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| |IMAGEComponent=Agriculture and land use; | | |IMAGEComponent=Agriculture and land use; |
| |KeyReference=IPCC, 2000 | | |KeyReference=IPCC, 2000 |
| |FrameworkElementType=driver
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| |Description=In order for the IMAGE model to explore future scenarios, exogenous assumptions need to be made for a range of factors that shape the direction and rate of change in key model variables and results. Together with the endogenous functional relationships and endogenous model parameters that jointly typify the behaviour of the model, these exogenous assumptions drive the outcome of any particular model calculation. In other words, these assumptions are the drivers that determine the model results subject to the assumed external conditions.
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| For IMAGE, six groups of assumptions are distinguished that make up the scenario drivers. In this chapter these six scenario drivers will be described. These drivers are the basis of any scenario and are generally rooted in a scenario narrative or storyline. This includes cases where current trends and dynamics are assumed to continue into the future; commonly referred to as reference or ‘business-as-usual’ cases. As a rule, scenario drivers do not constitute numerical model inputs, but they do govern in qualitative or semi-quantitative terms the more elaborate set of exogenous assumptions in terms of model input files for the various parts of the model framework. In other words, numerical model drivers for any specific scenario will need to be established on the basis of the six more generic scenario drivers. The model drivers for the various modules will be explained in more detail in the following Chapters 4 through 8. The scenario drivers and underlying narrative, together with the concrete quantitative model drivers form a scenario, inextricably linked with the results from the IMAGE scenario run
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| <h2>Scenario drivers</h2>
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| When considering how the world might unfold in the longer term, six key scenario drivers are distinguished: demographics, economics, culture and lifestyle, natural resources, technological development and the policy and governance environment. To a large degree these scenario drivers are interdependent, and their future direction is often inferred from a simple to very elaborate ‘storyline’ or narrative. Such storylines describe the type and function of the scenario at hand: a reference projection with no new policies, a single ‘best-guess’ projection combining trends from the past with assumptions about how they might unfold in the future, multiple contrasting scenarios that span a range of uncertainty about the future, or a specific or broad policy scenario aiming to improve future outcomes. For examples, see (Brink, 2010; OECD, 2008; OECD, 2012; UNEP, 2011; Westhoek et al., 2011)
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| <h3>Demographics</h3>
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| The future state of the world will depend on how many people are expected to populate the world, as all –average- per capita activities scales with the number of people to obtain regional and global demands for goods and services. Typically the population projections that are used as input to drive the IMAGE model are adopted from published sources such as UN and IIASA. Behind these numerical projections are economic, technical, educational and policy assumptions that influence the estimated future population as the net outcome of fertility and mortality, adjusted for migration flows. This provides internally consistent, or at least plausible, overall population scenarios on the basis of underlying demographic trends. At the level of model drivers, besides the total population size the breakdown over gender, income classes, urban/ rural split, and the level of education matter for such issues as consumption preferences and patterns, and access to goods and services. With use of a downscaling procedure, the population at country or regional level is also available at the grid level; see Chapter 2. Population data are used in the energy- and agricultural economics modeling, but also directly in various other modules of IMAGE such as water stress, nutrients, flood risks and human health.
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| <h3>Economics.</h3>
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| At the most aggregate level, the level of economic activity is typically described in terms of GDP (Gross Domestic Product) per capita. Models outside the IMAGE 3.0 framework estimate long-term GDP growth perspectives, following from development of the key production factors (e.g. capital, labour, natural resources) and the sectoral composition of the economy; such as the ENV-Growth model of the OECD. The overall GDP is composed of Value Added (VA) per relevant sector and private consumption, estimated with more detailed models accounting for intersectoral linkages, responses to prices, and others (Chateau, 2013). Economic variables are used as model drivers for the energy demand model (chapter 4.1.1), and the non-agricultural water demand contributing to water stress (Chapter 6.1.3). For the household energy demand model, the average income is broken down into urban and rural population, and for each into quintiles of income levels. The latter is derived from the assumed, uneven income distribution by means of the so-called GINI factor. The GINI factor is a measure of income disparity in a population. If all enjoy the same income, GINI equals unity. The lower the GINI is, the wider is the gap between the lowest and highest income groups. The agricultural economy model MAGNET is in fact an economy-wide Computable General Equilibrium (CGE) model, that is able to reproduce the exogenous GDP growth projections made with the less complex economic growth models; see section 4.2.1. The macro indicator GDP/capita is also used directly in various other IMAGE modules, such as nutrients in urban wastewater, human health, and flood risks.
