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The study advances an enhanced model encompassing psychological involvement, denoted as the psychological continuum model (PCM) and perceived customer service quality as intermediaries in the association between subjective customer knowledge (SCK) and behavioral loyalty. The purpose of this study is to assess the mediating role of psychological engagement and consumers' perceived service quality in the relationship between SCK and behavioral loyalty among members of nonprofit sports service organizations. Additionally, the study aims to examine the impact of membership duration on the relationship between consumer knowledge and behavioral loyalty.

The study used a quantitative research design, and primary data were collected through a structured questionnaire from 527 members of nonprofit Chinese sports clubs who were selected using a simple random sampling technique. A 5-point Likert scale questionnaire was developed to measure all constructs in the intended research model. The suitability of the measurement model was analyzed by performing confirmatory factor analysis (CFA). Structural equation modeling (SEM) was used to analyze the data using AMOS-24.

The results of the overall direct effect indicate a significant influence of subjective knowledge on perceived service quality, perceived service quality significantly and positively influences psychological engagement; psychological engagement was found to be an important predictor of consumer behavioral loyalty.

The results offer information for nonprofit sports club (NPSC) managers who seek to increase the attractiveness and retention of their clubs' members by establishing the importance of subjective consumer knowledge.

The purpose of this paper is to analyze the linear and nonlinear effects of market integration on carbon emissions and explore the direct and indirect paths of market integration on carbon emissions through path analysis.

The authors first conduct a measurement and contrastive study of the market integration and carbon emissions of China’s 28 provinces from year 1995 to 2015. Then, the linear effect of market integration on carbon emissions is analyzed by using the fixed-effect model. Next, based on the path analysis method, the direct and indirect paths of market integration’s impact on carbon emissions are explored. Finally, the panel threshold regression model is used to evaluate the effect of market integration on carbon emissions under different situations of geographic distance.

The results show that first, the improvement of market integration can increase carbon emissions in the form of a linear relationship. Second, market integration not only has a direct and positive impact on the carbon emissions, but also has an indirect and positive impact on carbon emissions through the level of economic development, and a negative impact on carbon emissions through technological level. Third, an increase in market integration can reduce its positive effect on carbon emissions, but the improvement of economic growth and technology level can both enhance the positive effect of market integration on carbon emissions.

This paper focuses on the impact of market integration on carbon emissions in 30 provinces in China, while, the authors do not conduct a comparative analysis of different regions, so there are certain limitations. In addition, policy interaction between regional governments is also a key factor affecting carbon emissions, but this paper does not consider the effect of policy interaction, future follow-up research will try to incorporate it into the analytical models.

An important practical implication of this research is that market integration should be regarded highly in China’s energy conservation and emission reduction efforts. The research results have important reference value for policy authorities to formulate relevant policies. That is, the government can play a more active role in the process of integration through breaking the regional blockade and interest barriers to comprehensively improve resource utilization efficiency and technical level, and ultimately achieve regional low-carbon development.

This paper explores the effects of market integration on China’s carbon emissions based on different methods and perspectives, and confirms that market integration plays a vital role in China’s carbon emissions through economic growth and technological progress. Notably, based on the studied results, some specific and practical suggestions are proposed in this paper so as to reduce carbon emission and realize the sustainable development of economy and society in China.

Looks at the problem of global warming from the viewpoint of wholeness. That is, the problem of global warming will be looked at in a comprehensive study considering several aspects of the cosmos, the Earth, and the phenomenon of life. With such a broad understanding in mind, first analyzes both the disadvantageous and advantageous aspects of the current global warming. Second, compares three typical environmental conditions in which humans have lived. Third, employing the concept of global warming, looks at the myth of how ancient civilizations appeared and disappeared. Then, considering our Earth system as an open system travelling in the universe, provides an explanation for the current global warming and for global climate changes. It is expected that the explanation presented can be applied to produce long‐term predictions for climate changes.

With the rapid increase in the number of installed wind turbines (WTs) worldwide, requirements and expenses of maintenance have also increased significantly. The condition monitoring (CM) of WT provides a strong “soft guarantee” for preventive maintenance. The supervisory control and data acquisition (SCADA) system records a huge amount of condition data, which has become an effective means of CM. The main objective of the present study is to summarize the application of SCADA data to fault detection in wind turbines, analyze its advantages and disadvantages and predict the potential of future investigations on the use of SCADA data for fault detection.

