IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i6p2425-d1609169.html
   My bibliography  Save this article

Comprehensive Assessment and Obstacle Analysis on Low-Carbon Development Quality of 30 Provincial Regions in China

Author

Listed:
  • Haoran Zhao

    (Business School, Beijing Information Science & Technology University, Beijing 100085, China)

  • Zhen Yang

    (Business School, Beijing Information Science & Technology University, Beijing 100085, China)

  • Shunan Wu

    (China Energy Capital Holdings Co., Ltd., Beijing 100044, China)

  • Zhuowen Zhang

    (Business School, Beijing Information Science & Technology University, Beijing 100085, China)

  • Chuan Li

    (Business School, Beijing Information Science & Technology University, Beijing 100085, China)

  • Chunhua Jin

    (Business School, Beijing Information Science & Technology University, Beijing 100085, China)

  • Sen Guo

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

Low-carbon development (LCD) in China has become the critical measure to achieve sustainable development and handle climate change. This investigation evaluates 30 provincial regions’ LCD quality from dimensions of low-carbon (LC) economy, resources utilization, LC environment, and LC society. According to the integrated weights combined subjective weights identified through the best–worst method (BWM) and objective weights attained through the anti-entropy weight (AEW) method, the top five sub-criteria in 2021 were coal consumption relative to total primary energy consumption, industrial sulfur dioxide (SO 2 ) emission, carbon dioxide emissions intensity, industrial dust emission, and forest coverage rate. According to the comprehensive evaluation results obtained through the MARCOS model, Beijing’s comprehensive score is far ahead, and its scores in resource utilization, LC environment, and LC economy are also in a leading position. Moreover, the level of LCD quality shows a gradually reduced pattern from east to west. The obstacle analysis demonstrates that the obstacle factors with high frequency of occurrence include real GDP, energy intensity, coal consumption relative to total primary energy consuming, carbon dioxide emissions intensity, industrial dust emission, industrial SO 2 emission, forest coverage rate, and the number of private vehicles. Suggestions are proposed based on the results, including increase infrastructure construction, optimize energy structure and develop renewable energy, protect the ecological environment with intensify efforts, and accelerate industrial transformation and upgrading to optimize industrial structure.

