or to enable technology observers to determine the current life cycle stage of a particular technology of interest
whereas Gao et al. 7 propose the development of a new forecasting approach to analysing technology life cycle of a particular technology of interest.
Gao et al. 7 proposes an approach to enable technology managers to determine the current life cycle stage of a particular technology.
they propose a model to calculate the TLC for a technology based on multiple patent-related indicators.
The right understanding where a certain technology is in its TLC is important to estimate its future development,
The authors claim that the first step for devising a technology strategy is to decide if the technology is worth investing in by better understanding how such technology might develop in the future.
In this context, the proposed model focuses on devising and assessing patent-based TLC indicators using a Nearest Neighbour Classifier,
to measure the technology life cycle stage of the selected technology. Clearly, different types of technologies may have different developing patterns, especially for those technologies close to basic science, such as biotechnology,
and future research should take this into account to test the validity of the proposed model.
there is a major limitation of this method to assess a given technology. It is an oftenobseerve fact that technologies change their course because of (unpredictable) changes in the broader socioeconomic context (fluctuations in demand
changes in regulation, changing/stronger ethical concerns, scarcity of natural resources, environmental issues, etc. as well as due to new combinations of existing and/or emerging technologies.
influences on future-oriented technology analysis, Tech. Anal. Strateg. Manag. 24 (8)( 2012) 753 767.3 L. Georghiou, J. C. Harper, Rising to the challenges Reflections on Future-oriented technology analysis, Technol.
and is now a senior advisor of STI (Science, Technology and Innovation policy and strategy at CGEE.
/Technological forecasting & Social Change 80 (2013) 386 397 identification of emerging clusters analysing citations and keywords for a particular technology field,
Järvanpää et al. 34 analyse the use of bibliometric data for distinguishing between science-based and conventional technologies,
Thorleuchter et al. 35 demonstrate that patent-based quantitative approaches to cross-impact analysis for the identification of relationships between technologies can be used instead of,
to monitor the convergence of adjacent technologies 37, or to identify Emerging s&t areas 38. Comparison of outcomes of qualitative and quantitative approaches Participants at the 2011 International Seville Conference on FTA raised the potential of the use of qualitative and quantitative methods for identifying
thus limiting the scale of failures (with a focus on market pull vis-à-vis the technology push approach).
In the NEEDS project (www. needs-project. org), the acceptability of future energy technology options was submitted to a multi-criteria assessment involving a panel of stakeholders, the results
and benefits that can be expected from the diffusion of the technologies under scrutiny (how will their diffusion affect quality of life, the conservation of natural resources, landscape integrity, ecosystem services etc.).
Change 78 (2011) 274 282.34 H. M. Järvanpää, S. J. Mäkinen, M. Seppänen, Patent and publishing activity sequence over a technology's life cycle, Technol.
and patent-based cross-impact analysis for identifying relationships between technologies, Technol. Forecast. Soc. Change 77 (2010) 1037 1050.36 R. Popper, Foresight methodology, in:
Change 78 (2011) 256 273.38 P. Lee, H. Su, F. Wu, Quantitative mapping of patented technology the case of electrical conducting polymer composite, Technol.
12th International Command and Control Research and Technology Symposium, US Naval War College, Newport R i. USA, June 19 21,2007, 2007, Available at:
School of Public policy, Georgia Institute of technology, Atlanta, GA 30332-0345, USA d College of Computer science & Technology, Huaqiao University, Xiamen, 361021, PR China e
Received 14 may 2011 Received in revised form 22 june 2012 Accepted 23 august 2012 Available online 28 november 2012 To estimate the future development of one technology
But using the patent application counts alone to represent the development of technology oversimplifies the situation.
In this paper, we build a model to calculate the TLC for an object technology based on multiple patent-related indicators.
Then we choose some technologies (training technologies) with identified life cycle stages, and finally compare the indicator features in training technologies with the indicator values in an object technology (test technology) using a nearest neighbour classifier,
which is used widely in pattern recognition to measure the technology life cycle stage of the object technology.
