Spatiotemporal Differences, Dynamic Evolution and Trend of the Coupled Coordination Relationship between Urbanization and Food Security in China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Variable Definition
- (1)
- Urbanization. Urbanization is a comprehensive system with multi-dimensional features such as population mobility, urban land growth and non-agricultural economy development [20]. A single-dimensional population urbanization is different to reflect the complex characteristics of urbanization [29,30]. With reference to existing studies and data acquisition at the prefecture-level, indicators are selected from population, land, and economy to assess the comprehensive level of urbanization [31,32]. Population urbanization is characterized by the urbanization rate of resident population. Land urbanization is equal to the proportion of the urban built-up area to land area. Economy urbanization is equal to the proportion of non-agricultural industries to GDP. The entropy method is used to assign weights to indicators and conduct comprehensive evaluation to avoid the subjectivity of artificial assignment [33]. Limited to space, the detailed calculation can be found in the cited literature. The relevant indicators are standardized by the polarization method before calculation [34].
- (2)
- Food security. Food security is also a comprehensive system containing multi-dimensional indicators such as self-sufficiency rate, reserve level, and per capita food possession [35,36]. However, some indicator data are difficult to obtain at the prefecture level, so from the perspective of per capita output scale, we use food output per capita (food production/total population) to portray food security. The reason is that the fundamental strategy to ensure food security remains to increase the output capacity per capita of food to stabilize supply. In addition, the international food security standard line is also measured by per capita food possession [37].
2.2. Data Sources
2.3. Research Methods
2.3.1. CDUFS Measurement: Coupling Coordination Degree Model
2.3.2. Spatiotemporal Characteristics: Exploratory Spatial Data Analysis (ESDA)
2.3.3. Regional Differences: Dagum–Gini Coefficient
2.3.4. Dynamic Distribution: Kernel Density Estimation (KDE)
2.3.5. Evolution Trend: Spatial Markov Chain
3. Results
3.1. Spatiotemporal Correlation and Differentiation
3.1.1. Global Spatial Autocorrelation
3.1.2. Local Spatial Autocorrelation
- (1)
- High-High Cluster (H-H). Areas with H-H are mainly distributed in the FPAs such as the Northeast Plain, the North China Plain, and the middle and lower reaches of the Yangtze River. The coverage of H-H in general also showed a steady expansion of changes. The H-H areas in 2000 were mainly located in the FPAs of Northeast Plain, North China Plain and the lower Yangtze River. By 2019, the distribution expanded significantly to the FPAs in the center region, especially Hubei and Henan. Overall, the distribution of H-H was more stable, and its distribution range was gradually expanding to the FPAs in the middle reaches of the Yangtze River.
- (2)
- Low-Low Cluster (L-L). Areas with L-L were steadily at the Northwest, Southwest and Southeast coast, concentrated in the FBAs and FMAs. In terms of changes in their coverage, it showed a stable expansion in the Northwest, expansion followed by contraction in the Southwest, and obvious expansion in the Southeast coastal. In general, the distribution pattern of H-H was relatively stable, but the southwest region shrunk significantly, and the distribution range gradually expanded to the FMAs of the Southeast coast.
- (3)
- High-Low Outlier and Low-High Outlier (H-L, L-H). Areas with H-L and L-H of CDUFS were less distributed and sporadic, concentrated in the peripheral neighboring areas of H-H and L-L. The interaction between areas weakened the CDUFS to a certain extent. Individual areas in Qinghai have changed from H-L to L-L, which means that its urbanization and food security are faced with great challenges. Other areas with H-L are mainly at Xinjiang, Yunnan, Guangxi, etc., concentrated in the periphery of L-L. The areas with H-L in Yunnan and Guangxi are gradually disappearing while areas with L-H include the Daxinganling in Heilongjiang, mainly at the periphery of H-H.
3.2. Regional Differences and Decomposition
3.2.1. Overall Regional Differences
3.2.2. Regional Differences and Their Decomposition
3.3. Dynamic Distribution
3.3.1. The National Level
- (1)
- Distribution location. The Kernel density curve at the national level showed an evolution process in which the curve center first shifted left and then right, and the peak height first increased and then decreased, indicating that the CDUFS generally showed a decreasing trend over time.
