Policymakers involved in climate change negotiations are key users of climate science. It is therefore vital to understand how to communicate scientific information most eectively to this group. We tested how a unique sample of policymakers and negotiators at the Paris COP21 conference update their beliefs on year 2100 global mean temperature increases in response to a statistical summary of climate models’ forecasts. We randomized the way information was provided across participants using three dierent formats similar to those used in Intergovernmental Panel on Climate Change reports2,3. In spite of having received all available relevant scientific information, policymakers adopted such information very conservatively, assigning it less weight than their own prior beliefs. However, providing individual model estimates in addition to the statistical range was more eective in mitigating such inertia. The experiment was repeated with a population of European MBA students who, despite starting from similar priors, reported conditional probabilities closer to the provided models’ forecasts than policymakers. There was also no eect of presentation format in theMBAsample. These results highlight the importance of testing visualization tools directly on the population of interest.
COP21 climate negotiators' responses to climate model forecasts
BOSETTI, VALENTINA;LIU, NING;
2017
Abstract
Policymakers involved in climate change negotiations are key users of climate science. It is therefore vital to understand how to communicate scientific information most eectively to this group. We tested how a unique sample of policymakers and negotiators at the Paris COP21 conference update their beliefs on year 2100 global mean temperature increases in response to a statistical summary of climate models’ forecasts. We randomized the way information was provided across participants using three dierent formats similar to those used in Intergovernmental Panel on Climate Change reports2,3. In spite of having received all available relevant scientific information, policymakers adopted such information very conservatively, assigning it less weight than their own prior beliefs. However, providing individual model estimates in addition to the statistical range was more eective in mitigating such inertia. The experiment was repeated with a population of European MBA students who, despite starting from similar priors, reported conditional probabilities closer to the provided models’ forecasts than policymakers. There was also no eect of presentation format in theMBAsample. These results highlight the importance of testing visualization tools directly on the population of interest.File | Dimensione | Formato | |
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