Spatial Spillover Effect of Rural Labor Transfer on the Eco-Efficiency of Cultivated Land Use: Evidence from China
Abstract
:1. Introduction
2. Theoretical Analysis and Research Hypothesis
3. Materials and Methods
3.1. Research Methods
3.1.1. Super-Efficiency EBM Model
3.1.2. Spatial Correlation Test Model
3.1.3. Spatial Durbin Model (SDM)
3.1.4. Spillover Effect
3.2. Data Sources and Variable Selection
- (1)
- Dependent variable: ECLU (TE). Based on the research of Chen et al. [40], we selected indicators to calculate the ECLU from the input–output perspective (Table 1). The pollutant generation coefficient, utilization rate, and loss rate of each indicator of cultivated land NPS pollution and the coefficient of each carbon emission indicator were previously reported [41,42,43,44,45];
- (2)
- Independent variable: rural labor transfer (Labor), which is the rural labor transferred from rural areas to urban and secondary and tertiary industries and can be characterized by the ratio of (rural laborers–laborers of rural primary industry) to rural laborers [46];
- (3)
- Control variables: Based on the studies of Li [47] and Dhehibi et al. [48] and considering the data availability, we selected seven indicators as control variables: cultivated land area per laborer (Land), cultivated land multiple cropping index (MCI), peasant’s net income per capita (PINC), irrigation index (IRR), average total power of agricultural machinery (ATP), average agricultural consumption of chemical fertilizers (by effective component, FER), and proportion of agricultural expenditure of the government (AGE).
4. Results and Analysis
4.1. Rural Labor Transfer and Spatial and Temporal Evolution Characteristics of ECLU
4.2. Spatial Spillover Effect
4.2.1. Spatial Correlation Test
4.2.2. SDM Regression Results
4.2.3. Effect Decomposition
4.3. Robustness Test
5. Discussion
6. Conclusions
7. Policy Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Tu, S.S.; Long, H.L.; Zhang, Y.N.; Ge, D.Z.; Qu, Y. Rural Restructuring at Village Level Under Rapid Urbanization in Metropolitan Suburbs of China and Its Implications for Innovations in Land Use Policy. Habitat Int. 2018, 77, 143–152. [Google Scholar] [CrossRef]
- Huang, J.K.; Wu, Y.H.; Rozelle, S. Moving off the Farm and Intensifying Agricultural Production in Shandong: A Case Study of Rural Labor Market Linkages in China. Agric. Econ. 2009, 40, 203–218. [Google Scholar] [CrossRef]
- Kimura, S.; Otsuka, K.; Sonobe, T.; Rozelle, S. Efficiency of Land Allocation through Tenancy Markets: Evidence from China. Econ. Dev. Cult. Change 2011, 59, 485–510. [Google Scholar] [CrossRef] [Green Version]
- De Brauw, A.; Rozelle, S. Migration and Household Investment in Rural China. China Econ. Rev. 2008, 19, 320–335. [Google Scholar] [CrossRef]
- Rosenthal, S.S.; Strange, W.C. Evidence on the Nature and Sources of Agglomeration Economies. Reg. Urban Econ. 2004, 4, 2119–2171. [Google Scholar] [CrossRef]
- Xu, H.Z.; Chen, T. Impact of farmers’ differentiation on farmland-use efficiency: Evidence from household survey data in rural China. Agric. Econ. 2013, 59, 227–234. [Google Scholar] [CrossRef] [Green Version]
- Niu, H.P.; Zhang, A.L. Reconstruction of Cultivated Land Use Benefit System and Analysis on its Externality. China Land Sci. 2009, 23, 25–29. (In Chinese) [Google Scholar]
- Lu, X.H.; Tang, Y.F.; Yi, J.L.; Jiang, X. Study on the Impact of Cultivated Land Use Transition on Agricultural Economic Growth Based on Spatial Econometric Model. China Land Sci. 2019, 33, 53–61. (In Chinese) [Google Scholar]
- Henderson, V. Externalities and Industrial Development. J. Urban Econ. 1997, 42, 449–470. [Google Scholar] [CrossRef] [Green Version]
- Acs, Z.J.; Armington, C. Employment Growth and Entrepreneurial Activity in Cities. Reg. Stud. 2004, 38, 911–927. [Google Scholar] [CrossRef] [Green Version]
- Lopez-Bazo, E.; Vaya, E.; Artis, M. Regional Externalities and Growth: Evidence from European Regions. J. Reg. Sci. 2004, 44, 43–73. [Google Scholar] [CrossRef]
- Fang, S.L.; Wei, L.B.; Wu, J.Q. Spatial Spillover Effect of Agricultural Mechanization and Its Distribution Rules from the Perspective of Agricultural Machinery Cross-Regional Services. Manag. World 2017, 11, 65–78, 187–188. (In Chinese) [Google Scholar]
- Houshyar, E.; Sheikh-Davoodi, M.J.; Nassiri, S.M. Energy Efficiency for Wheat Production Using Data Envelopment Analysis (DEA) Technique. J. Agric. Sci. Technol. 2010, 6, 663–673. Available online: https://www.thaiscience.info/journals/Article/IJAT/10842589.pdf (accessed on 8 May 2022).
