Drouet Laurent

Laurent Drouet is a senior scientist at EIEE.
He holds a PhD in Economics and Social Sciences from the University of Geneva, Switzerland and a Master in Applied Mathematics from the Institute of Applied Mathematics of Angers, France.
In 2006-2009, he was postdoctoral research fellow in the Research Lab of Economics and Environmental Management at the Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland. In 2009-2012, he was a researcher at the Public Research Center Tudor, Luxembourg.
From 2012 to 2018, he was a senior researcher at Fondazione Eni Enrico Mattei (FEEM). He developed algorithms to couple models of different kind (ie, economy vs climate, air pollution vs energy systems) in an optimization framework.
He also developed an open-source energy systems model ETEM which he implemented for Luxembourg. He currently co-leads the development of the WITCH model. He was involved in the implementation of many model components: energy system, economy, land-use, climate, air pollution, mitigation options and impacts.

LATEST PUBLICATIONS

  • The role of the discount rate for emission pathways and negative emissions
  • An inter-model assessment of the role of direct air capture in deep mitigation pathways
  • Taking some heat off the NDCs? The limited potential of additional short-lived climate forcers’ mitigation
  • The role of methane in future climate strategies: Mitigation potentials and climate impacts
  • NeatWork: A Tool for the Design of Gravity-Driven Water Distribution Systems for Poor Rural Communities
  • A Multi-Model Assessment of Food Security Implications of Climate Change Mitigation
  • Looking under the hood: A comparison of techno-economic assumptions across national and global integrated assessment models
  • Implications of various effort-sharing approaches for national carbon budgets and emission pathways
  • Enhancing global climate policy ambition towards a 1.5 °C stabilization: a short-term multi-model assessment
  • Water demand for electricity in deep decarbonisation scenarios: a multi-model assessment

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