Learning Pathways for Energy Supply Technologies: bridging
between Innovation Studies and Learning Rates
Mark Winskela,*, Nils Markussonb, Henry Jeffreya, Chiara Candelisec, Geoff Duttond, Paul
Howarthe, Sophie Jablonskic, Christos Kalyvasf and David Wardg
a
Institute of Energy Systems, School of Engineering, University of Edinburgh, EH9 3JL, UK.
b
School of Geosciences, University of Edinburgh, EH9 3JW, UK.
c
Imperial College Centre for Energy Policy and Technology, London, SW7 2AZ, UK.
d
STFC Rutherford Appleton Laboratory, Didcot, Oxfordshire, OX11 0QX, UK.
e
Dalton Nuclear Institute, University of Manchester, Manchester, M13 9PL, UK.
f
Department of Earth Science and Engineering, Imperial College, London SW7 2AZ, UK.
g
Culham Science Centre, Abingdon, Oxfordshire, OX14 3DB, UK.
*
Corresponding author: Email: [email protected]; Address: Institute for Energy
Systems, University of Edinburgh, Kings Buildings, Edinburgh EH9 3JL; Tel. +44 (0) 131 650
5594
Abstract
Supporting innovation and learning for different emerging low carbon energy supply
technology fields is a key issue for policymakers, investors and researchers. A range of
contrasting analytical approaches are used, often with little cross-over between them.
Energy systems modelling using learning rates provides abstracted, quantitative and output
oriented accounts, while innovation studies research offers contextualised, qualitative and
process oriented accounts. Drawing on research evidence and expert consultation on
learning for several different emerging energy supply technologies, this paper introduces a
‘learning pathways’ matrix to help bridge between the rich contextualisation of innovation
studies and the systematic comparability of learning rates. The learning pathways matrix
characterises technology fields by their relative orientation to radical or incremental
innovation, and to concentrated or distributed organisation. A number of archetypal
learning pathways are outlined to help learning rates analyses draw on innovation studies
research, and so better acknowledge the different niche origins and learning dynamics of
energy supply technologies. Finally, future research issues are outlined.
1
Keywords
Innovation; learning; niches; pathways; energy; electricity; technology
1. Introduction
National and international policy ambitions for greenhouse gas emissions reduction and low
carbon technology deployment have focused attention on energy system transformation [1,
2]. One of the key dynamics – and uncertainties – associated with this envisaged
transformation relates to the development and deployment of low carbon energy supply
technologies. While technological innovation holds out considerable promise as an enabler
of more affordable energy system change [3, 4], this promise is highly uncertain, particularly
over decades-long timescales.
Technological innovation in the energy sector is driven by a complex mix of incentives and
interests [5, 6], and there is now a large number of emerging low carbon energy supply
technology fields, each supported by particular policy initiatives, investment programmes,
developer firms and research institutions . Making sense of this activity – in terms of
systematic ordering, comparing and assessing its effectiveness and potential – has become a
major policy and research challenge in its own right [3, 7, 8].
A range of tools and frameworks are drawn on, including technology roadmaps [1, 9], energy
system models [3, 10] and explorative scenario planning techniques [11, 12]. Each provides
particular insights. Technology roadmaps specify the anticipated sequences involved in the
progressive commercialisation of emerging technologies in considerable detail. System
modelling provides ‘structured insights’ into the interactions and trade-offs between
different parts of the whole energy system [13, 14]. Explorative scenario exercises consider
alternative possible futures in the context of social and economic trends and potentially
disruptive events [15], and may therefore capture more diverse combinations of envisaged
social and technical futures than either system models or roadmaps.
2
These different techniques are not mutually exclusive, and indeed, they are sometimes used
in combination [16]. At the same time, each approach has its limitations and blind-spots.
Roadmaps may under represent the wider socio-technical context for innovation, including
interactions between different technologies and the more socio-political aspects of
innovation, factors which are often especially important for energy technologies. Different
roadmaps may also articulate inconsistent levels of optimism or ambition across different
technology communities. This is especially problematic in early stage technology assessment
because of a lack of any empirical track record, and a tendency to ‘appraisal optimism’ [17]
or even hype [18].
Energy system models, in elaborating a broad system-level view, may over over-simplify,
either by under-representing the uncertainties, contingencies and non-linearities of system
change, or by only allowing for highly aggregated, crude representations of key system
drivers such as technological innovation. Even relatively detailed bottom-up energy system
models tend to characterise supply technologies by a small set of parameters, such as capital
and operating cost, resource availability and conversion efficiency, with innovation dynamics
often represented by a single parameter: the experience (or learning) rate [19] (the term
‘learning rate’ is used in this paper because of its resonance with the concern here for
learning effects). Reducing down innovation processes to a single aggregated parameter
means that many their important properties go unrepresented, such as the qualitative
difference between early stage and later stage innovation dynamics [20], or the often key
role of market diversity, including niche markets, in early stage innovation [21].
Finally, explorative scenarios often provide only rather ‘broad-brush’ characterisations of
socio-technical trends or possible reconfigurations, and tend to lack any detailed account of
the causal mechanisms (agents, institutions and policies) by which their envisaged outcomes
may be realised [22, 23].
Alongside these widely used tools and methods is a body of mainly qualitative social science
research, innovation studies, which also analyses the dynamics of emerging technology
systems. Low carbon energy innovation studies has become a highly active research field
over recent years, developing and applying frameworks such as Technological Innovation
3
Systems (TIS) [24-26] and the multi-level perspective (MLP) on system transition [27-30].
Although TIS and MLP have distinctive strengths and weaknesses [31] both provide richly
contextualised and contingent accounts of innovation, in terms of interwoven and coevolving social and technical elements; case study research based on them has provided
many detailed accounts of the evolution of energy technology systems [20, 32, 33].
