We welcome contributions that:
Integrate SSH concepts and methods into integrated assessment models (IAMs), energy–economy–environment models, or other analytical frameworks
Use empirical and participatory approaches to inform model assumptions, structures, and constraints
Engage with normative dimensions such as fairness, feasibility, and societal acceptance
Connect justice issues to marginalized or disadvantaged communities, especially in the Global South
Address the role of governance, institutions, finance, and critically evaluate material and human needs in shaping transition pathways
Investigate social impacts of modeled scenarios (e.g., income, labor, or demand modeling)
Just-transitions and equity analyses linked to real supply-chain data & green industrial policy
We particularly encourage work that incorporates procedural, recognitional, transitional and other forms of justice, identifies how data gaps map onto justice gaps, and provides bi-directional feedback between social science and modeling communities. By convening these perspectives, the session seeks to advance interdisciplinary approaches that make climate and energy scenarios more relevant, inclusive, and impactful.
As the Intergovernmental Panel on Climate Change (IPCC) enters its seventh assessment cycle (AR7), the scientific community faces a pivotal moment of reflection regarding the role of global modelled scenarios in shaping the international climate policy landscape. The Sixth Assessment Report (AR6) highlighted pathways toward the Paris Agreement but also surfaced tensions between cost-optimal global scenarios and heterogeneous levels of national development, mitigation capabilities, and historical responsibilities for climate change. This invited presentation frames the session by interrogating current approaches to justice in climate mitigation research and proposes a research agenda for its transformation.
We first establish a typology of justice-related critiques on the current generation of scenarios. This typology distinguishes between three interrelated dimensions of the modelling process. Structural limitations of the research culture pertain to the geographic and disciplinary concentration of modelling expertise in the Global North, specifically in Europe, North America, and Japan, which has historically privileged certain epistemological contexts while perspectives from Low- and Middle-Income Countries (LMICs) and Small Island Developing States (SIDS) remain underexplored. This lack of diversity shapes narratives constructed and solutions deemed feasible. Next, we discuss methodological biases inherent in model architectures. Standard modelling approaches privilege scenarios that allocate high mitigation burdens to regions with high technical mitigation potential but low institutional and financial capacity, effectively neglecting the principle of common but differentiated responsibilities and respective capabilities. These choices effectively prioritise technoeconomic efficiency over intergenerational and interregional equity. Finally, we discuss epistemological boundaries limiting the breadth of indicators relevant to informing national policy, and the limited contextualisation of scenario outputs within heterogenous policy regimes that face differentiated costs of capital and risks.
Responding to these challenges, we propose a tiered research agenda designed to integrate considerations of justice into scenario design and use. Tier one advocates for incremental refinements within existing frameworks. This includes improving the transparency of model inputs, downscaling global results to policy-relevant national scales and for relevant indicators, and systematically integrating climate impacts and loss-and-damage considerations. Tier two calls for more fundamental advancements in scenario frameworks, including emerging work that replaces blind economic growth narratives with convergent pathways centred on Decent Living Standards (DLS) and multidimensional well-being. This involves reconceptualization of the solution space to prioritize demand-side transformations, sufficiency-based lifestyles, and protection of ecological thresholds that support both human and non-human life. We also emphasize the need for scenarios that explicitly model effort-sharing principles from the outset, incorporating differentiated carbon budgets and international climate finance flows as internal model objectives rather than ex-post calculations. Tier three focuses on procedural justice through participatory co-production. We argue that the legitimacy of future scenarios depends on the sustained engagement of a broader set of stakeholders, including social scientists, humanities scholars, and frontline communities, in the design and interpretation of narratives. This shift requires institutional reforms to support modelling capacity in the Global South and to move beyond tokenistic consultation toward genuine co-production of knowledge.
While models cannot fully capture equity and justice, strengthening them is essential to inform just collective action.
How to cite: Pachauri, S. and the Coauthors: Advancing representations of justice and the social sciences in climate mitigation futures, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18819, https://doi.org/10.5194/egusphere-egu26-18819, 2026.
Under the Paris Agreement, states must communicate Nationally Determined Contributions (NDCs) to the collective achievement of the long-term temperature goal. The International Court of Justice’s (ICJ) Advisory Opinion clarified that the ambition communicated in NDCs is not discretionary to the state. NDCs reflecting highest possible ambition (HPA), as explicitly mandated under Article 4 of the Paris Agreement, must be an adequate contribution to the 1.5° C temperature goal. HPA forms part of states’ due diligence standard to the Paris Agreement, imposing a procedural obligation for the conduct of formulating the NDC targets.
However, practical translation of how to operationalise states’ respective HPA in NDCs remains unexplored. This contribution bridges the gap between legal obligations and scenario modelling by proposing a framework that establishes a structured understanding of highest possible ambition across six elements, incorporating domestic, international and implementation considerations. These elements are translated into concrete operational terms through conduct and process expectations, including guidance for how scenario studies informing NDCs can be designed to inform the required faithful assessment the mitigation options that serves as a starting point for NDCs of HPA.
By clarifying how such assessment should be situated within explicit considerations of respective capabilities, equity, and implementation pathways, this framework may shape future NDCs by informing modelling approaches and documentation choices. It supports the systematic and transparent exploration of higher ambition scenarios, strengthening the alignment between legal obligations, scenario modelling, and states’ national climate pledges.
How to cite: Schönfeld, J., Beath, H., Pelz, S., and Rogelj, J.: Translating the Legal Expectation of Highest Possible Ambition in Scenario Study Design , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12714, https://doi.org/10.5194/egusphere-egu26-12714, 2026.
Climate change is a wicked problem characterized by high uncertainty, ambiguous goals, and diverse normative preferences among actors, which pose significant challenges to just and effective policy design. Cost-Benefit Integrated Assessment Models (CB-IAMs) are widely used to design global mitigation pathways and play a central role in informing and evaluating climate policies within the IPCC Working Group III. However, CB-IAMs reduce the complexity of climate policymaking into a deterministic, unidimensional policy optimization problem. They typically optimize mitigation policy under a single welfare objective, evaluated from the normative perspective of a single representative agent (typically utilitarianism), and consider only a single reference scenario for optimization. This unidimensional framing obscures normative preferences, leaving policymakers without robust or justice-focused information needed in real-world negotiation contexts.
To address these limitations, we introduce JUSTICE, an IAM framework that integrates methods from decision-making under deep and normative uncertainty and welfare economics. JUSTICE implements a general social welfare function (SWF) that explicates normative preferences across regions, time scales, and climate states. This general SWF enables the optimization of policy pathways consistent with multiple distributive justice principles relevant to climate justice discourse. Using JUSTICE, we reformulate the mitigation policy problem into a multi-objective, multi-justice-framing optimization problem. This optimization setup yields Pareto sets of solutions, one for each distributive justice framing. In addition, we conduct a sensitivity analysis of optimal mitigation levels across three types of uncertainty: stochastic, deep, and normative.
Results show that justice framing strongly shapes both the ambition and distribution of global mitigation pathways. The prioritarian framing recommends deeper emission reductions across all SSPs, placing greater normative weight on the temperature objective than the utilitarian framing. The utilitarian framing distributes the mitigation burden more uniformly across regions, whereas the prioritarian framing allocates greater mitigation responsibility to developed regions. Near-term (2050) mitigation ambition is highly sensitive to normative uncertainty, which explains over 50% of the variance in mitigation levels, followed by deep uncertainties arising from socioeconomic dynamics. Stochastic uncertainty originating from the climate's response to emissions has the least influence on the mitigation actions. We find that justice framing determines the spatial distribution of mitigation, while the selection of policy solutions along the Pareto frontier determines the level of ambition. Normative uncertainty is the dominant factor shaping near-term decisions, whereas deep uncertainty becomes increasingly influential towards the end of the century.
Overall, our results demonstrate the complex interaction of deep and normative uncertainties in mitigation planning. Explicitly disaggregating conflicting objectives and justice perspectives is essential for understanding the distributional consequences of optimal policies. Our methods also expand the range of possible decisions, clarify trade-offs, and ensure the representation of diverse stakeholder values, thereby directly addressing the tenets of procedural justice. When integrated into CB-IAMs, this approach supports the design of fairer climate policies, increases legitimacy, strengthens stakeholder engagement, and facilitates effective climate negotiations.
How to cite: Biswas, P., Zatarain Salazar, J., and Kwakkel, J.: Normative Uncertainty Dominates Near-Term Mitigation Policy Decisions in Integrated Assessment Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14012, https://doi.org/10.5194/egusphere-egu26-14012, 2026.
Carbon dioxide removal (CDR) is critical for achieving net-zero and net-negative CO2 emissions that can halt and potentially reverse global warming, respectively. However, reliable CDR is still costly and comes with considerable technological and ecological uncertainties. Considering global CDR employment from a fairness perspective serves as a starting point to inform national actions and international cooperation, as well as to provide guidance for the formulation and evaluation of nationally determined contributions (NDCs) and long-term low-emission development strategies (LT-LEDS) for which countries need to indicate how they represent a fair and ambitious contribution. Despite the centrality of equity, no integrated framework exists to equitably allocate responsibilities for CDR and residual emissions among countries.
