# Spatial Analysis of Agriculture Ecological Efficiency and Its Influence on Fiscal Expenditures

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Data Sources

#### Indicator selection for Input and Output

^{2}), agricultural sowing 312.6 (kg/km

^{2}) [18,19].

#### 2.2. Super-Efficient DEA Model

_{0}decision-making unit, ε is a non-Archimedes infinitesimal quantity. n is the number of decision-making units, each decision-making unit includes m input variables and s output variables, ${S}_{i}^{-}$, ${S}_{r}^{+}$ are the input and output slack variables, respectively x

_{ij}represents the value of the jth decision-making unit at the ith input index, y

_{ij}represents the value of the j decision unit on the rth output variable. λ

_{j}is the input and output index. The weight coefficient of θ, λ

_{j}, ${S}_{i}^{-}$, ${S}_{r}^{+}$ are unknown parameters, which can be solved by the model.

#### Window Analysis

#### 2.3. Spatial Spillover and Financial Expenditure Impact of Agriculture Ecological Efficiency

#### 2.3.1. Spatial Measurement Model

_{it}it is the agricultural ecological efficiency value in year t in region i, ρ is the spatial lag coefficient of the dependent variable, ${w}_{i}^{\prime}$ is the ith row of the spatial weight matrix y

_{t}is the dependent variable in year t in region i, ${x}_{it}^{\prime}$ is the independent variable in year t in the i region, β is the independent variable influence coefficient, ${d}_{i}^{\prime}$ is the ith row of the spatial weight matrix D, and x is the independent variable matrix. The 12 × 4 matrix of the four indicators of “agriculture science and technology financial expenditure and agricultural education financial expenditure”, δ is the spatial lag coefficient of the explanatory variable, u

_{i}is the individual effect, y

_{t}is the time effect, and ${m}_{i}^{\prime}$ is the disturbance term of the spatial matrix M. In row i, λ is the spatial error term coefficient, where W, D, and M are equal in this paper. Regression is carried out according to the constructed general model, and then the model is tested as follows: when λ = 0, the model is a Durbin model (SDM), and when ρ ≠ 0 and δ = 0, the model is a spatial autoregressive model (SLM); When δ = −β λ, the model is a spatial error model (SEM).

#### 2.3.2. Spatial Matrix Construction

#### 2.4. Indicator Selection for Agriculture Fiscal Expenditure Impact on Agricultural Eco-Efficiency

## 3. Results

#### 3.1. Temporal and Spatial Changes in China’s Agricultural Eco-Efficiency Value

#### 3.1.1. Analysis of Agriculture Ecological Efficiency at the National Level

#### 3.1.2. Analysis of Agriculture Ecological Efficiency at the Regional Level

#### 3.1.3. Analysis of Agriculture Ecological Efficiency at the Provincial Level

#### 3.2. Kernel Density Analysis

#### 3.3. Spatial Spillover and Agriculture Fiscal Expenditure Impact on Eco-Efficiency of Agriculture

#### 3.3.1. Spatial Autocorrelation Test

#### 3.3.2. Analysis of Spatial Durbin Model

## 4. Conclusions and Recommendation

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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First-Level Indicator | Secondary Indicators | Variable Description |
---|---|---|

Input Indicator | Labor input | Number of employees in agriculture, forestry, animal husbandry, and fishery × (gross agricultural output value/total output value of agriculture, forestry, animal husbandry, and fishery) (ten thousand people) |

Land input | The total sown area of crops (thousand hectares) | |

Irrigation input | Effective irrigated area (thousand hectares) | |

Mechanical input | Total power of agricultural machinery (10,000 kilowatts) | |

Diesel input | Agricultural diesel application amount (10,000 tons) | |

Input of draft animals | Number of large livestock at the end of the year (10,000 heads) | |

Fertilizer input | Agricultural chemical fertilizer application amount (10,000 tons) | |

Pesticide input | Pesticide application amount (10,000 tons) | |

Agricultural film input | Agricultural plastic film application amount (10,000 tons) | |

Output Indicator | Expected output | Gross agricultural output value (100 million yuan) (constant price in 1978) |

Undesired output | Agricultural non-point source pollution (cubic kilometers) | |

