3.3.5.3 Stabilization costs

Models use different metrics to report the costs of emission reductions. Top-down general equilibrium models tend to report GDP losses, while system-engineering partial equilibrium models usually report the increase in energy system costs or the net present value (NPV) of the abatement costs. A common cost indicator is also the marginal cost/price of emissions reduction (US$/tC or US$/tCO₂).

Figure 3.25 shows the relationship between stabilization targets and alternative measures of mitigation costs, comprising GDP losses, net present value of abatement, and carbon price in terms of US$ /tCO₂-eq.

Figure 3.25: Relationship between the cost of mitigation and long-term stabilization targets (radiative forcing compared to pre-industrial level, W/m² and CO₂-eq concentrations).

Notes: These panels show costs measured as a % loss of GDP (top), net present value of cumulative abatement costs (middle), and carbon price (bottom). The left-hand panels give costs for 2030, the middle panel for 2050, and the right-hand panel for 2100 repectively. Individual coloured lines denote selected studies with representative cost dynamics from very high to very low cost estimates. Scenarios from models sharing similar baseline assumptions are shown in the same colour. The grey-shaded range represents the 80th percentile of the TAR and post-TAR scenarios. NPV calculations are based on a discount rate of 5%. Solid lines show representative scenarios considering all radiatively active gases. CO₂ stabilization scenarios are added based on the relationship between CO₂ concentration and the radiative forcing targets shown in Figure 3.16. Dashed lines represent multi-gas scenarios, where the target is defined by the six Kyoto gases (other multi-gas scenarios consider all radiatively active gases).

Data sources: CCSP scenarios (USCCSP, 2006); IMCP scenarios (Edenhofer et al., 2006); Post-SRES (PS) scenarios (Morita et al., 2001); Azar et al., 2006; Riahi et al., 2006; Van Vuuren et al., 2007.

It is important to note that for the following reported cost estimates, the vast majority of the models assume transparent markets, no transaction costs, and thus perfect implementation of policy measures throughout the 21^st century, leading to the universal adoption of cost-effective mitigation measures, such as carbon taxes or universal cap and trade programmes. These assumptions generally result in equal carbon prices across all regions and countries equivalent to global, least-cost estimates. Relaxation of these modelling assumptions, alone or in combination (e.g. mitigation-only in Annex I countries, no emissions trading, or CO₂-only mitigation), will lead to an appreciable increase in all cost categories.

The grey shaded area in Figure 3.25 illustrates the 10^th–90^th percentile of the mitigation cost ranges of recent studies, including the TAR. The area includes only those recent scenarios in the literature that report cost estimates based on a comprehensive mitigation analysis, defined as those that have a sufficiently wide portfolio of mitigation measures.^[11] The selection was made on a case-by-case basis for each scenario considered in this assessment. The Figure also shows results from selected illustrative studies (coloured lines). These studies report costs for a range of stabilization targets and are representative of the overall cost dynamics of the full set of scenarios. They show cases with high-, intermediate- and low-cost estimates (sometimes exceeding the 80^th (i.e. 10^th–90^th) percentile range on the upper and lower boundaries of the grey-shaded area). The colour coding is used to distinguish between individual mitigation studies that are based on similar baseline assumptions. Generally, mitigation costs (for comparable stabilization targets) are higher from baseline scenarios with relatively high baseline emissions (brown and red lines). By the same token, intermediate or low baseline assumptions result in relatively lower cost estimates (blue and green lines).

Figure 3.25a shows that the majority of studies find that GDP losses increase with the stringency of the target, even though there is considerable uncertainty with respect to the range of losses. Barker et al. (2006) found that, after allowing for baseline emissions, the differences can be explained by:

• The spread of assumptions in modelling-induced technical change.
• The use of revenues from taxes and permit auctions.
• The use of flexibility mechanisms (i.e. emissions trading, multi-gas mitigation, and banking).
• The use of backstop technologies.
• Allowing for climate policy related co-benefits.
• Other specific modelling assumptions.

Weyant (2000) lists similar factors but also includes the number and type of technologies covered, and the possible substitution between cost factors (elasticities). A limited set of studies finds negative GDP losses (economic gains) that arise from the assumption that a model’s baseline is assumed to be a non-optimal pathway and incorporates market imperfections. In these models, climate policies steer economies in the direction of reducing these imperfections, for example by promoting more investment into research and development and thus achieving higher productivity, promoting higher employment rates, or removing distortionary taxes.