Here, we present a justice-based framework that separates out ethical considerations for equitably allocating gross emissions and gross CDR, addressing how these contributions shift before and after reaching global net-zero CO2 emissions. We distinguish between distributive justice, which refers to ethical principles guiding the fair allocation of scarce resources and rights from a forward-looking perspective, and corrective justice, which applies when losses and damages arising from the excessive use of environmental commons must be addressed. Building on distributive and corrective justice theories, the framework distinguishes between CDR delivered as a common good to reach a collective global climate outcome, and CDR that is used to pay off carbon debts due to emissions overconsumption. We apply the framework to 1.5 °C-consistent scenarios and national projections, covering 176 countries and focusing on durable, engineered CDR options.
Our results reveal substantial divergences between justice-based allocations and technically optimized IAM pathways. High-income regions are systematically assigned larger corrective CDR obligations, while in the Global South, technically modeled pathways generally project fewer residual emissions and larger potential for CDR deployment compared to the justice-based allocation benchmarks, principally in Africa, Southern Asia, and Latin America & the Caribbean. A limited amount of countries provide quantitative information regarding residual emissions and CDR in their LT-LEDS, and even fewer meet their equitable quota. Out of 26 residual emission pledge estimations, only Fiji and Ethiopia stay within their equitable allocation. Out of 38 CDR pledge estimations, 19 countries meet or exceed their equitable CDR allocation, showing a tendency to overly rely on CDR deployment in major countries.
In this work, we offer a new perspective for how nations with substantial historical responsibilities and emerging economies with increasing capacities can collaborate and equitably share the CDR burden, enhancing both international cooperation and national-level climate action.
How to cite: Li, M., Wang, R., Zheng, X., Wang, C., and Rogelj, J.: Sharing emissions and removals for meeting the Paris Agreement through a distributive and corrective justice lens, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20125, https://doi.org/10.5194/egusphere-egu26-20125, 2026.
Most global pathways generated using Integrated Assessment Models (IAMs) follow a cost-optimized approach, while national scenarios submitted by national models capture the heterogeneity of national circumstances, development priorities, and political realities. National analyses integrated with social sciences and justice insights are essential to close the ‘implementation gap’ between global mitigation pathways and actual mitigation progress. Effort-sharing approaches (also known as burden-sharing) serve as one type of conceptual and ethical bridge between global and national analyses.
Under the Paris Agreement, countries are encouraged to explain how their NDCs are ‘fair and ambitious’. Studies suggest that most parties declare their contributions fair without substantial rigorous metrics. The Enhanced Transparency Framework (ETF) and Global Stocktake (GST) scheduled every five years are designed to collect and analyze data to assess whether collective national efforts are sufficient to meet long term goals in the light of justice and equity. Additionally, the national pathways are developed through co-production of knowledge with stakeholders, strengthening the findings and building national capacity for long term planning.
In this study, we present national analyses from 10 countries (including emission intensive countries and a few African countries) exploring alternative mitigation pathways that captures each of the current policies, NDC, LTS, and Net-Zero using multiple model analysis. The analysis compares socio-economic assumptions, energy supply, energy demand, emission pathways, and subsequent technology change. Additionally, we compare the respective national carbon budgets with each of the effort-sharing carbon budgets of the selected countries from global models to assess the ‘implementation gap’ and estimate the need in the emission reduction of these countries to achieve the global temperature of 2C and well below 2C. Further, we plan to reflect the unique priorities in national plans and present enablers required from a global perspective to accelerate low-carbon transitions towards net-zero in these selected countries.
How to cite: Vishwanathan, S. S. and the Co-authors: Contribution of national analyses to Justice and Social Science Integration , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22813, https://doi.org/10.5194/egusphere-egu26-22813, 2026.
Large-scale electrification efforts in Sub-Saharan Africa have prioritized the expansion of formal electricity access, supported by substantial public and donor investment. Yet dominant energy access frameworks often misdiagnose lived energy deprivation in under-electrified contexts characterized by unreliable supply, high costs, and complex institutional arrangements. Indicator-based tools such as the Multi-Tier Framework capture technical service attributes but obscure how energy-related burdens interact and compound in everyday life. This study identifies the mechanisms through which lived energy deprivation is produced, moving beyond isolated indicators to examine how burdens co-occur and reinforce one another. Drawing on focus group discussions across 14 rural and peri-urban communities in Nigeria and South Africa (84 participants), we combine inductive qualitative coding with co-occurrence analysis to identify recurring configurations of energy-related stressors.
The analysis reveals an interactional system of energy precarity operating through three coupled conversion pathways. First, affordability pressure is converted into compound deprivation through reactive coping strategies, whereby forced trade-offs, psychosocial strain, and time loss interact to erode households’ capacity to pursue or sustain modern energy transitions. Second, reliability failures and high operating costs trigger non-linear transition dynamics, as households revert to traditional fuels or informal substitutes, producing cascading physical, temporal, and environmental burdens despite nominal access. Third, institutional and procedural frictions—manifested through administrative burden, opaque billing, and accountability gaps—act as structural amplifiers, intensifying both affordability and reliability stress by imposing additional time, cost, and emotional demands. These pathways emerge as stable clusters in the co-occurrence matrix, indicating patterned, reinforcing dynamics rather than isolated experiences.
We reconceptualize energy poverty as a dynamic, interactional process rather than a set of isolated deficits, explaining why linear transition models and indicator-based assessments systematically overestimate progress and underestimate vulnerability. Integrating lived mechanisms into energy access planning is essential to avoid mistaking nominal system functionality for meaningful energy access, and to prevent underperforming systems from being labeled as transition successes.
How to cite: Obinegbo, O., Osman, K. K., and Benson, S. M.: Beyond Access Frameworks: Mechanisms of Lived Energy Deprivation in Sub-Saharan Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5793, https://doi.org/10.5194/egusphere-egu26-5793, 2026.
Climate change affects mental health in various ways, now increasingly documented across health, social science, and environmental research, yet these impacts remain largely absent from climate assessments used to inform integrated transformation pathways. Empirical studies associate climate-related stressors, such as extreme heat, floods, food insecurity, displacement, and environmental degradation, with adverse mental health outcomes, including anxiety, depression, psychological distress, increased psychiatric hospitalizations, and elevated suicide risk. Evidence also suggests that relatively small increases in temperature, on the order of 1 °C, can negatively affect cognitive performance, decision-making, and emotional regulation, with implications for productivity, learning, and social functioning.
These impacts are unevenly distributed and often more pronounced in rural and peri-urban settings, where climate-sensitive livelihoods, environmental stress, and limited access to mental health services intersect. Certain groups face heightened vulnerability, including individuals with pre-existing mental health conditions, whose symptoms may intensify under repeated climate stress, and pregnant individuals, for whom climate-related stress can affect prenatal mental health with potential long-term consequences for child development. In contexts where health systems are already under-resourced, climate stressors can contribute to prolonged mental health crises and strain institutional capacity well beyond the immediate aftermath of climate events.
Despite this growing evidence base, mental health impacts are rarely treated as climate impacts in their own right within climate change assessments, which continue to prioritize physical health outcomes and economic damages. This narrow framing risks underestimating adaptation needs and overlooking important dimensions of non-economic loss and damage, particularly those related to long-term well-being, recovery, and resilience.
This study argues for a more systematic recognition of mental health in climate impact assessments and outlines a pragmatic pathway to do so that is consistent with existing assessment practices. We suggest a staged approach in which mental health impacts are first explicitly identified and characterized within the climate impact space, alongside physical health and economic damages, drawing on established epidemiological and social science evidence. These impacts can then be incorporated into broader assessment processes through several entry points, including scenario narratives that reflect psychosocial vulnerability and recovery, the expansion of impact categories to include mental health–related non-economic losses, and SSH-informed interpretation of assessment results that considers how mental health shapes adaptive capacity, societal readiness, and long-term resilience. Recognizing mental health as a climate impact in this way can help make climate assessments more comprehensive, realistic, and equity-aware, thereby improving their relevance for adaptation planning and long-term transformation pathways.
How to cite: Awais, M., Niazi, H., and Malik, A.: Recognizing Mental Health Impacts in Climate Change Assessments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19663, https://doi.org/10.5194/egusphere-egu26-19663, 2026.
Achieving a good life for all within planetary boundaries requires understanding what contributes to human flourishing, yet most macroeconomic models treat GDP as an end goal despite its poor correlation with well-being in high-income societies. Here we investigate key determinants of human well-being that are readily measurable and useful in advancing integrated assessment models (IAMs). We first compile and harmonize global survey data (World Values Survey, Gallup World Poll, Global Flourishing Study) to identify how socioeconomic, biophysical, and cultural markers codetermine human well-being. We then compare survey data to time use data from the Human Chronome Project and an array of material factors using advanced statistical methods (e.g. fixed-effects panel regression) and machine learning (e.g. random forests). We reveal robust patterns that challenge assumptions about relationships between material consumption and life satisfaction. We also interrogate the relationship between self-reported or subjective well-being and more normative understandings of the good life, including societal characteristics like whether wealth is fairly distributed (using inequality metrics e.g. Gini coefficient) or whether citizens have influence over collective decision-making (using e.g. “political voice” metrics from Raworth’s Doughnut). These findings are used to propose new empirically-derived well-being indices for use in macroeconomic models. Models incorporating these metrics provide a powerful tool for policymakers to target well-being outcomes directly, rather than relying on imprecise proxies like GDP. It is our hope that the next generation of IAMs—or environment–society-economy models, more broadly—incorporate these insights to help guide just transitions within and between countries.
