Skip to main content

Advertisement

Log in

LMDI decomposition analysis of industry carbon emissions in Henan Province, China: comparison between different 5-year plans

  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

Due to the rapid development of industrialization, carbon emission has been increased significantly in China. To comply with international obligation, carbon reduction and energy-saving are becoming more and more important. With Henan Province being the key part of Central Plains Economic Zone, the energy consumption will be further increased. Therefore, how to control and reduce energy use and subsequent combustion emissions is a serious challenge. Based on the calculation of carbon emission in 36 industries of industrial sector during 2001–2012, the main driving factors of changes in carbon emission were analyzed using Logarithmic Mean Divisia Index decomposition model. The results indicate that carbon emissions in industry sector have increased from 52.3 Mt in 2001 to 184.6 Mt. Economic scale plays a decisive role in the industrial carbon emissions, resulting in increased carbon emissions by 265.8 Mt between 2001 and 2012, with two industries of Production and Supply of Electric Power Steam and Water as well as Coal Mining and Washing accounting for 50 % of increased carbon emissions. Both internal structure and energy intensity play disincentive roles, reducing carbon emissions by 73.4 and 61.5 Mt, respectively. Also, the results for carbon reduction indicate that the progress made during the 11th 5-year plan. The reduction in carbon emissions is due to a series of energy-saving and emission reduction measures taken. Energy structure has little effect on carbon emission change due to coal-based structure. Nevertheless, there is a room to further reduce carbon emission by re-adjusting internal structure and reducing energy intensity for the energy-intensive industries following the internationally advanced level. The use of renewable energy should further enhance the effect of energy structure in reducing carbon emissions. The results of the present study can assist decision makers in proposing new policies and providing strategies for effective reducing industrial carbon emissions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

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

    Article  Google Scholar 

  • Ang BW (2005) The LMDI approach to decomposition analysis: a practical guide. Energy Policy 33:867–871

    Article  Google Scholar 

  • Ang BW, Choi KH (1997) Decomposition of aggregate energy and gas emission intensities for industry: a refined Divisia index method. Energy J 18:59–73

    Article  Google Scholar 

  • Ang BW, Liu N (2007) Handling zero values in the logarithmic mean Divisia index decomposition approach. Energy Policy 35:238–246

    Article  Google Scholar 

  • CG (2014) US and China agree historic climate change deal. http://www.climatechangenews.com/2014/11/12/us-and-china-agree-historic-climate-change-deal/

  • Chen LT, Hu AH (2012) Voluntary GHG reduction of industrial sectors in Taiwan. Chemosphere 88:1074–1082

    Article  Google Scholar 

  • Cornillie J, Fankhauser S (2004) The energy intensity of transition countries. Energy Econ 26:283–295

    Article  Google Scholar 

  • De la Rue du Can S, Hasanbeigi A, Sathaye J, (2012) Analysis of the energy intensity of industries in California. Lawrence Berkeley National Laboratory, LBNL-5869E, Berkeley, CA

  • Domingo G, Manuel M (2012) Changes in CO2 emission intensities in the Mexican industry. Energy Policy 51:149–163

    Article  Google Scholar 

  • Hoekstra R, Van den Bergh JCJM (2003) Comparing structural decomposition analysis and index. Energy Econ 25:39–64

    Article  Google Scholar 

  • HSY (2013) Henan statistical yearbook 2002–2013. China Statistical Publishing House, Beijing

    Google Scholar 

  • IEA (2014) CO2 Emissions from fuel combustion highlights 2014. International Energy Agency, Pairs

    Book  Google Scholar 

  • IPCC (2006) 2006 IPCC guidelines for national greenhouse gas inventories. In: Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K (eds) Prepared by the national greenhouse gas inventories programme. IGES, Hayama

    Google Scholar 

  • Jiang L, Folmer H, Ji MH (2014) The drivers of energy intensity in China: a spatial panel data approach. China Econ Rev 31:351–360

    Article  Google Scholar 

  • Jung S, An KJ, Dodbiba G, Fujita T (2012) Regional energy-related carbon emission characteristics and potential mitigation in eco-industrial parks in South Korea: logarithmic mean Divisia index analysis based on the Kaya identity. Energy 46:231–241

