Projections and Recommendations for Energy Structure and Industrial Structure Development in China through 2030: A System Dynamics Model
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Research Institute of Standards and Norms, Ministry of Housing and Urban-Rural Development of the People’s Republic of China, Beijing 100835, China
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School of Civil Engineering, Chongqing University, Chongqing 400044, China
3
School of Economics and Management, China University of Petroleum, Beijing 102249, China
4
Center for Economics and Management, IFP School, 92852 Paris, France
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(18), 4901; https://doi.org/10.3390/su11184901
Submission received: 26 June 2019
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Revised: 21 August 2019
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Accepted: 22 August 2019
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Published: 7 September 2019
(This article belongs to the Section Energy Sustainability)
Abstract
:In this research, we established a System Dynamics Model named “E&I-SD” to study the development of the energy structure and industrial structure in China from 2000 to 2030 using Vensim Simulation Software based on energy economy theory, system science theory and coordinated development theory. We used Direct Structure Test, Structure-oriented Behavior Test, and Behavior Pattern Test to ensure the optimal operation of the system. The model’s results showed that the indicators of total energy consumption, total added value of GDP after regulation, energy consumption per capita, and GDP per capita were on the rise in China, but emissions per unit of energy showed a downward trend. Separately, the model predicted average annual growth rates in China through 2030. Based on these findings, we proposed important policies for China’s sustainable development. Firstly, short- and long-term policy measures should be implemented to replace fossil fuels with clean energy. Secondly, the utilization efficiency of raw coal should be appraised future. The planning should provide for steady development and improvement of the primary, secondary, and tertiary sectors. Thirdly, the mid- and long-term plans for development and management of various industrial sectors and the corresponding energy consumption should be based on technological trends. Finally, a market-oriented pricing mechanism for energy should be established in China as soon as possible.
1. Introduction
Economic growth in China faces a conflict between the needs of the expanding industrial structure and the importance of a clean, modern energy system. The intensification of these competing demands has aroused widespread concern among researchers, and brought about increased national efforts to strengthen regulations and control macroeconomic policy. The 13th Five Year Plan [1] and the 19th National Congress of the Communist Party of China [2] provided important guidance with respect to the coordinated development of energy structure and industrial structure. Their published positions reflected a wide consensus that the long-term fundamentals of China’s economic development will not change over the next few years. To promote modernization, China must embrace the energy revolution actively, while vigorously developing its economy and optimizing its industrial structure. To augment its energy supply structure, China must enhance the efficiency of energy utilization, and build a clean (low-carbon), safe, and efficient energy system. However, the issues of imbalance and lack of coordination between economic growth and energy consumption are critical. The energy structure and industrial structure not only have their own characteristics to develop and change independently, but also have mutual influence and promotion to each other. Since both of these subsystems are an important part of the overall economic system, it is imperative that they confront the increasingly serious waste of resources, environmental pollution, and ecological destruction. Therefore, the future and systematic research on coordinated development of the energy structure and industrial structure has great significance for China’s sustainable economic development.
2. Literature Review
Energy shortage has been the bottleneck of economic and social sustainable development in China. The new task to improve energy efficiency has been put on the agenda of the “13th Five-Year” Plan. Therefore, numerous research papers have analyzed the relationship between economy and energy, with valuable results [3]. Most of the existing literature focused on two variables: energy consumption and economic growth [4,5,6,7,8]. Some of the studies focused on energy consumption and industrial structure change [9], while others looked at economic growth and the energy structure [10]. In response to rapid development of the world economy and the increasingly severe issues of energy supply and demand, population growth, and environmental pollution [11,12], an increased number of studies have applied econometrics analysis methods to analyze the development of China’s energy structure and industrial structure [13,14]. Many researchers analyzed the impact of energy structure, technological progress, and industrial structure on energy intensity with the logarithmic mean Divisia index technique, the results show that energy structure and industrial structure have different effects on energy intensity [15,16,17]. Cross-correlations between energy structure and industrial structure based on the well-known detrended cross-correlation analysis are shown to be significant and strong [18,19]. Some authors described the historical evolution of various end uses in the different energy sectors through mapping Sankey diagrams. They use the information from the energy flow analysis to project future scenarios [20,21]. Furthermore, the Granger causality test, input–output analysis, Grey correlation analysis, impulse response function and variance decomposition methods are also applied to analyze the influence relationship between the energy structure and industrial structure [22,23,24,25]. These studies on the interaction between energy structure and industrial structure provide massive meaning findings, but most of this work has come in the form of static studies [26].
