Nothing Special   »   [go: up one dir, main page]
Next Article in Journal
Sustainability of Aqua Feeds in Africa: A Narrative Review
Previous Article in Journal
The Impact of High-Tech Policies on Corporate ESG Performance: Evidence from China
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 16
Issue 23
10.3390/su162310322
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Multi-Scenario Simulation of the Production-Living-Ecological Spaces in Sichuan Province Based on the PLUS Model and Assessment of Its Ecological and Environmental Effects

by
Yu Fu
1,2,
Qian Li
1,3,*,
Julin Li
1,2,
Kun Zeng
1,2,
Liangsong Wang
4 and
Youhan Wang
1,2,*
1
The Engineering Laboratory of Land and Resources Utilization in Hilly Areas, China West Normal University, Nanchong 637009, China
2
School of Geographical Sciences, China West Normal University, Nanchong 637009, China
3
Business School, China West Normal University, Nanchong 637009, China
4
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
*
Authors to whom correspondence should be addressed.
Sustainability 2024, 16(23), 10322; https://doi.org/10.3390/su162310322
Submission received: 4 September 2024 / Revised: 21 November 2024 / Accepted: 22 November 2024 / Published: 26 November 2024
Browse Figures
Figure 1
<p>The geographical location of the study area.</p> "> Figure 2
<p>Technology roadmap.</p> "> Figure 3
<p>Driving factors of PLES expansion in Sichuan Province.</p> "> Figure 4
<p>Distribution of PLES in Sichuan Province under multiple scenarios in 2030.</p> "> Figure 5
<p>Spatial changes in the PLES in Sichuan Province during 2020–2030.</p> "> Figure 6
<p>Distribution of ecological environment quality in the PLES of Sichuan Province under different scenarios in 2030.</p> "> Figure 7
<p>Spatial changes in the ecological quality of the PLES in Sichuan Province under different scenarios from 2020 to 2030.</p> ">
Versions Notes

Abstract

:
Research investigates the transformations in production–living–ecological spaces (PLES) across diverse scenarios and their ecological effects, with the aim of offering advice for environmental preservation and long-term growth in Sichuan Province. Utilizing the PLUS model, we simulated the PLES configuration in Sichuan Province for the year 2030 and subsequently evaluated its ecological impacts using an ecological effect assessment model. The findings reveal that: (1) population and GDP are key drivers of the expansion of Industrial-Production Spaces (IMPS), Urban-Living Spaces (ULS), and Rural-Living Spaces (RLS), whereas altitude has a crucial influence on shaping the expansion of Agricultural-Production Spaces (APS), Forest-Ecological Spaces (FES), Grassland-Ecological Spaces (GES), Water-Ecological Spaces (WES), and Other-Ecological Spaces (OES); (2) significant changes in PLES are observed in Sichuan Province by 2030 across four scenarios, with notable distinctions between the production priority scenario and the other three; (3) variations in ecological quality exist among the four scenarios concerning PLES; (4) the reasons behind better or worse ecological conditions differ across scenarios. The research demonstrates that the PLUS model can effectively simulate PLES in Sichuan Province under multiple scenarios for 2030, offering various potential development pathways and their corresponding ecological effects, thereby aiding in the selection of optimal development pathways.

1. Introduction

In recent decades, China’s eastern, central, and western regions have witnessed rapid urbanization and industrialization, accompanied by significant economic achievements. However, this has also resulted in issues such as farmland loss, land conversion to non-agricultural uses, inefficient development of construction land, spatial disorder, structural imbalance, and functional degradation within territorial space [1]. Consequently, a series of ecological and environmental challenges, including ecosystem degradation, air pollution, habitat disruption, and soil contamination, have emerged, creating significant barriers to China’s pursuit of balanced and enduring social-economic growth and ecological health [2,3,4]. The vast western region, which serves as the source of China’s major rivers and boasts abundant natural resources like forests, grasslands, wetlands, and lakes, holds a pivotal ecological position [5]. Therefore, reinforcing the national western region’s ecological defenses has emerged as a crucial strategic imperative for fostering a fresh approach to western development in the contemporary era, with implications for China’s overall modernization efforts. Currently, China’s territorial space development is transitioning from a focus on building production areas to achieving balanced progress among PLES (production-living-ecological spaces). Furthermore, the reports of the 19th and 20th National Congress of the Communist Party of China have repeatedly highlighted the importance of refining territorial space layout and to scientifically arrange spaces for production, living, and ecology [6,7]. Hence, examining changes in spatial configurations and their ecological–environmental impacts in the western region with a focus on PLES holds immense importance for constructing a rational and organized territorial layout, reinforcing the country’s ecological defenses, and promoting a novel approach to pattern of western development in the current era.
Currently, research on PLES in academia primarily revolves around concepts and theories [8,9], classification and identification methods [10,11], spatio-temporal pattern evolution [12,13], conflicts and balancing mechanisms [14,15], driving forces [16], ecological and environmental impacts [17,18], as well as ecosystem service values [19]. Notably, several scholars have delved into the spatio-temporal pattern evolution of PLES, utilizing the Ecological Environmental Quality Index (EEQI) [20,21] and Ecological Contribution Rate (ECR) [22] as methods to assess the resultant ecological and environmental effects [23,24]. Temporally, the focus is on the evolution of PLES patterns [25]. However, research on the transformation of PLES in various future scenarios or conditions and the associated ecological and environmental impacts is lacking. In recent years, scholars have increasingly utilized diverse models to simulate the PLES. Research has revealed that the basic Markov model [26], while capable of predicting quantities and areas, lacks the ability to analyze the spatial changes within this space. Consequently, CLUE-S [27] and CA models [28] were introduced for spatial prediction, yet researchers found that these models struggled to represent the complexity of changes in the PLES. To overcome these limitations, the CA-Markov coupling model [29] and FLUS model [30] were employed, but even these models faced challenges in depicting the changes over time and space multiple patches, especially in scenarios involving extensive patch expansions. To address the limitations of existing models, the PLUS model [31] was developed. This model aims to identify the primary causes behind land expansion and landscape changes, enabling the simulation of individual land use areas changes in various land use types. Characterized by its refinement and integration, the PLUS model offers a more nuanced and comprehensive approach to understanding land use changes. Many scholars have employed the PLUS model to investigate the characteristics of PLES and its ecological-environmental effects under various future scenarios across diverse regions [32,33]. Nevertheless, these studies have predominantly concentrated on areas such as the Beijing–Tianjin–Hebei urban agglomeration [34], Zhengzhou City [35], and central provinces like Hubei [36]. Insufficient attention has been given to the future pattern of PLES and its potential ecological-environmental effects in significant provinces of the western region.
This paper takes Sichuan Province, the strategic hinterland of national development, as the study area and utilizes land resource utilization as the carrier. Considering four scenarios: natural development, production priority, living priority, and ecological priority, we conduct multi-scenario simulations of PLES. Furthermore, we calculate the EEQI related to changes in PLES, aiming to identify the primary PLES types that influence ecological environment quality changes and the dominant factors driving regional ecological environment variations. Consequently, we can predict the land resource utilization changes in Sichuan Province during the high-quality development stage. The anticipated research findings are expected to offer substantial data support and theoretical insights for territorial space planning and ecological environmental protection endeavors in Sichuan Province. Furthermore, these findings will significantly contribute to the reinforcement of ecological security frameworks in the upstream regions of the Yangtze and Yellow Rivers.

