JOURNAL OF NATURAL RESOURCES >
Evolution characteristics and formation mechanism of spatial correlation network of ecological well-being performance in China
Received date: 2021-09-27
Revised date: 2021-12-28
Online published: 2022-12-28
In this article, the ecological well-being performance of provincial-level regions in China from 2000 to 2019 is measured by using the super EBM model considering undesirable output. At the same time, with the help of the modified gravity model and social network analysis method, the evolution characteristics and formation mechanism of China's ecological well-being performance spatial correlation network are analyzed. The results show that: (1) The spatial correlation network of China's ecological well-being performance shows a spatial pattern of "dense in the east but sparse in the west". During the research period, the hierarchical network structure was gradually changed, and the associated network had a "flat" evolution trend. However, the compactness and stability of spatial association networks need to be improved. (2) The Beijing-Tianjin-Hebei region, Yangtze River Delta and Pearl River Delta are at the core of the spatial correlation network, showing obvious spillover effects, while the Northeast, Northwest and Southwest regions are located at the edge of the network. Guizhou and Gansu are the key nodes between the eastern region and the southwest and northwest provinces in the network. The transmission of ecological well-being performance among provinces is mostly realized through the mediating role of southern provinces. On the whole, the eastern coastal provinces are the "overflow highland" in the spatial correlation network, showing a significant "trickling-down effect" on the Northwest, Southwest and Northeast regions, and the benefit effect of the central provinces is not significant. (3) The results of block model analysis show that China's ecological well-being performance has significant spillover effects between provinces and regions, and the spillover effect between regions is the most important. Among them, Beijing, Tianjin and Shanghai play the role of "engine" in the correlation network, forming the net overflow plate. The Pearl River Delta and Jiangsu and Zhejiang belong to the broker plate, the middle and lower reaches of the Yangtze River and Southwest China are net overflow plates, and Northeast region, the middle and lower reaches of the Yellow River and Northwest region constitute two-way overflow plates. (4) Resource endowment difference, market regulation, government macro-control and science and technology progress are the main driving mechanisms for the evolution of spatial correlation network of ecological well-being performance in China. This study can provide an important reference for promoting the regional collaborative improvement of ecological well-being performance.
ZHAO Lin , CAO Nai-gang , HAN Zeng-lin , GAO Xiao-tong . Evolution characteristics and formation mechanism of spatial correlation network of ecological well-being performance in China[J]. JOURNAL OF NATURAL RESOURCES, 2022 , 37(12) : 3183 -3200 . DOI: 10.31497/zrzyxb.20221211
表1 生态福利绩效评价指标体系Table 1 The evaluation index system of ecological well-being performance |
表2 中国生态福利绩效测度结果Table 2 Measure result of ecological well-being performance in China |
省(市、自治区) | EWP指数 | 省(市、自治区) | EWP指数 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
2000年 | 2006年 | 2012年 | 2019年 | 增长率 | 2000年 | 2006年 | 2012年 | 2019年 | 增长率 | ||
北京 | 0.690 | 0.456 | 0.743 | 1.002 | 1.983 | 湖南 | 0.529 | 0.458 | 0.636 | 0.969 | 3.241 |
天津 | 0.467 | 0.454 | 0.660 | 0.602 | 1.337 | 广东 | 0.381 | 0.314 | 0.460 | 0.620 | 2.597 |
河北 | 0.428 | 0.399 | 0.587 | 0.748 | 2.986 | 广西 | 0.341 | 0.411 | 0.577 | 0.714 | 3.968 |
山西 | 0.491 | 0.518 | 0.651 | 0.749 | 2.251 | 海南 | 0.658 | 0.461 | 0.603 | 0.685 | 0.217 |
内蒙古 | 0.387 | 0.322 | 0.482 | 0.565 | 2.010 | 重庆 | 0.384 | 0.265 | 0.439 | 0.627 | 2.609 |
辽宁 | 0.347 | 0.343 | 0.457 | 0.446 | 1.322 | 四川 | 0.339 | 0.338 | 0.602 | 0.702 | 3.904 |
吉林 | 0.396 | 0.356 | 0.414 | 0.470 | 0.907 | 贵州 | 0.132 | 0.200 | 0.536 | 0.704 | 9.188 |
黑龙江 | 0.328 | 0.295 | 0.404 | 0.469 | 1.899 | 云南 | 0.339 | 0.285 | 0.489 | 0.837 | 4.874 |
上海 | 0.616 | 0.583 | 0.721 | 0.956 | 2.339 | 陕西 | 0.561 | 0.571 | 0.804 | 0.941 | 2.761 |
江苏 | 0.442 | 0.374 | 0.518 | 0.698 | 2.435 | 甘肃 | 0.295 | 0.319 | 0.460 | 0.602 | 3.831 |
浙江 | 0.361 | 0.405 | 0.593 | 0.744 | 3.872 | 青海 | 0.413 | 0.375 | 0.667 | 0.635 | 2.296 |
安徽 | 0.252 | 0.352 | 0.497 | 0.667 | 5.249 | 宁夏 | 0.308 | 0.203 | 0.253 | 0.352 | 0.708 |
福建 | 0.441 | 0.393 | 0.558 | 0.779 | 3.045 | 新疆 | 0.410 | 0.356 | 0.408 | 0.415 | 0.068 |
江西 | 0.406 | 0.347 | 0.587 | 0.666 | 2.635 | 东部 | 0.479 | 0.412 | 0.583 | 0.713 | 2.121 |
山东 | 0.435 | 0.349 | 0.517 | 0.567 | 1.403 | 中部 | 0.408 | 0.403 | 0.544 | 0.686 | 2.774 |
河南 | 0.459 | 0.445 | 0.602 | 0.816 | 3.076 | 西部 | 0.355 | 0.331 | 0.520 | 0.645 | 3.187 |
湖北 | 0.404 | 0.452 | 0.562 | 0.685 | 2.814 | 全国 | 0.415 | 0.380 | 0.550 | 0.681 | 2.646 |
图4 中国生态福利绩效溢出和受益关系的空间格局Fig. 4 Spatial pattern of spillover and benefit relationship of ecological well-being performance in China |
表3 中国生态福利绩效空间关联网络的中心性特征Table 3 Centrality of spatial correlation network of ecological well-being performance in China |
省(市、自治区) | 网络中心性指标 | 省(市、自治区) | 网络中心性指标 | ||||
---|---|---|---|---|---|---|---|
度数中心度 | 接近中心度 | 中介中心度 | 度数中心度 | 接近中心度 | 中介中心度 | ||
北京 | 89.655/89.655 | 87.879/90.625 | 9.113/7.681 | 湖北 | 20.690/31.034 | 3.448/37.662 | 0.000/0.252 |
天津 | 82.759/34.483 | 80.556/56.863 | 7.202/0.431 | 湖南 | 20.690/27.586 | 4.715/53.704 | 0.369/9.892 |
河北 | 13.793/17.241 | 49.153/50.877 | 0.059/3.038 | 广东 | 37.931/31.034 | 4.754/42.029 | 5.111/15.237 |
山西 | 17.241/24.138 | 49.153/50.877 | 0.100/1.790 | 广西 | 24.138/27.586 | 4.700/37.179 | 0.544/9.190 |
内蒙古 | 10.345/17.241 | 48.333/3.333 | 0.059/0.000 | 海南 | 17.241/24.138 | 4.708/32.955 | 0.062/0.059 |
辽宁 | 20.690/24.138 | 3.571/3.571 | 0.082/0.123 | 重庆 | 24.138/31.034 | 4.700/31.183 | 0.092/14.26 |
吉林 | 17.241/24.138 | 3.571/3.567 | 0.000/0.883 | 四川 | 20.690/24.138 | 3.333/23.967 | 0.000/0.038 |
黑龙江 | 17.241/24.138 | 3.333/3.333 | 0.000/0.000 | 贵州 | 44.828/27.586 | 3.333/32.955 | 0.000/2.704 |
上海 | 96.552/93.103 | 96.667/93.548 | 7.628/14.361 | 云南 | 27.586/24.138 | 3.448/32.955 | 0.000/2.509 |
江苏 | 34.483/79.310 | 60.417/76.316 | 0.510/8.291 | 陕西 | 13.793/24.138 | 4.915/21.014 | 0.021/0.185 |
浙江 | 34.483/62.069 | 59.184/69.048 | 0.000/1.901 | 甘肃 | 20.690/41.379 | 3.448/25.893 | 0.123/8.987 |
安徽 | 17.241/20.690 | 50.877/52.727 | 2.648/0.679 | 青海 | 13.793/24.138 | 3.448/20.714 | 0.000/0.000 |
福建 | 24.138/58.621 | 4.700/52.727 | 0.369/8.807 | 宁夏 | 10.345/27.586 | 3.333/3.333 | 0.000/0.000 |
江西 | 24.138/24.138 | 4.708/53.704 | 0.811/12.469 | 新疆 | 10.345/27.586 | 3.333/3.333 | 0.000/0.000 |
山东 | 13.793/20.690 | 49.153/3.333 | 0.059/0.000 | 均值 | 27.816/34.253 | 25.173/37.170 | 1.174/4.138 |
河南 | 13.793/20.690 | 48.333/51.786 | 0.261/0.372 | — | — | — | — |
注:分子和分母分别表示2000年和2019年数据。 |
表4 空间关联网络板块间的溢出效应Table 4 Spillover effects between the blocks of spatial correlation network |
板块类型 | 2000年 | 2019年 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
板块Ⅰ | 板块Ⅱ | 板块Ⅲ | 板块Ⅳ | 板块Ⅰ | 板块Ⅱ | 板块Ⅲ | 板块Ⅳ | |||
接收关系 | 板块内 | 3 | 0 | 6 | 7 | 2 | 0 | 14 | 9 | |
板块外 | 13 | 15 | 38 | 63 | 8 | 23 | 55 | 73 | ||
溢出关系 | 板块内 | 3 | 0 | 6 | 7 | 2 | 0 | 14 | 9 | |
板块外 | 74 | 32 | 13 | 10 | 61 | 60 | 11 | 27 | ||
期望内部关系比例/% | 6.897 | 10.345 | 37.931 | 34.483 | 6.897 | 10.345 | 34.483 | 37.931 | ||
实际内部关系比例/% | 3.900 | 0 | 31.579 | 41.176 | 3.175 | 0 | 56.000 | 25.000 |
表5 空间关联网络的影响因素回归结果Table 5 Regression results of influencing factors of spatial correlation network |
变量 | 2000年 | 2002年 | 2004年 | 2006年 | 2008年 | 2010年 | 2012年 | 2014年 | 2016年 | 2019年 |
---|---|---|---|---|---|---|---|---|---|---|
dis | 0.189*** (0.000) | 0.187*** (0.000) | 0.192*** (0.000) | 0.248*** (0.000) | 0.259*** (0.000) | 0.279*** (0.000) | 0.274*** (0.000) | 0.242*** (0.000) | 0.255*** (0.000) | 0.246*** (0.000) |
pgdp | 0.393*** (0.000) | 0.390*** (0.000) | 0.419*** (0.000) | 0.526*** (0.000) | 0.527*** (0.000) | 0.537*** (0.000) | 0.510*** (0.000) | 0.484*** (0.000) | 0.522*** (0.000) | 0.573*** (0.000) |
inds | 0.144** (0.016) | 0.123** (0.029) | 0.103** (0.041) | 0.011 (0.363) | 0.008 (0.420) | -0.014 (0.265) | 0.015 (0.304) | 0.026 (0.159) | -0.017 (0.306) | -0.122*** (0.005) |
open | 0.048* (0.084) | 0.098** (0.017) | 0.079** (0.044) | 0.042 (0.136) | 0.013 (0.265) | 0.010 (0.378) | 0.026 (0.206) | 0.039* (0.096) | 0.056* (0.070) | 0.042* (0.090) |
env | 0.009 (0.348) | -0.002 (0.510) | -0.013 (0.373) | -0.008 (0.429) | 0.001 (0.476) | -0.019 (0.175) | -0.046** (0.020) | -0.036** (0.022) | -0.011 (0.340) | -0.080*** (0.002) |
gov | -0.014 (0.365) | -0.001 (0.542) | 0.030 (0.179) | 0.038* (0.088) | 0.022 (0.196) | -0.016 (0.214) | -0.031* (0.085) | -0.017 (0.229) | 0.011 (0.369) | -0.008 (0.421) |
tech | 0.074** (0.046) | 0.026 (0.191) | 0.058* (0.066) | 0.066** (0.034) | 0.086*** (0.002) | 0.084*** (0.000) | 0.047** (0.027) | 0.051** (0.021) | 0.059*** (0.007) | 0.012 (0.374) |
R2 | 0.289 | 0.311 | 0.321 | 0.357 | 0.340 | 0.347 | 0.335 | 0.319 | 0.345 | 0.313 |
Adj-R2 | 0.285 | 0.306 | 0.317 | 0.352 | 0.336 | 0.343 | 0.330 | 0.317 | 0.340 | 0.309 |
注:变量的系数为标准化回归系数,*、**、***分别表示10%、5%、1%水平上显著,括号内数值表示显著性概率。 |
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