In recent years, some authors attend to build energy–economy–environment models have increased gradually as well [27,28,29,30]. However, there are few studies on the dynamic coordinated development of the energy structure and industrial structure. In our research, we proposed a system dynamics approach referred to as the Coordinated Development of Energy Structure and Industrial Structure in China (E&I-SD) model. This study has both theoretical and practical value for analyzing the development of clean energy and industry in China, and lays the foundation for planning future coordinated development as well. The contributions mainly include two aspects: (1) The dynamic simulation model of the coordinated development of energy structure and industrial structure based on system dynamics theory, energy economy theory and coordinated development theory is established. This E&I-SD model may serve as a guidance to policymakers towards their efforts to plan the coordinated development of energy structure and industrial structure strategies by using the information of simulation and prioritizing uncertainties through driving the E&I-SD mode from the economic, energy and environmental perspectives. (2) This study simulates, analyzes, and predicts the coordinated development of China’s energy structure and industrial structure in 2030 by using the E&I-SD mode. According to the results, the paper provides reasonable suggestions for the policy makers.
The remainder of this paper is structured as follows. Section 3 presents our methods and the data used in this study, along with a brief introduction to the system dynamics model. In Section 4, we describe the details of the E&I-SD model and the tests used to validate the model. Section 5 provides the E&I-SD model’s predictions of China’s development trends through 2030. Finally, Section 6 presents our conclusions and recommendations based on the model’s the results, and provides some concluding remarks.
3. Methods and Data
3.1. Objectives and Requirements of Modeling
The objective of this research was to explore the mechanism of the dynamic changes in the energy structure and industrial structure of China. For this purpose, we established the E&I-SD system dynamics model, which enabled us to run simulations. From the perspective of system dynamics, this paper analyzed China’s opportunities and challenges with respect to the coordinated development of its energy and industrial subsystems, with particular attention to problems of the environment and population growth. We explored the feedback relationship between the two subsystems, clarified the relationships among the internal variables, and described the operation mode and track of the subsystems. Having established the current status of China’s energy structure and industrial structure, and we studied the internal relations between energy consumption, the employed population, environmental pollution, and economic development. Finally, we specified the direction of China’s coordinated energy and economic development by creating a projection of the growth trends in these areas under the constraints of the anticipated environment in 2030.
3.2. Brief Introductin to the System Dynamics
System dynamics as a methodology was created by Professor Jay Forrester of the Massachusetts Institute of Technology in the late 1950s [31,32,33]. System dynamics is a branch of system science, and is a related discipline of communications in the fields of natural and social sciences. The value of system dynamics is that it provides an effective modeling technique for understanding and exploring complex systems and their inherent feedback mechanisms. System dynamics provides a method for developing computational simulations of large-scale systems that has been applied widely to the study of various industries, the economy, ecology, and the environment, among other areas. System dynamics has been used extensively in research focused on the relationships between energy supply, prediction of energy demand, and industrial development, particularly with respect to problems of periodicity, protracted nature, and insufficient data [34]. Essentially, the energy structure and industry structure together form a complex nonlinear system that is influenced mainly by economic growth, social development, and energy supply and demand. The system dynamics approach is popular for analyzing energy policies because it can link the observable patterns of a system to micro-level structures and the decision-making process [35].
3.3. Logical Framework of the E&I-SD Model
Establishing system boundaries is central for attaining a focused and accurate system model for research because the core issues of the system become more centralized and accurate. Clear boundaries are a prerequisite for developing the E&I-SD model and producing accurate analyses. The E&I-SD system includes three subsystems: the economy, energy, and society. China’s energy structure and industrial structure has been changing dynamically in response to the constant exchange of information, materials, and energy, along with the interrelationships and interactions between the variables of the three subsystems. Among them, the economic and energy subsystems are the basic components of the system model, and they are the core parts of the research on the coordinated development of China’s energy structure and industrial structure. The social subsystem is considered an external influence that includes external factors such as population, environment, employment, and similar considerations. The details are as follows:
(1) Sub-module reflecting of the impact of industrial structure on energy structure
The sub-module of the impact of industrial structure on energy structure explores the impact of three industries structure changes on primary energy consumption structure. It is mainly composed of 32 variables in energy structure and industrial structure—among which, the industrial structure is represented by the proportion of added value of primary industry, secondary industry and tertiary industry to GDP; energy structure only refers to the energy consumption structure, and it is represented by the proportion of raw coal, crude oil, natural gas and other energy consumption to the total energy consumption.
