https://models.pbl.nl/api.php?action=feedcontributions&feedformat=atom&user=EdelenboscoIMAGE - User contributions [en]2026-08-25T00:41:46ZUser contributionsMediaWiki 1.39.15https://models.pbl.nl/index.php?title=Energy_demand/Description&diff=36586Energy demand/Description2021-06-18T15:56:23Z<p>Edelenbosco: </p>
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<div>{{ComponentDescriptionTemplate<br />
|Reference=De Vries et al., 2001; Richels et al., 2004; Van Ruijven et al., 2016; Van Ruijven et al., 2011; Isaac and van Vuuren, 2009; Daioglou et al., 2014; Plotkin and Singh 2009;<br />
}}<br />
<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]]). Electric vehicle battery costs are updated based on Nykvist et al. [[Nykvist2015|2015]]), which is described in Edelenbosch et al. [[Edelenbosch et al. 2018|2018]].<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 />
</div></div>Edelenboscohttps://models.pbl.nl/index.php?title=Edelenbosch_et_al._2018&diff=36585Edelenbosch et al. 20182021-06-18T15:55:34Z<p>Edelenbosco: Created page with "{{ReferenceTemplate |Author=O.Y. Edelenbosch, A. F. Hof, B. Nykvist, B. Girod & D. P. van Vuuren |Year=2018 |Title=Transport electrification: the effect of recent battery cost..."</p>
<hr />
<div>{{ReferenceTemplate<br />
|Author=O.Y. Edelenbosch, A. F. Hof, B. Nykvist, B. Girod & D. P. van Vuuren<br />
|Year=2018<br />
|Title=Transport electrification: the effect of recent battery cost reduction on future emission scenarios<br />
|DOI=https://doi.org/10.1007/s10584-018-2250-y<br />
|PublicationType=Journal article<br />
|Journal=Climatic Change<br />
|Volume2=151<br />
|Pages2=95–108<br />
}}</div>Edelenboscohttps://models.pbl.nl/index.php?title=Nykvist2015&diff=36584Nykvist20152021-06-18T15:50:04Z<p>Edelenbosco: </p>
<hr />
<div>{{ReferenceTemplate<br />
|Author=B. Nykvist, and M. Nilsson<br />
|Year=2015<br />
|Title=Rapidly falling costs of battery packs for electric vehicles<br />
|DOI=https://doi.org/10.1038/nclimate2564<br />
|PublicationType=Journal article<br />
|Journal=Nature Climate Change<br />
|Volume2=5<br />
|Pages2=329–332<br />
}}</div>Edelenboscohttps://models.pbl.nl/index.php?title=Nykvist2015&diff=36583Nykvist20152021-06-18T15:44:04Z<p>Edelenbosco: Created page with "{{ReferenceTemplate |Author=Nykvist, Bjorn and Nilsson, Maans |Year=2015 |Title=Rapidly falling costs of battery packs for electric vehicles |DOI=https://doi.org/10.1038/nclim..."</p>
<hr />
<div>{{ReferenceTemplate<br />
|Author=Nykvist, Bjorn and Nilsson, Maans<br />
|Year=2015<br />
|Title=Rapidly falling costs of battery packs for electric vehicles<br />
|DOI=https://doi.org/10.1038/nclimate2564<br />
|PublicationType=Journal article<br />
|Journal=Nature Climate Change<br />
|Volume2=5<br />
|Pages2=329–332<br />
}}</div>Edelenboscohttps://models.pbl.nl/index.php?title=Forest_management/Data_uncertainties_limitations&diff=27833Forest management/Data uncertainties limitations2016-11-22T10:16:26Z<p>Edelenbosco: </p>
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<div>{{ComponentDataUncertaintyAndLimitationsTemplate<br />
|Reference=FAO, 2010; IEA, 2012; Brown, 2000; Carle and Holmgren, 2008; UNEP-INTERPOL, 2012; FAO, 2001a; FAO, 2008;<br />
|Description=<h2>Data, uncertainties and limitations</h2><br />
===Data===<br />
The main data source for the development and calibration of the forest management module is FAO Forest Resource Assessment ([[FAO, 2010]]), from which data on wood production and deforested areas are derived. In addition, statistics from the International Energy Agency ([[IEA, 2012]]]) are used to estimate the regional fuelwood production, based on household fuelwood and charcoal requirements in national energy statistics. Finally, national data were collected to parameterise the type and production parameters of forest management in world regions (see details in [[Arets et al., 2011]]) and establishment of new forest plantations was designed according to planting rates reported and projected by FAO ([[Brown, 2000]]; [[Carle and Holmgren, 2008]]).<br />
<br />
===Uncertainties===<br />
Several assumptions had to be made to project future production in forest management systems. These pinpoint the uncertainties in the forestry management model. Better data, monitoring and reporting would improve calibration of the IMAGE forest management module.<br />
<br />
FAO Forest Resource Assessment reports are published regularly on quantities of industrially produced wood and the areas of primary and secondary forests. However, these reports do not include the area from which these wood quantities are harvested, and the forest management system of these areas. The amount of wood produced in deforestation processes is not reported, probably due to the illegal nature of many such operations. <br />
<br />
Few data are available on the extent of illegal logging, they are not captured in the FAO statistics, but in satellite-based assessments, and only very rough estimates are available ([[UNEP-INTERPOL, 2012]]). In addition, few data are available on informal collection of fuelwood in forests in developing countries ([[FAO, 2001a]]; [[FAO, 2008]]). Estimates of total fuelwood demand are highly uncertain ([[IEA, 2012]]), and fuelwood demand is only partly met by the forestry operations in this IMAGE module. <br />
<br />
Another uncertainty is the starting point, which is the state of forest use by age cohort in 1970. As forests take several decades to a century to regrow after felling, the effect of historic uncertainties in forest-use extends far into the future.<br />
<br />
===Limitations===<br />
<div class="version changev31"><br />
Timber demand in IMAGE 3.0 is the sum of the demand for sawlogs, pulpwood and fuelwood. This total demand is than used in harvesting forests across the world, without knowing anymore the underlying wood types. So, forest plantations, for example, can used to fulfill the demand for either sawlogs of fuelwood. For specific assessments (e.g. for timber use for modern biomass), it would have been useful to remain the three wood types in the allocation. <br />
<br />
The timber demand in a region is the sum of local/regional demands and timber claims by other regions. The trade assumptions are adopted from external models, limiting the application of the model for cases with regional timber scarcity (which will change the total demand in a region).<br />
</div><br />
}}</div>Edelenboscohttps://models.pbl.nl/index.php?title=Forest_management/Data_uncertainties_limitations&diff=27832Forest management/Data uncertainties limitations2016-11-22T10:16:03Z<p>Edelenbosco: </p>
<hr />
<div>{{ComponentDataUncertaintyAndLimitationsTemplate<br />
|Reference=FAO, 2010; IEA, 2012; Brown, 2000; Carle and Holmgren, 2008; UNEP-INTERPOL, 2012; FAO, 2001a; FAO, 2008;<br />
|Description=<h2>Data, uncertainties and limitations</h2><br />
===Data===<br />
The main data source for the development and calibration of the forest management module is FAO Forest Resource Assessment ([[FAO, 2010]]), from which data on wood production and deforested areas are derived. In addition, statistics from the International Energy Agency ([[IEA, 2012]]]) are used to estimate the regional fuelwood production, based on household fuelwood and charcoal requirements in national energy statistics. Finally, national data were collected to parameterise the type and production parameters of forest management in world regions (see details in [[Arets et al., 2011]]) and establishment of new forest plantations was designed according to planting rates reported and projected by FAO ([[Brown, 2000]]; [[Carle and Holmgren, 2008]]).<br />
<br />
===Uncertainties===<br />
Several assumptions had to be made to project future production in forest management systems. These pinpoint the uncertainties in the forestry management model. Better data, monitoring and reporting would improve calibration of the IMAGE forest management module.<br />
<br />
FAO Forest Resource Assessment reports are published regularly on quantities of industrially produced wood and the areas of primary and secondary forests. However, these reports do not include the area from which these wood quantities are harvested, and the forest management system of these areas. The amount of wood produced in deforestation processes is not reported, probably due to the illegal nature of many such operations. <br />
<br />
Few data are available on the extent of illegal logging, they are not captured in the FAO statistics, but in satellite-based assessments, and only very rough estimates are available ([[UNEP-INTERPOL, 2012]]). In addition, few data are available on informal collection of fuelwood in forests in developing countries ([[FAO, 2001a]]; [[FAO, 2008]]). Estimates of total fuelwood demand are highly uncertain ([[IEA, 2012]]), and fuelwood demand is only partly met by the forestry operations in this IMAGE module. <br />
<br />
Another uncertainty is the starting point, which is the state of forest use by age cohort in 1970. As forests take several decades to a century to regrow after felling, the effect of historic uncertainties in forest-use extends far into the future.<br />
<br />
<div class="version changev31"><br />
===Limitations===<br />
Timber demand in IMAGE 3.0 is the sum of the demand for sawlogs, pulpwood and fuelwood. This total demand is than used in harvesting forests across the world, without knowing anymore the underlying wood types. So, forest plantations, for example, can used to fulfill the demand for either sawlogs of fuelwood. For specific assessments (e.g. for timber use for modern biomass), it would have been useful to remain the three wood types in the allocation. <br />
<br />
The timber demand in a region is the sum of local/regional demands and timber claims by other regions. The trade assumptions are adopted from external models, limiting the application of the model for cases with regional timber scarcity (which will change the total demand in a region).<br />
</div><br />
}}</div>Edelenboscohttps://models.pbl.nl/index.php?title=Energy_supply/Description&diff=27831Energy supply/Description2016-11-22T09:52:36Z<p>Edelenbosco: </p>
<hr />
<div>{{ComponentDescriptionTemplate<br />
|Reference=Hoogwijk, 2004; De Vries et al., 2007; New et al., 1997; Rogner, 1997; Mulders et al., 2006;<br />
|Description====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 supply cost 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. These submodules indicate how demand can be met by supply in a region and other regions through interregional trade.<br />
<br />
<table class="pbltable"><br />
<div class="thumbcaption dark">Table: Main assumptions on fossil fuel resources ([[Rogner, 1997]]; [[Mulders et al., 2006]])</div><br />
<tr><th><br />
</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. 1970-2005 production<br />
</td><td>4.4<br />
</td><td>2.1<br />
</td><td>1.6<br />
</td><td>1.1<br />
</td></tr><br />
<tr><td>Reserves<br />
</td><td>4.8<br />
</td><td>4.6<br />
</td><td>23.0<br />
</td><td>2.2<br />
</td></tr><br />
<tr><td>Other conventional resources<br />
</td><td>6.6<br />
</td><td>6.9<br />
</td><td>117.7<br />
</td><td>10.0<br />
</td></tr><br />
<tr><td>Unconventional resources (reserves)<br />
</td><td>2.9<br />
</td><td>6.9<br />
</td><td>25.0<br />
</td><td>233.5<br />
</td></tr><br />
<tr><td>Other unconventional resources<br />
</td><td>46.2<br />
</td><td>498.6<br />
</td><td>1.3<br />
</td><td>23.0<br />
</td></tr><br />
<tr><td>Total<br />
</td><td>65.0<br />
</td><td>519.2<br />
</td><td>168.6<br />
</td><td>270.0<br />
</td></tr><br />
</table><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 two to eight times the 1970–2005 production level. Production estimates for unconventional resources are much larger, albeit very 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 (a threshold of 60% is used) 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 />
===Bioenergy===<br />
The structure of the biomass submodule is similar to that for fossil fuel supply, but with the following differences ([[Hoogwijk, 2004]]): <br />
* Depletion of bioenergy is not governed by cumulative production but by the degree to which available land is used for commercial energy crops.<br />
