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Emissions-intensive, trade-exposed (EITE) industries must decarbonize to limit global warming to 1.5°C. This study explores how policy stringency and regional variability impact EITE industrial decarbonization. It uses Canada as a case study due to its heterogeneous industrial sector and high regional resource variability. The study has two scenarios: one with global climate action where the world pushes to limit warming to 1.5°C, and one where Canada acts to achieve net-zero emissions by 2050 and the rest of the world lags. The scenarios differ in three ways: the global price of oil, the pace of technological change for low emission technologies, and domestic climate policy. In the global action scenario, a carbon price of $430USD2020 was needed to achieve 75% decarbonization of EITE industries by 2050. In our global inaction scenario, EITE industries only decarbonize 25%, as domestic climate policy considered international competition and the risk of industrial shutdown. If competitiveness concerns persist as simulated in this scenario, Canada is highly unlikely to achieve deep industrial decarbonization by 2050. The results also show that regional variability plays a significant role in low emissions technology adoption. While all regions will need targeted innovation and commercialization support as well as market uptake mechanisms, we find that relative advantages and disadvantages in terms of resource availability and industrial mix play an important role in how regional decarbonization occurs. For instance, regions with inexpensive local fossil fuels and ready geology suitable for CO2 storage have a high uptake of carbon capture and storage. Regions with access to abundant hydroelectricity rely more on electrification as a decarbonization pathway. Regions with no relative resource advantages are at greater risk for industrial shutdown due to the higher cost of decarbonization. Achieving national deep decarbonization of EITE industries in Canada or elsewhere is highly unlikely without global climate action due to competitiveness concerns. Policymakers interested in addressing competitiveness concerns might do so either through policy mechanisms that support the uptake of low emission technologies, mechanisms that penalize regions without decarbonization policy, or both. Policymakers should consider relative regional advantages and disadvantages to EITE decarbonization by designing policies that account for industrial heterogeneity and regional resource availability. [ABSTRACT FROM AUTHOR]

According to intuition and theories of diffusion, consumer preferences develop along with technological change. However, most economic models designed for policy simulation unrealistically assume static preferences. To improve the behavioral realism of an energy-economy policy model, this study investigates the "neighbor effect," where a new technology becomes more desirable as its adoption becomes more widespread in the market. We measure this effect as a change in aggregated willingness to pay under different levels of technology penetration. Focusing on hybrid-electric vehicles, an online survey experiment collected stated preference (SP) data from 535 Canadian and 408 Californian vehicle owners under different hypothetical market conditions. Revealed preference (RP) data was collected from the same respondents by eliciting the year, make and model of recent vehicle purchases from regions with different degrees of HEV popularity: Canada with 0.17% new market share, and California with 3.0% new market share. We compare choice models estimated from RP data only with three joint SP-RP estimation techniques, each assigning a different weight to the influence of SP and RP data in coefficient estimates. Statistically, models allowing more RP influence outperform SP influenced models. However, results suggest that because the RP data in this study is afflicted by multicollinearity, techniques that allow more SP influence in the beta estimates while maintaining RP data for calibrating vehicle class constraints produce more realistic estimates of willingness to pay. Furthermore, SP influenced coefficient estimates also translate to more realistic behavioral parameters for CIMS, allowing more sensitivity to policy simulations.

The international workshop on Policy Modeling for Industrial Energy Use was jointly organized by EETA (Professional Network for Engineering Economic Technology Analysis) and INEDIS (International Network for Energy Demand Analysis in the Industrial Sector). The workshop has helped to layout the needs and challenges to include policy more explicitly in energy-efficiency modeling. The current state-of-the-art models have a proven track record in forecasting future trends under conditions similar to those faced in the recent past. However, the future of energy policy in a climate-restrained world is likely to demand different and additional services to be provided by energy modelers. In this workshop some of the international models used to make energy consumption forecasts have been discussed as well as innovations to enable the modeling of policy scenarios. This was followed by the discussion of future challenges, new insights in the data needed to determine the inputs into energy models, and methods to incorporate decisionmaking and policy in the models. Based on the discussion the workshop participants came to the following conclusions and recommendations: Current energy models are already complex, and it is already difficult to collect the model inputs. Hence, new approaches should be transparent and not lead to extremely complex models that try to "do everything". The model structure will be determined by the questions that need to be answered. A good understanding of the decision making framework of policymakers and clear communication on the needs are essential to make any future energy modeling effort successful. There is a need to better understand the effects of policy on future energy use, emissions and the economy. To allow the inclusion of policy instruments in models, evaluation of programs and instruments is essential, and need to be included in the policy instrument design. Increased efforts are needed to better understand the effects of innovative (non-monetary) policy instruments through evaluation and to develop approaches to model both conventional and innovative policies. The explicit modeling of barriers and decision making in the models seems a promising way to enable modeling of conventional and innovative policies. A modular modeling approach is essential to not only provide transparency, but also to use the available resources most effectively and efficiently. Many large models have been developed in the past, but have been abandoned after only brief periods of use. A development path based on modular building blocks needs the establishment of a flexible but uniform modeling framework. The leadership of international agencies and organizations is essential in the establishment of such a framework. A preference is given for "softlinks" between different modules and models, to increase transparency and reduce complexity. There is a strong need to improve the efficiency of data collection and interpretation efforts to produce reliable model inputs. The workshop participants support the need for the establishment of an (in-)formal exchanges of information, as well as modeling approaches. The development of an informal network of research institutes and universities to help build a common dataset and exchange ideas on specific areas is proposed. Starting with an exchange of students would be a relative low-cost way to start such collaboration. It would be essential to focus on specific topics. It is also essential to maintain means of regular exchange of ideas between researchers in the different focus points.