This research study examines the potential conflict between increasing plastic recycling rates (SDG 12) and achieving global carbon reduction targets (SDG 13). Although plastic recycling is widely promoted as a key circular economy strategy, its climate performance depends strongly on the carbon intensity of the electricity systems supporting recycling processes, a relationship that has received limited comparative attention. Using a desktop-based comparative environmental assessment, this study analyzes peer-reviewed literature and industrial reports published between 2015 and 2025 to determine the energy requirements and lifecycle carbon emissions of different waste management methods, ranging from mechanical recycling to chemical and advanced recycling. Mechanical recycling exhibited the lowest environmental burden, with approximately 130 kg CO₂-eq/t and an energy demand of about 1.8 GJ/t, whereas chemical recycling required 12–32 GJ/t and generated approximately 739 kg CO₂-eq/t. “Advanced recycling” demonstrated potential net climate benefits (− 348 kg CO₂-eq/t) under favourable lifecycle assumptions and low-carbon electricity systems. By comparing countries with renewable-dominant and fossil-fuel-dependent electricity grids, the study identifies the “sustainability threshold,” at which recycling becomes more carbon-intensive than virgin plastic production. The proposed comparative sustainability-threshold framework provides a practical basis for selecting recycling technologies according to national energy-system characteristics. The findings herein offer evidence-based guidance for policymakers and industry to align circular economy strategies with climate mitigation objectives, ensuring resource recovery does not compromise global decarbonization. Mechanical recycling exhibits lowest emissions ( 130 kg CO₂-eq/t) and energy use. Chemical recycling may reach 739 kg CO₂-eq/t with high energy demand (12–32 GJ/t). Electricity-grid carbon intensity determines whether recycling remains climate-beneficial. Sustainability thresholds identify when recycling emissions exceed virgin production. Recycling-driven circularity supports net-zero goals only under low-carbon energy systems.
This study examines whether blockchain technology adoption improves corporate sustainability performance and whether its effects differ across sectors. Using a panel of 491 S P 500 firms from 2015 to 2023, blockchain adoption is identified through natural language processing of 10-K filings, while sustainability is measured using Bloomberg ESG scores and greenhouse gas (GHG) emissions intensity. A staggered difference-in-differences framework with firm and year fixed effects is employed, alongside sectoral interaction models and robustness checks. The findings show no evidence that blockchain adoption enhances overall ESG performance. Across multiple specifications, blockchain disclosure is not significantly associated with higher composite ESG scores. At the pillar level, no effect is found for environmental performance, the social dimension shows weak negative tendencies, and governance exhibits only a marginally positive relationship. In contrast, blockchain adoption is associated with higher GHG emissions intensity. Sectoral results do not support stronger positive effects in digitally intensive industries. Instead, some sectors show negative associations. Overall, the results suggest that blockchain disclosure may reflect technological signaling rather than measurable sustainability improvements. Moreover, such disclosure may not accurately capture the adoption and reflect the depth or maturity of actual blockchain implementation.
Maintaining financial benefits and controlling the rising level of carbon emissions is difficult for organizations. To improve sustainable supply chain strategies, this study presents a production inventory model that incorporates inflation, imperfect production, and carbon emission costs. The model assumes an integrated model in which market forces affect the producer's demand, displaying stock dependence, and also affect the retailer’s demand under inflation. The study also promotes investment in preservation technology to reduce the rate of deterioration. To reduce system costs, the model determines the optimal production cycles and inventory levels. Furthermore, it efficiently manages shortages and backlogs, determining the optimum number of units to display to minimize system costs. Relevant numerical examples have been elaborated to validate the results. Sensitivity analysis illustrates the effects of significant parameters, providing managerial insights to help businesses strike a balance between economic success and their ecological responsibilities.
