Entropy Weight TOPSIS Method Research Articles

Comprehensive evaluation of “Three Waters” carrying capacity and path evolution study: A case of the Yellow River Basin

Research focusing solely on the carrying capacity of a single aspect of water resources, water environment, or water ecology is no longer sufficient to support the sustainable development and management of basin water systems. The study of basin carrying capacity should expand towards a comprehensive and holistic direction. Therefore, this study constructed an evaluation index system for carrying capacity based on water resources, water environment, and water ecology (“Three Waters”). Utilizing the entropy weight-TOPSIS method, System Comprehensive Index Evaluation, and ArcGIS tools, the comprehensive evaluation index of the “Three Waters” System Carrying Capacity (TWSCC) in the Yellow River Basin (YRB) from 2005 to 2020 was calculated. The evaluation index analyzed the spatiotemporal variation characteristics of subsystem carrying capacity and performed early warning identification and analysis of TWSCC. Four differentiated developmental pathways were designed based on the current status of basin carrying capacity. Leveraging System Dynamics (SD) modeling, the dynamic simulation, and emulation of carrying capacity trends in the YRB from 2020 to 2035 were conducted. The research findings indicate that from 2005 to 2020, the TWSCC levels across the nine provinces in the YRB consistently exhibited varying degrees of overload. The alert levels mostly remained in “Heavy warning” or “Medium warning” states. By 2035, TWSCC under the four development paths improved from 2020 levels, with the Green Environmental Protection-Oriented scheme reaching a safe carrying capacity. In summary, this paper offers theoretical and methodological support for developing basin-carrying capacity and the integrated governance of “Three Waters.”

Digital dividend or digital divide? – Evidence from China

ABSTRACT This paper examines the impact of the digital economy on the urban‒rural income gap. Based on Chinese provincial panel data from 2011 to 2022, this paper uses the entropy weight-TOPSIS method to construct a comprehensive index of the digital economy and explores the spatial spillover and heterogeneity of the impact of the digital economy on the urban‒rural income gap through the spatial Durbin model and partially linear functional-coefficient panel model. The results indicate that at the national level, the digital economy widens the urban‒rural income gap in the region and neighbouring regions. At the regional level, the digital economy contributes to widening the urban‒rural income gap in the eastern region and narrowing the gap in the western region. Furthermore, there is a nonlinear relationship between the digital economy and the urban‒rural income gap, and different dimensions of the digital economy have different impacts. This paper aims to provide insights and references for China and other developing countries to achieve integrated urban‒rural development with the support of the digital economy.

Research on Financial Risk Evaluation of Tobacco Companies Based on Entropy Weight TOPSIS

Domestic economic development has switched from high-speed growth to a new stage of high-quality development, which makes domestic enterprises have to study the connotation of high-quality development. Financial risk assessment is one of the important basis for high-quality development, which promotes the relationship between balanced development and risk. This paper takes tobacco enterprises as the research object, through collecting the existing financial risk evaluation content of tobacco enterprises, and building the financial risk evaluation system, using entropy weight TOPSIS method, calculates the advantages and disadvantages of the financial risk work of each company under the same standard system, promotes benchmarking learning between different tobacco companies, improves the level of financial risk control, and promotes the high-quality development of tobacco enterprises.

Spatial Correlation Structure and Influencing Mechanism of Development Level of Zero-waste Cities in China

Based on the entropy weight TOPSIS method to measure the development level of "zero-waste cities" in China from 2004 to 2021, the social network analysis method and spatial Durbin model were used to explore the spatial correlation network structure and impact mechanism of the development level of "zero-waste cities." The results showed that： ① The development level of "zero-waste cities" was generally on the decline in the whole country and the eastern and central regions. However, it was on the rise in the western regions. ② The spatial correlation of the development level of "zero-waste cities" presented a core-edge structure, with an overall upward trend in network density and a stable state in the overall network. ③ Beijing, Shanghai, Jiangsu, Zhejiang, Fujian, and Guangdong were at the center and dominant position of the network. ④ Beijing, Tianjin, Shanghai, and Jiangsu belonged to the "net benefit" sector； Zhejiang, Fujian, and Guangdong belonged to the "broker" sector； and the other provinces belonged to the "net overflow" sectors. ⑤ The level of urbanization, economic development, technological innovation, foreign investment, environmental regulations, government intervention, and population size had a significant impact on the development level of "zero-waste cities" in local or neighboring provinces, respectively. The research results can provide a reference for the proposal of policies for constructing and coordinating the development of "zero-waste cities" in various regions.

