Abstract
The limited land is under
unprecedented pressure from production, living and ecology. In order to
evaluate the land pressure in the
Yangtze River Delta region in 1995, 2000, 2005, 2010, 2015, and 2020
from the perspective of production, living, and ecology.
This study builds a land pressure
evaluation index system based on a fuzzy comprehensive evaluation model
using multi-source and multi-scale data.
In order to
investigate trade-offs and
synergies among production, living, and ecology pressures, we use the
mechanical equilibrium model in physics. We then
analyze land pressure model
reliability and uncertainty using Monte Carlo simulations.
The results show that (1) Our
model can effectively reveal the level of land pressure and reflect
the land pressure geographical
pattern of ”high in the east and low in the west, high in the south and
low in the north” that characterizes the Yangtze River Delta region. (2)
While living and ecology pressures are tending to rise, production
pressures are tending to decrease. (3) Except for Shanghai, the
trade-off areas are primarily concentrated in economically successful
regions with high production and living pressure and low ecology
pressure. The coordinated areas are primarily found in northern Jiangsu
Province and northern Anhui Province.
Keywords:Land pressure;
Production-living-ecology; Fuzzy comprehensive evaluation; Model
validation; Yangtze River Delta region.
Introduction
For
the continued existence of humans and for their ability to evolve, land
serves as a crucial material foundation (Verburg et al., 2009; Jiang et
al., 2022). People have increased from 2.6 billion in 1950 to 7.6
billion in 2021 as a result of increasing urbanization, and human
activities are deteriorating the state of the land cover and ecological
environments at an unprecedented rate, size, and spatial scale (Liu et
al., 2014; Lazzarini et al., 2015; Nathaniel and Khan, 2020; Yu et al.,
2021; Amponsah et al., 2022). The need to study and solve a variety of
issues brought on by rising pressure on the land has been underlined
(Zhu and He, 2010). With the rapid development of the economy and
society, human demand for land resources is increasing, and the
contradiction between humans and land is gradually accentuated and
deteriorating (Herrmann et al., 2020; Pereira da Silva and Schwingel,
2021). The world’s 6 million km2 of protected land is
already under a significant amount of pressure (Jones et al., 2018).
Almost every city in the world, regardless of its size, is affected by
land pressure, which is a result of the development of human society
itself. Land use in different regions naturally presents different
states and produces different effects, and changes with the change in
land pressure as a result of the natural endowment of land resources in
different regions and the pressure on economic development (Chen et al.,
2021; Liu et al., 2022), food production (Jiang et al., 2018; Liu et
al., 2020; Gao et al., 2022; Zhang et al., 2022), and ecological
protection (Hao and Li, 2014; Chen et al., 2021; Liu et al., 2022; Zhao
et al., 2022).
Population pressure (Herrmann et
al., 2020; Mammides, 2020), urbanization pressure (Quang and Kim, 2020;
Klusacek et al., 2022), and ecological pressure (Hirsh-Pearson et al.,
2022) have been the main topics of academic research on land pressure.
Early domestic studies calculated the pressure on cultivated land to
analyze land pressure (Liu et al., 2020). As the research develops, an
increasing number of academics are beginning to take into account the
combined effects of numerous causes on land pressure (Yang et al., 2020;
Lai et al., 2022) and to evaluate land pressure using diverse research
scales, indicators, and methodologies (Cheng et al., 2011; Hao and Li,
2014; Chen et al., 2017; Han et al., 2020; Zhang and Dong, 2022).
