Addressing uncertainty in decarbonisation policy mixes – Lessons learned from German and European bioenergy policy

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Original research article

Addressing uncertainty in decarbonisation policy mixes – Lessons learned from German and European bioenergy policy ⁎

Alexandra Purkusa, , Erik Gawela,b, Daniela Thränb,c,d Helmholtz Centre for Environmental Research – UFZ, Department of Economics, Permoserstr. 15, 04318 Leipzig, Germany University of Leipzig, Institute for Infrastructure and Resources Management, Grimmaische Str. 12, 04109 Leipzig, Germany c Helmholtz Centre for Environmental Research – UFZ, Department of Bioenergy, Permoserstr. 15, 04318 Leipzig, Germany d DBFZ Deutsches Biomasseforschungszentrum, Torgauer Str. 116, 04347 Leipzig, Germany a

b

A R T I C L E I N F O

A B S T R A C T

Keywords: Bioenergy policy Decarbonisation policy mixes Uncertainty New institutional economics

For promoting innovation in the context of sustainability transitions, research emphasizes the importance of combining technology-push and demand-pull instruments in a coordinated policy mix. Designing such policy mixes, however, remains challenging, due to path dependencies, interacting market failures, and uncertainty regarding eventual economic, environmental and societal impacts of innovations. This results in the need for a learning and flexible policy design, but simultaneously, stable political framework conditions are required to bring about lasting changes in production and consumption behaviour. This paper undertakes an economic assessment of how this trade-off between flexibility and stability has been addressed in practice, focussing on a case study of the European and German bioenergy policy mix which serves as a prime example for the challenges of dealing with uncertainty (e.g. regarding land use impacts, GHG balances, cost developments). Informed by the theory of second best, new institutional economics and the interdisciplinary policy mix literature, we identify dimensions for assessing whether relevant uncertainties, interactions between market failures and other constraints on first-best policy making have been handled in a rational manner. From the case study, we derive lessons for bioeconomy policy, as a further example of a decarbonisation policy mix faced by high uncertainty and complexity.

1. Introduction Technological and social innovation is a key component of realising a decarbonisation of economic production, which is urgently needed to mitigate climate change. For promoting such innovation, empirical evidence highlights the importance of combining technology-push and demand-pull instruments in a coordinated policy mix [1–3]. The economic theory of second best emphasizes that under certain circumstances, a policy mix is called for to address a single policy aim such as greenhouse gas (GHG) mitigation [4–6]. First, this is the case if there are interactions between multiple market failures. For example, environmental externalities cause the demand for emissions-intensive technologies to be higher than socially optimal; this market failure is exacerbated by knowledge and learning spillovers which cause investments in research and development (R & D) but also the diffusion of innovative low carbon technologies to be lower than optimal [7,8]. Technological path dependencies further distort competition between such technologies and incumbent, fossil fuel-based options [9,10]. Second, it may not be feasible to implement “first-best” interventions ⁎

which optimally address one market failure (e.g. emissions trading systems or carbon taxes which cause the full social costs of GHG emissions to be reflected in market prices), due to political and other constraints such as transaction costs, institutional path dependencies or uncertainty. The latter is particularly relevant in the decarbonisation context. Given the presence of ecological thresholds beyond which irreversible changes may occur, there is uncertainty about marginal damage costs of GHG emissions [11]. Also, there may be uncertainty about GHG balances of mitigation options, as in the case of bioenergy use [12], as well as about further environmental and social impacts. Under such circumstances, combining instruments which – imperfectly – internalize externalities (e.g. the European Emissions Trading System (EU-ETS) and R & D support) with further instruments to promote the diffusion of innovative low carbon technologies and safeguard sustainability can perform better than adopting a “one market failure – one instrument” approach [5,13–15]. The design of such “second-best” decarbonisation policy mixes, however, remains challenging. Uncertainties about eventual economic, environmental and societal impacts of innovative technologies result in

Corresponding author. E-mail address: [email protected] (A. Purkus).

http://dx.doi.org/10.1016/j.erss.2017.09.020 Received 15 February 2017; Received in revised form 12 September 2017; Accepted 15 September 2017 2214-6296/ © 2017 Elsevier Ltd. All rights reserved.

Please cite this article as: Purkus, A., Energy Research & Social Science (2017), http://dx.doi.org/10.1016/j.erss.2017.09.020

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the need for a learning policy design which provides flexibility to adapt to changing circumstances and new information [16–18]. Simultaneously, both innovation studies and economic policy literature stress the importance of stable political framework conditions for creating collective expectations regarding a path transition and incentivising lasting changes in production and consumption behaviour [1,19–21]. This is especially true if investments have long amortisation periods, like in the energy sector, and are highly asset specific in that their profitability depends on the continued existence of policy incentives [22,23]. Solving the trade-off between flexibility and stability therefore presents a key challenge for the design of innovation and transition policy mixes towards decarbonisation. This paper analyses how this trade-off has been addressed in practice, focussing on a case study of the European and German bioenergy policy mix. Section 2 presents the methodology and theoretical background of the case study analysis. Building on an economic policy assessment framework, we draw on the theory of second best, new institutional economics and the interdisciplinary policy mix literature to identify relevant dimensions for assessing policy mixes in the presence of interacting market failures, uncertainty and other barriers to firstbest policy interventions. Section 3 provides an overview of the German bioenergy policy mix in its European context. Section 4 evaluates this policy mix along the dimensions proposed in Section 2. Section 5 discusses what lessons can be learned from bioenergy policy for the design of bioeconomy policy, as an example of a further decarbonisation policy mix characterised by uncertainty and multiple interacting market failures. Section 6 concludes with a reflection on what insights can be drawn from the case study analysis for the design and evaluation of decarbonisation policy mixes. Bioenergy policy has interesting insights to offer as a case study, because its development over the last decade serves as a prime example for the conundrum of dealing with uncertainty in policy design. On the EU level and in member states such as Germany, the use of bioenergy in transport, electricity and heating sectors has been associated with high expectations regarding contributions to aims such as GHG mitigation, security of energy supply and rural development [24,25]. Its expansion has been supported by the EU Renewable Energy Directive’s 2020 targets for renewable energy sources (RES), which member states implement through a mix of technology-neutral instruments (e.g. carbon and energy taxes, EU-ETS) and technology-specific deployment support schemes, including biofuel quotas and incentives for bioelectricity and biomass-based heating technologies [26]. However, a critical debate ensued about direct and indirect land use change impacts of an increased biomass demand, associated negative environmental and socioeconomic effects, uncertainties in assessing GHG balances, and high GHG mitigation costs of biofuels and dedicated bioelectricity pathways [for overviews, see Refs. [27,28–30]]. On the EU level, this led to the introduction of a cap on agricultural crop-based biofuels’ contributions to the transport sector RES target, and continuing uncertainty about future biofuel policy design. In Germany, biofuel and bioelectricity support instruments were subject to a number of policy changes. In particular, remuneration rates for bioelectricity were significantly reduced in 2014 (see Section 3.2), following a debate on lower-than-expected decreases in electricity generation costs and the sustainability of energy crops [31,32]. Also, with increasingly high shares of the intermittent RES wind and photovoltaics, the systemic context of the energy transition has been changing. In sum, bioenergy policy has to deal not only with uncertainty regarding cost developments and environmental and socio-economic impacts of heterogeneous bioenergy pathways, but also with uncertainty about the future development of reference systems in relevant sectors (for example, an electricity system based on intermittent RES favours flexible bioelectricity plant concepts, rather than base load-oriented ones [33]). Given these circumstances, a case study assessment of how bioenergy policy has performed in handling the trade-offs between policy stability and flexibility promises valuable lessons for decarbonisation

