IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i14p10956-d1192726.html
   My bibliography  Save this article

Achieving Synergies of Carbon Emission Reduction, Cost Savings, and Asset Investments in China’s Industrial Sector: Towards Sustainable Practices

Author

Listed:
  • Xu Wang

    (School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang 212018, China)

  • Xiang Su

    (School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang 212018, China)

  • Ke Bi

    (School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang 212018, China)

Abstract

This study aims to investigate the dynamic correlations among carbon emission reduction, total cost savings, and asset investments in the industrial sector in China. This study uses the panel vector autoregressive (PVAR) model and the generalized method of moments (GMM) model to obtain three conclusions based on Chinese industrial industry data from 2005–2019. (1) The interaction between carbon emission reduction and cost reduction is bidirectional. A carbon emission decrease can result in persistent cost cutting, while measures in shrinking costs lead to reducing carbon emissions with lasting effects. Moreover, carbon emission decline has strong inertia, while cost reduction is softer. (2) Green investment promotes reducing carbon emissions and is efficient and sustainable. Conversely, completing carbon reduction milestones will inhibit asset expansion in the subsequent period. (3) China’s industrial sector has already achieved the “synergy of emission reduction and cost decrease” development model. The transmission chain “asset investment–carbon emission decline–cost decrease–carbon emission abatement” has been established. Nonetheless, a gap remains between the mature cycle of decarbonization, cost saving, and effectiveness. Finally, it is recommended that the government focuses on the synergistic effect of carbon and cost reduction, encourages continuous green investment, and systematically organizes decarbonization actions. This study provides a basis for increasing the interest of companies in transitioning to a low-carbon economy, contributing to the simultaneous realization of green development and economic benefits.

