Journal of Resources and Ecology ›› 2022, Vol. 13 ›› Issue (6): 986-998.DOI: 10.5814/j.issn.1674-764x.2022.06.004
• Resource Use and Resource Economy • Previous Articles Next Articles
LI Hongli1(), CHEN Yunping2,*(
)
Received:
2021-08-25
Accepted:
2022-02-01
Online:
2022-11-30
Published:
2022-10-12
Contact:
CHEN Yunping
About author:
LI Hongli,E-mail: 360299525@qq.com
Supported by:
LI Hongli, CHEN Yunping. Spatio-temporal Dynamic Evolution and the Factors Impacting Eco-efficiency in Chengdu-Chongqing Economic Circle[J]. Journal of Resources and Ecology, 2022, 13(6): 986-998.
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URL: http://www.jorae.cn/EN/10.5814/j.issn.1674-764x.2022.06.004
Grade 1 | Grade 2 | Definition of indicators |
---|---|---|
Inputs | Labor force (I1) | Number of social employees at the end of the year (104 person) |
Capital (I2) | The perpetual inventory method was used to calculate the capital stock to measure the capital investment, which is: Ki,t= Ki,t-1(1-δi,t) + Ii,t, where Ki,t and Ki,t-1 represent the stock of capital of city i in period t and t-1,respectively, δi,t stands for depreciation rate of city i in period t, and Ii,t is the total fixed assets investment of city i in period t (108 yuan) | |
Energy resource (I3) | The consumption of energy, which is converted into 10000 tons of standard coal (104 t) | |
Desirable outputs | GDP (O1) | GDP of cities at constant prices from 2004 (108 yuan) |
Greening coverage (O2) | The greening coverage rate of built-up area (%) | |
Undesirable outputs | Index of environmental pollution (UO1) | Comprehensive index of industrial wastewater, waste gas and solid waste based on the entropy method. |
Table 1 Variables of input and output indicators
Grade 1 | Grade 2 | Definition of indicators |
---|---|---|
Inputs | Labor force (I1) | Number of social employees at the end of the year (104 person) |
Capital (I2) | The perpetual inventory method was used to calculate the capital stock to measure the capital investment, which is: Ki,t= Ki,t-1(1-δi,t) + Ii,t, where Ki,t and Ki,t-1 represent the stock of capital of city i in period t and t-1,respectively, δi,t stands for depreciation rate of city i in period t, and Ii,t is the total fixed assets investment of city i in period t (108 yuan) | |
Energy resource (I3) | The consumption of energy, which is converted into 10000 tons of standard coal (104 t) | |
Desirable outputs | GDP (O1) | GDP of cities at constant prices from 2004 (108 yuan) |
Greening coverage (O2) | The greening coverage rate of built-up area (%) | |
Undesirable outputs | Index of environmental pollution (UO1) | Comprehensive index of industrial wastewater, waste gas and solid waste based on the entropy method. |
Variables | Indicators | Definition of Indicators |
---|---|---|
Explained variable | Eco-efficiency | Based on the previous evaluation—EE |
Control variables | Industrial structure | Refer to the calculation method of Fu Linghui (Fu, |
Technological level | Removal rate of sulfur dioxide—Tech | |
Urbanization level | Urban population / Local total population—Urban | |
