
长江经济带污染密集型产业集聚时空特征及其绿色经济效应
Spatial and temporal characteristics of pollution intensive industrial agglomeration and its green economy effect in the Yangtze River Economic Belt
污染治理是长江经济带生态保护与绿色发展的难点与痛点,而污染密集型产业集聚正是造成长江环境问题的重要诱因。从对绿色发展效率的作用机理出发,采用加权标准差椭圆等方法,识别长江经济带沿线11省份污染密集型产业集聚的时空演化格局并探究其绿色经济效应。结果显示:长江经济带污染密集型产业集聚深度弱于全国水平,产业结构朝绿色低碳化转型;长江经济带污染密集型产业集聚地区分异显著,中上游地区污染产能集聚能力较强;污染密集型产业集聚对长江经济带绿色发展能力在短期内具有抑制作用,但存在长期绿色转型机制,上中下游地区绿色转折速率梯度提升。研究结果可为推动长江经济带工业绿色集聚发展提供借鉴。
Pollution control is the difficulty and pain point of ecological protection and green development in the Yangtze River Economic Belt (YREB), and pollution intensive industrial agglomeration is the severe inducement of environmental problems. Based on the mechanism of green development efficiency, this paper uses the weighted standard deviation ellipse method and other methods to identify the spatial-temporal evolution pattern of pollution intensive industrial agglomeration in 11 provincial-level regions of the YREB, and then explores its green economy effect. The results show that: the degree of pollution intensive industrial agglomeration in the YREB is lower than the national level, the industrial structure is transforming towards green and low-carbon development; the pollution intensive industrial agglomeration areas in the YREB are significantly different, the pollution production capacity in the middle and upstream areas is strong; the pollution intensive industrial agglomeration has an inhibitory effect on the green development capacity of the YREB in the short term, however, the pollution intensive industry cluster has a long-term green transformation mechanism, and the transition rate in the upper, middle and lower reaches rises gradually. The results can provide reference for promoting the industrial green agglomeration in the study area.
污染密集型产业 / 长江经济带 / 产业集聚 / 绿色发展 / 影响效应 {{custom_keyword}} /
pollution intensive industry / YREB / industrial agglomeration / green development / influential effect {{custom_keyword}} /
表1 长江经济带污染密集型产业集聚绿色经济效应的相关变量描述性统计Table 1 Descriptive statistics of related variables on green economy effect of the agglomeration in the YREB |
变量 | 单位 | 符号 | 均值 | 标准差 | 最大值 | 最小值 | 样本数/个 |
---|---|---|---|---|---|---|---|
绿色发展效率 | — | efficiency | 0.737 | 0.189 | 1.402 | 0.445 | 165 |
产业集聚 | — | agg | 1.057 | 0.279 | 1.638 | 0.532 | 165 |
产业集聚二次项 | — | agg2 | 1.195 | 0.615 | 2.682 | 0.283 | 165 |
产业结构高级化 | % | industrial | 0.772 | 0.040 | 0.898 | 0.705 | 165 |
环境规制 | % | environment | 1.132 | 0.438 | 2.660 | 0.514 | 165 |
技术创新 | % | technique | 1.424 | 0.746 | 3.934 | 0.406 | 165 |
对外开放 | % | opening | 0.330 | 0.412 | 1.721 | 0.032 | 165 |
注:“—”表示该指标无单位。 |
表2 1999—2018年长江经济带上中下游地区污染密集型产业集聚水平Table 2 Concentration level of pollution intensive industries in the three reaches of the YREB from 1999 to 2018 |
