Annika Weiss, Karlsruhe Institute of Technology (KIT), Institute for Technology Assessment and Systems Analysis (ITAS) ; annikaweiss@gmx.net (corresponding author)
Witold-Roger Poganietz, Karlsruhe Institute of Technology (KIT), Institute for Technology Assessment and Systems Analysis (ITAS)
Dominik Poncette, Karlsruhe Institute of Technology (KIT), Institute for Technology Assessment and Systems Analysis (ITAS)
Abstract
Societal and technological developments are interlinked. This influences pathways for renewable fuel production in the transport sector. Using the Cross-Impact Balance (CIB) method, we developed scenarios considering these interdependencies for three technologies to produce synthetic natural gas: fermentation, gasification and power-to-gas (PtG). Descriptors for societal developments were combined with technological descriptors, such as resource demand or environmental impact. A matching scenario was only identified for PtG since limited resources hamper biomass-based fuels in scenarios where gas-cars are used – in prospering societies with environmental attitude. Gasification and PtG require an innovative societal setting in contrast to the matured process of fermentation.
Keywords: Cross-Impact Balance; Decision Support; Biogas; Renewable Fuel; Scenarios; Socio-Technical
Introduction
The transport sector needs to reduce its greenhouse gas (GHG) emissions in order to meet European climate goals (European Environment Agency). Reducing GHG emissions can be done by different strategies (Moriarty & Honnery, 2008), including the substitution of fossil fuels with renewable fuels (Lorenzi & Baptista, 2018; Ragauskas et al., 2006). Amongst the diversity of liquid and gaseous fuels that can be produced from renewable resources (Aresta, 2012; Climent et al., 2014; Codina Gironès et al., 2017), synthetic natural gas (SNG) seems to be a promising alternative (Millinger et al., 2018). It can be used in existing power trains, which are expected to have a relevant market share in the next two decades (European Commission, 2014). However, even when focusing on SNG as a strategy to reduce GHG emissions, it can be produced with different technologies, such as fermentation, gasification and power-to-gas (PtG).
In the fermentation process, sugar-rich feedstock such as plant material or agricultural or municipal waste are biochemically transformed into a mixture of gasses, including SNG (Kumar et al., 2013), under anaerobic conditions. During gasification, solid carbon-rich materials are first converted into SynGas under low-oxygen conditions at elevated temperatures and then methanated to SNG (Brown & Brown, 2014). ‘Power-to-gas’ describes concepts to convert electrical energy into a gaseous energy carrier by electrolysis. The resulting hydrogen can be converted into SNG via methanation (Wietschel et al., 2015).
Those technologies vary in their characteristics, such as the type of required resources, the maturity of the process or the environmental impacts (McKendry, 2002). These aspects are interlinked with societal developments and attitudes and therefore different production routes may thrive in different societal settings. The complex interactions between technology and society influence the development of a technology from niche level to mainstream application (Geels, 2002). Understanding these interactions is therefore necessary to support decisions on the appropriate pathways, e.g. for governmental strategies or businesses (Gausemeier et al., 1998; Heger & Rohrbeck, 2012). Their analysis is a highly complex process that requires sophisticated analytical frameworks (Geels, 2012). Scenarios are a useful and creative instrument for this purpose (Bezold, 2010; Grunwald, 2011; Ringland, 2010).
The combination of societal and technological factors in scenarios could show the interdependencies which support or hinder the respective technologies in the production of SNG. The following literature analysis shows the demand for research in this area.
Even though research linking societal and technical aspects is gaining more attention (Sovacool, 2014), studies analysing these interlinkages are still rare. Many scenarios dealing with biofuels or the transport system do not include the societal perspective (Gül et al., 2009; Krishnan et al., 2014; Pasaoglu et al., 2016; Turton, 2006). If at all, they only deal with societal conditions (Åhman, 2010) to justify techno-economic parameters (e.g. a driving behaviour justifies the fuel demand) or to set a general framework for economic development or public attitudes.