Level of opening | Actual utilization of foreign capital /Local GDP—Open | |
Environmental regulation | Expenditure for environmental protection/ local fiscal expenditure—RE |
Table 2 Influencing factors of eco-efficiency
Variables | Indicators | Definition of Indicators |
---|---|---|
Explained variable | Eco-efficiency | Based on the previous evaluation—EE |
Control variables | Industrial structure | Refer to the calculation method of Fu Linghui (Fu, |
Technological level | Removal rate of sulfur dioxide—Tech | |
Urbanization level | Urban population / Local total population—Urban | |
Level of opening | Actual utilization of foreign capital /Local GDP—Open | |
Environmental regulation | Expenditure for environmental protection/ local fiscal expenditure—RE |
Outputs/Inputs | Labor force | Capital | Energy resource |
---|---|---|---|
GDP | 0.881*** (0.0000) | 0.993*** (0.0000) | 0.959*** (0.0000) |
Greening coverage | 0.170*** (0.0085) | 0.221*** (0.0006) | 0.250*** (0.0001) |
Index of environmental pollution | -0.785*** (0.0000) | -0.548*** (0.0000) | -0.663*** (0.0000) |
Table 3 The results of the Pearson test for inputs and outputs
Outputs/Inputs | Labor force | Capital | Energy resource |
---|---|---|---|
GDP | 0.881*** (0.0000) | 0.993*** (0.0000) | 0.959*** (0.0000) |
Greening coverage | 0.170*** (0.0085) | 0.221*** (0.0006) | 0.250*** (0.0001) |
Index of environmental pollution | -0.785*** (0.0000) | -0.548*** (0.0000) | -0.663*** (0.0000) |
Year | Eco-efficiency | Pure technical efficiency | Scale efficiency |
---|---|---|---|
2004 | 0.870297 | 0.983623902 | 0.884786450 |
2005 | 0.801846 | 1.018703980 | 0.787123893 |
2006 | 0.839451 | 0.986933873 | 0.850564379 |
2007 | 0.918079 | 0.994288994 | 0.923352445 |
2008 | 0.971765 | 1.025956425 | 0.947179244 |
2009 | 0.972578 | 1.025964106 | 0.947965412 |
2010 | 0.946803 | 1.015201737 | 0.932625727 |
2011 | 0.948763 | 1.006303896 | 0.942819382 |
2012 | 0.939203 | 0.998493515 | 0.940619746 |
2013 | 0.927904 | 0.985206629 | 0.941836502 |
2014 | 0.938332 | 0.977123031 | 0.960300994 |
2015 | 0.941280 | 0.995825774 | 0.945225493 |
2016 | 0.927984 | 1.001682759 | 0.92642494 |
2017 | 0.880588 | 0.928576901 | 0.948320129 |
2018 | 0.932584 | 0.969394411 | 0.962027807 |
Table 4 Annual eco-efficiency values and their decomposition
Year | Eco-efficiency | Pure technical efficiency | Scale efficiency |
---|---|---|---|
2004 | 0.870297 | 0.983623902 | 0.884786450 |
2005 | 0.801846 | 1.018703980 | 0.787123893 |
2006 | 0.839451 | 0.986933873 | 0.850564379 |
2007 | 0.918079 | 0.994288994 | 0.923352445 |
2008 | 0.971765 | 1.025956425 | 0.947179244 |
2009 | 0.972578 | 1.025964106 | 0.947965412 |
2010 | 0.946803 | 1.015201737 | 0.932625727 |
2011 | 0.948763 | 1.006303896 | 0.942819382 |
2012 | 0.939203 | 0.998493515 | 0.940619746 |
2013 | 0.927904 | 0.985206629 | 0.941836502 |
2014 | 0.938332 | 0.977123031 | 0.960300994 |
2015 | 0.941280 | 0.995825774 | 0.945225493 |
2016 | 0.927984 | 1.001682759 | 0.92642494 |
2017 | 0.880588 | 0.928576901 | 0.948320129 |
2018 | 0.932584 | 0.969394411 | 0.962027807 |
State | 1 | 2 | 3 | 4 |
---|---|---|---|---|
1 | 0.6964 | 0.2143 | 0.0536 | 0.0357 |