- (2)
- Distribution pattern. The Kernel density curve showed a significant single-peak distribution with a relatively stable distribution trend. On the whole, the width of the main peak has increased, and the height of the main peak has decreased, indicating that the imbalance phenomenon in the CDUFS is still prominent, and the coordination gap between regions tends to expand, which is consistent with the typical characteristics in the previous section.
- (3)
- Distribution ductility. The Kernel density curve had a weak left trailing phenomenon, and the distribution ductility was broadened to a certain extent but did not show a convergence trend. The gap of the CDUFS between the regions with high coupling coordination and the average level was somewhat enlarged.
- (4)
- Polarization characteristics. The distribution of the Kernel density curve with a single-peak and a weak left trailing, indicating that the CDUFS did not show a polarization state and gradient effect, the single-peak distribution would continue to exist, and the gap of CDUFS between regions was more obvious.
3.3.2. Different Food Functional Areas
- (1)
- Distribution location. The centers of Kernel density curve in both FPAs and FBAs evolved leftward and then rightward, with an overall shift to the left, similar to the national level, while the center of Kernel density curve in FMAs showed an obvious leftward evolution, indicating that the CDUFS in different food functional areas have a decreasing trend over time.
- (2)
- Distribution pattern. The Kernel density curves of the three food functional areas were all significant single-peak patterns, with the width of the main peak increasing and the height of the main peak decreasing. Among them, the main peak height of FPAs showed a continuous decreasing process, while the main peak heights of FMAs and FBAs experienced a rising and then decreasing process, which generally indicates that the spatial imbalance of the CDUFS in different food functional areas is still prominent. The trends for this coordination gap widened.
- (3)
- Distribution ductility. The Kernel density curve of FPAs did not show an obvious trailing phenomenon, while FMAs and FBAs showed left trailing phenomenon, and the FMAs were more prominent with a certain widening of the distribution ductility. The Kernel density curves of the three food functional areas did not show a convergence trend, and the gap between the areas with high coordination and the average level widened, prominently in the FMAs.
- (4)
- Polarization characteristics. The Kernel density curves of different food functional areas with a single-peak indicates that the CDUFS of each food functional area did not have a polarized state and gradient effect, the single-peak distribution will continue to exist, and the coordination gap between regions is still obvious.
3.4. Evolution Trend
3.4.1. Traditional Markov Chain Analysis
- (1)
- The CDUFS had the stability of keeping the original state. All the elements on the diagonal were larger than those on the non-diagonal significantly, implying that the CDUFS had a smaller probability of state transfer. The fluidity among states was low with an obvious path-dependence. In addition, the probability of maintaining stability was greatest for the types at either end of the diagonal (types 1 and 4), while types 2 and 3 in the diagonal had a relatively smaller probability of maintaining stability. The CDUFS was more likely to be distributed at lower and higher levels, low and high distributions more prone to transfer.
- (2)
- Areas with a higher level of CDUFS were more likely to transfer to the lower level, while areas with a lower level of CDUFS had a higher likelihood of shifting to a higher level. For example, the probability of type 4 transition downward was 0.1091, the probability of type 3 transition downward was 0.1073, the probability of type 2 transition downward was 0.1013, 0.1091 > 0.1073 > 0.1013. It can be seen that areas in type 4 were more likely to transfer downward. Although the evolution trend was stable in areas with a high level of CDUFS, there is a certain risk and possibility of falling back, which requires vigilance and attention. In addition, the probability of type 2 transferring upward was greater than that of type 3 (0.1742 > 0.1171), indicating that areas with a relatively low CDUFS have higher room for growth.
- (3)
- The CDUFS was difficult to achieve a leapfrog evolution in the short term. The probability transition of the CDUFS occurred almost on both sides of the diagonal. For elements on both sides of the non-diagonal, the probability values were significantly smaller than those on both sides of the diagonal. For example, the probability of type 2 transferring upward to type 4 was 0.0083, which is obviously smaller than the probability of transferring to type 3 (0.1389). Among two consecutive years, the probability of achieving a leapfrog transfer was low (e.g., 1→3, 1→4), which means that the evolution of CDUFS in each region was a relatively stable and continuous process.
