Study uses TV news to measure climate risks affecting clean energy firms.
problem Understanding how climate risks impact clean energy firms' financial stability.
method Developed climate risk measures from TV news coverage and analyzed their effects on clean energy firms' risks.
result Increased TV news coverage of climate risks correlates with higher systematic risk and lower idiosyncratic risk for clean energy firms.
A framework uses deep learning for spatio-temporal data prediction.
problem Interpolation of continuous spatio-temporal fields on irregular points.
method Decomposes spatio-temporal processes into products of basis functions and spatial coefficients.
result Effectiveness in reconstructing coherent spatio-temporal fields.
Conventional economic analysis of stringent climate change mitigation policy generally concludes various levels of economic slowdown as a result of substantial spending on low carbon technology. Equilibrium economics however could not explain or predict the current economic crisis, which is of financial nature. Meanwhi…
Migration crisis, climate change or tax havens: Global challenges need global solutions. But agreeing on a joint approach is difficult without a common ground for discussion. Public spheres are highly segmented because news are mainly produced and received on a national level. Gain- ing a global view on international d…
First-best climate policy is a uniform carbon tax which gradually rises over time. Civil servants have complicated climate policy to expand bureaucracies, politicians to create rents. Environmentalists have exaggerated climate change to gain influence, other activists have joined the climate bandwagon. Opponents to cli…
We argue that the present crisis and stalling economy continuing since 2007 are rooted in the delusionary belief in policies based on a "perpetual money machine" type of thinking. We document strong evidence that, since the early 1980s, consumption has been increasingly funded by smaller savings, booming financial prof…
Proposes a new stochastic method to calibrate climate risks in financial models.
problem Estimating climate-related financial risks in bank loan portfolios.
method Stochastic forward-looking methodology to calibrate climate macro-correlation evolution from scientific data.
result A new framework to evaluate climate risks without specific scenario assumptions.
CE improves climate uncertainty quantification using GCM ensembles and observational data.
problem Uncertainty in climate projections due to model inadequacies and variability.
method Conformal ensembles integrating GCM ensembles and observational data.
result CE generates statistically rigorous, easy-to-interpret uncertainty estimates.
Climate-contingent finance helps adapt to uncertain climate risks.
problem Uncertainty in future climate scenarios makes proactive adaptation less feasible.
method Underwrite climate adaptation projects with repayment based on future climate scenarios.
result Optimal financing reduces over- and under-preparation risks.
Geospatial framework assesses climate risks for California's banking and exposed sectors.
problem Evaluating climate risks on banking and exposed sectors in California.
method Integrates hazard mapping, exposure analysis, and scenario-based financial risk assessment.
result Framework supports portfolio monitoring and institutional readiness under new standards.
Study identifies new stable climate states in climate model.
problem Understanding multistability and transitions in climate models.
method Combination of quasipotential theory and manifold learning.
result Discovery of a third stable climate state not previously known.
Framework identifies causal factors of climate change using correlations and machine learning.
problem Understanding socioeconomic factors influencing carbon emissions and climate change.
method Three-step framework: correlation analysis, causal discovery, LLM interpretations.
result Adaptable solutions for data-driven policy-making and strategic decision-making.
New framework bridges climate science and ML for easier climate model emulation.
problem High computational costs and mistrust of ML methods in climate models.
method Integrating climate science and machine learning perspectives to design easy-to-adopt emulators.
result Demonstrated reliability of emulators designed to address specific tasks.
ESN model helps understand climate event impacts.
problem Understanding complex climate event impacts.
method Feature importance methods for ESNs on spatio-temporal climate data.
result Characterized relationships between Mount Pinatubo eruption variables.
The study examines how climate risk influences sovereign debt default decisions.
problem The relationship between climate risk and sovereign debt default decisions.
method Calibration of a model to analyze the credit spreads of sovereign bonds and the impact of climate vulnerability on bond spreads.
result Climate risk does not significantly influence the decision to default on sovereign debt.
Future projection of climate is typically obtained by combining outputs from multiple Earth System Models (ESMs) for several climate variables such as temperature and precipitation. While IPCC has traditionally used a simple model output average, recent work has illustrated potential advantages of using a multitask lea…
CCVA adjusts for climate change impacts on financial valuation.
problem Climate change impacts on financial valuation are currently ignored.
method Flexible parameterization to capture climate impacts on hazard rates.
result Significant impacts on interest rate swaps even with slow climate change.
HECT tests climate model outputs for reproducibility.
problem Ensuring climate models accurately reflect physical processes.
method Probabilistic classifiers for high-dimensional spatio-temporal data.
result A principled way to assess statistical reproducibility of climate models.
CERM calculates climate risks in bank loans.
problem Estimating climate risks in bank credit portfolios.
method Adapts credit risk models to include physical and transition risks.
result Calculates incremental credit losses due to climate risks.
Develops a climate risk model for asset managers.
problem Climate-related risks affecting asset performance and productivity.
method Uses the Vasicek model with downward jumps to represent climate impacts on asset dynamics.
result Expected losses increase over time due to climate-related extreme events.
Investigates how extreme temperature events affect global equity portfolios.
problem Impact of extreme temperature events on global equity portfolios.
method Panel regression analysis and multi-objective portfolio optimization.
result Extreme temperature events negatively impact most sectors' returns.
Study shows climate change can cause a 'run on fossil fuels' affecting prices and production.
problem Impact of climate change expectations on fossil fuel markets and prices.
method Dynamic, general equilibrium model of climate-change-linked transition risk.
result Climate change expectations can lead to either increased or decreased fossil fuel prices, depending on economic responses.
Study predicts climate data at distant locations using machine learning.
problem Predict climate variables at distant locations where comprehensive data collection is not feasible.
method Uses reservoir computing and vector autoregression models for prediction.
result Machine learning improves prediction accuracy for highly correlated data.
Generative model emulates climate model for 100-year forecasts.
problem Challenges in accurately simulating long-term climate data.
method Integrates DYffusion with SFNO for stable, accurate climate simulations.
result Achieves near gold-standard performance for climate model emulation.
SPF uses a hierarchical approach to efficiently emulate climate changes.
problem Slow and unstable climate emulation for long horizons.
method Spatiotemporal Pyramid Flows (SPF) model data hierarchically across spatial and temporal scales.
result SPF outperforms flow matching baselines and pre-trained models on ClimateBench.
Study analyzes climate impact on agricultural prices, offering insurance solutions.
problem Financial risk from climate-induced agricultural price volatility.
method Historical and future climate projections, EGARCH and SARIMAX models, Black-Scholes framework.
result Improved agricultural risk modeling and insurance mechanisms.
Nostradamus links climate and stock market performance.
problem Understanding the impact of climate on stock prices.
method Analyzing historical data, climate indicators, and natural disasters.
result Significant correlation between climate and stock price fluctuations.
AutoML struggles with climate change data, but offers potential improvements.
problem Improving machine learning for climate change applications.
method Benchmarked AutoML libraries on climate modeling, wind power, and catalyst discovery.
result Current AutoML techniques fail to surpass human-designed models in climate change applications.
Developing a climate-aware pricing framework for XL reinsurance and CAT bonds under non-stationary catastrophe risk.
problem Pricing excess-of-loss (XL) reinsurance and catastrophe (CAT) bonds under climate uncertainty.
method Modeling catastrophe arrivals as a Cox process with a temperature-dependent stochastic intensity and aggregate losses following a compound Cox structure.
result Climate dependence materially changes the loss-generation mechanism and affects the valuation of catastrophe-linked contracts.
New method uses spherical convolutional Wasserstein distance to validate climate models.
problem Ensuring the accuracy of global climate models.
method Spherical convolutional Wasserstein distance to measure model differences.
result Phase 6 models show modest improvements in realistic climatologies.
Climate volatility reduces economic growth, especially in poorer countries.
problem Impact of climate volatility on economic growth.
method Exploiting data on 133 countries over 59 years, controlling for temperature changes.
result A 1 degree C increase in temperature volatility leads to a 0.3% decline in GDP growth.
Study combines variational inference and transformers for seasonal climate predictions.
problem Lack of robust seasonal predictions due to limited historical records and computational constraints.
method Combines variational inference with transformer models trained on climate model output.
result Method provides skilful predictions beyond climate change-induced trends in various regions.
MMformer improves forecasting of environmental time series data.
problem Accurately forecasting environmental change trends for policy-making.
method Meta-learning MTS model combining self-attention and adaptive transferable multi-head attention.
result MMformer outperforms other models in air quality and climate datasets, reducing prediction errors by 50% in MSE and 20% in MAE.
This paper corrects climate model biases using a factor model approach.
problem Systematic biases in GCM outputs due to unobserved confounders.
method Factor model approach to learn latent confounders from historical data and apply them to enhance bias correction.
result Significant improvements in the accuracy of precipitation outputs.
The paper examines spillovers between agriculture, crude oil, carbon, and climate markets.
problem Understanding dynamic spillovers between agriculture, crude oil, carbon emission, and climate markets.
method A novel R2 decomposed connectedness approach. result Overall spillovers are mainly contemporaneous, not lagged; climate change significantly impacts others; agricultural markets have heterogeneous effects; corn is a major risk contributor.
This paper analyzes machine learning workflows in climate modeling.
problem Challenges in integrating machine learning with climate modeling.
method Analysis of case studies focusing on design patterns and workflow structure.
result Synthesis of workflow design patterns across diverse projects in ML-enabled climate modeling.
Study predicts doubling of U.S. maize insurance claims due to climate change.
problem Climate change increases U.S. maize loss probability, impacting insurance claims.
method Neural Network Monte Carlo simulations to predict crop loss metrics.
result Doubling of annual probability of maize Yield Protection insurance claims by mid-century.
Deep learning improves stochastic downscaling of climate variables.
problem Accurately capturing climatic variability at local scales.
method Proposed improvements to GANs for stochastic downscaling of climate variables.
result Improved stochastic calibration of GANs for high-resolution climate predictions.
Climate projections suffer from uncertain equilibrium climate sensitivity. The reason behind this uncertainty is the resolution of global climate models, which is too coarse to resolve key processes such as clouds and convection. These processes are approximated using heuristics in a process called parameterization. Th…
Reservoir Computing enhances climate predictability studies.
problem Improving climate predictability using machine learning.
method Reservoir Computing applied to climate data.
result Reservoir Computing outperforms traditional LIM in predicting climate variables.
NESYM combines AI and Earth models for new climate insights.
problem Replacing traditional Earth models with AI.
method Neural Earth System Modelling (NESYM) integrating AI and climate models.
result Artificial intelligence may render traditional models obsolete.
Combines GANs and EVT for better modeling of spatial climate extremes.
problem Modeling dependencies between climate extremes, especially in high-dimensional spaces.
method Generative Adversarial Networks (GANs) combined with Extreme Value Theory (EVT).
result evtGAN outperforms classical GANs and statistical approaches in modeling spatial extremes.
Climate change impacts and adaptations are the subjects to ongoing issues that attract the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable devel…
New method preserves GCM spatial dependencies for better climate projections.
problem Systemic biases in GCM output and loss of spatial/temporal dependencies.
method SPECD approach using Vecchia approximation and semi-parametric quantile regression.
result SPECD preserves key marginal and joint distribution properties of precipitation and temperature.
Study evaluates deep learning methods for climate downscaling over Spain.
problem Deep learning methods' extrapolation capability for climate projections.
method Intercomparison experiment using PP and RCM emulation models.
result Existing models struggle with extrapolating unseen conditions.
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, w…
We consider the effects of the global financial crisis through a local Korean financial market around the 2008 crisis. We analyze 185 individual stock prices belonging to the KOSPI (Korea Composite Stock Price Index), cosidering three time periods: the time before, during, and after the crisis. The complex networks gen…
According to a recent investigation, an estimated 33-50% of the world's coral reefs have undergone degradation, believed to be as a result of climate change. A strong driver of climate change and the subsequent environmental impact are greenhouse gases such as methane. However, the exact relation climate change has to …