Regional Characteristics Research Articles

Regional aroma characteristics of spontaneously fermented Cabernet Sauvignon wines produced from seven sub-regions in Shangri-La of China.

Shangri-La is a promising wine region in China, which has great potential for producing high-quality wines with distinctive regional characteristics. In this work, the aroma properties of spontaneously fermented Cabernet Sauvignon wines produced from seven sub-regions of Shangri-La (Adong, Liutongjiang, Sinong, Xidang, and Nitong from Lancang River basin; Benzilan and Dari from Jinsha River Basin) were comprehensively analyzed using the headspace micro-extraction with gas chromatography-mass spectrometry, odor activity values, and olfactory evaluation. Results showed that a total of 59 volatiles belonging to seven groups were identified and quantified in all regional wines, with alcohols constituting the most abundant group, followed by esters and volatile fatty acids. Based on their odor activity values, six alcohols, seven esters, two terpenes, one C13-norisoprenoids, and three volatile fatty acids were identified as key volatiles which significantly contribute to the aroma of these wines. Principal component analysis showed the distinct compositions of these 19 key volatiles among the seven regional wines. Olfactory evaluation revealed certain differences in aroma profiles, particularly "Tropical fruit", "Dried fruit", "Vegetal", and "Sweet" among them. This study enhances our understanding on the unique terroir flavors of Shangri-La wines and is helpful for further producing high-quality wines with regional characteristics.

Translation of Earthliness: An Exploration of Translation Dislocation of Folk Language in Chin P'ing Mei in Late Ming Dynasty within the Framework of Aesthetic Reception

As the first full-lenght satiric novel, Jin Ping Mei (Chin P'ing Mei), written by Lanling Xiaoxiao Sheng at the end of the Ming Dynasty, elaborates the social and worldly affairs comprehensively through vivid language and skillful usage of literary devices. Due to the richness of the folk language for the language forms, and the differences and great colloquial and regional characteristics to the Western readers in the geographical and time facets, for the purpose of reception in the target culture, the translator gives adjustment or modification to folk languages forms like dialect, slang, idiom, and proverbs, etc., which engenders certain translation dislocation to some extent. Through qualitative and comparative analyses, this paper aims to offer a preliminary exploration of the basic paradigm, causes, and construction process of the translation dislocation of the folk language in those two English translation versions of Jin Ping Mei from the four dimensions of readers' expectation horizons, aesthetic distance, integration of horizons, and the construction of respond-inviting structures under the perspective of aesthetic reception theory, so as to provide an avenue for English readers to delve into the peculiar translation dislocation of the folk language in the translation of Chinese classical works.

Sustainable development of electric vehicles in China: A bibliometric analysis and systematic review, 2011 onward

To address the challenge of climate change, China declared that its carbon dioxide emissions would peak in 2030 and become carbon neutral by 2060. China is the largest energy supplier and electric vehicles market worldwide and will stop selling combustion vehicles by 2035 and completely phase them out by 2050. To provide a overview of the current research, a bibliometric analysis and systematic review were conducted. The bibliometric analysis utilized data from Web of Science and Scopus, selected based on the PRISMA guidelines. Focusing on studies conducted in China and related to social sciences, environmental sciences, business, or economics. A total of 188 articles met the inclusion criteria. The analysis was performed using VOS viewer software to map co-citations and keyword co-occurrences, identifying core research themes and prominent clusters in the field. However, most researchers have primarily focused on one or two aspects of sustainability, with the hot spots being subsidies, charging costs, charging infrastructure, charging stations, batteries, and emissions. Life cycle assessment is also the most applied theory. This study recommended future research to focus on the usage stage of EVs, incorporating all three sustainability pillars. Additionally, regional assessment should be conducted to consider regional characteristics such as temperature, how policy influences affect electric vehicle emissions, social impacts, and economic advantages. Before promoting electric vehicles, regional assessments should be conducted, so that stakeholders can make decisions.

