RESUMO
Global pathways limiting warming to 2 °C or below require deep carbon dioxide removal through a large-scale transformation of the land surface, an increase in forest cover, and the deployment of negative emission technologies (NETs). Government initiatives endorse bioenergy as an alternative, carbon-neutral energy source for fossil fuels. However, this carbon neutral assumption is increasingly being questioned, with several studies indicating that it may result in accounting errors and biased decision-making. To address this growing issue, we use a carbon budget model combined with an energy system model. We show that including forest sequestration in the energy system model alleviates the decarbonization effort. We discuss how a forest management strategy with a high sequestration capacity reduces the need for expensive negative emission technologies. This study indicates the necessity of establishing the most promising forest management strategy before investing in bioenergy with carbon capture and storage. Finally, we describe how a carbon neutrality assumption may lead to biased decision-making because it allows the model to use more biomass without being constrained by biogenic CO2 emissions. The risk of biased decision-making is higher for regions that have lower forest coverage, since available forest sequestration cannot sink biogenic emissions in the short term, and importing bioenergy could worsen the situation.
Assuntos
Dióxido de Carbono , Florestas , Biomassa , Dióxido de Carbono/análise , Combustíveis Fósseis , Sequestro de CarbonoRESUMO
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.
RESUMO
Global warming mitigation strategies are likely to affect human health and biodiversity through diverse cause-effect mechanisms. To analyze these effects, we implement a methodology to link TIMES energy models with life cycle assessment using open-source software. The proposed method uses a cutoff to identify the most relevant processes. These processes have their efficiencies, fuel mixes, and emission factors updated to be consistent with the TIMES model. The use of a cutoff criterion reduces exponentially the number of connection points between models, facilitating the analysis of scenarios with a large number of technologies involved. The method is used to assess the potential effects of deploying low-carbon technologies to reduce combustion emissions in the province of Quebec (Canada). In the case of Quebec, the reduction of combustion emissions is largely achieved through electrification of energy services. Global warming mitigation efforts reduce the impact on human health and ecosystem quality, mainly because of lower global warming, water scarcity, and metal contamination impacts. The TIMES model alone underestimated the reduction of CO2eq by 21% with respect to a full account of emissions.