https://www.iamcdocumentation.eu/api.php?action=feedcontributions&feedformat=atom&user=Johannes+Emmerling 2026-06-21T10:43:54Z User contributions MediaWiki 1.39.15 https://www.iamcdocumentation.eu/index.php?title=RICE50%2B&diff=15943 2023-06-12T22:47:04Z

<p>Johannes Emmerling: </p> <hr /> <div>{{ModelTemplate}}<br /> {{ModelInfoTemplate<br /> |Name=RICE50+<br /> |Version=2.0.0<br /> |ModelLink=https://github.com/witch-team/RICE50xmodel; https://www.eiee.org/tool/rice50/<br /> |participation=reference card only<br /> |processState=submitted<br /> }}<br /> {{InstitutionTemplate<br /> |abbr=CMCC<br /> |institution=Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici<br /> |link=https://www.cmcc.it/<br /> |country=Italy<br /> }}<br /> {{ScopeMethodTemplate<br /> |ModelTypeOption=CBA-integrated assessment model<br /> |GeographicalScopeOption=Global<br /> |Objective=Welfare function<br /> |SolutionConceptOption=General equilibrium (closed economy)<br /> |SolutionHorizonOption=Intertemporal optimization (foresight)<br /> |SolutionMethodOption=Optimization<br /> |BaseYear=2015<br /> |TimeSteps=5 years<br /> |Horizon=2300<br /> |Nr=57<br /> |SpatialText=57 regions is the default version. Also features an almost all country resolution with 160 ISO3 countries.<br /> |TimeDiscountingTypeOption=Discount rate endogenous<br /> |PoliciesOption=Emission tax; Emission pricing; Cap and trade<br /> }}<br /> {{Socio-economicTemplate<br /> |PopulationOption=Yes (exogenous)<br /> |PopulationAgeStructureOption=Yes (exogenous)<br /> |EducationLevelOption=Yes (exogenous)<br /> |UrbanizationRateOption=Yes (exogenous)<br /> |GDPOption=Yes (endogenous)<br /> |IncomeDistributionOption=Yes (endogenous)<br /> |EmploymentRateOption=Yes (exogenous)<br /> |TotalFactorProductivityOption=Yes (exogenous)<br /> |AutonomousEnergyEfficiencyImprovementsOption=Yes (exogenous)<br /> }}<br /> {{Macro-economyTemplate<br /> |CostMeasureOption=GDP loss; Welfare loss; Consumption loss<br /> |CategorizationByGroupOption=Income<br /> }}<br /> {{EnergyTemplate}}<br /> {{Land-useTemplate}}<br /> {{EmissionClimateTemplate<br /> |GHGOption=CO2 fossil fuels; CO2 cement; CO2 land use; CH4 energy; N2O energy<br /> |ClimateIndicatorOption=Concentration: CO2; Radiative forcing: CO2; Radiative forcing: Kyoto gases; Radiative forcing: aerosols; Radiative forcing: total; Temperature change; Sea level rise<br /> |CarbonDioxideRemovalOption=Direct air capture<br /> |ClimateChangeImpactsOption=Economic output; Built capital; Inequality<br /> |Co-LinkagesOption=Biodiversity<br /> }}</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=RICE50%2B&diff=15942 2023-06-12T22:45:48Z

<p>Johannes Emmerling: </p> <hr /> <div>{{ModelTemplate}}<br /> {{ModelInfoTemplate<br /> |Name=RICE50+<br /> |Version=2.0.0<br /> |ModelLink=https://www.eiee.org/tool/rice50/;https://github.com/witch-team/RICE50xmodel;https://iopscience.iop.org/article/10.1088/1748-9326/ac843b<br /> |participation=reference card only<br /> |processState=submitted<br /> }}<br /> {{InstitutionTemplate<br /> |abbr=CMCC<br /> |institution=Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici<br /> |link=https://www.cmcc.it/<br /> |country=Italy<br /> }}<br /> {{ScopeMethodTemplate<br /> |ModelTypeOption=CBA-integrated assessment model<br /> |GeographicalScopeOption=Global<br /> |Objective=Welfare function<br /> |SolutionConceptOption=General equilibrium (closed economy)<br /> |SolutionHorizonOption=Intertemporal optimization (foresight)<br /> |SolutionMethodOption=Optimization<br /> |BaseYear=2015<br /> |TimeSteps=5 years<br /> |Horizon=2300<br /> |Nr=57<br /> |SpatialText=57 regions is the default version. Also features an almost all country resolution with 160 ISO3 countries.<br /> |TimeDiscountingTypeOption=Discount rate endogenous<br /> |PoliciesOption=Emission tax; Emission pricing; Cap and trade<br /> }}<br /> {{Socio-economicTemplate<br /> |PopulationOption=Yes (exogenous)<br /> |PopulationAgeStructureOption=Yes (exogenous)<br /> |EducationLevelOption=Yes (exogenous)<br /> |UrbanizationRateOption=Yes (exogenous)<br /> |GDPOption=Yes (endogenous)<br /> |IncomeDistributionOption=Yes (endogenous)<br /> |EmploymentRateOption=Yes (exogenous)<br /> |TotalFactorProductivityOption=Yes (exogenous)<br /> |AutonomousEnergyEfficiencyImprovementsOption=Yes (exogenous)<br /> }}<br /> {{Macro-economyTemplate<br /> |CostMeasureOption=GDP loss; Welfare loss; Consumption loss<br /> |CategorizationByGroupOption=Income<br /> }}<br /> {{EnergyTemplate}}<br /> {{Land-useTemplate}}<br /> {{EmissionClimateTemplate<br /> |GHGOption=CO2 fossil fuels; CO2 cement; CO2 land use; CH4 energy; N2O energy<br /> |ClimateIndicatorOption=Concentration: CO2; Radiative forcing: CO2; Radiative forcing: Kyoto gases; Radiative forcing: aerosols; Radiative forcing: total; Temperature change; Sea level rise<br /> |CarbonDioxideRemovalOption=Direct air capture<br /> |ClimateChangeImpactsOption=Economic output; Built capital; Inequality<br /> |Co-LinkagesOption=Biodiversity<br /> }}</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=RICE50%2B&diff=15941 2023-06-12T22:43:32Z

