Digitalized bioeconomy : Planned obsolescence-driven circular economy enabled by Co-Evolutionary coupling
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This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY-NC-ND 4.0 https://creativecommons.org/licenses/by-nc-nd/4.0/ Digitalized bioeconomy : Planned obsolescence-driven circular economy enabled by CoEvolutionary coupling © 2018 Elsevier Ltd. Accepted version (Final draft) Watanabe, Chihiro; Naveed, Kashif; Neittaanmäki, Pekka Watanabe, C., Naveed, K., & Neittaanmäki, P. (2019). Digitalized bioeconomy : Planned obsolescence-driven circular economy enabled by Co-Evolutionary coupling. Technology in Society, 56, 8-30. https://doi.org/10.1016/j.techsoc.2018.09.002 2019
Accepted Manuscript Digitalized bioeconomy: Planned obsolescence-driven circular economy enabled by Co-Evolutionary coupling Chihiro Watanabe, Nasir Naveed, Pekka Neittaanmäki PII: S0160-791X(18)30120-9 DOI: 10.1016/j.techsoc.2018.09.002 Reference: TIS 1081 To appear in: Technology in Society Received Date: 8 May 2018 Revised Date: 6 August 2018 Accepted Date: 10 September 2018 Please cite this article as: Watanabe C, Naveed N, Neittaanmäki P, Digitalized bioeconomy: Planned obsolescence-driven circular economy enabled by Co-Evolutionary coupling, Technology in Society (2018), doi: https://doi.org/10.1016/j.techsoc.2018.09.002. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 1 Digitalized Bioeconomy: Planned Obsolescence-Driven Circular Economy Enabled by Co-Evolutionary Coupling Chihiro Watanabe a,b , Nasir Naveed a and Pekka Neittaanmäki a a Faculty of Information Technology, University of Jyväskylä, Finland b International Institute for Applied Systems Analysis (IIASA), Austria Abstract Driven by digital solutions, the bioeconomy is taking major steps forward in recent years toward achievement of the long-lasting goal of transition from a traditional fossil economy to a bioeconomy-based circular economy. The coupling of digitalization and bioeconomy is leading towards a digitalized bioeconomy that can satisfy the shift in consumers’ preferences for eco-consciousness, which in turn induces coupling of up-down stream operation in the value chain. Thus, the co-evolution of the coupling of digitalization and bioeconomy and of upstream and downstream operations is transforming the forest-based bioeconomy into a digital platform industry. Aiming at addressing this transformation, a model was developed that explains above mentioned dynamism and demonstrated its reliability through an empirical analysis focusing on the development trajectory of UPM (forest-based ecosystem leader in Europe and a world pioneer in the circular economy) over the last quarter century, highlighting its efforts towards planned obsolescence-driven circular economy. It was comprehended that with the advancement of digital innovations, UPM has incorporated a self-propagating function that accelerates digital solution. Furthermore, this self-propagating function was triggered by coupling with a downstream leader, Amazon, in the United States. The dynamism in transforming a forest-based bioeconomy into a digital platform industry is thus clarified, and new insights common to all industries in the digital economy are provided. Keywords: Digitalized bioeconomy; Digital-bio coupling; Upstream-downstream coupling; Circular economy; Planned obsolescence. Corresponding author Chihiro Watanabe ([email protected]) TIS_2018_105_Revised_Manuscript
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 2 1. Introduction The marked advancement of the Internet has generated the digital economy, changing the way we conduct business and our daily lives (Tapscott, 1994). This advancement has hence created a digital economy and also transformed the traditional bio-based economy 1 , particularly, in the present context, the forest-based bioeconomy, into a consolidated platform. This transformation is not associated to natural resources and technologies only but also complex future trajectories of societies, firms and individuals. These trajectories are informed by socio-cultural and ethical perspectives as well as techno-economic perspectives (VTT, 2017). Wider socio-economic transformations, not merely technological and material processes can be expanded by the service strategies of such an economy (Pelli et al., 2017). Thus, driven by digital solutions, the bioeconomy is taking major steps forward in recent years, enabling the potential achievement of the long-lasting goal of transitioning from a traditional fossil-based economy towards a bio-based circular economy (MISTRA, 2017). Consumer preferences have tended to shift towards supra-functionality and now go beyond basic economic value, encompassing social, cultural and emotional values (McDonagh, 2008; Watanabe et al., 2015). In this context, a circular economy ultimately seeks to decouple global economic development from infinite resource consumption (Ellen Macarthur Foundation, 2015). Accordingly, the coupling of digitalization and bioeconomy that emerges digitalized bioeconomy satisfies a downstream shift in consumer preferences (Watanabe et al., 2018b). Furthermore, these preferences induce further upstream coupling of the value chain. Thus, the co-evolutionary coupling of bioeconomy and digitalization and of upstream and downstream operations is transforming the forest-based bioeconomy into a digital platform industry. Mega trends: population growth, urbanization, demographic change, resource scarcity, role of renewables, digitalization, e-commerce, climate change, responsibility and compliance highlight the importance of bioeconomy-based circular economy. New emerging technologies such as industrial biotechnologies, 3D printing and energy technologies give new ways to create innovative solutions without compromising the sustainability. All these bring economic, social and environmental value to the stakeholders and the society. 1 Based on EC definition (Bioeconomy for Europe, 2012), here the bio-based economy (bioeconomy) has been defined and recognized as follows (Watanabe et al., 208b): The economy encompassing the production of renewable biological resources and their conversion into food, feed, bio-based products and bioenergy. The forest-based bioeconomy is an important sub-sector of the bioeconomy. Advancement of digitalization has transformed this economy and this transformation is not about natural resources and technologies but about the complex future potential trajectories of societies, firms, and individuals.
