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Final Report ‐ Pathway to 2020 for Increased Stringency in New Building Energy Efficiency Standards: Benefit Cost Analysis: Commercial Buildings: 2016 Update

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Document Details

Prepared for: Department of Industry, Innovation and Science

Client representative: Stanford Harrison

Date: 10 May 2016

Rev01

pitt&sherry ref: HB16076/Final Report 31‐P/Rev 01

Table of Contents

1. Summary of Analysis ...................................................................................................................................... 1 2. Results – All Australia ..................................................................................................................................... 3

2.1 No learning rate .................................................................................................................................. 3 2.2 Learning rate 3% p.a for 10 years ....................................................................................................... 4 2.3 Learning rate 100% after 7 years ........................................................................................................ 4 2.4 Breakeven (BCR 1.0) results by climate zone ...................................................................................... 5

3. Discussion of updated results ........................................................................................................................ 8 4. Comparison with 2012 results ....................................................................................................................... 9 5. Research Questions ........................................................................................................................................ 9

Prepared by:

Phil McLeod Date: 10th May 2016

Reviewed by:

Philip Harrington Date: 10th May 2016

© 2016 pitt&sherry This document is and shall remain the property of pitt&sherry. The document may only be used for the purposes for which it was commissioned and in accordance with the Terms of Engagement for the commission. Unauthorised use of this document in any form is prohibited.

pitt&sherry ref: HB16076/Final Report 31‐P/Rev 01 1

1. Summary of Analysis

Purpose

This purpose of this study is to update the findings of the building benefit/cost analysis, for commercial buildings, undertaken for the report Pathway to 2020 for Increased Stringency in New Building Energy Efficiency Standards: Benefit Cost Analysis (2012). The 2012 study analysed the range of cost‐effective savings in the energy consumption of new buildings that could be achieved in Australia by 2015 and 2020, relative to buildings compliant with the current, 2010 version of the Building Code of Australia (BCA2010), based on a number of defined scenarios. It was commissioned by the former Department of Climate Change and Energy Efficiency as a contribution to the National Building Energy Framework measure described in the former National Strategy on Energy Efficiency.

In December 2015 the COAG Energy Council agreed to the National Energy Productivity Plan which includes a measure to advance the building energy performance requirements in the National Construction Code (measure 31). In this context the Department of Industry, Innovation and Science commissioned an update on the benefit cost analysis contained in the 2012 report, for commercial buildings only, to help inform potential policy settings for future. This updated analysis largely repeats the 2012 study methodology, albeit with contemporary price and building stock observations and revised policy scenarios, as described below. The purpose of this study to help inform potential policy settings for building energy performance requirements in 2019 for Class 3 and 5 – 9 buildings in the National Construction Code. The original study and this updated analysis should be read in conjunction with each other. Due to the change in policy scenarios, the results of the 2012 study and 2016 Update are not strictly comparable. That said, some scenarios in the two reports overlap and, as discussed below, the findings of the two reports are consistent with each other. The performance levels identified here as being cost effective should be regarded as indicative only, as they are based on a point‐in‐time analysis of technical building performance improvement potentials and associated economic costs and benefits, for a limited number of building types, climate zones and policy scenarios. The scope of this project did not allow for revised cost estimation or new building simulation modelling. This would tend make the results conservative, as new materials, technologies, design concepts and cost reductions that have become available since 2012 would be likely to increase the scope of cost effective savings.

Approach

In summary this updated benefit cost analysis uses:

Commercial building forms, building specifications and climate zones as per the 2012 Report;

Contemporary energy price projections based on AEMO forecasts without a carbon price with two different shadow carbon prices i) based on latest ERF auction outcomes ($12.25 tonne) increasing at CPI and ii) the medium scenario from the Climate Change Authority (CCA) 2014 Targets and Progress Review; 1;

3 learning rates i) 0% (no learning) ii) 100% after 7 years (i.e the incremental cost falls to zero after 7 years), and iii) 3% per annum for 10 years.

Changes in incremental costs since the original study, and also changes in the ‘opportunity set’ due to new/improved materials or technologies, have not been accounted for here;

1 The shadow price of carbon in this scenario begins at $5.49/t CO2‐e in 2015 and rises to $30.14 in 2020, $36.67 in 2025, $44.61 in 2030 and $56.45 by 2036.


