Emissions Scenarios - IPCC

236 An Overview of Scenarios
the A2-A1-MiniCAM scenario) could even yield emissions in
the Medium-Low category. Thus, the A2 scenario family also
indicates that a wide range of emissions outcomes is possible
for any given development path of main scenario driving
forces, such as population and income per capita.
Finally, the categorization of scenarios in terms of their
(cumulative carbon) emission outcomes illustrates that similar
emission outcomes could arise from very different
developments of main scenario drivers. For instance. High
category cumulative emissions could arise from scenarios of
low population growth, combined with high incomes (and
energy use) and globalized technological developments that
favor accessibility and economics of fossil fuels (coal,
unconventional oil and gas; e.g., AlC and AIG scenario
groups). Alternatively, similar High category cumulative
emissions could also arise from scenarios of high population
growth combined with slower per capita income growth and
more regionally oriented technology development trends
(scenario family A2). A comparison of the Bl and AIT
scenario groups (see Table 4-20) also confirms this conclusion.
Both scenarios explore pathways that reduce current income
disparities between regions. They indicate that such a tendency
does not necessarily lead to high emissions, but could be
achieved with Low to Medium-Low category cumulative
emissions (as scenario groups AlC and AIG also indicate that
High category emission pathways are possible).
Perhaps the most important conclusion from the SRES multimodel,
open process, and the large number of scenarios it has
generated is the recognition that there is no simple, linear
relationship between scenario driving forces and outcomes or
between emission outcomes and scenario driving forces. High
or low population scenarios need not automatically lead to high
or low emissions; similar statements also hold for economic
growth and for closing regional income gaps.
References:
Alcamo, J., A. Bouwman, J Edmonds, A. Grubler, T Monta, and A
Sugandhy, 1995. An evaluation of the IPCC IS92 emission scenanos In
Climate Change ¡994, Radiative Forcing of Climate Change and An
Evaluation of the I P C C IS92 Emission Scenarios, J.T. Houghton, L G
Meira Filho, J. Bruce, Hoesung Lee, B.A. Callander, E. Haites, N. Hams
and K. Maskell (eds.), Cambridge University Press, Cambridge, pp 233-
304
Alcamo, J., E. Kreileman, M KJ-O1, R. Leemans, J Bollen, J van Minnen, M.
Schaefer, S. Toet, and B. de Vries, 1998: Global modelling of
environmental change: an overview of IMAGE 2.1. In Global
change
scenarios of the 21" centuiy Results from the I M A G E 2 ¡ Model. J
Alcamo, R. Leemans, E. Kreileman (eds.), Elsevier Science, Kidlington,
Oxford, pp. 3-94.
Ausubel, J H., A. Grubler, and N. Nakicenovic, 1988: Carbon dioxide
emissions m a methane economy. Climatic Change, 12, 245-263
BP (British Petroleum), various years, 1972-1999. BP Statistical Review of
World Energy BP, London. (http-//www.bpamoco.com/worldenergy/otl/
index.htm).
СРВ (Bureau for Economic Policy Analysis), 1992 Scanning the Future A
Long-teim study of the Woild Economy 1990-20¡5. Sdu Publishers, The
Hague.
Croft, TA , 1973- Burning waste gas in oil fields. Natuie, 245 (16 July),
375-376.
Croft, T.A., 1978: Nighttime images of the earth from space. Scientific
American, 239, 68-79
Croft, T.A., 1979 The brightness ofUghts on earth at night, digitally recoided
by D M S P satellites. Stanford Research Institute final report prepared for the
и S Geological Survey, SRI, Stantoid, CA
Demeny, P, 1990; Population In The Earth As Ti aiisfoi med by Human Action.
B.L. Turner II et al (eds.), Cambndge University Press. Cambridge, pp.
