Baseline Energy Consumption and Greenhouse Gas Emissions

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1 Baseline Energy Consumption and Greenhouse Gas Emissions In Commercial Buildings in Australia Part 1 - Report November 2012 Council of Australian Governments (COAG) National Strategy on Energy Efficiency

2 Baseline Energy Consumption and Greenhouse Gas Emissions in Commercial Buildings in Australia Part 1 - Report Prepared by pitt&sherry with input from BIS Shrapnel and Exergy Pty Ltd Published by the Department of Climate Change and Energy Efficiency ISBN: Commonwealth of Australia 2012 This work is licensed under the Creative Commons Attribution 3.0 Australia Licence. To view a copy of this license, visit The Department of Climate Change and Energy Efficiency asserts the right to be recognised as author of the original material in the following manner: or Commonwealth of Australia (Department of Climate Change and Energy Efficiency) IMPORTANT NOTICE PLEASE READ This document is produced for general information only and does not represent a statement of the policy of the Commonwealth of Australia. The Commonwealth of Australia and all persons acting for the Commonwealth preparing this report accept no liability for the accuracy of or inferences from the material contained in this publication, or for any action as a result of any person s or group s interpretations, deductions, conclusions or actions in relying on this material. Acknowledgment As part of the National Strategy on Energy Efficiency the preparation of this document was overseen by the Commercial Buildings Committee, comprising officials of the Department of Climate Change and Energy Efficiency, Department of Resources, Energy and Tourism and all State and Territory governments.

3 Table of Contents Index of Tables... iii Index of Figures... iv Glossary... v Abbreviations... xi 1. Executive Summary... 1 Overall Conclusions Introduction Background Project Objectives and Scope Policy Context The Project Team Overview of Methodology Stock Model Energy Consumption Data Data Analysis and Model Construction Model Validation Statistical Confidence Key Assumptions Key Issues The Building Stock Energy Performance Data Model Scope and Resolution Overarching Conclusions Offices Introduction Stock Estimates - Offices Energy Intensity - Standalone Offices Total Energy Consumption and Greenhouse Gas Emissions - Standalone Offices Energy End Use - Offices State and Territory Estimates - Standalone Offices Government Owned Standalone Offices Conclusions - Offices Hotels Introduction Stock Estimates - Hotels Energy Intensity - Hotels Total Energy Use and Greenhouse Gas Emissions - Hotels Energy End Use - Hotels State and Territory Results - Hotels Conclusions - Hotels Retail Buildings Introduction Stock Estimates - Retail Energy Intensity - Retail Total Energy Consumption and Greenhouse Gas Emissions - Retail Energy End Use - Retail States and Territory Estimates - Retail Conclusions - Retail Hospitals Introduction Stock Estimates - Hospitals Energy Intensity - Hospitals Total Hospital Energy Use and Greenhouse Gas Emissions Energy End Use - Hospitals State and Territory Estimates - Hospitals Conclusions - Hospitals Schools Introduction i

4 9.2 Stock Estimates - Schools Energy Intensity - Schools Total Energy Consumption and Greenhouse Gas Emissions - Schools Energy End Use - Schools State and Territory Estimates - Schools Conclusions - Schools Tertiary Education Buildings Introduction Stock Estimates - Tertiary Education Energy Intensity - Tertiary Education Buildings Total Energy Consumption and Greenhouse Gas Emissions - Tertiary Education Energy End Use - Universities States and Territory Estimates - Tertiary Education Conclusions - Tertiary Education Public Buildings Introduction Stock Estimates - Public Buildings Energy Intensity - Public Buildings Total Energy and Greenhouse Gas Emissions - Public Buildings Energy End Use - Public Buildings State and Territory Estimates - Public Buildings Conclusions - Public Buildings Appendix A Appendix B Appendix C Appendix D Appendix E Statement of Requirements Bibliography Model Documentation Top-down Model Validation Statistical Analysis ii