Currently, methods for measuring land pressure that are often used
include gray correlation analysis, Shannon entropy, principal component
analysis, and hierarchical analysis (Ou et al., 2017; Gupta et al.,
2018; Hu and Xu, 2019; Lin et al., 2020; Zhang et al., 2021). While the
principal component analysis and Shannon entropy methods have more
objective evaluation results and the hierarchical analysis method is too
subjective, all of these factors will have an impact on the accuracy and
reliability of the evaluation results. For the comprehensive index
method, the availability of data will limit the selection of some
important indicators. Fuzzy evaluation techniques, as opposed to
traditional methods, use fuzzy sets and fuzzy logic theory to evaluate
complex things affected by multiple factors as a whole (Cai et al.,
2019). This allows an indicator value to belong to multiple pressure
levels with different affiliation degrees at the same time, reducing the
influence of subjective and objective factors while better reflecting
the actual characteristics of indicators (Sun et al., 2018; Zahabi and
Kaber, 2019). However, very few scholars have assessed land pressure
using fuzzy evaluation methods from production-living-ecology aspects.
The Yangtze River Delta region (YRDR) is currently experiencing rapid
urban expansion and faces potential risks from urbanization and natural
disasters like climate change. The region is also economically
developed, densely populated, and relatively concentrated in urban
clusters (Pei et al., 2021; Yu et al., 2022). Scholars have mainly
focused on issues such as land-intensive use (Luo et al., 2022), urban
expansion (Luo and Lau, 2019), ecological efficiency (Li et al., 2022),
ecological security (Xiaobin et al., 2021; Zhang et al., 2022),
eco-environment pressure (Lin et al., 2020) in the YRDR, lacking
research on land pressure. Over-exploitation has resulted in a sharp
decline in the region’s primary agricultural land and green ecological
space, as well as a steadily worsening ecological environment. This has
seriously hampered the overall sustainable development of the region’s
land area, and the problem of land pressure is particularly acute. The
YRDR, one of China’s most developed regions, will be essential in
resolving the land pressure issue. The evaluation results are difficult
to express accurately in the current studies, and there is a lack of
quantitative analysis of land pressure based on the spatial scale of the
grid. These studies, however, primarily concentrate on large-scale areas
such as provinces, cities, counties, a few significant economic zones,
and watersheds. Therefore, ensuring the sustainable use of land
resources, assessing land pressure in the YRDR based on pertinent data
research, analyzing the spatial distribution of land pressure in the
YRDR, and exploring the synergistic and trade-off relationships of land
pressure have become important needs for the sustainable development of
the region.
We built a land pressure evaluation system using a fuzzy comprehensive
evaluation method. Our specific goals are: (1) to
evaluate the level of land
pressure in each 1 km grid in the YRDR, (2) to
characterize the spatial
distribution of production, living, and ecology pressure and their
tradeoff/synergy characteristics and (3) to
verify the reliability and
uncertainty of the land pressure evaluation model. Our findings offer a
method for evaluating land pressure quantitatively from the perspective
of urban agglomerations and have significant research ramifications for
the sustainable use of land resources.
Materials and Methods
Study Area
The YRDR is comprised of 41
cities in Shanghai, Jiangsu Province, Anhui Province, and Zhejiang
Province, which are distributed in the optimized and key development
areas of the national ”two horizontal and three vertical” urbanization
pattern. It is situated at the intersection of two highly urbanized
urban zones along the river and the coast of China (Figure 1). One of
China’s most economically active, urbanized, and population-absorbing
areas, the Yangtze River Delta will contribute 24% of the nation’s
population and gross domestic product in 2021 (Qiao et al.,
2021).
Figure 1. Study area:
(a) location of the YRDR in China. (b) The 41 cities and economic zones
in the YRDR. The abbreviations of city names refer to previous studies
(Yu et al., 2022).
Data
We assessed land pressure in the
YRDR using a variety of datasets, including data on land use,
topography, socioeconomics, environmental, and fossil energy consumption
from 1995 to 2020. All of the statistics utilized are yearly. Referring
to previous studies, we selected a total of 22 indicators from the
production, living, and ecology aspects, with P4, E3, E4, and E5 as
negative indicators and the rest as positive
indicators. Positive indicators
indicate that the greater the indicator, the higher the land pressure,
negative indicators indicate that the greater the indicator, the lower
the land pressure (Yang et al., 2020; Jiang et al., 2021; Liu et al.,
2022; Sun et al., 2022). Table S1 provides comprehensive explanations of
the data sources used for the various study objectives.
