https://models.pbl.nl/image/api.php?action=feedcontributions&user=Oostenrijr&feedformat=atomIMAGE - User contributions [en]2024-03-19T10:50:14ZUser contributionsMediaWiki 1.31.1https://models.pbl.nl/image/index.php?title=Key_policy_issues_overview&diff=36572Key policy issues overview2020-08-05T14:13:56Z<p>Oostenrijr: </p>
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}}</div>Oostenrijrhttps://models.pbl.nl/image/index.php?title=IMAGE_framework/IMAGE_3.0_in_a_nutshell&diff=36568IMAGE framework/IMAGE 3.0 in a nutshell2020-08-05T13:53:35Z<p>Oostenrijr: </p>
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<div class="page_standard"><br />
<h2>The IMAGE 3.0 model</h2><br />
IMAGE 3.0 is a comprehensive integrated modelling framework of interacting human and natural systems. The model framework is suited to large scale (mostly global) and long-term (up to the year 2100) assessments of interactions between human development and the natural environment, and integrates a range of sectors, ecosystems and indicators. The impacts of human activities on the natural systems and natural resources are assessed and how such impacts hamper the provision of ecosystem services to sustain human development. <br />
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The model identifies socio-economic pathways, and projects the implications for energy, land, water and other natural resources, subject to resource availability and quality. Unintended side effects, such as emissions to air, water and soil, climatic change, and depletion and degradation of remaining stocks (fossil fuels, forests), are calculated and taken into account in future projections.<br />
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===Features===<br />
IMAGE has been designed to be comprehensive in terms of human activities, sectors and environmental impacts, and where and how these are connected through common drivers, mutual impacts, and synergies and trade-offs. IMAGE 3.0 is the latest version of the IMAGE framework models, and has the following features:<br />
<br />
*Comprehensive and balanced integration of energy and land systems was a pioneering feature of IMAGE. Recently, other {{abbrTemplate|IAM}}s have been developed in similar directions and comprehensive IAMs are becoming more mainstream.<br />
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*Coverage of all emissions by sources/sinks including natural sources/sinks makes IMAGE appropriate to provide input to bio-geochemistry models and complex Earth System Models ({{abbrTemplate|ESMs}}). <br />
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*In addition to climate change, which is the primary focus of most IAMs, the IMAGE framework covers a broad range of closely interlinked dimensions. These include water availability and water quality, air quality, terrestrial and aquatic biodiversity, resource depletion, with competing claims on land and many ecosystem services.<br />
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*Rather than averages over larger areas, spatial modelling of all terrestrial processes by means of unique and identifiable grid cells captures the influence of local conditions and yields valuable results and insights for impact models.<br />
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*IMAGE is based on biophysical/technical processes, capturing the inherent constraints and limits posed by these processes and ensuring that physical relationships are not violated.<br />
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*Integrated into the IMAGE framework, [[MAGICC model|MAGICC-6]] is a simple climate model calibrated to more complex climate models. Using downscaling tools, this model uses the spatial patterns of temperature and precipitation changes, which vary between climate models. <br />
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*Detailed descriptions of technical energy systems, and integration of land-use related emissions and carbon sinks enable IMAGE to explore very low greenhouse gas emissions scenarios, contributing to the increasingly explored field of very low climate forcing scenarios.<br />
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*The integrated nature of IMAGE enables linkages between climate change, other <br />
environmental concerns and human development issues to be explored, thus contributing to informed discussion on a more sustainable future including trade-offs and synergies between stresses and possible solutions.<br />
<br />
===Model components===<br />
{{DisplayFigureTemplate|IMAGE framework schematic}}<br />
The components of the IMAGE framework are presented in the [[IMAGE framework schematic]] (the figure on the right), which also shows the information flow from the key driving factors to the impact indicators. An overview of the model components is provided in [[IMAGE_framework_summary]], and the model components are described<br />
in their component pages (via [[Framework overview|Components overview]]).<br />
<br />
Future pathways or scenarios depend on the assumed projections of key driving forces. Thus, all results can only be understood and interpreted in the context of the assumed future environment in which they unfold.<br />
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As a result of the exogenous drivers, IMAGE projects how human activities would develop in the Human system, namely in the energy and agricultural systems (see [[IMAGE framework schematic]]). Human activities and associated demand for ecosystem services are squared to the Earth system through the ‘interconnectors’ Land Cover and Land Use, and Emissions (see [[IMAGE framework schematic]]). <br />
<br />
Assumed policy interventions lead to model responses, taking into account all internal interactions and feedback. Impacts in various forms arise either directly from the model, for example the extent of future land-use for agriculture and forestry, or the average global temperature increase up to 2050. Other indicators are generated by activating additional models that use output from the core IMAGE model, together with other assumptions to estimate the effects, for example, biodiversity (GLOBIO; see Components [[Terrestrial biodiversity]] and [[Aquatic biodiversity]]) and [[flood risks]].<br />
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Currently, impacts emerging from additional models do not influence the outcome of the model run directly. The results obtained can reveal unsustainable or otherwise undesirable impacts, and induce exploration of alternative model assumptions to alleviate the problem. As the alternative is implemented in the linked models, synergies and trade-offs against other indicators are revealed.<br />
<br />
====State-of-the-world in 2005====<br />
To apply IMAGE 3.0, all model settings are adjusted so that the model reproduces the state-of-the-world in 2005. The model calculates the state in 2005 over the period starting in 1970, using exogenous data to calibrate internal parameters. From 2005 onwards, a range of model drivers rooted in more generic narratives and scenario drivers must be prepared either by experts or teams at PBL or in partner institutes to provide inputs, such as population, economic projections and food production (see [[Drivers]]. These steps are taken in consultation with stakeholders and sponsors of the studies, and with project partners.<br />
<br />
====IMAGE outputs====<br />
An IMAGE run produces a long list of outputs representing the results of the various parts of the framework, either as end indicator or as intermediate inputs driving operations further downstream. Together the outputs span the range from drivers to pressures, states and impacts. (See [[Variable overview]] and related overviews.)<br />
<br />
The IMAGE 3.0 model has a wide range of outputs, including:<br />
* energy use, conversion and supply; <br />
* agricultural production, land cover and land-use; <br />
* nutrient cycles in natural and agricultural systems; <br />
* emissions to air and surface water; <br />
* carbon stocks in biomass pools, soils, atmosphere and oceans; <br />
* atmospheric emissions of greenhouse gases and air pollutants; <br />
* concentration of greenhouse gases in the atmosphere and radiative forcing; <br />
* changes in temperature and precipitation; <br />
* sea level rise; <br />
* water use for irrigation.<br />
<br />
These standard outputs are complemented with additional impact models with indicators for biodiversity, human development, water stress, and flood risks. <br />
<br />
===Spatial resolution===<br />
{{DisplayFigureTemplate|Region classification map}}<br />
While IMAGE is designed to address global issues, impacts and challenges tend to occur at different geographic scales and to different degrees in different parts of the world. This depends on location-specific biophysical conditions, and on the level of human development (for example high income, industrialised versus low income, subsistence agriculture dominated regions, and all levels in between). It implies that indicators at the level of global totals or global averages are rarely adequate to reveal the real problems. Furthermore, policy interventions and governance structures are not uniform across scales and administrative entities, and are bound by cultural and political history.<br />
<br />
To capture spatial and multi-scale differences, IMAGE models socio-economic developments in 26 world regions (see the figure on the right). Land use, land cover, and associated biophysical processes are treated at grid level to capture local dynamics. The grid size has been reduced to 5 x 5 arcminutes in IMAGE 3.0 (corresponding to 10 x 10 km at the equator), from 30 x 30 arcminutes (0.5 x 0.5 degrees) in IMAGE 2. Operating within global boundaries, the regional approach provides insight to identify where specific problems manifest, where the driving factors are concentrated, and how changes in some regions influence other regions.<br />
<br />
===Areas of application===<br />
An integrated framework, such as IMAGE 3.0, covers a wide range of components of the Human and Earth systems, and contains variables in many domains. Development and applications of the IMAGE framework focus on two interrelated clusters: energy and climate; and food, land, water and biodiversity.<br />
<br />
There are many relationships between these two clusters in IMAGE. For instance, climate change has impacts on agriculture and nature, land use for bioenergy has implications for food prices, and water for irrigation competes with water for coolant in electric power plants. Synergies and trade-offs are interesting from the perspective of policy discussions with regard to the complicating effects of unintended and often undesirable impacts. IMAGE 3.0 has the capacity to generate a long and widely diverging set of indicators for different sectors and regions.<br />
<br />
===Modular structure===<br />
{{DisplayFigureTemplate|IMAGE framework schematic}}<br />
Over the years, various components of the IMAGE framework have been replaced by expert models developed outside IMAGE, which be used either as stand-alone models or within the IMAGE framework (see [[Computer models overview]]) . <br />
<br />
The IMAGE 3.0 core model comprises most processes in the Human system, the Earth system and their connectors Land cover/Land use and Emissions, and parts of the impacts (see the figure on the right). This core model consists of IMAGE/TIMER energy and IMAGE/Land & Climate. The latter also includes the LPJmL model, which is an essential component of any IMAGE model run, representing carbon, water, crop and vegetation dynamics. <br />
<br />
The IMAGE 3.0 framework contains other models that are employed to generate impacts (such as, [[GLOBIO model|GLOBIO]], [[GLOFRIS model|GLOFRIS]] and [[GISMO model|GISMO]]), and models that describe parts of the Human system, such as agro-economic models ([[MAGNET model|MAGNET]] and [[IMPACT model|IMPACT]]) to project future agricultural production requirements. Furthermore, policy models, such as [[FAIR model|FAIR]], are used in exploring effectiveness, efficiency and equity of climate policy regimes, and to provide input on emission constraints and price signals arising from climate policy proposals.<br />
</div></div>Oostenrijrhttps://models.pbl.nl/image/index.php?title=Schipper_et_al.,_2020&diff=36567Schipper et al., 20202020-07-24T10:12:20Z<p>Oostenrijr: </p>
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|Title=Projecting terrestrial biodiversity intactness with GLOBIO 4<br />
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|Title=Mapping the yields of lignocellulosic bioenergy crops from observations at the global scale<br />
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|Title=Afforestation for climate change mitigation: Potentials, risks and trade-offs<br />
|Year=2020<br />
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|Title=Mapping the yields of lignocellulosic bioenergy crops from observations at the global scale<br />
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|Year=2020<br />
|Title=Mapping the yields of lignocellulosic bioenergy crops from observations at the global scale<br />
|DOI=10.5194/essd-12-789-2020<br />
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|Year=2020<br />
|Title=Projecting terrestrial biodiversity intactness with GLOBIO 4<br />
|DOI=10.1111/gcb.14848<br />
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<div>{{ReferenceTemplate<br />
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|Title=Mapping the yields of lignocellulosic bioenergy crops from observations at the global scale<br />
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<div>{{ReferenceTemplate<br />
|Author=van Dijk M.; Gramberger M.; Laborde D.; Mandryk M.; Shutes L.; Stehfest E.; Valin H.; Faradsch K.<br />
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|Title=Afforestation for climate change mitigation: Potentials, risks and trade-offs<br />
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|Author=Schipper A.M.; Hilbers J.P.; Meijer J.R.; Antão L.H.; BenÃtez-López A.; de Jonge M.M.J.; Leemans L.H.; Scheper E.; Alkemade R.; Doelman J.C.; Mylius S.; Stehfest E.; van Vuuren D.P.; van Zeist W.-J.; Huijbregts M.A.J.<br />
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<div>{{ReferenceTemplate<br />
|Author=Molotoks A.; Henry R.; Stehfest E.; Doelman J.; Havlik P.; Krisztin T.; Alexander P.; Dawson T.P.; Smith P.<br />
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|Year=2020<br />
|Journal=Philosophical Transactions of the Royal Society B: Biological Sciences<br />
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|Issue=1794<br />
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<br />
The increase in material wealth, population and economic growth have led to a large demand for agricultural products and transformation of large parts of the land surface. The wide range of environmental issues related to agriculture and forestry include distorted nutrient balances, biodiversity loss, greenhouse gas emissions from land use and land-use change, soil degradation, and water stress due to agricultural water demand. These issues can be addressed from a sector perspective focusing on the respective system (e.g., [[Nutrients|nutrients]], [[water]], see the respective components). However, these issues are linked by demand for land-based products, and by land management. <br />
<br />
The IMAGE framework enables a systems approach to analyse policy interventions targeting the impacts of land use on biodiversity and climate change. To identify interventions that could reduce the impacts of agriculture and forestry on the environment, the system takes account of the chain linking demand for food, feed, wood, and bioenergy, to types of production systems and to landscape impacts. <br />
<br />
Policy interventions can target demand for commodities (Figure A), the production system, for instance, with respect to efficiency of natural resource use (Figure B and C), or a more systemic approach to regulating land use for different purposes within a landscape (Figure D). Regulation of land use implies managing the land resource base by designating areas to specific purposes, such as excluding protected natural areas from agricultural use, or preventing deforestation. Alternatively, regulation could be in the form of financial incentives to create value for currently non-market ecosystem services, such as emission reduction from deforestation combined with biodiversity conservation (e.g., {{abbrTemplate|REDD+}} schemes) and other forms of payment for ecosystem services ({{abbrTemplate|PES}}).<br />
<br />
While this section focuses on the impacts on biodiversity, climate change, water and nutrient balances, some policy interventions also have implications for other policy domains, such as food security, human health and animal welfare.<br />
<br />
==Model description==<br />
The interventions described in this section are implemented in different parts of the IMAGE 3.0 framework, and are also addressed in the components in which the respective processes are described. <br />
<br />
Policies that change demand for agricultural products ([[Land and biodiversity policies/Agricultural demand|Agricultural demand part]]) are implemented in the agricultural economic model, thus taking into account the impacts on trade and demand in other regions. In IMAGE 3.0, change in wood demand is addressed in the model via a simple relationship with GDP, or by using external input data on wood demand (see Component [[Agricultural economy]]). Demand for second-generation bioenergy crops is addressed in the [[Energy supply and demand|energy model]].<br />
<br />
Changes in production systems ([[Land and biodiversity policies/Agricultural production system|Agricultural production system part]]) are modelled in IMAGE using alternative input parameters. For the relevant inputs in e.g. the [[Land-use allocation|land-use allocation]], [[Livestock systems|livestock]], and [[Nutrients|nutrient]] modules, these changes are consistent with those in the [[Agricultural economy|agro-economic]] model, to ensure appropriate representation of the (cost) structure of production. Production system changes, for example those induced by taxes or scarcity of endowments, are implemented in the agro-economic model and adjusted in other modules, accordingly.<br />
<br />
Land-use regulation ([[Land and biodiversity policies/Forestry sector|Forestry sector]] and [[Land and biodiversity policies/Land-use regulation|Land-use regulation]] part), which is the regulation of land supply, is modelled as a consistent resource constraint in the [[Land-use allocation|land-use allocation]] model and the [[Agricultural economy|agro-economic model]]. This last model takes account of the economic effects of restricted land supply. For example, {{abbrTemplate|REDD+}} and {{abbrTemplate|PES}} are implemented not as additional productive functions, but by reducing the land supply in the agro-economic model. The spatial dimension of such land-use regulation, like the expansion of protected area, is taken into account in the [[Land-use allocation|agricultural systems]] module, and affects via the resulting land use pattern all down-stream processes.<br />
</div></div>Oostenrijrhttps://models.pbl.nl/image/index.php?title=Template:PolicyResponseComponentTemplate&diff=36537Template:PolicyResponseComponentTemplate2020-04-01T13:57:26Z<p>Oostenrijr: </p>
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<h2>Introduction</h2></div><br />
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==Introduction==</div><br />
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|Reference=Den Elzen et al., 2007; Van Vuuren et al., 2011a; Meinshausen et al., 2011b; Den Elzen et al., 2013; Hof et al., 2013; Roelfsema et al., 2014; Den Elzen et al., 2012a; Hof et al., 2012; Hof et al., 2008; Hof et al., 2010; De Bruin et al., 2009; Admiraal et al., 2016; Van den Berg et al., 2015; UNFCCC (2015b); Den Elzen et al., 2016; UNEP (2016); Rogelj et al., 2016; Den Elzen et al., 2015a; Kuramochi et al., 2016; Hof et al., 2016;<br />
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<br />
FAIR consists of six linked modules as presented in the flowchart and described briefly below. <br />
<br />
===Global pathfinder and climate module===<br />
The pathfinder module FAIR-SiMCaP calculates global emission pathways that are consistent with a long-term climate target ([[Den Elzen et al., 2007]]; [[Van Vliet et al., 2012]]; [[Van den Berg et al., 2015]]). Inputs are climate targets defined in terms of concentration levels, radiative forcing, temperature, and cumulative emissions. In addition, intermediate restrictions on overshoot levels or intermediate emission targets representing climate policy progress can be included. The model combines the FAIR mitigation costs model and a module that minimises cumulative discounted mitigation costs by varying the timing of emission reductions. For climate calculations, FAIR-SiMCaP uses the [[MAGICC model|MAGICC 6 model]], with parameter settings calibrated to reproduce the medium response in terms of time scale and amplitude of 19 IPCC {{abbrTemplate|AR}}4 General Circulation Models ([[Meinshausen et al., 2011b]]).<br />
<br />
===Policy evaluation module===<br />
<br />
The Policy evaluation module calculates emission levels resulting from the reduction [!CHANGE] proposals (pledges and NDCs) and mitigation actions submitted by developed and developing countries as part of the 2015 {{abbrTemplate|UNFCCC}} Paris agreement ([[Den Elzen et al., 2016]]; [[Rogelj et al., 2016]]). This module also collects the emission projections of current and planned policy scenarios as calculated by the IMAGE-TIMER model ([[Roelfsema et al., 2018]]). These scenarios take into account the impact of individual policies in different subsectors that are implemented in 25 major emitting countries ([[Kuramochi et al., 2018]]). [!CHANGE]The module is used in conjunction with a wide range of evaluation tools developed in cooperation with [[IIASA]] and [[JRC]], such as tools for analysing policy options for land-use credits and surplus emissions. The PBL [https://themasites.pbl.nl/climate-ndc-policies-tool/ Climate Pledge NDC tool] gives a summary of the greenhouse gas emission reduction proposals, domestic policies of major countries and the impact on the emissions by 2030.<br />
<br />
===Effort sharing module===<br />
The Effort sharing module calculates emission targets for regions and countries, resulting from different emission allocation or effort-sharing schemes ([[Den Elzen et al., 2012a]]; [[Hof et al., 2012]]; [[Van den Berg et al., 2019]]). Such schemes start either at the global allowed emission level, after which the effort-sharing approach allocates emission allowances across regions, or at the required global reduction level, after which various effort-sharing approaches allocate regional emission reduction targets. Both approaches use information from the Global Pathfinder and Climate module on the required global emission level or emission reductions. As an alternative, emission allowances can be allocated to regions without a predefined global reduction target, based on different effort-sharing approaches. The model includes effort-sharing approaches such as Contraction & Convergence, common-but-differentiated convergence, ability to pay, and a multi-stage approach.<br />
<br />
===Mitigation costs module===<br />
The Mitigation costs module is used for calculating the regional mitigation costs of achieving the targets calculated in the Policy Evaluation and/or the Effort Sharing modules, and to determine the buyers and sellers on the international emissions trading market ([[Den Elzen et al., 2011a]]; [[Hof et al., 2017]]). Inputs to the model are regional gas- and source-specific Marginal Abatement Cost (MAC) curves that reflect the additional costs of abating one extra tonne of CO<sub>2</sub> equivalent emissions. CO<sub>2</sub> MAC curves are derived from the energy and land-use modules of IMAGE. Non-CO<sub>2</sub> MAC curves are based on [[Lucas et al., 2007|Lucas et al. (2007)]] and [[Harmsen et al., 2019c|Harmsen et al. (2019c)]]. The {{abbrTemplate|MAC}} curves describe the potential and costs of the abatement options considered. The model uses aggregated regional permit demand and supply curves derived from the MAC curves to calculate the equilibrium permit price on the international trading market, its buyers and sellers, and the resulting domestic and external abatement per region. The design of the emissions trading market can include: constraints on imports and exports of emission permits; non-competitive behaviour; transaction costs associated with the use of emission trading; a less than fully efficient supply of viable {{abbrTemplate|CDM}} projects with respect to their operational availability; and the banking of surplus emission allowances. <br />
