Fashion e-commerce is highly image-intensive, as product images shape customer attention, perceived quality, and reduce uncertainty about physical attributes. Traditionally, this relies on professional photoshoots requiring studios, models, crews, logistics, equipment, and post-production. Recently, Generative AI (GenAI) has started to be adopted to create realistic on-model images, shifting workflows from physical operations to compute-intensive services with potential environmental impact reductions. However, both standalone assessments and robust comparisons remain difficult: traditional photoshoots vary widely across brands and contexts, while peer-reviewed evidence on GenAI workflows is still limited, hindering informed strategic decisions. This paper addresses these gaps by proposing a methodology and framework that translates these heterogeneous workflows into configurable stages and parameters, enabling repeatable Climate Change impact comparisons per publishable fashion product image. A hybrid Life Cycle Inventory (LCI) is built by combining primary activity data collected through structured surveys with fashion brands and a GenAI provider with secondary data. The methodology is operationalised through a configurable decision-support tool and validated through a real-case application. The proposed approach supports fashion brands’ strategic decision-making by providing a repeatable and transferable framework for environmental assessment of alternative imagery production workflows in fashion e-commerce.

Digitalized Deposit Return Schemes (DDRS) are a key enabler of circular economy and zero-waste models, but their effectiveness depends on efficient reverse logistics and consumer convenience. In this paper, we propose a distance-aware decentralized load-balancing algorithm that assigns consumers to smart bins through a local ``green signaling" mechanism based on a Poisson race. The signaling rate of each bin depends on its residual capacity and the consumer-bin distance, tuned by a parameter a that controls the trade-off between system-wide balancing and consumer walking distance. Using a discrete-event simulation of three smart bins in a paper cup return scheme, we compare four scenarios (no app, distance importance, balance importance, and trade-off). The results show that our approach minimizes waste and provides a tunable compromise between average walking distance and bins load imbalance that enables synchronized collections while maintaining consumer convenience. Accordingly, the proposed mechanism may be integrated within a DDRS scheme to enhance user engagement and improve recycling rates.

Growing importance of Artificial Intelligence (AI) for future society is starting to change strategies and priorities of nations and businesses world-wide. Although contemporary focus is in AI servers, the mobility aspect of future AI has been neglected so far: the scale of AI starts from stationary (computing centers, servers) to semi-stationary (containers, movable by trucks); space-orbit satellites; autonomous vehicles; drones; robots; wearable personal devices; implants; IoT devices; all the way to future nano-scale devices. Implementing power supply is one of the major challenges at each level. This paper presents a literature survey of the mobile AI power supply subsystems and analytic study of supply chain challenges of AI power supply subsystems’ critical raw-material elements. Paper also presents empirical case study: personal medical-AI system architecture for assisting epilepsy. Survey helps understand what are supply chain critical vulnerabilities and locistics bottlenecks which may jeopardise AI application diversity and growth. Analysis helps nations and businesses world-wide plan strategy and prepare for mitigation actions which are needed for sustainable development under a challenging geopolitical scenario presented earlier for critical element lithium.

The Lean and Green paradigm integrates operational efficiency with environmental sustainability. However, many automobile after-sales service centres lack systematic approaches to evaluate resource efficiency and implement sustainable practices. This research addresses this gap by developing a Lean and Green evaluation framework tailored to the automotive aftersales sector. The methodology began a comprehensive literature review to identify key Lean and Green elements. These findings informed an Analytic Hierarchy Process (AHP) model used to evaluate various practices at a South African facility. The results identified Kaizen, Value Stream Mapping (VSM), and 5S as the most impactful tools for enhancing service centre performance. In order to better bridge the gap between theory and practice, the Hoshin Kanri X-Matrix was utilised to align strategic Lean and Green objectives with long-term goals and specific Key Performance Indicators (KPIs). This framework was validated by surveys and promotes systematic and measurable improvements. This research culminates in a comprehensive Lean and Green artefact specifically designed for automotive aftersales environments. By providing practitioners and researchers with actionable tools to drive operational efficiency and minimise environmental impact, the model promotes a robust culture of sustainability.