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| |IsDocumentationOf=REMIND | | |IsEmpty=No |
| | |IsDocumentationOf=REMIND-MAgPIE |
| |DocumentationCategory=Energy end-use | | |DocumentationCategory=Energy end-use |
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| == Transport ==
| | Since version 1.7, REMIND-MAgPIE represents the transport, industry and residential/commercial end use sectors. In former REMIND-MAgPIE versions, residential/commercial and industry were represented as an aggregate stationary sector. |
| REMIND models the transport sector by using a hybrid approach combining top-down and bottom-up elements (see [[REMINDIMPORT/Macro-economy---REMIND_34379398#Macro-economy-REMIND-Figure3|Figure 3]]). Specifically, mobility demands for the 4 modeled transport sub-sectors (Passenger-light duty vehicles (LDV), Freight, Electric Rail, Passenger-Aviation and Buses) are derived in a top-down fashion, since they are input to a nested CES production function that ultimately produces GDP. For the LDV mode, three different technology options (internal combustion engine, battery electric vehicle, and fuel cell vehicle) compete against each other in a linear bottom-up technology model. | |
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| The transport sector requires input of final energy in different forms (liquids, electricity and hydrogen) and requires investments and operation and maintenance payments into the distribution infrastructure (infrastructure capacity grows linearly with distributed final energy) as well as into the vehicle stock. It generates emissions that go into the climate model and, depending on the scenario, can be taxed or limited by a budget. Furthermore, it is possible to consider taxes and subsidies on fuels. Material needs and embodied energy are not considered.
| | '''Table 1'''. Overview of energy carriers used in the various end-use sectors |
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| The main drivers/determinants of transport demand are GDP growth, the autonomous efficiency improvements (efficiency parameters of CES production function), and the elasticities of substitution between capital and energy and between stationary and transport energy forms. In more detail, mobility from the different modes is input to a CES function, the output of which is combined with stationary energy to generate a generalized energy good, which is combined with labor and capital in the main production function for GDP. Finally, inside a model run, different final energy prices (due to climate policy, different resource assumptions, etc.) can lead to substitution of different transport modes inside the CES function, or a total reduction of travel demand (see Pietzcker et al. (2014a) for a comparison of the different contributions to transport mitigation). For passenger transport we consider LDV (powered by liquids, electricity or hydrogen), Aviation and Bus (aggregated, only powered by liquids) and Electric Trains (only powered by electricity). For freight transport, there is only one generic mode based on liquid fuels. For the conversion technologies of primary energy sources into these secondary energy carriers, see Section [[REMINDIMPORT/Energy-conversion---REMIND_34379402#Energyconversion-REMIND-Conversion|Energy Conversion]].
| | <figtable id="tab:REMIND-MAgPIEtable_8"> |
| | | [[File:Table_8_Remind.PNG]] |
| The distribution of vehicles inside the LDV mode follows cost optimization (perfect linear substitutability), although with different non-linear constraints (learning curve, upper limits of 70% on share of battery-electric vehicles and 90% on Fuel Cell vehicles) that in most realizations lead to a technology mix.
| | </figtable> |
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| Efficiency, lifetime, investment costs, and fixed O&M costs parameters characterize all vehicle technologies. All these parameters, except investment costs for battery electric and fuel cell vehicles, are constant over time. Battery electric vehicles and fuel cell vehicles undergo learning-by-doing through a one-factor learning curve with floor costs that are asymptotically approached as cumulated capacity increases. Fuel prices are fully endogenous, as determined by the supply sector (intertemporal optimization with resource and capacity constraints as well as prices/constraints on emissions in policy scenarios).
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| REMIND calibrates the efficiencies of the transport CES leaves in such a way that when the baseline per-capita travel demand is plotted over per-capita GDP, travel demand in different regions shows a converging behavior. Regions with already very high affluence have mainly flat transport final energy per capita, in line with recent developments (USA, EU27, JPN), and a slow convergence towards a level in between that of the USA and EU27 today. REMIND assumes developed countries to show rising final energy per capita use with rising affluence, with some deviations in the exact path in the final energy per capita ? GDP per capita ? space due to differences in their recent history. They converge to a similar point like the average of OECD regions, but so slow that convergence would only happen after 2100. | |
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| Table 8. Overview of LDV technologies
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| [[File:54067642.jpg]] | |
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| == Residential and commercial sectors ==
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| The residential and commercial sectors and the industry sector are aggregated into one stationary sector in REMIND. For further details see section [[#other|other]].
