CBD Expansion: Feasibility Studycbd.gov.au/files/Energy Consult and Energy Action - CBD...

Energy Action (Australia) Pty Ltd EnergyConsult Pty Ltd ABN 23 103 365 199 ABN 18 090 579 365

CBD Expansion: Feasibility Study

Shopping Centres, Data Centres and Hotels

20 June 2018


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Prepared for:

Commercial Buildings Team

Appliance and Building Energy Efficiency Branch

Energy Security and Efficiency Division

Department of the Environment and Energy

Prepared by: Energy Action and EnergyConsult

Reference: REP08954-A-001

Quality Control

Author Paul Bannister, Paul Ryan

Reviewer Paul Ryan

Report Number REP08954-A-001

Job Number 08954

Edition Date 20 June 2018

Print Date


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Table of Contents Executive Summary............................................................................................ 1

1. Introduction ............................................................................................... 4

1.1 Background 4

1.2 Scope of Study 4

1.3 Approach 5

2. Sector Characteristics ................................................................................ 7

2.1 Shopping Centres 7

2.1.1 Scale of Sector ........................................................................................ 7

2.1.2 Ownership .............................................................................................. 7

2.1.3 Sales and Growth .................................................................................. 10

2.1.4 Rating Systems ...................................................................................... 11

2.1.5 NABERS Penetration ............................................................................. 11

2.1.6 Targeting ............................................................................................... 13

2.2 Data Centres 13

2.2.1 Scale of Sector ...................................................................................... 13

2.2.2 Ownership ............................................................................................ 14

2.2.3 Sales and Growth .................................................................................. 16

2.2.4 Rating Systems ...................................................................................... 17


2.2.6 Targeting ............................................................................................... 18

2.3 Hotels and Serviced Apartments 19

2.3.1 Scale of Sector ...................................................................................... 19

2.3.2 Sector breakdown ................................................................................ 19

2.3.3 Ownership ............................................................................................ 20

2.3.4 Sales and Growth .................................................................................. 21

2.3.5 Rating Systems ...................................................................................... 23


2.3.7 Targeting ............................................................................................... 25

3. Market Failures/Barriers and CBD Expansion Opportunities .................... 28


3.1.1 Principal-agent failures ......................................................................... 29

3.1.2 Information Asymmetry ....................................................................... 29

3.1.3 Search Costs.......................................................................................... 30

3.1.4 Targeting ............................................................................................... 30

3.2 Data Centres 30



3.2.3 Search Costs.......................................................................................... 31


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3.2.4 Targeting ............................................................................................... 31

3.3 Hotels and Serviced Apartments 32



3.3.3 Search costs .......................................................................................... 33

3.3.4 Targeting ............................................................................................... 33

3.4 Mandatory Disclosure for other Sectors 34

3.4.1 Rating Tools .......................................................................................... 34

4. High Level Cost Benefit Analysis ............................................................... 40

4.1 Approach 40

4.1.1 Costs and Benefits ................................................................................ 41

4.1.2 Energy Impacts ..................................................................................... 44

4.1.3 Market Segmentation ........................................................................... 46

4.1.4 Policy Case Parameters ........................................................................ 47

4.2 Inputs and Results 48

4.2.1 BAU Energy Consumption .................................................................... 49

4.2.2 Shopping Centres.................................................................................. 50

4.2.3 Data Centres ......................................................................................... 53

4.2.4 Hotels and Serviced Apartments .......................................................... 56

4.2.4.1 Annual Rating ......................................................................... 57

4.2.4.2 Rating on Sale ......................................................................... 59

4.3 Summary of Costs Benefits and Impacts 62

5. Implementation Issues and Scope ............................................................ 64

5.1 Legal Basis 64

5.2 Scope of Program 64

5.2.1 Shopping Centres.................................................................................. 64

5.2.2 Data Centres ......................................................................................... 65

5.2.3 Hotels .................................................................................................... 65

5.3 Administration 65

5.3.1 Site Capture and Compliance ............................................................... 65

5.3.2 Assessors .............................................................................................. 66

6. Appendix A: Energy Intensity Trends........................................................ 67


6.2 Data Centres 69

6.3 Hotels 72

7. Appendix B: Hotel Rating Systems ........................................................... 76

7.1 Green Globe 76

7.2 Earthcheck 79

7.3 Suitability for CBD 80


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Table of Figures

Figure 1: Cumulative distribution of shopping centre GLAR. __________________________________________ 7 Figure 2: Distribution of Shopping Centre ownership by company type and GLAR. ________________________ 8 Figure 3: Distribution of large portfolio owners by shopping centre count. ______________________________ 8 Figure 4: Distribution of large portfolio owners by shopping centre GLAR. ______________________________ 9 Figure 5: Distribution of Shopping Centre ownership by count and GLAR amongst large portfolio owners. ____ 9 Figure 6: Shopping centre transaction trends from 1996-2016. ______________________________________ 10 Figure 7: Distribution of buyer type in 2016 shopping centre acquisitions. _____________________________ 10 Figure 8: NABERS rated Shopping centres count and proportion of market. ____________________________ 12 Figure 9: Distribution of NABERS rated Shopping Centre ownership by company type and GLAR. ___________ 12 Figure 10: Cumulative distribution of data centre co-location area. ___________________________________ 14 Figure 11: Cumulative distribution of data centre power capacities. __________________________________ 14 Figure 12: Distribution of data centre owners by type. _____________________________________________ 15 Figure 13: Distribution of data centres' co-location area and total power capacities. _____________________ 15 Figure 14: Distribution of ownership of large data centre owners. ____________________________________ 16 Figure 15: Data centre transaction volumes 2015-2017.____________________________________________ 16 Figure 16: NABERS rated data centres count and proportion of market. _______________________________ 18 Figure 17: Distribution of NABERS rated data ownership by company and type. _________________________ 18 Figure 18: Hotel ownership by rooms and site count. ______________________________________________ 20 Figure 19: Distribution of large hotel owners in Australia by room count. ______________________________ 21 Figure 20: Hotels sales volumes 2006-2016. _____________________________________________________ 22 Figure 21: Major hotel owners and operators growth from 2000-2015. _______________________________ 23 Figure 22: NABERS rated hotels count and proportion of market. ____________________________________ 24 Figure 23: Distribution of NABERS rating and owner types who have rated. ____________________________ 25 Figure 24: Relationship between hotel star quality rating and average emissions per room. _______________ 26 Figure 25: Diagram Illustrating the Calculation of Energy, GHG Emission Impacts and Costs Benefit Analysis. _ 41 Figure 26: Conceptual energy use calculations. ___________________________________________________ 44 Figure 27: Market segmentation for BAU energy consumption. ______________________________________ 47 Figure 28: Market segmentation for policy energy consumption. ____________________________________ 48 Figure 29: BAU Energy Consumption for Each Sector. ______________________________________________ 49 Figure 30: Shopping Centres - Cumulative energy savings and emission reductions to 2030 for mid scenario. _ 52 Figure 31: Shopping Centres - Policy and BAU energy consumption and GHG emissions for mid scenario. ____ 53 Figure 32: Data Centres - Cumulative energy savings and emission reductions to 2030 for mid scenario. _____ 55 Figure 33: Data Centres: Policy and BAU energy consumption and GHG emissions for mid scenario. ________ 56 Figure 34: Hotels and Serviced Apartments – Annual Rating: Cumulative energy savings and emission reductions to 2030 for mid scenario. ___________________________________________________________ 58 Figure 35: Hotels and Serviced Apartments – Annual Rating: Policy and BAU energy consumption and GHG emissions for mid scenario. ___________________________________________________________________ 59 Figure 36: Hotels and Serviced Apartments – Rating on Sale: Cumulative energy savings and emission reductions to 2030 for mid scenario. ___________________________________________________________ 61 Figure 37: Hotels and Serviced Apartments – Rating on Sale: Policy and BAU energy consumption and GHG emissions for mid scenario. ___________________________________________________________________ 62 Figure 38: Energy intensity trends for shopping centres with multiple NABERS ratings. ___________________ 68 Figure 39: Earthcheck software in Green Globe certification process. _________________________________ 78 Figure 40: Green Globe certification standards. __________________________________________________ 78 Figure 41: Earthcheck certification standards. ____________________________________________________ 80


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Executive Summary

This report reviews the potential for the mandatory disclosure of energy ratings currently applied to office buildings in Australia under the Building Energy Efficiency Disclosure Act (2010) to be expanded to shopping centres, data centres and hotels/serviced apartments.

Shopping Centres

There are approximately 588 shopping centres of Gross Lettable Area (GLAR) greater than 12,000m2, with a total sector GLAR of 18 million m2. Larger shopping centres are most often owned by public companies or Real Estate Investment Trusts (REITs), while smaller centres are more often privately owned. Shopping centres have a high lease turnover rate and a moderate (10%) sale turnover rate.

Approximately 34% of shopping centres voluntarily rated using NABERS in 2017, and as such this is the recommended metric for disclosure.

The extension of the mandatory CBD for this sector is expected to reduce the market barriers relating to information asymmetry and address a lesser extent principal-agent market failure. A disclosed energy rating is of most use in informing investors as to the performance of a shopping centre, as tenants and customers are unlikely to be motivated by location and footfall issues. The expected benefit cost ratio for operating a mandatory disclosure scheme on sale or lease (effectively annual for this sector) for centres of area>20,000m2 is 2.23, indicating that such a scheme would produce good overall economic returns. On this basis, extension of CBD to shopping centres is supported.

Data Centres

This study has focussed on colocation data centres, where IT rack space is leased to third parties. There are approximately 240 colocation data centres in Australia, although only about 100 are believed to be of significant size. There is an even mix in ownership between public companies and private companies, although the larger owners are public companies. Wholesale colocation data centres have long lease terms (10-15 years) while retail colocation data centres have shorter lease terms (~3 years). Sale turnover of data centres appears high.

The dominant performance metric in this sector is the Power Usage Effectiveness (PUE), but this lacks a quality assurance framework and as a result is open to gaming. Around 4.5% of the sector rated using the NABERS for Data Centres Infrastructure rating up to 2017. This rating is in effect a PUE based metric with a quality assurance framework; as such, it is the recommended metric for disclosure.


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The extension of the mandatory CBD for this sector is expected to principally reduce the market barriers relating to information asymmetry. A disclosed energy rating is of high value to tenants of colocation data centres, while also having some value to inform sales transactions and investor decisions. The expected benefit cost ratio for operating a mandatory disclosure scheme on an annual rating basis for data centres of over 2MW rated facility capacity is 2.21, indicating that such a scheme would produce good overall economic returns. On this basis, extension of CBD to data centres is supported.

Hotels

There are around 250,000 hotel rooms in Australia in around 4400 hotels. Of these, the approximately 1300 hotels with more than 50 rooms, which are considered to be large enough to merit consideration for disclosure account for approximately half the total room count. Hotels are predominantly owned by private companies. Hotel owners may choose to operate the hotel themselves, let out a management contract (typically 3-5 year term), or lease the hotel to a hotel operator (typically 10-15 year lease term). Sale turnover of hotels is very low at around 1%.

There is little use of energy efficiency metrics in this sector, and only 4 NABERS for Hotels ratings were reported in 2017. Nonetheless, the NABERS metric appears to be the only rating system fit for CBD purposes and as such is recommended for disclosure. However it is noted that this rating, which was designed for business hotels, has not been validated for serviced apartments which comprise approximate half of the target market, or for resort hotels.

The extension of the mandatory CBD for this sector is expected to reduce the market barriers relating to information, primarily reducing search costs. A disclosed energy rating is of technical interest only to hotel owners and operators and is unlikely to inform transaction decisions by these parties. However, with appropriate supporting policy, there is potential for a disclosed energy rating to inform guest room booking decisions, particularly for large corporate and government clients. The expected benefit cost ratio for operating an annual mandatory disclosure scheme for hotel owners and serviced apartments over 50 rooms is 1.26, indicating that such a scheme would produce marginally positive overall economic returns. On this basis, extension of CBD to hotels and serviced apartments is only recommended if there is an intent to provide complementary policy, such as energy-efficiency requirements in government accommodation procurement guidelines. In this context it is noted that NSW is reportedly close to introducing such requirements.

Implementation Issues

The Building Energy Efficiency Disclosure Act (2010) is based on sale and lease transactions with a heavy emphasis on the capture of disclosure affected sites through monitoring of advertising. This mechanism is likely to be only moderately effective and certainly inefficient for the shopping centre and data centre sectors, where leasing advertisement follows significant different routes to offices. However, given the relatively small number of sites in these sectors, it is quite feasible to develop a registry of potential sites and monitor these on an annual basis for compliance.


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Data centres may also be captured via monitoring of their websites and a requirement for disclosure of energy ratings in context with offers of lettable space. Similarly for hotels, requirements for energy ratings to be disclosed on websites and in association with room offers appears viable, although extension of this disclosure into overseas room booking engines is unlikely to be feasible.

Estimated Impacts and High-Level Cost/Benefits

The estimated energy and GHG emission impacts of expanding the CBD program by sector are shown in the table below (based on implementation beginning in 2021).

Summary of cumulative impacts to 2030 by sector.

Sector Electricity (TJ) Natural Gas (TJ) Total Energy (TJ) GHG Emissions (kt CO2-e)

Shopping Centres 6,611 408 7,019 1,429 Data Centres 3,128 0 3,128 664 Sub Total 9,739 408 10,147 2,093 Hotels - Annual Rating 2,514 1,354 3,868 615 Hotels - On Sale Rating 480 270 750 118 Total* 12,253 1,762 14,015 2,708

*Total = Sum of Shopping Centres, Data Centres and Hotels – Annual Rating option

The estimated costs and benefits are assessed for expanding the CBD program by sector are shown in the table below.