The left-hand side panel of Figure 3.25a shows that for 2030, GDP losses in the vast majority of the studies (more than 90% of the scenarios) are generally below 1% for the target categories V and VI. Also in the majority of the category III and IV scenarios (70% of the scenarios) GDP losses are below 1%. However, it is important to note that for categories III and IV costs are higher, on average, and show a wider range than those for categories V and VI. For instance, for category IV the interval lying between the 10^th and 90^th percentile varies from about 0.6% gain to about 1.2% loss. For category III, this range is shifted upwards (0.2–2.5%). This is also indicated by the median GDP losses by 2030, which increases from below 0.2% for categories V and VI, to about 0.2% for the category IV scenarios, and to about 0.6% for category III scenarios. GDP losses of the lowest stabilization categories (I & II) are generally below 3% by 2030, however the number of studies are relatively limited and in these scenarios stabilization is achieved predominantly from low baselines. The absolute GDP losses by 2030 correspond on average to a reduction of the annual GDP growth rate of less than 0.06 percentage points for the scenarios of category IV, and less than 0.1 and 0.12 percentage points for the categories III and I&II, respectively.

GDP losses by 2050 (middle panel of Figure 3.25a) are comparatively higher than the estimates for 2030. For example, for category IV scenarios the range is between -1% and 2% GDP loss compared to baseline (median 0.5%), and for category III scenarios the range is from slightly negative to 4% (median 1.3%). The Stern review (2006), looking at the costs of stabilization in 2050 for a comparable category (500–550 CO₂-eq) found a similar range of between -2% and +5%. For the studies that also explore different baselines (in addition to multiple stabilization levels), Figure 3.25a also shows that high emission baselines (e.g. high SRES-A1 or A2 baselines) tend to lead to higher costs. However, the uncertainty range across the models is at least of a similar magnitude. Generally, models that combine assumptions of very slow or incremental technological change with high baseline emissions (e.g. IGSM-CCSP) tend to show the relatively highest costs (Figure 3.25a). GDP losses of the lowest stabilization categories (I & II) are generally below 5.5% by 2050, however the number of studies are relatively limited and in these scenarios stabilization is achieved predominantly from low baselines. The absolute GDP losses numbers for 2050 reported above correspond on average to a reduction of the annual GDP growth rate of less than 0.05 percentage points for the scenarios of category IV, and less than 0.1 and 0.12 percentage points for the categories III and I&II, respectively.

Finally, the most right-hand side panel of Figure 3.25a shows that GDP losses show a bigger spread and tend to be somewhat higher by 2100. GDP losses are between 0.3% and 3% for category V scenarios and -1.6% to about 5% for category IV scenarios. Highest costs are given by category III (from slightly negative costs up to 6.5%). The sample size for category I is not large enough for a statistical analysis. Similarly, for category II scenarios, the range is not shown as the stabilization scenarios of category II are predominantly based on low or intermediate baselines, and thus the resulting range would not be comparable to those from the other stabilization categories. However, individual studies indicate that costs become higher for more stringent targets (see, for example, studies highlighted in green and blue for the lowest stabilization categories in Figure 3.25a).^[12]

The results for the net present value of cumulative abatement costs show a similar picture (Figure 3.25b). However, given the fact that abatement costs only capture direct costs, this cost estimate is by definition more certain.^[13] The interval from the 10^th to the 90^th percentile in 2100 ranges from nearly zero to about 11 trillion US$. The highest level corresponds to around 2–3% of the NPV of global GDP over the same period. Again, on the basis of comparison across models, it is clear that costs depend both on the stabilization level and baseline emissions. In general, the spread of costs for each stabilization category seems to be of a similar order to the differences across stabilization scenarios from different baselines. In 2030, the interval covering 80% of the NPV estimates runs from around 0–0.3 trillion for category IV scenarios. The majority of the more stringent (category III) scenarios range between 0.2 to about 1.6 trillion US$. In 2050, typical numbers for category IV are around 0.1–1.2 trillion US$ and, for category III, this is 1–5 trillion US$ (or below about 1% of the NPV of GDP). By 2100 the NPV estimates increase further, with the range up to 5 trillion for category IV scenarios and up to 11 trillion for category III scenarios, respectively. The results of these studies, published since the TAR, are consistent with the numbers presented in the TAR, although the new studies extend results to substantially lower stabilization levels.

Finally, a similar trend is found for carbon price estimates. In 2030, typical carbon prices across the range of models and baselines for a 4.5 W/m² stabilization target (category IV) range from around 1–24 US$/tCO₂ (80% of estimates), with the median of about 11 US$/tCO₂. For category III, the corresponding prices are somewhat higher and range from 18–79 US$/tCO₂ (with the median of the scenarios around 45 US$/tCO₂). Most individual studies for the most stringent category cluster around prices of about 100 US$/tCO₂.^[14] Carbon prices by 2050 are comparatively higher than those in 2030. For example, costs of category IV scenarios by 2050 range between 5 and 65 US$/tCO₂, and those for category III range between 30 and 155 US$/tCO₂. Carbon prices in 2100 vary over a much wider range – mostly reflecting uncertainty in baseline emissions and technology development. For the medium target of 4.5 W/m², typical carbon prices in 2100 range from 25–200 US$/tCO₂ (80% of estimates). This is primarily a consequence of the nature of this metric, which often represents costs at the margin. Costs tend to slowly increase for more stringent targets – with a range between the 10^th and 90^th percentile of more than 35 to about 350 US$/tCO₂ for category III.