scholarly journals Spatial-Temporal Pattern and Evolution Trend of the Cultivated Land Use Eco-Efficiency in the National Pilot Zone for Ecological Conservation in China

2021 ◽  
Vol 19 (1) ◽  
pp. 111
Author(s):  
Zhenggen Fan ◽  
Chao Deng ◽  
Yuqi Fan ◽  
Puwei Zhang ◽  
Hua Lu
Keyword(s):  
Land Use ◽  
Spatial Patterns ◽  
Temporal Pattern ◽  
Spillover Effect ◽  
Cultivated Land ◽  
Ecological Civilization ◽  
Spatial Spillover ◽  
Ecological Conservation ◽  
Evolution Trend ◽  
Spatial Spillover Effect

The cultivated land use eco-efficiency (CLUE) is an important indicator to evaluate ecological civilization construction in China. Research on the spatial-temporal pattern and evolution trend of the CLUE can help to assess the level of ecological civilization construction and reveal associated demonstration and driving effects on surrounding areas. Based on the perspective of the CLUE, this paper obtains cultivated land use data pertaining to National Pilot Zones for Ecological Conservation in China and neighboring provinces from 2008 to 2018. In this study, the SBM-undesirable, Moran’s I, and Markov chain models are adopted to quantitatively measure and analyze the CLUE and its temporal and spatial patterns and evolution trend. The research results indicate that the CLUE in the whole study area exhibited the characteristics of one growth, two stable, and two decline stages, with a positive spatial autocorrelation that increased year by year, and a spatial spillover effect was observed. Geographical spatial patterns and spatial spillover effects played a major role in the evolution of the CLUE, and there occurred a higher probability of improvement in the vicinity of cities with high CLUE values. In the future, practical construction experience should be disseminated at the provincial level, and policies and measures should be formulated according to local conditions. In addition, a linkage model between prefecture-level cities should be developed at the municipal level to fully manifest the positive spatial spillover effect. Moreover, we should thoroughly evaluate the risk associated with CLUE transition from high to low levels and establish a low-level early warning mechanism.

scholarly journals Effect of Complex Road Networks on Intensive Land Use in China’s Beijing-Tianjin-Hebei Urban Agglomeration

Land ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 532
Author(s):  
Chen Zeng ◽  
Zhe Zhao ◽  
Cheng Wen ◽  
Jing Yang ◽  
Tianyu Lv
Keyword(s):  
Land Use ◽  
Road Network ◽  
Spillover Effect ◽  
Urban Agglomeration ◽  
Land Use Intensity ◽  
Spatial Econometric ◽  
Spatial Spillover ◽  
The Road ◽  
Intensive Land Use ◽  
Spatial Spillover Effect

Coupled with rapid urbanization and urban expansion, the spatial relationship between transportation development and land use has gained growing interest among researchers and policy makers. In this paper, a complex network model and land use intensity assessment were integrated into a spatial econometric model to explore the spatial spillover effect of the road network on intensive land use patterns in China’s Beijing–Tianjin–Hebei (BTH) urban agglomeration. First, population density, point of interest (POI) density, and aggregation index were selected to measure land use intensity from social, physical, and ecological aspects. Then, the indicator of average degree (i.e., connections between counties) was used to measure the characteristics of the road network. Under the hypothesis that the road network functions in shaping land use patterns, a spatial econometric model with the road network embedded spatial weight matrix was established. Our results revealed that, while the land use intensity in the BTH urban agglomeration increased from 2010 to 2015, the road network became increasingly complex with greater spatial heterogeneity. The spatial lag coefficients of land use intensity were positively significant in both years and showed a declining trend. The spatially lagged effects of sector structure, fixed asset investment, and consumption were also significant in most of our spatial econometric models, and their contributions to the total spillover effect increased from 2010 to 2015. This study contributes to the literature by providing an innovative quantitative method to analyze the spatial spillover effect of the road network on intensive land use. We suggest that the spatial spillover effect of the road network could be strengthened in the urban–rural interface areas by improving accessibility and promoting population, resource, and technology flows.

scholarly journals The Spatial Effect of Administrative Division on Land-Use Intensity

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 543
Author(s):  
Pengrui Wang ◽  
Chen Zeng ◽  
Yan Song ◽  
Long Guo ◽  
Wenping Liu ◽  
...  
Keyword(s):  
Land Use ◽  
Spillover Effect ◽  
County Level ◽  
Land Use Intensity ◽  
Spatial Interactions ◽  
Spatial Spillover ◽  
Administrative Division ◽  
Administrative Units ◽  
Relationship Of ◽  
Spatial Spillover Effect

Land-use intensity (LUI) is one of the most direct manifestations of regional land use efficiency. The study of cross-administrative LUI in urban agglomerations is of great importance for the sustainable development of land, new urbanization, and territorial spatial planning. In this study, the urban agglomeration in the middle reaches of the Yangtze River in China was used as the case study area to explore the spatial spillover effect through the administrative division, underlying driving mechanism, and spatial interactions or constraints of LUI. First, LUI was measured using the index of the proportion of construction land to the total area of the administrative region. Second, the adjacency relationship of the county-level administrative units was identified on the basis of the queen-type adjacency criterion under the county-level administrative division system. Thereafter, spatial weight matrix for spatial modeling was constructed. Last, a spatial model using the “Spatial adjacency matrix” was devised to examine the influencing factors and the potential spatial interactions or constraints of administrative units. Results revealed that the level of LUI of different county-level administrative units were quite different, and the gap of LUI among county-level administrative units widened from 2010 to 2017. The fixed asset investment per land (FAIL), gross domestic product per capital (PGDP), and proportion of tertiary sector (PTS) are the driving factors of LUI. County-level administrative units not only had a significant and increasing spatial interaction effect based on the relationship of cooperation, but also had an influence of restraint mutually which was caused by the competition. The direct spatial spillover effect was remarkable. In the future, the effect of interaction among administrative units under the administrative division should be considered to promote the reasonable use and optimal layout of regional urban land to realize the optimal allocation of land resources.

