Journal of Geographical Sciences >
Spatiotemporal mismatch of land use functions and land use efficiencies and their influencing factors: A case study in the Middle Reaches of the Yangtze River, China
Gao Yunxiao (1992), PhD, specialized in land use evaluation and territorial spatial planning. Email: Gyunxiao2021@cug.edu.cn 
Received date: 20221001
Accepted date: 20231107
Online published: 20240108
Supported by
Key Project of the National Social Science Foundation of China(23AZD058)
National Natural Science Foundation of China(72004209)
Identification of the spatial mismatch between land use functions (LUFs) and land use efficiencies (LUEs) is essential to regional land use policies. However, previous studies about LUFLUE mismatch and its driving factors have been insufficient. In this study, we explored the spatiotemporal mismatch of LUFs and LUEs and their influencing factors from 2000 to 2018 in the Middle Reaches of the Yangtze River (MRYR). Specifically, we used Spearman correlation analysis to reveal the tradeoff relationship between LUFs and LUEs and determine the direction of the influencing factors on the LUFLUE mismatch, adopted spatial mismatch analysis to measure the imbalance between LUFs and LUEs, and used the geographical detector model to analyze the factors influencing this spatial mismatch. The results showed that production function (PDF), living function (LVF), ecological function (ELF), agricultural production efficiency (APE), urban construction efficiency (UCE), and ecological services efficiency (ESE) all displayed significant spatial heterogeneity. The high tradeoff areas were widely distributed and longlasting in agricultural space and urban space, while gradually decreasing in ecological space. Wuhan and Changsha showed high spatial mismatch coefficients in urban space, but low spatial mismatch coefficients in agricultural space. Hunan generally presented high spatial mismatch coefficients in ecological space. Furthermore, the interaction of the proportion of cultivated area and transportation accessibility exacerbated the mismatch in agricultural space. The interaction effects of capital investment and technology innovation with other factors have the most intense impact on the mismatch in urban space. The internal factor for cultivated area interacts with other external factors to drastically affect ecological spatial mismatch.
GAO Yunxiao , WANG Zhanqi , CHAI Ji , ZHANG Hongwei . Spatiotemporal mismatch of land use functions and land use efficiencies and their influencing factors: A case study in the Middle Reaches of the Yangtze River, China[J]. Journal of Geographical Sciences, 2024 , 34(1) : 62 88 . DOI: 10.1007/s1144202421951
Figure 1 Location of the Middle Reaches of the Yangtze River, ChinaNote: Map Content Approval Number: GS (2019)1831, no modification 
Table 1 Data categories and sources 
Data category  Data description  Data source 

Geographic data  NPP; Raster; 1 km×1 km  Monthly NPP 1 km Raster Dataset of China’s Terrestrial Ecosystems (https://doi.org/10.3974/geodb.2019.03.02.V1) 
NDVI; Raster; 1 km×1 km  Global GIMMS NDVI3g v1 dataset (http://data.tpdc.ac.cn)  
Meteorological data  Total solar radiation, sunshine duration, temperature, and precipitation; Sites  National Meteorological Information Center (http://data.cma.cn/), and Local Meteorological Administration 
Land use/ land cover data  Land use/Land cover database; Raster; 30 m×30 m  Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn/) 
Socio economic data  Output value of primary industry secondary industry, tertiary industry; Statistics; The basic unit is the city  China Regional Economic Statistical Yearbook, China Rural Statistical Yearbook (https://data.cnki.net/), and Local Statistical Yearbook 
Population, Employment, Facilities. Electricity consumption, Fiscal expenditure, Industry pollution, Energy consumption; Statistics; The basic unit is the city  China Regional Economic Statistical Yearbook, China Rural Statistical Yearbook (https://data.cnki.net/), and Local Statistical Yearbook 
Figure 2 Theoretical framework 
Table 2 Evaluation index system and calculation method of LUFs 
Primary functions  Subfunctions (Weight)  Indicators  Formula  Key references 

