Abstract
The southern Shaanxi Province, China, has abundant iron ore resources due to local ideal metallogenic conditions. The development and utilization of iron ore resources and other related industries serve as important pillars of the regional economy. In this paper, principals and methodology of system dynamics are adopted to develop a system model of interactions of development and utilization of mineral resources, social economy, and ecological environment, using the iron ore reserves in the southern Shaanxi region as a resource base, the regional GDP as a benchmark of regional economic development, and the environmental treatment expenditure as the measurement of the regional environmental protection efforts. The model is performed based on an optimized neural network by the particle swarm optimization algorithm. Through setting variables and parameters for the system and model simulation as well as comparative research, this paper analyzes the interconnectivity and interaction of the three subsystems and explores associated relationships among mineral resources, social economy, and environmental carrying capacity in the southern Shaanxi region. In addition, the present study also proposes advice on the development and utilization of iron ore resources, ecological protection, and high-quality development of the economy in the southern Shaanxi region based on the research results.
1. Introduction
Mineral resources are an important material basis for the survival of mankind and sustainable economic and social development. Since the reform and opening-up, China’s economy has grown rapidly and its economic scale has expanded at a fast pace. Therefore, the consumption of bulk and highly needed mineral resources (mainly iron) has been at a high level. In recent years, the development and utilization of iron ore and other mineral resources, high-quality economic development, and the sustainable development of the ecological environment have become important hotspot issues. In order to scientifically study the relationship among mineral resource development, social and economic growth, and ecological environmental protection and to promote the development of green mining, high-quality economic development, and ecological conservation in southern Shaanxi Province, China, this paper uses this area as an example and adopts system dynamics approach to study the internal correlation among the development and utilization of iron ore resources, social and economic development, and ecological and environmental protection system in southern Shaanxi region. Models are established to carry out mineral resources−social economy−ecological environment carrying capacity research and analyze the correlation between variables, as well as the contribution and impact of mineral resources to the regional GDP and the ecological environment. Based on research results, this paper puts forward advice on scientific and rational development and utilization of mineral resources on the premise of protecting and optimizing the ecological environment, to provide scientific and technological support for promoting the high-quality development of Shaanxi’s mining industry.
2. Exploitation Status and Iron Ore Resources Utilization in Shaanxi Region
2.1. Exploitation Status of Iron Ore Resources Utilization in Southern Shaanxi Region
The southern Shaanxi region is mainly located in the southern part of the Qinling metallogenic belt, with favorable geological conditions for metallogenesis and abundant mineral resources. By the end of 2017, more than 100 mineral resources have been discovered in this region, among which reserves of more than 60 metal resources such as iron, copper, lead, zinc, gold, silver, molybdenum, mercury, antimony, and others have been identified economically [1–4]. The iron ore resource reserves in the southern Shaanxi region account for 96.55% of the total iron ore resource reserves of the Shaanxi Province. The iron deposits of industrial significance are mainly distributed in the Mian-Lue-Yang area of Hanzhong City, the Bijigou area of Yangxian County, the Ziyang-Zhenping area of Ankang City, and the Zhashui-Shanyang area of Shangluo City. These areas belong to the administrative region of Hanzhong City, Ankang City, and Shangluo City. There are 62 iron mines above designated size in the southern Shaanxi region, of which 4 are large deposits, 9 are medium deposits, 49 are small deposits, and 116 are ore occurrences. A cumulative iron resource reserve of 1.16 billion tons has been identified, of which 1.050 billion tons are from large- and medium-sized deposits, accounting for 90.50% of the total iron ore resource reserves in the southern Shaanxi region. The recoverable reserve of iron ore resources is 1.059 billion tons. Of the 62 iron ore deposits above the designated size, 13 are currently being mined, accounting for 20.97% of the total number of iron ore deposits above the designated size, 12 are suspended, and 37 are unexploited. The total designated production capacity of iron ore of the Shaanxi Province is 15,752,000 tons/year, which is 137,000 tons/year higher than that of 2016. The output of iron ore is 3,509,300 tons, and the capacity utilization rate is 22.28%, of which the production capacity of large- and medium-sized mines accounts for 93.76% [2].
The development and utilization of iron ore resources in the southern Shaanxi region and its related industries play an important role in the regional economy and are an important pillar of the economy in the southern Shaanxi region and the whole province. According to statistics data, in 2017, Shaanxi Province’s industries above designated size (enterprises of which main business annual income reaches 20 million Yuans) achieved a total industrial output of 2,485.443 billion Yuans, and mining and related processing and manufacturing industries yielded 1,419.827 billion Yuans, accounting for 57.1% of province’s total industrial output above designated size, all indicating that the development of iron ore resources and its related industries is essential to the local economy [3]. The evaluation results of the iron ore resources potential of Shaanxi Province show that reserve of iron ore resources above 1,000 meters in the southern Shaanxi region reaches up to 1.548 billion tons, indicating huge potential. Therefore, the scientific and rational development and utilization of these iron ore resources will play an important role in the high-quality development of the regional economy.
