From f8f6bdfcf0026479d15e73fdfecfd4a7e0ffc71e Mon Sep 17 00:00:00 2001 From: Fei Date: Fri, 16 Aug 2019 17:46:44 +0200 Subject: [PATCH 1/2] Revision of the Section "Energy end-use" and " Socio-economic Development" 1) minor language editing and typo correction 2) term consistency (like MESSAGE and MESSAGEix, etc.) --- source/energy/enduse/index.rst | 2 +- source/energy/enduse/industrial.rst | 10 +++++----- source/energy/enduse/resid_commerc.rst | 6 +++--- source/energy/enduse/transport.rst | 10 +++++----- source/socio_econ/beh_change.rst | 10 +++++----- source/socio_econ/narratives.rst | 2 +- source/socio_econ/pop_GDP.rst | 4 ++-- 7 files changed, 22 insertions(+), 22 deletions(-) diff --git a/source/energy/enduse/index.rst b/source/energy/enduse/index.rst index bb3aca9..284d87b 100644 --- a/source/energy/enduse/index.rst +++ b/source/energy/enduse/index.rst @@ -1,6 +1,6 @@ Energy end-use ================= -MESSAGE distinguishes three energy end-use sectors, i.e. transport, residential/commercial (also referred to as the buildings sector) and industry. Given the long-term nature of the scenarios, the model version used for the SSPs, represents these end-use sectors in a stylized way. For more detailed short-term analysis, a model version with a more detailed transport sector module that distinguishes different transport modes, vehicle classes amd consumer types exists (McCollum et al., 2016 :cite:`mccollum_transport_2016`). +MESSAGEix distinguishes three energy end-use sectors, i.e. transport, residential/commercial (also referred to as the buildings sector) and industry. Given the long-term nature of the scenarios, the model version used for the SSPs, represents these end-use sectors in a stylized way. For more detailed short-term analysis, a model version with a more detailed transport sector module that distinguishes different transport modes, vehicle classes amd consumer types exists (McCollum et al., 2016 :cite:`mccollum_transport_2016`). .. toctree:: :maxdepth: 1 diff --git a/source/energy/enduse/industrial.rst b/source/energy/enduse/industrial.rst index 6c53d36..14f430c 100755 --- a/source/energy/enduse/industrial.rst +++ b/source/energy/enduse/industrial.rst @@ -2,17 +2,17 @@ Industrial sector ----------------- -Similar to the residential and commercial sectors, 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). +Similar to the residential and commercial sectors, the industrial sector in MESSAGEix 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 :numref:`fig-indus` below). The specific industrial demand can be satisfied either by electricity from the grid or with decentralized electricity generation options such as fuel cells (including CHP). +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 :numref:`fig-indus` below). The specific industrial demand can be satisfied either by electricity from the grid or with decentralized electricity generation options such as fuel cells (including CHP). .. _fig-indus: .. figure:: /_static/industry_end-use.png - Schematic diagram of the industrial sector representation in MESSAGE. + Schematic diagram of the industrial sector representation in MESSAGEix. -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 +While cement production is not explicitly modeled at the process level in MESSAGEix, the amount of cement production is linked to industrial activity (more specifically the +industrial thermal demand in MESSAGEix) 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. :numref:`tab-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 :cite:`fricko_marker_2016`). diff --git a/source/energy/enduse/resid_commerc.rst b/source/energy/enduse/resid_commerc.rst index 3f0fb3a..f45fa01 100755 --- a/source/energy/enduse/resid_commerc.rst +++ b/source/energy/enduse/resid_commerc.rst @@ -2,16 +2,16 @@ 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). +The residential and commercial sector in MESSAGEix 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). The 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 :numref:`fig-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, including on-site CHP. .. _fig-rescom: .. figure:: /_static/residential-commercial_end-use.png - Schematic diagram of the residential and commercial sector representation in MESSAGE. + Schematic diagram of the residential and commercial sector representation in MESSAGEix. -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, as in the transport sector, the diffusion speed of alternative fuels is limited to mimic bottlenecks in the supply chains, not explicitly represented in MESSAGE (e.g., non-energy related infrastructure). +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, as in the transport sector, the diffusion speed of alternative fuels is limited to mimic bottlenecks in the supply chains, not explicitly represented in MESSAGEix (e.g., non-energy related infrastructure). :numref:`tab-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 :cite:`fricko_marker_2016`). diff --git a/source/energy/enduse/transport.rst b/source/energy/enduse/transport.rst index c9c535d..c538959 100755 --- a/source/energy/enduse/transport.rst +++ b/source/energy/enduse/transport.rst @@ -2,16 +2,16 @@ Transport sector ---------------- -The most commonly applied MESSAGE transport sector representation is stylized and essentially includes fuel