Existing socio-technical scenarios usually focus on broader aspects, e.g. the development of the transport sector as a whole (Elzen et al., 2004; Geels, 2012), biofuels in general (Ulmanen et al., 2009) or aviation biofuels (Kim et al., 2019). Scenarios for different transport biofuels were, for example, examined by Lorenzi & Baptista (2018), also including factors such as energy independence and local resources.
Even though socio-technical linkages in future scenarios are an exception, they have been considered in the analyses of the recent development of biofuels. Pfeiffer & Thrän (2018) for example reflect the history of biofuels in Germany with regard to political and historical events. Similarly Bomb et al. (2007) discuss the lessons learned from the socio-political context for the German and British biofuel industries. The political economy of fuels for road transport and the links between markets was investigated for the development of liquid bioenergy production in the U.S. (Rodríguez Morales & Rodríguez López, 2017).
Studies on the socio-technical reasons for the success or failure of certain fuel technologies are also retrospective. Raven (2004) provides examples for manure digestion in the Netherlands and Carolan (2010) for ethanol in the U.S. Ammenberg et al. (2018) identified important aspects of actor behaviour for the implementation of biogas mobility in the Stockholm region. These studies emphasise the importance of considering socio-technical linkages in future analyses too.
Scenarios can be created with a variety of methods using qualitative or quantitative approaches (Kosow & Gassner, 2008). Amer et al. (2013), for example, distinguish between three major scenario development techniques, i.e. intuitive logics, probabilistic modified trends and the French approach of La prospective. Yet, with increasingly complex systems, it becomes more difficult to thoroughly understand the implications of the narratives (Garb et al., 2008). Thus, approaches are needed that systemise and formalise the interrelationships and thus enhance the storylines of scenarios (Huss & Honton, 1987; Rounsevell & Metzger, 2010). The Cross-Impact Balance (CIB) method (Weimer-Jehle, 2006) systematically interlinks different types of developments and can thus be used for this purpose. To date, it has been applied in different research fields such as energy, sustainability, innovation, health, waste management and environmental research (Meylan et al., 2013; Schweizer & Kriegler, 2012; Weimer-Jehle et al., 2012). It has not yet been applied to identify relevant futures for different technologies, like synthetic natural fuels, and thus support strategic decisions for technology implementation. Due to restricted time and resources, only about two to five scenarios can be analysed in detail. Therefore approaches are needed to detect relevant scenarios (Amer et al., 2013).
To date, there is no paper that combines the above-mentioned aspects of addressing socio-technical interrelations in scenarios for one “product”, i.e. fuel type and different technologies to produce it. Yet, these societal-technical interrelations could influence the success factors for specific process technologies. This paper develops socio-technical scenarios for the implementation of three different technologies to produce SNG for the transport sector: fermentation, gasification and power-to-gas. For this purpose, we use the Cross-Impact Balance (CIB) method (Vögele et al., 2017; Weimer-Jehle, 2006) to interlink societal and technical developments. A distinct scenario is created for each technology to produce SNG using fermentation, gasification and PtG as possible process routes, highlighting the drivers and barriers resulting from the interrelations.
Section 2 describes how the CIB method is applied and adapted to identify the most relevant scenarios. Section 3 contains the results including the selected scenario descriptors and the technology-related context scenarios. The results are discussed in section 4 with regard to contents and methodology. Conclusions are drawn in Section 5.
Method
To create distinct scenarios for each SNG technology, socio-technical developments must be linked to technological properties. To meet this purpose, the CIB method (Weimer-Jehle, 2006) was applied and adapted in two ways, as explained in the following.
CIB Approach and Adaption
The four steps of the CIB include: (i) defining the general framework, (ii) determining conditions that influence future developments (so-called descriptors), (iii) assigning two or three potential futures to each descriptor (so-called descriptor states) and (iv) evaluating the mutual influences between the descriptors in the CIB matrix. Based on these interlinkages, the ‘SzenarionWizard’ software for the CIB approach (ZIRIUS) calculates scenarios in which the future developments (i.e. the descriptor states) are consistent with each other.