However, despite their common concern to take into account the role of innovation in sociotechnical system change, there is strikingly little cross-over between the abstracted
representations of learning rates and system modelling, and the contextualised, contingent
accounts of innovation studies. The premise for the learning pathways framing outlined here
is that there are missed opportunities for cross-disciplinary interaction here, and in
particular, that innovation studies offer a valuable research resource to enrich learning rates
analysis. For example, learning rates and system modelling tend to see techno-economic
performance, measured as unit cost, as the determining factor on technological change,
while innovation studies highlights a broader set of socio-cultural forces (such as the role of
knowledge flows and information sharing in early stage innovation, political legitimacy and
societal acceptability, and the enabling or blocking role of incumbent organisational
interests) [25, 32, 34].
The aim of this paper is to help bridge between system modelling and innovation studies by
developing a simple analytical framework that allows for systematic comparison of energy
technologies, while retaining some of the contextual richness of innovation studies. The
formidable task of developing a full synthesis of system modelling and innovation studies is
beyond our scope. Instead, we take technology-specific innovation studies as a starting
point, develop a 2x2 matrix to improve its comparability without losing too much specificity,
and then consider the cross-overs or implications for learning rates and system modelling.
Our efforts are inspired and informed by earlier attempts within innovation and
organisational studies at developing comparative frameworks for technology comparison
[35-41]. Within the ‘comparison-oriented’ stream of innovation and organisational studies,
the learning pathways approach pays particular attention to the differing niche origins of
emerging energy technologies and their different development pathways. By describing a set
4
of archetypical or generic learning pathways, the prospect is opened up of learning
narratives that are part-contextualised but which also allow technologies to be compared.
The paper proceeds as follows. Section 2 summarises and contrasts system modelling using
learning rates and innovation studies; Section 3 introduces the learning pathways matrix,
drawing on detailed analyses of a few energy supply technologies; Section 4 applies the
learning pathways matrix to compare the niche origins and learning paths of selected
technologies. Section 5 presents a set of archetypal learning pathways for use in technology
forecasting for energy system change. Section 6 summarises and concludes the paper, and
identifies some areas for future research.
2. Review: Perspectives on Technology Learning
2.1 Learning Rates
Learning rates first emerged from historical evidence of cost reduction with cumulative
production in manufacturing industries [42, 43]. The learning rate is the percentage
reduction in technology ‘unit costs’ associated with each doubling of installed cumulative
capacity [44]. Over recent years, in the context of policy targets for energy system change,
learning rates have been used in many energy system modelling exercises [45-47], either
formulated endogenously within the model, or factored-in exogenously using off-model
calculations [48].
The learning rate is a powerful analytical construct, given its apparent ability to capture and
quantify innovation, and project it forwards as a key part of wider socio-technical system
change. In practice, technological innovation is more complex and less predictable than this
suggests, and comparing different technologies on the basis of learning rates disguises
important differences. As a number of observers have recognised, using learning rates for
long-term energy system projections raises particular concerns [43, 49-56]; these include:

the assumed correlation between deployment and cost reduction is not always
observed: history shows examples of some energy technologies, such as nuclear power,
5
coal-fired steam turbines and offshore wind energy failing to lower costs despite
significant deployment [57-60].

even when a correlation is observed, the direction of causality is often unclear: unit cost
reductions may result from market growth, or be a driver of market growth [61].

over time, apparently small differences in learning rate estimates have dramatic impacts
on suggested investments needed for commercial breakthrough of individual
technologies, and, at the system level, optimal energy mixes [51, 56, 57].

cumulative deployment is a poor measure of learning for research-intensive
technologies. ‘Two-factor’ learning rates [62, 63] explicitly allow for learning by research,
alongside learning by doing, but they exacerbate the problem of input data
uncertainties. Junginger et al. [18] suggested that time rather than deployment may be a
better indicator of cost reduction potential for R&D intensive technologies.

using a single learning rate for a technology field is likely to disguise significant
contextual diversity over place, time and content:
o
energy innovation policies are still determined, to a significant degree, at the
national and sub-national level, and innovation dynamics differ across regions,
nations and organisations
o
discontinuities and step-changes in learning are often seen over time, through
different phases of development. Colpier and Cornland [64] distinguished
between price umbrella, shakeout and stability phases; Grubler [65] and Wilson
[66] identified four phases of development, including early phase
experimentation, unit scaling, industry scaling and global diffusion.
o
learning effects often differ considerably between the component parts of a
technology system [67].
While these differences are ironed-out over long-run global learning rate studies, they
are significant for those operating at any level of detail, including policymakers, business
strategists and research programme managers.
6
The need for improved representations of innovation in learning rates and energy system
modelling has been recognised by modelling researchers. Gielen et al. [68] outlined lines of
enquiry for a ‘technology learning research agenda’, including analysis of clustering of
interrelated technologies, identification of global and regional learning patterns, and
attention to underlying ‘autonomous trends’ such as increased computer capacity and
advanced materials. Berglund and Söderholm [69] suggested linking exogenous assumptions
about learning rates to different assumptions about policy, cumulative capacity and R&D
investment. Clarke et al. [61] (p593) called for study of ‘any distortions of policy conclusions
from models with limited representations of technological change’.
2.2 Innovation Studies
Learning rates are a widely adopted, influential tool, and their refinement has become a
highly active research area in recent years. At the same time, their well-documented flaws
suggest a need to draw on accounts of innovation able to retain greater complexity and
contextualisation. Reflecting its conceptual groundings in evolutionary economics and
sociology, innovation studies – especially in its more technology-rich strands – emphasises
the contingent nature of technological development, and the need to analyse technology
systems in their socio-historical context, rather than by reference to technical or economic
imperatives [70, 71]. A central insight offered by this body of research is that the various
elements that make up a technology system: technical artefacts and knowledge, and also
practices, institutions and expectations, interact together in co-evolutionary and pathdependent ways over time [31]. Foxon [72] identified five key domains for energy system coevolution: technologies, institutions, business strategies, natural ecosystems and social
practices.
