Unveiling the mechanisms and implications: how artificial intelligence drives green growth in China’s Huaihe River Ecological Economic Belt under the carbon neutrality agenda

Xin Zhao , Ziqi Wang , Laszlo Vasa , Hasan Dincer

Carbon Footprints ›› 2025, Vol. 4 ›› Issue (3) : 16

PDF
Carbon Footprints ›› 2025, Vol. 4 ›› Issue (3) :16 DOI: 10.20517/cf.2025.9
Original Article

Unveiling the mechanisms and implications: how artificial intelligence drives green growth in China’s Huaihe River Ecological Economic Belt under the carbon neutrality agenda

Author information +
History +
PDF

Abstract

Amidst the backdrop of global climate warming and China’s proactive chase of its carbon peak and carbon neutrality goals, the Huaihe River Basin (HRB), a region of significant strategic importance in the heartland and eastern expanse of the nation is confronted with formidable challenges, including high energy consumption and severe environmental pollution. Despite its substantial contributions to economic development, the traditional development model of the HRB conflicts with the principles of green development, necessitating the urgent exploration of innovative pathways to sustainable progress. Through a comprehensive review of scholarly literature and rigorous theoretical analysis, this study demonstrates that artificial intelligence (AI) can significantly drive green development by enhancing eco-innovation and optimizing industrial structures. Using a panel dataset from 27 cities in the Huaihe River Ecological Economic Belt (HEB) from 2010 to 2022, this study employs a bidirectional fixed-effects model to analyze the repercussions of AI on green development. The baseline regression results show that for every one-unit increase in AI development level (AIDL), HEB’s urban green development level significantly increases by 0.087. This positive influence is further confirmed through robustness tests. We found that AI can indirectly influence the mechanism and pathway of green development through intermediate variables. AI drives green development indirectly through two pathways: green technology innovation and the rationalization of the industrial structure, with a total explanatory power of 56.7% (R2 = 0.812). Based on these findings, we propose vigorously promoting the green effects of AI, refining industrial structures, and leveraging mediating effects to foster sustainable regional development. These insights offer novel perspectives for the green development of the HRB but also provide valuable references for the green transformation of other areas with similar challenges.

Keywords

Artificial intelligence / Huaihe River Ecological Economic Belt / green development / carbon neutrality

Cite this article

Download citation ▾
Xin Zhao, Ziqi Wang, Laszlo Vasa, Hasan Dincer. Unveiling the mechanisms and implications: how artificial intelligence drives green growth in China’s Huaihe River Ecological Economic Belt under the carbon neutrality agenda. Carbon Footprints, 2025, 4(3): 16 DOI:10.20517/cf.2025.9

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Huang J.Green AI-a multidisciplinary approach to sustainability.Environ Sci Ecotechnol2025;24:100536

[2]

Huang L,Papa A.Artificial intelligence augmenting human intelligence for manufacturing firms to create green value: Towards a technology adoption perspective.Technol Forecast Soc Change2025;213:124013

[3]

Zhong K.Artificial intelligence adoption and corporate green innovation capability.Financ Res Lett2025;72:106480

[4]

Li B,Xia H.Under the background of AI application, research on the impact of science and technology innovation and industrial structure upgrading on the sustainable and high-quality development of regional economies.Sustainability2022;14:11331

[5]

Jones N.How to stop data centres from gobbling up the world’s electricity.Nature2018;561:163-6

[6]

Lange S,Santarius T.Digitalization and energy consumption. Does ICT reduce energy demand?.Ecol Econ2020;176:106760

[7]

Liu Z,He G.Challenges and opportunities for carbon neutrality in China.Nat Rev Earth Environ2021;3:141-55.

[8]

Cheng G,Zhao W.Integrated study of the water-ecosystem-economy in the Heihe River basin.Nat Sci Revi2014;1:413-28

[9]

Yang D,Li J.Exploring the supply and demand imbalance of carbon and carbon-related ecosystem services for dualcarbon goal ecological management in the Huaihe River Ecological Economic Belt.Sci Total Environ2024;912:169169

[10]

Xu J,Mo Y,Li L.Assessing anthropogenic impacts on chemical and biochemical oxygen demand in different spatial scales with Bayesian networks.Water2020;12:246

[11]

Zhang C,Mao G,Hsu W.An empirical study on the ecological economy of the Huai River in China.Water2020;12:2162

[12]

Cheng Y.Spatial and temporal differentiation trends and attributions of high-quality development in the Huaihe Eco-Economic Belt.J Resour Ecol2023;14:517-32

[13]

Wang C,Zhang B.Environmental regulation, emissions and productivity: evidence from Chinese COD-emitting manufacturers.J Environ Econ Manage2018;92:54-73

[14]

Jalil A.The impact of growth, energy and financial development on the environment in China: a cointegration analysis.Energy Econ2011;33:284-91

[15]

Libman A.Natural resources and sub-national economic performance: DOEs sub-national democracy matter?.Energy Econ2013;37:82-99

[16]

Ma X,Song M.Off-office audit of natural resource assets and water pollution: a quasi-natural experiment in China.J Enterp Inf Manag2025;38:292-317

[17]

Liu P,Han N.Efficiency and equity: effect of urban agglomerations’ spatial structure on green development efficiency in China.Sustain Cities Soc2024;108:105504

[18]

Zhao X,Abedin M.Z, Shang Y, Alofaysan H.Public Money Manage2025;1-12.

