Descripción del proyecto
Estrategias de mitigación del cambio climático consistentes frente a incertidumbres negativas
Las estrategias para luchar contra el cambio climático deben ser resilientes a incertidumbres científicas y políticas negativas. En este sentido, los modelos matemáticos pueden ayudar a identificar opciones de acción consistentes, pero las restricciones epistémicas e informáticas limitan su capacidad de predicción. El objetivo del proyecto EUNICE, financiado con fondos europeos, es cuantificar y analizar incertidumbres en las estrategias de estabilización compatibles con la estabilización climática. Los investigadores utilizarán aprendizaje automático y simulaciones para examinar una amplia variedad de supuestos sobre el futuro lejano. El trabajo en EUNICE ayudará a identificar estrategias consistentes para reducir las emisiones y hacer frente al cambio climático abrupto, armonizando las predicciones a largo plazo con el panorama político y tecnológico de la política climática en rápida evolución.
Objetivo
Mathematical models have become central tools in global environmental assessments. To serve society well, climate change stabilization assessments need to capture the uncertainties of the deep future, be statistically sound and track near-term disruptions. Up to now, conceptual, computational and data constraints have limited the quantification of uncertainties of climate stabilization pathways to a narrow set, focused on the current century. The statistical interpretation of scenarios generated by multi-model ensembles is problematic due to availability biases and model dependencies. Scenario plausibility assessments are scant. Simplified, single-objective decision criteria frameworks are used to translate decarbonization uncertainties into decision rules whose understanding is not validated.
EUNICE aims to transform the methodological and experimental foundations of model-based climate assessments through quantification and debiasing of uncertainties in climate stabilization pathways. Our approach is threefold: construct, consolidate and convert. We first apply simulation and statistical methods for extending scenarios into the deep future (beyond the current century and status quo), quantifying and attributing deep uncertainties. We consolidate model ensembles through machine learning and human ingenuity to eliminate statistical biases, pin down near-term correlates of long-term targets, and identify early signals of scenario plausibility through prediction polls. Finally, we use decision-theoretic methods to convert model-generated maps of the future into resilient recommendations and experimentally test how to communicate them effectively. By advancing the state of the art in mathematical modelling, statistics, and behavioural decision-making, we strengthen the scientific basis of climate assessments, such as those of the IPCC. The approach and insights of EUNICE can be applied to other high-stakes environmental, social and technological evaluations.
Ámbito científico
Palabras clave
Programa(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régimen de financiación
HORIZON-ERC - HORIZON ERC GrantsInstitución de acogida
20133 Milano
Italia