Sustainable development trade-offs shape the acceptability of climate mitigation scenarios

Sustainable development trade-offs shape the acceptability of climate mitigation scenarios | 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 Sustainable development trade-offs shape the acceptability of climate mitigation scenarios Gonzalo Parrado-Hernando, Behzad Zamanipour, Ilkka Keppo, Noelia Ferreras-Alonso, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9151931/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 Climate mitigation scenarios are typically evaluated for technical and economic feasibility, yet their implementation depends on whether they are considered acceptable within broader development priorities. Sustainable Development Goal (SDGs) interactions create trade-offs that shape how mitigation strategies are perceived across regions. Combining integrated assessment modelling and stochastic multicriteria acceptability analysis, we assess the acceptability of four narratives achieving a common mid-century climate target. We find that pathways with similar climate outcomes differ markedly in acceptability and that global rankings often mask regional divergences. Technology-led decarbonization is more acceptable in the Global South, enabling rapid gains in energy access and development. Conversely, regions in the Global North favour strategies that protect natural carbon sinks, reflecting greater ecosystem restoration benefits. These divergent preferences stem from structural SDG trade-offs, challenging universal win–win narratives. Explicitly accounting for these differences is essential for designing politically feasible and effective climate pathways. Scientific community and society/Social sciences/Decision making Scientific community and society/Social sciences/Climate change Climate mitigation Sustainable Development Goals (SDGs) sustainability trade-offs scenario acceptability Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction International cooperation entities remember 2015 as a landmark milestone in advancing sustainability. First, the Paris Agreement 1 established a coordinated global action to address climate change. Second, all United Nations member states committed to a wider agenda to promote human prosperity, enable well-being and peace, and ensure harmony with natural ecosystems by 2030, consolidated in 17 sustainable development goals (SDGs) 2 . Since 2015, the integrated assessment modelling (IAM) community has engaged in the systematic analysis of the United Nations’ new agenda over long-term horizons, typically up to 2050 and 2100. These models combine knowledge from multiple disciplines into structured, formal representations to inform policy-making about the uncertainties associated with the impacts of policy agendas 3 . The scientific modelling community has incorporated the Paris Agreement objectives in the tools, defined wide array of technological deployments to mitigate emissions, and collaborated with stakeholders to shape policy commitments to limit global warming 4 . In parallel, the social sciences have contributed to IAMs through the endogenization of social dynamics and the use of innovative participatory research methods for scenario development 5 . However, the integration of the 17 SDGs into IAMs is far from complete, although; the literature reflects recent advances 6–8 . Science-based governance faces great challenges in looking for which pathways are more socially and politically acceptable 9–11 . Previous exercises have proposed a wide diversity of possible policies for a sustainability transition aiming to achieve both the SDGs and the Paris Agreement 6,12,13 ; yet, each has yielded different distribution of benefits and costs in terms of sustainable development outcomes and varying degrees of progress 14–16 across a world of finite resources 17 . The acceptability of these distributions is further complicated by historically entrenched power imbalances between the global North and South, which risk perpetuating inequality in future policy scenarios 18 . This concern echoes broader criticisms regarding the strong, often homogenizing economic assumptions in prevailing IAMs 19 and the lack of inclusiveness of perspectives from the Global South 20 – a situation that starkly contradicts the 2030 Agenda's foundational preamble of the SDGs as a universal "win-win" for international cooperation. In practice, the SDGs provide a widely shared normative framework that can be operationalized as a set of criteria to evaluate trade-offs and compare the acceptability of IAM pathways across regions. Consequently, although earlier studies have proposed deterministic solutions to identify an optimal pathway for balancing future priorities between climate change mitigation and sustainable economic development 6–8 , the acceptability of a pathway is ultimately a complex reality of policymaking and social participation 21 . When faced with the dilemma of having to prioritize certain SDGs over others, different policymakers weighed SDGs differently 22,23 and, considering a global, heterogenous pool of policy makers, it remains unclear which trade-offs they might be willing to accept. Moreover, environmental ceilings and social conditions have been already recognized as limiting factors in the scope of policy action 24 . As a result, a mitigation pathway that appears attractive from a global perspective may face resistance in specific regional contexts, undermining its political feasibility and the prospects for international cooperation. In particular, there is limited insight into whether certain mitigation narratives systematically align with the priorities of some regions while conflicting with those of others, and what this implies for the feasibility of shared global pathways 25 . Here, we investigate how regional trade-offs between SDGs may shape the acceptability of climate mitigation scenarios. We analyse four alternative global mitigation narratives that achieve a common mid-century climate target but differ in their underlying socio-economic and biophysical assumptions. To capture how the different and uncertain SDG outcomes might affect the preference of one narrative over another, we combine integrated assessment modelling with a stochastic multicriteria acceptability analysis, allowing us to assess the likelihood that specific scenarios and related SDG outcomes are preferred over others, under heterogeneous regional conditions. Rather than identifying a single preferred pathway, our approach focuses on revealing the structural drivers of acceptance and disagreement across regions. Results Defining scenario acceptability We assess the acceptability of mitigation scenarios using stochastic multicriteria acceptability analysis (SMAA; see Methods). In this context, acceptability represents the likelihood that a scenario ranks favourably compared to alternatives when accounting for sensitivities in model inputs, assumptions, and decision-maker preferences. A scenario with high acceptability thus performs robustly across a wide range of SDG weighing schemes, whereas low acceptability indicates that a scenario is preferred only under narrow, high specific, preference configurations. From a total of 72 pathways generated by four mitigation narratives (table 1; further details in Supplementary Note 1) under varying sensitivity assumptions, we select one per narrative that achieves a common 2050 climate target under harmonized assumptions, enabling a controlled comparison of sustainable development implications. Pathway outcomes are derived from simulations with the WILIAM integrated assessment model, which captures interactions across energy, economic, land-use and climate systems (see Methods). By evaluating scenarios through SDG outcomes rather than climate performance alone, acceptability becomes a measure of how mitigation pathways align with multiple development objectives simultaneously. In order to represent SDG indicators within WILIAM, we followed a targeted literature review, as documented in Supplementary Note 2. This framing allows us to examine whether scenarios that meet climate targets also remain attractive when broader sustainability priorities are considered. Table 1 . Summary of the scenarios applied in the study. Further information in Supplementary Note 1. Scenario name Accronym Brief narrative description Technology-led decarbonization S1-TLD Decarbonization relies on a technology-driven transformation across all sectors –fuelled by investment, deregulation, and carbon pricing– with mature innovations like renewables, electrification, and CCS enabling a fossil-free system by mid-century, while land-use shifts and industrialized agriculture support the transition amid unchanged consumption patterns. Demand-side reduction S2-DSR Decarbonization hinges on reducing consumption through lifestyle changes, regulation, and public investment in sustainable alternatives. By mid-century, demand growth stalls, governments expand social welfare, and sharing economies rise. Mobility shifts to public transit and non-motorized travel, while localized agriculture cuts waste. Promotion of natural carbon sinks S3-NCS Decarbonization prioritizes natural carbon sinks –via afforestation, forest protection, and regenerative agriculture– to sequester emissions while boosting climate resilience, soil health, and biodiversity. By mid-century, forests expand and agroecological practices are widespread. Carbon revenues fund land-use transformation and farmer incentives, backed by growing public investment in environmental protection Mixed strategy scenario S4-MSS Decarbonization follows a mixed, context-driven approach blending technological innovation, demand reduction, and natural carbon sinks. By mid-century, renewables, moderate electrification, and green hydrogen co-exist with "choice editing" policies that shape –not ban– consumption. Afforestation and regenerative agriculture are elevated to national security priorities. Carbon revenues fund diversified public investments across all sectors. Global climate mitigation scenarios differ markedly in acceptability Although all scenarios achieve a comparable mid-century climate outcome (1.83ºC, see Supplementary Note 1), they differ strikingly in their likelihood of being considered acceptable when evaluated through a broader sustainable development lens. Assuming uniform preferences across SDGs, we observe a clear separation in the overall acceptability, indicating that similar climate performance does not translate into similar prospects for decision-making support. Across global simulations, pathways prioritising the protection and enhancement of natural carbon sinks (S3-NCS) display the highest probability of acceptance (figure 1a,b). These pathways consistently outperform alternatives that rely primarily on demand-side reductions (S2-DSR) or exclusively on rapid technological substitution (S1-TLD). The latter and mixed-strategy scenarios (S4-MSS) occupy an intermediate position, while scenarios centred on large reductions in consumption exhibit the lowest overall acceptability. The separation between the most and least acceptable scenarios is substantial, suggesting that differences in development outcomes across SDGs are large enough to generate robust preferences. These differences arise because acceptability is grounded in SDG performance rather than climate outcomes alone. Scenarios with higher acceptability tend to combine emissions reductions with relatively favourable outcomes in economic activity, energy provision and environmental protection. By contrast, pathways that impose strong constraints on consumption often generate trade-offs affecting employment, income or institutional capacity, reducing their overall appeal. Building on this, we identify the following system-wide dynamics influencing the acceptability of global mitigation scenarios. Economic growth improves employment and income indicators but increases industrial emissions, revealing persistent mitigation–development tensions. Rapid fossil fuel phase-out and renewable scale-up face biophysical and system constraints, including curtailment peaks, land scarcity and thermodynamic efficiency limits. Energy transition strategies shape acceptability differently depending on how demand reduction affects labour markets and social outcomes. Clean energy expansion without forest protection intensifies land-use pressures, exposing trade-offs between SDG 7 (Clean Energy) and SDG 15 (Life on Land). Agricultural sustainability policies may induce short-term productivity losses before longer-term system stabilization. These conclusions are further explained in Supplementary Note 5. Importantly, the observed ranking does not reflect the superiority of any single mitigation narrative across all dimensions of sustainable development. Acceptability emerges from the balance of co-benefits and trade-offs across multiple SDGs, rather than from excellence in any single domain. Trade-offs among Sustainable Development Goals shape scenario acceptability To better understand how SDG outcomes relate to scenario rankings, we examine the structure of trade-offs across goals. Using central weight vectors from the SMAA (figure 1c), we explore which combinations of SDG weights are most commonly associated with a scenario achieving the top rank. These weight patterns should not be interpreted as normative priorities or as unique drivers of scenario performance; rather, they indicate typical preference structures under which scenarios emerge as favourable (see also SMAA information in Methods). Under uniform SDG preferences, scenarios that rank highly tend to display relatively balanced performance across multiple goals rather than strong gains in a single dimension. In particular, pathways that combine progress in climate mitigation with moderate economic performance, sustained public revenues and protection of environmental assets are more likely to be favoured under a holistic interpretation of the SDG framework. By contrast, scenarios that prioritise a narrow set of objectives—such as rapid reductions in energy demand or consumption—often entail adverse effects on other goals related to income generation, employment or institutional capacity, which reduce their overall appeal despite potential environmental benefits. Some pathways rely on a broad set of SDGs contributing moderately to their overall performance, making them robust to changes in preference structures. Others depend disproportionately on improvements in a small subset of goals (e.g., S2-DSR), rendering their acceptability more sensitive to how development priorities are weighted. That is, if a narrative performs well only for one or two SDGs, it will rank high only if these are weighted very high. The structure of SDG trade-offs differs systematically across mitigation narratives. Technology-led decarbonization pathways tend to score highly on goals associated with energy provision, infrastructure development and industrial activity, while placing greater pressure on land use and ecosystems. Conversely, scenarios centred on the protection of natural carbon sinks perform strongly on goals related to biodiversity, land and water systems, but may offer fewer short-term gains in indicators linked to industrial output or energy expansion. Mixed-strategy pathways distribute impacts more evenly, partially mitigating extreme trade-offs but rarely maximising performance across all goals. These patterns indicate that acceptability is not driven by the absolute performance of any single SDG, but by the internal balance between competing development dimensions. These patterns provide the foundation for understanding why global rankings become unstable once regional contexts and development priorities are explicitly considered, as explored in the following sections. Regional development contexts reverse global acceptability rankings Global rankings conceal substantial regional variation in scenario acceptability. When results are disaggregated by region, scenarios that perform well globally do not necessarily retain their relative position. Instead, regional socio-economic and biophysical conditions systematically reshape scenario rankings. Two broad patterns emerge across regions (figure 2). Regions commonly associated with the Global North tend to show higher acceptability for pathways centred on the protection and enhancement of natural carbon sinks (S3-NCS). In contrast, many Global South regions exhibit greater acceptability for technology-led decarbonization pathways (S1-TLD). These contrasting preferences arise despite the common climate ambition and policy assumptions across scenarios, indicating that they stem from how mitigation scenarios interact with region-specific development priorities and constraints. In Global North contexts, where infrastructure and energy systems are already highly developed, the marginal benefits of rapid technological expansion are relatively limited. Mitigation pathways that prioritise ecosystem restoration, forest protection and regenerative land use practices therefore generate comparatively larger co-benefits across environmental and social SDGs, increasing their overall acceptability. Conversely, technology-intensive or demand- reduction pathways tend to exacerbate trade-offs related to land competition, resource use, or economic activity. In many Global South regions, by contrast, lower baseline levels of energy access, infrastructure and industrial capacity make investments in renewable energy and electrification (S1-TLD) aligned with development goals. The greater remaining biophysical and technological potential can simultaneously support climate mitigation and progress on energy access, income and employment, increasing their perceived compatibility with broader SDGs. Regional rankings are often closely clustered, and in several cases no single scenario clearly dominates (figure S6; Supplementary Information). This proximity indicates that small differences in SDG performance or preference structures can shift regional rankings, reinforcing the idea that acceptability is not a binary property but a probabilistic outcome shaped by sensitivity and contextual factors. While this creates scope for compromise, it also implies that consensus requires acknowledging the underlying sources of divergence. Taken together, these findings show that regional contexts do not merely adjust global preferences but can fundamentally reshape them. Global rankings that suggest a dominant mitigation narrative may therefore be misleading if interpreted as guidance for regionally differentiated policy design, unless the SDG trade-offs underlying acceptability are made explicit. Distinct SDG trade-offs underpin regional preferences Regional differences in scenario acceptability are rooted in distinct configurations of SDG trade-offs. These trade-offs structurally explain why the same mitigation narrative can be attractive in some regions and problematic in others. In regions where technology-led pathways (S1-TLD) are more acceptable, scenario performance is driven primarily by gains in SDGs linked to energy access, infrastructure development and economic productivity (upper subplot in figure 3). These gains reinforce the perception that mitigation can support broader socio-economic advancement. Environmental trade-offs may still occur due to pressures on land use and ecosystems, but within these contexts they are often weighed against substantial socio-economic benefits. By contrast, regions that favour the acceptability of nature-based mitigation strategies (S3-NCS, bottom subplot in figure 3) exhibit trade-offs structures in which land, biodiversity, and ecosystem-related SDGs play a larger role. In these settings, the marginal benefits of additional technological expansion are smaller, while the co-benefits of ecosystem restoration and regenerative land use are more salient. These findings reinforce that acceptability is a systemic property arising from SDG interactions rather than a direct function of performance on individual goals. Understanding these interaction patterns helps explain why mitigation narratives resonate differently across regions and why global consensus around a single pathway remains elusive. Discussion Our results show that the acceptability of climate mitigation scenarios cannot be inferred from their climate performance alone. Instead, acceptability emerges from how mitigation strategies distribute co-benefits and trade-offs across SDGs within specific regional contexts. Even when global mitigation pathways achieve comparable temperature outcomes, they generate systematically different levels of support. This suggest that acceptability is not a secondary political constraint but a structural property of mitigation scenarios shaped by the 2030 Agenda. This finding speaks directly to ongoing debates on the political feasibility of climate action. Previous work has highlighted that mitigation pathways must be not only technically and economically viable but also politically implementable, existing a dynamic space defined by economic and institutional capacity of actors and the political and distributional costs of mitigation actions 26 . Our results add a development dimension to this perspective by showing that feasibility is closely related tied to how climate strategies interact with broader societal objectives. Mitigation pathways that align with regionally salient development priorities would be more likely to be perceived as legitimate and therefore politically durable. The systemic contrast observed between many Global North and Global South regions in the types of mitigation narratives they are more likely to accept illustrates this point. Technology-led mitigation pathways (S1-TLD) tend to align with contexts where expanding