IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v106y2022ics0140988322000044.html
   My bibliography  Save this article

Capturing the least costly measure of CO2 emission abatement: Evidence from the iron and steel industry in China

Author

Listed:
  • Xian, Yujiao
  • Yu, Dan
  • Wang, Ke
  • Yu, Jian
  • Huang, Zhimin

Abstract

Estimating the marginal abatement cost (MAC) of CO2 emission is critical in formulating emission reduction targets and policies. Existing studies rarely emphasized the impact of random noise on MAC estimation, and downscaling the production activity is conventionally applied as the only measure for emission abatement while the possibilities of other measures, such as increase the investment, are often neglected. This paper estimates the least MAC of CO2 for Chinese iron and steel enterprises using a stochastic semi-nonparametric method which considers both inefficiency and random noise. Multiple measures including downscaling the production activity and increasing the inputs investment, are all considered for identifying the least-cost measure for reducing emissions. In addition, the strategies corresponding to adjustment on production and response to environmental regulation of each enterprise are included in the estimation, which makes it possible for identifying the upper and lower bound of MACs. Empirical results indicate that i) the stochastic semi-nonparametric method provides a more consistent estimates with the production process, ii) the average MAC of CO2 emissions in China's iron and steel industry ranges from 2.07 to 2395 yuan/ton, and iii) increasing labor is identified as the least-cost abatement measures for most of the iron and steel enterprises listed in China's top 500 enterprise. Policy implications have been put forward to reduce the carbon abatement cost in China's iron and steel industry.