Multi-Modal Graph Aggregation Transformer for image captioning

The current image captioning directly encodes the detected target area and recognizes the objects in the image to correctly describe the image. However, it is unreliable to make full use of regional features because they cannot convey contextual information, such as the relationship between objects and the lack of object predicate level semantics. An effective model should contain multiple modes and explore their interactions to help understand the image. Therefore, we introduce the Multi-Modal Graph Aggregation Transformer (MMGAT), which uses the information of various image modes to fill this gap. It first represents an image as a graph consisting of three sub-graphs, depicting context grid, region, and semantic text modalities respectively. Then, we introduce three aggregators that guide message passing from one graph to another to exploit context in different modalities, so as to refine the features of nodes. The updated nodes have better features for image captioning. We show significant performance scores of 144.6% CIDEr on MS-COCO and 80.3% CIDEr on Flickr30k compared to state of the arts, and conduct a rigorous analysis to demonstrate the importance of each part of our design.

A classification and recognition model for multiple fruit tree leaf diseases

Abstract Fruit tree leaf diseases affect both fruit survival rate and orchard revenue. Rapid and accurate identification of tree leaf diseases not only prevents the spread of diseases but also ensures healthy tree growth and improves fruit quality. Existing models mostly focus on individual types of tree leaf diseases, with limited applications in the identification of multiple tree leaf diseases. In response to the insensitivity of current classification models to disease region features and the issue of increased error rates due to the presence of similar diseases, this study proposes a residual network model based on the ECA channel attention mechanism and meta-Acon adaptive activation function.The model employs ResNet34 as the backbone network and incorporates the ECA module after each residual block to focus on relevant information. Additionally, a new activation function called meta-Acon is introduced to enhance the model's generalization ability through its dynamic learning capability. Finally, the model's recognition performance is improved by fusing bottom-level features with features from other layers using the Feature Pyramid Network (FPN).Experimental results on a dataset augmented with Mosaic processing show that the FPEM-ResNet34 model achieves a classification accuracy of 98.46%. Compared to other common models such as VGG-16, Inception-V1,AlexNet, and ResNet50, the proposed method mentioned in this paper demonstrates more effective improvement in the accuracy of tree leaf classification, making it highly valuable for practical applications..

Peculiarities of length-weight somatometric indices in school-age children of the industrial center

Introduction. Children’s health is of particular value, being a medical, biological and social and labour potential for any state. Objective and informative health indices include parameters of the physical development. The aim of the study is to assess length-weight somatometric indices in children studying in general educational institutions of Ulan-Ude. Materials and methods. The sample of respondents included nine hundred thirty two school-age children. Variables were sampled for length and weight, body mass index was calculated and compared with WHO criteria. Descriptive statistics are expressed as mean and standard deviation, Student’s t-test was used to compare means. Results. Among children of the industrial center, persons with harmonious physical development predominate (51.9±2.6%). Among the variants of disharmonious physical development in boys and girls, excessive body weight (27.3±2.3% and 22.9±1.9) and high height (12.6±1.7% and 14±1.6%, respectively) prevailed. The proportion of other variants of disharmony (low length, body weight deficit and their combination) is small. The phenomena of sexual dimorphism are quite pronounced in children – acceleration of growth differed from the general patterns earlier by 2–3 years. Limitations. The results of the study have limitations in relation to children with various health disorders. Given the small number of age groups, as well as the fact that children of one industrial center were examined, the diagnostic significance of the data obtained may be less objective. Conclusion. Regional features of growth-weight somatometric indices are important to timely identify children with disharmonious development as early as possible to prevent long-term consequences.

