National Energy Savings Initiative Progress Report Chapter 11

11 Economic and energy market modelling In the Clean Energy Future plan the Australian Government committed to undertake further work – including quantification of costs and benefits – on a possible national Energy Savings Initiative. The commitment also indicated that a final decision on whether to adopt a national scheme would be subject to economic modelling and regulatory impact analysis.

This chapter provides an overview of the approach that will be applied to model the costs and benefits of a possible national scheme.

11.1 Modelling a national Energy Savings Initiative Scenario modelling is an established method for improving understanding of the strengths and weaknesses of a given policy option. Scenarios can provide information about potential policy impacts, how different policy designs compare, and the relative importance of different policy inputs or parameters to the modelling results.

Scenario modelling does not predict what will happen in the future, rather it is a projection of what could happen. This projection will be influenced by the structure of the model and its input data and assumptions (among other things) and does not include events that are very uncertain (such as future technology breakthroughs) or that are outside the boundaries of the model.

Previous modelling exercises for a national Energy Savings Initiative The potential economic and energy market impacts of a national Energy Savings Initiative have been examined through two previous modelling exercises. In 2010, the Prime Minister’s Task Group on Energy Efficiency commissioned modelling on the impacts of a highly stylised scheme with different targets and emissions abatement assumptions. In May 2011, the Department of Climate Change and Energy Efficiency commissioned further work to test the sensitivity of the approach used in the Task Group modelling to a range of assumptions concerning consumer behaviour and scheme coverage.

The Working Group has engaged Sinclair Knight Merz McLennan Magasanik Associates (SKM MMA) to model a range of scenarios for the regulatory impact analysis. This work will further build on and refine the methodology developed through the previous two modelling exercises.

An overview of this methodology is provided in Box 11.1.

Page | 119 Box 11.1 Overview of the SKM MMA modelling methodology

The SKM MMA modelling approach estimates the potential impacts of a national Energy Savings Initiative on Australia’s electricity and gas markets, for a given set of targets.

The potential impacts of a national Energy Savings Initiative on energy prices would largely flow from the price of the energy savings certificates that (under a typical white certificate scheme) would be acquitted by liable parties to meet their share of the chosen target. SKM MMA has developed a National Energy Efficiency Model (NEEM) to derive a plausible combination of energy efficiency activities that could be undertaken across households and businesses in order to generate certificates that are used to meet the scheme target. The NEEM draws from a wide inventory of possible activities, including appliances, equipment and processes available to the residential, SME, commercial and industrial sectors.

The NEEM assumes that liable parties would preference the cheapest available certificates, and therefore sequences low cost activities ahead of higher cost ones. However, the model also accounts for factors that could limit the availability of, and market for, low cost activities. For instance, the NEEM assumes that the take-up of different activities will be constrained by the size of the market for an activity, rates of take-up, and the degree to which energy efficiency is an important consideration for different households and businesses. By applying these assumptions, the model ensures that the combination of energy efficiency activities taken up is as realistic as possible.

The NEEM derives the scheme cost in any given year by multiplying the marginal certificate price (that is, the cost of the last certificate created to fulfil the target in a given year) by the scheme target for that year. Since scheme costs and benefits would likely be passed through to end consumers, the SKM MMA modelling approach also estimates the net costs and benefits of a national scheme on retail energy bills, by using established energy market models. The energy market models include quantification of private benefits (such as reduced energy costs for households and businesses that adopt energy efficiency improvements under the scheme) and shared benefits (such as reduced wholesale energy prices and deferred network infrastructure expenditure, which would be felt by all consumers).

More detail about the scenario modelling approach employed by SKM MMA can be found in the Modelling Assumptions Report, which is available on the website of the Department of Climate Change and Energy Efficiency.117

Improving the modelling approach The extent to which estimated costs and benefits can be relied on will depend on the quality of the input data and assumptions that underpin the models. The Working Group has sought views on the modelling approach, its assumptions and input data from a range of experts (including energy sector, industry, and academia) and the public. In December 2011, a Modelling Assumptions Report, outlining the modelling approach and key assumptions used in the 2011 exercise, was released for public comment.

Page | 120 A range of stakeholders provided feedback to the Working Group through formal submissions in response to this report and during targeted expert and public consultation workshops. In broad terms, many stakeholders at the workshops expressed support for the modelling approach as a sensible way to model the potential economic and energy market impacts of a national Energy Savings Initiative, and acknowledged the difficulties involved.

Stakeholders asked for further explanations about:

. whether the models could accurately project impacts on particular sectors or states;

. how energy market features are modelled, including wholesale prices, the impact of the Renewable Energy Target on electricity prices, and the patterns of network investment assumed; and

. whether the models include assumptions regarding administration costs for participating businesses.

Areas of concern raised by stakeholders included ensuring that:

. underlying data was as granular and accurate as possible;

. energy demand forecasts upon which the modelling is based are as up to date as practical and the modelling approach accounts for carbon prices (and potential rises) beyond 2025; and

. payback thresholds assumed in the modelling are an accurate proxy of decision- making in different sectors (see Box 11.2 for an explanation of how the modelling approach accounts for decision-making).

