Hype among low-carbon technologies: Carbon capture and storage in comparison

Global Environmental Change 41 (2016) 124–141

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Hype among low-carbon technologies: Carbon capture and storage in comparison Alfonso Martínez Arranz School of Social Sciences, Monash University, 900 Dandenong Rd., Caulfield East, VIC 3145, Australia

A R T I C L E I N F O

Article history: Received 17 February 2016 Received in revised form 30 August 2016 Accepted 3 September 2016 Available online xxx Keywords: Carbon capture and storage Hype R&D Climate policy Technology policy Solar thermal power

A B S T R A C T

Carbon dioxide capture and storage (CCS) technology has become a crucial part of climate change mitigation strategies around the world; yet its progress has been slow. Some have criticised CCS as a distracting hype, even as mainstream support continues. This article adapts the literature on technological hypes to develop a framework suitable for technologies with limited media/public exposure, such as CCS. It provides a qualitative context and analyses seven quantitative indicators of hype that are largely internal to the CCS technology regime. Throughout, the article contrasts results for CCS with those of comparable technologies. The main findings, are as follows. “Expectations” in the form of mounted rapidly project announcements for electricity applications of CCS and deployment forecasts in influential reports. However, announcements soon plummeted. “Commitments” remained high, nonetheless, judging by allocations in public budgets and number of peer-reviewed publications. Meanwhile, “outcomes”—in terms of patents, prototypes and estimated costs— reveal few if any improvements for CCS. Considering these findings and the characteristics of CCS, its development is likely to be more difficult than initially expected. Accordingly, this article calls for decisively prioritising CCS for industrial and, potentially, bioenergy uses. Coal- and gas-fired power plants may be replaced by non-CCS technologies, so power CCS development is far less pressing. ã 2016 Elsevier Ltd. All rights reserved.

1. Introduction Carbon dioxide capture and storage (CCS) is often considered an essential technology for climate change mitigation; yet it faces fundamental technical, economic and social uncertainties (Markusson et al., 2012a). Nonetheless, some insist on the transient nature of its problems or on the necessity of building from the small progress in essentially the same direction (Shackley and Evar, 2012; IEA, 2015b). Another view is that CCS has been a harmful technological “hype” drawing resources away from other technologies (Stephens, 2015). Several studies have remarked on the biased or conflicting beliefs in CCS discussions and policymaking (Hansson and Bryngelsson, 2009; Meadowcroft and Langhelle, 2011; Hansson, 2012; Martínez Arranz, 2015). This suggests a significant potential for hype. This article firstly builds a conceptual framework based on existing studies of technological hypes. It adapts some elements for the analysis of a low-carbon energy technology such as CCS. Then, the article uses this framework to test for a CCS hype. Finally,

E-mail addresses: [email protected], [email protected] (A. Martínez Arranz). http://dx.doi.org/10.1016/j.gloenvcha.2016.09.001 0959-3780/ã 2016 Elsevier Ltd. All rights reserved.

it derives some implications for CCS policy and directions for further hype research. 2. Conceptual framework Hype is often defined as a cycle of high-rising expectations and subsequent disappointment about a technology. Despite tendencies in the literature to dissociate hype from outcomes and to emphasize its positive effects (Borup et al., 2006; Bakker, 2010; Van Lente et al., 2013), the use of the term “hype” is implicitly (Jarvenpaa and Makinen, 2008; Jun, 2012) or explicitly (Brown, 2003; Borup et al., 2006; Van Lente, 2012; Van Lente et al., 2013) associated with poor outcomes at the system level. Uncovering a hype is only possible retrospectively (Jarvenpaa and Makinen, 2008; Bakker, 2010; Van Lente et al., 2013), but once this happens it can and should lead to a rethink of assumptions and directions for technological development. Hype results from the embeddedness of technology in society (Rip and Kemp, 1998). Interests and institutions can be powerful barriers to technological change, e.g. through higher financing requirements or commercialisation disadvantages for new technologies (Unruh, 2000). Thus, during early phases of

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development—in which CCS and most low-carbon technologies find themselves—the unavoidable risks and uncertainty are countered with claims about future reductions in social costs or future synergistic linkages with existing technologies, which are in turn backed by existing interest groups. Crucially, thanks to the “interpretive flexibility” of project results (Konrad, 2006), framing of early results as a step on the road to success is often difficult to disprove. It is only in more advanced phases that more objective techno-economic competition can play an important role (Geels, 2005; Bakker et al., 2011). A certain “desire for a miracle” in clean energy technology makes it particularly prone to hypes—not only among an ignorant public and politicians (Banholzer, 2012), swayed by sweet-talking marketers and narratives (Audin, 2002; Vel, 2014), but also among experts in their earnest pursuit of the results they wish for. Indeed, framing, as the socio-cognitive mechanism by which hypes become possible, is naturally applicable to the energy sector (Dosi, 1982; Scrase and Ockwell, 2010). Due to space constraints, the focus below is on the differences between existing studies of hype and the framework presented in this article. Section 5 draws on observations from this same literature to derive lessons for the case of CCS. 2.1. Existing studies of hype 2.1.1. Context, relevant indicators and their relationships Studies of hype often use interviews with experts and close textual analysis of media articles to provide a qualitative context within which to interpret fluctuations in attention or other quantitative indicators of hype. However, the selection of indicators is quite varied and inconsistent:  Qualitative analysis of roadmaps and expert interviews (Bakker et al., 2011).  Qualitative analysis of peer-reviewed publications (Van Lente and Bakker, 2010).  Counts of articles in prestigious newspapers (Konrad, 2006) combined with analyses of their content (Van Lente et al., 2013).

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 Counts of prototypes and (a) company statements on time remaining until market launch (Bakker, 2010) or (b) specialised magazine articles (Bakker et al., 2012).  Counts of items in science, engineering, and patent databases, as well as in the popular and business press (Jarvenpaa and Makinen, 2008).  Analysis of Internet search traffic, patent analysis, counts of news items, and market share (Jun, 2012).  Media attention and expert interviews (Konrad et al., 2012).  Media attention, expert interviews, conferences and R&D fairs, peer-reviewed publications and patents (Ruef and Markard, 2010). In general, the literature analyses both discursive indicators (such as media attention or stakeholder statements) and innovative indicators (such as patents or the building of prototypes). However, although both types are deemed to affect expectations (Konrad, 2006; Konrad et al., 2012), hype is also frequently noted to be the result of discursive activities only (Borup et al., 2006; Ruef and Markard, 2010; Van Lente et al., 2013). The hype cycle is therefore most often depicted by plotting the level of media activities as a proxy for expectations (Fig. 1). Nonetheless, the media have been rather passive regarding CCS (Boyd and Paveglio, 2014), and public opinion remains overwhelmingly ignorant about what CCS is and what it does (Riesch et al., 2013; Ashworth et al., 2015). In addition, media attention may well have little impact on actual innovation activities (Ruef and Markard, 2010). Thus, attention from mass media is likely to be a secondary aspect of hype for CCS, and probably many other technologies. Moreover, not all activities involved in hype yield a rising-falling plot as different activities play different roles in technology development (Konrad et al., 2012; Jun, 2012; Jarvenpaa and Makinen, 2008). Accordingly, the framework outlined in Section 2.2 defines hype as the result of rapid changes across both discursive and innovative quantitative indicators, which correspond with a plausible narrative drawn from contextual information.

Technological regime

Outside world

Hype H ype cycl cycl cle e Innovave acvies

Discursive acvies

Publicaons

Patents

Media acvies Protoypes

Public reacons

Market share

Expert statements

Fig. 1. Schematic of indicators of hype analysed in the literature, with media activities acting as the measure of technological expectations. Solid lines indicate direct influence, dashed lines indicate delayed influence.

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2.1.2. Thresholds and ranges Even strictly quantitative hype analyses typically contain no explicit discussion of how much change in one or more variables constitutes hype (e.g. Jarvenpaa and Makinen, 2008). Van Lente et al. argue that the “sharp peak” in media attention to superconductor research (within one year as opposed to two or three) likely led to deeper disappointment. Their graphs for superconductor, VoIP and gene therapy show 8–10-fold increases in attention for peaks compared to non-peak periods (Van Lente et al., 2013). This is a similar ratio to that found for hydrogen vehicle prototypes (Bakker, 2010), but internet traffic search and news for hybrid cars only double under a studied hype cycle (Jun, 2012). It seems impossible to establish a clear ratio of attention (‘inputs’) relative to usable products (‘outputs’) that unequivocally indicates hype. Therefore, this article compares CCS to competing technologies and their approximate input/output ratio. Such alternatives may not exist in all sectors and the comparability and competitiveness are likely to be only approximate, but alternative technologies provide a point of reference that allows a more confident diagnosis of “hype” with relevance at the system level. 2.2. Analysing hype in comparison 2.2.1. Context, indicators and their relationships The contextual evaluation in the “Results” Section 4.1 relies on published analyses of interviews with experts. I contrast their findings with a broader literature review. In addition, I trace the spread of ideas about CCS through key events and documents around the world. Quantitative indicators are grouped into three hype factors expectations, commitments and outcomes. The full justification for each indicator is in Section 3. Below follows a brief description of their relationships. Hype is founded upon high-rising expectations for a technology. This factor is the trigger for a hype, and forms a common departing point for the literature interested in hypes (Borup et al., 2006). This article focuses on expectations internal to the regime given the less public nature of energy generation technologies. Under overall enthusiasm and group pressure (Konrad, 2006), companies are tempted to make project announcements (Bakker, 2010). These

industry expectations are then incorporated into mitigation scenarios, which serve as the linkage with the following factor. High-rising expectations may trigger high-rising commitments concerning the technology. Public budgets are under pressure from these expectations, as are research activities as reported in peer-reviewed publications (Van Lente and Bakker, 2010). These activities are crucial in widening the interest group committed to and even dependent on the success of the technology. Nonetheless, it is possible for budgets to be reappropriated and for researchers to move to other technological fields. Finally, hype implies flat-lining outcomes, particularly relative to expectations. Low patenting activity denotes a poor level of translation between research and budget commitments to more concrete outcomes. It is linked to the unwillingness to risk large investments in prototypes. Those that are built struggle to reduce costs significantly. This factor helps ascertain the innovation effects of a hype. Fig. 2 below provides a summary of the conceptual framework used in this paper. It depicts the three factors of hype and their indicators within a “technological regime” (Rip and Kemp, 1998). As per the conventional division in the literature, indicators range from discursive (and easier to alter) to innovative (harder to alter). There is a delayed feedback between outcomes, on the one hand, and commitments and expectations, on the other. Indicators in a regime are more or less exposed to regime outsiders, and their relevance varies depending on the regime. Hence, contextual information is needed to make the necessary links and establish research priorities for researching hype factors. Thus, we can consider media activities and public reactions as alternative indicators of expectations, which lie outside the scope of the present analysis. 2.2.2. Making hype detection more relevant: competitors This article compares the evolution of CCS to other emerging low-carbon technologies, namely: (enhanced) geothermal electricity, marine electricity and solar thermal electricity. Similarly to CCS, these technologies have all been deployed to a (very) limited extent, requiring substantial financial support, and have limitations to delivering significant emissions reductions (Edenhofer et al., 2011; IEA, 2015b). However, each technology faces different starting points and issues:

Fig. 2. Summary schematic of the analytical framework. The present paper zooms in on the three hype factors on the left and their indicators (with coloured ellipses and lines). Solid lines indicate direct influence, dashed or dotted lines indicate delayed or indirect influence.