energy access, infrastructure and industrial capacity remain central development goals. Nature-based pathways (S3-NCS), by contrast, become more attractive in regions where technological systems are mature and where ecosystem restoration yields comparatively larger marginal benefits. These patterns do not reflect transient policy preferences but deeper differences in development trajectories and marginal benefits associated with alternative SDG trade-offs. These structural differences we observed broaden the scope of equity, extending well beyond language, knowledge production, funding, and partnerships 20 . Our findings also challenge the widespread framing of climate mitigation as a universal “win–win” strategy 27 . While many mitigation actions do produce co-benefits, these benefits are not distributed evenly across regions or development dimensions. Trade-offs remain intrinsic to sustainability transitions, particularly when land use, energy systems and economic activity interact under biophysical and social constraints. Recognising these trade-offs does not weaken the case for climate action; rather, it clarifies the conditions under which cooperation is more or less likely to emerge. We underscore a critical distinction between identifying technically feasible or welfare-optimal pathways and designing mitigation strategies that are acceptable across heterogeneous socio-economic and biophysical conditions. From a governance perspective, our results indicate that international cooperation on climate mitigation may benefit from a more pluralistic approach to scenario design. Rather than seeking a single globally optimal pathway, policymakers may need to acknowledge the legitimacy of regionally differentiated strategies that pursue a common climate objective through distinct development pathways. Thus, policy-driven scenarios can incorporate political and institutional constraints, producing outcomes that diverge from cost-optimal pathways 28 . For scenario analysis and integrated assessment modelling, these insights carry several implications. First, evaluating pathways solely through aggregated global indicators risks obscuring politically relevant heterogeneity. Explicitly examining SDG interactions and regional acceptability can reveal sources of potential disagreement that remain invisible in global averages. Second, in line with previous literature 29 , we emphasize that UN indicators should be mathematically reproducible and grounded in transparent data measurement, and that the underlying mechanisms should be clearly illustrated to enable open discussion on model assumptions, acceptability, and interpretation of results. While many mitigation pathways do generate co-benefits, our analysis highlights that such co-benefits are often context-dependent and may coexist with trade-offs that are unevenly distributed across regions and SDGs. The mechanisms represented in the WILIAM model provide a plausible structural basis for these patterns. Interactions among energy transitions, land-use dynamics and economic development create feedbacks that link climate mitigation to multiple SDGs simultaneously. For example, renewable energy expansion can support energy access and income while intensifying land competition, whereas ecosystem restoration can strengthen environmental outcomes with more limited short-term economic effects. Our analysis does not claim that these mechanisms are exhaustive, but they illustrate how systemic interactions can translate into differentiated development outcomes. Several limitations should be acknowledged. Our analysis assumes a uniform distribution of SDG preferences in the absence of explicit stakeholder input and relies on a particular representation of SDG indicators within one modelling framework. Alternative modelling choices 30 or participatory weighting schemes could shift scenario rankings, so scenario acceptability. Moreover, some dimensions of sustainability –such as institutional quality, social cohesion or political stability– remain difficult to capture endogenously 6,31,32 . Future research could address these limitations by combining model-based acceptability analysis with participatory processes and explore how preferences evolve over time under dynamic political and socio-economic change. Despite these caveats, incorporating acceptability explicitly into scenario analysis provides a useful lens for examining the feasibility of sustainability transitions. By linking SDG trade-offs to regional preferences, our framework helps explain why consensus around global mitigation pathways is often fragile and why regionally differentiated strategies may be necessary. Integrating acceptability into climate and sustainability assessments is therefore not only a methodological refinement but a step towards designing transitions that are environmentally effective, socially legitimate and politically durable. Methods Overview. The modelling framework used in this study is built around scenario development, integrated assessment modelling (WILIAM v1.4 33 ), and acceptability analysis (SMAA), as shown in figure 5. The representation of endogenous SDG indicators has been extended to 36. Scenarios . We use the word scenario to represent an exploration of potential future developments, built upon a set of qualitative and quantitative assumptions and reflecting an underlying narrative . A scenario provides insights into strategic decision-making by envisioning a possible future and evaluating its consequences under different policy settings 34,35 . The quantification of a scenario is thus preceded by the development of a qualitative narrative, to provide the narrative of which the quantitative scenario elements follow. Thus, a narrative is essential to provide a general and common context for questioning 36 . Finally, a pathway is a specific simulation or trajectory (time series) showing how the quantitative model variables evolve over time. Following van Vuuren et al. (2012) 36 , we identified attributes of four narratives of climate mitigation, quantitatively parametrised as four scenarios. On top of them, we included variations on three sensitivity parameters. The population dynamics, the minimum energy return on energy invested (EROI) of renewable technologies 37 , and activation or not of climate change impacts. In total, we have reached 72 different pathways. Supplementary Note 1 details the qualitative attributes of narratives in table S1, and consequent quantitative assumptions in table S2. WILIAM and SDG indicator space . Within Limits Integrated Assessment Model is developed under two integrated methodologies. On the one hand, system dynamics has been the general theory behind the modelling of most dimensions of our reality such as energy, materials, population, land uses or climate. On the other hand, a detailed representation of economic processes using a dynamic econometric Input-Output approach, consistently linking the economic and biophysical spheres in line with the principles of ecological macroeconomics. The overall objective of WILIAM is to provide global and regional policies on climate change mitigation and energy transition. It incorporates nine global regions with specific disaggregation for the European Union (27 countries). The allocation of countries by region can be consulted in Supplementary Note 4. The demography and economy modules represent 35 regions, while the energy and land modules do it for 9 regions, and materials is globally described as a whole region. System dynamics models like WILIAM are not primarily designed to evaluate the attainment of fixed targets. Instead, their strength lies in analysing the dynamic behavior of complex systems and in revealing the trade-offs and co-benefits that emerge over time as a consequence of feedback structures and policy interventions 38 . In coherency with this methodology, the selection of endogenous SDG indicators has been carried out through a literature review (see Supplementary Note 2). First, the exclusive selection of endogenous variables as in Moreno et al. (2023) 7 , avoiding scenario assumptions and post-processing tools to avoid exogenous information that has no sensitivity by definition. Second, the qualitative relationship between SDG indicators and IAM proxies as in Soergel et al. (2021) 6 . The information of SDGs has come from three sources: a) the original UN indicators, which are directly implemented where possible; b) references reviewed in this work (Supplementary Note 2, table S3); c) indicators proposed by van Vuuren et al. (2022) 39 ; and d) other WILIAM variables selected to cover more aspects of the SDGs. Consequently, our SDG indicator space is concluded in table S4 (Supplementary Note 2). We note that material footprint indicators are repeated in SDG 8 and 12 to better represent both, given the similarities they have in their official description of United Nations. Due to data and model limitations (see the information about WILIAM in Methods), certain indicators are not regionally available. Therefore, the analysis covers nine SDGs hereafter: 1, 2, 3, 6, 7, 9, 11, 15, and 17. Reducing the set of SDGs—and therefore the information considered—alters the results, making it less clear which scenario performs best at the global level. Stochastic multicriteria acceptability analysis (SMAA) 40 . A multicriteria decision problem is considered as a set of alternatives evaluated based on several criteria. The multicriteria decision analysis (MCDA) examines the weight space to show the preferences that would make an alternative the best choice (or any given rank) for the decision makers. In addition, the SMAA methods allow for assessing sensitivities in both preference information and criteria measurements. SMAA computes three measures for the alternatives: rank acceptability indices and central weight vectors. The rank acceptability indices are the share of all feasible weights that make the alternatives acceptable for a particular rank. This is computed as a multidimensional integral over the criteria distributions and favourable rank weights. The most acceptable (best) alternatives are those with high acceptability for the best ranks. The rank acceptability indices are within the range [0, 1], where zero shows the alternative will not obtain a given rank in any circumstances, and 1 indicates that it will always obtain the given rank no matter what the weights are. In this study, we used the SMAA-2 method 41 , and specifically, the software named JSMAA, which is an open-source software for SMAA computations 42 as a multicriteria assessment tool to address the sensitivity analysis. The SMAA-2 handles the sensitivity intervals of criteria values and weights, and also is able to consider the ordinal preference information of weights. In line with the 2030 Agenda for Sustainable Development, we assume a homogenous distribution of preferences across SDGs as “integrated and