Multi-Scenario Simulations of “Production–Living–Ecological” Functional Patterns and Ecological Effects in the Upper Reaches of Huaihe River

Abstract

Taking the upper reaches of Huaihe River (UHR) as a research area, based on land use types data with 30 m resolution from 1980 to 2020, the changes in “production–living–ecological space” (PLE) and eco-environmental quality (EQ) in UHR from 1980 to 2020 were analyzed by using the eco-environmental effect evaluation method. Meanwhile, the PLUS model was applied to simulate and forecast the future scenarios for the data of 2010 and 2020, and the data for 2030–2050 under three situations of business as usual, ecological protection, and production priority were obtained, and the changing pattern of PLE and the change in EQ under each scenario were analyzed. Results: (1) From 1980 to 2020, the production and ecological space area in UHR presented a downward–upward–downward tendency and the living space area continued to increase. (2) From 1980 to 2020, the eco-environmental quality index (EV) presented a down–up tendency, and the expansion of lower eco-quality areas was obvious. The conversion of agricultural production (AP) and forest ecological (FE) is the main factor affecting environmental quality change. (3) Under the business as usual and production priority scenarios, the production and ecological space continues to reduce, and the living space continues to augment, but the production space area in the production priority situation is the least in three scenarios. Under the ecological protection scenario, the production space keeps reducing, and the ecological and living space keep increasing. (4) The ecological protection situation has the uppermost EV in three scenarios. The research can provide a scientific basis for territorial spatial planning and sustainable development of UHR.

1. Introduction

The term “spatial planning” originated in Europe for the purpose of achieving balanced regional development and spatial balanced coordination. Some countries have conducted various studies on it according to their national conditions and formed a sound and typical spatial planning system [1]. With the continuous progress of industrialization, urbanization, and information technology in the world, the development and disturbance of human land space are increasing, and many effects such as urban sprawl, environmental pollution, and ecological degradation are frequent, posing severe challenges to the future survival and development of human beings [2,3,4,5,6,7]. Therefore, understanding the development of national land space and optimizing the layout of national land has always been the focus of attention of all countries [8]. Recently, the development of industrialization and urbanization has caused many problems in China [9], such as urban land crowding out ecological land and agricultural land, changes in the ecological environment, uncoordinated regional development, and extensive land. In order to deal with the above problems, taking into account food security, economic development, and ecological protection [10], the Chinese government in land space planning formulated a “production–living–ecological space” (PLE) strategy [11], and proposed a clear goal for the first time at the 18th CPC National Congress. After that, the 19th CPC National Congress proposed the scientific delineation of the PLE to coordinate production, life, and ecology development for sustainable economic and environmental growth [12].

PLE is a comprehensive land space zoning method, which is the production–life–ecological space; research on PLE can provide direction for the optimization and development of land space in the future. At present, the research on PLE mainly involves theoretical connotation and framework [13,14]; identification of leading actions of land use types [15,16,17,18]; the spatial optimization and spatial carrying capacity [19,20,21,22,23]; and spatio-temporal change in PLE land use pattern and eco-environmental effect analysis [24,25,26]. In recent years, the prediction and simulation of the evolution of the PLE pattern have gradually emerged, and the simulation combined with mathematical models is a typical and effective quantitative method, including the cellular automata model, Markov chain model, and FLUS and PLUS models. In the early days, the first two models were dominant. However, they did not explore the relationship between spatial change in land use and spatial driving factors, which may lead to low simulation accuracy [27,28], but now, the FLUS and PLUS models have become the most extensive models for land prediction, such as Li et al. [29] simulated and predicted the spatial changes in the PLE environment in Poyang Lake under various scenarios based on remote sensing and RF–Markov–FLUS models. Zhang et al. [30] simulated and predicted the spatial pattern of PLE in Changsha City in 2030 based on the DTTD-MCR-PLUS model. Compared with other models, PLUS uses the random forest algorithm to explain the correlation between land spatial change and spatial driving factors better and uses the adaptive inertia cycle mechanism to predict the spatial distribution of different land types in the future [30], which can obtain higher simulation accuracy and more similar landscapes [31,32,33]. Therefore, this study uses the PLUS model to simulate and predict the changes in the spatial pattern of the region and quantitatively analyzes the impact of spatial changes on future eco-environment in different scenarios.