Such study can be used in management practice to enable technology observers to determine the current life cycle stage of a particular technology of interest
and make their R&d strategy accordingly. 2012 Elsevier Inc. All rights reserved. Keywords: Technology life cycle Patent Indicator Cathode ray tube Thin film transistor liquid crystal display Nano-biosensor 1. Introduction The rapidly changing economic environment
technology, marketing, and user experience 1. Technology plays a key role among these three components 2. Before the product strategy is formulated,
a technology strategy must be developed to provide competitive products, materials, processes, or system technologies 3. The first step for devising a technology strategy is to decide
if the technology is worth the investment. Howwill the technology develop in the future? Will the technology flourish in the future
or will it decline? To answer these questions, one should know the current life cycle stage of the technology
in order to estimate future development trends to make informed decisions on whether to invest in it or not.
Within the Future-oriented technology analysis (FTA), technology forecasting traces back to the 1950's 4. One of its half dozen
or so basic techniques, dating from that time at least, is trend analysis. This includes both historical time series analyses
and fitting of growth models to project possible future trends 5. Most trend projection is naïve i e.,
, fitting a curve to the historical data under the assumption that whatever forces are collectively driving the trend will continue into the future unabated.
Another important technology forecasting technique 6 is the use of analogies. Herein, one anticipates growth in an emerging technology based on the pattern of growth observed in a somewhat related technology.
The stronger that relationship, the more likely the pattern will pertain. Technological forecasting & Social Change 80 (2013) 398 407 Corresponding author at:
& Social Change Another important predecessor approach upon which we draw is the identification of Technology Readiness Levels (TRLS).
but precise anticipation of when a given advanced technology will be ready for application is precarious.
In the growth stage, there are pacing technologies with high competitive impact that have not yet been integrated in new products or processes.
In the maturity stage, some pacing technologies turn into key technologies, are integrated into products or processes, and maintain their high competitive impact.
As soon as a technology loses its competitive impact it becomes a base technology. It enters the saturation stage
and might be replaced by a new technology. According to this definition, Ernst 10 developed a map to illustrate TLC (Fig. 1). The dominant approach to analysing TLC with an S-curve is to observe technological performance,
A research team from MIT 11 studied the development trends of power transmission technology and aero-engine technology by S-curve modelling.
But using patent application counts alone to represent the development of technology oversimplifies the situation. Accordingly, some multiple indicators are used to measure TLC.
These papers studied the indicators that would have different performance based on the changes of technology.
In this paper, we focus on combining multiple indicators to calculate the life cycle stages for an object technology
and hope that would help decision makers estimate its future development trends. 2. Methodology The model that we build to calculate the TLC for an object technology includes the following steps:
then we choose some technologies (training technologies) with identified life cycle stages, and finally we compare the indicator features in training technologies with the indicator values in an object technology Fig. 1. The S-curve concept of technology life cycle. 399 L. Gao et al./
/Technological forecasting & Social Change 80 (2013) 398 407 (test technology) via the nearest neighbour classifier,
which is used widely in pattern recognition, in order to measure the technology's life cycle stages. The research framework is designed as follows (Fig. 2). 2. 1. Indicators
and data source The most fundamental and challenging task is to select suitable indicators and data sources.
So in this paper, we choose the other two indicators to measure the development of technology:
and Popular Press abstracts Trends over time in number of items Technological needs noted Types of topics receiving attention Spin-off technologies linked Reinhard Haupt, Martin Kloyer
We count the respective numbers for each of these two indicators in DII by priority year. 2. 1. 3. Inventor This indicator indicates the amount of human resources invested in R&d of one particular technology.
Backward citations to science literature indicate a linkage between science and the patented technology. Backward citations to other patents may indicate a linkage between other technologies and the patented technology.
The number of these two kinds of references can be found on the front page of the patent documents.
established by the Strasbourg Agreement 1971, is the most widely used hierarchical classification system of patents based on the different areas of technologies to
The number of IPC codes represents howmany fields are involved in the development of a technology.
