Pub Date : 2024-11-02DOI: 10.1016/j.apgeog.2024.103457
Rui Ma, Min Zhang
Previous studies on the relationship between green spaces exposure and emotions paid limited attention to populations with varying mental health conditions, especially the pathways of influence on high-risk mental health populations are less clear. The current study explored the differences of pathways among people with varying mental health conditions using cross-sectional data from 859 individuals residing in 9 neighborhoods in Nanjing, China. Evident illustrate that the neighborhood safety associated with green spaces is important across all populations. The relationship between green spaces and emotional benefits is more complex in populations with good mental health Environmental attractiveness in serial mediation model was a crucial factor influencing later mediators. This study validated emotional benefits are more sensitive to buffer size. In future urban planning, coordinated interventions for different mental health populations should be emphasized.
{"title":"Pathways mediating neighborhood green spaces on emotions of populations with different mental health in Nanjing, China","authors":"Rui Ma, Min Zhang","doi":"10.1016/j.apgeog.2024.103457","DOIUrl":"10.1016/j.apgeog.2024.103457","url":null,"abstract":"<div><div>Previous studies on the relationship between green spaces exposure and emotions paid limited attention to populations with varying mental health conditions, especially the pathways of influence on high-risk mental health populations are less clear. The current study explored the differences of pathways among people with varying mental health conditions using cross-sectional data from 859 individuals residing in 9 neighborhoods in Nanjing, China. Evident illustrate that the neighborhood safety associated with green spaces is important across all populations. The relationship between green spaces and emotional benefits is more complex in populations with good mental health Environmental attractiveness in serial mediation model was a crucial factor influencing later mediators. This study validated emotional benefits are more sensitive to buffer size. In future urban planning, coordinated interventions for different mental health populations should be emphasized.</div></div>","PeriodicalId":48396,"journal":{"name":"Applied Geography","volume":"173 ","pages":"Article 103457"},"PeriodicalIF":4.0,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-31DOI: 10.1016/j.apgeog.2024.103458
Xiaochang Liu , Renlu Qiao , Zhiqiang Wu , Tianren Yang , Xiuning Zhang , Xueliang Zhang , Zhiliang Zhu
The relationship between urban built environment (UBE) and housing prices manifests as complex, exhibiting significant nonlinearities and spatial heterogeneity that remain inadequately understood. Taking Shanghai as a testbed, this study employs a novel ensemble learning approach, augmented by Bayesian optimization and Monte Carlo simulation, to decipher the intricate and nonlinear impacts of UBE factors on housing markets across diverse urban geographies. Our analysis unveils substantial spatial variations in how transit accessibility, amenities, residential density, and green/blue spaces influence real estate values. Proximity to metro stations and bike-sharing facilities exerts a more pronounced positive effect than bus stops. Moreover, residents in central areas demonstrate a higher willingness-to-pay for public service amenities, while those in outer suburbs prioritize access to public transportation infrastructure. Intriguingly, it invokes an optimal threshold range of urban density for properties in new cities, thereby increasing the vitality and dense socio-economic networks. Furthermore, the sprawling suburbs have identified an adverse economic impact of large conservation green/blue spaces. These insights can guide policymakers in crafting spatially-tailored strategies that harness localized built environment drivers to catalyse equitable and prosperous urban development. Tailored policies informed by this spatially explicit understanding of nonlinear built environment-housing interactions can foster more sustainable, liveable, and inclusive cities.