<p>Johannes Emmerling: </p> <hr /> <div>{{ModelTemplate}}<br /> {{ModelInfoTemplate<br /> |Name=RICE50+<br /> |Version=2.0.0<br /> |ModelLink=https://www.eiee.org/tool/rice50/;https://github.com/witch-team/RICE50xmodel;https://iopscience.iop.org/article/10.1088/1748-9326/ac843b<br /> |participation=reference card only<br /> |processState=in preparation<br /> }}<br /> {{ScopeMethodTemplate<br /> |ModelTypeOption=CBA-integrated assessment model<br /> |GeographicalScopeOption=Global<br /> |Objective=Welfare function<br /> |SolutionConceptOption=General equilibrium (closed economy)<br /> |SolutionHorizonOption=Intertemporal optimization (foresight)<br /> |SolutionMethodOption=Optimization<br /> |BaseYear=2015<br /> |TimeSteps=5 years<br /> |Horizon=2300<br /> |Nr=57<br /> |SpatialText=57 regions is the default version. Also features an almost all country resolution with 160 ISO3 countries.<br /> |TimeDiscountingTypeOption=Discount rate endogenous<br /> |PoliciesOption=Emission tax; Emission pricing; Cap and trade<br /> }}<br /> {{Socio-economicTemplate<br /> |PopulationOption=Yes (exogenous)<br /> |PopulationAgeStructureOption=Yes (exogenous)<br /> |EducationLevelOption=Yes (exogenous)<br /> |UrbanizationRateOption=Yes (exogenous)<br /> |GDPOption=Yes (endogenous)<br /> |IncomeDistributionOption=Yes (endogenous)<br /> |EmploymentRateOption=Yes (exogenous)<br /> |TotalFactorProductivityOption=Yes (exogenous)<br /> |AutonomousEnergyEfficiencyImprovementsOption=Yes (exogenous)<br /> }}<br /> {{Macro-economyTemplate<br /> |CostMeasureOption=GDP loss; Welfare loss; Consumption loss<br /> |CategorizationByGroupOption=Income<br /> }}<br /> {{EnergyTemplate}}<br /> {{Land-useTemplate}}<br /> {{EmissionClimateTemplate<br /> |GHGOption=CO2 fossil fuels; CO2 cement; CO2 land use; CH4 energy; N2O energy<br /> |ClimateIndicatorOption=Concentration: CO2; Radiative forcing: CO2; Radiative forcing: Kyoto gases; Radiative forcing: aerosols; Radiative forcing: total; Temperature change; Sea level rise<br /> |CarbonDioxideRemovalOption=Direct air capture<br /> |ClimateChangeImpactsOption=Economic output; Built capital; Inequality<br /> |Co-LinkagesOption=Biodiversity<br /> }}<br /> {{InstitutionTemplate<br /> |abbr=CMCC<br /> |institution=Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici<br /> |link=https://www.cmcc.it/<br /> |country=Italy<br /> }}</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=RICE50%2B&diff=15940 2023-06-12T22:40:22Z

<p>Johannes Emmerling: </p> <hr /> <div>{{ModelTemplate}}<br /> {{ModelInfoTemplate<br /> |Name=RICE50+<br /> |Version=2.0.0<br /> |ModelLink=https://www.eiee.org/tool/rice50/;https://github.com/witch-team/RICE50xmodel;https://iopscience.iop.org/article/10.1088/1748-9326/ac843b<br /> |participation=reference card only<br /> |processState=in preparation<br /> }}<br /> {{ScopeMethodTemplate<br /> |ModelTypeOption=CBA-integrated assessment model<br /> |GeographicalScopeOption=Global<br /> |Objective=Welfare function<br /> |SolutionConceptOption=General equilibrium (closed economy)<br /> |SolutionHorizonOption=Intertemporal optimization (foresight)<br /> |SolutionMethodOption=Optimization<br /> |BaseYear=2015<br /> |TimeSteps=5 years<br /> |Horizon=2300<br /> |Nr=57<br /> |SpatialText=57 regions is the default version. Also features an almost all country resolution with 160 ISO3 countries.<br /> |TimeDiscountingTypeOption=Discount rate endogenous<br /> |PoliciesOption=Emission tax; Emission pricing; Cap and trade<br /> }}<br /> {{Socio-economicTemplate<br /> |PopulationOption=Yes (exogenous)<br /> |PopulationAgeStructureOption=Yes (exogenous)<br /> |EducationLevelOption=Yes (exogenous)<br /> |UrbanizationRateOption=Yes (exogenous)<br /> |GDPOption=Yes (endogenous)<br /> |IncomeDistributionOption=Yes (endogenous)<br /> |EmploymentRateOption=Yes (exogenous)<br /> |TotalFactorProductivityOption=Yes (exogenous)<br /> |AutonomousEnergyEfficiencyImprovementsOption=Yes (exogenous)<br /> }}<br /> {{Macro-economyTemplate<br /> |CostMeasureOption=GDP loss; Welfare loss; Consumption loss<br /> |CategorizationByGroupOption=Income<br /> }}<br /> {{EnergyTemplate}}<br /> {{Land-useTemplate}}<br /> {{EmissionClimateTemplate}}<br /> {{InstitutionTemplate<br /> |abbr=CMCC<br /> |institution=Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici<br /> |link=https://www.cmcc.it/<br /> |country=Italy<br /> }}</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=RICE50%2B&diff=15939 2023-06-12T22:39:00Z