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| <h3>Culture and lifestyle</h3> For comparable levels of affluence, the observed behaviour of societies with respect to consumption differs strongly between countries and regions, and to a lesser extent also within countries. The modal split of passenger transport between walking, cycling, cars, buses, trains, boats and planes depend on income, but also on traditions and habits engrained in social groups. Preference for different foodstuffs depends on availability and affordability, but also strongly on cultural factors. These may be inspired by religion (no pork for Jewish and Islamic households; no beef or no meat at all for Hindus; etc.), but also by tradition, public moral and health concerns. In addition, concerns over environmental degradation, animal welfare, inter-regional and intergenerational equity and other issues can influence actual behavior, in accordance with dominant societal norms and values. Consumer preferences and lifestyles can change over time, as norms and values can change. The direction and rate of such changes can be inferred from the underlying scenario storyline. Policies may be put in place to enable, encourage or even enforce such changes, given sufficient public support.
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| <h3>Natural resource availability</h3>
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| Ultimate natural resources are considered a given and are not subject to adjustment by human activities, other than depletion of non-renewable resources by transforming them. For example extracting coal from underground deposits to burn them to generate thermal energy and a range of -mostly gaseous-substances- including carbon dioxide. Similarly, the total land surface is fixed, except for relatively limited man-made expansion of land into shallow coastal waters. Human activities such as agriculture and forestry, however, transform the land-cover and land-use and thereby alter the quantity of suitable land for agriculture..
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| What can be assumed as exogenous scenario drivers are assumptions about how much of the ultimate resource is available in future. This depends on assumptions about future technological capabilities and assumed policies reflecting human preferences. Technological optimism is associated with higher end estimates of non-conventional fossil and nuclear fuel reserves. Nature conservation and other concerns can limit the potential for conversion of natural land for agriculture, but may also impose limits to hydro-electricity generation.
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| In IMAGE 3.0 renewable and non-renewable energy resource by volume and price are explicitly modeled; see section 4.1.3. Similarly the potential for land to be used for agriculture, ranked by suitability and subject to nature conservation policies, sets boundaries on future land conversion; see section 4.2.3.
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| <h3>Technological change</h3>
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| At the overall scenario level, assumptions about technological progress are the all-important drivers of economic growth. Given an effective labour force, the increase in labour productivity delineates the potential for economic growth. It is generally assumed that the degree of technological progress at the scenario level is bound to be reflected in all instances where technology plays a role. In other words: rapid technological growth assumptions behind high economic growth rates imply that technologies in specific sectors (energy, agriculture) will also improve relatively quickly. Across and within sectors the directions of technological change may differ, however. For example, renewable energy technologies may improve much more rapidly than fossil fuel based technology, an uncertain factor.
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| In the energy sector, see section 4.1, the improvement in technologies over time is largely governed by an endogenous mechanism that links technology cost to the cumulative installed capacity: learning-by-doing. In agriculture technological factors are estimated exogenously, based on historical data and published projections of crop and livestock productivity, water and fertilizer use efficiency, irrigation systems performance, etc.
