Regional inequality and driving factors behind low-carbon energy transition in China’s provinces

Abstract

Accelerating the transition of China’s energy consumption structure toward low-carbon development is essential for achieving global carbon neutrality goals. As the country with the world’s largest share of energy-related emissions, China provides a critical case in which substantial provincial disparities remain. Using panel data from 30 provinces from 2012 to 2022, this study develops an integrated framework combining Geographically and Temporally Weighted Regression (GTWR), eXtreme Gradient Boosting (XGBoost), and SHapley Additive exPlanations (SHAP) to examine the driving mechanisms of low-carbon energy transition from a regional inequality perspective. The results reveal persistent east–west disparities, significant spatial clustering, and clear temporal shifts in the effects of key drivers. Results reveal pronounced spatiotemporal heterogeneity. Green technology innovation consistently showed the strongest positive effect, while industrialization and urban–rural income gaps exerted stronger negative impacts in central and western regions. Government intervention shifted from a negative factor in early years to a positive driver in later years, which reflects the evolving role of policy in steering decarbonization. Moreover, nonlinear threshold effects were identified, such as U-shaped impacts of government intervention and scale-sensitive effects of afforestation. Findings show that China’s low-carbon transition is evolving from regional heterogeneity toward policy convergence, yet inequalities remain significant. These results underscore the need for targeted strategies for reducing disparities, including technology diffusion and financial support in less-developed provinces, to ensure a more balanced and equitable energy transition. This study contributes new empirical insights to understanding low-carbon drivers and designing decarbonization policies for ensuring an equitable and coordinated national transition.

Keywords

Low-carbon energy consumption / Energy transition / Regional disparities / GTWR / Interpretable machine learning

References

[1] Ahmed Z, Ahmad M, Rjoub H, et al.. Economic growth, renewable energy consumption, and ecological footprint: Exploring the role of environmental regulations and democracy in sustainable development. Sustain Dev, 2022, 30: 595-605

[2] An K, Lu C, Tang X. Development levels of low-carbon economy at provincial scales in China. Resour Sci, 2011, 33: 612-619

[3] Anselin L. Local indicators of spatial association—LISA. Geogr Anal, 1995, 27: 93-115

[4] Aslam B, Hu J, Shahab S, et al.. The nexus of industrialization, GDP per capita and CO2 emission in China. Environ Technol Innov, 2021, 23 101674

[5] Aspiras AH, Zarrouk SJ, Winmill R, Kempa-Liehr AW. Real-time incident detection in geothermal drilling through machine learning. Renew Energy, 2025, 250 123260

[6] Bogdanov D, Ram M, Aghahosseini A, et al.. Low-cost renewable electricity as the key driver of the global energy transition towards sustainability. Energy, 2021, 227 120467

[7] Chen T, Guestrin C (2016) XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, New York, NY, USA, pp 785–794

[8] Chen D, Sun Y. The spatial–temporal evolution and correlation between China’s energy low-carbon transition and urban–rural income gap. Energy Convers Manag, 2025, 343 120195

[9] Chen H, Long R, Niu W, et al.. How does individual low-carbon consumption behavior occur? – An analysis based on attitude process. Appl Energy, 2014, 116: 376-386

[10] Cong L, Lu S, Jiang P, et al.. Research progress on CO2 as geothermal working fluid: a review. Energies, 2024, 17 5415

[11] Cong L, Lu S, Jiang P, et al.. Harnessing enhanced rock weathering for carbon neutrality: potential and challenges in China. Earth Sci Rev, 2025, 271 105309

[12] Ding Z, Jiang X, Liu Z, et al.. Factors affecting low-carbon consumption behavior of urban residents: a comprehensive review. Resour Conserv Recycl, 2018, 132: 3-15

[13] El Bilali A, Abdeslam T, Ayoub N, et al.. An interpretable machine learning approach based on DNN, SVR, Extra Tree, and XGBoost models for predicting daily pan evaporation. J Environ Manage, 2023, 327 116890

[14] Erin Bass A, Grøgaard B. The long-term energy transition: drivers, outcomes, and the role of the multinational enterprise. J Int Bus Stud, 2021, 52: 807-823

[15] Esseghir A, Haouaoui Khouni L. Economic growth, energy consumption and sustainable development: the case of the Union for the Mediterranean countries. Energy, 2014, 71: 218-225

[16] Fotheringham AS, Crespo R, Yao J. Geographical and temporal weighted regression (GTWR). Geogr Anal, 2015, 47: 431-452

[17] Gao S, Zhou P, Zhang H. Does energy transition help narrow the urban-rural income gap? Evidence from China. Energy Policy, 2023, 182 113759

[18] Ge T. Rising energy inequity and its driving factors to approach a just energy transition in China. Environ Impact Assess Rev, 2023, 103 107231

[19] Guo Q, Sun H. Spatial distribution of Chinese urban residents’ electricity consumption and its driving factors—an empirical study based on the MGWR model. Energy, 2025, 319 135076

