Ecosystem Services’ Supply–Demand Assessment and Ecological Management Zoning in Northwest China: A Perspective of the Water–Food–Ecology Nexus

Abstract

The coordinated development of the water–food–ecology (WFE) nexus is a practical issue that has to be addressed urgently for northwest China’s (WTL) sustainable development. Optimizing the linkage relationship and accomplishing the rational distribution of resources from the perspective of the supply and demand for ecosystem services (ESSD) are imperative. Thus, in this study, a numerical indicator system for ESSD from the perspective of the WFE nexus was constructed with the incorporation of the water and carbon footprint. Based on this premise, the ecological management zoning method was enhanced by integrating supply and demand risks, and optimization suggestions were proposed for various zones. The results showed that (1) carbon sequestration (CS), food production (FP), and water yield (WY) supply and demand significantly increased between 2000 and 2021. High ESSDs were concentrated in the west side of northwest China. Maize, wheat, cotton, vegetables, and garden fruits had a higher demand for ecosystem services (ESs). (2) The three ESSDs were bound in a synergistic relationship. The synergy between supply exhibited significant spatial heterogeneity, while the synergies between demand showed similar distribution patterns. (3) Regarding quantity matching, the supply for FP and CS surpassed demand, while the WY supply could not meet the demand. The three ESs’ supply and demand deficits rose. Ecological supply–demand ratio (ESDR) regional differentiation for the three ESs was apparent. Regarding spatial matching, FP and CS concentrated on low supply–low demand, while WY focused on high supply–high demand. FP risk was concentrated in Qaidam Basin, whereas WY risk was mostly in Hexi inland river basin (HX), the Yellow River Basin area (HH), and both sides of the “Qice line”. (4) The ecological management zones were formed by integrating WTL’s three dominant weak functional zones, four categorized strategy regions, and four governance models. This study can serve as a scientific benchmark for regional ecological management, which is significant in ensuring northwest China’s water, food, and ecological safety.

1. Introduction

Human civilization originating in ecosystems, water, and food are necessary conditions for human survival, development, and social progress [1]. These three elements are interdependent and influential, providing humans with essential ecosystem services (ESs). Nevertheless, due to multiple variables like climate change, population expansion, urbanization, and industrialization, global water and food demand has surged, and about 60% of ESs have deteriorated [2,3]. People realize the importance of integrated water, food, and ecological development for national and regional sustainability. Water availability, food production, and ecosystem conditions all have a direct impact on the attainment of the United Nations 2030 Sustainable Development Goals [4]. Ecosystem services’ supply and demand (ESSD) act as critical bridges connecting natural and society systems [5]. Therefore, systematically identifying the characteristics of ESSD based on water, food, and the ecosystem promotes coordinated water–food–ecology (WFE) nexus development and provides a new entry point for global sustainable development research.

Northwest China (WTL) is a vital hub for the Silk Road Economic Belt, a crucial food production reserve and ecological barrier, and a reception region for the “One Belt and One Road Initiative” [6]. It is critical for China’s social stability, food security, ecological security, and economic development [7]. However, it is an arid and water-scarce region, with water resources playing a significant role in restricting food production and promoting ecological environment degradation [8]. The Chinese government has released various documents, including “Guiding opinions on promoting the western development in the new era and forming a new pattern”, “National plan for implementing the 2030 agenda for sustainable development in China”, and others, to promote ecosystems and social harmony [9]. However, current research mainly focuses on resource allocation issues in WTL, with very limited research on ESSD. There is an urgent need to study the issue of balancing ESSD from the perspective of the WFE nexus to offer decision-makers scientific information related to managing the WFE nexus and guide regional development planning [10,11,12].

ESSD assessment methods are diverse and constantly evolving. Currently, land use estimation [13], qualitative evaluation [14], ecological process simulation, value evaluation, data space superposition [5], and ecological modeling [15] are typical approaches for quantifying supply and demand. Methods for supply–demand matching include correlation, supply–demand ratio, four-quadrant analysis, and supply–demand coordination [16]. However, these studies still have some limitations. Firstly, most studies analyze supply and demand independently, with few studies exploring the relationship between ESSD from the perspective of the WFE nexus, without reflecting on the complex interactions between supply and demand [5,17,18,19]. Secondly, the description of the demand for ESs is not very accurate [20,21]. Thirdly, most studies only focus on the spatiotemporal matching of supply and demand, neglecting the investigation of the risks associated with combining the current situation with changing trends [22]. Research on supply–demand relationships may be moved from a static to dynamic approach by the risk analysis of supply–demand matching and more comprehensively reflect the supply–demand relationship of ESs. Eventually, researchers will pay less attention to arid areas’ ecosystems, especially on the demand side of ESs [23,24].

To address the shortcomings of existing research and better promote the coordinated development of the WFE nexus in WTL, this study considers ESSD from the WFE nexus, divides ecological management zones, and proposes effective management strategies. The specific objectives are to (1) choose three crucial ESs from the perspective of the WFE nexus, propose more scientific evaluation indicators, and quantify ESSD; (2) assess the trade-offs and synergies between ESs regarding supply–supply and demand–demand; (3) explore the temporal–spatial and static–dynamic matching characteristics of ESSD and construct an ecological management framework; and (4) delineate management zones based on the established framework and suggest associated regulatory measures.

2. Materials and Methods

2.1. Study Area

Northwest China (73~120° E, 30~50° N, WTL) covers 3.45 million km² of land, encompassing 55 cities in 6 provinces (Figure 1). Its climates are semi-arid and arid, with 950.93 mm of potential evaporation and 191.81 mm of annual precipitation [25]. From the perspectives of terrain, hydrology, meteorology, and socio-economic factors, this area can be divided into eight subareas [7,26]. This region is renowned for its important position in crop production and ecological protection in China. From 2000 to 2021, food yields increased by 2.57 times, and a 34.51% improvement in soil retention per unit area was observed. However, the region faces acute water scarcity, with an average annual amount of water resources of only 1.593 × 10¹¹ m³, lower than the national benchmark [25]. Due to fast socio-economic growth, there is a growing demand for ESs, which has resulted in a conflict between supply and demand. Therefore, against the backdrop of promoting high-quality development in WTL, this region must identify and optimize ecological control methods based on assessing ESSD related to the WFE nexus.

2.2. Data Sources

The essential data required for this research include the NDVI, DEM, meteorological data, soil, land use, and some related statistical data.

Table 1. Research Data and Source.

