Drivers/Policy issues: Difference between revisions

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{{DriverPartTemplate
{{DriverPartTemplate
|Status=On hold
|PageLabel=Policy issues
|PageLabel=Drivers and types of scenarios
|Sequence=4
|Sequence=4
|Reference=Moss et al., 2010; Van Vuuren et al., 2012;
|Reference=Moss et al., 2010; Van Vuuren et al., 2012; Ebi et al., 2014a; Lutz and KC, 2010; Dellink et al., 2017; KC and Lutz, forthcoming; IIASA, 2013;
|Description=<h2>Scenario approaches </h2>
|Description=<h2>Baseline developments </h2>
Under baseline conditions scenario drivers are typically assumed to develop either along a pathway that is considered a best guess translation of current trends into the future. Alternatively, multiple contrasting scenarios can be considered to explore the range of plausible future trends. The former approach is chosen in many studies, such as the [[OECD Environmental Outlook to 2050 (2012)|OECD Environmental Outlook]] ([[OECD, 2012]]), with the specific aim to serve as starting point for policy interventions aiming to improve the baseline outcomes within and across sectors and domains. The latter approach recognizes structural uncertainties in how the world might unfold, and aims to explore how such uncertainties would play out in future ranges of outcomes. They also serve to investigate how robust policy interventions play out under different future conditions. Examples of multiple baseline studies are ([[IPCC, 2000]]), ([[MA, 2005]]). See Figures 3.2*** and 3.3*** for illustrative results  from the so-called Shared Socio-economic Pathways (SSPs), recently developed to support climate change research across different research communities. ([[Moss et al., 2010]]; [[Van Vuuren et al., 2012]]). An important element of the SSPs are the qualitative narratives or storylines characterizing alternative futures. From there, assumptions are made about internally coherent sets of scenario drivers, and finally key model drivers such as population and GDP growth factors.
Under baseline conditions, scenario drivers are assumed to develop either along the pathway considered the ‘best guess’ translation of current trends into the future, or multiple contrasting scenarios are considered to explore the range of plausible future trends. The first approach has been chosen for many studies, such as the OECD Environmental Outlook to 2050 ([[OECD, 2012]]), as starting point for policy interventions to improve the baseline outcomes within and across sectors and issues. The second approach recognises structural uncertainties in how the world may develop, and explores how such uncertainties would play out in a future range of outcomes. Multiple contrasting scenarios also serve to investigate how robust policy interventions play out under different future conditions. Examples of multiple baseline studies are the Special Report on Emissions Scenarios ([[IPCC, 2000]]) and the Millennium Ecosystem Assessment ([[MA, 2005]]).


===Scenario examples===
Recently, the Shared Socioeconomic Pathways ({{AbbrTemplate|SSP}}s) have been developed to support climate change research by different research communities ([[Moss et al., 2010]]; [[Van Vuuren et al., 2012]]; [[Ebi et al., 2014a]]). See both Figures below. The qualitative narratives or storylines characterising alternative futures are important elements of the SSPs From there, assumptions are made about internally coherent sets of scenario drivers, and key model drivers, such as population and {{AbbrTemplate|GDP}} growth ([[IIASA, 2013]])
Figure 3.2**** illustrates the wide range in the population size projected for the long term: by 2100 the world could see around the same number of people as today, or about twice as many. The projections are made by [[IIASA]], using a population ([[MA, 2005]]) modeling approach that links aggregate education levels to fertility and mortality rates per country, and together with migration flows these determine the future population size.