5 Index of Tables Table 1.1- Non-Residential, Non-Industrial Building Stock, Australia, (floor area in 000m 2 )... 2 Table Total Energy Use and Greenhouse Gas Emissions: Australia, , Non- Residential Buildings... 3 Table Australian Average Energy Intensity Trends by Building Type, Table Energy Data Records and Individual Building Counts by Building Type Table 3.2 Recommended Minimum Sample Sizes per Year Table Population Growth, 1999 to 2020 (millions) Table Stand-Alone Office Stock by State and Region, 1999 to 2020 ( 000 m 2 NLA) Table Non-Stand-Alone Office Stock by State and Region, 1999 to 2020 ( 000m 2 NLA) Table Energy Use and Greenhouse Gas Emissions, Standalone Offices by Sub-Type, Table Standalone Offices, Whole Buildings, Energy Consumption by Fuel, 1999 to 2020, Australia Table Standalone Office Tenancy Energy Consumption by State and Territory Table Standalone Office Base Building Energy Consumption by State and Territory Table Standalone Office Whole Buildings Energy Consumption by State and Territory Table Privately Owned Standalone Office Tenancies, Average Energy Intensity by State, Territory and Region (n > 50/year), Table Privately Owned Standalone Office Base Buildings, Average Energy Intensity by State, Territory and Region (n > 50/year), Table Privately Owned Standalone Office Whole Buildings, Average Energy Intensity by State, Territory and Region (n > 30/year), Table Government Owned Standalone Office Tenancies, Average Energy Intensity by State, Territory and Region (n > 50/year), Table Government Owned Standalone Office Whole Buildings, Average Energy Intensity by State, Territory and Region (n > 30/year), Table Sample Size Summary, Government-Owned Offices by State and Region, All Periods Table Sample Size Summary, Privately-Owned Offices by State and Region, All Periods 52 Table Hotel Stock by State and Region, 1999 to 2020 ( 000 m 2 NLA) Table Hotels, Energy Consumption by Fuel, and GHG Emissions 1999 to 2020, Australia. 56 Table Hotel Energy Consumption by State and Territory Table Hotels, Average Energy Intensity by State, Territory and Region (n > 5/year), Table 7.1- Shopping Centre Stock Estimates by State and Region, ( 000 m 2 GFA) 61 Table 7.2- Supermarket Stock Estimates by State and Region, ( 000 m 2 GFA) Table Retail Strip Stock Estimates by State and Region, ( 000 m 2 GFA) Table Retail: Energy Use and Greenhouse Gas Emissions, Table Shopping Centre Total Energy Consumption by State, 2009 and 2020, PJ Table Shopping Centre Retail Tenancies Energy Consumption by State, 2009 and 2020, PJ Table Shopping Centre Base Building Energy Consumption by State, 2009 and 2020, PJ. 70 Table Supermarket Total Energy Consumption by State, 2009 and 2020, PJ Table Stand-alone Supermarket Energy Consumption by State, 2009 and 2020, PJ Table 8.1 All Hospital Stock by State and Region, 1999 to 2020 ( 000 m 2 ) Table Total Energy Use and Greenhouse Gas Emissions, Hospitals, Table Hospital Energy Consumption by State and Territory Table Public Hospitals, Average Energy Intensity by State, Territory and Region (n >= 10/year), Table School Stock (public and private) by State and Region, 1999 to 2020 ( 000 m 2 NLA) Table Total Energy Consumption and Greenhouse Gas Emissions, Schools, Table Schools Energy Consumption by State and Territory Table Public Schools, Average Energy Intensity by State, Territory and Region (n >= 10/year), Table Stock Estimates, TAFE/VET Floor Area, , 000m Table Stock Estimates, University Floor Area, , 000m Table Total Energy Consumption and Greenhouse Gas Emissions, Vocational Education and Training (VET) Buildings, Australia, iii

6 Table Total Energy Consumption by Fuel and Greenhouse Gas Emissions, Universities, Australia, Table Vocational Education and Training Buildings, Total Energy Consumption by State, 1999, 2009, Table University Buildings, Total Energy Consumption by State, 1999, 2009, Table Public Building Stock by State and Region, 1999 to 2020 ( 000 m 2 NLA) Table Law Court Stock by State and Region, 1999 to 2020 ( 000 m 2 NLA) Table Correctional Centre Stock by State and Region, 1999 to 2020 ( 000 m 2 NLA) Table Public Buildings, Energy Consumption by Fuel, and GHG Emissions, 1999 to 2020, Australia Table Total Energy Consumption, Fuel Use and Greenhouse Gas Emissions, Law Courts, Australia, Table Public Buildings, Average Energy Intensity by State, Territory and Region (where n>= 10/year), Table Public Buildings, Energy Consumption by State, 1999, 2009, 2020 (PJ) Table Law Courts, Average Energy Intensity by State (where n>=6/year), Table Law Courts, Energy Consumption by State, 1999, 2009, 2020 (PJ) Index of Figures Figure Total Energy Consumption by Building Type, 2009 (PJ, % shares)... 5 Figure Total Energy Consumption by Building Type, 2020 (PJ, % shares)... 5 Figure Total Energy Consumption: Non-Residential, Non-Industrial Buildings, Australia, 2009 to 2020 (PJ)... 6 Figure Fuel Mix, All Buildings, 2009 (% shares)... 6 Figure Offices (All), Electricity End Use Shares, Figure Projected Greenhouse Gas Emissions, All Non-Residential, Non-Industrial Buildings, 2009 to Figure NRBuild Model Schematic Figure Greenhouse Gas Intensity of Electricity Supply by State, 2009 (kg CO 2 -e/kwh) Figure Standalone Office Stock by State Historical and Projections, 1999 to Figure Non-Standalone Office Stock by State, Historical and Projections, 1999 to Figure Total Energy Intensity versus Area, All Offices Figure Average Energy Intensity, Office Tenancies, Australia Figure Average Energy Intensity, Office Base Buildings, Australia Figure Whole Office Building Energy Intensity, Australia, cf Base Building + Tenancy Energy Intensity (MJ/m 2.a) Figure Whole Office Building Energy Intensity, Australia, cf Base Building + Tenancy Energy Intensity without OSCAR data Figure Office Tenancies, Electricity End Use Shares, Figure Office Base Buildings, Electricity End Use Shares, Figure Office Base Buildings, Natural Gas End Use Shares, Figure Offices (All), Electricity End Use Shares, Figure Offices (All), Natural Gas End Use Shares, Figure Hotel Energy Intensity, Australia (MJ/m 2.a) Figure Hotels- Electrical End Use Shares, Figure Hotels- Natural Gas End Use Shares, Figure Hospitals Energy Intensity, Australia, (MJ/m 2.a) Figure Hospitals- Electrical End Use Shares, Figure Hospitals-Gas End Use Shares, Figure Average Energy Intensity, Schools, Australia Figure ACT Schools, Electrical End Use Shares, Figure ACT Schools, Natural Gas End Use Shares, Figure Average Energy Intensity, VETs, Australia, Figure Average Energy Intensity, Universities, Australia, Figure VET Buildings Fuel Shares, Australia Figure University Buildings Fuel Shares, Australia, Figure Universities- Electrical End Use Shares, Australia, Figure Public Building Average Energy Intensity, Australia, (MJ/m2.a) Figure Average Energy Intensity, Law Courts, Australia, Figure Law Courts- Electrical End Use Shares, Australia, Figure Law Courts- Natural Gas End Use Shares, Australia, iv