Summary of High Level Cost Benefit Analysis by Sector (present value $, discount rate of 7%).

Sector Total Costs ($M) Total Benefits (SM) Net Benefits ($M) Benefit Cost Ratio

Shopping Centres 159.6 355.6 196.0 2.23 Data Centres 78.4 176.0 97.6 2.24 Sub Total 238.0 531.5 293.5 2.23 Hotels - Annual Rating 135.0 169.6 34.6 1.26 Hotels - On Sale Rating 5.6 7.0 1.4 1.26 Total* 373.0 701.1 293.5 1.88


The estimated summary impacts for the combined shopping centres and data centres sectors of 10,147 TJ cumulative energy savings and net benefits of $293M are of similar magnitude to the findings of the CBD Program Review (ACIL Allen 2015), which estimates the CBD offices program has a total cumulated saving of 18,250 TJ (inc energy savings post program evaluation date of 2019) and net benefits of $111M.


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1. Introduction

1.1 Background

The Council of Australian Governments (COAG) Energy Council is a Ministerial forum for the Commonwealth, states and territories and New Zealand, who work together in the pursuit of national energy reforms. The establishment of a national regime for the mandatory disclosure of commercial building energy efficiency was identified in the 2004 Energy White Paper, Securing Australia’s Energy Future. It was subsequently adopted by the Ministerial Council on Energy (MCE) in the implementation plans for Stage One of NFEE.

The Commercial Buildings Disclosure (CBD) program was introduced in 2010 through the Commonwealth’s Building Energy Efficiency Disclosure Act 2010. The review of the program in 2015 showed that it has been successful in inducing a change in the behaviour of building owners, operators and tenants in regards to commercial building energy efficiency.

The Department of the Environment and Energy (the Department), has engaged EnergyConsult and Energy Action to provide a concept report to examine the feasibility of expanding the CBD program to other commercial building classes, including: shopping centres, data centres and hotels (including serviced apartments).

1.2 Scope of Study

The aim of this concept report is to examine the feasibility of expanding the CBD program to other commercial building classes, including: shopping centres, data centres and hotels (including serviced apartments). The policy intent is to encourage cost-effective energy efficiency improvements which would not happen otherwise. This report also examines the key issues associated with the development and implementation of a national regime, including the examination of key technical, administrative and legal issues in the context of each commercial property sector listed above.

The report includes a high-level analysis of the costs and benefits for the most feasible options for expanding the program. This preliminary economic analysis will help determine whether there is a case for further investigating the expansion of the program, and a more detailed economic analysis will be undertaken at a later stage.


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As the Review of the CBD Program1 has shown, mandatory disclosure of energy efficiency information works well in the office market. The CBD Program currently utilises the NABERS Energy for Offices rating tool for measuring the energy performance of a building. NABERS rating tools for measuring energy currently exist for shopping centres, data centres, hotels and public hospitals, however consideration is also given to alternative rating tools if suitable. Consideration of a rating approach which could be applied to all other classes of commercial buildings would need to be included.

1.3 Approach

The project team undertook research to characterise the targeted sectors and obtain information to review the following key areas:

The availability of tools to provide ratings for the nominated building types (shopping centres,

data centres and hotels), and the suitability of these for application in a CBD environment

(cost, coverage, accuracy, industry acceptance). This will include a review of locally available

tools (NABERS, Green Star as well as international precedents for tools where the local tools

do not meet the requirements for CBD (e.g. Energy Star, UK DECs, UK EPCs, etc)

The technical and market drivers that a disclosed performance rating would support, including

shareholder/ethical investment drivers; cost reduction; emissions reduction; and

advertisement of environmental credentials to potential customers (tenants, guests,

shoppers, data centre lessees).

The transaction points or other timing points that could be used to trigger disclosure for each

building type, e.g. lease, sale, time period.

The ability of the Commonwealth to legislate requirements for the relevant sector via the

Companies Act route used for CBD.

The broader rating framework needed to extend CBD to all classes of buildings, including the

types of rating suitable for different building types, the potential stakeholders to the rating

information and the potential transaction points at which disclosure would be possible or

relevant.

The policy case was researched to identify and provide evidence of the market factors that might support government intervention. This included research of the problem, including the potential market failures, and the cost and benefits of government action. The high level cost benefit analysis (CBA) was developed within CBA methodological frameworks used to support RISs. This includes:

BAU energy consumption and projections, based on the commercial baseline study and

international research

1 Commercial Building Disclosure Program Review ACIL Allen 2015


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Energy efficiency measure energy savings (valued at customer energy tariffs as projected by

AEMO) and costs, applicable to each of the three sectors

Likely take up of measures under the policy option, including the estimated influence of the

policy and interaction of market/business operational characteristics of that sector

Scope and magnitude of costs (Industry regulatory burden costs, government costs)

GHG Emission reductions (annual and cumulative) based on agreed emission factors

NPV costs and benefits over agreed projection period

The CBA tested a number of key policy parameters under three scenarios.

The outcomes of this study are provided in the following sections.


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2. Sector Characteristics

2.1 Shopping Centres

2.1.1 Scale of Sector

There are over 1,753 shopping centres in Australia that exceed 1,000 m2 of GLAR (Gross Lettable Area Retail)2. 588 shopping centres of at least 12,000m2 GLAR formed a representative sample used in this analysis. The total GLAR for this sample is 18,137,968 m2.

Figure 1: Cumulative distribution of shopping centre GLAR.

2.1.2 Ownership

Private owners account for 74% of all shopping centre count but only 35% of all GLAR in this sample. The distribution in terms of GLAR is heavily in the favour of public and trusts compared to private ownership as seen in Figure 2. Foreign ownership is not specifically identified in the following figures, however these owners are included in the Private category.

2 Urbis, August 2015, Australian Shopping Centre Industry

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Figure 2: Distribution of Shopping Centre ownership by count and GLAR.

Large shopping centre portfolio owners (>=10 Shopping Centres) represent 45.2% of the sample in terms of centre count and 57.1% of the total sample GLAR.

Figure 3: Distribution of large portfolio owners by shopping centre count.

Private, 178, 74%

Public, 24, 10%

Trust, 37, 16%

Distribution of SC owners by Company type

Private, 6390921, 35%

Public, 7849462, 43%

Trust, 3897585, 22%

Distribution of ownership by GLAR

Federation Centres, 36, 14%

Colonial First State , 34, 13%

Westfield Group, 33, 12%

Stockland, 26, 10%

Charter Hall REIT, 17, 6%

Mirvac Property Trust, 15, 6%

QIC Global Real Estate, 15, 6%

ISPT (Core Fund), 13, 5%

AMP Capital Investors , 12, 4%

Lend Lease, 12, 4%

Retail Direct Property, 12, 4%

YFG Shopping Centres, 11, 4%

DEXUS (SAS Trustee Corporation), 10, 4%

Strata Plan, 10, 4%

Undisclosed Private Investor, 10, 4%

Number of Shopping Centres under each Major Ownership (>=10 centres)


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Figure 4: Distribution of large portfolio owners by shopping centre GLAR.

The largest owner by number of centres is Vicinity Centres who own a combined total of 49 centres (36-Federation Centres, 12-Retail Direct Property and 1-Centro Properties Group) and the largest owner by GLAR is Westfield at 2,873,113 m2. The ownership distribution amongst the top 15 owners is dominated by public and trusts as opposed to private.

Figure 5: Distribution of Shopping Centre ownership by count and GLAR amongst large portfolio owners.

Federation Centres, 1006538, 10%

Colonial First State , 1530823, 15%

Westfield Group, 2873113, 28%

Stockland, 754346, 7%

Charter Hall REIT, 269007, 3%

Mirvac Property Trust, 391826, 4%

QIC Global Real Estate, 982869, 9%

ISPT (Core Fund), 238780, 2%

AMP Capital Investors , 500233, 5%

Lend Lease, 375724, 4%

Retail Direct Property, 233830, 2%

YFG Shopping Centres, 340946, 3%

DEXUS (SAS Trustee Corporation), 526888, 5%

Strata Plan, 186134, 2%

Undisclosed Private Investor, 140472, 1%

GLAR of Shopping centres under each Major Ownership (>=10 centres)

Private, 46, 17%

Public, 158, 60%

Trust, 62, 23%

Distribution of top 15 owners by SC count

Private, 1650421, 16%

Public, 6539679, 63%

Trust, 2161429, 21%

Distribution of top 15 owners by GLAR


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2.1.3 Sales and Growth

Shopping centre transactions have been showing a generally increasing trend in the last 20 years in terms of transaction value. There is also a growing foreign acquisition.

Figure 6: Shopping centre transaction trends from 1996-2016.

2015 saw a record high 2213 (12.6%) centre transactions and 2016 is estimated to be 165 (9.5%).

There is also a shift in ownership towards public and trusts which accounted for 36% of all acquisition costs compared to the 22% from private investors.

Figure 7: Distribution of buyer type in 2016 shopping centre acquisitions.

3 JLL, 2017, Australian Shopping Centre Investment Review and Outlook 2016


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The shopping centre industry has been growing at an average of 24 new centres (>1,000m2 GLAR) per year since 19574.

2.1.4 Rating Systems

There are two environmental rating systems in common use in the shopping centre industry, being NABERS and Green Star.

NABERS provides a specific benchmark for in-operation energy use based on the base building services of the shopping centre. It is noted that the actual coverage of this varies significantly between different shopping centres, from those that provide little more than common area servicing to those that provide full air-conditioning services to tenants. NABERS generates a custom benchmark that allows for these service differences. This enables the rating to be applied across the diverse range of shopping centre configurations but does means that the coverage of the rating is not the same for each shopping centre.

Green Star is a broader based environmental rating of which energy in design is a subcomponent; similarly Green Star’s performance rating includes an allowance of an energy assessment as part of the total assessment. Other aspects of Green Star include indoor environment quality, waste, refrigerant emissions, and other such broader issues. Based on publicly available data5 there are 11 shopping centres registered under Green Star, although not all of these encompass the entire site. The extent of use of Green Star performance in the shopping centre sector is not known.

As the precedent from the office sector is the use of a single dimensional operational rating, it would appear that NABERS Energy is the logical tool to consider for this sector.

2.1.5 NABERS Penetration

NABERS rating data is available since FY 2010 for the number of shopping centres rated. There has been a steady increase since 2010. The data for FY2018 is incomplete as it is still in progress.

4 Shopping Centre Council of Australia, http://www.scca.org.au/industry-information/australian-shopping-centre-industry/ 5 https://www.gbca.org.au/project-directory.asp

http://www.scca.org.au/industry-information/australian-shopping-centre-industry/

http://www.scca.org.au/industry-information/australian-shopping-centre-industry/


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Figure 8: NABERS rated Shopping centres count and proportion of market.

45 owners (total portfolio of 222 centres covering 38% of the sample and 54.6% of the GLAR) were responsible for a total of 739 ratings for the whole data collection period. For the period FY2017, the total rated GLAR was 6,512,966m2 which accounts for 34% of the total sample GLAR. 13 of the top 15 owners have rated their centres. The distribution of these owners and the ratings are shown below:

Figure 9: Distribution of NABERS rated Shopping Centre ownership by count and GLAR.

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Percentage of all Shopping Centres Rated Number of Shopping Centre Ratings

Private, 18, 40%

Public, 15, 33%

Trust, 12, 27%

Distribution of NABERS rated Owners

Private, 94, 13%

Public, 393, 55%

Trust, 230, 32%

Distribution of NABERS ratings by Owner Type


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2.1.6 Targeting

For the purposes of this study, we have calculated the BAU energy use of shopping centres greater than 12,000m2, however scope of the mandatory disclosure impacts is calculated for shopping centres of more than 20,000m2 GLAR. This subgroup comprises 302 centres with a total GLAR of 13.9 million m2.

2.2 Data Centres

This study only considers colocation data centres, where space is leased to users, as opposed to owner-operated data centres. Colocation data centres can be categorised as either wholesale or retail. Wholesale colocation data centres lease out larger spaces for leases of typically longer than 10 years, whereas retail colocation data centres lease down to rack level on terms typically of the order of 3 years.


There are currently at least 240 co-location data centres in Australia listed on the online register cloudscene6 with the technical information on area and power capacities available for 100 of them. For the 100 data centres, the total co-location area is 365,874m2 and supplied power capacity 1003.64MW. The division between retail and wholesale centres is unknown.

Note that the colocation data centres form a relatively small proportion of the total data centre population which has been estimated at 50,0007, bearing in mind that this includes many small data rooms.

6 https://cloudscene.com/market/data-centers-in-australia/all 7http://www.computerworld.com.au/article/456761/australian_data_centre_spending_hit_2_billion_2013_analyst

https://cloudscene.com/market/data-centers-in-australia/all


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Figure 10: Cumulative distribution of data centre co-location area.

Figure 11: Cumulative distribution of data centre power capacities.

2.2.2 Ownership

Ownership is shared between publicly listed and private companies.

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Distribution of Data Centres Supplied Power Capacities


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Figure 12: Distribution of data centre owners by type.

For the 100 data centres with technical data, the even divide between public and private data centres is even more evident.

Figure 13: Distribution of data centres' co-location area and total power capacities.

All larger owners (>5 data centre portfolios) are public companies. The largest owner is TPG who owns a total of 28 data centres from AAPT, Intervolve and Pipenetworks.

Private, 142, 59%

Public, 97, 40%

Trust, 1, 1%

Distribution of Data Centre Ownership

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Distribution of Data Centre Co-location Area

Public, 502.95, 50%

Private, 499.59, 50%

Distribution of Data Centre Power Capacity


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Figure 14: Distribution of ownership of large data centre owners.