policy mixes in contexts characterised by high uncertainty and complexity. An important example is the emerging field of bioeconomy policy [34–36]. Similar to bioenergy policy, substituting fossil resources in material applications for renewable ones is associated with various policy aims, but the heterogeneity of production pathways is even greater than in the bioenergy context, and there is a high degree of uncertainty about sustainable biomass availability and the performance of innovative technologies. As a choice of case study, the German bioenergy policy mix has been selected, because policy makers have pursued an ambitious expansion of bioenergy use in transport, electricity and heating sectors simultaneously, amplifying resource competition problems and coordination needs [37]. German policy instruments are embedded in the European policy context, illustrating the multi-level character of the policy problem, and increasing the comparability to other European member states. 2. Evaluating decarbonisation policy mixes in the presence of multiple market failures, uncertainty and other constraints: methodology and theoretical background From a theoretical economic perspective, a policy mix should address market failures in such a way that the outcome is economically efficient, in order to achieve decarbonisation without using more societal resources than necessary. At the same time, it needs to be sustainable in a broader ecological and social sense – environmental “guard rails” [30] must not be exceeded, and distributive impacts need to be taken into account. In reality, however, achieving efficiency and sustainability is complicated by several factors. First, there are conflicts between various societal aims to consider, including aims relating to efficiency, ecological and social sustainability dimensions. Second, interactions between multiple market failures have to be taken into account, as well as constraints which prevent the implementation of first-best solutions (see Section 1). Third, the political process follows its own inherent rationality, and policy aim setting and instrument choices are influenced by the pursuit of variables such as political support or administrative budgets [38,39]. In particular, in a democratic setting, policies require political majorities to be adopted, and the necessity of building broad advocacy coalitions and addressing different interests may imply trade-offs between economic efficiency and political feasibility. To reflect these conditions of real-world policy making, we assess whether policies succeed in handling conflicts between aims, interactions between market failures, uncertainty and other constraints in a “rational” manner, while striving for efficient and sustainable outcomes [31]. This approach is informed by second-best thinking [4–6], and can be operationalized through assessment dimensions derived from the interdisciplinary policy mix literature and new institutional economic theory. The question here is what policy mix characteristics contribute to efficiency and sustainability [40], even if optimal outcomes where all relevant market failures are addressed by first-best interventions prove unattainable. Furthermore, conditions for second-best optima may remain unknown due to the complexity of the policy problem, and the position of sustainability guard rails may be uncertain. Relevant policy mix characteristics can be distinguished according to whether they refer to the setting of policy aims, the alignment of aims and measures, or the choice and design of policy instruments (see Table 1). 2.1. Defining a system of policy aims Sustainability and economic rationality requirements imply that a system of policy aims should be complete and consistent [41]. Completeness means that it should encompass all economic, social and environmental aims that are relevant in a given policy context (e.g. bioenergy policy), to be able to reflect synergies and trade-offs. Consistency requires aims not to contradict each other; if conflicts arise, a prioritisation is necessary to indicate how trade-offs are to be resolved. 2

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coherence, which can be improved, inter alia, by integrating different policy sectors into the policy process and accessing the knowledge of various stakeholders [40]. In our case study we focus on the outcome dimension of policy assessment. Furthermore, it is necessary to establish credible commitment that aims will be implemented and that appropriate measures will be adopted to do so [23,38,40]. Especially for a path transition under uncertainty, this kind of commitment is crucial to maintain a stable policy environment [19]. After all, individual policy instruments may need to be adjusted if they prove ineffective or if there is new information on their effects on various relevant policy aims. We therefore discuss whether aims, targets which operationalize aims and instruments are perceived as credible and overall reliable, even when allowing for policy learning.

Table 1 Dimensions of assessing the “rationality” of decarbonisation policy mixes under uncertainty (own compilation). Defining a system of policy aims Alignment of aims and measures

Choice and design of policy instruments

Completeness of the system of policy aims Consistency of the system of policy aims Appropriateness of instruments for meeting policy aims Consistency of the instrument mix Credible commitment towards implementing aims through appropriate measures Control over social costs of errors in price and quantity instruments Technology differentiation: impacts on search processes for cost-effective and sustainable solutions Policy adjustment: balance between adaptive efficiency and planning security

2.3. Choice and design of policy instruments If this is not the case, conflicts are likely to propagate to the setting of targets and choice of instruments, with the outcome that adopted measures may benefit one aim and negatively impact another. As a mix, measures might then counteract each other in their effects (see Section 2.2). Also, particularly where innovative technologies are concerned, long-term policy aims and targets can have an important impact on the guidance of technological search processes [21,42]. Different political priorities may imply different directions for search processes, and unclear and shifting priorities can negatively impact planning security for investors. On the other hand, in the policy making process, keeping the priority between aims ambiguous and emphasising synergies rather than trade-offs can be a rational strategy to secure political support, by enabling the formation of advocacy coalitions even among disparate interests [43]. A trade-off can therefore emerge between the prioritisation of conflicting aims as a precondition for consistency and the necessity to achieve political majorities. Keeping this trade-off in mind, we assess the completeness and consistency of the system of policy aims that has emerged as an outcome of the policy process, as indicated by strategy documents and the orientation of policy instruments.

Instrument choice and design opens up a range of policy assessment issues, starting with the selection and combination of instrument types such as economic, regulatory or information instruments (for an overview, see Ref. [40]). Here, we focus on three dimensions which gain special importance if there is uncertainty about the private and/or external costs and benefits of decarbonisation technologies [31]: the choice between price and quantity instruments for supporting the deployment of low carbon technologies, the degree and implementation of technology differentiation, and the implementation of policy adjustments. The choice between price and quantity instruments influences how alternatives perform in controlling the social costs of erroneous judgements about costs and benefits of technologies [47–49]. Under uncertainty about the marginal cost curve of RES technologies, for instance, quantity instruments (e.g. quotas, tendering schemes) ensure target achievement at uncertain costs. Price instruments (e.g. feed-in tariffs or premiums) offer better control over the costliness of adopted technologies, but meeting targets requires regular policy adjustments. Which instrument type is advantageous depends on the relative slopes of marginal cost and marginal benefit curves [47], but estimating these can be difficult. Under such conditions, hybrid instruments which combine price and quantity elements can have advantages [48]. If technologies are heterogeneous not only in their cost structure but also in their environmental impacts, then the implementation of technology differentiation has important implications for incentives to search for cost-effective and sustainable technology options. Different components of the instrument mix interact to influence technology choices, including environmental policy regulations and information instruments such as labelling. Additionally, technology differentiation can be implemented within deployment support instruments. Under a technology-neutral scheme, market actors choose technologies with the lowest private costs, given regulatory framework conditions. Technology-specific support instruments (e.g. differentiated RES deployment support) perform better in promoting innovative technologies whose costs may be higher from a static perspective, but whose diffusion is associated with learning curve effects which will lower future costs of achieving policy aims [50–52]. Moreover, policy makers can use technology-specific remuneration rates or eligibility requirements to control technology choice parameters which affect environmental impacts and contributions to multiple policy aims. However, the finer the degree of technology differentiation adopted, the higher the information requirements on policy makers. Alternatively, deployment support can be tied to sustainability certification requirements, where market actors retain a larger scope for technology choices as long as predefined sustainability criteria are met [53]. Furthermore, technology differentiation rules and price and quantity incentives need to adjust to changing framework conditions and respond to steering errors. The requirement of adaptive efficiency demands the potential reversibility of policy impacts, to avoid a lock-in