(2) Sub-module reflecting the impact of energy structure on industrial structure
The sub-module of the impact of energy structure on industrial structure consists of 59 variables in energy structure and industrial structure. In addition, it explores the impact of energy structure changes on three industries structure. Energy structure includes energy consumption structure, production structure, import structure and export structure, in which energy production structure is represented by the proportion of raw coal, crude oil, natural gas and other energy production to the total energy production; energy import structure is represented by the proportion of raw coal, crude oil, natural gas and other energy imports to the total energy imports; and energy export structure is represented by the proportion of raw coal, crude oil and natural gas and other energy exports to the total energy exports.
(3) Sub-module reflecting of social factors
The E&I-SD model introduces the necessary social factors as important indicators to restrict the coordinated development of energy structure and industrial structure in China. Solving the problem of employment is the fundamental task for the development of people’s livelihood. The employment structure has an important impact on the development of three industries and energy consumption. Therefore, the effective correlation between employment structure and industrial structure can make the analysis of the coordinated development of energy structure and industrial structure more complete. In addition, this social sub-module is mainly composed of 18 variables.
3.4. Data Source
The data for this study covered from 2000 to 2016 in China. First, the energy and industrial sectors were defined according to the China Statistical Yearbook 2016 [36] and China Energy Statistical Yearbook 2016 [37]. Primary energy was divided into raw coal, crude oil, natural gas, and other forms of energy. The category of “other energy” consisted mainly of hydro power, nuclear power, wind power, solar power, and biomass energy, among others. We considered the Chinese economy as divided into three sectors: primary, secondary, and tertiary industries. Among them, the primary sector comprises agriculture, forestry, animal husbandry, fisheries, and other industries directly involved with natural resources. The secondary sector comprises industry and construction. The tertiary sector comprises service industries: wholesale and retail trades; transport, storage, and postal services; hotels and catering services; and financial intermediation including real estate among others.
4. The E&I-SD Model Description and Validation Test
Using the E&I-SD model, we simulated the coordinated development of China’s energy structure and industrial structure from 2000 to 2030. We set the running time of the simulation at thirty years, with a time step of one year, beginning with the year 2000. For this work, VENSIM (VENSIM is an industrial strength simulation tool for improving the performance of real systems. Its rich feature set emphasizes model quality, connections to data, flexible distribution, and advanced algorithms.) was employed to write the simulation programs, and EVIEWS software was used to regress parts of the equations. There were 115 system dynamics equations and parameters established through repeated modification and improvement on the model, parts of typical variables and equations are shown in Appendix A Table A1.
4.1. Causal Loop Framework
The E&I-SD model links the important variables in the three subsystems of energy, economy, and society by defining the relationships between the variables. The causal framework of the model system is shown in Figure 1. The three subsystems are combined organically by the important variables of GDP, added value of different industries, total energy consumption, major pollutant emissions, total population, and so on. The arrows represent the relationships between variables, and the “+” or “-” sign at the end of the influence lines indicates the direction of the effect. The “+” indicates that the variables change in the same direction, and the “-” indicates the opposite.
The structure of a system in system dynamic methodology is captured by causal loop diagrams. A causal loop diagram represents the major feedback mechanisms. These mechanisms are either negative feedback or positive feedback loops. Table 1 shows typical feedback loops in the E&I-SD model.
4.2. Stock Flow Diagram
The stock flow diagram is the core of a system dynamics model, and represents the process of quantization and materialization of the causal loop framework. For this research, the stock flow diagram was constructed according to the causal loop framework of the E&I-SD model, which was composed of two level variables, three rate variables, and 107 auxiliary variables, as shown in Figure 2.
4.3. Hypotheses and Boundaries of the E&I-SD Model
The consensus in the literature holds that building a model entails understanding the essential features of a system through abstraction and induction. An excellent model should be as simple as possible based on a true reflection of the research questions. Accordingly, this study simplified the system while ensuring the integrity of the model. We made the following hypotheses and set the boundaries of the E&I-SD model as described:
- (1)
- Since the data of each variable and parameter were known only through 2016, we chose to project the 2016 standard values as constant from 2016 to 2030. The variables for the three economic sectors (primary, secondary, and tertiary) included variables such as the following:
- The proportion of GDP;
- Output of different forms of primary energy, Import of different forms of primary energy, Export of different forms of primary energy;
- Regulated proportion of different forms of primary energy;
- Natural growth rate, employment population;
- Investment in the treatment of environment pollution.
- (2)
- The data concerning environmental pollution were too extensive to include in its entirety. The data included pollution of wastewater, waste gas, and solid waste as well as noise, light, and radiation pollution. Since the units of measurement of each type of pollutant were not uniform, and the degree of correlation was very small, for this model, we chose to represent pollution by the total amount of wastewater.
