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

==== 2.5.2.1 Price of carbon emissions ====

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The price of carbon assessed here is fundamentally different from the concepts of optimal carbon price in a cost–benefit analysis, or the social cost of carbon (see Cross-Chapter Box 5 in this chapter and Chapter 3, Section 3.5.2). Under a cost-effectiveness analysis (CEA) modelling framework, prices for carbon (mitigation costs) reflect the stringency of mitigation requirements at the margin (i.e., cost of mitigating one extra unit of emission). Explicit carbon pricing is briefly addressed here to the extent it pertains to the scope of Chapter 2. For detailed policy issues about carbon pricing see Section 4.4.5.

Based on data available for this special report, the price of carbon varies substantially across models and scenarios, and their values increase with mitigation efforts (see Figure 2.26) ( ''high confidence'' ). For instance, undiscounted values under a Higher-2°C pathway range from 15–220 USD2010 tCO <sub>2-eq</sub> <sup>−1</sup> in 2030, 45–1050 USD2010 tCO <sub>2-eq</sub> <sup>−1</sup> in 2050, 120–1100 USD2010 tCO <sub>2-eq</sub><br />
<sup>−1</sup> in 2070 and 175–2340 USD2010 tCO <sub>2-eq</sub> <sup>−1</sup> in 2100. On the contrary, estimates for a Below-1.5°C pathway range from 135–6050 USD2010 tCO <sub>2-eq</sub> <sup>−1</sup> in 2030, 245–14300 USD2010 tCO <sub>2-eq</sub> <sup>−1</sup> in 2050, 420–19300 USD2010 tCO <sub>2-eq</sub> <sup>−1</sup> in 2070 and 690–30100 USD2010 tCO <sub>2-eq</sub> <sup>−1</sup> in 2100. Values for 1.5°C-low-OS pathway are relatively higher than 1.5°C-high-OS pathway in 2030, but the difference decreases over time, particularly between 2050 and 2070. This is because in 1.5°C-high-OS pathways there is relatively less mitigation activity in the first half of the century, but more in the second half. The low energy demand (LED, P1 in the Summary for Policymakers) scenario exhibits the lowest values across the illustrative pathway archetypes. As a whole, the global average discounted price of emissions across 1.5°C- and 2°C pathways differs by a factor of four across models (assuming a 5% annual discount rate, comparing to Below-1.5°C and 1.5°C-low-OS pathways). If 1.5°C-high-OS pathways (with peak warming 0.1–0.4°C higher than 1.5°C) or pathways with very large land-use sinks are also considered, the differential value is reduced to a limited degree, from a factor 4 to a factor 3. The increase in mitigation costs between 1.5°C and 2°C pathways is based on a direct comparison of pathway pairs from the same model and the same study in which the 1.5°C pathway assumes a significantly smaller carbon budget compared to the 2°C pathway (e.g., 600 GtCO <sub>2</sub> smaller in the CD-LINKS and ADVANCE studies). This assumption is the main driver behind the increase in the price of carbon (Luderer et al., 2018; McCollum et al., 2018) <sup>[[#fn:r558|558]]</sup> . <sup>[[#fn:14|14]]</sup>