Suggested Citation

  • Xian, Yujiao & Yu, Dan & Wang, Ke & Yu, Jian & Huang, Zhimin, 2022. "Capturing the least costly measure of CO2 emission abatement: Evidence from the iron and steel industry in China," Energy Economics, Elsevier, vol. 106(C).
    • Handle: RePEc:eee:eneeco:v:106:y:2022:i:c:s0140988322000044
    DOI: 10.1016/j.eneco.2022.105812
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988322000044
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2022.105812?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. David Dollar & Shang-Jin Wei, 2007. "Das (Wasted) Kapital: Firm Ownership and Investment Efficiency in China," IMF Working Papers 2007/009, International Monetary Fund.
    2. Xiting Gong & Sean X. Zhou, 2013. "Optimal Production Planning with Emissions Trading," Operations Research, INFORMS, vol. 61(4), pages 908-924, August.
    3. Afriat, Sidney N, 1972. "Efficiency Estimation of Production Function," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 13(3), pages 568-598, October.
    4. Choi, Yongrok & Zhang, Ning & Zhou, P., 2012. "Efficiency and abatement costs of energy-related CO2 emissions in China: A slacks-based efficiency measure," Applied Energy, Elsevier, vol. 98(C), pages 198-208.
    5. He, Weijun & Wang, Bo & Danish, & Wang, Zhaohua, 2018. "Will regional economic integration influence carbon dioxide marginal abatement costs? Evidence from Chinese panel data," Energy Economics, Elsevier, vol. 74(C), pages 263-274.
    6. Hall P. & Simar L., 2002. "Estimating a Changepoint, Boundary, or Frontier in the Presence of Observation Error," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 523-534, June.
    7. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    8. Wang, Yihan & Wen, Zongguo & Yao, Jianguo & Doh Dinga, Christian, 2020. "Multi-objective optimization of synergic energy conservation and CO2 emission reduction in China's iron and steel industry under uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    9. A. Delaigle & I. Gijbels, 2004. "Bootstrap bandwidth selection in kernel density estimation from a contaminated sample," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 56(1), pages 19-47, March.
    10. Kuosmanen, Timo, 2006. "Stochastic Nonparametric Envelopment of Data: Combining Virtues of SFA and DEA in a Unified Framework," Discussion Papers 11864, MTT Agrifood Research Finland.
    11. Du, Limin & Hanley, Aoife & Wei, Chu, 2015. "Estimating the Marginal Abatement Cost Curve of CO2 Emissions in China: Provincial Panel Data Analysis," Energy Economics, Elsevier, vol. 48(C), pages 217-229.
    12. Ke Wang & Linan Che & Chunbo Ma & Yi-Ming Wei, 2017. "The Shadow Price of CO2 Emissions in China's Iron and Steel Industry," CEEP-BIT Working Papers 105, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    13. Chen, Bin & Jin, Yingmei, 2020. "Adjusting productivity measures for CO2 emissions control: Evidence from the provincial thermal power sector in China," Energy Economics, Elsevier, vol. 87(C).
    14. Fare, Rolf & Grosskopf, Shawna & Weber, William L., 2006. "Shadow prices and pollution costs in U.S. agriculture," Ecological Economics, Elsevier, vol. 56(1), pages 89-103, January.
    15. Dougherty, Sean & Herd, Richard & He, Ping, 2007. "Has a private sector emerged in China's industry? Evidence from a quarter of a million Chinese firms," China Economic Review, Elsevier, vol. 18(3), pages 309-334.
    16. Noreen Beg & Jan Corfee Morlot & Ogunlade Davidson & Yaw Afrane-Okesse & Lwazikazi Tyani & Fatma Denton & Youba Sokona & Jean Philippe Thomas & Emilio L�bre La Rovere & Jyoti K. Parikh & Kirit Parikh , 2002. "Linkages between climate change and sustainable development," Climate Policy, Taylor & Francis Journals, vol. 2(2-3), pages 129-144, September.
    17. Chao Qi & Yongrok Choi, 2019. "A Study of the Feasibility of International ETS Cooperation between Shanghai and Korea from Environmental Efficiency and CO 2 Marginal Abatement Cost Perspectives," Sustainability, MDPI, vol. 11(16), pages 1-16, August.
    18. H. Fried & C. Lovell & S. Schmidt & S. Yaisawarng, 2002. "Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 17(1), pages 157-174, January.
    19. Andrew Johnson & Timo Kuosmanen, 2011. "One-stage estimation of the effects of operational conditions and practices on productive performance: asymptotically normal and efficient, root-n consistent StoNEZD method," Journal of Productivity Analysis, Springer, vol. 36(2), pages 219-230, October.
