Indirect CO2 Emission Implications of Energy System Pathways: Linking IO and TIMES Models for the UK

Material flow analysis and regional greenhouse gas emissions associated to permanent magnets and batteries used in electric vehicles

Clean technologies are rapidly increasing in the last decade. In the transport sector, market share of global electric car sales has changed from 0.0 % in 2010 to 3.2 % (2.1million) in 2020, and predictions show that sales could reach near 30 % in 2030. This drastic change is mainly encouraged by environmental goals set to reduce greenhouse gas emissions (GHG) expressed in CO2-eq, not emitted by electric vehicles (EVs) during the use phase. However, clean technologies might cause other impacts during manufacture and, while clearly reduce the dependency on oil, can increase the dependency on other materials. In this context, the objectives of our work are quantifying the critical raw materials needed by permanents magnets and batteries of EVs (neodymium, lithium, and cobalt); their supply risk, performing a material flow analysis; and studying their environmental impacts using the methodology "Environmentally-Extended Multi-Regional Input-Output Analysis". This methodology is used to quantify the produced impacts and the country where the impacts are being produced, in contrast to conventional methodologies that only calculate global impacts. Therefore, environmental impacts are estimated considering different scenarios, based on environmental objectives of the European Union and China. In most scenarios China shows a key role in mining and processing of metals, being the country where major impacts are produced. Obtained results are useful to assess which environmental proposals are more effective to reduce the environmental impact of EVs and set the ground to understand the geostrategic importance of key metals used for EVs manufacture.

Energy requirements and carbon emissions for a low-carbon energy transition

Achieving the Paris Agreement will require massive deployment of low-carbon energy. However, constructing, operating, and maintaining a low-carbon energy system will itself require energy, with much of it derived from fossil fuels. This raises the concern that the transition may consume much of the energy available to society, and be a source of considerable emissions. Here we calculate the energy requirements and emissions associated with the global energy system in fourteen mitigation pathways compatible with 1.5 °C of warming. We find that the initial push for a transition is likely to cause a 10-34% decline in net energy available to society. Moreover, we find that the carbon emissions associated with the transition to a low-carbon energy system are substantial, ranging from 70 to 395 GtCO₂ (with a cross-scenario average of 195 GtCO₂). The share of carbon emissions for the energy system will increase from 10% today to 27% in 2050, and in some cases may take up all remaining emissions available to society under 1.5 °C pathways.

Pricing indirect emissions accelerates low-carbon transition of US light vehicle sector

Large–scale electric vehicle adoption can greatly reduce emissions from vehicle tailpipes. However, analysts have cautioned that it can come with increased indirect emissions from electricity and battery production that are not commonly regulated by transport policies. We combine integrated energy modeling and life cycle assessment to compare optimal policy scenarios that price emissions at the tailpipe only, versus both tailpipe and indirect emissions. Surprisingly, scenarios that also price indirect emissions exhibit higher, rather than reduced, sales of electric vehicles, while yielding lower cumulative tailpipe and indirect emissions. Expected technological change ensures that emissions from electricity and battery production are more than offset by reduced emissions of gasoline production. Given continued decarbonization of electricity supply, results show that a large–scale adoption of electric vehicles is able to reduce CO₂ emissions through more channels than previously expected. Further, carbon pricing of stationary sources will also favor electric vehicles.

New research shows how large–scale adoption of electric vehicles due to expected technological change may not only reduce emissions from tailpipes, but also indirect emissions stemming from energy and battery production.

Considering Life Cycle Greenhouse Gas Emissions in Power System Expansion Planning for Europe and North Africa Using Multi-Objective Optimization

We integrate life cycle indicators for various technologies of an energy system model with high spatiotemporal detail and a focus on Europe and North Africa. Using multi-objective optimization, we calculate a pareto front that allows us to assess the trade-offs between system costs and life cycle greenhouse gas (GHG) emissions of future power systems. Furthermore, we perform environmental ex-post assessments of selected solutions using a broad set of life cycle impact categories. In a system with the least life cycle GHG emissions, the costs would increase by ~63%, thereby reducing life cycle GHG emissions by ~82% compared to the cost-optimal solution. Power systems mitigating a substantial part of life cycle GHG emissions with small increases in system costs show a trend towards a deployment of wind onshore, electricity grid and a decline in photovoltaic plants and Li-ion storage. Further reductions are achieved by the deployment of concentrated solar power, wind offshore and nuclear power but lead to considerably higher costs compared to the cost-optimal solution. Power systems that mitigate life cycle GHG emissions also perform better for most impact categories but have higher ionizing radiation, water use and increased fossil fuel demand driven by nuclear power. This study shows that it is crucial to consider upstream GHG emissions in future assessments, as they represent an inheritable part of total emissions in ambitious energy scenarios that, so far, mainly aim to reduce direct CO2 emissions.

