Does Tax Competition Among Local Governments Improve the Green Economic Efficiency in the Yellow River Basin?
Abstract
:1. Introduction
2. Literature Review
3. Research Hypotheses
4. Research Design
4.1. Green Economic Efficiency Measurement
4.2. Model Construction
4.3. Description of Variables
4.3.1. The Explained Variable
4.3.2. Core Explanatory Variable
4.3.3. Mechanism Variables
4.3.4. Control Variables
4.4. Data Source
5. Results
5.1. Baseline Regression Results
5.2. Robustness Test
5.3. Endogeneity Test
5.4. Heterogeneity Analysis
5.5. Mechanism Analysis
6. Conclusions and Policy Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Indicator Category | Name | Definition | Unit |
---|---|---|---|
Input | Capital input | Fixed capital stock (fixed asset investment adjusted using the perpetual inventory method based on 2000) | Hundred million (RMB) |
Labor input | Number of employees at the end of the year | Ten thousand persons | |
Energy input | Electricity consumption of the whole society | Hundred million kilowatt-hours | |
Water resource input | Total amount of urban water consumption | Ten thousand cubic meters | |
Land investment | Urban construction land area | Square kilometer | |
Expected output | Economic output | The real GDP (based on nominal GDP in 2000 converted from the GDP deflator index) | Hundred million (RMB) |
Environment indicator | Greening coverage rate of built-up areas | Percent | |
Unexpected output | Wastewater discharge | The amount of industrial wastewater emitted | Ten thousand tons |
emissions | emitted | Ten thousand tons | |
Waste dust discharge | The amount of industrial waste dust emitted | Ten thousand tons |
Shanxi | Inner Mongolia | Shandong | Henan | Sichuan | Shaanxi | Gansu | Qinghai | Ningxia | |
---|---|---|---|---|---|---|---|---|---|
2004 | 0.583 | 1.019 | 1.133 | 1.037 | 0.617 | 1.007 | 0.559 | 1.394 | 0.495 |
2005 | 0.581 | 1.030 | 1.138 | 1.035 | 0.630 | 0.643 | 1.002 | 1.203 | 0.513 |
2006 | 0.551 | 1.039 | 1.136 | 1.024 | 1.006 | 1.010 | 1.007 | 1.206 | 1.007 |
2007 | 0.553 | 1.079 | 1.130 | 1.011 | 1.008 | 1.013 | 1.010 | 1.206 | 1.031 |
2008 | 0.572 | 1.070 | 1.104 | 1.003 | 1.011 | 1.014 | 1.006 | 1.215 | 1.052 |
2009 | 0.533 | 1.064 | 1.099 | 1.003 | 1.008 | 1.020 | 0.557 | 1.205 | 1.039 |
2010 | 0.561 | 1.059 | 1.109 | 1.001 | 1.013 | 1.040 | 1.021 | 1.253 | 1.032 |
2011 | 0.570 | 1.070 | 1.079 | 1.001 | 1.034 | 1.044 | 0.568 | 1.335 | 1.014 |
2012 | 0.557 | 1.076 | 1.076 | 1.005 | 1.093 | 1.034 | 0.588 | 1.268 | 1.013 |
2013 | 0.572 | 1.070 | 1.077 | 1.001 | 1.110 | 1.040 | 0.623 | 1.193 | 1.023 |
2014 | 0.562 | 1.064 | 1.076 | 1.014 | 1.116 | 1.044 | 0.626 | 1.198 | 1.035 |
2015 | 0.567 | 1.069 | 1.075 | 1.011 | 1.109 | 1.035 | 0.652 | 1.317 | 1.056 |
2016 | 0.605 | 1.082 | 1.061 | 1.069 | 1.119 | 1.013 | 1.001 | 1.165 | 1.039 |
2017 | 0.652 | 1.058 | 1.065 | 1.123 | 1.086 | 0.765 | 1.001 | 1.179 | 1.170 |
2018 | 1.152 | 1.063 | 1.075 | 1.119 | 1.046 | 1.001 | 1.026 | 1.181 | 1.053 |
2019 | 1.192 | 1.047 | 1.090 | 1.158 | 1.034 | 0,705 | 1.083 | 1.170 | 1.055 |
2020 | 1.021 | 1.104 | 1.074 | 1.164 | 1.068 | 0.677 | 1.172 | 1.162 | 1.035 |
