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In my dissertation I explore conceptual and economic aspects of resilience, i.e. a system’s ability to maintain its basic functions and controls under disturbances. I provide methodological considerations on the conceptual level and general insights derived from stylized ecological-economic models. In doing so, I demonstrate how to frame resilience so as to economically evaluate and investigate it as an important property of ecological-economic systems. Is conceptual vagueness an asset or a liability? In chapter 1 I address this question by weighing arguments from philosophy of science and applying them to the concept of resilience. I first sketch the wide spectrum of resilience concepts that ranges from concise concepts to the vague perspective of “resilience thinking”. Subsequently, I set out the methodological arguments in favor and against conceptual vagueness. While traditional philosophy of science emphasizes precision and conceptual clarity as precondition for empirical science, alternative views highlight vagueness as fuel for creative and pragmatic problem-solving. Reviewing this discussion, I argue that a trade-off between vagueness and precision exists, which is to be solved differently depending on the research context. In some contexts research benefits from conceptual vagueness while in others it depends on precision. Assessing the specific example of “resilience thinking” in detail, I propose a restructuring of the conceptual framework which explicitly distinguishes descriptive and normative knowledge. Chapter 2 investigates the common assumption that the optimization problem within a simple selfprotection problem (spp) is convex. It is shown that the condition given in the literature to legitimate this assumption may have implausible consequences. Via a simple functional specification we analyze the (non-)convexity of the spp more thoroughly and find that for reasonable parameter values strict convexity may not be justified. In particular, we demonstrate numerically that full self-protection is often optimal. Neglecting these boundary solutions and analyzing only the comparative statics of interior maxima may entail misleading policy implications such as underinvestment in self-protection. Thus, we highlight the relevance of full self-protection as a policy option even for non-extreme losses. Chapter 3 starts from the observation that ecosystem resilience is often interpreted as insurance: by decreasing the probability of future drops in the provision of ecosystem services, resilience insures risk-averse ecosystem users against potential welfare losses. Using a general and stringent definition of “insurance” and a simple ecological-economic model, we derive the economic insurance value of ecosystem resilience and study how it depends on ecosystem properties, economic context, and the ecosystem user’s risk preferences. We show that (i) the insurance value of resilience is negative (positive) for low (high) levels of resilience, (ii) it increases with the level of resilience, and (iii) it is one additive component of the total economic value of resilience. Chapter 4 performs a model analysis to study the origins of limited resilience in coupled ecologicaleconomic systems. We demonstrate that under open access to ecosystems for profit-maximizing harvesting forms, the resilience properties of the system are essentially determined by consumer preferences for ecosystem services. In particular, we show that complementarity and relative importance of ecosystem services in consumption may significantly decrease the resilience of (almost) any given state of the system. We conclude that the role of consumer preferences and management institutions is not just to facilitate adaptation to, or transformation of, some natural dynamics of ecosystems. Rather, consumer preferences and management institutions are themselves important determinants of the fundamental dynamic characteristics of coupled ecological-economic systems, such as limited resilience. Chapter 5 describes how real option techniques and resilience thinking can be integrated to better understand and inform decision making around environmental risks within complex systems. Resilience thinking offers a promising framework for framing environmental risks posed through the non-linear responses of complex systems to natural and human-induced disturbance pressures. Real options techniques offer the potential to directly model such systems including consideration of the prospect that the passage of time opens new options while closing others. Examples are provided which illustrate the potential for integrated resilience and real options approaches to contribute to understanding and managing environmental risk.
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