Switzerland's net zero objective: quantifying impacts beyond borders

doi:10.21203/rs.3.rs-4915252/v1

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Switzerland's net zero objective: quantifying impacts beyond borders

https://doi.org/10.21203/rs.3.rs-4915252/v1

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National energy system models are vital to climate policy. However, they do not assess environmental impacts beyond territorial greenhouse gas (GHG) emissions. Here, we evaluate a territorial net zero GHG emissions energy scenario for Switzerland coupled with life-cycle assessment to quantify non-domestic environmental burdens. We stress the limited insights from considering territorial GHG emissions only. Indeed, significant GHG emissions persist outside of Switzerland by 2050 (~3-5 Mtons CO₂-eq./year) because of imports and energy related infrastructure, even though domestic emissions are reduced to net zero. Global climate policies influence the extra-territorial GHG emissions Switzerland is responsible for. Additionally, we must broaden the spectrum of environmental indicators in the context of many countries’ ambitions to achieve net zero goals. Our findings highlight the trade-offs involved, showing how environmental impacts other than those on climate change (ecosystem impacts, air pollution, natural resource use) could increase and shift from Switzerland to the rest of the world as the country electrifies its economy.

Earth and environmental sciences/Environmental sciences/Environmental impact

Earth and environmental sciences/Environmental social sciences/Climate-change policy

Life Cycle Assessment (LCA)

energy system models

energy system analysis

energy transition

sustainable development

pathways

decarbonization

net zero

The global energy landscape is undergoing a transformative shift. Historically dominated by fossil fuels, the global economy is now expected to evolve towards a future characterized by renewable and low-carbon energy technologies¹. Central to understanding and guiding this transition are energy system models (ESMs). ESMs are extensively used to analyze long-term transformation pathways of the energy system toward future states achieving specific energy and climate targets^2–4. ESMs have influenced climate and energy policy since the 1970s. Today, ESMs are vital in understanding how to reach climate goals and shaping associated policies^5,6.

Switzerland's commitment to the Paris Agreement objectives, through its long-term climate strategy to 2050, illustrates the practical implementation of national climate pledges⁷. Switzerland represents a relevant case study due to its profound energy system transformations driven by ambitious decarbonization targets and nuclear phase-out⁸, aiming to reduce greenhouse gas (GHG) emissions to net zero by mid-century⁷. Previous analyses of the pathways for the Swiss energy transition have been performed with ESMs. In the Swiss context, a prime example of ESM used for modeling domestic policy strategies is the Swiss TIMES energy model (STEM)⁹. However, while ESMs, like STEM, excel in analyzing technological and economic aspects, they fall short in assessing broader environmental impacts beyond territorial GHG emissions, such as those affecting human health and ecosystems¹⁰.

Moreover, reducing domestic GHG emissions may still result in GHG emissions and other environmental impacts associated with infrastructure and industrial activities beyond national borders⁸. To address this issue, cost-optimal ESM-based transition scenarios should be accompanied by life-cycle environmental externalities to detect spatial burden shifts associated with domestic production and consumption¹¹. In this way, decision-makers can be informed of potential offshore spillovers with ethical consequences in the energy transition scenarios¹². This underscores the need for more comprehensive models encompassing a wider array of environmental considerations and extending the assessment's geographical boundaries, ensuring a sustainable national energy transition.

Considering the growing necessity to expand the toolkit for addressing interdisciplinary challenges in the energy transition and the complementary strengths of ESM and life cycle assessment (LCA), combining these two approaches offers multiple benefits¹³. LCA provides information on various environmental and resource-related performance indicators for a variety of economic activities and processes. Furthermore, unlike ESM, LCA quantifies the environmental impacts of processes considering the value chain they contribute to (i.e., from raw material extraction to operation and end-of-life). This approach provides a granular view of potential environmental trade-offs and can be used to allocate impacts to end-consumers¹⁴. Therefore, applying LCA at the macroscale, including regions, nations, and markets, allows us to understand the broad implications and effectiveness of the energy transition¹⁵. Additionally, by incorporating geographic information into the life cycle inventory, LCA can identify the locations of impacts generated, thereby highlighting burden shifts across different regions¹⁶.