4.4. Validation Test of the E&I-SD Model
The E&I-SD model can be used to research China’s future coordinated development policy for its energy structure and industrial structure. To ensure the correctness and validity of the model, and establish the feasibility of forecasting future development [38], we applied three test methods: a direct structure test, structure-oriented behavior test, and a behavior pattern test.
4.4.1. Direct Structure Test
The direct structure test was designed to verify the rationality of the empirical and theoretical structure, causal relationships, stock-flow diagram, definition of variables, and parameter confirmation of our model. The research summarized the advantages and disadvantages of the model structure, and tried to perfect the model to match the actual situation, making the model scientifically logical and reasonable based on extensive data and reading a large amount of literature. It is necessary to judge the rationality of the model’s results after testing the dimension consistency to realize formal inspections of the model [39].
4.4.2. Structure-Oriented Behavior Test
The structure-oriented behavior test provides an advantage because it is more suitable for formalization and quantification than the direct structure test. A behavior sensitivity test can verify and track the correctness of the simulation model through its automatic identification function. The E&I-SD model is sensitive to parameter changes and inertial resistance to policy changes. Such changes are reflected in the lag of one factor over the other. Because of the large number of factors affecting a real-world system, and the complex relationship between the variables, realistic systems generally exhibit relative stability. It is vital to investigate the stability of the system model in the simulation test. If the values of the system indexes change greatly during different time intervals, it is probable that the system model is unstable. In that case, the model would not be able to complete the simulation correctly, and any predictions about the real-world system would demonstrate strong variability [40]. Therefore, the time steps for simulation analysis were set to one year, six months, and three months.
The variables of total energy consumption and death rate were selected as the test variables in the system. The graphs of the simulations are shown in Figure 3. The results show that the model system was stable, without violent shocks and fluctuations. The system’s performance was in line with the actual system, so that the model was operating in accordance with the actual system.
4.4.3. Behavior Pattern Test
The behavior pattern test aimed at justifying the reliability of the model and providing confidence for model application [41]. We used the E&I-SD model to simulate the results from 2000 to 2030. Then, we compared the model’s results for the test variables with actual historical data. We selected the following test variables for examination and comparison: GDP, added value of various industries, investment in the treatment of environmental pollution, total energy consumption, raw coal consumption in secondary industries, crude oil consumption in secondary industries, total consumption of different types of energy, death rate, total population, total employed population, major pollutant emissions, and differences in supply and demand for various types of energy.
Some important variables results of the behavior pattern test are shown in Appendix B Table A2. As can be seen, the deviation of test variables was within ±10% for the most part, demonstrating that the model met simulation requirements. Note that the error rate of the simulation value and the real value for individual years were slightly greater for a few variables. This finding can be explained by the many uncertainties in the coordinated development of China’s energy structure and industrial structure. For example, changes in national policies, natural disasters, or other external factors caused statistical data to fluctuate at certain points. In such cases, when using the linear regression model to determine the relationship between variables, there are larger individual errors in the variables. This conclusion was consistent with the findings of Zhu et al. [42]. Because of the rapid development of China’s economy, the demand for energy rose rapidly across the three economic sectors. The degree of fitting of the simulation data for the individual variables in the model from 2000 to 2002 years was low. Therefore, the model can be used to predict and analyze the coordinated development of energy and industry.
5. The Model Applications and the Result Analysis
Based on the E&I-SD model, the preliminary prediction results of the coordinated development of China’s energy structure and industrial structure in 2030 are shown in Table 2.
The model’s projection shows that the total added value of GDP after regulation, Energy consumption per capita, GDP per capita and other indicators have a different degree of upward trend and the pollution emissions per unit of energy consumption have a degree of downward trend compared with 2015. We also used the innovative O-S method (The orthogonal scenario analysis method combines orthogonal experimental design with scenario analysis methods. This approach selects the representative factors and levels in the comprehensive scenario planning group based on the orthogonal principle, and finally identifies the best scenario group of the corresponding indicators) to analyze the evolution trend and causes of the key factors in the current stage of China’s energy structure and industrial structure development, and summarized the important opportunities and challenges facing China’s future energy structure and industrial structure coordinated development depend on the predicted results as follows:
(1) The slowdown of the world and China’s economic growth forces the optimization and upgrading of energy structure and industrial structure in China.
Facing the increasingly severe situation of resource shortage, environmental deterioration, ecological destruction, population reduction and technological progress hindered, the traditional low-cost competitive advantages are gradually losing. Especially under the impact of the international financial crisis and the slowdown of economic growth in the world and China, resulting in a significant reduction in the total energy demand, which in turn forced the energy structure and industrial structure to be continuously upgraded and optimized.
