Geographic variations of ecosystem service intensity in Fuzhou City, China

Hu, Xisheng; Hong, Wei; Qiu, Rongzu; Hong, Tao; Chen, Can; Wu, Chengzhen

doi:10.1016/j.scitotenv.2015.01.035

Cited by 85 publications

(28 citation statements)

References 55 publications

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“…The relationships between ecosystem services and urbanization have recently attracted extensive attention; however, different urbanization indices reflected different aspects of social-economic development and caused different impacts on ecosystem services by region [16][17][18][19][20][21][22]. Our results revealed that three urbanization indices, i.e., economic urbanization, population urbanization, and land urbanization, had significantly negative relationships with TESV in Jiangsu (Table 5), which is consistent with the results of other previous work [4,7,21,28].…”

Section: The Relationships Between Tesv and Urbanization Indices In Jsupporting

confidence: 91%

Relationships between Ecosystem Services and Urbanization in Jiangsu Province, Eastern China

Wang

Zhang

et al. 2019

Sustainability

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Ecosystem services are comprehensive and quantitative indicators for describing ecosystem–human interactions. China has experienced rapid urbanization in the past 30 years, which has created a significant impact on regional ecosystem services. However, whether the impact is linear is not clear as yet. In this study, the Jiangsu province, a main body of the Chinese Yangtze River Delta city cluster, was chosen as a case study. Multi-source remotely-sensed geospatial data, including meteorological, land use, vegetation, and socio-economic data, were collected to estimate the total amount of ecosystem services (TESV) and urbanization levels. Subsequently, the relationships between TESV and urbanization indices (i.e., gross domestic product (GDP) per unit area, GPUA; population per unit area, PPUA; and built-up land proportion, BULP) were determined using the Pearson correlation analysis and piecewise linear regression. The primary findings of this study were as follows: (1) There was a distinct spatial pattern in TESV, which gradually increased from west to east with high-value areas located in eastern coastal areas of Jiangsu. Among different land use types, cropland and woodland contributed the most to TESV; (2) The three urbanization indices had spatial patterns, indicating higher urbanization in southern Jiangsu than in central or northern Jiangsu; and (3) Once GPUA and PPUA exceeded threshold values of 3719.55 × 104 yuan/km2 and 744.37 person/km2, respectively, TESV sharply decreased with an increase in these indices. However, the BULP showed a linear and significantly negative relationship with TESV at all values, which indicated that the impacts of economic and population growth on TESV lagged behind that of built-up land expansion. These findings provide a potentially significant reference for decision-makers to rationally enhance regional ecosystem services during rapid urbanization processes.

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Section: The Relationships Between Tesv and Urbanization Indices In Jsupporting

confidence: 91%

Relationships between Ecosystem Services and Urbanization in Jiangsu Province, Eastern China

Wang

Zhang

et al. 2019

Sustainability

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“…Both the global spatial autocorrelation (i.e., global Moran's I) and the local spatial autocorrelation (i.e., the local indicator of spatial association (LISA)) were calculated using the ArcGIS 10.0 program in this study. When executing the program, the method of inverse distance weighting was selected to conceptualize the spatial relationships between neighbors, and the default neighborhood sizes were adopted [53,54]. The reason for choosing inverse distance weighting is based on the first law of geography [55], which mandates that "everything is related to everything else, but near things are more related than distant things".…”

Section: Spatial Autocorrelation Analysismentioning

confidence: 99%

Spatial Paradigms in Road Networks and Their Delimitation of Urban Boundaries Based on KDE

Lin

et al. 2020

IJGI

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An in-depth analysis of urban road network distribution plays a critical role in understanding the urbanization process. However, effective ways to quantitatively analyze the spatial paradigms of road networks are still lacking, and few studies have utilized road networks to rapidly identify urban areas of a region. Thus, using a fast-developing region in the south-eastern costal region of China, Fuzhou City, as a case, we introduced kernel density estimation (KDE) to characterize road networks and quantified the area’s spatial heterogeneity using exploratory spatial data analysis (ESDA) and semivariance analysis (SA). The results show that there is an uneven spatial distribution of the networks both at the regional and downtown levels. At the regional level, there is a conspicuous polarization in the road distribution, with the KDE being much higher in the urban areas than in the rural areas; at the downtown level, the KDE gradually decreases from the center to the periphery. Quantitatively, the ranges of the spatial dependence of the networks are approximately 25 km for the entire study region and 12 km for the downtown area. Additionally, the spatial variations vary among different directions, with greater variations in the northeast–southwest and the southeast–northwest directions compared with the other directions, which is in line with the urban sprawl policy of the study area. Both the qualitative and quantitative results show that the distribution of road networks has a clear urban–rural dual structure, which indicates that road networks can be an active tool in identifying the urban areas of a region. To this end, we propose a quick and easy method to delimit urban areas using KDE. The extraction results of KDE are better than those of the index-based built-up index (IBI), indicating the effectivity and feasibility of our proposed method to identify the urban areas in the region. This research sheds new light on urbanization development research.

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“…Xu et al used ESDA to reveal the spatial-temporal evolution, spatial patterns, and spatial agglomeration characteristics of energy consumption [20]. Hu presented a spatial explicit and quantitative assessment of the geographic variation in ecosystems using ESDA and semivariance analysis [21]. Liu applied ESDA and the spatial Durbin model to analyze the distribution and variation of different natural and anthropogenic factors influencing the air quality of 289 prefecture-level cities in 2014 [22].…”

Section: Introductionmentioning

confidence: 99%

A Spatial-Temporal Approach to Evaluate the Dynamic Evolution of Green Growth in China

Guo

et al. 2018

Sustainability

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Green growth, a new growth mode to tackle resource and environment crises, is imperative in light of current environmental crises and resource depletion. Most evaluation strategies of green growth, while emphasizing the time dimension, ignore the spatial association and diffusion. This study provides a measurement framework of green growth with which to select a set of 18 indicators and evaluates the efficiency of green growth in different regions. The coefficient of variation and the regional Gini coefficient were applied to analyze the spatial variation of green growth. The Exploratory Spatial Data Analysis (ESDA) was used to identify the evolution of the geographical agglomeration in 30 administrative regions in mainland China. The results show that China's green growth capacity is constantly improving, and the gap between regions in this respect is shrinking. The spatial evolution trend of green growth is expanding horizontally from the East to the West and vertically from the central to the southwest and the northwest regions. Green growth in the eastern and central regions is active but poor in the northeast region. Compared with the continuous stability of the 22 provinces, eight provinces exhibited spatial activity and growth spillover, which affected the adjacent regions. Promoting the outflow of capital and technology is key to increasing green growth in the eastern and central region, while increasing investment and introducing technology through policy advantages to promote industrial transformation is an urgent task for the northeast region.

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Geographic variations of ecosystem service intensity in Fuzhou City, China

Cited by 85 publications

References 55 publications

Relationships between Ecosystem Services and Urbanization in Jiangsu Province, Eastern China

Relationships between Ecosystem Services and Urbanization in Jiangsu Province, Eastern China

Spatial Paradigms in Road Networks and Their Delimitation of Urban Boundaries Based on KDE

A Spatial-Temporal Approach to Evaluate the Dynamic Evolution of Green Growth in China

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