Impact Factors and Policy Effectiveness of Renewable Energy Generation in China
中国における再生可能エネルギー発電の影響要因と政策効果 (AI 翻訳)
Songyuan Liu, Shuaiqi Hu, Mei Wang, Yue Song, Yichuan Jin, Lingfeng Tan
🤖 gxceed AI 要約
日本語
本論文は、中国の再生可能エネルギー拡大の因果要因を解明するため、データ駆動型のK2構造学習と専門知識に基づくベイジアンネットワークを融合した分析枠組みを開発。特に太陽光発電では、再生可能エネルギーポートフォリオ基準（RPS）が自然放射量を超える主要因となったことを示し、政策転換の必要性を強調する。感度分析では、RPS欠如によりシステムの因果結合性が64%低下することが明らかになった。
English
This paper develops a hybrid causal framework combining K2 structural learning and Bayesian Networks to analyze drivers of renewable energy expansion in China. It finds a paradigm shift from resource-driven to institution-driven growth, with the Renewable Portfolio Standard (RPS) becoming the primary determinant for solar capacity. Absence of RPS leads to a 64% degradation in causal connectivity, highlighting the need for consumption-side mandates over price stimuli.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の再生可能エネルギー政策（FITからFIPへの移行、容量市場など）にも示唆を与える。特にRPSのような消費義務型政策の効果を因果推論で実証しており、日本の「非化石価値取引市場」や「再エネ特措法」改正の評価にも活用可能。
In the global GX context
This paper offers a rigorous causal methodology for evaluating renewable energy policy effectiveness, directly relevant to global transitions under the Paris Agreement. Its findings on the superiority of consumption mandates over subsidies inform ISSB-aligned transition planning and national energy strategies.
👥 読者別の含意
🔬研究者:Provides a validated hybrid causal framework (K2 + Bayesian Networks) applicable to analyzing renewable policy impacts in diverse contexts.
🏢実務担当者:Demonstrates that consumption-side mandates like RPS are more effective than subsidies for renewable expansion, guiding corporate energy procurement and policy engagement.
🏛政策担当者:Offers evidence that synergistic convergence of cost reductions and mandatory quotas is crucial for high-growth milestones, with a 64% causal connectivity loss if RPS is absent.
📄 Abstract（原文）
As China accelerates toward carbon neutrality, decrypting the causal drivers of renewable energy expansion is paramount for effective policy design. We develop a hybrid analytical framework bridging data-driven K2 structural learning with expert-informed Bayesian Networks to map the intricate interdependencies between policy instruments, resource endowments, and socio-economic variables. This causal mapping reveals a fundamental paradigm shift from resource-bound growth to institutional-steered expansion, particularly in the solar sector where the Renewable Portfolio Standard (RPS) has superseded natural radiation as the primary determinant for capacity scaling. Forward sensitivity and backward diagnostic analyses demonstrate that achieving high-growth milestones requires a synergistic convergence of technological cost reductions and mandatory consumption quotas; conversely, the absence of RPS leads to a 64% degradation in systemic causal connectivity. These findings underscore the necessity of transitioning from price-side stimuli to structural consumption-side mandates to ensure a resilient energy transition. Ultimately, this framework and the identified causal pathways provide a strategic blueprint for other emerging economies navigating the complex transition from subsidy-dependent to market-resilient renewable energy landscapes under stringent climate constraints.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18073519first seen 2026-05-17 06:57:30 · last seen 2026-05-18 04:59:41
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