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Journal of Geographical Sciences >

2023 , Vol. 33 >Issue 2: 373 - 391

DOI: https://doi.org/10.1007/s11442-023-2087-9

Special Issue: Land for High-quality Development

Simulation and prediction of multi-scenario evolution of ecological space based on FLUS model:A case study of the Yangtze River Economic Belt, China

  • LIU Xiaoqiong , 1, 2 ,
  • WANG Xu 3 ,
  • CHEN Kunlun , 1, 2, 4, * ,
  • LI Dan 5
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  • 1. School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
  • 2. China Institute of Mountaineering and Outdoor Sports, Wuhan 430074, China
  • 3. School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
  • 4. School of Physical Education, China University of Geosciences, Wuhan 430074, China
  • 5. Institute of Geography and Tourism, Guangdong University of Finance & Economics, Guangzhou 510320, China
*Chen Kunlun (1982-), Professor, specialized in urban geography and urban planning, sports geography. E-mail:

Liu Xiaoqiong (1992-), specialized in environmental planning and design. E-mail:

Received date: 2021-09-03

  Accepted date: 2021-12-07

  Online published: 2023-02-21

Supported by

The Fundamental Research Funds for the Central Universities, China University of Geosciences(Wuhan)(CUG2018123)

Copyright

© 2023
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Abstract

Building the Yangtze River Economic Belt (YREB) is one of China’s three national development policies in the new era. The ecological environment of the Yangtze River Economic Belt must be protected not only for regional economic development but also for regional ecological security and ecological progress in this region. This paper takes the ecological space of the Yangtze River Economic Belt as the research object, based on land use data in 2010 and 2015, and uses the FLUS model to simulate and predict the ecological space of the research area in 2035. The variation of the research area’s ecological space area and its four sub-zones has remarkable stability under diverse situations. Both the production space priority scenarios (S1) and living space priority scenarios (S2) saw a fall in ecological space area, with the former experiencing the highest reduction (a total reduction of 25,212 km2). Under the ecological space priority scenarios (S3) and comprehensive space optimization scenario (S4), the ecological space area increased, and the ecological space area expanded even more under the former scenario (a total growth of 23,648 km2). In Yunnan-Guizhou, the ecological space is relatively stable, with minimal signs of change. In Sichuan-Chongqing, the Sichuan Basin, Zoige Grassland, and Longmen Mountains were significant regions of area changes in ecological space. In the middle reaches of the Yangtze River, the ecological space changes mainly occur in the Wuyi Mountains, Mufu Mountains, and Dabie Mountains, as well as the surrounding waters of Dongting Lake. The Yangtze River Delta’s changes were mainly observed in the eastern Dabie Mountains and Jianghuai Hills.

Key words： ecological space; multi-scenario simulation; prediction; FLUS model; Yangtze River Economic Belt; extensive protection of the Yangtze River

Cite this article

LIU Xiaoqiong , WANG Xu , CHEN Kunlun , LI Dan . Simulation and prediction of multi-scenario evolution of ecological space based on FLUS model:A case study of the Yangtze River Economic Belt, China[J]. Journal of Geographical Sciences, 2023 , 33(2) : 373 -391 . DOI: 10.1007/s11442-023-2087-9

