This study explores the impact of artificial intelligence (AI) on achieving Sustainable Development Goal 7 (SDG 7), with a focus on AI's role in reducing energy vulnerability through a detailed spatial threshold analysis of government effectiveness. Specifically, the study investigates both the direct effect of AI on energy vulnerability and its spatial threshold effect via government effectiveness, utilizing panel data from 67 countries. The results show that AI significantly lowers global energy vulnerability and indirectly alleviates it by improving government effectiveness. The panel threshold model analysis uncovers that AI's mitigating effects are particularly pronounced in countries with lower levels of government effectiveness. Additionally, AI demonstrates significant spatial spillover effects, helping reduce energy vulnerability in both domestic and neighboring countries through technological diffusion and cooperation. The spatial threshold model further suggests that as government effectiveness improves, AI's impact on energy vulnerability in both domestic and neighboring countries diminishes. These findings are critical for advancing our understanding of AI's role in global energy policy, emphasizing the importance of accounting for the complexities and spatial effects of AI when designing effective energy security policies.
GershonOAsafoJKNyarko-AsomaniA, et al.Investigating the nexus of energy consumption, economic growth and carbon emissions in selected African countries. Energy Strategy Reviews2024; 51: 101269.
2.
JantaPLeeraphunNThapmaneeK, et al.Energy resilience assessment: incorporating consideration of recoverability and adaptability in risk assessment of energy infrastructure. Energy Sustain Dev2024; 81: 101506.
3.
GattoABusatoF. Energy vulnerability around the world: the global energy vulnerability index (GEVI). J Cleaner Prod2020; 253: 118691.
4.
UsmanOIoremberPTOzkanO, et al.Dampening energy security-related uncertainties in the United States: the role of green energy-technology investment and operation of transnational corporations. Energy2024; 289: 130006.
5.
IFR. World robotics 2024 report. 2024.
6.
HAI. Artificial intelligence Index report 2025. 2025.
7.
WangBWangJDongK, et al.How does artificial intelligence affect high-quality energy development? Achieving a clean energy transition society. Energy Policy2024; 186: 114010.
8.
AngelovPPSoaresEAJiangR, et al.Explainable artificial intelligence: an analytical review. WIRES Data Mining and Knowledge Discovery2021; 11: e1424.
9.
AhmadTZhangDHuangC, et al.Artificial intelligence in sustainable energy industry: status Quo, challenges and opportunities. J Cleaner Prod2021; 289: 125834.
10.
DivyaSPandaSHajraS, et al.Smart data processing for energy harvesting systems using artificial intelligence. Nano Energy2023; 106: 108084.
11.
KatareDPerinoDNurmiJ, et al.A survey on approximate edge AI for energy efficient autonomous driving services. IEEE Commun Surv Tutorials2023; 25: 2714–2754.
12.
DuHZhangRNiyatoD, et al.Exploring collaborative distributed diffusion-based AI-generated content (AIGC) in wireless networks. IEEE Network2024; 38: 178–186.
13.
HorowitzMCAllenGCSaravalleE, et al.Artificial intelligence and international security. Washington: Center for a New American Security, 2022.
14.
FeijóoCKwonYBauerJM, et al.Harnessing artificial intelligence (AI) to increase wellbeing for all: the case for a new technology diplomacy. Telecomm Policy2020; 44: 101988.
15.
YamashitaIMurakamiACairnsS, et al.Measuring the AI content of government-funded R&D projects: A proof of concept for the OECD Fundstat initiative. OECD Science, Technology and Industry Working Papers 2021; 09. DOI: https://doi.org/10.1787/7b43b038-en.
16.
YangCHuangC. Quantitative mapping of the evolution of AI policy distribution, targets and focuses over three decades in China. Technol Forecast Soc Change2022; 174: 121188.
17.
ChenPDagestaniAA. Urban planning policy and clean energy development Harmony- evidence from smart city pilot policy in China. Renewable Energy2023; 210: 251–257.
18.
ChenCHuYKaruppiahM, et al.Artificial intelligence on economic evaluation of energy efficiency and renewable energy technologies. Sustainable Energy Technologies and Assessments2021; 47: 101358.
19.
GnansounouE. Assessing the energy vulnerability: case of industrialised countries. Energy Policy2008; 36: 3734–3744.
20.
SimcockNJenkinsKEHLacey-BarnacleM, et al.Identifying double energy vulnerability: a systematic and narrative review of groups at-risk of energy and transport poverty in the global north. Energy Research & Social Science2021; 82: 102351.
