Abstract
Green innovation has now become an important component of high-quality development, but China’s provinces still face imbalances in green innovation development. Clarifying the current state of development and the causes of the imbalance in each province is urgently necessary. But only a single or overall indicator cannot well reflect the structural differences within each province. We used the multi-way efficiency analysis (MEA) method to analyze the overall and structural efficiency of green innovation in each province of China, thus overcoming the shortcomings of comprehensive indicators. In addition, based on the decomposition of regional differences, policy factors resulting in heterogeneity among provinces are analyzed using Geodetector. The research results reveal the diversity of green innovation systems, the severe symmetry in resource utilization, and the internal and external sources of regional differences. We categorize the provinces into four development models by combining the internal structural characteristics of green innovation efficiency, as a way to propose suitable green innovation policies for each province, to take into account, the different development environments of the provinces assessed. Our research has significant implications for effectively improving green innovation efficiency and guides the formulation of more precise policies.
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Data availability
The raw data that support the findings of this study are available in China Statistical Yearbook with the identifier http://www.stats.gov.cn/tjsj/ndsj/.
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Acknowledgements
The work was supported by the National Natural Science Foundation of China (NSFC) General Program (Grant numbers71974045) and the Fundamental Research Funds for the Central Universities (Grant numbers. HIT.HSS.DZ201906).
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XT performed conceptualization, methodology, project administration, writing—review & editing. QM provided resources, writing—review & editing, supervision. QZ did writing—review and editing, ML and SL did writing—review and editing. All authors have read and agreed to thepublished version of the manuscript.
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Appendices
Appendix I
Different regional green innovation overall efficiency in China.
Region | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Northeast region | LN | 0.60 | 0.78 | 0.77 | 0.65 | 0.76 | 0.66 | 0.69 | 0.71 | 0.71 | 0.82 | 0.84 | 0.79 | 0.76 |
JL | 0.59 | 0.66 | 0.62 | 0.55 | 0.56 | 0.59 | 0.50 | 0.46 | 0.53 | 0.67 | 0.76 | 0.75 | 0.75 | |
HL | 0.49 | 0.58 | 0.53 | 0.65 | 0.66 | 0.59 | 0.59 | 0.46 | 0.50 | 0.38 | 0.50 | 0.47 | 0.45 | |
Northeast region mean | 0.56 | 0.67 | 0.64 | 0.62 | 0.66 | 0.62 | 0.59 | 0.55 | 0.58 | 0.62 | 0.70 | 0.67 | 0.65 | |
Eastern region | BJ | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
