Abstract
Dynamic efficiency (or the ability of a policy instrument to generate a continuous incentive for technical improvements and cost reductions in technologies) is central to the assessment and choice of environmental and energy policies in long-run scenarios where innovation lock-in is relevant. This is also the case in instruments that support electricity from renewable energy sources (RES-E). In contrast with effectiveness and static efficiency assessment criteria, the innovation effects of such support have received much less attention from both a theoretical and an empirical perspective. Several theoretical perspectives have paid some attention to these innovation effects, including the traditional economics approach, the systems of innovation perspective and the literature on learning effects. The aims of this chapter are to provide an overview of those perspectives and to build bridges between them.
The work in this paper has been supported by a mobility grant awarded by the Spanish Ministry of Science and Innovation to Mercedes Bleda as a visiting Research Fellow at the CCHS, CSIC.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
However, some authors are doubtful about the relative importance of dynamic efficiency criteria compared with more traditional, static efficiency criteria. For example, Parry et al. (2003) stress that the welfare gain from innovation is sometimes not much greater than the welfare gain of efficiently abating pollutants by means of conventional technologies. Requate (2005) observes that resources to engage in R&D are scarce. Hence, environmental technological progress may crowd out other strands of welfare enhancing technological progress. Finally, Fischer and Newell (2008) argue that the underlying process of technological change turns out to be far less important than the incentives to use technology efficiently to reduce emissions.
- 2.
Following Marechal (2007), we use the word traditional to avoid the problems arising from the somewhat ambiguous use of the term neoclassical. Traditional economics refers to the Walrasian model of welfare economics, which can be defined as the theoretical synthesis of the Marshallian approach with marginal production theory (Marechal 2007).
- 3.
- 4.
Assessment in terms of system functions is one of the main approaches of the systems of innovation literature. Other innovation system studies have placed more emphasis on structural analyses (Carlsson et al. 2002; Jacobsson and Johnson 2000). Currently, some authors are concentrating on the integration of both approaches (Markard and Truffer 2008).
- 5.
For example, in his analysis of wind energy deployment and policy in Denmark, Spain and Sweden, Meyer (2007) provides empirical evidence of the role of the coalition of forces in encouraging wind energy in Spain.
- 6.
The assessment of Astrand and Neij (2006) shows that early inflexible steering of technology and market development, together with a lack of comprehensive, long-term strategy, lack of continuity in policy interventions and weak combinations of policy programmes and measures, have contributed to very limited wind power development in Sweden.
- 7.
For example, Junginger et al. (2006) show that for technologies developed on a local level (e.g. biogas plants), learning-by-using and learning-by-interacting are important learning mechanisms whereas for CHP plants utilising fluidised bed boilers, upscaling is probably one of the main mechanisms behind cost reductions. Nemet and Baker (2010) show that certain components of the costs of solar PV improved with R&D investment, whereas others responded to increased deployment of the technology.
- 8.
- 9.
- 10.
- 11.
Indeed, learning effects introduce nonlinearities and positive feedbacks into the models in which they are used (the more a technology is used, the greater the incentive for using it more) (McDonald and Schrattenholzer 2001).
- 12.
Watanabe et al. (2000) convincingly showed that the political environment behind Japanese government support for PV innovation was critical in developing the interindustry partnerships basic public research and broad-based market promotion for this fledging industry which in turn led to and was a result of learning effects. The authors analysed the Japanese solar PV Sunshine Project which aimed to encourage the broad involvement of cross-sectoral industry, stimulate inter-technology stimulation and cross-sectoral technology spillover and induce vigorous industry investment in PV R&D, leading to an increase in industry’s PV technology knowledge stock. They showed that an increase in this technology knowledge stock contributed to a dramatic increase in solar cell production. These increases led to a dramatic decrease in solar cell production price, and this decrease induced a further increase in solar cell production. An increase in solar cell production induced further PV R&D, thus creating a “virtuous cycle” between R&D, market growth, learning effects and price reduction.
- 13.
