Minimax regret strategies for greenhouse gas abatement: methodology and application

Uncertain climate thresholds and optimal economic growth

“…Expected utility maximization is used as a decision criterion to be consistent with previous studies [27,39,41]. Other decision criteria (such as minimizing the consumption loss of the worst-case scenario analogous to Loulou and Kandia [30]) are possible and could be investigated with our model.…”

Uncertainty and economic analysis of climate change: A survey of approaches and findings

“…Loulou and Kanudia [27,28] for example not only integrated sequential decision-making under uncertainty by means of stochastic programming into the bottom-up energy systems model MARKAL, but also implement a strategy that minimizes the maximal "regret" of a policy (minimax-regret strategy). Lange [29] combines expected utility and the maximin criterion for decision under uncertainty (maximize the minimal worst case outcome) in a two period model of optimal emissions.…”

“…Nevertheless, there is also the sunk cost effect and studies that consider both kinds of irreversibilities find that it is optimal to emit more in the short run if learning about uncertainties is possible [10,13,14,48]. Other studies find some evidence that it is optimal to chose an intermediate level of emission reductions, until uncertainty is resolved [27,28]. One policy recommendation that can be drawn is that in any case it makes sense to invest in flexible abatement measures that do not imply a large amount of sunk and irreversible investment.…”

Minimax Regret Analysis for Municipal Solid Waste Management: An Interval-Stochastic Programming Approach

“…[35][36][37][38][39][40][41][42][43] The MMR seeks to minimize the maximum regret among all possible states of the world. The regret value for a special state of the world (i.e., all scenario realizations) represents the difference between the cost of the alternative selected by decision-makers and that of the best alternative for that state of the world.…”

“…Thus, this method has an advantage in providing effective decision support under uncertainty; it only needs a list of possible scenarios without any assumption on their probabilities. 39 In the MMR, uncertainties may be presented as random variables and, at the same time, some random events can only be quantified as intervals with lower and upper bounds, leading to dual uncertainties. This requires that both MMR and IMP techniques be incorporated within a general framework to reflect the uncertainties with varied quality levels and presentation formats.…”