Comprehensive benefit evaluation of Carbon reduction in power transmission and transformation projects by TOPSIS method based on combination weighting

Abstract With the continuous promotion of carbon peak carbon neutrality, the investment and construction of new power systems have accelerated, and the construction of a low-carbon economy has become an inevitable trend of the transformation and development of power transmission and transformation projects. In this paper, the FAHP and entropy methods are used to evaluate the economic, social, and social benefits of carbon reduction in power transmission and transformation projects and finally establish a set of comprehensive benefit evaluation systems for carbon reduction in power transmission and transformation projects. Under this evaluation system, the comprehensive benefit of 220 kV power transmission and transformation project in A province is sorted by entropy weight-TOPSIS method. The comprehensive benefit of power transmission and transformation project B >C >A project is finally obtained. At the same time, in the sub-criteria of economic benefits, the index of “enterprise asset-liability ratio” has the largest comprehensive weight. The “using distributed energy generation” index has the largest comprehensive weight in the sub-criteria layer of carbon reduction benefit. In the subsequent construction of power transmission and transformation projects, enterprises can prioritize improving the above indicators to improve carbon reduction efficiency and economy.

Assessment of Rural Industry Integration Development, Spatiotemporal Evolution Characteristics, and Regional Disparities in Ethnic Regions: A Case Study of Inner Mongolia Autonomous Region Counties

Ethnic regions in China primarily focus on the development of agricultural and animal husbandry economies, which are relatively underdeveloped. Rural industry integration development (RIID) is considered the foundation and guarantee for ethnic regions to achieve high-quality modernization of agriculture. The purpose of this article is to measure the level of rural industrial integration in ethnic minority areas, analyze the spatial evolution and regional differences, and explore the actual situation of RIID in these regions. The aim is to provide a decision-making basis for local governments to effectively promote the development of rural industrial integration. Based on the improvement of the evaluation index system for rural industrial integration development, this paper takes the counties of the Inner Mongolia Autonomous Region as the research area. Utilizing panel data from the statistical yearbooks of 68 banners and counties in Inner Mongolia from 2011 to 2020, the panel entropy weight TOPSIS method is employed to assess the average level of rural industrial integration in the research area. The ArcGIS natural breakpoint method is employed to classify the level of RIID in county areas. Exploratory Spatial Data Analysis (ESDA) and GeoDa are utilized to analyze the spatial distribution characteristics of RIID. Finally, the Theil index is employed to analyze the regional differences in the level of RIID. The results show the following: (1) The overall level of RIID in ethnic regions is relatively low, with the contributions of the four dimensions in the evaluation index system as follows: integration path > integration foundation > integration sustainability > integration effect. The level of RIID in the study area is as follows: western region > eastern region > central region. (2) Spatially, there are positive correlations and significant spatial clustering in the level of RIID, with the spatial clustering effect of RIID weakening. (3) There are regional differences in the level of RIID, which are expanding. The inter-regional differences are decreasing, while the intra-regional differences are increasing. (4) The construction of agricultural processing facilities, financial investment, financial support, and talent policies are important influencing factors for the current stage of RIID in ethnic regions. Therefore, in the low-level development stage of RIID in ethnic regions, it is necessary to fully utilize the advantages of resource endowment, increase investment in rural infrastructure, and strengthen the guidance of talent flow into rural revitalization construction.

Research on the traction characteristic evaluation of urban rail vehicles based on entropy weight TOPSIS

