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Journal article
Bridging research and policy in China's energy sector: A semantic and reinforcement learning framework
Energy strategy reviews, Vol.59, 101770
2025
Abstract
Aligning academic research with policymaking is vital for addressing China's energy challenges. This study introduces an AI-driven framework combining BERTopic, semantic similarity analysis, and deep reinforcement learning (DRL) to evaluate alignment between 106,661 English-language academic papers and 618 national-level policy documents. Topic modeling reveals strong convergence in themes such as “Coal mining and geological formations,” which account for 15.73 % of academic publications, while “Safety regulations and worker protection” dominate policy texts at 13.35 %. In contrast, emerging topics like “Digital economy and carbon transformation” remain underrepresented, with a popularity score of only 0.13. Semantic similarity analysis across 22 policy and 27 academic topics yields an average cosine similarity of 0.23, with only 12.5 % of topic pairs exceeding 0.4, underscoring thematic misalignment. Structurally, policy networks are 15.9 times denser and exhibit 30 × higher clustering coefficients than scientific networks, indicating more centralized but less diversified discourse. DRL-based prioritization identifies “Power systems and renewable integration” as the top-performing theme (Q-value = 1.6225), highlighting opportunities for targeted energy transition policies. These quantitative results offer empirical evidence to guide theme-based policy adaptation and foster actionable science-policy integration.
•Integrates AI methods to assess research-policy alignment in energy governance.•Identifies thematic gaps in China's energy policy using NLP and deep learning.•Uses deep reinforcement learning for dynamic policy prioritization.•Demonstrates framework scalability across environmental and policy domains.•Provides actionable insights for evidence-based energy policy decisions.
Details
- Title
- Bridging research and policy in China's energy sector: A semantic and reinforcement learning framework
- Authors/Creators
- Yang Liu - Murdoch University, Perth, WA, 6150, Australia
- Publication Details
- Energy strategy reviews, Vol.59, 101770
- Publisher
- Elsevier Ltd.
- Number of pages
- 10
- Identifiers
- 991005783133707891
- Copyright
- © 2025 The Author.
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
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- Citation topics
- 6 Social Sciences
- 6.115 Sustainability Science
- 6.115.2292 Energy Security
- Web Of Science research areas
- Energy & Fuels
- ESI research areas
- Engineering