The Deployment of Low Carbon Technologies in Energy Intensive Industries: A Macroeconomic Analysis for Europe, China and India

Abstract

Industrial processes currently contribute 40% to global CO 2 emissions and therefore substantial increases in industrial energy efficiency are required for reaching the 2 • C target. We assess the macroeconomic effects of deploying low carbon technologies in six energy intensive industrial sectors (Petroleum, Iron and Steel, Non-metallic Minerals, Paper and Pulp, Chemicals, and Electricity) in Europe, China and India in 2030. By combining the GAINS technology model with a macroeconomic computable general equilibrium model, we find that output in energy intensive industries declines in Europe by 6% in total, while output increases in China by 11% and in India by 13%. The opposite output effects emerge because low carbon technologies lead to cost savings in China and India but not in Europe. Consequently, the competitiveness of energy intensive industries is improved in China and India relative to Europe, leading to higher exports to Europe. In all regions, the decarbonization of electricity plays the dominant role for mitigation. We find a rebound effect in China and India, in the size of 42% and 34% CO 2 reduction, respectively, but not in Europe. Our results indicate that the range of considered low-carbon technology options is not competitive in the European industrial sectors. To foster breakthrough low carbon technologies and maintain industrial competitiveness, targeted technology policy is therefore needed to supplement carbon pricing.

A review of technology and policy deep decarbonization pathway options for making energy-intensive industry production consistent with the Paris Agreement

“…hybridization techniques (Hourcade et al, 2006;Pye and Bataille, 2016) should be used to include transformative, deep decarbonization industrial detail in all modelling types used for <=2°C studies, be it directly as technologies in IAMs or bottom up stock turnover models, via production functions and elasticities in CGE models, or combinations thereof. Nabernegg et al (2017), which addressed a 50% reduction, and the 2017 IEA ETP (International Energy Agency (IEA), 2017b), are steps in this direction. A related recommendation is that better methods are required for including dynamic trade in global and national models, be it endogenously or through scenarios.…”

“…We refer to this as "decarbonization lite", focussing on efficiency, some fuel switching, energy cascading (where high quality waste heat from one industrial facility is reused by another or for general heating), destructive and non-destructive recycling, reduced demand and dematerialization (Allwood et al, 2010). Modelling studies, be they national or of a global integrated assessment model variety, typically incorporate only moderate (up to 50%) sector mitigation (Nabernegg et al, 2017) and simulate industry in an aggregate way that obscures sectoral complexity and capacities to abate (Edelenbosch et al, 2017). These approaches often lead to results and policy advice reflecting substantial unnecessary residual industrial emissions ("Mission 2020: Industry Milestones," 2017) requiring more reductions at higher costs from other sectors, and leading to the common perception that very deep targets are practically impossible.…”

Impact and Interactions of Policies for Mitigation of Air Pollutants and Greenhouse Gas Emissions in Korea

“…With such a reduction technology, the reduction cost can be greatly reduced. Nabernegg et al [41] also mentioned the importance of mitigation technology by applying a global CGE model. When the low carbon technologies (such as waste heat recovery) are deployed in developing countries (such as China and India), their global competitiveness increase due to energy saving and GHG reduction, in spite of additional cost of capital investment.…”

Energy-Saving Potential of China’s Steel Industry According to Its Development Plan

“…Formula (7) shows that the influence of energy structure and industry concentration on EI can be reflected by the comprehensive EI, while the influence of technology, production route can be reflected by the comparable EI, according to Formula (8). In this paper, the relationship between the comprehensive and comparable EIs should be further studied to conduct a comprehensive analysis of all the factors.…”

“…As the world's largest steel producing country, China is a prominent and important global and national case study for scenario analysis, and a number of energy-modeling approaches have been used to analyze its future energy intensity trends and to assess policy support for energy-saving [2][3][4][5][6][7][8]. These can be categorized into two types: bottom-up (BU) and top-down (TD) models.…”