https://models.pbl.nl/api.php?action=feedcontributions&feedformat=atom&user=GernaatdIMAGE - User contributions [en]2026-07-23T07:48:10ZUser contributionsMediaWiki 1.39.15https://models.pbl.nl/index.php?title=Energy_supply/Description&diff=36041Energy supply/Description2019-04-26T14:47:24Z<p>Gernaatd: Removed figure, not good enough</p>
<hr />
<div>{{ComponentDescriptionTemplate<br />
|Reference=Hoogwijk, 2004; De Vries et al., 2007; New et al., 1997; Rogner, 1997; Mulders et al., 2006;<br />
}}<br />
<div class="page_standard"><br />
==Model description of {{ROOTPAGENAME}}==<br />
===Fossil fuels and uranium===<br />
Depletion of fossil fuels (coal, oil and natural gas) and uranium is simulated on the assumption that resources can be represented by a long-term cost-supple curve, consisting of different resource categories with increasing costs levels. The model assumes that the cheapest deposits will be exploited first. For each region, there are 12 resource categories for oil, gas and nuclear fuels, and 14 categories for coal. <br />
<br />
A key input for each of the fossil fuel and uranium supply submodules is fuel demand (fuel used in final energy and conversion processes). Additional input includes conversion losses in refining, liquefaction, conversion, and energy use in the energy system. [!CHANGE] Upstream energy use is endogenously determined based energy carrier, region in which the energy carrier is produced, production rate, and resource category. These submodules indicate how demand can be met by supply in a region and other regions through interregional trade.<br />
<br />
<div class="thumbcaption dark">Table: [!CHANGE]Main assumptions on fossil fuel resources (in ZJ; coal does not have a distinction between conventional and unconventional; <nowiki>[[IEA, 2017]]</nowiki>, <nowiki>[[USGS, 2012]]</nowiki>; <nowiki>[[BGR, 2016]]</nowiki>; [[EDGAR database]]; <nowiki>[[Abundant Gas Project]]</nowiki>)</div><table class="pbltable"><br />
<br />
<tr><th></th><th>Oil<br />
</th><th>Natural gas<br />
</th><th>Underground coal<br />
</th><th>Surface coal<br />
</th></tr><br />
<tr><td>Cum. 1970-2015 production<br />
</td><td>6.5<br />
</td><td>3.4<br />
</td><td>2.4<br />
</td><td>1.5<br />
</td></tr><br />
<tr><td>Reserves<br />
</td><td>9.4<br />
</td><td>7.3<br />
</td><td>17<br />
</td><td>3.6<br />
</td></tr><br />
<tr><td>Other conventional resources<br />
</td><td>33<br />
</td><td>17<br />
</td><td>481<br />
</td><td>56<br />
</td></tr><br />
<tr><td>Unconventional resources (reserves)<br />
</td><td>2.0<br />
</td><td>0.30<br />
</td><td></td><td></td></tr><br />
<tr><td>Other unconventional resources<br />
</td><td>54<br />
</td><td>2023<br />
</td><td></td><td></td></tr><br />
<tr><td>Total<br />
</td><td>105<br />
</td><td>2051<br />
</td><td>501<br />
</td><td>61<br />
</td></tr><br />
</table><br />
<br />
Fossil fuel resources are aggregated to five resource categories for each fuel (the table above). Each category has typical production costs. The resource estimates for oil and natural gas supply imply that for conventional resources supply is limited to only [!CHANGE] about 7 times the 1970–2015 production level. Production estimates for unconventional resources are much larger, albeit speculative. Recently, some of the occurrences of these unconventional resources have become competitive such as shale gas and tar sands. For coal, even current reserves amount to almost ten times the production level of the last three decades. For all fuels, the model assumes that, if prices increase, or if there is further technology development, the energy could be produced in the higher cost resource categories. The values presented in the table above represent medium estimates in the model, which can also use higher or lower estimates in the scenarios. The final production costs in each region are determined by the combined effect of resource depletion and learning-by-doing.<br />
<br />
===Trade===<br />
Trade is dealt with in a generic way for oil, natural gas and coal. In the fuel trade model, each region imports fuels from other regions. The amount of fuel imported from each region depends on the relative production costs and those in other regions, augmented with transport costs, using multinomial logit equations. Transport costs are calculated from representative interregional transport distances and time- and fuel-dependent estimates of the costs per GJ per kilometre.<br />
<br />
To reflect geographical, political and other constraints in the interregional fuel trade, an additional 'cost' is added to simulate trade barriers between regions (this costs factor is determined by calibration). Natural gas is transported by pipeline or liquid-natural gas ({{abbrTemplate|LNG}}) tanker, depending on distance, with pipeline more attractive for short distances. In order to account for cartel behaviour, the model compares production costs with and without unrestricted trade. Regions that can supply at lower costs than the average production costs in importing regions are assumed to supply oil at a price only slightly below the production costs of the importing regions. Although also this rule is implemented in a generic form for all energy carriers, it is only effective for oil, where the behaviour of the OPEC cartel is simulated to some extent.<br />
<br />
===Renewable energy !CHANGE!===<br />
IMAGE model the supply of eight renewable energy options: utility-scale photovoltaic (PV), rooftop PV, concentrated solar power (CSP), onshore wind energy, offshore wind energy, first-generation bio-energy, lignocellulosic bio-energy, and hydropower is estimated generically as follows ([[Hoogwijk, 2004]]; [[De Vries et al., 2007]]; [[Gernaat et al., 2017]]; [[Köberle et al., 2015]]; [[Gernaat et al., 2014]]; [[Daioglou et al., 2019]]; [[Gernaat]]): <br />
Firstly, physical and geographical data are collected on a 0.5x0.5 degree grid. The characteristics of wind speed, insulation and monthly variation are taken from the digital databases. [[File:Physical climate data renewables.png|thumb|621x621px|'''Model mean (GFLD-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5) historical 30-year (1970–2000) average climate data used as input to calculate energy potentials as available in the ISIMIP2b database.''' '''a''', Solar irradiance (kWh m<sup>-2</sup> day<sup>-1</sup>). '''b''', Temperature (°C). '''c''', Wind speeds (m s<sup>-1</sup>). '''d''', Run-off (kg km<sup>-2</sup> s<sup>-1</sup>). '''e''', Sugar and maize yields (crop selected with highest yield per cell) (%). '''f''', Lignocellulosic crop yields (switchgrass and Miscanthus) (%).]]<br />
<br />
The methodology assumes that part of the grid cell can be used for energy production, given its physical–geographic (terrain, habitation) and socio-geographical (location, acceptability) characteristics. This leads to an estimate of the geographical potential. Several of these factors are scenario-dependent. The geographical potential for biomass production, for example, is estimated using suitability factors taking considering competing land-use options and the harvested rain-fed yield of energy crop. Next, we assume that only part of the geographical potential can be used due to limited conversion efficiency and maximum power density, This result of accounting for these conversion efficiencies is referred to as the technical potential. The final step is to relate the technical potential to on-site production costs. Information at grid level is sorted and used as supply cost curves to reflect the assumption that the lowest cost locations are exploited first. Supply cost curves are used dynamically and change over time as a result of the learning effect.<br />
<br />
The calculation of each renewable energy potential is explained in detail in separate published articles. Here, a short explanation is given introducing each.<br />
<br />
Utility-scale PV and CSP starts with the theoretical potential based on a global solar irradiation map (kWh m<sup>-2</sup> day<sup>-1</sup>) ([[Hoogwijk, 2004]]; [[Köberle et al., 2015]]). This is subsequently restricted by excluding unsuitable areas (e.g. areas with snow cover or steep mountainous terrain) to calculate the geographical potential. The area that remains is further restricted by suitability factors. The idea behind suitability factors is that only part of the land is physically available for solar applications to ensure that it may keep the land-use function that it has, such as agricultural crop production. To calculate the technical potential, conversion efficiencies are assumed that are explained in method section ‘Climate impacts on renewable energy’.<br />
<br />
Rooftop PV builds on the method of utility-scale PV, using the theoretical and technical aspects, but differentiates on the geographical potential (). For rooftop PV, the geographical potential is determined according to roof area. This area is estimated by dividing the living area per household by the number of floors per household, both of which are based on census data. The estimates distinguish between urban areas and rural areas, and are combined with an urban/rural population map to scale down the estimated roof areas to grid level. The technical calculations are similar as the ones used to calculate utility-scale PV and explained in method section ‘Climate impacts on renewable energy’.<br />
<br />
Calculations of onshore and offshore wind energy potential start with wind speeds (m s<sup>-1</sup>) ([[Hoogwijk, 2004]]; [[Gernaat et al., 2014]]). Then, similar as for solar power, areas are excluded and further restricted according to suitability factors. For the remaining geographical area, based on wind data, the electricity output is calculated using a Weibull distribution function and power curve of the turbine. For details on offshore wind methodology see Supplementary Text 7-S2.<br />
<br />
Bio-energy potential calculations start with primary biomass production, represented through yields (t ha<sup>-1</sup> y<sup>-1</sup>) ([[Hoogwijk, 2004]]; [[Daioglou et al., 2019]]). Potential primary biomass sources include maize, sugar, and lignocellulosic crops (trees, switchgrass, and Miscanthus). Land availability for bio-energy production is limited by agricultural production following a ‘food-first’ principle where agricultural lands are determined first and are off-limits for biomass production. The technical potential is further limited by excluding forests, nature reserves and water stressed areas. In principle, bio-energy can be produced on remaining unprotected lands but also on abandoned agricultural lands. Besides energy crops, residues from agricultural and forestry can also be used as a feedstock. The costs of primary bio-energy crops are calculated with a Cobb-Douglas economic growth model using labour , land rent and capital costs as inputs <ref><div style="clear:both float:right">The Cobb–Douglas production function is a particular functional form of the production function widely used to represent the technological relationship between the amounts of two or more inputs, particularly physical capital and labor, and the amount of output that can be produced by those inputs.</div></ref>. The land costs are based on average regional income levels per km2, which was found to be a reasonable proxy for regional differences in land rent costs. The production functions are calibrated to empirical data .This technical potential is converted to several secondary energy carriers (solids, liquids, electricity, hydrogen) that compete in the energy system with other secondary energy carriers, such as fossil fuels or renewables ([[Daioglou et al., 2019]]) for a full description of biomass supply and demand in IMAGE) <ref>Daioglou, V., Doelman, J.C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2019. Integrated assessment of biomass supply and demand in climate change mitigation scenarios. ''Global Environmental Change'', ''54'', pp.88-101.</ref><ref>Daioglou, V., Stehfest, E., Wicke, B., Faaij, A. and Van Vuuren, D.P., 2016. Projections of the availability and cost of residues from agriculture and forestry. ''Gcb Bioenergy'', ''8''(2), pp.456-470.</ref> <ref>Daioglou, V., Doelman, J.C., Stehfest, E., Müller, C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2017. Greenhouse gas emission curves for advanced biofuel supply chains. ''Nature Climate Change'', ''7''(12), p.920.</ref>.<br />
<br />
Calculations of hydropower potential start with run-off (kg km<sup>-2</sup> s<sup>-1</sup>) that flows from high elevation to low elevation (representing discharge). On the basis of these discharge maps, >3.8 million site-specific hydropower installations were evaluated, at a 25km interval for every river between 56° S and 60° N (the excluded area is due to unavailable topographic data). At each site, high-resolution topographic data (3” × 3”) were used to calculate the cost-optimal dam dimensions and associated production potential. In this way, 60,000 suitable sites were identified, which together represent the remaining technical potential (see [[Gernaat et al., 2017]] for a full description of the site selection process). <br />
<br />
[[File:Technical potential maps renewables.png|thumb|617x617px|'''Global maps showing technical potential of renewable energy sources for 2010.''' Calculated with climate data from HadGEM2-ES (30y-average 1970-2000) and the suitability factors of Table 5-1. One cell has an area of 0.5°×0.5°. '''a,''' Solar PV (utility-scale PV) (based on Chapter 3 and Hoogwijk (2004), Köberle et al. (2015)). '''b''', CSP (based on Köberle et al. (2015)). '''c''', Wind (onshore and offshore) (based on Chapter 2 and Hoogwijk (2004)). '''d''', Hydropower (defined as: remaining technical potential, explained and based on Chapter 4). '''e''', 1<sup>st</sup> generation bio-energy (based on Daioglou et al. (2019), Hoogwijk (2004)). '''f''', 2<sup>nd</sup> generation bio-energy (based on Daioglou et al. (2019), Hoogwijk (2004)). Note that the scales are different.]]<br />
<br />
