IAMC-Documentation - User contributions [en]

Fossil energy resources - MESSAGE-GLOBIOM

2020-12-18T14:38:07Z

Anique Carbados: Minor English edit to refer to other data sources in line with MESSAGEix 2020 update.

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=MESSAGE-GLOBIOM
|DocumentationCategory=Fossil energy resources
}}
==Fossil Fuel Reserves and Resources==

The availability and costs of fossil fuels influences the future direction of the energy system, and therewith future mitigation challenges. Understanding the variations in fossil fuel availability and the underlying extraction cost assumptions across the SSPs is hence important. Our fossil energy resource assumptions are derived from various sources, including global databases such as The Federal Institute for Geosciences and Natural Resources (BGR) and the U.S. Geological Survey (USGS), as well as market reports and outlooks provided by different energy institutes and agencies. The availability of fossil energy resources in different regions under different socio-economic assumptions are then aligned with the storylines of the individual SSPs (Rogner, 1997 [[CiteRef::MSG-GLB_rogner_assessment_1997]]; Riahi et al., 2012 [[CiteRef::MSG-GLB_riahi_chapter_2012]]). While the physical resource base is identical across the SSPs, considerable differences are assumed regarding the technical and economic availability of overall resources, for example, of unconventional oil and gas.

What ultimately determines the attractiveness of a particular type of resource is not just the cost at which it can be brought to the surface, but the cost at which it can be used to provide energy services. Assumptions about fossil energy resources should thus be considered together with those of related conversion technologies. In line with the narratives, technological change in fossil fuel extraction and conversion technologies is assumed to be slowest in SSP1, while comparatively faster technological change occurs in SSP3 thereby considerably enlarging the economic potential of coal and unconventional hydrocarbons ( <xr id="tab:MESSAGE-GLOBIOM_globff"></xr>, <xr id="fig:MESSAGE-GLOBIOM_supply"></xr>). However, driven by the tendency toward regional fragmentation, the focus in SSP3 is assumed to be on developing coal technologies which leads to a replacement of oil products by synthetic fuels in the longer-term based on coal-to-liquids technology. In contrast, for SSP2 we assume a continuation of recent trends, focusing more on developing extraction technologies for unconventional hydrocarbon resources, thereby leading to higher potential cumulative oil extraction than in the other SSPs (<xr id="fig:MESSAGE-GLOBIOM_supply"></xr>, middle panel).

<xr id="tab:MESSAGE-GLOBIOM_globff"></xr> shows the assumed total quantities of fossil fuel resources in the MESSAGE model for the base year 2005. <xr id="fig:MESSAGE-GLOBIOM_supply"></xr> gives these resource estimates as supply curves. In addition, the assumptions are compared with estimates from the Global Energy Assessment (Rogner et al., 2012 [[CiteRef::MSG-GLB_rogner_chapter_2012]]) and the databases mentioned earlier. Estimating fossil fuel reserves is built on both economic and technological assumptions. With an improvement in technology or a change in purchasing power, the amount that may be considered a “reserve” vs. a “resource” (generically referred to here as resources) can actually vary quite widely.

‘Reserves’ are generally defined as being those quantities for which geological and engineering information indicate with reasonable certainty that they can be recovered in the future from known reservoirs under existing economic and operating conditions. ‘Resources’ are detected quantities that cannot be profitably recovered with current technology, but might be recoverable in the future, as well as those quantities that are geologically possible, but yet to be found. The remainder are ‘Undiscovered resources’ and, by definition, one can only speculate on their existence. Definitions are based on Rogner et al. (2012) [[CiteRef::MSG-GLB_rogner_chapter_2012]].

<figtable id="tab:MESSAGE-GLOBIOM_globff">
{| class="wikitable"
|+<caption>Assumed global fossil fuel reserves and resources in the MESSAGE model. Estimates from the Global Energy Assessment also added for comparison</caption>
! Source
! MESSAGE (Rogner et al., 1997 [[CiteRef::MSG-GLB_rogner_assessment_1997]])
! colspan="2" | Rogner et al., 2012 [[CiteRef::MSG-GLB_rogner_chapter_2012]]
|-
|
| Reserves+Resources [ZJ]
| Reserves [ZJ]
| Resources [ZJ]
|-
| Coal
| 259
| 17.3 – 21.0
| 291 – 435
|-
| Conventional Oil
| 9.8
| 4.0 – 7.6
| 4.2 – 6.2
|-
| Unconventional Oil
| 23.0
| 3.8 – 5.6
| 11.3 – 14.9
|-
| Conventional Gas
| 16.8
| 5.0 – 7.1
| 7.2 – 8.9
|-
| Unconventional Gas
| 23.0
| 20.1 – 67.1
| 40.2 – 122
|}
</figtable>

The following table (<xr id="tab:MESSAGE-GLOBIOM_ffavail"></xr>) presents the fossil resource availability in ZJ (2010-2100) for coal, oil and gas, for SSP1, SSP2 and SSP3, respectively.

<figtable id="tab:MESSAGE-GLOBIOM_ffavail">
{| class="wikitable"
|+<caption>Fossil resource availability for SSP1, SSP2, and SSP3</caption>
! Type
! SSP1 [ZJ]
! SSP2 [ZJ]
! SSP3 [ZJ]
|-
| Coal
| 93
| 92
| 243
|-
| Oil
| 17
| 40
| 17
|-
| Gas
| 39
| 37
| 24
|}
</figtable>

Coal is the largest resource among fossil fuels; it accounts for more than 50% of total fossil reserve plus resource estimates even at the higher end of the assumptions, which includes
considerable amounts of unconventional hydrocarbons. Oil is the most vulnerable fossil fuel at less than 10 ZJ of conventional oil and possibly less than 10 ZJ of unconventional oil.
Natural gas is more abundant in both the conventional and unconventional categories.

<xr id="fig:MESSAGE-GLOBIOM_supply"></xr> presents the cumulative global resource supply curves for coal, oil and gas in the IIASA IAM framework. Green shaded resources are technically and economically extractable in all SSPs, purple shaded resources are additionally available in SSP1 and SSP2 and blue shaded resources are additionally available in SSP2. Coloured vertical lines
represent the cumulative use of each resource between 2010 and 2100 in the SSP baselines (see top panel for colour coding), and are thus the result of the combined effect of the assumptions on fossil resource availability and conversion technologies in the SSP baselines.

<div style=" overflow: auto;">
<figure id="fig:MESSAGE-GLOBIOM_supply">
[[File:GlobalResourceSupplyCurves.png|left|750px|thumb|<caption>Cumulative global resource supply curves for coal (top), oil (middle), and gas (bottom) in the IIASA IAM framework</caption>]]
</figure>
</div>

Conventional oil and gas are distributed unevenly throughout the world, with only a few regions dominating the reserves. Nearly half of the reserves of conventional oil is found in Middle East and North Africa, and close to 40% of conventional gas is found in Russia and the former Soviet Union states. The situation is somewhat different for unconventional oil of which North and Latin America potentially possess significantly higher global shares. Unconventional gas in turn is distributed quite well throughout the world, with North America holding most (roughly 25% of global resources). The distribution of coal reserves shows the highest geographical diversity which in the more fragmented SSP3 world contributes to increased overall reliance on this resource. Russia and the former Soviet Union states, Pacific OECD, North America, and Centrally Planned Asia and China all possess more than 10 ZJ of reserves.

Fossil energy resources - MESSAGE-GLOBIOM

2020-12-18T14:36:53Z

Anique Carbados: Minor English edits

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=MESSAGE-GLOBIOM
|DocumentationCategory=Fossil energy resources
}}
==Fossil Fuel Reserves and Resources==

The availability and costs of fossil fuels influences the future direction of the energy system, and therewith future mitigation challenges. Understanding the variations in fossil fuel availability and the underlying extraction cost assumptions across the SSPs is hence important. Our fossil energy resource assumptions are derived from various sources, including global databases such as The Federal Institute for Geosciences and Natural Resources (BGR) and the U.S. Geological Survey (USGS), as well as market reports and outlooks provided by different energy institutes and agencies. The availability of fossil energy resources in different regions under different socio-economic assumptions are then aligned with the storylines of the individual SSPs (Rogner, 1997 [[CiteRef::MSG-GLB_rogner_assessment_1997]]; Riahi et al., 2012 [[CiteRef::MSG-GLB_riahi_chapter_2012]]). While the physical resource base is identical across the SSPs, considerable differences are assumed regarding the technical and economic availability of overall resources, for example, of unconventional oil and gas.

What ultimately determines the attractiveness of a particular type of resource is not just the cost at which it can be brought to the surface, but the cost at which it can be used to provide energy services. Assumptions about fossil energy resources should thus be considered together with those of related conversion technologies. In line with the narratives, technological change in fossil fuel extraction and conversion technologies is assumed to be slowest in SSP1, while comparatively faster technological change occurs in SSP3 thereby considerably enlarging the economic potential of coal and unconventional hydrocarbons ( <xr id="tab:MESSAGE-GLOBIOM_globff"></xr>, <xr id="fig:MESSAGE-GLOBIOM_supply"></xr>). However, driven by the tendency toward regional fragmentation, the focus in SSP3 is assumed to be on developing coal technologies which leads to a replacement of oil products by synthetic fuels in the longer-term based on coal-to-liquids technology. In contrast, for SSP2 we assume a continuation of recent trends, focusing more on developing extraction technologies for unconventional hydrocarbon resources, thereby leading to higher potential cumulative oil extraction than in the other SSPs (<xr id="fig:MESSAGE-GLOBIOM_supply"></xr>, middle panel).

<xr id="tab:MESSAGE-GLOBIOM_globff"></xr> shows the assumed total quantities of fossil fuel resources in the MESSAGE model for the base year 2005. <xr id="fig:MESSAGE-GLOBIOM_supply"></xr> gives these resource estimates as supply curves. In addition, the assumptions are compared with estimates from the Global Energy Assessment (Rogner et al., 2012 [[CiteRef::MSG-GLB_rogner_chapter_2012]]) as of the year 2009. Estimating fossil fuel reserves is built on both economic and technological assumptions. With an improvement in technology or a change in purchasing power, the amount that may be considered a “reserve” vs. a “resource” (generically referred to here as resources) can actually vary quite widely.

‘Reserves’ are generally defined as being those quantities for which geological and engineering information indicate with reasonable certainty that they can be recovered in the future from known reservoirs under existing economic and operating conditions. ‘Resources’ are detected quantities that cannot be profitably recovered with current technology, but might be recoverable in the future, as well as those quantities that are geologically possible, but yet to be found. The remainder are ‘Undiscovered resources’ and, by definition, one can only speculate on their existence. Definitions are based on Rogner et al. (2012) [[CiteRef::MSG-GLB_rogner_chapter_2012]].

<figtable id="tab:MESSAGE-GLOBIOM_globff">
{| class="wikitable"
|+<caption>Assumed global fossil fuel reserves and resources in the MESSAGE model. Estimates from the Global Energy Assessment also added for comparison</caption>
! Source
! MESSAGE (Rogner et al., 1997 [[CiteRef::MSG-GLB_rogner_assessment_1997]])
! colspan="2" | Rogner et al., 2012 [[CiteRef::MSG-GLB_rogner_chapter_2012]]
|-
|
| Reserves+Resources [ZJ]
| Reserves [ZJ]
| Resources [ZJ]
|-
| Coal
| 259
| 17.3 – 21.0
| 291 – 435
|-
| Conventional Oil
| 9.8
| 4.0 – 7.6
| 4.2 – 6.2
|-
| Unconventional Oil
| 23.0
| 3.8 – 5.6
| 11.3 – 14.9
|-
| Conventional Gas
| 16.8
| 5.0 – 7.1
| 7.2 – 8.9
|-
| Unconventional Gas
| 23.0
| 20.1 – 67.1
| 40.2 – 122
|}
</figtable>

The following table (<xr id="tab:MESSAGE-GLOBIOM_ffavail"></xr>) presents the fossil resource availability in ZJ (2010-2100) for coal, oil and gas, for SSP1, SSP2 and SSP3, respectively.

<figtable id="tab:MESSAGE-GLOBIOM_ffavail">
{| class="wikitable"
|+<caption>Fossil resource availability for SSP1, SSP2, and SSP3</caption>
! Type
! SSP1 [ZJ]
! SSP2 [ZJ]
! SSP3 [ZJ]
|-
| Coal
| 93
| 92
| 243
|-
| Oil
| 17
| 40
| 17
|-
| Gas
| 39
| 37
| 24
|}
</figtable>

Coal is the largest resource among fossil fuels; it accounts for more than 50% of total fossil reserve plus resource estimates even at the higher end of the assumptions, which includes
considerable amounts of unconventional hydrocarbons. Oil is the most vulnerable fossil fuel at less than 10 ZJ of conventional oil and possibly less than 10 ZJ of unconventional oil.
Natural gas is more abundant in both the conventional and unconventional categories.

<xr id="fig:MESSAGE-GLOBIOM_supply"></xr> presents the cumulative global resource supply curves for coal, oil and gas in the IIASA IAM framework. Green shaded resources are technically and economically extractable in all SSPs, purple shaded resources are additionally available in SSP1 and SSP2 and blue shaded resources are additionally available in SSP2. Coloured vertical lines
represent the cumulative use of each resource between 2010 and 2100 in the SSP baselines (see top panel for colour coding), and are thus the result of the combined effect of the assumptions on fossil resource availability and conversion technologies in the SSP baselines.

<div style=" overflow: auto;">
<figure id="fig:MESSAGE-GLOBIOM_supply">
[[File:GlobalResourceSupplyCurves.png|left|750px|thumb|<caption>Cumulative global resource supply curves for coal (top), oil (middle), and gas (bottom) in the IIASA IAM framework</caption>]]
</figure>
</div>

Conventional oil and gas are distributed unevenly throughout the world, with only a few regions dominating the reserves. Nearly half of the reserves of conventional oil is found in Middle East and North Africa, and close to 40% of conventional gas is found in Russia and the former Soviet Union states. The situation is somewhat different for unconventional oil of which North and Latin America potentially possess significantly higher global shares. Unconventional gas in turn is distributed quite well throughout the world, with North America holding most (roughly 25% of global resources). The distribution of coal reserves shows the highest geographical diversity which in the more fragmented SSP3 world contributes to increased overall reliance on this resource. Russia and the former Soviet Union states, Pacific OECD, North America, and Centrally Planned Asia and China all possess more than 10 ZJ of reserves.

Fossil energy resources - MESSAGE-GLOBIOM

2020-12-18T14:29:52Z

Anique Carbados: English edit

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=MESSAGE-GLOBIOM
|DocumentationCategory=Fossil energy resources
}}
==Fossil Fuel Reserves and Resources==

The availability and costs of fossil fuels influences the future direction of the energy system, and therewith future mitigation challenges. Understanding the variations in fossil fuel availability and the underlying extraction cost assumptions across the SSPs is hence important. Our fossil energy resource assumptions are derived from various sources, including global databases such as The Federal Institute for Geosciences and Natural Resources (BGR) and the U.S. Geological Survey (USGS), as well as market reports and outlooks provided by different energy institutes and agencies. The availability of fossil energy resources in different regions under different socio-economic assumptions are then aligned with the storylines of the individual SSPs (Rogner, 1997 [[CiteRef::MSG-GLB_rogner_assessment_1997]]; Riahi et al., 2012 [[CiteRef::MSG-GLB_riahi_chapter_2012]]). While the physical resource base is identical across the SSPs, considerable differences are assumed regarding the technical and economic availability of overall resources, for example, of unconventional oil and gas.

What ultimately determines the attractiveness of a particular type of resource is not just the cost at which it can be brought to the surface, but the cost at which it can be used to provide energy services. Assumptions about fossil energy resources should thus be considered together with those of related conversion technologies. In line with the narratives, technological change in fossil fuel extraction and conversion technologies is assumed to be slowest in SSP1, while comparatively faster technological change occurs in SSP3 thereby considerably enlarging the economic potential of coal and unconventional hydrocarbons ( <xr id="tab:MESSAGE-GLOBIOM_globff"></xr>, <xr id="fig:MESSAGE-GLOBIOM_supply"></xr>). However, driven by tendency toward regional fragmentation the focus in SSP3 is assumed to be on developing coal technologies which in the longer term leads to a replacement of oil products by synthetic fuels based on coal-to-liquids technologies. In contrast, for SSP2 we assume a continuation of recent trends, focusing more on developing extraction technologies for unconventional hydrocarbon resources, thereby leading to higher potential cumulative oil extraction than in the other SSPs (<xr id="fig:MESSAGE-GLOBIOM_supply"></xr>, middle panel).