Two prominent conceptual frameworks have developed within innovation studies over the
last two decades: the Technological Innovation Systems (TIS) approach [20, 24-26, 32] and
the Multi-Level Perspective (MLP) on system transitions [27-30]. TIS studies emphasise
multiple agency and distributed learning in innovation processes. Rather than all-powerful
technologists, or linear knowledge flows, the focus tends to be on interaction and feedbacks
across different system elements: actors, networks and institutions [73]. Two broad phases
of technology development are often identified: an initial, formative phase, characterised by
7
the generation of technological variety, the testing of different designs, interactive learning,
niche markets, and efforts to establish organisational and political legitimacy; and a
subsequent market expansion phase, in which positive feedbacks between market growth,
learning by doing and scale economies enable diffusion of a dominant technology into mass
markets, allowing for the run down of public policy support mechanisms [20, 25, 32].
The MLP conceives technological innovation as an outcome of the interplay of social and
technical elements over three distinct levels of aggregation: micro-level niches, meso-level
regimes and macro-level societal landscapes [74, 75] Within this, ‘system innovations’
(innovations with the greatest impact on the design and character of systems, including their
environmental performance) are seen to originate mainly in radical niches, which over time
become stabilised in dominant designs, and which may subsequently break through to
reconfigure regimes [76]. Case research using the MLP has often involved long-term
historical studies, such as the transition from coal- to gas-based energy supply in The
Netherlands [77]. In the context of the ‘managed transitions’ of energy systems to meet
decarbonisation targets, Shackley and Green [78] suggested that the approach could usefully
supplement modelling and scenario-based analysis. Verbong and Geels [79], for example,
described three different prospective ‘transition pathways’ for the electricity system and
grid infrastructures, based on different niche-regime-landscape interactions. Foxon et al.
have elaborated different transition pathways for UK electricity system transition [16].
The rising prominence of TIS and the MLP in innovation studies provoked some criticism. For
TIS, and innovation systems studies more broadly, criticisms included inconsistencies across
different studies in terms of system delineation and measures of system performance, and
reliance on mainly ex-post qualitative analysis [80, 81]. Criticisms of the MLP included
inconsistent conceptual framing, a neglect of agency, an over-emphasis on niches as drivers
of system change [38, 82]. Later contributions to both theories have sought to respond to
these criticisms. In TIS, this involved the development of a more standard, prescriptive
analytical framework, based on a set of system ‘functions’ (including, for example,
knowledge development, market formation and resource mobilization) [24, 25]. In MLP
studies, in an effort to overcome ‘niche-driven bias’, Geels and Schot [28] introduced a small
set of archetypal transition pathways, based on particular niche-regime-landscape
relationships.
8
These later contributions have directed innovation studies towards greater comparability
between different cases. In offering these more standardised accounts, there is a danger of
under-emphasising significant differences of content or context between different
technologies. For example, TIS’s framing by system functions and inter-functional patterns
may fail to capture important differences in niche origin and learning paths (such as different
levels of importance of learning by doing, interacting, and researching, scale economies, or
learning by transfer from other industries [18, 61, 83-85]. Bergek et al. [25] identified a need
for a taxonomy of archetypal development pathways for emerging innovation systems;
Section 5 of this paper offers such a taxonomy.
Case research using the MLP is deeply concerned with niche context, and a particular strand
of transitions research based on ‘innovation journeys’ has paid particular attention to the
shifting dynamics of technology learning over time [33, 86]. Even so, the MLP tends to a
niche-led account of system innovation, with more significant system change arising from
radical and disruptive niches. By doing so, like TIS, it may under-represent the different
origins and learning paths of different technologies, or the powerful role, over time, of
incremental innovation. Smith et al. [87] called for greater attention to be paid to the
plurality of niches and ‘niche-regime’ interactions. The learning pathways matrix can be seen
as a response here, and also, to the prospect of regime-led system innovation under urgent
change imperatives [88]. Foxon [72] has noted the neglect of cost and economic factors in
much innovation studies based on the MLP; this strengthens the case for seeking bridges
between innovation studies and cost-based methods such as learning rates.
2.3 Summary
This section has outlined two contrasting accounts of technological innovation: learning
rates, a highly abstracted, quantitative representation, and innovation studies, a
contextualised and mainly qualitative account. While learning rates and energy system
modelling allow comparison between different technologists, and attention to wider energy
system effects, they are a grossly simplifying measure of complex socio-technical processes,
and well-recognised concerns about their use in technology forecasting. By contrast,
9
innovation studies, while offering rich insight on the socio-technical dynamics of innovation,
has developed standard explanations of socio-technical change which neglect the different
niche origins of technologies, and also, the influence of relative cost.
There has been some recent recognition, from both learning rates and innovation studies
scholars, of the potential rewards of establishing links between these two fields. Junginger et
al. [18] considered the possibilities here, both for historic and prospective studies. Van Sark
et al. [89] suggested that a combination of experience curves and innovation systems could
produce a useful ‘hybrid framework mixing quantitative and qualitative data’ (ibid., p265);
the rest of the paper introduces and applies of one such tool.
3. Method: the Learning Pathways Matrix
3.1 Characterising Technology Learning
The learning pathways matrix is built on detailed analysis of the niche origins and innovation
dynamics of a number of emerging energy supply technologies, developed from reviews of
relevant research literature (both innovation studies and the more economics-oriented
literature of learning rates and energy systems analysis), and also, from consultations with
experts in particular energy supply technologies.
Table 1 summarises some issues highlighted in the research literature and expert accounts
(some of the many research papers consulted are listed in the table). The technology fields
were selected because previous research [2, 10] has suggested their possibly significant role
in UK energy futures. (As well as the five technologies in Table 2, two others – bioenergy and
hydrogen fuel cells – were included in the overall analysis; they are omitted here for brevity).