年份/ 地区 | 上游 地区 | 中游 地区 | 下游 地区 | 长江 经济带 | 年份/ 地区 | 上游 地区 | 中游 地区 | 下游 地区 | 长江 经济带 |
---|---|---|---|---|---|---|---|---|---|
1999 | 1.019 | 1.138 | 0.939 | 0.983 | 2009 | 1.048 | 1.146 | 0.863 | 0.939 |
2000 | 1.019 | 1.162 | 0.933 | 0.980 | 2010 | 1.060 | 1.136 | 0.849 | 0.934 |
2001 | 1.013 | 1.123 | 0.944 | 0.979 | 2011 | 1.036 | 1.114 | 0.849 | 0.932 |
2002 | 1.037 | 1.120 | 0.938 | 0.977 | 2012 | 1.011 | 1.081 | 0.837 | 0.917 |
2003 | 1.084 | 1.192 | 0.896 | 0.959 | 2013 | 0.991 | 1.066 | 0.857 | 0.926 |
2004 | 1.218 | 1.275 | 0.865 | 0.956 | 2014 | 0.988 | 1.048 | 0.844 | 0.914 |
2005 | 1.121 | 1.229 | 0.901 | 0.972 | 2015 | 0.988 | 1.038 | 0.860 | 0.923 |
2006 | 1.137 | 1.228 | 0.893 | 0.969 | 2016 | 0.957 | 1.025 | 0.864 | 0.918 |
2007 | 1.122 | 1.228 | 0.874 | 0.958 | 2017 | 0.925 | 1.007 | 0.855 | 0.903 |
2008 | 1.081 | 1.177 | 0.868 | 0.947 | 2018 | 0.933 | 0.952 | 0.836 | 0.881 |
注:上游地区包括云贵川渝四省市,中游地区包括湘鄂赣三省,下游地区包括苏浙皖沪四省市,下同。 |
表3 1999—2018年长江经济带污染密集型产业集聚标准差椭圆的基本参数Table 3 Basic parameters of the SDE of the pollution intensive industry in the YREB from 1999 to 2018 |
年份/参数 | 重心坐标 | 长轴/km | 短轴/km | 扁率/% | 方位角/(°) | 面积/km2 |
---|---|---|---|---|---|---|
1999 | (112.68°E,29.44°N) | 1750.88 | 482.91 | 0.7242 | 76.84 | 663891.35 |
2000 | (112.66°E,29.42°N) | 1749.62 | 482.54 | 0.7242 | 76.58 | 662911.62 |
2001 | (112.67°E,29.41°N) | 1763.95 | 482.92 | 0.7262 | 76.66 | 668865.24 |
2002 | (112.58°E,29.39°N) | 1765.59 | 485.43 | 0.7251 | 76.60 | 672964.36 |
2003 | (112.36°E,29.30°N) | 1764.56 | 487.57 | 0.7237 | 76.26 | 675540.87 |
2004 | (111.88°E,29.14°N) | 1792.58 | 487.80 | 0.7279 | 75.40 | 686517.50 |
2005 | (112.20°E,29.22°N) | 1787.64 | 483.32 | 0.7296 | 75.69 | 678399.39 |
2006 | (112.08°E,29.16°N) | 1800.37 | 481.34 | 0.7326 | 75.34 | 680424.61 |
2007 | (112.01°E,29.12°N) | 1802.25 | 480.80 | 0.7332 | 75.18 | 680367.78 |
2008 | (112.03°E,29.12°N) | 1810.37 | 476.92 | 0.7366 | 75.05 | 677912.23 |
2009 | (112.02°E,29.12°N) | 1810.21 | 477.72 | 0.7361 | 75.13 | 678994.26 |
2010 | (111.92°E,29.08°N) | 1821.67 | 479.46 | 0.7368 | 75.15 | 685785.14 |
2011 | (111.91°E,29.05°N) | 1829.47 | 476.54 | 0.7395 | 75.08 | 684452.02 |
2012 | (111.95°E,29.07°N) | 1833.35 | 476.75 | 0.7400 | 75.08 | 686276.33 |
2013 | (111.99°E,29.06°N) | 1842.97 | 473.35 | 0.7432 | 74.92 | 684948.30 |
2014 | (111.91°E,29.03°N) | 1847.62 | 476.69 | 0.7420 | 74.94 | 691522.22 |
2015 | (111.98°E,29.07°N) | 1844.26 | 481.20 | 0.7391 | 75.20 | 696803.38 |
2016 | (112.04°E,29.09°N) | 1849.26 | 480.44 | 0.7402 | 75.18 | 697588.53 |
2017 | (112.07°E,29.06°N) | 1863.60 | 471.54 | 0.7470 | 74.68 | 689966.01 |
2018 | (111.99°E,29.04°N) | 1869.06 | 472.54 | 0.7472 | 74.60 | 693448.24 |
表4 2003—2017年长江经济带污染密集型产业集聚的驱动因素相关系数与方差膨胀因子Table 4 Correlation index and variance inflation factor of the driving factors in the YREB from 2003 to 2017 |