switching and price-elastic demands (via MACRO linkage) as the main responses to energy and climate policy (see :numref:`fig-trans`). +The most commonly applied MESSAGEix transport sector representation is stylized and essentially includes fuel switching and price-elastic demands (via MACRO linkage) as the main responses to energy and climate policy (see :numref:`fig-trans`). -In this stylized transport sector representation fuel switching is a key option to reduce emissions, 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 :numref:`fig-trans` below. +In this stylized transport sector representation fuel switching is a key option to reduce emissions, 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 :numref:`fig-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 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. +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 speed of alternative fuels is limited to mimic bottlenecks in the supply chains, not explicitly represented in MESSAGEix (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. .. _fig-trans: .. figure:: /_static/transport_end-use.png - Schematic diagram of the stylized transport sector representation in MESSAGE. + Schematic diagram of the stylized transport sector representation in MESSAGEix. 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. @@ -24,5 +24,5 @@ The demand for international shipping is modeled in a simplified way with a numb | | **SSP1** | **SSP2** | **SSP3** | +---------------+----------------------------------------+----------------------------------------+---------------------------------------+ | **Transport** | High electrification | Medium electrification | Low electrification | - | | (max. 75% of total transport possible) | (max. 50% of total transport possible) | (max 10% of total transport possible) | + | | (max. 75% of total transport possible) | (max. 50% of total transport possible) | (max. 10% of total transport possible) | +---------------+----------------------------------------+----------------------------------------+---------------------------------------+ diff --git a/source/socio_econ/beh_change.rst b/source/socio_econ/beh_change.rst index ecaea5e..e8d5c87 100644 --- a/source/socio_econ/beh_change.rst +++ b/source/socio_econ/beh_change.rst @@ -2,15 +2,15 @@ Behavioural change ==================== -With increasing affluence, consumers of final energy are more likely to demand technologies that are more convenient in their use, even if they cost more than less convenient energy forms. Examples of this empirically observed phenomenon are room heating with gas, electricity or district heat, which are more convenient than heating with coal. The affluent end-user does not like to fill up the coal furnace manually and is willing to pay more for a convenient technology. If MESSAGE is to correctly reflect this phenomenon, the model’s cost-minimizing behavior mustbe modified accordingly. As a model feature to accomplish this task, the concept of inconvenience factors has been introduced in the definition of end-use technologies. The inconvenience factors are specified for each end-use technology, time period and world region. The cost entry in the objective function is calculated as the monetary costs, multiplied by the inconvenience factor. The inconvenience factors for a given world region increase with the level of affluence (GDP per capita) in this region. Flexible and grid-dependent energy technologies, such as electricity, gas and district heating have low inconvenience factors. A second mechanism for taking into account non-monetary decision criteria in the end-use sectors is the application of implicit discount rates which change perceived upfront investment costs by consumers. These two concepts are predominantly applied in the consumer dominated energy end-use sectors transportation (see :ref:`transport`) and residential and commercial (see :ref:`resid_commerc`). Below, this is described in more detail for the MESSAGE-Access model, an extension of MESSAGE that focuses on residential energy services in developing countries which are characterized by high reliance on traditional fuels. +With increasing affluence, consumers of final energy are more likely to demand technologies that are more convenient in their use, even if they cost more than less convenient energy forms. Examples of this empirically observed phenomenon are room heating with gas, electricity or district heat, which are more convenient than heating with coal. The affluent end-user does not like to fill up the coal furnace manually and is willing to pay more for a convenient technology. If MESSAGEix is to correctly reflect this phenomenon, the model’s cost-minimizing behavior mustbe modified accordingly. As a model feature to accomplish this task, the concept of inconvenience factors has been introduced in the definition of end-use technologies. The inconvenience factors are specified for each end-use technology, time period and world region. The cost entry in the objective function is calculated as the monetary costs, multiplied by the inconvenience factor. The inconvenience factors for a given world region increase with the level of affluence (GDP per capita) in this region. Flexible and grid-dependent energy technologies, such as electricity, gas and district heating have low inconvenience