2.2. Alignment of aims and measures When it comes to aligning aims and measures, adopted instruments should be appropriate to effectively implement policy aims and targets, and, as an instrument mix, they should be internally consistent [40,44]. When assessing appropriateness, it is necessary to analyse if interactions between market failures and other constraints on first-best solutions imply that individual aims require a combination of instruments to be effectively implemented (e.g. in the case of decarbonisation, a combination of an internalisation instrument, R & D support and deployment support for innovative low carbon technologies, see Section 1). These instruments should be consistent in the incentives they set for decarbonisation – they should be mutually supportive and not contradict each other [40]. In particular, contradictions can arise when instruments are used to achieve several aims at once, and priorities in case of conflicts are not clear (see Section 2.1). Moreover, when assessing policy outcomes, an integrated approach is required that takes positive and negative impacts on all elements of the system of policy aims into account, because it cannot be assumed that they are all addressed by first-best policy interventions of their own [6,40,45,46]. To be consistent with the system of policy aims, it may be necessary to introduce additional measures into the instrument mix to alleviate conflicts or foster synergies (e.g. sustainability certification alongside deployment support and internalisation instruments). Alternatively, the design of individual instruments may need to be adjusted (e.g. by using technology differentiation rules, see Section 2.3). An integrated approach that systematically assesses impacts on various aims is also desirable for the process of policy making, to avoid unintended consequences. This can be understood as part of the requirement of policy 3

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into inefficient development pathways [54]. Moreover, instruments should provide incentives for experimentation and decentralised search processes, because strict constraints on technology choices increase lock-in risks [54,55]. Being able to adjust policies is a prerequisite for credible long-term commitment [22,23], but it also contributes to policy uncertainty. Accordingly, policy design needs to strike a balance between stability and flexibility. Transaction cost economics suggest that this balance should be informed by transaction characteristics such as asset specificity, which refers to the degree to which a transaction involves specialised and durable investments which “cannot be redeployed to alternative uses except at a loss of productive value” (Williamson). Examples are specialised physical assets (e.g. electricity generation plants), site-specific assets (e.g. heating grids) or human assets (e.g. training in the use of specific technologies) [56]. If the realisation of policy aims depends on asset specific investments, instruments have to offer contractual safeguards and coordinated adjustment responses to provide a sufficient degree of planning security. On the other hand, transactions with low asset specificity should be governed by more market-oriented instruments with fewer hierarchical elements, as these provide more intense incentives for cost reductions and decentralised search processes. In the case study, we therefore discuss how relevant instruments have performed in controlling social costs of errors and setting incentives for search processes for cost-effective and sustainable solutions, and how adjustment mechanisms balance adaptive efficiency and planning security.

In subsequent years, the treatment of indirect land use changes (ILUC) associated with biofuel production became a major issue in the EU bioenergy policy debate [57,58]. Particularly the contribution of energy crops grown on agricultural land is viewed critically, because food and feed production may be displaced to former natural areas such as forests and grasslands. In 2015, an amendment of RED and FQD was adopted (Directive (EU) 2015/1513), which, among other provisions, states that agricultural crop-based first generation biofuels can only account for 7% of the RED’s 10% transport sector target. Also, it requires member states to set non-binding targets for advanced (i.e. nonagricultural crop-based) biofuels with low ILUC impacts in 2017. Further changes were made with the EU’s 2030 climate and energy policy framework, which no longer includes binding national targets for RES expansion. Rather, it limits itself to national GHG emission reduction targets and a collective target of 27% RES share in the EU’s final energy consumption by 2030 [59]. In its proposal for a recast RED [60], the EC nonetheless asks member states to require fuel suppliers to provide an increasing share of energy from low carbon transport fuels (1.5% in the total amount of transport fuels supplied in 2021 increasing to at least 6.8% in 2030), but these are defined so as not to include food or feed crop-based first generation biofuels. For advanced biofuels and biogas from selected feedstock, a minimum contribution of at least 0.5% is envisioned by 2021, increasing to at least 3.6% by 2030. Furthermore, the proposal suggests extending sustainability and greenhouse gas savings criteria to solid and gaseous biomass fuels used in electricity and heating sectors, if plants exceed a 20 MW fuel capacity threshold.

3. The German and European bioenergy policy mix: case study background

3.2. Development of the German bioenergy policy mix Biomass still constitutes the most important RES in Germany, with a share of 58% in final energy consumption from RES (total 2016: 390 TWh) [61]. In the heating sector, it contributed 87% to final energy consumption from RES in 2016, in the transport sector the share was 89% [61]. In the electricity sector, wind and photovoltaics play a more important role and the share of biomass in gross electricity generation from RES amounted to 26% in 2016. The German bioenergy policy mix encompasses three broad categories (see Table 2). First, there are instruments which set framework conditions for biomass production, such as agricultural, forestry and environmental laws but also waste and recycling regulation. For growing energy feedstocks, the same regulations apply as for biomass intended for other uses, with few exceptions (e.g. short rotation coppices) [27]. Second, instruments such as the EU-ETS and energy taxes increase the costs of fossil fuel-based energy technologies, thereby supporting bioenergy use indirectly. In the transport sector, energy tax exemptions were a strong driver of biofuel expansion until 2007, when the biofuel quota was introduced and tax exemptions were gradually phased out [62]. In the electricity sector, tax rates do not differentiate between energy sources; in the heating sector, tax rates on energy carriers are comparatively low, limiting the effectiveness of tax incentives for RES use [62]. As for the EU-ETS, prices of emission

3.1. Development of European bioenergy policy The context of member states’ bioenergy policies is heavily influenced by EU-level climate and energy policies. For the use of biofuels and other renewable transport fuels, the EC already defined targets in 2003 (see 2003/30/EC, Article 3). In 2005, the European Commission (EC) adopted a biomass action plan [25] which encouraged member states to develop their bioenergy potential. Given security of supply concerns, the role of biofuels as “the only direct substitute for oil in transport” (EC 2005) was particularly emphasised. For operationalizing European bioenergy policy, the Renewable Energy Directive (RED, 2009/28/EC) and the Fuel Quality Directive (FQD, 2009/30/EC) are particularly relevant. The RED has established a 20% target for the share of RES in final EU energy consumption by 2020, which is translated into binding national targets (e.g. 18% for Germany). In the transport sector, each member state has to achieve a 10% RES share by 2020. The 2009 revision of the FQD requires fuel suppliers to reduce the GHG intensity of road transport fuels by up to 10% per unit of energy supplied by 2020. To count towards RED or FQD, biofuels and bioliquids must comply with sustainability criteria and certification requirements defined in the RED. Table 2 Major instruments of German bioenergy policy (based on [31,p. 213]). Framework conditions for biomass production

Agricultural, forestry and environmental policies Rural development policy Waste and recycling policy Import tariffs on biofuels and agricultural commodities

Utilisation-sided instruments Heating sector

Electricity sector

Transport sector

Energy tax incentives for biomass

EU-ETS

Mandatory minimum RES shares in new buildings (EEWärmeG) Grants & loans (Market Incentive Programme)

