Unveiling environmental implications of Canadian electricity system's low-carbon transitions: A multi-regional stochastic optimization-driven input-output model

Abstract

Low-carbon transition of the electricity system is essential for achieving greenhouse gas (GHG) emission mitigation goals. Nevertheless, such a transition involves various complexities and the related environmental impacts on various sectors need to be explored. In this study, a multi-regional stochastic optimization-driven input-output model (STIO) has been developed to unveil the environmental implications of the low-carbon transitions under various policy scenarios from a sectoral perspective and applied to Canada. STIO can (1) explore optimal decarbonization pathways under uncertainties through a stochastic electricity system optimization module; (2) link the outputs to a power generation technology-focused input-output module to examine sectoral-level environmental impacts; (3) reflect the interactive effects of multiple policy factors on system response through an interaction analysis module. Results find that higher carbon taxes would reduce direct and indirect emissions of the electricity sector by 38.3% and 33.1%, respectively, in 2050. Specifically, the significance of indirect emissions from the natural gas-fired power sector in Alberta is highlighted; its reduced production would indirectly curtail emissions in the oil and gas extraction (Canada's largest GHG emissions source) and services (the highest GDP contributor to Canada's economy) sectors by 15.9% and 16.1%, respectively. Further analysis shows that the interaction among a high-level carbon tax and increasing capital cost of natural gas-fired power technology would contribute to the electricity sector's indirect emission reductions. The findings can help policymakers gain insights into the environmental impacts of various decarbonization pathways on the whole economy.