<p>The extraction and utilization of crude oil are fundamental to global energy production, driving economies and fueling countless industries. However, wax deposition in pipelines and equipment creates several challenges, causing issues during the production, transportation, and refining of waxy crude oil. On the other hand, conventional chemicals such as alkylphenol ethoxylates (APEs) and volatile organic compounds (VOCs) used in the treatment have negative environmental and human health effects. Nanocomposites of polymers have emerged as promising solutions to mitigate wax damage. They represent a revolutionary class of nanocomposite hybridized polymer matrices. Moreover, to our knowledge, there has been a lack of comprehensive reviews of researchers who have combined and evaluated the effectiveness of these methods over the last decade. To gain a comprehensive understanding of the current state of knowledge and recognize emerging research trends, in this systematic review, we critically evaluated the published research on the role of polymer nanocomposites in the environmentally friendly management of wax deposition in crude oil systems. This review covers numerous topics, including (1) spatiotemporal distribution of research on polymer nanocomposites, (2) synthesis routes of millennium polymer nanocomposites, (3) reaction mechanisms for wax improvement, (4) common emerging trends in applications, (5) diverse polymer candidates for nanomaterials, (6) trending nanoparticle candidates for polymerization, and (7) future perspectives. However, further progress in understanding the effects of polymer nanocomposites on waxy crude oil is hindered by the lack of comparative studies on their reaction mechanisms and human health toxicity. However, despite these limitations, polymer nanocomposites continue to show great promise in addressing challenges related to waxy crude oil.</p>

This study employed co-integration methodology to explore the fundamental drivers of carbon price in the European Union Emissions Trading System (EU ETS) during the transition from phase Ⅲ to phase Ⅳ, focusing on the interactions between the carbon market, energy sector, and macroeconomic factors. A novel approach of bilaterally modifying dummy variables was used to include the impact of regulatory event announcements on the EU carbon price. Long-term analysis revealed that coal prices exhibited a positive but statistically insignificant impact, while natural gas and oil prices show substantial and significant influences. Short-term dynamics indicated a self-adjusting mechanism, with gas and oil prices playing crucial roles. We found that the European stock market has a dampening effect on short-term carbon prices, whereas the commodity market exerted a counteractive force. In the energy sector, variations in European gas prices and shifts in nonrenewable electricity generation significantly influence short-term carbon price. Regulatory event announcements exhibited an overall negative and statistically significant trend. However, the relatively small magnitude of these coefficients underscored the modest impact of regulatory event announcements on short-term carbon prices within the EU ETS. Additionally, our study estimated the long-term equilibrium relationship between variables using ridge regression, serving as a proxy for the EU's equilibrium carbon price. We examined the disparities between this equilibrium and market prices. Two distinct phases emerged in the analysis. Before April 2018, the market price consistently lagged behind the equilibrium, suggesting an overestimation of the value of carbon emission rights. Subsequently, the market price consistently surpassed the equilibrium. In particular, the sharp increase in carbon prices from April to June 2020, driven by the COVID-19 pandemic, highlighted the sensitivity of the EU carbon market to external shocks.

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.

To investigate the effect of Polyvinyl Alcohol (PVA) fiber incorporation on the erosion resistance of soil under different environmental conditions, this study selected fiber length and fiber content as variables and systematically compared the performance difference between fiber-reinforced soil and plain soil through dry and wet cycles, freeze-thaw cycles, sulfate erosion, and scouring tests. The fiber soil was found to have significant erosion resistance compared to the plain soil. The results showed that, for dry-wet cycling, the addition of PVA fibers effectively enhances the soil sample's resistance. Particularly, when the fiber content ranges from 0.5% to 0.8%, the soil sample's resistance to dry-wet cycles is significantly improved. Moreover, the influence of fiber content on enhancing the dry-wet cycling performance of the sample is more pronounced than that of fiber length. Shrinkage tests showed that the soil undergoes three distinct stages during the drying process: constant rate, deceleration, and residual phases. Under the action of freeze-thaw cycles, specimens containing PVA fibers demonstrate good deformation resistance and high unconfined compressive strength and do not appear to be damaged in the process of freezing and thawing, indicating good freeze-thaw resistance. Meanwhile, the effect of fiber length on the strength-loss rate is minor, suggesting that the freeze-thaw resistance of the specimens is relatively insensitive to fiber length. For the sulfate erosion resistance test, with an increase in fiber content, the strength of the specimen improves, and its strength loss rate is significantly reduced, whereas the influence of fiber length on the specimen remains limited. In the scouring test, soil samples show good scour resistance after fiber content, and the scour growth rate decreases with time.