<p>Johannes Emmerling: </p> <hr /> <div>{{ModelTemplate}}<br /> {{ModelInfoTemplate<br /> |Name=RICE50+<br /> |Version=2.0.0<br /> |ModelLink=https://www.eiee.org/tool/rice50/;https://github.com/witch-team/RICE50xmodel;https://iopscience.iop.org/article/10.1088/1748-9326/ac843b<br /> |participation=reference card only<br /> |processState=in preparation<br /> }}<br /> {{ScopeMethodTemplate<br /> |ModelTypeOption=CBA-integrated assessment model<br /> |GeographicalScopeOption=Global<br /> |Objective=Welfare function<br /> |SolutionConceptOption=General equilibrium (closed economy)<br /> |SolutionHorizonOption=Intertemporal optimization (foresight)<br /> |SolutionMethodOption=Optimization<br /> |BaseYear=2015<br /> |TimeSteps=5 years<br /> |Horizon=2300<br /> |Nr=57<br /> |SpatialText=57 regions is the default version. Also features an almost all country resolution with 160 ISO3 countries.<br /> |TimeDiscountingTypeOption=Discount rate endogenous<br /> |PoliciesOption=Emission tax; Emission pricing; Cap and trade<br /> }}<br /> {{Socio-economicTemplate<br /> |PopulationOption=Yes (exogenous)<br /> |PopulationAgeStructureOption=Yes (exogenous)<br /> |EducationLevelOption=Yes (exogenous)<br /> |UrbanizationRateOption=Yes (exogenous)<br /> |GDPOption=Yes (endogenous)<br /> |IncomeDistributionOption=Yes (endogenous)<br /> |EmploymentRateOption=Yes (exogenous)<br /> |TotalFactorProductivityOption=Yes (exogenous)<br /> |AutonomousEnergyEfficiencyImprovementsOption=Yes (exogenous)<br /> }}<br /> {{Macro-economyTemplate}}<br /> {{EnergyTemplate}}<br /> {{Land-useTemplate}}<br /> {{EmissionClimateTemplate}}<br /> {{InstitutionTemplate<br /> |abbr=CMCC<br /> |institution=Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici<br /> |link=https://www.cmcc.it/<br /> |country=Italy<br /> }}</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=RICE50%2B&diff=15938 2023-06-12T22:38:07Z

<p>Johannes Emmerling: </p> <hr /> <div>{{ModelTemplate}}<br /> {{ModelInfoTemplate<br /> |Name=RICE50+<br /> |Version=2.0.0<br /> |ModelLink=https://www.eiee.org/tool/rice50/;https://github.com/witch-team/RICE50xmodel;https://iopscience.iop.org/article/10.1088/1748-9326/ac843b<br /> |participation=reference card only<br /> |processState=in preparation<br /> }}<br /> {{ScopeMethodTemplate<br /> |ModelTypeOption=CBA-integrated assessment model<br /> |GeographicalScopeOption=Global<br /> |Objective=Welfare function<br /> |SolutionConceptOption=General equilibrium (closed economy)<br /> |SolutionHorizonOption=Intertemporal optimization (foresight)<br /> |SolutionMethodOption=Optimization<br /> |BaseYear=2015<br /> |TimeSteps=5 years<br /> |Horizon=2300<br /> |Nr=57<br /> |SpatialText=57 regions is the default version. Also features an almost all country resolution with 160 ISO3 countries.<br /> |TimeDiscountingTypeOption=Discount rate endogenous<br /> |PoliciesOption=Emission tax; Emission pricing; Cap and trade<br /> }}<br /> {{Socio-economicTemplate}}<br /> {{Macro-economyTemplate}}<br /> {{EnergyTemplate}}<br /> {{Land-useTemplate}}<br /> {{EmissionClimateTemplate}}<br /> {{InstitutionTemplate<br /> |abbr=CMCC<br /> |institution=Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici<br /> |link=https://www.cmcc.it/<br /> |country=Italy<br /> }}</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=RICE50%2B&diff=15932 2023-06-12T08:00:35Z

<p>Johannes Emmerling: </p> <hr /> <div>{{ModelTemplate}}<br /> {{ModelInfoTemplate<br /> |Name=RICE50+<br /> |Version=2.0.0<br /> |ModelLink=https://www.eiee.org/tool/rice50/;https://github.com/witch-team/RICE50xmodel;https://iopscience.iop.org/article/10.1088/1748-9326/ac843b<br /> |participation=reference card only<br /> |processState=in preparation<br /> }}<br /> {{InstitutionTemplate<br /> |abbr=CMCC<br /> |institution=Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici<br /> |link=https://www.cmcc.it/<br /> |country=Italy<br /> }}<br /> {{ScopeMethodTemplate}}<br /> {{Socio-economicTemplate}}<br /> {{Macro-economyTemplate}}<br /> {{EnergyTemplate}}<br /> {{Land-useTemplate}}<br /> {{EmissionClimateTemplate}}</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=Climate_damages,_temperature_changes_-_WITCH&diff=12527 2020-05-14T10:44:35Z

<p>Johannes Emmerling: added damages</p> <hr /> <div>{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=Climate damages, temperature changes<br /> }}<br /> By default, climate damages are not included in WITCH runs. However, a damage module is available. The damage functions implemented in the WITCH model are based on sectoral climate impact estimates from the ClimateCost project, described in detail in (Bosello and De Cian 2014), and they are integrated with estimates for the impact categories health and catastrophic events. For the category settlements and ecosystems, new estimates are computed using a Willingness-To-Pay approach.</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=Economic_activity_-_WITCH&diff=12526 2020-05-14T10:27:33Z

<p>Johannes Emmerling: </p> <hr /> <div>{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=Economic activity<br /> }}<br /> We calibrate Total Factor Productivity (TFP) to match the SSP baseline GDp projections in a no policy scenarios. In all other scenarios TFP is held constant and GDP reacts endogenously.<br /> <br /> [[Special:Categories|Category]]:</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=Technological_change_-_WITCH&diff=12525 2020-05-14T10:25:18Z

<p>Johannes Emmerling: Edited automatically from page WITCH setup.</p> <hr /> <div>{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=Technological change<br /> }}</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=Water_-_WITCH&diff=12524 2020-05-14T10:24:54Z

<p>Johannes Emmerling: Edited automatically from page WITCH setup.</p> <hr /> <div>{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=Water<br /> }}</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=Data_-_WITCH&diff=12523 2020-05-14T10:24:45Z

<p>Johannes Emmerling: Edited automatically from page WITCH setup.</p> <hr /> <div>{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=Data<br /> }}</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=Mathematical_model_description_-_WITCH&diff=12522 2020-05-14T10:24:33Z

<p>Johannes Emmerling: Edited automatically from page WITCH setup.</p> <hr /> <div>{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=Mathematical model description<br /> }}</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=Climate_damages,_temperature_changes_-_WITCH&diff=12521 2020-05-14T10:24:24Z

<p>Johannes Emmerling: Edited automatically from page WITCH setup.</p> <hr /> <div>{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=Climate damages, temperature changes<br /> }}</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=Agriculture_-_WITCH&diff=12520 2020-05-14T10:24:14Z

<p>Johannes Emmerling: Edited automatically from page WITCH setup.</p> <hr /> <div>{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=Agriculture<br /> }}</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=Land-use_change_-_WITCH&diff=12519 2020-05-14T10:24:12Z

