# China’s Energy Structure Optimization Based on Data Mining from the Perspective of Circular Economy: Take Qujing City as an Example

Authors: Jing Zhao; Qiongli Zhu
Journal: Mobile Information Systems
 (2022)

---

## Abstract

While China’s economic strength has steadily improved, China’s environment and energy development have also attracted much attention. On the basis of previous research results, this paper constructs the ESO (energy structure optimization) evaluation index system from the perspective of CE (circular economy). In data mining, correlation analysis is used to identify the factors that affect ESO. The carbon pinch method is used to identify the bottleneck of energy consumption, and the amount of clean energy that meets the carbon emission limit is determined by energy substitution. The research shows that ESO can only be realized under certain incentives and constraints under the carbon emission limit, and the key is to reduce the proportion of high-carbon energy. In the next 20 years of Qujing’s energy structure, primary power will enter a stage of rapid development, accounting for 5% of the total energy consumption by 2022. The development of CE is conducive to overcoming resource bottlenecks and reducing energy risks in Qujing, so the development of CE is the only way for Qujing to develop ESO.


---

## Body

## 1. Introduction

CE (circular economy) is a new scientific development concept that has become the international community’s development trend. It is an important realization form of sustainable development strategy. Understanding CE is critical to China’s current social and economic development as well as its long-term development. It is a critical input in the production of energy enterprises, the foundation of long-term industrial development, and a key consideration in regional economic development strategies [1]. To some extent, a country’s or region’s energy structure determines its industrial structure, and a change in the energy structure will inevitably lead to a change in the industrial structure and an adjustment in the economic development strategy. Because coal production and consumption dominate China’s energy structure, coal’s CO2 emissions rank first among all energy sources. Reduced coal consumption and energy structure optimization are important ways to reduce carbon emissions.China is currently undergoing rapid economic growth, industrialization, and urbanization. The industrial structure has a very high level of industrialization. Energy is required for economic growth, industrial production, and people’s daily lives. Economic growth and industrial structure will be significantly impacted by the transition from low-cost coal to low-carbon, expensive natural gas, electricity, and other clean energy consumption [2, 3]. Scholars’ current research on the economic impact of energy consumption is primarily focused on three aspects. First, using the time series method, the causality and long-term equilibrium between energy consumption and economic growth are investigated. Second, studies such as Ji et al. [4] and BGF [5] have looked at the impact of adjusting energy consumption structures or changing industrial structures under environmental energy constraints on the promotion of industrial optimization. Finally, Yuan et al. [6], Eriksen et al. [7], and others have looked into the impact of energy intensity and efficiency. However, the above research only looks at the economic chain effect of energy structure adjustment from the perspectives of economic growth, structural adjustment, and energy efficiency, and there is no systematic theoretical or empirical evidence for this effect. We can reduce our use of high-carbon fossil fuels by implementing CE’s recommendations for technological innovation, institutional innovation, renewable energy and new energy development, and energy utilization structure optimization. Reduce energy and greenhouse gas emissions, and create a win-win new economic development pattern that promotes both economic and social development while also protecting the environment.Scholars have been investigating; recognizing the close relationship between society, economy, energy, and the environment; and highlighting the energy shortage from the perspectives of ESO (energy structure optimization), optimization policy, and optimization direction [8]. As a result, examining the scale and future development trend of ESO in China is beneficial to the country’s long-term energy development. This paper examines energy based on theoretical achievements both at home and abroad, as well as CE theory, sustainable development theory, resource-saving society and environment-friendly society theory, and the benefits and drawbacks of Akai’s current energy power generation. The optimization analysis of Qujing’s consumption structure complements the regional energy sustainable development theoretical system. This paper proposes some measures for adjusting ESO under CE, based on an analysis of the industrial structure in employment, and provides practical guidance for CE’s positive development.


### 1.1. Innovation

(1)
Incorporate the development of CE into the analysis of influencing factors of ESO adjustment, and establish an ESO evaluation index system based on CE. Using the data mining analysis model, combined with the latest statistical data, this paper makes an empirical analysis of ESO in Qujing from the perspective of CE
(2)
Using CO2 emission pinch analysis technology, the pinch analysis model of energy consumption CO2 emission in Qujing city was established, and the energy consumption structure of Qujing city in 2022 was obtained to meet the needs of economic development and carbon emissionThe main research contents of this paper are the following: The first section introduces the research background and significance and then introduces the main work of this paper. The second section mainly introduces the related research status of ESO. The third section puts forward the concrete method and implementation of this research. The fourth section verifies the superiority and feasibility of this research model. The fifth section is the summary and prospect of the full text.


## 1.1. Innovation

(1)
Incorporate the development of CE into the analysis of influencing factors of ESO adjustment, and establish an ESO evaluation index system based on CE. Using the data mining analysis model, combined with the latest statistical data, this paper makes an empirical analysis of ESO in Qujing from the perspective of CE
(2)
Using CO2 emission pinch analysis technology, the pinch analysis model of energy consumption CO2 emission in Qujing city was established, and the energy consumption structure of Qujing city in 2022 was obtained to meet the needs of economic development and carbon emissionThe main research contents of this paper are the following: The first section introduces the research background and significance and then introduces the main work of this paper. The second section mainly introduces the related research status of ESO. The third section puts forward the concrete method and implementation of this research. The fourth section verifies the superiority and feasibility of this research model. The fifth section is the summary and prospect of the full text.


## 2. Related Work

### 2.1. Circular Economy

CE advocates the economic development model based on sustainable recycling of material resources. We believe that economic activities, like a natural ecosystem, should run in a continuous cycle. This process is composed of resources, products, consumption, and renewable resources and has the characteristics of low exploitation, high utilization rate, and low emission. CE is essentially an ecological economy, and it needs to use ecological laws to guide the economic activities of human society. Under normal circumstances, the natural ecosystem can achieve ecological balance through the laws of energy conversion and material circulation and can exist stably and continuously.Zhang and colleagues systematically introduced the basic principle of CE, pointing out that there are three ways to develop CE from energy in the relationship between energy and CE [9]: using renewable energy, developing existing energy recovery technology, and actively developing renewable energy. Nuclear fusion technology has a lot to offer. Natural resources were referred to as resource assets by Jesus and others, and the intergenerational balance model of resource asset sustainability was proposed. Natural resources were dubbed resource assets by Yong et al., who proposed the intergenerational balance model of resource asset sustainability [10]. The effect of CE on the environmental Kuznets curve was investigated by Song et al. [11]. According to Moktadir and others, there is no such thing as a “recycling industry” in CE, and what we need to do is “green” the existing industry in order to meet CE’s requirements [12]. In terms of industrial policy, Breure and others suggested that CE industrial policy be formally formulated to guide the industrial organization and deployment of CE core industries [13]. The industrial policy of CE development, according to AMN and others, should emphasize resource utilization efficiency, environmental protection, and economic competitiveness. The CE industrial system was established in the direction of industrial structure adjustment, optimization, and upgrading by ecological industry, ecological agriculture, and ecological tertiary industry [14]. CE, according to Ali and others, should be realized through institutional innovation, increased institutional supply, and institutional change as a new economic development model that is conducive to society’s long-term development [15].


