Global supply chains face mounting pressures for sustainability, necessitating a shift from Green Supply Chain Management (GSCM) towards regenerative supply chain management (RSCM) to address environmental concerns and enhance Resilience. This transition addresses environmental concerns while improving and supporting Resilience within supply networks. My aims were twofold: (1) To assess the resilience-enhancing mechanisms during the transition to RSCM through a comprehensive review process, and (2) to uncover critical factors and themes of the RSCM. The study employed qualitative interviews as the primary method to collect data using a structured questionnaire. The study adopted snowball sampling based on the referral and recommendation of the respondents. The study investigated vital strategies and challenges for adopting RSCM, explicitly focusing on environmental sustainability. The results indicated that the transition emphasizes a shift from harm reduction to ecosystem restoration, highlighting the importance of environmental restoration in RSCM. Additionally, RSCM places a pronounced emphasis on resilience-building strategies compared to GSCM, underscoring the need for more comprehensive integration of Resilience within supply chains during this transition, particularly in an environmental context. I also developed a framework illustrating the transition from GSCM to RSCM, emphasizing environmental considerations. Additionally, this study contributes novel insights into the dynamic landscape of sustainable supply chain management, emphasizing the importance of resilience-building strategies, particularly in an environmental context, during the shift to RSCM.

The transportation sector is considered among the major sources of greenhouse gas emissions. Given advancements in transportation technology, customers’ willingness to reduce carbon footprints, as well as policy incentives, Electric Vehicles (EVs) are becoming an increasingly important part of the passenger vehicle industry. Evaluation of Supply Chain (SC) performance in the EV industry seems to contribute significantly to the enhancement of the operational consequences across the supply chain tiers. The SCOR (Supply Chain Operations Reference) model was designed to help businesses optimize their supply chain operations, reduce costs, and improve customer satisfaction. Although many performance measurement models have been developed in the context of SC, there is no performance measurement model in relation to the EV supply chain based on indicators of customer perceived value (Reliability, Responsiveness and Agility) in the SCOR model. Therefore, we aimed to develop a new method to evaluate the performance of the EV supply chain using a set of critical SC performance evaluation indicators. Multi-criteria decision-making along with machine learning was used in order to develop a new method for evaluating SC performance. We used k-means clustering and fuzzy logic approaches in the development of the new method. An assessment of indicators’ importance level was performed using the fuzzy logic approach. The results of the method evaluation show that the proposed method is capable of predicting the performance of the EV supply chain accurately. According to the results, by optimizing their supply chain, companies can improve their ability to deliver products and services that meet or exceed customer expectations, resulting in higher customer perceived value and customer satisfaction.

This study aimed to develop an artificial neural network (ANN) capable of predicting the molar concentration of diisobutylene (DIB), 3,4,4-trimethyl-1-pentene (DIM), and tert-butyl alcohol (TBA) in the distillate and residue streams within three specific columns: reactive (CDC), high pressure (ADC), and low pressure (TDC). The process simulation was conducted using DWSIM, an open-source platform. Following its validation, a sensitivity analysis was performed to identify the operational variables that influenced the molar fraction of DIB, DIM, and TBA in the outputs of the three columns. The input variables included the molar fraction of isobutylene (IB) and 2-butene (2-Bu) in the butane (C4) feed, the temperature of the C4 and TBA feeds, and the operating pressure of the CDC, ADC, and TDC columns. The network's design, training, validation, and testing were performed in MATLAB using the Neural FittinG app. The network structure was based on the Bayesian regularization (BR) algorithm, that consisted of 7 inputs and seven outputs with 30 neurons in the hidden layer. The designed, trained, and validated ANN demonstrated a high performance, with a mean squared error (MSE) of 0.0008 and a linear regression coefficient (R) of 0.9946. The statistical validation using an analysis of variance (ANOVA) (p-value > 0.05) supported the ANN's capability to reliably predict molar fractions. Future research will focus on the in-situ validation of the predictions and explore hybrid technologies for energy and environmental optimization in the process.

The evolution of mega-ships and maritime alliances, in tandem with global changes, has intensified hub port competitiveness, increasing the need for innovation and adaptation, with a focus on sustainability. This study analyzed the competitiveness of the Mediterranean maritime hub port with Northern European ports. It provided a comprehensive analysis of the competitiveness of ten leading international hub ports in the Mediterranean and Northern Europe. Using the Hybrid F-AHP TOPSIS method, this research first identified and assigned weights to 10 criteria across five categories of port competitiveness from a shipping liner perspective in the Mediterranean and Northern Europe. It then ranked those ten leading ports based on the weighted criteria. The results showed that handling cost, operational efficiency, and infrastructure were the main factors for defining a competitive hub port, with growing emphasis on sustainability considerations. The results also highlight that Tangier Med, Algeciras, and Port Said were the leading ports in the region, excelling in technical efficiency and maritime costs compared to Northern European ports. The analysis further provided a holistic overview of competitiveness in the region, identifying the strengths and opportunities of each port based on 5 aspects with a special focus on the hub ports of the Southern Mediterranean. This research offers valuable insights for maritime shippers and port authorities in understanding hub port performance and competitiveness in a sustainable context.