The maps on technical potential for all renewables are combined with economic information to generate cost-supply curves. Assumptions on cost can be found in the separate articles but the general methodology is as follows. Each technology requires an investment before it can produce energy. This investment (in USD) is divided by the annual production (kWh) to calculate the production cost (USD kWh<sup>-1</sup>). This yields two global maps, a technical potential map (kWh) and a production cost map (USD kWh<sup>-1</sup>). Together they are used to generate a cost-supply curve, by sorting (in ascending order) the cells in the production cost map while simultaneously adding the same cells from the technical potential map.<br />
<br />
<div class="version newv31"></div><references /><br />
</div></div>Gernaatdhttps://models.pbl.nl/index.php?title=Energy_supply/Description&diff=36038Energy supply/Description2019-04-25T15:15:44Z<p>Gernaatd: </p>
<hr />
<div>{{ComponentDescriptionTemplate<br />
|Reference=Hoogwijk, 2004; De Vries et al., 2007; New et al., 1997; Rogner, 1997; Mulders et al., 2006;<br />
}}<br />
<div class="page_standard"><br />
==Model description of {{ROOTPAGENAME}}==<br />
===Fossil fuels and uranium===<br />
Depletion of fossil fuels (coal, oil and natural gas) and uranium is simulated on the assumption that resources can be represented by a long-term cost-supple curve, consisting of different resource categories with increasing costs levels. The model assumes that the cheapest deposits will be exploited first. For each region, there are 12 resource categories for oil, gas and nuclear fuels, and 14 categories for coal. <br />
<br />
A key input for each of the fossil fuel and uranium supply submodules is fuel demand (fuel used in final energy and conversion processes). Additional input includes conversion losses in refining, liquefaction, conversion, and energy use in the energy system. [!CHANGE] Upstream energy use is endogenously determined based energy carrier, region in which the energy carrier is produced, production rate, and resource category. These submodules indicate how demand can be met by supply in a region and other regions through interregional trade.<br />
<br />
<div class="thumbcaption dark">Table: [!CHANGE]Main assumptions on fossil fuel resources (in ZJ; coal does not have a distinction between conventional and unconventional; <nowiki>[[IEA, 2017]]</nowiki>, <nowiki>[[USGS, 2012]]</nowiki>; <nowiki>[[BGR, 2016]]</nowiki>; [[EDGAR database]]; <nowiki>[[Abundant Gas Project]]</nowiki>)</div><table class="pbltable"><br />
<br />
<tr><th></th><th>Oil<br />
</th><th>Natural gas<br />
</th><th>Underground coal<br />
</th><th>Surface coal<br />
</th></tr><br />
<tr><td>Cum. 1970-2015 production<br />
</td><td>6.5<br />
</td><td>3.4<br />
</td><td>2.4<br />
</td><td>1.5<br />
</td></tr><br />
<tr><td>Reserves<br />
</td><td>9.4<br />
</td><td>7.3<br />
</td><td>17<br />
</td><td>3.6<br />
</td></tr><br />
<tr><td>Other conventional resources<br />
</td><td>33<br />
</td><td>17<br />
</td><td>481<br />
</td><td>56<br />
</td></tr><br />
<tr><td>Unconventional resources (reserves)<br />
</td><td>2.0<br />
</td><td>0.30<br />
</td><td></td><td></td></tr><br />
<tr><td>Other unconventional resources<br />
</td><td>54<br />
</td><td>2023<br />
</td><td></td><td></td></tr><br />
<tr><td>Total<br />
</td><td>105<br />
</td><td>2051<br />
</td><td>501<br />
</td><td>61<br />
</td></tr><br />
</table><br />
<br />
Fossil fuel resources are aggregated to five resource categories for each fuel (the table above). Each category has typical production costs. The resource estimates for oil and natural gas supply imply that for conventional resources supply is limited to only [!CHANGE] about 7 times the 1970–2015 production level. Production estimates for unconventional resources are much larger, albeit speculative. Recently, some of the occurrences of these unconventional resources have become competitive such as shale gas and tar sands. For coal, even current reserves amount to almost ten times the production level of the last three decades. For all fuels, the model assumes that, if prices increase, or if there is further technology development, the energy could be produced in the higher cost resource categories. The values presented in the table above represent medium estimates in the model, which can also use higher or lower estimates in the scenarios. The final production costs in each region are determined by the combined effect of resource depletion and learning-by-doing.<br />
<br />
===Trade===<br />
Trade is dealt with in a generic way for oil, natural gas and coal. In the fuel trade model, each region imports fuels from other regions. The amount of fuel imported from each region depends on the relative production costs and those in other regions, augmented with transport costs, using multinomial logit equations. Transport costs are calculated from representative interregional transport distances and time- and fuel-dependent estimates of the costs per GJ per kilometre.<br />
<br />
To reflect geographical, political and other constraints in the interregional fuel trade, an additional 'cost' is added to simulate trade barriers between regions (this costs factor is determined by calibration). Natural gas is transported by pipeline or liquid-natural gas ({{abbrTemplate|LNG}}) tanker, depending on distance, with pipeline more attractive for short distances. In order to account for cartel behaviour, the model compares production costs with and without unrestricted trade. Regions that can supply at lower costs than the average production costs in importing regions are assumed to supply oil at a price only slightly below the production costs of the importing regions. Although also this rule is implemented in a generic form for all energy carriers, it is only effective for oil, where the behaviour of the OPEC cartel is simulated to some extent.<br />
<br />
===Renewable energy !CHANGE!===<br />
IMAGE model the supply of eight renewable energy options: utility-scale photovoltaic (PV), rooftop PV, concentrated solar power (CSP), onshore wind energy, offshore wind energy, first-generation bio-energy, lignocellulosic bio-energy, and hydropower is estimated generically as follows ([[Hoogwijk, 2004]]; [[De Vries et al., 2007]]; [[Gernaat et al., 2017]]; [[Köberle et al., 2015]]; [[Gernaat et al., 2014]]; [[Daioglou et al., 2019]]; [[Gernaat]]): <br />
Firstly, physical and geographical data are collected on a 0.5x0.5 degree grid. The characteristics of wind speed, insulation and monthly variation are taken from the digital databases. [[File:Physical climate data renewables.png|thumb|621x621px|'''Model mean (GFLD-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5) historical 30-year (1970–2000) average climate data used as input to calculate energy potentials as available in the ISIMIP2b database.''' '''a''', Solar irradiance (kWh m<sup>-2</sup> day<sup>-1</sup>). '''b''', Temperature (°C). '''c''', Wind speeds (m s<sup>-1</sup>). '''d''', Run-off (kg km<sup>-2</sup> s<sup>-1</sup>). '''e''', Sugar and maize yields (crop selected with highest yield per cell) (%). '''f''', Lignocellulosic crop yields (switchgrass and Miscanthus) (%).]]<br />
<br />
The methodology assumes that part of the grid cell can be used for energy production, given its physical–geographic (terrain, habitation) and socio-geographical (location, acceptability) characteristics. This leads to an estimate of the geographical potential. Several of these factors are scenario-dependent. The geographical potential for biomass production, for example, is estimated using suitability factors taking considering competing land-use options and the harvested rain-fed yield of energy crop. Next, we assume that only part of the geographical potential can be used due to limited conversion efficiency and maximum power density, This result of accounting for these conversion efficiencies is referred to as the technical potential. The final step is to relate the technical potential to on-site production costs. Information at grid level is sorted and used as supply cost curves to reflect the assumption that the lowest cost locations are exploited first. Supply cost curves are used dynamically and change over time as a result of the learning effect.<br />
<br />
The calculation of each renewable energy potential is explained in detail in separate published articles. Here, a short explanation is given introducing each.<br />
<br />
Utility-scale PV and CSP starts with the theoretical potential based on a global solar irradiation map (kWh m<sup>-2</sup> day<sup>-1</sup>) ([[Hoogwijk, 2004]]; [[Köberle et al., 2015]]). This is subsequently restricted by excluding unsuitable areas (e.g. areas with snow cover or steep mountainous terrain) to calculate the geographical potential. The area that remains is further restricted by suitability factors. The idea behind suitability factors is that only part of the land is physically available for solar applications to ensure that it may keep the land-use function that it has, such as agricultural crop production. To calculate the technical potential, conversion efficiencies are assumed that are explained in method section ‘Climate impacts on renewable energy’.<br />
<br />
Rooftop PV builds on the method of utility-scale PV, using the theoretical and technical aspects, but differentiates on the geographical potential (). For rooftop PV, the geographical potential is determined according to roof area. This area is estimated by dividing the living area per household by the number of floors per household, both of which are based on census data. The estimates distinguish between urban areas and rural areas, and are combined with an urban/rural population map to scale down the estimated roof areas to grid level. The technical calculations are similar as the ones used to calculate utility-scale PV and explained in method section ‘Climate impacts on renewable energy’.<br />
<br />
Calculations of onshore and offshore wind energy potential start with wind speeds (m s<sup>-1</sup>) ([[Hoogwijk, 2004]]; [[Gernaat et al., 2014]]). Then, similar as for solar power, areas are excluded and further restricted according to suitability factors. For the remaining geographical area, based on wind data, the electricity output is calculated using a Weibull distribution function and power curve of the turbine. For details on offshore wind methodology see Supplementary Text 7-S2.<br />
<br />
Bio-energy potential calculations start with primary biomass production, represented through yields (t ha<sup>-1</sup> y<sup>-1</sup>) ([[Hoogwijk, 2004]]; [[Daioglou et al., 2019]]). Potential primary biomass sources include maize, sugar, and lignocellulosic crops (trees, switchgrass, and Miscanthus). Land availability for bio-energy production is limited by agricultural production following a ‘food-first’ principle where agricultural lands are determined first and are off-limits for biomass production. The technical potential is further limited by excluding forests, nature reserves and water stressed areas. In principle, bio-energy can be produced on remaining unprotected lands but also on abandoned agricultural lands. Besides energy crops, residues from agricultural and forestry can also be used as a feedstock. The costs of primary bio-energy crops are calculated with a Cobb-Douglas economic growth model using labour , land rent and capital costs as inputs <ref><div style="clear:both float:right">The Cobb–Douglas production function is a particular functional form of the production function widely used to represent the technological relationship between the amounts of two or more inputs, particularly physical capital and labor, and the amount of output that can be produced by those inputs.</div></ref>. The land costs are based on average regional income levels per km2, which was found to be a reasonable proxy for regional differences in land rent costs. The production functions are calibrated to empirical data .This technical potential is converted to several secondary energy carriers (solids, liquids, electricity, hydrogen) that compete in the energy system with other secondary energy carriers, such as fossil fuels or renewables ([[Daioglou et al., 2019]]) for a full description of biomass supply and demand in IMAGE) <ref>Daioglou, V., Doelman, J.C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2019. Integrated assessment of biomass supply and demand in climate change mitigation scenarios. ''Global Environmental Change'', ''54'', pp.88-101.</ref><ref>Daioglou, V., Stehfest, E., Wicke, B., Faaij, A. and Van Vuuren, D.P., 2016. Projections of the availability and cost of residues from agriculture and forestry. ''Gcb Bioenergy'', ''8''(2), pp.456-470.</ref> <ref>Daioglou, V., Doelman, J.C., Stehfest, E., Müller, C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2017. Greenhouse gas emission curves for advanced biofuel supply chains. ''Nature Climate Change'', ''7''(12), p.920.</ref>.<br />
<br />
Calculations of hydropower potential start with run-off (kg km<sup>-2</sup> s<sup>-1</sup>) that flows from high elevation to low elevation (representing discharge). On the basis of these discharge maps, >3.8 million site-specific hydropower installations were evaluated, at a 25km interval for every river between 56° S and 60° N (the excluded area is due to unavailable topographic data). At each site, high-resolution topographic data (3” × 3”) were used to calculate the cost-optimal dam dimensions and associated production potential. In this way, 60,000 suitable sites were identified, which together represent the remaining technical potential (see [[Gernaat et al., 2017]] for a full description of the site selection process). <br />
<br />
[[File:Technical potential maps renewables.png|thumb|617x617px|'''Global maps showing technical potential of renewable energy sources for 2010.''' Calculated with climate data from HadGEM2-ES (30y-average 1970-2000) and the suitability factors of Table 5-1. One cell has an area of 0.5°×0.5°. '''a,''' Solar PV (utility-scale PV) (based on Chapter 3 and Hoogwijk (2004), Köberle et al. (2015)). '''b''', CSP (based on Köberle et al. (2015)). '''c''', Wind (onshore and offshore) (based on Chapter 2 and Hoogwijk (2004)). '''d''', Hydropower (defined as: remaining technical potential, explained and based on Chapter 4). '''e''', 1<sup>st</sup> generation bio-energy (based on Daioglou et al. (2019), Hoogwijk (2004)). '''f''', 2<sup>nd</sup> generation bio-energy (based on Daioglou et al. (2019), Hoogwijk (2004)). Note that the scales are different.]]<br />
<br />