This study focuses on a long-term scenario that achieves net zero territorial GHG emissions from Switzerland’s fuel combustion and industrial processes and a counterpart baseline scenario. We incorporate three background global scenarios from the REMIND model¹⁷: <1.5°C, < 2°C, and + 3.5°C, detailed in the Experimental procedures section. This case study illustrates the importance of decarbonization efforts beyond the national economy and evaluates environmental co-benefits and trade-offs because of reducing climate impacts. Here, we demonstrate the first application of the Python tool pathways¹⁸ to systematically calculate the life-cycle impacts of ESM scenarios (see Experimental procedures). Through the workflow outlined, we calculate the life-cycle impacts of each flow of final energy consumed, reporting these impacts by life-cycle stage and geographical origin. This approach captures direct territorial emissions for net zero and upstream impacts in different world regions.

Switzerland's journey to domestic net zero relies on renewable energy and carbon capture

We evaluate two scenarios to study Switzerland's energy transition: “Baseline” and “Net Zero” (see Experimental procedures). Baseline represents continuing current trends in energy consumption and use. Net Zero outlines the necessary steps to achieve net zero domestic GHG emissions by 2050, detailing the required configuration of the energy system to meet this target. Each scenario builds around a multi-dimensional storyline, spanning three key areas: (1) societal behaviors, along with circular economy practices in Switzerland; (2) technological advancements, learning rates, and the adoption and development rate of clean technologies; and (3) geopolitical factors, including carbon trading markets and Switzerland's integration in energy trade within the EU and globally. A solution satisfying the objectives and constraints of both scenarios is provided by the Swiss TIMES Energy system Model (STEM), a comprehensive cost-optimization energy system model covering a wide array of energy technologies and sectors¹⁹. Figure 1 illustrates the system configurations resulting from each scenario, including the primary energy demand, disaggregated by primary energy carrier, and the direct GHG emissions associated with each economic sector.

The Baseline scenario underscores the consequences of maintaining current trends, highlighting the need for more ambitious policies to achieve stringent climate targets. In this scenario, the energy system continues to rely on fossil fuels, with a reduced but persisting use of oil, and natural gas consumption increasing by almost 20% between 2020 and 2050. The limited adoption of synthetic liquid fuels, which reaches 6.8 PJ/year in 2050, further underscores the tendency of this pathway to maintain current trends. In contrast, the Net Zero scenario provides a pathway to domestic net zero emissions, emphasizing substantial changes in energy sources. The energy mix shifts away from fossil fuels, with oil consumption declining to zero by 2050. Fossil gas usage also decreases from 117 PJ/year in 2020 to 55 PJ/year by 2050, while synthetic liquid fuels usage increases to 26.1 PJ/year by 2050, indicating a move towards low-carbon energy alternatives.

Additionally, there is a notable reduction in primary energy consumption, mainly driven by electrification and the large-scale deployment of photovoltaics, along with more sustainable behaviours and greater efficiencies. An important aspect of the Net Zero scenario is the integration of carbon capture technologies and biogenic carbon flows, resulting in negative GHG emissions in specific sectors by 2050. For instance, power generation’s GHG emissions decrease from 2.57 Mton/year in 2020 to -0.05 Mton/year by 2050, and industry emissions fall from 6.5 Mton/year to -0.4 Mton/year in the same period, primarily due to the combined effects of moving away from combustion-based processes and implementing carbon capture and storage (CCS) on biomass- (i.e., BECCS) and fossil-based power sources. Additionally, synthetic fuel emissions reach − 1.5 Mton/year in 2050 when considering the life-cycle, largely due to hydrogen production via gasification of woody biomass combined with CCS. This highlights the significance of different CO₂ reduction and removal technologies in this scenario to achieve the net zero goal.

Emissions abroad are significant

Our modeling results show the life-cycle GHG emissions associated with the production, supply, and consumption of final energy in Switzerland for the Baseline and Net Zero scenarios. These emissions include the infrastructure to supply and use the energy. For example, a unit of energy used in a battery electric vehicle includes emissions related to the electricity grid construction, maintenance and losses as well as the charging infrastructure, electric motor and energy storage components of the vehicle.