<p>Johannes Emmerling: Edited automatically from page WITCH setup.</p> <hr /> <div>{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=Land-use change<br /> }}</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=Economic_activity_-_WITCH&diff=12518 2020-05-14T10:23:43Z

<p>Johannes Emmerling: Edited automatically from page WITCH setup.</p> <hr /> <div>{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=Economic activity<br /> }}</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=Socio-economic_drivers_-_WITCH&diff=12516 2020-05-14T10:11:35Z

<p>Johannes Emmerling: </p> <hr /> <div>{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=Socio-economic drivers<br /> }}<br /> Socio-economic drivers are typically informed by a scenario narrative that in qualitative terms describes the overall logic behind the scenarios. In the case of WITCH, the Shared Socio-economic Pathways (SSPs, see O’Neill et al., 2014) provide this overall scenario logic based on which the main socio-economic drivers, [[Population - WITCH|population]] and [[Economic activity - WITCH|GDP]], have been quantified. The subsections of this chapter describe how these quantitative drivers are used in WITCH.</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=WITCH&diff=11352 2020-03-03T13:36:24Z

<p>Johannes Emmerling: </p> <hr /> <div>{{ModelTemplate}}<br /> {{ModelInfoTemplate<br /> |Name=WITCH<br /> |participation=full<br /> |processState=published<br /> }}<br /> {{ScopeMethodTemplate<br /> |Objective=WITCH evaluates the impacts of climate policies on global and regional economic systems and provides information on the optimal responses of these economies to climate change. The model considers the positive externalities from leaning-by-doing and learning-by-researching in the technological change.<br /> |Concept=Hybrid: Economic optimal growth model, including a bottom-up energy sector and a simple climate model, embedded in a `game theory` framework.<br /> |SolutionMethod=Regional growth models solved by non-linear optimization and game theoretic setup solved by tatonnement algorithm (cooperative solution: Negishi welfare aggregation, non-cooperative solution: Nash equilibrium)<br /> |Anticipation=Perfect foresight<br /> |BaseYear=2005<br /> |TimeSteps=5<br /> |Horizon=2150<br /> |Nr=14<br /> |Region=cajaz: Canada, Japan, New Zeland; china: China, including Taiwan; easia: South East Asia; india: India; kosau: South Korea, South Africa, Australia; laca: Latin America, Mexico and Caribbean; indo: Indonesia; mena: Middle East and North Africa; neweuro: EU new countries + Switzerland + Norway; oldeuro: EU old countries (EU-15); sasia: South Asia; ssa: Sub Saharan Africa; te: Non-EU Eastern European countries, including Russia; usa: United States of America;<br /> |SpatialText=The number of regions is not fixed, as they can be aggregated or downscaled until country level.<br /> |PolicyImplementation=Quantitative climate targets (temperature, radiative forcing, concentration), carbon budgets, emissions profiles as optimization constraints.<br /> Carbon taxes.<br /> Allocation and trading of emission permits, banking and borrowing.<br /> Subsidies, taxes and penalty on energies sources.<br /> }}<br /> {{Socio-economicTemplate<br /> |ExogenousDriverOption=Total Factor Productivity; Labour Productivity; Capital Technical progress<br /> }}<br /> {{Macro-economyTemplate<br /> |EconomicSectorOption=Energy<br /> |EconomicSector=other;<br /> |EconomicSectorText=A single economy sector is represented. Production inputs are capital, labor and energy services, accounting for the Energy sector split into 8 energy technologies sectors (coal, oil, gas, wind&amp;solar, nuclear, electricity and biofuels).<br /> |CostMeasureOption=GDP loss; Welfare loss; Consumption loss; Energy system costs<br /> |TradeOption=Coal; Oil; Gas; Emissions permits<br /> }}<br /> {{EnergyTemplate<br /> |ResourceUseOption=Coal; Oil; Gas; Uranium; Biomass<br /> |ElectricityTechnologyOption=Coal; Gas; Oil; Nuclear; Biomass; Wind; Solar PV; CCS<br /> |GridInfrastructureOption=Electricity; CO2<br /> |TechnologySubstitutionOption=Expansion and decline constraints; System integration constraints<br /> |EnergyServiceSectorOption=Transportation<br /> }}<br /> {{Land-useTemplate<br /> |Land-use=Cropland; Forest;<br /> |Land-useText=Bioenergy related cost and emissions are obtained by an soft linking with the GLOBIOM model.<br /> }}<br /> {{OtherResourcesTemplate<br /> |OtherResourceOption=Water<br /> }}<br /> {{EmissionClimateTemplate<br /> |GHGOption=CO2; CH4; N2O; HFCs; CFCs; SF6<br /> |PollutantOption=NOx; SOx; BC; OC<br /> |ClimateIndicatorOption=CO2e concentration (ppm); Radiative Forcing (W/m&lt;sup&gt;2&lt;/sup&gt; ); Temperature change (°C); Climate damages $ or equivalent<br /> }}<br /> {{InstitutionTemplate<br /> |abbr=EIEE<br /> |institution=RFF-CMCC European Institute on Economics and the Environment<br /> |link=http://www.eiee.org<br /> |country=Italy<br /> }}<br /> {{InstitutionTemplate<br /> |abbr=CMCC<br /> |institution=Centro Euro-Mediterraneo sui Cambiamenti Climatici<br /> |link=http://www.cmcc.it<br /> |country=Italy<br /> }}</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=References_-_WITCH&diff=11349 2020-03-03T13:33:23Z