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| <h3>Policy and governance</h3>
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| At the scenario level it can make a big difference if –implicit or explicit- policies are assumed that reflect alternative directions that shape future development of human and natural systems. And it matters also how governance structures and institutional settings are assumed to evolve. Overall policy thinking can vary in many ways, but in the context of IMAGE important scenario bifurcations include more focus on the shorter term and/or on material wealth, versus longer term sustainability concerns. But also inter-regional and/or intergenerational equity may gain more or less weight as underlying future trend. As mentioned under culture and lifestyle, such assumed overall policy directions have the potential to influence pretty much all relevant scenario drivers, and model drivers alike.
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| In addition to the alternative policy directions, it matters how the governance structures and associated institutional arrangements develop over time in different regions of the world, or different groups of regions which share certain characteristics, such as high-income industrialized, medium income emerging or low income developing countries.
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| Besides the structural elements, the assumed policy and governance environment can also make certain policy measures and instruments more or less plausible. For example, a concerted and jointly implemented global climate mitigation strategy can hardly be imagined in a world of diverging regions primarily occupied with shorter term domestic interests, including barriers to international trade and weak intergovernmental bodies.
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| <h2>Relations between scenario drivers</h2>
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| As mentioned in the descriptions of the six scenario drivers, assumptions for each tend to depend to a lesser or greater extent on what is assumed for one or several of the other scenario drivers. Plausibility of the set of drivers, over and above the plausibility of each driver in its own right, therefore hinges on a careful consideration of the nature and direction of these relationships. An overarching story or narrative is proven to be helpful in choosing meaningful combinations across each of the six domains (IPCC, 2000); (Millennium_Ecosystem_Assessment, 2006).
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| <h2>Model drivers</h2>
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| Direct model drivers are inferred from the scenario drivers, and are used to set parameter values used in IMAGE 3.0. Their start values are estimated from literature and data, and future changes in values are inferred from the ultimate drivers. The resulting parameter values are used as input for different parts of IMAGE 3.0. A selection of key model drivers is given in Table 3.1, for further details and other model drivers not shown here; see Chapters 4 through 8.
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| Table 3.1: Model drivers
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| <h2>References</h2>
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| Brink, B.t., 2010. Rethinking Global Biodiversity Strategies. 500197001, PBL
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| Bruinsma, J.E., 2003. World agriculture: towards 2015/2030. An FAO perspective. Earthscan, London, 432 pp.
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| Chateau, J., R. Dellink, E. Lanzi and B. Magné 2013. An overview of the OECD ENV-Linkages model – version 3, OECD, Paris ( www.oecd.org/environment/modelling).
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| FAO, 2006. World Agriculture: towards 2030/2050, FAO, Rome
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| FAO, 2013. FAOSTAT database collections. Food and Agriculture Organization of the United Nations, Rome.
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| IPCC, 2000. Special Report on Emissions Scenarios.
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| Millennium_Ecosystem_Assessment (Ed.), 2006. Ecosystems and human well-being: scenarios (edited by Carpenter S.R., Pingali P.L., Bennett, E.M. and Zurek, M.B.), Volume 2. Island Press, Washington, D.C.
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| Moss, R.H. et al., The next generation of scenarios for climate change research and assessment. Nature, 463: 747-756,doi:10.1038/nature08823.
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| OECD, 2008. Environmental outlook to 2030. OECD Publications, Paris.
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| OECD, 2012. OECD Environmental Outlook to 2050: The Consequences of Inaction OECD Publishing, Paris.
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| UNEP, 2011. UNEP Bridging the Gap Report, http://www.unep.org/publications/ebooks/bridgingemissionsgap/
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| Vuuren, D.P.v. et al., 2012. A proposal for a new scenario framework to support research and assessment in different climate research communities. Global Environmental Change
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| Pages 21–35, Volume 22(Issue 1),http://dx.doi.org/10.1016/j.gloenvcha.2011.08.002.
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| Westhoek, H. et al., 2011. The Protein Puzzle PBL Netherlands Environmental Assessment Agency, Bilthoven
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