Effects of tourism development on economic growth: An empirical study of China based on both static and dynamic spatial Durbin models

2021 ◽  
pp. 135481662110211
Author(s):  
Honghong Liu ◽  
Ye Xiao ◽  
Bin Wang ◽  
Dianting Wu
Keyword(s):  
Economic Growth ◽  
Spillover Effect ◽  
Tourism Development ◽  
Spillover Effects ◽  
Policy Implications ◽  
Long Term Effects ◽  
Spatial Spillover ◽  
Domestic Tourism ◽  
Inbound Tourism ◽  
Spatial Spillover Effect

This study applies the dynamic spatial Durbin model (SDM) to explore the direct and spillover effects of tourism development on economic growth from the perspective of domestic and inbound tourism. The results are compared with those from the static SDM. The results support the tourism-led-economic-growth hypothesis in China. Specifically, domestic tourism and inbound tourism play a significant role in stimulating local economic growth. However, the spatial spillover effect is limited to domestic tourism, and the spatial spillover effect of inbound tourism is not significant. Furthermore, the long-term effects are much greater than the short-term impact for both domestic and inbound tourism. Plausible explanations of these results are provided and policy implications are drawn.

scholarly journals Spatial Spillover Effect and Influencing Factors of Information Flow in Urban Agglomerations—Case Study of China Based on Baidu Search Index

2021 ◽  
Vol 13 (14) ◽  
pp. 8032
Author(s):  
Chengzhuo Wu ◽  
Li Zhuo ◽  
Zhuo Chen ◽  
Haiyan Tao
Keyword(s):  
Influencing Factors ◽  
Information Flow ◽  
Spillover Effect ◽  
Spillover Effects ◽  
Urban Agglomeration ◽  
Coordinated Development ◽  
Spatial Spillover ◽  
Urban Agglomerations ◽  
Search Index ◽  
Spatial Spillover Effect

Cities in an urban agglomeration closely interact with each other through various flows. Information flow, as one of the important forms of urban interactions, is now increasingly indispensable with the fast development of informatics technology. Thanks to its timely, convenient, and spatially unconstrained transmission ability, information flow has obvious spillover effects, which may strengthen urban interaction and further promote urban coordinated development. Therefore, it is crucial to quantify the spatial spillover effect and influencing factors of information flows, especially at the urban agglomeration scale. However, the academic research on this topic is insufficient. We, therefore, developed a spatial interaction model of information flow (SIM-IF) based on the Baidu Search Index and used it to analyze the spillover effects and influencing factors of information flow in the three major urban agglomerations in China, namely Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) in the period of 2014–2019. The results showed that the SIM-IF performed well in all three agglomerations. Quantitative analysis indicated that the BTH had the strongest spillover effect of information flow, followed by the YRD and the PRD. It was also found that the hierarchy of cities had the greatest impact on the spillover effects of information flow. This study may provide scientific basis for the information flow construction in urban agglomerations and benefit the coordinated development of cities.

Spatial Patterns of Urban Innovation and Their Evolution from Perspectives of Capacity and Structure: Taking Shenzhen as an Example

2021 ◽  
Vol 11 (1) ◽  
pp. 7
Author(s):  
Erjie Hu ◽  
Di Hu ◽  
Handong He
Keyword(s):  
Spatial Distribution ◽  
Spatial Pattern ◽  
Distribution Pattern ◽  
Spatial Patterns ◽  
Spillover Effect ◽  
Tertiary Industry ◽  
Innovation Capacity ◽  
Structure Index ◽  
Secondary Industry ◽  
Spatial Spillover Effect

Innovation is a key factor for a country’s overall national strength and core competitiveness. The spatial pattern of innovation reflects the regional differences of innovation development, which can provide guidance for the regional allocation of innovation resources. Most studies on the spatial pattern of innovation are at urban and above spatial scale, but studies at urban internal scale are insufficient. The precision and index of the spatial pattern of innovation in the city needs to be improved. This study proposes to divide spatial units based on geographic coordinates of patents, designs the innovation capability and innovation structure index of a spatial unit and their calculation methods, and then reveals the spatial patterns of innovation and their evolutionary characteristics in Shenzhen during 2000–2018. The results show that: (1) The pattern of innovation capacity of secondary industry exhibited a pronounced spatial spillover effect with a positive spatial correlation. The innovation capacity and innovation structure index of the secondary industry evolved in a similar manner; i.e., they gradually extended from the southwest area to the north over time, forming a tree-like distribution pattern with the central part of the southwest area as the “root” and the northwest and northeast areas as the “canopy”. (2) The pattern of innovation capacity of tertiary industry also had a significant spatial spillover effect with a positive spatial correlation. There were differences between the evolutions of innovation capacity and innovation structure index of tertiary industry. Specifically, its innovation capacity presented a triangular spatial distribution pattern with three groups in the central and eastern parts of the southwest area and the south-eastern part of the northwest area as the vertices, while its innovative structure showed a radial spatial distribution pattern with the southwestern part of the southwest area as the source and a gradually sparse distribution toward the northeast. (3) There were differences between the evolution modes of secondary and tertiary industries. Areas with high innovation capacity in the secondary industry tended to be more balanced, while areas with high innovation capacity in the tertiary industry did not necessarily have a balanced innovation structure. Through the method designed in this paper, the spatial pattern of urban innovation can be more precise and comprehensive revealed, and provide useful references for the development of urban innovation.

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