(2) Optimizing the national energy structure strategy is an urgent requirement for expanding international cooperation.
Firstly, in order to reduce the rising price of crude oil caused by geopolitics and the security of energy supply, which have a significant impact on China’s economic development, the Chinese government has put forward new requirements for controlling the dependence of crude oil on foreign countries in the 12th Five Year Plan and the 13th Five Year Plan. Secondly, the government has put forward a new strategic thinking of energy reform. The ideas of energy development are from extensive to intensive and from fossil energy to renewable energy. The continuous expansion of international energy cooperation by the Chinese government and the diversification of natural gas and other energy import channels helps the economy resist political risks that may jeopardize supply stability. Thirdly, under the pressure of environmental pollution, the development of renewable energy such as wind power, hydropower, solar energy, biomass energy, nuclear power and other energy sources has become a new direction of energy market:
(3) New requirements of environmental protection on the development technology and consumption of raw coal.
The greenhouse gases produced by the combustion of raw coal are the main cause of climate warming. The promulgation and implementation of environmental protection laws and regulations were the main reasons for the increased cost of raw coal development. As the actual investment in the raw coal industry in resources, environment, safety, technology and other aspects, it will cause the energy cost to rise and the energy profit to decrease continuously. When the output of raw coal reaches a certain limit, the contribution of the increase in raw coal output to the China’s economy growth will gradually decrease:
(4) The improvement of energy efficiency has promoted the transformation and upgrading of China’s energy structure and industrial structure.
With the rapid progress of technology, the efficiency of energy processing and conversion, energy use efficiency and recycling level have been steadily improved, resulting in a decrease of total energy demand, and upgrading of China’s energy structure and industrial structure to a new mode of modern energy and economic development with clean, low-carbon, quality and efficiency.
(5) As domestic energy production continues to increase and new requirements for international renewable energy diversification, China’s industrial structure needs to be continuously optimized.
Since China’s reform and opening up, economic growth has been positively correlated with the overall trend of raw coal production. This is due to the natural resource endowment of China’s rich coal, which has formed an energy consumption structure led by raw coal consumption at this stage.
The development of the secondary industry encountered new bottlenecks. With the improvement of the specialization of the secondary industry and the level of product processing continuing to deepen, the product cost has been gradually increased. The share of China’s secondary industry in the international market is basically stable. In the future, the development direction of this industry is to eliminate backward production capacity, increase product added value, reduce environmental pollution and focus on the development of domestic strategic emerging industries. It will gradually transition from extensive and high speed development mode to a new development mode of high efficiency, high quality and stable secondary industry.
The potential and space for the development of tertiary industry are enormous. According to the Kuznets Theorem [43] and the existing research literature [44,45], the proportion of the tertiary industry surpassing the secondary industry is an important symbol of the industrial structure upgrading. With the increase of national income and urbanization level, the trend of the added value of the three industries is: the proportion of the primary industry decrease, the proportion of the secondary industry changed from rapid rise to decline, and the proportion of the tertiary industry showed a clear upward trend and eventually became the largest industry in the national economy.
From the experience of industrial structure development of various developed countries, the United States, Germany, Japan, South Korea and other countries have followed this development law. This further shows that the trend of China’s three industries conforms to the general law of industrial structure evolution. The contribution rate of the tertiary industry to economic growth will continue to increase and the development potential of service industry is huge in the future. China has entered a new stage of rapid development of the service industry.
The development of the tertiary industry and the secondary industry is mutually supportive and reinforcing. That is to say, the solid foundation of China’s primary and secondary industries has driven the rapid development of the tertiary sector headed by producer services. In recent years, with the promulgation of the national policy for the development of tertiary industry, the injection of foreign capital and the increase of national income, the tertiary industry has developed rapidly. Compared with the secondary industry, China’s tertiary industry is mainly oriented to the domestic market and its service level is relatively low. In the short term, it should focus on developing the domestic market, mastering its core technologies and creating and maintaining its tertiary industry with an international competitive advantage. This argument further clarifies the necessity of adhering to the national optimization policy of “steadily developing the primary industry, improving and upgrading the secondary industry and increasing the proportion of the tertiary industry”.