The Working Group is grateful for this feedback and has incorporated improvements to address these issues in the next modelling exercise. In particular, the Working Group has worked with SKM MMA and other consultants to improve and enhance the data and assumptions that underpin the models. As noted in Chapter 7, significant effort has been made to gather better data about energy consumption and energy efficiency in the industrial, commercial and small and medium enterprise (SME) sectors. SKM MMA has also worked with consultants to increase the resolution of the modelling approach so it can estimate the potential impacts of a national scheme on distribution networks (such as distribution infrastructure deferrals and possible changes to network tariffs).

Page | 121 Box 11.2: How the modelling approach considers decision-making

The modelling uses an ‘adjusted payback period’ approach to decision-making concerning energy efficiency activities. This is different to a conventional financial payback period, which simply compares the upfront cost of an investment with its expected financial returns to determine the period of time it takes for future returns to offset the upfront cost.

The ‘adjusted payback period’ approach builds on this basic financial tool to more accurately reflect observed consumer behaviour. This approach incorporates information from the extensive literature on consumer decision-making, including the known tendency of consumers to discount future gains or losses and inflate present gains or losses. For example, consumers frequently buy household appliances based on their sale price alone without considering the ongoing energy cost of running these appliances.

If this behaviour is taken into account when calculating the balance of costs and benefits around a decision, it appears that consumers have a very high ‘personal discount rate’ – that is, they will agree to decisions where the benefits outweigh the costs at or very close to the time of purchase, even though in the longer term the decision may make them worse off.

To simulate the operation of a national Energy Savings Initiative, the National Energy Efficiency Model (NEEM) must account for how decision-making by consumers changes in response to the incentives offered by the scheme. Not all households and businesses make decisions about energy efficiency in the same way. Rather, consumers’ decision-making is affected by a range of commercial considerations and behavioural factors, and these can increase or decrease the balance between costs and benefits that any given consumer is apparently willing to accept before adopting an energy efficiency improvement.

The NEEM assumes that consumers will purchase an efficient appliance if the energy savings provided by it balance out the upfront costs within a given period of time. This period of time (sometimes called the ‘payback threshold’) is determined by the range of factors mentioned above, including the average consumer’s apparent ‘personal discount rate’ – the extent to which present costs are inflated and future gains are discounted. This period is therefore likely to be significantly shorter than the period over which the capital cost of the appliance is offset by the value of the energy savings.

Within this context, energy savings certificates can reduce capital costs to the point where efficient appliances fall within the average consumer’s ‘payback threshold’. The NEEM applies different assumptions about this ‘payback threshold’ for different sectors and sub- sectors, based on what is known about decision-making in these sectors.

The modelling approach aims to simulate how an average consumer in a given sector or sub- sector may respond to the incentives offered by the scheme. These assumptions, like all those applied in the modelling, are intended to be plausible central estimates within a range of uncertainty.

Page | 122 As a result, the modelling approach and its underpinning data and assumptions have been improved in a number of ways:

. areas where the models can incorporate more accurate or up-to-date data have been identified, and steps have been taken to access and incorporate this data. As a result, the models’ inventory of energy efficiency activities available to the residential, commercial and industrial sectors is underpinned by more, and better, data, and the models now include take-up of energy efficiency activities by SMEs;

. the models now include energy market impacts at the resolution of distribution network zone (rather than state border), critical for analysing a scheme’s potential impact on peak demand; and

. a range of minor assumptions around the models’ treatment of different energy generation and network technologies have been improved.

11.2 Modelling inputs to the regulatory impact analysis The Working Group is now working with SKM MMA to model a range of scenarios. These examine how different scheme targets and sectoral and geographical coverage settings could affect the overall costs and benefits of a national scheme. The modelling work will also estimate the costs and benefits of a scheme where peak demand impacts are reflected in certificate incentives and a scheme that supports energy efficiency improvements in low-income households. The outputs of this scenario testing will be important inputs to the regulatory impact analysis.

As part of the modelling work, the Working Group will also test the models’ sensitivity to a number of key assumptions around which there is some uncertainty. In particular, a number of industry stakeholders have suggested that decision-making around energy efficiency is influenced by broader economic circumstances. For example, stakeholders highlighted that firms may have shorter payback thresholds for energy efficiency investments during times when broader economic circumstances reduce the availability of credit, but longer payback thresholds when business confidence is high.

A number of scenarios will therefore be tested to establish the degree to which the models’ outputs are sensitive to decision-making assumptions. The Working Group also intends to test the sensitivity of the modelling approach to other key assumptions, including consumer behaviours and the inclusion of distribution networks.

Page | 123 In addition to economic and energy market modelling, the Working Group will continue to gather other qualitative and quantitative analysis to inform the regulatory impact analysis. One important research task focuses on how compliance costs faced by obligated parties and firms that create certificates may change under two possible scenarios: if existing state schemes were harmonised; or if the government decided to pursue a stand-alone scheme. The Working Group is also supporting a survey by the Australian Industry Group to improve understanding concerning Australian businesses’ awareness of and attitudes to energy efficiency. Chapter 1 includes an overview of the analytical tasks currently commissioned by the Working Group.

Page | 124 117 SKM-MMA, Energy Market Modelling of National Eneryg Savings Initiative Scheme – Assumptions Report, 2011, http://www.climatechange.gov.au/en/government/initiatives/~/media/publications/energy-savings-initiative/20111215-skm-report-energy-marketing- modelling-pdf