IAM_COMPACT_D5.4_Modelling_out_of_ordinary_extremes
Year: 2024
Type: article
Abstract: The deliverable aims to enhance the scenario analyses in WP4 by exploring a broad spectrum of uncertainties, including extreme environmental, technological, policy, and societal shifts. It focuses on resilient pathways and technologies in both national and global contexts, under extreme conditions. The urgency of addressing climate change underscores the importance of integrated assessment models (IAMs) in decision-making, especially in energy. IAMs, however, struggle with the complexities of real-world systems and extreme events like sudden technological or policy changes and natural disasters. The study addresses these challenges by examining critical questions across dimensions, defining extremes, understanding geographical differences, identifying system vulnerabilities, assessing model adaptability, and integrating new frameworks and technologies, aiming to improve IAMs’ response to extreme events in the energy sector. The literature review discusses IAMs’ role in capturing extreme events, their types, impacts, and the need for IAMs to incorporate such conditions beyond conventional models. It categorises extremes, explores their origins and impacts, and proposes a scenario framework and narratives informed by recent studies and the NGFS scenario approach. This framework considers climate and socio-economic tipping points through transformational response and resilience, presenting four narratives: Business as Usual, Unadaptive Transformation, Effective Transformation, and Incremental Resilience. Expert interviews explore how technologies and measures can enhance resilience in transformation pathways during extreme events and identify feasible mitigation trajectories. Key themes from the interviews highlight the distinction between risk and uncertainty, technological impacts, abrupt policy changes, regional differences, vulnerabilities, and challenges in adapting IAMs. The interviews underscore the need for adaptable strategies, robust modelling, and interdisciplinary collaboration.
Authors: Khourdajie, Alaa Al, Fragkos, Panagiotis, Zisarou, Eleftheria, van de Ven, Dirk-Jan, Rinaldi, Lorenzo, Hawkes, Adam, Mittal, Shivika, Nieto Vega, Jaime
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: green
Funder: European Commission
OpenAlex ID: W6911784942
FWCI:
Cites:
Cited by:
Related to: