About

Jennifer (Jen) Burney is a Professor in Global Environmental Policy and Earth System Science in the Doerr School of Sustainability at Stanford University. Her research focuses on the coupled relationships between climate and food security, including measuring air pollutant emissions and concentrations, quantifying the effects of climate and air pollution on land use and food systems, understanding how food production and consumption contribute to climate change, and designing and evaluating technologies and strategies for adaptation and mitigation among the world’s farmers. Her research group employs methods from physics, ecology, statistics, remote sensing, economics, and policy to understand critical scientific uncertainties in this coupled system and to provide evidence for effective solutions to end hunger and stabilize the earth’s climate.

Research topics

• Geography
• Environmental science
• Ecology
• Meteorology
• Natural resource economics
• Medicine
• Economics
• Environmental health
• Environmental resource management
• Economic growth
• Environmental planning
• Business
• Biology
• Environmental protection
• Psychology
• Physical geography
• Agronomy
• Development economics
• Agricultural economics
• Agroforestry

Selected publications

• Modeling Food Market Dynamics Response to Climate Shocks in Karnataka to estimate Minimum Support Price Responses

  Stanford Digital Repository · 2026-04-21

  otherOpen accessSenior author

  This project develops an agent-based model of Karnataka’s food economy to investigate the mechanisms that generate observed distributions and temporal patterns of food prices. The model uses a data‑driven digital twin calibrated with district‑level economic indicators and historical price series, then perturbs production through varying growing conditions to examine how climate shocks propagate through markets. Within this framework, the study focuses on the role of the Minimum Support Price (MSP) and associated subsidies, quantifying how often market prices trigger MSP interventions, how large the resulting fiscal burden becomes, and how these public support costs evolve across normal years and monsoon‑related shocks. By comparing scenarios with and without MSP, the model measures who bears the incidence of price risk, how changes to MSP parameters affect producer and consumer welfare, and whether MSP stabilisation amplifies or mitigates inequality, using derivative statistics constructed from simulated price, income, and wealth distributions.

  PublisherDOI

• Crop water origins and hydroclimate vulnerability of global croplands

  Nature Sustainability · 2025-10-24 · 1 citations

  articleSenior author
  PublisherDOI

• Emulation of the Climate Response to Greenhouse Gas and Aerosol Emissions From High‐ and Low‐Income Nations

  Geophysical Research Letters · 2025-11-09 · 1 citations

  articleOpen access

  Abstract Global annual mean temperature change is a useful metric for tracking climate change, but climate impacts are felt locally and over relatively short time periods. It is therefore critical to predict the heterogeneous spatiotemporal pattern of future temperature changes. We use the CESM2 single‐forcing large ensemble to identify spatiotemporal changes in temperature associated with greenhouse gases and anthropogenic aerosols, leveraging the temporal offset of aerosol emissions from high income and developing economies to parse climate impacts of aerosols from these two groups of countries. Our results suggest that aerosol emissions from non‐OECD regions could still mask over 20% of greenhouse gas induced warming for over 40% of the global population in 2050. Parsing climatological impacts from different emission source regions improves our understanding of regional climate impacts and reduces the computational barrier to including regional impacts in simplified modeling frameworks.

  PublisherOA PDFDOI

• Eliminating hunger in a world serious about climate mitigation

  Proceedings of the National Academy of Sciences · 2025-08-04

  articleOpen access1st authorCorresponding

  What will it take to eliminate hunger in a world that is increasingly serious about climate change mitigation? For the past two decades, climate activism and mitigation efforts have largely focused on energy, industry, and transportation as the main contributors to anthropogenic warming. Although there is a long way to go to eliminate emissions from these sources, increasing international ambition means that attention is now turning to other hard-to-decarbonize sectors. Net zero food systems in particular pose unique challenges for humanity, given the moral dimensions of food security and the strong potential for vicious cycles: Globally hunger is back on the rise, the changing climate is already making food production for a growing population more difficult, and food shocks could lead to more land use change emissions that accelerate warming and feed back to negatively impact agricultural productivity and hunger. The dynamics of food production and consumption differ from other sectors and reveal several dimensions in which it will be critical to get policy right. Recent science suggests that two coordinated efforts will be needed to meet this challenge-a revamped focus on smallholder adaptation and productivity, and new "safety valve" institutions that sit at the intersection of food production and climate mitigation efforts and can operate at the relevant spatial and temporal scales to avoid negative tradeoffs in the quest to eliminate hunger and stabilize earth's climate.

