Chapter 24: Policies for the Energy Technology Innovation System (ETIS)

Innovation and technological change are integral to the energy system transformations described in the Global Energy Assessment (GEA) pathways. Energy technology innovations range from incremental improvements to radical breakthroughs and from technologies and infrastructure to social institutions and individual behaviors. This Executive Summary synthesizes the main policy-relevant findings of Chapter 24 . The innovation process involves many stages – from research through to incubation, demonstration, (niche) market creation, and ultimately, widespread diffusion. Feedbacks between these stages influence progress and likely success, yet innovation outcomes are unavoidably uncertain. Innovations do not happen in isolation; interdependence and complexity are the rule under an increasingly globalized innovation system. Any emphasis on particular technologies or parts of the energy system, or technology policy that emphasizes only particular innovation stages or processes (e.g., an exclusive focus on energy supply from renewables, or an exclusive focus on Research and Development [R&D], or feed-in tariffs) is inadequate given the magnitude and multitude of challenges represented by the GEA objectives. A first, even if incomplete, assessment of the entire global resource mobilization (investments) in both energy supply and demand-side technologies and across different innovation stages suggests current annual Research, Development & Demonstration (RD&D) investments of some US$50 billion, market formation investments (which rely on directed public policy support) of some US$150 billion, and an estimated US$1 trillion to US$5 trillion investments in mature energy supply and end-use technologies (technology diffusion). Major developing economies like Brazil, India and above all China, have become significant players in global energy technology RD&D, with public- and private-sector investments approaching US20 billion, or almost half of global innovation investments, which is significantly above the Organisation for Economic Co-operation and Development (OECD) countries’ public-sector energy RD&D investments (US13 billion). Important data and information gaps exist for all stages of the energy technology innovation investments outside public sector R&D funding in OECD countries, particularly in the areas of recent technology-specific private sector and non-OECD R&D expenditures, and energy end-use diffusion investments. Analysis of investment flows into different stages of the innovation process reveals an apparent mismatch of resource allocation and resource needs. Early in the innovation process, public expenditure on R&D is heavily weighted toward large-scale supply-side technologies. Of an estimated US$50 billion in annual investment globally, less than US$10 billion are allocated to energy end-use technologies and energy efficiency. Later in the innovation process, annual market (diffusion) investment in supply-side plant and infrastructure total roughly US 2005 $0.8 trillion, compared with a conservative estimate of some US$1–4 trillion spent on demand-side technologies. These relative proportions are, however, insufficiently reflected in market deployment investment incentives of technologies, which almost exclusively focus on supply-side options, to the detriment of energy end use in general and energy efficiency in particular foregoing also important employment and economic growth stimuli effects from end-use investments that are critical in improving energy efficiency. The need for investment to support the widespread diffusion of efficient end-use technologies is also clearly shown in the GEA pathway analyses. The demand side generally tends to contribute more than the supply-side options to realizing the GEA goals. This apparent mismatch suggests the necessity of rebalancing public innovation expenditure and policy incentives to include smaller-scale demand-side technologies within innovation portfolios . Given persistent barriers to the adoption of energy-efficient technologies even when they are cost competitive on a life cycle basis, technology policies need to move toward a more integrated approach, simultaneously stimulating the development as well as the adoption of energy efficiency technologies and measures. R&D initiatives that fail to incentivize consumers to adopt the outcomes of innovation efforts (e.g., promoting energy-efficient building designs without strengthened building codes, or Carbon Capture and Storage [CCS] development without a price on carbon) risk not only being ineffective but also precluding the market feedback and learning that are critical for continued improvements in technologies. Little systematic data are available for private-sector innovation inputs (including investments), particularly in developing countries. Information is patchy on innovation spillovers or transfers between technologies, between sectors, and between countries. It is also not clearly understood how fast knowledge generated by innovation investments may depreciate, although policy and investment volatility are recognized as critical factors. Technical performance and economic characteristics for technologies in the lab, in testing, and in the field are not routinely available. Innovation successes are more widely documented than innovation failures. Although some of the data constraints reflect legitimate concerns to protect intellectual property, most do not. Standardized mechanisms to collect, compile, and make data on energy technology innovation publicly available are urgently needed. The benefits of coupling these information