* The total amount of potentially available bioenergy is derived from bioenergy crop yields calculated on a 0.5x0.5 degree grid with the IMAGE [[Crops and grass|crop model]] for various land-use scenarios for the 21st century. Potential supply is restricted on the basis of a set of criteria, the most important of which is that bioenergy crops can only be on abandoned agricultural land and on part of the natural grassland. The costs of primary bioenergy crops (woody, maize and sugar cane) are calculated with a [[Cobb-Douglas economic growth model|Cobb-Douglas production function]] using labour , land rent and capital costs as inputs. The land costs are based on average regional income levels per km<sup>2</sup>, which was found to be a reasonable proxy for regional differences in land rent costs. The production functions are calibrated to empirical data ([[Hoogwijk, 2004]]).<br />
* The model describes the conversion of biomass (including residues, in addition to wood crops, maize and sugar cane) to two generic secondary fuel types: bio-solid fuels used in the industry and power sectors; and liquid fuel used mostly in the transport sector. <br />
* The trade and allocation of biofuel production to regions is determined by optimisation. An optimal mix of bio-solid and bio-liquid fuel supply across regions is calculated, using the prices of the previous time step to calculate the demand.<br />
<br />
The production costs for bioenergy are represented by the costs of feedstock and conversion. Feedstock costs increase with actual production as a result of depletion, while conversion costs decrease with cumulative production as a result of ‘learning by doing’. Feedstock costs include the costs of land, labour and capital, while conversion costs include capital, {{abbrTemplate|O&M}} and energy use in this process. For both steps, the associated greenhouse gas emissions (related to deforestation, N<sub>2</sub>O from fertilisers, energy) are estimated (see Component [[Emissions]]), and are subject to carbon tax, where relevant.<br />
<br />
<div class="version newv31"><br />
Besides the energy crops mentioned above, agricultural and forestry residues can also be used as a primary feedstock for modern bioenergy. The availability of residues is linked to the productivity of agriculture and forestry, taking into account the effect of changing yields (see [[Agricultural economy/Description|Agricultural economy]] description) or [[Forest management]] techniques. The available potential is limited by environmental constraints as well as competing uses (use of agricultural residues as feed for livestock, see [[Agricultural economy/Description|Agricultural economy]]). As with bioenergy crops, availability and costs of residues are calculated on a 0.5x0.5 degree grid. For further details see ([[Daioglou et al. 2016]]).<br />
</div><br />
<br />
===Other renewable energy===<br />
Potential supply of renewable energy (wind, solar and bioenergy) is estimated generically as follows ([[Hoogwijk, 2004]]; [[De Vries et al., 2007]]): <br />
# Physical and geographical data for the regions considered are collected on a 0.5x0.5 degree grid. The characteristics of wind speed, insulation and monthly variation are taken from the digital database constructed by the Climate Research Unit ([[New et al., 1997]]). <br />
# The model assesses the part of the grid cell that 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 from energy crops is estimated using suitability/availability factors taking account of competing land-use options and the harvested rain-fed yield of energy crops.<br />
# 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.<br />
# 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 />
}}</div>Edelenboscohttps://models.pbl.nl/index.php?title=Energy_supply/Description&diff=27830Energy supply/Description2016-11-22T09:49:24Z<p>Edelenbosco: </p>
<hr />
<div>{{ComponentDescriptionTemplate<br />
|Reference=Hoogwijk, 2004; De Vries et al., 2007; New et al., 1997; Rogner, 1997; Mulders et al., 2006;<br />
|Description====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 supply cost 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. These submodules indicate how demand can be met by supply in a region and other regions through interregional trade.<br />
<br />
<table class="pbltable"><br />
<div class="thumbcaption dark">Table: Main assumptions on fossil fuel resources ([[Rogner, 1997]]; [[Mulders et al., 2006]])</div><br />
<tr><th><br />
</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. 1970-2005 production<br />
</td><td>4.4<br />
</td><td>2.1<br />
</td><td>1.6<br />
</td><td>1.1<br />
</td></tr><br />
<tr><td>Reserves<br />
</td><td>4.8<br />
</td><td>4.6<br />
</td><td>23.0<br />
</td><td>2.2<br />
</td></tr><br />
<tr><td>Other conventional resources<br />
</td><td>6.6<br />
</td><td>6.9<br />
</td><td>117.7<br />
</td><td>10.0<br />
</td></tr><br />
<tr><td>Unconventional resources (reserves)<br />
</td><td>2.9<br />
</td><td>6.9<br />
</td><td>25.0<br />
</td><td>233.5<br />
</td></tr><br />
<tr><td>Other unconventional resources<br />
</td><td>46.2<br />
</td><td>498.6<br />
</td><td>1.3<br />
</td><td>23.0<br />
</td></tr><br />
<tr><td>Total<br />
</td><td>65.0<br />
</td><td>519.2<br />
</td><td>168.6<br />
</td><td>270.0<br />
</td></tr><br />
</table><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 two to eight times the 1970–2005 production level. Production estimates for unconventional resources are much larger, albeit very 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 (a threshold of 60% is used) 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 />
===Bioenergy===<br />
The structure of the biomass submodule is similar to that for fossil fuel supply, but with the following differences ([[Hoogwijk, 2004]]): <br />
* Depletion of bioenergy is not governed by cumulative production but by the degree to which available land is used for commercial energy crops.<br />