  PublisherDOI

• The Influence of Wildfire Smoke on Ambient PM_2.5 Chemical Species Concentrations in the Contiguous US

  Environmental Science & Technology · 2025-02-03 · 19 citations

  article

  Wildfires significantly contribute to ambient air pollution, yet our understanding of how wildfire smoke influences specific chemicals and their resulting concentration in smoke remains incomplete. We combine 15 years of daily species-specific PM2.5 concentrations from 700 air pollution monitors with satellite-derived ambient wildfire smoke PM2.5, and use a panel regression to estimate wildfire smoke’s contribution to the concentrations of 27 different chemical species in PM2.5. Wildfire smoke drives detectable increases in the concentration of 25 out of the 27 species with the largest increases observed for organic carbon, elemental carbon, and potassium. We find that smoke originating from wildfires that burned structures had higher concentrations of copper, lead, zinc, and nickel relative to smoke from fires that did not burn structures. Wildfire smoke is responsible for an increasing share of ambient concentrations of multiple species, some of which are particularly harmful to health. Using a risk assessment approach, we find that wildfire-induced enhancement of carcinogenic species concentrations could cause increases in population cancer risk, but these increases are very small relative to other environmental risks. We demonstrate how combining ground-monitored and satellite-derived data can be used to measure wildfire smoke’s influence on chemical concentrations and estimate population exposures at large scales.

  PublisherDOI

• Women-led sustainable irrigation improved indicators of nutritional status in children from Northern Benin

  Journal of Health Population and Nutrition · 2025-12-08

  articleOpen access

  BACKGROUND: Climate change threatens food security and child nutrition in Africa. This study assessed the impact of community-based, solar-powered drip irrigation gardens (SMGs) on the nutritional status of children in households reliant on local food production. METHODS: Women's groups (WGs) from 16 matched villages in Northern Benin were randomly assigned to receive SMGs (WG_SMG) or continue traditional irrigation (WG_C). Additional households without women's groups (NWG_SMG and NWG_C) were recruited from intervention and comparison villages. Children aged 1-5 years participated in baseline and one-year follow-up surveys. Outcomes included dietary diversity, anthropometry, anemia, and micronutrient status. Dietary diversity was assessed using the WHO minimum dietary diversity indicator, based on consumption from at least five of eight food groups in the previous 24 h. Anemia was defined as hemoglobin < 10.5 g/dL or < 11 g/dL, age dependent. Difference-in-differences analysis estimated treatment effects (TEs). RESULTS: WG_SMG participation increased the percentage of children meeting minimum dietary diversity compared with all groups (TE: 0.64; p < 0.001). Hemoglobin concentrations increased in WG_SMG children versus all groups (TE: 0.53; p < 0.05), with reductions in anemia compared with WG_C (TE: - 0.15; p < 0.05). WG_SMG membership also improved weight-for-age z-scores (TE: 0.35; p < 0.05) and reduced wasting (TE: - 0.04; p < 0.01). Effects remained statistically significant when clustering at child or village levels. CONCLUSIONS: Children from women-owned SMGs had improved dietary diversity, hemoglobin status, and growth outcomes while reducing anemia and wasting compared to controls. Expanding this technology may enhance food security and child nutrition in climate-vulnerable regions.

