- Best Practice Policies for Low Carbon & Energy Buildings Based on Scenario Analysis, Center for Climate Change and Sustainable Energy Policy (3CSEP), Central European University, commissioned by GBPN, 2012
- Buildings For Our Future, The Deep Path for Closing the Emissions Gap in the Building Sector, GBPN-KPMG, 2013
This section provides a detailed description of the methodology behind this work. [Data collection and analysis performed by the Centre for Climate Change and Sustainable Energy Policy (3CSEP), Central European University.
The tool was developed using the 3CSEP-HEB (High Efficiency Buildings) model, which is based on a performance-oriented approach to buildings energy analysis. As opposed to component-oriented methods, a systemic perspective is taken: the performance of whole buildings is studied and these performance values are used as key inputs in the scenarios.
The three main scenarios included in the 3CSEP model are presented in this tool. These three scenarios differ according to the level of ambition in building performance policy and technological developments: a scenario with very ambitious actions (Deep), a scenario with moderately ambitious actions (Moderate), and a scenario without further actions (Frozen).
Key assumptions for each of the three scenarios are described in this table:
The model provides projections for the entire world. Results are provided for the following key 11 regions: North America (NAM), Western Europe (WEU), Eastern Europe (EEU), Former Soviet Union (FSU), Latin America (LAC), Pacific OECD (PAO), Centrally Planned Asia (CPA), Pacific Asia (PAS), South Asia (SAS), Middle East and Africa (MEA), and Africa (AFR). The tool also provides results for the four key zones: China, Europe, India and the United Sates.
Within each region different climate zones are considered in order to capture the difference in building energy use caused by climate variations. The differentiation among different climate zones is based on several climatic factors in terms their influence on building energy demand for space heating, cooling and dehumidification, namely:
- Heating Degree Days (HDD)
- Cooling Degree Days (CDD)
- Relative Humidity of the warmest month (RH)
- Average Temperature of the warmest month (T)
These parameters have been processed by means of GIS tool - spatial analysis - and performed with ArcGIS 9.3 software.
GIS analysis facilitated the combination of the parameters outlined above, according to a certain set of criteria for each climate zone (see Table 2). Each selected geographical area corresponds to a certain climate zone categorized by energy needs for space heating, cooling and dehumidification.
In total there are 17 climate zones considered in this typology. Each of them is characterized in terms of heating and/or cooling demand, which varies from “low” to “very high” depending on the amount of average annual HDD and CDD in each area. The need for dehumidification is determined on the basis of the combination of values for relative humidity and average temperature of the warmest month. It is assumed that if relative humidity of the warmest month is higher than 50% and average temperature of the warmest month is higher or equals 23°C, than dehumidification in buildings is needed. Such a climate split gives the opportunity to capture variation in energy needs for heating, cooling and dehumidification in different geographical locations.
All climate zones are presented in Figure 1 and Table 2.
Building Types Classification
The Tool for Building Energy Performance Scenarios has a comprehensive multi-level building type classification. Building categories are distinguished by their location (urban, rural, areas of informal housing), building type (single-family, multifamily, commercial and public buildings with subcategories), and building vintage (existing, new, advanced new, retrofit, advanced retrofit).
The split between urban and rural building areas for residential buildings is made on the basis of projections of urbanization rates in each region and country. For commercial and public buildings a certain small share (5-10%) of floor area is assumed to be rural. Due to the lack of such data in open sources, these assumptions are based on expert judgments.
The Tool takes into account existence of informal settlements in developing regions. This is done on the basis of the share of urban population living in informal housing, according to UN-HABITAT statistics.
Residential urban buildings are split into single-family (SF; detached or attached) and multifamily (MF; 4 or more levels, terraced, etc.), according to the population living in each building type. Rural residential buildings are assumed to be only single-family ones. Commercial and public buildings both in urban and rural areas are divided into six sub-categories: hotels & restaurants, educational, hospitals, offices, retail buildings, and others, according to the share of the floor area for each commercial and public building type in the total commercial and public floor area. Such data have been found only for a limited number of regions and for other regions assumptions were made, based on the collected data.
In the energy scenarios, five building vintages have been considered: standard, new, retrofit, advanced new and advanced retrofit buildings. These vintages represent different levels of energy performance. Standard buildings are those buildings, which had been built in the country or region prior to the analysed period. As a result this vintage includes old buildings, which are usually the least efficient ones. New buildings are the ones constructed in the country or region during a particular year within the analysed period. Correspondingly, retrofit buildings are those renovated during a particular year within the analysed period. The same is applied to advanced new and advanced retrofit buildings with the only difference in specific energy use, as they consume much less.
A crucial step in producing scenarios is building floor area calculation. The building stock model is based on annual dynamics, including the following process in the existing building stock: demolition (a certain share of the building is demolished due to the end of the lifecycle or other reasons), renovation (a certain share of the building is renovated) and new construction (a certain number of new buildings is added every year).
