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| DOI | 10.1016/j.apenergy.2024.123007 |
| Navigating the community renewable energy landscape: An analytics-driven policy formulation | |
| 发表日期 | 2024 |
| ISSN | 0306-2619 |
| EISSN | 1872-9118 |
| 起始页码 | 362 |
| 卷号 | 362 |
| 英文摘要 | In an era where climate change and energy security have become paramount concerns, community renewable energy (CRE) projects have emerged as an essential tool for engaging citizens in the transition to sustainable energy sources. Despite growing interest in CRE, limited research has been conducted to statistically understand the non-economic social factors that along with the economic and technical factors influence adoption and investment in such initiatives. Addressing this knowledge gap, our study presents a data-driven approach to examining the demographic, attitudinal, and heterogeneous socio-behavioural drivers in decisions to participate in CRE, with the aim of designing evidence-based local energy policies. In our study, we leverage insights from a large-scale survey of 941 Australians, which investigated some possible non-economic and economic factors and employ unsupervised machine learning techniques. We introduce the Stratified Harmonic Clustering Framework (SHCF), a comprehensive analytical approach that examines five clustering classes across nine distinct methods, completing 235,420 hyperparameter tuning iterations to determine the optimal algorithm for identifying distinct groups. Here, we present our novel Adaptive Nested DBSCAN algorithm, which reveals three distinct clusters with varying priorities, motivations, and attitudes towards renewable energy (RE): a) Senior CRE Enthusiasts, b) Urban RE Adopters and Advocates, and c) Rural RE Investors and Sceptics. Our findings suggest that i) Tailoring outreach efforts to these different demographic clusters, ii) Prioritising community needs and concerns, iii) Fostering positive attitudes and trust, iv) Implementing supportive regulations, and v) Devising economic incentives, are all crucial for promoting CRE adoption. Based on these insights, we propose targeted CRE policies for each identified cluster, underscoring the importance of addressing the unique priorities and motivations of these various groups. The key benefit of this approach is the potential to address debates surrounding the changes in social formations arising from energy transition, and the opportunities they present for increased resilience. |
| 英文关键词 | Energy cooperation; Data science; Regional energy equity; Decentralised energy; Citizen -led finance; Interdisciplinary analytics; Community renewable energy |
| 语种 | 英语 |
| WOS研究方向 | Energy & Fuels ; Engineering |
| WOS类目 | Energy & Fuels ; Engineering, Chemical |
| WOS记录号 | WOS:001221354700001 |
| 来源期刊 | APPLIED ENERGY
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/304414 |
| 作者单位 | University of Sydney; Technische Universitat Dresden; University of Technology Sydney; University of Twente |
| 推荐引用方式 GB/T 7714 | . Navigating the community renewable energy landscape: An analytics-driven policy formulation[J],2024,362. |
| APA | (2024).Navigating the community renewable energy landscape: An analytics-driven policy formulation.APPLIED ENERGY,362. |
| MLA | "Navigating the community renewable energy landscape: An analytics-driven policy formulation".APPLIED ENERGY 362(2024). |
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