### 2.2. ESO Research

The optimization of the energy consumption structure is inextricably linked with the research achievements in the fields of resource economics, resources and environment, resources and sustainable development, energy strategy, energy security, energy prediction, energy technology, and energy system engineering. The influence of energy structure adjustment on the industrial structure is not only an important basic issue of China’s energy policy and energy regulatory policy but also a problem that must be solved to promote high-quality development of China’s economy.The premise of ESO adjustment is to study the characteristics and causes of China’s energy structure, as well as to analyze the problems that exist in the energy structure. This has both theoretical and practical implications for China’s energy structure transformation. Zhang and Liu summarized many issues in China’s energy structure, concluding that scarcity of energy resources, economic and social development needs, and the neglect of energy, environmental, and social costs were the main reasons for unreasonable energy resources [16]. The main factors hindering the rigid transformation of China’s energy structure, according to Xue et al., are resource holding conditions, socioeconomic development level, distorted energy price system, and bottleneck of renewable energy development, and energy consumption is considered one of the main factors affecting CO2 emission [17]. Liu et al. looked at the factors that influence energy consumption structure by path and found that carbon emission constraints had a greater direct impact on energy consumption structure optimization [18]. Using the grey system model G1,1, Chao and Miao predicted the energy production and consumption structure [19]. Scenario analysis is used by BFMA and others to investigate energy demand and structure in various scenarios [20]. To systematically and comprehensively predict the medium- and long-term energy supply and demand situation in China, Cai et al. used a system dynamics model, a grey system model, a vector autoregressive model, and a variable weight combination forecasting model [21]. Chen et al. developed a strategic energy structure adjustment model that took into account the constraints of energy conservation and carbon emissions and then built a general equilibrium model to assess the macroeconomic impact of various energy structure costs [22].


## 2.1. Circular Economy

CE advocates the economic development model based on sustainable recycling of material resources. We believe that economic activities, like a natural ecosystem, should run in a continuous cycle. This process is composed of resources, products, consumption, and renewable resources and has the characteristics of low exploitation, high utilization rate, and low emission. CE is essentially an ecological economy, and it needs to use ecological laws to guide the economic activities of human society. Under normal circumstances, the natural ecosystem can achieve ecological balance through the laws of energy conversion and material circulation and can exist stably and continuously.Zhang and colleagues systematically introduced the basic principle of CE, pointing out that there are three ways to develop CE from energy in the relationship between energy and CE [9]: using renewable energy, developing existing energy recovery technology, and actively developing renewable energy. Nuclear fusion technology has a lot to offer. Natural resources were referred to as resource assets by Jesus and others, and the intergenerational balance model of resource asset sustainability was proposed. Natural resources were dubbed resource assets by Yong et al., who proposed the intergenerational balance model of resource asset sustainability [10]. The effect of CE on the environmental Kuznets curve was investigated by Song et al. [11]. According to Moktadir and others, there is no such thing as a “recycling industry” in CE, and what we need to do is “green” the existing industry in order to meet CE’s requirements [12]. In terms of industrial policy, Breure and others suggested that CE industrial policy be formally formulated to guide the industrial organization and deployment of CE core industries [13]. The industrial policy of CE development, according to AMN and others, should emphasize resource utilization efficiency, environmental protection, and economic competitiveness. The CE industrial system was established in the direction of industrial structure adjustment, optimization, and upgrading by ecological industry, ecological agriculture, and ecological tertiary industry [14]. CE, according to Ali and others, should be realized through institutional innovation, increased institutional supply, and institutional change as a new economic development model that is conducive to society’s long-term development [15].


## 2.2. ESO Research

The optimization of the energy consumption structure is inextricably linked with the research achievements in the fields of resource economics, resources and environment, resources and sustainable development, energy strategy, energy security, energy prediction, energy technology, and energy system engineering. The influence of energy structure adjustment on the industrial structure is not only an important basic issue of China’s energy policy and energy regulatory policy but also a problem that must be solved to promote high-quality development of China’s economy.The premise of ESO adjustment is to study the characteristics and causes of China’s energy structure, as well as to analyze the problems that exist in the energy structure. This has both theoretical and practical implications for China’s energy structure transformation. Zhang and Liu summarized many issues in China’s energy structure, concluding that scarcity of energy resources, economic and social development needs, and the neglect of energy, environmental, and social costs were the main reasons for unreasonable energy resources [16]. The main factors hindering the rigid transformation of China’s energy structure, according to Xue et al., are resource holding conditions, socioeconomic development level, distorted energy price system, and bottleneck of renewable energy development, and energy consumption is considered one of the main factors affecting CO2 emission [17]. Liu et al. looked at the factors that influence energy consumption structure by path and found that carbon emission constraints had a greater direct impact on energy consumption structure optimization [18]. Using the grey system model G1,1, Chao and Miao predicted the energy production and consumption structure [19]. Scenario analysis is used by BFMA and others to investigate energy demand and structure in various scenarios [20]. To systematically and comprehensively predict the medium- and long-term energy supply and demand situation in China, Cai et al. used a system dynamics model, a grey system model, a vector autoregressive model, and a variable weight combination forecasting model [21]. Chen et al. developed a strategic energy structure adjustment model that took into account the constraints of energy conservation and carbon emissions and then built a general equilibrium model to assess the macroeconomic impact of various energy structure costs [22].


