Economic “Normalization” of Disaster Losses 1998-2020: A Literature Review and Assessment

Please email me for a pre-publication copy of this new paper …

Roger Pielke Jr.
University of Colorado Boulder
Environmental Hazards (in press, 2021)

Nowadays, in the aftermath of every weather disaster quickly follow estimates of economic loss. Quick blame for those losses, or some part of them, is often placed on claims of more frequent or intense weather events. However, understanding what role changes in climate may have played in increasing weather-related disaster losses is challenging because, in addition to changes in climate, society also undergoes dramatic change. Increasing development and wealth influence exposure and vulnerability to loss – typically increasing exposure while reducing vulnerability. In recent decades a scientific literature has emerged that seeks to adjust historical economic damage from extreme weather to remove the influences of societal change from economic loss time series in order to estimate what losses past extreme events would cause under present-day societal conditions. In regions with broad exposure to loss, an unbiased economic normalization will exhibit trends consistent with corresponding climatological trends in related extreme events, providing an independent check on normalization results. This paper reviews 54 normalization studies published 1998 to 2020 and finds little evidence to support claims that any part of the overall increase in global economic losses documented on climate time scales can be attributed to human-caused changes in climate, reinforcing conclusions of recent assessments of the Intergovernmental Panel on Climate Change.

Good Evidence? A 2013 Panel Discussion

A reader shared this with me (thanks MP), I was unaware that it was online. From 7 years ago …

Recording of a debate held at the Institute of Physics, 4th Feb 2013. Co-organised by Science Policy Research Unit, University of Sussex and the UCL’s department of Science & Technology Studies.

Policymakers often talk up the importance of evidence-based policy, with increasing calls for randomised controlled trials (RCTs) as the best way of testing whether particular interventions work. But finding and applying evidence in policy is anything but straightforard. Evidence alone rarely wins complex political arguments. Often this merely shifts the locus of debate to what counts as evidence.

Speakers: Roger Pielke Jr, Professor of Environmental Studies, University of Colorado at Boulder; Richard Horton, Editor of The Lancet; Georgina Mace, Professor of Biodiversity and Ecosystems, University College London; Jonathan Breckon, Alliance for Useful Evidence.

Chair: James Wilsdon, Professor in Science and Democracy, SPRU, University of Sussex.

Papers on Use and Misuse of Climate Scenarios


Non-technical overview: Pielke, Jr. R. (2018). Opening up the climate policy envelope. Issues in Science and Technology34(4), 30-36.

Detailed history and critique: Pielke, Jr. R. and Ritchie, J., Systemic Misuse of Scenarios in Climate Research and Assessment (April 21, 2020). Available at SSRN:

Quantitative evaluation (GDP and CO2): Burgess, M. G., Ritchie, J., Shapland, J., & Pielke, R., Jr. (2020, February 18). IPCC baseline scenarios over-project CO2 emissions and economic growth.

Quantitative evaluation (energy intensity and carbon intensity, AR5): Stevenson, S., & Pielke Jr, R. (2018). Assumptions of Spontaneous Decarbonization in the IPCC AR5 Baseline Scenarios. (PDF)

Quantitative evaluation (energy intensity and carbon intensity, AR4): Pielke, R., Wigley, T., & Green, C. (2008). Dangerous assumptions. Nature, 452(7187), 531-532. (PDF)

Case study (tropical cyclones): Pielke Jr, R. A. (2007). Future economic damage from tropical cyclones: sensitivities to societal and climate changesPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences365(1860), 2717-2729.

Case study (disaster loss projections): Pielke Jr, R. (2007). Mistreatment of the economic impacts of extreme events in the Stern Review Report on the Economics of Climate Change. Global Environmental Change, 17(3-4), 302-310.

Case study (tropical cyclones): Pielke Jr, R. A., Klein, R., & Sarewitz, D. (2000). Turning the big knob: An evaluation of the use of energy policy to modulate future climate impactsEnergy & Environment11(3), 255-275.

More general discussion: Pielke Jr, R. A. (2003). The role of models in prediction for decision. Models in ecosystem science, 111-135. (PDF)

Covid-19 Resources for Research and Teaching: Models and Forecasts

MIDAS – Online Portal for COVID-19 Modeling Research (link)

U.K.  Scientific Pandemic Influenza Group on Modelling (SPI-M) Modelling
Summary (2018 report, link) (advisory subcommittee)

Public Health Agency of Canada, 2020. COVID-19 in Canada: Using data and modelling to inform public health action: Technical Briefing for Canadians, 9 April (PDF).

Begley, S. 2020. Influential Covid-19 model uses flawed methods and shouldn’t guide U.S. policies, critics say, Stat, 17 April.

Bender, M. and R. Ballhaus, 2020. Trump’s Coronavirus Focus Shifts to Reopening Economy, Defending His Response, The Washington Post, 17 April.

Wan, W. and C. Johnson, 2020. America’s most influential coronavirus model just revised its estimates downward. But not every model agrees. The Washington Post, 8 April.

Wan, W. 2020. Experts and Trump’s advisers doubt White House’s 240,000 coronavirus deaths estimate, The Washington Post, 2 April.

Koerth et al. 2020. Why It’s So Freaking Hard To Make A Good COVID-19 Model, FiveThirtyEight, 31 March.

Wan, W. and A. Blake, 2020. Coronavirus modelers factor in new public health risk: Accusations their work is a hoax, The Washington Post, 27 March.

IHME Covid-19 Projections (link) based on: IHME COVID-19 health service utilization forecasting team. Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator days and deaths by US state in the next 4 months. MedRxiv. 26 March 2020.

Enserink, M. and K. Kupferschmidt, 2020. Mathematics of life and death: How disease models shape national shutdowns and other pandemic policies, Science, 25 March.

Rivers, C. et al. 2020. Modernizing and Expanding Outbreak Science to Support Better Decision Making During Public Health Crises: Lessons for COVID-19 and Beyond, Johns Hopkins Center for Health Security, 24 March. (PDF).

Rivers, C., Chretien, J.P., Riley, S., Pavlin, J.A., Woodward, A., Brett-Major, D., Berry, I.M., Morton, L., Jarman, R.G., Biggerstaff, M. and Johansson, M.A., 2019. Using “outbreak science” to strengthen the use of models during epidemicsNature communications10(1), pp.1-3.

Chowell, G., Sattenspiel, L., Bansal, S., & Viboud, C. (2016). Mathematical models to characterize early epidemic growth: A reviewPhysics of life reviews18, 66-97.

Glasser, J. W., Hupert, N., McCauley, M. M., & Hatchett, R. (2011). Modeling and public health emergency responses: Lessons from SARSEpidemics3(1), 32-37.