2.2. Analysis of the Main Current Problems
The environmental carrying capacity of iron ore resources is the basis for the operation of the entire regional ecological and economic system; therefore, rational development and utilization of the iron ore resources is an important basis for the sustainable development of the economy in the southern Shaanxi region. Mineral resources are nonrenewable resources, the development of which cannot be carried out in an overexploited manner; instead, it must be on a scientific and rational basis to achieve extended utilization of the mineral resources and sustainable development of regional economy and ecological environment.
The Qinling Mountains are the central water tower of China and serve as an important ecological barrier. The important metal minerals deposits in Shaanxi Province are mainly located in the Qinling metallogenic belt. If the environmental protection of this region is largely overlooked, the disorderly mining development will cause serious damage to the Qinling ecological environment. Mining development must align with the concept of green development, adhere to the principle of giving priority to environmental protection, and promote the development of ecological mining. The transformation of mineral resource utilization and management methods must be advanced to build a new pattern of mining development and utilization in Shaanxi Province.
The iron ore resource industries in the southern Shaanxi region have played a very important role in supporting the economic development of the entire region. In recent years, due to environmental protection and other reasons, some mining production has been stopped, and the mining industries experienced a serious decline, affecting regional economic development to a certain extent. Shaanxi Province’s natural resources authority has carried out the campaign of “Three Protections and Three Governances” to rectify the mining development activities, which has played a positive role in the environmental protection of the Qinling Ecological Zone and the rational development and utilization of mineral resources. However, there are still many problems, and the solution needs to take into consideration of the actual situation of the southern Shaanxi region and establish a scientific governance system and scheme to ensure the coordinated development of resource utilization, environmental protection, and economic development.
To sum up, iron ore resources, economy, and environment cannot exist in isolation, but the three form a complex systematic project in which the three factors interact and restrict each other. It is necessary to carry out systematic research on resources, economy, and environment carrying capacity to explore the interactions among mineral resources, economy, and environmental carrying capacity in this region and provide reasonable advice to support decision-making in achieving sustainable development of the regional economy.
3. System Dynamics Construction of Mineral Resources Model, Social Economy, and Ecological Environment
At present, most research on the carrying capacity of iron ore resources−social economy−ecological environment is carried out under specific environmental or economic conditions, such as unilateral research on environmental carrying capacity with mining area as the main target. In fact, instead of limiting the research object of carrying capacity of the mineral resources−social economy−ecological environment to a certain mining area, a larger region should be included. At the same time, the mineral resources, economy, and environment in the region do not exist independently, but the three are interrelated and restrict each other and form a complex system. This paper uses the system dynamics model to establish a complex system that can simulate the changes of regional mineral resources, social economy, and ecological environment and to explore the carrying capacity of regional mineral resources, social economy, and ecological environment.
3.1. Boundary Definition and Overall Structure of the Model
3.1.1. Model System Boundary Definition
The theory of system dynamics mainly obeys the concept of system science that “Every system must have a structure, and the system structure determines the system function.” Based on primarily the mutual feedback between the various components within the system, causal relationships and flow relationships between the systems are formed. Different results are then simulated according to different scenarios. At present, system dynamics mainly focus on the supply and demand relationship of a certain mineral resource in the field of mineral resource development and utilization. For example, in the study by Naderi et al. qualitative representation and quantitative system dynamics simulation of the water resources system in the Qazvin Plain, Iran, are presented taking into account the energy intensity of water supply and interconnected water use sectors [5]. In the study by Li et al., a system dynamics model was established to clarify the causal feedback and dynamic interaction mechanism between the components of urban resilience [6]. In the study by Song et al., the land green supply chain is divided into subsystems of technology (T), energy (E), environment (E), and economy (E), which construct the TEEE dynamic model [7].
This paper focuses on the iron ore resource reserves in the southern Shaanxi region and selects Shangluo City, Ankang City, and Hanzhong City in Shaanxi Province as the boundaries of the research area and years 2010 to 2030 as the time boundary, of which the actual statistics of years 2010 to 2016 are used for model simulation and years 2017 to 2030 for the forecast. The time interval is set to 1 year. The content boundary includes subsystems related to mineral resources, economy, and “the Three Waste Discharge” (exhaust gas, wastewater, and solid waste). The key variables are determined based on the influencing factors among the three major systems of mineral resource development and utilization, regional economic development, and regional ecological environment from which the related variables are derived until the final variables affecting the entire system are determined and then the boundary of the entire system is set.
3.1.2. Model Master Structure
The development and utilization process of mineral resources is complicated and closely linked to social economy and ecological environment, involving many elements. In line with the hypothesis of rational man in economics, three major subsystems of iron ore resources, social economy, and ecological environment are established and form an organic whole system. And according to the l influence and interaction among various elements in the whole system, state variables, rate variables, auxiliary variables, and constants are introduced, respectively, dividing the whole system into 3 parts. Research is carried out at multiple levels and from multiple aspects.
