scholarly journals Spatio-temporal variations of ecosystem services in the urban agglomerations in the middle reaches of the Yangtze River, China

2020 ◽  
Vol 115 ◽  
pp. 106394 ◽  
Author(s):  
Xin Dai ◽  
Lunche Wang ◽  
Chunbo Huang ◽  
Lulu Fang ◽  
Shaoqiang Wang ◽  
...  
Keyword(s):  
Ecosystem Services ◽  
Yangtze River ◽  
Temporal Variations ◽  
The Yangtze River ◽  
Urban Agglomerations ◽  
Spatio Temporal

scholarly journals Assessing Ecosystem Services Supply-Demand (Mis)Matches for Differential City Management in the Yangtze River Delta Urban Agglomeration

2021 ◽  
Vol 18 (15) ◽  
pp. 8130
Author(s):  
Wenbo Cai ◽  
Wei Jiang ◽  
Hongyu Du ◽  
Ruishan Chen ◽  
Yongli Cai
Keyword(s):  
Ecosystem Services ◽  
Yangtze River ◽  
Yangtze River Delta ◽  
River Delta ◽  
Urban Agglomeration ◽  
City Management ◽  
The Yangtze River ◽  
Social Demand ◽  
Urban Agglomerations ◽  
The Yangtze River Delta

With the global increase in population and urban expansion, the simultaneous rise of social demand and degradation of ecosystems is omnipresent, especially in the urban agglomerations of China. In order to manage environmental problems and match ecosystem supply and social demand, these urban agglomerations promoted regional socio-ecological integration but ignored differential city management during the process of integration. Therefore, it is necessary to design a general framework linking ecosystem supply and social demand to differential city management. In addition, in previous studies, ecosystem services supply–demand amount (mis)match assessment was emphasized, but ecosystem services supply–demand type (mis)match assessment was ignored, which may lead to biased decisions. To deal with these problems, this study presented a general ecosystem services framework with six core steps for differential city management and developed a double-indices (amount and type) method to identify ecosystem services supply–demand (mis)matches in an urban agglomeration. This framework and the double-indices method were applied in the case study of the Yangtze River Delta Urban Agglomeration. Ecosystem supply–demand amount and type (mis)match levels and spatial pattern of twenty-six cities were identified. Twenty-six cities in the YRDUA were classified into five kinds of cities with different levels of ES supply–demand (mis)matches for RS, three kinds of cities for PS, and four kinds of cities for CS. Differential city management strategies were designed. Despite its limitations, this study can be a reference to giving insights into ES supply–demand (mis)match assessment and management.

scholarly journals Spatio-Temporal Patterns and Impacts of Sediment Variations in Downstream of the Three Gorges Dam on the Yangtze River, China

2018 ◽  
Vol 10 (11) ◽  
pp. 4093 ◽  
Author(s):  
Jilong Chen ◽  
Xinrui Fang ◽  
Zhaofei Wen ◽  
Qiao Chen ◽  
Maohua Ma ◽  
...  
Keyword(s):  
Suspended Sediment ◽  
Yangtze River ◽  
Temporal Variations ◽  
Three Gorges Dam ◽  
Three Gorges ◽  
The Yangtze River ◽  
The Three Gorges ◽  
Spatio Temporal ◽  
The Government ◽  
The Three Gorges Dam

Spanning the Yangtze River of China, the Three Gorges Dam (TGD) has received considerable concern worldwide with its potential impacts on the downstream side of the dam. This work investigated the spatio-temporal variations of suspended sediment concentration (SSC) at the downstream section of Yichang-to-Chenglingji from 2002 to 2015. A random forest model was developed to estimate SSC using MODIS ground reflectance products, and the spatio-temporal distributions of SSC were retrieved with this model to investigate the characteristics of water-silt variation. Our results revealed that, relatively, SSC before 2003 was evenly distributed in the downstream Yangtze River, while this spatial distribution pattern changed ce 2003 when the dam started storing water. Temporally, the SSC demonstrated a W-shaped curve of seasonal variation as one peak occurred in September and two troughs in March and November, and showed a significantly decreasing trend after three-stage impoundment. After official operation of the TGD in 2009, the SSC was reduced by over 40% than before 2003. Spatially, the most significant changes occurred in the upper Jingjiang section, where the SSC dropped by 45%. During all stages of impoundment, the water impoundment to 135 m in 2003 had the most significant impact on suspended sediment. The decreased SSC has led to emerging risks of bank failure, aggravated erosion of water front and aggressive down-cutting erosion along the downstream of the dam, as well as other ecological and environmental issues that require urgent attention by the government.