2021 | 1.011 | 1.066 | 1.108 | 1.249 | 1.025 | 0.610 | 0.633 | 1.146 | 1.044 |
2022 | 1.018 | 1.062 | 1.109 | 1.176 | 1.025 | 0.647 | 0.625 | 1.157 | 1.035 |
Variable Name | Variable Symbol | Descriptions |
---|---|---|
Green economic efficiency | Gee | Super-SBM model |
Tax competition | Taxc | Tax competition index |
Industrial structure upgrading | Ins | (Primary Sector’s GDP Share × 1) + (Secondary Sector’s GDP Share × 2) + (Tertiary Sector’s GDP Share × 3) |
Industrial agglomeration | Ind | Regional entropy |
Environment regulation | Er | The proportion of the completed investment in industrial pollution control accounts for GDP |
Per capita GDP | Pgdp | Logarithm of per capita GDP |
The level of industrialization | Il | The proportion of regional industrial added value accounts for GDP |
GDP growth rate | Growth | The difference between the GDP index and 100 |
Human capital | Hc | The number of higher education students per ten thousand persons |
Infrastructure construction level | Icl | Logarithm of road mileage |
Variable | Observations | Mean | Median | Minimum | Maximum |
---|---|---|---|---|---|
Gee | 171 | 0.987 | 1.039 | 0.495 | 1.394 |
Taxc | 171 | 0.935 | 0.934 | 0.895 | 0.996 |
Er | 171 | 0.209 | 0.161 | 0.005 | 0.992 |
Pgdp | 171 | 10.370 | 10.490 | 8.695 | 11.480 |
Il | 171 | 0.397 | 0.393 | 0.244 | 0.530 |
Growth | 171 | 0.098 | 0.096 | 0.002 | 0.238 |
Hc | 171 | 5.065 | 5.127 | 4.002 | 5.798 |
Icl | 171 | 2.695 | 2.691 | 1.968 | 3.332 |
Ins | 171 | 2.315 | 2.314 | 2.122 | 2.494 |
Ind | 171 | 0.961 | 0.969 | 0.536 | 1.469 |
Variable | (1) | (2) |
---|---|---|
Gee | Gee | |
Taxc | −5.440 *** (−2.886) | −5.979 *** (−4.553) |
Er | −0.160 * (−1.841) | |
Pgdp | −0.207 (−1.562) | |
Il | 0.852 *** (2.637) | |
Growth | 1.532 ** (2.518) | |
Hc | 0.592 *** (3.274) | |
Icl | 0.119 (1.006) | |
Constant | 0.632 *** (5.174) | 4.950 *** (2.697) |
Observations | 171 | 171 |
Province FE | YES | YES |
Year FE | YES | YES |
R-squared | 0.609 | 0.689 |
Variable | (1) | (2) | (3) | (4) |
---|---|---|---|---|
Gee | Gee | Gee | Gee | |
Com | −0.381 ** (−2.277) | |||
L.Taxc | −7.427 *** (−6.022) | |||
Tax | −3.580 *** (−4.231) | −6.759 *** (−4.147) | ||
Er | −0.184 * (−1.706) | −0.194 *** (−3.357) | −0.134 (−1.525) | −0.171 * (−1.932) |
Pgdp | −0.134 (−0.953) | −0.118 (−1.312) | −0.163 (−1.305) | −0.215 (−1.221) |
Il | 0.846 ** (2.507) | 0.522 ** (2.389) | 0.780 ** (2.484) | 1.283 *** (3.040) |
Growth | 1.519 ** (2.487) | 1.509 *** (3.818) | 1.463 ** (2.599) | 1.164 * (1.661) |
Hc | 0.537 *** (2.737) | 0.661 *** (5.127) | 0.384 ** (2.088) | 0.359 * (1.774) |
Icl | 0.270 ** (2.096) | 0.206 ** (2.398) | 0.143 (1.194) | 0.097 (0.824) |
Constant | −1.160 (−0.781) | 1.325 (1.246) | 6.868 *** (3.988) | 6.849 *** (2.632) |
Observations | 171 | 171 | 162 | 144 |
Province FE | YES | YES | YES | YES |
Year FE | YES | YES | YES | YES |
R-squared | 0.663 | 0.904 | 0.701 | 0.743 |
Variable | (1) | (2) |
---|---|---|
Taxc | Gee | |
Taxc | −17.753 *** (−3.205) | |
L.Taxc#Num | 0.014 *** (3.355) | |
Er | −0.009 * (−1.848) | −0.232 * (−1.878) |
Pgdp | 0.008 (0.902) | 0.067 (0.342) |
