Temporal-Spatial Differentiation and Optimization Analysis of Cultivated Land Green Utilization Efficiency in China
Abstract
:1. Introduction
2. Literature Review
3. Model Construction and Index Selection
3.1. Super-Efficiency SBM-VRS Model
3.2. The Input and Output Indicators of the GUECL
4. Analysis on the Temporal-Spatial Differentiation of the GUECL
4.1. The Overall Evaluation of the GUECL
4.2. Temporal-Spatial Differentiation of the GUECL
5. Analysis of the Factors Influencing the GUECL
5.1. Indicators
5.2. Analysis of Empirical Results
6. Analysis of the Optimization of the GUECL
7. Conclusions, Discussion and Policy Recommendation
7.1. Conclusions and Discussion
7.2. Policy Implications
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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1 | The carbon emission coefficients of chemical fertilizer (West and Marland, 2002), pesticide (Post and Kwon, 2000), agricultural film (Li et al., 2011), agricultural diesel oil (Wang et al., 2018), agricultural irrigation (Li et al., 2011), agricultural tilling (Wu et al., 2007), and agricultural machinery (West and Marland, 2002) were 0.896 kg/kg, 4.934 kg/kg, 5.180 kg/kg, 0.593 kg/kg, 20.476 kg/hm, 312.6 kg/hm, and 0.18 kg/kw, respectively. |
2 | According to Lai et al.’s (2004) unit analysis method, the nitrogen loss rate and phosphorus loss rate from chemical fertilizer in Jiangsu and Beijing were 30% and 7%, respectively; the nitrogen loss rate and phosphorus loss rate in Tianjin, Guangdong, Zhejiang and Shanghai were 30% and 4%, respectively; the loss rates in Hubei, Fujian and Shandong were 20% and 7%, respectively; the loss rates in Hebei, Shaanxi, Liaoning, Yunnan, Ningxia, Hunan, Jilin, Inner Mongolia and Guizhou were 20% and 4%, respectively; the loss rates in Henan and Heilongjiang were 10% and 7%, respectively; the loss rates in Anhui, Hainan, Xinjiang, Shanxi, Guangxi, Gansu, Sichuan, Jiangxi, Chongqing, Qinghai and Xizang were 10% and 4%, respectively. |
3 | The classification method for efficiency groups is the same as that in Section 4.1. |
4 | The elasticity of the agricultural environmental system refers to the agricultural resource utilization system’s ability to recover, self-regulate, and resist various pressures and disturbances. When internal or external disturbances or pressures upon the system have not exceeded its elastic limit (agricultural resources carrying capacity), the system can recover from the deviation and return to its original status. However, the elastic force is limited. When an external force is too powerful and exceeds the system’s elastic limit, shifting the elastic force from its original equilibrium position, then the resource utilization system changes from one status to another because it is unable to restore to the original status. The higher the system elasticity is, the more room for human activities, the more opportunities for choice, the greater the resistance to natural disasters, etc. |
Primary Indices | Secondary Indices | Variables and Descriptions |
---|---|---|
Inputs | Agricultural labor input | AFAHF × (Total agricultural output/TO) (ten thousand people) |
Agricultural land input | Total sown area of crops (thousand hectare) | |
Agricultural capital input | OVPFA (Yuan/per person) | |
Consumption of chemical fertilizers (ten thousand tons) | ||
Consumption of pesticide (ten thousand tons) | ||
Consumption of agricultural film (ten thousand tons) | ||