This paper aims to investigate the influence of different speed at the end of the constant power section of the traction characteristic curve (TCC) on the vehicle dynamic performance and evaluate the TCC. A dynamics co-simulation model is developed in SIMPACK and MATLAB for dynamic analysis of the urban rail vehicle (URV) under different traction characteristics. Then it is used to investigate how acceleration distance and vehicle dynamic performance varies with the speed at the end of the constant power section of the TCC under different starting torques. Moreover, the entropy weight TOPSIS method which is widely used to solve the multi-objective decision-making problem is chosen to evaluate the traction characteristics based on various dynamic performance indexes over 25 test sections and acceleration distance. The results show that under traction conditions, the starting torque has effects on the lateral and vertical accelerations of car body, lateral and vertical wheel-rail contact forces, derailment coefficient and wheel unloading factor. The higher the torque, the greater the value of each index, that means the poorer the vehicle ride characteristics and running safety. For a given starting torque, the dynamic performance indexes increase with the increase of the speed at the end of the constant power section from 50 km/h to 80 km/h during the vehicle speed-up process. The maximum growth rate of the lateral and vertical accelerations of car body, lateral and vertical wheel-rail contact forces are 17%, 8%, 8% and 2%, respectively, when the speed at the end of the constant power section increases from 50 km/h to 80 km/h. Last, the comprehensive evaluation of traction characteristics using entropy-weight TOPSIS method reveals when the starting torque is 800 [Formula: see text], 1000 [Formula: see text], 1200 [Formula: see text] and 1400 [Formula: see text], the corresponding optimal speed at the end of the constant power section is 80 km/h, 80 km/h, 70 km/h and 50 km/h, respectively. The result of the study can provide theoretical support for traction characteristic design and traction control for URVs.

Energy retrofitting of hospital buildings considering climate change: An approach integrating automated machine learning with NSGA-III for multi-objective optimization

In the context of sustainable development, energy-saving renovation of existing buildings can not only effectively reduce energy consumption but also improvement the residential comfort and office environments. This study proposes an optimization method for building energy-saving renovation based on automated machine learning and the NSGA-III algorithm, aiming at efficiently and accurately identifying the best energy-saving renovation schemes. The results showed that automated machine learning could provide a predictive model for multi-objective optimization analysis in a relatively short training time, with an average prediction accuracy of over 95%. And the Stacked Ensemble algorithm and Gradient Boosting Machine algorithm perform the best among all the algorithms considered in this study. Additionally, climate change significantly affects the energy-saving benefits and thermal comfort performance of renovation measures, thereby altering the choice of optimal schemes. The final renovation schemes obtained through the NSGA-III and entropy weight TOPSIS method can achieve an energy saving rate of 22.06% to 26.48% under typical climate scenarios, while also improving thermal comfort time by 31.87% to 43.73%. In future climate scenarios, an energy saving rate of 32.73% to 33.02% and a thermal comfort time of 40.20% to 42.29% can be achieved. Combining these two technologies can quickly and effectively find the best balance among energy consumption, whole life cycle cost, and thermal comfort time. The method proposed in this paper offers new perspectives and tools for researchers in the field of building energy conservation, demonstrating the application potential of automated machine learning and the NSGA-III algorithm in the energy-saving renovation of hospital buildings.

Driving risk identification of urban arterial and collector roads based on multi-scale data

Urban arterial and collector roads, while interconnected within the urban transportation network, serve distinct purposes, leading to different driving risk profiles. Investigating these differences using advanced methods is of paramount significance. This study aims to achieve this by primarily collecting and processing relevant vehicle trajectory data alongside driver-vehicle–road-environment data. A comprehensive risk assessment matrix is constructed to assess driving risks, incorporating multiple conflict and traffic flow indicators with statistically temporal stability. The Entropy weight-TOPSIS method and the K-means algorithm are employed to determine the risk scores and levels of the target arterial and collector roads. Using risk levels as the outcome variables and multi-scale features as the explanatory variables, random parameters models with heterogeneity in means and variances are developed to identify the determinants of driving risks at different levels. Likelihood ratio tests and comparisons of out-of-sample and within-sample prediction are conducted. Results reveal significant statistical differences in the risk profiles between arterial and collector roads. The marginal effects of significant parameters are then calculated separately for arterial and collector roads, indicating that several factors have different impacts on the probability of risk levels for arterial and collector roads, such as the number of movable elements in road landscape pictures, the standard deviation of the vehicle’s lateral acceleration, the average standard deviation of speed for all vehicles on the road segment, and the number of one-way lanes on the road segment. Some practical implications are provided based on the findings. Future research can be implemented by expanding the collected data to different regions and cities over longer periods.

Dynamic changes and improvement paths of China's emergency logistics response capabilities under public emergencies-research based on the entropy weight TOPSIS method.