<xr id="tab:MESSAGE-GLOBIOM_globff"></xr> shows the assumed total quantities of fossil fuel resources in the MESSAGE model for the base year 2005. <xr id="fig:MESSAGE-GLOBIOM_supply"></xr> gives these resource estimates as supply curves. In addition, the assumptions are compared with estimates from the Global Energy Assessment (Rogner et al., 2012 [[CiteRef::MSG-GLB_rogner_chapter_2012]]) as of the year 2009. Estimating fossil fuel reserves is built on both economic and technological assumptions. With an improvement in technology or a change in purchasing power, the amount that may be considered a “reserve” vs. a “resource” (generically referred to here as resources) can actually vary quite widely.

‘Reserves’ are generally defined as being those quantities for which geological and engineering information indicate with reasonable certainty that they can be recovered in the future from known reservoirs under existing economic and operating conditions. ‘Resources’ are detected quantities that cannot be profitably recovered with current technology, but might be recoverable in the future, as well as those quantities that are geologically possible, but yet to be found. The remainder are ‘Undiscovered resources’ and, by definition, one can only speculate on their existence. Definitions are based on Rogner et al. (2012) [[CiteRef::MSG-GLB_rogner_chapter_2012]].

<figtable id="tab:MESSAGE-GLOBIOM_globff">
{| class="wikitable"
|+<caption>Assumed global fossil fuel reserves and resources in the MESSAGE model. Estimates from the Global Energy Assessment also added for comparison</caption>
! Source
! MESSAGE (Rogner et al., 1997 [[CiteRef::MSG-GLB_rogner_assessment_1997]])
! colspan="2" | Rogner et al., 2012 [[CiteRef::MSG-GLB_rogner_chapter_2012]]
|-
|
| Reserves+Resources [ZJ]
| Reserves [ZJ]
| Resources [ZJ]
|-
| Coal
| 259
| 17.3 – 21.0
| 291 – 435
|-
| Conventional Oil
| 9.8
| 4.0 – 7.6
| 4.2 – 6.2
|-
| Unconventional Oil
| 23.0
| 3.8 – 5.6
| 11.3 – 14.9
|-
| Conventional Gas
| 16.8
| 5.0 – 7.1
| 7.2 – 8.9
|-
| Unconventional Gas
| 23.0
| 20.1 – 67.1
| 40.2 – 122
|}
</figtable>

The following table (<xr id="tab:MESSAGE-GLOBIOM_ffavail"></xr>) presents the fossil resource availability in ZJ (2010-2100) for coal, oil and gas, for SSP1, SSP2 and SSP3, respectively.

<figtable id="tab:MESSAGE-GLOBIOM_ffavail">
{| class="wikitable"
|+<caption>Fossil resource availability for SSP1, SSP2, and SSP3</caption>
! Type
! SSP1 [ZJ]
! SSP2 [ZJ]
! SSP3 [ZJ]
|-
| Coal
| 93
| 92
| 243
|-
| Oil
| 17
| 40
| 17
|-
| Gas
| 39
| 37
| 24
|}
</figtable>

Coal is the largest resource among fossil fuels; it accounts for more than 50% of total fossil reserve plus resource estimates even at the higher end of the assumptions, which includes
considerable amounts of unconventional hydrocarbons. Oil is the most vulnerable fossil fuel at less than 10 ZJ of conventional oil and possibly less than 10 ZJ of unconventional oil.
Natural gas is more abundant in both the conventional and unconventional categories.

<xr id="fig:MESSAGE-GLOBIOM_supply"></xr> presents the cumulative global resource supply curves for coal, oil and gas in the IIASA IAM framework. Green shaded resources are technically and economically extractable in all SSPs, purple shaded resources are additionally available in SSP1 and SSP2 and blue shaded resources are additionally available in SSP2. Coloured vertical lines
represent the cumulative use of each resource between 2010 and 2100 in the SSP baselines (see top panel for colour coding), and are thus the result of the combined effect of the assumptions on fossil resource availability and conversion technologies in the SSP baselines.

<div style=" overflow: auto;">
<figure id="fig:MESSAGE-GLOBIOM_supply">
[[File:GlobalResourceSupplyCurves.png|left|750px|thumb|<caption>Cumulative global resource supply curves for coal (top), oil (middle), and gas (bottom) in the IIASA IAM framework</caption>]]
</figure>
</div>

Conventional oil and gas are distributed unevenly throughout the world, with only a few regions dominating the reserves. Nearly half of the reserves of conventional oil is found in Middle East and North Africa, and close to 40% of conventional gas is found in Russia and the former Soviet Union states. The situation is somewhat different for unconventional oil of which North and Latin America potentially possess significantly higher global shares. Unconventional gas in turn is distributed quite well throughout the world, with North America holding most (roughly 25% of global resources). The distribution of coal reserves shows the highest geographical diversity which in the more fragmented SSP3 world contributes to increased overall reliance on this resource. Russia and the former Soviet Union states, Pacific OECD, North America, and Centrally Planned Asia and China all possess more than 10 ZJ of reserves.

Fossil energy resources - MESSAGE-GLOBIOM

2020-12-18T14:29:03Z

Anique Carbados: Minor English edits

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=MESSAGE-GLOBIOM
|DocumentationCategory=Fossil energy resources
}}
==Fossil Fuel Reserves and Resources==

The availability and costs of fossil fuels influences the future direction of the energy system, and therewith future mitigation challenges. Understanding the variations in fossil fuel availability and the underlying extraction cost assumptions across the SSPs is hence important. Our fossil energy resource assumptions are derived from various sources, including global databases such as The Federal Institute for Geosciences and Natural Resources (BGR) and the U.S. Geological Survey (USGS), as well as market reports and outlooks provided by different energy institutes and agencies. The availability of fossil energy resources in different regions under different socio-economic assumptions are then aligned with the storylines of the individual SSPs (Rogner, 1997 [[CiteRef::MSG-GLB_rogner_assessment_1997]]; Riahi et al., 2012 [[CiteRef::MSG-GLB_riahi_chapter_2012]]). While the physical resource base is identical across the SSPs, considerable differences are assumed regarding the technical and economic availability of overall resources, for example, of unconventional oil and gas.

What ultimately determines the attractiveness of a particular type of resource is not just the cost at which it can be brought to the surface, but the cost at which it can be used to provide energy services. Assumptions about fossil energy resources should thus be considered together with those of related conversion technologies. In line with the narratives, technological change in fossil fuel extraction and conversion technologies is assumed to be slowest in SSP1, while comparatively faster technological change occurs in SSP3 thereby considerably enlarging the economic potentials of coal and unconventional hydrocarbons ( <xr id="tab:MESSAGE-GLOBIOM_globff"></xr>, <xr id="fig:MESSAGE-GLOBIOM_supply"></xr>). However, driven by tendency toward regional fragmentation the focus in SSP3 is assumed to be on developing coal technologies which in the longer term leads to a replacement of oil products by synthetic fuels based on coal-to-liquids technologies. In contrast, for SSP2 we assume a continuation of recent trends, focusing more on developing extraction technologies for unconventional hydrocarbon resources, thereby leading to higher potential cumulative oil extraction than in the other SSPs (<xr id="fig:MESSAGE-GLOBIOM_supply"></xr>, middle panel).

<xr id="tab:MESSAGE-GLOBIOM_globff"></xr> shows the assumed total quantities of fossil fuel resources in the MESSAGE model for the base year 2005. <xr id="fig:MESSAGE-GLOBIOM_supply"></xr> gives these resource estimates as supply curves. In addition, the assumptions are compared with estimates from the Global Energy Assessment (Rogner et al., 2012 [[CiteRef::MSG-GLB_rogner_chapter_2012]]) as of the year 2009. Estimating fossil fuel reserves is built on both economic and technological assumptions. With an improvement in technology or a change in purchasing power, the amount that may be considered a “reserve” vs. a “resource” (generically referred to here as resources) can actually vary quite widely.

‘Reserves’ are generally defined as being those quantities for which geological and engineering information indicate with reasonable certainty that they can be recovered in the future from known reservoirs under existing economic and operating conditions. ‘Resources’ are detected quantities that cannot be profitably recovered with current technology, but might be recoverable in the future, as well as those quantities that are geologically possible, but yet to be found. The remainder are ‘Undiscovered resources’ and, by definition, one can only speculate on their existence. Definitions are based on Rogner et al. (2012) [[CiteRef::MSG-GLB_rogner_chapter_2012]].

<figtable id="tab:MESSAGE-GLOBIOM_globff">
{| class="wikitable"
|+<caption>Assumed global fossil fuel reserves and resources in the MESSAGE model. Estimates from the Global Energy Assessment also added for comparison</caption>
! Source
! MESSAGE (Rogner et al., 1997 [[CiteRef::MSG-GLB_rogner_assessment_1997]])
! colspan="2" | Rogner et al., 2012 [[CiteRef::MSG-GLB_rogner_chapter_2012]]
|-
|
| Reserves+Resources [ZJ]
| Reserves [ZJ]
| Resources [ZJ]
|-
| Coal
| 259
| 17.3 – 21.0
| 291 – 435
|-
| Conventional Oil
| 9.8
| 4.0 – 7.6
| 4.2 – 6.2
|-
| Unconventional Oil
| 23.0
| 3.8 – 5.6
| 11.3 – 14.9
|-
| Conventional Gas
| 16.8
| 5.0 – 7.1
| 7.2 – 8.9
|-
| Unconventional Gas
| 23.0
| 20.1 – 67.1
| 40.2 – 122
|}
</figtable>

The following table (<xr id="tab:MESSAGE-GLOBIOM_ffavail"></xr>) presents the fossil resource availability in ZJ (2010-2100) for coal, oil and gas, for SSP1, SSP2 and SSP3, respectively.

<figtable id="tab:MESSAGE-GLOBIOM_ffavail">
{| class="wikitable"
|+<caption>Fossil resource availability for SSP1, SSP2, and SSP3</caption>
! Type
! SSP1 [ZJ]
! SSP2 [ZJ]
! SSP3 [ZJ]
|-
| Coal
| 93
| 92
| 243
|-
| Oil
| 17
| 40
| 17
|-
| Gas
| 39
| 37
| 24
|}
</figtable>

Coal is the largest resource among fossil fuels; it accounts for more than 50% of total fossil reserve plus resource estimates even at the higher end of the assumptions, which includes
considerable amounts of unconventional hydrocarbons. Oil is the most vulnerable fossil fuel at less than 10 ZJ of conventional oil and possibly less than 10 ZJ of unconventional oil.
Natural gas is more abundant in both the conventional and unconventional categories.

<xr id="fig:MESSAGE-GLOBIOM_supply"></xr> presents the cumulative global resource supply curves for coal, oil and gas in the IIASA IAM framework. Green shaded resources are technically and economically extractable in all SSPs, purple shaded resources are additionally available in SSP1 and SSP2 and blue shaded resources are additionally available in SSP2. Coloured vertical lines
represent the cumulative use of each resource between 2010 and 2100 in the SSP baselines (see top panel for colour coding), and are thus the result of the combined effect of the assumptions on fossil resource availability and conversion technologies in the SSP baselines.

<div style=" overflow: auto;">
<figure id="fig:MESSAGE-GLOBIOM_supply">
[[File:GlobalResourceSupplyCurves.png|left|750px|thumb|<caption>Cumulative global resource supply curves for coal (top), oil (middle), and gas (bottom) in the IIASA IAM framework</caption>]]
</figure>
</div>

Conventional oil and gas are distributed unevenly throughout the world, with only a few regions dominating the reserves. Nearly half of the reserves of conventional oil is found in Middle East and North Africa, and close to 40% of conventional gas is found in Russia and the former Soviet Union states. The situation is somewhat different for unconventional oil of which North and Latin America potentially possess significantly higher global shares. Unconventional gas in turn is distributed quite well throughout the world, with North America holding most (roughly 25% of global resources). The distribution of coal reserves shows the highest geographical diversity which in the more fragmented SSP3 world contributes to increased overall reliance on this resource. Russia and the former Soviet Union states, Pacific OECD, North America, and Centrally Planned Asia and China all possess more than 10 ZJ of reserves.

Fossil energy resources - MESSAGE-GLOBIOM

2020-12-18T14:27:55Z

Anique Carbados: U to u in Survey

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=MESSAGE-GLOBIOM
|DocumentationCategory=Fossil energy resources
}}
==Fossil Fuel Reserves and Resources==

The availability and costs of fossil fuels influences the future direction of the energy system, and therewith future mitigation challenges. Understanding the variations in fossil fuel availability and the underlying extraction cost assumptions across the SSPs is hence important. Our fossil energy resource assumptions are derived from various sources, including global databases such as The Federal Institute for Geosciences and Natural Resources (BGR) and the U.S. Geological Survey (USGS), as well as market reports and outlooks provided by different energy institutes and agencies. The availability of fossil energy resources in different regions under different socio-economic assumptions are then aligned with the storylines of the individual SSPs (Rogner, 1997 [[CiteRef::MSG-GLB_rogner_assessment_1997]]; Riahi et al., 2012 [[CiteRef::MSG-GLB_riahi_chapter_2012]]). While the physical resource base is identical across the SSPs, considerable differences are assumed regarding the technical and economic availability of overall resources, for example, of unconventional oil and gas.

What ultimately determines the attractiveness of a particular type of resource is not just the cost at which it can be brought to the surface, but the cost at which it can be used to provide energy services. Assumptions on fossil energy resources should thus be considered together with those on related conversion technologies. In line with the narratives, technological change in fossil fuel extraction and conversion technologies is assumed to be slowest in SSP1, while comparatively faster technological change occurs in SSP3 thereby considerably enlarging the economic potentials of coal and unconventional hydrocarbons ( <xr id="tab:MESSAGE-GLOBIOM_globff"></xr>, <xr id="fig:MESSAGE-GLOBIOM_supply"></xr>). However, driven by tendency toward regional fragmentation the focus in SSP3 is assumed to be on developing coal technologies which in the longer term leads to a replacement of oil products by synthetic fuels based on coal-to-liquids technologies. In contrast, for SSP2 we assume a continuation of recent trends, focusing more on developing extraction technologies for unconventional hydrocarbon resources, thereby leading to higher potential cumulative oil extraction than in the other SSPs (<xr id="fig:MESSAGE-GLOBIOM_supply"></xr>, middle panel).

<xr id="tab:MESSAGE-GLOBIOM_globff"></xr> shows the assumed total quantities of fossil fuel resources in the MESSAGE model for the base year 2005. <xr id="fig:MESSAGE-GLOBIOM_supply"></xr> gives these resource estimates as supply curves. In addition, the assumptions are compared with estimates from the Global Energy Assessment (Rogner et al., 2012 [[CiteRef::MSG-GLB_rogner_chapter_2012]]) as of the year 2009. Estimating fossil fuel reserves is built on both economic and technological assumptions. With an improvement in technology or a change in purchasing power, the amount that may be considered a “reserve” vs. a “resource” (generically referred to here as resources) can actually vary quite widely.

‘Reserves’ are generally defined as being those quantities for which geological and engineering information indicate with reasonable certainty that they can be recovered in the future from known reservoirs under existing economic and operating conditions. ‘Resources’ are detected quantities that cannot be profitably recovered with current technology, but might be recoverable in the future, as well as those quantities that are geologically possible, but yet to be found. The remainder are ‘Undiscovered resources’ and, by definition, one can only speculate on their existence. Definitions are based on Rogner et al. (2012) [[CiteRef::MSG-GLB_rogner_chapter_2012]].

<figtable id="tab:MESSAGE-GLOBIOM_globff">
{| class="wikitable"
|+<caption>Assumed global fossil fuel reserves and resources in the MESSAGE model. Estimates from the Global Energy Assessment also added for comparison</caption>
! Source
! MESSAGE (Rogner et al., 1997 [[CiteRef::MSG-GLB_rogner_assessment_1997]])
! colspan="2" | Rogner et al., 2012 [[CiteRef::MSG-GLB_rogner_chapter_2012]]
|-
|
| Reserves+Resources [ZJ]
| Reserves [ZJ]
| Resources [ZJ]
|-
| Coal
| 259
| 17.3 – 21.0
| 291 – 435
|-
| Conventional Oil
| 9.8
| 4.0 – 7.6
| 4.2 – 6.2
|-
| Unconventional Oil
| 23.0
| 3.8 – 5.6
| 11.3 – 14.9
|-
| Conventional Gas
| 16.8
| 5.0 – 7.1
| 7.2 – 8.9
|-
| Unconventional Gas
| 23.0
| 20.1 – 67.1
| 40.2 – 122
|}
</figtable>

The following table (<xr id="tab:MESSAGE-GLOBIOM_ffavail"></xr>) presents the fossil resource availability in ZJ (2010-2100) for coal, oil and gas, for SSP1, SSP2 and SSP3, respectively.

<figtable id="tab:MESSAGE-GLOBIOM_ffavail">
{| class="wikitable"
|+<caption>Fossil resource availability for SSP1, SSP2, and SSP3</caption>
! Type
! SSP1 [ZJ]
! SSP2 [ZJ]
! SSP3 [ZJ]
|-
| Coal
| 93
| 92
| 243
|-
| Oil
| 17
| 40
| 17
|-
| Gas
| 39
| 37
| 24
|}
</figtable>

Coal is the largest resource among fossil fuels; it accounts for more than 50% of total fossil reserve plus resource estimates even at the higher end of the assumptions, which includes
considerable amounts of unconventional hydrocarbons. Oil is the most vulnerable fossil fuel at less than 10 ZJ of conventional oil and possibly less than 10 ZJ of unconventional oil.
Natural gas is more abundant in both the conventional and unconventional categories.