3.4.2. Spatial Markov Chain Analysis
- (1)
- Geospatial pattern plays an important role in the dynamic evolution of the CDUFS, and spatial Markov chain analysis can provide a spatially meaningful interpretation of the single-peak distribution pattern of the CDUFS. Under the spatial effect, the type of transition probability of the CDUFS in each area was not the same, was also not equal to the corresponding traditional Markov transition probability matrix, otherwise, the effect of spatial lag would not exist (Figure 6). For example, when the spatial effect was not considered, the transition probability from type 2 to type 3 in an area was P23 = 0.1389. When the area was adjacent to an area in type 2, P23|2 = 0.1019, when it was adjacent to an area in type 3, P23|3 = 0.1529, and when it was adjacent to an area in type 4, P23|4 = 0.2237. It can be seen that it is necessary to consider the spatial background when analyzing the spatial evolution of the CDUFS. An area is adjacent to the areas of different types, the state transition probability of the CDUFS in this area will be different. In general, for a certain area, the probability of upward transfer of its type was greater when adjacent to areas with higher coordination, while the probability of downward transfer of its type was greater when adjacent to an area with lower coordination.
- (2)
- The evolution of the CDUFS remained more stable in its original state; the possibility of jump transfer is low. This is because the elements on the diagonal were still larger than the elements on the non-diagonal after considering the spatial background. As the spatial lag type rises, there are differences in the stability and transition probability of the evolution of the CDUFS. For areas with a lower CDUFS (type 1), their stability decreased as the spatial lag type increased, but the probability of upward transfer was increasing. For areas with a low CDUFS (type 2), their stability showed an increase and then decrease with the increase of spatial lag type, while the probability of upward transfer showed a decrease and then increase, and the probability of downward transfer in decreasing. For areas with a high CDUFS (type 3), their stability also showed an increase and then a decrease with the increase of spatial lag type, but the probability of upward transfer was increasing and the probability of downward transfer was decreasing. For areas with a higher CDUFS (type 4), their stability decreased and then increased with the increase of the spatial lag type, while the probability of downward transfer showed an increase and then a decrease.
4. Discussion
- (1)
- Changes in spatial use of territory land led to the conflict between urbanization and food security. It is necessary to plan the urban-rural land use scientifically and reasonably, clarify the limit of urban land, and slow down the expansion speed of urban land. In addition, we should strengthen the policy of farmland protection in the urbanization process, strictly guard the red line of cropland use, and optimize the balance management system of farmland occupation and supplement to maintain the dynamic balance of total farmland, so as to guarantee the high-quality development of urbanization and sustainable food production.
- (2)
- Urbanization not only leads to the adjustment in production resources and grain-growing behavior, but also provides an opportunity for large-scale, mechanization and efficient operation of food production [55]. Therefore, the process of high-quality urbanization involves guiding households to transfer management rights of land orderly to bring into play the scale effect of land. Improving food production efficiency and yield capacity through intensive land use and technological progress can also provide useful support for urbanization, which promote the coordinated development between urbanization and food security.
- (3)
- Differences in industrial division of labor make regional differences in the CDUFS, and there is no convergence in the long-term evolution process. Therefore, the strategies of urbanization development and ensuring food security by different regions should be adapted to local conditions, and in line with their own development positioning based on industrial characteristics.
- (4)
- The FPAs need to grasp the room and direction in the process of urbanization, avoid the irregular expansion of urban space, ensure the free mobility of factors between urban areas and rural areas, adapt the utilization structure of water-soil resources, enhance the efficiency of land use, and drive the intensive management of food productive through urbanization. The FMAs have a high level of urbanization, and with the industrial structure upgrading, the comparative advantage of agricultural production has declined obviously. It should focus on weighing the intrinsic structure of food production against the extrinsic changes triggered by urbanization and improve the factor agglomeration and labor productivity through technological progress. The urbanization of FBAs is relatively backward, but grain producing and marketing basically maintain a balance. Therefore, the potential for coordination between urbanization and food security is greater. The trade-off between the government’s guiding effect and the market’s allocation effect should be focused on to optimize the regional layout of urbanization and food production by effective cooperation between government and market.
