IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i18p7985-d1476859.html
   My bibliography  Save this article

Temporal–Spatial Characteristics of Carbon Emissions and Low-Carbon Efficiency in Sichuan Province, China

Author

Listed:
  • Qiaochu Li

    (School of Economics and Management, Southwest Petroleum University, Chengdu 610500, China)

  • Peng Zhang

    (School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China)

Abstract

Clarifying the temporal and spatial characteristics of regional carbon emissions and low-carbon efficiency is of great significance for the realization of carbon peaking and carbon neutrality. This study calculated the carbon emissions in Sichuan Province from 2015 to 2022 based on four major units: energy activity, industrial production, forestry activity, and waste disposal, and its time evolution characteristics and key sources were investigated. Meanwhile, based on the Super-SBM-Undesirable model, the low-carbon efficiency of Sichuan Province and its 21 cities (states) was evaluated, and its spatial heterogeneity characteristics were investigated. The empirical results reveal the following: (1) energy activity was the main contributor to regional carbon emissions, with thermal power generation and industrial energy terminal consumption as the key sectors. Inter-regional power allocation could indirectly reduce the regional emission intensity. The carbon emissions of industrial production showed significant aggregation in cement and steel production. The forest carbon sink had a significant effect on alleviating the regional greenhouse effect. The carbon emissions of waste disposal were small. (2) From 2015 to 2022, the low-carbon efficiency of Sichuan Province showed an overall upward trend. Chengdu had a high level of economic development, a reasonable industrial organization, and a continuous increase in its urban greening rate. Heavy industrial cities such as Panzhihua and Deyang made great efforts to eliminate backward production capacity and low-carbon transformation of key industries. Therefore, they were the first mover advantage regions of low-carbon transformation. Zigong, Mianyang, Suining, and Leshan enjoyed favorable preferential policies and energy-saving space, and were developmental regions of low-carbon transformation. But they need to actively deal with the problem of industrial solidification. The low-carbon efficiency of plateau areas in western Sichuan was relatively low, but they have unique resource endowment advantages in clean energy such as hydropower, so the development potential is strong. Cities such as Ya’an and Bazhong faced a series of challenges such as weak geographical advantages and the risk of pollution haven. They were potential regions of low-carbon transformation.

