Countries worldwide are focusing on energy efficiency, economic sustainability, and responsible resource management to address climate change and meet sustainable development goals (SDGs). This study investigates how factors such as artificial intelligence, renewable energy, green human capital, geopolitical risk, natural resource rent, and information and communication technology influenced CO₂ emissions in 36 countries between 2000 and 2021. The study also explores how institutional quality moderates these relationships. We employed advanced econometric techniques to address this gap, including panel-correlated standard errors (PCSE) and the Driscoll–Kraay estimations (DKSE) models. A two-step system GMM approach was also used to strengthen the robustness of our findings. The findings reveal that green human capital, renewable energy consumption, and institutional quality can significantly reduce CO₂ emissions. Conversely, artificial intelligence, geopolitical risk, natural resource rent, and information communication technology contribute to increased CO₂ emissions. Institutional quality enhances the positive impact of green human capital and renewable energy on emission reduction. However, it has the opposite effect on artificial intelligence, leading to an even greater increase in CO₂ emissions. These findings underscore the importance of green policies in achieving sustainable development goals. We recommend that policymakers prioritize investing in clean energy and green human capital while strengthening institutional quality to effectively mitigate carbon emissions and meet SDGs. They also regulate AI and ICT carbon footprints and address geopolitical risks through energy diversification and international cooperation.

The study examined the decoupling relationship between industry carbon emissions and economic growth using the most current updated data. We collected data from the industry sector of Pakistan from 1991 to 2018. This study used two techniques, Tapio’s elasticity and fluctuation index, to test the decoupling relationship and the relationship’s stability between industry carbon emissions and economic growth. First, we analyzed the decoupling relationship between industry carbon emissions and economic growth in Pakistan using Tapio’s decoupling model, decoupling the elasticity of technology, scale and structure. Second, the decoupling volatility index was used to determine the stability of the technology, scale and structure elasticity. The Tapio decoupling elasticity technique results confirmed the significant relationship between industry CO₂ emissions, economic growth and added value comes out to be a simple feature of each stage and undergoes expansive negative and strong decoupling processes. The outcomes of elastically decoupled volatility indexes suggest that three out of four indices are high, indicating unstable decoupling conditions. This study concludes that growth and volatility were the crucial characteristics of industrial carbon emissions during 1991–2018. Hence, the industry’s carbon emissions were increased from 16.67 million tons in 1991 to 55.70 million tons in 2018, an average yearly growth of 4.56%.

Adopting innovative, systematically structured, and sustainable human resource management (SHRM) practices is essential for enhancing logistics agility and deriving sustainable development in logistics operations. This study examines the influence of sustainable human resources management practices on logistics agility with a mediating role of artificial intelligence (AI) in China’s logistics industry. Given the rapid growth and technological advancements in China’s logistics sector, this study employed quantitative research and convenience sampling techniques to collect data from 341 employees working in the industry. Smart PLS was used to test the proposed hypotheses through structural equation modeling (SEM). The study’s findings reveal that reward management, training and development, job appraisal, and teamwork significantly enhance logistics agility, while recruitment and selection show an insignificant impact. Similarly, the results reveal that sustainable HRM practices and artificial intelligence positively and significantly influence logistics agility. In addition, artificial intelligence substantially mediates the relationship between sustainable HRM practices and logistics agility. These findings offer valuable insights for HRM and logistics management, highlighting how AI can strengthen sustainable HRM practices to foster agility and improve logistics performance. The findings are particularly relevant for practitioners and policymakers aiming to enhance sustainability and efficiency in the logistics sector.