Exploring Political Factors in Clean Energy Transition Using Machine Learning Technique

Abstract: A nationwide transition to clean energy still faces persistent political challenges in the past decade. It is because the public support for clean energy policies remains deeply polarized among partisan and ideological lines. While there is an established scientific consensus of climate change, the urgence to change the status quo fails to trigger existential insecurity of individual Americans. While extensive studies have examined the role of partisanship, regional economy, and media framing in shaping these divisions, scholars know very little about the emotional foundation that drive individual voter’s clean energy preferences and behaviors. A major challenge for the scholarship is due to the lack of longitudinal data that contains measurement of both individual emotions as well as clean energy ideologies over time. This study introduces a simulation-based methodology that combining machine learning with traditional survey analysis to examine how anxiety and fear, two emotions related to existential security, shape clean energy policy ideologies and behaviors in the net of social and political factors. Our analytical strategy proceeds in three stages. First, utilizing multiple waves of cross-sectional data collected by Chapman University since 2014, we train a semi-parametric model to estimate the relationship between commonly used apolitical demographic features and anxiety and fear. We will also run the robustness check to make the model is time invariant. Second, we apply the model to two of most recent probabilistic samples collected by Pew Research Center where it contains clean energy ideology and behavior items but lacks emotional measures. And finally, we analyze how these simulated emotional predispositions interact with a range of political and social factors to predict support for clean energy initiatives. Preliminary findings suggest that political and partisan preference may suppress the effective size of existential insecurity to the support of clean energy. And we find this impact is also varied across different clean energy behaviors. The study makes several key contributions. First and methodologically, the study demonstrates how machine learning can bridge gaps between datasets with different focuses and enables more comprehensive analysis of clean energy related studies with nationally representative data. Second and theoretically, it advances our understanding of how emotions related to existential insecurity shape clean energy policy behaviors. Future research could extend this framework to examine how different aspects of policy framing might differentially affect the suppression of climate-related fears. Additionally, longitudinal applications of this approach combining with large language modeling could help track how the relationship between political framing and negative emotions evolves in response to changing environmental conditions and policy-making.

Keywords: Machine Learning, Clean Energy Transition, Political Emotion, Public Opinion

DOI: 10.54941/ahfe1006396

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