How to cite: Horen Greenford, D., Kaye, M., Al Faisal, A., and Galbraith, E.: Modelling well-being to aid in social–ecological transitions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15530, https://doi.org/10.5194/egusphere-egu26-15530, 2026.
The Paris Agreement identifies renewable energy technologies (RETs) as essential to avoiding catastrophic climate change. Since 2010, the global electricity mix has evolved rapidly, with renewables as the fastest‑growing source. However, ambitious renewable targets can produce significant social impacts at the local level. To understand these impacts holistically, we need to examine their implications for human development and how well‑being concerns shape local acceptance of RETs.
Norway has some of the best wind resources in Europe, but wind development has been contested: licensing has been revoked following opposition from nature‑conservation groups, recreational users, Sámi reindeer herders, and local communities. This paper operationalises the Capability Approach to integrate well‑being and other socio‑technical considerations into energy‑systems modelling (ESM). It explores the challenges and trade‑offs involved in evaluating well‑being outcomes from RETs, with a particular focus on capturing the voices of people who live near wind infrastructure and using their conceptions of well‑being to define system boundaries, identify priorities and amelioration options, and inform scenario design.
We apply this approach to two Norwegian municipalities: one in Finnmark on Sámi reindeer‑herding land, and one in Østfold near Oslo. Building on capability‑identification methods (Alkire 2002, 2013; Clark 2003; Ibrahim 2008; Uyan‑Semerci 2007), we visualise relationships between well‑being and energy projects and embed those relationships into ESM scenarios.
Our mixed‑methods co‑creation process involved Sámi, Norwegian, and other scholars and consisted of semi‑structured interviews and focus groups (59 participants, July–August 2024) and four participatory workshops (34 participants, October–November 2025) to validate and extend findings. From these engagements, we developed six decarbonisation scenarios that reflect human development and social‑justice priorities: four scenarios directly associated with well‑being dimensions (nature protection; contribution to local industry; protection of traditional economic activities; and Friluftsliv — outdoor life) and two indirectly (reduced energy consumption and technology preferences).
Findings highlight the importance of inclusive energy planning that addresses information asymmetries, acknowledges historical land uses, and creates pathways for restorative justice, local employment, and nature protection. This paper contributes to theory and practice by demonstrating how locally defined capabilities can be operationalised within ESM to better integrate social priorities and justice considerations. We argue that this methodology can help to configure future renewable projects so they prioritise both sustainability and the well‑being of affected communities.
How to cite: Velasco Herrejon, P., Valenzuela Venegas, G., Javed, M. S., and Zeyringer, M.: From Capabilities to Scenarios: A Mixed‑Methods Approach to Socially Responsive Energy Systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19392, https://doi.org/10.5194/egusphere-egu26-19392, 2026.
The transition to climate-friendly energy supply is highly contested and increasingly influenced by a rapidly changing geopolitical order. This paper provides an overview of how this energy transition is influencing the distribution of power between major powers, and, conversely, how major powers are seeking to shape the speed and direction of this transition. It takes an analytical perspective that distinguishes broader geopolitical interests from the geoeconomic competition within emerging clean energy supply chains.
It begins by reviewing the relative asset base of China, the US and Europe within the existing fossil-dominated energy system and a potential future one, dominated by renewable energy and characterized by increasing levels of electrification. It then goes on to review the role of new energy supply chains in enabling the economic rise of China and how it is affecting the geoeconomics positions of the US and the EU. It then moves on to the role of these major powers in actively seeking to shape the energy transition. Building on Quitzow and Zabanova (2025), it presents and applies a conceptual framework for analyzing the main channels of influence and how they are being deployed by the three major powers to influence the speed and direction of the energy transition. It discusses how the increasing geopolitical confrontation between the US and China is leading to the development of novel strategies, alliances and institutions. Finally, it also briefly discusses implications and strategic choices of fossil-fuel exporting countries and selected emerging economies.
Reference: Quitzow, R., & Zabanova, Y. (2025). Geoeconomics of the transition to net-zero energy and industrial systems: A framework for analysis. Renewable and sustainable energy reviews, 214: 115492. doi:10.1016/j.rser.2025.115492.
How to cite: Quitzow, R. and Scholten, D.: Great Power Rivalry, Geoeconomic Competition and the Transition to a Net-Zero Energy System, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1874, https://doi.org/10.5194/egusphere-egu26-1874, 2026.
This article examines how green state interventionism and geopolitical rivalry affect the spatio-organizational dynamics of global production networks (GPNs), using solar photovoltaic (PV) as a case. Drawing on the GPN 2.0 approach but incorporating a stronger conceptualization of the role of states, institutions and (geo)politics, our conceptual framework specifies how two policy instruments that are gaining prominence in the current geopolitical conjuncture – tariffs and subsidies – reshape the structural imperatives facing firms and, thus, incentivize a swathe of reconfiguration strategies with direct consequences for the spatial organization of GPNs. Based on interviews with solar PV manufacturers and other stakeholders, policy documents, trade and investment data, a systematic review of the industry press, and corporate financial reports, we present a detailed analysis of the restructuring of the global solar PV industry in response to successive interventions by the United States (US) and the European Union (EU) – particularly targeting Chinese solar PV manufacturers – since 2012. The analysis not only documents a reciprocal, ‘whack-a-mole’-like interplay, in which changing US and EU policies drive a continuous geographical reconfiguration of solar PV GPNs, shifting production from China to Southeast Asia and beyond; it also shows that this restructuring is embedded in a deeper remapping of market, cost-capability and financial imperatives in the solar PV industry, induced by escalating trade and industrial policy interventions. In so doing, the article contributes to narrowing the ‘politics gap’ of GPN research and improving our understanding of GPN dynamics in an era of increasing geopolitical tensions.
How to cite: Hansen, U. E.: Green state interventionism and the reconfiguration of global production networks in the era of geopolitical rivalry, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2799, https://doi.org/10.5194/egusphere-egu26-2799, 2026.
Ammonia-based synthetic nitrogen fertilizers are indispensable for global food security, yet today’s supply is dominated by fossil-fuel-based, centralized production (largely natural gas and coal) and long-distance transport. This structure creates simultaneous climate, affordability, and resilience challenges: ammonia production is highly energy- and carbon-intensive, while supply disruptions and high delivered prices disproportionately affect import-dependent regions, particularly in the Global South, widening yield gaps between potential and actual crop production.
We present a spatially explicit modelling framework to assess low-carbon, small-scale and decentralized ammonia supply options through the full delivered-cost lens, focusing on electrified pathways based on electrolytic hydrogen as alternatives to conventional fossil routes. Using high-resolution geospatial representations of ammonia supply (including a harmonized dataset of >400 existing plants and major import hubs), nitrogen fertilizer demand, and transport infrastructure and costs, we formulate a mixed-integer linear program that allocates supply to demand and selects least-cost routing to quantify delivered ammonia prices. By explicitly separating production and logistics components, the framework identifies where transport markups and supply-chain fragility create favorable conditions for smaller-scale, decentralized production, even when production costs are higher.
Results show that transportation is a major (and highly uneven) driver of delivered fertilizer prices. Globally, transport adds ~23% to delivered costs, but in many countries in Latin America and Sub-Saharan Africa it exceeds 50%; in remote regions, transportation alone can approximately double end-user prices as ammonia travels thousands of kilometers. In these settings, decentralized electrified production could improve access, reduce exposure to disruptions and price volatility, and support sustainable agricultural intensification, but cost competitiveness hinges on local electricity prices: decentralized electrolytic ammonia becomes viable only below roughly 30–60 USD/MWh, implying the need for targeted financial support, infrastructure upgrades, or policy mechanisms that lower effective power costs.
A U.S. specific case-study illustrates how the same framework can benchmark centralized versus decentralized (grid- and renewables-powered) pathways in a mature market, highlighting the central role of electricity prices and logistics in determining competitiveness. Overall, the approach supports integrated assessment of climate–cost–resilience trade-offs to guide sustainable fertilizer and energy transition planning.
How to cite: Mingolla, S. and Rosa, L.: Mapping Opportunities for Small-Scale Electrified Ammonia to Improve Fertilizer Access and Supply-Chain Resilience, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6460, https://doi.org/10.5194/egusphere-egu26-6460, 2026.
The global basic material industries (e.g., steel, chemicals) are a crucial bottleneck in the transition towards climate neutrality. Renewable electricity and hydrogen can become a key enabler. However, as the renewable resources are distributed heterogeneously across locations, both the global supply chains and trade will likely reshape in the net-zero transition.1,2 The resulting global geography of this future climate-neutral production remains uncertain. This uncertainty is further fuelled by an increasingly complex international trade landscape (e.g., geopolitical developments, trade frictions, carbon tariffs, industrial policy). Global shifts in material production in turn determine regional energy and infrastructure demands and associated regional transition bottlenecks.
To derive long-term transition pathways to climate neutrality for the globe, including for basic material industries, Integrated Assessment Models (IAMs) are the methodological standard. While the representation of international trade in IAMs has historically focused on primary energy carriers, more recently some modelling teams have introduced material trade in stylised “pool-trade” form (i.e., without bilateral routing and corridor constraints).3 However, a detailed representation of bilateral material trade flows is required to capture potential reconfigurations of global material supply chains and trade, while accounting for various trade frictions. Hence, there is no modelling framework that analyses the global energy and industry transformation, while accounting for a potential global reconfiguration of material supply chains and trade.
