Environmental and material implications of global climate mitigation pathways

Environmental and material implications of global climate mitigation pathways | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Environmental and material implications of global climate mitigation pathways Alvaro Jose Hahn Menacho, David Bantje, Christian Bauer, Nico Bauer, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9527853/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Reaching the goals of the Paris Agreement requires a rapid transformation of global energy systems. Yet energy supply and use exert multiple pressures on ecosystems, human health, and natural resources, raising questions about the broader environmental consequences of alternative decarbonization pathways and the resources they require. Here, we analyse 24 climate mitigation pathways from three Integrated Assessment Models using a life-cycle perspective. This enables a system-wide comparison of environmental impacts and resource requirements of global final energy provision from 2020 to 2100. We find that deep decarbonization delivers substantial co-benefits for human health and ecosystem quality, but does not systematically reduce pressures on land, water, or mineral resource extraction. For metals central to electrification, climate change mitigation ambition primarily reshapes the timing of extraction rather than cumulative volumes. Pathways achieving similar climate targets can impose markedly different environmental and resource pressures, shaping the feasibility and desirability of alternative transitions. Earth and environmental sciences/Environmental sciences/Environmental impact Physical sciences/Energy science and technology/Energy modelling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Human activities are increasingly destabilising the Earth system, pushing key biophysical processes beyond conditions under which civilisations have historically developed 1,2 . Central to these pressures are fossil fuel combustion and greenhouse gas (GHG) emissions, which drive climate change and trigger feedback loops that exacerbate ocean acidification, land-system change, and other planetary boundary transgressions 3,4 . In response, decarbonization of the energy system has emerged as a key pillar of international climate policy, with more than 130 countries adopting net-zero targets 5 . Translating these commitments into actionable strategies relies heavily on Integrated Assessment Models (IAMs), which generate long-term energy system transformation pathways consistent with specific climate targets 6 . By linking assumptions about socio-economic development, energy supply and demand, land use, and the climate system, these models provide internally consistent scenarios that inform national and international climate policy 7 . Despite this critical role, IAMs are primarily designed to explore climate mitigation pathways and energy-economy dynamics. They do not explicitly represent many non-climate environmental impacts, or capture them only in aggregated form 8 . Complementary approaches are needed to resolve the technology- and supply chain-specific environmental implications of alternative decarbonization pathways in greater detail 9,10 . As a result, key questions raised by the energy transition remain insufficiently explored, including how decarbonization pathways redistribute environmental pressures across impact categories 11,12 , how specific technologies affect individual Earth system processes 13 , and how the deployment of low-carbon technologies may influence the demand for critical raw materials 14 . Here, we assess environmental impacts beyond climate change across 24 mitigation pathways derived from three widely used and structurally distinct IAMs: IMAGE 15 , MESSAGEix 16,17 , and REMIND 18 . These models span a broad range of modelling paradigms and representations of energy, land-use, and techno-economic dynamics. The 24 pathways cover multiple Shared Socioeconomic Pathways (SSP) 19 and end-of-century global mean surface temperature (GMST) outcomes, reflecting diverse assumptions about socio-economic development, GHG emissions mitigation ambitions, and energy system configurations ( Fig. 1 ). They differ substantially in underlying socio-economic conditions, such as population trajectories, as well as in key structural drivers of the transition, including final energy demand, electrification levels, hydrogen deployment, and the scale of engineered carbon dioxide removal represented within the energy system. By coupling IAM-based pathways with environmental Life Cycle Assessment (LCA), we quantify the environmental implications of global final energy provision, and examine how alternative decarbonization strategies reshape human health, ecosystem quality, land and water use, and critical raw material demand alongside GHG emissions mitigation. This harmonised multi-model framework enables a system-wide comparison of the complete set of energy system transformations across multiple IAMs and socio-economic pathways, rather than focusing on individual sectors, technologies, or single-model pathways. Using this framework, we evaluate whether structurally distinct pathways with comparable climate policy ambition that achieve similar temperature outcomes also converge in their broader environmental consequences. In doing so, we distinguish the role of climate change mitigation ambition from the influence of underlying socio-economic assumptions and energy system configurations. Specifically, we address three questions: whether pathways consistent with deep decarbonization systematically reduce environmental pressures beyond climate change, how alternative energy system configurations redistribute impacts across sectors and technologies, and how climate change mitigation ambition influences the scale and timing of critical raw material extraction associated with the transition. Environmental impacts beyond greenhouse gas emissions Assessing environmental impacts beyond GHG emissions reveals clear co-benefits of decarbonization for human health ( Fig. 2 ). Particulate matter formation declines across all pathways below 2.0 °C by 5-75%, while photochemical ozone formation is reduced by 12-70%. These improvements emerge early in the transition. By mid-century, particulate matter formation has already declined by a median of 48% and photochemical ozone formation by 43% in pathways below 2.0 °C. Aggregate human health burdens, measured in disability-adjusted life years (DALYs), decrease by a median of 8% by 2100 in pathways below 2.0 °C, whereas they increase by a median of 65% in pathways above 2.5 °C. These reductions reflect the air-quality benefits of reduced fossil-fuel combustion 20,21 . Within SSP2 pathways, where population and socio-economic assumptions are held constant, more ambitious climate policy consistently improves health outcomes relative to less ambitious policy within the same model. Total ecosystem quality, which aggregates multiple environmental pressures affecting terrestrial, marine, and freshwater ecosystems, shows a similar aggregate response to climate change mitigation ambition. It declines by 23-68% by 2100 in pathways below 2.0 °C but increases by up to 119% under warming above 2.5 °C. Within SSP2, ecosystem quality improves consistently with climate ambition across all three models, with SSP2 pathways below 2.0 °C showing a median reduction of 41% compared with a median increase of 19% in pathways above 2.5 °C. These changes reflect the combined effects of declining climate change damages and lower combustion-related emissions as fossil-fuel use declines. However, this aggregate improvement masks an exception. Freshwater ecotoxicity increases across all pathways analysed, including the most sustainability-oriented SSP1 scenarios, where it rises by 33-111% by 2100. Contribution analysis ( Fig. 3 ) shows that the dominant driver for freshwater ecotoxicity impacts is the expansion of electrified transport, which contributes an average of 70 percentage points (range: 37-130) to its increase in pathways below 2.0 °C. These impacts arise not at the point of energy use but along upstream supply chains, including battery production, electric motor manufacturing, and the copper-intensive grid and charging infrastructure required to deliver electricity to vehicles. In the underlying life cycle impact assessment model, ecotoxicity characterisation factors are particularly sensitive to metal emissions, which means that increased mining and metal processing strongly influence this indicator. Even as fossil fuel phase-out reduces ecotoxicity from coal mining (up to -20 percentage points) and oil refining (up to -15 percentage points), these reductions are outweighed by metal supply associated with electrification. Nevertheless, freshwater ecotoxicity remains substantially lower in pathways below 2.0 °C than in those above 2.5 °C, indicating that inadequate decarbonization would lead to even greater pressures on ecosystem toxicity. This pattern suggests that while electrification is essential for climate mitigation, additional measures to manage mining impacts and metal supply chains might be needed to limit freshwater ecotoxicity. Figure 2 | Annual environmental impacts of global final energy provision across climate mitigation pathways. Percentage change in annual impacts relative to 2020, from 2020 to 2100, across ten environmental indicators and 24 mitigation pathways. Panels are organised by area of protection: ecosystem quality (total ecosystem quality in local species loss over time, acidification, freshwater ecotoxicity, freshwater eutrophication), human health (total human health in DALYs, particulate matter formation, photochemical ozone formation), and natural resources (land use, freshwater use, scarcity-weighted mineral resource use). The horizontal dashed line marks zero change relative to the 2020 baseline. Colours indicate end-of-century global mean surface temperature (GMST) outcomes, marker shapes denote IAM models, and line styles represent SSP scenarios, all as in Fig. 1, with SSP2 scenarios emphasised with higher opacity. Nevertheless, differences in environmental outcomes are not determined by climate ambition alone. Within pathways with similar climate ambition levels, the socio-economic development narrative strongly influences environmental performance. Among pathways staying below 2.0 °C, SSP1-based scenarios reduce total human health impacts by a median of 21%, compared with a median increase of 6% under SSP2. For ecosystem quality, SSP1 pathways achieve median reductions of 59%, compared with 41% under SSP2. These differences reflect SSP1's higher efficiency gains, lower population growth, consequent lower total final energy demand, and reduced reliance on large-scale bioenergy, which collectively moderate the upstream pressures associated with infrastructure expansion. Natural resource indicators display a fundamentally different pattern from human health and ecosystem quality, further illustrating how strongly environmental outcomes depend on the configuration of the energy transition. Land occupation is shaped by two opposing dynamics of biomass use. The phase-out of traditional biomass in buildings for cooking and heating reduces land use relative to 2020 across all pathways. In pathways below 2.0 °C, however, the expansion of large-scale bioenergy, including transport biofuels, biomass-derived industrial fuels, and bioenergy with carbon capture and storage (BECCS), partially or fully offsets these reductions. Net land occupation by 2100 consequently ranges from −13% to +354% across pathways below 2.0 °C, a spread of 367 percentage points driven almost entirely by the scale of biomass deployment. SSP2-based pathways exhibit the largest increases (median +194%), compared to SSP1 (median +67%), reflecting differences in bioenergy reliance. Freshwater use also increases across nearly all pathways. In contrast to ecotoxicity, which is driven by toxic emissions from metal extraction, increases in freshwater use primarily reflect direct water consumption in mining, material processing, and cooling across expanding electricity supply chains. Electrification of transport, buildings, and industry is the dominant driver, collectively accounting for a median of 64% of all positive contributions to freshwater use increases in pathways below 2.0 °C. Hydrogen contributes a median of 7% to total freshwater use in pathways below 2.0 °C (range: 3-12%), while BECCS accounts for a median of 2.5% (range: 0.1-11%). Both remain secondary to the water required by electrification itself. These increases show no consistent relationship with climate ambition within SSP2 pathways, indicating that they arise from the structure of the energy system configuration rather than the level of mitigation or the socio-economic narrative. Figure 3 | Sectoral and energy supply alternative contributions to changes in environmental impacts by 2100. Stacked bar charts show the relative contribution of consuming sectors and energy supply options to the percentage change in five environmental indicators between 2020 and 2100. Environmental indicators include ‘Total: Ecosystem Quality’, ‘Total: human health’, ‘Freshwater Ecotoxicity’, ‘Land use’, and ‘Water use’. Values are expressed as percentage change relative to the total 2020 impacts. For a given pathway and indicator, each stacked segment represents the specific contribution of a specific sector and energy supply option to the overall change in 2100. Segments above zero increase relative impacts compared to 2020, whereas segments