1. Introduction
Since the 21st century, the contradiction between global economic and social development and resources and environment has become more prominent, and the international community has paid more and more attention to sustainable development. The United Nations has proposed global SDGs (sustainable development goals), and major countries in the world have also proposed national action plans. Sustainable development emphasizes the coordinated relationship between population, resources, environment, and development, and creates a healthy and sustainable resource and environmental foundation for future generations [
1,
2]. Biocapacity evaluation has become an important part of regional sustainable development research.
The theory of biocapacity and ecological footprint was first put forward by Rees et al. [
3]. It emphasizes the material and energy basis of the operation of life systems, ecosystems, and human social systems [
4]. In this theory, biocapacity quantifies the ability of ecology, environment, and resources to support the survival of humans and other organisms, and is a core indicator for evaluating the basis of regional sustainable development [
5]. In recent years, scholars have studied biocapacity from different scales and perspectives, such as regional biocapacity [
6], national biocapacity [
7], global biocapacity [
8], spatial-temporal changes of biocapacity [
9], and future simulation of biocapacity [
10]. More importantly, scholars have conducted in-depth and extensive research on the effects of biocapacity stability and its changes, such as the restriction of biocapacity on economic growth and human capital [
11,
12], the impact of biocapacity changes on ecosystem services [
13], the relationship between biocapacity and environmental management level [
14], and the relationship between biocapacity and subjective well-being index [
15]. These studies have enriched the theory and application of biocapacity, indicating that the stability of biocapacity plays an important role in the stability of ecological services, environmental improvement, and regional economic development. Their research results can provide a scientific basis for targeted governance by national and local governments, and are of great significance for the promotion of regional and global sustainable development.
In the calculation of biocapacity, the area and productivity of biologically productive land are basic elements [
16]. Regional land use/cover change (LUCC) will directly cause changes in biocapacity and determine the spatial-temporal change pattern of biocapacity [
17]. Therefore, historical land use data and future scenario data play an important role in the evaluation of biocapacity. Carrying out remote sensing interpretation of land use, or selecting land use data products with feasible accuracy and continuous time, to simulate future scenarios of LUCC, is the key to biocapacity prediction research.
In terms of land use simulation methods, the most widely used models include the CLUE-S (Conversion of Land Use and its Effects at Small Region Extent) model [
18], Agent–CA (Agent–Cellular Automata) model [
19], FLUS (Future Land Use Simulation) model [
20], and CA–Markov (Cellular Automata–Markov) model [
21]. The CLUE-S model integrates spatial analysis technology and the system theory method. It is a mature and widely used dynamic model, especially on a small area scale [
22]. However, the CLUE-S model sets land use demand and elasticity coefficients based on personal experience, and its results are easily affected by subjective factors [
23]. The Agent–CA model adds human and social factors to the natural and continuity simulation of the CA (Cellular Automata) model through the ABM (Agent-Based Model), which can easily explore urban land development scenarios under different policies [
24]. However, the ABM relies on survey data to define the agent’s behavioral rules, and the simulation results are highly subjective [
23]. The FLUS model uses an ANN (Artificial Neural Network) to calculate land suitability, and then uses a CA model with an adaptive inertial mechanism to simulate land use changes [
25,
26]. The ANN brings the land suitability assessment closer to human thinking, but there is also a “black box” problem, which is that researchers cannot know the mechanism of land use change. The CA–Markov model organically combines the long-term prediction advantage of the Markov model with the complex system simulation capability of the CA model [
27,
28]. It uses the Markov area transfer matrix as the area evolution target of the CA model [
29], which solves the key problem that LUCC simulation has difficulty achieving spatial-temporal synchronization, and the simulation effect is excellent [
30].