Figure 2 demonstrates that while the Swiss energy system achieves net zero GHG emissions by 2050, life-cycle emissions beyond its borders remain substantial. Evaluating the GHG flows, we observe that the main contributors to gross emissions within Switzerland are the heat and transport sectors, which account for 12 Mtons of emissions in 2050. These emissions are primarily offset by biogenic carbon flows into carbon sinks inside Switzerland and a fraction of the CCS and forestry activities occurring in Europe, as accounted for by the ESM. This substantial balancing act shows progress within national borders. However, life-cycle emissions from activities abroad—mainly related to transport, manufacturing, and heat—result in 4 Mtons of net emissions when considering the < 2°C global scenario on the international level. These 4 Mtons constitute nearly 15% of the net domestic GHG emissions in 2020 and almost 20% of the total net emissions projected in the baseline scenario for 2050.

Breaking down the results by activity over time, as shown in Fig. 3.a, illustrates that while the heat and transport sectors experience the most significant reductions relative to 2020, they remain the main contributors to GHG emissions. In 2020, emissions from heat generation and transport activities to satisfy the Swiss demand were 39 Mtons, with 36 Mtons emitted within Switzerland and 3 Mtons abroad. By 2050, under the Net Zero scenario, total gross GHG emissions from heat generation and transport are projected to be 14 Mtons, with 12 Mtons emitted within Switzerland. Although the Swiss fraction is offset mainly by industrial carbon capture and biomass or electricity-based fuels, more than 2 Mtons of CO₂-eq (net) are emitted from heat and transport activities outside Switzerland.

Figure 3.b shows the breakdown of life-cycle GHG emissions by final energy consumer, highlighting the sectors driving these emissions. The case of the transport sector underscores the importance of considering global supply chain impacts when evaluating the environmental implications of policies. In 2020, passenger and goods transport emissions amounted to 27 Mtons, with 20 Mtons emitted within Switzerland. By 2050, the transport sector will see an uptake of synthetic fuels, battery electric, and fuel cell vehicles combined with the deployment of renewable sources of electricity, significantly reducing domestic GHG emissions. However, this sector's net life-cycle GHG emissions remain at 1.5 Mtons that year, which explains more than 30% of the total net emissions.

Finally, Fig. 3.c shows that in 2020, emissions within Switzerland are 33 Mtons, increasing to 45 Mtons when accounting for supply chain emissions abroad. This indicates that almost 75% of the total emissions can currently be addressed through national policies. However, by 2050, while territorial GHG emissions are projected to be reduced to net zero, life-cycle emissions outside of Switzerland remain substantial. Achieving net zero emissions from a life-cycle perspective would require an additional yearly mitigation of 3, 4 and 5 Mtons of GHG under the < 1.5°C, < 2°C, and + 3.5°C global scenarios, respectively. This analysis fundamentally shifts the decarbonization paradigm. While national efforts can significantly reduce emissions today, achieving further reductions as Switzerland reaches net zero emissions requires understanding and addressing global supply chain emissions through a life-cycle perspective.

Reducing direct GHG emissions leads to trade-offs and co-benefits

Figure 4 presents the life-cycle impact assessment of the Net Zero scenario in conjunction with the < 2°C global scenario. The analysis extends beyond global warming (Fig. 4.a) to include other environmental indicators representing stressors on human health and ecosystems, showing both reduction and increase in impacts over time. The impacts are initially categorized by final energy consumers (left bars). Within each consumer category, impacts are further detailed by supply chain activities (middle bars). Lastly, the results are further specified by region of impact for each supply chain activity (right bars).

Figure 4.b illustrates the abiotic depletion of minerals and metals (i.e., not the physical volume demanded). This indicator, based on current production and reserves data, serves as a proxy global elemental scarcity²¹. For example, this indicator weighs platinum scarcity 1,600 times higher than copper’s; copper’s almost 26,000 times higher than iron’s, and antimony, the reference element is weighted 700 times more scarce than copper. This indicator rises by more than two times between 2020 and 2050, illustrating the reliance of Switzerland’s ambitious energy transition on relatively-scarce metals.

The increased demand for mining and related mining waste treatment activities, which occur outside Switzerland, leads to significant impacts in other environmental categories. Notably, the toxicity impact on freshwater ecosystems (Fig. 4.c) is projected to double, predominantly caused by the leaching of metal-rich mine tailings into surrounding water bodies²². It is important to note, however, that we did not project a change in mining practices, which may partially mitigate the impacts quantified²³.

