JOURNAL OF NATURAL RESOURCES >
Research on measurement of industrial structural transformation and upgrading level in resource-exhausted cities and its influencing factors:Based on panel data of 24 prefecture-level cities of China
Received date: 2020-05-24
Revised date: 2020-10-31
Online published: 2021-10-28
Copyright
Industrial structural transformation and upgrading is an inevitable path for resource-exhausted cities to realize sustainable development, and researches on measurement of industrial structural transformation and upgrading level in such cities and its influencing factors are important bases of accurate formulation of industrial transformation policies. Based on panel data of 24 resource-exhausted cities in China from 2008 to 2017, conditions of their industrial structural transformations and upgrades are measured from three aspects, namely, direction, velocity and level. In addition, the fixed effect model is adopted to explore influencing factors of industrial structural transformation and upgrading level. The results indicate that: (1) Most of the resource-exhausted cities in China have mainly transformed to the tertiary industry while a small number of cities focus on cultivating the transitional model of multi-industry integration. (2) Compared with the period 2008-2012, the speed of industrial structural transformations and upgrades from 2013 to 2017 was higher, which showed an accelerating trend, but there were differences between regions and cities. (3) Industrial structural transformation and upgrading levels in resource-exhausted cities were on the rise, industrial structures were constantly optimizing and upgrading, and the integral level was lower than the national average level, presenting a spatial pattern of higher in the eastern region, but lower in the central, western and northeastern regions. (4) Total employment, proportion of fiscal expenditures in GDP, total retail sales of consumer goods and proportion of urban population had positive effects on industrial structural transformations and upgrades of resource-exhausted cities, while the total amount of fixed-asset investments had a negative effect on it. The impact of the number of authorized patents is of insignificance.
HUANG Tian-neng , XU Jin-long , XIE Ling-ling . Research on measurement of industrial structural transformation and upgrading level in resource-exhausted cities and its influencing factors:Based on panel data of 24 prefecture-level cities of China[J]. JOURNAL OF NATURAL RESOURCES, 2021 , 36(8) : 2065 -2080 . DOI: 10.31497/zrzyxb.20210812
表1 指标体系构建Table 1 Construction of index system |
变量设置 | 变量名称 | 衡量指标 | |
---|---|---|---|
因变量 | 产业结构转型升级水平(IND) | 产业结构高级化指数(IH) | |
自变量 | 供给因素 | 人力资本水平(LAP) | 就业总人数/万人 |
财政投入水平(FI) | 财政支出/GDP/% | ||
技术发展水平(TEC) | 授权专利数/项 | ||
需求因素 | 社会消费需求(CON) | 社会消费品零售总额/亿元 | |
社会投资需求(INV) | 固定资产投资总额/亿元 | ||
城镇化需求(URB) | 城镇人口/总人口/% |
表2 24座资源枯竭地级市三次产业超前系数Table 2 The leading coefficients of three industries in resource-exhausted cities in 24 prefecture-level cities of China |
区域 | 城市 | T1=2008—2012年 | T2=2013—2017年 | Tall=2008—2017年 | ||||||
---|---|---|---|---|---|---|---|---|---|---|
一产 | 二产 | 三产 | 一产 | 二产 | 三产 | 一产 | 二产 | 三产 | ||
东北 | 阜新市 | 1.022 | 1.724 | 0.219 | 0.953 | 5.212 | -5.062 | 0.836 | -1.372 | 3.614 |
盘锦市 | -0.029 | 0.539 | 3.539 | 1.878 | 7.975 | -18.818 | -0.563 | -1.638 | 12.978 | |
抚顺市 | 1.190 | 1.340 | 0.436 | 3.350 | 2.125 | -1.482 | -0.905 | 0.285 | 2.462 | |
辽源市 | -0.958 | 1.656 | 0.766 | -3.990 | -0.329 | 4.696 | -1.054 | 1.375 | 1.249 | |
白山市 | -0.624 | 1.392 | 0.921 | -0.986 | -12.367 | 26.286 | -0.474 | 0.471 | 2.484 | |
伊春市 | 4.626 | 0.236 | -0.819 | -9.538 | 22.885 | -9.637 | 6.947 | -4.578 | 1.314 | |
七台河市 | 1.779 | 0.938 | 0.886 | -5.711 | 20.984 | -19.658 | 10.990 | -6.209 | 11.376 | |
鹤岗市 | 2.497 | 1.587 | -0.908 | -2.941 | 10.846 | -11.539 | 4.570 | -1.858 | 2.194 | |
双鸭山市 | 1.242 | 1.354 | 0.108 | -0.206 | 11.723 | -7.467 | 2.094 | -2.703 | 5.878 | |
均值 | 1.194 | 1.196 | 0.572 | -1.910 | 7.673 | -4.742 | 2.493 | -1.803 | 4.839 | |
东部 | 枣庄市 | 0.437 | 0.194 | 2.950 | -1.527 | -0.570 | 4.120 | -0.195 | -0.042 | 3.674 |
韶关市 | 0.939 | -0.102 | 2.434 | 0.773 | -0.981 | 2.949 | 0.509 | -0.180 | 2.691 | |
均值 | 0.688 | 0.046 | 2.692 | -0.377 | -0.776 | 3.535 | 0.157 | -0.111 | 3.183 | |
中部 | 淮北市 | -0.136 | 1.727 | -0.071 | -1.260 | -1.404 | 8.299 | -0.403 | 0.745 | 1.966 |
铜陵市 | -0.803 | 1.629 | -0.237 | 13.293 | -0.280 | 3.749 | 3.601 | 0.692 | 1.459 | |
萍乡市 | -0.039 | 0.815 | 1.702 | -2.386 | -1.146 | 5.503 | -0.626 | 0.130 | 3.352 | |
景德镇市 | -0.139 | 1.117 | 1.122 | -2.359 | -1.329 | 5.755 | -0.600 | 0.341 | 2.671 | |
新余市 | -0.489 | 0.897 | 1.647 | 1.000 | -1.021 | 4.270 | 0.014 | 0.034 | 3.489 | |
焦作市 | 0.632 | 1.193 | 0.602 | -2.203 | -0.541 | 6.196 | -0.465 | 0.403 | 3.071 | |
濮阳市 | 1.000 | 0.817 | 1.619 | -1.394 | -1.053 | 9.080 | -0.220 | 0.107 | 4.871 | |
黄石市 | 1.510 | 2.104 | -0.606 | 0.732 | -0.100 | 3.286 | 1.464 | 1.306 | 0.993 | |
均值 | 0.192 | 1.287 | 0.722 | 0.678 | -0.859 | 5.767 | 0.346 | 0.470 | 2.734 | |
西部 | 乌海市 | -0.304 | 1.567 | -0.104 | 25.275 | -8.062 | 18.329 | 1.022 | 0.375 | 2.269 |
泸州市 | -1.049 | 2.525 | -0.029 | -0.916 | -0.385 | 5.135 | -0.797 | 1.389 | 1.604 | |
铜川市 | 0.553 | 1.560 | 0.074 | 2.496 | -10.517 | 29.605 | 0.698 | 0.372 | 2.224 | |
白银市 | 0.276 | 1.121 | 1.070 | -30.697 | 39.704 | 2.671 | 0.025 | -1.174 | 4.522 | |
石嘴山市 | 0.596 | 0.181 | 3.828 | 0.026 | -0.401 | 4.166 | 0.440 | 0.102 | 4.130 | |
均值 | 0.014 | 1.391 | 0.968 | -0.763 | 4.068 | 11.981 | 0.278 | 0.213 | 2.950 |
表3 24个资源枯竭地级市产业结构变化速率Table 3 The changes in the industrial structure of resource-exhausted cities in 24 prefecture-level cities of China |
区域 | 城市 | 产业结构变动向量夹角α/(°) | 产业结构年均变动率 | ||||
---|---|---|---|---|---|---|---|
T1 | T2 | Tall | T1 | T2 | Tall | ||