The Loess Plateau in Northern Shaanxi is the core area of China’s Loess Plateau. The Cenozoic red soil layer and the loose loess layer, with a thickness of dozens to more than 100 m, cover the Mesozoic bedrock foundation. After a long period of water cutting, soil erosion, and human activities, the unique landform of “hundreds and thousands of hills and valleys” is finally formed. On the whole, the vegetation in this area has been widely destroyed, the soil erosion is serious, and the ecological environment is fragile. In 1999, the Chinese government launched the GGP (the Grain to Green Program), trying to restore part of the farmland to forest and grassland to improve the regional ecological environment [
31]. In recent years, scholars have studied the effects of the GGP implementation and changes in the regional ecological environment from multiple aspects such as vegetation coverage [
32], soil and water conservation [
33], and ecological services [
34]. Relevant results show that, since 2000, the soil and water conservation capacity of the Loess Plateau has been significantly enhanced, vegetation restoration and soil erosion control have achieved good results [
33], regional hydrological regulation capacity has been significantly improved, carbon storage has increased [
34], and vegetation coverage and vegetation quality has improved [
32]. These in-depth and detailed studies provide a scientific basis for the government to formulate policies for ecological construction, regional planning, and sustainable development. However, the existing research also has some shortcomings, such as focusing more on historical processes rather than future trends, single ecosystem types and processes rather than complex changes in multiple ecosystem types, and environmental protection policy and environmental performance evaluation rather than quantitative study of biocapacity.
Given the insufficient quantitative analysis of biocapacity, insufficient understanding of spatial distribution laws, and insufficient clarity of future development trends in the study of sustainable development of the Loess Plateau, this article applies the logistic regression method to grasp the laws of LUCC based on authoritative land use data. Furthermore, we apply the CA–Markov model and biocapacity model to carry out a quantitative and spatial analysis of the future land use and biocapacity of the Loess Plateau in Northern Shaanxi. The research aims to answer the following three key questions:
- (1)
How to construct a set of biocapacity evaluation models suitable for the Loess Plateau in Northern Shaanxi?
- (2)
What will be the spatial distribution pattern of the biocapacity of the Loess Plateau in Northern Shaanxi in the future?
- (3)
In response to future changes in the biocapacity, what strategies should the government take?
4. Discussion
4.1. Simulation Accuracy Analysis
In the process of the CA–Markov simulation, the cycle number and CA filter are important parameter settings [
49]. After many experiments, the authors finally decided to use a 5 × 5 filter and 10 cycles, taking into account the running speed and simulation accuracy. Before the scenario simulation of land use/cover in 2030, the authors used the historical data of 2000 and 2010 to simulate the land use/cover in 2020 and compared it with the satellite remote sensing interpretation data (Globeland30 2020 data). The results show that, compared with the reference Globeland30 2020 data, the Kappa coefficient of the simulated data in 2020 is 0.9033 (it is generally believed that the simulation results are reliable when the Kappa coefficient is not less than 0.75 [
50]), and there is no significant difference in the spatial distribution pattern between the simulation results and the satellite remote sensing interpretation data.
Further accuracy analysis on a pixel scale (
Table 5) shows that the overall accuracy (OA) of the simulation results in 2020 is 93.77%, the user accuracy and producer accuracy of farmland, forest, and grassland are higher than 90%, the user accuracy and producer accuracy of unused land are higher than 88%, and the user accuracy of built-up land and water area is relatively low (76.67% and 79.83%, respectively). This is because the area base of built-up land and water area in the study area is very small, and a small area difference will lead to a large error ratio. Because farmland, forest, and grassland constitute the main ecosystem type of the study area (the total area of the three accounts for more than 97%), it can be considered that the CA–Markov model can meet the requirements of this research, and the selected driving factors, filter, and cycle times are reliable.
In the simulations performed by scholars using other models, the OA of the CLUE-S model was 79.25% [
51], the OA of the FLUS model reached 83% [
52], and the OA of the Agent–CA model reached 90.40% [
53]. In this study, the OA of the simulation result of the CA–Markov model is higher than 93.77%, which shows that the CA–Markov model has an excellent effect.
4.2. Uncertainty Analysis
In terms of the selection of driving factors for LUCC, we refered to previous studies [
40,
41,
42], and comprehensively considered the actual economic and social development of the study area. A total of 10 driving factors in four categories, including population density, per capita GDP, elevation, slope, precipitation, temperature, and distance to the road network, were selected. Some unconsidered secondary factors (such as agricultural industry structure, soil types, nutrients, and real estate prices) may also affect potential changes in land use. However, if new impact factors are added in the future, they may still have an impact on the results [
54].
