Unlocking marginal SDG dynamics: A KRLS machine learning analysis of AI technologies and solar energy

Abstract

Studies quantifying AI’s impact on Sustainable Development Goals (SDGs) often rely on proxies that inaccurately reflect AI progress. Moreover, focusing solely on environmental and growth indicators provides an incomplete picture of AI’s overall contribution to the SDGs, as the SDG framework encompasses a broader set of interconnected goals. Therefore, this study unveils the marginal impacts of AI and solar energy (SEN) directly on the SDG Index (SDGI) by using the Kernel-Based Regularized Least Squares (KRLS) machine learning approach for the 10 largest economies from 2000 2022. While the study found an overall positive average marginal impact of AI on the SDG Index, indicating significant progress driven by AI technologies, the analysis across different quantiles revealed variability. Specifically, at the 25th quantile, AI appears to hinder SDG progress. This could be due to negative externalities from AI adoption, like its use in accelerating non-renewable energy production and resource-intensive consumption, or from countries’ insufficient technological application capabilities. However, at higher quantiles (likely representing countries with better SDG achievement and greater AI maturity), the marginal effects of AI become increasingly positive, suggesting its beneficial use in areas that support SDGs. Marginal effects of SEN on SDGI are found to be positive, showing a positive connection between SDGs’ achievements and solar energy adoption. The marginal effects of economic globalization (EGB) and institutional productive capacity (INP) on SDGI are found to be positive. Finally, policies to boost AI and solar energy adoption, as well as exploring potential applications of AI across various sectors for sustainable development, are discussed.

Keywords

Artificial intelligence / Kernel-based regularized least squares / machine learning approach / Institutional productivity / Sustainable development / Solar power

CRediT authorship contribution statement

Zahoor Ahmed: Writing - original draft, Visualization, Methodology, Formal analysis, Conceptualization. Stefania Pinzon: Writing - review & editing, Writing - original draft, Methodology. Muhammad Qamar Rasheed: Writing - review & editing, Writing - original draft, Visualization, Data curation, Formal analysis.

Declaration of competing interest

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.

References

[1] Abdalla, A.N., Nazir, M.S., Tao, H., Cao, S., Ji, R., Jiang, M., Yao, L., 2021. Integration of energy storage system and renewable energy sources based on artificial intelligence: An overview. J. Energy Storage 40, 102811. https://doi.org/10.1016/j.est.2021.102811.

[2] Ahmad, M., Peng, T., Awan, A., Ahmed, Z., 2023. Policy framework considering resource curse, renewable energy transition, and institutional issues: Fostering sustainable development and sustainable natural resource consumption practices. Resour. Policy 86, 104173. https://doi.org/10.1016/j.resourpol.2023.104173.

[3] Ahmed, Z., 2024. Assessing the interplay between political globalization, social globalization, democracy, militarization, and sustainable development: evidence from G 7 economies. Environ. Sci. Pollut. Res. 31, 11261-11275. https://doi.org/10.1007/s11356-024-31854-9.

[4] Ahmed, Z., Ahmad, M., Caglar, A.E., Pinzon, S., 2025. Achieving carbon neutrality and SDGs: Assessing roles of solar energy, government stability, and population aging in greenhouse gas emissions. Int. J. Sustain. Dev. World Ecol. 32, 127-141. https://doi.org/10.1080/13504509.2024.2414377.

[5] Ahmed, Z., Cary, M., Le, H.P., 2021. Accounting asymmetries in the long-run nexus between globalization and environmental sustainability in the United States: an aggregated and disaggregated investigation. Environ. Impact Assess. Rev. 86, 106511. https://doi.org/10.1016/j.eiar.2020.106511.

[6] Balsalobre-Lorente, D., Abbas, J., He, C., Pilarˇ L., Shah, S.A.R., 2023. Tourism, urbanization and natural resources rents matter for environmental sustainability: the leading role of AI and ICT on sustainable development goals in the digital era. Resour. Policy 82, 103445. https://doi.org/10.1016/j.resourpol.2023.103445.

[7] Bekun, F.V., Alola, A.A., Sarkodie, S.A., 2019. Toward a sustainable environment: Nexus between CO₂ emissions, resource rent, renewable and nonrenewable energy in 16-EU countries. Sci. Total Environ. 657, 1023-1029. https://doi.org/10.1016/j.scitotenv.2018.12.104.