Data Types	Type and Resolution	Period	Database Sources
Soil	Raster, 1 km	-	Resource and Environment Science and Data Center http://www.resdc.cn/
Land use	Raster, 30 m	2000, 2005, 2010, 2015, 2020
DEM	Raster, 30 m	-
Effective irrigation area	Statistical data	2000~2021	Water resource bulletins of provinces http://slt.shaanxi.gov.cn/ https://slt.gansu.gov.cn/ https://slt.nmg.gov.cn/ http://slt.nx.gov.cn/ https://slt.xinjiang.gov.cn/ http://slt.qinghai.gov.cn/
Irrigation quota		2000~2021
Water consumption		2000~2021
Precipitation	Raster, 1 km	2000~2021	National Tibetan Plateau Scientific Data Center https://data.tpdc.ac.cn/
Potential evapotranspiration	Raster, 1 km	2000~2021
Crop production	Statistical data	2000~2021	China Rural Statistical Yearbook and Statistical Yearbooks of provinces https://www.stats.gov.cn/ http://tjj.shaanxi.gov.cn/ http://tjj.gansu.gov.cn/ http://tj.nmg.gov.cn/ http://tj.nx.gov.cn/ http://tjj.xinjiang.gov.cn/ http://tjj.qinghai.gov.cn/
Crop area		2000~2021
Population		2000~2021
Population density		2000~2021
Per capita food consumption		2000~2021
Carbon emission factor	Statistical data	-	China Products Carbon Footprint Database http://lca.cityghg.com/ the China Emission Accounts and Datasets
Crop coefficient	Statistical data	-	[6]

displays detailed information of all the data. Time series data were from 2000 to 2021. Raster data were resampled to 1 km spatial resolution and converted to WGS_1984_Albers in ArcGIS 10.2.

2.3. Research Framework

This research constructed a methodological framework to divide ecological management zones by evaluating the matching characteristics of ESSD associated with the WFE nexus. The structure has four stages. Initially, water yield (WY), carbon sequestration (CS), and food production (FP) were chosen to assess supply and demand based on the requirements of the WFE nexus. In the second stage, we evaluated the interrelationships among ESSD, specifically finding possible trade-offs and synergistic links. In the third step, the matching characteristics of ESSD were evaluated. Ultimately, based on steps 2 and 3, we designated ecological management zones and proposed targeted management strategies (Figure 2).

2.4. Methods

2.4.1. Quantification of ESSD

(1)

FP

FP, a vital supply service for the ecosystem, is crucial for ensuring human life and advancing sustainable development. In this research, the total output of the 10 main crops in WTL (wheat, maize, rice, tubers, bean, cotton, garden fruit, melon, oil, and vegetables) was used to characterize the FP supply.

Currently, research on FP demand is mainly expressed as population density and the product of per capita consumption [2,9]. But this only represents the direct demand for food, not the total demand. Therefore, based on the differences in consumption patterns, this research conducted a detailed analysis of the FP demand in WTL [20,27]. The calculation formulae are as follows:

$${FP}_{demand}={FP}_{zdemand}+{FP}_{jdemand}+{FP}_{sedmand}+{FP}_{lodemand}$$

(1)

$${FP}_{zdemand}=U_{perfood}\times P_{urban}+R_{perfood}\times P_{rural}$$

(2)

$${FP}_{jdemand}={\sum }_{j=1}^7{P}_j\times c_j\times r_j$$

(3)

$${FP}_{sedemand}=c_{seed}\times A$$

(4)

$${FP}_{lodemand}=0.05\times TY$$

(5)

where FP_demand is the total demand of food production (t); FP_zdemand, FP_jdemand, FP_sedemand, and FP_lodemand represent the direct, indirect, seed, and losses’ food production demand (t); U_perfood and R_perfood represent urban and rural residents’ per capita food consumption (t/person); P_urban and P_rural represent the urban and rural population (person); c_j is the feed conversion ratio of j; j is the type of livestock, poultry, eggs, and aquatic products, including seven categories: pork, beef, lamb, poultry, milk, eggs, and aquatic products; r_j is the proportion of crops of j (%); P_j is the production yield of j (t); c_seed is the seed usage per unit area (kg/hm²); A is the crop sowing area (hm²); TY is the crop yield (t).

(2)

WY

Freshwater is essential for supporting production activities and maintaining ecological balance [28]. To better evaluate water resources, Falkenmark proposed the notions of green and blue water. Blue water refers to the combined amount of surface and groundwater flow from precipitation [29], while green water is the sum of green water flow and storage [30,31]. Since blue water can be more directly utilized by production than green water, more focus has been placed on blue water supply and less on green water in previous research [9,11]. Actually, green water is essential for sustaining terrestrial ecological environments and supporting agricultural production [32,33,34]. Thus, this research comprehensively evaluated WY from blue and green water perspectives.

Firstly, the blue water yield (BWY) supply was quantified using the InVEST 3.13.0 Water Yield module [2,35]. Then, fully considering the actual situation of WTL, 50% was taken as a reasonable threshold for developing and utilizing local water yield, and the amount of BWY that can be supplied for agriculture was determined by the local agricultural water consumption ratio [36].

$$W_{blue}=0.5\times Y_i\times {\displaystyle \frac{AWU}{WU}}$$

(6)

where W_blue is the blue water yield supply for agriculture (m³); AWU is agricultural water consumption (m³); WU is the total water consumption (m³); Y_i is the blue water yield supply (m³).

The green water yield (GWY) supply for agriculture is quantified by the effective precipitation on the cultivated land area [6,37].

$$W_{green}=10\times P_{eff}\times CA$$

(7)

where W_green is the green water yield supply for agriculture (m³); CA is the cultivated land area (hm²); P_eff is effective precipitation (mm); 10 is a constant factor.

The consumption of water resources by crop production is considered as the WY demand. The water footprint, proposed by Hoekstra and Hung (2002) [38], can comprehensively reflect water resource usage in crop production [39]. Therefore, the WY demand was determined using the crop water footprint. The calculation formulae are as follows [40]:

$${WF}_{total}={WF}_{blue}+{WF}_{green}$$

(8)

$${WF}_{green}={WFP}_{green}\times TY$$

(9)

$${WFP}_{green}=10\times {ET}_{green}/Y$$

(10)

$${ET}_{green}=\min ({ET}_c,P_{eff})$$

(11)

$${WF}_{blue}={WFP}_{blue}\times TY$$

(12)

$${WFP}_{blue}=IRC/Y$$

(13)

$$IRC={\displaystyle \frac{W_A\times P_w^i}{I_A^i}}$$

(14)

$$P_w^i={\displaystyle \frac{\left({ET}_c^i-P_{eff}^i\right)\times I_A^i}{{\sum }_{i=1}^n\left[\left({ET}_c^i-P_{eff}^i\right)\times I_A^i\right]}}$$

(15)

where WF_total, WF_blue, and WF_green represent the total, blue, and green water footprint (m³); WFP_blue and WFP_green represent the blue and green water footprint of per unit crop production (m³/kg); TY is the crop yield (kg); ET_green is the green water evapotranspiration (mm); Y is the crop yield per unit (kg/hm²); ET_c and P_eff represent crop evapotranspiration (mm) and effective precipitation (mm), which were obtained by [6]. IRC is the irrigation water consumption of the crop per unit area (m³/hm²); W_A is the total irrigation water use of crops in WTL (m³); Iⁱ_A is the irrigation area of the crop (hm²); Pⁱ_W is the proportion of irrigation water consumption for crop i to the total irrigation water consumption in WTL [41].

