EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 209 (2020) E3S Web Conf., 209 (2020) 02022 References
Open Access
Issue
E3S Web Conf.
Volume 209, 2020
ENERGY-21 – Sustainable Development & Smart Management
Article Number 02022
Number of page(s) 5
Section Session 1. Towards Intelligent Energy Systems
DOI https://doi.org/10.1051/e3sconf/202020902022
Published online 23 November 2020
  1. V. Coates, M. Faroque, R. Klavins et al. On the future of technological forecasting. Technol. Forecast. Soc. Change, 67 (1), 2001. pp. 1 – 17. https://doi.org/10.1016/S0040-1625(00)00122-0 [Google Scholar]
  2. Technology Futures Analysis Methods Working Group, Technology Futures Analysis: Toward Integration of the Field and New Methods, Technological Forecasting and Social Change, Vol. 71, 2004. pp. 287-303. [Google Scholar]
  3. Firat, A.K., Woon, W.L., Madnick, S. Technological Forecasting – A Review, Working Paper, Massachusetts Institute of Technology, Report, USA, 2008. [Google Scholar]
  4. Haleem, A., Mannan, B., Luthra, S., Kumar, S. and Khurana, S. Technology forecasting (TF) and technology assessment (TA) methodologies: a conceptual review, Benchmarking: An International Journal, Vol. 26 No. 1, 2019, pp. 48-72. https://doi.org/10.1108/BIJ-04-2018-0090 [CrossRef] [Google Scholar]
  5. Grübler, A., Nakićenović, N., & Victor, D. G. Dynamics of energy technologies and global change. Energy Policy, 27(5), 1999. pp. 247–280. https://doi.org/10.1016/S0301-4215(98)00067-6 [Google Scholar]
  6. Olawumi, T. O., & Chan, D. W. M. A scientometric review of global research on sustainability and sustainable development. Journal of Cleaner Production, 183, 2018. Pp. 231–250. https://doi.org/10.1016/J.JCLEPRO.2018.02.162 [Google Scholar]
  7. Van Eck, N. J., & Waltman, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 2010. 523–538. https://doi.org/10.1007/s11192-009-0146-3 [CrossRef] [PubMed] [Google Scholar]
  8. Chen, C., Song, M. Visualizing a field of research: A methodology of systematic scientometric reviews. PLoS ONE, 14(10), 2019. https://doi.org/10.1371/journal.pone.0223994 [Google Scholar]
  9. McDonald, A., & Schrattenholzer, L. Learning rates for energy technologies. Energy Policy, 29(4), 2001. pp. 255–261. https://doi.org/10.1016/S0301-4215(00)00122-1 [Google Scholar]
  10. Nemet, G. F. (2006). Beyond the learning curve: factors influencing cost reductions in photovoltaics. Energy Policy, 34(17), 3218–3232. https://doi.org/10.1016/j.enpol.2005.06.020 [Google Scholar]
  11. Rubin, E. S., Azevedo, I. M. L., Jaramillo, P., & Yeh, S. (2015). A review of learning rates for electricity supply technologies. Energy Policy, 86, 198–218. https://doi.org/10.1016/j.enpol.2015.06.011 [Google Scholar]
  12. Acemoglu, D., Aghion, P., Bursztyn, L., Hemous, D. (2012). The Environment and Directed Technical Change. American Economic Review, 102(1), 131–166. [CrossRef] [Google Scholar]
  13. Wüstenhagen, R., Wolsink, M., & Bürer, M. J. (2007). Social acceptance of renewable energy innovation: An introduction to the concept. Energy Policy, 35(5), 2683–2691. https://doi.org/10.1016/j.enpol.2006.12.001 [Google Scholar]
  14. Zhong, B., Wu, H., Li, H., Sepasgozar, S., Luo, H., & He, L. (2019). A scientometric analysis and critical review of construction related ontology research. Automation in Construction, 101, 2019. pp. 17–31. https://doi.org/10.1016/J.AUTCON.2018.12.013 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.