<xr id="fig:MESSAGE-GLOBIOM_supply"></xr> presents the cumulative global resource supply curves for coal, oil and gas in the IIASA IAM framework. Green shaded resources are technically and economically extractable in all SSPs, purple shaded resources are additionally available in SSP1 and SSP2 and blue shaded resources are additionally available in SSP2. Coloured vertical lines
represent the cumulative use of each resource between 2010 and 2100 in the SSP baselines (see top panel for colour coding), and are thus the result of the combined effect of the assumptions on fossil resource availability and conversion technologies in the SSP baselines.

<div style=" overflow: auto;">
<figure id="fig:MESSAGE-GLOBIOM_supply">
[[File:GlobalResourceSupplyCurves.png|left|750px|thumb|<caption>Cumulative global resource supply curves for coal (top), oil (middle), and gas (bottom) in the IIASA IAM framework</caption>]]
</figure>
</div>

Conventional oil and gas are distributed unevenly throughout the world, with only a few regions dominating the reserves. Nearly half of the reserves of conventional oil is found in Middle East and North Africa, and close to 40% of conventional gas is found in Russia and the former Soviet Union states. The situation is somewhat different for unconventional oil of which North and Latin America potentially possess significantly higher global shares. Unconventional gas in turn is distributed quite well throughout the world, with North America holding most (roughly 25% of global resources). The distribution of coal reserves shows the highest geographical diversity which in the more fragmented SSP3 world contributes to increased overall reliance on this resource. Russia and the former Soviet Union states, Pacific OECD, North America, and Centrally Planned Asia and China all possess more than 10 ZJ of reserves.

Fossil energy resources - MESSAGE-GLOBIOM

2020-12-18T12:53:13Z

Anique Carbados:

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=MESSAGE-GLOBIOM
|DocumentationCategory=Fossil energy resources
}}
==Fossil Fuel Reserves and Resources==

The availability and costs of fossil fuels influences the future direction of the energy system, and therewith future mitigation challenges. Understanding the variations in fossil fuel availability and the underlying extraction cost assumptions across the SSPs is hence important. Our fossil energy resource assumptions are derived from various sources, including global databases such as The Federal Institute for Geosciences and Natural Resources (BGR) and the U.S. Geological SUrvey (USGS), as well as market reports and outlooks provided by different energy institutes and agencies. The availability of fossil energy resources in different regions under different socio-economic assumptions are then aligned with the storylines of the individual SSPs (Rogner, 1997 [[CiteRef::MSG-GLB_rogner_assessment_1997]]; Riahi et al., 2012 [[CiteRef::MSG-GLB_riahi_chapter_2012]]). While the physical resource base is identical across the SSPs, considerable differences are assumed regarding the technical and economic availability of overall resources, for example, of unconventional oil and gas.

What ultimately determines the attractiveness of a particular type of resource is not just the cost at which it can be brought to the surface, but the cost at which it can be used to provide energy services. Assumptions on fossil energy resources should thus be considered together with those on related conversion technologies. In line with the narratives, technological change in fossil fuel extraction and conversion technologies is assumed to be slowest in SSP1, while comparatively faster technological change occurs in SSP3 thereby considerably enlarging the economic potentials of coal and unconventional hydrocarbons ( <xr id="tab:MESSAGE-GLOBIOM_globff"></xr>, <xr id="fig:MESSAGE-GLOBIOM_supply"></xr>). However, driven by tendency toward regional fragmentation the focus in SSP3 is assumed to be on developing coal technologies which in the longer term leads to a replacement of oil products by synthetic fuels based on coal-to-liquids technologies. In contrast, for SSP2 we assume a continuation of recent trends, focusing more on developing extraction technologies for unconventional hydrocarbon resources, thereby leading to higher potential cumulative oil extraction than in the other SSPs (<xr id="fig:MESSAGE-GLOBIOM_supply"></xr>, middle panel).

<xr id="tab:MESSAGE-GLOBIOM_globff"></xr> shows the assumed total quantities of fossil fuel resources in the MESSAGE model for the base year 2005. <xr id="fig:MESSAGE-GLOBIOM_supply"></xr> gives these resource estimates as supply curves. In addition, the assumptions are compared with estimates from the Global Energy Assessment (Rogner et al., 2012 [[CiteRef::MSG-GLB_rogner_chapter_2012]]) as of the year 2009. Estimating fossil fuel reserves is built on both economic and technological assumptions. With an improvement in technology or a change in purchasing power, the amount that may be considered a “reserve” vs. a “resource” (generically referred to here as resources) can actually vary quite widely.

‘Reserves’ are generally defined as being those quantities for which geological and engineering information indicate with reasonable certainty that they can be recovered in the future from known reservoirs under existing economic and operating conditions. ‘Resources’ are detected quantities that cannot be profitably recovered with current technology, but might be recoverable in the future, as well as those quantities that are geologically possible, but yet to be found. The remainder are ‘Undiscovered resources’ and, by definition, one can only speculate on their existence. Definitions are based on Rogner et al. (2012) [[CiteRef::MSG-GLB_rogner_chapter_2012]].

<figtable id="tab:MESSAGE-GLOBIOM_globff">
{| class="wikitable"
|+<caption>Assumed global fossil fuel reserves and resources in the MESSAGE model. Estimates from the Global Energy Assessment also added for comparison</caption>
! Source
! MESSAGE (Rogner et al., 1997 [[CiteRef::MSG-GLB_rogner_assessment_1997]])
! colspan="2" | Rogner et al., 2012 [[CiteRef::MSG-GLB_rogner_chapter_2012]]
|-
|
| Reserves+Resources [ZJ]
| Reserves [ZJ]
| Resources [ZJ]
|-
| Coal
| 259
| 17.3 – 21.0
| 291 – 435
|-
| Conventional Oil
| 9.8
| 4.0 – 7.6
| 4.2 – 6.2
|-
| Unconventional Oil
| 23.0
| 3.8 – 5.6
| 11.3 – 14.9
|-
| Conventional Gas
| 16.8
| 5.0 – 7.1
| 7.2 – 8.9
|-
| Unconventional Gas
| 23.0
| 20.1 – 67.1
| 40.2 – 122
|}
</figtable>

The following table (<xr id="tab:MESSAGE-GLOBIOM_ffavail"></xr>) presents the fossil resource availability in ZJ (2010-2100) for coal, oil and gas, for SSP1, SSP2 and SSP3, respectively.

<figtable id="tab:MESSAGE-GLOBIOM_ffavail">
{| class="wikitable"
|+<caption>Fossil resource availability for SSP1, SSP2, and SSP3</caption>
! Type
! SSP1 [ZJ]
! SSP2 [ZJ]
! SSP3 [ZJ]
|-
| Coal
| 93
| 92
| 243
|-
| Oil
| 17
| 40
| 17
|-
| Gas
| 39
| 37
| 24
|}
</figtable>

Coal is the largest resource among fossil fuels; it accounts for more than 50% of total fossil reserve plus resource estimates even at the higher end of the assumptions, which includes
considerable amounts of unconventional hydrocarbons. Oil is the most vulnerable fossil fuel at less than 10 ZJ of conventional oil and possibly less than 10 ZJ of unconventional oil.
Natural gas is more abundant in both the conventional and unconventional categories.

<xr id="fig:MESSAGE-GLOBIOM_supply"></xr> presents the cumulative global resource supply curves for coal, oil and gas in the IIASA IAM framework. Green shaded resources are technically and economically extractable in all SSPs, purple shaded resources are additionally available in SSP1 and SSP2 and blue shaded resources are additionally available in SSP2. Coloured vertical lines
represent the cumulative use of each resource between 2010 and 2100 in the SSP baselines (see top panel for colour coding), and are thus the result of the combined effect of the assumptions on fossil resource availability and conversion technologies in the SSP baselines.

<div style=" overflow: auto;">
<figure id="fig:MESSAGE-GLOBIOM_supply">
[[File:GlobalResourceSupplyCurves.png|left|750px|thumb|<caption>Cumulative global resource supply curves for coal (top), oil (middle), and gas (bottom) in the IIASA IAM framework</caption>]]
</figure>
</div>

Conventional oil and gas are distributed unevenly throughout the world, with only a few regions dominating the reserves. Nearly half of the reserves of conventional oil is found in Middle East and North Africa, and close to 40% of conventional gas is found in Russia and the former Soviet Union states. The situation is somewhat different for unconventional oil of which North and Latin America potentially possess significantly higher global shares. Unconventional gas in turn is distributed quite well throughout the world, with North America holding most (roughly 25% of global resources). The distribution of coal reserves shows the highest geographical diversity which in the more fragmented SSP3 world contributes to increased overall reliance on this resource. Russia and the former Soviet Union states, Pacific OECD, North America, and Centrally Planned Asia and China all possess more than 10 ZJ of reserves.

Fossil energy resources - MESSAGE-GLOBIOM

2020-12-18T12:52:54Z

Anique Carbados: /* Fossil Fuel Reserves and Resources */

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=MESSAGE-GLOBIOM
|DocumentationCategory=Fossil energy resources
}}
==Fossil Fuel Reserves and Resources==

The availability and costs of fossil fuels influences the future direction of the energy system, and therewith future mitigation challenges. Understanding the variations in

fossil fuel availability and the underlying extraction cost assumptions across the SSPs is hence important. Our fossil energy resource assumptions are derived from various sources, including global databases such as The Federal Institute for Geosciences and Natural Resources (BGR) and the U.S. Geological SUrvey (USGS), as well as market reports and outlooks provided by different energy institutes and agencies. The availability of fossil energy resources in different regions under different socio-economic assumptions are then aligned with the storylines of the individual SSPs (Rogner, 1997 [[CiteRef::MSG-GLB_rogner_assessment_1997]]; Riahi et al., 2012 [[CiteRef::MSG-GLB_riahi_chapter_2012]]). While the physical resource base is identical across the SSPs, considerable differences are assumed regarding the technical and economic availability of overall resources, for example, of unconventional oil and gas.

What ultimately determines the attractiveness of a particular type of resource is not just the cost at which it can be brought to the surface, but the cost at which it can be used to provide energy services. Assumptions on fossil energy resources should thus be considered together with those on related conversion technologies. In line with the narratives, technological change in fossil fuel extraction and conversion technologies is assumed to be slowest in SSP1, while comparatively faster technological change occurs in SSP3 thereby considerably enlarging the economic potentials of coal and unconventional hydrocarbons ( <xr id="tab:MESSAGE-GLOBIOM_globff"></xr>, <xr id="fig:MESSAGE-GLOBIOM_supply"></xr>). However, driven by tendency toward regional fragmentation the focus in SSP3 is assumed to be on developing coal technologies which in the longer term leads to a replacement of oil products by synthetic fuels based on coal-to-liquids technologies. In contrast, for SSP2 we assume a continuation of recent trends, focusing more on developing extraction technologies for unconventional hydrocarbon resources, thereby leading to higher potential cumulative oil extraction than in the other SSPs (<xr id="fig:MESSAGE-GLOBIOM_supply"></xr>, middle panel).

<xr id="tab:MESSAGE-GLOBIOM_globff"></xr> shows the assumed total quantities of fossil fuel resources in the MESSAGE model for the base year 2005. <xr id="fig:MESSAGE-GLOBIOM_supply"></xr> gives these resource estimates as supply curves. In addition, the assumptions are compared with estimates from the Global Energy Assessment (Rogner et al., 2012 [[CiteRef::MSG-GLB_rogner_chapter_2012]]) as of the year 2009. Estimating fossil fuel reserves is built on both economic and technological assumptions. With an improvement in technology or a change in purchasing power, the amount that may be considered a “reserve” vs. a “resource” (generically referred to here as resources) can actually vary quite widely.

‘Reserves’ are generally defined as being those quantities for which geological and engineering information indicate with reasonable certainty that they can be recovered in the future from known reservoirs under existing economic and operating conditions. ‘Resources’ are detected quantities that cannot be profitably recovered with current technology, but might be recoverable in the future, as well as those quantities that are geologically possible, but yet to be found. The remainder are ‘Undiscovered resources’ and, by definition, one can only speculate on their existence. Definitions are based on Rogner et al. (2012) [[CiteRef::MSG-GLB_rogner_chapter_2012]].

<figtable id="tab:MESSAGE-GLOBIOM_globff">
{| class="wikitable"
|+<caption>Assumed global fossil fuel reserves and resources in the MESSAGE model. Estimates from the Global Energy Assessment also added for comparison</caption>
! Source
! MESSAGE (Rogner et al., 1997 [[CiteRef::MSG-GLB_rogner_assessment_1997]])
! colspan="2" | Rogner et al., 2012 [[CiteRef::MSG-GLB_rogner_chapter_2012]]
|-
|
| Reserves+Resources [ZJ]
| Reserves [ZJ]
| Resources [ZJ]
|-
| Coal
| 259
| 17.3 – 21.0
| 291 – 435
|-
| Conventional Oil
| 9.8
| 4.0 – 7.6
| 4.2 – 6.2
|-
| Unconventional Oil
| 23.0
| 3.8 – 5.6
| 11.3 – 14.9
|-
| Conventional Gas
| 16.8
| 5.0 – 7.1
| 7.2 – 8.9
|-
| Unconventional Gas
| 23.0
| 20.1 – 67.1
| 40.2 – 122
|}
</figtable>

The following table (<xr id="tab:MESSAGE-GLOBIOM_ffavail"></xr>) presents the fossil resource availability in ZJ (2010-2100) for coal, oil and gas, for SSP1, SSP2 and SSP3, respectively.

<figtable id="tab:MESSAGE-GLOBIOM_ffavail">
{| class="wikitable"
|+<caption>Fossil resource availability for SSP1, SSP2, and SSP3</caption>
! Type
! SSP1 [ZJ]
! SSP2 [ZJ]
! SSP3 [ZJ]
|-
| Coal
| 93
| 92
| 243
|-
| Oil
| 17
| 40
| 17
|-
| Gas
| 39
| 37
| 24
|}
</figtable>

Coal is the largest resource among fossil fuels; it accounts for more than 50% of total fossil reserve plus resource estimates even at the higher end of the assumptions, which includes
considerable amounts of unconventional hydrocarbons. Oil is the most vulnerable fossil fuel at less than 10 ZJ of conventional oil and possibly less than 10 ZJ of unconventional oil.
Natural gas is more abundant in both the conventional and unconventional categories.

<xr id="fig:MESSAGE-GLOBIOM_supply"></xr> presents the cumulative global resource supply curves for coal, oil and gas in the IIASA IAM framework. Green shaded resources are technically and economically extractable in all SSPs, purple shaded resources are additionally available in SSP1 and SSP2 and blue shaded resources are additionally available in SSP2. Coloured vertical lines
represent the cumulative use of each resource between 2010 and 2100 in the SSP baselines (see top panel for colour coding), and are thus the result of the combined effect of the assumptions on fossil resource availability and conversion technologies in the SSP baselines.

<div style=" overflow: auto;">
<figure id="fig:MESSAGE-GLOBIOM_supply">
[[File:GlobalResourceSupplyCurves.png|left|750px|thumb|<caption>Cumulative global resource supply curves for coal (top), oil (middle), and gas (bottom) in the IIASA IAM framework</caption>]]
</figure>
</div>

Conventional oil and gas are distributed unevenly throughout the world, with only a few regions dominating the reserves. Nearly half of the reserves of conventional oil is found in Middle East and North Africa, and close to 40% of conventional gas is found in Russia and the former Soviet Union states. The situation is somewhat different for unconventional oil of which North and Latin America potentially possess significantly higher global shares. Unconventional gas in turn is distributed quite well throughout the world, with North America holding most (roughly 25% of global resources). The distribution of coal reserves shows the highest geographical diversity which in the more fragmented SSP3 world contributes to increased overall reliance on this resource. Russia and the former Soviet Union states, Pacific OECD, North America, and Centrally Planned Asia and China all possess more than 10 ZJ of reserves.

