Editing IPCC:AR6/WGIII/Chapter-3 (section)

=== 3.3.1 Socio-economic Drivers of Emissions Scenarios ===

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Greenhouse gas (GHG) emissions mainly originate from the use and transformation of energy, agriculture, land use (change) and industrial activities. The future development of these sources is influenced by trends in socio-economic development, including population, economic activity, technology, politics, lifestyles, and climate policy. Trends for these factors are not independent, and scenarios provide a consistent outlook for these factors together ( [[#3.2|Section 3.2]] ). [[#Marangoni--2017|Marangoni et al. (2017)]] show that in projections, assumptions influencing energy intensity (e.g., structural change, lifestyle and efficiency) and economic growth are the most important determinants of future CO 2 emissions from energy combustion. Other critical factors include technology assumptions, preferences, resource assumptions and policy ( [[#van%20Vuuren--2008|van Vuuren et al. 2008]] ). As many of the factors are represented differently in specific models, the model itself is also an important factor – providing a reason for the importance of model diversity ( [[#Sognnaes--2021|Sognnaes et al. 2021]] ). For land use, [[#Stehfest--2019|Stehfest et al. (2019)]] show that assumptions on population growth are more dominant given that variations in per capita consumption of food are smaller than for energy. Here, we only provide a brief overview of some key drivers. We focus first on so-called reference scenarios (without stringent climate policy) and look at mitigation scenarios in detail later. We use the SSPs to discuss trends in more detail. The SSPs were published in 2017, and by now, some elements will have to be updated ( [[#O’Neill--2020b|O’Neill et al. 2020b]] ). Still, the ranges represent the full literature relatively well.

Historically, population and GDP have been growing over time. Scenario studies agree that further global population growth is likely up to 2050, leading to a range of possible outcomes of around 8.5–11 billion people (Figure 3.9a). After 2050, projections show a much wider range. If fertility drops below replacement levels, a decline in the global population is possible (as illustrated by SSP1 and SSP5). This typically includes scenarios with rapid development and investment in education. However, median projections mostly show a stabilisation of the world population (e.g., SSP2), while high-end projections show a continued growth (e.g., SSP3). The UN Population Prospects include considerably higher values for both the medium projection and the high end of the range than the SSP scenarios ( [[#KC--2017|KC and Lutz 2017]] ; [[#UN--2019|UN 2019]] ). The most recent median UN projection reaches almost 11 billion people in 2100. The key differences are in Africa and China: here, the population projections are strongly influenced by the rate of fertility change (faster drop in SSPs). Underlying these differences, the UN approach is more based on current demographic trends while the SSPs assume a broader range of factors (including education) driving future fertility.

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'''Figure 3.9 | Trends in key scenario characteristics and driving forces as included in the SSP scenarios (showing 5–95th percentiles of the reference scenarios as included in the database in grey shading).''' Reference (dotted lines) refers to the UN low-, medium- and high-population scenarios ( [[#UN--2019|UN 2019]] ), the OECD long-term economic growth scenario ( [[#OECD--2021|OECD 2021]] ), the scenarios from the IEA’s World Energy Outlook ( [[#IEA--2019|IEA 2019]] ), and the scenarios in the FAO assessment ( [[#FAO--2018|FAO 2018]] ).

