IAM_COMPACT_Study_3_Geopolitics

This dataset contains the underling raw data of IAM COMPACT "Study 3 - Geopolitics". 

This study aims to respond to the research question “What are the different cost, energy security and resilience metrics and how do they compare for different scenarios?” (it groups 4 policy questions) by assessing the effectiveness of transformational responses to Climate Change through a comparative analysis of socio-economic outcomes across different resilience scenarios.

Disruptive events pose significant risks to socio-economic systems, necessitating a comprehensive framework to understand and address their potential impacts. Part of this study builds upon van Ginkel et al. proposed dimensions of transformational response and resilience to socio-economic impact, to create a novel matrix for exploring climate-related extremes and their socio-economic consequences.

The resulting scenario matrix yields four distinct narratives: Business as Usual, Unadaptive Transformation, Incremental Resilience, and Effective Transformation. These narratives represent varying combinations of transformational response and resilience to socio-economic impacts, offering a nuanced perspective on potential future pathways. The Business as Usual scenario depicts a society with low transformation and resilience, while the Unadaptive Transformation narrative illustrates the pitfalls of narrow focus in climate change mitigation efforts. Incremental Resilience represents a society that achieves high resilience through expanding existing capacities rather than transformative changes. Finally, the Effective Transformation scenario embodies the ideal resilient future, characterized by significant, well-rounded transformations and high resilience to socio-economic impacts.

This framework provides a valuable tool for policymakers, researchers, and stakeholders to assess the complex interplay between climate change tipping points and socio-economic systems, facilitating more informed decision-making and strategy development in the face of climate uncertainties.

Results of the study have been documented in D5.4 - Modelling out-of-ordinary extremes (DOI 10.5281/zenodo.13840866)