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 3 To date, many studies have analyzed the systems nature of the forest-based bioeconomy. Several studies have pointed out the possibility of creative disruption by stemming from the abovementioned mega trends confronting the pulp and paper industry (PPI) as opportunities rather than threats (e.g., Hetemaki et al., 2014; Hetemaki, 2016). The possibility of achieving digital solutions has accelerated these studies. Therefore, we attempt to demonstrate the above hypothetical view by conducting an empirical analysis of the core business activities at the forefront of both the upstream and downstream operations of the forest-based bioeconomy chain. In particular, we showed a transformative stream resulting in the construction of a creative disruption platform and the embracement of digital solutions (Watanabe et al., 2017). While this analysis provides new insight into a forest-based bioeconomy within a digital economy, digitalization does not stop the transformation stream as the PPI produces more diversified products (Toppinen et al., 2017) such as biofuels, biocomposites, biochemical and renewable materials for plastic industry. In addition, actors from different sectors interact and play different roles (Giurca et al., 2017), and all stakeholders involved in the forest-based bioeconomy should be considered (Mustalahti, 2018). Consequently, new insight is provided into the forest-based bioeconomy as well as numerous industries in the digital economy that are constructing a creative disruption platform by embracing digital solutions. Based on these expectations, we attempted to trace this transformation by developing a model that explains abovementioned dynamism and demonstrated its reliability through an empirical analysis focusing on the development trajectory of UPM (forest-based ecosystem leader in Europe and a world pioneer in the circular economy) over the last quarter century since after the emergence of the digital economy, highlighting its efforts towards planned obsolescence-driven circular economy. The process of consolidating a platform similar to a super computer called ‘super digital biofore computer’ by consolidating upstream and downstream operations as well as producers and consumers (prosumer) was envisioned, wherein ‘biofore’ implies a sustainably growing bio-forest (Watanabe et al., 2018b). Since an enabling mechanism of this ‘super computer’ has been strongly required, the operationalization of the aforementioned concept of the co-evolutionary coupling of bioeconomy and digitalization and of upstream and downstream operations is also necessary. Therefore, this paper attempts to operationalize the dynamism of this co-evolution by tracing the above mentioned transformation dynamism. In order to demonstrate the reliability of the model used for the analysis, an empirical analysis focusing on the development trajectory of UPM over the last quarter century was conducted, with special attention placed on the resurgence of the planned, obsolescence-driven circular economy. In long-lasting debate on planned obsolescence (e.g., Swan, 1972; Bulow, 1986;
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 4 Aladeojebi, 2013), many industries focused on a profit maximization strategy and produced goods and services with intentionally shorter economic lives to stimulate consumers to make repeated purchases in a shorter period of time. While some recent analyses (e.g., Fabian, 2016; Satyro et al., 2018) discussed a significance of planned obsolescence toward a circular economy, no analysis has undertaken planned obsolescence strategy in the context of the development stage of the industry toward a circular economy. In light of the transformation of a fossil economy towards a bioeconomy-based circular economy amidst an abundance of digital solutions, this paper attempts to explore new insights into creative disruptive platform that is expected to be initiated by a planned obsolescence-driven circular economy enabled by the co-evolution of the coupling of bioeconomy and digitalization and of upstream and downstream operations. In line with an orientation towards a planned obsolescence-driven circular economy, UPM has incorporated a self-propagating function that accelerates digital solutions. Furthermore, this self-propagating function was promoted through coupling with a downstream leader, Amazon, in the United States. The dynamism in transforming a forest-based bioeconomy into a digital platform industry is thus clarified, and new insights common to all industries in the digital economy are provided. The structure of this paper is as follows: Section 2 explores the dynamism in constructing a consolidated platform ecosystem. Section 3 reviews the co-evolution of the dual couplings of the bioeconomy and digitalization, and of upstream and downstream operations. Section 4 analyzes sustainable development with an orientation towards planned, obsolescence-driven circular economy. Section 5 briefly summarizes noteworthy findings, implications and suggestions for future works. 2. Dynamism in Constructing a Consolidated Platform Ecosystem 2.1 Digital Solution for Transformation into a Creative Disruption Platform We previously demonstrated that the advancement of digital innovation transforms the value chain of the forest industry into a creative disruption platform in a stepwise manner creating a consolidated platform ecosystem (Watanabe et al., 2018b). The conventional linear supply chain from forestry to consumption (step 1) transforms into creative disruption platforms within the upstream and downstream industries (step 2). Disruption in upstream is induced (step 3) by the espousal of digital solutions and creation of new business systems in the downstream (step 4). This is how we can expect the emergence of creative disruption in the value chain of forest-based bioeconomy. Moreover, diversified production to address the people’s shifting preferences for eco-consciousness, different actors’ varying roles and interaction in the value chain as well as stakeholders involvement accelerate the consolidation of up-downstream operations and thus leads forest-based bioeconomy into a digital platform industry (step 5).
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 5 The forest-based ecosystem leader in Europe, UPM in Finland, has undertaken a strong initiative to restructure its business model and has moved towards digital-driven solutions and an eco-design approach since the beginning of the second decade of this century, whereas Amazon has aggressively embraced digital solutions to diversify its products and to improve users’ experience on the downstream side. Thus, similar to the conspicuous business accomplishments of Amazon on the downstream side, UPM has demonstrated notable accomplishments on the upstream side as evidenced in Figs. 1 and 2. Fig. 1. Top 20 Global Forest, Paper and Packaging Industry Firms in the World by Net Income (2015). Source: PWC (2016). Fig. 1 demonstrates UPM’s notable accomplishment of gaining the world’s highest net income within the paper and packaging industry in 2015. Fig. 2 demonstrates its resurgence from the beginning of the second decade of this century, as the company has maintained sustainable market capitalization growth since 2012. Fig. 2. Trend in UPM’s Market Capitalization (1990 – 2017) – 2010 fixed price. Source: UPM annual reports (annual issues).