## 3. Methodology

### 3.1. ESO Index System Based on CE

On the one hand, the evaluation index system for CE construction should be based on the existing statistical system and data; on the other hand, it should be based on the existing statistical system and data. Simply copy, add, and accumulate the original indicators, but organically synthesize, refine, sublimate, and innovate them to some extent. Each region’s evaluation is based on its economic development level, ecological environment status, and existing environmental problems, as well as a comprehensive consideration and assessment of various indicators, and finally the determination of the region’s specific evaluation indicators [1].Although ESO indicators are considered and defined in the context of promoting and rationalizing the industrial structure, from the perspective of CE, these indicators are clearly insufficient to achieve industrial structure optimization and adjustment. The ESO index only considers the economic benefits of structural optimization, whereas the CE index considers both the economic benefits and the quality of economic development. The following indicators can be used to determine whether the energy consumption structure is optimized:(1)
Energy Synergy. The energy-economy-society-environment synergy can be calculated by the energy-economy-society-environment joint evaluation model. The higher the degree of cooperation, the more optimized the energy system, especially the energy consumption structure.(2)
Relative Number of Energy Structure. The relative quantity of energy structure is used to analyze the proportion of various energy sources in the total energy. ESO is to reduce the share of high-cost, low-efficiency, and high-pollution energy (such as coal) more and more, thus reducing the share of noncoal energy, especially new energy such as solar energy, geothermal energy, and wind energy. In-growth mainly includes the relative quantity of production capacity structure and the relative quantity of energy consumption structure.(3)
Energy Self-Sufficiency Rate. The energy self-sufficiency rate is the ratio of energy consumption to energy production, which can be calculated by converting primary energy into coal or oil equivalent. A higher energy self-sufficiency rate optimizes the energy system and energy consumption structure.(4)
Energy Consumption Elasticity Coefficient. The energy consumption elasticity coefficient indicates the ratio of the growth rate of primary energy consumption in a certain year to the economic growth rate of a country or region. The economic growth rate generally adopts the growth rate of GDP or GDP and national income. It reflects the relationship between energy and economic growth.(5)
Energy Efficiency. The higher the energy efficiency, the more optimized the energy system and the more optimized the energy consumption structure.(6)
Carbon Emissions. The lower the carbon emissions or the more carbon emission reductions, the more optimized the energy system of a country or region, and to a large extent, the more optimized the energy consumption structure.According to the above analysis, the indicators of CE development should be fully considered in the ESO process, and the comprehensive evaluation indicators of ESO based on CE can be summarized in Figure1.Figure 1
Comprehensive evaluation index of ESO based on CE.


### 3.2. ESO Based on Data Mining

#### 3.2.1. Multiobjective Decision Model

It is a significant investment as well as a competitive advantage for modern technology firms. Traditional Qujing industries are mostly energy- and labor-intensive, with low technology content, low added value, high pollution, and high energy consumption. The market and the government are increasingly restricting the growth of these businesses. Increase investment in technological innovation, encourage enterprise technological innovation, and use information technology to improve traditional industries’ equipment levels, optimize technological processes, and improve enterprises’ intelligence, automation, and informatization levels.Because pollution does not far exceed the environment’s self-purification ability at a low level of economic development, people use fewer resources, produce less waste, and have better environmental quality. People’s demand for a pleasant environment and investment in environmental improvement will rise as the economy develops and per capita national income rises, resulting in a reduction in pollution, an improvement in quality of life, and environmentally and economically sustainable development. As a result of CE development, Qujing city can not only cross the peak of the environmental Kuznets curve ahead of schedule under low per capita economic conditions but also directly determine the height to cross, which is very realistic in the future. The key to moving the vertex of the environmental Kuznets curve vertically downward is to implement CE.In real life, people often have to consider multiple goals when making decisions, some of which are contradictory and bring certain difficulties to decision-making. Multiobjective decision-making is divided into continuous and discrete decision-making technologies according to the continuous or discrete states of decision variables in multiobjective decision-making. The purpose of this paper is to realize the coordinated development of social economy and environment by optimizing the energy structure.It is the material basis of the economic and social development of energy, so it is necessary to focus on the harmonious state and development trend within the energy subsystem. Realizing the harmonious development of energy, economy, society, and environment through the optimization of energy structure is a decision-making problem with infinite plans and continuous decision variables. The model considers the energy existing in the energy system, so that the energy consumption can meet the requirements of economic development and reach the expected value of GDP. The target function expression is(1)max∑i1naixi.Among them,ai is the contribution rate of various energy sources per unit to the unit gross national product.Total energy consumption is the most obvious manifestation of related energy in an economy. According to the principle of CE reduction, the lowest total energy consumption is an important aspect of pursuing the highest energy efficiency, so the objective function of the lowest total energy consumption is(2)min∑i1nbixi.Among them,bi refers to the standard coal conversion coefficient of the first energy source.According to the CE concept, we insist on prevention first and comprehensive treatment, strengthen pollution prevention and control from the source, protect the ecological environment, and resolutely change the pollution situation. Therefore, the energy target plan takes the environmental target as one of the model targets. Taking CO2 as an example, the environmental objectives are
(3)min∑i1ncixi.Among them,ci refers to the CO2 emission coefficient of the i energy source, which is converted into the same calculation unit.In order to solve this problem, it is more appropriate to use the “weighting method,” because the weight is selected according to the energy observation constraints and the composition and importance of the objective function in the predetermined target.The weighting method is to weight different objective functions to make them a single objective function:(4)Max=dominateZX=Z1X,Z2X,⋯,ZPXs.t.giX≤0,i=1,2,⋯,m,XjX≥0,j=1,2,⋯,n.The weighting formula is(5)Max=ZX=w1Z1x+w2Z2x+⋯+wpZpxs.t.giX≤0,i=1,2,⋯,m,XjX≥0,j=1,2,⋯,n.If some functions or constraints are nonlinear, the above problem is a nonlinear programming problem. At this time, the optimal solution of (5), that is, a noninferior solution of (4), can be obtained by K−T condition or other methods.Because of the different values ofwk, the objective function of equation (5) is different, so its optimal solution may be different; that is, other noninferior solutions of equation (4) can be obtained. Therefore, some or all noninferior solutions to the original multiobjective problem can be obtained by changing the values of wk.