Fossil energy resources - MESSAGE-GLOBIOM

2020-12-18T12:46:10Z

Anique Carbados: /* Fossil Fuel Reserves and Resources */

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=MESSAGE-GLOBIOM
|DocumentationCategory=Fossil energy resources
}}
==Fossil Fuel Reserves and Resources==

The availability and costs of fossil fuels influences the future direction of the energy system, and therewith future mitigation challenges. Understanding the variations in
fossil fuel availability and the underlying extraction cost assumptions across the SSPs is hence important. Our fossil energy resource assumptions are derived from various sources, including global databases such as The Federal Institute for Geosciences and Natural Resources (BGR) and the U.S. Geological SUrvey (USGS), as well as market reports and outlooks provided by different energy institutes and agencies. The availability of fossil energy resources in different regions under different socio-economic assumptions are then aligned with the storylines of the individual SSPs (Rogner, 1997 [[CiteRef::MSG-GLB_rogner_assessment_1997]]; Riahi et al., 2012 [[CiteRef::MSG-GLB_riahi_chapter_2012]]). While the physical resource base is identical across the SSPs, considerable differences are assumed regarding the technical and economic availability of overall resources, for example, of unconventional oil and gas. What ultimately determines the attractiveness of a particular type of resource is not just the cost at which it can be brought to the surface, but the cost at which it can be used to provide energy services. Assumptions on fossil energy resources should thus be considered together with those on related conversion technologies. In line with the narratives, technological change in fossil fuel extraction and conversion technologies is assumed to be slowest in SSP1, while comparatively faster technological change occurs in SSP3 thereby considerably enlarging the economic potentials of coal and unconventional hydrocarbons ( <xr id="tab:MESSAGE-GLOBIOM_globff"></xr>, <xr id="fig:MESSAGE-GLOBIOM_supply"></xr>). However, driven by tendency toward regional fragmentation the focus in SSP3 is assumed to be on developing coal technologies which in the longer term leads to a replacement of oil products by synthetic fuels based on coal-to-liquids technologies. In contrast, for SSP2 we assume a continuation of recent trends, focusing more on developing extraction technologies for unconventional hydrocarbon resources, thereby leading to higher potential cumulative oil extraction than in the other SSPs (<xr id="fig:MESSAGE-GLOBIOM_supply"></xr>, middle panel).

<xr id="tab:MESSAGE-GLOBIOM_globff"></xr> shows the assumed total quantities of fossil fuel resources in the MESSAGE model for the base year 2005. <xr id="fig:MESSAGE-GLOBIOM_supply"></xr> gives these resource estimates as supply curves. In addition, the assumptions are compared with estimates from the Global Energy Assessment (Rogner et al., 2012 [[CiteRef::MSG-GLB_rogner_chapter_2012]]) as of the year 2009. Estimating fossil fuel reserves is built on both economic and technological assumptions. With an improvement in technology or a change in purchasing power, the amount that may be considered a “reserve” vs. a “resource” (generically referred to here as resources) can actually vary quite widely.

‘Reserves’ are generally defined as being those quantities for which geological and engineering information indicate with reasonable certainty that they can be recovered in the future from known reservoirs under existing economic and operating conditions. ‘Resources’ are detected quantities that cannot be profitably recovered with current technology, but might be recoverable in the future, as well as those quantities that are geologically possible, but yet to be found. The remainder are ‘Undiscovered resources’ and, by definition, one can only speculate on their existence. Definitions are based on Rogner et al. (2012) [[CiteRef::MSG-GLB_rogner_chapter_2012]].

<figtable id="tab:MESSAGE-GLOBIOM_globff">
{| class="wikitable"
|+<caption>Assumed global fossil fuel reserves and resources in the MESSAGE model. Estimates from the Global Energy Assessment also added for comparison</caption>
! Source
! MESSAGE (Rogner et al., 1997 [[CiteRef::MSG-GLB_rogner_assessment_1997]])
! colspan="2" | Rogner et al., 2012 [[CiteRef::MSG-GLB_rogner_chapter_2012]]
|-
|
| Reserves+Resources [ZJ]
| Reserves [ZJ]
| Resources [ZJ]
|-
| Coal
| 259
| 17.3 – 21.0
| 291 – 435
|-
| Conventional Oil
| 9.8
| 4.0 – 7.6
| 4.2 – 6.2
|-
| Unconventional Oil
| 23.0
| 3.8 – 5.6
| 11.3 – 14.9
|-
| Conventional Gas
| 16.8
| 5.0 – 7.1
| 7.2 – 8.9
|-
| Unconventional Gas
| 23.0
| 20.1 – 67.1
| 40.2 – 122
|}
</figtable>

The following table (<xr id="tab:MESSAGE-GLOBIOM_ffavail"></xr>) presents the fossil resource availability in ZJ (2010-2100) for coal, oil and gas, for SSP1, SSP2 and SSP3, respectively.

<figtable id="tab:MESSAGE-GLOBIOM_ffavail">
{| class="wikitable"
|+<caption>Fossil resource availability for SSP1, SSP2, and SSP3</caption>
! Type
! SSP1 [ZJ]
! SSP2 [ZJ]
! SSP3 [ZJ]
|-
| Coal
| 93
| 92
| 243
|-
| Oil
| 17
| 40
| 17
|-
| Gas
| 39
| 37
| 24
|}
</figtable>

Coal is the largest resource among fossil fuels; it accounts for more than 50% of total fossil reserve plus resource estimates even at the higher end of the assumptions, which includes
considerable amounts of unconventional hydrocarbons. Oil is the most vulnerable fossil fuel at less than 10 ZJ of conventional oil and possibly less than 10 ZJ of unconventional oil.
Natural gas is more abundant in both the conventional and unconventional categories.

<xr id="fig:MESSAGE-GLOBIOM_supply"></xr> presents the cumulative global resource supply curves for coal, oil and gas in the IIASA IAM framework. Green shaded resources are technically and economically extractable in all SSPs, purple shaded resources are additionally available in SSP1 and SSP2 and blue shaded resources are additionally available in SSP2. Coloured vertical lines
represent the cumulative use of each resource between 2010 and 2100 in the SSP baselines (see top panel for colour coding), and are thus the result of the combined effect of the assumptions on fossil resource availability and conversion technologies in the SSP baselines.

<div style=" overflow: auto;">
<figure id="fig:MESSAGE-GLOBIOM_supply">
[[File:GlobalResourceSupplyCurves.png|left|750px|thumb|<caption>Cumulative global resource supply curves for coal (top), oil (middle), and gas (bottom) in the IIASA IAM framework</caption>]]
</figure>
</div>

Conventional oil and gas are distributed unevenly throughout the world, with only a few regions dominating the reserves. Nearly half of the reserves of conventional oil is found in Middle East and North Africa, and close to 40% of conventional gas is found in Russia and the former Soviet Union states. The situation is somewhat different for unconventional oil of which North and Latin America potentially possess significantly higher global shares. Unconventional gas in turn is distributed quite well throughout the world, with North America holding most (roughly 25% of global resources). The distribution of coal reserves shows the highest geographical diversity which in the more fragmented SSP3 world contributes to increased overall reliance on this resource. Russia and the former Soviet Union states, Pacific OECD, North America, and Centrally Planned Asia and China all possess more than 10 ZJ of reserves.

Energy resource endowments - MESSAGE-GLOBIOM

2020-11-19T13:38:05Z

Anique Carbados: Added a note orienting readers to correct pages.

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|IsDocumentationOf=MESSAGE-GLOBIOM
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}}
Information about the MESSAGEix-GLOBIOM energy resource endowments can be found in the following subsections:
* [[Fossil energy resources - MESSAGE-GLOBIOM|Fossil energy resources - MESSAGEix-GLOBIOM]]
* [[Uranium and other fissile resources - MESSAGE-GLOBIOM|Uranium and other fissile resources - MESSAGEix-GLOBIOM]]
* [[Bioenergy - MESSAGE-GLOBIOM|Bioenergy - MESSAGEix-GLOBIOM]]
* [[Non-biomass renewables - MESSAGE-GLOBIOM|Non-biomass renewables - MESSAGEix-GLOBIOM]]

Energy - MESSAGE-GLOBIOM

2020-11-18T11:49:14Z

Anique Carbados: Updated to reflect MESSAGEix 2020 release

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|IsEmpty=No
|IsDocumentationOf=MESSAGE-GLOBIOM
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The MESSAGEix modelling framework (Model for Energy Supply Strategy Alternatives and their General Environmental Impact), MESSAGEix for short, is a linear programming (LP) energy engineering model with global coverage. As a systems engineering optimization model, MESSAGEix is used for medium- to long-term energy system planning, energy policy analysis, and scenario development (Huppman et al., 2019<ref>Daniel Huppmann, Matthew Gidden, Oliver Fricko, Peter Kolp, Clara Orthofer, Michael Pimmer, Nikolay Kushin, Adriano Vinca, Alessio Mastrucci, Keywan Riahi, and Volker Krey. The messageix integrated assessment model and the ix modeling platform (ixmp): an open framework for integrated and cross-cutting analysis of energy, climate, the environment, and sustainable development. ''Environmental Modelling & Software'', 112:143–156, 2019. doi:10.1016/j.envsoft.2018.11.012.</ref>; Messner and Strubegger, 1995<ref>Sabine Messner and Manfred Strubegger. User’s Guide for MESSAGE III. 1995. URL: <nowiki>http://pure.iiasa.ac.at/id/eprint/4527/1/WP-95-069.pdf</nowiki>.</ref> [[CiteRef::MSG-GLB_messner_users_1995]]). The model provides a framework for representing an energy system with all its interdependencies from resource extraction, imports and exports, conversion, transport, and distribution, to the provision of energy end-use services such as light, space conditioning, industrial production processes, and transportation. In addition, MESSAGEix links to GLOBIOM (GLObal BIOsphere Model, cf. Section [[Land-use_-_MESSAGE-GLOBIOM|Land-use of MESSAGEix-GLOBIOM]]) to consistently assess the implications of utilizing bioenergy of different types and to integrate the GHG emissions from energy and land us,e and to the aggregated macro-economic model MACRO (cf. Section [[Macro-economy_-_MESSAGE-GLOBIOM|Macro-economy of MESSAGEix-GLOBIOM]]) to assess economic implications and to capture economic feedbacks.

MESSAGEix covers all greenhouse gas (GHG)-emitting sectors, including energy, industrial processes as well as - through its linkage to GLOBIOM - agriculture and forestry. The emissions of the full basket of greenhouse gases, including CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O and F-gases (CF<sub>4</sub>, C<sub>2</sub>F<sub>6</sub>, HFC125, HFC134a, HFC143a, HFC227ea, HFC245ca and SF6), as well as other radiatively active gases, such as NO<sub>x</sub>, volatile organic compounds (VOCs), CO, SO<sub>2</sub>, and BC/OC, is represented in the model. MESSAGEix is used in conjunction with MAGICC (Model for Greenhouse gas Induced Climate Change) version 6.8 (cf. Section [[Climate_-_MESSAGE-GLOBIOM|Climate of MESSAGEix-GLOBIOM]]) to calculate atmospheric concentrations, radiative forcing, and annual-mean global surface air temperature increase.

The model is designed to formulate and evaluate alternative energy supply strategies consistent with user-defined constraints such as limits on new investment, fuel availability and trade, environmental regulations and policies as well as diffusion rates of new technology. Environmental aspects can be analysed by accounting for, and if necessary limiting, the amount of pollutants emitted by various technologies at various steps in energy supply. This helps to evaluate the impact of environmental regulations on energy system development.

Its principal results comprise, among others, estimates of technology-specific multi-sector response strategies for specific climate stabilization targets. The model thus identifies the least-cost portfolio of mitigation technologies. The choice of individual mitigation options across gases and sectors is driven by the relative economics of abatement measures, assuming full temporal and spatial flexibility (i.e., emissions-reduction measures are assumed to occur when and where they are cheapest to implement).

The Reference Energy System (RES) defines the total set of available energy conversion technologies. In MESSAGEix terms, energy conversion technology refers to all types of energy technology, from resource extraction to transformation, transport, distribution of energy carriers, and end-use technologies.

Because few conversion technologies convert resources directly into useful energy, the energy system in MESSAGEix is divided into 5 energy levels:

*Resource (r) - raw resources (e.g., coal, oil, natural gas in the ground or biomass on the field)
*Primary (a) energy - raw product at a generation site (e.g., crude oil input to the refinery)
*Secondary (x) energy - final product at a generation site (e.g., gasoline or diesel fuel output from the refinery)
*Final (f) energy - final product at its consumption point (e.g., gasoline in the tank of a car or electricity leaving a socket)
*Useful (u) energy - final product satisfying demand for services (e.g., heating, lighting or moving people)
Technologies can take in energy commodities from one level and put them out at another level (e.g., refineries produce refined oil products at a secondary level from crude oil input at the primary level) or at the same level (e.g., hydrogen electrolyzers produce hydrogen at the secondary energy level from electricity at the secondary level). The energy forms defined in each level can be envisioned as a transfer hub that the various technologies feed into or pump away from. The useful energy demand is given as a time series. Technology characteristics generally vary over time periods.

The mathematical formulation of MESSAGEix ensures that the flows are consistent: demand is met, inflows equal outflows and constraints are not exceeded. In other words, MESSAGEix is itself a partial equilibrium model. However, through its linkag to MACRO, general equilibrium effects are taken into account.

Macro-economy - MESSAGE-GLOBIOM

2020-11-16T14:53:35Z

Anique Carbados: Updated MESSAGE to MESSAGEix, minor editing to reflect 2020 release documentation.

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The detailed energy supply model (MESSAGEix) is soft-linked to an aggregated, single-sector macro-economic model (MACRO) which has been derived from the so-called Global 2100 or
ETA-MACRO model (Manne and Richels, 1992 [[CiteRef::MSG-GLB_manne_buying_1992]]), a predecessor of the [http://www.stanford.edu/group/MERGE/ MERGE] model. The two models are linked to consistently reflect the influence of energy supply costs, as calculated by MESSAGEix, in the mix of production factors considered in MACRO, and the effect of energy price changes on energy service demand. The combined MESSAGEix-MACRO model (Messner and Schrattenholzer, 2000 [[CiteRef::MSG-GLB_messner_messagemacro:_2000]]) can generate a consistent economic response to changes in energy prices and estimate the overall economic consequences (e.g., changes in GDP or household consumption) of energy or climate policies.

ACRO is a macroeconomic model maximizing the intertemporal utility function of a single representative producer-consumer in each world region. The optimization result is a sequence of optimal savings, investment, and consumption decisions. The main variables of the model are capital stock, available labor, and energy inputs, which together determine an economy's total output according to a nested CES (constant elasticity of substitution) production function. End-use service demand in the (commercial) demand categories of MESSAGEix (see [[Energy_demand_-_MESSAGE-GLOBIOM|Energy demand of MESSAGEix-GLOBIOM]]) is determined within the MACRO model, and is consistent with energy supply from MESSAGEix, which is an input to the model.

The model’s most important driving input variables are the projected growth rates of total labor, i.e., the combined effect of labor force and labor productivity growth, and the annual rates of reference energy intensity reduction, i.e. the so-called autonomous energy efficiency improvement (AEEI) coefficients. The latter are calibrated to the developments in a MESSAGEix baseline scenario to ensure consistency between the two models. Labor supply growth is also referred to as reference or potential GDP growth. In the absence of price changes, energy demands grow at rates that are the approximate result of potential GDP growth rates, reduced by the rates of overall energy intensity reduction. Price changes in the six demand categories, for example induced by energy or climate policies, can alter this path significantly.

MACRO's production function includes six commercial energy demand categories represented in MESSAGEix. To optimize, MACRO requires cost information for each demand category. The exact definitions of these costs as a function over all positive quantities of energy cannot be given in closed form because each point of the function would be a result of a full MESSAGEix run. However, the optimality conditions implicit in the formulation of MACRO only require the functional values and its derivatives at the optimal point to be consistent between the two models. Since these requirements are therefore only local, most functions with this feature will simulate the combined energy-economic system in the neighborhood of the optimal point. The regional costs (of energy use and imports) and revenues (from energy exports) of providing energy in MACRO are approximated by a Taylor expansion to the first order of the energy system costs as calculated by MESSAGEix. From an initial MESSAGEix model run, the total energy system cost (including costs/revenues from energy trade) and additional abatement costs (e.g., abatement costs from non-energy sources) as well as the shadow prices of the six commercial demand categories by region are passed to MACRO. In addition to the economic implications of energy trade, the data exchange from MESSAGEix to MACRO may also include the revenues or costs of trade if GHG permits.

For a more elaborate description of MACRO, including the system of equations and technical details of the implementation, please consult the annex presenting the mathematical formulation of MACRO in [[Appendices_-_MESSAGE-GLOBIOM|Appendices of MESSAGEix-GLOBIOM]].

MESSAGE-GLOBIOM

2020-11-16T13:25:05Z

Anique Carbados: Added behavioural change

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Population - MESSAGE-GLOBIOM

2020-11-16T13:16:15Z

Anique Carbados: Minor English edit - difficulties to adapt to >> difficulty adapting to

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Demographic development has, next to economic growth, strong implications for the anticipated mitigation and adaptation challenges. For example, a larger, poorer population will have more difficulty adapting to the detrimental effects of climate change (O’Neill et al., 2014 [[CiteRef::MSG-GLB_oneill_new_2014]]). The primary drivers of future energy demand in MESSAGEix are projections of total population and GDP at purchasing power parity, denoted as GDP (PPP). In addition to total population, the urban/rural split of population is relevant for the MESSAGEix-Access version of the model which distinguishes rural and urban population with different household incomes in developing country regions.