7 Glossary Abatement Activity Base Building Baseline Behaviour Bottom-up model Carbon dioxide equivalent (CO 2 e) Co-generation Confidence level Cost-effective Database (or set) Duty cycle Embedded generation Emissions An activity that leads to a reduction in greenhouse gas emissions. In the context of energy efficiency, activity refers to the output associated with energy use when the output is not a physical product. An example is space heating or cooling in the residential and commercial sectors. The common areas of a building which are served by central services. A projected level of future emissions or energy use against which reductions by project activities could be determined; or the emissions or energy use that would occur without policy intervention. Energy user or equipment operator behaviours that affect energy consumption. A method of estimation whereby the individual components that make up a project are estimated separately. The individual results are then aggregated to produce an estimate of the entire project. In the context of this study, estimates of the energy use of individual buildings are aggregated to estimate the total energy use of all the relevant building stock. Greenhouse gases include carbon dioxide, methane, and nitrous oxide, with each gas having different physical properties and global warming potential. It is conventional to express all gas emissions in equivalent amounts of carbon dioxide where equivalent means having the same global warming potential over a period of 100 years. Combined production of electricity and useful heat (for hot water or space heating) from the same process. Also known as combined heat and power. Using sample data to make conclusions and estimates about the population is not always going to be correct. For this reason, a measure of reliability has been built into the statistical inference. The confidence level is the proportion of times that an estimating procedure will be correct. In this project, the minimum sample size is that required in order to ensure that estimates based on this energy data will be correct 95% of the time. A measure is cost effective when the present value of the benefits attributable to the measure exceeds the present value of the costs at a given discount rate. When these two values are expressed as a ratio (a benefit cost ratio or BCR), a cost effective measure will have a BCR of at least 1. A collection of data records relating, in this context, to a particular building type. The work or actions which an appliance or piece of equipment performs over a set period of time which is representative of the pattern of work or actions performed over the whole life of the appliance or equipment. The annual energy use of equipment is a function of numerous variables one of the most important is the duty cycle. Production of electricity from power stations which are connected to the distribution network (as opposed to the transmission network). Generally these range from small household solar PV systems to medium scale with capacity less than 30 MW. In Australia, distributed generation most often relates to diesel, gas (including cogeneration) or renewables (including solar, wind, micro hydro or biomass). Also referred to as distributed generation, or on-site generation. The release of greenhouse gases to the atmosphere. v