factors. A second mechanism for taking into account non-monetary decision criteria in the end-use sectors is the application of implicit discount rates which change perceived upfront investment costs by consumers. These two concepts are predominantly applied in the consumer dominated energy end-use sectors transportation (see :ref:`transport`) and residential and commercial (see :ref:`resid_commerc`). Below, this is described in more detail for the MESSAGEix-Access model, an extension of MESSAGEix that focuses on residential energy services in developing countries which are characterized by high reliance on traditional fuels. -Behavioral change in MESSAGE-Access +Behavioral change in MESSAGEix-Access ------------------------------------ -MESSAGE-Access is a variant of the MESSAGE model that provides a detailed representation of energy use for the residential sector in developing country regions. It is fully integrated with the MESSAGE supply side model, but not in call scenarios is the the detailed demand-side representation used, but instead a more aggregated formulation with just seven demand categories is used (see :ref:`demand`) which is parametrized off the detailed MESSAGE-Access formulation. The objective function maximizes household utility by choosing an energy-equipment combination for an individual household group that meets a particular energy service demand at lowest cost. The model is calibrated with data on existing household energy use patterns, derived from national household surveys and energy statistics and balances for the base year 2005. Assumptions regarding urbanization, income growth and changes in income distributions over time drive the model outcomes in the future. In its current version the model is implemented only for 3 of the 11 MESSAGE regions (see :ref:`spatial`), SAS, PAS and AFR, that are developing regions where access to modern energy remains the most limited. +MESSAGEix-Access is a variant of the MESSAGEix model that provides a detailed representation of energy use for the residential sector in developing countries. It is fully integrated with the MESSAGEix supply side model, but not in call scenarios is the detailed demand-side representation used, but instead a more aggregated formulation with just seven demand categories is used (see :ref:`demand`) which is parametrized off the detailed MESSAGEix-Access formulation. The objective function maximizes household utility by choosing an energy-equipment combination for an individual household group that meets a particular energy service demand at lowest cost. The model is calibrated with data on existing household energy use patterns, derived from national household surveys and energy statistics and balances for the base year 2005. Assumptions regarding urbanization, income growth and changes in income distributions over time drive the model outcomes in the future. In its current version the model is implemented only for 3 of the 11 MESSAGE regions (see :ref:`spatial`), SAS, PAS and AFR, that are developing regions where access to modern energy remains the most limited. The model distinguishes between two primary energy end-uses in the residential sector – (1) thermal, largely cooking demand and (2) electricity demand for lighting and appliance use. Several alternative fuel and technology options can be specified in the model to meet each of these respective service demands. To reflect heterogeneity among consumers, the household or residential sector is further disaggregated into several sub-groups that distinguish among rural and urban households and five or more expenditure classes within the rural and urban sub-sectors (Figure 2.3). -The methodology for modeling energy choices in the residential sector using this model is described in detail in Ekholm et al. (2010) :cite:`ekholm_determinants_2010` and in the Supplementary Materials section of Pachauri et al. (2013) :cite:`pachauri_pathways_2013`. In addition to energy prices, technology costs and performance parameters, and income level of a household determining the least-cost energy-equipment combination that meets a specific energy need, two additional parameters determine choices in the model. The first is referred to as the “inconvenience cost”. An inconvenience cost is a cost related to the inconveniences associated with obtaining and using certain types of fuels. For example, gathering firewood involves an opportunity cost for the time spent in collecting it and a dis-utility to users from exposure to the smoke they inhale when it is combusted. This non-monetary cost is captured by estimating an inconvenience cost (see Ekholm et al. (2010) :cite:`ekholm_determinants_2010` for further details regarding the methodology) for each household group and fuel. This is considered an additional cost that must be taken into account by the household in making a decision regarding the choice of fuels. The second parameter that also determines energy choices for households is income dependent implicit discount rates that determine the annualized capital costs of equipment depending on their individual lifetimes. +The methodology for modeling energy choices in the residential sector of this model is described in detail in Ekholm et al. (2010) :cite:`ekholm_determinants_2010` and in the Supplementary Materials section of Pachauri