The traditional CIB approach was adjusted in two ways: First, two different sets of descriptors (in step (ii)) were defined, based on literature and stakeholder discussions.
a) The first set of socio-economic descriptors (Dsoc in Figure 1) includes general developments in society and – as our focus is on fuel technologies – developments related to the transport sector such as mobility behaviour, infrastructure and personal attitudes.
b) The second set of technology-related descriptors (Dtech in Figure 1) represents the characteristics of the three gas-producing technologies that could be relevant for their success, considering technological challenges, operation characteristics and environmental aspects.
As a second adjustment, the descriptor states of the technology-related descriptors (Dtech) were used to identify the technology-related scenarios from the number of consistent scenarios as described in the next chapter.
The scope of the study was the development of the European Economic Area (EEA) until 2040. For some aspects (e.g. resources), the worldwide situation was considered. Descriptors were chosen based on stakeholder discussions; an expert panel, which could also be part of the CIB approach (Weimer-Jehle, 2015), was not implemented.
Selecting Technology-related Scenarios
To select the technology-related scenarios from all consistent scenarios, the technology-related descriptor states were analysed. For PtG, gasification or fermentation respectively, the required technology-related descriptor states (Dtech A/B/C in Figure 1) were defined based on literature analysis: It was chosen, whether one specific descriptor state is compulsory (red) for the respective technology, whether a tendency is sufficient (i.e. two out of three options, yellow) or whether the descriptor is not of major importance for the technology and thus any descriptor state is possible (green) (Figure 1).
The consistent scenarios (identified with the CIB software) were then scanned whether they contain the descriptor states required for one or several SNG technologies. If no full match was identified, the scenario with the closest match was analysed. Storylines were then developed for each of the identified scenarios. Based on the combination of socio-economic and technology-related descriptors, drivers and barriers of market success were analysed.
Figure 1: Approach to identify technology-related scenarios (Dtech: technology-related descriptors, Dsoc: socio-economic descriptors, Sc: scenarios)
Results
Results of the analysis include the two sets of socio-economic and technology-related descriptors, the required descriptor states for each technology and the scenarios deriving from these definitions.
Relevant Descriptors and Descriptor States
In total, twenty descriptors were selected. The first set of eleven socio-economic descriptors includes general societal boundary conditions as well as aspects specifically related to mobility, e.g. infrastructural aspects and personal attitudes. The socio-economic descriptors are: ‘D.Demographic change’, ‘I.Income development’, ‘P.Policy in the European Economic area’, ‘E.Quality of education system’, ‘L.Environmental legislation’, ‘DW.Digitalisation of working life’, ‘DP.Digitalisation of private life’, ‘EN.Attitude towards the environment’, ‘PT.Availability of public transport’, ‘CS.Availabilty of car sharing schemes’ and ‘G.Availability of gas cars and infrastructure’. To identify scenarios with a preference for gas cars, the descriptor state of ‘G.Availability of gas cars and infrastructure’ should be ‘medium’ or ‘high’. The second set of nine descriptors represents characteristics of the three gas-producing technologies that can be linked to the societal aspects defined in the first set (e.g. the impact of the technology on the environment). The technological descriptor states were all defined as ‘low’, ‘medium’ or ‘high’. A full list of the 20 descriptors, their definitions, relevance and descriptor states is provided in the Annex.
Technology-related Descriptor States
For each of the three SNG technologies, a literature research indicated which of the technological descriptor states are required (Table 1). These descriptor states are needed to select the technology-related scenarios from the list of consistent scenarios. For example, in order to support PtG technologies, sufficient electricity must be available while biomass is not required. For fermentation, it is the other way round since biomass is the main resource. The descriptor states for ‘EF. Final efficiency’ and ‘OP. Operating costs’ describe the ‘distance to target’ of the respective technologies; the companies’ potential to achieve improvement is evaluated. For example, as the current efficiency for power-to-gas is low, it must increase to succeed on the market.