The transaction activity for data centres saw a record high $20b in 2017 with 48 deals, each of which could comprise one or more data centres; as a result, the ownership churn rate appears high. This is to be expected in a rapidly growing industry: Frost and Sullivan8 state that “Australian Data Centre Services Market [is expected to] to grow at 12.4% CAGR to 2022; will reach A$2.055 Billion by 2021”.

Figure 15: Data centre transaction volumes 2015-2017.

8 Frost and Sullivan, 2016, Australian Data Centre Services Market 2016


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There are two energy benchmarks used in the data centre industry, being PUE and NABERS.

The PUE, or power usage effectiveness, is the dominant method of assessing data centre energy efficiency. The PUE is the ratio of total data centre energy use to IT energy use. However, the industry use of this figure lacks accreditation or quality control, leading to considerable doubt as to the veracity of much of the published information. Specific areas that are open to manipulation are the timeframe and timing of measurement (a whole year, or just a particularly advantageous day?) and the boundaries (what is counted within the IT equipment energy and what is not). While some protocols exist around these issues9, there is no mechanism for enforcement. A further issue with PUE is that it has no mechanism for assigning different weights to electricity and gas.

NABERS Energy for Data Centres (Infrastructure rating) is in essence a PUE calculation with greater regulation of the timing (one year) and boundaries (equivalent to Green Grid Type 2 PUE measurement). NABERS weights electricity and gas based on greenhouse emissions and factors this into its PUE-like calculation.

We also note that in the US, Energy Star has a rating tool for data centres10, which is in many ways similar to NABERS; but we have not heard of this tool being used in Australia. Given the quality assurance issues with PUE, it would appear that NABERS Energy is the logical tool to consider for this sector; any alternative would essentially need to achieve the same level of quality assurance to be credible.


NABERS rating data is available since FY 2014. There has also been a small increase in ratings since its introduction.

9 E.g. GreenGrid is a US industry organisation that has resources available on this issue www.greengrid.com 10 https://www.energystar.gov/buildings/tools-and-resources/energy-star-score-data-centers


18

Figure 16: NABERS rated data centres count and proportion of market.

6 owners (4 public, 2 private) were responsible for 35 of the ratings and their distribution shown below:

Figure 17: Distribution of NABERS rated data ownership by company and type.

2.2.6 Targeting

For the purposes of this study, we have calculated the BAU energy use of colocation data centres, however scope of the mandatory disclosure impacts is calculated for colocation data centres with a total rated facility capacity of more than 2 MW. This subgroup comprises 67 centres with a total rated facility capacity of 970MW.

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

4.00%

4.50%

5.00%

0

2

4

6

8

10

12

FY14 FY15 FY16 FY17 FY18

Percentage of all Data Centres Rated Number of Ratings

Private, 4, 11%

Public, 31, 89%

Distribution of NABERS ratings by Owner Type

Interactive, 3, 8%

Metronode, 8, 23%

Fujistu, 19, 54%

Digital Reality, 1, 3%

NextDC, 3, 9%Equinix, 1, 3%

Distribution of NABERS ratings by Company


19

2.3 Hotels and Serviced Apartments


There are at least 248,573 accommodation rooms in Australia as of June 201511 and the top 15 owners account for 12.5% of all rooms. The last audit of hotel count in Australia in 2009 estimated at least 6,80712 and growing, although as discussed in Section 2.3.2, this figure is probably inflated by hotels providing very limited accommodation.

2.3.2 Sector breakdown

There is some ambiguity about the boundaries of what is defined as a hotel. However ABS 8635.0 Tourist accommodation, Australia identifies the following data13:

Year

Licenced hotels establishments, >= 15 rooms

Licenced hotels rooms available, >=15 rooms

Motels establishments, >= 15 rooms

Motels rooms available, >= 15 rooms

Serviced apart-ments establishments

Serviced apart-ments, number of rooms

2009 859 85,181 2,477 87,455 974 54,001

2010 857 86,489 2,464 86,857 972 54,409

2011 841 86,193 2,440 86,120 962 55,110

2012 858 88,362 2,416 85,315 979 55,679

2013 850 88,416 2,392 85,086 985 56,928

2014 956 97,864 2,372 84,428 1,071 63,793

2015 963 97,560 2,435 86,757 1,078 65,276

2016 967 98,315 2,404 86,636 1,074 65,180

As this matches the hotel rooms data but not the hotel count data, it seems probable that the AHA figure of 6807 hotels includes some hotels that provide very limited accommodation. The ABS figure of 4445 hotels/motels and serviced apartments in 2016 should be used as the reference.

11 JLL, 2016, Top Owners and Operators Australian Accommodation Survey 2015 12 AHA, 2009, An Overview of the Australian Hotels Industry 13 Selected data from ABS 8635.0. Data presented are from the December quarter, except for 2016, where the presented

data are from the June quarter


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2.3.3 Ownership

The vast majority of hotel owners are private owners as shown in Figure 18. This is also the case amongst the top 15 largest hotel portfolio owners by rooms.

Figure 18: Hotel ownership by rooms and site count.

The largest hotel owner is the foreign state fund Abu Dhabi Investment Authority. However, the shift in ownership is largely due to publicly listed companies such as Ascendas14, Mirvac,15 Intercontinental16 selling off assets.

14The Australian, 2015, Ascendas’s $1.4bn hotels portfolio for sale 15 SMH, 2011, Mirvac offloads $327m hotel assets 16 Hospitalitynet, 2005, InterContinental Hotels Group to sell hotels in Australia, New Zealand and Fiji

Public, 8126, 30%

Private, 17179, 64%

Trust, 1702, 6%

Distribution of Hotel Rooms Ownership

Public, 38, 28%

Private, 91, 67%

Trust, 6, 5%

Distribution of Hotel Count Ownership


21

Figure 19: Distribution of large hotel owners in Australia by room count.


Hotel transactions have also been generally increasing over the last 10 years with both 2015 and 2016 seeing around 70 (1.03%) hotels changing owners17.

17 Savills, 2017, Savills Hotels Market Report

Abu Dhabi Investment Authority

19%

Meriton Serviced Apartments

8%

Schwartz Family Company

7%

Event Hospitality and Entertainment Limited

(formerly Amalgamated Holdings Limited )

11%

Toga Far East Hotels11%

Crown Limited3%

Tucker Box Hotel Group7%

TCC4%

Ascendas Hospitality Trust4%

Hotel Grand Central5%

StayWell Group7%

iProsperity4%

Stamford Land Corp4%

Hawaiian2%

BG Investment Holdings4%

Distribution of Top 15 Hotel room Owners


22

Figure 20: Hotels sales volumes 2006-2016.

There is also consistent growth in the number of hotel rooms being added to the market. ABS reported that “accommodation rose by 3.9% in June 2016…followed increases of 4.5% in March 2016 and December 2015 and 4.3% in September 2015”18

18 ABS, 2016, http://www.abs.gov.au/ausstats/[email protected]/mf/8635.0/


23

Figure 21: Major hotel owners and operators growth from 2000-2015.


There are multiple environmental ratings for hotels. Examples include:

Green Star is a broader based environmental rating of which energy in design is a subcomponent; similarly Green Star’s performance rating includes an allowance of an energy assessment as part of the total assessment. Other aspects of Green Star include indoor environment quality, waste, refrigerant emissions, and other such broader issues. Based on publicly available data19 there are 6 hotels registered under Green Star. The extent of use of Green Star performance in the shopping centre sector is not known.

There are several tourism/accommodation sector specific rating tools including Earth Check20, Green Globe21. These adopt a mix of practice and measurement, based approaches across a wide range of sustainability criteria. These are described in more detail in Appendix B.

In the US, Energy Star has a rating for hotels which is substantially similar to NABERS Energy for hotels22

19 https://www.gbca.org.au/project-directory.asp 20 https://earthcheck.org/products-services/certification/benchmarking-and-certification/ 21 https://greenglobe.com/green-globe-certification/ 22 https://www.energystar.gov/buildings/tools-and-resources/energy-star-score-hotels


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NABERS Energy for hotels provides a rating of operational performance, with normalization for hotel star quality rating, room numbers and other key facilities. NABERS was developed solely based on data for business hotels, and its applicability to resort hotels and serviced apartments has not been established. No equivalent rating for these sites is known.

NABERS is the only rating for which we have detailed data on market penetration, although the general indication is that no tool has widespread use.


NABERS rating data is available since FY 2009. There number of ratings peaked in 2012 but has been very low in the past 4 years, and has never been particularly significant as a fraction of the market.

Figure 22: NABERS rated hotels count and proportion of market.

35 owners were responsible for 158 ratings and 7 of the top 15 owners are amongst them. 76 unique hotels have been rated which covers 1.71% of the market. These 76 hotels account for 20,633 rooms which covers 8.30% of the hotel market.

0.00%

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0.40%

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0

5

10

15

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25

30

35

FY09 FY10 FY11 FY12 FY13 FY14 FY15 FY16 FY17 FY18

Percentage of all Hotels Rated Number of Ratings


25

Figure 23: Distribution of NABERS rating and owner types who have rated.

Private owners are the dominant majority amongst those who have NABERS ratings.

2.3.7 Targeting

Unlike data centres and shopping centres, the hotels sector is very diverse, which creates risks in relation to the application of mandatory disclosure.

The June 2016 ABS data set23 defines Tourist Accommodation as a sum of the subsets of motels/private hotels/guest houses (2404 sites), hotels of greater than 15 rooms (967 sites), and serviced apartments (1074 sites). As the first category only contains small establishments, it is of limited interest for mandatory disclosure and can be discarded. Hotels and serviced apartments are also differentiated by quality rating, as measured by the ASRS star rating system. The latest star grading distribution for hotels and serviced apartments is obtained from a June 2013 ABS data set24 which highlights the number of establishments per star category and also the room count for each star category and is summarized in Table 1.

23 ABS, 2015-2016 June, 8635 Tourist Accommodation 24 ABS, 2013, 8635 Tourist Accommodation Small Area Data Table 1


26

Table 1: Hotel and serviced apartment population distribution by star quality rating.

Star Grade 1 2 3 4 5

Hotels 17 109 265 308 74

Rooms 661 3,119 16,381 45,561 19502

Rooms/hotel 39 29 62 148 264

Percentage of total sites 2% 14% 34% 40% 10%

Percentage of rooms 1% 4% 19% 53% 23%

Serviced Apartments 0 12 298 609 38

Rooms 0 380 11,230 39,212 4,153

Rooms/site 0 32 38 64 109

Percentage of total establishments 0% 1% 31% 64% 4%

Percentage of rooms 0% 1% 20% 71% 8%

Setting a notional minimum of 50 rooms for mandatory disclosure, it can be seen that this directs the majority of the sample at 3-5 star hotels and 4-5 star serviced apartments, which collectively comprise 84% of hotel sites and 95% of hotel rooms, and 68% of serviced apartment sites and 79% of serviced apartment rooms. A further weighting factor arises from the fact that the average emissions per room is strongly linked to the quality, as shown in Figure 24 below for hotels25.

Figure 24: Relationship between hotel star quality rating and average emissions per room.

Extrapolating the relationship in Figure 24 to serviced apartments, it is possible to estimate the percentage of emissions arising from each star quality rating category, as shown in Table 2.

25 Source: NABERS for Hotels: Benchmark Development Exergy Australia report (Ref XA-CR-329) for NABERS 15 October 2008. Used with permission


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Table 2. Hotel and serviced apartments emissions by star quality rating

Star 1 2 3 4 5

Hotels 17 109 265 308 74

Rooms 661 3,119 16,381 45,561 19,502

Emissions (tonnes) 1,322 7,798 139,239 683,415 438,795

% of total hotels emissions 0.1 0.6 11 53.8 34.5

Serviced Apartments 0 12 298 609 38

Rooms 0 380 11,230 39,212 4,153

Emissions (tonnes) 0 950 95,455 588,180 93,443

% of total serviced apartment emissions 0 0.1 12.3 75.6 12.0

Based on the above it can be seen that the target group of larger hotels and serviced apartments comprises 1,294 sites and 95.1% of the relevant emissions, at an average rate of 15,489 kgCO2/room/year for hotels and 15,718 kgCO2/room/year for serviced apartments.


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3. Market Failures/Barriers and CBD Expansion Opportunities

The purpose of this concept report is to examine the feasibility of expanding the CBD program to other commercial building classes, including: shopping centres, data centres and hotels (including serviced apartments). The intent of the CBD program is to encourage energy efficiency improvements that would not otherwise occur. Therefore, to test the extent of the feasibility of expanding the program, it must be evaluated on how well the policy instrument will address the market failures and barriers, in each of the sectors under consideration.

The prime market failures and barriers that have been identified are:

Principal-agent market failures causing non-optimal lowest life cycle cost outcomes. This is the clearly the case where the building owner is responsible for the energy efficiency investments however the tenant receives the benefits through lower outgoings. While it is in theory for the owner to increase rent to balance the reduction in outgoings, this typically takes some time to implement. As a result, this issue can significantly reduce the motivation for the owner to invest in energy efficiency.

Information asymmetry. An information failure exists when buyers lack sufficient information about a product or service, or the information between buyers and sellers is asymmetrical - leading to sub-optimal buyer choices. An example of this failure occurs when a purchaser of a lease or building is not aware of the ongoing energy performance of the building and has a choice of properties to purchase or lease, however they cannot compare the building’s comparable energy performance. Related to this failure is also the lack of information on the GHG emissions, which are not generally reported, but may be an important factor for certain buyers.

Search Costs. This barrier is related to time and cost barriers associated with gathering and analysing information in order to inform a decision; in the present case, this relates to the effort required to uncover information on building energy performance that is not disclosed by the seller or owner to a prospective purchaser or tenant. These costs of time and effort are often perceived as larger than the benefit of obtaining the information. In the context of this report, search costs also include the internal barriers of time and effort that prevent building owners and operators from quantifying and understanding their own energy performance.


