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Green is the background color of high-quality development of Chinese cities. By constructing a theoretical model of the driving force and driving mechanism of China’s urban green development, this paper simulates the green development path of Chinese cities based on the panel data of Chinese cities from 2010 to 2020. The empirical results show that, firstly, the driven force formed by scientific and technological in novation, the action force formed by structural adjustment and the driven force formed by environmental regulation can drive Chinese cities to achieve green development. Secondly, the pressure of different paths on the ecological environment showed a gradual downward trend, the ecological environment pressure under the baseline scenario was the highest, and the ecological environment pressure driven by single factors may be lower than that driven by two factors. Finally, the comprehensive driven path formed by the combination of innovation driven, structure driven and environment a driven is a better path for China’s urban green development. Therefore, the green development of Chinese cities requires policy intervention, different cities need to formulate green development plans according to local conditions, and should consider the combination of policies. In the future, Chinese cities need to increase investment in science and technology, guide all entities to develop in the direction of resource conservation and pollution reduction, adjust the industrial structure, promote their development in the direction of cleanliness, increase environmental protection investment, standardize the development of all entities in the direction of environmental protection, and give full play to the synergy of the three major policies to achieve green development.

Deep eutectic electrolytes (DEEs) have attracted significant interest due to the unique physiochemical properties, yet challenges persist in achieving satisfactory Li anode compatibility through a binary DEE formula. In this study, we introduce a nonflammable binary DEE electrolyte comprising of lithium bis(trifluoro‐methane‐sulfonyl)imide (LiTFSI) and solid butadiene sulfone (BdS), which demonstrates enhanced Li metal compatibility while exhibiting high Li+ ion migration number (0.52), ionic conductivity (1.48 mS ⋅ cm−1), wide electrochemical window (~4.5 V vs. Li/Li+) at room temperature. Experimental and theoretical results indicate that the Li compatibility derives from the formation of a LiF‐rich SEI, attributed to the undesirable adsorption and deformation of BdS on Li surface that facilitates the preferential reactions between LiTFSI and Li metal. This stable SEI effectively suppresses dendrites growth and gas evolution reactions, ensuring a long lifespan and high coulombic efficiency in both the Li||Li symmetric cells, Li||LiCoO2 and Li||LiNi0.8Co0.1Mn0.1O2 full cells. Moreover, the BdS eutectic strategy exhibit universal applicability to other metal such as Na and Zn by pairing with the corresponding TFSI‐based salts. [ABSTRACT FROM AUTHOR]

The Green Light Optimal Speed Advisory (GLOSA) system suggests speeds to vehicles to assist them pass intersections during green intervals. In application, drivers can ultimately decide whether to change speed based on their driving experience. However, with the gradual popularization of autonomous driving, drivers are gradually leaving the driving area. Therefore, the central algorithms in on-board systems need to be trained more intelligently for autonomous decision-making. Specifically, we found that the frequency of decision making can significantly affect the performance of the GLOSA system, but this issue has not been discussed in previous research. In this paper, we propose an adaptive frequency GLOSA (AF-GLOSA) model based on deep reinforcement learning (DRL) algorithm. Different from traditional models, this model can extract effective features from raw data and learn decision-making experience through constant interaction with simulated environments. By using parameterized action spaces, we divided the GLOSA task into two parts: frequency control and speed consultation. The frequency control module helps filter out unnecessary operations, and the speed consultation module provides acceleration suggestions based on the results of the upper model. In addition, we have designed a novel reward function to balance fuel consumption and travel efficiency. Finally, the AF-GLOSA model was evaluated in both single intersection and multi-intersection scenarios in SUMO. The results indicate that the model can effectively reduce fuel consumption and carbon dioxide emissions in both cases. In term of the number of stops, the single-intersection outperforms the state-of-the-art method and the multi-intersection approaches the state-of-the-art method. The final results also demonstrate the necessity of considering decision-making frequency. [ABSTRACT FROM AUTHOR]

