The problem and complication arise from the growing environmental inefficiencies and concerns in traditional supply chains, for instance, poor accountability, excessive waste, and lack of transparency. The green supply chain practices aim to reduce or minimize the environmental impact of supply chain activities, but these efforts often face problems, for example, difficulty in monitoring sustainability performance, data manipulation, and limited traceability across numerous stakeholders. The main problem is that without effective techniques to verify and track eco-friendly practices, enterprises struggle to utilize and enforce green initiatives reliably. The blockchain technique is being derived as a solution because of its capability to give decentralized, transparent, and immutable records of processes and transactions. By integrating the blockchain into green supply chain practices, we aim to design the model of linguistic (p, q)-rung orthopair fuzzy Z-number sets with algebraic and Sugeno-Weber operational laws for the construction of the power weighted averaging operator and power weighted geometric operator. These operators can be used in the utilization of the multi-attributive border approximation area comparison model, which is also explained step-by-step with the help of examples to simplify the supremacy and validity of the invented model by comparing their ranking values with the ranking values of the existing approaches.

The unanimous focus of the sustainable technological development is energy conservation and environmental friendly production. Power management is an essential aspect of sustainable development. It not only support energy production and conservation, but also increases the life time of domestic appliances and thereby reducing the global electronic wastage. The existing systems involving Artificial Intelligence (AI) were mere prediction models, without the evidence on the detailing behind the prediction. Traditional AI systems have focused on predictive analysis but often lack transparency in decision-making and limiting consumer trust. This study proposes a solution combining remote power monitoring with the ZigBee module and Explainable Artificial Intelligence (XAI) to offer both predictive accuracy and interpretability. XAI models are more consumer oriented in every area of application, similar to the problem discussed, which tells about the impact of various parameters in power management in domestic appliances. Local Interpretable Model Agonistic Explainer(LIME) and SHAP explainer are used in the proposed work, providing explainability in the local and global surrogates. The proposed work applies various regression models such as Decision Tree (DT), Random Forest(RF), Support Vector Regressor (SVR), Gradient Boost Regressor (GBR) and Extreme Graident Boost Regressor (XGBR). The RF provides the best R2-Score of 94.71% , which is 1.5%–3.0% more than the rest of the models, and also with variance score of 68.82% , had been chosen for explainability. This study demonstrates how XAI can improve transparency and reliability in AI-powered domestic energy systems, offering actionable insights for more sustainable power consumption.