Search Results (99)

In recent years, China has actively advanced the new energy vehicle industry to achieve its “dual carbon” objectives via a green revolution. The growth of green technical innovation by new energy vehicle enterprises has emerged as a significant national support project, and it has implemented a number of new energy vehicle promotion policies. Therefore, it is essential to investigate if promotional policies encourage the development of green technologies in businesses. China’s 2016 “New Energy Vehicle Promotion Catalogue” serves as the policy’s temporal shock point, and data from Chinese-listed new energy vehicle companies from 2011 to 2022 are used in this study. The effect and mechanism of the new energy vehicle promotion strategy on developing green technologies in businesses are investigated using a double difference model. As per the research, the promotion policy substantially enhances the green technological innovation of new energy vehicle firms. It can augment the level of R&D investment and alleviate financing constraints for enterprises, and enterprises’ social responsibility can act as a positive moderator for the promotion policy and enterprise green technological innovation. Finally, it has a more apparent positive impact on the green technological innovation of major companies and non-state-owned enterprises compared to state-owned firms. Additionally, it is more evident that enterprises are raising green technology innovation in the eastern and central regions. Full article

The variable valve mechanism, as a critical component for the efficient and low-carbon development of internal combustion engines, faces increasingly stringent requirements regarding its driving efficiency, output force, precision, and energy consumption. To address the limitations of existing technologies, a new composite electromagnetic valve train is proposed, characterized by a high force-to-power ratio, fast response, and high precision, along with a unique single/double drive mode, which offers greater flexibility in controlling valve timing parameters; however, it also introduces complex coupling relationships and increases the difficulty of optimization design. To this end, this paper establishes a thermodynamic model of the engine based on the composite electromagnetic valve mechanism. First, it analyzes the effects of different valve timing parameters and drive modes on engine performance; second, a multi-objective game theory optimization algorithm is employed to optimize the valve timing parameters and obtain the optimal solution set; finally, taking into account the energy consumption of the valve mechanism, engine emissions, and performance, a control strategy for valve timing parameters is developed based on an entropy-weighted method combined with a superiority and inferiority solution distance analysis. The results indicated that, under all the operating conditions of the engine, the average torque increased by 2.56%, the effective fuel consumption rate decreased by 6.23%, and nitrogen oxide emissions reduced by 9.86%. Meanwhile, an efficient and economical operational mode for the variable valve mechanism was obtained, providing new insights for the development of variable valve timing technology. Full article

The residues of the herbicides aminopyralid and iodosulfuron-methyl-sodium are phytotoxic to rotational crops. Their behaviour therefore needs to be studied under different agronomic practises and climatic conditions. The objective of this work was to use controlled laboratory conditions to study the effect of the following: (i) the application of green compost (GC) to agricultural soil, (ii) herbicide dose, (iii) soil moisture, and (iv) soil microbial activity on the degradation rate of aminopyralid and iodosulfuron-methyl-sodium. Moreover, the formation of two iodosulfuron-methyl-sodium metabolites (metsulfuron-methyl and 2-amino-4-methyl-4-methoxy methyl-triazine) and the dissipation mechanism of labelled ¹⁴C-iodosulfuron-methyl-sodium under the same conditions were also studied. Aminopyralid and iodosulfuron-methyl showed slower degradation and half-life values (DT₅₀) that were up to 4.6 and 1.4 times higher, respectively, in soil amended with GC, as the higher organic carbon (OC) content of this soil increased herbicide adsorption. The DT₅₀ values were up to 2.6 and 1.9 times higher for aminopyralid and iodosulfuron-methyl sodium, respectively, in soils treated with the double herbicide dose compared to soils treated with the agronomic dose. The DT₅₀ values for aminopyralid were up to 2.3 times higher in soils with moisture equal to 25% (H25%) of their water-holding capacity (WHC) than in soils with H50%. However, the DT₅₀ values for iodosulfuron-methyl-sodium were slightly lower in soils with H25% than in soils with H50%, due to the formation of bound residues. A biodegradation process significantly contributes to the dissipation of both herbicides. Higher amounts of metabolite metsulfuron-methyl were formed in the GC-amended soil in all cases. The percentages of ¹⁴C extractable in soils treated with both doses of herbicide under H25% were slightly higher than in soils under higher soil moisture (H50%) over time, due to the slower degradation of ¹⁴C-(iodosulfuron-methyl+metabolites). The higher persistence of the herbicides and their metabolites when the doses were applied at a high rate in soil amended with GC and under low moisture content may have negative consequences for the rotational crop. In the case of adverse conditions leading to the persistence of herbicides in the soil during the primary crop, the intervals for crop rotation should be increased. Full article