The focus here is on electricity generating technologies; while this excludes important nonelectricity energy supplies, there are suggestions that electricity may become more
pervasive part of future energy systems [3, 10].
10
Field
Origins and Development
Key Learning Effects
Wind
[60, 85,
90-94]
- Onshore wind emerged in the 1970s,
mainly from small scale experimental
devices which were gradually upscaled.
- Devices underwent rapid upscaling and
improvement from the mid-1990s
- Offshore wind is much less mature, and
has experienced cost increases typical of
early stage technologies
- Onshore wind evolved over decades of learning-byexperience, with later incorporation of learning-by-research.
- Offshore wind innovation dynamics are still unclear, but are
likely to differ from onshore, given differences in physical
environment, social attitudes and relative costs of capital
and operation (with greater emphasis, for example on scale
economies).
Marine
(Wave
and
Tidal
Flow)
[91, 9597]
- The marine energy field emerged in the
1970s. It re-emerged in the early 2000s,
largely by small private developer firms.
- The field is still immature, with limited
experiences in operating conditions.
- The field spans a wide variety of designs,
especially for wave power, though with
some generic aspects.
- Investment focuses on a small number of full-scale
prototype devices, which make use of relatively conventional
designs and components.
- Given limited learning-by-doing opportunities, many
developers focus on learning-by-research though smallscale testing and modelling.
- There is potential for knowledge / technology transfer from
related industries, but these face commercial barriers.
Solar PV
[32, 98102]
- The solar PV field emerged in the 1960s
within the NASA space programme.
- the field has since greatly diversified its
production technologies and market
applications.
- PV systems are highly modular, with
distinctive system components: modules
and ‘balance of systems’ (BoS).
- Learning effects differ for different generations, and between
modules and BoS components.
- For 1st generation technology, innovation efforts focus on
feedstock, manufacturing and economies of scale. For 2nd
and 3rd generation technologies, emphasis is on R&D, e.g.
advanced materials for improved efficiencies.
- BoS costs are location and application-specific, and driven
by learning-by-experience.
Carbon
Capture
and
Storage
(CCS)
[88, 103108]
- CCS as a power plant technology only
emerged relatively recently, in the 1990s.
- CCS is an assembly of technologies from
chemical processing, power generation
and oil and gas
- It involves large scale capital and
infrastructure, creating an investment
threshold; its development is led by large
manufacturing and energy firms
- Learning involves the scaling-up of capture technology, and
the integration of capture and power generation.
- Integration with the wider energy system poses multiple
technical, economic, organisational and regulatory
challenges.
- Three different capture technology types are emerging:
post- and pre-combustion, and oxyfuel capture; these
present differing levels of continuity or disruptiveness for
established fossil fuel interests.
Nuclear
Power
[58, 59,
109]
- Civilian nuclear power emerged from post
WWII nuclear weapons development
programmes
- Until the 1980s, nuclear fission technology
development was carried out by stateowned ‘national champions’.
- Since the 1990s, a small number of
international producers have emerged.
- Nuclear power is a large scale technology system suited to
centralised generation and transmission, and ‘fleet build’
economies using standard plant designs.
- It has a poor record of learning with deployment due to
changing designs, high construction costs, weak financial
scrutiny, complexity of safety systems and costs of
regulatory compliance.
- Nuclear fusion involves experimental prototypes developed
in highly co-ordinated international public programmes.
Table 2: Niche Origins and Learning Paths of selected Electricity Supply
Technologies
3.2 Generic Issues in Energy Innovation
Although there are deep-rooted technical, organisational and institutional differences
between the technologies in Table 2, a comparative reading of the research literature and
11
expert accounts revealed a number of common themes related to their innovation
dynamics; these include:

Design variety or consensus: emerging energy technologies tend to span a wide
variety of designs, more mature ones to design consensus and standardisation.
Innovation studies case research has identified an early stage trade-off between
‘variety and volume’, and has recommended an emphasis on variety rather than
scale [20]

Distributed or concentrated organisations: some emerging energy technology
systems are organisationally distributed, while others are highly concentrated.
Innovation studies research has suggested that successful early-stage innovation is
associated with organisationally distributed, highly co-ordinated fields, with strong
feedbacks between developers, users, testers and regulators [85, 90].

Radical or incremental innovation: more mature energy technologies tend to be
characterised by incremental innovation, while less mature fields may emphasise
step-change improvements from radical innovation; however, energy case research
highlights a powerful role of incremental innovation over time [35, 37, 110].

Scale or modularity: innovation studies research points to the advantages of smaller
scale, more modular technologies, which tend to offer greater opportunities for
learning by experience [55], and have greater variety of applications. Larger scale
systems tend to design and application standardisation and replication economies.
While scaling dynamics vary by technology, there is some evidence of historical
patterns and a generic sequence of scaling effects, from small-scale experiments,
technology units, industry sectors and global diffusion patterns [65, 66].

Dedicated learning or technology transfer: technology and knowledge transfer have
been historically important mechanisms for technologies such as wind turbines
(transfer from agricultural machinery [90] and gas turbines (transfer from aerospace
jet engine programmes [111]. However, there are often powerful barriers to
transfer, such as adaptation costs and intellectual property rights.

Niche or mainstream markets: niches offer key opportunities for supporting early
phase learning [21]. Where niches are absent, either though lack of application
variety or dedicated support mechanisms, learning may be limited [33, 98]. Given
12
the lack of natural product differentiation, innovation investments for electricity
technologies may be predicated on capturing mainstream market share.

Innovation support policies: innovation studies case research has emphasised the
benefits of market-pull and technology-push policies to be run in parallel, with
strong feedbacks between deployment and research [112]. There is a suggested
tendency to over-emphasise technology push and neglect commercial prospects,
and wider societal influences [33, 98]. Regulations, such as performance standards,
have also been key policies for creating demand for environmental technologies [5].

