Scientia Silvae Sinicae ›› 2024, Vol. 60 ›› Issue (5): 177-190.doi: 10.11707/j.1001-7488.LYKX20220205
• Research papers • Previous Articles Next Articles
Yunhao Sun,Nanyang Cheng,Wenxing Shen*
Received:
2022-04-01
Online:
2024-05-25
Published:
2024-06-14
Contact:
Wenxing Shen
CLC Number:
Yunhao Sun,Nanyang Cheng,Wenxing Shen. Analysis of Spatial Correlation and Influencing Factors of Urban Forest Construction in the Yangtze River Delta Region[J]. Scientia Silvae Sinicae, 2024, 60(5): 177-190.
Table 1
Evaluation index system of urban forest construction level in Yangtze River Delta"
目标层Target layer | 一级指标 Primary index | 二级指标Secondary index | 权重Weight |
城市森林建设水平评价指标体系 Evaluation index system of urban forest construction level | 森林状态 State of the forest | 建成区人均绿化覆盖面积 Greening coverage area per capita in built-up areas | 0.100 6 |
建成区人均园林绿地面积Green space per capita in built-up areas | 0.106 6 | ||
人均公园绿地面积Green space per capita | 0.109 3 | ||
绿化覆盖率Greenery coverage | 0.008 2 | ||
森林发展 Forest development | 园林绿化投资Landscaping investment | 0.010 3 | |
人工更新造林面积Artificial reforestation area | 0.015 0 | ||
环境友好程度Environmentally friendly level | 0.014 3 | ||
城市森林认可度Urban forest recognition | 0.013 7 | ||
森林效益 Forest benefits | 林业总产值Total forestry output | 0.005 6 | |
城市植被固碳量Carbon sequestration by urban vegetation | 0.338 5 | ||
每万人享有公园个数Number of parks per 10 000 people | 0.277 6 |
Table 2
Evaluation index of forest construction level in major cities from 2011 to 2019"
城市City | 城市森林建设水平指数Urban forest construction level index | ||||||||
2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | |
安庆Anqing | 0.172 | 0.168 | 0.191 | 0.200 | 0.201 | 0.202 | 0.204 | 0.208 | 0.250 |
滁州Chuzhou | 0.257 | 0.278 | 0.302 | 0.303 | 0.311 | 0.308 | 0.315 | 0.325 | 0.367 |
淮南Huainan | 0.098 | 0.093 | 0.097 | 0.092 | 0.088 | 0.088 | 0.093 | 0.091 | 0.090 |
黄山Huangshan | 0.318 | 0.328 | 0.361 | 0.359 | 0.353 | 0.338 | 0.337 | 0.333 | 0.347 |
宣城Xuancheng | 0.083 | 0.084 | 0.096 | 0.136 | 0.134 | 0.119 | 0.119 | 0.121 | 0.122 |
常州Changzhou | 0.200 | 0.197 | 0.212 | 0.213 | 0.211 | 0.211 | 0.213 | 0.215 | 0.238 |
淮安Huai’an | 0.123 | 0.123 | 0.133 | 0.133 | 0.135 | 0.134 | 0.137 | 0.140 | 0.166 |
南京Nanjing | 0.339 | 0.331 | 0.351 | 0.352 | 0.358 | 0.356 | 0.357 | 0.363 | 0.360 |
苏州Suzhou | 0.641 | 0.597 | 0.649 | 0.638 | 0.650 | 0.634 | 0.636 | 0.642 | 0.696 |
扬州Yangzhou | 0.174 | 0.168 | 0.180 | 0.177 | 0.181 | 0.269 | 0.312 | 0.301 | 0.329 |
镇江Zhenjiang | 0.243 | 0.248 | 0.275 | 0.278 | 0.278 | 0.270 | 0.270 | 0.281 | 0.282 |
杭州Hangzhou | 0.380 | 0.378 | 0.395 | 0.355 | 0.375 | 0.370 | 0.362 | 0.365 | 0.361 |
嘉兴Jiaxing | 0.390 | 0.383 | 0.420 | 0.424 | 0.427 | 0.421 | 0.421 | 0.398 | 0.438 |
宁波Ningbo | 0.414 | 0.419 | 0.435 | 0.427 | 0.437 | 0.470 | 0.432 | 0.439 | 0.447 |
绍兴Shaoxing | 0.414 | 0.415 | 0.262 | 0.262 | 0.260 | 0.251 | 0.261 | 0.282 | 0.313 |
温州Wenzhou | 0.198 | 0.294 | 0.309 | 0.308 | 0.332 | 0.317 | 0.320 | 0.353 | 0.364 |
芜湖Wuhu | 0.197 | 0.197 | 0.203 | 0.184 | 0.183 | 0.183 | 0.189 | 0.188 | 0.198 |
合肥Hefei | 0.303 | 0.297 | 0.310 | 0.327 | 0.326 | 0.325 | 0.324 | 0.320 | 0.331 |
上海Shanghai | 0.693 | 0.687 | 0.668 | 0.659 | 0.653 | 0.664 | 0.668 | 0.696 | 0.679 |
Table 3
Structural matching, effective correlation test of spatial matrix"
矩阵 Matrix | 均值匹配检验 Means matching test | 方差匹配检验 Variance matching test | 有效相关检验 Valid correlation test | |||||
列数 Columns | 行数 Rows | 列数 Columns | 行数 Rows | 相关系数 Correlation coefficient | T统计量 T-statistic | |||
STW1 | 574 | 574 | 574 | 574 | 0.295 41 | 177.49*** | ||
STW2 | 574 | 574 | 574 | 574 | 0.515 59 | 345.4*** | ||
STW3 | 574 | 574 | 574 | 574 | 0.303 44 | 182.79*** |
Table 4
Model setting test"
检验方法 Test method | W1 | W2 | W3 | |||||
统计量Statistics | 概率Prob. | 统计量Statistics | 概率Prob. | 统计量Statistics | 概率Prob. | |||
LM(lag) | 164.67 | 0.000 | 221.74 | 0.000 | 166.16 | 0.000 | ||
R-LM(lag) | 33.86 | 0.000 | 44.23 | 0.000 | 31.91 | 0.000 | ||
LM(error) | 148.70 | 0.000 | 254.82 | 0.000 | 154.22 | 0.000 | ||
R-LM(error) | 17.88 | 0.000 | 77.319 | 0.000 | 19.97 | 0.000 | ||
Wald-lag | 18.56 | 0.005 | 17.41 | 0.007 | 12.89 | 0.045 | ||
Wald-er | 17.76 | 0.006 | 11.61 | 0.071 | 11.32 | 0.079 | ||
LR-d | 272.18 | 0.000 | 271.78 | 0.000 | 257.77 | 0.000 | ||
Hausman | 60.93 | 0.000 | 41.28 | 0.000 | 65.31 | 0.000 |
Table 5
Results of dynamic spatial panel model with three weight matrices"
解释变量 Explanatory variable | 被解释变量 Dependent variable | ||
W1 | W2 | W3 | |
F(-1) | 1.215 0*** (0.017 6) | 1.167 0*** (0.015 6) | 1.167 0*** (0.016 7) |
W×F | 0.254 0*** (0.021 8) | 0.172 0*** (0.047 0) | 0.081 2*** (0.020 2) |
ln pgdp | ?0.016 8*** (0.002 6) | ?0.014 0*** (0.002 5) | ?0.014 3*** (0.002 4) |
ln pop | ?0.004 5* (0.002 3) | ?0.001 6 (0.002 1) | 0.000 7 (0.001 6) |
ln umt | ?0.018 4*** (0.001 7) | ?0.017 3*** (0.001 6) | ?0.015 7*** (0.001 9) |
ln road | 0.019 3*** (0.002 2) | 0.015 3*** (0.001 9) | 0.013 1*** (0.002 0) |
industy | ?0.000 528*** (0.000 16) | 0.000 192 (0.000 16) | 0.000 108 (0.000 15) |
fcity | 0.007 1*** (0.002 5) | 0.007 7*** (0.002 2) | 0.007 8*** (0.002 3) |
W×解释变量Explanatory variable | 控制Control | 控制Control | 控制Control |
Sigma2 | 0.000 42*** (0.000 1) | 0.000 42*** (0.000 1) | 0.000 42*** (0.000 1) |
N | 533 | 533 | 533 |
R2 | 0.878 | 0.945 | 0.961 |
Table 6
Short-term direct and indirect effect estimates"
矩阵Matrix | 效应Effects | ln pgdp | ln pop | ln umt | ln road | industy | fcity |
W1 | 直接Direct | ?0.017 6*** (0.002 4) | ?0.006 5*** (0.002 4) | ?0.020 5*** (0.001 6) | 0.021 2*** (0.002 2) | ?0.000 6*** (0.000 2) | 0.008 5*** (0.002 5) |
间接Indirect | ?0.013 5** (0.006 3) | ?0.048 7*** (0.008 2) | ?0.044 2*** (0.005 9) | 0.039 7*** (0.005 9) | ?0.002 3*** (0.000 4) | 0.033 6*** (0.003 5) | |
总效应Total | ?0.031 1*** (0.005 0) | ?0.055 2*** (0.009 5) | ?0.064 6*** (0.005 4) | 0.060 9*** (0.006 8) | ?0.002 9*** (0.000 4) | 0.042 2*** (0.003 8) | |
W2 | 直接Direct | ?0.014 2*** (0.002 5) | ?0.001 8 (0.002 1) | ?0.017 6*** (0.001 6) | 0.016 0*** (0.002 0) | 0.000 2* (0.000 2) | 0.007 7*** (0.002 3) |
间接Indirect | ?0.000 2 (0.009 0) | ?0.026 6*** (0.009 6) | ?0.018 3** (0.008 5) | 0.052 4*** (0.014 2) | ?0.002 1*** (0.000 6) | 0.002 5 (0.007 3) | |
总效应Total | ?0.014 4* (0.007 6) | ?0.028 4*** (0.010 5) | ?0.035 9*** (0.008 5) | 0.068 4*** (0.015 2) | ?0.002 0*** (0.000 6) | 0.010 2* (0.007 2) |
Table 7
Long-term direct and indirect effect estimates"
矩阵Matrix | 效应Effects | ln pgdp | ln pop | ln umt | ln road | industy | fcity |
W1 | 直接Direct | 0.095 5*** (0.019 1) | ?0.021 5* (0.013 8) | 0.074 5*** (0.012 1) | ?0.085 5*** (0.015 0) | 0.001 0 (0.001 1) | ?0.011 8 (0.017 7) |
间接Indirect | ?0.045 6** (0.022 7) | 0.110 0*** (0.021 5) | 0.029 3** (0.013 9) | ?0.012 2 (0.018 4) | 0.003 7*** (0.001 2) | ?0.056 0** (0.018 9) | |
总效应Total | 0.049 8*** (0.007 1) | 0.088 6*** (0.015 2) | 0.104 0*** (0.008 7) | ?0.097 7*** (0.011 0) | 0.004 7*** (0.000 6) | ?0.067 8** (0.006 9) | |
W2 | 直接Direct | 0.091 4*** (0.016 9) | 0.000 2 (0.012 7) | 0.106 0*** (0.010 8) | ?0.081 3*** (0.012 5) | ?0.002 0** (0.001 0) | ?0.049 3** (0.016 3) |
间接Indirect | ?0.056 6** (0.026 4) | 0.070 5*** (0.026 6) | ?0.017 0 (0.018 6) | ?0.088 8*** (0.030 5) | 0.006 9*** (0.001 6) | 0.023 6 (0.026 2) | |
总效应Total | 0.034 7** (0.016 5) | 0.070 7*** (0.024 9) | 0.089 3*** (0.016 5) | ?0.170 0*** (0.028 5) | 0.004 8*** (0.001 3) | ?0.025 8* (0.018 7) |
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