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Specifications of Social Welfare in Economic Studies of Climate Policy: Overview of Criteria and Related Policy Insights

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Abstract

An important assumption in economic studies of climate policy is the social welfare function. This paper shows that applying distinct decision or social welfare criteria can result in different optimal policies of climate control, notably if climate change impacts are uncertain. First, decision criteria in current climate-economic studies are reviewed. Next, the most important alternatives are discussed, including their (mathematical) formalization and incorporation in economy-climate models. Most of these alternative criteria suggest more stringent climate policies to be optimal than the standard discounted utilitarianism approach. However, several important welfare criteria have not or hardly seen any application in the economic analysis of climate policy. We conclude that there is clear need for systematic research on this theme, for which the current review provides a solid basis.

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Notes

  1. Froyn (2005) examines several decision criteria to determine strategies for abating GHG emissions in a simple game. She discusses their characteristics, and argues that deciding upon a single, superior criterion as the basis for climate change decision-making is difficult because of value judgments involved as well as pros and cons associated with each criterion.

  2. This problem may be overcome by designing financial mechanisms that transfer money across generations, but the practical implementation of such mechanisms is likely to be very difficult (Lind 1995; Howarth 2001b; Howarth and Norgaard 1995; Gerlagh 1998).

  3. Here we refer to utility as “well-being” as is increasingly common among economists (Kahneman and Krueger 2006), although some, notably Dasgupta (2008), prefer the term “felicity”.

  4. Moreover, the axioms rule out the maximin criterium that is explained in Sect. 3 (Roemer 2011).

  5. This central SCC value of $21 per ton of \(\hbox {CO}_{2}\), which was later on updated to about $36 (Interagency Working Group on Social Cost of Carbon 2010, 2013), has been used in a variety of CBAs by US government institutions (Kopp and Mignone 2012).

  6. The specification of the life-cycle utility function of consumers rules out intergenerational altruism as a basis for private decisions regarding saving and investment. However, public decision makers can consider questions of intergenerational fairness in the design of climate policy (Howarth 2001a).

  7. Sometimes expected utility theory is implemented pragmatically in IAMs by using mean or “most likely” values of the model parameters (Nordhaus 2008).

  8. The ambiguity-adjusted second order probabilities are defined as \({\hat{p}}_m (a^{*})=\frac{\phi ^{\prime }(EU_m (a^{*}))p_m }{\sum _n {\phi ^{\prime }(EU_n (a^{*}))p_n } }\) where \(\alpha ^{*}\) is the optimal abatement level. In this model, the marginal costs of abatement should equal the sum of its marginal utility benefits over all models weighted by \( {\hat{p}}_m \) (Millner et al. 2010).

  9. These are described in Woodward and Bishop (1997) and are defined as independence of irrelevant alternatives, relabeling, irrelevance of repetitive states, and dominance.

  10. This criterion is incorrectly referred to as the minimax regret approach in Hof et al. (2010).

  11. Where possible the authors use the 95th percentile of the upper range of these input scenarios that have been defined in the literature, although they realize that these probabilities distributions not fully account for the real uncertainty associated with these parameters.

  12. Loomes and Sugden (1982) provide a counterpart of the minimax regret social welfare function for explaining individual decision making under risk called regret theory.

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This research has been funded by The Netherlands Organisation for Scientific Research (NWO).

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Botzen, W.J.W., van den Bergh, J.C.J.M. Specifications of Social Welfare in Economic Studies of Climate Policy: Overview of Criteria and Related Policy Insights. Environ Resource Econ 58, 1–33 (2014). https://doi.org/10.1007/s10640-013-9738-8

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