- Zewdie, M.C.; Moretti, M.; Tenessa, D.B.; Ayele, Z.A.; Nyssen, J.; Tsegaye, E.A.; Minale, A.S.; Van Passel, S. Agricultural Technical Efficiency of Smallholder Farmers in Ethiopia: A Stochastic Frontier Approach. Land 2021, 10, 246. [Google Scholar] [CrossRef]
- Zhang, C.Z.; Su, Y.Y.; Yang, G.Q.; Chen, D.L.; Yang, R.X. Spatial-Temporal Characteristics of Cultivated Land Use Efficiency in Major Function-Oriented Zones: A Case Study of Zhejiang Province, China. Land 2020, 9, 114. [Google Scholar] [CrossRef]
- Tian, Y.P.; Liu, X.Y. Evaluation of Regional Green Development Efficiency Based on Super-eficiency SBM-DEA and Spatial Analysis. J. Stat. Inf. 2021, 36, 56–65. (In Chinese) [Google Scholar]
- Yin, N.; Wang, Y.P. Impacts of Rural Labor Resource Change on the Technical Efficiency of Crop Production in China. Agriculture 2017, 7, 26. [Google Scholar] [CrossRef] [Green Version]
- Bjørnsen, H.M.; Mishra, A.K. Labor Allocation in a Household and Its Impact on Production Efficiency: A Comparison of Panel Modeling Approaches. Essays Honor Jerry Hausman 2012, 29, 269–303. [Google Scholar]
- Xu, Q.H.; Zhang, G.S. The Spatial Spillover Effects of Rural Labor Migration on the Agricultural Production Efficiency of Counties: An Empirical Analysis Based on a Panel Data of 1832 Counties. Res. Agric. Mod. 2020, 41, 407–416. (In Chinese) [Google Scholar]
- Hou, M.Y.; Yao, S.B. Spatial Spillover Effects and Threshold Characteristics of Rural Labor Transfer on Agricultural Eco-Efficiency in China. Resour. Sci. 2018, 40, 2475–2486. (In Chinese) [Google Scholar]
- Wang, P.; Liu, Q.; Xie, H.L. Spatial Econometric Analysis of Cultivated Land Change and Its Influencing Factors in the Poyang Lake Eco-Economics Zone. Adv. Mater. Res. 2014, 864, 2659–2664. [Google Scholar] [CrossRef]
- Wang, L.J.; Li, H. Cultivated Land Use Efficiency and the Regional Characteristics of Its Influencing Factors in China: By Using a Panel Data of 281 Prefectural Cities and the Stochastic Frontier Production Function. Geogr. Res. 2014, 33, 1995–2004. (In Chinese) [Google Scholar]
- Fang, F.; He, R.W.; He, Y. Effects of Off-Farm Employment on Farmland Use Efficiency in Beijing-Tianjin-Hebei Region. Res. Agric. Mod. 2019, 40, 234–242. (In Chinese) [Google Scholar]
- Jiang, H.; Yang, H.R.; Wu, Q. The Spatial and Temporal Differentiation of the Farmland Utilization Efficiency in the Eastern Coastal Economic Zone. Res. Agric. Mod. 2020, 41, 321–330. (In Chinese) [Google Scholar]
- Liu, M.B.; Zhang, A.L.; Wen, G.H. Temporal and Spatial Pattern and Evolution Trend of Cultivated Land Use Ecological Efficiency in the Main Grain Producing Areas in the Lower Yangtze Region. China Land Sci. 2021, 35, 50–60. (In Chinese) [Google Scholar]
- Skevas, T.; Lansink, A.O. Reducing Pesticide Use and Pesticide Impact by Productivity Growth: The Case of Dutch Arable Farming. J. Agric. Econ. 2014, 65, 191–211. [Google Scholar] [CrossRef]
- Li, L.B.; Liu, B.L. Efficiency Evaluation and Cause Identification of China’s High-Tech Industry. Econ. Perspect. 2014, 9, 56–65. (In Chinese) [Google Scholar]
- Wang, W.; Sun, F.C. Analysis on the Three-Stage Innovation Efficiency Change of High-Tech Industry—Based on Internal Undesirable Output of SBM Model and EBM Model. Sci. Technol. Prog. Policy 2018, 35, 67–71. (In Chinese) [Google Scholar]
- Wu, Z.H.; Dang, J.Q.; Pang, Y.P.; Xu, W. Threshold effect or spatial spillover? The impact of agricultural mechanization on grain production. J. Appl. Econ. 2021, 24, 478–503. [Google Scholar] [CrossRef]
