Conference Agenda

This paper examines how temperature shocks influence both the level and volatility

of key macroeconomic variables across a broad panel of countries. We develop a novel global-to-local identification strategy that uses exogenous variation from the El Nino-Southern Oscillation (ENSO) to isolate temperature anomalies at the country level. Our results show that a 1°C rise in temperature anomalies leads to persistent inflationary pressures and a decline in economic activity, with effects particularly pronounced in developing economies – where inflation can increase by up to 1.2%. Incorporating a stochastic volatility-in-mean framework, we find that these shocks also elevate macroeconomic uncertainty by raising the volatility of GDP growth and consumer price inflation. Together, these findings highlight the importance of modeling climate shocks not only through their direct effects on prices and output, but also through their second-moment consequences, which may warrant policy attention.

We construct a comprehensive set of climate indices for European countries that account for several variables related to weather, atmospheric conditions, and water availability. Our dataset includes monthly gridded climate observations from ERA5-Land, aggregated at the country level. Employing a Multilevel Dynamic Factor Model, we disentangle a global indicator, capturing overall climate dynamics across Europe, from country-specific local indices. While most empirical studies proxy climate through temperature or precipitation, our approach acknowledges that other atmospheric dimensions, such as humidity, radiation, and evaporation, jointly shape climatic variability and its economic effects. The global index primarily reflects temperature patterns common to most European countries, whereas the local indicators capture other meteorological phenomena and variations in water reserves. Finally, we show, via panel local projections, that different filtering and detrending procedures used to construct climate anomalies influence the estimated effects of climate shocks on economic activity.

Inconsistent and incomplete climate records remain a major constraint for environmental and

resource economics, particularly in regions with sparse or interrupted meteorological

observations. In Brazil, missing data in national weather station networks introduce measurement

error, bias climate damage functions, and increase basis risk in applications ranging from

agricultural insurance to climate-informed public planning. This paper presents an AI-based

climate data infrastructure designed to improve the continuity, auditability, and economic

usability of climate exposure data. The proposed framework combines global reanalysis products

and local surface observations through a staged pipeline that separates physical validation from

operational imputation. A Transformer-based model trained on ERA5 reanalysis data under

controlled masking is used to learn physically coherent spatiotemporal structures, while Dense

Neural Networks (DNNs) provide operational baselines for imputing missing values in Brazil's

INMET station network. While qualitative experiments demonstrate that Transformer-based

models preserve physically consistent spatial gradients in reanalysis fields, the application of this

architecture to the INMET network is currently in progress. Preliminary results show that the

implemented DNN baselines substantially outperform classical methods such as K-Nearest

Neighbors and linear regression across key climate variables. Beyond methodological

contributions, the study introduces a prototype national-scale climate intelligence platform that

delivers harmonized, imputed, and metadata-rich climate series. By reducing information costs

and improving the reliability of climate exposure measures, the proposed infrastructure supports

more credible economic analysis, climate risk assessment, and evidence-based policy design in

Brazil.

This paper quantifies climate stranding risk for French residential properties exposed to clay shrinkage-swelling (CSS). We measure the potential for stranding by estimating the wedge between the actuarial present value of future CSS damages and the extent to which these expected costs are capitalized into current house prices. We train an XGBoost classifier on roughly 30,000 CatNat-CSS recognition events in metropolitan France over 2000-2024 and project municipality-level recognition probabilities through 2100 under TRACC climate scenarios. We then map projected recognition risk into expected CatNat claim counts and monetary losses, and compute net present values (NPVs) of future CSS damages. Aggregate projected losses amount to about 81 billion € through 2100, or 37 billion € when discounted at a 2% real rate (base year 2025). Merging these NPVs with 800,000 housing transactions, hedonic regressions show that CSS risk is only weakly capitalized: doubling the projected NPV is associated with a price decrease of only a few tenths of a percent. The resulting gap between forward-looking expected losses and observed price discounts points to substantial unpriced chronic climate risk and suggests meaningful scope for future asset stranding as information, regulation, or insurance conditions evolve.