Despite great academic interest in global green supply chain management (GSCM) practices, its effectiveness for environmental management systems (EMS) and market competitiveness during COVID-19 remains untapped. Existing literature suggests that a fundamental link between GSCM, EMS, and market competitiveness is missing, as supply management is critical to maintain market competitiveness. To fill this gap in the literature, this study examines whether environmental management systems influence the link between GSCM practice and market competitiveness in China. We also propose the articulating role of big data analytics and artificial intelligence (BDA-AI) and environmental visibility toward these associations in the context of the COVID-19 pandemic. We evaluated the proposed model using regression-based structural equation modeling (SEM) with primary data (n = 330). This result provides empirical evidence of the impact of GSCM on EMS and market competitiveness. Moreover, the results show that the BDA-AI and the environmental visibility enhanced the positive relationship between GSCM-EMS and EMS and market competitiveness in China. Recent research shows that supply chain professionals, policymakers, managers, and researchers are turning to formal EMS, BDA-AI, and environmental visibility to help their organizations achieve the competitiveness that the market indicates they need.

The foremost aim of this research was to forecast the performance of three stock market indices using the multilayer perceptron (MLP), recurrent neural network (RNN), and autoregressive integrated moving average (ARIMA) on historical data. Moreover, we compared the extrapolative abilities of a hybrid of ARIMA with MLP and RNN models, which are called ARIMA‐MLP and ARIMA‐RNN. Because of the complicated and noisy nature of financial data, we combine novel machine‐learning techniques such as MLP and RNN with ARIMA model to predict the three stock market data. The data used in this study are taken from the Pakistan Stock Exchange, National Stock Exchange India, and Sri Lanka Stock Exchange. In the case of Pakistan, the findings show that the ARIMA‐MLP and ARIMA‐RNN beat the individual ARIMA, MLP, and RNN models in terms of accuracy. Similarly, in the case of Sri Lanka and India, the hybrid models show more robustness in terms of forecasting than individual ARIMA, MLP, and RNN models based on root‐mean‐square error and mean absolute error. Apart from this, ARIMA‐MLP outperformed the ARIMA‐RNN in the case of Pakistan and India, while in the context of Sri Lanka, ARIMA‐RNN beat the ARIMA‐MLP in forecasting. Our findings reveal that the hybrid models can be regarded as a suitable option for financial time‐series forecasting.

The objective of this paper is to ascertain the impact of Chinese FDI on economic growth in Pakistan. This study documents the exploration of the determinants of economic growth in Pakistan by emphasizing the significant role played by Chinese FDI and investments in renewable energy in particular. This paper employs time series data analysis to examine the relationship between GDP and Chinese FDI, inflation, trade openness, exchange rates, interest rates, remittances, and renewable energy consumption from 1990 to 2019. The study involved performing the ARDL bounds test, and it was determined that the dependent and independent variables are linked in the long term. Furthermore, the error correction model is negative and noteworthy, which checks the long-run relationship between variables. According to the findings of the autoregressive distributed lag (ARDL) model, Chinese FDI has a substantial favorable effect on Pakistan’s economic growth. Furthermore, renewable energy usage has a long-term favorable and significant association with Pakistan’s economic growth. This study established that FDI, and particularly renewable energy, will stimulate the economic growth of Pakistan. Our research has substantial policy implications, especially when it comes to the relationship between FDI and renewable energy.

Fossil fuel and electricity-powered irrigation techniques boost the water availability expense and increase greenhouse gas emissions. Especially in developing countries, solar-powered irrigation is becoming more popular as a response to the growing energy and environmental issues associated with agriculture systems. The existing study used data from 1080 wheat farmers in Balochistan, Pakistan, to investigate the impact of solar-powered irrigation system (SPIS) usage on the technical efficiency (TE) of wheat production. The TE of wheat production is estimated using a stochastic frontier production function, and the potential self-selectivity bias is addressed using an endogenous switching regression model. The findings indicated that 13.7% of the wheat cultivators assessed used SPISs to produce their crops. Using an endogenous switching regression model shows that SPIS usage increases the TE of wheat growers by 6.657%, after controlling for self-selection bias. While using SPISs, wheat farmers with large farms and farming familiarity had stronger positive effects on TE. The results highlight the need for more investigation and research into evidence-based good practice for SPIS solutions at the site level to ensure that the rollout of modern equipment not only drives the energy sector forward but also contributes significantly to our level playing field and sustainable environment.

The purpose of this research paper is to conducts a comprehensive examination of social media engagement in Pakistan, focusing on user trends across diverse platforms. [1]A longitudinal observational research design was adopted to capture temporal variations in user interactions on major social media platforms. The data collection process involved sourcing monthly engagement metrics from publicly available platform analytics, ensuring accuracy and reliability. The analysis begins with Facebook, revealing distinctive fluctuations in engagement percentages over the observed months. Peaks in November 2022 and subsequent recoveries underscore the impact of seasonality, content dynamics, and platform updates on user behavior. Twitter, as a microblogging platform, maintains a consistent but comparatively lower level of engagement, resiliently serving audiences seeking real-time and concise content. Visual-centric platforms like YouTube and Instagram exhibit dynamic patterns, with YouTube's fluctuating percentages indicating a diverse content landscape and an uptick in Instagram engagement signaling evolving user preferences for visually-driven experiences. Beyond mainstream platforms, the study extends its focus to niche audiences on platforms such as Pinterest, LinkedIn, Reddit, and others, revealing the coexistence of platforms catering to specific interests. the findings contribute to a distinctive understanding of user behavior, uncovering the impact of various factors on engagement percentages across different social media platforms in Pakistan. Future research endeavors can build upon these insights, exploring additional dimensions of social media engagement and it's evolving trends within the digital landscape of Pakistan.