Total agricultural machinery power (ten thousand kw) | ||
Effective irrigation area (thousand hectares) | ||
Desirable Outputs | Total agricultural output | Total agricultural output (ten thousand Yuan) |
Undesirable Outputs | Pollution emission | agricultural pollution emission index integrated from the loss of nitrogen and phosphorus from fertilizer (ten thousand tons), pesticide loss (ten thousand tons), and agricultural film residuals (ten thousand tons) 1 |
Carbon emission | The total carbon emissions of chemical fertilizer, pesticide, agricultural film, agricultural diesel oil, agricultural irrigation, agricultural tilling and agricultural machinery (ten thousand tons) |
Influencing Factors | Representing Variables and Units | Predictive Effect |
---|---|---|
Agricultural human capital | Proportion of population above high school education level in the rural population aged six and above (%) | Positive |
Farmer occupational differentiation | Amount of agricultural employment/total rural employment (%) | Undefined |
The extent of farmers’ dependence on cultivated land | Rural household net farmland income per capita/rural household net income per capita (%) | Undefined |
Agricultural added value | Agricultural added value (ten thousand Yuan) | Undefined |
Agricultural machinery density | Agricultural machinery gross power/agricultural crop sown area (KW/hectare) | Undefined |
Degree of agricultural scale | Cultivated land area per capita (hectare/per capita) | Undefined |
Agricultural disaster rate | Crop disaster area/agricultural crop sown area (%) | Negative |
Explanatory Variables | Coef. | Std. Err. | p > |z| |
---|---|---|---|
Agricultural human capital | −0.01 | 0.08 | 0.861 |
Farmer occupational differentiation | 0.50 *** | 0.19 | 0.009 |
The extent of farmers’ dependence on cultivated land | 0.83 *** | 0.18 | 0.000 |
Agricultural added value | 3.11 *** | 2.25 | 0.000 |
Agricultural machinery density | −6.89 ** | 3.10 | 0.026 |
Degree of agricultural scale | 0.02 | 0.01 | 0.184 |
Agricultural disaster rate | −0.04 * | 0.02 | 0.063 |
C | 0.42 | 0.09 | 0.000 |
rho | 0.77 | 0.05 | |
Wald chi2 (7) | 464.74 | ||
Prob > chi2 | 0.0000 |
Province | Inputs Slack/% | Outputs Slack/% | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Land | Labor | OVPFA | Fertilizer | Pesticide | Film | Machinery | Irrigation Area | Agricultural Output | Pollution Emission | Carbon Emission | |
China | −50.81 | −37.82 | −31.37 | −36.27 | −31.69 | −41.11 | −42.70 | −41.41 | 0.53 | −34.43 | −42.71 |
E | −45.09 | −29.33 | −27.16 | −34.87 | −33.57 | −38.24 | −41.16 | −36.96 | 2.41 | −30.55 | −41.09 |
C | −72.64 | −53.86 | −49.69 | −52.13 | −47.23 | −54.67 | −64.33 | −61.13 | −0.29 | −55.60 | −59.22 |
W | −40.64 | −34.65 | −22.25 | −26.14 | −18.51 | −34.10 | −28.51 | −31.51 | −0.75 | −22.90 | −32.34 |
Beijing | −19.16 | −1.13 | −1.35 | −10.65 | −9.05 | −13.32 | −17.48 | −10.09 | 12.22 | −0.09 | −14.36 |
Jiangsu | −55.46 | −27.61 | −40.28 | −48.56 | −33.55 | −44.73 | −43.22 | −56.05 | −0.99 | −40.41 | −48.40 |
Zhejiang | −46.85 | −41.06 | −19.80 | −24.62 | −50.49 | −46.80 | −59.30 | −52.51 | 0.00 | −49.39 | −51.14 |
Anhui | −83.29 | −61.72 | −75.12 | −64.42 | −53.81 | −67.31 | −78.54 | −77.36 | 0.00 | −44.73 | −69.33 |
Fujian | −52.19 | −38.54 | −16.26 | −44.78 | −48.84 | −41.15 | −39.34 | −41.63 | −1.64 | −20.77 | −48.64 |
Jiangxi | −82.79 | −57.29 | −49.67 | −53.50 | −69.85 | −70.80 | −73.70 | −73.85 | 0.00 | −85.71 | −66.30 |