8 Emissions intensity End use technology/process data Energy conversion Energy efficiency (general) Energy end use Energy intensity Energy performance Energy services Equipment level energy end use Environmental data Explanatory data Field Finite population An amount of emissions (CO 2 -e) per a specified unit of output (e.g. GDP, sales revenue or goods produced). Data regarding the nature of the stock of end-use and conversion technologies. The process of converting energy from one form to another. Power stations for instance convert primary fuels or renewable energy into secondary or final forms, such as electricity. A boiler transforms coal, gas, electricity or other fuels into heat in the form of steam. Each conversion process involves losses of useful energy, with the greater the loss, the lower the energy efficiency. The amount of useful work that can be performed by an energy using system per unit of energy consumption. It is generally expressed as a ratio: useful output to energy input. A piece of equipment or system is described as more energy efficient to the extent that it performs more useful work for the same energy consumption, or else performs the same amount of useful work for less energy consumption. The concept is only applicable to narrowly defined energy using systems. For complex systems, such as a manufacturing plant, or a whole sector the number of energy using processes and variables affecting energy use are too great to measure energy efficiency in this strict sense. The point at which energy is used in the provision of a final product or service, rather than producing another form of energy. The ratio of energy input to useful output. Measurable results relating to energy use and consumption. The term includes energy efficiency, energy intensity, energy conservation, fuel choice and greenhouse gas emissions resulting directly and indirectly from energy use. Useful energy or work provided by an energy-using system. The services may include heating, cooling, mechanical work or electrical system outputs (computing, communications, etc). The consumption of energy measures at the level of individual pieces of equipment at a site. The term is most commonly used to describe energy use, in residential and commercial buildings, manufacturing facilities or mine sites, disaggregated to the level of, for instance, a refrigerator, a chiller, a boiler, a kiln, or a grinding mill. Also referred to as equipment energy end use. A range of environmental or climate variables that may affect the efficiency of energy-using processes. Examples include degree days as a proxy for external heat loads on a structure, or relative humidity that may affect combustion efficiency. A broad term covering information about factors that may influence energy efficiency and energy intensity, such as weather, duty cycle, prices, exchange rates and many other factors. Data point of a certain type such as Financial Year or Street Address. Data records are comprised of such fields. Where the population is not infinitely large. Generally, if the sample size is greater than 1% of the population, the population is assumed to be finite. In the calculations for the minimum number of buildings required, assuming an infinite population means that the minimum number of buildings required to achieve the prescribed confidence level and accuracy could exceed the actual number of buildings available in a particular region. In such a situation, the population is considered finite and the associated calculations assume finite population size. vi

9 Fully Enclosed Covered Area (FECA) Final energy use Fuel mix Greenhouse gases GreenPower Gross Floor Area (GFA) Gross Lettable Floor Area Retail (GLAR) Infinite population Mean Metrics Minimum energy performance standards (MEPS) Net Lettable Area (NLA) The sum of all such areas at all building floor levels, including basements (except unexcavated portions), floored roof spaces and attics, garages, penthouses, enclosed porches and attached enclosed covered ways alongside buildings, equipment rooms, lift shafts, vertical ducts, staircases and any other fully enclosed spaces and usable areas of the building, computed by measuring from the normal inside face of exterior walls but ignoring any projections such as plinths, columns, piers, and the like which project from the normal inside face of exterior walls. It shall not include open courts, light wells, connecting or isolated covered ways and net open areas of upper portions of rooms, lobbies, halls, interstitial spaces and the like, which extend through the storey being computed (Altus Page Kirkland, 2012). The total amount of energy consumed in the final or end use energy sectors. It is equal to primary energy use less energy consumed or lost in conversion, transmission and distribution. The mix of fuel types within a given amount of energy consumption. The atmospheric gases responsible for causing global warming and climate change. The major greenhouse gases are carbon dioxide (CO 2 ), methane (CH 4 ), nitrous oxide (N 2 0), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulphur hexafluoride (SF 6 ). Certified renewable energy that is delivered to an end user by an energy supplier. The sum of fully enclosed covered area and unenclosed covered area as defined. The floor space contained within a retail tenancy measured from the internal finished surface of external building walls or passageways, but excluding features such as balconies and verandahs. Where the population is assumed to be infinitely large. Generally, if the sample size is less than 1% of the population, the population is assumed to be infinite. In computing numerical descriptive measures of the data, interest usually focuses on two measures: (1) a measure of the central, or average, value of the data and (2) a measure of the degree to which the observations are spread out about this average value. The mean measures the central location of the data, also expressed as the average in this project. Measurement units associated with a quantitative measure, such as thousands of square metres of floor area, or Petajoules (PJ) of energy. Regulatory requirements for appliances or equipment manufactured or imported to Australia to ensure a set level of energy efficiency performance is met or exceeded. MEPS typically cover appliances such as refrigerators, air conditioners and televisions. The sum of all lettable areas within a commercial type building, measured from the internal finished surfaces of permanent walls and from the internal finished surfaces of dominant portions of the permanent outer building walls, and including the area occupied by structural columns and engaged perimeter columns, as defined by the Property Council of Australia. vii