Il | −0.046 * (−1.760) | 0.195 (0.346) |
Growth | 0.009 (0.233) | 1.801 ** (2.143) |
Hc | 0.016 * (1.772) | 0.696 ** (2.612) |
Icl | −0.019 *** (−2.837) | 0.030 (0.191) |
Constant | 0.678 *** (7.939) | |
Observation | 162 | 162 |
R-squared | 0.848 | −0.011 |
Kleibergen–Paap rk LM | 18.280 *** (0.000) | |
Kleibergen–Paap Wald rk F statistic | 11.258 |
Variable | (1) | (2) | (3) |
---|---|---|---|
Gee | Gee | Gee | |
Taxc | −6.545 *** (−3.209) | −12.737 (−1.368) | 4.845 (1.037) |
Er | −0.101 (−0.763) | −0.458 * (−1.872) | 0.070 (0.328) |
Pgdp | 0.270 (1.306) | −0.497 (−0.901) | 0.157 (1.309) |
Il | 1.271 *** (2.747) | 3.244 ** (2.748) | −0.549 (−1.455) |
Growth | 0.870 (0.744) | 1.265 (1.189) | −0.669 (−1.004) |
Hc | 0.317 (0.645) | 2.160 *** (3.175) | 0.316 ** (2.361) |
Icl | −0.077 (−0.463) | −0.040 (−0.111) | 0.079 (0.813) |
Constant | 2.473 (1.165) | 4.840 (0.354) | −6.712 (−1.639) |
Observation | 95 | 38 | 38 |
Province FE | YES | YES | YES |
Year FE | YES | YES | YES |
r2 | 0.693 | 0.948 | 0.972 |
Variable | (1) | (2) | (3) | (4) | (5) |
---|---|---|---|---|---|
Gee | Ins | Gee | Ind | Gee | |
Ins | 1.657 *** (3.027) | ||||
Ind | −0.413 *** (−3.595) | ||||
Taxc | −5.979 *** (−4.553) | 0.926 *** (4.837) | −7.514 *** (−4.953) | −2.117 ** (−1.996) | −6.853 *** (−5.304) |
Er | −0.160 * (−1.841) | −0.008 (−0.635) | −0.147 * (−1.741) | 0.058 (1.104) | −0.137 (−1.604) |
Pgdp | −0.207 (−1.562) | 0.051 *** (2.735) | −0.291 ** (−2.198) | −0.394 *** (−3.752) | −0.370 ** (−2.515) |
Il | 0.852 *** (2.637) | −0.677 *** (−12.960) | 1.974 *** (3.989) | 1.914 *** (6.641) | 1.641 *** (4.208) |
Growth | 1.532 ** (2.518) | −0.154 (−1.437) | 1.788 *** (2.806) | −0.310 (−0.631) | 1.404 ** (2.280) |
Hc | 0.592 *** (3.274) | 0.087 *** (3.896) | 0.447 ** (2.417) | 0.041 (0.406) | 0.609 *** (3.413) |
Icl | 0.119 (1.006) | −0.048 *** (−2.909) | 0.198 * (1.729) | −0.030 (−0.386) | 0.107 (0.887) |
Constant | 4.950 *** (2.697) | 0.895 *** (3.189) | 3.467 ** (2.107) | 6.159 *** (3.535) | 7.491 *** (4.009) |
Observation | 171 | 171 | 171 | 171 | 171 |
Province FE | YES | YES | YES | YES | YES |
Year FE | YES | YES | YES | YES | YES |
R-squared | 0.689 | 0.953 | 0.710 | 0.814 | 0.718 |
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Sun, J.; Sun, X.; Wang, Y. Does Tax Competition Among Local Governments Improve the Green Economic Efficiency in the Yellow River Basin? Sustainability 2025, 17, 5165. https://doi.org/10.3390/su17115165
Sun J, Sun X, Wang Y. Does Tax Competition Among Local Governments Improve the Green Economic Efficiency in the Yellow River Basin? Sustainability. 2025; 17(11):5165. https://doi.org/10.3390/su17115165
Chicago/Turabian StyleSun, Jile, Xiao Sun, and Yihan Wang. 2025. "Does Tax Competition Among Local Governments Improve the Green Economic Efficiency in the Yellow River Basin?" Sustainability 17, no. 11: 5165. https://doi.org/10.3390/su17115165
APA StyleSun, J., Sun, X., & Wang, Y. (2025). Does Tax Competition Among Local Governments Improve the Green Economic Efficiency in the Yellow River Basin? Sustainability, 17(11), 5165. https://doi.org/10.3390/su17115165