2. Materials and Methods

2.1. Study Area

Sichuan, located centrally in the heartland of southwest China, located between 26°03′ N and 34°19′ N, and 97°21′ E and 108°12′ E, encompassing an expansive territory of 486,000 square kilometers. The province extends across the Qinghai–Tibet Plateau and the Sichuan-Yunnan ecological barriers of the Loess Plateau, featuring a diverse range of landform types, including mountains, hills, plains, and plateaus, ranging from a maximum of 7556 m to a minimum of 188 m. Sichuan Province is characterized by three distinct climatic zones: the Sichuan Basin’s mid-subtropical humid climate, the subtropical semi-humid climate prevalent in the southwestern mountainous region, and the alpine-plateau cold climate found in northwestern Sichuan. Additionally, Sichuan serves as a vital water conservation area and recharge zone for the upper reaches of both the Yangtze and Yellow Rivers. Notably, 96.6 percent of Sichuan’s water systems are part of the Yangtze River system, which comprises roughly one-third of the river’s total flow volume. Administratively, Sichuan Province encompasses 21 prefecture-level units, including 18 cities and three autonomous prefectures. Demographically, as of the end of 2020, the number of permanent residents in Sichuan approached approximately 83.67 million individuals (Figure 1).

2.2. Data Sources and Pre-Processing

This study primarily employs data on land utilization and influential factors sourced from Sichuan Province. (1) Land use data, sourced from the Resource and Environmental Science Data Platform, are utilized to analyze and simulate the spatial arrangement of a specific subject in Sichuan Province. The data have a resolution of 30 m × 30 m. For this research, data from three distinct time points, the years 2000, 2010, and 2020, were selected for examination. (2) Driving factor data. These data are utilized to establish the likelihoods of transitions among different land use categories based on their suitability. Incorporated within the PLUS model, these data are crucial for two key objectives: assessing the model’s precision and modeling the regional distribution of PLES. They serve as the essential data for validating the model’s accuracy and simulating the distribution patterns of PLES within the region [37]. Drawing upon relevant research findings by Dong [38] and Wang [39], and others, while considering data availability and representativeness, six natural environmental factors including DEM, slope, topographic relief, and Normalized Difference Vegetation Index (NDVI) were chosen. Additionally, five socio-economic factors were selected, including population, GDP, and proximity to highways, main roads, and expressways. (The above eleven data sources are detailed in Table 1.) These data primarily originate from platforms such as Geospatial Data Cloud, National Science and Technology Infrastructure, the European Centre for Medium-Range Weather Forecasts Released ERA5-Land dataset, and OpenStreetMap. All driving factor data were resampled into raster format with resolutions of either 30 m or 1 km. Among these factors, the road distance factor (representing the distance to highways, main roads, and expressways) was computed during the data preprocessing stage using Euclidean Distance.

2.3. Research Methodology

Firstly, the land use data of Sichuan Province are reclassified into PLES according to their primary functional categories. Secondly, utilizing the Markov module embedded in the PLUS model, we forecast the demand for PLES in Sichuan Province under different preset development scenarios for 2030. Thirdly, the LEAS (Land Expansion Analysis Strategy) module of the PLUS model is employed to analyze how eleven key factors influence the growth patterns of diverse land use types. Subsequently, we simulate the distribution pattern of PLES across Sichuan Province for a future scenario, drawing upon historical land use data from the years 2000 and 2010 as a basis. We then compare these simulated data with the actual land use data from 2020, and perform an assessment of its accuracy using methodologies associated with the PLUS model. After conducting the accuracy evaluation, we utilize the CARS (Conversion Analysis and Rule Setting) module to forecast the distribution of PLES in Sichuan Province. This prediction is based on different development scenarios, achieved by altering the transition matrix and neighborhood weights. Finally, we employ the EEQI and ECR to quantitatively evaluate the ecological and environmental impacts of Sichuan Province in 2030, considering various development scenarios. The specific technical route is illustrated in Figure 2.

2.3.1. Classification of PLES

The classification of PLES serves as the fundamental prerequisite for constructing a rational distribution pattern of these spaces. Based on the dominant functions of land use and with reference to the PLES classification system suggested by scholars including Yang [40] and Lu [41], among others, we categorize the 24 second-level classifications of land use status in Sichuan Province into APS, IMPS, ULS, RLS, FES, GES, WES, and OES. Furthermore, these categories are consolidated into Production space (PS), Living space (LS), and Ecological space (ES). Meanwhile, referencing the research findings of numerous scholars, including Li [42] and Cui [43], on the valuation of ecological attributes within second-level land use categories, this paper assigns values to the ecological attributes corresponding to the second-level classifications within the PLES framework in Sichuan Province, using the area-weighted method. The detailed assignments are presented in Table 2.

2.3.2. Markov-Chain Model

Before simulating the future pattern of PLES across the region of interest, forecasting the upcoming requirements for diverse land cover categories is crucial. Given that shifts in land cover can be perceived as stochastic processes akin to Markovian processes [44], the application of the Markov Chain model becomes feasible for anticipating the future needs of various spatial configurations in Sichuan Province. The specific model can be expressed as [45]:
S t + 1 = P m n × S t
P m n = P 11 P 12 ⋯ P 1 i P 21 P 22 ⋯ P 2 i ⋮ ⋮   ⋮ P i 1 P i 2 ⋯ P i i
P m n ∈ 0 , 1 , ∑ i = 1 n P m n = 1 , m , n = 1 , 2 , 3 , ⋯ , i
In the formula: S t and S t + 1 present the state matrices of the PLES in Sichuan Province for years t + 1 and t , respectively. P m n denotes the transition probability matrix from spatial type m into spatial type n . Here i denotes the number of PLES types, which is 8.

2.3.3. Multi-Scenario Settings

Multi-scenarios encompass diverse potential future visions for the region, expressed through both the amount required and the geographical arrangement of various land use categories. A crucial task in multi-scenario simulation of PLES is to ascertain the requirements for land use across various scenarios within the study area. Subsequently, these demand data are utilized within the PLUS framework to generate the spatial arrangements of PLES for various scenarios. The academic community primarily utilizes a stochastic model to forecast land cover requirements in scenarios of natural growth. Based on these predictions, adjustments are made to the probabilities of land cover transitions, taking into account regional planning objectives, to ascertain land use demands in alternative scenarios. This paper primarily sets up four scenarios: natural development, production priority, living priority, and ecological priority.
(1)
Natural development scenario: utilizing the patterns of transition and evolution observed in PLES in Sichuan Province from 2000 to 2020, and without applying any restrictive constraints, we employ a stochastic forecasting model to predict the requirements for various spatial allocations in Sichuan Province by 2030 [46].
(2)
Production priority scenario: with reference to the development goals for production land in the “Overall Land Use Planning of Sichuan Province (2006–2020)” and “Territorial Spatial Planning of Sichuan Province (2021–2035),” this scenario proposes a 60% reduction in the transition from PS to LS and a 20% increase in the transition from other ES to PS [47], relative to the natural development scenario.
(3)
Living priority scenario: Based on the development goals for living land outlined in the “Implementation Plan for the 14th Five-Year Plan of New-Type Urbanization in Sichuan Province,” and starting from the natural development scenario, this paper proposes to enhance the likelihood of conversions from PS and ES into LS by 20%, while simultaneously reducing the probability of transitions from LS to ES by 30% [48]. These adjustments reflect the anticipated land use requirements for diverse spaces in Sichuan Province’s living priority scenario by the year 2030.
(4)
Ecological priority scenario: Considering that the “14th Five-Year Plan for Ecological and Environmental Protection in Sichuan Province” indicates that the current achievements in ecological environment governance are not yet stable, ensuring ecological security and continuously improving environmental quality are essential. Therefore, under the natural development scenario, this paper proposes a 50% reduction in the transition probabilities from FES and GES toLS, along with a 30% decrease in the transition probability from PS to LS. Additionally, it advocates for a 30% increase in the transition probabilities from PS and GES to FES, and a 10% increase in the transition probability from LS to FES [49]. These adjustments reflect the estimated land use demands for diverse spaces in Sichuan Province’s ecological priority scenario by the year 2030.