10 Network losses Online System for Comprehensive Activity Reporting (OSCAR) Peak demand Population Precinct Primary energy Process energy use Quality factors R 2 Record Energy losses incurred in transporting energy over a network. It can include: heat lost through resistance in electricity wires, gas leaks, metering errors and theft. It can also include energy used in operating the network such as gas consumed to run compressors in gas pipelines. A web based data tool to record energy and emissions data for government program reporting. OSCAR standardises reporting from corporations and government. OSCAR calculates greenhouse gas emissions based on energy and emissions data. The maximum demand recorded in a given area. In the electricity market, to ensure reliability, supply capacity (generation and network) must be greater than the peak demand. Peak demand may only occur a few hours a year and is often driven by temperature due to heating and cooling loads. The term peak load is used interchangeably. A population is the set of all items of interest in a statistical problem. For example, the population referred to in this project will be the actual number of buildings within a prescribed category, e.g. actual number of government owned office buildings in NSW. A collection of buildings at the same location, often but not necessarily with the same owner. Schools, hospitals, universities, airports are all examples of precincts. Importantly for this study, building types (by function) may well vary within a precinct, and this variation is often not captured in statistical data. The total energy consumed of each primary fuel (in energy units) in both the transformation and end use sectors. It includes the use of primary fuels in transformation activities notably the consumption of fuels used to produce petroleum products and electricity. It also includes own use and losses in the energy transformation sector. It excludes the consumption of secondary energy sources such as electricity and petroleum products. The level at which energy is used by individual systems or processes at a site. The term is most commonly used to describe energy use, in commercial buildings, manufacturing facilities or mine sites, disaggregated to the level of, for instance, cooling, steam production and grinding. Also referred to as system level. Changes in the nature, composition, performance specifications of inputs, processes or outputs. In this context, changes in qualitative factors or specifications can significantly affect measured energy consumption, particularly over longer periods of time. For example, it not strictly correct to compare the energy consumption of a house or a car from 1940 with one from 2012, as the nature of the house and car (in terms of the services they provide) has itself changed through time. A statistical measure that indicates the proportion of the variance in one data series that is attributable to the variance in another. Generally it is applied in this report to indicate the extent to which a best fit trendline (e.g., for average energy intensity) explains the variance in calculated data points. A collection of data points or fields relating, in this context, to a single building in a single year. viii

11 Regression Renewables Sample Standard deviation Standard Error Stationary energy Stock Structural data Top-down estimation T-test Regression is used to predict the value of one variable on the basis of other variables. The coefficient of determination, denoted R 2, measures the strength of the linear relationship between two variables. In this project, R 2 is often predominantly used to describe the linear relationship between financial years and average EUI. The higher the value of R 2, the better the model fits the data. Energy sources that are constantly renewed by natural processes over a short recharge cycle. These include flow resources, such as solar, wind, wave and tidal energy, and some storage resources, including hydropower and some forms of biomass. Recharge cycles are generally limited to one year, to allow for seasonal restoration of dam storages and biomass resources, also this definition is contested. A sample is a set of data drawn from the population. In this project, the sample data is the energy data collating for each category. A descriptive measure of a sample is called a statistic. We use statistics to make inferences about the population (e.g., use the proportion of commercial buildings energy data collected to make inferences about general characteristics of all commercial buildings in Australia). The standard deviation is a measure of variability that is expressed in the same units as the original data/observations, as is the mean. It is merely the square root of variance, which measures the variability of a set of quantitative data. The standard error referred to in this report is the standard deviation of the mean. It is also referred to as accuracy in this report. Energy produced and used by stationary equipment. Includes energy used for electricity generation; and fuels consumed in other sectors such as gas in the manufacturing and mining sectors and wood in the residential sector. A measure of the physical extent of buildings in Australia, such as the number or area of buildings. Data that reveal, at the level of sectoral disaggregation being examined, changes in the composition of activity or the mix of production. For example data on end use equipment stocks layered by size, efficiency, age or other parameters. Structural factors vary by sector. In the context of this study, it is a method for estimating the overall or aggregate energy use of all the relevant building stock. Unlike a bottomup estimation, it does not rely on estimating the individual components of a project and then adding them up. A t-test tests and estimates the difference between two population means by assuming the distribution is normal. T-tests are conducted wherever we have claimed or drawn conclusions about two means (e.g. the average EUI of capital cities buildings is higher than regional buildings) to test the difference between the two means. If the p-value of the test is small, we can conclude that there is sufficient evidence to infer that the average EUI of data set 1 is higher/lower (different) from the average EUI of data set 2. However, note that a t-test does not validate the source of the data or reliability of the data set, e.g. if certain numbers are self-reported without any clear standards or rules to which energy use is reported, the data might not be reliable at all. ix