TJ | 0.78 | 1.00 | 0.84 | 0.88 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.84 | 1.00 | |
HE | 0.69 | 0.80 | 0.74 | 0.73 | 0.81 | 0.93 | 0.61 | 0.69 | 0.67 | 0.80 | 0.81 | 0.81 | 0.60 | |
SH | 0.86 | 1.00 | 0.85 | 1.00 | 1.00 | 0.95 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
JS | 1.00 | 0.94 | 0.83 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.93 | 0.99 | 1.00 | 1.00 | 1.00 | |
ZJ | 1.00 | 0.97 | 1.00 | 1.00 | 1.00 | 0.89 | 0.76 | 0.89 | 1.00 | 1.00 | 1.00 | 0.81 | 0.94 | |
FJ | 1.00 | 0.81 | 1.00 | 0.73 | 0.82 | 0.70 | 0.47 | 0.88 | 0.78 | 0.88 | 0.73 | 0.59 | 0.88 | |
SD | 1.00 | 1.00 | 0.98 | 0.66 | 1.00 | 0.87 | 0.88 | 0.90 | 0.67 | 0.81 | 1.00 | 1.00 | 1.00 | |
GD | 0.96 | 1.00 | 0.91 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.99 | 1.00 | 0.93 | 1.00 | 1.00 | |
HI | 0.62 | 0.63 | 0.61 | 0.84 | 0.33 | 1.00 | 1.00 | 0.85 | 0.96 | 0.63 | 1.00 | 1.00 | 1.00 | |
Eastern region mean | 0.89 | 0.92 | 0.88 | 0.88 | 0.90 | 0.93 | 0.87 | 0.92 | 0.90 | 0.91 | 0.95 | 0.90 | 0.94 | |
Central region | SX | 0.77 | 0.77 | 0.60 | 0.66 | 0.58 | 0.77 | 0.74 | 0.72 | 0.75 | 0.84 | 0.82 | 0.80 | 1.00 |
AH | 0.71 | 0.98 | 0.86 | 0.80 | 1.00 | 1.00 | 0.88 | 0.84 | 0.77 | 1.00 | 1.00 | 1.00 | 1.00 | |
JX | 0.78 | 0.75 | 0.75 | 1.00 | 0.84 | 0.63 | 0.80 | 0.73 | 0.73 | 0.73 | 0.73 | 0.73 | 0.60 | |
HA | 0.84 | 0.83 | 0.74 | 0.93 | 0.82 | 0.71 | 0.62 | 0.66 | 1.00 | 1.00 | 1.00 | 0.72 | 0.89 | |
HB | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.91 | 0.83 | 0.74 | 0.82 | 0.71 | 0.80 | 0.74 | |
HN | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.97 | 0.99 | 0.95 | 0.85 | 0.76 | 0.75 | 0.73 | 0.69 | |
Central region mean | 0.85 | 0.89 | 0.82 | 0.90 | 0.87 | 0.85 | 0.82 | 0.79 | 0.81 | 0.86 | 0.83 | 0.80 | 0.82 | |
Western region | NM | 0.66 | 0.68 | 0.83 | 0.65 | 0.66 | 0.71 | 0.67 | 0.57 | 0.52 | 0.53 | 0.76 | 0.66 | 0.64 |
GX | 0.45 | 0.80 | 0.53 | 0.78 | 1.00 | 1.00 | 1.00 | 0.57 | 1.00 | 0.73 | 1.00 | 0.61 | 1.00 | |
CQ | 0.76 | 0.84 | 0.81 | 0.82 | 0.60 | 0.76 | 0.74 | 0.51 | 0.54 | 0.40 | 0.59 | 0.71 | 0.81 | |
SC | 0.63 | 0.77 | 0.72 | 0.73 | 0.63 | 0.75 | 0.68 | 0.73 | 0.78 | 0.76 | 0.78 | 1.00 | 0.85 | |
GZ | 0.69 | 0.64 | 0.65 | 0.70 | 0.63 | 0.60 | 0.76 | 0.45 | 0.38 | 0.52 | 0.66 | 0.35 | 0.77 | |
YN | 0.55 | 0.60 | 0.48 | 0.53 | 0.39 | 0.44 | 0.45 | 0.56 | 0.61 | 0.65 | 0.52 | 0.58 | 0.75 | |
SN | 0.51 | 0.79 | 0.76 | 0.77 | 0.64 | 1.00 | 0.87 | 0.84 | 0.81 | 1.00 | 1.00 | 1.00 | 1.00 | |
GS | 0.58 | 0.65 | 0.69 | 0.67 | 0.54 | 0.59 | 0.60 | 0.70 | 0.78 | 0.70 | 0.75 | 0.62 | 0.48 | |
QH | 1.00 | 1.00 | 0.92 | 1.00 | 1.00 | 0.84 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
NX | 0.47 | 0.48 | 0.77 | 0.59 | 0.54 | 0.62 | 0.56 | 0.68 | 0.55 | 0.64 | 0.64 | 0.65 | 0.44 | |
XJ | 1.00 | 1.00 | 0.88 | 0.83 | 0.84 | 0.53 | 1.00 | 0.83 | 0.73 | 0.69 | 0.52 | 0.54 | 0.58 | |
Western region means | 0.66 | 0.75 | 0.73 | 0.73 | 0.68 | 0.71 | 0.76 | 0.68 | 0.70 | 0.69 | 0.75 | 0.70 | 0.76 | |
Appendix II
Decomposition of regional differences in output efficiency.