below zero reduce relative impacts. The diamond marker indicates the net percentage change, equal to the sum of all stacked segments for that pathway. Bars are grouped by end-of-century global mean surface temperature (GMST) outcome, with columns representing pathways below 2.0 degrees Celsius, between 2.0 and 2.5 degrees Celsius, and above 2.5 degrees Celsius. Within each column, pathways are grouped by Integrated Assessment Model (IAM) and Shared Socioeconomic Pathway (SSP). Labels further group scenarios into temperature outcome categories within each SSP, with suffixes distinguishing individual scenarios that fall within the same category. Colours indicate energy supply options, hatching denotes consuming sectors. Only the main contributing sector and option combinations are shown explicitly, with remaining contributions aggregated as ‘Other’. Taken together, these results reveal a structured pattern. Total human health impacts and aggregate ecosystem quality improve consistently as climate change mitigation ambition increases. In contrast, freshwater ecotoxicity, land occupation, freshwater use, and mineral resource scarcity do not scale with climate policies. Instead, they vary widely across pathways that achieve similar temperature targets. The spread within pathways of similar climate ambition can be as large as the difference between pathways with different ambition levels. These results demonstrate that the multidimensional environmental performance of energy transitions is not determined by climate ambition alone, but by the configuration of the energy system, including the extent of electrification 22 , the scale of biomass deployment 23 , hydrogen use 24 , and overall energy demand, as well as broader socio-economic conditions that shape demand for energy services and natural resources. Timing and scale of metal extraction demand across energy pathways Mineral extraction increases across all pathways as energy systems expand and electrify. The rapid deployment of renewable electricity, electrified transport, and energy storage has heightened attention to the scale of materials required to defossilise the energy system, with a focus on the availability of critical metals 25,26 . Metals such as copper, nickel, lithium, cobalt, and rare earth elements are central to power generation, grid infrastructure, batteries, and electrified end-uses, raising questions about both near-term supply constraints and long-term resource availability. As described in the Methods , the trajectories shown ( Fig. 4 ) represent primary metal extraction embedded in global final energy provision and its associated supply chains, excluding non-energy-driven demand. These physical extraction trajectories are distinct from the aggregate mineral resource scarcity indicator shown in Fig. 2, which reflects scarcity-weighted resource use across the full life cycle inventory. Demand trajectories differ markedly in timing across mitigation pathways, with more ambitious mitigation pathways associated with accelerated near-term extraction requirements for copper, nickel, cobalt, and silver. In pathways below 2.0 °C, annual copper demand embedded in final energy provision alone exceeds 2023 global production levels (22.6 Mt yr⁻¹) by 2035-2040 across all models, reaching a median of 43 Mt yr⁻¹ by mid-century. Under pathways above 2.5 °C, this threshold is crossed up to 10 years later, with a median demand of 33 Mt yr⁻¹ at mid-century. The near-term acceleration is most pronounced for battery metals and silver. By 2040, median lithium demand under pathways below 2.0 °C is about 60% higher than under pathways above 2.5 °C, and cobalt demand is about 50% higher. Nickel and silver show similar near-term increases of around 50%. This pattern is consistent across models and socio-economic narratives. Platinum group metals exhibit a distinct trajectory. Demand declines in the near term as internal combustion engine vehicles are phased out, notably reducing demand for exhaust treatment systems. In pathways with substantial hydrogen deployment, platinum demand increases later in the century due to its use in proton exchange membrane electrolyzers and fuel cells. This dynamic illustrates how technology substitution within the energy system reshapes metal demand profiles over time. In contrast, manganese demand associated with final energy provision remains modest relative to its broader industrial use, particularly in steel production, highlighting that not all battery-relevant metals exhibit comparable sensitivity to energy transition dynamics. Figure 4 | Timing and scale of metal demand across energy system pathways. Trajectories of annual primary metal extraction from 2020 to 2100 across 24 energy system pathways, shown for bulk metals (copper, iron, nickel, zinc), battery metals (lithium, cobalt, manganese), and precious metals and rare earth elements (platinum group metals, silver, rare earth elements). Insets show cumulative primary extraction from 2020 to 2100 for each pathway. Horizontal solid lines indicate 2023 global primary metal production, and horizontal dashed lines indicate 2023 reserves for each metal 27 . For a given metal, a trajectory that rises above the solid line implies that annual extraction embedded in final energy provision exceeds current global production in that year. In the inset, if a pathway’s cumulative bar approaches the dashed line, cumulative extraction from 2020 to 2100 is of the same order as currently reported reserves. Colours indicate end-of-century global mean surface temperature (GMST) outcomes, marker shapes denote Integrated Assessment Models (IAMs), and line styles represent Shared Socioeconomic Pathways (SSPs), all as in Fig. 1, with SSP2 scenarios emphasised with higher opacity. Despite pronounced differences in annual trajectories, cumulative extraction from 2020 to 2100 varies only modestly across mitigation pathways with differing levels of climate ambition. For copper, pathways below 2.0 °C require 3,400-5,100 Mt cumulatively, compared with 2,300-5,100 Mt under pathways above 2.5 °C. This represents nearly identical upper bounds despite markedly different deployment trajectories. For nickel and cobalt, the overlap across pathways with differing levels of climate ambition spans 60–70% of the total range. Within SSP2, however, the overlap is smaller for some metals: cumulative copper and nickel demand is consistently higher in SSP2 pathways below 2.0 °C than above 2.5 °C, reflecting the material intensity of electrification that is only partially offset by lower long-term energy demand under these pathways. Cumulative extraction approaches or exceeds currently reported reserves for several metals regardless of the climate policy. Copper cumulative demand exceeds reserves by a factor of 2-5 across all pathways, nickel by 3-7 times, cobalt by 1-7 times, silver and zinc by 1.6-4 times, and lithium by 0.4-4 times. Only iron and rare earth elements (REEs) extraction demand driven by the energy system remains well below reserve estimates 27 . Reported reserves reflect current economics and geological knowledge and are used here as a benchmark for scale rather than as fixed physical limits. Long-term material availability challenges are therefore not confined to the most ambitious mitigation pathways but accompany energy system expansion more broadly across pathways. These LCA results reflect the modelling assumptions underlying the IAM pathways, which incorporate endogenous responses of the energy system to prices and policy, influencing final energy demand (see Fig. 1) and, in turn, indirectly affecting material demand. However, they do not represent feedback from material scarcity itself, such as price-driven substitution between metals, accelerated recycling, or technological innovation triggered by constrained supply. Even under highly optimistic assumptions regarding future recycling rates, explored in the Supplementary Information , the short-term acceleration of metal demand in more ambitious pathways remains pronounced, and cumulative extraction patterns across pathways are only partially moderated. Ambitious mitigation pathways thus exhibit two counteracting dynamics: faster near-term growth in metal demand driven by rapid electrification, and lower long-term final energy demand resulting from efficiency improvements and structural changes in energy consumption. Together, these results indicate that cumulative material pressures are governed primarily by the overall scale of energy service provision, whereas climate ambition primarily reshapes the timing of extraction rather than the total volumes required over the century. Environmental sustainability of energy transitions Deep decarbonization delivers clear environmental benefits, particularly for ecosystem quality, air quality, and human health. Yet pathways that achieve similar temperature outcomes can diverge substantially in their broader environmental performance. Across the scenarios analysed here, improvements in combustion-related impacts occur consistently as fossil fuel use declines, but pressures on land, water, and mineral resources vary widely depending on how energy systems are reconfigured. These differences arise from a limited set of structural transition levers. High levels of electrification consistently reduce combustion-related impacts but increase upstream pressures associated with metal extraction and water use, creating trade-offs between climate mitigation and resource use. For metals central to electrification, climate ambition primarily reshapes the timing of extraction rather than cumulative volumes, accelerating near-term demand. This near-term acceleration may increase the risk of supply constraints, price volatility, and geopolitical pressures in critical mineral supply chains 14,25 . Cumulative volumes show substantial overlap across mitigation pathways with differing levels of climate ambition, although within SSP2 pathways, more ambitious scenarios show modestly higher totals for some metals. Beyond electrification, other technology choices introduce additional trade-offs. Hydrogen-based strategies reshape demand for platinum group metals and, when produced through electrolysis, increase electricity demand and associated upstream pressures. Extensive biomass deployment can help mitigate fossil fuel emissions and provide carbon dioxide removal in the case of climate overshoot, but substantially increases land occupation. The scale and configuration of carbon dioxide removal further influence both energy and material requirements. By allowing pathways to meet stringent climate targets with higher residual emissions, carbon dioxide removal shapes the configuration of the rest of the energy system. Different removal strategies introduce distinct environmental pressures, with biomass-based approaches such as BECCS increasing demand for land and feedstock, contributing to the land-use trade-offs identified above, whereas direct air capture relies on additional electricity and water inputs. This underscores the importance of diversifying carbon dioxide removal portfolios to avoid concentrating pressures on specific resources 28 . At the same time, reliance on energy-intensive removal options can increase overall system demand, reinforcing the importance of low-demand pathways in moderating both environmental impacts and material requirements. By linking structurally distinct IAM pathways to LCA, this study enables a system-wide comparison of the environmental implications of alternative transition strategies. Previous IAM-LCA integration efforts have assessed the power sector 10,29 or national energy systems 30 , but have not extended to the full global final energy system across multiple models and climate ambition levels. Comparing pathways within SSP2 further isolates the role of energy system configuration from differences in population and socio-economic development. Climate ambition largely determines the pace of transformation, but the configuration of the energy system determines how environmental pressures are redistributed across sectors, technologies, and supply chains. As a result, convergence in temperature outcomes does not imply convergence in environmental sustainability. Meaningful assessment of mitigation pathways requires considering not only global warming performance, but also broader environmental implications and resource requirements, which ultimately shape the feasibility and desirability of different transition strategies. Several limitations should be considered when interpreting these results. The material and environmental outcomes reported here reflect the technology portfolios and assumptions embedded in the underlying IAM pathways. The framework does not include endogenous responses to material scarcity, such as price-driven substitution, accelerated recycling, or technological innovation beyond those represented in the scenarios. Recent work that explicitly accounts for material availability constraints indicates that such dynamics can substantially reshape technology deployment and expose supply bottlenecks in standard IAM projections 31 . Life-cycle inventories also remain subject to uncertainty, particularly for emerging technologies and future production processes. Differences in technological granularity between IAM outputs and LCA datasets further require assumptions regarding sub-technology representation 32 . In addition, while reported metal reserves provide a useful benchmark for scale, they do not represent fixed physical limits and are expected to evolve with exploration, technological change, and economic