12 Time of use Trigeneration Unenclosed Covered Area (UCA) Useful output Z-score Refers to the period, in the 24 hour day and the 7 day week, in which energy use occurs. Time of use is particularly important in the context of the contribution of a particular energy demand to overall peak load and, hence, to overall energy system cost. The simultaneous production of electricity, useful heat (e.g. for domestic hot water or space heating) and useful coolth (generally, by feeding waste heat into an absorption chiller) for space cooling. Trigeneration systems can achieve extremely high conversion efficiencies of 90% or higher (meaning that less than 10% of the energy in the fuel is wasted). The sum of all such areas at all building floor levels, including roofed balconies, open verandahs, porches and porticos, attached open covered ways alongside buildings, undercrofts and usable space under buildings, unenclosed access galleries (including ground floor) and any other trafficable covered areas of the building which are not totally enclosed by full height walls, computed by measuring the area between the enclosing walls or balustrade (i.e. from the inside face of the U.C.A. excluding the wall or balustrade thickness). When the covering element (i.e. roof or upper floor) is supported by columns, is cantilevered or is suspended, or any combination of these, the measurements shall be taken to the edge of the paving or to the edge of the cover, whichever is the lesser. U.C.A. shall not include eaves overhangs, sun shading, awnings and the like where these do not relate to clearly defined trafficable covered areas, nor shall it include connecting or isolated covered ways (Altus Page Kirkland 2012). The output that an energy-using system or process is intended to produce; that is, not the waste or byproducts generated by the process. For example, a lamp produces both light (the useful output) and heat (generally a waste byproduct). Since in physics all energy is ultimately conserved, in one form or another, useful energy refers to the fraction of the conserved energy that is able to provide energy services. Z-score is also called a standard score. It has the effect of transforming the original distribution to one in which the mean becomes zero and the standard deviation becomes 1. A negative Z-score means that the original observation was below the mean. A positive Z-score means that the original observation was above the mean. The actual value corresponds to the number of standard deviations the observation is from the mean in that direction (positive or negative). The Z-score is dependent on the confidence level and standard error. x

13 Abbreviations ABARES Australian Bureau of Agricultural and Resource Economics and Sciences ABS Australian Bureau of Statistics AES Australian Energy Statistics ANZSIC Australian and New Zealand Standard Industrial Classification BREE Bureau of Resource and Energy Economics BCA Building Code of Australia COAG Council of Australian Governments CO 2 Carbon Dioxide CO 2 e Carbon Dioxide Equivalent DCCEE Department of Climate Change and Energy Efficiency DEWHA Department of Environment, Water, Heritage and the Arts GHG Greenhouse Gas GJ Gigajoule GWh Gigawatt hour HVAC Heating, ventilation and air conditioning EEDaF Energy Efficiency Data Framework EEO Energy Efficiency Opportunities program EWES Energy, Water and Environment Survey GFA Gross Floor Area GLAR Gross Lettable Area Retail HECS Household Energy Consumption Survey IEA International Energy Agency kwh Kilowatt hour kt Kilotonnes, or thousand tonnes LPG Liquefied petroleum gas MEPS minimum energy performance standards MJ Megajoule Mt Megatonne, or million tonnes MWh Megawatt hour NABERS National Australian Built Environment Rating System NEM National Energy Market NFEE National Framework for Energy Efficiency NLA Net Lettable Area NSEE National Strategy on Energy Efficiency NGERS National Greenhouse Energy Reporting Scheme OSCAR Online System for Comprehensive Activity Reporting PCA Property Council of Australia PJ Petajoule RET Department of Resources, Energy and Tourism TAFE Technical and Further Education TJ Terajoule TWh Terawatt hour UCA Unenclosed covered area UFA Usable Floor Area VET Vocational Education and Training xi

14 1. Executive Summary Context This project was commissioned by the Australian Government Department of Climate Change and Energy Efficiency (DCCEE) as part of a joint Commonwealth, State and Territory Government work program under the National Strategy on Energy Efficiency (NSEE). It aims to improve the availability of quantitative information on commercial buildings 1 in Australia, their energy use and associated greenhouse emissions. It is intended to help ground the work of policy makers, analysts, industry, governments, researchers and a wide range of interested stakeholders in a well-founded and shared information base. Terms of reference for this project may be found at Appendix A. The need for improved data on energy use and efficiency in Australia has been recognised for some time. For example, in the National Framework on Energy Efficiency (NFEE) Stage 2 Consultation Report that was released in 2007, it was noted: Scope Fundamental to the development and successful implementation of any new measures under the NFEE will be a comprehensive set of energy efficiency data. Currently energy efficiency data is limited, with little information available about energy use in important parts of the economy, for example commercial buildings. 2 This report covers the majority of commercial building types in Australia including stand-alone offices (base buildings, tenancies, whole buildings), hotels, shopping centres (base buildings, tenancies, whole buildings), supermarkets (tenancies, whole buildings), hospitals, schools, vocational education and training (VET) buildings, universities and public buildings (including galleries, museums, libraries and law courts). This report includes estimates for the building stock, energy consumption by fuel and end use (where possible), and greenhouse gas emissions by State/Territory and region, from 1999 to 2020, with 2009 as the base year. Key Findings Building Stock In 2009, the stock of commercial buildings that fall within the scope of this study amounted to just over 134 million m 2 (see Table 1.1). A further 22 million m 2 of nonstand-alone office space was estimated to be in use in that same year (refer to Chapter 5 for details). The stock increased by 20% over the decade from 1999 and is projected to grow by a further 23% over the 11 years from 2009 to It should be noted that an attempt has been made to standardise area definitions to a net lettable area (NLA), and in the case of retail buildings, gross lettable area-retail (GLAR). The difference between the Gross Floor Area and NLA of Buildings in the CBD could be as much as 25%. 1 Often referred to as commercial buildings, however in the building industry this phrase refers to buildings that are designed to earn a commercial rate of return on investment for their owners, whereas the set of buildings covered in this study includes many public buildings which do not share such an objective. 2 NFEE (2007), p