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 CCS is a process consisting of the separation of CO2 from industrial and energy-related sources, transport to a storage location and long-term isolation from the atmosphere. The transport and storage parts have long been mastered but the capture component is still very energy intensive, leading to high costs.  Geothermal refers to the use of energy from geological processes inside the Earth. However, generating geothermal electricity is currently economical only from the few available high-temperature sources near the surface.  Marine electricity comes from the periodical kinetic energy possessed by waves or tides, requiring mostly quite immature technologies for conversion.  Solar thermal electricity is generated by heating a liquid, solid or gas through concentration of direct-beam solar irradiance and transforming this heat through a typical steam cycle. Usability is limited to rather arid regions with high solar irradiance. Nonetheless, increased deployment of the three renewable technologies above has often been conceived in competition with baseload sources (such as CCS and nuclear) in the electricity sector (Jacobson et al., 2015; Couture and Leidreiter, 2014; Krewitt et al., 2007; Singer, 2011). In particular, solar thermal has been touted as a source of reliable electricity generation thanks to its capacity for energy storage (Lilliestam et al., 2012). Hence, this article relies on solar thermal for more specific comparisons with CCS. Other emerging low-carbon technologies do not qualify as straightforward competitors to CCS because they are: (a) hard to differentiate from more advanced technologies, e.g. concentrated photovoltaics and offshore wind; (b) too general-use, e.g. low-loss power transmission, smart grids, energy storage, or (c) not really competing in the same areas, e.g. biofuels. Nonetheless, their development is naturally relevant for the future of the whole system (with or without CCS) and I take it into account in the overall assessment. Finally, nuclear fission power offers low-carbon baseload generation and thus could be seen as a very direct competitor to CCS. However, nuclear comes with additional risks regarding waste, safety and proliferation that neither CCS nor renewables have on a similar magnitude. Therefore, it cannot be said to compete on comparable terms. Of course, these technologies are only competitors for applications of CCS in electricity generation. Far fewer options are available for industrial applications of CCS (IEA and UNIDO, 2011). The discussion of the results and the conclusion of this article reflect this fact. 3. Materials The selection of data sources for indicators in the subsections below takes into account the need to allow for inter-comparability between these lines of evidence and, to the extent possible, across technologies. 3.1. Context Several studies have carried out interviews of CCS experts, seeking to ascertain their framing of the technology. These studies fulfil here the function of media content analysis in other articles. They reveal the dynamics of expectation formation, which a review of general CCS literature (authored by such CCS experts) then confirms. In addition, to provide a better grounding for the final discussion and policy recommendations, this section uses this general CCS literature and a broader set of climate and energy policy works to trace the spread of ideas about CCS.

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3.2. Expectations 3.2.1. Project announcements Announcements generate “peer pressure” in a business setting (Konrad, 2006), often by their mere numbers but also because of their contents in terms of timelines and directions. They are thus an appropriate variable to test for exaggerated expectations (Bakker, 2010). I compiled a comprehensive database of CCS projects by putting together and expanding existing public databases (MIT, 2015; ZEROCO2.no, 2015; NETL, 2015; Parsons et al., 2009; ZEP, 2008a; IEA, 2006). Importantly, this list unifies metrics and uses reputable sources to add consistently missing information, such as year of commitment to the project. To the best of my knowledge, databases for marine technologies and (enhanced) geothermal installations do not exist, and overall deployment of these technologies is rather limited (REN21, 2015). Therefore, I focus on the existing US National Renewable Energy Laboratory database for solar thermal (NREL, 2016), complemented under similar principles to the CCS database. 3.2.2. Mitigation scenarios Many research and government organisations produce reports that are influential in translating industrial visions into funding. Key to this grey literature in the energy sector are econometric models of future mitigation scenarios, which make significant assumptions and therefore reveal (exaggerated) expectations— rather than unavoidable facts—about the future of the technology (Ekins et al., 2011). In models, CCS has been portrayed as one of the most expensive mitigation options but also one with the largest mitigation potential. To explore the expectations behind this, I take as a reference the expected deployment of each technology by the year 2050 in two competing understandings of the future of the energy sector. The 2015 Energy Technology Perspectives (ETP) by the International Energy Agency (IEA, 2015b) is a good comparison against the Worldwide Fund for Nature 2011 Energy Report (WWF et al., 2011). The IEA has long been an influential voice in the energy sector, and increasingly in the climate change debate. Its 2015 ETP represents its most conservative take on CCS yet. WWF’s assessment stands in for those of others in the environmentalist movement, which often pose a stark contrast to IEA analyses (Zervos et al., 2010; Couture and Leidreiter, 2014). 3.3. Commitments 3.3.1. Public research funding The response of government to expectations for energy technologies can be gauged from the relative level of allocation in public research funding. Investments in energy research and development (R&D) have been correlated with innovation (and patenting) activity. Indeed, a core topic of debate about energy R&D has been the decrease in investment in the face of rising needs for innovation (Margolis and Kammen, 1999). Public R&D expenditure is particularly important, as it may be more readily devoted to innovative technologies than that of established companies. This article draws on IEA statistics on public energy research, development and deployment (RD&D) in its 29 member states, which allows for a detailed comparison by technology. Other major countries, such as China, invest much less in RD&D (Tan, 2010). See Table 1 for IEA RD&D budget codes and descriptions that correspond to each technology (IEA, 2011). IEA RD&D data lacks a separate category for geothermal electricity generation as opposed to heating applications. However,

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Table 1 IEA Energy Research, Development and Demonstration budget database items for technologies used in this analysis. Shorthand

IEA RD&D Budget Data code

IEA heading titles and included subheadings

CCS Geothermal Marine Solar thermal

32 35 33 313

CO2 capture and storage (incl. capture/separation, transport, storage, and others) Geothermal energy (incl. hydrothermal, hot dry rock system, advanced drilling, and others) Ocean energy (incl. tidal, wave, salinity grading power, etc.) Solar thermal power and high temperature applications

extrapolating from the American Recovery and Reinvestment Act, the total is a good approximation for electricity-relevant spending (80% of total). Data extracted for this section covers the period 1990 until 2013, i.e. explicitly excluding 2014 because of its considerable number of missing data points. The start year 1990 is an approximation on the year when action on climate change first became a political goal. Finally, deployment aid—relevant for CCS and solar thermal— such as feed-in-tariffs, tax concessions and exemptions, etc. is, unsurprisingly, not included in the database (IEA, 2011). Given widely different taxation systems and electricity regimes, it would be difficult to use this type of data for comparison. Nonetheless, if authorities back a technology (generously) in the RD&D stages, they likely intend to facilitate its deployment if it develops sufficiently. Indeed, the two countries where solar thermal has been deployed—Spain and in particular the US—have also passed legislation and carried out substantial efforts to make deployment of CCS possible. 3.3.2. Peer-reviewed publications Another commitment indicator are peer-reviewed publications. Peer-review guarantees a minimum degree of quality and truthfulness, but it does not constrain the direction of research. This direction is, ideally, the result of myriad decisions by

researchers going after promising new grounds. However, it can also be determined by the availability of research funding, leading to accusations that certain research only takes place because of politically motivated decisions, not least in high-profile energyrelated research programmes (Romm, 2004). Hype dynamics can thus be gleaned from a rise in publication numbers after a funding increase where no interest was present previously. I searched for journal articles related to the technologies of interest in the comprehensive Scopus database, focusing on journals classified as dealing with “Energy” (697 journals). To avoid false positives, I restricted searches to the “Keywords” field. Initial searches drew on descriptions of the technologies as laid out in IPCC Special Reports (e.g. IPCC, 2005; Edenhofer et al., 2011), and used terms from Engineering Information’s “Compendex” thesaurus as well as from the Cooperative Patent Classification system (introduced in Section 3.5.5 below). In order to avoid false negatives, I added results from specialised journals through the search results analysis tool in Scopus. In the end, only the International Journal of Greenhouse Gas Control, for CCS technologies, qualified. After a first iteration of this sequence, I checked for coherence within the title and abstract of the first 200 results when listed according to “Relevance” on the Scopus result screen, as well as the entire list of Keywords. Thereafter, I identified smaller issues and

Table 2 Description of the setup of journal article searches in the Scopus database by technology field. Shorthand

Flagship journal(s) Included keywords (any characters beyond an asterisk (*) will still yield a match)

CCS

"carbon dioxide capture" OR "carbon sequestration" OR "carbon dioxide sequestration" OR "CO2 sequestration" OR "carbon capture" OR "co2 capture" OR ccs OR "carbon storage" OR "carbon dioxide storage" OR "CO2 storage" OR “postcombustion capture” OR “precombustion capture” OR “oxy-fuel capture” geothermal OR "earth coil heat" OR "hot dry rock"

Geothermal

Marine

Solar thermal

International Journal of Greenhouse Gas Control was created in 2007 with the express intention of publishing CCS research

Geothermics is by far the most common journal but it also contains references to geothermal heating "tidal energy" OR "wave energy" OR Sources are general energy "ocean energy" OR "marine energy" journals not specifically connected OR "tidal power" OR "wave power" OR to marine energy such as "marine power" OR "ocean power" OR Renewable Energy,Energy and even "oscillating water column" OR OWC the Acta Energiae Solaris Sinica. OR OTEC OR "ocean thermal energy " OR “ocean curren*” OR “salinity gradient” "solar thermal" OR "fresnel lens*" OR Most frequent sources also include "concentrated solar power" OR csp articles on photovoltaic OR "trough concentrato*" OR "tower technology, which is a very concentrato*" OR "dish collecto*" OR different technological set. "solar rankine" OR "solar stirling" OR "stirling solar" OR "rankine solar" OR "solar engin*" OR "solar towe*" or "solar concentrato*

Issues

Solution

Keyword search finds journal articles related to increased sequestration through biochar processes, which are not considered part of CCS (and have very different technological pathways).

Excluded keywords “forestry” and “agriculture”. This change excluded less than 0.13% of items (19 out of 1557) in main journal and maintained references to bioenergy.

Limited search so that results include The technology and its keywords overlap with geothermal heating and the following string: “AND (power OR cooling. electricity)” Original keyword search yields a Limited error by manually excluding handful of references related to keywords such as “offshore oil wells”, biomass (algae farms), offshore wind and “fossil fuels” power (describing damage or others), and diesel systems on ships (called Marine power)

The technology and its keywords overlap with solar heating and cooling.

Limited search so that results include the following string: “AND (power OR electricity)

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introduced amendments. Table 2 contains the search terms and workarounds.

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to expectations regarding budgets and level of technology deployment, drawing on the IEA’s (2009) CCS Roadmap and Greenpeace et al.’s (2009) Concentrating Solar Power Outlook.

3.4. Outcomes 3.4.1. Patents While patenting data is not without its problems and may itself suffer from hype dynamics (Jun, 2012), there is a long-standing consensus that it is a good proxy for actual innovation, including in the energy sector and for CCS in particular (Albino et al., 2014; Duch-Brown and Costa-Campi, 2015; Popp et al., 2013). Data for this indicator comes from the “Spring 2016” online version of the PATSTAT database of the European Patent Office. Queries relied on the Cooperative Patent Classification (CPC)’s “Y02 classification scheme [that collects] in one convenient location selected technologies, which control, reduce or prevent greenhouse gases (GHG) emissions of anthropogenic origin, as set forth by the Kyoto Protocol” (Veefkind et al., 2012). See Table 3 for the codes and descriptions. The availability of a technological classification makes the analysis more robust than reliance on keywords selected ad hoc (Albino et al., 2014). This article focuses on invention rather than mere patenting by using “patent families”, which gather all patent applications at different offices for the same invention (DuchBrown and Costa-Campi, 2015). Considering applications are only published 18 months after submission and adding a 12-month safety margin to allow for data collection and gathering from all the offices; patent data presented here is complete until mid-2013 (EPO, 2015). The most fundamental data of patent activity and efficacy are the number of patent families per year and the percentage of those that are granted (WIPO, 2009; Duch-Brown and Costa-Campi, 2015; Albino et al., 2014; Popp et al., 2013). This is because patenting is a relatively costly activity that companies do not carry out lightly and a basic criterion for granting patents is whether they are innovative enough. This forms the main part of the evidence for this section. For this paper, I also analysed data commonly used to evaluate the “quality” of patents, such as percentage of families receiving more than one citation, average number of citations, average number of applications filed for same invention (i.e. “family size”), and percentage of families with more than 20 citations. However, “successful” patents are often hard to distinguish from a “normal” one by focusing on these citations (Popp et al., 2013). Furthermore, comparing these factors across fields may show effects from citing habits or even hype in the case of family size. The Discussion considers these data accordingly. 3.4.2. Prototyping activities In addition to patenting, companies may feel confident enough to build large-scale prototypes for the technology. In terms of prototyping activities, for CCS, information on active projects comes from my database introduced in the Announcements section. Solar thermal data stems from the aforementioned NREL database (NREL, 2016) and the Renewable Energy Network 21 (REN21, 2015). I contrast the characteristics of prototypes relative

