Spatio-Temporal Differentiation and Driving Mechanism of the “Resource Curse” of the Cultivated Land in Main Agricultural Production Regions: A Case Study of Jianghan Plain, Central China
Abstract
:1. Introduction
2. Literature Review
3. Materials and Methods
3.1. The Connotation between Abundant Cultivated Land Resources and Economic Development
3.2. Study Area and Data Sources
3.3. Research Methodology
3.3.1. Coefficient of the Cultivated Land Resource Curse
3.3.2. Kernel Density Estimation (KDE)
3.3.3. Model Set for the Factors Driving the Cultivated Land Resource Curse
Selection of Driving Factors
- (1)
- Material capital investment level: Material capital is an important driving force of economic development. This study used the entire society’s fixed-asset investment per hectare of land (FI) to measure this index. The larger the index is, the greater the investment in infrastructure in the region, which is conducive to developing various industries;
- (2)
- Regional population’s status: The relationship between regional population status and economic development is close. This study used the urbanization rate (UR) to measure this index. The larger this index is, the more people are concentrated in cities to engage in high-value-added industries;
- (3)
- Cultivated land protection policy: The cultivated land protection policy is a basic national policy in China. It makes it difficult for areas with abundant arable land resources to be transformed into other land types. This study selected the per capita cultivated land area (PCA) to measure this index;
- (4)
- Industrial upgrading level: The local industrial structure reflects the composition, connection, and proportion of each industry’s regional economic system. This study selected the proportion of the output value of secondary and tertiary industries in GDP (STGD) as a model factor;
- (5)
- Agricultural intensification level: The intensification of agriculture makes the best possible use of the cultivated land per unit area. This study used the gross agricultural product per hectare of land (GA) and the grain output per hectare of land (GO) as indicators to measure the level of local agricultural intensification;
- (6)
- Agricultural mechanization level: Agricultural mechanization is an important agricultural infrastructure. This study selected the total power of machinery on land (TPM) to reflect the level of local agricultural mechanization;
- (7)
- Spatial correlation: Regional economic development is not independent of each other but influences each other. This study considered the spatial correlation and introduced the resource curse coefficient’s first-order spatial lag term as an independent variable. It can be expressed as follows:
Regression Model of Driving Factors
4. Results
4.1. Temporal and Spatial Evolution of the Cultivated Land Resource Curse in Jianghan Plain
4.1.1. Temporal Evolution of the Cultivated Land Resource Curse in Jianghan Plain
4.1.2. Spatial Evolution of the Cultivated Land Resource Curse in Jianghan Plain
4.2. Driving Factors of the Cultivated Land Resource Curse in Jianghan Plain
- No cultivated land resource curse zone: The curse coefficient presented a significant negative correlation with the variable Y1, which represented the spatial correlation;
- Potential cultivated land resource curse zone: The curse coefficient of the cultivated land resource in each county was negatively correlated with Y1. A significant positive correlation was observed between PCA and curse coefficient. A significant negative correlation between STGD, GA, GO, and TPM and the curse coefficient of cultivated land resources were observed;
- Slightly cultivated land resource curse zone: The curse coefficient of cultivated land resources in each county was positively correlated with Y1 and passed the general significance test. The curse coefficient of each county was significantly positively correlated with FI; generally significantly positively correlated with STGD, PCA, and UR; and generally significantly negatively correlated with TPM;
- Severe cultivated land resource curse zone: The resource curse coefficient of this zone was positively correlated with FI, Y1, UR, PCA, and STGD but negatively correlated with GA and TPM.
5. Discussion
5.1. Transmission Mechanism of the Cultivated Land Resource Curse in Jianghan Plain
5.2. Policy Implications of the Cultivated Land Resource Curse
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Zone | Threshold | Characteristics |
---|---|---|
No cultivated land resource curse zone | The economy develops faster than the development determined by cultivated land resource endowments. No risk of developing cultivated land resource curse is observed. | |
Potential cultivated land resource curse zone | The economy develops faster than the development determined by cultivated land resource endowments. A risk of developing a slightly cultivated land resource curse is observed. | |
Slightly cultivated land resource curse zone | The cultivated land resource curse starts manifesting. It is at a low level. A risk of developing a more severe cultivated land resource curse is observed. | |
Severe cultivated land resource curse zone | The cultivated land resource advantage does not fully transform into an economic advantage. |
2001 | 2003 | 2005 | 2007 | 2009 | 2011 | 2013 | 2015 | 2017 | Mean | Variance | |
---|---|---|---|---|---|---|---|---|---|---|---|
Caidian | 0.379 | 0.718 | 0.565 | 0.501 | 0.436 | 0.363 | 0.325 | 0.307 | 0.367 | 0.429 | 0.017 |
Dangyang | 0.596 | 0.652 | 0.799 | 0.780 | 0.648 | 0.562 | 0.623 | 0.616 | 0.597 | 0.653 | 0.005 |
Zhijiang | 1.093 | 0.883 | 0.771 | 0.760 | 0.688 | 0.595 | 0.540 | 0.510 | 0.382 | 0.694 | 0.035 |
Jingshan | 1.137 | 1.074 | 1.030 | 1.003 | 0.869 | 1.007 | 1.045 | 1.087 | 1.190 | 1.048 | 0.005 |
Shayang | 1.652 | 1.590 | 1.424 | 1.269 | 1.594 | 1.538 | 1.605 | 1.677 | 1.651 | 1.564 | 0.011 |
Zhongxiang | 1.018 | 1.035 | 1.071 | 1.031 | 0.987 | 0.986 | 1.013 | 1.062 | 1.534 | 1.053 | 0.015 |
Yunmeng | 0.662 | 0.633 | 0.589 | 0.585 | 0.562 | 0.591 | 0.595 | 0.623 | 0.583 | 0.613 | 0.002 |
Yingcheng | 0.525 | 0.521 | 0.734 | 0.771 | 0.792 | 0.768 | 0.765 | 0.775 | 0.834 | 0.711 | 0.012 |
Hanchuan | 0.735 | 0.684 | 0.760 | 0.792 | 0.772 | 0.803 | 0.767 | 0.759 | 0.692 | 0.752 | 0.001 |
Jiangling | 3.052 | 3.158 | 2.201 | 2.335 | 2.524 | 2.770 | 2.803 | 2.671 | 3.055 | 2.714 | 0.082 |
Gongan | 1.682 | 1.594 | 1.686 | 1.716 | 1.733 | 1.822 | 1.848 | 1.838 | 1.914 | 1.753 | 0.009 |
Jianli | 2.423 | 2.378 | 2.296 | 2.575 | 2.486 | 2.682 | 2.866 | 2.883 | 2.397 | 2.598 | 0.039 |
Shishou | 0.743 | 0.692 | 0.817 | 0.891 | 1.005 | 1.236 | 1.361 | 1.337 | 1.452 | 1.055 | 0.075 |
Honghu | 1.153 | 1.146 | 1.312 | 1.389 | 1.397 | 1.594 | 1.593 | 1.569 | 1.182 | 1.394 | 0.030 |
Songzi | 1.247 | 1.198 | 1.409 | 1.502 | 1.454 | 1.436 | 1.349 | 1.310 | 1.290 | 1.357 | 0.010 |
Xiantao | 0.892 | 0.833 | 0.663 | 0.685 | 0.734 | 0.730 | 0.728 | 0.726 | 0.610 | 0.740 | 0.006 |
Qianjiang | 0.788 | 0.765 | 0.659 | 0.573 | 0.585 | 0.569 | 0.593 | 0.622 | 0.672 | 0.645 | 0.006 |
Tianmen | 1.070 | 1.073 | 1.046 | 1.022 | 1.136 | 1.217 | 1.218 | 1.200 | 1.168 | 1.129 | 0.006 |
No Cultivated Land Resource Curse Zone | Potential Cultivated Land Resource Curse Zone | Slightly Cultivated Land Resource Curse Zone | Severe Cultivated Land Resource Curse Zone | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Caidian | Dangyang | Zhijiang | Yunmeng | Qianjiang | Yingcheng | Hanchuan | Xiantao | Jingshan | Shayang | Zhongxiang | Gongan | Shishou | Honghu | Songzi | Tianmen | Jiangling | Jianli | |
FI | 0.041 | 0.137 | 0.188 | −0.033 | 0.496 | 0.291 | −0.045 | −0.099 | 0.083 | 1.113 ** | 0.111 * | 0.245 * | 0.715 | 0.592 | 0.102 * | 0.208 ** | 2.228 *** | 4.993 *** |
pro | (0.697) | (0.526) | (0.415) | (0.759) | (0.207) | (0.202) | (0.881) | (0.765) | (0.356) | (0.040) | (0.099) | (0.100) | (0.372) | (0.256) | (0.195) | (0.016) | (0.001) | (0.009) |