Table 1: Required descriptor-states for scenarios that support the development of fermentation, gasification or power-to-gas technology.1
| Power-to-gas | Gasification | Fermentation | ||
| B. Availability of biomass | Any | B3 high or B2 medium | B3 high | |
| No biomass is required. | Biomass is the main resource; imports of lignocellulosic biomass are possible (Skytte et al., 2006). | Biomass is the main resource. | ||
| EL. Availability of (renewable) electricity | EL3 high | EL3 high or EL2 medium | Any | |
| Electricity is the main resource. | Electricity is an important resource besides biomass. | Electricity could potentially be produced on-site. | ||
| LU. Land use competition | Any (with marginal land use for electricity) | LU1 low or LU2 medium | LU1 low | |
| Marginal land could be used to produce renewable electricity; wind power can be partly produced offshore. | The production of ligno-cellulosic biomass requires land. | The production of sugar-rich biomass competes with food production. | ||
| AC. Acceptance in the living environment | AC3 high or AC2 medium | Any | AC3 high or AC2 medium | |
| Renewable electricity production requires acceptance (Sütterlin & Siegrist, 2017). | The production of lignin-rich feedstock has little direct impact on the living environment (Sansaniwal et al., 2017). | Fermentation can cause odour (mostly accepted in rural areas) and monocultures (Hijazi et al., 2016). | ||
| KN. New know-how | KN3 high or KN2 medium | KN3 high or KN2 medium | Any | |
| PtG is a complex technology not yet fully developed, but pilot plants exist. | Gasification is a complex technology not yet fully developed, but pilot plants exist. | Fermentation is a comparably uncomplex and mature technology already applied on a large scale. | ||
| IN. Independence from imports | Any | IN3 high or IN2 medium | Any | |
| Electricity could be produced within the EEA, relevant imports from outside the EEA (e.g. Middle East, North Africa) are rather improbable. | The demand for lignin-rich feedstock can probably not be satisfied by the EEA. Imports of wood etc. from Russia, Canada and the U.S. are expected (Skytte et al., 2006). | Feedstocks are traditionally produced regionally. International imports are unlikely due to the low energy density and the high specific transport costs. | ||
| EF. Final efficiency | EF3 high | EF3 high or EF2 medium | Any | |
| PtG has a low estimated final efficiency so that society must provide the conditions for achieving a high efficiency increase (Walker et al., 2016). | The estimated final efficiency of gasification is lower than that of fermentation, but higher than that of PtG; the conditions for an at least medium increase of the efficiency must be provided. | Fermentation has a comparably high resource efficiency (Hijazi et al., 2016); no significant increase is required. | ||
| OP. Operating costs | OP1 low | OP1 low or OP2 medium | Any | |
| Very high estimated costs 3‑5 €/Nm3, estimated 1-2 €/Nm3 requires a considerable lowering (Götz et al., 2016). | Currently high costs require a target of low or medium operating costs (expected costs 0.5 €/Nm3) (Leible et al., 2012). | Mature technology, lowering (~1€/Nm3) rather unlikely (Leible et al., 2012). | ||
| IE. Impact on environment | Any | IE2 medium or IE3 high | IE3 high | |
| Low when using renewable electricity (Zhang et al., 2017). | Potential impacts from lignocellulosic biomass production (water stress, fertiliser); can be lowered by imports and use of residuals. | Methane losses (Reinelt et al., 2017), potential impacts from sugar-rich biomass cultivation (monocultures, water stress, fertiliser), requires a lowering of the environmental impact. | ||
| 1 Colour code: Red: one specific descriptor state is required, yellow: a tendency (i.e. two out of three options) is sufficient, green: any descriptor state is possible | ||||
Technology-related Scenarios
The ScenarioWizard software calculated 18 consistent scenarios resulting from the defined descriptor states and their interdependencies (c.f. annex). Only one of these scenarios contains all the required descriptor states for the PtG technology, two other scenarios contain most of the requirements for the gasification and fermentation technology.
Comparing the three selected scenarios (Table 2), shows that four societal descriptors (income development, digitalisation of working life, attitude towards environment and availability of car sharing schemes) and two technological descriptors (availability of biomass and impact on environment) are equal in all scenarios. Moreover, the fermentation-related scenario differs from the other two. Scenarios for PtG and gasification are nearly identical; they differ in only three of 20 descriptor state combinations: the availability of gas cars, the land use competition and the final efficiency (all are medium instead of high). However, the respective framework conditions have different implications for the three technologies as discussed in the following.
