Emissions Scenarios

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4.1. Introduction

In Chapter 4 the main characteristics of the scenarios developed for the Special Report on Emissions Scenarios (SRES scenarios) are presented. These scenarios cover a wide range of driving forces from demographic to social and economic developments, and they encompass a wide range of future greenhouse gas (GHG) emissions (see Chapter 5). Chapters 2 and 3 provide an overview and assessment of the scenario literature, the main driving forces of future GHG emissions, and their relationships. How the driving forces are combined to produce a set of scenarios that cover the ranges of GHG emissions from the literature is described in this chapter.

Chapters 2 and 3 demonstrate the large uncertainty in the literature that surrounds both future emissions and the possible developments of their underlying driving forces. The uncertainties range from inadequate scientific understanding of the problems, through data gaps or lack of data, to the inherent uncertainties of future events in general. Hence alternative scenarios are used to describe the range of possible future emissions.

The SRES approach involved the development of a set of four alternative scenario "families" (see Chapter 1, Section 1.7.2). Each family of SRES scenarios includes a descriptive part (called a "storyline") and a number of alternative interpretations and quantifications of each storyline developed by six different modeling approaches (see also Box 1-1 on terminology). Each storyline describes a demographic, social, economic, technological, and policy future for each of the scenario families. Within each family different scenarios explore variations of global and regional developments and their implications for GHG, ozone precursors, and sulfur emissions. Each of these scenarios is consistent with the broad framework specified by the storyline of the scenario family.

Each storyline is basically a short "history" of a possible future development expressed as a combination of key scenario characteristics. These descriptions are stylized and designed to facilitate specification and further interpretation of scenario quantifications. The storylines identify particular dynamics, visible in the world today, that might have important influences on future GHG emissions. They deliberately explore what might happen if social, economic, technical, and policy developments take a particular direction at the global level; they also pay attention to regional differences and interactions, especially between developing and industrialized countries.

Figure 4-1: The four SRES scenario families that share common storylines are illustrated as branches of a two-dimensional tree. The two dimensions indicate the relative orientation of the different scenario storylines toward economic or environmental concerns and global and regional scenario development patterns, respectively. There is no implication that these two are mutually exclusive or incompatible. In reality, the four scenarios share a space of a much higher dimensionality given the numerous driving forces and other assumptions needed to define any given scenario in a particular modeling approach. The A1 storyline branches out into different groups of scenarios to illustrate that alternative development paths are possible within one scenario family.

The broad consensus among the SRES writing team is that the current literature analysis suggests the future is inherently unpredictable and so views will differ as to which of the storylines and representative scenarios could be more or less likely. Therefore, the development of a single "best guess" or "business-as-usual" scenario is neither desirable nor possible. Nor should the storylines and scenarios be taken as policy recommendations. The storylines represent the playing out of certain social, economic, technological, and environmental paradigms, which will be viewed positively by some people and negatively by others. The SRES writing team decided on four storylines - an even number helps to avoid the impression of a "central" or "most likely" case. The team wanted more than two storylines to help illustrate that the future depends on many different underlying dynamics; the team wanted no more than four to avoid complicating the process with too many alternatives. The scenarios cover a wide range of, but not all possible, futures. In particular, it was decided that possible "surprises" would not be considered and that there would be no "disaster" scenarios. The team decided to carry out sensitivity tests within some of the storylines by considering alternative scenarios with different fossil-fuel reserves, rates of economic growth, or rates of technical change. These sensitivity analyses resulted in groups of scenarios within a given scenario family and alternative scenario interpretations within a scenario group or family (see Section 4.2 below for a description of scenario terminology and taxonomy).

The titles of the storylines are deliberately simple - A1, A2, B1, and B2. There is no particular order among the storylines (they are listed alphabetically). Figure 4-1 shows that the SRES scenarios build on the main driving forces of GHG emissions. Each scenario family is based on a common specification of the main driving forces.

All four storylines and scenario families describe future worlds that are generally more affluent compared to the current situation. They range from very rapid economic growth and technologic change to high levels of environmental protection, from low-to-high global populations, and from high-to-low GHG emissions. Perhaps more importantly, all the storylines describe dynamic changes and transitions in generally different directions. The storylines do not include specific climate-change policies, but they do include numerous other socio-economic developments and non-climate environmental policies. As time progresses, the storylines diverge from each other in many of their characteristic features. In this way they span the relevant range of GHG emissions and different combinations of their main sources.