- Su, Q.M.; Jiang, X.Q. Evaluate the economic and environmental efficiency of land use from the perspective of decision-makers’ subjective preferences. Ecol. Indic. 2021, 129, 107984. [Google Scholar] [CrossRef]
- Guan, B.; Li, X. Analysis of Rural Labor Transfer Based on Network Data Mining and Financial Time Series Statistics; Springer: Singapore, 2020; pp. 1799–1807. [Google Scholar] [CrossRef]
- Zhang, W.K.; Liang, H.Y.; Chen, Z.; Zhang, J. The Rural Labor Transfer with Respect to the Development of Farmland Leasing Market: Evidence from Rural China. Man Econ. 2021, 8, 99–128. [Google Scholar] [CrossRef]
- Di, J.P.; Li, Y.J. Rural labor transfer mode and regional nonequilibrium degree comparative study in Shandong and Henan provinces. China Popul. Resour. Environ. 2008, 18, 189–193. [Google Scholar] [CrossRef]
- Cheng, G. Data Envelopment Analysis: Methods and MaxDEA Software; Intellectual Property Press: Beijing, China, 2014. (In Chinese)
- Tone, K.; Tsutsui, M. An Epsilon-Based Measure of Efficiency in DEA—A Third Pole of Technical Efficiency. Eur. J. Oper. Res. 2010, 207, 1554–1563. [Google Scholar] [CrossRef]
- Andersen, P.; Petersen, N.C. A Procedure for Ranking Efficient Units in Data Envelopment Analysis. Manag. Sci. 1993, 39, 1261–1264. [Google Scholar] [CrossRef]
- Moran, P.A. A Test for the Serial Independence of Residuals. Biometrika 1950, 37, 178–181. [Google Scholar] [CrossRef]
- Anselin, L. Spatial econometrics: Methods and models. Stud. Oper. Reg. Sci. 1988, 85, 310–330. [Google Scholar] [CrossRef]
- Lesage, J.P.; Pace, R.K. Introduction to Spatial Econometrics; Taylor & Francis Group: Boca Raton, FL, USA, 2009. [Google Scholar]
- Chen, Y.; Li, S.R.; Cheng, L. Evaluation of Cultivated Land Use Efficiency with Environmental Constraints in the Dongting Lake Eco-Economic Zone of Hunan Province, China. Land 2020, 9, 440. [Google Scholar] [CrossRef]
- Wouterse, F. Migration and Technical Efficiency in Cereal Production: Evidence from Burkina Faso. Agric. Econ. 2010, 41, 385–395. [Google Scholar] [CrossRef] [Green Version]
- Madu, I.A. Spatial impacts of rural population pressure on agricultural land use in Nigeria. Appl. Spat. Anal. Policy 2012, 5, 123–135. [Google Scholar] [CrossRef]
- Chen, Q.R.; Xie, H.L. Temporal-Spatial Differentiation and Optimization Analysis of Cultivated Land Green Utilization Efficiency in China. Land 2019, 8, 158. [Google Scholar] [CrossRef] [Green Version]
- Luo, X.; Ao, X.H.; Zhang, Z.; Wan, Q.; Liu, X. Spatiotemporal variations of cultivated land use efficiency in the Yangtze River Economic Belt based on carbon emission constraints. J. Geogr. Sci. 2020, 30, 535–552. [Google Scholar] [CrossRef]
- Dubey, A.; Lal, R. Carbon footprint and sustainability of agricultural production systems in Punjab, India and Ohio, USA. J. Crop Improv. 2009, 23, 332–350. [Google Scholar] [CrossRef]
- Su, X.; Liu, H.L. The Effects of Farmers’ Specialized Cooperatives on Agricultural Production Efficiency within the Context of Rural Labor Migration. China Rural Econ. 2017, 5, 58–72. (In Chinese) [Google Scholar]
- Li, M.; Sicular, T. Aging of the Labor Force and Technical Efficiency in Crop Production: Evidence from Liaoning Province, China. China Agric. Econ. Rev. 2013, 5, 342–359. [Google Scholar] [CrossRef]
- Dhehibi, B.; Almari, A.; Haddad, N.; Aw-Hassan, A. Technical Efficiency and Its Determinants in Food Crop Production: A Case Study of Farms in West Bank, Palestine. J. Agric. Sci. Technol. 2014, 16, 717–730. Available online: https://jast.modares.ac.ir/browse.php?a_id=5941&sid=23&slc_lang=en&ftxt=1 (accessed on 8 May 2022).