Youth Not in Employment, Education or Training (NEET): Evidence from National Labor Force Survey 2018

This study aims to examine and analyze the socio-demographic, socio-economic, and regional characteristics that influence an individual on being Not in Employment, Education, or Training (NEET). The data is from the February 2018 National Labor Force Survey (Sakernas) and analysed using binary logistic regression with descriptive and inferential approach. The descriptive results show that socio-demographic characteristics such as female, married, and having disability tend to become NEET. Socio-economic characteristics such as graduating from high school and living with an employed household head also tend to become NEET. Furthermore, regional characteristics such as living in rural area and living in area outside Java Island tend to become an NEET. Meanwhile, the inferential results show that female, married, having disability, not graduating from school, living with an unemployed household head, living in rural area, and living in Java Island have significant effect on the chances of becoming NEET

From Local Farms to Supermarket Foods: The Story of the Homogenization of the Argentine Diet Told by the IsotopeRatios of Modern Teeth.

Nutritional changes over the last century, driven by globalization, hypermarketization, and malnutrition, are global in scale. Large countries in the Global South might be resilient to dietary homogenization due to their natural diversity of regions and ecosystems, which might have prevented the adoption of supermarket diets. Argentina has a wide array of ecosystems and historically different subsistence diets dependent on regional characteristics. We analyzed the spatiotemporal variation of stable isotope values in Argentina using modern teeth to test for regional dietary patterns and its consistence over time. We collected teeth from voluntary donors born between 1940 and 2010, from 72 locations across Argentina. A total of 119 teeth were analyzed for the markers δ13Cdentine, δ13Cenamel, δ15N, and δ34S. A reconstruction of isotopic niches was performed to estimate dietary patterns across different regions and time periods. This study is the first to analyze changes in modern dietary patterns in Argentina using isotopic data measured in contemporary teeth. We showed latitudinal, longitudinal, and temporal differences in isotopic values, reflecting the variation in available resources within the country. Changes in the diet were observed over time, including declining δ15N values, a reduction in δ34S range, and a trend toward homogenization of δ13Cenamel values. Conversely, δ13Cdentine values remained constant over time, maintaining latitudinal patterns and regional differences across regions. This study increases our understanding of modern population dietary patterns both spatially and over the last 70 years. Our findings suggest that the Argentine population has shifted toward a supermarket diet in recent years.

Research on Park Landscape Design Strategy Based on Urban Cultural Brand -- A Case Study of Xinyang City

This paper aims to explore the strategy of park landscape design based on urban cultural brand, taking Xinyang City of China as an example to conduct in-depth research. First, the paper analyzes the historical and cultural background, regional characteristics and the importance of Xinyang city brand construction. Through combing the unique natural resources and cultural landscape of Xinyang, it is proposed that the park landscape design should fully integrate the local cultural elements to enhance the cultural identity of the city. Secondly, the paper discusses the core strategy of park landscape design, mainly including the implantation of cultural symbols, and displaying Xinyang's cultural characteristics through landmark buildings and art installations; The protection and promotion of ecological environment, combined with the local natural landscape, to create a harmonious ecological space; The design method of community participation encourages residents to participate in the planning and construction of the park to enhance the sense of community belonging. Finally, through the case analysis, it shows how successful park landscape design can enhance the cultural brand of the city, promote the development of tourism and improve the quality of life of citizens. The research results show that park landscape design based on urban cultural brand can not only enrich the cultural connotation of the city, but also provide new impetus for the sustainable development of the city.

Image segmentation for pest detection of crop leaves by improvement of regional convolutional neural network