The Huaihe River Basin (HR), China’s food and population-intensive areas, is the transition zone between the north and south climates. As the source of the Huai River, the upper reaches of Huaihe River Basin (UHR) have a variety of terrains such as mountains, plains, and hills, and the distribution of its land use structure affects the regional eco-environment and thus has an impact on the ecological development of the lower reaches. Therefore, the future development of this region is of great significance to the Huaihe River Basin. The Huaihe River Basin is rich in water systems, so there are many studies on water resources, but the research on land pattern evolution and future simulation analysis is not perfect. At the same time, some scholars have conducted a comprehensive study of the whole basin ecosystem [34,35], such as Qiao et al. [35] used the InVEST and GIS spatial analysis method to evaluate the temporal and spatial variation characteristics of ecosystem services in the Huaihe River Basin from 1995 to 2020. However, the impact of land changes in the UHR on the future ecological environment remains unclear. Therefore, in order to understand the future multi-scenario changes in UHR and their impact on eco-environmental quality (EQ), based on 30 m resolution land use data from 2010 to 2020, the PLUS model was applied to simulate and predict land use data of UHR from 2030 to 2050 under three scenarios of business as usual, ecological protection, and production priority. Then, we analyzed and evaluated the changes in PLE and EQ in historical periods and various scenarios. This research provides a basis for the optimization of territorial space and the construction of ecological civilization in UHR.

2. Data and Methods

2.1. Study Area

This paper selects the basin above Wangjiaba Hydrological Station in the Huai River as the study area (Figure 1), covering an area of 30,630 km². The upper reaches of the Huai River Basin (UHR) span across the climate transition zone between the north and south of China. The northern part of the UHR belongs to the warm temperate semi-humid zone, while the southern part of UHR belongs to the subtropical humid zone. The climate in the basin is mild, with an average annual temperature ranging from 9.9 °C to 16 °C. The spatial difference in temperature is significantly influenced by the terrain. The average annual rainfall in UHR is 1145 mm, and the spatial distribution of rainfall shows a gradually decreasing trend from southwest to north. The distribution of rainfall within the year is uneven. Rainfall gradually increases in April and May, and the flood season is from June to August, with 60% of the rainfall concentrated during the flood season.

2.2. Data

This article uses the land use data of 1980–2020, a resolution of 30 m. According to the comprehensive consideration, the driving factors selected a total of 14 socio-economic factors and climate and environmental factors (

Table 1. Data source.

Data Type	Data Name	Data Format	Data Source
Land use data	Land use types from 1980 to 2020	Grid 30 m resolution	Environmental Data Center of Chinese Academy of Sciences (http://www.resdc.cn/data, accessed on 1 January 2025)
Socio-economic factors	GDP	Grid 1 km resolution	Environmental Data Center of Chinese Academy of Sciences (http://www.resdc.cn/data, accessed on 1 January 2025)
	population
	Distance from City
	Distance from town
	Distance from National Highway
	Distance from Provincial Road
	Distance from highway
	Distance from railway
Climate and environmental factors	DEM	Grid 1 km resolution	Environmental Data Center of Chinese Academy of Sciences (http://www.resdc.cn/data, accessed on 1 January 2025)
	Slop
	Aspect
	Soil salt	Grid 1 km resolution	Harmonized World Soil Database (http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/, accessed on 1 January 2025)
	Soil workability
	Soil oxygen

). We unified the coordinate system of the above data, extracted the data of the study area, and used the PLUS model to simulate the future. Compared with the actual data, the accuracy is higher, and the reliability of the selected data can be verified. All data sources are shown in Table 1. These data are widely used, and most scholars use these data for research [36,37].

Land use classification in land use data includes first-level and second-level classification. According to the secondary classification of land dominant function, it is divided into production space (agricultural production (AP); industrial and mining production (IMP)), living space (urban living (UL); rural living (RL)), and ecological space (forest ecological (FE); grassland ecological (GE); water ecological (WE); other ecological (OE)) [38].

2.3. Research Methods

2.3.1. PLUS Model

The research methods for simulating land use change mainly include the CA–Markov model, CLUE-S model, and FLUS model [39,40,41]. However, the existing models do not perform well in exploring the mechanism of land use change, and the results of patch dynamic simulation are not ideal. The PLUS model, which mines the probability of land use change based on the random forest algorithm, outperforms other models in terms of quantity, location, cell scale change, and landscape pattern similarity [42]. Meanwhile, it can better explore the causes of land use change through land expansion analysis strategies and can better model the evolution of multiple land use patches over time and space. It has the advantages of high simulation accuracy and fast data processing speed [31].

PLUS is a simulation model of land use change based on patch generation, which mainly includes a land expansion analysis strategy module (LEAS) and cellular automata module (CARS) of a multi-type random patch seed mechanism. LEAS can extract two phases of land expansion and use driving factors to generate land development potential [43]. CARS can combine random seed generation and threshold diminishing mechanism, combined with the development potential, according to the corresponding transfer matrix and domain weight, and ultimately generate simulated patches [44].

