Spatiotemporal Dynamics and Spatial Spillover Effects of Resilience in China’s Agricultural Economy
Abstract
:1. Introduction
1.1. Literature Review
1.2. Summarizing and Researching Gaps
2. Research Materials and Methods
2.1. Study Object and Data Sources
2.2. Indicator System Construction
2.2.1. Evaluation Indicators for the Resilience Level of China’s Agricultural Economy
2.2.2. Spatial Durbin Model Explanatory Variables
2.3. Methods
2.3.1. Entropy Method
2.3.2. Theil Index
2.3.3. Kernel Density Estimation (KDE) Method
2.3.4. Spatial Autocorrelation Analysis
2.3.5. Spatial Durbin Model (SDM)
3. Results and Analysis
3.1. Measurement of Rural Economic Resilience in China
3.2. Measurement of Regional Disparities
3.3. Dynamic Evolution Based on Kernel Density Estimation (KDE)
3.4. Analysis of Spatial Autocorrelation Test Results
3.5. Spatial Durbin Model Estimation Results
3.5.1. Model Construction
3.5.2. Decomposition Analysis of Spatial Effects
3.5.3. Robustness Tests
4. Conclusions and Discussion
4.1. Conclusions
4.2. Discussion and Recommendations
4.3. Limitations and Prospects
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Primary Indicator | Secondary Indicator | Explanation | Indicator Properties | Weight |
---|---|---|---|---|
Resistance (P) | Ability of agriculture to resist disasters | Agricultural disaster-affected area/total cropped area of crops | − | 0.0043 |
Power of agricultural machinery per unit area | Total power of agricultural machinery/total cropped area of crops | + | 0.0385 | |
Total agricultural output | Total output value of the primary industry | + | 0.0558 | |
Number of rural residents engaged in agriculture | Number of people primarily engaged in agricultural-related work in rural areas | + | 0.0195 | |
Grain yield per unit of cultivated land | Total grain production/total area planted with grain crops | + | 0.0215 | |
Disposable income per capita in rural areas | Reflects the level of income that rural residents’ households can freely dispose of over a certain period | + | 0.0354 | |
Rural living guarantee expenditure | Government and societal investment in basic living security for rural residents | − | 0.0107 | |
Adaptation (S) | Fertilizer usage intensity | Amount of chemical fertilizer used in agriculture/total cropped area for agricultural crops | − | 0.0106 |
Intensity of agricultural plastic film usage | Amount of agricultural plastic film used/total cropped area for agricultural crops | − | 0.0064 | |
Number of rural health clinics per ten thousand people | Rural population count/number of rural health clinics | + | 0.0507 | |
Effective irrigation rate | Effective irrigated area/total cropped area for agricultural crops | + | 0.0399 | |
Per capita consumption expenditure in rural areas | Average annual expenditure per capita on goods and services in rural areas | + | 0.0362 | |
Per capita grain yield | Total grain production/total population of the region | + | 0.0645 | |