3.4.3. Costs Cost information is extremely sensitive for businesses, so that “real” data is hard to come by. Site-specific negotiations on the price of electricity as well as fiscal, regulatory and financial decisions are important for the deployment of new technologies. Nonetheless, these factors are arguably disturbed by expectations and peer-pressure. To avoid as much as possible this interference and enable comparability, I focus on raw technological costs based on publicly available information. US data from financial advisory firm Lazard’s levelised cost of energy (LCOE) avoids many hype-prone assumptions by focusing on investment at the margin (Lazard, 2015). In turn, US’s Energy Information Administration (EIA) includes some hype-prone “learning” estimates but also considers additional costs to the system from the integration of non-dispatchable technologies and their financing issues. For this purpose, the EIA calculates a more complex metric derived from LCOE (EIA, 2015). Marine energy is not included in either, given that its current costs are far above the other technologies. Both these approaches explicitly avoid taking into account the environmental and social costs (or benefits) of these technologies, and are often based on extrapolation (e.g.: Lazard’s pulverised coal CCS estimates refer to 600 MWe plants that have only been proposed but never built). Therefore, to give an idea of actual costs and carbon emissions reduction performance, I use the formula below to calculate total life-cycle costs (TLCC) of real CCS and solar thermal facilities over a 30-year operation period, which I then divide by (a) the amount of avoided CO2 and (b) electricity produced without emitting CO2. TLCC = Capital costs + Fixed operation & maintenance (O&M) + Variable O&M + Fuel costs + CCS-specific costs The CCS facility is SaskPower’s pulverised coal unit 3 at Boundary Dam in Saskatchewan, Canada, representing a first-of-a-kind facility for pulverised coal with CCS. The solar thermal facility is Andasol-1 in southern Spain, which became the world’s first solar thermal (parabolic trough) plant with molten salt energy storage in 2009. This article presents two sets of estimates for Boundary Dam: Saskwind’s, which is a pro-wind organization critical of Boundary Dam, and a derived set with my adjustments. Both use Lazard figures for many basic values (Glennie, 2015; Lazard, 2014). The adjustment for capture lies in the opportunity cost for the parasitic load of capture equipment, which Saskwind calculates at USD 65.25 MWh. I consider no such “opportunity” because the goal is to decarbonise the plant. For transport and storage, both estimates use the US National Energy Technology Laboratory estimates for North Dakota: USD 16/t CO2. Lastly, Saskwind’s values for net CO2 avoided at Boundary Dam are 59.8% and this article’s 69.6% of captured total. I deduct the additional CO2 escaped while using it for enhanced oil recovery but not the 10% of emissions still released by CCS equipment because the reference plant for these emissions

Table 3 Cooperative Patent Classification codes used in this analysis. “%” is the wildcard character in the search language. Shorthand

CPC codes

Official description of selected code and included subcodes

CCS Geothermal Marine Solar thermal

Y02C Y02E Y02E Y02E

CO2 capture or storage (incl. biological, chemical and membrane capture, as well as subterranean and submarine storage) Geothermal energy (incl. earth coil heat exchangers, hot dry rock system, etc.) Energy from sea (incl. water column, thermal exchange, wave and tidal energy, etc.) Solar thermal energy (incl. tower, dish, Fresnel trough concentrators and collectors, etc.)

1% 10/1% 10/3% 10/4%

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reduction calculations is a coal-fired power station. Deducting the 10% would be double-counting. Andasol estimates use NREL (2013) data, which in turn derives from public data. Lazard provides the heat rate needed to calculate the fuel costs of its auxiliary gas turbine, which is missing from NREL’s data. To allow comparison, the 2006 figures for capital costs and O&M are adjusted to 2014 using Spain’s official capital goods inflation index. In terms of emissions, I assume Andasol displaces a plant like Boundary Dam, minus any emissions from Andasol’s complementary gas turbine. Grid integration costs can be deemed to be equivalent, even if of different nature, to Boundary Dam’s (Milligan et al., 2011).

Political arguments used in support of the technology have followed the expert view, regularly omitting major potential alternatives (Hansson, 2012). Other inconsistencies in the political argumentation include those regarding the potential of CCS to (a) deploy rapidly and achieve lower mitigation costs under market conditions despite its still requiring “demonstration”, its large scale and its lack of business model independent of decarbonisation, (b) address energy security while being geographically constrained and requiring increased energy consumption, or (c) its potential to become a successful export technology while being tied to a global commons problem (Meadowcroft and Langhelle, 2011; Martínez Arranz, 2015).

4. Results

4.1.2. Process-tracing After being ignored by the climate policy community (Working Group III, 1990; Watson et al., 1995), bullish political statements about, in particular, coal-fired power plants with CCS can be traced back to the US FutureGen project, which aimed for energy autarky through coal starting under the 2000–2004 George Bush presidency NETL, 2004. A focus on coal was further justified by the meteoric rise of Chinese consumption starting around 2002 (Fergusson, 2008). This same “coal for electricity” discourse (often rendered as “clean coal” in the media and in marketing) became disseminated by reputable “boundary organisations” Guston, 1999 such as the IPCC (2005) and the IEA (2004). Even the G8 took CCS up in 2005 under the UK presidency, which featured a BP-backed CCS project at Peterhead in Scotland (UK Government, 2005). Optimism about CCS was echoed in the influential Stern Review (Stern and Treasury, 2006). The Review outsourced its technology analysis, but, nonetheless, followed the popular “Princeton wedges” view of mitigation (Anderson, 2006). The original ‘wedges’ article acknowledged it had no justification for using more CCS than, e.g., renewables ‘wedges’ (Pacala and Socolow, 2004). These Princeton University researchers were part of the BPsponsored Carbon Mitigation Initiative, which featured CCS prominently. Nonetheless, optimism about CCS also reached well-known environmentalists, such as Monbiot (2006), who argued for extensive deployment on gas-fired power plant. All highlighted “power” ahead of any other application of CCS, just like the original disseminators (IEA, 2004; IPCC, 2005). In Europe in particular, framing CCS largely as a clean coal, absolutely essential technology became a “hegemonic discourse” (Martínez Arranz, 2015). Barely three years after the first mention of CCS in an EU document—by reference to the ‘Princeton wedges’ (DG Environment Com, 2005)—this discourse helped secure an unprecedented EU-level support package for the technology. Arguments included energy security, technological revival and cost-savings (if demonstration was successful). The success of this discourse is explained by (a) a lack of other strong discourses, such as on alternatives to baseload generation, (b) a renewed impetus for climate action after the entry into force of the Kyoto Protocol, and (c) the influence of CCS advocates, which included former oil, gas and electricity monopolies, notably BP and Vattenfall, as well as European Union structures inherited from the days of the Coal and Steel Community (Martínez Arranz, 2015). Europe is also where the lack of progress of CCS relative to expectations is most evident. Pilot projects and engineering studies have been funded, but not one large-scale (coal) power plant with CCS has been built in the EU out of the hoped-for 12 by 2020. While the low carbon price yielded less money for CCS finance than expected, this should in principle only have reduced the number of plants built rather than completely obliterate the programme. Indeed, advocates of the demonstration programme only contemplated financing needs for “additional operational costs” (ZEP, 2008b). Furthermore, a mirror programme for

4.1. Context 4.1.1. Expert appraisals CCS experts have tended to express high confidence in the value and future of the technology. They have been simultaneously aware of significant uncertainties internal to the technology, regarding costs, life-cycle effects, and storage capacity. However, they did not frequently reflect those uncertainties in the scenarios they created or worked with. Explanations include organisational attachment to the technology and cognitive bias towards positive, stable future scenarios (Hansson and Bryngelsson, 2009; Evar, 2011). Indeed, most research has focused on how to remove external “barriers” towards rapid deployment of CCS in the power sector, which was the upshot of the aforementioned IPCC (2005) Special Report. Thus, this literature explores “rollouts” of (parts of) the CCS system or provides recommendations for “effective support policies” either in purely techno-economic analyses (Gibbins and Chalmers, 2008; Groenenberg and De Coninck, 2008; Krah et al., 2013; Middleton and Eccles, 2013; Selosse et al., 2013; Zhang et al., 2014; Renner, 2014; Luo et al., 2014; Lee et al., 2014; Eckhause and Herold, 2014; Eccles and Pratson, 2014; Lupion and Herzog, 2013), or more comprehensive socio-technical analyses (Van Alphen et al., 2009a,b, 2010; Wilson et al., 2011; Stephens et al., 2008; Ragland et al., 2011; Von Stechow et al., 2011; Scott, 2013; Watson et al., 2014). Notably, many techno-economic analyses— and the policies they inform—have seen little change in their understanding of CCS as a “one-decade-away” mitigation solution for the power sector. This focus remains despite growing awareness of technical and other challenges (Nykvist, 2013; IEA 2009, 2013), the increasing competitiveness of other electricity sector options (Viebahn et al., 2012; Lund and Mathiesen, 2012), and the absence of technical breakthroughs to justify optimism (LI et al., 2013; Leung et al., 2014; National Coal Council, 2015; Liu et al., 2012a; Liang et al., 2015; Zhao et al., 2016). In a conference paper, Hawkins and colleagues concluded that the signs of CCS hype they focused on (cancelled projects vs. expectations of deployment) were caused by circumstantial factors, viz. financing, public support, and regulation (Hawkins et al., 2009). However, they did not contemplate possible competitors to CCS nor its performance against broader sustainability indicators. Rather, they focused on its good fit with the current energy generation system. Under climate constraints and without allowing any other type of technological developments, CCS can easily be seen as absolutely unavoidable across many sectors now dependent on fossil fuels. Yet such scenario-building reflects tunnel vision regarding the reality of mitigation options. Crucially, research shows that broader questions on the relation of CCS to other potential technological futures have been consistently neglected by CCS researchers and advocates (Corry and Reiner, 2011; Markusson et al., 2012b).

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Fig. 3. Comparison of expectations and actual operation of projects. The left panel and axis represent additional annual capacity to capture emissions, for CCS, or to avoid them, for solar thermal projects, by their (expected) start year. Solid lines represent projects that had entered in operation as of 2016, dash-dot lines are originally expected projects. The right panel and axis show worldwide deployment projections for 2050 in the IEA and WWF publications. CCS 2050 projections are divided into power (black) and industry (green). Industry represents 61% of total energy but only half of the estimated CCS mitigation potential. Data respectively from Section 3.2.1 and Section 3.2.2. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

“innovative renewables”—intended to match the additional funding to CCS—was deployed without problems. As noted in Section 2.1.2, CCS is largely unknown among the public (Ashworth et al., 2015). General public hostility cannot have been a key factor in the slow progress of CCS, except possibly in the Netherlands, where Shell’s Barendrecht project had already failed due to local protests (Vergragt, 2011), and certainly in Germany, where local opposition undermined the transposition of CCS legislation (WBR/REUTERS/DPA, 2011). This legislation passed without problems elsewhere. Potential CCS projects in France (Pietralunga, 2012), Spain (ICAL, 2010) and the UK (Turley, 2015) had explicit local backing. Outside the EU, Canadian and US legislation encountered no issues and projects were generally well received by the affected, often fossil fuel-dependent constituencies after Barendrecht triggered keen attention to local engagement (Jaccard and Sharp, 2011; Stephens, 2011). Most notably, Norway’s CCS programme—with stronger financing and supported by legal requirements to install—also failed to build its large-scale demonstration plant. The Norwegian audit office blamed this failure on unrealistic cost estimates from around 2006 (OAGN, 2013). By contrast, the reactions to the UK government’s withdrawal of funds for CCS—the last remaining national support scheme in Europe—omitted any such discussions and forwent any mention to CCS research in other applications (Carrington, 2015). The discourse of CCS advocates in North America is similar (US DoE, 2016; Saskpower, 2016; DE Chant, 2015). Even Shell, which recently opened a large, industrial CCS project, supports its position with electricity-focused IEA

figures (Shell Canada, 2016). A May 2016 UK study explicitly recognised the importance of CCS for industrial activities but gingerly favoured a “power-led approach” while acknowledging different opinions on this matter (Poyry, 2016). As this article goes to press in September, a parliamentary report based on this study only contains concrete funding proposals for power CCS (PAG-CCS, 2016). 4.2. Expectations Using data from the aforementioned CCS and solar thermal databases, the left panel on Fig. 3 below shows the discrepancy between announced starts and actual starts each year for CCS and solar thermal projects. The basis for comparison is the CO2 emissions captured/avoided by projects coming online each year. The fluctuation and discrepancy for CCS is significantly higher. During the peak period 2014–2016, an average of 43.42 million tonnes of CO2 per annum (Mt/a) of capture capacity were expected to come online, but only an average of 1.66 Mt/a did. In 2015, 61 Mt/ a were expected, but none came online. The largest difference for solar thermal was in 2013 when only 3.68 out of 10.84 Mt/a were avoided (assuming displacement of a coal-fired power plant similar to Boundary Dam and taking into account capacity factors). As shown by the bars on the right panel, these expectations were reflected in the high projections for both power (black) and industrial applications (green) of CCS in the influential (IEA, 2009), IEA (2013) reports. By contrast, the WWF report reflects well the environmentalist claim that mitigating climate change without

