Unité mixte de recherche 7235

The land use change time-accounting failure

Marion Dupoux

[en]Land use change (LUC) is the second largest human-induced source of greenhouse gases. While LUC impacts are mostly immediate, policy makers consider it to be evenly spread over time. In the context of public evaluation of projects, I theoretically show that, as long as the discounting process perfectly offsets the rise of carbon prices, cost-benefit analysis outcomes are not affected. When this condition does not hold, which is particular to the global warming issue, the uniform time-accounting of LUC distorts present values by emphasizing both the discounting process and the increase in the carbon price over time. This induced bias is quantified in a case study of bioethanol in France. Depending on the type of impact and discounting and carbon pricing assumptions, a downward/upward bias between + or – 15% and + or – 30% of the LUC value is found. Two simple decision tools are provided to improve accounting of LUC impacts.[/en]


mardi 17 mai 2022

Rencontres économiques

9h30 à 11h30

Comment se débarrasser des énergies fossiles et développer les énergies renouvelables dans un monde en crise ?

jeudi 19 mai 2022


Christophe Blot

Are all central bank asset purchases the same?

lundi 23 mai 2022

Law, Institutions and Economics in Nanterre (LIEN)

Clara Jean (Grenoble Ecole de Management)

The Value of Your Data: Privacy and Personal Data Exchange Networks

mardi 24 mai 2022

Recherche et Economie et Socioéconomie Politique, des Institutions et des Régulations (RESPIR)

Alice Sindzingre (CEPN) et Fabrice Tricou

De 13h30 à 15h30

Six forms of hierarchy for a theoretical analysis of capitalism

lundi 30 mai 2022

Law, Institutions and Economics in Nanterre (LIEN)

Antoine Dubus (ETH Zurich)

Salle G110

Data Driven Mergers and Acquisitions with Information Synergies

mardi 31 mai 2022

Series of Webinars on Economics of Environment, Energy and Transport (SWEEET)

Juan Pablo Montero (PUC)


jeudi 9 juin 2022


Rémi Generoso


jeudi 9 juin 2022

Groupe de travail « Intelligence artificielle »

Hugo Le Picard (IFRI)

Salle G614B

Le deep learning au service de l’analyse des énergies renouvelables en Afrique

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