Nonlinear environmental impacts of digital transformation in China’s mega-urban agglomerations

Abstract

Amidst rapid digitalization and pressing environmental challenges, understanding the environmental implications of digital transformation is crucial for sustainable urban development. Yet, the complex, potentially nonlinear digitalization-environment relationships remain underexplored. This study has two objectives: first, to quantify the nonlinear causal impacts of digital transformation on pollution mitigation and carbon reduction; and second, to unravel the mediating pathways that drive these outcomes. We employ Double Machine Learning (DML) on panel data from 2013 to 2022 across China’s four mega-urban agglomerations to identify the nonlinear environmental impacts of digital transformation. Mediation analysis is then used to examine the technology, structure, governance, and scale pathways. Despite overall progress in both digital transformation and environmental performance, significant regional variations persist. Our DML analysis reveals distinct nonlinearities: an S-shaped relationship between digital transformation and pollution mitigation, and a more complex N-shaped curve for the digital transformation-carbon reduction nexus. Mediation analysis further reveals complex mechanism: while the structure path consistently promotes environmental benefits, technology and scale factors show negative effects, and governance impacts diverge, promoting pollution mitigation but hindering carbon reduction. Translating digital transformation into environmental benefits necessitates a multi-pronged strategy. Key imperatives include prioritizing green technological innovation over sheer digital expansion to mitigate adverse scale effects, and restructuring energy systems towards renewable sources. Furthermore, digital governance must be wielded judiciously, with accountability to enhance specific environmental goals. This research reveals the intricate and context-dependent nature of digital transformation’s environmental effects, providing data-driven insights for regional policies aiming to leveraging digitalization for environmental sustainability, particularly in urban contexts.

Keywords

Digital transformation / Environmental sustainability / Pollution mitigation / Carbon reduction / Double machine learning

Data availability statement

The data and code that supports the findings of this research is available from the corresponding author upon request.

CRediT authorship contribution statement

Zehui Chen: Writing - review & editing, Writing - original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Chuanglin Fang: Writing - review & editing, Validation, Supervision, Funding acquisition, Conceptualization. Zhitao Liu: Writing - review & editing, Visualization, Conceptualization. Lingyu Meng: Writing - review & editing, Validation.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

We thank the anonymous reviewers for their incisive comments and suggestions to earlier versions of the paper. This study is supported by the Innovative Research Group Project of the National Natural Science Foundation of China (Grant No. 42121001).

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.geosus.2026.100416.

References

[1] Arshad Z., Robaina M., Botelho A., 2020. The role of ICT in energy consumption and environment: an empirical investigation of Asian economies with cluster analysis. Environ. Sci. Pollut. Res. 27, 32913-32932. doi: 10.1007/s11356-020-09229-7.

[2] Berkhout F., Hertin J., 2004. De-materialising and re-materialising: digital technologies and the environment. Futures 36, 903-920. doi: 10.1016/j.futures.2004.01.003.

[3] Chandrappa R., Das D.B., 2024. Waste from electrical and electronic equipment. In: Chandrappa R., Das D.B. ( Solid Waste Management:Eds.), Principles and Practice. Springer, Cham, pp. 343-367. doi: 10.1007/978-3-031-50442-6_8.

[4] Charfeddine L., Umlai M., 2023. ICT sector, digitization and environmental sustainability: a systematic review of the literature from 2000 to 2022. Renew. Sustain. Energy Rev. 184, 113482. doi: 10.1016/j.rser.2023.113482.

[5] Cheng Y., Zhang Y., Wang J.J., Jiang J.X., 2023. The impact of the urban digital economy on China’s carbon intensity: spatial spillover and mediating effect. Resour. Conserv. Recycl. 189, 106762. doi: 10.1016/j.resconrec.2022.106762.

[6] Chernozhukov V., Chetverikov D., Demirer M., Duflo E., Hansen C., Newey W., Robins J., 2018. Double/debiased machine learning for treatment and structural parameters. Econom. J. 21 (1), C1-C68. doi: 10.1111/ectj.12097.