Generally, the top 5 or top 10 IPCS represent the main technology subjects. IPC code has been used as an indicator to measure the technology life cycle 26.
The technology structure is also different: MC and IPC are used complementary codes in this paper to measure technology subjects.
We count the number of MCS in DII by priority year for the MC indicator;
/Technological forecasting & Social Change 80 (2013) 398 407 2. 2. TLC stages of CRT and TFT-LCD It is better to choose a training technology with four TLC stages.
So we choose another similar technology, the Thin Film Transistor Liquid crystal display (TFT-LCD), as the second training technology.
Nano-biosensor (NBS) is chosen as the test technology. We then focus on CRT and TFT-LCD technologies and assess their life cycle stages.
We developed the questionnaires based on the concept of TLC given by Arthur D. Little 9. Ten experts in CRT,
TFT-LCD or display fields were asked to give the time periods of four stages for TFT-LCD and CRT.
Table 3 shows the TLC stages of CRT and TFT-LCD as given by the experts and literature. 2. 3. Search query The search terms for each technology are defined simply
We divide NBS technology into two parts: one is related a nano technology and the other is related a biosensor technology.
A query strategy for nanotechnology has been developed by TPAC at the Georgia Institute of technology 30. We refine our search terms for biosensors based on our earlier research 31
and add some keywords related to functions of biosensors, including test (or similar keywords, such as measure*,monitor*)and nucleic acid*(or some other bio-related keywords, such as lactate or cholesterol), and sensor*.
we develop a map for 13 indicators of each training technology. Numbers of inventors suggest very interesting changes in different stages.
Since the indicators show different trends in different stages, it might be better to combine all 13 indicators to measure the change of technology rather than using one single indicator.
And according to the definition of TLC, in a technology's growth stage, there are pacing technologies with high competitive impact that have not yet been integrated into new products or processes.
however, at the moment, these technologies need more work in order to resolve key problems. The most successful commercial biosensor technology surface plasmon resonance does not have a very good limit of detection (LOD),
the nanoparticle based SPR (or local SPR) can provide excellent LOD. However, the current fabrication technology is expensive 32.
Therefore, the fabrication technology is one of the pacing technologies of NBS. In this stage, a lot of challenging problems must be overcome, such as enhancement of gene array and protein array,
and some new and promising technologies are still under research 33. Technology observers can make their R&d investment decision by using the proposed approach.
The result shows that NBS is in a growth stage. Itmeans that there are many technologies still in development,
including SPR. Technology managers might informtheir NBS R&d investments by analysing patent application data from 1997 to the present to identify hot research topics or technological gaps.
For somenbs related companies that have enough money for R&d, it is a good time to invest in NBS to pursue potentialmarkets. 4. Conclusions How might technology life cycle analysis based on patents contribute to FTA?
This approach to gauge a technology's growth trend provides a more robust projection. However, as mentioned in Section 1,
including new combinations of technologies (existing and/or emerging), and many socioeconomic forces (e g.,, fluctuations in demand, regulations, ethical or environmental concerns.
it adopts 13 indicators that can be quantified to measure the TLC stages of an object technology.
In this study, we take TFT-LCD and CRT as the training technologies and NBS as the test technology.
This means that there are pacing technologies with high competitive impact that have not yet been integrated into new products or processes.
First, only two technologies serve as the training technologies to calculate the similarity feature with the object technology (test technology.
This is due to the lack of ideal training technologies with four TLC stages. So, this study resembles a laboratory test.
we still need to find more technologies and obtain more data to validate the method.
Second, we did not consider the technology type. TFT-LCD and CRT are categorised as single-technology type,
but NBS is a multi-technology: it involves nanotechnology and biotechnology, with diverse application possibilities.
Different types of technologies may have different developing patterns, especially for those technologies close to basic science, such as biotechnology.
Future research should also take this into account. Third, the classifier we used in this paper is the nearest neighbour classifier.
