This chapter documents baseline and stabilization scenarios in the literature since the publication of the IPCC Special Report on Emissions Scenarios (SRES) (Nakicenovic et al., 2000) and Third Assessment Report (TAR, Morita et al., 2001). It reviews the use of the SRES reference and TAR stabilization scenarios and compares them with new scenarios that have been developed during the past five years. Of special relevance is how ranges published for driving forces and emissions in the newer literature compare with those used in the TAR, SRES and pre-SRES scenarios. This chapter focuses particularly on the scenarios that stabilize atmospheric concentrations of greenhouse gases (GHGs). The multi-gas stabilization scenarios represent a significant change in the new literature compared to the TAR, which focused mostly on carbon dioxide (CO2) emissions. They also explore lower levels and a wider range of stabilization than in the TAR.
The foremost finding from the comparison of the SRES and new scenarios in the literature is that the ranges of main driving forces and emissions have not changed very much (high agreement, much evidence). Overall, the emission ranges from scenarios without climate policy reported before and after the SRES have not changed appreciably. Some changes are noted for population and economic growth assumptions. Population scenarios from major demographic institutions are lower than they were at the time of the SRES, but so far they have not been fully implemented in the emissions scenarios in the literature. All other factors being equal, lower population projections are likely to result in lower emissions. However, in the scenarios that used lower projections, changes in other drivers of emissions have offset their impact. Regional medium-term (2030) economic projections for some developing country regions are currently lower than the highest scenarios used in the SRES. Otherwise, economic growth perspectives have not changed much, even though they are among the most intensely debated aspects of the SRES scenarios. In terms of emissions, the most noticeable changes occurred for projections of SOx and NOx emissions. As short-term trends have moved down, the range of projections for both is currently lower than the range published before the SRES. A small number of new scenarios have begun to explore emission pathways for black and organic carbon.
Baseline land-related CO2 and non-CO2 GHG emissions remain significant, with continued but slowing land conversion and increased use of high-emitting agricultural intensification practices due to rising global food demand and shifts in dietary preferences towards meat consumption. The post-SRES scenarios suggest a degree of agreement that the decline in annual land-use change carbon emissions will, over time, be less dramatic (slower) than those suggested by many of the SRES scenarios. Global long-term land-use scenarios are scarce in numbers but growing, with the majority of the new literature since the SRES contributing new forestry and biomass scenarios. However, the explicit modelling of land-use in long-term global scenarios is still relatively immature, with significant opportunities for improvement.
In the debate on the use of exchange rates, market exchange rates (MER) or purchasing power parities (PPP), evidence from the limited number of new PPP-based studies indicates that the choice of metric for gross domestic product (GDP), MER or PPP, does not appreciably affect the projected emissions, when metrics are used consistently. The differences, if any, are small compared to the uncertainties caused by assumptions on other parameters, e.g. technological change (high agreement, much evidence).
The numerical expression of GDP clearly depends on conversion measures; thus GDP expressed in PPP will deviate from GDP expressed in MER, more so for developing countries. The choice of conversion factor (MER or PPP) depends on the type of analysis or comparison being undertaken. However, when it comes to calculating emissions (or other physical measures, such as energy), the choice between MER-based or PPP-based representations of GDP should not matter, since emission intensities will change (in a compensating manner) when the GDP numbers change. Thus, if a consistent set of metrics is employed, the choice of MER or PPP should not appreciably affect the final emission levels (high agreement, medium evidence). This supports the SRES in the sense that the use of MER or PPP does not, in itself, lead to significantly different emission projections outside the range of the literature (high agreement, much evidence). In the case of the SRES, the emissions trajectories were the same whether economic activities in the four scenario families were measured in MER or PPP.
Some studies find differences in emission levels between using PPP-based and MER-based estimates. These results critically depend on, among other things, convergence assumptions (high agreement, medium evidence). In some of the short-term scenarios (with a horizon to 2030) a ‘bottom-up’ approach is taken, where assumptions about productivity growth and investment and saving decisions are the main drivers of growth in the models. In long-term scenario models, a ‘top-down’ approach is more commonly used, where the actual growth rates are more directly prescribed based on convergence or other assumptions about long-term growth potentials. Different results can also be due to inconsistencies in adjusting the metrics of energy efficiency improvement when moving from MER-based to PPP-based calculations.