1 Introduction

Rapid human activity has caused ecological space to be continuously encroached upon and ecological functions to be continuously degraded, affecting the sustainable development of China’s ecological environment. With the accelerated industrialization and urbanization, a number of problems such as ecological damage, environmental pollution, and disorderly development of national land space have come to the fore (Jin et al., 2019; Gao et al., 2021). In the face of China’s future development challenges, such as increased environmental pollution and ecosystem degradation, the 18th National Congress of the Communist Part of China in 2012 proposed optimizing the spatial pattern of land as the primary task of ecological civilization construction. The current reform of land space management requires the formation of a competitive and sustainable spatial pattern of land and the transformation of land space development from production space-led to “production-living-ecology” space coordination, that is, to optimize the “production-living-ecology” space to achieve the harmonious coexistence of human and nature (Liu, 2017; Lan et al., 2018). The preservation and modification of ecological space is an important part of the process of optimizing the “production-living-ecology” space to address the problem of human and land use (Wang et al., 2020).
In the Western world, “ecological space” is derived from the term “green space”. Landscape ecology, ecosystems, and urban planning are the primary fields of study on the topic of ecological space in other countries. The study of ecological space patterns, which describes (Olehowski, 2008) the spatial shape characteristics of the ecosystem itself and the spatial distribution laws of distinct ecosystems (Syphard and Franklin, 2004), is the main focus of ecological space research in the field of landscape ecology. The main object of study in common ecosystem research is to adjust and control features and shapes optimization, primarily through the ecological system’s time and space structure evolution analysis, comprehensive understanding of the ecological system’s spatial distribution pattern and their interaction process, and the number of types of ecosystems to adjust and control features and shapes optimization, in order to realize the greatest ecological benefit (Gerling, 2019; Ingrid, 2019). Relevant studies in the field of urban planning, on the other hand, focus on examining the development of green space from the perspective of cities, as well as residents’ perception and use of green space, as well as the interaction between residents and green space (Tim et al., 2020), arguing that urban green space provides ecological services for residents (Marisa, 2018), promotes urban public health development, maintains social sustainable development, and has positive significance for urban and community construction (Popham, 2007; Roe et al., 2013; Aram et al., 2019), whose distribution reflects fairness and justice (Wolch and Byrne, 2014; Rutt and Gulsrud, 2016). Meanwhile, other researchers believe that urban green space has a significant impact on inhabitants’ physical and mental health (Matthew, 2017), but that it can also have negative consequences such as disease and crime (Bogar and Beyer, 2016).
Ecological space refers to national land space with natural attributes and whose main function is to provide ecological services or products (http://www.xinhuanet.com/legal/ 2017-02/08/c_1120429292.htm) (Hu et al., 2019), including forests, grasslands, wetlands, rivers, lakes, mudflats, shorelines, oceans, wastelands, deserts, Gobi, glaciers, alpine tundra, and uninhabited islands, according to China’s Several Opinions on Delineating and Strictly Observing the Red Line of Ecological Protection. The classification of ecological space, pattern evolution of ecological space, and management of ecological space are the three dimensions on which related studies on ecological space are generally undertaken. Ecological space is classified primarily based on the current condition of land use and land cover, with spatial analysis and factor analysis identifying many ecological space categories (Chi et al., 2018; Xie et al., 2018). The patch-corridor-matrix principle, ecological suitability and sensitivity theory, landscape indices, and other landscape ecology ideas and tools are commonly used in studies on the pattern evolution of ecological space (Jin et al., 2018; Li et al., 2018; Zou et al., 2020). Landscape indices, for example, are used to investigate the inter-annual variations and spatial heterogeneity patterns of ecological space using multi-period remote sensing images/land use status data. Furthermore, some studies employ the ecological space theory to propose feasible solutions for regulating ecological space uses, to determine the scope of ecological space protection zoning based on the importance of ecosystem service functions and ecological sensitivity, to make targeted regulatory recommendations, and to obtain preliminary research results (Liang et al., 2018; Peng et al., 2019; Huang et al., 2020).
At present, domestic and foreign research on ecological space mainly focuses on the analysis of the current situation because the evolution of ecological space is determined by the dynamic evolution of each land type within the “production-living-ecology” space and the elements within the ecological space, which have the characteristics of spatial and temporal heterogeneity and multidimensionality, which leads to a great deal of uncertainty about the future evolution of ecological space. However, using ecological space to analyze the current situation does not fully reflect the evolution mechanism of ecological space. Therefore, research on the simulation and prediction of ecological space under multi-scenario models is urgent and necessary, as it can provide complementary and necessary proof for the evaluation of ecological space status as well as reveal the complete pattern of ecological space evolution from a long time series scale.
The YREB is not only one of the most important regions in China’s territorial space development but also an important core area in China’s ecological space layout and plays a key strategic role in China’s overall regional development pattern (Ha et al., 2020; Zheng et al., 2020). While pursuing spatial development and economic growth, China’s spatial development strategy places more emphasis on the carrying and supporting capacities of resources and the environment as the “Yangtze River Economic Belt Strategy” becomes part of the national strategy of the Belt and Road Initiative (Lan et al., 2018; Chen et al., 2020). In 2019, the state policy proposes to realize the sustainable spatial pattern of the national land so that by 2035, the space for production is used intensively and efficiently, the living space is livable and proper in size, and the ecological space is unspoiled and beautiful. Under such conditions, an optimization simulation study of the ecological space can improve the ecological protection red line’s scientific and targeted delineation results, establish differentiated regional ecological protection actions and rules for regulating uses, achieve the rebuilding of spatial order and pattern optimization, and have a positive impact on the enhancement of regional ecological protection functions. The YREB is especially important for protecting the ecological environment and repairing the ecological space of this belt because it is an inland river economic belt with global influence, a coordinated development belt for East-Middle-West interaction and cooperation, a coastal and riverside belt for domestic and international opening, and a pilot demonstration economic belt for ecological civilization construction.
Based on the LUCC data in 2010 and 2015, this study simulates and predicts the ecological spatial quantity and distribution patterns of the YREB under different development scenarios in twenty years (2035) from different scales. On the one hand, it is hoped that the simulation and prediction of ecological space (or land use) at the watershed and economic belt scales can effectively enrich the scale connotation of the current ecological space research, while the related research can help enrich the content of the traditional ecological space research framework and help improve the theory of optimal development of national land space. On the other hand, the simulation of ecological space in the YREB can provide a more scientific understanding of the spatial and temporal evolution pattern and the promotion mechanism of ecological space in the study area, thus providing an effective reference for future territorial spatial planning, ecological civilization construction, Yangtze River protection, and even sustainable economic development in the region.