Due to global industrialization and urbanization, natural disasters, accidents, and public health emergencies happen frequently. These events cause significant loss of life and property damage to countries worldwide. In the context of frequent public emergencies, enhancing emergency logistics response capabilities is crucial, ensuring rapid supply of rescue materials and support for rescue personnel, thereby saving lives and reducing economic losses. In order to identify the changes and enhancement paths of the emergency logistics response capability of Chinese regions under the shocks of public emergencies, this paper innovatively constructs emergency logistics response capability measurement indicators. This paper uses the entropy weight TOPSIS method and panel quantile regression model to quantify the change and enhancement paths of China's regional emergency logistics response capability under different events. It is found that: (1) The gap in emergency logistics response capability among Chinese regions is widening, with the internal difference in the eastern region higher than that in the west, while the difference in the central region is relatively low. (2) China's emergency management department can effectively transform social logistics into emergency logistics, thereby promoting the improvement of emergency logistics response capabilities. (3) Sudden geological disasters break through the limits of social logistics resources when they cross lower scales, resulting in the failure of emergency logistics response capabilities. This paper expands research on assessing emergency logistics capabilities, addressing issues in existing assessments such as reliance on single indicators and subjective measurement methods. Additionally, it quantifies the dynamic changes in China's regional emergency logistics response capabilities under public emergencies by extending the study of event content, types, and impacts. This enhances discussions on the effects of public emergencies. Finally, from an empirical perspective, the paper explores pathways to enhance regional emergency logistics response capabilities in China. In practice, this paper assists countries worldwide in assessing whether different regions of China can effectively provide emergency support for various resources in direct investments, thus providing a scientific basis for investment decisions.

Research on the Influencing Factors of Cultural and Tourism Service Quality in Huizhou Area

This study investigates the elements that influence the quality of cultural and tourism services in the Huizhou region, seeking to improve the region’s tourism competitiveness and revive the growth of the local rural tourism industry based on the background of cultural and tourism integration. This article builds an evaluation index system for the influencing variables of culture and tourism service quality by choosing 20 indicators from three categories: public service quality, cultural service quality, and tourism industry performance. The entropy weight Topsis method is employed to assess the service quality of three districts and five counties in the Huizhou area, which serves as the research object. Research has shown that (1) the number of five-star hotels, the number of A-level scenic spots, and the number of overseas tourists received have a significant weight, and these have become important factors affecting the effective supply of high-quality rural tourism; (2) there is a significant difference in the quality of cultural and tourism services in different areas across the Huizhou region, with Tunxi District and Yi County having the highest level of service quality, while Qimen County and Jixi County have the lowest level; (3) The overall quality of cultural and tourism services in Huizhou region is relatively high, with six districts and counties rated medium or above, accounting for about 75% of the total. Therefore, it is necessary to improve the infrastructure construction, improve the quality of public services, maintain the local and regional culture, accelerate the integration of culture and tourism, and enhance the high-quality development of regional tourism in Huizhou.

Biochar Application Improved Sludge-Amended Landscape Soil Fertility Index but with No Added Benefit in Plant Growth

Co-application of sewage sludge (SS) with biochar in landscape/forestry soil is a common strategy for enhancing soil fertility and reducing the bioavailability of potential toxic elements (PTEs) derived from SS, such as Cd, Pb, Cu, Zn, and Ni. However, due to variability of biochar quality and uncertainties in responses of different plant species, whether the co-application benefits the landscape/forestry plant system remains elusive. Here, we tested the effectiveness of three types of biochar (SS-derived biochar (SB), rice straw-derived biochar (RB), and litter-derived biochar (LB)), which were added to soil amended with SS at 50% (w/w) at rates of 1.5%, 3%, and 4.5% as growth media for the landscape plant Aglaonema modestum (A. modestum). We analyzed the substrate’s physicochemical properties and assessed the alleviation of phytotoxicity by biochar application. A significant increase in the fertility index of substrate was observed in all the treatments with biochar addition. The addition of biochar reduced the potential mobility of PTEs while increasing their residual fraction in media. Nonetheless, it has been found that the addition of biochar has ineffective or even negative effects on A. modestum growth (height, biomass, root length) and nutrient absorption. Importantly, the reduction in root biomass and the increased activity of root antioxidant enzymes (SOD, POD, CAT, and MDA) indicate contamination stress of biochar on the roots of A. modestum. Toxic elements of concern—namely Cu, Cd, and Pb—were not significantly higher in tissues of A. modestum saplings planted in biochar-SS-amended soil. However, elevated levels of other elements that may pose toxicity concerns, such as Ni and Zn, increased in tissues at high biochar dosages. Based on the Entropy–Weight TOPSIS method, it was further confirmed that compared to the treatment without biochar, all treatments except for 3.0% LB application resulted in poorer A. modestum comprehensive growth. Our results emphasize the need for detailed research on the response of specific plants to biochar in specific environments, including plant adaptability and the unexplored toxicity of biochar, to understand the large variations and mechanisms behind these ineffective or negative effects before the large-scale co-utilization of SS and biochar in landscape/forestry soils.