2.3.4. PLUS Model

The PLUS model serves as a cellular automata tool for simulating changes in land cover at the patch scale [31,50]. Compared to previous cellular automata models, it offers more convenient data acquisition, higher accuracy, and landscape patterns that more closely resemble realistic landscapes. As such, it serves as an effective tool for simulating regional future land use patterns [51]. This model primarily comprises two modules: LEAS and CARS.
LEAS module: Initially, the land use data from 2010 and 2020 are overlaid to extract spatial expansion statistics for this period. The land use change expansion is then integrated with corresponding natural factors (DEM, Slope, Topographic relief, NDVI, Temperature, and Precipitation) and societal and economic variables (such as Population, GDP, distance to roads, etc.). Utilizing the Random Forest ensemble method, we independently assess land use transformation and its contributing factors, aiming to explore the connection between the spatial expansion of each category and various influencing variables, ultimately identifying the potential for expansion of PLES in Sichuan Province. Based on the research of relevant scholars, we have established 20 number of regression trees, set the default sampling rate to 0.01, and chosen an mtry value of 11, which remains within the total count of driving factors.
CARS module: this model integrates the expansion probabilities of PLES derived from the LEAS module with the predicted quantitative structures of future PLES obtained through the Markov chain model. Utilizing the 2020 data on PLES and considering neighborhood weights, the CARS module produces simulated results of PLES under four scenarios for the year 2030.
Setting of neighborhood weight parameters (Table 3): The neighborhood weights, which vary from 0 to 1, reflect a greater propensity for land type expansion as the value rises. Using land use data for Sichuan Province spanning the years 2010 to 2020, the intensity of this expansion is determined through a specific formula [52]:
W i = H i − H m i n H m a x − H m i n
In this formula, W i indicates the neighborhood weight of the I land category within the PLES, H i represents the area of growth for the I land category, while H m i n and H m a x denote the minimum and maximum expansion areas, respectively, across all land types within the PLES.
Table 3. The setting of neighborhood weight parameters.
Table 3. The setting of neighborhood weight parameters.
PLESAPSIMPSULSRLSFESGESWESOES
neighborhood weight0.1010.730.660.600.440.740.58
In reality, all land has the potential for expansion. Therefore, the APS, which should naturally have a neighborhood weight normalized to 0, are artificially set to 0.1.

2.3.5. Ecological Environment Effect Model

The ecological environment effect model is a commonly used approach for assessing regional ecological environment conditions [53]. It can be composed of an EEQI and an ECR. The EEQI for ecological environment quality evaluates the spatial aspects of ecology within the designated area, providing a numerical indication of the overall ecological quality. The purpose of calculating the ECR is to delve deeper into how various spatial transfers impact ecological quality changes across the entire study area [54].
(1)
Ecological Environment Quality Index (EEQI). The EEQI varies for different spatial land uses across various study areas, primarily due to changes in the proportion and overall size of these land uses within the study area. This study employs Sichuan Province specific land use categorization its associated EEQI to evaluate the general condition of the province’s ecological environment. The calculation formula can be expressed as [55]:
E V i = ∑ i = 1 n A k i A k R i
In the formula: E V i denotes the EEQI for the region being analyzed; A k i indicates the area occupied by land use category i within the boundaries of spatial category k ; A k represents the total area of spatial category k ; R i indicates the ecological environment index for land use category i ; n represents the total number of land use categories; k represents the secondary classification of the PLES, with k = 1 … 8 ; i denotes the secondary classification of the current land use status, with i = 1 … 24 .
(2)
Ecological Contribution Rate (ECR). The ECR measures how shifts in land use within spatial types in the study area affect the regional ecological environment quality. It measures how shifts in land use within spatial types in the study area affect the regional ecological environment quality [56].
L E I = R t + 1 − R t L A / A
In the formula, L E I signifies the ecological impact of various spatial land use transitions; R t + 1 and R t indicate the ecological environment quality indices after and before the change, respectively; L A stands for the size of the specific change type; A is the overall area of Sichuan Province.

3. Results

3.1. Drivers of Expansion of the PLES

This study incorporated the extracted expansion data for various PLES in Sichuan Province over the years 2010 to 2020 into the LEAS component of a modeling framework. Firstly, we identified the regions where changes occurred in each PLES type. Subsequently, by identifying the areas where transformations took place within each PLES category and integrating these areas with the Random Forest algorithm within the LEAS module, we derived the spatial arrangement and respective significance of the influential elements contributing to the expansion of various PLES categories. The results are presented in Figure 3.
The primary drivers of APS expansion are elevation and NDVI, occurring predominantly at the junction of eastern Sichuan’s plains and hills, northern river valleys, and the fringes of southern Sichuan’s basin, where hills and mountains meet. For IMPS, population growth and NDVI serve as key determinants, with industrial and commercial activities congregating towards urban centers amidst accelerated urbanization, primarily surrounding the downtown regions of Sichuan’s municipalities and prefectures. ULS expansion is predominantly driven by population, concentrated in Chengdu and its surrounding municipalities, particularly in their respective urban centers. RLS expansion is mainly attributed to GDP growth and population increase, occurring in rural regions adjacent to cities or along major transportation routes, particularly around Chengdu. FES expansion is driven by NDVI and elevation, occurring in mountainous areas surrounding the Sichuan Basin and on the western plateau. GES expansion is influenced by elevation and temperature, prevalent on the western plateau and mountains, with some distribution in southern and northern Sichuan. WES expansion is driven by surface relief and elevation, occurring in regions traversed by rivers like the Jialing, Dadu, and Jinsha. The expansion of OES is primarily influenced by population and elevation, taking place in ecologically sensitive zones in western Sichuan. Population and GDP are the key factors influencing IMPS, ULS and RLS expansion, while slope and surface relief have minimal impact. Conversely, elevation is the dominant factor influencing APS, FES, GES, WES, and OES expansion, with distance to major roads having the least influence.

3.2. Multi-Scenario Simulation of PLES in 2030 and Its Ecological and Environmental Effects

3.2.1. Model Accuracy Validation

Drawing upon the PLUS model, we leveraged land cover information from Sichuan Province for the period encompassing 2000 up to 2010 to forecast the spatial arrangement of PLES across the province in 2020. Subsequently, we contrasted these simulated outcomes for 2020 with the actual patterns observed in that year to assess the model’s accuracy. This comparison yielded validation results indicating the model’s overall accuracy stood at 0.909, with a Kappa coefficient of 0.869. Since a Kappa coefficient exceeding 0.8 implies a high degree of prediction accuracy for the model it indicates, indicating its good applicability for multi-scenario simulations of PLES in Sichuan Province.

