Lessons learned from the COVID-19 pandemic in planning the future energy systems of developing countries using an integrated MCDM approach in the off-grid areas of Bangladesh.

The COVID-19 epidemic is impeding energy development in developing countries and exacerbating the problems associated with energy planning in off-grid locations. To address such complicated decision-making issues and consider scenarios during this long-lasting pandemic, this study proposes a novel integrated MCDM (Multi-Criteria Decision Making) approach using the Delphi based FO-BWM (Fuzzy Optimistic Best-Worst Method), IDOCRIW (Integrated Determination of Objective Criteria Weights) and the Aggregated Weighting Method integrated with the CoCoSo method under different normalization methods based on a case study of the off-grid areas in Bangladesh. The results of Delphi analysis showed that a total of five criteria were agreed upon by the expert panel. After integrating 5 normalization methods with CoCoSo and using three weighting methods separately, a total of 15 MCDM models were constructed. Finally, the 8 sorted MCDM models demonstrate that Solar Home System (SHS) and Mini-Grid systems need to be prioritized, and the criterion Opportunity of Local Funding (OLF) is essential for choosing between SHS and Mini-Grid systems. Sensitivity analysis showed that the proposed method is effective for easing the dilemmas of energy planning in off-grid areas and provides useful insight to address the impacts of future pandemics on energy planning.

Heterogeneous links among urban concentration, non-renewable energy use intensity, economic development, and environmental emissions across regional development levels.

This study examined the long-run and short-run heterogeneous links among urban concentration, non-renewable energy use intensity, economic development, and environmental emissions index across the regional development levels of 31 Chinese provinces. By employing the augmented mean group method and Dumitrescu-Hurlin causality, the following results are drawn: Firstly, a bidirectional positive linkage was existent between the economic development and urban concentration in both the long-run and short-run across regional development levels. Secondly, a unidirectional positive linkage emerged from non-renewable energy use intensity to environmental emissions index, with the most influential effect in EER China (highest development level). Thirdly, bidirectional mixed linkages prevailed between economic development and non-renewable energy use intensity. Economic development mitigated the non-renewable energy use intensity (inverted U-shaped curve) in the national data set and EER China (highest development level); nevertheless, the linear linkage was observed in IER China (medium development level) and WER China (lowest development level). Fourthly, unidirectional mixed linkages were found from urban concentration to non-renewable energy use intensity and environmental emissions index. Urban concentration demonstrated a U-shaped linkage with non-renewable energy use intensity and environmental emissions index in the national data set and EER China. But it unveiled a linear linkage with both variables in IER China and WER China. Fifthly, economic development showed an environmental Kuznets curve with environmental emissions index in the national data set and EER China. Conversely, it showed a linear linkage with the environmental emissions index in IER China and WER China. In turn, the environmental emissions index linearly hampered the economic development in the national data set as well as regional samples. Finally, the long-run and short-run effects showed homogeneity of the linkages' nature; yet, the degree of effects in the long-run surpassed those in the short-run for all development levels.