In today’s competitive and environmentally conscious industries, the ability of organizations to adapt and respond is more important than ever. This study focuses on overcoming the obstacles faced by the Iranian automobile sector by highlighting the significance of incorporating green supply chain techniques. The research intends to integrate organizational operations with environmental sustainability goals by utilizing a MULTIMOORA strategy for supplier selection. The Iranian automobile sector, facing substantial environmental challenges, requires a strategy framework for selecting environmentally friendly suppliers in order to sustain competitiveness and fulfill ecological obligations. The study develops a supplier selection model based on extensive research and expert knowledge. The Delphi and MULTIMOORA techniques are employed to assess and prioritize suppliers according to green criteria, assuring conformity with environmental goals. Data are collected by conducting a comprehensive analysis of the existing literature and engaging in conversations with industry experts in order to acquire information for the construction of the model. The results emphasize the crucial significance of trust-based relationships with suppliers, rigorous compliance with quality standards in new product development, and substantial investment in employee training and development. Sector analysts view these characteristics as crucial for promoting sustainability and gaining a competitive advantage in the Iranian vehicle sector. This study provides firms with strategic instruments to effectively negotiate the intricacies of green supply chain management, with a particular focus on the need for adopting sustainable practices while selecting suppliers in the dynamic and competitive context of the Iranian automobile industry.

The transition toward a circular economy (CE) in global manufacturing is hindered by challenges such as limited traceability, fragmented waste flows, and lack of compliance enforcement. Blockchain technology, with its capabilities in transparency, immutability, and decentralization, presents a promising solution to address these gaps. This study develops a decision-oriented framework to evaluate blockchain-enabled strategies for circular waste management. Drawing on expert insights collected through a Delphi process and applying the Best-Worst Method (BWM) and TOPSIS, the research prioritizes strategic alternatives based on key criteria including traceability, regulatory compliance, and ease of integration. The results reveal that material traceability platforms and smart contracts for reverse logistics are the most viable blockchain applications for enhancing circularity. A case study in the electronics sector and sensitivity analysis further validate the robustness and applicability of the findings. This research contributes to both theory and practice by offering a structured, replicable model for aligning blockchain adoption with sustainable manufacturing goals.

Although economic profitability and food security often outweigh water conservation priorities in arid and semi-arid regions, this study investigates irrigation practices by evaluating water footprint and economic feasibility through a comparative analysis of water-intensive and water-efficient crops. In this context, an optimal irrigation disparity framework integrated with Internet of Things (IoT) and Machine Learning (ML) mechanisms is proposed to evaluate the effectiveness of water conservation, thereby assessing the potential for enhancing economic profitability. IoT-enabled components are employed to monitor real-time environmental—soil moisture, temperature, and weather—conditions between March and November 2023. This data is processed using a hybrid modeling approach that integrates KNN, GBT, and LSTM algorithms to predict both the duration of cultivation and the water requirements. Finally, the predicted parameters are incorporated into a multi-objective framework aimed at minimizing the disparity in water allocation per net benefit. The final results indicate that saffron required substantially less water—ranging from (19.87 to 28.65 ∗ 10⁶ m³)—compared to watermelon, which consumed (34.61 to 47.07 ∗ 10⁶ m³), while achieving a higher average net profit (33 ∗ 10⁹ IRR) relative to watermelon (31 ∗ 10⁹ IRR). Moreover, saffron consistently approached optimal values across disparity-based objective functions, averaging (0.404). These findings emphasize the dual advantages of saffron as a value-added, water-efficient crop in achieving substantial water conservation while enhancing profitability, offering actionable insights for authorities to incentivize water-efficient crop adoption through subsidies, market mechanisms, or regulatory frameworks. These strategies operationalize technical insights into actionable pathways for balancing food security, economic growth, and environmental resilience.