Christopher Knittel

About

Professor Christopher R. Knittel is the Associate Dean for Climate and Sustainability and the George P. Shultz Professor of Energy Economics in the Sloan School of Management at MIT. He directs the Center for Energy and Environmental Policy Research, established in 1977 as the hub for social science work related to energy and the environment at MIT. He is also the director of MIT’s Climate Policy Center and co-directs the Environmental and Energy Economics Program at the National Bureau of Economic Research, alongside Meredith Fowlie at UC Berkeley. His research studies consumer and firm decision-making and their implications for the benefits and costs of environmental and energy policy. He often interacts with policymakers to discuss his research findings and current research needs in the field. His current work involves studying how the costs of climate change policy vary across households and firms and how these differences influence policy choices. Professor Knittel employs a variety of empirical methods, including large-scale randomized control trials, machine learning techniques, and structural models, to advance understanding in energy economics and environmental policy.

Research topics

• Computer Science
• Economics
• Artificial Intelligence
• Microeconomics
• Engineering
• Econometrics
• Mathematics
• Political Science
• Statistics
• Market economy

Selected publications

• What Does and Does Not Correlate with COVID-19 Death Rates

  2020 · 83 citations

  1st authorCorresponding

  We correlate county-level COVID-19 death rates with key variables using both linear regression and negative binomial mixed models, although we focus on linear regression models.We include four sets of variables: socio-economic variables, county-level health variables, modes of commuting, and climate and pollution patterns.Our analysis studies daily death rates from April 4, 2020 to May 27, 2020.We estimate correlation patterns both across states, as well as within states.For both models, we find…

  DOI

• Using Machine Learning to Target Treatment: The Case of Household Energy Use

  The Economic Journal · 2025-05-19 · 9 citations

  article1st authorCorresponding

  Abstract We test the ability of causal forests to improve, through selective targeting, the effectiveness of a randomised program providing repeated behavioural nudges towards household energy conservation. The average treatment effect of the program is a monthly electricity reduction of 9 kilowatt hours (kWh), but the full distribution of predicted reductions ranges from roughly 1 to 33 kWh. Pre-treatment electricity consumption and home value are the strongest predictors of differential treatm…

  PublisherDOI

• Challenges to expanding EV adoption and policy responses

  Edward Elgar Publishing eBooks · 2025-06-19 · 9 citations

  book-chapter1st authorCorresponding
  PublisherDOI

• Two Wrongs Can Sometimes Make a Right: The Environmental Benefits of Market Power in Oil

  National Bureau of Economic Research · 2024-11-01 · 8 citations

  reportOpen accessSenior author

  Market power reduces equilibrium quantities and distorts production, typically causing welfare losses.However, as Buchanan (1969) noted, market power may mitigate overproduction from negative externalities.This paper examines this in the global oil market, where OPEC's market power affects oil production and carbon intensity.We estimate that from 1970 to 2021, OPEC's market power reduced emissions by over 67 GtCO2, equating to $4,073 billion in climate damages and 17.8% of the carbon budget need…

  PublisherDOI

• Charging Uncertainty: Real-Time Charging Data and Electric Vehicle Adoption

  National Bureau of Economic Research · 2025-01-01 · 4 citations

  reportOpen access

  Charging infrastructure is critical to electric vehicle (EV) adoption, but for chargers to be most useful, EV drivers need to know in real time where they are and whether they are working and available. We investigate the availability of real-time data from DC fast chargers on six major US Interstates and model the impacts of expanding access to real-time data to all DC fast chargers near highways. On average, between March and August 2024, 32.9% of DC fast charging stations adjacent to those si…

  PublisherDOI

Frequent coauthors

• Kenneth Gillingham

  128 shared

• Κωνσταντίνος Μεταξόγλου

  Carleton University

  87 shared

• Arthur van Benthem

  83 shared

• Mark R. Jacobsen

  83 shared

• James Sallee

  77 shared

• André Trindade

  59 shared

• Stephen P. Holland

  Yale University

  52 shared

• Jose-Miguel Abito

  Cornell University

  49 shared

Awards & honors

• Professor of the Year Award (2024)
• IJIO Best Empirical Paper Award (2020)

Similar researchers at Massachusetts Institute of Technology

• Robert S. Pindyckh-72
• Roberto Rigobonh-58
• Robert Gibbonsh-47