[7] Coyle D., 1998. The Weightless World:Strategies for Managing the Digital Economy. MIT Press, Boston.

[8] Crippa M., Guizzardi D., Pagani F., Banja M., Muntean M., Schaaf E., Becker W., Monforti-Ferrario F., Quadrelli R., Risquez Martin A., Taghavi-Moharamli P., Köykkä J., 2023. GHG emissions of all world countries. Publications Office of the European Union, Luxembourg.

[9] Danish, Ulucak, R., 2022. Analyzing energy innovation-emissions nexus in China: a novel dynamic simulation method. Energy 244, 123010. doi: 10.1016/j.energy.2021.123010.

[10] Di K.S., Chen W.D., Shi Q.M., Cai Q.L., Liu S.C., 2024b. Analysing the impact of coupled domestic demand dynamics of green and low-carbon consumption in the market based on SEM-ANN. J. Retail. Consum. Serv. 79, 103856. doi: 10.1016/j.jretconser.2024.103856.

[11] Di K.S., Chen W.D., Shi Q.M., Cai Q.L., Zhang B., 2024a. Digital empowerment and win-win co-operation for green and low-carbon industrial development: analysis of regional differences based on GMM-ANN intelligence models. J. Clean. Prod. 445, 141332. doi: 10.1016/j.jclepro.2024.141332.

[12] Di K.S., Chen W.D., Zhang X.N., Shi Q.M., Cai Q.L., Li D.L., Liu C.P., Di Z.S., 2023. Regional unevenness and synergy of carbon emission reduction in China’s green low-carbon circular economy. J. Clean. Prod. 420, 138436. doi: 10.1016/j.jclepro.2023.138436.

[13] Dogan E., Ulucak R., Kocak E., Isik C., 2020. The use of ecological footprint in estimating the Environmental Kuznets Curve hypothesis for BRICST by considering cross-section dependence and heterogeneity. Sci. Total Environ. 723, 138063. doi: 10.1016/j.scitotenv.2020.138063.

[14] Dong F., Hu M.Y., Gao Y.J., Liu Y.J., Zhu J., Pan Y.L., 2022. How does digital economy affect carbon emissions? Evidence from global 60 countries. Sci. Total Environ. 852, 158401. doi: 10.1016/j.scitotenv.2022.158401.

[15] Du W.H., Liu X.N., Liu Y.Y., Xie J.P., 2025. Digital Economy and carbon emission efficiency in three major urban agglomerations of China: a Ushaped journey towards green development. J. Environ. Manage. 373, 123571. doi: 10.1016/j.jenvman.2024.123571.

[16] Egan M., Schaltegger S., 2023. Accounting for corporate environmental rebounds. A conceptual approach. J. Clean. Prod. 419, 138175. doi: 10.1016/j.jclepro.2023.138175.

[17] Erdoğan S., Yıldırım S., Yıldırım D.Ç., Gedikli A., 2020. The effects of innovation on sectoral carbon emissions: evidence from G20 countries. J. Environ. Manage. 267, 110637. doi: 10.1016/j.jenvman.2020.110637.

[18] Fang C.L., Chen Z.H., Liao X., Sun B., Meng L.Y., 2024. Urban-rural digitalization evolves from divide to inclusion: empirical evidence from China. npj Urban Sustain. 4, 51. doi: 10.1038/s42949-024-00187-4.

[19] Fankhauser S., Smith S.M., Allen M., Axelsson K., Hale T., Hepburn C., Kendall J.M., Khosla R., Lezaun J., Mitchell-Larson E., Obersteiner M., Rajamani L., Rickaby R., Seddon N., Wetzer T., 2022. The meaning of net zero and how to get it right. Nat. Clim. Chang. 12 (1), 15-21. doi: 10.1038/s41558-021-01245-w.