A 7% real discount rate. The benefit cost analysis assumes that new performance requirements are introduced in FY2019‐20, consistent with an assumed start date of May 2019. It applies to a ‘cohort’ of buildings constructed between 2020 – 20242. All buildings are assumed to have an economic life of 40 years and the benefit cost analysis is conducted over this period. Cost effective levels of energy savings are calculated on a breakeven basis (benefit‐cost ratio of 1) and benefit‐cost ratios of 1.2 and 1.5.

Table 1 below shows the effect the different carbon price scenarios have on commercial electricity price forecasts in NSW (as an example). The carbon prices are relatively modest, so there is not a great difference in electricity price forecasts, particularly between the no and low carbon price scenario. Table 1: Commercial (medium business) electricity price forecasts under different carbon price scenarios

Electricity price NSW ($/GJ)

2020

2021

2022

2023

2024

2025

2026

2027

2030

2031

2032

2032

2033

2034

2035

2036

No carbon price 42.9 42.8 42.9 44.3 45.8 47.4 47.4 47.4 47.4 47.4 47.4 47.4 47.4 47.4 47.4 47.4

Low carbon price ($12.25 tonne rising with CPI)

45.9

45.9

46.0

47.4

48.9

50.5

50.5

50.5

50.5

50.5

50.5

50.5

50.5

50.5

50.5

50.5

Medium carbon price (based on CCA targets and progress review)

50.4

50.6

51.0

52.8

54.6

56.6

56.9

57.3

57.7

58.1

58.6

59.0

59.5

60.0

60.5

61.0

Table 2 shows how the incremental cost rate varies under the different learning rate scenarios. Learning rates refer to the rate of reduction in incremental costs through time, consequent upon at least two factors: first, innovation in designs, methods, tools, techniques and know‐how; and second, reductions in the unit costs of components, particularly those induced by the measure (for example, regulation may lead to increased economies of scale or induce innovation in the supply chain).

The example below is the additional cost to achieve a 40% energy reduction (compared to BCA 2010) for a 3‐storey office building in Sydney. Under the no learning rate scenario incremental costs remain unchanged for the 5‐year cohort of buildings. On the other hand, under the highest learning rate scenario, incremental costs are assumed to fall from $153/m2 in 2020 to $31/m2 by 2024. We note that there is uncertainty regarding the ‘correct’ value for the learning rate, hence the sensitivity analysis on a range of possible values. Ideally further quantitative research would be undertaken in this area. Table 2: Difference in incremental cost rates under different learning rate scenarios

Learning rate 2020 2021 2022 2023 2024

No learning rate $153/m2 $153/m2 $153/m2 $153/m2 $153/m2

3% p.a for 10 years $153/m2 $148/m2 $144/m2 $139/m2 $135/m2

Falling to 0% after 7 years (5% learning rate for the first two years, 15% in years three and four, 10% in year five, then 5% for years six and seven).

$153/m2

$115/m2

$77/m2

$54/m2

$31/m2

2 As per the original report, the working assumption is that Code revisions may occur every 5 years, and therefore only a 5‐year cohort is modelled.


Key Findings

This updated analysis confirms the findings of the 2012 study that there are very significant cost effective opportunities for energy savings in new commercial buildings in 2020 relative to (BCA) 2010, particularly when a BCR target of 1 or 1.2 is selected.

For example, even if no learning is assumed, average savings of 35% are cost effective with a low shadow carbon price and a BCR of 1. With a higher shadow carbon price and a target BCR of 1, savings of 47% on average are cost effective, even with no learning. If learning is set at 100% over 7 years, then savings of between 50% and up to 80% are shown to be cost‐effective, depending upon the scenario selected. At the other end of the spectrum, if a target BCR of 1.5 is specified and no learning, then savings (of 14%) are cost effective only with the ‘medium’ carbon price assumption, and none at all with lower carbon price assumptions.

This summary illustrates the sensitivity of what is deemed to be ‘cost effective’ as a function of key parameter assumptions, and notably the learning rate. We conclude from this that careful and evidence‐based research, and not assumptions, should be used as the basis for determining key values – or better, ranges of values – when undertaking full benefit‐cost analysis and regulatory impact assessment.