Feed-in tariffs/premiums (EEG)

Energy tax incentives for biofuels (until 2015) EU-ETS for aviation

Priority grid access for RES (EEG) Sustainability standards for liquid biomass Priority access to the gas grid for biogas Support for R & D

4

Biofuels quota Sustainability standards for biofuels

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Fig. 1. Gross electricity generation from RES in Germany, 2000–2016 (own illustration, based on [61]. Note: geothermal power is not depicted due to small quantities).

administered FIT or FIP. Besides instrument types, revisions have affected reference prices and the differentiation between installations sizes, bioelectricity technologies and feedstocks [32]. Notably, the EEG 2004 introduced bonuses for renewable resource use, innovative technologies and cogeneration, which led to a rapid increase in energy crop-based biogas production [64]. To counter this development and curtail the costs of bioelectricity support, the EEG 2012 abolished most bonuses and generally decreased FIT/FIP rates [29,64]. This was followed by even more substantial remuneration cuts in the EEG 2014. For example, for new biogas or solid biomass plants with up to 150 kW installed electric capacity, reference prices according to the EEG 2012 were 14.3–22.3 ct/ kWh, depending on the feedstock used (see § 27 EEG 2012). This changed to 13.66 ct/kWh with the EEG 2014, independent of the feedstock (see § 44 EEG 2014). For larger installation sizes, lower remuneration rates apply. Meanwhile, remuneration for fermentation of bio-degradable waste and manure remained comparatively stable. The reform intended to focus further bioelectricity expansion more strongly on low cost wastes and residues [65], even though critics argued that new remuneration rates were insufficient to allow a viable exploitation of remaining waste and residue potentials [66]. Furthermore, the EEG 2014 introduced a quantity constraint to limit annual gross expansion of electric biomass capacity to 100 MW. If exceeded, reference price values for new plants undergo an accelerated decrease (to reflect technological progress, there is a planned decrease of 0.5% at the beginning of each quarter of a year, but if 100 MW are exceeded within the year preceding the 5th month before the beginning of a quarter, this decrease is raised to 1.27%, see § 28 EEG 2014). Overall, the reform significantly reduced incentives for investments in new bioelectricity plants: between August 2014, when the EEG 2014

allowances have been too low and volatile to make investments in dedicated biomass plants viable [63]. Rather, past bioenergy expansion in Germany (see Figs. 1–3) has been primarily driven by the third instrument category, technology- and sector-specific direct deployment support. The extent and characteristics of bioelectricity expansion have closely followed changes in the Renewable Energy Sources Act (ErneuerbareEnergien-Gesetz, EEG) which was introduced in 2000 and underwent major revisions in 2004, 2009, 2012, 2014 and 2017. Under the law, new dedicated bioelectricity plants with a maximum installed capacity of 20 MW are eligible for remuneration over a 20 year period. Originally, the EEG offered fixed feed-in tariffs (FIT) per kWh, which incentivised base load-oriented bioelectricity concepts maximising the amount of electricity produced. With the 2012 and 2014 revisions, the political focus shifted to demand-oriented plant concepts capable of balancing the fluctuating supply of intermittent RES. FIT were gradually replaced by sliding feed-in premiums (FIP), which cover the difference between set reference prices and average market values of RES feed-in. In the FIP scheme, plant operators have to directly market their electricity or commission intermediaries, whereas in the FIT scheme marketing is handled centrally by transmission system operators. Alongside the FIP, the EEG 2012 introduced a capacity-oriented flexibility premium for biogas plants to compensate for investments in additional storage and production capacities required for plant flexibilisation. The EEG 2017 changed from administratively set reference prices to a technology-specific tendering scheme. Here, both new projects as well as existing bioelectricity plants nearing the end of their guaranteed remuneration period compete for FIP payments. For 2017–2022, tendered quantities amount to 150–200 MW per year. Small-scale biomass plants (≤150 kW) are still eligible for

Fig. 2. Final energy consumption of heat from RES, 2003–2016 (own illustration, based on [61]).

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Fig. 3. Final energy consumption from RES in the transport sector, 2000–2016 (own illustration, based on [61]).

based share of biofuels from wastes and residuals reached 17% in 2014 (mainly due to the use of used cooking oil and fats) [72]. In 2017, a further relevant change took place: hydrogen and methane produced from renewable electricity of non-biogenic origin can now count towards the quota [74]. Moreover, an ordinance which would allow a share of the quota to be met by upstream emission reductions in fossil fuel production (i.e. emission reductions before fossil resources are processed to fuel) is currently being controversially debated [75]. Major developments in direct bioenergy deployment support instruments (beyond indirect instruments such as energy taxes and the EU ETS) are summarised in Table 3.

entered into force, and April 2017, newly registered bioelectricity plants contributed only 120 MW to the gross expansion of installed electric capacity [67]. Meanwhile, existing plants receiving remuneration according to older EEG versions added 343 MW of installed capacity, to be able to produce flexibly and qualify for the flexibility premium [67]. Perspectively, the Combined Heat and Power Law (Kraft-Wärme-Kopplungsgesetz, KWKG) will gain relevance for biomass plants. RES-based combined heat and power (CHP) systems have not been its focus so far, but the introduction of a tendering scheme for innovative CHP systems which includes requirements on renewable heat use is currently being prepared [68]. Bioheat use is primarily supported by the Renewable Energy Heating Act (Erneuerbare-Energien-Wärmegesetz, EEWärmeG) and the Market Incentive Programme. The EEWärmeG was adopted in 2009 and prescribes minimum RES shares to be met in new buildings (and fundamentally renovated public buildings). RES obligations vary depending on the RES technologies used, and can also be met by compensatory measures such as energy efficiency improvements or the use of cogenerated heat. However, as the instrument does not affect heating investments in the building stock, its scope of influence is limited [69]. Renewable heating investments which do not count towards the renewables obligation are supported by grants and low interest loans under the Market Incentive Programme [70]. Depending on price developments of fossil fuel energy carriers, wood-based bio-heat installations can at times already be cost-competitive to fossil fuel-based alternatives [71,p. 941ff.] . However, in recent years, low heating oil and gas prices have dampened market incentives for small bio-heat installations in particular [70]. Moreover, demand for installations as well as actual heat production (see Fig. 2) is contingent on trends in weather conditions [70]. For biofuel use, the biofuel quota which is anchored in § 37a of the Federal Immission Control Act (Bundes-Immissionsschutzgesetz, BImSchG) constitutes the primary biofuel support instrument. From 2007 to 2014, the quota required fuel suppliers to provide an increasing share of biofuels in the energy content of diesel and petrol fuels brought into circulation. From 2015, fuel suppliers have to ensure that biofuel use achieves a net reduction in the GHG emissions of transport fuels, with targets increasing up to 6% in 2020. In both cases, liquid and gaseous biofuels have to comply with minimum sustainability requirements as defined in the Biofuel Sustainability Ordinance (BiokraftNachV). The change to a GHG-based quota was adopted in 2009; at the same time, a reduction in 2009’s quota target was implemented [72]. Adjustments of GHG-based quota requirements followed in 2015. Between 2007 and 2014, the biofuel quota has been exceeded each year [73], but in absolute numbers, biofuel use has been stagnating since its introduction, and even decreased in 2015 (see Fig. 3). So far, agricultural crop-based biofuels dominate, although the energy content-