[20] Feng X.Y., Ma X.Y., Lu J.B., Tang Q.Y., Chen Z.H., 2025. Assessing the impact of the digital economy on sustainable development in the underdeveloped regions of western China. Cities 156, 105552. doi: 10.1016/j.cities.2024.105552.

[21] Grossman G.M., Krueger A.B., 1991. Environmental impacts of a North American Free Trade Agreement (No. w3914). National Bureau of Economic Research, Cambridge, MA. doi:10.3386/w3914.

[22] Grossman G.M., Krueger A.B., 1995. Economic growth and the environment. Q. J. Econ. 110, 353-377. doi: 10.2307/2118443.

[23] Guo C.Y., Zhou Z.Q., Liu X.Y., Liu X.R., Meng J., Dai H.C., 2023. The unintended dilemma of China’s target-based carbon neutrality policy and provincial economic inequality. Energy Econ. 126, 107002. doi: 10.1016/j.eneco.2023.107002.

[24] Gupta S., Gupta P., 2018. Digitization for reliable and efficient manufacturing. Life Cycle Reliab. Saf. Eng. 7 (4), 245-250. doi: 10.1007/s41872-018-0051-y.

[25] Hampton S.E., Strasser C.A., Tewksbury J.J., Gram W.K., Budden A.E., Batcheller A.L., Duke C.S., Porter J.H., 2013. Big data and the future of ecology. Front. Ecol. Environ. 11 (3), 156-162. doi: 10.1890/120103.

[26] Han Y.G., Li Z.T., Feng T.C., Qiu S.L., Hu J., Yadav K.K., Obaidullah A.J., 2024. Unraveling the impact of digital transformation on green innovation through microdata and machine learning. J. Environ. Manage. 354, 120271. doi: 10.1016/j.jenvman.2024.120271.

[27] Hilty L.M., Arnfalk P., Erdmann L., Goodman J., Lehmann M., Wäger P.A., 2006. The relevance of information and communication technologies for environmental sustainability -a prospective simulation study. Environ. Model. Softw. 21 (11), 1618-1629. doi: 10.1016/j.envsoft.2006.05.007.

[28] Howson P., Oakes S., Baynham-Herd Z., Swords J., 2019. Cryptocarbon: the promises and pitfalls of forest protection on a blockchain. Geoforum 100, 1-9. doi: 10.1016/j.geoforum.2019.02.011.

[29] Istrate R., Tulus V., Grass R.N., Vanbever L., Stark W.J., Guillén-Gosálbez G., 2024. The environmental sustainability of digital content consumption. Nat. Commun. 15, 3724. doi: 10.1038/s41467-024-47621-w.

[30] Jiang S.R., Li Y.Z., Lu Q.Y., Hong Y.M., Guan D.B., Xiong Y., Wang S.Y., 2021. Policy assessments for the carbon emission flows and sustainability of Bitcoin blockchain operation in China. Nat. Commun. 12, 1938. doi: 10.1038/s41467-021-22256-3.

[31] Jiang S., Zhou J., Qiu S., 2022. Digital agriculture and urbanization: mechanism and empirical research. Technol. Forecast. Soc. Change 180, 121724. doi: 10.1016/j.techfore.2022.121724.

[32] Jiang T., 2022. Mediating effects and moderating effects in causal inference. China Ind. Econ. 5, 100-120. doi: 10.19581/j.cnki.ciejournal.2022.05.005, (in Chinese).

[33] Jiang Y.C., Sun J.S., 2025. Does smart city construction promote urban green development? Evidence from a double machine learning model. J. Environ. Manage. 373, 123701. doi: 10.1016/j.jenvman.2024.123701.

[34] Jin L., Duan K., Tang X., 2018. What is the relationship between technological innovation and energy consumption? Empirical analysis based on provincial panel data from China. Sustainability 10 (1), 145. doi: 10.3390/su10010145.

[35] Jin Z.R., Zhu S.J., 2025. Combinatorial innovation and lifecycle analysis of low-carbon energy technologies in China. Geogr. Sustain. 6, 100236. doi: 10.1016/j.geosus.2024.08.013.