Suggested Citation

  • Xu Wang & Xiang Su & Ke Bi, 2023. "Achieving Synergies of Carbon Emission Reduction, Cost Savings, and Asset Investments in China’s Industrial Sector: Towards Sustainable Practices," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:10956-:d:1192726
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/14/10956/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/14/10956/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Jinying & Li, Sisi, 2020. "Energy investment, economic growth and carbon emissions in China—Empirical analysis based on spatial Durbin model," Energy Policy, Elsevier, vol. 140(C).
    2. Bersalli, Germán & Menanteau, Philippe & El-Methni, Jonathan, 2020. "Renewable energy policy effectiveness: A panel data analysis across Europe and Latin America," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    3. Tamazian, Artur & Bhaskara Rao, B., 2010. "Do economic, financial and institutional developments matter for environmental degradation? Evidence from transitional economies," Energy Economics, Elsevier, vol. 32(1), pages 137-145, January.
    4. Zhang, Xinhua & Gan, Dongmei & Wang, Yali & Liu, Yu & Ge, Jiali & Xie, Rui, 2020. "The impact of price and revenue floors on carbon emission reduction investment by coal-fired power plants," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    5. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, vol. 56(6), pages 1371-1395, November.
    6. Wang, Jing & Rickman, Dan S. & Yu, Yihua, 2022. "Dynamics between global value chain participation, CO2 emissions, and economic growth: Evidence from a panel vector autoregression model," Energy Economics, Elsevier, vol. 109(C).
    7. Han, Chirok & Phillips, Peter C.B., 2013. "First difference maximum likelihood and dynamic panel estimation," Journal of Econometrics, Elsevier, vol. 175(1), pages 35-45.
    8. Andrew Wordsworth & Michael Grubb, 2003. "Quantifying the UK's incentives for low carbon investment," Climate Policy, Taylor & Francis Journals, vol. 3(1), pages 77-88, March.
    9. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    10. Piecyk, Maja I. & McKinnon, Alan C., 2010. "Forecasting the carbon footprint of road freight transport in 2020," International Journal of Production Economics, Elsevier, vol. 128(1), pages 31-42, November.
    11. Cui, Lian-Biao & Fan, Ying & Zhu, Lei & Bi, Qing-Hua, 2014. "How will the emissions trading scheme save cost for achieving China’s 2020 carbon intensity reduction target?," Applied Energy, Elsevier, vol. 136(C), pages 1043-1052.
    12. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
    13. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
    14. Anderson, Jeffrey J. & Rode, David & Zhai, Haibo & Fischbeck, Paul, 2021. "Transitioning to a carbon-constrained world: Reductions in coal-fired power plant emissions through unit-specific, least-cost mitigation frontiers," Applied Energy, Elsevier, vol. 288(C).
    15. He, Yong & Fu, Feifei & Liao, Nuo, 2021. "Exploring the path of carbon emissions reduction in China’s industrial sector through energy efficiency enhancement induced by R&D investment," Energy, Elsevier, vol. 225(C).
    16. Yue Liu & Lixin Tian & Zhuyun Xie & Zaili Zhen & Huaping Sun, 2021. "Option to survive or surrender: carbon asset management and optimization in thermal power enterprises from China," Papers 2104.04729, arXiv.org.
    17. Cheng, Fei & Chen, Tong & Chen, Qiao, 2022. "Cost-reducing strategy or emission-reducing strategy? The choice of low-carbon decisions under price threshold subsidy," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    18. Chang, Ting-Huan & Huang, Chien-Ming & Lee, Ming-Chih, 2009. "Threshold effect of the economic growth rate on the renewable energy development from a change in energy price: Evidence from OECD countries," Energy Policy, Elsevier, vol. 37(12), pages 5796-5802, December.
    19. Chen, Wenbo, 2018. "Retailer-driven carbon emission abatement with consumer environmental awareness and carbon tax: Revenue-sharing versus Cost-sharingAuthor-Name: Yang, Huixiao," Omega, Elsevier, vol. 78(C), pages 179-191.
    20. Tobias S. Schmidt, 2014. "Low-carbon investment risks and de-risking," Nature Climate Change, Nature, vol. 4(4), pages 237-239, April.
    21. Lee, Jungwoo & Yang, Jae-Suk, 2019. "Global energy transitions and political systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    22. Guang Zhu & Gaozhi Pan & Weiwei Zhang, 2018. "Evolutionary Game Theoretic Analysis of Low Carbon Investment in Supply Chains under Governmental Subsidies," IJERPH, MDPI, vol. 15(11), pages 1-27, November.
    23. Hu, Yucai & Ren, Shenggang & Wang, Yangjie & Chen, Xiaohong, 2020. "Can carbon emission trading scheme achieve energy conservation and emission reduction? Evidence from the industrial sector in China," Energy Economics, Elsevier, vol. 85(C).
    24. Elena Podrecca & Gaetano Carmeci, 2001. "Fixed investment and economic growth: new results on causality," Applied Economics, Taylor & Francis Journals, vol. 33(2), pages 177-182.
    25. 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.
    26. Liu, Bingsheng & Xu, Yinghua & Yang, Yang & Lu, Shijian, 2021. "How public cognition influences public acceptance of CCUS in China: Based on the ABC (affect, behavior, and cognition) model of attitudes," Energy Policy, Elsevier, vol. 156(C).
    Full references (including those not matched with items on IDEAS)