Table 2: Technology-related scenarios for power-to-gas, gasification and fermentation
| Power-to-gas | Gasification | Fermentation |
| D. Demographic change: D2 continuous (young) |
D. Demographic change: D3 sharp (young) |
|
| I. Income development: I3 increasing |
||
| P. Policy EEA: P1 cooperation |
P. Policy EEA: P2 ignorance |
|
| E. Quality of the education system: E3 improving |
E. Quality of the education system: E1 worsening |
|
| L. Environmental legislation: L1 strict |
L. Environmental legislation: L2 no change |
|
| DW. Digitalisation of working life: DW2 high |
||
| DP. Digitalisation of private life – life style: DP3 very high |
DP. Digitalisation of private life – life style: DP2 high |
|
| EN. Attitude towards environment: EN1 eco |
||
| PT. Availability of public transport: PT3 high |
PT. Availability of public transport: PT2 medium |
|
| CS. Availability of car sharing schemes: CS3 high |
||
| G. Availability of gas cars and infrastructure: G3 high | G. Availability of gas cars and infrastructure: G2 medium |
|
| B. Availability of biomass: B1 low |
B. Availability of biomass: B1 low |
B. Availability of biomass: B1 low |
| EL. Availability of (renewable) electricity: EL3 high |
EL. Availability of (renewable) electricity: EL3 high |
EL. Availability of (renewable) electricity: EL1 low |
| LU. Land use competition: LU3 high |
LU. Land use competition: LU2 medium |
LU. Land use competition: LU1 low |
| AC. Acceptance in the living environment: AC3 high |
AC. Acceptance in the living environment: AC3 high |
AC. Acceptance in the living environment: AC2 medium |
| NK. New know-how: NK3 high |
NK. New know-how: NK3 high |
NK. New know-how: KN2 medium |
| IN. Independence from imports: IN3 high |
IN. Independence from imports: IN3 high |
IN. Independence from imports: IN1 low |
| EF. Final efficiency: EF3 high |
EF. Final efficiency: EF2 medium |
EF. Final efficiency: EF2 medium |
| OP. Operating costs: OP1 low |
OP. Operating costs: OP1 low |
OP. Operating costs: OP2 medium |
| IE. Impact on environment: IE1 low |
IE. Impact on environment: IE1 low |
IE. Impact on environment: IE1 low |
Only the scenario identified for power-to-gas contained all descriptor states required for the technology. The main reason is the low biomass availability; while this is a problem for gasification and fermentation, it is not relevant for power-to-gas. The storyline for PtG related scenario, based on the interdependencies between descriptors, is exemplarily described below, the storylines for gasification and fermentation are provided in the annex.
Storyline power-to-gas
The political and societal framework conditions for the PtG technology are a prosperous economy with cooperative EEA policy, high income and a young, continuously growing society (P1, I3, D2). A cooperative policy with no trade barriers, open borders and transnational law and regulations could simplify the exchange of knowledge and extend the possible market for the PtG technology or products. In larger markets, economies of scale could be realised sooner than later, ‘riding the experience curve’ and shortening the time span until a successful market entry. These are also the preconditions to provide a good education system (E3) and generate new know-how (NK3), leading to a faster improvement of PtG technologies. Investments in R&D activities supported by a rising income also make new know-how available. This leads to an increasing efficiency and lower operating costs (EF3 high, OP1 low) of the, to date, rather immature technology.
Due to the high level of education (E3) and scarce resources (resulting in high resource costs), society is aware of environmental issues. Due to these factors and despite their growing income (I3), people accept disturbances caused by the generation of electricity from renewable energies (AC3 high). Therefore, most of the electricity for PtG can be produced nationally (IN3) (mainly on buildings, marginal land or offshore, see 4.1) and the impact on the environment is low (IE1).
