About

Luke Sanford is an Assistant Professor of Environmental Policy and Governance at Yale School of the Environment. He graduated in 2021 with a PhD in Political Science and International Affairs from the Political Science department and the School of Global Policy and Strategy at the University of California San Diego. His work focuses on environmental policymaking, particularly how political institutions mediate the relationship between the environmental preferences of constituents and the incentives and actions of policymakers. Sanford also studies the distributional consequences over space and time of decisions about natural resources and how these influence policymaking. He develops methods for utilizing new sources of digital data, including text and satellite images, to measure individual and group preferences and observe outcomes on the ground. His research aims to understand policy preferences and the effects of different policies. Sanford's expertise includes business and the environment, environmental markets and finance, international negotiations, climate science and policy, environmental politics, and land cover and land use change. He is currently on leave for the Spring 2026 semester and is not accepting PhD students.

Research topics

• Geography
• Environmental science
• Business
• Political science
• Natural resource economics

Selected publications

• Electoral Cycles and Distributive Politics: Evidence from Deforestation

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Causal Carbon: Baselines and Additionality with Potential Outcomes

  2025-02-27 · 1 citations

  preprintOpen access

  Recent work has questioned the credibility of forest carbon offsets as a climate solution. This threatens both investor confidence and genuine climate mitigation efforts in the voluntary carbon market, which has contracted by over 75% since 2021. Despite updated methodologies and widespread advice to invest only in high-integrity or high-quality credits, it remains unclear which credits genuinely meet these criteria. Here, we draw on the fields of statistics and causal inference to develop a generalized analytical framework for evaluating the additionality of credits generated by carbon offset protocols, addressing persistent ambiguities and limitations in current approaches. This framework comes from a systematic evaluation of all existing forest carbon offset protocols. We translated each protocol's baseline methodology into a statistical causal estimator and derived the assumptions necessary for it to produce accurate estimates of generated credits. By translating those assumptions back into the language of forest carbon credits we provide a set of conditions that buyers should believe in order to conclude that the credits they purchase are additional. We demonstrate that strategic enrollment, combined with even minor measurement errors in carbon stocks, can lead to significant market distortions, undermining both environmental integrity and financial reliability of offsets. Our analysis highlights an inherent tradeoff between ensuring accurate carbon measurement and expanding participation in offset programs. This framework clarifies the assumptions purchasers of carbon offsets must accept for credits under each protocol to reliably represent genuine climate impacts, providing a transparent basis for buyers, sellers, critics, and advocates to constructively engage and reconcile divergent views.

  PublisherOA PDFDOI

• The Importance of Ground-Truthing in Local Biomass Models for REDD+ – a Case Study in the Chiloe Island, Chile

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• The importance of local calibration in biomass models for REDD+ – a case study in the Chiloe Island, Chile

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Quantifying impacts of policy and practice interventions on biodiversity and climate

  2025-11-27

  preprintOpen access

  There is urgent demand for ecosystem management interventions – targeted actions through policies and practices – that meaningfully address climate change and biodiversity loss while sustaining ecosystem delivery of water, food, fibre and fuel. Rigorous quantification of intervention outcomes is required for decision makers to identify, promote and scale effective interventions. Yet quantification of intervention effectiveness – i.e. their real-world impact – is hampered by limited use in ecology of causal approaches that generate counterfactual, empirical evidence at the scales of policy and practice actions. Here, we review the historical development of causal approaches and ecological experimentation, and emerging efforts to reunite the two. Reunification requires ecology to broaden its philosophical consideration of the validity and generalisability of evidence and to expand its experimental framework. Such an ‘applied causal ecology’ promises evidence that builds confidence that policy and practice interventions will sustain ecosystem services and achieve biodiversity and climate goals.

  PublisherOA PDFDOI

• Elevating Spatial Evaluation: Satellite-Driven Confounder Adjustment

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Addressing Missingness in Serialized Bureaucratic Data: The Case of Chinese Courts

  Sociological Methods & Research · 2025-06-12 · 1 citations

  articleOpen accessSenior author

  Courts around the world are putting their data online, making information about caseloads, parties, and decisions available to the public. Yet, this data is far from complete, and often only reflects a portion of courts’ dockets. We offer and validate a set of tools for leveraging serialized bureaucratic data from courts to estimate the proportion of cases available to the public and the time courts take to make decisions. Using data from more than 3,000 courts in China, our methods allow us to assess patterns of missingness in court data across provinces and cities by type of case and to conduct the largest quantitative analysis to date on court delay in China. By providing an extensive validation of both new and existing tools for estimating missingness and delay, we provide a set of recommendations for researchers looking to augment incomplete bureaucratic data around the world.