<p>Johannes Emmerling: add climate references</p> <hr /> <div>{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=References<br /> }}<br /> Anderson, D. (2006). Costs and finance of carbon abatement in the energy sector, Paper for the Stern Review.<br /> <br /> Barreto, L. and S. Kypreos (2004). Endogenizing R&amp;amp;D and market experience in the &amp;quot;bottom-up&amp;quot; energy-systems ERIS model, Technovation 2, 615-629.<br /> <br /> Bosetti, V., C. Carraro, M. Galeotti, E. Massetti and M. Tavoni (2006). WITCH: A World Induced Technical Change Hybrid Model, The Energy Journal. Special&lt;br /&gt; Issue on Hybrid Modeling of Energy-Environment Policies: Reconciling Bottom-up and Top-down: 13-38.<br /> <br /> Bosetti, V., C. Carraro, E. Massetti and M. Tavoni (2008). International energy R&amp;amp;D spillovers and the economics of greenhouse gas atmospheric stabilization,Energy Economics, 30 (6) Pages 2912-2929.<br /> <br /> Criqui, P., G. Klassen and L. Schrattenholzer (2000). The efficiency of energy R&amp;amp;D expenditures. Economic modeling of environmental policy and endogenous technical change, Amsterdam, November 16-17, 2000.<br /> <br /> Eliasch J. (2008) Climate Change: Financing Global Forests. The Eliasch Review, available at http://www.occ.gov.uk/activities/eliasch.htm<br /> <br /> EIA (2008). Annual Energy Outlook. Energy Information Administration, Washington, DC.<br /> <br /> EIA (2008b). International Energy Outlook. Energy Information Administration, Washington, DC. ENERDATA (2008). Energy Statistics.<br /> <br /> EPA Report 430-R-06-003, June 2006. http://www.epa.gov/climatechange/economics/mitigation.html.<br /> <br /> Farrell A.E. and Brandt, A.R.(2006). Risks of the oil transition, Environmental Research Letters, 1 (1).<br /> <br /> Havlik, P., H. Valin, M. Herrero, M. Obersteiner, E. Schmid, M. C. Rufino, A. Mosnier, et al. 2014. “Climate Change Mitigation Through Livestock System Transitions.” ''Proceedings of the National Academy of Sciences'' 111 (10): 3709–14.<br /> <br /> Herrero, Mario, Petr Havlik, J McIntire, Amanda Palazzo, and Hugo Valin. 2014. “African Livestock Futures: Realizing the Potential of Livestock for Food Security, Poverty Reduction and the Environment in Sub-Saharan Africa.”<br /> <br /> IEA (2007). World Energy Outlook 2007. OECD/IEA, Paris.<br /> <br /> IEA (2005). Resources to Reserves ? Oil &amp;amp; Gas Technologies for the Energy Markets of the Future. OECD/IEA, Paris.<br /> <br /> IPCC (2007) Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment.<br /> <br /> IPCC, (2007b): Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [B. Metz, O.R. Davidson, P.R. Bosch, R. Dave, L.A. Meyer (eds)], Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA., XXX pp.<br /> <br /> Jamasab, T. (2007). Technical change theory and learning curves: patterns of progress in electric generation technologies, The Energy Journal 28 (3).<br /> <br /> Junginger, M., A. Faaij and W. C. Turkenburg (2005). Global experience curves for wind farms, Energy Policy 33: 133-150.<br /> <br /> Kahouli-Brahmi, S. (2008). Technological learning in energy-environment-economy modelling: a survey, Energy Policy 36 : 138-162.<br /> <br /> Klassen, G., A. Miketa, K. Larsen and T. Sundqvist (2005). The impact of R&amp;amp;D on innovation for wind energy in Denmark, Germany and the United Kingdom, Ecological Economics 54 (2-3): 227-240.<br /> <br /> Kouvaritakis, N., A. Soria and S. Isoard (2000). Endogenous Learning in World Post-Kyoto Scenarios: Application of the POLES Model under Adaptive Expectations, International Journal of Global Energy Issues 14 (1-4): 228-248.<br /> <br /> Kypreos, S. (2007). A MERGE model with endogenous technical change and the cost of carbon stabilisation, Energy Policy 35 : 5327-5336.<br /> <br /> McDonald, A. and L. Schrattenholzer (2001). Learning rates for energy technologies, Energy Policy 29 (4): 255-261.<br /> <br /> Meinshausen, M., S. C. B. Raper, and T. M. L. Wigley. 2011. “Emulating Coupled Atmosphere-Ocean and Carbon Cycle Models with a Simpler Model, Magicc6: Part I – Model Description and Calibration.” ''Atmospheric Chemistry and Physics'' 11: 1417–56.<br /> <br /> Nemet, G. F. (2006). Beyond the learning curve: factors influencing cost reductions in photovoltaics, Energy Policy 34 (17): 3218-3232.<br /> <br /> Nemet, G. F. and D. M. Kammen (2007). U.S. energy research and development: declining investment, increasing need, and the feasibility of expansion, Energy Policy 35 (1): 746-755.<br /> <br /> Nordhaus, William D., and Zili Yang. 1996. “A Regional Dynamic General-Equilibrium Model of Alternative Climate-Change Strategies.” ''The American Economic Review'' 86 (4): 741–65.<br /> <br /> Nordhaus, William, and Paul Sztorc. 2013. ''User’s Manual for Dice-2013R''.<br /> <br /> Schock, R. N., W. Fulkerson, M. L. Brown, R. L. San Martin, D. L. Greene and J. Edmonds (1999). How much is energy research &amp;amp; development worth as insurance? Annual Review of Energy and the Environment 24 : 487-512.<br /> <br /> Söderholm, P. and G. Klassen (2007). Wind power in Europe: a simultaneous innovation-diffusion model, Environmental and Resource Economics 36 (2): 163-190.<br /> <br /> Tavoni, M., B. Sohngen and V. Bosetti (2007), Forestry and the carbon market response to stabilise climate, Energy Policy, 35 : 5346--5353.<br /> <br /> UN (2004), World Population to 2300, Report No. ST/ESA/SER.A/236, Department of Economic and Social Affairs, Population Division, New York</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=Climate_-_WITCH&diff=11346 2020-03-03T13:31:41Z

<p>Johannes Emmerling: </p> <hr /> <div>{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=Climate<br /> }}<br /> WITCH included an internal climate module, which translates the regional emissions into global temperature through atmospheric concentrations. It has been building upon the DICE climate equations (Nordhaus and Sztorc 2013). Alternatively, and to make the climate outcome comparable with other models, it allows a soft link with the MAGICC6 climate model (Meinshausen, Raper, and Wigley 2011) for reporting a number of climate outcomes based on this widely used model.</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=References_-_WITCH&diff=11343 2020-03-03T13:27:41Z