5. Conclusions
- (1)
- The CDUFS in China showed a downward trend in fluctuating within the low coordination range, and food security continues to lag behind urbanization. The regional differences of the CDUFS are obvious with a continuously enhanced positive correlation in space. This exhibits a stable distribution pattern of H-H in the Northeast Plain and Huang-Huaihai Plain, and L-L in the Northwest, Southwest and Southeast coast.
- (2)
- There were obvious regional differences in the CDUFS in China, and the overall differences have expanded. For different food functional areas, inter-regional differences were the main source of contribution to the overall differences, with the highest regional differences between FPAs and FMAs, and their spatial imbalance gradually prominent. The contribution of intra-regional differences was the second, with the highest of internal differences in FBAs, and its expanding trend of internal differences was more obvious. The contribution of transvariation density was the smallest.
- (3)
- The main peak of the distribution curve of the CDUFS in China has increased in width and decreased in height. The imbalance of the CDUFS between regions is still prominent, but it does not show a convergence trend, and the single-peak distribution will continue to exist. The CDUFS in the three food function areas show a significant leftward shift of the single-peak distribution process, and the distribution curve of the FPAs do not show a trailing phenomenon. While the FMAs and FBAs show the left trailing, the FMAs are more obvious.
- (4)
- The CDUFS in China have the stability of maintaining the original state, obviously path dependent. The trend of the CDUFS transferring to a high level is not obvious, but areas with higher CDUFS have a higher possibility of transferring to a low level. It is difficult to achieve a leapfrog evolution of the in the short term. Geospatial pattern plays an important role in the dynamic evolution of CDUFS, and there are differences in the stability and transfer probability of the CDUFS evolution under the different spatial lag type. The long-term evolution trend of CDUFS in China is influenced by the geospatial effect, but the single-peak pattern is relatively stable and does not exhibit significant convergence in the long term.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
CDUFS | coupled coordination degree between urbanization and food security |
FPAs | food main producing areas |
FMAs | food main marketing areas |
FBAs | food balanced areas |
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Variables | Variable Desceition | Mean | Std. | ||
---|---|---|---|---|---|
CDUFS | Urbanization | Population urbanization | urbanization rate of resident population | 0.2824 | 0.0631 |
Land urbanization | urban built-up area/land area | ||||
Economy urbanization | non-agricultural industries/GDP | ||||
Food security | Food output per capita | food production/total population |
Year | Moran’s I | Z | Year | Moran’s I | Z | Year | Moran’s I | Z | Year | Moran’s I | Z |
---|---|---|---|---|---|---|---|---|---|---|---|
2000 | 0.473 | 13.615 | 2005 | 0.623 | 17.904 | 2010 | 0.655 | 18.812 | 2015 | 0.652 | 18.734 |
2001 | 0.353 | 10.189 | 2006 | 0.617 | 17.74 | 2011 | 0.587 | 16.894 | 2016 | 0.654 | 18.782 |