Table 4 Expected deployment in EJ/a of low-carbon technologies by 2050 in IEA and WWF publications to contain global warming below 2 C. Shorthand

IEA 2015 Energy Technology Perspectives

WWF 2011 Energy Report

CCS Geothermal Marine Solar thermal

80.69 (from potentially 126a ) 5.46 1.41 12.408

0 (from potentially 63.5b ) 16.2 0.9 43

a Derived from statements saying 2/3 of coal power plants, 1/3 of gas power plants, 1/5 of biomass power plants as well as 1/2 of all industrial uses of gas and coal. Achieve a 6 Gt CO2/a reduction targeted. b Refers to suitable large-scale stationary sources still using fossil fuels and biomass in this scenario.

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CCS is possible. It relies largely on solar thermal and geothermal instead. Beyond the represented technologies, WWF also argues for further efforts on energy efficiency, photovoltaics and, to a lesser extent, wind. This approach has been increasingly acknowledged in the mainstream but criticised as expensive (IEA, 2012, 2015b; AEMO, 2013; Danish Energy Agency, 2014). See Table 4 for further details. Drawing on the richer data from the CCS database compiled for this article, Fig. 4 shows that between 2005 and 2011 announcements of CCS projects peaked at almost 16 times the average of 4.5 projects per year during the rest of the era of international climate change mitigation efforts (1993–2004/2012–2014). The different subtypes of CCS technology are divided into (Metz et al., 2005)  ‘Simulations and tests’ (SIM): the former may simulate the integrated process but the latter refer to a (small-scale) captureor storage-only project.  ‘Mature applications of CCS’ (MAT): ammonia and urea production, coal to synfuels and chemicals, ethanol, fertilisers, food industry, oil and gas processing, other chemicals, refinery hydrogen, and steam-methane reformers.  ‘Immature applications of CCS’ (IMM): algae, cement, coal to hydrogen, iron & steel-making and multi-source plant, as well as Power projects (coal power plant, gas power plant, multi-fuel power plant, and oil power plant). Their distribution over time corresponds most clearly to a classic “hype cycle”. Despite the common perception, the issue is not that most of the announced CCS projects depicted above are failing. A high failure rate should be expected—and could be accepted – for any emerging technology. Rather, it is the sharply rising and falling shape of announcements for a first batch of CCS projects that reveals hyped expectations. In turn, the direction of technology development as seen in the type of project deserves close analysis. It may be reasonable for projects to focus on IMM because total MAT emissions are comparatively small. However, electricity applications take up 82% of all IMM announcements, even though industrial IMM was soon noted to be able to contribute about half of all emissions reductions (IEA, 2009). Furthermore, the fundamental need for prospection of suitable geological reservoirs means that a larger proportion of test projects would seem advisable (IEA, 2009; Leung et al., 2014). Another key issue is the rapid upscaling targeted in the projects. Boundary Dam came online in 2014 aiming to capture 1 Mt/a. In its first year of operation, technical problems caused it to capture only

0.4 Mt (MIT, 2016a). Until then, the only IMM in operation stored 0.12 Mt/a. By contrast, among all announced integrated IMM projects 61% are larger than 1.2 Mt/a. The average across all IMM is 2.21 Mt/a. This reinforces research suggesting that the challenges of a ten-fold upscaling were underestimated (Nykvist, 2013). Historically, upscaling has occurred either through market standardisation, viz. coal after a decades-long formative phase with very small increments, or through governments picking winners and backing them with massive public investment, viz. nuclear after a short formative phase (Wilson, 2012). The CCS “demonstration” plans openly aimed for state-supported but market-led standardisation and upscaling in just over a decade with little evidence on why CCS would be different to other energy-sector technologies (ZEP, 2008b). Finally, a hype-relevant variable within this indicator is the expected lead time from commitment until deployment. Established technology can be expected to have well-known lead times, whereas hyped technology will see far more uncertainty (cf. Bakker, 2010). Thus, for instance, recent construction lead-times for new coal power plants in Europe and the USA were consistently assumed at four years despite changing circumstances (Tidball et al., 2010; Bustreo, 2013). Indeed, this seems to be an absolute minimum “time until capture” in nearly all announcements of projects capturing and storing CO2 from coal power plants. Common initial expectations, i.e. not considering delays through major legal or technical challenges, were anything from 5 to 8 years. Captured amounts, the politico-legislative environment, capture technology, and commitment before or after the financial crisis did not explain the variation. In the context of other results, this suggests great uncertainty about the requirements to get the technology up and running. Fig. 5 showcases the concentration of announced coal power CCS projects in large scale and the small correlation between size and lead times. 4.3. Commitments 4.3.1. Public research, development and demonstration budgets The lines in the left panel of Fig. 6 show that, after an almost total lack of dedicated funding for nearly 14 years, CCS RD&D funding levels exceeded those of emerging technologies within two years (2004–2005). The subsequent six-fold increase in public RD&D funding for CCS from 2008 onwards represents significantly heightened commitments regarding this technology. Adhering to the aforementioned mitigation scenarios (see right panel), public RD&D support for other technologies has remained

Fig. 4. Total number of CCS projects announced worldwide divided into subtypes: immature applications (IMM), mature applications (MAT) and simulations and tests (SIM). See text above this figure for definitions. The total includes some 34 projects (mostly earlier ones) whose announcement date could only be estimated. These projects have been evenly distributed across the estimated period. Data from Section 3.2.1.

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0

2

4

6

Years unl capture starts 8 10 12

14

16

133

18

20

16.00 8.00 4.00

Captured CO2 amounts (log2)

2.00 1.00 0.50 0.25 0.13 0.06 0.03 0.02 0.01 0.00 Fig. 5. Plot of the announced values for all 79 integrated coal-power CCS projects in terms of size in Mt CO2/a (y axis, log2 scale) and in number of years until CO2 capture starts (x axis). Data from Section 3.2.1.

Fig. 6. Public Research, Development & Demonstration funds for selected technologies in OECD countries 1990–2013 (left axis and panel), compared to worldwide deployment projections for 2050 in IEA and WWF (right axis and panel as in Fig. 4). Data from Sections 3.3.1 and 3.2.2, respectively.

comparatively low, with the brief exception of geothermal under the US 2009 economic recovery plan.

comparatively little independent expertise when attention to CCS took off to its 2015 peak, after a short fall during 2014.

4.3.2. Peer-reviewed publications The number of journal articles for low-carbon technologies received a boost from 2005 onwards (Fig. 7). Previously, only geothermal had attracted a modest level of attention. However, the progression of CCS is particularly remarkable. The conceptual idea for CCS had existed since the 1970s (Marchetti, 1976) and an IEA “implementing agreement” exclusively for CCS research, the IEAGHG, had been created in 1991. However, by 2000, there were about 55 peer-reviewed papers around the world dealing with CCS. By the year of publication of the influential IPCC Special Report on CCS in 2005, this had increased to some 132. Thus, there was

4.4. Outcomes 4.4.1. Patents An RD&D boost ought to bring about a noticeable increase of patenting activity (Czarnitzki and Hussinger, 2004), particularly if it builds on a solid innovation system like that of CCS (Van Alphen et al., 2010). However, Fig. 8 below reveals that the number of CCS patent families has experienced but a gentle increase since about 2006. Marine electricity—and to a lesser extent geothermal— started an upward trend around the same time. For its part, solar

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800

Number of journal arcles

700 CCS 600 Geothermal 500

Marine

400

Solar thermal

300 200 100 0 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014

Fig. 7. Numbers of peer-reviewed journal articles in Scopus database by technology, marked as belonging in the “Energy” field. Data from Section 3.3.2.

7000

6000 CCS 5000 No. of patent families

Geothermal 4000 Marine

3000

Solar thermal

2000

1000

0 1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

Fig. 8. Evolution of patent family counts (1990–2013). Data from Section 3.4.1.

thermal has displayed a remarkable patenting increase, only recently slowing down. Comparing patent data across technology fields can be complicated. Nonetheless, higher patent filing and granting are generally welcome developments (OECD, 2013). The situation with respect to low-carbon technologies and in particular CCS is no different (GCCSI, 2011). In this context, solar thermal not only benefits from much more patenting activity, 66% of these patents were granted between 2005 and 2012. CCS meanwhile has lingered with the other two technologies, geothermal and marine energy, with granting percentages of 55%, 53% and 55%, respectively. CCS scores are noticeably higher than other technologies’ in terms of citations, which could indicate the potential for a breakthrough but also a concentration of citations on few usable patents. In the crucial aspect of CO2 separation (Haszeldine, 2009), a general review finds that “no current technologies for removing CO2 from large sources like coal-based power plants exist which satisfy the needs of safety, efficiency, and economy; further enhancement and innovation are much needed” (LI et al., 2013). This assessment is corroborated by reviews of advances across capture technologies (Kenarsari et al., 2013) and within the most

common capture types: amine-based solvents used in Boundary Dam (Liang et al., 2015), sorbent (Liu et al., 2012a,b), and membrane (Zhao et al., 2016). Another recent analysis stated the “need for fresh, transformational ideas” in general CCS research (National Coal Council, 2015). 4.4.2. Prototyping activities From 2005 to 2015, according to the database, seven plants carrying out the full cycle of capture and storage were built, which contrasts with the 103 projects that were announced to start within that period. Indeed, the IEA (2009) CCS Roadmap called for 100 projects by 2020, most of them in power generation. In addition, this roadmap suggested the minimum size for a coal-fired “pilot” plant would be 200 MW with “demonstration” requiring 300 MW. However, the only operative such plant is Boundary Dam with nominal capacity of 160 MW (equivalent to 1 Mt/a). Larger projects by SaskPower had been scrapped earlier because of escalating costs and the Boundary Dam refurbishment and CCS retrofit itself was 9% more expensive than projected (Glennie, 2015). Nonetheless, the IEA still hailed the “historic launch” of Boundary Dam (IEA, 2014a). Six other projects were under