There is great interest to understand and reduce the massive carbon footprint of the concrete industry. Recent descriptions of microbes incidentally living inside concrete materials (“concrete endoliths”) raised questions about how much carbon is either stored in or released from concrete by these microbes. We generated preliminary global estimates of how much organic carbon is stored within the living biomass of concrete endoliths (biomass-carbon) and much CO₂ is released from respiring concrete endoliths. Between 2020–2022, we collected widely varying samples of Portland cement-based concrete from Lubbock, Texas. After quantifying endolith DNA from 25 concrete samples and estimating the current global mass of concrete, we calculated that the global concrete endolith biomass-carbon as low as 5191.9 metric tons (suggesting that endoliths are a negligible part of concrete’s carbon profile) or as high as 1141542.3 tons (suggesting that concrete endoliths are a pool of carbon that could equal or offset some smaller sources of concrete-related carbon emissions). Additionally, we incubated concrete samples in air-tight microcosms and measured changes in the CO₂ concentrations within those microcosms. Two out of the ten analyzed samples emitted small amounts of CO₂ due to the endoliths. Thus, “concrete respiration” is possible, at least from concrete materials with abundant endolithic microbes. However, the remaining samples showed no reliable respiration signals, indicating that concrete structures often do not harbor enough metabolically active endoliths to cause CO₂ emissions. These results are preliminary but show that endoliths may alter the carbon dynamics of solid concrete and, thus, the carbon footprint of the concrete industry.

Cassava (Manihot esculenta Crantz) has garnered global attention due to its importance as a crucial raw material for ethanol and other derivative production. Nonetheless, its agroindustry generates a substantial amount of residues. We examined the potential utilization of co–products from both agricultural and industrial sectors concerning starch extraction processes. A total of 319 million tons of fresh cassava roots are globally produced, yielding up to 55% of agricultural co–products during harvesting. For every ton of starch extracted, 2.5 tons of bagasse, along with 100 to 300 kg of peel per ton of fresh processed cassava, and 17.4 m³ of residual liquid tributaries are generated. Consequently, both solid agricultural biomass and solid/liquid residues could be directed towards cogenerating bioenergy such as bioethanol, biobutanol, biodiesel, bio–oil, charcoal, and other bioproducts. In conclusion, the conversion of cassava agroindustrial co–products into food and non–food products with high added value could be promoted, thus fostering a circular economy to enhance profitability, sustainability, and crop promotion.

As electric vehicles (EVs) continue to advance, there is a growing emphasis on sustainability, particularly in the area of effectively managing the lifecycle of EV batteries. In this study, an efficient and novel optimization model was proposed for designing a circular supply chain network for EV batteries. In doing so, a comprehensive, bi-objective, mixed-integer linear programming model was employed. It is worth noting that the current model outlined in this paper involved both forward and reverse flows, illustrating the process of converting used batteries into their constituent materials or repurposing them for various applications. In line with the circular economy concept, the current model also minimized the total costs and carbon emission to develop an inclusive optimization framework. The LP-metric method was applied to solve the presented bi-objective optimization model. We simulated six problems with different sizes using data and experts' knowledge of a lithium-ion battery manufacturing industry in Canada, and evaluated the performance of the proposed model by simulated data. The results of the sensitivity analysis process of the objective functions coefficients showed that there was a balance between the two objective functions, and the costs should be increased to achieve lower emissions. In addition, the demand sensitivity analysis revealed that the increase in demand directly affects the increase in costs and emissions.

Sago starch and cellulose fiber can be used as a raw material for making degradable plastics to replace commercial plastics. This research used corn cob fiber and sugarcane bagasse fiber as filler to improve the properties of degradable plastic. The research method consists of several stages, cellulose fiber preparation, degradable plastic synthesis and plastic characterization. The result showed tensile strength of plastic with corn cob and sugarcane bagasse filler was 6.37–11.5 MPa and 9.70–16.47 MPa, respectively. The compound composition test through Fourier Transformation Infra-Red (FTIR) exhibited hydrophilic behavior, hence plastic easily degraded by soil. Differential Scanning Calorimetry (DSC) shows that degradable plastic with corn cob fiber filler denoted a melting point of 163.84 °C, while sugarcane bagasse fiber filler at 163.47 °C. Thermogravimetric Analysis (TGA) analysis indicated degradable plastic had a good thermal stability. Scanning Electron Microscopy (SEM) exhibited few white lumps and indentations on the surface, indicating that the solubility was not homogeny and could be due to the influence of stirring process. In term of ability to absorb water, both plastic with fiber filler had a small water absorption with a range of 5–6%. Melt Flow Rate (MFR) analysis of degradable plastics with corn cob fiber and sugarcane bagasse pointed the value that was suitable for injection molding processing technique. The degradation of both plastics ranged about 50–85 days to completely decomposed naturally in soil, as supported by FTIR analysis.

In this study, Mn-doped ZnO composite carbon nanofibers (Mn-ZnO/CNFs) were prepared via a simple blending and electrospinning (ES) method, followed by a thermal treatment. These fibers were used to investigate the photocatalytic degradation of an organic dye under UV and visible light irradiation. The results showed that Mn-ZnO/CNFs were successfully prepared under the same conditions used for CNFs preparation conditions, which induced a morphological change from a smooth to a rough surface compared to the CNFs. Energy dispersive X-ray (EDX), x-ray diffraction (XRD), and x-ray photoelectron spectroscopy (XPS) analyses confirmed the formation of Mn-doped ZnO on the CNFs’ surface. Furthermore, the addition of the catalyst significantly increased in the specific surface area, and a N₂ adsorption-desorption isotherm analysis revealed that all samples had mesoporous characteristics with a type IV isotherm index. The photocatalytic activity of the Mn-ZnO/CNFs carbonized at 650 ℃ using methylene blue (MB) dye as a model pollutant was investigated. All prepared samples effectively removed the MB with a degradation rate of 70-90%. The kinetic reaction rate was described using the simplified Langmuir-Hinshelwood equation. Overall, the CNFs and composites nanofibers developed through moderate thermal treatment processes possessed a high specific surface area and oxygen vacancy, enabling their potential use as adsorbents and as a catalyst support for reactions at room-to-elevated temperatures, as well as photocatalysts for the removal of organic contaminants.