[8] Cai, D., Aziz, G., Sarwar, S., Alsaggaf, M.I., Sinha, A., 2024. Applicability of denoising-based artificial intelligence to forecast the environmental externalities. Geosci. Front. 15, 101740. https://doi.org/10.1016/j.gsf.2023.101740.

[9] Destek, M.A., Aslan, A., 2020. Disaggregated renewable energy consumption and environmental pollution nexus in G-7 countries. Renew. Energy 151, 1298-1306. https://doi.org/10.1016/j.renene.2019.11.138.

[10] Dikshit, A., Pradhan, B., Matin, S.S., Beydoun, G., Santosh, M., Park, H.J., Maulud, K.N.A., 2024. Artificial Intelligence: a new era for spatial modelling and interpreting climate-induced hazard assessment. Geosci. Front. 15, 101815. https://doi.org/10.1016/j.gsf.2024.101815.

[11] Dinda, S., 2004. Environmental Kuznets Curve hypothesis: a survey. Ecol. Econ. 49, 431-455. https://doi.org/10.1016/j.ecolecon.2004.02.011.

[12] Dumitrescu, E.I., Hurlin, C., 2012. Testing for Granger non-causality in heterogeneous panels. Econ. Model. 29, 1450-1460. https://doi.org/10.1016/j.econmod.2012.02.014.

[13] Ferwerda, J., Hainmueller, J., Hazlett, C.J., 2017. Kernel-based regularized least squares in R (KRLS) and Stata (KRLS). J. Stat. Softw. 79 (3), 1-26. https://doi.org/10.18637/jss.v079.i03.

[14] Güney, T., 2022. Solar energy, governance and CO₂ emissions. Renew. Energy 184, 791-798. https://doi.org/10.1016/j.renene.2021.11.124.

[15] Güney, T., Ince, D., 2023. Solar energy and CO₂ emissions: CCEMG estimations for 26 countries. J. Knowl. Econ. 15, 2383-2400. https://doi.org/10.1007/s13132-023-01337-2.

[16] Ha, L.T., 2024. A transition to sustainable marine living resources: why does digital transformation matter? Technol. Forecast. Soc. Change 203, 123336. https://doi.org/10.1016/j.techfore.2024.123336.

[17] Hainmueller, J., Hazlett, C., 2014. Kernel regularized least squares: reducing misspecification bias with a flexible and interpretable machine learning approach. Polit. Anal. 22, 143-168. https://doi.org/10.1093/pan/mpt019.

[18] Khaliq, A., Mamkhezri, J., 2024. The impact of renewable portfolio standards and land-use governance on CO₂ and gross total emissions in four U.S. regions. Appl. Energy 372, 123832. https://doi.org/10.1016/j.apenergy.2024.123832.

[19] Lyu, W., Liu, J., 2021. Artificial intelligence and emerging digital technologies in the energy sector. Appl. Energy 303, 117615. https://doi.org/10.1016/j.apenergy.2021.117615.

[20] Investopedia, 2024. The Top 25 Economies in the World ( accessed 10 December 2024).

[21] OECD, 2024. Patents by Technology, Technologies Related to AI (accessed 10 March 2025).

[22] Pesaran, H., Yamagata, T., 2008. Testing slope homogeneity in large panels. J. Econom. 142, 50-93. https://doi.org/10.1016/j.jeconom.2007.05.010.

[23] Pesaran, M.H., 2021. General diagnostic tests for cross-sectional dependence in panels. Empir. Econ. 60, 13-50. https://doi.org/10.1007/s00181-020-01875-7.

[24] Pesaran, M.H., 2007. A simple panel unit root test in the presence of cross section dependence. J. Appl. Econom. 47, 265-312. https://doi.org/10.1002/jae.

[25] Rasheed, M.Q., Yuhuan, Z., Ahmed, Z., Haseeb, A., Saud, S., 2024a. Information communication technology, economic growth, natural resources, and renewable energy production: evaluating the asymmetric and symmetric impacts of artificial intelligence in robotics and innovative economies. J. Clean. Prod. 447, 141466. https://doi.org/10.1016/j.jclepro.2024.141466.