{"title":"Unveiling the spatially varied nonlinear effects of urban built environment on housing prices using an interpretable ensemble learning model","authors":"Xiaochang Liu , Renlu Qiao , Zhiqiang Wu , Tianren Yang , Xiuning Zhang , Xueliang Zhang , Zhiliang Zhu","doi":"10.1016/j.apgeog.2024.103458","DOIUrl":"10.1016/j.apgeog.2024.103458","url":null,"abstract":"<div><div>The relationship between urban built environment (UBE) and housing prices manifests as complex, exhibiting significant nonlinearities and spatial heterogeneity that remain inadequately understood. Taking Shanghai as a testbed, this study employs a novel ensemble learning approach, augmented by Bayesian optimization and Monte Carlo simulation, to decipher the intricate and nonlinear impacts of UBE factors on housing markets across diverse urban geographies. Our analysis unveils substantial spatial variations in how transit accessibility, amenities, residential density, and green/blue spaces influence real estate values. Proximity to metro stations and bike-sharing facilities exerts a more pronounced positive effect than bus stops. Moreover, residents in central areas demonstrate a higher willingness-to-pay for public service amenities, while those in outer suburbs prioritize access to public transportation infrastructure. Intriguingly, it invokes an optimal threshold range of urban density for properties in new cities, thereby increasing the vitality and dense socio-economic networks. Furthermore, the sprawling suburbs have identified an adverse economic impact of large conservation green/blue spaces. These insights can guide policymakers in crafting spatially-tailored strategies that harness localized built environment drivers to catalyse equitable and prosperous urban development. Tailored policies informed by this spatially explicit understanding of nonlinear built environment-housing interactions can foster more sustainable, liveable, and inclusive cities.</div></div>","PeriodicalId":48396,"journal":{"name":"Applied Geography","volume":"173 ","pages":"Article 103458"},"PeriodicalIF":4.0,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1016/j.apgeog.2024.103455
Xiaozhen Qin , Eddie Chi-Man Hui , Jianfu Shen
Industry 4.0 technologies have emerged as a focal point of regional technological competition in the Fourth Industrial Revolution. However, research on the emergence and determinants of innovation in Industry 4.0 technologies remains limited, especially in developing countries. Using Chinese patent data from 2001 to 2020, this study identifies the emergence of Industry 4.0 technologies across Chinese cities and explores the driving roles of relatedness/cross-relatedness and industrial policy in this process. Our findings indicate that relatedness/cross-relatedness between new technologies and Industry 4.0, Industry 3.0, and other technologies significantly increase the probability of a city developing a new specialization in Industry 4.0 technologies. The positive impact of relatedness within Industry 4.0 technologies indicates a self-reinforcing characteristic in their development. Furthermore, cross-relatedness between Industry 4.0 and Industry 3.0 technologies only promotes the specialization in Industry 4.0 technologies related to Industry 3.0 and benefits cities with a substantial Industry 3.0 foundation. Industrial policy also plays a crucial role, directly fostering specialization in Industry 4.0 technologies and indirectly enhancing the effects of relatedness/cross-relatedness. This study offers valuable policy insights for developing regional strategies to foster innovation in Industry 4.0 technologies.
{"title":"The emergence of Industry 4.0 technologies across Chinese cities: The roles of technological relatedness/cross-relatedness and industrial policy","authors":"Xiaozhen Qin , Eddie Chi-Man Hui , Jianfu Shen","doi":"10.1016/j.apgeog.2024.103455","DOIUrl":"10.1016/j.apgeog.2024.103455","url":null,"abstract":"<div><div>Industry 4.0 technologies have emerged as a focal point of regional technological competition in the Fourth Industrial Revolution. However, research on the emergence and determinants of innovation in Industry 4.0 technologies remains limited, especially in developing countries. Using Chinese patent data from 2001 to 2020, this study identifies the emergence of Industry 4.0 technologies across Chinese cities and explores the driving roles of relatedness/cross-relatedness and industrial policy in this process. Our findings indicate that relatedness/cross-relatedness between new technologies and Industry 4.0, Industry 3.0, and other technologies significantly increase the probability of a city developing a new specialization in Industry 4.0 technologies. The positive impact of relatedness within Industry 4.0 technologies indicates a self-reinforcing characteristic in their development. Furthermore, cross-relatedness between Industry 4.0 and Industry 3.0 technologies only promotes the specialization in Industry 4.0 technologies related to Industry 3.0 and benefits cities with a substantial Industry 3.0 foundation. Industrial policy also plays a crucial role, directly fostering specialization in Industry 4.0 technologies and indirectly enhancing the effects of relatedness/cross-relatedness. This study offers valuable policy insights for developing regional strategies to foster innovation in Industry 4.0 technologies.</div></div>","PeriodicalId":48396,"journal":{"name":"Applied Geography","volume":"173 ","pages":"Article 103455"},"PeriodicalIF":4.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-28DOI: 10.1016/j.apgeog.2024.103454
Xiaoyang Deng , Zilong Zhao , Luliang Tang , Hong Yang , Yi Yu , Guineng Liao
Cycling is a promising solution to transportation decarbonization and urban sustainability. To enhance the cycling environment, it's essential to assess the user-friendliness of cycling networks. Existing related studies lack large-scale quantitative assessments and typically focus on segments, which are cost-ineffective for extensive spatial coverage and disregard the comprehensive impact of the cycling network system. Therefore, we propose a multi-scale user-friendliness evaluation approach for cycling networks utilizing multi-source geospatial data. First, we construct the cycling network using bike-sharing trajectory data. Then, to assess the user-friendliness of individual cycling segments as transportation spaces, we derive and evaluate six fine-grained quantitative indicators of bike lanes (separation, shade, width, pavement, slope undulation, and connectivity) from streetview images, DEM, and the aforementioned cycling topological network. Thirdly, cycling communities, representing inhabitants' daily living spaces, are detected based on trajectory density that informs cycling patterns, and their user-friendliness is evaluated by combining the performance of cycling segments with the spatial distribution of POI facilities within each community. An empirical study in Xiamen, China demonstrates the effectiveness of the proposed method in pinpointing specific indicators at particular segments/underdeveloped communities, thus guiding investment and optimization of cycling infrastructure to promote greater adoption of cycling as a mode of transportation.
