Armstrong, J. Scott and Green, Kesten C. and Soon, Willie (2007): Polar Bear Population Forecasts: A Public-Policy Forecasting Audit.
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The extinction of polar bears by the end of the 21st century has been predicted and calls have been made to list them as a threatened species under the U.S. Endangered Species Act. The decision on whether or not to list rests upon forecasts of what will happen to the bears over the 21st Century. Scientific research on forecasting, conducted since the 1930s, has led to an extensive set of principles—evidence-based procedures—that describe which methods are appropriate under given conditions. The principles of forecasting have been published and are easily available. We assessed polar bear population forecasts in light of these scientific principles. Much research has been published on forecasting polar bear populations. Using an Internet search, we located roughly 1,000 such papers. None of them made reference to the scientific literature on forecasting. We examined references in the nine unpublished government reports that were prepared “…to Support U.S. Fish and Wildlife Service Polar Bear Listing Decision.” The papers did not include references to works on scientific forecasting methodology. Of the nine papers written to support the listing, we judged two to be the most relevant to the decision: Amstrup, Marcot and Douglas et al. (2007), which we refer to as AMD, and Hunter et al. (2007), which we refer to as H6 to represent the six authors. AMD’s forecasts were the product of a complex causal chain. For the first link in the chain, AMD assumed that General Circulation Models (GCMs) are valid. However, the GCM models are not valid as a forecasting method and are not reliable for forecasting at a regional level as being considered by AMD and H6, thus breaking the chain. Nevertheless, we audited their conditional forecasts of what would happen to the polar bear population assuming that the extent of summer sea ice will decrease substantially in the coming decades. AMD could not be rated against 26 relevant principles because the paper did not contain enough information. In all, AMD violated 73 of the 90 forecasting principles we were able to rate. They used two un-validated methods and relied on only one polar bear expert to specify variables, relationships, and inputs into their models. The expert then adjusted the models until the outputs conformed to his expectations. In effect, the forecasts were the opinions of a single expert unaided by forecasting principles. Based on research to date, approaches based on unaided expert opinion are inappropriate to forecasting in situations with high complexity and much uncertainty. Our audit of the second most relevant paper, H6, found that it was also based on faulty forecasting methodology. For example, it extrapolated nearly 100 years into the future on the basis of only five years of data – and data for these years were of doubtful validity. In summary, experts’ predictions, unaided by evidence-based forecasting procedures, should play no role in this decision. Without scientific forecasts of a substantial decline of the polar bear population and of net benefits from feasible policies arising from listing polar bears, a decision to list polar bears as threatened or endangered would be irresponsible.
|Item Type:||MPRA Paper|
|Original Title:||Polar Bear Population Forecasts: A Public-Policy Forecasting Audit|
|Keywords:||adaptation, bias, climate change, decision making, endangered species, expert opinion, evaluation, evidence-based principles, expert judgment, extinction, forecasting methods, global warming, habitat loss, mathematical models, scientific method, sea ice|
|Subjects:||C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods; Simulation Methods
H - Public Economics > H0 - General
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling
C - Mathematical and Quantitative Methods > C0 - General
H - Public Economics > H2 - Taxation, Subsidies, and Revenue > H23 - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics
|Depositing User:||Kesten Green|
|Date Deposited:||16. Dec 2007 05:02|
|Last Modified:||12. Feb 2013 03:34|
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