Analysis examines CO2 emissions determinants in various sectors, highlighting implications for carbon-reduction policies.
This study examines the determinants of U.S. CO2 emissions and provides evidence to inform more effective carbon-reduction policies. Using Autoregressive Distributed Lag (ARDL) and Nonlinear ARDL (NARDL) models, the analysis covers January 1997 to February 2022 across four end-use sectors: Residential, Commercial, Industrial, and Transportation. The models capture both long-run equilibria and short-run adjustments between emissions and key drivers, including industrial production, interest rates, climate policy uncertainty (CPU), and energy prices. Results indicate a long-run asymmetric relationship in which economic growth and interest rates differentially affect total emissions, while CPU exerts a significant negative influence only in the transportation sector. Methodologically, the combined ARDL–NARDL approach offers robust evidence of nonlinear and asymmetric effects of macroeconomic and policy variables on emissions. These findings underscore the need to integrate economic and financial conditions into climate policy design and suggest that sector-specific measures—particularly targeting transportation—may substantially improve the effectiveness of carbon-mitigation strategies.
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Shan-Heng Fu (2026) studied this question.
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