3.2.2. Multi-Scenario Simulation of the PLES

Drawing upon the PLUS model, utilizing the distribution patterns of PLES in Sichuan Province for 2020 as a foundation, combined with data on land use demands for various spaces under different scenarios, we simulated the distribution patterns of PLES in Sichuan Province for 2030 under different scenarios. Using ArcGIS software for statistical analysis, we obtained the corresponding area statistics table (Table 4) and maps depicting distribution patterns and spatial changes (Figure 4 and Figure 5). From Table 4 and Figure 4 and Figure 5, it can be observed that there are patch-level changes in the PLES’ patterns under the scenarios of natural development, production priority, living priority, and ecological priority in Sichuan Province in 2030 compared to 2020. Comparing the PLES in 2030 with those in 2020, we can find:
(1)
In the natural development scenario, Sichuan Province is projected to experience a decline in PS by 964.75 km2 and an increase in LS by 473.83 km2 along with an augmentation of ES by 312.97 km2 by 2030. This simulation predicts a decline in APS and an escalation in urbanization, with expansions in IMPS and ULS to accommodate the growing urban population and new construction demands during urbanization. The primary changes in PS occur in the Sichuan Basin and Panxi Region, while those in LS are more pronounced in Chengdu, and ES changes are concentrated in the Micang Mountain and Daba Mountain area.
(2)
In the production priority scenario, Sichuan Province will gain 275.29 km2 in PS, lose 46.99 km2 in LS, and lose 406.24 km2 in ES by 2030. This scenario simulates an increased allocation of resources towards production areas, resulting in an expansion of land utilization for both agricultural and non-farming activities. This expansion is designed to fully meet the requirements of economic growth and industrial advancement. As a result, specific environmentally sensitive zones may be converted into production sites. Consequently, this may result in diminished ecosystem service performance and a loss of biodiversity. The major PS changes occur in the Ruoergai Grassland of Aba Prefecture; changes in LS are primarily in Chengdu; and ES changes are mainly in the western Sichuan Plateau’s Aba and Ganzi Prefectures.
(3)
In the living priority scenario, Sichuan Province will see an increase of 683.67 km2 in LS, 448.65 km2 in ES, and a decrease of 1310.27 km2 in PS by 2030. In this scenario simulation, Sichuan Province is dedicated to optimizing the urban spatial layout and enhancing urban infrastructure and service levels, resulting in an expansion of land use for LS and a decrease in land use for APS. This effectively fosters the integrated development of urban and rural settings. Additionally, an expansion in ES contributes to the enhancement of ecosystem service functions. PS changes mostly occur in the Sichuan Basin and southwestern Sichuan, while LS changes are concentrated in Chengdu and its adjacent cities, and ES changes are widely distributed across the province.
(4)
In the ecological priority scenario, Sichuan Province will gain 2605.39 km2 in ES, increase 117.3 km2 in LS, and lose 2900.64 km2 in PS by 2030. This simulated scenario indicates a rise in the land use area dedicated to ES and a significant reduction in that allocated to PS. This scenario emphasizes environmental protection and ecological construction, encourages the development of green and circular economies, and contributes to enhancing the region’s sustainable development capabilities. The spatial distribution characteristics of changes in PS, LS and ES align with those observed in the living priority scenario.

3.2.3. Ecological Environmental Effects of PLES Under Multiple Scenarios

  • EEQI
Based on four different scenarios for 2030: natural development, production priority, living priority, and ecological priority, we employed the ArcGIS natural breaks classification method to categorize the EEQI of Sichuan Province’s second-level land use classification into five distinct levels. These levels are defined as follows: low-quality areas are identified by EV values of 0.163 or below, lower-quality areas have EV values between 0.163 and 0.2, medium-quality areas exhibit EV values ranging from above 0.2 to 0.267, higher-quality areas have EV values between 0.267 and 0.506, and high-quality areas possess EV values exceeding 0.506. The objective of this study is to objectively capture the influence of various types of PLES on ecological environment quality, while also facilitating data analysis and visualization. Consequently, we obtain spatial distribution maps of ecological environment quality for Sichuan Province’s PLES in 2030 under different scenarios (Figure 6). From this figure, it is evident that ecological environment quality across the PLES in Sichuan Province for 2030 generally exhibits a consistent spatial pattern across the four scenarios: natural development, production priority, living priority, and ecological priority. However, notable variations can be observed at the patch level within these scenarios.
Employing Formula (5), we calculated the EEQI for the four scenarios in 2020 and 2030. The results showed that the ecological priority scenario yielded the highest EEQI score, specifically 0.521. In contrast, the production priority scenario exhibited the lowest EEQI, measuring 0.517. The natural development and living priority scenarios followed closely, both with an EEQI of 0.518. Compared to 2020, the year 2030 will witness changes in the ecological environment quality in Sichuan Province. Specifically: Under the ecological priority scenario, there will be an improvement in the EEQI, with an increase of 0.003. In contrast, under the production priority scenario, the EEQI will decline by 0.001. The natural development and living priority scenarios will exhibit no change in their EEQI values. Further examination of the PLES’ EEQI changes from 2020 to 2030 under different scenarios (Figure 7) reveals that compared to 2020, the EEQI in most regions of Sichuan Province remains stable under all four scenarios. Distinct differences in EEQI improvement and deterioration are observed among scenarios, particularly in certain areas.
2.
ECR
This paper further integrates the concept of ECR, which arises from the mutual transformations among PLES in Sichuan Province using Formula (6). It analyzes the impact of transformations among various land uses on ecological quality over the period from 2020 to 2030, with the findings summarized in Table 5. The analysis reveals that:
The primary PLES land use transitions that facilitate the enhancement of ecological environment quality involve the conversion of APS into FES, GES, and WES. This significant enhancement is primarily attributed to the enforcement of measures like the Grain for Green Program and initiatives encouraging the shift from farmland to grassland, which provide incentives for farmers to convert some of their agricultural land into forest or grassland. Additionally, the establishment of ecological parks and wetland conservation areas works towards enhancing the regional ecological conditions. While the types of transition that result in better ecological conditions remain largely consistent across scenarios, the production priority scenario differs in that it focuses more on the transformation of OES into water and GES to enhance ecological environment quality. The primary PLES land use transitions causing ecological environment degradation under different scenarios are more dispersed, with varying degrees of ecological contribution and contribution rates. The conversion of GES into OES is the primary cause of ecological degradation under both natural development and the production priority scenario, with contribution rates of 24.8% and 74.09%, respectively. This is primarily because the inherent fragility of grassland ecosystems in certain regions, which are susceptible to interference and destruction from external factors, thus resulting in the impairment or reduction of ecosystem functions, and ultimately leading to a decline in ecological condition. Notably, the transformation of GES into IMPS significantly contributes to ecological degradation under the natural development scenario, accounting for 30.03% of the total. This transformation may arise from the excessive pursuit of economic interests, resulting in the overexploitation of natural resources. To accommodate this, people may convert GES into IMPS, thereby disrupting the original ecosystem balance. In the living priority scenario, the transition from FES to GES has the greatest impact on regional ecological environment quality degradation, contributing 59.15% to the total degradation. This is partly attributed to the fragility of forestland ecosystems in some areas and their susceptibility to external interference. Additionally, as people’s demand for LS continues to escalate, FES is converted into grassland on a substantial scale, significantly disrupting the original ecosystem balance. In the ecological priority scenario, the conversions of APS into IMPS and WES into GES are the primary factors contributing to the decline of regional ecological condition, accounting for 72.66% of the total negative impact. To meet the demands of industrialization and urbanization, a substantial amount of APS is converted into IMPS, while WES also faces the threat of overexploitation and is forced to shift to grassland or alternative land uses.

