Elsevier

Energy Policy

Volume 119, August 2018, Pages 282-293
Energy Policy

Time to get ready: Conceptualizing the temporal and spatial dynamics of formative phases for energy technologies

https://doi.org/10.1016/j.enpol.2018.04.015Get rights and content

Highlights

  • Operational definition of the formative phase applicable to any energy technology.

  • parametric analysis of drivers of formative phase duration.

  • Non-parametric analysis of spatial patterns of formative phase duration.

  • Substitutability of technologies has stronger effect on formative phases than price.

  • Acceleration tendency only observed in latecomer regions.

Abstract

Implementing the Paris agreement to prevent dangerous climate change requires energy system transformation and rapid diffusion of low-carbon innovations. In this paper we investigate both the temporal and spatial dynamics of formative phases by which energy technologies prepare for growth. Drawing on a review of diverse literatures, we offer a definition of the formative phase which clarifies its scope and duration, and identifies its main technological and economic determinants. We use parametric hazard models to assess the relative strengths of these determinants on formative phase durations for a sample of 15 energy technologies diffusing over time in their respective initial markets. We find that substitutability has stronger effects in accelerating the end of formative phases than installed capacity and prices. We extend our analysis using nonparametric models to analyze the spatial diffusion of formative phase durations from initial to follower markets. We find that formative phase durations are long outside initial markets as well, showing only signs of acceleration in latecomer regions. Our results imply risks for policies trying to accelerate the diffusion of large innovations without ready markets in both initial and follower markets.

Introduction

The historical diffusion of energy technologies shows long periods of emergence within changing energy systems (Fouquet, 2016, Grubler et al., 2016). Energy technologies often take several decades in the early phase of their life-cycle prior to mass commercialization (Fouquet, 2014, Smil, 2010, Smil, 2016). This period is also known as the formative phase which can be defined in the following terms: a period marked by high uncertainties (Van de Ven, 2017), during which the conditions (standardization, performance improvement, etc.) are created for a new technology to emerge and prepare for large-scale commercialization (Jacobsson and Lauber, 2006, Arthur, 2009, Bento and Wilson, 2016). This interactive process of testing and improvement, and aligning market and user needs, tends to occur in a small number of initial markets. At the end of the formative phase the technology becomes ready to leave the initial markets and diffuse out into new markets (Binz and Anadon, 2018; Binz et al., 2017, Grubler, 2012). Understanding both the temporal and spatial dynamics that shape the formative phase is important in the debate on how to accelerate energy innovation for climate change mitigation (Winskel and Radcliffe, 2014).

Different strands of the literature cover the dynamics and determinants of the formative phase. These include the identification of key changes in the type of innovation (e.g., product vs process) (Huenteler et al., 2016, Taylor and Taylor, 2012), the strategic management of new industries around innovations (e.g. changes in companies’ demography) (Peltoniemi, 2011, Gustafsson et al., 2016), and the dynamics of emerging systems in socio-technical transitions (Bergek et al., 2015, Markard et al., 2012, Geels, 2005).

In terms of what determines the duration of formative phase, studies in management science emphasize the role of demand variables, such as heterogeneity in price sensitivity and adopters’ risk avoidance (Golder and Tellis, 1997, Tellis et al., 2003, 2012; Peres et al., 2010). The diffusion of innovations literature shows that diffusion rates depend on the characteristics of both the technology and the adoption environment (Rogers, 2003). These factors include: relative advantage (Mansfield, 1968, Chandrasekaran et al., 2013); compatibility and complexity (Arthur, 2009); disruptiveness, inter-relatedness and infrastructural needs (Grubler et al., 1999); and market size (Wilson, 2012).

Technology growth out of the initial markets is typically investigated with the focus on the constraints to adoption like distance in economic geography (e.g. Comin et al., 2012; Griffith et al., 2013), or interactions with existing contextual structures in system theories (Bergek et al., 2015, Hansen and Coenen, 2015).

In this paper we pose the question: What determines the duration of formative phases for energy innovations in different markets? We are interested both in initial markets (also: core, lead, first mover, early adopter) where formative phases prepare technologies for mass commercialization, and in follower markets (also: periphery, lag, late adopter) where accelerated formative phases may benefit from diffusion and spillovers. To understand the temporal dynamics of energy innovation within initial markets (growth over time), we apply a hazard model to a time series dataset of 15 diverse energy technologies (including both new and old, energy supply and end-use). To understand the spatial dynamics of energy technology diffusion between markets (growth through space), we use Kaplan-Meier curves to compare the dynamics of formation in follower regions.

The paper is structured as follows. Section 2 reviews the relevant literature on formative phases to identify definitions, patterns and determinants. Section 3 explains the methodology including data sources, model and variables. Section 4 applies the concepts and methods presented in the previous sections to measure formative phase durations across regions and to estimate the effect of the determinants in accelerating formative periods. Section 5 concludes and derives policy implications.

Section snippets

Definition

The term formative phase appears in the technological innovation system literature to designate the early period of diffusion during which new technologies are first used, improved and prepared for commercialization: “the value of this very first phase” is “in the opportunities [given] for experimentation, learning and the formation of visions” (Jacobsson and Lauber, 2006: 271). A similar concept is’era of ferment’ which is used in the industry life-cycle literature to designate the period of

Models

We assess the determinants of formative phase duration of technologies in different markets using parametric and non-parametric survival analysis. We use parametric analysis in core markets with lengthy formative phases as data is more available. We then use non-parametric analysis to compare formative phase durations in follower regions.

The (parametric) hazard model explains the event of finishing the formative phase conditional on the change of covariates shown in Fig. 2. We use Cox's (1972)

Formative phase duration in initial markets

Fig. 3 compares key innovation measures across the sample of 15 technologies at end points of their respective formative phases in initial markets only. Cumulative capacity and number of units provide information on experimentation and system size. Average unit scale indicates the complexity of technology production and usage (here shown relatively to the maximum unit scale identified ex post). Price is indexed to the introductory level when technologies were first commercialized and shows the

Discussion and conclusion

Different strands of the innovation literature cover the dynamics and determinants of formation and diffusion. In this paper, we develop a coherent theoretical framework on formative phase duration. We apply this framework to estimate the duration of the formative phase for a diverse sample of energy technologies, and test the determinants of varying durations using a hazard model. Table 6 summarizes the key definitions and findings.

The paper confirms that certain drivers of formative phase

Acknowledgments

The research on which this article is based was supported by a grant from the International Institute for Applied Systems Analysis (IIASA), and Harvard Kennedy School. Nuno Bento also acknowledges the post-doctoral grant (ref.SFRH/BPD/91183/2012) received from Fundação para a Ciência e a Tecnologia (FCT). Laura Diaz Anadon would like to acknowledge funding from the European Union’s Horizon 2020 research and innovation programme (grant agreement 730403 – INNOPATHS). In addition, the authors

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