Innovation (R) | Fixed asset investment in agriculture | Total investment by rural residents in purchasing, constructing, and renovating agricultural fixed assets | + | 0.0697 |
Application and authorization of new plant variety rights in agriculture | Breeding and discovery of new varieties in agriculture | + | 0.1063 | |
Number of valid patents domestically | The number of patents still valid in China | + | 0.1496 | |
Number of township cultural stations per ten thousand people | Rural population count in the region/number of township cultural stations in the region | + | 0.0409 | |
Per capita electricity consumption in rural areas | Total electricity consumption in rural areas of the region/rural population of the region | + | 0.2394 |
Year | Intra-Regional Difference | Contribution Rate of Intra-Regional Difference | ||||||
---|---|---|---|---|---|---|---|---|
Total Difference | East | Central | West | Contribution Rate of Total Difference | East | Central | West | |
2013 | 0.03475 | 0.02173 | 0.00693 | 0.00608 | 64.18% | 40.14% | 12.80% | 11.23% |
2014 | 0.03264 | 0.02152 | 0.00678 | 0.00435 | 62.69% | 41.32% | 13.03% | 8.35% |
2015 | 0.02957 | 0.01900 | 0.00643 | 0.00415 | 58.62% | 37.66% | 12.74% | 8.22% |
2016 | 0.03067 | 0.01895 | 0.00681 | 0.00491 | 57.08% | 35.26% | 12.68% | 9.14% |
2017 | 0.03478 | 0.02072 | 0.00858 | 0.00548 | 61.22% | 36.48% | 15.10% | 9.65% |
2018 | 0.03523 | 0.02130 | 0.00806 | 0.00588 | 63.26% | 38.24% | 14.47% | 10.56% |
2019 | 0.03490 | 0.02087 | 0.00861 | 0.00542 | 62.44% | 37.33% | 15.40% | 9.70% |
2020 | 0.03829 | 0.02159 | 0.01070 | 0.00600 | 63.64% | 35.89% | 17.78% | 9.98% |
2021 | 0.03001 | 0.01179 | 0.01063 | 0.00760 | 69.56% | 27.32% | 24.63% | 17.61% |
2022 | 0.03087 | 0.01298 | 0.00899 | 0.00889 | 69.15% | 29.08% | 20.15% | 19.92% |
Mean | 0.03317 | 0.01904 | 0.00825 | 0.00588 | 63.18% | 35.87% | 15.88% | 11.44% |
Year | Overall Theil Index | Inter-Regional Difference | Contribution Rate of Inter-Regional Difference |
---|---|---|---|
2013 | 0.05415 | 0.01940 | 35.82% |
2014 | 0.05207 | 0.01943 | 37.31% |
2015 | 0.05044 | 0.02087 | 41.38% |
2016 | 0.05374 | 0.02306 | 42.92% |
2017 | 0.05680 | 0.02203 | 38.78% |
2018 | 0.05569 | 0.02046 | 36.74% |
2019 | 0.05590 | 0.02100 | 37.56% |
2020 | 0.06017 | 0.02188 | 36.36% |
2021 | 0.04314 | 0.01313 | 30.44% |
2022 | 0.04463 | 0.01377 | 30.85% |
Mean | 0.05267 | 0.01950 | 36.82% |
Year | Moran’s I | p-Value | Z-Value |
---|---|---|---|
2013 | 0.1523 | 0.0000 | 5.2346 |
2014 | 0.1603 | 0.0000 | 5.4121 |
2015 | 0.1577 | 0.0000 | 5.3065 |
2016 | 0.1585 | 0.0000 | 4.3319 |
2017 | 0.1239 | 0.0000 | 3.5678 |
2018 | 0.0964 | 0.0004 | 3.3506 |
2019 | 0.0884 | 0.0008 | 2.9614 |
2020 | 0.0742 | 0.0031 | 2.5842 |
2021 | 0.0601 | 0.0098 | 2.5842 |
2022 | 0.0351 | 0.0559 | 1.9115 |
Inspection Index | Test Method | Statistic | p-Value |
---|---|---|---|
LM test | Robust LM no test spatial lag | 31.145 | 0.000 |
