About

Frances Moore is a professor whose research interests include climate change, hazards, and natural disasters. Her work involves understanding the impacts of climate-related extreme weather on community and ecological resilience. She is actively engaged in research that explores how climate change affects natural systems and human communities, contributing to the broader field of environmental science and policy.

Research topics

• Ecology
• Geography
• Biology
• Economics
• Computer Science
• Environmental science
• Agronomy
• Natural resource economics
• Business
• Environmental resource management
• Agroforestry
• Economic growth
• Algorithm
• Development economics
• Chemistry
• Meteorology
• Neoclassical economics
• Biochemistry
• Agricultural economics
• Environmental planning

Selected publications

• Critical perspectives on climate change economics

  Elsevier eBooks · 2026-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Climate-induced range shifts support local plant diversity but don’t reduce extinction risk

  Science · 2026-05-07 · 1 citations

  article

  INTRODUCTION Anthropogenic climate change is reshaping where plants can live. As temperature and precipitation patterns shift, many species are moving to stay within suitable environmental conditions. Predicting how these range shifts will affect future biodiversity requires knowing both where suitable habitats will occur and whether species can reach them. The latter is challenging because dispersal abilities differ widely among species and depend on landscape structure, anthropogenic barriers, and climatic conditions. Large-scale biodiversity forecasts therefore often rely on overly simple assumptions-such as no dispersal, unlimited dispersal, or identical movement rates for all species-thus adding major uncertainty to projections and conservation planning. RATIONALE We used the largest global database of observed plant range shifts (BioShifts; 14,488 records across 6579 plant species) to build models that predict species-specific range shift velocities. Combining 6.8 million plant occurrence records, an ensemble of two top-performing habitat models, and climate projections from 10 global circulation models, we mapped current and future suitable habitats-areas with favorable climate, soil, and land use-at 8 × 8 km resolution for each species. Our analysis covers 18% of known vascular plant species under four greenhouse-gas emissions scenarios for 2081 to 2100. We then overlaid the projected future suitable habitats with species-specific range shift velocities to determine where each species is likely to persist or expand by the end of this century. From these results, we estimated global extinction risks, changes in local species richness, and temporal species turnover in community composition. RESULTS Overall, 7 to 16% of modeled plant species are projected to lose >90% of their range across emissions scenarios, placing them at high risk of extinction. Most of these losses (70 to 80%) stem from suitable habitats disappearing as a result of climate change, rather than from dispersal limitations, indicating that climate-induced habitat loss, rather than an inability to keep pace with changing climate, is the primary threat. Although range shifts are unlikely to prevent many global extinctions, they will strongly reshape local species composition. Plant movements into newly suitable habitats are expected to increase local species richness across 28% of Earth's land surface, maintain latitudinally averaged species richness in the tropics and subtropics (35°S to 35°N), and generate substantial species turnover in mid-latitudes (30° to 50° in both hemispheres). By contrast, in regions north of 50°N, warming is so rapid that most plants cannot keep pace, leading to widespread local extirpations and sharp declines in species richness. CONCLUSION Range shifts can help sustain local species richness but are unlikely to provide much relief from global extinctions. To reduce extinction risks, identifying and protecting climate change refugia to safeguard biodiversity, and expanding ex situ conservation efforts, such as global seed bank and botanic garden networks, may be more effective than facilitating migrations. At the same time, conservation strategies should anticipate changing community compositions and ecosystem functioning as new species arrive and ecosystems reorganize. In high-latitude regions where dispersal lags considerably behind the rapid warming, improving habitat connectivity, reducing human-made barriers, and where appropriate, assisting species movement could help maintain local species richness, ecosystem productivity, carbon sequestration, and ecosystem stability. [Figure: see text].

  PublisherDOI

• Spatial Selection Undermines Flood Protection in U.S. Wetland Markets

  Research Square · 2026-03-05

  preprintOpen accessSenior author
  PublisherOA PDFDOI

• Spatial Selection Undermines Flood Protection in U.S. Wetland Markets

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Learning, Catastrophic Risk, and Ambiguity in the Climate-Change Era

