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When replacing equipment at its end of life, industries often have the option to select higher efficiency technologies. The choice to invest in an improved piece of equipment rather than a simple in-kind replacement is driven by many factors, including costs, performance, and familiarity with new technology options. Engineering models that account for equipment stock turnover, such as ENERGY 2020, typically assume that this decision is primarily driven by the difference in marginal costs: higher up front capital costs must be, at minimum, balanced by lower lifetime energy costs for an upgrade to be pursued. Stock turnover analysis requires detailed data inputs regarding the costs and performance of new equipment. For simplicity, a common approach is to develop assumed correlations that reflect the trade-off between marginal capital cost and efficiency and compare these to energy prices to select an efficiency level for new equipment. In this study, we present a novel method to develop such trade-off curves based on a technology-explicit approach rather than a qualitative correlation assumption. We generate trade-off curves for two common types of industrial devices: electric machine drive and natural gas steam generation for the case study of the Canadian pulp and paper sector. The curves demonstrate that the efficiency of new devices can be expected to vary significantly based on energy prices. At current energy prices, we find that newly purchased machine drive and steam generation devices would have an optimal efficiency level of 91% and 75%, respectively. [ABSTRACT FROM AUTHOR]

The global demand of steel production is growing, and so are the carbon dioxide emissions from the sector. Research has confirmed that 15 to 20% further energy efficiency improvement is possible, which would reduce the sector's greenhouse gas emissions and help meet global emission reduction initiatives. In this context, this research aims at developing a bottom-up system-based model to evaluate the long-term potential of energy efficiency alternatives in greenhouse gas mitigation. A case study was conducted for the Canadian iron and steel sector. The developed framework involves review of the state of the art in iron and steel production technologies, demand tree development, energy-environmental modeling, scenario development, and analysis. Twenty-six mitigation scenarios were developed in planning horizons ending in 2030 and 2050. A 13% improvement in Canadian iron and steel energy efficiency could be achieved by increasing the production share of the electric arc furnace route from 41% in the base year to 56% by 2050. The results of the analysis suggest that 19 and 38 million tonnes of greenhouse gas emission reduction are achievable in the 2010–2030 and 2010–2050 planning horizons, respectively. This translates to an approximately 6% reduction in emissions compared to the baseline scenarios at a cost of − $76 and − $51 per tonne of carbon dioxide equivalent in the 2010–2030 and 2010–2050 time periods, respectively. The results also reveal that more than 85% of the potential emission reductions are achievable with negative costs and are economically attractive. The study provides insights to decision makers at different levels of industry and government. [ABSTRACT FROM AUTHOR]