Optimization of H-type Anti-slide Pile Support Structure Based on RSM-NSGA-II

The parameters of an h-type anti-slide pile are optimized and analyzed with respect to its structural characteristics. Based on laboratory model tests, further analysis of its stress characteristics is carried out through numerical simulation methods. Then, the response surface methodology (RSM) combined with the NSGAII algorithm was employed to perform multiobjective optimization of the reinforcement performance and engineering cost for h-type anti-slide piles regarding landslide consideration. Finally, the entropy weight TOPSIS method was used for comprehensive evaluation to obtain the optimal structural parameters. The results show that the bending moment distribution of the front and rear rows of h-type piles first increases and then decreases with increasing depth, and the front soil pressure of the pile gradually increases. Using RSM-NSGA-II instead of finite element modeling can shorten the optimization time, and the accuracy of the surrogate model reaches 94.8%. NSGA-II is much stronger than that of the particle swarm optimization algorithm. The entropy weight method combined with the TOPSIS method can obtain the unique optimal solution from the Pareto solution set without prior knowledge. The h-type anti-slide pile with the best structural parameters reduces the cost by 75.19%, while the slope safety factor increases by 47.39%. This method provides a new idea for structural optimization.

Optimizing Split-reduced drip fertigation schemes of Arabica coffee based on soil microcosms, bean yield, quality and flavor in dry-hot region of southwest China

As a characteristic cash crop in Yunnan Province, China, Arabica coffee ranks first in the country in terms of cultivation area and production, but has always suffered from poor soil nutrient management and a lack of scientific basis for fertilization. It is not clear how to improve the soil microecological environment using split-reduced drip fertigation at different reproductive stages of Arabica coffee to improve bean yield and quality. Therefore, a two-year (2020–2022) field experiment was conducted on 5-year-old Arabica coffee trees in a dry-hot region with five split-drip fertigation patterns, F450, F600, F750, F900 and F1050. Using the local conventional fertilization (Fertilizer of 900 kg ha−1 year−1 only in berry expansion, and no fertilization in early flowering and berry ripening) as the control (CK). The effects of different fertilization modes on soil microecological environment, bean yield, nutritional quality and characteristic flavor were studied, and a comprehensive benefit evaluation model to assess bean yield and nutritional quality under different fertilization modes was established. The results showed that F900 significantly increased soil nitrate nitrogen, available phosphorus, available potassium, microbial population and enzyme activities at early flowering and berry ripening stages compared to CK, while F900 reduced soil nitrate nitrogen, available phosphorus and available potassium but increased soil enzyme activity and microbial population at the berry expansion stage. Compared with CK, F450 decreased bean yield, caffeine and chlorogenic acid content by 31.83 %, 23.73 % and 13.48 %, respectively, while F750 increased two-year average yield, raw bean fat, caffeine and chlorogenic acid content by 20.52 %, 12.37 %, 5.48 % and 11.17 %, respectively. The two-year average yield of F600 showed less difference from CK. Marginal yield was positive and then negative with increasing fertilizer application. Dry bean yield decreased at a total fertilizer application of 900 kg ha−1 year−1. Correlation analyses showed a positive correlation between soil microbial environment and nutrient quality of coffee beans, while different correlations were found between nutrients and volatiles at dark and medium roasting levels. Cluster analysis revealed that the relative contents of aldehydes, alcohols, phenols, pyridines, and pyrroles were higher under dark roasted condition, while the relative contents of ketones, esters, acids, and pyrazines were higher under medium roasted condition. Based on the Entropy weight-TOPSIS method, F750 had the highest combined benefits in terms of yield increase and quality improvement, and the total fertilizer application decreased by 16.67 % compared to CK, thus F750 treatment was the most suitable split-drip fertigation strategy for Arabica coffee in the dry-hot region of Yunnan. This study provided a reference for drip fertigation systems of Arabica coffee in dry and hot regions.