骑自行车是实现交通脱碳和城市可持续发展的一个前景广阔的解决方案。要改善骑行环境，就必须评估骑行网络的用户友好性。现有的相关研究缺乏大规模的定量评估，通常只关注片段，对于大范围的空间覆盖而言成本效益不高，而且忽视了自行车网络系统的综合影响。因此，我们提出了一种利用多源地理空间数据对自行车网络进行多尺度用户友好性评估的方法。首先，我们利用共享单车的轨迹数据构建自行车网络。然后，为了评估单个自行车路段作为交通空间的用户友好性，我们从街景图像、DEM 和上述自行车拓扑网络中得出并评估了自行车道的六个细粒度量化指标（分隔、遮阳、宽度、路面、坡度起伏和连通性）。第三，代表居民日常生活空间的自行车社区是根据轨迹密度检测出来的，轨迹密度为自行车骑行模式提供了信息，通过将自行车路段的性能与每个社区内 POI 设施的空间分布相结合来评估其用户友好性。在中国厦门进行的一项实证研究表明，所提出的方法能够有效地确定特定路段/欠发达社区的具体指标，从而指导自行车基础设施的投资和优化，促进更多的人将自行车作为一种交通方式。
{"title":"A multi-scale user-friendliness evaluation approach on cycling network utilizing multi-source data","authors":"Xiaoyang Deng , Zilong Zhao , Luliang Tang , Hong Yang , Yi Yu , Guineng Liao","doi":"10.1016/j.apgeog.2024.103454","DOIUrl":"10.1016/j.apgeog.2024.103454","url":null,"abstract":"<div><div>Cycling is a promising solution to transportation decarbonization and urban sustainability. To enhance the cycling environment, it's essential to assess the user-friendliness of cycling networks. Existing related studies lack large-scale quantitative assessments and typically focus on segments, which are cost-ineffective for extensive spatial coverage and disregard the comprehensive impact of the cycling network system. Therefore, we propose a multi-scale user-friendliness evaluation approach for cycling networks utilizing multi-source geospatial data. First, we construct the cycling network using bike-sharing trajectory data. Then, to assess the user-friendliness of <strong>individual cycling segments as transportation spaces</strong>, we derive and evaluate six fine-grained quantitative indicators of bike lanes (separation, shade, width, pavement, slope undulation, and connectivity) from streetview images, DEM, and the aforementioned cycling topological network. Thirdly, <strong>cycling communities, representing inhabitants' daily living spaces,</strong> are detected based on trajectory density that informs cycling patterns, and their user-friendliness is evaluated by combining the performance of cycling segments with the spatial distribution of POI facilities within each community. An empirical study in Xiamen, China demonstrates the effectiveness of the proposed method in pinpointing specific indicators at particular segments/underdeveloped communities, thus guiding investment and optimization of cycling infrastructure to promote greater adoption of cycling as a mode of transportation.</div></div>","PeriodicalId":48396,"journal":{"name":"Applied Geography","volume":"173 ","pages":"Article 103454"},"PeriodicalIF":4.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-28DOI: 10.1016/j.apgeog.2024.103453
Jiaomin Wei , Zihan Kan , Mei-Po Kwan , Dong Liu , Lixian Su , Yanyan Chen
Individual movements within transport networks create activity spaces that shape travel communities. However, few studies have examined the spatial structures within bus travel and the associated factors across different geographic areas, which may overlook the underlying travel patterns and characteristics within these communities. Taking one-month bus smart card data in Beijing, China as a case study, we first build spatial interaction networks for older and younger adults, and conduct analysis on various network measures. Then we detect travel communities using Leiden algorithm and further investigate the determinants for bus flows across different communities based on Poisson regression models. The findings indicate that older adults have a shorter peak interval, more localized activity spaces, lower network connectivity, and weaker interaction strength, suggesting limited mobility in bus travel compared to younger adults. The study highlights that travel duration and land use mix are important predictors for both groups regardless of geographic areas, and there are also differences in the factors influencing bus travel across various regional communities. The results of this study could better portray the mobility patterns, travel networks, activity structures, and determinants impacting bus travel flows among older and younger adults, thereby providing nuanced and efficient strategic support for urban transportation development.