2 Materials and methods

2.1 Study area

The YREB (21°8°35''-35°7°34''N, 97°21°6''-122°8°18''E), which stretches along the Yangtze River from Shanghai, Jiangsu, and Zhejiang in the east to Yunnan and Guizhou in the west, includes 11 provinces and municipalities, straddles the three parts of China from east, middle, and west, and covers an area of about 205 km2, and thus plays a critical strategic role in China’s regional development (Figure 1). This zone has more than 40% of the nation’s population and economic output with 21% of the national land area. It is one of the development axes in the “two verticals and three horizontals” pattern (http://www.gov.cn/zhuanti/xxczh/) (Li et al., 2018; Jin et al., 2018) of China. This zone generated 45,780.517 billion yuan in GDP in 2019, accounting for 46.24% of the country’s overall GDP. It has already established itself as one of China’s most powerful areas in terms of overall strength and strategic support.
Figure 1 Location and land use of the Yangtze River Economic Belt
Due to natural conditions such as complex and diverse landforms and climates and the intervention of human activities such as dense population, economic distribution, and massive resource consumption, the core problems faced by the ecological environment in the YREB include the increasing threat of disasters, aggravating environmental pollution, and accelerated ecological degradation. The ecological security pattern is confronted with serious challenges (Luo et al., 2020). During the past two decades, the ecosystem pattern of the YREB has changed dramatically in the Yangtze River Delta, where nearly 20% of the ecosystem types have changed, the area of farmland, forests, grasslands, water, wetlands, and other ecosystems has decreased to varying degrees, and the area of farmland, wetlands, and forests encroached upon by urban expansion from 2000 to 2009 exceeded 1000 km2, roughly equivalent to the size of the Hong Kong Region in China.
Due to the complexity and diversity of the natural and social conditions in the YREB, the research area has been divided into four sub-zones according to the most authoritative classification in China: Yunnan-Guizhou, Sichuan-Chongqing, the middle reaches of the Yangtze River (Hubei, Hunan, and Jiangxi), and the Yangtze River Delta (Shanghai, Jiangsu, Zhejiang, and Anhui), in order to analyze the ecological and spatial simulation in this zone in a targeted and practical manner. This study involves simulation prediction and analysis for each of the four sub-zones to ensure scientific accuracy and precision.

2.2 Data sources

The following data was used in this study (Table 1): (1) administrative boundary data for the YREB; (2) historical land use data, largely including the land use data of the YREB in 2010 and 2015; (3) data on natural features including topography (digital elevation model, slope direction, slope), soil (farmland production potential, root oxygen utilization rate, nutrient availability), meteorology (annual precipitation, annual evaporation, annual average temperature) and vegetation (vegetation coverage, primary productivity of vegetation) to drive the changes of the “production-living-ecology” space in the YREB; (4) socio-economic data, including human activity indicators such as population, gross national product, lights at night, city location and road network to drive the changes that characterize the “production- living-ecology” space. The data are mainly sourced from the CAS website (http://www. resdc.cn), Harmonized World Soil Database v 1.2 (http://webarchive.iiasa.ac.at/Research), WorldClim version 2.0 (http://www.worldclim.org/) and the official website of the National Bureau of Statistics (http://www.stats.gov.cn/).
Table 1 Data source information
Data type Data name
Basic data LUCC Land use data (2010, 2015)
Administrative boundaries Administrative boundaries of the Yangtze River Economic Belt, and provinces in the zone
Driving factors Natural factors Digital elevation model, slope, slope direction, farmland potential productivity, root oxygen availability, nutrient availability, annual precipitation, annual evaporation, average annual air temperature, vegetation cover, plant primary productivity
Socio-economic factors Population, GDP, nighttime lights, city location, road network

2.3 Research methodology

(1) Model selection and presentation
The FLUS model was constructed by integrating the Artificial Neural Networks (ANNs) algorithm and the Roulette Wheel Selection mechanism, which are both based on the System Dynamics (SD) model and the Meta-Cellular Automata model (Li et al., 2017; Liu et al., 2017). Multiple driving aspects, such as socioeconomic and the natural environment, are integrated in the FLUS model, which employs a neural network algorithm approach to analyze land use data from the base era. This study uses training to obtain the conversion probabilities of various land use types in the study area, then combines the conversion probabilities, neighborhood factor, and conversion cost to derive the overall conversion probability, and finally predicts the future demand for various land use types based on previous year’s land use data. Finally, the final simulation results that approximate the land use type goal are produced within a given number of conversions utilizing the Roulette Wheel Selection mechanism. To confirm the correctness of the result, the simulation accuracy is validated using the Kappa coefficient with the simulated and predicted land use data. To assess the conversion probability of land pixels and create more accurate land use type forecast data, the model can combine socio-economic and natural environmental components with land use data from the base period.
(2) Model parameter setting
① Total number of future pixels
Based on the land use data from 2015, the Markov Chain (Li et al., 2017; Liu et al., 2017) is used to calculate the total number of pixels for each land use type and predict the total number of pixels for different land use types in the YREB in 2035:
S(t+1) = Pab×S(t)
where S(t), S(t+1) are the state matrices of the land use types in the study area at t, t+1; Pab denotes the transition probability matrix of the conversion from type a to type b.
② Neighborhood factor parameters
With a threshold value range of [01], neighborhood factor parameters refer to the expansion strength of a land type, or the capacity of each land type to expand itself in response to external factors. The value closer to 1 denotes the stronger expansion capacity of the land type (Liu et al., 2017). The development of land use types is often represented by an increase in the number of sectors as well as an increase in area. The expansion capacity of construction land > unused land > waters > grassland = farmland > forest land in descending order, based on existing authoritative studies and the actual land use status in the YREB and its four sub-zones. Construction land has the largest expansion capacity, while forest land has the lowest, and the parameters are set at 0.95 and 0.15, respectively, due to the influence of human activities (Table 2). When the combined effects of natural and social driving forces are taken into account, unused land has a moderate expansion capacity, whereas other types of land are allocated based on the current circumstances. Furthermore, the domain factor parameters are chosen based on the natural and social circumstances of the four sub-zones of the YREB.
Table 2 Neighborhood factor parameters (Wk)
Land use type Farmland Forest land Grassland Waters Construction land Unused land
YREB (Wk) 0.2 0.15 0.2 0.4 0.95 0.5
Sichuan-Chongqing (Wk) 0.2.5 0.15 0.3 0.3 0.90 0.50
Middle reaches of the Yangtze River (Wk) 0.3 0.2 0.2 0.4 0.95 0.55
Yangtze River Delta (Wk) 0.3 0.15 0.2 0.4 1 0.45
Yunnan-Guizhou (Wk) 0.2 0.15 0.25 0.3 0.90 0.50
③ Cost matrix and restricted development area
The cost matrix, also known as the rules of change between different land types, is used to determine if certain land types may be converted into each other (Deng et al., 2016). Theoretically, land conversion across land types should theoretically be unrestricted. When combined with the reality of land use changes, present economic and technological conditions can turn any land type into construction land. Converting construction land to other land types, on the other hand, is highly complex and expensive. In reality, such circumstances are quite prevalent. As a result, this study has made the temporary decision that construction land will not be converted to other land types as much as possible (Table 3). However, because it is still impossible to objectively determine whether there are mutual conversion relationships among waters and beaches, farmland, forest land, grassland, and other land types, so the cost matrix parameter settings of the model only limit the conversion of construction land to other land types, but not the mutual conversion between the other land types.
Table 3 Conversion cost matrix
a b c d e f a b c d e f a b c d e f a b c d e f
A 1 0 0 0 0 0 1 0 0 0 1 0 1 1 1 0 0 0 1 1 1 1 0 0
B 1 1 1 0 1 0 1 1 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0
C 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 0 0 1 1 1 1 1 0
D 1 0 1 1 1 1 1 1 1 1 1 0 0 1 0 1 0 0 1 0 1 1 1 0
E 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0
F 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1
S1 S2 S3 S4