Methods
Based on the fuzzy comprehensive
evaluation method, this study evaluates land pressure in the YRDR from
three perspectives: production, living, and ecology pressure. It also
examines the characteristics of land pressure’s spatial and temporal
distribution, identifies trade-offs and synergistic effects, and
assesses the reliability of the evaluation model. Figure 2 displays the
flow of the analysis.
Figure 2. Flowchart of
the land pressure evaluation study
Developing the land pressure
assessment model
The real output pressure on one side of the land that corresponds to
human needs is known as land pressure (Zhu, 2010). By drawing on the
index system of land evaluation research and combining the
quantifiability and accessibility of data to evaluate land pressure,
this study constructs an index system for quantitative assessment of
land pressure by considering the demand for land in three aspects:
production, living, and ecology.
Currently, most land pressure evaluation scales use large-scale research
with cities as the evaluation units. However, these scales are unable to
account for variations in land pressure within individual cities. As a
result, the raster is chosen as the evaluation unit in this study, and
the evaluation unit is ultimately decided to be 1km*1km. We transform
the vector data into 1km resolution raster data for the indicators P1,
P2, P3, P5, L2, L4, L5, L6, L8, L9, L10, E1, E2, E4, E6, and we resample
the data into 1km resolution raster data for the indications P4, L3, L7,
E3, and E5 using the ArcGIS resampling tool. To calculate the weights of
the indicators using the index data scales, we choose the entropy
weighting method (Wen et al.,
2021; Dong and Lyu, 2022).
Land pressure comprehensive evaluation
The fuzzy comprehensive evaluation method uses fuzzy mathematical theory
to produce an overall assessment of items exposed to various conditions.
By using fuzzy association functions, inference rules, and
defuzzification techniques, it translates qualitative assessment into
quantitative evaluation and turns raw data values into output evaluation
scores (Yu et al., 2020). Fuzzification, fuzzy inference, and
defuzzification are the three processes that make up the fuzzy
comprehensive assessment process (Cai et al., 2019).
Using the fuzzy inference system toolbox of the MATLAB program, we first
design the Mamdani FIS model for each indicator and fuzzily the
indicator values (Akbari et al., 2019). The Mamdani FIS model’s
deterministic input values are converted through a process called
”fuzzification” into the equivalent fuzzy linguistic variables (e.g.,
land pressure levels). Production pressure, living pressure, and ecology
pressure are divided into four levels using the natural fracture method:
high pressure, medium pressure, low pressure, and very low pressure.
Based on the intrinsic properties of the data, the natural fracture
method can group data that have the most similarities.
Second, using the affiliation function and fuzzy inference rules, we get
the fuzzy evaluation scores for each index. Positive indicators indicate
that the greater the indicator, the higher the land pressure score,
while a negative indicator is an opposite. The affiliation function is
applicable to describe the fuzziness of things. According to earlier
research, the S- and Z-shaped affiliation functions are best for
defining fuzzy concepts with high and low-value fuzzy states, whereas
the triangle affiliation function is best for explaining fuzzy concepts
with intermediate fuzzy states (Ustaoglu and Aydinoglu, 2020). To create
the affiliation functions of 22 indicators in 1995, 2000, 2005, 2010,
2015, and 2020 and convert the indicator data into affiliation degrees,
we choose the triangular affiliation function, S-shaped affiliation
function, and Z-shaped affiliation function (Figure S1). The affiliation
value of the intersection of two adjacent affiliation functions is 0.5.
To link the input and output variables for each indication, we build
fuzzy inference rules. Finally, defuzzification based on the centroid
approach is used to produce the fuzzy assessment score for each index,
which provides smoother output inference control, and the output changes
for tiny changes in the input data (Figure S2).