needs to public policy support have been clearly demonstrated. A positive policy example is provided by the early US Solar Thermal Electricity Program, which required formal, non-proprietary documentation of cost improvements resulting from public R&D support for the technology. The energy technology innovation system is founded on knowledge generation and flows. These are increasingly global, but this global knowledge needs to be adapted, modified, and applied to local conditions. The generation of knowledge requires independent and stable institutions to balance the competing needs and interests of the market, policy makers, and the R&D community. The technology roadmaps and the policy regime that characterize innovation in end-use technologies in the Japanese Top Runner program are a good example of the actor coordination and knowledge exchange needed to stimulate technological innovation. Generated knowledge needs to spread through the innovation system. Knowledge flows and feedbacks create and strengthen links between different actors. This can take place formally or informally. Policies that are overly focused on the development of technological “hardware” should be rebalanced to support interactions and learning between actors. The provision of test facilities in the early years of the Danish wind industry is a good example of how policy can support knowledge flows and the strengthening of collaborative links within networks of actors in an innovation system (energy companies, turbine manufacturers, local owners). Long-term, consistent, and credible institutions underpin investments in knowledge generation, particularly from the private sector, and consistency does not preclude learning. Knowledge institutions must be responsive to experience and adaptive to changing conditions. Although knowledge flows through international cooperation and experience sharing cannot presently be analyzed in detail, the scale of the innovation challenge emphasizes their importance alongside efforts to develop the capacity to absorb and adapt knowledge to local needs and conditions. The current global cooperation in energy technology innovation is well illustrated by the International Energy Agency (IEA) technology cooperation programs reviewed in Section 4.4 ; all invariably show a sparse involvement from developing countries. Clear, stable, and consistent expectations about the direction and shape of the innovation system are necessary for innovation actors to commit time, money, and effort with only the uncertain promise of distant returns. To date, policy support for the innovation system has been characterized by volatility, changes in emphasis, and a lack of clarity. The debilitating consequences on innovation outcomes of stop-go policies are well illustrated by the wind and solar water heater programs in the United States through the 1980s, as well as the large-scale (but fickle) US efforts to develop alternative liquid fuels (Synfuels). The legacy of such innovation policy failures can be long lasting. The creation of a viable and successful Brazilian ethanol industry through consistent policy support over several decades, including agricultural R&D, guaranteed ethanol purchase prices, and fuel distribution infrastructures, as well as vehicle manufacturing (flex fuel cars), is a good example of a stable, aligned, and systemic technology policy framework. It is worth noting that even in this highly successful policy example, it has taken some three decades for domestic renewable ethanol to become directly cost competitive with imported gasoline. Policies need also to be aligned . Innovation support through early research and development is undermined by an absence of support for their demonstration to potential investors and their subsequent deployment in potential markets. Policies to support innovations in low-carbon technologies are undermined by subsidies to support carbon-intensive technologies. Fuel efficiency standards that set minimum (static) efficiency floors fail to stimulate continuous technological advances, meaning innovations in efficiency stagnate once standards are reached. As a further example of misalignment, the lack of effective policies to limit the demand for mobility mean efficiency improvements can be swamped by rising activity levels. Policies should support a wide range of technologies. However seductive they seem, “silver bullets” do not exist without the benefit of hindsight. Innovation policies should use a portfolio approach under a risk-hedging and “insurance policy” decision-making paradigm. Portfolios need to recognize also that innovation is inherently risky. Failures vastly outnumber successes. Experimentation, often for prolonged periods (decades rather than years), is critical to generate the applied knowledge necessary to support the scaling up of innovations to the mass market. The whole energy system should be represented in innovation portfolios, not only particular groups or types of technologies; the entire suite of innovation processes should be included, not just particular stages or individual mechanisms. Less capital-intensive, smaller-scale (i.e., granular ) technologies or projects are less of a drain on scarce resources, and failure has less serious consequences. Granular projects and technologies with smaller scales (MW rather than GW) therefore should figure prominently in any innovation portfolio. Finally, public technology policy should not be beholden to incumbent interests that favor support for particular technologies that either perpetuate the lock-in of currently dominant technologies or transfer all high innovation risks of novel concepts to the public sector