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construction in 2016. The paragraphs below discuss the three IMM ones below, which are expected online during 2016. Firstly, Kemper County, a 528 MW coal gasification plant with CCS, is running 3.5 years late and USD 4.46 billion over its original USD 2.2 billion budget. In deviation from the strived for “standard” of 90% CO2 capture, Kemper County captures 65% of its flue CO2 or 3.3 Mt/a (Lacey, 2015). Secondly, a 240 MW slipstream from pulverised coal Petra Nova power plant in Texas is expected to capture 1.4 Mt/a. Petra Nova was postponed four years, but the targeted size increased. Just like the other two North American projects, its economics are dominated by high nearby demand for CO2 for oil recovery (Wang, 2014). Therefore, low oil prices threatened the commercial appeal of the plant (Poszywak, 2015). Finally, the Abu Dhabi CCS project for steelmaking aims for 0.8 Mt/ a. The finances of the Abu Dhabi project are unknown, and progress or delays have not been reported, but it is running one year behind the originally announced date (MIT, 2016b). Overall, there is no indication yet of a “dominant design” for future plants, although the high costs of Kemper County may deter investment in gasification. By comparison, according to the NREL’s database, solar thermal installations went from 0.39 GW to 4.66 GW between 2005 and 2015, with a further 1.37 GW under construction and expected to come online by 2018. This is above Greenpeace’s 2009 reference deployment scenario: 4.07 GW total installed capacity by 2015. Since no additional support has been agreed for solar thermal since 2009, this corresponds to an achievement of expectations. The implicit growth rate is second only to photovoltaics among power technologies. Power purchase agreements reported in the NREL database as of May 2016 averaged USD 0.17/kWh, similar to the 1.15 RMB/kWh feed-in-tariff announced by China in September 2016 (Cheng, 2016). This compares favourably with the inflationadjusted USD2015 0.42/kWh Spanish feed-in-tariff under which Andasol-1 came online in 2007. In terms of performance and delivery, Indian plants have incurred significant delays and the USA’s Ivanpah plant has faced performance issues since its start (CSPPLAZA, 2016). However, there are no reports of problems with

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schedule and operation in other plants. Finally, over this period, the database shows an increasing focus on plants with energy storage and either parabolic trough or solar tower technologies on a large scale. 4.4.3. Costs Fig. 9 below shows the evolution of Lazard’s minimum LCOE for the main technologies reviewed in this study—plus utility-scale photovoltaics as an example of a technology that is quickly becoming cheaper. For CCS, I add a 10% cost increase to cover CO2 transport and storage costs. Fig. 6 also shows cost calculations for carbon-free electricity at Andasol-1 and Boundary Dam Unit 3, estimated for 2014. In addition, cost calculations per tonne of avoided CO2 are $133.6 for Andasol, and $146.41 for Boundary Dam using this article’s estimates and $196.63 using Saskwind’s estimates. The EIA’s special metrics for 2020 are not compatible with this representation but have a similar relative distribution: in terms of minimum cost, CCS is ranked below solar thermal but still a long way from being profitable. Nonetheless, the EIA makes learning assumptions, which, in light of the indicators above, may not be justified. For instance, the EIA assumes capital costs in 2020 for solar thermal to be more than double those of coal CCS, even though Lazard currently estimates them as barely different. In addition, the EIA calculates solar thermal costs for a capacity factor of just 11–20% with no energy storage. Lazard and the NREL model solar thermal with storage by default, as in most current projects. Accordingly, the capacity factor doubles to some 40%. Expectations of future large-scale CCS deployment in the electricity sector rely on rapid cost-cutting. The comparison with photovoltaics showcases the ambition of such expectations. In its 2015 report on CCS, the IEA expects it to be on par in costs with PV and cover a similar amount of electricity sector mitigation burden by 2050 (IEA, 2015b). CCS is (still?) far from this trend. While we cannot rule out serendipitous breakthroughs or steady improvement through learning by doing for CCS, the same applies to any

20 15.52

18

14.35 13.23

Nominal USD cents/kWh

16 14 12 10 8 6 4 2 2008

2009

2010

2011

2012

2013

2014

Gasified coal CCS

Pulverised coal CCS

Geothermal

Solar thermal

Photovoltaics (ulity)

Boundary Dam (Saskwind)

Boundary Dam (Authors)

Andasol

2015

Fig. 9. Minimum cost estimates for selected technologies according to financial firm Lazard (lines) and authors’ calculation of carbon-free costs at representative CCS and solar thermal facilities (markers and labels). Data from Section 3.4.3.

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research shows that such complex technologies are prone to cost overruns and general management difficulties (Rai et al., 2010; Sovacool et al., 2014).

other technology, which is why it is important to discuss its competitors. 4.5. Competitors

5. Discussion The previous sections show hype trends appearing in all factors under scrutiny here. This could be simply accepted as part of the innovation cycle if CCS were fundamentally preferable because of either its better environmental performance or the technical hurdles of using competing options. In its industrial applications, CCS seems to have few competitors other than efficiency (either in producing or in consuming industrial goods). However, for electricity applications, the situation changes. In terms of environmental performance, independently of climate change, the reliance of CCS on extractive industries implies greater impacts on the environment than other power generation technologies (Schreiber et al., 2010; IEA et al., 2011; Hertwich et al., 2015). Furthermore, CCS leaves a monitoring burden on future generations, which may well be manageable but is one that its competitors do not have. Technical issues are more intricate. The WWF report and other publications in its vein (e.g. Czisch, 2005; Blarke and Jenkins, 2013; Jacobson et al., 2015; Bogdanov and Breyer, 2016) argue that changing the grid configuration could deal with the variability of renewables and render CCS totally unnecessary. Although mitigation-oriented grid reconfiguration is arguably only underway in Europe, objectives have become more ambitious over time (ENTSO-E, 2010, 2016). In addition, the grid technologies that enable higher penetration of renewables seem to be developing fast with comparatively little public spending. The data file accompanying this article shows that patenting activity has grown fast in “transmission”, “energy storage” and “smart grids” fields (40,000 patents in 2005–2012) with comparatively little public funding (some USD2014 9 billion from 1990 to 2013). Furthermore, the previous sections show that a key generation technology for this alternative grid configuration, solar thermal (WWF et al., 2011), has seen far greater innovative activity and faster deployment than CCS for electricity. While solar thermal potential is restricted geographically, which may diminish its value in the absence of low-loss power transmission, so is CCS due to its need for adequate storage. Finally, profitable CCS would be far more complex and larger on average than most of its renewable competitors. Empirical

Fig. 10 sums up the order and magnitude of cycles across the three hype factors for CCS, which reveals a clear hype in contrast with solar thermal, plotted on Fig. 11. Indicators are standardised with data from both CCS and solar thermal. For example, each RD&D expenditure value in either graph is standardised using the mean and standard deviation of yearly RD&D expenditures across both CCS and solar thermal. For CCS, announcements are the first to peak, shortly after the endorsement of CCS by the IPCC in 2005 and the first IEA estimates in 2004 (IEA, 2004; Metz et al., 2005). Budgetary allocations followed, increasing sharply with US, EU and other “economic recovery” programmes starting in 2008. Paradoxically, announcements plummeted thereafter. By contrast, the increase in academic papers has been more progressive. It is worth highlighting that most peer-reviewed publications appeared after the first IPCC and IEA assessments. The influential IPCC report relied mostly on research carried out by the IEAGHG, which was made up of public and private fossil fuel organisations and companies from IEA member states. Notably, that “major industrial technology vendors are confident in their ability to deliver commercial-scale CO2capture facilities for power plants” was assumed to translate not only into ‘increasingly cheaper CCS’ over time but also cheaper than other technologies (e.g. Scott et al., 2013). This was and remains a hope rather than a fact. The lack of external challenges to these assumptions helps explain the appearance and persistence of the early IPCC/IEA focus on (coal) power: 82% of CCS announcements aimed at electricity applications, even though, shortly after, these were expected to account for no more than 50% of emissions reduction (IEA, 2009). The optimism about the possibilities of upscaling was also clear: more than 61% of announcements of immature CCS applications (e.g. power and some industrial sources) proposed projects more than 10 times larger than existing ones. Nonetheless, expected lead times showed uncertainty: coal-fired power plants with CCS were estimated to take anything between 5 and 8 years with no significant correlation with captured amounts, legislative

8 7

Standard score

6 5 4 3 2 1 0 1993

1996

1999

Migaon announced

2002 RD&D

2005 Academic

2008

2011 Patents

Fig. 10. Plot of indicators across all hype factors for CCS. The right axis uses a standardised score derived from the mean and standard deviation across both CCS and solar thermal data for each indicator. Expectations: Mt CO2 captured/avoided; commitments: RD&D budget in million USD, and academic article count, and outcomes: patent count.

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8 7

Standard score

6 5 4 3 2 1 0 1993

1999 1996 Migaon announced

2002 RD&D

2005 2008 2011 Academic Patents

Fig. 11. Plot of indicators across all hype factors for solar thermal. Data as in Fig. 10.

environment, capture technology and commitment before or after the 2008 financial crisis. In contrast to the early optimism, Fig. 10 and Fig. 11 highlight that CCS patenting activity has been modest over this period compared with expectations and the level of commitment. It has stayed largely at the levels of marine or geothermal technologies. This is also consistent with limited prototyping activity and persistent high costs. The other technologies have not fared much better but they did not enjoy increases in funding, nor a wave of political enthusiasm, nor the setup of numerous research and support organisations (SCCS, 2005; UKCCSRC, 2012; BIGCCS, 2013; de Coninck and Bäckstrand, 2011). In summary, the evidence discussed here suggests that a CCS hype was driven by the expectations and commitments of the close-knit community of expert-advocates that formed around CCS in the early to mid-2000s. This community was dominated by US views (Stephens et al., 2011), which at the time seemed to justify a focus on coal for electricity generation. However, thanks to the attractiveness of CCS for large oil and gas companies, it included global actors such as BP (Van Alphen et al., 2010), which were key in influencing policy also in Europe (Dixon, 2006; Martínez Arranz, 2015). 5.1. Implications for CCS policy Hype literature has produced general observations on the likely consequences of hype, but tends to avoid recommending any course of action after establishing hype for one technology (Ruef and Markard, 2010; Konrad et al., 2012; Van Lente et al., 2013). This stance is eminently defensible for general, commercial technologies, particularly in the absence of comparison, but does not seem justified for mitigation technologies. In dealing with climate change, time is of the essence and resources are scarce. The paragraphs below contrast the general observations in the literature to the CCS case. These can be used to make betterinformed decisions about CCS technology, and about others that may be in a similar situation. Pronounced expectation peaks, such as those seen by CCS, have been followed by sharp disappointment in technologies that did not have a very specific, immediate realm of application (Van Lente et al., 2013). However, given climate change will not abate on its own, CCS and other low-carbon technologies are unlikely to suffer

this sharp disappointment. Furthermore, support structures built around CCS during the hype phase will help counter it (Ruef and Markard, 2010). This resilience is reinforced by the continued interest of the fossil fuel industry and associated bureaucracies (IEA, EIA, etc.), who may not see many alternatives for a smooth continuation of their activities (Konrad et al., 2012). This situation overall explains the very selective rejection of expectations, and a likely continuation of contextual hype dynamics (Ruef and Markard, 2010). Indeed, the reaction among CCS expert-advocates has largely been an extension of initial efforts, this time towards a later deadline of 2030. The recommendations repeat the recipe that brought about the first wave of hype: public CCS demonstration programmes, a robust carbon price, and limiting the financial, political and legal challenges for CCS. Most worrying are: (a) the lingering priority for electricity generation, despite acknowledgement of industrial opportunities, and (b) the disregard for non-CCS mitigation options (Nykvist, 2013; IEA, 2015a; Poyry and Elementenergy, 2015; ZEP, 2014, 2015; Bellona Foundation et al., 2013; Bellona Europa, 2014; Poyry, 2016; PAG-CCS, 2016). The foregoing analysis leads me to different recommendations. While CCS in electricity generation may be the only option under certain conditions, in general, it has not reduced costs to compensate for its environmental disadvantages. The first wave of hype identified in this paper proves that reducing costs for CCS is harder than initially expected. Thus, econometric modelling of CCS should assume rather humble learning rates. Accordingly (Torvanger and Meadowcroft, 2011), the current reassessment of public CCS spending and research ought to take seriously both the potential of CCS competitors in the electricity sector (Viebahn et al., 2012) and the urgency to consider longerterm mitigation measures in the non-electricity sector. The difficulties to achieve the latter are not reason enough to develop power-CCS first instead (cf. Poyry, 2016; PAG-CCS, 2016). Funds for emerging technologies should ideally be increased overall (BNEF, 2016). However, the proportion of funding previously devoted to fossil fuel electrical applications of CCS should preferably be directed to industrial CCS, bioenergy with CCS, or those non-CCS technologies that require a boost. There are few reasons not to explore CCS for industrial applications (Abdul Quader et al., 2016; Rootz and Johnsson, 2015; Al-Salem, 2015), and it is hard to reject the potential of

