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==Transport==
The most commonly applied MESSAGE transport sector representation is very stylized and essentially includes fuel switching and price-elastic demands (via MACRO linkage) as the main responses to energy and climate policy (see <xr id="fig:MESSAGE-GLOBIOM_trans"/>).
In this stylized transport sector representation fuel switching is a main option, i.e. different final energy forms that provide energy for transportation can be chosen from. In addition to the alternative energy carriers that serve as input to these stylized transportation options, their relative efficiencies are also different. The useful energy demand in the transportation sector is specified as internal combustion engine (ICE) equivalent demands which therefore by definition has a conversion efficiency of final to useful energy of 1. Relative to that the conversion efficiency of alternative fuels is higher, for example, electricity in 2010 has about a factor of three higher final to useful efficiency than the regular oil-product based ICE. The overall efficiency improvements of the ICE in the transportation sector and modal switching over time is implicitly included in the demand specifications, coming from the scenario generator (see section on '''demand'''). Additional demand reduction in response to price increases in policy scenarios then occurs via the fuel switching option (due to the fuel-specific relative efficiencies) as well as via the linkage with the macro-economic model MACRO as illustrated in <xr id="fig:MESSAGE-GLOBIOM_trans"/> below.
Limitations of switching to alternative fuels may occur for example as a result of restricted infrastructure availability (e.g., rail network) or some energy carriers being unsuitable for certain transport modes (e.g., electrification of aviation). To reflect these limitations, share constraints of energy carriers (e.g., electricity) and energy carrier groups (e.g., liquid fuels) are used in the transport sector. In addition, the diffusion of speed of alternative fuels is limited to mimic bottlenecks in the supply chains, not explicitly represented in MESSAGE (e.g., non-energy related infrastructure). Both the share as well as the diffusion constraints are usually parametrized based on transport sector studies that analyze such developments and their feasibility in much greater detail.
<div style=" overflow: auto;">
<figure id="fig:MESSAGE-GLOBIOM_trans">
[[File:35291304.png|left|thumb|<caption>Schematic diagram of the stylized transport sector representation in MESSAGE</caption>]]
</figure>
</div>
The demand for international shipping is modeled in a simplified way with a number of different energy carrier options (light and heavy fuel oil, biofuels, natural gas, and hydrogen). The demand for international shipping is coupled to global GDP development with an income elasticity, but to date no demand response in mitigation scenarios is implemented.
<xr id="tab:MESSAGE-GLOBIOM_trans"/> presents the quantitative translation of the the storyline elements of SSP1, SSP2 and SSP3 in terms of electrification rate for transport (Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]]).
<figtable id="tab:MESSAGE-GLOBIOM_trans">
{| class="wikitable"
|+<caption>Electrification rate within transport for SSP1, SSP2 and SSP3 (Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]]). The indicators apply to 2010-2100; Intensity improvements are presented in Final Energy (FE)/GDP annually</caption>
!
! SSP1
! SSP2
! SSP3
|-
| Transport
| High electrification (max. 75% of total transport possible)
| Medium electrification (max. 50% of total transport possible)
| Low electrification (max 10% of total transport possible)
|}
</figtable>
==Residential and commercial sectors==
The residential and commercial sector in MESSAGE distinguishes two demand categories, thermal and specific. Thermal demand, i.e. low temperature heat, can be supplied by a variety of different energy carriers while specific demand requires electricity (or a decentralized technology to convert other energy carriers to electricity).
This stylized residential and commercial thermal energy demand includes fuel switching as the main option, i.e. different choices about final energy forms to provide thermal energy. In addition to the alternative energy carriers that serve as input to these thermal energy supply options, their relative efficiencies also vary. For example, solid fuels such as coal have lower conversion efficiencies than natural gas, direct electric heating or electric heat pumps. Additional demand reduction in response to price increases in policy scenarios is included via the fuel switching option (due to the fuel-specific relative efficiencies) as well as via the linkage with the macro-economic model MACRO (see <xr id="fig:MESSAGE-GLOBIOM_rescom"/> below). The specific residential and commercial demand can be satisfied either by electricity from the grid or with decentralized electricity generation options such as fuel cells or CHP.
<div style=" overflow: auto;">
<figure id="fig:MESSAGE-GLOBIOM_rescom">