Biohydrogen production through hybrid dark and photofermentation is a promising sustainable energy approach, but substrate choice and optimization are still significant challenges. In this study, the biohydrogen potential of Rhodopseudomonas palustris was assessed by constraint-based metabolic modeling and statistical optimization. A genome-scale metabolic model was evaluated with the COBRApy toolbox, where hydrogen production was set as the objective function. Waste-derived substrates were modeled as relative acetate availability to facilitate comparative analysis. Flux Balance Analysis predicted the substrate-dependent difference in hydrogen production, where rice straw and wheat bran had the highest potential, followed by sugarcane bagasse, but the lowest performance was observed in fruit waste. A three-factor Central Composite Design was used to assess the impact of acetate availability, nitrogenase activity, and carbon uptake rate on hydrogen production. Statistical analysis revealed that acetate availability was the most important factor, followed by carbon uptake rate and nitrogenase activity, with a significant interaction between acetate availability and carbon uptake rate. These results clearly show that carbon availability from substrates and metabolic flux distribution are the key factors that determine biohydrogen production in R. palustris. This study emphasizes the importance of combining constraint-based modeling with statistical optimization for hybrid biohydrogen research.
To achieve a high level of quality of the product, safety of operation, and corrosion prevention of refinery systems, hydrogen sulfide (H₂S) has to be removed from liquefied petroleum gas (LPG). In the research, an intensive Aspen HYSYS model of the LPG sweetening unit of Basrah Refinery was created and fitted to the data of the plant, and the results were found to be in very good agreement (R² = 99.97
This study develops an integrated three-stage supply chain model for two substitutable and deteriorating products produced from raw material under a carbon cap-and-trade regulatory framework. The three-stage supply chain system considers a single manufacturer for production and delivery, a single retailer for ordering and sales, and supplier for material supply. Shortage-driven substitution is incorporated to reflect realistic consumer behaviour when preferred products are unavailable. Both the manufacturer and retailer operate under carbon emission caps, with the option to trade carbon allowances, thereby linking operational decisions with environmental compliance. The model aims to determine the optimal material supply quantities, production and delivery policies for the manufacturer, and replenishment strategies for the retailer in order to maximize the integrated supply chain profit while considering the carbon reduction benefits of the solutions. The proposed framework contributes to sustainable supply chain management by integrating operational decision-making with carbon-emission reduction objectives under a cap-and-trade regulatory environment. The findings demonstrate the potential of coordinated replenishment and substitution strategies to improve both profitability and environmental performance. A mathematical programming framework is used to derive optimal solutions, and numerical analyses are presented to compare system performance under substitution and non-substitution scenarios. The results demonstrate the significance of coordinated decision-making and highlight how substitution, deterioration, shortages, and carbon regulations jointly influence supply chain profitability and sustainability.
Evaluating the performance of sustainable supply chain innovation is essential for monitoring improvements in sustainability initiatives; however, it remains challenging due to the involvement of multiple performance indicators. Therefore, identifying the most relevant indicators for performance assessment is necessary. This study aims to analyze and prioritize sustainable supply chain innovation performance indicators (SSCIPIs). Based on an extensive literature review and expert consultation, twenty SSCIPIs were identified and categorized into four groups: Economic-Centric Process and Function Innovation (ECPFI), Environmental Process and Footprint Innovation (ENPFI), Societal and People-Focused Innovation (SPFI), and Operational Process and Flexibility Innovation (OPFI). The relative importance of these indicators was evaluated using the neutrosophic analytic hierarchy process (N-AHP), which enables handling uncertainty and ambiguity in expert judgments. The results reveal that ECPFI (0.3513) is the most significant category, followed by ENPFI (0.2820), OPFI (0.2168), and SPFI (0.1499). Among the sub-indicators, resource-efficient cost savings (0.1180), innovation-driven sales growth (0.0991), and enhanced return on investment through innovation (0.0762) were found to be the most influential. The findings, derived from a case study conducted in an Indian steel and automotive manufacturing organization, provide a decision-support framework for managers and policymakers to integrate sustainability into supply chain innovation strategies. Managers can utilize the prioritized indicators to align innovation initiatives with measurable performance goals, improve resource efficiency, and monitor sustainability-related investments, while policymakers can use them to establish industry-specific sustainability benchmarks and supportive policies.