We weigh the comprehensive evaluation scores for each dimension in 1995,
2000, 2005, 2010, 2015, and 2020 before adding the scores for production
pressure, living pressure, and ecology pressure to get the comprehensive
evaluation scores for land pressure over the six years in the Yangtze
River Delta.
where S represents the composite score of a dimension, for production
pressure and ecology pressure dimension q=6, for living pressure
dimension q=10, wi and xirepresent the weight and fuzzy evaluation score of indicator i,
respectively.
Tradeoff/synergy analysis based on Mechanical Equilibrium Model
The force balance model can be used to measure the coordination within
the system (Zhang et al., 2019). In this study, the vector connection
between three forces operating in opposing directions in the Cartesian
coordinate system is used to abstract the production pressures, living
pressure, and ecology pressure. The total vector forces and the quadrant
in which they are positioned indicate the state and characteristics of
the system under the impact of variously directed forces objectively.
The combined force is zero and is situated at the zero point in Figure
3, if all three forces succeed in achieving the intended outcome,
signifying the coordinated growth of each subsystem. The three
subsystems are in an unbalanced condition, as seen by the total force’s
deviation from point o in Figure 3.
The combined force F’s size may be used to gauge how much land pressures
conflict with one another, and the higher the value of F, the less
coordinated production pressure, living pressure, and ecology pressure
are. The combined force’s angle of deviation can indicate which of the
three forces is the most prominent feature and can also reveal details
about the coordination. In the model, the letters OA, OB, and OC stand
for the pressures on production, ecology, and living, respectively. The
angle between them is 2/3π (Yang et al., 2019). In the actual
calculation, polar coordinatesare used to represent the coordination
state of land pressure. is the polar radius to represent the
coordination degree, andis the polar angle to represent the deviation
direction.
In polar coordinates, the orientation angles of OA, OB, and OC are
defined as π/2 π, 11π/6π, and 7π/6π, respectively. We determine the
combined force of OA, OB, and OC and extend OA, OB, and OC in the other
direction to split the area into six equal quadrants, as shown in Table
S2. The calculation formulas are as follows:
where F1 is the resultant force of OA and OB,α is the angle between F1 and OA, Fis the resultant force of F1 and OC, and is the
angle between F and X axis.
Figure 3. Conceptual model of production, living, and ecology
pressure deviation levels
Monte Carlo simulation for validation
The Monte Carlo simulation method, also known as the stochastic
simulation method, estimates the probability of an event occurring by
the frequency of its occurrence (Yang et al., 2020). It is frequently
utilized in numerous domains, including biology, sociology, and ecology
(Ewertowska et al., 2017; Zaroni et al., 2019). To eliminate the
uncertainty induced by weight selection, we utilized the Monte Carlo
method to randomly simulate the weights of 22 land pressure indicators
1000 times. We created 1000 sets of indicator weights for the 22 land
pressure indicators by utilizing the identified weights as means and
10% of the identified weights as standard deviations based on a normal
distribution (Cheng et al., 2022). The land pressure fraction for each
pixel in 2020 was calculated using a variety of indicators based on 1000
simulated weights. Finally, we express the uncertainty of each raster by
calculating the 95% and 5% quartiles of the confidence interval of the
land pressure scores, as well as the difference between them, as well as
the variable ratio to the original assessment scores at the 90%
confidence level, with higher uncertainty at higher values.
Results
Evaluation of production,
living, and ecology pressure
The study of production, living,
and ecology pressure in the YRDR improves understanding of land pressure
disparities, which is conducive to focused measures to reduce land
pressure in the YRDR. Figure 4 depicts the spatial and temporal changes
in production, living conditions, and ecology constraints in the YRDR at
five-year intervals from 1995 to 2020.
There were notable regional disparities in production pressure, with a
general geographical pattern of high in the southeast and low in the
northwest from 1995 to 2020. The majority of regions in the YRDR had
medium to high levels of ecology pressure, with low-level areas mostly
concentrated in the region’s northwest in 1995. The initially
continuously dispersed high-level locations in the northeast and
southeast were sporadically distributed in 2000 as the low-level
production pressure slowly migrated to those regions. The dispersed
distribution of high-grade regions steadily showed a tendency of
development along the coast, reaching the maximum expansion area in
2015, which included SH as well as the entirety of Zhejiang Province
except HU from 2005 to 2010. The YRDR as a whole to HZ-SH as the
dividing line pressure grade high and low distribution is obvious, and
in the majority of the high-level locations in Zhejiang Province in 2020
pressure has reduced.