Year | Index | Overall | Intra- Regional differences | Inter- Regional differences | Northeast Region | Eastern Region | Central Region | Western Region |
|---|---|---|---|---|---|---|---|---|
2008 | Y1 | 0.031 | 0.018 | 0.013 | 0.003 | 0.013 | 0.009 | 0.034 |
0.595 | 0.405 | 0.010 | 0.132 | 0.051 | 0.361 | |||
Y2 | 0.035 | 0.020 | 0.015 | 0.029 | 0.009 | 0.002 | 0.045 | |
0.572 | 0.428 | 0.038 | 0.063 | 0.007 | 0.261 | |||
C | 0.003 | 0.002 | 0.001 | 0.000 | 0.002 | 0.001 | 0.003 | |
0.640 | 0.360 | 0.005 | 0.173 | 0.033 | 0.347 | |||
2009 | Y1 | 0.001 | 0.001 | 0.000 | 0.000 | 0.002 | 0.001 | 0.003 |
0.880 | 0.120 | 0.007 | 0.027 | 0.015 | 0.491 | |||
Y2 | 0.027 | 0.018 | 0.008 | 0.034 | 0.007 | 0.005 | 0.035 | |
0.689 | 0.311 | 0.069 | 0.071 | 0.031 | 0.319 | |||
C | 0.001 | 0.001 | 0.001 | 0.000 | 0.000 | 0.000 | 0.002 | |
0.669 | 0.331 | 0.012 | 0.063 | 0.035 | 0.493 | |||
2010 | Y1 | 0.001 | 0.001 | 0.000 | 0.000 | 0.000 | 0.000 | 0.001 |
0.807 | 0.193 | 0.056 | 0.179 | 0.069 | 0.421 | |||
Y2 | 0.028 | 0.022 | 0.006 | 0.023 | 0.012 | 0.009 | 0.039 | |
0.780 | 0.220 | 0.045 | 0.112 | 0.051 | 0.314 | |||
C | 0.003 | 0.002 | 0.001 | 0.002 | 0.001 | 0.001 | 0.004 | |
0.808 | 0.188 | 0.073 | 0.150 | 0.054 | 0.430 | |||
2011 | Y1 | 0.002 | 0.001 | 0.001 | 0.000 | 0.000 | 0.002 | 0.002 |
0.699 | 0.301 | 0.017 | 0.056 | 0.201 | 0.368 | |||
Y2 | 0.029 | 0.016 | 0.013 | 0.001 | 0.004 | 0.006 | 0.042 | |
0.561 | 0.440 | 0.001 | 0.038 | 0.033 | 0.321 | |||
C | 0.003 | 0.002 | 0.001 | 0.001 | 0.000 | 0.003 | 0.004 | |
0.730 | 0.270 | 0.018 | 0.042 | 0.190 | 0.404 | |||
2012 | Y1 | 0.002 | 0.002 | 0.001 | 0.000 | 0.000 | 0.003 | 0.003 |
0.769 | 0.232 | 0.002 | 0.046 | 0.242 | 0.401 | |||
Y2 | 0.054 | 0.041 | 0.013 | 0.025 | 0.051 | 0.006 | 0.061 | |
0.760 | 0.240 | 0.023 | 0.222 | 0.017 | 0.213 | |||
C | 0.004 | 0.003 | 0.001 | 0.000 | 0.000 | 0.005 | 0.006 | |
0.788 | 0.215 | 0.002 | 0.031 | 0.235 | 0.419 | |||
2013 | Y1 | 0.002 | 0.001 | 0.001 | 0.000 | 0.000 | 0.001 | 0.002 |
0.649 | 0.345 | 0.004 | 0.015 | 0.140 | 0.430 | |||
Y2 | 0.031 | 0.019 | 0.011 | 0.016 | 0.000 | 0.011 | 0.047 | |
0.626 | 0.374 | 0.030 | 0.008 | 0.052 | 0.340 | |||
C | 0.003 | 0.002 | 0.001 | 0.000 | 0.000 | 0.001 | 0.004 | |
0.671 | 0.332 | 0.004 | 0.010 | 0.102 | 0.471 | |||
2014 | Y1 | 0.002 | 0.002 | 0.000 | 0.000 | 0.000 | 0.003 | 0.002 |
0.796 | 0.204 | 0.002 | 0.075 | 0.248 | 0.382 | |||