<br />
===Damage and Cost-Benefit Analysis modules===<br />
The Damage and Cost-Benefit Analysis modules calculate the consumption loss resulting from climate change damage, and compare these with the consumption losses of adaptation and mitigation costs ([[Hof et al., 2008]]; [[Hof et al., 2009|2009]]; [[Hof et al., 2010|2010]]; [[Admiraal et al., 2016]]). Estimates of adaptation costs and residual damage (defined as the damage that remains after adaptation) [!CHANGE]are based either on the [[AD RICE model]] ([[De Bruin et al., 2009]]), which are based on total damage projections made by the [[RICE model]], or on output from sectoral impact models[!CHANGE]. Calibration of the regional adaptation cost functions of AD RICE is based on an assessment of each impact category described in the RICE model, using relevant studies and with expert judgement where necessary. The optimal level of adaptation can be calculated by the model, but may also be set to a non-optimal level by the user. <br />
<br />
===Estimation of consumption losses===<br />
Consumption losses due to mitigation, adaptation and climate change damage are estimated based on a simple Cobb–Douglas production function.<ref><div style="clear:both float:right">The Cobb–Douglas production function is a particular functional form of the production function widely used to represent the technological relationship between the amounts of two or more inputs and the amount of output that can be produced by those inputs.</div></ref> [!CHANGE]The production factors are labour and capital. Regional changes in labour over time are based on the projected changes in total regional population. Initial regional capital stocks are based on the Investment and Capital Stock Dataset of the IMF. Future capital stocks depend on depreciation (set at 5% per year) and investments. Investments depend on the savings rate and initial savings rates are taken from the same IMF source. Total factor productivity of each region is calibrated to the exogenous GDP path without damage or mitigation costs. In a second step, damages, adaptation and abatement costs are subtracted from investment or consumption to determine both the direct replacement effect on consumption and the indirect effect from replacing productive investments.[!CHANGE]<br />
<references /><br />
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The supply of ecosystem services is quantified using other models in the IMAGE 3.0 framework, and where necessary combined with relationships between environmental variables and ecosystem services supply, derived from literature reviews (Figure Flowchart). <br />
<br />
Ecosystem services derived directly from other IMAGE models include the food provision from agricultural systems; water availability; carbon sequestration; and flood protection. Estimation of the services, wild food provision, erosion risk reduction, pollination, pest control and attractiveness for nature-based tourism, requires additional environmental variables and relationships ([[Maes et al., 2012]]; [[Schulp et al., 2012]]). A key variable for these services is fine-scale land use intensity data from the [[GLOBIO model]].<br />
<br />
The supply of ecosystem services can be evaluated and aggregated in several ways. Some studies have constructed hotspot maps based on the number of services delivered ([[Egoh et al., 2008]]; [[Egoh et al., 2009]]; [[O'Farrell et al., 2010]]). Others translate service provision into monetary values for ecosystems ([[Costanza et al., 1997]]; [[TEEB, 2010b]]; [[UNEP-WCMC, 2011]]). <br />
<br />
The main shortcoming of hotspot maps and monetising is the lack of information whether sufficient ecosystem services are delivered to fulfil human requirements ([[Burkhard et al., 2012]]). Here, the service supply is compared to the potential requirement (minimum quantity required by humans) in order to assess surpluses and deficiencies. This translates into minimum quantities of food and water to stay healthy, or the minimum quantity of natural elements in a landscape to potentially pollinate all crops. The relation between supply and potential requirement is the ecosystem services budget. These budgets are relevant at different spatial scales, because some services can only be provided locally, while others (mainly goods) can be transported longer distances. The most relevant assessment scale for each ecosystem service was determined given the underlying modelling approaches (the table below).<br />
<br />
<div class="thumbcaption dark">Table: Assessment scale for each ecosystem service analysed</div><br />
<table class="pbltable"><br />
<tr><br />
<th>Category</th><br />
<th>Ecosystem service</th><br />
<th>Assessment scale</th><br />
</tr><br />
<tr><br />
<td rowspan="5" valign="top">Provisioning services</td><br />
<td>Food from agro-ecosystems</td><br />
<td>IMAGE region</td><br />
</tr><br />
<tr><br />
<td>Wild food </td><br />
<td>Country, local market </td><br />
</tr><br />
<tr><br />
<td>Fish</td><br />
<td>Country</td><br />
</tr><br />
<tr><br />
<td>Wood</td><br />
<td>IMAGE region</td><br />
</tr><br />
<tr><br />
<td>Water</td><br />
<td>River basin</td><br />
</tr><br />
<tr><br />
<td rowspan="5" valign="top">Regulating services</td><br />
<td>Carbon sequestration</td><br />
<td>Global </td><br />
</tr><br />
<tr><br />
<td>Erosion risk reduction</td><br />
<td>0.5°x0.5° grid </td><br />
</tr><br />
<tr><br />
<td>Pollination</td><br />
<td>0.5°x0.5° grid </td><br />
</tr><br />
<tr><br />
<td>Pest control</td><br />
<td>0.5°x0.5° grid </td><br />
</tr><br />
<tr><br />
<td>Flood protection</td><br />
<td>30 sec x 30 sec grid </td><br />
</tr><br />
<tr><br />
<td valign="top"><br />
Provisioning services<br />
</td><br />
<td><br />
Cultural services<br />
</td><br />
<td><br />
0.5°x0.5° grid <br />
</td></tr><br />
</table><br />
<br />
===Cultural services===<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
<br />
====Food====<br />
Food supply is broken down into three components: food produced on agricultural land (crops and livestock); fish from marine fish landings and aquaculture; and wild food from hunting and gathering. The food supply is converted to energy content (kcal nutritional value) and proteins (g) provided by the aggregate agricultural products, fish and wild food, and summed per IMAGE geographical region. <br />
<br />
Agricultural ecosystems are modified by human interventions such as mechanisation, fertiliser application, irrigation, and agro-chemicals for pest and disease control. The degree of modification varies significantly between agricultural production systems, from slight in an extensive grazing system to heavy in modern agriculture ([[Van Asselen et al., 2013]]). Even in the most heavily modified systems, the production of crops and livestock depends on ecological processes in agricultural ecosystems.<br />
<br />
For crops and livestock products, data from the IMAGE crop and livestock module are used (Components [[Agriculture and land use]] and [[Nutrients]]). Production volumes are modified for products used as feed and for post-harvest losses to estimate the food quantity consumed by humans. Marine fish landings per country are derived from the Sea around Us project ([[Sea around us project, 2013]]). The amount of fish derived from aquaculture is not yet included due to data limitations. Wild food can be an important part of local diets and includes game, mushrooms and berries. Local availability depends on the land cover and natural productivity of the ecosystem, and is determined from national and international hunting statistics for each land cover type ([[EFI, 2007]]; [[Schulp et al., 2012]]). Accessibility also influences availability of the wild food, and depends on the time people spend in collection including travel time ([[Nelson, 2008]]).<br />
<br />
The potential food requirement is estimated by multiplying the minimum amount of energy and protein required per year on average to stay healthy ([[FAO, 2013b]]) and by the number of inhabitants per IMAGE geographic region.<br />
<br />
The budget is determined by subtracting the regional requirement from the total regional supply, indicating whether regional ecosystems supply sufficient food to meet the human requirement. The Ecosystem service ‘food’ relates to average numbers over a large population with very different standards of living, and access to affordable and healthy food. Therefore, even if total supply equals or exceeds demand, a larger or smaller proportion of the population in the region may suffer from malnutrition or hunger.<br />
<br />
====Water====<br />
Water availability is essential for natural vegetation, agricultural production, human settlements and industry. The renewable water supply is the water availability in a river basin and is derived from the IMAGE hydrology module (Component [[Water]]). The water surplus of each grid cell is aggregated to watersheds to form river discharge. These flows are influenced by dams and reservoirs for irrigation and/or for hydropower production ([[Biemans et al., 2011]]).<br />
<br />
The water requirement is also derived from the IMAGE hydrology module (Component [[Water]]), and is determined by adding together water use for agriculture (irrigation and livestock), industry, electricity and for domestic purposes in each river basin ([[Alcamo et al., 2003]]).<br />
<br />
The budget is illustrated by the water stress in each river basin as the ratio of average annual water demand and supply ([[Vörösmarty et al., 2000]]). According to literature, medium water stress is indicated when >20% of the available water is extracted, and is considered severe when more than 40% of the available water is extracted. The ecosystem services ‘water’is considered to be sufficiently supplied when there is less than medium water stress, below 20% extraction ([[Vörösmarty et al., 2000]]).<br />
<br />
===Regulating services===<br />
====Carbon sequestration====<br />
CO<sub>2</sub> emissions are a key driver of climate change. By halting the increase of CO<sub>2</sub> concentration in the atmosphere, the consequences of global warming could be limited in the longer term. Natural vegetation and oceans can sequester CO<sub>2</sub> from the atmosphere and thus influence CO<sub>2</sub> concentration. <br />
<br />
The amount of CO<sub>2</sub> sequestered by vegetation and oceans is considered an ecosystem service. A proxy for CO<sub>2</sub> sequestration by vegetation is net ecosystem production (NEP), which is the difference between net primary productivity (NPP) and plant and soil respiration. NEP values and ocean sequestration are adopted from IMAGE (Section [[Carbon cycle and natural vegetation]] and [[Atmospheric composition and climate]]). The supply values are averaged over ten years to account for year to year model fluctuation ([[Crossman et al., 2013]]).<br />
<br />
Total CO<sub>2</sub> emissions from all sources would need to be sequestered to prevent increase in CO<sub>2</sub> concentration. Thus, the service requirement is the total CO<sub>2</sub> emissions from industry, energy and land use change (agriculture, deforestation and fires) from IMAGE, also averaged over ten years. The budget is established by subtracting emissions from CO<sub>2</sub> sequestered.<br />
<br />
====Erosion risk reduction====<br />
Erosion is the loss of topsoil by wind and water, and is a natural process. However, agricultural practices can accelerate erosion rates, reducing productivity and leading to loss of arable land. The model considers topsoil erosion related to water and agricultural practices (Component [[Land degradation]]).<br />
<br />
The erosion risk depends on topography, precipitation and agricultural practices, including crop type (see Component [[Land degradation]]). The risk can be reduced by natural vegetation serving as buffer zones, erosion prevention strips and uphill soil retention cover. To determine the supply of ecosystem services, the erosion risk index from IMAGE is linearly reduced by the percentage of natural elements in a grid cell, derived from the land use and intensity map from GLOBIO (see Component [[Land degradation]]). <br />
<br />
Erosion prevention is needed in all cultivated areas. The ecosystem services budget indicates whether natural vegetation is sufficient to protect the area from erosion risk. According to ([[Hootsmans et al., 2001]]), an erosion index value greater than 0.15 indicates moderate erosion risk, and an index value in excess of 0.30 indicates high erosion risk. It is assumed the ecosystem services are adequate when the nature-corrected index value is below 0.15.<br />
<br />
====Pollination====<br />
Pollination is required for fruit setting for a large variety of oil crops, pulses and fruits. Pollinator-dependent crops require insects such as bees, bumblebees and flies, but also bats and birds ([[Gallai et al., 2009]]). To secure adequate pollination for such crops, sufficient habitat for wild pollinators is needed. The abundance of pollinators is shown to decrease with decreasing percentage of natural elements, which reduces the pollination efficiency and yield ([[Steffan-Dewenter and Tscharntke, 1999]]; [[Kleijn and Langevelde, 2006]]; [[Schulp et al., 2012]]).<br />
<br />
The supply of this ecosystem service is derived from the relationship between the percentage of nature in a grid cell and the percentage of pollinator-dependent yield produced ([[Morandin et al., 2007]]; [[Klein et al., 2011]]; [[Schulp and Alkemade, 2011]]). Only yield produced by wild pollinators is included, but in practice yield is also influenced by managed- and self-pollination. When a grid cell contains 60% of nature, there are sufficient wild pollinators for all the plants and thus 100% of pollinator dependent yield is produced. However, when the percentage of nature decreases to 20% per grid cell, wild pollinators can still sustain 90% of pollinator-dependent yield. Less than 20% nature in a grid cell causes a sharp decline in yield ([[Morandin et al., 2007]]). Pollination is only needed in croplands, and we assume that in grid cells containing cropland and at least 20% of natural elements pollination is sufficient. <br />
<br />
The budget is the cropland area with sufficient natural elements, and divided by the total cropland area. <br />
<br />
====Pest control====<br />
Natural pest control reduces pest occurrence in agriculture fields as a result of the presence of predator species ([[Thies et al., 2003]]; [[Boccaccio and Petacchi, 2009]]; [[Rusch et al., 2011]]). This leads to higher yields than in fields without natural or technical pest control. Natural pest control requires sufficient natural elements to house predator species close to agricultural fields. Pest control is assumed to be effective on agricultural fields within 2 km of forests and other natural elements ([[Bianchi et al., 2005]]). This can be translated into a correlation between the percentage of natural elements in a grid cell and the effectiveness of biological pest control ([[Thies et al., 2003]]; [[Boccaccio and Petacchi, 2009]]; [[Rusch et al., 2011]]). <br />
<br />
Hawkins and Cornell ([[Hawkins and Cornell, 1994|Hawkins and Cornell|1994]]) indicate that natural pest control is no longer successful when the percentage of pest insects killed falls below 32 to 36%. This corresponds with 37 to 43% of nature in a grid cell. The model does not consider natural pest control by soil fauna present in the field. <br />
<br />
The supply of natural pest control is determined by the percentage of nature in a grid cell All cropland is assumed to potentially require effective natural pest control. The pest control budget is calculated by dividing the cropland area in cells with more than 40% of natural elements by the total cropland area. <br />
<br />
====Flood protection====<br />
Flooding is the most frequent and costly natural hazard, affecting most countries worldwide on a regular basis ([[UNISDR, 2011]]; [[IPCC, 2012]]). Therefore, flood risk assessment is an important issue for policymakers. While there are different levels of flood risk, the risk most often used by policymakers is the100-year flood event which indicates a flood event occurring with a 1% likelihood in every year ([[Bell and Tobin, 2007]]). Protection against 100-year flood events, but not against less likely events, is considered a reasonable compromise between protecting the public and overly stringent regulation. <br />
<br />
The flood risk is taken from the model GLOFRIS, which combines the flood extent and depth of river and coastal flood events, (Component [[Flood risks]]).<br />
<br />
Vegetation and soil affect inundation extent and depth because upstream vegetation can retain water and reduce flood risks. To calculate reduction in flood risk by ecosystems, flood risk is determined for a situation without conversion of natural vegetation and compared to flood risk in the current situation. The requirements for the service flood protection is estimated as the the depth of the 100-year flood event with the historical land use, vegetation and soil type. <br />
The budget illustrates whether change in vegetation and soil increases or decreases flood protection on urban and cultivated areas. Since the spatial variability of flood risk may be large, the 100-year flood event and the cultivated and urban areas are determined in 30x30 arcsec grid cells. The budget is aggregated to 0.5°x0.5° grid cell. <br />
<br />
===Cultural services===<br />
====Nature-based tourism====<br />
The ecosystem service “nature-based tourism” is rather complex and there is limited knowledge about this service on a global scale. Drawing upon expert knowledge, we have listed several indicators influencing the supply of nature-based tourism ([[Van Kolck et al., in preparation]]). To be able to provide one single indicator for the ecosystem service supply, the indicators were grouped into three categories: <br />
* an esthetic factor based on the climate-dependent tourism comfort index, scenic quality, land cover, and relief; <br />
* a habitat factor based on distance to river, coasts, waterfalls and lakes, and the amount of protected area; <br />
* a deterrent factor based on extent of urban and cultivated area, accessibility, rate of traffic disturbances, GDP and safety. <br />
The three groups of indicators are normalized between 0-1 and summed to form one single supply indicator.<br />
<br />
In the absence of expert judgment, it was not possible to estimate a demand for nature based tourism. Hence, to indicate whether the ecosystem service supply meets a certain level, and to determine an ecosystem service budget, we have set an arbitrary threshold at 1.40. The budget is the area in grid cells with an index value above 1.40, divided by the total area.<br />
<br />
====Aggregation====<br />
To aggregate ecosystem services, the budget of each service is set to a binary scale. Where zero indicates a service is not sufficiently delivered or not requested, and one indicates the supply meets the requirement, and thus is sufficiently delivered. These ecosystem service budgets on a binary scale can be summed to indicate the number of services sufficiently delivered in each grid cell.<br />
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<div>{{ComponentDescriptionTemplate<br />
|Reference=Bartholome et al., 2004; UNEP-WCMC, 2005; Dixon et al., 2001; Visconti et al., 2011; Alkemade et al., 2012; Alkemade et al., 2011a; Bouwman et al., 2002b; Bobbink et al., 2010; DMA, 1992; Meijer and Klein Goldewijk, 2009; Benitez-Lopez et al., 2010; UNEP, 2001; Verboom et al., 2014; Faith et al., 2008; Musters et al., submitted;<br />
}}<div class="page_standard"><br />
<br />
The GLOBIO model calculates changes in biodiversity in terrestrial ecosystems, based on seven drivers of biodiversity change: land-use change, land-use intensity, climate change, atmospheric nitrogen deposition, infrastructural development, encroachment and fragmentation. <br />
Four steps in the model are distinguished:<br />
# Drivers of biodiversity change derived from IMAGE results are combined with additional data; <br />
# Mean Species Abundance (MSA) is calculated for each driver and year, using empirical relationships between driver and change in MSA ([[Alkemade et al., 2009]]); <br />
# MSA values for each driver are aggregated to obtain one MSA values;<br />
# Two additional indicators are calculated: Wilderness area, and Species Richness Index (Figure Flowchart). <br />
<br />
MSA expresses the relationship of mean species abundance between a disturbed or managed ecosystem and an undisturbed ecosystem, on a scale from 1 (undisturbed or pristine) to 0 (complete loss). This concept is applicable for most ecosystems and dynamics of biodiversity loss, and allows to compare and aggregate across ecosystems and drivers. However, it ignores possible increase in species abundance due to natural processes or in certain agricultural systems, such as European high nature value farmland.<br />
<br />
===Land use and land-use intensity ===<br />
Changes in land use and land cover are major drivers of biodiversity change. Land use includes all human activities with a spatial component, such as forestry, agriculture, infrastructure and urban development. The impact of land use on biodiversity ranges from small (where the habitat quality is too poor for a limited subset of species) to large (where complete conversion of ecosystems results in habitat loss for a large number of species).<br />
<br />
GLOBIO calculates maps of land-use categories and intensities for the year 2000. The starting point is land-cover data from GLC2000 ([[Bartholome et al., 2004]]) on the major types of forests, rangelands and agricultural land areas, at around 30 arc seconds resolution (1x1km near the equator). These data are combined with the World Database on Protected Areas ([[WDPA database]]; [[UNEP-WCMC, 2005]]) that distinguishes protected and non-protected areas. The land-cover classes obtained are summarised as proportions of cropland, forest and pasture for IMAGE grid cells of 5x5 minutes resolution.<br />
<br />
For the period after 2000, changes in land use and land-use intensity from IMAGE are used as regional totals and allocated to the starting map. Data on cropland areas derived from the [[land-use allocation]] model are used as a total claim for each region. Three intensity classes are distinguished on the basis of management intensity ([[land-use allocation]]) for each region, calibrated with areas of irrigated, extensive and intensive croplands from the farming system typology from the FAO ([[Dixon et al., 2001]]). Data on three forestry management types are derived from the [[forest management]] module, and data on two grazing intensities from the [[livestock systems]] module. <br />
<br />
The pastoral grassland areas are allocated in natural rangelands. Grazing in mixed systems is assumed on managed pastures, where the natural vegetation would be densely forested biomes. The remaining grassland areas (e.g., semi-arid and arid grasslands, tundra) are considered natural areas. All regional cropland, forests and grazing areas are geographically distributed per land-use intensity class by adjusting the proportion per grid cell, avoiding protected areas ([[Visconti et al., 2011]]). <br />
<br />
MSA values for all land-use types are derived from the literature (Alkemade et al., 2009; Alkemade et al., 2012) and applied to the land-use map, with proportions of each land-use intensity class to yield the MSA land-use map for the year considered.<br />
<br />
===Climate ===<br />
Climate is a key determinant of ecosystems and biodiversity. Climate change causes shifts in species occurrence and abundance, and ultimately may lead to local species extinction. Species distribution models ({{abbrTemplate|SDM}}) are used to describe relationships between climate variables and species distribution.<br />