et al. (2013) :cite:`pachauri_pathways_2013`. In addition to energy prices, technology costs and performance parameters, and income level of a household determining the least-cost energy-equipment combination that meets a specific energy need, two additional parameters determine choices in the model. The first is referred to as the “inconvenience cost”. An inconvenience cost is a cost related to the inconveniences associated with obtaining and using certain types of fuels. For example, gathering firewood involves an opportunity cost for the time spent in collecting it and a dis-utility to users from exposure to the smoke they inhale when it is combusted. This non-monetary cost is captured by estimating an inconvenience cost (see Ekholm et al. (2010) :cite:`ekholm_determinants_2010` for further details regarding the methodology) for each household group and fuel. This is considered an additional cost that must be taken into account by the household in making a decision regarding the choice of fuels. The second parameter that also determines energy choices for households is income dependent implicit discount rates that determine the annualized capital costs of equipment depending on their individual lifetimes. -.. image:: /_static/MESSAGE-Access_groups.png +.. image:: /_static/MESSAGEix-Access_groups.png **Figure 2.3**: Split of residential energy demand into different spatial (urban/rural) and income (1-5) categories. diff --git a/source/socio_econ/narratives.rst b/source/socio_econ/narratives.rst index 2517c8c..27998ae 100755 --- a/source/socio_econ/narratives.rst +++ b/source/socio_econ/narratives.rst @@ -2,7 +2,7 @@ SSP narratives =============== -Narratives have been developed for the Shared Socioeconomic Pathways (SSPs) (O’Neill et al., 2015 :cite:`oneill_roads_2015`). These descriptions of alternative futures of societal development span a range of possible worlds that stretch along two climate-change-related dimensions: mitigation and adaptation challenges. The SSPs reflect five different developments of the world that are characterized by varying levels of global challenges (see `Riahi et al., 2016 `_ :cite:`riahi_shared_2016` for an overview). In the following, the three narratives that have been translated into quantitative scenarios with MESSAGE-GLOBIOM are presented (Fricko et al., 2016 :cite:`fricko_marker_2016`): +Narratives have been developed for the Shared Socioeconomic Pathways (SSPs) (O’Neill et al., 2015 :cite:`oneill_roads_2015`). These descriptions of alternative futures of societal development span a range of possible worlds that stretch along two climate-change-related dimensions: mitigation and adaptation challenges. The SSPs reflect five different developments of the world that are characterized by varying levels of global challenges (see `Riahi et al., 2016 `_ :cite:`riahi_shared_2016` for an overview). In the following, the three narratives that have been translated into quantitative scenarios with MESSAGEix-GLOBIOM are presented (Fricko et al., 2016 :cite:`fricko_marker_2016`): SSP1 Narrative: Sustainability — Taking the green road ---------------------------------------------------- diff --git a/source/socio_econ/pop_GDP.rst b/source/socio_econ/pop_GDP.rst index 65be8e3..b7edc1b 100755 --- a/source/socio_econ/pop_GDP.rst +++ b/source/socio_econ/pop_GDP.rst @@ -1,7 +1,7 @@ Population and GDP ==================== -Population and economic developments have strong implications for the anticipated mitigation and adaptation challenges. For example, a larger, poorer and less educated population will have more difficulties to adapt to the detrimental effects of climate change (O’Neill et al., 2014 :cite:`oneill_new_2014`). The primary drivers of future energy demand in MESSAGE are projections of total population and GDP at purchasing power parity exchange rates, denoted as GDP (PPP). In addition to total population, the urban/rural split of population is relevant for the `MESSAGE-Access `_ version of the model which distinguishes rural and urban population with different household incomes in developing country regions. +Population and economic developments have strong implications for the anticipated mitigation and adaptation challenges. For example, a larger, poorer and less educated population will have more difficulties to adapt to the detrimental effects of climate change (O’Neill et al., 2014 :cite:`oneill_new_2014`). The primary drivers of future energy demand in MESSAGEix are projections of total population and GDP at purchasing power parity exchange rates, 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 countries. -Understanding how population and economic growth develops in the SSPs gives a first layer of understanding of the multiple mitigation and adaptation challenges. 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 :cite:`kc_human_2014`). Gross Domestic Product (GDP) follows regional historical trends (Dellink et al., 2015 :cite:`dellink_long-term_2015`). In SSP2, 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. 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, both the population and GDP developments in SSP2 are designed to be situated in the middle of the road between SSP1 and SSP3, see KC and Lutz (2015) :cite:`kc_human_2014` and Dellink et al (2015) :cite:`dellink_long-term_2015` for details. (Fricko et al., 2016 :cite:`fricko_marker_2016`) +Understanding how population and economic growth develops in the SSPs gives a first layer of understanding of the multiple mitigation and adaptation challenges. 