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| == Industrial sector ==
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| The residential and commercial sectors and the industry sector are aggregated into one stationary sector in REMIND. For further details see section [[#other|other]].
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| == Other ==
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| In its present version, REMIND, represents an aggregate ?Stationary sector? that embodies residential, commercial and Industrial energy demand (see [[REMINDIMPORT/Macro-economy---REMIND_34379398#Macro-economy-REMIND-Figure3|Figure 3]]). We plan a disaggregation of the buildings (residential & commercial) and industry sectors for future REMIND versions.
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| Demand for energy types used in the stationary sector (electricity, solids, liquids, gas, district heat, and hydrogen) is modeled in a top-down fashion: they are input to a nested CES production function that produces GDP. Also, provision of these final energies is modeled in a bottom-up energy model, where detailed capital stocks of conversion technologies convert primary energies to secondary and final energies, with full substitutability between technologies.
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| The stationary sector requires input of final energy in different forms (electricity, solids, liquids, gas, district heat, and hydrogen) and requires investments and operation and maintenance payments into the distribution infrastructure (generic capacity constraint). It generates emissions that go into the climate model and, depending on the scenario, are taxed or limited by a budget.
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| The indirect energy use and material needs for production of appliances is not explicitly included, only implicitly embedded in the main CES production function via the total energy demand of a region. On the final energy provision side, REMIND represents all energy use for extraction and conversion up to the distribution of final energies.
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| The main drivers in the stationary sector are GDP growth, the autonomous efficiency improvements (efficiency parameters of CES production function), the elasticities of substitution between capital and energy and between stationary and transport energy forms. These drivers influence demand, in a similar manner as described for the transport sector, i.e. final energy types are input to a CES function, the output of which is combined with transport energy in a CES function to generate a generalized energy good, which is combined with labor and capital in the main production function for GDP. REMIND calibrates the efficiencies of the stationary CES leaves in such a way that when the baseline per-capita stationary energy demand is plotted over per-capita GDP, stationary energy demand in different regions shows a converging behavior. REMIND assumes developing countries to show rising final energy per capita use with rising affluence, with some deviations in the exact path in the FE/cap ? GDP/cap space due to differences in their recent history. For electricity demand, the per-capita levels converge to a similar point like the average of OECD regions, but convergence would only happen after 2100. For heat demand, the per-capita heat demand of a region levels off at a point based on a rough estimation of climate/heating demand in a region. Inside a model run, different FE prices (due to climate policy, different resource assumptions, etc.) can lead to substitution of different stationary energy types inside the CES function, or a total reduction of stationary energy demand. There is no direct price elasticity of demand in the model, the nested CES function results in different price elasticities at different points in time/system configurations.
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| The stationary sector differentiates between two explicit energy functions: electricity used for appliances, and all other inputs (gas, solids, district heat, liquids, and hydrogen) used for heating purposes. [[REMINDIMPORT/Electricity---REMIND_34379403#Electricity-REMIND-Table4|Table 4]], [[REMINDIMPORT/Heat---REMIND_34379404#Heat-REMIND-Table7a|Table 7a]] and [[REMINDIMPORT/Other-conversion---REMIND_34379405#Otherconversion-REMIND-Table7b|Table 7b]] show the primary energy sources that can be used to supply electricity or the other carriers used for heating. Combined heat and power plants using coal, gas or biomass are cross cutting along these two energy uses.
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| Technology choice follows cost optimization based on investment costs, fixed and variable operation and maintenance costs, fuel costs, emission costs, efficiencies, lifetimes, and learning rates. Endogenous technological change (learning-by-doing) influences wind and solar investment costs For fossil fuel power plants, some exogenous time-dependent improvement of efficiency parameters until 2050 and convergence of efficiencies that are regionally calibrated to observed 2005 values are implemented. REMIND assumes full substitutability between different technologies producing one final energy type.
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Note: The documentation of REMIND-MAgPIE is and is not yet 'published'!
Note: The documentation of REMIND-MAgPIE is and is not yet 'published'!
</figtable>