There is a clear and strong correlation between the CO2-equivalent concentrations (or radiative forcing) of the published studies and the CO2-only concentrations by 2100, because CO2 is the most important contributor to radiative forcing. Based on this relationship, to facilitate scenario comparison and assessment, stabilization scenarios (both multi-gas and CO2-only studies) have been grouped in this chapter into different categories that vary in the stringency of the targets, from low to high radiative forcing, CO2-equivalent concentrations and CO2-only concentrations by 2100, respectively.
Essentially, any specific concentration or radiative forcing target, from the lowest to the highest, requires emissions to eventually fall to very low levels as the removal processes of the ocean and terrestrial systems saturate. For low to medium targets, this would need to occur during this century, but higher stabilization targets can push back the timing of such reductions to beyond 2100. However, to reach a given stabilization target, emissions must ultimately be reduced well below current levels. For achievement of the very low stabilization targets from many high baseline scenarios, negative net emissions are required towards the end of the century. Mitigation efforts over the next two or three decades will have a large impact on opportunities to achieve lower stabilization levels (high agreement, much evidence).
The timing of emission reductions depends on the stringency of the stabilization target. Lowest stabilization targets require an earlier peak of CO2 and CO2-equivalent emissions. In the majority of the scenarios in the most stringent stabilization category (a stabilization level below 490 ppmv CO2-equivalent), emissions are required to decline before 2015 and are further reduced to less than 50% of today’s emissions by 2050. For somewhat higher stabilization levels (e.g. below 590 ppmv CO2-equivalent) global emissions in the scenarios generally peak around 2010–2030, followed by a return to 2000 levels, on average around 2040. For high stabilization levels (e.g. below 710 ppmv CO2-equivalent) the median emissions peak around 2040 (high agreement, much evidence).
Long-term stabilization scenarios highlight the importance of technology improvements, advanced technologies, learning-by-doing, and induced technological change, both for achieving the stabilization targets and cost reduction (high agreement, much evidence). While the technology improvement and use of advanced technologies have been employed in scenarios largely exogenously in most of the literature, new literature covers learning-by-doing and endogenous technological change. The latter scenarios show different technology dynamics and ways in which technologies are deployed, while maintaining the key role of technology in achieving stabilization and cost reduction.
Decarbonization trends are persistent in the majority of intervention and non-intervention scenarios (high agreement, much evidence). The medians of scenario sets indicate decarbonization rates of around 0.9 (pre-TAR) and 0.6 (post-TAR) compared to historical rates of about 0.3% per year. Improvements of carbon intensity of energy supply and the whole economic need to be much faster than in the past for the low stabilization levels. On the upper end of the range, decarbonization rates of up to 2.5% per year are observed in more stringent stabilization scenarios, where complete transition away from carbon-intensive fuels is considered.
The scenarios that report quantitative results with drastic CO2 reduction targets of 60–80% in 2050 (compared to today’s emission levels) require increased rates of energy intensity and carbon intensity improvement by 2–3 times their historical levels. This is found to require different sets of mitigation options across regions, with varying shares of nuclear energy, carbon capture and storage (CCS), hydrogen, and biomass.
The costs of stabilization crucially depend on the choice of the baseline, related technological change and resulting baseline emissions; stabilization target and level; and the portfolio of technologies considered (high agreement, much evidence). Additional factors include assumptions with regard to the use of flexible instruments and with respect to revenue recycling. Some literature identifies low-cost technology clusters that allow for endogenous technological learning with uncertainty. This suggests that a decarbonized economy may not cost any more than a carbon-intensive one, if technological learning is taken into account.
There are different metrics for reporting costs of emission reductions, although most models report them in macro-economic indicators, particularly GDP losses. For stabilization at 4–5 W/m2 (or ~ 590–710 ppmv CO2-equivalent) macro-economic costs range from -1 to 2% of GDP below baseline in 2050. For a more stringent target of 3.5–4.0 W/m2 (~ 535–590 ppmv CO2-equivalent) the costs range from slightly negative to 4% GDP loss (high agreement, much evidence). GDP losses in the lowest stabilization scenarios in the literature (445-535 ppmv CO2-equivalent) are generally below 5.5% by 2050, however the number of studies are relatively limited and are developed from predominantly low baselines (high agreement, medium evidence).
Multi-gas emission-reduction scenarios are able to meet climate targets at substantially lower costs compared to CO2-only strategies (for the same targets, high agreement, much evidence). Inclusion of non-CO2 gases provides a more diversified approach that offers greater flexibility in the timing of the reduction programme.