#### 3.2.2. Analysis of Influencing Factors Based on Data Mining

With the rapid development of data collection and data storage technology, various organizations are accumulating a large amount of data [2–5]. How to find valuable information in these data and help decision-makers make decisions has become a task that many data owners are eager to complete. Data mining is an iterative process, a process of interaction and cooperation between people and computers. People can discover valuable new information and valuable information in a large amount of data.Data mining is closely related to knowledge discovery. To understand the process of data mining, we must first understand the concept of knowledge discovery. Knowledge discovery is the whole process of discovering useful knowledge from data. Knowledge discovery is an iterative process. From the technical point of view, data mining is an important link in the process of knowledge discovery. Essentially, descriptive operations such as cluster analysis, association analysis, evolutionary analysis, and sequential pattern mining are exploratory operations.Clustering analysis is a very active research topic in data mining. It is different from the traditional classification. The classification model is sample data, and its category label is known. The data array adopts the object-attribute structure.Assuming that there arep objects in total, each object is represented by Xii=1,2,⋯,p, and each object has q variables, and xiji=1,2,⋯,q is represented as the value of the j variable in the ith object; then, the q variable values of p objects can be regarded as p×q matrix, as shown in
(6)x11⋯x1q⋮⋱xp1⋯xpqThe measure of similarity in clustering refers to determining the similarity between objects, and the standard of judgment is generally the distance between objects. At present, the most commonly used distance calculation formula is Euclidean distance in(7)di,j=dxi1,xj12+dxi2,xj22+⋯+dxiq,xjq2,where i=xi1,xi2,⋯,xiq,j=xj1,xj2,⋯,xjq, respectively, represents a q-dimensional data object.SVM (Support Vector Machine) is a supervised learning method. It builds learning functions from labeled training data sets, which have a good theoretical basis. SVM introduces the loss function, which adopts the new loss function typeε proposed by Vapnik, insensitive loss function Ly,fx, and the expression of insensitive loss function is as follows:
(8)Lεy,fx=0,ify−fx≤ε,y−fx−ε,otherwise,where ε is the insensitive coefficient.Carbon pinch analysis achieves the carbon emission target through the substitution between clean energy and high carbon energy. The research shows that the conversion process between energy sources is very orderly, but the substitution process is relatively slow. Therefore, based on the substitutability of energy sources, the ESO problem under the constraint of China’s carbon emissions can be interpreted as the determination that the carbon emission limit of clean energy sources is met.In the carbon emission constraint, linear programming can be expressed as determining the minimum amount of clean energy in a specific region given the total energy and CO2 emission limit targets. Energy substitution under carbon emission constraints is shown as follows:
(9)Wi+Fi=Si,∀iZ+Fi=D,∀i∑iCout,iFi≤CinD,∀iWi,Fi,Z≥0,∀i.In the above formula,Si is the i supply of energy; D is the total regional energy demand; Wi is the i surplus supply of energy; Fi is the supply quantity of energy i to meet regional energy demand; Z is the clean energy quantity to meet the regional energy demand; Cout,i is the i energy supply and emission factor; Cin is the regional energy demand emission factor, Cin=L/D; and L is the regional CO2 emission constraint.In some cases, low-carbon energy sources (such as biodiesel and methanol) are considered potential carbon-neutral or zero-carbon energy sources in their life cycle, but their production and utilization will still produce some trace amounts of CO2 or other energy sources. Therefore, by calculating the emission factor value, the basic ESO problem can be expressed as Figure 2.Figure 2
CO2 emission pinch analysis system.The flow into each different demand receiverD1,D2,D3,D4 is a combination of different energy resource supplies. The energy resource provided by each supply S1,S2,S3,S4 can also be provided to different demand receivers. The goal is to determine the minimum amount of low-carbon and zero-carbon energy resources, which can meet both energy demand and emission restrictions.Convergence analysis can effectively test the convergence and divergence of the ESO degree in different regions during the research period. The methods of convergence tests are mainly divided intoσ convergence and β convergence.σ coefficient reflects the variation trend of ESO degree deviation with time. When the deviation shows a downward trend, it indicates that there is σ convergence in the ESO degree; otherwise, it does not exist. σ coefficient is calculated as follows:
(10)σ=∑ilnck,t−lnct¯2N.Among them,ck,t represents the ESO degree of regional pk in t period, lnct¯ represents the average ESO degree in t period, and N represents the number of regions.β convergence reflects the trend that the ESO level increases with time, that is, whether the areas with lower initial ESO levels increase at a faster rate. According to Yuan et al.’s research [6], the formula for obtaining β coefficient is as follows:
(11)1Tlnck,t+Tck,t=α+βlnck,t+εi,t,where T represents the time interval, the growth rate of ESO degree from period t to period t+T; α,β is the estimation coefficient; and εi,t is the error term.Through clustering analysis and screening of the indicators affecting ESO, we found that Pearson correlation analysis was used to study six indicators affecting ESO. Run Poly Analyst software to generate project files for correlation analysis. Open the generated file and add a Microsoft Excel data source node in the editing window, load the data that affects ESO, and change the node name to the appropriate category.The data analysis node can study the correlation between different attributes and display the results interactively in the data analysis node option of correlation analysis. By adjusting the size of the correlation coefficient, the attribute columns larger than the correlation coefficient will be displayed quickly. The connection diagram of impact factor analysis is shown in Figure3.Figure 3
Connection diagram of influencing factor analysis.For coal resources, it is necessary to control the whole life cycle from the source to the process to the terminal, increase the ratio of raw coal washing to raw coal, reduce the output and direct combustion of raw coal, accelerate the development of high-efficiency coal conversion technology, and reduce pollution emissions in the conversion process. Realize the clean utilization of products, coal and coal-based products.


## 3.1. ESO Index System Based on CE

On the one hand, the evaluation index system for CE construction should be based on the existing statistical system and data; on the other hand, it should be based on the existing statistical system and data. Simply copy, add, and accumulate the original indicators, but organically synthesize, refine, sublimate, and innovate them to some extent. Each region’s evaluation is based on its economic development level, ecological environment status, and existing environmental problems, as well as a comprehensive consideration and assessment of various indicators, and finally the determination of the region’s specific evaluation indicators [1].Although ESO indicators are considered and defined in the context of promoting and rationalizing the industrial structure, from the perspective of CE, these indicators are clearly insufficient to achieve industrial structure optimization and adjustment. The ESO index only considers the economic benefits of structural optimization, whereas the CE index considers both the economic benefits and the quality of economic development. The following indicators can be used to determine whether the energy consumption structure is optimized:(1)
Energy Synergy. The energy-economy-society-environment synergy can be calculated by the energy-economy-society-environment joint evaluation model. The higher the degree of cooperation, the more optimized the energy system, especially the energy consumption structure.(2)
Relative Number of Energy Structure. The relative quantity of energy structure is used to analyze the proportion of various energy sources in the total energy. ESO is to reduce the share of high-cost, low-efficiency, and high-pollution energy (such as coal) more and more, thus reducing the share of noncoal energy, especially new energy such as solar energy, geothermal energy, and wind energy. In-growth mainly includes the relative quantity of production capacity structure and the relative quantity of energy consumption structure.(3)
Energy Self-Sufficiency Rate. The energy self-sufficiency rate is the ratio of energy consumption to energy production, which can be calculated by converting primary energy into coal or oil equivalent. A higher energy self-sufficiency rate optimizes the energy system and energy consumption structure.(4)
Energy Consumption Elasticity Coefficient. The energy consumption elasticity coefficient indicates the ratio of the growth rate of primary energy consumption in a certain year to the economic growth rate of a country or region. The economic growth rate generally adopts the growth rate of GDP or GDP and national income. It reflects the relationship between energy and economic growth.(5)
Energy Efficiency. The higher the energy efficiency, the more optimized the energy system and the more optimized the energy consumption structure.(6)
Carbon Emissions. The lower the carbon emissions or the more carbon emission reductions, the more optimized the energy system of a country or region, and to a large extent, the more optimized the energy consumption structure.According to the above analysis, the indicators of CE development should be fully considered in the ESO process, and the comprehensive evaluation indicators of ESO based on CE can be summarized in Figure1.Figure 1
Comprehensive evaluation index of ESO based on CE.


