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Environmental Science and Pollution Research

, Volume 25, Issue 10, pp 9626–9635 | Cite as

Identification of the driving factors’ influences on regional energy-related carbon emissions in China based on geographical detector method

  • Xinlin Zhang
  • Yuan Zhao
Research Article

Abstract

To investigate the influences of different factors on spatial heterogeneity of regional carbon emissions, we firstly studied the spatial-temporal dynamics of regional energy-related carbon emissions using global Moran’s I and Getis-Ord Gi and applied geographical detector model to explain the spatial heterogeneity of regional carbon emissions. Some conclusions were drawn. Regional carbon emissions showed significant global and local spatial autocorrelation. The carbon emissions were greater in eastern and northern regions than in western and southern regions. Fixed assets investment and economic output had been the main contributing factors over the study period, and economic output had been decreasing its influence. Industrial structure’s influence showed a decrease trend and became smaller in 2015. The results of the interaction detections in 2015 can be divided into two types: enhance and nonlinear, and enhance and bivariate. The interactive influences between technological level and fixed assets investment, economic output and technological level, population size and technological level, and economic output and economic development were greater than others. Some policy recommendations were proposed.

Keywords

Carbon emissions Spatial heterogeneity Driving factors Geographical detector model Regions 

Notes

Funding information

The current work is supported by the National Natural Science Foundation of China (No. 41371518) and Scientific Research Innovation Projects of Graduate Students in Jiangsu Province (No. KYLX16_1272).