21.
Calama-GonzálezCMEscandónRSuárezR, et al.Household energy vulnerability evaluation in southern Spain through parametric energy simulation models and socio-economic data. Sustainable Cities and Society2024; 103: 105276.
22.
VafadarnikjooATavanaMChalvatzisK, et al.A socio-economic and environmental vulnerability assessment model with causal relationships in electric power supply chains. Socioecon Plann Sci2022; 80: 101156.
23.
LiuYDongKJiangQ. Assessing energy vulnerability and its impact on carbon emissions: a global case. Energy Economics2023; 119: 106557.
24.
GouveiaJPPalmaPSimoesSG. Energy poverty vulnerability index: a multidimensional tool to identify hotspots for local action. Energy Reports2019; 5: 187–201.
25.
MattioliGPhilipsIAnableJ, et al.Vulnerability to motor fuel price increases: socio-spatial patterns in England. J Transp Geogr2019; 78: 98–114.
26.
PanYDongF. Design of energy use rights trading policy from the perspective of energy vulnerability. Energy Policy2022; 160: 112668.
27.
GunnarsdottirIDavidsdottirBWorrellE, et al.Review of indicators for sustainable energy development. Renewable Sustainable Energy Rev2020; 133: 110294.
28.
MalekpoorHChalvatzisKMishraN, et al.Integrated grey relational analysis and multi objective grey linear programming for sustainable electricity generation planning. Ann Oper Res2018; 269: 475–503.
29.
DoganEMadalenoMTaskinD. Which households are more energy vulnerable? Energy poverty and financial inclusion in Turkey. Energy Economics2021; 99: 105306.
30.
HankeFLowitzschJ. Empowering vulnerable consumers to join renewable energy communities—towards an inclusive design of the clean energy package. Energies13: 1615. DOI: https://doi.org/10.3390/en13071615.
31.
AbdallaANNazirMSTaoH, et al.Integration of energy storage system and renewable energy sources based on artificial intelligence: an overview. Journal of Energy Storage2021; 40: 102811.
32.
SerbanACLytrasMD. Artificial intelligence for smart renewable energy sector in Europe—smart energy infrastructures for next generation smart cities. IEEE Access2020; 8: 77364–77377.
33.
LyuWLiuJ. Artificial intelligence and emerging digital technologies in the energy sector. Appl Energy2021; 303: 117615.
34.
RochaHROHonoratoIHFiorottiR, et al.An artificial intelligence based scheduling algorithm for demand-side energy management in smart homes. Appl Energy2021; 282: 116145.
35.
HannanMAAl-ShetwiAQKerPJ, et al.Impact of renewable energy utilization and artificial intelligence in achieving sustainable development goals. Energy Reports2021; 7: 5359–5373.
36.
HimeurYGhanemKAlsalemiA, et al.Artificial intelligence based anomaly detection of energy consumption in buildings: a review, current trends and new perspectives. Appl Energy2021; 287: 116601.
37.
StrubellEGaneshAMcCallumA. Energy and policy considerations for deep learning in NLP. arXiv preprint arXiv:190602243 2019. DOI: https://doi.org/10.48550/arXiv.1906.02243.
38.
AlonsoCBergAKothariS, et al.Will the AI revolution cause a great divergence?J Monet Econ2022; 127: 18–37.
39.
AhnMJChenY-C. Digital transformation toward AI-augmented public administration: the perception of government employees and the willingness to use AI in government. Gov Inf Q2022; 39: 101664.
40.
KuziemskiMMisuracaG. AI Governance in the public sector: three tales from the frontiers of automated decision-making in democratic settings. Telecomm Policy2020; 44: 101976.
41.
Valle-CruzDCriadoJISandoval-AlmazánR, et al.Assessing the public policy-cycle framework in the age of artificial intelligence: from agenda-setting to policy evaluation. Gov Inf Q2020; 37: 101509.
42.
FatimaSDesouzaKCDenfordJS, et al.What explains governments interest in artificial intelligence? A signaling theory approach. Econ Anal Policy2021; 71: 238–254.
43.
ChlebnaCMattesJ. The fragility of regional energy transitions. Environmental Innovation and Societal Transitions2020; 37: 66–78.
44.
WangYHeZZouJ, et al.Energy transition for sustainable economy: what is the role of government governance and public concern?Finance Research Letters2024; 69: 106087.
45.