Demographic projections used in MESSAGEix-GLOBIOM are based on the Shared Socio-economic Pathways (SSPs) at the country level [https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=welcome SSP database]. Population growth evolves in response to how fertility, mortality, migration, and education of various social strata are assumed to change over time. In SSP2, global population peaks at 9.4 billion people around 2070, and slowly declines thereafter (KC and Lutz, 2015 [[CiteRef::MSG-GLB_kc_human_2014]]). However, modest improvements of educational attainment levels result in declines in education-specific fertility rates, leading to incomplete economic convergence across different world regions. This is particularly an issue for Africa. Overall, the population development in SSP2 is designed to be situated in the middle of the road between SSP1 and SSP3, see KC and Lutz (2015) [[CiteRef::MSG-GLB_kc_human_2014]] for details. (Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]])

Economic activity - MESSAGE-GLOBIOM

2020-11-16T13:15:21Z

Anique Carbados: Updated MESSAGE to MESSAGEix

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In addition to population economic development has a strong impact on the challenges to mitigation and adaptation. Generally, a poorer, less educated population will have more difficulty adapting to the detrimental effects of climate change (O’Neill et al., 2014 [[CiteRef::MSG-GLB_oneill_new_2014]]). The primary drivers of future energy demand in MESSAGEix are projections of total population and GDP per capita at purchasing power parity, denoted as GDP (PPP). In the MESSAGEix-Access version of the model households are represented by income level (and rural/urban split) in developing country regions.

MESSAGEix-GLOBIOM utilizes GDP (PPP) projections based on the Shared Socio-economic Pathways (SSPs) that are available at the country level [https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=welcome SSP database]. In the SSPs, GDP development follows regional historical trends (Dellink et al., 2015 [[CiteRef::MSG-GLB_dellink_long-term_2015]]). In SSP2 specifically, average income grows by a factor of six and reaches about 60,000 USD/capita by the end of the century (all GDP/capita figures use USD2005 and purchasing-power-parity – PPP). The SSP2 GDP projection is situated in-between the estimates for SSP1 and SSP3, which reach global average income levels of 82,000 USD2005 and 22,000 USD2005, respectively, by the end of the century. SSP2 depicts a future of global progress where developing countries achieve significant economic growth. Today, average per capita income in the global North is about five times higher than in the global South. In SSP2, developing countries reach today’s average income levels of the OECD between 2060 and 2090, depending on the region. Overall, the GDP developments in SSP2 are designed to be situated in the middle of the road between SSP1 and SSP3, see Dellink et al (2015) [[CiteRef::MSG-GLB_dellink_long-term_2015]] for details. (Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]])

Population - MESSAGE-GLOBIOM

2020-11-16T13:14:00Z

Anique Carbados: Updated MESSAGE to MESSAGEix

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Demographic development has, next to economic growth, strong implications for the anticipated mitigation and adaptation challenges. For example, a larger, poorer population will have more difficulties to adapt to the detrimental effects of climate change (O’Neill et al., 2014 [[CiteRef::MSG-GLB_oneill_new_2014]]). The primary drivers of future energy demand in MESSAGEix are projections of total population and GDP at purchasing power parity, denoted as GDP (PPP). In addition to total population, the urban/rural split of population is relevant for the MESSAGEix-Access version of the model which distinguishes rural and urban population with different household incomes in developing country regions.

Demographic projections used in MESSAGEix-GLOBIOM are based on the Shared Socio-economic Pathways (SSPs) at the country level [https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=welcome SSP database]. Population growth evolves in response to how fertility, mortality, migration, and education of various social strata are assumed to change over time. In SSP2, global population peaks at 9.4 billion people around 2070, and slowly declines thereafter (KC and Lutz, 2015 [[CiteRef::MSG-GLB_kc_human_2014]]). However, modest improvements of educational attainment levels result in declines in education-specific fertility rates, leading to incomplete economic convergence across different world regions. This is particularly an issue for Africa. Overall, the population development in SSP2 is designed to be situated in the middle of the road between SSP1 and SSP3, see KC and Lutz (2015) [[CiteRef::MSG-GLB_kc_human_2014]] for details. (Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]])

Socio-economic drivers - MESSAGE-GLOBIOM

2020-11-16T13:07:17Z

Anique Carbados: Added citations

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Socio-economic drivers are typically informed by a scenario narrative that describes in qualitative terms the overall logic behind the scenarios. In MESSAGEix-GLOBIOM, the Shared Socio-economic Pathways (SSPs, see O’Neill et al., 2014 [[CiteRef::MSG-GLB_oneill_new_2014]]) provide the overall scenario logic with which the main socio-economic drivers, [[Population_-_MESSAGE-GLOBIOM|population]] and [[Economic_activity_-_MESSAGE-GLOBIOM|GDP]] have been quantified.

== <u><big>SSP Narratives</big></u> ==
Narratives have been developed for the SSPs (O'Neill et al., 2015)<ref name=":0">Brian C O’Neill, Elmar Kriegler, Kristie L Ebi, Eric Kemp-Benedict, Keywan Riahi, Dale S Rothman, Bas J van Ruijven, Detlef P van Vuuren, Joern Birkmann, and Kasper Kok. The roads ahead: narratives for shared socioeconomic pathways describing world futures in the 21st century. ''Global Environmental Change'', 2015.</ref>. These descriptions of alternative future societal developments span a range of possible worlds that stretch along two climate change-related dimensions: mitigation and adaptation challenges. The SSPs reflect five different development pathways for the world that are characterized by varying levels of glboal challenges (see Riahi et al., 2017)<ref>Keywan Riahi, Detlef P. van Vuuren, Elmar Kriegler, Jae Edmonds, Brian O’Neill, Shinichiro Fujimori, Nico Bauer, Katherine Calvin, Rob Dellink, Oliver Fricko, Wolfgang Lutz, Alexander Popp, Jesus Crespo Cuaresma, Samir KC, Marian Leimbach, Leiwen Jiang, Tom Kram, Shilpa Rao, Johannes Emmerling, Kristie Ebi, Tomoko Hasegawa, Petr Havlik, Florian Humpenoder, Lara Aleluia Da Silva, Steve Smith, Elke Stehfest, Valentina Bosetti, Jiyong Eom, David Gernaat, Toshihiko Masui, Joeri Rogelj, Jessica Strefler, Laurent Drouet, Volker Krey, Gunnar Luderer, Mathijs Harmsen, Kiyoshi Takahashi, Lavinia Baumstark, Jonathan Doelman, Mikiko Kainuma, Zbigniew Klimont, Giacomo Marangoni, Hermann Lotze-Campen, Michael Obersteiner, Andrzej Tabeau, and Massimo Tavoni. The Shared Socioeconomic Pathways and their Energy, Land Use, and Greenhouse Gas Emissions Implications. ''Global Environmental Change'', 42:153–168, 2017. URL: <nowiki>http://pure.iiasa.ac.at/13280/</nowiki>, doi:10.1016/j.gloenvcha.2016.05.009.</ref>. The three narratives that have been translated into quantitative scenarios with MESSAGEix-GLOBIOM are presented below and in Fricko et al. (2017)<ref>Oliver Fricko, Petr Havlik, Joeri Rogelj, Zbigniew Klimont, Mykola Gusti, Nils Johnson, Peter Kolp, Manfred Strubegger, Hugo Valin, Markus Amann, Tatiana Ermolieva, Nicklas Forsell, Mario Herrero, Chris Heyes, Georg Kindermann, Volker Krey, David L. McCollum, Michael Obersteiner, Shonali Pachauri, Shilpa Rao, Erwin Schmid, Wolfgang Schoepp, and Keywan Riahi. The marker quantification of the shared socioeconomic pathway 2: a middle-of-the-road scenario for the 21st century. ''Global Environmental Change'', 42:251–267, 2017.</ref>.

==== SSP1 - Sustainability - Taking the green road ====
<blockquote>“The world shifts gradually, but pervasively, toward a more sustainable path, emphasizing more inclusive development that respects perceived environmental boundaries. Increasing evidence of and accounting for the social, cultural, and economic costs of environmental degradation and inequality drive this shift. Management of the global commons slowly improves, facilitated by increasingly effective and persistent cooperation and collaboration of local, national, and international organizations and institutions, the private sector, and civil society. Educational and health investments accelerate the demographic transition, leading to a relatively low population. Beginning with current high-income countries, the emphasis on economic growth shifts toward a broader emphasis on human well-being, even at the expense of somewhat slower economic growth over the longer term. Driven by an increasing commitment to achieving development goals, inequality is reduced both across and within countries. Investment in environmental technology and changes in tax structures lead to improved resource efficiency, reducing overall energy and resource use and improving environmental conditions over the longer term. Increased investment, financial incentives and changing perceptions make renewable energy more attractive. Consumption is oriented toward low material growth and lower resource and energy intensity. The combination of directed development of environmentally friendly technologies, a favorable outlook for renewable energy, institutions that can facilitate international cooperation, and relatively low energy demand results in relatively low challenges to mitigation. At the same time, the improvements in human well-being, along with strong and flexible global, regional, and national institutions imply low challenges to adaptation.” (O'Neill et al., 2015).<ref name=":0" /></blockquote>

==== SSP2 - Middle of the road ====
<blockquote>“The world follows a path in which social, economic, and technological trends do not shift markedly from historical patterns. Development and income growth proceed unevenly, with some countries making relatively good progress while others fall short of expectations. Most economies are politically stable. Globally connected markets function imperfectly. Global and national institutions work toward but make slow progress in achieving sustainable development goals, including improved living conditions and access to education, safe water, and health care. Technological development proceeds apace, but without fundamental breakthroughs. Environmental systems experience degradation, although there are some improvements and overall the intensity of resource and energy use declines. Even though fossil fuel dependency decreases slowly, there is no reluctance to use unconventional fossil resources. Global population growth is moderate and levels off in the second half of the century as a consequence of completion of the demographic transition. However, education investments are not high enough to accelerate the transition to low fertility rates in low-income countries and to rapidly slow population growth. This growth, along with income inequality that persists or improves only slowly, continuing societal stratification, and limited social cohesion, maintain challenges to reducing vulnerability to societal and environmental changes and constrain significant advances in sustainable development. These moderate development trends leave the world, on average, facing moderate challenges to mitigation and adaptation, but with significant heterogeneities across and within countries.” (O'Neill et al., 2015).<ref name=":0" /></blockquote>

==== SSP3 - Regional rivalry - A rocky road ====
<blockquote>“A resurgent nationalism, concerns about competitiveness and security, and regional conflicts push countries to increasingly focus on domestic or, at most, regional issues. This trend is reinforced by the limited number of comparatively weak global institutions, with uneven coordination and cooperation for addressing environmental and other global concerns. Policies shift over time to become increasingly oriented toward national and regional security issues, including barriers to trade, particularly in the energy resource and agricultural markets. Countries focus on achieving energy and food security goals within their own regions at the expense of broader-based development, and in several regions move toward more authoritarian forms of government with highly regulated economies. Investments in education and technological development decline. Economic development is slow, consumption is material-intensive, and inequalities persist or worsen over time, especially in developing countries. There are pockets of extreme poverty alongside pockets of moderate wealth, with many countries struggling to maintain living standards and provide access to safe water, improved sanitation, and health care for disadvantaged populations. A low international priority for addressing environmental concerns leads to strong environmental degradation in some regions. The combination of impeded development and limited environmental concern results in poor progress toward sustainability. Population growth is low in industrialized and high in developing countries. Growing resource intensity and fossil fuel dependency along with difficulty in achieving international cooperation and slow technological change imply high challenges to mitigation. The limited progress on human development, slow income growth, and lack of effective institutions, especially those that can act across regions, implies high challenges to adaptation for many groups in all regions.” (O'Neill et al., 2015)<ref name=":0" /></blockquote>

Model scope and methods - MESSAGE-GLOBIOM

2020-11-16T13:01:40Z

Anique Carbados: Added citations

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MESSAGEix<ref>Daniel Huppmann, Matthew Gidden, Oliver Fricko, Peter Kolp, Clara Orthofer, Michael Pimmer, Nikolay Kushin, Adriano Vinca, Alessio Mastrucci, Keywan Riahi, and Volker Krey. The messageix integrated assessment model and the ix modeling platform (ixmp): an open framework for integrated and cross-cutting analysis of energy, climate, the environment, and sustainable development. ''Environmental Modelling & Software'', 112:143–156, 2019. doi:10.1016/j.envsoft.2018.11.012.</ref> represents the core of the IIASA IAM framework and its main task is to optimize the energy system so that it can satisfy specified energy demand at the lowest cost. MESSAGEix carries out this optimization in an iterative setup with MACRO, which provides estimates of the macro-economic demand response that results from energy system and services costs computed by MESSAGEix. For the six commercial end-use demand categories depicted in MESSAGEix (see [[Energy_demand_-_MESSAGE-GLOBIOM|Demand section of MESSAGEix-GLOBIOM]]), MACRO adjusts useful energy demand based on demand prices until the two models have reached equilibrium (see [[Macro-economy_-_MESSAGE-GLOBIOM|Macro-economy section of MESSAGEix-GLOBIOM]]). It thus reflects price-induced energy efficiency improvements that can occur when energy prices change. MESSAGEix can represent different energy- and climate-related policies (see [https://www.iamcdocumentation.eu/index.php/Policy_-_MESSAGE-GLOBIOM Policy section of MESSAGEix-GLOBIOM)].

GLOBIOM provides MESSAGEix with information on land use and its implications, like the availability and cost of bio-energy, and the availability and cost of emission mitigation in the AFOLU (Agriculture, Forestry and Land Use) sector (see [[Land-use_-_MESSAGE-GLOBIOM|Land-use of MESSAGEix-GLOBIOM]]). To reduce computational costs, MESSAGEix iteratively queries a GLOBIOM emulator which provides an approximation of land-use outcomes during the optimization process instead of requiring the GLOBIOM model to be rerun iteratively. Once the iteration between MESSAGEix and MACRO has converged, the resulting bioenergy demands along with corresponding carbon prices are used for a concluding analysis with the full-fledged GLOBIOM model. This ensures full consistency in the results from MESSAGEix and GLOBIOM, and also allows a more extensive set of land-use related indicators, including spatially explicit information on land use, to be reported.

Air pollution implications of the energy system are computed in MESSAGEix by applying technology-specific pollution coefficients from GAINS (see [[Pollutants_and_non-GHG_forcing_agents_-_MESSAGE-GLOBIOM|Pollutants and non-GHG forcing agents for MESSAGEix-GLOBIOM]] and [[Air_pollution_and_health_-_MESSAGE-GLOBIOM|Air pollution and health of MESSAGEix-GLOBIOM]]). This approach had been applied to the SSP process (Rao et al. 2017)<ref>S. Rao, Z. Klimont, S.J. Smith, R. Van Dingenen, F. Dentener, L. Bouwman, K. Riahi, M. Amann, B.L. Bodirsky, D.P. van Vuuren, L. Aleluia Reis, K. Calvin, L. Drouet, O. Fricko, S. Fujimori, D. Gernaat, P. Havlik, M. Harmsen, T. Hasegawa, C. Heyes, J. Hilaire, G. Luderer, T. Masui, E. Stehfest, J. Strefler, S. van der Sluis, and M. Tavoni. Future air pollution in the shared socio-economic pathways. ''Global Environmental Change'', 42:346–358, 2017. doi:10.1016/j.gloenvcha.2016.05.012.</ref>. Alternatively, GAINS can be run ex-post based on MESSAGEix-GLOBIOM scenarios to estimate air pollution emissions, concentrations and the related health impacts. This approach allows for the analysis of different air polllution policy packages (e.g., current legislation, maximum feasible reduction), including the estimation of costs for air pollution control measures. Examples for applying this way of linking MESSAGEix-GLOBIOM and GAINS can be found in McCollum et al (2018)<ref>D.L. McCollum, W. Zhou, C. Bertram, H.-S. De Boer, V. Bosetti, S. Busch, J. Després, L. Drouet, J. Emmerling, M. Fay, O. Fricko, S. Fujimori, M. Gidden, M. Harmsen, D. Huppmann, G. Iyer, V. Krey, E. Kriegler, C. Nicolas, S. Pachauri, S. Parkinson, M. Poblete-Cazenave, P. Rafaj, N. Rao, J. Rozenberg, A. Schmitz, W. Schoepp, D. Van Vuuren, and K. Riahi. Energy investment needs for fulfilling the paris agreement and achieving the sustainable development goals. ''Nature Energy'', 3(7):589–599, 2018. doi:10.1038/s41560-018-0179-z.</ref> and Grubler et al. (2018)<ref>A. Grubler, C. Wilson, N. Bento, B. Boza-Kiss, V. Krey, D.L. McCollum, N.D. Rao, K. Riahi, J. Rogelj, S. De Stercke, J. Cullen, S. Frank, O. Fricko, F. Guo, M. Gidden, P. Havlík, D. Huppmann, G. Kiesewetter, P. Rafaj, W. Schoepp, and H. Valin. A low energy demand scenario for meeting the 1.5 °c target and sustainable development goals without negative emission technologies. ''Nature Energy'', 3(6):515–527, 2018. doi:10.1038/s41560-018-0172-6.</ref>.