For example, in the analysis of the Dutch wind-offshore sector, Smit et al. (2007) argued that there was weak learning-by-interacting by the actors from the industrial part of the technology system, who should get better access to actors in academia and actors in the oil and gas industry. The authors showed that there were several barriers hindering this interaction process. In contrast, they also showed that in the Danish case, learning-by-interacting occurred between knowledge institutes, component suppliers, project operators and turbine manufacturers and Danish policies contributed to the formation of these interactions.
References
Arrow, K. J. (1962). The economic implications of learning by doing. Review of Economic Studies, 29, 155–173.
Astrand, K., & Neij, L. (2006). An assessment of governmental wind power programmes in Sweden—using a systems approach. Energy Policy, 34, 277–296.
Bergek, A., Jacobsson, S., Carlsson, B., Lindmark, S., & Rickne, A. (2008). Analyzing the functional dynamics of technological innovation systems – A scheme of analysis. Research Policy, 37(3), 407–429.
Carlsson, B., Jacobsson, S., Holmen, M., & Rickne, A. (2002). Innovation systems: Analytical and methodological issues. Research Policy, 31(2), 233–245.
Del Río, P. (2009). The empirical analysis of the determinants for environmental technological change: A research agenda. Ecological Economics, 68(3), 861–878.
Del Río, P., & Gual, M. (2004). The promotion of green electricity in Europe: Present and future. European Environment Journal, 14, 219–234.
Del Río, P., & Unruh, G. (2007). Overcoming the lock-out of renewable energy technologies in Spain: The cases of wind and solar electricity. Renewable and Sustainable Energy Review, 11(7), 1498–1513.
Edenhofer, O., Carraro C., Hourcade J.-C., Neuhoff K., Luderer G., Flachsland C., Jakob M., Popp A., Steckel J., Strohschein J., Bauer N., Brunner S., Leimbach M., Lotze-Campen H., Bosetti V., de Cian E., Tavoni M., Sassi O., Waisman H., Crassous-Doerfler R., Monjon S., Dröge S., van Essen H., del Río P., Türk A. et al. (Ed.) (2009). The Economics of decarbonization (Report of the RECIPE Project). Potsdam: Potsdam-Institute for Climate Impact Research.
Edquist, C. (2005). Systems of innovation: Perspectives and challenges. In J. Fagerberg, D. Mowery, & R. Nelson (Eds.), Oxford handbook of innovation (pp. 181–208). Oxford: Oxford University Press.
Edquist, C., & Johnson, B. (1997). Institutions and organisations in systems of innovation. In C. Edquist (Ed.), Systems of innovation: Technologies, institutions and organizations. London/Washington: Pinter/Cassell Academic.
Faber, A., & Frenken, K. (2009). Models in evolutionary economics and environmental policy: Towards an evolutionary environmental economics. Technological Forecasting and Social Change, 76, 462–470.
Fischer, C., & Newell, R. (2008). Environmental and technology policies for climate mitigation. Journal of Environmental Economics and Management, 55(2), 142–162.
Foxon, T., & Andersen, M. (2009). The greening of innovation systems for eco-innovation – Towards an evolutionary climate mitigation policy. Paper presented at the Summer DRUID Conference, Copenhagen Business School.
Foxon, T., Gross, R., Chase, A., Howes, J., Arnall, A., & Anderson, D. (2005). U.K. Innovation systems for new and renewable energy technologies: Drivers, barriers and systems failures. Energy Policy, 33, 2123–2137.
Geels, F., & Schot, J. (2007). Typology of sociotechnical transition pathways. Research Policy, 36, 399–417.
Grübler, A., Nakicenovic, N., & Victor, D. (1999). Dynamics of energy technologies and global change. Energy Policy, 27(5), 247–280.
Hekkert, M., & Negro, S. (2009). Functions of innovation systems as a framework to understand sustainable technological change: Empirical evidence from earlier claims. Technological Forecasting and Social Change, 76, 584–594.
Hekkert, M., Suurs, R., Negro, S., Kuhlmann, S., & Smits, R. (2007). Functions of innovation systems: A new approach for analysing technological change. Technological Forecasting and Social Change, 74, 413–432.
