Marshall Burke's research while affiliated with Stanford University and other places
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Publications (144)
We use three quantitative case studies to argue that ubiquitous and universal condemnation of fossil fuel subsidies is myopic and does not adequately consider subsidizing gas for cooking as a potential strategy to improve public health and reduce greenhouse gas emissions. Ecuador offers a view into the long-run impacts of gas subsidies, having made...
We review current knowledge on the trends and drivers of global wildfire activity, advances in the measurement of wildfire smoke exposure, and evidence on the health effects of this exposure. We describe methodological issues in estimating the causal effects of wildfire smoke exposures on health and quantify their importance, emphasizing the role o...
Steady improvements in ambient air quality in the USA over the past several decades, in part a result of public policy1,2, have led to public health benefits1–4. However, recent trends in ambient concentrations of particulate matter with diameters less than 2.5 μm (PM2.5), a pollutant regulated under the Clean Air Act¹, have stagnated or begun to r...
Despite increasing exposure to flooding and associated financial damages, estimates suggest more than two-thirds of flood-exposed properties are currently uninsured. This low adoption rate could undermine the climate resilience of communities and weaken the financial solvency of the United States National Flood Insurance Program (NFIP). We study wh...
Air pollution negatively affects a range of health outcomes. Wildfire smoke is an increasingly important contributor to air pollution, yet wildfire smoke events are highly salient and could induce behavioral responses that alter health impacts. We combine geolocated data covering all emergency department (ED) visits to nonfederal hospitals in Calif...
Household electrification is thought to be an important part of a carbon-neutral future and could also have additional benefits to adopting households such as improved air quality. However, the effectiveness of specific electrification policies in reducing total emissions and boosting household livelihoods remains a crucial open question in both de...
The western United States has experienced severe drought in recent decades, and climate models project increased drought risk in the future. This increased drying could have important implications for the region's interconnected, hydropower-dependent electricity systems. Using power-plant level generation and emissions data from 2001 to 2021, we qu...
Global outdoor biomass burning is a major contributor to air pollution, especially in low- and middle-income countries. Recent years have witnessed substantial changes in the extent of biomass burning, including large declines in Africa. However, direct evidence of the contribution of biomass burning to global health outcomes remains limited. Here,...
We report small-sample evidence from a randomized experiment among a set of urban Ecuadorian households who owned both electric induction and gas stoves. We randomly assigned households to cook only with one stove during a prescribed two-day monitoring period, and then cook only with the other stove in a subsequent two-day period. The order of stov...
We review current knowledge on the trends and drivers of global wildfire activity, advances in the measurement of wildfire smoke exposure, and evidence on the health effects of this exposure. We discuss methodological issues in estimating the causal effects of wildfire smoke exposures on health. We conduct a systematic review and meta-analysis of t...
Rapidly changing wildfire regimes across the Western US has driven more frequent and severe wildfires, resulting in wide-ranging societal threats from the wildfires themselves and the smoke that they generate. However, common measures of fire severity focus on what is burned and do not account for the societal impacts of the smoke generated from ea...
Household electrification is thought to be an important part of a carbon neutral future, and could also have additional benefits to adopting households such as improved air quality. However, the effectiveness of specific electrification policies in reducing total emissions and boosting household livelihoods remains a crucial open question in both d...
The western United States has experienced severe drought in recent decades, and climate models project increased drought risk in the future. This increased drying could have important implications for the region's interconnected, hydropower-dependent electricity systems. Using power-plant level generation and emissions data from 2001-2021, we quant...
Building coverage statistics provide crucial insights into the urbanization, infrastructure, and poverty level of a region, facilitating efforts towards alleviating poverty, building sustainable cities, and allocating infrastructure investments and public service provision. Global mapping of buildings has been made more efficient with the incorpora...
Steady improvements in ambient air quality in the US over the past several decades have led to large public health benefits. However, recent trends in PM2.5 concentrations, a key pollutant, have stagnated or begun to reverse throughout much of the US. We quantify the contribution of wildfire smoke to these trends and find that since 2016, wildfire...
In many regions of the world, sparse data on key economic outcomes inhibit the development, targeting and evaluation of public policy1,2. We demonstrate how advancements in satellite imagery and machine learning (ML) can help ameliorate these data and inference challenges. In the context of an expansion of the electrical grid across Uganda, we show...