indivisible” 2 rather than a prioritised list to balance economic, social and environmental development. Thus, the scheme employed in this study assumes that there is no information on the preferences of decision makers regarding the SDG indicators, thus, a uniform range [0, 1]. To apply the SMAA to our case, WILIAM is run multiple times for each mitigation scenario, considering the sensitivities in EROI, population and climate change impacts, creating 18 paths ( S ) for each scenario (a total of 72 paths). Subsequently, the results of each path ( s ) are used to derive the SDG criteria. Table S5 (Supplementary Note 3) presents the equations used to calculate the criteria for SDGs. In the first step, each SDG variable in WILIAM is used to calculate its corresponding indicator ( I ). The central weight vectors represent the preferences of a typical decision maker supporting a specific alternative. It is also computed as a multidimensional integral over the criteria and weight distributions. By presenting the central weight vectors to the decision makers, an inverse approach for decision support can be applied, in the sense that instead of eliciting preferences and building a solution to the problem, the decision makers can learn what kind of preferences lead to which alternatives without providing any preference information. As described above, in order to implement the SMAA method, one needs to compute several multidimensional integrals that are practically impossible to calculate analytically. Therefore, Tervonen et al. 43 suggest Monte Carlo simulation as a solution to this problem and discuss their algorithm for this purpose. Scenario Acceptability Index (SAI) . The scenario acceptability index (SAI) has been measured with a simple multiplication of decreasing numbers times the rank acceptability indices. SAI is defined in equation 1, where m ∈ z is the total number of ranks (scenarios); r =1, 2, …, m is the rank position; b r i is the rank acceptability index for scenario i at rank r . Rank acceptability indices cover the entire probabilistic space of scenarios. SAI is calculated to summarize this space into an overall performance, where higher values indicate higher likelihood of acceptance. The multiplying vector is appropriately chosen to favour higher ranks. In summary, higher SAI values are designed to correspond to a greater probability of being accepted. SAI can take values from zero to m (maximum possible SAI for any scenario occurs when all probability mass is concentrated at the best rank, the first one). Declarations Funding This work was supported by the European Commission Horizon Europe project “IAM COMPACT”, under Grant Agreement No. 101056306. The sole responsibility for the content of this paper lies with the authors; the paper does not necessarily reflect the opinions of the European Commission or the granting authorities. P.P. acknowledges the European Research Council (ERC) funding for the BeyondSDG project (Project number 101077492). L.J.M.G. acknowledges the European Union’s Horizon Europe funding for the NEVERMORE project (Project number 101056858). Data availability The datasets used in this research article are accessible through the following Zotero repository: https://doi.org/10.5281/zenodo.19066849 Code availability The version of WILIAM (v1.4) used in this study is public, available under the following link: https://github.com/LOCOMOTION-h2020/WILIAM_model_VENSIM/releases/tag/WILIAM_v1.4 All figures were created in Python Notebooks, accessible in the following Zotero repository: https://doi.org/10.5281/zenodo.19066849 References United Nations. Paris Agreement . (United Nations, 2015). United Nations. 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Defining a sustainable development target space for 2030 and 2050. One Earth 5 , 142–156 (2022). Lahdelma, R. & Salminen, P. Stochastic Multicriteria Acceptability Analysis (SMAA). in Trends in Multiple Criteria Decision Analysis (eds Ehrgott, M., Figueira, J. R. & Greco, S.) vol. 142 285–315 (Springer US, Boston, MA, 2010). Lahdelma, R. & Salminen, P. SMAA-2: Stochastic Multicriteria Acceptability Analysis for Group Decision Making. Operations Research 49 , 444–454 (2001). Tervonen, T. JSMAA: open source software for SMAA computations. International Journal of Systems Science 45 , 69–81 (2014). Tervonen, T. & Lahdelma, R. Implementing stochastic multicriteria acceptability analysis. European Journal of Operational Research 178 , 500–513 (2007). Additional Declarations There is NO Competing Interest. 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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-9151931","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":610469304,"identity":"9de21252-ae9a-40b0-9acc-a76f20228cbb","order_by":0,"name":"Gonzalo 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Athens","correspondingAuthor":false,"prefix":"","firstName":"Natasha","middleName":"","lastName":"Frilingou","suffix":""},{"id":610469312,"identity":"e0d81fdd-109b-48b8-b6f4-9162be2fa746","order_by":8,"name":"Alexandros Nikas","email":"","orcid":"https://orcid.org/0000-0002-6795-3848","institution":"National Technical University of Athens","correspondingAuthor":false,"prefix":"","firstName":"Alexandros","middleName":"","lastName":"Nikas","suffix":""},{"id":610469313,"identity":"13df8350-28e1-4f7a-94c2-5f8aebfae43f","order_by":9,"name":"Daniel García-Yustos","email":"","orcid":"","institution":"University of Valladolid","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"García-Yustos","suffix":""},{"id":610469314,"identity":"e8adbb01-ca42-436a-b353-0172b023c6c2","order_by":10,"name":"Luis Javier Miguel González","email":"","orcid":"","institution":"University of Valladolid","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"Javier Miguel","lastName":"González","suffix":""},{"id":610469315,"identity":"ab4685ec-c8e0-49d8-8970-29d284117f40","order_by":11,"name":"José María Enríquez-Sánchez","email":"","orcid":"","institution":"University of Valladolid","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"María","lastName":"Enríquez-Sánchez","suffix":""},{"id":610469316,"identity":"f3d47f5a-5ece-48ac-8f45-1ca95ccf2464","order_by":12,"name":"Risto Lahdelma","email":"","orcid":"https://orcid.org/0000-0001-7882-2918","institution":"Aalto University","correspondingAuthor":false,"prefix":"","firstName":"Risto","middleName":"","lastName":"Lahdelma","suffix":""},{"id":610469317,"identity":"1fe1b3fb-bc10-4807-a6ba-a16801dc670e","order_by":13,"name":"Prajal Pradhan","email":"","orcid":"https://orcid.org/0000-0003-0491-5489","institution":"University of Groningen","correspondingAuthor":false,"prefix":"","firstName":"Prajal","middleName":"","lastName":"Pradhan","suffix":""}],"badges":[],"createdAt":"2026-03-17 19:01:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9151931/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9151931/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105904391,"identity":"6e350c4b-c5e6-4107-be58-e56052f877e8","added_by":"auto","created_at":"2026-04-01 10:07:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":194527,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal mitigation scenarios differ markedly in acceptability despite similar climate outcomes.\u003c/strong\u003e a. Rank acceptability indices showing the probability that each mitigation scenario occupies a given position in the overall ranking when Sustainable Development Goals (SDGs) are weighted uniformly. Rank positions (1st, 2nd, 3rd, 4th) refer to the probability or frequency with which a given alternative (see SMAA information in Methods). b. Scenario Acceptability Index (SAI), summarizing overall performance across rankings, where higher values indicate higher likelihood of acceptance. c. Relative contribution of SDGs to scenario acceptability (first position), illustrating how different mitigation narratives rely on distinct configurations of SDG outcomes. Although all scenarios achieve a comparable mid-century temperature target2, their likelihood of being considered acceptable differs substantially. The scenarios are: S1-TLD (blue), S2-DSR (orange), S3-NCS (green), and S4-MSS (red).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9151931/v1/fd1edd5075dc9716075e436c.png"},{"id":105904652,"identity":"3a6eb1e1-2430-4640-803a-073a6f5f56a9","added_by":"auto","created_at":"2026-04-01 10:10:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56589,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegional development contexts reverse global acceptability rankings. \u003c/strong\u003eScenario Acceptability Indices (SAI) across world regions reveal pronounced regional heterogeneity in preferred mitigation pathways. Regions commonly associated with the Global North show higher acceptability for scenarios centred on natural carbon sinks, whereas many Global South regions favour technology-led decarbonization pathways. The proximity of acceptability scores in several regions indicates that preferences are context-dependent and sensitive to underlying SDG trade-offs, rather than reflecting a single dominant global pathway.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9151931/v1/72ac0d54491aedab80554709.png"},{"id":105791993,"identity":"409d64b6-8e0a-4a15-8362-dcdc7fd0c445","added_by":"auto","created_at":"2026-03-31 07:52:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":542537,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSDG trade-offs underpin regional preferences for technology-led mitigation (S1-TLD, top) and nature-based mitigation (S3-NCS, bottom).\u003c/strong\u003e Central weight vectors from the acceptability analysis indicate the Sustainable Development Goals that contribute most strongly to the acceptability of scenarios across regions. SDGs are plotted until they cumulate more than 50% of the weight preference (ascending order). Regarding S1-TLD, the dominance of goals related to energy provision, infrastructure and economic activity in several regions of the Global South explains the higher acceptability of technology-centred mitigation strategies in these contexts. Regarding S3-NCS, in regions where ecosystem restoration, land use and environmental protection goals dominate, nature-based pathways emerge as more acceptable despite offering fewer short-term gains in technology-driven development indicators.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9151931/v1/859169d87fef27b6a2627bb4.png"},{"id":105791994,"identity":"9238fcff-3390-4f04-9210-99a7296474d2","added_by":"auto","created_at":"2026-03-31 07:52:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":321657,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProcedure followed in the study to perform the acceptability analysis\u003c/strong\u003e. First, conceptualization and quantification of mitigation scenarios. Second, representation of sustainable development indicators in the model, WILIAM. Third, calculation of acceptability indices through the stochastic multicriteria acceptability analysis.