Background/Objectives: High-throughput metabolomics data provide a detailed molecular window into biological processes. We consider the problem of assessing how association of metabolite levels with individual (sample) characteristics, such as sex or treatment, depend on metabolite characteristics such as pathways. Typically, this is done using a two-step process. In the first step, we assess the association of each metabolite with individual characteristics. In the second step, an enrichment analysis is performed by metabolite characteristics. Methods: We combine the two steps using a bilinear model based on the matrix linear model (MLM) framework previously developed for high-throughput genetic screens. Our method can estimate relationships in metabolites sharing known characteristics, whether categorical (such as type of lipid or pathway) or numerical (such as number of double bonds in triglycerides). Results: We demonstrate the flexibility and interoperability of MLMs by applying them to three metabolomic studies. We show that our approach can separate the contribution of the overlapping triglyceride characteristics, such as the number of double bonds and the number of carbon atoms. Conclusion: The matrix linear model offers a flexible, efficient, and interpretable framework for integrating external information and examining complex relationships in metabolomics data. Our method has been implemented in the open-source Julia package, MatrixLM. Data analysis scripts with example data analyses are also available. Full article

As people’s environment awareness increases and the “double carbon” policy is implemented, the new energy vehicle (NEV) becomes a popular form of transformation and more and more car manufacturers start to produce NEVs. Thus, how to choose an appropriate type of NEVs from many brands is an interesting topic for customers, which can be regarded as a multiple-attribute decision-making (MADM) problem because customers often concern several different factors such as the price, endurance mileage, appearance and so on. This paper proposes a possible degree-based D–S evidence theory method for helping customers select a proper type of NEVs in the probabilistic linguistic environment. In order to derive decision information reflecting customer demands, online customer reviews (OCRs) are crawled from multiple websites and converted into five-granularity probabilistic linguistic term sets (PLTSs). Afterwards, by maximizing deviation and minimizing the information uncertainty, a bi-objective programming model is built to determine attribute weights. Furthermore, a possible degree-based D–S evidence theory method in the PLTS environment is proposed to rank alternatives in each website. For fusing these ranking results, a 0–1 programming model is set up by maximizing the consensus between the comprehensive ranking and individual ones in each website. At length, a case study of selecting a type of NEVs is provided to show the application and validity of the proposed method. Full article

Transformers in the distribution network must have their carbon emissions analyzed in order to meet the “double carbon” goal. Using an oil-immersed distribution transformer as the research object, this paper develops a “cradle-to-grave” carbon accounting model. A life cycle assessment (LCA)-based methodology is proposed to account for carbon emissions and total energy demand over the full life cycle and further analyze the carbon emissions within each stage. A case study is presented using data from a 200 kVA transformer manufactured by a transformer plant in Xinjiang. According to the data, there are 112.18 t of carbon emissions and 798.21 GJ of total energy consumed. Distribution network transformers have carbon emissions of 282 kg, 782 kg, 122.96 kg, 11,079.64 kg, and −88.6 kg throughout the manufacture and manufacturing stage, transportation stage, construction and installation stage, operation stage, and waste recycling stage, respectively. There are 4.12 GJ, 9.06 GJ, 1.34 GJ, 785.97 GJ, and −0.28 GJ in total energy requirements. According to the study, which covered 99.03% of the entire life cycle, the operation stage had the largest percentage of carbon emissions. In addition to streamlining the production process and using more energy-efficient equipment, the waste recovery stage successfully decreased the environmental impact of carbon emissions. Sensitivity analysis shows that the silicon steel sheet and transformer oil has a significant impact on the carbon emissions of distribution network transformers during the life cycle, and the higher the grade of silicon steel sheet, the lower the carbon emissions, and the synthetic ester transformer oil has the most comprehensive performance and the lowest carbon emissions. Full article