Wildfires have increased in frequency and severity over the past two decades, threatening to undo substantial air quality improvements. We investigate the relationship between wildfire smoke exposure and learning outcomes across the United States using standardized test scores from 2009–2016 for nearly 11,700 school districts and satellite-derived...
The magnitude and distribution of physical and societal impacts from long-lived greenhouse gases are insensitive to the emission source location; the same is not true for major coemitted short-lived pollutants such as aerosols. Here, we combine novel global climate model simulations with established response functions to show that a given aerosol e...
Smoke from wildfires is a growing health risk across the US. Understanding the spatial and temporal patterns of such exposure and its population health impacts requires separating smoke-driven pollutants from non-smoke pollutants and a long time series to quantify patterns and measure health impacts. We develop a parsimonious and accurate machine l...
Crop residue burning is a major source of air pollution in many parts of the world, notably South Asia. Policymakers, practitioners and researchers have invested in both measuring impacts and developing interventions to reduce burning. However, measuring the impacts of burning or the effectiveness of interventions to reduce burning requires data on...
Unsupervised pre-training methods for large vision models have shown to enhance performance on downstream supervised tasks. Developing similar techniques for satellite imagery presents significant opportunities as unlabelled data is plentiful and the inherent temporal and multi-spectral structure provides avenues to further improve existing pre-tra...
Pollution from wildfires constitutes a growing source of poor air quality globally. To protect health, governments largely rely on citizens to limit their own wildfire smoke exposures, but the effectiveness of this strategy is hard to observe. Using data from private pollution sensors, cell phones, social media posts and internet search activity, w...
Object detection in high-resolution satellite imagery is emerging as a scalable alternative to on-the-ground survey data collection in many environmental and socioeconomic monitoring applications. However, performing object detection over large geographies can still be prohibitively expensive due to the high cost of purchasing imagery and compute....
Global outdoor biomass burning is a major contributor to air pollution, especially in low and middle-income countries. Recent years have witnessed substantial changes in the extent of biomass burning, including large declines in Africa. However, direct evidence on the contribution of biomass burning to global health outcomes remains limited. Here w...
Automated tracking of urban development in areas where construction information is not available became possible with recent advancements in machine learning and remote sensing. Unfortunately, these solutions perform best on high-resolution imagery, which is expensive to acquire and infrequently available, making it difficult to scale over long tim...
Object detection in high-resolution satellite imagery is emerging as a scalable alternative to on-the-ground survey data collection in many environmental and socioeconomic monitoring applications. However, performing object detection over large geographies can still be prohibitively expensive due to the high cost of purchasing imagery and compute....
Wildfires and associated smoke exposure have increased in frequency and severity over the past two decades, threatening to undo decades of air quality improvements. Our understanding of the impacts of these growing exposures on a range of societal outcomes remains incomplete. Building on emerging evidence that environmental exposures can negatively...
The magnitude and distribution of physical and societal impacts from long-lived greenhouse gases are insensitive to the emission source location; the same is not true for major co-emitted short-lived pollutants like aerosols. Here we combine novel global climate model simulations with established response functions to show that identical aerosols e...
![Figure A5: An example from the cropland mapping dataset [102], showing...](profile/Sherrie-Wang-3/publication/356026148/figure/fig3/AS:1088217822564353@1636462755632/Figure-A5-An-example-from-the-cropland-mapping-dataset-102-showing-a-an-example_Q320.jpg)
![Figure A6: An example from the crop type mapping dataset [83]. The left...](profile/Sherrie-Wang-3/publication/356026148/figure/fig4/AS:1088217822568449@1636462755688/Figure-A6-An-example-from-the-crop-type-mapping-dataset-83-The-left-image-represents_Q320.jpg)
Progress toward the United Nations Sustainable Development Goals (SDGs) has been hindered by a lack of data on key environmental and socioeconomic indicators, which historically have come from ground surveys with sparse temporal and spatial coverage. Recent advances in machine learning have made it possible to utilize abundant, frequently-updated,...