Acknowledgement This research was undertaken at Georgia Tech, drawing on support from the National science Foundation (NSF) through the Center for Nanotechnology in Society (Arizona State university;
Award no. 0531194) and the Science of Science policy Program Measuring and Tracking Research Knowledge Integration (Georgia Tech;
References 1 D. A. Norman, The life cycle of a technology: why it is so difficult for large companies to innovate?
/Technological forecasting & Social Change 80 (2013) 398 407 2 H. X. G. Ming, W. F. Lu, C. F. Zhu, Technology challenges
for product lifecycle management, Technical Report, STR/04/058/SP, Singapore Institute of Manufacturing Technology, 2004.3 T. A. Vijay, Challenges in product strategy
J. Banks, Forecasting and Management of Technology, 2nd Edition John Wiley, New york, NY, 2011.6 A l. Porter, M. Rader, Fitting future-oriented technology analysis methods to study
Strategic intelligence for an Innovative economy, Springer, Berllin, 2008, pp. 149 162.7 W. L. Nolte, B c. Kennedy, R. J. Dziegiel, Technology readiness level calculator
Online at, http://www. hq. nasa. gov/office/codeq/trl/trlchrt. pdf. 9 A d. Little, The strategic management of technology.
the diffusion of CNC-technology in the machine tool industry, Small Bus. Econ. 9 (4)( 1997) 361 381.11 T. H. Lee, N. Nakicenovic, Life cycle of technology
. Zhu, X. F. Wang, Chinese patent analysis of IC package technology, J. Mod. Inf. 9 (2006) 160 166.18 C. M. Chu, Using technology life cycle to analysis the developing trend of thin-film photovoltaic industry, Ph d. dissertation, National Central
Serv. 11 (2009) 59 63.20 H. L. Yu, Analysis of the particleboard technology based on TRIZ and S-Curve technique evolution law, Forest.
Technol. 34 (4)( 2009) 57 60.21 Y. C. Wu, T. C. Yen, RFID technology innovations:
use of patent data, IEEE in Beijing, 2008.22 M. Meyer, Does science push technology? Patents citing scientific literature, Res.
Market Manage. 21 (1)( 1992) 23 31.36 E. Hajime, The suitability of technology forecasting/foresight methods for decision systems and strategy:
Appl. 39 (3)( 2012) 2927 2938.38 E. Hajime, Obstacles for the acceptance of technology foresight to decision makers, lessons from complaint analysis of technology forecasting, Int. J. Foresight Innov.
and of Public policy, at Georgia Tech, where he remains Co-director of the Technology policy and Assessment Center.
He is also a Director of R&d for Search Technology, Inc.,Norcross, GA. He is the author of some 220 articles and books,
including Tech Mining (Wiley, 2005) and Forecasting and Management of Technology (Wiley, 2011. Jing Wang is an Associate professor of Huaqiao University.
and technology and he mainly focuses on Pattern Recognition, Neurocomputing et al. He is the author of more than 20 articles.
Her specialty is science and technology management, particularly the study of technology forecasting and assessment. She is focusing on a research on emerging science and technology topics. 407 L. Gao et al./
/Technological forecasting & Social Change 80 (2013) 398 407
Evolutionary theory of technological change: State-of-the-art and new approaches Tessaleno C. Devezas Technological forecasting and Innovation theory Working group, University of Beira Interior, Covilha, Portugal Received 13 may 2004;
accepted 6 october 2004 Abstract It is well known the fact that the world of technology is full of biological metaphors,
and has won the status of a dnatural lawt of technology diffusion due to its considerable success as an empirically descriptive and heuristic device capturing the essential changing nature of technologies, products, markets and industries.