Economic growth is even more uncertain than the population projections (Figure 3.9c). The average growth rate of GDP was about 2.8% per year (constant USD) in the 1990–2019 period ( [[#The%20World%20Bank--2021|The World Bank 2021]] ). In 2020, the COVID-19 crisis resulted in a considerable drop in GDP (estimated around 4–5%) ( [[#IMF--2021|IMF 2021]] ). After a recovery period, most economic projections assume growth rates to converge back to previous projections, although at a lower level ( [[#IMF--2021|IMF 2021]] ; [[#OECD--2021|OECD 2021]] ) (see also Box 3.2). In the long term, assumptions on future growth relate to political stability, the role of the progress of the technology frontier and the degree to which countries can catch up ( [[#Johansson--2013|Johansson et al. 2013]] ). The SSP scenarios cover an extensive range, with low per-capita growth in SSP3 and SSP4 (mostly in developing countries) and rapid growth in SSP1 and SSP5. At the same, however, also scenarios outside the range have some plausibility – including the option of economic decline ( [[#Kallis--2012|Kallis et al. 2012]] ) or much faster economic development ( [[#Christensen--2018|Christensen et al. 2018]] ). The OECD long-term projection is at the global level reasonably consistent with SSP2. Equally important economic parameters include income distribution (inequity) and the type of growth (structural change, i.e., services vs manufacturing industries). Some projections (like SSP1) show a considerable convergence of income levels within and across countries, while in other projections, this does not occur (e.g., SSP3). Most scenarios reflect the suggested inverse relationship between the assumed growth rate for income and population growth (Figure 3.9e). SSP1 and SSP5 represent examples of scenarios with relatively low population increase and relatively high-income increase over the century. SSP3 represents an example of the opposite – while SSP2 and SSP4 are placed more in the middle. Nearly all scenarios assessed here do not account for climate impacts on growth (mostly for methodological reasons). As discussed in [[#3.5|Section 3.5]] these impacts can be considerable. An emerging area of literature emphasises the possibility of stabilisation (or even decline) of income levels in developed countries, arguing that such a trend would be preferred or even needed for environmental reasons ( [[#Anderson--2013|Anderson and Larkin 2013]] ; [[#Hickel--2020|Hickel and Kallis 2020]] ; [[#Kallis--2020|Kallis et al. 2020]] ; [[#Hickel--2021|Hickel et al. 2021]] ; [[#Keyßer--2021|Keyßer and Lenzen 2021]] ) (see also Chapter 5). Such scenarios are not common among IAM outcomes, that are more commonly based on the idea that decarbonisation can be combined with economic growth by a combination of technology, lifestyle and structural economic changes. Still, such scenarios could result in a dramatic reduction of energy and resource consumption.

Scenarios show a range of possible energy projections. In the absence of climate policy, most scenarios project the final energy demand to continue to grow to around 650–800 EJ yr –1 in 2100 (based on the AR6 Scenarios Database, Figure 3.9b). Some projections show a very high energy demand up to 1000 EJ yr –1 (comparable to SSP5). The scenario of the IEA lies within the SSP range but near the SSP1 projection. However, it should be noted that the IEA scenario includes current policies (most reference scenarios do not) and many scenarios published before 2021 did not account for the COVID-19 crisis. Several researchers discuss the possibility of decoupling material and energy demand from economic growth in the literature, mainly in developed countries ( [[#Kemp-Benedict--2018|Kemp-Benedict 2018]] ) (decoupling here refers to either a much slower increase in demand or even a decrease). In the scenario literature, this is reflected by scenarios with very low demand for final energy based on increased energy efficiency and less energy-intensive lifestyles (e.g., SSP1 and the LED scenario) ( [[#Grubler--2018|Grubler et al. 2018]] ; [[#van%20Vuuren--2018|van Vuuren et al. 2018]] ). While these studies show the feasibility of such pathways, their energy efficiency improvement rates are considerably above the historic range of around 2% ( [[#Gütschow--2018|Gütschow et al. 2018]] ; [[#Jeffery--2018|Jeffery et al. 2018]] ; [[#Vrontisi--2018|Vrontisi et al. 2018]] ; [[#Haberl--2020|Haberl et al. 2020]] ; [[#Roelfsema--2020|Roelfsema et al. 2020]] ; [[#Giarola--2021|Giarola et al. 2021]] ; [[#Höhne--2021|Höhne et al. 2021]] ; [[#IEA--2021a|]] [[#IEA--2021|IEA 2021]] a ; [[#Höhne--2021|Höhne et al. 2021]] ; [[#Sognnaes--2021|Sognnaes et al. 2021]] ). These scenarios also show clear differences in food consumption and the amount of land used for agriculture. Food demand in terms of per-capita caloric intake is projected to increase in most scenarios (Figure 3.9d). However, it should be noted that there are large differences in dietary composition across the scenarios (from more meat-intensive in scenarios such as SSP5 to a decrease in meat consumptions in other scenarios such as SSP1). Land-use projections also depend on assumed changes in yield and the population scenarios (Figure 3.9f). Typically, changes in land use are less drastic than some other parameters (in fact, the 5–95th percentile database range is almost stable). Agriculture land is projected to increase in SSP3, SSP2, and SSP4 – it is more-or-less stable in SSP5 and is projected to decline in SSP1.

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