The impacts of environmental change on human outcomes often depend on local exposures and behavioral responses that are challenging to observe with traditional administrative or sensor data. We show how data from private pollution sensors, cell phones, social media posts, and internet search activity yield new insights on exposures and behavioral r...
In many regions of the world, sparse data on key economic outcomes inhibits the development, targeting, and evaluation of public policy. We demonstrate how advancements in satellite imagery and machine learning can help ameliorate these data and inference challenges. In the context of an expansion of the electrical grid across Uganda, we show how a...
Wildfires have increased in frequency and severity over the past two decades, especially in the Western United States. Beyond physical infrastructure damage caused by these wildfire events, researchers have increasingly identified harmful impacts of particulate matter generated by wildfire smoke on respiratory, cardiovascular, and cognitive health....
Background
Prior studies have found that residential proximity to upstream oil and gas production is associated with increased risk of adverse health outcomes. Emissions of ambient air pollutants from oil and gas wells in the preproduction and production stages have been proposed as conferring risk of adverse health effects, but the extent of air p...
Although most landslides are precipitation-triggered, a number of other complex conditions simultaneously predispose any given slope to failure, with the impact of urbanization posing particular scientific challenges. We use panel regression with fixed effects—which controls for observed and unobserved time-variant and time-invariant influences—to...
Satellite data offer great promise for improving measures related to sustainable development goals. However, assessing satellite estimates is complicated by the fact that traditional ground-based measures of these same outcomes are often very noisy, leading to underestimation of satellite performance. Here, we quantify the amount of noise in tradit...
Quantification of the sector-specific financial impacts of historical global warming represents a critical gap in climate change impacts assessment. The multiple decades of county-level data available from the U.S. crop insurance program-which collectively represent aggregate damages to the agricultural sector largely borne by U.S. taxpayers-presen...
There is limited population-scale evidence on the burden of exposure to wildfire smoke during pregnancy and its impacts on birth outcomes. In order to investigate this relationship, data on every singleton birth in California 2006–2012 were combined with satellite-based estimates of wildfire smoke plume boundaries and high-resolution gridded estima...
High-resolution satellite imagery has proven useful for a broad range of tasks, including measurement of global human population, local economic livelihoods, and biodiversity, among many others. Unfortunately, high-resolution imagery is both infrequently collected and expensive to purchase, making it hard to efficiently and effectively scale these...
Major decisions from governments and other large organizations rely on measurements of the populace's well-being, but making such measurements at a broad scale is expensive and thus infrequent in much of the developing world. We propose an inexpensive, scalable, and interpretable approach to predict key livelihood indicators from public crowd-sourc...
The combination of high-resolution satellite imagery and machine learning have proven useful in many sustainability-related tasks, including poverty prediction, infrastructure measurement, and forest monitoring. However, the accuracy afforded by high-resolution imagery comes at a cost, as such imagery is extremely expensive to purchase at scale. Th...
Significance
Monitoring compliance with environmental regulations is a global challenge. It is particularly difficult for governments in low-income countries, where informal industry is responsible for a large amount of pollution, because the governments lack the ability to locate and monitor large numbers of dispersed polluters. This study demonst...
Significance
Understanding whether markets efficiently price environmental risk is critical to policy design, particularly as key climate risks change rapidly. We conduct a nationwide analysis of the extent to which the US housing market prices information about flood risk contained in publicly available flood maps. Using data on millions of home s...
Background. Prior studies have found that residential proximity to upstream oil and gas production is associated with increased risk of adverse health outcomes. Emissions of ambient air pollutants from oil and gas wells in the preproduction and production stages have been proposed as conferring risk of adverse health effects, but the extent of air...
Satellite monitoring of development
Recent years have witnessed rapid growth in satellite-based approaches to quantifying aspects of land use, especially those monitoring the outcomes of sustainable development programs. Burke et al. reviewed this recent progress with a particular focus on machine-learning approaches and artificial intelligence met...
Significance
Precipitation extremes have increased in many regions of the United States, suggesting that climate change may be exacerbating the cost of flooding. However, the impact of historical precipitation change on the cost of US flood damages remains poorly quantified. Applying empirical analysis to historical precipitation and flood damages,...