In general, cumulative global GHG emissions from all sectors are constrained at different levels, with equivalent pricing applied to other GHGs, to reach the desired radiative forcing levels (cf. right-hand side). <xr id="fig:MESSAGE-GLOBIOM_iiasaiam"></xr>The climate constraints are thus taken up in the coupled MESSAGEix-GLOBIOM optimization, and the resulting carbon price is fed back to the full-fledged GLOBIOM model for full consistency. Finally, the combined results for land use, energy, and industrial emissions from MESSAGEix and GLOBIOM are merged and fed into MAGICC (see [[Climate_-_MESSAGE-GLOBIOM|Climate of MESSAGEix-GLOBIOM]]), a global carbon-cycle and climate model, which then provides estimates of the climate implications in terms of atmospheric concentrations, radiative forcing, and global-mean temperature increase. Importantly, climate impacts and impacts of the carbon cycle are - depending on the specific application - only partly accounted for in the IIASA IAM framework. The entire framework is linked to an online database infrastructure which allows straightforward visualisation, analysis, comparison and dissemination of results (Riahi et al., 2017)<ref>Keywan Riahi, Detlef P. van Vuuren, Elmar Kriegler, Jae Edmonds, Brian O’Neill, Shinichiro Fujimori, Nico Bauer, Katherine Calvin, Rob Dellink, Oliver Fricko, Wolfgang Lutz, Alexander Popp, Jesus Crespo Cuaresma, Samir KC, Marian Leimbach, Leiwen Jiang, Tom Kram, Shilpa Rao, Johannes Emmerling, Kristie Ebi, Tomoko Hasegawa, Petr Havlik, Florian Humpenoder, Lara Aleluia Da Silva, Steve Smith, Elke Stehfest, Valentina Bosetti, Jiyong Eom, David Gernaat, Toshihiko Masui, Joeri Rogelj, Jessica Strefler, Laurent Drouet, Volker Krey, Gunnar Luderer, Mathijs Harmsen, Kiyoshi Takahashi, Lavinia Baumstark, Jonathan Doelman, Mikiko Kainuma, Zbigniew Klimont, Giacomo Marangoni, Hermann Lotze-Campen, Michael Obersteiner, Andrzej Tabeau, and Massimo Tavoni. The Shared Socioeconomic Pathways and their Energy, Land Use, and Greenhouse Gas Emissions Implications. ''Global Environmental Change'', 42:153–168, 2017. URL: <nowiki>http://pure.iiasa.ac.at/13280/</nowiki>, doi:10.1016/j.gloenvcha.2016.05.009.</ref>.

The scientific software underlying the global MESSAGEix-GLOBIOM model is called the MESSAGEix framework, an open-source, versatile implementation of a linear optimization problem, with the option of coupling to the computable general equilibrium (CGE) model MACRO to incorporate the effect of price changes on economic activity and demand for commodities and resources. MESSAGEix is integrated with the ''ix modelling platform (ixmp)'', a "data warehouse" for version control of reference timeseries, input data and model results. ixmp provides interfaces to the scientific programming languages Python and R for efficient, scripted workflows for data processing and visualisation of results (Huppmann et al., 2019)<ref>Daniel Huppmann, Matthew Gidden, Oliver Fricko, Peter Kolp, Clara Orthofer, Michael Pimmer, Nikolay Kushin, Adriano Vinca, Alessio Mastrucci, Keywan Riahi, and Volker Krey. The messageix integrated assessment model and the ix modeling platform (ixmp): an open framework for integrated and cross-cutting analysis of energy, climate, the environment, and sustainable development. ''Environmental Modelling & Software'', 112:143–156, 2019. doi:10.1016/j.envsoft.2018.11.012.</ref>.

<div style=" overflow: auto;">
<figure id="fig:MESSAGE-GLOBIOM_iiasaiam">
[[File:iiasaiam.png|left|900px|thumb|<caption>Overview of the IIASA IAM framework. Coloured boxes represent respective specialized disciplinary models which are integrated for generating internally consistent scenarios. Figure from Riahi et al. (2016).</caption>]] [[CiteRef::MSG-GLB_riahi_shared_2016]]
</figure>
</div>

Socio-economic drivers - MESSAGE-GLOBIOM

2020-11-16T12:52:37Z

Anique Carbados: Added SSP narratives - still need to add full citations.

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Socio-economic drivers are typically informed by a scenario narrative that describes in qualitative terms the overall logic behind the scenarios. In MESSAGEix-GLOBIOM, the Shared Socio-economic Pathways (SSPs, see O’Neill et al., 2014 [[CiteRef::MSG-GLB_oneill_new_2014]]) provide the overall scenario logic with which the main socio-economic drivers, [[Population_-_MESSAGE-GLOBIOM|population]] and [[Economic_activity_-_MESSAGE-GLOBIOM|GDP]] have been quantified.

== <u><big>SSP Narratives</big></u> ==
Narratives have been developed for the SSPs (O'Neill et al., 2015). These descriptions of alternative future societal developments span a range of possible worlds that stretch along two climate change-related dimensions: mitigation and adaptation challenges. The SSPs reflect five different development pathways for the world that are characterized by varying levels of glboal challenges (see Riahi et al., 2017). The three narratives that have been translated into quantitative scenarios with MESSAGEix-GLOBIOM are presented below and in Fricko et al. (2017).

==== SSP1 - Sustainability - Taking the green road ====
<blockquote>“The world shifts gradually, but pervasively, toward a more sustainable path, emphasizing more inclusive development that respects perceived environmental boundaries. Increasing evidence of and accounting for the social, cultural, and economic costs of environmental degradation and inequality drive this shift. Management of the global commons slowly improves, facilitated by increasingly effective and persistent cooperation and collaboration of local, national, and international organizations and institutions, the private sector, and civil society. Educational and health investments accelerate the demographic transition, leading to a relatively low population. Beginning with current high-income countries, the emphasis on economic growth shifts toward a broader emphasis on human well-being, even at the expense of somewhat slower economic growth over the longer term. Driven by an increasing commitment to achieving development goals, inequality is reduced both across and within countries. Investment in environmental technology and changes in tax structures lead to improved resource efficiency, reducing overall energy and resource use and improving environmental conditions over the longer term. Increased investment, financial incentives and changing perceptions make renewable energy more attractive. Consumption is oriented toward low material growth and lower resource and energy intensity. The combination of directed development of environmentally friendly technologies, a favorable outlook for renewable energy, institutions that can facilitate international cooperation, and relatively low energy demand results in relatively low challenges to mitigation. At the same time, the improvements in human well-being, along with strong and flexible global, regional, and national institutions imply low challenges to adaptation.” (O'Neill et al., 2015)</blockquote>

==== SSP2 - Middle of the road ====
<blockquote>“The world follows a path in which social, economic, and technological trends do not shift markedly from historical patterns. Development and income growth proceed unevenly, with some countries making relatively good progress while others fall short of expectations. Most economies are politically stable. Globally connected markets function imperfectly. Global and national institutions work toward but make slow progress in achieving sustainable development goals, including improved living conditions and access to education, safe water, and health care. Technological development proceeds apace, but without fundamental breakthroughs. Environmental systems experience degradation, although there are some improvements and overall the intensity of resource and energy use declines. Even though fossil fuel dependency decreases slowly, there is no reluctance to use unconventional fossil resources. Global population growth is moderate and levels off in the second half of the century as a consequence of completion of the demographic transition. However, education investments are not high enough to accelerate the transition to low fertility rates in low-income countries and to rapidly slow population growth. This growth, along with income inequality that persists or improves only slowly, continuing societal stratification, and limited social cohesion, maintain challenges to reducing vulnerability to societal and environmental changes and constrain significant advances in sustainable development. These moderate development trends leave the world, on average, facing moderate challenges to mitigation and adaptation, but with significant heterogeneities across and within countries.” (O'Neill et al., 2015)</blockquote>

==== SSP3 - Regional rivalry - A rocky road ====
<blockquote>“A resurgent nationalism, concerns about competitiveness and security, and regional conflicts push countries to increasingly focus on domestic or, at most, regional issues. This trend is reinforced by the limited number of comparatively weak global institutions, with uneven coordination and cooperation for addressing environmental and other global concerns. Policies shift over time to become increasingly oriented toward national and regional security issues, including barriers to trade, particularly in the energy resource and agricultural markets. Countries focus on achieving energy and food security goals within their own regions at the expense of broader-based development, and in several regions move toward more authoritarian forms of government with highly regulated economies. Investments in education and technological development decline. Economic development is slow, consumption is material-intensive, and inequalities persist or worsen over time, especially in developing countries. There are pockets of extreme poverty alongside pockets of moderate wealth, with many countries struggling to maintain living standards and provide access to safe water, improved sanitation, and health care for disadvantaged populations. A low international priority for addressing environmental concerns leads to strong environmental degradation in some regions. The combination of impeded development and limited environmental concern results in poor progress toward sustainability. Population growth is low in industrialized and high in developing countries. Growing resource intensity and fossil fuel dependency along with difficulty in achieving international cooperation and slow technological change imply high challenges to mitigation. The limited progress on human development, slow income growth, and lack of effective institutions, especially those that can act across regions, implies high challenges to adaptation for many groups in all regions.” (O'Neill et al., 2015)</blockquote>

Socio-economic drivers - MESSAGE-GLOBIOM

2020-11-16T10:23:26Z

Anique Carbados: Adding SSP Narratives and updating to match 2020 release of MESSAGEix. More changes will be needed in the over-all structuring of this "Socio-economic drivers" section

Policy - MESSAGE-GLOBIOM

2020-11-16T10:14:09Z

Anique Carbados: Minor English edits, updated MESSAGE to MESSAGEix

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A number of different energy- and climate-related policies are, depending on the scenario setup and the research question, explicitly represented in MESSAGEix. This includes the following list of policies:

*GHG emission pricing
*GHG emission caps and trading permits
*Renewable energy portfolio standards (e.g., share of renewable energy in electricity generation)
*Renewable energy and other technology capacity targets
*Energy import taxes
*Fuel subsidies and micro-financing for achieving universal access to modern energy services in developing countries
*Air pollution legislation packages (fixed legislation, current and planned legislation, stringent legislation, maximum feasible reduction)

In general, these policies are implemented via constraints or cost coefficients (negative and positive) in the optimization problem. For air pollution policies, the different legislation packages are implemented via a set of emission coefficients and associated costs derived from the [[Pollutants_and_non-GHG_forcing_agents_-_MESSAGE-GLOBIOM|GAINS model]]. The cost coefficients are, however, not part of the optimization procedure, but instead allow an ex-post quantification of air pollution policy costs for a specific energy scenario.

Spatial dimension - MESSAGE-GLOBIOM

2020-11-12T14:44:56Z

Anique Carbados:

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The combined MESSAGEix-GLOBIOM framework has global coverage and divides the world into 11 regions which are also the native regions of the MESSAGEix model (see Table below). GLOBIOM itseld has 30 regions which are aggregated to the 11 MESSAGEix regions when the two models are linked. In some scenarios, the MESSAGEix region of FSU (Former Soviet Union) is disaggregated into four sub-regions resulting in a 14-region MESSAGEix model.

In addition to the 11 geographical regions, there is a global trade region in MESSAGEix for market clearing of global energy markets and which also represents international shipping bunker fuel demand, uranium resource extraction and the nuclear fuel cycle. <figure id="fig:MESSAGEix-GLOBIOM_regions">
[[File:35291297.png|left|800px|thumb|<caption>Map of 11 MESSAGEix-GLOBIOM regions</caption>]]
</figure>

<figtable id="tab:MESSAGEix-GLOBIOM_regions">
{| class="wikitable"
|+<caption>Country definitions of the 11 MESSAGEix regions</caption>
! 11 MESSAGE regions
! Definition
! List of countries
|-
| NAM
| North America
| Canada, Guam, Puerto Rico, United States of America, Virgin Islands
|-
| WEU
| Western Europe
| Andorra, Austria, Azores, Belgium, Canary Islands, Channel Islands, Cyprus, Denmark, Faeroe Islands, Finland, France, Germany, Gibraltar, Greece, Greenland, Iceland, Ireland, Isle of Man, Italy, Liechtenstein, Luxembourg, Madeira, Malta, Monaco, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, Turkey, United Kingdom
|-
| PAO
| Pacific OECD
| Australia, Japan, New Zealand
|-
| EEU
| Central and Eastern Europe
| Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, The former Yugoslav Rep. of Macedonia, Hungary, Poland, Romania, Slovak Republic, Slovenia, Yugoslavia, Estonia, Latvia, Lithuania
|-
| FSU
| Former Soviet Union
| Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Republic of Moldova, Russian Federation, Tajikistan, Turkmenistan, Ukraine, Uzbekistan
|-
| CPA
| Centrally Planned Asia and China
| Cambodia, China (incl. Hong Kong), Korea (DPR), Laos (PDR), Mongolia, Viet Nam
|-
| SAS
| South Asia
| Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, Sri Lanka
|-
| PAS
| Other Pacific Asia
| American Samoa, Brunei Darussalam, Fiji, French Polynesia, Gilbert-Kiribati, Indonesia, Malaysia, Myanmar, New Caledonia, Papua, New Guinea, Philippines, Republic of Korea, Singapore, Solomon Islands, Taiwan (China), Thailand, Tonga, Vanuatu, Western Samoa
|-
| MEA
| Middle East and North Africa
| Algeria, Bahrain, Egypt (Arab Republic), Iraq, Iran (Islamic Republic), Israel, Jordan, Kuwait, Lebanon, Libya/SPLAJ, Morocco, Oman, Qatar, Saudi Arabia, Sudan, Syria (Arab Republic), Tunisia, United Arab Emirates, Yemen
|-
| LAM
| Latin America and the Caribbean
| Antigua and Barbuda, Argentina, Bahamas, Barbados, Belize, Bermuda, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominica, Dominican Republic, Ecuador, El Salvador, French Guyana, Grenada, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Netherlands Antilles, Nicaragua, Panama, Paraguay, Peru, Saint Kitts and Nevis, Santa Lucia, Saint Vincent and the Grenadines, Suriname, Trinidad and Tobago, Uruguay, Venezuela
|-
| AFR
| Sub-Saharan Africa
| Angola, Benin, Botswana, British Indian Ocean Territory, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Comoros, Cote d’Ivoire, Congo, Democratic Republic of Congo, Djibouti, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Reunion, Rwanda, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, Saint Helena, Swaziland, Tanzania, Togo, Uganda, Zambia, Zimbabwe
|}
</figtable>

Spatial dimension - MESSAGE-GLOBIOM

2020-11-12T14:44:10Z

Anique Carbados: Updated MESSAGE to MESSAGEix

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The combined MESSAGEix-GLOBIOM framework has global coverage and divides the world into 11 regions which are also the native regions of the MESSAGEix model (see Table 1 below). GLOBIOM itseld has 30 regions which are aggregated to the 11 MESSAGEix regions when the two models are linked. In some scenarios, the MESSAGEix region of FSU (Former Soviet Union) is disaggregated into four sub-regions resulting in a 14-region MESSAGEix model.