In terms of resources, electricity, the most important source for PtG, is available (EL3). No biomass is required since industrial processes, wastes and direct air capture could provide CO2. A precondition for the latter is the increased efficiency (EF3 high) based on the new know-how. It is assumed that electricity production does not compete with food or feed production for arable land; the electricity for PtG is produced on marginal land or offshore. A high land use competition (LU3) is enhanced by strict legislation (L1) and environmental attitude, inhibiting the exploitation of natural resources for fuel production (e.g. the use of landscape conservation areas for electricity production). Nevertheless, the limited resources and high demand for mobility (see 4.1) require a very efficient (EF3) and effective use of resources. To meet this requirement, public transport is extensively used and car sharing is relevant (PT3, CS3), causing a low environmental impact per individual traveller.
Discussion
From the consistent scenarios, three could be assigned to a specific technology. However, only the scenario for PtG met all conditions required for this technology. The reasons for and background of this result are discussed with regard to the societal framework conditions and their interactions with the technological factors. Finally, the strengths and limitations of the approach are discussed.
Common Framework Conditions for the Investigated Technologies
The analysis of SNG for the transport sector requires scenarios in which gas cars are used. The political and societal environment that supports the use of gas cars is determined by three factors, which are fulfilled in all scenarios (Table 2): People travel and commute to work, want to use environmentally friendly transport and can afford gas cars. Their environmental attitude (EN1) leads to a demand for environmentally friendly transport. Increasing income (I3) promotes a positive evaluation of the natural environment and people can also afford cars with a corresponding power train. This setting could support gas cars as a comparably expensive transport option with low emissions, leading to a high or medium availability or market penetration of gas cars and infrastructure (G3, G2). These expectations are in line with the Energy Technology Perspectives where bioenergy for transport grows strongly in all scenarios (International Energy Agency, 2017) (Figure 7.5, p. 323). The trend is reinforced by the fact that several EU member states already supported a Danish proposal of 2019 to phase out fossil fuel vehicles by 2030 (Ekblom, 2019).
Despite a high availability of gas cars, car sharing is very popular (CS3) in all scenarios. Reasons are not only the environmental attitude, but also the growing and young population (D2 or D3) who prefers flexible and digitalised mobility over car ownership (Kuhnimhof et al., 2012; Sourbati & Behrendt, 2020). Sharing is also facilitated by high or very high digitalisation (DP2 or DP3) and smart devices, which help to evaluate and organise different transport options (Geels, 2012). Public transport (PT3 high or PT2 medium) accounts for a considerable part of the modal split as it reduces land use, health impacts and air pollution (Smargiassi et al., 2020) which is in line with the environmental attitude. Private digitalisation will generally reduce the demand for individual mobility, e.g. when goods and food are ordered online. Furthermore, fewer people need to commute as the working life is highly digitalised in all scenarios (DW2).
The societal setting of the transport sector is similar to the scenario ‘Modern Jazz’ (World Energy Council, 2019) including digitalisation and on-demand mobility services, both of which drive a smarter integration of public transport systems and encourage working from home and ride-sharing. It also resembles the sustainable transformation scenario of the International Energy Agency (International Energy Agency, 2020) (p.65) where transport systems shift towards modes of travel with less emissions.
The socio-economic demands of electric cars are similar to those of gas cars and they might be used in parallel in the scenarios. However, electric cars require further infrastructure and a different power train. This calls for innovation strategies in the car industry, i.e. with regard to battery electric vehicles (Geels, 2012). Therefore, gas cars could dominate if only two of the three drivers of the transition of the transport system identified by Geels (2012) are fulfilled. These are (a) the considered public environmental concerns (EN1 attitude towards environment: eco) and (b) innovation concepts aimed at the ‘greening’ of cars (in this study reflected by the increased knowledge which is promoted by the improved education system). Since this study focuses on SNG for the transport sector, this option is not analysed in detail.
Common to all scenarios is a scarcity of biomass (B1). This is triggered by the same reasons that support the use of gas cars in the first place: growing income, growing population and environmental attitude (I3, D2/D3, EN1). Biomass is needed to feed the growing population and could – driven by an environmentally friendly lifestyle – also gain importance for other applications like construction material, fire wood or biochemicals (Berndes et al., 2003; Bringezu et al., 2009; Jerrold E. Winandy et al., 2008). Consequently, all available resources must be used very efficiently, which is the reason why gas cars are combined with other transport options.
