Sustainability in shipping
Literature and research on GHG-reduction alternatives in shipping reflect that towards 2050 oil-based fuels will constitute 47% of energy for shipping, with a respective proliferation of gas-based fuels to 32% (DNV GL 2017, pp. 3,10). The remaining 21% are to be provided by carbon-neutral energy sources, such as biofuel and electricity (DNV GL 2017, p. 10). In order to achieve this fossil-fuel transition, environmental innovation is required, which De Marchi refers to as the development of “new or modified processes, techniques, practices, systems and products to avoid or reduce environmental harms” (De Marchi 2011, p. 2). In a study by De Marchi, it is further defined that environmental innovation is primarily triggered by regulations and externalities, resulting in an increased importance of R&D cooperation with external partners (De Marchi 2011, p. 1). Moreover, most empirical analyses have assumed an uptake of a broad spectrum of decarbonization alternatives with no “one size fits all” solution evolving over time (IMO 2018, p. 4; DNV GL 2017, p. 29; Joules n.d.-a, p. 3).
According to the IMO, short-term and medium-term objectives primarily refer to a wide-ranging knowledge building process, the implementation of operational efficiencies and the development of rules and regulations (IMO 2019, pp. 3, 5–6; IMO 2018, pp. 7–9). Therefore, the implementation of break-through CO2 reduction solutions is found in the IMO long-term perspective (Erdmann 2019; IMO 2019, p. 2; IMO 2018, p. 9). In parallel, the growing importance of market-based measures is increasingly covered in literature as the IMO Environmental Committee indicated that “technical and operational measures would not be sufficient to satisfactorily reduce the amount of GHG emissions from international shipping in view of the growth projections of world trade” (Erdmann 2019; Kristiansen 2019; IMO n.d.). Respectively, a market sentiment jointly recommends the implementation of a “fuel levy” in order to source an R&D fund for commercially viable decarbonization solutions in technological-engineering and energy-based focused fields of study (Kristiansen 2019; IMO n.d.). Apart from such measures, the course of action to achieve the IMO GHG reduction target can be generally subdivided into the three categories of technological-engineering measures, energy-based measures and operational measures (Erdmann 2019; von Berlepsch 2019; Hapag-Lloyd 2019f, p. 59; Hapag-Lloyd 2016).
Technological-engineering measures
DNV GL estimates the fuel consumption per vessel to decline by 18% due to hull and machinery energy efficiency measures (DNV GL 2017, p. 53, 55). Such measures include the limitation of a vessel’s shaft power, the installation of waste-heat recovery systems or retrofits to optimize primary energy converters (Adamopoulos 2019a; Joules n.d.-a, p. 8). In addition, researchers consider fuel cells, while the solution horizon towards 2050 is likely to be even further extended and differences in the way how ships are bunkered and how multi-fuel compatible engines can be designed have to be evaluated on a holistic scale (Becker 2019; Brünggen 2019; Nagel 2019; Smith 2019; Hapag-Lloyd 2019d, p.1; MAN Energy Solutions n.d.-b; Ship Technology Global n.d.).
Energy-based measures
IMO reinforces the need for liquid fuels for long-range shipping, with energy-density per ton being the primary criterion in technological research for evaluating, whether a fuel alternative should be specifically considered towards 2050 (Dörner 2019; Timmerberg 2019; IMO 2019, p. 5). To outline the respective energy-based CO2 reduction alternatives, the IMO further differentiates between fossil-free fuels (synthetic energy carriers produced from non-fossil renewable energy sources), zero-carbon fuels (“energy carrier that does not release any CO2 when being used in internal combustion engines”) and low carbon fuels (IMO 2019, p. 2). Literature and research on fossil-free fuels emphasize the uptake of liquid fuels generated out of renewable energies and CO2 (Expert Conference of the Association of German Engineers 2019; IMO 2019, p. 2; Deutscher Bundestag 2018, pp. 4–6). These fuel alternatives are titled “PtX” (Power-to-Anything), including both PtL (Power-to-Liquid) and PtG (Power-to-Gas). Shell further refers to the high energy density and convenient storability of PtX fuels and states the potential to use existing infrastructure (Expert Conference of the Association of German Engineers 2019; Warnecke 2019, p. 21). Adding to these studies, recent research projects considering fossil-free fuels refer furthermore to challenges in the energy extraction and fuel transport. This underlines the requirement for a life-cycle assessment of the whole well-to-propeller value chain (Erdmann 2019; Smith 2019; Timmerberg 2019; International Maritime Organization 2019, p. 2; Joules n.d.-b, p. 10; Ship Technology Global n.d.). In contrast to above outlined fossil-free alternatives, a different point of view is taken by researchers evaluating the direct onboard use of electricity, which, based on DNV GL, will cover 1/30 of global energy demand for shipping towards 2050, next to further marginal measures (Timmerberg 2019; IMO 2019, p. 2; DNV GL 2017, p. 53; Joules n.d.-a, p. 9). Nevertheless, on an international liner scale, most analyses assume the applicability of electricity only as a hybrid or auxiliary propulsion (Timmerberg 2019; Joules n.d.-a, pp. 9, 12, 20; Joules n.d.-b, p. 26). Additionally, LNG is considered as an energy-based alternative on an interim level, as it will account for 32% of total energy use in shipping by 2050 (Adamopoulos 2019b; DNV GL 2017, p. 53; Technological and Environmental Forum LNG 2019). Reflecting solely the vessels’ combustion phase, LNG can reduce GHG emissions by 28% for two-stroke engines, which is argued to be insufficient for the depicted IMO decarbonization goals (Adamopoulos 2019b). However, recent studies insinuate to not empirically illuminate the role of potential CO2 reductions in the production chain of LNG (Grötsch 2019; Timmerberg 2019).