Sources of finance: private finance is disinclined to support longer term or more
radical innovation, and typically requires high returns over relatively short
timescales [113]. Private capital also tends to favour less capital intensive, more
modular technologies, presenting barriers for scale intensive energy technologies.

System integration: low carbon energy supply technologies have significantly
different operating characteristics than established technologies, with implications
for network management, storage and infrastructure. The technical, organisational
and institutional challenges of system integration may be under-appreciated.
3.3 The Learning Pathways Matrix
To enable comparison between technology fields, the rich socio-technical data outlined in
Sections 3.1 and 3.2 was simplified and abstracted by developing a 2x2 matrix. While this
inevitably involves a loss of case detail, it still allows for important differences between
different cases (in our case, technology fields) to be represented. In their seminal paper,
Abernathy and Clark [34] described this as ‘depict[ing] the pattern of effects [by using]
composite … scales for each domain as the axes of a two-dimensional diagram … with four
quadrants representing a different kind of innovation’ (p.7). The aim here is not to precisely
define the size and position of the cases on the scales, but to use the matrix to
approximately locate technologies with respect to key socio-technical features, trace how
these have changed over time, and compare between different technology cases.
The scales chosen for the learning pathways matrix are (i) the relative emphasis on
incremental or radical innovation; and (ii) the relative degree of organisational concentration
or distribution. Other scales could have been chosen, but as we go on to show, we have
13
found that these two are able to capture important aspects of the socio-technical character
of technology fields, and to describe their niche origins and development paths of
technology fields. Clearly, the two dimensions of the LP matrix need to be complemented
with additional information for any comprehensive telling of each technology’s history, but
we believe that the matrix helpfully focuses the analytical gaze onto important features.
Though it draws on several similar framings within innovation studies and organisational
studies, the LP matrix is distinctive. For example, the unit of analysis of the LP matrix is
technology fields, rather than the firms, organisations or more specific technology systems
in organisational studies contributions [35-37, 114, 115]). Also, unlike similar matrices
devised for non-energy sectors, the LP matrix focuses mostly on differences in production
systems rather than application or product differences, since one defining feature of
electricity systems is the lack of product differentiation. (As discussed in Section 4, however,
application or market diversity is important for some electricity technologies, such as solar
PV, and representing this has involved some modification to the LP matrix).
Other 2x2 framings conceived in innovation studies, especially sustainable innovation
studies, share our focus on energy technologies. Even here, there are differences. For
example, while Smith et al. [38] focus on sectoral level transitions, the LP’s attention is on
the lower level of aggregation of technology fields. Also, where Smith et al. [38] distinguish
between different resourcing for transitions (internal or external to regimes), we distinguish
instead between the relative radicalness of different technology fields: these are related
parameters, but the latter is more appropriate for our level of analysis.
Unlike Raven [40] and Kemp [41], the LP matrix makes no upfront distinction between
disrupting niche-led and sustaining regime-led innovations. Instead, following Abernathy and
Clark [35], we seek to represent technology fields as ‘pathways through emerging
landscapes’ which have complex, changing relationships with niche and regime agents and
structures. The LP matrix allows for the possibility that technology fields span different
combinations of disruptiveness or continuity at any point in time, and that these
combinations change over time.
14
4. Application: using the Learning Pathways Matrix
4.1 Introduction
In this section we use the LP matrix to trace the historical development of different power
supply technologies, drawing on relevant innovation studies research literature.
Technologies are represented in the matrix as bounded and relatively coherent sociotechnical domains, or fields. Typically, the major share of resources and activities within a
field are directed to more mature, incremental designs, with lesser effort on more radical
designs; this is depicted in the matrix as fat incremental ‘bodies’ with thin radical ‘tails’.
Following Green et al.’s tracing of the evolution of food production and consumption
systems [39], the matrix is also used to show the evolution of technologies over time, shown
as arrows between fields at different times. The position of the fields and the direction of
the arrows in the diagrams is based on research evidence from innovation studies, with
some references provided in the main text; this was supplemented by consultation with
energy technology experts from within our research team.
In Section 5 we use the LP matrix to support discussion of future energy innovation, partly as
a response to the need for rapid decarbonisation of the energy system. This leads to the
presentation of a typology of archetypal learning pathways for different emerging energy
technologies, emphasising that there can be no ‘one-size-fits-all’ policy model, or innovation
theory, for energy supply innovation.
4.2 Historic Learning Pathways
Figure 1 shows the evolution of the onshore windpower technology field, from its
emergence in the 1970s as a mainly distributed and incremental field, to its current status as
a concentrated, mature field, dominated by a small number of international manufacturers
and a dominant design. Historically, the successful innovation system for windpower (with
15
origins in Denmark) was characterised, initially, by small-scale developers and the relatively
incremental adoption of conventional components drawn from other sectors [34, 90]. Over
time, this predominantly incremental system was able to incorporate technology and
knowledge from more radical development programmes [85, 91].
Radical
T-Transfer from
within the
windpower field
Concentrated
Distributed
1970s
and
1980s
T-Transfer from
other fields
2010
Incremental
Figure 1: Onshore windpower learning (Incremental pathway)
While windpower has developed via a predominantly incremental pathway, from an
organisationally distributed to a concentrated field, Figure 2 characterises nuclear (fission)
power’s development in terms of high levels of organisational concentration, from its radical
origins in the 1940s and 1950s, to its current status as relatively mature technology [4, 109].
Nuclear fusion remains a radical, highly concentrated technology field.
