2024 / Predrag Pilipovic, Adeline Samson, Susanne Ditlevsen

Parameter estimation in nonlinear multivariate stochastic differential equations based on splitting schemes

The Annals of Statistics, Vol. 52, No. 2, 842-867
2024 / Catarina Midões, Enrica De Cian, Giacomo Pasini, Sara Pesenti, and Malcolm N. Mistry

SHARE-ENV: A Data Set to Advance Our Knowledge of the Environment–Wellbeing Relationship

Environ. Health, 2, 95-104
Climate change interacts with other environmental stressors and vulnerability factors. Some places and, owing to socioeconomic conditions, some people, are far more at risk. The data behind current assessments of the environment–wellbeing nexus is coarse and regionally aggregated, when considering multiple regions/groups; or, when granular, comes from ad hoc samples with few variables. To assess the impacts of climate change, we require data that are granular and comprehensive, both in the variables and population studied. We build a publicly accessible data set, the SHARE-ENV data set, which fulfills these criteria. We expand on EU representative, individual-level, longitudinal data (the SHARE survey), with environmental exposure information about temperature, radiation, precipitation, pollution, and flood events. We illustrate through four simplified multilevel linear regressions, cross-sectional and longitudinal, how full-fledged studies can use SHARE-ENV to contribute to the literature. Such studies would help assess climate impacts and estimate the effectiveness and fairness of several climate adaptation policies. Other surveys can be expanded with environmental information to unlock different research avenues.
2024 / Daniel Torren-Peraire, Ivan Savin and Jeroen van den Bergh

An Agent-Based Model of Cultural Change for a Low-Carbon Transition

Journal of Artificial Societies ans Social Simulation, 27(1) 13
Meeting climate goals requires radical changes in the consumption behaviour of individuals. This necessitates an understanding of how the diffusion of low-carbon behaviour will occur. The speed and interdependency of these changes in behavioural choices may be modulated by individuals’ culture. We develop an agent-based model to study how behavioural decarbonisation interacts with longer-term cultural change, composed of individuals with multiple behaviours that evolve due to imperfect social learning in a social network. Using the definition of culture as socially transmitted information, we represent individuals’ environmental identity as an aggregation of attitudes towards multiple relevant behaviours. The strength of interaction between individuals is determined by the similarity in their environmental identity, leading to inter-behavioural dependency and spillovers in green attitudes. Our results show that the initial distribution of agent attitudes towards behaviours and asymmetries in social learning, such as confirmation bias, are the main drivers of model dynamics, helping to generate awareness of what roadblocks may appear to deep decarbonisation. To assess the impact of culture beyond a purely diffusive regime, we introduce green influencers as a minority of individuals who broadcast a green attitude. The greatest emissions reduction is achieved with the inclusion of culture, relative to a behavioural independence case, and with low confirmation bias. However, green influencers fail to achieve deep behavioural decarbonisation through solely voluntary action. We identify areas for further research regarding how culture, through inter-behavioural dependence, may be leveraged for climate policy.
2023 / Peter Ditlevsen, Susanne Ditlevsen

Warning of a forthcoming collapse of the Atlantic meridional overturning circulation

Nature Communications, 14 Art. No.: 4254
2023 / Catarina Midões, Denis de Crombrugghe

Assumption-light and computationally cheap inference on inequality measures by sample splitting: the Student t approach

The Journal of Economic Inequality
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