The maps on technical potential for all renewables are combined with economic information to generate cost-supply curves. Assumptions on cost can be found in the separate articles but the general methodology is as follows. Each technology requires an investment before it can produce energy. This investment (in USD) is divided by the annual production (kWh) to calculate the production cost (USD kWh<sup>-1</sup>). This yields two global maps, a technical potential map (kWh) and a production cost map (USD kWh<sup>-1</sup>). Together they are used to generate a cost-supply curve, by sorting (in ascending order) the cells in the production cost map while simultaneously adding the same cells from the technical potential map, such as the hydropower example below.<br />
<br />
[[File:Article Figure 1 Global and regional cost-supply curves and their geographic locations.png|thumb|900x900px|'''Global and regional cost-supply curves and their geographic locations.''' '''a-f,''' The global (a) and regional (Africa (b), Asia Pacific (c), Europe (d), Central and North America (e) and South America (f)) cost-supply curves showing the remaining technical potential below 0.50 $ kWh<sup>-1</sup>. The red numbers indicate the total river power potential, the blue numbers the total diversion canal power potential and the black numbers the sum of both. The dot-dashed line indicates the remaining economic potential below a cost of 0.10 $ kWh<sup>-1</sup>. '''g''', The geographic locations of the two hydropower systems.]]<br />
<br />
<div class="version newv31"></div><references /><br />
</div></div>Gernaatdhttps://models.pbl.nl/index.php?title=Gernaat_2019&diff=36035Gernaat 20192019-04-25T15:14:43Z<p>Gernaatd: Created page with "{{ReferenceTemplate |Author=Gernaat, DEHJ |Year=2019 |Title=Phd Thesis: The role of renewable energy in long-term energy and climate scenarios |PublicationType=Book }}"</p>
<hr />
<div>{{ReferenceTemplate<br />
|Author=Gernaat, DEHJ<br />
|Year=2019<br />
|Title=Phd Thesis: The role of renewable energy in long-term energy and climate scenarios<br />
|PublicationType=Book<br />
}}</div>Gernaatdhttps://models.pbl.nl/index.php?title=Energy_supply/Description&diff=36029Energy supply/Description2019-04-25T15:04:26Z<p>Gernaatd: References</p>
<hr />
<div>{{ComponentDescriptionTemplate<br />
|Reference=Hoogwijk, 2004; De Vries et al., 2007; New et al., 1997; Rogner, 1997; Mulders et al., 2006;<br />
}}<br />
<div class="page_standard"><br />
==Model description of {{ROOTPAGENAME}}==<br />
===Fossil fuels and uranium===<br />
Depletion of fossil fuels (coal, oil and natural gas) and uranium is simulated on the assumption that resources can be represented by a long-term cost-supple curve, consisting of different resource categories with increasing costs levels. The model assumes that the cheapest deposits will be exploited first. For each region, there are 12 resource categories for oil, gas and nuclear fuels, and 14 categories for coal. <br />
<br />
A key input for each of the fossil fuel and uranium supply submodules is fuel demand (fuel used in final energy and conversion processes). Additional input includes conversion losses in refining, liquefaction, conversion, and energy use in the energy system. [!CHANGE] Upstream energy use is endogenously determined based energy carrier, region in which the energy carrier is produced, production rate, and resource category. These submodules indicate how demand can be met by supply in a region and other regions through interregional trade.<br />
<br />
<div class="thumbcaption dark">Table: [!CHANGE]Main assumptions on fossil fuel resources (in ZJ; coal does not have a distinction between conventional and unconventional; <nowiki>[[IEA, 2017]]</nowiki>, <nowiki>[[USGS, 2012]]</nowiki>; <nowiki>[[BGR, 2016]]</nowiki>; [[EDGAR database]]; <nowiki>[[Abundant Gas Project]]</nowiki>)</div><table class="pbltable"><br />
<br />
<tr><th></th><th>Oil<br />
</th><th>Natural gas<br />
</th><th>Underground coal<br />
</th><th>Surface coal<br />
</th></tr><br />
<tr><td>Cum. 1970-2015 production<br />
</td><td>6.5<br />
</td><td>3.4<br />
</td><td>2.4<br />
</td><td>1.5<br />
</td></tr><br />
<tr><td>Reserves<br />
</td><td>9.4<br />
</td><td>7.3<br />
</td><td>17<br />
</td><td>3.6<br />
</td></tr><br />
<tr><td>Other conventional resources<br />
</td><td>33<br />
</td><td>17<br />
</td><td>481<br />
</td><td>56<br />
</td></tr><br />
<tr><td>Unconventional resources (reserves)<br />
</td><td>2.0<br />
</td><td>0.30<br />
</td><td></td><td></td></tr><br />
<tr><td>Other unconventional resources<br />
</td><td>54<br />
</td><td>2023<br />
</td><td></td><td></td></tr><br />
<tr><td>Total<br />
</td><td>105<br />
</td><td>2051<br />
</td><td>501<br />
</td><td>61<br />
</td></tr><br />
</table><br />
<br />
Fossil fuel resources are aggregated to five resource categories for each fuel (the table above). Each category has typical production costs. The resource estimates for oil and natural gas supply imply that for conventional resources supply is limited to only [!CHANGE] about 7 times the 1970–2015 production level. Production estimates for unconventional resources are much larger, albeit speculative. Recently, some of the occurrences of these unconventional resources have become competitive such as shale gas and tar sands. For coal, even current reserves amount to almost ten times the production level of the last three decades. For all fuels, the model assumes that, if prices increase, or if there is further technology development, the energy could be produced in the higher cost resource categories. The values presented in the table above represent medium estimates in the model, which can also use higher or lower estimates in the scenarios. The final production costs in each region are determined by the combined effect of resource depletion and learning-by-doing.<br />
<br />
===Trade===<br />
Trade is dealt with in a generic way for oil, natural gas and coal. In the fuel trade model, each region imports fuels from other regions. The amount of fuel imported from each region depends on the relative production costs and those in other regions, augmented with transport costs, using multinomial logit equations. Transport costs are calculated from representative interregional transport distances and time- and fuel-dependent estimates of the costs per GJ per kilometre.<br />
<br />
To reflect geographical, political and other constraints in the interregional fuel trade, an additional 'cost' is added to simulate trade barriers between regions (this costs factor is determined by calibration). Natural gas is transported by pipeline or liquid-natural gas ({{abbrTemplate|LNG}}) tanker, depending on distance, with pipeline more attractive for short distances. In order to account for cartel behaviour, the model compares production costs with and without unrestricted trade. Regions that can supply at lower costs than the average production costs in importing regions are assumed to supply oil at a price only slightly below the production costs of the importing regions. Although also this rule is implemented in a generic form for all energy carriers, it is only effective for oil, where the behaviour of the OPEC cartel is simulated to some extent.<br />
<br />
===Renewable energy !CHANGE!===<br />
IMAGE model the supply of eight renewable energy options: utility-scale photovoltaic (PV), rooftop PV, concentrated solar power (CSP), onshore wind energy, offshore wind energy, first-generation bio-energy, lignocellulosic bio-energy, and hydropower is estimated generically as follows ([[Hoogwijk, 2004]]; [[De Vries et al., 2007]]; [[Gernaat et al., 2017]]; [[Köberle et al., 2015]]; [[Gernaat et al., 2014]]; [[Daioglou et al., 2019]]): <br />
Firstly, physical and geographical data are collected on a 0.5x0.5 degree grid. The characteristics of wind speed, insulation and monthly variation are taken from the digital databases. [[File:Physical climate data renewables.png|thumb|621x621px|'''Model mean (GFLD-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5) historical 30-year (1970–2000) average climate data used as input to calculate energy potentials as available in the ISIMIP2b database.''' '''a''', Solar irradiance (kWh m<sup>-2</sup> day<sup>-1</sup>). '''b''', Temperature (°C). '''c''', Wind speeds (m s<sup>-1</sup>). '''d''', Run-off (kg km<sup>-2</sup> s<sup>-1</sup>). '''e''', Sugar and maize yields (crop selected with highest yield per cell) (%). '''f''', Lignocellulosic crop yields (switchgrass and Miscanthus) (%).]]<br />
<br />
The methodology assumes that part of the grid cell can be used for energy production, given its physical–geographic (terrain, habitation) and socio-geographical (location, acceptability) characteristics. This leads to an estimate of the geographical potential. Several of these factors are scenario-dependent. The geographical potential for biomass production, for example, is estimated using suitability factors taking considering competing land-use options and the harvested rain-fed yield of energy crop. Next, we assume that only part of the geographical potential can be used due to limited conversion efficiency and maximum power density, This result of accounting for these conversion efficiencies is referred to as the technical potential. The final step is to relate the technical potential to on-site production costs. Information at grid level is sorted and used as supply cost curves to reflect the assumption that the lowest cost locations are exploited first. Supply cost curves are used dynamically and change over time as a result of the learning effect.<br />
<br />
The calculation of each renewable energy potential is explained in detail in separate published articles. Here, a short explanation is given introducing each.<br />
<br />
Utility-scale PV and CSP starts with the theoretical potential based on a global solar irradiation map (kWh m<sup>-2</sup> day<sup>-1</sup>) ([[Hoogwijk, 2004]]; [[Köberle et al., 2015]]). This is subsequently restricted by excluding unsuitable areas (e.g. areas with snow cover or steep mountainous terrain) to calculate the geographical potential. The area that remains is further restricted by suitability factors. The idea behind suitability factors is that only part of the land is physically available for solar applications to ensure that it may keep the land-use function that it has, such as agricultural crop production. To calculate the technical potential, conversion efficiencies are assumed that are explained in method section ‘Climate impacts on renewable energy’.<br />
<br />
Rooftop PV builds on the method of utility-scale PV, using the theoretical and technical aspects, but differentiates on the geographical potential (). For rooftop PV, the geographical potential is determined according to roof area. This area is estimated by dividing the living area per household by the number of floors per household, both of which are based on census data. The estimates distinguish between urban areas and rural areas, and are combined with an urban/rural population map to scale down the estimated roof areas to grid level. The technical calculations are similar as the ones used to calculate utility-scale PV and explained in method section ‘Climate impacts on renewable energy’.<br />
<br />
Calculations of onshore and offshore wind energy potential start with wind speeds (m s<sup>-1</sup>) ([[Hoogwijk, 2004]]; [[Gernaat et al., 2014]]). Then, similar as for solar power, areas are excluded and further restricted according to suitability factors. For the remaining geographical area, based on wind data, the electricity output is calculated using a Weibull distribution function and power curve of the turbine. For details on offshore wind methodology see Supplementary Text 7-S2.<br />
<br />
Bio-energy potential calculations start with primary biomass production, represented through yields (t ha<sup>-1</sup> y<sup>-1</sup>) ([[Hoogwijk, 2004]]; [[Daioglou et al., 2019]]). Potential primary biomass sources include maize, sugar, and lignocellulosic crops (trees, switchgrass, and Miscanthus). Land availability for bio-energy production is limited by agricultural production following a ‘food-first’ principle where agricultural lands are determined first and are off-limits for biomass production. The technical potential is further limited by excluding forests, nature reserves and water stressed areas. In principle, bio-energy can be produced on remaining unprotected lands but also on abandoned agricultural lands. Besides energy crops, residues from agricultural and forestry can also be used as a feedstock. The costs of primary bio-energy crops are calculated with a Cobb-Douglas economic growth model using labour , land rent and capital costs as inputs <ref><div style="clear:both float:right">The Cobb–Douglas production function is a particular functional form of the production function widely used to represent the technological relationship between the amounts of two or more inputs, particularly physical capital and labor, and the amount of output that can be produced by those inputs.</div></ref>. The land costs are based on average regional income levels per km2, which was found to be a reasonable proxy for regional differences in land rent costs. The production functions are calibrated to empirical data .This technical potential is converted to several secondary energy carriers (solids, liquids, electricity, hydrogen) that compete in the energy system with other secondary energy carriers, such as fossil fuels or renewables ([[Daioglou et al., 2019]]) for a full description of biomass supply and demand in IMAGE) <ref>Daioglou, V., Doelman, J.C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2019. Integrated assessment of biomass supply and demand in climate change mitigation scenarios. ''Global Environmental Change'', ''54'', pp.88-101.</ref><ref>Daioglou, V., Stehfest, E., Wicke, B., Faaij, A. and Van Vuuren, D.P., 2016. Projections of the availability and cost of residues from agriculture and forestry. ''Gcb Bioenergy'', ''8''(2), pp.456-470.</ref> <ref>Daioglou, V., Doelman, J.C., Stehfest, E., Müller, C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2017. Greenhouse gas emission curves for advanced biofuel supply chains. ''Nature Climate Change'', ''7''(12), p.920.</ref>.<br />
<br />
