Project description DEENESFRITPL Integrated assessment models to improve climate decision-making Climate change is the most complicated global environmental problem today. Integrated assessment models (IAMs) can support climate policy by offering insights into future greenhouse gas emissions and quantifying mitigation scenarios. However, IAMS are not free from uncertainty. In this context, the EU-funded MANET project will define the sources of uncertainty, either due to IAMs inputs or IAMs structure. The methodology will be embodied in an emulator of IAMs, formulated using machine learning techniques to reproduce IAMs outputs. It will be a flexible tool for policymakers and scientists to directly compare IAMs with no limitation of the solution domain. The focus will be on the uptake of key decarbonisation technologies. Show the project objective Hide the project objective Objective Curbing greenhouse gas emissions is a challenge of the utmost importance for our society future and requires urgent decisions on the implementation of clear-cut climate economic policies. Integrated Assessment Models (IAMs) allow to explore alternative energy scenarios in the next 30-70 years. They are key to support the design of climate policies as they highlight the nexus between climate modelling, social science, and energy systems. However, the use of IAMs to inform climate policies does not come free of controversial aspects. Primarily, the inherent uncertainty of IAMs long-term outputs has created several difficulties for the integration of the modelling insights in the policy design. Modelling outputs diverge across IAMs models quite dramatically when they are asked for example to quantify the uptake of key technologies for the decarbonisation, such as renewables and carbon capture and storage. Uncertainty in IAMs descends from lack of knowledge of the future and from IAMs incomplete representations of the future. Uncertainty cannot be removed, but reduced, understood, and conveyed appropriately to policy makers to avoid that different projections cause delayed actions. This project aims to fill this gap providing a methodology which defines the sources of uncertainty, either due to IAMs inputs or IAMs structure, and quantify their relative importance. The methodology will be embodied in an emulator of IAMs, MANET (the eMulAtor of iNtegratAd assEssmenT models) formulated using machine learning techniques to reproduce IAMs outputs. The project will provide a proof of concept of MANET focusing on the uptake of key decarbonisation technologies. The emulator will provide a simplified version of the IAM outputs as a response surface of the model to any variation of the inputs. MANET will be a flexible tool for policy makers and scientists for a direct comparison of IAMs with no limitation of the solution domain. Fields of science engineering and technologyenvironmental engineeringenergy and fuelsrenewable energyengineering and technologyenvironmental engineeringcarbon capture engineeringnatural sciencescomputer and information sciencesartificial intelligencemachine learning Programme(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Topic(s) MSCA-IF-2020 - Individual Fellowships Call for proposal H2020-MSCA-IF-2020 See other projects for this call Funding Scheme MSCA-IF-EF-ST - Standard EF Coordinator POLITECNICO DI MILANO Net EU contribution € 171 473,28 Address PIAZZA LEONARDO DA VINCI 32 20133 Milano Italy See on map Region Nord-Ovest Lombardia Milano Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 171 473,28