    Article  Google Scholar 

  • Kang JD, Zhao T, Liu N, Zhang X, Xu XS, Lin T (2014) A multi-sectoral decomposition analysis of city-level greenhouse gas emissions: case study of Tianjin, China. Energy 68:562–571

    Article  Google Scholar 

  • Kaya Y (1990) Impact of carbon dioxide emission control on GNP growth: interpretation of proposed scenarios. Paper presented at the IPCC energy and industry subgroup, response strategies working group, Paris, France

  • Liu CL, Fan Y, Wu G, Wei YM (2007) Using LMDI method to analyze the change of China’s industrial CO2 emission from final fuel use: an empirical analysis. Energy Policy 35:5892–5900

    Article  Google Scholar 

  • Luciano CF, Shinji K (2011) Decomposing the decoupling of CO2 emissions and economic growth in Brazil. Ecol Econ 70:1459–1469

    Article  Google Scholar 

  • Ma CB, Stern DI (2008) China’s changing energy intensity trend: a decomposition analysis. Energy Econ 30:1037–1053

    Article  Google Scholar 

  • Margarita RA, Victor M (2013) Decomposition analysis and innovative accounting approach for energy-related CO2 (carbon dioxide) emissions intensity over 1996–2009 in Portugal. Energy 57:775–787

    Article  Google Scholar 

  • NBS (2014) China statistical yearbooks. National Bureau of Statistics. China Statistics Press, Beijing

    Google Scholar 

  • Rose A, Casler S (1996) Input–output structural decomposition analysis: a critical appraisal. Econ Syst Res 8:33–62

    Article  Google Scholar 

  • Schahczenski J, Hill H (2009) Agriculture, climate change and carbon sequestration. ATTRA, Melbourne

    Google Scholar 

  • State Council of the People’s Republic of China (SCPRC) (2011) The 12th Five-Year Plan outline of national economy and social development of People’s Republic of China. http://news.xinhuanet.com/politics/2011-03/16/c_121193916.htm. Accessed 16 Mar 2011

  • Subject Group of 2050 China Energy and CO2 Emissions Research (2009) 2050 China energy and CO2 emissions report. Science Press, Beijing

    Google Scholar 

  • Tadhg OM (2013) Decomposition of Ireland’s carbon emissions from 1990 to 2010: an extended Kaya identity. Energy Policy 59:573–581

    Article  Google Scholar 

  • Wang C, Chen JN, Zou J (2005) Decomposition of energy-related CO2 emission in China: 1957–2000. Energy 30:73–83

    Article  Google Scholar 

  • Wang WW, Liu R, Zhang M, Li HN (2013) Decomposing the decoupling of energy-related CO2 emissions and economic growth in Jiangsu Province. Energy Sustain Dev 17:62–71

    Article  Google Scholar 

  • Wood R, Lenzen M (2006) Zero-value problems of the logarithmic mean Divisia index decomposition method. Energy Policy 34:1326–1331

    Article  Google Scholar 

  • Xu JH, Fan Y, Yu SM (2014) Energy conservation and CO2 emission reduction in China’s 11th Five-Year Plan: a performance evaluation. Energy Econ 46:348–359

    Article  Google Scholar 

  • Zhang GX, Liu MX (2014) The changes of carbon emission in China’s industrial sectors from 2002 to 2010: a structural decomposition analysis and input-output subsystem. Discrete Dyn Nat Soc 10:1–9

    Google Scholar 

  • Zhao M, Tan LR, Zhang WG, Ji MH, Liu Y, Yu LZ (2010) Decomposing the influencing factors of industrial carbon emissions in Shanghai using the LMDI method. Energy 35:2505–2510

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by Energy Foundation (G-1410-22231).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ruiqin Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, L., Wang, S., Wang, K. et al. LMDI decomposition analysis of industry carbon emissions in Henan Province, China: comparison between different 5-year plans. Nat Hazards 80, 997–1014 (2016). https://doi.org/10.1007/s11069-015-2009-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-015-2009-y

Keywords

Navigation