Taking the population projections and the underlying educational attainment per age cohort, long term economic growth models are used to project the associated economic development expressed as GDP per capita. Together with the population the total GDP is calculated. This was done by three different teams at [[OECD]], [[IIASA]] and [[PIK]], each using their own models. Figure 3.3a*** shows the GDP projections until 2100 from the OECD model ENV-Growth, differing by a factor 3.7 in 2100. As the most populous scenarios are at the lower end of the GDP range, the differences in GDP per capita will be even bigger than that. Differences in population and economic growth rates between different countries and regions make that the distribution of total economic assets over different parts of the world are bound to shift, with Asia and later Africa and to a lesser extent Latin America becoming dominant in the world; see Figure 3.3b***.
===Population projections===
The wide range of long-term populations projections is presented in the figure below. By 2100, the world population could be either about the same as today or double. The projections were made by IIASA using a population modelling approach ([[Lutz and KC, 2010]]) that links aggregate education levels to fertility and mortality rates per country. Together with migration flows, these rates determine the size of the future population. ([[Dellink et al., 2017]]; [[KC and Lutz, forthcoming]]).


As mentioned, different models were used by teams at different institutes, allowing to investigate how different model structures and assumptions, but also different interpretations of the qualitative scenario story lines in model parameters, lead to quite different results (Figure 3.3c***). The projections for the SSP3 scenario not only differ with respect to the level in 2100, but also in the pathway over the century.
Using population projections and the underlying educational attainment per age cohort, long-term economic growth models project economic development expressed as GDP per capita. For the SSPs, economic development up to 2100 has been calculated by three different teams at [[OECD]], [[IIASA]] and [[PIK]], using their own models. GDP projections from the OECD model [[ENV-Growth model|ENV-Growth]] ([[Dellink et al., 2017]]) differ by a factor of up to 3.7 (see the figure at the bottom, left). The differences in population and economic growth rates between countries and regions mean that the distribution of total economic assets is likely to shift, with Asia in the lead, followed later by Africa and, to a lesser extent, by Latin America (see the figure at the bottom, middle).


===Baseline and policy interventions in the framework===
=== Models, scenario story lines and results===
For more information on how Baseline scenarios play out in more detail in economic, social and ecological terms, see the results per component of the IMAGE 3.0 framework in the 'policy issue' pages of the component.
As different models have been used, we can investigate how different model structures and assumptions and different interpretations of the qualitative scenario storylines in model parameters lead to quite different results (see the figure at the bottom, right). Projections for the SSP3 scenario made by different teams not only differ with respect to the levels projected for 2100, but also with respect to the profile over the century.
Once Baseline scenarios are implemented, modifications at various levels can be made that reflect policy interventions aiming to diverge from the trends emerging under baseline conditions. This can take many different shapes and forms, depending on the subject, scale, timeframe and policy levers under consideration. Reducing climate change impacts is one obvious example, but also reducing nutrient loading of coastal sees, slowing down the rate of biodiversity loss, reducing water stress, and many other options to alleviate anticipated future problems have been explored with IMAGE during the last decade. Again, for more information on policies, instruments and goals pursued, see the individual components (framework overview).
 
For more information on baseline scenarios in economic, social and ecological terms, see the results obtained in the IMAGE 3.0 framework in the 'Policy issues' pages of the [[Framework overview|Components]].
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}
 
==Policy interventions==
Once baseline scenarios are implemented, modifications can be made at various levels to reflect policy interventions diverging from the trends emerging under baseline conditions. These modifications can vary depending on the subject, scale, timeframe and policy levers under consideration, for instance, reducing climate change impacts, reducing the nutrient loading of coastal waters, slowing down the rate of biodiversity loss, and reducing water stress. These and many other options to alleviate anticipated future problems have been explored with IMAGE. More information on policies, instruments and goals is provided in the 'Policy issue' pages of the [[Framework overview|Components]]. The [[Policy interventions and components overview]] shows a list of all policy interventions and their affected components.
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}
}}
}}

Revision as of 12:25, 1 August 2017

Link to framework components overview

Parts of Drivers/Policy issues

  1. Introduction page
  2. Scenario drivers
  3. Model drivers
  4. Policy issues
  5. Overview of references
Projects/Applications
Models/Databases
Relevant overviews
Key publications
References
Scenario development and model drivers for IMAGE 3.0
Flowchart Drivers. Model drives are inferred from scenario storylines taking into account external data sources, such as time series, cross-sector data, and literature sources.
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