6. Recommendations and Conclusions
In this research, we utilized the E&I-SD model to simulate, analyze, and predict China’s coordinated energy and industrial growth from 2016 through 2030. According to the results, we propose the following conclusions and policy suggestions:
- (1)
- The E&I-SD model’s forecast showed that China’s primary energy consumption of raw coal and crude oil is on a sharp uptrend. Effective short- and long-term policy measures to replace fossil fuels with clean energy should be proposed and implemented across economic sectors. First, efforts to optimize China’s energy structure should start by reducing the percentage of raw coal in energy consumption, simultaneously increasing the proportion of natural gas and other primary energy sources. However, it is unrealistic to expect China to substitute low carbon energy sources for fossil fuels quickly. Second, the utilization efficiency of raw coal should be appraised further. In the short term, policies should aim to accelerate the popularity and application of technologies such as pithead power, clean coal, and coal-based polygeneration. In addition, it is necessary to increase investment in the innovation and utilization of new energy technologies, improve the utilization rate of terminal energy, and achieve the effective replacement of fossil fuels with clean energy over the medium and long term.
- (2)
- Mid- and long-term plans for managing the development of various economic sectors should be based on technological progress in China. The research groundwork can be accomplished through further adjustment and improvement of the E&I-SD model. Based on the simulation results, policies could be proposed for the healthy development of China’s energy structure and industrial structure, along with energy management planning suitable for China’s development.
- (3)
- A market-oriented pricing mechanism for energy should be established in China as soon as possible. The Chinese government’s intervention in energy prices, along with the oil companies’ monopoly of the energy market, have led to the non-marketization of energy prices. Currently, energy prices fail to reflect energy scarcity, and also seriously hinder the coordinated development of energy structure and the economic structure. Therefore, one of the most effective ways to promote the coordinated development of the energy structure and industrial structure would be by strengthening the regulation and control of the energy pricing system. A sound pricing system can inhibit the increase of total energy consumption. This conclusion is consistent with the work of Wang [46].
- (4)
- The optimization of China’s industrial structure relies mainly on the steady development of the primary industries, improvement and upgrading of the secondary industries, and promotion of the tertiary industries. Currently, the secondary industries participate in international industrial specialization, while the tertiary sector remains oriented toward the domestic market in China. Most of the production that requires high energy consumption comes from the secondary industries. The increasing amount of activity in secondary industries will become the main reason for a rise in energy intensity in China. For China’s secondary industries to maintain their share of the international markets, they should accelerate the rate at which they upgrade their output from low value-added to high value-added products. In addition, China should encourage elimination of the backward production capacity associated with high energy consumption and serious pollution, and promote the development of advanced manufacturing industries and emerging sectors of strategic importance.
In contrast with the secondary economic sector, the tertiary industries primarily face the domestic market, and the quality of service remains lower. In the short term, China should focus on the growth of the domestic market, along with greater innovation in core technologies. This approach would allow China to grasp the key core technologies, and create and maintain the competitive advantage of the tertiary sector. In the medium and long term, China needs to establish the national core-tech brand of China’s service industry, and gradually participate in international service industrial specialization. Only by realizing coordination and steady development of all three economic sectors can China reduce its energy intensity effectively, and coordinate the development of China’s energy structure and industrial structure harmoniously.
This study did encounter certain limitations. Due to the length of the article, the concept of the self-innovated O-S method and its application principle are not described in detail. Using the self-innovated O-S method and E&I-SD model, we were able to simulate multiple scenarios, and put forward important and feasible policy proposals. The proposal portion of our work has been completed another paper titled, “Scenario Analysis of the Coordinated Development of China’s Energy Structure and Industry Structure in 2030 based on the System Dynamics Model and Orthogonal Experimental Design Method,” that will be published at a future date.
Author Contributions
Conceptualization, Methodology and Writing—original draft, S.H.; Supervision, B.Z.; Project administration, C.L.; Validation, A.F.
Funding
This research was funded by National Key R&D Program of China, grant number 2018YFC0704400. And the APC was funded by Research Institute of Standards and Norms, Ministry of Housing and Urban-Rural Development of the People’s Republic of China.
Acknowledgments
The authors are also grateful to professors from the IFP School for helpful suggestions that improve this paper. The authors gratefully acknowledge the editors for kind help and the anonymous reviewers for their beneficial comments and encouragement.
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A
Table A1.
The variables and equations in VENSIM software.