In addition to the 11 geographical regions, there is a global trade region in MESSAGEix for market clearing of global energy markets and which also represents international shipping bunker fuel demand, uranium resource extraction and the nuclear fuel cycle. <figure id="fig:MESSAGEix-GLOBIOM_regions">
[[File:35291297.png|left|800px|thumb|<caption>Map of 11 MESSAGE-GLOBIOM regions</caption>]]
</figure>

<figtable id="tab:MESSAGEix-GLOBIOM_regions">
{| class="wikitable"
|+<caption>Country definitions of the 11 MESSAGE regions</caption>
! 11 MESSAGE regions
! Definition
! List of countries
|-
| NAM
| North America
| Canada, Guam, Puerto Rico, United States of America, Virgin Islands
|-
| WEU
| Western Europe
| Andorra, Austria, Azores, Belgium, Canary Islands, Channel Islands, Cyprus, Denmark, Faeroe Islands, Finland, France, Germany, Gibraltar, Greece, Greenland, Iceland, Ireland, Isle of Man, Italy, Liechtenstein, Luxembourg, Madeira, Malta, Monaco, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, Turkey, United Kingdom
|-
| PAO
| Pacific OECD
| Australia, Japan, New Zealand
|-
| EEU
| Central and Eastern Europe
| Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, The former Yugoslav Rep. of Macedonia, Hungary, Poland, Romania, Slovak Republic, Slovenia, Yugoslavia, Estonia, Latvia, Lithuania
|-
| FSU
| Former Soviet Union
| Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Republic of Moldova, Russian Federation, Tajikistan, Turkmenistan, Ukraine, Uzbekistan
|-
| CPA
| Centrally Planned Asia and China
| Cambodia, China (incl. Hong Kong), Korea (DPR), Laos (PDR), Mongolia, Viet Nam
|-
| SAS
| South Asia
| Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, Sri Lanka
|-
| PAS
| Other Pacific Asia
| American Samoa, Brunei Darussalam, Fiji, French Polynesia, Gilbert-Kiribati, Indonesia, Malaysia, Myanmar, New Caledonia, Papua, New Guinea, Philippines, Republic of Korea, Singapore, Solomon Islands, Taiwan (China), Thailand, Tonga, Vanuatu, Western Samoa
|-
| MEA
| Middle East and North Africa
| Algeria, Bahrain, Egypt (Arab Republic), Iraq, Iran (Islamic Republic), Israel, Jordan, Kuwait, Lebanon, Libya/SPLAJ, Morocco, Oman, Qatar, Saudi Arabia, Sudan, Syria (Arab Republic), Tunisia, United Arab Emirates, Yemen
|-
| LAM
| Latin America and the Caribbean
| Antigua and Barbuda, Argentina, Bahamas, Barbados, Belize, Bermuda, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominica, Dominican Republic, Ecuador, El Salvador, French Guyana, Grenada, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Netherlands Antilles, Nicaragua, Panama, Paraguay, Peru, Saint Kitts and Nevis, Santa Lucia, Saint Vincent and the Grenadines, Suriname, Trinidad and Tobago, Uruguay, Venezuela
|-
| AFR
| Sub-Saharan Africa
| Angola, Benin, Botswana, British Indian Ocean Territory, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Comoros, Cote d’Ivoire, Congo, Democratic Republic of Congo, Djibouti, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Reunion, Rwanda, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, Saint Helena, Swaziland, Tanzania, Togo, Uganda, Zambia, Zimbabwe
|}
</figtable>

Spatial dimension - MESSAGE-GLOBIOM

2020-11-12T14:42:59Z

Anique Carbados: Updated to match the 2020 release of MESSAGEix

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The combined MESSAGEix-GLOBIOM framework has global coverage and divides the world into 11 regions which are also the native regions of the MESSAGEix model (see Table 1 below). GLOBIOM itseld has 30 regions which are aggregated to the 11 MESSAGEix regions when the two models are linked. In some scenarios, the MESSAGEix region of FSU (Former Soviet Union) is disaggregated into four sub-regions resulting in a 14-region MESSAGEix model.

In addition to the 11 geographical regions, there is a global trade region in MESSAGEix for market clearing of global energy markets and which also represents international shipping bunker fuel demand, uranium resource extraction and the nuclear fuel cycle. <figure id="fig:MESSAGE-GLOBIOM_regions">
[[File:35291297.png|left|800px|thumb|<caption>Map of 11 MESSAGE-GLOBIOM regions</caption>]]
</figure>

<figtable id="tab:MESSAGE-GLOBIOM_regions">
{| class="wikitable"
|+<caption>Country definitions of the 11 MESSAGE regions</caption>
! 11 MESSAGE regions
! Definition
! List of countries
|-
| NAM
| North America
| Canada, Guam, Puerto Rico, United States of America, Virgin Islands
|-
| WEU
| Western Europe
| Andorra, Austria, Azores, Belgium, Canary Islands, Channel Islands, Cyprus, Denmark, Faeroe Islands, Finland, France, Germany, Gibraltar, Greece, Greenland, Iceland, Ireland, Isle of Man, Italy, Liechtenstein, Luxembourg, Madeira, Malta, Monaco, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, Turkey, United Kingdom
|-
| PAO
| Pacific OECD
| Australia, Japan, New Zealand
|-
| EEU
| Central and Eastern Europe
| Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, The former Yugoslav Rep. of Macedonia, Hungary, Poland, Romania, Slovak Republic, Slovenia, Yugoslavia, Estonia, Latvia, Lithuania
|-
| FSU
| Former Soviet Union
| Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Republic of Moldova, Russian Federation, Tajikistan, Turkmenistan, Ukraine, Uzbekistan
|-
| CPA
| Centrally Planned Asia and China
| Cambodia, China (incl. Hong Kong), Korea (DPR), Laos (PDR), Mongolia, Viet Nam
|-
| SAS
| South Asia
| Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, Sri Lanka
|-
| PAS
| Other Pacific Asia
| American Samoa, Brunei Darussalam, Fiji, French Polynesia, Gilbert-Kiribati, Indonesia, Malaysia, Myanmar, New Caledonia, Papua, New Guinea, Philippines, Republic of Korea, Singapore, Solomon Islands, Taiwan (China), Thailand, Tonga, Vanuatu, Western Samoa
|-
| MEA
| Middle East and North Africa
| Algeria, Bahrain, Egypt (Arab Republic), Iraq, Iran (Islamic Republic), Israel, Jordan, Kuwait, Lebanon, Libya/SPLAJ, Morocco, Oman, Qatar, Saudi Arabia, Sudan, Syria (Arab Republic), Tunisia, United Arab Emirates, Yemen
|-
| LAM
| Latin America and the Caribbean
| Antigua and Barbuda, Argentina, Bahamas, Barbados, Belize, Bermuda, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominica, Dominican Republic, Ecuador, El Salvador, French Guyana, Grenada, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Netherlands Antilles, Nicaragua, Panama, Paraguay, Peru, Saint Kitts and Nevis, Santa Lucia, Saint Vincent and the Grenadines, Suriname, Trinidad and Tobago, Uruguay, Venezuela
|-
| AFR
| Sub-Saharan Africa
| Angola, Benin, Botswana, British Indian Ocean Territory, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Comoros, Cote d’Ivoire, Congo, Democratic Republic of Congo, Djibouti, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Reunion, Rwanda, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, Saint Helena, Swaziland, Tanzania, Togo, Uganda, Zambia, Zimbabwe
|}
</figtable>

Temporal dimension - MESSAGE-GLOBIOM

2020-11-12T11:52:06Z

Anique Carbados: Updated the information to include GLOBIOM, and reflect MESSAGEix name change.

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|DocumentationCategory=Temporal dimension
}}
MESSAGEix models the time horizon from 2010 to 2110, generally in 10-year periods (2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100, 2110), using 2010 as the base year. The 2020 period is partly calibrated so far, with some recent trends included in this time period, but some flexibility remains. The reporting years are the final years in periods, which implies that investments that lead to the capacity in the reporting year are the average annual investments over the entire period the reporting year belongs to. In some model versions, the model has been calibrated to 2015, running with 5-year modeling periods to roughly the middle of the century (2020, 2025, 2030, 2035, 2040, 2045, 2050, 2055, 2060) and 10-year periods between 2060 and 2110.

MESSAGEix can both operate perfect foresight over the entire time horizon, limited foresight (e.g., two or three periods into the future) or myopically, optimizing one period at a time (Keppo and Strubegger, 2010 [[CiteRef::MSG-GLB_keppo_short_2010]]). Most frequently MESSAGEix is run with perfect foresight, but for specific applications such as delayed participation in a global climate regime without anticipation (Krey and Riahi, 2009 [[CiteRef::MSG-GLB_krey_implications_2009]]; O'Neill et al., 2010 [[CiteRef::MSG-GLB_oneill_mitigation_2010]]), limited foresight is used.

GLOBIOM models the time horizon from 2000 to 2100 in 10-year time steps (2000, 2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100), with the year 2000 as the base year. The model is recursive-dynamic, i.e. it is solved for each period individually and then passes on results to the subsequent periods. The linkage between MESSAGEix and GLOBIOM relies on the model results of the periods 2020 to 2100.

Model scope and methods - MESSAGE-GLOBIOM

2020-11-12T11:43:15Z

Anique Carbados: Added information to air pollution section, and updated last three paragraphs to include MESSAGEix 2020 update information

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=MESSAGE-GLOBIOM
|DocumentationCategory=Model scope and methods
}}
MESSAGEix represents the core of the IIASA IAM framework <xr id="fig:MESSAGE-GLOBIOM_iiasaiam"></xr> and its main task is to optimize the energy system so that it can satisfy specified energy demand at the lowest cost. MESSAGEix carries out this optimization in an iterative setup with MACRO, which provides estimates of the macro-economic demand response that results from energy system and services costs computed by MESSAGEix. For the six commercial end-use demand categories depicted in MESSAGEix (see [[Energy_demand_-_MESSAGE-GLOBIOM|Demand section of MESSAGEix-GLOBIOM]]), MACRO adjusts useful energy demand based on demand prices until the two models have reached equilibrium (see [[Macro-economy_-_MESSAGE-GLOBIOM|Macro-economy section of MESSAGEix-GLOBIOM]]). It thus reflects price-induced energy efficiency improvements that can occur when energy prices change. MESSAGEix can represent different energy- and climate-related policies (see [https://www.iamcdocumentation.eu/index.php/Policy_-_MESSAGE-GLOBIOM Policy section of MESSAGEix-GLOBIOM)].

GLOBIOM provides MESSAGEix with information on land use and its implications, like the availability and cost of bio-energy, and the availability and cost of emission mitigation in the AFOLU (Agriculture, Forestry and Land Use) sector (see [[Land-use_-_MESSAGE-GLOBIOM|Land-use of MESSAGEix-GLOBIOM]]). To reduce computational costs, MESSAGEix iteratively queries a GLOBIOM emulator which provides an approximation of land-use outcomes during the optimization process instead of requiring the GLOBIOM model to be rerun iteratively. Once the iteration between MESSAGEix and MACRO has converged, the resulting bioenergy demands along with corresponding carbon prices are used for a concluding analysis with the full-fledged GLOBIOM model. This ensures full consistency in the results from MESSAGEix and GLOBIOM, and also allows a more extensive set of land-use related indicators, including spatially explicit information on land use, to be reported.

Air pollution implications of the energy system are computed in MESSAGEix by applying technology-specific pollution coefficients from GAINS (see [[Pollutants_and_non-GHG_forcing_agents_-_MESSAGE-GLOBIOM|Pollutants and non-GHG forcing agents for MESSAGEix-GLOBIOM]] and [[Air_pollution_and_health_-_MESSAGE-GLOBIOM|Air pollution and health of MESSAGEix-GLOBIOM]]). This approach had been applied to the SSP process (Rao et al. 2017). Alternatively, GAINS can be run ex-post based on MESSAGEix-GLOBIOM scenarios to estimate air pollution emissions, concentrations and the related health impacts. This approach allows for the analysis of different air polllution policy packages (e.g., current legislation, maximum feasible reduction), including the estimation of costs for air pollution control measures. Examples for applying this way of linking MESSAGEix-GLOBIOM and GAINS can be found in McCollum et al (2018) and Grubler et al. (2018).

In general, cumulative global GHG emissions from all sectors are constrained at different levels, with equivalent pricing applied to other GHGs, to reach the desired radiative forcing levels (cf. right-hand side). <xr id="fig:MESSAGE-GLOBIOM_iiasaiam"></xr>The climate constraints are thus taken up in the coupled MESSAGEix-GLOBIOM optimization, and the resulting carbon price is fed back to the full-fledged GLOBIOM model for full consistency. Finally, the combined results for land use, energy, and industrial emissions from MESSAGEix and GLOBIOM are merged and fed into MAGICC (see [[Climate_-_MESSAGE-GLOBIOM|Climate of MESSAGEix-GLOBIOM]]), a global carbon-cycle and climate model, which then provides estimates of the climate implications in terms of atmospheric concentrations, radiative forcing, and global-mean temperature increase. Importantly, climate impacts and impacts of the carbon cycle are - depending on the specific application - only partly accounted for in the IIASA IAM framework. The entire framework is linked to an online database infrastructure which allows straightforward visualisation, analysis, comparison and dissemination of results (Riahi et al., 2017).

The scientific software underlying the global MESSAGEix-GLOBIOM model is called the MESSAGEix framework, an open-source, versatile implementation of a linear optimization problem, with the option of coupling to the computable general equilibrium (CGE) model MACRO to incorporate the effect of price changes on economic activity and demand for commodities and resources. MESSAGEix is integrated with the ''ix modelling platform (ixmp)'', a "data warehouse" for version control of reference timeseries, input data and model results. ixmp provides interfaces to the scientific programming languages Python and R for efficient, scripted workflows for data processing and visualisation of results (Huppmann et al., 2019).

<div style=" overflow: auto;">
<figure id="fig:MESSAGE-GLOBIOM_iiasaiam">
[[File:iiasaiam.png|left|900px|thumb|<caption>Overview of the IIASA IAM framework. Coloured boxes represent respective specialized disciplinary models which are integrated for generating internally consistent scenarios. Figure from Riahi et al. (2016).</caption>]] [[CiteRef::MSG-GLB_riahi_shared_2016]]
</figure>
</div>

Model scope and methods - MESSAGE-GLOBIOM

2020-11-10T15:01:49Z

Anique Carbados:

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=MESSAGE-GLOBIOM
|DocumentationCategory=Model scope and methods
}}
MESSAGEix represents the core of the IIASA IAM framework <xr id="fig:MESSAGE-GLOBIOM_iiasaiam"></xr> and its main task is to optimize the energy system so that it can satisfy specified energy demand at the lowest cost. MESSAGEix carries out this optimization in an iterative setup with MACRO, which provides estimates of the macro-economic demand response that results from energy system and services costs computed by MESSAGEix. For the six commercial end-use demand categories depicted in MESSAGEix (see [[Energy_demand_-_MESSAGE-GLOBIOM|Demand section of MESSAGEix-GLOBIOM]]), MACRO adjusts useful energy demand based on demand prices until the two models have reached equilibrium (see [[Macro-economy_-_MESSAGE-GLOBIOM|Macro-economy section of MESSAGEix-GLOBIOM]]). It thus reflects price-induced energy efficiency improvements that can occur when energy prices change. MESSAGEix can represent different energy- and climate-related policies (see [https://www.iamcdocumentation.eu/index.php/Policy_-_MESSAGE-GLOBIOM Policy section of MESSAGEix-GLOBIOM)].

GLOBIOM provides MESSAGEix with information on land use and its implications, like the availability and cost of bio-energy, and the availability and cost of emission mitigation in the AFOLU (Agriculture, Forestry and Land Use) sector (see [[Land-use_-_MESSAGE-GLOBIOM|Land-use of MESSAGEix-GLOBIOM]]). To reduce computational costs, MESSAGEix iteratively queries a GLOBIOM emulator which provides an approximation of land-use outcomes during the optimization process instead of requiring the GLOBIOM model to be rerun iteratively. Once the iteration between MESSAGEix and MACRO has converged, the resulting bioenergy demands along with corresponding carbon prices are used for a concluding analysis with the full-fledged GLOBIOM model. This ensures full consistency in the results from MESSAGEix and GLOBIOM, and also allows a more extensive set of land-use related indicators, including spatially explicit information on land use, to be reported.

Air pollution implications of the energy system are computed in MESSAGEix by applying technology-specific pollution coefficients from GAINS (see [[Pollutants_and_non-GHG_forcing_agents_-_MESSAGE-GLOBIOM|Pollutants and non-GHG forcing agents for MESSAGEix-GLOBIOM]] and [[Air_pollution_and_health_-_MESSAGE-GLOBIOM|Air pollution and health of MESSAGEix-GLOBIOM]]).

In general, cumulative global GHG emissions from all sectors are constrained at different levels to reach the forcing levels (cf. right-hand side). <xr id="fig:MESSAGE-GLOBIOM_iiasaiam"></xr>The climate constraints are thus taken up in the coupled MESSAGE-GLOBIOM optimization, and the resulting carbon price is fed back to the full-fledged GLOBIOM model for full consistency. Finally, the combined results for land use, energy, and industrial emissions from MESSAGE and GLOBIOM are merged and fed into MAGICC (see [[Climate_-_MESSAGE-GLOBIOM|Climate of MESSAGE-GLOBIOM]]), a global carbon-cycle and climate model, which then provides estimates of the climate implications in terms of atmospheric concentrations, radiative forcing, and global-mean temperature increase. Importantly, climate impacts and impacts of the carbon cycle are currently not accounted for in the IIASA IAM framework. The entire framework is linked to an online database infrastructure which allows straightforward visualisation, analysis, comparison and dissemination of results (Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]]).

<div style=" overflow: auto;">
<figure id="fig:MESSAGE-GLOBIOM_iiasaiam">
[[File:iiasaiam.png|left|900px|thumb|<caption>Overview of the IIASA IAM framework. Coloured boxes represent respective specialized disciplinary models which are integrated for generating internally consistent scenarios. Figure from Riahi et al. (2016).</caption>]] [[CiteRef::MSG-GLB_riahi_shared_2016]]
</figure>
</div>

Model scope and methods - MESSAGE-GLOBIOM

2020-11-05T11:53:23Z

Anique Carbados: Updated paragraph to match MESSAGEix 2020 release documentation.