WangHDengWZhangZ, et al.Does government's environmental attention improve urban energy efficiency?Int Rev Financ Anal2024; 91: 103046.
46.
UlnicaneIKnightWLeachT, et al.Framing governance for a contested emerging technology:insights from AI policy. Policy Soc2020; 40: 158–177.
47.
LvZLouRSinghAK. AI Empowered communication systems for intelligent transportation systems. IEEE Trans Intell Transp Syst2021; 22: 4579–4587.
48.
SolukJ. Organisations’ resources and external shocks: exploring digital innovation in family firms. Indus Innovat2022; 29: 792–824.
49.
LeiYLiangZRuanP. Evaluation on the impact of digital transformation on the economic resilience of the energy industry in the context of artificial intelligence. Energy Rep2023; 9: 785–792.
50.
AnđelkovićASBajatovićD. Integration of weather forecast and artificial intelligence for a short-term city-scale natural gas consumption prediction. J Cleaner Prod2020; 266: 122096.
51.
TaoWWengSChenX, et al.Artificial intelligence-driven transformations in low-carbon energy structure: evidence from China. Energy Econom2024; 136: 107719.
52.
Halhoul MerabetGEssaaidiMBen HaddouM, et al.Intelligent building control systems for thermal comfort and energy-efficiency: a systematic review of artificial intelligence-assisted techniques. Renewable Sustainable Energy Rev2021; 144: 110969.
53.
YussufROAsfourOS. Applications of artificial intelligence for energy efficiency throughout the building lifecycle: an overview. Energy Build2024; 305: 113903.
54.
Haibe-KainsBAdamGAHosnyA, et al.Transparency and reproducibility in artificial intelligence. Nature2020; 586: E14–E16.
55.
von EschenbachWJ. Transparency and the black box problem: why we do not trust AI. Philos Technol2021; 34: 1607–1622.
56.
TanLGuoJMohanarajahS, et al.Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices. Nat Hazards2021; 107: 2389–2417.
57.
SabaCSNgepahN. The impact of artificial intelligence (AI) on employment and economic growth in BRICS: does the moderating role of governance matter?Research in Globalization2024; 8: 100213.
58.
BakhshSZhangWAliK, et al.Strategy towards sustainable energy transition: the effect of environmental governance, economic complexity and geopolitics. Energy Strategy Reviews2024; 52: 101330.
59.
ZhaoLZhangLSunJ, et al.Can public participation constraints promote green technological innovation of Chinese enterprises? The moderating role of government environmental regulatory enforcement. Technol Forecast Soc Change2022; 174: 121198.
60.
WangQZhangFLiR, et al.Does artificial intelligence promote energy transition and curb carbon emissions? The role of trade openness. J Cleaner Prod2024; 447: 141298.
61.
LiJMaSQuY, et al.The impact of artificial intelligence on firms’ energy and resource efficiency: empirical evidence from China. Resour Policy2023; 82: 103507.
62.
LeeC-CQinSLiY. Does industrial robot application promote green technology innovation in the manufacturing industry?Technol Forecast Soc Change2022; 183: 121893.
63.
DauvergneP. Is artificial intelligence greening global supply chains? Exposing the political economy of environmental costs. Rev Int Political Econ2020; 29: 696–718.
64.
HansenMT. The search-transfer problem: the role of weak ties in sharing knowledge across organization subunits. Adm Sci Q1999; 44: 82–111.
65.
SongYWangZSongC, et al.Impact of artificial intelligence on renewable energy supply chain vulnerability: evidence from 61 countries. Energy Econ2024; 131: 107357.
66.
WangHCuiHZhaoQ. Effect of green technology innovation on green total factor productivity in China: evidence from spatial durbin model analysis. J Cleaner Prod2021; 288: 125624.
67.
ElhorstJP. Spatial econometrics from cross-sectional data to spatial panels. Heidelberg: Springer, 2014.
68.
LiJLiS. Energy investment, economic growth and carbon emissions in China—empirical analysis based on spatial Durbin model. Energy Policy2020; 140: 111425.
69.
LeSageJPPaceRK. Spatial econometric models. In: FischerMMGetisA (eds) Handbook of applied spatial analysis: software tools, methods and applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp.355–376.
70.
FengYLiuYYuanH. The spatial threshold effect and its regional boundary of new-type urbanization on energy efficiency. Energy Policy2022; 164: 112866.
71.
AthariSA. The impact of financial development and technological innovations on renewable energy consumption: do the roles of economic openness and financial stability matter in BRICS economies?Geol J2024; 59: 288–300.