Including land-use mitigation options as abatement strategies provides greater flexibility and cost-effectiveness for achieving stabilization (high agreement, medium evidence). Even if land activities are not considered as mitigation alternatives by policy, consideration of land (land-use and land cover) is crucial in climate stabilization for its significant atmospheric inputs and withdrawals (emissions, sequestration, and albedo). Recent stabilization studies indicate that land-use mitigation options could provide 15–40% of total cumulative abatement over the century. Agriculture and forestry mitigation options are projected to be cost-effective abatement strategies across the entire century. In some scenarios, increased commercial biomass energy (solid and liquid fuel) is a significant abatement strategy, providing 5–30% of cumulative abatement and potentially 1–15% of total primary energy over the century.
Decision-making concerning the appropriate level of mitigation in a cost-benefit context is an iterative risk-management process that considers investment in mitigation and adaptation, co-benefits of undertaking climate change decisions and the damages due to climate change. It is intertwined with development decisions and pathways. Cost-benefit analysis tries to quantify climate change damages in monetary terms as the social cost of carbon (SCC) or time-discounted damages. Due to considerable uncertainties and difficulties in quantifying non-market damages, it is difficult to estimate SCC with confidence. Results depend on a large number of normative and empirical assumptions that are not known with any certainty. SCC estimates in the literature vary by three orders of magnitude. Often they are likely to be understated and will increase a few percent per year (i.e. 2.4% for carbon-only and 2–4% for the social costs of other greenhouse gases (IPCC, 2007b, Chapter 20). SCC estimates for 2030 range between 8 and 189 US$/tCO2-equivalent (IPCC, 2007b, Chapter 20), which compares to carbon prices between 1 to 24 US$/tCO2-equivalent for mitigations scenarios stabilizing between 485-570 ppmv CO2-equivalent) and 31 to 121 US$/tCO2-equivalent for scenarios stabilizing between 440-485 ppmv CO2-equivalent, respectively (high agreement, limited evidence).
For any given stabilization pathway, a higher climate sensitivity raises the probability of exceeding temperature thresholds for key vulnerabilities (high agreement, much evidence). For example, policymakers may want to use the highest values of climate sensitivity (i.e. 4.5°C) within the ‘likely’ range of 2–4.5°C set out by IPCC (2007a, Chapter 10) to guide decisions, which would mean that achieving a target of 2°C (above the pre-industrial level), at equilibrium, is already outside the range of scenarios considered in this chapter, whilst a target of 3°C (above the pre-industrial level) would imply stringent mitigation scenarios, with emissions peaking within 10 years. Using the ‘best estimate’ assumption of climate sensitivity, the most stringent scenarios (stabilizing at 445–490 ppmv CO2-equivalent) could limit global mean temperature increases to 2–2.4°C above the pre-industrial level, at equilibrium, requiring emissions to peak before 2015 and to be around 50% of current levels by 2050. Scenarios stabilizing at 535–590 ppmv CO2-equivalent could limit the increase to 2.8–3.2°C above the pre-industrial level and those at 590–710 CO2-equivalent to 3.2–4°C, requiring emissions to peak within the next 25 and 55 years, respectively (high agreement, medium evidence).
Decisions to delay emission reductions seriously constrain opportunities to achieve low stabilization targets (e.g. stabilizing concentrations from 445–535 ppmv CO2-equivalent), and raise the risk of progressively more severe climate change impacts and key vulnerabilities occurring.
The risk of climate feedbacks is generally not included in the above analysis. Feedbacks between the carbon cycle and climate change affect the required mitigation for a particular stabilization level of atmospheric CO2 concentration. These feedbacks are expected to increase the fraction of anthropogenic emissions that remains in the atmosphere as the climate system warms. Therefore, the emission reductions to meet a particular stabilization level reported in the mitigation studies assessed here might be underestimated.
Short-term mitigation and adaptation decisions are related to long-term climate goals (high agreement, much evidence). A risk management or ‘hedging’ approach can assist policymakers to advance mitigation decisions in the absence of a long-term target and in the face of considerable uncertainties relating to the cost of mitigation, the efficacy of adaptation and the negative impacts of climate change. The extent and the timing of the desirable hedging strategy will depend on the stakes, the odds and societies’ attitudes to risks, for example with respect to risks of abrupt change in geo-physical systems and other key vulnerabilities. A variety of integrated assessment approaches exist to assess mitigation benefits in the context of policy decisions relating to such long-term climate goals. There will be ample opportunity for learning and mid-course corrections as new information becomes available. However, actions in the short term will largely determine what future climate change impacts can be avoided. Hence, analysis of short-term decisions should not be decoupled from analysis that considers long-term climate change outcomes (high agreement, much evidence).