A novel approach to discounting has been developed at IIASA in relation to economic evaluation of long-term projects, e.g., such as catastrophic floods management (construction of dams and dikes) and climate change mitigation projects. Debates on proper discount rates have a long-standing history. Indeed, how can we justify investments into mitigation efforts, which may possibly turn into benefits over long and uncertain time horizons in the future? Misperception of discounting may provoke catastrophes. Most traditional models assume the discount rate is the same as the rate of return in capital market. Such choice of discounting rate equal to market return rate is linked with the assets having a lifespan of only a few decades. This may substantially reduce the impacts that investments may have beyond these intervals. For example, market interest rates of 3.5% corresponds to approximately 30 years, which may have no correspondence with expected, say, 300-year extreme events. The IIASA approach links discount factors with the occurrences of “stopping time” random events (e.g. catastrophes) determining a discount-related evaluation horizon. Conversely, any stopping time associated with the first occurrence of a random event induces a discounting. A set of random events, e.g., 1000-, 500-, 250-, and 100- year floods, induces discounting with time-declining discount rates.
The methodology has been developed for addressing food, water, energy, social security issues. It has been applied in numerous studies on catastrophic risks management; for planning social security and health provision under risks; sustainable agriculture planning; for the development of a prototype model of robust emission trading markets; in model-based planning for secure energy provision; in water pricing methodology; etc.
Long-term and uncertain horizons of catastrophic events pose new challenges for the choice of proper discount rates. Catastrophes often create so-called endogenous, unknown (with the lack and even absence of adequate observations) and interdependent systemic risks (Arrow, 1996; Arrow et al., 1996; Ermolieva and Ermoliev, 2005; Ermolieva et al., 2003; Heal and Kristrom, 2002). Evaluation and management of catastrophic risks require development of spatially explicit catastrophe models (Ermolieva, 1997; Ermolieva and Ermoliev, 2005; Ermoliev et al., 2000; Weitzman, 1999). In these models, catastrophes are characterized by a random “stopping time” moment associated with the first occurrence of a catastrophic (“killing”) or “stopping time” event. The concept of random stopping time criterion in catastrophe management models induces social discounting that focuses on occurrence time of catastrophic events rather then the lifetime of market products. Since risk management decisions affect the occurrence of disasters in time and space, the induced discounting may depend on spatio-temporal distributions of extreme events and feasible sets of decisions. This endogeneity of induced spatio-temporal discounting calls for the use of stochastic optimization methods, which allow also to address the variability of discounted criteria.
The methodology is applied in integrated catastrophe management analysis; in planning agricultural production in the presence of risks and uncertainties; for planning social security and health provision under risks; in modeling of robust carbon trading markets; in models for planning secure energy provision, etc.
Last edited: 22 July 2013
International Institute for Applied Systems Analysis (IIASA)
Schlossplatz 1, A-2361 Laxenburg, Austria
Phone: (+43 2236) 807 0 Fax:(+43 2236) 71 313