Shandong | −46.29 | −45.63 | −44.93 | −42.15 | −41.87 | −64.62 | −54.94 | −45.22 | −0.72 | −49.46 | −45.74 |
Henan | −58.77 | −60.09 | −49.26 | −51.01 | −28.88 | −42.76 | −54.73 | −50.36 | −0.47 | −51.99 | −50.45 |
Hubei | −61.07 | −38.61 | −46.52 | −57.58 | −59.45 | −46.41 | −48.20 | −49.38 | 0.00 | −64.50 | −55.49 |
Hunan | −67.12 | −52.78 | −30.42 | −36.28 | −45.28 | −62.42 | −77.92 | −59.59 | 0.00 | −66.82 | −47.21 |
Guangdong | −48.52 | −42.86 | −14.81 | −39.01 | −41.16 | −28.14 | −37.78 | −33.04 | −1.50 | −18.48 | −41.23 |
Tianjin | −50.62 | −16.57 | −39.21 | −45.67 | −5.48 | −54.66 | −61.46 | −47.01 | 15.09 | −73.31 | −50.50 |
Guangxi | −20.79 | −18.64 | −0.53 | −16.24 | −11.44 | −3.89 | −10.49 | −16.13 | −1.67 | 0.22 | −16.53 |
Hainan | −47.46 | −35.36 | −20.51 | −40.89 | −53.57 | −19.87 | −20.18 | −2.62 | −1.83 | −25.12 | −43.79 |
Chongqing | −65.05 | −55.74 | −41.65 | −37.42 | −19.33 | −48.98 | −39.69 | −31.99 | 0.00 | −58.42 | −45.33 |
Sichuan | −64.41 | −55.49 | −32.36 | −34.38 | −16.59 | −62.81 | −35.73 | −47.49 | −0.61 | −20.74 | −45.65 |
Guizhou | −67.46 | −71.34 | −15.76 | −33.63 | −46.51 | −56.34 | −49.89 | −51.53 | −1.60 | −62.01 | −48.64 |
Yunnan | −13.30 | −7.76 | −3.89 | −10.53 | −7.74 | −12.37 | −5.84 | −8.02 | −1.19 | 0.11 | −12.09 |
Shaanxi | −55.56 | −52.52 | −5.14 | −40.82 | −32.45 | −38.83 | −41.00 | −50.93 | −1.30 | −8.45 | −44.77 |
Gansu | −19.51 | −26.79 | −20.26 | −0.17 | −30.51 | −23.19 | −19.88 | −5.53 | 0.10 | 0.53 | −14.46 |
Qinghai | −2.51 | 0.31 | −3.66 | 0.07 | 1.95 | −4.71 | −3.42 | −4.30 | 0.00 | −4.01 | −2.14 |
Ningxia | −49.04 | −24.85 | −29.02 | −45.40 | 2.23 | −27.02 | −35.94 | −40.05 | 0.00 | −40.96 | −45.05 |
Hebei | −52.14 | −36.58 | −39.60 | −36.56 | −35.63 | −49.73 | −62.90 | −56.20 | −1.94 | −1.02 | −49.17 |
Xinjiang | −9.92 | −10.92 | −10.06 | −11.83 | −10.59 | −19.71 | −8.45 | −17.33 | −1.98 | 0.64 | −13.22 |
Shanxi | −85.59 | −73.19 | −40.97 | −59.29 | −33.29 | −76.30 | −80.39 | −69.43 | 0.00 | −70.36 | −69.56 |
Inner Mongolia | −79.49 | −57.42 | −82.38 | −57.25 | −32.58 | −77.30 | −63.25 | −73.30 | 0.00 | −58.79 | −67.82 |
Liaoning | −72.21 | −33.66 | −56.98 | −41.35 | −37.36 | −48.87 | −58.65 | −54.73 | −0.12 | −53.17 | −51.28 |
Jilin | −88.19 | −51.61 | −73.59 | −68.07 | −41.39 | −37.62 | −70.06 | −69.01 | 0.00 | −58.95 | −71.03 |
Heilongjiang | −54.30 | −35.59 | −31.94 | −26.90 | −45.86 | −33.72 | −31.11 | −40.06 | −1.86 | −1.75 | −44.36 |
Shanghai | −5.12 | −3.60 | −5.05 | −9.34 | −12.23 | −8.79 | 2.45 | −7.42 | 7.92 | −4.85 | −7.73 |
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Chen, Q.; Xie, H. Temporal-Spatial Differentiation and Optimization Analysis of Cultivated Land Green Utilization Efficiency in China. Land 2019, 8, 158. https://doi.org/10.3390/land8110158
Chen Q, Xie H. Temporal-Spatial Differentiation and Optimization Analysis of Cultivated Land Green Utilization Efficiency in China. Land. 2019; 8(11):158. https://doi.org/10.3390/land8110158
Chicago/Turabian StyleChen, Qianru, and Hualin Xie. 2019. "Temporal-Spatial Differentiation and Optimization Analysis of Cultivated Land Green Utilization Efficiency in China" Land 8, no. 11: 158. https://doi.org/10.3390/land8110158
APA StyleChen, Q., & Xie, H. (2019). Temporal-Spatial Differentiation and Optimization Analysis of Cultivated Land Green Utilization Efficiency in China. Land, 8(11), 158. https://doi.org/10.3390/land8110158