Production optimization in the time of pandemic: an SIS-based optimal control model with protection effort and cost minimization

The COVID-19 pandemic wreaks havoc in supply chains by reducing the production capacity of some essential suppliers, closure of production facilities or the absence of infected workers. In this paper, we present three decision support models for a plant manager to help in deciding on (a) the level of protection of the workforce against the spread of the virus in the absence of regional protection measures, (b) on the duration of the protection, and (c) the level of protection of the workforce with regional protection measures enforced by health authorities. These decision models are based on a SIS epidemiological model which takes into account the possibility that a worker can infect others but also that even when recovered can be infected again. The first and third models prescribe how, in time, the protection effort in terms of prophylactic measures must be deployed. The second model extends the first one as it also determines the length the protection effort must be deployed. The proposed models have been applied to the case of a meat processing plant that must satisfy the demand of a large-scale retailer. Clearly, to achieve production targets and satisfy customers' demand, plants in this labor-intensive industry rely on the number of healthy workers and the service level of suppliers. Our results indicate that these models provide managers with the tools to understand and measure the impact of an infection on production and the corresponding cost. Along the way, this work illustrates the ripple effect as suppliers affected by the pandemic are unable to fulfill the processing plant requirements and so the retailer's orders. Our findings provide normative guidance for supply chain decision support systems under risk of pandemic induced disruptions using a quantitative model-based approach

Balancing mitigation policies during pandemics: economic, health, and environmental implications

The strategies implemented to contain the spread of COVID-19 have clearly shown the existence of a nontrivial relation between epidemiological and environmental outcomes. On the one hand, mitigation policy generates unclear pollution effects, since social distancing measures favor a reduction in industrial emissions while health regulations and recommendations contribute to increase it. On the other hand, increased pollution exposes individuals to a higher chance of severe symptoms increasing their probability of death due to respiratory diseases. In order to understand how balancing the different goals in the design of effective containment policies we develop a normative approach to account for their consequences on the economy, health and the environment by analyzing the working mechanisms of social distancing in a pollution-extended macroeconomic-epidemiological framework with healthenvironment feedback effects. By limiting social contacts and thus disease incidence, social distancing favors health and environmental outcomes at the cost of a deterioration inmacroeconomic conditions.We show that social distancing alone is not enough to reverse the growth pattern of both disease prevalence and pollution and thus it is optimal to reduce the disease spread even if this generates a deterioration in environmental conditions.We also extend our baseline model to account for the role of strategic interactions between neighbor economies in which both pollution and disease prevalence are transboundary. In this context we show that free-riding induces sizeable efficiency losses, quantifiable in about 5% excess disease prevalence and 10% excess pollution at the end of the epidemic management program in the case of only two interacting economies

Sustainability and intertemporal equity: a multicriteria approach

In (macro)economics literature, the need to consider sustainability and intertemporal equity issues leads to propose different criteria (discounted utilitarianism, green golden rule, Chichilnisky criterion) in order to define social welfare. We compare and assess the outcomes associated to such alternative criteria in a simple macroeconomic model with natural resources and environmental concern (Chichilnisky et al. in Econ Lett 49:174-179, 1995), by relying on a multicriteria approach. We show that among these three criteria, the green golden rule (discounted utilitarianism) yields the highest (lowest) welfare level, while the Chichilnisky criterion leads to an intermediate welfare level which turns out to be increasing in the weight attached to the asymptotic utility. These results suggest that completely neglecting finite-time utilities and focusing only on the asymptotic utility is not only more sensible from a sustainability point of view but also from a social welfare maximization standpoint

Pollution diffusion and abatement activities across space and over time

We analyze the spatio-temporal dynamics of capital and pollution in an economic growth model with purposive environmental protection activities. The production process of a unique homogeneous good generates pollution, thus the increases in output associated with economic growth tend to rise the stock of pollution. Pollution is a negative production externality which thus feeds back on the economy lowering the level of output; in order to compensate for such a negative effect associated with economic development, pollution is reduced by publicly funded abatement activities. We firstly consider a Solow-type framework in which economic and environmental policies are completely exogenous, and then we move to a Ramsey-type context in which they are endogenously determined. We analyze the spatio-temporal dynamics of the model economy through numerical simulations, and we consider two different specifications of the production function (a globally concave and a convex-concave technology) in order to stress the role that eventual poverty traps might play on both economic and environmental outcomes. We show that in the convex-concave technology framework, whenever rich regions are substantially rich diffusion can help poor regions to escape their poverty traps; if however they are not rich enough diffusion might condemn also rich regions to collapse. However, even if rich regions are particularly rich whenever the pollution externality is strong, the whole spatial economy might be doomed to collapse

Pollution-induced poverty traps via Hopf bifurcation in a minimal integrated economic-environment model

In this paper we present a minimal integrated environment-economic growth model. The accumulations of capital and pollution are connected by reciprocal feedbacks. Pollution is an undesirable but inevitable by-product of production, whose efficiency is hindered by pollution. The nexus between pollution and production is embodied in a damage function. The evolution of capital is enriched by the specific technology adopted here, an S-shaped production function. The model dynamics is represented by a couple of nonlinear differ- ential equations, whose long run behavior gives rise to multiple stationary points. A global analysis underlines the importance of a meditated choice of the relevant policy parameters. Pollution induced poverty traps emerge for low level of saving ratio and share of abated emission. Periodic behaviour of the economic and environmental variables represents an early signal of the imminent risk of being trapped in an unsatisfactory level of economic performance. Numerical examples corroborate the theoretical results of the paper

Staring at the Abyss: a neurocognitive grounded agent-based model of collective-risk social dilemma under the threat of environmental disaster

AbstractIncreasingly visible climate change consequences challenge carbon-based economies worldwide. While expert knowledge on climate change percolates through political initiatives and public awareness, its translation into large-scale policy actions appears limited. Climate change consequences unequally target regions, countries and social classes, a vital issue for social cooperation. When facing an imminent ecological collapse, in which conditions can self-interested agents gain environmental awareness and settle on a sustainable path of actions when their knowledge of the imminent collapse is bounded? This cooperation emerges from the interaction between individuals and the interaction of various cognitive processes within individuals. This article develops an agent-based model for this emergence of cooperation enriched with the Agent Zero neurocognitive grounded cognitive architecture. We investigate when agents endowed with deliberative, affective and social modules can settle on actions that safeguard their environment through numerical simulations. Our results show that cooperation on sustainable actions is the strongest when the system is at the edge of collapse. Policy measures that increase the environment’s resilience become internalized by the agents and undermine awareness of the ecological catastrophe. Depending on the cognitive channels activated, agent behaviors and reactions to specific interventions significantly vary. Our analysis suggests that taking different cognitive channels, deliberative, affective, social, and others into account, significantly impact results. The complexity of agent cognition deserves more attention to assess parameter sensitivity in social simulation models. </jats:p