Assessing energy efficiency towards carbon emissions in Yangtze River Economic Belt from a configurational perspective

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Abstract

Green economy has been attracting attention around the world. The "dual carbon" policy has been proposed to emphasize the pursuit of high-quality economy in China, and improving energy efficiency is considered an effective way to achieve this strategic target. Moreover, the Yangtze River Economic Belt acts as the principal carrier for China's economic development; therefore, analyzing the energy efficiency is crucial. Three phases constituted the analysis in this paper: a) Using the LDA topic model to conduct the evaluation system of energy efficiency based on the "dual carbon" policy texts; b) using the SBM-Undesirable model to measure the energy efficiency of 11 provinces and cities in the Yangtze River Economic Belt between 2011 and 2020; and c) using the fsQCA method to further analyze the configurational impact of influential factors on the energy efficiency of the Yangtze River Economic Belt. Through systematic research, the evolving trends within the Yangtze River Economic Belt could be examined from two perspectives. Chronologically, energy efficiency within the region exhibited fluctuating development patterns. From a geographical perspective, overall energy efficiency exhibited a tiered distribution pattern of "downstream > midstream > upstream", indicating that the midstream and upstream sectors possessed greater potential for improvement compared to the downstream sector. Therefore, four configuration paths that affect energy efficiency were finally identified toward achieving the "dual carbon" target.

Keywords

energy efficiency / carbon emissions / fsQCA / SBM-Undesirable model / LDA

CHANGE HISTORY

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References

[1] Tan X, Wang R, Choi Y, et al. (2024) Does Korea's Carbon Emissions Trading Scheme Enhance Efficiency for Sustainable Energy and Utilities? Util Policy 88: 101752. https://doi.org/10.1016/j.jup.2024.101752

[2] Xin Y, Chang X, Zhu J (2024) How Does the Digital Economy Affect Energy Efficiency? Empirical Research on Chinese Cities. Energy Environ 35: 1703-1728. https://doi.org/10.1177/0958305X221143411

[3] Hu H, Lv T, Zhang X, et al. (2022) Spatiotemporal Dynamics and Decoupling Mechanism of Economic Growth and Carbon Emissions in an Urban Agglomeration of China. Environ Monit Assess 194: 616. https://doi.org/10.1007/s10661-022-10195-5

[4] Wang L, Shao J (2023) Digital Economy, Entrepreneurship and Energy Efficiency. Energy 269: 126801. https://doi.org/10.1016/j.energy.2023.126801

[5] Zhang L, Mu R, Zhan Y, et al. (2022) Digital Economy, Energy Efficiency, and Carbon Emissions: Evidence from Provincial Panel Data in China. Sci Total Environ 852: 158403. https://doi.org/10.1016/j.scitotenv.2022.158403

[6] Liu Q, Hao J (2022) Regional Differences and Influencing Factors of Carbon Emission Efficiency in the Yangtze River Economic Belt. Sustainability 14: 4814. https://doi.org/10.3390/su14084814

[7] Wang J, Fang W, An H, et al. (2026) Estimation of Regional Heterogeneity and Synergistic Drivers of Ecological Boundary Transgression in the Yangtze River Economic Belt. Environ Monit Assess 198: 192. https://doi.org/10.1007/s10661-026-15036-3

[8] Lu C, Jiang G, Zhang X, et al. (2023) Evaluation of Energy Utilization Efficiency in the Yangtze River Economic Belt. Sustainability 15: 1601. https://doi.org/10.3390/su15021601

[9] Zhang C, Chen P (2022) Applying the Three-Stage Sbm-Dea Model to Evaluate Energy Efficiency and Impact Factors in Rcep Countries. Energy 241: 122917. https://doi.org/10.1016/j.energy.2021.122917

[10] Mengxuan T, Khan K, Cifuentes-Faura J, et al. (2024) Technological Innovation and Energy Efficiency in Central Eastern European Countries. Util Policy 88: 101761. https://doi.org/10.1016/j.jup.2024.101761

[11] Otsuka A (2023) Industrial Electricity Consumption Efficiency and Energy Policy in Japan. Util Policy 81: 101519. https://doi.org/10.1016/j.jup.2023.101519

[12] von Loessl V, Wetzel H (2022) Revenue Decoupling, Energy Demand, and Energy Efficiency: Empirical Evidence from the Us Electricity Sector. Util Policy 79: 101416. https://doi.org/10.1016/j.jup.2022.101416

[13] Barrera-Santana J, Marrero G A, Ramos-Real F J (2022) Energy Efficiency and Energy Governance: A Stochastic Frontier Analysis Approach. Energy J 43: 243-83. https://doi.org/10.5547/01956574.43.6.jbar

[14] Maziotis A, Mocholi-Arce M, Sala-Garrido R, et al. (2023) Energy Efficiency of Drinking Water Treatment Plants: A Methodological approach for Its Ranking Br. Sci Total Environ 862: 160840. https://doi.org/10.1016/j.scitotenv.2022.160840

[15] Piao S R, Li J, Ting C J (2019) Assessing Regional Environmental Efficiency in China with Distinguishing Weak and Strong Disposability of Undesirable Outputs. J Clean Prod 227: 748-759. https://doi.org/10.1016/j.jclepro.2019.04.207

