., 2016b. The relationship between economic growth, energy consumption, and CO2 emissions: Empirical evidence from China. Science of the Total Environment, 542: 360–371.Google Scholar
  45. Wang S J, Liu X P, 2017. China’s city-level energy-related CO2 emissions: Spatiotemporal patterns and driving forces. Applied Energy, 200: 204–214.Google Scholar
  46. Wang S J, Liu X P, Zhou C S et al., 2017. Examining the impacts of socioeconomic factors, urban form, and transportation networks on CO2 emissions in China’s megacities. Applied Energy, 185: 189–200.Google Scholar
  47. Wang Y, Cheng X, Yin P H et al., 2013. Research on regional characteristics of China’s carbon emission performance based on entropy method and cluster analysis. Journal of Natural Resources, 28(7): 1106–1116. (in Chinese)Google Scholar
  48. Wang Z, Ma C F, Wang Y et al., 2003. A geographical investigation into knowledge spillovers between regions. Acta Geographica Sinica, 58(5): 773–780. (in Chinese)Google Scholar
  49. Wu J X, Guo Z Y, 2016. Research on the convergence of carbon dioxide emissions in China: A continuous dynamic distribution approach. Statistical Research, 33(1): 54–60. (in Chinese)Google Scholar
  50. Xiao Y F, Wan Z J, Liu H G, 2014. An empirical study of carbon emission tranfer and carbon leakage in regional industrial transfer in China: Analysis based on inter-regional input-output model in 2002 and 2007. Journal of Finance and Economics, 40(2): 75–84. (in Chinese)Google Scholar
  51. Xie R, Fang J Y, Liu C J, 2017. The effects of transportation infrastructure on urban carbon emissions. Applied Energy, 196: 199–207.Google Scholar
  52. Xu G Y, 2010. The convergence in carbon dioxide emissions: Theoretical hypotheses and empirical research in China. The Journal of Quantitative & Technical Economics, (9): 31–42. (in Chinese)Google Scholar
  53. Xu J H, 1996. Mathematical Methods in Contemporary Geography. Beijing: Higher Education Press. (in Chinese)Google Scholar
  54. Yan Y M, Wang Z, Wu L Y et al., Analysis of the determinants of CEI on regional differences. Acta Scientiae Circumstantiae, 36(9): 3436–3444. (in Chinese)Google Scholar
  55. York R, Rosa E A, Dietz T, 2003. STIRPAT, IPAT and ImPACT: Analytic tools for unpacking the driving forces of environmental impacts. Ecological Economics, 46(3): 351–365.Google Scholar
  56. Zeng G, Shang Y M, Si Y F, 2015. The convergent evolution of China’s regional economic development models. Geographical Research, 34(11): 2005–2020. (in Chinese)Google Scholar
  57. Zhang G Y, 2010. Economic development pattern change impact on China’s CEI. Economic Research Journal, (4): 120–133. (in Chinese)Google Scholar
  58. Zhang L F, 2011. Relations among the industry structure, energy structure and carbon emissions. Journal of Arid Land Resources and Environment, 25(5): 1–7. (in Chinese)Google Scholar
  59. Zhang L J, Liu G L, Qin Y C, 2014. Multi-scale integrated assessment of urban energy use and CO2 emissions. Journal of Geographical Sciences, 24(4): 651–668.Google Scholar
  60. Zhang T X, Zeng A Z, 2013. Spatial econometrics analysis on China transport carbon emissions. Urban Development Studies, 20(10): 14–20. (in Chinese)Google Scholar
  61. Zhang X L, 2012. Has transport infrastructure promoted regional economic growth? With an analysis of the spatial spillover effects of transport infrastructure. Social Sciences in China, (3): 60–77. (in Chinese)Google Scholar
  62. Zhao G M, Chen L Z, Sun L C et al., 2017. Markov steady state prediction of CEI in China, based on the perspective of spatial differentiation. Science and Technology Management Research, 37(22): 228–233. (in Chinese)Google Scholar
  63. Zhao Q Z, Yan Q Y, Zhao H R, 2018. Research on spatial characteristics and influencing factors of provincial carbon emissions in China. Journal of Beijing Institute of Technology (Social Sciences Edition), 20(1): 9–16. (in Chinese)Google Scholar
  64. Zhao R Q, Huang X J, Xu H et al., 2009. Progress in the research of carbon cycle and management of urban system. Journal of Natural Resources, (10): 1847–1859. (in Chinese)Google Scholar
  65. Zhao R Y, Qiu Z Z, 2014. Review on the relationship between industrial structure and carbon emission. Economic Review, (10): 110–113. (in Chinese)Google Scholar
  66. Zhao Y T, Huang X J, Zhong T Y et al., 2011. Spatial pattern evolution of CEI from energy consumption in China. Environmental Science, 32(11): 3145–3152. (in Chinese)Google Scholar
  67. Zheng C D, Liu S, 2011. Industrial structure and carbon emission: An empirical analysis based on China provincial panel data. Research on Development, (2): 26–33. (in Chinese)Google Scholar
  68. Zhou J Q, Wang T S, 2014. Convergence of regional economic growth and CEI difference and its mechanism: An empirical analysis based on Chinese provincial panel data. Social Science Research, (5): 66–73. (in Chinese)Google Scholar

Copyright information

© Science in China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and PlanningSun Yat-sen UniversityGuangzhouChina

Personalised recommendations