<p>Johannes Emmerling: </p> <hr /> <div>{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=References<br /> }}<br /> Anderson, D. (2006). Costs and finance of carbon abatement in the energy sector, Paper for the Stern Review.<br /> <br /> Barreto, L. and S. Kypreos (2004). Endogenizing R&amp;amp;D and market experience in the &amp;quot;bottom-up&amp;quot; energy-systems ERIS model, Technovation 2, 615-629.<br /> <br /> Bosetti, V., C. Carraro, M. Galeotti, E. Massetti and M. Tavoni (2006). WITCH: A World Induced Technical Change Hybrid Model, The Energy Journal. Special&lt;br /&gt; Issue on Hybrid Modeling of Energy-Environment Policies: Reconciling Bottom-up and Top-down: 13-38.<br /> <br /> Bosetti, V., C. Carraro, E. Massetti and M. Tavoni (2008). International energy R&amp;amp;D spillovers and the economics of greenhouse gas atmospheric stabilization,Energy Economics, 30 (6) Pages 2912-2929.<br /> <br /> Criqui, P., G. Klassen and L. Schrattenholzer (2000). The efficiency of energy R&amp;amp;D expenditures. Economic modeling of environmental policy and endogenous technical change, Amsterdam, November 16-17, 2000.<br /> <br /> Eliasch J. (2008) Climate Change: Financing Global Forests. The Eliasch Review, available at http://www.occ.gov.uk/activities/eliasch.htm<br /> <br /> EIA (2008). Annual Energy Outlook. Energy Information Administration, Washington, DC.<br /> <br /> EIA (2008b). International Energy Outlook. Energy Information Administration, Washington, DC. ENERDATA (2008). Energy Statistics.<br /> <br /> EPA Report 430-R-06-003, June 2006. http://www.epa.gov/climatechange/economics/mitigation.html.<br /> <br /> Farrell A.E. and Brandt, A.R.(2006). Risks of the oil transition, Environmental Research Letters, 1 (1).<br /> <br /> Havlik, P., H. Valin, M. Herrero, M. Obersteiner, E. Schmid, M. C. Rufino, A. Mosnier, et al. 2014. “Climate Change Mitigation Through Livestock System Transitions.” ''Proceedings of the National Academy of Sciences'' 111 (10): 3709–14.<br /> <br /> Herrero, Mario, Petr Havlik, J McIntire, Amanda Palazzo, and Hugo Valin. 2014. “African Livestock Futures: Realizing the Potential of Livestock for Food Security, Poverty Reduction and the Environment in Sub-Saharan Africa.”<br /> <br /> IEA (2007). World Energy Outlook 2007. OECD/IEA, Paris.<br /> <br /> IEA (2005). Resources to Reserves ? Oil &amp;amp; Gas Technologies for the Energy Markets of the Future. OECD/IEA, Paris.<br /> <br /> IPCC (2007) Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment.<br /> <br /> IPCC, (2007b): Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [B. Metz, O.R. Davidson, P.R. Bosch, R. Dave, L.A. Meyer (eds)], Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA., XXX pp.<br /> <br /> Jamasab, T. (2007). Technical change theory and learning curves: patterns of progress in electric generation technologies, The Energy Journal 28 (3).<br /> <br /> Junginger, M., A. Faaij and W. C. Turkenburg (2005). Global experience curves for wind farms, Energy Policy 33: 133-150.<br /> <br /> Kahouli-Brahmi, S. (2008). Technological learning in energy-environment-economy modelling: a survey, Energy Policy 36 : 138-162.<br /> <br /> Klassen, G., A. Miketa, K. Larsen and T. Sundqvist (2005). The impact of R&amp;amp;D on innovation for wind energy in Denmark, Germany and the United Kingdom, Ecological Economics 54 (2-3): 227-240.<br /> <br /> Kouvaritakis, N., A. Soria and S. Isoard (2000). Endogenous Learning in World Post-Kyoto Scenarios: Application of the POLES Model under Adaptive Expectations, International Journal of Global Energy Issues 14 (1-4): 228-248.<br /> <br /> Kypreos, S. (2007). A MERGE model with endogenous technical change and the cost of carbon stabilisation, Energy Policy 35 : 5327-5336.<br /> <br /> McDonald, A. and L. Schrattenholzer (2001). Learning rates for energy technologies, Energy Policy 29 (4): 255-261.<br /> <br /> Nemet, G. F. (2006). Beyond the learning curve: factors influencing cost reductions in photovoltaics, Energy Policy 34 (17): 3218-3232.<br /> <br /> Nemet, G. F. and D. M. Kammen (2007). U.S. energy research and development: declining investment, increasing need, and the feasibility of expansion, Energy Policy 35 (1): 746-755.<br /> <br /> Schock, R. N., W. Fulkerson, M. L. Brown, R. L. San Martin, D. L. Greene and J. Edmonds (1999). How much is energy research &amp;amp; development worth as insurance? Annual Review of Energy and the Environment 24 : 487-512.<br /> <br /> Söderholm, P. and G. Klassen (2007). Wind power in Europe: a simultaneous innovation-diffusion model, Environmental and Resource Economics 36 (2): 163-190.<br /> <br /> Tavoni, M., B. Sohngen and V. Bosetti (2007), Forestry and the carbon market response to stabilise climate, Energy Policy, 35 : 5346--5353.<br /> <br /> UN (2004), World Population to 2300, Report No. ST/ESA/SER.A/236, Department of Economic and Social Affairs, Population Division, New York</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=Carbon_dioxide_removal_(CDR)_options_-_WITCH&diff=11340 2020-03-03T13:23:09Z

<p>Johannes Emmerling: </p> <hr /> <div>The CDR options modelled in WITCH are BECCS and Direct Air Capture (DAC).{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=Carbon dioxide removal (CDR) options<br /> }}</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=Pollutants_and_non-GHG_forcing_agents_-_WITCH&diff=11337 2020-03-03T13:20:55Z

<p>Johannes Emmerling: </p> <hr /> <div>{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=Pollutants and non-GHG forcing agents<br /> }}<br /> The WITCH model represents the black carbon (BC), carbon monoxide (CO), ammoniac (NH3), nitrogen oxides (NOx), organic carbon (OC), sulfur dioxide (SO2), volatile organic compounds (VOCs).<br /> <br /> The air quality module relates the pollution economic activities to emission levels of the most important air pollutants. It allows the assessment of air pollution emissions in baseline scenarios or under a climate or pollution regulation scenario. The implementation originates from the LIMITS project and its emission factors have been calculated from the GAINS model in the context of the EMF30 exercise. In the WITCH model we use information on both fuel use and the type of electricity generation technologies employed.<br /> <br /> In WITCH we do not model all the activities that generate air pollution, therefore the non-energy-related pollution is accounted for exogenously. For this non-modelled sector the emissions are taken directly from available databases and mapped into the (SNAP sectors), which are generally sector categories for reporting air pollutant levels. The emissions of the exo-sectors (sectors that are related to energy but are not accounted in the model directly, see the table in appendix), from the EMF30 database. The non-energy sectors, such as solvents, waste (landfills, waste water, non-energy incineration), agriculture waste burning on fields, agriculture, Grassland burning and Forest burning and the ammonia emissions follow the RCP8.5 emissions from the RCP database.</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=Land-use_-_WITCH&diff=11334 2020-03-03T13:15:23Z