2002 | 0.483 | 13.914 | 2007 | 0.603 | 17.337 | 2012 | 0.669 | 19.223 | 2017 | 0.667 | 19.156 |
2003 | 0.452 | 13.044 | 2008 | 0.637 | 18.292 | 2013 | 0.665 | 19.099 | 2018 | 0.674 | 19.358 |
2004 | 0.607 | 17.449 | 2009 | 0.621 | 17.847 | 2014 | 0.642 | 18.452 | 2019 | 0.649 | 18.670 |
Food Functional Areas | 2000 | 2003 | 2006 | 2009 | 2012 | 2015 | 2018 | 2019 | Mean | |
---|---|---|---|---|---|---|---|---|---|---|
Intra-regional difference | FPAs | 0.0742 | 0.0848 | 0.0950 | 0.0983 | 0.1008 | 0.0965 | 0.0948 | 0.0882 | 0.0933 |
FMAs | 0.0750 | 0.0812 | 0.0838 | 0.0956 | 0.0968 | 0.0992 | 0.1116 | 0.1126 | 0.0958 | |
FBAs | 0.0959 | 0.1255 | 0.1217 | 0.1352 | 0.1261 | 0.1133 | 0.1185 | 0.1003 | 0.1181 | |
Inter-regional differences | FPAs-FMAs | 0.0860 | 0.1000 | 0.1254 | 0.1602 | 0.1772 | 0.1814 | 0.2065 | 0.1938 | 0.1538 |
FPAs-FBAs | 0.1210 | 0.1293 | 0.1449 | 0.1540 | 0.1475 | 0.1420 | 0.1481 | 0.1429 | 0.1414 | |
FMAs-FBAs | 0.0970 | 0.1087 | 0.1060 | 0.1200 | 0.1211 | 0.1177 | 0.1334 | 0.1186 | 0.1157 | |
Contribution rate/% | Intra-regional | 35.42 | 37.15 | 36.35 | 35.18 | 35.26 | 34.44 | 32.92 | 32.07 | 35.07 |
Intra-regional | 47.55 | 35.25 | 46.16 | 48.22 | 51.14 | 53.78 | 57.11 | 60.11 | 49.40 | |
Transvariation density | 17.03 | 27.61 | 17.49 | 16.60 | 13.60 | 11.78 | 9.97 | 7.82 | 15.53 |
Type | n | 1 | 2 | 3 | 4 |
---|---|---|---|---|---|
1 | 1614 | 0.8482 | 0.1363 | 0.0105 | 0.0050 |
2 | 1570 | 0.1013 | 0.7516 | 0.1389 | 0.0083 |
3 | 1537 | 0.0078 | 0.0995 | 0.7755 | 0.1171 |
4 | 1549 | 0.0039 | 0.0077 | 0.0975 | 0.8909 |
Spatial Lag | Type | n | 1 | 2 | 3 | 4 |
---|---|---|---|---|---|---|
1 | 1 | 250 | 0.9120 | 0.0840 | 0.0040 | 0 |
2 | 116 | 0.1121 | 0.7586 | 0.1293 | 0 | |
3 | 41 | 0.0244 | 0.2683 | 0.6829 | 0.0244 | |
4 | 11 | 0 | 0 | 0.0909 | 0.9091 | |
2 | 1 | 736 | 0.8641 | 0.1209 | 0.0095 | 0.0054 |
2 | 589 | 0.1121 | 0.7827 | 0.1019 | 0.0034 | |
3 | 233 | 0.0258 | 0.1760 | 0.7425 | 0.0558 | |
4 | 95 | 0.0211 | 0.0211 | 0.1263 | 0.8316 | |
3 | 1 | 595 | 0.8151 | 0.1681 | 0.0118 | 0.0050 |
2 | 713 | 0.1010 | 0.7377 | 0.1529 | 0.0084 | |
3 | 833 | 0.0060 | 0.0960 | 0.8019 | 0.0960 | |
4 | 595 | 0.0050 | 0.0118 | 0.1193 | 0.8639 | |
4 | 1 | 33 | 0.6061 | 0.3030 | 0.0606 | 0.0303 |
2 | 152 | 0.0526 | 0.6908 | 0.2237 | 0.0329 | |
3 | 430 | 0 | 0.0488 | 0.7512 | 0.2000 | |
4 | 848 | 0.0012 | 0.0035 | 0.0790 | 0.9163 |
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Liu, S.; Hou, M. Spatiotemporal Differences, Dynamic Evolution and Trend of the Coupled Coordination Relationship between Urbanization and Food Security in China. Foods 2022, 11, 2526. https://doi.org/10.3390/foods11162526
Liu S, Hou M. Spatiotemporal Differences, Dynamic Evolution and Trend of the Coupled Coordination Relationship between Urbanization and Food Security in China. Foods. 2022; 11(16):2526. https://doi.org/10.3390/foods11162526
Chicago/Turabian StyleLiu, Shan, and Mengyang Hou. 2022. "Spatiotemporal Differences, Dynamic Evolution and Trend of the Coupled Coordination Relationship between Urbanization and Food Security in China" Foods 11, no. 16: 2526. https://doi.org/10.3390/foods11162526
APA StyleLiu, S., & Hou, M. (2022). Spatiotemporal Differences, Dynamic Evolution and Trend of the Coupled Coordination Relationship between Urbanization and Food Security in China. Foods, 11(16), 2526. https://doi.org/10.3390/foods11162526