东北 | 阜新市 | 7.746 | 25.711 | 17.641 | 2.309 | 7.720 | 2.700 |
盘锦市 | 5.750 | 21.921 | 28.097 | 2.494 | 7.680 | 5.200 | |
抚顺市 | 3.346 | 8.152 | 4.975 | 1.189 | 3.000 | 0.920 | |
辽源市 | 6.121 | 3.035 | 6.209 | 2.480 | 1.160 | 1.280 | |
白山市 | 3.481 | 10.912 | 9.851 | 1.400 | 3.700 | 1.820 | |
伊春市 | 10.554 | 13.991 | 21.172 | 3.400 | 4.720 | 3.180 | |
七台河市 | 0.753 | 11.588 | 20.636 | 0.320 | 3.760 | 3.274 | |
鹤岗市 | 11.028 | 17.120 | 15.422 | 3.760 | 5.520 | 2.520 | |
双鸭山市 | 4.558 | 28.277 | 26.824 | 1.560 | 8.588 | 4.380 | |
均值 | 5.926 | 15.634 | 16.759 | 2.101 | 5.094 | 2.808 | |
东部 | 枣庄市 | 5.954 | 6.964 | 14.362 | 2.685 | 2.504 | 2.600 |
韶关市 | 7.966 | 8.394 | 15.606 | 2.480 | 2.360 | 2.560 | |
均值 | 6.960 | 7.679 | 14.984 | 2.583 | 2.432 | 2.580 | |
中部 | 淮北市 | 5.797 | 12.624 | 6.530 | 2.388 | 4.739 | 1.260 |
铜陵市 | 5.734 | 9.632 | 4.845 | 2.520 | 4.360 | 1.100 | |
萍乡市 | 3.036 | 11.860 | 16.571 | 1.200 | 4.280 | 3.060 | |
景德镇市 | 1.519 | 11.168 | 12.858 | 0.663 | 4.000 | 2.380 | |
新余市 | 3.017 | 9.115 | 17.281 | 1.200 | 3.080 | 3.120 | |
焦作市 | 1.140 | 10.015 | 9.767 | 0.520 | 3.920 | 1.920 | |
濮阳市 | 1.229 | 15.269 | 16.164 | 0.480 | 5.760 | 3.080 | |
黄石市 | 10.300 | 4.931 | 4.784 | 3.640 | 1.744 | 0.860 | |
均值 | 3.972 | 10.577 | 11.100 | 1.576 | 3.985 | 2.098 | |
西部 | 乌海市 | 6.925 | 8.727 | 9.243 | 2.880 | 3.280 | 1.700 |
泸州市 | 12.027 | 9.634 | 9.777 | 4.800 | 3.520 | 1.860 | |
铜川市 | 6.142 | 16.999 | 10.128 | 2.400 | 6.040 | 1.760 | |
白银市 | 1.182 | 18.212 | 21.840 | 0.480 | 6.236 | 3.472 | |
石嘴山市 | 7.843 | 4.546 | 12.869 | 3.160 | 1.691 | 2.520 | |
均值 | 6.824 | 11.624 | 12.771 | 2.744 | 4.153 | 2.262 |
表4 24个资源枯竭地级市产业结构高级化指数Table 4 The advanced industrial structure index of resource-exhausted cities in 24 prefecture-level cities of China (°) |
区域 | 城市 | 产业结构高级化(IH)指数 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
2008年 | 2009年 | 2010年 | 2011年 | 2012年 | 2013年 | 2014年 | 2015年 | 2016年 | 2017年 | ||
东北 | 阜新市 | 125.169 | 123.834 | 117.572 | 117.361 | 116.576 | 116.825 | 122.305 | 127.494 | 140.346 | 141.002 |
盘锦市 | 101.233 | 104.296 | 108.752 | 107.102 | 107.839 | 108.443 | 120.233 | 123.279 | 132.484 | 130.855 | |
抚顺市 | 122.112 | 123.445 | 120.003 | 118.197 | 118.693 | 118.822 | 124.558 | 130.524 | 129.807 | 127.229 | |
辽源市 | 119.520 | 118.857 | 119.124 | 117.358 | 117.380 | 117.762 | 118.278 | 119.603 | 120.584 | 120.966 | |
白山市 | 117.420 | 118.274 | 114.577 | 116.352 | 116.679 | 116.978 | 117.427 | 119.526 | 122.007 | 128.121 | |
伊春市 | 114.063 | 113.864 | 111.860 | 111.086 | 110.892 | 113.290 | 113.776 | 121.398 | 121.009 | 121.325 | |
七台河市 | 117.195 | 112.878 | 110.965 | 112.214 | 116.803 | 128.633 | 134.510 | 138.017 | 139.934 | 140.036 | |
鹤岗市 | 116.742 | 111.806 | 107.822 | 103.837 | 99.758 | 104.946 | 113.872 | 117.554 | 119.080 | 120.557 | |
双鸭山市 | 103.151 | 103.865 | 102.587 | 96.397 | 95.875 | 96.827 | 102.250 | 123.596 | 128.404 | 129.448 | |