The southeast and province capitals had higher living pressure levels
than the northwest, according to research from 1995 to 2020. With time,
the region of high living pressure extends from a faceted scattered
patchy distribution to a faceted concentrated continuous distribution,
and the intensity of living pressure is rising. The strong provincial
capital plan may have a role in the living pressure in HF in Anhui
province, where the gap with other cities in the province is
progressively rising. Living pressure is typically lower in northern
Jiangsu and most of Anhui Province due to less intense agricultural,
social, and economic activities, in contrast to the generally increasing
trend of living pressure in southern Jiangsu Province and Zhejiang
Province.
The YRDR’s northwest and central-east region experience high ecology
pressure and ecology pressure are typically stronger in the north than
in the south. As urbanization progresses, the high-grade areas exhibit a
pattern of progressive growth. It is important to note that in the
high-level region, typical resource-based cities like XZ, HN, and HB as
well as economically developed regions like NJ, JX, and SH are
constantly under greater ecology pressure. However, the pressure in SH
and NJ has significantly decreased over the past five years. The cities
of FY, BZ, SQ, CA, SU, and YC eventually develop into others that are
under high ecology pressure.
Figure 4. Production, living, and ecology pressure spatial
distributions in the YRDR in 1995, 2000, 2005, 2010, 2015, and 2020
Spatial distribution of land
pressure
The land pressure in the YRDR is
divided into five categories based on the results of the research: slow,
low, medium, rapid, and high. Overall, the land pressure in the YRDR
from 1995 to 2020 reveals high land pressure that SH is the region’s
center, followed by XZ in northern Anhui, NJ, CA, WX, and SU in southern
Jiangsu, HZ, JX, NB, and WZ in Zhejiang, and low land pressure
throughout the remainder of the region. Grid level statistics from 1995
to 2020 show that from low to high five levels, the number of land
pressure grids in the YRDR declined by 17.06%, rose by 17.88%, reduced
by 34.77%, grew by 12.24%, and increased by 29.47%, respectively
(Figure 5).
Figure 5. Spatial
distribution pattern of land pressure in the YRDR in 1995, 2000, 2005,
2010, 2015, and 2020
The six years of land pressure are specifically
2015>2020>1995>2010>2005>2000
in declining order in the YRDR (Figure 6). Among the provinces, SH has a
substantially higher land pressure score than the others, reaching a
mean maximum of 60.24 in 2005, while Anhui has the lowest land pressure
score, reaching a mean maximum of 39.40 in 2015. Land pressure scores
were greater in Jiangsu before 2000 and higher in Zhejiang after 2000,
with Jiangsu and Zhejiang having the highest mean land pressure scores
in 2015, at 43.65 and 46.07, respectively (Figure S3).
Figure 6. Land pressure scores in the Yangtze River Delta in
1995, 2000, 2005, 2010, 2015, and 2020
3.3 Land pressure
trade-off/synergy characteristics
By using a mechanical equilibrium
model, we computed the polar angleand examined the predominant traits of
the production pressure, living pressure, and ecology pressure in the
quadrant where the polar angleis located. Calculating the combined force
F using Equation 10–12 yields the trade-off/synergy of production,
living, and ecology pressure, which is then classified into high
coordination, basic coordination, out of coordination, and over-out of
coordination. The synergy includes high coordination and basic
coordination, while the trade-off includes out of coordination, and over
out of coordination.
