This study examined the integration of Industry 4.0 technologies with environmental, social, and governance (ESG) strategies to improve organizational sustainability and competitiveness. Industry 4.0 technologies, including blockchain, artificial intelligence (AI), the Internet of Things (IoT), and big data analytics, offer transformative capabilities for ESG reporting, resource optimization, and operational efficiency. By addressing sustainability challenges and advancing governance practices, these technologies enable businesses to meet their environmental and governance responsibilities while achieving economic growth. However, implementing Industry 4.0 in ESG frameworks presents challenges, such as high costs, poor data quality, and resistance to change. This paper analyzed existing literature to identify opportunities and barriers to aligning ESG with Industry 4.0, providing insights for managers, researchers, and policymakers. The report adds to the body of knowledge on sustainable innovation by pointing out the advantages and disadvantages of this integration. It also provides a roadmap for achieving sustainability-implementing goals over the long run in a rapidly evolving digital landscape.

Air pollution has very serious health impacts as well as detrimental effects on ecosystems. Addressing this issue will require operational real-time monitoring and management, specifically in urban environments. Potential sensors that could provide the necessary high accuracy are too costly for mass deployment in the Internet of Things (IoT) environment. On the other hand, low-cost sensors usually have noisy, unreliable, and less accurate outputs, preventing them from being effective tools in real-time applications. Therefore, there is a lack of cost-effective, accurate, and scalable solutions to enhance the reliability of low-cost sensors such that they can improve air quality monitoring and control in real-time. Consequently, this research proposes a novel approach that advances the use of embedded machine learning with edge computing to enhance the accuracy and real-time actions at the local level. In the system, the Raspberry Pi was utilized for processing sensor information as the embedded edge device to monitor multiple real-time air pollutants, including SO₂, NO₂, and PM_2.5. In this research, the one-rank cuckoo search-driven adaptive support vector machine (ORCS-ASVM) was proposed to improve the prediction accuracy of low-cost sensors with noise. The ORCS-ASVM machine learning model predicts air quality and reduces the amount of computing power required while providing immediate knowledge about the amount of air pollution present. This model strives to enhance the accuracy of low-cost sensor readings, thus accommodating erroneous sensor readings in pollution prediction. Data on air pollution that is continuously collected from the sensors will be monitored at regular intervals to provide real-time monitoring. Sensor data often contains noise, requiring filtering and preprocessing techniques to correct sensor errors. Smoothing and normalization are the most common preprocessing methods used to correct sensor errors. Compared with traditional prediction algorithms, the proposed model outperformed traditional methods in terms of error reduction. Air pollutants such as PM_2.5, SO₂, and NO₂ achieved detection accuracies of 94.2%, 95.2%, and 94.8%, respectively. An embedded machine learning system approach for air quality monitoring and in situ pollution mitigation is a low-cost method that can be extended to other industries, making it suitable for smart city infrastructure, smart agriculture, and other important areas. An embedded machine learning approach is applicable to many areas as it has great scalability and flexibility and is therefore very suitable for widespread adoption by IoT-based pollution monitoring networks.

This article examined the research landscape of energy justice within the social sciences, providing a detailed overview of key developments, trends, and methodologies in this emerging field of study. The study began by identifying the leading journals in the field and mapping co-authorship networks, followed by an analysis of keyword co-occurrences to pinpoint the central themes in the literature. The findings highlight key areas of energy injustice, including accessibility, affordability, and environmental and technological factors. The article further inspected the underlying drivers of these disparities and the actions being taken to address them, emphasizing the critical role of emerging community pressures in shaping policy decisions. Additionally, the study reviewed five case studies that underscore the need for ongoing policy adaptation and illustrate the significant impact of advocacy in the development and implementation of equitable energy policies. The paper concludes by stressing the importance of rethinking current energy policy approaches to create a more inclusive, sustainable, and just energy future.

Assessing heavy metal contamination in aquatic ecosystems is crucial to understanding its effects on macroinvertebrate abundance and diversity, as these organisms serve as sensitive indicators of water quality. Among other heavy metals, cadmium and copper pose significant risks to aquatic ecosystems. We aimed to analyze the relationship of cadmium (mg/kg) and copper (mg/kg) contamination on macroinvertebrate communities using research articles published between 2014 and 2024. A total of 2,491 articles were identified from Scopus, ProQuest, and Google Scholar, and the selection process, guided by the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) criteria, narrowed the studies down to 28 articles that met the inclusion requirements. The studies reported cadmium concentrations ranging from 0.001 mg/kg to 21.90 mg/kg, with the highest concentration recorded in streams and the lowest in lagoon and rivers. copper levels varied from 0.001 mg/kg to 745.0 mg/kg, with the highest concentrations found in lakes, and the lowest observed in estuaries, streams, rivers, and lagoons. Findings revealed that the lowest concentrations (0.001mg/kg) of cadmium and copper corresponded with a high abundance of macroinvertebrates, reaching over 77,000 individuals. Conversely, at the highest levels of copper, macroinvertebrate abundance dropped to as low as 22, while at elevated cadmium levels in separate ecosystem, the abundance was 6707. Thus, this study highlights the possible influence of cadmium and copper contamination on the abundance, diversity, and ecological roles of macroinvertebrate communities in aquatic ecosystems.