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 6 2.2 Planned Obsolescence-Driven Circular Economy 1) UPM’s Endeavor Towards Developing a Circular Economy UPM’s resurgence can be attributed to its strategic shift towards a planned obsolescence-driven circular economy that is enabled by digital solutions integrating both upstream and downstream operations. It has taken strong initiative to shift from the fossil economy towards a circular economy. UPM was established in 1995 by the merging of the Kymmene Corporation and Repola Ltd with its subsidiary United Paper Mills Ltd, as illustrated in Fig. 3. The new company started operations on May 1, 1996. Fig. 3. Development Trajectory of UPM. Source: Elaborated by the authors based on UPM (2016). UPM has a long tradition in the Finnish forest products industry. The group’s first mechanical pulp mill, paper mills and sawmills started operations at the beginning of the 1870s. Pulp production began in the 1880s and pulp was converted to paper by the 1920s, with plywood production beginning the following decade. The present group comprises approximately 100 production facilities that originally functioned as independent companies. Among others, the following companies and forest industry operations were merged into the group: Kymi, United Paper Mills, Kaukas, Kajaani Schauman, Rosenlew, Raf. Haarla and Rauma-Repola. In 2008, UPM adopted a new, market driven business structure comprising three business groups: Energy and Pulp, Paper, and Engineered Materials (UPM, 2008). Later in 2013, UPM once again implemented a new business structure to drive a clear change in profitability. Thus, UPM further developed its business portfolio and changed from a
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 7 vertically integrated forest industry to a company with six separate business areas: UPM Biorefining, UPM Energy, UPM Raflatac, UPM Paper Asia, UPM Paper Europe and UPM Plywood (UPM, 2013). While UPM started as a resource intensive firm, it recognized the potentially fatal shift from a fossil economy to a non-fossil economy within the emerging context of sustainable development, as illustrated in Fig. 4. Fig. 4. Transforming Direction of Business Model Toward Bioeconomy-based Circular Economy. Source: UPM Annual Report (2017). Circular economy thinking addresses two crucial global issues i.e. climate change and scarcity of natural resources. In circular economy, materials, production waste and products are recycled several times to create added-value through smart solutions. Moreover, waste generation is avoided by maximizing the use of renewable energy and materials. UPM aims to achieve the target of no process waste to landfills by 2030. Fig. 5 demonstrates the UPM’s circular economy approach. Fig. 5. Scheme of a UPM’s Circular Economy. Source: UPM Circular Economy (2018).
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 14 By means of a correlation analysis between market value of UPM and the governing factors of both upstream and downstream flows over the 1998-2016 period, we previously demonstrated the significance of the forgoing dynamism enabled by the coupling of upstream and downstream operations (Watanabe et al., 2018). Fig. 11. Coupling of Upstream and Downstream Operations – A Case of UPM’s Market Capitalization Effect (1998-2016). In Fig. 11 and abovementioned correlation analysis, we noted that UPM has been sustaining an increase in market capitalization since 2012, as reviewed earlier (Fig. 2). The effects of R&D and economic environment on transforming market capitalization into sustainable increases were not entirely remarkable, yet this transformation can largely be attributed to the improvement in operating income and also the stockprice increase of Amazon in the downstream. The former contribution can be primarily attributed to the coupling of bioeconomy and digitalization, as outlined in section 3.1, and the latter contribution can definitely be attributed to the coupling of upstream and downstream operations initiated by the virtual link e-commerce link, as shown in Fig. 10. Furthermore, spiral increases in both couplings thereafter convince us that the co-evolution between these two couplings is well functioning. Own profit Innovation efforts External market condition s Coupling effect of the business performance in downstream side
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 15 4. Sustainable Development Based on a Planned Obsolescence Driven Circular Economy Orientation 4.1 Analytical Framework UPM’s resurgent development by means of a planned obsolescence driven circular economy enabled by co-evolutionary coupling is demonstrated as following. Given the Internet commercialization in 1991 which triggered the digital economy, this has provided significant impact on UPM’s development strategy in shifting from natural resources dependent structure to digital solution seeking structure. This shift can be examined by digital solution substitution for natural resources as depicted as follows: ln =+ ln where I: Internet dependence as a proxy of digital solution 3 ; E: energy consumption as a proxy of natural resources dependence; p i : Internet price; p e : energy prices; : elasticity of Internet substitution for energy; and a: coefficient. In this equation, in case when > 1, I substitution for E can be demonstrated 4 . Table 1 summarizes the result of the above correlation analysis in UPM over the period of 1991-2017 which demonstrates statistically significant. Table 1 Correlation of UPM’s Digital Sollution – Resources Dependence Ratio and their Prices Ratio (1991-2017) ln =−1.697+1.264ln !" !# + 0.498D 1 - 0.577D 2 D: Dummy variables D 1 : 1998, 2016, 2017 =1, others = 0; D 2 : 1991, 1992 = 1, others = 0. The figures in parentheses indicate t-statistics: All are significant at the 1% level. Table 1 demonstrates digital solution (I) substitution for natural resources (E) which endorses that UPM has been endeavouring to shifting from natural resources dependent structure to digital solution seeking structure for its survival in the digital economy. With this pre-analysis, UPM’s resurgent development trajectory analysis was conducted focusing on its digital solution seeking trajectory. 3 Since the Internet has been permeating into broad ICT in the digital economy, Internet dependency can be considered a proxy of digital solution (Watanabe et al., 2018c). 3 $≡&'()*')()+,)'-.(/*) 0')*12)+,)'-.(/*) = &∙, . 0⋅, ) ln5=ln −ln! " ! # = a + 67 −1ln 6 7 When > 1, p e increase (or p i decrease) reacts to Internet expenditure increase (I substitutes for E). ( - 19.68) (34.57) (4.08) ( - 3.32) adj. R 2 0.986 DW 1.19
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 16 The framework of the analysis is outlined in Figs. 12 and 13 (see the details of the numerical model in Appendices 1 and 2). Fig. 12. Framework of a Planned Obsolescence-Driven Circular Economy Enabled by Co-Evolutionary Couplings. Simple Logistic Growth ( SLG ) Logistic Growth within a Dynamic Carrying Capacity (LGDCC) Planned obsolescence strategy
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 17 Fig. 13. Framework of Planned Obsolescence Management.
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 18 4.2 Data Construction Data utilized for the analysis are tabulated in Table 2 (see the details of the data in Appendices 3 and 4). Table 2 Data for the Analysis Category of Data References Primary data UPM sales (nominal, real) UPM annual reports (1997-2017) UPM R&D expenditure (nominal, real) UPM annual reports (1997-2017) UPM R&D intensity UPM annual reports (1997-2017) UPM energy cost (nominal, real) UPM annual reports (1997-2017) GDP deflator World B ank national accounts and OECD national accounts data files Long-term interest rate OECD, long-term interest rates (2018) Finland internet dependence International Telecommunication Union (ITU), statistics (2018) Constructed data Rate of obsolescence of technology UPM annual reports and ITU data L ead time between R&D and commercialization UPM technology stock and ITU data R ate of depreciation of sold goods and services UPM technology stock and ITU data UPM technology knowledge stock Computed - based UPM annual reports data (1997-2017) UPM market value Computed - based UPM annual reports data (1997-2017) Fig. 14 illustrates trends in UPM’s sales, R&D expenditure and R&D intensity. Fig. 14. Trends in UPM Sales, R&D expenditure and R&D Intensity ( 1990-2017; 2010 real price). Source: UPM Annual Reports.