4. Discussion

This study’s prediction results indicate diverse trends in ecological environment quality in Sichuan Province between 2020 and 2030 under various development scenarios. Under the natural development scenario, Sichuan’s PLES evolve according to their inherent laws. Land use changes may remain relatively stable, although with potential risks of overexploitation of resources and environmental degradation. However, this scenario may also maintain ecological balance to a certain extent, serving as a baseline for formulating environmental protection policies. In contrast, the production priority scenario emphasizes economic development and production activities, potentially leading to farmland expansion, overconsumption of resources, and environmental damage. This therefore underscores the need for policymakers to balance economic growth with environmental protection to achieve sustainable development. The living priority scenario focuses on improving the living environment and ensuring living needs. However, urban land expansion may result in increased environmental issues, such as household waste emissions, necessitating policies that consider both people’s livelihood and environmental protection. By comparison, the ecological priority scenario prioritizes ecological protection, anticipating an increase in ecological land use and potential ecological transformation of production land. This scenario restricts activities harmful to the ecological environment, resulting in notable enhancements in the ecological ecosystem, despite at the potential cost of reduced economic growth and social welfare levels. These findings emphasize the importance of ecological environment protection and provide a rationale for formulating stringent environmental protection policies. They also highlight the importance of striking a compromise between safeguarding the ecological environment and economic development. Overall, considering the research results, if Sichuan Province plans the utilization of its PLES according to the natural development, living priority, and ecological priority scenarios, its ecological environment quality will improve. Conversely, if it pursues the production priority scenario, it may lead to ecological degradation due to APS expansion compressing grassland areas. The simulation results assist governments in identifying areas with improving and deteriorating ecological environment quality, enabling them to formulate management policies more scientifically, avoid blind development and overexploitation of resources, thus achieving sustainable development and ensuring that the functional positioning and development direction of the PLES meet both the dual needs of ecological protection and economic development.
Sichuan Province has a vast geographical area, exhibiting significant regional variations in ecological and economic conditions. Therefore, tailored promotion strategies should be formulated based on regional realities. For vulnerable ecological zones such as the western plateau and mountainous areas and the Sichuan Basin periphery, the ecological priority scenario should be promoted, emphasizing ecological protection and restoration, including the promotion of sustainable development models such as green agriculture and forestry. In the Chengdu Plain and low-mountain hilly areas, agricultural production layouts should be optimized, and circular economy and resource-saving industries should be promoted to reduce the risk of environmental pollution and ecological damage. At the same time, efforts should be made to enhance urban development quality and standards through strengthened planning and management. For low-quality areas, governance plans targeting severe environmental issues should be formulated, with enhanced supervision and stricter environmental protection standards. For areas of lower-quality, environmental governance and ecological protection plans aimed at promoting industrial upgrading and transformation should be developed, with a focus on reducing pollution emissions, promoting environmental protection technologies and green production methods, and facilitating sustainable development. For medium-quality areas, the promotion and support of eco-friendly industries should be encouraged and supported to foster a green economy, strengthen ecological protection and restoration efforts, maintain ecological balance, and reduce environmental pollution. For higher-quality areas, policies focusing on ecological protection should be formulated to ensure continuous improvement of ecological environment quality, with strict measures taken to protect the ecological environment and prevent human-induced damage and pollution. For high-quality areas, policies oriented towards sustainable development should be established to encourage green innovation and the development of green industries, strengthen ecological protection efforts, and ensure that their ecological environment quality remains excellent in the long term.
The research offers a detailed examination of the precise factors contributing to both the advancement and degradation of the ecological environment, focusing on the interplay between different types of PLES. It acknowledges that comprehensive factors, including the natural and socioeconomic circumstances, contribute to shaping changes in the ecological environment. Methodologically, the current study utilizes the PLUS model to simulate various land cover categories within the PLES. However, future research could integrate additional land cover and environmental conservation models to enhance simulation accuracy and better serve ecological environment effect evaluations. Additionally, future research is expected to not only integrate economic and social development factors (such as economic policies and social dynamics) and indicator factors like soil type and precipitation in Sichuan Province into the model to deeply analyze the driving mechanisms of PLES changes, but also expand the research scope to other regions with similar geographical, climatic, and economic characteristics for cross-regional comparisons and analyses. Meanwhile, it is advisable to consider incorporating key aspects of Sichuan Province’s territorial spatial planning into the model, simulating land use types within the PLES under existing territorial spatial planning. This would provide data support for national macro-strategic decision-making in a bottom-up manner, scientifically guiding the spatio-temporal structural changes of various land uses within the planning period in Sichuan Province. Furthermore, it would guarantee enhanced ecological optimization of key watersheds in the Yangtze and Yellow River basins, and facilitate the elevation of Sichuan Province’s status in the hinterland of national development strategies, as well as facilitate the smooth progression of the new regional development blueprint for the west.

5. Conclusions

This paper employs the PLUS model to simulate the spatial configurations of PLES in Sichuan Province by 2030, under four scenarios: natural development, production priority, living priority, and ecological priority. Furthermore, we assess the ecological and environmental impacts of these scenarios using the EEQI and ECR. The main conclusions are as follows:
(1)
Population and GDP are the key factors influencing IMPS, ULS, and RLS expansion, while slope and surface relief have minimal impact. Conversely, elevation is the dominant factor influencing APS, FES, GES, WES, and OES expansion, with distance to major roads having the least influence.
(2)
The simulated changes in the PLES of Sichuan Province in 2030 under the four scenarios are pronounced, with the changes under the production priority scenario differing from those under the other three scenarios. Compared to 2020, the PS area increases under the production priority scenario, while the LS and ES areas both decrease. Conversely, the changes in PLES areas under the other three scenarios are opposite to this; under the natural development, living priority, and ecological priority scenarios, the changes in LS are more significant, with the LS area increasing by 10.83%, 15.63%, and 2.68%, respectively, mainly distributed in the Chengdu Plain and its surrounding areas.
(3)
In Sichuan Province by 2030, variations in ecological environment quality are observed within the PLES across four different scenarios. Specifically, the ecological environment quality is optimal under the ecological priority scenario, followed by the natural development and living priority scenarios, while it is the worst under the production priority scenario. Compared to 2020, the EEQI decreased by 0.01 under the production priority scenario, while it increased by 0.68, 0.04, and 0.03, respectively, under the ecological priority, living priority, and natural development scenarios.
(4)
In Sichuan Province by 2030, the factors contributing to the enhancement and degradation of environmental quality vary across four distinct scenarios. Specifically, under the natural development, living priority, and ecological priority scenarios, the primary driver of environmental quality improvement is the conversion of APS into ES, such as forests and grasslands. Under the production priority scenario, the primary factor driving the enhancement of ecological conditions is the conversion of OES, including swamps and bare lands, into WES and GES.
In scenarios of natural development and production priority, the decline in ecological environment quality is primarily attributed to the transformation of GES into OES. Notably, the conversion of GES into IMPS significantly contributes to this decline under natural development scenarios. In contrast, under living priority scenarios, the primary determinant of ecological degradation is the shift from FES to GES. Under ecological priority scenarios, the main drivers of ecological environment quality deterioration are the conversion of APS into IMPS, as well as the transformation of WES into GES.

