Editing IPCC:AR6/SR15/Chapter-2 (section)

====== Lead Authors ======

* Luis Mundaca (Sweden, Chile)
* Mustafa Babiker (Sudan)
* Johannes Emmerling (Italy, Germany)
* Sabine Fuss (Germany)
* Jean-Charles Hourcade (France)
* Elmar Kriegler (Germany)
* Anil Markandya (Spain, United Kingdom)
* Joyashree Roy (Thailand, India)
* Drew Shindell (United States)

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Two approaches have been commonly used to assess alternative emissions pathways: '''cost-effectiveness analysis (CEA)''' and '''cost–benefit analysis (CBA)''' . '''CEA''' aims at identifying emissions pathways minimising the total mitigation costs of achieving a given warming or GHG limit (Clarke et al., 2014) <sup>[[#fn:r528|528]]</sup> . '''CBA''' has the goal to identify the optimal emissions trajectory minimising the discounted flows of abatement expenditures and monetized climate change damages (Boardman et al., 2006; Stern, 2007) <sup>[[#fn:r529|529]]</sup> . A third concept, the '''Social Cost of Carbon (SCC)''' measures the total net damages of an extra metric ton of CO <sub>2</sub> emissions due to the associated climate change (Nordhaus, 2014; Pizer et al., 2014; Rose et al., 2017a) <sup>[[#fn:r530|530]]</sup> . Negative and positive impacts are monetized, discounted and the net value is expressed as an equivalent loss of consumption today. The SCC can be evaluated for any emissions pathway under policy consideration (Rose, 2012; NASEM, 2016, 2017) <sup>[[#fn:r531|531]]</sup> .

Along the optimal trajectory determined by CBA, the SCC equals the discounted value of the marginal abatement cost of a metric ton of CO <sub>2</sub> emissions. Equating the present value of future damages and marginal abatement costs includes a number of critical value judgements in the formulation of the social welfare function (SWF), particularly in how non-market damages and the distribution of damages across countries and individuals and between current and future generations are valued (Kolstad et al., 2014) <sup>[[#fn:r532|532]]</sup> . For example, since climate damages accrue to a larger extent farther in the future and can persist for many years, assumptions and approaches to determine the social discount rate (normative ‘prescriptive’ vs. positive ‘descriptive’) and social welfare function (e.g., discounted utilitarian SWF vs. undiscounted prioritarian SWF) can heavily influence CBA outcomes and associated estimates of SCC (Kolstad et al., 2014; Pizer et al., 2014; Adler and Treich, 2015; Adler et al., 2017; NASEM, 2017; Nordhaus, 2017; Rose et al., 2017a) <sup>[[#fn:r533|533]]</sup> .

In CEA, the marginal abatement cost of carbon is determined by the climate goal under consideration. It equals the shadow price of carbon associated with the goal which in turn can be interpreted as the willingness to pay for imposing the goal as a political constraint. Emissions prices are usually expressed in carbon (equivalent) prices using the GWP-100 metric as the exchange rate for pricing emissions of non-CO <sub>2</sub> GHGs controlled under internationally climate agreements (like CH <sub>4</sub> , N <sub>2</sub> O and fluorinated gases, see Cross-Chapter Box 2 in Chapter 1). <sup>[[#fn:13|13]]</sup> Since policy goals like the goals of limiting warming to 1.5°C or well below 2°C do not directly result from a money metric trade-off between mitigation and damages, associated shadow prices can differ from the SCC in a CBA. In CEA, value judgments are to a large extent concentrated in the choice of climate goal and related implications, while more explicit assumptions about social values are required to perform CBA. For example, in CEA assumptions about the social discount rate no longer affect the overall abatement levels now set by the climate goal, but the choice and timing of investments in individual measures to reach these levels.

Although CBA-based and CEA-based assessment are both subject to large uncertainty about socio-techno-economic trends, policy developments and climate response, the range of estimates for the SCC along an optimal trajectory determined by CBA is far wider than for estimates of the shadow price of carbon in CEA-based approaches. In CBA, the value judgments about inter- and intra-generational equity combined with uncertainties in the climate damage functions assumed, including their empirical basis, are important (Pindyck, 2013; Stern, 2013; Revesz et al., 2014) <sup>[[#fn:r534|534]]</sup> . In a CEA-based approach, the value judgments about the aggregate welfare function matter less, and uncertainty about climate response and impacts can be tied into various climate targets and related emissions budgets (Clarke et al., 2014) <sup>[[#fn:r535|535]]</sup> .