均值 | 115.178 | 114.569 | 112.585 | 111.100 | 111.166 | 113.614 | 118.579 | 124.555 | 128.184 | 128.838 | |
东部 | 枣庄市 | 113.124 | 114.237 | 116.410 | 118.312 | 119.428 | 120.853 | 122.537 | 126.212 | 128.050 | 128.091 |
韶关市 | 125.074 | 134.860 | 132.980 | 131.806 | 133.335 | 136.997 | 137.457 | 140.617 | 141.409 | 141.572 | |
均值 | 119.099 | 124.549 | 124.695 | 125.059 | 126.382 | 128.925 | 129.997 | 133.415 | 134.730 | 134.832 | |
中部 | 淮北市 | 115.254 | 113.847 | 111.128 | 109.611 | 110.038 | 109.032 | 109.822 | 116.647 | 121.771 | 122.165 |
铜陵市 | 114.207 | 113.387 | 109.031 | 107.334 | 108.542 | 109.469 | 110.332 | 113.496 | 120.400 | 118.633 | |
萍乡市 | 113.778 | 114.480 | 113.300 | 111.183 | 117.007 | 118.822 | 119.549 | 122.047 | 124.068 | 130.998 | |
景德镇市 | 117.447 | 118.009 | 116.015 | 113.838 | 117.913 | 119.408 | 120.029 | 121.596 | 123.010 | 130.923 | |
新余市 | 112.608 | 119.937 | 114.693 | 111.759 | 115.642 | 121.222 | 121.737 | 124.042 | 125.239 | 130.397 | |
焦作市 | 109.399 | 110.345 | 107.510 | 106.814 | 108.353 | 109.186 | 109.498 | 117.086 | 119.845 | 119.699 | |
濮阳市 | 102.792 | 104.556 | 102.649 | 101.783 | 104.152 | 104.110 | 103.792 | 117.040 | 118.597 | 121.212 | |
黄石市 | 125.436 | 126.422 | 120.611 | 115.008 | 114.856 | 115.393 | 117.237 | 121.784 | 122.005 | 120.452 | |
均值 | 113.865 | 115.123 | 111.867 | 109.666 | 112.063 | 113.330 | 114.000 | 119.217 | 121.867 | 124.310 | |
西部 | 乌海市 | 116.199 | 113.685 | 110.830 | 109.567 | 109.285 | 116.728 | 118.909 | 126.417 | 126.893 | 125.445 |
泸州市 | 114.977 | 116.521 | 112.797 | 109.783 | 109.516 | 110.641 | 111.198 | 112.670 | 113.763 | 121.496 | |
铜川市 | 117.745 | 116.955 | 115.258 | 113.567 | 111.681 | 110.932 | 112.721 | 118.917 | 127.099 | 128.091 | |
白银市 | 116.568 | 117.999 | 118.682 | 116.751 | 116.874 | 119.817 | 125.140 | 130.274 | 135.714 | 137.144 | |
石嘴山市 | 106.158 | 113.423 | 116.119 | 114.427 | 114.178 | 114.674 | 113.947 | 115.240 | 116.488 | 119.293 | |
均值 | 114.329 | 115.717 | 114.737 | 112.819 | 112.307 | 114.558 | 116.383 | 120.704 | 123.991 | 126.294 | |
全国平均值 | 127.709 | 130.740 | 131.133 | 131.230 | 133.143 | 135.019 | 137.372 | 140.159 | 141.343 | 141.030 |
表5 变量多重共线性诊断结果Table 5 The variable multicollinearity diagnosis results |
Variable | CON | LAB | INV | TEC | URB | FI |
---|---|---|---|---|---|---|
VIF | 5.75 | 4.54 | 3.43 | 2.85 | 2.42 | 1.49 |
表6 模型统计学检验结果Table 6 The model statistical test results |
检验名称 | 统计量 | P值 |
---|---|---|
F检验 | 12.48 | 0.000 |
LM检验 | 166.35 | 0.000 |
豪斯曼检验 | 30.83 | 0.001 |
表7 模型回归结果Table 7 The model regression results |
变量 | 系数 | t值 | P值 |
---|---|---|---|
LAP | 2.37 | 3.31 | 0.00*** |
FI | 0.74 | 8.62 | 0.00*** |
TEC | -0.5 | -0.66 | 0.51 |
CON | 0.56 | 4.19 | 0.00*** |
INV | -0.50 | -4.89 | 0.00*** |
URB | 0.65 | 4.02 | 0.00*** |
截距项 | -0.9 | -3.14 | 0.00*** |
注:***表示在1%的水平上显著;R2=0.52。 |
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