Research on the Efficiency Measurement and Spatial Spillover Effect of China’s Regional E-Commerce Poverty Alleviation from the Perspective of Sustainable Development
Abstract
:1. Introduction
2. Literature Review
2.1. Research on E-Commerce Development in Urban and Rural Areas
2.2. Research on E-Commerce Poverty Alleviation
2.3. Research on the Spatial Effect of E-Commerce Poverty Alleviation
3. Methods
3.1. Super-Efficiency DEA Evaluation Method
- (1)
- Model construction
- (2)
- Selection of input and output indicators
3.2. Spatial Measurement
3.2.1. Spatial Correlation Analysis
3.2.2. Panel Spatial Econometric Model
- (1)
- Spatial lag model (SLM)
- (2)
- Spatial Error Model
- (3)
- Spatial Durbin model (SDM)
3.2.3. Spatial Spillover Effect Decomposition
4. Results
4.1. Results of Poverty Alleviation Efficiency of E-Commerce in Various Regions of China
4.2. Spatial Correlation Analysis Results
4.3. Results of Spatial Spillover Effect Decomposition of China’s E-Commerce Poverty Alleviation Efficiency
4.3.1. Model Checking
- (1)
- Model selection test
- (2)
- Model Form Test
4.3.2. Spatial Durbin Model Estimation Results
- (1)
- Indicator selection
- (2)
- Model construction
- (3)
- Estimation results
- (4)
- Spatial spillover effect decomposition results of e-commerce poverty alleviation efficiency
5. Conclusions and Applications
5.1. Conclusions
- (1)
- From the perspective of space, the efficiency of e-commerce poverty alleviation varies greatly among regions, with Tianjin, Beijing, and Shanghai being the most efficient regions. In general, the efficiency of e-commerce poverty alleviation is the highest in the eastern region of China and the lowest in the western region of China.
- (2)
- There is a significant spatial autocorrelation effect in e-commerce poverty alleviation efficiency. The Moran’s I index values are all greater than 0.5; that is, e-commerce poverty alleviation efficiency among neighboring regions is high. Mutual influences have positive spatial correlations, while economic factors contribute to this spatial correlation.
- (3)
- From the regression results of influencing factors, the influence of the level of communication facilities is the most significant, and the influence coefficient reaches 0.331, which is the basic premise for the rapid development of e-commerce. At the same time, the level of transportation infrastructure is also at the core of promoting the rapid development of e-commerce. The elasticity coefficient factor had values up to 0.112. If the other factors remain fixed, the impact of the financial environment on the efficiency of poverty alleviation is minimal, but it is worth noting that it has a positive impact. Only by starting research in the medium and long terms can it be clear that these factors have a positive effect on poverty alleviation efficiency.
- (4)
- From the regression results of the influencing factors, the influence of the level of communication facilities is the most significant, and the influence coefficient reaches 0.331, which is the basic premise for the rapid development of e-commerce. At the same time, the level of transportation infrastructure is also at the core of promoting the rapid development of e-commerce. The elasticity coefficient factor has values up to 0.112. If the other factors remain fixed, the impact of the financial environment on the efficiency of poverty alleviation is minimal, but it is worth noting that it has a positive impact.
- (5)
- From the decomposition of the spillover effects, the three most significant factors from the perspective of direct effects are the level of communication facilities, transportation infrastructure, and the level of human capital. It can be seen that the levels of communication facilities, transportation infrastructure, and human capital are major factors affecting the future development of e-commerce and are the basic premise for its rapid development. It is worth noting that the direct effect of financial conditions is the least significant factor.