Technology evolution; Technological change; Complex systems; Universal Darwinism 1. Introductory thoughts The main objective of this seminar concerns the exploitation of the powerful new capabilities provided by the Information technology Era to advance Future-oriented technology analysis (TFA), both product and process.
following his optimistic view of a strong, confident technology-driven scenario, which would bring a renewed thrust toward new methods in technological forecasting (Fig. 1). The picture suggests that the chaotic phase transition might be behind us
and the manifold convergence of information and molecular technologies that are contributing enormously to new insights in simulation methods and evolutionary programming.
and technology remains beyond reach, with some people even doubting whether it T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1138 can ever be achieved.
technique, technology and (technological innovation. An evolutionary approach within the framework of danthropology of techniquet is a necessary step to grasp adequately these concepts.
and has won the status of a dnatural lawt of technology diffusion due to its considerable success as an empirically descriptive and heuristic device capturing the essential changing nature of technologies, products, markets and industries.
All this is to say that the use of biological approaches in analyzing the evolution of technology
and we can say that a lot of work remains to be done to make evolution a viable strategy and school of thought in the study of technology.
T in the way paved by the German philosopher of technology Hans Sachsse 18 almost three decades ago.
1 Technique precedes technology, not only in human history, but also under a pure evolutionary point of view. Technique (or routine,
because nature owns the basic structure (then a fundamental law) of over shortcuts to reach easily the goals immediately ahead. 5 Technology is a recent human achievement that flourished conceptually in the 18th century,
or genes transmission it had evolved not to technology; if technology had favored not human pool of genes
or human genes transmission it had evolved not continually toward more and more complex technological systems; the human massive capacity for culture (and technology) may be seen as a very strong capacity of adaptation to respond to very quick spatial and temporal variations, observed in the Earth homeland since the Pleistocene;
T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1147 the coevolutionary complexity of managing two inheritance systems (the vertical, genetic,
NK technology landscapes, initially proposed by Stuart Kauffman 26 and further pursued by other researchers of the Santa fe Institute, like Jose'Lobo 27 and Walter Fontana 28;
Scale-free networks pervade technology: the Internet, power-grids and transportation systems are but a few examples. For a review on this field I suggest the reading of two recent review articles 29,30,
It is a numerical simulation method for the search of complex technology spaces based on percolation theory,
In technology and science GAS have been used as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems,
technology must be viewed as the further improvement of this process by intelligent means (then allowing too for intentionality),
human technology is a part of a biologically co-evolved massive capacity for culture, managing two inheritance systems, vertical (twofold in scope, genetic and Lamarckian) and horizontal (pure Lamarckian in scope),
References 1 TFA Methods Working group, Technology futures analysis: toward integration of the fields and new methods, Technol.
, An Evolutionary theory of Economic Change, Beknap of Harvard university Press, Boston, 1982.17 G. Baslalla, The Evolution of Technology, Cambridge university Press, 1988.18 H. Sachsse, Anthropologie der Technik
extremal search on a technology landscape, SFI-Working Paper 03-02-003,2003. 28 J. Lobo, J. H. Miller, W. Fontana, Neutrality in technological landscapes
Change 71 (2004) 881 896.35 G. Silverberg, B. Verspagen, A percolation model of innovation in complex technology spaces, J. Econ.
Given ignorance about the possible side effects of technologies under development, he argues that one should strive for correctability of decisions, extensive monitoring of effects, and flexibility.
Energy transitions are characterized by many deep uncertainties related to transition mechanisms, to the various competing technologies, and to human and organizational decision-making 45.
Here we focus on the competition between technologies. 3. 1. Introduction to the energy transition case
Energy is a crucial domain inwhich a fundamental transition toward clean generation technologies is desirable 47 for environmental and security reasons.
The current energy systemis mainly dominated by fossil energy generation technologies which are being challenged by rapidly evolving emerging technologies.
Although new sustainable energy technologies are entering the market, their contribution to the total amount of energy generation is still relatively small.
For example, precise lifetimes of technologies are known not and expected values are used in planning decisions. Also, since the installation of new capacity mostly happens in large chunks,
and expectations related to them, in turn influence the adoption and survival of technologies during the transition.
