Quantitative Approach of Policy Drivers in Clean Energy Transition: Unveiling the Interconnected Pathway

Abstract

As the global community grapples with the imperative of transitioning to clean energy sources, policymakers, utilities, and other stakeholders face the challenge of navigating a complex landscape of interrelated factors. The situation necessitates a comprehensive examination of the various factors, and the interaction of those factors, which shape the clean energy transition. This paper presents a quantitative approach to better understand clean energy transition, to uncover the critical pathways and variables at play. Drawing upon our experience in data analytics and computational modeling, this research delves into the intricate web of influences that impact the adoption and diffusion of clean energy technologies. We collect data from multiple sources, including Residential Energy Consumption Survey, and Pew’s American Trends Survey to drive insights. Policy levels including Inflation Reduction Act are analyzed, and simulations are conducted to test different scenario. Preliminary result indicates recipient of energy assistance policy for low-income households significantly predicts energy consumption, and solar panel installations. Viewing the transition to clean energy as part of an interconnected system, the paper offers insights into how interactions between key factors correlates with the adoption of clean energy, ultimately shedding light on the most effective policy strategies. The findings aim to provide policymakers, utilities, stakeholders, and researchers with a comprehensive understanding of the quantitative aspects of clean energy transition, facilitating informed decision-making in the pursuit of a sustainable energy future.

Keywords: Sustainability, Clean Energy Transition, Policy, Statistical Simulation

DOI: 10.54941/ahfe1005023