| No. | System | Equation | Unit |
|---|---|---|---|
| 1 | Parameter | INITIAL TIME = 2000 | Year |
| 2 | Parameter | FINAL TIME = 2030 | Year |
| 3 | Parameter | Time step = 1 | Year |
| 4 | Economy | GDP = INTEGER (The added value of GDP, 100280.1) | 100 million yuan |
| 5 | Economy | The added value of GDP = GDP × Growth rate of GDP | 100 million yuan |
| 6 | Economy | The proportion of GDP in tertiary industry = WITH LOOKUP ((Time,[(2000,0)(2030,1)],(2000,0.398),(2001,0.412),(2002,0.422),(2003,0.42),(2004,0.412),(2005,0.413),(2006,0.418),(2007,0.429),(2008,0.428),(2009,0.443),(2010,0.441),(2011,0.442),(2012,0.453),(2013,0.467),(2014,0.478),(2015,0.502),(2016,0.502),(2017,0.502),(2018,0.502),(2019,0.502),(2020,0.502),(2021,0.502),(2022,0.502),(2023,0.502),(2024,0.502),(2025,0.502),(2026,0.502),(2027,0.502),(2028,0.502),(2029,0.502),(2030,0.502)) | Dmnl |
| 7 | Economy | The added value of secondary industry = GDP × The proportion of GDP in secondary industry | 100 million yuan |
| 8 | Economy | GDP per capita=GDP/Total population | 100 million yuan/10000 persons |
| 9 | Energy | Raw coal consumption in primary industry = 0.372699 × The added value of primary industry − 2920.026 | 10,000 tce |
| 10 | Energy | Crude oil consumption in secondary industry = 0.082784 × The added value of secondary industry + 12785.54 | 10,000 tce |
| 11 | Energy | Natural gas consumption in secondary industry = 0.064632 × The added value of secondary industry − 600.3673 | 10,000 tce |
| 12 | Energy | Other energy consumption in secondary industry = 0.091 × The added value of secondary industry | 10,000 tce |
| 13 | Energy | Raw coal consumption in tertiary industry = 0.165659 × The added value of tertiary industry + 20593.55 | 10,000 tce |
| 14 | Energy | Crude oil consumption in tertiary industry = 0.229194 × The added value of tertiary industry + 7904.51 | 10,000 tce |
| 15 | Energy | Natural gas consumption = Natural gas consumption in primary industry + Natural gas consumption in secondary industry + Natural gas consumption in tertiary industry | 10,000 tce |
| 16 | Energy | Total energy consumption = Raw coal consumption + Crude oil consumption + Natural gas consumption + Other energy consumption | 10,000 tce |
| 17 | Energy | Natural gas ratio of energy consumption = Natural gas consumption/Total energy consumption | 10,000 tce |
| 18 | Energy | Other energy ratio of energy consumption = Other energy consumption/Total energy consumption | 10,000 tce |
| 19 | Energy | Energy consumption per capita = Total energy consumption/Total population | 10,000 tce /10,000 persons |
| 20 | Energy | Energy intensity = Total energy consumption/GDP | 10,000 tce /100 million yuan |
| 21 | Energy | Supply of raw coal = Output of raw coal + Import of raw coal − Export of raw coal | 10,000 tce |
| 22 | Energy | Supply of other energy = Output of other energy + Import of other energy − Export of other energy | 10,000 tce |
| 23 | Energy | Total energy supply and demand difference = Raw coal supply and demand difference + Crude oil supply and demand difference + Natural gas supply and demand difference + Other energy supply and demand difference | 10,000 tce |
| 24 | Energy | Natural gas supply and demand difference = Supply of natural gas − Natural gas consumption | 10,000 tce |
| 25 | Energy | Other energy supply and demand difference = Supply of other energy − Other energy consumption | 10,000 tce |
| 26 | Energy | Regulated consumption of energy in teritary industry = Regulated consumption of raw coal in tertiary industry + Regulated consumption of crude oil in teritary industry + Regulated consumption of natural gas in teritary industry + Regulated consumption of other energy in teritary industry | 10,000 tce |
| 27 | Energy | Regulated consumption of crude oil in primary industry = Crude oil supply and demand difference × Regulated proportion of crude oil in primary industry | 10,000 tce |
| 28 | Energy | Regulated consumption of natural gas in primary industry = Natural gas supply and demand difference × Regulated proportion of nature gas in primary industry | 10,000 tce |
| 29 | Energy | Regulated consumption of crude oil in secondary industry = Crude oil supply and demand difference × Regulated proportion of crude oil in secondary industry | 10,000 tce |