  PublisherDOI

• Estimating the impacts of climate change: reconciling disconnects between physical climate and statistical models

  Climatic Change · 2025-02-01

  articleOpen accessSenior author

  Abstract Climate impacts studies often rely on empirical statistical methods to isolate the effects of changing environmental factors on human outcomes of interest. However, this research may not always account for the ways in which the underlying structure of climate might influence estimates from such models. Here we show how the different characteristic spatial scales of temperature (T) and precipitation (P), as well as the physical relationship between T and P, lead to biased historical and projected impact estimates in standard statistical approaches. Furthermore, we demonstrate how contemporary statistical research designs that exploit local fluctuations may unintentionally mis-characterize the influence of shifts in mean climate on outcomes of interest. Drawing on data from published studies we demonstrate these three issues; we focus primarily on the economic growth literature, though our results also apply to other contexts. We also propose several avenues to correct and bound the magnitude of the identified biases.

  PublisherOA PDFDOI

• Empirical modeling of agricultural climate risk

  Proceedings of the National Academy of Sciences · 2024-04-08 · 7 citations

  articleOpen access1st authorCorresponding

  Effective policies for adaptation to climate change require understanding how impacts are related to exposures and vulnerability, the dimensions of the climate system that will change most and where human impacts will be most draconian, and the institutions best suited to respond. Here, we propose a simple method for more credibly pairing empirical statistical damage estimates derived from recent weather and outcome observations with projected future climate changes and proposed responses. We first analyze agricultural production and loan repayment data from Brazil to understand vulnerability to historical variation in the more predictable components of temperature and rainfall (trend and seasonality) as well as to shocks (both local and over larger spatial scales). This decomposed weather variation over the past two decades explains over 50% of the yield variation in major Brazilian crops and, critically, can be constructed in the same way for future climate projections. Combining our estimates with bias-corrected downscaled climate simulations for Brazil, we find increased variation in yields and revenues (including more bad years and worse outcomes) and higher agricultural loan default at midcentury. Results in this context point to two particularly acute dimensions of vulnerability: Intensified seasonality and local idiosyncratic shocks both contribute to worsening outcomes, along with a reduced capacity for spatially correlated ("covariate") shocks to ameliorate these effects through prices. These findings suggest that resilience strategies should focus on institutions such as water storage, financial services, and reinsurance.

  PublisherOA PDFDOI

• Age-Dependent Variations in Kawasaki Disease Incidence in Japan

  JAMA Network Open · 2024-02-06 · 18 citations

  articleOpen access

  Importance: The etiology of Kawasaki disease (KD) remains elusive, with immunologic and epidemiologic data suggesting different triggers in individuals who are genetically susceptible. KD remains the most common cause of acquired heart disease in pediatric patients, and Japan is the country of highest incidence, with an increasing number of cases. Objective: To investigate whether an analysis of the epidemiologic KD record in Japan stratified by age and prefecture (subregion) may yield new clues regarding mechanisms of exposure to etiologic agents associated with KD. Design, Setting, and Participants: This cross-sectional study was conducted using a dataset of patients with KD with detailed information on location and age at onset created through nationwide surveys of hospitals caring for pediatric patients with KD throughout Japan. Pediatric patients hospitalized in Japan for KD from 1970 to 2020 were included. Data were analyzed from January 2022 to January 2024. Exposure: Pediatric patients with KD. Main Outcomes and Measures: The KD dataset was analyzed by patient age (infants [aged <6 months], toddlers [aged 6 to <24 months], children aged 2 years [aged 24 to <36 months], and children and adolescents aged 3 years or older [aged ≥36 months]), with investigations of seasonal cycles, interannual variations, and correlations across regions. Results: Among 422 528 pediatric patients (243 803 males [57.7%] and 178 732 females [42.3%]; median [IQR] age, 23.69 [11.96-42.65] months), infants, toddlers, and patients aged 3 years or older exhibited different rates of increase in KD incidence, seasonality, and degrees of coherence of seasonality across prefectures. Although the mean (SD) incidence of KD among infants remained relatively stable over the past 30 years compared with older patients (1.00 [0.07] in 1987-1992 to 2.05 [0.11] in 2011-2016), the mean (SD) incidence rate for children and adolescents aged 3 years or older increased 5.2-fold, from 1.00 (0.08) in 1987 to 1992 to 5.17 (0.46) in 2014 to 2019. Patients aged 3 years or older saw a reduction in mean (SD) incidence, from peaks of 5.71 (0.01) in October 2014 through June 2015 and July 2018 through March 2019 to 4.69 (0.11) in 2016 to 2017 (17.8% reduction) not seen in younger children. The seasonal cycle varied by age group; for example, mean (SD) incidence peaked in July and August (5.63 [0.07] cases/100 000 individuals) for infants and in December and January (4.67 [0.13] cases/100 000 individuals) for toddlers. Mean (SD) incidence changed dramatically for toddlers beginning in the early 2010s; for example, the normalized mean (SD) incidence among toddlers for October was 0.74 (0.03) in 1992 to 1995 and 1.10 (0.01) in 2016 to 2019. Across Japan, the seasonal cycle of KD incidence of older children and adolescents exhibited mean (SD) correlation coefficients between prefectures as high as 0.78 (0.14) for prefecture 14 among patients aged 3 years or older, while that of infants was much less (highest mean [SD] correlation coefficient, 0.43 [0.23]). Conclusions and Relevance: This study found distinct temporal signatures and changing spatial consistency of KD incidence across age groups, suggesting different age-related mechanisms of exposure. Some results suggested that social factors may modulate exposure to etiologic agents of KD; however, the increase in KD incidence in older children coupled with the correlation across prefectures of KD incidence suggest that the intensity of an environmental exposure that triggers KD in this age group may have increased over time.