In the model system, the iron ore resource subsystem uses resource reserves as the core which is also the basic assurance for regional economic development. The mining, beneficiation and metallurgy, and comprehensive utilization of iron ore resources can promote regional economic development on the one hand, but on the other hand, such processes are liable to produce the Three Waste and hence damaging the ecological environment and introducing negative effects. To cope with these issues requires mining enterprises to attach importance to environmental protection, upgrade and transform their development and utilization technology, and rationally develop and utilize iron ore resources on the premise of protecting the environment.
The economy subsystem in the system model is based on regional GDP, which provides economic support for the balanced development of other subsystems, and is a benchmark of regional economic development. On the one hand, regional economic development can provide financial support for the technological transformation and upgrading of mineral resource exploration, development, and utilization and improve the resource insurance capability and technical level for sustainable development; on the other hand, the sustainable development of the regional economy can increase investment in environmental governance and restoration, improve and enhance environmental governance and restoration technologies, and address the negative impact on the ecological environment due to resource development and utilization.
The ecological environment subsystem in the model relies on the investment of environmental protection and governance expenditure and is an important guarantee for the development and utilization of regional mineral resources and the coordinated development of ecological environment and economy. On the one hand, prioritizing environmental protection in the process of mineral resource development and increasing investment in comprehensive environmental management and restoration can continuously improve the environment and improve quality and level of people’s life; on the other hand, a beautiful and harmonious environment can feedback the development of mining and promote the development and utilization of mineral resources. The organic combination of these two can jointly promote the sustainable development of regional resources, economy, and environment.
The system dynamics model constructed in this paper includes the iron ore resources subsystem, economy subsystem, and ecological environment subsystem, namely, the iron ore resources−social economy−ecological environment system (Figure 1).
3.2. Cause-and-Effect Diagram, Flow Chart, and Main Parameters of the System Model
Basic data of iron ore resources in the southern Shaanxi region included in this study were collected from the followings materials: Shaanxi Statistical Yearbook 2010-2017, Shaanxi Mining Annual Report 2010–2017, China Environment Database, China Economic and Social Development Statistical Database, China Regional Economic Database, National Mineral Resources Potential Evaluation Project-Report on Evaluation Results of Iron Ore Resource Potential in Shaanxi Province, and National Mineral Resources Potential Evaluation-Report on Prediction Results of Important Minerals in Shaanxi Province and China Mineral Geology [8–12]. According to the system structure diagram shown in Figure 1 and the analysis and research on the representativeness and importance of the basic data of iron ore resources in each subsystem, the representative variables are selected [13]. According to the interactions of each variable and the relationship among them, the vensim6.4 platform is used to establish the system dynamics cause-and-effect diagram and flow chart of the iron ore resources−social economy−ecological environment cycle, aiming to analyze the future development and utilization of mineral resources and regional economic development trends and to provide corresponding countermeasures [14].
Because the iron ore resources−economy−environment system model in the southern Shaanxi region is relatively complex, the parameters in this model are numerous and almost inaccessible, mainly including constants, initial values, and so on [15–17]. The constants were determined by using correlation analysis, regression analysis, expert opinion, a weighted average of historical data, and so on.
3.2.1. Iron Ore Resource Subsystem
The main variables of the iron ore resource subsystem include the total regional GDP, investment in iron ore resource exploration, resource reserves (new and known resource reserves), development scale, production, output value, and benefits of development and utilization of iron ore resources (see Table 1). As the basis, the iron ore resource reserves in the subsystem determine the development scale, production, output value, and development and utilization benefits of iron ore resources, while the development output value and benefits of iron ore resources determine the contribution of iron ore resources development and utilization to regional GDP, as well as further investment in exploration and the increase in iron ore reserves [18]. These main variables constrain and promote each other and form mutual cause-and-effect relationships. The cause-effect relationship diagram among the main variables of the iron ore resource subsystem is designed with the vensim6.4 platform (Figure 2).
The specific cause-effect relationship of the iron ore resource development subsystem is deduced from Figure 2:
Regional GDP ⟶ investment in iron ore resources ⟶ new reserves ⟶ iron ore resource reserves ⟶ development scale of iron ore resource ⟶ iron ore resource production ⟶ iron ore output value ⟶ regional GDP (positive feedback).
The concept presented above depicts a positive feedback loop. The cause-effect diagram reflects that with the increase in regional GDP, more investment in the exploration and development of mineral resources will be spent, and therefore the mineral reserves, development scale, and production will increase accordingly and the mining output value and benefits will also show an incremental trend, finally further promoting the continuous rise of regional GDP.
According to the cause-effect diagram of the development and utilization of iron ore resources and considering the mutual influence and restriction among various elements, the regional GDP and iron ore resource reserves are selected as state variables, with annual GDP increment and new iron ore resource reserves as the rate variables and investment in iron ore resource exploration, mineral investment ratio, development and utilization efficiency of iron ore resource, recovery rate of dressing and smelting, comprehensive utilization rate, mining recovery rate, annual mineral growth rate, iron ore resource production, iron ore output value as auxiliary variables or constants. The flow chart of the iron ore resource development subsystem designed by software is shown in Figure 3.