* The total amount of potentially available bioenergy is derived from bioenergy crop yields calculated on a 0.5x0.5 degree grid with the IMAGE [[Crops and grass|crop model]] for various land-use scenarios for the 21st century. Potential supply is restricted on the basis of a set of criteria, the most important of which is that bioenergy crops can only be on abandoned agricultural land and on part of the natural grassland. The costs of primary bioenergy crops (woody, maize and sugar cane) are calculated with a [[Cobb-Douglas economic growth model|Cobb-Douglas production function]] using labour , land rent and capital costs as inputs. The land costs are based on average regional income levels per km<sup>2</sup>, which was found to be a reasonable proxy for regional differences in land rent costs. The production functions are calibrated to empirical data ([[Hoogwijk, 2004]]).<br />
* The model describes the conversion of biomass (including residues, in addition to wood crops, maize and sugar cane) to two generic secondary fuel types: bio-solid fuels used in the industry and power sectors; and liquid fuel used mostly in the transport sector. <br />
* The trade and allocation of biofuel production to regions is determined by optimisation. An optimal mix of bio-solid and bio-liquid fuel supply across regions is calculated, using the prices of the previous time step to calculate the demand.<br />
<br />
The production costs for bioenergy are represented by the costs of feedstock and conversion. Feedstock costs increase with actual production as a result of depletion, while conversion costs decrease with cumulative production as a result of ‘learning by doing’. Feedstock costs include the costs of land, labour and capital, while conversion costs include capital, {{abbrTemplate|O&M}} and energy use in this process. For both steps, the associated greenhouse gas emissions (related to deforestation, N<sub>2</sub>O from fertilisers, energy) are estimated (see Component [[Emissions]]), and are subject to carbon tax, where relevant.<br />
<br />
<div class="version newv31"><br />
Besides the energy crops mentioned above, agricultural and forestry residues can also be used as a primary feedstock for modern bioenergy. The availability of residues is linked to the productivity of agriculture and forestry, taking into account the effect of changing yields (see [[Agricultural economy/Description|Agricultural economy]] description) or [[Forest management]] techniques. The available potential is limited by environmental constraints as well as competing uses (use of Agricultural residues as feed for livestock, see [[Agricultural economy/Description|Agricultural economy]]). As with bioenergy crops, availability and costs of residues are calculated on a 0.5x0.5 degree grid. For further details see ([[Daioglou et al. 2016]]).<br />
</div><br />
<br />
===Other renewable energy===<br />
Potential supply of renewable energy (wind, solar and bioenergy) is estimated generically as follows ([[Hoogwijk, 2004]]; [[De Vries et al., 2007]]): <br />
# Physical and geographical data for the regions considered are collected on a 0.5x0.5 degree grid. The characteristics of wind speed, insulation and monthly variation are taken from the digital database constructed by the Climate Research Unit ([[New et al., 1997]]). <br />
# The model assesses the part of the grid cell that 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 from energy crops is estimated using suitability/availability factors taking account of competing land-use options and the harvested rain-fed yield of energy crops.<br />
# 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.<br />
# 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 />
}}</div>Edelenboscohttps://models.pbl.nl/index.php?title=Energy_demand/Description&diff=27781Energy demand/Description2016-11-08T09:22:05Z<p>Edelenbosco: </p>
<hr />
<div>{{ComponentDescriptionTemplate<br />
|Reference=De Vries et al., 2001; Richels et al., 2004; Van Ruijven et al., 2016; Van Ruijven et al., 2011; Isaac and van Vuuren, 2009; Daioglou et al., 2014; Plotkin and Singh 2009;<br />
|Description=The energy demand module has aggregated formulations for some sectors and more detailed formulations for other sectors. In the description that follows, the generic model is presented which is used for the service sector, part of the industry sector (light) and in the category other sectors. Next, the more technology detailed sectors of residential energy use, heavy industry and transport are discussed in relation to the elements of the generic model.<br />
<br />
In the generic module, demand for final energy is calculated 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 />
===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 />
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 was include for the steel and cement sectors ([[Van Ruijven et al., 2016]]). These two sectors represented about 8% of global energy use and 13% of global anthropogenic greenhouse gas emissions in 2005. The generic structure of the energy demand module was adapted as follows:<br />
*Activity is described in terms of production of tonnes cement and steel. The regional demand for these commodities is determined by a relationship similar to the formulation of the structural change discussed above. Both cement and steel can be traded but this is less important for cement. 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 technologies. Each technology is characterised by costs and energy use per unit of production, both of which decline slowly over time. The actual mix of technologies used to produce steel and cement in the model is derived from a multinominal logit equation, and results in a larger market share for the technologies with the lowest costs. The autonomous improvement of these technologies leads to an autonomous increase in energy efficiency. The selection of technologies 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 technology because some technologies can only use specific energy carriers (e.g., electricity for electric arc furnaces). <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 is 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 />
<div class="version newv31"><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 />