Production function (PDF)  Crop supply (0.441)  Crop yield  $G{Y}_{i}={{\displaystyle \sum}}^{\text{}}G{Y}_{ij}/S$  (Fan et al., 2021) 
Poultry and egg supply (0.323)  Poultry and egg yield  $P{E}_{i}={{\displaystyle \sum}}^{\text{}}P{E}_{ij}/S$  (Fan et al., 2021)  
Aquatic product supply (0.237)  Aquatic product yield  $A{P}_{i}={{\displaystyle \sum}}^{\text{}}A{P}_{ij}$  (Fan et al., 2021)  
Living function (LVF)  Population carrier (0.449)  Population density  $P{D}_{i}={P}_{i}/{S}_{i}$  (Ren et al., 2021) 
Employment security (0.308)  Employed population in urban  $E{M}_{i}={{\displaystyle \sum}}^{\text{}}E{M}_{ij}/{S}_{i}$  (Liu et al., 2021)  
Transport support (0.243)  Traffic coverage level  $T{C}_{i}={\displaystyle \sum}_{k=1}^{n}{w}_{k}\times {r}_{k,i}/{A}_{i}$  (Li et al., 2023)  
Ecological function (ELF)  Gas regulation (0.270)  Carbon sequestration  $CS=N\times \beta \times {{\displaystyle \sum}}^{\text{}}NPP$  (Zhu et al., 2021) 
Water regulation (0.411)  Water yield  $Y\left(i\right)$= $Y\left(i\right)$ $Y\left(i\right)$ $Y\left(i\right)$ $Y\left(i\right)$. $Y\left(i\right)$ $Y\left(i\right)$  (Zhang et al., 2001; Zhang et al., 2004)  
Ecological conservation (0.319)  Vegetation coverage index  ${C}_{i}=\frac{NDVINDV{I}_{s}}{NDV{I}_{v}NDV{I}_{s}}$  (Meng et al., 2019) 
Table 3 Evaluation index system and calculation method of LUEs 
Classification  Indicators of inputs and outputs  Formula  Key references  

Agricultural production efficiency (APE)  Input  Sown area of crops  $SA{C}_{i}={{\displaystyle \sum}}^{\text{}}SA{C}_{ij}$  (Kuang et al., 2020) 
Employees in the primary industry  $EP{I}_{i}={{\displaystyle \sum}}^{\text{}}EP{I}_{ij}$  (Kuang et al., 2020)  
Agricultural capital stock  $AC{S}_{i}={{\displaystyle \sum}}^{\text{}}AC{S}_{ij}$  (Yang et al., 2021a)  
Agricultural machinery power  $AM{P}_{i}={{\displaystyle \sum}}^{\text{}}AM{P}_{ij}$  (Kuang et al., 2020)  
Desirable output  Agricultural GDP  $AGD{P}_{i}={{\displaystyle \sum}}^{\text{}}AGD{P}_{ij}$  (Kuang et al., 2020)  
Undesirable output  Carbon emissions  $AC{E}_{i}={{\displaystyle \sum}}^{\text{}}VAC{E}_{ij}\times \gamma $  (Kuang et al., 2020)  
Urban construction efficiency (UCE)  Input  Urban construction land  $ULUC{C}_{i}={{\displaystyle \sum}}^{\text{}}SULU{T}_{ij}$  (Song et al., 2022) 
Fiscal expenditure  $F{E}_{i}={{\displaystyle \sum}}^{\text{}}FE{I}_{ij}$.  (Song et al., 2022)  
Employees in the secondary and tertiary industries  $ES{T}_{i}={{\displaystyle \sum}}^{\text{}}ES{T}_{ij}$  (Lu et al., 2018)  
Desirable output  Nonagricultural GDP  $NAGD{P}_{i}={{\displaystyle \sum}}^{\text{}}NAGD{P}_{ij}$  (Lu et al., 2018)  
Undesirable output  Industrial waste  $I{W}_{i}={{\displaystyle \sum}}^{\text{}}I{W}_{ij}$  (Lu et al., 2018)  
Indusial sulfur dioxide  $IS{D}_{i}={{\displaystyle \sum}}^{\text{}}IS{D}_{ij}$  (Xue et al., 2022)  
Ecological service efficiency (ESE)  Input  Ecological land  $E{L}_{i}={{\displaystyle \sum}}^{\text{}}E{L}_{ij}$  (Jin et al., 2022) 
Energy consumption per unit of GDP  $ECP{G}_{i}=E{C}_{i}/{{\displaystyle \sum}}^{\text{}}GD{P}_{ij}$.  (Cheng et al., 2014)  
Desirable output  Ecolical service value  $ES{V}_{i}={{\displaystyle \sum}}^{\text{}}SES{V}_{ij}\times \beta $  (Xie et al., 2015a; Xie et al., 2015b)  
Carbon sink  $EC{S}_{i}={{\displaystyle \sum}}^{\text{}}SEC{S}_{ij}\times \delta $  (Sun et al., 2015) 
Table 4 Classification of spatial mismatch coefficients 
SMI  Spatial mismatch (Low efficiency)  Spatial match  Spatial mismatch (High efficiency) 

PDFAPE mismatch  <0.75  0.751.00  >1.00 
LVFUCE mismatch  <1.78  1.781.13  >1.13 
ELFESE mismatch  <0.94  ‒0.941.03  >1.03 
Table 5 Index system of influencing factors 
Dimension  Influencing factors  Unit  Quantification method/Sources 