(1) Model parameter setting and scenario design.

Based on the land data of 2010 and 2020, the study simulates and predicts future scenarios. When setting the weights of various types of land use, the study assigns them according to the proportion of different types of land expansion area [45,46] and finally obtains the neighborhood weights (

Table 2. Neighborhood weights.

AP	IMP	UL	RL	FE	GE	WE	OE
0.28	0.14	0.17	0.27	0.08	0.01	0.05	0

).

The transition matrix represents a variety of inter-class conversion constraints and can be used in a variety of scenarios [47]. After careful consideration, this paper sets three future scenarios: business-as-usual, ecological protection, and production priority. The business-as-usual scenario is a continuation of history, that is, without considering any planning policy, using the Markov chain to predict future land use type. The ecological protection scenario is based on the protection of ecological land and restricted conversion of ecological land. In this scenario, the conversion probability of ecological land to other land use is reduced by 20%, and the conversion probability of IMP and living land to ecological land is increased by 20%. In the production priority scenario, considering the growth of population and economic development that leads to an increased demand for production land, the probability of converting ecological land to production land is increased by 10%, while the probability of converting production land to ecological land is reduced by 20%. According to the above provisions, the corresponding transfer matrix is developed (

Table 3. Multi-scenario land transfer matrix.

	Business as Usual								Ecological Protection								Production Priority
	a	b	c	d	e	f	g	h	a	b	c	d	e	f	g	h	a	b	c	d	e	f	g	h
a	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	1	1	1	1	0	0	0	0
b	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
c	1	0	1	1	1	1	1	0	1	1	1	1	0	1	1	1	1	1	1	1	0	1	0	0
d	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	1	0	0
e	1	1	1	1	1	1	1	0	0	0	0	0	1	0	0	0	1	1	1	0	1	1	0	0
f	1	1	1	1	1	1	1	1	0	0	0	0	1	1	0	0	1	1	1	1	1	1	1	1
g	1	1	1	1	1	1	1	1	0	0	0	0	0	0	1	0	1	0	0	0	1	1	1	1
h	1	1	0	0	0	1	1	1	0	0	0	0	0	0	1	1	1	1	0	0	0	1	1	1

a, b, c, d, e, f, g, and h stand for AP, IMP, UL, RL, FE, GE, WE, and OE, respectively.

).

(2) Simulation accuracy verification. In order to ensure that the model can be used to simulate and predict future land use in the study area, the actual 2020 data in the upper reaches of the Huaihe River are compared with the simulated 2020 data to obtain the Kappa coefficient and FoM value. The Kappa coefficient is 0.93, and the FoM value is 0.08. The relevant research shows that its accuracy is high [48,49].

2.3.2. Eco-Environmental Effect Model

(1)

Eco-environmental quality index (EV)

Considering the EQ and area ratio of each land type, from the perspective of the eco-environment, the EQ of each period is quantitatively characterized [50,51].

$${EV}_t=\sum _{k=1}^n{\displaystyle \frac{S_{uk}}{S_u}}{R}_k$$

(1)

EV_t is the EV of t period; n is the number of land types; S_uk is the area of land type k in uth ecological unit; S_u is the total area of uth ecological unit; R_k represents EV of kth land use type.

According to the secondary classification of land use and the corresponding EV, considering the ecological quality and area ratio of various land types, the EV of 8 land types was calculated by Formula (1) (

Table 4. EV.

Land Type	EV
AP	0.2609
IMP	0.15
UL	0.2
RL	0.2
FE	0.8159
GE	0.5622
WE	0.55
OE	0.0265

) so as to quantitatively characterize the eco-environment quality of different land types [52]. At present, most scholars use this method to evaluate the eco-environment quality of regional land types [53,54].

(2)

Ecological contribution rate

Ecological contribution rate refers to the change in EQ caused by the change in land use function [15]. Based on the land use data of two adjacent periods, this paper uses the land transfer matrix to obtain the mutual conversion area (LA) of eight land types. According to the following expression, the ecological environment quality change caused by the conversion of one type of land to another type of land is calculated, that is, the ecological contribution rate.

$$LEI=\left({LE}_t-{LE}_i\right)LA/TA$$

(2)

LEI is the ecological contribution rate; LE_t and LE_i are the EV of t-period land type and i-period land type, respectively. LA is the conversion area of t-period land type and i-period land type; TA is the total area of the region.