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The main market failure that the CBD program addresses is lack of information. The CBD program provides a comparable rating on the energy performance and hence operating costs of the facility, which a buyer can use as part of their decision-making process. As each of these sectors have different characteristics of ownership, operation, purchase/lease arrangements and business motivations, it is important to assess the effectiveness of the CBD program in the context of each sector.


3.1.1 Principal-agent failures

Shopping centres generally pass their energy costs through to their tenants, and as a result the savings from energy efficiency investments by the owners are not directly recovered by the owners, although it is feasible for an owner to adjust, over time, the balance between outgoings and rent to create improved return on the asset via efficiency investment. Overall, however, the classic principal-agent failure model applies.

3.1.2 Information Asymmetry

Tenants are unlikely to be motivated by a disclosed NABERS rating, as the shopping centre location and footfall are of paramount importance. Similarly shoppers are unlikely to be motivated by a NABERS rating, and indeed, allowing for transport costs, it is not clear that such motivation would produce a net environmental benefit. As a result the information asymmetry is not considered to be a major issue for these stakeholders.

There is evidence for this sector suggests that investors (purchasers) are motivated to discover and improve the energy performance and lower the GHG emissions. This is supported by the larger proportion of the market who are exposed to investor sentiment (public and trust ownership companies) that are voluntarily rating shopping centres with NABERS (87% of area rated from 2010 to 2017).

The expansion of the CBD to this sector would enable all potential purchasers to obtain the information on energy performance and compare buildings publicly, which would reduce information asymmetry for this important transaction group. However, privately owned shopping centres have limited exposure to investor sentiment are would be little affected on this basis as a result.


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3.1.3 Search Costs

All ownership types of shopping centres have the potential to receive the benefit of lower search costs through a disclosed mandatory rating, as this will provide them with a direct signal as to the efficiency of their building. This may encourage additional investment in energy efficiency in order to lower outgoings and increase their yield. In the case of the mandatory CBD program in the office sector, a large segment of the market that were not rating before the program and were required to disclose provided the majority of the benefits (ACIL Allen 2015)26.

3.1.4 Targeting

The modelling in Chapter 4. High Level Cost Benefit Analysis assumes that only shopping centres with at least 20,000 m2 of GLAR are included in the scope. This limit is set as the vast majority of investor owned shopping centres are above this size, and it will reduce the burden on smaller privately owned shopping centres. The CBA also assumes that of these shopping centres that are not currently rating, that 75% will improve their energy efficiency as a result of the expansion of the CBD program.

As the implementation of CBD would require the disclosure of the rating at the lease of space, and retail leases are continuously offered in shopping centres, the implementation would effectively require shopping centres to rate annually.

3.2 Data Centres


Energy costs for infrastructure operation are on-charged by colocation data centre owners to tenants. Tenants also pay for their own energy use at rack level. This means that infrastructure energy efficiency investments by the owners benefit the tenants rather than the owner. In a retail data centre environment, the short duration of leases means that it is possible for an owner to claw back such savings by increasing rent and decreasing outgoings over time. However for wholesale leased data centres, this is not possible, and the tenant may not be motivated or even empowered to make investments themselves.

Owner operated data centres do not have a principal-agent dynamic and thus do not suffer this issue.

26 Commercial Building Disclosure Program Review ACIL Allen 2015


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As the costs of data centre operation are passed on to tenants, information on this is of value to tenants but is not generally available in a comparable format. Where data is available, it is frequently available in the form of a PUE. However, the use of PUE as an efficiency measures is open to significant abuse due to the lack of a formal, certified definition of PUE. Thus, a data centre may advertise a PUE based not on their annual PUE but under a short period of high efficiency operation; most potential tenants would be none the wiser.

The high sales turnover in this sector supports the value of information on performance being available to purchasers at the time of purchase in order to inform investment decisions.

3.2.3 Search Costs

For data centre owners of all varieties, the availability of performance information carries some potential to drive action to improve efficiency. As noted previously, the lack of a standardised protocol for assessment of PUE leads to considerable ambiguity in this respect, thereby providing additional value for a quality assured rating such as NABERS. For data centre users (tenants), the availability of a publicly verified NARERS rating would dramatically lower their search costs and enable easier comparisons.

For owner-operated data centres, the effective cost of assessment of any form of infrastructure rating is often very high due to the lack of suitable metering; while whole-facility assessment has some potential for this sector, it only addresses a search cost issue and has little potential to inform transactions or market driven behaviours.

3.2.4 Targeting

Targeting of mandatory disclosure at colocation data centres makes good sense because it addresses multiple market failures. Colocation data centres appear to be relatively well identified in the market, but it will be necessary to provide a minimum size factor, probably assessed in terms of connected IT electrical capacity.

For retail colocation data centres, lease transactions are sufficiently frequent that a requirement to rate on lease is effectively a requirement for an annual rating. For wholesale data centres, there is value in a more frequent declaration of efficiency as the lease periods are long by comparison to the technical half-life of the site performance. Together these factors favour an annual rating.

The modelling in Chapter 4. High Level Cost Benefit Analysis, assumes that only colocation data centres (who offer leased space for IT equipment) with at least 2MW of total rated facility load are included in the scope. This limit is set as the vast majority of colocation owned data centres are above this size, while nonetheless excluding many smaller data centres.


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3.3 Hotels and Serviced Apartments


Hotels operate in a variety of business models, including:

Strata owned units with a management contract (particularly in the serviced apartments sector)

Owner operated

Leased – typically for periods in excess of 10 years.

Owned with a management contract.

In most instances:

Management contracts are of shorter duration (3-5 years) and energy costs and investments are solely the responsibility of the owner.

Leased hotels pass responsibility for investment and energy cost payment to the lessee

Thus as a whole, the costs and benefits of energy efficiency investment are in the domain of a single party. However, evidence from a range of energy audits conducted by Energy Action suggests that capital for upgrades is prioritised towards guest-facing facilities ahead of back-of-house engineering. Furthermore, although leases may be 10-15 years, plant life is longer than this and lessees have little motivation to make long term investments that they may not see the full value from. As a result, this is a sector where capital investment behaviour mimics the outcomes of a principal-agent failure even though the energy efficiency cashflow pathways would not seem to support this.


Although there is scope for a disclosed rating to affect the decisions of a potential buyer or lessee, the low churn rate for sales and leases means that this is a very slow process; in the context that a rating requires market presence to gain credibility as valuable information, a rating on sale or lease would develop too slowly to change market behaviour on any reasonable timeframe. Conversely, an annual rating would carry a high overhead of ratings that do not inform any sale or lease transactions.

The one area where a mandatory rating could affect information asymmetry is for hotel guests – particularly under large government and corporate contracts. Availability of disclosed ratings could be used by these agents to target procurement at more efficient hotels. Given the importance of guest numbers to hotels, this has potential to be a powerful driver for efficiency investment.


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3.3.3 Search costs

Lack of information on hotel efficiency affects hotel operators, lessees, owners and guests alike. That there is a desire for such information is evident from the existence of other hotel rating systems as well as the emphasis placed on issues such as laundry minimisation. Availability of a disclosed rating has the potential to inform all these stakeholders in a manner that could create positive outcomes for efficiency.

3.3.4 Targeting

The high level of diversity in the hotels sector means that mandatory disclosure would need to be well targeted. As discussed in Section 2.3.7, it has been assessed that a minimum size of around 50 rooms provides a reasonable minimum for mandatory disclosure.

Two options are modelled in Chapter 4. High Level Cost Benefit Analysis:

Annual Rating – applied to all hotels and service apartments of greater than 50 rooms to

enable a system of guest selection of hotels.

Rating on Sale - applied to all hotels and service apartments. We assume that all hotels and

serviced apartments of greater than 50 rooms are required to obtain and disclose their rating

on sale. However, the very low proportion of sales means that only 1% pa of the stock are

included each year.


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3.4 Mandatory Disclosure for other Sectors

Although the primary purpose of this report is to review the prospects for expansion of mandatory disclosure to shopping centres, data centres and hotels, the brief includes the question of how other building sectors could be addressed.

3.4.1 Rating Tools

NABERS rating tools have been identified as the preferred rating tool for shopping centres, hotels and data centres. This has been because of the following key features:

1. Existence. For shopping centres and hotels there is no comparable energy rating available.

2. Quality assurance. For data centres, while the PUE metric has wider use than NABERS, it lacks a quality assurance system, with the result that reported PUE figures are unreliable.

These two factors are critical for the core sectors covered in this report. However, these are not the only factors that are relevant when considering the suitability of ratings. In a more general sense, ratings must also meet the following criteria:

1. Measurability. It must be possible to assess the rating without imposing undue costs in measurement or assessment.

2. Normalisation and relevance. All performance based ratings require some form of normalisation to be able to be use as an assessment of efficiency rather than, for instance, scale. Furthermore, there is a need to be able to correct for function, e.g. NABERS for Offices Base Building ratings are corrected for area, hours and climate. However, for retail shops, for instance, such a correction for function could be quite complex, taking into consideration the wide range of different types of shop. There is little value in a rating that says that all supermarkets are “bad” and all hardware warehouses are “good” because of their relative energy intensity figures.


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3. Relationship to outcomes and transactions. In most cases, the question in the mind of a prospective buyer or lessee is “how much will this cost to run?” For most commercial building types, this is best answered using a rating that is based on actual energy use. This contrasts with the approach used in many overseas jurisdictions, where mandatory disclosure most frequently focuses on the design features of the building rather than its actual operation. The UK and EU EPC systems take this line. As the relationship between design and performance is intermediated by a wide range of factors, there is no guarantee a good design will create a good performance and indeed some evidence that even quite poorly designed buildings can be made to perform relatively well. As a result, these design based systems are not well suited to provide the information needed by prospective purchasers of lessees27.

4. Target audience. For many commercial building types, measured energy use can be converted into a meaningful measure of technical efficiency if one is looking at the base building. This is because the base building performance is dominated by technical considerations, and to the extent that it is behavioural, this behaviour often relates to technical personnel. By contrast, the energy use for commercial occupants is dominated by institutional behaviours (IT policy) and personal behaviours (manual equipment switching) and for residential occupants it is almost entirely personal behaviour. For these occupancy-based assessments, a performance based rating is substantially a rating of behaviour and is relevant to the modification of that behaviour. However, this same rating tells a future owner or occupier of the same space about the likely energy use of the space once they move in. Thus in some situations, a rating based on the energy assets in the building rather than the actual energy use is more appropriate.

With these principles in mind it is possible to develop concepts for how CBD could be expanded to other sectors, as outlined in Table 3. The table outlines major sectors in terms of the technical difficulty/feasibility of developing a CBD compatible rating approach. The likely value of CBD expansion needs to be overlaid on this, allowing for the potential costs of implementation and scale of impacts.

27 Energy Action is currently working with the Better Buildings Partnership in London to introduce a NABERS like rating

system, to a significant extent because of the failure of the EPC system to deliver the scale of benefits achieved by

NABERS in Australia.


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Table 3. Outline of potential CBD approaches for other sectors

Sector Rating Type Coverage Notes Transaction point

Less Difficult Sectors

Schools Performance Whole building US Energy Star precedent, and some NABERS work. High level of comparability across the majority of the sector, although state-by-state differences in school year ranges may affect comparability. Private schools may be more difficult to benchmark due to greater diversity of facilities.

Internal reporting, student engagement for public schools. No commercial transaction point. Limited potential to affect private school commercial transactions. Annual rating.

Supermarkets Performance Light, power (including refrigeration) and air-conditioning

US Energy Star precedent. Highly concentrated sector ownership and vertical integration means that there is a high risk of major players having identifiable different average ratings.

Public interaction – benefit relates to interaction with customers. Annual rating. May drive investor behavior.

Hospitals Performance Campus NABERS precedent. Existing benchmarks may not be valid for private hospitals; ability to produce meaningful comparison for more diverse private hospital types not known.

Internal reporting for public hospitals. No commercial transaction point. Annual rating.

Office tenancies

Performance Tenancy energy use

NABERS precedent, although some further work required. Rating informs organizational behavior mainly rather than technical efficiency

No commercial transaction point. Value lies in providing information to the occupier to inform decisions and behaviours. May help inform corporate ESD and drive investor behavior.


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Apartments Performance Base building NABERS Precedent. Highly diverse sector with a “base building” services range from close to nil to full air-conditioning and lifts.

Sale or lease transaction for potential apartment owners.

Homes/apartments

Asset Asset features covering heating, cooling, DHW, lighting and on-site generation

Partially serviced by NatHERS but a broader rating framework is required. Mandatory declaration of NatHERS currently in operation in ACT

Sale or lease transaction for potential home/apartment owners

Health centres Performance Whole building Smaller building type but high numbers. Reasonable comparability of generic GP centres but specialist centres probably not covered.

Public interaction – benefit relates to interaction with customers. May also drive investment for listed companies. Annual rating

Smaller hotels/motels

Performance Whole Building High numbers, reasonable comparability. As per hotels, limited sale/lease transactions means that this only works in interaction with guest demand

Moderately difficult sectors

Shops Performance Light and power for tenancy, whole building for pad shops.

Extensive subdivision of sector required to capture comparable groups of shops.

Public interaction – benefit relates to interaction with customers. Annual rating

Pubs and clubs

Performance Whole building Likely to require several subsector ratings to capture diversity.



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Industrial office/warehouse

Performance Whole building/base building

Sector often not metered at base building level. Allowance required for diversity of use in warehouse area

A base building rating has potential to inform a sale or lease transaction

Mixed use buildings

Performance Base building/whole building

In theory possible to create a blended rating based on multiple NABERS rating types provided these cover the building types present

Where a base building possible, could inform a sale/lease transaction.

Homes/apartments

Performance Light, power and air-conditioning within the dwelling

Basically a behavioural rating for the occupants

No commercial transaction value – informs occupants of their performance.