    20. Wang, Ke & Wei, Yi-Ming, 2014. "China’s regional industrial energy efficiency and carbon emissions abatement costs," Applied Energy, Elsevier, vol. 130(C), pages 617-631.
    21. Lee, Myunghun & Zhang, Ning, 2012. "Technical efficiency, shadow price of carbon dioxide emissions, and substitutability for energy in the Chinese manufacturing industries," Energy Economics, Elsevier, vol. 34(5), pages 1492-1497.
    22. Timo Kuosmanen, 2008. "Representation theorem for convex nonparametric least squares," Econometrics Journal, Royal Economic Society, vol. 11(2), pages 308-325, July.
    23. Hailu, Atakelty & Veeman, Terrence S., 2000. "Environmentally Sensitive Productivity Analysis of the Canadian Pulp and Paper Industry, 1959-1994: An Input Distance Function Approach," Journal of Environmental Economics and Management, Elsevier, vol. 40(3), pages 251-274, November.
    24. Timo Kuosmanen & Andrew L. Johnson, 2010. "Data Envelopment Analysis as Nonparametric Least-Squares Regression," Operations Research, INFORMS, vol. 58(1), pages 149-160, February.
    25. Aldanondo-Ochoa, Ana M. & Casasnovas-Oliva, Valero L. & Almansa-Sáez, M. Carmen, 2017. "Cross-constrained Measuring the Cost-environment Efficiency in Material Balance Based Frontier Models," Ecological Economics, Elsevier, vol. 142(C), pages 46-55.
    26. Qingwei Shi & Hong Ren & Weiguang Cai & Jingxin Gao, 2020. "How to Set the Proper CO 2 Reduction Targets for the Provincial Building Sector of China?," Sustainability, MDPI, vol. 12(24), pages 1-22, December.
    27. Lee, Chia-Yen & Zhou, Peng, 2015. "Directional shadow price estimation of CO2, SO2 and NOx in the United States coal power industry 1990–2010," Energy Economics, Elsevier, vol. 51(C), pages 493-502.
    28. Xian, Yujiao & Wang, Ke & Wei, Yi-Ming & Huang, Zhimin, 2019. "Would China’s power industry benefit from nationwide carbon emission permit trading? An optimization model-based ex post analysis on abatement cost savings," Applied Energy, Elsevier, vol. 235(C), pages 978-986.
    29. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    30. Mekaroonreung, Maethee & Johnson, Andrew L., 2012. "Estimating the shadow prices of SO2 and NOx for U.S. coal power plants: A convex nonparametric least squares approach," Energy Economics, Elsevier, vol. 34(3), pages 723-732.
    31. Timo Kuosmanen & Mika Kortelainen, 2012. "Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints," Journal of Productivity Analysis, Springer, vol. 38(1), pages 11-28, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen Shi & Yujiao Xian & Zhixin Wang & Ke Wang, 2023. "Marginal abatement cost curve of carbon emissions in China: a functional data analysis," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 28(2), pages 1-25, February.
    2. Chen, Yufeng & Xu, Jing & Miao, Jiafeng, 2023. "Dynamic volatility contagion across the Baltic dry index, iron ore price and crude oil price under the COVID-19: A copula-VAR-BEKK-GARCH-X approach," Resources Policy, Elsevier, vol. 81(C).
    3. Xian, Yujiao & Hu, Zhihui & Wang, Ke, 2023. "The least-cost abatement measure of carbon emissions for China's glass manufacturing industry based on the marginal abatement costs," Energy, Elsevier, vol. 284(C).
    4. Kuosmanen, Natalia & Kuosmanen, Timo & Maczulskij, Terhi & Zhou, Xun, 2024. "Least-cost Decarbonization Pathways for Electricity Generation in Finland: A Convex Quantile Regression Approach," ETLA Working Papers 114, The Research Institute of the Finnish Economy.
    5. Yu, Jian & Liu, Peng & Fu, Dahai & Shi, Xunpeng, 2023. "How do power shortages affect CO2 emission intensity? Firm-level evidence from China," Energy, Elsevier, vol. 282(C).
    6. Wu, F. & Wang, S.Y. & Zhou, P., 2023. "Marginal abatement cost of carbon dioxide emissions: The role of abatement options," European Journal of Operational Research, Elsevier, vol. 310(2), pages 891-901.
    7. Quinn, Barry & Gallagher, Ronan & Kuosmanen, Timo, 2023. "Lurking in the shadows: The impact of CO2 emissions target setting on carbon pricing in the Kyoto agreement period," Energy Economics, Elsevier, vol. 118(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wei, Xiao & Zhang, Ning, 2020. "The shadow prices of CO2 and SO2 for Chinese Coal-fired Power Plants: A partial frontier approach," Energy Economics, Elsevier, vol. 85(C).