Calculations of hydropower potential start with run-off (kg km<sup>-2</sup> s<sup>-1</sup>) that flows from high elevation to low elevation (representing discharge). On the basis of these discharge maps, >3.8 million site-specific hydropower installations were evaluated, at a 25km interval for every river between 56° S and 60° N (the excluded area is due to unavailable topographic data). At each site, high-resolution topographic data (3” × 3”) were used to calculate the cost-optimal dam dimensions and associated production potential. In this way, 60,000 suitable sites were identified, which together represent the remaining technical potential (see [[Gernaat et al., 2017]] for a full description of the site selection process). <br />
<br />
[[File:Technical potential maps renewables.png|thumb|617x617px|'''Global maps showing technical potential of renewable energy sources for 2010.''' Calculated with climate data from HadGEM2-ES (30y-average 1970-2000) and the suitability factors of Table 5-1. One cell has an area of 0.5°×0.5°. '''a,''' Solar PV (utility-scale PV) (based on Chapter 3 and Hoogwijk (2004), Köberle et al. (2015)). '''b''', CSP (based on Köberle et al. (2015)). '''c''', Wind (onshore and offshore) (based on Chapter 2 and Hoogwijk (2004)). '''d''', Hydropower (defined as: remaining technical potential, explained and based on Chapter 4). '''e''', 1<sup>st</sup> generation bio-energy (based on Daioglou et al. (2019), Hoogwijk (2004)). '''f''', 2<sup>nd</sup> generation bio-energy (based on Daioglou et al. (2019), Hoogwijk (2004)). Note that the scales are different.]]<br />
<br />
The maps on technical potential for all renewables are combined with economic information to generate cost-supply curves. Assumptions on cost can be found in the separate articles but the general methodology is as follows. Each technology requires an investment before it can produce energy. This investment (in USD) is divided by the annual production (kWh) to calculate the production cost (USD kWh<sup>-1</sup>). This yields two global maps, a technical potential map (kWh) and a production cost map (USD kWh<sup>-1</sup>). Together they are used to generate a cost-supply curve, by sorting (in ascending order) the cells in the production cost map while simultaneously adding the same cells from the technical potential map, such as the hydropower example below.<br />
<br />
[[File:Article Figure 1 Global and regional cost-supply curves and their geographic locations.png|thumb|900x900px|'''Global and regional cost-supply curves and their geographic locations.''' '''a-f,''' The global (a) and regional (Africa (b), Asia Pacific (c), Europe (d), Central and North America (e) and South America (f)) cost-supply curves showing the remaining technical potential below 0.50 $ kWh<sup>-1</sup>. The red numbers indicate the total river power potential, the blue numbers the total diversion canal power potential and the black numbers the sum of both. The dot-dashed line indicates the remaining economic potential below a cost of 0.10 $ kWh<sup>-1</sup>. '''g''', The geographic locations of the two hydropower systems.]]<br />
<br />
<div class="version newv31"></div><references /><br />
</div></div>Gernaatdhttps://models.pbl.nl/index.php?title=Energy_supply/Description&diff=36024Energy supply/Description2019-04-25T14:50:46Z<p>Gernaatd: Format minor</p>
<hr />
<div>{{ComponentDescriptionTemplate<br />
|Reference=Hoogwijk, 2004; De Vries et al., 2007; New et al., 1997; Rogner, 1997; Mulders et al., 2006;<br />
}}<br />
<div class="page_standard"><br />
==Model description of {{ROOTPAGENAME}}==<br />
===Fossil fuels and uranium===<br />
Depletion of fossil fuels (coal, oil and natural gas) and uranium is simulated on the assumption that resources can be represented by a long-term cost-supple curve, consisting of different resource categories with increasing costs levels. The model assumes that the cheapest deposits will be exploited first. For each region, there are 12 resource categories for oil, gas and nuclear fuels, and 14 categories for coal. <br />
<br />
A key input for each of the fossil fuel and uranium supply submodules is fuel demand (fuel used in final energy and conversion processes). Additional input includes conversion losses in refining, liquefaction, conversion, and energy use in the energy system. [!CHANGE] Upstream energy use is endogenously determined based energy carrier, region in which the energy carrier is produced, production rate, and resource category. These submodules indicate how demand can be met by supply in a region and other regions through interregional trade.<br />
<br />
<div class="thumbcaption dark">Table: [!CHANGE]Main assumptions on fossil fuel resources (in ZJ; coal does not have a distinction between conventional and unconventional; <nowiki>[[IEA, 2017]]</nowiki>, <nowiki>[[USGS, 2012]]</nowiki>; <nowiki>[[BGR, 2016]]</nowiki>; [[EDGAR database]]; <nowiki>[[Abundant Gas Project]]</nowiki>)</div><table class="pbltable"><br />
<br />
<tr><th></th><th>Oil<br />
</th><th>Natural gas<br />
</th><th>Underground coal<br />
</th><th>Surface coal<br />
</th></tr><br />
<tr><td>Cum. 1970-2015 production<br />
</td><td>6.5<br />
</td><td>3.4<br />
</td><td>2.4<br />
</td><td>1.5<br />
</td></tr><br />
<tr><td>Reserves<br />
</td><td>9.4<br />
</td><td>7.3<br />
</td><td>17<br />
</td><td>3.6<br />
</td></tr><br />
<tr><td>Other conventional resources<br />
</td><td>33<br />
</td><td>17<br />
</td><td>481<br />
</td><td>56<br />
</td></tr><br />
<tr><td>Unconventional resources (reserves)<br />
</td><td>2.0<br />
</td><td>0.30<br />
</td><td></td><td></td></tr><br />
<tr><td>Other unconventional resources<br />
</td><td>54<br />
</td><td>2023<br />
</td><td></td><td></td></tr><br />
<tr><td>Total<br />
</td><td>105<br />
</td><td>2051<br />
</td><td>501<br />
</td><td>61<br />
</td></tr><br />
</table><br />
<br />
Fossil fuel resources are aggregated to five resource categories for each fuel (the table above). Each category has typical production costs. The resource estimates for oil and natural gas supply imply that for conventional resources supply is limited to only [!CHANGE] about 7 times the 1970–2015 production level. Production estimates for unconventional resources are much larger, albeit speculative. Recently, some of the occurrences of these unconventional resources have become competitive such as shale gas and tar sands. For coal, even current reserves amount to almost ten times the production level of the last three decades. For all fuels, the model assumes that, if prices increase, or if there is further technology development, the energy could be produced in the higher cost resource categories. The values presented in the table above represent medium estimates in the model, which can also use higher or lower estimates in the scenarios. The final production costs in each region are determined by the combined effect of resource depletion and learning-by-doing.<br />
<br />
===Trade===<br />
Trade is dealt with in a generic way for oil, natural gas and coal. In the fuel trade model, each region imports fuels from other regions. The amount of fuel imported from each region depends on the relative production costs and those in other regions, augmented with transport costs, using multinomial logit equations. Transport costs are calculated from representative interregional transport distances and time- and fuel-dependent estimates of the costs per GJ per kilometre.<br />
<br />
To reflect geographical, political and other constraints in the interregional fuel trade, an additional 'cost' is added to simulate trade barriers between regions (this costs factor is determined by calibration). Natural gas is transported by pipeline or liquid-natural gas ({{abbrTemplate|LNG}}) tanker, depending on distance, with pipeline more attractive for short distances. In order to account for cartel behaviour, the model compares production costs with and without unrestricted trade. Regions that can supply at lower costs than the average production costs in importing regions are assumed to supply oil at a price only slightly below the production costs of the importing regions. Although also this rule is implemented in a generic form for all energy carriers, it is only effective for oil, where the behaviour of the OPEC cartel is simulated to some extent.<br />
<br />
===Renewable energy !CHANGE!===<br />
IMAGE model the supply of eight renewable energy options: utility-scale photovoltaic (PV), rooftop PV, concentrated solar power (CSP), onshore wind energy, offshore wind energy, first-generation bio-energy, lignocellulosic bio-energy, and hydropower is estimated generically as follows ([[Hoogwijk, 2004]]; [[De Vries et al., 2007]]): <br />
Firstly, physical and geographical data are collected on a 0.5x0.5 degree grid. The characteristics of wind speed, insulation and monthly variation are taken from the digital databases. [[File:Physical climate data renewables.png|thumb|621x621px|'''Model mean (GFLD-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5) historical 30-year (1970–2000) average climate data used as input to calculate energy potentials as available in the ISIMIP2b database.''' '''a''', Solar irradiance (kWh m<sup>-2</sup> day<sup>-1</sup>). '''b''', Temperature (°C). '''c''', Wind speeds (m s<sup>-1</sup>). '''d''', Run-off (kg km<sup>-2</sup> s<sup>-1</sup>). '''e''', Sugar and maize yields (crop selected with highest yield per cell) (%). '''f''', Lignocellulosic crop yields (switchgrass and Miscanthus) (%).]]<br />
<br />
The methodology assumes that part of the grid cell can be used for energy production, given its physical–geographic (terrain, habitation) and socio-geographical (location, acceptability) characteristics. This leads to an estimate of the geographical potential. Several of these factors are scenario-dependent. The geographical potential for biomass production, for example, is estimated using suitability factors taking considering competing land-use options and the harvested rain-fed yield of energy crop. Next, we assume that only part of the geographical potential can be used due to limited conversion efficiency and maximum power density, This result of accounting for these conversion efficiencies is referred to as the technical potential. The final step is to relate the technical potential to on-site production costs. Information at grid level is sorted and used as supply cost curves to reflect the assumption that the lowest cost locations are exploited first. Supply cost curves are used dynamically and change over time as a result of the learning effect.<br />
<br />
The calculation of each renewable energy potential is explained in detail in separate published articles. Here, a short explanation is given introducing each.<br />
<br />
Utility-scale PV and CSP starts with the theoretical potential based on a global solar irradiation map (kWh m<sup>-2</sup> day<sup>-1</sup>) (Köberle et al. 2015). This is subsequently restricted by excluding unsuitable areas (e.g. areas with snow cover or steep mountainous terrain) to calculate the geographical potential. The area that remains is further restricted by suitability factors. The idea behind suitability factors is that only part of the land is physically available for solar applications to ensure that it may keep the land-use function that it has, such as agricultural crop production. To calculate the technical potential, conversion efficiencies are assumed that are explained in method section ‘Climate impacts on renewable energy’.<br />
<br />
Rooftop PV builds on the method of utility-scale PV, using the theoretical and technical aspects, but differentiates on the geographical potential (Gernaat et al. submitted). For rooftop PV, the geographical potential is determined according to roof area. This area is estimated by dividing the living area per household by the number of floors per household, both of which are based on census data. The estimates distinguish between urban areas and rural areas, and are combined with an urban/rural population map to scale down the estimated roof areas to grid level. The technical calculations are similar as the ones used to calculate utility-scale PV and explained in method section ‘Climate impacts on renewable energy’.<br />
<br />
Calculations of onshore and offshore wind energy potential start with wind speeds (m s<sup>-1</sup>) (Gernaat et al. 2014, Hoogwijk 2004). Then, similar as for solar power, areas are excluded and further restricted according to suitability factors. For the remaining geographical area, based on wind data, the electricity output is calculated using a Weibull distribution function and power curve of the turbine. For details on offshore wind methodology see Supplementary Text 7-S2.<br />
<br />
Bio-energy potential calculations start with primary biomass production, represented through yields (t ha<sup>-1</sup> y<sup>-1</sup>) (Daioglou et al. 2019, Hoogwijk 2004). Potential primary biomass sources include maize, sugar, and lignocellulosic crops (trees, switchgrass, and Miscanthus). Land availability for bio-energy production is limited by agricultural production following a ‘food-first’ principle where agricultural lands are determined first and are off-limits for biomass production. The technical potential is further limited by excluding forests, nature reserves and water stressed areas. In principle, bio-energy can be produced on remaining unprotected lands but also on abandoned agricultural lands. Besides energy crops, residues from agricultural and forestry can also be used as a feedstock. The costs of primary bio-energy crops are calculated with a Cobb-Douglas economic growth model using labour , land rent and capital costs as inputs <ref><div style="clear:both float:right">The Cobb–Douglas production function is a particular functional form of the production function widely used to represent the technological relationship between the amounts of two or more inputs, particularly physical capital and labor, and the amount of output that can be produced by those inputs.</div></ref>. The land costs are based on average regional income levels per km2, which was found to be a reasonable proxy for regional differences in land rent costs. The production functions are calibrated to empirical data (Hoogwijk, 2004).This technical potential is converted to several secondary energy carriers (solids, liquids, electricity, hydrogen) that compete in the energy system with other secondary energy carriers, such as fossil fuels or renewables (see Daioglou et al. (2019)) for a full description of biomass supply and demand in IMAGE) <ref>Daioglou, V., Doelman, J.C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2019. Integrated assessment of biomass supply and demand in climate change mitigation scenarios. ''Global Environmental Change'', ''54'', pp.88-101.</ref><ref>Daioglou, V., Stehfest, E., Wicke, B., Faaij, A. and Van Vuuren, D.P., 2016. Projections of the availability and cost of residues from agriculture and forestry. ''Gcb Bioenergy'', ''8''(2), pp.456-470.</ref> <ref>Daioglou, V., Doelman, J.C., Stehfest, E., Müller, C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2017. Greenhouse gas emission curves for advanced biofuel supply chains. ''Nature Climate Change'', ''7''(12), p.920.</ref>.<br />