3. Results Analysis

3.1. Analysis of Spatial and Temporal Pattern Changes in PLE in the UHR from 1980 to 2020

According to the data (Figure 2), the production space in the UHR exceeds 60% of the total area, the ecological space only accounts for about 22%, and the proportion of living space does not exceed 10%. In terms of time, from 1980 to 2020, the production space in the UHR presented a down–up–downward tendency. In 40 years, the production space area decreased by 873.24 km², the proportion of AP was the most, while the proportion of IMP was less than 2%, but it continued to increase. The ecological space presented a down-up-downward tendency from 1980 to 2020, with a total increase of 340.90 km². Among them, FE was the main ecological land, about 79%. In 40 years, FE, WE, and OE increased while GE decreased. The living space area continued to increase from 1980 to 2020, of which RL was the main one, and UL accounted for less than 20% of living space. Both types of land continued to grow, but the proportion of RL in living space decreased. It can be seen that the growth rate of RL is smaller than that of UL, which also shows an improvement in the urbanization level in the region.

In order to more clearly understand the conversion of the area of PLE in the UHR from 1980 to 2020 among each land use type, this paper uses ArcGIS to overlay land use data from 1980 to 2020 in UHR and obtains land use transfer matrix from 1980 to 2020, and visualizes it (Figure 3). From 1980 to 2020, except for OE, the rest of the land has been converted, the most obvious of which is the conversion of AP. At the same time, the AP has gradually developed from the exchange of ecological land to the exchange of living land. It can be seen that in recent years, the UHR has shifted from developing agriculture and economy to focusing on ecological and economic development.

In terms of space (Figure 4), the distribution of various types of land from 1980 to 2020 is relatively consistent. AP is distributed in most areas of the UHR, and IMP is less distributed, mainly in the southwest and northwest regions. The distribution range of UL is large, and the area is concentrated. It is embedded in the AP in blocks, mostly distributed in southwest and northwest regions, while RL is sporadically distributed. FE is distributed along the border from the northwest to the southeast. The grassland ecosystem is distributed around the FE, mainly in the northwest and eastern. WE are mainly the Huaihe River and the Suyahu Reservoir in Zhumadian City, Henan Province. OE areas are small and do not explain too much. From 1980 to 2020, various types of land use have changed greatly or slightly. The changes are more obvious in UL and RL, mainly in the northwest and central regions. In the last 40 years, the population living demand in the UHR has been increasing, and urban and rural land has been expanding.

3.2. Analysis of Eco-Environmental Effects in the UHR from 1980 to 2020

3.2.1. Ecological Environment Quality Analysis

The EV of UHR was calculated by formula (2), and it was divided into low eco-quality (≤0.15), lower eco-quality (0.15~0.20), medium eco-quality (0.20~0.26), higher eco-quality (0.26~0.56) and high eco-quality (>0.56) by ArcGIS natural breakpoint method. The EQ distribution map of UHR from 1980 to 2020 was obtained (Figure 5). The UHR is mainly dominated by medium eco-quality areas; the areas above the medium level are mainly distributed in the northwest to southeast regions, which are basically ecological land. Among them, the FE is a high eco-quality area, and the grassland and WE are high eco-quality areas. The lower eco-quality areas are mainly UL and RL, and the low eco-quality areas are less, mainly IMP and OE. From 1980 to 2020, the lower eco-quality area increased by 530.15 km², the medium eco-quality area decreased by 1043.83 km², and the other three types of area increased by no more than 230 km². From 1980 to 2000, the environmental quality index of the UHR continued to decline, from 0.3698 in 1980 to 0.3679 in 2000, and from 2000 to 2010, EV rose to 0.3769. The reason may be that the policy of returning farmland to forest and grassland has played an effective role. By 2020, EV has fallen to 0.3750. It can be seen that the eco-environment in UHR has been effectively improved in recent years, but the future eco-environment needs to be maintained and further improved.

Figure 6 shows the change in EQ from 1980 to 2020. It can be seen that the change in EQ from 1980 to 1990 was not obvious, and the change in EQ from 1990 to 2000 was mainly distributed in the northwest and south, and the change was small. From 2000 to 2010, EQ changed greatly; the most obvious was the improvement of EQ in the south, and the deterioration of EQ was mainly distributed in the middle, and the range was scattered. From 2010 to 2020, the deterioration area of EQ was more than the improvement area, and the improvement of EQ was mainly distributed in the southwest. On the whole, the improvement of EQ is mainly in the southern and northern mountains and waters, while the deterioration of EQ is mainly in the central plains, mostly living land occupying production land.

3.2.2. Ecological Contribution Rate Analysis

EV can only judge the environmental situation of the region as a whole. According to Formula (2), this paper calculates the variation in ecological contribution caused by the mutual conversion of each land type in UHR from 1980 to 2020 (

Table 5. Eco-environmental effects of UHR from 1980 to 2020.