72.
LiuJChangHForrestJY-L, et al.Influence of artificial intelligence on technological innovation: evidence from the panel data of China's manufacturing sectors. Technol Forecast Soc Change2020; 158: 120142.
73.
ShangYZhouSZhuangD, et al.The impact of artificial intelligence application on enterprise environmental performance: evidence from microenterprises. Gondwana Res2024; 131: 181–195.
74.
DingTLiJShiX, et al.Is artificial intelligence associated with carbon emissions reduction? Case of China. Resources Policy2023; 85: 103892.
75.
DubeyRBrydeDJDwivediYK, et al.Dynamic digital capabilities and supply chain resilience: the role of government effectiveness. Int J Prod Econ2023; 258: 108790.
76.
MizrahiSVigoda-GadotECohenN. How well do they manage a crisis? The government's effectiveness during the COVID-19 pandemic. Public Adm Rev2021; 81: 1120–1130. DOI: https://doi.org/10.1111/puar.13370.
77.
AthariSA. Global economic policy uncertainty and renewable energy demand: does environmental policy stringency matter? Evidence from OECD economies. J Cleaner Prod2024; 450: 141865.
78.
AppiahMLiMNaeemMA, et al.Greening the globe: uncovering the impact of environmental policy, renewable energy, and innovation on ecological footprint. Technol Forecast Soc Change2023; 192: 122561.
79.
WangQLiYLiR. Ecological footprints, carbon emissions, and energy transitions: the impact of artificial intelligence (AI). Humanities and Social Sciences Communications2024; 11: 1043.
80.
Balsalobre-LorenteDTopalogluEENurT, et al.Exploring the linkage between financial development and ecological footprint in APEC countries: a novel view under corruption perception and environmental policy stringency. J Cleaner Prod2023; 414: 137686.
81.
WangQSunTLiR. Does artificial intelligence (AI) enhance green economy efficiency? The role of green finance, trade openness, and R&D investment. Humanities Social Sciences Communications2025; 12: 1–22.
82.
LiuYSadiqFAliW, et al.Does tourism development, energy consumption, trade openness and economic growth matters for ecological footprint: testing the environmental kuznets curve and pollution haven hypothesis for Pakistan. Energy2022; 245: 123208.
83.
ZeFYuWAliA, et al.Influence of natural resources, ICT, and financial globalization on economic growth: evidence from G10 countries. Resources Policy2023; 81: 103254.
84.
Ongo NkoaBESongJSMinkoue BikoulaB. Natural resource rents in developing countries: is the positive influence on the fragilities real?Resources Policy2024; 89: 104541.
85.
SongMPanHShenZ, et al.Assessing the influence of artificial intelligence on the energy efficiency for sustainable ecological products value. Energy Econ2024; 131: 107392.
86.
DohaleVAkarteMGunasekaranA, et al.Exploring the role of artificial intelligence in building production resilience: learnings from the COVID-19 pandemic. Int J Prod Res2022; 62: 5472–5488.
87.
WangQSunTLiR. Does artificial intelligence promote green innovation? An assessment based on direct, indirect, spillover, and heterogeneity effects. Energy Environ2023. DOI: https://doi.org/10.1177/0958305X231220520.
88.
WangQZhangFLiR. Artificial intelligence and sustainable development during urbanization: perspectives on AI R&D innovation, AI infrastructure, and AI market advantage. Sustain Dev2025; 33: 1136–1156.
89.
AthariSA. Does the sovereign environmental, social, and governance sustainability activities jeopardize the banking sector's stability: evidence from the Arab economies. Sustain Futures2024; 7: 100204.
90.
DragoCGattoA. Policy, regulation effectiveness, and sustainability in the energy sector: a worldwide interval-based composite indicator. Energy Policy2022; 167: 112889.
91.
RaduR. Steering the governance of artificial intelligence: national strategies in perspective. Policy and Society2021; 40: 178–193.
92.
AthariSASalibaCAbboudE, et al.Examining the quadratic impact of sovereign environmental, social, and governance practices on firms’ profitability: new insights from the financial industry in gulf cooperation council countries. Sustainability2024; 16: 2783.
93.
WangQLiYLiR. Integrating artificial intelligence in energy transition: a comprehensive review. Energy Strategy Reviews2025; 57: 101600.
94.
KempA. Competitive advantage through artificial intelligence: toward a theory of situated AI. Acad Manage Rev2024; 49: 618–635.