4. Lessons from German and European bioenergy policy: results and discussion In the following section, we evaluate the German bioenergy policy mix along the dimensions proposed in Section 2 and discuss what lessons can be learned from it. 4.1. Defining a system of policy aims The lack of a consistent and complete system of policy aims has strongly shaped the development of German and European bioenergy policy. The EC’s biomass action plan stressed bioenergy’s positive and supposedly synergistic contributions to the aims GHG mitigation, security of supply and rural employment and value creation, while leaving their prioritisation unclear [25]. This neglected the potential for adverse side effects on other societal aims as well as conflicts among these three aims – this is particularly problematic as different priorities imply different bioenergy support strategies [30,76]. For example, a focus on rural value creation favours an expansion of energy crop use, whereas under GHG mitigation aspects waste and residuals-based pathways perform particularly well. During bioenergy policy implementation, conflicts and side effects became apparent, leading to a broadening of the system of policy aims discussed in relation with bioenergy [24] (see Fig. 4). However, the prioritisation of aims still needs to be inferred from actual policy implementation, and remains subject to change. This is particularly apparent in the treatment of energy crops. In EU biofuel policy, the expansion of agricultural crop-based first generation biofuels was consistent with an initial policy focus on security of supply and rural value creation aims [77]. However, the debate on land use changes’ adverse impacts on GHG balances, nature conservation aims and social sustainability aspects has shifted the policy focus towards advanced biofuels and other low carbon transport options. In Germany, the change to a GHG-based biofuel quota reflects an increasing emphasis on biofuels’ contributions to GHG mitigation. In German 6

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Table 3 Major developments in German bioenergy deployment support (own compilation, based on successive versions of EEG, EEWärmeG, BImSchG and KWKG). Bioelectricity deployment support 2000 2004 2009 2012 2014

2017

EEG introduced: fixed FIT, differentiated by installation size. EEG 2004: bonuses for renewable resource and slurry use, innovative technologies and cogeneration. EEG 2009: continues bonuses, additional cogeneration eligibility requirement for plants > 5 MW. EEG 2012: abolishes bonuses except for processing of biogas to biomethane; introduces differentiation of remuneration by substrate class. Minimum shares for cogeneration or slurry use; cap on share of maize and cereal grain kernels. Introduces optional FIP and flexibility premium. EEG 2014: direct marketing and FIP obligatory for all new plants > 500 kW from 2015 and > 100 kW from 2016. Abolishes differentiation by substrate class, gas processing bonus, cogeneration and slurry minimum shares and maize cap. 100 MW cap on annual gross capacity expansion introduced. To ensure that plants are capable of producing flexibly, remuneration is limited to the annual electricity production corresponding to a power rating of 50% of the electric capacity installed (for plants > 100 kW). EEG 2017: change from administered to tendered FIP; maize cap reintroduced. KWKG: introduction of tendering scheme for innovative CHP systems in preparation.

Bioheat deployment support 1999 2009

Market Incentive Programme introduced. EEWärmeG introduced: if biogas is used, it has to cover at least 30% of heat and cooling consumption, for solid or liquid biomass this value is 50%. Market Incentive Programme continues for RES investments not counting towards RES minimum shares.

Biofuel deployment support 2007–2015 2007 2009 2015 2017

Gradual phase-out of energy tax exemptions for biofuels. Biofuel quota introduced: Minimum biofuel share in the energy content of transport fuels brought into circulation 6.25% in 2009 increasing to 8% from 2015 onwards, with separate quotas for share in diesel and petrol fuels starting in 2007. Quota target changed to 5.25% in 2009 and 6.25% 2010–2014. Change to GHG-based quota from 2015 onwards. Targets for reduction in GHG emissions of transport fuels: 3% from 2015, 4.5% from 2017, 7% from 2020. Adjustment of GHG-based quota target: 3.5% GHG emissions reduction from 2015, 4% from 2017, 6% from 2020. GHG-based quota is opened for renewable electricity-based hydrogen and methane; negotiations on inclusion of upstream emission reductions and recast RED are ongoing.

As discussed in Section 2.1, leaving the prioritisation of policy aims unclear can be a valid political strategy to maximise political support. However, the case of bioenergy policy shows the problems of such an approach when it comes to supporting the diffusion of low carbon technologies. In effect, changes in biofuel and bioelectricity policies which followed changing priorities between aims have led to significant uncertainty on the side of market actors. Besides potential negative impacts on the future willingness to invest in response to bioenergy

bioelectricity policy, high remuneration rates for energy crop-based biogas plants in 2004 and 2009 versions of the EEG also reflect a strong influence of the rural value creation aim. However, the EEG 2014 and 2017 reforms in particular were shaped strongly by a debate on the cost-effectiveness of RES supply [65,78]. In consecutive reforms, efforts were also made to ensure larger GHG mitigation contributions, e.g. through minimum heat use requirements in the EEG 2012 and attempts to incentivise the use of wastes and residuals.

Fig. 4. Major policy fields and aims impacted by bioenergy use (based on [31,p. 195]).

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infrastructures. Security of supply contributions are limited in size by sustainable biomass availability, but could be relevant for applications with a high reliance on energy-dense and easily storable fuels such as aviation [84]. Taking knowledge and learning externalities into account, a rationale can be derived for incentivising the diffusion of advanced biofuels combining high GHG mitigation and innovation potentials with low environmental impacts, despite comparatively high current costs [72,85]. For first generation biofuels with lower innovation potential, allowing a greater degree of competition with other GHG mitigation options would be preferable. Opening the biofuels quota for renewable electricity-based fuels can be viewed positively in this light, but it remains to be seen whether the GHG-based quota succeeds in incentivising technologies with high innovation potentials but comparatively high current costs. Also, coordination with incentives for electric vehicles and absolute reductions in transport energy consumption remains an issue. In the heating sector, building characteristics influence strongly what GHG mitigation options (e.g. different RES technologies or energy efficiency improvements) are feasible. Moreover, the asset specificity of bio-heat investments is low, given their proximity to commercial competitiveness. This makes comparatively technology-open instruments advantageous. By providing scope for decentralised choices, EEWärmeG and Market Incentive Programme perform well in this regard. Extending incentives to the building stock, however, remains challenging [69]. Overall, these considerations show that once interactions between market failures are taken into account, the German bioenergy policy mix performs better than a neoclassical economicsbased critique focussing on the internalisation of GHG externalities alone leads to expect. However, particularly in the transport sector, scope remains for improving the alignment of aims and measures. Again, credibility and long-term reliability of measures depend on the rationale for deployment support being made transparent and remaining stable, which has not been the case with German biofuel and bioelectricity policies. At least in the electricity sector, a credible transition perspective exists, backed by long-term RES targets in the EEG (by 2050, RES shall cover at least 80% of gross electricity consumption), a strong advocacy coalition [86] and transition-oriented infrastructure and market design adjustments [83]. In the heating and transport sectors, a comprehensive transition dynamic has yet to enfold [69,87], and sectoral targets exist only for 2020 (for the heating sector, a 14% RES-share in final energy consumption is envisioned). Meanwhile, experiences with the RED’s largely biofuels-based RES target for the transport sector show the importance of defining targets sufficiently technology open, to ensure their credibility. If targets’ achievement depends on the success of individual technologies, adjustments are required if the assessment of that technology changes. Also, narrowly formulated targets constrain search processes for technological alternatives. For meeting RES targets in the electricity sector, a broad range of feasible options exist, but RES targets in the transport and heating sector rely strongly on biomass-based options so far. Here, sectoral GHG mitigation targets would provide greater flexibility, because energy efficiency improvements and energy savings would be incentivised as well. At the same time, planning security for investors in decarbonisation options would still be higher than with undifferentiated national GHG mitigation targets, by strengthening sectoral transition perspectives.