## 3.2. ESO Based on Data Mining

### 3.2.1. Multiobjective Decision Model

It is a significant investment as well as a competitive advantage for modern technology firms. Traditional Qujing industries are mostly energy- and labor-intensive, with low technology content, low added value, high pollution, and high energy consumption. The market and the government are increasingly restricting the growth of these businesses. Increase investment in technological innovation, encourage enterprise technological innovation, and use information technology to improve traditional industries’ equipment levels, optimize technological processes, and improve enterprises’ intelligence, automation, and informatization levels.Because pollution does not far exceed the environment’s self-purification ability at a low level of economic development, people use fewer resources, produce less waste, and have better environmental quality. People’s demand for a pleasant environment and investment in environmental improvement will rise as the economy develops and per capita national income rises, resulting in a reduction in pollution, an improvement in quality of life, and environmentally and economically sustainable development. As a result of CE development, Qujing city can not only cross the peak of the environmental Kuznets curve ahead of schedule under low per capita economic conditions but also directly determine the height to cross, which is very realistic in the future. The key to moving the vertex of the environmental Kuznets curve vertically downward is to implement CE.In real life, people often have to consider multiple goals when making decisions, some of which are contradictory and bring certain difficulties to decision-making. Multiobjective decision-making is divided into continuous and discrete decision-making technologies according to the continuous or discrete states of decision variables in multiobjective decision-making. The purpose of this paper is to realize the coordinated development of social economy and environment by optimizing the energy structure.It is the material basis of the economic and social development of energy, so it is necessary to focus on the harmonious state and development trend within the energy subsystem. Realizing the harmonious development of energy, economy, society, and environment through the optimization of energy structure is a decision-making problem with infinite plans and continuous decision variables. The model considers the energy existing in the energy system, so that the energy consumption can meet the requirements of economic development and reach the expected value of GDP. The target function expression is(1)max∑i1naixi.Among them,ai is the contribution rate of various energy sources per unit to the unit gross national product.Total energy consumption is the most obvious manifestation of related energy in an economy. According to the principle of CE reduction, the lowest total energy consumption is an important aspect of pursuing the highest energy efficiency, so the objective function of the lowest total energy consumption is(2)min∑i1nbixi.Among them,bi refers to the standard coal conversion coefficient of the first energy source.According to the CE concept, we insist on prevention first and comprehensive treatment, strengthen pollution prevention and control from the source, protect the ecological environment, and resolutely change the pollution situation. Therefore, the energy target plan takes the environmental target as one of the model targets. Taking CO2 as an example, the environmental objectives are
(3)min∑i1ncixi.Among them,ci refers to the CO2 emission coefficient of the i energy source, which is converted into the same calculation unit.In order to solve this problem, it is more appropriate to use the “weighting method,” because the weight is selected according to the energy observation constraints and the composition and importance of the objective function in the predetermined target.The weighting method is to weight different objective functions to make them a single objective function:(4)Max=dominateZX=Z1X,Z2X,⋯,ZPXs.t.giX≤0,i=1,2,⋯,m,XjX≥0,j=1,2,⋯,n.The weighting formula is(5)Max=ZX=w1Z1x+w2Z2x+⋯+wpZpxs.t.giX≤0,i=1,2,⋯,m,XjX≥0,j=1,2,⋯,n.If some functions or constraints are nonlinear, the above problem is a nonlinear programming problem. At this time, the optimal solution of (5), that is, a noninferior solution of (4), can be obtained by K−T condition or other methods.Because of the different values ofwk, the objective function of equation (5) is different, so its optimal solution may be different; that is, other noninferior solutions of equation (4) can be obtained. Therefore, some or all noninferior solutions to the original multiobjective problem can be obtained by changing the values of wk.


### 3.2.2. Analysis of Influencing Factors Based on Data Mining

With the rapid development of data collection and data storage technology, various organizations are accumulating a large amount of data [2–5]. How to find valuable information in these data and help decision-makers make decisions has become a task that many data owners are eager to complete. Data mining is an iterative process, a process of interaction and cooperation between people and computers. People can discover valuable new information and valuable information in a large amount of data.Data mining is closely related to knowledge discovery. To understand the process of data mining, we must first understand the concept of knowledge discovery. Knowledge discovery is the whole process of discovering useful knowledge from data. Knowledge discovery is an iterative process. From the technical point of view, data mining is an important link in the process of knowledge discovery. Essentially, descriptive operations such as cluster analysis, association analysis, evolutionary analysis, and sequential pattern mining are exploratory operations.Clustering analysis is a very active research topic in data mining. It is different from the traditional classification. The classification model is sample data, and its category label is known. The data array adopts the object-attribute structure.Assuming that there arep objects in total, each object is represented by Xii=1,2,⋯,p, and each object has q variables, and xiji=1,2,⋯,q is represented as the value of the j variable in the ith object; then, the q variable values of p objects can be regarded as p×q matrix, as shown in
(6)x11⋯x1q⋮⋱xp1⋯xpqThe measure of similarity in clustering refers to determining the similarity between objects, and the standard of judgment is generally the distance between objects. At present, the most commonly used distance calculation formula is Euclidean distance in(7)di,j=dxi1,xj12+dxi2,xj22+⋯+dxiq,xjq2,where i=xi1,xi2,⋯,xiq,j=xj1,xj2,⋯,xjq, respectively, represents a q-dimensional data object.SVM (Support Vector Machine) is a supervised learning method. It builds learning functions from labeled training data sets, which have a good theoretical basis. SVM introduces the loss function, which adopts the new loss function typeε proposed by Vapnik, insensitive loss function Ly,fx, and the expression of insensitive loss function is as follows:
(8)Lεy,fx=0,ify−fx≤ε,y−fx−ε,otherwise,where ε is the insensitive coefficient.Carbon pinch analysis achieves the carbon emission target through the substitution between clean energy and high carbon energy. The research shows that the conversion process between energy sources is very orderly, but the substitution process is relatively slow. Therefore, based on the substitutability of energy sources, the ESO problem under the constraint of China’s carbon emissions can be interpreted as the determination that the carbon emission limit of clean energy sources is met.In the carbon emission constraint, linear programming can be expressed as determining the minimum amount of clean energy in a specific region given the total energy and CO2 emission limit targets. Energy substitution under carbon emission constraints is shown as follows:
(9)Wi+Fi=Si,∀iZ+Fi=D,∀i∑iCout,iFi≤CinD,∀iWi,Fi,Z≥0,∀i.In the above formula,Si is the i supply of energy; D is the total regional energy demand; Wi is the i surplus supply of energy; Fi is the supply quantity of energy i to meet regional energy demand; Z is the clean energy quantity to meet the regional energy demand; Cout,i is the i energy supply and emission factor; Cin is the regional energy demand emission factor, Cin=L/D; and L is the regional CO2 emission constraint.In some cases, low-carbon energy sources (such as biodiesel and methanol) are considered potential carbon-neutral or zero-carbon energy sources in their life cycle, but their production and utilization will still produce some trace amounts of CO2 or other energy sources. Therefore, by calculating the emission factor value, the basic ESO problem can be expressed as Figure 2.Figure 2
CO2 emission pinch analysis system.The flow into each different demand receiverD1,D2,D3,D4 is a combination of different energy resource supplies. The energy resource provided by each supply S1,S2,S3,S4 can also be provided to different demand receivers. The goal is to determine the minimum amount of low-carbon and zero-carbon energy resources, which can meet both energy demand and emission restrictions.Convergence analysis can effectively test the convergence and divergence of the ESO degree in different regions during the research period. The methods of convergence tests are mainly divided intoσ convergence and β convergence.σ coefficient reflects the variation trend of ESO degree deviation with time. When the deviation shows a downward trend, it indicates that there is σ convergence in the ESO degree; otherwise, it does not exist. σ coefficient is calculated as follows:
(10)σ=∑ilnck,t−lnct¯2N.Among them,ck,t represents the ESO degree of regional pk in t period, lnct¯ represents the average ESO degree in t period, and N represents the number of regions.β convergence reflects the trend that the ESO level increases with time, that is, whether the areas with lower initial ESO levels increase at a faster rate. According to Yuan et al.’s research [6], the formula for obtaining β coefficient is as follows:
(11)1Tlnck,t+Tck,t=α+βlnck,t+εi,t,where T represents the time interval, the growth rate of ESO degree from period t to period t+T; α,β is the estimation coefficient; and εi,t is the error term.Through clustering analysis and screening of the indicators affecting ESO, we found that Pearson correlation analysis was used to study six indicators affecting ESO. Run Poly Analyst software to generate project files for correlation analysis. Open the generated file and add a Microsoft Excel data source node in the editing window, load the data that affects ESO, and change the node name to the appropriate category.The data analysis node can study the correlation between different attributes and display the results interactively in the data analysis node option of correlation analysis. By adjusting the size of the correlation coefficient, the attribute columns larger than the correlation coefficient will be displayed quickly. The connection diagram of impact factor analysis is shown in Figure3.Figure 3
Connection diagram of influencing factor analysis.For coal resources, it is necessary to control the whole life cycle from the source to the process to the terminal, increase the ratio of raw coal washing to raw coal, reduce the output and direct combustion of raw coal, accelerate the development of high-efficiency coal conversion technology, and reduce pollution emissions in the conversion process. Realize the clean utilization of products, coal and coal-based products.


