| Session | ||
Risk, uncertainty, and climate adaptation
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| Presentations | ||
Early Warning Systems 1RPTU Kaiserslautern-Landau, Germany; 2Chair of Integrative Risk Management and Economics, ETH Zurich; 3Centre for Climate Resilience, University of Augsburg; 4Department of Economics, University of Copenhagen; 5CESifo Research Network, Munich; 6RFF‐CMCC European Institute on Economics and the Environment (EIEE), Centro Euro‐Mediterraneo sui Cambiamenti Climatici, Milan, Italy Complex systems can undergo sudden, irreversible changes when critical thresholds—known as tipping points—are crossed. While statistical indicators can serve as early warning signals (EWS) of such transitions, their integration into decision-making frameworks remains limited. This paper develops a theoretical framework connecting early warning signals to optimal management of systems prone to tipping. We translate early warning indicators into the formal language of management science, introduce an analytically tractable approximation of tipping indicators, and derive practical insights about the optimal design and value of early warning systems. Our analysis reveals how the optimal design and value of these systems depend on both the magnitude of potential damages and decision-maker risk preferences, providing a foundation for incorporating early warning signals into management decisions. Exporters' behaviour in the face of climate volatility 1Toulouse School of Economics; 2Bordeaux School of Economics This study examines how exporters make export decisions when faced with production and demand shocks. Using a unique dataset of French wine shipments from 2001 to 2020 across 134 Protected Denomination of Origin (PDO) regions, and daily weather data from M\'et\'eo-France, we employ gravity estimations to show that extreme weather affects both trade intensive and extensive margins, while favorable weather boosts them. A heterogeneity analysis reveals that exports to core markets are less sensitive than peripheral markets to extreme weather, indicating market prioritization by exporters. Our theoretical analysis explains how climate-induced production shock volatility shapes export behavior, leading firms to reallocate resources to most attractive markets and streamline their destination markets portfolios by exiting less favorable ones. Managing ecological systems with tipping points under exogenous shocks 1Western Norway University of Applied Sciences, Norway; 2Department of Economics, Tilburg University, the Netherlands, and Beijer Institute for Ecological Economics, Royal Swedish Academy of Sciences The lake is a good example of an ecological system with tipping points. Polluting the lake by the release of phosphorus from agriculture not only damages the lake gradually, but can cause the lake to tip with a sudden big loss of ecosystem services. This paper extends the analysis of optimally managing the lake with uncertain outside shocks to the stock of phosphorus. The conclusion is that when it is optimal to keep the lake in a state with a high level of ecosystem services, optimal management becomes precautionary. However, the set of initial levels of the stock of phosphorus for which it is optimal to keep the lake in such a state becomes smaller because of the additional risk for higher levels of phosphorus. Temperature Shocks and Climate Change: A Conceptual Analysis University of Oslo, Norway This paper addresses the challenge of accurately modeling and estimat- ing climate change damages. Time-series approaches rely on weather shocks, while cross-sectional analyses capture climatic differences but suffer from omitted variable bias. Climate is defined as the statistical pattern of weather over time, allowing for adaptation to predictable changes, unlike unpredictable weather realizations. To assess econometric approaches, I integrate forward-looking adaptation into a detailed, quantitative integrated assessment model of climate change. I analyze how adaptation affects observed damages under time-series and cross-sectional approaches and compare this information with the requirements for calculating the social costs. While neither approach fully captures climate change costs, time-series methods focusing on weather shocks outperform cross-sectional methods in several key respects. | ||