Another key aspect is the formation of particulate matter (Fig. 4.f). Decarbonizing the economy through the reduced use of combustion vehicles, furnaces, and boilers for industrial and heating purposes halves the domestic formation of particulate matter (i.e., from 10 kilotons to 5 kilotons of PM_2.5-eq. between 2020 and 2050). Considering the health benefits related to a decrease in exposure to particulate matter, a domestic reduction of 5 kilotons of these pollutants by the year 2050 can be monetarily valued at 740 M€₂₀₁₀/year. Furthermore, with respect to the Baseline scenario, the Net Zero scenario provides cumulative national savings estimated at approximately 20,000 M€₂₀₁₀ between 2020 and 2050. These valuations are calculated using the mean unit costs per kilogram of pollutant from Schmid et al. (2019)²⁴, informed by EcoSense²⁵ results. However, unlike the substantial domestic reductions, emissions abroad see a modest decline, decreasing only from 22 kilotons to 19 kilotons by 2050. To illustrate this geographic shift, we focus on the passenger car sector. Figure 5 provides a flow diagram of particulate matter formation related to the main hotspot i.e. passenger cars operation in Switzerland, comparing the years 2020 and 2050. In 2020, life-cycle particulate matter formation amounted to 10 kton PM_2.5-eq, arising from the 1,400 PJ energy consumption by passenger cars. By 2050, final energy use is reduced by a factor of three (i.e., to 460 PJ) thanks to efficient electric drivetrains, and domestic emissions are nearly eliminated. However, global life-cycle emissions only decrease to 5 kilotons PM_2.5-eq. These emissions persist abroad because of the mining requirements to supply copper, graphite, iridium, and platinum for producing battery cells and fuel cell stacks, despite 1) the doubling of the energy density of battery cells between 2020 and 2050 (i.e., the battery mass halves for a same range autonomy), and 2) the reduced use of iridium and platinum in electrolyzers and fuel cell stacks. Overall, while the total formation of particulate matter is reduced, the shift in emissions sources highlights the complexity of global supply chains. In 2020, mining activities are responsible for 30% of 32 kilotons of particulate matter emissions, increasing to 50% of 24 kilotons by 2050. This underscores the spatial environmental trade-offs involved in the transition to a net zero GHG economy. Yet, given the often-isolated location of mining sites, the extent to which local populations are affected by this increase in particulate matter emissions is hard to quantify without a detailed regionalized impact assessment.

While global impacts are significant, domestic environmental changes are equally important. Land use (Fig. 4.d), measured in square kilometers-year, reflects the total surface area occupied over time. This indicator shows an increase of over 70% between 2020 and 2050, with most land used in Switzerland. Forestry activities, linked to biodiversity impacts²⁶, play a significant role in this category, supporting electricity production, combined heat and power (CHP) units for district heating, and providing wood-based hydrogen and ethanol for vehicles.

Likewise, freshwater use (Fig. 4.e) increases by 25%, with 90% of the total demand occurring within Switzerland. Electricity generation is the major contributor in this category; hydroelectric reservoirs play a significant role in Switzerland’s energy mix, resulting in considerable water evaporation. In relative terms, the water demand for electrolysis remains modest, and biomass-based fuels carry a limited water footprint (as the feedstock is from biomass residuals).

This study applies a life-cycle perspective to evaluate a long-term scenario that achieves net zero territorial GHG emissions from Switzerland’s energy supply and industrial processes. While the scenario achieves the domestic net zero goal and significant reductions of overall GHG emissions, a life-cycle perspective points to additional mitigation requirements. Thus, overlooking life-cycle emissions can undermine the net decarbonization results driven by national energy system models.

Furthermore, GHG emissions reduction can lead to an increase and/or spatial shift in other environmental burdens. For instance, transitioning to electric mobility will reduce local air pollution, but it partially displaces burdens like particulate matter formation from urban to mining areas and increases toxicity impacts in these regions. This shift highlights the complexity of global supply chains and the necessity for a life-cycle perspective that considers environmental impacts beyond global warming when planning national energy system transitions. It also shows that climate and broader environmental policies must be implemented globally – otherwise, effectiveness cannot be ensured and globally just transition might be compromised. Additionally, it underscores the importance of regionalized impact assessments to fully understand the effects of changes in the energy system on human health and ecosystem damages¹⁵.