Pest detection is important for crop cultivation. Crop leaf is the main place of pest invasion. Current technologies to detect crop pests have constraints, such as low efficiency, storage demands and limited precision. Image segmentation is a fast and efficient computer-aided detection technology. High resolution image capture solidly supports the crucial processes in discerning pests from images. Study of analytical methods help parse information in the images. In this paper, a regional convolutional neural network (R-CNN) architecture is designed in combination with the radial bisymmetric divergence (RBD) method for enhancing the efficiency of image segmentation. As a special application of RBD, the hierarchical mask (HM) is produced to endorse detection and classification of the leaf-dwelling pests, offering enhanced efficiency and reduced storage requirements. Moreover, to deal with some mislabeled data, a threshold variable is introduced to adjust a fault-tolerant mechanism into HM, to generate a novel threshold-based hierarchical mask (TbHM). Consequently, the hierarchical mask R-CNN (HM-R-CNN) model and the threshold-based hierarchical mask R-CNN (TbHM-R-CNN) model are established to detect various types of healthy and pest-invasive crop leaves to select the regional image features that are rich in pest information. Then simple linear iterative clustering (SLIC) method is incorporation to finish the image segmentation for the classification of pest invasion. The models are tuned and optimized, then validated. The most optimized modeling results are from the TbHM-R-CNN model, with the classification accuracy of 96.2%, the recall of 97.5% and the F1 score of 0.982. Additionally, the HM-R-CNN model observed appreciable results second only to the best model. These results indicate that the proposed methodologies are well-suited for training and testing a dataset of plant diseases, offering heightened accuracy in pest classification. This study revealed that the proposed methods significantly outperform the existing techniques, marking a substantial improvement over current methods.

Social Problems of Islam in Modern Indonesia in Muslim Academic Periodicals

The purpose of the article is to analyse the modern Muslim intellectual tradition in Indonesia in the context of analysing the social problems of Islam. The novelty of the study lies in a comprehensive and comparative analysis of the main trends and directions in the development of Muslim social thought in modern Indonesia. Methodologically, the article is based on the principles proposed in intellectual and social history, which allows us to perceive Muslim intellectuals involved in the study of the social problems of the Ummah as an imagined community, and their ideas as invented traditions. The article analyses 1) the theoretical and methodological foundations of modern Indonesian sociology of Islam, 2) national specifics and regional features of the development of Islam in Indonesia in modern Muslim thought, 3) the problems of transformation and contradictions of the radicalization of Islam through the prism of understanding and interpreting this dimension of the modern social history of Islam. It is assumed that Muslim intellectuals make a significant contribution to the consolidation of modern identity by analysing both the social characteristics and characteristics of the Ummah, as well as the threats faced by the Indonesian model of Islam in a secular society in the context of the actualization of the ideas of radical Islam. The author's main conclusions include several points: 1) professional Muslim intellectuals make a major contribution to the development of modern Muslim sociology, 2) specialised journals published by Islamic universities play a leading role in the production and relaying of social knowledge based on Islam, 3) modern Muslim intellectual tradition is the subject to fragmentation, but, in general, the scientific tradition of the Ummah actualizes moderate trends in Islamic Sociology, based on the principles of interdisciplinarity.

Structural topology optimization based on deep learning

In the mechanical design of structures, traditional topology optimization methods involve numerous finite element iterative analyses, leading to a significant expenditure of computational resources. Therefore, the improved multi-scale gradient generative adversarial networks topology optimization technique is proposed. The topology optimization condition parameters are compressed into a low-dimensional latent space feature representation using the encoder, allowing the model to better extract features from these parameters. To speed up model training, the generator and discriminator networks use lightweight residual convolutional blocks. The hybrid attention mechanism extracts prominent region features from the topology optimization structure map. The model training process is guided by a multi-dimensional fusion loss function to enhance the quality of generated model samples. Finally, transferring the parameters of the low-resolution topology optimization model to the high-resolution model enables complete training on a limited amount of high-resolution topology optimization datasets. The experimental data on the low- and high-resolution topology optimization datasets demonstrate that, when compared to alternative methods, this method produces better-quality topology optimization structure maps. Additionally, it can generate high-resolution topology optimization structure maps in minimal time, enabling real-time topology optimization.

Can regionalization enhance the performance of land-use change models in rapidly urbanizing areas?