[26] Rasheed, M.Q., Yuhuan, Z., Haseeb, A., Ahmed, Z., Saud, S., 2024b. Asymmetric relationship between competitive industrial performance, renewable energy, industrialization, and carbon footprint: does artificial intelligence matter for environmental sustainability? Appl. Energy 367, 123346. https://doi.org/10.1016/j.apenergy.2024.123346.

[27] Rasheed, M.Q., Yuhuan, Z., Nazir, M., Ahmed, Z., Yu, X., 2025. How do semiconductors, artificial intelligence, geopolitical risk, and their moderating effects shape renewable energy production in leading semiconductor manufacturing countries? Technol. Soc. 80, 102761. https://doi.org/10.1016/j.techsoc.2024.102761.

[28] Ren, F.R., Tian, Z., Liu, J., Shen, Y.T., 2020. Analysis of CO₂ emission reduction contribution and efficiency of China’s solar photovoltaic industry: based on Input-output perspective. Energy 199, 117493. https://doi.org/10.1016/j.energy.2020.117493.

[29] Sachs, J.D., Lafortune, G., Fuller, G., 2024. The SDGs and the UN Summit of the Future. Sustainable Development Report 2024. Dublin University Press, Dublin, pp. 1-83.

[30] Sarkodie, S.A., Adams, S., 2018. Renewable energy, nuclear energy, and environmental pollution: Accounting for political institutional quality in South Africa. Sci. Total Environ. 643, 1590-1601. https://doi.org/10.1016/j.scitotenv.2018.06.320.

[31] Saud, S., Chen, S., Haseeb, A., Sumayya, 2020. The role of financial development and globalization in the environment: Accounting ecological footprint indicators for selected one-belt-one-road initiative countries. J. Clean. Prod. 250, 119518. https://doi.org/10.1016/j.jclepro.2019.119518.

[32] Shahbaz, M., Mallick, H., Mahalik, M.K., Loganathan, N., 2015. Does globalization impede environmental quality in India? Ecol. Indic. 52, 379-393. https://doi.org/10.1016/j.ecolind.2014.12.025.

[33] Sharif, A., Meo, M.S., Chowdhury, M.A.F., Sohag, K., 2021. Role of solar energy in reducing ecological footprints: an empirical analysis. J. Clean. Prod. 292, 126028. https://doi.org/10.1016/j.jclepro.2021.126028.

[34] OWD, 2024. Global primary energy consumption by source. Our World in Data (accessed 10 March 2025).

[35] Soto, G.H., 2024. The role of outward foreign direct investment as a filter for high energy intensity economies in the European Union. Appl. Energy 361, 122941. https://doi.org/10.1016/j.apenergy.2024.122941.

[36] Solar, 2024. 5 Ways that solar energy benefits the environment. Solar Pros and Cons (accessed 12 March 2025).

[37] STATISTICA, 2024. Distribution of carbon dioxide emissions worldwide in 2023, by select country (accessed 12 December 2024).

[38] UN, 2024. Sustainable Development Report (accessed 5 February 2025).

[39] UN, 2020. The Sustainable Development Agenda (accessed 15 February 2025).

[40] UNCTAD, 2024. Productive capacities index, annual, 2000-2022. UN trade and development. ( accessed 10 February 2025).

[41] Wang, E.Z., Yang, M., 2022. Green complexity and CO₂ emission: does institutional quality matter? Energy Econ. 110, 106022. https://doi.org/10.1016/j.eneco.2022.106022.

[42] Yu, J., Tang, Y.M., Chau, K.Y., Nazar, R., Ali, S., Iqbal, W., 2022. Role of solar-based renewable energy in mitigating CO₂ emissions: evidence from quantile-on-quantile estimation. Renew. Energy 182, 216-226. https://doi.org/10.1016/j.renene.2021.10.002.

[43] Zhao, C., Dong, K., Wang, K., Nepal, R., 2024a. How does artificial intelligence promote renewable energy development? the role of climate finance. Energy Econ. 133, 107493. https://doi.org/10.1016/j.eneco.2024.107493.

[44] Zhao, Q., Wang, L., Stan, S.E., Mirza, N., 2024b. Can artificial intelligence help accelerate the transition to renewable energy? Energy Econ. 134, 107584. https://doi.org/10.1016/j.eneco.2024.107584.