conditions 27 . Finally, assessing multiple environmental dimensions does not establish whether any pathway remains within absolute environmental limits or planetary boundaries 33,34 . The indicators used here quantify relative pressures associated with alternative transition strategies rather than cumulative system-wide thresholds. Integrating boundary-based or absolute sustainability frameworks with scenario-based life-cycle assessment remains an important direction for future research. Making configuration-dependent environmental effects visible enables comparison of transition strategies before large-scale infrastructure and material commitments are locked in and clarifies the structural decisions that ultimately determine whether decarbonization aligns not only with climate stabilisation but with broader Earth system stability. Methods Overview of the modelling framework We combine IAM scenarios with prospective LCA to quantify the environmental impacts associated with the global final energy supply from 2020 to 2100. The modelling workflow integrates scenario-specific energy system projections with the LCA database ecoinvent 3.11 35,36 , and performs time-resolved impact calculations for each scenario and year across the regions defined by each model. Related IAM-LCA applications have previously been used to assess national energy transitions 30,32 . Here we extend this framework to a global, multi-model context. For each scenario and time step, the Python package Premise (v2.3.7) 9 generates a modified version of the background LCA database that reflects projected regional changes in electricity generation, fuel production, and industrial processes. These scenario-specific databases are stored in data packages that contain the transformed life-cycle inventories of activities related to energy supply and use, IAM scenario production volumes for each year across regions, and mappings between scenario variables and corresponding LCA activities. Environmental impacts are calculated using the Python package Pathways (v2.0) 37 , which builds on Brightway2 38 , to compute life-cycle impacts and resource use at each time step, using scenario production volumes as demand vectors. Calculations are performed at the level of individual supply chain activities and regions, preserving the technological and geographical resolution of each IAM model. Results are subsequently aggregated for global analysis. This workflow enables consistent integration of multiple IAM scenarios into a collection of process-based LCA databases while maintaining transparency in scenario mapping, database transformation, and time-resolved impact calculation. IAM scenarios We analyze 24 mitigation pathways derived from three IAMs: REMIND, IMAGE, and MESSAGEix-GLOBIOM-GAINS (hereafter MESSAGE). REMIND 18 is a multiregional general equilibrium-based energy-economy-climate model that accounts for macroeconomic-energy interactions with a high level of process detail in sectoral transformation dynamics 39–41 , and representation of crucial inertias and path dependencies, such as capital stock inertia in supply and demand sectors, as well as technological learning and inertias in technology up-scaling 42 . Land-use interactions are emulated based on the MAgPIE model 43 . The model distinguishes 12 world regions. IMAGE 15 is a recursive-dynamic simulation model linking the land and energy systems. Land cover and land use are modelled at a grid-scale and are hard-linked to the LPJml model which simulates biophysical processes, allowing explicit analysis of climate-ecosystem interactions. The energy system module has an explicit representation of energy supply, conversion and demand across 26 regions. MESSAGE 16,17 couples the energy-system optimization model MESSAGEix, the land use model GLOBIOM and the air pollution and GHG model GAINS, providing detailed energy-system pathways linked to land use and air quality dynamics. The energy system model consists of a bottom-up model with high level of technological detail. The recent integration of the MESSAGEix-Materials has introduced a process-based representation of heavy industry and explicit, endogenous material flow modelling 44 . The coupling of energy and land-use systems is implemented via an emulator approach. The linkage between GAINS and MESSAGEix is achieved by a bilateral model soft-link. The integrated model aggregates the system at a resolution of 12 macro regions. Together, these models provide complementary representations of energy-economy, land-use, and technological dynamics. Mapping between IAM variables and LCA Technology and sector variables reported by each IAM are mapped to corresponding activities in the LCA database. For each IAM region and time step, reported final energy demands are mapped to datasets that reflect both the energy carrier consumed and the infrastructure required to deliver and use it. For example, each megajoule of electricity supplied to passenger cars is associated with a proportional share of the infrastructure required for its use, including charging infrastructure, onboard batteries, and electric motors. These datasets are linked to scenario-modified upstream supply chains, including region-specific electricity mixes, fuel production systems, industrial processes, and conversion efficiencies. As a result, the life-cycle representation captures not only shifts in final energy demand but also structural transformations of the entire energy supply chain consistent with IAM projections. The complete mapping between scenario variables and LCA datasets is provided in the online repository 45 . Differences in technological granularity between IAM outputs and the LCA database are addressed through structured assumptions. IAM categories often represent aggregated technologies, such as battery energy storage or photovoltaic electricity, whereas the LCA database distinguishes multiple chemistries, module types, or system configurations. In such cases, either time-dependent sub-technology mixes or representative datasets are applied, as illustrated by the following examples. For battery technologies, dynamic sub-technology mixes are constructed exogenously. Mobile battery chemistries include multiple NMC variants, NCA, LFP, sodium-ion, lithium-sulfur, and lead-acid systems, while stationary storage includes NMC variants, LFP, vanadium redox flow batteries, sodium-sulfur, and lead-acid systems. Future mobile battery shares follow projections from Degen et al. (2023) 46 , historical shares prior to 2021 are based on Orangi et al. (2024) 47 , and stationary storage shares are derived from Schlichenmaier et al. (2022) 48 . These shares are implemented through market datasets that evolve over time while remaining consistent with IAM-level deployment volumes. Photovoltaic electricity is represented using country-specific, technology-resolved inventories 49 . Installed capacity is disaggregated by installation type, module technology, and mounting configuration, and converted to per-kWh life-cycle inventories using country-specific productivity data and fixed lifetime assumptions. Where IAM scenarios distinguish residential and commercial PV, separate low-voltage and high-voltage production mixes are implemented. Module efficiencies are adjusted over time to reflect expected technological improvements. For technologies where IAM scenarios do not distinguish sub-technologies and no robust projections of future market shares are available, a single representative configuration is selected. For example, hydrogen fuel cells are represented using proton exchange membrane technology as a widely deployed baseline configuration. Previous work shows that technological granularity strongly influences material demand and can shift pressures across resources. Its effect on environmental impacts is more limited and concentrated in categories linked to material extraction, such as freshwater ecotoxicity, while effects on overall decarbonization outcomes remain very limited 32 . Scenario alignment and consistency Impacts are quantified using environmental LCA 50 . By accounting for upstream resource extraction, energy conversion processes, infrastructure requirements, and end-of-life treatment, LCA captures direct and indirect environmental impacts across life-cycle phases, thereby complementing scenario-level indicators with environmental detail along the supply chain 10,30,51 . For each pathway, life cycle inventories are dynamically aligned using Premise with projected changes in energy technologies, fuel mixes, and conversion efficiencies from 2020 to 2100. Life cycle-derived carbon dioxide emissions and primary energy demand closely track the corresponding IAM outputs across all pathways ( Fig. 5 ), indicating that scenario-specific energy system configurations are consistently reflected in the environmental inventories. Residual differences arise from structural distinctions between IAM accounting conventions and life-cycle system boundaries. In particular, LCA results include emissions and removals associated with engineered carbon dioxide removal technologies represented within the energy system, but do not independently reproduce carbon sequestration processes embedded in IAM climate modules, such as afforestation. Consequently, life-cycle net CO 2 emissions exceed IAM-reported emissions in low-carbon scenarios where non-energy carbon sinks contribute substantially to mitigation. Differences in primary energy accounting mainly reflect methodological conventions. For biomass in particular, differing treatment of feedstock energy content and upstream inputs leads to systematic differences in reported totals, while preserving consistency in relative scenario trends. LCA modelling Functional unit The functional unit is defined as the global final energy supply and use for the period 2020 to 2100 under each mitigation pathway. Engineered carbon dioxide removal technologies represented within the energy system pathways are included, as they allow the final energy supply and use to align with the enforced climate trajectories. These comprise direct air capture (using solvents and sorbents), BECCS, synthetic fuels with CCS, biofuels with CCS, enhanced weathering, ocean alkalinity enhancement, and biochar application. Land-use change and other carbon dynamics represented in the IAMs' climate modules are not modelled independently within the LCA framework. Life cycle inventory The background life cycle inventory is based on ecoinvent v3.11 (system model: allocation, cut-off by classification). Additionally, Premise enriches the database by adding inventories for emerging technologies relevant to the energy system. To improve the representation of critical raw materials, additional inventories are incorporated, and material intensities are updated for technologies central to the energy transition, including vehicles, wind turbines, photovoltaic systems, batteries, fuel cells, and electrolysers 30,32 . Material intensities are calibrated to present-day technologies, and projected efficiency improvements are introduced over time, in line with technology-learning assumptions. All scenario-driven database transformations are documented in the online repository 45 . To track demand for primary metal extraction, multi-output mining processes are modified to ensure the physical consistency of extracted metal quantities. Extraction of individual elements from the ore is fully attributed to the respective metal based on mass balances, whereas other elementary and intermediate flows (e.g., electricity and diesel) retain economic allocation consistent with the default ecoinvent approach. This hybrid allocation procedure ensures mass-balance consistency while preserving the structure of upstream production processes 52 . Recycling rates are not modified beyond those represented in the underlying IAM pathways, as dynamically coupling recycling availability, secondary supply, and additional energy requirements would require structural feedbacks not consistently represented across models. Adjusting recycling rates independently within the LCA framework may alter associated energy requirements, causing final energy demand to diverge from the IAM scenarios. A sensitivity analysis assuming optimistic global recycling rates by 2050 confirms that the main conclusions regarding environmental impacts, and timing and cumulative material extraction remain unchanged ( Supplementary Information ). Life cycle impact assessment Environmental impacts are quantified using the Environmental Footprint (EF) 3.1 midpoint method for acidification (mol H + -eq), photochemical ozone formation (kg NMVOC-eq), particulate matter formation (disease incidence), freshwater eutrophication (kg P-eq), and freshwater ecotoxicity (CTUe) 53 . Total human health impacts (DALYs) and total ecosystem quality (species-years) are also evaluated using the ReCiPe 2016 endpoint method (Hierarchist perspective) 54 . Land occupation (m 2 -year) and net freshwater use (m 3 ) are reported as total flows without scarcity or other weighting. Mineral resource depletion is quantified using a crustal scarcity indicator (kg Si-eq), which characterizes mineral demand based on elemental crustal concentrations as a proxy for long-term global resource scarcity 55 . Limitations Several limitations arise from integrating IAM scenarios with process-based LCA. Technological granularity differs between IAM outputs and life-cycle inventories, necessitating the introduction of sub-technology resolution at the LCA level through structured assumptions. This introduces uncertainty in the mapping between aggregated