Note: a, b, c, d, e, and f represent “farmland”, “forest land”, “grassland”, “water”, “construction land”, and “unused land”, respectively. (0 means not being able to convert, 1 means conversion permitted)

(3) Multi-scenario simulation of land use types in 2035
The FLUS model’s adaptive inertial competition CA based on roulette selection is a critical module. It can be used in conjunction with neighborhood factors and conversion rules to provide an acceptable spatial distribution of the total number of pixels for various land use types based on the suitability probability distribution of each land type and to mimic land use changes. Finally, to estimate the overall conversion probability of the units occupied by a certain land use type, the following formula is used:
$TP_{p,k}^{t}={{P}_{p,k}}\times \Omega _{p,k}^{t}\times I_{k}^{t}\times s{{c}_{c\to k}}$
where $TP_{p,k}^{t}$ denotes the overall conversion probability of a cell unit p from the original land use type to the land use type k at the time t; ${{P}_{p,k}}$ means the suitability probability of converting a cell unit p to convert to the land use type k; $\Omega _{p,k}^{t}$ denotes the neighborhood impact factor; $I_{k}^{t}$ is the adaptive inertia coefficient; and $s{{c}_{c\to k}}$ means the conversion cost of converting the land use type c to the land use type k.

3 Results analysis

3.1 Simulation accuracy verification analysis

The Markov model was used to predict the land demand for the “production-living-ecology” space in 2015 using land use data from the YREB in 2010. The scenario conversion cost matrix for comprehensive spatial optimization is then used to apply the demand to the FLUS model and simulate the land use prediction results of the YREB’s “production-living-ecology” space in 2015, which are compared with the actual land use data in 2015. It can be observed that the FLUS model’s simulation results of the “production-living-ecology” space of cities are largely congruent with real data (Figure 2).
Figure 2 Comparisons of actual and simulated land use in the Yangtze River Economic Belt from 2010 to 2015
To further check the accuracy of the simulation findings of the FLUS model, the overall accuracy and Kappa coefficient are introduced to analyze the simulation accuracy. According to existing research, the better the simulation’s accuracy, the closer the total accuracy and Kappa coefficient are to 1, and the optimum accuracy is attained when the Kappa coefficient varies between 0.8 and 1. In order to increase the accuracy of the simulation results in this study, the simulation accuracy was tested for the four sub-zones of the YREB. The calculated overall accuracy of the four sub-zones is above 0.93 and the Kappa coefficient is above 0.95 (Table 4), indicating that the FLUS model used in this work can reach a high level of accuracy and accurately reflect the development status of the “production-living-ecology” space in the YREB.
Table 4 Accuracy of simulation scenario Kappa in 2015
Kappa accuracy Overall accuracy
Yangtze
River
Delta		 Middle reaches
of the Yangtze
River		 Sichuan-
Chongqing		 Yunnan-
Guizhou		 Yangtze
River Delta		 Middle reaches
of the Yangtze
River		 Sichuan-
Chongqing		 Yunnan-
Guizhou
S1 0.955443 0.96713 0.986221 0.98676 0.97132 0.981493 0.990421 0.992112
S2 0.959168 0.966249 0.982045 0.98473 0.973503 0.980979 0.987488 0.990876
S3 0.970615 0.973749 0.9863 0.986184 0.980924 0.985184 0.990475 0.991736
S4 0.93099 0.964735 0.957676 0.981285 0.955018 0.98018 0.970522 0.988868