Hicks, J. (1932). The theory of wages. London: Macmillan.
IEA. (2008). Energy technology perspectives. Paris: IEA.
Jacobsson, S. (2008). The emergence and troubled growth of a ‘biopower’ innovation system in Sweden. Energy Policy, 36, 1491–1508.
Jacobsson, S., & Bergek, A. (2004). Transforming the energy sector: The evolution of technology systems in renewable energy technology. Industrial and Corporate Change, 13(5), 815–849.
Jacobsson, S., & Johnson, A. (2000). The diffusion of renewable energy technology: An analytical framework and key issues for research. Energy Policy, 28(9), 625–640.
Jaffe, A. B., Newell, R., & Stavins, R. (2002). Environmental policy and technological change. Environment and Resource Economics, 22(1–2), 41–69.
Jaffe, A. B., Newell, R., & Stavins, R. (2005). A tale of two market failures: Technology and environmental policy. Ecological Economics, 54(2–3), 164–174.
Junginger, M., de Visser, E., Hjort-Gregersen, K., Koornneef, J., Raven, R., Faaij, A., & Turkenburg, W. (2006). Technological learning in bioenergy systems. Energy Policy, 34, 4024–4041.
Kahouli-Brahmi, S. (2008). Technological learning in energy–environment–economy modelling: A survey. Energy Policy, 36, 138–162.
Kalowekamo, J.,& Baker, E. (2009). Estimating the manufacturing cost of purely organic solar cells. Solar Energy, 83(8), 1224–1231.
Kemp, R. (1996). The transition from hydrocarbons: The issues for policy. In S. Faucheux, D. W. Pearce, & J. Proops (Eds.), Models of sustainable development (pp. 151–175). Cheltenham: Edward Elgar.
Kneese, A., & Schulze, C. (1975). Pollution, prices, and public policy. Washington, DC: Brookings Institute.
Köhler, J., Grubb, M., Popp, D., & Edenhofer, O. (2006). The transition to endogenous technical change in climate-economy models: A technical overview to the innovation modelling comparison project. Energy Journal, 27, 17–56.
Lee, B., Lliev, L., & Preston, F. (2009). Who owns our low carbon future? Intellectual property and energy technologies. London: Chatham House Report.
Lundvall, B. (1988). Innovation as an interactive process: From user-producer interaction to the national system of innovation. In G. Dosi et al. (Eds.), Technical change and economic theory. London: Pinter Publishers.
Lundvall, B. (1992). National systems of innovation. London: Printer Publisher.
Lundvall, B., & Johnson, B. (1994). The learning economy. Journal of Industry Studies, 1(2), 23–42.
Malerba, F. (2004). Sectoral systems of innovation: Basic concepts. In F. Malerba (Ed.), Sectoral systems of innovation: Concepts, issues and analyses of six major sectors (pp. 9–41). Cambridge: Cambridge University Press.
Marechal, K. (2007). The economics of climate change and the change for climate in economics. Energy Policy, 35(10), 5181–5194.
Markard, J., & Truffer, B. (2008). Technological innovation systems and the multi-level perspective: Towards an integrated Framework. Research Policy, 37, 596–615.
Markard, J., Stadelmann, M., & Truffer, B. (2009). Analysis of variation in innovation systems. Identifying potential development options for biogas in Switzerland. Research Policy, 38(4), 655–667.
McDonald, A., & Schrattenholzer, L. (2001). Learning rates for energy technologies. Energy Policy, 29(4), 255–261.
Meyer, N. (2007). Learning from wind energy policy in the EU: Lessons from Denmark, Sweden and Spain. European Environment, 17, 347–362.
Negro, S., Hekkert, M., & Smits, R. (2007). Explaining the failure of the Dutch innovation system for biomass digestion—A functional analysis. Energy Policy, 35, 925–938.
Nemet, G., & Baker, E. (2010). Demand subsidies versus R&D: Comparing the uncertain impacts of policy on a pre-commercial low-carbon energy technology. The Energy Journal, 30(4), 49–80.