Further analysis of the goodness of fit (
Table 6) of the simplified land change driving model in this study shows that the area under the ROC curve of the logistic regression results of each category is greater than 0.87. It is believed that the explanatory power of the regression results is very good, and the selected 10 driving factors are reliable (it is generally believed that when the area under the ROC curve is not less than 0.7, the explanatory power of the regression results is good [
55]).
The basis of the CA–Markov model simulation is to determine future land targets based on historical change laws of land use [
21]. This foundation is based on the assumption that the comprehensive environmental conditions remain unchanged. If the central and local governments make major adjustments to land use policies on the Loess Plateau in Northern Shaanxi in the future, or the economic and sociological basis of land use changes undergo major changes, the simulation results of the CA–Markov model will have major deviations. Taking into account the determination of the Chinese government to carry out the construction of ecological civilization and the steady development of China’s regional economy and society, the authors believe that the GGP in the Loess Plateau will continue to be consolidated, and the level of regional economic and social development will also be steadily improved. Therefore, the environmental conditions of the Loess Plateau in Northern Shaanxi will not change significantly, and the simulation results of the CA–Markov model will be feasible. This is consistent with the research conclusions of scholars on the Loess Plateau [
10,
40].
In biocapacity calculations, the yield factor, the equivalence factor, and land use/cover type are the key factors. In this study, the average yield factor for China is used to represent the yield level of the Loess Plateau in Northern Shaanxi. Verification by the China Statistical Yearbook shows that China’s average production level is slightly higher than that of the Loess Plateau in Northern Shaanxi. In future research, this can be optimized in terms of regional downscaling correction of yield factors, or field measurement of yield levels. On the other hand, this study assumes that the yield factor and equivalence factor in 2030 will remain at the 2020 level. With the development of the national economy and science and technology, the overall level of biological production of various types of land will continue to increase, and the growth rate will be different. Therefore, it can be expected that the actual biocapacity in 2030 will be slightly higher than the predicted results of this study. However, on a 10-year scale, the authors believe that the changes of the above factors will not have a significant impact on the changing trend and spatial distribution pattern of biocapacity.
4.3. Policy Suggestions
From 2000 to 2020, the forest area of the Loess Plateau in Northern Shaanxi increased from 1.45 × 106 ha to 1.46 × 106 ha, an increase of 0.88%. In the next 10 years, it is predicted that the forest will continue to increase by 0.43%, and the biocapacity of the forest will correspondingly increase by about 9707 gha. This is the main source of the increase in the biocapacity of the Loess Plateau in Northern Shaanxi. Grassland is the ecosystem with the highest area in the Loess Plateau in Northern Shaanxi. From 2000 to 2030, the area of grassland has not changed much, but the area transferred from other land types (especially farmland and unused land) is the same as the area transferred from grassland (especially forest). This reflects the natural process of arable land being abandoned first and then naturally forming shrubs.
These changes were combined with the ecological restoration and construction project of the GGP in the Loess Plateau from 2000 to 2020 for analysis. Strongly promoted ecological protection policies are conducive to improving the coverage and biocapacity of regional forest and grassland and have a positive effect on reducing regional soil erosion, improving the local climate and the overall ecological environment [
26]. It is also a prerequisite for improving the capacity of regional sustainable development and an important measure to build a “Beautiful China”. Therefore, from the perspective of environmental protection, the authors recommend that the central and local governments continue to coordinate relevant policies and continue to stably implement ecological projects such as the GGP.
However, it should be noted that ecological restoration and ecological management projects, and expansion of built-up land will lead to a decrease in farmland, especially high-quality farmland around cities. Our research shows that from 2000 to 2020, the area of farmland on the Loess Plateau in Northern Shaanxi reduced from 2.57 × 106 ha to 2.55 × 106 ha. In the next 10 years, it is predicted that the farmland will be reduced by about 745 ha, and the biocapacity of farmland will be reduced by 3763 gha. This is an important reason hindering the improvement of the biocapacity of the Loess Plateau in Northern Shaanxi. Therefore, to ensure regional food security and the stability of biocapacity, it is necessary to scientifically and rationally plan the conversion ratios of farmland, grassland, and forest, while increasing the degree of intensification of built-up land, and reducing the occupation of surrounding farmland by urban and rural construction.