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1.INTRODUCTIONSince 1980, urbanization in China has undergone rapid development, particularly in the past two decades of this century. In the economically developed Pearl River Delta region, with the rapid improvement of regional infrastructure, the spatial expansion has resulted in the formation of urban corridors, with some villages located on the outskirts of urban built-up areas, transitioning from urban to suburban areas, and becoming known as “villages on the urban fringe.” In recent years, China has launched the New Rural Construction movement, which has brought attention to governance issues in villages on the urban fringe, especially as the economic development level of these villages on the urban fringe has improved. The livability of these villages has become a focal point of concern. Many practices that were implemented in urban areas have been applied to rural areas. As a result, there is a new research question on how to evaluate the built environment in the context of the New Rural Construction movement, aiming to reflect the goal of creating livable rural environments. This study adopted the post-occupancy evaluation (POE) method to evaluate the built environment of a building and conducted a systematic evaluation of the designed and used facilities from the perspective of users1. The advantage of the POE is that it focuses on the acquisition of data sources2, support for two important investigation routes: technical and functional3, and feedback from multiple groups. Objectively analyzing the interaction between various elements and the characteristics of the environment can predict the possibility of future construction. Therefore, this study uses the POE technology and functional route content integration in response to the unique characteristics of rural environments from the perspective of sustainable rural development, introduces regional adaptability, and establishes a new evaluation route. 2.REGIONAL ADAPTABILITYRegional adaptability refers to a combination of the natural environment, socio-economic factors, and local cultural characteristics of a specific geographical area4. After several decades of urbanization, China has entered a phase of urban support for rural areas placing increased emphasis on coordinated urban-rural development5. Regional characteristics have received widespread attention in the context of rural construction projects. Research on the regional adaptability theory revolves around rural environmental construction and is manifested in several aspects. In terms of adaptability to natural environment, scholars have conducted empirical studies to verify that climate is the primary factor influencing architectural forms in rural areas6. Regarding adaptability to the social environment, socio-economic development is closely intertwined with the evolution of rural areas7. Factors such as village construction8, industrial layout9, and the improvement of environmental quality10 have resulted in changes in the spatial structure of rural areas. In terms of concerns around adaptability to the cultural environment, researchers have emphasized analyzing the dual adaptability and degree of cultural adjustment in both natural and social aspects11. In addition, implemented project cases, such as Germany’s “Rural Renewal”12 France’s “Rural Revival”13 and Japan’s “Satoyama Movement”14 have reference value for their search on regional adaptability theory in rural contexts. Currently, there are several trends in the research on regional adaptability theory and rural construction. First, the research scope has expanded from an early analysis of the relationship between rural architectural, forms and the natural environment to encompassing social, economic, political, and cultural aspects. Second, the introduction of new methods has deepened research on rural construction. A quantitative analysis of the comprehensive evaluations of rural construction from various perspectives have supplemented the qualitative analysis, thereby enhancing the persuasiveness of their search. Third, the application of regional adaptability emphasizes, regional differences and particularities in rural construction. Given the rapid development of social economy and urbanization, the present study, focuses on areas like the insufficient study of natural and economic differences in rural areas, limited research on the renewal of rural environments in the context of urban-rural integration, and insufficient research on the evaluation of newly constructed rural environments. 3.MATERIALS AND METHODS3.1.Study areaJingxia Village has a history of nearly 300 years since its establishment during the Qing Dynasty. The village center of Jingxia has undergone three important historical development stages in rural public activities, The first stage was the ancient village period, centered on the ancestral hall. During this period, the ancestral hall served as a bond to construct the village cultural community, and the ancestral hall buildings became the primary venue for communal activities (Figure 1). The second stage was the period of village self-governance and grassroots village organizations centered around the village office and village committee. In the early 20th century, it was called the village office, and after the establishment of the People’s Republic of China, it was renamed the People’s Commune. Since the 1980s, it has been renamed as the village committee. With the political guidance of rural development, the village office and committee served as bonds to construct a village political community and became administrative and office spaces, respectively, for village activities (Figure 2). The third stage was the new rural period, centered on comprehensive community services. The Huinong Centre project is located on the southern side of Zhushan Road, Jingxia Village, Huangpu District, Guangzhou City, Guangdong Province, China. It is a comprehensive community service center constructed by the Guangzhou Municipal Government and the Guangzhou Science City Group. The Huinong Center was completed in 2018 and occupies an area of over 3,000 m2 with a building area of approximately 310 m2. The total investment exceeded RMB 20 million. It is a comprehensive public service building that integrates functions, such as rural e-commerce, poverty alleviation through industry, and cultural heritage. The architectural style of Huinong Center combines traditional rural architecture with modern elements featuring a concise and vibrant overall design and bright colors that align with the characteristics of modern urban architecture (Figure 3). 3.2.Data collectionThis study attempted to obtain opinions from various parties using a questionnaire to assess the effects of integrating modern communities into traditional rural environments (Table 1). Questionnaires were distributed offline on April 29, 2023 at four locations: within the Jingxia Rural Comprehensive Service Center, among the roaming villagers of Jingxia Village, individual merchants in Jingxia Village, and the Straw Hat Farm. The participants were randomly selected from four locations using stratified sampling, A total of 100 questionnaires were distributed and 93 were collected resulting in a response rate of 93%. The primary target groups for the survey were tourists and residents of Jingxia. Among the original data samples, 60% were aged between 30-50, and over 75% were non-local tourists. The questionnaire was divided into sections for respondent demographics and building acceptance. The Likert five-point scale was used, with statements ranging from “very satisfied, satisfied, neutral dissatisfied, very dissatisfied” with corresponding to scores of 5, 4, 3, 2 and 1, respectively. Table 1.Evaluation of acceptance intention for the Huinong Center.
