The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Artificial Intelligence and emerging digital technologies in the energy sector
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Citations
Cites
The future of employment: How susceptible are jobs to computerisation?
Frey, C. B., & Osborne, M. A. (2017) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2016.08.019 · Cited by 8,727
The Skill Content of Recent Technological Change: An Empirical Exploration
Autor, D. H., Levy, F., & Murnane, R. J. (2003) · The Quarterly Journal of Economics · doi:10.1162/003355303322552801 · Cited by 6,205
Why Are There Still So Many Jobs? The History and Future of Workplace Automation
Autor, D. H. (2015) · Journal of Economic Perspectives · doi:10.1257/jep.29.3.3 · Cited by 3,917
Skills, Tasks and Technologies: Implications for Employment and Earnings
Acemoglu, D., & Autor, D. (2011) · Handbook of Labor Economics · doi:10.1016/s0169-7218(11)02410-5 · Cited by 3,799
Blockchain technology in the energy sector: a systematic review of challenges and opportunities
Andoni, M., Robu, V., Flynn, D., Abram, S., Geach, D., Jenkins, D., McCallum, P., & Peacock, A. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.10.014 · Cited by 2,426
Measuring and Explaining Management Practices Across Firms and Countries
Bloom, N., & Van Reenen, J. (2007) · The Quarterly Journal of Economics · doi:10.1162/qjec.2007.122.4.1351 · Cited by 2,373
Skills, Tasks and Technologies: Implications for Employment and Earnings
Acemoglu, D., & Autor, D. (2010) · doi:10.3386/w16082 · Cited by 2,180
Machine Learning: An Applied Econometric Approach
Mullainathan, S., & Spiess, J. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.87 · Cited by 1,989
On big data, artificial intelligence and smart cities
Allam, Z., & Dhunny, Z. A. (2019) · Cities · doi:10.1016/j.cities.2019.01.032 · Cited by 1,178
Financing Innovation: Evidence from R&D Grants
Howell, S. T. (2017) · American Economic Review · doi:10.1257/aer.20150808 · Cited by 1,138
Cited by
Heymann, F., Milojevic, T., Covatariu, A., & Verma, P. (2023) · Energy · doi:10.1016/j.energy.2022.125521 · Cited by 60
Zhao, G., Cui, Z., Xu, J., Liu, W., & Ma, S. (2022) · Energy · doi:10.1016/j.energy.2022.124492 · Cited by 41
De Silva, D., Jayatilleke, S., El-Ayoubi, M., Issadeen, Z., Moraliyage, H., & Mills, N. (2024) · Machine Learning and Knowledge Extraction · doi:10.3390/make6020051 · Cited by 14
Metadata
- Authors
- Lyu, W., & Liu, J.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2021-12
- Volume
- 303
- Pages
- 117615
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W3194691748
- Cited by
- 313
- References
- 18
- FWCI
- 33.44
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Artificial Intelligence and emerging digital technologies in the energy sector. https://app.cochat.ai/papers/10.1016/j.apenergy.2021.117615
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- Lyu, W., & Liu, J. (2021). Artificial Intelligence and emerging digital technologies in the energy sector. Applied Energy. https://doi.org/10.1016/j.apenergy.2021.117615
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