<br />
Regression equations are derived for each biome by applying a large number of SDMs to a series of climate scenarios, and calculating the proportion of remaining species per grid cell (0.5x0.5 degrees). The average proportion of remaining species per grid cell is related to the global mean temperature increase (GMTI) from IMAGE for the scenario considered ([[Alkemade et al., 2011a]]). The regression equation between GMTI and the proportion of remaining species is used to derive the map of MSA levels related to climate change for a given year.<br />
<br />
===Nitrogen=== <br />
Nitrogen is a plant nutrient that stimulates growth, but some species benefit more than others and become more dominant with higher nitrogen availability. Thus, nitrogen deposition affects the species composition, mainly of plant and invertebrate species. Ecosystems can take up nitrogen without observable effects up to the level at which the assimilative capacity of the ecosystem is exceeded. This level of N input is defined as the critical load ({{abbrTemplate|CL}}). <br />
<br />
Deposition rates of atmospheric nitrogen for current and future years are derived from IMAGE (Components [[Emissions]] and [[Nutrients]]), and the map of critical loads is based on Bouwman et al. ([[Bouwman et al., 2002b|2002b]]). The nitrogen exceedance is calculated by subtracting the critical load from the estimated deposition. For forested and grassland ecosystems, the {{abbrTemplate|MSA}} map for nitrogen is derived from the regression equation between nitrogen exceedance and the proportion of remaining species. Regression equations are derived from published impact studies on the effects of a nitrogen surplus on species composition ([[Bobbink et al., 2010]]).<br />
<br />
===Infrastructure and Encroachment===<br />
The construction and use of infrastructure, such as roads, railroads and built-up area, may have multiple impacts on biodiversity. Roads have a direct impact on species, for example as the result of traffic disturbance, road kills and habitat fragmentation (see below). There are also indirect impacts, such as increased human access to natural areas, increasing hunting, gathering and tourism. Traffic disturbance reduces the breeding success of bird and mammal species, reducing their abundance close to infrastructure. Hunting and gathering reduce populations when intensity exceeds threshold values. <br />
<br />
Data on infrastructure are derived from globally available road maps, such as the Digital Chart of the World ([[DMA, 1992]]) and the GRIP, Global Roads Inventory Project ([[Meijer and Klein Goldewijk, 2009]]). Direct impacts occur in a 500 m zone on both sides of roads and an MSA value is derived from a meta-analysis on disturbance effects ([[Benitez-Lopez et al., 2010]]). <br />
<br />
Human settlements are the major access points to natural areas, and are likely to correlate with agricultural areas. Thus, 20 km impact zones are calculated around cropland areas and assigned as encroachment areas. Based on literature review of hunting activities, an MSA value of 0.7 is attributed to such zones. The MSA map for infrastructure and encroachment is obtained by combining the MSA map for direct (infrastructure) and indirect (encroachment) effects. In projections, the impact zone of direct effects is broadened according to the GLOBIO2.0 procedure ([[UNEP, 2001]]). Future impact zones for indirect effects are determined by the projections for agricultural areas. <br />
<br />
===Ecosystem fragmentation ===<br />
Conversion of natural land to intensive cropping and road construction change vast areas of contiguous wilderness into a fragmented landscape with remnants of natural areas remaining as isolated islands. These relatively small patches are likely to house fewer species than could be expected from their habitat quality, because the individual patches may be too small to sustain viable populations of some species. Based on literature data on minimum area requirements of species, a relationship is constructed between patch size and relative number of species compared to a non-fragmented situation, known as the minimum area requirement (MAR) curve ([[Verboom et al., 2014]]). The relative number of species in a certain patch according to this MAR curve is used as a proxy for mean species abundance (MSA).<br />
<br />
The area of natural vegetation patches is calculated by reclassifying the GLC2000 Global Land Cover data into two classes: human-dominated land (including croplands and urban areas) and natural land. Contiguous cells of natural land are grouped together and with an overlay of main roads (see above) are used to produce a map of natural land patches. <br />
<br />
In scenario projections, patch sizes change as agricultural land use expands and as new roads emerge ([[Verboom et al., 2014]]). Changes in patch sizes also change the relative number of species and the MSA biodiversity indicator. <br />
<br />
===Aggregation===<br />
Total MSA values per area unit are calculated by multiplying the individual MSA values related to the separate drivers of biodiversity change (Figure Flowchart) to arrive at the total effect of all drivers. The contribution of individual drivers to biodiversity loss is also calculated. <br />
Wilderness areas are defined as natural areas with high (>0.8) MSA values. The Species Richness Index (SRI) is calculated by applying species–area relationships according to Faith et al. ([[Faith et al., 2008|2008]]), and using MSA values as a proxy for their intactness parameter. Aggregation from regional to global species richness is based on species lists in the Wildfinder database to avoid double counting ([[Musters et al., submitted]]).<br />
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<div>{{ComponentDescriptionTemplate<br />
|Reference=Nilsson et al., 2005; Alcamo et al., 2003; Davies et al., 2013; Pastor et al., 2014; Bondeau et al., 2007; Sitch et al., 2003; Gerten et al., 2004; Rost et al., 2008; Biemans et al., 2013;<br />
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<br />
In IMAGE, the hydrological cycle is represented by LPJmL ([[Sitch et al., 2003]]; [[Bondeau et al., 2007]]; [[Gerten et al., 2013]]), which simulates the global hydrological cycle as part of the dynamics of natural vegetation and agricultural production systems. Because LPJmL is linked to IMAGE, there is consistency in the way the [[Carbon cycle and natural vegetation|carbon cycle, natural vegetation]] dynamics, [[Crops and grass|crop growth and production]], [[land-use allocation]] and the water balance can be modelled. <br />
<br />
Data on annual [[land cover and land use]] are used as input to LPJmL, including information on the location of irrigated areas and crop types (Figure Flowchart and Input/Output Table at [[Water|Introduction part]]). This affects the amount of water that evaporates and runs off, as well as the amount of water needed for irrigated areas during the (simulated) growing season of crops. Similarly, information on water availability calculated by LPJmL is taken into account in the [[Land-use allocation]] model to identify suitable locations to expand irrigated areas.<br />
<br />
Climate is used as input in LPJmL to determine potential evapotranspiration, and the precipitation input to the water balance ([[Gerten et al., 2004]]). The [[Crops and grass]] module, which is also part of LPJmL, calculates irrigation water demand based on crop characteristics, soil moisture and climate. If the amount of water available for irrigation is limited, water stress will occur which leads to reduction of crop yields calculated by the [[Crops and grass|crop and grassland]] model.<br />
<br />
===The natural hydrological cycle===<br />
The Hydrology module in LPJmL consists of a vertical water balance ([[Gerten et al., 2004]]) and a lateral flow component ([[Rost et al., 2008]]) which are run at 0.5 degree resolution in daily time steps (Figure Flowchart). The soil in each grid cell is represented by a two-layer soil column of 0.5 and 1.0 m depth, partly covered with natural vegetation or crops.<br />
<br />
The potential evapotranspiration rate in each grid cell depends primarily on net radiation and temperature, and is calculated using the Priestley-Taylor approach ([[Gerten et al., 2004]]). The actual evapotranspiration is calculated as the sum of three components: evaporation of water stored in the canopy (interception), bare soil evaporation and plant transpiration ([[Gerten et al., 2004]]). Water storage in the canopy is a function of vegetation type, leaf area index ({{abbrTemplate|LAI}}) and precipitation amount. Plant transpiration is modelled as the minimum of atmospheric demand and plant water supply. Plant water supply depends on the plant-dependent maximum transpiration rate and relative soil moisture. Soil evaporation occurs in the proportion of land in the grid cell that is not covered by vegetation. It equals potential evaporation when the soil moisture of the upper 20 cm is at field capacity, and declines linearly with relative soil moisture. <br />
<br />
Precipitation reaching the soil (throughfall, precipitation minus interception) either accumulates as snow or infiltrates into the soil. Snowmelt is calculated using a simple degree-day method ([[Gerten et al., 2004]]). The soil is parameterised as a bucket model. The status of soil moisture of the two soil layers is updated daily, accounting for throughfall, snowmelt, evapotranspiration, percolation and runoff. Percolation rates for the two soil layers depend on soil type and decline exponentially with soil moisture. Total runoff is calculated as water in excess of field capacity from the two soil layers and water percolating through the second soil layer. The current version of LPJmL has no explicit representation of groundwater recharge, but a groundwater scheme is under development. The daily (subsurface) runoff includes the renewable fraction of groundwater, but without any time delay.<br />
<br />
All runoff is routed daily through a gridded river network, representing a system of rivers, natural lakes and reservoirs, using a simple routing algorithm ([[Rost et al., 2008]]). Local runoff is added to surface water storage in the cell, and subsequently flows downstream at a constant flow velocity of 1 m s-1 until reaching a lake or reservoir. Water accumulates in lakes and reservoirs, and outflow depends on actual storage relative to the maximum storage capacity (for lakes) and the operational purpose of the reservoir ([[Biemans et al., 2011]]). For man-made reservoirs, see further below ([[Biemans et al., 2011]])<br />
<br />
===Supply and demand for irrigation water===<br />
Water availability and demand in agriculture is simulated with LPJmL’s irrigation algorithm and an algorithm to simulate the operation of large reservoirs to supply water to irrigated areas ([[Biemans et al., 2013]]). <br />
<br />
The irrigation demand submodel (Figure Flowchart) is described in detail by Rost et al. ([[Rost et al., 2008|2008]]). Crop net irrigation demand is defined as the minimum atmospheric evaporative demand and the amount of water needed to fill the soil to field capacity. The irrigation withdrawal demand – the gross demand – is subsequently calculated as the product of the crop irrigation demand and a country-specific irrigation efficiency factor that reflects the type and efficiency of prevailing irrigation systems ([[Rost et al., 2008]]). The efficiency, i.e. the losses of withdrawn water during transport between withdrawal point and irrigated field depends on the type of conveyance system (e.g., open channel or pipeline). Thus, the quantity of water demanded by crops (water consumption) is always less than the quantity withdrawn (water use). <br />
<br />
Irrigation water is extracted from the rivers and lakes in the grid cell or a neighbouring grid cell. If these local surface water sources cannot meet the total demand, water is extracted from nearby reservoirs, if available. Finally, water can be supplied from an unlimited source that can be interpreted as non-sustainable groundwater or water imported from another basin. By excluding these water sources in a series of model runs, irrigation water supply and crop production can be attributed to different water sources.<br />
<br />
===Large reservoirs===<br />
Some 50% of global river systems are regulated by dams, most of which are in basins where there is irrigation and economic activity ([[Nilsson et al., 2005]]). The main purpose of approximately one-third of all large reservoirs is irrigation. Thus, in estimating agricultural water use, man-made reservoirs have to be taken into account. <br />
The reservoir operation module in LPJmL ([[Biemans et al., 2011]]) distinguishes three types of reservoirs: reservoirs used primarily for irrigation; reservoirs used primarily for other purposes (e.g., hydropower and flood control) but also for irrigation; and reservoirs not used for irrigation. Each type of reservoir is managed differently. The outflow of irrigation reservoirs follows the temporal pattern of irrigation demand, whereas the other reservoirs are intended to release equal quantities of water throughout the year. Water from irrigation reservoirs is supplied to downstream irrigated areas.<br />
<br />
===Water demand in other sectors===<br />
IMAGE-LPJmL only calculates agricultural water demand internally, and water demand in other sectors is calculated separately. For household and manufacturing sectors, data and algorithms are adopted from the WaterGAP model ([[Alcamo et al., 2003]]). For the electricity sector, a process-based estimation is used based on the study by Davies et al. ([[Davies et al., 2013|2013]]), and Livestock water demand follows from the number of animals estimated in the [[Livestock systems]] model, with the water demand per head adjusted for climate conditions. Domestic demand is a function of population size and per capita income, corrected for the proportion of the population without access to a piped water supply (see Component [[Human development]]). Manufacturing demand is a function of industrial value added, corrected for changes in sector composition, such as the structural change factor used for [[Energy demand]]. <br />
<br />
For the electricity sector, a technology-based approach was adopted from the study by Davies et al. ([[Davies et al., 2013|2013]]).The type of power plant (e.g., standard steam cycle, combined steam cycle) determines the demand for cooling capacity. As plants cogenerating heat and power require less cooling capacity, demand is also corrected for these plants. In addition, the type of cooling facility determines the quantity of water required. Once-through cooling systems use large volumes of surface water that are returned almost entirely to the water body from which they were extracted, albeit at an elevated temperature. Wet cooling towers exploit the evaporation heat capacity of water and, thus require much lower water volumes. However, a significant part of the cooling water evaporates during the process and does not return to the original water body. In some regions, cooling ponds are used, where cooling water is pumped and recycled in a closed loop, with water demand somewhere between the once-through and wet tower cooling systems. Finally, dry cooling systems are deployed that use air as a coolant and thus do not require cooling water. Based on data from Davies et al. ([[Davies et al., 2013|2013]]), market share for types of cooling systems – for each power plant type distinguished in [[TIMER model|TIMER]] in each world region – are combined with energy input requirements to obtain the total water demand for the electricity sector.<br />
<br />
===Water extractions===<br />
Water requirements in other sectors are extracted from local surface water, if available (rather than from reservoirs). Meeting the demand from these sectors receives priority over water withdrawal for irrigation.<br />
<br />
The current version of IMAGE-LPJmL does not take into account the water needs of ecosystems, or other uses, such as shipping and recreation. However, a new module to calculate environmental flow requirements is under development ([[Pastor et al., 2014]]). This module, which constrains water withdrawals so that a minimum environmental flow is guaranteed, will be used to identify possible areas of conflict between water users.<br />
</div>{{DisplayFigureTemplate|Baseline figure Water}}<br />
<div class="page_standard"><br />
===Impact indicators===<br />
<br />
Water stress is often presented as a spatial and temporal average water withdrawal-to-availability ratio at basin or country level. The population living with water stress is estimated by overlaying such a water-stress (or water availability) map with a population density map. These indicators are used to present IMAGE-LPJmL results (for instance, in the [[OECD Environmental Outlook to 2050 (2012) project|OECD Environmental Outlook]], see Figure) but they mask the potential occurrence of water shortages in the short-term or on sub-basin scale. Thus, water stress should also be calculated at higher spatial and temporal resolutions, as can principally be done with LPJmL (see [[Biemans, 2012]]). <br />
<br />
The impacts of water stress differ per sector, but the indicators described above do not provide deeper insight into these impacts. In addition to the general water stress indicators, the model also considers production reduction in irrigated agriculture due to limited water availability as an indicator of agricultural water stress ([[Biemans, 2012]]).<br />
</div></div>Oostenrijrhttps://models.pbl.nl/image/index.php?title=Land_degradation/Description&diff=36530Land degradation/Description2020-04-01T13:34:43Z<p>Oostenrijr: Text replacement - "==Model description of {{ROOTPAGENAME}}==" to ""</p>
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<div>{{ComponentDescriptionTemplate<br />
|Reference=Oldeman et al., 1991; Batjes, 1997; Harris et al., 2013; Batjes, 2009; FAO et al., 2009;<br />
}}<div class="page_standard"><br />
<br />
Water erosion is the main cause of land degradation (1049 million hectares (Mha), followed by wind erosion (548 Mha), chemical degradation (239 Mha) and physical degradation (83 Mha) (GLASOD; [[Oldeman et al., 1991|Oldeman et al. (1991)]]). IMAGE assesses soil erosion by water ([[Hootsmans et al., 2001]]) by calculating a water erosion sensitivity index, ranging from zero (no erosion risk) to one (extremely high erosion risk). This risk is calculated for each grid cell as the compounded result from the following indices:<br />
==A. Risk of soil erosion caused by water==<br />
Water erosion is the main cause of land degradation (1049 million hectares (Mha), followed by wind erosion (548 Mha), chemical degradation (239 Mha) and physical degradation (83 Mha) (GLASOD; [[Oldeman et al., 1991|Oldeman et al. (1991)]]). IMAGE assesses soil erosion by water ([[Hootsmans et al., 2001]]) by calculating a water erosion sensitivity index, ranging from zero (no erosion risk) to one (extremely high erosion risk). This risk is calculated for each grid cell as the compounded result from the following indices:<br />
<br />
* ''terrain erodibility index'': terrain erodibility represents the water erosion characteristics of the terrain in an index that combines surface relief and soil properties, expressed as index numbers. The relief index is a landform characteristic derived from a digital elevation model, calculated from the difference between minimum and maximum altitude in a 10 minute grid cell. The index is 1 for a difference of 300 m or more and zero for no altitude differences, with a linear relationship assumed between the two extremes. The soil erodibility index is derived from indices on soil texture, bulk density and soil depth. Soil characteristics were deduced from the 0.5x0.5 degree resolution in the [[WISE database]] ([[Batjes, 1997]]).<br />
<br />
* ''rainfall erosivity index'': this index represents exposure to heavy rainfall, derived from the month of the year with the highest precipitation and number of wet (rainy) days in each month. Rainfall erosivity is largely determined by the intensity of rainfall events, because soil loss only occurs during periods of intense rainfall. Monthly rainfall intensities of between 0 and 2 mm per day are assigned an index value of zero, and days exceeding 20 mm receive a value of one, with a linear relationship assumed between these two end points. Climate data are used for the historical period ([[Harris et al., 2013]]). For future years, predictions are based on changes in precipitation according to scenarios generated by the climate model, see Component [[Atmospheric composition and climate]]. The number of wet days per month is assumed to be constant over time.<br />
<br />
* ''land-use/land-cover index'': this index presents the level of protection against water erosion offered by various types of natural vegetation and crops. The basis for this index is the geographic distribution of land-cover types generated by the land-cover model. Most types of natural vegetation provide a high degree of protection against water erosion, while agriculture, and arable agriculture in particular, increases the vulnerability of the soil surface. A composite value is used for grid cells that contain agriculture, based on the distribution of agricultural crops in that world region. <br />
<br />
All intermediate and resulting factors are expressed as dimensionless indices from zero to one, and so too is the end indicator, Water Erosion Sensitivity Index.<br />
<br />
The susceptibility and sensitivity indices are calculated according to:<br />
<br />
:<math>T = (Ia+ SE)/2 </math><br />
:<math>Ep = (T+R)/2</math><br />
:<math>WES = Ep*V</math><br />
<br />
with:<br />
: <math>Ia</math> = relief index (-)<br />
: <math>SE</math> = soil erodibility index (-)<br />
: <math>T</math> = terrain erodibility index (-)<br />
: <math>R</math> = rainfall erosivity index (-)<br />
: <math>Ep</math> = water erosion susceptibility index (-)<br />
: <math>V</math> = land-use/land-cover index (-)<br />
: <math>WES</math> = Water Erosion Sensitivity Index (-)<br />
<br />
Management systems are in use around the world to reduce the risk of erosion, such as building terraces, zero tillage, planting or conserving protective vegetation zones around fields, and high capacity drainage systems. The Water Erosion Sensitivity Index cannot capture all these and other interventions for the current situation, let alone into the future. The index only indicates areas potentially under threat. Impacts on crop production and soil quality cannot be derived directly from the indicator.<br />
<br />
Comparison of the calculation above and the GLASOD degradation status maps by [[Oldeman et al., 1991|Oldeman et al. (1991)]] shows maximum correspondence with use of the classification in the table below. This classification can be used as a guide in analysing the water erosion sensitivity indicator.<br />
<br />
<div class="thumbcaption dark">Classification of the Water Erosion Sensitivity Index</div><br />
<table class="pbltable"><br />
<tr><br />
<th>Water Erosion Sensitivity Index</th><br />
<th>GLASOD soil degradation caused by water erosion</th><br />
</tr><br />
<tr><br />
<td>< 0.15</td><br />
<td>no/low</td><br />
</tr><br />
<tr><br />
<td>0.15 - 0.30</td><br />
<td>moderate</td><br />
</tr><br />
<tr><br />
<td>0.30 - 0.45</td><br />
<td>high</td><br />
</tr><br />
<tr><br />
<td>> 0.45</td><br />
<td>very high</td><br />
</tr><br />
</table><br />
<br />
==B. Human-induced soil changes==<br />
Soil degradation is mostly reflected in changes in soil properties, such as soil depth, soil organic matter ({{abbrTemplate|SOM}}) content, and texture. Land cover and land use drive changes in soil properties. Land cover protects the soil against wind and water erosion, and provides organic matter to the soil. Land use tends to remove part of the biomass with harvested crops and residues and may increase mineralisation of SOM through tillage. <br />
<br />
An empirical model denominated S-World has been developed that relates change in soil properties to topography, climate (average annual temperature and total annual precipitation), land management and land use, and land cover (as vegetation cover) ([[Stoorvogel, 2014]]; [[Stoorvogel et al., 2017]]). The following soil properties are considered:<br />