Eco-Environmental Effects	Year	The Change in Land Types	Conversion Area/km2	Ecological Contribution Rate	Ecological Contribution Ratio/%
Environmental quality improvement	1980~1990	AP—WE	13.52	0.000129	61.94
		AP—FE	3.22	0.000059	28.33
		Total	16.74	0.000189	90.27
	1990~2000	AP—WE	88.07	0.000843	43.77
		GE—FE	94.17	0.000791	41.06
		AP—FE	9.13	0.000168	8.71
		Total	191.36	0.001802	93.54
	2000~2010	AP—FE	675.68	0.012417	77.98
		AP—WE	161.53	0.001546	9.71
		RL—AP	540.46	0.001090	6.85
		Total	1377.67	0.015054	94.54
	2010~2020	AP—FE	51.55	0.000947	58.12
		AP—WE	28.38	0.000272	16.67
		RL—AP	81.47	0.000164	10.08
		Total	161.40	0.001384	84.87
Environmental quality deterioration	1980~1990	WE—AP	27.78	−0.000266	55.30
		FE—AP	6.24	−0.000115	23.85
		AP—UL	26.47	−0.000053	11.11
		Total	60.49	−0.000434	90.27
	1990~2000	FE—AP	100.67	−0.001850	51.89
		GE—AP	123.52	−0.001232	34.57
		AP—UL	66.37	−0.000134	3.76
		Total	290.56	−0.003216	90.21
	2000~2010	FE—AP	138.36	−0.002543	37.24
		AP—RL	575.85	−0.001162	17.02
		WE—AP	109.91	−0.001052	15.41
		FE—RL	14.38	−0.000293	4.30
		GE—AP	27.24	−0.000272	3.98
		FE—WE	29.81	−0.000262	3.84
		Total	895.55	−0.005584	81.80
	2010~2020	FE—AP	94.69	−0.001740	48.29
		AP—RL	203.81	−0.000411	11.41
		AP—IMP	82.95	−0.000305	8.46
		FE—IMP	13.71	−0.000302	8.39
		WE—AP	31.07	−0.000298	8.26
		Total	426.23	−0.003056	84.80

). Among them, the positive value of LEI indicates the improvement of environmental quality, and the negative value of the ecological contribution rate indicates the deterioration of EQ. At the same time, the proportion of LEI of all land conversions is obtained and used to indicate the impact of land conversion on ecology. Because there are many land conversions and some of the ecological contribution rates are small, this paper mainly selects the land conversion with a large ecological contribution rate for analysis.

From the perspective of environmental quality improvement, from 1980 to 1990 and 1990 to 2000, the largest proportion of environmental quality improvement was the conversion of AP to WE; the former accounted for 61.94%, and the latter accounted for 43.77%. At the same time, from 1990 to 2000, the conversion of GE to FE accounted for more than 40%. The land use change types that occupied a large proportion in the improvement of environmental quality from 2000 to 2010 and 2010 to 2020 were the same, and the transition of AP to FE occupied the largest proportion, with the former accounting for 77.98% and the latter accounting for 58.12%, while the contribution rates of the other two land use conversion were no more than 20%. From the perspective of environmental quality deterioration, WE was mainly converted into AP from 1980 to 1990, and FE was mainly converted into AP from 1990 to 2000, accounting for more than 50%. From 2000 to 2010, there were the most types of land change in the deterioration of environmental quality, and the proportion was relatively small. Among them, the highest proportion was the conversion of FE to AP, but it only accounted for 37.24%, and the rest of the land conversion did not exceed 20% of the deterioration of environmental quality. From 2010 to 2020, the largest proportion of EQ deterioration was the conversion of FE into AP. Therefore, land production and ecological transformation are the main causes of changes in environmental quality.

3.3. Analysis of the Change in PLE in UHR Under Multiple Scenarios in the Future

Based on land use data of the UHR from 2010 to 2020, this study uses the PLUS model to simulate and predict land use type data of the UHR from 2030 to 2050 under three scenarios, PLE in the UHR under three future situations is obtained (Figure 7).

Under the business-as-usual situation, the production space area in the UHR continued to decrease; from 2020 to 2050, a total of 528.52 km² was reduced, of which AP continued to decrease, while IMP continued to increase from 2020 to 2050. The living space area kept increasing from 2020 to 2050, and the urban and RL increased by 699.36 km². From 2020 to 2050, the ecological space area kept reducing, but the degree of change was smaller than that of production and living space, with a total decrease of 170.85 km², of which FE decreased the most.