[20] Gyimah J, Yao X, Tachega MA, et al.. Renewable energy consumption and economic growth: new evidence from Ghana. Energy, 2022, 248 123559

[21] Hepburn C, Qi Y, Stern N, et al.. Towards carbon neutrality and China’s 14th Five-Year Plan: Clean energy transition, sustainable urban development, and investment priorities. Environ Sci Ecotechnol, 2021, 8: 100130

[22] International Energy AgencyIEA. Enhancing China’s ETS for carbon neutrality: Focus on Power Sector, 2022, Paris, IEA

[23] Jiang Z, Lyu P, Ye L, Zhou YW. Green innovation transformation, economic sustainability and energy consumption during China’s new normal stage. J Clean Prod, 2020, 273 123044

[24] Jiang Y, Fan M, Fan Y. Does energy transition policy enhance urban green innovation capabilities?–a quasi-natural experiment based on China’s new energy demonstration city policy. Front Environ Sci, 2024, 12 12

[25] Jiang P, Yu Z, Lu S, Cong L. Interaction effects and threshold analysis of enterprise digital service innovation on environmental performance: evidence from interpretable machine learning models. J Clean Prod, 2025, 520 146113

[26] Jiang P, Yu Z, Lu S, Cong L. Carbon emissions and population migration preferences: the impact of China’s carbon emissions and low-carbon policies on regional population migration. Clim Change, 2026, 179: 34

[27] Kalnins A, Hill KP. The VIF score. What is it good for? Absolutely nothing.. Organ Res Methods, 2023

[28] Kanger L, Sovacool BK. Towards a multi-scalar and multi-horizon framework of energy injustice: A whole systems analysis of Estonian energy transition. Polit Geogr, 2022, 93 102544

[29] Li Z. Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost. Comput Environ Urban Syst, 2022, 96 101845

[30] Li Q, Li L, Lei Y, Wu S. The impact of energy transition on economy and health and its fairness. J Clean Prod, 2023, 425 138953

[31] Li W, Cao N, Xiang Z. Drivers of renewable energy transition: the role of ICT, human development, financialization, and R&D investment in China. Renew Energy, 2023, 206: 441-450

[32] Liao J, Liu X, Zhou X, Tursunova NR. Analyzing the role of renewable energy transition and industrialization on ecological sustainability: Can green innovation matter in OECD countries. Renew Energy, 2023, 204: 141-151

[33] Liu Z, Deng Z, He G, et al.. Challenges and opportunities for carbon neutrality in China. Nat Rev Earth Environ, 2022, 3: 141-155

[34] Lu S, Yu Z, Zhang Y, Xu T. Review of non-isothermal processes in CCUS from a geomechanical perspective. Earth-Sci Rev, 2024, 255 104848

[35] Lu S, Jiang P, Cong L, et al.. Review of geochemical processes in CCUS: Mechanisms, processes, and implications. Gondwana Res, 2025, 146: 200-215

[36] Ma L, Wang Q, Shi D, Shao Q. Spatiotemporal patterns and determinants of renewable energy innovation: evidence from a province-level analysis in China. Humanit Soc Sci Commun, 2023, 10: 357

[37] Maghyereh A, Boulanouar Z, Essid L. The dynamics of green innovation and environmental policy stringency in energy transition investments. J Clean Prod, 2025, 487 144649

[38] Mosca E, Szigeti F, Tragianni S et al (2022) SHAP-based explanation methods: A Review for NLP interpretability. In: Calzolari N, Huang C-R, Kim H,. (eds) Proceedings of the 29th International Conference on Computational Linguistics. International Committee on Computational Linguistics, Gyeongju, Republic of Korea, pp 4593–4603

[39] Omer AM. Energy, environment and sustainable development. Renew Sustain Energy Rev, 2008, 12: 2265-2300

[40] Qiao R, Liu X, Gao S, et al.. Industrialization, urbanization, and innovation: nonlinear drivers of carbon emissions in Chinese cities. Appl Energy, 2024, 358 122598

[41] Rogerson PA, Fotheringham AS (2008) The SAGE Handbook of Spatial Analysis. 1–528

[42] Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell, 2019, 1: 206-215

[43] Shan K, Cong L, Yu Z, Ye X. Artificial intelligence empowering geothermal energy development: a full-lifecycle review from exploration to operation. Renewable and Sustainable Energy Reviews, 2026, 226 116468

[44] Shen L, Sun Y. Review on carbon emissions, energy consumption and low-carbon economy in China from a perspective of global climate change. J Geogr Sci, 2016, 26: 855-870

[45] Su X, Tan J. Regional energy transition path and the role of government support and resource endowment in China. Renew Sustain Energy Rev, 2023, 174 113150

[46] Su M, Wang Q, Li R, Wang L. Per capita renewable energy consumption in 116 countries: The effects of urbanization, industrialization, GDP, aging, and trade openness. Energy, 2022, 254 124289

[47] Sun C, Sun S, Yue X. Does the transition to low-carbon energy alleviate urban-rural energy inequality? The case of China. Heliyon, 2024, 10 e31355

