ArticleScientific reports2026
AI readiness, STEM education, economic growth, and climate transition in China: a long-run systems analysis.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study has examined the long-run relationships among system-level AI readiness, STEM human capital capacity, economic performance, and climate transition in China over the period of 1980–2024. Rather than evaluating the classroom-level learning outcomes, analysis conceptualizes the AI-driven personalized learning systems (AI-PLS) as a macro-level proxy for the national capacity to deploy AI-enabled education and innovation infrastructures. Multidimensional indices are constructed using the principal component analysis, and long-run and short-run dynamics are examined through the Johansen cointegration, vector error correction models, impulse response functions, forecast error variance decomposition, and wavelet coherence analysis. Results have indicated that AI readiness and STEM capacity are strongly and positively associated with the economic modernization over long run, reflecting complementarities between digital capability, human capital formation, and growth. At same time, AI readiness exhibits the negative long-run association with climate transition index, which is more plausibly interpreted as reflecting energy- and emissions-intensive nature of the economy-wide digitalization and industrial upgrading, rather than environmental effects of the educational technologies per se. Climate transition dynamics adjust more slowly than economic and STEM indicators, underscoring structural rigidities in the energy systems and longer time horizons required for decarbonization. Overall, findings suggest that while the AI-enabled education and innovation capacity can support the economic growth, sustainability benefits are conditional on the complementary energy, governance, and climate policies. Policy implications have highlighted importance of the aligning AI adoption strategies with the low-carbon development pathways, strengthening linkages between STEM education and the green innovation, and adopting the integrated governance frameworks that recognize the interdependencies across education, technology, economy, and environment. Study’s scope is limited by its focus on the single country, reliance on macro-level secondary data, and the linear modeling assumptions. Future research may has extended to this framework through the cross-country comparisons, nonlinear and structural-break analyses, and incorporation of the micro-level evidence.
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