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Pakistan is facing energy crises due to localized shortages, market manipulation, infrastructure disruption, rising demand, governance issues, climate and geopolitical events. In this situation Demand Side Management (DSM) is a promising solution to overcome the problem of energy crises. DSM strategy helps to manage consumer demand through energy conservation rather than to addition of new power capacity. In this study, Low Emissions Analysis Platform (LEAP) develops an energy model for Pakistan for the period 2022-2050. Three scenarios has been to constructed namely Baseline (BAS), Green Energy Policy (GEP), and Energy Efficiency (ENE) to predict the future energy demand, production, carbon emissions and the investment cost which covers capital, operational and maintenance costs. The model results suggest that DSM targets should be achieved through the implementation of ENE scenario. Predicted energy production and consumption under the ENE scenario are substantially less than those under the BAS scenario. The country can meet its 635.83,000 GWh energy demand with its 747.15,000 GWh energy production. Non-renewable sources produce 171.27,000 GWh, whilst renewable sources produce 575.88,000 GWh. According to this scenario, by 2050, CO 2 emissions will be produced around 93.16 million metric tons, requiring an investment cost of $46.80 billion for building new power capacity. The study provides a roadmap with a suggestive optimal balance between energy saving with DSM approach and utilizing renewable energy production to meet energy demand for different sectors of the economy., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

The potential of solid waste as an energy source is clear, owing to its wide availability and renewable properties, which provide a critical answer for energy security. This can be especially effective in reducing the environmental impact of fossil fuels. Countries that rely heavily on coal should examine alternatives such as electricity from solid waste to provide a constant energy supply while also contributing to atmospheric restoration. In this regards, Low Emissions Analysis Platform (LEAP) is used for simulation the entire energy system in Pakistan and forecasted its capital cost and future CO 2 emissions in relation to the use of renewable and fossil fuel resources under the different growth rates of solid waste projects like 20%, 30% and 40% for the study period 2023-2053. The results revealed that, 1402.97 TWh units of energy are generated to meet the total energy demand of 1193.93 TWh until 2053. The share of solid waste based electricity in total energy mix is increasing from a mere 0.81% in 2023 to around 9.44% by 2053 under the 20% growth rate, which then increase to 39.67% by 2053 under the 30% growth rate and further increases to 78.33% by 2053 under the 40% growth rate. It is suggested that 40% growth rate for solid waste based electricity projects is suitable for Pakistan until 2053 because under this condition, renewable sources contributes 95.2% and fossil fuels contributed 4.47% in the total energy mix of Pakistan. Hence, CO 2 emissions are reduced from 148.26 million metric tons to 35.46 million metric tons until 2053 but capital cost is increased from 13.23 b$ in 2023 to 363.11 b$ by 2053., (© 2024. The Author(s).)

In today's modern era, chronic kidney disease stands as a significantly grave ailment that detrimentally impacts human life. This issue is progressively escalating in both developed and developing nations. Precise and timely identification of chronic kidney disease is imperative for the prevention and management of kidney failure. Historical methods of diagnosing chronic kidney disease have often been deemed unreliable on several fronts. To distinguish between healthy individuals and those afflicted by chronic kidney disease, dependable and effective non-invasive techniques such as machine learning models have been adopted. In our ongoing research, we employ various machine learning models, encompassing logistic regression, random forest, decision tree, k-nearest neighbor, and support vector machine utilizing four kernel functions (linear, Laplacian, Bessel, and radial basis kernels), to forecast chronic kidney disease. The dataset used constitutes records from a case-control study involving chronic kidney disease patients in district Buner, Khyber Pakhtunkhwa, Pakistan. For comparative evaluation of the models in terms of classification and accuracy, diverse performance metrics, including accuracy, Brier score, sensitivity, Youden's index, and F1 score, were computed., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Khan, Raza, Mirjat, Balouch, Abbas, Yousef and Touti.)

This paper investigates the long-run dynamic association of carbon dioxide emissions and economic progress using the environmental Kuznets curve (EKC) hypothesis for Pakistan from 1972 to 2019. The other variables used along with the above indicators are urbanization, financial development, manufacturing, oil and gas energy consumption, and trade openness. The ARDL bounds testing methodology has been used and the results of the bound testing showed that a 1% increase in economic growth and financial development would increase CO 2 emissions in the country by 4.277 and 0.0787%, respectively. Urbanization and manufacturing would increase CO 2 emissions by 0.648 and 0.171%, respectively. Oil and gas energy consumption would also increase CO 2 emissions by 0.130 and 0.1935%, respectively. Trade openness is the only factor found in the study which decreases emissions in the country in the long run by -0.087%. Besides, findings confirm the EKC hypothesis with the reversed U-shaped relation for per capita CO 2 emissions and economic progress. However, the threshold point of the economy is approximately $2735.19 per capita, where economic progress can mitigate the impact of pollution lies beyond the study period, and GDP growth still increases emissions of the country and degrades the environment of Pakistan, as it is not yet reached at the turning level of the economy. As a result, GDP growth is still harming the economy of Pakistan as it has not yet reached the turning point of the economy., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)