Y2 | 0.036 | 0.029 | 0.007 | 0.046 | 0.023 | 0.011 | 0.040 | |
0.795 | 0.205 | 0.061 | 0.162 | 0.045 | 0.267 | |||
C | 0.004 | 0.003 | 0.001 | 0.000 | 0.002 | 0.003 | 0.006 | |
0.835 | 0.165 | 0.003 | 0.179 | 0.110 | 0.427 | |||
2015 | Y1 | 0.001 | 0.001 | 0.000 | 0.000 | 0.001 | 0.001 | 0.001 |
0.766 | 0.234 | 0.010 | 0.237 | 0.133 | 0.308 | |||
Y2 | 0.043 | 0.025 | 0.018 | 0.077 | 0.001 | 0.008 | 0.057 | |
0.582 | 0.418 | 0.078 | 0.007 | 0.026 | 0.260 | |||
C | 0.002 | 0.002 | 0.001 | 0.000 | 0.002 | 0.001 | 0.002 | |
0.773 | 0.228 | 0.014 | 0.244 | 0.112 | 0.311 | |||
2016 | Y1 | 0.001 | 0.001 | 0.001 | 0.001 | 0.000 | 0.000 | 0.002 |
0.574 | 0.426 | 0.069 | 0.044 | 0.050 | 0.362 | |||
Y2 | 0.047 | 0.036 | 0.011 | 0.126 | 0.007 | 0.004 | 0.070 | |
0.765 | 0.235 | 0.137 | 0.040 | 0.013 | 0.320 | |||
C | 0.002 | 0.002 | 0.001 | 0.002 | 0.001 | 0.001 | 0.002 | |
0.720 | 0.280 | 0.087 | 0.195 | 0.039 | 0.315 | |||
2017 | Y1 | 0.001 | 0.001 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
0.517 | 0.483 | 0.028 | 0.046 | 0.056 | 0.343 | |||
Y2 | 0.043 | 0.033 | 0.010 | 0.119 | 0.010 | 0.004 | 0.057 | |
0.773 | 0.227 | 0.158 | 0.064 | 0.015 | 0.285 | |||
C | 0.001 | 0.001 | 0.001 | 0.000 | 0.000 | 0.000 | 0.001 | |
0.507 | 0.493 | 0.050 | 0.037 | 0.044 | 0.326 | |||
2018 | Y1 | 0.001 | 0.001 | 0.000 | 0.000 | 0.000 | 0.000 | 0.001 |
0.615 | 0.385 | 0.009 | 0.048 | 0.041 | 0.471 | |||
Y2 | 0.022 | 0.015 | 0.008 | 0.059 | 0.000 | 0.001 | 0.027 | |
0.654 | 0.346 | 0.180 | 0.004 | 0.010 | 0.302 | |||
C | 0.002 | 0.001 | 0.001 | 0.000 | 0.000 | 0.000 | 0.002 | |
0.633 | 0.367 | 0.015 | 0.041 | 0.031 | 0.487 | |||
2019 | Y1 | 0.001 | 0.001 | 0.000 | 0.000 | 0.000 | 0.000 | 0.001 |
0.680 | 0.320 | 0.014 | 0.100 | 0.076 | 0.424 | |||
Y2 | 0.033 | 0.028 | 0.005 | 0.016 | 0.018 | 0.002 | 0.059 | |
0.850 | 0.150 | 0.035 | 0.147 | 0.011 | 0.424 | |||
C | 0.002 | 0.002 | 0.001 | 0.000 | 0.000 | 0.000 | 0.003 | |
0.670 | 0.326 | 0.033 | 0.087 | 0.057 | 0.415 | |||
2020 | Y1 | 0.001 | 0.001 | 0.000 | 0.000 | 0.000 | 0.000 | 0.001 |
0.794 | 0.216 | 0.043 | 0.068 | 0.117 | 0.496 | |||
Y2 | 0.032 | 0.028 | 0.004 | 0.018 | 0.001 | 0.019 | 0.066 | |
0.878 | 0.122 | 0.041 | 0.014 | 0.091 | 0.519 | |||
C | 0.002 | 0.002 | 0.000 | 0.001 | 0.000 | 0.001 | 0.003 | |
0.797 | 0.198 | 0.043 | 0.050 | 0.111 | 0.523 |
Appendix III
Driver detector results for 2008–2020.