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9151931/v1/eaaec3041edeca7a2bd9bc9b.png"},{"id":106401667,"identity":"763682ea-1186-47fc-b856-42b8cdcbf97e","added_by":"auto","created_at":"2026-04-08 09:08:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1873310,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9151931/v1/a3af6b2c-3bc0-4762-a218-a1ece2fc819e.pdf"},{"id":105791992,"identity":"36f591ae-b589-4cf7-85b6-31c1be70f8cc","added_by":"auto","created_at":"2026-03-31 07:52:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1881402,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-9151931/v1/a64eaca8cee9c2f19ce450ba.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Sustainable development trade-offs shape the acceptability of climate mitigation scenarios","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInternational cooperation entities remember 2015 as a landmark milestone in advancing sustainability. First, the Paris Agreement\u003csup\u003e1\u003c/sup\u003e established a coordinated global action to address climate change. Second, all United Nations member states committed to a wider agenda to promote human prosperity, enable well-being and peace, and ensure harmony with natural ecosystems by 2030, consolidated in 17 sustainable development goals (SDGs)\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSince 2015, the integrated assessment modelling (IAM) community has engaged in the systematic analysis of the United Nations\u0026rsquo; new agenda over long-term horizons, typically up to 2050 and 2100. These models combine knowledge from multiple disciplines into structured, formal representations to inform policy-making about the uncertainties associated with the impacts of policy agendas\u003csup\u003e3\u003c/sup\u003e. The scientific modelling community has incorporated the Paris Agreement objectives in the tools, defined wide array of technological deployments to mitigate emissions, and collaborated with stakeholders to shape policy commitments to limit global warming\u003csup\u003e4\u003c/sup\u003e. In parallel, the social sciences have contributed to IAMs through the endogenization of social dynamics and the use of innovative participatory research methods for scenario development\u003csup\u003e5\u003c/sup\u003e. However, the integration of the 17 SDGs into IAMs is far from complete, although; the literature reflects recent advances\u003csup\u003e6\u0026ndash;8\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eScience-based governance faces great challenges in looking for which pathways are more socially and politically acceptable\u003csup\u003e9\u0026ndash;11\u003c/sup\u003e. Previous exercises have proposed a wide diversity of possible policies for a sustainability transition aiming to achieve both the SDGs and the Paris Agreement\u003csup\u003e6,12,13\u003c/sup\u003e; yet, each has yielded different distribution of benefits and costs in terms of sustainable development outcomes and varying degrees of progress\u003csup\u003e14\u0026ndash;16\u003c/sup\u003e across a world of finite resources\u003csup\u003e17\u003c/sup\u003e. The acceptability of these distributions is further complicated by historically entrenched power imbalances between the global North and South, which risk perpetuating inequality in future policy scenarios\u003csup\u003e18\u003c/sup\u003e. This concern echoes broader criticisms regarding the strong, often homogenizing economic assumptions in prevailing IAMs\u003csup\u003e19\u003c/sup\u003e and the lack of inclusiveness of perspectives from the Global South\u003csup\u003e20\u003c/sup\u003e \u0026ndash; a situation that starkly contradicts the 2030 Agenda\u0026apos;s foundational preamble of the SDGs as a universal \u0026quot;win-win\u0026quot; for international cooperation. In practice, the SDGs provide a widely shared normative framework that can be operationalized as a set of criteria to evaluate trade-offs and compare the acceptability of IAM pathways across regions.\u003c/p\u003e\n\u003cp\u003eConsequently, although earlier studies have proposed deterministic solutions to identify an optimal pathway for balancing future priorities between climate change mitigation and sustainable economic development\u003csup\u003e6\u0026ndash;8\u003c/sup\u003e, the acceptability of a pathway is ultimately a complex reality of policymaking and social participation\u003csup\u003e21\u003c/sup\u003e. When faced with the dilemma of having to prioritize certain SDGs over others, different policymakers weighed SDGs differently\u003csup\u003e22,23\u003c/sup\u003e and, considering a global, heterogenous pool of policy makers, it remains unclear which trade-offs they might be willing to accept. Moreover, environmental ceilings and social conditions have been already recognized as limiting factors in the scope of policy action\u003csup\u003e24\u003c/sup\u003e. As a result, a mitigation pathway that appears attractive from a global perspective may face resistance in specific regional contexts, undermining its political feasibility and the prospects for international cooperation. In particular, there is limited insight into whether certain mitigation narratives systematically align with the priorities of some regions while conflicting with those of others, and what this implies for the feasibility of shared global pathways\u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHere, we investigate how regional trade-offs between SDGs may shape the acceptability of climate mitigation scenarios. We analyse four alternative global mitigation narratives that achieve a common mid-century climate target but differ in their underlying socio-economic and biophysical assumptions. To capture how the different and uncertain SDG outcomes might affect the preference of one narrative over another, we combine integrated assessment modelling with a stochastic multicriteria acceptability analysis, allowing us to assess the likelihood that specific scenarios and related SDG outcomes are preferred over others, under heterogeneous regional conditions. Rather than identifying a single preferred pathway, our approach focuses on revealing the structural drivers of acceptance and disagreement across regions. \u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDefining scenario acceptability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe assess the acceptability of mitigation scenarios using stochastic multicriteria acceptability analysis (SMAA; see Methods). In this context, acceptability represents the likelihood that a scenario ranks favourably compared to alternatives when accounting for sensitivities in model inputs, assumptions, and decision-maker preferences. A scenario with high acceptability thus performs robustly across a wide range of SDG weighing schemes, whereas low acceptability indicates that a scenario is preferred only under narrow, high specific, preference configurations. From a total of 72 pathways generated by four mitigation narratives (table 1; further details in Supplementary Note 1) under varying sensitivity assumptions, we select one per narrative that achieves a common 2050 climate target under harmonized assumptions, enabling a controlled comparison of sustainable development implications. Pathway outcomes are derived from simulations with the WILIAM integrated assessment model, which captures interactions across energy, economic, land-use and climate systems (see Methods). By evaluating scenarios through SDG outcomes rather than climate performance alone, acceptability becomes a measure of how mitigation pathways align with multiple development objectives simultaneously. In order to represent SDG indicators within WILIAM, we followed a targeted literature review, as documented in Supplementary Note 2. This framing allows us to examine whether scenarios that meet climate targets also remain attractive when broader sustainability priorities are considered.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e. Summary of the scenarios applied in the study.\u0026nbsp;\u003c/strong\u003eFurther information in Supplementary Note 1.\u003c/p\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eScenario name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAccronym\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBrief narrative description\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTechnology-led decarbonization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS1-TLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDecarbonization relies on a technology-driven transformation across all sectors\u0026nbsp;\u0026ndash;fuelled by investment, deregulation, and carbon pricing\u0026ndash;\u0026nbsp;with mature innovations like renewables, electrification, and CCS enabling a fossil-free system by mid-century, while land-use shifts and industrialized agriculture support the transition amid unchanged consumption patterns.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDemand-side reduction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS2-DSR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDecarbonization hinges on reducing consumption through lifestyle changes, regulation, and public investment in sustainable alternatives. By mid-century, demand growth stalls, governments expand social welfare, and sharing economies rise. Mobility shifts to public transit and non-motorized travel, while localized agriculture cuts waste.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePromotion of natural carbon sinks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS3-NCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDecarbonization prioritizes natural carbon sinks\u0026nbsp;\u0026ndash;via afforestation, forest protection, and regenerative agriculture\u0026ndash;\u0026nbsp;to sequester emissions while boosting climate resilience, soil health, and biodiversity. By mid-century, forests expand and agroecological practices are widespread. Carbon revenues fund land-use transformation and farmer incentives, backed by growing public investment in environmental protection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMixed strategy scenario\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS4-MSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDecarbonization follows a mixed, context-driven approach blending technological innovation, demand reduction, and natural carbon sinks. By mid-century, renewables, moderate electrification, and green hydrogen co-exist with \u0026quot;choice editing\u0026quot; policies that shape \u0026ndash;not ban\u0026ndash; consumption. Afforestation and regenerative agriculture are elevated to national security priorities. Carbon revenues fund diversified public investments across all sectors.