scholarly journals Ecosystem Services and Their Driving Forces in the Middle Reaches of the Yangtze River Urban Agglomerations, China

2020 ◽  
Vol 17 (10) ◽  
pp. 3717
Author(s):  
Wanxu Chen ◽  
Guangqing Chi ◽  
Jiangfeng Li
Keyword(s):  
Ecosystem Services ◽  
Yangtze River ◽  
Driving Forces ◽  
Spatial Regression ◽  
Spillover Effect ◽  
Spillover Effects ◽  
The Yangtze River ◽  
Road Density ◽  
Urban Agglomerations ◽  
The Impact

The impact of human activities on ecosystems can be measured by ecosystem services. The study of ecosystem services is an essential part of coupled human and natural systems. However, there is limited understanding about the driving forces of ecosystem services, especially from a spatial perspective. This study attempts to fill the gap by examining the driving forces of ecosystem services with an integrated spatial approach. The results indicate that more than US$430 billion of ecosystem services value (ESV) is produced annually in the Middle Reaches of the Yangtze River Urban Agglomerations (MRYRUA), with forestland providing the largest proportion of total ESV (≥75%) and hydrological regulation function accounting for the largest proportion of total ESV (≥15%). The average ESV in the surrounding areas is obviously higher than those in the metropolitan areas, in the plains areas, and along major traffic routes. Spatial dependence and spatial spillover effects were observed in the ecosystem services in the MRYRUA. Spatial regression results indicate that road density, proportion of developed land, and river density are negatively associated with ecosystem services, while distance to a socioeconomic center, proportion of forestland land, elevation, and precipitation are positively associated with ecosystem services. The findings in this study suggest that these driving factors and the spillover effect should be taken into consideration in ecosystem protection and land-use policymaking in urban agglomerations.

scholarly journals Spatio-Temporal Coupling Characteristics and the Driving Mechanism of Population-Land-Industry Urbanization in the Yangtze River Economic Belt

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 400
Author(s):  
Liejia Huang ◽  
Peng Yang ◽  
Boqing Zhang ◽  
Weiyan Hu
Keyword(s):  
Economic Development ◽  
Spatial Correlation ◽  
Yangtze River ◽  
Three Dimensional ◽  
Driving Mechanism ◽  
The Yangtze River ◽  
Central Cities ◽  
Urban Agglomerations ◽  
Development Level ◽  
Coordination Degree

The purpose of this paper is to probe into the coupled coordination of urbanization in population, land, and industry to improve urbanization quality. A coupled coordination degree model, spatial analysis method and spatial metering model are employed. The study area is 110 prefecture-level cities in the Yangtze River Economic Belt. The study shows that: (1) the coupling degree of the population-land-industry urbanization grew very slowly between 2006 and 2016. On the whole, the three-dimensional urbanization is in a running-in period, and land-based urbanization dominates, while population-based urbanization and industry-based urbanization are relatively lagging behind. (2) The three major urban agglomerations, the Chengdu-Chongqing, the middle reaches of the Yangtze River and the Yangtze River Delta, are parallel to the whole area in terms of the coupling degree of the three dimensional urbanization with a well-ordered structure, especially in the central cities of the three major urban agglomerations. (3) There is significant spatial correlation in the coupling degree and coordination degree of the three-dimensional urbanization. The high value of coupling degree and coordination degree are clustered continuously in developed cities, provincial capitals, and central cities of the downstream reaches of the Yangtze River. (4) The coordinated degree has significant positive spatial autocorrelation, showing obvious spatial agglomeration characteristics: H-H agglomeration areas are concentrated in the downstream developed areas such as Jiangsu, Zhejiang, and Shanghai. L-L agglomeration areas are mainly concentrated in upstream undeveloped areas, but the number of their cities shows a decreasing trend. (5) The coordination degree of the three-dimensional urbanization is the result of the comprehensive effect of economic development level, the government’s decision-making behavior, and urban location. Among them, the economic development level, urbanization investment, traffic condition, and urban geographical location play a decisive role. This paper contributes to the existing literatures by exploring urbanization quality, spatial correlation and influencing factors from the perspectives of the three-dimensional urbanization in the Yangtze River Economic Belt. The conclusion might be helpful to promote the coupling and coordinated development of urbanization in population-land-industry, and ultimately to improve urbanization quality in the Yangtze River Economic Belt.