Production pressure, which is greater than both living pressure and
ecology pressure, predominates in Quadrant III. The cities of SH, AQ,
CI, HS, WZ, and TZ made up the majority of the 5.29% of the YRDR that
was situated in quadrant III in 1995. SH dominated the areas with the
highest production pressure between 2000 and 2005. As production
pressure decreases and living pressure increases in the YRDR from 2000
to 2020, the proportion of production pressure patches in quadrant III
falls to 0%, with living pressure dominating the whole region.
The impact of living pressure is greater than the impact of production
pressure and ecology pressure in the areas of quadrants IV and V.
Quadrant IV represents locations with high living pressure but low
ecology pressure, indicating positive living pressure and negative
ecology pressure. In general, regions with high living pressure but
relatively low production pressure are covered by Quadrant V, which
implies positive living pressure and negative production pressure.
Quadrant IV contained 86.43% of the YRDR’s land area, while quadrant V
contained 8.27% in 1995. Quadrant IV held 66.46% of the land area in
the YRDR, while quadrant V held 33.54%. This is due to the steady
transition of areas in quadrant IV to quadrant V between 2000 and 2020
as a result of regional production pressure becoming progressively less
significant than ecology pressure (Figure 7). Living pressure generally
predominates in the YRDR, with the dominance fluctuating from 94.71% to
100%. The YRDR does not have an ecology pressure-dominated area,
primarily because the local economy has developed and the living
pressure is considerably more than the production pressure and the
ecology pressure combined.
Figure 7. Spatial pattern analysis of production, living and
ecology pressure deviation results and coordination in the YRDR in 1995,
2000, 2005, 2010, 2015, and 2020
Geographically, the high coordinated and basic coordinated areas where
production pressure, living pressure, and ecology pressure are most
closely synchronized with one another are primarily found in
northeastern Jiangsu Province and northern Anhui Province. Most of
Zhejiang Province, southern Jiangsu Province, and southern Anhui
Province are related to the out of coordination and over out of
coordination (Figure 7). The influence of living pressures in the region
is significantly greater than the impact of production and ecology
pressures, and there is a dissonance in which living pressures are
stronger than production and ecology pressures. This is mostly due to
natural and socioeconomic factors. In general, from 1995 to 2020, the
incoherence between production, living, and ecology pressure in the YRDR
is progressively increasing. There is a transition from high
coordination, basic coordination, and out of coordination to over out of
coordination, but in particular regions, including LYG, SQ, HA, HN, WH,
and MAS, there is also a transition from out of coordination and over
out of coordination to high and basic coordination. Quantitatively, from
1995 to 2020, there was a decline in coordination, as indicated by the
mean and standard deviation of coordination. The proportion of trade-off
areas grew from 29.66% to 50.27%, whereas the proportion of
synergistic regions declined from 70.34% to 49.73% (Table S3). There
is a critical need to coordinate production, living, and ecology
pressure, and a pressing need for pressure adjustment and alleviation in
the YRDR.
3.4 Model validation of land
pressure
We used a Monte Carlo simulation
to confirm the uncertainty of the model. Based on a Monte Carlo
simulation, Figure 8 depicts the distribution of land pressure fraction
values for a grid in 2020. It shows that there is a 4.65 difference
between the land pressure score in the 5% quantile and the land
pressure score in the 95% quantile. In 2020, this pixel’s real land
pressure fraction will be 47.10, a difference of 9.87% from the real
land pressure score, which is a very minor amount.
Figure 8. The distribution of land pressure scores in the YRDR
for a grid in 2020 based on the simulation weight calculation of the
Monte Carlo method
Figure 9 displays the land pressure scores for the whole research region
and the scores within the 5% and 95% confidence intervals, as well as
the ratio of the land pressure scores to the scores from the original
assessment at the 90% confidence level. The range of values between the
5% and 95% quartiles for the entire region is between 2.53 and 6.11,
and the percent difference is substantially less than the range between
8.74% and 11.95% of the real land pressure evaluation score. Figures
9a and 9b show that the 5% and 95% quintile values are spatially
similar and significantly different, with the Southeast scores being
significantly higher than the Northwest scores. As a result, the Monte
Carlo uncertainty test validates the model evaluation and strengthens
the veracity of the model
results.
