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Cost reductions in nascent forms of Renewable Energy Technology (RET) are essential for them to contribute to the energy mix. Policy intervention can facilitate this cost reduction; however, this may require a significant investment from the public sector. These cost reductions fall into two broad categories: (1) incremental cost reductions through continual improvements to existing technologies, and (2) radical innovation where technologies that significantly differ from the incumbents are developed. This study presents a modelling methodology to integrate radical innovation in RET experience curve and learning investment analysis, using wave energy as an example nascent RET. This aims to quantify the potential effects of radical innovation on the learning investment, allowing the value of successful innovation to be better analysed. The study highlights the value offered by radical innovations in long-term deployment scenarios for wave energy. This suggests that high-risk R&D efforts in nascent RET sectors, even with low success rates, could still present significant expected value in offsetting future revenue support.

Learning is an essential part of life through which we accumulate experience, which then can help us to improve our ability to solve problems. In economics experience can improve productivity. The concept of the experience curve as a form of organizational learning was developed during the second half of the 20th century. It was especially promoted by the Boston Consulting Group and found its way into most textbooks on management and strategic planning; empirical data have been published for a number of industries. In the construction industry, however, very few data areavailable to support the concept. The largest obstacle for all researchers is to obtain the cost information as these are typically not shared by companies. Consequentially we have gathered cost data from four very large expressway bridges to study experience effects in construction projects. These were built in Bangkok, Thailand, by the same joint venture for the same owner during the periodfrom 1990 to 2000 using segmental bridge construction technology. The square meters of bridge deck serve as basic units and the four projects comprise some threemillion of these, so that impacts of increasing experience could be observed. The data for all four projects have been evaluated and adjusted for inflation. They support the idea that a costreduction of 20% can be achieved. The cost reductions must be actively pursued, there exists no automatic process.

Systematic literature review on experience curve in software development.Enhanced version of SIMPLE cost model for SPLE adoption with experience curve factors.Industrial case study on SPLE ROI analysis.Practical and general guidelines for SPLE adoption considering experience curve effect. ContextExperience curve is a well-known concept in management and education science, which explains the phenomenon of increased worker efficiency with repetitive production of a good or service. ObjectiveWe aim to analyze the impact of the experience curve effect on the Return on Investment (ROI) in the software product line engineering (SPLE) process. MethodWe first present the results of a systematic literature review (SLR) to explicitly depict the studies that have considered the impact of experience curve effect on software development in general. Subsequently, based on the results of the SLR, the experience curve effect models in the literature, and the SPLE cost models, we define an approach for extending the cost models with the experience curve effect. Finally, we discuss the application of the refined cost models in a real industrial context. ResultsThe SLR resulted in 15 primary studies which confirm the impact of experience curve effect on software development in general but the experience curve effect in the adoption of SPLE got less attention. The analytical discussion of the cost models and the application of the refined SPLE cost models in the industrial context showed a clear impact of the experience curve effect on the time-to-market, cost of development and ROI in the SPLE adoption process. ConclusionsThe proposed analysis with the newly defined cost models for SPLE adoption provides a more precise analysis tool for the management, and as such helps to support a better decision making.

This study produces an attempt to estimate the cost of future global energy supplies. The approach chosen to address this concern relies on a comparative static exercise of estimating the cost of three energy scenarios representing different energy futures. The first scenario, the business as usual scenario, predicts the future energy-mix based on the energy plans held by major countries. The second scenario is the renewable energy scenario, where as much of the primary energy supply as possible is replaced by renewable energy by 2050. The cost of the renewable energy generating technologies and their theoretical potential are taken into account in order to create a plausible scenario. The third scenario, the nuclear case, is based on the use of nuclear and renewable energy to replace fossil-fuels by 2050. Endogenous learning rates for each technology are modeled using an innovative approach where learning rates are diminishing overtime. It results from the analysis that going fully renewable would cost between −0.4 and 1.5% of the global cumulated GDP over the period 2009–2050 compared to a business as usual strategy. An extensive use of nuclear power can greatly reduce this gap in costs.

Experience curves are useful for understanding technology development and can aid in the design and analysis of market transformation programs. Here, we employ a novel approach to create experience curves, to examine both global and North American compact fluorescent lamp (CFL) data for the years 1990–2007. We move away from the prevailing method of fitting a single, constant, exponential curve to data and instead search for break points where changes in the learning rate may have occurred. Our analysis suggests a learning rate of approximately 21% for the period of 1990–1997, and 51% and 79% in global and North American datasets, respectively, after 1998. We use price data for this analysis; therefore our learning rates encompass developments beyond typical “learning by doing”, including supply chain impacts such as market competition. We examine correlations between North American learning rates and the initiation of new programs, abrupt technological advances, and economic and political events, and find an increased learning rate associated with design advancements and federal standards programs. Our findings support the use of segmented experience curves for retrospective and prospective technology analysis, and may imply that investments in technology programs have contributed to an increase of the CFL learning rate.

Progress ratios (PRs) derived from historical data in experience curves are used for forecasting development of many technologies as a means to model endogenous technical change in for instance climate–economy models. These forecasts are highly sensitive to uncertainties in the progress ratio. As a progress ratio is determined from fitting data, a coefficient of determination R2 is frequently used to show the quality of the fit and accuracy of PR. Although this is instructive, we recommend using the error σPR in PR, which can be directly determined from fitting the data. In this paper we illustrate this approach for three renewable energy technologies, i.e., wind energy, bio-ethanol, and photovoltaics.

Experience curve approach has been used before to predict cost reductions in renewable power technologies. For wind and PV, this prediction came true but not for Concentrating Solar Thermal (CST). The CST has not been able to enjoy sufficiently high deployment rates and therefore has not been able to achieve significant cost reductions. This paper examines the technical reasons for the relatively slow CST uptake over the last two decades and how this can be remedied in the future using different technology.