  Environmental and Energy Policy and the Economy · 2025-01-01 · 2 citations

  article1st authorCorresponding

  Key methodologies used for managing weather risks have relied on the assumption that climate is not changing and that the historic weather record is therefore representative of current risks. Anthropogenic climate change upends this assumption, effectively reducing the information available to actors and increasing ambiguity in the estimated climate distribution, with associated costs for weather risk management and risk-averse decision makers. These costs result purely from the knowledge that the climate could be changing, may arise abruptly, are additional to any direct costs or benefits from actual climate change, and are, to date, entirely unquantified. Using a case study of extreme rainfall-related flood damages in New York City, this paper illustrates how these ambiguity-related costs arise. Greater uncertainty over the current climate distribution interacts with a steeply nonlinear damage function to greatly increase the mean and variance of the posterior loss distribution. This is a systemic information shock that cannot be diversified within the insurance sector, producing higher and more volatile premiums and higher reinsurance costs. These effects are consistent with recent developments in US property insurance markets, where premium increases, bankruptcies, and insurer withdrawals have been linked to the growing costs of natural disasters.

  PublisherDOI

• Natural Disasters Assistance: Beliefs, Preferences, and Impacts

  AEA Randomized Controlled Trials · 2025-02-21

  dataset
  PublisherDOI

• Natural Disasters Assistance: Beliefs, Preferences, and Impacts

  AEA Randomized Controlled Trials · 2025-02-21

  dataset
  PublisherDOI

• A value of information framework for climate adaptation and risk-management

  Environmental Research Climate · 2025-07-04

  articleOpen access1st authorCorresponding

  Abstract The demand for information required to manage weather-related risks under changing climate conditions is growing. In response there have been efforts within both the public and private sectors to provide information from climate science to support adaptation decisions. Such information provision is complicated by the wide range of potential adaptation use-cases, the variable spatial and temporal scales of information required, and the lack of a systematic understanding of how scientific uncertainties propagate through adaptation decisions to affect the value to users. Here we propose value of information (VOI) as a useful conceptual framework for analyzing information designed to support adaptive decision-making. We describe the VOI framework and its usefulness for the adaptation context. We illustrate using a stylized set of decisions and a set of climate information states based on a modified Lorenz model.

  PublisherDOI

• Author response for "A value of information framework for climate adaptation and risk-management"

  2025-05-13

  peer-review1st authorCorresponding
  PublisherDOI

• Synthesis of Evidence Yields High Social Cost of Carbon Due to Structural Model Variation and Uncertainties

  National Bureau of Economic Research · 2024-06-01 · 16 citations

  reportOpen access1st authorCorresponding

  Estimating the cost to society from a ton of CO2 - termed the social cost of carbon (SCC) - requires connecting a model of the climate system with a representation of the economic and social effects of changes in climate, and the aggregation of diverse, uncertain impacts across both time and space. Increasingly a growing literature has examined the effect of fundamental structural elements of the models supporting SCC calculations. This work has accumulated in piecemeal fashion, leaving their relative importance unclear. Here we perform a comprehensive synthesis of the evidence on the SCC, combining 1823 estimates of the SCC from 147 studies with a survey of authors of these studies. The distribution of published 2020 SCC values is wide and substantially right-skewed, showing evidence of a heavy right tail (truncated mean of $132). Analysis of variance reveals important roles for the inclusion of persistent damages, representation of the Earth system, and distributional weighting. However, our survey reveals that experts believe the literature is biased downwards due to an under-sampling of structural model variations and biases in damage-function and discount-rate. To address this imbalance, we train a random forest model on variation in the literature and use it to generate a synthetic SCC distribution that more closely matches expert assessments of appropriate model structure and discounting. This synthetic distribution has a mean of $284 per ton CO2, respectively, for a 2020 pulse year (5%–95% range: $32–$874), higher than all official government estimates, including a 2023 update from the U.S. EPA.

  PublisherDOI

Recent grants

• CNH2-S: Understanding the Coupling Between Climate Policy and Ecosystem Change

  NSF · $750k · 2019–2025

Frequent coauthors

• Thomas W. Hertel

  15 shared

• Uris Lantz C. Baldos

  Purdue University West Lafayette

  14 shared

• Steven J. Davis

  University of California, Irvine

  11 shared

• Moritz A. Drupp

  Hamburg School of Business Administration

  10 shared

• Bernardo A. Bastien-Olvera

  University of California, San Diego

  9 shared

• Richard S.J. Tol

  9 shared

• Delavane Diaz

  Electric Power Research Institute

  8 shared

• Michael C. MacCracken

  Sphere Institute

  8 shared

Labs

• Fran Moore LabPI

Education

• Ph.D., Agricultural and Resource Economics

  University of California, Davis