* topsoil depth,<br />
* soil depth,<br />
* soil organic matter in the topsoil and subsoil , and <br />
* soil texture (sand and clay content).<br />
<br />
S-World is based on the global Harmonised World Soil Database ([[HWSD database|HWSD]]; ([[FAO et al., 2009]]) and the [[WISE database|WISE soil profile database]] ([[Batjes, 2009]]). The compound mapping units in HWSD were disaggregated using detailed terrain information, so that each grid cell could be linked to a unique soil type described in the WISE database. For each soil type, ranges for the main soil characteristics described above were assessed on the basis of the WISE soil profiles. The range of variable, i.e., soil property v for every soil type s is subsequently defined as [v<sub>ls</sub>..v<sub>hs</sub>] in which v<sub>ls</sub> corresponds to the 1<sup>st</sup> decile and v<sub>hs</sub> to the 9<sup>th</sup> decile. S-World downscales each soil property v based on 5 landscape properties or explanatory factors [''p<sub>1</sub>,p<sub>2</sub>… p<sub>5</sub>'']. These explanatory factors are: temperature, precipitation, slope, land management, and land cover. The land management is set to:<br />
* 1.0 for cropland, <br />
* 0.5 for mosaics of cropland and pasture or natural vegetation, <br />
* 0.3 for pasture, and <br />
* 0.0 for natural vegetation; <br />
Land cover is characterised by a remotely sensed {{abbrTemplate|NDVI}} map. <br />
<br />
The soil property v at location x with soil s is estimated as: {{FormulaAndTableTemplate|Formula1 Land degradation}} <br />
with w<sub>x </sub>being a weight w∈ [0..1] that determines where v is in the range [v<sub>ls</sub>..v<sub>hs</sub> ]. Different explanatory factors represented by the landscape properties determine w. The weight at location x is calculated as: {{FormulaAndTableTemplate|Formula4 Land degradation}}<br />
The weight w<sub>px</sub> for landscape property p is calculated as: {{FormulaAndTableTemplate|Formula2 Land degradation}}<br />
In which c<sub>pv</sub> is a constant that indicates the relative importance of the landscape property p for a soil property v. The sign of c<sub>pv</sub> indicates whether there is a positive or negative relationship between the landscape property and the soil property. <br />
<br />
When: {{FormulaAndTableTemplate|Formula3 Land degradation}} <br />
and all the w∈ [0..1] then all values in the range [v<sub>ls</sub>..v<sub>hs</sub> ] are possible based on the landscape properties. Although in practice c is specific for each landscape property, soil type, and soil property, data are lacking to estimate c at that level of specificity. Therefore the model assumes that c is constant per soil and landscape property, or, in other words, the relative impact of landscape properties on a specific soil property is assumed to be constant over the different soil types.<br />
<br />
The soil properties are estimated based on land management and land use. This allows for the estimation of soil properties under pristine conditions. For future years, the NDVI map is changed as a function of land use, forest management and assumptions on degradation. To assess pristine conditions, soil properties are calculated with land use set at natural, and land cover represented by the NDVI under pristine conditions. <br />
<br />
With this procedure, a change in soil properties (topsoil depth, soil depth, SOM in topsoil and subsoil, and soil texture) can be calculated as a result of land use and land cover. Subsequently, additional soil characteristics, such as water holding capacity and water infiltration rate, can be derived from these soil property values by using pedo-transfer functions ([[Van Beek, 2012]]). These soil characteristics can be used in other models in the IMAGE framework, such as [[LPJmL model|LPJmL]] (Component [[Carbon cycle and natural vegetation]] ) and [[GLOFRIS model|GLOFRIS]] (Component [[Flood risks]]), as alternative input to assess the consequences of historical or future land degradation.<br />
|=1〗, the w∈ [0..1] and all values in the range [v_ls..v_hs ] are possible based on the landscape properties. Although in practice c is specific for each landscape property, soil type, and soil property, data are lacking to estimate c at that level of specificity. Therefore the model assumes that c is constant per soil and landscape property, or, in other words, the relative impact of landscape properties on a specific soil property is assumed to be constant over the different soil types.<br />
The soil properties are estimated based on land management and land use. This allows for the estimation of soil properties under pristine conditions. For future years, the NDVI map is changed as a function of land use, forest management and assumptions on degradation. To assess pristine conditions, soil properties are calculated with land use set at natural, and land cover represented by the NDVI under pristine conditions. <br />
With this procedure, a change in soil properties (topsoil depth, soil depth, SOM in topsoil and subsoil, and soil texture) can be calculated as a result of land use and land cover. Subsequently, additional soil characteristics, such as water holding capacity and water infiltration rate, can be derived from these soil property values by using pedo-transfer functions (Van Beek, 2012). These soil characteristics can be used in other models in the IMAGE framework, such as LPJmL (Section 6.1) and GLOFRIS (Section 7.4), as alternative input to assess the consequences of historical or future land degradation.<br />
<div></div>Oostenrijrhttps://models.pbl.nl/image/index.php?title=Human_development/Description&diff=36531Human development/Description2020-04-01T13:34:43Z<p>Oostenrijr: Text replacement - "==Model description of {{ROOTPAGENAME}}==" to ""</p>
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|Reference=Hilderink, 2000; UNDP, 1990; UNDP, 2010; WHO, 2002; Cairncross and Valdmanis, 2006; Mathers and Loncar, 2006; Craig et al., 1999; Smith and Haddad, 2000; De Onis and Blossner, 2003; FAO, 2003; Mathers and Loncar, 2006; Pandey et al., 2006; Dockery et al., 1993; Pope et al., 1995; Ravallion et al., 2008;<br />
}}<div class="page_standard"><br />
<br />
GISMO assesses the impacts of global environmental change on human development through the impacts on human health either directly, for example the impact of climate change on malaria, or indirectly through, for instance, the impact of climate change on food availability. In addition to environmental factors, human health is also driven by socioeconomic factors, including income and education levels.<br />
<br />
To take account of the interrelationships between the various factors, GISMO consists of three modules that address human health, poverty and education (Figure Flowchart). The modules are linked through a cohort component population model that includes endogenous fertility and mortality (for details see [[Hilderink, 2000]]). Fertility levels are modelled using a convergence level that is determined by female educational levels, and speed of convergence determined by the human development index, and mortality rates by the health module. Future trends in migration, including urbanisation, are exogenous inputs to the model (for details see [[Hilderink, 2000]]).<br />
<br />
The Human development index (HDI), which was introduced in the UNDP Human Development Report 1990 to rank development achievements, is a composite index of life expectancy, education, and income indices ([[UNDP, 1990]]; [[UNDP, 2010]]). While the underlying indicators have been refined several times, the three elements have remained the same. The index links to the three GISMO model components.<br />
<br />
===GISMO health module===<br />
This module describes the causal chains between health-risk factors and health outcomes (morbidity and mortality) and takes into account the effect of health services. The mortality rate is modelled by a risk-factor-attributable component and a non-attributable component. Historically, the non-attributable component represents mortality not covered by the risk factors included. For future projections, this component is assumed to reduce by the average regional historical rates of reduction. <br />
<br />
The risk-factor-attributable component is based on a multi-state approach that distinguishes exposure, disease and death ([[WHO, 2002]]; [[Cairncross and Valdmanis, 2006]]). This implies that incidence and case fatality rates (ratio of the number of deaths from a specific disease to the number of diagnosed cases) are taken into account for various health-risk factors. Case fatality rates are modified by the level of health services. This method is used for malaria, diarrhoea and pneumonia. The method for projecting mortality due to other causes (non-communicable chronic diseases, other communicable diseases and injuries) follows the global burden of disease ({{abbrTemplate|GBD}}) approach. This method uses a parsimonious regression technique to relate mortality rates with GDP, smoking behaviour and human capital, in ten major disease clusters ([[Mathers and Loncar, 2006]]). This method is also used in determining death related to urban air pollution.<br />
<br />
<div class="thumbcaption dark">Table: Cause of death and environmental risk factors </div><br />
<table class="pbltable"><br />
<tr><br />
<th>Cause of death</th><br />
<th>Risk factors</th><br />
</tr><br />
<tr><br />
<td>Malaria</td><br />
<td>Climate suitable for malaria vectors</td><br />
</tr><br />
<tr><br />
<td>Protein deficiency</td><br />
<td>Prevalence of underweight</td><br />
</tr><br />
<tr><br />
<td>Diarrhoea</td><br />
<td>Lack of safe drinking water and basic sanitation</td><br />
</tr><br />
<tr><br />
<td>Pneumonia, Chronic obstructive pulmonary disease (COPD), Lung cancer</td><br />
<td>Use of solid fuels (traditional biomass or coal) for cooking and heating</td><br />
</tr><br />
<tr><br />
<td>Lung cancer, Cardiopulmonary diseases, Acute respiratory infections (ARI)<br />
</td><br />
<td>Exposure to PM10 and PM2.5, related to NO<sub>x</sub>, SO<sub>2</sub> and black carbon emissions<br />
</td><br />
</tr><br />
</table><br />
<br />
The GISMO health module takes into account mortality due to a range of diseases and conditions. These include:<br />
* malaria; <br />
* communicable and infectious diseases associated with undernourishment, limited access to safe drinking water and basic sanitation and poor indoor air quality; <br />
* diseases caused by poor outdoor air quality; <br />
* HIV-AIDS; <br />
* chronic diseases including high blood pressure and obesity. <br />
Only the first three causes of mortality are considered because these are linked to environmental factors. The mortality rate due to a specific disease is a multiplication of the incidence rate (fraction of the population with the specific disease) and the case fatality rate (the fraction of people who die from a specific disease), distinguishing for the two sexes and five-year age cohorts. These mortality rates can then be used to calculated age-specific life expectancy (for details see [[Hilderink, 2000]]).<br />
<br />
====Malaria risk====<br />
Incidence rates of malaria are determined by the areas suitable for the malaria mosquito, based on monthly temperature and precipitation, see Component [[Water]] ([[Craig et al., 1999]]). Incidence rates are decreased by the level of insecticide treated bed nets and indoor residual spraying, modelled separately as potential policy options. The case fatality rate of malaria is increased by level of underweight people and decreased by case management (treatment).<br />
<br />
===Access to food, water and energy=== <br />
GISMO relates incidence and case fatality rates for specific diseases to access to food, water and energy (the table above). Access is defined by per capita food availability, access to safe drinking water and improved sanitation, and access to modern energy sources for cooking and heating. The future per capita food availability (Kcal/cap/day) is obtained from IMAGE (Component [[Agriculture and land use]]). The levels of access to safe drinking water and improved sanitation are modelled separately by applying linear regression. The explanatory variables include GDP per capita, urbanisation rate and population density. Improvements in water supply are assumed to be implemented ahead of sanitation. Access to water supply and sanitation follows a pathway from no sustainable access to safe drinking water and basic sanitation, to improved water supply only, improved water supply and sanitation, household connection for water supply, to household connection to water supply and sanitation. Three levels of access to modern energy sources for cooking and heating are distinguished: traditional biomass and coal on traditional stoves; traditional biomass and coal on improved stoves; and use of modern energy carriers (electricity, natural gas, LPG, kerosene, modern biofuels and decentralised renewable sources). Trends in access to modern energy sources are taken from the TIMER [[energy demand]] module.<br />
<br />
====Underweight children and prevalence of undernourishment====<br />
For children under the age of five, undernourishment is expressed as underweight (measured as weight-for-age), and prevalence of undernourishment is used for the rest of the population. The direct effect of undernourishment is protein deficiency, which for mortality rates of the under fives is scaled to their underweight status and for other age groups to the level of undernourishment. Undernourishment indirectly increases the incidence of diarrhoea and pneumonia, and the case fatality of malaria, diarrhoea and pneumonia. These indirect effects are only modelled for children under the age of five. Underweight children as the result of chronic undernourishment is modelled as a function of improvements in average food intake, ratio of female to male life expectancy at birth, female enrolment in secondary education and access to clean drinking water ([[Smith and Haddad, 2000]]). Based on a normal distribution, the total number of underweight children is divided into three groups of mildly, moderately and severely underweight ([[De Onis and Blossner, 2003]]).<br />
<br />
The prevalence of undernourishment is calculated from per-capita food availability and minimum energy requirements ([[FAO, 2003]]). The calculations use a lognormal distribution function determined by mean food consumption and a coefficient of variation, which decreases over time as a function of per capita GDP. The minimum requirement of dietary energy is derived by aggregating region-specific, sex-age energy requirements weighted by the proportion of each sex and age group in the total population, including a pregnancy allowance. <br />
Incidence rates of pneumonia, chronic obstructive pulmonary disease ({{abbrTemplate|COPD}}) and lung cancer are increased by indoor air pollution caused by cooking and heating with traditional biomass and coal. Simultaneously, incidence rates and case fatality rates are increased by child underweight levels. Incidence rates of diarrhoea depend on levels of access to drinking water and sanitation, levels of underweight children, and also on climate change. Case fatality rates are increased by underweight levels and decreased by the level of oral rehydration therapy. <br />
<br />
====Mortality associated with urban air pollution====<br />
Mortality rates of lung cancer, cardiopulmonary diseases and acute respiratory infections due to urban air pollution (PM10 and PM2.5 concentration levels) are derived using the {{abbrTemplate|GBD}} method ([[Mathers and Loncar, 2006]]). Based on emissions of NO<sub>x</sub>, SO<sub>2</sub> and black carbon (Component [[Emissions]]), PM10 concentration levels are determined using the Global Urban Air quality Model ([[GUAM model]]). This model originates from the GMAPS model ([[Pandey et al., 2006]]), which determines PM10 concentration levels by economic activity, population, urbanisation and meteorological factors. PM2.5 concentrations are obtained using a region-specific PM10–PM2.5 ratio. Based on these levels and the exposed population, mortality attributable to causes of death is derived using relative risks obtained from epidemiology studies ([[Dockery et al., 1993]]; [[Pope et al., 1995]]).<br />
<br />
===GISMO poverty module===<br />
The poverty line is commonly defined as the level at which consumption or income levels fall below that required to meet basic needs. In the model, the poverty head count (people living below the poverty line) is conducted by applying a log-normal distribution using per-capita income and a GINI coefficient to describe poverty distribution over a population. The poverty module can assess the number of people living below a poverty line, including the international poverty line defined as USD 1.25 per day, at 2005 {{abbrTemplate|PPP}}, by the World Bank ([[Ravallion et al., 2008]]). <br />
<br />
===GISMO education module===<br />
The education module assesses future developments in school enrolment and educational attainment, including literacy rates at three levels of education: primary, secondary and tertiary. The model tracks the proportion of the highest level of education completed and the average number of years of schooling per cohort. The enrolment ratios per educational level are determined using cross-sectional relationships with per-capita GDP (PPP). The age at which a certain educational level is attained is assumed to be identical in all regions. Literacy rates are determined by the proportion of the population over the age of 15 who have completed at least primary education. Furthermore, to take account of autonomous increases in literacy levels, literacy levels of the population between the age of 15 and 65 is increased by 0.3%, annually.<br />
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<br />
===Fossil fuels and uranium===<br />
Depletion of fossil fuels (coal, oil and natural gas) and uranium is simulated on the assumption that resources can be represented by a long-term cost-supple curve, consisting of different resource categories with increasing costs levels. The model assumes that the cheapest deposits will be exploited first. For each region, there are 12 resource categories for oil, gas and nuclear fuels, and 14 categories for coal. <br />
<br />
A key input for each of the fossil fuel and uranium supply submodules is fuel demand (fuel used in final energy and conversion processes). Additional input includes conversion losses in refining, liquefaction, conversion, and energy use in the energy system. [!CHANGE] Upstream energy use is endogenously determined based energy carrier, region in which the energy carrier is produced, production rate, and resource category. These submodules indicate how demand can be met by supply in a region and other regions through interregional trade.<br />
<br />
<div class="thumbcaption dark">Table: [!CHANGE]Main assumptions on fossil fuel resources (in ZJ; coal does not have a distinction between conventional and unconventional; <nowiki>[[IEA, 2017]]</nowiki>, <nowiki>[[USGS, 2012]]</nowiki>; <nowiki>[[BGR, 2016]]</nowiki>; [[EDGAR database]]; <nowiki>[[Abundant Gas Project]]</nowiki>)</div><table class="pbltable"><br />
<br />
<tr><th></th><th>Oil<br />
</th><th>Natural gas<br />
</th><th>Underground coal<br />
</th><th>Surface coal<br />
</th></tr><br />
<tr><td>Cum. 1970-2015 production<br />
</td><td>6.5<br />
</td><td>3.4<br />
</td><td>2.4<br />
</td><td>1.5<br />
</td></tr><br />
<tr><td>Reserves<br />
</td><td>9.4<br />
</td><td>7.3<br />
</td><td>17<br />
</td><td>3.6<br />
</td></tr><br />
<tr><td>Other conventional resources<br />
</td><td>33<br />
</td><td>17<br />
</td><td>481<br />
</td><td>56<br />
</td></tr><br />
<tr><td>Unconventional resources (reserves)<br />
</td><td>2.0<br />
</td><td>0.30<br />
</td><td></td><td></td></tr><br />
<tr><td>Other unconventional resources<br />
</td><td>54<br />
</td><td>2023<br />
</td><td></td><td></td></tr><br />
<tr><td>Total<br />
</td><td>105<br />
</td><td>2051<br />
</td><td>501<br />
</td><td>61<br />
</td></tr><br />
</table><br />
<br />
Fossil fuel resources are aggregated to five resource categories for each fuel (the table above). Each category has typical production costs. The resource estimates for oil and natural gas supply imply that for conventional resources supply is limited to only [!CHANGE] about 7 times the 1970–2015 production level. Production estimates for unconventional resources are much larger, albeit speculative. Recently, some of the occurrences of these unconventional resources have become competitive such as shale gas and tar sands. For coal, even current reserves amount to almost ten times the production level of the last three decades. For all fuels, the model assumes that, if prices increase, or if there is further technology development, the energy could be produced in the higher cost resource categories. The values presented in the table above represent medium estimates in the model, which can also use higher or lower estimates in the scenarios. The final production costs in each region are determined by the combined effect of resource depletion and learning-by-doing.<br />
<br />
===Trade===<br />
Trade is dealt with in a generic way for oil, natural gas and coal. In the fuel trade model, each region imports fuels from other regions. The amount of fuel imported from each region depends on the relative production costs and those in other regions, augmented with transport costs, using multinomial logit equations. Transport costs are calculated from representative interregional transport distances and time- and fuel-dependent estimates of the costs per GJ per kilometre.<br />
<br />
To reflect geographical, political and other constraints in the interregional fuel trade, an additional 'cost' is added to simulate trade barriers between regions (this costs factor is determined by calibration). Natural gas is transported by pipeline or liquid-natural gas ({{abbrTemplate|LNG}}) tanker, depending on distance, with pipeline more attractive for short distances. In order to account for cartel behaviour, the model compares production costs with and without unrestricted trade. Regions that can supply at lower costs than the average production costs in importing regions are assumed to supply oil at a price only slightly below the production costs of the importing regions. Although also this rule is implemented in a generic form for all energy carriers, it is only effective for oil, where the behaviour of the OPEC cartel is simulated to some extent.<br />
<br />
===Renewable energy !CHANGE!===<br />
IMAGE model the supply of eight renewable energy options: utility-scale photovoltaic (PV), rooftop PV, concentrated solar power (CSP), onshore wind energy, offshore wind energy, first-generation bio-energy, lignocellulosic bio-energy, and hydropower is estimated generically as follows ([[Hoogwijk, 2004]]; [[De Vries et al., 2007]]; [[Gernaat et al., 2017]]; [[Köberle et al., 2015]]; [[Gernaat et al., 2014]]; [[Daioglou et al., 2019]]; [[Gernaat]]): <br />
Firstly, physical and geographical data are collected on a 0.5x0.5 degree grid. The characteristics of wind speed, insulation and monthly variation are taken from the digital databases. [[File:Physical climate data renewables.png|thumb|621x621px|'''Model mean (GFLD-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5) historical 30-year (1970–2000) average climate data used as input to calculate energy potentials as available in the ISIMIP2b database.''' '''a''', Solar irradiance (kWh m<sup>-2</sup> day<sup>-1</sup>). '''b''', Temperature (°C). '''c''', Wind speeds (m s<sup>-1</sup>). '''d''', Run-off (kg km<sup>-2</sup> s<sup>-1</sup>). '''e''', Sugar and maize yields (crop selected with highest yield per cell) (%). '''f''', Lignocellulosic crop yields (switchgrass and Miscanthus) (%).]]<br />
<br />