Important Combinations of Societal and Technological Factors
The study is based on the assumption that SNG will be a relevant fuel in the future and thus, gas cars are demanded. As described in the previous paragraph, this precondition already determines the common societal and political framework conditions – which in turn influence most other technology-relevant criteria such as resource demand and know-how (this could be visualised with the software (ZIRIUS), see supplementary information). The relation between the societal framework conditions and the specific technological requirements then determines the success of each technology. In the following, these interrelations and their implications are discussed.
Resources: demand and availability
The availability of resources is an important driver and enabler for market success. The scarcity of biomass prevented a fully matching scenario for the gasification and fermentation technologies. Whether resources are available can – in contrast to other technological properties such as environmental performance – only partially be improved by research and development. It depends largely on societal conditions. For example, population growth and demographic change have an influence on whether sufficient land is available for biomass production (or the generation of electricity); the political situation determines whether resources can be imported and legislation and public acceptance influence whether biomass can be used for fuel (Chin et al., 2014). With regard to the last aspect, gasification seems to have an advantage over fermentation since it uses lignocellulosic biomass, which does not compete with food production. Strict legislation or trade regulations can still limit the use of lignocellulosic biomass.
Technological maturity: state and potential improvement
Whether a technology is competitive in a given setting depends on its maturity and environmental performance. For immature technologies such as gasification and PtG, the political and societal environment for their further development must be provided. This insight is supported by results from the Sustainable Development Scenario of the IEA (International Energy Agency, 2020), where concerted policy efforts speed up innovation timelines for new energy technologies to ensure the efficient transmission of knowledge. These conditions are indirectly met in the current scenario and, in the long run, via the ‘E. Quality of education system’. It provides new know-how to increase the efficiency and lower the operating costs. It is also the precondition for a high income to enable investments in technology and infrastructure, e.g. as recommended by IRENA (2019). The quality of the education system might also influence other suggested measures of innovation such as public R&D and market-led private innovation (International Energy Agency, 2020) (p. 173).
Environmental performance: driver and barrier
Environmental legislation is the main driver for the three technologies, mainly invented to reduce GHG emissions. It sets the framework (or is a part of the goal system) for research and development activities and market performance. Strict legislation supports environmentally friendly technologies, giving them, above all, a competitive edge over established, more polluting technologies. For example, regulations on nitrous oxides emissions (as a measure to reduce forest decline due to acid rain in the 1980s) supported the implementation of the catalytic converter (O’Riordan, 2013). Therefore, strict legislation is a precondition for ‘green’ technologies such as renewable fuels production. Yet, strict environmental legislation could also hinder the application of renewable fuels if other impacts on the environment are high (Scharlemann & Laurance, 2008). The GHG emissions and thus potential savings of SNG technologies vary widely, depending on the used substrate, supply chain, credits, etc. (Holmgren et al., 2015; Rönsch & Kaltschmitt, 2012). If no GHG could be saved, as indicated by Wagner et al. (2014), even the initial environmental motivation lapses and other options to decarbonise the transport sector should be investigated, e.g. as described by Banister (2008). This was not the focus of our study.
Table 3 summarises the most important interrelations of societal and technological conditions.
