Conference Agenda

The majority of people worldwide lives in cities (78% in Germany), and for them urban green spaces (UGS) provide key points with nature and biodiversity. UGS has been shown to contribute to human well-being by improving physical and mental health, fostering social cohesion, and providing essential ecosystem services. Among UGS attributes, naturalness—reflecting biodiversity and aesthetic quality—and proximity—determining accessibility—are particularly valued by urban residents. Understanding preferences and benefits associated with UGS is crucial for sustainable urban planning, especially in light of the Kunming-Montreal target 14, to integrate biodiversity in decision-making at every level. However, willingness-to-pay (WTP) estimates for urban green space remain uncertain, and different methods lead to different results.

Here, we analyze survey data from 22 German cities collected during the summer of 2020 to estimate the WTP for improvements in UGS naturalness and proximity, using three different valuation approaches: a stated preferences approach by means of a discrete choice experiment (DCE), a revealed preferences approach by means of the travel cost method (TCM), and an experienced utility approach by means of the life satisfaction approach (LSA), all using the same data set. We exploit exogenous income shocks caused by the COVID-19 pandemic, to address endogeneity concerns often associated with traditional income measures in LSA. Our results show that environmental quality is positively associated with higher life satisfaction, with significantly lower and more conservative WTP estimates derived from income shock data compared to standard income models. We show that the income shock-adjusted estimates from the LSA align more closely with values derived from the stated and revealed preference methods, and propose a synthesis estimate for the WTP derived from the stated, revealed, and experienced utility approaches combined.

Consider an event of interest B and another event A which is viewed as information for B. When a decision maker (DM) evaluates the effect of A on B, she evaluates the degree to which she asserts the indicative conditional "if A then B", written as A —→ B. In the context of the standard Bayesian confirmation theory, the degree of assertability, As(A —→ B) is given by DM's subjective conditional probability P (B | A). However, the Bayesian interpretation is not the only rational interpretation of As(A —→ B). An alternative interpretation is that As(A —→ B) goes by the probability that the proposition A —→ B is true, that is by DM's unconditional probability P (A —→ B). It is now widely accepted that there is no interpretation of "—→" that ensures the genral validity of P (A —→ B) = P (B | A). Hence, there are multiple truth-conditional interpretations of "—→" each corresponding to a distinct way of information processing. One of these interpretations, namely the material implication of the Propositional Logic, competes favorably with the Bayesian interpretation on normative grounds. As a result, two decision makers can disagree about their posterior probabilities of B even if they share the same information A and have identical prior probability functions.

This study examines carbon market volatility within the European Union Emissions Trading System (EU ETS) by applying the Omori law, traditionally used in seismology, to analyze financial market responses to external events. We assess pre-shock and after-shock volatility patterns of EUAs (European Union Allowances) futures contracts from 2008 to 2024, categorizing events into five classes - Financial Events, Policy and Institutional Milestones, Extreme Environmental Events, Geopolitical Tensions and Disruptions, and Energy Market Shocks - and enabling a comprehensive analysis of market reactions across different types of shocks. Results highlight significant differences in market responses, largely driven by event predictability. Financial Events, i.e., the December EUA futures expirations, and Policy and Institutional Milestones show strong anticipatory behavior and rapid post-shock stabilization. Conversely, unforeseeable events, such as Extreme Environmental Events or Geopolitical Tensions and Disruptions, are associated with delayed market reactions and prolonged volatility dissipation. The analysis highlights the critical role of market structure and participant behavior in modulating volatility dynamics, shedding light on the resilience of the EU ETS to external disturbances.

This paper develops an integrated optimal control framework to address the dual challenges of rapid decarbonization and social equity in the context of a Just Transition. We construct a dynamic two-sector labor market model that captures the decline of the coal industry and the expansion of the green economy. By introducing a dedicated state variable, \( U_1^{tr}(t) \), to track coal-sector workers undergoing retraining, the model explicitly accounts for the delays and costs associated with labor market transitions. Policy interventions are represented by three control variables: \( u_{12} \) for retraining, \( u_{13} \) for job-matching support, and \( u_{22} \) for green-sector vacancy creation. The objective is to minimize a cost functional that penalizes unemployment—placing greater weight on coal-sector unemployment—and the direct costs of policy implementation. Using a Hamiltonian formulation, we derive the necessary optimality conditions and provide explicit characterizations of the time-dependent optimal policies. Numerical simulations, conducted with a forward–backward iterative scheme in Julia, illustrate the dynamic interplay between unemployment, employment, and sectoral transitions, and highlight key phase transitions in policy interventions over time. Our results offer actionable insights for policymakers aiming to balance the imperatives of climate change mitigation with the socio-economic needs of workers in declining industries.