(3)

CS

In this study, crop carbon carrying capacity was used to estimate the supply of CS. It denotes the capacity of crops to assimilate and sequester carbon dioxide via photosynthesis [42]. The calculation formula is as follows:

$$S={\sum }_{i=1}^{10}{C}_i\times Y_i\times (1-W_c)/E_f$$

(16)

where CS is the carbon sequestration of crops (t); C_i is the carbon uptake rate of crop i (%); Y_i is the economic yield of crop i (t); E_f is the economic coefficient of crop i; W_c is the water content of crop i (%). The carbon storage parameters of different crops are shown in

Table 2. Estimated parameters of CS for different crops.

Crop	Ci	Wc	Ef
rice	0.41	0.12	0.45
wheat	0.49	0.12	0.4
maize	0.47	0.13	0.4
bean	0.45	0.13	0.34
potato	0.42	0.7	0.7
oil	0.45	0.1	0.25
cotton	0.45	0.08	0.1
vegetable	0.45	0.9	0.6
garden fruit	0.45	0.9	0.7
melon	0.45	0.9	0.7

[43,44].

As for the demand for CS, the crop carbon footprint was used as the evaluation indicator, which was assessed by the total quantity of carbon emissions emitted across the crop’s life-cycle [45]. This can be calculated as follows:

$$\begin{gathered} {CF}_{sc}={CE}_{turn}+{CE}_{seed}+{CE}_{fertilizer}+{CE}_{pesticide}+{CE}_{agricultural film} \\ +{CE}_{machine}+{CE}_{irrigation}+{CE}_{\mathrm{o}\mathrm{i}\mathrm{l}} \end{gathered}$$

(17)

$${CE}_{turn}=SA\times {EF}_{turn}÷{10}^7$$

(18)

$${CE}_{seed}=SA\times {Unit}_{seed}\times {EF}_{seed}÷{10}^7$$

(19)

$${CE}_{fertilizer}=SA\times {Unit}_{fertilizer}\times {EF}_{fertilizer}÷{10}^7$$

(20)

$${CE}_{\mathrm{p}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{d}\mathrm{e}}=\mathrm{T}\mathrm{Y}\times {Unit}_{\mathrm{p}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{d}\mathrm{e}}\times {EF}_{\mathrm{p}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{d}\mathrm{e}}÷{10}^7$$

(21)

$${CE}_{\mathrm{a}\mathrm{g}\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{u}\mathrm{l}\mathrm{t}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{l} \mathrm{f}\mathrm{i}\mathrm{l}\mathrm{m}}=SA\times {Unit}_{\mathrm{a}\mathrm{g}\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{u}\mathrm{l}\mathrm{t}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{l} \mathrm{f}\mathrm{i}\mathrm{l}\mathrm{m}}\times {EF}_{\mathrm{a}\mathrm{g}\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{u}\mathrm{l}\mathrm{t}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{l} \mathrm{f}\mathrm{i}\mathrm{l}\mathrm{m}}÷{10}^7$$

(22)

$${CE}_{\mathrm{o}\mathrm{i}\mathrm{l}}=SA\times {Unit}_{\mathrm{o}\mathrm{i}\mathrm{l}}\times {EF}_{\mathrm{o}\mathrm{i}\mathrm{l}}÷{10}^7$$

(23)

$${CE}_{\mathrm{m}\mathrm{a}\mathrm{c}\mathrm{h}\mathrm{i}\mathrm{n}\mathrm{e}}=SA\times {Unit}_{\mathrm{d}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{l}}\times {EF}_{\mathrm{d}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{l}}÷{10}^7$$

(24)

$${CE}_{\mathrm{i}\mathrm{r}\mathrm{r}\mathrm{i}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}={WF}_{blue}\times \left(U_{oil}\times {EF}_{oil}+U_{\mathrm{e}\mathrm{l}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{t}\mathrm{y}}\times {EF}_{\mathrm{e}\mathrm{l}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{t}\mathrm{y}}\right)÷{10}^7$$

(25)

where CF_sc is the total quantity of carbon storage (t); CE_turn, CE_seed, CE_fertilizer, CE_pesticide, CE_{agricultural film}, CE_machine, CE_irrigation, and CE_oil are the carbon emission of farmland tillage, seed, fertilizers, pesticides, agricultural films, agricultural machinery, irrigation, and agricultural diesel oil usage, respectively; EF_i is the carbon emission factor of i (

Table 3. Carbon emission factors for different elements.

Element		Carbon Emission Factor	Element		Carbon Emission Factor
Seed	wheat	0.66 kgCO2/kg	Fertilizer	Nitrogen	1.53 kgCO2/kg
	maize	0.59 kgCO2/kg		Phosphate	1.63 kgCO2/kg
	bean	0.37 kgCO2/kg		Potash	0.65 kgCO2/kg
	potato	0.27 kgCO2/kg		Compound	1.77 kgCO2/kg
	oil	0.63 kgCO2/kg	Pesticides		4.93 kgCO2/kg
	melon	0.32 kgCO2/kg	Agricultural diesel		3.15 kgCO2/kg
	vegetable	0.18 kgCO2/kg	Agricultural machinery		0.18 kgCO2/kwh
	garden fruit	0.24 kgCO2/kg	Electricity for agricultural irrigation		0.97 kgCO2/kwh
	rice	1.84 kgCO2/kg	Agricultural film		5.18 kgCO2/kg
	cotton	0.35 kgCO2/kg	Farmland tillage		312.60 kgCO2/km2

) [46]; $SA$ is the planted area of crops (km²); Unit_i is the amount of i used per unit area of crop production.