Figure 9. Spatial
distribution of uncertainty in land pressure fractions based on Monte
Carlo simulation (a) Land pressure score confidence interval 5%
quantile fraction (b) Land pressure score confidence interval 95%
quantile fraction (c) Difference between the values in the 5% and 95%
quantile of the land pressure score confidence interval (d) Ratio of
variables to the original evaluation score at 90% confidence level
Discussion
4.1 Evaluation of Land pressure method
In terms of research methodology,
this study integrated the three aspects of production, living, and
ecology land pressures covering food production pressure, economic
development pressure (Zhu and He, 2010), and arable land pressure (Chen
et al., 2019) considered by previous research scholars. In comparison to
previous fuzzy evaluation studies (Wen et al., 2021; Lu et al., 2022),
this study avoided the shortcomings of subjectivity and arbitrariness
inherent in the traditional expert scoring scheme by calculating the
evaluation scores of each raster cell using fuzzy mathematical methods
and fuzzy rules that are more objective and easy to extend, and finally
obtaining the land pressure score through weighted summation. Model
validation is necessary to clarify the reliability and validity of the
assessment outcomes, but only a few research have conducted it. Our
model was assessed in this study using the Monte Carlo model, and the
findings revealed that the reliability and validity of the evaluation
results of this study were excellent.
4.2 Spatial and temporal
distribution of land pressure
Our study provides more reliable
information for the measurement of land pressure in the YRDR. The
geography of the YRDR’s land pressure distribution features and temporal
evolution pattern were highlighted.
We noted that the pattern of land pressure in the YRDR is ”high in the
east and low in the west, high in the south and low in the north”
consistent with Hu‘s findings (Hu et al., 2020), and the high-pressure
zones of land pressure are grouped (Cheng et al., 2022). While land
pressure in NT exhibits a continual diminishing trend, which is
consistent with Wang’s findings, it is substantially higher in SH, NB,
and HZ than it is in other locations (Wang et al., 2020). We discovered
that land pressure in LYG and YC exhibits a consistent declining pattern
between 1995 and 2010, followed by a growing trend, which supports
Wang’s findings (Hu et al., 2020). Additionally, we observed that
although the rest of the cities had a constant or changing pattern of
growing pressure, CA, FY, TA, YZ, ZJ, TL, LA, and AQ displayed a
continual declining trend in land pressure. Moreover, we revealed
considerable variations in land pressure between economic zones, with
Ningbo, Hangzhou, and Suxichang economic zones usually experiencing
higher levels of land pressure as a result of greater demographic and
socioeconomic pressures (Li and Lang, 2010; Liu et al., 2020). Due to
higher living and production demands, urban regions experience higher
levels of land pressure than rural ones (Liu et al., 2017). There is a
clear relationship between regional development and ecology pressure
status, with SH experiencing the highest ecology pressure and SX, NB,
and JH experiencing the lowest levels by 2015 which is consistent with
Zhang’s findings (Zhang et al., 2022). The ecology pressure on the YRDR
is also typically rising (Lin et al., 2020; Zhang et al., 2022).
Therefore, to reduce land pressure, population and industrial growth
should be reasonably controlled, pressure should be gradually
transferred to low-pressure areas, the level of response should be
improved while reducing high pressure, the ratio of population resources
in the YRDR should be adjusted, and a reasonable spatial optimization
and control policy should be developed.
4.3 Land pressure
trade-off/synergy relationship
To achieve a balance between
production, living, and ecology pressure in the YRDR, it is important to
investigate the trade-off/synergy relationship of land pressure in that
region. This research also offers new ideas for measuring the degree to
which production, living, and ecology pressure are coupled in the YRDR.