Year | Index | Statistics | X1 | X2 | X3 | X4 | X5 |
|---|---|---|---|---|---|---|---|
2008 | Overall efficiency | q-statistic | 0.046 | 0.045 | 0.121 | 0.166 | 0.048 |
Technical output | q-statistic | 0.079 | 0.018 | 0.293 | 0.184 | 0.151 | |
Economic output | q-statistic | 0.046 | 0.039 | 0.126 | 0.150 | 0.066 | |
Environmental output | q-statistic | 0.073 | 0.008 | 0.272 | 0.191 | 0.142 | |
2009 | Overall efficiency | q-statistic | 0.052 | 0.041 | 0.105 | 0.144 | 0.045 |
Technical output | q-statistic | 0.043 | 0.021 | 0.032 | 0.211 | 0.113 | |
Economic output | q-statistic | 0.023 | 0.042 | 0.142 | 0.148 | 0.061 | |
Environmental output | q-statistic | 0.071 | 0.006 | 0.283 | 0.128 | 0.185 | |
2010 | Overall efficiency | q-statistic | 0.046 | 0.027 | 0.283 | 0.173 | 0.078 |
Technical output | q-statistic | 0.057 | 0.006** | 0.048 | 0.125 | 0.194 | |
Economic output | q-statistic | 0.027 | 0.089 | 0.067 | 0.142 | 0.139 | |
Environmental output | q-statistic | 0.046 | 0.017 | 0.272 | 0.138 | 0.145 | |
2011 | Overall efficiency | q-statistic | 0.049 | 0.024 | 0.221* | 0.321 | 0.048 |
Technical Output | q-statistic | 0.091 | 0.038 | 0.321** | 0.194 | 0.151 | |
Economic output | q-statistic | 0.038 | 0.047 | 0.139 | 0.242 | 0.076 | |
Environmental output | q-statistic | 0.086 | 0.012 | 0.322 | 0.215 | 0.136 | |
2012 | Overall efficiency | q-statistic | 0.061 | 0.046 | 0.197 | 0.204 | 0.028 |
Technical output | q-statistic | 0.062 | 0.019 | 0.319 | 0.174 | 0.128 | |
Economic output | q-statistic | 0.031 | 0.062 | 0.148 | 0.214 | 0.084 | |
Environmental output | q-statistic | 0.092 | 0.057 | 0.287 | 0.235* | 0.204 | |
2013 | Overall efficiency | q-statistic | 0.071 | 0.043 | 0.129 | 0.161 | 0.051 |
Technical output | q-statistic | 0.085 | 0.023 | 0.313 | 0.203* | 0.149 | |
Economic output | q-statistic | 0.059 | 0.104* | 0.113 | 0.155 | 0.188 | |
Environmental output | q-statistic | 0.071 | 0.013 | 0.321** | 0.084 | 0.025 | |
2014 | Overall efficiency | q-statistic | 0.114 | 0.111 | 0.183 | 0.145 | 0.084 |
Technical output | q-statistic | 0.077 | 0.037 | 0.212 | 0.184 | 0.112 | |
Economic output | q-statistic | 0.094 | 0.124 | 0.156 | 0.132 | 0.177 | |
Environmental output | q-statistic | 0.083 | 0.028 | 0.281 | 0.068 | 0.088 | |
2015 | Overall efficiency | q-statistic | 0.081 | 0.213 | 0.185 | 0.113 | 0.089 |
Technical output | q-statistic | 0.089 | 0.137 | 0.178** | 0.193 | 0.094 | |
Economic output | q-statistic | 0.064 | 0.067 | 0.087 | 0.114 | 0.166 | |
Environmental output | q-statistic | 0.144 | 0.106 | 0.219 | 0.073 | 0.107 | |
2016 | Overall efficiency | q-statistic | 0.106 | 0.118 | 0.104 | 0.095 | 0.109 |
Technical output | q-statistic | 0.081 | 0.033* | 0.115 | 0.132 | 0.116 | |
Economic output | q-statistic | 0.096 | 0.09 | 0.142 | 0.099** | 0.19 | |
Environmental output | q-statistic | 0.08 | 0.052 | 0.147 | 0.192 | 0.049 | |
2017 | Overall efficiency | q-statistic | 0.168 | 0.122* | 0.141 | 0.140 | 0.121 |
Technical output | q-statistic | 0.068* | 0.022 | 0.214* | 0.201 | 0.098 | |
Economic output | q-statistic | 0.047 | 0.074 | 0.099 | 0.224 | 0.146 | |
Environmental output | q-statistic | 0.068 | 0.064 | 0.193 | 0.107 | 0.127 | |
2018 | Overall efficiency | q-statistic | 0.189 | 0.167 | 0.174 | 0.142* | 0.086 |
Technical output | q-statistic | 0.065 | 0.031 | 0.134* | 0.156 | 0.124 | |
Economic output | q-statistic | 0.019 | 0.082 | 0.135 | 0.089 | 0.085 | |
Environmental output | q-statistic | 0.035 | 0.122 | 0.188 | 0.126 | 0.116 | |
2019 | Overall efficiency | q-statistic | 0.222 | 0.189 | 0.182 | 0.081 | 0.099 |
Technical output | q-statistic | 0.074 | 0.023 | 0.164 | 0.074 | 0.082 | |
Economic output | q-statistic | 0.163 | 0.094 | 0.118* | 0.062** | 0.156 | |
Environmental output | q-statistic | 0.083 | 0.063 | 0.151 | 0.051 | 0.097 | |
2020 | Overall efficiency | q-statistic | 0.221 | 0.208 | 0.193 | 0.075 | 0.113 |
Technical output | q-statistic | 0.073 | 0.037 | 0.106 | 0.062* | 0.102 | |
Economic output | q-statistic | 0.177 | 0.121 | 0.178 | 0.043 | 0.186 | |
Environmental output | q-statistic | 0.085 | 0.051 | 0.118 | 0.043* | 0.111 |
Appendix IV
Interaction detector results for Green Innovation Efficiency.