Gen I
1940s
and
1950s
Radical
2010
Distributed
Gen IV
Concentrated
Gen III+
Gen III
Incremental
Figure 2: Nuclear Power Learning (Breakthrough pathway)
16
While some energy technology fields, such as windpower and nuclear power can be shown
as single, bounded spaces, other fields show more diverse patterns of production and
application. For example, the solar PV field, in commercialising away from its highly
concentrated national public programme niche origin, has diversified in terms of both
product design and market application [99]. Figure 3 shows that solar PV has distinctive
production and application domains, with relatively centralised production fields, of more or
less radical or disruptive character for different generations of PV technology. In essence,
the highly modular character of PV technology has enabled a wide range of product designs
and applications. At the same time, production systems for first generation PV have close
associations with the semiconductor industry: a global high technology field supported by
large commercial and state interests [8, 116].
c-Si
1960s
Radical
3rd Gen
2nd Gen
Distributed
Concentrated
1st Gen
2010
Incremental
Figure 3: Solar PV Learning (Diversification pathway)
The matrix was also used to illustrate the learning dynamics of more conventional power
supply technologies. As with solar PV, the combined cycle gas turbine (CCGT) technology
field drew on a state sponsored high technology niche: the jet aero engine. The major
growth phase of the CCGT field from the late-1980s involved a combination of the jet aero
engine field with industrial gas turbines (the latter was a long-established incremental
technology field [111, 117] The CCGT learning pathway can therefore be characterised by
technology transfer and combination (what Hargadon [118] referred to as ‘recombination’)
across two distinctive fields: aerospace and power generation, and two distinctive learning
pathways: incremental and breakthrough. This proved to be a powerful combination, and
since the late-1980s, CCGT has been a mainstay of power generation internationally. The
17
mature CCGT learning pathway involves highly co-ordinated incremental innovation (Figure
7).
1940s
Radical
Jet aeroengines
1980s
Distributed
Concentrated
Combined
Cycle Gas
Turbines
Industrial
Gas
Turbines
1930s
2010
Incremental
Figure 4: CCGT Learning Pathway (Recombination pathway)
In the late-2000s, an increasingly urgent policy imperative for decarbonisation stimulated
efforts at accelerated innovation for low carbon energy supply technologies, and this has
reconfigured the learning pathways of emerging fields. For example, the marine energy field
first emerged as a radical high-technology niche in the nationalised and concentrated energy
systems of the 1970s, in the wake of the first energy crisis [119]. By the mid-1980s, this
initial dynamism subsided and innovation activity remained low for many years. More
recently, the field re-emerged, but in the much more organisationally distributed energy
system of the late 1990s [97]. In the 2000s, the marine field was characterised by small
developer firms progressing more conventional systems and components, with more radical
devices and components developed in university-based public R&D programmes. More
recently, under a gathering innovation imperative, larger public-private development
programmes have encouraged the participation of utilities and international power
equipment manufacturers [96].
Figure 5 shows the changing learning pathways for the marine energy field. For much of the
2000s, the marine field followed a predominantly incremental pathway, with small firm
18
developers gradually improving prototypes through learning-by-doing and gradual upscaling;
in parallel, a more radical pathways were pursued in relatively small university-based R&D
programmes. More recently, an accelerated incremental pathway for mature devices has
emerged via strengthened market-pull incentives, and in parallel, a breakthrough pathway
for more radical 2nd and 3rd generation concepts and components has emerged, via
expanded RD&D programmes; the latter shares some breakthrough aspirations of the initial
niche activity of the 1970s and 1980s.
More Radical
Devices and
Components
Radical
2010
Initial
Niche
2000
Breakthrough
Pathway
1970s
1980s
Distributed
Reemerged
Niche
More
Conventional
Devices and
Components
Concentrated
2000
2010
Accelerated
Incremental
Pathway
Incremental
Figure 5: Marine Energy Learning Pathways
5. Prospective Learning Pathways
5.1 Introduction
The marine energy case exemplifies a wider contemporary phenomenon: under a global
accelerated innovation policy imperative, more concentrated and highly co-ordinated
innovation systems for energy supply technologies are being developed nationally and
internationally, as increased resources and incentives attract the participation of larger
private and public organisations. This is a significant shift: for much of its recent history, the
energy sector had little interest in technological innovation, other than the incremental
development of conventional plant, and in some countries, the gradual progression of more
mature renewables technologies such as onshore wind.
19
While this is an appropriate response – the decades long timescales for incremental learning
in renewables innovation since the 1970s reflected a less urgent system context – history
highlights the risks and potential pitfalls of more co-ordinated ‘top-down’ efforts. In the case
of windpower, the incremental pathway pursued by some countries: distributed, interactive
systems with high levels of early stage design variety – what Garud and Karnøe [90] referred
to as a ‘bricolage’ style of innovation – proved, over time, more successful than the
breakthrough style pursued elsewhere. As others have noted [5, 8] a breakthrough style of
energy innovation may not be suitable responses to the decarbonisation imperative.
5.2 Archetypal Pathways
The analysis presented in this paper suggests that there is no ‘one best way’ response to the
accelerated energy innovation imperative. Table 3 shows the relative strengths and
weaknesses of a number of archetypal or generic learning pathways, articulated with the
help of the learning pathways matrix and by reference to the research literature survey and
expert consultations. The pathways are not mutually exclusive, and may co-exist within a
technology field; for example, the early onshore wind energy field spanned both incremental
and breakthrough pathways. However, the pathways do represent distinctive, coherent and
at times durable socio-technical configurations.
Because different technology fields emerge from distinctive socio-technical niche origins, not
all pathways are feasible for all emerging technologies. For example, the diversification
learning pathway is much more credible for modular technologies with multiple prospective
applications and markets.
Given the major technical, economic and socio-political uncertainties facing energy system
transition, and the absence of any technology ‘silver bullet’ easily able to reconcile
imperatives for decarbonisation, security and affordability, any coherent energy innovation
system should span a range of more emergent and more mature technologies. The added
implication of the analysis presented here is that energy innovation policies – and innovation
20
theories – must also take into account the different learning pathways associated with
different technologies within such a portfolio. This supports other recent suggestions of the
need for more technology specificity in energy innovation policy [120]. Efforts to consolidate
energy innovation institutions and organisations into integrated, unified ‘best-practice’
arrangements [121] are valuable, but recognise the need for a variety of institutional
arrangements for technology-specific learning.