To address this gap, we present a proof-of-concept study for coupling a trade model for materials to an IAM. More concretely, we couple an Armington-CES structural gravity model to the REMIND material flow analysis (REMIND-MFA)4 of the IAM REMIND framework5. We (i) calibrate the model to historic bilateral trade flows, supply and demand, by adjusting behavioural parameters so that the model reproduces the data, then (ii) take regional supply and demand estimates from the REMIND energy supply system and the REMIND-MFA, respectively, (iii) calculate bilateral trade flows and material prices with the trade model and return them to REMIND. Crucially, transport costs are included as per-unit rates to conserve quantities, as opposed to iceberg costs – the common practice in Comuptable General Equilibrium (CGE modelling. Lastly, (iv) as the trade model enables us to also represent policy changes, geopolitical fragmentation and other modelled shocks, we analyse them and assess their impact on bilateral trade flows in comparison with the previous REMIND-MFA trade. At the conference we present the overall framework and a one-way coupled prototype for steel trade.
Figure: Overview of the linkages between the REMIND-MFA and the trade model
References
1 Verpoort, P. C. Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains. Nature Energy9, 491–503 (2024).
2 Eicke, L. & Quitzow, R. Toward a Renewables-Driven Industrial Landscape: Evidence on investment decisions in the Chemical and Steel Sectors. Preprint at https://doi.org/10.21203/rs.3.rs-5519615/v1 (2025).
3 Ünlü, G. et al.MESSAGEix-Materials v1.1.0: representation of material flows and stocks in an integrated assessment model. Geosci. Model Dev.17, 8321–8352 (2024).
4 Dürrwächter, J., Hosak, M., Weiss, B. & Ueckerdt, F. Model documentation: REMIND-MFA framework. https://remind-mfa.readthedocs.io/.
5 Luderer, G. et al.Impact of declining renewable energy costs on electrification in low-emission scenarios. Nat Energy7, 32–42 (2022).
How to cite: Hu, X., Horster, P., Verpoort, P., and Ueckerdt, F.: Material value chains in a fragmented world: modelling reconfigurations and trade strategies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19034, https://doi.org/10.5194/egusphere-egu26-19034, 2026.
Carbon fiber reinforced polymer (CFRP) is a critical material for lightweighting strategies in aviation, enabling substantial emission reductions over the aircraft life cycle. However, the manufacturing value chain of CFRP is highly energy-intensive and costly. While its operational fuel-saving potential is well recognized, integrated assessments that systematically weigh upstream value-chain environmental and economic burdens against downstream application benefits remain limited. This gap is particularly critical in the context of China, which now accounts for over 50% of global carbon fiber production capacity. Such concentration raises concerns regarding value chain resilience, systemic risk exposure, and the uneven distribution of environmental and economic burdens across regions.
We develop a comprehensive cradle-to-gate life cycle assessment (LCA) and cost accounting model based on primary data from more than 20 Chinese enterprises, collectively representing approximately 60% of China’s carbon fiber production capacity. The analysis covers the full CFRP supply chain, including acrylonitrile (AN) synthesis, polyacrylonitrile (PAN) polymerization and spinning, precursor stabilization and carbonization, and final CFRP processing. To assess value-chain resilience and trade-offs, we introduce the concept of break-even flight distance, defined as the operational threshold at which fuel-saving benefits offset production-stage environmental and economic burdens.
Results reveal an asymmetry between environmental and economic resilience. The cradle-to-gate carbon footprint of aerospace-grade CFRP reaches 114 kg CO2 per kg, substantially higher than that of aluminum alloys. Environmentally, CFRP substitution is highly resilient: operational fuel savings offset production-related emissions within approximately two years of aircraft operation. Economically, however, the CFRP value chain appears fragile. Ultra-high manufacturing costs and market prices (exceeding 2400 CNY/kg) drive the economic break-even distance into the range of tens of millions of kilometers, comparable to the aircraft’s service lifetime.
These findings highlight a critical mismatch within clean-tech value chains, where environmental benefits coexist with significant upstream economic risks. The results underscore the need for cost-reduction technologies and carefully designed green industrial policies to enhance value chain resilience, rebalance risk distribution, and align economic feasibility with climate mitigation goals.
How to cite: Liu, Z., Wang, F., and Zhang, S.: Asymmetric Resilience and Trade-offs in Value Chains of Carbon Fiber Composites for Aviation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20024, https://doi.org/10.5194/egusphere-egu26-20024, 2026.
Curbing carbon emissions to meet the targets set in the Paris Agreement requires the global deployment of low-carbon technologies (LCTs), including solar photovoltaics, wind turbines, bioenergy systems, electric vehicles, and carbon capture and storage (CCS). The positive impact of global LCT trade on environmental performance has been widely confirmed, but quantifying its influence on national energy structures remains a critical and pressing task. This study quantifies the impact of global LCT trade on greenhouse gas emissions and energy structure transformation under shared socio-economic pathway scenarios (SSPs). The results indicate that: (1) the emission reduction potential of global LCT trade is uneven. Developed regions can achieve effective carbon reduction through LCT trade, while developing regions generate higher greenhouse gas emissions as a result of participation in LCT trade; (2) LCT trade promotes the green transformation of energy structures in developed regions. By 2050, the share of renewable energy in developed countries is projected to increase by nearly 15% under the influence of global LCT trade; (3) trade in LCTs can improve overall social welfare while reducing carbon emissions, but this sustainable development effect is observed primarily in developed regions; and (4) the technological sophistication of traded products leads to heterogeneous carbon reduction effects across regions. This study highlights the need to reduce tariffs, promote the liberalization of LCT trade, and enhance the technological content of traded products to facilitate the global dissemination of green technologies.
How to cite: Wang, P., Mao, X., and Huang, X.: Impact of global low-carbon technology trade on future energy structure transformation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14453, https://doi.org/10.5194/egusphere-egu26-14453, 2026.
Just transition has emerged as a central concept in international climate discourse and is increasingly framed as a necessary precondition for accelerating climate action. These debates are unfolding alongside a critical evolution in climate and energy modelling: a shift away from an exclusive focus on carbon management towards a broader interrogation of the social, economic, and political dimensions that shape real-world policy choices. This evolution reflects a growing recognition that modelling insights must be easily interpretable and aligned with stakeholder priorities if they are to meaningfully inform decision-making.
Our work at the research–policy interface highlights a gap between modelling outputs and policy uptake. Scenarios that are perceived by stakeholders as abstract, overly technical, or misaligned with political, institutional and local realities frequently fail to be integrated into policy processes. By contrast, an implementation-focused and participatory approach, grounded in systematic stakeholder engagement, can surface concrete priorities and constraints, which are essential for translating modelling insights into collaborative climate action.
This paper presents key conclusions from the Just Transition Compass, a co-creative manual for action designed to support the implementation of just transitions. The Compass was developed through an extensive consultation process, including four international events held across three continents, culminating in its launch at COP30. More than 300 stakeholders, including government negotiators, policymakers, practitioners, private sector representatives, and civil society actors, participated in the process. This enabled a structured exploration of how just transition principles are interpreted across regions and governance levels, and how these principles can be transformed in concrete governance frameworks, policy interventions and financing opportunities.
The key takeaways from the Compass speak directly to urgent political, economic, and social debates that ought to be better reflected in climate and energy modelling. Stakeholders emphasised the importance of recognising the co-benefits and economic opportunities of the transition; ensuring climate action acts as an enabler of the Sustainable Development Goals rather than a competing agenda; addressing cross-border impacts of mitigation measures; reframing industrial policy as a basis for multilateral cooperation; reversing historical injustices by tackling inequalities embedded in global supply chains; and supporting economic diversification and energy security, particularly in transition-dependent economies.
These diverse insights point to a shared lesson: advancing global climate action requires first understanding people’s vision for a prosperous, just transition. This implies moving beyond modelling frameworks centred solely on emissions trajectories, towards approaches that integrate multiple dimensions of justice, development, and governance. Emerging initiatives, such as the NEWPATHWAYS Horizon Europe project, demonstrate the potential of such co-creative approaches. We argue that, at a time of increasing global fragmentation, participatory modelling can become a critical tool to unblock negotiations and support effective, future-proof climate policies.
How to cite: Campanelli, G.: Advancing an Implementation- and Stakeholder-Focused Approach to Modelling Just Transitions , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18551, https://doi.org/10.5194/egusphere-egu26-18551, 2026.
The process of transition scenarios design and the construction of policy narratives based on them are often criticized for lacking proper participation of key stakeholders. For the AFOLU sectors, deep transformations face challenges from sociopolitical dynamics which are often underrepresented in scenario design. Further, integrated assessment models lack transparency due to highly complex structures and opaque assumptions that limit their credibility with key stakeholder groups with power to implement the changes needed. The FABLE Calculator was developed to address these criticisms and to enable broad participation of non-technical users. Developed by the FABLE consortium, it is a user-friendly Excel-based tool that links food demand, agricultural production, land-use change, trade, and sustainability indicators to greenhouse gas emissions in five-year steps from 2000 to 2050. It allows for designing and running agriculture and land use scenarios for climate change mitigation such as those exploring outcomes of Nationally Determined Contributions (NDCs) to the Paris Agreement. This study uses the FABLE calculator, applying a Brazil-adapted version with multiple adjustments to reflect national data and policy context, to support Brazil’s alignment of short- and medium-term climate actions with long-term strategies (LTS) and climate-neutrality objectives. The approach translates Brazil’s updated Nationally Determined Contribution (NDC), with a focus on Agriculture, Forestry and Other Land Use (AFOLU), into quantitative pathways using the FABLE Calculator.