2.4.2. Trade-Offs and Synergy Analysis of ESs

(1)

Temporal trade-off and synergy analysis

Spearman correlation analysis is normally utilized to assess trade-offs and synergies between two sets of ESs [47]. Synergistic effects refer to two sets of ESs changing simultaneously. Trade-offs are in the opposite direction [48]. In this study, this analysis was conducted using the R2024.04.0 package.

(2)

Spatial trade-off and synergy analysis

To gain a more in-depth understanding of the patterns in the spatial distribution of ESs, this study used geographically weighted regression (GWR) to capture the spatial interaction correlations of ESs [49,50,51]. Positive regression coefficients indicate spatial synergies, and negative regression coefficients indicate spatial trade-offs.

2.4.3. Matching Evaluation of ESSD

(1)

Quantity matching

The ecological supply–demand ratio (ESDR) was used to assess the ESSD quantity matching status [17].

$$ESDR={\displaystyle \frac{S-D}{\left(S_{max}+D_{max}\right)/2}}$$

(26)

where S and D are, respectively, each ecosystem services’ supply and demand.

(2)

Spatial matching

This study explored the spatial matching patterns of ESSD using the four-quadrant model. Firstly, the Z-score standardization method was used to standardize ESSD [18]. Secondly, a two-dimensional coordinate system was created with standardized supply and demand on the X- and Y-axes. Thirdly, according to the four quadrants’ differentiation, the spatial matching pattern was divided into high supply–high demand (quadrant I), low supply–high demand (quadrant II), low supply–low demand (quadrant III), and high supply–low demand (quadrant IV).

(3)

Risk identification

Risk identification integrates the ESDR with the trend of supply and demand over time, identifying areas that continue to change within a specific time range [22]. The supply–demand balance (C_v) is a metric that reflects the dynamic changes in the matching relationship of ESSD and is calculated as follows:

$$C_v=\sqrt{{\displaystyle \frac{S\times D}{{\left[\left(D\times S\right)/2\right]}^2}}}$$

(27)

when C_v approaches 1, coordination is the best; when approaching 0, coordination is the worst. Moreover, when ΔC_v > 0, the deficit or surplus gap decreases and vice versa.

Based on the results of the ESDR and ΔC_v, the risk level of ecological service supply and demand matching was divided into 5 levels: (1) moderate security (ESDR > 0, ΔC_v > 0); (2) high security (ESDR > 0, ΔC_v ≤ 0); (3) supply and demand balance (ESDR = 0); (4) moderate risk (ESDR < 0, ΔC_v > 0); and (5) high risk (ESDR < 0, ΔC_v ≤ 0).

2.4.4. Ecological Management Zoning Method

In this study, a comprehensive management zoning model for ecosystem services was constructed. This model was based on the principle of “partition control–classification strategy–hierarchical governance”, which combines ESs, integrates quantity and spatial matching characteristics between supply and demand, and considers supply and demand risk levels. The precise sequence of actions is as follows:

Step 1: Regarding partition control, three leading weak service functional areas were divided based on the standardized values of ESs’ supply, including the food production weak zone (F), carbon sequestration weak zone (C), and water yield weak zone (W).

Step 2: Regarding the classification strategy, based on the established four-quadrant model, the corresponding ecological strategy areas for the main weak ESs were divided, and the dominant direction of environmental restoration was clarified. Specifically, (1) the ecological avoidance zone (C) has a high supply and high demand; (2) the ecological reconstruction zone (R) has a low supply and high demand; (3) the ecological enhancement zone (I) has a low supply and low demand; and (4) the ecological protection zone (P) has a high supply and low demand.

Step 3: Regarding hierarchical governance, four ecological governance areas were divided based on identifying the supply and demand risks of the main weak ESs, including the critical governance zone (K), minor governance zone (M), potential governance zone (P), and governance buffer zone (S).

3. Result

3.1. Spatiotemporal Evolution of ESSD

3.1.1. Ess’ Supply

(1)

FP

Temporally, the FP supply in WTL first grew and subsequently dropped during 2000–2021, with the supply climbing from 65.31 × 10⁶ tons (2000) to 178.20 × 10⁶ tons (2016) and then declining to 167.82 × 10⁶ tons (2021). Different crop supplies showed varying patterns of change. The supply change rules of maize, cotton, melon, vegetables, garden fruits, and oil exhibited the same pattern, with a notable initial increase followed by a minor decline in the latter phase. And their changes were in line with the fluctuations in the overall FP supply. The supply of wheat, rice, and tubers showed a fluctuating trend. However, the supply of beans decreased over the past 22 years.

From the proportion of specific crop supply to the total, the supply of vegetables, maize, wheat, and garden fruits was significantly higher than other crops, making them the main contributors to the FP supply in WTL. Among them, vegetables were the main contributors, accounting for 30.14% to 39.48% of the FP supply. Maize and garden fruits followed, with average annual proportions of 17.89% and 16.35%, respectively (

Table 4. Specific crop supply in WTL from 2000 to 2021, unit: million tons.

Year	Rice	Bean	Wheat	Tubers	Maize	Vegetables	Cotton	Oil	Melon	Garden Fruits	Total
2000	0.96	1.05	12.91	3.77	11.67	19.69	1.73	2.18	3.60	7.76	65.31
2005	1.34	1.12	13.26	4.15	16.16	30.53	2.47	2.12	5.14	11.29	87.58
2010	1.49	1.04	16.74	4.48	24.06	52.55	3.03	2.81	9.92	22.40	138.52
2015	1.46	0.80	16.91	5.14	33.13	65.96	4.93	3.16	12.84	31.30	175.63
2020	1.45	0.77	13.19	4.96	30.39	57.22	5.22	2.95	11.79	34.20	162.14
2021	1.52	0.73	13.81	5.27	31.81	58.39	5.16	2.94	12.05	36.14	167.82

).

The distribution of FP supply in WTL showed significant variation. Lower values were mostly found in the northeastern parts of BGH, CDM, and QCXDB, while high values were primarily found in eastern HH, central parts of QCXXN, and QCXDN. Low-value regions dropped, while high-value areas significantly grew during the past 22 years. The importance of Xinjiang and the Hexi Corridor in ensuring local and even national food security was becoming progressively prominent (Figure 3a).

(2)

WY

Temporally, the BWY supply in WTL first increased and then decreased during 2000–2021, with the supply significantly (p < 0.05) increasing from 77.85 × 10⁹ m³ (2000) to 87.73 × 10⁹ m³ (2013) and then reducing to 84.58 × 10⁹ m³ (2021). The GWY supply fluctuated, changing from 21.85 × 10⁹ m³ (2000) to 28.04 × 10⁹ m³ (2021), an increase rate of 3.89 × 10⁹ m³/a. The WY supply showed an upward trend, followed by fluctuating changes. The average value was 105.27 billion m³, ranging from 96.98 to 110.55 billion m³. The highest and lowest values were seen in 2019 and 2004, respectively (Figure 4).