{"title":"Uncovering travel communities among older and younger adults using smart card data","authors":"Jiaomin Wei , Zihan Kan , Mei-Po Kwan , Dong Liu , Lixian Su , Yanyan Chen","doi":"10.1016/j.apgeog.2024.103453","DOIUrl":"10.1016/j.apgeog.2024.103453","url":null,"abstract":"<div><div>Individual movements within transport networks create activity spaces that shape travel communities. However, few studies have examined the spatial structures within bus travel and the associated factors across different geographic areas, which may overlook the underlying travel patterns and characteristics within these communities. Taking one-month bus smart card data in Beijing, China as a case study, we first build spatial interaction networks for older and younger adults, and conduct analysis on various network measures. Then we detect travel communities using Leiden algorithm and further investigate the determinants for bus flows across different communities based on Poisson regression models. The findings indicate that older adults have a shorter peak interval, more localized activity spaces, lower network connectivity, and weaker interaction strength, suggesting limited mobility in bus travel compared to younger adults. The study highlights that travel duration and land use mix are important predictors for both groups regardless of geographic areas, and there are also differences in the factors influencing bus travel across various regional communities. The results of this study could better portray the mobility patterns, travel networks, activity structures, and determinants impacting bus travel flows among older and younger adults, thereby providing nuanced and efficient strategic support for urban transportation development.</div></div>","PeriodicalId":48396,"journal":{"name":"Applied Geography","volume":"173 ","pages":"Article 103453"},"PeriodicalIF":4.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-25DOI: 10.1016/j.apgeog.2024.103451
Dong Liu , Zihan Kan , Jinhyung Lee
The chrono-urbanism framework suggests that urban life quality decreases with increased time spent in transportation, particularly motorized modes. The 15-min city concept, aligned with chrono-urbanism, emphasizes the importance of reaching essential urban functions within a short active travel time and has gained global attention. This study aims to develop a 15-min city composite index score (CIS) using the chrono-urbanism framework. The CIS integrates spatial accessibility to six key urban functions, providing a holistic assessment of the 15-min city status in Wuhan, China. Urban function accessibility is computed based on visitor volume inferred from GPS trajectory data using the Bayesian framework. Results reveal CIS hotspots in Hongshan, Wuchang, Jiangan, and Qingshan districts, with Hongshan having the highest concentration. However, Wuhan faces challenges in achieving comprehensive 15-min city status, as hotspots are mainly concentrated in specific areas like university towns and traditional city centers. The proposed assessment approach is applicable to accurately evaluate the 15-min status in other urban contexts using GPS trajectory data. The study's findings can assist policymakers in understanding CIS hotspot distribution and developing future planning policies to enhance the overall 15-min city status.