The study combines (i) the development and systematic validation of the model with Brazilian national datasets to enhance transparency, acceptability and policy relevance; (ii) Brazil-specific spatial downscaling to explore the territorial implications of pathway assumptions and identify potential feasibility constraints, and (iii) a structured translation of the AFOLU NDC components of Brazil’s NDC into explicit scenario levers, such as deforestation limits, restoration trajectories, agricultural productivity, land livestock and demand assumptions to create NDC-consistent pathways. Both the model development and scenario design is informed and validated by stakeholder-oriented processes designed to obtain context-specific evidence, challenge unrealistic parameter choices, and facilitate bi-directional feedback between SSH-informed insights and model structures. The research systematically documents stakeholder responses to modellers’ choices and explores how they align or disagree, and why. Results will inform future studies and provide useful information for the broader modelling community engaged in land use scenario design for Brazil and elsewhere. The study will draw from past workshops already conducted with Brazilian stakeholders and two more scheduled for the first months of 2026.
This research demonstrates how the integration of participatory and empirical inputs into scenario design and validation advances interdisciplinary practice and procedural justice in policy-relevant scenario research. As a result, it enhances the realism, transparency, and acceptability of land-use and climate pathways in decision-making processes for a major Global South agricultural exporter such as Brazil, which is also the most biodiverse country in the world and home to the largest remaining area of primary tropical rainforest .
This work is supported by FCT, I.P./MCTES through national funds (PIDDAC): LA/P/0068/2020 - https://doi.org/10.54499/LA/P/0068/2020 , UID/50019/2025, https://doi.org/10.54499/UID/PRR/50019/2025, UID/PRR2/50019/2025. This work is also supported by the German Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection.
How to cite: Galvez Gutierrez, S. J., Costa, W., and Köberle, A.: From NDC to Pathways: Translating Brazil’s AFOLU Climate Commitments into Scenarios with the FABLE Calculator, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20710, https://doi.org/10.5194/egusphere-egu26-20710, 2026.
Accelerating decarbonization requires significant shifts in financial investments. However, dominant approaches in sustainable finance and climate modeling have primarily emphasized policy, regulation, and risk-based mechanisms. Relatively little attention has been given to how societal dynamics influence financial decision-making processes and how these processes can be incorporated into analytical frameworks used to explore transition pathways. This paper examines the effects of societal dynamics, such as changing social norms, collective action, and climate-related litigation, on financial markets and the resulting feedback loops that can either accelerate or impede low-carbon transitions.
We conduct an exploratory qualitative synthesis of the empirical literature to identify robust evidence on how societal processes influence financial system behavior. Our results reveal links between societal pressures and financial outcomes, including balancing and reinforcing feedback loops. Shifts in social norms and perceptions of legitimacy affect investor preferences and expectations, altering the valuation of carbon-intensive and low-carbon assets. Collective action and climate litigation introduce reputational and legal risks reflected in asset pricing and financing conditions, thereby reinforcing capital reallocation dynamics. Meanwhile, countervailing forces, such as incumbent interests and advocacy, can dampen or delay these processes. Together, these interactions may produce nonlinear dynamics that lead to tipping behavior in investment patterns once critical thresholds are reached.
The framework identifies and links the empirical relationships identified. It highlights that financial markets are shaped not only by formal policy signals, but by societal influences and pressures that affect perceptions of risk, acceptability, and future profitability. The framework clarifies how governance arrangements, institutional legitimacy, and societal acceptance influence the feasibility of transition pathways. It does so by making these mechanisms explicit. We present an initial structured approach to representing society-finance interactions in climate modeling. This has implications for the pace and direction of decarbonization.
This study advances the integration of social science insights by translating scattered empirical evidence into a coherent conceptual framework that can inform future modeling efforts. The results identify leverage points within the society-finance system and provide a structured basis for future empirical research and quantitative modeling that can progressively capture feedback between society and finance in climate transitions.
How to cite: Leoncio Hehl, D., Koberle, A. C., Schoenberg, W., Prawitz, H., Tan, R. Y. W., and Eker, S.: Integrating Societal Dynamics into Financial Pathways for Decarbonization, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20365, https://doi.org/10.5194/egusphere-egu26-20365, 2026.
The establishment of the Loss and Damage Fund at COP27 raised expectations for supporting climate vulnerable countries, yet its implementation has been hindered by several unresolved questions on contribution rules, eligibility, and evidence needed to assess its effectiveness. Addressing these issues requires an analytical framework that links social and economic development conditions with impacts of financial transfers. As an exploration, this study develops a scenario-based approach to examine how socioeconomic pathways shape both the scale and effects of Loss and Damage transfers on energy, water and agricultural systems.
We focus on Shared Socioeconomic Pathways, SSP1, SSP2 and SSP5, to quantify how differences in growth, vulnerability and sectoral structures influence the size and allocation of the Loss and Damage Fund. For simplification, climate damages are imposed on national GDP to derive allocation patterns. Using the Global Change Analysis Model (GCAM), we simulate how fund inflows affect national CO2 emissions, energy use, agricultural production and water withdrawals for both donors and recipients. Across all scenarios, we find that Loss and Damage transfers lead to measurable changes in sectoral activity, but their magnitude is small relative to the variation driven by socioeconomic development. For example, primary energy use in vulnerable recipient regions in 2050 differs by about 70.8 EJ between SSP1 and SSP5, whereas the difference between fund and no fund cases within SSP5 is roughly 7.9 EJ. Sectoral structures also diverge substantially by pathway. In 2050 fossil fuel shares in recipient regions reach 71 percent in SSP5 compared with 61 percent in SSP1, and fund transfers alone do not shift these trajectories. In some cases, fund inflows raise local energy, food and water prices, indicating potential distributional pressures that may increase inequality.
Fig.1 Research framework. E7 and E35 are donor groupings based on historical cumulative CO2 emissions, representing the top 7 (60% of global emissions) and top 35 countries (90%) respectively. G7 refers to the Group of Seven. VH refers to Very High climate-vulnerable countries, and VHH refers to Very High and High climate-vulnerable countries.
Our results show that the performance and equity of the Loss and Damage Fund depend strongly on the socioeconomic context in which transfers are deployed. Therefore, climate finance assessment requires a better consideration of social and economic development pathways and their interactions with the broader system. Our work aims to integrate social science perspectives into modeling by demonstrating how vulnerability, equity and development conditions shape modeled outcomes and influence the design and governance of climate finance mechanisms.
How to cite: Shi, J., Ou, Y., Niazi, H., and Guo, C.: Socioeconomic Development Shapes the Effectiveness and Equity of Loss and Damage Finance, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-447, https://doi.org/10.5194/egusphere-egu26-447, 2026.
Decarbonization is essential to combat climate change, but policies may unintentionally exacerbate inequities between communities. Although energy policy increasingly acknowledges equity concerns, most studies focus narrowly on distributional equity, often overlooking its procedural and contextual dimensions. Further, existing analytical tools used to inform policymaking rarely integrate all three aspects of equity systematically.
This study addresses these limitations by developing a framework for incorporating distributional, procedural, and contextual equity into decision-support models. The framework is applied to inform a strategy for the phaseout of natural gas power plants in California. Key equity-relevant metrics are identified through a structured literature review, and a large language model (LLM) is used with carefully designed prompts and operational definitions to weigh the relative importance of these metrics under different resource allocation (or shapes of justice) principles. This LLM-enabled procedure is used as a scalable, transparent method to rapidly synthesize the literature by systematically surfacing the range of interpretations reported in prior work and representing uncertainty in metric weights (rather than aiming for one optimized value). The resulting metric set is incorporated into a multicriteria decision-making (MCDM) model that assesses how different shapes of justice principles and equity metrics influence phaseout priorities. The framework is designed to accommodate broader stakeholder input and address common critiques of technocratic, top-down approaches. Together, these contributions introduce a novel methodological framework for integrating multiple dimensions of equity into energy transition decision-support models.
How to cite: Chowdhury, S.: Equity Consideration in Analytical Models Used for Decision Making: Conceptual Framework, and Case Study, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2694, https://doi.org/10.5194/egusphere-egu26-2694, 2026.
As international pressure to achieve carbon neutrality intensifies, electric vehicle (EV) adoption has become a pivotal policy instrument for decarbonizing the transportation sector. While governments have accelerated this transition through subsidies, the shift is causing a structural erosion of fuel tax revenues, threatening the sustainability of transportation infrastructure funding. Furthermore, the concentration of EV adoption among high-expenditure households skews policy benefits toward upper-income groups, while the fuel tax burden remains disproportionately on lower-expenditure households, raising concerns about distributional equity.
This study empirically analyzes the impact of EV expansion on the fiscal sustainability and distributional equity of transportation tax systems. Using survey data from 700 South Korean vehicle-owning households in 2024, we conducted dynamic simulations through 2050, integrating household-level EV adoption intentions and transition timing. We compared two scenarios: maintaining the current fuel tax system versus a full transition to a vehicle miles traveled (VMT) tax.