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“negative emissions” of bioenergy with CCS (Klein et al., 2014; Kriegler et al., 2014; Fuss et al., 2014). However, garnering broad support for these ideas will be harder the longer the “coal for electricity” hype remains unacknowledged by key players (Van Lente et al., 2013). 6. Conclusion and directions for future research This article has refined a hype analysis framework that takes into account the delayed and diminished media exposure of energy generation technologies such as CCS, and the importance of comparison with competing technologies. Notably, the analysis has focused on indicators of expectations internal to the technological regime, such as project announcements. After discussing the enthusiasm among CCS expert-advocates starting in the 2000s and the spread of their ideas, this article has demonstrated that indicators of expectations, commitment and outcomes all show signs of hype. This work can be improved upon in several ways to better understand both low-carbon hypes and the CCS hype in particular. Firstly, the specification of indicators for this article is more comprehensive than in most of the literature; however, more research is needed to establish a set of necessary and optional indicators (and a clearer understanding of what they indicate). Secondly and in this vein, although media reactions to CCS were central to neither the evolution of the technology nor its hype, it is now necessary to contrast the results presented in this article with the evolution in media attention and assessment of CCS. Media attention could still turn out to be a useful proxy for CCS hype. Thirdly, this closer evaluation could also be extended to peerreview publications. Last but not least, while some regression analysis has been carried out between different indicators (Jun, 2012), better tools of statistical analysis could be tested, such as path analysis (Duncan, 1966) or structural equation model (Ullman and Bentler, 2003). All these extensions would benefit from a comparative angle, which would enable better generalisations and theorising. For this purpose, collaboration among researchers is essential. Acknowledgements This research was made possible by the 2015 Taiwan Fellowship. I would like to thank the anonymous reviewers for their helpful and extensive engagement with the text, and Dimitri Lafleur for his useful comments and timely help. I am particularly indebted to Tracy Huang for her support above and beyond the academic realm. Nevertheless, any errors in the present article remain my sole responsibility. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j. gloenvcha.2016.09.001. References Abdul Quader, M., Ahmed, S., Dawal, S.Z., Nukman, Y., 2016. Present needs: recent progress and future trends of energy-efficient Ultra-Low Carbon Dioxide (CO2) Steelmaking (ULCOS) program. Renew. Sustain. Energy Rev. 55, 537–549. AEMO, 2013. 100 Per Cent Renewables Study—Modelling Outcomes. Australian Energy Market Operator, Melbourne, AU. Al-Salem, S.M., 2015. Carbon dioxide (CO2) emission sources in Kuwait from the downstream industry: critical analysis with a current and futuristic view. Energy 81, 575–587. Albino, V., Ardito, L., Dangelico, R.M., Messeni Petruzzelli, A., 2014. Understanding the development trends of low-carbon energy technologies: a patent analysis. Appl. Energy 135, 836–854.

Anderson, D., 2006. Costs and finance of carbon abatement in the energy sector. In: Treasury, U. (Ed.), Stern Review. UK Government, London. Ashworth, P., Wade, S., Reiner, D., Liang, X., 2015. Developments in public communications on CCS. Int. J. Greenhouse Gas Control 40 (September), 449– 458. Audin, L., 2002. Green power: how real is it? Cogeneration Competitive Power J. 17, 59–63. BIGCCS, 2013. BIGCCS Vision & Goals [Online]. Sintef, Trondheim http://bigccs.no/ background/bigccs-vision-goals/ (accessed 04.09.15). BNEF, 2016. New Energy Outlook. Bloomberg New Energy Finance. Bakker, S., Van Lente, H., Meeus, M., 2011. Arenas of expectations for hydrogen technologies. Technol. Forecast. Soc. Change 78, 152–162. Bakker, S., Van Lente, H., Engels, R., 2012. Competition in a technological niche: the cars of the future. Technol. Anal. Strateg. Manage. 24, 421–434. Bakker, S., 2010. The car industry and the blow-out of the hydrogen hype. Energy Policy 38, 6540–6544. Banholzer, W.F., 2012. Practical limitations and recognizing hype. Energy Environ. Sci. 5, 5478–5480. Bellona Europa, 2014. CCS: Solving the Climate Puzzle for Industry and Energy. Bellona, Oslo. Bellona Foundation E3G & Zero Emissions Resource Organisation, 2013. Moving CCS Forward in Europe. ENGO Network on CCS, Oslo. Blarke, M.B., Jenkins, B.M., 2013. SuperGrid or SmartGrid: competing strategies for large-scale integration of intermittent renewables? Energy Policy 58, 381–390. Bogdanov, D., Breyer, C., 2016. North-East Asian Super Grid for 100% renewable energy supply: optimal mix of energy technologies for electricity, gas and heat supply options. Energy Convers. Manage. 112, 176–190. Borup, M., Brown, N., Konrad, K., Van Lente, H., 2006. The sociology of expectations in science and technology. Technol. Anal. Strateg. Manage. 18, 285–298. Boyd, A.D., Paveglio, T.B., 2014. Front page or buried beneath the fold? Media coverage of carbon capture and storage. Public Underst. Sci. 23, 411–427. Brown, N., 2003. Hope against hype: accountability in biopasts presents and futures. Sci. Stud. 16, 3–21. Bustreo, C., 2013. Development & Application of ETM Dissemination Strategies: Review and Update of the Power Sector. EURATOM Fusion Association/ Consorcio RFX, Padova, Italy. CSPPLAZA. 2016. Ivanpah Caught on Fire, and That’s the Least of Its Problems. CSPPlaza, May (24). 2016. Carrington, D. 2015. UK cancels pioneering £1bn carbon capture and storage competition. The Guardian, 26 November 2015. Cheng, W., 2016. China Released the Feed-in Tariff for CSP Demonstration Projects. China National Solar Thermal Energy Alliance. URL: http://en.nafste.org/index. php?m=content&c=index&a=show&catid=6&id=490. Corry, O., Reiner, D., 2011. Evaluating global Carbon Capture and Storage (CCS) communication materials: a survey of global CCS communications. In: School, C. J.B. (Ed.), Work Package 1. CSIRO publishing, Cambridge, MA, US. Couture, T.D., Leidreiter, A., 2014. How to Achieve 100% Renewable Energy. World Future Council. Czarnitzki, D., & Hussinger, K., 2004. The link between R&D subsidies, R&D spending and technological performance. ZEW-Centre for European Economic Research Discussion Paper. Czisch, G., 2005. Scenarios for a Future Electricity Supply. Cost-Optimised Variations on Supplying Europe and Its Neighbours with Electricity from Renewable Energies. Institute of Engineering & Technology, Herts, UK. DE Chant, T., 2015. The Power Plants That Can Reverse Climate Change. PBS Nova Next, 9 December 2015. de Coninck, H., Bäckstrand, K., 2011. An international relations perspective on the global politics of carbon dioxide capture and storage. Global Environ. Change 21, 368–378. DG Environment Com, 2005. 35 Winning the Battle Against Global Climate Change. European Commission, Brussels. Danish Energy Agency, 2014. An Energy System Without Fossil Fuels Is Technically Possible [Online]. DEA, Copenhagen http://www.ens.dk/en/info/news-danishenergy-agency/energy-system-without-fossil-fuels-technically-possible (accessed 14.08.14). Dixon, T., 2006. In: Trade, U.D.O.I.A. (Ed.), UK Work on CCS and the EU Emissions Trading Scheme. UK Government, London. US DoE, 2016. Carbon Capture. (Online]. Available: http://www.netl.doe.gov/ research/coal/carbon-capture (Accessed 31.05.16). Dosi, G., 1982. Technological paradigms and technological trajectories: a suggested interpretation of the determinants and directions of technical change. Res. Policy 11, 147–162. Duch-Brown, N., Costa-Campi, M.T., 2015. The diffusion of patented oil and gas technology with environmental uses: a forward patent citation analysis. Energy Policy 83, 267–276. Duncan, O.D., 1966. Path analysis: sociological examples. Am. J. Sociol. 72, 1–16. EIA, 2015. Levelized Cost and Levelized Avoided Cost of New Generation Resources in the Annual Energy Outlook. U.S. Energy Information Administration. ENTSO-E, 2010. Pilot Ten-Year Network Development Plan. European Network of Transmission System Operators for Electricity, Brussels. ENTSO-E, 2016. Ten-Year Network Development Plan. European Network of Transmission System Operators for Electricity, Brussels. http://tyndp.entsoe.eu/. Eccles, J.K., Pratson, L., 2014. A carbonshed assessment of small- vs. large-scale CCS deployment in the continental US. Appl. Energy 113, 352–361.

A. Martínez Arranz / Global Environmental Change 41 (2016) 124–141 Eckhause, J., Herold, J., 2014. Using real options to determine optimal funding strategies for CO2 capture: transport and storage projects in the European Union. Energy Policy 66, 115–134. Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Seyboth, K., Matschoss, P., Kadner, S., Zwickel, T., Eickemeier, P., Hansen, G., Schl Mer, S., Stechow, C.V. (Eds.), 2011. IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation. Cambridge, UK, U.P, New Yor. Ekins, P., Kesicki, F., Smith, A.Z.P., 2011. Marginal Abatement Cost Curves: A Call for Caution. UCL Energy Institute, London. EPO. 2015, PATSTAT Online Sample Queries and Tips. Evar, B., 2011. Conditional inevitability: expert perceptions of carbon capture and storage uncertainties in the UK context. Energy Policy 39, 3414–3424. Fergusson, I.F., 2008. FutureGen: a brief history and issues for congress. In: SERVICE, C.R. (Ed.), CRS Report for Congress. US Congress, Washington, DC, US. Fuss, S., Canadell, J.G., Peters, G.P., Tavoni, M., Andrew, R.M., Ciais, P., Jackson, R.B., Jones, C.D., Kraxner, F., Nakicenovic, N., LE Quere, C., Raupach, M.R., Sharifi, A., Smith, P., Yamagata, Y., 2014. Betting on negative emissions. Nat. Clim. Change 4, 850–853. GCCSI, 2011. Carbon Dioxide Capture and Storage Demonstration in Developing Countries: Analysis of Key Policy Issues and Barriers. Global CCS Institute. Geels, F.W., 2005. Technological Transitions and System Innovations: A CoEvolutionary and Socio-Technical Analysis. Edward Elgar, Cheltenham, UK. Gibbins, J., Chalmers, H., 2008. Preparing for global rollout: a ‘developed country first’ demonstration programme for rapid CCS deployment. Energy Policy 36, 501–507. Glennie, J., 2015. Analysis of the Cash and Carbon Flows of Boundary Dam Coal-fired Power Station. Saskatchewan Community Wind (Saskwind), Saskatoon. Greenpeace, Solarpaces and Estela, 2009. Concentrating Solar Power Global Outlook 09. Groenenberg, H., De Coninck, H., 2008. Effective EU and member state policies for stimulating CCS. Int. J. Greenhouse Gas Control 2, 653–664. Guston, D.H., 1999. Stabilizing the boundary between US politics and science: the role of the Office of Technology Transfer as a boundary organization. Soc. Stud. Sci. 29, 87–111. Hansson, A., Bryngelsson, M., 2009. Expert opinions on carbon dioxide capture and storage—a framing of uncertainties and possibilities. Energy Policy 37, 2273– 2282. Hansson, A., 2012. Colonizing the future: the case of CCS. In: Markusson, N., Shackley, S., Evar, B. (Eds.), The Social Dynamics of Carbon Capture and Storage: Understanding CCS Representations, Governance and Innovation. Routledge, Oxford, UK. Haszeldine, R.S., 2009. Carbon capture and storage: how green can black Be? Science 325, 1647–1652. Hawkins, D., Peridas, G., Steelman, J., 2009. Twelve years after Sleipner: moving CCS from hype to pipe. Energy Procedia 1, 4403–4410. Hertwich, E.G., Gibon, T., Bouman, E.A., Arvesen, A., Suh, S., Heath, G.A., Bergesen, J. D., Ramirez, A., Vega, M.I., Shi, L., 2015. Integrated life-cycle assessment of electricity-supply scenarios confirms global environmental benefit of lowcarbon technologies. Proc. Natl. Acad. Sci. 112, 6277–6282. ICAL. 2010. La planta de captura de CO2 de Cubillos del Sil iniciará su actividad en 2011. El Mundo, 22 October 2010. IEA & UNIDO, 2011. Carbon Capture and Storage in Industrial Applications. OECD Publishing House, Paris. IEA, OPEC, OECD, BANK, 2011. Joint Report on Fossil-Fuel and Other Energy Subsidies: An Update of the G20 Pittsburgh and Toronto Commitments. IEA, OPEC, OECD, World Bank. IEA, 2004. Prospects for CO2 Capture and Storage. OECD Publishing House, Paris. IEA, 2006. Focus On Clean Coal. OECD, Paris. IEA, 2009. CCS Technology Roadmap. OECD Publishing House, Paris. IEA, 2011. IEA Guide to Reporting Energy RD&D Budget/Expenditure Statistics. OECD, Paris. IEA, 2012. Energy Technology Perspectives : Pathways to a Clean Energy System. OECD Publishing House, Paris. IEA, 2013. Carbon Capture and Storage: Technology Roadmap, 2013 edition OECD Publishing House, Paris. IEA, 2014a. IEA Hails Historic Launch of Carbon Capture and Storage Project [Online]. OECD Publishing House, Paris Available: https://www.iea.org/ newsroomandevents/pressreleases/2014/october/iea-hails-historic-launch-ofcarbon-capture-and-storage-project.html (accessed 01.06.15). IEA, 2015a. Development Prospects of the ASEAN Power Sector. OECD, Paris. IEA, 2015b. Energy Technology Perspectives 2015: Mobilising Innovation to Accelerate Climate Action. OECD Publishing House, Paris. IPCC, 2005. Summary for policymakers. In: Metz, B., Davidson, O., Coninck, H.D., Loos, M., Meyer, L. (Eds.), IPCC Special Report on Carbon Dioxide Capture and Storage. Cambridge, New York, UK, U.P. Jaccard, M., Sharp, J., 2011. CCS in Canada. In: Meadowcroft, J., Langhelle, O. (Eds.), Caching the Carbon: the Politics and Policy of Carbon Capture and Storage. Edward Elgar, Cheltenham, UK, Northhampton. Jacobson, M.Z., Delucchi, M.A., Bazouin, G., Bauer, Z.A.F., Heavey, C.C., Fisher, E., Morris, S.B., Piekutowski, D.J.Y., Vencill, T.A., Yeskoo, T.W., 2015. 100% clean and renewable wind, water, and sunlight (WWS) all-sector energy roadmaps for the 50 United States. Energy Environ. Sci. 8, 2093–2117. Jarvenpaa, H., Makinen, S.J., 2008. Empirically detecting the Hype Cycle with the life cycle indicators: an exploratory analysis of three technologies Industrial Engineering and Engineering Management, 2008. IEEM 2008. IEEE International Conference, On, 8–11 Dec, pp. 12–16.