{"title":"The proposal of a 15-minute city composite index through integrating GPS trajectory data-inferred urban function attraction based on the Bayesian framework","authors":"Dong Liu , Zihan Kan , Jinhyung Lee","doi":"10.1016/j.apgeog.2024.103451","DOIUrl":"10.1016/j.apgeog.2024.103451","url":null,"abstract":"<div><div>The chrono-urbanism framework suggests that urban life quality decreases with increased time spent in transportation, particularly motorized modes. The 15-min city concept, aligned with chrono-urbanism, emphasizes the importance of reaching essential urban functions within a short active travel time and has gained global attention. This study aims to develop a 15-min city composite index score (CIS) using the chrono-urbanism framework. The CIS integrates spatial accessibility to six key urban functions, providing a holistic assessment of the 15-min city status in Wuhan, China. Urban function accessibility is computed based on visitor volume inferred from GPS trajectory data using the Bayesian framework. Results reveal CIS hotspots in Hongshan, Wuchang, Jiangan, and Qingshan districts, with Hongshan having the highest concentration. However, Wuhan faces challenges in achieving comprehensive 15-min city status, as hotspots are mainly concentrated in specific areas like university towns and traditional city centers. The proposed assessment approach is applicable to accurately evaluate the 15-min status in other urban contexts using GPS trajectory data. The study's findings can assist policymakers in understanding CIS hotspot distribution and developing future planning policies to enhance the overall 15-min city status.</div></div>","PeriodicalId":48396,"journal":{"name":"Applied Geography","volume":"173 ","pages":"Article 103451"},"PeriodicalIF":4.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1016/j.apgeog.2024.103439
Jiaxing Xin , Jun Yang , Huisheng Yu , Jiayi Ren , Wenbo Yu , Nan Cong , Xiangming Xiao , Jianhong (Cecilia) Xia , Xueming Li , Zhi Qiao
Global urbanization and climate change have a profound influence on the ecological quality (EQ) of China. In this study, utilizing the Google Earth Engine, we calculated the spatiotemporal heterogeneity of the China Remote Sensing Ecological Index (RSEI) for the period 2001–2020. We analyzed its drivers using land use, socioeconomic, and climate data. According to the results, the national average RSEI values for 2001, 2010, 2016, and 2020 were 0.39, 0.41, 0.46, and 0.45, respectively, and the proportions of the moderate and upper grades were 54 % in 2001, 64 % in 2010, 76 % in 2016, and 73 % in 2020. The RSEI value in the forest cover area was higher than that in the urban built-up and non-vegetation cover area by 0.1–0.2. The correlation coefficients between each variable and RSEI presented a ladder distribution (along the trend distribution of the Huanyong line). Moreover, maximum temperature (Tmmx) consistently contributed the most to RSEI (the contribution rate was between 35 % and 40 %), followed by precipitation accumulation (Pre, the contribution rate was between 18 % and 28 %), and then DEM, GDP, population (PPP), and wind speed (VS), all with relatively lower contributions around 10 %. Furthermore, temperature surpassing 24 °C, precipitation below 90 mm, population exceeding 50, or GDP above 10,000 showed a negative correlation with RSEI. This study analyzed the regional differences in RSEI drivers in different regions of China, providing a reference for local targeted improvement measures.
{"title":"Towards ecological civilization: Spatiotemporal heterogeneity and drivers of ecological quality transitions in China (2001–2020)","authors":"Jiaxing Xin , Jun Yang , Huisheng Yu , Jiayi Ren , Wenbo Yu , Nan Cong , Xiangming Xiao , Jianhong (Cecilia) Xia , Xueming Li , Zhi Qiao","doi":"10.1016/j.apgeog.2024.103439","DOIUrl":"10.1016/j.apgeog.2024.103439","url":null,"abstract":"<div><div>Global urbanization and climate change have a profound influence on the ecological quality (EQ) of China. In this study, utilizing the Google Earth Engine, we calculated the spatiotemporal heterogeneity of the China Remote Sensing Ecological Index (RSEI) for the period 2001–2020. We analyzed its drivers using land use, socioeconomic, and climate data. According to the results, the national average RSEI values for 2001, 2010, 2016, and 2020 were 0.39, 0.41, 0.46, and 0.45, respectively, and the proportions of the moderate and upper grades were 54 % in 2001, 64 % in 2010, 76 % in 2016, and 73 % in 2020. The RSEI value in the forest cover area was higher than that in the urban built-up and non-vegetation cover area by 0.1–0.2. The correlation coefficients between each variable and RSEI presented a ladder distribution (along the trend distribution of the Huanyong line). Moreover, maximum temperature (Tmmx) consistently contributed the most to RSEI (the contribution rate was between 35 % and 40 %), followed by precipitation accumulation (Pre, the contribution rate was between 18 % and 28 %), and then DEM, GDP, population (PPP), and wind speed (VS), all with relatively lower contributions around 10 %. Furthermore, temperature surpassing 24 °C, precipitation below 90 mm, population exceeding 50, or GDP above 10,000 showed a negative correlation with RSEI. This study analyzed the regional differences in RSEI drivers in different regions of China, providing a reference for local targeted improvement measures.</div></div>","PeriodicalId":48396,"journal":{"name":"Applied Geography","volume":"173 ","pages":"Article 103439"},"PeriodicalIF":4.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1016/j.apgeog.2024.103448