Carbon emissions (tons) |

Index | Meaning | Expected | |
---|---|---|---|

Explained Variable | Agriculture ecological efficiency | Obtained by calculating the input and output of agricultural production in each province and city | - |

Core Explanatory Variables | Agricultural financial support | Local financial expenditure on agriculture, forestry, and water affairs | Negative |

Other Explanatory Variables | Financial support for rural environmental governance | Gross agricultural output/Gross regional product × local environmental protection expenditure | Positive |

Rural science and technology financial support | Gross Agricultural Output/Gross Regional Product × Local Financial Science and Technology Expenditure | Positive | |

Financial support for rural education | Gross agricultural output/Gross regional product × local financial education expenditure | Uncertain |

**Table 3.**Statistics on the 21-year average value of agricultural ecological efficiency in each region.

Efficiency | 0.9–1 | 0.8–0.9 | 0.7–0.8 | 0.6–0.7 | 0.5–0.6 |
---|---|---|---|---|---|

East | Shanghai, Hainan, Jiangsu | Beijing, Zhejiang, Guangdong | Fujian and Tianjin | Liaoning, Hebei | Shandong |

Central | - | - | Hubei and Hunan | Henan, Heilongjiang, Jiangxi | Jilin, Anhui, Shanxi |

West | - | Guizhou, Chongqing, Xinjiang, Shaanxi | Sichuan, Qinghai, Guangxi | Inner Mongolia | Ningxia, Yunnan, Gansu |

Area | Mean | Full Distance | Growth Rate | Area | Mean | Full Distance | Growth Rate | Area | Mean | Full Distance | Growth Rate |
---|---|---|---|---|---|---|---|---|---|---|---|

Shanghai | 0.992 | 1.445 | 6.20% | Sichuan | 0.787 | 0.700 | 0.30% | Hebei | 0.620 | 0.669 | 1.10% |

Hainan | 0.933 | 1.563 | −2.30% | Hubei | 0.769 | 0.666 | 1.60% | Ningxia | 0.590 | 0.709 | 1.80% |

Jiangsu | 0.923 | 2.371 | 4.60% | Qinghai | 0.766 | 0.745 | 2.00% | Shandong | 0.583 | 0.616 | 3.00% |

Guizhou | 0.887 | 2.195 | 2.80% | Guangxi | 0.764 | 0.598 | 2.10% | Jilin | 0.578 | 0.515 | −2.60% |

Beijing | 0.871 | 1.324 | 6.00% | Hunan | 0.764 | 0.871 | 5.50% | Anhui | 0.573 | 0.482 | 1.70% |

Zhejiang | 0.865 | 2.252 | 3.20% | Tianjin | 0.736 | 0.641 | 0.20% | Yunnan | 0.532 | 0.483 | 1.80% |

Guangdong | 0.860 | 1.185 | 3.10% | Henan | 0.667 | 0.703 | 2.10% | Gansu | 0.510 | 0.436 | 0.10% |

Chongqing | 0.847 | 2.249 | 6.80% | Heilongjiang | 0.664 | 0.847 | 2.60% | Shanxi | 0.502 | 0.586 | 1.00% |

Xinjiang | 0.809 | 1.005 | 2.30% | Inner Mongolia | 0.658 | 0.726 | −1.00% | National | 0.762 | - | 2.20% |

Shaanxi | 0.806 | 1.832 | 4.60% | Jiangxi | 0.650 | 0.499 | 0.30% | ||||

Fujian | 0.789 | 1.142 | 4.60% | Liaoning | 0.633 | 0.550 | 0.90% |

Years | 1999 | 2000 | 2001 | 2002 | 2003 | 2005 | 2006 |
---|---|---|---|---|---|---|---|

Adjacency Matrix | 0.148 * | 0.184 * | 0.223 ** | 0.202 ** | 0.218 ** | 0.209 ** | 0.161 |

Capital Matrix | 0.020 * | 0.038 ** | 0.032 ** | 0.029 ** | 0.023 | 0.037 ** | 0.060 *** |

Economic Matrix | 0.029 ** | 0.044 ** | 0.039 ** | 0.039 ** | 0.041 ** | 0.055 *** | 0.076 *** |

Years | 2007 | 2008 | 2009 | 2010 | 2011 | 2015 | 2018 |

Adjacency Matrix | 0.254 ** | 0.081 | 0.265 ** | 0.159 | 0.121 | 0.105 | 0.208 ** |