Background/Objectives: Facial bone density, including the jawbone, declines earlier than that of the lumbar spine and calcaneus. Calcium maltobionate is reported to mitigate bone resorption and maintain bone density of the lumbar spine in post-menopausal women, but its effects on facial bone density remain understudied. Therefore, this study compared variations in facial bone mineral density with variations in calcaneal bone mineral density and bone resorption markers among healthy women, examining differences between pre- and post-menopause and the effects of continuous calcium maltobionate intake. Methods: This randomized, double-blind, placebo-controlled, parallel-group trial involved 48 healthy Japanese women aged 30–69 years, divided into two groups. The test food group received tablets containing calcium maltobionate, while the placebo group received tablets containing a maltose and calcium carbonate mixture for 24 weeks. Calcaneal and facial bone densities were measured pre- and post-intervention in both groups. Results: Post-intervention calcaneal bone mineral density and bone resorption marker deoxypyridinoline (DPD) showed no statistical difference between groups in pre-menopausal women. However, in post-menopausal women, the test food group exhibited significantly higher calcaneal bone density and lower DPD levels compared with the placebo group. Facial bone mineral density increased significantly in the test food group compared with the placebo group in post-menopausal participants, with similar trends observed in pre-menopausal participants. Conclusions: Facial bone mineral density could serve as a useful indicator for monitoring bone health from middle age onward. Moreover, continuous calcium maltobionate intake appears to mitigate bone density decline in pre- and post-menopausal women, contributing to osteoporosis prevention (UMIN-CTR ID: 000046391). Full article

In recent years, research on farmland soil stability has gained attention due to climate change. Studying the thermal stability of soil enzymes at key crop growth stages in response to increased CO₂, drought, and warming is critical for evaluating climate change impacts on crop production and soil ecosystem stability. Despite its importance, research on the thermal stability of soil nutrient cycling enzymes remains limited. A pot experiment was conducted using the soil of winter wheat (Triticum aestivum L.), one of China’s main grain crops, as the research object. An artificial climate chamber was used to simulate four growth stages of winter wheat (jointing stage, flowering stage, grain filling stage, and maturity stage). Different levels of CO₂ concentration (400 and 800 μmol mol⁻¹), temperature conditions (current temperature and 4 °C higher), and water conditions (80% and 60% of field water capacity) were set, and their interactions were examined. By analyzing the temperature sensitivity (Q₁₀) of soil enzyme activities related to soil carbon (C), nitrogen (N), and phosphorous (P) cycles in response to different treatments, the results showed that doubling CO₂ concentration decreased soil C cycle enzyme Q₁₀ and increased soil N and P cycle enzyme Q₁₀ significantly. Additionally, soil C cycle enzyme Q₁₀ decreased with increasing temperature, while other enzymes showed inconsistent responses. Mild drought significantly decreased the soil N-cycling enzyme Q₁₀ in the early growth stage of winter wheat and the soil P-cycling enzyme Q₁₀ in each growth stage, but significantly increased the soil N-cycling enzyme Q₁₀ in the mature stage. The interaction between CO₂ concentration doubling and warming exhibited a single-factor superimposed effect in reducing soil C cycle enzyme Q₁₀. Moreover, doubling CO₂ concentration offset the effect of mild drought stress on soil P cycle enzyme Q₁₀. Above-ground biomass, soil total dissolved nitrogen, and nitrate nitrogen were identified as the primary factors influencing soil C, N, and P cycling enzyme Q₁₀. This study is of great significance in exploring the effects of global warming on food production and the mechanism of soil ecosystem functional stability under future climate change. Full article

