7 September 2010 – In part one of a three part series, Anthony Szatow of the CSIRO presents the results of the three year Intelligent Grid Project, an investigation into how distributed energy can reduce both carbon emissions and energy costs.

The Australian energy market has been developed around a centrally planned and controlled system. As a result, Australia has well-established systems for planning, investment, designing, building and operating centralised generation assets and associated infrastructure. Now distributed energy, a collective term that includes local generation of energy, energy efficiency and better management of supply and demand, promises to significantly reduce greenhouse gas emissions and avoid significant financial loss over time.

CSIRO recently completed a three year research project which found that if Australia followed the 450 parts per million trajectory outlined by Professor Ross Garnaut in the Garnaut Report, DE could deliver savings of up to $130 billion in today’s money and reduce carbon emissions by 4 metric tonnes carbon dioxide equivalent a year by 2020 and 40 Mt CO2e a year by 2050. Simply put, distributed energy represents a means of saving money while reducing emissions.

Significantly, this value remains substantial under different policy scenarios, including two different Carbon Pollution Reduction Scheme targets (CPRS 5: $105b in savings; CPRS 15 : $115b in savings) and the higher emission Garnaut scenario of 550ppm ($115b in savings). These values are determined by comparing energy scenarios with and without the use of distributed energy, using CSIRO’s Energy Sector Model, a bottom-up economic model of Australia’s stationary and transport energy sectors.

Realising this value necessitates change.

To inform what change may be necessary, the CSIRO Intelligent Grid research included economic modelling, technical engineering studies, case studies, social research to profile potential uptake and an analysis of energy policy, regulation and market frameworks. In a series of three articles we aim to cut through some of the complexity to show how that value can be realised and to discuss implications for stakeholders involved in the generation and consumption of energy in Australia.

The most common starting point for reducing emissions has typically been through policy and regulation. Economists tell us externalities need to be priced, and that businesses or consumers won’t move voluntarily at the scale required to address climate change. We will challenge that notion later in this article series, but assume it stands for now.

Policies can set strategic direction and create mechanisms such as financial incentives, education, research and development, or funding for pilot projects that enable new directions to be realised. One aspect of our research included interviews with 47 energy market stakeholders combined with a literature review, to identify and distill key barriers and enablers of distributed energy.

Our findings suggest a hierarchy of enablers for distributed energy, including:

  • Policy and regulation needs to allow proponents to capture some portion of the value of DE where it reduces emissions or costs that are otherwise socialised.
  • Policy and regulation must have long term certainty to give DE proponents and investors the confidence to pursue DE.
  • Consumers, industry and governments all need to be educated on the value of DE and how it works, to overcome cultural bias towards mains grid energy supply.

Comparing this hierarchy with literature, we found a strong corroboration around the themes of policy certainty and clear investment signals, but also market access (particularly ease of grid connection) and stakeholder education. Our policy research focused on cataloguing barriers, analysing the progress of policy and regulation against perceived barriers, as well as examining the process of good policy-making as an enabler of good outcomes.

Evidence-based policy making is said to be an approach that “helps people make well informed decisions about policies, programs and projects by putting the best available evidence from research at the heart of policy development and implementation” (Davies, 2004). Explicitly, it aims to avoid the use of “best hunches” and “educated guesses” in the policy development process.

Evidence-based policy development has an obvious intuitive logic, but implementing it is not always easy. Research by Campbell et al (2007) in the United Kingdom points to issues such as the demands of political cycles, inadequate resources and political culture that can undermine the use of evidence based research in policy development. So how can these issues be overcome?

In the United States, research by Allison (2005) has highlighted the importance of policy networks in shaping public policy relating to distributed generation. Ostrom et al. (1990, cited in Allison, 2005) state that:

“Policy networks coordinate public and private actors who are increasingly bound by shared values, common discourse and dense exchanges of information…”

In this way, policy networks can be used to overcome some of the difficulties of implementing evidence based policy development by ensuring a degree of continuity across political cycles and by encouraging sharing of resources and collaboration across institutions and interest groups.

When trying to drive improved sustainability of resource and energy use, there is always a risk that policies can inadvertently reinforce the status quo albeit with marginal efficiency improvements, or marginal pollution reduction. By reinforcing the status quo, competing and potentially transformative technologies may be locked out and remain immature or underdeveloped – technologies that may be necessary to make the transition to a desired, long term policy objective. So how can that risk be mitigated?


Backcasting is a policy development technique that can be used in parallel with policy networks to overcome the risk of incremental change. Backcasting involves envisioning a desired future objective based on need, and then assessing what is required to get there. Whereas forecasting attempts to determine future scenarios based on information and data analysis today, backcasting attempts to determine what change is necessary to achieve a desired future scenario (Jansen, 2001).

In this way, it could be said that backcasting requires an implicit moral or ethical judgement. It forces us to ask what world should be created. This type of thinking dominated the development of the Dutch Sustainable Technology Development Programme in the 1990s and led to models for strategic planning that were applied across government and the private sector.

Backcasting helps policy makers think in terms of what is necessary to meet some important future objective, not what appears possible given today’s circumstance. In the Dutch context, backcasting has been coupled with interdisciplinary partnerships between government, private enterprise, financiers, research and education institutions and end users of technology. By involving a range of stakeholders in policy development in this way, policy becomes more than just a consultative process to determine the detail of policy delivery, it can galvanise a collective strategy based on a shared policy objective (Van de Meulen, 1999).

Of course, effective policy networks and tight collaborative partnerships between diverse organisations that impact on energy outcomes are easy to discuss in theory, but difficult to achieve in practice. Any process of change, let alone game changing innovation, is delicate. However, the message from climate science research is clear; incremental change is high risk.

Anecdotally, we find more and more corporations and investors appear to be seeing climate change as a business continuity risk that requires action ahead of policy and regulation and there appears to be a growing recognition that we cannot wait for political consensus. Transformative change may appear difficult, but it also appears necessary.

Backcasting suggests transformation can start by rethinking our objectives as businesses, researchers, government agencies or communities and asking tough questions: Who do we want to be? What does the future demand of us? What should our future look like? Collaborative partnerships and supportive networks can help organisations work through transformative change by enabling data sharing, knowledge transfer and risk spreading, but it starts with building trust and a shared vision.

In our next article in the series  we will discuss what transformative change might look like in the energy sector, including potential new business models for deploying distributed energy, and ways of packaging technologies to make the whole greater than the sum of the parts in a way that could enable business to move ahead of policy and regulatory change.

This article draws on research from the Intelligent Grid project. Details of research published to date can be found here

Allison, J. (2005), Distributed generation of electricity: the role of academic research and advice in California’s clean DG’ policy network, International Environmental Agreements: Politics, Law and Economics, 5 (4): 405-414.?Campbell, S, S Benita, E Coates, P. Davies and G. Penn (2007), Analysis for Policy: Evidence-based Policy in Practice, Cabinet Office, Government Social Research Unit, HM Treasury, London, UK.?Davies, P. (2004), Is Evidence-Based Policy Possible? The Jerry Lee Lecture, Campbell Collaboration Colloquium, Washington. Jansen, L. (2001), The challenge of sustainable development,

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