<br />
Calculations of hydropower potential start with run-off (kg km<sup>-2</sup> s<sup>-1</sup>) that flows from high elevation to low elevation (representing discharge). On the basis of these discharge maps, >3.8 million site-specific hydropower installations were evaluated, at a 25km interval for every river between 56° S and 60° N (the excluded area is due to unavailable topographic data). At each site, high-resolution topographic data (3” × 3”) were used to calculate the cost-optimal dam dimensions and associated production potential. In this way, 60,000 suitable sites were identified, which together represent the remaining technical potential (see Gernaat et al. (2017) for a full description of the site selection process). <br />
<br />
[[File:Technical potential maps renewables.png|thumb|617x617px|'''Global maps showing technical potential of renewable energy sources for 2010.''' Calculated with climate data from HadGEM2-ES (30y-average 1970-2000) and the suitability factors of Table 5-1. One cell has an area of 0.5°×0.5°. '''a,''' Solar PV (utility-scale PV) (based on Chapter 3 and Hoogwijk (2004), Köberle et al. (2015)). '''b''', CSP (based on Köberle et al. (2015)). '''c''', Wind (onshore and offshore) (based on Chapter 2 and Hoogwijk (2004)). '''d''', Hydropower (defined as: remaining technical potential, explained and based on Chapter 4). '''e''', 1<sup>st</sup> generation bio-energy (based on Daioglou et al. (2019), Hoogwijk (2004)). '''f''', 2<sup>nd</sup> generation bio-energy (based on Daioglou et al. (2019), Hoogwijk (2004)). Note that the scales are different.]]<br />
<br />
The maps on technical potential for all renewables are combined with economic information to generate cost-supply curves. Assumptions on cost can be found in the separate articles but the general methodology is as follows. Each technology requires an investment before it can produce energy. This investment (in USD) is divided by the annual production (kWh) to calculate the production cost (USD kWh<sup>-1</sup>). This yields two global maps, a technical potential map (kWh) and a production cost map (USD kWh<sup>-1</sup>). Together they are used to generate a cost-supply curve, by sorting (in ascending order) the cells in the production cost map while simultaneously adding the same cells from the technical potential map, such as the hydropower example below.<br />
<br />
[[File:Article Figure 1 Global and regional cost-supply curves and their geographic locations.png|thumb|900x900px|'''Global and regional cost-supply curves and their geographic locations.''' '''a-f,''' The global (a) and regional (Africa (b), Asia Pacific (c), Europe (d), Central and North America (e) and South America (f)) cost-supply curves showing the remaining technical potential below 0.50 $ kWh<sup>-1</sup>. The red numbers indicate the total river power potential, the blue numbers the total diversion canal power potential and the black numbers the sum of both. The dot-dashed line indicates the remaining economic potential below a cost of 0.10 $ kWh<sup>-1</sup>. '''g''', The geographic locations of the two hydropower systems.]]<br />
<br />
<div class="version newv31"></div><references /><br />
</div></div>Gernaatdhttps://models.pbl.nl/index.php?title=Energy_supply&diff=36021Energy supply2019-04-25T14:47:22Z<p>Gernaatd: Text improvements</p>
<hr />
<div>{{ComponentTemplate2<br />
|Application=Roads from Rio+20 (2012) project; ADVANCE project;<br />
|IMAGEComponent=Drivers; Land cover and land use; Crops and grass; Climate policy; Atmospheric composition and climate;<br />
|KeyReference=De Vries et al., 2007; Van Vuuren et al., 2008; Van Vuuren et al., 2009;<br />
|InputVar=Technology development of energy supply; Energy resources; Trade restriction; Demand for primary energy; Potential bioenergy yield - grid; Land supply for bioenergy - grid; Learning rate;<br />
|Parameter=Initial production costs;<br />
|OutputVar=Primary energy price; Carbon storage price; Energy security indicators; Total primary energy supply; Marginal abatement cost; Energy and industry activity level; Bioenergy production;<br />
|ComponentCode=ES<br />
|AggregatedComponent=Energy supply and demand<br />
|FrameworkElementType=pressure component<br />
}}<br />
<br />
<div class="page_standard"><br />
==Introduction==<br />
!CHANGE! The energy supply model simulates long-term trends in energy supply. This model describes the investments in, and the use of, different types of energy carriers by technology development and resource depletion. Technological development is implemented in form of learning curves for most fuels and renewable energy options. Costs decrease endogenously as a function of the cumulative energy capacity. On the other hand, resource costs increase as they get depleted which is based on cost-supply curves. <br />
<br />
!CHANGE! Energy demand is assumed to always meet energy supply and, because regions are sometimes unable to meet their own demand, energy carriers, such as coal, oil and gas, are traded. The impact of depletion and technology development lead to changes in primary fuel prices, which influence investment decisions in the end-use and energy-conversion modules. Linkages to other parts of IMAGE framework include available land for bio-energy production and emissions of greenhouse gases and air pollutants. <br />
<br />
{{InputOutputParameterTemplate}}<br />
</div></div>Gernaatdhttps://models.pbl.nl/index.php?title=Energy_supply/Data_uncertainties_limitations&diff=36018Energy supply/Data uncertainties limitations2019-04-25T14:39:55Z<p>Gernaatd: Text improvements</p>
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<div>{{ComponentDataUncertaintyAndLimitationsTemplate<br />
|Reference=Mulders et al., 2006; Van Vuuren et al., 2009; Van Vuuren et al., 2010; Hoogwijk, 2004; Hendriks et al., 2004b; IPCC, 2005; van Vuuren et al., 2008; WEC, 2010;<br />
}}<br />
<div class="page_standard"><br />
==Data, uncertainty and limitations==<br />
===Data===<br />
Main data for the supply side of TIMER are the size of the resources available at different production costs (the table below).<br />
<div class="thumbcaption dark">Table: Main data sources for the TIMER energy supply module</div><table class="pbltable"><br />
<br />
<tr><th>Data input<br />
</th><th>Sources<br />
</th></tr><br />
<tr><td>Fossil-fuel resources and costs<br />
</td><td>[!CHANGE] <nowiki>[[IEA, 2017]]</nowiki>, <nowiki>[[USGS, 2012]]</nowiki>; <nowiki>[[BGR, 2016]]</nowiki>; [[EDGAR database]]; <nowiki>[[Abundant Gas Project]]</nowiki>. Costs mainly based on Rogner et al. <nowiki>[[(2018, in prep.)]]</nowiki><br />
</td></tr><br />
<tr><td>Nuclear fuel data (uranium and thorium)<br />
</td><td>[[WEC-Uranium]] ([[WEC, 2010]])<br />
</td></tr><br />
<tr><td>Bio-energy potential and costs<br />
</td><td>PBL calculations ([[Van Vuuren et al., 2009]]; [[Van Vuuren et al., 2010]])<br />
</td></tr><br />
<tr><td>Solar, wind, and hydropower potential <br />
</td><td>PBL calculations ([[Hoogwijk, 2004]])<br />
</td></tr><br />
<tr><td>CCS potential<br />
</td><td>Based on ([[Hendriks et al., 2004b]]; [[IPCC, 2005]])<br />
</td></tr><br />
</table><br />
<br />
===Uncertainties===<br />
One of the main uncertainties with respect to long-term supply is the size of the resource estimates at various production costs. Estimates of energy resources vary significantly, especially non-conventional resource estimates for oil and natural gas. Equally important uncertainties are the nature and rate of technological advances, and the design and implementation of energy policies in different regions.<br />
<br />
Various PBL publications have analysed the sensitivity of the model to supply uncertainties. The Monte Carlo uncertainty analysis of various scenarios ([[Van Vuuren et al., 2008]]) identified model parameters as important determinants of the future supply such as oil and natural gas resources and renewable energy learning rates. Some of these factors were only important for a subset of scenario output. For instance, size of oil resources was found to directly influence future oil production, but had limited impact on future CO<sub>2</sub> emissions. The main reason is that oil production in the medium-term is constrained by competition from other fossil fuels and bio-energy. The results were also shown to be scenario dependent. Fossil fuel related uncertainties were more important in a scenario that resulted in a high rather than low fossil-fuel demand.<br />
<br />
===Limitations===<br />
The general limitations of TIMER also apply to energy supply modules with a few specific limitations. As a global model, TIMER specifies resource availability in [[Region classification map|26 global regions]]. However, to some degree this does not take into account the underlying geographical dimensions of individual countries and specific areas. For fossil fuels, this issue leads to heterogeneity within a region (e.g., due to different tax systems), but is more important for renewable energy. A key factor can be transport from one area to another, and calculations require the use of other models. <br />
<br />
Another main limitation concerns the focus on production costs in describing energy markets. Although long-term developments may be expected to be driven by long-term supply costs over the last few decades, issues related to capacity constraints and market formation over longer time periods have lead to fossil fuels prices that differ from production costs.<br />
</div></div>Gernaatdhttps://models.pbl.nl/index.php?title=Energy_supply/Policy_issues&diff=36015Energy supply/Policy issues2019-04-25T14:36:59Z<p>Gernaatd: Text improvements</p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
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}}<br />
<br />
== Baseline developments ==<br />
<div class="page_standard"><br />
Under the baseline scenario, demand for energy increases rapidly, and as a consequence, supply is projected to increase in the coming decades for all energy supply options. Energy demand is, however, mostly met by fossil fuels: coal increases in production for most regions; oil also increases and moves to unconventional sources from Canada and South America; natural gas is expected to rise faster in production because it is presumed to be more abundant and increasingly cost competitive, with unconventional sources becoming increasingly important. Main gas producers are the United States, the former Soviet Union and increasingly the Middle Eas. <br />
<br />
Production of modern bio-energy is constantly increasing in different parts of the world. For solar and wind, the most rapid increase so far has been in western Europe, the United States, and China. In the future, parts of South America and India are expected to produce large amounts of renewable energy. Nuclear power is expected to remain roughly at the same level, and uranium production to remain more or less stable and rather evenly distributed across world regions. Finally, hydropower capacity shows a modest increase under the baseline scenario with large remaining potential is Asia, South America, and Africa.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
The model can simulate various policies on the supply side:<br />
* Carbon tax. As discussed, a carbon tax can lead to significant changes in the demand for fuels and therefore, also supply. <br />
* Restrictions on fuel trade. As part of energy security policies, fuel trade between different regions can be blocked.<br />
* Sustainability criteria for bio-energy production may restrict production in water-scarce areas.<br />
* Production targets are mostly set to force (renewable) technologies through a learning curve.<br />
The influence of stringent climate policy on production of primary energy resources is shown in the figure below. Climate policy leads to a major shift from a system mostly based on fossil fuels to an increase in the use of nuclear power, renewable energy, bio-energy and {{abbrTemplate|CCS}} technology. The choice of these alternative options depends on assumptions made in the model, as shown in the scenarios in the study [[Roads from Rio+20 (2012) project|Roads from Rio+20]] ([[PBL, 2012]]). Three pathways based on different initial assumptions emphasizes different combinations of primary energy carriers, each time within a stringent emission constraint.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Gernaatdhttps://models.pbl.nl/index.php?title=Energy_supply/Description&diff=36012Energy supply/Description2019-04-25T14:06:54Z<p>Gernaatd: Bug fix and added !change!</p>
<hr />
<div>{{ComponentDescriptionTemplate<br />
|Reference=Hoogwijk, 2004; De Vries et al., 2007; New et al., 1997; Rogner, 1997; Mulders et al., 2006;<br />
}}<br />
<div class="page_standard"><br />
==Model description of {{ROOTPAGENAME}}==<br />
===Fossil fuels and uranium===<br />
Depletion of fossil fuels (coal, oil and natural gas) and uranium is simulated on the assumption that resources can be represented by a long-term cost-supple curve, consisting of different resource categories with increasing costs levels. The model assumes that the cheapest deposits will be exploited first. For each region, there are 12 resource categories for oil, gas and nuclear fuels, and 14 categories for coal. <br />
<br />
A key input for each of the fossil fuel and uranium supply submodules is fuel demand (fuel used in final energy and conversion processes). Additional input includes conversion losses in refining, liquefaction, conversion, and energy use in the energy system. [!CHANGE] Upstream energy use is endogenously determined based energy carrier, region in which the energy carrier is produced, production rate, and resource category. These submodules indicate how demand can be met by supply in a region and other regions through interregional trade.<br />