The analysis reveals that higher-expenditure households adopt EVs earlier and prioritize replacing secondary vehicles, confirming structural heterogeneity in transition behavior. Under the current fuel tax regime, transportation tax revenue is projected to decline by 10% by 2050, with the Kakwani index deteriorating from -0.611 to -0.644, indicating significant intensification of regressivity. While the VMT tax ensures superior revenue stability, it exhibits even stronger initial regressivity (Kakwani index of -0.645) compared to the fuel tax (-0.611) under identical driving patterns.
These findings challenge the conventional wisdom that VMT taxes inherently improve equity. In the Korean context, even technologically neutral instruments can exacerbate inequity due to heterogeneous mobility structures and transition pathways. We conclude that future transportation tax reforms must move beyond merely selecting taxation methods and instead focus on sophisticated institutional designs that account for income-specific mobility patterns and transition speeds.
How to cite: Lee, Y. and Woo, J.: Fiscal Sustainability and Distributional Equity of Transport Taxes under Electric Vehicle Transition: A Micro-Simulation Study of Fuel and VMT Taxes in South Korea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3893, https://doi.org/10.5194/egusphere-egu26-3893, 2026.
The mining industry is a nexus of global climate, nature, and economic challenges. The global energy transition depends on a range of minerals and metals, and on securing vital ecosystem values in the mining process. Failure to do so could disrupt supply chains and undermine confidence and momentum in the transition. Mongolia has one of the world’s richest endowments of minerals and metals and is among the last mining frontiers: less than one-third of its territory has been geologically surveyed and only 1% licensed for mining exploration. Exploiting this potential is crucial to realizing the country’s economic potential. However, to meet sustainability goals, Mongolia also needs to address the social and ecological risks from the expansion of industrial mining.
Our research provides strategies for transitioning Mongolia’s mining sector towards sustainability by incorporating natural capital assessments and valuations into the planning and operation of mining projects. Scalable industry standards for climate and land stewardship in the mining sector could be identified based on these analyses. We deploy the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) data and software platform to quantify how mining impacts critical ecosystem services – such as carbon sequestration, flood mitigation, maintenance of water quality, rangeland production, and sandstorm protection – and the resulting social and economic implications.
This is the first deployment of these tools to analyze mining-related impacts on natural capital, as well as the first application of asset-specific footprinting for mining supply chains. Quantifying these impacts and developing policies to mitigate them is crucial for the sustainability of the mining sector in Mongolia and many other countries.
How to cite: Wu, T., Zhu, M., Li, Y., and Fendorf, E.: Mainstreaming Natural Capital for Sustainable Mining in Mongolia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4123, https://doi.org/10.5194/egusphere-egu26-4123, 2026.
As the world struggles to limit emissions, Solar Radiation Management (SRM) has been proposed as a potential climate intervention. However, its implications for economic inequality and broader socioeconomic outcomes remain uncertain. To explore these questions, we used the coupled climate-economy model NorESM2-DIAM to simulate an idealised SRM experiment. NorESM2 is an Earth system model, while DIAM is a cost-benefit integrated assessment model using perfect foresight. The two models exchange temperature and CO2 emissions annually at a 1x1-degree resolution: temperatures from NorESM2 are passed to DIAM, where they affect economic productivity and the economic agents’ decisions. In DIAM, the agents make decisions about savings and energy use based on temperature, the current economic situation, and their expectations for the future. The resulting CO2 emissions are passed back to NorESM2.
In the experiment, we reduced solar insolation by 1% from 2030 onward, at which point the economic agents adjusted their expectations to account for SRM. The results suggest that SRM reduces economic inequality compared to no intervention. However, this outcome is accompanied by higher CO₂ emissions, which imply additional environmental and societal risks.
While this is an idealised experiment, it demonstrates potential trade-offs between geoengineering and socioeconomic outcomes. The high spatial resolution of the model offers future potential to assess regional inequalities and other distributional outcomes in greater detail. In addition, we plan to explore more realistic SRM scenarios and add additional climate–economy interactions.
How to cite: Bjordal, J., Van Dijk, E., Cornec, H., Smith, Jr., A. A., and Storelvmo, T.: Can Solar Radiation Management Reduce Economic Inequality? Insights from a Coupled Climate–Economy Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3980, https://doi.org/10.5194/egusphere-egu26-3980, 2026.
Large-scale Energy Storage Systems (ESS) are increasingly recognized as a cornerstone for grid flexibility and the expansion of renewable energy. Consequently, the Levelized Cost of Storage (LCOS) has been widely adopted as a key economic indicator across various electricity markets. While conventional LCOS methodologies effectively serve energy-oriented markets, they exhibit significant limitations in capacity-based contractual environments, where specific operational constraints and rigid capacity maintenance requirements are enforced. This study proposes an advanced LCOS estimation framework that explicitly incorporates two critical constraints: the mandatory maintenance of discharge capacity throughout the contract period and the prohibition of mid-term capacity expansion. To meet these requirements, the model integrates a 'preemptive oversizing strategy' at the initial installation phase to compensate for expected degradation. Furthermore, the framework endogenously reflects the dynamic feedback loop between capacity fading and degradation rates; specifically, it accounts for the gradual increase in the effective Depth of Discharge (DoD) required to maintain constant discharge energy as the system ages, which in turn accelerates cycle-life depletion. Comparative analysis using a simplified grid-scale ESS case demonstrates that traditional LCOS approaches systematically overestimate the economic feasibility of ESS under capacity-based contracts by neglecting the coupled effects of oversizing costs and DoD-induced lifespan reduction. This research clarifies that cost metrics must be tailored to the specific market structure and provides a robust methodological expansion to support consistent design, operation, and investment decision-making for ESS in evolving electricity markets.
How to cite: Choi, J. and Woo, J.: LCOS Methodology for Energy Storage Systems Incorporating Discharge Capacity Maintenance Constraints under Capacity-Based Contracts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4604, https://doi.org/10.5194/egusphere-egu26-4604, 2026.
The global climate crisis compels nations to pursue clean and sustainable energy sources to meet the demands of both economic and decarbonisation goals. Geothermal energy is a critical component of this transition, yet its utilisation is often hindered by varying public perceptions. News media content plays a pivotal role in shaping risk perceptions of deep geothermal energy exploration and production. Despite research into how text-based news media influences public perception, there is a notable gap in understanding the extent to which visual framing shapes public perceptions and attitudes towards geothermal energy. This research employs an image cluster approach to analyse how geothermal energy is visually framed in news media in the Philippines, Kenya, Germany, and Australia. We also examine whether visual representations include or marginalize local communities. By categorizing visual motifs ranging from industrial techno-optimism to localized environmental impacts and comparing across various contexts, we explore how visual narratives may shape perceived acceptability of deep geothermal projects. This work advocates the inclusion of social science within transition pathway design, ensuring that modelled scenarios of the energy transition are grounded on social reality, making them not only technically feasible but socially just and inclusive.
How to cite: Samantela, S., Handley, H., Bruns, C., and Dijkstra, A.: Beyond the Text: Visual Framing of Geothermal Energy in News Media, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7666, https://doi.org/10.5194/egusphere-egu26-7666, 2026.
The rapid integration of Artificial Intelligence (AI) into Earth system science promises a transformative revolution in predictive speed and fidelity, yet this technological prowess rests on a fragile and unequal foundation. We argue that the current trajectory of AI development risks automating and amplifying the historical North-South divide in the global climate information system. The systemic inequalities are manifested and compounded across the three primary stages of the AI modeling pipeline: input, process, and output.
At the input level, we highlight the risks of relying on global datasets, such as ERA5, which inadvertently inherit and reinforce geographic biases and observational gaps in the Global South; most notably in the Amazon and Sub-Saharan Africa. At the process level, we detail a profound compute sovereignty gap, where the concentration of exascale High Performance Computing infrastructure in the Global North gatekeeps the development of foundation models. Finally, at the output level, we demonstrate that AI-powered forecasting improvements are unevenly distributed, with wealthy regions seeing significantly greater skill gains than the vulnerable populations most in need of accurate early warning systems.
To steer this revolution toward just outcomes, we call for a move toward Climate Digital Public Infrastructure. By prioritizing data-centric AI, human-cost evaluation metrics, and knowledge co-production, we can ensure that the AI revolution fosters genuine systemic resilience rather than exacerbating global inequity.
How to cite: Mozaffari, A., Duarte, A., Teckentrup, L., Materia, S., Charnley, G. E. C., Palma, L., Baulenas Serra, E., Bojovic, D., Checchia, P., Carreric, A., and Doblas-Reyes, F.: The Rise of AI in Weather and Climate Information and Its Impact on Global Inequity, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7693, https://doi.org/10.5194/egusphere-egu26-7693, 2026.
Accelerating climate-tech innovation in the formative phase is crucial to meeting climate goals. However, effective green industrial policies require an understanding of when and where to target policy interventions within the value chain. We conceptualize nascent value chains for climate-tech as product clusters and explore innovation patterns within and across them. We analyze 14 climate-tech sectors using early-stage private investments in over 3,600 North American firms (2006-2021). In terms of product clusters, only 15% of firms develop end products, while 59% provide components, manufacturing, or optimization products, and 26% develop services. Investment evolution reveals three patterns of innovation: maturing innovation (e.g., energy efficiency), ongoing innovation (e.g., energy storage), and emerging innovation (e.g., agriculture). This characterization of nascent value chains offers an analytical basis for green industrial policy, identifying critical structural segments for intervention and illustrating how different value chain positions can create varied opportunities and pathways for regional benefit.