<p>Johannes Emmerling: </p> <hr /> <div>{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=Land-use<br /> }}<br /> Land-use in WITCH is taken into account through soft-linking the model to the [http://www.globiom.org/ GLOBIOM] model. Given the importance of land use emissions, of the link between agriculture, biomass energy and forest management, modelling land-use is of key importance in integrated assessment models. Rather than being modelled in its full detail, land-use in WITCH is represented by the mean response functions produced by the Global Biosphere Management Model (GLOBIOM) land-use model (Havlik et al. 2014). GLOBIOM is a partial equilibrium model that covers agriculture and forestry, including bioenergy. It is used for analysing land-use scenarios over many years. In GLOBIOM, the world is divided into 30 economic regions, in which consumer behaviour is modelled through isoelastic demand functions. Commodity uses “Simulation Units”, which are aggregates of 5 to 30 arcmin pixels belonging to the same altitude, slope, and soil class in the same country. For crops, grass, and forest products, Leontief production functions covering alternative production systems are calibrated from biophysical models including EPIC (Izaurralde et al. 2006). Economic optimization is based on a spatial equilibrium approach and regional price-quantity equilibria are computed.</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=Transport_-_WITCH&diff=11331 2020-03-03T13:12:08Z

<p>Johannes Emmerling: /* Light Duty Vehicles */</p> <hr /> <div>{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=Transport<br /> }}<br /> The transport sector is part of the [[#non-electric sector|non-electric sector]].<br /> == Light Duty Vehicles ==<br /> <br /> WITCH model has been designed to incorporate a range of competing vehicle types to assist in the determination&lt;br /&gt; of the dominant modes of light duty vehicles LDV transport that will tend to be selected to adequately satisfy demand for mobility.<br /> <br /> Transport has been included in the model through the incorporation of the impact of investments in LDVs and fuel expenditures on the level of consumption. This means that increased LDV travel (in terms of kilometres travelled per vehicle) as well as the costs of the vehicle and fuel expenditure directly impact utility through the corresponding effect of decreasing consumption on other goods and services.<br /> <br /> Demand for vehicles is set exogenously based on the assumption that constant travel patterns correspond to given levels and growth rates of GDP and population. This assumption is important as the demand for private transport will likely continue to be high and have a strong correlation with national income, unless a significant change in the way public transport is provided occurs.<br /> <br /> The possibility of introducing a &quot;Travel Elasticity Switch&quot; and a ?&quot;Vehicle - ownership Elasticity Switch&quot; provides feedback effects which test the sensitivity of these constraints ( these elasticity impacts will be reviewed in future and are not imposed within this analysis ). Figure 3.3.1 shows the transportation module within the WITCH model structure. As noted, the model separates consumption in transport from the rest of consumption, which allows for the direct modelling of the costs involved in switching between vehicles and fuels for a given demand of mobility. Investments in vehicle capital and supplementary costs decrease the level of consumption. A Leontief production function (LDV Trans in Figure 3.3.1) represents the fixed proportions of operation &amp;amp; maintenance (O&amp;amp;M) costs, fuel and investment cost required for each technological type. Fuel demand and fuel category depend upon the vehicle chosen.<br /> <br /> '''Figure 3.3.1 The transport module'''&lt;br /&gt;[[File:36405636.png|600x600px]]</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=Macro-economy_-_WITCH&diff=11328 2020-03-03T13:10:30Z

<p>Johannes Emmerling: </p> <hr /> <div>{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=Macro-economy<br /> }}<br /> == Production function ==<br /> <br /> The production side of the economy is very aggregated. Each region produces one single commodity that can be used for consumption or investments. The final good is produced using&lt;br /&gt; capital, labour and energy services. In the first place capital and labor are aggregated using a Cobb-Douglas production function. This nest is then aggregated with energy services with a Constant Elasticity of Substitution production function (CES), see Figure 2.3.1 .<br /> <br /> The optimal path of consumption is determined by optimising the intertemporal social welfare function, which is defined as the an isoelastic utility function of per capita consumption, weighted by regional population. The pure rate of time is set at 1% per year.<br /> <br /> Energy services, in turn, are given by a combination of the physical energy input and a stock of energy efficiency knowledge. This way of modelling energy services allows for endogenous improvements in energy efficiency. Energy efficiency increases with investments in dedicated energy R&amp;amp;D, which build up the stock of knowledge. The stock of knowledge can then replace (or substitute) physical energy in the production of energy services.<br /> <br /> Energy used in final production is a combination of electric and non electric energy. Electric energy can be generated using a set of different technology options and non electric energy also entails different fuels. Each region will choose the optimal intertemporal mix of technologies and R&amp;amp;D investments in a strategic way<br /> <br /> '''Figure 2.3.1 The nested production function'''&lt;br /&gt;[[File:WITCH CES Production Function.png|600x600px]]</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=File:WITCH_CES_Production_Function.png&diff=11327 2020-03-03T13:09:50Z

<p>Johannes Emmerling: </p> <hr /> <div>WITCH CES Production Function</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=Spatial_dimension_-_WITCH&diff=11324 2020-03-03T11:49:05Z

