AI innovation and inclusive finance are linked to improved environmental carrying capacity in G7 economies, while globalization and urbanisation exert downward pressure; the results support a nonlinear income–environment (Load Capacity) relationship.

Artificial Intelligence, Financial Access, and the Path to Sustainable Development: Insights from G-7 Countries

Mohammad Ridwan, Kabaly P. Subramanian, Jaheer Mukthar K.P., Binata Rani Sen, Minghui Jiang, Mujeeb Saif Mohsen Al Absy · June 12, 2026 · Journal of Data Science and Intelligent Systems

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Using panel ARDL estimates for the G7 (1990–2019), the paper finds that AI innovation and greater financial accessibility are associated with higher ecological carrying capacity (LCF), while globalization and urbanization reduce it, supporting a nonlinear Load Capacity Curve between income and environmental sustainability.

Achieving sustainable development in advanced economies requires reconciling rapid technological progress with growing environmental constraints. This study examines whether artificial intelligence (AI) innovation can enhance environmental sustainability alongside economic growth in the G-7 countries over the period 1990–2019. Using the load capacity curve (LCC) framework and the load capacity factor (LCF) as a measure of ecological sustainability, the analysis integrates AI innovation, financial accessibility, globalization, urbanization, and income dynamics. This study contributes to the literature by providing the first evidence on the role of AI innovation in shaping environmental carrying capacity within the LCC framework for advanced economies. Panel autoregressive distributed lag estimates reveal strong support for the LCC hypothesis, indicating a nonlinear income–environment relationship. AI innovation and financial accessibility significantly improve the LCF, suggesting that technological progress and inclusive finance can promote sustainability through efficiency gains and cleaner production. In contrast, globalization and urbanization reduce environmental carrying capacity, particularly at higher levels of ecological pressure. These findings highlight the importance of AI-driven innovation and green finance in sustainability strategies, while underscoring the need for stronger environmental governance to manage globalization and urban expansion in advanced economies. Received: 15 January 2026 | Revised: 10 March 2026 | Accepted: 26 March 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are publicly available from the following sources: 1) Load Capacity Factor (LCF): Global Footprint Network – https://data.footprintnetwork.org 2) Economic Growth (GDP) and Urbanization: World Bank, World Development Indicators (WDI) – https://databank.worldbank.org/source/world-development-indicators 3) Artificial Intelligence (AI) Patent Data: Our World in Data – https://ourworldindata.org 4) Financial Accessibility: Global Financial Inclusion (Global Findex) Database, World Bank – https://globalfindex.worldbank.org 5) Globalization Index: KOF Swiss Economic Institute – https://kof.ethz.ch/en/forecasts-and-indicators/indicators/kof-globalisation-index.html All datasets are openly accessible and do not require special permission for academic use. Author Contribution Statement Mohammad Ridwan: Conceptualization, Methodology, Software, Investigation, Writing – review & editing. Kabaly P Subramanian: Conceptualization, Software, Validation, Resources, Writing – original draft, Writing – review & editing. Jaheer Mukthar K.P.: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Binata Rani Sen: Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing. Minghui Jiang: Conceptualization, Formal analysis, Writing – review & editing, Visualization. Mujeeb Saif Mohsen Al Absy: Investigation, Resources, Data curation, Writing – review & editing.

Summary

Main Finding

AI innovation and improved financial accessibility significantly increase ecological carrying capacity (measured by the Load Capacity Factor, LCF) in G‑7 countries (1990–2019). The analysis supports a nonlinear income–environment relationship consistent with the Load Capacity Curve (LCC) hypothesis. By contrast, globalization and urbanization are associated with reduced LCF, especially under higher ecological pressure.

Key Points

Outcome metric: Load Capacity Factor (LCF) — a composite measure of ecological sustainability/carrying capacity.
Main explanatory variables: AI innovation (proxied by AI patent activity), financial accessibility (Global Findex), globalization (KOF index), urbanization (urban population share), and income dynamics (GDP measures).
Econometric result: Panel ARDL estimates provide strong support for the LCC hypothesis (a nonlinear income–environment link).
Positive effects: AI innovation and financial accessibility both raise LCF, interpreted as enabling efficiency gains and cleaner production pathways.
Negative effects: Globalization and urbanization reduce the LCF, with larger adverse effects observed at higher levels of ecological pressure.
Policy emphasis: Findings point to the value of AI-driven innovation and green/inclusive finance, coupled with stronger environmental governance to manage globalization and urban expansion.

Data & Methods

Sample: G‑7 countries, annual data 1990–2019.
Data sources (public):
- LCF: Global Footprint Network
- GDP and urbanization: World Bank WDI
- AI patents: Our World in Data
- Financial accessibility: World Bank Global Findex
- Globalization index: KOF Swiss Economic Institute
Empirical approach:
- Load Capacity Curve (LCC) framework to model a nonlinear income–environment relationship.
- Panel autoregressive distributed lag (panel ARDL) estimation to capture short-run dynamics and long-run relationships among LCF and covariates.
- Controls and model setup integrate AI innovation, finance, globalization, urbanization, and GDP dynamics.
Availability: All datasets used are openly accessible without special permission.

