Nature scenario plausibility: A dynamic Bayesian network approach

Chiara Colesanti Senni and Skand Goel

Ecological Economics, 2025, vol. 236, issue C

Abstract: To cope with the lack of quantifiable knowledge about the occurrence of nature-related risks, scenario analysis has emerged as a way to investigate possible futures. We argue that expressing scenario narratives as causal models – leveraging causal Bayesian graphs – opens up new avenues for designing and using scenarios. As one use case of this approach, we show how dynamic Bayesian networks to assess the plausibility of high-dimensional quantitative scenarios. We provide an algorithm that probabilistically evaluates whether a quantitative scenario is consistent with a certain narrative about nature-economy linkages. This can allow the user to choose among several available scenarios using a data-driven approach. As a demonstration, we apply this approach to data from an integrated assessment model.

Keywords: Nature scenarios; Plausibility; Dynamic Bayesian networks; Financial sector (search for similar items in EconPapers)
JEL-codes: E17 G21 Q20 Q56 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolec:v:236:y:2025:i:c:s0921800925001302

DOI: 10.1016/j.ecolecon.2025.108647

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