变量 | agg | agg2 | industrial | environment | technique | opening | VIF |
---|---|---|---|---|---|---|---|
agg | 1.000 | 0.256 | -0.270 | -0.142 | -0.779 | -0.595 | 4.228 |
agg2 | 0.256 | 1.000 | 0.324 | -0.157 | -0.073 | 0.172 | 1.405 |
industrial | -0.270 | 0.324 | 1.000 | -0.174 | 0.656 | 0.661 | 3.447 |
environment | -0.142 | -0.157 | -0.174 | 1.000 | -0.021 | -0.055 | 1.083 |
technique | -0.779 | -0.073 | 0.656 | -0.021 | 1.000 | 0.700 | 5.784 |
opening | -0.595 | 0.172 | 0.661 | -0.055 | 0.700 | 1.000 | 2.785 |
注:VIF列数字为方差膨胀因子,其他数字为相关系数。 |
表5 长江经济带污染密集型产业集聚的绿色经济效应Table 5 Green economic effect of pollution intensive industrial agglomeration in the YREB |
变量 | 模型1 | 模型2 | 模型3 | 模型4 | 模型5 |
---|---|---|---|---|---|
agg | -1.451*** (0.510) | -1.599*** (0.571) | -1.059* (0.559) | -0.785* (0.461) | -2.473*** (0.394) |
agg2 | 0.719*** (0.230) | 0.778*** (0.252) | 0.514** (0.248) | 0.462* (0.246) | 1.208*** (0.175) |
industrial | -0.613 (0.523) | -0.681 (0.508) | -1.827*** (0.691) | -1.895*** (0.554) | |
environment | -0.128*** (0.029) | -0.128*** (0.029) | -0.065** (0.028) | ||
technique | 0.106** (0.044) | 0.070* (0.038) | |||
opening | 0.177*** (0.048) | ||||
cons | 1.412*** (0.271) | 1.972*** (0.609) | 1.913*** (0.591) | 2.418*** (0.627) | 3.287*** (0.457) |
F统计量 | 12.44*** [0.002] | 12.97*** [0.005] | 33.31*** [0.000] | 40.40*** [0.000] | 92.79*** [0.000] |
R2 | 0.354 | 0.272 | 0.193 | 0.246 | 0.380 |
Hausman 统计量 | 34.09*** [0.000] | 31.64*** [0.000] | 35.56*** [0.000] | 40.98*** [0.000] | 84.13*** [0.000] |
样本量/个 | 165 | 165 | 165 | 165 | 165 |
注:*、**、***分别表示通过10%、5%、1%的显著性检验,小括号内为标准误,中括号内为双侧伴随概率p值,下同。 |
表6 长江经济带上中下游地区污染密集型产业集聚的绿色经济效应Table 6 Green economic effects of the industrial agglomeration in the three reaches of the YREB |
变量 | 上游地区_FE | 上游地区_RE | 中游地区_FE | 中游地区_RE | 下游地区_FE | 下游地区_RE | |
---|---|---|---|---|---|---|---|
agg | -2.752*** (0.484) | -1.010 (0.993) | -4.802** (2.300) | 2.281 (2.126) | -1.112* (0.591) | 0.965 (2.277) | |
agg2 | 1.044*** (0.199) | 0.251 (0.383) | 2.022** (0.904) | -0.766 (0.831) | 0.810* (0.433) | -0.053 (1.115) | |
industrial | -2.340*** (0.542) | -4.199*** (1.051) | -1.949*** (0.656) | 6.541*** (1.749) | 0.799*** (0.283) | 1.507 (2.192) | |
environment | -0.081** (0.034) | -0.080** (0.040) | -0.048 (0.043) | -0.056* (0.032) | -0.135* (0.058) | -0.068** (0.034) | |
technique | -0.449*** (0.064) | -0.116 (0.135) | -0.318*** (0.115) | -0.616*** (0.100) | 0.119* (0.060) | 0.017 (0.077) | |
opening | -0.544** (0.301) | -0.361 (0.306) | 0.361 (0.617) | -1.469** (0.604) | 0.078** (0.039) | -0.345 (0.258) | |
cons | 4.795*** (0.392) | 4.997 (0.964) | 5.320*** (1.669) | -4.948** (2.341) | 0.341 (1.268) | -0.928 (1.995) | |
F统计量 | 184.47*** [0.000] | 103.1*** [0.000] | 92.3*** [0.000] | ||||
Wald统计量 | 10.75*** [0.000] | 32.39*** [0.000] | 8.75*** [0.000] | ||||