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eGlobal climate mitigation scenarios differ markedly in acceptability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough all scenarios achieve a comparable mid-century climate outcome (1.83\u0026ordm;C, see Supplementary Note 1), they differ strikingly in their likelihood of being considered acceptable when evaluated through a broader sustainable development lens. Assuming uniform preferences across SDGs, we observe a clear separation in the overall acceptability, indicating that similar climate performance does not translate into similar prospects for decision-making support.\u003c/p\u003e\n\u003cp\u003eAcross global simulations, pathways prioritising the protection and enhancement of natural carbon sinks (S3-NCS) display the highest probability of acceptance (figure 1a,b). These pathways consistently outperform alternatives that rely primarily on demand-side reductions (S2-DSR) or exclusively on rapid technological substitution (S1-TLD). The latter and mixed-strategy scenarios (S4-MSS) occupy an intermediate position, while scenarios centred on large reductions in consumption exhibit the lowest overall acceptability. The separation between the most and least acceptable scenarios is substantial, suggesting that differences in development outcomes across SDGs are large enough to generate robust preferences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese differences arise because acceptability is grounded in SDG performance rather than climate outcomes alone. Scenarios with higher acceptability tend to combine emissions reductions with relatively favourable outcomes in economic activity, energy provision and environmental protection. By contrast, pathways that impose strong constraints on consumption often generate trade-offs affecting employment, income or institutional capacity, reducing their overall appeal. Building on this, we identify the following system-wide dynamics influencing the acceptability of global mitigation scenarios. Economic growth improves employment and income indicators but increases industrial emissions, revealing persistent mitigation\u0026ndash;development tensions. Rapid fossil fuel phase-out and renewable scale-up face biophysical and system constraints, including curtailment peaks, land scarcity and thermodynamic efficiency limits. Energy transition strategies shape acceptability differently depending on how demand reduction affects labour markets and social outcomes. Clean energy expansion without forest protection intensifies land-use pressures, exposing trade-offs between SDG 7 (Clean Energy) and SDG 15 (Life on Land). Agricultural sustainability policies may induce short-term productivity losses before longer-term system stabilization. These conclusions are further explained in Supplementary Note 5.\u003c/p\u003e\n\u003cp\u003eImportantly, the observed ranking does not reflect the superiority of any single mitigation narrative across all dimensions of sustainable development. Acceptability emerges from the balance of co-benefits and trade-offs across multiple SDGs, rather than from excellence in any single domain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrade-offs among Sustainable Development Goals shape scenario acceptability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo better understand how SDG outcomes relate to scenario rankings, we examine the structure of trade-offs across goals. Using central weight vectors from the SMAA (figure 1c), we explore which combinations of SDG weights are most commonly associated with a scenario achieving the top rank. These weight patterns should not be interpreted as normative priorities or as unique drivers of scenario performance; rather, they indicate typical preference structures under which scenarios emerge as favourable (see also SMAA information in Methods).\u003c/p\u003e\n\u003cp\u003eUnder uniform SDG preferences, scenarios that rank highly tend to display relatively balanced performance across multiple goals rather than strong gains in a single dimension. In particular, pathways that combine progress in climate mitigation with moderate economic performance, sustained public revenues and protection of environmental assets are more likely to be favoured under a holistic interpretation of the SDG framework. By contrast, scenarios that prioritise a narrow set of objectives\u0026mdash;such as rapid reductions in energy demand or consumption\u0026mdash;often entail adverse effects on other goals related to income generation, employment or institutional capacity, which reduce their overall appeal despite potential environmental benefits. Some pathways rely on a broad set of SDGs contributing moderately to their overall performance, making them robust to changes in preference structures. Others depend disproportionately on improvements in a small subset of goals (e.g., S2-DSR), rendering their acceptability more sensitive to how development priorities are weighted. That is, if a narrative performs well only for one or two SDGs, it will rank high only if these are weighted very high.\u003c/p\u003e\n\u003cp\u003eThe structure of SDG trade-offs differs systematically across mitigation narratives. Technology-led decarbonization pathways tend to score highly on goals associated with energy provision, infrastructure development and industrial activity, while placing greater pressure on land use and ecosystems. Conversely, scenarios centred on the protection of natural carbon sinks perform strongly on goals related to biodiversity, land and water systems, but may offer fewer short-term gains in indicators linked to industrial output or energy expansion. Mixed-strategy pathways distribute impacts more evenly, partially mitigating extreme trade-offs but rarely maximising performance across all goals. These patterns indicate that acceptability is not driven by the absolute performance of any single SDG, but by the internal balance between competing development dimensions.\u003c/p\u003e\n\u003cp\u003eThese patterns provide the foundation for understanding why global rankings become unstable once regional contexts and development priorities are explicitly considered, as explored in the following sections.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegional development contexts reverse global acceptability rankings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlobal rankings conceal substantial regional variation in scenario acceptability. When results are disaggregated by region, scenarios that perform well globally do not necessarily retain their relative position. Instead, regional socio-economic and biophysical conditions systematically reshape scenario rankings.\u003c/p\u003e\n\u003cp\u003eTwo broad patterns emerge across regions (figure 2). Regions commonly associated with the Global North tend to show higher acceptability for pathways centred on the protection and enhancement of natural carbon sinks (S3-NCS). In contrast, many Global South regions exhibit greater acceptability for technology-led decarbonization pathways (S1-TLD). These contrasting preferences arise despite the common climate ambition and policy assumptions across scenarios, indicating that they stem from how mitigation scenarios interact with region-specific development priorities and constraints.\u003c/p\u003e\n\u003cp\u003eIn Global North contexts, where infrastructure and energy systems are already highly developed, the marginal benefits of rapid technological expansion are relatively limited. Mitigation pathways that prioritise ecosystem restoration, forest protection and regenerative land use practices therefore generate comparatively larger co-benefits across environmental and social SDGs, increasing their overall acceptability. Conversely, technology-intensive or demand- reduction pathways tend to exacerbate trade-offs related to land competition, resource use, or economic activity.\u003c/p\u003e\n\u003cp\u003eIn many Global South regions, by contrast, lower baseline levels of energy access, infrastructure and industrial capacity make investments in renewable energy and electrification (S1-TLD) aligned with development goals. The greater remaining biophysical and technological potential can simultaneously support climate mitigation and progress on energy access, income and employment, increasing their perceived compatibility with broader SDGs.\u003c/p\u003e\n\u003cp\u003eRegional rankings are often closely clustered, and in several cases no single scenario clearly dominates (figure S6; Supplementary Information). This proximity indicates that small differences in SDG performance or preference structures can shift regional rankings, reinforcing the idea that acceptability is not a binary property but a probabilistic outcome shaped by sensitivity and contextual factors. While this creates scope for compromise, it also implies that consensus requires acknowledging the underlying sources of divergence.\u003c/p\u003e\n\u003cp\u003eTaken together, these findings show that regional contexts do not merely adjust global preferences but can fundamentally reshape them. Global rankings that suggest a dominant mitigation narrative may therefore be misleading if interpreted as guidance for regionally differentiated policy design, unless the SDG trade-offs underlying acceptability are made explicit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDistinct SDG trade-offs underpin regional preferences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegional differences in scenario acceptability are rooted in distinct configurations of SDG trade-offs. These trade-offs structurally explain why the same mitigation narrative can be attractive in some regions and problematic in others.\u003c/p\u003e\n\u003cp\u003eIn regions where technology-led pathways (S1-TLD) are more acceptable, scenario performance is driven primarily by gains in SDGs linked to energy access, infrastructure development and economic productivity (upper subplot in figure 3). These gains reinforce the perception that mitigation can support broader socio-economic advancement. Environmental trade-offs may still occur due to pressures on land use and ecosystems, but within these contexts they are often weighed against substantial socio-economic benefits.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy contrast, regions that favour the acceptability of nature-based mitigation strategies (S3-NCS, bottom subplot in figure 3) exhibit trade-offs structures in which land, biodiversity, and ecosystem-related SDGs play a larger role. In these settings, the marginal benefits of additional technological expansion are smaller, while the co-benefits of ecosystem restoration and regenerative land use are more salient.\u003c/p\u003e\n\u003cp\u003eThese findings reinforce that acceptability is a systemic property arising from SDG interactions rather than a direct function of performance on individual goals. Understanding these interaction patterns helps explain why mitigation narratives resonate differently across regions and why global consensus around a single pathway remains elusive.