Table 3: Combination of societal and technological factors resulting in drivers of and barriers to market success
| Power-to-gas | Gasification | Fermentation | |
| Societal conditions | An environmental attitude and high income ensure a general demand for gas cars and gaseous fuels in all scenarios, but also a high demand for resources. The cars are mainly shared, due to resource scarcity and high digitalisation of working and private life. | ||
| Political conditions | The political stability and cooperation to provide and exchange new know-how (and thus to further develop immature technologies) is given. | Less favourable political conditions are reflected by a non-cooperative EEA policy and a worsening education system. | |
| Technological maturity | Technology under development | Technology under development | Mature technology |
| Availability of resources | The main resource, electricity, is available. | Electricity is available but biomass as second important input is not. | Biomass as the main resource is not available (electricity could be produced on-site). |
| Environmental performance | Low expected impact on the environment | Medium expected impact on the environment | High expected impact on the environment |
| Conclusions | The scenario meets all conditions for a market success of the PtG technology. | The scenario does not meet the requirements for the gasification technology. In a scenario where gas cars are used, high environmental standards prevent the use of sufficient lignocellulosic biomass. | The scenario does not meet the requirements for the fermentation technology. Although the comparably mature technology could also thrive under less favourable political conditions, the lack of biomass hinders its establishment. R&D is required to lower the environmental impact (methane slip). |
Strengths and Limitations of the Approach
Like other foresight methods, the presented approach cannot predict the future, but gives an idea of the future using today’s knowledge (Grunwald, 2011; Vecchiato, 2012). For example, it remains uncertain whether a future society could provide the relevant know-how and economic strength (Archibugi, 2010) to further develop a rather immature technology such as power-to-gas. Even if this condition is met, a technological take-off is not guaranteed (van den Ende et al., 1998). Likewise, the question remains open whether or at what costs power-to-gas will reach the expected efficiency or save GHG emissions. This must be subject to further analyses.
The advantage of this CIB-based approach is the systematic combination of factors that are usually treated separately (Weimer-Jehle et al., 2016), thus reflecting relevant ‘feedback loops’. For example, it has already been shown that the use of biomass for fuel competes with other applications and can cause environmental problems (Groom et al., 2008; Meyer, 2017). Moreover, the present analysis suggests that a technology that relies on biomass is not compatible with a future in which gas cars evolve – namely in scenarios where environmental attitudes prevail and where strict environmental legislation provides an incentive for low-emission technologies.
Methodologically, the approach defines two sets of interacting descriptors, one for societal developments and one for technological characteristics which are interrelated but not equivalent. For example, while the education system depends mainly on policy, funding and demography, the available know-how for a technology can also be influenced by environmental legislation (increasing the need for skilled workers to develop better products) and also feeds back on societal parameters such as the income development. As the CIB matrix grows quadratically with the number of descriptors, defining two sets of descriptors significantly raises the efforts to evaluate interrelations as well as the calculation time (Weimer-Jehle, 2006). Approaches to limit descriptors, e.g. by developing separate matrices (Schweizer & Kriegler, 2012; Vögele et al., 2017), could be applied if technical and societal descriptors are only partially interrelated.
A limitation and uncertainty of the study is the evaluation of the semi-quantitative interrelation between the different descriptor states (Weimer-Jehle et al., 2016). Although the interdependencies were carefully analysed based on stakeholder involvement and literature research, other sources, interpretations or ideas could lead to different interdependencies and thus to different scenarios. An expert panel could be consulted to receive further evaluations (Weimer-Jehle et al., 2020). Sensitivity analyses may be necessary to identify the crucial assumptions.
Conclusions
This study combined societal developments with technological characteristics to create scenarios for three technologies for the production of gaseous fuels. It identified combinations of social and technical factors that support or hinder the success of the respective technologies.
The results indicate that the societal and political conditions interact with the technological characteristics, with consequences for resource demand and availability, the technological maturity and potential improvement, and the environmental performance of a technology. In the investigated case of SNG production, the use of gas cars is more likely to thrive in a political and societal environment with an environmental attitude, high income and a young and growing population. However, the same conditions limit the production of biomass-based fuels as land is scarce and biomass could be needed for other purposes such as food or construction material. Consequently, the only matching scenario for gas-driven cars was determined for the power-to-gas technology, which does not need biomass as a resource.
The approach helps linking technological and societal aspects, thereby identifying socio-technical circumstances under which specific technologies will thrive or be restricted. This could for example support decision making when comparing technology options with diverse societal impacts, such as different power trains or transport modes.
Acknowledgements
The work has been funded by Toyota Motor Europe (TME). We would like to thank Marleen De Weser (TME) for her valuable contributions to the choice of descriptors and the first draft of the paper.
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