• Energy Research

Global fossil fuel reserves are declining due to differential uses, especially for power generation. Everybody can help to do their bit for the environment by using solar energy. Geographically, Bangladesh is a potential zone for harnessing solar energy. In March 2021, the renewable generation capacity in Bangladesh amounted to 722.592 MW, including 67.6% from solar, 31.84% from hydro, and 0.55% from other energy sources, including wind, biogas, and biomass, where 488.662 MW of power originated from over 6 million installed solar power systems. Concurrently, over 42% of rural people still suffer from a lack of electricity, where solar energy can play a vital role. This paper highlights the present status of various forms of solar energy progress in Bangladesh, such as solar parks, solar rooftops, solar irrigation, solar charging stations, solar home systems, solar-powered telecoms, solar street lights, and solar drinking water, which can be viable alternative sources of energy. This review will help decision-makers and investors realize Bangladesh’s up-to-date solar energy scenario and plan better for the development of a sustainable society.

AbstractAt the global and regional scales, green vegetation cover has the ability to affect the climate and land surface fluxes. Climate is an important factor which plays an important role in vegetation cover. This research aimed to study the changes in land cover and relation of different vegetation indices with temperature using multi-temporal satellite data in Sahiwal region, Pakistan. Supervised classification method (maximum likelihood algorithm) was used to achieve the land cover classification based on ground-truthing. Our research denoted that during the last 24 years, almost 24,773.1 ha (2.43%) of vegetation area has been converted to roads and built-up areas. The built-up area increased in coverage from 43,255.54 ha (4.24%) from 1998 to 2022 in study area. Average land surface temperature (LST) values were calculated at 16.6 °C and 35.15 °C for winter and summer season, respectively. In Sahiwal region, the average RVI, DVI, TVI, EVI, NDVI and SAVI values were noted as 0.19, 0.21, 0.26, 0.28, 0.30 and 0.25 respectively. For vegetation indices and LST relation, statistical linear regression analysis indicated that kappa coefficient values were R2 = 0.79 for RVI, 0.75 for DVI, 0.78 for DVI, 0.81 for EVI, 0.83 for NDVI and 0.80 for SAVI related with LST. The remote sensing (RS) technology can be used to monitor changes in vegetation indices values over time, providing valuable information for sustainable land use management. Even though the findings on land cover provide significant references for reasoned and optimal use of land resources through policy implications.

In recent years, there has been a surge in research pertaining to sustainable urban development. Importance–performance analysis (IPA) has emerged as one of the most widely used methods. However, few studies have combined IPA with structural equation modelling (SEM). This study introduces and evaluates an integrated IPA–SEM approach to assess the impact of perceived importance and performance of sustainable city dimensions on residents’ satisfaction and loyalty. The data for this study were collected from 425 survey respondents residing in Siliguri City of West Bengal, India. The results indicate that the ‘Keep Up the Good Work’ quadrant, consisting of social quality, connectivity conditions, and environmental quality, significantly and positively affected satisfaction. On the other hand, the ‘Concentrate Here’ quadrant concerning environmental pollution presented a negative yet insignificant relationship with satisfaction while both the ‘Low Priority’ and ‘Potential Overkill’ quadrants demonstrated no notable influence. Furthermore, a robust positive correlation between satisfaction and loyalty is confirmed. Overall, the findings offer valuable implications for urban planning, policy-making, and strategies aimed at enhancing Siliguri residents’ quality of life.

Renewable energy sources are the most necessitated natural energy to reduce fossil fuels globally. Fossil fuel is the most valuable and limited resource on the planet, but on the other hand, renewable energy creates less pollution. Solar energy is the most effective renewable resource for daily use. Solar power plants are necessary for domestic and daily use. Remote sensing and geographic information technology (GIS) were used for this study to delineate the possible site selection of solar power plants in Kolkata and the surrounding area in West Bengal, India. The analytical hierarchy process (AHP) and the multi-criteria decision-making process (MCDA) were used for each weight calculation and ArcGIS v10.8 was applied for weighted overlay analysis (WOA) for delineation of the result. The site suitability map was developed using a pairwise comparison matrix and the weights were calculated for each criterion. The suitability map was divided into five categories, from not suitable to very highly suitable. A total of 474.21 km2 (10.69%) of the area was classified as very highly suitable whereas 249.54 km2 (5.62%) area was classified as not suitable because of the water area and east Kolkata wetland. A total of 1438.15 km2 (32.43%) of the area was classified as highly suitable for a solar power plant. The Kolkata megacity and water body locations were identified as moderate to not suitable sites. Very high and high-potential sites were identified 2 to 5 km from the central business district (CBD) location, which is Dharmotala. Renewable energy source is needed in the megacity of Kolkata. If solar power plants are contracted then the demand for fossil fuel will be reduced one day, and that will help the environment as well as the society in terms of sustainable development. This study result is helpful for administrators, urban planners, developers, and other stakeholders for the implementation and development of a new solar power plant in the study area.