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 19 Upon examining Fig. 14 we noted that sales generally increased at the beginning of this century (except during Asian financial crisis in 1998), as the industry enjoyed the benefits of this emerging economy. Thereafter, sales generally decreased as the industry shifted to a matured economy. This continued decrease was once again modified to an increasing trend during the early part of the second decade of this century, allowing UPM to generate the world’s top net income in 2015, as shown in Fig. 1. This recovery can be attributed to the shift towards a circular economy, as described in Section 2. R&D investment was demonstrated to be generally subject to trends in sales. Figs. 15 and 16 illustrate trends in Internet dependence in Finland, Japan, Singapore and the USA and in the long-term interest rate in European countries over the 1990-2017 period, respectively. Fig. 15. Trends in Internet Dependence (%; 1990-2017). Source: International Telecommunication Union. Fig. 15 shows the high dependence of Finland on the Internet, particularly in this century, and demonstrates the marked role of the Internet in the planned obsolescence strategy. Fig. 16. Trends in the Long-Term Interest Rate in European-Countries (1990-2017) . Source: OECD. Fig. 16 demonstrates trends in the long-term interest rate in European countries over the 1990-2017 period. This declining trend has induced an extension of technology life corresponding to a circular economy within a matured economy (see the details of this mechanism in Appendices 2 and 4).
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 20 4.3 Empirical Analysis 4.3.1 Creation of Technology Knowledge Stock and Market Value Measurement of technology knowledge stock and market value is essential for the analysis of UPM’s technology-driven development trajectory towards a circular economy. Griliches (1980) postulated that technology knowledge stock can be measured as a cumulative stock of R&D investment by considering the obsolescence rate ( for commercialization (m), as illustrated in Fig. 12 (see the details of the equation in Appendix 1). It was previously identified that firms attempt to manage this lead time depending on the rate of obsolescence of technology (Watanabe, 1999). Similarly, market value can be measured as the cumulative stock of sales taking into account their depreciation rate (:). In this case, contrary to technology knowledge stock, lead time can be considered negligibly small (see also Fig. 12 and Appendix 1). Generally, ‘depreciation’ in a broad sense (sometimes also called ‘obsolescence’) can be defined as the loss in service value incurred in connection with the consumption or prospective retirement of a property (NARUC, 1996). It generally results from two principle classes of losses: (i) traditional mortality forces, such as wear and tear from usage, deterioration with age and accidental or chance destruction, and (ii) technological obsolescence (Barreca, 2000). These losses can be derived from either physical obsolescence or technical obsolescence mechanisms (Aladeojebi, 2013). Before the 1970s, the overwhelming drivers of mortality for utility property were traditional mortality forces before the 1970s (Barreca, 2000); however, technological obsolescence has a primary role as technology advances. When technological obsolescence is present, mortality rates increase with the passage of time (Barreca, 2000), leading to a much higher obsolescence/depreciation rate than that of stemming from traditional mortality forces. In this paper, in line with Griliches (1980), the depreciation rate of technology is framed as the rate of obsolescence of technology, while the depreciation rate of sales is framed as the rate of depreciation of sold goods and services. 4.3.2 Planned Obsolescence Strategy The above postulates suggest that the rates of obsolescence of technology and depreciation of sold goods and services play decisive roles in measuring technology knowledge stock and market value. Particularly, careful attention should be paid to these rates of obsolescence and depreciation in the context of the resurgence of UPM and its planned obsolescence-driven circular economy. 1) Rate of Obsolescence of Technology Confronting the stagnating trend in the forest-based bioeconomy of Europe as a consequence of the advancement of the digital economy and growing inertia in emerging economies, the planned obsolescence management strategy has become important in leading European pulp and paper firms (MISTRA, 2017). Leading pulp and paper firms in Europe have taken sincere consideration of planned obsolescence as a technology strategy (Mubareka et al., 2016) and have implemented digital solutions as an additional strategy stemming from the dramatic advancement of digital innovations (Mustalahti, 2018). Traditionally, the purpose of this strategy was to force consumers to purchase newer products by shortening the natural end of life of the current products owned by
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 21 consumers (Packard, 1960; Swan, 1972; Bulow, 1986; Slade, 2006; Aladeojebi, 2013). However, in line with increasing concerns over the environment and consumer preferences, some concerns with respect to ‘planned longevity’ have arisen (e.g. Swan, 1972; Bulow, 1986; Keeble, 2013; EMF, 2015; Fabian, 2016; Satyro et al., 2018). In light of the metabolic change in the forest-based bioeconomy, particularly in UPM’s transformative development trajectory towards a circular economy, an optimal obsolescence management strategy encompassing both possibilities of shortening and extending technology life (UPM, 2017a) was sought in this paper based on the principle of profitable R&D with minimum R&D investment and maximum utilization of digital solutions, as demonstrated in Fig. 7. In addition, the state of the development trajectory was taken into account (see the details of mathematical analyses in Appendix 2). There are two forms of obsolescence: external obsolescence and functional obsolescence (Barecca, 2000). The former can be attributed to external circumstances including the development, diffusion and utilization of technology, whereas, the latter results from a flaw in structures, materials or design that diminishes the function, utility and value of an asset (Barecca, 2000). Here, flaw refers to any deficiency in an asset that negatively impact its ability to perform the desired function per customer expectation. With the rapid advancement of technology, technological obsolescence is the principle cause of functionality obsolescence today, overshadowing external obsolescence, which is relevant for the analysis of the rate of obsolescence of technology for measuring technology knowledge stock. Hence, technology-driven functionality obsolescence was focused on in this paper in the analysis of the rate of obsolescence of technology. It was previously demonstrated that firms’ rate of obsolescence of technology can be depicted as a function of their technology knowledge stock level (Watanabe, 1999). UPM’s endeavour to shift towards a planned obsolescence strategy within a circular economy corresponds with a digital solution strategy primarily initiated by the effective utilization and advancement of digital innovation led by the advancement of the internet (I) (Tieto, 2017). This preceding approach was utilized through developing Internet dependence rather than inducing general technology knowledge stock. This dependence represents a general trend in co-evolutionary coupling. As reviewed earlier, profit maximization can be attained by increasing the rate of obsolescence of technology ((rapid substitution with new technology) in an emerging economy, while the reverse occurs in a matured economy. In a matured economy, an extension of the technology life cycle (decrease) is required, corresponding with the principles of a circular economy (EMF, 2015; UPM, 2017a). In addition, as reviewed in Fig. 12, given firms’ planned obsolescence management strategy, their rate of obsolescence of technology is subject to the long-term interest rate, as demonstrated in Fig. 16 (see the detailed mechanism in Appendices 1 and 2).