Significantly difficult sectors

Fast food outlets; restaurants; cafes

Performance Light, power (including refrigeration and cooking) and air-conditioning

Challenging to develop a rating that captures the diverse range of cooking styles. Likely to involve a significant number of sub-sector ratings


Universities Performance Whole campus Challenging to develop a rating that captures the diverse range of facilities provided on different campuses

Public interaction – benefit relates to interaction with potential students and staff. Annual rating


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Laboratories Performance Whole building Challenging to develop a rating that captures the different types of laboratory, given the lack of base building differentiation

No commercial value – informs building users only


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4. High Level Cost Benefit Analysis

The intention of the Cost Benefit Analysis (CBA) is to provide sufficient information on the likely costs and benefits of the potential expansion of the CBD program for decision makers to consider. The approach uses available information on the sectors and assumptions about the percent of the market that will improve the energy efficiency of their facilities and the rate of that improvement.

The assumptions are tested in a number scenario to determine the sensitivity of the assumption to the outcomes of the CBA.

4.1 Approach

The approach to calculation of the Energy/GHG Emission impacts and Cost Benefit Analysis is summarised conceptually in Figure 25. Firstly, the market is defined in terms of the size and segments that are voluntarily disclosing their energy performance rating (typically using NABERS), then the energy use is estimated using a measure of the energy intensity metric (energy use per size or other metric) and finally the cost and benefits are calculated of the policy option compared to the Business as Usual (BAU). The policy option impacts are estimated based on the segment of the market that is applicable to the policy intervention. This includes the percentage of the market required to mandatorily disclose their rating, the estimated percentage undertaking energy efficiency upgrades due to the disclosure and the estimated percentage reduction in energy intensity. The costs of the upgrades, mandatory rating costs and government costs are then calculated along with the benefits of the energy savings and GHG emission reduction.


41

Figure 25: Diagram Illustrating the Calculation of Energy, GHG Emission Impacts and Costs Benefit Analysis.

4.1.1 Costs and Benefits

The basis of the costs and benefits are as follows:

Costs

Owners

Costs of the ratings to the building owners


42

Costs of energy efficiency upgrades

Government

Development and research

Ongoing Administration

Benefits

Owners

Energy savings using retail tariffs

Society

Carbon abatement value

Retail prices for energy are used to value the benefits as these represent long run resource cost savings. This approach is used for valuing benefits of other energy efficiency policy interventions, including those for the E3 program and the recent consultation RIS proposing changes affecting the commercial sector in the National Construction Code (NCC). Retail costs of building efficiency improvements are also used to represent the resource costs.

The general parameters for the calculation of the high-level costs and benefits are:

The policy options are modelled with an implementation date of 2021 and is evaluated for a

period of 10 years (until 2030). Ongoing benefits of energy savings are included post 2030 as

described below.

The costs are included in the year they are estimated to be incurred and the benefits of

energy savings are summed for a period of 10 years (estimated average life) of the efficiency

upgrades. This is likely to be a conservative estimate of the life, as many measures will have a

life longer than 10 years, and hence conservatively underestimates the full benefits.

All costs and benefits are based on real 2018 dollars and future values are discounted at the

rate of 7%, in accordance with the OBPR RIS guidelines.

The baseline energy use intensity is assumed to continue to decrease at a rate that includes

the impact of those owners who are voluntarily disclosing energy ratings.

The costs of ratings are estimated at $6000 per facility for all sectors. This assumption is based

on average market prices for the rating, however if the same assessor is conducting

assessments for the same facility on an annual basis, there might be lower costs in future

years.

The costs of energy efficiency upgrades are based on the assumption that owners implement

measures with an average four-year simple payback period (using a tariff of $0.18/kWh for

electricity and 2.54 c/MJ for NG, the payback can be converted to a cost per energy saving

unit). Again this is a conservative estimate that may overestimate the costs of energy


43

efficiency upgrades, as many buildings can be improved by 20% to 30% with lower payback

periods28.

For each sector, the Government costs are estimated at $500,000 over 2019 and 2020 to

undertake additional research, legal advice, implementation design, consultation and

development of the Regulatory Impact Statement. Ongoing costs of $250,000 pa per sector

are included to cover compliance, administration and education. It is assumed that the

program administration infrastructure established for the offices sector would be easily

extended to the other sectors.

The common sources of information for the calculation of national costs and benefits are as follows:

Energy tariffs – retail tariffs as projected by AEMO for the commercial businesses from 2016

to 203729

Australian average emission factors for electricity and natural gas are based on Scope 1,2 and

3 emission factors derived from the National Carbon Accounts and projections supplied by the

Department of Environment and Energy

Options to value of carbon abatement, as follows:

Option A: No value

Option B: constant real value of $13.08/t CO2-e used by the Emission Reduction Fund30

Option C: constant real value $25/t CO2-e (this is used as a medium value for E3 RISs)

Option D: viable real value starting at $60/t CO2-e in 2020 and increasing to $86/t CO2-e in 2040 based on the Social Cost of Carbon31, Medium (3% discount rate)

Option B is used to value carbon abatement for the policy analysis, as this represents the

current policy settings.

28 Low Carbon Growth Plan for Australia Retail Sector Summary Report, ClimateWorks Australia 2011; Energy efficient

office buildings: Transforming the mid-tier sector, Sustainable Victoria, 2016 29 AEMO 2016: Gas Retail Price Projections - 2016-12-14.xlsx, AEMO 2017 EFI: AEMO Wholesale and retail prices

EFI 2017.xlsx 30 http://www.cleanenergyregulator.gov.au/ERF/Auctions-results/December-2017 31 US EPA 2017, The Social Cost of Carbon: Estimating the Benefit of Reducing Greenhouse Gas Emissions,

https://19january2017snapshot.epa.gov/climatechange/social-cost-carbon_.html

https://19january2017snapshot.epa.gov/climatechange/social-cost-carbon_.html


44

4.1.2 Energy Impacts

The basic formulae for calculations of energy use are shown in Figure 26. For each of the sectors under consideration, information is sourced to provide the size of the market and estimated growth over the period 2010 to 2030. The energy use metric (by fuel type) is also estimated for the market over the same period, including the change in metric. Finally, the energy use for the sector is calculated for each year. Evidence to support the changes in energy metric are provided in Appendix A: Energy Intensity Trends.

Figure 26: Conceptual energy use calculations.

For each of the sectors, the key metrics used to determine the BAU are described below. The detailed calculations and sources for the energy metric values are found in Appendix A: Energy Intensity Trends.

Shopping centres

Size is estimated in square meters of gross lettable area – retail (GLAR) from the CBBS 32to 2020 and forecast to grow at the average rate of 3 % pa (based on year 2020 in the CBBS).

Energy use metric is the energy use intensity per square meter GLAR in MJ/m2, from the CBBS for the base building only

Baseline change in EUI over time is a decrease of 0.03% pa from CBBS (average 1999- 2020) and assumed to be the same for the period to 2030

32 Pitt&Sherry, 2012, Baseline Energy Consumption and Greenhouse Gas Emissions In Commercial Buildings in

Australia

Sect

or Hotels

Shopping Centre

Data Centre

Size

Met

ric Rooms

m2 GLAR

kW IT load

Een

rgy

Met

ric EUI=MJ/

Room

EUI =MJ/m2

PUE

Tota

l En

ergy Size x EUI

Size x EUI

Size and function of PUE


45

Data centres

Size is estimated in rated IT power (MW) from the sample of colocation data centres. The forecast is growth in rated IT power is estimated at 12% pa to 2020, then reducing to 4% by 2023 and remaining at 4% till 2030. Industry publications suggest that forecast growth in the co-lo data centres is 12% to 2020. The reduced growth rate post 2020 assumes that IT power will not grow as fast as efficiencies improve (due of greater use of virtualisation and hyperscale data centres, as well as improved processing power per watt of power input).

Energy use metric is the Power Usage Effectiveness (PUE) which enables the calculation of the energy use of the infrastructure (non-IT facility) used to meet the demand of IT equipment. PUE equal to 1 + non-IT facility energy use/IT equipment energy use. The PUE is estimated at 1.92 in 2010 in the USA (Shehabi et al., 2016) 33, and 1.8 in 2017 in the EU (Avgerinou, Bertoldi and Castellazzi, 2017)34. There is no available historical data for Australia, but these values are reasonable when compared to the values used in the report: Energy Efficiency Policy Options for Australian and New Zealand Data Centres (Department of Industry, E3 2014)35

Baseline change in PUE over time is a decrease of 1.0% pa from the United States Data Center Energy Usage Report 2016 and assumed to apply for the period to 2018 to 2030.

Hotels/serviced apartments

Size is estimated in rooms and is based on the number of rooms for hotels and serviced apartments from the ABS data shown in Section 2.3 Hotels. The compound annual growth rate in terms of increase in total rooms is 2.5 % from 2010 to 2016, which is assumed to apply for the period 2016 – 2030.

Energy use metric is the energy use intensity per room (EUI) in MJ/room, derived from NABERS 2008 research (see Figure 24.

Baseline change in EUI over time is a decrease of 0.78% pa from USA, EIA, 2003 and 2012, Commercial Buildings Energy Consumption Survey and assumed to be the same for the period to 2030

33 Shehabi, A., Smith, S.J., Sartor, D.A., Brown, R.E., Herrlin, M., Koomey, J.G., Masanet, E.R., Horner, N., Azevedo,

I.L., Lintner, W., 2016. United States Data Center Energy Usage Report. http://eta-

publications.lbl.gov/sites/default/files/lbnl-1005775_v2.pdf 34 Avgerinou, M., Bertoldi, P., Castellazzi, L., 2017. Trends in Data Centre Energy Consumption under the European

Code of Conduct for Data Centre Energy Efficiency. Energies, MDPI 10, 1–18. https://doi.org/10.1016/j.rser.2014.10.035 35 http://energyrating.gov.au/document/report-energy-efficiency-policy-options-australian-and-new-zealand-data-centres

http://eta-publications.lbl.gov/sites/default/files/lbnl-1005775_v2.pdf

http://eta-publications.lbl.gov/sites/default/files/lbnl-1005775_v2.pdf

https://doi.org/10.1016/j.rser.2014.10.035

http://energyrating.gov.au/document/report-energy-efficiency-policy-options-australian-and-new-zealand-data-centres


46

4.1.3 Market Segmentation

The market is segmented for each of the sectors to account for the changes to the energy use. The detailed calculations and sources for the energy metric values are found in Appendix A: Energy Intensity Trends. The baseline values of energy metric are adjusted to account for the buildings that are voluntarily rating with NABERS by segmenting the market size into two categories:

Regularly Rating Segment

Shopping centres with at least 3 or more NABERS rating per unique property over the period 2010 -2018, which is 6M m2, or 30% of the total market. This proportion is assumed to increase over time by an additional 2% each year from 30% in 2018 to 54% of the total market in 2030.

Energy metric change over time is derived is assumed to -2.88% (all those with at least one rating average is -2.88%, while 3 or more is -2.98%, so conservatively using the lower value)

Data centres with at least one NABERS rating represent 4.58% of the total number of relevant data centres over the period 2016-2018. We assumed that about double this number are rating their centres but not publicly reporting the result. This implies 10% of the market is rating regularly in 2017. The proportion that are regularly rating is assumed to increase by 1% each year from 2018, leading to 23% of the total market rating regularly by 2030

Energy metric change over time is taken from the EU Code of Conduct, which suggests a change of -1.58% pa in PUE (Avgerinou, Bertoldi and Castellazzi, 2017).

Hotels with at least one NABERS rating represent 20% of the total number of hotel rooms over the period 2009-2018. With about 50% of the hotels with NABERS ratings rating at least 3 or more times. This suggests that approximately 6% of the hotel and serviced apartment market is rating regularly, however the trend in the last 3 years has indicated a much lower number of NABERS ratings (less than 0.3% of the total number of hotels). We assumed that conservatively about 5% of the market is rating regularly and assume that this proportion will not change over the period to 2030.

Energy metric change over time is derived from the USA data36 is (-3.25%)

Not Rating Segment

For all sectors, the Not Rating Segment is the difference of the total size and the Regularly Rating Segment.

The energy metric that is derived for the baseline is used for this segment

36 US Dept of Energy, 2015, New York City Benchmarking and Transparency Policy Impact Evaluation Report


47

The total sector BAU Energy use is then estimated from the energy consumption resulting from these assumptions of market size and change in energy metric by segment over time. This is explained conceptually in Figure 27.

Figure 27: Market segmentation for BAU energy consumption.

4.1.4 Policy Case Parameters

The policy case parameters are designed to test the impacts of the policy action. The mandatory disclosure policy is assumed to encourage facility owners to upgrade the energy efficiency of the property. However, depending on the implementation approach and the underlying motivations/capacity to upgrade equipment or change practices, a proportion of owners will not do so. We have also assumed that the scope of the policy will apply to a certain proportion of the market based on the size of the facility, and the legal requirement to disclose the energy performance.

In estimating the policy impact, only those that are not rating regularly are included. Those building owners who are voluntarily rating, and estimated to continue to do so over time, are not included in the impacts. As shown in Figure 28, the policy impacts are estimated on the basis of:

Not rating Segment – the percentage of the market that are within the scope of the policy and

are required to disclose the rating. This is defined in terms of:

Shopping Centres: m2 of GLAR

Hotels/Serviced Apartments: number of rooms

Data Centres: Total Rated load of the facility (MW)

Total Market: BAU

Not Rating Segment

Size = remaining market

Energy Metric Improve = BAU

Rating Regularly Segment

Size = % of Market from NABERS

Energy Metric Improve = from

NABERS or International


48

Percentage of the owners that are taking actions to upgrade the energy efficiency of the

property. This is an estimated variable that based on the likely impact of the rating on

motivated owners.