[36] Jones E.R., Bierkens M.F.P., van Puijenbroek P.J.T.M., van Beek L.P.H., Wanders N., Sutanudjaja E.H., van Vliet M.T.H., 2023. Sub-Saharan Africa will increasingly become the dominant hotspot of surface water pollution. Nat. Water 1 (7), 602-613. doi: 10.1038/s44221-023-00105-5.

[37] Jones N., 2018. How to stop data centres from gobbling up the world’s electricity. Nature 561 (7722), 163-166. doi: 10.1038/d41586-018-06610-y.

[38] Kunkel S., Tyfield D., 2021. Digitalisation, sustainable industrialisation and digital rebound -asking the right questions for a strategic research agenda. Energy Res. Soc. Sci. 82, 102295. doi: 10.1016/j.erss.2021.102295.

[39] Li B., Du Y., Chen G., 2025. Dynamic nexus between urban digitization and pollutant discharge: RBC theory and empirical evidence from China. Technol. Forecast. Soc. Change 211, 123915. doi: 10.1016/j.techfore.2024.123915.

[40] Li Y., Yang X.D., Ran Q.Y., Wu H.T., Irfan M., Ahmad M., 2021. Energy structure, digital economy, and carbon emissions: evidence from China. Environ. Sci. Pollut. Res. 28 (45), 64606-64629. doi: 10.1007/s11356-021-15304-4.

[41] Li Z.G., Wang J., 2022. The dynamic impact of digital economy on carbon emission reduction: evidence city-level empirical data in China. J. Clean. Prod. 351, 131570. doi: 10.1016/j.jclepro.2022.131570.

[42] Liang C.L., Chen X.L., Di Q.B., 2024. Path to pollution and carbon reduction synergy from the perspective of the digital economy: fresh evidence from 292 prefecture-level cities in China. Environ. Res. 252, 119050. doi: 10.1016/j.envres.2024.119050.

[43] Liu H.M., Wang X.Y., Wang Z.Y., Cheng Y., 2024. Does digitalization mitigate regional inequalities? Evidence from China. Geogr. Sustain 5 (1), 52-63. doi: 10.1016/j.geosus.2023.09.007.

[44] Ma Q., Tariq M., Mahmood H., Khan Z., 2022. The nexus between digital economy and carbon dioxide emissions in China: the moderating role of investments in research and development. Technol. Soc. 68, 101910. doi: 10.1016/j.techsoc.2022.101910.

[45] Ma Z.H., Zhang P.D., 2025. Myth of the digital economy: can it continually contribute to a low-carbon status and sustainable development? Environ. Impact Assess. Rev. 110, 107688. doi: 10.1016/j.eiar.2024.107688.

[46] Masanet E., Shehabi A., Lei N.A., Smith S., Koomey J., 2020. Recalibrating global data center energy-use estimates. Science 367 (6481), 984-986. doi: 10.1126/science.aba3758.

[47] Peng Z.Z., Dan T., 2023. Digital dividend or digital divide? Digital economy and urban-rural income inequality in China. Telecommun. Policy 47 (9), 102616. doi: 10.1016/j.telpol.2023.102616.

[48] Pörtner H.-O., Roberts D.C., Poloczanska E.S., Mintenbeck K., Tignor M., Alegría A., Craig M., Langsdorf S., Löschke S., Möller V., Okem A., 2022. Climate change 2022:impacts, adaptation and vulnerability. Contribution of Working Group II to the Sixth Assessment Report. Interlaken.

[49] Preacher K.J., Hayes A.F., 2004. SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behav. Res. Meth. Instrum. Comput. 36 (4), 717-731. doi: 10.3758/BF03206553.

[50] Ran Q.Y., Yang X.D., Yan H.C., Xu Y., Cao J.H., 2023. Natural resource consumption and industrial green transformation: does the digital economy matter? Resour. Policy 81, 103396. doi: 10.1016/j.resourpol.2023.103396.

