Incorporating different endogenous learning formulations in MERGE

This paper presents the implementation of endogenous technological change, following either a one-factor or a two-factor learning curve, in the MERGE model. We have indeed developed a new version of this model, called MERGE-ETL, to analyse the dynamics of endogenous technological learning (ETL) in the energy system. This paper describes first the basic formulation of MERGE-ETL, then the solving techniques used for this model, and presents finally some numerical results in the context of policies designed to mitigate a global climate change.carbon control policies; endogenous technological change; MERGE; optimisation techniques.

The coupling of optimal economic growth and climate dynamics

In this paper, we study optimal economic growth programs coupled with climate change dynamics. The study is based on models derived from MERGE, a well established integrated assessment model (IAM). We discuss first the introduction in MERGE of a set of “tolerable window” constraints which limit both the temperature change and the rate of temperature change. These constraints, obtained from ensemble simulations performed with the Bern 2.5-D climate model, allow us to identity a domain intended to preserve the Atlantic thermohaline circulation. Next, we report on experiments where a two-way coupling is realized between the economic module of MERGE and an intermediate complexity “3-D-” climate model (C-GOLDSTEIN) which computes the changes in climate and mean temperature. The coupling is achieved through the implementation of an advanced “oracle based optimization technique” which permits the integration of information coming from the climate model during the search for the optimal economic growth path. Both cost-effectiveness and cost-benefit analysis modes are explored with this combined “meta-model” which we refer to as GOLDMERGE. Some perspectives on future implementations of these approaches in the context of “collaborative” or “community” integrated assessment modules are derived from the comparison of the different approaches