The performance of Land Use Change (LUC) models is influenced by the regional spatial characteristics that trigger the changes. However, the literature on LUC models generally reports validation results for entire regions without considering subregions that differ significantly in their LUC drivers. This research explores how the LUC driving forces differ among subregions and whether regionalization can improve the performance of LUC models in areas undergoing rapid urbanization. We analyzed the Geomod, Cellular Automata-Markov, and Land Change Modeler models across rural and urbanized subregions on the western edge of Mexico City. Regionalization significantly enhanced the overall accuracy of the models and the concordance of spatial patterns with the reference data in rural regions but was of limited benefit in urbanized regions. This shows the need to consider regionalized modeling to improve the performance of LUC models when there are noticeable differences in LUC drivers between subregions. These findings will enhance the usefulness of LUC models for urban planning and land management policies, promoting more precise and effective decision-making.

Effects of traditional Chinese medicine on outcomes and costs of dementia care: results from a retrospective real-world study

ObjectivesThis study aims to assess the impact of Traditional Chinese Medicine (TCM) on dementia patients, utilizing real-world data. Specifically, it seeks to evaluate how TCM influences clinical outcomes by examining changes in the Clinical Dementia Rating (CDR) and Mini-Mental State Examination (MMSE) scores, as well as its effect on medical expenses over a two-year period. Data from a multi-center research database spanning from 2004 to 2021 will be used to achieve these objectives, addressing the current gap in empirical data concerning intuitive outcomes and cognitive function assessments.MethodsPropensity score matching was adopted to improve comparability among the intervention and control groups. Due to repeated dependent variable measurements, the generalized estimating equation was used to control for socio-demographic characteristics, regional characteristics, and Western medicine treatments for dementia.ResultsAfter propensity score matching, a total of 441 research subjects were included: 90 in the TCM intervention group and 351 in the non-TCM intervention group. The results of multivariate regression analysis showed that compared with the non-TCM intervention group, the MMSE scores in the TCM intervention group increased by 0.608 points each year. The annual change in CDR scores in the TCM intervention group was 0.702 times that of the non-TCM utilization group. After TCM intervention, annual outpatient expenses increased by US$492.2, hospitalization expenses increased by US$324.3, and total medical expenses increased by US$815.9, compared with the non-intervention group.ConclusionsTCM interventions significantly decelerate cognitive decline in dementia patients, evidenced by slower reductions in MMSE scores and mitigated increases in CDR scores. However, these benefits are accompanied by increased medical expenses, particularly for outpatient care. Future healthcare strategies should balance the cognitive benefits of TCM with its economic impact, advocating for its inclusion in dementia care protocols.

Development and activation of the innovative potential of the Russian economy: regional features, structural priorities, levels of implementation

Relevance. The idea of developing and implementing innovative solutions in various spheres of society implies the existence of ways to find such solutions, their active use and improvement. For this reason, the essence of innovative thinking on the scale of both a single enterprise and the state as a whole requires appropriate provision.The purpose of the research is a comprehensive analysis of the chronological cross-section of the level of innovative development of the subjects of the Russian Federation in the context of multiple global economic and political crises and the internal activation of strategic innovative trends in the formats of lawmaking and modernization.Objectives. To achieve this goal, within the framework of this study, the current legislative situation regarding the state's innovation policy and its projected consequences were considered; the factors of growth in the level of innovative development of the regions of the Russian Federation in the context of statistical dominants were studied; the complex results of regional innovation activity and the all-Russian state of the innovative sphere of development, production, export and import of goods and services were analyzed.Methodology. The methodological basis of this study is the method of chronological extrapolation analysis of trends with constant adjustment in conditions of the selective nature of the analytical data bank; in addition, a theoretical generalization of the indexed result and a priori trend forecasting within the observation period are used.Results. The general principles of the innovation segment development are the same for the whole state, but their applicability in different regions depends on the degree of their convergence with border territorial entities — the basis of the innovation development strategy is the assessment of regional potential in the sum of all indicators. Nevertheless, there is always the possibility of finding a thematic statistical trend.Conclusions. It is fundamentally important for the innovative development of the state to take into account the specifics of each individual region. With all the variety of means and methods of innovative development at all levels of management, priority is given to only a few. In this case, rating systems are an auxiliary element of management structures.