For these reasons, efforts are necessary to integrate a life-cycle perspective into ESMs to comprehensively address all environmental impacts, allocating impacts to consumers. This study demonstrates the first step towards this integration by developing the tools and workflow to systematically and effectively evaluate the life-cycle impacts of ESM scenarios. The next step involves incorporating these life-cycle considerations into the cost-optimized engine of the ESM.

While we are in the early stages of integrating LCA into ESMs, several methods have been proposed in the literature. However, widely accepted and reproducible integration tools are still lacking. Existing alternatives include single-objective optimization, where environmental impacts are monetized and different weighting factors for costs are defined across environmental categories to find the least-cost scenario²⁸; and multi-objective optimization, as applied in Vandepaer et al. (2020)²⁸, where the epsilon-constrained method is applied. Another approach involves introducing caps on LCA indicators to represent absolute cumulative limits on certain environmental impacts²⁹. These approaches broaden assessment metrics beyond energy, cost, and GHG emissions to include impact assessment endpoint indicators such as biodiversity, human health impacts, and resource depletion. Implementing such methodologies would enable benchmarking scenarios against planetary boundaries, revealing potential co-benefits and trade-offs between decarbonization and other environmental concerns systematically and comprehensively.

Furthermore, building on the insights provided by the ESM, it becomes evident that the transition encompasses more than just a change in energy sources. This energy transition is intrinsically linked to a materials transition: the shift towards renewable energy systems coupled with the electrification of transport and industrial processes results in a significantly higher demand for minerals and metals than conventional fossil fuel systems³⁰. Our results indicate an increased demand for scarce metals, which poses significant challenges. The availability of these raw materials may affect the feasibility of energy transition scenarios. This increased demand – even when considering advancements in circularity – has profound implications for raw material extraction and the global competition for their access³¹. Recent reports from institutions such as the European Comimission³² and the U.S. Department of Energy³³ have emphasized the strategic imperative of securing a reliable and adequate supply of critical raw materials. Hence, while ESMs (and integrated assessment models) provide a high-level perspective on potential energy futures, they would benefit from higher technological and material resolutions to capture the environmental and material implications of deploying a given technology over another.

Future research should aim at advancing the combined use of material flow analysis (to track the flows and stocks of materials within the economy) and LCA (to evaluate the sustainability of potential interventions). This would create opportunities for designing more effective policy strategies by identifying and addressing potential material bottlenecks and related environmental burdens³⁴.

Current energy models, including STEM, typically do not specify a broad range of sub-technologies within a technology class. For example, the demand for photovoltaics or mobile batteries is quantified, but further specifications such as c-Si or CIGS for photovoltaics or NMC, NCA, or LFP chemistries for Li-ion batteries are lacking. Yet, environmental impacts and material demand across sub-technologies can vary significantly^35–37. Future research should incorporate greater detail regarding sub-technologies and develop the tools to evaluate the influence of future market shares and potential trends.

Resource availability

Lead contact

Alvaro Jose Hahn Menacho, [email protected]

Materials availability

No materials were used in this study.

Data and code availability

The code required to reproduce the results presented in this study, and the results themselves, can be found in the following code repository: https://github.com/premise-community-scenarios/sweet_sure-2050-switzerland. The workflow requires premise and pathways, both open-sourced and installable via the Python package repository Pypi. However, the workflow also requires a valid license to the ecoinvent LCA database, which should be obtained from the Ecoinvent Association (https://ecoinvent.org/).

STEM modeling framework

The energy system modeling framework, upon which this work is built, consists of a large set of mathematical models orchestrated to provide a comprehensive view of the transformation of the Swiss economy and energy system towards achieving net zero GHG emissions by 2050. Detailed descriptions of the STEM model and the evaluated pathways can be found in Panos et al. (2023)³⁸ and Panos et al. (2022)³⁹, respectively. The pathways evaluated in this study correspond to the SURE Pathway Scenarios (SPS) 1 and 4. "SPS1: Team Sprint. Focus on Sustainability" aligns with the Net Zero scenario, while "SPS4: Walk & Talk. Current Trends and Policies" serves as the Baseline scenario. STEM includes only GHG from energy and industrial processes and not all GHG emissions (e.g., from agriculture). Regarding the transport sector, it follows the territorial approach. Thus, GHG emissions are accounted to the country of purchase. The detailed results used for this study can be found in the supplemental data repository.

