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Data and method for assessing the sustainability of electricity generation sectors in the south Asia growth quadrangle
The research article “Khan I, Sustainability challenges for the south Asia growth quadrangle: A regional electricity generation sustainability assessment, Journal of Cleaner Production. 243 (2020), 118639, 1–13. DOI: https://doi.org/10.1016/j.jclepro.2019.118639” [1] is linked to this data article....
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6909054/ https://www.ncbi.nlm.nih.gov/pubmed/31871973 http://dx.doi.org/10.1016/j.dib.2019.104808 |
Sumario: | The research article “Khan I, Sustainability challenges for the south Asia growth quadrangle: A regional electricity generation sustainability assessment, Journal of Cleaner Production. 243 (2020), 118639, 1–13. DOI: https://doi.org/10.1016/j.jclepro.2019.118639” [1] is linked to this data article. The electricity generation related data were collected from the electricity authorities of Bangladesh, Bhutan, India, and Nepal annual reports, which were publicly available through their websites. Two methods of sustainability assessment, the ‘global’ and ‘multi-criteria decision analysis (MCDA)’ were employed. These two methods were adopted from recent literature. Related data were thus also collected from previous studies in the literature. These two models were explicitly used through a step-by-step calculation using the collected data. These data and methods will allow the researchers to replicate the methods readily. The use of this data and method will also enhance applying a similar approach to other related datasets. Overall, this dataset and method of calculation allow the researcher or analyst to avoid a number of issues: (i) it eliminates considering a large volume of electricity generation data from a myriad of sources for the four countries; (ii) this dataset is ready to be used for any further related sustainability assessment, thus reducing the steps by breaking large datasets down in a way that makes the analysis much easier, and (iii) the calculation steps are ready to be used for any other similar dataset. |
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