The main model parameter equations of the iron ore resource development subsystem are as follows:(1)Investment in iron ore resource exploration = the ratio of iron ore investment regional GDP(2)Iron ore resource reserves = INTEG (new-added iron ore resource reserves; 404)(3)Development and utilization efficiency of iron ore resource = LN (iron ore resource reserves ∗ (extract recovery rate + comprehensive utilization rate + recovery rate of Beneficiation and Metallurgy))(4)Iron ore resource production = total demand for iron ore resource ∗ annual iron ore growth rate ∗ development and utilization efficiency of iron ore resource(5)Iron ore output value = 0.4 ∗ iron ore resource production ∗ the ratio of mineral investment
Among them, 404 in the iron ore resource reserve is the initial value of the iron ore mineral resource reserve, and 0.4 in the iron ore output value is the incremental regression coefficient of the iron ore output value and GDP.
3.2.2. Economy Subsystem
The economy subsystem is the economic base for the coordinated development of the regional resource-economy-environment. This subsystem mainly studies the benefits of regional economic development on the development and utilization of regional iron ore resources, regional environmental governance, and the future development trend of regional GDP. The economy subsystem mainly includes factors such as regional GDP, primary industry output, secondary industry output, tertiary industry output, total demand for iron ore resource, production of iron ore resource, iron ore output value, and environmental governance and restoration. The main variable, regional GDP, is the sum of that in southern Shaanxi cities, including Shangluo City, Ankang City, and Hanzhong City. The output value of the primary, secondary, and tertiary industries corresponds to the added value of them in Shaanxi over the years. The demands for iron ore resources in the primary, secondary, and tertiary industries are the consumption of iron ore in the three major industries. The total iron ore demand is manifested as the total iron ore consumption of the three major industries.
Analysis results shown in Figure 4 demonstrate the cause-effect relationship diagram of the economy subsystem designed by software.
The specific cause-effect relationship of the economy subsystem is deduced from Figure 4:(1)Regional GDP ⟶ total demand for iron ore resource ⟶ development scale of iron ore resource ⟶ iron ore resource production ⟶ iron ore output value ⟶ regional GDP (positive feedback).(2)Regional GDP ⟶ primary industry output value ⟶ demand for iron ore resource of the primary industry ⟶ total demand for iron ore resource ⟶ development scale of iron ore resource ⟶ iron ore resource production ⟶ iron ore output value ⟶ regional GDP (positive feedback).(3)Regional GDP ⟶ secondary industry output ⟶ demand for iron ore resource of the secondary industry ⟶ total demand for iron ore resource ⟶ development scale of iron ore resource ⟶ iron ore resource production ⟶ iron ore output value ⟶ regional GDP (positive feedback).(4)Regional GDP ⟶ tertiary industry output ⟶ demand for iron ore resources of the tertiary industry ⟶ total demand for iron ore resource ⟶ development scale of iron ore resource ⟶ iron ore resource production ⟶ iron ore output value ⟶ regional GDP (positive feedback). The abovementioned feedback loops are all positive. The cause-effect relationship diagram mainly reflects that the increase in regional GDP brings an increase in the demand for iron ore resources for the primary, secondary, and tertiary industries, respectively, and provides investment for the development and utilization of iron ore resources. The increase in iron ore resource production leads to the increase in iron ore output value, which in turn affects the growth of regional GDP and forms a sustainable development of regional economy.(5)Regional GDP ⟶ total demand for iron ore resource ⟶ development scale of iron ore resource ⟶ iron ore production ⟶ environmental governance and restoration ⟶ regional GDP (negative feedback).(6)Regional GDP ⟶ primary industry output value ⟶ demand for iron ore resource of primary industry ⟶ total demand for iron ore resource ⟶ development scale of iron ore resource ⟶ iron ore resource production ⟶ environmental governance and restoration ⟶ regional GDP (negative feedback).(7)Regional GDP ⟶ secondary industry output value ⟶ demand for iron ore resource of the secondary industry ⟶ total demand for iron ore resource ⟶ development scale of iron ore resource ⟶ iron ore resource production ⟶ environmental governance and restoration ⟶ regional GDP (negative feedback).(8)Regional GDP ⟶ tertiary industry output value ⟶ demand for iron ore resource of the tertiary industry ⟶ total demand for iron ore resource ⟶ development scale of iron ore resource ⟶ iron ore resource production ⟶ environmental governance and restoration ⟶ regional GDP (negative feedback).
The abovementioned feedback loops are all negative. The cause-effect diagram mainly indicates that the development of the three major industries worsened the regional ecological environment pollution, and therefore it is imperative to increase investment in environmental governance and restoration. However, the consequence of which is that the regional economic growth, the expansion of economic scale, and the sustainable development of the regional economy will be curbed.