[51] Romer P.M., 1990. Endogenous technological change. J. Polit. Econ. 98, S71-S102. doi: 10.1086/261725.

[52] Sachs J.D., Schmidt-Traub G., Mazzucato M., Messner D., Nakicenovic N., Rockström J., 2019. Six transformations to achieve the sustainable development goals. Nat. Sustain. 2 (9), 805-814. doi: 10.1038/s41893-019-0352-9.

[53] Shahbaz M., Hye Q.M.A., Tiwari A.K., Leitão N.C., 2013. Economic growth, energy consumption, financial development, international trade and CO₂ emissions in Indonesia. Renew. Sust. Energ. Rev. 25, 109-121. doi: 10.1016/j.rser.2013.04.009.

[54] Shahbaz M., Ali Nasir M., Hille E., Mahalik M.K.,2020. UK’s net-zero carbon emissions target: Investigating the potential role of economic growth, financial development, and R&D expenditures based on historical data (1870-2017). Technol. Forecast. Soc. Change 161, 120255. doi: 10.1016/j.techfore.2020.120255.

[55] Shahbaz M., Nejati M., Du A.M., Jiao Z.L., 2024. A recursive dynamic CGE approach to investigate environmental Kuznets curve. J. Environ. Manage. 370, 122359. doi: 10.1016/j.jenvman.2024.122359.

[56] Shahbaz M., Wang J., Dong K., Zhao J., 2022. The impact of digital economy on energy transition across the globe: the mediating role of government governance. Renew. Sust. Energ. Rev. 166, 112620. doi: 10.1016/j.rser.2022.112620.

[57] Shao S., Guo L.F., Yu M.L., Yang L.L., Guan D.B., 2019. Does the rebound effect matter in energy import-dependent mega-cities? Evidence from Shanghai (China). Appl. Energy 241, 212-228. doi: 10.1016/j.apenergy.2019.03.007.

[58] Shen N., Zhou J., Zhang G., Wu L., Zhang L., 2025. How does data factor marketization influence urban carbon emission efficiency? A new method based on double machine learning. Sust. Cities Soc. 119, 106106. doi: 10.1016/j.scs.2024.106106.

[59] Song Z., Wang C., Bergmann L., 2020. China’s prefectural digital divide: spatial analysis and multivariate determinants of ICT diffusion. Int. J. Inf. Manage. 52, 102072. doi: 10.1016/j.ijinfomgt.2020.102072.

[60] Sun T., Di K.S., Hu J., Shi Q.M., Irfan M., 2025. Digitally empowered green public services in environmentally vulnerable areas: insights from SEM-ANN analysis. J. Retail. Consum. Serv. 84, 104216. doi: 10.1016/j.jretconser.2024.104216.

[61] Sun T., Di K.S., Shi Q.M., Hu J., Zhang X.H., 2024. Study on the development path of low-carbon retail clusters empowered by digital empowerment. J. Retail. Consum. Serv. 81, 104006. doi: 10.1016/j.jretconser.2024.104006.

[62] Tang H.L., Liu J.M., Wu J.G., 2020. The impact of command-and-control environmental regulation on enterprise total factor productivity: a quasi-natural experiment based on China’s “Two Control Zone ” policy. J. Clean Prod. 254, 120011. doi: 10.1016/j.jclepro.2020.120011.

[63] Tang K., Wang Y.Y., Wang H.J., 2024. The impact of innovation capability on green development in China’s urban agglomerations. Technol. Forecast. Soc. Chang. 200, 123128. doi: 10.1016/j.techfore.2023.123128.

[64] Tozer L., 2023. Water pollution ‘timebomb’ threatens global health. Nature doi: 10.1038/d41586-023-02337-7, 18 July 2023.

[65] Ulucak R., Bilgili F., 2018. A reinvestigation of EKC model by ecological footprint measurement for high, middle and low income countries. J. Clean. Prod. 188, 144-157. doi: 10.1016/j.jclepro.2018.03.191.