Biotechnology and the Politics of Truth : From the Green Revolution to an Evergreen Revolution

This paper investigates why and how issues around the diffusion of GM technologies and products to developing countries have become so central to a debate which has shifted away from technical issues of cost-benefit optimisation in a context of uniform mass production and consumption in the North, to the moral case for GM crops to feed the hungry and aid ‘development’ in the South. Using comparison between agricultural biotechnology and the ‘Green Revolution’ as a cross cutting theme, the contributions of this paper are threefold. Firstly, by analysing biotechnology as a set of overlapping frames within a discursive formation, four frames are identified which summarise key challenges presented by biotechnology era. Secondly, the use of Foucault's concept of bio-power to synthesise key themes from the frame analysis illuminates the ‘revolutionary’ nature of the biotech revolution. Thirdly, the potential of actor-network theory to provide a tools for the empirical study of processes of (re)negotiation of nature/society relations in the context of agricultural biotechnology controversies is explored

Who leads research productivity growth? Guidelines for R&D policy-makers

[EN] This paper evaluates to what extent policy-makers have been able to promote the creation and consolidation of comprehensive research groups that contribute to the implementation of a successful innovation system. Malmquist productivity indices are applied in the case of the Spanish Food Technology Program, finding that a large size and a comprehensive multi-dimensional research output are the key features of the leading groups exhibiting high efficiency and productivity levels. While identifying these groups as benchmarks, we conclude that the financial grants allocated by the program, typically aimed at small-sized and partially oriented research groups, have not succeeded in reorienting them in time so as to overcome their limitations. We suggest that this methodology offers relevant conclusions to policy evaluation methods, helping policy-makers to readapt and reorient policies and their associated means, most notably resource allocation (financial schemes), to better respond to the actual needs of research groups in their search for excellence (micro-level perspective), and to adapt future policy design to the achievement of medium-long term policy objectives (meso and macro-level).Jiménez Saez, F.; Zabala Iturriagagoitia, JM.; Zofio, JL. (2013). Who leads research productivity growth? Guidelines for R&D policy-makers. Scientometrics. 94(1):273-303. doi:10.1007/s11192-012-0763-0S273303941Abbring, J. H., & Heckman, J. J. (2008). Dynamic policy analysis. In L. Mátyás & P. Sevestre (Eds.), The econometrics of panel data (3rd ed., pp. 795–863). Heidelberg: Springer.Acosta Ballesteros, J., & Modrego Rico, A. (2001). Public financing of cooperative R&D projects in Spain: the concerted projects under the national R&D plan. Research Policy, 30, 625–641.Arbel, A. (1981). Policy evaluation in the dynamic input–output model. International Journal of Systems Science, 12, 255–260.Arnold, E. (2004). Evaluation research and innovation policy: A systems world needs systems evaluations. Research Evaluation, 13, 3–17.Arrow, J. K. (1962). Economic welfare and the allocation of resources for inventions. In R. Nelson (Ed.), The rate and direction of inventive activity: Economic and social factor (pp. 609–625). Princeton: Princeton University Press and NBER.Autio, E. (1997). New, technology-based firms in innovation networks symplectic and generative impacts. Research Policy, 26, 263–281.Balk, B. (2001). Scale efficiency and productivity change. Journal of Productivity Analysis, 15, 153–183.Balzat, M., & Hanusch, H. (2004). Recent trends in the research on national innovation systems. Journal of Evolutionary Economics, 14, 197–210.Berg, S. A., Førsund, F. R., & Jansen, E. S. (1992). Malmquist indices of productivity growth during the deregulation of Norwegian banking. Scandinavian Journal of Economics, 94, S211–S228.Bergek, A., Carlsson, B., Lindmark, S., Rickne, A., & Jacobsson, S. (2008). Analyzing the functional dynamics of technological innovation systems: A scheme of analysis. Research Policy, 37, 407–429.Bonaccorsi, A., & Daraio, C. (2005). Exploring size and agglomeration effects on public research productivity. Scientometrics, 63(1), 87–120.Buisseret, T. J., Cameron, H., & Georghiou, L. (1995). What difference does it make? Additionality in the public support of R&D in large firms. International Journal of Technology Management, 10, 587–600.Bustelo, M. (2006). The potential role of standards and guidelines in the development of an evaluation culture in Spain. Evaluation, 12, 437–453.Chavas, J. P., & Cox, T. M. (1999). A generalized distance function and the analysis of production efficiency. Southern Economic Journal, 66, 295–318.CICYT. (1987). Programa Nacional de Tecnología de los Alimentos. Madrid: Ministerio de Educación y Ciencia.CICYT (1988). Plan Nacional de Investigación Científica y Desarrollo Tecnológico 1988–1991. Ministerio de Educación y Ciencia, Secretaría de Estado de Universidades e Investigación, Madrid.Cooper, W. W., Seiford, L. M., & Tone, K. (2000). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-software. Boston: Kluwer Academic Publishers.David, P., Mowery, D., & Steinmueller, W. E. (1994). Analyzing the economic payoffs from basic research. In D. Mowery (Ed.), Science and technology policy in interdependent economies (pp. 57–78). Boston: Kluwer Academic Publishers.Dopfer, K., Foster, J., & Potts, J. (2004). Micro-meso-macro. Journal of Evolutionary Economics, 14, 263–279.Edquist, C., & Hommen, L. (2008). Comparing national systems of innovation in Asia and Europe: Theory and comparative framework. In C. Edquist & L. Hommen (Eds.), Small country innovation systems: Globalisation, change and policy in Asia and Europe (pp. 1–28). Cheltenham: Edward Elgar.Färe, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review, 84, 66–83.Farrell, M. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A, General, 120(3), 253–281.Førsund, F. R. (1993). Productivity growth in Norwegian ferries. In H. O. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency: Techniques and applications (pp. 352–373). New York: Oxford University Press.Førsund, F. R. (1997). The Malmquist productivity index, TFP and scale. University of Oslo, Oslo: Working Paper, Department of Economics and Business Administration.Freeman, C. (1987). Technology policy and economic performance: Lessons from Japan. London: Printer Publishers.García-Martínez, M., & Briz, J. (2000). Innovation in the Spanish food & drink industry. International Food and Agribusiness Management Review, 3, 155–176.Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, M. (1994). The new production of knowledge: The dynamics of science and research in contemporary societies. London: Sage Publications.Grammatikopoulos, V., Kousteiios, A., Tsigilis, N., & Theodorakis, Y. (2004). Applying dynamic evaluation approach in education. Studies in Educational Evaluation, 30, 255–263.Grifell-Tatjé, E., & Lovell, C. A. K. (1999). A generalized Malmquist productivity index. Top, 7(1), 81–101.Grimpe, C., & Sofka, W. (2007). Search patterns and absorptive capacity: A comparison of low- and high-technology firms from thirteen European countries. Discussion paper no. 07-062. Centre for European Economic Research (ZEW), Mannheim, Germany.Guan, J., & Wang, J. (2004). Evaluation and interpretation of knowledge production efficiency. Scientometrics, 59(1), 131–155.Hekkert, M. P., Suurs, R. A. A., Negro, S. O., Kuhlmann, S., & Smits, R. E. H. M. (2007). Functions of innovation systems: A new approach for analysing technological change. Technological Forecasting and Social Change, 74, 413–432.Jiménez-Sáez, F. (2005). Una Evaluación del Programa Nacional de Tecnología de Alimentos: análisis de la articulación fomentada sobre el Sistema Alimentario de Innovación en España. PhD dissertation, Servicio de Publicaciones de la Universidad Politécnica de Valencia, Valencia.Jiménez-Sáez, F., Zabala-Iturriagagoitia, J. M., Zofío, J. L., & Castro-Martínez, E. (2011). Evaluating research efficiency within National R&D Programmes. Research Policy, 40, 230–241.Kao, C. (2008). Efficiency analysis of university departments: An empirical study. OMEGA, 36, 653–664.Kuhlmann, S. (2003). Evaluation of research and innovation policies: A discussion of trends with examples from Germany. International Journal of Technology Management, 26, 131–149.Laitinen, E. K. (2002). A dynamic performance measurement system: Evidence from small Finnish technology companies. Scandinavian Journal of Management, 18, 65–99.Laranja, M., Uyarra, E., & Flanagan, K. (2008). Policies for science, technology and innovation: Translating rationales into regional policies in a multi-level setting. Research Policy, 37(5), 823–835.Lee, T.-L., & von Tunzelman, N. (2005). A dynamic analytic approach to national innovation systems: The IC industry in Taiwan. Research Policy, 34, 425–440.Lipsey, R., & Carlaw, K. (1998). A structuralist assessment of technology policies: Taking Schumpeter seriously on policy. Ottawa: Industry Canada Research Publications Program.Lipsey, R., Carlaw, K., & Bekar, C. (2005). Economic transformations: General purpose technologies and long term economic growth. Oxford: Oxford University Press.Lundvall, B. Å. (1992). National systems of innovation: Toward a theory of innovation and interactive learning. London: Printer Publishers.Lundvall, B. Å., Johnson, B., Andersen, E. S., & Dalum, B. (2002). National systems of production, innovation and competence building. Research Policy, 31, 213–231.Markard, J., & Truffer, B. (2008). Actor-oriented analysis of innovation systems: Exploring micro-meso level linkages in the case of stationary fuel cells. Technology Analysis & Strategic Management, 20, 443–464.Metcalfe, J. S. (2002). Equilibrium and evolutionary foundations of competition and technology policy: New perspectives on the division of labour and the innovation process. CRIC Working Papers series, University of Manchester.Miettinen, R. (1999). The riddle of things. Activity theory and actor network theory as approaches of studying innovations. Mind, Culture and Activity, 6, 170–195.Molas-Gallart, J., & Davies, A. (2006). Toward theory-led evaluation: The experience of European science, technology, and innovation policies. American Journal of Evaluation, 27, 64–82.Mytelka, L. K., & Smith, K. (2002). Policy learning and innovation theory: An interactive and co-evolving process. Research Policy, 31, 1467–1479.Olazarán, M., Lavía, C., & Otero, B. (2004). ¿Hacia una segunda transición en la ciencia? Política científica y grupos de investigación. Revista Española de Sociología, 4, 143–172.Potts, J. (2007). The innovation system & economic evolution. Productivity commission submission, public support for science & innovation, productivity commission, Camberra.Ray, S., & Desli, E. (1997). Productivity growth, technical progress, and efficiency change in industrialized countries: Comment. American Economic Review, 87(5), 1033–1039.Rip, A., & Nederhof, A. J. (1986). Between dirigism and laissez-faire: Effects of implementing the science policy priority for biotechnology in the Netherlands. Research Policy, 15, 253–268.Schmidt, E. K., Graversen, E. K., & Langberg, K. (2003). Innovation and dynamics in public research environments in Denmark: A research-policy perspective. Science and Public Policy, 30, 107–116.Schmoch, U., & Schubert, T. (2009). Sustainability of incentives for excellent research—The German case. Scientometrics, 81(1), 195–218.Shephard, R. (1970). Theory of cost and production functions. New Jersey: Princeton University Press.Simar, L., & Wilson, P. W. (1998). Productivity growth in industrialized countries. Discussion paper 9810, Universite Catholique de Louvain, Belgium.Van Raan, A. F. J. (2000). R&D evaluation at the beginning of the new century. Research Evaluation, 8, 81–86.Zofio, J. L. (2007). Malmquist productivity index decompositions: A unifying framework. Applied Economics, 39, 2371–2387.Zofio, J. L., & Lovell, C. A. K. (1998). Yet another Malmquist productivity index decomposition. Working paper, Department of Economics, University of Georgia, Athens, GA 30602, USA.Zofio, J. L., & Lovell, C. A. K. (2001). Graph efficiency and productivity measures: An application to US agriculture. Applied Economics, 33(10), 1433–1442.Zofio, J. L., & Prieto, A. M. (2006). Return to dollar, generalized distance function and the Fisher productivity index. Spanish Economic Review, 8, 113–138