## 3.2.1. Multiobjective Decision Model

It is a significant investment as well as a competitive advantage for modern technology firms. Traditional Qujing industries are mostly energy- and labor-intensive, with low technology content, low added value, high pollution, and high energy consumption. The market and the government are increasingly restricting the growth of these businesses. Increase investment in technological innovation, encourage enterprise technological innovation, and use information technology to improve traditional industries’ equipment levels, optimize technological processes, and improve enterprises’ intelligence, automation, and informatization levels.Because pollution does not far exceed the environment’s self-purification ability at a low level of economic development, people use fewer resources, produce less waste, and have better environmental quality. People’s demand for a pleasant environment and investment in environmental improvement will rise as the economy develops and per capita national income rises, resulting in a reduction in pollution, an improvement in quality of life, and environmentally and economically sustainable development. As a result of CE development, Qujing city can not only cross the peak of the environmental Kuznets curve ahead of schedule under low per capita economic conditions but also directly determine the height to cross, which is very realistic in the future. The key to moving the vertex of the environmental Kuznets curve vertically downward is to implement CE.In real life, people often have to consider multiple goals when making decisions, some of which are contradictory and bring certain difficulties to decision-making. Multiobjective decision-making is divided into continuous and discrete decision-making technologies according to the continuous or discrete states of decision variables in multiobjective decision-making. The purpose of this paper is to realize the coordinated development of social economy and environment by optimizing the energy structure.It is the material basis of the economic and social development of energy, so it is necessary to focus on the harmonious state and development trend within the energy subsystem. Realizing the harmonious development of energy, economy, society, and environment through the optimization of energy structure is a decision-making problem with infinite plans and continuous decision variables. The model considers the energy existing in the energy system, so that the energy consumption can meet the requirements of economic development and reach the expected value of GDP. The target function expression is(1)max∑i1naixi.Among them,ai is the contribution rate of various energy sources per unit to the unit gross national product.Total energy consumption is the most obvious manifestation of related energy in an economy. According to the principle of CE reduction, the lowest total energy consumption is an important aspect of pursuing the highest energy efficiency, so the objective function of the lowest total energy consumption is(2)min∑i1nbixi.Among them,bi refers to the standard coal conversion coefficient of the first energy source.According to the CE concept, we insist on prevention first and comprehensive treatment, strengthen pollution prevention and control from the source, protect the ecological environment, and resolutely change the pollution situation. Therefore, the energy target plan takes the environmental target as one of the model targets. Taking CO2 as an example, the environmental objectives are
(3)min∑i1ncixi.Among them,ci refers to the CO2 emission coefficient of the i energy source, which is converted into the same calculation unit.In order to solve this problem, it is more appropriate to use the “weighting method,” because the weight is selected according to the energy observation constraints and the composition and importance of the objective function in the predetermined target.The weighting method is to weight different objective functions to make them a single objective function:(4)Max=dominateZX=Z1X,Z2X,⋯,ZPXs.t.giX≤0,i=1,2,⋯,m,XjX≥0,j=1,2,⋯,n.The weighting formula is(5)Max=ZX=w1Z1x+w2Z2x+⋯+wpZpxs.t.giX≤0,i=1,2,⋯,m,XjX≥0,j=1,2,⋯,n.If some functions or constraints are nonlinear, the above problem is a nonlinear programming problem. At this time, the optimal solution of (5), that is, a noninferior solution of (4), can be obtained by K−T condition or other methods.Because of the different values ofwk, the objective function of equation (5) is different, so its optimal solution may be different; that is, other noninferior solutions of equation (4) can be obtained. Therefore, some or all noninferior solutions to the original multiobjective problem can be obtained by changing the values of wk.