  PublisherDOI

• Causal Carbon: Baselines and Additionality with Potential Outcomes

  2025-03-07

  preprintOpen access

  Recent work has questioned the credibility of forest carbon offsets as a climate solution. This threatens both investor confidence and genuine climate mitigation efforts in the voluntary carbon market, which has contracted by over 75% since 2021. Despite updated methodologies and widespread advice to invest only in high-integrity or high-quality credits, it remains unclear which credits genuinely meet these criteria. Here, we draw on the fields of statistics and causal inference to develop a generalized analytical framework for evaluating the additionality of credits generated by carbon offset protocols, addressing persistent ambiguities and limitations in current approaches. This framework comes from a systematic evaluation of all existing forest carbon offset protocols. We translated each protocol's baseline methodology into a statistical causal estimator and derived the assumptions necessary for it to produce accurate estimates of generated credits. By translating those assumptions back into the language of forest carbon credits we provide a set of conditions that buyers should believe in order to conclude that the credits they purchase are additional. We demonstrate that strategic enrollment, combined with even minor measurement errors in carbon stocks, can lead to significant market distortions, undermining both environmental integrity and financial reliability of offsets. Our analysis highlights an inherent tradeoff between ensuring accurate carbon measurement and expanding participation in offset programs. This framework clarifies the assumptions purchasers of carbon offsets must accept for credits under each protocol to reliably represent genuine climate impacts, providing a transparent basis for buyers, sellers, critics, and advocates to constructively engage and reconcile divergent views.

  PublisherOA PDFDOI

• Enhanced Forecasting Methods for Extreme Weather Events and Vulnerable Populations

  2025-10-06

  preprintOpen access

  Extreme weather event prediction remains a critical challenge in operational forecasting, with conventional methods demonstrating limited accuracy for intensity change detection. This study presents complementary advances through integrated machine learning frameworks and experimental mathematical analysis techniques for enhanced environmental prediction capabilities. We developed computational systems by combining neural network architectures with multi-source meteorological datasets from comprehensive Atlantic Basin storm records spanning a decade, representing near-complete population coverage. Machine learning algorithms achieved substantial overall accuracy with improved detection rates for critical intensity changes, significantly exceeding historical operational capabilities. Neural network trajectory prediction demonstrated competitive performance with mean absolute errors comparable to current operational standards across intensity categories. Most significantly, experimental mathematical analysis achieved enhanced prediction accuracy, representing substantial improvements over conventional environmental parameter methods. Using advanced geometric techniques on multi-dimensional atmospheric state representations, we identified structural patterns with the majority of critical events preceded by characteristic transitions within operationally relevant timeframes. This approach provided novel precursor signals for dangerous intensity changes. Feature analysis revealed that dynamic atmospheric variables dominated prediction importance versus static measurements, challenging traditional approaches. Validation identified optimal behavioral classifications corresponding to distinct meteorological regimes. These advances offer immediate operational value for weather forecasting while establishing theoretical foundations for understanding atmospheric systems as dynamical processes with discrete organizational phases, providing enhanced prediction capabilities essential for protecting vulnerable communities from climate change.

  PublisherOA PDFDOI

• Disparate air pollution reductions during California’s COVID-19 economic shutdown

  Nature Sustainability · 2022-04-07 · 43 citations

  articleOpen access
  PublisherOA PDFDOI

Frequent coauthors

• Jennifer Burney

  Scripps Institution of Oceanography

  5 shared

• Katharine Ricke

  University of California, San Diego

  4 shared

• Kyle S. Hemes

  4 shared

• Morgan Levy

  Swiss Federal Institute of Aquatic Science and Technology

  4 shared

• Pascal Polonik

  Scripps Institution of Oceanography

  3 shared

• Richard Bluhm

  3 shared

• Bin Dong

  Technical Institute of Physics and Chemistry

  2 shared

• Jonathan Lautze

  2 shared

Labs

• Yale School of the EnvironmentPI

Education

• Doctor of Political Science and International Affairs, Political Science

  University of California San Diego

  2021