That said, while there are variations in the degree of cost effective savings by climate zone and by building type, and by policy scenario, these variations are around mean values which are high and quite robust in the face of the sensitivity analyses included in this study. This overall result is attributed primarily to the relatively low stringency for commercial buildings in BCA20103, which means that many opportunities for energy savings that were cost effective at that time were not taken up. Secondly, energy prices have risen more strongly than was anticipated in the 2012 study, offsetting the reduction in actual/shadow carbon prices.

We stress that these findings are based on a limited‐scope update of certain modelling parameters. Further and more comprehensive analysis would be required to support a regulation impact assessment, for example. That said, we consider it likely that if an updated ‘savings opportunity set’ were modelled, along with updated incremental costs, higher savings than those reported here would be deemed cost effective (for each policy scenario).

2. Results – All Australia

The results below are presented under the 3 learning rate scenarios. For each scenario results are then shown for the 3 different energy prices modelled. Percentage energy reductions from BCA 2010 are weighted averages for all relevant commercial buildings in Australia. Energy intensities as a result of the energy savings are also shown. The energy intensities are also weighted averages based on the proportions of the total Australia stock in each climate zone.

2.1 No learning rate

Table 1 below shows that even with no learning rate, energy savings can be achieved under each energy price scenario at break‐even and BCR 1.2 levels. At higher energy costs because of a carbon price, the more

3 The 2012 study found that the BCA2010 stringency level was associated with a BCR of around 2.2, meaning that very many cost effective savings were not taken up at that time. Generally the level of cost effective savings tends to increase through time, due to at least three factors: real energy price increases; real cost declines (typically but not always) in key energy using equipment, designs and materials; and adoption of new techniques and ‘industry standards’ over time for market/non‐regulatory reasons, such as meeting NABERS or Green Star targets.


cost effective energy savings become. Energy savings of 14% at the benefit cost ratio of 1.5 can be achieved under the medium carbon price scenario.

Table 3: All Australia results, no learning rate

No carbon price ERF (low) carbon price Medium carbon price

BCR % energy reduction from BCA2010

MJ/m2.a % energy reduction from BCA2010


MJ/m2.a

1.0 29% 358 35% 328 47% 268

1.2 13% 436 20% 401 34% 330

1.5 * * 14% 428

* Can’t be achieved for this scenario

2.2 Learning rate 3% p.a for 10 years

Table 2 below shows the results where the learning rate is a modest 3% per annum for 10 years. So after 10 years the incremental cost has reduced by 30% and thereafter there is no further reduction. Break even energy savings of up to 53% can be achieved under the medium carbon price scenario. Energy savings at a BCR of 1.5 are realized for the low and medium carbon price scenarios; 7% and 23% respectively.

Table 4: All Australia results, learning rate 30% after 10 years

No carbon price scenario ERF carbon price Medium carbon price




MJ/m2.a

1.0 37% 320 42% 292 53% 238

1.2 22% 390 28% 358 41% 294

1.5 * 7% 462 23% 384

* Can’t be achieved for this scenario

2.3 Learning rate 100% after 7 years

Table 3 below shows the results where the learning rate increases to 100% after 7 years. Significant break‐even energy savings, ranging from 70% to 80% depending on energy prices, can be achieved. Under this learning rate scenario energy savings of at least 50% can be achieved for a BCR of 1.5.

Table 5: All Australia results, learning rate 100% after 7 years

No carbon price scenario ERF (low) carbon price Medium carbon price




MJ/m2.a

1.0 70% 150 73% 134 80% 102

1.2 62% 189 66% 170 73% 133

1.5 50% 250 54% 228 63% 182


2.4 Breakeven (BCR 1.0) results by climate zone

The tables below show a breakdown of results by climate zone for the break‐even energy savings for each of the scenarios described above. There is a reasonable spread of results by climate zone, with the hotter climate zones with greater electricity use for space conditioning, and also those with higher electricity prices, tending to show more cost effective savings. At the highest learning rate (tables 10‐12) the level of break‐even energy savings is the greatest.