[66] UNCTAD, 2024. Digital economy report 2024: shaping an environmentally sustainable and inclusive digital future. United Nations Conference on Trade and Development, New York.

[67] UNDP,2022. Digital strategy 2022- 2025. United Nations Development Programme, New York.

[68] UNEP, 2024a. Annual report 2023:keep the promise. United Nations Environment Programme, Nairobi.

[69] UNEP, 2024b. Navigating new horizons: a global foresight report on planetary health and human wellbeing. United Nations Environment Programme, Nairobi.

[70] UNEP, 2023. Emissions gap report 2023:broken record-temperatures hit new highs, yet world fails to cut emissions (again). United Nations Environment Programme, Nairobi.

[71] Wang J.D., Dong K.Y., Dong X.C., Taghizadeh-Hesary F., 2022a. Assessing the digital economy and its carbon-mitigation effects: the case of China. Energy Econ. 113, 106198. doi: 10.1016/j.eneco.2022.106198.

[72] Wang J.B., Qiao L., Zhu G.F., Di K.S., Zhang X.H., 2025. Research on the driving factors and impact mechanisms of green new quality productive forces in high-tech retail enterprises under China’s Dual Carbon Goals. J. Retail. Consum. Serv. 82, 104092. doi: 10.1016/j.jretconser.2024.104092.

[73] Wang X.X., Xu Z.S., Qin Y., Skare M., 2022b. Innovation, the knowledge economy, and green growth: is knowledge-intensive growth really environmentally friendly? Energy Econ. 115, 106331. doi: 10.1016/j.eneco.2022.106331.

[74] Wei J., Li Z.Q., Lyapustin A., Sun L., Peng Y.R., Xue W.H., Su T.N., Cribb M., 2021. Reconstructing 1-km-resolution high-quality PM_2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sens. Environ. 252, 112136. doi: 10.1016/j.rse.2020.112136.

[75] Wei J., Li Z.Q., Wang J., Li C., Gupta P., Cribb M., 2023. Ground-level gaseous pollutants (NO2, SO 2, and CO) in China: daily seamless mapping and spatiotemporal variations. Atmos. Chem. Phys. 23 (2), 1511-1532. doi: 10.5194/acp-23-1511-2023.

[76] Weitzman M.L., 1997. Sustainability and technical progress. Scand. J. Econ. 99 (1), 1-13. doi: 10.1111/1467-9442.00043.

[77] Wu D.S., Xie Y., Lyu S.J., 2023. Disentangling the complex impacts of urban digital transformation and environmental pollution: evidence from smart city pilots in China. Sust. Cities Soc. 88, 104266. doi: 10.1016/j.scs.2022.104266.

[78] Xiong L.X., Ning J.J., Dong Y.H., 2022. Pollution reduction effect of the digital transformation of heavy metal enterprises under the agglomeration effect. J. Clean. Prod. 330, 129864. doi: 10.1016/j.jclepro.2021.129864.

[79] Xu Y.F., Peng J.C., Tian J.L., Fu S.Y., Hu C., Fu S.K., Feng Y.Z., 2024. The impact and mechanism analysis of Clean Development Mechanism on the synergistic effects of pollution mitigation and carbon reduction. Environ. Res. 260, 119659. doi: 10.1016/j.envres.2024.119659.

[80] Yan X.H., Deng Y.J., Peng L.N., Jiang Z.D., 2023. Study on the impact of digital economy development on carbon emission intensity of urban agglomerations and its mechanism. Environ. Sci. Pollut. Res. 30 (12), 33142-33159. doi: 10.1007/s11356-022-24557-6.

[81] Yang J.-C., Chuang H.-C., Kuan C.-M., 2020. Double machine learning with gradient boosting and its application to the Big N audit quality effect. J. Econom. 216 (1), 268-283. doi: 10.1016/j.jeconom.2020.01.018.