</div><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 MJUE/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 />
}}</div>Edelenboscohttps://models.pbl.nl/index.php?title=Van_Ruijven_et_al.,_2016&diff=27780Van Ruijven et al., 20162016-11-08T09:21:22Z<p>Edelenbosco: Created page with "{{ReferenceTemplate |Author=B.J. van Ruijven, D.P. van Vuuren, W. Boskaljon, M. Neelis, D. Saygin, M.K. Patel |Year=2016 |Title=Long-term model-based projections of energy use..."</p>
<hr />
<div>{{ReferenceTemplate<br />
|Author=B.J. van Ruijven, D.P. van Vuuren, W. Boskaljon, M. Neelis, D. Saygin, M.K. Patel<br />
|Year=2016<br />
|Title=Long-term model-based projections of energy use and CO2 emissions from the global steel and cement industries<br />
|DOI=http://dx.doi.org/10.1016/j.resconrec.2016.04.016<br />
|PublicationType=Journal article<br />
|Volume5=<br />
|Publisher=<br />
|City=<br />
|ISBN=<br />
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}}</div>Edelenboscohttps://models.pbl.nl/index.php?title=Energy_demand/Description&diff=27779Energy demand/Description2016-11-08T09:15:35Z<p>Edelenbosco: </p>
<hr />
<div>{{ComponentDescriptionTemplate<br />
|Reference=De Vries et al., 2001; Richels et al., 2004; Van Ruijven et al., 2013; Van Ruijven et al., 2011; Isaac and van Vuuren, 2009; Daioglou et al., 2014; Plotkin and Singh 2009;<br />
|Description=The energy demand module has aggregated formulations for some sectors and more detailed formulations for other sectors. In the description that follows, the generic model is presented which is used for the service sector, part of the industry sector (light) and in the category other sectors. Next, the more technology detailed sectors of residential energy use, heavy industry and transport are discussed in relation to the elements of the generic model.<br />
<br />
In the generic module, demand for final energy is calculated 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 />
===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 />
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 was include for the steel and cement sectors ([[Van Ruijven et al., 2013]]). These two sectors represented about 8% of global energy use and 13% of global anthropogenic greenhouse gas emissions in 2005. The generic structure of the energy demand module was adapted as follows:<br />
*Activity is described in terms of production of tonnes cement and steel. The regional demand for these commodities is determined by a relationship similar to the formulation of the structural change discussed above. Both cement and steel can be traded but this is less important for cement. 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 technologies. Each technology is characterised by costs and energy use per unit of production, both of which decline slowly over time. The actual mix of technologies used to produce steel and cement in the model is derived from a multinominal logit equation, and results in a larger market share for the technologies with the lowest costs. The autonomous improvement of these technologies leads to an autonomous increase in energy efficiency. The selection of technologies 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 technology because some technologies can only use specific energy carriers (e.g., electricity for electric arc furnaces). <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 is 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 />
<div class="version newv31"><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 />
</div><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 MJUE/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 />
}}</div>Edelenboscohttps://models.pbl.nl/index.php?title=Energy_demand/Description&diff=27722Energy demand/Description2016-11-04T13:54:33Z<p>Edelenbosco: </p>
<hr />
<div>{{ComponentDescriptionTemplate<br />
|Reference=De Vries et al., 2001; Richels et al., 2004; Van Ruijven et al., 2013; Van Ruijven et al., 2011; Isaac and van Vuuren, 2009; Daioglou et al., 2014; Plotkin and Singh 2009;<br />
|Description=The energy demand module has aggregated formulations for some sectors and more detailed formulations for other sectors. In the description that follows, the generic model is presented which is used for the service sector, part of the industry sector (light) and in the category other sectors. Next, the more technology detailed sectors of residential energy use, heavy industry and transport are discussed in relation to the elements of the generic model.<br />
<br />
In the generic module, demand for final energy is calculated 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 />
===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 />
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 was include for the steel and cement sectors ([[Van Ruijven et al., 2013]]). These two sectors represented about 8% of global energy use and 13% of global anthropogenic greenhouse gas emissions in 2005. The generic structure of the energy demand module was adapted as follows:<br />
*Activity is described in terms of production of tonnes cement and steel. The regional demand for these commodities is determined by a relationship similar to the formulation of the structural change discussed above. Both cement and steel can be traded but this is less important for cement. 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 technologies. Each technology is characterised by costs and energy use per unit of production, both of which decline slowly over time. The actual mix of technologies used to produce steel and cement in the model is derived from a multinominal logit equation, and results in a larger market share for the technologies with the lowest costs. The autonomous improvement of these technologies leads to an autonomous increase in energy efficiency. The selection of technologies 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 technology because some technologies can only use specific energy carriers (e.g., electricity for electric arc furnaces). <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 is 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 />
<div class="version newv31"><br />
<br />