The coupling of ESM and LCA

A prospective LCA approach is used to quantify environmental burdens of global supply chains in line with ESM and integrated assessment model scenarios. Using premise⁴⁰, Swiss life cycle inventories are projected into the future by integrating results from STEM (i.e., Baseline and Net Zero scenarios), while processes in other world regions are projected using the REMIND integrated assessment model (IAM)¹⁷. Three REMIND scenarios are considered in this study: SSP2-NPi, SSP2-PkBudg1150, and SSP2-PkBudg500. The first scenario reflects the current trajectory based on existing national policies (NPi), under which global mean temperature could rise over 3.5 degrees Celsius by the end of the century. The second scenario limits global warming to approximately 2 degrees Celsius, with a global carbon budget of 1150 gigatons of CO₂ for the entire century. The third scenario is intended to limit global warming to 1.5 degrees Celsius, with a global carbon budget of 500 gigatons of CO₂. These scenarios capture different levels of ambition and risk associated with transitioning to a low-carbon future, providing a comprehensive range of potential pathways and their impacts⁴¹.

Integrating insights from ESM and IAM into LCA to extend present-day life-cycle inventories into the future has become a structured approach, establishing the methodological foundation for prospective LCA. This integration, initiated by Mendoza Beltran et al.⁴² and formalized by the Python library premise⁴⁰, allows LCA to benefit from process and technology-specific forecasts in ESM and IAM, including learning curves, technological developments, and the evolution of electricity mixes and other dynamic parameters over time. Conversely, numerous efforts exist in the literature to perform LCA of ESM-based scenarios to gain a more comprehensive understanding of the sustainability of different energy system pathways^8,10,43–46. However, these efforts are often fragmented and ad hoc, with varying scopes and methodologies, highlighting the need for a standardized approach applicable across different ESM, much like the early stages of prospective LCA. As was the case then, there is a need to consolidate the workflow, streamline the methodology, and develop tools that ensure reproducibility, computational efficiency, and methodological clarity.

In this study, we demonstrate the first application of the Python package pathways¹⁸ to systematically calculate the life-cycle impacts of an ESM scenario. This open-source library builds on the LCA framework brightway⁴⁷. While we use premise together with scenarios from STEM, REMIND and the ecoinvent life-cycle inventory database⁴⁸, pathways is model-agnostic. This ensures replicability, allowing the same workflow to be reproduced using different data sources and ESMs. The inputs necessary for pathways to perform the LCA calculations include a data package containing (1) a 'datapackage.json' metadata file describing the contents of the data package and (2) a 'mapping. yaml' file describing the links between the scenario variables and the LCA datasets; (3) the ESM scenario-based technosphere and biosphere LCA matrices for each time step as CSV files, produced by premise in this case; and (4) the ESM scenario data with production volumes for each time step as a CSV file. Files used in this study, based on the STEM scenarios, and necessary to produce the data package read by pathways, can be found at https://github.com/premise-community-scenarios/sweet_sure-2050-switzerland. pathways carries out calculations using bw2calc, the calculation module of brightway. Finally, some post-processing to address potential double-counting issues is performed. The tool returns the environmental impacts broken down by environmental indicator, product or service consumer, geographical location of consumer, time step, geographical origin of impacts, and life-cycle stage. See Data Availability to access the data package and the script used to produce the results presented in this study.

Mapping between STEM variables and life-cycle assessment

The mapping between final energy consumers in STEM and LCA datasets is described in the scenario data package's mapping file mapping.yaml found under the folder configuration_file in the online repository. The LCA datasets used for each final energy consumer of the STEM scenario are available in the file lci-sweet_sure.xlsx under the folder inventories in the online repository.