Operational measures
Following these studies, further empirical efforts have been carried out concerning operational GHG-reduction measures as precursor to the outlined decarbonization objectives (Adamopoulos 2019a). In this context, studies by DNV GL estimate such measures to reduce fuel consumption per ton-mile by 35–40% until 2050 (DNV GL 2017, p. 10). In line with this, DNV GL makes the hypothesis of vessel speed declining by 5% towards 2050, resulting in bunker consumption reductions of 10% (Becker 2019; Guntermann 2019; Smith 2019; DNV GL 2017, p. 53). In addition, research conducted by Fraunhofer Institute proposes environmental data analysis i.a. for weather routing as further operational setscrew (Fraunhofer n.d. (b)).
Taking all outlined GHG-reduction solutions into account, shipping companies need to evaluate how to combine and implement the depicted alternatives. In this regard, it is reflected in econometric research studies that environmentally innovative firms collaborate on technology development with external partners more frequently than other innovative companies (De Marchi 2011, p. 1). The underlying principles for such inter-company cooperation models have been broadly analysed in economic literature and key findings are outlined in the following paragraphs.
Inter-company cooperation
Kermani et al. define inter-company cooperation as a “collaboration of different parts of a cooperative process in order to meet a common purpose or seize an opportunity in the market” (Kermani et al. n.d., p. 1). Fett & Spiering propose that these “parts of a cooperative process” can be further subdivided into a horizontal or vertical scale (Fett and Spiering 2015, p. 24). Badillo and Morena define horizontal partnerships as “cooperation agreements with competitors or enterprises of the same sector”, while referring to vertical cooperation models as “cooperation agreements with suppliers … or with customers or clients” (Badillo and Moreno 2016, p. 5). Bouncken et al. disentangle further the analysis by considering coopetition as an “inter-organizational relationship that combines cooperation and competition” (Bouncken et al. 2015, p. 1). Moreover, recent literature explores the configuration of lateral or institutional cooperation agreements with “consultants, commercial labs, private R&D institutes, universities, other higher education institutions, the government, public research institutes or technology centers” (Badillo and Moreno 2016, p. 5).
Following above outlined structural cooperation setups, supplementing empirical efforts have been conducted to evaluate the resource requirements for various collaboration models (Fett and Spiering 2015, p. 2). Recent studies find empirical evidence on the importance of technological and knowledge capacities of the potential partner company and identify headcount requirements as a primary driver in the formation of a cooperation model (Fett and Spiering 2015, pp. 2–3, 16). In this regard, literature refers to “input-relative motives” to search for collaboration partners, and Fett and Spiering underline the substantial relevance of mutual value addition to pursue a joint market entry (Franco and Gussoni n.d., p. 10; Fett and Spiering 2015, p. 3, 15). Fett and Spiering further outline the reduction of the underlying financial exposure and the respective risk mitigation as critically relevant considerations before entering a cooperative partnership (Fett and Spiering 2015, p. 2, 14). A different view is taken by studies focusing on the areas of tension within a cooperation model. Such areas of tension include i.a. the corporate culture, underlying rationale, trust and confidentiality of the cooperation partners (Fett and Spiering 2015, pp. 3–5, 14, 17; Moss Kanter 2010).
In order to depict the characteristics of R&D cooperation, a staggered approach was adopted, dividing the comprehensive body of economic and social science consecutively into the subjects of R&D, R&D cooperation and environmental R&D cooperation.