Regional background conditions  Elevation (Elve)  m  DEM 
Terrain slope (Slope)  °  Slope Tool in ArcGIS  
Annual precipitation (AP)  mm  China Meteorological Administration  
Annual temperature (AT)  ℃  
Traffic accessibility (TA)  km/km^{2}  Road length/total area  
Distance to water area (Dwater)  m  Near Tool in ArcGIS  
Land reclamation rate (LRR)  %  Cultivated area/total area  
External influences  Per capita GDP (PCGDP)  yuan/pop.  China City Statistical Yearbook 
Proportion of secondary and tertiary industries (PSTI)  %  
Urbanization rate (UR)  %  
Proportion of rural population (PRP)  %  
Total investment in scientific research (TISR)  10^{4} yuan  
Sewage treatment rate (STR)  % 
Table 6 The interactive categories of two factors 
Interaction relationship  Interaction 

$q\left({X}_{1}\cap {X}_{2}\right)<Min\left(q\left({X}_{1}\right),q\left({X}_{2}\right)\right)$  Weak; nonlinear 
$Min\left(q\left({X}_{1}\right),q\left({X}_{2}\right)\right)<q\left({X}_{1}\cap {X}_{2}\right)<Max\left(q\left({X}_{1}\right),q\left({X}_{2}\right)\right)$  Weak; univariate 
$q\left({X}_{1}\cap {X}_{2}\right)>Max\left(q\left({X}_{1}\right),q\left({X}_{2}\right)\right)$  Enhanced; bivariate 
$q\left({X}_{1}\cap {X}_{2}\right)=q\left({X}_{1}\right)+q\left({X}_{2}\right)$  Independent 
$q\left({X}_{1}\cap {X}_{2}\right)>q\left({X}_{1}\right)+q\left({X}_{2}\right)$  Enhanced; nonlinear 
Figure 3 Spatiotemporal changes of LUFs in the Middle Reaches of the Yangtze River from 2000 to 2018 
Figure 4 Spatiotemporal changes of LUEs in the Middle Reaches of the Yangtze River from 2000 to 2018 
Figure 5 Spatial mismatch coefficient between PDF and APE in the Middle Reaches of the Yangtze River from 2000 to 2018 
Figure 6 Spatial mismatch coefficient between LVF and UCE in the Middle Reaches of the Yangtze River from 2000 to 2018 
Figure 7 Spatial mismatch coefficient between ELF and ESE in the Middle Reaches of the Yangtze River from 2000 to 2018 
Figure 8 Spatial distribution of tradeoffs and synergies among the mismatch of LUFs and LUEs in the Middle Reaches of the Yangtze River from 2000 to 2018 
Table 7 Determinant power (q) and Spearman correlation coefficient (ρ) of factors about LUFLUE mismatch in 2018 
Influencing factors  PDFAPE mismatch  LVFUCE mismatch  ELFESE mismatch  

q  ρ  q  ρ  q  ρ  
Elve  0.117*  0.189  0.282**  0.345***  0.374***  0.328*** 
Slope  0.121**  0.311***  0.307***  0.420***  0.346***  0.423*** 
AP  0.320***  0.320***  0.028  0.081  0.140***  0.210*** 
AT  0.108  0.170  0.072  0.114  0.065  0.149 
TA  0.239***  0.221***  0.361***  0.406***  0.158*  0.055 
Dwater  0.136**  0.352***  0.279**  0.459***  0.456***  0.545*** 
LRR  0.338***  0.445***  0.185**  0.436***  0.578***  0.447*** 
PCGDP  0.237***  0.354***  0.660***  0.417***  0.119*  0.060 
PSTI  0.410***  0.628***  0.634***  0.428***  0.341***  0.351*** 
UR  0.399***  0.483***  0.463***  0.442***  0.135*  0.079 
PRP  0.392***  0.449***  0.457***  0.425***  0.160*  0.067 
TISR  0.135**  0.297***  0.562***  0.088  0.185**  0.384*** 
STR  0.074  0.158  0.076  0.016  0.062  0.024 
Note: ***, **, and * represent the 1%, 5%, and 10% significance levels, respectively. 
Figure 9 Interaction effect (q) between influencing factors on LUFLUE mismatch in 2018 (a. PDFAPE mismatch; b. LVFUCE mismatch; c. ELFESE mismatch) 
Figure 10 The trend of determinant power (q) and Spearman’s correlation coefficient (ρ) for influencing factors on LUFLUE mismatch from 2000 to 2018 (a. PDFAPE mismatch; b. LVFUCE mismatch; c. ELFESE mismatch) 
Figure 11 Mechanisms of influence of LUFs and LUEs 
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