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 :cite:`kc_human_2014`). Gross Domestic Product (GDP) follows regional historical trends (Dellink et al., 2015 :cite:`dellink_long-term_2015`). In SSP2, 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 that 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. 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, both the population and GDP developments in SSP2 are designed to be situated in the middle of the road between SSP1 and SSP3, see KC and Lutz (2015) :cite:`kc_human_2014` and Dellink et al (2015) :cite:`dellink_long-term_2015` for details. (Fricko et al., 2016 :cite:`fricko_marker_2016`) The full quantitative data set of demographic and economic projections for the SSPs can be found in an online database (`SSP database `_). \ No newline at end of file From 000c0e10c87306e659e0d2fbfb2203f71e2ec310 Mon Sep 17 00:00:00 2001 From: GUO Fei Date: Thu, 10 Oct 2019 14:46:16 +0200 Subject: [PATCH 2/2] Reflecting the new comments for the "End Use" and "Socio-economic" --- source/energy/enduse/industrial.rst | 6 +++--- source/energy/enduse/resid_commerc.rst | 2 +- source/socio_econ/beh_change.rst | 6 +++--- source/socio_econ/index.rst | 1 + source/socio_econ/pop_GDP.rst | 6 +++--- 5 files changed, 11 insertions(+), 10 deletions(-) diff --git a/source/energy/enduse/industrial.rst b/source/energy/enduse/industrial.rst index 14f430c..2afef4d 100755 --- a/source/energy/enduse/industrial.rst +++ b/source/energy/enduse/industrial.rst @@ -4,15 +4,15 @@ Industrial sector ----------------- Similar to the residential and commercial sectors, the industrial sector in MESSAGEix 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 :numref:`fig-indus` below). The specific industrial demand can be satisfied either by electricity from the grid or with decentralized electricity generation options such as fuel cells (including CHP). +This stylized industrial 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. 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 :numref:`fig-indus` below). The specific industrial demand can be satisfied either by electricity from the grid or with decentralized electricity generation options such as fuel cells and on-site CHP. .. _fig-indus: .. figure:: /_static/industry_end-use.png Schematic diagram of the industrial sector representation in MESSAGEix. -While cement production is not explicitly modeled at the process level in MESSAGEix, the amount of cement production is linked to industrial activity (more specifically the -industrial thermal demand in MESSAGEix) and the associated CO2 emissions from the calcination process are accounted for explicitly. In addition, adding carbon capture and storage to +While cement production is not explicitly modeled at the process level in MESSAGEix, the amount of cement production is linked to industrial activity (more specifically the +industrial thermal demand in MESSAGEix) 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. :numref:`tab-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 :cite:`fricko_marker_2016`). diff --git a/source/energy/enduse/resid_commerc.rst b/source/energy/enduse/resid_commerc.rst index f45fa01..a3b0527 100755 --- a/source/energy/enduse/resid_commerc.rst +++ b/source/energy/enduse/resid_commerc.rst @@ -4,7 +4,7 @@ Residential and commercial sectors ---------------------------------- The residential and commercial sector in MESSAGEix 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). -The 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 :numref:`fig-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, including on-site CHP. +The 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 :numref:`fig-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 and on-site CHP. .. _fig-rescom: .. figure:: /_static/residential-commercial_end-use.png diff --git a/source/socio_econ/beh_change.rst b/source/socio_econ/beh_change.rst index e8d5c87..63a05b5 100644 --- a/source/socio_econ/beh_change.rst +++ b/source/socio_econ/beh_change.rst @@ -2,15 +2,15 @@ Behavioural change ==================== -With increasing affluence, consumers of final energy are more likely to demand technologies that are more convenient in their use, even if they cost more than less convenient energy forms. Examples of this empirically observed phenomenon are room heating with gas, electricity or district heat, which are more convenient than heating with coal. The affluent end-user does not like to fill up the coal furnace manually and is willing to pay more for a convenient technology. If MESSAGEix is to correctly reflect this phenomenon, the model’s cost-minimizing behavior mustbe modified accordingly. As a model feature to accomplish this task, the concept of inconvenience factors has been introduced in the definition of end-use technologies. The inconvenience factors are specified for each end-use technology, time period and world region. The cost entry in the objective function is calculated as the monetary costs, multiplied by the inconvenience factor. The inconvenience factors for a given world region increase with the level of affluence (GDP