41-54
De Jong, A., and G Zalm, 1991: Scanning the Future. A long-term scenario
study ot the world economy 1990-2015 In Long-term Prospects of the
World Econoniv OECD, Pans, pp 27-74
De Vries, H / M., J G.J. Olivier, R.A. van den Wijngaait. G J J. Kieiieman, and
A.M.C Toet, 1994: Model foi calculating regional energy use, industrial
production and greenhouse gas emissions for evaluating global climate
scenarios Watei, Air Soil Pollution, 16, 79-131
De Vries, В , M. Janssen, and A. Beusen, 1999' Perspectives on global eneigy
futures - simulations with the TIME model. Energy Policy, 27, 477-494.
De Vries, В., J Bollen, L Bouwman, M. den Elzen, M. Janssen, and E.
Kreileman, 2000: Greenhouse gas emissions in an equity-, environmentand
service-onented world: An IMAGE-based scenario for the next
century Technological Foiecasting & Social Change, 63(2-3) (In press)
Dietz, J , С D. Elvidge, A Gntsevskyi, and A Grublei, 2000; Night satellite
imagei у data and socio-economic activity vai tables Current patterns and
long-teim simulations IR-OO-XX, International Institute for Applied
Systems Analysis, Laxenburg, Austria (http.//www nasa ас at/
Publications/Catalogue),
Durand, J.D., 1967' The modern expansion of world population. Proceedings
of the American Philosophical Society, 111(3), 136-159.
Edmonds, J., M Wise, and C. MacCracken, 1994: Advanced energy
echnologies and climate change An Analysis Using the Global Change
Assessment Model (GCAM). PNL-9798, UC-402, Pacific Northwest
Laboratory, Richland, WA.
Edmonds, J, M Wise, H Pitcher, R Richels, T. Wigley, and С MacCracken,
1996a; An integrated assessment of climate change and the acceleiated
introduction of advanced energy technologies: An application of MiniCAM
1 0. Mitigauon and Adaptation Stiategies joi Global Change, 1(4), 311-
339.
Edmontis, J. M. Wise, R. Sands, R Brown, and H. Kheshgi, 1996b:
Agricultuie, land-use, and commercial biomass energy A Preliminaty
integiated analysis of the potential role of Biomass Eneigy for Reducing
Futuie Greenhouse Related Emissions PNNL-11155, Pacific Northwest
National Laboratories, Washington, DC.
EIA (Eneigy Information Administration), 1997: International Energy
Outlook EIA, Washington, DC.
EIA (Energy Information Administration). 1999' International Energy
Outlook EIA, Washington, DC
Elvidge, CD., K.E. Baugh, E.A. Kihn, H W. Kroehl, E.R. Davis, and C. Davis,
1997a; Relation between satellite obseived visible to neai infraied
emissions, population, and energy consumption. Intei national Joui nal of
Remote Sen.sing. 18, 1373-1379
Elvidge, С D., K.E. Baugh, K.E Hobson, E A Kihn, H.W. Kroehl, H W,
Davis, E.R, Davis, and D. Cocero, 1997b. Satellite inventory of human
settlements using nocturnal radiation emissions' A contribution foi the
global toolchest. Global Change Biology, 3, 387-395
Elvidge, С D., K.E. Baugh, E.A. Kihn, H W Kroehl, and E.R. Davis, 1997c-
Mapping of city lights using DMSP Operational Linescan System data.
Photogrammetric Engineering and Remote Sensing. 63,121-1ЪА.
Elvidge, CD , K.E. Baugh, J B. Dietz.T Bland, PC. Sutton, and H.W. Kroelil,
1999: Radiance cahbration ot DMSP-OLS low-light imaging data of
human settlements. Remote Sensing of Environment, 68, 77-88.
Foster, J L., 1983 Observations of the earth using nighttime visible imageiy.
International Journal of Remote Sensing, 4, 785-791.
Gallo, K.P, J.D Tarpley, AL. McNab, and T.R. Karl. 1995; Assessment of
uiban heat islands: a satellite perspective. Atmospheiic Reseaich, 37, 37-
43.