Suggested Citation

  • Qiaochu Li & Peng Zhang, 2024. "Temporal–Spatial Characteristics of Carbon Emissions and Low-Carbon Efficiency in Sichuan Province, China," Sustainability, MDPI, vol. 16(18), pages 1-28, September.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:18:p:7985-:d:1476859
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/18/7985/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/18/7985/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lee, Chien-Chiang & He, Zhi-Wen & Yuan, Zihao, 2023. "A pathway to sustainable development: Digitization and green productivity," Energy Economics, Elsevier, vol. 124(C).
    2. Nehru, Vikram & Swanson, Eric & Dubey, Ashutosh, 1995. "A new database on human capital stock in developing and industrial countries: Sources, methodology, and results," Journal of Development Economics, Elsevier, vol. 46(2), pages 379-401, April.
    3. Charnes, Abraham & Gallegos, Armando & Li, Hongyu, 1996. "Robustly efficient parametric frontiers via Multiplicative DEA for domestic and international operations of the Latin American airline industry," European Journal of Operational Research, Elsevier, vol. 88(3), pages 525-536, February.
    4. de la Rue du Can, Stephane & Price, Lynn & Zwickel, Timm, 2015. "Understanding the full climate change impact of energy consumption and mitigation at the end-use level: A proposed methodology for allocating indirect carbon dioxide emissions," Applied Energy, Elsevier, vol. 159(C), pages 548-559.
    5. Liu, Zhu, 2016. "National carbon emissions from the industry process: Production of glass, soda ash, ammonia, calcium carbide and alumina," Applied Energy, Elsevier, vol. 166(C), pages 239-244.
    6. Silva, José Maria Cardoso & Araujo, Leonardo Schultz & Torres, Roger Rodrigues & Barbosa, Luis Claudio Fernandes, 2024. "The sustainability of development pathways and climate change vulnerability in the Americas," Ecological Economics, Elsevier, vol. 220(C).
    7. Lin, Huaxing & Zhou, Ziqian & Chen, Shun & Jiang, Ping, 2023. "Clustering and assessing carbon peak statuses of typical cities in underdeveloped Western China," Applied Energy, Elsevier, vol. 329(C).
    8. Igor Davydenko & Meike Hopman & Ruben Fransen & Jorrit Harmsen, 2022. "Mass-Balance Method for Provision of Net Zero Emission Transport Services," Sustainability, MDPI, vol. 14(10), pages 1-20, May.
    9. Wentao Lu & Guixiang Zhang, 2023. "Green development efficiency of urban agglomerations in a developing country: evidence from Beijing-Tianjin-Hebei in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6939-6962, July.
    10. Narayan, Paresh Kumar & Narayan, Seema, 2010. "Carbon dioxide emissions and economic growth: Panel data evidence from developing countries," Energy Policy, Elsevier, vol. 38(1), pages 661-666, January.
    11. Tian, Chuyin & Huang, Guohe & Xie, Yulei, 2021. "Systematic evaluation for hydropower exploitation rationality in hydro-dominant area: A case study of Sichuan Province, China," Renewable Energy, Elsevier, vol. 168(C), pages 1096-1111.
    12. Tone, Kaoru & Tsutsui, Miki, 2009. "Network DEA: A slacks-based measure approach," European Journal of Operational Research, Elsevier, vol. 197(1), pages 243-252, August.
    13. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    14. Luo, Shihua & Hu, Weihao & Liu, Wen & Xu, Xiao & Huang, Qi & Chen, Zhe & Lund, Henrik, 2021. "Transition pathways towards a deep decarbonization energy system—A case study in Sichuan, China," Applied Energy, Elsevier, vol. 302(C).
    15. Shamsuzzaman, Mohammad & Shamsuzzoha, Ahm & Maged, Ahmed & Haridy, Salah & Bashir, Hamdi & Karim, Azharul, 2021. "Effective monitoring of carbon emissions from industrial sector using statistical process control," Applied Energy, Elsevier, vol. 300(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. Khezrimotlagh, Dariush & Kaffash, Sepideh & Zhu, Joe, 2022. "U.S. airline mergers’ performance and productivity change," Journal of Air Transport Management, Elsevier, vol. 102(C).
    2. Wen-Min Lu & Qian Long Kweh & Chung-Wei Wang, 2021. "Integration and application of rough sets and data envelopment analysis for assessments of the investment trusts industry," Annals of Operations Research, Springer, vol. 296(1), pages 163-194, January.
    3. Yongqi Feng & Haolin Zhang & Yung-ho Chiu & Tzu-Han Chang, 2021. "Innovation efficiency and the impact of the institutional quality: a cross-country analysis using the two-stage meta-frontier dynamic network DEA model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3091-3129, April.
    4. Patricija Bajec & Danijela Tuljak-Suban, 2019. "An Integrated Analytic Hierarchy Process—Slack Based Measure-Data Envelopment Analysis Model for Evaluating the Efficiency of Logistics Service Providers Considering Undesirable Performance Criteria," Sustainability, MDPI, vol. 11(8), pages 1-18, April.
    5. Karlaftis, Matthew G. & Tsamboulas, Dimitrios, 2012. "Efficiency measurement in public transport: Are findings specification sensitive?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(2), pages 392-402.
    6. Chi-Yo Huang & Min-Jen Yang & Jeen-Fong Li & Hueiling Chen, 2021. "A DANP-Based NDEA-MOP Approach to Evaluating the Patent Commercialization Performance of Industry–Academic Collaborations," Mathematics, MDPI, vol. 9(18), pages 1-26, September.
    7. Yang, Guo-liang & Fukuyama, Hirofumi & Chen, Kun, 2019. "Investigating the regional sustainable performance of the Chinese real estate industry: A slack-based DEA approach," Omega, Elsevier, vol. 84(C), pages 141-159.
    8. Chiou, Yu-Chiun & Lan, Lawrence W. & Yen, Barbara T.H., 2012. "Route-based data envelopment analysis models," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(2), pages 415-425.
    9. Kao, Chiang & Liu, Shiang-Tai, 2020. "A slacks-based measure model for calculating cross efficiency in data envelopment analysis," Omega, Elsevier, vol. 95(C).
    10. Zhicheng Lai & Lei Li & Zhuomin Tao & Tao Li & Xiaoting Shi & Jialing Li & Xin Li, 2023. "Spatio-Temporal Evolution and Influencing Factors of Ecological Well-Being Performance from the Perspective of Strong Sustainability: A Case Study of the Three Gorges Reservoir Area, China," IJERPH, MDPI, vol. 20(3), pages 1-25, January.
    11. Aparicio, Juan & Kapelko, Magdalena, 2019. "Accounting for slacks to measure dynamic inefficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 278(2), pages 463-471.
    12. Loske, Dominic & Klumpp, Matthias, 2021. "Human-AI collaboration in route planning: An empirical efficiency-based analysis in retail logistics," International Journal of Production Economics, Elsevier, vol. 241(C).
    13. Alperovych, Yan & Amess, Kevin & Wright, Mike, 2013. "Private equity firm experience and buyout vendor source: What is their impact on efficiency?," European Journal of Operational Research, Elsevier, vol. 228(3), pages 601-611.
    14. Fatemeh Boloori & Rashed Khanjani-Shiraz & Hirofumi Fukuyama, 2021. "Relative partial efficiency: network and black box SBM DEA interpretations in multiplier form," Operational Research, Springer, vol. 21(4), pages 2689-2718, December.
    15. Liang-Han Ma & Jin-Chi Hsieh & Ying Li & Yung-Ho Chiu, 2021. "Evaluating Efficiency Change in Taiwan’s Financial Industry," SAGE Open, , vol. 11(2), pages 21582440211, April.
    16. Ming-Fu Hsu & Ying-Shao Hsin & Fu-Jiing Shiue, 2022. "Business analytics for corporate risk management and performance improvement," Annals of Operations Research, Springer, vol. 315(2), pages 629-669, August.
    17. Giokas, Dimitris I., 2001. "Greek hospitals: how well their resources are used," Omega, Elsevier, vol. 29(1), pages 73-83, February.
    18. Zhen Shi & Shijiong Qin & Yung-ho Chiu & Xiaoying Tan & Xiaoli Miao, 2021. "The impact of gross domestic product on the financing and investment efficiency of China’s commercial banks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-23, December.
    19. Azadi, Majid & Yousefi, Saeed & Farzipoor Saen, Reza & Shabanpour, Hadi & Jabeen, Fauzia, 2023. "Forecasting sustainability of healthcare supply chains using deep learning and network data envelopment analysis," Journal of Business Research, Elsevier, vol. 154(C).
    20. Day‐Yang Liu & Hsin‐Hsin Yao & Wen‐Min Lu & Cheng‐Hsien Lin, 2020. "Impulse response function analysis of the impacts of land value‐added tax policy on government performance," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(6), pages 1020-1032, September.

    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:16:y:2024:i:18:p:7985-:d:1476859. 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.