IMAGE framework summary/Description: Difference between revisions
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==The IMAGE framework== | ==The IMAGE framework== | ||
The IMAGE integrated assessment framework is structured around the chain of key global sustainability problems (see figure on the right) . Similar to other integrated assessment models, it contains two main subcomponents: | The IMAGE integrated assessment framework is structured around the chain of key global sustainability problems (see figure on the right) . Similar to other integrated assessment models, it contains two main subcomponents: | ||
#the human system, describing the long-term development of human activities relevant for sustainable development issues; | |||
# the earth system, describing changes in the natural environment. | |||
The two systems are coupled in two directions: via the impact of human activities on the environmental system, and via the impacts of environmental change back on the human system. | |||
Now we briefly describe the various components of the IMAGE system, following the structure of the IMAGE framework. Each of these will be described in far more detail in the IMAGE framwork component pages. The IMAGE model can be used to explore the possible consequences of policy inaction (so-called baseline or reference scenarios) and to study the potential impact of different policy interventions. Throughout this report, we will use the baseline scenario used in the Rio+20 study to illustrate main model output. Here, while introducing the IMAGE model – we illustrate the key outcomes of (sub-)components resulting from this scenario. | Now we briefly describe the various components of the IMAGE system, following the structure of the IMAGE framework. Each of these will be described in far more detail in the IMAGE framwork component pages. The IMAGE model can be used to explore the possible consequences of policy inaction (so-called baseline or reference scenarios) and to study the potential impact of different policy interventions. Throughout this report, we will use the baseline scenario used in the Rio+20 study to illustrate main model output. Here, while introducing the IMAGE model – we illustrate the key outcomes of (sub-)components resulting from this scenario. | ||
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Key inputs for the model are projections describing direct and indirect drivers of global environmental change ([[Drivers of framework]]). Most of these drivers (such as technology and lifestyle assumptions) are used as input in various subcomponents of IMAGE (see figure on the right). Clearly, the exogenous assumptions made on these factors need to be consistent. To ensure this, so-called storylines are used, brief stories about how the future may unfold, that can be used to derive internally consistent assumptions for main driving forces. Important categories of scenario drivers include demographic factors, economic development, lifestyle, and technology change. Among these, population and economic development form a special category as they can be dealt with in quantitative sense as exogenous model drivers. Other drivers mostly concern assumptions in different subcomponents of IMAGE, for examplee.g. both the yield assumptions in the crop growth model and the performance of solar power production in the energy model depend on a more generic description of the rate of technology change. | Key inputs for the model are projections describing direct and indirect drivers of global environmental change ([[Drivers of framework]]). Most of these drivers (such as technology and lifestyle assumptions) are used as input in various subcomponents of IMAGE (see figure on the right). Clearly, the exogenous assumptions made on these factors need to be consistent. To ensure this, so-called storylines are used, brief stories about how the future may unfold, that can be used to derive internally consistent assumptions for main driving forces. Important categories of scenario drivers include demographic factors, economic development, lifestyle, and technology change. Among these, population and economic development form a special category as they can be dealt with in quantitative sense as exogenous model drivers. Other drivers mostly concern assumptions in different subcomponents of IMAGE, for examplee.g. both the yield assumptions in the crop growth model and the performance of solar power production in the energy model depend on a more generic description of the rate of technology change. | ||
For population, the IMAGE model mostly uses exogenous assumptions (total population per region, household size and urbanization rate). Also the population projections of the [[PBL]] [[GISMO model]] can be used, which allows, in principle, to also account for feedback of environmental factors (e.g. air pollution and undernourishment) on population growth (see [[Impacts]]). For that purpose, population can be downscaled to the grid level. For economic variables such as [[GDP]], usually also exogenous assumptions are used. In most studies the economic projections are developed by macro-economic models based on the same storylines as the rest of the IMAGE model to ensure consistency. Sector specific economics and household consumption can be derived directly by such models, in addition the latter can be broken down into income categories, reflecting the so-called GINI coefficient (a measure of the disparity in income distribution). | For population, the IMAGE model mostly uses exogenous assumptions (total population per region, household size and urbanization rate). Also the population projections of the [[PBL]] [[GISMO model]] can be used, which allows, in principle, to also account for feedback of environmental factors (e.g. air pollution and undernourishment) on population growth (see [[Impacts]]). For that purpose, population can be downscaled to the grid level. For economic variables such as [[GDP]], usually also exogenous assumptions are used. In most studies the economic projections are developed by macro-economic models based on the same storylines as the rest of the IMAGE model to ensure consistency. Sector specific economics and household consumption can be derived directly by such models, in addition the latter can be broken down into income categories, reflecting the so-called [[GINI]] coefficient (a measure of the disparity in income distribution). | ||
Assumptions for future scenarios start from observed trends in recent decades and this is also the base for the baseline scenario used in the Rio+20 study ([[PBL, 2012]])). The global population is based on the UN medium projection and grows to about 9 billion people in 2050, the increase mostly occurring in developing countries. The economic projection shows that developing countries increasingly dominate the world economy in terms of total GDP. For the [[OECD]] countries, the baseline scenario assumes a long-term economic growth rate of 1-2% per year over the whole scenario period. In the short term, per capita growth rates in Asia and Latin America are much higher, but they start to converge gradually to a long-term growth rates of around 2% per year. Africa, in contrast, shows a later peak in economic growth. | Assumptions for future scenarios start from observed trends in recent decades and this is also the base for the baseline scenario used in the Rio+20 study ([[PBL, 2012]])). The global population is based on the UN medium projection and grows to about 9 billion people in 2050, the increase mostly occurring in developing countries. The economic projection shows that developing countries increasingly dominate the world economy in terms of total GDP. For the [[OECD]] countries, the baseline scenario assumes a long-term economic growth rate of 1-2% per year over the whole scenario period. In the short term, per capita growth rates in Asia and Latin America are much higher, but they start to converge gradually to a long-term growth rates of around 2% per year. Africa, in contrast, shows a later peak in economic growth. | ||
Revision as of 13:00, 11 December 2013
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