The wide range of values depends on numerous aspects, including methodologies, projected energy service demands, mitigation targets, fuel prices and technology availability ( ''high confidence'' ) (Clarke et al., 2014; Kriegler et al., 2015b; Rogelj et al., 2015c; Riahi et al., 2017; Stiglitz et al., 2017) <sup>[[#fn:r559|559]]</sup> . The characteristics of the technology portfolio, particularly in terms of investment costs and deployment rates, play a key role (Luderer et al., 2013, 2016a; Clarke et al., 2014; Bertram et al., 2015a; Riahi et al., 2015; Rogelj et al., 2015c) <sup>[[#fn:r560|560]]</sup> . Models that encompass a higher degree of technology granularity and that entail more flexibility regarding mitigation response often produce relatively lower mitigation costs than those that show less flexibility from a technology perspective (Bertram et al., 2015a; Kriegler et al., 2015a) <sup>[[#fn:r561|561]]</sup> . Pathways providing high estimates often have limited flexibility of substituting fossil fuels with low-carbon technologies and the associated need to compensate fossil-fuel emissions with CDR. The price of carbon is also sensitive to the non-availability of BECCS (Bauer et al., 2018) <sup>[[#fn:r562|562]]</sup> . Furthermore, and due to the treatment of future price anticipation, recursive-dynamic modelling approaches (with ‘myopic anticipation’) exhibit higher prices in the short term but modest increases in the long term compared to optimization modelling frameworks with ‘perfect foresight’ that show exponential pricing trajectories (Guivarch and Rogelj, 2017) <sup>[[#fn:r563|563]]</sup> . The chosen social discount rate in CEA studies (range of 2–8% per year in the reported data, varying over time and sectors) can also affect the choice and timing of investments in mitigation measures (Clarke et al., 2014; Kriegler et al., 2015b; Weyant, 2017) <sup>[[#fn:r564|564]]</sup> . However, the impacts of varying discount rates on 1.5°C (and 2°C) mitigation strategies can only be assessed to a limited degree. The above highlights the importance of sampling bias in pathway analysis ensembles towards outcomes derived from models which are more flexible, have more mitigation options and cheaper cost assumptions and thus can provide feasible pathways in contrast to other who are unable to do so (Tavoni and Tol, 2010; Clarke et al., 2014; Bertram et al., 2015a; Kriegler et al., 2015a; Guivarch and Rogelj, 2017) <sup>[[#fn:r565|565]]</sup> . All CEA-based IAM studies reveal no unique path for the price of emissions (Bertram et al., 2015a; Kriegler et al., 2015b; Akimoto et al., 2017; Riahi et al., 2017) <sup>[[#fn:r566|566]]</sup> .

Socio-economic conditions and policy assumptions also influence the price of carbon ( ''very high confidence'' ) (Bauer et al., 2017; Guivarch and Rogelj, 2017; Hof et al., 2017; Riahi et al., 2017; Rogelj et al., 2018) <sup>[[#fn:r567|567]]</sup> . A multimodel study (Riahi et al., 2017) <sup>[[#fn:r568|568]]</sup> estimated the average discounted price of carbon (2010–2100, 5% discount rate) for a 2°C target to be nearly three times higher in the SSP5 marker than in the SSP1 marker. Another multimodel study (Rogelj et al., 2018) <sup>[[#fn:r569|569]]</sup> estimated the average discounted price of carbon (2020–2100, 5%) to be 35–65% lower in SSP1 compared to SSP2 in 1.5°C pathways. Delayed near-term mitigation policies and measures, including the limited extent of international global cooperation, result in increases in total economic mitigation costs and corresponding prices of carbon (Luderer et al., 2013; Clarke et al., 2014) <sup>[[#fn:r570|570]]</sup> . This is because stronger efforts are required in the period after the delay to counterbalance the higher emissions in the near term. Staged accession scenarios also produce higher mitigation costs than immediate action mitigation scenarios under the same stringency level of emissions (Kriegler et al., 2015b) <sup>[[#fn:r571|571]]</sup> .