5.2. Suggestions
5.3. Limitations and Prospects
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Region | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | Mean | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
East | Beijing | 2.012 | 2.015 | 2.018 | 2.112 | 2.132 | 2.146 | 2.157 | 2.188 | 2.167 | 2.233 | 2.256 | 2.269 | 2.142 |
Tianjin | 1.793 | 1.799 | 1.813 | 1.823 | 1.848 | 1.986 | 1.995 | 2.018 | 2.117 | 2.165 | 2.167 | 2.178 | 1.975 | |
Hebei | 0.489 | 0.493 | 0.499 | 0.503 | 0.501 | 0.511 | 0.516 | 0.522 | 0.518 | 0.533 | 0.536 | 0.522 | 0.512 | |
Liaoning | 0.401 | 0.413 | 0.424 | 0.436 | 0.441 | 0.447 | 0.449 | 0.458 | 0.467 | 0.472 | 0.478 | 0.385 | 0.439 | |
Shanghai | 1.986 | 1.994 | 1.999 | 2.012 | 2.018 | 2.119 | 2.134 | 2.144 | 2.156 | 2.167 | 2.189 | 2.199 | 2.093 | |
Jiangsu | 0.811 | 0.815 | 0.819 | 0.826 | 0.838 | 0.849 | 0.864 | 0.877 | 0.889 | 0.895 | 0.915 | 0.933 | 0.861 | |
Zhejiang | 0.798 | 0.803 | 0.816 | 0.828 | 0.837 | 0.854 | 0.869 | 0.889 | 0.898 | 0.901 | 0.905 | 0.916 | 0.860 | |
Fujian | 0.765 | 0.789 | 0.797 | 0.805 | 0.811 | 0.818 | 0.829 | 0.833 | 0.852 | 0.866 | 0.883 | 0.898 | 0.829 | |
Sahndong | 0.465 | 0.469 | 0.471 | 0.477 | 0.487 | 0.495 | 0.501 | 0.513 | 0.522 | 0.534 | 0.544 | 0.556 | 0.503 | |
Guangdong | 0.891 | 0.899 | 0.911 | 0.927 | 0.936 | 0.949 | 0.959 | 0.969 | 0.989 | 0.995 | 0.999 | 1.101 | 0.960 | |
Hainan | 0.613 | 0.615 | 0.618 | 0.624 | 0.627 | 0.635 | 0.647 | 0.688 | 0.701 | 0.733 | 0.762 | 0.782 | 0.670 | |
Central | Shanxi | 0.412 | 0.415 | 0.425 | 0.433 | 0.437 | 0.453 | 0.476 | 0.488 | 0.498 | 0.517 | 0.542 | 0.573 | 0.472 |
Jilin | 0.501 | 0.506 | 0.511 | 0.514 | 0.516 | 0.515 | 0.517 | 0.521 | 0.524 | 0.528 | 0.533 | 0.542 | 0.519 | |
Heilong jiang | 0.572 | 0.573 | 0.582 | 0.587 | 0.586 | 0.590 | 0.592 | 0.598 | 0.603 | 0.608 | 0.612 | 0.619 | 0.594 | |
Anhui | 0.611 | 0.617 | 0.623 | 0.632 | 0.639 | 0.648 | 0.655 | 0.667 | 0.678 | 0.682 | 0.687 | 0.693 | 0.653 | |
Jinagxi | 0.711 | 0.719 | 0.714 | 0.721 | 0.722 | 0.719 | 0.725 | 0.729 | 0.737 | 0.753 | 0.765 | 0.768 | 0.732 | |
Henan | 0.501 | 0.513 | 0.515 | 0.517 | 0.519 | 0.527 | 0.524 | 0.529 | 0.535 | 0.538 | 0.544 | 0.553 | 0.526 | |
Hubei | 0.476 | 0.481 | 0.488 | 0.491 | 0.494 | 0.498 | 0.503 | 0.508 | 0.514 | 0.519 | 0.517 | 0.525 | 0.501 | |
Hunan | 0.651 | 0.655 | 0.664 | 0.674 | 0.672 | 0.683 | 0.689 | 0.691 | 0.693 | 0.698 | 0.707 | 0.718 | 0.683 | |
West | Neimenggu | 0.578 | 0.581 | 0.589 | 0.584 | 0.588 | 0.593 | 0.596 | 0.603 | 0.608 | 0.609 | 0.612 | 0.616 | 0.596 |
Guangxi | 0.603 | 0.609 | 0.611 | 0.614 | 0.619 | 0.617 | 0.618 | 0.619 | 0.624 | 0.627 | 0.626 | 0.635 | 0.619 | |
Chonhqing | 0.503 | 0.505 | 0.511 | 0.513 | 0.519 | 0.518 | 0.522 | 0.529 | 0.527 | 0.535 | 0.538 | 0.544 | 0.522 | |
Sichaun | 0.515 | 0.518 | 0.523 | 0.528 | 0.531 | 0.536 | 0.534 | 0.541 | 0.547 | 0.552 | 0.558 | 0.566 | 0.537 | |
Guizhou | 0.316 | 0.319 | 0.326 | 0.336 | 0.341 | 0.348 | 0.364 | 0.373 | 0.383 | 0.389 | 0.394 | 0.399 | 0.357 | |
