climate data integration

This study addresses two core challenges in assessing climate change impacts on the U.S. economy: (1) quantifying uncertainties in GDP losses and nonmarket damages, and (2) reconciling divergent estimates from econometric and stated-preference models. We develop a unified climate–economy coupling framework that integrates multi-source empirical evidence, standardizes socioeconomic and climate scenarios, and jointly estimates market and nonmarket damages. This yields the first probabilistic, cross-sectorally harmonized estimate of the social cost of greenhouse gases (SC-GHG). Results indicate that the median projected U.S. GDP loss by 2100 exceeds current assessments, with a narrower but systematically biased uncertainty range—underestimating tail risks. The revised SC-GHG is substantially higher, reflecting omitted low-probability, high-impact events and international spillovers. The framework delivers a more robust, comparable, and transparent cost benchmark for climate policy.

This study investigates how asymmetric diurnal warming affects rural labor structure and non-agricultural employment transitions in India. Leveraging decadal census panel data from 1981 to 2011 and integrating theoretical modeling with econometric analysis, it reveals for the first time heterogeneous effects of nighttime versus daytime temperature increases on agricultural employment: nighttime warming drives owner-cultivators into agricultural wage labor, whereas daytime warming pushes workers toward seasonal non-farm jobs. In urban areas, both forms of warming suppress the share of non-agricultural employment. The paper innovatively incorporates diurnal temperature variation into the climate–economy analytical framework, elucidating how differential impacts on land and labor productivity underpin structural transformation across rural and urban sectors.

In agricultural and applied economics, randomized experiments are often infeasible for causal inference, necessitating credible identification strategies grounded in observational data. This paper synthesizes major quasi-experimental methods—including instrumental variables, difference-in-differences, regression discontinuity design, and matching—and develops a domain-specific empirical research framework tailored to agricultural economics. The framework emphasizes explicit articulation of identification assumptions, their empirical testability, and transparent reporting. Its key contribution is the first domain-specific causal design selection guide for agricultural economics, embedding methodological principles within concrete research contexts and illustrating assumption validation through canonical empirical examples. By standardizing identification logic, diagnostic checks, and reporting conventions, the framework substantially enhances the rigor, reproducibility, and cross-study comparability of causal effect estimates derived from observational data—thereby addressing a critical gap in methodological guidance for the field. (149 words)

This paper addresses model misspecification in two-way fixed effects (TWFE) estimators under nonlinear continuous treatment effects. We propose a semiparametric estimator for panel data that enables unbiased identification of the average partial derivative (APD). Methodologically, we extend the double/debiased machine learning (DML) framework—previously developed for cross-sectional settings—to continuous-treatment panel models with unit fixed effects, integrating high-dimensional regressions (e.g., Lasso or random forests) and robust standard error construction, and formally establish the asymptotic normality of the estimator. Empirically, we apply the method to estimate the impact of extreme heat on maize yields. Results reveal a nonlinear, marginally diminishing dose–response relationship: linear TWFE estimates underestimate heat-induced yield losses by 50%, implying an additional annual loss of USD 3.17 billion by 2050.

This study investigates the causal mechanisms through which meteorological factors drive price volatility of soybean and eggplant in India, focusing on Madhya Pradesh and Odisha. To model conditional price volatility, an Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) framework is employed; Granger causality tests—extended to capture nonlinear dependencies—are applied to identify statistically significant meteorological drivers. Furthermore, a meteorology-augmented hybrid SARIMAX-LSTM forecasting architecture is developed. The study provides the first systematic, regionally granular evidence in India demonstrating statistically significant causal effects of rainfall and temperature on both perishable and staple crop prices (p < 0.01). The proposed hybrid paradigm integrates econometric rigor with machine learning interpretability, achieving 18–23% lower Mean Absolute Error (MAE) relative to standard benchmarks. These findings deliver actionable quantitative insights for designing climate-resilient agricultural finance instruments, supporting smallholder risk management decisions, and optimizing crop rotation policies.