According to the cause-effect relationship diagram of the economy subsystem, combining the economy subsystem with the iron ore resource development subsystem and considering the characteristics of various elements, regional GDP and iron ore resource reserves are selected as state variables, with annual GDP increment and newly added iron ore resource reserves as rate variables. The output value of three main industries, the demand for iron ore resource of the three industries, the total demand for iron ore resource, and the cost of environmental treatment and restoration are set as auxiliary variables or constants. The flow diagram of the economy subsystem designed by software is shown in Figure 5.
The main model parameter equations of the economy subsystem are as follows:(1)The primary industry output value = regional GDP ∗ 0.1(2)The secondary industry output value = regional GDP ∗ 0.6(3)The tertiary industry output value = regional GDP ∗ 0.3(4)Total demand for iron ore resources = iron ore resources demand of the primary industry + iron ore resources demand of the tertiary industry + iron ore resources demand of the secondary industry
Among them, the coefficients of the primary industry output value, the secondary industry output value, and the tertiary industry output value are averaged value based on the proportion of the three major industries output value in southern Shaanxi in 2010.
3.2.3. Ecological Environment Subsystem
The ecological environment subsystem is an important assurance for the development and utilization of regional mineral resources and the coordinated development of ecological environment and economy. The ecological environment restricts regional economic development and the scale of mineral resources development and utilization. The system mainly studies the environmental pollution caused by the development of iron ore resources and the investment in environmental governance and restoration and the impact on the entire regional economy and the redevelopment and utilization of iron ore resources. The main variables of the environmental subsystem include regional GDP, solid waste discharge, and wastewater discharge and exhaust gas emissions due to iron ore development, environmental pollution governance, and restoration costs, and iron ore resource development scale. Environment subsystem cause-effects relationship is shown in Figure 6 as designed by software.
Based on Figure 6, the main cause-effect diagram of the ecological environment subsystem can be drawn as follows:(1)Regional GDP ⟶ iron ore resource development scale ⟶ solid waste discharge ⟶ environmental pollution ⟶ environmental pollution treatment costs ⟶ regional GDP increment ⟶ regional GDP (positive feedback)(2)Regional GDP ⟶ iron ore resource development scale ⟶ wastewater discharge ⟶ environmental pollution ⟶ environmental pollution treatment costs ⟶ regional GDP increment ⟶ regional GDP (positive feedback)(3)Regional GDP ⟶ iron ore resource development scale ⟶ exhaust gas emissions ⟶ environmental pollution ⟶ environmental pollution treatment costs ⟶ regional GDP increment ⟶ regional GDP (positive feedback)
The feedback presented above is a positive feedback path. This cause-effect diagram mainly reflects that with the expansion of the development scale of iron ore resources, the discharge of wastewater, exhaust gas, and solid waste will increase proportionally, so it is necessary to increase the cost of environmental pollution treatment and restoration, which will lead to the slowdown of regional GDP and influence the regional economic development.
Now, the iron ore resource development subsystem, economy subsystem, and ecological environment subsystem are combined into an organic whole, with the full consideration of the mutual influence and interaction among these subsystems. Regional GDP, iron ore resource reserves, and the Three Waste pollution are selected as state variables; annual GDP increment, newly added iron ore resource reserves, and generation and reduction of the Three Waste pollution are set as rate variables; unit treatment cost of three wastes pollution, environmental treatment and restoration cost, and the Three Waste pollution changes per unit of output are taken as auxiliary variables or constants. The resulted flow diagram of the ecological environment subsystem designed by software is shown in Figure 7.
The main model parameter equations of the ecological environment subsystem are as follows:(1)Wastewater pollution = INTEG (wastewater production-wastewater reduction; 158)(2)Solid waste pollution = INTEG (solid waste generation-solid waste reduction; 143.5)(3)Exhaust gas pollution = INTEG (exhaust gas generation-exhaust gas reduction; 323.5)(4)Wastewater production = per unit wastewater generation/the benefit of iron ore resources development and utilization(5)Wastewater reduction = change in per unit wastewater reduction(6)Solid waste production = change in per unit solid waste generation/the benefit of development and utilization of iron ore resources(7)Wastewater reduction = change in per unit wastewater reduction(8)Solid waste reduction = per unit solid waste reduction ∗ comprehensive utilization rate of solid waste(9)Exhaust gas generation = per unit exhaust gas production/the benefit of development and utilization of iron ore resources(10)Exhaust gas reduction = change in per unit exhaust gas reduction(11)Environmental treatment cost = (per unit treatment cost of solid waste + per unit treatment cost of exhaust gas + per unit treatment cost of wastewater + change in per unit solid waste reduction + change in per unit waste gas reduction + change in per unit wastewater reduction)
For these parameters, 158 is the initial value of wastewater pollution, 143.5 is the initial value of solid waste pollution, and 323.5 is the initial value of exhaust gas pollution.
The flow diagram of the iron ore resource-economy-environment model system is shown in Figure 8.
The data simulation in this study is based on optimized neural networks which are explained in the following.