Under the ecological protection situation, the production space area in UHR keeps reducing while the living and ecological space area continues to increase. From 2020 to 2050, the AP in the production space decreased by 819.80 km², while the IMP increased by 147.70 km². From 2020 to 2050, the living space area increased by 405.18 km², and the urban and RL increased. The ecological space area continues to grow, which is in line with the development of this scenario. From 2020 to 2050, the ecological space area has increased by 266.92 km². Among them, the increased area of FE was the largest, and the other three types of ecological land have changed little.

In a production priority situation, the production land is mainly developed, but due to social development, the production space area is still reduced. From 2020 to 2050, the production space area in the UHR decreased by 435.09 km². However, compared to the other two scenarios, the declining trend of production space in this scenario was effectively alleviated. The living space area kept increasing, with an augment of 702.40 km² from 2020 to 2050, of which the increase in urban and RL exceeds 300 km². The ecological land area continued to reduce; from 2020 to 2050, a total of 267.31 km² was reduced, of which the ecological land of forest land decreased by more than 200 km², and the remaining three kinds of ecological land did not exceed 32 km².

3.4. Analysis of Eco-Environmental Effects in the UHR Under Multiple Scenarios in the Future

3.4.1. Eco-Environment Quality Analysis

The EV of UHR under the three scenarios was calculated by Formula (2). According to the EV, the EQ distribution maps of the three scenarios from 2030 to 2050 were obtained (Figure 8). The spatial distribution of EQ in UHR remained mostly unchanged across the three scenarios. Under the business-as-usual scenario, from 2020 to 2050, the EV of UHR continued to decline, from 3.750 in 2020 to 0.3698 in 2050. The expansion of the lower eco-quality area is more obvious from the map. From 2020 to 2050, the area of the lower eco-quality area increased by 699.37 km², while the low eco-quality area increased by 212.56 km². On the contrary, the high eco-quality and higher eco-quality areas were reduced, and the medium eco-quality area decreased by 741.50 km².

Under the ecological protection scenario, the EV of UHR continues to rise, from 0.3750 in 2020 to 0.3786 in 2050. The ecological protection scenario is the best EQ of the three scenarios, and it is also the only scenario in which EV continues to rise. Under this scenario, the high eco-quality and high eco-quality areas increased. From 2020 to 2050, the high eco-quality area increased by 266.12 km², and the higher eco-quality area has changed weakly. The low eco-quality area and the lower eco-quality area both had a growth of 147.62 km² and 405.18 km², and the medium eco-quality area decreased by 819.80 km². This shows that under this scenario, the expansion of low eco-quality areas has been effectively alleviated, and the expansion of high eco-quality areas has also gradually improved the EQ in UHR.

Under the production priority scenario, the EV of UHR continued to decline, from 0.3750 in 2020 to 0.3682 in 2050, which was the lowest in the three scenarios. From 2020 to 2050, the expansion area of low eco-quality and lower eco-quality areas in UHR reached 924.98 km², the high eco-quality area decreased by 222.58 km², the higher eco-quality area decreased far more than that of natural situation, and the medium eco-quality area decreased by 658.10 km². It can be seen that in this scenario, the low eco-quality areas are too much outward expansion, crowding out the medium eco-quality and high eco-quality areas, resulting in a gradual decline in the EQ in UHR.

In order to understand the spatial changes in EQ from 2020 to 2050 under various scenarios, the EQ in 2020 and 2050 are superimposed to obtain the EQ changes from 2020 to 2050 (Figure 9). In the business-as-usual scenario, there were more areas with deteriorating EQ in the UHR from 2020 to 2050 than those with improved EQ. The areas with deteriorating EQ are scattered throughout the UHR, mainly living land, and a small number of areas in the south have improved ecological environment quality. Under the ecological protection situation, from 2020 to 2050, the improvement areas of EQ in the UHR are mainly distributed from the west to the southern edge of the UHR, while the deterioration areas are mainly distributed in the north. Under the production priority scenario, from 2020 to 2050, the EQ deterioration area in UHR will be far more than the improvement area, mainly scattered everywhere.

3.4.2. Ecological Contribution Rate Analysis

According to Formula (3), this paper calculates the change in UHR’s ecological contribution from 2020 to 2050 (

Table 6. LEI of the UHR under various scenarios from 2020 to 2050.