Functional units and Life cycle inventory (LCI)

As described above, each STEM variable representing a final energy consumer is matched with an LCA dataset. The LCA datasets that represent the consumption of final energy in vehicles include the supply of energy to the vehicle (e.g., gasoline, diesel, electricity, hydrogen) but also the storage component needed to store the energy on the road (e.g., fuel tank, hydrogen tank, battery) and the components needed to turn it into kinetic energy (e.g., internal combustion engine, electric motor). Using the kilometric lifetime of the vehicle and its tank-to-wheel energy consumption, we derive the fraction of the energy storage component needed per megajoule of energy consumed. The other vehicle components (e.g., glider, suspension, etc.) are left outside the energy system's scope.

For vehicles equipped with a battery (i.e., passenger cars, buses, trucks, two-wheelers, etc.), the “Mixed” scenario from Degen et al. (2023)⁴⁹ is used. As shown in Fig. 6, it foresees the continued market deployment of lithium-ion batteries (i.e., Li-NMC and Li-LFP) until 2030 with a tendency toward nickel-rich and cobalt-poor chemistries (i.e., Li-NMC 955) and silicon-coated anodes (e.g., Li-NMC900-Si). After 2030, the scenario introduces post-lithium chemistries (e.g., solid-state, sodium-ion), which reach a 39% market share by 2040. We add the market shares of solid-state batteries to that of Sodium-ion batteries because of a lack of inventory data. For 2050, market shares for 2040 are used. Concurrently, the energy density of the cells also improves over time, as shown in Table 1.

Table 1

Current and projected battery energy density values.
	Current			2050
Type	Specific energy density [kWh/kg cell]	Battery energy density [kWh/kg battery]	Source	Specific energy density [kWh/kg cell]	Battery energy density [kWh/kg battery]	Source
Li-ion, NMC111	0.18	0.13	Hasselwander et al. (2023)⁵⁰	0.2	0.15	Maximum interval value for current energy density used as future mean
Li-ion, NMC523	0.2	0.15		0.22	0.16
Li-ion, NMC622	0.24	0.18		0.26	0.19
Li-ion, NMC811	0.28	0.2		0.34	0.24
Li-ion, NMC955	0.34	0.24		0.38	0.27
Li-ion, NCA	0.28	0.2		0.34	0.24
Li-ion, LFP	0.16	0.13		0.22	0.18
Li-ion, LiMn2O4	0.11	0.09	Ecoinvent 3.10⁴⁸	0.11	0.08	No improvement considered
Li-ion, LTO	0.05	0.03	Wang et al. (2020)⁵¹	0.05	0.03	No improvement considered
Li-sulfur, Li-S	0.15	0.11	Wickerts et al. (2023)⁵²	0.34	0.26	Duffner et al. (2021)⁵³
Li-oxygen, Li-O2	0.36	0.2	Duffner et al. (2021)⁵³	0.93	0.51	Duffner et al. (2021)⁵³

Other datasets representing the consumption of final energy by a specific consumer include the infrastructure needed to consume the energy (e.g., boiler, furnace, heat pump) and the supply of one megajoule of energy entering the conversion system. In all instances of combustion, the LCA dataset includes the combustion products. For example, modeling the consumption of one megajoule of wood pellets in an 80% efficient wood pellet stove is achieved by requiring the supply of 0.8 megajoule of heat from a wood pellet-fired stove.

As described above, the LCA datasets used for each final energy consumer of the STEM scenario are available in the file lci-sweet_sure.xlsx under the folder inventories in the online repository.

The ecoinvent LCA database v.3.10 (system model “allocation, cut-off by classification”) serves as the primary source for background inventory data, further modified by premise to align with a combination of global and Swiss scenarios. Complementing this, the premise library enriches the database by including inventories for emerging technologies and mining activities. Current LCI databases often exhibit gaps, particularly in representing critical raw materials, such as rhenium, rare earth oxides, and platinum group metals. Though used in small quantities, these materials play a pivotal role in various technologies and are frequently underrepresented or completely absent in existing inventories. Building on the work by Schlichenmaier and Naegler (2022)⁵⁴, additional data is collected for electric and internal combustion engine vehicles, wind turbines, photovoltaic panels, concentrated solar power projects, nuclear plants, electric batteries, fuel cells, and electrolyzers.

