The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Technological challenges of green innovation and sustainable resource management with large scale data
Abstract
This Special Volume (SV) focuses on finding effective ways to achieve sustainable development. This introductory article contains an overview of the articles, divided into four topics: (a) sustainable natural resource management based on large scale data (b) environmental pollution and prevention through large scale data, (c) technological challenges involving green innovation, and (d) big data modeling and mining involving green growth. Taking into account the seriousness of environmental pollution, sustainable development papers based on big data are clearly the most important contributions of this SV. Some authors reported on the relationship between environmental pollution and influencing factors, such as shelling, traffic density, e-commerce environmental regulation, technological innovation and green supply chain behavior. Some authors investigated sustainable natural resource management based on large scale data, including energy, generation and agricultural land. Some authors studied green innovation from various perspectives, and built different models to analyze green growth problems. The findings and recommendations are positive and encouraging. However, it is clear that many additional challenges must be solved in order to establish and support systems which will guide and monitor transformations into sustainable, livable, and low pollution. Much work remains, but the wisdom in the articles in this SV will help societies transition.
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Citations
Cites
Wu, X., Zhu, X., Wu, G.-Q., & Ding, W. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.109 · Cited by 2,410
Can big data and predictive analytics improve social and environmental sustainability?
Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., Luo, Z., Wamba, S. F., & Roubaud, D. (2019) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2017.06.020 · Cited by 860
Bibri, S. E. (2018) · Sustainable Cities and Society · doi:10.1016/j.scs.2017.12.034 · Cited by 799
Resource management in big data initiatives: processes and dynamic capabilities
Braganza, A., Brooks, L., Nepelski, D., Ali, M., & Moro, R. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2016.08.006 · Cited by 339
Hofman, A., Aravena, C., & Aliaga, V. (2016) · Telecommunications Policy · doi:10.1016/j.telpol.2016.02.002 · Cited by 105
Cited by
Ahmad, M., Jiang, P., Majeed, A., Umar, M., Khan, Z., & Muhammad, S. (2020) · Resources Policy · doi:10.1016/j.resourpol.2020.101817 · Cited by 773
Ghobakhloo, M., Iranmanesh, M., Grybauskas, A., Vilkas, M., & Petraitė, M. (2021) · Business Strategy and the Environment · doi:10.1002/bse.2867 · Cited by 462
AlNuaimi, B. K., Khan, M., & Ajmal, M. M. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.120808 · Cited by 170
Zhang, W., Zhang, N., & Yu, Y. (2019) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2018.12.014 · Cited by 157
Pandey, D. K., Hunjra, A. I., Bhaskar, R., & Al-Faryan, M. A. S. (2023) · Resources Policy · doi:10.1016/j.resourpol.2023.104250 · Cited by 68
Metadata
- Authors
- Song, M., Fisher, R., & Kwoh, Y.
- Venue
- Technological Forecasting and Social Change
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 144
- Pages
- 361-368
- ISSN
- 0040-1625 (ISSN-L)
- OpenAlex
- W2887178505
- Cited by
- 428
- References
- 5
- FWCI
- 54.89
- Type
- journal article
- Language
- en
- Open access
- closed
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- Song, M., Fisher, R., & Kwoh, Y. (2019). Technological challenges of green innovation and sustainable resource management with large scale data. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2018.07.055
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.