The ecology of animal species’ behaviors in a Solow-type model

 

n light of the rapidly expanding body of literature examining the adverse effects of human-induced changes in animal behavior on ecological dynamics, biodiversity conservation, and human well-being, this study investigates a bio-economic dynamic model characterized by a system of three ordinary differential equations. The model incorporates three state variables: K , x , and y . The variable K represents the stock of productive capital in the economy. The variable x denotes the size of the population of animals (from a representative species) exhibiting “typical” behavior; that is, the one that animals would adopt in the absence of human interference (e.g., hunting prey). The variable y denotes the size of the population displaying “non-typical” behavior, adopted either to mitigate the harmful impacts of human activities or to opportunistically exploit anthropogenic resources, such as food by-products. The dynamics of x and y are modelled starting from a system of Lotka-Volterra equations and augmenting it with a component depending on the difference in the payoffs (fitness) of the two behaviors. The stock of capital follows a Solow-type dynamics incorporating defensive expenditure aimed at protecting animals that adopt typical behavior. Our study reveals that the interaction between the dynamics of animal behavior adoption and economic growth can lead to a wide range of equilibrium outcomes. In some equilibria, the animal population fully specializes by adopting either the typical or the non-typical behavior, while in others, a coexistence is observed. We derive policy recommendations to minimize the adoption of non-typical behavior, finding that the optimal ecological outcome is not achieved by maximizing expenditure, but rather by striking a precise balance between ecological defense and economic viability.

 

Carbon leakage in 3D: on the dynamics of green, dirty and relocating firms under the ETS

 

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The debate on the carbon leakage risk of unilateral climate policies is gaining momentum along with the increase in carbon prices and in the ambition of emissions reduction targets. While empirical evidence on carbon leakage is weak or absent so far, more firms might decide to delocalize their activity in the future due to higher carbon prices. To investigate this issue, we propose a simple theoretical model which analyses the choices of a population of firms subject to an Emissions Trading System (ETS). Each firm has three alternative strategies at disposal: (i) "green": stop polluting by investing in a clean technology, (ii) "dirty": keep polluting by purchasing the correspondent emission allowances under the ETS, (iii) "relocating": keep polluting by relocating its activities to an ETS-free jurisdiction.

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Urban Mobility on Spatial Networks: Congestion, Accessibility, and Targeted Policy Design

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Urban mobility systems are characterized by the joint presence of congestion, spatial heterogeneity in accessibility, and heterogeneous user behavior. This paper develops a simulation-based model of urban mobility defined on a spatial transportation network to analyze the interaction between these features under alternative policy regimes. We evaluate a set of policy scenarios, including congestion pricing, mobility bonds with performance-based refunds, public transport improvements, spatially differentiated pricing, and targeted reinvestment of policy revenues. Policy performance is assessed in terms of both efficiency, measured by congestion reduction, and equity, measured by accessibility in peripheral areas. The results show that uniform pricing policies reduce congestion but fail to address spatial disparities, while mobility bonds have limited impact on behavioral outcomes. Uniform public transport improvements improve overall efficiency but do not significantly reduce spatial inequality. In contrast, policies combining pricing with targeted reinvestment in low-accessibility areas consistently outperform all other interventions. We further show that this ranking is robust across a wide range of welfare weight specifications, indicating that the superiority of targeted reinvestment does not depend on a specific efficiency–equity trade-off. These findings highlight the importance of spatial targeting in the design of effective and equitable urban mobility policies.

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When bacteria and physicians play public good games

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Antimicrobial resistance (AMR) is increasingly recognized as a global public health crisis arising from the interaction between biological processes and human behavior. This paper develops a dynamic model of the strategic interplay between physicians’ prescribing decisions and the evolution of bacterial resistance over time. Physicians face an intertemporal trade-off between responsible prescribing (i.e. in line with international “best practice” guidelines) and over-prescribing, which may generate short-term benefits for individual patients but accelerates the emergence and spread of resistance. Since the costs of resistance are diffuse and delayed, whereas the benefits of over-prescribing are immediate and privately appropriated, AMR constitutes a behavioral public-good problem prone to collective action failure.The model shows that this interaction can generate multiple long-run equilibria, such that small differences in initial conditions, institutional incentives, or environmental pressures may lead to markedly different outcomes. Even when structural conditions favor responsible prescribing and low resistance, high-resistance, over-prescribing equilibria may persist and prove difficult to escape. Sensitivity analysis identifies the key parameters driving these dynamics. A central finding is that policy interventions become less effective the longer action is delayed: restoring low-resistance outcomes may require disproportionately stronger and more coordinated efforts once a high-resistance equilibrium has become entrenched.These results underscore the importance of early, incentive-compatible interventions targeting prescribing behavior directly and highlight AMR as a socio-ecological feedback system whose complexity must be explicitly accounted for in policy design.

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