policy incentives, the unclear and unstable hierarchy between aims has resulted in a lack of guidance as to the direction of investments in R & D and technology diffusion. For a stable policy environment, acknowledging trade-offs between policy aims early on and establishing a clear hierarchy emerges as an important prerequisite. For bioenergy policy, a prioritisation of GHG mitigation can be recommended, given the urgency of limiting climate change and the heterogeneous performance of bioenergy pathways with regard to this aim [27,30]. Moreover, such a focus does not preclude positive contributions to further aims, whereas a focus e.g. on security of supply does not necessarily guarantee GHG mitigation contributions [27]. To build political majorities for decarbonisation policy mixes, emphasising and actively encouraging cobenefits of GHG mitigation activities might therefore be a more promising approach than keeping the hierarchy between GHG mitigation and potentially conflicting aims deliberately ambiguous. 4.2. Alignment of aims and measures The lack of prioritisation between policy aims makes it difficult to assess either the individual measures’ effectiveness or the consistency of the policy mix. In particular, the lack of a common alignment of the German bioenergy policy mix with GHG mitigation potentials and costs has frequently been criticised [27,29,79,80]. Indeed, policy advice based on neoclassical economics suggests rephrasing bioenergy policy in the context of a cost-effective GHG mitigation strategy – this would entail the phase-out of technology-specific deployment support for bioenergy options [81], or even for RES in general [82]. Instead, bioenergy pathways would compete with other GHG mitigation options in a reformed emissions trading system, potentially in combination with carbon taxes for non-EU-ETS sectors [79,80]. However, in assessing the alignment of aims and measures, interactions between market failures have to be taken into account. The diffusion of low carbon innovations is not only impaired by GHG mitigation externalities, but also by knowledge and learning externalities associated with investments in innovative technologies and path dependencies acting in favour of incumbent technologies (see Section 1). At the same time, different GHG mitigation options perform differently with regard to security of supply externalities (e.g. intermittent vs. dispatchable RES), or other environmental externalities beyond climate impacts [51]. This implies that aligning policy instruments only with a static minimisation of GHG mitigation costs across sectors is not always appropriate. Rather, from a second-best perspective, it can be more efficient to adopt a mix of instruments which ameliorate other unresolved market failures or avoid worsening others, even if this increases GHG mitigation costs compared to a first-best approach where only the most cost-effective options are implemented [4–6]. This can provide a rationale for combining instruments like emission trading systems and taxes with sector- and technology-specific targets and deployment support [50,51]. Also, by addressing several aims jointly (including distributive ones, such as rural value creation), the political feasibility of decarbonisation measures may be improved. In the bioenergy case, different interacting market failures prove relevant in electricity, transport and heating sectors. In the electricity sector, GHG externalities interact with security of supply externalities and path dependencies. Currently, the German electricity system is characterised by conventional overcapacities, limiting market incentives for investments in low carbon flexibility options [83]. This can provide a rationale for incentivising the continued technology development of flexible bioelectricity plants, to reflect their option value for security of supply provision in a future electricity system based predominantly on intermittent RES. Following this rationale, deployment support should be aligned with flexibility provision, besides GHG mitigation; since the EEG 2012, increasing efforts have been undertaken in this direction. In the transport sector, path dependencies are less relevant, because biofuels are compatible with established mineral oil-based

4.3. Choice and design of policy instruments In discussing instrument choice and design, we focus on biofuel and bioelectricity deployment support – given high asset specificity of dedicated bioelectricity plants and biofuel refineries, finding a balance between policy stability and flexibility is particularly challenging for these cases. Investments in bioheat installations which are closer to commercial competitiveness are characterised by lower asset specificity, making a greater degree of technology openness preferable. 8

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4.3.2. Technology differentiation: impacts on search processes for costeffective and sustainable solutions With its eligibility requirements and differentiation of remuneration rates by installation sizes, technologies and feedstocks, the EEG represents a fairly hierarchical approach to steering technology choices. The expansion of energy crop-based biogas plants which followed the introduction of the renewable resource bonus in 2004 [64] is a striking example of how closely market actors align technology and feedstock choices with policy incentives. Moreover, as higher remuneration rates apply for smaller plants, plant sizing decisions are strongly influenced by size categories, rather than cost-effectiveness concerns which would favour a more pronounced use of economies of scale. In principle, various elements of technology differentiation represent the influence of various policy aims. For example, besides rural value creation, support for small-scale plants can be associated with the aim of promoting actor diversity in the energy sector, to support the social acceptability of the energy transition. To improve the environmental balance of bioelectricity production, different bonuses and eligibility requirements have been used over time – examples are the EEG 2009’s bonus for slurry use in biogas plants, which the EEG 2012 replaced with minimum slurry shares or heat use requirements as prerequisites for remuneration. The problem with this hierarchical approach is that information requirements on policy makers are high. If policy incentives do not result in cost-effective and sustainable technology choices, society bears the costs of steering errors. Following an increasing emphasis on costeffectiveness (see Section 4.1), the degree of technology differentiation has been decreased in 2012, 2014 and 2017 revisions of the EEG (see Section 3.2). Notably, the EEG 2017’s tendering scheme allows for competition between different installation sizes. Ensuring sustainability incentives remains challenging, however, as exemplified by the EEG 2017’s reintroduction of the EEG 2012’s cap on the permissible maize and cereal crop share, which had not featured in the EEG 2014. Sustainability certification represents an alternative option for technology differentiation. Here, policy makers define minimum criteria (e.g. with regard to GHG emission reductions) and safeguards (e.g. by excluding certain types of land for biomass production), but market actors decide how to meet these criteria at lowest costs. However, transaction costs of implementing a certification scheme are higher than with a hierarchical approach, particularly for small-scale producers. Moreover, bioelectricity value chains in Germany are so far predominantly regional [32], so that uncertainty about production conditions is lower than with transnational biofuel value chains. In combination with sustainability certification, the GHG-based biofuel quota adopts a more market-oriented technology differentiation mechanism, leaving a broader scope for search processes at least within the technology group of biofuels. However, as market actors search for the most cost-effective compliance options, it is not surprising that so far the quota is mainly fulfilled by agricultural crop-based first generation biofuels, rather than more costly advanced ones [72,90]. The EU cap on agricultural crop-based biofuels can be understood as a response to this development. These examples show that for fostering co-benefits and reducing sustainability risks of low carbon technologies, eligibility requirements can prove advantageous if (i) key influence factors for desired outcomes can be easily identified, (ii) transaction costs of a certification system are high and (iii) policy makers have control over relevant environmental and social framework regulations. Sustainability certification shows its strengths when (i) transnational value chains play an important role and (ii) there is greater uncertainty about how best to provide a certain outcome (such as a certain GHG emission reduction) at least costs. Nevertheless, sustainability certification is of limited effectiveness when it comes to dealing with indirect land use changes [53], and this needs to be taken into account when implementing deployment support instruments with large potential impacts on transnational biomass flows.