Scenarios	Eco-Environmental Effects	The Change in Land Types	Conversion Area/km2	Ecological Contribution Rate	Ecological Contribution Rate/%
Business as usual	Environmental quality improvement	AP—FE	53.06	0.000975	85.99
		IMP—FE	1.56	0.000034	3.03
		GE—FE	3.80	0.000032	2.81
		IMP—AP	7.41	0.000027	2.40
		Total	65.82	0.001069	94.23
	Environmental quality deterioration	FE—AP	192.19	−0.003532	55.78
		AP—UL	397.12	−0.000801	12.65
		AP—IMP	204.64	−0.000752	11.87
		AP—RL	300.56	−0.000606	9.58
		FE—IMP	16.47	−0.000363	5.74
		FE—UL	4.63	−0.000094	1.49
		Total	1115.61	−0.006149	97.11
Ecological protection	Environmental quality improvement	AP—FE	262.14	0.004818	96.53
		IMP—FE	3.43	0.000076	1.51
		Total	265.56	0.004893	98.05
	Environmental quality deterioration	AP—RL	301.16	−0.000608	42.92
		AP—IMP	157.70	−0.000579	40.92
		AP—UL	111.54	−0.000225	15.90
		Total	570.39	−0.001412	99.74
Production priority	Environmental quality improvement	GE—FE	5.90	0.000050	27.76
		WE—FE	4.32	0.000038	21.34
		IMP—FE	1.63	0.000036	20.19
		IMP—AP	7.53	0.000028	15.51
		UL—AP	6.67	0.000013	7.54
		RL—AP	3.45	0.000007	3.90
		Total	29.50	0.000172	96.25
	Environmental quality deterioration	FE—AP	208.86	−0.003838	55.50
		AP—UL	399.01	−0.000805	11.64
		AP—IMP	211.66	−0.000778	11.24
		AP—RL	308.33	−0.000622	9.00
		FE—IMP	17.88	−0.000394	5.70
		Total	1145.74	−0.006438	93.08

). In the business-as-usual scenario, the conversion of AP to FE accounted for 85.99%, which was the main reason for the improvement of EQ. The other three types of land conversion accounted for no more than 5% of the improvement of EQ. In the deterioration of EQ, the conversion of FE to AP accounted for 55.78%, followed by the conversion of AP to UL and IMP, accounting for about 12%. In the ecological protection scenario, the conversion of AP to FE is the main factor for the improvement of EQ. The conversion area is 262.14 km², accounting for 96.53%. In the deterioration of EQ, the conversion area of AP to RL and IMP is 301.16 km² and 111.54 km², and the contribution rate of both is more than 40%. Under the production priority scenario, there are many types of land use changes to improve EQ, and the contribution rate is not more than 30%. Among them, grassland, WE, and IMP are converted into FE, accounting for more than 20%, and the rest accounts for less than 20%. In the deterioration of EQ, the conversion of FE into AP accounts for 55.50%, and the remaining four land use changes do not exceed 12% of the deterioration of EQ. It can be seen that the changes in AP and FE are the main reasons for the improvement and deterioration of EQ. At the same time, the proportion of ecological contribution rate is not necessarily large, which is also related to land use types.

In general, under the improvement of the eco-environment, the ecological contribution rate of business as usual and production priority scenarios is mainly in the range of 0.000028–0.000975, and LEI of ecological protection is mainly in 0.004818. The ecological contribution rate of business as usual and production priority scenarios under the deterioration of eco-environment is mainly in 0.000752–0.003838, and LEI of ecological protection is mainly in 0.000225–0.000608. It can be seen that the ecological protection scenario has a higher ecological contribution rate in the improvement of the eco-environment than the other two scenarios, while the ecological contribution rate in the deterioration of the ecological environment is lower than the other two scenarios.

4. Discussion and Conclusions

4.1. Discussion

This paper studies and analyzes the changes in PLE and EQ in UHR in historical periods and different future scenarios. From 1980 to 2020, the production space in UHR was occupied, the living space was expanding, and the ecological space was also changing. However, due to the policy of returning farmland to forest and grass, the ecological space as a whole showed an increasing state. Because the region is dominated by agriculture, the area of AP is the largest, mainly distributed in the central and northern plains of UHR. Living space is distributed near the production land, and most of it is rural. At the same time, it continues to expand outwards, mainly occupying the production land. Ecological space is mainly distributed in the western and southern mountainous areas, and its area has an expansion trend. It can be seen that the effective implementation of the policy and the regional emphasis on the eco-environment are the same as the research results of Li et al. [55], who proposed that the ecological space is mainly distributed in the western and southern mountainous areas, and the expanded area is mainly concentrated in the eastern and western slopes of Laoshan Mountain, Funiu Mountain, Dabie Mountain, and other areas. At the same time, EQ in UHR has been effectively improved in recent years, which further shows that the region pays attention to the eco-environment.