3.3.Evaluation and analysis methods3.3.1.Construction of theoretical evaluation modelA model to evaluate regional adaptability was constructed based on the theory of regional adaptability (Figure 4). The model consisted of three levels: A, B, and C. The first level (A) included three elements: A1-Natural Factors, A2-Social Factors, and A3-Humanistic Factors. The second level (B) was divided into B1-Climate, B2-Geography, B3-Material, B4-Economy, B5-Management, B6-Religion, B7-Culture, and B8-Art. The third level (C1-C20) represented specific subfactors derived from the second level. Through factor analysis of C1-C20, it was found that the data met the basic prerequisites; however, adjustments were required for the analysis. After adjusting for the corresponding items, factor extraction and information condensation was performed using a biased classification, nine factors influencing satisfaction were identified: appearance, usage, location, consumption, hygiene, parking, sensory experience agricultural services, and services (Figure 5). 3.3.2.Data processing and analysisFrom the model summary table (Table 2), it can be observed that as the nine independent variables were gradually introduced, the model’s fit continually improved until the introduction of the final independent variable, Hygiene Satisfaction (H). The model could no longer introduce new variables, so the computation was stopped. Model 4 is the final model, with a high fit of 73.9%, meaning that the four independent variables introduced in the stepwise regression model can explain 73.9% of the “intention to return” by users, and are related to Agricultural Services Satisfaction (AS), Sensory Experience Satisfaction (SE), Usage Satisfaction (U), and Hygiene Satisfaction (H) (R2=73.9%). Since this questionnaire survey used stepwise linear regression analysis, the coefficients table (Table 3) only needs to analyze the coefficients in Model 4, which includes the 4 independent variables of “Agricultural Services Satisfaction (AS), Sensory Experience Satisfaction (SE), Usage Satisfaction (U), and Hygiene Satisfaction (H).” This means that these four variables are the final variables that can significantly affect “intention to return.” For example, the regression coefficient of “Agricultural Services Satisfaction (AS)” was 0.265>0, indicating that an increase of 1 point in “Agricultural Services Satisfaction (AS)” can lead to a 0.265 increase in “intention to return,” and Agricultural Services Satisfaction (AS)” has a significant positive influence on “intention to return.” Table 2.Summary of multiple linear regression models.
Note: a: Predictive variable: (Constant), Agricultural services (AS); b: Predictive variable: (Constant), Agricultural services (AS), Sensory experience (SE); c: Predictive variable:(Constant), Agricultural services (AS), Sensory experience (SE), Usage (U); d: Predictive variable: (Constant), Agricultural services (AS), Sensory experience (SE), Usage (U), Hygiene (H) Table 3.Regression coefficients of acceptance of Huinong Center in Jingxia Village.
Based on the coefficient table in Model 4, the B values for AS, SE, U, and H are all positive. This indicates a positive correlation between the satisfaction levels of the independent variables and acceptance of the dependent variable. For example, higher U corresponds to a higher willingness to revisit an agricultural center. A negative B-value indicates a negative correlation between the satisfaction levels of the independent variables and the willingness of the dependent variable. All significance values were <0.05, indicating that the four independent variables had a significant influence on the dependent variable. he variance inflation factor values were all <10, indicating no multicollinearity among the four independent variables (Table 3). Based on the above analysis, the willingness equation can be derived as follows: where A: Acceptance of the Huangpu Agricultural Service Center (Huinong Center in Jingxia Village). (AS): Agricultural Service satisfaction (SE): Sensory Experience satisfaction (U): Use satisfaction (H): Hygiene satisfaction According to the results of regression models, the changes of the acceptance of Huinong Center in Jingxia Village were affected by Agricultural Services Satisfaction (AS), Sensory Experience Satisfaction (SE), Usage Satisfaction (U), and Hygiene Satisfaction (H), respectively. This indicates that there is a positive correlation between the satisfaction of the independent variable and the acceptance of the dependent variable. Whenever the comprehensive score of four influencing factors increases, it proves that people’s “intention to return” increases. 4.CONCLUSIONSThis study addresses the challenges faced in evaluating the built environment of new rural areas and proposes an evaluation framework based on “regional adaptability” for assessing the built environment in these areas. Through empirical research on the Jingxia Huinong Center, it was found that user acceptance is primarily influenced by four factors: agricultural benefits, sensory effects, usability, and satisfaction with hygiene. By proposing a regional adaptability evaluation model, further analysis of the acceptance of the built environment of the Jingxia Huinong Center is conducted from three dimensions: natural climate, socio-economic factors, and humanistic arts. This analysis reveals that in terms of natural adaptability, attention should be given to the connection between the building’s ventilation, lighting, and sensory aspects and local traditions. In terms of social adaptability, it is important to provide services that benefit the livelihoods of villagers while also ensuring cleanliness and maintaining orderly conditions. In terms of humanistic adaptability, emphasis should be placed on the integration of landscape functionality with cultural history and religious customs, rather than focusing solely on visual artistic decoration. The approach of regional adaptability evaluation proposed in this study contributes to a better understanding of the differences in perceptions between users of rural and urban environments, enabling the development of targeted strategies for rural environmental development. ACKNOWLEDGEMENTThis Paper was supported by the Research Fund: Philosophy and Social Science Planning Project of Guangdong Province in 2023 (No. GD23CYS19). REFERENCESPreiser, W., Rabinowitz, H. and White, E., Post-Occupancy Evaluation, 233
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