An investigation into the role of Residents' cognitive preferences in distributed renewable energy development

Donglong Wu, Dequn Zhou, Qingyuan Zhu and Liangpeng Wu

Applied Energy, 2024, vol. 372, issue C, No S0306261924011978

Abstract: During the development and deployment of distributed renewable energy, enhancing residents' cognitive preferences towards renewable energy can effectively mitigate resistance. To examine the effect of residents' cognitive preferences on the development of distributed renewable energy, we first qualitatively and quantitatively analyzed residents' cognitive preferences through literature review and big data analysis. Subsequently, a two-way fixed effects model with time and region were utilized to test the relationship between residents' cognitive preferences and distributed renewable energy. However, the selected sample may lead to biased estimation results due to omitted variable and self-selection bias. To ensure the reliability of the results, we used the instrumental variable method and the Heckman correction method to eliminate these biases. Finally, we used the spatial autoregressive model to explore the spatial interaction relationship between surrounding cognitive activities and residents' cognitive preferences. The results show that residents' cognitive preferences significantly promote the development of distributed renewable energy. Moreover, residents living in areas with higher levels of air pollution, income, education, and informatization are more receptive to the development of distributed renewable energy. Additionally, we found a “herd effect” in residents' cognitive preferences in neighbouring regions, which is limited to adjacent areas. These findings provide an important reference for the government to effectively guide residents' cognition to form the “herd effect”, and then promote the large-scale development of distributed renewable energy.

Keywords: Residents' cognitive preferences; Renewable energy; Fixed effect; Herd effect (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.123814

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