Recent dramatic and deadly increases in global wildfire activity have increased attention on the causes of wildfires, their consequences, and how risk from wildfire might be mitigated. Here we bring together data on the changing risk and societal burden of wildfire in the United States. We estimate that nearly 50 million homes are currently in the...
Human health is increasingly threatened by rapid and widespread changes in the environment and climate, including rising temperatures, air and water pollution, disease vector migration, floods, and droughts. In the United States, many medical schools, the American Medical Association, and the National Academy of Sciences have published calls for ph...
Contrastive learning methods have significantly narrowed the gap between supervised and unsupervised learning on computer vision tasks. In this paper, we explore their application to remote sensing, where unlabeled data is often abundant but labeled data is scarce. We first show that due to their different characteristics, a non-trivial gap persist...
Estimation of pollution impacts on health is critical for guiding policy to improve health outcomes. Estimation is challenging, however, because economic activity can worsen pollution but also independently improve health outcomes, confounding pollution–health estimates. We leverage variation in exposure to local particulate matter of diameter <2.5...
Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. We synthesize the growing literature that uses satellite imagery to understand these outcomes, with a focus on approaches that combine imagery with machine learning. We quantify the paucity of ground data on k...
Restrictions to reduce human interaction have helped to avoid greater suffering and death from the COVID-19 pandemic, but have also created socioeconomic hardship. This disruption is unprecedented in the modern era of global observing networks, pervasive sensing and large-scale tracking of human mobility and behaviour, creating a unique test bed fo...
The potential links between climate and conflict are well studied, yet disagreement about the specific mechanisms and their significance for societies persists. Here, we build on assessment of the relationship between climate and organized armed conflict to define crosscutting priorities for future directions of research. They include (1) deepening...
Accurate local-level poverty measurement is an essential task for governments and humanitarian organizations to track the progress towards improving livelihoods and distribute scarce resources. Recent computer vision advances in using satellite imagery to predict poverty have shown increasing accuracy, but they do not generate features that are int...
Major decisions from governments and other large organizations rely on measurements of the populace's well-being, but making such measurements at a broad scale is expensive and thus infrequent in much of the developing world. We propose an inexpensive, scalable, and interpretable approach to predict key livelihood indicators from public crowd-sourc...
The combination of high-resolution satellite imagery and machine learning have proven useful in many sustainability-related tasks, including poverty prediction, infrastructure measurement, and forest monitoring. However, the accuracy afforded by high-resolution imagery comes at a cost, as such imagery is extremely expensive to purchase at scale. Th...
Background:
Recent studies report an association between preterm birth and exposure to unconventional oil and gas wells. There has been limited previous study on exposure to conventional wells, which are common in California. Our objective was to determine whether exposure to well sites was associated with increased odds of spontaneous preterm bir...
Accurate and comprehensive measurements of economic well-being are fundamental inputs into both research and policy, but such measures are unavailable at a local level in many parts of the world. Here we train deep learning models to predict survey-based estimates of asset wealth across ~ 20,000 African villages from publicly-available multispectra...
Farm parcel delineation provides cadastral data that is important in developing and managing climate change policies. Specifically, farm parcel delineation informs applications in downstream governmental policies of land allocation, irrigation, fertilization, green-house gases (GHG's), etc. This data can also be useful for the agricultural insuranc...
Accurate local-level poverty measurement is an essential task for governments and humanitarian organizations to track the progress towards improving livelihoods and distribute scarce resources. Recent computer vision advances in using satellite imagery to predict poverty have shown increasing accuracy, but they do not generate features that are int...
Many mountainous and high‐latitude regions have experienced more precipitation as rain rather than snow due to warmer winter temperatures. Further decreases in the annual snow fraction are projected under continued global warming, with potential impacts on flood risk. Here, we quantify the size of streamflow peaks in response to both seasonal and e...
The advent of multiple satellite systems capable of resolving smallholder agricultural plots raises possibilities for significant advances in measuring and understanding agricultural productivity in smallholder systems. However, since only imperfect yield data are typically available for model training and validation, assessing the accuracy of sate...
Organized intergroup violence is almost universally modeled as a calculated act motivated by economic factors. In contrast, it is generally assumed that non-economic factors, such as an individual’s emotional state, play a role in many types of interpersonal violence, such as “crimes of passion.” We ask whether non-economic factors can also explain...
