The Energy Modeling Forum (EMF34)

Description of the project

The Energy Modeling Forum (EMF34) working group focuses on how an integrated North American (Canada, USA, Mexico) market of tradable commodities such as oil, natural gas and electric power might operate under various world oil and macroeconomic conditions.


The scenario evaluation involves primarily these metrics: production, consumption, prices, and energy trade between Canada, Mexico and USA and extends from 2015 through 2050 due to the long lead times for investment and planning.


Key findings from the core North American scenarios in the EMF34 intermodel comparison, Energy Policy, Volume 144, September 2020

North American energy system responses to natural gas price shocks, Energy Policy, Volume 149, February 2021

Paris Reinforce

MUSE is one of the 5 global integrated assessment models used in the PARIS REINFORCE project, an H2020 project started in June 2019 where 18 partners are involved to innovate the dialogue between modellers and stakeholders in the design of future policies consistent with the Paris Agreement.

Finding novel ways to communicate the modelling assumptions and explain the model output is the core principle inspiring the I2AM PARIS platform, one of the pillars of the project, an interactive interface between modellers, researchers and policy-makers where all the teams will present their modelling inputs and results in a transparent fashion.

In the Paris Reinforce project, MUSE is used to generate plausible transitions of the energy systems with an enhanced stakeholders’ engagement (co-created scenarios), at a global level using its world discretization into 28 regions. The scenarios are formulated adopting a model inter-comparison approach which follows a rigorous protocol for the harmonisation of the data inputs across models. This unique feature of PARIS REINFORCE is essential to ensure the robustness of the modelling outputs and justify their variance.

The modelled scenarios will define the effectiveness gap of existing carbon mitigation policies and Nationally Determined Contributions compared to the Paris Agreement and will be assessed to inform the 2023 Global Stocktake.

Find out more about the Paris Reinforce project.

Building an agent-based model for CCS in Brazil found on a stakeholders (agents) survey to stablish relationship and learning capabilities among actors

In Brazil, the uncertainties surrounding CCS are steep. Despite the Brazilian geological potential for carbon dioxide injection (Netto et al., 2020), and the country’s current activities in Enhanced Oil Recovery (EOR), with a total storage of 7 Mt of CO2 by 2017 (Global CCS Institute, 2020), it is far from the estimated geological capacity in the country of 2035 Gt of CO2 (Rockett et al., 2011). Furthermore, the country performs very low (9 out of 100) in the CCS Policy Indicator created by the Global CCS Institute.

Based on the exposed above, this project aims to identify the best measures to develop the CCS industry in Brazil, considering the country’s capture and storage options, including climate engineering and natural-based solution. It will be developed in two main activities: Data collection for the available CCS technologies applied to the different regions of Brazil, including reforestation and model the CCS module in MUSE using agent-based modeling.

Global energy system modelling for the decarbonisation of the residential building sector

Buildings comprise between 20% and 40% of overall energy consumption depending on the economic development, cultural, geographical features and climate conditions of a country or region. Heating and cooling of households can represent up to 80% of the total energy consumed in buildings. This research aims to address: 1. The investment behaviour when choosing energy technologies, and 2. The spatio-temporal dimension of the demand drivers.

 Whereas Geographic Information Systems (GIS) approaches capture the spatio-temporal dimension of energy demand drivers (weather conditions, population density, demand distribution, income, etc.) [1], Agent-Based Modelling (ABM) approaches are key to model agent’s investment behaviour [2]. Agents are independent with a wide range of heterogeneous attributes in terms of decision-making processes, investment objectives and technology preferences [3]. In general, integrated assessment of energy technology deployment applying ABM and GIS approaches to develop pathways for the decarbonisation of the residential sector at a global scale has not yet been assessed.

 For illustration purposes, a Canada case study is presented here. The Figure below shows preliminary results of the combination of these approaches to assess the diffusion of heating technologies in the residential sector in Canada, disaggregating the country into 14 subnational regions. The subnational regions were spatially calculated using three attributes at 1 km2-resolution: income per capita, heat demand density and head demand per capita. Then, a range of attributes are extracted in each subnational region to produce MUSE inputs. Future research will assess GIS-Agent-Based scenarios for 165 countries aggregated in 28 regions to study the decarbonisation of the residential sector globally.