The methodology assumes that part of the grid cell can be used for energy production, given its physical–geographic (terrain, habitation) and socio-geographical (location, acceptability) characteristics. This leads to an estimate of the geographical potential. Several of these factors are scenario-dependent. The geographical potential for biomass production, for example, is estimated using suitability factors taking considering competing land-use options and the harvested rain-fed yield of energy crop. Next, we assume that only part of the geographical potential can be used due to limited conversion efficiency and maximum power density, This result of accounting for these conversion efficiencies is referred to as the technical potential. The final step is to relate the technical potential to on-site production costs. Information at grid level is sorted and used as supply cost curves to reflect the assumption that the lowest cost locations are exploited first. Supply cost curves are used dynamically and change over time as a result of the learning effect.<br />
<br />
The calculation of each renewable energy potential is explained in detail in separate published articles. Here, a short explanation is given introducing each.<br />
<br />
Utility-scale PV and CSP starts with the theoretical potential based on a global solar irradiation map (kWh m<sup>-2</sup> day<sup>-1</sup>) ([[Hoogwijk, 2004]]; [[Köberle et al., 2015]]). This is subsequently restricted by excluding unsuitable areas (e.g. areas with snow cover or steep mountainous terrain) to calculate the geographical potential. The area that remains is further restricted by suitability factors. The idea behind suitability factors is that only part of the land is physically available for solar applications to ensure that it may keep the land-use function that it has, such as agricultural crop production. To calculate the technical potential, conversion efficiencies are assumed that are explained in method section ‘Climate impacts on renewable energy’.<br />
<br />
Rooftop PV builds on the method of utility-scale PV, using the theoretical and technical aspects, but differentiates on the geographical potential (). For rooftop PV, the geographical potential is determined according to roof area. This area is estimated by dividing the living area per household by the number of floors per household, both of which are based on census data. The estimates distinguish between urban areas and rural areas, and are combined with an urban/rural population map to scale down the estimated roof areas to grid level. The technical calculations are similar as the ones used to calculate utility-scale PV and explained in method section ‘Climate impacts on renewable energy’.<br />
<br />
Calculations of onshore and offshore wind energy potential start with wind speeds (m s<sup>-1</sup>) ([[Hoogwijk, 2004]]; [[Gernaat et al., 2014]]). Then, similar as for solar power, areas are excluded and further restricted according to suitability factors. For the remaining geographical area, based on wind data, the electricity output is calculated using a Weibull distribution function and power curve of the turbine. For details on offshore wind methodology see Supplementary Text 7-S2.<br />
<br />
Bio-energy potential calculations start with primary biomass production, represented through yields (t ha<sup>-1</sup> y<sup>-1</sup>) ([[Hoogwijk, 2004]]; [[Daioglou et al., 2019]]). Potential primary biomass sources include maize, sugar, and lignocellulosic crops (trees, switchgrass, and Miscanthus). Land availability for bio-energy production is limited by agricultural production following a ‘food-first’ principle where agricultural lands are determined first and are off-limits for biomass production. The technical potential is further limited by excluding forests, nature reserves and water stressed areas. In principle, bio-energy can be produced on remaining unprotected lands but also on abandoned agricultural lands. Besides energy crops, residues from agricultural and forestry can also be used as a feedstock. The costs of primary bio-energy crops are calculated with a Cobb-Douglas economic growth model using labour , land rent and capital costs as inputs <ref><div style="clear:both float:right">The Cobb–Douglas production function is a particular functional form of the production function widely used to represent the technological relationship between the amounts of two or more inputs, particularly physical capital and labor, and the amount of output that can be produced by those inputs.</div></ref>. The land costs are based on average regional income levels per km2, which was found to be a reasonable proxy for regional differences in land rent costs. The production functions are calibrated to empirical data .This technical potential is converted to several secondary energy carriers (solids, liquids, electricity, hydrogen) that compete in the energy system with other secondary energy carriers, such as fossil fuels or renewables ([[Daioglou et al., 2019]]) for a full description of biomass supply and demand in IMAGE) <ref>Daioglou, V., Doelman, J.C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2019. Integrated assessment of biomass supply and demand in climate change mitigation scenarios. ''Global Environmental Change'', ''54'', pp.88-101.</ref><ref>Daioglou, V., Stehfest, E., Wicke, B., Faaij, A. and Van Vuuren, D.P., 2016. Projections of the availability and cost of residues from agriculture and forestry. ''Gcb Bioenergy'', ''8''(2), pp.456-470.</ref> <ref>Daioglou, V., Doelman, J.C., Stehfest, E., Müller, C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2017. Greenhouse gas emission curves for advanced biofuel supply chains. ''Nature Climate Change'', ''7''(12), p.920.</ref>.<br />
<br />
Calculations of hydropower potential start with run-off (kg km<sup>-2</sup> s<sup>-1</sup>) that flows from high elevation to low elevation (representing discharge). On the basis of these discharge maps, >3.8 million site-specific hydropower installations were evaluated, at a 25km interval for every river between 56° S and 60° N (the excluded area is due to unavailable topographic data). At each site, high-resolution topographic data (3” × 3”) were used to calculate the cost-optimal dam dimensions and associated production potential. In this way, 60,000 suitable sites were identified, which together represent the remaining technical potential (see [[Gernaat et al., 2017]] for a full description of the site selection process). <br />
<br />
[[File:Technical potential maps renewables.png|thumb|617x617px|'''Global maps showing technical potential of renewable energy sources for 2010.''' Calculated with climate data from HadGEM2-ES (30y-average 1970-2000) and the suitability factors of Table 5-1. One cell has an area of 0.5°×0.5°. '''a,''' Solar PV (utility-scale PV) (based on Chapter 3 and Hoogwijk (2004), Köberle et al. (2015)). '''b''', CSP (based on Köberle et al. (2015)). '''c''', Wind (onshore and offshore) (based on Chapter 2 and Hoogwijk (2004)). '''d''', Hydropower (defined as: remaining technical potential, explained and based on Chapter 4). '''e''', 1<sup>st</sup> generation bio-energy (based on Daioglou et al. (2019), Hoogwijk (2004)). '''f''', 2<sup>nd</sup> generation bio-energy (based on Daioglou et al. (2019), Hoogwijk (2004)). Note that the scales are different.]]<br />
<br />
The maps on technical potential for all renewables are combined with economic information to generate cost-supply curves. Assumptions on cost can be found in the separate articles but the general methodology is as follows. Each technology requires an investment before it can produce energy. This investment (in USD) is divided by the annual production (kWh) to calculate the production cost (USD kWh<sup>-1</sup>). This yields two global maps, a technical potential map (kWh) and a production cost map (USD kWh<sup>-1</sup>). Together they are used to generate a cost-supply curve, by sorting (in ascending order) the cells in the production cost map while simultaneously adding the same cells from the technical potential map.<br />
<br />
<div class="version newv31"></div><references /><br />
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<div>{{ComponentDescriptionTemplate<br />
|Reference=Hertel, 1997; Britz, 2003; Armington, 1969; Huang et al., 2004; Helming et al., 2010; Banse et al., 2008; Bruinsma, 2003; Woltjer et al., 2011; Van Meijl et al., 2006; Eickhout et al., 2009; Overmars et al., 2014; Alexandratos and Bruinsma, 2012;<br />
}}<br />
<div class="page_standard"><br />
<br />
The MAGNET model ([[Woltjer et al., 2011]]; [[Woltjer et al., 2014]]) is based on the standard GTAP model ([[Hertel, 1997]]), which is a multi-regional, static, applied computable general equilibrium ({{abbrTemplate|CGE}}) model based on neoclassical microeconomic theory. Although the model covers the entire economy, there is a special focus on agricultural sectors. It is a further development of GTAP regarding land use, household consumption, livestock, food, feed and energy crop production, and emission reduction from deforestation.<br />
<br />
===Demand and supply===<br />
Household demand for agricultural products is calculated based on changes in income, income elasticities, preference shift, price elasticities, cross-price elasticities, and the commodity prices arising from changes in the supply side. Demand and supply are balanced via prices to reach equilibrium. Income elasticities for agricultural commodities are consistent with FAO estimates ([[Britz, 2003]]), and dynamically depend on purchasing power parity ({{abbrTemplate|PPP}}) corrected GDP per capita. The supply of all commodities is modelled by an input–output structure that explicitly links the production of goods and services for final consumption via different processing stages back to primary products (crops and livestock products) and resources. At each production level, input of labour, capital, and intermediate input or resources (e.g., land) can be substituted for one another. For example, labour, capital and land are input factors in crop production, and substitution of these production factors is driven by changes in their relative prices. If the price of one input factor increases, it is substituted by other factors, following the price elasticity of substitution.<br />
<br />
===Regional aggregation and trade=== <br />
MAGNET is flexible in its regional aggregation (129 regions). In linking with IMAGE, MAGNET closely matches the regions in IMAGE (Figure [[Region classification map|IMAGE regions]]). Similar to most other {{abbrTemplate|CGE}} models, MAGNET assumes that products traded internationally are differentiated according to country of origin. Thus, domestic and foreign products are not identical, but are imperfect substitutes (Armington assumption; [[Armington, 1969]]). <br />
<br />
===Land use===<br />
In addition to the standard [[GTAP database|GTAP model]], MAGNET includes a dynamic land-supply function ([[Van Meijl et al., 2006]]) that accounts for the availability and suitability of land for agricultural use, based on information from IMAGE (see below). A nested land-use structure accounts for the differences in substitutability of the various types of land use ([[Huang et al., 2004]]; [[Van Meijl et al., 2006]]). In addition, MAGNET includes international and EU agricultural policies, such as production quota and export/import tariffs ([[Helming et al., 2010]]). <br />
<br />
===Biofuel crops===<br />
MAGNET includes ethanol and biodiesel as first-generation biofuels made from wheat, sugar cane, maize, and oilseeds ([[Banse et al., 2008]]) and the use of by-products ({{abbrTemplate|DDGS}}, oilcakes) from biofuel production in the livestock sector.<br />
<br />
===Livestock===<br />
MAGNET distinguishes the livestock commodities of beef cattle, dairy cattle, other cattle (sheep & goats), dairy cattle, poultry, and pig and other animal products. The first three are the ruminant sectors which are grass and crop fed, while the poultry and pigs sectors are crop fed. Modelling the livestock sector includes different feedstuffs, such as feed crops, co-products from biofuels (oil cakes from rapeseed-based biofuel, or distillers grain from wheat-based biofuels), and grass ([[Woltjer, 2011]]). Grass may be substituted by feed from crops for ruminants. <br />
<br />
===Land supply===<br />
In MAGNET, land supply is calculated using a land-supply curve that relates the area in use for agriculture to the land price. Total land supply includes all land that is potentially available for agriculture, where crop production is possible under soil and climatic conditions, and where no other restrictions apply such as urban or protected area designations (see also Component Land-use allocation). In the IMAGE model, total land supply for each region is obtained from potential crop productivity and land availability on a resolution of 5x5 arcminutes. The supply curve depends on total land supply, current agricultural area, current land price, and estimated price elasticity of land supply in the starting year. Recently, the earlier land supply curve ([[Eickhout et al., 2009]]) has been updated with a more detailed assessment of land resources and total land supply in IMAGE ([[Mandryk et al., 2015]]), and with literature data on current price elasticities. Regions differ with regard to the proportion of land in use, and with regard to change in land prices in relation to changes in agricultural land use.<br />
<br />
===Reduced land availability===<br />
By restricting land supply in IMAGE and MAGNET, the models can assess scenarios with additional protected areas, or reduced emissions from deforestation and forest degradation ({{abbrTemplate|REDD}}). These areas are excluded from the land supply curve in MAGNET, leading to lower elasticities, less land-use change and higher prices, and are also excluded from the allocation of agricultural land in IMAGE (e.g., [[Overmars et al., 2014]]).<br />
<br />
===Intensification of crop and pasture production===<br />
Crop and pasture yields in MAGNET may change as a result of the following four processes:<br />
# autonomous technological change (external scenario assumption); <br />
# intensification due to the substitution of production factors (endogenous);<br />
# climate change (from IMAGE);<br />
# change in agricultural area affecting crop yields (such as, decreasing average yields due to expansion into less suitable regions; from IMAGE).<br />
<br />
Biophysical yield effects due to climate and area changes are calculated by the IMAGE crop model and communicated to MAGNET. Likewise, also the potential yields and thus the yield gap can be assessed with the crop model in IMAGE. External assumptions on autonomous technological changes are mostly based on FAO projections ([[Alexandratos and Bruinsma, 2012]]) [ Update REF FAO 2018 ], which describe per region and commodity, the assumed future changes in yields for a wide range of crop types. In MAGNET, the biophysical yield changes are combined with the autonomous technological change to give the total exogenous yield change. In addition, during the simulation period, MAGNET calculates an endogenous intensification as a result of price-driven substitution between labour, land and capital. In IMAGE, regional yield changes due to autonomous technological change and endogenous intensification according to MAGNET are used in the spatially explicit allocation of land use (Component [[Land-use allocation]]).<br />
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|Reference=Van Asselen and Verburg, 2013; Alexandratos and Bruinsma, 2012; Klein Goldewijk et al., 2010; O'Neill, 2013; Lambin et al., 2000; IIASA and FAO, 2012; Nelson, 2008; Klein Goldewijk et al., 2011; Letourneau et al., 2012; Doelman et al., 2018;<br />
}}<div class="page_standard"><br />
<br />
IMAGE 3.0 uses a regression-based suitability assessment to determine future land-use patterns. Optionally, the IMAGE allocation module can be coupled to CLUMondo ([[Van Asselen and Verburg, 2013]]) providing a more detailed representation of land-use systems and their dynamics.<br />
<br />
Land-use allocation is driven by regional crop and grassland production and their respective intensity levels, as calculated by the IMAGE agro-economic model ([[Agricultural economy]]). Agricultural land use is allocated to grid cells in an iterative process until the required regional production of crops and grass is met. Land use in IMAGE is modelled using dominant land use per grid cell on a 5 x 5 minute resolution, distinguishing extensive grasslands, agricultural and non-agricultural grid cells, and within agricultural land areas fractions of grass, seven rain-fed and seven irrigated crop types, and bioenergy crops.<br />
<br />
In each time step, maps of actual crop yields are computed by combining the potential crop and grassland yields calculated by the crop model ([[Crops and grass]]), and the regional management intensity from the agro-economic model ([[Agricultural economy]]). Starting with the land-cover and land-use map of the previous time step, actual yields are used to determine crop and grassland production on current agricultural land. This is compared to the required regional crop and grassland production. If the demand exceeds calculated production, the agricultural area needs to be expanded at the cost of natural vegetation. If the calculated production of current cropland exceeds the required production, agricultural land is abandoned to adjust to the production required.<br />
<br />
Crop and grassland is either abandoned or expanded until the required production is met. Since actual yields are taken into account, changes in crop yields in time due to technological change, climate change and land heterogeneity are included. If yields in the new agricultural areas are lower than average in the current area, relatively more agricultural land is required compared to the production increase.<br />
<br />
In determining the location of agricultural expansion or abandonment, all grid cells are assessed and ranked on suitability, based on an empirical regression analysis, and optionally based on [[CLUMondo model|CLUMondo]] (see additional page:[[Model description IMAGE-CLUMondo]]).<br />
<br />
Additionally, a few other rules are applied in determining the location of new agricultural land. For instance, agricultural expansion is not permitted in protected areas, and in areas otherwise protected, such as in assumed {{abbrTemplate|REDD}} (reducing emissions from deforestation and degradation) schemes. A grid cell is only regarded suitable for agriculture if the potential rain-fed production is at least 10% of the global maximum attainable crop yield. Grid cells with a production potential between 0.01 and 10% of the maximum attainable are still assumed suitable for extensive grassland.<br />
<br />
Irrigated areas are increased on a regional scale, prescribed by external scenario dependent assumptions, such as based on FAO ([[Alexandratos and Bruinsma, 2012]]). In each time-step, more irrigated areas are allocated in agricultural land based on the need for irrigation (the difference in rain-fed and irrigated yields), and water availability.<br />
<br />
In agricultural areas, the fraction of specific crops is determined based on the initial fractions, and modified annually based on changes in regional demand and local crop yields. As a result, the land-use fraction of a certain crop increases when the demand for this crop increases faster than for other crops, or if the potential yield in this grid cell increases more than for other crops. <br />
<br />
The land use allocation model enables new land-use and land cover maps to be created ([[Land cover and land use]]). These land-use maps specify agricultural land, extensive grassland, and, land for sustainable bio-energy production. Crop fractions are allocated for 16 food and other non-energy crop types in IMAGE (wheat, rice, maize, tropical cereals, other temperate cereals, pulses, soybeans, temperate oil crops, tropical oil crops, temperate roots & tubers, tropical roots & tubers, sugar crops, palm oil, vegetables & fruits, other non-food, plant-based fibres, both rain-fed and irrigated), for grass and for five dedicated bio-energy crop types (sugar cane, maize, oil crops, wood biomass and grass biomass). These data are calculated on a 5 minute resolution, and aggregated to proportional land use on 30 minute resolution of the carbon, crop and water model [[LPJmL model|LPJmL]].<br />
<br />
==Empirical regression analysis to determine land use suitability==<br />
<br />
Land-use change is determined by various factors, such as climate and climate variability, soil and terrain characteristics, and socio-economic variables, such as population density and accessibility ([[O'Neill, 2013]]). Land-use change dynamics differ substantially between regions ([[Lambin et al., 2000]]). These characteristics are taken into account in IMAGE 3.0 in a regional suitability assessment based on an empirical multiple linear regression analysis.<br />
The suitability assessment includes data on two biophysical determinants: the potential yield which covers effects of climate and soil ([[Crops and grass]]), and [[Slope - grid|the terrain slope index]] based on {{abbrTemplate|SRTM}} elevation data (Shuttle Radar Topography Mission) from NASA. Two socio-economic determinants are included: population density ([[Klein Goldewijk et al., 2010]]), and the accessibility index from JRC ([[Nelson, 2008]]), which is defined as minutes travel time to major cities (>50,000 inhabitants). <br />
<br />
These four independent variables are used in multiple linear regression analysis to investigate the relationship between these land-use determinants and current land use (fractions of crop and grassland in 2005 from [[Klein Goldewijk et al., 2011]]). The analysis is performed separately for each IMAGE region, and takes into account the logarithmic relationship found for all independent variables except for potential crop yield.<br />
<br />
For each region, between two and four variables are found to be significant explanatory factors for 2005 land use. For example, population density is a significant determinant in almost all regions. Terrain slope is a key determinant in many regions, including North America, Europe and Asia; accessibility in South America, Africa and Australia; and potential yield in the Americas, Europe and North Africa.<br />
<br />
The region-specific regression models are used in IMAGE to calculate the suitability of land areas in annual time-steps. In addition to the suitability assessment, a random factor is included to account for inherent uncertainty and non-deterministic behaviour of land-use change processes, allowing the emergence of new agricultural patches. Agricultural land is expanded according to the final suitability ranking. Extensive pastures located in areas where the natural vegetation is grassland are assumed to be rather constant over time, and thus do not expand and are only abandoned as a result of climate change.<br />
<br />
Land use in IMAGE is modelled using dominant land use types per grid cell on a 5 x 5 minute resolution. In reality, land use is more heterogeneous. For some applications, dominant land use on 5 x 5 minute resolution, or the derived proportional land use on a 30 x 30 minute resolution may be sufficient. However, many applications require higher resolution and additional data, such as studies on biodiversity and agricultural intensification ([[Verburg et al., 2013]]).<br />
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===Model core===<br />
[[GLOFRIS model|GLOFRIS]] estimates the effect of land cover and climate change on global flood risks in river catchments and coastal areas ([[Winsemius et al., 2012]]; [[Ward et al., 2013]]). Global flood risks are expressed as the projected number of people affected annually and as GDP value. GLOFRIS uses land-cover input from IMAGE and climate time series, such as the IPCC GCM projections. These input data drive the global hydrological model, [[PCR-GLOBWB model|PCR-GLOBWB]], the computational core of the module. PCR-GLOBWB calculates where and when flooding events may occur, and calculates the inundation extent and inundation depth needed to estimate flood risks. PCR-GLOBWB has features, namely daily time steps and proper accounting of the relationship between non-linear soil moisture and run-off, that make it appropriate for simulating flooding events. The spatial resolution currently used by the model is 0.5x0.5 degrees. The model steps of GLOFRIS are shown in the flowchart.<br />
<br />
===Land cover===<br />