2 | 0.1755 | 0.6491 | 0.1754 | 0.0000 |
3 | 0.0364 | 0.1636 | 0.6727 | 0.1273 |
4 | 0.0179 | 0.0000 | 0.1428 | 0.8393 |
Table 5 Eco-efficiency transfer probability matrix
State | 1 | 2 | 3 | 4 |
---|---|---|---|---|
1 | 0.6964 | 0.2143 | 0.0536 | 0.0357 |
2 | 0.1755 | 0.6491 | 0.1754 | 0.0000 |
3 | 0.0364 | 0.1636 | 0.6727 | 0.1273 |
4 | 0.0179 | 0.0000 | 0.1428 | 0.8393 |
Period | DMU | Eco-efficiency score | Pure technical efficiency score | Scale efficiency score | Scale effect score | Period | DMU | Eco-efficiency score | Pure technical efficiency score | Scale efficiency score | Scale effect score |
---|---|---|---|---|---|---|---|---|---|---|---|
2004 | Chengdu | 1.574 | 2.276 | 0.691 | Increasing | 2009 | Chengdu | 1.144 | 1.161 | 0.985 | Increasing |
2004 | Dazhou | 0.611 | 0.638 | 0.957 | Increasing | 2009 | Dazhou | 1.042 | 1.049 | 0.993 | Decreasing |
2004 | Deyang | 1.051 | 1.053 | 0.998 | Decreasing | 2009 | Deyang | 1.045 | 1.045 | 1.000 | Decreasing |
2004 | Guang'an | 0.667 | 0.790 | 0.845 | Increasing | 2009 | Guang'an | 1.002 | 1.007 | 0.995 | Increasing |
2004 | Leshan | 1.044 | 1.106 | 0.944 | Increasing | 2009 | Leshan | 1.026 | 1.032 | 0.994 | Increasing |
2004 | Luzhou | 0.813 | 1.010 | 0.805 | Decreasing | 2009 | Luzhou | 0.939 | 1.001 | 0.938 | Increasing |
2004 | Meighan | 0.685 | 0.782 | 0.875 | Increasing | 2009 | Meishan | 0.780 | 1.014 | 0.769 | Increasing |
2004 | Mianyang | 1.080 | 1.082 | 0.998 | Decreasing | 2009 | Mianyang | 1.027 | 1.033 | 0.994 | Increasing |
2004 | Neijiang | 0.677 | 1.020 | 0.663 | Increasing | 2009 | Neijiang | 0.793 | 0.832 | 0.953 | Increasing |
2004 | Nanchong | 0.695 | 0.697 | 0.997 | Decreasing | 2009 | Nanchong | 0.788 | 0.816 | 0.965 | Decreasing |
2004 | Suining | 0.634 | 0.721 | 0.879 | Increasing | 2009 | Suining | 1.047 | 1.049 | 0.998 | Decreasing |
2004 | Ya'an | 1.155 | 1.600 | 0.722 | Increasing | 2009 | Ya'an | 1.028 | 1.479 | 0.696 | Increasing |
2004 | Yibin | 0.768 | 0.786 | 0.976 | Increasing | 2009 | Yibin | 0.830 | 0.832 | 0.998 | Increasing |
2004 | Chongqing | 1.108 | 1.108 | 1.000 | Constant | 2009 | Chongqing | 1.219 | 1.228 | 0.993 | Decreasing |
2004 | Ziyang | 0.707 | 0.733 | 0.965 | Increasing | 2009 | Ziyang | 0.877 | 0.903 | 0.971 | Increasing |
2004 | Zigong | 1.236 | 1.284 | 0.963 | Increasing | 2009 | Zigong | 1.114 | 1.118 | 0.996 | Increasing |
Mean | 0.870 | 0.984 | 0.885 | - | Mean | 0.973 | 1.026 | 0.948 | - | ||
2014 | Chengdu | 1.105 | 1.107 | 0.999 | Increasing | 2018 | Chengdu | 1.070 | 1.071 | 0.998 | Decreasing |
2014 | Dazhou | 0.561 | 0.562 | 0.998 | Decreasing | 2018 | Dazhou | 0.627 | 0.633 | 0.989 | Increasing |
2014 | Deyang | 1.031 | 1.039 | 0.993 | Increasing | 2018 | Deyang | 1.074 | 1.074 | 1.000 | Decreasing |
2014 | Guang'an | 1.013 | 1.015 | 0.998 | Decreasing | 2018 | Guang'an | 0.806 | 0.830 | 0.971 | Increasing |
2014 | Leshan | 0.820 | 0.845 | 0.970 | Increasing | 2018 | Leshan | 0.906 | 0.954 | 0.949 | Increasing |
2014 | Luzhou | 0.787 | 0.791 | 0.994 | Increasing | 2018 | Luzhou | 0.773 | 0.777 | 0.996 | Increasing |
2014 | Meishan | 1.003 | 1.005 | 0.998 | Increasing | 2018 | Meishan | 1.018 | 1.033 | 0.986 | Increasing |