Pattern of situation of tuberculosis in the Islamic republic of Afghanistan

Purpose of the study: to study the incidence of tuberculosis depending on gender, age and regional characteristics over the past 6 years (2017-2022) in the Islamic Republic of Afghanistan. Material and methods: Data from the official statistics of the National Tuberculosis Program of Afghanistan, as well as the WHO assessment of the incidence of tuberculosis in the Islamic Republic of Afghanistan over the past 6 years (2017-2022) were analyzed. Results: In the Islamic Republic of Afghanistan, the situation with tuberculosis remains a cause for concern. Over the past 6 years, the difference in the frequency of detection of new cases of tuberculosis between the WHO estimate and official statistics ranges from 26.7% to 36.9%. It should be noted that in 2017-2019, the detection of tuberculosis improved slightly; in 2019-2020, when the COVID-19 pandemic occurred, the detection of new cases of tuberculosis decreased, and in 2020-2022 it increased again.. Over the past 6 years More women than men suffer from tuberculosis. The difference in incidence is 4490 cases in 2017, 7936 in 2018, 10990 in 2019, 4230 in 2020, 12453 in 2021 and 5690 cases in 2022 and is significant. In 2022, as in other years, more (40%) were diagnosed with tuberculosis in young people aged 15-34 years. Moreover, about 11% of new cases (5562 out of 51654 cases) were registered in the age group 0-4 years, which should be paid special attention to. The most serious situation regarding the incidence of tuberculosis is in the Khost, Nangarhar and Nimroz regions, which are 350, 324 and 315 per 100 thousand population, respectively. The best situation for tuberculosis incidence is in the Panjshir, Maidan-Wardak and Bamiyan regions, which are 47, 72 and 83 per 100,000 population, respectively. Conclusions: The difference in the incidence of new cases of tuberculosis between the WHO estimate and official statistics ranges from 26.7% to 36.9%. In 2019-2020, when the COVID-19 pandemic occurred, the number of new cases of tuberculosis decreased, and in 2020-2022 it increased again. Women are more prone to tuberculosis than men. 40% of new cases of tuberculosis are registered among young people aged 15-34 years, about 11% of new cases are registered in the age group 0-4 years. Among the 34 provinces of Afghanistan, the worst situation is in the provinces of Khost, Nangarhar and Nimroz, and the best situation for tuberculosis is in the provinces of Panjshir, Maidan Wardak and Bamiyan.

Shallow Reverse Moderate Earthquakes in the Weiyuan Shale Gas Field, Sichuan Basin, China, Related to Hydraulic Fracturing

Abstract The Weiyuan shale gas field in the stable southern Sichuan basin, China, has experienced increasing seismicity since systematic hydraulic fracturing (HF) operations in 2015. Three Mw≥4.4 shallow earthquakes occurred in the Weiyuan area between September 2019 and February 2020, yet their seismogenic faults, rupture models, and relationship with HF are unknown. In this study, we first obtain the high-resolution coseismic deformation fields of these three events and then invert their slip distribution. The result shows that all three events are shallow high-dip reverse events under the contractional Weiyuan anticline environment with peak slips of 158, 68, and 34 cm and at depths of 4, 3, and 1.6 km, respectively. The spatial relationship between seismogenic faults, horizontal wells, as well as geological data reveals that pore-pressure diffusion due to the HF may be the main mechanism of the 8 September 2019 and the 18 December 2019 events, whereas the 16 February 2020 event may be attributed to the poroelastic stress perturbation caused by the HF. Our study highlights that HF activities and regional geological characteristics jointly influence the properties of earthquakes in the Sichuan basin.