Basis for the parameters in PCR-GLOBWB is the land-cover map Global Land Cover Characterization (GLCC), which express the hydrological characteristics of various land-cover types. IMAGE and PCR-GLOBWB are linked by lookup tables that translate the IMAGE land-cover classification into that of GLCC ([[Loveland et al., 2000]]). <br />
<br />
PCR-GLOBWB requires data on daily precipitation, potential evaporation and temperature that are consistent with the IMAGE scenario ([[Van Beek et al., 2011]]; [[Wada et al., 2011]]). Daily data are required because these reflect inter-monthly and inter-annual climate variability and the effect on flood risk. <br />
<br />
===Flood hazard===<br />
PCR-GLOBWB includes a routing component on river flooding that estimates inundation proportions and average inundation depths on a time-step basis to estimate flood risk. GLOFRIS scenarios typically cover a 30-year or longer climatological model run. From this time series, annual extreme values of the inundated proportions and water depths are derived and summarised in an extreme value probability distribution. This probability distribution is subsequently used for annual projections on the damage of flood risk. <br />
<br />
GLOFRIS estimates flood risk on two scales 0.5x0.5 degrees for global analyses, and 1x1 km<sup>2</sup> for specific case studies. On a global scale, the extreme value probability distribution is directly combined with data on population and GDP, using a linear flood level–damage relationship. Thus for each year of simulation, the most extreme water level and inundated proportion from PCR-GLOBWB is used to calculate the maximum damage (in GDP or population) per grid cell. <br />
<br />
An algorithm is implemented to scale down the 0.5x0.5 degrees maps of the extent and depth of annual maximum inundation to 1x1 km<sup>2</sup>, using a high-resolution digital elevation model. A scale down is needed because the spatial variability of flood hazards and flood exposure may be large and not well represented on the coarser scales in IMAGE and PCR-GLOBWB. A more accurate estimation of flood risk is obtained by converting the results to a higher resolution. The downscaling procedure may also include the risk of coastal flooding (see the flowchart, bottom).<br />
<br />
===Downscaling===<br />
For scaling down in river catchments, annual extreme values of inundation depths and proportions are transformed to bank-full volumes and excess volumes per 0.5 degree cell. The bank-full volume represents the volumetric capacity of a river channel in a grid cell and is estimated according to flood volume in a user-defined return period in which flood volumes do not exceed the bank-full volume (return period threshold in the flowchart, bottom) under current climate and land-cover conditions. The excess bank-full volume for each year is scaled down by estimating a water level from identified river pixels. This is determined by the user-defined stream threshold (see the flowchart, bottom) that generates a flood volume in the surrounding connected pixels, resulting in the same flood volume estimated from the 0.5x0.5 degree results. The method is mass conservative with respect to the PCR-GLOBWB results on 0.5x0.5 degree cells. <br />
<br />
===Coastal flood===<br />
Coastal flood hazard maps are established using the [[DIVA model|DIVA]] database. DIVA contains estimates on 1-, 10-, 100- and 1000-year water levels along a large number of coasts worldwide ([[Hinkel and Klein, 2009]]). These coastal flood probabilities are combined with those on river flooding by finding the upstream connected pixels on the high-resolution elevation map that are lower than the coastal water levels. It is assumed that the height of a wave reduces as it moves inland and that the water spreads over the surface, resulting in lower water levels inland than on the coast.<br />
<br />
===Flood risk===<br />
After the high-resolution flood hazard maps have been established, the annual extreme values can be combined to form average annual flood hazard maps and flood risk maps. At this scale, more local detail can be added about cropland locations, high-resolution maps on population and GDP and other exposure data of interest. The resulting flood hazard maps can be combined with these high-resolution maps and, if possible, in more localised damage models. <br />
<br />
More information about GLOFRIS, its underlying models and methods, and the downscaling module is available in [[Winsemius et al., 2012]] and [[Ward et al., 2013]].<br />
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<div>{{ComponentDescriptionTemplate<br />
|Reference=IPCC, 2006; Cofala et al., 2002; Stern, 2003; Smith et al., 2005; Van Ruijven et al., 2008; Carson, 2010; Smith et al., 2011; Bouwman et al., 1993; Velders et al., 2009; Kreileman and Bouwman, 1994; Bouwman et al., 1997; Bouwman et al., 2002a; Velders et al., 2009; Harnisch et al., 2009; Braspenning Radu et al., 2016; Velders et al., 2015; Lucas et al., 2007; Harmsen et al., 2019c; EC-JRC/PBL, 2016; Hoesly et al., 2018; Rao et al., 2016; Rao et al., 2017;<br />
}}<br />
<div class="page_standard"><br />
<br />
===General approaches===<br />
Air pollution emission sources included in IMAGE are listed in [[Emission table]], and emissions transported in water (nitrate, phosphorus) are discussed in Component [[Nutrients]]. In approach and spatial detail, gaseous emissions are represented in IMAGE in four ways: <br />
<br />
1) ''World number (W)''<br />
<br />
:The simplest way to estimate emissions in IMAGE is to use global estimates from the literature. This approach is used for natural sources that cannot be modelled explicitly ([[Emission table]]).<br />
<br />
2) ''Emission factor (EF)'' <br />
<br />
:Past and future developments in anthropogenic emissions are estimated on the basis of projected changes in activity and emissions per unit of activity (Figure Flowchart). <br />
<br />
:The equation for this emission factor approach is:<br />
::<math>Emission_{r,i,t} = Activity_{r,i,t} * EFbase_{r,i,t} * AF_{r,i,t}</math> (Equation 1) <br />
:where:<br />
:* <math>Emission</math> is the emission of the specific gas or aerosol;[[Amann et al., 2011]] <br />
:* <math>Activity</math> is the energy input or agricultural activity; r is the index for region; <br />
:* <math>i</math> is the index for further specification (sector, energy carrier); <br />
:* ''t'' is the index for time (years). All factors are time-dependent; <br />
:* <math>EFbase</math> is the emission factor in the baseline; <br />
:* <math>AF</math> is the abatement factor (reduction in the baseline emission factor as a result of climate policy). <br />
:<br />
:<br />
:Following Equation 1, there is a direct relationship between level of economic activity and emission level. Shifts in economic activity (e.g., use of natural gas instead of coal) may influence total emissions. These activities are calculated in various sub-modules of the model (e.g. related to energy supply and demand, food production and industry). Finally, emissions can change as a result of changes in emission factors (EF) and climate policy (AF). Emission factors indicate the emission rate of an air pollutant per activity level and differences in time represent technological changes in air pollution control. This information is generally obtained from specialized research groups and databases, such as GAINS ([[Amann et al., 2011]]) , EDGAR ([[EC-JRC/PBL, 2016]]) and CEDS ([[Hoesly et al., 2018]]). Abatement factors are applied to non-CO<sub>2</sub> greenhouse gases (methane (CH<sub>4</sub>), nitrous oxide (N<sub>2</sub>O) and fluorinated gases (F-gases: HFCs, PFCs and SF<sub>6</sub>)) and are determined in the climate policy model FAIR (see Component [[Climate policy]]). <br />
:The emission factor approach has some limitations, the most important of which is not capturing the consequences of specific emission control technologies (or management action) for multiple species, either synergies or trade-offs. <br />
: <br />
3) ''Gridded emission factor with spatial distribution (GEF)''<br />
<br />
:GEF is a special case of the EF method, where a proxy distribution is used to present gridded emissions. This is done for a number of sources, such as emissions from livestock ([[Emission table]]).<br />
<br />
4) ''Gridded process model (GPM)'' <br />
:Land-use related emissions of NH<sub>3</sub>, N<sub>2</sub>O and NO are calculated with grid-specific models (Figure Flowchart). The models included in IMAGE are simple regression models that generate an emission factor (Figure Flowchart). For comparison with other models, IMAGE also includes the N<sub>2</sub>O methodology generally proposed by {{abbrTemplate|IPCC}} ([[IPCC, 2006]]).<br />
<br />
The approaches used to calculate emissions from energy production and use, industrial processes and land-use related sources are discussed in more detail below.<br />
<br />
===Emissions from energy production and use===<br />
Emission factors ([[#General approaches|Equation 1]]) are used for estimating emissions from the energy-related sources ([[Emission table]]). In general, the Tier 1 approach from IPCC guidelines ([[IPCC, 2006]]) is used. In the energy system, emissions are calculated by multiplying energy use fluxes by time-dependent emission factors. Changes in emission factors represent, for example, technology improvements and end-of-pipe control techniques, fuel emission standards for transport, and clean-coal technologies in industry.<br />
<br />
The emission factors for the historical period for the energy system and industrial processes are calibrated with the EDGAR emission model described by Braspenning Radu et al. ([[Braspenning Radu et al., 2016]]). Calibration to the EDGAR database is not always straightforward because of differences in aggregation level. The general rule is to use weighted average emission factors for aggregation. However, where this results in incomprehensible emission factors (in particular, large differences between the emission factors for the underlying technologies), specific emission factors were chosen. <br />
<br />
Future emission factors are based on the following rules:<br />
<br />
* Emission factors can follow an exogenous scenario, which can be based on the storyline of the scenario. In some cases, exogenous emission factor scenarios are used, such as those developed by GAINS/IIASA ([[Amann et al., 2011]]). These represent the policies in different regions for the 2000–2030 and 2030-2100 periods. Future emission factors for air pollutants have been set in accordance with the SSP storylines ([[Rao et al., 2016]]). <br />
<br />
* Alternatively, emission factors can be derived from generic rules, one of which in IMAGE is the {{abbrTemplate|EKC}}: Environmental Kuznets Curve ([[Stern, 2003]]; [[Smith et al., 2005]]; [[Van Ruijven et al., 2008]]; [[Carson, 2010]]; [[Smith et al., 2011]]). EKC suggests that starting from low-income levels, per-capita emissions will increase with increasing per-capita income and will peak at some point and then decline. The last is driven by increasingly stringent environmental policies, and by shifts within sectors to industries with lower emissions and improved technology. Although such shifts do not necessarily lead to lower absolute emissions, average emissions per unit of energy use decline. See below, for further discussion of EKC.<br />
<br />
* Combinations of the methods described above for a specific period, followed by additional rules based on income levels. <br />
<br />
In IMAGE, {{abbrTemplate|EKC}} is used as an empirically observed trend, as it offers a coherent framework to describe overall trends in emissions in an Integrated Assessment context. However , it is accepted that many driving forces other than income influence future emissions. For instance, more densely populated regions are likely to have more stringent air quality standards. Moreover, technologies developed in high-income regions often tend to spread within a few years to developing regions. The generic equations in IMAGE can capture this by decreasing the threshold values over time. For CO<sub>2</sub> and other greenhouse gases, such as halogenated gases for which there is no evidence of {{abbrTemplate|EKC}} behaviour, IMAGE uses an explicit description of fuel use and deforestation.<br />
<br />
The methodology for EKC scenario development applied in the energy model is based on two types of variables: income thresholds (2–3 steps); and gas- and sector-dependent reduction targets for these income levels. The income thresholds are set to historical points: the average {{abbrTemplate|OECD}} income at which air pollution control policies were introduced in these countries; and current income level in OECD countries. The model assumes that emission factors will start to decline in developing countries, when they reach the first income threshold, reflecting more efficient and cleaner technology. It also assumes that when developing countries reach the second income threshold, the emission factors will be equal to the average level in OECD regions. Beyond this income level, the model assumes further reductions, slowly converging to the minimum emission factor in OECD regions by 2030, according to projections made by {{abbrTemplate|IIASA}} under current legislation (current abatement plans). The IMAGE rules act at the level of regions, this could be seen as a limitation, but as international agreements lead countries to act as a group, this may not be an important limitation.<br />
<br />
===Emissions from industrial processes===<br />
For the industry sector, the energy model includes three categories:<br />
<br />
# Cement and steel production. IMAGE-TIMER includes detailed demand models for these commodities (Component [[Energy supply and demand]]). Similar to those from energy use, emissions are calculated by multiplying the activity levels to exogenously set emission factors.<br />
# Other industrial activities. Activity levels are formulated as a regional function of industry value added, and include copper production and production of solvents. Emissions are also calculated by multiplying the activity levels by the emission factors.<br />
# For halogenated gases, the approach used was developed by Harnisch et al. ([[Harnisch et al., 2009|2009]]), which derived relationships with income for the main uses of halogenated gases (HFCs, PFCs, SF<sub>6</sub>). In the actual use of the model, slightly updated parameters are used to better represent the projections as presented by Velders et al. ([[Velders et al., 2009|2009]], [[Velders et al., 2015|2015]]). The marginal abatement cost curve per gas still follows the methodology described by Harnisch et al. ([[Harnisch et al., 2009|2009]]).<br />
<br />
===Land-use related emissions===<br />
CO<sub>2</sub> exchanges between terrestrial ecosystems and the atmosphere computed by the LPJ model are described in [[Carbon cycle and natural vegetation]]. The land-use emissions model focuses on emissions of other compounds, including greenhouse gases (CH<sub>4</sub>, N<sub>2</sub>O), ozone precursors (NO<sub>x</sub>, CO, NMVOC), acidifying compounds (SO<sub>2</sub>, NH<sub>3</sub>) and aerosols (SO<sub>2</sub>, NO<sub>3</sub>, BC, OC).<br />
<br />
For many sources, the emission factor ([[#General approaches|Equation 1]]) is used ([[Emission table]]). Most emission factors for anthropogenic sources are from the [[EDGAR database]], with time-dependent values for historical years. In the scenario period, most emission factors are constant, except for explicit climate abatement policies (see below). <br />
<br />
There are some other exceptions: Various land-use related gaseous nitrogen emissions are modelled in grid-specific models (see further), and in several other cases, emission factors depend on the assumptions described in other parts of IMAGE. For example, enteric fermentation CH<sub>4</sub> emissions from non-dairy and dairy cattle are calculated on the basis of energy requirement and feed type (see Component [[Livestock systems]]). High-quality feed, such as concentrates from feed crops, have a lower CH<sub>4</sub> emission factor than feed with a lower protein level and a higher content of components of lower digestibility. This implies that when feed conversion ratios change, the level of CH<sub>4</sub> emissions will automatically change. Pigs, and sheep and goats have IPCC 2006 emission factors, which depend on the level of development of the countries. In IMAGE, agricultural productivity is used as a proxy for the development. For sheep and goats, the level of development is taken from EDGAR.<br />
<br />
Constant emission factors may lead to decreasing emissions per unit of product, for example, when the emission factor is specified on a per-head basis. An increasing production per head may lead to a decrease in emissions per unit of product. For example, the CH<sub>4</sub> emission level for animal waste is a constant per animal, which leads to a decrease in emissions per unit of meat or milk when production per animal increases.<br />
<br />
A special case is N<sub>2</sub>O emissions after forest clearing. After deforestation, litter remaining on the soil surface as well as root material and soil organic matter decompose in the first years after clearing, which may lead to pulses of N<sub>2</sub>O emissions. To mimic this effect, emissions in the first year after clearing are assumed to be five times the flux in the original ecosystem. Emissions decrease linearly to the level of the new ecosystem in the tenth year, usually below the flux in the original forest. For more details, see Kreileman and Bouwman ([[Kreileman and Bouwman, 1994|1994]]).<br />
<br />
Land-use related emissions of NH<sub>3</sub>, N<sub>2</sub>O and NO are calculated withgrid-specific models.N<sub>2</sub>O from soils under natural vegetation is calculated with the model developed by Bouwman et al. (1993). This regression model is based on temperature, a proxy for soil carbon input, soil water and oxygen status, and for net primary production. Ammonia emissions from natural vegetation are calculated from net primary production, C:N ratio and an emission factor. The model accounts for in-canopy retention of the emitted NH<sub>3</sub> ([[Bouwman et al., 1997]]).<br />
<br />
For N<sub>2</sub>O emissions from agriculture, the determining factors in IMAGE are N application rate, climate type, soil organic carbon content, soil texture, drainage, soil pH, crop type, and fertiliser type. The main factors used to calculate NO emissions include N application rate per fertiliser type, and soil organic carbon content and soil drainage (for detailed description, see Bouwman et al. ([[Bouwman et al., 2002a|2002a]]). For NH<sub>3</sub> emissions from fertilised cropland and grassland, the factors used in IMAGE are crop type, fertiliser application rate per type and application mode, temperature, soil pH, and CEC ([[Bouwman et al., 2002a]]).<br />
<br />
For comparison with other models, IMAGE also includes the N<sub>2</sub>O methodology proposed by IPCC ([[IPCC, 2006|2006]]). This methodology represents only anthropogenic emissions. For emissions from fertilizer fields this is the emission from a fertilized plot minus that from a control plot with zero fertilizer application. For this reason, soil emissions calculated with this methodology cannot be compared with the above model approaches, which yields total N<sub>2</sub>O emissions.<br />
<br />
===Emission abatement===<br />
Emissions from energy, industry, agriculture, waste and land-use sources are also expected to vary in future years, as a result of climate policy. This is described using abatement coefficients, the values of which depend on the scenario assumptions and the stringency of climate policy described in the climate policy component. In scenarios with climate change or sustainability as the key feature in the storyline, abatement is more important than in business-as-usual scenarios. Abatement factors are used for CH<sub>4</sub> emissions from fossil fuel production and transport, N<sub>2</sub>O emissions from transport, CH<sub>4</sub> emissions from enteric fermentation and animal waste, and N<sub>2</sub>O emissions from animal waste according to the IPCC method. These abatement files are calculated in the IMAGE climate policy sub-model FAIR (Component [[Climate policy]]) by comparing the costs of non-CO<sub>2</sub> abatement in agriculture and other mitigation options.<br />
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<div>{{ComponentDescriptionTemplate<br />
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<br />
===Livestock production===<br />
IMAGE distinguishes two livestock production systems, namely pastoral systems, and mixed and industrial systems, based on FAO ([[Seré and Steinfeld, 1996]]). Pastoral systems are mostly dominated by extensive ruminant production, while mixed and industrial systems are more intensive with animal husbandry comprising grazing ruminants and monogastrics. The distribution of livestock production in the two systems is constructed from historical data for the years up to the present, and for future years will depend on the scenario selected.<br />
<br />
===Livestock===<br />
IMAGE distinguishes five types of livestock: beef, dairy cattle (large ruminants), the category sheep & goats (small ruminants), pigs, and poultry (monogastrics). The numbers of animals and the proportion per production system are calculated from data on domestic livestock production per region provided by the agro-economic model MAGNET ([[Agricultural economy]]). The number of animals in each of the five livestock types is calculated from the total production per region and the characteristics of the livestock systems in that region. <br />
Stocks of dairy cows (POP) per country and world region are obtained from total milk production (PROD) and milk production per animal (MPH).<br />
<math> POP = PROD / MPH </math><br />
<br />
Animal stocks per region of beef cattle, pigs, and sheep and goats are obtained from production and carcass weight (CW) and off-take rate (OR):<br />
<math>POP = PROD/(OR*CW)</math><br />
<br />
Historical data on milk production per cow, off-take rate, and carcass weight are obtained from statistics, and values for future years will depend on the scenario selected.<br />
<br />
===Energy requirements===<br />
For dairy cattle, the energy requirements are calculated for maintenance (based on body weight), feeding (based on the proportion of grass in feed rations), lactation (based on milk production per cow) and pregnancy (based on the number of calves per year). The amount of feed dry matter is calculated on the basis of the proportion of digestible energy in the total energy intake, and the energy content of biomass.<br />
<br />
Energy requirements for cattle are based on animal activity and production, and for pigs, poultry, sheep and goats on Feed Conversion Ratios (FCR). This is the amount of feed (kg dry matter) required to produce one kilogram of milk or meat. The {{AbbrTemplate|FCR}} values are based on historical data and values for future years will depend on the scenario selected.<br />
<br />
===Cropland and grassland required===<br />
Areas for feed crop production and grass are calculated on the basis of feed crop and grass requirements ([[Land-use allocation]]), which are calculated from total feed requirement and diet composition (feed rations, see below). <br />
Composition of animal feed<br />
IMAGE distinguishes five feed categories: <br />
#grass, including hay and grass silage; <br />
#food crops and processing by-products; <br />
#crop residues in the field after harvesting, and fodder crops; <br />
#animal products; <br />
#foraging including roadside grazing, scavenging household waste, and feedstuffs from backyard farming.<br />
<br />
In pastoral ruminant production systems, the feed is almost entirely grass except in developing regions where foraging constitutes a larger but variable proportion of the total feed. Pigs and poultry are fed feed crops and by-products, crop residues and fodder. Since these animals are mainly farmed in mixed systems, the contribution of feed crops and residues to the total feed in these systems is much higher than in pastoral systems.<br />
<br />