Background/Objectives: Improved global data allow for a new understanding of what impact the food we produce, eat and dispose of has on the environment, human health and Nature’s resources. The overall goal is to guide decision-makers and individuals by providing in-depth knowledge about the effects of their dietary preferences on human and environmental health. Methods: The method is to investigate ways to reduce environmental degradation and to secure healthy food supplies in an urbanizing world, and to quantify the options. Results: Reviewed articles show that by eating less meat-based food and more plant-based and soilless food, as well as reducing food waste and recycling urban-disposed nutrients as fertilizers, we could reduce agriculture’s land requirement by 50% to 70% while still securing a healthy food supply. Less land under cultivation and pasture would reduce global emissions to air and water to a similar extent, and allow Nature to reclaim freed areas in order to catch more carbon and rejuvenate biodiversity. Thus, we could avoid further environmental degradation such as the current clearing of new fields needed under a business-as-usual regime. Presently, some 17 million people die each year due to poor diets, which is more than double the 7 million deaths since the onset of the COVID-19 pandemic. A return to more plant-based diets with unchanged intake of proteins but less calories, sugar, salt and fat combined with less red meat and ultra-processed food would reduce foremost non-communicable diseases by up to 20% and prolong life. The article suggests that the international focus has gradually turned to the food sector’s big contribution to climate change, biodiversity loss and harmful chemicals as well as to poor human health. It argues that this century’s rapid population growth and urbanization give urban residents a pivotal role in food’s impact on agricultural areas, which today cover half of the globe’s inhabitable areas. Their food demand, rather than the activities of farmers, fishermen or loggers, will guide remedial measures to be taken by individuals, industry and the public sector. A tool to calculate the potential environmental footprints of individual or societal measures is presented. Conclusions: Measures to make the agrifood sector more sustainable are still pending full recognition in international fora such as the UN COP Summits. Smart cities fitted with infrastructures to recycle macro- and micro-nutrients and organic matter have the potential to ameliorate human-induced impacts such as emissions to air and water bodies, crossing planetary boundaries, and polluting extraction of N (nitrogen), P (phosphorus) and K (potassium). Rapid results are within reach since dietary change and the turn-around time of nutrients in food is short compared to decades or centuries for recycled materials in cars or buildings. Full article

To achieve the regional goal of “double carbon”, it is necessary to map the carbon stock prediction for a wide area accurately and in a timely fashion. This paper introduces a long- and short-term memory network algorithm called the Self-Attention Convolutional Long and Short-Term Memory Network (SA-ConvLSTM). This paper takes the Wuhan urban circle of China as the research object, establishes a carbon stock AI prediction model, constructs a carbon stock change evaluation system, and investigates the correlation between carbon stock change and land use change during urban expansion. The results demonstrate that (1) the overall accuracy of the ConvLSTM and SA-ConvLSTM models improved by 4.68% and 4.70%, respectively, when compared to the traditional metacellular automata prediction methods (OS-CA, Open Space Cellular Automata Model), and for small sample categories such as barren land, shrubs, and grassland, the accuracy of SA-ConvLSTM increased by 17.15%, 43.12%, and 51.37%, respectively; (2) from 1999 to 2018, the carbon stock in the Wuhan urban area showed a decreasing trend, with an overall decrease of 6.49 × 10⁶ MgC. The encroachment of arable land due to rapid urbanization is the main reason for the decrease in carbon stock in the Wuhan urban area. From 2018 to 2023, the predicted value of carbon stock in the Wuhan urban area was expected to increase by 9.17 × 10⁴ MgC, mainly due to the conversion of water bodies into arable land, followed by the return of cropland to forest; (3) the historical spatial error model (SEM) indicates that for each unit decrease in carbon stock change, the Single Land Use Dynamic Degree (SLUDD) of water bodies and impervious surfaces will increase by 119 and 33 units, respectively. For forests, grasslands, and water bodies, the future spatial error model (SEM) indicated that for each unit increase in carbon stock change, the SLUDD would increase by 55, 7, and −305 units, respectively. This study demonstrates that we can use deep neural networks as a new method for predicting land use expansion, revealing the key impacts of land use change on carbon stock change from both historical and future perspectives and providing valuable insights for policymakers. Full article