Between times, WY supply spatial differential patterns were similar. BWY supply exhibited a geographical distribution characterized by high values in the west and low values in the east. The places with high value were mostly concentrated on both sides of the “Qice line” and the Hexi Corridor, while the areas with low value were generally found in CDM (Figure 3b). GWY supply was high in the edge and low in the center. The majority of the high-value regions were found in QCXXB, HH, and BGH, which was strongly correlated with the basin’s cultivated land distribution and precipitation (Figure 3c). WY supply followed a similar geographical trend to BWY supply. The high-value areas were primarily concentrated on both sides of the “Qice line” and the Hexi Corridor, which had a high agricultural development degree (Figure 3d).

(3)

CS

From 2000 to 2021, the supply of CS increased, with an average of 66.60 million tons and a range of 40.61 to 88.44 million tons. The highest and lowest values were seen in 2017 and 2001, respectively. Different crop carbon storage showed different change characteristics. The variation pattern of carbon storage in maize, cotton, melon, vegetables, garden fruits, and oil was similar, with a notable increasing tendency in the early stage and a later modest drop. The carbon storage of wheat, rice, and tubers showed a fluctuating increasing trend, while the supply of beans decreased in the past 22 years. From the proportion of specific crop carbon storage to the total, wheat, maize, and cotton had significantly higher carbon storage than others, making them the main contributors to CS in WTL. Among them, maize had the highest contribution to CS, accounting for 28.14% to 39.21%. Next were wheat and cotton, with an average annual proportion of 23.96% and 22.10%, respectively (

Table 5. Specific crop carbon storage in WTL from 2000 to 2021, unit: million tons.

Year	Rice	Bean	Wheat	Tubers	Maize	Vegetables	Cotton	Oil	Melon	Garden Fruits	Total
2000	0.77	1.21	13.92	0.68	11.93	1.48	7.18	3.52	0.23	0.50	41.41
2005	1.08	1.29	14.30	0.75	16.52	2.29	10.22	3.44	0.33	0.73	50.93
2010	1.20	1.19	18.05	0.81	24.59	3.94	12.55	4.56	0.64	1.44	68.96
2015	1.17	0.92	18.23	0.93	33.87	4.95	20.40	5.12	0.83	2.01	88.42
2020	1.16	0.89	14.22	0.89	31.07	4.29	21.60	4.78	0.76	2.20	81.86
2021	1.22	0.84	14.88	0.95	32.51	4.38	21.36	4.76	0.77	2.32	84.01

).

The spatial distribution pattern of CS in various times differed insignificantly, showing a distribution feature of “high in the edge and low in the center”. High-value regions were mostly in HH, QCXXB, and QCXXN, whereas low-value areas were in CDM. This distribution pattern was closely related to the size of the regional crop planting area. Moreover, the differences between the primary agricultural areas and non-agricultural planting areas became increasingly significant over time (Figure 3e).

3.1.2. Ess’ Demand

(1)

FP

The FP demand in WTL significantly increased from 50.63 × 10⁶ tons (2000) to 96.37 × 10⁶ tons (2021), an average annual growth rate of 4.11% (p < 0.05). Specifically, the demand for vegetables, fruits, and tubers increased yearly from 2000 to 2021. The demand for oil, cotton, beans, rice, wheat, and maize showed a significant upward trend in the early stage and slightly decreased later. From the proportion of specific crop demand to the total, the demand for vegetables, maize, and rice was significantly higher than for other crops, with average annual proportions of 46.89%, 14.95%, and 11.80%, respectively. However, it should be noted that the proportion of grain demand showed a downward trend, indicating that although food consumption in WTL was mainly based on plant-based and grain consumption, more emphasis had been placed on the balance of dietary structure and nutrition in recent years (

Table 6. Specific crop demand in WTL from 2000 to 2021, unit: million tons.

Year	Rice	Beans	Wheat	Tubers	Maize	Vegetables	Cotton	Oil	Fruits	Total
2000	9.80	1.80	5.18	1.57	5.34	20.95	0.48	2.10	3.40	50.63
2005	10.57	2.14	5.64	1.70	7.65	27.26	0.66	2.30	3.93	61.85
2010	10.75	2.34	6.18	1.75	9.33	33.81	0.72	2.59	5.13	72.61
2015	11.13	2.98	6.56	1.94	11.24	42.11	0.94	3.78	6.40	87.09
2020	10.92	2.81	6.15	2.27	9.86	48.60	0.97	3.76	7.84	93.18
2021	11.07	3.02	6.29	2.25	10.58	49.95	0.96	3.87	8.39	96.37

Fruit is the sum of garden fruits and melon.

).

The demand for FP in WTL exhibited a distribution characterized by high levels at the edge and low levels at the center. The locations experiencing significant demand were primarily located in areas with dense populations or high levels of urbanization (Figure 5a).

(2)

WY

The overall demand for WY had a fluctuating upward trend, increasing from 83.30 × 10⁹ m³ (2000) to 97.66 × 10⁹ m³ (2021), representing a substantial rise of 38.59%. BWY demand increased by 12.96% from 69.38 × 10⁹ m³ (2000) to 78.38 × 10⁹ m³ (2021). GWY demand showed an inverted U-shaped trend, rising from 13.92 × 10⁹ m³ (2000) to 24.16 × 10⁹ m³ (2016), then gradually decreasing to 19.28 × 10⁹ m³ (2021). The annual average proportion of BWY was as high as 80.33% (

Table 7. Specific crop water demand in WTL from 2000 to 2021, unit: billion m³.