15 Table 1.1- Non-Residential, Non-Industrial Building Stock, Australia, (floor area in 000m 2 ) Standalone offices Hotels Retail (Shopping Centres) Hospitals Schools Universities VET Buildings Public Buildings Law Courts ,586 9,547 12,584 12,045 34,622 5,561 6,435 1, , ,200 9,825 13,330 11,840 34,932 5,561 6,494 1, , ,814 10,065 13,873 11,651 35,192 6,247 6,530 1, , ,122 9,964 14,484 11,565 35,575 6,686 6,562 1, , ,392 10,305 14,903 11,790 35,958 7,011 6,628 1,729 1, , ,179 10,381 15,608 11,973 36,438 7,034 6,623 1,733 1, , ,751 10,500 16,076 12,295 36,781 7,099 6,582 1,732 1, , ,526 10,438 16,505 12,335 37,375 7,353 6,691 1,738 1, , ,254 10,499 17,461 12,329 38,021 7,640 6,648 1,766 1, , ,271 10,565 18,239 12,462 38,548 8,011 6,686 1,753 1, , ,645 10,692 18,270 12,406 39,248 8,837 6,802 1,772 1, , ,844 10,761 18,658 12,459 40,024 9,312 6,917 1,780 1, , ,316 10,662 19,133 12,508 40,817 9,763 6,964 1,790 1, , ,970 10,826 19,648 12,790 41,134 9,997 7,009 1,800 1, , ,471 11,003 20,234 13,086 41,611 10,233 7,066 1,800 1, , ,064 11,206 20,837 13,506 42,194 10,474 7,142 1,800 1, , ,911 11,424 21,451 13,747 42,763 10,721 7,208 1,800 1, , ,067 11,608 22,036 13,977 43,370 10,974 7,272 1,800 1, , ,403 11,787 22,599 13,984 44,023 11,233 7,338 1,800 1, , ,480 11,970 23,318 14,079 44,690 11,498 7,403 1,800 1, , ,223 12,156 24,039 14,220 45,360 11,769 7,470 1,800 1, , ,736 12,345 24,763 14,451 46,033 12,047 7,537 1,800 1, ,970 Source - BIS Shrapnel Note: area is standardised to a net lettable area concept, excluding external walls, building cores and standard service areas such as toilets, access passageways, storerooms, etc TOTAL 2

16 Table Total Energy Use and Greenhouse Gas Emissions: Australia, , Non-Residential Buildings Stand Alone Totals Hotels Retail Hospitals Education Offices Total Energy Use GHG Total Energy Use GHG Total Energy Use GHG Total Energy Use GHG Total Energy Use GHG Total Energy Use GHG Public Buildings FY: PJ Mt CO 2- Mt CO PJ 2- Mt CO PJ 2- Mt CO PJ 2- Mt CO PJ 2- Mt CO PJ 2- Mt CO PJ 2- e e e e e e e Total Ener gy Use GHG 3

17 The modelled stock growth is a function of many factors, which vary between building types. These are described in chapters 5 to 11. However, a key underlying factor is the expectation of continued growth in Australia s population and economy (see Section 3.6). Estimates of floor area per capita by building type are reported in the body of this Report. While there are variations by state and region in such calculations, they generally show consistent trends through time, providing a reasonable indicator of likely growth in the demand for floor area with a rising population. Other factors, such as changing population demographics, are also taken account, notably in the projections for hospital floor space (see Chapter 10 Hospitals). For background on the stock model, please refer to Appendix C. Total Energy Consumption Total energy consumption 3 in commercial buildings covered by this study is estimated to have been some 135 PJ in 2009, as shown in Table 1.2 above. This figure represents around 3.5% of the 3,907 PJ of gross final energy consumption in Australia in that year. 4,5 Total energy consumption is expected to rise by 24% over the period 2009 to 2020, reaching just under 170 PJ by This reflects a combination of factors including a rising population, rising economic activity, a growing stock of commercial buildings and energy intensity trends that vary considerably by building type. Retail buildings accounted for the largest share of energy consumption in commercial buildings in 2009, consuming approximately 47 PJ or 35% of the total (see Figure 1.1). Office buildings represented the second largest share in 2009, with nearly 34 PJ or 25% of the total energy consumption. However, as discussed in Chapter 5, if non-stand-alone offices are also considered, total energy consumption in offices in Australia could be significantly higher by approximately 26 PJ, and this would result in the total energy in all office building types in Australia above that of retail buildings. By 2020, the share of total energy consumption attributable to stand-alone offices is projected to fall to 23%, while retail s share increases modestly (see Figure 1.2 below). This reflects the projection that energy intensity in offices may fall over the period to 2020, while growth in the office stock (25% by 2020 cf 2009) is slower than retail. By contrast, retail energy intensity is projected to increase, while at the same time as the retail stock grows more rapidly than offices (37% by 2020 cf 2009). The energy consumption shares attributable to other building types are expected to remain largely static. Expected growth in energy consumption by building type over the period from 2009 to 2020 is presented in Figure 1.3 below. 3 Note that all references to energy consumption in this Report relate to the consumption of final energy sources, including electricity: conversion losses associated with the transformation of primary fuels into electricity are not included. 4 ABARES (2011), p This figure is just under half that reported by ABARES for the total energy consumption of the commercial and services sector of the economy in that year (287.4 PJ). However ABARES data includes energy consumption that is unrelated to buildings (such as transportation and process energy consumption), including significant energy using sectors such as waste water/sewage treatment. By contrast, this study only describes building-related energy use and does not cover all commercial building types. Appendix D provides further analysis of top-down data, contrasting this with the bottom-up findings of this Report. 4