How to cite: Surana, K., Thomas, Z., Williams, E., and Edwards, M.: Green industrial policy for accelerating innovation in nascent value chains of climate-mitigating technologies , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10794, https://doi.org/10.5194/egusphere-egu26-10794, 2026.
Since the adoption of the first circular economy action plan in the European Union (EU) in 2015, this subject has become even more important by the years passing on. In 2019 the EU’s Commission implemented the European Green Deal as it’s flagship initiative, as a growth strategy which set the EU on the path to a green transition, with the ultimate goal of reaching climate neutrality by 2050. However, amids of recent geopolitics turmoils besides climate neutrality, green transition has been seen more and more as a tool for energy security by its contribution to energy diversification, a connenction clearly stated out by the EU’s so called REPowerEU Plan published after the break out of the Russian-Ukrainian war. Meanwhile, accelerating green transition means growing demand for some distinguished technologies like solar panels, wind turbines or accumulators, just like for a set of raw materials which are essential building blocks of these technologies. Nevertheless, the overall value chain network of these technologies in the EU tends to be heavily import dependent for example because there is a general lack of availability for many of these raw materials within its territory. The EU itself realised both the economic and geopolitical consequences of this situation and brought up its master plan the so called Critical Raw Materials Act (CRMA) in 2024 to mitigate it by improving capacities all along the supply chains. Taking into account the lack of raw materials just like the occasionally strong but eventually a small global industrial share in the vast majority of cases, recycling as part of the wider circular economy concept could be a key feature to improve availability of these important scarce elements. In this paper the authors' aims are threefold. First, they would like to outline the evolution of the EU’s circular economy policy, focusing on raw materials. Second, besides the general lack of raw materials in the EU, they would present the different devices and their respective raw materials needs as well as their recycling tendencies, changes, prospects, concerning for example their end-of-life recycling rate (EOL-RR) and end-of-life recycling input rate (EOL-RIR). During this process, a special focus will be put onto rare earth elements (REEs) and permanent magnets. The reason behind this choice is the fact that these permanent magnets (PMs) have a wide range of applications including industry, energy and defense sectors. This means that PMs are in the very heart of the most pressing questions of the EU like green transition, competitiveness, reindustrialisation and rearmament. Finally, authors would like to present the current state of the act of recycling which encompasses some future prospects. For all of these, official EU documents will be analysed in depth. Besides, a special attention will put on some implementation of the PMs in depth as well. The first set of so-called Strategic Projects related to strategic raw materials approved by the EU Commission under the CRMA in 2025 will be also discussed from the angle of recycling.
How to cite: Papp, G. and Magda PhD, Dr. R.: The role of circular economy in the EU’s strategy for critical raw materials, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11498, https://doi.org/10.5194/egusphere-egu26-11498, 2026.
Climate change mitigation can save lives and improve health through multiple pathways, such as reducing air pollution, promoting active transport, and facilitating healthier diets. These immediate health co-benefits can provide stronger incentives for climate action beyond mitigating health risks associated with global warming and extreme weather events. Thus, a comprehensive stocktake of mitigation costs, health co-benefits, and their consequent cost-effectiveness is critical to better prioritize health gains while achieving Nationally Determined Contribution (NDC) goals. This study first synthesizes findings on mitigation costs, health co-benefits, and cost-effectiveness of climate actions from global and regional health-included Cost-Benefit Analysis (CBA) studies. It then conducts an in-depth analysis of challenges in designing and implementing health-considered climate policies in real-world contexts, and finally proposes strategies for the scientific community to advance health-considered or even health-centered mitigation targets, technology pathways, and implementation strategies.
Global evidence indicates that air pollution-related health co-benefits of climate policies usually outweigh mitigation costs, with Benefit/Cost Ratios (BCRs) ranging from 1.10 to 2.45, meaning each $1 invested in mitigation yields $1.10~2.45 in health co-benefits (Figure 1). Notably, regional BCRs vary by up to 40-fold. Regions with high air pollution and population density (e.g., China and India) have greater health co-benefits, while developed regions (e.g., Europe, USA) with stringent pollution controls show lower co-benefits.
Figure 1 (a) Total carbon reductions (10 Mt), mitigation costs and health benefits (billion 2015 USD) from different original studies (n=332), and (b) regional distribution of annual BCR from different original studies.
Key recommendations include: (1) adopting policy-relevant methods (e.g., the Cost of Illness method, which incorporates tangible region-specific healthcare expenditure data to quantify the reduction of healthcare system burden) to monetize health co-benefits, replacing the Value of a Statistical Life (VSL)-based approaches; (2) fostering interdisciplinary collaboration (involving economists, political scientists, and sociologists alongside climate and health researchers) and strengthening cross-sector policy engagement, particularly engaging high-level decision-makers to establish interdepartmental collaboration frameworks that bridge fragmented governance; (3) conducting more national, subnational (especially in the Global South), or city-level localized studies and enhancing inter-study comparability through unified modeling frameworks and transparent data disclosure protocols (e.g., the Pathfinder Initiative, which integrates health impact data across pathways and regions); and (4) exploring health-optimized mitigation pathways (Figure 2) by addressing three core policy questions (i.e., target allocation across regions/sectors, optimal technology selection, and regionally tailored implementation), and incorporating health co-benefits into model objective functions to shift decision-making from traditional cost minimization to net benefit maximization. This work aims to provide actionable scientific guidance for integrating health co-benefits into climate mitigation modelling and policymaking, ultimately enhancing both climate and public health outcomes.
Figure 2 Conceptual framework for optimizing health-considered mitigation pathways. (a) 3 different research questions mentioned in the text. (b) Additional methods (in color red) to optimize climate policy with health co-benefits compared with current one-way assessment studies. (c) The mechanism of how the differentiated health benefits (in the color red) impact climate policymaking. (Source: Shen et al., Improving cost–benefit analyses for health-considered climate mitigation policymaking, Nature Climate Change, 2025)
How to cite: Shen, J. and Cai, W.: Integrating Health Co-benefits into Climate Mitigation Policymaking, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16575, https://doi.org/10.5194/egusphere-egu26-16575, 2026.
Incorporating climate action, resource efficiency, and circularity performance within the EU’s industrial transition is a well understood necessity—especially in an environment contested by geopolitical developments and competitiveness concerns. However, the transformations and profound energy and material reconfigurations required towards a coordinated industrial transition are often hampered by divergent regional strategies and potential spatial inequalities. Research in support of these policy processes is often constrained by disciplinary boundaries; notably, energy- and climate-economy models typically used to enable assessments of decarbonisation efforts across multiple industrial value-chains and technologies lack the necessary spatial explicitness and often fail to represent the industrial sector with adequate granularity to address the physical realities and diverse needs of different industrial clusters. Here, we adopt a triangulation approach for informing the industrial low-carbon, circular transition in a transdisciplinary setting that revolves around co-creation and Systems of Innovation perspectives, with the aim to output actionable insights for quantitative systems modelling. Our approach is applied to four representative industrial clusters in Europe. We first establish a stakeholder engagement process with regional and EU actors, to produce key policy- and industry-relevant guiding questions. We then apply socio-technical analysis using integrated frameworks comprising the Multi-Level Perspective and Technological Innovation Systems, to uncover enabling mechanisms for, and hurdles to, the transition. Towards informing place-based scenarios that respond to industrial needs, societal expectations, and climate targets, we highlight aspects that modelling scenarios alone cannot capture without spatiotemporally refined inter- and trans-disciplinary methods, including the role of game-changing disruptions, cross-sectoral cooperation, and industrial symbiosis.
How to cite: Tigka, C., Koasidis, K., Ruß, M., Hermwille, L., Rizos, V., Righetti, E., Nipius, L., Sütő, G. M. (., Shen, L., Gorczyca, A., Bialas, P., Ziecina, A., Muñoz Mateos, I., Garcia Gusano, D., Jimenez Iturriza, I., Efthymiades, P., Baka, M.-I., Domenech Aparisi, T., and Nikas, A.: Stakeholder insights and socio-technical perspectives for analysing sustainable transitions in energy-intensive industrial regions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17455, https://doi.org/10.5194/egusphere-egu26-17455, 2026.
High-resolution climate model products are increasingly embedded in climate impact analyses (CIA) and adaptation planning across diverse societal sectors. While advances in regional climate modelling and statistical downscaling methods have improved the spatial granularity of climate information, recent studies demonstrate that model reliability and bias characteristics vary substantially by region, variable, and modelling framework. These variations raise critical questions not only about scientific robustness and the reliability of impact analyses, but also about the equity and fairness in how climate information is produced, made available, and applied in decision-making.
Responding to growing calls within the climate science community to integrate social science perspectives and justice considerations into climate modelling, this study develops and applies a climate data justice framework to assess the equity and efficacy of downscaled climate data for CIA across Europe. Rather than proposing a normative definition of “just climate data,” we identify sector-specific contexts through which climate data must be generated, evaluated, and stewarded to ensure fair premises for adaptation to changing climate and extreme weather.
We first map sectoral climate data needs by identifying key climate risks, required variables, and temporal resolutions relevant to societally critical sectors. We then compile a comprehensive inventory of publicly available high-resolution climate datasets (including EURO-CORDEX, NEX-GDDP, and Climate Impact Lab products), documenting metadata on spatial and temporal resolution, ensemble composition, scenario coverage, accessibility, and licensing. A crosswalk analysis is used to match sectoral requirements with available datasets.