per capita) in this region. Flexible and grid-dependent energy technologies, such as electricity, gas and district heating have low inconvenience factors. A second mechanism for taking into account non-monetary decision criteria in the end-use sectors is the application of implicit discount rates which change perceived upfront investment costs by consumers. These two concepts are predominantly applied in the consumer dominated energy end-use sectors transportation (see :ref:`transport`) and residential and commercial (see :ref:`resid_commerc`). Below, this is described in more detail for the MESSAGEix-Access model, an extension of MESSAGEix that focuses on residential energy services in developing countries which are characterized by high reliance on traditional fuels. +With increasing affluence, consumers of final energy are more likely to demand technologies that are more convenient in their use, even if they cost more than less convenient energy forms. Examples of this empirically observed phenomenon are room heating with gas, electricity or district heat, which are more convenient than heating with coal. The affluent end-user does not like to fill up the coal furnace manually and is willing to pay more for a convenient technology. If MESSAGEix is to correctly reflect this phenomenon, the model’s cost-minimizing behavior must be modified accordingly. As a model feature to accomplish this task, the concept of inconvenience factors has been introduced in the definition of end-use technologies. The inconvenience factors are specified for each end-use technology, time period and world region. The cost entry in the objective function is calculated as the monetary costs, multiplied by the inconvenience factor. The inconvenience factors for a given world region increase with the level of affluence (GDP per capita) in this region. Flexible and grid-dependent energy technologies, such as electricity, gas and district heating have low inconvenience factors. A second mechanism for taking into account non-monetary decision criteria in the end-use sectors is the application of implicit discount rates which change perceived upfront investment costs by consumers. These two concepts are predominantly applied in the consumer dominated energy end-use sectors transportation (see :ref:`transport`) and residential and commercial (see :ref:`resid_commerc`). Below, this is described in more detail for the MESSAGEix-Access model, an extension of MESSAGE that focuses on residential energy services in developing countries which are characterized by high reliance on traditional fuels. Behavioral change in MESSAGEix-Access ------------------------------------ -MESSAGEix-Access is a variant of the MESSAGEix model that provides a detailed representation of energy use for the residential sector in developing countries. It is fully integrated with the MESSAGEix supply side model, but not in call scenarios is the detailed demand-side representation used, but instead a more aggregated formulation with just seven demand categories is used (see :ref:`demand`) which is parametrized off the detailed MESSAGEix-Access formulation. The objective function maximizes household utility by choosing an energy-equipment combination for an individual household group that meets a particular energy service demand at lowest cost. The model is calibrated with data on existing household energy use patterns, derived from national household surveys and energy statistics and balances for the base year 2005. Assumptions regarding urbanization, income growth and changes in income distributions over time drive the model outcomes in the future. In its current version the model is implemented only for 3 of the 11 MESSAGE regions (see :ref:`spatial`), SAS, PAS and AFR, that are developing regions where access to modern energy remains the most limited. +MESSAGEix-Access is a variant of the MESSAGEix model that provides a detailed representation of energy use for the residential sector in developing country regions. It is fully integrated with the MESSAGEix supply side model, but not in call scenarios is the the detailed demand-side representation used, but instead a more aggregated formulation with just seven demand categories is used (see :ref:`demand`) which is parametrized off the detailed MESSAGEix-Access formulation. The objective function maximizes household utility by choosing an energy-equipment combination for an individual household group that meets a particular energy service demand at lowest cost. The model is calibrated with data on existing household energy use patterns, derived from national household surveys and energy statistics and balances for the base year 2005. Assumptions regarding urbanization, income growth and changes in income distributions over time drive the model outcomes in the future. In its current version the model is implemented only for 3 of the 11 MESSAGEix regions (see :ref:`spatial`), SAS, PAS and AFR, that are developing regions where access to modern energy remains the most limited. The model distinguishes between two primary energy end-uses in the residential sector – (1) thermal, largely cooking demand and (2) electricity demand for lighting and appliance use. Several alternative fuel and technology options can be specified in the model to meet each of these respective service