Emissions Scenarios - IPCC

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206 An Overview of Scenarios turnover rates (GDP growth of 1.6% per year between 1990 and 2100). The final energy demand across regions is determined by the product of regional GDP growth and energy intensity improvements. For example, the higher energy intenshies in ASIA compared to the ALM region lead to a higher absolute final energy demand in the former in spite of a lower GDP. This is explained by differences in initial conditions and by delayed diffusion of more efficient energy end-use technologies, because of lower GDP per capita growth. 4.4.5.4. Harmonized and Other A2 Scenarios The global primary energy use and final energy use in the A2 scenarios created with the AIM, MESSAGE, and MiniCAM models are quite close to those of the marker scenario, while A2-IMAGE scenario projects lower primary and final energy use compared to the A2 marker. As mentioned above, the A2- Al-MiniCAM scenario explores a very different unfolding of driving forces in terms of population and GDP growth, combined with an assumption about saturating energy demand at current Western European levels. Combined, these assumptions translate into the lowest energy intensity improvement rates across the SRES scenario set. Global primary energy use per unit of GDP (intensities) improves from 14.7 MJ/US$90 in 1990 to 8.9 MJ/US$90 in 2100. This reflects mainly the low per capita GDP (productivity) growth of this scenario which (other factors being equal) translates into low rates of energy intensity improvement. Nonetheless, the resultant energy intensities are comparable to current Westem European levels, as are income and energy use per capita. In other words, the scenario describes a global picture by 2100 quite similar to that of Westem Europe of today, 100 years earlier. 4.4.5.5. Bl Scenarios Energy intensity improvements in the В1 marker result from energy efficiency investments brought about by increases in fuel and electricity prices and technological innovations (including assumptions on taxes and perceived premium values for clean fuels). The rather high rates in energy intensity reduction in В1 stem also from the explicit assumption that less industrialized regions catch-up. Another factor is the assumption that monetary economic growth in less developed regions initially largely replaces activities in the informal economy, which leads to a replacement of traditional noncommercial energy forms by high-efficiency modern applications and fuels - and hence substantial energy intensity improvements. In the developed regions the high economic growth in the В1 scenario may, for instance, be in the form of increasing monetization of human activities previously not included in GDP accounts (e.g., childcare, household work). Such monetary GDP growth does not result in additional demands for energy services, and hence again results in significant energy intensity improvements. The demand for electricity is assumed to rise faster than that for non-electricity energy, and may pose one of the capital availability constraints in this scenario. 4.4.5.6. Harmonized and Other Bl Scenarios Various altemative scenario quantifications were developed for В1 by the modeling teams. For the fhst four to five decades most model runs show a global final energy use within the proposed bounds of the Bl marker, except Bl-ASF which is higher. By 2100 most scenarios assume higher final energy use than the marker run, except ВI-MESSAGE which reproduces closely the final energy use of the Bl marker (and is correspondingly classified as a "fully harmonized" scenario). In parficular, BI-MARlA, BlHigh-MiniCAM, and BlHigh- MESSAGE show a global final energy use in 2100 nearly twice that of the marker. These scenarios explored the implications for energy demand of less rapid "dematerialization" tendencies of the economy, especially for developing countries, with trends in line with historical energy intensity experiences in the OECD countries. Regional trends differ most dramatically for the MiniCAM and MARIA runs for ASIA and ALM, with the MiniCAM simulations assuming a saturating (converging) energy use on a per capita basis at 125 GJ/capita. However, current knowledge about rates and direction of dematerialization of economic activities is limited. Therefore, both historical OECD trends and their applicability to the future economies of currently developing countries may not necessarily reflect future developments. The use of altemative modeling approaches in the quantification of the Bl scenario storyline has helped to shed light on this important area of uncertainty of the future. 4.4.5.7. B2 Scenarios Final energy demand for the B2 marker was derived by applying efficiencies of end-use technologies to the demand of electric and non-electric energy services. These in tum depend on the economic development rates, income levels, and sectoral economic stmcture of each region. The evolution of the final energy demand levels and structure in the developing regions follows patterns that are similar to the historical development in the now-industriafized regions of the world. This again is consistent with the "dynamics-as-usual" interpretation of the B2 storyline. By successive iterations with a macro-economic model, marginal cost increases are taken into account in the energy demand projections of the MESSAGE model for the B2 marker (see Appendix IV). The resuh is an aggregate energy-intensity improvement rate at the global level of about 1% per year until 2100, about the same as has prevailed over the past 100 years in countries for which such long-term time series data are available (see Chapter 3). This aggregate global improvement rate masks important differences in the temporal and spatial evolution of energy intensities. Improvements are generally higher in regions far away from the energy-intensity frontier and also faster in those for which the capital turnover rate (i.e., GDP growth) is higher. Consistent with the more imperfect realization of future trends characteristic of the B2 scenario, energy intensity improvements are slower than in the Al or ВI scenario families, but higher than in the A2 scenario family.