  PublisherOA PDFDOI

• Satellite Observations of Water Origins Reveal New Dimensions of Hydroclimate Vulnerability of Global Croplands

  Research Square · 2024-05-31

  preprintOpen accessSenior author

  <title>Abstract</title> Food security depends on water availability; understanding the sources of atmospheric moisture for different crop producing regions is thus critical. Here, using Earth observations and physical and statistical models over the period 2003-2019, we explore the extent to which atmospheric moisture for the world’s major crops originates from oceans versus land. By integrating precipitation isotope observations and two satellite-derived atmospheric water isotope products, we find that the percentage of crop water originating from land varies from ~25% to 75% over the year, with strong variation over crop growing seasons and crop phenological stages. Although patterns vary by region, ocean-originating moisture tends to provide most precipitation during the vegetative and reproductive stages of the main crop growing season. Linking source and water supply information shows that crop water stress becomes more likely later during the reproductive period, due to continuous and increasing soil moisture depletion, and this phenomenon is much stronger for croplands dependent on land-originating moisture. This stress is highly associated with crop productivity as measured by greenness (NIRv): we find that croplands with ≥40% of moisture derived from land are more susceptible to water stress than those with &lt;40%. Importantly, when disaggregated by crop type, we find that over 40% of the world’s maize and over 50% of the world’s wheat receive more than 40% of their moisture from land during the critical reproductive stage. For these areas, simple hydrological metrics -- integrated rainwater supply and root-zone soil moisture -- explain up to 60% of inter-annual variability in crop greenness. These results thus provide valuable insights for understanding dimensions of the vulnerability of global crop production and potential adaptation responses, including protection of local land moisture sources, selection of suitable crops, and design of smart irrigation strategies at sub-seasonal scales.

  PublisherOA PDFDOI

Recent grants

• CNH-L: The Coupled Climate and Institutional Dynamics of Short-Lived Local Pollutants and Long-Lived Global Greenhouse Gases

  NSF · $1.5M · 2017–2022

Frequent coauthors

• Simone Chess

  University of California, San Diego

  36 shared

• Alice Inwood

  Wells College

  36 shared

• Steven Dansky

  University of California, San Diego

  36 shared

• J. Dailey

  36 shared

• Kayden Arias

  Brooklyn College

  36 shared

• Julie Coley Gray

  Wells College

  36 shared

• Barbara Barnes

  36 shared

• Moa Hiscox

  Los Angeles LGBT Center

  36 shared

Labs

• Burney Research GroupPI

  Understanding emissions from agriculture and land use change, characterizing climate and air pollution risks to food security, and helping farmers and food systems adapt to environmental changes.

Awards & honors

• National Geographic Emerging Explorer (2011)