Spatio-Temporal Evolution and Driving Factors of Tourism Economic Resilience in the Yili River Valley

The tourism economy plays an essential role in supporting and driving tourism development. Therefore, studying its resilience is crucial for promoting sustainable and high-quality tourism development. The purpose of this study is to construct a tourism economic resilience evaluation index system and assess the tourism economic resilience of 10 counties and cities in the Ili River Valley from 2010 to 2019 using the entropy weight TOPSIS method. The results show that (1) the tourism economic resilience shows a “W”-shaped time-series dynamic development trend, especially a decline in 2012–2013 and 2015–2016; (2) the spatial pattern shows a non-equilibrium characteristic, with the northwestern part of the valley represented by Yining City showing a high level of resilience, and the central to southeastern counties and cities showing a lower level of resilience; (3) the spatio-temporal evolution steadily points to “northwest–southeast”, and there are five toughness convergence clubs, with obvious stability and neighborhood effects; (4) and the analysis of the driving factors shows that the number of employees in the tertiary industry, especially the input of high-quality talents, is crucial to improving the quality of services, strengthening the ability to withstand risks and the potential for development, and its impact is significant. These results provide an important reference for the formulation of tourism development strategies and promote the sustainable development of tourism.

Difference in regional logistics development level in China based on entropy weight TOPSIS and Dagum models

The high-quality development of the logistics industry, which is an essential and strategic industry supporting the national economic operation and a fundamental component of modern industrial system construction, is not only a key component of the high-quality development of the national economy but also the main driving force for the high-quality growth of the national economy. As the supporting industry of the national economy, the logistics industry will also face spatial disequilibrium during development. Therefore, to achieve the coordinated development of the logistics industry, the high-quality development and the spatial-temporal unbalanced development status of the logistics industry in each province must be figured out first. This research established a comprehensive evaluation system for the logistics industry development, which included 14 basic indexes based on the provincial-level panel data of 30 provinces in China during 2009–2020. Then, the regional logistics development level score in China was measured using the entropy weight TOPSIS method, and the differences in the regional logistics development level in China and the dynamic evolution law of their distribution were deeply explored through the Dagum Gini coefficient model. The research results revealed that the evaluation index system (14 basic indexes) for the regional logistics industry development level in China was relatively scientific and reasonable; the regional logistics industry development level in China was increasing year by year, showing a steady upward trend, and the imbalance in the eastern, central, and western regions regarding the regional logistics development was shrinking year by year; the average intergroup contribution rate was 36.33%, the intragroup contribution rate was 31.49%, and the contribution rate of intensity of trans variation was 32.19%, proving that the regional differences exerted a most extraordinary influence on the spatial differences in the regional logistics industry development level in China. The research results have important reference value for summarizing the meaning of high-quality logistics industry development, constructing the evaluation index system for logistics industry development, and exploring the reasons for the temporal and spatial differences in logistics industry development in China.

The influence of NaOH/urea treatment on the transfer law of water in cotton fabric

The moisture absorption and water conductivity of fabrics determine the thermal and moisture comfort of textiles and clothing. In this study, the water transfer law in cotton fabrics treated with a NaOH/urea system was investigated. The morphological changes and structural properties of the cotton fabrics before and after treatment were analyzed using scanning electron microscopy, Fourier transform infrared spectroscopy, and X-ray diffraction. Additionally, the relationship between the gaseous and liquid water transfer performance and the fiber morphology and structure was studied. The results indicate that after the NaOH/urea treatment, the surface of the cotton fibers gradually became rougher, and the fibers swelled slightly along the cross-sectional direction. With an increase in treatment time, the axial twist of the fibers decreased, and a trend of dissolution was observed. However, after 5 min of treatment, the cotton fabric tended to adhere to the partially dissolved fibers at the junction of the warp and weft yarns. Although the chemical structure of the cotton fibers did not show significant changes after the treatment, their crystallinity gradually decreased. Following treatment, the air permeability and moisture permeability of the fabric increased, while the wicking height and water retention rate initially increased and then decreased, resulting in an interesting pattern of change. The NaOH/urea treatment also increased the dyeability of the cotton fabrics, and the fastness to rubbing and washing of the fabrics reached grades ≥ 3. Prolonged NaOH/urea treatment enhanced the breaking strength of the cotton fabrics. The establishment of the entropy weight-TOPSIS method to evaluate the moisture transfer performance of fabrics treated with the NaOH/urea system has theoretical significance for the comprehensive analysis of cotton fabrics treated with NaOH/urea and related product development. This study provides a new basis for the comprehensive analysis and evaluation of the water transfer and moisture comfort performance of the fabrics, further advancing the application of NaOH/urea treatment to cotton fabrics, which has promising prospects for the future.

