Influential Effect and Mechanism of Digital Finance on Urban Land Use Efficiency in China
Abstract
:1. Introduction
- Does DF have any effect on ULUE?
- If yes, will this effect be influenced by other variables?
- What is the influencing mechanism between DF and ULUE?
2. Mechanism Analysis and Research Hypotheses
2.1. The Overall Influence of DF on ULUE
2.2. The Indirect Impact of DF on ULUE
2.3. The Threshold Impact of DF on ULUE
3. Methods and Variables
3.1. Measurement of ULUE and DF
3.1.1. ULUE
3.1.2. DF
3.2. Model Setting
3.2.1. The Reference Effect Model
3.2.2. Intermediate Effect Model
3.2.3. Threshold Effect Model
3.3. Variable Selection
3.4. Sample and Data Source
4. Results Analysis
4.1. The Descriptive Statistics of the Variables
4.2. Evolutionary Trends of ULUE and DF in China
4.3. Direct Effect Analysis
4.4. Mediating Effect Analyses
4.5. Robustness Test
4.6. Heterogeneity Analysis
4.7. Threshold Effect Analysis
5. Conclusions and Discussion
5.1. Conclusions
5.2. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Categories | Dimensions | Indicators |
---|---|---|
Inputs | Capital | Total investment in fixed assets (105 Yuan) |
Labor | Number of employees in the secondary and tertiary industries (104 person) | |
Land | Construction land area (km2) | |
Desirable outputs | Economic benefit | Added value of the secondary and tertiary industries (105 yuan) |
Social benefit | Average salary of urban workers (Yuan) | |
Environment benefit | Green coverage rate of built-up area (%) | |
Undesirable outputs | Pollution output | Sulfur dioxide emissions (tons) |
Industrial wastewater discharge (105 tons) | ||
Industrial soot emissions (tons) |
Types of Variables | Variable Name | Symbol | Mean | Standard Deviation | Minimum | Maximum |
---|---|---|---|---|---|---|
Dependent variable | Urban land use efficiency | ULUE | 0.371 | 0.270 | 0.003 | 2.425 |
Independent variable | Digital Finance | lnDF | 5.056 | 0.514 | 2.834 | 5.813 |
Mediating variable | Industrial structure upgrading | ISU | 0.257 | 0.099 | 0.001 | 1.000 |
Control variable | Urban economic development | lnEd | 10.959 | 0.565 | 8.327 | 15.675 |
Population density | lnPi | 5.737 | 0.934 | 0.683 | 7.882 | |
Urban transportation development | lnRs | 0.283 | 0.394 | −4.515 | 2.216 | |
Local government financial support | lnGov | 2.909 | 0.434 | 1.479 | 4.517 |
Variables | (1) | (2) | (3) | (4) |
---|---|---|---|---|
ULUE | ULUE | ULUE | ULUE | |
lnDF | 0.082 *** | |||
(8.24) | ||||
lnBre | 0.067 *** | |||
(7.36) | ||||
lnDep | 0.101 *** | |||
(10.37) | ||||
lnDig | 0.030 *** | |||
(4.12) | ||||
lnEd | 0.125 *** | 0.134 *** | 0.111 *** | 0.172 *** |
(8.00) | (8.64) | (7.35) | (11.79) | |
lnPi | −0.056 *** | −0.054 *** | −0.061 *** | −0.048 *** |
(−4.68) | (−4.47) | (−5.10) | (−3.96) | |
lnRs | 0.117 *** | 0.120 *** | 0.108 *** | 0.132 *** |
(8.61) | (8.83) | (7.99) | (9.69) | |
lnGov | 0.140 *** | 0.160 *** | 0.116 *** | 0.205 *** |
(6.11) | (7.16) | (5.13) | (9.36) | |
Constant | −1.525 *** | −1.621 *** | −1.371 *** | −2.034 *** |
(−7.61) | (−8.09) | (−7.00) | (−10.54) | |
sigma_u | 0.183 *** | 0.184 *** | 0.182 *** | 0.184 *** |
(21.10) | (21.10) | (21.10) | (21.14) | |
sigma_e | 0.178 *** | 0.178 *** | 0.176 *** | 0.180 *** |
(66.21) | (66.21) | (66.21) | (66.23) | |
N | 2830 | 2830 | 2830 | 2830 |
Log likelihood | 125.516 | 118.771 | 144.672 | 100.433 |
Variables | Reference Regression | Intermediate Effect Regression | |
---|---|---|---|
(1) ULUE | (2) ISU | (3) ULUE | |
lnDF | 0.082 *** | 0.046 *** | 0.030 *** |
(8.24) | (19.26) | (2.91) | |
ISU | 1.089 *** | ||
(14.30) | |||
Control variables | control | control | control |
sigma_u | 0.183 *** | 0.078 *** | 0.199 *** |
(21.10) | (22.91) | (20.88) | |
sigma_e | 0.178 *** | 0.042 *** | 0.169 *** |
(66.21) | (71.26) | (65.99) | |
N | 2830 | 2830 | 2830 |
Log likelihood | 125.516 | 4452.801 | 228.852 |
