Figure 57. Structure of ETP industry model

Note: Refer to Annex III for further details on the methodology for materials demand and the impact of material efficiency strategies on material demand assumptions.

Based on socio-economic assumptions, historical trends, expert views and statistical information, exogenous material demand projections are used to determine the final energy consumption and direct CO2 emissions of the sector, depending on the energy performance of process technologies and technology choice within each of the available production routes.

Global buildings sector model

The buildings sector is modelled using a global simulation stock accounting framework, split into residential and non-residential subsectors across 35 countries and regions (Figure 58). The residential subsector includes all energy-using activities in apartments and houses, including space and water heating, cooling, ventilation, lighting, and the use of appliances and other electrical plug loads. The non-residential subsector includes activities related to trade, finance, real estate, public administration, health, food and lodging, education and other commercial services. This is also commonly referred to as the commercial and public services sector. It covers energy used for space and water heating, cooling, ventilation, lighting and a range of other miscellaneous energy-consuming equipment such as commercial appliances, office equipment, cooking devices and medical equipment.

For both subsectors, the model uses socio-economic drivers, such as population, GDP, income (approximated by gross national income [GNI] per capita), urbanisation and electrification rates, to project the major buildings energy demand drivers, including residential and non-residential floor area, number of households and residential appliance ownership. As far as

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possible, country statistics are used for historical energy balances by end use, floor area, appliance ownership rates, and other building-related technical data and efficiency rates (e.g. technology stock and sales data). These data can be difficult to obtain across many developing countries. Therefore, in several cases, the historical driver parameters for the ETP buildings sector model have been estimated using a series of applied logistic functions relative to GDP, GNI per capita, urbanisation and electrification, or another combination of proxies as defined by multilinear regressions. Those functions are applied to individual countries, or, in cases where few data are available, to country clusters designed to be as homogeneous as possible within the cluster and as heterogeneous as possible among cluster categories. The functions differentiate the applied energy indicators by year to 2060 and across the 35 model countries and regions. The indicators are then applied within a stock accounting framework, which is distinguished by annual vintages, and the technology (or buildings stock) lifetimes are spread using a Weibull distribution.

Whenever possible, historical data and buildings sector information, such as buildings energy codes or minimum energy performance standards for end-use equipment, are applied within the model. Depending on the end use or technology, multiple categories are included (or estimated) within the model. For example, the global buildings stock is broken down into three categories, including near-zero energy buildings (nZEBs), code-compliant buildings and buildings that do not meet a code or do not have an applicable buildings energy code. Buildings end-use technologies (e.g. major household appliances) are similarly broken down into categories where applicable, such as best in class, median market performance and minimum energy performance technologies.

Using the annually differentiated stock accounting framework by country or region, historical useful energy intensity is estimated across the various buildings end uses based on assumed technology shares and efficiencies. Buildings stock characteristics (e.g. nZEB and codecompliant buildings energy intensity) are applied with heating and cooling equipment to estimate historical and then projected annual demand for space heating and cooling per unit of floor area (i.e. useful energy services delivered). The model also takes into account the ageing, refurbishment or reconstruction of buildings through degradation, improvement, renovation rates or specific lifetime distributions. For the other end uses (e.g. water heating, lighting, appliances and cooking), the useful energy demand is similarly estimated through a differentiated stock accounting framework to determine the useful (or delivered) energy service by end use. Across all end uses and countries/regions, useful energy demand can vary over time (e.g. relative to average GNI per capita growth), where some convergence (in useful energy service) is assumed across similar countries/regions, depending on the buildings ETP scenario.

For each of the derived useful energy demands, a suite of technology and fuel options are represented in the model reflecting current techno-economic characteristics (e.g. efficiencies, costs and lifetimes) as well as their assumed evolution to 2060 in the applied ETP scenario. Depending on the technology stock, as well as assumptions on the penetration and market share of new technologies in the future, the ETP buildings sector model allows exploration of strategies that meet the different useful energy demands and the quantification of the resulting developments by final energy consumption and related CO2 emissions. Detailed annual results from the model are also applied within a logarithmic mean Divisia index analysis. This allows indepth tracking of changes in activity, technology and energy performance over time with respect to the various scenarios.

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