Unveiling the Spatio-Temporal Dynamics and Driving Mechanism of Rural Industrial Integration Development: A Case of Chengdu–Chongqing Economic Circle, China

China’s urban–rural dichotomy has resulted in a widening gap between urban and rural areas, posing significant challenges to rural development. This study aims to investigate the spatio-temporal differentiation and driving mechanisms of rural industry integration within the Chengdu–Chongqing Economic Circle in China. Using panel data from 2011 to 2020, we employed the entropy weight TOPSIS method to construct a comprehensive index that charts the evolution of rural industry integration across various districts and counties. Additionally, we utilized fixed-effect and spatio-temporally weighted regression models to analyze the underlying driving forces behind this integration. Our findings reveal a dynamic and varied landscape of rural industry integration, with different levels of depth and breadth across various subsystems. Spatially, we observed a transition from a dispersed to a more concentrated agglomeration pattern within the Chengdu–Chongqing Economic Circle. This shift suggests a diffusion effect emanating from core metropolitan areas, as well as an attracting force exerted by adjacent metropolitan circles. In terms of drivers, market demand, openness level, financial development, policy support, and agricultural insurance breadth significantly contribute to rural industry integration. However, technological progress and rural human capital exhibit a weaker correlation. Notably, our models identified pronounced spatial–temporal heterogeneity among these influencing factors, highlighting a nuanced and dynamic relationship between them. Overall, our study emphasizes the crucial role of rural industry integration in bridging the urban–rural divide and fostering sustainable agricultural development and rural revitalization. The insights gained from this research provide valuable guidance for policymakers and stakeholders seeking to optimize rural development strategies and unlock the potential of integrated rural industries.

Research on content analysis and quality evaluation for online reviews of new energy vehicles

The development of China's new energy vehicle industry has been gradually transformed from past policy support to the survival of enterprises determined by the market, which forces the new energy vehicle industry to accelerate towards the market-oriented direction. We collected review data from online automotive forums and generated a corpus after pre-processing. Next, we extracted the statistical and topic semantic features of online reviews for new energy vehicles and clarified the quality evaluation indicators for online reviews. Then, the entropy weight-TOPSIS method was used to determine the indicators’ weights and assign values to the quality of online reviews. Finally, the quality scoring rules of online reviews were sorted out from multidimensional perspectives. The research results have shown that the statistical features and topic semantic features of online reviews on new energy vehicles can be used as evaluation indicators to construct a quality evaluation system. It was also found that the quality scores of lower-priced vehicle reviews tend to be consistent in different price groups, while the quality scores of mid-priced vehicle reviews are the most dispersed. The quality of online reviews varies significantly among other groups or models. The study results provide a simple criterion for consumers’ cognition of the value of online reviews. The results will enhance the ability of new energy vehicle companies to carry out online marketing with the help of online reviews and transform the purchase intention of potential consumers into actual purchase behavior.

Construction and Application of Regional Carbon Performance Evaluation Index System: The Case of Chinese Provinces

As global warming becomes increasingly severe, reducing carbon emissions and promoting low-carbon development has become an international consensus. Against this backdrop, evaluating regional carbon performance helps better understand the carbon emission status, emission reduction capabilities, and low-carbon development levels, providing a scientific basis for formulating targeted carbon emission reduction policies. This study constructed a “5E” regional carbon performance evaluation index system from five dimensions: economy, effectiveness, efficiency, environmentality, and equity. Then, this study evaluated and analyzed the carbon performance of 30 provinces in China from 2008 to 2021 using the entropy weight TOPSIS method. The research results indicated that (1) during the sample period, China’s carbon performance ranged from 0.416 to 0.504, exhibiting a steady upward trend; the highest score among the first-level indicators was Effectiveness, while the lowest was Economy; (2) in terms of carbon performance among China’s three major regions, it showed a decreasing pattern from east to west, with the growth potential of the central and western regions being greater than that of the eastern region; (3) in 2033, the carbon performance of China in the eastern region, the central region, and the western region will reach 0.602, 0.612, 0.613, and 0.582, respectively. A carbon performance evaluation carries significant practical and strategic implications. Our study can provide a reference for policymakers to assess carbon emission performance and improve carbon management efficiency and decision-making levels.