<p>Patchouli cultivation in Lhokseumawe, Aceh Province, produces a significant amount of biomass, with approximately 96%–98.5% of the raw material ending up as solid residue after the oil distillation process. This waste is often discarded or burned, leading to environmental problems. However, this residue is rich in compounds derived from the breakdown of lignocellulose and can be pyrolyzed to produce liquid smoke. Liquid smoke contains valuable phenols, acids, and carbonyls but may also generate harmful byproducts such as polycyclic aromatic hydrocarbons (PAHs). This study aimed to optimize the production of grade A liquid smoke through purification using a distillation and adsorption process. Pyrolysis of patchouli residue was conducted at 400 ℃ to produce liquid smoke. Furthermore, the raw liquid smoke was purified by distillation and adsorption. The distillation process was conducted at a temperature range (X<sub>1</sub>) of 150–200 ℃ for 30–90 min (X<sub>2</sub>). The weight of activated biochar used during the adsorption process varied between 5 and 15 g (X<sub>3</sub>). The experimental design was carried out using Design Expert version 13. Research findings indicate that the optimal condition for liquid smoke was achieved with the equation Y = 1.26 + 0.53X<sub>1</sub> + 0.0319X<sub>2</sub> + 0.8023X<sub>3</sub> – (4.91X<sub>1</sub>)<sup>2</sup> – (0.0015X<sub>2</sub>)<sup>2</sup> – (0.2147X<sub>3</sub>)<sup>2</sup> – 0. 0644 X<sub>1</sub>X<sub>2</sub> – 0.0327X<sub>1</sub>X<sub>3</sub> + 2.15X<sub>2</sub>X<sub>3</sub>. The best results were obtained at a distillation temperature of 175 ℃, a duration of 90 minutes, and using 13.4 g of activated carbon, which resulted in a high total phenol yield of 8.06%. The produced liquid smoke was classified as grade A, with a yield of 74.33%, a density of 0.9928 g/m³, a viscosity of 1.6203 cP, and a pH of 3.32. This research highlights an eco-friendly solution for valorizing patchouli waste, transforming it into a product with a wide range of applications as a food preservative, flavor enhancer, and medicinal agent.</p>

In this study, we focused on the rapid land use and land cover (LULC) changes in Bankura in 1990, 2000, 2010, and 2024, employing an integrated remote sensing, geospatial, and statistical approach to track land use changes. The supervised classification technique and change detection analysis were applied with the Supper Vector Machine (SVM), Maximum Likelihood (ML), and Random Forest (RF) methods to identify land use classes in various categories like Dense Forest, Open Forest, water body, agricultural land, settlement, barren land, and sand. The Kappa Coefficient was used for the accuracy assessment, which revealed that the overall accuracy of 1990 was 93.33%, 2000 was 93.23%, 2010 was 93.43%, and 2024 was 90%. The analysis revealed a significant increase in built-up land from agricultural and forested areas, with a higher percentage of agricultural land converted to built-up areas observed between 1990 and 2024. During this interval, the built-up land area increased by approximately 13.6%, primarily due to the conversion of agricultural land and forest cover. Agricultural land decreased by 11.45%, while dense forest cover declined by 7.75%, indicating a significant anthropogenic influence on landscape transformation. Our findings underscore the importance of sustainable land use planning, conservation efforts, and policy interventions in mitigating environmental degradation, leveraging the effectiveness of space-based inputs and geospatial techniques. The research emphasizes the need for continuous monitoring and further investigation into socio-economic drivers and environmental consequences to ensure resilient urban management and sustainable development. This reveals the importance of reforestation, preserving water bodies, and developing ecologically sensitive infrastructure. Moreover, the study highlights the importance of sustainable land use planning in mitigating adverse environmental impacts and preserving ecological balance.