The required feed crop production per animal is calculated from feed rations, and this information is incorporated into the agro-economic model ([[Agricultural economy]]). The proportion of grass in feed rations determines total grass consumption. The amount of grassland area required, and the corresponding grazing intensity are based on the[[Agricultural economy]] module.<br />
<br />
===Scenario definition===<br />
A scenario includes assumptions on milk production per animal for dairy cattle, carcass weight and off-take rate for beef cattle, pigs, poultry, sheep and goats, and feed conversion rates ({{abbrTemplate|FCR}}) for pigs, poultry, sheep and goats. The changes in these parameters are generally based on the scenario, and on the economic growth scenario.<br />
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<br />
===Vegetation types===<br />
LPJmL is a Dynamic Global Vegetation Model ({{abbrTemplate|DGVM}}) that was developed initially to assess the role of the terrestrial biosphere in the global carbon cycle ([[Prentice et al., 2007]]). DGVMs simulate vegetation distribution and dynamics, using the concept of multiple plant functional types ({{abbrTemplate|PFT}}s) differentiated according to their bioclimatic (e.g. temperature requirement), physiological, morphological, and phenological (e.g. growing season) attributes, and competition for resources (light and water). <br />
<br />
To aggregate the vast diversity of plant species worldwide, with respect to major differences relevant to the carbon cycle, [[LPJmL model|LPJmL]] distinguishes nine plant functional types. These include e.g. tropical evergreen trees, temperate deciduous broad-leaved trees and C3 herbaceous plants. Plant dynamics are computed for each PFT present in a grid cell. As IMAGE uses the concept of biomes (natural land cover types), combinations of PFTs in an area/grid cell are translated into a natural land cover (biome) type (see [[Plant functional types and natural land cover types]]).<br />
<br />
===Carbon dynamics===<br />
IMAGE-LPJmL covers the carbon cycle processes, and tracks all carbon fluxes between the atmosphere and the biosphere. Carbon cycle dynamics of the terrestrial biosphere are computed as carbon uptake and release in plants (photosynthesis, autotrophic respiration), transfer of plant carbon to the soil (shedding of leaves, turnover, mortality) and mineralisation of soil organic matter (heterotrophic respiration; see Figure Flowchart). Because these processes are closely related to weather conditions, they are computed in daily time steps. <br />
<br />
The composition of natural vegetation depends on slower processes, such as the inter-annual and inter-seasonal variability in weather conditions and disturbances, such as natural fires. Thus, vegetation dynamics including competition between plant functional types, mortality, turnover, and fire disturbances are computed in annual time steps. <br />
<br />
Allocation of newly established biomass is computed in annual time steps for perennial plants (natural grasses, trees) and in daily time steps for annual plants (crops). Allocation to plant organs (represented by a carbon pool for each) distinguishes up to four living plant carbon pools, depending on plant type. For grasses, the model distinguishes carbon pools of leaves and roots only, and for trees, there are two additional woody carbon pools (hardwood and sapwood). For agricultural crops, the pools are categorised as leaves, roots, storage organs, stems, and a mobile reserve pool. <br />
<br />
To simulate mineralisation rates of soil organic carbon, the model distinguishes three soil carbon pools for litter, fast soil organic matter (10-year turnover rate) and slow soil organic matter (100-year turnover rate). All carbon from harvested products (crops, grass, biofuels) is assumed to be released to the atmosphere as CO<sub>2</sub> after consumption (food, feed, energy) in the same year. Residues are either left in the fields to enter the litter pool or are removed to subsequently decompose.<br />
<br />
During wood harvesting, a proportion of the plant pools is cut down and harvested, as determined in the [[forest management]] model . The waste is left to enter the soil litter pool as dead biomass. Three classes of wood products are distinguished to account for differences in lifespan:<br />
# Pulp and paper has fast turnover rates; <br />
# timber products, such as furniture, have longer turnover rates ([[Lauk et al., 2012]]); <br />
# traditional biomass used as an energy source and emitted within the same year. <br />
<br />
The IMAGE land-use module (Component [[Agriculture and land use]]) determines annual land-use dynamics, including expansion or abandonment of pastures, cropland and bioenergy plantations, and wood harvested from natural vegetation. <br />
<br />
===Model linkage and simulation procedure===<br />
The [[LPJmL model]] has multiple links to other IMAGE components and uses IMAGE data on climate, atmospheric CO<sub>2</sub> concentration, land use (including wood demand), and timber use and deforestation (cutting and burning). LPJmL supplies other IMAGE components with information on annual carbon fluxes, net CO<sub>2</sub> exchange between biosphere and atmosphere, size of carbon pools, and natural land cover (biome) classes (see [[Carbon_cycle_and_natural_vegetation|Input/output Table]] at Introduction part ). <br />
<br />
LPJmL and IMAGE are linked via an interface and starts in the simulation year of 1970. Before 1970, vegetation and soil carbon pools need to be initialised. This is done by using LPJmL first in a 1000-year spin up to initialise the natural ecosystems and their carbon pools and fluxes, followed by a 390-year spin up, in which agricultural land is gradually expanded based on historical [[HYDE database|HYDE]] land-use data ([[Klein Goldewijk et al., 2011]]). The pool sizes of timber products for 1970 are based on literature estimates ([[Lauk et al., 2012]]). <br />
<br />
The linked IMAGE-LPJmL simulations start in 1970 with observed climate, followed by simulated climate from 2005 onwards (Component [[Atmospheric composition and climate]]). As the inter-annual variability in weather conditions is needed for the simulation of vegetation dynamics in IMAGE-LPJmL, smooth annual climate trends from IMAGE are superimposed with inter-annual variability fields, extracted from observed climate over the 1971–2000 period. To avoid repeating climate trends in these 30-year periods, annual anomalies are ordered at random before superimposition.<br />
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<div class="page_standard"><br />
<br />
The energy demand module represents the total of all subsectors in the economy using energy, such as industry, transport, residential and services, etc. Each subsector is represented via either an aggregated formulation (used for the service sector, light industry and 'other' energy demand) or detailed modelling of specific processes (transport, residential and commercial and energy-intensive manufacturing industries). <br />
<br />
The generic formulation calculates total demand for final energy for each region (R), sector (S) and energy form (F, heat or electricity) according to:<br />
<br />
{{FormulaAndTableTemplate|Formula1 Energy demand}}<br />
<br />
Equation 1, in which: <br />
*SE represents final energy; <br />
*POP represents population; <br />
*ACT/POP the sectoral activity per capita; <br />
*[[HasAcronym::SC]] a factor capturing intra-sectoral structural change;<br />
*[[HasAcronym::AEEI]] the autonomous energy efficiency improvement;<br />
*[[HasAcronym::PIEEI]] the price-induced energy efficiency improvement.<br />
<br />
In the denominator: <br />
*η is the end-use efficiency of energy carriers used in, for example, boilers and stoves;<br />
*MS represents the share of each energy carrier. <br />
<br />
Population and economic activity levels are exogenous inputs into the module. Each of the other dynamic factors in equation 1 are briefly discussed below.<br />
<br />
===Structural change (SC)=== <br />
In each sector, the mix of activities changes as a function of development and time. These changes, referred to as structural change, may influence the energy intensity of a sector. For instance, using more private cars for transport instead of buses tends to increase energy intensity. Historically, in several sectors, as a consequence of the structural changes in the type of activities an increase in energy intensity can be observed followed by a decrease. Evidence of this trend is more convincing in industry with shifts from very basic to heavy industry and finally to industries with high value-added products than in other sectors, such as transport where historically, energy intensity has mainly been increasing ([[De Vries et al., 2001]]).<br />
<br />
Based on the above, in ''generic model formulations'', energy intensity is driven by income, assuming a peak in energy intensity, followed by saturation of energy demand at a constant per capita energy service level. In the calibration process, the choice of parameters may lead, for instance, to a peak in energy intensity higher than current income levels. In the technology-detailed energy demand (see below), structural change is captured by other equations that describe the underlying processes explicitly (e.g., modal shift in transport).<br />
<br />
===Autonomous Energy Efficiency Increase (AEEI)===<br />
This is a multiplier used in the generic energy demand module to account for efficiency improvement as a result of technology improvement, independent of prices. In general, current appliances are more efficient than those available in the past. <br />
<br />
The autonomous energy efficiency increase for new capital is a fraction (f) of the economic growth rate based on the formulation of Richels et al. ([[Richels et al., 2004|2004]]). The fraction varies between 0.45 and 0.30 (based on literature data) and is assumed to decline with time because the scope for further improvement is assumed to decline. Efficiency improvement is assumed for new capital. Autonomous increase in energy efficiency for the average capital stock is calculated as the weighted average value of the AEEI values of the total in capital stock, using the vintage formulation. In the ''technology-detailed submodules'', the autonomous energy efficiency increase is represented by improvement in individual technologies over time. <br />
<br />
===Price-Induced Energy Efficiency Improvement (PIEEI)===<br />
This multiplier is used to describe the effect of rising energy costs in the form of induced investments in energy efficiency by consumers. It is included in the ''generic formulation'' using an energy conservation cost curve. In the ''technology-detailed submodules'', this multiplier is represented by competing technologies with different efficiencies and costs. <br />
<br />
===Substitution===<br />
Demand for secondary energy carriers is determined on the basis of demand for energy services and the relative prices of the energy carriers. For each energy carrier, a final efficiency value (η) is assumed to account for differences between energy carriers in converting final energy into energy services. The indicated market share ([[HasAcronym::IMS]]) of each fuel is determined using a multinomial logit model that assigns market shares to the different carriers (i) on the basis of their relative prices in a set of competing carriers (j). <br />
<br />
{{FormulaAndTableTemplate|Formula2 Energy demand}}<br />
<br />
IMS is the indicated market share of different energy carriers or technologies and c is their costs. In this equation, λ is the so-called logit parameter, determining the sensitivity of markets to price differences. <br />
<br />
The equation takes account of direct production costs and also energy and carbon taxes and premium values. The last two reflect non-price factors determining market shares, such as preferences, environmental policies, infrastructure (or the lack of infrastructure) and strategic considerations. The premium values are determined in the model calibration process in order to correctly simulate historical market shares on the basis of simulated price information. The same parameters are used in scenarios to simulate the assumption on societal preferences for clean and/or convenient fuels. However, the market shares of traditional biomass and secondary heat are determined by exogenous scenario parameters (except for the residential sector discussed below). Non-energy use of energy carriers is modelled on the basis of exogenously assumed intensity of representative non-energy uses (chemicals) and on a price-driven competition between the various energy carriers ([[Daioglou et al., 2014]]).<br />
<br />
===Heavy industry===<br />
The heavy industry submodule includes representations for the steel, cement, non-energy (chemicals), pulp & paper and food processing sectors ([[Van Ruijven et al., 2016]]). The generic structure of the energy demand module was adapted as follows:<br />
<br />
*Activity is described in terms of production of tonnes of product. The regional demand for these commodities is determined by a relationship similar to the formulation of the structural change discussed above. Cement and steel can be traded. Historically, trade patterns have been prescribed but future production is assumed to shift slowly to producers with the lowest costs. <br />
*The demand after trade can be met from production that uses a mix of production processes. Each production process is characterised by costs and energy use per unit of production, both of which decline slowly over time. The actual mix of production process used to produce feedstock or end product in the model is derived from a multinominal logit equation, and results in a larger market share for the production processes with the lowest costs. The autonomous improvement of these production processes leads to an autonomous increase in energy efficiency. The selection of production processes represents the price-induced improvement in energy efficiency. Fuel substitution is partly determined on the basis of price, but also depends on the type of production process used because some production processes can only use specific energy carriers (e.g., electricity for electric arc furnaces). <br />
<br />
More detailed information for specific manufacturing industries can be found in the Expert level of model documentation: http://image.pbl.local/index.php/Expert:Energy_demand_-_Industry<br />
<br />
===Transport===<br />
The transport submodule consists of two parts - passenger and freight transport. A detailed description of the passenger transport (TRAVEL) is provided by Girod et al. ([[Girod et al., 2012|2012]]). There are seven modes - foot, bicycle, bus, train, passenger vehicle, high-speed train, and aircraft. The structural change (SC) processes in the transport module are described by an explicit consideration of the modal split. Two main factors govern model behaviour, namely the near-constancy of the travel time budget (TTB), and the travel money budget (TMB) over a large range of incomes. These are used as constraints to describe transition processes among the seven main travel modes, on the basis of their relative costs and speed characteristics and the consumer preferences for comfort levels and specific transport modes.<br />
<br />
The freight transport submodule contains a simpler structure. Service demand is projected with constant elasticity of the industry value added for each transport mode. In addition, demand sensitivity to transport prices is considered for each mode, depending on its share of energy costs in the total service costs.<br />
<br />
The efficiency changes in both passenger and freight transport represent the autonomous increase in energy efficiency, and the price-induced improvements in energy efficiency improvement parameters. These changes are described by substitution processes in explicit technologies, such as vehicles with different energy efficiencies, costs and fuel type characteristics compete on the basis of preferences and total passenger-kilometre costs, using a multinomial logit equation. The efficiency of the transport fleet is determined by a weighted average of the full fleet (a vintage model, giving an explicit description of the efficiency in all single years). As each type of vehicle is assumed to use only one fuel type, this process also describes the fuel selection.<br />
<br />
Since Girod et. al ([[Girod et al., 2012|2012]]) the LDV projected vehicle costs and efficiency have been revised to incorporate more recent projections of LDV vehicle technology development. The vehicle characteristics are based on the in depth study performed by the Argonne National Laboratory ([[Plotkin and Singh 2009|2009]]).<br />
<br />
<br />
===Residential energy use===<br />
The residential submodule describes the energy demand from household energy functions of cooking appliances, space heating and cooling, water heating and lighting. These functions are described in detail elsewhere ([[Daioglou et al., 2012]]; [[Van Ruijven et al., 2011]]). <br />
<br />
Structural change in energy demand is presented by modelling end-use household functions: <br />
*Energy service demand for space heating is modelled using correlations with floor area, heating degree days and energy intensity, the last including building efficiency improvements. <br />
*Hot water demand is modelled as a function of household income and heating degree days. <br />
*Energy service demand for cooking is determined on the basis of an average constant consumption of 3 MJ<sub>UE</sub>/capita/day. <br />
*Energy use related to appliances is based on ownership, household income, efficiency reference values, and autonomous and price-induced improvements. Space cooling follows a similar approach, but also includes cooling degree days (Isaac and Van Vuuren, 2009). <br />
*Electricity use for lighting is determined on the basis of floor area, wattage and lighting hours based on geographic location. <br />
<br />
Efficiency improvements are included in different ways. Exogenously driven energy efficiency improvement over time are used for appliances, light bulbs, air conditioning, building insulation and heating equipment, Price-induced energy efficiency improvements (PIEEI) occur by explicitly describing the investments in appliances with a similar performance level but with different energy and investment costs. For example, competition between incandescent light bulbs and more energy-efficient lighting is determined by changes in energy prices.<br />
<br />
The model distinguishes five income quintiles for both the urban and rural population. After determining the energy demand per function for each population quintile, the choice of fuel type is determined on the basis of relative costs. This is based on a multinomial logit formulation for energy functions that can involve multiple fuels, such as cooking and space heating. In the calculations, consumer discount rates are assumed to decrease along with household income levels, and there will be increasing appreciation of clean and convenient fuels ([[Van Ruijven et al., 2011]]). For developing countries, this endogenously results in the substitution processes described by the energy ladder. This refers to the progressive use of modern energy types as incomes grow, from traditional bioenergy to coal and kerosene, to energy carriers such as natural gas, heating oil and electricity.<br />
<br />
The residential submodule also includes access to electricity and the associated investments ([[Van Ruijven et al., 2012]]). Projections for access to electricity are based on an econometric analysis that found a relation between level of access , and GDP per capita and population density. The investment model is based on population density on a 0.5 x 0.5 degree grid, from which a stylised power grid is derived and analysed to determine investments in low-, medium- and high-voltage lines and transformers. See additional info on [[Grid and infrastructure]]<br />
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<div>{{ComponentDescriptionTemplate<br />
|Reference=Weijters et al., 2009; Harper, 1992; Bouwman et al., 2013c; Lehner and Döll, 2004; Kourzeneva, 2010; Ladson and White, 1999; Biemans et al., 2011; Kuiper et al., 2014;<br />
}}<br />
<div class="page_standard"><br />
<br />
GLOBIO aquatic assesses biodiversity intactness,expressed as mean species abundance (MSA) in inland aquatic ecosystems: rivers and streams, deep and shallow lakes and wetlands such as floodplain wetlands, marshes, and isolated wetlands. See Figure Flowchart.<br />
<br />
The model calculates the effects of land use changes in catchment areas in each of the aquatic ecosystems listed. For rivers and floodplain wetlands, the model also describes the effect of human interventions (e.g., through dam construction or climate change) on the hydrology on biodiversity. From a biodiversity perspective, reservoirs are considered as heavily modified river stretches. GLOBIO is also used to compute the probability of the dominance of harmful algal blooms of cyanobacteria (blue-green algae) in lakes, which often coincides with shifts in food webs and biodiversity loss, and which interferes with the human use of these systems.<br />
<br />
The land-use effect on MSA in streams, rivers and wetlands is based on the type and proportion of human land use in the upstream catchment areas. Data from studies on biodiversity in rivers and streams in catchment and sub-catchment areas with different types of land use (e.g., forest, agriculture, urban) were combined in a meta-analysis. The data were expressed in MSA and fitted by linear regression ([[Weijters et al., 2009]]). A comparable meta-analysis was performed for wetlands (Janse et al., submitted). The analysis for lakes was based on phosphorus and nitrogen loadings, because the effects of eutrophication on lakes are well established, and nutrient loading to surface waters correlates closely with the type and intensity of land use ([[Harper, 1992]]; [[Bouwman et al., 2013c]]). Data from literature on the relationship between biodiversity and P and N concentrations were combined and fitted by logistic regression for deep and shallow lakes ([[Janse et al., submitted]]).<br />
<br />
Local concentration levels are calculated from nutrient discharges to surface water (Component [[Nutrients]]) and their accumulation and processing along the river network (see Component [[Water]]), currently using 0.5x0.5 degree resolution data. The model uses data on water bodies (streams, rivers, lakes, reservoirs and several types of wetlands) from the GLWD Global Lakes and Wetlands Database ([[Lehner and Döll, 2004]]) to calculate the proportion of each water body type in each grid cell. Lake depth categories were derived where possible from the Flake database ([[Kourzeneva, 2010]]). <br />
<br />
The river network and GLWD map were combined in an overall water network map to estimate nutrient loadings to water bodies. Some wetland types are assumed to be isolated from the river network and thus only influenced by the land-use and nutrient emissions in the specific grid cell. An adapted wetland map can be used to take account of historical or projected wetland conversions to other land-use ([[Van Asselen et al., 2013]]).<br />
<br />
For rivers and riverine wetlands, GLOBIO also considers the effect of hydrological changes on biodiversity. Monthly river discharges in pristine and in present or future situations (affected by climate change, dams and water abstraction) are derived from the hydrology module in LPJ model ((Component [[Nutrients]]). These monthly discharge patterns are used to calculate the deviation between affected and natural seasonal pattern, referred to as the Amended Annual Proportional Flow Deviation ([[Ladson and White, 1999]]; [[Biemans et al., 2011]]). Literature data on biodiversity in rivers under different regulation (e.g., by dams) were combined and expressed as a change in MSA ([[Janse et al., submitted]]). A comparable analysis was performed on the effects of flow deviation of biodiversity in riverine wetlands ([[Kuiper et al., 2014]]).<br />
<br />