    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. Dogan, Eyup & Chishti, Muhammad Zubair & Karimi Alavijeh, Nooshin & Tzeremes, Panayiotis, 2022. "The roles of technology and Kyoto Protocol in energy transition towards COP26 targets: Evidence from the novel GMM-PVAR approach for G-7 countries," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    2. Yongfu Huang, 2011. "Private investment and financial development in a globalized world," Empirical Economics, Springer, vol. 41(1), pages 43-56, August.
    3. Reed, W. Robert & Zhu, Min, 2017. "On estimating long-run effects in models with lagged dependent variables," Economic Modelling, Elsevier, vol. 64(C), pages 302-311.
    4. Artūras Juodis & Yiannis Karavias & Vasilis Sarafidis, 2021. "A homogeneous approach to testing for Granger non-causality in heterogeneous panels," Empirical Economics, Springer, vol. 60(1), pages 93-112, January.
    5. Bittencourt, Manoel, 2011. "Inflation and financial development: Evidence from Brazil," Economic Modelling, Elsevier, vol. 28(1), pages 91-99.
    6. Kamiar Mohaddes & M. Hashem Pesaran, 2013. "One Hundred Years of Oil Income and the Iranian Economy: A Curse or a Blessing?," Working Papers 771, Economic Research Forum, revised Sep 2013.
    7. Biørn, Erik, 2012. "The Measurement Error Problem in Dynamic Panel Data Analysis: Modeling and GMM Estimation," Memorandum 02/2012, Oslo University, Department of Economics.
    8. Ryo Okui, 2017. "Misspecification in Dynamic Panel Data Models and Model-Free Inferences," The Japanese Economic Review, Japanese Economic Association, vol. 68(3), pages 283-304, September.
    9. Cheng Hsiao & M. Hashem Pesaran, 2004. "Random Coefficient Panel Data Models," CESifo Working Paper Series 1233, CESifo.
    10. Na Yu & Jianghua Chen & Lei Cheng, 2022. "Evolutionary Game Analysis of Carbon Emission Reduction between Government and Enterprises under Carbon Quota Trading Policy," IJERPH, MDPI, vol. 19(14), pages 1-22, July.
    11. Awaworyi Churchill, Sefa & Inekwe, John & Ivanovski, Kris & Smyth, Russell, 2023. "Corrigendum to “The environmental Kuznets curve in the OECD: 1870–2014” [Energy Economics 75 (2018) 389–399]," Energy Economics, Elsevier, vol. 124(C).
    12. G. Everaert & L. Pozzi & -, 2010. "The Stickiness of Aggregate Consumption Growth in OECD Countries: A Panel Data Analysis," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/654, Ghent University, Faculty of Economics and Business Administration.
    13. Mariam Camarero & Sergi Moliner & Cecilio Tamarit, 2025. "Which are the long-run determinants of US outward FDI? Evidence using large long-memory panels," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 34(2), pages 259-290, February.
    14. Diana Weinhold, 1996. "Tests de causalité sur données de panel : une application à l'étude de la causalité entre l'investissement et la croissance," Économie et Prévision, Programme National Persée, vol. 126(5), pages 163-175.
    15. Sangyup Choi & Kimoon Jeong & Jiseob Kim, 2023. "One Monetary Policy and Two Bank Lending Standards: A Tale of Two Europes," Working papers 2023rwp-209, Yonsei University, Yonsei Economics Research Institute.
    16. Bussière, Matthieu & Ca' Zorzi, Michele & Chudik, Alexander & Dieppe, Alistair, 2010. "Methodological advances in the assessment of equilibrium exchange rates," Working Paper Series 1151, European Central Bank.
    17. Dang, Viet Anh & Kim, Minjoo & Shin, Yongcheol, 2015. "In search of robust methods for dynamic panel data models in empirical corporate finance," Journal of Banking & Finance, Elsevier, vol. 53(C), pages 84-98.
    18. Cavalcanti, Tiago V. de V. & Mohaddes, Kamiar & Raissi, Mehdi, 2011. "Growth, development and natural resources: New evidence using a heterogeneous panel analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(4), pages 305-318.
    19. Juodis, Artūras & Sarafidis, Vasilis, 2022. "An incidental parameters free inference approach for panels with common shocks," Journal of Econometrics, Elsevier, vol. 229(1), pages 19-54.
    20. Tarek Ghazouani & Samir Maktouf, 2024. "Impact of natural resources, trade openness, and economic growth on CO2 emissions in oil‐exporting countries: A panel autoregressive distributed lag analysis," Natural Resources Forum, Blackwell Publishing, vol. 48(1), pages 211-231, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:gam:jsusta:v:15:y:2023:i:14:p:10956-:d:1192726. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.