Index	Year	Rice	Bean	Wheat	Tubers	Maize	Vegetables	Cotton	Oil	Melon	Garden Fruits	Total
Total water	2000	4.65	3.80	23.00	4.16	12.40	5.25	12.62	9.70	0.99	6.71	83.30
	2005	4.14	3.91	19.89	4.48	16.95	7.01	15.02	7.86	1.45	12.82	93.52
	2010	3.67	2.81	19.07	4.89	17.97	8.56	14.57	8.33	1.91	17.80	99.59
	2015	3.09	1.85	18.88	3.81	21.74	9.95	18.28	6.93	2.16	18.26	104.94
	2020	2.73	1.37	15.71	3.31	23.30	8.85	18.43	6.23	1.52	14.22	95.66
	2021	2.46	1.43	16.81	3.23	25.55	8.19	18.73	5.65	1.57	14.02	97.66
Blue water	2000	4.56	3.13	18.97	2.51	10.37	4.05	11.65	8.39	0.90	4.84	69.38
	2005	4.02	3.20	16.43	2.85	14.23	5.37	13.63	6.68	1.31	10.29	78.01
	2010	3.53	2.24	14.65	2.85	13.64	6.19	13.06	6.87	1.70	13.79	78.52
	2015	2.97	1.44	14.39	2.12	16.97	6.95	15.97	5.49	1.92	13.79	82.01
	2020	2.60	0.93	12.76	1.56	18.26	6.43	16.74	4.85	1.26	10.38	75.76
	2021	2.34	0.95	13.20	1.37	20.12	6.55	17.30	4.24	1.26	11.04	78.38
Green water	2000	0.09	0.67	4.03	1.65	2.03	1.2	0.97	1.31	0.09	1.87	13.92
	2005	0.12	0.71	3.46	1.63	2.72	1.64	1.39	1.18	0.14	2.53	15.51
	2010	0.14	0.57	4.42	2.04	4.33	2.37	1.51	1.46	0.21	4.01	21.07
	2015	0.12	0.41	4.49	1.69	4.77	3	2.31	1.44	0.24	4.47	22.93
	2020	0.13	0.44	2.95	1.75	5.04	2.42	1.69	1.38	0.26	3.84	19.9
	2021	0.12	0.48	3.61	1.86	5.43	1.64	1.43	1.41	0.31	2.98	19.28

). These indicated that although GWY significantly contributed to agriculture development in WTL, crop production still relies on BWY. Irrigated agriculture was still the main force of agricultural output in WTL and the cornerstone of ensuring regional food security.

Maize (18.84 billion m³), wheat (18.52 billion m³), garden fruits (14.37 billion m³), and cotton (16.69 billion m³) contribute the most to the WY demand in WTL, accounting for 70.97%. A further analysis of the water demand trend for these crops found that wheat’s water demand marginally dropped from 23.00 ×10⁹ m³ (2000) to 16.81 ×10⁹ m³ (2021). Meanwhile, the water demand for maize, garden fruits, and cotton significantly increased by 2.06, 2.09, and 1.48 times, respectively. In addition, these same crops were also the main contributors to the blue water demand in WTL, accounting for 71.99% of the total. Based on the water demand composition structure, the blue water demand was the main component of the ten typical crops in WTL (Table 7).

The demand for WY exhibits significant heterogeneity in spatial distribution. The regions with high values of total yield, BWY, and GWY were predominantly situated on the eastern and western sides of WTL. Conversely, the places with poor value were primarily concentrated in the center of WTL. Influenced by the geographical distribution of water resources and the degree of agricultural growth, areas with higher levels of agricultural planting had higher crop water consumption, leading to relatively high water demand (Figure 5b–d).

(3)

CS

CS demand increased from 17.95 × 10⁶ tons (2000) to 33.07 × 10⁶ tons (2015) and then declined to 32.74 × 10⁶ tons (2021). This decrease was primarily due to the “Weight Loss and Drug Reduction” launch by the Ministry of Agriculture and Rural Affairs in 2015, which aimed to control carbon emissions. Wheat, maize, vegetables, and cotton had significantly higher CS demand than other crops. Among them, maize had the highest CS demand, accounting for 13.82% to 24.65% of the total. Next were wheat and cotton, with an average annual proportion of 18.82% and 14.59%, respectively. Except for the CS demand for beans, which declined during the research period, the CS demand for other crops showed a notable increase in the first stage and minor decrease in the latter stage (Figure 6).

The demand for CS was not evenly distributed in WTL from 2000 to 2021. The high-value regions were predominantly located in HH, QCXXB, and QCXXN, whereas the low-value areas were mainly in CDM. Among these areas, HH had the highest CS demand, accounting for 39.48% to 46.84% of the total in WTL. This was mainly due to HH being one of China’s crucial agricultural production areas, which plays a substantial role in safeguarding national food security. But, due to the continuous investment in pesticides and fertilizers to promote the rapid development of its planting industry, there have been prominent pollution problems and increased carbon emissions from the industry. On the contrary, the CS demand in CDM was less than 1% of the total, mainly due to the fact that the region was mainly industrial, resulting in lower carbon emissions from crop production (Figure 5e).

3.2. Trade-Offs and Synergies of ESs

3.2.1. Supply Trade-Offs and Synergies between ESs

There were some substantial positive connections between the supply of ESs during 2000~2021. Notably, FP displayed the most significant synergistic effect on CS (p < 0.05). However, over time, the synergistic interaction among ESs supply declined, while the trade-off effect increased. There was even a trade-off effect between WY and FP in 2020, suggesting a reciprocal causality between the paucity of water resources and food production in WTL (Figure 7).

Although the various ESs’ supply demonstrated a synergistic interaction at the city size, the geographical distribution pattern differed. Specifically, the strong synergistic effects of FP and CS were mainly distributed in HH. FP and WY had a strong synergistic relationship on both sides of the “Qice line”. WY and CS had a trade-off relationship in QCXXN and HH and had a strong synergistic relationship in BGH (Figure 8).

3.2.2. Demand Trade-Offs and Synergies between ESs

A strong synergistic link was seen between demands for FP, WY, and CS (p < 0.05). From 2000 to 2021, the synergistic relationship between demands for WY and CS and FP and WY showed an increasing trend followed by a slight decrease, while the synergistic relationship between demands for FP and CS increased (Figure 7). Different ecosystem service demand pairs exhibited a similar spatial pattern. The area with a high synergistic association was mostly found in HH and BGH, whereas the area with a weak synergistic relationship was located on both sides of the “Qice line” (Figure 8).

3.3. Supply–Demand Matching Characteristics of ESs

3.3.1. Quantity Matching

During the research period, the ESDR for FP and CS in WTL were all above zero, showing that these two ESs had a surplus supply and could satisfy internal demand or consumption. The ESDR for WY was less than 0, indicating that the WY supply could not meet the demand. In particular, the ESDR for FP increased from 0.11 in 2000 to 0.65 in 2016, then decreased to 0.52 in 2021. The ESDR for WY declined from −0.03 in 2000 to −0.17 in 2021, indicating that the degree of supply shortage was gradually expanding. This situation occurred for two reasons: firstly, the water supply was becoming progressively insufficient, and secondly, the scale of crop cultivation was gradually expanding. However, the ESDR for CS increased from 0.39 in 2000 to 0.84 in 2021, indicating that the crop ecosystem in WTL was a carbon sink system, and the carbon sink function was gradually increasing. This increase was primarily due to (1) China’s execution of the carbon emission reduction strategy, and (2) the increase in the crop sowing area promoted carbon absorption. In general, the annual average ESDR for these three ESs was sorted as CS (0.66) > FP (0.39) > WY (0.15). And the decrease range of WY was the largest. This suggested that the water resource constraints faced were further tightening, which may exacerbate environmental deterioration (

Table 8. ESDR for ecosystem services in WTL from 2000 to 2021.