demands. To reflect heterogeneity among consumers, the household or residential sector is further disaggregated into several sub-groups that distinguish among rural and urban households and five or more expenditure classes within the rural and urban sub-sectors (Figure 2.3). The methodology for modeling energy choices in the residential sector of this model is described in detail in Ekholm et al. (2010) :cite:`ekholm_determinants_2010` and in the Supplementary Materials section of Pachauri et al. (2013) :cite:`pachauri_pathways_2013`. In addition to energy prices, technology costs and performance parameters, and income level of a household determining the least-cost energy-equipment combination that meets a specific energy need, two additional parameters determine choices in the model. The first is referred to as the “inconvenience cost”. An inconvenience cost is a cost related to the inconveniences associated with obtaining and using certain types of fuels. For example, gathering firewood involves an opportunity cost for the time spent in collecting it and a dis-utility to users from exposure to the smoke they inhale when it is combusted. This non-monetary cost is captured by estimating an inconvenience cost (see Ekholm et al. (2010) :cite:`ekholm_determinants_2010` for further details regarding the methodology) for each household group and fuel. This is considered an additional cost that must be taken into account by the household in making a decision regarding the choice of fuels. The second parameter that also determines energy choices for households is income dependent implicit discount rates that determine the annualized capital costs of equipment depending on their individual lifetimes. -.. image:: /_static/MESSAGEix-Access_groups.png +.. image:: /_static/MESSAGE-Access_groups.png **Figure 2.3**: Split of residential energy demand into different spatial (urban/rural) and income (1-5) categories. diff --git a/source/socio_econ/index.rst b/source/socio_econ/index.rst index 058d9f6..95208de 100755 --- a/source/socio_econ/index.rst +++ b/source/socio_econ/index.rst @@ -4,5 +4,6 @@ Socio-economic development .. toctree:: :maxdepth: 1 + beh_change narratives pop_GDP diff --git a/source/socio_econ/pop_GDP.rst b/source/socio_econ/pop_GDP.rst index b7edc1b..088a441 100755 --- a/source/socio_econ/pop_GDP.rst +++ b/source/socio_econ/pop_GDP.rst @@ -1,7 +1,7 @@ Population and GDP ==================== -Population and economic developments have strong implications for the anticipated mitigation and adaptation challenges. For example, a larger, poorer and less educated population will have more difficulties to adapt to the detrimental effects of climate change (O’Neill et al., 2014 :cite:`oneill_new_2014`). The primary drivers of future energy demand in MESSAGEix are projections of total population and GDP at purchasing power parity exchange rates, 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 countries. +Population and economic developments have strong implications for the anticipated mitigation and adaptation challenges. For example, a larger, poorer and less educated population will have more difficulties to adapt to the detrimental effects of climate change (O’Neill et al., 2014 :cite:`oneill_new_2014`). The primary drivers of future energy demand in MESSAGEix are projections of total population and GDP at purchasing power parity exchange rates, 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. -Understanding how population and economic growth develops in the SSPs gives a first layer of understanding of the multiple mitigation and adaptation challenges. 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 :cite:`kc_human_2014`). Gross Domestic Product (GDP) follows regional historical trends (Dellink et al., 2015 :cite:`dellink_long-term_2015`). In SSP2, 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 that 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. 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, both the population and GDP developments in SSP2 are designed to be situated in the middle of the road between SSP1 and SSP3, see KC and Lutz (2015) :cite:`kc_human_2014` and Dellink et al (2015) :cite:`dellink_long-term_2015` for details. (Fricko et al., 2016 :cite:`fricko_marker_2016`) +Understanding how population and economic growth develops in the SSPs gives a first layer of understanding of the multiple mitigation and adaptation challenges. 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 :cite:`kc_human_2014`). Gross Domestic Product (GDP) follows regional historical trends (Dellink et al., 2015 :cite:`dellink_long-term_2015`). In SSP2, 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. 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, both the population and GDP developments in SSP2 (Fricko et al., 2016 :cite:`fricko_marker_2016`) are designed to be situated in the middle of the road between SSP1 and SSP3, see KC and Lutz (2015) :cite:`kc_human_2014` and Dellink et al (2015) :cite:`dellink_long-term_2015` for details. -The full quantitative data set of demographic and economic projections for the SSPs can be found in an online database (`SSP database `_). \ No newline at end of file +The full quantitative data set of demographic and economic projections for the SSPs can be found in an online database (`SSP database `_).