Skip to main content
SpringerLink
Log in

Spatial–temporal heterogeneity and driving factors of carbon emissions in China

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

Recently, exploring the driving factors behind carbon emission (CE) change in China has achieved increasing attention. As the determinants of CEs are likely to be affected by both spatial and temporal heterogeneities, we propose an extended production-theoretical decomposition analysis (PDA) approach based on global meta-frontier data envelopment analysis (DEA) to resolve heterogeneity problem. Then, by combing the extended PDA and index decomposition analysis (IDA) approaches, CE changes are decomposed into nine factors. And using panel data from China’s 30 provinces during 2005–2015, the main results provide findings as follows. (1) The national total CEs are continuous increasing from 2005 to 2012, and then remain stable in 2012–2015. (2) Potential energy intensity and carbon emission temporal heterogeneity result in reduction of CEs. (3) Economic activity is the dominant driving factor for increasing the CEs, while temporal catch-up effect of carbon emission helps decrease the CEs in almost all provinces.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

The datasets used during the current study are available from the corresponding author on reasonable request.

References

  • An Q, Wu Q, Li J, Xiong B, Chen X (2019) Environmental efficiency evaluation for Xiangjiang River basin cities based on an improved SBM model and Global Malmquist index. Energy Econ. 81:95–103

    Article  Google Scholar 

  • Ang BW (2004) Decomposition analysis for policy making in energy: which is the preferred method? Energy Policy 32(9):1131–1139

    Article  Google Scholar 

  • Ang BW, Su B (2016) Carbon emission intensity in electricity production: a global analysis. Energy Policy 94:56–63

    Article  CAS  Google Scholar 

  • Battese GE, Rao DSP, O’Donnell CJ (2004) A meta-frontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies. J. Prod. Anal. 21(1):91–103

    Article  Google Scholar 

  • Chang N, Lahr ML (2016) Changes in China’s production-source CO2 emissions: insights from structural decomposition analysis and linkage analysis. Econ. Syst. Res. 28(2):224–242

    Article  Google Scholar 

  • Chang YF, Lin SJ (1998) Structural decomposition of industrial CO2 emission in Taiwan: an input-output approach. Energy Policy 26:5–12

    Article  Google Scholar 

  • Ding T, Chen Y, Wu H, Wei YQ, Liang L (2018) Centralized fixed cost and resource allocation considering technology heterogeneity: a DEA approach. Ann Oper Res. 268(1-2):497–511

    Article  Google Scholar 

  • Ding T, Wu HQ, Jia JJ, Wei YQ, Liang L (2020) Regional assessment of water-energy nexus in China’s industrial sector: an interactive meta-frontier DEA approach. J. Clean Prod. 244:118797. https://doi.org/10.1016/j.jclepro.2019.118797

    Article  Google Scholar 

  • Feng C, Wang M, Liu GC, Huang JB (2017) Sources of economic growth in China from 2000–2013 and its further sustainable growth path: a three-hierarchy meta-frontier data envelopment analysis. Econ. Model. 64:334–348

    Article  Google Scholar 

  • Gao CC, Ge HQ (2020) Spatiotemporal characteristics of China’s carbon emissions and driving forces: a Five-Year Plan perspective from 2001 to 2015. Journal of Cleaner Production 248:119280

    Article  Google Scholar 

  • Geng Y, Zhao H, Liu Z, Xue B, Fujita T, Xi F (2013) Exploring driving factors of energy-related CO2 emissions in Chinese provinces: a case of Liaoning. Energy Policy 60:820–826

    Article  Google Scholar 

  • Guan D, Hubacek K, Weber CL, Peters GP, Reiner DM (2008) The drivers of Chinese CO2 emissions from 1980 to 2030. Glob. Environ. Change 18(4):626–634

    Article  Google Scholar 

  • Hao Y, Chen H, Wei YM, Li YM (2016) The influence of climate change on CO2 (carbon dioxide) emissions: an empirical estimation based on Chinese provincial panel data. J. Clean Prod. 131:667–677

    Article  Google Scholar 

  • Lan J, Malik L, Lenzen M, McBain D, Kanemoto K (2019) A structural decomposition analysis of global energy footprints. Applied Energy 163:436–451

