calculated in a single metric, comparable across
countries.
We use a wild binary segmentation algorithm
to identify significant changes in the fuel mix.
This algorithm identifies out of the ordinary
changes in the variance of the rate of change of
the fuel mix. That is to say, the algorithm identifies when there is a statistically significant
change in the rate at which the fuel mix changes
relative to its normal rate of change. Such a test is
necessary. The magnitude of significant change
is identified by summation of the rates of change
in fuels’ share of the energy flow between the
years identified as significant by the algorithm.
For each fuel in a flow, such as primary energy to
power, the absolute rate of change in its share of
the fuel mix is calculated between the years
identified as significant by the algorithm. The
change metric for the flow is then the summation
of these absolute rates of change. The change
metric for the entire energy system is the sum of
weighted change metrics for each component in
the energy system, where weights are the ratio of
absolute energy in that flow to the absolute
energy in final consumption.
We identified the common characteristics of
cases where technology played a major role in an
energy system change by analysing the largest
energy system changes in the G20 since 1970.
We used the change metric dataset to identify the
quantifiably largest energy system changes
across countries and over time. We then investigated these periods of greatest change to
understand which of the factors identified in our
analysis of innovation and deployment (technology, demand, supply or markets) triggered the
change. We then combined this analysis of trigger factors with an assessment of the characteristics of technology that played a major role in
the set of greatest changes, even when technology was not a trigger factor. Our findings from
this assessment were developed into a framework
that describes the conditions that, in international
experience, have enabled innovative technology
to be successfully applied at a scale that revolutionises an energy system.
5.2.2 Findings from International
Experience
We analysed the changes in G20 countries across
five components of the energy system—primary
energy, power, industry, transport and refining,
and buildings to determine patterns of change.
Additional results for the G7 subset are presented
to demonstrate how patterns of change have
altered as industrial economies mature into
service-led economies over this time period.
(1) Factors driving the successful application
of energy technology
The gap between hype, research effort and
deployment varies by technology, which suggests that some technologies have supporting
factors that others do not. First, hype tends to
peak before R&D spending, which peaks before
deployment. This suggests that technology
eventually has its impact on the energy system
many years after its potential is perceived, and
that changes flowing from actual deployment do
not gather as much notice as the initial innovation. Second, there is significant variation in the
gaps between hype, R&D and deployment across
technologies. Renewable power is often perceived as driving an energy revolution. However,
since 1974, nuclear power has received 1.6 times
more citations, 7.6 times more R&D funding and
generated 89 times more power.
Technologies that achieve high levels of
deployment benefit from a supporting set of
factors in addition to their technological development. These include technology innovation to
a level such that deployment is feasible, supply
of the inputs the technology requires, demand for
the services the technology provides, and markets that incentivise deployment. All the factors
must be present and aligned if a technology is to
move from innovation, through application to
changing the energy system.
The case of US tight gas illustrates how the
sequencing of supply, demand, market and
technology factors can determine when revolutionary change is triggered. US tight gas
318
S. Zifeng and N. Dickens
countries.
We use a wild binary segmentation algorithm
to identify significant changes in the fuel mix.
This algorithm identifies out of the ordinary
changes in the variance of the rate of change of
the fuel mix. That is to say, the algorithm identifies when there is a statistically significant
change in the rate at which the fuel mix changes
relative to its normal rate of change. Such a test is
necessary. The magnitude of significant change
is identified by summation of the rates of change
in fuels’ share of the energy flow between the
years identified as significant by the algorithm.
For each fuel in a flow, such as primary energy to
power, the absolute rate of change in its share of
the fuel mix is calculated between the years
identified as significant by the algorithm. The
change metric for the flow is then the summation
of these absolute rates of change. The change
metric for the entire energy system is the sum of
weighted change metrics for each component in
the energy system, where weights are the ratio of
absolute energy in that flow to the absolute
energy in final consumption.
We identified the common characteristics of
cases where technology played a major role in an
energy system change by analysing the largest
energy system changes in the G20 since 1970.
We used the change metric dataset to identify the
quantifiably largest energy system changes
across countries and over time. We then investigated these periods of greatest change to
understand which of the factors identified in our
analysis of innovation and deployment (technology, demand, supply or markets) triggered the
change. We then combined this analysis of trigger factors with an assessment of the characteristics of technology that played a major role in
the set of greatest changes, even when technology was not a trigger factor. Our findings from
this assessment were developed into a framework
that describes the conditions that, in international
experience, have enabled innovative technology
to be successfully applied at a scale that revolutionises an energy system.
5.2.2 Findings from International
Experience
We analysed the changes in G20 countries across
five components of the energy system—primary
energy, power, industry, transport and refining,
and buildings to determine patterns of change.
Additional results for the G7 subset are presented
to demonstrate how patterns of change have
altered as industrial economies mature into
service-led economies over this time period.
(1) Factors driving the successful application
of energy technology
The gap between hype, research effort and
deployment varies by technology, which suggests that some technologies have supporting
factors that others do not. First, hype tends to
peak before R&D spending, which peaks before
deployment. This suggests that technology
eventually has its impact on the energy system
many years after its potential is perceived, and
that changes flowing from actual deployment do
not gather as much notice as the initial innovation. Second, there is significant variation in the
gaps between hype, R&D and deployment across
technologies. Renewable power is often perceived as driving an energy revolution. However,
since 1974, nuclear power has received 1.6 times
more citations, 7.6 times more R&D funding and
generated 89 times more power.
Technologies that achieve high levels of
deployment benefit from a supporting set of
factors in addition to their technological development. These include technology innovation to
a level such that deployment is feasible, supply
of the inputs the technology requires, demand for
the services the technology provides, and markets that incentivise deployment. All the factors
must be present and aligned if a technology is to
move from innovation, through application to
changing the energy system.
The case of US tight gas illustrates how the
sequencing of supply, demand, market and
technology factors can determine when revolutionary change is triggered. US tight gas
318
S. Zifeng and N. Dickens