Thursday, March 12, 2026 | 5:00 - 6:00 PM
Politecnico di Milano - Leonardo Campus
Alpha Room (Building 24)
Speakers: Davide Stucchi, Jacopo Ghirri
Contacts: phd-step@polimi.it
Abstract
Join us for the sixth seminar in our Meet the STEP-CHANGErs series, a platform showcasing the innovative research of PhD students in the Science, Technology, and Policy for Sustainable Change program. Each session explores cutting-edge sustainability challenges and solutions, offering insights into how emerging research shapes real-world systemic change. The seminars also provide a valuable networking opportunity, with an aperitif following each session.This session features two exciting talks.
An Integrated Modeling Framework to Guide Performance-Based Urban Forestry Under Climate Stress
Davide Stucchi
Urban trees are crucial for climate-resilient cities, providing essential ecosystem services as local climate regulation and stormwater management. However, the effective planning of these biological assets is affected by modeling frameworks that treat vegetation as static infrastructure, overlooking the dynamic physiological responses of plants to the unique stressors of the urban environemnt.
Thus, the study begins with a systematic review to identify how much ecosystem condition is guiding ecosystem service assessments. To address these limitations resolve, this doctoral research developes a dynamical and mechanistic modeling framework designed to simulate core biological functions at an individual level tree, and so the performance under non-stationary environmental conditions. This biological engine was subsequently coupled with a high-resolution hydrological framework to bridge the gap between decadal biomass growth and sub-daily urban hydrology.
The results demonstrate that traditional modeling approaches often suffer from an optimistic bias by neglecting high-frequency physiological stressors. Furthermore, the findings highlight a critical establishment gap for juvenile trees in sealed environments, suggesting that urban greening success is strictly bounded by hydrological carrying capacity. Ultimately, this work advocates for a transition from quantity-based targets toward performance-based planning and the strategic preservation of mature biological assets.
Climate Policy Under Fear of Model Misspecification
Jacopo Ghirri
This research addresses a central challenge in climate policy: how should decision-makers act when the models they rely on may be fundamentally inadequate? Climate policy evaluation depends on quantitative models to inform its expected impacts, yet these models are simplified representations of systems whose behaviour remains poorly understood. Standard uncertainty analyses consider variability within models and disagreement across them, but typically assume that the correct model is among those considered.
This work goes further by incorporating model misspecification, the concern that none of the available models may be correct, into the policy evaluation process. Drawing on recent advances in decision theory, the study develops a methodology that allows policymakers to formally hedge against this deeper form of uncertainty. The framework is applied to ten climate-economy model combinations, built from five integrated assessment models and two damage function paradigms from IPCC-reviewed scenarios, evaluating the effects of long-term climate targets defined by carbon budgets and emission overshoots.
The results show that when policymakers take model inadequacy seriously, they should pursue more stringent climate targets, whereas accounting only for model ambiguity without considering misspecification can paradoxically lead to less ambitious policies, suggesting that a partial treatment of uncertainty may be misleading.
Please register here.
This research addresses a central challenge in climate policy: how should decision-makers act when the models they rely on may be fundamentally inadequate? Climate policy evaluation depends on quantitative models to inform its expected impacts, yet these models are simplified representations of systems whose behaviour remains poorly understood. Standard uncertainty analyses consider variability within models and disagreement across them, but typically assume that the correct model is among those considered.
This work goes further by incorporating model misspecification, the concern that none of the available models may be correct, into the policy evaluation process. Drawing on recent advances in decision theory, the study develops a methodology that allows policymakers to formally hedge against this deeper form of uncertainty. The framework is applied to ten climate-economy model combinations, built from five integrated assessment models and two damage function paradigms from IPCC-reviewed scenarios, evaluating the effects of long-term climate targets defined by carbon budgets and emission overshoots.
The results show that when policymakers take model inadequacy seriously, they should pursue more stringent climate targets, whereas accounting only for model ambiguity without considering misspecification can paradoxically lead to less ambitious policies, suggesting that a partial treatment of uncertainty may be misleading.
Please register here.