Journal of Geographical Sciences >
Traffic accessibility and the coupling degree of ecosystem services supply and demand in the middle reaches of the Yangtze River urban agglomeration, China
Chen Wanxu (1989), PhD, specialized in resource and environment assessment and regional economic analysis. Email: cugcwx@cug.edu.cn 
Received date: 20210729
Accepted date: 20211222
Online published: 20221025
Supported by
National Natural Science Foundation of China(42001187)
National Natural Science Foundation of China(41701629)
The spatial relationships between traffic accessibility and supply and demand (S&D) of ecosystem services (ESs) are essential for the formulation of ecological compensation policies and ESs regulation. In this study, an ESs matrix and coupling analysis method were used to assess ESs S&D based on landuse data for 2000, 2010, and 2020, and spatial regression models were used to analyze the correlated impacts of traffic accessibility. The results showed that the ESs supply and balance index in the middle reaches of the Yangtze River urban agglomeration (MRYRUA) continuously decreased, while the demand index increased from 2000 to 2020. The Gini coefficients of these indices continued to increase but did not exceed the warning value (0.4). The coupling degree of ESs S&D continued to increase, and its spatial distribution patterns were similar to that of the ESs demand index, with significantly higher values in the plains than in the montane areas, contrasting with those of the ESs supply index. The results of global bivariate Moran’s I analysis showed a significant spatial dependence between traffic accessibility and the degree of coupling between ESs S&D; the spatial regression results showed that an increase in traffic accessibility promoted the coupling degree. The present results provide a new perspective on the relationship between traffic accessibility and the coupling degree of ESs S&D, representing a case study for similar future research in other regions, and a reference for policy creation based on the matching between ESs S&D in the MRYRUA.
CHEN Wanxu , BIAN Jiaojiao , LIANG Jiale , PAN Sipei , ZENG Yuanyuan . Traffic accessibility and the coupling degree of ecosystem services supply and demand in the middle reaches of the Yangtze River urban agglomeration, China[J]. Journal of Geographical Sciences, 2022 , 32(8) : 1471 1492 . DOI: 10.1007/s1144202220065
Figure 1 Location of the middle reaches of the Yangtze River urban agglomeration in China 
Figure 2 Framework of this study 
Figure 3 A typical Lorenz curve 
Figure 4 Spatial distribution of ESSI at 5 km grid scale in the middle reaches of the Yangtze River urban agglomeration 
Figure 5 Spatial distribution of ESSI at 10 km grid scale in the middle reaches of the Yangtze River urban agglomeration 
Figure 6 Spatial distribution of ESDI at 5 km grid scale in the middle reaches of the Yangtze River urban agglomeration 
Figure 7 Spatial distribution of ESDI at 10 km grid scale in the middle reaches of the Yangtze River urban agglomeration 
Figure 8 Lorenz curve of ecosystem services supply and demand in 2000, 2010, and 2020 at 5 km grid scale 
Figure 9 Lorenz curve of ecosystem services supply and demand in 2000, 2010, and 2020 at 10 km grid scale 
Figure 10 Lorenz curve of ecosystem services balance in 2000, 2010, and 2020 at 5 km and 10 km grid scales 
Figure 11 Spatial distribution of SDCD at 5 km grid scale in the in the middle reaches of the Yangtze River urban agglomeration 
Figure 12 Spatial distribution of SDCD at 10 km grid scale in the in the middle reaches of the Yangtze River urban agglomeration 
Table 1 Regression results of the ordinary least squares (OLS) 
Variable  5 km grid scale  10 km grid scale  

2000  2010  2020  2000  2010  2020  
Traffic accessibility  0.220*** (0.012)  0.220*** (0.012)  0.299*** (0.013)  0.206*** (0.019)  0.196*** (0.020)  0.270*** (0.020) 
Population density  0.559*** (0.041)  0.550*** (0.044)  0.335*** (0.046)  0.580*** (0.062)  0.573*** (0.066)  0.406*** (0.068) 
Elevation  0.929*** (0.017)  0.934*** (0.011)  0.939*** (0.010)  0.816*** (0.017)  0.833*** (0.017)  0.844*** (0.017) 
Constant  0.763*** (0.005)  0.759*** (0.005)  0.727*** (0.006)  0.752*** (0.009)  0.756*** (0.011)  0.726*** (0.011) 
Moran’s I (error)  0.620***  0.626***  0.601***  0.621***  0.628***  0.599*** 
LM (lag)  15238.516***  15410.364***  14136.079***  3379.456***  3415.517***  3027.331*** 
Robust LM (lag)  36.419***  21.949***  25.780***  12.996***  5.576*  3.641 
LM (error)  18445.007***  18772.058***  17334.164***  4572.310***  4655.374***  4252.143*** 
Robust LM (error)  3242.909***  3383.642***  3223.865***  1205.850***  1255.433***  1228.453*** 
LM (lag and error)  18481.425***  18794.006***  17359.944***  4585.307***  4670.950***  4225.784*** 
Measures of fit  
Log likelihood  6150.210  6092.970  6189.220  2090.020  2048.960  2074.740 
AIC  12292.400  12177.900  12370.400  4172.040  4089.920  4141.470 
SC  12262.600  12148.100  12340.600  4147.640  4065.520  4117.070 
Rsquared  0.501  0.498  0.507  0.576  0.568  0.581 
N  12712  12712  12712  3299  3299  3299 
Note: The study uses the queen’s contiguity weight matrix. ***p≤0.001, *p≤0.05. Standard errors are in parentheses. LM = Lagrange multiplier. AIC = Akaike information criterion. SC = Schwarz criterion. 
Table 2 Regression results of the spatial error models with lag dependence in 2000, 2010, and 2020 
Variable  5 km grid scale  10 km grid scale  