[82] Yang Y.Y., Gu R.D., Ma S.B., Chen W.K., 2023. How does digital technology empower urban green development efficiency in the Beijing-Tianjin-Hebei region— mechanism analysis and spatial effects. Environ. Sci. Pollut. Res. 30 (11), 31471-31488. doi: 10.1007/s11356-022-24368-9.

[83] Yang Z., Gao W.J., Han Q., Qi L.Y., Cui Y.J., Chen Y.Q., 2022. Digitalization and carbon emissions: how does digital city construction affect China’s carbon emission reduction? Sust. Cities Soc. 87, 104201. doi: 10.1016/j.scs.2022.104201.

[84] Yi M., Liu Y.F., Sheng M.S., Wen L., 2022. Effects of digital economy on carbon emission reduction: new evidence from China. Energy Policy 171, 113271. doi: 10.1016/j.enpol.2022.113271.

[85] Yuan H., Peng G., Song C., Wang L., Lu S., 2025. Enhancing digital economy: optimizing export enterprise markup and resource allocation efficiency. J. Knowl. Econ. 16 (1), 4185-4220. doi: 10.1007/s13132-024-02142-1.

[86] Zhai X.Q., An Y.F., 2020. Analyzing influencing factors of green transformation in China’s manufacturing industry under environmental regulation: a structural equation model. J. Clean. Prod. 251, 119760. doi: 10.1016/j.jclepro.2019.119760.

[87] Zhang N., Bi Y.R., Gao W.Y., Deng J., Tang E.Z., 2024b. Heterogeneous effects of environmental regulations on green technology innovation: measurement by using patents and evidence from China’s prefecture-level cities. Scientometrics 129 (12), 7667-7698. doi: 10.1007/s11192-024-05200-2.

[88] Zhang X.W., Fang C.L., Ma H.T., Hu X.Q., 2024a. How does digital economy affect urban-rural integration? An empirical study from China. Habitat Int. 154, 103229. doi: 10.1016/j.habitatint.2024.103229.

[89] Zhao B.Y., Hu H.Q., 2025. Digital economy as a new engine for synergy of multi-pollutant mitigation: evidence from a new perspective of marginal cost. Energy Policy 196, 114417. doi: 10.1016/j.enpol.2024.114417.

[90] Zhao J., Wang Y.Y., Lei Y.L., Huang H.Y., 2024. How does digital economy empower pollution mitigation and carbon reduction? Evidence from Chinese cities. Urban Clim. 55, 101946. doi: 10.1016/j.uclim.2024.101946.

[91] Zhao Y.M., Wang W.P., 2025. The multiple empowerment effects of digital transformation on carbon emissions in manufacturing industry from the prospective of factor allocation: theoretical analysis and empirical evidence. Environ. Impact Assess. Rev. 110, 107698. doi: 10.1016/j.eiar.2024.107698.

[92] Zhong W.L., Liu Y., Dong K.Y., Ni G.H., 2024. Assessing the synergistic effects of artificial intelligence on pollutant and carbon emission mitigation in China. Energy Econ. 138, 107829. doi: 10.1016/j.eneco.2024.107829.

[93] Zhou M.L., Jiang K.Q., Zhang J., 2023. Environmental benefits of enterprise digitalization in China. Resour. Conserv. Recycl. 197, 107082. doi: 10.1016/j.resconrec.2023.107082.

[94] Zhou X.Y., Zhou D.Q., Wang Q.W., Su B., 2019. How information and communication technology drives carbon emissions: a sector-level analysis for China. Energy Econ. 81, 380-392. doi: 10.1016/j.eneco.2019.04.014.

[95] Zhu Q., Ma D., He X., 2023. Digital transformation and firms’ pollution emissions. Technol. Forecast. Soc. Chang. 197, 122910. doi: 10.1016/j.techfore.2023.122910.

[96] Zhu Y.H., Xu Y.Q., Yin S.G., 2024. How does digital technology innovation drive synergies for reducing pollution and carbon emissions? Sust. Cities Soc. 116, 105932. doi: 10.1016/j.scs.2024.105932.