Energy Transition Pathways for Deep Decarbonization of the Greater Montreal Region: An Energy Optimization Framework

Energies (Basel). 2022 May 20;15(10):3760. doi: 10.3390/en15103760. eCollection 2022 May.

Abstract

More than half of the world's population live in cities, and by 2050, it is expected that this proportion will reach almost 68%. These densely populated cities consume more than 75% of the world's primary energy and are responsible for the emission of around 70% of anthropogenic carbon. Providing sustainable energy for the growing demand in cities requires multifaceted planning approach. In this study, we modeled the energy system of the Greater Montreal region to evaluate the impact of different environmental mitigation policies on the energy system of this region over a long-term period (2020-2050). In doing so, we have used the open-source optimization-based model called the Energy-Technology-Environment Model (ETEM). The ETEM is a long-term bottom-up energy model that provides insight into the best options for cities to procure energy, and satisfies useful demands while reducing carbon dioxide (CO2) emissions. Results show that, under a deep decarbonization scenario, the transportation, commercial, and residential sectors will contribute to emission reduction by 6.9, 1.6, and 1 million ton CO2-eq in 2050, respectively, compared with their 2020 levels. This is mainly achieved by (i) replacing fossil fuel cars with electric-based vehicles in private and public transportation sectors; (ii) replacing fossil fuel furnaces with electric heat pumps to satisfy heating demand in buildings; and (iii) improving the efficiency of buildings by isolating walls and roofs.

Keywords: ETEM; bottom–up energy model; cities; deep decarbonization; energy policy.

Grants and funding

This research was jointly funded by the Canadian IVADO program (VORTEX project), and the Natural Sciences and Engineering Research Council of Canada (grant holders are Olivier Bahn and Erick Delage, with grant numbers RGPIN-2016-04214 and RGPIN-2016-05, respectively).