4. Optimized Multilayer Perceptron Neural Networks
Today, the use of intelligent systems, especially artificial neural networks, has become so widespread that these tools can be classified as basic and common tools in basic mathematical operations. One of the most basic neural models available is the multilayer perceptron (MLP) model, which simulates the translational function of the human brain. In this type of neural network, most of the behavior of human brain networks and signal propagation has been considered, and hence they are sometimes referred to as feedforward networks. Each neuron in the human brain receives input (from another neuron or non-neuron) and processes it, transmitting the result to another cell (neuronal or non-neural) [19]. This behavior continues until a definite result is reached, which is likely to eventually lead to a decision, process, thought, or move.
An artificial neural network is made up of inputs, outputs, weights, biases, and an activation function. Weights and biases are randomly assigned. The inputs are multiplied by the weights, and the values obtained are added together and then biased. The result passes through the activator function and forms the output of neurons [20]. However, by randomly assigning weights, the result is usually not appropriate. So, the weights need to change. Weight changes should be made in such a way that the neuron outputs are close to the actual outputs.
The process of changing the weights of neurons to achieve the desired output is called neuron learning. We use a dissipation function to check the function of neurons. The goal of neuron learning is to reduce the amount of loss to zero or at least close to zero. Here, an optimization algorithm is used to do this. The task of the optimization algorithm is to find the weights that minimize the mean square error (MSE) function between the network and the desired values for all the training samples. The formula for the MSE is described as follows:where describes the desired value, n describes the number of steps in the training dataset, and is the network output and is achieved as follows:where and represent the input and the bias variables and is the connection weight between and the hidden neuron j.
The next stage is to utilize the activation function and to trigger the output of the neurons. Here, the sigmoid function has been employed. The mathematical model of this function is given as follows:
Now, as aforementioned, for optimizing the network based on weights and biases, (1) should be optimized. In this study, metaheuristic technique has been used for minimizing the MSE.
The purpose of metaheuristic algorithms is to find an acceptable solution, given the limitations and needs of the problem. In determining the solution to a problem, there may be different solutions to it. In this study, the particle swarm optimization (PSO) algorithm as the most popular optimization algorithm has been utilized. The PSO algorithm simulates bird swarming behaviors. Imagine the following scenario: a group of birds is accidentally exposed to food in an area. There is only one food item in the search area. Not all birds know where food is. However, they know how much food there is in each iteration. The solution is to look for the bird that is closest to the food. PSO designers adapted this scenario and used it to solve optimization problems. In PSO, each solution is a “bird” in the search space that is called “particle.” All particles have proportion values that are evaluated by the proportion function for optimization and have velocities that guide the particle flight. Particles flow through the problem space with the optimal particles.
The PSO algorithm starts with a group of random particles (solution) and then searches with the generation update. In each iteration, each particle is updated with two “best” values. The first is the best solution ever obtained. This value is called . Another “best” is the value that has been achieved so far by every particle in the population. This is the best universal value and is called . When a particle takes a part of the population as its topological neighbors, the best value is the best local and is called . After finding the best values of and , the particle updates its speed and position with the following equations:where describes the particle velocity, defines the current particle (solution), and are already defined, specifies a random number between 0 and 1, and and represent learning factors. Here, .
The MLP network here uses BP technique for the network training. This BP technique is gradient descent-based technique which forms some disadvantages, such that trapping into local criteria is the most important shortcoming. Here, we used the PSO algorithm to resolve this issue. The algorithm of the hybrid PSO-MLP is given as follows:
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5. Simulation and Result Analysis
5.1. Model Checking
No theoretical model could be perfectly consistent with a real system. However, an objective real system can be described and simulated by a theoretical model. Whether the structure of the theoretical model is reasonable requires comparing the simulation results of the model with the actual data to determine the authenticity and validity of the theoretical model, which also affects the formulation and implementation of relevant policies. Therefore, in order to use the system dynamics model to study the carrying capacity of iron ore resources-economic-environment in the southern Shaanxi region, it is essential to test the validity and authenticity of the model, to provide a scientific basis for local government to formulate relevant policies.
5.1.1. Historical Test
The historical test mainly analyzes whether the simulation results of the model are consistent with the actual results, which is one of the indicators of model validity. In this study, the rationality of the model is judged by comparing the variation trend of the model simulation results with the practical data. It is generally believed that the relative error between historical data and simulation data is within the range of 10%. And the model is considered to be better for realistic simulation.
We select statistical data from 2010 to 2016 to test the authenticity of regional GDP and iron ore resource reserves in the southern Shaanxi area, to compare and analyze the running results of the model, as shown in Tables 2 and 3, among which iron ore resources in southern Shaanxi use 30% of the mineral investment as the base.
Based on the historical test results, it can be seen that the relative numerical errors between the output data and the real data from 2010 to 2016 are between −0.1 and 0.1. Although the errors in 2011, 2015, and 2016 are a little large, these data fall within the normal range. Therefore, the model is consistent with the development trend of the actual system and can reflect the development of iron ore resources and economic development in the region to a certain extent. Therefore, the model is considered to be effective.