Building on data justice theory and recent work on defining successful climate services for adaptation, we operationalize the concept of climate data justice across three dimensions: procedural (transparency and accuracy), rights-based (availability and accessibility), and instrumental (applicability and usability for decision-making). A battery of questions with scoring enables quantification and systematic comparison of climate datasets and their availability, accessibility accuracy, and applicability with respect to specific geographic region and industry. The framework is demonstrated through representative sector–region case studies, including agriculture in Ukraine, healthcare in the Nordics, tourism in the Alps, and manufacturing in Portugal.
The results provide a justice-oriented assessment identifying where current climate data infrastructures underserve specific sectors or regions. The study delivers a reproducible framework for evaluating climate data utility, contributes to the integration of justice perspectives in climate modelling, and offers actionable guidance for climate impact analysts, data providers, and funders seeking to strengthen equitable and effective climate adaptation across Europe.
How to cite: Hulkkonen, M., Leppänen, S., Laakso, A., McCarty, J. L., Kokkola, H., and Mielonen, T.: A Data Justice Framework for Evaluating Accessibility, Accuracy and Applicability of High-Resolution Climate Model Data for Climate Impact Analysis in Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17488, https://doi.org/10.5194/egusphere-egu26-17488, 2026.
In order to mitigate climate change, cooperation is needed among actors with different levels of power and vulnerability to climate harms. We propose a minimal model for climate mitigation, a two-player continuous-time (differential) game. Each player starts with a fossil fuel stock that determines their contribution to a global emissions pool. Both players suffer damage from climate change due to total cumulative emissions. Each player can pay to reduce their individual fossil stock, which in turn prevents future harm for both players; this is thus a public goods game wherein we label fossil stock reductions as cooperation. We compute the optimal strategies of the players under two forms of inequality: inequality in the players' vulnerability to climate harms, and inequality in their starting fossil fuel stock. Both types of inequality lead to reduced cooperation and greater total emissions, and the least cooperation resulting when both types of inequality are present. We provide simple mechanistic explanations for this result within the context of the model. We also analyse a version of the game where players may invest in renewable energy and find qualitatively similar conclusions.
How to cite: Bach, E., Tafreshi, A. G., and Akçay, E.: Inequality can prevent cooperation in a minimal differential game for climate mitigation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20028, https://doi.org/10.5194/egusphere-egu26-20028, 2026.
Marine carbon dioxide removal (mCDR) is increasingly included in integrated assessment model (IAM) scenarios, particularly in pathways that allow for temperature overshoot or delayed emissions reductions. While these scenarios explore the technical contribution of mCDR to long-term climate targets, they often leave implicit key assumptions about where deployment occurs, over what time horizons climate benefits are realized, and who bears responsibility for long-term oversight and risk.
This contribution presents ongoing research that applies a justice-informed framework to the interpretation of mitigation scenarios including mCDR, using justice as an internal evaluative dimension rather than an external critique of models. Drawing on recent work in justice-oriented scenario analysis, the framework specifies justice concerns along three axes: spatial scale, temporal scale, and the scope of affected entities.
Spatially, the analysis examines how scenario representations obscure the geographic distribution of mCDR deployment and governance responsibility, with particular attention to transboundary impacts and implications for regions with limited regulatory capacity, many of which are located in the Global South. Temporally, the framework assesses whether assumed climate benefits are aligned with the durability of storage and the long-term monitoring and liability obligations imposed on future generations, highlighting intergenerational justice concerns. Finally, where scenarios imply large-scale or irreversible impacts on marine ecosystems, justice-based assessment is complemented by environmental ethical considerations that extend beyond an exclusively anthropocentric focus.
Using selected overshoot and net-zero pathways as illustrative cases, the paper shows how mitigation scenarios may appear technically coherent while relying on ethically fragile assumptions about governance capacity, permanence, and long-term responsibility. By making these assumptions explicit and comparable across scenarios, the contribution aims to support closer integration of social science and normative insights into climate modeling, improving the transparency and policy relevance of scenario-based assessments of emerging mitigation options such as marine CDR.
How to cite: Sehdev, G.: Justice Dimensions in Climate Mitigation Scenarios: Insights from Marine Carbon Dioxide Removal, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20270, https://doi.org/10.5194/egusphere-egu26-20270, 2026.
How to cite: Wang, F.: Unlocking cost-competitive synthetic graphite in Saudi Arabia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20420, https://doi.org/10.5194/egusphere-egu26-20420, 2026.
Participatory approaches to integrated assessment modelling seek to bring a more diverse range of views into the modelling process, to build a better understanding of the societal context while supporting more inclusive and fairer decision-making. This need for co-creation reflects a growing trend towards societally engaged and action-oriented research across sustainability science. Efforts to integrate stakeholder inputs in IAM-based research to strengthen legitimacy and transparency of the scientific process and the desirability of its results have remained sparse, with participation often limited to top-down formats in which stakeholders are consulted but rarely able to directly shape choices, outputs, or policy prescriptions. Furthermore, there has been little practical how-to guidance for well-structured participatory IAM processes in a domain that has long acknowledged the added value. Any such process should go beyond well-structured procedures and offer flexibility to quickly adapt to a changing policy landscape and thus to shifting stakeholder priorities.
We designed and implemented a participatory process intended to support acceptable, robust, and durable transition strategies, while strengthening trust between modelling researchers and stakeholders. The process was designed to produce outputs that are intelligible in terms of real-world implications and actionable in terms of concrete policy recommendations. In practice, the process began by scoping and prioritising relevant stakeholder groups and policy questions; it then engaged stakeholders in co-designing the analytical approach used to address these questions; interim results were iteratively refined based on stakeholder feedback; and dedicated discussions supported shared interpretation of findings, which were distilled into policy briefs.
A key lesson from implementing this multi-stage process was the overly thematic structure: framing exchanges around broad “climate and energy transition” topics often diluted sector-specific dynamics and actionable insights. Going forward, engagement could be organised around key drivers and barriers within each sectoral system (e.g., infrastructure and technology constraints, investment and competitiveness, regulatory bottlenecks, distributional impacts, and feasibility). To operationalise this shift, we propose a re-design of the participatory approach into iterative sectoral conversations that enable continuous exchange between the research process and relevant international debates, drawing on prior experience with knowledge co-production and multidisciplinary transition research and aligning with established scholarship on knowledge co-production for sustainability research. The participation proceeds along two integration tracks: (i) interdisciplinary synthesis linking country-level sectoral findings with global-level analysis, and (ii) transdisciplinary exchanges with a small, carefully selected group of international sector experts, complemented by broader expert-facing events.
How to cite: Frilingou, N., Keliauskaite, U., Broer, R., Jüngling, E., Zachmann, G., Heussaff, C., Obergassel, W., Venjakob, M., Holtz, G., Ortiz, W., Briand, Y., Guazzini, V., Xexakis, G., Koasidis, K., and Nikas, A.: Advancing participatory modelling for climate policy assessments: toward policy-relevant, conversation-driven climate-economy modelling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21460, https://doi.org/10.5194/egusphere-egu26-21460, 2026.
Integrated Assessment Models (IAMs) have traditionally relied on exogenous assumptions of human behaviour, rather than representing endogenously the social systems dynamics that influence decision-making under climate change. Within this context, the FRIDA model addresses a key limitation of conventional IAMs by introducing a fully endogenous behavioural change modelling framework, allowing behavioural representations to be extended across multiple domains.
This study aims to extend the FRIDA behaviour change module by advancing the endogenous representation of agricultural decision-making. It extends the FRIDA decision-making framework to develop a producers’ behaviour submodule that will extend FRIDA’s endogenous representation of decision-making in agriculture, including crop and the livestock sectors. A fertilizer demand submodule, structured in line with existing behaviour change components, explicitly focuses on perceived accessibility, reflecting economic and systemic constraints that can limit fertilizer use. To represent these dynamics, the submodule is dynamically linked to the Energy module of FRIDA, allowing fertilizer demand to respond endogenously to changes in natural gas prices and availability. For the livestock sector, this study incorporates key drivers of decision-making, including attitudes toward practices, perceived climate change risk, habits and social norms, which have been shown to affect the adoption of sustainable land-use strategies, such as integrated systems (IRs) and sustainable animal housing systems.
Preliminary results show a reduction in fertilizer demand that is endogenously driven, avoiding the need for exogenous caps. Results for the livestock sector are pending additional model development.
This work is supported by FCT, I.P./MCTES through national funds (PIDDAC): LA/P/0068/2020 - https://doi.org/10.54499/LA/P/0068/2020 , UID/50019/2025, https://doi.org/10.54499/UID/PRR/50019/2025, UID/PRR2/50019/2025. This work has also received funding from the European Union’s Horizon 2.5 – Climate Energy and Mobility programme under grant agreement No. 101081661 through the 'WorldTrans – TRANSPARENT ASSESSMENTS FOR REAL PEOPLE' project.
How to cite: Costa, W., Schoenberg, W., Rajah, J., Blanz, B., Mahú, F., and Köberle, A.: Behavioural Dynamics in Agriculture within IAMs: Extending the FRIDA Model with Fertilizer and Livestock Decision Modules, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21053, https://doi.org/10.5194/egusphere-egu26-21053, 2026.
