Climate change is a pressing global issue with severe consequences for the planet and human health. The Earth’s temperature has risen by 2 °C from 1901 to 2023, and this warming trend is expected to continue, causing potentially dangerous shifts in climate. Climate change impacts are already visible, with more frequent and severe heat waves, droughts, intense rain, and floods becoming increasingly common. Therefore, hydropower can contribute to addressing the global climate change issue and help to achieve global energy transition and stabilize global energy security. A Long Short-Term Memory (LSTM)-based model implemented in Python for global and regional hydropower forecasting was developed for a study period of 2023 to 2060 by taking the input data from 1980 to 2022. The results revealed that Asian countries have greater hydropower potential, which is expected to increase from 1926.51 TWh in 2023 to 2318.78 TWh in 2030, 2772.06 TWh in 2040, 2811.41 TWh in 2050, and 3195.79 TWh in 2060, as compared with the other regions of the world like the Middle East, Africa, Asia, Common Wealth of Independent State (CIS), Europe, North America, and South and Central America. The global hydropower potential is also expected to increase from 4350.12 TWh in 2023 to 4806.26 TWh in 2030, 5393.80 TWh in 2040, 6003.53 TWh in 2050, and 6644.06 TWh in 2060, which is sufficient for achieving energy transition and energy security goals. Furthermore, the performance and accuracy of the LSTM-based model were found to be 98%. This study will help in the efficient scheduling and management of hydropower resources, reducing uncertainties caused by environmental variability such as precipitation and runoff. The proposed model contributes to the energy transition and security that is needed to meet the global climate targets.

Fossil fuel-based energy systems are mostly used for supplying energy that creates negative impacts on the environment, so in this study, the development of a 100% renewable energy system is evaluated for Pakistan for sustainable climate change. This study modeled three scenarios, namely, ongoing (ONG), energy saving policy (ESP), and green energy policy (GEP) scenarios using low emission analysis platform (LEAP) software for the study period 2022 to 2050 for Pakistan. The results revealed that a 100% renewable energy supply could be achieved through the GEP scenario. Model results show that the share of renewable sources in the total energy mix is 1117.08 TWh and non-renewable sources contribute only 18.12 TWh to meet the energy demand of 966.05 TWh until 2050. Non-renewable production leads to the generation of 8.85 million metric tons of carbon emissions, which is too low compared with the 135.47 million metric tons under the ONG scenario. The USD 1482.46 billion investment cost required for adding renewable energy capacity until 2050 is too high as compared with the USD 46.80 billion under the ONG scenario. Energy demand and production requirements are reduced by 34.18% under the ESP scenario until 2050. This approach can also be applicable to the majority of nations worldwide.

Thar desert is located in south-east Pakistan carries 175 billion tons of coal and is the biggest coalfield in Asia. In this paper mathematical framework is developed for the accurate estimation of power production from the available Thar coal capacity. Annual TWh possible energy production the period 2020 to 2040 followed by detailed analysis of capacity factor and Maximizing the share percentage of coal in total energy mix at least 40% by 2040 is presented. The findings reveal that Thar coal has the capability of producing 100,000 MW power up to the next 250 years. The energy generation for the years 2020, 2032, and 2040 is calculated 744 TWh per year, 700 TWh per year, and 665 TWh per year respectively and it produces negligible negative impacts on the environment. The long-term energy planning presented in this paper would attract foreign investment and Pakistan's energy needs would be fulfilled for sustainable development of the country.