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<p>This study evaluates how achieving higher levels of renewable energy adoption—as measurable outcomes of effective policy implementation—affects national energy risks. Rather than directly measuring policy enactment or stringency, we adopt an outcome-based approach: using median adoption levels as a threshold to identify countries with successful policy outcomes. We combine difference-in-differences (DID), the synthetic control method (SCM), and spatial econometrics to assess the effectiveness of policies based on their realized results across OECD countries from 1990 to 2022. We quantify how high adoption levels reduce national energy risks, with countries achieving above-median renewable adoption experiencing 15–20% lower energy import dependency, with effectiveness peaking at median adoption thresholds. SCM counterfactuals estimate that risks would be 30–40% higher had these countries not achieved high adoption levels, validating the causal impacts. Spatial models reveal significant cross-border spillovers driven by geographic proximity and energy structure similarity, underscoring the value of regional coordination. Heterogeneous effects across the 25th, 50th, and 75th percentile thresholds emphasize the need for tailored policy optimization to maximize system resilience. This multi-method approach enhances causal inference and resource allocation strategies, bridging temporal, spatial, and intensity dimensions. It offers policymakers actionable insights to strengthen energy security and mitigate risks in interconnected energy networks, advancing sustainable system optimization.</p>

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Evaluating renewable energy policy effectiveness: a quantitative approach for decision support and risk mitigation

  • 期刊论文

  • 发表日期：

    2026年5月30日

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  • Sheng-Wei Lin

摘要

This study evaluates how achieving higher levels of renewable energy adoption—as measurable outcomes of effective policy implementation—affects national energy risks. Rather than directly measuring policy enactment or stringency, we adopt an outcome-based approach: using median adoption levels as a threshold to identify countries with successful policy outcomes. We combine difference-in-differences (DID), the synthetic control method (SCM), and spatial econometrics to assess the effectiveness of policies based on their realized results across OECD countries from 1990 to 2022. We quantify how high adoption levels reduce national energy risks, with countries achieving above-median renewable adoption experiencing 15–20% lower energy import dependency, with effectiveness peaking at median adoption thresholds. SCM counterfactuals estimate that risks would be 30–40% higher had these countries not achieved high adoption levels, validating the causal impacts. Spatial models reveal significant cross-border spillovers driven by geographic proximity and energy structure similarity, underscoring the value of regional coordination. Heterogeneous effects across the 25th, 50th, and 75th percentile thresholds emphasize the need for tailored policy optimization to maximize system resilience. This multi-method approach enhances causal inference and resource allocation strategies, bridging temporal, spatial, and intensity dimensions. It offers policymakers actionable insights to strengthen energy security and mitigate risks in interconnected energy networks, advancing sustainable system optimization.