scholarly journals Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt

2021 ◽  
Vol 18 (5) ◽  
pp. 2222
Author(s):  
Jin-Wei Yan ◽  
Fei Tao ◽  
Shuai-Qian Zhang ◽  
Shuang Lin ◽  
Tong Zhou
Keyword(s):  
Geographically Weighted Regression ◽  
Yangtze River ◽  
National Level ◽  
National Development ◽  
Urban Agglomeration ◽  
Spatiotemporal Distribution ◽  
Weighted Regression ◽  
Series Data ◽  
The Yangtze River ◽  
Urban Agglomerations

As part of one of the five major national development strategies, the Yangtze River Economic Belt (YREB), including the three national-level urban agglomerations (the Cheng-Yu urban agglomeration (CY-UA), the Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and the Yangtze River Delta urban agglomeration (YRD-UA)), plays an important role in China’s urban development and economic construction. However, the rapid economic growth of the past decades has caused frequent regional air pollution incidents, as indicated by high levels of fine particulate matter (PM2.5). Therefore, a driving force factor analysis based on the PM2.5 of the whole area would provide more information. This paper focuses on the three urban agglomerations in the YREB and uses exploratory data analysis and geostatistics methods to describe the spatiotemporal distribution patterns of air quality based on long-term PM2.5 series data from 2015 to 2018. First, the main driving factor of the spatial stratified heterogeneity of PM2.5 was determined through the Geodetector model, and then the influence mechanism of the factors with strong explanatory power was extrapolated using the Multiscale Geographically Weighted Regression (MGWR) models. The results showed that the number of enterprises, social public vehicles, total precipitation, wind speed, and green coverage in the built-up area had the most significant impacts on the distribution of PM2.5. The regression by MGWR was found to be more efficient than that by traditional Geographically Weighted Regression (GWR), further showing that the main factors varied significantly among the three urban agglomerations in affecting the special and temporal features.

scholarly journals Research on the Spatio-Temporal Dynamic Evolution Characteristics and Influencing Factors of Electrical Power Consumption in Three Urban Agglomerations of Yangtze River Economic Belt, China Based on DMSP/OLS Night Light Data

2021 ◽  
Vol 13 (6) ◽  
pp. 1150
Author(s):  
Yang Zhong ◽  
Aiwen Lin ◽  
Chiwei Xiao ◽  
Zhigao Zhou
Keyword(s):  
Influencing Factors ◽  
Yangtze River ◽  
Electrical Power ◽  
Random Forest Algorithm ◽  
Temporal Dynamic ◽  
Urban Agglomerations ◽  
Evolution Characteristics ◽  
Spatio Temporal ◽  
Electrical Power Consumption ◽  
Gwr Model

In this paper, based on electrical power consumption (EPC) data extracted from DMSP/OLS night light data, we select three national-level urban agglomerations in China’s Yangtze River Economic Belt(YREB), includes Yangtze River Delta urban agglomerations(YRDUA), urban agglomeration in the middle reaches of the Yangtze River(UAMRYR), and Chengdu-Chongqing urban agglomeration(CCUA) as the research objects. In addition, the coefficient of variation (CV), kernel density analysis, cold hot spot analysis, trend analysis, standard deviation ellipse and Moran’s I Index were used to analyze the Spatio-temporal Dynamic Evolution Characteristics of EPC in the three urban agglomerations of the YREB. In addition, we also use geographically weighted regression (GWR) model and random forest algorithm to analyze the influencing factors of EPC in the three major urban agglomerations in YREB. The results of this study show that from 1992 to 2013, the CV of the EPC in the three urban agglomerations of YREB has been declining at the overall level. At the same time, the highest EPC value is in YRDUA, followed by UAMRYR and CCUA. In addition, with the increase of time, the high-value areas of EPC hot spots are basically distributed in YRDUA. The standard deviation ellipses of the EPC of the three urban agglomerations of YREB clearly show the characteristics of “east-west” spatial distribution. With the increase of time, the correlations and the agglomeration of the EPC in the three urban agglomerations of the YREB were both become more and more obvious. In terms of influencing factor analysis, by using GWR model, we found that the five influencing factors we selected basically have a positive impact on the EPC of the YREB. By using the Random forest algorithm, we found that the three main influencing factors of EPC in the three major urban agglomerations in the YREB are the proportion of secondary industry in GDP, Per capita disposable income of urban residents, and Urbanization rate.

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