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=MESSAGE-GLOBIOM
|DocumentationCategory=Model scope and methods
}}
MESSAGEix represents the core of the IIASA IAM framework <xr id="fig:MESSAGE-GLOBIOM_iiasaiam"></xr> and its main task is to optimize the energy system so that it can satisfy specified energy demand at the lowest cost. MESSAGEix carries out this optimization in an iterative setup with MACRO, which provides estimates of the macro-economic demand response that results from energy system and services costs computed by MESSAGEix. For the six commercial end-use demand categories depicted in MESSAGEix (see [[Energy_demand_-_MESSAGE-GLOBIOM|Demand section of MESSAGEix-GLOBIOM]]), MACRO adjusts useful energy demand based on demand prices until the two models have reached equilibrium (see [[Macro-economy_-_MESSAGE-GLOBIOM|Macro-economy section of MESSAGEix-GLOBIOM]]). It thus reflects price-induced energy efficiency improvements that can occur when energy prices change. MESSAGEix can represent different energy- and climate-related policies (see [https://www.iamcdocumentation.eu/index.php/Policy_-_MESSAGE-GLOBIOM Policy section of MESSAGEix-GLOBIOM)].

GLOBIOM provides MESSAGEix with information on land use and its implications, like the availability and cost of bio-energy, and the availability and cost of emission mitigation in the AFOLU (Agriculture, Forestry and Land Use) sector (see [[Land-use_-_MESSAGE-GLOBIOM|Land-use of MESSAGEix-GLOBIOM]]). To reduce computational costs, MESSAGEix iteratively queries a GLOBIOM emulator which provides an approximation of land-use outcomes during the optimization process instead of requiring the GLOBIOM model to be rerun iteratively. Once the iteration between MESSAGEix and MACRO has converged, the resulting bioenergy demands along with corresponding carbon prices are used for a concluding analysis with the full-fledged GLOBIOM model. This ensures full consistency in the results from MESSAGEix and GLOBIOM, and also allows a more extensive set of land-use related indicators, including spatially explicit information on land use, to be reported.

Air pollution implications of the energy system are computed in MESSAGEix by applying technology-specific pollution coefficients from GAINS (see [[Pollutants_and_non-GHG_forcing_agents_-_MESSAGE-GLOBIOM|Pollutants and non-GHG forcing agents for MESSAGEix-GLOBIOM]] and [[Air_pollution_and_health_-_MESSAGE-GLOBIOM|Air pollution and health of MESSAGEix-GLOBIOM]]).

In general, cumulative global GHG emissions from all sectors are constrained at different levels to reach the forcing levels (cf. right-hand side <xr id="fig:MESSAGE-GLOBIOM_iiasaiam"></xr>). The climate constraints are thus taken up in the coupled MESSAGE-GLOBIOM optimization, and the resulting carbon price is fed back to the full-fledged GLOBIOM model for full consistency. Finally, the combined results for land use, energy, and industrial emissions from MESSAGE and GLOBIOM are merged and fed into MAGICC (see [[Climate_-_MESSAGE-GLOBIOM|Climate of MESSAGE-GLOBIOM]]), a global carbon-cycle and climate model, which then provides estimates of the climate implications in terms of atmospheric concentrations, radiative forcing, and global-mean temperature increase. Importantly, climate impacts and impacts of the carbon cycle are currently not accounted for in the IIASA IAM framework. The entire framework is linked to an online database infrastructure which allows straightforward visualisation, analysis, comparison and dissemination of results (Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]]).

<div style=" overflow: auto;">
<figure id="fig:MESSAGE-GLOBIOM_iiasaiam">
[[File:iiasaiam.png|left|900px|thumb|<caption>Overview of the IIASA IAM framework. Coloured boxes represent respective specialized disciplinary models which are integrated for generating internally consistent scenarios. Figure from Riahi et al. (2016).</caption>]] [[CiteRef::MSG-GLB_riahi_shared_2016]]
</figure>
</div>

Model scope and methods - MESSAGE-GLOBIOM

2020-11-05T11:20:27Z

Anique Carbados: Edited link to direct to correct page.

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=MESSAGE-GLOBIOM
|DocumentationCategory=Model scope and methods
}}
MESSAGEix represents the core of the IIASA IAM framework <xr id="fig:MESSAGE-GLOBIOM_iiasaiam"></xr> and its main task is to optimize the energy system so that it can satisfy specified energy demand at the lowest cost. MESSAGEix carries out this optimization in an iterative setup with MACRO, which provides estimates of the macro-economic demand response that results from energy system and services costs computed by MESSAGEix. For the six commercial end-use demand categories depicted in MESSAGEix (see [[Energy_demand_-_MESSAGE-GLOBIOM|Demand of MESSAGE-GLOBIOM]]), MACRO adjusts useful energy demand based on demand prices until the two models have reached equilibrium (see [[Macro-economy_-_MESSAGE-GLOBIOM|Macro-economy section of MESSAGE-GLOBIOM]]). It thus reflects price-induced energy efficiency improvements that can occur when energy prices change. MESSAGEix can represent different energy- and climate-related policies (see [https://www.iamcdocumentation.eu/index.php/Policy_-_MESSAGE-GLOBIOM Policy section of MESSAGEix-GLOBIOM)].

GLOBIOM provides MESSAGEix with information on land use and its implications, like the availability and cost of bio-energy, and availability and cost of emission mitigation in the AFOLU (Agriculture, Forestry and Land Use) sector (see [[Land-use_-_MESSAGE-GLOBIOM|Land-use of MESSAGE-GLOBIOM]]). To reduce computational costs, MESSAGEix iteratively queries a GLOBIOM emulator which can provide possible land-use outcomes during the optimization process instead of requiring the GLOBIOM model to be rerun continuously. Only once the iteration between MESSAGEix and MACRO has converged, the resulting bioenergy demands along with corresponding carbon prices are used for a concluding online analysis with the full-fledged GLOBIOM model. This ensures full consistency in the modelled results from MESSAGEix and GLOBIOM, and also allows the production of a more extensive set of reporting variables.

Air pollution implications of the energy system are computed in MESSAGEix by applying technology-specific pollution coefficients from GAINS (see [[Pollutants_and_non-GHG_forcing_agents_-_MESSAGE-GLOBIOM|Pollutants and non-GHG forcing agents for MESSAGE-GLOBIOM]] and [[Air_pollution_and_health_-_MESSAGE-GLOBIOM|Air pollution and health of MESSAGE-GLOBIOM]]).

In general, cumulative global GHG emissions from all sectors are constrained at different levels to reach the forcing levels (cf. right-hand side <xr id="fig:MESSAGE-GLOBIOM_iiasaiam"></xr>). The climate constraints are thus taken up in the coupled MESSAGE-GLOBIOM optimization, and the resulting carbon price is fed back to the full-fledged GLOBIOM model for full consistency. Finally, the combined results for land use, energy, and industrial emissions from MESSAGE and GLOBIOM are merged and fed into MAGICC (see [[Climate_-_MESSAGE-GLOBIOM|Climate of MESSAGE-GLOBIOM]]), a global carbon-cycle and climate model, which then provides estimates of the climate implications in terms of atmospheric concentrations, radiative forcing, and global-mean temperature increase. Importantly, climate impacts and impacts of the carbon cycle are currently not accounted for in the IIASA IAM framework. The entire framework is linked to an online database infrastructure which allows straightforward visualisation, analysis, comparison and dissemination of results (Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]]).

<div style=" overflow: auto;">
<figure id="fig:MESSAGE-GLOBIOM_iiasaiam">
[[File:iiasaiam.png|left|900px|thumb|<caption>Overview of the IIASA IAM framework. Coloured boxes represent respective specialized disciplinary models which are integrated for generating internally consistent scenarios. Figure from Riahi et al. (2016).</caption>]] [[CiteRef::MSG-GLB_riahi_shared_2016]]
</figure>
</div>

Model scope and methods - MESSAGE-GLOBIOM

2020-11-05T11:19:09Z

Anique Carbados: Adding link to Policy section

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=MESSAGE-GLOBIOM
|DocumentationCategory=Model scope and methods
}}
MESSAGEix represents the core of the IIASA IAM framework <xr id="fig:MESSAGE-GLOBIOM_iiasaiam"></xr> and its main task is to optimize the energy system so that it can satisfy specified energy demand at the lowest cost. MESSAGEix carries out this optimization in an iterative setup with MACRO, which provides estimates of the macro-economic demand response that results from energy system and services costs computed by MESSAGEix. For the six commercial end-use demand categories depicted in MESSAGEix (see [[Energy_demand_-_MESSAGE-GLOBIOM|Demand of MESSAGE-GLOBIOM]]), MACRO adjusts useful energy demand based on demand prices until the two models have reached equilibrium (see [[Macro-economy_-_MESSAGE-GLOBIOM|Macro-economy section of MESSAGE-GLOBIOM]]). It thus reflects price-induced energy efficiency improvements that can occur when energy prices change. MESSAGEix can represent different energy- and climate-related policies (see [[Policy section of MESSAGEix-GLOBIOM)]].

GLOBIOM provides MESSAGEix with information on land use and its implications, like the availability and cost of bio-energy, and availability and cost of emission mitigation in the AFOLU (Agriculture, Forestry and Land Use) sector (see [[Land-use_-_MESSAGE-GLOBIOM|Land-use of MESSAGE-GLOBIOM]]). To reduce computational costs, MESSAGEix iteratively queries a GLOBIOM emulator which can provide possible land-use outcomes during the optimization process instead of requiring the GLOBIOM model to be rerun continuously. Only once the iteration between MESSAGEix and MACRO has converged, the resulting bioenergy demands along with corresponding carbon prices are used for a concluding online analysis with the full-fledged GLOBIOM model. This ensures full consistency in the modelled results from MESSAGEix and GLOBIOM, and also allows the production of a more extensive set of reporting variables.

Air pollution implications of the energy system are computed in MESSAGEix by applying technology-specific pollution coefficients from GAINS (see [[Pollutants_and_non-GHG_forcing_agents_-_MESSAGE-GLOBIOM|Pollutants and non-GHG forcing agents for MESSAGE-GLOBIOM]] and [[Air_pollution_and_health_-_MESSAGE-GLOBIOM|Air pollution and health of MESSAGE-GLOBIOM]]).

In general, cumulative global GHG emissions from all sectors are constrained at different levels to reach the forcing levels (cf. right-hand side <xr id="fig:MESSAGE-GLOBIOM_iiasaiam"></xr>). The climate constraints are thus taken up in the coupled MESSAGE-GLOBIOM optimization, and the resulting carbon price is fed back to the full-fledged GLOBIOM model for full consistency. Finally, the combined results for land use, energy, and industrial emissions from MESSAGE and GLOBIOM are merged and fed into MAGICC (see [[Climate_-_MESSAGE-GLOBIOM|Climate of MESSAGE-GLOBIOM]]), a global carbon-cycle and climate model, which then provides estimates of the climate implications in terms of atmospheric concentrations, radiative forcing, and global-mean temperature increase. Importantly, climate impacts and impacts of the carbon cycle are currently not accounted for in the IIASA IAM framework. The entire framework is linked to an online database infrastructure which allows straightforward visualisation, analysis, comparison and dissemination of results (Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]]).

<div style=" overflow: auto;">
<figure id="fig:MESSAGE-GLOBIOM_iiasaiam">
[[File:iiasaiam.png|left|900px|thumb|<caption>Overview of the IIASA IAM framework. Coloured boxes represent respective specialized disciplinary models which are integrated for generating internally consistent scenarios. Figure from Riahi et al. (2016).</caption>]] [[CiteRef::MSG-GLB_riahi_shared_2016]]
</figure>
</div>

Model scope and methods - MESSAGE-GLOBIOM

2020-11-05T11:17:15Z

Anique Carbados: Edited first paragraph to match the 2020 release documentation on Read the Docs. Updated MESSAGE name to MESSAGEix

{{ModelDocumentationTemplate
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|IsDocumentationOf=MESSAGE-GLOBIOM
|DocumentationCategory=Model scope and methods
}}
MESSAGEix represents the core of the IIASA IAM framework <xr id="fig:MESSAGE-GLOBIOM_iiasaiam"></xr> and its main task is to optimize the energy system so that it can satisfy specified energy demand at the lowest cost. MESSAGEix carries out this optimization in an iterative setup with MACRO, which provides estimates of the macro-economic demand response that results from energy system and services costs computed by MESSAGEix. For the six commercial end-use demand categories depicted in MESSAGEix (see [[Energy_demand_-_MESSAGE-GLOBIOM|Demand of MESSAGE-GLOBIOM]]), MACRO adjusts useful energy demand based on demand prices until the two models have reached equilibrium (see [[Macro-economy_-_MESSAGE-GLOBIOM|Macro-economy section of MESSAGE-GLOBIOM]]). It thus reflects price-induced energy efficiency improvements that can occur when energy prices change. MESSAGEix can represent different energy- and climate-related policies.

GLOBIOM provides MESSAGEix with information on land use and its implications, like the availability and cost of bio-energy, and availability and cost of emission mitigation in the AFOLU (Agriculture, Forestry and Land Use) sector (see [[Land-use_-_MESSAGE-GLOBIOM|Land-use of MESSAGE-GLOBIOM]]). To reduce computational costs, MESSAGEix iteratively queries a GLOBIOM emulator which can provide possible land-use outcomes during the optimization process instead of requiring the GLOBIOM model to be rerun continuously. Only once the iteration between MESSAGEix and MACRO has converged, the resulting bioenergy demands along with corresponding carbon prices are used for a concluding online analysis with the full-fledged GLOBIOM model. This ensures full consistency in the modelled results from MESSAGEix and GLOBIOM, and also allows the production of a more extensive set of reporting variables.

Air pollution implications of the energy system are computed in MESSAGEix by applying technology-specific pollution coefficients from GAINS (see [[Pollutants_and_non-GHG_forcing_agents_-_MESSAGE-GLOBIOM|Pollutants and non-GHG forcing agents for MESSAGE-GLOBIOM]] and [[Air_pollution_and_health_-_MESSAGE-GLOBIOM|Air pollution and health of MESSAGE-GLOBIOM]]).

In general, cumulative global GHG emissions from all sectors are constrained at different levels to reach the forcing levels (cf. right-hand side <xr id="fig:MESSAGE-GLOBIOM_iiasaiam"></xr>). The climate constraints are thus taken up in the coupled MESSAGE-GLOBIOM optimization, and the resulting carbon price is fed back to the full-fledged GLOBIOM model for full consistency. Finally, the combined results for land use, energy, and industrial emissions from MESSAGE and GLOBIOM are merged and fed into MAGICC (see [[Climate_-_MESSAGE-GLOBIOM|Climate of MESSAGE-GLOBIOM]]), a global carbon-cycle and climate model, which then provides estimates of the climate implications in terms of atmospheric concentrations, radiative forcing, and global-mean temperature increase. Importantly, climate impacts and impacts of the carbon cycle are currently not accounted for in the IIASA IAM framework. The entire framework is linked to an online database infrastructure which allows straightforward visualisation, analysis, comparison and dissemination of results (Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]]).

<div style=" overflow: auto;">
<figure id="fig:MESSAGE-GLOBIOM_iiasaiam">
[[File:iiasaiam.png|left|900px|thumb|<caption>Overview of the IIASA IAM framework. Coloured boxes represent respective specialized disciplinary models which are integrated for generating internally consistent scenarios. Figure from Riahi et al. (2016).</caption>]] [[CiteRef::MSG-GLB_riahi_shared_2016]]
</figure>
</div>

Model Documentation - MESSAGE-GLOBIOM

2020-11-05T10:46:24Z

Anique Carbados: Updated MESSAGE to MESSAGEix

{{ModelDocumentationTemplate
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|DocumentationCategory=Model Documentation
}}
The IIASA IAM framework consists of a combination of five different models or modules - the energy model MESSAGEix, the land use model GLOBIOM, the air pollution and GHG model GAINS, the aggregated macro-economic model MACRO, and the simple climate model MAGICC - which complement each other and are specialized in different areas. All models and modules together build the IIASA IAM framework, also referred to as MESSAGEix-GLOBIOM because the energy model MESSAGEix and the land use model GLOBIOM are its most important components. The five models provide input to and iterate between each other during a typical SSP scenario development cycle.

Model Documentation - MESSAGE-GLOBIOM

2020-11-05T10:45:52Z

Anique Carbados: I renamed the MESSAGE model to MESSAGEix in line with the 2020 release and made minor changes for concision.

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=MESSAGE-GLOBIOM
|DocumentationCategory=Model Documentation
}}
The IIASA IAM framework consists of a combination of five different models or modules - the energy model MESSAGEix, the land use model GLOBIOM, the air pollution and GHG model GAINS, the aggregated macro-economic model MACRO, and the simple climate model MAGICC - which complement each other and are specialized in different areas. All models and modules together build the IIASA IAM framework, also referred to as MESSAGEix-GLOBIOM because the energy model MESSAGE and the land use model GLOBIOM are its most important components. The five models provide input to and iterate between each other during a typical SSP scenario development cycle.