• Energy Research

Land use and land cover (LULC) changes are significantly impacting the natural environment. Human activities and population growth are negatively impacting the natural environment. This negative impact directly relates to climate change, sustainable agriculture, inflation, and food security at local and global levels. Remote sensing and GIS tools can provide valuable information about change detection. This study examines the correlation between population growth rate and LULC dynamics in three districts of South Punjab, Pakistan—Multan, Bahawalpur, and Dera Ghazi Khan—over a 30-year period from 2003 to 2033. Landsat 7, Landsat 8, and Sentinel-2 satellite imagery within the Google Earth Engine (GEE) cloud platform was utilized to create 2003, 2013, and 2023 LULC maps via supervised classification with a random forest (RF) classifier, which is a subset of artificial intelligence (AI). This study achieved over 90% overall accuracy and a kappa value of 0.9 for the classified LULC maps. LULC was classified into built-up, vegetation, water, and barren classes in Multan and Bahawalpur, with an additional “rock” class included for Dera Ghazi Khan due to its unique topography. LULC maps (2003, 2013, and 2023) were prepared and validated using Google Earth Engine. Future predictions for 2033 were generated using the MOLUSCE model in QGIS. The results for Multan indicated substantial urban expansion as built-up areas increased from 8.36% in 2003 to 25.56% in 2033, with vegetation and barren areas displaying decreasing trends from 82.96% to 70% and 7.95% to 3.5%, respectively. Moreover, areas containing water fluctuated and ultimately changed from 0.73% in 2003 to 0.9% in 2033. In Bahawalpur, built-up areas grew from 1.33% in 2003 to 5.80% in 2033, while barren areas decreased from 79.13% to 74.31%. Dera Ghazi Khan expressed significant increases in built-up and vegetation areas from 2003 to 2033 as 2.29% to 12.21% and 22.53% to 44.72%, respectively, alongside reductions in barren and rock areas from 32.82% to 10.83% and 41.23% to 31.2%, respectively. Population projections using a compound growth model for each district emphasize the demographic impact on LULC changes. These results and findings focus on the need for policies to manage unplanned urban sprawl and focus on environmentally sustainable practices. This study provides critical awareness to policy makers and urban planners aiming to balance urban growth with environmental sustainability.

In recent decades, the fortunes of energy economies have been closely linked in Pakistan. A major energy inefficiency issue was found in Pakistan due to the mismatch between horsepower (HP) requirements and bore depth. Keeping this in view, a total of 194 tubewells were chosen for an energy audit in the Multan region, Pakistan. The Terrameter SAS 4000 was used to measure the accurate demand of the head during the resistivity surveys at all of the selected locations. The results showed that the tubewell sets were installed arbitrarily at high power, irrespective of the provided flow and head, and these pumps used more energy for their flow. The results revealed that the efficiency of the tubewell sets increased from 35 to 54%, from 55 to 80%, from 49 to 80%, and from 48 to 75% for centrifugal pumps with electric motors and diesel engines and for turbines with electric motors and diesel engines, respectively. A weighted overlay analysis indicated that the efficiency of tubewells covering 838.12, 1131.8, and 2077.1 km2 for centrifugal pumps with electric motors, diesel engines, and turbines, respectively, was enhanced for the study area. Similarly, the energy saved for the study area covered 1423.8, 1161.1, and 1131.1 km2, as shown by the overlay analysis. The results revealed that the annual energy saving was found of 3486 kw for 194 tubewells, resulted in the saving of USD 0.204 million in operational costs over one year. The overall results indicate the strong need to adopt proper investigations of the head and power requirements before installing a system in the study area.