The YRDR has a significant regional heterogeneity in the land pressure
trade-off/synergy relationship, which alternates throughout time (Lin et
al., 2020). Except for SH, places with high production and living
demands and low ecology pressures are mostly where regions with high
levels of conflict (strong trade-offs) are located. This is in agreement
with Zhang’s results (Zhang et al., 2019). In agreement with the
findings of Chen’s study, the YRDR’s production-living-ecology pressure
synergy rapidly worsened. The degree of synergy fell from 70.35% in
1995 to 49.74% in 2020 (Chen and Zhu, 2022). The YRDR’s
production-living-ecology pressure progressively shifts from synergistic
development to trade-off development and represents various trade-offs
under the influence of economic and social growth as well as regional
variances (Huang et al., 2017). Production, living, and ecology
pressures are now traded off at a high stage, with production and living
pressures being higher than ecology pressures. This does not mean that
ecology pressures are lessening, only that they are increasing at a much
slower rate than production and living pressures. As urbanization
advances, production and living pressures are elevated and living
pressures are particularly prominent. The findings of earlier research
by academics for the Hengduan Mountains (Shi et al., 2018), the Three
Gorges reservoir area (Li et al., 2018), and the eastern and southern
high hilly sections of Zhangjiakou (Liu et al., 2018) differ slightly
from those presented here. This is primarily due to the mountainous
region in the southwest, which has a complex topography and an improved
rate of urbanization. The living pressure will be reduced due to the
expansion of construction land and the urbanization process, but the
YRDR’s high level of urbanization, which is characterized by a stark
contrast between human and land, production development, and
environmental protection, will exert pressure on the area’s
already-saturated living space, increasing living pressure (Li et al.,
2019). To promote socioeconomic transformation development and advance
coordinated and sustainable development, it is required to modify the
interaction between production, living, and ecology in the process in
future.
4.4 Innovations and limitations
This study combines objective
empowerment and raster cells to create a land pressure evaluation index
system based on the production-living-ecology perspective in the YRDR.
This study’s results can be used as an exploratory supplement to the
current research findings of the evaluation category to deepen the
comprehensive understanding of the impact of development on land
pressure in the YRDR in the context of regional integration. The chosen
quantitative indicators must be further enhanced and augmented,
nevertheless, because of regional variations in resource endowments and
the abundance of complicated indicators. In the meanwhile, further
research has to be done on the fusion of data at various scales and how
to increase the precision of rasterizing socioeconomic data. More
research is required on the mechanisms underlying land pressure
generation and its motivating elements.
Conclusions
This study builds a land pressure
evaluation model for the Yangtze River Delta region based on the
perspective of production, living, and ecology pressure and the fuzzy
comprehensive evaluation method. We evaluated the land pressure in the
Yangtze River Delta region and examined the patterns of its regional and
temporal evolution in 1995, 2000, 2005, 2010, 2015, and 2020. The
trade-off/synergy relationship between production, living, and ecology
constraints in the YRDR was examined using a mechanical equilibrium
model. The following are the main results and recommendations.
- The land pressure evaluation
model constructed from three aspects of production-living-ecology can
effectively reflect the level of land pressure in the Yangtze River
Delta region. A general pattern of ”high in the east and low in the
west, high in the south and low in the north” can be seen in the
Yangtze River Delta region’s spatial distribution of land pressure,
and the six-year range from high to low is as follows:
2015>2020>1995>2010>2005>2000.
- The level of production pressure in the Yangtze River Delta region
shows a declining trend, the level of living and ecology pressure
shows a rising trend, the spatial distribution of production pressure
is consistent with the spatial distribution of land pressure, the
level of living pressure shows a trend of high in the southeast and
provincial capital cities - lower in the northwest and the ecology
pressure changes from high ecology pressure in the northwest and low
ecology pressure in the southeast.
- The Yangtze River Delta
region’s incoherence between production, living, and ecology pressure
progressively got worse during 1995 - 2020, mostly in the form of high
coordination, basic coordination, and out of coordination into out of
coordination.
Provide a reference for the spatial distribution characteristics of land
pressure in the Yangtze River Delta region, which helps reduce land
disputes, enhances the region’s sustainable development, and serves as a
useful guide for research in other cities.