## 3.2.2. Analysis of Influencing Factors Based on Data Mining

With the rapid development of data collection and data storage technology, various organizations are accumulating a large amount of data [2–5]. How to find valuable information in these data and help decision-makers make decisions has become a task that many data owners are eager to complete. Data mining is an iterative process, a process of interaction and cooperation between people and computers. People can discover valuable new information and valuable information in a large amount of data.Data mining is closely related to knowledge discovery. To understand the process of data mining, we must first understand the concept of knowledge discovery. Knowledge discovery is the whole process of discovering useful knowledge from data. Knowledge discovery is an iterative process. From the technical point of view, data mining is an important link in the process of knowledge discovery. Essentially, descriptive operations such as cluster analysis, association analysis, evolutionary analysis, and sequential pattern mining are exploratory operations.Clustering analysis is a very active research topic in data mining. It is different from the traditional classification. The classification model is sample data, and its category label is known. The data array adopts the object-attribute structure.Assuming that there arep objects in total, each object is represented by Xii=1,2,⋯,p, and each object has q variables, and xiji=1,2,⋯,q is represented as the value of the j variable in the ith object; then, the q variable values of p objects can be regarded as p×q matrix, as shown in
(6)x11⋯x1q⋮⋱xp1⋯xpqThe measure of similarity in clustering refers to determining the similarity between objects, and the standard of judgment is generally the distance between objects. At present, the most commonly used distance calculation formula is Euclidean distance in(7)di,j=dxi1,xj12+dxi2,xj22+⋯+dxiq,xjq2,where i=xi1,xi2,⋯,xiq,j=xj1,xj2,⋯,xjq, respectively, represents a q-dimensional data object.SVM (Support Vector Machine) is a supervised learning method. It builds learning functions from labeled training data sets, which have a good theoretical basis. SVM introduces the loss function, which adopts the new loss function typeε proposed by Vapnik, insensitive loss function Ly,fx, and the expression of insensitive loss function is as follows:
(8)Lεy,fx=0,ify−fx≤ε,y−fx−ε,otherwise,where ε is the insensitive coefficient.Carbon pinch analysis achieves the carbon emission target through the substitution between clean energy and high carbon energy. The research shows that the conversion process between energy sources is very orderly, but the substitution process is relatively slow. Therefore, based on the substitutability of energy sources, the ESO problem under the constraint of China’s carbon emissions can be interpreted as the determination that the carbon emission limit of clean energy sources is met.In the carbon emission constraint, linear programming can be expressed as determining the minimum amount of clean energy in a specific region given the total energy and CO2 emission limit targets. Energy substitution under carbon emission constraints is shown as follows:
(9)Wi+Fi=Si,∀iZ+Fi=D,∀i∑iCout,iFi≤CinD,∀iWi,Fi,Z≥0,∀i.In the above formula,Si is the i supply of energy; D is the total regional energy demand; Wi is the i surplus supply of energy; Fi is the supply quantity of energy i to meet regional energy demand; Z is the clean energy quantity to meet the regional energy demand; Cout,i is the i energy supply and emission factor; Cin is the regional energy demand emission factor, Cin=L/D; and L is the regional CO2 emission constraint.In some cases, low-carbon energy sources (such as biodiesel and methanol) are considered potential carbon-neutral or zero-carbon energy sources in their life cycle, but their production and utilization will still produce some trace amounts of CO2 or other energy sources. Therefore, by calculating the emission factor value, the basic ESO problem can be expressed as Figure 2.Figure 2
CO2 emission pinch analysis system.The flow into each different demand receiverD1,D2,D3,D4 is a combination of different energy resource supplies. The energy resource provided by each supply S1,S2,S3,S4 can also be provided to different demand receivers. The goal is to determine the minimum amount of low-carbon and zero-carbon energy resources, which can meet both energy demand and emission restrictions.Convergence analysis can effectively test the convergence and divergence of the ESO degree in different regions during the research period. The methods of convergence tests are mainly divided intoσ convergence and β convergence.σ coefficient reflects the variation trend of ESO degree deviation with time. When the deviation shows a downward trend, it indicates that there is σ convergence in the ESO degree; otherwise, it does not exist. σ coefficient is calculated as follows:
(10)σ=∑ilnck,t−lnct¯2N.Among them,ck,t represents the ESO degree of regional pk in t period, lnct¯ represents the average ESO degree in t period, and N represents the number of regions.β convergence reflects the trend that the ESO level increases with time, that is, whether the areas with lower initial ESO levels increase at a faster rate. According to Yuan et al.’s research [6], the formula for obtaining β coefficient is as follows:
(11)1Tlnck,t+Tck,t=α+βlnck,t+εi,t,where T represents the time interval, the growth rate of ESO degree from period t to period t+T; α,β is the estimation coefficient; and εi,t is the error term.Through clustering analysis and screening of the indicators affecting ESO, we found that Pearson correlation analysis was used to study six indicators affecting ESO. Run Poly Analyst software to generate project files for correlation analysis. Open the generated file and add a Microsoft Excel data source node in the editing window, load the data that affects ESO, and change the node name to the appropriate category.The data analysis node can study the correlation between different attributes and display the results interactively in the data analysis node option of correlation analysis. By adjusting the size of the correlation coefficient, the attribute columns larger than the correlation coefficient will be displayed quickly. The connection diagram of impact factor analysis is shown in Figure3.Figure 3
Connection diagram of influencing factor analysis.For coal resources, it is necessary to control the whole life cycle from the source to the process to the terminal, increase the ratio of raw coal washing to raw coal, reduce the output and direct combustion of raw coal, accelerate the development of high-efficiency coal conversion technology, and reduce pollution emissions in the conversion process. Realize the clean utilization of products, coal and coal-based products.