The CEA- and CBA-based carbon cost estimates are derived with a different set of tools. They are all summarised as integrated assessment models (IAMs) but in fact are of very different nature (Weyant, 2017) <sup>[[#fn:r536|536]]</sup> . Detailed process IAMs such as AIM (Fujimori, 2017) <sup>[[#fn:r537|537]]</sup> , GCAM (Thomson et al., 2011; Calvin et al., 2017) <sup>[[#fn:r538|538]]</sup> , IMAGE (van Vuuren et al., 2011b, 2017b) <sup>[[#fn:r539|539]]</sup> , MESSAGE-GLOBIOM (Riahi et al., 2011; Havlík et al., 2014; Fricko et al., 2017) <sup>[[#fn:r540|540]]</sup> , REMIND-MAgPIE (Popp et al., 2010; Luderer et al., 2013; Kriegler et al., 2017) <sup>[[#fn:r541|541]]</sup> and WITCH (Bosetti et al., 2006, 2008, 2009) <sup>[[#fn:r542|542]]</sup> include a process-based representation of energy and land systems, but in most cases lack a comprehensive representation of climate damages, and are typically used for CEA. Diagnostic analyses across CBA-IAMs indicate important dissimilarities in modelling assembly, implementation issues and behaviour (e.g., parametric uncertainty, damage responses, income sensitivity) that need to be recognized to better understand SCC estimates (Rose et al., 2017a) <sup>[[#fn:r543|543]]</sup> .

CBA-IAMs such as DICE (Nordhaus and Boyer, 2000; Nordhaus, 2013, 2017) <sup>[[#fn:r544|544]]</sup> , PAGE (Hope, 2006) <sup>[[#fn:r545|545]]</sup> and FUND (Tol, 1999; Anthoff and Tol, 2009) <sup>[[#fn:r546|546]]</sup> attempt to capture the full feedback from climate response to socio-economic damages in an aggregated manner, but are usually much more stylised than detailed process IAMs. In a nutshell, the methodological framework for estimating SCC involves projections of population growth, economic activity and resulting emissions; computations of atmospheric composition and global mean temperatures as a result of emissions; estimations of physical impacts of climate changes; monetization of impacts (positive and negative) on human welfare; and the discounting of the future monetary value of impacts to year of emission (Kolstad et al., 2014; Revesz et al., 2014; NASEM, 2017; Rose et al., 2017a) <sup>[[#fn:r547|547]]</sup> . There has been a discussion in the literature to what extent CBA-IAMs underestimate the SCC due to, for example, a limited treatment or difficulties in addressing damages to human well-being, labour productivity, value of capital stock, ecosystem services and the risks of catastrophic climate change for future generations (Ackerman and Stanton, 2012; Revesz et al., 2014; Moore and Diaz, 2015; Stern, 2016) <sup>[[#fn:r548|548]]</sup> . However, there has been progress in ‘bottom-up’ empirical analyses of climate damages (Hsiang et al., 2017) <sup>[[#fn:r549|549]]</sup> , the insights of which could be integrated into these models (Dell et al., 2014) <sup>[[#fn:r550|550]]</sup> . Most of the models used in Chapter 2 on 1.5°C mitigation pathways are detailed process IAMs and thus deal with CEA.

An important question is how results from CEA- and CBA-type approaches can be compared and synthesized. Such synthesis needs to be done with care, since estimates of the shadow price of carbon under the climate goal and SCC estimates from CBA might not be directly comparable due to different tools, approaches and assumptions used to derive them. Acknowledging this caveat, the SCC literature has identified a range of factors, assumptions and value judgements that support SCC values above $100 tCO <sub>2</sub> <sup>−1</sup> that are also found as net present values of the shadow price of carbon in 1.5°C pathways. These factors include accounting for tipping points in the climate system (Lemoine and Traeger, 2014; Cai et al., 2015; Lontzek et al., 2015) <sup>[[#fn:r551|551]]</sup> , a low social discount rate (Nordhaus, 2007a; Stern, 2007) <sup>[[#fn:r552|552]]</sup> and inequality aversion (Schmidt et al., 2013; Dennig et al., 2015; Adler et al., 2017) <sup>[[#fn:r553|553]]</sup> .

The SCC and the shadow price of carbon are not merely theoretical concepts but used in regulation (Pizer et al., 2014; Revesz et al., 2014; Stiglitz et al., 2017) <sup>[[#fn:r554|554]]</sup> . As stated by the report of the High-Level Commission on Carbon Pricing (Stiglitz et al., 2017) <sup>[[#fn:r555|555]]</sup> , in the real world there is a distinction to be made between the implementable and efficient explicit carbon prices and the implicit (notional) carbon prices to be retained for policy appraisal and the evaluation of public investments, as is already done in some jurisdictions such as the USA, UK and France. Since 2008, the U.S. government has used SCC estimates to assess the benefits and costs related to CO <sub>2</sub> emissions resulting from federal policymaking (NASEM, 2017; Rose et al., 2017a) <sup>[[#fn:r556|556]]</sup> .

The use of the SCC for policy appraisals is, however, not straightforward in an SDG context. There are suggestions that a broader range of polluting activities than only CO <sub>2</sub> emissions, for example emissions of air pollutants, and a broader range of impacts than only climate change, such as impacts on air quality, health and sustainable development in general (see Chapter 5 for a detailed discussion), would need to be included in social costs (Sarofim et al., 2017; Shindell et al., 2017a) <sup>[[#fn:r557|557]]</sup> . Most importantly, a consistent valuation of the SCC in a sustainable development framework would require accounting for the SDGs in the social welfare formulation (see Chapter 5).

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