Karstic terrains are usually dominated by aquifer systems and/or underground cavities. Overexploitation of groundwater in such areas often induces land subsidence and sometimes causes sinkholes. The Cheria basin in Algeria suffers from severe land subsidence issues, and this phenomenon has been increasing in recent years due to population expansion and uncontrolled groundwater exploitation. This work uses GPS data and persistent scatterer interferometry synthetic aperture radar (PS-InSAR) techniques to monitor the land subsidence rate by employing Sentinel-1 satellite data for the period from 2016 to 2022. Our results demonstrate that the Cheria basin experiences both uplift and subsidence in places, with an overall substantial change in the land surface. The total cumulative subsidence over 6 years reached a maximum of 500 mm. Comparison of land deformation between PSI and GPS showed root mean square error (RMSE) values of about 2.83 mm/year, indicating that our analyzed results are satisfactorily reproducing the actual changes. Nonetheless, these results can be used to extract the susceptible zones for vertical ground displacement and evaluate the surface deformation inventory map of the region for reducing damages (e.g., human losses, economic impact, and environmental degradation) that may occur in the future (e.g., sinkholes) and can be further utilized in perspective for a sinkhole early warning system.

Hydrological reaction to climate change anticipates water cycle alterations. To ensure long-term water availability and accessibility, it is essential to develop sustainable water management strategies and better hydrological models that can simulate peak flow. These efforts will aid in water resource planning, management, and climate change mitigation. This study develops and compares Sacramento, Australian Water Balance Model (AWBM), TANK, and SIMHYD conceptual models to simulate daily streamflow at Rajegaon station of the Pranhita subbasin in the Godavari basin of India. The study uses daily Indian Meteorological Department (IMD) gridded rainfall and temperature datasets. For 1987–2019, 70% of the models were calibrated and 30% validated. Pearson correlation (CC), Nash Sutcliffe efficiency (NSE), Root mean square error (RMSE), and coefficient of determination (CD) between the observed and simulated streamflow to evaluate model efficacy. The best conceptual (Sacramento) model selected to forecast future streamflow for the SSP126, SSP245, SSP370, and SSP585 scenarios for the near (2021–2040), middle (2041–2070), and far future (2071–2100) using EC-Earth3 data was resampled and bias-corrected using distribution mapping. In the far future, the SSP585 scenario had the most significant relative rainfall change (55.02%) and absolute rise in the annual mean temperature (3.29 °C). In the middle and far future, the 95th percentile of monthly streamflow in the wettest July is anticipated to rise 40.09% to 127.06% and 73.90% to 215.13%. SSP370 and SSP585 scenarios predicted the largest streamflow increases in all three time periods. In the near, middle, and far future, the SSP585 scenario projects yearly relative streamflow changes of 72.49%, 93.80%, and 150.76%. Overall, the findings emphasize the importance of considering the potential impacts of future scenarios on water resources to develop effective and sustainable water management practices.

AbstractGlobally, green spaces are considered as a fundamental part of biodiversity to balance the environment in an ecosystem particularly in urban areas. Contrary to this, urban green is declining in the least developing nations particularly due to rapid urbanization. This impact alter the condition of environmental services in term of non-provision and insufficient urban green spaces for the local population. This study takes Shahdara Town as an area of interest that is a congested urban area of Lahore Pakistan, and currently facing social injustice in terms of a dense urban area with few green spaces. The study uses Geographical Information Science for the identification of green spaces, calculation of their total area, and quantification of green cover extent by using NDVI, to comparing them with population to analyzed the environmental justice. To check the social perspective of the population regarding green areas and their importance among urban people, a comparison has been made with the presence of parks in the area, their association with living conditions and quality of life of locals. This social prospective was investigated by applying the Multiple Linear Regression Model (MLRM) by taking the availability of park/urban green in area as dependent variable and accessibility to parks, its total distance from a house, and frequency of visit as independent variables. The resultant analysis shows that according to international standards, per capita green spaces are less in this town, of Lahore. This imbalance provision of environmental services is a challenging task, for policymakers and a hurdle for urban sustainability. Finally, the outcomes of the research provide a documented base evidence and social analysis for environmental sustainability in terms of provisions of environmental services for the urban dwellers in the region.

Accurate streamflow modeling is crucial for effective water resource management. This study used five machine learning models (support vector regressor (SVR), random forest (RF), M5-pruned model (M5P), multilayer perceptron (MLP), and linear regression (LR)) to simulate one-day-ahead streamflow in the Pranhita subbasin (Godavari basin), India, from 1993 to 2014. Input parameters were selected using correlation and pairwise correlation attribution evaluation methods, incorporating a two-day lag of streamflow, maximum and minimum temperatures, and various precipitation datasets (including Indian Meteorological Department (IMD), EC-Earth3, EC-Earth3-Veg, MIROC6, MRI-ESM2-0, and GFDL-ESM4). Bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets were utilized in the modeling process. Model performance was evaluated using Pearson correlation (R), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and coefficient of determination (R2). IMD outperformed all CMIP6 datasets in streamflow modeling, while RF demonstrated the best performance among the developed models for both CMIP6 and IMD datasets. During the training phase, RF exhibited NSE, R, R2, and RMSE values of 0.95, 0.979, 0.937, and 30.805 m3/s, respectively, using IMD gridded precipitation as input. In the testing phase, the corresponding values were 0.681, 0.91, 0.828, and 41.237 m3/s. The results highlight the significance of advanced machine learning models in streamflow modeling applications, providing valuable insights for water resource management and decision making.