139

Jun, S.-P., 2012. A comparative study of hype cycles among actors within the sociotechnical system: with a focus on the case study of hybrid cars. Technol. Forecast. Soc. Change 79, 1413–1430. Kenarsari, S.D., Yang, D., Jiang, G., Zhang, S., Wang, J., Russell, A.G., Wei, Q., Fan, M., 2013. Review of recent advances in carbon dioxide separation and capture. RSC Adv. 3, 22739–22773. Klein, D., Luderer, G., Kriegler, E., Strefler, J., Bauer, N., Leimbach, M., Popp, A., Dietrich, J.P., Humpen Der, F., Lotze-Campen, H., Edenhofer, O., 2014. The value of bioenergy in low stabilization scenarios: an assessment using REMINDMAgPIE. Clim. Change 123, 705–718. Konrad, K., Markard, J., Ruef, A., Truffer, B., 2012. Strategic responses to fuel cell hype and disappointment. Technol. Forecast. Soc. Change 79, 1084–1098. Konrad, K., 2006. The social dynamics of expectations: the interaction of collective and actor-specific expectations on electronic commerce and interactive television. Technol. Anal. Strateg. Manage. 18, 429–444. Krah, M., Heidug, W., Ward, J., Smale, R., 2013. From demonstration to deployment: an economic analysis of support policies for carbon capture and storage. Energy Policy 60, 753–763. Krewitt, W., Simon, S., Graus, W., Teske, S., Zervos, A., Sch Fer, O., 2007. The 2  C scenario—a sustainable world energy perspective. Energy Policy 35, 4969– 4980. Kriegler, E., Weyant, J.P., Blanford, G.J., Krey, V., Clarke, L., Edmonds, J., Fawcett, A., Luderer, G., Riahi, K., Richels, R., Rose, S.K., Tavoni, M., Van Vuuren, D.P., 2014. The role of technology for achieving climate policy objectives: overview of the EMF 27 study on global technology and climate policy strategies. Clim. Change 123, 353–367. LI, B., Duan, Y., Luebke, D., Morreale, B., 2013. Advances in CO2 capture technology: a patent review. Appl. Energy 102, 1439–1447. Lacey, S., 2015. Owner of first clean coal plant in US faces bankruptcy over soaring CCS costs. RenewEconomy 20 (August) . Lazard. 2014. Lazard’s Levelised Cost of Energy Analysis. Versions 8.0. Lazard. 2008–2015. Lazard’s Levelised Cost of Energy Analysis. Versions 1. 0-9.0. Lee, J.Y., Tan, R.R., Chen, C.L., 2014. A unified model for the deployment of carbon capture and storage. Appl. Energy 121, 140–148. Leung, D.Y.C., Caramanna, G., Maroto-Valer, M.M., 2014. An overview of current status of carbon dioxide capture and storage technologies. Renew. Sustain. Energy Rev. 39, 426–443. Liang, Z.H., Rongwong, W., Liu, H., FU, K., Gao, H., Cao, F., Zhang, R., Sema, T., Henni, A., Sumon, K., 2015. Recent progress and new developments in post-combustion carbon-capture technology with amine based solvents. Int. J. Greenhouse Gas Control 40, 26–54. Lilliestam, J., Bielicki, J.M., Patt, A.G., 2012. Comparing carbon capture and storage (CCS) with concentrating solar power (CSP): potentials, costs, risks, and barriers. Energy Policy 47, 447–455. Liu, J., Thallapally, P.K., Mcgrail, B.P., Brown, D.R., Liu, J., 2012a. Progress in adsorption-based CO2 capture by metal–organic frameworks. Chem. Soc. Rev. 41, 2308–2322. Liu, W., A.N, H., Qin, C., Yin, J., Wang, G., Feng, B., X.U, M., 2012b. Performance enhancement of calcium oxide sorbents for cyclic CO2 capture—a review. Energy Fuels 26, 2751–2767. Lund, H., Mathiesen, B.V., 2012. The role of carbon capture and storage in a future sustainable energy system. Energy 44, 469–476. Luo, X., Wang, M., Oko, E., Okezue, C., 2014. Simulation-based techno-economic evaluation for optimal design of CO2 transport pipeline network. Appl. Energy 132, 610–620. Lupion, M., Herzog, H.J., 2013. NER300: lessons learnt in attempting to secure CCS projects in Europe. Int. J. Greenhouse Gas Control 19, 19–25. MIT, 2015. Carbon Capture and Sequestration Project Database [Online]. MIT Press, Boston, MA, US Available: http://sequestration.mit.edu/tools/projects/ boundary_dam.html (Accessed 22.07.15). MIT, 2016a. Boundary Dam Fact Sheet [Online]. MIT Press, Boston, MA, US Available: http://sequestration.mit.edu/tools/projects/boundary_dam.htm (accessed 13.05.16). MIT, 2016b. ESI CCS Project Fact Sheet [Online]. MIT Press, Boston, MA, US Available: https://sequestration.mit.edu/tools/projects/esi_ccs.html (accessed 13.05.16). Marchetti, C., 1976. On geoengineering and the CO2 problem. Clim. Change 1, 59–68. Margolis, R.M., Kammen, D.M., 1999. Evidence of under-investment in energy R&D in the United States and the impact of federal policy. Energy Policy 27, 575–584. Markusson, N., Kern, F., Watson, J., Arapostathis, S., Chalmers, H., Ghaleigh, N., Heptonstall, P., Pearson, P., Rossati, D., Russell, S., 2012a. A socio-technical framework for assessing the viability of carbon capture and storage technology. Technol. Forecast. Soc. Change 79, 903–918. Markusson, N., Shackley, S., Evar, B., 2012b. The Social Dynamics of Carbon Capture and Storage: Understanding CCS Representations, Governance and Innovation. Routledge, Oxford, UK. Martínez Arranz, A., 2015. Carbon capture and storage: frames and blind spots. Energy Policy 82, 249–259. Meadowcroft, J., Langhelle, O., 2011. The politics and policy of CCS: the uncertain road ahead. In: Meadowcroft, J., Langhelle, O. (Eds.), Caching the Carbon: the Politics and Policy of Carbon Capture and Storage. Edward Elgar, Cheltenham, UK, Northhampton, MA, USA. Metz, B., Davidson, O., Coninck, H.D., Loos, M., Meyer, L., 2005. IPCC Special Report on Carbon Dioxide Capture and Storage. Cambridge, Cambridge, UK, New York, U.P. Middleton, R.S., Eccles, J.K., 2013. The complex future of CO2 capture and storage: variable electricity generation and fossil fuel power. Appl. Energy 108, 66–73.

