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. 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.


a b s t r a c t
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, 1e13. 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 E-mail addresses: ikr_ece@yahoo.com, i.khan@just.edu.bd.   Table   Subject Energy (Sustainability) Specific subject area Regional sustainability assessment was conducted using two different sustainability assessment methods. Type of data Equation

Data
All the data are stored in one excel file containing a number of sheets (see supplementary data in the Appendix A). The first sheet 'Equations' shows the equations used for the sustainability assessment for the global model and MCDA model. The next ten sheets namely SI-Coal, SI-Oil, SI-Gas, SI-Small Hydro, SI-Large Hydro, SI-Solar, SI-Wind, SI-Biomass, SI-Geothermal, and SI-Nuclear show step-wise detail calculation associated with data for the global model for sustainability assessment. The sheet 'Global SI' presents the calculated sustainability index for the different electricity generation technologies. The next five sheets Bangladesh, India, Bhutan, Nepal, and SAGQ used the calculated sustainability index from the global model and applied it to either total generated electricity or total installed capacity for the years 2014, 2015, and 2016. The sheet 'Country-SAGQ SI' shows country-specific and regional (i.e., SAGQ) sustainability index obtained from the global model. The last two sheets 'Eco & Env' and 'Social' present the data used for the MCDA model to assess sustainability. These are listed in Table 1.

Experimental design, materials, and methods
The calculation equations and use of data are listed in Table 1.
Step by step calculation methods for the Global and MCDA models are illustrated in Figs. 1 and 2, respectively.
The method of calculation for the global sustainability index model comprises four basic steps and is illustrated in Fig. 1. In the first step, different values for different factors (i.e., A i , n i , m i ) were used from [2,3] and each indicator in social, economic, and environmental criteria was calculated using equation (1). The sustainability index for each generation technology was calculated in the second step. References [2,3] were also used for the values of a i , b i , and g i using equation (2) to obtain the technologyspecific sustainability index. The final two steps calculate the historical and future sustainability index using equations (3) and (4), respectively.
On the other hand, there are five steps to calculate the sustainability index of the power generation system through the MCDA model. In the first step, social, economic, and environmental indicators were collected from the literature. The list of articles used can be found in the excel file in the sheets named as-'Eco & Env' and 'Social'. SMARTER (Simple Multi-Attribute Rating Technique Exploiting Ranks) method was then employed to assign a weight to the indicators using the equation (5) as shown in Table 1. Details of this weighting method can be found in Refs. [4,5]. Using equation (6), total weights for each technology and criteria (i.e., social, economic, and environmental), were calculated. The sustainability index for each technology was then calculated through equation (7). Finally, the system sustainability index was calculated using equation (8). All these steps are depicted in Fig. 2.

Value of the Data
These data can be used to assess the global sustainability of any electricity generation system. Sustainability policymakers, researchers, engineers, and academics can use the data along with the detailed step-by-step calculation to obtain the global sustainability of any electricity generation systems. The data are presented along with the detailed calculation steps for the global model, thus changing the related values at the respective field will generate new results for the system under consideration. These detailed calculation steps and data for the global sustainability assessment model will reduce the time-consuming calculation steps for any researcher who intends to use this model. For the multi-criteria decision analysis model, data are collected from the literature. This will reduce the time for other researchers who are in search of sustainability assessment-related indicators' data in the literature. Sustainability assessment is a dynamic process and needs to be evaluated at certain time intervals to check its status. Therefore, this dataset can be used as a reference for any future electricity generation related sustainability assessment for the south Asia growth quadrangle In the excel file, sheets: SI-Coal, SI-Oil, SI-Gas, SI-Small Hydro, SI-Large Hydro, SI-Solar, SI-Wind, SI-Biomass, SI-Geothermal, SI-Nuclear, and Global SI In the excel file, sheets: Bangladesh, India, Bhutan, Nepal, and SAGQ [3] MCDA Model For weight assignment: if N is the number of total technologies considered, then the weight (w) of the indicator for kth technology will be: Here, w 1 ! w 2 ! : : : : : : : : ! w k , w 1 þ w 2 þ : : : : : : : þ w k ¼ 1; N ¼ 10, and k ¼ 1 to 10.
Detailed method was adopted from [4]. For this, data were used from sheets: 'Eco & Env' and 'Social'. [4,5] Where: W c T : Total score reflecting the performance of technology T on criterion c. T: Generation technology; e.g. coal, gas, oil, hydro. c: Criterion; e.g. economic, environmental, social. w k : Indicator specific assigned weight obtained from equation (5).
Adopted from [4]. [4] I. Khan / Data in brief 28 (2020) 104808 Nc : Number of indicators for criterion c. W c T : Total score reflecting the performance of technology T on criterion c.
Where: SI System : Overall electricity generation system sustainability index. C T : Output capacity (MW) of the technology in the future (or present) year. C Tot : Total system capacity (MW) in that future (or present) year.