3.2 General characteristics of the ecological space of the YREB

The YREB’s ecological space covered 1,351,776 km2 in 2015, accounting for 66% of the entire area. The middle reaches of the Yangtze River, Sichuan-Chongqing, and Yunnan-Guizhou all have more than 65% of ecological space, with Yunnan-Guizhou having the highest rate of 78.3%. In comparison to the other three sub-zones, the Yangtze River Delta’s ecological space accounted for only 38.8% of the total area (Table 5). In terms of spatial distribution patterns, ecological space is primarily found in the Hengduan Mountains, the Western Sichuan Plateau, and the Yunnan-Guizhou Plateau in the upper reaches, and the Qinba Mountains, Western Hubei Mountains, Dabie Mountains, and Wuyi Mountains in the middle and lower reaches, which are less developed and have better environmental protection, as well as natural waters, such as Dongting Lake and Poyang Lake.
Table 5 The current situation and simulation of “production-living-ecology” space area in the Yangtze River Economic Belt (km2)
Sichuan-Chongqing Middle reaches of the Yangtze River Yangtze River Delta Yunnan-Guizhou
2015
Area
Status		 Ecology space 403308 (71.3%) 376045 (66.7%) 135565 (38.8%) 436858 (78.3%)
Production space 156153 (27.6%) 171658 (30.5%) 173588 (49.7%) 116465 (20.9%)
Living space 6396 (1.1%) 15880 (2.8%) 40323 (11.5%) 4519 (0.8%)
S1 Ecology space 396450 (70.1%) 366677 (65%) 131248 (37.6%) 432460 (77.5%)
Production space 162588 (28.7%) 178527 (31.7%) 190116 (54.4%) 120645 (21.6%)
Living space 6819 (1.2%) 18379 (3.3%) 28112 (8%) 4737 (0.9%)
S2 Ecology space 396897 (70.1%) 366125 (65%) 131244 (37.6%) 432298 (77.5%)
Production space 160903 (28.5) 167893 (29.8%) 164949 (47.2%) 114805 (20.6%)
Living space 8057 (1.4%) 29565 (5.2%) 53283 (15.2%) 10739 (1.9%)
S3 Ecology space 408751 (72.2%) 385295 (68.4%) 140563 (40.2%) 440815 (79%)
Production space 150710 (26.7%) 162391 (28.8%) 168588 (48.2%) 112506 (20.2%)
Living space 6396 (1.1%) 15897 (2.8%) 40325 (11.6%) 4521 (0.8%)
S4 Ecology space 404601 (71.5%) 382413 (67.9%) 139649 (40%) 440134 (78.9%)
Production space 154583 (27.3%) 163351 (29%) 162550 (46.5%) 112587 (20.2%)
Living space 6672 (1.2%) 17819 (3.1%) 47277 (13.5%) 5121 (0.9%)
The modeling results (Table 5) demonstrate that under S1 and S2, the YREB’s ecological space will shrink dramatically by 2035, whereas it will expand under S3 and S4. Except for S1, the production space decreases under the other three scenarios, with the greatest reduction under S3 and S4. The living space shows an increasing trend under the other three scenarios, except for a small decrease under S1. In terms of spatial change patterns, the ecological space in the upper reaches does not show obvious changes, but the changes in the middle and lower reaches are visible, especially in regions with intensive human activities, such as the Wuhan City Circle, Nanyang Basin, Changsha-Zhuzhou-Xiangtan Ring City Cluster, Nanchang Metropolitan Area, Yangtze River Delta City Cluster, and Huaihai Economic Zone.

