人工知能、気候政策の不確実性、再生可能エネルギー転換：持続可能なエネルギー・レジリエンスに関する非線形エビデンス

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🔬研究者:AIと政策不確実性の非対称・非線形効果をNARDLで識別する実証設計は、エネルギー転換研究の手法参照になる。

🏢実務担当者:政策不確実性の高まりが再エネ投資リスクと導入遅延につながる点は、長期調達・投資計画の感度分析に活用できる。

🏛政策担当者:政策の安定性と予見可能性の確保が再エネ投資とデジタル投資の実効性を左右することを示唆する。

Abstract The transition toward renewable energy is central to achieving sustainable development and climate mitigation objectives. At the same time, artificial intelligence (AI) and climate governance have emerged as critical forces shaping the pace and resilience of clean energy transformation. Despite growing attention to these factors, limited evidence exists regarding their nonlinear and asymmetric influences on renewable energy performance. This study investigates the asymmetric effects of AI development and climate policy uncertainty (CPU) on renewable energy transition using monthly data spanning 2013 to 2023. Employing a nonlinear autoregressive distributed lag (NARDL) framework, AI and CPU are decomposed into positive and negative partial shocks to capture heterogeneous short-run and long-run responses. The empirical results reveal pronounced asymmetries in the renewable energy transition process. Negative shocks to AI development generate larger and more persistent adverse effects on renewable energy performance than positive AI shocks generate benefits, indicating that disruptions in digital innovation substantially weaken the resilience of sustainable energy systems. Likewise, adverse climate policy uncertainty shocks exert stronger and longer-lasting impacts than favorable policy developments, increasing investment risk, delaying clean energy deployment, and weakening confidence in long-term low-carbon planning. Dynamic multiplier analysis confirms gradual adjustment toward long-run equilibrium, highlighting the presence of transition costs and delayed adaptation mechanisms within renewable energy markets. Furthermore, financial conditions and conventional energy prices significantly influence renewable energy performance through market-based transmission channels. Overall, the findings demonstrate that resilient renewable energy transitions depend on sustained technological innovation, stable climate governance, and supportive financial environments. By uncovering the asymmetric interactions between AI, policy uncertainty, and renewable energy dynamics, this study contributes to the emerging literature on sustainable development and offers policy insights for strengthening energy transition resilience and accelerating progress toward long-term decarbonization goals.

• openalex https://doi.org/10.1038/s41598-026-72800-8first seen 2026-10-25 04:56:46 · last seen 2026-11-06 05:03:21