[48] Tcvetkov P. Climate policy imbalance in the energy sector: Time to focus on the value of CO2 utilization. Energies, 2021, 14: 411

[49] Wan Y, Sheng N, Wei X, Su H. Study on the spatial spillover effect and path mechanism of green finance development on China’s energy structure transformation. Journal of Cleaner Production, 2023, 415 137820

[50] Wang X, Xie T. Study on the impact of carbon finance on the transformation of energy consumption structure. Mod Industrial Econ Informationization, 2025, 15: 13-16

[51] Wang Z, Zhu Y, Zhu Y, Shi Y. Energy structure change and carbon emission trends in China. Energy, 2016, 115: 369-377

[52] Wang S, Shi C, Fang C, Feng K. Examining the spatial variations of determinants of energy-related CO2 emissions in China at the city level using geographically weighted regression model. Appl Energy, 2019, 235: 95-105

[53] Wang F, Wang Y, Zhang K, et al.. Spatial heterogeneity modeling of water quality based on random forest regression and model interpretation. Environ Res, 2021, 202 111660

[54] Wang X-C, Yang L, Wang Y, et al.. Imbalances in virtual energy transfer network of China and carbon emissions neutrality implications. Energy, 2022, 254 124304

[55] Wang L, Yu Z, Zhang Y, Yao P. Review of machine learning methods applied to enhanced geothermal systems. Environ Earth Sci, 2023, 82 69

[56] Wang Q, Fan J, Kwan M-P, et al.. Examining energy inequality under the rapid residential energy transition in China through household surveys. Nat Energy, 2023, 8: 251-263

[57] Wang H, Liang Q, Hancock JT, Khoshgoftaar TM. Feature selection strategies: a comparative analysis of SHAP-value and importance-based methods. J Big Data, 2024, 11: 44

[58] Wang Z, Xi Y, Li L, et al.. The spatial-temporal characterization of the driving factors of China’s energy transition and prediction of future transition potential. Renew Energy, 2025, 249 123234

[59] Xiao Y, Feng Z, Li X, Wang S. Low-carbon transition and energy poverty: quasi-natural experiment evidence from China’s low-carbon city pilot policy. Humanit Soc Sci Commun, 2024, 11: 84

[60] Xu S. The paradox of the energy revolution in China: a socio-technical transition perspective. Renew Sustain Energy Rev, 2021, 137 110469

[61] Xu G, Yang M, Li S, et al.. Evaluating the effect of renewable energy investment on renewable energy development in China with panel threshold model. Energy Policy, 2024, 187 114029

[62] Xue C, Shahbaz M, Ahmed Z, et al.. Clean energy consumption, economic growth, and environmental sustainability: what is the role of economic policy uncertainty?. Renew Energy, 2022, 184: 899-907

[63] Yan J, Li Y, Su B, Ng TS. Contributors and drivers of Chinese energy use and intensity from regional and demand perspectives, 2012-2015-2017. Energy Econ, 2022, 115 106357

[64] Yang J, Yu S, Sun Y-F. Restructuring effects of industrial and energy structures on sectoral CO2 emission peak trajectories in China. iScience, 2024, 27 110541

[65] Yu B, Zhao Z, Wei Y-M, et al.. Approaching national climate targets in China considering the challenge of regional inequality. Nat Commun, 2023, 14 8342

[66] Zhang Z, Chen H. Dynamic interaction of renewable energy technological innovation, environmental regulation intensity and carbon pressure: evidence from China. Renew Energy, 2022, 192: 420-430

[67] Zhang D, Kong Q. Green energy transition and sustainable development of energy firms: an assessment of renewable energy policy. Energy Econ, 2022, 111 106060

[68] Zhang W, Wang J, Xu Y, et al.. Analyzing the spatio-temporal variation of the CO2 emissions from district heating systems with Coal-to-Gas transition: evidence from GTWR model and satellite data in China. Sci Total Environ, 2022, 803 150083

[69] Zhang Y, Teoh BK, Zhang L. Exploring driving force factors of building energy use and GHG emission using a spatio-temporal regression method. Energy, 2023, 269 126747

[70] Zhang Z, Xiao Y, Zhang K, et al.. The impact of China pilot carbon market policy on electricity carbon emissions. Sci Rep, 2025, 15 16415

[71] Zheng L, Fu J, Li J (2011) Evaluation on the development level and spatial progress of low-carbon economy at provincial scale in China. China Population, Resources and Environment

[72] Zhong F, Tian J, Zhao C, et al.. Assessing energy justice in climate change policies: an empirical examination of China’s energy transition. Clim Policy, 2024, 24: 362-377

Funding

University of Vaasa

RIGHTS & PERMISSIONS

The Author(s)

Just Accepted

This article has successfully passed peer review and final editorial review, and will soon enter typesetting, proofreading and other publishing processes. The currently displayed version is the accepted final manuscript. The officially published version will be updated with format, DOI and citation information upon launch. We recommend that you pay attention to subsequent journal notifications and preferentially cite the officially published version. Thank you for your support and cooperation.