The authors first review the means of WT CM and summarize the characteristics of CM based on SCADA data. To ensure the quality of SCADA data, data preprocessing methods are analyzed and compared. Then, the failure modes of the key components are discussed and the SCADA data used for fault detection of each component are compared. Moreover, the fault detection methods for WT are classified and a general framework for fault detection is proposed. Finally, the issues in the WT fault detection method based on SCADA data are reviewed.

Based on the performed analyses, it is found that although the fault detection accuracy based on SCADA data is relatively poor, it has low capital expenses and low computational cost. More specifically, when there is scarce fault data, the normal SCADA data can be used to detect the fault time. However, the specific fault type cannot be identified in this way. When a large amount of fault data are accumulated in the SCADA system, it can not only detect the occurrence time of the fault but also identify the specific fault type.

The main contribution of the present study is to summarize the pre-processing methods for SCADA data, the data required for fault detection of key components and the characteristics of the fault detection model. Then we propose a general fault detection framework for wind turbines based on SCADA data, where the maintenance workers can choose the appropriate fault detection method according to different fault detection requirements and data resources. This article is expected to provide guidance for fault detection based on time-series sensor signals and be of interest to researchers, maintenance workers and managers.

Development of urban-rural integration is essential to fulfill sustainable development goals worldwide, and comprehension about urban-rural integration types has been highlighted as increasingly relevant for an efficient policy design. This paper aims to utilize an unsupervised machine learning approach to identify urban-rural integration typologies based on multidimensional metrics regarding economic, population and social integration in China.

The study introduces partitioning around medoids (PAM) for the identification of urban-rural integration typologies. PAM is a powerful tool for clustering multidimensional data. It identifies clusters by the representative objects called medoids and can be used with arbitrary distance, which help make clustering results more stable and less susceptible to outliers.

The study identifies four clusters: high-level urban-rural integration, urban-rural integration in transition, low-level urban-rural integration and early urban-rural integration in backward stage, showing different characteristics. Based on the clustering results, the study finds continuous improvement in urban-rural integration development in China which is reflected by the changes in the predominate type. However, the development still presents significant regional disparities which is characterized by leading in the east regions and lagging in the western and central regions. Besides, achievement in urban-rural integration varies significantly across provinces.

The machine learning techniques could identify urban-rural integration typologies in a multidimensional and objective way, and help formulate and implement targeted strategies and regionally adapted policies to boost urban-rural integration.

This is the first paper to use an unsupervised machine learning approach with PAM for the identification of urban-rural integration typologies from a multidimensional perspective. The authors confirm the advantages of this machine learning techniques in identifying urban-rural integration types, compared to a single indicator.

The digital economy has become a key force supporting the high-quality development of tourism. This paper discusses the coupling coordination relationship and spatiotemporal evolution path of digital economy and tourism in China's provinces.

This paper uses the entropy method to measure the development level of digital economy and tourism, and establishes coupling coordination model and spatial autocorrelation model to study the interaction between the two industries.

Results show that the development levels of the two industries are rising, which spatially show a progressively decreasing pattern of east-middle-northeast-west. The coupling coordination degrees of the two industries have increased steadily, but the overall level is still near maladjusted. Spatially, the positive correlation is increasing, but the incongruity of spatial agglomeration is still significant. The coupling coordination evolution path in the provinces shows differentiated characteristics. The migration path is mainly concentrated in Zones I and II. The eastern region has an obvious trend of extending to Zone III, where the tourism industry was the most affected by the pandemic.

The study helps clarify the industrial coupling and coordination relationship in various regions and formulate regional tourism digital transformation strategies to promote the high-quality development of China's tourism industry.

This paper enriches the research on the relationship between digital economy and tourism from the perspective of industrial integration. The development commonality of China's tourism digital transformation summarized provides theoretical reference and demonstration for the coordinated development of China's tourism.

The purpose of this paper is to identify the effects earthquakes may have on rates of marriage and divorce in China, a country strongly affected by losses due to earthquakes.