Existing research on assessing the socioeconomic impacts of climate disasters using textual data often suffers from ambiguous impact definitions, inadequate handling of spatiotemporal biases, and inconsistent modeling strategies, which undermine result transparency and comparability. This study addresses these limitations by systematically integrating large-scale textual sources—including news articles, social media posts, and official reports—and proposes the first standardized methodological framework tailored to this task. The framework explicitly defines impact criteria, corrects for spatiotemporal biases, and standardizes model selection. Leveraging natural language processing and large language models within a “text-as-data” paradigm, the work introduces a reproducible, transparent, and comparable set of best practices for extracting and quantifying disaster-related information, thereby significantly enhancing the accuracy of climate disaster impact assessment and attribution studies.

This study addresses the lack of high-resolution, site-scale spatiotemporal analyses of land surface temperature in Ghana, which has hindered understanding of local climate characteristics and their implications for agriculture and public health. Leveraging daily maximum and minimum temperature records from 22 meteorological stations spanning 1983–2021, this work presents the first multi-site, long-term, high-resolution trend analysis for the country. Data were rigorously quality-controlled and homogenized following World Meteorological Organization (WMO) standards and integrated with AgERA5 reanalysis data. Modified Mann-Kendall tests and Sen’s slope estimator revealed pronounced asymmetric diurnal warming: minimum temperatures rose significantly faster than maximum temperatures, leading to a persistent decline in diurnal temperature range. These findings underscore the urgent need for site-specific, seasonally tailored climate adaptation strategies.

This study addresses the limitations of traditional climate analysis methods, which struggle to integrate socioeconomic knowledge and lack the capacity for interpretable, adaptive analysis of the complex interactions between human behavior and climate change. To bridge this gap, the authors propose ClimateAgents—a novel research assistant built on a multi-agent collaborative framework that introduces multi-agent systems into socio-climatic research for the first time. By orchestrating domain-specific agents to jointly perform hypothesis generation, multimodal data retrieval (incorporating authoritative sources such as the United Nations and World Bank), statistical modeling, and automated reasoning, ClimateAgents enables interdisciplinary, interpretable, and context-aware analysis. This approach significantly enhances the flexibility and depth of exploring relationships among climate indicators, social variables, and environmental outcomes, thereby improving the efficiency of interdisciplinary research on complex socio-environmental systems.

This study quantifies the impact of extreme climatic factors on cause-specific mortality across diverse U.S. populations to address uncertainties in physical climate risk. Innovatively integrating compositional data analysis (CODA) into climate–mortality research, the authors employ principal component analysis (PCA) to reduce dimensionality of climate variables and construct generalized additive models (GAMs) to capture nonlinear relationships between factors such as temperature and sea-level rise and the proportional distribution of causes of death. The findings reveal that elevated temperatures and rising sea levels significantly increase the proportion of deaths attributable to hypertensive heart disease, with individuals aged 55–95 exhibiting heightened sensitivity. Moreover, the study identifies climate-driven natural hedging effects among different causes of death, offering novel insights for climate risk modeling and the design of insurance products.

This study addresses the limitations of traditional time series methods in disentangling genuine multi-scale periodic effects—such as daily, weekly, and annual cycles—from random fluctuations, particularly under multivariate and unbalanced experimental designs where interpretability is often compromised. For the first time, the authors systematically apply ANOVA Simultaneous Component Analysis (ASCA) to observational time series, integrating analysis of variance with principal component analysis into a unified framework that effectively models cyclostationary structures across multiple temporal scales. The proposed approach explicitly accounts for autocorrelation and unbalanced designs while accommodating both univariate and multivariate settings. Its efficacy is demonstrated through applications to lake water temperature data from the Sierra Nevada in Spain and a 30-year pollen concentration record from Granada, where it substantially enhances the interpretability and precision of extracted periodic patterns.