Yunnan | 0.502 | 0.505 | 0.509 | 0.513 | 0.519 | 0.526 | 0.527 | 0.534 | 0.536 | 0.535 | 0.538 | 0.547 | 0.524 | |
Shanxi | 0.442 | 0.445 | 0.451 | 0.456 | 0.464 | 0.476 | 0.481 | 0.487 | 0.495 | 0.498 | 0.501 | 0.507 | 0.475 | |
Gansu | 0.209 | 0.213 | 0.215 | 0.216 | 0.223 | 0.226 | 0.231 | 0.237 | 0.239 | 0.241 | 0.352 | 0.254 | 0.238 | |
Qinghai | 0.363 | 0.365 | 0.369 | 0.373 | 0.379 | 0.386 | 0.388 | 0.389 | 0.391 | 0.393 | 0.392 | 0.398 | 0.382 | |
Ningxia | 0.423 | 0.427 | 0.435 | 0.436 | 0.438 | 0.445 | 0.463 | 0.477 | 0.489 | 0.498 | 0.534 | 0.565 | 0.469 | |
Xinjiang | 0.341 | 0.345 | 0.349 | 0.354 | 0.361 | 0.369 | 0.375 | 0.381 | 0.388 | 0.387 | 0.394 | 0.397 | 0.370 |
Year | Poverty Alleviation Efficiency | Year | Poverty Alleviation Efficiency | ||
---|---|---|---|---|---|
Moran | p-Value | Moran | p-Value | ||
2010 | 0.5034 | 0.0003 | 2016 | 0.5233 | 0.0004 |
2011 | 0.5512 | 0.0011 | 2017 | 0.5245 | 0.0013 |
2012 | 0.5034 | 0.0009 | 2018 | 0.5278 | 0.0004 |
2013 | 0.5316 | 0.0012 | 2019 | 0.5363 | 0.0004 |
2014 | 0.5019 | 0.0014 | 2020 | 0.5378 | 0.0009 |
2015 | 0.5176 | 0.0011 | 2021 | 0.5344 | 0.0016 |
Test Method | Statistical Value | p-Value |
---|---|---|
Wald spatial lag | 77.091 | 0.001 |
Wald spatial error | 75.223 | 0.001 |
LR spatial lag | 66.129 | 0.002 |
LR spatial error | 63.339 | 0.001 |
Hausman Test | Chi-Sq. Statistic | Chi-Sq. d.f. | Prob. |
---|---|---|---|
Cross-section random | 21.31 | 10 | 0.022 |
Variable | Regression Coefficient | Variable | Regression Coefficient |
---|---|---|---|
LnJT | 0.112 *** | W × LnJT | 0.145 *** |
LnTX | 0.331 *** | W × LnTX | 0.361 *** |
LnCZ | 0.034 *** | W × LnCZ | 0.112 *** |
LnJJ | 0.219 * | W × LnJJ | 0.204 ** |
LnRL | 0.003 *** | W × LnRL | 0.012 *** |
LnJR | 0.009 | W × LnJR | 0.013 * |
W*dep.var | 0.1765 *** | ||
R-squared | 0.9877 | ||
log-likelihood | 461.092 |
Variable | Direct Effect | Indirect Effect | Total Effect |
---|---|---|---|
LnJT | 0.2312 *** | 0.1113 *** | 0.3425 *** |
lnTX | 0.2933 *** | 0.0528 *** | 0.3461 *** |
lnCZ | 0.0431 *** | 0.0127 | 0.0558 *** |
lnJJ | 0.0899 *** | 0.2134 *** | 0.3033 * |
lnRL | 0.1198 *** | −0.0132 | 0.1066 *** |
LnJR | 0.2312 | 0.0146 | 0.2458 * |
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Xu, G.; Zhao, T.; Wang, R. Research on the Efficiency Measurement and Spatial Spillover Effect of China’s Regional E-Commerce Poverty Alleviation from the Perspective of Sustainable Development. Sustainability 2022, 14, 8456. https://doi.org/10.3390/su14148456
Xu G, Zhao T, Wang R. Research on the Efficiency Measurement and Spatial Spillover Effect of China’s Regional E-Commerce Poverty Alleviation from the Perspective of Sustainable Development. Sustainability. 2022; 14(14):8456. https://doi.org/10.3390/su14148456
Chicago/Turabian StyleXu, Guoyin, Tong Zhao, and Rong Wang. 2022. "Research on the Efficiency Measurement and Spatial Spillover Effect of China’s Regional E-Commerce Poverty Alleviation from the Perspective of Sustainable Development" Sustainability 14, no. 14: 8456. https://doi.org/10.3390/su14148456