Search

The social cost of carbon dioxide (SC-CO2) measures the monetized value of the damages to society caused by an incremental metric tonne of CO2 emissions and is a key metric informing climate policy. Used by governments and other decision-makers in benefit-cost analysis for over a decade, SC-CO2 estimates draw on climate science, economics, demography and other disciplines. However, a 2017 report by the US National Academies of Sciences, Engineering, and Medicine1 (NASEM) highlighted that current SC-CO2 estimates no longer reflect the latest research. The report provided a series of recommendations for improving the scientific basis, transparency and uncertainty characterization of SC-CO2 estimates. Here we show that improved probabilistic socioeconomic projections, climate models, damage functions, and discounting methods that collectively reflect theoretically consistent valuation of risk, substantially increase estimates of the SC-CO2. Our preferred mean SC-CO2 estimate is $185 per tonne of CO2 ($44-$413 per tCO2: 5%-95% range, 2020 US dollars) at a near-term risk-free discount rate of 2%, a value 3.6 times higher than the US government's current value of $51 per tCO2. Our estimates incorporate updated scientific understanding throughout all components of SC-CO2 estimation in the new open-source Greenhouse Gas Impact Value Estimator (GIVE) model, in a manner fully responsive to the near-term NASEM recommendations. Our higher SC-CO2 values, compared with estimates currently used in policy evaluation, substantially increase the estimated benefits of greenhouse gas mitigation and thereby increase the expected net benefits of more stringent climate policies.

The recently published Intergovernmental Panel on Climate Change (IPCC) projections to 2100 give likely ranges of global temperature increase in four scenarios for population, economic growth and carbon use1. However these projections are not based on a fully statistical approach. Here we use a country-specific version of Kaya's identity to develop a statistically-based probabilistic forecast of CO2 emissions and temperature change to 2100. Using data for 1960-2010, including the UN's probabilistic population projections for all countries2-4, we develop a joint Bayesian hierarchical model for GDP per capita and carbon intensity. We find that the 90% interval for cumulative CO2 emissions includes the IPCC's two middle scenarios but not the extreme ones. The likely range of global temperature increase is 2.0-4.9°C, with median 3.2°C and a 5% (1%) chance that it will be less than 2°C (1.5°C). Population growth is not a major contributing factor. Our model is not a "business as usual" scenario, but rather is based on data which already show the effect of emission mitigation policies. Achieving the goal of less than 1.5°C warming will require carbon intensity to decline much faster than in the recent past.

Summary: Professor Adrian E. Raftery is the Boeing International Professor of Statistics and Sociology and an adjunct professor of Atmospheric Sciences at the University of Washington in Seattle. He was born in Dublin, Ireland, and obtained a BA in Mathematics (1976) and an MSc in Statistics and Operations Research (1977) at Trinity College Dublin. He obtained a doctorate in mathematical statistics in 1980 from the Université Pierre et Marie Curie in Paris, France, under the supervision of Paul Deheuvels. He was a lecturer in statistics at Trinity College Dublin from 1980 to 1986, and then an associate (1986–1990) and full (1990‐present) professor of statistics and sociology at the University of Washington. He was the founding Director of the Center for Statistics and Social Sciences (1999–2009). Professor Raftery has published over 200 articles in peer‐reviewed statistical, sociological and other journals. His research focuses on Bayesian model selection and Bayesian model averaging, model‐based clustering, inference for deterministic simulation models, and the development of new statistical methods for demography, sociology, and the environmental and health sciences. He is a member of the United States National Academy of Sciences, a Fellow of the American Academy of Arts and Sciences, an Honorary Member of the Royal Irish Academy, a member of the Washington State Academy of Sciences, a Fellow of the American Statistical Association, a Fellow of the Institute of Mathematical Statistics, and an elected Member of the Sociological Research Association. He has won the Population Association of America's Clifford C. Clogg Award, the American Sociological Association's Paul F. Lazarsfeld Award for Distinguished Contribution to Knowledge, the Jerome Sacks Award for Outstanding Cross‐Disciplinary Research from the National Institute of Statistical Sciences, the Parzen Prize for Statistical Innovation, and the Science Foundation Ireland St. Patrick's Day Medal. He is also a former Coordinating and Applications Editor of the Journal of the American Statistical Association and a former Editor of Sociological Methodology. He was identified as the world's most cited researcher in mathematics for the decade 1995–2005 by Thomson‐ISI. Thirty‐three students have obtained PhD's working under Raftery's supervision, of whom 21 hold or have held tenure‐track university faculty positions. He has over 150 academic descendants. This interview took place over two sessions in March 2023. [ABSTRACT FROM AUTHOR]

B Professor Adrian E. Raftery b (University of Washington): It is a pleasure to congratulate the authors of these two papers on the important topic of statistical aspects of climate change. For forecasting, numerical weather prediction models did forecast the 2021 heatwave quite successfully even though it was outside the range of all previous experience. They developed new statistical methods for climate problems ranging from the integrated assessment of future global climate change to the detailed modelling of granular atmospheric processes. The purpose is clear, to provide probabilistic forecasts of future water damage for any building in Norway, both with current climate and accounting for future climate change, for insurance purposes. [Extracted from the article]