<br />
<div class="thumbcaption dark">Table: [!CHANGE]Main assumptions on fossil fuel resources (in ZJ; coal does not have a distinction between conventional and unconventional; <nowiki>[[IEA, 2017]]</nowiki>, <nowiki>[[USGS, 2012]]</nowiki>; <nowiki>[[BGR, 2016]]</nowiki>; [[EDGAR database]]; <nowiki>[[Abundant Gas Project]]</nowiki>)</div><table class="pbltable"><br />
<br />
<tr><th></th><th>Oil<br />
</th><th>Natural gas<br />
</th><th>Underground coal<br />
</th><th>Surface coal<br />
</th></tr><br />
<tr><td>Cum. 1970-2015 production<br />
</td><td>6.5<br />
</td><td>3.4<br />
</td><td>2.4<br />
</td><td>1.5<br />
</td></tr><br />
<tr><td>Reserves<br />
</td><td>9.4<br />
</td><td>7.3<br />
</td><td>17<br />
</td><td>3.6<br />
</td></tr><br />
<tr><td>Other conventional resources<br />
</td><td>33<br />
</td><td>17<br />
</td><td>481<br />
</td><td>56<br />
</td></tr><br />
<tr><td>Unconventional resources (reserves)<br />
</td><td>2.0<br />
</td><td>0.30<br />
</td><td></td><td></td></tr><br />
<tr><td>Other unconventional resources<br />
</td><td>54<br />
</td><td>2023<br />
</td><td></td><td></td></tr><br />
<tr><td>Total<br />
</td><td>105<br />
</td><td>2051<br />
</td><td>501<br />
</td><td>61<br />
</td></tr><br />
</table><br />
<br />
Fossil fuel resources are aggregated to five resource categories for each fuel (the table above). Each category has typical production costs. The resource estimates for oil and natural gas supply imply that for conventional resources supply is limited to only [!CHANGE] about 7 times the 1970–2015 production level. Production estimates for unconventional resources are much larger, albeit speculative. Recently, some of the occurrences of these unconventional resources have become competitive such as shale gas and tar sands. For coal, even current reserves amount to almost ten times the production level of the last three decades. For all fuels, the model assumes that, if prices increase, or if there is further technology development, the energy could be produced in the higher cost resource categories. The values presented in the table above represent medium estimates in the model, which can also use higher or lower estimates in the scenarios. The final production costs in each region are determined by the combined effect of resource depletion and learning-by-doing.<br />
<br />
===Trade===<br />
Trade is dealt with in a generic way for oil, natural gas and coal. In the fuel trade model, each region imports fuels from other regions. The amount of fuel imported from each region depends on the relative production costs and those in other regions, augmented with transport costs, using multinomial logit equations. Transport costs are calculated from representative interregional transport distances and time- and fuel-dependent estimates of the costs per GJ per kilometre.<br />
<br />
To reflect geographical, political and other constraints in the interregional fuel trade, an additional 'cost' is added to simulate trade barriers between regions (this costs factor is determined by calibration). Natural gas is transported by pipeline or liquid-natural gas ({{abbrTemplate|LNG}}) tanker, depending on distance, with pipeline more attractive for short distances. In order to account for cartel behaviour, the model compares production costs with and without unrestricted trade. Regions that can supply at lower costs than the average production costs in importing regions are assumed to supply oil at a price only slightly below the production costs of the importing regions. Although also this rule is implemented in a generic form for all energy carriers, it is only effective for oil, where the behaviour of the OPEC cartel is simulated to some extent.<br />
<br />
===Renewable energy !CHANGE!===<br />
IMAGE model the supply of eight renewable energy options: utility-scale photovoltaic (PV), rooftop PV, concentrated solar power (CSP), onshore wind energy, offshore wind energy, first-generation bio-energy, lignocellulosic bio-energy, and hydropower is estimated generically as follows ([[Hoogwijk, 2004]]; [[De Vries et al., 2007]]): <br />
Firstly, physical and geographical data are collected on a 0.5x0.5 degree grid. The characteristics of wind speed, insulation and monthly variation are taken from the digital databases. [[File:Physical climate data renewables.png|thumb|621x621px|'''Model mean (GFLD-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5) historical 30-year (1970–2000) average climate data used as input to calculate energy potentials as available in the ISIMIP2b database.''' '''a''', Solar irradiance (kWh m<sup>-2</sup> day<sup>-1</sup>). '''b''', Temperature (°C). '''c''', Wind speeds (m s<sup>-1</sup>). '''d''', Run-off (kg km<sup>-2</sup> s<sup>-1</sup>). '''e''', Sugar and maize yields (crop selected with highest yield per cell) (%). '''f''', Lignocellulosic crop yields (switchgrass and Miscanthus) (%).]]<br />
<br />
The methodology assumes that part of the grid cell can be used for energy production, given its physical–geographic (terrain, habitation) and socio-geographical (location, acceptability) characteristics. This leads to an estimate of the geographical potential. Several of these factors are scenario-dependent. The geographical potential for biomass production, for example, is estimated using suitability factors taking considering competing land-use options and the harvested rain-fed yield of energy crop. Next, we assume that only part of the geographical potential can be used due to limited conversion efficiency and maximum power density, This result of accounting for these conversion efficiencies is referred to as the technical potential. The final step is to relate the technical potential to on-site production costs. Information at grid level is sorted and used as supply cost curves to reflect the assumption that the lowest cost locations are exploited first. Supply cost curves are used dynamically and change over time as a result of the learning effect.<br />
<br />
The calculation of each renewable energy potential is explained in detail in separate published articles. Here, a short explanation is given introducing each.<br />
<br />
Utility-scale PV and CSP starts with the theoretical potential based on a global solar irradiation map (kWh m<sup>-2</sup> day<sup>-1</sup>) (Köberle et al. 2015). This is subsequently restricted by excluding unsuitable areas (e.g. areas with snow cover or steep mountainous terrain) to calculate the geographical potential. The area that remains is further restricted by suitability factors. The idea behind suitability factors is that only part of the land is physically available for solar applications to ensure that it may keep the land-use function that it has, such as agricultural crop production. To calculate the technical potential, conversion efficiencies are assumed that are explained in method section ‘Climate impacts on renewable energy’.<br />
<br />
Rooftop PV builds on the method of utility-scale PV, using the theoretical and technical aspects, but differentiates on the geographical potential (Gernaat et al. submitted). For rooftop PV, the geographical potential is determined according to roof area. This area is estimated by dividing the living area per household by the number of floors per household, both of which are based on census data. The estimates distinguish between urban areas and rural areas, and are combined with an urban/rural population map to scale down the estimated roof areas to grid level. The technical calculations are similar as the ones used to calculate utility-scale PV and explained in method section ‘Climate impacts on renewable energy’.<br />
<br />
Calculations of onshore and offshore wind energy potential start with wind speeds (m s<sup>-1</sup>) (Gernaat et al. 2014, Hoogwijk 2004). Then, similar as for solar power, areas are excluded and further restricted according to suitability factors. For the remaining geographical area, based on wind data, the electricity output is calculated using a Weibull distribution function and power curve of the turbine. For details on offshore wind methodology see Supplementary Text 7-S2.<br />
<br />
Bio-energy potential calculations start with primary biomass production, represented through yields (t ha<sup>-1</sup> y<sup>-1</sup>) (Daioglou et al. 2019, Hoogwijk 2004). Potential primary biomass sources include maize, sugar, and lignocellulosic crops (trees, switchgrass, and Miscanthus). Land availability for bio-energy production is limited by agricultural production following a ‘food-first’ principle where agricultural lands are determined first and are off-limits for biomass production. The technical potential is further limited by excluding forests, nature reserves and water stressed areas. In principle, bio-energy can be produced on remaining unprotected lands but also on abandoned agricultural lands. Besides energy crops, residues from agricultural and forestry can also be used as a feedstock. The costs of primary bio-energy crops are calculated with a Cobb-Douglas economic growth model using labour , land rent and capital costs as inputs <ref><div style="clear:both float:right">The Cobb–Douglas production function is a particular functional form of the production function widely used to represent the technological relationship between the amounts of two or more inputs, particularly physical capital and labor, and the amount of output that can be produced by those inputs.</div></ref>. The land costs are based on average regional income levels per km2, which was found to be a reasonable proxy for regional differences in land rent costs. The production functions are calibrated to empirical data (Hoogwijk, 2004).This technical potential is converted to several secondary energy carriers (solids, liquids, electricity, hydrogen) that compete in the energy system with other secondary energy carriers, such as fossil fuels or renewables (see Daioglou et al. (2019)) for a full description of biomass supply and demand in IMAGE) <ref>Daioglou, V., Doelman, J.C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2019. Integrated assessment of biomass supply and demand in climate change mitigation scenarios. ''Global Environmental Change'', ''54'', pp.88-101.</ref><ref>Daioglou, V., Stehfest, E., Wicke, B., Faaij, A. and Van Vuuren, D.P., 2016. Projections of the availability and cost of residues from agriculture and forestry. ''Gcb Bioenergy'', ''8''(2), pp.456-470.</ref> <ref>Daioglou, V., Doelman, J.C., Stehfest, E., Müller, C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2017. Greenhouse gas emission curves for advanced biofuel supply chains. ''Nature Climate Change'', ''7''(12), p.920.</ref>.<br />
<br />
Calculations of hydropower potential start with run-off (kg km<sup>-2</sup> s<sup>-1</sup>) that flows from high elevation to low elevation (representing discharge). On the basis of these discharge maps, >3.8 million site-specific hydropower installations were evaluated, at a 25km interval for every river between 56° S and 60° N (the excluded area is due to unavailable topographic data). At each site, high-resolution topographic data (3” × 3”) were used to calculate the cost-optimal dam dimensions and associated production potential. In this way, 60,000 suitable sites were identified, which together represent the remaining technical potential (see Gernaat et al. (2017) for a full description of the site selection process). [[File:Technical potential maps renewables.png|thumb|617x617px|'''Global maps showing technical potential of renewable energy sources for 2010.''' Calculated with climate data from HadGEM2-ES (30y-average 1970-2000) and the suitability factors of Table 5-1. One cell has an area of 0.5°×0.5°. '''a,''' Solar PV (utility-scale PV) (based on Chapter 3 and Hoogwijk (2004), Köberle et al. (2015)). '''b''', CSP (based on Köberle et al. (2015)). '''c''', Wind (onshore and offshore) (based on Chapter 2 and Hoogwijk (2004)). '''d''', Hydropower (defined as: remaining technical potential, explained and based on Chapter 4). '''e''', 1<sup>st</sup> generation bio-energy (based on Daioglou et al. (2019), Hoogwijk (2004)). '''f''', 2<sup>nd</sup> generation bio-energy (based on Daioglou et al. (2019), Hoogwijk (2004)). Note that the scales are different.]]<br />
<br />
The maps on technical potential for all renewables are combined with economic information to generate cost-supply curves. Assumptions on cost can be found in the separate articles but the general methodology is as follows. Each technology requires an investment before it can produce energy. This investment (in USD) is divided by the annual production (kWh) to calculate the production cost (USD kWh<sup>-1</sup>). This yields two global maps, a technical potential map (kWh) and a production cost map (USD kWh<sup>-1</sup>). Together they are used to generate a cost-supply curve, by sorting (in ascending order) the cells in the production cost map while simultaneously adding the same cells from the technical potential map, such as the hydropower example below.[[File:Article Figure 1 Global and regional cost-supply curves and their geographic locations.png|thumb|900x900px|'''Global and regional cost-supply curves and their geographic locations.''' '''a-f,''' The global (a) and regional (Africa (b), Asia Pacific (c), Europe (d), Central and North America (e) and South America (f)) cost-supply curves showing the remaining technical potential below 0.50 $ kWh<sup>-1</sup>. The red numbers indicate the total river power potential, the blue numbers the total diversion canal power potential and the black numbers the sum of both. The dot-dashed line indicates the remaining economic potential below a cost of 0.10 $ kWh<sup>-1</sup>. '''g''', The geographic locations of the two hydropower systems.]]<br />
<br />
<div class="version newv31"></div><references /><br />
</div></div>Gernaatdhttps://models.pbl.nl/index.php?title=Energy_supply/Description&diff=36009Energy supply/Description2019-04-25T14:05:44Z<p>Gernaatd: Added renewable energy supply options in a structured way with the most up to date information from David's thesis</p>