Implications for AI Economics

AI as an environmental multiplier: Evidence that AI innovation (measured via patents) can improve national ecological carrying capacity suggests AI can be a technology of decarbonization/resource efficiency when deployed appropriately.
Complementarity with finance: The positive interaction of financial accessibility with LCF highlights the importance of inclusive/green finance in diffusing AI-enabled clean technologies and enabling adoption by firms and households.
Distributional and governance considerations: Benefits of AI for sustainability are conditional — globalization and urbanization can counteract gains. Policy must align AI deployment with environmental regulation, urban planning, and trade/production standards to avoid leakage or scaling of environmentally intensive activities.
Research directions: Further work should (a) unpack causal channels (diffusion vs. invention; firm-level adoption), (b) refine AI-innovation measures (beyond patents), (c) test heterogeneous effects across sectors and regions, and (d) extend analysis post-2019 to capture recent AI advances and policy responses.
Policy takeaway: Promote AI R&D targeted at green technologies, expand green finance and inclusion, and strengthen environmental governance to ensure AI-led growth is environmentally sustainable.

Assessment

Paper Typecorrelational Evidence Strengthmedium — The study uses long-run panel time-series methods over 1990–2019 and explicitly models nonlinear income–environment dynamics (LCC), which strengthens inference about persistent associations; however, identification relies on observational variation in seven countries without exogenous variation or instruments, leaving results vulnerable to endogeneity, omitted variables, reverse causality, and measurement error (e.g., patents as an AI proxy). Methods Rigormedium — Appropriate and modern panel econometric techniques (panel ARDL, cointegration, nonlinear specification) are applied to a multi-decade panel, and multiple relevant covariates are included; but the small cross-sectional sample (G7), limited discussion of causal threats (instrumentation, dynamic panel bias, country-specific confounders), and reliance on aggregate patent counts reduce methodological rigor for causal claims. SampleAnnual country-level panel of the G7 advanced economies (7 countries) over 1990–2019; dependent variable is the Load Capacity Factor (LCF) from the Global Footprint Network; key regressors include AI patent counts (Our World in Data), GDP and urbanization (World Bank WDI), financial accessibility (Global Findex), and KOF globalization index; likely N≈7, T≈30. Themesinnovation governance IdentificationPanel autoregressive distributed lag (ARDL) framework with panel cointegration and nonlinear (Load Capacity Curve) specification to estimate long-run and short-run associations between AI patenting, financial accessibility, globalization, urbanization, income, and the Load Capacity Factor (LCF); no exogenous instruments, natural experiments, or policy discontinuities are used to secure causal identification. GeneralizabilityResults are limited to advanced G7 economies and may not apply to emerging or developing countries with different institutional and technological contexts., Country-level aggregate analysis cannot be extrapolated to firm-, sector-, or worker-level outcomes (ecological fallacy)., AI measured via patent counts captures innovation activity but not diffusion, adoption, or intensity of AI use across firms and sectors., The study period ends in 2019 and does not cover post-2019 rapid advances in generative AI and deployment patterns., Small cross-sectional sample (7 countries) limits heterogeneity analysis and external validity across diverse policy environments.

Claims (7)

Claim	Direction	Outcome	Confidence & Evidence	Details
Panel autoregressive distributed lag estimates reveal strong support for the load capacity curve (LCC) hypothesis, indicating a nonlinear income–environment relationship. Consumer Welfare	mixed	Load Capacity Factor (LCF) / environmental carrying capacity (income–environment relationship)	Reading fidelity high Study strength medium	n=7 0.3
AI innovation significantly improves the Load Capacity Factor (LCF), suggesting technological progress promotes ecological sustainability through efficiency gains and cleaner production. Consumer Welfare	positive	Load Capacity Factor (LCF) / environmental carrying capacity	Reading fidelity high Study strength medium	n=7 0.3
Financial accessibility significantly improves the Load Capacity Factor (LCF), indicating inclusive finance can promote sustainability. Consumer Welfare	positive	Load Capacity Factor (LCF) / environmental carrying capacity	Reading fidelity high Study strength medium	n=7 0.3
Globalization reduces environmental carrying capacity (LCF), particularly at higher levels of ecological pressure. Consumer Welfare	negative	Load Capacity Factor (LCF) / environmental carrying capacity	Reading fidelity high Study strength medium	n=7 0.3
Urbanization reduces environmental carrying capacity (LCF), with reductions more pronounced under higher ecological pressure. Consumer Welfare	negative	Load Capacity Factor (LCF) / environmental carrying capacity	Reading fidelity high Study strength medium	n=7 0.3
AI-driven innovation and green finance should be prioritized in sustainability strategies for advanced economies, while stronger environmental governance is needed to manage the environmental impacts of globalization and urban expansion. Governance And Regulation	positive	Policy-relevant environmental sustainability strategy (inferred from LCF findings)	Reading fidelity medium Study strength speculative	n=7 0.03
This study provides the first evidence on the role of AI innovation in shaping environmental carrying capacity within the LCC framework for advanced economies. Governance And Regulation	null_result	Novelty of literature contribution (first evidence on AI within LCC for advanced economies)	Reading fidelity high Study strength low	n=7 0.15