scenario variables and specific technological configurations. In particular, alternative technology developments for batteries, photovoltaic modules, and hydrogen conversion systems, such as faster adoption of low cobalt and nickel battery chemistries, widespread deployment of silver-lean or silver-free photovoltaic technologies, or a transition to hydrogen systems that do not rely on platinum group metals, would alter demand for specific metals. While such substitutions would redistribute material demand across individual elements, they would not remove the broader increase in mineral requirements associated with rapid electrification and infrastructure expansion 32 . The framework also does not incorporate endogenous feedback between material availability, prices, and technology deployment. Recycling rates and secondary supply are represented only to the extent they are reflected in the IAM pathways, and dynamic coupling between material scarcity and energy system transformation is beyond the present scope. Spatial resolution of environmental impacts is constrained by the regional aggregation inherent in IAMs and by the availability of regionalised inventories. Consequently, localised burdens associated with water use, land transformation, or other region-specific pressures may not be fully captured. Furthermore, as is conventional in LCA, infrastructure-related material demand is spread over technology lifetimes, thereby smoothing short-term installation peaks. While cumulative material demand is preserved, short-term surges may be underestimated. A further limitation arises from the use of present-day life cycle impact characterisation factors to evaluate future environmental interventions. Characterisation models in EF 3.1 and ReCiPe 2016 are based on current or historical environmental conditions, exposure pathways, and background concentrations. Applying these factors to future emission profiles implicitly assumes that fate, exposure, vulnerability, and environmental thresholds remain constant over time. Potential changes in climate, land use, population distribution, and baseline environmental quality are therefore not reflected in the impact assessment 56,57 . Declarations Code and data availability The code required to reproduce the results presented in this study is available in the following repository: https://github.com/polca/premise/tree/2.3.7.dev.lca-of-iam-scenarios/examples/MIC_exercise. The workflow requires Premise and Pathways , both open-source 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/). Acknowledgments A.J.H.M., R.S., and C.B. acknowledge the financial support of their work through the Swiss State Secretariat for Education, Research and Innovation (SERI) under the Horizon Europe projects PRISMA (grant agreement no. 101081604) and RAWCLIC (grant agreement no. 101183654). D.B. and G.L. acknowledge support through the Ariadne project from the German Federal Ministry of Education and Research (funding label 03SFK5A0-2) as well as through Horizon Europe projects RAWCLIC (grant agreement no. 101183654) and PRISMA (grant agreement no. 101081604). V.D acknowledges support through the Horizon Europe projects PRISMA (grant agreement no. 101081604) and UPTAKE (grant agreement no. 101081521). F.M. and V.K. acknowledge support through the Horizon Europe project PRISMA (grant agreement no. 101081604). Author contributions A.J.H.M. wrote the manuscript with input from all authors. A.J.H.M. performed the analyses and created the figures. R.S. conceived the study and initiated the development of the open-source tools. A.J.H.M., D.B., and R.S. contributed to the development of the tools. A.J.H.M., C.B., and R.S. developed the conceptual framework. D.B., F.M., and V.D. collaborated with A.J.H.M. and R.S. to integrate the IAM pathways. References Rockström, J. et al. A safe operating space for humanity. Nature 461 , 472–475 (2009). Ripple, W. J. et al. The risk of a hothouse Earth trajectory. One Earth 9 , 101565 (2026). Sakschewski, B. et al. Planetary Health Check 2025: A Scientific Assessment of the State of the Planet . Planetary Boundaries Science (PBScience) doi: 10.48485/PIK.2025.017 (2025). Van Vuuren, D. P. et al. Exploring pathways for world development within planetary boundaries. Nature 641 , 910–916 (2025). OECD. The Climate Action Monitor 2025 . 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Coupling the Ecoinvent Database with Projections from Integrated Assessment Models (IAM). v2.3.7 . https://github.com/polca/premise. Degen, F., Winter, M., Bendig, D. & Tübke, J. Energy consumption of current and future production of lithium-ion and post lithium-ion battery cells. Nat Energy 8 , 1284–1295 (2023). Orangi, S. et al. Historical and prospective lithium-ion battery cost trajectories from a bottom-up production modeling perspective. Journal of Energy Storage 76 , 109800 (2024). Schlichenmaier, S. & Naegler, T. May material bottlenecks hamper the global energy transition towards the 1.5 °C target? Energy Reports 8 , 14875–14887 (2022). Frischknecht, R., Stolz, P., Krebs, L., Wild-Scholten, M. D., & Parikhit Sinha. Life Cycle Inventories and Life Cycle Assessments of Photovoltaic Systems 2020 Task 12 PV Sustainability. (2020). Handbook on Life Cycle Assessment: Operational Guide to the ISO Standards . vol. 7 (Springer Netherlands, Dordrecht, 2002). Hellweg, S., Benetto, E., Huijbregts, M. A. J., Verones, F. & Wood, R. Life-cycle assessment to guide solutions for the triple planetary crisis. Nat Rev Earth Environ 4 , 471–486 (2023). Berger, M. et al. Mineral resources in life cycle impact assessment: part II– recommendations on application-dependent use of existing methods and on futuremethod development needs. Int J Life Cycle Assess 25 , 798–813 (2020). European Commission. Joint Research Centre. Updated Characterisation and Normalisation Factors for the Environmental Footprint 3.1 Method. (Publications Office, LU, 2023). Huijbregts, M. A. J. et al. ReCiPe2016: a harmonised life cycle impact assessment method at midpoint and endpoint level. Int J Life Cycle Assess 22 , 138–147 (2017). Arvidsson, R. et al. A crustal scarcity indicator for long-term global elemental resource assessment in LCA. Int J Life Cycle Assess 25 , 1805–1817 (2020). Barbosa Watanabe, M. D. & Cherubini, F. Prospective Characterization Factors for Assessing Climate Change Impacts in Life Cycle Assessments. Environ. Sci. Technol. 60 , 3202–3215 (2026). Van Der Giesen, C., Cucurachi, S., Guinée, J., Kramer, G. J. & Tukker, A. A critical view on the current application of LCA for new technologies and recommendations for improved practice. Journal of Cleaner Production 259 , 120904 (2020). Additional Declarations There is NO Competing Interest. Supplementary Files Supplementaryinformation.docx Supplementary information Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9527853","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":635997891,"identity":"e0a32092-e57d-469d-a09f-0a4fc1a94e71","order_by":0,"name":"Alvaro Jose Hahn 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19:10:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9527853/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9527853/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108754329,"identity":"c125bcf9-6b3a-41c8-b4ea-143e1b6aa13c","added_by":"auto","created_at":"2026-05-08 04:39:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1857907,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKey drivers of climate mitigation pathways analysed in this study.\u003c/strong\u003e Trajectories of \u003cstrong\u003eA\u003c/strong\u003e, global final energy demand, \u003cstrong\u003eB, \u003c/strong\u003eglobal population, \u003cstrong\u003eC\u003c/strong\u003e, electricity share in final energy, \u003cstrong\u003eD\u003c/strong\u003e, hydrogen share in final energy, and \u003cstrong\u003eE\u003c/strong\u003e, deployment of engineered carbon dioxide removal across 24 climate mitigation pathways derived from Integrated Assessment Models (IAM) between 2020 and 2100. Colours indicate end-of-century global mean surface temperature (GMST) outcomes, with dark green for less than 2.0 degrees Celsius, orange for 2.0 to 2.5 degrees Celsius, and dark red for more than 2.5 degrees Celsius. Marker shapes denote models, with circles for IMAGE, triangles for MESSAGE, and squares for REMIND. Line styles represent Shared Socioeconomic Pathways (SSP), with dashed lines for SSP1, solid lines for SSP2, dotted lines for SSP3, and dash-dot lines for SSP5. SSP2 scenarios are emphasised with higher opacity. Population trajectories are governed by SSP assumptions and are similar across models and policy variants within each SSP family. All values are shown for the global aggregate.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9527853/v1/06f5cde16f55a857c1b82276.png"},{"id":108754334,"identity":"d23f322a-d820-4b2b-b7bf-af07529768e8","added_by":"auto","created_at":"2026-05-08 04:39:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2377739,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnnual environmental impacts of global final energy provision across climate mitigation pathways.\u003c/strong\u003e Percentage change in annual impacts relative to 2020, from 2020 to 2100, across ten environmental indicators and 24 mitigation pathways. Panels are organised by area of protection: ecosystem quality (total ecosystem quality in local species loss over time, acidification, freshwater ecotoxicity, freshwater eutrophication), human health (total human health in DALYs, particulate matter formation, photochemical ozone formation), and natural resources (land use, freshwater use, scarcity-weighted mineral resource use). The horizontal dashed line marks zero change relative to the 2020 baseline. Colours indicate end-of-century global mean surface temperature (GMST) outcomes, marker shapes denote IAM models, and line styles represent SSP scenarios, all as in Fig. 1, with SSP2 scenarios emphasised with higher opacity.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9527853/v1/fe6d0fb9efcfdc497a5ac5b6.png"},{"id":108754345,"identity":"c4c6e943-5ece-4ee7-bcec-866c9a4f9a52","added_by":"auto","created_at":"2026-05-08 04:40:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":874740,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSectoral and energy supply alternative contributions to changes in environmental impacts by 2100.\u003c/strong\u003e Stacked bar charts show the relative contribution of consuming sectors and energy supply options to the percentage change in five environmental indicators between 2020 and 2100. Environmental indicators include ‘Total: Ecosystem Quality’, ‘Total: human health’, ‘Freshwater Ecotoxicity’, ‘Land use’, and ‘Water use’. Values are expressed as percentage change relative to the total 2020 impacts. For a given pathway and indicator, each stacked segment represents the specific contribution of a specific sector and energy supply option to the overall change in 2100. Segments above zero increase relative impacts compared to 2020, whereas segments below zero reduce relative impacts. The diamond marker indicates the net percentage change, equal to the sum of all stacked segments for that pathway. Bars are grouped by end-of-century global mean surface temperature (GMST) outcome, with columns representing pathways below 2.0 degrees Celsius, between 2.0 and 2.5 degrees Celsius, and above 2.5 degrees Celsius. Within each column, pathways are grouped by Integrated Assessment Model (IAM) and Shared Socioeconomic Pathway (SSP). Labels further group scenarios into temperature outcome categories within each SSP, with suffixes distinguishing individual scenarios that fall within the same category. Colours indicate energy supply options, hatching denotes consuming sectors. Only the main contributing sector and option combinations are shown explicitly, with remaining contributions aggregated as ‘Other’.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9527853/v1/19cb41656d4377677eb36f81.png"},{"id":108754330,"identity":"2e2c218e-a805-44cf-9f75-446eb85992ef","added_by":"auto","created_at":"2026-05-08 04:39:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3692876,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTiming and scale of metal demand across energy system pathways. \u003c/strong\u003eTrajectories of annual primary metal extraction from 2020 to 2100 across 24 energy system pathways, shown for bulk metals (copper, iron, nickel, zinc), battery metals (lithium, cobalt, manganese), and precious metals and rare earth elements (platinum group metals, silver, rare earth elements). Insets show cumulative primary extraction from 2020 to 2100 for each pathway. Horizontal solid lines indicate 2023 global primary metal production, and horizontal dashed lines indicate 2023 reserves for each metal\u003csup\u003e27\u003c/sup\u003e. For a given metal, a trajectory that rises above the solid line implies that annual extraction embedded in final energy provision exceeds current global production in that year. In the inset, if a pathway’s cumulative bar approaches the dashed line, cumulative extraction from 2020 to 2100 is of the same order as currently reported reserves. Colours indicate end-of-century global mean surface temperature (GMST) outcomes, marker shapes denote Integrated Assessment Models (IAMs), and line styles represent Shared Socioeconomic Pathways (SSPs), all as in Fig. 1, with SSP2 scenarios emphasised with higher opacity.