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 22 Based on the foregoing review and the historical review with international comparisons (Schott, 1978; Bosworth, 1978; Japan Science and Technology Agency, 1985; Mitsubishi Research Institute, 1991; Watanabe, 1992; Watanabe, 1999; OECD, annual issues), the rate of obsolescence of technology in UPM over the 1990-2017 period was estimated, as illustrated in Fig. 17 (see the details of the estimation approach and estimated values in Appendices 3 and 4). Fig. 17. Trend in the Rate of Obsolescence of Technology in UPM (1990-2017). Dynamic change in rate of obsolescence of technology, as reviewed in Fig. 12, evidence the capacity of firms to change lead time between R&D and commercialization (m), as illustrated in Fig. 18 (see the details of estimated values in Appendix 4). This dynamic change in lead time was reflected in the measurement of the technology knowledge stock. Fig. 18. Trend in Lead Time between R&D and Commercialization (1990-2017) . dT
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 23 2) Rate of Depreciation of Sold Goods and Services In principle, the strategy of firms for the planned depreciation of market value is based on the similar strategy of planned obsolescence of technology (Keeble, 2013). However, it largely depends on external obsolescence and is more sensitive to factors other than digital solutions (Disney et al., 2003; Obeng et al., 2014). It is subject to the advancement of the Internet, similar to , yet it is also subject to the sales trend (S) or, specifically, the popularity of sold goods and services in the market. Yet notably, it is decisive factor of firms’ planned depreciation strategy (Rodriguez et al., 2015). In addition, technology knowledge stock (T) cannot be overlooked as it may produce new goods and services that substitute existing ones. Furthermore, advancement of the internet accelerates dissemination of goods and services in new emerging markets and extends their life cycle leading, to decreasing :. Hence, the rate of depreciation of sold goods and services : can be depicted by a function describing dependence on the Internet, the sales trend and technology knowledge stock. Contrary to rate of obsolescence of technology, the depreciation of sold goods and services is primarily governed by traditional mortality forces, such as wear and tear and deterioration. These forces are typically a constant function of the age of the assets and do not change with the passage of time (Barreca, 2000) 5 . In addition, compared to technological obsolescence, which increases the mortality rate over time, leading to a higher rate of obsolescence, the rate of depreciation of sold goods and services is considered to be much lower as it stems from traditional mortality forces. Based on the foregoing review in addition to the lifecycles of UPM’s leading products, the rate of depreciation of goods and services sold by UPM over the 1990-2017 period was estimated, as illustrated in Fig. 19 (see the details of the estimation approach and estimated values in Appendices 3 and 4). Fig. 19. Trend in the Rate of Depreciation of Sold Goods and Services (1990-2017). 5 Barreca (2000) demonstrated as follows: 5 years ago, 10 year pld assets may have had a 3% retirement rate (depreciation rate); today, 10 year old assets would still have a 3% retirement rate; and 30 years from now, 10 year old assets would still have a 3% retirement rate.
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 30 Appendix 1. Model Constructed for the Analysis Market value created by firms in a competitive environment can be described as follows: B =CD,E (1) where V: market value; X: traditional production factors; and T: technology knowledge stock. Given the emphasis on internet-driven digital solutions, the internet permeates T as follows: B =CD,E (2) where I: internet dependence. Furthermore, I embodies X in an internet of things (IoT) society as follows: B =CD,E (3) Thus, the market value of firms seeking digital solutions in an IoT society is governed by the following function: B ≈CE (4) Technology knowledge stock at time t, T t can be depicted as follows: E G =H GIJ +1−E GIK , E L =H KIJ +M ⁄ (5) where R t : R&D expenditure at time t; m: time lag between R&D to commercialization; : rate of obsolescence of technology; and g: increasing rate of R&D expenditure at the initial period. Given the creative disruption caused by embracing digital solutions, these solutions can be represented by the decreasing rate of obsolescence of technology (i.e. prolongment of the life time of technology) initiated by the advancement of the internet as follows: G = L GO (6) where P: learning coefficient for digital R&D. Corresponding to change in ,m also changes as follows (Watanabe, 1999): Q G = RSTU=U ⁄IRS<VWX RSKWX +1 (7) Similarly, market value at time t V t can be depicted as follows: B G =Y G + (1 -:B GIK , B L =Y K :+Z ⁄ (8) where S t : sales at time t; :: rate of depreciation of sold goods and services; and k: growth rate of sales at the initial period. Given the logistic nature of the growth of internet, equation (4) can be developed by the following T-driven logistic growth function: B G ≈C[E\, ;] ;= = ^] ^= ∙ ;= ;= = ^] ^= =B1− ] _ (9) where N: carrying capacity and a: velocity of diffusion. Equation (9) develops the following simple logistic growth function (SLG): B G E G = _ KW`abcV (10) where b: coefficient indicating the initial level of diffusion.