140

A. Martínez Arranz / Global Environmental Change 41 (2016) 124–141

Milligan, M., Ela, E., Hodge, B.-M., Kirby, B., Lew, D., Clark, C., Decesaro, J., Lynn, K., 2011. Cost-Causation and Integration Cost Analysis for Variable Generation Task No. WE110820. National Renewable Energy Laboratory and U.S. Department of Energy, Springfield,VA. Monbiot, G., 2006. Heat: How to Stop the Planet from Burning. Allen Lane Penguin press, London. NETL, 2004. FtureGen: Integrated Hydrogen, Electric Power Production and Carbon Sequestration Research Initiative. US Department of Energy National Energy Technology Laboratory, Washington, DC, US. NETL, 2015. NETL’s Carbon Capture and Storage Database [Online]. Online: National Energy Technology Laboratory Available: http://www.netl.doe.gov/research/ coal/carbon-storage/strategic-program-support/database (accessed 22.07.15). NREL, 2013. System Advisor Model (SAM) Case Study: Andasol-1. National Renewable Energy Laboratory. NREL, 2016. Concentrating Solar Power Projects [Online]. US Department of Energy Available: http://www.nrel.gov/csp/solarpaces/ (accessed 25.05.16). National coal council, 2015. Fossil Forward: Revitalizing CCS Bringing Scale and Speed to CCS Deployment. Fossil Forward. Nykvist, B., 2013. Ten times more difficult: quantifying the carbon capture and storage challenge. Energy Policy 55, 683–689. OAGN, 2013. Cost Management of CCS Must Be Improved [Online]. Office of the Auditor General of Norway, Oslo Available: https://www.riksrevisjonen.no/en/ Formedia/PressReleases/Pages/CCS.aspx (accessed 15.01.15). OECD, 2013. Climate and Carbon Aligning Prices and Policies. OECD Environment Policy Paper. Organisation for Economic Cooperation and Development. Pacala, S., Socolow, R., 2004. Stabilization wedges: solving the climate problem for the next 50 years with current technologies. Science 305, 968–972. Parsons, W., Baker, S., Epri, M., 2009. Report 1: status of carbon capture and storage projects globally. In: GCCSI (Ed.), Strategic Analysis of the Global Status of Carbon Capture and Storage. Global CCS Institute. PAG-CCS, 2016. Lowest Cost Decarbonisation for the UK: The Critical Role of CCS. Report to the Secretary of State for Business, Energy and Industrial Strategy. Parliamentary Advisory Group on Carbon Capture and Storage, London. Pietralunga, C., 2012. A la recherche d'une troisième voie pour Florange. Le Monde 30 (11), 2012. Popp, D., Santen, N., Fisher-Vanden, K., Webster, M., 2013. Technology variation vs. R&D uncertainty: what matters most for energy patent success? Resour. Energy Econ. 35, 505–533. Poszywak, A., 2015. Hitting the reset button: NRG targets debt reduction, stock buybacks with restructuring. SNL Canada Energy Week, 28 September 2015. Poyry, 2016. A Strategic Approach for Developing CCS in the UK. Report No. 2016/ 346. The Committee on Climate Change, London. Poyry & Elementenergy, 2015. Potential CCS Cost Reduction Mechanisms: Final Report Summary. London: Committee on Climate Change. REN21, 2015. Renewable global status report update. In: El-ashry, M. (Ed.), Renewable Energy Policy Network for the 21st Century. REN21 Secretariat, Paris. Ragland, C.J., Feldpausch-Parker, A., Peterson, T.R., Stephens, J., Wilson, E., 2011. Socio-political dimensions of CCS deployment through the lens of social network analysis. Energy Procedia 4, 6210–6217. Rai, V., Victor, D.G., Thurber, M.C., 2010. Carbon capture and storage at scale: lessons from the growth of analogous energy technologies. Energy Policy 38, 4089– 4098. Renner, M., 2014. Carbon prices and CCS investment: a comparative study between the European Union and China. Energy Policy 75, 327–340. Riesch, H., Oltra, C., Lis, A., Upham, P., Pol, M., 2013. Internet-based public debate of CCS: lessons from online focus groups in Poland and Spain. Energy Policy 56, 693–702. Rip, A., Kemp, R., 1998. Technological change. In: Malone, E.L. (Ed.), Human Choice and Climate Change. Battelle Press, Columbus, OH, US. Romm, J.J., 2004. The hype about hydrogen. Issues Sci. Technol. 20, 74–81. Rootz, N.J., Johnsson, F., 2015. CO2 emissions abatement in the Nordic carbonintensive industry – an end-game in sight? Energy 80, 715–730. Ruef, A., Markard, J., 2010. What happens after a hype? How changing expectations affected innovation activities in the case of stationary fuel cells. Technol. Anal. Strateg. Manage. 22, 317–338. SCCS, 2005. About SCCS [Online]. British Geological Survey, University of Edinburgh, Edinburgh Available: http://www.sccs.org.uk/about/sccs (accessed 04.09.15). Saskpower, 2016. Capturing Carbon and the World’s Attention [Online]. Available: http://www.saskpower.com/our-power-future/innovating-today-to-powertomorrow/capturing-carbon-and-the-worlds-attention/ (accessed 29.05.16). Schreiber, A., Zapp, P., Markewitz, P., V. Gele, S., 2010. Environmental analysis of a German strategy for carbon capture and storage of coal power plants. Energy Policy 38, 7873–7883. Scott, V., Gilfillan, S., Markusson, N., Chalmers, H., Haszeldine, R.S., 2013. Last chance for carbon capture and storage. Nature Clim. Change 3, 105–111. Scott, V., 2013. What can we expect from Europe’s carbon capture and storage demonstrations? Energy Policy 54, 66–71. Scrase, J.I., Ockwell, D.G., 2010. The role of discourse and linguistic framing effects in sustaining high carbon energy policy—an accessible introduction. Energy Policy 38, 2225–2233. Selosse, S., Ricci, O., MA ZI, N., 2013. Fukushima’s impact on the European power sector: the key role of CCS technologies. Energy Econ. 39, 305–312. Shackley, S., Evar, B., 2012. Up and down with CCS: the issue-attention cycle and the political dynamics of decarbonisation. In: Markusson, N., Shackley, S., Evar, B.

(Eds.), The Social Dynamics of Carbon Capture and Storage: Understanding CCS Representations, Governance and Innovation. Routledge, Oxford, UK. Shell Canada, 2016. Quest Carbon Capture and Storage Project [Online]. Available: http://www.shell.ca/en/aboutshell/our-business-tpkg/downstream/oil-sands/ quest.html(accessed 31.05.16). Singer, S., 2011. The Energy Report: 100% Renewable Energy by 2050. WWF, Gland, Switzerland. Sovacool, B.K., Gilbert, A., Nugent, D., 2014. Risk, innovation, electricity infrastructure and construction cost overruns: testing six hypotheses. Energy 74, 906–917. Stephens, J.C., Wilson, E.J., Peterson, T.R., 2008. Socio-Political Evaluation of Energy Deployment (SPEED): an integrated research framework analyzing energy technology deployment. Technol. Forecast. Soc. Change 75, 1224–1246. Stephens, J.C., Hansson, A., Liu, Y., DE Coninck, H., Vajjhala, S., 2011. Characterizing the international carbon capture and storage community. Global Environ. Change 21, 379–390. Stephens, J.C., 2011. Technology leader, policy laggard: CCS development for climate mitigation in the US political context. In: Meadowcroft, J., Langhelle, O. (Eds.), Caching the Carbon: the Politics and Policy of Carbon Capture and Storage. Edward Elgar, Cheltenham, UK, Northhampton, MA, USA. Stephens, J.C., 2015. Carbon capture and storage: a controversial climate mitigation approach. Int. Spect. 50, 74–84. Stern, N.H., Treasury, H., 2006, 2006. Chapter 16 accelerating technological innovation. In: Stern, N.H., Great Britain. Treasury (Eds.), Stern Review on the Economics of Climate Change. HM Treasury, London. Tan, X., 2010. Clean technology R&D and innovation in emerging countries— experience from China. Energy Policy 38, 2916–2926. Tidball, R., Bluestein, J., Rodriguez, N., Knoke, S., 2010. Cost and Performance Assumptions for Modeling Electricity Generation Technologies. NREL. Torvanger, A., Meadowcroft, J., 2011. The political economy of technology support: making decisions about carbon capture and storage and low carbon energy technologies. Global Environ. Change 21, 303–312. Turley, A. 2015. Local Labour MPs call on government to step up support for CCS project on Teesside. Labour and Co-operative, 18 November 2015. UK Government, 2005. Gleneagles Plan of Action [Online]. HM Publishing Office, London Available: https://www.gov.uk/government/uploads/system/uploads/ attachment_data/file/48584/gleneagles-planofaction.pdf(accessed 10.07.13). UKCCSRC, 2012. Home Page of UK Carbon Capture and Storage Research Centre, London. Available: https://ukccsrc.ac.uk./ [Online](accessed 04.0915). Ullman, J.B., Bentler, P.M., 2003. Structural Equation Modeling. Wiley Online Library. Unruh, G.C., 2000. Understanding carbon lock-in. Energy Policy 28, 817–830. Van Alphen, K., Hekkert, M.P., Turkenburg, W.C., 2009a. Comparing the development and deployment of carbon capture and storage technologies in Norway, the Netherlands, Australia, Canada and the United States-an innovation system perspective. Energy Procedia 1, 4591–4599. Van Alphen, K., Van Ruijven, J., Kasa, S., Hekkert, M., Turkenburg, W., 2009b. The performance of the Norwegian carbon dioxide: capture and storage innovation system. Energy Policy 37, 43–55. Van Alphen, K., Noothout, P.M., Hekkert, M.P., Turkenburg, W.C., 2010. Evaluating the development of carbon capture and storage technologies in the United States. Renew. Sustain. Energy Rev. 14, 971–986. Van Lente, H., Bakker, S., 2010. Competing expectations: the case of hydrogen storage technologies. Technol. Anal. Strateg. Manage. 22, 693–709. Van Lente, H., Spitters, C., Peine, A., 2013. Comparing technological hype cycles: towards a theory. Technol. Forecast. Soc. Change 80, 1615–1628. Van Lente, H., 2012. Navigating foresight in a sea of expectations: lessons from the sociology of expectations. Technol. Anal. Strateg. Manage. 24, 769–782. Veefkind, V., Hurtado-Albir, J., Angelucci, S., Karachalios, K., Thumm, N., 2012. A new EPO classification scheme for climate change mitigation technologies. World Patent Inform. 34, 106–111. Vel, J., 2014. Trading in discursive commodities: biofuel brokers’ roles in perpetuating the Jatropha hype in Indonesia. Sustainability (Switzerland) 6, 2802–2821. Vergragt, J., 2011. CCS in the Netherlands: glass half empty or half full? In: Meadowcroft, J., Langhelle, O. (Eds.), Caching the Carbon: the Politics and Policy of Carbon Capture and Storage. Edward Elgar, Cheltenham, UK, Northhampton, MA, USA. Viebahn, P., Daniel, V., Samuel, H., 2012. Integrated assessment of carbon capture and storage (CCS) in the German power sector and comparison with the deployment of renewable energies. Appl. Energy 97, 238–248. Von Stechow, C., Watson, J., Praetorius, B., 2011. Policy incentives for carbon capture and storage technologies in Europe: a qualitative multi-criteria analysis. Global Environ. Change 21, 346–357. WBR/REUTERS/DPA, 2011. Klimaschutz: bundesrat verhindert gesetz zur CEntsorgung. Spiegel Online. WIPO, 2009. Patent-based Technology Analysis Report – Alternative Energy Technology. World Intellectual Property Organization, Geneva. WWF Ecofys & OMA, 2011. In: Singer, S., Denruyter, J.-p., Jeffries, B. (Eds.), The Energy Report. WWF – World Wide Fund For Nature, Gland, CH. http://wwf. panda.org/what_we_do/footprint/climate_carbon_energy/ energy_solutions22/renewable_energy/sustainable_energy_report/. Wang, U., 2014. NRG's $1B Bet to Show How Carbon Capture Could Be Feasible for Coal Power Plants. Forbes. Watson, R.T., Zinyowera, M.C., Moss, R.H., 1995. Climate Change. Impacts, Adaptations and Mitigation of Climate Change: Scientific-Technical Analyses, Cambridge. University of Cambridge, New York and Melbourne II, W. G..

A. Martínez Arranz / Global Environmental Change 41 (2016) 124–141 Watson, J., Kern, F., Markusson, N., 2014. Resolving or managing uncertainties for carbon capture and storage: lessons from historical analogues. Technol. Forecast. Soc. Change 81, 192–204. Wilson, E., Zhang, D., Zheng, L., 2011. The socio-political context for deploying CCS in China and the U.S. Global Environ. Change 21 (2), 324–335. Wilson, C., 2012. Up-scaling, formative phases, and learning in the historical diffusion of energy technologies. Energy Policy 50, 81–94. Working Group III, 1990. The Climate Response Strategies. Cambridge University Press, Cambridge, UK, New York, NY, USA and Melbourne, Australia. ZEP, 2008a. In: Advisory Council (Ed.), EU Demonstration Programme for CO2 Capture and Storage (CCS): ZEP's Proposal. European Technology Platform for Zero Emissions Fossil Fuel Power Plants, Brussels. ZEP, 2008b. The EU Flagship Programme for CO2 Capture and Storage (CCS): ZEP Recommendations and Funding. European Technology Platform for Zero Emissions Fossil Fuel Power Plants, Brussels.

141

ZEP, 2014. CCS and the Electricity Market – Modelling the Lowest-cost Route to Decarbonising Europe. European Technology Platform for Zero Emissions Fossil Fuel Power Plants, Brussels. ZEP, 2015. CCS for Industry – Modelling the Lowest-cost Route to Decarbonising Europe. European Technology Platform for Zero Emissions Fossil Fuel Power Plants, Brussels. ZEROCO2. NO, 2015. Projects [Online]. Zero Emission Resource Organisation, Oslo Available: http://www.zeroco2.no/projects (accessed 22.07.15). Zervos, A., Lins, C., Muth, J., 2010. RE- Thinking 2050: A 100% Renewable Energy Vision for the European Union. European Renewable Energy Council, Brussels. Zhang, X., Wang, X., Chen, J., Xie, X., Wang, K., Wei, Y., 2014. A novel modeling based real option approach for CCS investment evaluation under multiple uncertainties. Appl. Energy 113, 1059–1067. Zhao, S., Feron, P.H., Deng, L., Favre, E., Chabanon, E., Yan, S., Hou, J., Chen, V., QI, H., 2016. Status and progress of membrane contactors in post-combustion carbon capture: a state-of-the-art review of new developments. J. Membr. Sci. 511, 180–206.