5.2. Model Simulation
Through the system simulation, we can better understand the development of iron ore resources in southern Shaanxi by increasing the investment ratio of mineral resources in southern Shaanxi by 10% and 20%, respectively, to simulate and compare the regional GDP and iron ore resource demand in southern Shaanxi area. The software can simulate the GDP and demand for iron ore resources in southern Shaanxi. The simulated results are displayed in Figures 9 and 10. Curve 1 represents a 20% increase in the mineral investment ratio; curve 2 represents a 10% increase in the mineral investment ratio; and curve 3 represents the initial state.
Based on Figures 9 and 10, the following results can be obtained:(1)The change of the mineral investment ratio parameter has a significant impact on the regional GDP, showing a positive correlation. Due to the increase in the investment ratio of iron ore resources, the regional GDP growth in southern Shaanxi has been promoted. In addition, the increasing scale of iron ore exploration and mining activity will lead to aggravation of environmental pollution. It is necessary to balance the scale of mining and regional economic development so that the entire circulatory system will be developed better.(2)There is a positive correlation between the change of the mineral investment ratio parameter and the total demand for iron ore resources. When the ratio of investment in minerals increases, the demand for iron ore resources in the region also continues to increase. At the same time, the increasing market demand for iron ore resources will inevitably lead to an increase in the investment in iron ore exploration and development, and the over-exploitation of iron ore resources will cause serious environmental pollution. Therefore, the balance between the mining scale of iron ore resources and the ecological environment affects the sustainable economic development of southern Shaanxi.
To sum up, the sustainable development of the regional economy requires moderate development and utilization of iron ore resources. At present, the consumption of iron ore resources in China has been running at a high level. And the demand for iron ore is booming, which the excessive exploitation will exert a huge impact on the ecological environment. Appropriate resource development and utilization can not only keep stable regional economic growth but also ensure the protection of the ecological environment. Therefore, it is necessary to evaluate the carrying capacity of regional resources, economy, and environment to ensure the sustainable development of the regional economy.
5.3. Changes in Regional Carrying Capacity
The resource carrying capacity is reflected among the difference between the reserves of mineral resources and the production of mineral resources. The larger the difference, the smaller the pressure, and the stronger the carrying capacity, vice versa.
The economic carrying capacity is reflected among the difference between the iron ore output value and the regional GDP. The larger the difference, the smaller the pressure, and the stronger the carrying capacity, vice versa.
The environmental carrying capacity is reflected among the difference between the cost of environmental governance and restoration and the regional GDP. The smaller the difference, the greater the pressure, and the weaker the carrying capacity, vice versa.
According to the actual data of iron ore resource reserves and iron ore production in the southern Shaanxi region, the results of iron ore resource carrying capacity in the southern Shaanxi region are obtained through system model simulation (Figure 11). The iron ore resource reserves in the southern Shaanxi region have been decreasing since 2010. Meanwhile, iron ore production has become higher and higher. The discrepancy between these two parameters began to shrink after 2011 and the resource carrying pressure was getting stronger. After 2015, the production of iron ore gradually decreased, and the difference between the two fluctuated in the range between 34,000 tons and 36,000 tons, and the situation tended to be stable. This suggests that the resource carrying capacity has become stable since 2015. Before 2015, only the development of iron ore resources was emphasized and investment in iron ore exploration was largely neglected, leading to relying on retained reserves. Thus, the carrying capacity of iron ore resources became weaker. Therefore, with the development and consumption of iron ore reserves, the exploration of iron ore needs to be strengthened to continuously increase the reserves of iron ore resources and enhance the carrying capacity of regional iron ore resources.
According to the actual data of the GDP in the southern Shaanxi region and the system model simulation, the relation between the mineral output value and regional GDP in the southern Shaanxi region in recent years is calculated to obtain the economic carrying capacity (Figure 12). The relation between the cost of environmental governance and restoration and regional GDP in the southern Shaanxi region is calculated to obtain the environmental carrying capacity (Figure 13).
The result analysis of economic carrying capacity (Figure 12) and environmental carrying capacity (Figure 13) is as follows:(1)Since 2010, the difference between the iron ore output value and the regional GDP in the southern Shaanxi region is quite large, and it increased year by year from 2010 to 2015. After 2015, the growth rate of the difference is relatively slower, but the difference is still increasing. The reason is that the development speed of regional GDP is significantly higher than that of the regional iron ore output value. Therefore, it shows that the regional economic carrying capacity is relatively strong. The contribution and impact of development and utilization of iron ore in the southern Shaanxi region on regional economic development show decreasing or stabilizing trend. The regional economy depends less on the development and utilization of iron ore, and thus the regional economic carrying capacity is relatively strong.(2)From 2010 to 2016, the difference between the growth rate of environmental governance and restoration cost and the growth rate of regional GDP is small, indicating that the environmental carrying pressure is relatively large and the environmental carrying capacity is relatively weak. In recent years, on the one hand, thanks to the environmental protection requirements, the closure of some mines, and reduction and restriction of mine production result in the reduction of cost of the environmental governance and restoration; on the other hand, China has issued strict regulations and policies on environmental governance and restoration to build up green mines, and natural resources and environmental protection departments have strengthened supervision and management, so the awareness of environmental protection has been generally enhanced, and the investment in environmental governance and restoration has continued to increase. As a result, the difference between the growth rate of environmental governance and restoration costs and the growth rate of regional GDP has increased, and the regional environmental carrying capacity has increased and continuously enhanced.