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9527853/v1/ca8d6d3301839029e0f8290b.png"},{"id":108754350,"identity":"c0a333e3-336b-45a8-907a-3be170dd30b1","added_by":"auto","created_at":"2026-05-08 04:40:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2461940,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConsistency between life cycle assessment (LCA) results and Integrated Assessment Models (IAM) outputs.\u003c/strong\u003e Comparison of IAM outputs and LCA results for primary energy and carbon dioxide emissions across 24 climate mitigation pathways from 2020 to 2100. Panels \u003cstrong\u003eA-C\u003c/strong\u003e show primary energy, and panels \u003cstrong\u003eD-F\u003c/strong\u003e show carbon dioxide emissions. \u003cstrong\u003eA\u003c/strong\u003e, IAM-defined primary energy trajectories. \u003cstrong\u003eB\u003c/strong\u003e, Primary energy demand calculated from life cycle inventories of global final energy provision. \u003cstrong\u003eC\u003c/strong\u003e, \u0026nbsp;IAM-defined primary energy (x-axis) versus LCA-derived primary energy (y-axis), with the dashed line indicating one-to-one correspondence. \u003cstrong\u003eD\u003c/strong\u003e, IAM-defined net carbon dioxide emissions trajectories. \u003cstrong\u003eE\u003c/strong\u003e, Life-cycle carbon dioxide emissions from final energy provision and associated carbon dioxide removal. \u003cstrong\u003eF\u003c/strong\u003e, IAM-defined carbon dioxide emissions (x-axis) versus LCA-derived carbon dioxide emissions (y-axis), with the dashed line indicating one-to-one correspondence. Colours, marker shapes, and line styles are as in Fig. 1.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9527853/v1/a371161ac1a257b8aca57111.png"},{"id":108807695,"identity":"5c4bec4c-dc83-4272-bcce-6ffb6c8a9a5d","added_by":"auto","created_at":"2026-05-08 15:31:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10491062,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9527853/v1/cb4d5cc2-1643-49f3-b2cc-b861a01dd5e3.pdf"},{"id":108754333,"identity":"d97704b7-8586-461b-a013-1f6e94658747","added_by":"auto","created_at":"2026-05-08 04:39:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3038136,"visible":true,"origin":"","legend":"Supplementary information","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-9527853/v1/17baeb677de438bb8d90cd4c.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Environmental and material implications of global climate mitigation pathways","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHuman activities are increasingly destabilising the Earth system, pushing key biophysical processes beyond conditions under which civilisations have historically developed\u003csup\u003e1,2\u003c/sup\u003e. Central to these pressures are fossil fuel combustion and greenhouse gas (GHG) emissions, which drive climate change and trigger feedback loops that exacerbate ocean acidification, land-system change, and other planetary boundary transgressions\u003csup\u003e3,4\u003c/sup\u003e. In response, decarbonization of the energy system has emerged as a key pillar of international climate policy, with more than 130 countries adopting net-zero targets\u003csup\u003e5\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTranslating these commitments into actionable strategies relies heavily on Integrated Assessment Models (IAMs), which generate long-term energy system transformation pathways consistent with specific climate targets\u003csup\u003e6\u003c/sup\u003e. By linking assumptions about socio-economic development, energy supply and demand, land use, and the climate system, these models provide internally consistent scenarios that inform national and international climate policy\u003csup\u003e7\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eDespite this critical role, IAMs are primarily designed to explore climate mitigation pathways and energy-economy dynamics. They do not explicitly represent many non-climate environmental impacts, or capture them only in aggregated form\u003csup\u003e8\u003c/sup\u003e. Complementary approaches are needed to resolve the technology- and supply chain-specific environmental implications of alternative decarbonization pathways in greater detail\u003csup\u003e9,10\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAs a result, key questions raised by the energy transition remain insufficiently explored, including how decarbonization pathways redistribute environmental pressures across impact categories\u003csup\u003e11,12\u003c/sup\u003e, how specific technologies affect individual Earth system processes\u003csup\u003e13\u003c/sup\u003e, and how the deployment of low-carbon technologies may influence the demand for critical raw materials\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHere, we assess environmental impacts beyond climate change across 24 mitigation pathways derived from three widely used and structurally distinct IAMs: IMAGE\u003csup\u003e15\u003c/sup\u003e, MESSAGEix\u003csup\u003e16,17\u003c/sup\u003e, and REMIND\u003csup\u003e18\u003c/sup\u003e. These models span a broad range of modelling paradigms and representations of energy, land-use, and techno-economic dynamics. The 24 pathways cover multiple Shared Socioeconomic Pathways (SSP)\u003csup\u003e19\u003c/sup\u003e and end-of-century global mean surface temperature (GMST) outcomes, reflecting diverse assumptions about socio-economic development, GHG emissions mitigation ambitions, and energy system configurations (\u003cstrong\u003eFig. 1\u003c/strong\u003e). They differ substantially in underlying socio-economic conditions, such as population trajectories, as well as in key structural drivers of the transition, including final energy demand, electrification levels, hydrogen deployment, and the scale of engineered carbon dioxide removal\u0026nbsp;represented within the energy system.\u003c/p\u003e\n\u003cp\u003eBy coupling IAM-based pathways with environmental Life Cycle Assessment (LCA), we quantify the environmental implications of global final energy provision, and examine how alternative decarbonization strategies reshape human health, ecosystem quality, land and water use, and critical raw material demand alongside GHG emissions mitigation. This harmonised multi-model framework enables a system-wide comparison of the complete set of energy system transformations across multiple IAMs and socio-economic pathways, rather than focusing on individual sectors, technologies, or single-model pathways.\u003c/p\u003e\n\u003cp\u003eUsing this framework, we evaluate whether structurally distinct pathways with comparable climate policy ambition that achieve similar temperature outcomes also converge in their broader environmental consequences. In doing so, we distinguish the role of climate change mitigation ambition from the influence of underlying socio-economic assumptions and energy system configurations. Specifically, we address three questions: whether pathways consistent with deep decarbonization systematically reduce environmental pressures beyond climate change, how alternative energy system configurations redistribute impacts across sectors and technologies, and how climate change mitigation ambition influences the scale and timing of critical raw material extraction associated with the transition.\u003c/p\u003e\n\u003ch2\u003eEnvironmental impacts beyond greenhouse gas emissions\u003c/h2\u003e\n\u003cp\u003eAssessing environmental impacts beyond GHG emissions reveals clear co-benefits of decarbonization for human health (\u003cstrong\u003eFig. 2\u003c/strong\u003e). Particulate matter formation declines across all pathways below 2.0 \u0026deg;C by 5-75%, while photochemical ozone formation is reduced by 12-70%. These improvements emerge early in the transition. By mid-century, particulate matter formation has already declined by a median of 48% and photochemical ozone formation by 43% in pathways below 2.0 \u0026deg;C. Aggregate human health burdens, measured in disability-adjusted life years (DALYs), decrease by a median of 8% by 2100 in pathways below 2.0 \u0026deg;C, whereas they increase by a median of 65% in pathways above 2.5 \u0026deg;C. These reductions reflect the air-quality benefits of reduced fossil-fuel combustion\u003csup\u003e20,21\u003c/sup\u003e. Within SSP2 pathways, where population and socio-economic assumptions are held constant, more ambitious climate policy consistently improves health outcomes relative to less ambitious policy within the same model.\u003c/p\u003e\n\u003cp\u003eTotal ecosystem quality, which aggregates multiple environmental pressures affecting terrestrial, marine, and freshwater ecosystems, shows a similar aggregate response to climate change mitigation ambition. It declines by 23-68% by 2100 in pathways below 2.0 \u0026deg;C but increases by up to 119% under warming above 2.5 \u0026deg;C. Within SSP2, ecosystem quality improves consistently with climate ambition across all three models, with SSP2 pathways below 2.0 \u0026deg;C showing a median reduction of 41% compared with a median increase of 19% in pathways above 2.5 \u0026deg;C. These changes reflect the combined effects of declining climate change damages and lower combustion-related emissions as fossil-fuel use declines. However, this aggregate improvement masks an exception. Freshwater ecotoxicity increases across all pathways analysed, including the most sustainability-oriented SSP1 scenarios, where it rises by 33-111% by 2100.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eContribution analysis (\u003cstrong\u003eFig. 3\u003c/strong\u003e) shows that the dominant driver for freshwater ecotoxicity impacts is the expansion of electrified transport, which contributes an average of 70 percentage points (range: 37-130) to its increase in pathways below 2.0 \u0026deg;C. These impacts arise not at the point of energy use but along upstream supply chains, including battery production, electric motor manufacturing, and the copper-intensive grid and charging infrastructure required to deliver electricity to vehicles. In the underlying life cycle impact assessment model, ecotoxicity characterisation factors are particularly sensitive to metal emissions, which means that increased mining and metal processing strongly influence this indicator. Even as fossil fuel phase-out reduces ecotoxicity from coal mining (up to -20 percentage points) and oil refining (up to -15 percentage points), these reductions are outweighed by metal supply associated with electrification. Nevertheless, freshwater ecotoxicity remains substantially lower in pathways below 2.0 \u0026deg;C than in those above 2.5 \u0026deg;C, indicating that inadequate decarbonization would lead to even greater pressures on ecosystem toxicity. This pattern suggests that while electrification is essential for climate mitigation, additional measures to manage mining impacts and metal supply chains might be needed to limit freshwater ecotoxicity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2 | Annual environmental impacts of global final energy provision across climate mitigation pathways.\u003c/strong\u003e Percentage change in annual impacts relative to 2020, from 2020 to 2100, across ten environmental indicators and 24 mitigation pathways. Panels are organised by area of protection: ecosystem quality (total ecosystem quality in local species loss over time, acidification, freshwater ecotoxicity, freshwater eutrophication), human health (total human health in DALYs, particulate matter formation, photochemical ozone formation), and natural resources (land use, freshwater use, scarcity-weighted mineral resource use). The horizontal dashed line marks zero change relative to the 2020 baseline. Colours indicate end-of-century global mean surface temperature (GMST) outcomes, marker shapes denote IAM models, and line styles represent SSP scenarios, all as in Fig. 1, with SSP2 scenarios emphasised with higher opacity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNevertheless, differences in environmental outcomes are not determined by climate ambition alone. Within pathways with similar climate ambition levels, the socio-economic development narrative strongly influences environmental performance. Among pathways staying below 2.0 \u0026deg;C, SSP1-based scenarios reduce total human health impacts by a median of 21%, compared with a median increase of 6% under SSP2. For ecosystem quality, SSP1 pathways achieve median reductions of 59%, compared with 41% under SSP2. These differences reflect SSP1\u0026apos;s higher efficiency gains, lower population growth, consequent lower total final energy demand, and reduced reliance on large-scale bioenergy, which collectively moderate the upstream pressures associated with infrastructure expansion.\u003c/p\u003e\n\u003cp\u003eNatural resource indicators display a fundamentally different pattern from human health and ecosystem quality, further illustrating how strongly environmental outcomes depend on the configuration of the energy transition. Land occupation is shaped by two opposing dynamics of biomass use. The phase-out of traditional biomass in buildings for cooking and heating reduces land use relative to 2020 across all pathways. In pathways below 2.0 \u0026deg;C, however, the expansion of large-scale bioenergy, including transport biofuels, biomass-derived industrial fuels, and bioenergy with carbon capture and storage (BECCS), partially or fully offsets these reductions. Net land occupation by 2100 consequently ranges from \u0026minus;13% to +354% across pathways below 2.0 \u0026deg;C, a spread of 367 percentage points driven almost entirely by the scale of biomass deployment. SSP2-based pathways exhibit the largest increases (median +194%), compared to SSP1 (median +67%), reflecting differences in bioenergy reliance.