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 31 The critical level informing firms’ decision to undertake R&D should satisfy the following balance (see Note): ^] ^= =1+Qdd+ (11) where r: interest rate. Similar to as a function of internet-driven learning for digital solutions, : can be depicted as a function of the internet-driven learning in an IoT society as follows: : G =: L Ge (12) where f: learning coefficient for sales promotion. This rate is also subject to the trends in sales (S) and technology stock (T). In addition, advancement of the internet expands the market leading to the extension of the life time of goods and services. Thus, δis governed by hY,E, as depicted in equation (13). : G =: L Ge ∙hY,E, (13) Note: Internal rate of return of R&D investment The critical level that informs firms’ decision to undertake R&D is defined by the following discount rate (r: internal rate of return [IRR] of R&D investment), which should not be lower than the interest rate (Watanabe et al., 1997). iB iE ＝ 1 + Qd d + ） Thus, the critical level informing firms’ decision to undertake R&D should satisfy the following balance given marginal technology productivity:
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 32 Appendix 2. Planned Obsolescence in a Matured Industry Shifting Toward Digital Solutions From equation (1) firms’ technology productivity can be developed as follows: ;] ;= = ^] ^j ∙ ;j ;= + ^] ^= ∙ ;= ;= = ^] ^j ∙ ;j ;= + ^] ^= (14) Φ≡ ð] ^j ∙ ;j ;= = ;] ;= − ^] ^= (15) From equations (9) and (11), the components of equation (15) can be provided as follows: m≡ ;] ;= =B1− ] _ (9) n≡ ^] ^= =1+Qdd+ (11) Provided that firms seek profitable R&D with minimum R&D investment and maximum utilization of digital solutions, the planned obsolescence strategy can be depicted as follows 6 : ;o ;< = 0 (16) pΦ p =pm p−pn p =qpB pr1−B st−B s∙pB pu−pn p =⋅ ;] ;< v1− w] _ x− ;y ;< =⋅ ;] ;= ⋅ ;= ;< v1− w] _ x− ;y ;< =∙Bv1− ] _ xv1− w] _ x ;= ;< − ;y ;< =0 ;< ;= = z { ]KI | }~a{| } •€ •• (17) Provided that interest rate r is given by an external market independent from management, the following holds true: pn p = p p1+Qdd+=d⋅pQ pd++1+Qd = IK RSKWX ∙ ‚<W‚ <WX +1+Qd≈− ‚<W‚ X<WX +1+Qd In the case that H ≡ X<WX ‚<W‚ 1+Qd>1, ;y ;< >0 , then ;< ;= <0 The numerator of equation (17) is negative in a matured economy (N > V > N/2) whereas the opposite occurs in an emerging economy (V < N/2). In the case that g > r, X<WX ‚<W‚ 1+Qd> ‚<W‚ ‚<W‚ 1+Qd=1+Qd>1, ;< ;= <0. 6 The internal rate of return of R&D investment r in equation (11) should be no lower than the interest rate, and maximum digital substitution for traditional production factor X should be sought.
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 33 Appendix 3. Rates of Obsolescence of Technology and Depreciation of Sold Goods and Services A3.1 Rate of Obsolescence of Technology On the basis of a historical review comparing different countries which is tabulated in Table 4, the rate of obsolescence of technology in the Finnish PPI 7 in 1995 can be estimated as 7%. Table A1 Rate of Obsolescence of Technology in the Manufacturing Industry in the 1980s and 1990s 1980s average (1985) 1995 Average growth rate References Japan 9.8 *1 12.3 *2 2.5 % p.a *1 Japan Science & Technology Agency (1985) Watanabe (1992) *2 Watanabe (1999) USA 6.7 *3 9.0 *4 3.0 % p.a *4 *3 Mitsubishi Research Institute (1991) *4 Higher pace than Japan during a period of new economy Europe 5-10 *5 6.5 *6 8.0 *7 2.0% p.a *7 *5 Schott and Bosworth (1978). *6 Pace of technological change is similar to the USA (OECD). *7 Pace of technological change is similar to Japan (OECD) Electronics/machinery 9%, Chemicals 8% Pulp & paper/food 7% As reviewed in Section 2, UPM’s development trajectory over the last quarter century has been shifting from an emerging economy to a matured economy informed by a circular economic strategy within a resurgent economy. This trajectory shift corresponds to the advancement of the digital economy, and the countermeasures to this shift largely depend on digital solutions led by the advancement of the internet (Tieto, 2017). With this understanding, UPM’s rate of obsolescence of technology as part of its planned obsolescence strategy can be considered a function of the advancement of digital innovation represented by internet dependence (I) as follows: G = L∙ O (18) where L : the rate of obsolescence of technology of the base year and P: coefficient. As reviewed in Section 2, ;< ; > 0 in an emerging economy, while ;< ; <0 in a matured economy. Finally, ;< ;ƒ < 0→ ;< ; > 0 in circular economy based on a resurgent economy 8 . In addition, as reviewed in Fig. 13, given firms’ planned obsolescence management 7 Since pulp and paper industry is non-R&D intensive industry as demonstrated below, its pace of technological change is among the lowest group in the manufacturing industry. R&D intensity in 2014 (Japan’s case): Pharmaceutical 7.8%, electronics 6.5%, chemicals 3.9%, machinery 3.7%, textiles 2.4%, pulp & paper 0.7%, food 0.6%. 8 ;< ;= = ;< ; ∙ ; ;= and ; ;= >0 in a digital economy.
