Generative AI offers promising potential to promote environmental sustainability through personalized recommendations that influence individual behavior. This study examines the factors influencing the adoption and actual use of generative AI recommendations for environmental sustainability among Gen Z users in the Philippines by integrating the Theory of Planned Behavior (TPB) and the Technology–Environmental, Economic, and Social Sustainability Theory (T-EESST) with key generative AI attributes, together with trust and perceived risk. Survey data were collected from 531 Gen Z users in higher education institutions in the National Capital Region (NCR), Philippines, and analyzed using a hybrid SEM and ANN approach. Results from SEM indicate that key AI attributes, namely perceived anthropomorphism, perceived intelligence, and perceived animacy, significantly influenced users’ attitude towards generative AI recommendations. Attitude, perceived behavioral control, and trust emerged as significant predictors of behavioral intention, which have an eventual positive relation to actual use and environmental sustainability outcomes. In contrast, subjective norms and perceived risk did not significantly affect behavioral intention, which may suggest that Gen Z users’ engagement with generative AI for environmental sustainability is primarily driven by internal evaluations, perceived capability, and trust rather than social pressure or risk concerns. Complementing these findings, the ANN analysis identified perceived behavioral control, attitude, and trust as the most important factors, reinforcing the robustness of the SEM results. Overall, this study integrates existing sustainability and technology-adoption literature by demonstrating how generative AI recommendations can support environmental sustainability among Gen Z users by combining behavioral theory, sustainability theory, and AI attributes through a hybrid SEM–ANN approach in the context of a developing country.

Artificial intelligence (AI) has reshaped various aspects of human lives, particularly through its capabilities to address complex sustainability challenges. Despite the rapid expansion of AI applications, their contribution to cleaner production and sustainable development remains underexplored, especially in developing nations. In Southeast Asia (SEA), where AI adoption has grown substantially across environmental, economic, and social dimensions, research that examines its role in cleaner production outcomes remains fragmented. In view of this gap, this study conducts a systematic literature review (SLR) of AI applications related to cleaner production and sustainable development by examining relevant themes, application areas, and sustainability dimensions addressed by AI, while evaluating the maturity of AI methodologies, alignment with cleaner production outcomes, and integration with circular economy and resource efficiency goals. Moreover, it investigates the barriers and challenges that constrain AI application and offers future research directions to advance AI deployment for cleaner production and sustainable development across SEA countries.

In the automotive industry, reducing vehicle weight while maintaining durability is crucial for improving fuel efficiency, reducing emissions, and enhancing overall vehicle performance. The wheel rim is a vital component that supports the vehicle’s load and influences unsprung mass, directly affecting ride quality and safety. Therefore, designing an optimized wheel rim that minimizes weight without compromising strength under heavy load conditions is essential. This research investigates three materials—aluminum alloy, magnesium alloy, and titanium alloy—known for their high strength-to-weight ratios and suitability for automotive applications. Three key geometric design variables were considered: spoke thickness (t), spoke width (w), and spoke radius (r), each tested at three different levels. Finite Element Analysis (FEA) was conducted using ANSYS software to evaluate stress distribution and deformation of various design configurations under typical operational loads. Optimization was performed using the Simple Additive Weighting (SAW) multi-criteria decision-making method to identify the most effective design balancing multiple objectives. To ensure the reliability of results, statistical methods including the Taguchi method, analysis of variance (ANOVA), interaction analysis, and regression analysis were applied for validation. The optimized wheel rim design achieved an 11% weight reduction compared to the baseline model, with maximum stress and deformation values of 29.106 MPa and 0.0459 mm, respectively. These closely matched the predicted values, showing errors of only 0.66% and 0.31%, confirming the accuracy of the model. This study demonstrates a successful approach to producing a lightweight, durable wheel rim that can contribute to improved vehicle efficiency and reduced manufacturing costs.

Trong nghiên cứu này, tính hợp lý của ba định luật về chuyển động của Newton và phạm vi áp dụng của chúng được phân tích một cách chi tiết. Kết quả cho thấy định luật I không đơn thuần là định luật quán tính, mà nó đặt ra hệ quy chiếu, điểm nhìn để xem xét chuyển động của các vật. Đồng thời, nghiên cứu khẳng định hệ quy chiếu phi quán tính không phải là hạn chế của định luật II và định luật II cũng không phải là định nghĩa của lực. Bên cạnh đó, bài báo cáo đề cập tới những nhầm lẫn khi áp dụng định luật III Newton và chứng minh rằng định luật này không phải là tuyệt đối. Cụ thể, lực Coulomb dựa trên tính toán lý thuyết trong thuyết tương đối hẹp không phù hợp với phát biểu về định luật III của Newton.