[16] Yang G, Gui Q, Supanyo P, et al. (2023) Temporal and Spatial Changes and Influencing Factors of Low-Carbon Economy Efficiency in China. Environ Monit Assess 195: 55. https://doi.org/10.1007/s10661-022-10599-3

[17] Zheng J, Dang Y, Assad U (2024) Household Energy Consumption, Energy Efficiency, and Household Income-Evidence from China. Appl Energy 353: 122074. https://doi.org/10.1016/j.apenergy.2023.122074

[18] Li Y, Yang X, Ma S (2025) The Efficiency Measurement and Spatial Spillover Effect of Green Technology Innovation in Chinese Industrial Enterprises. Sustainability 17: 3162. https://doi.org/10.3390/su17073162

[19] Lin T Y, Chiu Y, Chen C H, et al. (2025) Renewable Energy Consumption Efficiency, Greenhouse Gas Emission Efficiency, and Climate Change in Europe. Geoenergy Sci Eng 247: 213665. https://doi.org/10.1016/j.geoen.2025.213665

[20] Karbasioun M, Gholamalipour A, Safaie N, et al. (2023) Developing Sustainable Power Systems by Evaluating Techno-Economic, Environmental, and Social Indicators from a System Dynamics Approach. Util Policy 82: 101566. https://doi.org/10.1016/j.jup.2023.101566

[21] Lu W, Jiang S, Zhu H (2025) Spatial-Temporal Evolution and Determinants of Energy Environmental Efficiency in China: An Integrated Analytical Approach. Environ Dev Sustain: 1-31. https://doi.org/10.1007/s10668-025-06828-7

[22] Pamula A, Gontar Z, Gontar B, et al. (2023) Latent Dirichlet Allocation in Public Procurement Documents Analysis for Determining Energy Efficiency Issues in Construction Works at Polish Universities. Energies 16: 4596. https://doi.org/10.3390/en16124596

[23] Ramírez-Cando L J, Alvarez C I, Gualotuña D (2025) Overview Based on Latent Dirichlet Allocation: The Case of Study Climate Change Research Efforts in Latin America. Environ Monit Assess 197: 829. https://doi.org/10.1007/s10661-025-14291-0

[24] Bai D, Dong Q, Khan S A R, et al. (2022) Spatial Analysis of Logistics Ecological Efficiency and Its Influencing Factors in China: Based on Super-Sbm-Undesirable and Spatial Dubin Models. Environ Sci Pollut Res Int 29: 10138-10156. https://doi.org/10.1007/s11356-021-16323-x

[25] Niu H, Zhang Z, Xiao Y, et al. (2022) A Study of Carbon Emission Efficiency in Chinese Provinces Based on a Three-Stage Sbm-Undesirable Model and an Lstm Model. Int J Environ Res Public Health 19: 5395. https://doi.org/10.3390/ijerph19095395

[26] Yang B, Wang Z, Zou L, et al. (2021) Exploring the Eco-Efficiency of Cultivated Land Utilization and Its Influencing Factors in China's Yangtze River Economic Belt, 2001-2018. J Environ Manag 294: 112939. https://doi.org/10.1016/j.jenvman.2021.112939

[27] Pappas I O, Woodside A G (2021) Fuzzy-Set Qualitative Comparative Analysis (Fsqca): Guidelines for Research Practice in Information Systems and Marketing. Int J Inf Manag 58: 102310. https://doi.org/10.1016/j.ijinfomgt.2021.102310

[28] Sun X, Zhang R, Yu Z, et al. (2024) Revisiting the Porter Hypothesis within the Economy-Environment-Health Framework: Empirical Analysis from a Multidimensional Perspective. J Environ Manag 349: 119557. https://doi.org/10.1016/j.jenvman.2023.119557

[29] Ni Q, Zhang H, Lu Y (2023) Way to Measure Intangible Capital for Innovation-Driven Economic Growth: Evidence from China. Econ Anal Policy 78: 156-172. https://doi.org/10.1016/j.eap.2023.03.003

[30] Ma L, Hong Y, Chen X, et al. (2022) Can Green Innovation and New Urbanization Be Synergistic Development? Empirical Evidence from Yangtze River Delta City Group in China. Sustainability 14: 5765. https://doi.org/10.3390/su14105765

[31] Zhao C, Liu M, Wang K (2022) Monetary Valuation of the Environmental Benefits of Green Building: A Case Study of China. J Clean Prod 365: 132704. https://doi.org/10.1016/j.jclepro.2022.132704

[32] Krupnik S, Szczucka A, Woźniak M, et al. (2023) The Potential of Consecutive Qualitative Comparative Analysis as a Systematic Strategy for Configurational Theorizing. Evaluation 29: 451-467. https://doi.org/10.1177/13563890231200292

[33] Rihoux, Beno Charles Ragin. 2009. Configurational Comparative Methods: Qualitative Comparative Analysis (Qca) and Related Techniques. Thousand Oaks, California. Retrieved 2024/04/05. (https://methods.sagepub.com/book/configurational-comparative-methods). https://doi.org/ 10.4135/9781452226569

[34] Baumgartner M, Ambühl M (2023) Optimizing Consistency and Coverage in Configurational Causal Modeling. Sociol Methods Res 52: 1288-1320. https://doi.org/10.1177/0049124121995554