\u003c/p\u003e\n\u003cp\u003eFreshwater use also increases across nearly all pathways. In contrast to ecotoxicity, which is driven by toxic emissions from metal extraction, increases in freshwater use primarily reflect direct water consumption in mining, material processing, and cooling across expanding electricity supply chains. Electrification of transport, buildings, and industry is the dominant driver, collectively accounting for a median of 64% of all positive contributions to freshwater use increases in pathways below 2.0 \u0026deg;C. Hydrogen contributes a median of 7% to total freshwater use in pathways below 2.0 \u0026deg;C (range: 3-12%), while BECCS accounts for a median of 2.5% (range: 0.1-11%). Both remain secondary to the water required by electrification itself. These increases show no consistent relationship with climate ambition within SSP2 pathways, indicating that they arise from the structure of the energy system configuration rather than the level of mitigation or the socio-economic narrative.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3 | Sectoral and energy supply alternative contributions to changes in environmental impacts by 2100.\u003c/strong\u003e Stacked bar charts show the relative contribution of consuming sectors and energy supply options to the percentage change in five environmental indicators between 2020 and 2100. Environmental indicators include \u0026lsquo;Total: Ecosystem Quality\u0026rsquo;, \u0026lsquo;Total: human health\u0026rsquo;, \u0026lsquo;Freshwater Ecotoxicity\u0026rsquo;, \u0026lsquo;Land use\u0026rsquo;, and \u0026lsquo;Water use\u0026rsquo;. Values are expressed as percentage change relative to the total 2020 impacts. For a given pathway and indicator, each stacked segment represents the specific contribution of a specific sector and energy supply option to the overall change in 2100. Segments above zero increase relative impacts compared to 2020, whereas segments below zero reduce relative impacts. The diamond marker indicates the net percentage change, equal to the sum of all stacked segments for that pathway. Bars are grouped by end-of-century global mean surface temperature (GMST) outcome, with columns representing pathways below 2.0 degrees Celsius, between 2.0 and 2.5 degrees Celsius, and above 2.5 degrees Celsius. Within each column, pathways are grouped by Integrated Assessment Model (IAM) and Shared Socioeconomic Pathway (SSP). Labels further group scenarios into temperature outcome categories within each SSP, with suffixes distinguishing individual scenarios that fall within the same category. Colours indicate energy supply options, hatching denotes consuming sectors. Only the main contributing sector and option combinations are shown explicitly, with remaining contributions aggregated as \u0026lsquo;Other\u0026rsquo;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTaken together, these results reveal a structured pattern. Total human health impacts and aggregate ecosystem quality improve consistently as climate change mitigation ambition increases. In contrast, freshwater ecotoxicity, land occupation, freshwater use, and mineral resource scarcity do not scale with climate policies. Instead, they vary widely across pathways that achieve similar temperature targets. The spread within pathways of similar climate ambition can be as large as the difference between pathways with different ambition levels. These results demonstrate that the multidimensional environmental performance of energy transitions is not determined by climate ambition alone, but by the configuration of the energy system, including the extent of electrification\u003csup\u003e22\u003c/sup\u003e, the scale of biomass deployment\u003csup\u003e23\u003c/sup\u003e, hydrogen use\u003csup\u003e24\u003c/sup\u003e, and overall energy demand, as well as broader socio-economic conditions that shape demand for energy services and natural resources.\u003c/p\u003e\n\u003ch2\u003eTiming and scale of metal extraction demand across energy pathways\u003c/h2\u003e\n\u003cp\u003eMineral extraction increases across all pathways as energy systems expand and electrify. The rapid deployment of renewable electricity, electrified transport, and energy storage has heightened attention to the scale of materials required to defossilise the energy system, with a focus on the availability of critical metals\u003csup\u003e25,26\u003c/sup\u003e. Metals such as copper, nickel, lithium, cobalt, and rare earth elements are central to power generation, grid infrastructure, batteries, and electrified end-uses, raising questions about both near-term supply constraints and long-term resource availability. \u0026nbsp;As described in the \u003cem\u003eMethods\u003c/em\u003e, the trajectories shown (\u003cstrong\u003eFig. 4\u003c/strong\u003e)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003erepresent primary metal extraction embedded in global final energy provision and its associated supply chains, excluding non-energy-driven demand. These physical extraction trajectories are distinct from the aggregate mineral resource scarcity indicator shown in Fig. 2, which reflects scarcity-weighted resource use across the full life cycle inventory.\u003c/p\u003e\n\u003cp\u003eDemand trajectories differ markedly in timing across mitigation pathways, with more ambitious mitigation pathways associated with accelerated near-term extraction requirements for copper, nickel, cobalt, and silver. In pathways below 2.0 \u0026deg;C, annual copper demand embedded in final energy provision alone exceeds 2023 global production levels (22.6 Mt yr⁻\u0026sup1;) by 2035-2040 across all models, reaching a median of 43 Mt yr⁻\u0026sup1;\u0026nbsp;by mid-century. Under pathways above 2.5\u0026nbsp;\u0026deg;C, this threshold is crossed up to 10 years later, with a median demand of 33 Mt yr⁻\u0026sup1;\u0026nbsp;at mid-century. The near-term acceleration is most pronounced for battery metals and silver. By 2040, median lithium demand under pathways below 2.0 \u0026deg;C is about 60% higher than under pathways above 2.5 \u0026deg;C, and cobalt demand is about 50% higher. Nickel and silver show similar near-term increases of around 50%. This pattern is consistent across models and socio-economic narratives.\u003c/p\u003e\n\u003cp\u003ePlatinum group metals exhibit a distinct trajectory. Demand declines in the near term as internal combustion engine vehicles are phased out, notably reducing demand for exhaust treatment systems. In pathways with substantial hydrogen deployment, platinum demand increases later in the century due to its use in proton exchange membrane electrolyzers and fuel cells. This dynamic illustrates how technology substitution within the energy system reshapes metal demand profiles over time. In contrast, manganese demand associated with final energy provision remains modest relative to its broader industrial use, particularly in steel production, highlighting that not all battery-relevant metals exhibit comparable sensitivity to energy transition dynamics.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4 | Timing and scale of metal demand across energy system pathways.\u0026nbsp;\u003c/strong\u003eTrajectories of annual primary metal extraction from 2020 to 2100 across 24 energy system pathways, shown for bulk metals (copper, iron, nickel, zinc), battery metals (lithium, cobalt, manganese), and precious metals and rare earth elements (platinum group metals, silver, rare earth elements). Insets show cumulative primary extraction from 2020 to 2100 for each pathway. Horizontal solid lines indicate 2023 global primary metal production, and horizontal dashed lines indicate 2023 reserves for each metal\u003csup\u003e27\u003c/sup\u003e. For a given metal, a trajectory that rises above the solid line implies that annual extraction embedded in final energy provision exceeds current global production in that year. In the inset, if a pathway\u0026rsquo;s cumulative bar approaches the dashed line, cumulative extraction from 2020 to 2100 is of the same order as currently reported reserves. Colours indicate end-of-century global mean surface temperature (GMST) outcomes, marker shapes denote Integrated Assessment Models (IAMs), and line styles represent Shared Socioeconomic Pathways (SSPs), all as in Fig. 1, with SSP2 scenarios emphasised with higher opacity.\u003c/p\u003e\n\u003cp\u003eDespite pronounced differences in annual trajectories, cumulative extraction from 2020 to 2100 varies only modestly across mitigation pathways with differing levels of climate ambition. For copper, pathways below 2.0 \u0026deg;C require 3,400-5,100 Mt cumulatively, compared with 2,300-5,100 Mt under pathways above 2.5 \u0026deg;C. This represents nearly identical upper bounds despite markedly different deployment trajectories. For nickel and cobalt, the overlap across pathways with differing levels of climate ambition spans 60\u0026ndash;70% of the total range. Within SSP2, however, the overlap is smaller for some metals: cumulative copper and nickel demand is consistently higher in SSP2 pathways below 2.0 \u0026deg;C than above 2.5 \u0026deg;C, reflecting the material intensity of electrification that is only partially offset by lower long-term energy demand under these pathways.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCumulative extraction approaches or exceeds currently reported reserves for several metals regardless of the climate policy. Copper cumulative demand exceeds reserves by a factor of 2-5 across all pathways, nickel by 3-7 times, cobalt by 1-7 times, silver and zinc by 1.6-4 times, and lithium by 0.4-4 times. Only iron and rare earth elements (REEs) extraction demand driven by the energy system remains well below reserve estimates\u003csup\u003e27\u003c/sup\u003e. Reported reserves reflect current economics and geological knowledge and are used here as a benchmark for scale rather than as fixed physical limits. Long-term material availability challenges are therefore not confined to the most ambitious mitigation pathways but accompany energy system expansion more broadly across pathways.\u003c/p\u003e\n\u003cp\u003eThese LCA results reflect the modelling assumptions underlying the IAM pathways, which incorporate endogenous responses of the energy system to prices and policy, influencing final energy demand (see Fig. 1) and, in turn, indirectly affecting material demand. However, they do not represent feedback from material scarcity itself, such as price-driven substitution between metals, accelerated recycling, or technological innovation triggered by constrained supply. Even under highly optimistic assumptions regarding future recycling rates, explored in the \u003cem\u003eSupplementary Information\u003c/em\u003e, the short-term acceleration of metal demand in more ambitious pathways remains pronounced, and cumulative extraction patterns across pathways are only partially moderated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmbitious mitigation pathways thus exhibit two counteracting dynamics: faster near-term growth in metal demand driven by rapid electrification, and lower long-term final energy demand resulting from efficiency improvements and structural changes in energy consumption. Together, these results indicate that cumulative material pressures are governed primarily by the overall scale of energy service provision, whereas climate ambition primarily reshapes the timing of extraction rather than the total volumes required over the century.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eEnvironmental sustainability of energy transitions\u003c/h2\u003e\n\u003cp\u003eDeep decarbonization delivers clear environmental benefits, particularly for ecosystem quality, air quality, and human health. Yet pathways that achieve similar temperature outcomes can diverge substantially in their broader environmental performance. Across the scenarios analysed here, improvements in combustion-related impacts occur consistently as fossil fuel use declines, but pressures on land, water, and mineral resources vary widely depending on how energy systems are reconfigured.\u003c/p\u003e\n\u003cp\u003eThese differences arise from a limited set of structural transition levers. High levels of electrification consistently reduce combustion-related impacts but increase upstream pressures associated with metal extraction and water use, creating trade-offs between climate mitigation and resource use. For metals central to electrification, climate ambition primarily reshapes the timing of extraction rather than cumulative volumes, accelerating near-term demand. This near-term acceleration may increase the risk of supply constraints, price volatility, and geopolitical pressures in critical mineral supply chains\u003csup\u003e14,25\u003c/sup\u003e. Cumulative volumes show substantial overlap across mitigation pathways with differing levels of climate ambition, although within SSP2 pathways, more ambitious scenarios show modestly higher totals for some metals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBeyond electrification, other technology choices introduce additional trade-offs. Hydrogen-based strategies reshape demand for platinum group metals and, when produced through electrolysis, increase electricity demand and associated upstream pressures. Extensive biomass deployment can help mitigate fossil fuel emissions and provide carbon dioxide removal in the case of climate overshoot, but substantially increases land occupation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe scale and configuration of carbon dioxide removal further influence both energy and material requirements. By allowing pathways to meet stringent climate targets with higher residual emissions, carbon dioxide removal shapes the configuration of the rest of the energy system. Different removal strategies introduce distinct environmental pressures, with biomass-based approaches such as BECCS increasing demand for land and feedstock, contributing to the land-use trade-offs identified above, whereas direct air capture relies on additional electricity and water inputs. This underscores the importance of diversifying carbon dioxide removal portfolios to avoid concentrating pressures on specific resources\u003csup\u003e28\u003c/sup\u003e. At the same time, reliance on energy-intensive removal options can increase overall system demand, reinforcing the importance of low-demand pathways in moderating both environmental impacts and material requirements.