3.3 Production priority scenario

Because farmland is the most important land use type in S1, basic farmland protection has been added as a constraint in this simulation scenario process to strictly limit the conversion of production space to living space and ecological space, and raise its conversion cost to reduce the conversion probability of the production space and thus realize the production priority principle. The Sichuan Basin, the middle-lower Yangtze River Plain, and the Huang-Huai-Hai Plain are among the YREB’s basic farmland protection regions, which are predominantly concentrated on contiguous farmland. Under the influence of the basic farmland protection policy, the four sub-zones of the YREB are mostly characterized by the conversion of ecological space and living space to production space, mostly ecological space to production space, and the degree of replacement for ecological space in the middle reaches of the Yangtze River and the Yangtze River Delta is greater than that for ecological space in Sichuan-Chongqing and Yunnan-Guizhou in the upper reaches under the influence of the basic farmland protection policy.
In terms of quantitative changes, the ecological space in the study area under S1 has decreased in all four sub-zones, with the highest decrease of 1.7% in the middle reaches of the Yangtze River, which has a high proportion of basic farmland area, and the lowest decrease of 0.8% in Yunnan-Guizhou, which has the best ecological environment endowment. The ecological space in Sichuan-Chongqing and the Yangtze River Delta has decreased by 1.2%. Under this scenario, the middle reaches of the Yangtze River and Yunnan-Guizhou are primarily transferred out of the ecological space, with the former shifting primarily to the production space and partially to the living space, and the latter shifting primarily to the production space. The transition in Sichuan-Chongqing and the Yangtze River Delta is complicated, with the expansion of the production space not only pointing to the ecological space but also occupying the living space. The growth of production space not only points to ecological space, but also occupies living space, with the quantity of occupied living space in the Delta being the most relevant in this scenario (12,211 km2).
In terms of geographical alterations (Figure 3), ecological space in the Yangtze River Delta will be converted to production space, primarily in the Dabie Mountains, with the replaced ecological space being near to the original production space; the conversion of living space to production space will primarily occur in the Huang-Huai-Hai Plain and the middle-lower Yangtze River Plain. Ecological space in the middle reaches of the Yangtze River is primarily converted to production space in the Poyang Lake Plain and Jianghan Plain, which are the sub-commercial zone’s grain bases, while a small amount of ecological space is also converted to production space in the surrounding mountains, and ecological space is converted to living space in areas along lakes and rivers. The Sichuan Basin and Daba Mountains in northwest Sichuan will see the most conversion from ecological space to production space, while the Zoige Grassland and Panzhihua in northwest Sichuan will see the most conversion from ecological space to living space. The conversion of ecological space to production space in Yunnan-Guizhou will be concentrated mostly in Guiyang and its surroundings.
Figure 3 Spatial simulation results of production priority scenario in the Yangtze River Economic Belt
In general, ecological space will still occupy 65.14% of the total area of the YREB in 2035 under S1, with the greatest increase in production space compared to other control circumstances. The vast majority of the conversions will be from ecological space to production space, with some ecological space also being converted to living space. It can be seen that once other space types have occupied production space, the YREB’s requirement for urban growth can only be relocated to ecological space, which will become the primary source for conversion to living space and production space. According to the spatial change pattern, the regions where ecological space is converted into production space under S1 will be concentrated on the commodity grain bases in the four sub-zones, such as the Sichuan Basin in Sichuan-Chongqing, the Poyang Lake Plain, and the Jianghan Plain in the middle reaches, and the plains in the middle and lower reaches of the Yangtze River, while ecological space will be converted into living space along rivers (tributaries of the Yangtze River) and major transportation routes, primarily because these regions have good social conditions for economic development, and future city expansion under this scenario will be forced to occupy the surrounding ecological land. In summary, under S1, the YREB’s production development and city expansion will consume a huge amount of ecological space in the zone, negatively impacting the environment and jeopardizing the ecological sustainability of the communities.

3.4 Living priority scenario

The conversion of living space to other space types is restricted according to the level of city development needs in the simulation of S2, and the conversion probability of other space types to living space is increased appropriately to realize the living priority. The YREB is primarily characterized by the conversion of ecological space to production space and production space to living space. City expansion primarily encroaches on production space, while insufficient production space tends to occupy ecological space, and finally, city expansion will have negative effects on both production space and living space in this scenario.
In terms of quantitative changes, the trend and proportion of ecological space area turned out in the study region under S2 are remarkably comparable to those under S1, with the exception that the turning out direction differs. From the perspective of the transformation of the “production-living-ecology” space, Sichuan-Chongqing and Yunnan-Guizhou mainly shift out of ecological space, with a large amount of ecological space shifting to living space and a small amount shifting to production space in the former, and ecological space mainly pointing to living space and a very small amount of production space pointing to living space in the latter. The transformation in the Yangtze River Delta and the middle reaches of the Yangtze River is more intricate, primarily demonstrating a shift from ecological space to production space, and from production space to living space.
In terms of spatial changes (Figure 4), ecological space in the Yangtze River Delta will continue to be converted to production space, mostly in the Dabie Mountains. However, under S1, the regions where production space is converted to living space will largely overlap with regions where living space is converted to production space, particularly in the Huang-Huai-Hai Plain and the plains in the middle and lower reaches of the Yangtze River. In the middle reaches of the Yangtze River, the regions where ecological space is transformed into production space will be dispersed among the mountains around the middle reaches. As evidenced by the encroachment of water bodies by construction land expansion, a small number of regions where ecological space is directly converted to living space will be distributed in the Hanjiang River basin and Wuhan City Circle. The Sichuan Basin will see the most dramatic conversion of ecological space to production space in Sichuan-Chongqing region. The increase in living space in Chengdu, one of the main cities in the Chengdu-Chongqing City Cluster, is enormous and extensive, consuming a wider area of ecological space. Whereas in Yunnan-Guizhou, the regions where ecological space is converted to living space will be centered mostly in Yunnan’s Kunming, Yuxi, Qujing, Lijiang, and Dali, with Guizhou’s Guiyang and Bijie playing a smaller role.
Figure 4 Spatial simulation results of living priority scenario in the Yangtze River Economic Belt
In general, the percentage of ecological space in the YREB in 2035 will be 65.13% under S2, the most significant growth in living space compared to other control conditions, and both ecological space and production space are sources of conversion to production space. It is worth noting that in this scenario, some regions will be embodied in the conversion of ecological space to production space and production space to living space, resulting in an indirect conversion from ecological space to living space (typically in the middle reaches of the Yangtze River). Therefore, cities keep growing at the expense of the environment. According to the spatial change patterns, regions where ecological space is converted to living space will be predominantly concentrated around the key cities of each sub-zone (such as Chengdu in Sichuan-Chongqing) under S2, whereas the regions where production space is converted to living space will be mainly in the plains (such as the Huang-Huai-Hai Plain) and around the core city clusters of each sub-zone (such as Wuhan City Circle) because these regions are well developed and can better radiate the surrounding areas, thus promoting the expansion of living space. This is also consistent with the long-term development strategy of the YREB, which intends to further enhance the comprehensive carrying capacity and service capability of Wuhan, Changsha, and Nanchang and build the city clusters.

3.5 Ecology priority scenario

The main purpose of S3 is to prevent further encroachment on ecological space, and the appropriate land types will not be reduced further. Meanwhile, the conversion of other land types into ecological space is supported and pushed due to the necessity to repair the environment. As a result, the YREB’s natural reserves are used as a constraint in the simulation, ensuring that the conversion of ecological space-related land to living space and production space-related land is strictly controlled and that the conversion of production space and living space-related land to ecological space is appropriately increased. In this scenario, production space in all four sub-zones of the YREB is converted to ecological space, and living space in these four sub-zones continues to expand to varying degrees. Due to the irreversible urban growth process and the high cost, as well as the difficulty of recovering land for construction, the ecological restoration in the YREB is primarily centered on farmland.
The ecological space in the research area under S3 expanded in all four sub-zones, with the middle reaches of the Yangtze River (1.7%) and Yangtze River Delta (1.4%) showing the most growth, followed by Sichuan-Chongqing (0.9%) and Yunnan-Guizhou (0.7%). In terms of the “production-living-ecology” space, all four sub-zones in this scenario show a shift from production to ecological space, with a small amount of production space converted to living space in the Yangtze River Delta and Yunnan-Guizhou but no obvious change in living space in the middle reaches of the Yangtze River and Yunnan-Guizhou.
In terms of spatial changes (Figure 5), the Yangtze River Delta’s production space will primarily be converted to ecological space in the east part of the Dabie Mountains and Jianghuai Hills, with a small amount of production space being converted to ecological space in the middle-lower Yangtze River Plain. Western Hubei, Western Hunan, the Wuyi Mountains, Mufu Mountains, Dabie Mountains, Jianghan Plain, Dongting Lake Plain, Poyang Lake Plain, and other sites in the Yangtze River’s middle reaches are the most common places where production space is converted to ecological space. Production space will be converted to ecological space in Sichuan-Chongqing region, mostly in sparsely populated regions such as the Qinba Mountains and the Western Sichuan Plateau, as well as near rivers and lakes (such as the Jialing River Basin). In Yunnan-Guizhou, ecological space shows a trend of decentralized expansion, but only on a local scale and without any obvious spatial manifestation.
Figure 5 Spatial simulation results of ecology priority scenario in the Yangtze River Economic Belt
In general, the ecological space area in the YREB will account for 67.53% of the total area in 2035 under S3, and will show the greatest increase when compared to other control circumstances. Under such a scenario, the transfer will primarily be from production space to ecological space, and living space will remain largely constant with few modifications. It can be observed that in S3, ecological space expansion based on demand for environmental preservation and ecological restoration would predominantly utilize production space, with little impact on living space. The city’s expansion will be irreversible, and changing the living space once it has been constructed would be difficult and costly. In terms of spatial change patterns, the regions where production space is converted to ecological space under S3 will primarily be found in mountainous regions and near rivers and lakes, particularly in the Yangtze River’s upper reaches. This is owing to the national policy direction of ensuring ecological security in the upper reaches of the Yangtze River.

3.6 Comprehensive optimization scenario

Many needs of the “production-living-ecology” space are stressed more in S4, leading to more intricate land use modification and more frequent mutual conversions of the “production-living-ecology” space, aligning with the comprehensive development trend of future land space and ecological space. With the goals of implementing the Yangtze River’s Extensive Protection Strategy and assuring the YREB’s sustainable growth, the YREB’s development under S4 takes into account the harmony between human and land, as well as the concept of regional green development. In this scenario, the transition from production space to ecological space and living space characterizes the entire YREB. The expansion of ecological space in Sichuan-Chongqing and the middle reaches of the Yangtze River is more pronounced due to the good ecological foundation than in Yunnan-Guizhou, which has a fragile ecological environment, and the Yangtze River Delta, which has a relatively small scale of ecological space, but the expansion of living space in the Yangtze River Delta with the most developed urbanization has the most significant expansion of living space.
The ecological space of the study area under S4 shows growth in all the four sub-zones, with the middle reaches of the Yangtze River and the Yangtze River Delta having the highest growth ratio of 1.2%, while Sichuan-Chongqing and Yunnan-Guizhou have lower growth ratios of 0.2% and 0.6%, respectively. In terms of changes in the “production-living- ecology” space, the Yangtze River Delta and the middle reaches of the Yangtze River have shifted from production space to ecological space and living space, while the Sichuan-Chongqing and Yunnan-Guizhou have shifted from production space to ecological space. Living space has also increased in this scenario, but the changes in area are minor.
In terms of spatial changes (Figure 6), production space in the Yangtze River Delta will primarily be converted to ecological space in the eastern Dabie Mountains and Jianghuai Hills, while production space will primarily be converted to living space in the Suzhou- Wuxi-Changzhou City Cluster, occupying a portion of waters and other ecological space. The conversion of production space to ecological space will primarily occur in the Wuyi Mountains, Mufu Mountains, and Dabie Mountains in the Yangtze River’s middle reaches, with only a few exceptions in the Dongting Lake Plain and Poyang Lake Plain. In terms of spatial changes, production space in Sichuan-Chongqing will primarily be converted to ecological space in the Jinjiang and Jialing River basins, as well as in Zigong, Yibin, and Neijiang in Sichuan, while production space will primarily be converted to living space near the Jialing River basin. In Yunnan-Guizhou, the ecological space reveals a pattern of widespread expansion, albeit on a small scale and with limited spatial embodiment. The expansion of living space, on the other hand, will be centered in Kunming and Guiyang, the capital cities of two provinces in the sub-zone.
Figure 6 Spatial simulation results of comprehensive optimization scenario in the Yangtze River Economic Belt
In general, the ecological space area in the YREB will account for 67.11% of the total area in 2035, owing mostly to the conversion of production space to ecological space and living space under S4. The upper reaches of the Yangtze River, such as Yunnan-Guizhou and Sichuan-Chongqing, will experience fewer changes than the Yangtze River’s middle reaches and the Yangtze River Delta, which will experience more developed urbanization. In regions where production space is divided and marginalized, production space will primarily be converted to ecological space (such as mountains, plateaus, and areas around rivers and lakes). Because these regions do not have ideal agricultural circumstances or a significant ecological status, the farmland conversion policy can take into account the zone’s economic and ecological benefits. Production space will be converted to living space primarily in the Yangtze River Delta City Cluster, city clusters in the middle reaches of the Yangtze River, and major and medium-sized cities such as Chengdu, Chongqing, Guiyang, and Kunming. As a result of the YREB’s sustainable development, large and medium-sized cities will be further developed, promoting an increase in living space and taking up additional production space.