- Tian, X.H.; Zhang, X.S. The Identification Problem of Spatial Externalities. Stat. Res. 2013, 30, 94–100. (In Chinese) [Google Scholar]
- Long, X.N.; Zhu, Y.L.; Cai, W.X.; Li, S.M. An Empirical Analysis of Spatial Tax Competition Among Chinese Counties Based on Spatial Econometric Models. Econ. Res. J. 2014, 49, 41–53. (In Chinese) [Google Scholar]
- Elhorst, J.P. Applied Spatial Econometrics: Raising the Bar. Spat. Econ. Anal. 2010, 5, 9–28. [Google Scholar] [CrossRef]
- Xu, D.; Huang, Z.F.; Hou, G.L.; Zhang, C. The spatial spillover effects of haze pollution on inbound tourism: Evidence from mid-eastern China. Tour. Geogr. 2020, 22, 83–104. [Google Scholar] [CrossRef]
- Gao, J.; Song, G.; Sun, X.Q. Does Labor Migration Affect Rural Land Transfer? Evidence from China. Land Use Policy 2020, 99, 105096. [Google Scholar] [CrossRef]
- Lu, H.; Xie, H.L.; Yao, G.R. Impact of Land Fragmentation on Marginal Productivity of Agricultural Labor and Non-Agricultural Labor Supply: A Case Study of Jiangsu, China. Habitat Int. 2019, 83, 65–72. [Google Scholar] [CrossRef]
- Zeng, J.J.; Han, J.Y.; Qu, J.S.; Maraseni, T.N.; Xu, L.; Li, H.J.; Liu, L.N. Ecoefficiency of China’s Agricultural Sector: What Are the Spatiotemporal Characteristics and How Are They Determined? J. Clean. Prod. 2021, 325, 129346. [Google Scholar] [CrossRef]
- Xie, R.; Fang, J.Y.; Liu, C.J. Impact and Spatial Spillover Effect of Transport Infrastructure on Urban Environment. Energy Procedia 2016, 104, 227–232. [Google Scholar] [CrossRef]
- Chen, W.X.; Chi, G.Q. Urbanization and Ecosystem Services: The Multi-Scale Spatial Spillover Effects and Spatial Variations. Land Use Policy 2022, 114, 105964. [Google Scholar] [CrossRef]
Indicator | Variable Name | Indicator | Variable Name |
---|---|---|---|
Input indicators | Agricultural laborers (104 people) | Dependent variable | ECLU (TE) |
Total sown area of farm crops (103 hectares) | Independent variables | Labor transfer (Labor; %) | |
Total power of agricultural machinery (104 kW) | Squared term of labor transfer (Labor2; %) | ||
Effective irrigation area/103 hectares | Control variables | Cultivated land area per laborer (Land; hectare) | |
Agricultural consumption of chemical fertilizers by effective component (104 tons) | Multiple cropping index (MCI; %) | ||
Consumption of pesticides (104 tons) | Peasant’s net income per capita (PINC; yuan) | ||
Consumption of agricultural plastic film (104 tons) | Irrigation index (IRR; %) | ||
Desirable output indicators | Gross crop production (104 tons) | Average total power of agricultural machinery (ATP; kW) | |
Undesirable output indicators | Carbon emissions (104 tons) | Average agricultural consumption of chemical fertilizers (by effective component, FER; ton) | |