\u003c/p\u003e\n\u003cp\u003eBy linking structurally distinct IAM pathways to LCA, this study enables a system-wide comparison of the environmental implications of alternative transition strategies. Previous IAM-LCA integration efforts have assessed the power sector\u003csup\u003e10,29\u003c/sup\u003e or national energy systems\u003csup\u003e30\u003c/sup\u003e, but have not extended to the full global final energy system across multiple models and climate ambition levels. Comparing pathways within SSP2 further isolates the role of energy system configuration from differences in population and socio-economic development. Climate ambition largely determines the pace of transformation, but the configuration of the energy system determines how environmental pressures are redistributed across sectors, technologies, and supply chains. As a result, convergence in temperature outcomes does not imply convergence in environmental sustainability. Meaningful assessment of mitigation pathways requires considering not only global warming performance, but also broader environmental implications and resource requirements, which ultimately shape the feasibility and desirability of different transition strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be considered when interpreting these results. The material and environmental outcomes reported here reflect the technology portfolios and assumptions embedded in the underlying IAM pathways. The framework does not include endogenous responses to material scarcity, such as price-driven substitution, accelerated recycling, or technological innovation beyond those represented in the scenarios. Recent work that explicitly accounts for material availability constraints indicates that such dynamics can substantially reshape technology deployment and expose supply bottlenecks in standard IAM projections\u003csup\u003e31\u003c/sup\u003e. Life-cycle inventories also remain subject to uncertainty, particularly for emerging technologies and future production processes. Differences in technological granularity between IAM outputs and LCA datasets further require assumptions regarding sub-technology representation\u003csup\u003e32\u003c/sup\u003e.\u0026nbsp;In addition, while reported metal reserves provide a useful benchmark for scale, they do not represent fixed physical limits and are expected to evolve with exploration, technological change, and economic conditions\u003csup\u003e27\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFinally, assessing multiple environmental dimensions does not establish whether any pathway remains within absolute environmental limits or planetary boundaries\u003csup\u003e33,34\u003c/sup\u003e. The indicators used here quantify relative pressures associated with alternative transition strategies rather than cumulative system-wide thresholds. Integrating boundary-based or absolute sustainability frameworks with scenario-based life-cycle assessment remains an important direction for future research. Making configuration-dependent environmental effects visible enables comparison of transition strategies before large-scale infrastructure and material commitments are locked in and clarifies the structural decisions that ultimately determine whether decarbonization aligns not only with climate stabilisation but with broader Earth system stability.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eOverview of the modelling framework\u003c/h2\u003e\n\u003cp\u003eWe combine IAM scenarios with prospective LCA to quantify the environmental impacts associated with the global final energy supply from 2020 to 2100. The modelling workflow integrates scenario-specific energy system projections with the LCA database ecoinvent 3.11\u003csup\u003e35,36\u003c/sup\u003e, and performs time-resolved impact calculations for each scenario and year across the regions defined by each model. Related IAM-LCA applications have previously been used to assess national energy transitions\u003csup\u003e30,32\u003c/sup\u003e. Here we extend this framework to a global, multi-model context.\u003c/p\u003e\n\u003cp\u003eFor each scenario and time step, the Python package \u003cem\u003ePremise (v2.3.7)\u003c/em\u003e\u003csup\u003e9\u003c/sup\u003e generates a modified version of the background LCA database that reflects projected regional changes in electricity generation, fuel production, and industrial processes. These scenario-specific databases are stored in data packages that contain the transformed life-cycle inventories of activities related to energy supply and use, IAM scenario production volumes for each year across regions, and mappings between scenario variables and corresponding LCA activities.\u003c/p\u003e\n\u003cp\u003eEnvironmental impacts are calculated using the Python package \u003cem\u003ePathways (v2.0)\u003c/em\u003e\u003csup\u003e37\u003c/sup\u003e, which builds on \u003cem\u003eBrightway2\u003c/em\u003e\u003csup\u003e38\u003c/sup\u003e\u003cem\u003e,\u003c/em\u003e to compute life-cycle impacts and resource use at each time step, using scenario production volumes as demand vectors. Calculations are performed at the level of individual supply chain activities and regions, preserving the technological and geographical resolution of each IAM model. Results are subsequently aggregated for global analysis.\u003c/p\u003e\n\u003cp\u003eThis workflow enables consistent integration of multiple IAM scenarios into a collection of process-based LCA databases while maintaining transparency in scenario mapping, database transformation, and time-resolved impact calculation.\u003c/p\u003e\n\u003ch2\u003eIAM scenarios\u003c/h2\u003e\n\u003cp\u003eWe analyze 24 mitigation pathways derived from three IAMs: REMIND, IMAGE, and MESSAGEix-GLOBIOM-GAINS (hereafter MESSAGE).\u003c/p\u003e\n\u003cp\u003eREMIND\u003csup\u003e18\u003c/sup\u003e is a multiregional general equilibrium-based energy-economy-climate model that accounts for macroeconomic-energy interactions with a high level of process detail in sectoral transformation dynamics\u003csup\u003e39\u0026ndash;41\u003c/sup\u003e, and representation of crucial inertias and path dependencies, such as capital stock inertia in supply and demand sectors, as well as technological learning and inertias in technology up-scaling\u003csup\u003e42\u003c/sup\u003e. Land-use interactions are emulated based on the MAgPIE model\u003csup\u003e43\u003c/sup\u003e. The model distinguishes 12 world regions.\u003c/p\u003e\n\u003cp\u003eIMAGE\u003csup\u003e15\u003c/sup\u003e is a recursive-dynamic simulation model linking the land and energy systems. Land cover and land use are modelled at a grid-scale and are hard-linked to the LPJml model which simulates biophysical processes, allowing explicit analysis of climate-ecosystem interactions. The energy system module has an explicit representation of energy supply, conversion and demand across 26 regions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMESSAGE\u003csup\u003e16,17\u003c/sup\u003e couples the\u0026nbsp;energy-system optimization model\u0026nbsp;MESSAGEix, the land use model GLOBIOM and the air pollution and GHG model GAINS, providing detailed energy-system pathways linked to land use and air quality dynamics. The energy system model consists of a bottom-up model with high level of technological detail. The recent integration of the MESSAGEix-Materials has introduced a process-based representation of heavy industry and explicit, endogenous material flow modelling\u003csup\u003e44\u003c/sup\u003e. The coupling of energy and land-use systems is implemented via an emulator approach. The linkage between GAINS and MESSAGEix is achieved by a bilateral model soft-link. The integrated model aggregates the system at a resolution of 12 macro regions.\u003c/p\u003e\n\u003cp\u003eTogether, these models provide complementary representations of energy-economy, land-use, and technological dynamics.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eMapping between IAM variables and LCA\u003c/h2\u003e\n\u003cp\u003eTechnology and sector variables reported by each IAM are mapped to corresponding activities in the LCA database. For each IAM region and time step, reported final energy demands are mapped to datasets that reflect both the energy carrier consumed and the infrastructure required to deliver and use it. For example, each megajoule of electricity supplied to passenger cars is associated with a proportional share of the infrastructure required for its use, including charging infrastructure, onboard batteries, and electric motors. These datasets are linked to scenario-modified upstream supply chains, including region-specific electricity mixes, fuel production systems, industrial processes, and conversion efficiencies. As a result, the life-cycle representation captures not only shifts in final energy demand but also structural transformations of the entire energy supply chain consistent with IAM projections. The complete mapping between scenario variables and LCA datasets is provided in the online repository\u003csup\u003e45\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eDifferences in technological granularity between IAM outputs and the LCA database are addressed through structured assumptions. IAM categories often represent aggregated technologies, such as battery energy storage or photovoltaic electricity, whereas the LCA database distinguishes multiple chemistries, module types, or system configurations. In such cases, either time-dependent sub-technology mixes or representative datasets are applied, as illustrated by the following examples.\u003c/p\u003e\n\u003cp\u003eFor battery technologies, dynamic sub-technology mixes are constructed exogenously. Mobile battery chemistries include multiple NMC variants, NCA, LFP, sodium-ion, lithium-sulfur, and lead-acid systems, while stationary storage includes NMC variants, LFP, vanadium redox flow batteries, sodium-sulfur, and lead-acid systems. Future mobile battery shares follow projections from Degen et al. (2023)\u003csup\u003e46\u003c/sup\u003e, historical shares prior to 2021 are based on Orangi et al. (2024)\u003csup\u003e47\u003c/sup\u003e, and stationary storage shares are derived from Schlichenmaier et al. (2022)\u003csup\u003e48\u003c/sup\u003e. These shares are implemented through market datasets that evolve over time while remaining consistent with IAM-level deployment volumes.\u003c/p\u003e\n\u003cp\u003ePhotovoltaic electricity is represented using country-specific, technology-resolved inventories\u003csup\u003e49\u003c/sup\u003e. Installed capacity is disaggregated by installation type, module technology, and mounting configuration, and converted to per-kWh life-cycle inventories using country-specific productivity data and fixed lifetime assumptions. Where IAM scenarios distinguish residential and commercial PV, separate low-voltage and high-voltage production mixes are implemented. Module efficiencies are adjusted over time to reflect expected technological improvements.\u003c/p\u003e\n\u003cp\u003eFor technologies where IAM scenarios do not distinguish sub-technologies and no robust projections of future market shares are available, a single representative configuration is selected. For example, hydrogen fuel cells are represented using proton exchange membrane technology as a widely deployed baseline configuration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevious work shows that technological granularity strongly influences material demand and can shift pressures across resources. Its effect on environmental impacts is more limited and concentrated in categories linked to material extraction, such as freshwater ecotoxicity, while effects on overall decarbonization outcomes remain very limited\u003csup\u003e32\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eScenario alignment and consistency\u003c/h2\u003e\n\u003cp\u003eImpacts are quantified using environmental LCA\u003csup\u003e50\u003c/sup\u003e. By accounting for upstream resource extraction, energy conversion processes, infrastructure requirements, and end-of-life treatment, LCA captures direct and indirect environmental impacts across life-cycle phases, thereby complementing scenario-level indicators with environmental detail along the supply chain\u003csup\u003e10,30,51\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor each pathway, life cycle inventories are dynamically aligned using \u003cem\u003ePremise\u003c/em\u003e with projected changes in energy technologies, fuel mixes, and conversion efficiencies from 2020 to 2100. Life cycle-derived carbon dioxide emissions and primary energy demand closely track the corresponding IAM outputs across all pathways (\u003cstrong\u003eFig. 5\u003c/strong\u003e), indicating that scenario-specific energy system configurations are consistently reflected in the environmental inventories.