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In this paper we standardize, compare, and aggregate results from thirteen surveys of technology experts, performed over a period of five years using a range of different methodologies, but all aiming at eliciting expert judgment on the future cost of five key energy technologies and how future costs might be influenced by public R&D investments. To enable researchers and policy makers to use the wealth of collective knowledge obtained through these expert elicitations we develop and present a set of assumptions to harmonize them. We also aggregate expert estimates within each study and across studies to facilitate the comparison. The analysis showed that, as expected, technology costs are expected to go down by 2030 with increasing levels of R&D investments, but that there is not a high level of agreement between individual experts or between studies regarding the technology areas that would benefit the most from R&D investments. This indicates that further study of prospective cost data may be useful to further inform R&D investments. We also found that the contributions of additional studies to the variance of costs in one technology area differed by technology area, suggesting that (barring new information about the downsides of particular forms of elicitations) there may be value in not only including a diverse and relatively large group of experts, but also in using different methods to collect estimates.

This paper analyses optimal investments in innovation when dealing with a stringent climate target and with the uncertain effectiveness of R&D. The innovation needed to achieve the deep cut in emissions is modelled by a backstop carbon-free technology whose cost depends on R&D investments. To better represent the process of technological progress, we assume that R&D effectiveness is uncertain. By means of a simple analytical model, we show how accounting for the uncertainty that characterizes technological advancement yields higher investments in innovation and lower policy costs. We then confirm the results via a numerical analysis performed with a stochastic version of WITCH, an energy-economy-climate model. The results stress the importance of a correct specification of the technological change process in economy-climate models.

This is the second in a series of four articles on price formulae/determination. In the first a simulation model was developed and the criterion of evaluating the model, the internal rate of return, was introduced. In this article two price formulae are simulated and analysed in accordance with principles discussed in the first article. Significant differences emerge between the two formulae. The internal rate of return of formula B (which is expressed in terms of replacement values) is continuously more than 100 % higher and more stable than that of formula A (which is expressed in terms of historical values). It also appears that it can be misleading to judge the profitability of a price formula against the allowed rate of profitability. The adequacy of the price formulae for inflation is judged by examining the internal rate of return in a situation with and without inflation and by calculating the ratio between depreciation allowed and replacement investments. Finally, the effect of the formulae on financial structure is analysed by calculating a liquidity (based on cash flow) and a solvability ratio. In conclusion, it appears that formula B is superior to formula A, mainly as a result of differences between the price formulae.

This study investigates how information and communication technology (ICT) complements globalisation in order to influence CO2 emissions in 44 Sub-Saharan African countries over the period 2000-2012. ICT is measured with internet penetration and mobile phone penetration whereas globalisation is designated in terms of trade and financial openness. The empirical evidence is based on the generalised method of moments. The findings broadly show that ICT can be employed to dampen the potentially negative effect of globalisation on environmental degradation like CO2 emissions. Practical, policy and theoretical implications are discussed.

AbstractThis paper presents effect of thermo-physical properties of soil on performance of an Earth Air Tunnel Heat Exchanger (EATHE). The analysis has been carried out using a validated three-dimensional, transient numerical model for three different types of soil. The governing equations, based on the k–ε model and energy equation were used to describe the turbulence and heat transfer phenomena, are solved by using finite volume method. Comparisons were made in terms of temperature drop, heat transfer rate and COP of the EATHE system by operating it continuously for 12 h duration. The study reveals that each soil exhibits different rate of heat dissipation and thermal saturation over a period of continuous operation, which adversely affects the performance of EATHE. Dissipation of heat from the EATHE pipes to its surrounding soil and subsequently to the outer subsoil region is mainly found to be depending upon the thermal conductivity of soil; even of their thermal diffusivity is of different order.

One of the recurring themes in the sustainability literature has been the legitimacy of using an economic framework to account for natural resources. This paper examines the potential for substituting between different inputs in the generation of income, where the inputs include natural resources such as land and energy resources. A nested constant elasticity of substitution (CES) production function is used to allow flexibility in the estimated elasticities of substitution. Also, with this specification, natural resources and other inputs are combined in different levels of the function, thus allowing for different levels of substitutability. Institutional and economic indicators are also incorporated in the production function estimated. Results show that the elasticities derived from functions involving land resources were generally around one or greater, implying a fairly high degree of substitutability. Furthermore, changes in trade openness and private sector investment have a statistically significant and direct relationship with income generation. No statistically significant relationship between income and any of the institutional indicators was found.

Storage devices and demand control may constitute beneficial tools to optimize electricity generation with a large share of intermittent resources through inter-temporal substitution of load. We quantify the related cost reductions in a simulation model of a simplified stylized medium-voltage grid (10kV) under uncertain demand and wind output. Benders Decomposition Method is applied to create a two-stage stochastic program. The model informs an optimal investment sizing decision as regards specific 'smart grid' applications such as storage facilities and meters enabling load control. Model results indicate that central storage facilities are a more promising option for generation cost reductions as compared to demand management. Grid extensions are not appropriate in any of our scenarios. A sensitivity analysis is applied with respect to the market penetration of uncoordinated Plug-In Electric Vehicles which are found to strongly encourage investment into load control equipment for `smart` charging and slightly improve the case for central storage devices.