Since metals are primarily traded globally, we model world markets for the various metals assessed. In these markets, the contribution of different mining and refining regions corresponds to their current market shares. Following this approach, the supply from different regions for a specific metal will be directly proportional to the country-level contributions to the global market. We derive these shares from various sources, mainly the British Geological Survey (BGS)⁵⁵ and the United States Geological Survey (USGS)⁵⁶, in addition to data from van den Brink et al. (2022)⁵⁷ for antimony refining. For certain metals where data is accessible (e.g., lithium, cobalt), we incorporate projections from BloombergNEF regarding the development of future mining and refining projects to forecast the market shares’ evolution up to 2030⁵⁸. The metal markets also account for the average transport distances and modalities of the metal product from producer to consumer. This data is retrieved from The United Nations Conference on Trade and Development (UNCTAD)⁵⁹.

These data and its implementation can be found in the premise online code repository (https://github.com/polca/premise). The data pertaining to the implementation of the STEM SPS scenarios into LCA, including the mapping between STEM technology variables and LCA datasets, are contained in the scenario online repository (https://github.com/premise-community-scenarios/sweet_sure-2050-switzerland).

Endogeneity and double-counting

In the context of combining LCA with ESM, double-counting needs addressing as ESM typically generate aggregate demand scenarios without distinguishing between intermediate and final energy demand. When these aggregate demands are used as inputs for LCA, there is a risk of counting the same resource use more than once. This is particularly problematic when assessing the environmental impacts of interconnected systems where the outputs of one process serve as inputs for another. If the demand for these materials is not carefully allocated between processes, the total environmental impact can be overestimated, resulting in misleading conclusions about the performance of the scenarios under analysis. Hence, the focus is on evaluating the total quantities yielded by the ESM's final output rather than the quantities provided by examining the supply chain as provided by LCA. To achieve this, the original life cycle inventory database is modified by zeroing out all regional energy inputs that are considered and potentially required by the energy system model throughout its life cycle, adhering to the rationale presented in Volkart et al. (2018)¹⁰. In this case, we focus on the inventories mapped as final energy providers. This approach is implemented in a systematic and reproducible manner, effectively isolating the quantities of interest and preventing the inflation of resource use that would otherwise occur due to supply chain overlaps. Consequently, this method enhances the accuracy of the life-cycle impact assessment.

However, the issue of double-counting is not completely eliminated and requires a more complex algorithm to be fully addressed throughout the LCA database. Indeed, double-counting persists because of how energy service demands in ESM, particularly global ones, are evaluated. Double-counting occurs when calculating energy service demands in ESM (e.g., tons of steel and chemicals) because mining and construction activities are also represented and included in an aggregated form. These calculations do not consider adjustments from a LCA perspective. Specifically, the tons of steel assumed in ESMs when calculating the energy used for production may not align with and have not been adjusted to reflect LCA calculations.

Acknowledgments

The authors gratefully acknowledge the financial support from the Swiss Federal Office of Energy (SFOE) via the SWEET-SURE program for developing the premise and pathways tools and thereby coupling the STEM model with the LCA framework. They also thank the Swiss State Secretariat for Education, Research and Innovation (SERI) under the Horizon Europe project PRISMA (grant agreement no. 101081604) for supporting the work carried out in relation to materials and metals consumption flows representation in net zero CO₂ scenarios. Finally, the authors thank Prof. Claudia R. Binder and Dr. Nino J.D. Jordan for the review work and time provided.

Author contributions

Conceptualization and methodology, A.J.H.M. and R.S.; formal analysis, A.J.H.M.; software, R.S., and A.J.H.M.; validation, A.J.H.M., and R.S.; writing – original draft, A.J.H.M.; writing – review and editing, R.S.; writing – feedback, C.B., C.M., E.P., and P.B.; funding acquisition, C.B. and P.B.

Declaration of interests

The authors declare no competing interests.

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Switzerland's net zero objective: quantifying impacts beyond borders

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Introduction

Results

Switzerland's journey to domestic net zero relies on renewable energy and carbon capture

Emissions abroad are significant

Reducing direct GHG emissions leads to trade-offs and co-benefits

Discussion

Experimental procedures

Resource availability

Lead contact

Materials availability

Data and code availability

STEM modeling framework

The coupling of ESM and LCA

Mapping between STEM variables and life-cycle assessment

Functional units and Life cycle inventory (LCI)

Endogeneity and double-counting

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References

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