<p>Predicting river water quality in the Special Region of Yogyakarta (DIY) is crucial. In this research, we modeled a river water quality prediction system using the artificial neural network (ANN) backpropagation method. Backpropagation is one of the developments of the multilayer perceptron (MLP) network, which can reduce the level of prediction error by adjusting the weights based on the difference in output and the desired target. Water quality parameters included biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), dissolved oxygen (DO), total phosphate, fecal coliforms, and total coliforms. The research object was the upstream, downstream, and middle parts of the Oya River. The data source was secondary data from the DIY Environment and Forestry Service. Data were in the form of time series data for 2013–2023. Descriptive data results showed that the water quality of the Oya River in 2020–2023 was better than in previous years. However, increasing community and industrial activities can reduce water quality. This was concluded based on the prediction results of the ANN backpropagation method with a hidden layer number of 4. The prediction results for period 3 in 2023 and period 1 in 2024 are that 1) the concentrations of BOD, fecal coli, and total coli will increase and exceed quality standards, 2) COD and TSS concentrations will increase but will still be below quality standards, 3) DO and total phosphate concentrations will remain constant and still on the threshold of quality standards. The possibility of several water quality parameters increasing above the quality standards remains, so the potential for contamination of the Oya River is still high. Therefore, early prevention of river water pollution is necessary.</p>

<p>Short-term reductions in social welfare, expected to be caused by a currently imposed carbon tax, are among the obstacles to a rapid transition to a low-carbon economy. Using an extended DICE model, we studied the potential of green bonds to both accelerate this transition and smoothen welfare losses and gains in a socially optimal way. We showed that green bonds can indeed accelerate the transition to a low-carbon economy and that lower interest rates on bonds speed up this acceleration. Moreover, bonds can reduce short-term welfare losses; however, to eliminate welfare losses, additional compensation mechanisms are needed. For example, bonds at a 3% interest rate can decrease the peak atmospheric carbon concentrations by about 20% and shorten the initial time, during which society is worse off from 75 to 45 years. Retaining at least the same consumption level as in the no-mitigation scenario, without using bonds, is possible only through a decrease in abatement efforts. Green bonds of sufficiently low interest rates allow improving intertemporal welfare as well as achieving a more pronounced climate change mitigation with respect to both mitigation and no-mitigation scenarios without bonds.</p>

<p>Environmental concern is a determinant in acquiring new green innovation. We aimed to investigate the relationship between environmental concern, consumer attitude, and behavioral intention to switch in the context of drone delivery. The motivation that comes from a green perspective is believed to create a behavior that is also keen on innovative green products. One of the examples is the implementation of drones in delivering parcels, which is believed to cut the carbon footprint. Our purpose was to analyze the direct impact of environmental concern on consumers' behavioral intentions regarding e-commerce drone delivery. Additionally, we aimed to examine the mediating role of consumers' attitudes toward innovation in the relationship between environmental concern and behavioral intention. We sought to provide insights into how environmental awareness and the adoption of innovative delivery technologies like drones can influence consumer behavior, contributing to more sustainable and eco-friendly e-commerce practices. Structured questionnaires were provided to e-commerce users, reliability and validity tests were confirmed, and structural equation modeling (SEM) was used to analyze the relationships among variables. The results of the SEM analysis proved that environmental concern and consumer attitudes have a positive impact on behavioral intention. Customer attitude mediates the relationship between environmental concern and behavioral intention. This research provides a deeper understanding of how environmental concerns influence consumer behavior towards drone delivery innovation within the e-commerce sector. The implications integrate environmental concerns with consumer behavior and innovation adoption, providing a comprehensive view that goes beyond traditional marketing and consumer research.</p>