Since Girod et. al ([[Girod et al., 2012|2012]]) the LDV projected vehicle costs and efficiency have been revised to incorporate the most 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 />
</div><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 MJUE/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 />
}}</div>Edelenboscohttps://models.pbl.nl/index.php?title=Energy_demand/Description&diff=27717Energy demand/Description2016-11-04T13:43:07Z<p>Edelenbosco: </p>
<hr />
<div>{{ComponentDescriptionTemplate<br />
|Reference=De Vries et al., 2001; Richels et al., 2004; Van Ruijven et al., 2013; Van Ruijven et al., 2011; Isaac and van Vuuren, 2009; Daioglou et al., 2014; Plotkin and Singh 2009; <br />
|Description=The energy demand module has aggregated formulations for some sectors and more detailed formulations for other sectors. In the description that follows, the generic model is presented which is used for the service sector, part of the industry sector (light) and in the category other sectors. Next, the more technology detailed sectors of residential energy use, heavy industry and transport are discussed in relation to the elements of the generic model.<br />
<br />
In the generic module, demand for final energy is calculated 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 />
===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 />
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 was include for the steel and cement sectors ([[Van Ruijven et al., 2013]]). These two sectors represented about 8% of global energy use and 13% of global anthropogenic greenhouse gas emissions in 2005. The generic structure of the energy demand module was adapted as follows:<br />
*Activity is described in terms of production of tonnes cement and steel. The regional demand for these commodities is determined by a relationship similar to the formulation of the structural change discussed above. Both cement and steel can be traded but this is less important for cement. 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 technologies. Each technology is characterised by costs and energy use per unit of production, both of which decline slowly over time. The actual mix of technologies used to produce steel and cement in the model is derived from a multinominal logit equation, and results in a larger market share for the technologies with the lowest costs. The autonomous improvement of these technologies leads to an autonomous increase in energy efficiency. The selection of technologies 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 technology because some technologies can only use specific energy carriers (e.g., electricity for electric arc furnaces). <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 is 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 />
<div class="version newv31"><br />
<br />
Since Girod et. al ([[Girod et al., 2012|2012]]) the LDV projected vehicle costs and efficiency have been revised to incorporate the most 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 />
</div><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 MJUE/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 />
}}</div>Edelenboscohttps://models.pbl.nl/index.php?title=ADVANCE_project&diff=27711ADVANCE project2016-11-04T13:35:01Z<p>Edelenbosco: </p>
<hr />
<div>{{ApplicationTemplate<br />
|Website=http://fp7-advance.eu/<br />
|ApplicationType=5. Scientific research<br />
|IMAGERoleDescription=The IMAGE team will lead work on the improved representation of energy demand. In addition, IMAGE will participate in all other work packages, covering topics like: model documentation, heterogeneity, subsidies, taxes, uncertainty, technological learning, renewable integration, life cycle assessment, water energy nexus, infrastructure and policy relevance.<br />
|Summary=The ADVANCE project aims to improve the representations of complex system interactions and to thoroughly validate model behavior in order to increase confidence in climate policy assessments.<br />
|Partners=PIK; IIASA; FEEM; JRC; UCL; SMASH; UEA; ICCS/E3MLab; UPMF-EDDEN; NTNU; DLR; UU; Enerdata;<br />
|KeyReference=ADVANCE publications; Edelenbosch et al. 2016; <br />
|Description=ADVANCE project<br />
Integrated Assessment Models (IAMs) have become central tools used in forming long-term global and regional climate mitigation strategies. However, sound policy advice requires improved representations of complex system interactions and thorough validation of model behaviour, in order to increase confidence in climate policy assessments.<br />
To respond to this demand, the ADVANCE project has the following objectives:<br />
* Development of a new generation of IAMs for the analysis of climate change mitigation policies;<br />
* Improving the level of confidence that politicians have in the results of IAMs by increasing transparency;<br />
* Model validation with the aim of evaluating their strengths and limitations;<br />
* Improvement of the representation of energy demand: especially energy services, technologies, and consumer behaviour;<br />
* Enhanced representation of technological innovation, uncertainty, and system integration;<br />
* Evaluation of the impacts of mitigation policies on economic sectors in the EU and beyond;<br />
* Creation of a platform for sharing methodologies and input data sets in the modelling community.<br />
Based on improved IAMs, the ADVANCE project will contribute answers to the following key questions:<br />
* What is the role of energy efficiency improvements for climate change mitigation?<br />
* What are the bottlenecks for the development of a low-carbon energy supply system?<br />
* What are broader sustainability implications of alternative mitigation pathways?<br />
* How does uncertainty about technological innovation affect optimal innovation policies?<br />
* How can climate change mitigation targets and energy access objectives be reconciled?<br />
<br />
Model Documentation<br />