2.4.1 No learning rate

No carbon price

Table 6: No learning rate, no carbon price results

Climate zone

% Energy

reduction from

BCA 2010

MJ/m2.a

Sydney (CZ5) 35% 326 Darwin (CZ1) 68% 233 Brisbane (CZ2) 46% 285 Adelaide (CZ5) 30% 350 Hobart (CZ7) 26% 374 Melbourne (CZ6) 7% 468 Perth (CZ5) 40% 297 Canberra (CZ7) 32% 363 Weighted average 29% 358

ERF carbon price

Table 7: No learning rate, ERF carbon price results

Climate zone % Energy

reduction from

BCA 2010

MJ/m2.a



Medium carbon price

Table 8: No learning rate, medium carbon price results


reduction from

BCA 2010

MJ/m2.a


2.4.2 Learning rate 3% per annum

No carbon price

Table 9: Learning rate 3% per annum, no carbon price results


reduction from

BCA 2010

MJ/m2.a


ERF carbon price

Table 10: Learning rate 3% per annum, ERF carbon price results


reduction from

BCA 2010

MJ/m2.a



Medium carbon price

Table 11: Learning rate 3% per annum, medium carbon price results

2.4.3 Learning rate 100% after 7 years

No carbon price

Table 12: Learning rate 100% after 7 years, no carbon price results

Climate zone

% Energy

reduction from

BCA 2010

MJ/m2.a

Sydney (CZ5) 70% 149

Darwin (CZ1) 93% 55 Brisbane (CZ2) 80% 106 Adelaide (CZ5) 73% 135 Hobart (CZ7) 65% 176 Melbourne (CZ6) 62% 193 Perth (CZ5) 76% 116 Canberra (CZ7) 67% 173 Weighted average 70% 150

ERF carbon price

Table 13: Learning rate 100% after 7 years, ERF carbon price results

Climate zone

% Energy

reduction from

BCA 2010

MJ/m2.a

Sydney (CZ5) 74% 131 Darwin (CZ1) 93% 49 Brisbane (CZ2) 82% 93 Adelaide (CZ5) 75% 124 Hobart (CZ7) 60% 203 Melbourne (CZ6) 66% 169 Perth (CZ5) 77% 111 Canberra (CZ7) 71% 154

Climate zone

% Energy

reduction from

BCA 2010

MJ/m2.a



Climate zone

% Energy

reduction from

BCA 2010

MJ/m2.a

Weighted average 73% 134

Medium carbon price

Table 14: Learning rate 100% after 7 years, medium carbon price results

Climate zone

% Energy

reduction from

BCA 2010

MJ/m2.a


3. Discussion of updated results

While the different energy price scenarios affect the results to some degree, the biggest influence on the varying level of energy savings is the learning rate. Under the no carbon price scenario, for example, break‐even energy savings range from 29% (all Australia results) where the learning rate is zero, to 70% where the learning rate is 100% after 7 years. As reported in 2012, a key driver of the generally high level of cost effective energy savings for commercial building results is the relatively low starting point implicit in BCA2010. Further, it is generally accepted in the building industry that industry standards and practices have moved on significantly in recent years, in part due to the influence of programs such as NABERS and Green Star, but also due to new technologies and a growing appreciation of the value of energy efficiency. Due to relatively modest performance requirements being set in 2010, many savings opportunities that were cost effective at that time were not taken up. As a result, they remain available, and this significantly increases the overall level of savings that are available now. In addition, energy prices for electricity and gas, and also the mix of fuels used in different building types and climate zones, impact on the results. These effects are accentuated in commercial as distinct from residential buildings due to their significantly higher energy intensity (energy use per square metre). The relatively lower level of cost effective savings in Canberra, Hobart and Melbourne is largely attributable to higher gas use in these cooler climates, with gas savings being less valuable than electricity savings, and also due to relatively lower electricity prices in those states. By contrast, the hotter climate zones with greater electricity use for space conditioning, and also those with higher electricity prices, tend to show more cost effective savings.


4. Comparison with 2012 results

The 2012 study called for an analysis of energy savings at ‘break‐even’ benefit cost ratio (BCR = 1), although some scenarios also tested a BCR of 1.2. The 2012 study used two different discount rates, and for this section we report only those with a 7% real discount rate. In terms of policy scenarios, the 2012 study specification required that scenarios reflected a mix of different carbon prices and differing learning rates rather than treating these independently.

