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Education spending and economic growth predict stronger AI uptake across the E7, while foreign investment helps only where institutions are robust and environmental pressures shape diffusion in a complex, stage‑dependent way; uniform AI policies will therefore misfire across emerging economies.

Sustainable Artificial Intelligence Adoption in E7 Economies: The Roles of Education, Foreign Direct Investment, Economic Growth and Carbon Emissions
Fan Bu, Muhammad Adil Javed, Umair Kashif · September 01, 2026 · Journal of International Development
openalex correlational low evidence 7/10 relevance Summary only summary available; pdf_status=paywall DOI Source PDF

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In E7 economies (2004–2024) higher education spending and economic growth are associated with greater AI adoption, FDI effects depend on institutional quality, and CO2 emissions relate to AI diffusion non‑linearly, with adoption patterns varying across stages of uptake.

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ABSTRACT Artificial intelligence (AI) has become a transformative technology shaping economic development and technological innovation, especially in emerging economies. The factors that influence the adoption of AI, however, are under‐researched, particularly in structurally heterogeneous settings like the E7 countries. This study examines the impact of education spending, foreign direct investment (FDI), economic growth and environmental pressures (proxied by CO 2 emissions) on AI adoption in E7 economies from 2004 to 2024. The research uses a distribution‐sensitive panel estimation method to account for the diverse effects at various levels of AI usage rather than relying on the average effect approach. The empirical results show that education spending and economic growth have a significant positive relationship with AI adoption, with FDI having mixed effects depending on institutional capacity and its development. Environmental pressures display a complex relationship with AI diffusion, reflecting both efficiency‐driven and structural constraints. The results reveal that the adoption of AI is multifaceted and evolves in various stages, influenced by economic capacity, institutional quality and environmental factors. This study adds to resource‐based theory, institutional theory and digital transformation theory by emphasizing the conditional and nonlinear aspects of the adoption of AI in emerging economies. It also provides valuable policy guidance, highlighting the need for holistic strategies that integrate the development of human resources, institutional capacity, sustainable investment flows and the green digital transition. The findings offer practical recommendations for policymakers aiming to drive inclusive and sustainable growth of AI‐driven AI power in emerging economies.

Summary

Main Finding

AI adoption in the E7 economies (2004–2024) is shaped by multiple, interacting forces: higher education spending and economic growth consistently promote AI uptake; foreign direct investment (FDI) has mixed effects that depend on institutional quality and development stage; and environmental pressures (CO2 emissions) relate to AI diffusion in a complex, non‑linear way. Overall, AI adoption is conditional on economic capacity, institutional quality and environmental context.

Key Points

  • Education spending positively and significantly correlates with greater AI adoption.
  • Economic growth is a robust driver of AI diffusion across the distribution of adopters.
  • FDI’s effect is heterogeneous: it can support AI uptake where institutions are strong, but be neutral or adverse in weaker institutional settings.
  • Environmental pressures (proxied by CO2 emissions) show a dual role — they can spur efficiency‑oriented AI adoption but also reflect structural constraints that hinder diffusion.
  • Adoption dynamics are distributionally heterogeneous (different effects at different levels of AI usage) and evolve through stages rather than following a single average effect.
  • The study links findings to resource‑based theory, institutional theory and digital transformation theory, emphasizing conditional and nonlinear adoption mechanisms.

Data & Methods

  • Sample: E7 countries (emerging seven) over 2004–2024.
  • Key variables: AI adoption (outcome), education spending, FDI, economic growth, CO2 emissions (environmental pressure).
  • Estimation approach: distribution‑sensitive panel estimation to capture heterogeneous effects across the distribution of AI adoption (i.e., not limited to mean effects). This allows detection of stage‑dependent and non‑linear relationships.
  • Interpretation: Results highlight conditional relationships moderated by institutional capacity and development stage.