As part of the ADVANCE project, harmonised model documentation has been elaborated for all energy-economic and Integrated Assessment Models (IAMs) included in the project. The documentation is to enhance the understanding of models, as well as the comparability and interpretability of their results. To achieve comparability, model-specific reference cards have been made for all models. The [[reference card]] for IMAGE 3.0 is also presented on this website. If you are interested in model comparison, visit the [http://fp7-advance.eu/content/model-documentation model documentation] page of the ADVANCE project.<br />
}}</div>Edelenboscohttps://models.pbl.nl/index.php?title=Energy_demand/Description&diff=27707Energy demand/Description2016-11-04T13:17:27Z<p>Edelenbosco: </p>
<hr />
<div>{{ComponentDescriptionTemplate<br />
|Reference=De Vries et al., 2001; Richels et al., 2004; Van Ruijven et al., 2013; Van Ruijven et al., 2011; Isaac and van Vuuren, 2009; Daioglou et al., 2014;<br />
|Description=The energy demand module has aggregated formulations for some sectors and more detailed formulations for other sectors. In the description that follows, the generic model is presented which is used for the service sector, part of the industry sector (light) and in the category other sectors. Next, the more technology detailed sectors of residential energy use, heavy industry and transport are discussed in relation to the elements of the generic model.<br />
<br />
In the generic module, demand for final energy is calculated 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 />
===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 />
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 was include for the steel and cement sectors ([[Van Ruijven et al., 2013]]). These two sectors represented about 8% of global energy use and 13% of global anthropogenic greenhouse gas emissions in 2005. The generic structure of the energy demand module was adapted as follows:<br />
*Activity is described in terms of production of tonnes cement and steel. The regional demand for these commodities is determined by a relationship similar to the formulation of the structural change discussed above. Both cement and steel can be traded but this is less important for cement. 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 technologies. Each technology is characterised by costs and energy use per unit of production, both of which decline slowly over time. The actual mix of technologies used to produce steel and cement in the model is derived from a multinominal logit equation, and results in a larger market share for the technologies with the lowest costs. The autonomous improvement of these technologies leads to an autonomous increase in energy efficiency. The selection of technologies 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 technology because some technologies can only use specific energy carriers (e.g., electricity for electric arc furnaces). <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 is 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 />
<div class="version newv31"><br />
<br />
Since Girod et. al ([[Girod et al., 2012|2012]]) the LDV projected vehicle costs and efficiency have been revised to incorporate the most 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 />
</div><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 MJUE/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 />
}}</div>Edelenboscohttps://models.pbl.nl/index.php?title=Energy_demand/Description&diff=27706Energy demand/Description2016-11-04T13:17:01Z<p>Edelenbosco: </p>
<hr />
<div>{{ComponentDescriptionTemplate<br />
|Reference=De Vries et al., 2001; Richels et al., 2004; Van Ruijven et al., 2013; Van Ruijven et al., 2011; Isaac and van Vuuren, 2009; Daioglou et al., 2014;<br />
|Description=The energy demand module has aggregated formulations for some sectors and more detailed formulations for other sectors. In the description that follows, the generic model is presented which is used for the service sector, part of the industry sector (light) and in the category other sectors. Next, the more technology detailed sectors of residential energy use, heavy industry and transport are discussed in relation to the elements of the generic model.<br />
<br />
In the generic module, demand for final energy is calculated 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 />
===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 />
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 was include for the steel and cement sectors ([[Van Ruijven et al., 2013]]). These two sectors represented about 8% of global energy use and 13% of global anthropogenic greenhouse gas emissions in 2005. The generic structure of the energy demand module was adapted as follows:<br />
*Activity is described in terms of production of tonnes cement and steel. The regional demand for these commodities is determined by a relationship similar to the formulation of the structural change discussed above. Both cement and steel can be traded but this is less important for cement. 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 technologies. Each technology is characterised by costs and energy use per unit of production, both of which decline slowly over time. The actual mix of technologies used to produce steel and cement in the model is derived from a multinominal logit equation, and results in a larger market share for the technologies with the lowest costs. The autonomous improvement of these technologies leads to an autonomous increase in energy efficiency. The selection of technologies 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 technology because some technologies can only use specific energy carriers (e.g., electricity for electric arc furnaces). <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 is 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 />
<div class="version changev31"><br />
<br />
Since Girod et. al ([[Girod et al., 2012|2012]]) the LDV projected vehicle costs and efficiency have been revised to incorporate the most 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 />
</div><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 MJUE/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 />