UR | 0.062 | −0.041 | −0.062 | 0.007 | −0.046 | 0.103 | 0.052 | −0.028 | 0.035 | 0.013 | 0.030 | 0.307* | 0.022 | 0.098 | 0.002 | 0.042 * | 0.012 | 0.104 * |
pro | (0.511) | (0.778) | (0.529) | (0.957) | (0.738) | (0.229) | (0.684) | (0.636) | (0.635) | (0.885) | (0.729) | (0.073) | (0.891) | (0.206) | (0.988) | (0.078) | (0.696) | (0.129) |
PCA | 0.062 | −0.033 | 0.019 | −0.028 | −0.137 | 0.331 * | 0.511 * | 0.213 * | 0.056 * | 0.072 * | 0.145 ** | 0.035 * | 0.031 | 0.301 ** | 0.078 *** | 0.018 | 0.339 ** | 0.178 * |
pro | (0.847) | (0.800) | (0.945) | (0.949) | (0.213) | (0.080) | (0.117) | (0.170) | (0.146) | (0.184) | (0.035) | (0.086) | (0.927) | (0.014) | (0.001) | (0.356) | (0.038) | (0.158) |
STGD | −0.492 | −0.240 | −0.349 | 0.069 | −0.107 | −0.296 ** | −0.004 * | −0.396 ** | 0.109 | 0.006 | 0.171 | 0.243 * | 0.391 | 0.081 | 0.356 ** | 0.043 * | 0.958 *** | 0.734 ** |
pro | (0.511) | (0.285) | (0.403) | (0.814) | (0.770) | (0.019) | (0.189) | (0.036) | (0.513) | (0.974) | (0.487) | (0.130) | (0.490) | (0.605) | (0.015) | (0.035) | (0.000) | (0.014) |
GA | −0.150 | −0.176 | −0.283 | 0.052 | −0.110 | −0.325 * | −0.031 | −0.357 * | −0.148 | 0.200 | −0.184 | −0.536 | −0.688 | −0.256 | −0.938 | −0.006 | −3.814 *** | −3.241 *** |
pro | (0.278) | (0.319) | (0.336) | (0.823) | (0.739) | (0.054) | (0.927) | (0.153) | (0.561) | (0.659) | (0.608) | (0.382) | (0.518) | (0.418) | (0.416) | (0.990) | (0.000) | (0.006) |
GO | 0.027 | −0.049 | 0.246 | −0.049 | −0.046 | 0.005 | −0.002 * | −0.154 | −0.067 | 0.025 | −0.064 | 0.020 | −0.231 | 0.261 | −0.302 | −0.286 | −0.469 *** | −0.199 * |
pro | (0.895) | (0.678) | (0.567) | (0.592) | (0.661) | (0.952) | (0.178) | (0.375) | (0.518) | (0.837) | (0.711) | (0.903) | (0.266) | (0.321) | (0.373) | (0.405) | (0.000) | (0.151) |
TPM | 0.375 | 0.904 | −4.896 | −1.048 | −7.639 | −1.182 | 2.238 | −8.247 * | −3.445 * | −11.073 ** | 0.781 | −4.715 * | −4.867 * | −0.045 ** | −12.429 *** | −8.401 * | −15.737 *** | −7.108 |
pro | (0.942) | (0.869) | (0.362) | (0.927) | (0.286) | (0.842) | (0.665) | (0.099) | (0.158) | (0.021) | (0.857) | (0.120) | (0.098) | (0.040) | (0.002) | (0.164) | (0.000) | (0.449) |
Y1 | −0.529 *** | −0.115 * | −0.200 ** | −0.154 * | −0.240 | −0.538 * | −0.787 * | −0.276 | 0.196 * | 1.014 * | 0.094 * | 0.073 | 0.055 | 0.112 * | 0.188 | 1.311 * | 1.505 ** | 0.571 *** |
pro | (0.001) | (0.055) | (0.018) | (0.125) | (0.382) | (0.140) | (0.115) | (0.275) | (0.134) | (0.129) | (0.074) | (0.321) | (0.265) | (0.141) | (0.374) | (0.131) | (0.039) | (0.001) |
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Zhu, Y.; Zhou, X.; Gan, Y.; Chen, J.; Yu, R. Spatio-Temporal Differentiation and Driving Mechanism of the “Resource Curse” of the Cultivated Land in Main Agricultural Production Regions: A Case Study of Jianghan Plain, Central China. Int. J. Environ. Res. Public Health 2021, 18, 858. https://doi.org/10.3390/ijerph18030858
Zhu Y, Zhou X, Gan Y, Chen J, Yu R. Spatio-Temporal Differentiation and Driving Mechanism of the “Resource Curse” of the Cultivated Land in Main Agricultural Production Regions: A Case Study of Jianghan Plain, Central China. International Journal of Environmental Research and Public Health. 2021; 18(3):858. https://doi.org/10.3390/ijerph18030858
Chicago/Turabian StyleZhu, Yuanyuan, Xiaoqi Zhou, Yilin Gan, Jing Chen, and Ruilin Yu. 2021. "Spatio-Temporal Differentiation and Driving Mechanism of the “Resource Curse” of the Cultivated Land in Main Agricultural Production Regions: A Case Study of Jianghan Plain, Central China" International Journal of Environmental Research and Public Health 18, no. 3: 858. https://doi.org/10.3390/ijerph18030858