Inclusive integrated assessment methodology for decarbonisation policies through the lens of systemic disruptions, climate finance, and equity

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Researchers in climate policy modelling will find a coherent methodological chain for making IAMs more policy-relevant, including best practices for stakeholder engagement, model coupling, and stress-testing under disruptions.

🏛政策担当者:Policymakers will benefit from the demonstration that recovery packages and diversified strategies can accelerate decarbonisation, and from tools to assess distributional impacts of carbon pricing (e.g., regressivity) and the cost implications of technology constraints.

Under the Paris Agreement, countries are required to design and implement progressively stronger climate policies through successive Nationally Determined Contributions (NDCs), yet both current policies and the first rounds of NDCs remain insufficient to meet the Agreement’s temperature objectives. This ambition gap—together with the expectation of more stringent post-2030 commitments—places unprecedented weight on climate – economy modelling to support credible policy packages spanning technology, markets, institutions, and social change. Integrated assessment models (IAMs) have become central to this evidence base, but their practical value is often constrained by persistent legitimacy and usability challenges: stakeholders are rarely embedded in scenario processes in ways that build trust and ownership, and demand-side behaviour and broader sustainability objectives are still commonly treated as external narratives rather than core drivers. Recent disruptions, notably COVID-19, the energy crisis, and the emissions shifts associated with societal and economic shocks, further underscore that durable mitigation depends on the co-evolution of structural, behavioural, and policy change—making it imperative that model-based analysis be transparent, socially grounded, robust under uncertainty, and actionable. Building on this premise, the dissertation develops and demonstrates an integrated framework for producing climate – policy evidence that is legitimate, transparent, robust, and decision-relevant, with IAMs as a central analytical backbone. It argues that credibility is not secured by technical sophistication alone, but by a coherent methodological chain that aligns modelling with policy needs, makes assumptions and workflows traceable and reusable, stress-tests insights across models and scales within real policy architectures, expands modelling to include disruptions, finance, equity, and sustainability trade-offs that shape feasibility, and translates results into clear, actionable explanations and a future research agenda. The framework is organised into five intertwined pillars: Listening, Exchanging, Modelling, Expanding, and Explaining.First, the “Listening” pillar institutionalises co-creation as a formal element of scenario analysis. The dissertation designs a structured mechanism for iterative two-way engagement between modelling teams and stakeholders, through which priorities and constraints are translated into research questions, scenario narratives, and technical assumptions, strengthening trust and a sense of “ownership” of the results. This pillar is complemented by expert co-design tools such as Fuzzy Cognitive Maps (FCMs) and an open-source web-based FCM software application that supports Monte Carlo uncertainty analysis. Together, these elements serve as a practical bridge from consultation and experiential knowledge to quantitative scenario analysis, especially when available quantitative data are limited.Second, within the “Exchanging” pillar, an operational protocol of open and reusable practices is proposed (harmonised definitions, documentation, data management, interoperability) to address transparency and comparability gaps that often weaken the policy usefulness of IAMs. This protocol combines: (i) harmonised definitions and inputs to ensure comparability; (ii) data management consistent with the FAIR principles, technical documentation, and—where feasible—open-source code to enable traceability and reuse; and (iii) shared protocols for model linking, to ensure conceptual and temporal consistency in coupled modelling systems. In addition, best practice in model coupling is advanced by making explicit the technical and conceptual choices, i.e., variable definitions, spatial–temporal resolution, depth of coupling, that decisively shape the interpretation of results, and by developing a practical “checklist” to support well-documented decisions when linking climate–economy models. Third, the “Modelling” pillar demonstrates how these foundations can be applied in analyses across multiple scales and economic sectors, linking climate–economy models and leveraging ensembles of approaches to make conclusions more robust and policy-relevant. The dissertation makes a substantive contribution to the EU climate-policy literature by filling a critical gap in the assessment of the Fit-for-55 package in relation to the targets of updated National Energy and Climate Plans (NECPs), through analysis at the Member State level and with a detailed representation of the electricity sector. It develops a global database of COVID-19 recovery packages and translates them into explicit inputs across multiple IAMs, showing that recovery funding can accelerate decarbonisation in key sectors, but is insufficient to sustain a Paris-consistent pathway without complementary structural reforms. In the context of the European energy crisis after 2022, an expert-based FCM analysis is combined with quantitative IAM studies, first within a single IAM and subsequently across a model ensemble, to examine policies that jointly address mitigation, affordability, and electricity-system reliability, as well as disruptions in Russian natural gas supply through the simulation of alternative strategies for its full replacement. Finally, constraints on critical low-emission technologies, arising from geopolitics, supply chains, or social acceptance, are examined, showing how they can increase costs, reinforce dependence on fossil fuels, and jeopardize 2050 targets. Particular emphasis is placed on the “Expanding” pillar, which constitutes the dissertation’s fourth pillar, aiming to capture drivers of real-world feasibility that often remain underrepresented, such as disruptions, the Sustainable Development Goals (SDGs), finance, distributional impacts, and climate justice. The Disruptive Event-Resilient Pathways (DERP) framework is introduced to move beyond “smooth transition” assumptions by mapping pathways along mitigation ambition and technoeconomic resilience, and by subjecting strategies to “stress tests” under disruptions. Results show that socioeconomic resilience is a prerequisite for cost-effective deep decarbonization; that narrow reliance on a single technological solution creates hidden vulnerabilities and delays structural change; and that diversified strategies can offset disruption risks at negligible additional cost. At the same time, by combining IAMs with multi-objective optimisation algorithms, a framework is developed that explicitly incorporates performance on SDG indicators and maps the corresponding Pareto frontiers, revealing critical trade-offs by sector (energy, land use, industry, transport, buildings) and ambition level (1.5–2°C). Financial “realism” is incorporated by exploring trajectories of the cost of capital by region and technology and through a “corrective justice” policy that recycles a windfall-profit tax to support risk-reduction for regions facing higher costs of capital, illustrating how financial conditions transform investments and emissions. Equity is assessed by linking a European IAM with a high-resolution distributional tool, showing that EU carbon pricing can be cost-effective yet regressive, whereas nationally tailored policy mixes reduce regressivity; intersecting dimensions such as gender and the urban–rural divide further shape vulnerability. Finally, climate finance is analysed through the lens of accounting boundaries, geographic allocation, and governance incentives, revealing a portfolio of global flows oriented toward mitigation and geographically concentrated, weak alignment with countries’ economic capacity, and patterns consistent with intermediation incentives that favour bank-financed debt over grants and adaptation actions. The fifth “Explaining” pillar synthesises the dissertation’s contributions and conclusions and proposes a forward-looking research agenda. Explanation is treated as a methodological requirement: assumptions must be traceable, uncertainties communicable, and trade-offs explicit, in order to support informed and legitimate decision-making. The dissertation concludes with priorities for advancing policy support through IAMs, including deeper integration of financial-system behaviour, stronger representation of sociopolitical feasibility, richer treatment of risks and disruptions, and the development of model openness tools that enable a cumulative, trustworthy science of mitigation scenarios. Overall, the dissertation provides a comprehensive blueprint for policy-oriented climate and energy modelling that links co-creation, open science, multi-model robustness, and expanded representations of disruptions, climate finance, equity, and sustainability into a coherent, end-to-end approach for actionable transition guidance.

• openalex http://hdl.handle.net/10442/hedi/61163first seen 2026-08-02 18:45:10