4 Conclusions and discussion

4.1 Results of simulating the ecological spatial change trend of the YREB by the FLUS model

In terms of quantitative changes, the area changes of the ecological space in the YREB and its four sub-zones under different scenarios are consistent. Both S1 and S2 show a falling tendency, with S2 showing the largest reduction; both S3 and S4 show an increasing trend, with S3 showing the largest increase. In terms of space changes, the ecological space in Yunnan-Guizhou, which has a vulnerable ecological environment, is stable and shows no symptoms of change. Changes in the ecological space of Sichuan-Chongqing occur primarily in the Sichuan Basin, the Zoige Grassland, and the Longmen Mountains in northwest Sichuan. Changes in the ecological space in the middle reaches of the Yangtze River occur primarily in the Wuyi Mountains, Mufu Mountains, Dabie Mountains, and around Dongting Lake, Poyang Lake, East Lake, and other places; changes in the Yangtze River Delta’s ecological space occur primarily in the eastern part of the Dabie Mountains and Jianghuai Hills.
To summarize, the simulation conditions of S1 are consistent with the region’s current and future development patterns and planning orientation, and the development results are, to some extent, consistent with the YREB’s regional development strategy, and also we take into account the ecological protection requirements of the ecological civilization, green development, and Yangtze River protection strategies in the region. In other words, it will be critical in the future to concentrate not just on high-quality economic development but also on scientifically and rationally controlling and gradually repairing natural space.

4.2 Rationality and scientificity of the FLUS model used to simulate and predict the change of ecological space

This study simulates the distribution pattern of ecological space in the study area and its four sub-zones in 2015 using 18 driving factors (including natural and human aspects). The results show that the model is very suitable for simulating and predicting future ecological space in the YREB. The overall accuracies are higher than 0.93 and the Kappa coefficients are higher than 0.95. Meanwhile, this study not only simulates and analyzes the future ecological space layout of the YREB but also introduces policy factors from multiple perspectives for differentiated simulation and comparison, in order to determine the influence and effects of various policies on the ecological space layout and obtain more practical results.

4.3 Implication of the FLUS model used to simulate and predict the change of ecological space

Studies using the FLUS for ecological land optimization and simulation are currently primarily conducted at national or provincial scales, with few studies on the economic belt at the meso-scale. This work supports the appropriateness of adopting the FLUS model for meso-scale investigations by analyzing the ecological space optimization and simulation in the YREB, thus providing some reference value for comparison. From a global viewpoint, the “Yangtze River Economic Belt Strategy” is one of the key national strategies of the Belt and Road Initiative, and it is closely linked to the environment and economy of the countries along the Belt and Road. Simulation and prediction of its ecological space will be useful to comparable regions in Asian, European, and African continental countries involved in the Belt and Road Initiative and will have a positive impact on the East Asian Economic Circle and the European Economic Circle’s long-term economic development and ecological civilization construction. In this work, the FLUS model was used to simulate quantitative and spatial changes in the ecological space of the YREB in 2035, which corresponds to the region’s anticipated development tendency. As can be observed, the FLUS model is scientific and appropriate for the simulation and prediction of ecological space, and it has some reference value for analogous studies in China and other countries, as can be observed.

4.4 Limitations of the FLUS model used to simulate and predict the change of ecological space

It is becoming more common to include quantitative optimization, space optimization, and benefits optimization in the structural optimization analysis of future ecological space is becoming increasingly popular. Because there are many elements that influence the distribution of ecological space, this study focused on only 18 significant driving factors, including natural and human impacts, based on authoritative research results in this field, the YREB’s regional characteristics, and data availability. This system incorporates some elements (such as the geological conditions of the study area). Future studies will include a range of parameters in simulation trials to increase simulation accuracy, make the study more rational and scientific, and better meet practical needs.

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