Climate

The escalating frequency and severity of extreme weather events globally have underscored the critical importance of addressing anthropogenic climate change. Countries that contribute disproportionately to global warming relative to their population size bear an urgent responsibility to mitigate climate risks. However, effecting substantive policy change requires a broad public consensus to compel legislative action, a process fundamentally dependent on risk perception. It is theorized that individuals, households, and communities with higher levels of climate change risk perception are more inclined to adopt mitigation behaviors and support collective action. Such perception, however, varies considerably across social dimensions. This study aims to examine how sociodemographic factors shape climate change risk perception among Americans and how intersectionality reveals nuanced patterns beyond single-axis analysis. Using data from the 2023 National Survey of Health Attitudes, the analysis demonstrates that gender, race/ethnicity, educational attainment, religiosity, marital status, and geographic region serve as strong predictors of climate risk perception. Further intersectional analysis reveals that individuals with multiple marginalized social identities, such as race, gender, and socioeconomic status, perceive climate risk distinctly from those without such compounded identities. The study concludes that effective climate communication and policy interventions must prioritize sociodemographic diversity and integrate an intersectional lens to address differential vulnerabilities and perceptions equitably.

This study presents a comparative flood frequency analysis of annual maximum discharges for major Romanian river basins, assessing the performance of the Halphen-A distribution relative to the Pearson Type III distribution, the reference model in Romanian hydrological practice. Four long-term discharge series from the Siret, Ialomița, and Danube rivers are analyzed, covering diverse hydroclimatic conditions. Distribution parameters are estimated using the method of moments and maximum likelihood estimation. Model performance is evaluated using RMSE and MAE, complemented by an analysis of extreme quantile behavior. The results show that both distributions fit the observed data well, with only minor differences in global error metrics. However, for high return periods (T > 100 years), Halphen-A exhibits smoother extrapolation and yields more stable extreme quantile estimates, particularly when estimated by MLE. Although Pearson III often achieves slightly lower metrics values, its upper tail is more constrained and sensitive to skewness and record length. The study concludes that classical goodness-of-fit measures alone are insufficient for selecting models for design floods and that Halphen-A provides a robust complementary alternative for extreme flood estimation.

Drought is a slow-onset hazard whose economic impacts can propagate across sectors with multi-year delays. This study develops a non-linear autoregressive model with exogenous drought inputs (ARX) to assess whether U.S. drought severity, measured by the Drought Severity and Coverage Index (DSCI), contains incremental predictive information for monthly stock returns. Using weekly DSCI and stock price data from 2013 to 2023, we constructed monthly compound returns and multi-year drought lags spanning 1–5 years for four sector-representative firms: a water utility (American Water Works, AWK), two food service firms (Chipotle Mexican Grill, CMG; Starbucks, SBUX), and an industrial manufacturer (Tesla, TSLA). We compared regularized linear ARX baselines (Elastic Net, Ridge) with a non-linear Histogram Gradient Boosting Regressor (HGB) ARX model and used permutation importance to diagnose drought-relevant lag horizons. Results showed a clear, delayed drought signal for the water utility, with a dominant ~48-month drought lag, consistent with multi-year transmission through operations, regulation, and investment cycles. In contrast, drought lags added limited or unstable information for the food service firms and negligible information for TSLA, whose dynamics were dominated by non-drought drivers. Overall, the findings indicate that drought–return relationships are sector-specific and can emerge at multi-year lags, and that non-linear ARX models provide a flexible tool for detecting these delayed climate-risk signals.