R2 | 0.777 | 0.563 | 0.731 | 0.335 | 0.6352 | 0.512 | |
Hausman 统计量 | 10.11** [0.017] | 18.11*** [0.000] | 15.55*** [0.001] | ||||
样本量/个 | 60 | 60 | 45 | 45 | 60 | 60 |
注:FE表示固定效应模型,RE表示随机效应模型。 |
表7 长江经济带污染密集型产业集聚的绿色经济效应稳健性检验结果Table 7 Robustness test results of green economy effect of the industrial agglomeration in the YREB |
变量 | 模型1 | 模型2 | 模型3 | 模型4 | 模型5 | 模型6 |
---|---|---|---|---|---|---|
agg | -3.185*** (0.911) | -2.146** (0.957) | -2.521*** (0.388) | -1.910** (0.969) | -3.724*** (0.771) | -0.319** (0.138) |
agg2 | 2.525*** (0.846) | 1.480* (0.831) | 1.222*** (0.173) | 0.669* (0.387) | 1.742*** (0.327) | 0.282* (0.158) |
industrial | -2.774*** (0.745) | -1.939*** (0.536) | -2.630** (1.256) | -1.846* (1.020) | 0.339 (1.024) | |
environment | -0.121*** (0.023) | -0.060** (0.027) | -0.248*** (0.082) | -0.027 (0.031) | -0.024 (0.042) | |
technique | -0.025 (0.043) | 0.055** (0.027) | -0.375*** (0.098) | 0.077 (0.075) | 0.212** (0.097) | |
opening | -0.652*** (0.092) | 0.186*** (0.047) | 0.295** (0.139) | 0.095 (0.110) | 0.061 (0.153) | |
cons | 1.641*** (0.235) | 3.927*** (0.592) | 3.364*** (0.446) | 4.593*** (1.136) | 3.852*** (0.823) | 0.078 (0.905) |
F统计量 | 21.16*** [0.000] | 23.17*** [0.000] | 97.25*** [0.000] | 23.3*** [0.000] | 42.04*** [0.000] | 20.35*** [0.002] |
R2 | 0.189 | 0.484 | 0.381 | 0.365 | 0.678 | 0.4925 |
Hausman 统计量 | 17.97*** [0.000] | 94.65*** [0.000] | 84.15*** [0.000] | 17.94** [0.012] | 13.16* [0.068] | 12.25* [0.093] |
样本量/个 | 165 | 165 | 165 | 55 | 55 | 55 |
表8 长江经济带污染密集型产业集聚的绿色经济效应内生性检验结果Table 8 Endogenous tests on green economy effect of the industrial agglomeration in the YREB |
变量 | 模型1 | 模型2 | 模型3 | 模型4 | 模型5 |
---|---|---|---|---|---|
efficiency.l | 1.290*** (0.273) | 1.200*** (0.358) | 1.172*** (0.426) | 1.222*** (0.415) | 0.121** (0.049) |
agg | -1.937* (1.181) | -2.226* (1.193) | -4.071** (2.018) | -11.240* (6.907) | -52.228** (24.194) |
agg2 | 0.599** (0.299) | 0.760* (0.405) | 1.419* (0.847) | 4.211** (1.874) | 19.673* (10.672) |
industrial | 0.742 (1.974) | 0.368 (2.073) | 1.565 (2.249) | -2.445** (1.147) | |
environment | -0.024* (0.013) | -0.007 (0.023) | 0.022 (0.033) | ||
technique | -0.351* (0.217) | -1.636 (3.444) | |||
opening | -0.686 (1.235) | ||||
cons | 1.074 (1.203) | 0.605 (4.107) | 1.732** (0.672) | 5.375 (3.672) | 33.021* (19.986) |
Wald统计量 | 737.02*** [0.000] | 402.28*** [0.000] | 329.12*** [0.000] | 2370.33*** [0.000] | 469.70*** [0.000] |
AR(1) | 1.621* [0.105] | -2.242** [0.025] | -1.697* [0.0869] | 1.869* [0.616] | -1.710* [0.087] |
AR(2) | 0.745 [0.456] | 0.793 [0.428] | 0.529 [0.597] | -1.318 [0.187] | 0.286 [0.775] |
Sargan统计量 | 7.235 [1.000] | 7.070 [1.000] | 5.648 [1.000] | 2.940 [1.000] | 4.028 [1.000] |
样本量/个 | 154 | 154 | 154 | 154 | 154 |
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