Xiaoyuan Zhang , Minrui Zheng , Peipei Wang , Yin Ma , Xinqi Zheng
The COVID-19 pandemic has fundamentally reshaped global socio-economic structures, precipitating a profound transformation in people's lifestyles. An in-depth analysis of the disruptions in post-pandemic population returns patterns and the evolving driving factors can facilitate socio-economic recovery, development, and macro-control. This paper employs social network analysis to examine the spatial patterns and evolving urban locational attributes of the Population Mobility Network (PMN) during post-holiday returns in major Chinese cities amid the pandemic, assessing the impact of varying proximities on the PMN. The results indicate that: (1) During the pandemic, the geographic patterns of population mobility in China underwent significant fluctuations, with distinct regional and temporal variations, while intercity connections increasingly shifted toward shorter trips. (2) From 2019 to 2022, major Chinese cities experienced notable changes in status and connectivity, evolving into a multi-centric structure within the national population mobility network. (3) Community clusters predominantly adhere to provincial demarcations, maintaining stable structures within major urban agglomerations despite intense intra-regional competition, with clear distinctions in community stability between northern and southern regions. (4) Beyond traditional socio-economic factors, the level of digital finance has emerged as a new driver of population mobility, with the varied attributes of inter-city relationships also significantly influencing the PMN. Studying spatial patterns and urban location from the perspective of population mobility can inform post-pandemic optimization of national resource allocation and urban recovery strategies.
{"title":"Analyzing disruptions in post-pandemic population return patterns: A network perspective on Chinese cities","authors":"Xiaoyuan Zhang , Minrui Zheng , Peipei Wang , Yin Ma , Xinqi Zheng","doi":"10.1016/j.apgeog.2024.103448","DOIUrl":"10.1016/j.apgeog.2024.103448","url":null,"abstract":"<div><div>The COVID-19 pandemic has fundamentally reshaped global socio-economic structures, precipitating a profound transformation in people's lifestyles. An in-depth analysis of the disruptions in post-pandemic population returns patterns and the evolving driving factors can facilitate socio-economic recovery, development, and macro-control. This paper employs social network analysis to examine the spatial patterns and evolving urban locational attributes of the Population Mobility Network (PMN) during post-holiday returns in major Chinese cities amid the pandemic, assessing the impact of varying proximities on the PMN. The results indicate that: (1) During the pandemic, the geographic patterns of population mobility in China underwent significant fluctuations, with distinct regional and temporal variations, while intercity connections increasingly shifted toward shorter trips. (2) From 2019 to 2022, major Chinese cities experienced notable changes in status and connectivity, evolving into a multi-centric structure within the national population mobility network. (3) Community clusters predominantly adhere to provincial demarcations, maintaining stable structures within major urban agglomerations despite intense intra-regional competition, with clear distinctions in community stability between northern and southern regions. (4) Beyond traditional socio-economic factors, the level of digital finance has emerged as a new driver of population mobility, with the varied attributes of inter-city relationships also significantly influencing the PMN. Studying spatial patterns and urban location from the perspective of population mobility can inform post-pandemic optimization of national resource allocation and urban recovery strategies.</div></div>","PeriodicalId":48396,"journal":{"name":"Applied Geography","volume":"173 ","pages":"Article 103448"},"PeriodicalIF":4.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1016/j.apgeog.2024.103452
Jing Shen , Lu Cui , Yehua Dennis Wei , Shaogu Wang
This study analyses regional industrial sustainability transitions from global and local perspectives. We measure industrial eco-efficiency (IEE) of 280 cities in China from 2010 to 2020 and analyze potential influencing factors using geographical and temporal weighted regression model. We find that foreign investment and international trade are positively associated with IEE with diminishing effects, and then local household consumption enhances IEE on the basis of local environmental governance. The effects of international environmental regulations and technology spillover on IEE are positive, as they align with China's drive for innovation. We further categorize cities into five distinct types based on the influences of both global and local factors—dominated by local environmental regulations, driven by international standards, propelled by foreign investment, shaped by local market dynamics, and affected by multiple factors. We conclude that regional industrial sustainability transitions in China are influenced by forces operating at various scales and exhibit distinct spatiotemporal variation. The drivers of these transitions have shifted from a global scale to a local scale, and both regional conditions and the stage of industrialization significantly affect the intensity of actions undertaken by different scale factors. Finally, we propose policy recommendations, considering regional heterogeneity and the stages of industrialization.