The Innovation Systems of Latin America and the Caribbean

It is now widely recognized that we live in a knowledge-based economy; in fact, knowledge is the driving factor behind productivity growth. The share of knowledge-intensive sectors in the world economy`s value-added and employment has been rising for a number of years. This trend is particularly pronounced in the developed countries, where by 1999 knowledge-based industries share of GDP was already above 50 percent, up from 45 percent in 1985 (OECD, 1999; OECD, 2000a). Furthermore, knowledge-driven innovation has become a decisive factor in the competitiveness of both nations and firms

Skipping of Exons by Premature Termination of Transcription and Alternative Splicing within Intron-5 of the Sheep SCF Gene: A Novel Splice Variant

Stem cell factor (SCF) is a growth factor, essential for haemopoiesis, mast cell development and melanogenesis. In the hematopoietic microenvironment (HM), SCF is produced either as a membrane-bound (−) or soluble (+) forms. Skin expression of SCF stimulates melanocyte migration, proliferation, differentiation, and survival. We report for the first time, a novel mRNA splice variant of SCF from the skin of white merino sheep via cloning and sequencing. Reverse transcriptase (RT)-PCR and molecular prediction revealed two different cDNA products of SCF. Full-length cDNA libraries were enriched by the method of rapid amplification of cDNA ends (RACE-PCR). Nucleotide sequencing and molecular prediction revealed that the primary 1519 base pair (bp) cDNA encodes a precursor protein of 274 amino acids (aa), commonly known as ‘soluble’ isoform. In contrast, the shorter (835 and/or 725 bp) cDNA was found to be a ‘novel’ mRNA splice variant. It contains an open reading frame (ORF) corresponding to a truncated protein of 181 aa (vs 245 aa) with an unique C-terminus lacking the primary proteolytic segment (28 aa) right after the D175G site which is necessary to produce ‘soluble’ form of SCF. This alternative splice (AS) variant was explained by the complete nucleotide sequencing of splice junction covering exon 5-intron (5)-exon 6 (948 bp) with a premature termination codon (PTC) whereby exons 6 to 9/10 are skipped (Cassette Exon, CE 6–9/10). We also demonstrated that the Northern blot analysis at transcript level is mediated via an intron-5 splicing event. Our data refine the structure of SCF gene; clarify the presence (+) and/or absence (−) of primary proteolytic-cleavage site specific SCF splice variants. This work provides a basis for understanding the functional role and regulation of SCF in hair follicle melanogenesis in sheep beyond what was known in mice, humans and other mammals