In this research, we explored the synthesis and application of iron oxide magnetic nanoparticles functionalized with watermelon peel extract (NP-FeO-CL) using an eco-friendly approach. These nanoparticles were characterized by FT-IR, SEM, and UV-Visible spectroscopy, confirming their structure, magnetic properties, and purity. Their efficiency was evaluated in removing lead (Pb) ions from aqueous solutions under various experimental conditions, including variations in contact time, adsorbent mass, pH, temperature, and initial Pb concentration. The results showed a maximum Pb removal of 91.40% under optimum conditions (pH=4, temperature=25 °C, and contact time=5 min). Adsorption isotherms indicated an excellent fit to the Freundlich model (R²=0.94, KF=16.99 mg/g, n=1.97), reflecting heterogeneous and multi-layered adsorption. Likewise, a good fit to the Langmuir model (R²=0.91) was observed, suggesting a single-layer adsorption with a maximum adsorption capacity q_max of 47.16 mg/g. As for the kinetics, the pseudo-second order model adequately described the process (R²=0.99), suggesting that the adsorption rate is controlled by the availability of active sites. Thermodynamic analysis evidenced that the process is spontaneous and favorable at moderate temperatures, although the efficiency decreases at higher temperatures. This study highlights the potential of NP-FeO-CL as a sustainable and efficient solution for the remediation of water contaminated with heavy metals. Moreover, the green synthesis method used not only minimizes the environmental impact but also promotes the reuse of agro-industrial waste, contributing to the circular economy. Tests conducted under real conditions, such as in mining waters fortified with Pb, validated its practical applicability, achieving efficiencies above 90% in the first minutes of the process. These results position NP-FeO-CL as a versatile tool for treating contaminated water, with a significant impact on the sustainable management of water resources and environmental protection.

The desire to balance environmental sustainability and food security is causing a significant upheaval in the agricultural sector. This study investigated how prepared stakeholders are to embrace agriculture in Industry 4.0, which integrates green, intelligent, and sustainable technologies like blockchain, IoT, and AI. Although these advances promise increased resilience and efficiency, they present significant obstacles to implementation, such as inadequate infrastructure, socioeconomic inequality, and a lack of technology awareness. This study highlights these technologies' theoretical and managerial implications and finds critical gaps using bibliometric and thematic analysis. To overcome adoption obstacles, it emphasizes the significance of inclusive policies, strategic alliances, and capacity-building programs. The study concludes that these impediments must be addressed to secure food systems for future generations and align agriculture with global sustainability goals. Longitudinal studies, stakeholder-centric methodologies, and investigating emergent technology synergies are among the suggestions for future research.

The purpose of this study was to investigate the effects of the addition of rice straw (RIS), corn straw (CS), pepper straw (PS), and rape straw (RAS) on the release and speciation of heavy metals in coal gangue. Results showed that the addition of rape straw and corn straw significantly increased the pH value of the leachate. Straw addition increased the iron (Fe), manganese (Mn), copper (Cu), zinc (Zn), and cadmium (Cd) concentrations in leachate but inhibited the release of lead (Pb) from coal gangue, with RIS having a particularly significant inhibitory effect. Straw addition promoted the transformation of heavy metals from a reducible to an oxidizable state in coal gangue, with significant differences in the impact on exchangeable heavy metals. Rape straw reduced the exchangeable Fe and Mn concentrations, whereas RIS decreased the exchangeable Zn concentration. The results of the RAC risk assessment indicated that by the end of the trials, RAS significantly reduced the ecological risks of Fe, Mn, Cu, Pb, and Cd. In summary, when using straw to remediate and improve coal gangue, dynamic changes in the activity of heavy metals need to be considered, and the use of RAS should be prioritized to avoid potential secondary environmental pollution.

Deteriorating water ecosystems pose a critical constraint to high-quality development in the Yangtze River Economic Belt (YREB). In this study, we analyzed the grey water footprint (GWF) in the YREB from 2009 to 2022 using panel data from 11 provinces/municipalities. A multi-sector pollutant load model was employed to calculate the GWF. Driving mechanisms were investigated using Random Forest (RF) and SHapley Additive exPlanations (SHAP) analysis, while spatial dynamics were assessed via standard deviational ellipse. The results indicated: (1) The regional GWF peaked in 2015 (489.9 billion m³), then decreased by 35.5% after 2016 due to the Yangtze River Protection initiative. Agriculture became the dominant contributor (53.1% share), especially in midstream provinces such as Hubei and Jiangxi. (2) Spatially, the GWF exhibited a northeast– southwest axial pattern. The ellipse area initially contracted to 80.22 km² (2018) before rebounding, while the distribution centroid shifted 105 km northwestward. (3) The RF–SHAP model revealed key non-linear drivers; the GWF significantly decreased when the secondary/tertiary industry share exceeded 70% or solid waste utilization surpassed 85%. Environmental investment showed diminishing marginal returns beyond 35% of GDP. Urban green space per capita had an optimal GWF-reduction range of 12–16 m²; beyond 16 m², it increased the GWF due to ecological encroachment effects. We recommend targeted agricultural non-point source control, industrial circular economy upgrades, and region-specific ecological management to achieve sustainable development.