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 34 strategy, the rate of obsolescence of technology is also subject to the long-term interest rate (r). Taking all these factors into account, UPM’s rate of obsolescence of technology over the 1990-2017 period was estimated by the following equation. The coefficient was identified by a heuristic approach, satisfying all conditions necessary for the planned obsolescence strategy, as illustrated in Fig. 13. K„„… =0.07, G = K„„… ∙ L.K (1990-2001), G†<{UU~ IL.K (2002-2017) A3.2 Rate of Depreciation of Sold Goods and Services While firms’ strategy for planned depreciation of market value is based on the similar strategy of planned obsolescence of technology in principle (Keeble, 2013), it is more sensitive to factors other than digital solutions (Disney et al., 2003; Obeng et al., 2014). As reviewed in Section 4, it is subject to the advancement of the internet, similar to ,yet it is also subject to the trends in sales (S) and technology stock (T). Both generally react to saturate and/or substitute goods and services, leading to an increase in the rate of depreciation of market value : (Bosworth, 1978). In addition, advancement of the internet reacts by extending the life cycle, leading to decreasing :. Therefore : G can be depicted by the following equation: : G =: L Ge ∙hY,E, (13) ‡ G =ˆ⋅Y ‰ E Š ‹ (19) where A: scale factor and Œ,•and•: coefficients. Following the estimated rate of obsolescence of technology, the primary governing factor : L Ge can be estimated as follows 9 : : K„„… =0.04, : G =: K„„… ∙ L.K (1990-2001), : G =: wLLK ∙ IL.K (2002-2017) By means of a heuristic approach satisfying all conditions necessary for the planned obsolescence strategy, as illustrated in Fig. 12, the coefficients of equation (19) were identified as follows: h G =0.25∙Y L.’“ E L.”w IL.K“ (1990-2001), h G =0.29∙Y L.’“ E L.”w IL.K„ (2002-2017) Consequently, : G was estimated as follows: : G =0.01⋅ IL.L“ Y L.’“ E L.”w (1990-2001), 0.013∙ IL.w„ Y L.’“ E L.”w (2002-2017): G 9 The resurgence of the forest industry has been expected to disseminate goods and services in existing industries through innovation (Technology Strategy Board, 2014): Formable plywood (developed by UPM) and wood-based casting plaster (developed by Onbone) are typical examples in the 2010s, with lifetimes of 30 years (Finnish Forest Industries Federation, 2012). Accordingly, their : wLKL• was estimated as 0.033. Given the decreasing trend in: G since the early 1990s, : K„„… was estimated to be 0.04.
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 35 Appendix 4. Key Data for Analyses (1): Primary Data GDP deflator value for 2017 was estimated based on the growth rate of the last 20 years. R&D values for 1992-1994 were estimated based on the growth rate of the following 5 years. The internet dependence value for 2017 was adjusted based on 2013-2015 growth rate. Internet price p i was estimated by the following learning equation: G =55.3 G -0.6 (Tou et al., 2018). UPM energy cost: UPM’s annual reports. Values for 1990-1994 were based on backward estimation. Energy Price: Average annual OPEC crude oil price (Statista, 2018); energy cost and prices were deflated by GDP deflator . Year UPM Sales (Mil. Euro) (nominal) UPM Sales (Mil. Euro) (real) UPM R&D (Mil. Euro) (nominal) UPM R&D (Mil. Euro) (real) UPM R&D intensity (R&D/ Sales)(%) UPM market capitalization (Mil. Euro) (nominal) UPM market capitalization (Mil. Euro) (Real) GDP deflator (base year 2010) Internet dependence Finland (%) Internet price Finland (Real) UPM energy cost (Mil. Euro) (Real) Energy price US$/barrel (Real) 1990 6224.0 8629.5 21.5 29.8 0.35 2002 2776 72.13 0.4 95.83 747.51 30.86 1991 6007.0 8200.7 23.6 32.2 0.39 1894 2586 73.25 1.4 45.19 743.84 25.42 1992 6283.0 8498.6 25.7 34.8 0.41 1968 2662 73.93 1.9 37.62 744.72 24.94 1993 7394.0 9824.1 27.8 37.0 0.38 3852 5118 75.26 2.6 31.17 739.11 21.70 1994 8067.0 10524.3 29.9 39.1 0.37 3948 5151 76.65 4.9 21.31 733.20 20.26 1995 9206.0 11526.7 33.0 41.3 0.36 3690 4620 79.87 13.9 11.40 741.23 21.11 1996 8705.0 10910.3 35.0 43.9 0.40 4340 5439 79.79 16.8 10.18 736.96 25.43 1997 8478.0 10405.3 35.0 43.0 0.41 4957 6084 81.48 19.5 9.30 704.49 23.15 1998 8365.0 9956.8 36.0 42.9 0.43 6630 7892 84.01 25.5 7.92 651.09 14.62 1999 8261.0 9740.7 41.0 48.3 0.50 10663 12573 84.81 32.3 6.87 633.19 20.56 2000 9583.0 11118.3 44.0 51.0 0.46 9502 11024 86.19 37.3 6.31 718.17 32.02 2001 9918.0 11135.7 45.0 50.5 0.45 9681 10870 89.06 43.1 5.78 715.21 25.96 2002 10475.0 11648.1 46.0 51.2 0.44 7960 8851 89.93 62.4 4.63 765.05 27.09 2003 9948.0 11038.5 48.0 53.3 0.48 7917 8785 90.12 69.2 4.35 745.66 31.18 2004 9820.0 10830.6 47.0 51.8 0.48 8578 9461 90.67 72.4 4.24 843.73 39.76 2005 9348.0 10215.7 50.0 54.6 0.53 8665 9469 91.51 74.5 4.16 885.19 55.29 2006 10022.0 10853.2 44.0 47.6 0.44 10005 10835 92.34 79.7 4.00 1012.55 66.06 2007 10035.0 10574.9 50.0 52.7 0.50 7084 7465 94.89 80.8 3.97 916.81 72.75 2008 9461.0 9672.4 49.0 50.1 0.52 4680 4785 97.81 82.5 3.92 858.77 96.20 2009 7719.0 7746.1 48.0 48.2 0.62 4326 4341 99.65 83.7 3.88 818.86 69.10 2010 8924.0 8924.0 45.0 45.0 0.50 6874 6874 100.00 86.9 3.80 836.00 77.38 2011 10068.0 9814.4 50.0 48.7 0.50 4466 4354 102.58 88.7 3.75 965.06 104.75 2012 10492.0 9934.3 45.0 42.6 0.43 4633 4387 105.61 89.9 3.72 885.30 103.63 2013 10054.0 9282.7 38.0 35.1 0.38 6497 5999 108.31 91.5 3.68 839.27 97.75 2014 9868.0 8959.3 35.0 31.8 0.35 7266 6597 110.14 92.4 3.67 892.48 87.42 2015 10138.0 9023.3 37.0 33.0 0.37 9192 8181 112.35 92.7 3.65 704.92 44.05 2016 9812.0 8652.3 40.0 35.4 0.41 12452 10980 113.40 93.4 3.63 589.93 35.87 2017 10010.0 8818.4 51.0 44.9 0.48 13818 12173 113.51 94.0 3.62 523.29 46.26