<p>Johannes Emmerling: </p> <hr /> <div>{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=Spatial dimension<br /> }}<br /> Countries included within the model are grouped into 17 regions clustered by geography, income and the structure of energy demand. <br /> <br /> The regional disaggregation is now flexible and the model can be run on different model configurations, typically from 5 to 17 regions, or more.<br /> <br /> [[File:WITCH default regional aggregation.png|600x600px]]&lt;br /&gt;'''Figure 1.1: Regions of the WITCH model'''<br /> <br /> The 17 regions are:<br /> <br /> {| class=&quot;wikitable&quot;<br /> ! Region<br /> ! Countries<br /> |-<br /> |brazil<br /> |Brazil<br /> |-<br /> |canada<br /> |Canada<br /> |-<br /> |china<br /> |China, including Taiwan<br /> |-<br /> |europe<br /> |Europe<br /> |-<br /> |seasia<br /> |South East Asia<br /> |-<br /> |india<br /> |India<br /> |-<br /> |indonesia<br /> |Indonesia<br /> |-<br /> |jpnkor<br /> |Japan and South Korea<br /> |-<br /> |laca<br /> |Latin America and the Caribbean<br /> |-<br /> |mena<br /> |Middle East and North Africa<br /> |-<br /> |mexico<br /> |Mexico<br /> |-<br /> |oceania<br /> |Australia and New Zealand<br /> |-<br /> |sasia<br /> |South Asia<br /> |-<br /> |ssa<br /> |Sub Saharan Africa<br /> |-<br /> |southafrica<br /> |South Africa<br /> |-<br /> |te<br /> |Non-EU Eastern European countries, including Russia<br /> |-<br /> |usa<br /> |United States of America<br /> |}<br /> <br /> WITCH features any regional aggregations in the so-called coalitions. Some common coalitions are:<br /> <br /> * regional coalitions : each region is mapped to a coalition containing only this region.<br /> * world coalition : a coalition containing all the world regions.<br /> <br /> Coalitions and regions interact with each others because of the presence of economic (technology, exhaustible natural resources) and environmental global externalities.</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=File:WITCH_default_regional_aggregation.png&diff=11323 2020-03-03T11:48:41Z

<p>Johannes Emmerling: </p> <hr /> <div>WITCH default regional aggregation</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=Spatial_dimension_-_WITCH&diff=11320 2020-03-03T11:39:12Z

<p>Johannes Emmerling: </p> <hr /> <div>{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=Spatial dimension<br /> }}<br /> Countries included within the model are grouped into 17 regions clustered by geography, income and the structure of energy demand. <br /> <br /> The regional disaggregation is now flexible and the model can be run on different model configurations, typically from 5 to 17 regions, or more.<br /> <br /> [[File:WITCH Regional Aggregation.png|600x600px]]&lt;br /&gt;'''Figure 1.1: Regions of the WITCH model'''<br /> <br /> The 17 regions are:<br /> <br /> {| class=&quot;wikitable&quot;<br /> ! Region<br /> ! Countries<br /> |-<br /> |brazil<br /> |Brazil<br /> |-<br /> |canada<br /> |Canada<br /> |-<br /> |china<br /> |China, including Taiwan<br /> |-<br /> |europe<br /> |Europe<br /> |-<br /> |seasia<br /> |South East Asia<br /> |-<br /> |india<br /> |India<br /> |-<br /> |indonesia<br /> |Indonesia<br /> |-<br /> |jpnkor<br /> |Japan and South Korea<br /> |-<br /> |laca<br /> |Latin America and the Caribbean<br /> |-<br /> |mena<br /> |Middle East and North Africa<br /> |-<br /> |mexico<br /> |Mexico<br /> |-<br /> |oceania<br /> |Australia and New Zealand<br /> |-<br /> |sasia<br /> |South Asia<br /> |-<br /> |ssa<br /> |Sub Saharan Africa<br /> |-<br /> |southafrica<br /> |South Africa<br /> |-<br /> |te<br /> |Non-EU Eastern European countries, including Russia<br /> |-<br /> |usa<br /> |United States of America<br /> |}<br /> <br /> WITCH features any regional aggregations in the so-called coalitions. Some common coalitions are:<br /> <br /> * regional coalitions : each region is mapped to a coalition containing only this region.<br /> * world coalition : a coalition containing all the world regions.<br /> <br /> Coalitions and regions interact with each others because of the presence of economic (technology, exhaustible natural resources) and environmental global externalities.</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=Spatial_dimension_-_WITCH&diff=11317 2020-03-03T11:26:39Z

<p>Johannes Emmerling: update to witch17</p> <hr /> <div>{{ModelDocumentationTemplate<br /> |IsDocumentationOf=WITCH<br /> |DocumentationCategory=Spatial dimension<br /> }}<br /> Countries included within the model are grouped into 17 regions clustered by geography, income and the structure of energy demand. <br /> <br /> Regional disaggregation is now flexible and the model can be run on different model configurations, typically from 5 to 17 regions, or more.<br /> <br /> [[File:WITCH Regional Aggregation.png]]&lt;br /&gt;'''Figure 1.1: Regions of the WITCH model (except that India and Indonesia are not detached from their former regions, sasia and easia, respectively).'''<br /> <br /> The 17 regions are:<br /> <br /> {| class=&quot;wikitable&quot;<br /> ! Region<br /> ! Countries<br /> |-<br /> |cajaz<br /> |Canada, Japan, New Zealand<br /> |-<br /> |china<br /> |China, including Taiwan<br /> |-<br /> |easia<br /> |South East Asia<br /> |-<br /> |india<br /> |India<br /> |-<br /> |indonesia<br /> |Indonesia<br /> |-<br /> |kosau<br /> |South Korea, South Africa, Australia<br /> |-<br /> |laca<br /> |Latin America, Mexico and Caribbean<br /> |-<br /> |mena<br /> |Middle East and North Africa<br /> |-<br /> |neweuro<br /> |EU new countries + Switzerland + Norway<br /> |-<br /> |oldeuro<br /> |EU old countries (EU-15)<br /> |-<br /> |sasia<br /> |South Asia<br /> |-<br /> |ssa<br /> |Sub Saharan Africa<br /> |-<br /> |te<br /> |Non-EU Eastern European countries, including Russia<br /> |-<br /> |usa<br /> |United States of America<br /> |}<br /> <br /> WITCH features any regional aggregations in the so-called coalitions. Some common coalitions are:<br /> <br /> * regional coalitions : each region is mapped to a coalition containing only this region.<br /> * world coalition : a coalition containing all the world regions.<br /> <br /> Coalitions and regions interact with each others because of the presence of economic (technology, exhaustible natural resources) and environmental global externalities.</div>

Johannes Emmerling https://www.iamcdocumentation.eu/index.php?title=File:WITCH_Regional_Aggregation.png&diff=11316 2020-03-03T11:26:08Z

<p>Johannes Emmerling: </p> <hr /> <div>WITCH Regional Aggregation</div>

Johannes Emmerling