This study investigates the integration of sustainability into the Lean, Agile, Resilient, and Green (LARG) supply chain framework, resulting in the development of the LARGS model. It aims to examine how combining these five paradigms enhances sustainable competitive advantage in supply chain management. A mixed-methods design was applied, using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) to map causal interactions among LARGS components and the Analytic Network Process (ANP) to determine their relative importance. Expert surveys provided data for these analyses. Structural Equation Modeling (SEM) was then employed to empirically assess the impact of the LARGS model on supply chain competitiveness. Results show that sustainability maintains two-way interactions with all LARG components, creating a synergistic system. Among the five paradigms, sustainability ranked fourth, supporting the formulation of an expanded LARGS model. SEM analysis confirms that the LARGS model significantly and positively influences Supply chain competitiveness. This research presents the first empirically validated LARGS framework that integrates sustainability with LARG paradigms in a systematic, causally ranked, and statistically tested model. It contributes a novel approach for achieving sustainable competitive advantage through holistic supply chain integration. The LARGS model offers organizations a strategic pathway for enhancing supply chain performance while embedding sustainability principles. Effective implementation requires capacity-building initiatives, particularly employee training, to align operational practices with sustainability objectives.
The swift adoption of solar photovoltaic (PV) power systems around the world has led to a growing demand for smart maintenance solutions that can enhance operational performance, minimize degradation, and lower lifecycle costs. However, traditional maintenance practices such as reactive and scheduled preventive maintenance are becoming insufficient in the case of PV installations because of the complexity of fault mechanisms, the variability of environmental conditions, and the increasing scale of distributed solar power installations. Artificial intelligence (AI) has proven to show a lot of promise in fault diagnosis, predictive maintenance, and performance optimization, but most of the research studied has been based on the use of a single algorithm and not a whole autonomous system for maintenance. This review systematically and critically explores the AI-based autonomous health management methods for solar PV systems, which encompass fault detection and isolation, predictive maintenance, self-optimization, digital twins, edge intelligence, reinforcement learning, and explainable artificial intelligence. The review covers current state-of-the-art methodologies, benchmark datasets, challenges for deployment and industrial readiness from a diagnostic accuracy, real-time adaptability, scalability and interpretability perspective. Moreover, major obstacles to fully autonomous PV maintenance, such as data availability, model generalization, computing power, cyber security, and trustworthiness are thoroughly discussed. A future research roadmap is suggested to address the research gaps identified for the realization of a self-learning, self-adaptive, and self-optimizing PV system that can make autonomous decisions and operate resiliently. This review aims to offer a holistic approach to the development of PV maintenance systems that go beyond the traditional PV fault detection to intelligent and autonomous PV health management, which will be essential to build reliable and sustainable PV infrastructures.
Food loss and waste (FLW) emerges from interconnected upstream production inefficiencies and downstream household behaviors. This study develops a process-integrated machine learning framework that combines producer- and consumer-side evidence to support FLW reduction in circular food supply chains. Two datasets were analyzed: 150 food and beverage SMEs and 150 household respondents. The producer module examined circular economy practices, environmental practices, circular material use, collaboration, managerial routines, and a constructed relative FLW risk index. The consumer module examined shopping frequency and quantity, planning, storage, disposal frequency and quantity, and six food-waste item categories. K-Means clustering identified heterogeneous producer and consumer segments, Apriori revealed within-cluster food-item co-occurrence, and Decision Trees generated interpretable classification rules. Producer-side results showed that routine reduce, reuse, and recycle practices, recovery, remanufacturing, repair, circular material use, collaboration, and managerial routines differentiated the relative FLW risk classes. On the consumer side, planning level formed the root of the Decision Tree, followed by shopping quantity, storage level, disposal frequency, shopping frequency, and disposal quantity, indicating that the three household food-waste behavior classes are differentiated by interacting planning, purchasing, storage, and disposal conditions. Association rules further identified cluster-specific combinations of staple foods, fruits, vegetables, bakery products, packaged foods, and meat residues. The main contribution is an integrated producer-consumer decision-support architecture that translates complementary upstream and downstream analytical outputs into actor-specific and cross-actor interventions. Integration is achieved at the process, framework, and intervention-design levels rather than through respondent-level data matching.
