The Nonlinear Nexus of Climate Policy Uncertainty and Renewable Energy Consumption in the United States of America: A Markov-Switching Approach

Setiastuti, Sekar Utami and Rajendra, Mohammad Arief (2024) The Nonlinear Nexus of Climate Policy Uncertainty and Renewable Energy Consumption in the United States of America: A Markov-Switching Approach. International Journal of Energy Economics and Policy, 14 (5). 321 - 334. ISSN 2146-4553

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Abstract

We employ a Markov-Switching regression model using monthly US data from April 1987 to August 2022 to investigate the effect of climate policy uncertainty (CPU) on renewable energy consumption (REC). The main findings suggest the presence of a nonlinear relationship between CPU and REC. The baseline analysis using the CPU index reveals an adverse effect in regimes characterized by high levels of uncertainty. However, the effect is not statistically significant in a low uncertainty regime. To account for potential variations in the results, we perform a robustness analysis that considers the effect of CPU on REC, which may fluctuate based on the authorities contextual perspectives (i.e., being in favor or against climate policy) and also the effect of CPU on REC by household. In addition, we incorporate a robustness check by utilizing the environmental policy uncertainty index developed by Noailly et al. (2022). The robustness test results confirm the results obtained from the baseline estimation. © 2024 Elsevier B.V., All rights reserved.

Item Type: Article
Additional Information: Cited by: 0; All Open Access; Gold Open Access
Uncontrolled Keywords: Climate Policy Uncertainty; Environmental Policy Uncertainty; Markov-switching Regression; Renewable Energy Demand
Subjects: H Social Sciences > HC Economic History and Conditions
Divisions: Faculty of Economics & Business > Doctoral Program in Accounting, Economics, and Management
Depositing User: Maryatun MARYATUN
Date Deposited: 27 Oct 2025 01:53
Last Modified: 27 Oct 2025 01:53
URI: https://ir.lib.ugm.ac.id/id/eprint/23589

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