Variables | Add Control Variables | Replacement Period | Exclude Municipalities | Instrumental Variable Regression | |
---|---|---|---|---|---|
(1) ULUE | (2) ULUE | (3) ULUE | (4) lnDF | (5) ULUE | |
lnDF | 0.072 *** | 0.397 *** | 0.079 *** | 0.192 *** | |
(7.28) | (21.21) | (8.00) | (11.37) | ||
L.lnDF | 0.649 *** | ||||
(248.71) | |||||
lnEd | 0.147 *** | 0.072 *** | 0.126 *** | 0.040 *** | 0.069 *** |
(9.46) | (4.33) | (8.09) | (12.97) | (5.15) | |
lnPi | −0.034 *** | −0.071 *** | −0.052 *** | 0.009 *** | −0.038 *** |
(−2.67) | (−5.60) | (−4.33) | (6.54) | (−6.26) | |
lnRs | 0.111 *** | 0.089 *** | 0.113 *** | 0.008 *** | 0.063 *** |
(8.45) | (5.12) | (8.33) | (2.53) | (4.71) | |
lnGov | −0.033 | 0.054 ** | 0.148 *** | 0.018 *** | 0.162 *** |
(−1.26) | (2.13) | (6.41) | (4.32) | (9.18) | |
lnFis | 0.001 | ||||
(0.12) | |||||
lnRe | 0.259 *** | ||||
(12.41) | |||||
Constant | −1.592 *** | −2.265 *** | −1.566 *** | 1.386 *** | −1.651 *** |
(−8.03) | (−10.69) | (−7.77) | (32.92) | (−9.52) | |
sigma_u | 0.190 *** | 0.191 *** | 0.181 *** | ||
(20.87) | (20.55) | (20.94) | |||
sigma_e | 0.171 *** | 0.167 *** | 0.177 *** | ||
(66.11) | (57.75) | (65.77) | |||
N | 2830 | 2264 | 2790 | 2547 | 2547 |
Log likelihood | 210.493 | 146.049 | 144.361 |
Variables | (1) Eastern Region | (2) Central Region | (3) Western Region | (4) Provincial Capital | (5) Non-Provincial Capital |
---|---|---|---|---|---|
lnDF | 0.075 *** | 0.073 *** | 0.091 *** | 0.238 *** | 0.073 *** |
(4.53) | (4.57) | (4.21) | (5.16) | (7.34) | |
control variable | control | control | control | control | control |
sigma_u | 0.206 *** | 0.192 *** | 0.204 *** | 0.111 *** | 0.179 *** |
(14.18) | (13.99) | (12.13) | (5.27) | (19.96) | |
sigma_e | 0.174 *** | 0.157 *** | 0.189 *** | 0.216 *** | 0.172 *** |
(37.95) | (39.01) | (34.73) | (21.54) | (62.59) | |
N | 1000 | 1000 | 830 | 300 | 2530 |
Log likelihood | 19.939 | 142.027 | −44.585 | −24.760 | 191.214 |
Threshold Variable | Threshold Type | F-Value | Threshold Value | BS Times | Self-Sampling Critical Value | ||
---|---|---|---|---|---|---|---|
1% | 5% | 10% | |||||
lnEd | Single | 67.10 *** | 11.719 | 300 | 43.469 | 35.885 | 30.153 |
Double | 23.39 | 300 | 46.893 | 35.169 | 31.216 | ||
Triple | 14.45 | 300 | 84.603 | 70.922 | 64.127 | ||
lnDF | Single | 371.65 *** | 5.315 5.363 | 300 | 172.939 | 150.923 | 137.725 |
Double | 283.59 *** | 300 | 78.620 | 64.891 | 57.558 | ||
Triple | 52.04 | 300 | 259.821 | 189.013 | 178.385 |
Variables | Threshold Variable | Variables | Threshold Variable |
---|---|---|---|
lnEd | lnDF | ||
0.082 *** (7.84) | −0.004 (−0.43) | ||
0.110 *** (9.81) | 0.023 ** (2.30) | ||
0.074 *** (7.38) | |||
lnEd | 0.094 *** (5.24) | lnEd | 0.078 *** (4.97) |
lnPi | −0.102 *** (−3.97) | lnPi | −0.078 *** (−3.32) |
lnRs | 0.132 *** (9.18) | lnRs | 0.102 *** (7.74) |
lnGov | 0.104 *** (3.77) | lnGov | 0.027 * (1.07) |
Constant | −0.846 *** (−3.40) | Constant | −0.203 (−0.90) |
R2 | 0.235 | R2 | 0.371 |
N | 2830 | N | 2830 |
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Qiu, H.; Li, X.; Zhang, L. Influential Effect and Mechanism of Digital Finance on Urban Land Use Efficiency in China. Sustainability 2023, 15, 14726. https://doi.org/10.3390/su152014726
Qiu H, Li X, Zhang L. Influential Effect and Mechanism of Digital Finance on Urban Land Use Efficiency in China. Sustainability. 2023; 15(20):14726. https://doi.org/10.3390/su152014726
Chicago/Turabian StyleQiu, Haiyang, Xin Li, and Long Zhang. 2023. "Influential Effect and Mechanism of Digital Finance on Urban Land Use Efficiency in China" Sustainability 15, no. 20: 14726. https://doi.org/10.3390/su152014726
APA StyleQiu, H., Li, X., & Zhang, L. (2023). Influential Effect and Mechanism of Digital Finance on Urban Land Use Efficiency in China. Sustainability, 15(20), 14726. https://doi.org/10.3390/su152014726