Uncertainties Description Type Range or categories Initial capacities Starting value of the installed capacity of a technology Parametric Varying between 1 and 16,000 MW for different technologies Lifetimes Expected
lifetime of a technology Parametric Varying between 15 and 50 years for different technologies Delay orders of lifetimes Orders of the decommissioning delays Categorical 1st, 3rd, 10th,
1000th Initial decommissioned capacities Initial values of the total decommissioned capacities of the technologies Parametric Varying between 10 and 10,000,
0000 MW for different technologies Planning and construction periods Average period for planning and constructing new capacity for a technology Parametric Varying between 1 and 5 years for different technologies Progress ratios Ratio for determining cost reduction due to learning curve Parametric Varying
between 70%and 95%for four different technologies Initial costs Initial investment cost of new capacity of a particular technology Parametric Varying between €500, 000 and €10 million per MW Economic growth Economic growth
rate Parametric Randomly fluctuating between-0. 01 and 0. 035 (smoothed concatenation of 10-year random growth values) Investment preference structures Preference criteria
and weights for investing in new capacity of each of the technologies Parametric weights and categorical switches Preference for (more) familiar technologies called here the Preference‘Against unknown';
the main structures driving the competition among four energy technologies. Technology 1 represents old dominant nonrenewable technologies.
The other three technologies are at the start of the simulation relatively new, more sustainable, and more expensive.
Since fast and relatively simple models are needed for EMA the more sustainable technologies (2, 3 and 4) are considered to be generic for the sake of simplicity.
The four technologies compete with each other in order to increase their share of energy generation, driven by mechanisms such as total energy demand, investment costs and the effect of learning curves on costs.
A more detailed explanation of the model can be found in 3 . And the uncertainties taken into consideration
The lower range of the‘lifetime of Technology 1'is relevant for all three subspaces,
i e. the adoption of new sustainable technologies is hampered in combination with the other uncertainties of the subspaces by longer lifetimes of the dominant technology.
Although a low performance of Technology 2 on the‘CO2 avoidance'criterion, a high performance of Technology 1 on the‘expected cost per MWE'criterion
a short lifetime for Technology 3, and a short planning and construction time for Technology 1 also hinder the transition toward sustainability,
none of these uncertainties and their ranges are as unambiguous as the lifetime of Technology 1 (for all regions, not the lower ranges).
Shortening the lifetime of Technology 1 therefore seems to be a promising basic policy, i e. a policy that will be implemented in any case from the start. 3. 2. 2. Basic policy Shortening the lifetime of Technology 1 could be achieved by increasing its decommissioning,
for as long as the fraction of new technologies remains below a particular target fraction, say 0. 8,
assuming that 80%is a reasonable target for the fraction of sustainable technologies. To assess the performance of this basic policy,
the same 9349 experiments used for exploring the no policy case are executed now with the basic policy.
The upward shift of the sustainable fraction in Fig. 4 means that the need for new capacity resulting from the additional decommissioning of Technology 1 is to a large extent filled by new technologies.
Hence, the basic policy stimulates the transition from Technology 1 to new technologies, at least to some extent.
Although there is an improvement in terms of the fraction of sustainable technologies there is still a room for further improvement.
The basic policy aimed at increasing the decommissioning of the dominant technology, since all PRIM boxes indicated decreasing the negative effect of the lifetime of Technology 1 would help to increase the fraction of new technologies.
The second iteration PRIM results show there are three very different troublesome regions in the basic policy ensemble:
the first region relates to the performance of the technologies on the CO2 avoidance criterion,
the second region relates to the underperformance of Technology 2, and the third region is determined by uncertainties related to economic growth
The main drivers of the first region are the CO2 avoidance performance values for Technologies 1, 2,
and 3. If the CO2 avoidance performance of the dominant technology is high, while it is low for the new technologies,
Additionally, the region shows that higher performance for expected cost per MWE of the dominant technology also limits the transition.
it means that the old dominant technology outperforms the other technologies in terms of expected investment costs and CO2 avoidance, which,
The second region is driven mainly by uncertainties related to Technology 2. A shorter lifetime, lower performance of CO2 avoidance,
and longer planning and construction period for Technology 2, lead to low fractions of sustainable technologies.