Interaction variables | Overall efficiency cross-tabulation analysis | Technical output efficiency cross-tabulation analysis | ||
|---|---|---|---|---|
q-statistic | Interaction type | q-statistic | Interaction type | |
LX2 ∩ LX1 | 0.223 | Nonlinear enhancement | 0.127 | Nonlinear enhancement |
LX3 ∩ LX1 | 0.284 | Dual-factor enhancement | 0.168 | Dual-factor enhancement |
LX3 ∩ LX2 | 0.231 | Dual-factor enhancement | 0.182 | Nonlinear enhancement |
LX4 ∩ LX1 | 0.318 | Dual-factor enhancement | 0.228 | Nonlinear enhancement |
LX4 ∩ LX2 | 0.224 | Dual-factor enhancement | 0.091 | Nonlinear enhancement |
LX4 ∩ LX3 | 0.296 | Dual-factor enhancement | 0.199 | Nonlinear enhancement |
LX5 ∩ LX1 | 0.243 | Dual-factor enhancement | 0.180 | Dual-factor enhancement |
LX5 ∩ LX2 | 0.277 | Dual-factor enhancement | 0.147 | Nonlinear enhancement |
LX5 ∩ LX3 | 0.310 | Dual-factor enhancement | 0.172 | Dual-factor enhancement |
LX5 ∩ LX4 | 0.316 | Dual-factor enhancement | 0.196 | Nonlinear enhancement |
Interaction variables | Economic output efficiency cross-tabulation analysis | Environmental output efficiency cross-tabulation analysis | ||
q-statistic | Interaction type | q-statistic | Interaction type | |
LX2 ∩ LX1 | 0.279 | Nonlinear enhancement | 0.144 | Nonlinear enhancement |
LX3 ∩ LX1 | 0.315 | Dual-factor enhancement | 0.194 | Dual-factor enhancement |
LX3 ∩ LX2 | 0.221 | Dual-factor enhancement | 0.177 | Nonlinear enhancement |
LX4 ∩ LX1 | 0.337 | Nonlinear enhancement | 0.223 | Nonlinear enhancement |
LX4 ∩ LX2 | 0.192 | Nonlinear enhancement | 0.089 | Nonlinear enhancement |
LX4 ∩ LX3 | 0.268 | Dual-factor enhancement | 0.198 | Nonlinear enhancement |
LX5 ∩ LX1 | 0.303 | Dual-factor enhancement | 0.186 | Dual-factor enhancement |
LX5 ∩ LX2 | 0.254 | Nonlinear enhancement | 0.144 | Nonlinear enhancement |
LX5 ∩ LX3 | 0.322 | Dual-factor enhancement | 0.166 | Dual-factor enhancement |
LX5 ∩ LX4 | 0.276 | Dual-factor enhancement | 0.198 | Nonlinear enhancement |
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Tian, X., Mai, Q., Zhang, Q. et al. Analyzing provincial imbalances in green innovation development in china: multi-way efficiency analysis and geodetector approach. Environ Dev Sustain (2023). https://doi.org/10.1007/s10668-023-03719-7
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DOI: https://doi.org/10.1007/s10668-023-03719-7