Robust LM no test spatial error | 4.564 | 0.033 | |
Wald test | Wald test for SAR | 14.57 | 0.0239 |
Wald test for SEM | 13.12 | 0.0412 | |
LR test | LR test for SAR | 14.16 | 0.0279 |
LR test for SEM | 12.80 | 0.0463 | |
Hausman test | Hausman test | 13.33 | 0.0381 |
Ind | Time | Both | |
---|---|---|---|
Main | |||
APE | 0.0497 *** (15.44) | 0.0859 *** (23.94) | 0.0518 *** (14.99) |
UL | 0.121 * (2.11) | 0.115 *** (4.91) | 0.118 * (2.06) |
AM | 0.0130 (1.90) | 0.0121 *** (6.92) | 0.00905 (1.32) |
ACS | −0.0244 * (−1.99) | −0.0372 *** (−3.55) | −0.0214 (−1.77) |
NC | −0.00583 (−1.38) | −0.00768 (−1.28) | −0.00651 (−1.55) |
AEA | 0.00104 (0.60) | 0.00471 *** (5.12) | 0.00100 (0.56) |
Wx | |||
APE | 0.0118 (0.57) | 0.127 *** (3.80) | 0.0699 * (2.26) |
UL | 0.269 * (2.34) | 0.998 *** (6.32) | 0.611 (1.63) |
AM | 0.00996 (0.43) | 0.0794 *** (6.28) | 0.0747 * (2.01) |
ACS | 0.133 * (1.98) | −0.0501 (−1.01) | 0.155 * (2.24) |
NC | −0.00413 (−0.33) | −0.00921 (−0.26) | −0.0258 (−1.07) |
AEA | −0.00249 (−0.36) | −0.0343 *** (−5.09) | −0.00354 (−0.26) |
Spatial autoregression coefficient | 0.125 (0.75) | −0.645 ** (−2.58) | −0.410 (−1.72) |
Spatial error factor | 0.000126 *** (12.24) | 0.000282 *** (12.26) | 0.000117 *** (12.21) |
0.836 | 0.882 | 0.784 |
Variables | Direct | Indirect | Total |
---|---|---|---|
APE | 0.0509 *** (0.00346) | 0.0349 * (0.0180) | 0.0858 *** (0.0184) |
UL | 0.101 * (0.0529) | 0.385 * (0.228) | 0.486 ** (0.203) |
AM | 0.00841 (0.00773) | 0.0505 * (0.0288) | 0.0589 ** (0.0252) |
ACS | −0.0235 * (0.0135) | 0.118 ** (0.0543) | 0.0947 * (0.0549) |
NC | −0.00697 (0.00500) | −0.0154 (0.0202) | −0.0223 (0.0200) |
AEA | 0.00112 (0.00168) | −0.00202 (0.0110) | −0.000900 (0.0114) |
Variables | Direct | Indirect | Total |
---|---|---|---|
APE | 0.0508 *** (0.00329) | 0.0163 ** (0.00807) | 0.0670 *** (0.00911) |
UL | 0.113 ** (0.0494) | 0.147 (0.127) | 0.260 ** (0.113) |
AM | 0.0196 *** (0.00739) | −0.0104 (0.0145) | 0.00923 (0.0124) |
ACS | −0.0373 *** (0.0139) | 0.0767 ** (0.0300) | 0.0394 (0.0306) |
NC | −0.00642 (0.00481) | −0.0178 (0.0123) | −0.0243 * (0.0132) |
AEA | 0.00164 (0.00171) | 0.00300 (0.00510) | 0.00464 (0.00591) |
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Share and Cite
Luo, L.; Nie, Q.; Jiang, Y.; Luo, F.; Wei, J.; Cui, Y. Spatiotemporal Dynamics and Spatial Spillover Effects of Resilience in China’s Agricultural Economy. Agriculture 2024, 14, 1522. https://doi.org/10.3390/agriculture14091522
Luo L, Nie Q, Jiang Y, Luo F, Wei J, Cui Y. Spatiotemporal Dynamics and Spatial Spillover Effects of Resilience in China’s Agricultural Economy. Agriculture. 2024; 14(9):1522. https://doi.org/10.3390/agriculture14091522
Chicago/Turabian StyleLuo, Liang, Qi Nie, Yingying Jiang, Feng Luo, Jie Wei, and Yong Cui. 2024. "Spatiotemporal Dynamics and Spatial Spillover Effects of Resilience in China’s Agricultural Economy" Agriculture 14, no. 9: 1522. https://doi.org/10.3390/agriculture14091522