## 4. Experiment and Results

In some cases, it is necessary to meet the energy demand and CO2 emission limit of the whole region, as well as the energy demand and CO2 emission limit of individual regions. When the overall regional emission limit is reached, if the emission of one region is lower than the limit, the emission of another department or region will exceed the limit. In other words, the low emission level in energy demand areas leads to high emissions. Table 1 summarizes the energy distribution results that meet the energy demand and emission limits while reducing carbon emissions in various regions.Table 1
Energy consumption structure under regional emission restrictions.
Coal
Petroleum
Natural gas
Low carbon energy
Total demand
Area A
Energy quantity (105TJ)
0
5.24
1.03
10.25
16.52
Discharge amount (106tCO2)
0
44.68
7.01
1.01
52.7
Area B
Energy quantity (105TJ)
4.21
0.36
1
3.27
8.84
Discharge amount (106tCO2)
41.67
2.25
0
1.68
45.6The above energy distribution method is not unique. This is a simulation analysis, and there are many alternative methods to choose from, because it depends on the specific numerical settings of different regions. CO2 pinch analysis is a very useful supplementary energy planning for the early stage.It is assumed that only China’s overall carbon emission limits are considered, and regional carbon emission limits are not considered. To achieve the goal of reducing carbon emissions by 40%~45% in 2022, the overall energy structure and carbon emissions of Qujing are shown in Figure4.Figure 4
Overall energy structure and carbon emissions of Qujing city under carbon emission targets.As shown in Figure4, to achieve the carbon emission target, it is necessary to increase the energy with low carbon emission and reduce the energy with high carbon emission. When the carbon emission of clean energy is zero, comparing the emission reduction costs of coal, oil, and natural gas, we can see that the marginal emission reduction cost of coal is the lowest [12]. Clean energy not only cannot meet the energy demand but also can be used to replace high-carbon energy coal, taking into account the cost of reducing emissions and limiting carbon emissions.In the model calculation, the unit energy investment cost of coal, oil, natural gas, and primary electricity is in the primary energy structure of the province with energy transfer and energy self-balance and the unit investment cost of primary electricity and primary electricity transferred by energy. Calculation of energy investment cost uses thermal power market price. Ignore the energy import and export from the national point of view, and replace the price with the unit energy investment cost when calculating the total energy investment cost. According to the above conditions, the data is substituted into the model for calculation, and the result is shown in Figure5.Figure 5
Optimized energy structure of Qujing city in 2022.Despite the fact that it will be difficult to compete with thermal power in terms of economic benefits in the near future due to the large initial investment in primary power generation, the proportion of coal consumption in coal-fired power generation is decreasing and will gradually decrease to 50-60 percent by 2022; with the increasing cost of pollution prevention and control of coal-fired power generation and limited pollution emission, the proportion of coal consumption in coal-fired power generation is decreasing and will gradually decrease to 50-60 percent by 2022, but the proportion of it is responsible for 70% of all coal consumption. Primary electricity will enter a phase of rapid development over the next 20 years, accounting for 5% of total energy consumption by 2022. The energy structure is shifting from coal-to-coal-to-coal-to-coal-to-coal-to-coal-to-coal to various structures that use renewable energy sources like nuclear and wind as important alternative energy sources.Combined with the present situation of resource monitoring, the strategic goal of economic development, and the primary goal of reducing environmental protection in Qujing, the primary energy structure of Qujing is estimated by a statistical method. Considering the requirements of social and economic sustainable development for the total energy consumption and energy consumption structure, it is increasing at an average annual rate of 10%. Therefore, the overall energy consumption trend assumes that the proportion of natural gas consumption in total energy consumption will increase by about 2%, with an average annual increase of 0.21%. Shown in Figure6, starting from 2010, is the change range of energy consumption structure in the next 11 years.Figure 6
Estimation of energy consumption structure.The purpose of establishing the optimization model of the energy consumption structure is to obtain the optimal energy consumption that can meet both economic growth and environmental control indicators. Based on the optimization results of the model and the predicted value of conventional power generation, the change of optimal power generation and conventional power generation can be compared. According to the results obtained by the model, we can get all kinds of optimized energy consumption and its proportion to the total energy consumption, as shown in Figure7.Figure 7
Comparison between conventional development and optimized development of energy consumption structure in Qujing city.Other energy sources are becoming a larger part of total consumption. In Qujing’s future economic development, coal will likely replace oil, natural gas, and electricity in terms of energy consumption for a long time. As a result, we must pay close attention to oil and electricity supply and make plans for oil and electricity energy. The energy structure of Qujing has been improved, and pollution has been reduced as a result of optimization. Promote the implementation of clean energy projects and environmental protection as a priority, as this is the only way to ensure Qujing’s long-term viability. As shown in Figure8, the σ coefficient of the ESO chart of Qujing city first rose and then decreased from 2015 to 2021, and the overall performance volatility showed a downward trend.Figure 8
σ convergence of ESO degree.From the perspective of three major regions, the overallσ coefficient of the central and western regions showed a downward trend, and all showed σ convergence, so ESO of the central and western regions also showed a downward trend with time. However, the overall change of σ coefficient in the eastern region is relatively stable, and there is no obvious downward trend, so the difference in the ESO level in the eastern region has not changed significantly, and the σ convergence effect is not significant.After data correlation analysis of six attribute elements of ESO factor data, ther value of each attribute correlation coefficient ranges from 0.68 to 0.96, and the fields with values greater than 0.7 are selected in this paper, as shown in Table 2.Table 2
Correlation coefficient of factors affecting ESO.
Influencing factor
Correlation coefficient
Energy synergy
0.86
Energy consumption ratio
0.94
Energy self-sufficiency rate
0.61
Energy consumption elasticity coefficient
0.96
Energy intensity of GDP
0.63
Carbon emission reduction
0.88Natural gas is Qujing city’s future strategic energy, and the natural gas industry is destined to become a strategic emerging industry for the city’s economic development. Despite the government’s aggressive promotion, Qujing has achieved some success in natural gas promotion and infrastructure construction, but natural gas utilization remains low. It is necessary to strengthen various support for the rapid development of the natural gas industry in order to fully exploit the economic, social, and ecological benefits of natural gas energy and promote the economic and social development of Qujing city.Despite the fact that the Qujing municipal government has shut down many coal enterprises with high energy consumption and pollution in order to meet the state’s general capacity and inventory reduction requirements, the current industries in Qujing are still dominated by these high-energy-consumption industries. The inevitability of a highly developed regional economy is to break down barriers between the three industries; maximize the linkage effect of the first, second, and third industries; and expand the industrial chain in response to CE demand. Through waste exchange, recycling, element combination, and the industrial ecological chain, Qujing Eco-industrial Park’s eco-industrial chain system should be an interdependent, closely related, and interdependent network of eco-agriculture, eco-industry, and eco-service industry. A collaborative eco-industrial system can create a relatively complete and closed eco-industrial network, allowing for better resource allocation, waste utilization, and pollution reduction; attracting green and low-carbon industries, such as biotechnology, energy conservation and environmental protection, information technology, new materials technology, automotive new energy technology, and other related emerging industries; entering Qujing city with natural gas as an energy source; rapidly entering emerging industries; and realizing the scale of emerging industries and rapid growth through complex construction.


## 5. Conclusions

This paper finds that the adjustment of the energy structure not only promotes the optimization of the interindustry structure but also promotes the optimization of the intraindustry structure and accelerates the development of service-oriented industries in Qujing. For the CE energy structure, in the next 20 years, primary power will enter a stage of rapid development, accounting for 5% of the total energy consumption by 2022. The method of energy distribution is not unique. This means that there are many alternative methods to choose from, because they depend on the specific energy consumption value and the setting of carbon emission limits in different regions, which is very useful for decision-makers to make baselines. In addition, the finer the division of emission reduction areas, the better the emission reduction effect. Therefore, from the perspective of CE, the government should focus on promoting cross-regional coordinated development, expanding ESO radiation areas, advocating ESO high areas to lead other regions to coordinate development, and accelerating the ESO process.



---

*Source: 7905576-2022-05-11.xml*