Addressing temporal considerations in life cycle assessment

In life cycle assessment (LCA), temporal considerations are usually lost during the life cycle inventory calculation, resulting in an aggregated "snapshot" of potential impacts. Disregarding such temporal considerations has previously been underlined as an important source of uncertainty, but a growing number of approaches have been developed to tackle this issue. Nevertheless, their adoption by LCA practitioners is still uncommon, which raises concerns about the representativeness of current LCA results. Furthermore, a lack of consistency can be observed in the used terms for discussions on temporal considerations. The purpose of this review is thus to search for common ground and to identify the current implementation challenges while also proposing development pathways. This paper introduces a glossary of the most frequently used terms related to temporal considerations in LCA to build a common understanding of key concepts and to facilitate discussions. A review is also performed on current solutions for temporal considerations in different LCA phases (goal and scope definition, life cycle inventory analysis and life cycle impact assessment), analysing each temporal consideration for its relevant conceptual developments in LCA and its level of operationalisation. We then present a potential stepwise approach and development pathways to address the current challenges of implementation for dynamic LCA (DLCA). Three key focal areas for integrating temporal considerations within the LCA framework are discussed: i) define the temporal scope over which temporal distributions of emissions are occurring, ii) use calendar-specific information to model systems and associated impacts, and iii) select the appropriate level of temporal resolution to describe the variations of flows and characterisation factors. Addressing more temporal considerations within a DLCA framework is expected to reduce uncertainties and increase the representativeness of results, but possible trade-offs between additional data collection efforts and the increased value of results from DLCAs should be kept in mind.

Heat Decarbonisation Modelling Approaches in the UK: An Energy System Architecture Perspective

Energy models have been widely applied to the analysis of energy system decarbonisation to assess the options and costs of a transition to a low carbon supply. However, questions persist as to whether they are able to effectively represent and assess heat decarbonisation pathways for the buildings sector. A range of limitations have been identified, including a poor spatio-temporal resolution, limited representation of behaviour, and restricted representation of the full technical option set. This paper undertakes a review of existing energy models for heat decarbonisation in the UK, applying the novel perspective of energy system architecture (ESA). A set of ESA-related features are identified (including evolvability, flexibility, robustness, and feasibility), and models are reviewed against these features. The review finds that a range of models exist that have strengths across different features of ESA, suggesting that multiple modelling approaches are needed in order to adequately address the heat decarbonisation challenge. However, opportunities to improve existing models and develop new approaches also exist, and a research agenda is therefore proposed.

Optimizing the Chinese Electricity Mix for CO2 Emission Reduction: An Input-Output Linear Programming Model with Endogenous Capital

This study develops an input-output linear programming (IO-LP) model to identify a cost-effective strategy to reduce the economy-wide carbon dioxide (CO₂) emissions in China from 2020 to 2050 through a shift in the electricity generation mix. In particular, the fixed capital formation of electricity technologies (FCFE) is endogenized so that the capital-related CO₂ emissions of various generation technologies can be captured in the model. The modeling results show that low-carbon electricity, e.g., hydro, nuclear, wind, and solar, is associated with lower operation-related CO₂ emissions but higher capital-related CO₂ emissions compared to coal-fired electricity. A scenario analysis further reveals that a shift in the electricity generation mix could reduce the accumulated economy-wide CO₂ emissions in China by 20% compared to the business-as-usual (BAU) level and could help peak China's CO₂ emissions by 2030. The emission reduction is mainly due to a drop in operation-related CO₂ emissions of electricity, contributing to a decrease in accumulated economy-wide emissions by 21.4%. The infrastructure expansion of electricity, on the other hand, causes a rise in the accumulated emissions by 1.4%. The proposed model serves as an effective tool to identify the optimal technology choice in the electricity system with the consideration of both direct and indirect CO₂ emissions in the economy.

Human Health and Ecosystem Impacts of Deep Decarbonization of the Energy System

Global warming mitigation strategies are likely to affect human health and biodiversity through diverse cause-effect mechanisms. To analyze these effects, we implement a methodology to link TIMES energy models with life cycle assessment using open-source software. The proposed method uses a cutoff to identify the most relevant processes. These processes have their efficiencies, fuel mixes, and emission factors updated to be consistent with the TIMES model. The use of a cutoff criterion reduces exponentially the number of connection points between models, facilitating the analysis of scenarios with a large number of technologies involved. The method is used to assess the potential effects of deploying low-carbon technologies to reduce combustion emissions in the province of Quebec (Canada). In the case of Quebec, the reduction of combustion emissions is largely achieved through electrification of energy services. Global warming mitigation efforts reduce the impact on human health and ecosystem quality, mainly because of lower global warming, water scarcity, and metal contamination impacts. The TIMES model alone underestimated the reduction of CO_2eq by 21% with respect to a full account of emissions.