Capital Matrix | 0.030 * | −0.011 | 0.032 ** | 0.014 | 0.02 | 0.008 | 0.002 |

Economic Matrix | 0.058 *** | 0.034 ** | 0.044 ** | 0.039** | 0.041** | 0.028* | 0.004 |

(1) | (2) | (3) | (4) | (5) | ||
---|---|---|---|---|---|---|

Two-Way Fixed | Random Effects | East | Central | West | ||

W × AEE (rho) | −0.448 *** | 0.518 *** | 0.321 *** | 0.0884 | 0.248 *** | |

Agricultural expenditure | Direct effect (Main) | −1.423 *** | −1.123 ** | −0.913 | 3.167 *** | −4.284 *** |

(0.489) | (0.485) | (1.071) | (0.746) | (0.723) | ||

Indirect effects (W × Agricultural Support) | 10.11 *** | 7.350 *** | 1.229 | −3.427 *** | 9.246 *** | |

(3.378) | (2.320) | (3.659) | (1.083) | (1.693) | ||

total effect | 8.688 ** | 6.227 *** | 0.317 | - | 4.962 *** | |

(3.506) | (2.342) | (4.146) | - | (1.553) | ||

Fiscal Expenditures for Agricultural Environmental Governance | Direct effect (Main) | 3.064 *** | 2.368 ** | 2.950 | 0.662 | 0.613 |

(0.921) | (0.934) | (1.931) | (1.665) | (1.724) | ||

Indirect effects (W × Environmental Support) | −10.25 ** | −16.62 ** | 2.095 | −6.711 ** | −4.418 | |

(4.534) | (6.967) | (7.319) | (2.860) | (4.173) | ||

total effect | −7.181 * | −14.25 ** | 5.045 | - | −3.805 | |

(4.454) | (6.906) | (8.100) | - | (3.675) | ||

Agricultural Science and Technology Fiscal Expenditure | Direct effect (Main) | 2.510 ** | 2.759 *** | 7.911 *** | 4.385 *** | 23.27 *** |

(1.031) | (1.072) | (1.733) | (1.526) | (4.971) | ||

Indirect effects (W × Tech Support) | 6.599 | 21.33 *** | 18.99 ** | 8.229 *** | 43.87 *** | |

(4.920) | (6.176) | (7.407) | (2.759) | (15.38) | ||

total effect | 9.110 * | 24.09 *** | 26.90 *** | - | 67.14 *** | |

(5.039) | (6.322) | (8.200) | - | (16.34) | ||

Agricultural Education Support | Direct effect (Main) | 1.370 *** | 0.622 | 1.521 ** | −1.597 ** | 0.613 |

(0.435) | (0.435) | (0.759) | (0.775) | (1.724) | ||

Indirect effects (W × Agricultural Support) | 0.473 | −4.334 *** | −6.343 *** | 2.190 ** | −4.418 | |

(2.493) | (1.028) | (1.650) | (0.880) | (4.173) | ||

total effect | 1.843 | −3.712 *** | −4.822 *** | - | −3.805 | |

(2.457) | (1.000) | (1.711) | - | (3.675) | ||

sigma2_e | 0.0175 *** | 0.0219 *** | 0.0276 *** | 0.00725 *** | 0.0204 *** | |

R-squared | 0.133 | 0.270 | 0.294 | 0.358 | 0.468 |

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**MDPI and ACS Style**

Wu, G.; Fan, Y.; Riaz, N.
Spatial Analysis of Agriculture Ecological Efficiency and Its Influence on Fiscal Expenditures. *Sustainability* **2022**, *14*, 9994.
https://doi.org/10.3390/su14169994

**AMA Style**

Wu G, Fan Y, Riaz N.
Spatial Analysis of Agriculture Ecological Efficiency and Its Influence on Fiscal Expenditures. *Sustainability*. 2022; 14(16):9994.
https://doi.org/10.3390/su14169994

**Chicago/Turabian Style**

Wu, Guoyong, Yang Fan, and Noman Riaz.
2022. "Spatial Analysis of Agriculture Ecological Efficiency and Its Influence on Fiscal Expenditures" *Sustainability* 14, no. 16: 9994.
https://doi.org/10.3390/su14169994