References

  1. Ang BW, Su B, Wang H (2016) A spatial–temporal decomposition approach to performance assessment in energy and emissions. Energ Econ 60:112–121.  https://doi.org/10.1016/j.eneco.2016.08.024 CrossRefGoogle Scholar
  2. Anselin L (1988) Spatial econometrics: methods and models. Stud Oper Reg Sci 85:310–330Google Scholar
  3. Chen L, Xu L, Xu Q, Yang Z (2016) Optimization of urban industrial structure under the low-carbon goal and the water constraints: a case in Dalian, China. J Clean Prod 114:323–333.  https://doi.org/10.1016/j.jclepro.2015.09.056 CrossRefGoogle Scholar
  4. Cheng Y, Wang Z, Ye X, Wei YD (2014) Spatiotemporal dynamics of carbon intensity from energy consumption in China. J Geogr Sci 24(4):631–650.  https://doi.org/10.1007/s11442-014-1110-6 CrossRefGoogle Scholar
  5. Ding Y, Li F (2017) Examining the effects of urbanization and industrialization on carbon dioxide emission: evidence from China’s provincial regions. Energy 125:533–542.  https://doi.org/10.1016/j.energy.2017.02.156 CrossRefGoogle Scholar
  6. Donglan Z, Dequn Z, Peng Z (2010) Driving forces of residential CO2 emissions in urban and rural China: an index decomposition analysis. Energ Policy 38(7):3377–3383.  https://doi.org/10.1016/j.enpol.2010.02.011 CrossRefGoogle Scholar
  7. Fan T, Luo R, Xia H, Li X (2015) Using LMDI method to analyze the influencing factors of carbon emissions in China’s petrochemical industries. Nat Hazards 75(S2):319–332.  https://doi.org/10.1007/s11069-014-1226-0 CrossRefGoogle Scholar
  8. Fragkos P, Tasios N, Paroussos L, Capros P, Tsani S (2017) Energy system impacts and policy implications of the European Intended Nationally Determined Contribution and low-carbon pathway to 2050. Energ Policy 100:216–226.  https://doi.org/10.1016/j.enpol.2016.10.023 CrossRefGoogle Scholar
  9. Getis A, Ord JK (1992) The analysis of spatial association by use of distance statistics. Geogr Anal 24:189–206CrossRefGoogle Scholar
  10. Guan D, Liu Z, Geng Y, Lindner S, Hubacek K (2012) The gigatonne gap in China’s carbon dioxide inventories. Nat Clim Chang 2(9):672–675.  https://doi.org/10.1038/nclimate1560 CrossRefGoogle Scholar
  11. Han X, Jiao J, Liu L, Li L (2017) China’s energy demand and carbon dioxide emissions: do carbon emission reduction paths matter? Nat Hazards 6:1333–1345CrossRefGoogle Scholar
  12. Huang J, Wang J, Bo Y, Xu C, Hu M, Huang D (2014) Identification of health risks of hand, foot and mouth disease in China using the geographical detector technique. Int J Env Res Pbu He 11(3):3407–3423.  https://doi.org/10.3390/ijerph110303407 CrossRefGoogle Scholar
  13. Irandoust M (2016) The renewable energy-growth nexus with carbon emissions and technological innovation: evidence from the Nordic countries. Ecol Indic 69:118–125.  https://doi.org/10.1016/j.ecolind.2016.03.051 CrossRefGoogle Scholar
  14. Ji X, Chen Z, Li J (2014) Embodied energy consumption and carbon emissions evaluation for urban industrial structure optimization. Front Earth Sci 8:32–43CrossRefGoogle Scholar
  15. Jiang J, Ye B, Xie D, Li J, Miao L, Yang P (2016) Sector decomposition of China’s national economic carbon emissions and its policy implication for national ETS development. Renew Sust Energ Rev 75:855–867CrossRefGoogle Scholar
  16. Johnson JM, Franzluebbers AJ, Weyers SL, Reicosky DC (2007) Agricultural opportunities to mitigate greenhouse gas emissions. Environ Pollut 150(1):107–124.  https://doi.org/10.1016/j.envpol.2007.06.030 CrossRefGoogle Scholar
  17. Li Q, Wei Y-N, Dong Y (2016) Coupling analysis of China’s urbanization and carbon emissions: example from Hubei Province. Nat Hazards 81(2):1333–1348.  https://doi.org/10.1007/s11069-015-2135-6 CrossRefGoogle Scholar
  18. Long R, Shao T, Chen H (2016) Spatial econometric analysis of China’s province-level industrial carbon productivity and its influencing factors. Appl Energ 166:210–219.  https://doi.org/10.1016/j.apenergy.2015.09.100 CrossRefGoogle Scholar
  19. Luo W, Jasiewicz J, Stepinski T, Wang J, Xu C, Cang X (2015) Spatial association between dissection density and environmental factors over the entire conterminous United States. Geophys Res Lett 43:692–700CrossRefGoogle Scholar
  20. Malakoff D (2014) China’s peak carbon pledge raises pointed questions. Science 346(6212):903.  https://doi.org/10.1126/science.346.6212.903 CrossRefGoogle Scholar
  21. Mi Z, Wei YM, Wang B, Meng J, Liu Z, Shan Y, Liu J, Guan D (2017) Socioeconomic impact assessment of China’s CO2 emissions peak prior to 2030. J Clean Prod 142:2227–2236.  https://doi.org/10.1016/j.jclepro.2016.11.055 CrossRefGoogle Scholar
  22. Nduagu EI, Gates ID (2016) Economic assessment of natural gas decarbonization technology for carbon emissions reduction of bitumen recovery from oil sands. Int J Greenh Gas Con 55:153–165.  https://doi.org/10.1016/j.ijggc.2016.10.011 CrossRefGoogle Scholar
  23. Ohlan R (2015) The impact of population density, energy consumption, economic growth and trade openness on CO2 emissions in India. Nat Hazards 79:1–20CrossRefGoogle Scholar
  24. Qi T, Winchester N, Karplus VJ, Zhang X (2014) Will economic restructuring in China reduce trade-embodied CO2 emissions? Energ Econ 42:204–212.  https://doi.org/10.1016/j.eneco.2013.12.011 CrossRefGoogle Scholar
  25. Qiu J (2008) China asks world to step up on climate. Nature 456(7219):151.  https://doi.org/10.1038/456151a CrossRefGoogle Scholar
  26. Qiu J (2009) China’s climate target: is it achievable? Nature 462(7273):550–551.  https://doi.org/10.1038/462550a CrossRefGoogle Scholar