Author Contributions

Y.F. contributed to data processing, data analysis, graphing, thesis writing, and thesis revision; J.L., Y.W., K.Z., L.W. and Q.L. contributed to the guidance of graph optimization and thesis revision. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Social Science Fund of China (No.19XJY008), the Startup Project of Doctoral Research by China West Normal University (No. 20E034).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Zhu, C.M.; Dong, B.Y.; Li, S.N.; Lin, Y.; Shahtahmassebi, A.; You, S.X.; Zhang, J.; Gan, M.Y.; Yang, L.X.; Wang, K. Identifying the trade-offs and synergies among land use functions and their influencing factors from a geospatial perspective: A case study in Hangzhou, China. J. Clean. Prod. 2021, 314, 128026. [Google Scholar] [CrossRef]
  2. Liu, X.H.; Xue, Z.J.; Liu, J.F.; Zhao, X.F.; Fu, Y.J.; Wang, R.; Luo, X.P.; Xing, L.Y.; Wang, C.; Zhao, H.H. Spatio-Temporal Evolution and Multi-Scenario Modeling Based on Terrestrial Carbon Stocks in Xinjiang. Land 2024, 13, 1454. [Google Scholar] [CrossRef]
  3. Xu, X.; Tian, H.; Yang, G.; Li, X.; He, X.; Li, Y.; Gao, Y.; Li, F.; Li, P.; Liu, B.; et al. Spatial and temporal changes in land and water resources on the northern slopes of the Tianshan mountains from the perspective of “production-living-ecological space”. Front. Environ. Sci. 2023, 11, 1121248. [Google Scholar] [CrossRef]
  4. Lin, J.Y.; Chen, Q.T. Analyzing and Simulating the Influence of a Water Conveyance Project on Land Use Conditions in the Tarim River Region. Land 2023, 12, 2073. [Google Scholar] [CrossRef]
  5. Jiang, Z.; Luo, Y.; Wen, Q.; Shi, M.; Ayyamperumal, R.; Wang, M. Achieving Sustainable Land Use Allocation in High-Altitude Area by 2030: Insights from Circle Structure and Scenario Predictions for Production-Living-Ecological Land in Xining Marginal Area, China. Land 2024, 13, 1241. [Google Scholar] [CrossRef]
  6. Pan, X.C. Research on Ecological Civilization Construction and Environmental Sustainable Development in the New Era. IOP Conf. Ser. Earth Environ. Sci. 2018, 153, 062080. [Google Scholar]
  7. Bao, S.; Lu, L.; Zhi, J.; Li, J. An Optimization Strategy for Provincial “Production-Living-Ecological” Spaces under the Guidance of Major Function-Oriented Zoning in China. Sustainability 2024, 16, 2248. [Google Scholar] [CrossRef]
  8. Huang, J.; Lin, H.; Qi, X. A literature review on optimization of spatial development pattern based on ecological-production-living space. Prog. Geogr. 2017, 36, 378–391. [Google Scholar]
  9. Lin, G.; Jiang, D.; Fu, J.; Zhao, Y. A Review on the Overall Optimization of Production-Living-Ecological Space: Theoretical Basis and Conceptual Framework. Land 2022, 11, 345. [Google Scholar] [CrossRef]
  10. Liu, J.; Liu, Y.; Li, Y. Classification evaluation and spatial-temporal analysis of “production-living-ecological” spaces in China. Acta Geogr. Sin. 2017, 72, 1290–1304. [Google Scholar]
  11. Huang, A.; Xu, Y.; Lu, L.; Liu, C.; Zhang, Y.; Hao, J.; Wang, H. Research progress of the identification and optimization of production-living-ecological spaces. Prog. Geogr. 2020, 39, 503–518. [Google Scholar] [CrossRef]
  12. Wang, Y.n.; Xiao, X.; Pu, J.; Wang, S.; Wang, W.; Wang, W. Spatial and Temporal Evolution Characteristics of Production-Living-Ecological Space in Yangtze River Economic Belt in Past 40 Years. Trans. Chin. Soc. Agric. Mach. 2022, 53, 215–225. [Google Scholar]
  13. Zhang, Y.; Chen, Y.; Zhang, Y.; Zhang, Y.; Gong, J. Coulpling Coordination and Spatio-temporal Pattern Analysis of the “Production-Living-Ecological” Space in Chengdu-Chongqing Urban Agglomeration. Areal Res. Dev. 2022, 41, 119–125. [Google Scholar]
  14. Lin, G.; Fu, J.Y.; Jiang, D. Production-Living-Ecological Conflict Identification Using a Multiscale Integration Model Based on Spatial Suitability Analysis and Sustainable Development Evaluation: A Case Study of Ningbo, China. Land 2021, 10, 383. [Google Scholar] [CrossRef]
  15. Lin, G.; Jiang, D.; Fu, J.Y.; Cao, C.L.; Zhang, D.W. Spatial Conflict of Production-Living-Ecological Space and Sustainable-Development Scenario Simulation in Yangtze River Delta Agglomerations. Sustainability 2020, 12, 2175. [Google Scholar] [CrossRef]
  16. Zhu, J.H.; Shang, Z.Y.; Long, C.; Lu, S. Functional Measurements, Pattern Evolution, and Coupling Characteristics of “Production-Living-Ecological Space” in the Yangtze Delta Region. Sustainability 2023, 15, 16712. [Google Scholar] [CrossRef]
  17. Li, X.; Liu, Z.; Li, S.; Li, Y. Multi-Scenario Simulation Analysis of Land Use Impacts on Habitat Quality in Tianjin Based on the PLUS Model Coupled with the InVEST Model. Sustainability 2022, 14, 6923. [Google Scholar] [CrossRef]
  18. Wei, Q.; Abudureheman, M.; Halike, A.; Yao, K.; Yao, L.; Tang, H.; Tuheti, B. Temporal and spatial variation analysis of habitat quality on the PLUS-InVEST model for Ebinur Lake Basin, China. Ecol. Indic. 2022, 145, 109632. [Google Scholar] [CrossRef]
  19. Du, Y.; Li, X.; He, X.; Li, X.; Yang, G.; Li, D.; Xu, W.; Qiao, X.; Li, C.; Sui, L. Multi-Scenario Simulation and Trade-Off Analysis of Ecological Service Value in the Manas River Basin Based on Land Use Optimization in China. Int. J. Environ. Res. Public Health 2022, 19, 6216. [Google Scholar] [CrossRef]
  20. Li, K.; Zhang, B.Y.; Xiao, W.D.; Lu, Y. Land Use Transformation Based on Production-Living-Ecological Space and Associated Eco-Environment Effects: A Case Study in the Yangtze River Delta Urban Agglomeration. Land 2022, 11, 1076. [Google Scholar] [CrossRef]
  21. Pang, R.; Hu, N.; Zhou, J.; Sun, D.; Ye, H. Study on Eco-Environmental Effects of Land-Use Transitions and Their Influencing Factors in the Central and Southern Liaoning Urban Agglomeration: A Production-Living-Ecological Perspective. Land 2022, 11, 937. [Google Scholar] [CrossRef]
  22. Pan, F.; Shu, N.; Wan, Q.; Huang, Q. Land Use Function Transition and Associated Ecosystem Service Value Effects Based on Production-Living-Ecological Space: A Case Study in the Three Gorges Reservoir Area. Land 2023, 12, 391. [Google Scholar] [CrossRef]
  23. Wang, Y.; Wang, Y.; Xu, W.Z.; Sheng, Z.Y.; Zhu, Z.; Tang, H.S. Eco-environmental effect and driving factors of changing “production-living-ecological space” in northern Xinjiang, China. Front. Ecol. Evol. 2023, 11, 1248702. [Google Scholar] [CrossRef]
  24. Li, Y.; Li, J.; Chu, J. Research on land-use evolution and ecosystem services value response in mountainous counties based on the SD-PLUS model. Ecol. Evol. 2022, 12, e9431. [Google Scholar] [CrossRef]
  25. Zou, Y.; Zhang, S.; Xie, Y.; Du, Y. Spatial distribution and evolution characters of production-living-ecological spaces in Xuzhou city. Sci. Surv. Mapp. 2020, 45, 154–162. [Google Scholar]
  26. Xiaofan, Z.; Wenbo, C.; Limin, D.A.I. The Application of Markov and Gray Model in Land Use Prediction. Res. Soil Water Conserv. 2007, 14, 19–21. [Google Scholar]
  27. Li, Y.; Ye, C.; Huang, X. Temporal-Spatial Evolution and Scenario Simulation of Production-Living- Ecological Space in Nanchang Based on CLUE-S Model. Res. Soil Water Conserv. 2021, 28, 325–332. [Google Scholar]
