Sourav Batabyal

This study examines the relationship between climate policy uncertainty (CPU) and residential housing prices across U.S. metropolitan areas using the U.S. CPU index developed by Gavriilidis in 2021 and monthly S&P CoreLogic Case-Shiller Home Price Indices, covering January 1991 to May 2024. Employing a Fourier-augmented Toda–Yamamoto causality framework that accounts for both abrupt and gradual structural breaks, we document significant CPU → housing prices transmission in multiple metropolitan markets, with bidirectional transmission dynamics emerging in Los Angeles, New York, San Diego, and San Francisco, as well as at the U.S. national level. The results reveal substantial spatial heterogeneity across various market types. Coastal high-exposure markets exhibit strong CPU sensitivity, which may reflect the influence of physical climate risks and regulatory uncertainty; inland growth markets display housing prices → CPU feedback, likely operating through political economy channels; Midwest extreme-weather markets show persistent transmission despite their non-coastal locations; recession-sensitive markets become CPU-responsive following the Great Recession; and insulated markets show no significant transmission. The findings indicate that CPU operates as a priced systematic risk factor requiring integration into housing finance oversight, macroprudential frameworks, and investment strategies. These results have important implications for financial stability monitoring, mortgage credit risk assessment, and climate policy design as markets navigate transition risks in a low-carbon economy.

Purpose: This paper examines the early pandemic experience in a large metropolitan area to differentiate the roles of the lifestyle and built environment factors associated with differing case rates across neighborhoods. Design/methodology/approach: This paper develops a simple empirical methodology for sorting out the separate effects of lifestyle and the built environment factors along with their interactive effects when individuals’ behaviors not only reflect their observable characteristics but also are influenced by the physical environments in which they live and work, indirect connections implied by the early insights of Jacobs (1961) and more recently Hawley and Turnbull (2019). Findings: The results demonstrate that lifestyle factors tied to employment show the strongest association with COVID-19 cases. Other lifestyle choices, built environment features, and demographic attributes such as household size, principal cities, highway connectivity, and population density also affect COVID-19 transmission at the onset of the disease outbreak. The analysis reveals a surprising spatial pattern; employment-related lifestyle factors on case rates in outlying neighborhoods are stronger than in neighborhoods within primary cities after accounting for various built environment factors. Originality/value: This research addresses important questions and the perplexing outcomes related to lifestyle and the built environment’s multi-faceted role in spreading COVID-19. In addition, this study represents a pioneering effort in disentangling the pure lifestyle effect on virus transmission after eliminating potentially confounding impacts of built environment factors on household behavior that in turn influence virus transmission.

Using the GARCH model and quantile regression with dummy variables, we investigate the hedging and safe haven properties of carbon futures and clean energy stocks against the U.S. climate policy uncertainty (CPU). We discover that carbon futures and clean energy stocks have a weak hedge and a semi-strong safe haven in different market conditions. Carbon futures exhibit a strong safe haven in both bull and bear markets, depending on the degree of uncertainty. Clean energy stocks, on the other hand, possess a weak hedge across market conditions and a strong safe haven in bull markets. Sub-sample analyses of prior- and post-Paris Agreement of 2016 also exhibit consistent results for safe haven properties of carbon futures and clean energy stocks.

Using monthly data from January 2000 to August 2018, this paper examines how the Canadian oil and gas industry and individual firms’ equity prices react to oil price fluctuations, which are measured by the traditional West Texas Intermediate (WTI) benchmark and the Canada-specific Western Canadian Select (WCS) benchmark. The findings provide support for the view that oil price movements are an important factor in explaining the equity returns of the overall industry and for many individual oil and gas firms in Canada. Both WTI and WCS measures provide statistically significant evidence, but the results support that WTI may still be the more relevant measure for Canadian-based firms. We also find that the spread between WTI and WCS has a minimal impact on the firms’ equity returns. Additional tests for asymmetric impacts of oil price movements on Canadian oil and gas equity returns have provided little evidence, whereas time-varying impacts are found for a handful of firms. The empirical findings predicated on the holistic view of the impacts of oil price fluctuations on equity market returns will enhance investor confidence and strengthen the Canadian economy.