In the future scenario simulation, due to the dense population in UHR, the future development trend will still be dominated by population increase, so the living space will continue to increase. Because of the influence of geographical location and social background, the future living space will occupy most of the production space. In the three scenarios, it can also be seen that the living space increases and the production space decreases, but in the production priority scenario, the decline in production land will be alleviated, which is the same as the existing research [56]. Yang et al. [56] pointed out that under the cultivated land protection scenario, although the overall number of cultivated land is still reduced, the reduction is controlled. Ecological land only increases in the ecological protection scenario, and its EQ also increases accordingly, while the other two scenarios also lead to a decline in EQ due to the reduction in high-efficiency areas. This is consistent with Yang et al. [57], who pointed out that the dominant factor leading to the improvement or degradation of EQ is the conversion between forest land and other land use.

In this study, three future scenarios were set up, which can summarize the future development simulation, but there is still a certain gap with the reality, which cannot include all the future development. In the future, the scenario should be adjusted appropriately according to the national policy. In the selection of driving factors, only the more important factors are selected, but the contribution rate of each factor to the land types is different (

Table 7. Contribution rate of driving factors in each category.

	AP	IMP	UL	RL	FE	GE	WE	OE
Aspect	0.0439	0.0317	0.0213	0.0382	0.0207	0.0150	0.0540	0.0000
DEM	0.0654	0.0798	0.0584	0.1392	0.1476	0.2915	0.0819	0.3820
Distance from city	0.0642	0.1188	0.0738	0.0564	0.1288	0.0605	0.1271	0.4150
Distance from town	0.0401	0.0513	0.0276	0.0679	0.0834	0.0463	0.0351	0.0228
GDP	0.2079	0.1252	0.2420	0.2115	0.2513	0.1426	0.2389	0.0257
Distance from highway	0.0587	0.1184	0.0291	0.0435	0.0454	0.0542	0.0404	0.0000
Distance from national highway	0.0787	0.0665	0.0549	0.0508	0.0340	0.1342	0.0464	0.0399
population	0.1469	0.1178	0.2559	0.1708	0.1305	0.1231	0.1952	0.0000
Distance from provincial road	0.0898	0.0638	0.0985	0.0432	0.0407	0.0423	0.0593	0.0064
Distance from railway	0.0605	0.0654	0.0801	0.0612	0.0443	0.0422	0.0331	0.0257
Slope	0.0939	0.0777	0.0381	0.1016	0.0524	0.0415	0.0573	0.0825
Soil salt	0.0416	0.0069	0.0181	0.0078	0.0039	0.0051	0.0131	0.0000
Soil workability	0.0013	0.0695	0.0000	0.0041	0.0131	0.0000	0.0122	0.0000
Soil oxygen	0.0070	0.0074	0.0021	0.0039	0.0040	0.0015	0.0063	0.0000

). From Table 7, social factors such as population and GDP contribute greatly to the land types. The contribution rate of different driving factors to the land types is different, and the driving factors will not be suitable for all land types. For example, the contribution rate of Slope to RL is 0.1016, while the contribution rate to UL is only 0.0381; therefore, in future research, it is necessary to find the factors that are more matched with the land type as the driving factors, and the policy and other factors can be added to improve the simulation accuracy.

UHR is dominated by agriculture, and agricultural land will generally show a downward trend in the future. Therefore, we need to increase the total output value and efficiency per unit area by strengthening the management and investment in production space and optimizing the structure of agricultural land. Due to human growth and social development, living space will further occupy production land. In the construction of towns, we should pay attention to the ‘rigid and flexible’ control boundary and closely integrate urban construction with the protection of landscape, forest, farmland, lake, and grass systems. Additionally, we should also coordinate the ecological land, make full use of the western and southern mountains in UHR, protect the forest land and grassland in the region, and carry out reasonable development and renovation to ensure the ecological quality of the source of the Huaihe River.

4.2. Conclusions

(1)

From 1980 to 2020, the production space area in UHR decreased first, then increased, and finally decreased, with an overall decrease of 873.24 km²; AP had the highest proportion. The ecological space area showed a downward–upward–downward trend, of which FE increased the most. The living space area has continued to increase.

(2)

The EV of UHR reduced from 0.3698 in 1980 to 0.3679 in 2000, increased to 0.3769 in 2010, and finally reduced to 0.3750 in 2020. The overall EQ has been effectively improved. The conversion of production and ecological land is the primary reason for the change in EQ from 1980 to 2020.

(3)

From 2020 to 2050, under the three scenarios, the production space area continues to decrease, but the reduction under the production priority is the least in the three scenarios, only 435.09 km², of which the AP decreases and the IMP increases. Under the business-as-usual and production priority scenario, the ecological space keeps decreasing, and the ecological protection scenario is the opposite; the living space under the three scenarios keeps increasing.

(4)

From 2020 to 2050, the EQ of the ecological protection scenario is the best, and its EV continues to increase, while the EV of the other two scenarios continues to decrease. The conversion of production and ecological land has a great impact on the EQ.