The Textile and Apparel (T A) industry, being a prominent global economic driver, is consistently heading towards sustainability measures to address its endangering fast fashion trend and alarming impact on the environment. Consequently, the T A industry is about to adopt various circular production practices and lean philosophies to buttress overall industrial sustainability. Nevertheless, these fast-forwarding, environment-conscious approaches can amplify sustainability performance; these principles are becoming mandatory for foreign fashion retailers. In this context, it is necessary to examine the concurrent impact of these principles on different organizational concerns. To fill this gap, this study attempts to investigate the impact of Organizational Context (OC) on Sustainability Performance (SP), considering the mediating effect of Circular Economy (CE) and Lean manufacturing (LM), and the moderating impact of Total Quality Management (TQM). The Partial Least Squares-Structural Equation Modeling (PLS-SEM) technique was deployed in this study to analyze survey data collected in a structured questionnaire format from the T A industry of Bangladesh, consisting of 279 valid responses from backward linkage facilities, key technical departments, and management professionals. Nine hypotheses associated with the conceptual research framework were justified using SmartPLS (version 4) software. The results reveal a significant direct impact of OC on SP, while CE and LM positively mediate the relationship between OC and SP. Moreover, TQM has been outlined to have a negative impact on SP in both ways, through CE and LM. Previously, some scholarly works have investigated the impact of CE and LM in the emerging industrial concern, but to the author’s best knowledge, this is the first attempt to realize its sustainability implications from an organizational background for the T A industry. This study also delineates the first empirical investigation in justifying the impact of CE and LM on SP, with the moderation of TQM in a specific industry context.
Food and beverage small and medium-sized enterprises (SMEs) are critical for local food supply, employment, and regional economic development. Yet, their operations often uneven adoption of digital technologies. Existing sustainability maturity assessment models primarily target large-scale industries or conventional triple-bottom-line evaluation, offering limited guidance for SMEs. This study proposes a Fuzzy CRITIC-based data-driven framework to assess sustainability maturity and prioritize improvements, integrating 44 indicators for industry 4.0-enabled sustainability maturity assessment. Empirical data from 150 SMEs were transformed into a numerical decision matrix. The Fuzzy CRITIC method used to calculate overall and pillar-level maturity scores, classify SMEs into maturity levels, and generate an improvement priority index. Results show the industry 4.0 pillar carried the highest weight, followed by environmental, economic, and social pillars, highlighting digital capability as a key differentiator. Classification results revealed 91 SMEs at Managed, 46 at Developing, 13 at Advanced, and none at Initial. High-priority improvement areas include work accident reduction, productivity loss mitigation, IoT adoption, energy recovery, AI adoption, waste-to-value practices, and cloud-based data management. These findings reveal asymmetric development across pillars, where environmental and digital practices are more advanced, while economic and social dimensions lag. By linking objective weighting, maturity mapping, and improvement prioritization, the framework provides a practical decision-support tool for SMEs to enhance sustainability and digital integration. This study contributes to sustainability assessment literature by extending fuzzy multi-criteria methods to a data-driven SME context and by positioning Industry 4.0 as an enabler of resource efficiency, waste reduction, circular economy practices, and sustainability-oriented process optimization.
