Sustainability leadership aims at balancing short-term economic goals with long-term sustainable development goals by considering the interests of all stakeholders instead of just shareholders and focusing on a triple bottom line: people, planet, and profit. The existing research on sustainability leadership has mainly focused on the role of individual competencies without considering other meso and macro level factors that can impact the enactment of sustainable leadership. The studies that have considered these micro, meso, and macro levels have conceptualized these levels as stratified and discrete, assuming a hierarchical relationship between them. Such a conceptualization constitutes an impediment to the dynamic communication and engagement that is necessary to the achievement of sustainability goals. Drawing on stakeholder theory, this study investigates the key factors impacting the practice of sustainability leadership in a contextually relevant manner. More specially, we propose a multi-level, multi-stakeholder framework for sustainability leadership that is data driven and supported by evidence. This framework is meant to portray a holistic model that is dynamic and reciprocal in the manner in which micro, meso and macro factors impact each other. Qualitative research methods and purposive sampling were used for four stages of data collection, from 39 individuals with diverse profiles across the sustainable-engineering sector. The data collected were analyzed thematically, and the findings formed the basis of the dynamic inclusive business model for sustainability proposed in this paper, which challenges the traditional hierarchical business models. The data-driven, multi-level, multi-stakeholder framework proposed in this work extends the literature by providing insights on the key factors that impact the practice of sustainability leadership in the context of SMEs, operating in an emerging market. This framework demonstrates that the effective practice of sustainability leadership by SMEs is influenced by the interplay of factors at micro, meso and macro levels, as represented by individuals, organizations/firms, and governments.

This study aims to improve the performance of organic to recyclable waste through deep learning techniques. Negative impacts on environmental and Social development have been observed relating to the poor waste segregation schemes. Separating organic waste from recyclable waste can lead to a faster and more effective recycling process. Manual waste classification is a time-consuming, costly, and less accurate recycling process. Automated segregation in the proposed work uses Improved Deep Convolutional Neural Network (DCNN). The dataset of 2 class category with 25077 images is divided into 70% training and 30% testing images. The performance metrics used are classification Accuracy, Missed Detection Rate (MDR), and False Detection Rate (FDR). The results of Improved DCNN are compared with VGG16, VGG19, MobileNetV2, DenseNet121, and EfficientNetB0 after transfer learning. Experimental results show that the image classification accuracy of the proposed model reaches 93.28%.