4.3.1. Control over social costs of errors in price and quantity instruments The development of EEG and biofuel quota demonstrate the problems that both price and quantity instruments face when there is uncertainty about private and external costs and benefits of bioenergy pathways. With administered FIT and FIP, setting reference prices under asymmetric information is challenging, and subject to political negotiation and lobbying [88]. Particularly for biogas, remuneration rate adjustments in EEG revisions have led to a very volatile expansion dynamic. For example, following an increase in FIT rates in the EEG 2009 revision, the annual growth in installed biogas capacity jumped from 203 MW in 2008 to 1,103 MW in 2009 [61]. As a price instrument aligned with technology-specific electricity generation costs, the FIT/ FIP scheme offers high control over the private costs of bioelectricity options adopted. However, as the expansion level resulting from price incentives is uncertain, control over total support costs is limited. The same is true for external costs, which can be assumed to increase with the level of expansion as resource competition intensifies (e.g. as a result of agricultural intensification). In the EEG 2017’s tendering scheme, competing market actors reveal information about what FIP is required to fulfil certain targets (although strategic bidding behaviour is possible [89]). This shifts the information problem to setting bioelectricity expansion targets, which become the new subject of political negotiation processes. If set too low, benefits of bioelectricity production are foregone, and low demand for new plants might stall further technology development. If too high, expensive bids may be successful, resulting in higher than expected support costs. In effect, both EEG 2014 and EEG 2017 attempt to increase control over error costs by using hybrid elements: the EEG 2014’s quantity constraint makes remuneration dependent on expansion levels, while the tendering scheme encompasses price caps to limit permissible bids. Again, information problems apply to configuring these hybrid elements. If price caps are set too low, quantity targets will be missed. The quantity constraint, meanwhile, allows for some expansion after the accelerated decrease in remuneration rates is triggered, until remuneration rates equal producers’ actual marginal costs. With the biofuel quota as a quantity instrument, it is also not necessary to centrally assess marginal costs of biofuel provision, because market actors will choose the most cost-effective options to fulfil quota obligations. This can still result in high private costs of target achievement, and a high willingness to pay for biomass which impacts competition with alternative resource and land uses. As penalties for missing quotas act as buyout prices, a hybrid element is once more used to guard against higher than expected implementation costs. Nonetheless, the problem of setting targets and penalties also applies to biofuel quota design. If too low, diffusion-related learning curve effects and economies of scale will not set in, as investments in biofuel refineries would not be viable. If too high, implementation costs and distortionary impacts may be large. Trade-offs are reflected in quota target adjustments undertaken in 2009 and 2015, but in combination with the change to a GHG-based quota this contributed to policy uncertainty. Meanwhile, a minimisation of private costs does not imply that external costs are minimised. The change to the GHG-based quota brought an important improvement in this respect, because unlike its energy content-based precursor it directly incentivises the minimisation of biofuels’ GHG mitigation costs. This has contributed to a significant increase in average GHG emission reductions of biofuels used to fulfil quota obligations [72]. Overall, lessons from bioenergy policy indicate that hybrid elements can play an important role in controlling cost of errors in both quantity and price instruments. However, their design can significantly impact the effectiveness of instruments and is also subject to information constraints, enhancing the importance of adequate adjustment mechanisms. Here, the impact of rule-based adjustment approaches on planning security merits further investigation, with the EEG 2014’s quantity constraint which ties remuneration rate adjustments to expansion dynamics as an example.

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security, secure and sustainable resource supply, nature conservation) and sectors involved (e.g. agriculture, forestry and marine industries, energy, food, wood processing, chemicals, pharmaceuticals, biotechnology) [91]. Accordingly, there is much greater heterogeneity of biomass utilisation pathways and associated private and external costs and benefits, and many uncertainties remain concerning the comparative assessment of alternatives, future technology cost developments and sustainability impacts. With regard to the system of policy aims, the need for a clear and credible prioritisation of aims proves an important insight also for bioeconomy policy. So far even the interpretation of the bioeconomy as a growth and industrial policy concept or a sustainability transition concept is unclear [92,93]. This entails a problematic lack of guidance for investment strategies. While bioenergy policy focusses on the energy sector, the bioeconomy encompasses policy mixes applying to diverse material and energy sectors (even though so far, energetic uses clearly dominate as far as innovative biomass applications are concerned [94]). Given the different challenges that different sectors face, defining a single aim as a priority aim will neither be possible nor appropriate – this reinforces the importance of making at least the embeddedness of the bioeconomy concept in an overarching sustainability transitioncontext clear. Clarifying priority aims that the bioeconomy should contribute to in different sectoral applications can further help in guiding search processes, as well as credibly committing to binding, overarching sustainability safeguards (e.g. no negative impacts on food security or critical natural capital). In particular, limited additional biomass availability suggests placing a clear focus on innovative applications with high potential contributions towards sustainable development and cascading use concepts [95]. The diverse nature of the bioeconomy policy field also affects the operationalization of aims through targets, which need to be aligned with sectoral priorities. Lessons from bioenergy policy indicate that defining sectoral targets for biomass use is not promising. In Germany, the simultaneous expansion of bioenergy use in electricity, heating and transport sectors was sufficient to trigger a critical debate about resource competition and sustainability impacts. If multiple sectoral biotargets were defined (e.g. for bio-based chemicals, or building materials), coordination requirements and distortionary impacts on competition for biomass resources would be significant. Moreover, the relevance of direct and indirect LUC impacts of an increasing biomass demand shows the importance of incentivising also the use of innovative non-bio-based renewable resources, resource efficiency improvements and absolute reductions in resource demand. This argues for a more technology-open definition of targets. As to the alignment between aims and measures, the example of bioenergy policy shows that developing policy mixes for bioeconomy applications requires a differentiated analysis of what market failures inhibit the use of bio- and non-bio-based renewable resources, and how they interact within and across sectors. For policy design, a further relevant question is how efficiency-oriented aims interact with distributive aims, and whether instrument mixes which address them in combination can make the promotion of a sustainability transition more feasible. As a second step, it is necessary to analyse where it is feasible to strengthen or introduce technology-neutral instruments in support of bioeconomy applications. Examples are carbon taxes for non-EU-ETS energetic but also material applications, or reforms of waste and recycling regulations. Lastly, it needs to be discussed where interactions between market failures and political feasibility constraints make more technology-specific approaches necessary. However, the learning process that bioelectricity policy has gone through shows the problems involved with implementing a detailed degree of technology differentiation, with high information requirements on policy makers. If bioeconomy applications were supported by technology- and/or feedstock-specific price or quantity instruments (e.g. price incentives or quotas for bioplastics) with a high impact on market and technology development, there would be the risk of steering