  28. Ye, D.; Gao, L.; Ma, Z.; Peng, L. Simulation Research on Cultivated Land Planning Based on Cellular Automata Model. Southwest China J. Agric. Sci. 2019, 32, 899–904. [Google Scholar]
  29. Liu, Y.; Ren, Y.; Zhou, Y.; Liu, C. Dynamic simulation of future ecosystem services value in jianghan plain based on ca-markova case study of qianjiang city. J. China Agric. Resour. Reg. Plan. 2020, 41, 159–167. [Google Scholar]
  30. Jiang, X.; Zhai, S.; Wang, Z.; Liu, H.; Chen, J.; Zhu, Y. Simulating the Production-Living-Ecological space and analyzing eco-environmental effects based on FLUS model in Zhengzhou, China. Acta Ecol. Sin. 2023, 43, 6225–6242. [Google Scholar]
  31. Liang, X.; Guan, Q.; Clarke, K.C.; Liu, S.; Wang, B.; Yao, Y. Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan, China. Comput. Environ. Urban Syst. 2021, 85, 101569. [Google Scholar] [CrossRef]
  32. Li, X.; Fu, J.; Jiang, D.; Lin, G.; Cao, C. Land use optimization in Ningbo City with a coupled GA and PLUS model. J. Clean. Prod. 2022, 375, 134004. [Google Scholar] [CrossRef]
  33. Zhang, X.; Li, A.; Nan, X.; Lei, G.; Wang, C. Multi-scenario Simulation of Land Use Change Along China-Pakistan Economic Corridor through Coupling FLUS Model with SD Model. J. Geo-Inf. Sci. 2020, 22, 2393–2409. [Google Scholar]
  34. Yang, Y.; Bao, W.; Li, Y.; Wang, Y.; Chen, Z. Land Use Transition and Its Eco-Environmental Effects in the Beijing-Tianjin-Hebei Urban Agglomeration: A Production-Living-Ecological Perspective. Land 2020, 9, 285. [Google Scholar] [CrossRef]
  35. Guo, P.; Wang, H.; Qin, F.; Miao, C.; Zhang, F. Coupled MOP and PLUS-SA Model Research on Land Use Scenario Simulations in Zhengzhou Metropolitan Area, Central China. Remote Sens. 2023, 15, 3762. [Google Scholar] [CrossRef]
  36. Wang, X.; Ma, B.; Li, D.; Chen, K.; Yao, H. Multi-scenario simulation and prediction of ecological space in Hubei province based on FLUS model. J. Nat. Resour. 2020, 35, 230–242. [Google Scholar]
  37. Xie, X.; Lin, X.; Wang, Y.; Tu, R.; Zhang, J. Multi-scenario Simulation of Land Use in Nanchuan District of Chongqing Based on PLUS Model. J. Yangtze River Sci. Res. Inst. 2023, 40, 86–92, 113. [Google Scholar]
  38. Dong, K.; Wang, H.; Luo, K.; Yan, X.; Yi, S.; Huang, X. The Use of an Optimized Grey Multi-Objective Programming-PLUS Model for Multi-Scenario Simulation of Land Use in the Weigan-Kuche River Oasis, China. Land 2024, 13, 802. [Google Scholar] [CrossRef]
  39. Wang, L.; Zhou, S.; Ouyang, S. The spatial prediction and optimization of production-living-ecological space based on Markov-PLUS model: A case study of Yunnan Province. Open Geosci. 2022, 14, 481–493. [Google Scholar] [CrossRef]
  40. Yang, Q.; Duan, X.; Wang, L.; Jin, Z. Land Use Transformation Based on Ecological-production-living Spaces and Associated Eco-environment Effects: A Case Study in the Yangtze River Delta. Sci. Geogr. Sin. 2018, 38, 97–106. [Google Scholar]
  41. Lu, C.; Qi, X.; Zheng, Z.; Jia, K. PLUS-Model Based Multi-Scenario Land Space Simulation of the Lower Yellow River Region and Its Ecological Effects. Sustainability 2022, 14, 6942. [Google Scholar] [CrossRef]
  42. Li, X.; Fang, C.; Huang, J.; Mao, H. The urban land use transformations and associated effects on eco-environment in northwest china arid region:a case study in ehxi region, gansu province. Quat. Sci. 2003, 23, 280–290. [Google Scholar]
  43. Cui, J.; Zang, S. Regional disparities of land use changes and their eco-environmental effects in Harbin-Daqing-Qiqihar Industrial Corridor. Geogr. Res. 2013, 32, 848–856. [Google Scholar]
  44. Mo, J.; Sun, P.; Shen, D.; Li, N.; Zhang, J.; Wang, K. Simulation Analysis of Land-Use Spatial Conflict in a Geopark Based on the GMOP-Markov-PLUS Model: A Case Study of Yimengshan Geopark, China. Land 2023, 12, 1291. [Google Scholar] [CrossRef]
  45. Xiong, S.; Yang, F.; Zhang, J.; Tang, Y. An integrated model chain for diagnosing and predicting conflicts between production-living-ecological space in lake network regions: A case of the Dongting Lake region, China. Ecol. Indic. 2024, 166, 112237. [Google Scholar] [CrossRef]
  46. Chen, L.; Cai, H.; Zhang, T.; Zhang, X.; Zeng, H. Land use multi-scenario simulation analysis of Rao River Basin based on Markov-FLUS model. Acta Ecol. Sin. 2022, 42, 3947–3958. [Google Scholar]
  47. Li, J.; Yang, D.; Wu, F.; Chen, R.; He, W. Dynamic Simulation of Land Use Changes and Assessment of Carbon Storage in Kunming City Based on PLUS and InVEST Models. Bull. Soil Water Conserv. 2023, 43, 378–387. [Google Scholar]
  48. Yang, S.; Su, H.; Zhao, G. Multi-scenario simulation of urban ecosystem service value based on PLUS model: A case study of Hanzhong city. J. Arid Land Resour. Environ. 2022, 36, 86–95. [Google Scholar]
  49. La, L.; Gou, M.; Li, L.; Wang, N.; Hu, J.; Liu, C.; Xiao, W. Spatiotemporal Dynamics and Scenarios Analysis on Trade-offs between Ecosystem Service in Three Gorges Reservoir Area: A Case Study of Zigui County. J. Ecol. Rural Environ. 2021, 37, 1368–1377. [Google Scholar]
  50. Luo, F.; Pan, A.; Chen, Z.; Wang, Y. Spatiotemporal Pattern Change of Cultivated Land and Its Driving Forces in Yibin City, Sichuan Province During 1980-2018. Bull. Soil Water Conserv. 2021, 41, 336–344. [Google Scholar]
  51. Lin, T.; Yang, M.; Wu, D.; Liu, F.; Yang, J.; Wang, Y. Spatial correlation and prediction of land use carbon storage based on the InVEST-PLUS model- A case study in Guangdong Province. China Environ. Sci. 2022, 42, 4827–4839. [Google Scholar]
  52. Wang, B.; Liao, J.; Zhu, W.; Qiu, Q.; Wagn, L.; Tang, L. The weight of neighborhood setting of the FLUS model based on a historical scenario: A case study of land use simulation of urban agglomeration of the Golden Triangle of Southern Fujian in 2030. Acta Ecol. Sin. 2019, 39, 4284–4298. [Google Scholar]
  53. Feng, Y.; Zhai, S.; Jiang, X.; Luo, J.; Yan, P.; Dong, C.; Han, J. Simulating spatio-temporal pattern of production-living-ecological space and evaluating eco-environmental effects in Zhongyuan Urban Agglomeration based on FLUS model and shared socioeconomic pathways. Acta Ecol. Sin. 2023, 43, 8292–8308. [Google Scholar]
  54. Yuan, S.; Tang, Y.; Shentu, C. Spatiotemporal Change of Land-Use Transformation and Its Eco-Environmental Response: A Case of 127 Counties in Yangtze River Economic Belt. Econ. Geogr. 2019, 39, 174–181. [Google Scholar]
  55. Dong, J.; Zhang, Z.; Da, X.; Zhang, W.; Feng, X. Eco-environmental effects of land use transformation and its driving forces from the perspective of production-living-ecologicalspaces: A case study of Gansu Province. Acta Ecol. Sin. 2021, 41, 5919–5928. [Google Scholar]
  56. Lu, L.; Zhou, S.; Zhou, B.; Dai, L.; Chang, T.; Bao, G.; Zhou, H.; Li, Z. Land Use Transformation and Its Eco-environmental Response in Process of the Regional Development:A Case Study of Jiangsu Province. Sci. Geogr. Sin. 2013, 33, 1442–1449. [Google Scholar]
Sustainability 16 10322 g001
Figure 1. The geographical location of the study area.
Figure 1. The geographical location of the study area.
