Carbon emissions constitute a large portion of greenhouse gases that are responsible for global warming and climate change. This study examines the impact of export variety on carbon emissions along with foreign direct investment (FDI) and technological development as determinants of environmental degradation in Pakistan. Moreover, this study is conducted in the context of the environmental Kuznets curve hypothesis (EKC). This study applies dynamic ordinary least squares and error correction models for long-term and short-term estimates, respectively. The results indicate that the EKC hypothesis is valid in the long term. This implies that Pakistan’s economy reached the threshold level of income, after which an increase in income was not responsible for environmental degradation. Export variety restrains environmental degradation in the short term and is not a significant factor in the long term. Energy consumption has aggravated environmental degradation, while FDI and technological development are restraining environmental degradation. Policy measures are recommended to curb environmental degradation in Pakistan.

The COVID-19 pandemic is considered to be the world’s most urgent health disaster and the greatest challenge humanity has faced since World War II. One of the most significant concerns is the effectiveness of the extraordinary efforts undertaken around the world to prevent the spread of the coronavirus. This study applied the Westerlund cointegration test and the Pooled Mean Group–Autoregressive Distributed Lag (PMG-ARDL) model to investigate the effectiveness of government policy measures in reducing the COVID-19 pandemic using weekly data for the period 1 January 2020 to 31 May 2021. The results of the Westerlund cointegration test validate the presence of a cointegration relationship between government policy measures and the COVID-19 pandemic. In addition, results of PMG-ARDL show that economic support, stringency, and health and containment measures play a significant role in reducing the COVID-19 pandemic. Therefore, this study concluded that pharmaceutical and non-pharmaceutical measures have a substantial impact on mitigating the COVID-19 pandemic in South Asia. Policy implications such as government financial support for poor families, stringency, and health and containment measures must be adopted during the pandemic to curb/reduce the spread of COVID-19. Furthermore, it is also recommended to provide vaccines free of charge to developing countries and a stimulus package to support health research to expand the capacity for rapid vaccine in this region.

Pakistan is facing an energy crisis and is also severely affected by climate change. Moreover, Pakistan is not doing well as far as health outcome indicators are concerned. The causal nexus among energy, environment, and health outcomes is well-established in literature. Besides, financial development also grabs the attention of health outcome literature as financial development can play a significant role in improving health outcomes. Thus, this study was conducted to test the causal nexus among energy consumption, environmental degradation, financial development, and health outcomes in the case of Pakistan. This study proxies health outcomes with life expectancy and infant mortality. Time series data have been analyzed through different econometric techniques, such as unit root tests, cointegration techniques, causality techniques, and cointegration regressions. Moreover, this study not just discovers the causal direction among variables but also determines the strength of causality through variance decomposition. Results of the study confirm that all variables of the study are cointegrated in the long run. The causality analysis reveals that unidirectional causality is running from energy consumption and environmental degradation to health outcomes, whereas bidirectional causality is found between financial development and health outcomes in the long run. Besides, this study also determines the effect of energy, environmental degradation, and financial development in the health outcome model and finds that energy and financial development can help Pakistan to improve health outcomes. Policy implications are recommended for Pakistan.

Online reviews play a crucial role in shaping consumer decisions, especially in the context of e-commerce. However, the quality and reliability of these reviews can vary significantly. Some reviews contain misleading or unhelpful information, such as advertisements, fake content, or irrelevant details. These issues pose significant challenges for recommendation systems, which rely on user-generated reviews to provide personalized suggestions. This article introduces a recommendation system based on a Passer Learning Optimization-enhanced Bidirectional Long Short-Term Memory network classifier applicable to e-commerce recommendation systems with improved accuracy and efficiency compared to state-of-the-art models. More specifically, the proposed model achieves a high accuracy of 98.03%, F1 score of 98.03%, precision of 98.49%, recall of 97.57%, and minimum mean square error of 1.97 based on training percentage using the patio lawn garden dataset. These results, made possible by advanced graph embedding for effective knowledge extraction and fine-tuning of classifier parameters by the hybrid PLO algorithm, establish the suitability of the proposed PLO-Bi-LSTM model in various e-commerce environments. Received: 9 April 2025 | Revised: 26 June 2025 | Accepted: 24 August 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The Amazon product data that support the findings of this study are openly available at https://cseweb.ucsd.edu/~jmcauley/datasets/amazon/links.html. Author Contribution Statement Hemn Barzan Abdalla: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Mehdi Gheisari: Conceptualization, Software, Data curation. Awder Ahmed: Conceptualization, Methodology, Software, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization. Bahtiyar Mehmed: Formal analysis, Investigation, Resources. Maryam Cheraghy: Validation, Resources, Writing – review & editing. Yang Liu: Validation, Investigation, Resources.