2026/02/11 by Shonali Pachauri, Elina Brutschin, Mathew J. Gidden +20 · 1 voice · 1 citation
Environmental Science · Social Sciences · Agricultural and Biological Sciences · #Sustainability and Climate Change Governance #Climate Change, Adaptation, Migration #Climate change impacts on agriculture
paper · doi:10.1371/journal.pclm.0000763
openalex created_date 2026/02/11 · openalex publication_date 2026/02/11 · openalex updated_date 2026/07/28
In this work, we conduct a narrative review of pressing equity and justice issues within global modelled scenarios and propose a new research agenda to strengthen their consideration in future model developments and applications. We begin by introducing a typology of equity and justice limitations in climate mitigation scenarios, distinguishing among structural, methodological, and epistemological issues that shape what integrated assessment models (IAMs) can reveal at policy-relevant scales. Reflecting on these concerns, we develop a research agenda that describes new avenues of work and draws together distinct emerging initiatives, ranging from incremental improvements to structural reforms and alternative participatory approaches. Drawing on reflexive insights from integrated assessment practitioners, this agenda prioritizes embedding equity principles directly into scenario design through differentiated effort sharing and finance flows, developing new frameworks that incorporate sufficiency and demand transformations while protecting decent living, and establishing genuine co-production with underrepresented communities beyond mere consultation. Underlying this research agenda is a recognition that modeling communities must engage more critically with the implicit assumptions in scenario and model design and use that have equity and justice implications. Achieving equitable climate futures will require transformative actions that integrate diverse justice concerns, advance sustainable development, and confront systemic inequities across both human and ecological dimensions. Although models will never capture all these aspects, these can be significantly enhanced to support more informed discussion and practical application. Our contribution proposes a way forward to achieving this goal.