Fullerene C₆₀ is by far the most studied of all allotropic modifications of carbon. Chemical modification of the double bond over the years has led to the emergence of a variety of fullerene derivatives. These derivatives have now found numerous applications in medicine, materials and supramolecular chemistry, and as efficient electron acceptors in organic photovoltaic devices. The main method for the functionalization of C₆₀ fullerenes, which makes it possible to obtain its derivatives in a preparative volume, is the Bingel–Hirsch reaction. But this method makes it possible to obtain fullerocyclopropanes containing only carboxyl substituents at the bridging carbon atom. Therefore, in order to obtain new materials, we began to study the interaction with organic carbonates in combination with Grignard reagents in the presence of Ti-containing complex catalysts. We hope that replacing the olefin in the Kulinkovich reaction with a C₆₀ fullerene molecule will lead to new and hard-to-find functionalization products of the latter. Organic carbonates were chosen as the object of study due to the fact that they are used in the industry as solvents for natural and synthetic resins, cellulose ethers, dispersants, blowing agents, emulsifiers, absorbents of hydrogen sulfide and carbon dioxide, starting materials for the industrial synthesis of fibers and plastics, as well as plasticizers, pharmaceuticals and plant protection products. Full article

China has implemented a “dual-carbon” policy in response to the Paris Agreement’s global climate change objectives. Hohhot, Baotou, and Ordos (HBO-UA) is a resource-based urban agglomeration that is noteworthy for having significant heavy industry in China. Based on the extended STRIPAT model, which broadens the study indicators into six aspects—population, economics, technology, urbanization, industrial energy, and industrial structure—this paper develops a research framework of “Driving–Predicting–Simulating” for carbon emissions. According to the “one formula for one city” principle, driver models were constructed for Hohhot, Baotou, and Ordos, respectively. The following conclusions were drawn: (1) Population and urbanization are the dominant factors of carbon emissions in HBO-UA, following the economy and industrial energy. (2) Carbon emissions are multifactor-driven in Hohhot, double-factor-driven in Baotou, and single-factor-driven in Ordos. (3) Hohhot can achieve its carbon emissions peak under more efficient and lower policy costs, while Ordo is under great pressure to reduce carbon emissions. (4) We suggest multiple strategies to accomplish the “dual-carbon” goals for resource-based urban agglomeration with industrial clusters. These strategies include fostering diversified consumption by continuously enhancing urban functions, directing the transformation of the industrial structure, and fostering the growth of emerging industries. Full article

Logistics, as a significant field for achieving energy-saving and carbon reduction goals, is recognized as a crucial direction for realizing the global “double carbon” objective, while vehicle path optimization is an effective method for promoting energy efficiency and reducing carbon emissions. In this paper, an improved genetic algorithm is proposed for optimizing logistics and distribution paths concerning the carbon emissions of fuel vehicles throughout the logistics and distribution process, and a low-carbon logistics and distribution path model is constructed based on time windows, vehicle loading, and carbon emissions. The INNC method is adopted to initialize the population, and an enhanced genetic algorithm (GA-LNS) is designed to solve the model in conjunction with a large-scale neighborhood vector search algorithm. The results indicate that the initialization of the population using the INNC method produces a higher-quality initial solution. Compared to traditional genetic algorithms and particle swarm optimization, the GA-LNS algorithm exhibits superior robustness, effectively addressing the limitations of traditional genetic algorithms that rely on initial solutions and are prone to local optima. By comparing the computational results of the low-carbon logistics distribution path model constructed in this study with those of traditional optimization objective models, it is demonstrated that this model effectively balances the trade-offs between objectives and benefits, achieving the lowest total logistics distribution cost while promoting sustainable low-carbon logistics. The research findings provide a theoretical foundation for optimizing logistics vehicle paths and formulating energy-saving and carbon reduction implementation plans in China. Full article