Learning
Pathway
Typical Learning Effects
Pathway Strengths
Pathway Weaknesses
Gradual learning over time.
Strong feedbacks between
developers, users, testers,
policymakers and public.
Can support design variety
and flexibility, avoiding
early ‘lock in’.
Supported by significant
institutional, organisational
and financial resources
within the regime; builds on
established capital assets
and knowledge bases.
Capable of step-change
improvements across or
within technology fields.
Can support innovation in
underpinning / enabling
technologies (e.g. IT,
materials).
Capable of radical /
disruptive innovation. Small
scale, modular systems
offer many opportunities for
learning by experience.
Multiple niche markets offer
diversity and flexibility, so
learning is more likely to be
sustained over time.
Long development
timescales; no rapid stepchange improvements.
Niches vulnerable to
changing policy context or
emergence of rival
technologies
Emphasis on incremental
improvements may offer
diminishing returns; may
offer an inadequate
response in a rapidly
changing context.
Risk of early failure, or
failure to commercialise
over longer term. Weak
links to wider society, so
risk of public backlash.
Needs sustained high
levels of public funding.
Limited core resources, so
may tend to ‘start-stop’
learning. High cost modules
may not commercialise.
State-sponsored niches
vulnerable to changing
policies / rival technologies.
May be ‘locked out’ by
large scale incumbents.
Able to ‘piggy back’
learning investments from
other fields and sectors.
Novel combinations may
enable step change
improvements over
relatively short timescales.
Transferred technology
may be disruptive in its new
context; incumbents may
resist transfer. Adaptation
and collaboration barriers /
costs (e.g. IP barriers) may
be under-appreciated.
(with examples)
Incremental,
early stage
(e.g. early
onshore wind,
marine energy)
Small firms developing
adapting components and
systems from other sectors.
Learning by transfer, by
experience, by interacting,
gradual upscaling.
Incremental,
mature stage
(e.g. coal and
gas fired turbine
plant, nuclear
fission)
Gradual improvement of
mature technology systems
by incumbent organisations
(utilities, large equipment
manufacturers and affiliated
research bodies).
Breakthrough
(e.g. nuclear
power, jet
engines,
possibly CCS)
Highly co-ordinated and
concentrated e.g. defence
programmes. Formalised
learning by R,D&D..
Diversification
(e.g. advanced
PV, fuel cells,
and advanced
bioenergy)
Small, high technology
research groups, firms and
networks. Emphasis on
learning by research for
modules. For applications,
emphasis on learning by
experience via small scale
trials. Multiple niche markets
may exist in parallel.
Recombination
(e.g. early
CCGTs,
possibly CCS)
Combinations of
technologies, practices or
knowledge from multiple
fields or sectors. Learning by
formal transfer / adaptation,
and by experience.
Table 2: Generic Learning Pathways for Electricity Technologies
21
5.3 Learning Scenarios
Reflecting calls by Junginger et al. [18] and van Sark [89] for the incorporation of innovation
theory insights into learning rates analysis, the generic learning pathways described in Table
2 may be used to construct ‘learning scenarios’: coherent, qualitative socio-technical
narratives used as a basis for quantitative datasets of future technology cost-performance
trends. Alternative scenarios could be devised which place more or less emphasis on
different learning pathways or learning effects (R&D, demonstration, deployment,
replication and scaling effects) for different technologies.
Such scenarios should reflect a detailed assessment of the relative prospects of different
pathways for different technologies. For example, credible incremental learning curve
scenarios should not assume cost reductions during the (perhaps long) period of early phase
experimentation. Greater possibilities for step change unit cost improvements may be
associated with a breakthrough pathways, but also, for early-phase cost increases (as more
ambitious socio-technical challenges are confronted), and also, of the prospect of premature
lock-in to high cost designs or societal opposition, manifested in cost terms as increased
costs of regulatory or planning compliance. Figure 6 shows two examples of stylised
incremental and breakthrough learning curves reflecting these dynamics; other learning
curve scenarios should be developed in any systematic analysis.
Breakthrough
Pathway
Unit
cost
Incremental
Pathway
Cumulative Investment or Deployment
Figure 6: Stylised Learning Curves for Incremental and Breakthrough Pathways
22
Following Berglund and Söderholm’s [69] suggestion, these scenarios should also be
informed by explicit assumptions on the role policy, regulation and organisational strategy in
promoting particular learning effects and pathways. Useful insight can be provided here on
the diversity of national policies, with technology fields such as CCS and smart grids being
developed in different ways reflecting local orientations to incremental or breakthrough
pathways. Technology learning pathways do reflect particular niche origins, but also, the
social arrangements for their development.
As Sahal [36] described, the set of regulations and institutions which incline technology fields
to different learning paths may be conceived as a socio-technical topography (Figure 7).
Particular technology fields are suited to different landscape conditions: for example, the
solar PV field is well-suited to a decentralised landscape. The relationship between
technology learning paths and the wider energy and economy landscape is a key area for
further research.
Radical
Incremental
(a) Concentrated System Landscape
Concentrated
Distributed
Distributed
Concentrated
Radical
Incremental
(b) Distributed System Landscape
Figure 7 : Sociotechnical Landscapes for Learning Pathways
6. Summary and Conclusion
Technological innovation holds out a compelling promise as an enabler of the envisaged
transformation of energy systems. However, this promise is highly uncertain, and there are
multiple ways of analysing and comparing the dynamics of emerging technology systems.
This paper considered two contrasting methods for comparing emerging technology
systems: learning rates, a quantitative, abstracted and output-orientated approach, and
innovation studies, a qualitative, contextual and process-oriented approach.