Research & Development
The majority of studies define R&D analogous to Hall as “activities undertaken by firms and other entities … in order to create new or improved products and processes … increase the stock of knowledge … and use this knowledge to devise new applications” (Appendix A) (Hall 2006, pp. 1–2; Cambridge Dictionary n.d. (b)). R&D as such does not directly provide a “homogeneous activity” but can be subdivided into its components “Research” and “Development” (Hottenrott and Lopes-Bento 2014, p. 39). Hall, based on work by Nelson, classifies R&D into three primary stages of activity, reflecting the basic research phase, the applied research phase and the development phase, covering both, the acquisitive manner of knowledge generation and the testing and conceptualization of new products and processes (Hall 2006, pp. 1–3). Supplementing studies evaluate R&D with regard to the underlying market cycle, referring to Hud & Hussinger, who state the pro-cyclicality of R&D with a higher required ROR for credit-constrained firms. A slightly different view is taken by Añón-Higón et al., outlining the comparatively low opportunity costs in times of economic downturn (Beck et al. 2017, pp. 4, 17).
R&D cooperation
An increasing amount of studies on R&D cooperation underline the aspects of inter- and intra-sectoral knowledge spillovers and ex-post sharing of research results, depicting a company’s opportunity to “use knowledge created by another firm with no cost or with less cost than the value of the knowledge” (Beck et al. 2017, p. 18 / Katz and Ordover 1990, pp. 3, 10, 22). In this context, a societal rate of return is indicated by Comin et al. and Beck et al. compiled of the “spillover effect of knowledge creation” added to the private R&D ROR, resulting in societal returns of 20–60%, as assumed by most empirical analyses (Beck et al. 2017, pp. 7, 12, 23, 29; Border 2002, p. 3; Katz and Ordover 1990, p. 1) (Fig. 1). In contrast, several supplementing studies have been published on the underlying uncertainty about R&D outcomes, defining it as primary risk factor. Literature i.a. brings forward the potential “lack of appropriability” of R&D outcomes, asymmetric information exchange, antitrust constraints, or intellectual property requirements (Hall 2006, p. 4; Katz and Ordover 1990, pp. 2, 33) (Fig. 1).
An additional theoretical approach to examine the characteristics of R&D cooperation is grounded in the innovation economics literature (Badillo and Moreno 2016, p. 1). Referring to Perkmann & Walsh, research partnerships can be defined as “formal collaborative arrangements among organizations with the objective to cooperate on research and development activities” (Beck et al. 2017, p. 70; Perkmann and Walsh 2007, p. 268). In line with De Marchi, such “formal collaborative arrangements” might result in substitutional effects between a company’s internal R&D activities and external R&D partnerships (De Marchi 2011, pp. 1, 3). Beck et al. however also raise the awareness that R&D cooperation across sectors might be rather heterogeneous and differs along aspects, such as the cooperation partner size and age, the commercial potential of R&D projects and the duration of a cooperative partnership (Beck et al. 2017, pp. 15, 16, 35). In this regard, recent studies further differentiate between leading and laggard industries, where the latter ordinarily generates more benefits from R&D cooperation models (Beck et al. 2017, p. 16).
With reference to the incentives and underlying reasons for public and private entities to contribute to R&D partnerships, empirical analyses highlight that “the stock of knowledge created by doing R&D makes one more productive in acquiring additional knowledge” (Hall 2006, p. 4). The relevance of this “absorptive capacity” is reinforced in a systematic study carried out by De Marchi, specifically stating the resulting ability to “identify, assimilate and exploit the knowledge coming from external sources”. This becomes even more essential with increasing market uncertainty and technological turbulence (Beck et al. 2017, p. 46; De Marchi 2011, p. 3).
Further principal motivations to cooperate on R&D include the incorporation of external knowledge, risk mitigation or the sharing of complementary capabilities and resources (Beck et al. 2017, pp. 4; 15; Badillo and Moreno 2016, p. 3). In line with this, R&D cooperation models can also be beneficial in raising financial subsidies or creating intellectual property (Beck et al. 2017, p. 23). Although primarily focused in this study, specialized R&D cooperation configurations, including partnerships with governmental organizations or less flexible equity-based R&D Joint Ventures have been investigated as well (Marinucci 2012, p. 9; De Marchi 2011, p. 3; Katz and Ordover 1990, pp. 20, 38).
Environmental R&D Cooperation
In the field of R&D cooperation, a specific differentiation between environmentally related and non-environmental R&D has to be considered, with this study focusing on the former (De Marchi 2011, p. 3). De Marchi reinforces the need for R&D cooperation in the context of environmental innovations, due to the underlying “credence and complex character” (De Marchi 2011, pp. 1–2). Literature asserts the requirement for restrictive policy intervention leading to a “regulatory push and pull effect”, which becomes even more important concerning “radical changes of technological systems towards the greening of industries” (De Marchi 2011, pp. 1, 2; Katz and Ordover 1990, p. 1). De Marchi reaches the conclusion that continuous information exchanges within a broad stakeholder scope and an in-depth capability development are highly important to attain environmental targets (De Marchi 2011, pp. 1–3, 5).