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<p>The study examines the impact of artificial intelligence (AI) adoption on poverty reduction and inclusive economic growth in BRICS economy in the 2008–2023 period through the lenses of the endogenous growth theory and the capability approach developed by Sen. The research adopts second-generation econometric methods including the Pesaran cross-sectional dependence test, CIPS panel unit root test, Westerlund cointegration and the Cross-Sectionally Augmented Autoregressions Distributed Lag (CS-ARDL) model. The empirical findings indicate a statistically significant and strong connection between AI adoption and poverty reduction, with 1% of AI adoption resulting in 0.14 and 0.39% poverty reduction in the short run and long run, respectively, indicating that the poverty-reducing benefits of AI increase over time. Poverty reduction is further reinforced by human development, access to clean cooking fuels, economic growth, and effective governance, but income inequality has the opposite effect. The error correction value is negative and highly significant, indicating the stability of the long-run equilibrium relationship. Robustness checks confirm the consistency of the findings on an alternative measure of poverty. In the course of the research, AI can support poverty alleviation as a general-purpose technology, provided robust governance systems and supplementary funding for human development are in place. The results provide significant policy implications for using AI to achieve SDG 1 in developing economies.</p>

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AI for poverty reduction (SDG 1): driving inclusive economic growth in BRICS countries

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月15日

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  • Vikas Garg,
  • Ernesto D.R. Santibanez Gonzalez,
  • Pooja Kaushik,
  • Arun Kumar

摘要

The study examines the impact of artificial intelligence (AI) adoption on poverty reduction and inclusive economic growth in BRICS economy in the 2008–2023 period through the lenses of the endogenous growth theory and the capability approach developed by Sen. The research adopts second-generation econometric methods including the Pesaran cross-sectional dependence test, CIPS panel unit root test, Westerlund cointegration and the Cross-Sectionally Augmented Autoregressions Distributed Lag (CS-ARDL) model. The empirical findings indicate a statistically significant and strong connection between AI adoption and poverty reduction, with 1% of AI adoption resulting in 0.14 and 0.39% poverty reduction in the short run and long run, respectively, indicating that the poverty-reducing benefits of AI increase over time. Poverty reduction is further reinforced by human development, access to clean cooking fuels, economic growth, and effective governance, but income inequality has the opposite effect. The error correction value is negative and highly significant, indicating the stability of the long-run equilibrium relationship. Robustness checks confirm the consistency of the findings on an alternative measure of poverty. In the course of the research, AI can support poverty alleviation as a general-purpose technology, provided robust governance systems and supplementary funding for human development are in place. The results provide significant policy implications for using AI to achieve SDG 1 in developing economies.