An Overview of Scenarios 207 100 Sí Oí и
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E M I S S I O N S S C E N A R I O S
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PUBLISHED BY THE PRESS SYNDICATE OF
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Contents Foreword Preface Summary f
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Preface The Intergovernmental Panel
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C O N T E N T S Why new Intergovern
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4 Summary for Policymakers Box SPM-
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6 Summary for Policymaker -3.5 i i
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8 Summary for PoUcymake. b Scenario
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lu Summary for Policymakers 0 I I I
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Summary for Policymakers such as fe
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14 Summary for Policymakers CQ so t
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16 Summary for Policymakers CQ ^ Ч
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18 Summary for Policymakers s I, I
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Summary for Policymakers P3 M ^ NO
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CONTENTS 1. Introduction and Backgr
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24 Technical Summai-y intended to b
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26 Technical Summary 1900 1950 2000
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28 Technical Summary Figure TS-2: S
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30 Technical Summaiy Box TS-3: SRES
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32 Technical Summary Figure TS-3: P
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34 Technical Summary Renewables/Nuc
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36 Technical Summary Figure TS-5 il
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38 Technical Summar 1900 1950 2000
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40 Technical Summary 40 1990 2000 2
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42 Technical Summary family). Chapt
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44 Technical Summary 1930 1960 1990
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46 Technical Summary does not exist
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48 Technical Summary CQ CP 00 00 l>
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50 Technical Summary n rí M •л
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52 Technical Summary pa Ю un ^ Ч
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54 Technical Summary (S ее CQ fN
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56 Technical Summary References: Al
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1 Background and Overview
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Background and Overview 61 1.1. Int
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Background and Overview 63 Box 1-1:
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Background and Overview 65 interven
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Background and Overview 67 powerful
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Background and Overview 69 1900 195
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Background and Overview 71 SRES Sce
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Background and Overview 73 1900 195
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Background and Overview characteriz
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2 An Overview of the Scenario Liter
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An Overview of the Scenario Literat
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hn Overview of the Scenario Literat
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3 Scenario Driving Forces
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Scenario Driving Forces 105 3.1. In
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Scenario Driving Forces 107 Section
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Scenario Driving Forces J 09 UN 199
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Scenario Driving Forces 111 deficit
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Scenario Driving Forces 113 recent
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Scénario Driving Forces lis Box 3-
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Scénario Driving Forces 117 Table
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Scenario Driving Forces 119 о I ht
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Scenario Driving Forces 121 in biol
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Scenario Driving Forces 123 0.15 0.