NPS pollution (104 tons) | Proportion of agricultural expenditure of government (AGE; %) |
Year | Moran’s I | Year | Moran’s I |
---|---|---|---|
1990 | 0.188 *** | 2005 | 0.071 ** |
1991 | 0.191 *** | 2006 | 0.014 |
1992 | 0.198 *** | 2007 | −0.068 |
1993 | 0.051 | 2008 | −0.158 |
1994 | 0.011 | 2009 | −0.155 |
1995 | 0.019 * | 2010 | −0.181 |
1996 | 0.107 *** | 2011 | −0.182 |
1997 | 0.127 *** | 2012 | −0.192 |
1998 | 0.101 *** | 2013 | −0.157 |
1999 | 0.148 *** | 2014 | −0.149 |
2000 | 0.199 *** | 2015 | −0.124 |
2001 | 0.201 *** | 2016 | −0.075 |
2002 | 0.126 *** | 2017 | −0.092 |
2003 | 0.071 ** | 2018 | −0.089 |
2004 | 0.127 *** | 1990–2018 | 0.055 *** |
Variables | Whole Country | East | Central | West | Northeast |
---|---|---|---|---|---|
Labor | −0.751 *** | −0.668 | 0.16 | −1.825 *** | −3.955 * |
Labor2 | 1.332 *** | 0.992 | 0.248 | 3.181 *** | 4.530 * |
Land | 0.174 ** | 1.222 *** | 0.311 * | 0.260 * | −0.00639 |
MCI | 0.236 *** | 0.448 *** | 0.179 *** | 0.159 *** | −0.562 |
Ln.PINC | −0.00791 | 0.277 * | 0.233 *** | −0.035 | 1.717 *** |
IRR | −0.345 *** | −0.201 | −0.515 *** | −0.399 ** | −2.512 *** |
ATP | 0.00864 ** | 0.0219 *** | −0.00237 | 0.000971 | 0.201 ** |
FER | −0.293 *** | −0.862 *** | 0.142 | −0.361 * | 7.284 *** |
AGE | −0.846 ** | 0.441 | −2.766 *** | −2.326 *** | 4.198 ** |
W × Labor | −0.488 | −0.807 | −1.681 | −6.591 * | −7.833 ** |
W × Labor2 | 1.089 *** | 0.192 | 3.656 * | 9.095 | 4.946 |
ρ | −0.0583 | −0.0585 | −1.196 *** | −0.974 *** | −0.833 *** |
Sigma | 0.0157 *** | 0.0108 *** | 0.0004 *** | 0.0110 *** | 0.0010 *** |
Variables | Whole Country | East | Central | West | Northeast | |
---|---|---|---|---|---|---|
Direct effect | Labor | −0.740 *** | −0.639 | 0.540 * | −1.456 ** | −1.828 |
Labor2 | 1.314 *** | 0.956 | −0.433 | 2.716 ** | 3.920 ** | |
Land | 0.178 ** | 1.279 *** | 0.422 *** | −0.065 | 0.0241 | |
MCI | 0.236 *** | 0.450 *** | 0.141 *** | 0.0557 | −0.241 | |
Ln.PINC | −0.0146 | 0.268 * | 0.247 *** | −0.189 * | 1.266 *** | |
IRR | −0.341 *** | −0.179 | −0.398 *** | −0.337 ** | −1.254 ** | |
ATP | 0.00844 ** | 0.0208 *** | −0.00122 | −0.00393 | 0.0457 | |
FER | −0.291 *** | −0.863 *** | 0.141 ** | −0.0815 | 4.669 *** | |
AGE | −0.810 ** | 0.525 | −1.812 *** | −1.868 *** | 3.560 *** | |
Indirect effect | Labor | −0.42 | −0.644 | −1.198 * | −2.702 | −4.494 ** |
Labor2 | 0.962 *** | −0.0711 | 2.144 ** | 3.259 | 1.028 | |
Land | 0.345 | −1.831 | −0.32 | 2.605 *** | −0.0409 | |
MCI | 0.071 | −0.0907 | 0.129 | 0.801 *** | −0.665 | |
Ln.PINC | 0.668 *** | 0.479 | −0.0384 | 1.172 ** | 0.967 * | |
IRR | −0.0053 | −1.919 *** | −0.376 * | −0.365 | −2.760 *** | |
ATP | 0.0158 * | 0.123 *** | −0.00418 | 0.0356 | 0.339 ** | |
FER | −0.410 *** | −0.122 | −0.000534 | −2.217 *** | 5.544 *** | |