Year	FP	CS	WY
2000	0.11	0.39	−0.03
2001	0.08	0.37	0.02
2002	0.12	0.44	0.07
2003	0.13	0.42	0.03
2004	0.18	0.49	−0.25
2005	0.19	0.51	−0.05
2006	0.22	0.54	−0.15
2007	0.27	0.46	−0.18
2008	0.35	0.60	−0.26
2009	0.46	0.64	−0.31
2010	0.48	0.68	−0.01
2011	0.49	0.68	−0.18
2012	0.51	0.75	−0.21
2013	0.54	0.79	−0.36
2014	0.59	0.90	−0.48
2015	0.64	0.91	−0.33
2016	0.65	0.89	−0.09
2017	0.57	0.92	−0.09
2018	0.44	0.77	−0.04
2019	0.51	0.77	−0.12
2020	0.50	0.81	−0.16
2021	0.52	0.84	−0.17

).

The regions exhibiting a negative ESDR for FP were mostly concentrated in HH, CDM, and the Urumqi urban agglomeration in Xinjiang. These regions were either economically developed areas with high human activity or crucial ecological functional areas for water source conservation in China. The demand for FP services was high, leading to a prominent imbalance between supply and demand. The regions exhibiting a positive ESDR for FP were mostly concentrated in the southern part of the “Qice line”, HX, and the northeast part of HH, which are the primary agricultural production areas in WTL (Figure 9a). For WY, about half of the cities in WTL were in deficits, mainly involving central irrigated agriculture and oasis agriculture areas. From 2000 to 2021, the ESDR for WY in 24 cities showed a significant downward trend, mainly concentrated in HX and on both sides of the Qice line (Figure 9b). For CS, most cities were in surplus. The regions with high-value ESDRs for CS were primarily situated in QCXXB, QCXDB, and HX. From 2000 to 2021, the ESDR for CS in 42 cities showed an increasing trend, while that for 13 cities showed a decreasing trend, mainly concentrated near urban clusters within HH (Figure 9c).

3.3.2. Spatial Matching

In 2000, the areas of low supply–low demand, high supply–high demand, low supply–high demand, and high supply–low demand for FP accounted for 71.97%, 20.61%, 3.04%, and 4.03% of the total area, respectively. The regions with low supply and low demand were mostly located in QCXDB, QCXDN, CDM, the western part of HH, and the northeast and west parts of BGH. The high supply–high demand areas were mainly clustered in the Tianshan and Tarim River systems in Xinjiang, southeastern Gansu, central Inner Mongolia, and Shaanxi. High supply–low demand areas only existed in Tacheng, Shihezi, and Zhangye. Meanwhile, low supply–low demand areas were scattered in the transition zone of high supply–high demand areas. In 2021, the areas of low supply–low demand, high supply–high demand, low supply–high demand, and high supply–low demand for FP accounted for 43.83%, 16.45%, 13.18%, and 26.54%. Compared to 2000, the proportion of high supply–low demand areas significantly increased and expanded towards Xinjiang along the Hexi Corridor (Figure 10a).

The WY in WTL during the research period was dominated by high supply–high demand areas, followed by low supply–low demand areas, high supply–low demand areas, and low supply–high demand areas. The geographical differentiation patterns remained largely consistent over several periods. Taking 2021 as an example, the high supply–high demand areas were distributed in Xinjiang, covering 39.61% of the total area. The low supply–low demand areas were mainly concentrated in QCXDB, BGH, and the central and eastern parts of HH, accounting for 33.58%. The high supply–low demand areas were mainly clustered in CDM, accounting for 17.17%. The low supply–high demand areas were primarily located in the central part of BGH and the northwest part of HX, accounting for 9.64%. In comparison to 2000, the Tacheng prefecture shifted from high supply–low demand to high supply–high demand in 2021, leading to increasingly prominent supply–demand contradictions (Figure 10b).

The CS in WTL was characterized by low supply–low demand areas, followed by high supply–high demand areas. In 2000, the low supply–low demand areas were mostly situated in the eastern region of the “Qice line”, the western part of HH, CDM, and HX. The high supply–high demand areas were scattered in the western region of the “Qice line”, the central part of BGH, and the southeast part of HH. The low supply–high demand areas were primarily located in QCXXN and the eastern part of HH. Compared to 2000, seven cities shifted from low supply–high demand or high supply–low demand to low supply–low demand areas or high supply–high demand in 2021 (Figure 10c).

3.3.3. Risk Identification

For FP, the supply and demand security areas covered 74.46%. This result means that in most cities of WTL, the supply of FP exceeded the demand, and the risk of supply and demand imbalance was relatively low. The high-risk areas were mainly distributed in Qinghai, Xi’an, Urumqi, and BGH, accounting for 25.54%. For WY, 20 cities were at high risk, primarily situated in HH and both sides of the “Qice line”, accounting for 23.99%. Twelve cities were at moderate risk, mainly involving QCXDB and BGH, accounting for 22.65%. Four cities were in moderate security, mainly situated in Qinghai. Nineteen cities in CDM, QCXXN, QCXDN, and the middle part of HH were in high security, accounting for 42.92%. As for CS, the demand of all 55 cities was less than the supply, and the surplus gap increased, indicating that the CS in these 55 cities was in a high-security state (Figure 11).

3.4. Ecological Management Zoning

Using the “partition control–classification strategy–hierarchical governance” principle, this research identified ecological management zones, combining weak ecosystem service and supply–demand matching characteristics.

In terms of partition control, according to the standardized values of ESs’ supply, we divided it into the three leading weak service functional areas, including the F zone, C zone, and W zone. Thirty cities belong to the W zone, mostly located in Xinjiang, HX, and the northeast part of HH, covering 58.42% of the total area. Twenty-four cities belong to the F zone, mainly distributed in CDM, BGH, and the western part of the “Qice line”, accounting for 39.94%. Only Ulanqab City was in the C zone (Figure 12a).