Improvement is the Energy Metric (EUI or PUE) is based on the same value that is used for the

Regularly Rating segment

Figure 28: Market segmentation for policy energy consumption.

4.2 Inputs and Results

For each of the sectors being examined, the key policy input parameters are tested in three scenarios:

Low: the key policy input parameters that result in a benefit cost ratio (BCR) of 1.0

Mid: the input policy parameters that are judged to be appropriate based on the evidence

High: the best-case input policy parameters that are judged to be achievable based on the

evidence

The key policy input parameters tested are either: (1) the proportion of facilities that are influenced by the mandatory disclosure (shown as % Taking Action in Figure 28); or (2) the rate of decrease in EUI for those facilities that influenced by the mandatory disclosure (shown as Energy Metric Improve in Figure 28).

Total Market: Policy

Not Rating Segment

Size = remaining market

Policy scope limits

% Out = Size < m2, MW or Rooms

Energy Metric Improve = BAU

% In = Size > m2, MW or Rooms

% Taking Action

Energy Metric Improve

Rating Regularly Segment

Size = % of Market from NABERS

Energy Metric Improve = from NABERS or

International


49

The mid scenario is the scenario chosen for reporting the summary results.

4.2.1 BAU Energy Consumption

Based on the modelling parameters and the scope of the sectors being examined, the BAU energy consumption for each sector is shown in Figure 29. The BAU energy consumption is calculated for each sector using the following scope:

Shopping Centres: greater than 12,000m2 GLAR

Data Centres: Colocation DCs with no minimum rated facility load

Hotels and Serviced Apartments: greater than 50 rooms per establishment

Figure 29: BAU Energy Consumption for Each Sector.

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030

BAU Energy Use (PJ)

Shopping Centres Data Centres Hotels


50

Energy consumption is gradually increasing with the increase in the number of shopping centres and hotels/serviced apartments. Energy consumption of colocation data centres is increasing rapidly from 2010 to 2020, mainly due to the high growth of this segment. It is then projected to increase at a lower rate as the capacity of these data centres is filled, infrastructure cooling efficiency increases due to technology advances and computer processing density increases. The value for 2013 energy consumption in the BAU is equal to the estimated energy consumption value reported for colocation data centres in Energy Efficiency Policy Options for Australian and New Zealand Data Centres (Department of Industry, E3 2014). Our projections are based on the more recent number and size of colocation data centres from data shown earlier (see section 2.2 Data Centres)

The sector BAU energy consumption calculated for shopping centres, data centres and hotels/serviced apartments represents similar scale to the scope of the Offices CBD mandatory program. Considering that the scope of each of the sectors BAU energy consumption relates only to the scope of the sectors (i.e., as described above, only shopping centres greater than 12,000m2

GLAR, etc), the appropriate comparison would be with the scope of the Offices CBD mandatory program. The majority of the ratings for the CBD Offices is for base building energy use (in 2016/17 office base building is 6.1 PJ and whole building is 1.4 PJ37). While the estimated energy use of all base building stand-alone offices in 2017 is 21 PJ according to the CBBS, which includes owned and leased office space.

The comparison shown in Table 4, illustrates that the potential expansion of the CBD program in each sector would address similar energy consumption as the CBD Office sector.

Table 4: Comparison of Sectors Considered with the CBD Office Sector

Sector (scope of sector covered) Estimated Energy Consumption (PJ) in 2017

Shopping Centres (>12,000m2 GLAR) 8.5

Data Centres (colocation only) 6.3

Hotels/Serviced Apartments (>50 rooms) 10.7

NABERS Rated Offices (base building only) 6.1

4.2.2 Shopping Centres

The key policy parameters and estimated impacts are shown in Table 5 and based on the following scope:

CBD Scope: GLAR is greater than 20,000 m2 , which means that 77% of the GLAR is captured (of

shopping centres above 12,000 m2)

37 NABERS Annual Report 2016/17, https://nabers.gov.au/AnnualReport/2016-2017/nabers-energy-for-offices.html

https://nabers.gov.au/AnnualReport/2016-2017/nabers-energy-for-offices.html


51

CDB Rating Frequency: Annual rating, as it is assumed that retail leases are offered

continually to the market

Table 5: Shopping Centre: Key Assumptions and CBA Outcomes by Scenario.

Scenario Name/ Parameter or Output High Mid Low

BAU Efficiency Change -0.03% -0.03% -0.03%

Mandatory Rating Segment Efficiency Change -6.80% -2.88% -0.20%

Percent of Mandatory Rating Segment Taking Action 100% 100% 100%

Net Benefits ($M) 418 196 0

Benefit Cost Ratio 2.34 2.23 1.00

Cumulative Energy Savings (TJ to 2030) 14,912 7,019 467

Cumulative GHG Reduction (kt CO2-e to 2030) 3,039 1,429 95

As the shopping centre market is regularly rating over 30% of the GLAR voluntarily and there is a large share of investor owners, it is considered that 100% of the market will respond to the mandatory CBD rating and take action to improve their energy efficiency. The sensitivity of the mandatory rating segment efficiency change is therefore tested for shopping centres. For the mid scenario, the average efficiency change that has been demonstrated from the NABERS data is used as the most likely parameter. For the high scenario, we have applied the EUI change that was determined as the average change for offices as a result of the mandatory CBD requirement from the review of the program (ACIL Allen 2015). The low scenario shows that to achieve a BCR of 1.0, the annual rate of improvement in EUI would need to be at least 0.2% pa.

Table 6 provides the detailed present value costs and benefits of the mid scenario; and Figure 30 and Figure 31 shows the energy and emission impacts over the period to 2030.

Table 6: Shopping Centres: Detailed costs and benefit breakdown for the mid scenario.

PV Costs and Benefits ($M)

Owner Efficiency Upgrade Costs $145.3.0

Owner Rating Costs $12.3

Government Costs $2.0

Total Costs $123.3

Energy Saving Benefits $255.5

Carbon Reduction Benefits $11.2

Total Benefits $266.7

Net Benefits $143.4


52

Figure 30: Shopping Centres - Cumulative energy savings and emission reductions to 2030 for mid scenario.

- 500 1,000 1,500 2,000

Electricity (GWh)

NG (TJ)

GHG (kt CO2-e)

Cumulative Savings 2030


53

Figure 31: Shopping Centres - Policy and BAU energy consumption and GHG emissions for mid scenario.

4.2.3 Data Centres

The key policy parameters and estimated impacts are shown in Table 7 and based on the following scope:

0.0

2.0

4.0

6.0

8.0

10.0

12.0

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

Energy Use (PJ)

Policy BAU

0

500

1,000

1,500

2,000

2,500

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

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21

20

22

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23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

Emissions (kt CO2-e)

Policy BAU


54

CBD Scope: IT rated load is greater than 2 MW, which means that 66% of the estimated total

colocation data centre capacity is captured.

CDB Rating Frequency: Annual rating, as it is assumed that colocation space is offered

continually to the market.

Table 7: Data Centres - Key Assumptions and CBA Outcomes by Scenario.







Cumulative Energy Savings (TJ to 2030) 4,698 3,128 221

Cumulative GHG Reduction (kt CO2-e to 2030) 997 664 47

The energy metric (PUE) change of the mandatory rating segment is considered to be a reasonable assumption based on the evidence presented in Appendix A: Energy Intensity Trends. However, there is uncertainty in how much of the mandatory rating segment will responding to the mandatory CBD rating. Mandatory CBD will address many of the market barriers identified in the earlier section 3.2 Data Centres; however differences in the ownership and types of leases means that not all colocation data centres may take action and improve their facility rating. Therefore, the Percent of Mandatory Rating Segment Taking Action is chosen as the policy parameter to test the sensitivity for the various scenarios. The mid scenario assumes that 75% of the market segment will take action and improve their facility rating, which is based on the high level of competition for colocation data centre services in the larger sized facilities. In the high scenario, it is assumed that 100% of the market will respond and take action to improve their facility rating. For the low scenario, only 5% of the market is required to respond for the BCR to be greater than 1.0.

Table 8 provides the detailed present value costs and benefits of the mid scenario; and Figure 32and Figure 33 shows the energy and emission impacts over the period to 2030.


55

Table 8: Data Centres - Detailed costs and benefit breakdown for the mid scenario.


Owner Efficiency Upgrade Costs $71.6 Owner Rating Costs $4.8 Government Costs $2.0 Total Costs $78.4 Energy Saving Benefits $168.5 Carbon Reduction Benefits $7.4 Total Benefits $176.0 Net Benefits $97.6

Figure 32: Data Centres - Cumulative energy savings and emission reductions to 2030 for mid scenario.

- 200 400 600 800 1,000

Electricity (GWh)

NG (TJ)

GHG (kt CO2-e)



56

Figure 33: Data Centres: Policy and BAU energy consumption and GHG emissions for mid scenario.

4.2.4 Hotels and Serviced Apartments

The hotels and serviced apartments sector results are provided for two options: Annual Rating and Rating on Sale, to examine the impact of different implementation mechanisms.

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

Energy Use (PJ)

Policy BAU

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030


Policy BAU


57

4.2.4.1 Annual Rating

The key policy parameters and estimated impacts are shown in Table 9Table 5 and based on the following scope:

CBD Scope: Number of rooms per establishment is greater than 50, which means that 89% of

rooms and 75% of establishments are captured (of all hotels and serviced apartments)

CDB Rating Frequency: Annual rating, as it is assumed disclosure is required to enable

selection of accommodation on the basis of the building energy rating by guests and

corporate procurement programs.

Table 9: Hotels and Serviced Apartments – Annual Rating: Key Assumptions and CBA Outcomes by Scenario.







Cumulative Energy Savings (TJ to 2030) 15,471 3,868 2,496

Cumulative GHG Reduction (kt CO2-e to 2030) 2,459 615 397

The energy metric (EUI) change of the mandatory rating segment is considered to be a reasonable assumption based on the evidence presented in Appendix A: Energy Intensity Trends. However, there is uncertainty in how much of the mandatory rating segment will responding to the mandatory CBD rating. Therefore, the Percent of Mandatory Rating Segment Taking Action is chosen as the policy parameter to test the sensitivity for the various scenarios. The mid scenario assumes that 25% of the affected market segment will take action and improve their facility rating, which is based on the current evidence that only a small percentage of the market are voluntarily rating their facilities. It is considered that some brands will increase their marketing activities in terms of energy ratings, which might encourage up to 25% of the market to participate. In the high scenario, it is assumed that 100% of the affected market segment will respond and take action to improve their facility rating. For the low scenario, 16% of the affected market segment is required to respond for the BCR to be greater than 1.0.

There are a number of uncertainties relating to the potential response of the hotels/serviced apartments sector to a mandatory CBD, and hence the mid scenario policy parameters are conservative. This is justified as:

There are currently far fewer voluntary participants compared to the other sectors;


58

The barriers relating to the market sector described earlier are not easily addressed by this policy action; and

There is potential a need for complementary policies by government/large corporate accommodation buyers to specify higher rating facilities.


Table 10: Hotels and Serviced Apartments – Annual Rating: Detailed costs and benefit breakdown for the mid scenario.



The table shows that the owners’ costs of rating their facilities has a significant impact on the total net benefits. The modelling shows that these rating costs are a higher proportion of the total costs compared to other sectors examined due to the assumption that a smaller proportion (25%) of the affected market segment will take action and upgrade the efficiency of their buildings.

Figure 34: Hotels and Serviced Apartments – Annual Rating: Cumulative energy savings and emission reductions to 2030 for mid scenario.

- 200 400 600 800 1,000 1,200 1,400 1,600

Electricity (GWh)

NG (TJ)

GHG (kt CO2-e)



59

Figure 35: Hotels and Serviced Apartments – Annual Rating: Policy and BAU energy consumption and GHG emissions for mid scenario.

4.2.4.2 Rating on Sale

The key policy parameters and estimated impacts are shown in Table 9Table 5 and based on the following scope:

CBD Scope: Number of rooms per establishment is greater than 50, which means that 89% of

rooms and 75% of establishments are captured (of all hotels and serviced apartments)

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

Energy Use (PJ)

Policy BAU

0

500

1,000

1,500

2,000

2,500

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030


Policy BAU


60

CDB Rating Frequency: Rating on Sale, as it is assumed disclosure is only required on the sale

of the property. This results in approximately 1% of the stock being captured each year (see

Section 2.3 Hotels and Serviced Apartments) by the mandatory rating.

Table 11: Hotels and Serviced Apartments – Rating on Sale: Key Assumptions and CBA Outcomes by Scenario.

Scenario Name/ Parameter or Output

Mid Low

BAU Efficiency Change -0.78% -0.78%

Mandatory Rating Segment Efficiency Change -3.25% -2.41%

Percent of Mandatory Rating Segment Taking Action 100% 100%

Net Benefits ($M) 1 0

Benefit Cost Ratio 1.26 1.01

Cumulative Energy Savings (TJ to 2030) 159 105

Cumulative GHG Reduction (kt CO2-e to 2030) 25 17

As the rating will apply to all the market within scope, the Percent of Mandatory Rating Segment Taking Action is set to 100%, however the disclosure only occurs at sale. Only a mid and low scenario is shown as the scale of the net benefits are very small and a high scenario would not significantly impact this result. The energy metric (EUI) change of the mandatory rating segment for the mid scenario is based on the evidence presented in Appendix A: Energy Intensity Trends. For the low scenario, the annual change in EUI is required to be at least -2.41% for the BCR to be greater than 1.0.

The results suggest that mandatory CBD on sale of hotels and serviced apartments is cost effective (a BCR of 1.26), but the net benefits are very low of $1.4M.


Table 12: Hotels and Serviced Apartments – Rating on Sale: Detailed costs and benefit breakdown for the mid scenario.