Sustainability 16 10322 g001
Sustainability 16 10322 g002
Figure 2. Technology roadmap.
Figure 2. Technology roadmap.
Sustainability 16 10322 g002
Sustainability 16 10322 g003
Figure 3. Driving factors of PLES expansion in Sichuan Province.
Figure 3. Driving factors of PLES expansion in Sichuan Province.
Sustainability 16 10322 g003
Sustainability 16 10322 g004
Figure 4. Distribution of PLES in Sichuan Province under multiple scenarios in 2030.
Figure 4. Distribution of PLES in Sichuan Province under multiple scenarios in 2030.
Sustainability 16 10322 g004
Sustainability 16 10322 g005
Figure 5. Spatial changes in the PLES in Sichuan Province during 2020–2030.
Figure 5. Spatial changes in the PLES in Sichuan Province during 2020–2030.
Sustainability 16 10322 g005
Sustainability 16 10322 g006
Figure 6. Distribution of ecological environment quality in the PLES of Sichuan Province under different scenarios in 2030.
Figure 6. Distribution of ecological environment quality in the PLES of Sichuan Province under different scenarios in 2030.
Sustainability 16 10322 g006
Sustainability 16 10322 g007
Figure 7. Spatial changes in the ecological quality of the PLES in Sichuan Province under different scenarios from 2020 to 2030.
Figure 7. Spatial changes in the ecological quality of the PLES in Sichuan Province under different scenarios from 2020 to 2030.
Sustainability 16 10322 g007
Table 1. Main data and data sources.
Table 1. Main data and data sources.
Data TypeSpecific DataResolutionData Sources
Land use dataLand use data in Sichuan Province for the three periods 2000, 2010 and 202030 mResource and Environmental Science Data Platform
(https://www.resdc.cn, accessed on 28 December 2023)
Climate and
environmental
factors		DEM30 mGeospatial Data Cloud (www.gscloud.cn, accessed on 27 December 2023)
Slope30 m
Topographic relief30 m
NDVI 30 mNational Science and Technology Infrastructure (http://www.nesdc.org.cn/, accessed on 29 December 2023)
Temperature30 mEuropean Centre for Medium-Range Weather Forecasts Released ERA5-Land dataset (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=overview, accessed on 29 December 2023)
Precipitation30 m
Social and
economic factors		Population1 kmResource and Environmental Science Data Platform
(https://www.resdc.cn, accessed on 29 December 2023)
GDP1 km
Distance to highways1 kmOpenStreetMap (http://www.geofabrik.de/, accessed on 29 December 2023)
Distance to main roads1 km
Distance to expressways1 km
Table 2. Classification of PLES and its ecological environment quality index.
Table 2. Classification of PLES and its ecological environment quality index.
Production-Living-Ecological Spaces ClassificationSecondary Type Land Use ClassificationEEQI
1st Level ClassesSubclasses
Production space (PS)APSPaddy field, dry land0.267
IMPSOther construction land0.150
Living space (LS)ULSUrban land0.200
RLSRural residential areas0.200
Ecological space (ES)FESForest land, shrub land, open woodland and other forest land0.750
GESHigh coverage grassland, medium coverage grassland, low coverage grassland0.506
WESRiver canal, lake, reservoir pond, permanent glacial snow land, tidal flat, beach0.628
OESDesert, saline-alkali land, swamp, bare land, bare rock, other0.163
Table 4. Area of PLES in Sichuan Province in 2020 and 2030 (unit: km2).
Table 4. Area of PLES in Sichuan Province in 2020 and 2030 (unit: km2).
ScenariosPSLSES
APSIMPSULSRLSFESGESWESOES
Status of PLES in 2020area117,797.631869.681997.132378.35168,999.90170,562.164749.8817,677.07
percent24.24%0.38%0.41%0.49%34.77%35.09%0.98%3.64%
2030 Natural developmentarea116,490.372212.182320.522528.79168,898.88170,713.495045.4417,644.16
percent23.98%0.46%0.48%0.52%34.76%35.14%1.04%3.63%
2030 Production priorityarea117,946.081996.512040.062288.42168,912.34170,020.655043.9217,605.84
percent24.28%0.41%0.42%0.47%34.77%34.99%1.04%3.62%
2030 Living priorityarea116,296.742060.302333.442725.71168,859.69170,912.015022.5817,643.37
percent23.94%0.42%0.48%0.56%34.76%35.18%1.03%3.63%
2030 Ecological priorityarea114,840.001926.672106.892385.89173,280.21168,937.134751.9717,625.09
percent23.64%0.40%0.43%0.49%35.67%34.77%0.98%3.63%
Table 5. Transformation of major spatial types and their ecological contribution to the PLES in Sichuan Province during 2020–2030.
Table 5. Transformation of major spatial types and their ecological contribution to the PLES in Sichuan Province during 2020–2030.
ImprovementDegradation
ScenariosMain PLES ChangeECECRScenariosMain PLES ChangeECECR
natural developmentAPS—GES0.546 × 10−344.29%natural developmentGES—IMPS−0.27 × 10−330.03%
OES—GES0.235 × 10−319.05%GES—OES−0.223 × 10−324.80%
OES—WES0.171 × 10−313.84%GES—APS−0.191 × 10−321.16%
APS—WES0.124 × 10−310.07%GES—ULS−0.059 × 10−36.53%
production priorityOES—WES0.274 × 10−351.91%production priorityGES—OES−0.411 × 10−374.09%
OES—GES0.232 × 10−343.90%GES—APS−0.065 × 10−311.75%
GES—WES0.022 × 10−34.16%FES—APS−0.03 × 10−35.33%
OES—ULS0 × 10−30.02%WES—IMPS−0.027 × 10−34.83%
living priorityAPS—FES1.081 × 10−340.82%living priorityFES—GES−1.437 × 10−359.15%
GES—FES0.974 × 10−336.78%GES—IMPS−0.271 × 10−311.17%
OES—GES0.192 × 10−37.25%FES—APS−0.236 × 10−39.72%
APS—GES0.149 × 10−35.64%GES—OES−0.186 × 10−37.65%
ecological priorityAPS—FES2.161 × 10−359.63%ecological priorityAPS—IMPS−0.03 × 10−340.96%
GES—FES1.062 × 10−329.29%WES—GES−0.023 × 10−331.70%
APS—GES0.193 × 10−35.32%APS—RLS−0.014 × 10−319.49%
APS—WES0.085 × 10−32.34%APS—ULS−0.006 × 10−37.84%
In this table, we present the top four categories of spatial land use transitions that contribute the most to the changes in the ecological environment quality of the PLES within the study area, sorted from the largest to the smallest contribution.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Fu, Y.; Li, Q.; Li, J.; Zeng, K.; Wang, L.; Wang, Y. Multi-Scenario Simulation of the Production-Living-Ecological Spaces in Sichuan Province Based on the PLUS Model and Assessment of Its Ecological and Environmental Effects. Sustainability 2024, 16, 10322. https://doi.org/10.3390/su162310322

AMA Style

Fu Y, Li Q, Li J, Zeng K, Wang L, Wang Y. Multi-Scenario Simulation of the Production-Living-Ecological Spaces in Sichuan Province Based on the PLUS Model and Assessment of Its Ecological and Environmental Effects. Sustainability. 2024; 16(23):10322. https://doi.org/10.3390/su162310322

Chicago/Turabian Style

Fu, Yu, Qian Li, Julin Li, Kun Zeng, Liangsong Wang, and Youhan Wang. 2024. "Multi-Scenario Simulation of the Production-Living-Ecological Spaces in Sichuan Province Based on the PLUS Model and Assessment of Its Ecological and Environmental Effects" Sustainability 16, no. 23: 10322. https://doi.org/10.3390/su162310322

APA Style

Fu, Y., Li, Q., Li, J., Zeng, K., Wang, L., & Wang, Y. (2024). Multi-Scenario Simulation of the Production-Living-Ecological Spaces in Sichuan Province Based on the PLUS Model and Assessment of Its Ecological and Environmental Effects. Sustainability, 16(23), 10322. https://doi.org/10.3390/su162310322

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop