Spatial Heterogeneity in Climate Risk and Human Flourishing: An Exploration with Generative AI
- URL: http://arxiv.org/abs/2601.20880v1
- Date: Mon, 26 Jan 2026 23:35:45 GMT
- Title: Spatial Heterogeneity in Climate Risk and Human Flourishing: An Exploration with Generative AI
- Authors: Stefano Maria Iacus, Haodong Qi, Devika Jain,
- Abstract summary: This study develops a spatial framework to examine how cumulative climate risk relates to multidimensional human flourishing across U.S. counties.<n>High-resolution climate hazard indicators are integrated with a Human Flourishing Geographic Index (HFGI), an index derived from classification of 2.6 billion geotagged tweets.<n>These indicators are aggregated to the US county-level and mapped to a structural equation model to infer overall climate risk and human flourishing dimensions.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advances in Generative Artificial Intelligence (AI), particularly Large Language Models (LLMs), enable scalable extraction of spatial information from unstructured text and offer new methodological opportunities for studying climate geography. This study develops a spatial framework to examine how cumulative climate risk relates to multidimensional human flourishing across U.S. counties. High-resolution climate hazard indicators are integrated with a Human Flourishing Geographic Index (HFGI), an index derived from classification of 2.6 billion geotagged tweets using fine-tuned open-source Large Language Models (LLMs). These indicators are aggregated to the US county-level and mapped to a structural equation model to infer overall climate risk and human flourishing dimensions, including expressed well-being, meaning and purpose, social connectedness, psychological distress, physical condition, economic stability, religiosity, character and virtue, and institutional trust. The results reveal spatially heterogeneous associations between greater cumulative climate risk and lower levels of expressed human flourishing, with coherent spatial patterns corresponding to recurrent exposure to heat, flooding, wind, drought, and wildfire hazards. The study demonstrates how Generative AI can be combined with latent construct modeling for geographical analysis and for spatial knowledge extraction.
Related papers
- Population synthesis with geographic coordinates [1.6419687521433917]
It is increasingly important to generate synthetic populations with explicit coordinates rather than coarse geographic areas.<n>We propose a population synthesis algorithm that maps spatial coordinates into a more regular latent space.<n>We demonstrate the method by generating synthetic homes with the same statistical properties of real homes in 121 datasets.
arXiv Detail & Related papers (2025-10-08T13:36:13Z) - A Self-Evolving AI Agent System for Climate Science [59.08800209508371]
We introduce EarthLink, the first self-evolving AI agent system designed as an interactive "copilot" for Earth scientists.<n>Through natural language interaction, EarthLink automates the entire research workflow by integrating planning, code execution, data analysis, and physical reasoning.<n>It exhibits human-like cross-disciplinary analytical ability and proficiency comparable to a junior researcher in expert evaluations on core large-scale climate tasks.
arXiv Detail & Related papers (2025-07-23T08:29:25Z) - ClimateBench-M: A Multi-Modal Climate Data Benchmark with a Simple Generative Method [61.76389719956301]
We contribute a multi-modal climate benchmark, i.e., ClimateBench-M, which aligns time series climate data from ERA5, extreme weather events data from NOAA, and satellite image data from NASA.<n>Under each data modality, we also propose a simple but strong generative method that could produce competitive performance in weather forecasting, thunderstorm alerts, and crop segmentation tasks.
arXiv Detail & Related papers (2025-04-10T02:22:23Z) - Regional climate risk assessment from climate models using probabilistic machine learning [12.737495484442443]
GenFocal is a general-purpose, end-to-end generative framework for complex climate processes interacting at finetemporal scales.<n>It more accurately assesses extreme risk in the current climate than leading approaches.<n>GenFocal shows compelling results consistent with the literature on projecting climate impact on decadal timescales.
arXiv Detail & Related papers (2024-12-11T03:52:17Z) - Causal Representation Learning in Temporal Data via Single-Parent Decoding [66.34294989334728]
Scientific research often seeks to understand the causal structure underlying high-level variables in a system.
Scientists typically collect low-level measurements, such as geographically distributed temperature readings.
We propose a differentiable method, Causal Discovery with Single-parent Decoding, that simultaneously learns the underlying latents and a causal graph over them.
arXiv Detail & Related papers (2024-10-09T15:57:50Z) - Combining deep generative models with extreme value theory for synthetic
hazard simulation: a multivariate and spatially coherent approach [0.0]
Generative adversarial networks (GANs) are well-suited to such a problem due to their ability to implicitly learn the distribution of data in high-dimensional settings.
We employ a GAN to model the dependence structure for daily maximum wind speed, significant wave height, and total precipitation over the Bay of Bengal.
Once trained, the model can be used to efficiently generate thousands of realistic compound hazard events.
arXiv Detail & Related papers (2023-11-30T12:55:51Z) - Federated Prompt Learning for Weather Foundation Models on Devices [37.88417074427373]
On-device intelligence for weather forecasting uses local deep learning models to analyze weather patterns without centralized cloud computing.
This paper propose Federated Prompt Learning for Weather Foundation Models on Devices (FedPoD)
FedPoD enables devices to obtain highly customized models while maintaining communication efficiency.
arXiv Detail & Related papers (2023-05-23T16:59:20Z) - Using Artificial Intelligence to aid Scientific Discovery of Climate
Tipping Points [1.521140899164062]
We propose a hybrid Artificial Intelligence (AI) climate modeling approach that enables climate modelers in scientific discovery.
We describe how this methodology can be applied to the discovery of climate tipping points and, in particular, the collapse of the Atlantic Meridional Overturning Circulation (AMOC)
We show preliminary results of neuro-symbolic method performance when translating between natural language questions and symbolically learned representations.
arXiv Detail & Related papers (2023-02-14T06:00:39Z) - Multi-scale Digital Twin: Developing a fast and physics-informed
surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
Climate change exacerbates the long-term soil management problem of groundwater contamination.
We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO)
In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
arXiv Detail & Related papers (2022-11-20T06:46:35Z) - Spatiotemporal modeling of European paleoclimate using doubly sparse
Gaussian processes [61.31361524229248]
We build on recent scale sparsetemporal GPs to reduce the computational burden.
We successfully employ such a doubly sparse GP to construct a probabilistic model of paleoclimate.
arXiv Detail & Related papers (2022-11-15T14:15:04Z) - Deep generative model super-resolves spatially correlated multiregional
climate data [5.678539713361703]
We show an adversarial network-based machine learning enables us to correctly reconstruct the inter-regional spatial correlations in downscaling.
The proposed method has a potential application to the inter-regionally consistent assessment of the climate change impact.
We present the outcomes of another variant of the deep generative model-based downscaling approach in which the low-resolution precipitation field is substituted with the pressure field.
arXiv Detail & Related papers (2022-09-26T05:45:16Z) - Methodological Foundation of a Numerical Taxonomy of Urban Form [62.997667081978825]
We present a method for numerical taxonomy of urban form derived from biological systematics.
We derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form.
After framing and presenting the method, we test it on two cities - Prague and Amsterdam.
arXiv Detail & Related papers (2021-04-30T12:47:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.