Resources For The Future

We present empirical evidence of option pricing in a created market to address stock externalities in a common-pool resource. Using unique data on New Zealand’s quota fisheries, we show that tradable quota can carry an option premium of over 10 percent (i.e., the quota price is relatively higher at the start of the fishing year). By exploiting differences in species that are exported fresh and frozen, we show that an option premium arises only if a fisher can earn higher profits by aligning harvesting with high market prices. Since quota represents a forward-looking option to produce throughout the year, the quota price accounts for the potential value of using quota later in the year, when it may be more profitable.

Abstract Solar radiation modification (SRM) presents important challenges to risk regulation and governance, arising from the array of multiple risks that SRM may influence. SRM would not simply reverse climate change, but could pose further ancillary impacts, depending on the method of SRM, such as stratospheric aerosol injection (SAI), marine cloud brightening (MCB), or a space-based planetary sunshade system (PSS). We identify multiple risks that SRM may influence, both biophysical and sociopolitical, to be compared to the multiple risks that may be affected by greenhouse gas (GHG) mitigation and climate adaptation. This multi-risk framework helps analysts and decision makers identify, evaluate, and compare multiple risks holistically; helps identify affected groups to overcome problems of disregard and omitted voice; helps compare policy options and map the array of risks to corresponding (or missing) governance mechanisms; and seeks risk-superior policies that would reduce multiple risks in concert. We then examine governance frameworks: uncoordinated, coordinated and comprehensive. We suggest two key mechanisms that can help build up from uncoordinated toward more coordinated or even comprehensive approaches, and that can gain support from SRM advocates, observers and critics alike: a series of international assessments of SRM, and a transparent international monitoring system for SRM.

ABSTRACTThis study evaluates five scoring rules, or measures of statistical accuracy, for assessing uncertainty estimates from expert judgment studies and model forecasts. These rules — the Continuously Ranked Probability Score (), Kolmogorov‐Smirnov (), Cramer‐von Mises (), Anderson Darling (), and chi‐square test — were applied to 6864 expert uncertainty estimates from 49 Classical Model (CM) studies. We compared their sensitivity to various biases and their ability to serve as performance‐based weight for expert estimates. Additionally, the piecewise uniform and Metalog distribution were evaluated for their representation of expert estimates because four of the five rules require interpolating the experts' estimates. Simulating biased estimates reveals varying sensitivity of the considered test statistics to these biases. Expert weights derived using one measure of statistical accuracy were evaluated with other measures to assess their performance. The main conclusions are (1) overlooks important biases, while chi‐square and behave similarly, as do and . (2) All measures except agree that performance weighting is superior to equal weighting with respect to statistical accuracy. (3) Neither distributions can effectively predict the position of a removed quantile estimate. These insights show the behavior of different scoring rules for combining uncertainty estimates from expert or models, and extent the knowledge for best‐practices.