After the basic features and driving forces for each of the four storylines had been determined, the team quantified the storylines into individual scenarios with the help of formal (computer) models. While the writing team and the modeling groups included experts from around the world, all six modeling groups are based in Europe, North America, and Japan. As indicated above, each model quantification of a storyline constitutes a scenario, and all scenarios of one storyline constitute a "scenario family." The six models are representative of different approaches to emissions-scenario modeling and different integrated assessment frameworks in the literature, and include so-called top-down and bottom-up models. The use of different models reflects the SRES Terms of Reference call for methodologic pluralism and for an open process (see Appendix I). The number and type of models chosen in the open process was on a voluntary basis. In January 1997 the Intergovernmental Panel on Climate Change Working Group II Technical Support Unit (IPCC WGII TSU) sent letters to Governments asking for nominations of modeling teams to contribute to SRES and advertised in a number of scientific journals for modelers to participate in SRES. Six different modeling groups from Europe, North America, and Japan volunteered to participate in the formulation and development of the scenarios in response to the call. It is fortunate that they are from three different continents and also include different methodological approaches used in the literature to develop quantitative emissions scenarios.

The six models have different regional aggregations. The writing team decided to group the various global regions into four "macro-regions" common to all the regional aggregations across the six models (Box 4-1).

Box 4-1: Four SRES World Regions

The six modeling frameworks used to develop the SRES scenarios have different regional aggregations. The writing team decided to group the various global regions into four "macro-regions" common to all the different regional aggregations across the six models (Figure 4.2; see Appendix IV, Table IV-1). The individual scenarios were formulated with the respective regional aggregation of each model. Afterward, the input assumptions and results were summed to correspond to the four macro-regions:

  • OECD90 region groups together all member countries of the Organization for Economic Cooperation and Development as of 1990, the base year of the participating models, and corresponds to the Annex II countries originally defined in UNFCCC (1992).
  • REF region consists of countries undergoing economic reform and groups together the East and Central European countries and the Newly Independent States of the former Soviet Union; it roughly corresponds to Annex I outside the Annex II countries as defined in UNFCCC (1992).
  • ASIA region stands for all developing (non-Annex I) countries in Asia (excluding the Middle East).
  • ALM region stands for the rest of the world and corresponds to developing (non-Annex I) countries in Africa, Latin America, and Middle East

In other words, the OECD90 and REF regions together roughly correspond to Annex I or industrialized (developed) countries (IND), while the ASIA and ALM regions together roughly correspond to the non-Annex I, or developing countries (DEV). Developing, or non-Annex I countries (i.e., ASIA and ALM), are sometimes referred to in the text as the "South" to distinguish them from the industrialized, or Annex I countries, of the "North" (i.e., OECD90 and REF). A detailed description of each region is provided in Appendix III.

Figure 4-2: SRES world regions ALM, ASIA, OECD90, and REF. The developing (DEV) countries, comprising the ALM and ASIA regions, roughly correspond to non-Annex I countries of the UNFCCC (1992). The industrialized (IND) countries, comprising the OECD90 and REF regions, roughly correspond to Annex I countries of the UNFCCC.

In response to a number of requests from potential user groups within IPCC and in accordance with a decision of the IPCC Bureau in 1998 to release draft scenarios to climate modellers for their input in the Third Assessment Report, the writing team chose one model run to characterize each scenario family. Scenarios resulting from these runs are called "marker" scenarios or simply "markers." There are four marker scenarios, each considered characteristic for one of the four scenario families. The rationale and process for designating marker scenarios is discussed in more detail in Section 4.4.1.

The SRES scenario quantifications of the main indicators (such as population and economic growth, characteristics of the energy system, and the associated GHG emissions) all fall within the range of studies published in the literature and scenarios documented in the SRES database (see Chapter 2). Quantitative indicators form an important part of each scenario description. These indicators include gross world product, population, supply and demand for principal energy forms, energy resource characteristics, the breakdown of land use, and emissions of various GHGs. The scenarios are designed so that the evolution of their indicators over the 21 st century falls well within the range represented by scenarios from the literature and included in the SRES database (see Chapter 2 and Morita and Lee, 1998; Nakic´┐Żenovic´┐Ż, et al., 1998). More importantly, they correspond to the qualitative characteristics of the respective storylines. Also, they were revised iteratively within the six modeling approaches to achieve internal consistency on the basis of inputs from the entire SRES writing team and the SRES open process.

Each storyline was characterized initially by two quantitative "targets," namely global population (15, 10, and 7 billion by 2100 in scenarios A2, B2, and both A1 and B1, respectively) and global gross domestic product (GDP) by 2100 (in 1990 US dollars, US$550 trillion for A1, US$250 trillion for A2, US$350 trillion for B1, and US$250 trillion for B2). These quantitative targets guided the subsequent quantification of the SRES scenarios with different model approaches. Generally, the orders of magnitude of these original quantitative scenario "guideposts" are reflected in the final SRES scenarios (see Table 4-2) and have been adopted in a majority of SRES scenarios. Evidently, the quantitative characteristics of the four SRES scenario families comprise many more dimensions than this, in particular regional patterns, differences in resource and technology availability, land-use changes, non-carbon dioxide (CO2) GHGs, etc. These are discussed in the subsequent Sections.

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