This paper studies the effect of earthquakes on marriage and divorce rates in China between 2000 and 2011, using panel data from 31 provinces as well as from Sichuan at the prefecture level, a province that has a high frequency of earthquakes.

The results show that when controlling for demographic, economic, and social factors, losses due to earthquakes are found to be associated with increases in both marriage and divorce rates. While the estimated elasticities are low, amounting to 1.92×10−2 and 6.102×10−2, respectively, they are highly significant, suggesting that a doubling of losses due to earthquakes increases marriages by 1.92 percent and divorces by 6.102 percent with a lag of one year. Since the first elasticity is smaller than the second, losses due to earthquakes may influence familial instability. Moreover, these effects increase in the second year but cannot be traced beyond three years after the disaster.

In view of the cost imposed on society by instable family relationships, these findings point to a need to provide relief to families after earthquake disasters.

The purpose of this paper is to describe the trends in residential solid waste collection (RSWC) services in rural China over the past decade and analyze the determinants of these services using nationally representative data.

The authors draw on panel data from three rounds of village-level surveys of 101 villages. The three surveys were conducted in 2005, 2008, and 2012 in five provinces. The authors used fixed-effected regression approach to analyze the determinants of these services.

The results show that in the aftermath of increased investment and policy attention at the national level, the proportion of villages providing RSWC services in rural China increased significantly from 1998 to 2011. However, half of all villages in rural China still did not provide RSWC services as of 2011. Based on econometrics analysis, the authors show that villages that are richer, more populous, and villages with more small hamlets are more likely to provide RSWC services.

The analyses are based on primary survey data and the first to quantify trends in waste management services in the beginning of the twentieth century. The authors believe that the results will have significant policy implications for China in its continuing quest for better waste management policy.

The purpose of this paper is to construct a digital collection and database of traditional clothing that is convenient for the digital dissemination and application of traditional clothing and provide resources for research on clothing fashion, traditional clothing techniques, clothing culture, history and clothing teaching.

A real object analysis method was used in this paper, based on 15 core elements of the internationally common DC metadata standard, and with consideration to the characteristics of clothing products and clothing industry application specifications, the core elements of DC are expanded to facilitate the detailed record of the characteristic information of clothing, especially the implicit clothing culture. A code symbol compilation method was developed to give each piece of clothing a unique number, facilitating identification, classification and recording. At last, a metadata construction scheme for traditional clothing was developed. A traditional embroidered children's hat and Mamianqunt serve as examples to demonstrate the metadata elements.

The clothing meta-database provides a main body of traditional clothing while also paying attention to the collection of cultural elements. It is composed of five layers of classified data, source data, characteristic data, connotation data and management data, as well as 28 data elements, providing ease of sharing and interoperation.

This paper expands the subset of fashion metadata by describing traditional clothing metadata, especially the excavation of clothing cultural elements, and developing code compilation methods so that each clothing product can obtain a unique identification number, thereby building a traditional clothing metadata construction scheme consisting of five data layers and containing 28 data elements. This scheme records the information about each layer of traditional clothing in detail and provides shared data for discipline research and industry applications.

There are certain differences in the production products of enterprises. What are the impacts of product differentiation on the iron and steel industry? Based on the macro background of CO₂ emission reduction, this paper aims to analyze the economic benefits and environmental changes of the iron and steel industry under the dual influence of CO₂ emission reduction policy and product differentiation policy.

Taking the basic data of iron and steel industry in six regions of China as an example, this paper constructed an extended two-stage dynamic game model to analyze the impact of product differentiation and carbon tax policy on the production, economic indicators and CO₂ emission levels for the overall industry and regional enterprises.

As the CO₂ emission reduction target increased, the unit carbon tax and total tax increased, whereas the macro-environmental losses, social welfare, consumer surplus and outputs decrease. Emission reduction pressures and other economic indicators showed obvious regional differences. Differentiated products promoted various indicators of enterprises and industries; higher degrees of product differentiation resulted in greater promoting effects on economic indicators.

This paper constructed multiple emission reduction and production backgrounds, and discusses the impact of the comprehensive implementation of these policies, which has been practically absent in previous studies. The results of this study are consistent with the current industrial policy for stable production and environmental protection, and also provides a reference for the formulation of detailed policies in the future.