  27. Salahuddin M, Gow J, Ozturk I (2015) Is the long-run relationship between economic growth, electricity consumption, carbon dioxide emissions and financial development in Gulf Cooperation Council Countries robust? Renew Sust Energ Rev 51:317–326.  https://doi.org/10.1016/j.rser.2015.06.005 CrossRefGoogle Scholar
  28. Wang M, Feng C (2017) Decomposition of energy-related CO2 emissions in China: an empirical analysis based on provincial panel data of three sectors. Appl Energ 190:772–787.  https://doi.org/10.1016/j.apenergy.2017.01.007 CrossRefGoogle Scholar
  29. Wang JF, Hu Y (2012) Environmental health risk detection with GeogDetector. Environ Model Softw 33:114–115.  https://doi.org/10.1016/j.envsoft.2012.01.015 CrossRefGoogle Scholar
  30. Wang C, Wang F (2017) China can lead on climate change. Science 57:764.1–76764CrossRefGoogle Scholar
  31. Wang T, Watson J (2010) Scenario analysis of China’s emissions pathways in the 21st century for low carbon transition. Energ Policy 38(7):3537–3546.  https://doi.org/10.1016/j.enpol.2010.02.031 CrossRefGoogle Scholar
  32. 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.  https://doi.org/10.1080/13658810802443457 CrossRefGoogle Scholar
  33. Wang J, Feng L, Davidsson S, Höök M (2013) Chinese coal supply and future production outlooks. Energy 60:204–214.  https://doi.org/10.1016/j.energy.2013.07.031 CrossRefGoogle Scholar
  34. Wang C, Wang F, Zhang H, Ye Y, Wu Q (2014) China’s carbon trading scheme is a priority. Environ Sci Technol 48(23):13559.  https://doi.org/10.1021/es505198t CrossRefGoogle Scholar
  35. Wang Z, Zhu Y, Zhu Y, Shi Y (2016) Energy structure change and carbon emission trends in China. Energy 115:369–377.  https://doi.org/10.1016/j.energy.2016.08.066 CrossRefGoogle Scholar
  36. Wang C, Wang F, Zhang X, Yang Y, Su Y, Ye Y, Zhang H (2017) Examining the driving factors of energy related carbon emissions using the extended STIRPAT model based on IPAT identity in Xinjiang. Renew Sust Energ Rev 67:51–61.  https://doi.org/10.1016/j.rser.2016.09.006 CrossRefGoogle Scholar
  37. Wu CB, Huang GH, Liu ZP, Zhen JL, Yin JG (2017) Scenario analysis of carbon emissions’ anti-driving effect on Qingdao’s energy structure adjustment with an optimization model, part II: energy system planning and management. J Env Manage 188:120–136CrossRefGoogle Scholar
  38. Xu D, Gao L (2016) Research on the influence of forestry carbon storage incremental from forestry investment in fixed assets in China. For Econ 11:1–10Google Scholar
  39. Xu B, Lin B (2017) Factors affecting CO2 emissions in China’s agriculture sector: evidence from geographically weighted regression model. Energ Policy 104:404–414.  https://doi.org/10.1016/j.enpol.2017.02.011 CrossRefGoogle Scholar
  40. Zeng N, Ding Y, Pan J, Wang H, Gregg J (2008) Climate change: the Chinese challenge. Science 319(5864):730–731.  https://doi.org/10.1126/science.1153368 CrossRefGoogle Scholar
  41. Zhang YJ, Da YB (2015) The decomposition of energy-related carbon emission and its decoupling with economic growth in China. Renew Sust Energ Rev 41:1255–1266.  https://doi.org/10.1016/j.rser.2014.09.021 CrossRefGoogle Scholar
  42. Zhang C, Tan Z (2016) The relationships between population factors and China’s carbon emissions: does population aging matter? Ren Sust Energ Rev 65:1018–1025.  https://doi.org/10.1016/j.rser.2016.06.083 CrossRefGoogle Scholar
  43. Zhang Y, Zhang J, Yang Z, Li S (2011) Regional differences in the factors that influence China’s energy-related carbon emissions, and potential mitigation strategies. Energ Policy 39(12):7712–7718.  https://doi.org/10.1016/j.enpol.2011.09.015 CrossRefGoogle Scholar
  44. Zhang X, Zhao Y, Sun Q, Wang C (2017a) Decomposition and attribution analysis of industrial carbon intensity changes in Xinjiang, China. Sustain 9(3):459–475.  https://doi.org/10.3390/su9030459 CrossRefGoogle Scholar
  45. Zhang X, Zhao Y, Xu X, Wang C (2017b) Urbanization effect on energy-related carbon emissions in Jiangsu Province from the perspective of resident consumption. Pol J Environ Stud 26(4):1875–1884.  https://doi.org/10.15244/pjoes/68953 CrossRefGoogle Scholar
  46. Zhang YJ, Peng H-R, Su B (2017c) Energy rebound effect in China’s industry: an aggregate and disaggregate analysis. Energ Econ 61:199–208.  https://doi.org/10.1016/j.eneco.2016.11.011 CrossRefGoogle Scholar
  47. Zhao Y, Li H, Zhang Z, Zhang Y, Wang S, Liu Y (2017) Decomposition and scenario analysis of CO2 emissions in China’s power industry: based on LMDI method. Nat Hazards 86:1–24CrossRefGoogle Scholar
  48. Zhu X, Pfueller S, Whitelaw P, Winter C (2010) Spatial differentiation of landscape values in the Murray River region of Victoria, Australia. Environ Manag 45(5):896–911.  https://doi.org/10.1007/s00267-010-9462-x CrossRefGoogle Scholar
  49. Zhu H, Liu J, Chen C, Lin J, Tao H (2015) A spatial-temporal analysis of urban recreational business districts: a case study in Beijing, China. J Geogr Sci 25(12):1521–1536.  https://doi.org/10.1007/s11442-015-1249-9 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Geography, Geomatics and PlanningJiangsu Normal UniversityXuzhouChina
  2. 2.School of Geographic ScienceNanjing Normal UniversityNanjingChina
  3. 3.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and ApplicationNanjingChina
  4. 4.Ginling CollegeNanjing Normal UniversityNanjingChina

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