[[File:35815641.png|left|thumb|<caption>Schematic diagram of the residential and commercial sector representation in MESSAGE</caption>]]
</figure>
</div>
To reflect limitations of switching to alternative fuels, for example as a result of limited infrastructure availability (e.g., district heating network) or some energy carriers being unsuitable for certain applications, share constraints of energy carriers (e.g., electricity) and energy carrier groups (e.g., liquid fuels) are used in the residential and commercial sector. In addition, the diffusion of speed of alternative fuels is limited to mimic bottlenecks in the supply chains, not explicitly represented in MESSAGE (e.g., non-energy related infrastructure).
<xr id="tab:MESSAGE-GLOBIOM_rescom"/> presents the quantitative translation of the the storyline elements of SSP1, SSP2 and SSP3 in terms of electrification rate for the residential and commercial sectors. These indicators apply to 2010-2100; Intensity improvements are in FE/GDP annually (Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]]).
<figtable id="tab:MESSAGE-GLOBIOM_rescom">
{| class="wikitable"
|+<caption>Electrification rate within the residential and commercial sectors for SSP1, SSP2 and SSP3 (Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]])</caption>
!
! SSP1
! SSP2
! SSP3
|-
| Residential & Commercial
| High electrification rate:1.44% (Regional range from 0.35% to 4%)
| Medium electrification rate: 1.07% (Regional range from 0.23% to 3%)
| Low electrification rate: 0.87% (Regional range from 0.37% to 2%)
|}
</figtable>
==Industrial sector==
The industrial sector in MESSAGE distinguishes two demand categories, thermal and specific. Thermal demand, i.e. heat at different temperature levels, can be supplied by a variety of different energy carriers while specific demand requires electricity (or a decentralized technology to convert other energy carriers to electricity).
This stylized industrial thermal energy demand includes fuel switching as the main option, i.e. different final energy forms that provide energy for thermal energy can be chosen from. In addition to the alternative energy carriers that serve as input to these thermal energy supply options, their relative efficiencies also vary. For example, solid fuels such as coal have lower conversion efficiencies than natural gas, direct electric heating or electric heat pumps. To account for the fact that some technologies cannot supply temperature at high temperature levels (e.g., electric heat pumps, district heat), the share of these technologies in the provision of industrial thermal demand is constrained. Additional demand reduction in response to price increases in policy scenarios is included via the fuel switching option (due to the fuel-specific relative efficiencies) as well as via the linkage with the macro-economic model MACRO (see <xr id="fig:MESSAGE-GLOBIOM_indus"/> below). The specific industrial demand can be satisfied either by electricity from the grid or with decentralized electricity generation options (including CHP) such as fuel cells.
<div style=" overflow: auto;">
<figure id="fig:MESSAGE-GLOBIOM_indus">
[[File:35815642.png|left|thumb|<caption>Schematic diagram of the industrial sector representation in MESSAGE</caption>]]
</figure>
</div>
While cement production is not explicitly modeled at the process level in MESSAGE, the amount of cement of cement production is linked to industrial activity (more specifically the
industrial thermal demand in MESSAGE) and the associated CO2 emissions from the calcination process are accounted for explicitly. In addition, adding carbon capture and storage to
mitigate these process-based CO2 emission is available.
<xr id="tab:MESSAGE-GLOBIOM_indus"/> presents the quantitative translation of the the storyline elements of SSP1, SSP2 and SSP3 in terms of electrification rate for industry and feedstocks. These indicators apply to 2010-2100; Intensity improvements are in FE/GDP annually (Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]]).
.. _tab-indus:
.. table::
<figtable id="tab:MESSAGE-GLOBIOM_indus">
{| class="wikitable"
|+<caption>Electrification rate within industry and feedstocks for SSP1, SSP2 and SSP3 (Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]])</caption>
!
! SSP1
! SSP2
! SSP3
|-
| Industry and feedstocks
| High electrification rate: 0.56% (Regional range from 0.2% to 1.2%)
High feedstock reduction rate: -0.33% (Regional range from -0.51 to 0.59%)
| Medium electrification rate: 0.47% (Regional range from 0.07% to 1.08%)
Medium feedstock reduction rate: -0.27% (Regional range from -0.45% to 0.64%)
| Low electrification rate: 0.12% (Regional range from -0.03% to 0.71%)
Low feedstock reduction rate: -0.24% (Regional range from -0.38% to 0.51%)
|}

Latest revision as of 16:14, 23 June 2020

Model Documentation - MESSAGE-GLOBIOM

Corresponding documentation
Previous versions
Model information
Model link
Institution International Institute for Applied Systems Analysis (IIASA), Austria, http://data.ene.iiasa.ac.at.
Solution concept General equilibrium (closed economy)
Solution method Optimization
Anticipation
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