<hr />
<div>{{ComponentDescriptionTemplate<br />
|Reference=Hoogwijk, 2004; De Vries et al., 2007; New et al., 1997; Rogner, 1997; Mulders et al., 2006;<br />
}}<br />
<div class="page_standard"><br />
==Model description of {{ROOTPAGENAME}}==<br />
===Fossil fuels and uranium===<br />
Depletion of fossil fuels (coal, oil and natural gas) and uranium is simulated on the assumption that resources can be represented by a long-term cost-supple curve, consisting of different resource categories with increasing costs levels. The model assumes that the cheapest deposits will be exploited first. For each region, there are 12 resource categories for oil, gas and nuclear fuels, and 14 categories for coal. <br />
<br />
A key input for each of the fossil fuel and uranium supply submodules is fuel demand (fuel used in final energy and conversion processes). Additional input includes conversion losses in refining, liquefaction, conversion, and energy use in the energy system. [!CHANGE] Upstream energy use is endogenously determined based energy carrier, region in which the energy carrier is produced, production rate, and resource category. These submodules indicate how demand can be met by supply in a region and other regions through interregional trade.<br />
<br />
<div class="thumbcaption dark">Table: [!CHANGE]Main assumptions on fossil fuel resources (in ZJ; coal does not have a distinction between conventional and unconventional; <nowiki>[[IEA, 2017]]</nowiki>, <nowiki>[[USGS, 2012]]</nowiki>; <nowiki>[[BGR, 2016]]</nowiki>; [[EDGAR database]]; <nowiki>[[Abundant Gas Project]]</nowiki>)</div><table class="pbltable"><br />
<br />
<tr><th></th><th>Oil<br />
</th><th>Natural gas<br />
</th><th>Underground coal<br />
</th><th>Surface coal<br />
</th></tr><br />
<tr><td>Cum. 1970-2015 production<br />
</td><td>6.5<br />
</td><td>3.4<br />
</td><td>2.4<br />
</td><td>1.5<br />
</td></tr><br />
<tr><td>Reserves<br />
</td><td>9.4<br />
</td><td>7.3<br />
</td><td>17<br />
</td><td>3.6<br />
</td></tr><br />
<tr><td>Other conventional resources<br />
</td><td>33<br />
</td><td>17<br />
</td><td>481<br />
</td><td>56<br />
</td></tr><br />
<tr><td>Unconventional resources (reserves)<br />
</td><td>2.0<br />
</td><td>0.30<br />
</td><td></td><td></td></tr><br />
<tr><td>Other unconventional resources<br />
</td><td>54<br />
</td><td>2023<br />
</td><td></td><td></td></tr><br />
<tr><td>Total<br />
</td><td>105<br />
</td><td>2051<br />
</td><td>501<br />
</td><td>61<br />
</td></tr><br />
</table><br />
<br />
Fossil fuel resources are aggregated to five resource categories for each fuel (the table above). Each category has typical production costs. The resource estimates for oil and natural gas supply imply that for conventional resources supply is limited to only [!CHANGE] about 7 times the 1970–2015 production level. Production estimates for unconventional resources are much larger, albeit speculative. Recently, some of the occurrences of these unconventional resources have become competitive such as shale gas and tar sands. For coal, even current reserves amount to almost ten times the production level of the last three decades. For all fuels, the model assumes that, if prices increase, or if there is further technology development, the energy could be produced in the higher cost resource categories. The values presented in the table above represent medium estimates in the model, which can also use higher or lower estimates in the scenarios. The final production costs in each region are determined by the combined effect of resource depletion and learning-by-doing.<br />
<br />
===Trade===<br />
Trade is dealt with in a generic way for oil, natural gas and coal. In the fuel trade model, each region imports fuels from other regions. The amount of fuel imported from each region depends on the relative production costs and those in other regions, augmented with transport costs, using multinomial logit equations. Transport costs are calculated from representative interregional transport distances and time- and fuel-dependent estimates of the costs per GJ per kilometre.<br />
<br />
To reflect geographical, political and other constraints in the interregional fuel trade, an additional 'cost' is added to simulate trade barriers between regions (this costs factor is determined by calibration). Natural gas is transported by pipeline or liquid-natural gas ({{abbrTemplate|LNG}}) tanker, depending on distance, with pipeline more attractive for short distances. In order to account for cartel behaviour, the model compares production costs with and without unrestricted trade. Regions that can supply at lower costs than the average production costs in importing regions are assumed to supply oil at a price only slightly below the production costs of the importing regions. Although also this rule is implemented in a generic form for all energy carriers, it is only effective for oil, where the behaviour of the OPEC cartel is simulated to some extent.<br />
<br />
===Renewable energy===<br />
IMAGE model the supply of eight renewable energy options: utility-scale photovoltaic (PV), rooftop PV, concentrated solar power (CSP), onshore wind energy, offshore wind energy, first-generation bio-energy, lignocellulosic bio-energy, and hydropower is estimated generically as follows ([[Hoogwijk, 2004]]; [[De Vries et al., 2007]]): <br />
Firstly, physical and geographical data are collected on a 0.5x0.5 degree grid. The characteristics of wind speed, insulation and monthly variation are taken from the digital databases. [[File:Physical climate data renewables.png|thumb|621x621px|'''Model mean (GFLD-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5) historical 30-year (1970–2000) average climate data used as input to calculate energy potentials as available in the ISIMIP2b database.''' '''a''', Solar irradiance (kWh m<sup>-2</sup> day<sup>-1</sup>). '''b''', Temperature (°C). '''c''', Wind speeds (m s<sup>-1</sup>). '''d''', Run-off (kg km<sup>-2</sup> s<sup>-1</sup>). '''e''', Sugar and maize yields (crop selected with highest yield per cell) (%). '''f''', Lignocellulosic crop yields (switchgrass and Miscanthus) (%).]]The methodology assumes that part of the grid cell can be used for energy production, given its physical–geographic (terrain, habitation) and socio-geographical (location, acceptability) characteristics. This leads to an estimate of the geographical potential. Several of these factors are scenario-dependent. The geographical potential for biomass production, for example, is estimated using suitability factors taking considering competing land-use options and the harvested rain-fed yield of energy crop. Next, we assume that only part of the geographical potential can be used due to limited conversion efficiency and maximum power density, This result of accounting for these conversion efficiencies is referred to as the technical potential. The final step is to relate the technical potential to on-site production costs. Information at grid level is sorted and used as supply cost curves to reflect the assumption that the lowest cost locations are exploited first. Supply cost curves are used dynamically and change over time as a result of the learning effect.<br />
<br />
The calculation of each renewable energy potential is explained in detail in separate published articles. Here, a short explanation is given introducing each.<br />
<br />
Utility-scale PV and CSP starts with the theoretical potential based on a global solar irradiation map (kWh m<sup>-2</sup> day<sup>-1</sup>) (Köberle et al. 2015). This is subsequently restricted by excluding unsuitable areas (e.g. areas with snow cover or steep mountainous terrain) to calculate the geographical potential. The area that remains is further restricted by suitability factors. The idea behind suitability factors is that only part of the land is physically available for solar applications to ensure that it may keep the land-use function that it has, such as agricultural crop production. To calculate the technical potential, conversion efficiencies are assumed that are explained in method section ‘Climate impacts on renewable energy’.<br />
<br />
Rooftop PV builds on the method of utility-scale PV, using the theoretical and technical aspects, but differentiates on the geographical potential (Gernaat et al. submitted). For rooftop PV, the geographical potential is determined according to roof area. This area is estimated by dividing the living area per household by the number of floors per household, both of which are based on census data. The estimates distinguish between urban areas and rural areas, and are combined with an urban/rural population map to scale down the estimated roof areas to grid level. The technical calculations are similar as the ones used to calculate utility-scale PV and explained in method section ‘Climate impacts on renewable energy’.<br />
<br />
Calculations of onshore and offshore wind energy potential start with wind speeds (m s<sup>-1</sup>) (Gernaat et al. 2014, Hoogwijk 2004). Then, similar as for solar power, areas are excluded and further restricted according to suitability factors. For the remaining geographical area, based on wind data, the electricity output is calculated using a Weibull distribution function and power curve of the turbine. For details on offshore wind methodology see Supplementary Text 7-S2.<br />
<br />
Bio-energy potential calculations start with primary biomass production, represented through yields (t ha<sup>-1</sup> y<sup>-1</sup>) (Daioglou et al. 2019, Hoogwijk 2004). Potential primary biomass sources include maize, sugar, and lignocellulosic crops (trees, switchgrass, and Miscanthus). Land availability for bio-energy production is limited by agricultural production following a ‘food-first’ principle where agricultural lands are determined first and are off-limits for biomass production. The technical potential is further limited by excluding forests, nature reserves and water stressed areas. In principle, bio-energy can be produced on remaining unprotected lands but also on abandoned agricultural lands. Besides energy crops, residues from agricultural and forestry can also be used as a feedstock. The costs of primary bio-energy crops are calculated with a Cobb-Douglas economic growth model using labour , land rent and capital costs as inputs <ref><div style="clear:both float:right">The Cobb–Douglas production function is a particular functional form of the production function widely used to represent the technological relationship between the amounts of two or more inputs, particularly physical capital and labor, and the amount of output that can be produced by those inputs.</div></ref>. The land costs are based on average regional income levels per km2, which was found to be a reasonable proxy for regional differences in land rent costs. The production functions are calibrated to empirical data (Hoogwijk, 2004).This technical potential is converted to several secondary energy carriers (solids, liquids, electricity, hydrogen) that compete in the energy system with other secondary energy carriers, such as fossil fuels or renewables (see Daioglou et al. (2019)) for a full description of biomass supply and demand in IMAGE) <ref>Daioglou, V., Doelman, J.C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2019. Integrated assessment of biomass supply and demand in climate change mitigation scenarios. ''Global Environmental Change'', ''54'', pp.88-101.</ref><ref>Daioglou, V., Stehfest, E., Wicke, B., Faaij, A. and Van Vuuren, D.P., 2016. Projections of the availability and cost of residues from agriculture and forestry. ''Gcb Bioenergy'', ''8''(2), pp.456-470.</ref> <ref>Daioglou, V., Doelman, J.C., Stehfest, E., Müller, C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2017. Greenhouse gas emission curves for advanced biofuel supply chains. ''Nature Climate Change'', ''7''(12), p.920.</ref>.<br />
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Calculations of hydropower potential start with run-off (kg km<sup>-2</sup> s<sup>-1</sup>) that flows from high elevation to low elevation (representing discharge). On the basis of these discharge maps, >3.8 million site-specific hydropower installations were evaluated, at a 25km interval for every river between 56° S and 60° N (the excluded area is due to unavailable topographic data). At each site, high-resolution topographic data (3” × 3”) were used to calculate the cost-optimal dam dimensions and associated production potential. In this way, 60,000 suitable sites were identified, which together represent the remaining technical potential (see Gernaat et al. (2017) for a full description of the site selection process). [[File:Technical potential maps renewables.png|thumb|617x617px|'''Global maps showing technical potential of renewable energy sources for 2010.''' Calculated with climate data from HadGEM2-ES (30y-average 1970-2000) and the suitability factors of Table 5-1. One cell has an area of 0.5°×0.5°. '''a,''' Solar PV (utility-scale PV) (based on Chapter 3 and Hoogwijk (2004), Köberle et al. (2015)). '''b''', CSP (based on Köberle et al. (2015)). '''c''', Wind (onshore and offshore) (based on Chapter 2 and Hoogwijk (2004)). '''d''', Hydropower (defined as: remaining technical potential, explained and based on Chapter 4). '''e''', 1<sup>st</sup> generation bio-energy (based on Daioglou et al. (2019), Hoogwijk (2004)). '''f''', 2<sup>nd</sup> generation bio-energy (based on Daioglou et al. (2019), Hoogwijk (2004)). Note that the scales are different.]]The maps on technical potential for all renewables are combined with economic information to generate cost-supply curves. Assumptions on cost can be found in the separate articles but the general methodology is as follows. Each technology requires an investment before it can produce energy. This investment (in USD) is divided by the annual production (kWh) to calculate the production cost (USD kWh<sup>-1</sup>). This yields two global maps, a technical potential map (kWh) and a production cost map (USD kWh<sup>-1</sup>). Together they are used to generate a cost-supply curve, by sorting (in ascending order) the cells in the production cost map while simultaneously adding the same cells from the technical potential map, such as the hydropower example below.[[File:Article Figure 1 Global and regional cost-supply curves and their geographic locations.png|thumb|900x900px|'''Global and regional cost-supply curves and their geographic locations.''' '''a-f,''' The global (a) and regional (Africa (b), Asia Pacific (c), Europe (d), Central and North America (e) and South America (f)) cost-supply curves showing the remaining technical potential below 0.50 $ kWh<sup>-1</sup>. The red numbers indicate the total river power potential, the blue numbers the total diversion canal power potential and the black numbers the sum of both. The dot-dashed line indicates the remaining economic potential below a cost of 0.10 $ kWh<sup>-1</sup>. '''g''', The geographic locations of the two hydropower systems.]]<br />