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur results show that the acceptability of climate mitigation scenarios cannot be inferred from their climate performance alone. Instead, acceptability emerges from how mitigation strategies distribute co-benefits and trade-offs across SDGs within specific regional contexts. Even when global mitigation pathways achieve comparable temperature outcomes, they generate systematically different levels of support. This suggest that acceptability is not a secondary political constraint but a structural property of mitigation scenarios shaped by the 2030 Agenda.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis finding speaks directly to ongoing debates on the political feasibility of climate action. Previous work has highlighted that mitigation pathways must be not only technically and economically viable but also politically implementable, existing a dynamic space defined by economic and institutional capacity of actors and the political and distributional costs of mitigation actions\u003csup\u003e26\u003c/sup\u003e. Our results add a development dimension to this perspective by showing that feasibility is closely related tied to how climate strategies interact with broader societal objectives. Mitigation pathways that align with regionally salient development priorities would be more likely to be perceived as legitimate and therefore politically durable. The systemic contrast observed between many Global North and Global South regions in the types of mitigation narratives they are more likely to accept illustrates this point. Technology-led mitigation pathways (S1-TLD) tend to align with contexts where expanding energy access, infrastructure and industrial capacity remain central development goals. Nature-based pathways (S3-NCS), by contrast, become more attractive in regions where technological systems are mature and where ecosystem restoration yields comparatively larger marginal benefits. These patterns do not reflect transient policy preferences but deeper differences in development trajectories and marginal benefits associated with alternative SDG trade-offs. These structural differences we observed broaden the scope of equity, extending well beyond language, knowledge production, funding, and partnerships\u003csup\u003e20\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOur findings also challenge the widespread framing of climate mitigation as a universal \u0026ldquo;win\u0026ndash;win\u0026rdquo; strategy\u003csup\u003e27\u003c/sup\u003e. While many mitigation actions do produce co-benefits, these benefits are not distributed evenly across regions or development dimensions. Trade-offs remain intrinsic to sustainability transitions, particularly when land use, energy systems and economic activity interact under biophysical and social constraints. Recognising these trade-offs does not weaken the case for climate action; rather, it clarifies the conditions under which cooperation is more or less likely to emerge. We underscore a critical distinction between identifying technically feasible or welfare-optimal pathways and designing mitigation strategies that are acceptable across heterogeneous socio-economic and biophysical conditions. From a governance perspective, our results indicate that international cooperation on climate mitigation may benefit from a more pluralistic approach to scenario design. Rather than seeking a single globally optimal pathway, policymakers may need to acknowledge the legitimacy of regionally differentiated strategies that pursue a common climate objective through distinct development pathways. Thus, policy-driven scenarios can incorporate political and institutional constraints, producing outcomes that diverge from cost-optimal pathways\u003csup\u003e28\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFor scenario analysis and integrated assessment modelling, these insights carry several implications. First, evaluating pathways solely through aggregated global indicators risks obscuring politically relevant heterogeneity. Explicitly examining SDG interactions and regional acceptability can reveal sources of potential disagreement that remain invisible in global averages. Second, in line with previous literature\u003csup\u003e29\u003c/sup\u003e, we emphasize that UN indicators should be mathematically reproducible and grounded in transparent data measurement, and that the underlying mechanisms should be clearly illustrated to enable open discussion on model assumptions, acceptability, and interpretation of results. While many mitigation pathways do generate co-benefits, our analysis highlights that such co-benefits are often context-dependent and may coexist with trade-offs that are unevenly distributed across regions and SDGs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe mechanisms represented in the WILIAM model provide a plausible structural basis for these patterns. Interactions among energy transitions, land-use dynamics and economic development create feedbacks that link climate mitigation to multiple SDGs simultaneously. For example, renewable energy expansion can support energy access and income while intensifying land competition, whereas ecosystem restoration can strengthen environmental outcomes with more limited short-term economic effects. Our analysis does not claim that these mechanisms are exhaustive, but they illustrate how systemic interactions can translate into differentiated development outcomes.\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be acknowledged. Our analysis assumes a uniform distribution of SDG preferences in the absence of explicit stakeholder input and relies on a particular representation of SDG indicators within one modelling framework. Alternative modelling choices\u003csup\u003e30\u003c/sup\u003e or participatory weighting schemes could shift scenario rankings, so scenario acceptability. Moreover, some dimensions of sustainability \u0026ndash;such as institutional quality, social cohesion or political stability\u0026ndash; remain difficult to capture endogenously\u003csup\u003e6,31,32\u003c/sup\u003e. Future research could address these limitations by combining model-based acceptability analysis with participatory processes and explore how preferences evolve over time under dynamic political and socio-economic change.\u003c/p\u003e\n\u003cp\u003eDespite these caveats, incorporating acceptability explicitly into scenario analysis provides a useful lens for examining the feasibility of sustainability transitions. By linking SDG trade-offs to regional preferences, our framework helps explain why consensus around global mitigation pathways is often fragile and why regionally differentiated strategies may be necessary. Integrating acceptability into climate and sustainability assessments is therefore not only a methodological refinement but a step towards designing transitions that are environmentally effective, socially legitimate and politically durable.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eOverview.\u0026nbsp;\u003c/strong\u003eThe modelling framework used in this study is built around scenario development, integrated assessment modelling (WILIAM v1.4\u003csup\u003e33\u003c/sup\u003e), and acceptability analysis (SMAA), as shown in figure 5. The representation of endogenous SDG indicators has been extended to 36.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScenarios\u003c/strong\u003e. We use the word\u003cem\u003e\u0026nbsp;scenario\u0026nbsp;\u003c/em\u003eto represent an exploration of potential future developments, built upon a set of qualitative and quantitative assumptions and reflecting an underlying \u003cem\u003enarrative\u003c/em\u003e. A scenario provides insights into strategic decision-making by envisioning a possible future and evaluating its consequences under different policy settings\u003csup\u003e34,35\u003c/sup\u003e. The quantification of a scenario is thus preceded by the development of a qualitative \u003cem\u003enarrative,\u003c/em\u003e to provide the narrative of which the quantitative scenario elements follow. Thus, a narrative is essential to provide a general and common context for questioning\u003csup\u003e36\u003c/sup\u003e. Finally, a \u003cem\u003epathway\u003c/em\u003e is a specific simulation or trajectory (time series) showing how the quantitative model variables evolve over time. Following van Vuuren et al. (2012)\u003csup\u003e36\u003c/sup\u003e, we identified attributes of four narratives of climate mitigation, quantitatively parametrised as four scenarios. On top of them, we included variations on three sensitivity parameters. The population dynamics, the minimum energy return on energy invested (EROI) of renewable technologies\u003csup\u003e37\u003c/sup\u003e, and activation or not of climate change impacts. In total, we have reached 72 different pathways. Supplementary Note 1 details the qualitative attributes of narratives in table S1, and consequent quantitative assumptions in table S2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWILIAM and SDG indicator space\u003c/strong\u003e. Within Limits Integrated Assessment Model is developed under two integrated methodologies. On the one hand, system dynamics has been the general theory behind the modelling of most dimensions of our reality such as energy, materials, population, land uses or climate. On the other hand, a detailed representation of economic processes using a dynamic econometric Input-Output approach, consistently linking the economic and biophysical spheres in line with the principles of ecological macroeconomics. The overall objective of WILIAM is to provide global and regional policies on climate change mitigation and energy transition. It incorporates nine global regions with specific disaggregation for the European Union (27 countries). The allocation of countries by region can be consulted in Supplementary Note 4. The demography and economy modules represent 35 regions, while the energy and land modules do it for 9 regions, and materials is globally described as a whole region.