Kafait Ullah

This research explores the effect of green human resource management practices, CSR activities, and green banking practices on the sustainable performance of banks in developing economies, with employee green behavior as a mediator and financial technology adoption as a moderator. Data were gathered for the study from 620 branch managers in the banking sector using self-administered printed and online questionnaires. We applied partial least squares structural equation modelling (PLS-SEM) to examine direct and indirect associations among the variables, and the results indicated that green banking practices significantly impact banks' sustainable performance. Moreover, it was discovered that FinTech partially moderates the relationship between green banking practices and sustainable performance, and the adoption of GHRM practices and CSR activities improves banks' sustainable performance. This study stands out by looking at the combined effects of green human resource management practices, CSR initiatives, and green banking practices on sustainability. This area has not received much attention in previous studies. It emphasizes how crucial employee green behavior and FinTech adoption are to maximizing banks' sustainable performance. Furthermore, this study invoked the Social Exchange Theory, Resource-Based View, AMO Theory, Stakeholder Theory, and Institutional Theory to examine the relationship of study variables. Prominently, this research strengthens the 2030 Plan of the United Nations for Sustainable Development, particularly in relation to SDG 8, SDG 9, and SDG 13.

Sustainable energy infrastructure relies on the adoption of advanced low carbon and affordable technologies. In developing countries like Pakistan, sustainability of the energy system has become a challenge because of the gap between electricity demand and supply, inefficient energy generation, dependence on carbon- intensive technologies, low electrification rate and increased effects of climate change. Measuring the sustainability of energy systems is vital to ascertain the level of sustainability, innovativeness and the reliability. Various studies have proposed indices for measuring the sustainability of energy systems. Rarely can any index boast of providing a comprehensive framework for measuring all aspects of sustainability. This study has reviewed the existing indices and developed a framework based on strengths and weaknesses of the existing indices. The framework has divided the sustainability into four main segments with further division into thirteen sub-segments which consist of forty-nine indicators. Later, we have applied the newly proposed framework to comparatively measure the sustainability level of energy system in Pakistan, USA and European Union. The results provide a vital input to the policy makers to focus on critical areas for enhancing the sustainability of energy systems in developing countries. Keywords: Energy systems, national energy policy, sustainability assessment framework.

Sustainable energy infrastructure relies on the adoption of advanced low carbon and affordable technologies. In developing countries like Pakistan, sustainability of the energy system has become a challenge because of the gap between electricity demand and supply, inefficient energy generation, dependence on carbon- intensive technologies, low electrification rate and increased effects of climate change. Measuring the sustainability of energy systems is vital to ascertain the level of sustainability, innovativeness and the reliability. Various studies have proposed indices for measuring the sustainability of energy systems. Rarely can any index boast of providing a comprehensive framework for measuring all aspects of sustainability. This study has reviewed the existing indices and developed a framework based on strengths and weaknesses of the existing indices. The framework has divided the sustainability into four main segments with further division into thirteen sub-segments which consist of forty-nine indicators. Later, we have applied the newly proposed framework to comparatively measure the sustainability level of energy system in Pakistan, USA and European Union. The results provide a vital input to the policy makers to focus on critical areas for enhancing the sustainability of energy systems in developing countries. Keywords: Energy systems, national energy policy, sustainability assessment framework.

Net metering (NM) is among the potent regulatory tools used globally for supporting distributed generation and renewable energy sources. This paper examines the trajectory of NM in a developing country such as Pakistan, analyzing the impact of regulatory changes, confidence-building strategies, hindering factors, and technical/financial issues. The three-stage methodology involves three components, namely techno-economic analysis, stakeholder engagement surveys, and impact analysis of financing mechanisms. This study emphasizes the importance of clear regulatory and financial frameworks, grid upgrades, and public–private partnerships for technology distribution in the context of a developing country with weak grid utilities and an import–export energy ratio. It also explores the role of financial incentives, such as tax breaks and subsidies, to encourage investment in NM systems from the perspective of lucrative rates, impact on paybacks, and return on investments, and proposes concrete solutions to enhance financial inclusion for ambitious renewable energy goals. Until April 2023, over 56,000 NM/distributed generation facilities were commissioned, with an installed capacity of 950 MW. By May 2024, the number of NM consumers reached ~100,000, with a 1950 MW capacity, nearly doubling. However, the import and export ratio of IESCO changed most, with 61% exports and 39% imports, directly impacting the revenue stream. A total of 60% of banks have adopted actions linked with green banking criteria, aiming to limit their environmental impact. The change in tariff will result in reduced ROI for NM consumers to 20%, and increase the payback period from less than 4 years to 13 years. Government subsidies, tax breaks, and green financing frameworks are proposed to encourage investment, but have been abruptly halted, and were previously at a 6% interest rate. This research aims to provide insights into effective market evaluation methodologies for NM programs and offer policy recommendations to strengthen legislative and institutional frameworks governing NM.

With the emergence of advanced computational technologies, the capacity to process data for developing machine learning-based predictive models has increased multifold. However, reliance on the model’s mere accuracy has swiftly shifted attention away from its interpretability. Resultantly, a need has emerged amongst forecasters and academics to have predictive models that are not only accurate but also interpretable as well. Therefore, to facilitate energy forecasters, this paper advances the knowledge of short-term load forecasting through generalized regression analysis using high degree polynomials and cross terms. To predict the irregularly changing energy demand at the consumer level, the proposed model uses a time series of an hourly load of three years of an electricity distribution company in Pakistan. Two variants of regression analysis are used: (a) generalized linear regression model (GLRM), and (b) generalized linear regression model with polynomials and cross-terms (GLRM-PCT) for comparative reasons. Experiments revealed that GLRM-PCT showed higher forecasting accuracy across a variety of performance metrics such as mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and r-squared values. Moreover, the enhanced interpretability of GLRM-PCT also explained a wide range of combinations of weather variables, public holidays, as well as lagged load and climatic variables.

In the above article [1] , Fig. 3 (a) and (b) and Fig. 5 (a) and (b) were inadvertently duplicated during the proofreading and typesetting stage. This resulted in Fig. 3 (a) and (b) in [1] not representing the correlation of load with dry bulb temperature and dewpoint temperature as intended and stated in the figure caption. The correct representation of the correlation of load with both the dry bulb temperature and dewpoint temperature to be considered is shown below in Fig. 3 . However, Fig. 5 (a) and (b) in [1] already correctly represents the average dry bulb temperature of eight weather stations and the average dew point temperature of eight weather stations. Therefore, Fig. 3 given below should replace Fig. 3 (a) and (b) in [1] .

Load forecasts are fundamental inputs for the reliable and resilient operation of a power system. Globally, researchers endeavor to improve the resulting forecast accuracies. However, the lack of studies detailing a standardized model development process remains a major issue. In this regard, this paper advances the knowledge of the systematic development of a short-term load forecast (STLF) model for electric utilities. The proposed model has been developed by using hourly load (time series) of five years of an electric power utility in Pakistan. Following the investigation of previously developed forecast models, this study addresses the challenges of STLF by utilizing multiple linear regression, bootstrap aggregated decision trees, and artificial neural networks (ANNs) as mutually competitive forecasting techniques. The study also highlights both rudimentary and advanced elements of data extraction, synthetic weather station development, and the use of elastic nets for feature space development to upscale its reproducibility at the global level. Simulations showed the superior forecasting prowess of ANNs over other techniques in terms of mean absolute percentage error (MAPE), root mean squared error (RMSE) and R2 score. Furthermore, an empirical approach has been taken to underline the effects of data recency, climatic events, power cuts, human activities, and public holidays on the model’s overall performance.