policymakers demonstrate

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prioritization of low carbon infrastructure, perhaps through a strategic infrastructure
plan that sets out the criteria that ensure that infrastructure investments
are compatible with long-term green economy objectives. This would enable
a prioritization of those infrastructures that are required for a green economy
(such as sufficient transmission capacity to incorporate renewable electricity
into the power system), ‘smarter grids’ to facilitate its management, and materials
management facilities to delay or prevent resources from becoming wastes.
A National Infrastructure Bank with green criteria embedded within its mandate,
could finance large infrastructure and demonstration projects. In addition,
the capacity of local authorities to drive green infrastructure locally could be
bolstered by enabling the establishment of green municipal bonds and a collective
municipal bond agency owned by participating local authorities.
Innovation
Change in the energy sector since the industrial revolution has been rapid
and dramatic, with a huge range of energy demand technologies and associated
energy consumption practices being invented, developed and adopted
as new, more convenient and versatile energy sources became widely available
and cheaper. The extent of cost-reducing innovation is often described
through learning or experience curves, and associated ‘learning rates’, the percentage
reduction in unit cost for each doubling of installed cumulative capacity.
Azevedo et al. (2013 p. vii) give learning rates for different electric power
generation technologies from a literature review of different studies. Nuclear
and coal have relatively low learning rates (rates for the former technology have
been negative), whilst of the renewables technologies, the narrowest range of
estimates is for hydropower. High rates of learning have been estimated for natural
gas, onshore wind, solar PV and bio-power. In future, further innovation
in low-carbon energy supply technologies, particularly innovation that reduces
their costs, will be crucial.
The literature often characterizes innovation as having several distinctive
stages – from research and development (R&D) to prototyping, demonstration,
commercialization and deployment. Early conceptions of innovation tended
to emphasize a linear process of moving through these stages from R&D to
deployment. However, this ‘linear model’ is now regarded as too simplistic.
Models of innovation have therefore evolved to reflect empirical observations
of innovation processes, including feedback between innovation stages (a process
that is sometimes referred to as ‘learning by doing’), and the increasingly
networked character of innovation (including parallel activities by different
functional departments within innovating firms, closer relationships between
technology suppliers and customers, and a focus on speed and flexibility of
product development to respond to changing needs). This increasingly sophisticated
understanding of innovation is further enhanced by a recognition that