The prolonged utilization of copper (Cu)-containing fungicides results in Cu accumulation and affects soil ecological health. Thus, a pot experiment was conducted using Citrus reticulata cv. Shatangju with five Cu levels (38, 108, 178, 318, and 388 mg kg−1) to evaluate the impacts of the soil microbial processes, chemistry properties, and citrus growth. These results revealed that, with the soil Cu levels increased, the soil total Cu (TCu), available Cu (ACu), organic matter (SOM), available potassium (AK), and pH increased while the soil available phosphorus (AP) and alkali-hydrolyzable nitrogen (AN) decreased. Moreover, the soil extracellular enzyme activities related to C and P metabolism decreased while the enzymes related to N metabolism increased, and the expression of soil genes involved in C, N, and P cycling was regulated. Moreover, it was observed that tolerant microorganisms (e.g., p_Proteobacteria, p_Actinobacteria, g_Lysobacter, g_Sphingobium, f_Aspergillaceae, and g_Penicillium) were enriched but sensitive taxa (p_Myxococcota) were suppressed in the citrus rhizosphere. The citrus biomass was mainly positively correlated with soil AN and AP; plant N and P were mainly positively correlated with soil AP, AN, and acid phosphatase (ACP); and plant K was mainly negatively related with soil β−glucosidase (βG) and positively related with the soil fungal Shannon index. The dominant bacterial taxa p_Actinobacteriota presented positively correlated with the plant biomass and plant N, P, and K and was negatively correlated with plant Cu. The dominant fungal taxa p_Ascomycota was positively related to plant Cu but negatively with the plant biomass and plant N, P, and K. Notably, arbuscular mycorrhizal fungi (p_Glomeromycota) were positively related with plant P below soil Cu 108 mg kg−1, and pathogenic fungi (p_Mortierellomycota) was negatively correlated with plant K above soil Cu 178 mg kg−1. These findings provided a new perspective on soil microbes and chemistry properties and the healthy development of the citrus industry at increasing soil Cu levels. [ABSTRACT FROM AUTHOR]

This study mainly analyzed the keyword extraction of English text. First, two commonly used algorithms, the term frequency–inverse document frequency (TF–IDF) algorithm and the keyphrase extraction algorithm (KEA), were introduced. Then, an improved TF–IDF algorithm was designed, which improved the calculation of word frequency, and it was combined with the position weight to improve the performance of keyword extraction. Finally, 100 English literature was selected from the British Academic Written English Corpus for the analysis experiment. The results showed that the improved TF–IDF algorithm had the shortest running time and took only 4.93 s in processing 100 texts; the precision of the algorithms decreased with the increase of the number of extracted keywords. The comparison between the two algorithms demonstrated that the improved TF–IDF algorithm had the best performance, with a precision rate of 71.2%, a recall rate of 52.98%, and an F 1 score of 60.75%, when five keywords were extracted from each article. The experimental results show that the improved TF–IDF algorithm is effective in extracting English text keywords, which can be further promoted and applied in practice.

Carbon emissions trading policies play a crucial role in facilitating the transition to high-end products within high-carbon enterprises. Nevertheless, current empirical analyses of the carbon emissions trading market exhibit a lack of precision and are susceptible to bias in their findings. Limited research has been conducted on the influence of product quality as a potential constraint on the impact of carbon trading on product bargaining power. This study presents a double-difference model utilizing data on emission-control enterprises in China's carbon market to examine the influence of the carbon emissions trading mechanism on the bargaining power of high-carbon products. Empirical analysis is conducted using financial data from listed companies in China spanning the years 2010 to 2020. The findings indicate that the implementation of carbon emissions trading policies has a dampening impact on the product bargaining power of high-carbon enterprises. Moreover, carbon emissions trading policies have heterogeneous effects on the product bargaining power of high-carbon firms with different life cycles, with mature high-carbon firms receiving a boost and declining high-carbon firms receiving a dampening effect. Mechanism test finds that the incomplete transmission effect of cost shocks resulting from carbon emissions trading policies has negatively affect the product bargaining power of high-carbon enterprises. Further research finds that product quality is a key factor in determining the effect of the carbon emissions trading policy, and that the impact of the carbon emissions trading policy on the bargaining power of products of high-carbon firms takes on a "U" shape due to product quality. Once the product quality exceeds the bottleneck value of 0.5956, the policy significantly increases the bargaining power of products. The study confirms that the establishment of carbon markets can effectively increase the bargaining power of superior products. These results offer a comprehensive theoretical and practical foundation for nations to advance the development of carbon markets and facilitate the achievement of sustainable development by high-carbon enterprises. [ABSTRACT FROM AUTHOR]