23
While learning rates and innovation studies both address the uncertain promise of
innovation, they are almost entirely unrelated research fields. The premise in this paper has
been that there are opportunities here for bridge-building, and especially, that innovation
studies can inform and enrich learning rates and system modelling. As a contribution to this
developing agenda, and drawing on detailed accounts of technology specific learning, a
number of generic issues were identified, and two of these – the orientation to radical or
incremental innovation, and organisational concentration – were selected as the axes of a
learning pathways matrix.
The learning pathways matrix sets out a socio-technical landscape for locating and
comparing the origins and learning paths of different technologies. Though simplifying and
abstracting, it retains sufficient complexity to capture important differences in the sociotechnical character of different technologies. Drawing on historic pathways, a small number
of generic learning pathways were elaborated to bridge over to learning rates scenarios.
There are limitations to the framework outlined here. More attention is needed on how to
represent the prospect of radical change such as highly decentralised electricity production
and consumption, of changes in ‘enabling’ technologies such as electricity storage, and for
demand-side technologies, such as more or less centrally co-ordinated arrangements for
smart metering. More work is also needed on systematically differentiating between the
production and application parts of technology systems. Finally, the landscape would need
further development to explore the multi-regime aspects of socio-technical transitions [122,
123].
Nevertheless, the learning pathways approach has highlighted important differences in the
niche origins and learning dynamics of energy supply technologies which tend to be
neglected within learning rates and also, innovation studies. For both policymakers and
innovation theorists, the implied need is to take seriously the specificities of energy
innovation, and the varied conditions for learning for different technologies.
24
Acknowledgements
This research was undertaken as part of the research programme of the UK Energy Research
Centre, supported by the UK Research Councils under Natural Environment Research Council
awards NE/C513169/1 and NE/G007748/1.
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Biographical Notes
Mark Winskel is an applied social scientist working on energy supply and energy systems
problems based at the Institute for Energy Systems, University of Edinburgh. His research
addresses the dynamics of innovation in energy systems, especially the relationship between
technological change, investment and policy. He is as Research Co-ordinator for the UK
Energy Research Centre (UKERC); he led UKERC’s research on Technology Acceleration for
Future Sources of Energy, part of UKERC’s whole energy system research programme.
Nils Markusson is a sociologist of technology, with 16 years worth of experience of studying
technological innovation, and with a background in engineering and innovation studies. He
works as a researcher at the University of Edinburgh, mainly at the Scottish Carbon Capture
and Storage research centre, studying carbon capture and storage and other low-carbon
innovations. He is predominantly a qualitative researcher, and favoured data sources include
documents and interviews, analysed as case studies, but also has experience of, for example,
statistical analysis, scenario-based work and foresight methodologies. He mainly use
concepts and models from Science and Technology Studies (STS) and Innovation Studies.
Henry Jeffrey is a Senior Research Fellow at the Institute for Energy Systems, University of
Edinburgh. He has extensive energy sector experience of working in marine renewable
energy, both in academia and industry. He was a key member of the project team that
installed the worlds first commercial, grid connected wave energy generator. He has
extensive experience and demonstrable success in delivering project results, and co-wrote
the UK Energy Research Centre road map for marine energy that has been widely adopted
by the sector across Europe, Canada and the USA.
31
Chiara Candelise is an experienced energy economist. Her research interests span from
techno-economic assessment of PV technologies to wider economic and policy analysis of
energy and climate change issue. She has contributed to several research projects including
the UK academic and industrial consortium PV Supergen 21 – PV Materials for the 21st
Century, several UK Energy Research Centre (UKERC) projects and Intelligence Energy
Europe (IEE) BioSolEsco Project. Prior to that she has built up sound experience on
economics and policy. She worked as economist for several private and public institutions,
including the UK Department for Environment, Food and Rural Affairs (Defra).
Dr Geoff Dutton is a senior research engineer in the Energy Research Unit at STFC Rutherford
Appleton Laboratory in the UK. He is a Chartered Engineer, a member of the Institution of
Mechanical Engineers, and a theme leader in the Supergen Wind Energy Technologies
project. He provided technical expertise on wind energy to the UK Energy Research Centre’s
Energy 2050 project.
Paul Howarth is Managing Director of the UK’s National Nuclear Laboratory. Paul co-founded
the Dalton Nuclear Institute at the University of Manchester. Prior to working at the
University, Paul spent eleven years with the BNFL Group and progressed from Commercial
Manager to Head of Technology for Nuclear Generation and eventually Programme Director
for Advanced Reactors and Head of Group Science & Skills Strategy.
Sophie Jablonski is an Energy Engineer at the European Investment Bank, where she focuses
on investments in renewable energy and energy efficiency. She holds a PhD in Energy
Technology and Policy from Imperial College London, an M.Phil in Environmental Science
from the University of Cambridge, and an M.Sc in Engineering from Ecole Centrale Paris
(France). Sophie was previously a Research Associate at Imperial College Centre for
Environmental Policy in London. She also worked at the World Bank in Washington DC,
where she was dealing with energy and transport infrastructure operations in the Middle
East & North Africa region.
Christos Kalyvas is Research Fellow, holding an EPSRC/NPL Post-Doctoral Research
Fellowship at Imperial College London, focussing on novel diagnostic techniques for the
study of fuel cells. Previously, Christos was a Research Associate at the Department of Earth
Science and Engineering, Imperial College, specialising in the performance of catalysts for
solid oxide fuel cells.
David Ward is a Senior Researcher at the Culham Centre for Fusion Energy (CCFE) and
Visiting Fellow at the Smith School of Enterprise and the Environment, University of Oxford.
He has worked in energy research for over 20 years as part of the UK fusion research
programme and the wider European programme. Much of this time he worked on
secondment to JET, the main European fusion experiment, based at Culham Science Centre
in Oxfordshire. David now leads the UK work on DEMO, a demonstration power station. He
also takes a strong interest in developments in other future energy technologies and leads
UK work on socio-economic aspects of fusion.
32