Suggested Citation

  • Haoran Zhao & Zhen Yang & Shunan Wu & Zhuowen Zhang & Chuan Li & Chunhua Jin & Sen Guo, 2025. "Comprehensive Assessment and Obstacle Analysis on Low-Carbon Development Quality of 30 Provincial Regions in China," Sustainability, MDPI, vol. 17(6), pages 1-27, March.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:6:p:2425-:d:1609169
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/6/2425/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/6/2425/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ying QU & Yue LIU, 2017. "Evaluating the low-carbon development of urban China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 19(3), pages 939-953, June.
    2. Glaeser, Edward L. & Kahn, Matthew E., 2010. "The greenness of cities: Carbon dioxide emissions and urban development," Journal of Urban Economics, Elsevier, vol. 67(3), pages 404-418, May.
    3. Haixiang Guo & Xiao Liu & Yijing Li & Deyun Wang & Xiaohong Chen, 2015. "Comparison Analysis and Evaluation of Urban Competitiveness in Chinese Urban Clusters," Sustainability, MDPI, vol. 7(4), pages 1-23, April.
    4. Rezaei, Jafar, 2016. "Best-worst multi-criteria decision-making method: Some properties and a linear model," Omega, Elsevier, vol. 64(C), pages 126-130.
    5. Rezaei, Jafar, 2015. "Best-worst multi-criteria decision-making method," Omega, Elsevier, vol. 53(C), pages 49-57.
    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. James J. H. Liou & Perry C. Y. Liu & Huai-Wei Lo, 2020. "A Failure Mode Assessment Model Based on Neutrosophic Logic for Switched-Mode Power Supply Risk Analysis," Mathematics, MDPI, vol. 8(12), pages 1-19, December.
    2. Liang, Fuqi & Brunelli, Matteo & Rezaei, Jafar, 2020. "Consistency issues in the best worst method: Measurements and thresholds," Omega, Elsevier, vol. 96(C).
    3. Salimi, Negin & Rezaei, Jafar, 2018. "Evaluating firms’ R&D performance using best worst method," Evaluation and Program Planning, Elsevier, vol. 66(C), pages 147-155.
    4. Ravindra Singh Saluja & Varinder Singh, 2023. "Attribute-based characterization, coding, and selection of joining processes using a novel MADM approach," OPSEARCH, Springer;Operational Research Society of India, vol. 60(2), pages 616-655, June.
    5. Junli Zhang & Guoteng Wang & Zheng Xu & Zheren Zhang, 2022. "A Comprehensive Evaluation Method and Strengthening Measures for AC/DC Hybrid Power Grids," Energies, MDPI, vol. 15(12), pages 1-20, June.
    6. Hamzeh Soltanali & Mehdi Khojastehpour & Siamak Kheybari, 2023. "Evaluating the critical success factors for maintenance management in agro-industries using multi-criteria decision-making techniques," Operations Management Research, Springer, vol. 16(2), pages 949-968, June.
    7. Yossi Hadad & Baruch Keren & Dima Alberg, 2023. "An Expert System for Ranking and Matching Electric Vehicles to Customer Specifications and Requirements," Energies, MDPI, vol. 16(11), pages 1-18, May.
    8. Ebrahim Sharifi & Saman Hassanzadeh Amin & Liping Fang, 2025. "Assessing sustainability of food supply chains by using a novel method integrating group multi-criteria decision-making and interval Type-2 fuzzy set," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(2), pages 3665-3705, February.
    9. Vieira, Fabiana C. & Ferreira, Fernando A.F. & Govindan, Kannan & Ferreira, Neuza C.M.Q.F. & Banaitis, Audrius, 2022. "Measuring urban digitalization using cognitive mapping and the best worst method (BWM)," Technology in Society, Elsevier, vol. 71(C).
    10. Ridvan Ertugrul Yildirim & Aziz Sisman, 2025. "Spatial Decision Support for Determining Suitable Emergency Assembly Places Using GIS and MCDM Techniques," Sustainability, MDPI, vol. 17(5), pages 1-28, March.
    11. Besharati Fard, Moein & Moradian, Parisa & Emarati, Mohammadreza & Ebadi, Mehdi & Gholamzadeh Chofreh, Abdoulmohammad & Klemeŝ, Jiří Jaromír, 2022. "Ground-mounted photovoltaic power station site selection and economic analysis based on a hybrid fuzzy best-worst method and geographic information system: A case study Guilan province," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    12. Negin Salimi & Jafar Rezaei, 2016. "Measuring efficiency of university-industry Ph.D. projects using best worst method," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1911-1938, December.
    13. Chun-Chieh Tseng & Jun-Yi Zeng & Min-Liang Hsieh & Chih-Hung Hsu, 2022. "Analysis of Innovation Drivers of New and Old Kinetic Energy Conversion Using a Hybrid Multiple-Criteria Decision-Making Model in the Post-COVID-19 Era: A Chinese Case," Mathematics, MDPI, vol. 10(20), pages 1-25, October.
    14. Tavana, Madjid & Khalili Nasr, Arash & Mina, Hassan & Michnik, Jerzy, 2022. "A private sustainable partner selection model for green public-private partnerships and regional economic development," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    15. Jairo Ortega & Sarbast Moslem & János Tóth & Tamás Péter & Juan Palaguachi & Mario Paguay, 2020. "Using Best Worst Method for Sustainable Park and Ride Facility Location," Sustainability, MDPI, vol. 12(23), pages 1-18, December.
    16. Vineet Kaushik & Ashwani Kumar & Himanshu Gupta & Gaurav Dixit, 2022. "Modelling and prioritizing the factors for online apparel return using BWM approach," Electronic Commerce Research, Springer, vol. 22(3), pages 843-873, September.
    17. Amelia Bilbao-Terol & Mar Arenas-Parra & Raquel Quiroga-García & Celia Bilbao-Terol, 2022. "An extended best–worst multiple reference point method: application in the assessment of non-life insurance companies," Operational Research, Springer, vol. 22(5), pages 5323-5362, November.
    18. Sarbast Moslem & Muhammet Gul & Danish Farooq & Erkan Celik & Omid Ghorbanzadeh & Thomas Blaschke, 2020. "An Integrated Approach of Best-Worst Method (BWM) and Triangular Fuzzy Sets for Evaluating Driver Behavior Factors Related to Road Safety," Mathematics, MDPI, vol. 8(3), pages 1-20, March.
    19. Anchal Patil & Vipulesh Shardeo & Ashish Dwivedi & Noor Ulain Rizvi & Sanjoy Kumar Paul, 2024. "A framework to evaluate the temporary hospital locations in wake of COVID-19 pandemic: implications to healthcare operations," Operations Management Research, Springer, vol. 17(2), pages 438-452, June.
    20. Sahar Moazzeni & Sobhan Mostafayi Darmian & Lars Magnus Hvattum, 2023. "Multiple criteria decision making and robust optimization to design a development plan for small and medium-sized enterprises in the east of Iran," Operational Research, Springer, vol. 23(1), pages 1-32, March.

    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:17:y:2025:i:6:p:2425-:d:1609169. 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.