Cost-Energy Optimum Pathway for the UK Food Manufacturing Industry to Meet the UK National Emission Targets

This paper investigates and outlines a cost-energy optimised pathway for the UK food manufacturing industry to attain the national Greenhouse Gas (GHG) emission reduction target of 80%, relative to 1990 levels, by 2050. The paper employs the linear programming platform TIMES, and it models the current and future technology mix of the UK food manufacturing industry. The model considers parameters such as capital costs, operating costs, efficiency and the lifetime of technologies to determine the cheapest pathway to achieve the GHG emission constraints. The model also enables future parametric analyses and can predict the influence of different economic, trade and dietary preferences and the impact of technological investments and policies on emissions. The study showed that for the food manufacturing industry to meet the emission reduction targets by 2050 the use of natural gas as the dominant source of energy in the industry at present, will have to be replaced by decarbonised grid electricity and biogas. This will require investments in Anaerobic Digestion (AD), Combined Heat and Power (CHP) plants driven by biogas and heat pumps powered by decarbonised electricity.

Regional distribution and losses of end-of-life steel throughout multiple product life cycles-Insights from the global multiregional MaTrace model

Substantial amounts of post-consumer scrap are exported to other regions or lost during recovery and remelting, and both export and losses pose a constraint to desires for having regionally closed material cycles. To quantify the challenges and trade-offs associated with closed-loop metal recycling, we looked at the material cycles from the perspective of a single material unit and trace a unit of material through several product life cycles. Focusing on steel, we used current process parameters, loss rates, and trade patterns of the steel cycle to study how steel that was originally contained in high quality applications such as machinery or vehicles with stringent purity requirements gets subsequently distributed across different regions and product groups such as building and construction with less stringent purity requirements. We applied MaTrace Global, a supply-driven multiregional model of steel flows coupled to a dynamic stock model of steel use. We found that, depending on region and product group, up to 95% of the steel consumed today will leave the use phase of that region until 2100, and that up to 50% can get lost in obsolete stocks, landfills, or slag piles until 2100. The high losses resulting from business-as-usual scrap recovery and recycling can be reduced, both by diverting postconsumer scrap into long-lived applications such as buildings and by improving the recovery rates in the waste management and remelting industries. Because the lifetimes of high-quality (cold-rolled) steel applications are shorter and remelting occurs more often than for buildings and infrastructure, we found and quantified a tradeoff between low losses and high-quality applications in the steel cycle. Furthermore, we found that with current trade patterns, reduced overall losses will lead to higher fractions of secondary steel being exported to other regions. Current loss rates, product lifetimes, and trade patterns impede the closure of the steel cycle.

Harvested wood products and REDD+: looking beyond the forest border

BACKGROUND

The focus of REDD+ is sensu stricto on maintaining forest carbon stocks. We extend the scope of sustainable management of forest from forests to timber utilization, and study carbon offsets resulting from the utilization of harvested timber for bio energy or harvested wood products (HWPs). The emission budget of harvesting operations depends on the loss of standing biomass by timber extracted from the forest site and logging losses on the one side, and on the other on the wood end use and the utilization of processing residues. We develop two scenarios to quantify the magnitude of CO₂ emissions by (1) energetic utilization, and (2) energetic and material utilization of harvested timber and compare the substitution effects for different fossil energy sources.

RESULTS

The direct energetic use of harvested timber does not compensate for the losses of forest carbon stock. Logging residuals and displacement factors reflecting different wood use constitute by far the most important factor in potential emission reductions. Substitution effects resulting from energetic use of mill residuals and from HWPs have only a subordinated contribution to the total emissions as well as the type of fossil fuel utilized to quantify substitution effects. Material substitution effects associated with harvested wood products show a high potential to increase the climate change benefits.

CONCLUSIONS

The observation and perception of REDD+ should not be restricted to sustainable management and reduced impact logging practices in the forest domain but should be extended to the utilization of extracted timber. Substitution effects from material and energetic utilization of harvested timber result in considerable emission reductions, which can compensate for the loss of forest carbon, and eventually contribute to the overall climate change mitigation benefits from forestry sector.

Cultured Construction: Global Evidence of the Impact of National Values on Renewable Electricity Infrastructure Choice

Renewable electricity is an important tool in the fight against climate change, but globally these technologies are still in the early stages of diffusion. To contribute to our understanding of the factors driving this diffusion, I study relationships between national values (measured by Hofstede's cultural dimensions) and renewable electricity adoption at the national level. Existing data for 66 nations (representing an equal number of developed and developing economies) are used to fuel the analysis. Somewhat dependent on limited available data on controls for grid reliability and the cost of electricity, I discover that three of Hofstede's dimensions (high uncertainty avoidance, low masculinity-femininity, and high individualism-collectivism) have significant exponential relationships with renewable electricity adoption. The dimension of uncertainty avoidance appears particularly appropriate for practical application. Projects or organizations implementing renewable electricity policy, designs, or construction should particularly attend to this cultural dimension. In particular, as the data imply that renewable technologies are being used to manage risk in electricity supply, geographies with unreliable grids are particularly likely to be open to renewable electricity technologies.