    Article  Google Scholar 

  • Lin B, Du K (2014) Decomposing energy intensity change: a combination of index decomposition analysis and production-theoretical decomposition analysis. Appl. Energy 129:158–165

    Article  Google Scholar 

  • Lin B, Ouyang X (2014) Analysis of energy-related CO2 (carbon dioxide) emissions and reduction potential in the Chinese non-metallic mineral products industry. Energy 68:688–697

    Article  CAS  Google Scholar 

  • Liu DN, Guo XD (2019) What causes growth of global greenhouse gas emissions? Evidence from40 countries. Science of the Total Environment. 661:750–766

    Article  CAS  Google Scholar 

  • Liu X, Zhou D, Zhou P, Wang Q (2017) What drives CO2 emissions from China’s civil aviation? An exploration using a new generalized PDA method. Transp Res. Part A Policy Pract 99:30–45

    Article  Google Scholar 

  • Liu B, Shi J, Wang H, Su XL, Zhou P (2019) Driving factors of carbon emissions in China: a joint decomposition approach based on meta-frontier. Appl. Energy 256:113986

    Article  Google Scholar 

  • Mahony TO, Zhou P, Sweeney J (2012) The driving forces of change in energy-related CO2 emissions in Ireland: a multi-sectoral decomposition from 1990 to 2007. Energy Policy 44:256–267

    Article  Google Scholar 

  • Malik A, Lan J (2016) The role of outsourcing in driving global carbon emissions. EconSyst Res 28:168–182

    Google Scholar 

  • Pastor JT, Lovell CK (2005) A global Malmquist productivity index. Econ. Lett. 88(2):266–271

    Article  Google Scholar 

  • Qi T, Weng Y, Zhang X, He J (2016) An analysis of the driving factors of energy related CO2 emission reduction in China from 2005 to 2013. Energy Econ. 60:15–22

    Article  Google Scholar 

  • Shahiduzzaman M, Layton A, Alam K (2015) Decomposition of energy-related CO2 emissions in Australia: challenges and policy implications. Econ. Anal. Policy 45:100–111

    Article  Google Scholar 

  • Song Y, Huang JB, Feng C (2018) Decomposition of energy-related CO2 emissions in China’s iron and steel industry: a comprehensive decomposition framework. Resour. Policy 59:103–116

    Article  Google Scholar 

  • Su B, Ang BW (2012) Structural decomposition analysis applied to energy and emissions: some recent developments. Energy Econ. 34(1):177–188

    Article  Google Scholar 

  • Su B, Ang BW (2017) Multiplicative structural decomposition analysis of aggregate embodied energy and emission intensities. Energy Econ. 65:137–147

    Article  Google Scholar 

  • Sueyoshi T, Li A, Liu X (2019) Exploring sources of China’s CO2 emission: decomposition analysis under different technology changes. Eur. J. Oper. Res. 279:984–995

    Article  Google Scholar 

  • Sun J, Li G, Wang Z (2019) Technology heterogeneity and efficiency of China’s circular economic systems: a game meta-frontier DEA approach. Resour. Conserv. Recycl. 337–347

  • Tan RP, Lin BQ (2018) What factors lead to the decline of energy intensity in China’s energy intensive industries? Energy Econ. 71:213–221

    Article  Google Scholar 

  • U.S.-China Joint Announcement on Climate Change (2014) http://www.whitehouse.gov/the-%20press-office/2014/11/11/us-china-jointannouncement-climatechange

  • Wang Z, Feng C (2015) Sources of production inefficiency and productivity growth in China: a global data envelopment analysis. Energy Econ. 49:380–389

    Article  Google Scholar 

  • Wang M, Feng C (2017) Understanding China’s industrial CO2: a comprehensive decomposition framework. J. Clean Prod. 166:1335–1346

    Article  Google Scholar 

  • Wang M, Feng C (2020a) The impacts of technological gap and scale economy on the low-carbon development of China’s industries: an extended decomposition analysis. Technological Forecasting & Social Change. 157:120050