\u003c/p\u003e\n\u003cp\u003eResidual differences arise from structural distinctions between IAM accounting conventions and life-cycle system boundaries. In particular, LCA results include emissions and removals associated with engineered carbon dioxide removal technologies represented within the energy system, but do not independently reproduce carbon sequestration processes embedded in IAM climate modules, such as afforestation. Consequently, life-cycle net CO\u003csub\u003e2\u003c/sub\u003e emissions exceed IAM-reported emissions in low-carbon scenarios where non-energy carbon sinks contribute substantially to mitigation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDifferences in primary energy accounting mainly reflect methodological conventions. For biomass in particular, differing treatment of feedstock energy content and upstream inputs leads to systematic differences in reported totals, while preserving consistency in relative scenario trends.\u003c/p\u003e\n\u003ch2\u003eLCA modelling\u003c/h2\u003e\n\u003ch3\u003eFunctional unit\u003c/h3\u003e\n\u003cp\u003eThe functional unit is defined as the global final energy supply and use for the period 2020 to 2100 under each mitigation pathway. Engineered carbon dioxide removal technologies represented within the energy system pathways are included, as they allow the final energy supply and use to align with the enforced climate trajectories. These comprise direct air capture (using solvents and sorbents), BECCS, synthetic fuels with CCS, biofuels with CCS, enhanced weathering, ocean alkalinity enhancement, and biochar application. Land-use change and other carbon dynamics represented in the IAMs\u0026apos; climate modules are not modelled independently within the LCA framework.\u003c/p\u003e\n\u003ch3\u003eLife cycle inventory\u003c/h3\u003e\n\u003cp\u003eThe background life cycle inventory is based on ecoinvent v3.11 (system model: allocation, cut-off by classification). Additionally, \u003cem\u003ePremise\u0026nbsp;\u003c/em\u003eenriches the database by adding inventories for emerging technologies relevant to the energy system.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo improve the representation of critical raw materials, additional inventories are incorporated, and material intensities are updated for technologies central to the energy transition, including vehicles, wind turbines, photovoltaic systems, batteries, fuel cells, and electrolysers\u003csup\u003e30,32\u003c/sup\u003e. Material intensities are calibrated to present-day technologies, and projected efficiency improvements are introduced over time, in line with technology-learning assumptions. All scenario-driven database transformations are documented in the online repository\u003csup\u003e45\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo track demand for primary metal extraction, multi-output mining processes are modified to ensure the physical consistency of extracted metal quantities. Extraction of individual elements from the ore is fully attributed to the respective metal based on mass balances, whereas other elementary and intermediate flows (e.g., electricity and diesel) retain economic allocation consistent with the default ecoinvent approach. This hybrid allocation procedure ensures mass-balance consistency while preserving the structure of upstream production processes\u003csup\u003e52\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eRecycling rates are not modified beyond those represented in the underlying IAM pathways, as dynamically coupling recycling availability, secondary supply, and additional energy requirements would require structural feedbacks not consistently represented across models. Adjusting recycling rates independently within the LCA framework may alter associated energy requirements, causing final energy demand to diverge from the IAM scenarios. A sensitivity analysis assuming optimistic global recycling rates by 2050 confirms that the main conclusions regarding environmental impacts, and timing and cumulative material extraction remain unchanged (\u003cem\u003eSupplementary Information\u003c/em\u003e).\u003c/p\u003e\n\u003ch3\u003eLife cycle impact assessment\u003c/h3\u003e\n\u003cp\u003eEnvironmental impacts are quantified using the Environmental Footprint (EF) 3.1 midpoint method for acidification (mol H\u003csup\u003e+\u003c/sup\u003e-eq), photochemical ozone formation (kg NMVOC-eq), particulate matter formation (disease incidence), freshwater eutrophication (kg P-eq), and freshwater ecotoxicity (CTUe)\u003csup\u003e53\u003c/sup\u003e. Total human health impacts (DALYs) and total ecosystem quality (species-years) are also evaluated using the ReCiPe 2016 endpoint method (Hierarchist perspective)\u003csup\u003e54\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eLand occupation (m\u003csup\u003e2\u003c/sup\u003e-year) and net freshwater use (m\u003csup\u003e3\u003c/sup\u003e) are reported as total flows without scarcity or other weighting. Mineral resource depletion is quantified using a crustal scarcity indicator (kg Si-eq), which characterizes mineral demand based on elemental crustal concentrations as a proxy for long-term global resource scarcity\u003csup\u003e55\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003eLimitations\u003c/h2\u003e\n\u003cp\u003eSeveral limitations arise from integrating IAM scenarios with process-based LCA. Technological granularity differs between IAM outputs and life-cycle inventories, necessitating the introduction of sub-technology resolution at the LCA level through structured assumptions. This introduces uncertainty in the mapping between aggregated scenario variables and specific technological configurations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn particular, alternative technology developments for batteries, photovoltaic modules, and hydrogen conversion systems, such as faster adoption of low cobalt and nickel battery chemistries, widespread deployment of silver-lean or silver-free photovoltaic technologies, or a transition to hydrogen systems that do not rely on platinum group metals, would alter demand for specific metals. While such substitutions would redistribute material demand across individual elements, they would not remove the broader increase in mineral requirements associated with rapid electrification and infrastructure expansion\u003csup\u003e32\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe framework also does not incorporate endogenous feedback between material availability, prices, and technology deployment. Recycling rates and secondary supply are represented only to the extent they are reflected in the IAM pathways, and dynamic coupling between material scarcity and energy system transformation is beyond the present scope.\u003c/p\u003e\n\u003cp\u003eSpatial resolution of environmental impacts is constrained by the regional aggregation inherent in IAMs and by the availability of regionalised inventories. Consequently, localised burdens associated with water use, land transformation, or other region-specific pressures may not be fully captured. Furthermore, as is conventional in LCA, infrastructure-related material demand is spread over technology lifetimes, thereby smoothing short-term installation peaks. While cumulative material demand is preserved, short-term surges may be underestimated.\u003c/p\u003e\n\u003cp\u003eA further limitation arises from the use of present-day life cycle impact characterisation factors to evaluate future environmental interventions. Characterisation models in EF 3.1 and ReCiPe 2016 are based on current or historical environmental conditions, exposure pathways, and background concentrations. Applying these factors to future emission profiles implicitly assumes that fate, exposure, vulnerability, and environmental thresholds remain constant over time. Potential changes in climate, land use, population distribution, and baseline environmental quality are therefore not reflected in the impact assessment\u003csup\u003e56,57\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eCode and data availability\u003c/p\u003e\n\u003cp\u003eThe code required to reproduce the results presented in this study is available in the following repository: https://github.com/polca/premise/tree/2.3.7.dev.lca-of-iam-scenarios/examples/MIC_exercise. The workflow requires \u003cem\u003ePremise\u003c/em\u003e and \u003cem\u003ePathways\u003c/em\u003e, both open-source 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/).\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eA.J.H.M., R.S., and C.B. acknowledge the financial support of their work through the Swiss State Secretariat for Education, Research and Innovation (SERI) under the Horizon Europe projects PRISMA (grant agreement no. 101081604) and RAWCLIC (grant agreement no. 101183654). D.B. and G.L. acknowledge support through the Ariadne project from the German Federal Ministry of Education and Research (funding label\u0026nbsp;03SFK5A0-2) as well as through Horizon Europe projects RAWCLIC (grant agreement no. 101183654) and PRISMA (grant agreement no. 101081604). V.D acknowledges support through the Horizon Europe projects PRISMA (grant agreement no. 101081604) and UPTAKE (grant agreement no. 101081521). F.M. and V.K. acknowledge support through the Horizon Europe project PRISMA (grant agreement no. 101081604).\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;A.J.H.M. wrote the manuscript with input from all authors. A.J.H.M. performed the analyses and created the figures. R.S. conceived the study and initiated the development of the open-source tools. A.J.H.M., D.B., and R.S. contributed to the development of the tools. A.J.H.M., C.B., and R.S. developed the conceptual framework. D.B., F.M., and V.D. collaborated with A.J.H.M. and R.S. to integrate the IAM pathways. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRockstr\u0026ouml;m, J. \u003cem\u003eet al.\u003c/em\u003e A safe operating space for humanity. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e461\u003c/strong\u003e, 472\u0026ndash;475 (2009).\u003c/li\u003e\n\u003cli\u003eRipple, W. J. \u003cem\u003eet al.\u003c/em\u003e The risk of a hothouse Earth trajectory. \u003cem\u003eOne Earth\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 101565 (2026).\u003c/li\u003e\n\u003cli\u003eSakschewski, B. \u003cem\u003eet al.\u003c/em\u003e \u003cem\u003ePlanetary Health Check 2025: A Scientific Assessment of the State of the Planet\u003c/em\u003e. \u003cem\u003ePlanetary Boundaries Science (PBScience)\u003c/em\u003e doi: 10.48485/PIK.2025.017 (2025).\u003c/li\u003e\n\u003cli\u003eVan Vuuren, D. 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A critical view on the current application of LCA for new technologies and recommendations for improved practice. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e \u003cstrong\u003e259\u003c/strong\u003e, 120904 (2020).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9527853/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9527853/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Reaching the goals of the Paris Agreement requires a rapid transformation of global energy systems. Yet energy supply and use exert multiple pressures on ecosystems, human health, and natural resources, raising questions about the broader environmental consequences of alternative decarbonization pathways and the resources they require. Here, we analyse 24 climate mitigation pathways from three Integrated Assessment Models using a life-cycle perspective. This enables a system-wide comparison of environmental impacts and resource requirements of global final energy provision from 2020 to 2100. We find that deep decarbonization delivers substantial co-benefits for human health and ecosystem quality, but does not systematically reduce pressures on land, water, or mineral resource extraction. For metals central to electrification, climate change mitigation ambition primarily reshapes the timing of extraction rather than cumulative volumes. Pathways achieving similar climate targets can impose markedly different environmental and resource pressures, shaping the feasibility and desirability of alternative transitions.","manuscriptTitle":"Environmental and material implications of global climate mitigation pathways","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-08 04:39:23","doi":"10.21203/rs.3.rs-9527853/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-energy","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nenergy","sideBox":"Learn more about [Nature Energy](http://www.nature.com/nenergy/)","snPcode":"","submissionUrl":"","title":"Nature Energy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"984b34a8-c81d-483b-89b5-d2e69d63b00b","owner":[],"postedDate":"May 8th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-05-08T12:06:54+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-05-08T12:05:18+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"4","date":"2026-05-07T07:51:01+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67688462,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":67688463,"name":"Physical sciences/Energy science and technology/Energy modelling"}],"tags":[],"updatedAt":"2026-05-08T04:39:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-08 04:39:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9527853","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9527853","identity":"rs-9527853","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}