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Greenhouse gas (GHG) emissions are currently at the crux of political, environmental, technological, and cultural discussions due to climate change. A drastic reduction of GHG emissions is needed in order to mitigate potentially catastrophic climate change impacts. Thus, thoroughly understanding emission sources is imperative. A disaggregated analysis of Canada’s future GHG emission projections has not yet been conducted. The objectives in this paper are to assess disaggregated GHG emissions in Canada for the years 2014, 2030, and 2050, and analyze the results through Sankey diagrams. Emissions are calculated using a bottom-up multi-regional accounting-based Long-range Energy Alternative Planning systems model. Each major economic sector in Canada is analyzed including the electricity generation, residential, commercial and institutional, industrial, transportation, and agriculture sectors. The emissions released in these sectors are traced to the resources and end-uses responsible. Results are presented for Canada and for provinces individually. GHG emissions contained in exported resources are evaluated. Results show that Canada’s business-as-usual GHG emissions will grow from 732 million tonnes in 2014 to 780 and 798 million tonnes in 2030 and 2050, respectively. Canada exports more emissions contained in resources than it emits. Per capita emissions intensity will fall by 14% between 2014 and 2050. Results are compared to climate targets and key areas of GHG mitigation potential are identified. Alberta’s oil and gas sector and Ontario’s transportation sector are the two single largest sectoral sources of emissions by 2050. This research can help policy makers, innovators, and the public better understand GHG emissions, which can lead to more effective GHG mitigation. [ABSTRACT FROM AUTHOR]

The paper proceeds with a discussion of the interplay of scale and intensity in determining greenhouse gas emissions. This is followed by the presentation of several macroeconomic scenarios using LowGrow, a simulation model of the Canadian economy. The scenarios considered are ‘business as usual’ which is a projection into the future of past trends, ‘selective growth’ in which differential growth rates are applied to parts of the economy according to their direct and indirect greenhouse gas emissions, and ‘degrowth’ where the average GDP/capita of Canadians is reduced towards a level more consistent with a world economy the size of which respects global environmental limits. The paper ends with a comparison of the scenarios. [Copyright &y& Elsevier]

Through tests conducted at the Toronto and Region Conservation Authority's (TRCA) Archetype Sustainable Houses, a hybrid residential space heating system with a supervisory controller was monitored and studied to evaluate its performance and effectiveness for the heating season. A high efficiency natural gas furnace (NGF) and an electric air source heat pump (ASHP) were coupled together to meet the space heating demand of the house. This integrated system is called the cloud based Smart Dual Fuel Switching System (SDFSS) that considers time-of-use (TOU) pricing, fuel cost, short-term weather forecast, and equipment efficiencies and capacities. This multi-variable decision-making process defines an optimal schedule for the hybrid system to run more cost effectively. This paper analyses two separate SDFSSs. According to these analyses, the SDFSS systems showed lower operating cost with respect to the furnace or the ASHP system alone with various carbon tax (CT) levels from $0 to $250/tonne of CT with an increment of $10/tonne of CT that were simulated, along with a significant GHG emission reduction relative to the conventional heating systems. Furthermore, with these technologies, Canada's residential sector could potentially meet Canada's Paris Agreement goals. The SDFSS technology is a is flexible, user friendly, ubiquitous technology for the smooth transition from today's natural gas dominated space heating system to the future's low carbon infrastructure powered by a heat pump and renewable energy. [ABSTRACT FROM AUTHOR]

Summary: Uncertainty estimation of climate change impacts has been given a lot of attention in the recent literature. It is generally assumed that the major sources of uncertainty are linked to General Circulation Models (GCMs) and Greenhouse Gases Emissions Scenarios (GGES). However, other sources of uncertainty such as the choice of a downscaling method have been given less attention. This paper focuses on this issue by comparing six downscaling methods to investigate the uncertainties in quantifying the impacts of climate change on the hydrology of a Canadian (Quebec province) river basin. The downscaling methods regroup dynamical and statistical approaches, including the change factor method and a weather generator-based approach. Future (2070–2099, 2085 horizon) hydrological regimes simulated with a hydrological model are compared to the reference period (1970–1999) using the average hydrograph, annual mean discharge, peak discharge and time to peak discharge as criteria. The results show that all downscaling methods suggest temperature increases over the basin for the 2085 horizon. The regression-based statistical methods predict a larger increase in autumn and winter temperatures. Predicted changes in precipitation are not as unequivocal as those of temperatures, they vary depending on the downscaling methods and seasons. There is a general increase in winter discharge (November–April) while decreases in summer discharge are predicted by most methods. Consistently with the large predicted increases in autumn and winter temperature, regression-based statistical methods show severe increases in winter flows and considerable reductions in peak discharge. Across all variables, a large uncertainty envelope was found to be associated with the choice of a downscaling method. This envelope was compared to the envelope originating from the choice of 28 climate change projections from a combination of seven GCMs and three GGES. Both uncertainty envelopes were similar, although the latter was slightly larger. The regression-based statistical downscaling methods contributed significantly to the uncertainty envelope. Overall, results indicate that climate change impact studies based on only one downscaling method should be interpreted with caution. [Copyright &y& Elsevier]