In 3CSEP-HEB model behind this Tool demolition rates vary from one region to another in the range of 0.3 – 1%. For most regions 0.5% is used as the demolition rate.
Retrofit rates are assumed between 0.7% - 2%, and the 1.4% value (corresponding to approximately 70-year building stock turnover rate) is considered as a normal retrofit rate in developed countries, which is increasing to 2020 to different levels in case of the Moderate Efficiency and the Deep Efficiency scenarios in all regions for quicker mitigation. In case of the Frozen Efficiency scenario the retrofit rate remains fixed (at 1.4% level) in all regions for the whole analysed period.
Residential floor area growth is based on floor area per capita estimates and population projections for each region with the assumptions that the developing world will have approximately the same standard of living in terms of living space per capita as OECD countries by 2050. This is then coupled with the urbanization rate to produce a total floor area for rural and urban buildings. In case of declining population the immediate removal of building stock is assumed to be unrealistic since capital stock typically retains value even with no occupancy, and demolition can be more costly than leaving buildings unoccupied. However, in terms of energy consumption this building stock does not exist since energy consumption in unoccupied buildings is negligible and is therefore removed from the model.
Building floor area is also calculated for each climate zone by applying share of population for each climate zone within each region/country. Share of population for each climate zone was calculated by means of GIS analysis through overlaying created climate split with population grid.
Commercial and Public Buildings
This group of buildings includes all non-residential buildings, except for industrial ones.
The main driver for commercial floor area calculation is GDP (MER – market exchange rate) projections for each region. Commercial and public floor area in 2005 is divided by GDP in 2005, which yields “commercial and public floor area elasticity”. This constant, when multiplied by GDP for a given year gives the commercial and public floor area demanded by the economy. Since the developing world has a higher ratio of commercial and public floor area to GDP than the developed OECD countries, the ratio is assumed to decrease over time and eventually achieve an average OECD level of floor area elasticity, representing a shift to higher GDP output per unit floor area synonymous with completed economic development.
Thermal comfort combines space heating and cooling needs to maintain an acceptable indoor air temperature.
Different regions have different demand for cooling and heating energy. As the model uses a performance-based approach combined energy consumption for space heating and cooling is taken as the main input data for the analysis.
Advanced buildings, according to the model’s logic, have a state-of-the-art design, which allows for a significant reduction of thermal energy demand in most climate zones (up to 90%). This assumption is also in line with the concept of a passive house, which often does not include any “active” heating or cooling systems, with the usual energy performance for space heating and cooling presented at the level of 15 kWh/m2 year in final energy.
Key assumptions for input data on specific energy consumption for space heating and cooling
- Energy consumption for space heating and cooling of residential buildings in rural areas is assumed to be 30% lower than in urban ones in developing regions and at the same level in developed ones.
- Retrofit buildings consume 30% less than standard buildings in Moderate & Deep Efficiency Scenarios and 10% less in Frozen Efficiency Scenario for the regions in general.
- Energy performance of advanced buildings is determined by best practices, which can be achieved in a particular climate zone, according to a number of case studies. Most of data are approximately at the level of 15-30 kWh/m2, depending on the region.
- Informal buildings consume 70% less than single-family buildings, in the climate zones, which require heating (as people in such areas usually use very inefficient fuel solution for heating), and 95% less for the climate zones where only cooling is needed.
Energy use for space heating and cooling is calculated by multiplying the estimated floor area values for each region, climate zone, building type, building vintages in each year by specific energy consumption figures of exemplary buildings (in kWh/m2 year) for the same categories. These results can be summed up in order to get the results for each region and then for the whole world.
In order to calculate energy for water heating assumptions on regional technology mix and the efficiencies of the technologies in these mixes are made to determine regional average efficiency levels. Therefore, to assess the energy savings potentials in the case of water heating, 2005 residential and commercial and public hot water energy use values are needed, plus the improvement potentials of regional average energy performance values must be estimated. In addition, the volume of hot water consumption is also expected to change in some regions, so this is also considered in the scenarios.
Current average water heating efficiencies are estimated on the basis of the technology mix and the efficiencies of current technologies in each region. Future efficiencies are estimated on the basis of efficiencies of advanced water heating systems and potentials of different technologies. For each scenario, a potentially achievable technology mix was assumed and the efficiencies of technologies used in a given scenario were averaged to obtain advanced efficiency values.
In addition to technological change, consumption levels also affect final hot water energy demands. To give account of changes in consumption, floor area was used as a proxy. Population predictions in the estimation of residential hot water consumption and assumed that per capita hot water consumption changes like per capita floor area. Water saving technologies with a potential to reduce consumption were considered in determining future efficiencies.