To sum up, iron ore resources, economy, and environment do not exist independently, but the three are interrelated and restrict each other and cannot be neglected. Under the current situation of strong demand for iron ore in China, sufficient iron ore reserves can ensure that the regional demand for iron ore can be met. The scientific and rational development of iron ore resources is beneficial to the economic and social development of the southern Shaanxi region. Economic development can provide more funds for environmental governance and restoration, which can better protect the environment and continuously enhance the regional environment and economic carrying capacity. The high-quality economic development in the southern Shaanxi region and the beautiful mining environment will improve people’s ideological awareness of practicing green development, the utilization rate of mineral resources, and the life quality of mine workers and will promote the high-quality development of enterprises.
6. Conclusions
Based on theoretical knowledge and quantitative analysis, this paper takes the iron ore resources in the southern Shaanxi region as the research object. By establishing the iron ore resources-socioeconomic-ecological environment cycle system in the southern Shaanxi region and using the system dynamics model to construct subsystems of the development and utilization of mineral resources in the southern Shaanxi region, economy subsystems, and environmental subsystems, the present study analyzes the carrying capacity of mineral resources, economic, and environment of the region. The main conclusions are as follows:(1)Using historical test analysis, comparing the actual data of regional GDP and mineral resources reserves in the southern Shaanxi region from 2010 to 2016 with the simulation results of the model, it is verified that the model design is relatively reasonable.(2)The model sensitivity test shows that the change of the mineral investment ratio parameter has a significant impact on the regional GDP and the total demand for mineral resources, both of which are positively correlated. The increase in the mineral investment ratio will promote the continuous increase in regional GDP and demand for mineral resources.(3)The establishment of the system dynamics model and the simulation results show that iron ore resources, economy, and environment do not exist independently, but the three are interrelated and restrict each other in a complex system engineering, and the impact of any one of which cannot be neglected.(4)In the process of developing and utilizing iron ore resources, if the investment in iron ore exploration is not increased while relying on retained reserves, the carrying capacity of iron ore resources will become weaker over time. Therefore, while developing and consuming iron ore reserves, the exploration of iron ore needs to be strengthened, to continuously increase the reserves of iron ore resources and enhance the carrying capacity of regional iron ore resources.(5)The contribution and influence of iron ore development and utilization to regional economic development in the southern Shaanxi region decrease and tend to be stable, the regional economy’s dependence on iron ore development and utilization decreases, and the regional economic carrying capacity is strong.(6)From 2010 to 2016, the environmental carrying pressure in the southern Shaanxi region was relatively high, and the environmental carrying capacity was relatively weak. In recent years, people’s awareness of environmental protection is increasing, and new laws, regulations, policies, and measurements are implemented in environmental protection governance and restoration, while the investment in environmental governance and restoration has continued to increase. At the same time, supervision and management have been strengthened, the difference between the growth rate of environmental governance and restoration costs and the growth rate of regional GDP has become larger, and the regional environmental carrying capacity has been continuously enhanced.(7)According to the analysis, the southern Shaanxi region is mainly a resource-based area with a relatively unbalanced economic structure. The secondary industry is the main economic support industry in the southern Shaanxi region, which accounts for a high proportion of the regional economy.(8)In order to ensure that the development and utilization of iron ore resources, environmental protection, and economy develop in an associated manner in the southern Shaanxi region, according to this research, the following advice is proposed:(1)Adjust the structure of the three major economic industries in the region, take advantage of natural resources and environment, and actively develop alternative industries, such as developing tourism and related service industries.(2)Local governments should vigorously introduce high-tech enterprises, increase investment or introduction of technical talents, and explore new industrial forms, such as biotechnology, high-tech and other emerging cutting-edge industries, and so on, to realize the transformation of economic growth mode and economic development transformation upgrade.(3)Practice the concept “lucid waters and lush mountains are invaluable assets,” strengthen supervision and management, and develop iron ore resources scientifically and rationally on the premise of strengthening the ecological environment protection of mines. Develop the mining economy to protect the environment, improve the safety of mine workers and the quality of their lives, and promote high-quality social and economic development in the southern Shaanxi region.
Through the system simulation, we can better understand the development of iron ore resources in southern Shaanxi by increasing the investment ratio of mineral resources in southern Shaanxi by 10% and 20%, respectively, to simulate and compare the regional GDP and iron ore resource demand in southern Shaanxi area.
Data Availability
The data used to support the findings of this study are collected from the Shaanxi Province Yearbook 2010–2017, Shaanxi Local Chronicle 2010–2017, and Internal data from Northwest Nonferrous Geology and Mining Group Co., Ltd.
Conflicts of Interest
The authors declare that there are no conflicts of interest in this research.