2014 | Mianyang | 1.004 | 1.008 | 0.997 | Increasing | 2018 | Mianyang | 1.007 | 1.008 | 1.000 | Decreasing |
2014 | Neijiang | 1.053 | 1.071 | 0.984 | Increasing | 2018 | Neijiang | 0.858 | 0.932 | 0.921 | Increasing |
2014 | Nanchong | 0.734 | 0.750 | 0.979 | Decreasing | 2018 | Nanchong | 1.010 | 1.021 | 0.989 | Decreasing |
2014 | Suining | 0.832 | 0.865 | 0.961 | Increasing | 2018 | Suining | 0.757 | 0.805 | 0.940 | Increasing |
2014 | Ya'an | 1.149 | 1.633 | 0.704 | Increasing | 2018 | Ya'an | 1.185 | 1.652 | 0.717 | Increasing |
2014 | Yibin | 0.806 | 0.811 | 0.994 | Increasing | 2018 | Yibin | 0.762 | 0.769 | 0.991 | Increasing |
2014 | Chongqing | 1.700 | 1.938 | 0.877 | Increasing | 2018 | Chongqing | 1.185 | 1.190 | 0.996 | Increasing |
2014 | Ziyang | 0.801 | 0.813 | 0.985 | Increasing | 2018 | Ziyang | 1.077 | 1.080 | 0.997 | Increasing |
2014 | Zigong | 1.058 | 1.072 | 0.987 | Increasing | 2018 | Zigong | 1.039 | 1.041 | 0.997 | Decreasing |
Mean | 0.938 | 0.977 | 0.960 | - | Mean | 0.933 | 0.969 | 0.962 | - |
Table 6 List of eco-efficiency values for cities in the Chengdu-Chongqing Economic Circle
Period | DMU | Eco-efficiency score | Pure technical efficiency score | Scale efficiency score | Scale effect score | Period | DMU | Eco-efficiency score | Pure technical efficiency score | Scale efficiency score | Scale effect score |
---|---|---|---|---|---|---|---|---|---|---|---|
2004 | Chengdu | 1.574 | 2.276 | 0.691 | Increasing | 2009 | Chengdu | 1.144 | 1.161 | 0.985 | Increasing |
2004 | Dazhou | 0.611 | 0.638 | 0.957 | Increasing | 2009 | Dazhou | 1.042 | 1.049 | 0.993 | Decreasing |
2004 | Deyang | 1.051 | 1.053 | 0.998 | Decreasing | 2009 | Deyang | 1.045 | 1.045 | 1.000 | Decreasing |
2004 | Guang'an | 0.667 | 0.790 | 0.845 | Increasing | 2009 | Guang'an | 1.002 | 1.007 | 0.995 | Increasing |
2004 | Leshan | 1.044 | 1.106 | 0.944 | Increasing | 2009 | Leshan | 1.026 | 1.032 | 0.994 | Increasing |
2004 | Luzhou | 0.813 | 1.010 | 0.805 | Decreasing | 2009 | Luzhou | 0.939 | 1.001 | 0.938 | Increasing |
2004 | Meighan | 0.685 | 0.782 | 0.875 | Increasing | 2009 | Meishan | 0.780 | 1.014 | 0.769 | Increasing |
2004 | Mianyang | 1.080 | 1.082 | 0.998 | Decreasing | 2009 | Mianyang | 1.027 | 1.033 | 0.994 | Increasing |
2004 | Neijiang | 0.677 | 1.020 | 0.663 | Increasing | 2009 | Neijiang | 0.793 | 0.832 | 0.953 | Increasing |
2004 | Nanchong | 0.695 | 0.697 | 0.997 | Decreasing | 2009 | Nanchong | 0.788 | 0.816 | 0.965 | Decreasing |
2004 | Suining | 0.634 | 0.721 | 0.879 | Increasing | 2009 | Suining | 1.047 | 1.049 | 0.998 | Decreasing |
2004 | Ya'an | 1.155 | 1.600 | 0.722 | Increasing | 2009 | Ya'an | 1.028 | 1.479 | 0.696 | Increasing |
2004 | Yibin | 0.768 | 0.786 | 0.976 | Increasing | 2009 | Yibin | 0.830 | 0.832 | 0.998 | Increasing |
2004 | Chongqing | 1.108 | 1.108 | 1.000 | Constant | 2009 | Chongqing | 1.219 | 1.228 | 0.993 | Decreasing |
2004 | Ziyang | 0.707 | 0.733 | 0.965 | Increasing | 2009 | Ziyang | 0.877 | 0.903 | 0.971 | Increasing |
2004 | Zigong | 1.236 | 1.284 | 0.963 | Increasing | 2009 | Zigong | 1.114 | 1.118 | 0.996 | Increasing |