The MSA value for each water body (river, lake, wetland) is calculated by multiplying the values for the relevant drivers. The final indicator, aquatic MSA, is calculated by area-weighted averaging of MSA values for rivers, lakes and wetlands. In addition to MSA, the probability of dominance of harmful algal blooms of cyanobacteria in lakes is calculated as a biodiversity indicator, based on P concentration, N:P ratio, and water temperature ([[Håkanson et al., 2007]]. The results are expressed as the proportion of lakes with a cyanobacteria biomass above the WHO standard.<br />
<br />
A more detailed description of the model is published in ([[Alkemade et al., 2011b]]; [[Janse et al., submitted]]).<br />
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<div>{{ComponentDescriptionTemplate<br />
|Reference=Bouwman et al., 2013c; Van Drecht et al., 2009; Cleveland et al., 1999; Salvagiotti et al., 2008; Beusen et al., 2014; Beusen et al., 2015; Beusen et al., 2016;<br />
}}<div class="page_standard"><br />
<br />
===General===<br />
The IMAGE-Global Nutrient Model (GNM) is a global distributed spatially explicit model using hydrology as the basis for describing nitrogen (N) and phosphorus (P) delivery to surface water and transport and in-stream retention in rivers, lakes, wetlands and reservoirs. IMAGE-GNM is coupled to the PCR-GLOBWB global hydrological model ([[Van Beek et al., 2011]]). In the IMAGE-GNM model, grid cells receive water with dissolved and suspended N and P from upstream grid cells; inside grid cells, N and P are delivered to water bodies via diffuse sources (surface runoff, shallow and deep groundwater, riparian zones; litterfall in floodplains; atmospheric deposition) and point sources (wastewater); N and P retention in a water body is calculated on the basis of the residence time of the water and nutrient uptake velocity; subsequently, water and nutrients are transported to downstream grid cells. <br />
<br />
===Wastewater===<br />
Urban wastewater contains N and P emitted by households and industries that are connected to a sewerage system, and households with sanitation but without a sewerage connection.<br />
<br />
N discharges to surface water (''E<sub>sw</sub><sup>N</sup>'' in kg per person per year) are calculated as follows ([[Van Drecht et al., 2009]]; [[Morée et al., 2013]]):{{FormulaAndTableTemplate|Formula1 Nutrients}}where: <br />
*''E<sub>hum</sub><sup>N</sup>'' is human N emissions (kg per person per year), <br />
* D is the proportion of the total population connected to public sewerage systems (no dimension), <br />
*R N is the overall removal of N through wastewater treatment (no dimension). <br />
<br />
Total P emissions to surface water are calculated in a similar way, but also include estimates of P emissions to surface water resulting from the use of P-based dishwasher and laundry detergents. Nutrient removal by wastewater treatment R is based on the relative contribution of four classes of treatment (none, primary, secondary and tertiary treatment). D is calculated from the proportion of households with improved sanitation. D and R by treatment class are scenario variables.<br />
<br />
===Soil nutrient budget===<br />
The soil budget approach ([[Bouwman et al., 2009]]; [[Bouwman et al., 2013c]]) considers all N and P inputs and outputs for IMAGE grid cells. N input terms in the budgets include application of synthetic N fertiliser (N<sub>fert</sub>) and animal manure (N<sub>man</sub>), biological N fixation (N<sub>fix</sub>), and atmospheric N deposition (N<sub>dep</sub>). Output terms include N withdrawal from the field through crop harvesting, hay and grass cutting, and grass consumed by grazing animals (N<sub>withdr</sub>). <br />
<br />
The soil N budget (N<sub>budget</sub>) is calculated as follows:{{FormulaAndTableTemplate|Formula2 Nutrients}} <br />
The same approach is used for P, with input terms being animal manure and fertiliser. The soil nutrient budget does not include nutrient accumulation in soil organic matter for a positive budget (surplus), or nutrient depletion due to soil organic matter decomposition and mineralisation. With no accumulation, a surplus represents a potential loss to the environment. For N this includes NH<sub>3</sub> volatilisation (see Component [[Emissions]]), denitrification, surface runoff and leaching. For P, this is surface runoff.<br />
<br />
For spatial allocation of the nutrient input to IMAGE grid cells, grass and the crop groups in IMAGE (temperate cereals, rice, maize, tropical cereals, pulses, roots and tubers, oil crops, other crops, energy crops) and grass are aggregated to five broad groups. These groups are grass, wetland rice, leguminous crops, other upland crops and energy crops for both mixed and pastoral production systems (see [[Livestock systems]]).<br />
<br />
====Fertiliser====<br />
Fertiliser use is based on nutrient use efficiency, representing crop production in kilograms of dry matter per kilogram of fertiliser N (NUE) and P (PUE). NUE and PUE vary between countries because of differences in crop mix, attainable yield potential, soil quality, amount and form of N and P application and management. In constructing scenarios on fertiliser use, data on the 1970–2005 period serve as a guide to distinguish countries with an input exceeding crop uptake (positive budget or surplus) from countries with a deficit. Generally, farmers in countries with a surplus are assumed to be increasingly efficient in fertiliser use (increasing NUE and PUE). In countries with nutrient deficits, an increase in crop yields is only possible with an increase in the nutrient input. Initially, this will lead to decreasing NUE and PUE, showing a decrease in soil nutrient depletion due to increased fertiliser use.<br />
<br />
====Manure====<br />
Total manure production is computed from animal stocks and N and P excretion rates (Figure Flowchart, middle). IMAGE uses constant N and P excretion rates per head for dairy and non-dairy cattle, buffaloes, sheep and goats, pigs, poultry, horses, asses, mules and camels. Constant excretion rates imply that the N and P excretion per unit of product decreases with increased milk and meat production per animal.<br />
<br />
N and P in the manure for each animal category are spatially allocated to mixed and pastoral systems. In each country and system, the manure is distributed over three management systems: grazing; storage in animal housing and storage systems; and manure used outside the agricultural system for fuel or other purposes. The quantity of manure assigned to grazing is based on the proportion of grass in feed rations (Figure Flowchart, middle).<br />
<br />
Stored animal manure available for cropland and grassland application includes all stored and collected manure, excluding ammonia volatilisation from animal houses and storage systems. In general, IMAGE assumes that 50% of available animal manure from storage systems is applied to arable land and the rest to grassland in industrialised countries. In most developing countries, 95% of the available manure is spread on croplands and 5% on grassland, thus accounting for the lower economic importance of grass compared to crops in these countries. In the European Union, maximum manure application rates are 170 to 250 kg N per ha , reflecting current regulations.<br />
<br />
====Biological N<sub>2</sub> fixation====<br />
Data on biological N<sub>2</sub> fixation by leguminous crops (pulses and soybeans) are obtained from the N in the harvested product (see nutrient withdrawal) following the approach of ([[Salvagiotti et al., 2008]]). Thus any change in the rate of biological N<sub>2</sub> fixation by legumes is the result of yield changes for pulses and soybeans. In addition to leguminous crops, IMAGE uses an annual rate of biological N<sub>2</sub> fixation of 5 kg N per ha for non-leguminous crops and grass, and 25 kg N per ha for wetland rice. N fixation rates in natural ecosystems were based on the low estimates for areal coverage by legumes ([[Cleveland et al., 1999]]) as described by Bouwman et al. ([[Bouwman et al., 2013a]]).<br />
<br />
====Atmospheric deposition==== <br />
Deposition rates for historical and future years are calculated by scaling N deposition field for 2000 (obtained from atmospheric chemistry transport models), using emission inventories for the historical period and N gas emissions in the scenario considered. IMAGE does not include atmospheric P deposition.<br />
<br />
====Nutrient withdrawal==== <br />
Withdrawal of N and P in harvested products is calculated from regional crop production in IMAGE and the N and P content for each crop, which is aggregated to the broad crop categories (wetland rice, leguminous crops, upland crops and energy crops). IMAGE also accounts for uptake by fodder crops. N withdrawal through grass consumption and harvest is assumed to amount to 60% of all N input (manure, fertiliser, deposition, N fixation), excluding NH<sub>3</sub> volatilisation. P withdrawal through grazing or grass cutting is calculated as a proportion of 87.5% of fertiliser and manure P input. The rest is assumed to be lost through surface runoff. In calculating spatially nutrient withdrawal, a procedure is used to downscale regional crop production data from IMAGE to country estimates for nutrient withdrawal based on distributions in 2005.<br />
<br />
===Nutrient environmental fate===<br />
Nutrient losses from the plant-soil system to the soil-hydrology system are calculated from the soil nutrient budgets ([[Bouwman et al., 2013a]]). For N, the budget is corrected for ammonia volatilisation from grazing animals and from fertiliser and manure spreading (see Component [[Emissions]]). P not taken up by plants is generally bound to soil particles, with the only loss pathway being surface runoff. N is more mobile and is transported via surface runoff and through soil, groundwater and riparian zones to surface water.<br />
<br />
====Soil denitrification and leaching====<br />
Denitrification is calculated as a proportion of the soil N budget surplus based on the effect of temperature and residence time of water and nitrate in the root zone, and the effects of soil texture, soil drainage and soil organic carbon content. In a soil budget deficit, IMAGE assumes that denitrification does not occur. Leaching is the complement of the soil N budget.<br />
<br />
====Groundwater transport, surface runoff and denitrification====<br />
Two groundwater subsystems are distinguished. One is the shallow groundwater system representing interflow and surface runoff for the upper 5 m of the saturated zone, with short travel times for the water to enter local surface water at short distances or to infiltrate the deep groundwater system. The other is the deep system with a thickness of 50 m with generally long travel times draining to larger streams and rivers. Deep groundwater is assumed to be absent in areas of non-permeable, consolidated rocks or in the presence of surface water. Denitrification during groundwater transport is based on the travel time and the half-life of nitrate. The half-life depends on the lithological class (1 year for schists and shales containing pyrite, 2 years for alluvial material, and 5 years for all other lithological classes). Flows of water and nitrate from shallow groundwater to riparian zones are assumed to be absent in areas with surface water bodies, where the flow is assumed to bypass riparian zones flowing directly to streams or rivers.<br />
<br />
====Denitrification in riparian areas====<br />
The calculation of denitrification in riparian areas is similar to that in soils, but with two differences: <br />
# a biologically active layer of 0.3 m thickness is assumed instead of 1 m for other soils; <br />
# the approach includes the effect of pH on denitrification.<br />
<br />
====Nutrients from vegetation in floodplains====<br />
NPP from the LPJ model [[Carbon cycle and natural vegetation]] for wetlands and floodplains are used. Part of annual NPP is assumed to be deposited in the water during flooding, and where flooding is temporary, the litter from preceeding periods is assumed to be available for transport in the flood water. 50% of total NPP is assumed to end in the surface water.<br />
<br />
====Other direct sources of nutrients====<br />
Other sources include aquaculture, weathering and atmospheric deposition. Deposition is from the same data as used for the land nutriënt budgets. Aquaculture is taken from data from two recent studies ([[Bouwman et al., 2011]]; [[Bouwman et al., 2013c]]), and weathering. The calculation of P release from weathering is based on a recent study ([[Hartmann et al., 2014]]) which uses the lithological classes distinguished by ([[Dürr et al., 2005]]). The lithological classes are available on a 5 by 5 minute resolution, hence the weighted average P concentration within each 0.5 by 0.5 degree grid cell is calculated.<br />
<br />
====In-stream nutrient retention====<br />
The water that enters streams and rivers through surface runoff and discharges from groundwater and riparian zones is routed through stream and river channels, and passes through lakes, wetlands and reservoirs. The history of the construction of reservoirs during the 20th century is based on data from ([[Lehner et al., 2011]]). The nutrient retention in each of these systems is calculated on the basis of the nutrient spiralling ecological concept, which is based on residence time and temperature as described in ([[Beusen et al., 2014]]; [[Beusen et al., 2015]]).<br />
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|Reference=Hoogwijk, 2004; Van Vuuren, 2007; Hendriks et al., 2004b; Van Ruijven et al., 2007; Ueckerdt et al., 2016; Gernaat et al., 2014; Köberle et al., 2015; De Boer and Van Vuuren, 2017;<br />
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<div class="page_standard"><br />
<br />
[[TIMER model|TIMER]] includes two main energy conversion modules: Electric power generation and hydrogen generation. Below, electric power generation is described in detail. In addition, the key characteristics of the hydrogen generation model, which follows a similar structure, are presented.<br />
<br />
===Electric power generation===<br />
[!CHANGE] In TIMER, electricity can be generated by 30 technologies. These include the VRE sources solar utility scale photovoltaics (PV), residential photocoltaics (RPV), concentrated solar power (CSP), and onshore and offshore wind power. Other technology types are natural gas-, coal-, biomass- and oil-fired power plants. These power plants come in multiple variations: conventional, combined cycle, carbon capture and storage (CCS) and combined heat and power (CHP). The electricity sector in TIMER also describes the use of nuclear, other renewables (mainly geothermal power) and hydroelectric power. ([[De Boer and Van Vuuren, 2017]])<br />
<br />
[!CHANGE] As shown in the [[Flowchart Energy conversion|flowchart]], two key elements of the electric power generation are the investment strategy and the operational strategy in the sector. A challenge in simulating electricity production in an aggregated model is that in reality electricity production depends on a range of complex factors, related to costs, reliance, and technology ramp rates. Modelling these factors requires a high level of detail and thus IAMs, such as TIMER, concentrate on introducing a set of simplified, meta relationships ([[Hoogwijk, 2004]]; [[Van Vuuren, 2007]]; [[De Boer and Van Vuuren, 2017]]).<br />
<br />
====Total demand for new capacity====<br />
<div class="version changev31"><br />
The electricity generation capacity required to meet the demand per region is based on a forecast of the maximum annual electricity demand plus a reserve margin. The reserve margin consists of a general reserve margin of 10-20% on peak demand plus a compensation for imperfect capacity credits (the reliability of a plant type to supply power during the peak hours) of existing capacity. The maximum annual demand is calculated on the basis of an assumed shape of the load duration curve (LDC) and the gross electricity demand. The latter comprises the net electricity demand from the end-use sectors plus electricity trade and transmission losses. An LDC shows the distribution of load over a certain timespan in a downward form. The peak load is plotted to the left of the LDC and the lowest load is plotted to the right. The shape of the LDC is based on work by Ueckerdt et al. ([[Ueckerdt et al., 2016|2016]]), who derived regional normalized residual LDCs (RLDC) for different solar and wind shares, including the application of optimized electricity storage. <br />
<br />
The final demand for new generation capacity is equal to the difference between the required and existing capacity. Power capacity is assumed to be replaced at the end of its lifetime, which varies from 25 to 80 years, depending on the technology.<br />
<br />
Capacity can also be decommissioned before the end of the technical life time. This so-called early retirement can occur if the operation of the capacity has become relatively expensive compared to the operation and construction of new capacity. The operational costs include fixed O&M, variable O&M, fuel and CCS costs. Capacity will not be retired early if the capacity has a backup role, characterized by a low load factor resulting in low operational costs. ([[De Boer and Van Vuuren, 2017]])<br />
</div><br />
<br />
====Decisions to invest in specific options ====<br />
In the model, the decision to invest in generation technologies is based on the levelized cost of electricity (LCOE; in USD/kWhe) produced per technology, using a multinomial logit equation that assigns larger market shares to the lower cost options.<br />
<br />
An important variable used in determining the LCOE is the expected amount of electricity generated. Often, the LCOEs of technologies are compared at maximum full load hours. However, only a limited share of the installed capacity will actually generate electricity at full load. This effect is captured in a heuristic: 10 different load bands have been introduced to link the investment decision to expected dispatch. The different load bands are distributed among the LDC, resulting in a load factor for each load band. The inclusion of different load factors for each load band means that less capital-intensive technologies are attractive to use for lower load factor load bands. These are likely to be gas-fired peaker plants. For load bands with higher load factors, the electricity submodule chooses technologies with lower operational costs. These are likely to be base load plants, such as coal-fired or nuclear power plants. [!CHANGE]VRE load factors per load band are derived from the marginal load band contributions resulting from the RLDC. A system with more VRE sources will result in lower residual load factors and therefore in a higher demand for peak or mid load technologies. ([[De Boer and Van Vuuren, 2017]])<br />
<br />
The standard costs of each option can be broken down into several categories: investment or capital cost; fuel cost; fixed and variable operational and maintenance costs; construction costs; and carbon capture and storage costs. <br />
* [!CHANGE] The capital costs of power generating technologies can be exogenously described, but they can also develop as a result of endogenous learning mechanisms explained [[Technical learning|here]]. For the endogenous method, technologies are split up in different cost components. These components have individual learning characteristics, like learning rate, floor costs and start costs. However, spillovers are possible between technologies and regions. Technology spillovers occur when technologies share a component.<br />
* Fuel cost result from the supply modules described [[Energy supply|here]].<br />
* [!CHANGE]Fixed and variable operation and maintenance costs develop according to the same principles as the capital costs<br />
* Construction costs result from interest paid during construction. Construction times vary among the technologies.<br />
* More information on carbon capture and storage cost can be found [[Carbon capture and storage|here]].<br />
Also, additional costs are distinguished: backup costs; curtailment costs; VRE load factor decline; storage costs; and transmission and distribution costs.<br />
* Backup costs have been added to represent the additional costs required in order to meet the capacity and energy production requirements of a load band. Backup costs are usually higher for technologies with low capacity credits. Backup costs include all standard cost components for the chosen backup technology. [!CHANGE]This backup capacity is installed together with regular investments in load bands<br />
* Curtailment costs are only relevant for VRE technologies and CHP. Curtailments occur when the supply exceeds the demand. The degree to which curtailment occurs depends on VRE share, storage use and the regional correlation between electricity demand and VRE or CHP supply. Curtailment influences the LCOE by reducing the potential amount of electricity that could be generated.<br />
* [!CHANGE]Load factor reduction results from the utilisation of resource sites with less favourable environmental conditions, such as lower wind speeds, lower water discharge or less solar irradiation. This results in a lower potential load influencing the LCOE by reducing the potential electricity generation. The development of load factor reduction is captured in cost supply curves. For more information on the TIMER cost supply curves see: Hoogwijk ([[Hoogwijk, 2004|2004]]), Gernaat et al., ([[Gernaat et al., 2014|2014]]), Koberle et al., ([[Köberle et al., 2015|2015]]) and Gernaat et al., (<nowiki>[[2018]]</nowiki>)<br />
* Storage use has been optimised in the RLDC data set. For more information on storage use, see Ueckerdt et al. (n.d.).<br />
* Transmission and distribution costs are simulated by adding a fixed relationship between the amount of capacity and the required amount of transmission and distribution capital. VRE cost supply curves contain additional transmission costs resulting from distance between VRE potential and demand centres.<br />
<br />
[!CHANGE]The exceptions are ''other renewables'' and CHP. ''Other renewables'' are exogenously prescribed, because of a lack of available data. The demand for CHP capacity is heat demand driven. ([[De Boer and Van Vuuren, 2017]])<br />
<br />
Finally, in the equations, some constraints are added to account for limitations in supply, for example restrictions on biomass availability. For a more detailed description on electricity sector investments in TIMER, see [[De Boer and Van Vuuren, 2017]]).<br />
<br />
====Operational strategy====<br />
<br />
The demand for electricity is met by the installed capacity of power plants. The available capacity is used according to the merit order of the different types of plants; technologies with the lowest variable costs are dispatched first, followed by other technologies based on an ascending order of variable costs. This results in a cost-optimal dispatch of technologies. The dispatch of VRE is described by the RLDC dataset. CHP dispatch is distributed based on monthly heating degree days. Within each month, the CHP load stays constant. [!CHANGE] Hydropower has a monthly dispatch potential. This limited availability of hydropower is distributed so that so that it creates most system benefits. Generally, this has a peak shaving effect on residual demand for electricity.<br />
<br />
===Hydrogen generation===<br />
The structure of the hydrogen generation submodule is similar to that for electric power generation ([[Van Ruijven et al., 2007]]) but with following differences:<br />
#There are only eleven supply options for hydrogen production:<br />
#* coal, oil, natural gas and bioenergy, with and without carbon capture and storage (8 plants); <br />
#* hydrogen production from electrolysis, direct hydrogen production from solar thermal processes; <br />
#* small methane reform plants. <br />
#No description of preferences for different power plants is taken into account in the operational strategy. The load factor for each option equals the total production divided by the capacity for each region.<br />
#Intermittence does not play an important role because hydrogen can be stored to some degree. Thus, there are no equations simulating system integration.<br />
#Hydrogen can be traded. A trade model is added, similar to those for fossil fuels described in [[Energy supply]].<br />
<br />
See the additional info on [[Grid and infrastructure]].<br />
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