4.3.3. Policy adjustment: balance between adaptive efficiency and planning security Biofuel quota and bioelectricity support differ in their balance of adaptive efficiency and planning security. With the EEG, plants are entitled to remuneration according to the version of the law they became operational under until the end of their remuneration period. Consequently, policy makers bear uncertainty about changes in technology assessment: even though the focus on bioelectricity support has shifted to waste and residuals-based pathways and flexible plant concepts, implementing such changes remains voluntary for existing plants or has to be incentivised by positive incentives (e.g. participation in balancing markets with additional income opportunities is possible only for plants in the FIP scheme, not the FIT scheme). This provides a high planning security for bioelectricity plants, once operational. However, it also results in low adaptive efficiency, due to a low reversibility of policy impacts and low incentives to implement environmental improvements or cost reductions for existing plants as long as profits are satisfactory. Furthermore, to limit the impact of steering errors, frequent revisions of the law are required, increasing policy uncertainty for project and technology developers. With the biofuel quota, market actors bear higher price and volume risks, as remuneration is determined on markets in dependence of annual quota targets. This results in continuing incentives for cost reductions, to be able to compete. Moreover, it is up to market actors to proof compliance with sustainability criteria and meet GHG emission reduction requirements which increase in stringency over time. Unlike with the EEG, policy changes (e.g. with regard to quota targets) have a direct impact on operating biofuel refineries. Longer term prospects are also affected by uncertainty about biofuel policy design beyond 2020, and the further development of sustainability requirements and GHG accounting methodology. Overall, the ability to make actors adapt to policy adjustments results in a higher adaptive efficiency, but at the cost of lower planning security. That said, investments in biofuel refineries are somewhat less asset specific than is the case for dedicated bioelectricity plants, as biofuels are also exported to other countries with support policies. Conversely, the quota can also be fulfilled by imports, so that policy uncertainty is more likely to stall investments in new biofuel refinery capacity than result in quotas being missed (this seems to be confirmed by the development of domestic biodiesel and bioethanol capacities which have decreased between 2010 and 2015 [72]). The bioelectricity and biofuel policy cases show that a careful analysis is required of how much planning security has to be offered to existing plants for investments to take place, taking asset specificity into account. If an adjustment mechanism with low adaptive efficiency and high planning security for existing installations is chosen even if uncertainty about a technology’s impacts are high, then a gradual approach to policy implementation gains in importance, to limit the social cost of errors and allow for a timely adjustment of incentives for new installations. This should be accompanied by adequate monitoring mechanisms and a transparent communication of policy evaluation criteria, to reduce uncertainty for developers of new projects. 5. Implications of bioenergy policy lessons for bioeconomy policy As an example for a decarbonisation policy mix under conditions of high uncertainty and complexity, the following section discusses what implications bioenergy policy has to offer for the comparatively new policy field of bioeconomy policy. The bioeconomy concept widens the scope from energetic to material biomass uses; the German Bioeconomy Council defines it as “the knowledge-based production and utilization of biological resources to provide products, processes and services in all sectors of trade and industry within the framework of a sustainable economic system”. Compared to bioenergy use, it is characterised by an even wider range of relevant policy aims (e.g. economic growth and employment, health, climate change mitigation, food and water 10

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for search processes for cost-effective and sustainable solutions, and the balance between adaptive efficiency and planning security. Applied to the case of German and European bioenergy policy, this approach highlights the importance of establishing credible commitment at the level of political priorities, as a prerequisite for creating a stable policy environment and consistent policy mixes. For example, a credible prioritisation of GHG mitigation in bioenergy policy would ensure that incentives for long-term search processes remained stable, even if the assessment of individual technologies changed in the light of new information and instruments were adjusted accordingly. However, such a prioritisation has long been absent, and has also not yet emerged in the case of the bioeconomy concept where conflicts between associated aims need to be addressed much more explicitly. What instruments to adopt as part of a decarbonisation policy mix must follow from an assessment of which market failures inhibit specific low carbon technologies. Also, technologies’ asset specificity and resulting needs for investment safeguards should be taken into account. In the bioenergy context, we identified two cases with theoretical rationales for combining technology-neutral instruments such as EU-ETS and carbon taxes with technology-specific interventions on behalf of biomass-based pathways: flexible bioelectricity plants with security of supply benefits in an electricity system based on intermittent RES, and advanced biofuels with high innovation and GHG mitigation potentials. What instrument type, hybrid elements and degree of technology differentiation are appropriate is again highly dependent on the specific policy context. When it comes to generating new and potentially significant demands for biomass with uncertain impacts on land use and resource competition, case study experiences warn against overestimating centrally available knowledge when trying to steer innovation processes. This emphasises the importance of effective mechanisms for limiting costs of errors and implementing policy adjustments. Also, when designing technology-specific deployment support, potential distortionary impacts on resource competition have to be taken into account. In the bioeconomy context, for example, manifold heterogeneous applications compete for limited biomass supplies. As a result, the potential for policy-induced distortions is high if price or quantity instruments are used to promote selected bioeconomy technologies on a significant scale. Under such conditions, it is worthwhile to explore if combining technology-specific niche support on a smaller scale and technology-neutral instruments which allow for a greater openness of search processes can be a feasible and effective option for creating demand-pull for innovative applications.

technology developments in directions which might turn out to be economically or environmentally unsustainable or socially not accepted. The development of German biofuel and bioelectricity support instruments demonstrates the challenges of controlling social costs of errors in price and quantity instruments, even if hybrid elements are introduced. In effect, instruments have gone through a succession of trial-and-error adjustments, with lasting negative impacts on planning security. For technology-specific bioeconomy support instruments, it therefore seems more beneficial to focus first of all on niches to generate learning, rather than introducing broad deployment support which creates a large-scale demand for specific biomass-based pathways with uncertain consequences. The diffusion of bioeconomy technologies requiring large and comparatively asset specific investments, such as biorefineries, may prove more challenging in the absence of technology-specific price or quantity instruments [96]. However, given the impacts of bioenergy policy adjustments on policy uncertainty, ensuring the credibility of such instruments would also be challenging. Offering strong investments safeguards, as those implemented in the EEG, would be an option, but these would negatively affect adaptive efficiency. Against this background, combining technology-push instruments (e.g. R & D support, investment support for demonstration projects, public-private partnerships) with focussed niche support and technology-neutral instruments could be more promising (see [97] for a more detailed discussion). Exemplary niche support instruments are public procurement, accompanied by support for networks to promote knowledge and learning diffusion, as well as information and moral suasion instruments promoting niche market development (e.g. voluntary or mandatory labelling) [2,3,40]. Importantly, to support economies of scale and diffusion-based learning, effective technology-neutral instruments which exert a demand-pull for bio-based and non-bio-based technologies alike would need to be in place. Besides carbon pricing and waste and recycling regulation which increase the costs of fossil resourcebased processes and products [97], regulatory requirements with regard to life cycle emissions could be an option, e. g. in the context of “green building” policies [98,99]. That said, policies which impose direct costs on producers and consumers tend to have it more difficult to find political majorities than support policies which offer positive incentives and distribute costs over the wider public [36]. To differentiate between bioeconomy pathways according to environmental characteristics, combining demand-pull measures with sustainability certification requirements may prove advisable, particularly if resource supply is based on transnational value chains. But also domestically, broadening demands on biomass provision increase the importance of further developing biomass production framework conditions, such as environmental and agricultural policies, to increase the stringency of sustainability safeguards.

Acknowledgements This research received general funding from the Helmholtz Association of German Research Centres. We would like to thank two anonymous reviewers for valuable comments.

6. Conclusions References To generate realistic policy advice, the relevance of conflicting policy aims, interacting market failures and uncertainty about policy impacts has to be taken into account when assessing decarbonisation policy mixes. Focussing on first-best solutions for individual market failures, such as an emissions trading system whose design conforms to theoretical ideals, would be misleading, but the “muddling through” approach of actual policy making is also problematic. This is demonstrated by the case of German and European bioenergy policy, where the combination of ambitious biofuel and bioelectricity support policies with frequent and substantial policy adjustments has resulted in significant policy uncertainty. Under such conditions, assessing whether policy mixes handle interacting market failures, uncertainty and other constraints in a rational manner while striving for efficient and sustainable outcomes can be a promising approach. Examples for relevant dimensions of such an assessment are the consistency and credibility of aims and policy measures, control over social costs of errors, incentives

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