Background/Objectives: The folate Recommended Daily Allowance (RDA) for pregnant women is 600 μg/day dietary folate equivalents, which is equivalent to approximately 400 μg folic acid. Many prenatal supplements contain much higher doses of folic acid. The body’s ability to reduce synthetic folic acid to the metabolically active form may be exceeded with high levels of supplementation. The objective of this double-blinded randomized controlled intervention trial was to determine changes in unmetabolized folic acid and other biomarkers of folate and one-carbon metabolism in maternal and cord blood in response to a folic acid dose commonly found in prenatal supplements (800 μg/day) compared to the dose equivalent of the RDA (400 μg/day). Methods: Healthy pregnant women were randomized and provided supplements from their first prenatal visit (<12 weeks gestation) through delivery. Maternal blood was collected at baseline and delivery. Umbilical cord blood from the mothers was collected at delivery. Results: A repeated measures analysis of variance revealed that there was a significant group supplemental dose effect (p = 0.0225) on serum unmetabolized folic acid concentration in mothers but no difference in cord blood unmetabolized folic acid concentrations between groups. Mixed effects analysis found a significant overall effect of pre-pregnancy BMI (p = 0.0360) and length of previous folic acid supplementation (p = 0.0281) on serum folate concentrations. No treatment effect was seen in RBC folate concentrations. Choline concentrations were higher in cord blood from the 800 μg/day group compared to the 400 μg/day group, but there was no group effect in maternal choline concentrations. Conclusions: The results indicate that folic acid dose during pregnancy affects certain folate and one-carbon biomarkers, and these effects are not consistent between maternal and cord blood. Potential long-term effects of these results on both mothers and offspring are unknown and merit further investigation. Full article

Artificial intelligence serves as the fundamental catalyst for a new wave of technological innovation and industrial transformation. It holds vital importance in reaching carbon reduction targets and the objectives of “carbon peak and neutrality”. This factor contributes significantly to the reduction in carbon emissions in the industrial domain. This article utilizes panel data from 30 provinces in China, covering the years 2013 to 2021, to develop an evaluation framework for assessing the progress of artificial intelligence development. Through the use of double fixed-effect models, mediation effect models, and threshold effect models, the empirical analysis examines the industrial carbon reduction effects of artificial intelligence and its operating mechanisms. Research indicates that the advancement of AI can significantly reduce carbon emission intensity within the industrial sector. This conclusion remains valid following comprehensive robustness tests. Furthermore, there exists temporal and regional variability in AI’s impact on industrial carbon reduction, particularly more pronounced after 2016 and in central and western regions. AI influences carbon emission reduction in China’s industrial sector through the advancement and optimization of industrial structures. Here, the increase in senior-level operations acts as a partial masking effect, while optimization serves as a partial mediator. The relationship between AI and industrial carbon emission intensity is non-linear, being influenced by the threshold of government intervention; minimal intervention weakens AI’s effect on carbon intensity reduction. These findings enhance our understanding of the factors influencing industrial carbon emissions and contribute to AI-related research. They also lay a solid empirical groundwork for promoting carbon emission reduction in the industrial domain via AI. Additionally, the results offer valuable insights for formulating policies aimed at the green transformation of industry. Full article