61

Figure 36: Hotels and Serviced Apartments – Rating on Sale: Cumulative energy savings and emission reductions to 2030 for mid scenario.

- 10 20 30 40 50 60

Electricity (GWh)

NG (TJ)

GHG (kt CO2-e)



62

Figure 37: Hotels and Serviced Apartments – Rating on Sale: Policy and BAU energy consumption and GHG emissions for mid scenario.

4.3 Summary of Costs Benefits and Impacts

The summary of the estimated impacts of the expansion of the CBD program to Shopping Centres, Data Centres and Hotels/Apartments is shown in Table 13.

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

Energy Use (PJ)

Policy BAU

0

500

1,000

1,500

2,000

2,500

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030


Policy BAU


63

Table 13: Summary of High Level Cost Benefit Analysis by Sector (present value $, discount rate of 7%).

Sector Total Costs ($M) Total Benefits (SM) Net Benefits ($M) Benefit Cost Ratio

Shopping Centres 159.6 355.6 196.0 2.23 Data Centres 78.4 176.0 97.6 2.24 Sub Total 238.0 531.5 293.5 2.23 Hotels - Annual Rating 135.0 169.6 34.6 1.26 Hotels - On Sale Rating 5.6 7.0 1.4 1.26 Total* 373.0 701.1 293.5 1.88


The energy and GHG emission impacts are shown in Table 14.

Table 14: Summary of cumulative impacts to 2030 by sector.

Sector Electricity (TJ) Natural Gas (TJ) Total Energy (TJ) GHG Emissions (kt CO2-e)

Shopping Centres 6,611 408 7,019 1,429 Data Centres 3,128 0 3,128 664 Sub Total 9,739 408 10,147 2,093 Hotels - Annual Rating 2,514 1,354 3,868 615 Hotels - On Sale Rating 480 270 750 118 Total* 12,253 1,762 14,015 2,708


The estimated summary impacts for the combined shopping centres and data centres sectors of 10,147 TJ cumulative energy savings and net benefits of $293M are of similar magnitude to the findings of the CBD Program Review (ACIL Allen 2015), which estimates the CBD offices program has a total cumulated saving of 18,250 TJ (inc energy savings post program evaluation date of 2019) and net benefits of $111M.


64

5. Implementation Issues and Scope

5.1 Legal Basis

The Building Energy Efficiency Disclosure Act 2010 (BEED) draws on the power of the Companies Act to enable the Australian government to regulate in this area. As the owners of shopping centres, data centres and hotels all typically would classify as constitutional corporations under BEED, the extension of mandatory disclosure is not expected to yield any significant difficulties from this perspective.

However, it is noted that the provisions of BEED are framed entirely in terms of sale or lease and do not contain any provisions for mandating an annual rating, as has been suggested for hotels, unless it can be construed that letting a room to a guest is covered by the Act. As noted, mandatory disclosure on sale or lease in conventional hotels is unlikely to be practical owing to the low transaction rate.

A further related note is that the current legislation is strongly linked to advertising related to individual leases, which is not ideal for the three sectors considered in this report:

For shopping centres, leasing is not necessarily conducted as publicly as it is for office buildings;

For data centres, leasing advertisements are generally not specific to an individual lease;

For hotels, it is not known whether leases are advertised; if they are, it is not a public exercise.

Means of managing this issue are discussed in Section 5.3.

5.2 Scope of Program

The recommended scope of the program is as follows

5.2.1 Shopping Centres

For the purpose of the program, a shopping centre is a purpose built complex of containing shops, food outlets and other facilities for the use of pedestrians38, owned by a constitutional corporation and of gross lettable area greater than 20,000 m2.

38 This definition is adapted from the Collins Dictionary


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Transactions that trigger disclosure will be the sale of the shopping centre or a lease of a space within the shopping centre. Note that this will be equivalent to an annual rating for most shopping centres due to the turnover of leases.

5.2.2 Data Centres

For the purpose of the program, a data centre is a facility that provides the air-conditioning, power conditioning and other services required to support the housing of computing equipment of third parties who lease space within the facility, with a rated minimum total facility load of 2MW.

Transactions that trigger disclosure will be the sale of the data centre or a lease of a space within the data centre. Note that this will be equivalent to an annual rating for retail data centres but for wholesale data centres it may result in infrequent ratings.

It would be preferable, but require modification of legislation, to mandate an annual rating for all affected centres.

5.2.3 Hotels

For the purpose of the program a hotel is an establishment providing accommodation, meals, and other services for travellers and tourists39. This may include serviced apartments, noting that the availability of a valid rating for these has not been established. For the purposes of the program, the hotel must have a minimum of 50 rooms.

An annual rating is required for hotels, as sale or lease transactions occur too infrequently to create a workable scheme. This would require modification to the legislation unless it can be construed that a transaction with a guest can be treated as a lease.

5.3 Administration

5.3.1 Site Capture and Compliance

As noted in Section 5.1, the nature of lease transactions in the affected subsectors is sufficiently different to that in the offices sector that the mechanisms in the BEED Act are not well suited. This is because the Act relies on public disclosure relating to individual leases, while the proposed new sectors tend to have a less public leasing process than offices. However, it is also notable that these proposed sectors are also considerably smaller in number than the offices sector, which makes other approaches feasible.

39 Adapted from the Oxford Dictionary


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In particular it is recommended that the core administration mechanism is built around a national registry of affected sites. This can be readily assembled given the relatively small number of sites involved – indeed for this study we were able to obtain reasonably comprehensive listings of colocation data centres and shopping centres; hotels have the benefit of requiring a significant public profile and thus are not difficult to track down.

Irrespective of whether a sale/lease trigger or an annual trigger is used, a national registry approach can provide a viable mechanism for enforcement; having identified a site, the site would be required once a year to disclose either a current rating or (if an annual rating is not required) a statutory declaration that no sale or leasing activity has occurred.

For data centres and hotels, however, it would also be preferable for the rating to be required to be disclosed on non-lease specific advertising materials, such as websites, as this is the means by which most potential customers make contact. Note however that it is unlikely to be possible to require third party (often overseas operated) hotel booking engines to publish disclosed energy ratings.

5.3.2 Assessors

As noted, for all three sectors, the NABERS rating provides the most applicable rating tool for use in mandatory disclosure, with the exception of serviced apartments and resort hotels, where the application of NABERS Hotels has not been validated.

Significant numbers of NABERS assessors are already trained and active in shopping centre ratings, with 131 ratings conducted in 2016-7; relative to a projected affected population of around 300. However, activity in hotels and data centres is considerably lower, at 4 and 12 ratings in 2016-17 respectively, relative to potentially disclosure affected populations of 1300 and 70 respectively. In this context it is noted that hotel ratings are not generally seen as particularly difficult; however, the sub-metering and data issues associated with data centre ratings are significant.

On this basis it is expected that while there is a moderate ramp-up required for shopping centre assessors, there is likely to be a significant capacity building exercise required to service mandatory disclosure for hotels and data centres.


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6. Appendix A: Energy Intensity Trends


The energy intensity for shopping centres is defined here as the total annual energy use divided by the net lettable area. Using actual and projected data from the Baseline Energy Consumption and Greenhouse Gas Emissions40 report, the baseline energy intensity trend was extracted. The baseline annual percentage change in energy intensity was also calculated from averaging the energy intensity changes for each year. This baseline was found to be -0.03%/year.

Table 15: CBBS data on shopping centre energy intensity.

Year Energy Intensity MJ/m2 Percentage Change In Energy Intensity

2009 496.0 2010 492.3 -0.76%

2011 492.8 0.12%

2012 492.4 -0.09%

2013 496.3 0.79%

2014 493.7 -0.53%

2015 491.0 -0.55%

2016 494.6 0.74%

2017 493.1 -0.30%

2018 493.7 0.11%

2019 494.1 0.10%

2020 494.5 0.08%

Average: -0.03%/year

To investigate if NABERS ratings have an effect on increasing the reduction of shopping centre energy intensity, the average energy intensity for NABERS rated shopping centres was calculated for each year from 2010 to 2018 (sample size n=737). From the data, the annual percentage change in energy intensity of NABERS rated shopping centres was found to be -2.88%/year.

40Pitt&Sherry, 2012, Baseline Energy Consumption and Greenhouse Gas Emissions In Commercial Buildings in Australia


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Table 16: NABERS data on shopping centre energy intensity.

Year Average Energy Intensity

MJ/m2 Percentage Change In Energy

Intensity

2010 444 2011 365 -17.69%

2012 395 7.96%

2013 394 -0.11%

2014 364 -7.62%

2015 361 -0.73%

2016 358 -0.94%

2017 371 3.60%

2018 343 -7.48%


The NABERS data set also contained a significant proportion of shopping centres with multiple ratings. Repeating the aforementioned process, the annual percentage change in energy intensity for shopping centres with multiple NABERS ratings is shown below to be generally consistent around -3%/year except for those which rate almost every year (>7 ratings).

Figure 38: Energy intensity trends for shopping centres with multiple NABERS ratings.


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Based on the above data, shopping centres undertaking regular NABERS ratings appear to have an energy efficiency improvement rate of approximately 2.5% p.a. better than the market average. It is recommended that this is used as the incremental benefit of mandatory disclosure in the analysis.

6.2 Data Centres

Information on data centre energy intensity trends in Australia is not yet readily available, hence, PUE and other energy intensity indicator trends from the US, Europe and China are used to establish a baseline case.

A 2016 report on data centres in the US41 provided PUE values for 6 different types of data centres and their projected 2020 PUEs in 3 different scenarios: current trends, improvement management and best practice. The average annual percentage change in PUE was calculated by assuming a constant annual reduction in PUE from the 2014 value to the projected 2020 value.

Table 17: PUE trends of US data centres by type and scenario.

Calculated average annual percentage change in PUE by scenario %/year

Data Centre Type Current Trends Improved Management Best practice

Closet 0.00 0.00 0.00

Room -1.03 -6.22 -8.15

Localised -1.02 -2.67 -4.68

Midtier -0.99 -1.84 -4.96

High End -1.01 -1.96 -4.37

Hyper Scale -1.00 -1.00 -1.44

Averaged -0.84%/year -2.28%/year -3.93%/year

A recent paper42 reported the average PUE from 2009 through to 2016 (sample size n=286). However, the data centres included in this sample were participants in the EU Code of Conduct for Energy Efficiency in Data Centres which is a voluntary initiative where energy consumption reduction commitments are supported43. Therefore, the calculated average PUE change of -1.58%/year may be greater than the baseline case if significant energy efficiency measures were involved.

41 Lawrence Berkeley National Laboratory, 2016, United States Data Center Energy Usage Report 42 Maria Avgerinou et al, 2017, Trends in Data Centre Energy Consumption under the European Code of Conduct for

Data Centre Energy Efficiency 43 https://ec.europa.eu/jrc/en/energy-efficiency/code-conduct/datacentres

https://ec.europa.eu/jrc/en/energy-efficiency/code-conduct/datacentres


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Table 18: PUE trend of EU Code of Conduct participant data centres.

Year PUE Percentage Change In PUE

2009 1.87 2010 1.96 4.81%

2011 1.73 -11.73%

2012 1.9 9.83%

2013 1.78 -6.32%

2014 1.86 4.49%

2015 1.72 -7.53%

2016 1.64 -4.65%


Survey results from the 2014 report44 by Uptime Institute collected self-reported PUE values from over 1,000 data centres for the period 2011 through to 2014. The average PUE change of -3.40%/year is significantly greater than the previous sources but there may be considerable bias in the data from self-reporting idealised PUE values.

Table 19 PUE trend in data centres from Uptime Institute survey

Year PUE Percentage Change In PUE

2011 1.89 2012 1.8 -4.76%

2013 1.67 -7.22%

2014 1.7 1.80%


The last source of data centre energy intensity is a report45 on Chinese data centre markets by the US Department of Energy. Data on white space and power of data centres is provided for 2012 to 2015. Hence, an indicative energy intensity figure can be estimated by dividing the power by area to obtain a GW/km2 intensity value. The average change in energy intensity was calculated to be -0.21%/year. However this in not a PUE value and in hence only indicative.

Table 20: Energy intensity trend of Chinese data centres.

Year White space (km2)

Power (GW)

Energy Intensity

(GW/km2) Percentage Change In Energy Intensity

2012 1.21 1.56 1.29 2013 1.5 1.79 1.19 -7.44%

2014 1.79 2.12 1.18 -0.75%

44 UptimeInstitute, 2015, 2014 Data Centre Industry Survey 45 US Dept of Energy, 2015, The Emerging Chinese Market for Energy Efficient Data Centers


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2015 2.08 2.65 1.27 7.57%


The NABERS data set on rated data centres is relatively small (n=35). Energy intensity figures (units unknown) on rated data centres from 2014 through to 2018 are available and the average calculated for each year. The resulting annual percentage change in energy intensity of NABERS rated data centres was found to be -1.70%/year.

Table 21: Energy intensity trend in NABERS rated data centres.

Year Average Energy Intensity Percentage Change In Energy Intensity

2014 2.14 2015 2.12 -1.11%

2016 2.06 -3.04%

2017 1.96 -4.84%

2018 2.00 2.20%

Average -1.70%/year

This figure is similar to the trend from the EU data centres participating in an energy reduction scheme and in the ballpark of the Improved Management scenario from the US data set.

The baseline case is difficult to determine for data centres in Australia but taking into account the US figure figures applicable to the colocation market (Mid-tier = -0.99% p.a., High end =-1.01% p.a.) of an average of -1.00 % pa for the current measures scenario and the Chinese data centre trend of -0.21% p.a., it is likely to be around in the region of -0.5% to -1% p.a. Taking this as a baseline, the NABERS improvement for data centres may be in the order of approximately 1%. This is similar to the US study’s improved management case which had an incremental improvement of 1.34% above the current trend. The EU CoC data suggest that a program of action (although voluntary) creates an average decrease of 1.58% pa in the PUE. We have therefore assumed that annual change in the baseline PUE is -1.0% based on the US LBL report and the annual change for mandatory rating is - 1.6%.