The results indicate that Technology 1 becomesmore preferable than Technology 2, which is initially themain alternative to Technology 1. In this situation,
a reasonable defensive action would be to focus on the other sustainable technologies, in order to promote the transition toward these technologies instead.
To address this vulnerability a signpost tracking the progress of Technologies 2, 3 and 4 could be used.
The pointwhere the performance of Technology 3 or 4 equals the performance of Technology 2 could be the trigger for Table 2 PRIM results for the no policy ensemble.
Preference against unknown Average planning and construction period Tech. 1 Lifetime of Technology 1 Lifetime of Technology 3 CO2 avoidance performance of Technology 2 Expected
cost per MWE performance of Technology 1 Lower Upper Lower Upper Lower Upper Lower Upper Lower Upper Lower Upper Original 2 5
1 5 30 50 15 40 1 5 1 2 Region1 2 5 1 5 34.4 50 15 37.5 1
4. 2 1. 1 1. 8 Region2 2 5 1 4. 8 33 7 50 15 37.5 1 4. 4
the corrective action would be to stop investing in Technology 2 and to shift investments to Technologies 3 and 4 instead.
So, we modified our basic policy by adding the monitoring and corrective actions and reran the experiments.
the installed capacities of Technologies 3 and 4 increase. Thismeans that the defensive action developed for the second region served its purpose by steering the commissioning toward Technologies 3 and 4. The third region shows that certain combinations of economic growth factors
and preference for the expected progress criterion may also hinder the energy transition. Each of the economic growth parameters indicated in the third region corresponds to the value of economic development for ten years
or more sustainable technologies for some time to make them competitive. Hence, the costs of Technologies 2, 3 and 4 are monitored over time
and when their costs are close enough to the cost of the dominant technology, a 20%cost reduction of the new technologies is triggered over a period of 10 years.
To further address this vulnerability, we also add a hedging action to the basic policy in the form of additional commissioning of Technologies 3 and 4 in their early years.
These actions together aim at making the sustainable technologies more cost efficient once their costs are reasonably affordable levels
and to promote the transition toward new technologies in their early years. The economic action is successful in promoting sustainable technologies
and increasing the total fraction after the first 10 years (around 2020). The adoption of the new technologies in later years is also higher than under the basic policy,
without forcing a transition to new technologies upon situations that do not require a transition to take place (e g. in case of a cheap and environmentally friendly dominant technology) or for
Policy analysis, Delft University of Technology, Delft, 2010.6 P. Eykhoff, System Identification: Parameter and State Estimation, Wiley Interscience, London, 1974.7 W. E. Walker, V. A w. J. Marchau, D. Swanson, Addressing deep uncertainty using adaptive policies:
I. Miles, M. Mogee, A. Salo, F. Scapolo, R. Smits, W. Thissen, Technology futures analysis: toward integration of the field and new methods, Technol.
Integrating Science and Politics for the Environment, Island Press, Washington, 1993.33 D. Collingridge, The Social control of Technology, Frances Pinter Publisher, London, UK, 1980.34 J. P. Brans
Technology policy and Management, Delft University of Technology, Delft, 2008, p. 285.37 E. Pruyt, J. Kwakkel, A bright future for system dynamics:
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/Technological forecasting & Social Change 80 (2013) 408 418 Caner Hamarat is a Phd researcher at the Faculty of technology, Policy and Management of Delft University of Technology.
Jan Kwakkel received a Ph d. from Delft University of Technology. His research focused on the treatment of uncertainty in long-term airport planning.
He currently works as a postdoc on the treatment of uncertainties in model-based decision support for fresh water supply in The netherlands at Delft University of Technology.
Next to this research, he also has an interest in scientometrics and tech-mining. Erik Pruyt is the Assistant professor of System Dynamics and Policy analysis at the Faculty of technology, Policy and Management of Delft University of Technology.
He obtained a master's degree in Commercial Engineering and a Phd degree from the Faculty of economics, Social and Political sciences & Solvay Business school of the Free University of Brussels. His research focuses mainly on the multidimensional dynamics of complex systems,
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