18 Figure Total Energy Consumption by Building Type, 2009 (PJ, % shares) 17.2, 13% 2.3, 2% 19.1, 14% 33.6, 25% Stand Alone Offices Hotels Retail Hospitals 15.2, 11% Education Public Buildings 47.2, 35%. Figure Total Energy Consumption by Building Type, 2020 (PJ, % shares) 23.2, 14% 2.2, 1% 38.1, 23% Stand Alone Offices 24.2, 14% Hotels Retail 20.4, 12% Hospitals Education Public Buildings 61.6, 36%. 5

19 Figure Total Energy Consumption: Non-Residential, Non-Industrial Buildings, Australia, 2009 to 2020 (PJ) Energy (PJ) Public Buildings Education Hospitals Retail Hotels Stand Alone Offices Year Fuel Mix Electricity dominates the fuel mix for all commercial buildings in Australia, with a share of almost 83% in 2009 (see Figure 1.4 below). Given the relatively high average greenhouse gas intensity of electricity supply in Australia, this result largely explains why buildings exhibit a larger share of Australia s greenhouse gas emissions than their share of energy use. Natural gas accounted for over 17% of the fuel mix in 2009, while LPG and diesel shares amounted to less than 1% in total. Figure Fuel Mix, All Buildings, 2009 (% shares) 0.6% 0.1% 17.0% Electricity Gas LPG Diesel 82.4%. 6

20 While electricity is the dominant fuel (or energy source) for all the building types studied, the fuel mix does vary considerably by building type. Supermarkets, on average, use close to 100% electricity for their energy needs, while hospitals have the smallest electricity share, on average, at just over 49% in 2009 (balanced by a greater than 47% natural gas share). 6 Offices are also electricity intensive, with an almost 90% electricity share in 2009, while shopping centres are similarly high at nearly 98% electricity. The fuel mix in offices has been largely static since 1999, with only a minor increase in the share of electricity at the expense of natural gas. Schools increased their share of electricity use on average from 72% in 1999 to more than 87% in Public buildings also increased their electricity use as a share of total energy, on average, from just under 60% in 1999 to a little over 70% by 2009, with natural gas shares falling in the same proportion. Looking forward to 2020, we expect the overall fuel mix across the stock of commercial buildings to be similar in 2020 as in the 2009 base year, with electricity continuing to hold around an 83% share of the fuel mix. Further analysis of the fuel mix by building type is provided in the Chapters While a few of the data sets available to this study included records indicating use of GreenPower and/or on-site generation of renewable energy, there was insufficient data to draw statistically significant conclusions. Similarly, the data sets included no statistically significant information on the extent of cogeneration or trigeneration in buildings in Australia. Energy End Use Energy end use is discussed by building type in the Chapters 5-11 below. Consistent with other studies, heating, ventilation and air conditioning (HVAC) is generally the largest end-use of electricity, with lighting and equipment following behind, while space heating is the dominant end use for gas. With respect to the minor fuels, the limited information available suggests that diesel is likely to be used almost exclusively for back-up power generation, while LPG is likely to be used in a wider range of applications - particularly in regions without access to natural gas including use for cooking, water heating (including for swimming pools in hotels), and some space heating. As a typical result, office electrical end use shares are shown in Figure 1.5. Figure Offices (All), Electricity End Use Shares, Average all periods, n=1150 2% 10% 20% 43% HVAC Lighting Total Equipment Domestic hot water Other electrical process 26%. 6 For hospitals and several other building types, no significant time series trend for fuel mix was evident, and therefore, the values reported are averages over the period. Where significant trends are evident, these are reported and modelled in NRBuild. 7

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