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The theoretical background bases on the big data analytics-artificial intelligence (BDA-AI) technologies and supply chain ambidexterity (SCAX) in the firms to assess their sustainability endeavors such as green supply chain management (GSCM) to improve their green communication and corporate image.

Around 220 participants in the manufacturing firms are participants' industry expertise, diverse roles, and representation as key stakeholders.

The results show BDA-AI and SCAX affected on GSCM and found the significant relationships with green communication and corporate image. Green communication was discovered to impact corporate image significantly.

Prior studies are neglected to address the relationship among the AI, powered by rapid computational and BDA breakthroughs, redefines cognitive tasks, achieving feats previously deemed impossible-making implicit judgments, simulating emotions, and driving operations. This study selects manufacturing firms as respondents due to their forefront of BDA-AI and supply chain ambidexterity adoption to benefit the operational efficiency and competitiveness. The firms intricate supply chains, diverse stakeholders, and strategic emphasis on corporate image make it an ideal context to examine the nuanced impact of these technologies.

This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.

This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.

The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.

This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

In the last few years, environmental issues have become a matter of survival. In this sense, e-waste management is among the major problems since it may be a way of mitigating mineral depletion. In this context, the literature lacks e-waste supply chain studies that systematically map supply chain challenges and risks concerning material recovery.

Given this context, the authors' paper conducted a systematic literature review (SLR) to build a framework to identify the constructs of e-waste supply chain risk management.

The paper revealed the theoretical relationship between important variables to achieve e-waste supply chain risk management via a circular economy (CE) framework. These variables include reverse logistics (RL), closed-loop supply chains (CLSC), supply chain risk management, supply chain resilience and smart cities.

The literature contributions of this paper are as follows: (1) a complete list of the risks of the e-waste supply chains, (2) the techniques being used to identify, assess and mitigate e-waste supply chain risks and (3) the constructs that form the theoretical framework of e-waste supply chain risk management. In addition, the authors' results address important literature gaps identified by researchers and serve as a guide to implementation.