Newell, R. (2008). A U.S. innovation strategy for climate change mitigation (Discussion Paper 2008-15; Hamilton Project). Washington, D.C.: Brookings Institution.
Newell, R. (2010). The role of markets and policies in delivering innovation for climate change mitigation. Oxford Review of Economic Policy, 26(2), 253–269.
Nill, J., & Kemp, R. (2009). Evolutionary approaches for sustainable innovation policies: From niche to paradigm. Research Policy, 38(4), 668–680.
Palmer, K., & Burtraw, D. (2005). Cost-effectiveness of renewable electricity policies. Energy Economics, 27(6), 873–894.
Parry, I., Pizer, W., & Fischer, C. (2003). How large are the welfare gains from technological innovation induced by environmental policies? Journal of Regulatory Economics, 23(3), 237–255.
Popp, D. (2010). Innovation and climate policy (NBER Working Paper 15673). Cambridge: NBER. http://www.nber.org/papers/w15673
Requate, T. (2005). Dynamic incentives by environmental policy instruments—A survey. Ecological Economics, 54, 175–195.
Rip, A., & Kemp, R. (1998). Technological change. In S. Rayner & E. Malone (Eds.), Human choice and climate change (Vol. 2, pp. 327–399). Columbus: Battelle.
Rogge, K., & Hoffmann, V. (2010). The impact of the EU ETS on the sectoral innovation system of power generation technologies – Findings for Germany. Energy Policy, 38, 7639–7652.
Rosenberg, N. (1982). Inside the black box: Technology and economics. Cambridge: Cambridge University Press.
Sagar, A., & van der Zwaan, B. (2006). Technological innovation in the energy sector: R&D, deployment and learning-by-doing. Energy Policy, 34, 2601–2608.
Smit, T., Junginger, M., & Smits, R. (2007). Technological learning in offshore wind energy: Different roles of the government. Energy Policy, 35, 6431–6444.
Smits, R., & Kuhlmann, S. (2004). The rise of systemic instruments in innovation policy. International Journal of Foresight and Innovation Policy, 1(1), 4–32.
Suurs, R., & Hekkert, M. (2009). Cumulative causation in the formation of a technological innovation system: The case of biofuels in the Netherlands. Technological Forecasting and Social Change, 76, 1003–1020.
Taylor, M. (2008). Beyond technology-push and demand-pull: Lessons from California’s solar policy. Energy Economics, 30, 2829–2854.
Unruh, G. (2000). Understanding carbon lock-in. Energy Policy, 28(12), 817–830.
Unruh, G. (2002). Escaping carbon lock-in. Energy Policy, 30, 317–325.
Van Mierlo, B., Leeuwis, C., Smiths, R., & Woolthuis, R. (2010). Learning towards system innovation: Evaluating a systemic instrument. Technological Forecasting and Social Change, 77(2), 318–334.
Verbruggen, A. (2009). Performance evaluation of renewable energy support policies, applied on Flanders’ tradable certificates system. Energy Policy, 37(4), 1385–1394.
Walz, R., & Schleich, J. (2009). The economics of climate change policies: Macroeconomic effects, structural adjustments and technological change. Heidelberg: Physica Verlag, Springer.
Watanabe, C., Wakabayashi, K., & Miyazawa, T. (2000). Industrial dynamism and the creation of a virtuous cycle between R&D, market growth and price reduction. The case of Photovoltaic Power Generation (PV) development in Japan. Technovation, 20(6), 299–312.
Woolthuis, R., Lankhuizen, M., & Gilsing, V. (2005). A system failure framework for innovation policy design. Technovation, 25, 609–661.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Del Río, P., Bleda, M. (2012). Theoretical Approaches to Dynamic Efficiency in Policy Contexts: The Case of Renewable Electricity. In: Costantini, V., Mazzanti, M. (eds) The Dynamics of Environmental and Economic Systems. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5089-0_3
Download citation
DOI: https://doi.org/10.1007/978-94-007-5089-0_3
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-5088-3
Online ISBN: 978-94-007-5089-0
eBook Packages: Business and EconomicsEconomics and Finance (R0)