}}</div>Edelenboscohttps://models.pbl.nl/index.php?title=Energy_demand/Description&diff=27705Energy demand/Description2016-11-04T13:14:08Z<p>Edelenbosco: </p>
<hr />
<div>{{ComponentDescriptionTemplate<br />
|Reference=De Vries et al., 2001; Richels et al., 2004; Van Ruijven et al., 2013; Van Ruijven et al., 2011; Isaac and van Vuuren, 2009; Daioglou et al., 2014;<br />
|Description=The energy demand module has aggregated formulations for some sectors and more detailed formulations for other sectors. In the description that follows, the generic model is presented which is used for the service sector, part of the industry sector (light) and in the category other sectors. Next, the more technology detailed sectors of residential energy use, heavy industry and transport are discussed in relation to the elements of the generic model.<br />
<br />
In the generic module, demand for final energy is calculated 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 />
===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 />
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 was include for the steel and cement sectors ([[Van Ruijven et al., 2013]]). These two sectors represented about 8% of global energy use and 13% of global anthropogenic greenhouse gas emissions in 2005. The generic structure of the energy demand module was adapted as follows:<br />
*Activity is described in terms of production of tonnes cement and steel. The regional demand for these commodities is determined by a relationship similar to the formulation of the structural change discussed above. Both cement and steel can be traded but this is less important for cement. 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 technologies. Each technology is characterised by costs and energy use per unit of production, both of which decline slowly over time. The actual mix of technologies used to produce steel and cement in the model is derived from a multinominal logit equation, and results in a larger market share for the technologies with the lowest costs. The autonomous improvement of these technologies leads to an autonomous increase in energy efficiency. The selection of technologies 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 technology because some technologies can only use specific energy carriers (e.g., electricity for electric arc furnaces). <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 is 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 />
<div class=“version newv31”><br />
Since Girod et. al ([[Girod et al., 2012|2012]]) the LDV projected vehicle costs and efficiency have been revised to incorporate the most 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 />
</div><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 MJUE/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 />
}}</div>Edelenboscohttps://models.pbl.nl/index.php?title=Plotkin_and_Singh_2009&diff=27703Plotkin and Singh 20092016-11-04T13:11:55Z<p>Edelenbosco: Created page with "{{ReferenceTemplate |Author=Plotkin, S. and M. Singh |Year=2009 |Title=Multi-path transportation futures study: vehicle characterization and scenario analyses. |DOI=www.ipd.an..."</p>
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<div>{{ComponentDescriptionTemplate<br />
|Reference=De Vries et al., 2001; Richels et al., 2004; Van Ruijven et al., 2013; Van Ruijven et al., 2011; Isaac and van Vuuren, 2009; Daioglou et al., 2014;<br />
|Description=The energy demand module has aggregated formulations for some sectors and more detailed formulations for other sectors. In the description that follows, the generic model is presented which is used for the service sector, part of the industry sector (light) and in the category other sectors. Next, the more technology detailed sectors of residential energy use, heavy industry and transport are discussed in relation to the elements of the generic model.<br />
<br />
In the generic module, demand for final energy is calculated 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 />
===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 />
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 was include for the steel and cement sectors ([[Van Ruijven et al., 2013]]). These two sectors represented about 8% of global energy use and 13% of global anthropogenic greenhouse gas emissions in 2005. The generic structure of the energy demand module was adapted as follows:<br />
*Activity is described in terms of production of tonnes cement and steel. The regional demand for these commodities is determined by a relationship similar to the formulation of the structural change discussed above. Both cement and steel can be traded but this is less important for cement. 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 technologies. Each technology is characterised by costs and energy use per unit of production, both of which decline slowly over time. The actual mix of technologies used to produce steel and cement in the model is derived from a multinominal logit equation, and results in a larger market share for the technologies with the lowest costs. The autonomous improvement of these technologies leads to an autonomous increase in energy efficiency. The selection of technologies 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 technology because some technologies can only use specific energy carriers (e.g., electricity for electric arc furnaces). <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 is 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 />
<div class=“version newv31”><br />
Since Girod et. al ([[Girod et al., 2012|2012]]) the LDV projected vehicle costs and efficiency have been revised to incorporate the most recent projections of LDV vehicle technology development. The vehicle characteristics are based on the in depth study performed by the Argonne National Laboratory.<br />
</div><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 MJUE/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 />
}}</div>Edelenboscohttps://models.pbl.nl/index.php?title=Model_description_IMAGE-CLUMondo&diff=27695Model description IMAGE-CLUMondo2016-11-04T11:04:32Z<p>Edelenbosco: </p>
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