[19]

Makridakis S.The forthcoming artificial intelligence (AI) revolution: its impact on society and firms.Futures2017;90:46-60

[20]

Sarkar M.How does an industry reduce waste and consumed energy within a multi-stage smart sustainable biofuel production system?.J Clean Prod2020;262:121200

[21]

Wheeldon A,Rahman T,Yakovlev A.Learning automata based energy-efficient AI hardware design for IoT applications.Phil Trans R Soc A2020;378:20190593 PMCID:PMC7536019

[22]

Liu X,Yang X.The green innovation effect of industrial robot applications: evidence from Chinese manufacturing companies.Technol Forecast Social Change2025;210:123904

[23]

Bonfiglioli A,Gancia G.Artificial intelligence and jobs: evidence from US commuting zones.Economic Policy2025;40:145-94

[24]

Tariq G,Ali S.Environmental footprint impacts of green energies, green energy finance and green governance in G7 countries.Carbon Footprints2024;3:5

[25]

Li X,Tang Y.The impact of artificial intelligence development on urban energy efficiency-based on the perspective of smart city policy.Sustainability2024;16:3200

[26]

Yin K,Huang C.How does artificial intelligence development affect green technology innovation in China?.Environ Sci Pollut Res Int2023;30:28066-90

[27]

Zhou W,Chen Y.How does artificial intelligence affect pollutant emissions by improving energy efficiency and developing green technology.Energy Economics2024;131:107355

[28]

Li Y,Pan A,Veglianti E.Carbon emission reduction effects of industrial robot applications: heterogeneity characteristics and influencing mechanisms.Technol Soc2022;70:102034

[29]

Lin J,Wu S.How does artificial intelligence affect the environmental performance of organizations?.Inf Manag2024;61:103924

[30]

Wang Q,Li R.Does artificial intelligence promote green innovation?.Energy Environ2025;36:1005-37

[31]

Chen M,Wang X.How does artificial intelligence impact green development?.Sustainability2024;16:1260

[32]

Yigitcanlar T,Corchado JM.Green artificial intelligence: towards an efficient, sustainable and equitable technology for smart cities and futures.Sustainability2021;13:8952

[33]

Lin S,Jing C.The influence of AI on the economic growth of different regions in China.Sci Rep2024;14:9169

[34]

Li T,Liang K,Ma L.Synergy of patent and open-source-driven sustainable climate governance under green AI: a case study of TinyML.Sustainability2023;15:13779

[35]

Zhao X,Jabeen F,Jia W.Does green innovation induce green total factor productivity?.Technol Forecast Social Change2022;185:122021

[36]

Qian Y,Shi L,Yang Z.Can artificial intelligence improve green economic growth?.Environ Sci Pollut Res Int2023;30:16418-37

[37]

Luan F,Chen Y.Industrial robots and air environment: a moderated mediation model of population density and energy consumption.Sustain Prod Consum2022;30:870-88

[38]

Vinuesa R,Leite I.The role of artificial intelligence in achieving the sustainable development goals.Nat Commun2020;11:233 PMCID:PMC6957485

[39]

Budde M,Vogl J,Abecker A.NiMo 4.0 - enabling advanced data analytics with AI for environmental governance in the water domain.At - Autom2024;72:564-78

[40]

Bera M,Garai S.Advancing energy efficiency: innovative technologies and strategic measures for achieving net zero emissions.Carbon Footprints2025;4:3

[41]

Tang R.Digital economy drives tourism development-empirical evidence based on the UK.Econ Res Ekon Istraž2023;36:2003-20

[42]

Wang X,Chen N.How artificial intelligence affects the labour force employment structure from the perspective of industrial structure optimisation.Heliyon2024;10:e26686 PMCID:PMC10907740

[43]

Feng Q,Zhou G.Fixed cost allocation considering the input-output scale based on DEA approach.Comput Ind Eng2021;159:107476

[44]

Babina T,He A.Artificial intelligence, firm growth, and product innovation.J Financ Econ2024;151:103745

[45]

Du K,Yan Z.Do green technology innovations contribute to carbon dioxide emission reduction?.Technol Forecast Soc Change2019;146:297-303

[46]

Chen H,Chen A,Yang J.Green technology innovation and CO2 emission in China: evidence from a spatial-temporal analysis and a nonlinear spatial Durbin model.Energy Policy2023;172:113338

[47]

Yu W,Zhang Y.Global estimates of daily ambient fine particulate matter concentrations and unequal spatiotemporal distribution of population exposure: a machine learning modeling study.Lancet Planet Health2023;7:e209-18

[48]

Chen G,Knibbs LD.A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information.Sci Total Environ2018;636:52-60

[49]

Lu H.Hybrid decision tree-based machine learning models for short-term water quality prediction.Chemosphere2020;249:126169

AI Summary AI Mindmap
PDF

139

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/