2000  2010  2020  2000  2010  2020  
Traffic accessibility  0.031*** (0.006)  0.036*** (0.006)  0.037*** (0.007)  0.047*** (0.011)  0.050*** (0.012)  0.074*** (0.013) 
Population density  0.102*** (0.021)  0.098***(0.023)  0.047* (0.024)  0.231*** (0.038)  0.215*** (0.041)  0.152*** (0.043) 
Elevation  0.049*** (0.006)  0.048*** (0.006)  0.052*** (0.006)  0.114*** (0.012)  0.109*** (0.013)  0.126*** (0.013) 
Spatial lag term  0.475*** (0.019)  0.465*** (0.019)  0.470*** (0.019)  0.278*** (0.035)  0.294*** (0.036)  0.264*** (0.035) 
Spatial error term  0.999*** (0.005)  0.999*** (0.005)  1.000*** (0.005)  0.935*** (0.011)  0.943*** (0.011)  0.929*** (0.011) 
Constant  0.007 (0.004)  0.010* (0.004)  0.011* (0.005)  0.036*** (0.010)  0.028** (0.010)  0.029** (0.037) 
Log likelihood  11999.890  11988.570  11707.046  3448.866  3415.243  3310.822 
AIC  23989.800  23967.100  23404.100  6887.730  6820.490  6611.640 
SC  23952.500  23929.900  23366.800  6857.22  6789.980  6581.140 
R^{2}  0.807  0.807  0.799  0.816  0.814  0.804 
N  12712  12712  12712  3299  3299  3299 
Note: The study uses the queen’s contiguity weight matrix. ***p≤0.001, **p≤0.01, *p≤0.05. Standard errors are in parentheses. AIC = Akaike information criterion. SC = Schwarz criterion. 
Table S1 Regression results of the spatial lag model (SLM) and spatial error model (SEM) at 5k m grid scale in 2000, 2010, and 2020 
Variable  2000  2010  2020  

SLM  SEM  SLM  SEM  SLM  SEM  
Traffic accessibility  0.079*** (0.008)  0.111***(0.010)  0.084*** (0.008)  0.123*** (0.010)  0.104*** (0.009)  0.132*** (0.012) 
Population density  0.217*** (0.027)  0.283***(0.034)  0.213*** (0.029)  0.298**(0.038)  0.130*** (0.031)  0.224*** (0.041) 
Elevation  0.256*** (0.010)  0.939*** (0.018)  0.254*** (0.010)  0.940*** (0.019)  0.271*** (0.010)  0.969*** (0.019) 
Spatial lag term  0.792*** (0.007)  0.794*** (0.006)  0.782*** (0.007)  
Spatial error term  0.853***(0.006)  0.855*** (0.006)  0.847*** (0.006)  
Constant  0.152*** (0.006)  0.802***(0.007)  0.146***(0.06)  0.796*** (0.007)  0.148*** (0.006)  0.798*** (0.008) 
Measures of fit  
Log likelihood  10815.800  11348.963  10805.500  11361.028  10555.500  11093.583 
AIC  21621.600  22689.900  21601.100  22714.100  21101.000  22179.200 
SC  21584.400  22660.100  21563.800  22684.300  21063.800  22149.400 
R^{2}  0.788  0.811  0.788  0.743  0.780  0.804 
N  12712  12712  12712  12712  12712  12712 
Note: The study uses the queen’s contiguity weight matrix. ***p≤0.001, **p≤0.01. Standard errors are in parentheses. AIC = Akaike information criterion. SC = Schwarz criterion. 
Table S2 Regression results of the spatial lag model (SLM) and spatial error model (SEM) at 10 km grid scale in 2000, 2010, and 2020 
Variable  2000  2010  2020  

SLM  SEM  SLM  SEM  SLM  SEM  
Traffic accessibility  0.086*** (0.013)  0.124*** (0.015)  0.088*** (0.014)  0.141*** (0.016)  0.124*** (0.014)  0.172*** (0.017) 
Population density  0.310*** (0.042)  0.304*** (0.044)  0.287*** (0.044)  0.279** (0.048)  0.212*** (0.048)  0.218*** (0.051) 
Elevation  0.273*** (0.017)  0.849*** (0.025)  0.277*** (0.017)  0.859*** (0.025)  0.297*** (0.017)  0.879*** (0.026) 
Spatial lag term  0.749*** (0.013)  0.752*** (0.013)  0.732*** (0.014)  
Spatial error term  0.857*** (0.011)  0.857*** (0.011)  0.842*** (0.012)  
Constant  0.178*** (0.012)  0.792*** (0.012)  0.174*** (0.012)  0.785*** (0.013)  0.174*** (0.012)  0.774*** (0.013) 
Measures of fit  
Log likelihood  3219.170  3467.230  3180.950  3436.602  3093.230  3332.365 
AIC  6428.340  6926.460  6351.900  6865.200  6176.450  6656.730 
SC  6397.840  6902.050  6321.400  6840.800  6145.950  6632.320 
R^{2}  0.808  0.843  0.805  0.841  0.796  0.832 
N  3299  3299  3299  3299  3299  3299 
Note: The study uses the queen’s contiguity weight matrix. ***p≤0.001, **p≤0.01. Standard errors are in parentheses. LM = Lagrange multiplier. AIC = Akaike information criterion. SC = Schwarz criterion. 
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