Specifically, Scenario 1 in the 2012 study assumed no carbon prices and no learning and so is comparable to the similar scenario from this study. Scenario 2 in the 2012 study assumed 3% p.a. learning rate and a ‘low’ carbon price assumption. The carbon price assumptions for that study were based on the “Government policy” (Scenario 2) and “High price” (Scenario 3) based respectively on scenarios in the Treasury’s 2011 economic modelling of the Clean Energy Future legislative package. We note that this carbon price assumption was higher than the ‘medium’ carbon price scenario in this report.. Scenario 3 in the 2012 study assumed a 5% p.a. learning rate over 10 years (that is, 50% of the incremental costs remain after 10 years) and a carbon price trajectory of $27.50 in 2013, $62/t in 2020, and $147.80 by 2035.

With no carbon price and no learning, the break‐even (BCR = 1) 2016 results are lower than the 2012 ones. 29% energy savings would be cost effective in 2016, compared to 44% savings in 2012 for a similar scenario. The lower savings in 2016 are due to the difference in forecast electricity prices used for the original and updated study (see table 15 below as an example). The electricity price forecasts used in 2012 were for medium/small consumers. The 2016 prices are for medium consumers and are lower than the 2012 prices. We believe the owner of a medium size office building or small shopping mall is more likely to pay the medium consumer price, although the issue of commercial prices, and who pays what, is increasingly less transparent in the wake of full retail contestability. Another factor leading to lower savings in 2016 is that assumptions about network costs were higher in 2012 than now, reflecting the expected lower level of network infrastructure investment in coming years when compared with the past five years or more. Table 15: Comparison of forecast electricity prices used in 2012 and 2016 (NSW)

Electricity price

NSW ($/GJ)

2020

2021

2022

2023

2024

2025

2026

2027

2030

2031

2032

2032

2033

2034

2035

2036

2016 update 42.9 42.8 42.9 44.3 45.8 47.4 47.4 47.4 47.4 47.4 47.4 47.4 47.4 47.4 47.4 47.4 2012 study

56.0 56.2 56.4 56.5 56.7 57.3 58.2 57.1 57.3 55.6 55.9 56.2 56.5 56.4 56.0 56.2

In the 2012 study, the learning rate was not able to be varied independently of other variables, and nor were carbon prices, whereas in 2016 we have treated these variables separately, to better illustrate their relative contributions to the overall results. Therefore we can see that even in the ‘no carbon price’ scenario, the cost effective level of energy performance is highly contingent on the learning rate assumption. A very modest learning rate of 3% per year – which we consider unrealistically low – already lifts the cost effective improvement from 29% to 37%, while an assumption of 100% learning over 7 years lifts the figure to 70%.

5. Research Questions

The comparison of the 2012 and 2016 study results reinforces the need to address some key research questions.

First, amongst these is the choice of an appropriate learning rate. Since this is clearly a significant variable, an evidence‐based, rather than assumption‐based, approach is critical. Targeted research using sound


methodologies needs to be undertaken to establish a value or plausible range of values for modelling purposes. Industry stakeholders may be able to contribute to this research, but there is a clear risk of strategic bias that must be understood and designed into the research methodology. Second, the choice of benefit cost ratio for regulation impact assessment purposes also requires a rigorous treatment. There is widely held belief that higher benefit cost ratios are ‘better’ than lower ones, or, secondly, that a BCR > 1 should be used as a ‘confidence margin’, or to manage possible uncertainty in results. These have no basis in economic or public finance theory. Setting a policy target where the BCR > 1 by definition means that cost‐effective savings opportunities are being foregone, creating an opportunity cost of foregone economic welfare. Where there is uncertainty in values, the first‐best solution is conduct evidence‐based research to reduce that uncertainty. Second, for remaining and legitimate uncertainty, appropriate valuation techniques such as the use of probability‐weighted or ‘expected’ values, or ranges of results, should be used rather than varying benefit cost ratios as a proxy. Third, we note again that this quick update has not updated underlying cost assumptions, the energy savings opportunity set or the cost/savings opportunities in mechanical equipment and lighting, etc.

Fourth, both the 2012 and 2016 studies have an implicit assumption that NCC energy performance requirements are in fact complied with. However, our 2014 National Energy Efficient Buildings Project Phase 1 report raises material doubt about that. As recommended in that report, we note that significant quantitative research will be required to establish the extent and materiality of non‐compliances with respect to their impact on the actual energy performance of the building stock.

Contact: Philip Harrington Senior Principal, Carbon & Energy [email protected] (03) 6210 1489 0419 106 449

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Final Report Pathway to for Increased Stringency Building ... · Buildings: 2016 Update transport | community | mining | industrial | food & beverage | carbon & energy