In terms of the classification strategy, the C zone (high supply–high demand) was mainly distributed within Xinjiang and Shaanxi, accounting for 30.26%. The R zone (low supply–high demand) accounted for 21.62%, sporadically distributed in HH, HX, and BGH. The I zone (low supply–low demand) constituted a significant amount, reaching up to 46.61%, and was concentrated in 29 cities in HH, CDM, and other regions (Figure 12b).

In terms of hierarchical governance, on the city scale, the number of K zones was the highest (33 cities), covering 45.03% of the total area. The next highest was the S zone, with 13 cities accounting for 35.25%. Finally, the M zone included only nine cities (Figure 12c).

Overall, the C zone mainly comprised the critical and buffer governance zone of WY. The R zone mainly comprised the governance buffer zone of FP and the critical governance zone of WY. The I zone was mainly based on critical treatment in FP and WY (Figure 12d).

4. Discussion

4.1. Improved Approach to Assessing Ecosystem Services’ Supply and Demand

Although several studies have been conducted on ESs’ supply, demand quantification is still in its incipient stage. Theories and methods in this field are still immature, and many aspects need further exploration [5,16]. Firstly, previous studies have often used the InVEST model to calculate the supply of WY [9,23] and agricultural water consumption as the demand for WY [11,52,53]. However, referring to the concept of water resources, WY can be divided into BWY and GWY. However, the water production calculated by the InVEST model only represents the supply of BWY [2,35]. And agricultural water consumption amplifies crop demand for WY [54]. Secondly, the demand for FP should not only consider direct consumption demand but also indirect consumption demand, seed use, and loss demand [20,27]. Thirdly, the current description of carbon sequestration services is too rough [12,55]. However, some indicators may not fully and accurately represent the relevant supply and demand [56,57,58]. Thus, building high adaptability and matching ES demand indicators has become a difficult and key point in evaluating supply–demand relationships. To enhance the precision of the findings, this research opted for more precise and novel measurement indicators of ESSD. These measurement indicators can be used not only in northwest China but also globally, providing new insights for measuring the supply and demand of ecosystem services within the WFE context.

4.2. Rationality of Ecological Management Zoning

By calculating ESSD, social and natural ecosystems’ actual supply and demand situation can be more accurately reflected [59]. Therefore, it is essential to analyze the correlation between the supply and demand of ESs and establish ecological management zones to optimize the coordinated growth of ecosystems and societal systems. Currently, research on the matching of ESSD mainly includes quantitative evaluation, trade-offs/synergies, spatial matching, flow transmission paths, and more [60,61,62]. Only a limited number of studies have incorporated ESSD risk issues into ecological management zoning [22,63]. However, compared to research that only examines a single matching indicator [64,65], the zoning method based on the combination of supply and demand risk, quantity, and spatial matching characteristics can comprehensively reflect the complex relationship between natural systems and human society from time–space and static–dynamic perspectives. Furthermore, this research’s zoning result overlaps with China’s ecological function zoning, which also indicates the rationality of the zoning method used in this research.

4.3. Governance Strategies for Diverse Zones

Research on ESSD and their zoning management can help shift regional ecological governance from theory to practice [9,11]. This study not only considered the spatiotemporal matching characteristics of supply and demand but also used supply and demand risk identification as the principle for ecological management zoning judgment for the first time. This study also proposed appropriate management recommendations according to the characteristics of different zones.

In the ecological prevention zone, the contradiction between water scarcity and maintaining food supply capacity is acute. It is important to effectively manage and regulate water usage, establish a green and sustainable agriculture development system, and foster a mutually beneficial relationship between ecology and economy. In the ecological reconstruction zone, the critical issue is low supply–high demand imbalances, and the contradiction is sharpening. Hence, this region should prioritize reducing the interference of human activities on natural ecosystems [9], optimizing land use structure, and improving regional ecosystem services’ supply capacity [35]. In addition, this region is also the main input area for ecological services in WTL and can ensure local ecosystem services’ supply stability through coordinated ecosystem services’ flow between regions [23]. As for the ecological enhancement zone, it is the primary food production “cold spot” in WTL, but it has abundant natural reserves [22,26]. The supply–demand balance of food in this zone can be achieved through coordinated food flow between regions. Therefore, our main recommendations are as follows: (1) Stringent conservation and oversight procedures must be enforced in established nature reserves, lakes and wetlands, and other sites necessitating specific safeguarding. (2) The pace of ecological barrier construction should be accelerated. (3) Ecotourism and ecological agriculture should be developed to reap green dividends and explore a win–win path of ecological protection and significant ecological and economic benefits [17].

Meanwhile, the phenomenon of the cross-regional flow of ecological service supply in WTL cannot be ignored [26]. Thus, we also suggest establishing a differentiated, market-oriented, and diversified ecological compensation mechanism [66,67] to promote inter-regional collaborative ecosystem governance, which will also be the primary area of attention for future study.

5. Conclusions

This study improved the research method of ESSD from the perspective of the WFE nexus, evaluated the supply–demand status of ESs, constructed an ecological management zoning framework based on the time–space and static–dynamic characteristic of supply–demand matching, and proposed corresponding governance strategies. The primary findings are as follows:

(1)

Throughout the research period, except for the inverted U-shaped change in demand for GWY and the supply for BWY, all other ESSDs significantly increased. Maize, wheat, cotton, vegetables, and garden fruits had a higher demand for ESs. Spatially, the supply and demand of ESs exhibited significant heterogeneity, with most showing a pattern of “high in the west and low in the middle”.

(2)

All three ESs exhibited a synergistic relationship. But over time, the synergistic effect weakened. Spatially, the strong synergistic relationship between FP supply and WY supply, FP supply and CS supply, and WY supply and CS supply was mainly distributed in Xinjiang, HH, and BGH. Meanwhile, the synergistic relationship between ESs’ demands all presented the spatial pattern of “high in the east and low in the west”.

(3)

The ESDR for FP and CS in WTL increased, while the ESDR for WY decreased, and the supply was smaller than the demand. Spatially, the distribution pattern of the ESDR for FP and WY was the opposite. CS showed a supply and demand surplus in most cities. The FP and CS were dominated by the low supply–low demand space match, whereas the WY was dominated by the high supply–high demand space match. Further, FP was the highest risk in CDM, and WY was the highest risk in HX, HH, and both sides of the “Qice line”, while CS was highly safe in all cities.

(4)

Based on the features and matching patterns of ESSD, a spatial management framework was constructed to delineate ecosystem management zones and provide suggestions for different zones. Meanwhile, the proposed framework and adopted indicators offer new insights into understanding ecological governance under the WFE nexus.