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Scenario Driving Forces 125 GDP per
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Scenario Driving Forces 127 aluminu
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Scenario Driving Forces 129 Table 3
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Scenario Driving Forces 131 cooling
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Scenario Driving Forces 133 saved f
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Scenario Driving Forces 135 give re
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Scenario Driving Forces 137 3.4.3.3
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Scenario Driving Forces 139 economi
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Scenario Driving Forces 141 growth"
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Scenario Driving Forces 143 (a) (b)
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Scenario Driving Forces 145 the ext
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Scénario Driving Forces 147 Global
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Scénario Driving Forces 149 anywhe
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Scenario Driving Forces 151 (a) Sul
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Scenario Driving Forces 153 Nakicen
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Page 175 and 176: 4 An Overview of Scenarios
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Page 247 and 248: Emission Scenarios
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Emission Scénarios 257 is between
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Emission Scenarios 259 700 600 Bl F
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Emission Scénarios 261 The range o
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Emission Scénarios 263 oi • •
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Emission Scénarios 265 Table 5-7:
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Emission Scénarios 267 Table 5-9:
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Emission Scenarios 269 technically
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Emission Scénarios 271 ^ — BIT M
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Emission Scénarios 273 4000 AlB AI
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Emission Scenarios 275 M B v/ы —
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Emission Scenarios 277 Box 5-3: Sul
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Emission Scenarios 279 Box 5-4: Fut
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Emission Scenarios о ON — 00 Ч
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Emission Scenarios QS ON NO со ON
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Emission Scenarios 285 Table 5-14:
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Emission Scenarios 287 -OECD90 REF
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Emission Scenarios 289 1200 oU, \ \
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Emission Scenarios 291 Box 5-5: Gri
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6 Summary Discussions and Recommend
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Summary Discussions and Recommendat
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SPECIAL REPORT ON EMISSIONS SCENARI
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324 SRES Terms of Reference: New IP
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II SRES Writing Team and SRES Revie
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SRES Writmg Team ami SRES Reviewers
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Ill Definition of SRES World Re
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Definition of SRES World Regions Me
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336 Six Modeling Approaches Six Mod
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338 Six Modeling Approaches e < .S
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340 Six Modeling Approaches Populat
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342 Six Modeling Approaches Table I
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344 Six Modeling Approaches Table I
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346 Six Modeling Approaches for dev
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348 Data Description Database Descr
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350 Data Description Table V-2. Lis
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Open Process
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Open Process 355 Table VI'2: SRES w
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Open Process 357 1Г)-ч);ОООЧ
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Open Process 359 Preliminary Al Mar
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Open Process 361 Preliminary Al Mar
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Open Process 363 Table VI-4b: Stand
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Open Process 365 Preliminary A2 Mar
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Open Process 367 Preliminary A2 Mar
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Open Process 369 Preliminary Bl Mar
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Open Process 371 Preliminary Bl Mar
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Open Process 373 Table VI-4d: Stand
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Open Process 375 Preliminary B2 Mar
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Open Process 377 Preliminary B2 Mar
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380 Statistical Table Table VII.l:
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3S2 Statistical Table Marker Scenar
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384 Statistical Table Marker Scenar
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386 Statistical Table Scenario AlB-
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392 Statistical Table Scenario AIB-
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4U0 Statistical Table Scenario AlB-
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412 Statistical Table Scenario AlC-
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416 Statistical Table Scenario AIC-
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426 Statistical Table Scenario AIG-
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436 Statistical Table Illustrative
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442 Statistical Table Scenario AIT-
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452 Statis-tical Table Scenario AIT
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456 Statistical Table Scenario Alvl
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462 Statistical Table Scenario Alv2
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464 Statistical Table Scenario Alv2
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466 Statistical Table Scenario A2-A
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476 Statistical Table Scenario A2G-
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482 Statistical Table Scenario A2-I
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484 Statistical Table Scenario A2-M
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496 Statistical Table Scenario Bl-A
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500 Statistical Table Scenario В1-
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512 Statistical Table Scenario Bl-M
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522 Statistical Table OECDTO^^"'^'"
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526 Statistical Table Scenario BIT-
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532 Statistical Table Scenario BlHi
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542 Statistical Table Scenario B2-A
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554 Statistical Table Scenario B2-I
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556 Statistical Table Scenario B2-M
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558 Statistical Table Scenario B2-1
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572 Statistical Table Scenario B2C-
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574 Statistical Table Scenario B2C-
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576 Statistical Table Scenario B2Hi
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578 Statistical Table Scenario BZHi
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580 Statistical Table Scenario B2Hi
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582 Acronyms and Abbreviations Acro
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584 Acronyms and Abbreviations USCB
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586 Chemical Symbols Chemical Symbo
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588 Units Table X-1: SI (Système I
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590 Glossary of Terms Glossary of T
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592 Glossary of Terms Fuel Switchin
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594 Glossary of Terms standards for
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XII List of Major IPCC Reports
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List of Major IPCC Reports 599 Tech
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