AGE | −1.320 * | 4.594 | −2.917 *** | −3.18 | 1.675 | |
Total effect | Labor | −1.160 *** | −1.283 | −0.658 | −4.158 ** | −6.322 * |
Labor2 | 2.276 *** | 0.885 | 1.711 | 5.975 * | 4.948 | |
Land | 0.523 ** | −0.552 | 0.102 | 2.540 *** | −0.0168 | |
MCI | 0.307 *** | 0.359 * | 0.270 ** | 0.857 *** | −0.906 | |
Ln.PINC | 0.654 *** | 0.747 | 0.208 | 0.983 ** | 2.233 *** | |
IRR | −0.346 * | −2.099 *** | −0.774 *** | −0.702 | −4.013 *** | |
ATP | 0.0242 *** | 0.144 *** | −0.00541 | 0.0317 | 0.385 ** | |
FER | −0.702 *** | −0.985 ** | 0.14 | −2.299 *** | 10.21 *** | |
AGE | −2.130 *** | 5.119 | −4.729 *** | −5.048 *** | 5.235 * |
Variables | Adjacency Matrix | Direct Effect | Indirect Effect | Total Effect | Economic Matrix | Direct Effect | Indirect Effect | Total Effect |
---|---|---|---|---|---|---|---|---|
Labor | −0.751 *** | −0.740 *** | −0.42 | −1.160 *** | −0.576 *** | −0.581 *** | −0.576 ** | −1.157 *** |
Labor2 | 1.332 *** | 1.314 *** | 0.962 *** | 2.276 *** | 1.196 *** | 1.217 *** | 1.443 *** | 2.659 *** |
Land | 0.174 ** | 0.178 ** | 0.345 | 0.523 ** | 0.135 * | 0.146 ** | 0.230 ** | 0.376 *** |
MCI | 0.236 *** | 0.236 *** | 0.071 | 0.307 *** | 0.235 *** | 0.233 *** | −0.105 *** | 0.128 *** |
Ln.PINC | −0.00791 | −0.0146 | 0.668 *** | 0.654 *** | −0.0563 | −0.0463 | 0.506 *** | 0.460 *** |
IRR | −0.345 *** | −0.341 *** | −0.0053 | −0.346 | −0.307 *** | −0.299 *** | 0.159 | −0.14 |
ATP | 0.00864 ** | 0.00844 ** | 0.0158 * | 0.0242 *** | 0.0121 *** | 0.0121 *** | 0.00335 | 0.0154 *** |
FER | −0.293 *** | −0.291 *** | −0.410 *** | −0.702 *** | −0.460 *** | −0.462 *** | 0.0353 | −0.427 *** |
AGE | −0.846 ** | −0.810 ** | −1.320 * | −2.130 *** | −1.153 *** | −1.085 *** | 1.896 *** | 0.811 |
W × Labor | −0.488 | −0.513 ** | ||||||
W × Labor2 | 1.089 *** | 1.313 *** | ||||||
sigma | 0.0157 *** | 0.0141 *** |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zou, X.; Xie, M.; Li, Z.; Duan, K. Spatial Spillover Effect of Rural Labor Transfer on the Eco-Efficiency of Cultivated Land Use: Evidence from China. Int. J. Environ. Res. Public Health 2022, 19, 9660. https://doi.org/10.3390/ijerph19159660
Zou X, Xie M, Li Z, Duan K. Spatial Spillover Effect of Rural Labor Transfer on the Eco-Efficiency of Cultivated Land Use: Evidence from China. International Journal of Environmental Research and Public Health. 2022; 19(15):9660. https://doi.org/10.3390/ijerph19159660
Chicago/Turabian StyleZou, Xiuqing, Meihui Xie, Zhiyuan Li, and Kaifeng Duan. 2022. "Spatial Spillover Effect of Rural Labor Transfer on the Eco-Efficiency of Cultivated Land Use: Evidence from China" International Journal of Environmental Research and Public Health 19, no. 15: 9660. https://doi.org/10.3390/ijerph19159660