Energy Use

Unlike other sectors, energy use data, particularly specific to the colocation subsector of data centres, was difficult to obtain. In order to make an assessment of this we located energy data in relation to one of the colocation data centre operators and used this to calculate a connected load to energy use conversion factor as follows.

The energy use of a data centre can be characterised in terms of the following equation:

𝑇𝑜𝑡𝑎𝑙 𝑒𝑛𝑒𝑟𝑔𝑦 𝑢𝑠𝑒 = 𝑇𝑜𝑡𝑎𝑙 𝐼𝑇 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 ∗ ℎ𝑜𝑢𝑟𝑠 𝑜𝑓 𝑡ℎ𝑒 𝑦𝑒𝑎𝑟 ∗ 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑢𝑡𝑖𝑙𝑖𝑠𝑎𝑡𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟 ∗ 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑃𝑈𝐸


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The portfolio in question had four data centres with a stated total IT electrical capacity of 36.05 MW and total connected capacity of 55.5 MW.

The average utilisation rate for these data centres is estimated to be 45% from the recent US data centre report46 figures for 2014 and considering that all of the data centres for the identified operator are hyperscale by definition47. The average annual PUE is also taken to be 1.8 which is reasonable for 2014 from the trend analysis figures.

Table 22: Derivation of total energy use of data centres in 2014 for a single identified operator.

Total IT capacity (MW)

Hours Average Utilisation factor

Average PUE

Total Energy Use (MWh)

36.05 8760 0.45 1.8 255796

Taking the total energy use for 2014 and the total power capacity of the four power data centres as 55.5MW, the ratio between the annual energy use (MWh) per MW of capacity is 255796/55.5=4609.

The ratio of total facility rated power capacity to total rated IT power capacity is 55.5/36.05 = 1.54. This ratio was used to calculate the rated IT power capacity of the data from the Cloudscene (which used total facility rated power as the unit of measurement). This ratio is used the modelling shown in Chapter 3.4 to determine the rated IT power capacity.

6.3 Hotels

The baseline case energy intensity trend for hotels is calculated using data also from the Baseline Energy Consumption and Greenhouse Gas Emissions report and using the same units as shopping centres (MJ/m2 of net lettable area). For hotels, actual and projected data is available from 1999 through to 2020. This baseline was found to be positive at 1.52%/year.

Table 23: CBBS data on hotels energy intensity.

Year Energy Intensity MJ/m2 Percentage Change In Energy Intensity

1999 1205

2000 1232 2.24%

2001 1252 1.65%

2002 1275 1.82%

2003 1291 1.26%

2004 1310 1.51%

2005 1333 1.77%

2006 1360 2.03%

2007 1381 1.52%

46 Berkeley National Laboratory, 2016, United States Data Center Energy Usage Report 47 https://recap-project.eu/news/hyperscale-data-center/

https://recap-project.eu/news/hyperscale-data-center/


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2008 1401 1.43%

2009 1422 1.48%

2010 1440 1.32%

2011 1463 1.58%

2012 1487 1.64%

2013 1500 0.84%

2014 1526 1.76%

2015 1549 1.53%

2016 1568 1.19%

2017 1586 1.19%

2018 1612 1.63%

2019 1629 1.02%

2020 1652 1.45%

Average: 1.52%/year

To investigate if NABERS ratings have an effect on increasing the reduction of hotel energy intensity, the average energy intensity for NABERS rated hotels was calculated for each year from 2009 to 2018 (sample size n=158). From the data, the annual percentage change in energy intensity of NABERS rated hotels was found to be -2.20%/year, although the low sample count means that there is significant uncertainty in this figure, as is evident from the volatile changes from year to year.

It should be noted that the NABERS data set used different energy intensity units. However, since we are concerned with only the trend in energy intensity, the comparison is still valid.

Table 24: NABERS data on hotel energy intensity.

Year Average Energy Intensity Percentage Change In Energy

Intensity

2009 91527

2010 60369 -34.04%

2011 99685 65.13%

2012 57408 -42.41%

2013 53515 -6.78%

2014 88313 65.02%

2015 64479 -26.99%

2016 51455 -20.20%

2017 58769 14.21%

2018 38958 -33.71%



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Unlike the case for shopping centres, data for hotels with repeated NABERS ratings showed a worsening annual percentage change in energy intensity. This may have been due to the small sample size spread over a relatively longer time span, causing yearly averages to be highly sensitive to outlier energy intensity values. This is indicated by the highly volatile figures from year to year.

Table 25: Energy intensity trends for hotels with multiple NABERS ratings.

Year

Average Energy Intensity for hotels with >2 ratings

n=128 Percentage

Change

Average Energy Intensity for hotels

with >3 ratings n=88

Percentage Change

2009 100668

2010 62307 -38.11% 55309 26.21%

2011 97327 56.20% 69805 -26.39%

2012 54855 -43.64% 51385 -2.26%

2013 51680 -5.79% 50225 13.94%

2014 76081 47.22% 57228 -8.92%

2015 61564 -19.08% 52124 1.56%

2016 51455 -16.42% 52936 -3.82%

2017 54117 5.17% 50916 22.87%

2018 47203 -12.78% 62559 26.21%

Average: 1.36%/year Average: 2.90%/year

In order to check whether the disparity in the average annual energy intensity change between the baseline and NABERS hotels data sets is reasonable, data for hotels in the US was also analysed. Two sources of data48,49 were used to determine the energy intensity for hotels (MJ/m2) in the US from 2003 through to 2015. Detailed data (n>2,000) was available for the years 2003, 2012-2015 and interpolation was used to fill in the gaps. The baseline case was found to be -0.78%/year. This figure is similar to the results from another report50 stating the average change in energy intensity of the commercial buildings sector to be -0.55%/year for the period 2000-2011.

Table 26: Energy intensity trend of US hotels from US data sources.

Year Average Energy Intensity

(MJ/m2) Percentage Change In Energy

Intensity

2003 1137

2004 1132 -0.36%

2005 1128 -0.36%

2006 1124 -0.37%

2007 1120 -0.37%

48 EIA, 2003 and 2012, COMMERCIAL BUILDINGS ENERGY CONSUMPTION SURVEY 49 Greenview, 2015-2017, Cornell Hotel Sustainability Benchmarking Index 50 Pacific Northwest National Laboratory, 2014, A Comprehensive System of Energy Intensity Indicators for the U.S.:

Methods, Data and Key Trends


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2008 1116 -0.37%

2009 1112 -0.37%

2010 1108 -0.37%

2011 1104 -0.37%

2012 1099 -0.37%

2013 1114 1.30%

2014 1057 -5.11%

2015 1033 -2.25%


The reported effects of the Benchmarking and Transparency policy in New York51 which is similar to CBD mandatory disclosure, displayed a 3% and 3.5% savings in gross energy in the hotels sector for the periods 2011-2012 and 2012-2013 respectively. The formula used to calculate savings in gross energy is proportional to the average annual energy intensity for the hotels sector. Hence, a figure for the average annual change in energy intensity for hotels of -3.25%/year can be compared to the baseline of -0.78%/year in the US. Another report52 shows that the Median Energy Star score was higher in cities with higher disclosure compliance rates. However, no quantitative data on energy usage was given so it only provided an indicative improvement in building energy efficiency from disclosure laws.

The low sample sizes and high degree of year-on-year variation means that no specific conclusions can be drawn from the NABERS hotel data as to the potential impact of a mandatory disclosure program. However, given the apparent net 2.5%/year in energy intensity reduction trend seen from the US hotels data in association with mandatory disclosure, this figure appears reasonable for use in this study as an estimate of the impact of mandatory disclosure in Australia.

51 US Dept of Energy, 2015, New York City Benchmarking and Transparency Policy Impact Evaluation Report 52 Palmer et Walls, 2015, Can Benchmarking and Disclosure Laws Provide Incentives for Energy Efficiency

Improvements in Buildings?


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7. Appendix B: Hotel Rating Systems

7.1 Green Globe

Green Globe53 is an internationally recognized standard that covers multiple sectors from hotels, tourist destinations and airports. However, it is virtually non-existent in Australia (only ICC Sydney is listed as a certified member on the site)54.

Green Globe has four main criteria each with multiple sub criteria which are rated against unpublished benchmarks. Each sub criteria has indicators which are only available to Green globe membership paying members ($750-$5,000 depending on size). Typically, a hotel will be rated by a Green Globe accredited auditor who makes on site audits. For consecutive ratings, on site audits are only required in alternating years, with desktop audits sufficient in between on-site visits. The auditor then publishes a report which provides a review of achievements and compliance indicator score against each sub criteria.

The criteria are:

A. Sustainable Management

A.1. Implement a Sustainability Management System

A.2 Legal Compliance

A.3 Employee Training

A.4 Customer Satisfaction

A.5 Accuracy of Promotional Materials

A.6.1 Design and Construction – Compliance with Legal Requirements

A.6.2 Sustainable Design and Construction of Buildings and Infrastructure – New Buildings (Constructed within the last 5 years) & Existing Buildings

A.7 Experiential or Interpretative Tourism

A.8 Communications Strategy

A.9 Health and Safety

A.10 Disaster Management & Emergency Response

Social Economic

B.1 Community Development

B.2 Local Employment

B.3 Fair Trade

B.4 Support Local Entrepreneurs

B.5 Respect Local Communities

53 https://greenglobe.com 54 https://greenglobe.com/members/asia/#australia

https://greenglobe.com/

https://greenglobe.com/members/asia/#australia


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B.6 Exploitation

B.7 Equitable Hiring

B.8 Employee Protection

B.9 Access to Basic Services

B.10 Local Livelihoods

B.11 Bribery & Corruption

Cultural Heritage

C.1 Code of Behavior

C.2 Historical Artifacts

C.3 Protection of Sites

C.4 Incorporation of Culture

Environmental

D.1.1 Purchasing Policy

D.1.2 Consumable Goods

D.1.3 Energy Consumption

D.1.4 Water Consumption

D.1.5 Food & Beverage

D.1.6 Green Meetings

D.2.1 Greenhouse Gas

D.2.2 Wastewater

D.2.3 Waste Management Plan

D.2.3.1 Plan and Reduce

D.2.3.2 Reuse

D.2.3.3 Recycle

D.2.4 Harmful Substances

D.2.5 Other Pollutants

D.3.1 Wildlife Species

D.3.2 Wildlife in Captivity

D.3.3 Landscaping

D.3.4 Biodiversity Conservation

D.3.5 Interactions with Wildlife

Details defining each criteria can be found in the attached example auditor report. Both quantitative and qualitative data is involved, and often Earthcheck software is used for the quantitative energy, water and waste usage analysis55.

55 Wiberg et Baker, 2010, A Comparison Of Five Certification Schemes for the Hotel Sector -Green Globe, Nordic Swan,

EU Flower, Green Hospitality Award and LEED-EB.


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Figure 39: Earthcheck software in Green Globe certification process.

Once a hotel has been certified against all requisite criteria and achieved more than 50% of the criteria’s related indicators, it is awarded the Green Globe certified member status which allows it to display the symbol on its marketing material.

For members who have been awarded certified status for 5 consecutive years and completed the independent and mandatory onsite and desktop audits which are required in alternating years, they are awarded a gold status.

For members who have been awarded certified status and completed the alternating audit requirements, they are awarded the platinum status.

Figure 40: Green Globe certification standards.


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7.2 Earthcheck

Earthcheck is a software rating platform developed by the Australian Government Sustainable Tourism Co-operative Research Centre since 1987 based on the Agenda 21 principles to help hotels achieved desired outcomes for sustainable tourism. Destinations can also be certified by Earthcheck.

The certification process follows an initial benchmarking process:

The process56 begins with registration and payment of the membership fee. ($2,800 p.a. for Benchmarking and $4,800 p.a. for Certified)

An Environmental and Social Sustainability Policy is developed using Earthcheck’s company standard document (see attached documents) as a framework.

Quantitative and qualitative data collected and analysed from the following 10 areas are measured and benchmarked against competitors and climate zones.

Energy Consumption

Greenhouse Gas Emissions

Potable Water Consumption

Water Savings

Waste Sent to Landfill

Waste Recycling

Community Commitment

Community Contributions

Paper Products

Cleaning Products

Pesticide Products

Corporate Social Responsibility

56 https://earthcheck.org/products-services/certification/benchmarking-and-certification/


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Once benchmarked, a detailed report of performance and standing in industry provided along with the right to market the “Bronze Benchmarked” Earthcheck seal.

The certification process includes the following stages:

Comply with all the requirements and laws for certification.

A risk assessment, Environmental Action Plan and Environmental Management System to manage and improve overall environmental and social performance across all Key Performance Areas need to be developed.

Measurement of hotel’s performance metrics against the 10 key performance areas.

Communicate environmental and social commitment, goals and objectives.

Onsite certification audit undertaken by Earthcheck Auditor.

Once all passed, audit report providing qualitative evaluation of hotel against key performance areas provided and Silver Certified seal allowed to be used for marketing.

Progressive certification leads to higher awards (Gold, Platinum and Master). Certification must be renewed every 12 months.

Figure 41: Earthcheck certification standards.

Earthcheck ROI marketing material only promotes the savings from reduced energy, water and waste as the economic benefit rather than increased traffic.

7.3 Suitability for CBD

Neither of the above ratings is well suited to the purposes of CBD, due primarily to:

1. Limited market penetration;

2. Lack of a single focus on energy use


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3. Private ownership of the rating.

As a result, the preferred tool is NABERS for Hotels.

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