Revamping Image-Recipe Cross-Modal Retrieval with Dual Cross Attention Encoders

The image-recipe cross-modal retrieval task, which retrieves the relevant recipes according to food images and vice versa, is now attracting widespread attention. There are two main challenges for image-recipe cross-modal retrieval task. Firstly, a recipe’s different components (words in a sentence, sentences in an entity, and entities in a recipe) have different weight values. If a recipe’s different components own the same weight, the recipe embeddings cannot pay more attention to the important components. As a result, the important components make less contribution to the retrieval task. Secondly, the food images have obvious properties of locality and only the local food regions matter. There are still difficulties in enhancing the discriminative local region features in the food images. To address these two problems, we propose a novel framework named Dual Cross Attention Encoders for Cross-modal Food Retrieval (DCA-Food). The proposed framework consists of a hierarchical cross attention recipe encoder (HCARE) and a cross attention image encoder (CAIE). HCARE consists of three types of cross attention modules to capture the important words in a sentence, the important sentences in an entity and the important entities in a recipe, respectively. CAIE extracts global and local region features. Then, it calculates cross attention between them to enhance the discriminative local features in the food images. We conduct the ablation studies to validate our design choices. Our proposed approach outperforms the existing approaches by a large margin on the Recipe1M dataset. Specifically, we improve the R@1 performance by +2.7 and +1.9 on the 1k and 10k testing sets, respectively.

Analysis on the Evolution Characteristics of Rural Tourism Public Service System from the Perspective of Digitalization—Empirical Evidence from the Silk Road Economic Belt

The integration of digitalization with public tourism services has emerged as a new model for the development of the rural tourism public services. The Silk Road Economic Belt is a crucial area for the future development of the tourism industry. Exploring the coupling of the digitalization and the development trends of the rural tourism public services is significant for advancing rural tourism in this region. This study applies Complex Adaptive Systems theory and Synergy theory to the development of the digitization of rural tourism public services, clarifying the structure of the digitization of the rural tourism public service system. Based on this, a collaborative development evaluation system for the digitization of rural tourism public services is constructed, focusing on the Silk Road Economic Belt. By using the SMI-P evaluation model, ArcGIS spatial visualization, Gini coefficient, and kernel density methods, the collaborative development of the digitization of the rural tourism public service system was measured and analyzed in this paper. The findings reveal the following: (1) The collaborative development of the digitization of the rural tourism public service system in the Silk Road Economic Belt shows a gradual upward trend, yet the overall level of the collaboration is transitioning from a state of imbalance. (2) Compared to the Southwest, the Northwest exhibits a lower overall level of collaboration, with growing disparities in collaborative development indices among provinces, although there is a trend toward higher levels of aggregation. (3) The development of the rural tourism public services is trending toward a favorable state of multi-dimensional collaboration, characterized by significant regional features, including multi-polarization and unbalanced development. Finally, optimization paths for the digital development of the rural tourism public services were proposed in this study, which can provide guidance for improving the construction of the digitization of the rural tourism public service system and exploring clear development pathways.

The role of geographic distances in green industrial pilot policies evaluation: A sustainability transition perspective.

Green industrial policies (GIPs) aim to promote the adoption ofgreen technology within a sustainability framework. While previous evaluations of GIP have focused more on the policy itself and the impacts within the policy boundaries, this paper further introduces the geographical factor to analyze the impact of different spatial geographic distances on implementing GIP. By integrating geographic distances into a spatial difference-in-differences analysis to assess the effects of municipal green industry policies (referred to as green industry pilot policies in this paper) enacted in 11 cities in China from 2006 to 2010, we find that these policies not only improve environmental and economic outcomes in targeted regions but also have spillover effects that may affect neighboring areas negatively, highlighting the importance of geographic considerations. The effectiveness of green industrial policies varies across cities, influenced by local socio-technical systems and regional characteristics such as infrastructure and information technology. These findings suggest that policy impacts are complex and multifaceted, requiring a comprehensive understanding of geographic interdependencies. By incorporating geographic factors, this research contributes to sustainability transition theory by offering insights into the spatial and temporal dynamics of socio-technical systems. The results underscore the need for policymakers to consider spatial-temporal aspects and potential secondary effects on adjacent regions when designing and implementing green industrial policies.