\u003c/p\u003e\n\u003cp\u003eSystem dynamics models like WILIAM are not primarily designed to evaluate the attainment of fixed targets. Instead, their strength lies in analysing the dynamic behavior of complex systems and in revealing the trade-offs and co-benefits that emerge over time as a consequence of feedback structures and policy interventions\u003csup\u003e38\u003c/sup\u003e. In coherency with this methodology, the selection of endogenous SDG indicators has been carried out through a literature review (see Supplementary Note 2). First, the exclusive selection of endogenous variables as in Moreno et al. (2023)\u003csup\u003e7\u003c/sup\u003e, avoiding scenario assumptions and post-processing tools to avoid exogenous information that has no sensitivity by definition. Second, the qualitative relationship between SDG indicators and IAM proxies as in Soergel et al. (2021)\u003csup\u003e6\u003c/sup\u003e. The information of SDGs has come from three sources: a) the original UN indicators, which are directly implemented where possible; b) references reviewed in this work (Supplementary Note 2, table S3); c) indicators proposed by van Vuuren et al. (2022)\u003csup\u003e39\u003c/sup\u003e; and d) other WILIAM variables selected to cover more aspects of the SDGs. Consequently, our SDG indicator space is concluded in table S4 (Supplementary Note 2). We note that material footprint indicators are repeated in SDG 8 and 12 to better represent both, given the similarities they have in their official description of United Nations. Due to data and model limitations (see the information about WILIAM in Methods), certain indicators are not regionally available. Therefore, the analysis covers nine SDGs hereafter: 1, 2, 3, 6, 7, 9, 11, 15, and 17. Reducing the set of SDGs\u0026mdash;and therefore the information considered\u0026mdash;alters the results, making it less clear which scenario performs best at the global level.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStochastic multicriteria acceptability analysis (SMAA)\u003c/strong\u003e\u003csup\u003e40\u003c/sup\u003e. A multicriteria decision problem is considered as a set of alternatives evaluated based on several criteria.\u0026nbsp;The multicriteria decision analysis (MCDA) examines the weight space to show the preferences that would make an alternative the best choice (or any given rank) for the decision makers. In addition, the SMAA methods allow for assessing sensitivities in both preference information and criteria measurements.\u0026nbsp;SMAA computes three measures for the alternatives: rank acceptability indices and central weight vectors.\u0026nbsp;The rank acceptability indices are the share of all feasible weights that make the alternatives acceptable for a particular rank. This is computed as a multidimensional integral over the criteria distributions and favourable rank weights. The most acceptable (best) alternatives are those with high acceptability for the best ranks. The rank acceptability indices are within the range [0, 1], where zero shows the alternative will not obtain a given rank in any circumstances, and 1 indicates that it will always obtain the given rank no matter what the weights are. In this study, we used the SMAA-2 method\u003csup\u003e41\u003c/sup\u003e, and specifically, the software named JSMAA, which is an open-source software for SMAA computations\u003csup\u003e42\u003c/sup\u003e as a multicriteria assessment tool to address the sensitivity analysis. The SMAA-2 handles the sensitivity intervals of criteria values and weights, and also is able to consider the ordinal preference information of weights. In line with the 2030 Agenda for Sustainable Development, we assume a homogenous distribution of preferences across SDGs as \u0026ldquo;integrated and indivisible\u0026rdquo;\u003csup\u003e2\u003c/sup\u003e rather than a prioritised list to balance economic, social and environmental development. Thus, the scheme employed in this study assumes that there is no information on the preferences of decision makers regarding the SDG indicators, thus, a uniform range [0, 1]. To apply the SMAA to our case, WILIAM is run multiple times for each mitigation scenario, considering the sensitivities in EROI, population and climate change impacts, creating 18 paths (\u003cem\u003eS\u003c/em\u003e) for each scenario (a total of 72 paths). Subsequently, the results of each path (\u003cem\u003es\u003c/em\u003e) are used to derive the SDG criteria. Table S5 (Supplementary Note 3) presents the equations used to calculate the criteria for SDGs. In the first step, each SDG variable in WILIAM is used to calculate its corresponding indicator (\u003cem\u003eI\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eThe central weight vectors represent the preferences of a typical decision maker supporting a specific alternative. It is also computed as a multidimensional integral over the criteria and weight distributions. By presenting the central weight vectors to the decision makers, an inverse approach for decision support can be applied, in the sense that instead of eliciting preferences and building a solution to the problem, the decision makers can learn what kind of preferences lead to which alternatives without providing any preference information.\u003c/p\u003e\n\u003cp\u003eAs described above, in order to implement the SMAA method, one needs to compute several multidimensional integrals that are practically impossible to calculate analytically. Therefore, Tervonen et al.\u003csup\u003e43\u003c/sup\u003e suggest Monte Carlo simulation as a solution to this problem and discuss their algorithm for this purpose.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScenario Acceptability Index (SAI)\u003c/strong\u003e. The scenario acceptability index (SAI) has been measured with a simple multiplication of decreasing numbers times the rank acceptability indices. SAI is defined in equation 1, where\u003cem\u003e\u0026nbsp;m\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026isin;\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003csup\u003ez\u003c/sup\u003e\u003c/em\u003e\n \u003cv:shape id=\"_x0000_i1025\" type=\"#_x0000_t75\"\u003e\u0026nbsp;\u003cv:imagedata src=\"file:///C%3A/Users/btr8097/AppData/Local/Packages/oice_16_974fa576_32c1d314_21eb/AC/Temp/msohtmlclip1/01/clip_image002.png\" o:title=\"\" chromakey=\"white\"\u003e\u0026nbsp;\u003c/v:imagedata\u003e\n \u003c/v:shape\u003e is the total number of ranks (scenarios); \u003cem\u003er =1, 2, \u0026hellip;, m\u003c/em\u003e is the rank position; \u003cem\u003eb\u003csup\u003er\u003c/sup\u003e\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\n \u003cv:shape id=\"_x0000_i1025\" type=\"#_x0000_t75\"\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\n \u003cv:imagedata src=\"file:///C%3A/Users/btr8097/AppData/Local/Packages/oice_16_974fa576_32c1d314_21eb/AC/Temp/msohtmlclip1/01/clip_image003.png\" o:title=\"\" chromakey=\"white\"\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u003c/v:imagedata\u003e\n \u003c/v:shape\u003e is the rank acceptability index for scenario \u003cem\u003ei\u003c/em\u003e at rank \u003cem\u003er\u003c/em\u003e. Rank acceptability indices cover the entire probabilistic space of scenarios. SAI is calculated to summarize this space into an overall performance, where higher values indicate higher likelihood of acceptance. The multiplying vector is appropriately chosen to favour higher ranks. In summary, higher SAI values are designed to correspond to a greater probability of being accepted. SAI can take values from zero to \u003cem\u003em\u003c/em\u003e (maximum possible SAI for any scenario occurs when all probability mass is concentrated at the best rank, the first one).\n\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1774943203.png\" width=\"742\" height=\"124\"\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the European Commission Horizon Europe project \u0026ldquo;IAM COMPACT\u0026rdquo;, under Grant Agreement No. 101056306. The sole responsibility for the content of this paper lies with the authors; the paper does not necessarily reflect the opinions of the European Commission or the granting authorities. P.P. acknowledges the European Research Council (ERC) funding for the BeyondSDG project (Project number 101077492). L.J.M.G. acknowledges the European Union\u0026rsquo;s Horizon Europe funding for the NEVERMORE project (Project number 101056858).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in this research article are accessible through the following Zotero repository: https://doi.org/10.5281/zenodo.19066849\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe version of WILIAM (v1.4) used in this study is public, available under the following link: https://github.com/LOCOMOTION-h2020/WILIAM_model_VENSIM/releases/tag/WILIAM_v1.4\u003c/p\u003e\n\u003cp\u003eAll figures were created in Python Notebooks, accessible in the following Zotero repository: https://doi.org/10.5281/zenodo.19066849\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eUnited Nations. \u003cem\u003eParis Agreement\u003c/em\u003e. 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Implementing stochastic multicriteria acceptability analysis. \u003cem\u003eEuropean Journal of Operational Research\u003c/em\u003e \u003cstrong\u003e178\u003c/strong\u003e, 500\u0026ndash;513 (2007). \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":"Climate mitigation, Sustainable Development Goals (SDGs), sustainability trade-offs, scenario acceptability","lastPublishedDoi":"10.21203/rs.3.rs-9151931/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9151931/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Climate mitigation scenarios are typically evaluated for technical and economic feasibility, yet their implementation depends on whether they are considered acceptable within broader development priorities. Sustainable Development Goal (SDGs) interactions create trade-offs that shape how mitigation strategies are perceived across regions. Combining integrated assessment modelling and stochastic multicriteria acceptability analysis, we assess the acceptability of four narratives achieving a common mid-century climate target. We find that pathways with similar climate outcomes differ markedly in acceptability and that global rankings often mask regional divergences. Technology-led decarbonization is more acceptable in the Global South, enabling rapid gains in energy access and development. Conversely, regions in the Global North favour strategies that protect natural carbon sinks, reflecting greater ecosystem restoration benefits. These divergent preferences stem from structural SDG trade-offs, challenging universal win–win narratives. Explicitly accounting for these differences is essential for designing politically feasible and effective climate pathways.","manuscriptTitle":"Sustainable development trade-offs shape the acceptability of climate mitigation scenarios","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-31 07:52:35","doi":"10.21203/rs.3.rs-9151931/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8289800d-e3db-471c-b64b-d54b7646373f","owner":[],"postedDate":"March 31st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64948402,"name":"Scientific community and society/Social sciences/Decision making"},{"id":64948403,"name":"Scientific community and society/Social sciences/Climate change"}],"tags":[],"updatedAt":"2026-03-31T07:52:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-31 07:52:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9151931","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9151931","identity":"rs-9151931","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}