It has been long argued that an explicit carbon pricing mechanism (whether via a tax or cap-and-trade scheme) can theoretically achieve cost-effective emission reductions (Nordhaus, 2007b; Stern, 2007; Aldy and Stavins, 2012; Goulder and Schein, 2013; Somanthan et al., 2014; Weitzman, 2014; Tol, 2017) <sup>[[#fn:r572|572]]</sup> . Whereas the integrated assessment literature is mostly focused on the role of carbon pricing to reduce emissions (Clarke et al., 2014; Riahi et al., 2017; Weyant, 2017) <sup>[[#fn:r573|573]]</sup> , there is an emerging body of studies (including bottom-up approaches) that focuses on the interaction and performance of various policy mixes (e.g., regulation, subsidies, standards). Assuming global implementation of a mix of regionally existing best-practice policies (mostly regulatory policies in the electricity, industry, buildings, transport and agricultural sectors) and moderate carbon pricing (between 5–20 USD2010 tCO <sub>2</sub> <sup>−1</sup> in 2025 in most world regions and average prices around 25 USD2010 tCO <sub>2</sub> <sup>−1</sup> in 2030), early action mitigation pathways are generated that reduce global CO <sub>2</sub> emissions by an additional 10 GtCO <sub>2</sub> e in 2030 compared to the NDCs (Kriegler et al., 2018a) <sup>[[#fn:r574|574]]</sup> (see Section 2.3.5). Furthermore, a mix of stringent energy efficiency policies (e.g., minimum performance standards, building codes) combined with a carbon tax (rising from 10 USD2010 tCO <sub>2</sub> <sup>−</sup> <sup>1</sup> in 2020 to 27 USD2010 tCO <sub>2</sub> <sup>−</sup> <sup>1</sup> in 2040) is more cost-effective than a carbon tax alone (from 20 to 53 USD2010 tCO <sub>2</sub> <sup>−</sup> <sup>1</sup> ) to generate a 1.5°C pathway for the U.S. electric sector (Brown and Li, 2018) <sup>[[#fn:r575|575]]</sup> . Likewise, a policy mix encompassing a moderate carbon price (7 USD2010 tCO <sub>2</sub> <sup>−</sup> <sup>1</sup> in 2015) combined with a ban on new coal-based power plants and dedicated policies addressing renewable electricity generation capacity and electric vehicles reduces efficiency losses compared with an optimal carbon pricing in 2030 (Bertram et al., 2015b) <sup>[[#fn:r576|576]]</sup> . One study estimates the carbon prices in high energy-intensive pathways to be 25–50% higher than in low energy-intensive pathways that assume ambitious regulatory instruments, economic incentives (in addition to a carbon price) and voluntary initiatives (Méjean et al., 2018) <sup>[[#fn:r577|577]]</sup> . A bottom-up approach shows that stringent minimum performance standards (MEPS) for appliances (e.g., refrigerators) can effectively complement explicit carbon pricing, as tightened MEPS can achieve ambitious efficiency improvements that cannot be assured by carbon prices of 100 USD2010 tCO <sub>2</sub> <sup>−</sup> <sup>1</sup> or higher (Sonnenschein et al., 2018) <sup>[[#fn:r578|578]]</sup> . In addition, the revenue recycling effect of carbon pricing can reduce mitigation costs by displacing distortionary taxes (Baranzini et al., 2017; OECD, 2017; McFarland et al., 2018; Sands, 2018; Siegmeier et al., 2018) <sup>[[#fn:r579|579]]</sup> , and the reduction of capital tax (compared to a labour tax) can yield greater savings in welfare costs (Sands, 2018) <sup>[[#fn:r580|580]]</sup> . The effect on public budgets is particularly important in the near term; however, it can decline in the long term as carbon neutrality is achieved (Sands, 2018) <sup>[[#fn:r581|581]]</sup> . The literature indicates that explicit carbon pricing is relevant but needs to be complemented with other policies to drive the required changes in line with 1.5°C cost-effective pathways ( ''low to'' ''medium evidence'' , ''high agreement'' ) (see Chapter 4, Section 4.4.5) (Stiglitz et al., 2017; Mehling and Tvinnereim, 2018; Méjean et al., 2018; Michaelowa et al., 2018) <sup>[[#fn:r582|582]]</sup> .

In summary, new analyses are consistent with AR5 and show that the price of carbon increases significantly if a higher level of stringency is pursued ( ''high confidence'' ). Values vary substantially across models, scenarios and socio-economic, technology and policy assumptions. While an explicit carbon pricing mechanism is central to prompt mitigation scenarios compatible with 1.5°C pathways, a complementary mix of stringent policies is required.

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'''Figure 2.26'''

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'''Global price of carbon emissions consistent with mitigation pathways.'''
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[[File:23e135d56afd2dc5c4933599ebaceccb Figure-2.26-815x1024.jpg]]

Panels show (a) undiscounted price of carbon (2030–2100) and (b) average price of carbon (2030–2100) discounted at a 5% discount rate to 2020 in USD2010. AC: Annually compounded. NPV: Net present value. Median values in floating black line. The number of pathways included in box plots is indicated in the legend. Number of pathways outside the figure range is noted at the top.

Original Creation for this Report using IAMC 1.5°C Scenario Data hosted by IIASA

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