{"title":"Domesticating sustainability transitions: Spatial and temporal variation of industrial eco-efficiency in China","authors":"Jing Shen , Lu Cui , Yehua Dennis Wei , Shaogu Wang","doi":"10.1016/j.apgeog.2024.103452","DOIUrl":"10.1016/j.apgeog.2024.103452","url":null,"abstract":"<div><div>This study analyses regional industrial sustainability transitions from global and local perspectives. We measure industrial eco-efficiency (IEE) of 280 cities in China from 2010 to 2020 and analyze potential influencing factors using geographical and temporal weighted regression model. We find that foreign investment and international trade are positively associated with IEE with diminishing effects, and then local household consumption enhances IEE on the basis of local environmental governance. The effects of international environmental regulations and technology spillover on IEE are positive, as they align with China's drive for innovation. We further categorize cities into five distinct types based on the influences of both global and local factors—dominated by local environmental regulations, driven by international standards, propelled by foreign investment, shaped by local market dynamics, and affected by multiple factors. We conclude that regional industrial sustainability transitions in China are influenced by forces operating at various scales and exhibit distinct spatiotemporal variation. The drivers of these transitions have shifted from a global scale to a local scale, and both regional conditions and the stage of industrialization significantly affect the intensity of actions undertaken by different scale factors. Finally, we propose policy recommendations, considering regional heterogeneity and the stages of industrialization.</div></div>","PeriodicalId":48396,"journal":{"name":"Applied Geography","volume":"173 ","pages":"Article 103452"},"PeriodicalIF":4.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-22DOI: 10.1016/j.apgeog.2024.103449
Xiaohan Zhong, Yingcheng Li
With the rapid development of knowledge economy, much attention has been paid to establishing university-based innovation zones, which are often believed to facilitate knowledge spillovers from universities to enterprises. However, relatively little is known about the impact of micro-geographical proximity between universities and firms on knowledge spillovers. This paper employs university relocation as a quasi-natural experiment to explore the effective scale and the causal impact of geographical proximity on knowledge spillovers of 98 Chinese universities, as represented by patent citations. Empirical results, based on difference-in-difference strategy and an instrumental variable approach, suggest that university relocation significantly promotes knowledge spillovers to both local and non-local firms. Moreover, universities that have established their new campuses in areas with more local enterprises tend to generate more knowledge spillovers after relocation. The effect of micro-geographical proximity is further verified through a comparative analysis of two specific cases.
{"title":"Does micro-geographical proximity matter for knowledge spillovers? Evidence from the quasi-natural experiment of university relocation in China","authors":"Xiaohan Zhong, Yingcheng Li","doi":"10.1016/j.apgeog.2024.103449","DOIUrl":"10.1016/j.apgeog.2024.103449","url":null,"abstract":"<div><div>With the rapid development of knowledge economy, much attention has been paid to establishing university-based innovation zones, which are often believed to facilitate knowledge spillovers from universities to enterprises. However, relatively little is known about the impact of micro-geographical proximity between universities and firms on knowledge spillovers. This paper employs university relocation as a quasi-natural experiment to explore the effective scale and the causal impact of geographical proximity on knowledge spillovers of 98 Chinese universities, as represented by patent citations. Empirical results, based on difference-in-difference strategy and an instrumental variable approach, suggest that university relocation significantly promotes knowledge spillovers to both local and non-local firms. Moreover, universities that have established their new campuses in areas with more local enterprises tend to generate more knowledge spillovers after relocation. The effect of micro-geographical proximity is further verified through a comparative analysis of two specific cases.</div></div>","PeriodicalId":48396,"journal":{"name":"Applied Geography","volume":"173 ","pages":"Article 103449"},"PeriodicalIF":4.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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