Resilience to specified kinds of disasters is an active area of research and practice. However, rare or unprecedented disturbances that are unusually intense or extensive require a more broad-spectrum type of resilience. General resilience is the capacity of social-ecological systems to adapt or transform in response to unfamiliar, unexpected and extreme shocks. Conditions that enable general resilience include diversity, modularity, openness, reserves, feedbacks, nestedness, monitoring, leadership, and trust. Processes for building general resilience are an emerging and crucially important area of research.

Non-point-source (NPS) pollution refers to a form of pollution in which neither the source nor the size of specific emissions can be observed or identified with sufficient accuracy. In NPS pollution the ambient concentration of pollutants associated with the individually unobserved emissions is typically observed. NPS pollution due to agricultural runoff is a major source of water pollution, eutrophication, and hypoxia. Due to informational asymmetries and stochastic effects, the use of traditional environmental policy instruments such as emissions taxes or tradable quotas to regulate NPS pollution is very difficult. This article reviews the main theoretical approaches, up to the present, to the regulation of NPS pollution—input-based schemes, ambient schemes, and endogenous monitoring—and discusses issues associated with NPS pollution regulation and their relation to the theoretically proposed instruments.

This paper extends Turing analysis to standard recursive optimal control frameworks in economics and applies it to dynamic bioeconomic problems where the interaction of coupled economic and ecological dynamics under optimal control over space creates (or destroys) spatial heterogeneity. We show how our approach reduces the analysis to a tractable extension of linearization methods applied to the spatial analog of the well known costate/state dynamics. We explicitly show the existence of a non-empty Turing space of diffusive instability by developing a linear-quadratic approximation of the original non-linear problem. We apply our method to a bioeconomic problem, but the method has more general economic applications where spatial considerations and pattern formation are important. We believe that the extension of Turing analysis and the theory associated with the dispersion relationship to recursive infinite horizon optimal control settings is new.

Mechanisms generating patterns in spatial domains have been extensively studied in biology, but also in economics in the context of new economic geography. The Turing mechanism or Turing diffusion-induced instability has been central to the understanding of forces which endogenously generate spatial patterns, but in a context where agents do not explicitly optimize an objective. The present paper reviews tools to study, in the spirit of Turing's analysis, a mechanism generating optimal diffusion-induced instability where optimizing agents generate optimal agglomerations. By extending the maximum principle to the optimal control of partial differential equations, it is shown how under certain conditions it will be optimal to design controls so that the price-quantity system implied by the costate–state functions of the optimal control of distributed parameter systems induces optimal spatial patterns. These methods might be useful in studying pattern formation both in problems of resource management and of economic development.