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 36 Appendix 4. Key Data for Analyses (2): Constructed Data EG =HGIJ +1−EGIK, EL =HKIJ +M ⁄ , where g = 0.05 (average of growth rate of 1996-2000). For G , see Appendix 3, and for m t, see Appendix 1. BG=YG+ (1 -:BGIK, BL=YK:+Z ⁄ , where k = 0.01 (average growth rate of 1995-2000), For:?, see Appendix 3. Year Rate of obsolescence of technology ρ t Rate of depreciation of sold goods and services : :: : t UPM technology knowledge stock (Mil. Eur.) T t UPM market value (Mil. Eur.) V t Lead time between R&D and commerciali zation m t Internet dependence Finland (standardized 1995 = 1) I t UPM energy consumption (Barrel) E t 1990 6.9 0.037 244.4 211857.9 3.5 0.03 24.2 1991 6.9 0.034 259.6 212865.8 3.4 0.10 29.3 1992 6.9 0.035 274.8 213935.1 3.4 0.14 29.9 1993 7.0 0.041 290.0 215065.9 3.4 0.19 34.1 1994 7.0 0.043 305.2 216257.7 3.3 0.35 36.2 1995 7.0 0.040 308.0 216484.6 3.3 1.00 35.1 1996 7.0 0.045 323.4 217759.0 3.2 1.21 29.0 1997 7.0 0.041 339.6 219176.5 3.2 1.40 30.4 1998 7.1 0.038 356.9 220747.7 3.1 1.83 44.5 1999 7.1 0.036 375.3 222496.1 3.1 2.32 30.8 2000 7.2 0.043 391.4 224076.2 3.0 2.68 22.4 2001 7.2 0.043 406.0 225556.6 2.9 3.10 27.6 2002 7.2 0.043 425.3 227549.9 3.0 4.49 28.2 2003 7.1 0.039 446.0 229751.5 3.1 4.98 23.9 2004 7.0 0.038 465.1 231815.9 3.2 5.21 21.2 2005 7.0 0.035 483.9 233867.3 3.4 5.36 16.0 2006 6.9 0.037 503.9 236048.8 3.5 5.73 15.3 2007 6.8 0.037 521.3 237948.0 3.6 5.81 12.6 2008 6.8 0.032 540.6 240038.8 3.7 5.94 8.9 2009 6.7 0.027 551.9 241250.8 3.8 6.02 11.9 2010 6.7 0.030 567.6 242914.3 3.8 6.25 10.8 2011 6.7 0.035 579.8 244180.2 3.8 6.38 9.2 2012 6.7 0.037 589.2 245152.5 3.8 6.47 8.5 2013 6.7 0.035 594.9 245734.2 3.9 6.58 8.6 2014 6.7 0.033 604.0 246650.6 3.9 6.61 10.2 2015 6.7 0.036 606.4 246888.2 3.9 6.67 16.0 2016 6.7 0.032 613.8 247622.4 3.9 6.72 16.4 2017 6.7 0.033 619.8 248217.5 3.9 6.76 11.3
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 37 Appendix 5. Trend in Marginal Productivity of Technology Corresponding with Internal Rate of Return of R&D Investment (1990-2017) Fig. A1. Trend in UPM’s Marginal Productivity of Technology (1990-2017). Table A2 Trend in Marginal Productivity of Technology Corresponding with Internal Rate of Return of R&D Investment (1990-2017) Year r ρ m Q = (1 + mr)(r + ρ) H = g(ρ + g)(1 + mr)/r(r + ρ) 1990 0.109 0.069 2.789 0.232 0.401 1991 0.102 0.069 2.835 0.220 0.442 1992 0.098 0.069 2.880 0.215 0.465 1993 0.084 0.070 2.926 0.192 0.576 1994 0.082 0.070 3.284 0.192 0.613 1995 0.087 0.070 3.249 0.202 0.561 1996 0.072 0.071 3.217 0.177 0.720 1997 0.060 0.072 3.145 0.157 0.924 1998 0.047 0.074 3.063 0.139 1.247 1999 0.047 0.076 3.003 0.140 1.257 2000 0.054 0.077 2.933 0.153 1.030 2001 0.050 0.078 2.991 0.148 1.141 2002 0.049 0.060 3.066 0.126 1.178 2003 0.042 0.060 3.214 0.115 1.476 2004 0.041 0.059 3.369 0.115 1.494 2005 0.034 0.059 3.485 0.105 1.899 2006 0.039 0.059 3.573 0.111 1.647
MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 38 2007 0.043 0.059 3.670 0.118 1.426 2008 0.044 0.059 3.765 0.119 1.419 2009 0.040 0.058 3.803 0.114 1.571 2010 0.038 0.058 3.824 0.110 1.706 2011 0.043 0.058 3.838 0.118 1.444 2012 0.031 0.058 3.857 0.099 2.236 2013 0.030 0.058 3.861 0.098 2.273 2014 0.023 0.058 3.871 0.088 3.190 2015 0.013 0.058 3.879 0.074 6.313 2016 0.009 0.058 3.886 0.070 8.947 2017 0.005 0.058 5.4713 0.064 19.185 pB pE =iB iE =B G E G r1−B G E G st= 1+Q G d G d G + G r: Long term interest rate 10 for euro area expressed as percent per annum (Source: OECD). ρ: Rate of obsolescence of technology. m: Lead time between R&D and commercialization. g: Increase in rate of R&D at initial stage. In the case that H > 1, p/pE < 0 (see Appendix 2). Table 2 demonstrates the stability of H, in which H > 1after 2003. 10 ‘ Long-term interest rates refer to government bonds maturing in ten years. Rates are mainly determined by the price charged by the lender, the risk from the borrower and the fall in the capital value. Long-term interest rates are generally averages of daily rates, measured as a percentage. These interest rates are implied by the prices at which the government bonds are traded on financial markets, not the interest rates at which the loans were issued. In all cases, they refer to bonds whose capital repayment is guaranteed by governments. Long-term interest rates are one of the determinants of business investment. Low long-term interest rates encourage investment in new equipment and high interest rates discourage it. Investment is, in turn, a major source of economic growth’ (OECD).
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