    2. Lee, Chia-Yen & Wang, Ke, 2019. "Nash marginal abatement cost estimation of air pollutant emissions using the stochastic semi-nonparametric frontier," European Journal of Operational Research, Elsevier, vol. 273(1), pages 390-400.
    3. Zhou, P. & Zhou, X. & Fan, L.W., 2014. "On estimating shadow prices of undesirable outputs with efficiency models: A literature review," Applied Energy, Elsevier, vol. 130(C), pages 799-806.
    4. Kuosmanen, Timo & Johnson, Andrew, 2017. "Modeling joint production of multiple outputs in StoNED: Directional distance function approach," European Journal of Operational Research, Elsevier, vol. 262(2), pages 792-801.
    5. Eskelinen, Juha & Kuosmanen, Timo, 2013. "Intertemporal efficiency analysis of sales teams of a bank: Stochastic semi-nonparametric approach," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5163-5175.
    6. Dai, Sheng & Zhou, Xun & Kuosmanen, Timo, 2020. "Forward-looking assessment of the GHG abatement cost: Application to China," Energy Economics, Elsevier, vol. 88(C).
    7. Kuosmanen, Timo, 2012. "Stochastic semi-nonparametric frontier estimation of electricity distribution networks: Application of the StoNED method in the Finnish regulatory model," Energy Economics, Elsevier, vol. 34(6), pages 2189-2199.
    8. Di Maria, Corrado & Zarkovic, Maja & Hintermann, Beat, 2020. "Are Emissions Trading Schemes Cost-effective?," Working papers 2020/13, Faculty of Business and Economics - University of Basel.
    9. Keshvari, Abolfazl & Kuosmanen, Timo, 2013. "Stochastic non-convex envelopment of data: Applying isotonic regression to frontier estimation," European Journal of Operational Research, Elsevier, vol. 231(2), pages 481-491.
    10. Wang, Juan & Li, Ziming & Wang, Yanan, 2024. "How does China's energy-consumption trading policy affect the carbon abatement costs? An analysis based on spatial difference-in-differences method," Energy, Elsevier, vol. 294(C).
    11. Mekaroonreung, Maethee & Johnson, Andrew L., 2012. "Estimating the shadow prices of SO2 and NOx for U.S. coal power plants: A convex nonparametric least squares approach," Energy Economics, Elsevier, vol. 34(3), pages 723-732.
    12. Tang, Kai & Yang, Lin & Zhang, Jianwu, 2016. "Estimating the regional total factor efficiency and pollutants’ marginal abatement costs in China: A parametric approach," Applied Energy, Elsevier, vol. 184(C), pages 230-240.
    13. Cristina Polo & Julián Ramajo & Alejandro Ricci‐Risquete, 2021. "A stochastic semi‐non‐parametric analysis of regional efficiency in the European Union," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(1), pages 7-24, February.
    14. Wu, Yinyin & Yu, Jie & Song, Malin & Chen, Jiandong & Hou, Wenxuan, 2021. "Shadow prices of industrial air pollutant emissions in China," Economic Modelling, Elsevier, vol. 94(C), pages 726-736.
    15. Layer, Kevin & Johnson, Andrew L. & Sickles, Robin C. & Ferrier, Gary D., 2020. "Direction selection in stochastic directional distance functions," European Journal of Operational Research, Elsevier, vol. 280(1), pages 351-364.
    16. Ke Wang & Linan Che & Chunbo Ma & Yi-Ming Wei, 2017. "The Shadow Price of CO2 Emissions in China's Iron and Steel Industry," CEEP-BIT Working Papers 105, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    17. Alexander Arévalo S & Víctor Giménez G & Diego Prior J, 2022. "Análisis de eficiencia en educación: una aplicación del método StoNED," Revista Desarrollo y Sociedad, Universidad de los Andes,Facultad de Economía, CEDE, vol. 92(2), pages 45-91, October.
    18. Lee, Chia-Yen & Johnson, Andrew L. & Moreno-Centeno, Erick & Kuosmanen, Timo, 2013. "A more efficient algorithm for Convex Nonparametric Least Squares," European Journal of Operational Research, Elsevier, vol. 227(2), pages 391-400.
    19. Quaranta, Anna Grazia & Raffoni, Anna & Visani, Franco, 2018. "A multidimensional approach to measuring bank branch efficiency," European Journal of Operational Research, Elsevier, vol. 266(2), pages 746-760.
    20. Ma, Chunbo & Hailu, Atakelty & You, Chaoying, 2019. "A critical review of distance function based economic research on China’s marginal abatement cost of carbon dioxide emissions," Energy Economics, Elsevier, vol. 84(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:106:y:2022:i:c:s0140988322000044. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.