The objective of the study is to review the applications of sustainable production methods that decrease the environmental impacts of the production process. The different methods, such as carbon capture and storage (CCS), alternative fuels and alternative raw materials, supplementary cementitious materials (SCMs), recycling, and energy optimization systems, effectively decarbonize the manufacturing processes. The study adopts PRISMA-based systematic analysis from 137 research studies and gives a comparative techno-economic and technology readiness level (TRL) analysis. The reviewed studies mostly focus on emission analysis, waste development, energy expenditure, and raw material and resource consumption in the manufacturing system. The results of the study indicate that the CCS system significantly decreases CO₂ level to 90
Planning for sustainable development involves balancing a number of competing priorities and objectives that are inextricably linked, such as economic development, employment generation, use of energy and environmental protection. These are interdependent, and it is necessary to have optimization methods that can consider trade-offs between various components of the system in order to plan effectively. In this study, an integrated goal programming (GP) framework is introduced, which is based on weighted goal programming and fuzzy goal programming and is optimized in a single model. The weighted component allows the achievement of the set targets based on their relative weight, and the fuzzy component allows for the partial fulfilment of the established goals, thereby dealing with the uncertainty. A major shortcoming of traditional fuzzy goal programming is that when the performance of one goal is good, the performance of the other goal may be poor, so the proposed framework adds a penalty term, which clearly penalizes low performance of the individual goals. The model is designed to give a balanced and practically meaningful solution for policy formulation because it simultaneously rewards higher level of goal satisfaction and penalizes underperformance. The effectiveness of the proposed approach is shown with a numerical example, covering significant dimensions of sustainable development such as economic performance, employment, energy use and environmental impact. The findings revealed that the framework can provide a more balanced compromise between the competing objectives than conventional methods and that it leads to a better outcome on key sustainability indicators, while causing minimal negative impact on the other indicators. The proposed framework is flexible and generally applicable, and hence it is a valuable decision support methodology for integrated sustainable development planning as well as a contribution to the field of multi-objective optimization and process integration in general.
The rapid growth of global trade and industrialization has intensified environmental concerns, particularly the rise in carbon emissions contributing to climate change. Achieving sustainability in supply chain operations has become a critical challenge for manufacturers and retailers. This study proposes a production framework for a manufacturer-retailer supply chain for non-instantaneous deteriorating products while incorporating green technology investment. The study develops a two-echelon supply chain model considering price-dependent customer demand and controllable carbon emissions, designed to mitigate overall emissions from warehousing and transportation. Pricing strategies along with an advance payment approach is incorporated to maximize business profitability. This primary goal is to identify the optimal green technology investment, retail price and inventory planning that maximize the total profit of the supply chain. A nonlinear mathematical model is formulated, and a corresponding solution approach is introduced. The proposed framework is validated using a numerical example, and a sensitivity analysis is carried out to assess the effects of parameter variations. The numerical results indicate that a supply chain incorporating green technology investment outperforms one without such investment. This research uniquely links deterioration, carbon reduction, and financing policies under a unified decision-making approach, offering managerial insights for designing profitable and environmentally responsible supply chains.
Strategic distribution center (DC) location selection is a long-term decision that should simultaneously consider sustainability, resilience, and future uncertainty. However, existing studies rarely integrate these aspects within a unified AI-driven decision-making framework. To address this gap, this study proposes a novel hybrid framework that combines the Goal Programming-based Stochastic Best–Worst Method (GPSBWM), Weighted Fuzzy Inference System (WFIS), Random Forest (RF), and SHAP explainability analysis. GPSBWM is first employed to determine robust criteria weights under multiple future scenarios. Subsequently, WFIS generates reliable labels for previously unlabeled data, enabling the development of a supervised Random Forest classifier. Finally, SHAP is applied to explain the contribution of individual criteria and enhance model transparency. The proposed framework is validated using a real-world case study involving 200 candidate distribution center locations. The results identify security (0.0923), costs (0.0801), accessibility (0.0774), capacity expansion (0.0764), and flexibility (0.0769) as the most influential criteria. Furthermore, the developed framework successfully classifies candidate locations into Selected, Saved, and Rejected categories, with 10 locations identified as the most suitable alternatives for distribution center establishment. Comparative analyses demonstrate the effectiveness and robustness of the proposed approach, while SHAP provides transparent managerial insights into the contribution of each evaluation criterion. The proposed framework offers both methodological and practical contributions for sustainable and resilient distribution center location selection under uncertainty.