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</div></div>Gernaatdhttps://models.pbl.nl/index.php?title=File:Article_Figure_1_Global_and_regional_cost-supply_curves_and_their_geographic_locations.png&diff=36008File:Article Figure 1 Global and regional cost-supply curves and their geographic locations.png2019-04-25T14:02:48Z<p>Gernaatd: </p>
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<div>Global and regional cost-supply curves and their geographic locations. a-f, The global (a) and regional (Africa (b), Asia Pacific (c), Europe (d), Central and North America (e) and South America (f)) cost-supply curves showing the remaining technical potential below 0.50 $ kWh-1. The red numbers indicate the total river power potential, the blue numbers the total diversion canal power potential and the black numbers the sum of both. The dot-dashed line indicates the remaining economic potential below a cost of 0.10 $ kWh-1. g, The geographic locations of the two hydropower systems.</div>Gernaatdhttps://models.pbl.nl/index.php?title=File:Technical_potential_maps_renewables.png&diff=36007File:Technical potential maps renewables.png2019-04-25T13:54:45Z<p>Gernaatd: </p>
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<div>Global maps showing technical potential of renewable energy sources for 2010. Calculated with climate data from HadGEM2-ES (30y-average 1970-2000) and the suitability factors of Table 5-1. One cell has an area of 0.5°×0.5°. a, Solar PV (utility-scale PV) (based on Chapter 3 and Hoogwijk (2004), Köberle et al. (2015)). b, CSP (based on Köberle et al. (2015)). c, Wind (onshore and offshore) (based on Chapter 2 and Hoogwijk (2004)). d, Hydropower (defined as: remaining technical potential, explained and based on Chapter 4). e, 1st generation bio-energy (based on Daioglou et al. (2019), Hoogwijk (2004)). f, 2nd generation bio-energy (based on Daioglou et al. (2019), Hoogwijk (2004)). Note that the scales are different.</div>Gernaatdhttps://models.pbl.nl/index.php?title=File:Physical_climate_data_renewables.png&diff=36006File:Physical climate data renewables.png2019-04-25T13:41:36Z<p>Gernaatd: </p>
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<div>Model mean (GFLD-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5) historical 30-year (1970–2000) average climate data used as input to calculate energy potentials as available in the ISIMIP2b database. a, Solar irradiance (kWh m-2 day-1). b, Temperature (°C). c, Wind speeds (m s-1). d, Run-off (kg km-2 s-1). e, Sugar and maize yields (crop selected with highest yield per cell) (%). f, Lignocellulosic crop yields (switchgrass and Miscanthus) (%).</div>Gernaatdhttps://models.pbl.nl/index.php?title=Energy_supply&diff=36003Energy supply2019-04-25T13:30:01Z<p>Gernaatd: Cleaned up the description</p>
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<div>{{ComponentTemplate2<br />
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<div class="page_standard"><br />
==Introduction==<br />
!CHANGE! The energy supply model simulates long-term trends in energy supply. This model describes the investments in, and the use of, different types of energy carriers by technology development and resource depletion. Technological development is implemented in form of learning curves for most fuels and renewable energy options. Costs decrease endogenously as a function of the cumulative energy capacity. On the other hand, resource costs increase as they get depleted which is based on cost-supply curves. <br />
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!CHANGE! It is assumed that all demand is always met. Because regions are usually unable to meet all of their own demand, energy carriers, such as coal, oil and gas, are traded. The impact of depletion and technology development lead to changes in primary fuel prices, which influence investment decisions in the end-use and energy-conversion modules Linkages to other parts of IMAGE framework include available land for bio-energy production, emissions of greenhouse gases and air pollutants, and the use of land for bio-energy production. <br />
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</div></div>Gernaatdhttps://models.pbl.nl/index.php?title=K%C3%B6berle_et_al.,_2015&diff=27720Köberle et al., 20152016-11-04T13:49:47Z<p>Gernaatd: Created page with "{{ReferenceTemplate |Author=Alexandre C. Köberle, David E.H.J. Gernaat, Detlef P. van Vuuren |Year=2015 |Title=Assessing current and future techno-economic potential of conce..."</p>
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|Author=Alexandre C. Köberle, David E.H.J. Gernaat, Detlef P. van Vuuren<br />
|Year=2015<br />
|Title=Assessing current and future techno-economic potential of concentrated solar power and photovoltaic electricity generation<br />
|PBL-link=http://www.pbl.nl/en/publications/assessing-current-and-future-techno-economic-potential-of-concentrated-solar-power-and-photovoltaic-electricity<br />
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CSP and PV technologies represent energy sources with large potentials. We present cost-supply curves for both technologies using a consistent methodology for 26 regions, based on geoexplicit information on solar radiation, land cover type and slope, exploring individual potential and interdependencies. For present day, both CSP and PV supply curves start at $0.18/kWh, in North Africa, South America, and Australia. Applying accepted learning rates to official capacity targets, we project prices to drop to $0.11/kWh for both technologies by 2050. In an alternative “fast-learning” scenario, generation costs drop to $0.06–0.07/kWh for CSP, and $0.09/kWh for PV. Competition between them for best areas is explored along with sensitivities of their techno-economic potentials to land use restrictions and land cover type. CSP was found to be more competitive in desert sites with highest direct solar radiation. PV was a clear winner in humid tropical regions, and temperate northern hemisphere. Elsewhere, no clear winner emerged, highlighting the importance of competition in assessments of potentials. Our results show there is ample potential globally for both technologies even accounting for land use restrictions, but stronger support for RD&D and higher investments are needed to make CSP and PV cost-competitive with established power technologies by 2050.</div>Gernaatdhttps://models.pbl.nl/index.php?title=Gernaat_et_al.,_2014&diff=27719Gernaat et al., 20142016-11-04T13:46:00Z<p>Gernaatd: Created page with "{{ReferenceTemplate |Author=David E.H.J. Gernaat, Detlef P. van Vuuren, Jasper van Vliet, Patrick Sullivan, Douglas J. Arent |Year=2014 |Title=Global long-term cost dynamics o..."</p>
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<div>{{ReferenceTemplate<br />
|Author=David E.H.J. Gernaat, Detlef P. van Vuuren, Jasper van Vliet, Patrick Sullivan, Douglas J. Arent<br />
|Year=2014<br />
|Title=Global long-term cost dynamics of offshore wind electricity generation<br />
|PBL-link=http://www.pbl.nl/node/61165<br />
|DOI=http://dx.doi.org/10.1016/j.energy.2014.08.062<br />
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Using the IMAGE/TIMER (The Targets IMage Energy Regional) long-term integrated assessment model, this paper explores the regional and global potential of offshore wind to contribute to global electricity production. We develop long-term cost supply curve for offshore wind, a representation of the potential suitable for inclusion in global integrated assessment models. For this, we combine available data on resource potential and cost estimates to estimate regional and global characteristics of offshore wind electricity generation. We find that for 2050, a baseline scenario would include about 4% of the total electricity production based on offshore wind. The findings also show that in most regions, technical potential is not a limiting factor. In some regions, that have a seriously constrained resource base for onshore wind, offshore wind could provide a key source of renewable energy, including South-East Asia, Indonesia and Brazil.</div>Gernaatdhttps://models.pbl.nl/index.php?title=Gernaat_et_al.,_2015&diff=27715Gernaat et al., 20152016-11-04T13:39:34Z<p>Gernaatd: </p>
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|Author=David E.H.J. Gernaat, Katherine Calvin, Paul L. Lucas, Gunnar Luderer, Sander Otto, Rao AC, Shilpa, Jessica Strefler, Detlef P. van Vuuren<br />
|Year=2015<br />
|Title=Understanding the contribution of non-carbon dioxide gases in deep mitigation scenarios<br />
|PBL-link=http://www.pbl.nl/en/publications/understanding-the-contribution-of-non-carbon-dioxide-gases-in-deep-mitigation-scenarios<br />
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In 2010, the combined emissions of methane (CH4), nitrous oxide (N2O) and the fluorinated gasses (F-gas) accounted for 20–30% of Kyoto emissions and about 30% of radiative forcing. Current scenario studies conclude that in order to reach deep climate targets (radiative forcing of 2.8 W/m2) in 2100, carbon dioxide (CO2) emissions will need to be reduced to zero or negative. However, studies indicated that non-CO2 emissions seem to be have less mitigation potential. To support effective climate policy strategies, an in-depth assessment was made of non-CO2 greenhouse gas emission and their sources in achieving an ambitious climate target. Emission scenarios were assessed that had been produced by six integrated assessments models, which contributed to the scenario database for the fifth IPCC report. All model scenarios reduced emissions from energy-related sectors, largely resulting from structural changes and end-of-pipe abatement technologies. However, emission reductions were much less in the agricultural sectors. Furthermore, there were considerable differences in abatement potential between the model scenarios, and most notably in the agricultural sectors. The paper shows that better exploration of long-term abatement potential of non-CO2 emissions is critical for the feasibility of deep climate targets.</div>Gernaatdhttps://models.pbl.nl/index.php?title=Gernaat_et_al.,_2015&diff=27714Gernaat et al., 20152016-11-04T13:39:17Z<p>Gernaatd: </p>
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<div>{{ReferenceTemplate<br />
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|Title=Understanding the contribution of non-carbon dioxide gases in deep mitigation scenarios<br />
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In 2010, the combined emissions of methane (CH4), nitrous oxide (N2O) and the fluorinated gasses (F-gas) accounted for 20–30% of Kyoto emissions and about 30% of radiative forcing. Current scenario studies conclude that in order to reach deep climate targets (radiative forcing of 2.8 W/m2) in 2100, carbon dioxide (CO2) emissions will need to be reduced to zero or negative. However, studies indicated that non-CO2 emissions seem to be have less mitigation potential. To support effective climate policy strategies, an in-depth assessment was made of non-CO2 greenhouse gas emission and their sources in achieving an ambitious climate target. Emission scenarios were assessed that had been produced by six integrated assessments models, which contributed to the scenario database for the fifth IPCC report. All model scenarios reduced emissions from energy-related sectors, largely resulting from structural changes and end-of-pipe abatement technologies. However, emission reductions were much less in the agricultural sectors. Furthermore, there were considerable differences in abatement potential between the model scenarios, and most notably in the agricultural sectors. The paper shows that better exploration of long-term abatement potential of non-CO2 emissions is critical for the feasibility of deep climate targets.</div>Gernaatdhttps://models.pbl.nl/index.php?title=Gernaat_et_al.,_2015&diff=27713Gernaat et al., 20152016-11-04T13:37:35Z<p>Gernaatd: Created page with "{{ReferenceTemplate |Author=Gernaat, David EHJ, Calvin, Katherine, Lucas, Paul L, Luderer, Gunnar, Otto, Sander AC, Rao, ShilpaStrefler, Jessica, van Vuuren, Detlef P |Year=20..."</p>
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|Year=2015<br />
|Title=Understanding the contribution of non-carbon dioxide gases in deep mitigation scenarios<br />
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In 2010, the combined emissions of methane (CH4), nitrous oxide (N2O) and the fluorinated gasses (F-gas) accounted for 20–30% of Kyoto emissions and about 30% of radiative forcing. Current scenario studies conclude that in order to reach deep climate targets (radiative forcing of 2.8 W/m2) in 2100, carbon dioxide (CO2) emissions will need to be reduced to zero or negative. However, studies indicated that non-CO2 emissions seem to be have less mitigation potential. To support effective climate policy strategies, an in-depth assessment was made of non-CO2 greenhouse gas emission and their sources in achieving an ambitious climate target. Emission scenarios were assessed that had been produced by six integrated assessments models, which contributed to the scenario database for the fifth IPCC report. All model scenarios reduced emissions from energy-related sectors, largely resulting from structural changes and end-of-pipe abatement technologies. However, emission reductions were much less in the agricultural sectors. Furthermore, there were considerable differences in abatement potential between the model scenarios, and most notably in the agricultural sectors. The paper shows that better exploration of long-term abatement potential of non-CO2 emissions is critical for the feasibility of deep climate targets.</div>Gernaatd