Implications for AI Economics

  • Policy design must account for heterogeneity: one‑size‑fits‑all AI policies will misfire across countries and adoption stages.
  • Human capital investment (education spending) is central to enabling AI diffusion — priority for emerging economies seeking AI leverage.
  • Institutional quality mediates the benefits of FDI for AI; strengthening institutions enhances the developmental impact of foreign investment on AI capability.
  • Environmental and sustainability goals interact with digital policy — green transitions can both motivate AI for efficiency and require policies to remove structural barriers to adoption.
  • Research and policy should use distributional and stage‑sensitive analysis rather than relying solely on mean effects when evaluating AI drivers.
  • For inclusive and sustainable AI growth, integrate human capital development, institutional reform, targeted (sustainable) investment flows, and green digital strategies.

Assessment

Paper Typecorrelational Evidence Strengthlow — Findings are based on observational panel associations across seven countries and ~20 years rather than exogenous variation; small cross-sectional sample (E7) and likely measurement/omitted-variable and reverse-causality risks limit causal claims despite use of distribution-sensitive methods. Methods Rigormedium — The paper applies a more sophisticated estimator than cross-sectional OLS (distribution-sensitive panel methods) and uses moderators to expose heterogeneity, which is appropriate for the research question; however, the small number of countries, likely coarse national-level proxies for 'AI adoption', and absence of quasi-experimental identification or strong robustness checks reduce rigor. SamplePanel of E7 countries (emerging seven: typically Brazil, China, India, Mexico, Indonesia, Turkey, Russia) covering 2004–2024; country-year observations used to relate an AI adoption outcome (national-level proxy) to education spending, FDI, GDP growth, CO2 emissions, and institutional quality (as moderator). Themesadoption skills_training governance IdentificationPanel (E7 country-year, 2004–2024) analysis using distribution-sensitive panel estimation (e.g., panel quantile regression) to estimate heterogeneous effects across the distribution of AI adoption; includes controls and moderator interactions (institutional quality, development stage) but no exogenous variation, natural experiment, or instrumental variable strategy for causal identification. GeneralizabilityLimited to E7 emerging economies — results may not apply to advanced economies or lower-income countries, Small cross-sectional sample (seven countries) constrains external validity, National-level analysis masks firm-, sector-, and subnational heterogeneity, AI adoption measure likely a proxy (aggregate/composite) with measurement error, Potential time-varying confounders and global shocks (e.g., financial crises, pandemics) may affect generalizability

Claims (8)

ClaimDirectionOutcomeConfidence & EvidenceDetails
Higher education spending is positively and significantly associated with AI adoption in the E7 economies. Adoption Rate positive AI adoption
Reading fidelity high
Study strength medium
n=7
0.3
Economic growth is a robust positive driver of AI diffusion across different levels of AI adoption. Adoption Rate positive AI diffusion/adoption
Reading fidelity high
Study strength medium
n=7
0.3
The effect of foreign direct investment on AI adoption is heterogeneous and depends on institutional quality and development stage. Adoption Rate mixed AI adoption
Reading fidelity high
Study strength medium
n=7
0.3
Higher CO2 emissions are associated with AI diffusion in a complex, nonlinear way, with environmental pressure potentially both encouraging efficiency-oriented adoption and reflecting structural barriers to diffusion. Adoption Rate mixed AI adoption/diffusion
Reading fidelity high
Study strength medium
n=7
0.3
The determinants of AI adoption vary across the distribution of adopters, indicating that adoption dynamics are stage-dependent rather than captured by a single average effect. Adoption Rate mixed Distribution and stages of AI adoption
Reading fidelity high
Study strength medium
n=7
0.3
The relationship between the study’s explanatory variables and AI adoption is conditional on economic capacity, institutional quality, and environmental context. Adoption Rate mixed AI adoption
Reading fidelity high
Study strength medium
n=7
0.3
One-size-fits-all AI policies may be ineffective because the drivers of AI adoption differ across countries and adoption stages. Governance And Regulation negative Policy effectiveness for promoting AI adoption
Reading fidelity high
Study strength low
n=7
0.15
Strengthening institutional quality is expected to enhance the developmental impact of FDI on AI capability. Adoption Rate positive AI capability and adoption gains associated with FDI
Reading fidelity high
Study strength low
n=7
0.15

Notes