Mean | 0.870 | 0.984 | 0.885 | - | Mean | 0.973 | 1.026 | 0.948 | - | ||
2014 | Chengdu | 1.105 | 1.107 | 0.999 | Increasing | 2018 | Chengdu | 1.070 | 1.071 | 0.998 | Decreasing |
2014 | Dazhou | 0.561 | 0.562 | 0.998 | Decreasing | 2018 | Dazhou | 0.627 | 0.633 | 0.989 | Increasing |
2014 | Deyang | 1.031 | 1.039 | 0.993 | Increasing | 2018 | Deyang | 1.074 | 1.074 | 1.000 | Decreasing |
2014 | Guang'an | 1.013 | 1.015 | 0.998 | Decreasing | 2018 | Guang'an | 0.806 | 0.830 | 0.971 | Increasing |
2014 | Leshan | 0.820 | 0.845 | 0.970 | Increasing | 2018 | Leshan | 0.906 | 0.954 | 0.949 | Increasing |
2014 | Luzhou | 0.787 | 0.791 | 0.994 | Increasing | 2018 | Luzhou | 0.773 | 0.777 | 0.996 | Increasing |
2014 | Meishan | 1.003 | 1.005 | 0.998 | Increasing | 2018 | Meishan | 1.018 | 1.033 | 0.986 | Increasing |
2014 | Mianyang | 1.004 | 1.008 | 0.997 | Increasing | 2018 | Mianyang | 1.007 | 1.008 | 1.000 | Decreasing |
2014 | Neijiang | 1.053 | 1.071 | 0.984 | Increasing | 2018 | Neijiang | 0.858 | 0.932 | 0.921 | Increasing |
2014 | Nanchong | 0.734 | 0.750 | 0.979 | Decreasing | 2018 | Nanchong | 1.010 | 1.021 | 0.989 | Decreasing |
2014 | Suining | 0.832 | 0.865 | 0.961 | Increasing | 2018 | Suining | 0.757 | 0.805 | 0.940 | Increasing |
2014 | Ya'an | 1.149 | 1.633 | 0.704 | Increasing | 2018 | Ya'an | 1.185 | 1.652 | 0.717 | Increasing |
2014 | Yibin | 0.806 | 0.811 | 0.994 | Increasing | 2018 | Yibin | 0.762 | 0.769 | 0.991 | Increasing |
2014 | Chongqing | 1.700 | 1.938 | 0.877 | Increasing | 2018 | Chongqing | 1.185 | 1.190 | 0.996 | Increasing |
2014 | Ziyang | 0.801 | 0.813 | 0.985 | Increasing | 2018 | Ziyang | 1.077 | 1.080 | 0.997 | Increasing |
2014 | Zigong | 1.058 | 1.072 | 0.987 | Increasing | 2018 | Zigong | 1.039 | 1.041 | 0.997 | Decreasing |
Mean | 0.938 | 0.977 | 0.960 | - | Mean | 0.933 | 0.969 | 0.962 | - |
Fig. 8 Spatial distribution of regression coefficients of ecological efficiency using the GWR Model in the Chengdu- Chongqing Economic Circle in 2004 and 2018
Development suggestions | |||||
---|---|---|---|---|---|
Improve urbanization | Level up opening | Innovate technology | Enforce environmental regulations | Optimize the industrial structure | |
Chongqing | | | |||
Chengdu | | ||||
Zigong | | | |||
Luzhou | | ||||
Deyang | | ||||
Mianyang | | | |||
Suining | | | | ||
Neijiang | | | | | |
Leshan | | | |||
Nanchong | | | | ||
Meishan | | | |||
Yibin | | | |||
Guang'an | | ||||
Dazhou | | | |||
Ya'an | | | |||
Ziyang | | |
Table 7 Development suggestions for each city in the Chengdu-Chongqing Economic Circle
Development suggestions | |||||
---|---|---|---|---|---|
Improve urbanization | Level up opening | Innovate technology | Enforce environmental regulations | Optimize the industrial structure | |
Chongqing | | | |||
Chengdu | | ||||
Zigong | | | |||
Luzhou | | ||||
Deyang | | ||||
Mianyang | | | |||
Suining | | | | ||
Neijiang | | | | | |
Leshan | | | |||
Nanchong | | | | ||
Meishan | | | |||
Yibin | | | |||
Guang'an | | ||||
Dazhou | | | |||
Ya'an | | | |||
Ziyang | | |
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