    Article  Google Scholar 

  • Wang M, Feng C (2020b) The consequences of industrial restructuring, regional balanced development, and market-oriented reform for China’s carbon dioxide emissions: a multi-tier meta-frontier DEA-based decomposition analysis. Technological Forecasting & Social Change. 164:120507

    Article  Google Scholar 

  • Wang H, Zhou P (2018) Multi-country comparisons of CO2, emission intensity: the production-theoretical decomposition analysis approach. Energy Econ. 74:310–320

    Article  Google Scholar 

  • Wang Q, Chiu YH, Chiu CR (2015) Driving factors behind carbon dioxide emissions in China: a modified production-theoretical decomposition analysis. Energy Econ. 51:252–260

    Article  Google Scholar 

  • Wang Q, Hang Y, Su B, Zhou P (2018) Contributions to sector-level carbon intensity change: an integrated decomposition analysis. Energy Econ. 70:12–25

    Article  Google Scholar 

  • Wang H, Zhou P, Xie BC, Zhang N (2019) Assessing drivers of CO2 emissions in China’s electricity sector: a metafrontier production-theoretical decomposition analysis. Eur. J. Oper. Res. 275(3):1096–1107

    Article  Google Scholar 

  • Xie SC (2014) The driving forces of China’s energy use from 1992 to 2010: an empirical study of input-output and structural decomposition analysis. Energy Policy 73:401–415

    Article  Google Scholar 

  • Xu SC, Zhang L, Liu YT, Zhang WW, He ZX, Long RY, Chen H (2017) Determination of the factors that influence increments in CO2 emissions in Jiangsu, China using the SDA method. J. Clean. Prod. 142:3061–3074

    Article  CAS  Google Scholar 

  • Yan Q, Zhang Q, Zou X (2016) Decomposition analysis of carbon dioxide emissions in China’s regional thermal electricity generation, 2000–2020. Energy 112:788–794

    Article  Google Scholar 

  • Yu J, Zhou K, Yang S (2019) Regional heterogeneity of China’s energy efficiency in “new normal”: a meta-frontier Super-SBM analysis. Energy Policy 134:110941

    Article  Google Scholar 

  • Zhang XP, Tan YK, Tan QL, Yuan JH (2012) Decomposition of aggregate CO2 emissions within a joint production framework. Energy Econ. 34(4):1088–1097

    Article  Google Scholar 

  • Zhang W, Li K, Zhou D, Zhang W, Gao H (2016) Decomposition of intensity of energy-related CO2 emission in Chinese provinces using the LMDI method. Energy Policy 92:369–381

    Article  Google Scholar 

  • Zhou P, Ang BW (2008) Decomposition of aggregate CO2 emissions: a production-theoretical approach. Energy Econ. 30(3):1054–1067

    Article  Google Scholar 

Download references

Funding

This research is supported by the National Natural Science Foundation of China under Grant (Nos. 71801068 and 71871081) and the Fundamental Research Funds for the Central Universities of China (Nos. JZ2019HGTB0096 and JZ2020HGQA0178).

Author information

Authors and Affiliations

  1. School of Economics, Hefei University of Technology, 193, Tunxi Road, Hefei, 230009, Anhui, People’s Republic of China

    Tao Ding, Yufei Huang & Delin Zhuang

  2. School of Economics and Management, University of Science and Technology Beijing, Beijing, 100083, People’s Republic of China

    Weijun He

Authors
  1. Tao Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yufei Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weijun He
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Delin Zhuang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Tao Ding contributes to the conception of the study and manuscript preparation; Delin Zhuang performs the analysis with constructive discussions; Yufei Huang performs the data analyses; Weijun He performs the experiment.

Corresponding author

Correspondence to Delin Zhuang.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Research involving human participants and/or animals

Not applicable.

Informed consent

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Additional information

Responsible Editor: Philippe Garrigues

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ding, T., Huang, Y., He, W. et al. Spatial–temporal heterogeneity and driving factors of carbon emissions in China. Environ Sci Pollut Res 28, 35830–35843 (2021). https://doi.org/10.1007/s11356-021-13056-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-021-13056-9

Keywords

Search

Navigation