This study proposed an integrated inventory model for perishable products with selling price, advertisement and stock level dependent demand which reflects the realistic behaviour of the market model. The model also allows the possibility of partial shortage with a backlogging policy. To handle the uncertainty of cost parameters, trapezoidal and pentagonal fuzzy numbers are incorporated and the decision makers are given more flexibility. A one-level trade credit strategy is considered and the impact of carbon emission costs and green technology investment is considered for providing more sustainable operations. The paper aims to optimize the profit function by determining the depletion time, cycle time, selling price and green investment cost. Mathematical illustrations and sensitivity analysis are utilized to explain the impact of the system parameters on the optimal decisions. The results reveal that the total profit without fuzzy environment is 2857.58 and 3329.25 for Case I and Case II respectively. While, the total profit with trapezoidal fuzzy number is 3731.64 and 4245.12 and with pentagonal fuzzy number is 3972.76 and 4494.24 for Case I and Case II respectively. The numerical results indicate that pentagonal fuzzy representation yields higher estimated profit values than trapezoidal and crisp representations for the considered parameter settings. The numerical results show that, with the proposed model, profit increases and environmental impact are reduced.
Sustainability has become a critical objective in modern manufacturing systems due to increasing environmental concerns and resource limitations. This study develops a sustainable manufacturing model by incorporating system reliability under a ramp-type time-dependent demand pattern. The production system operates at two variable production rates Apλ and Aapλ. Demand follows a ramp-type pattern during production and shortage periods and stabilizes at constant rates. Shortages are permitted and partially backlogged, reflecting practical customer purchasing behaviour. The model considers item deterioration governed by a Weibull distribution to enhance real-world applicability; inflation effects are incorporated into the cost structure. In addition, sustainability and energy-efficiency considerations are integrated into the manufacturing process to reduce environmental impact and support green production practices. The objective is to determine the optimal production and inventory policies that minimize the total system cost while maintaining reliability, operational efficiency, and environmental sustainability. Numerical analyses demonstrate the influence of reliability, deterioration, inflation, and demand dynamics on system performance, providing valuable managerial insights for achieving sustainable and cost-effective manufacturing operations in competitive markets. In this study, sensitivity analysis is carried out, the best solution is found, and the software (MATHEMATICA) is operated to numerically demonstrate the accuracy of a model. The results are illustrated using numerical examples. Graphical representation is used to compare how different parameters influence the optimum output.
Liquefied natural gas (LNG) regasification terminals dissipate large amount of cryogenic energy during regasification, while simultaneously requiring effective boil-off gas (BOG) management. LNG cold energy recovery along with effective BOG management can significantly enhance the economics of LNG value chain. Thus, it is important to determine the most economical process for recovering the available cold energy. To address this, a comparative technoeconomic analysis of two competing waste cold energy valorization processes i.e., power generation (LNG_cold-PG) and hydrogen liquefaction (LNG_cold-HL), integrated with BOG management is presented in this study. A simulation-based optimization framework is developed to maximize the net present value (NPV) of both these processes. Impact of different terminal conditions on technoeconomics of proposed configurations are also investigated. The results indicate that both LNG cold energy recovery pathways are economically viable under diverse regasification terminal conditions. Comparative assessment reveals that the LNG_cold-HL configuration offers greater economic benefits owing to its enhanced recovery and utilization of LNG cold energy. These findings demonstrate that the proposed integrated hydrogen liquefaction process emerges as an effective pathway for waste cold energy valorization, thereby enhancing the technoeconomics of LNG regasification terminals. The proposed framework can further aid in decision-making for policymakers seeking to valorize the available LNG cold energy via sustainable integrated processes.