| 30 | Energy | Regulated consumption of natural gas in teritary industry = Natural gas supply and demand difference × Regulated proportion of natural gas in tertiary industry | 10,000 tce |
| 31 | Economy | The added value after regulation in primary industry = 2.81367 × Regulated consumption of energy in primary industry | 100 million yuan |
| 32 | Economy | The added value after regulation in secondary industry = 0.559261 × Regulated consumption of energy in secondary industry | 100 million yuan |
| 33 | Economy | The added value after regulation in teritary industry = 1.331794 × Regulated consumption of energy in teritary industry | 100 million yuan |
| 34 | Economy | Total added value of GDP after regulation = The added value after regulation in primary industry + The added value after regulation in secondary industry + The added value after regulation in teritary industry | 100 million yuan |
| 35 | Energy | Output of raw coal = WITH LOOKUP (Time, [(2000,0) − (2030,300000)],(2000,101009),(2001,107045),(2002,114307),(2003,135052),(2004,158116),(2005,177304),(2006,189881),(2007,205446),(2008,213024),(2009,219564),(2010,237839),(2011,264574),(2012,267464),(2013,270507),(2014,266230),(2015,260801),(2016,260800),(2017,260800),(2018,260800),(2019,260800),(2020,260800),(2021,260800),(2022,260800),(2023,260800),(2024,260800),(2025,260800),(2026,260800),(2027,260800),(2028,260800),(2029,260800),(2030,260800)) | 10,000 tce |
| 36 | Energy | Output of crude oil = WITH LOOKUP (Time, (2000,0) − (2030,40000)],(2000,23299.6),(2001,23368),(2002,23900.4),(2003,24183.3),(2004,25141.5),(2005,25984.3),(2006,26359.4),(2007,26687.8),(2008,27088.2),(2009,27106.7),(2010,28891.1),(2011,29074.1),(2012,29718.3),(2013,29944.2),(2014,30253.4),(2015,30682.5),(2016,30680),(2017,30680),(2018,30680),(2019,30680),(2020,30680),(2021,30680),(2022,30680),(2023,30680),(2024,30680),(2025,30680),(2026,30680),(2027,30680),(2028,30680),(2029,30680),(2030,30680)) | 10,000 tce |
| 37 | Energy | Import of crude oil = WITH LOOKUP (Time, [(2000,0) − (2030,60000)],(2000,13603.8),(2001,12653),(2002,14248.8),(2003,18516.3),(2004,24320.2),(2005,24043.4),(2006,27175.1),(2007,29401.3),(2008,32151.3),(2009,35918.3),(2010,40908.6),(2011,43943.7),(2012,46124.8),(2013,47754.3),(2014,50693.3),(2015,55573.3),(2016,55570),(2017,55570),(2018,55570),(2019,55570),(2020,55570),(2021,55570),(2022,55570),(2023,55570),(2024,55570),(2025,55570),(2026,55570),(2027,55570),(2028,55570),(2029,55570),(2030,55570)) | 10,000 tce |
| 38 | Energy | Export of crude oil = WITH LOOKUP (Time, [(2000,0) − (2030,8000)],(2000,2933.64),(2001,2719.57),(2002,2849.2),(2003,3350.98),(2004,2712.32),(2005,3539.46),(2006,3094.74),(2007,3139.38),(2008,3496.08),(2009,4757.34),(2010,4804.85),(2011,4707.05),(2012,4400.77),(2013,4924.6),(2014,4993.02),(2015,6288.28),(2016,6288),(2017,6288),(2018,6288),(2019,6288),(2020,6288),(2021,6288),(2022,6288),(2023,6288),(2024,6288),(2025,6288),(2026,6288),(2027,6288),(2028,6288),(2029,6288),(2030,6288)) | 10,000 tce |
| 39 | Population | Natural growth rate = WITH LOOKUP ((Time, [(2000,0) − (2030,0.01)],(2000,0.0076),(2001,0.007),(2002,0.0065),(2003,0.006),(2004,0.0059),(2005,0.0059),(2006,0.0053),(2007,0.0052),(2008,0.0051),(2009,0.0049),(2010,0.0048),(2011,0.0048),(2012,0.005),(2013,0.0049),(2014,0.0052),(2015,0.005),(2016,0.005),(2017,0.005),(2018,0.005),(2019,0.005),(2020,0.005),(2021,0.005),(2022,0.005),(2023,0.005),(2024,0.005),(2025,0.005),(2025,0.005),(2026,0.005),(2027,0.005),(2028,0.005),(2029,0.005),(2030,0.005)) | Dmnl |
| 40 | Population | Death rate = −0.45369 × Natural growth rate + 0.0000000303 × Investment in the treatment of environment pollution + 0.000000000031 × Major pollutant emissions + 0.008951 | Dmnl |
| 41 | Population | Deaths per year = Total population × Death rate | 10,000 persons |
| 42 | Population | Birth rate=WITH LOOKUP ((Time,[(2000,0) − (2030,0.1)],(2000,0.014),(2001,0.0134),(2002,0.0129),(2003,0.0124),(2004,0.0123),(2005,0.0124),(2006,0.0121),(2007,0.0121),(2008,0.0121),(2009,0.012),(2010,0.0119),(2011,0.0119),(2012,0.0121),(2013,0.0121),(2014,0.0124),(2015,0.0121),(2016,0.0121),(2017,0.0121),(2018,0.0121),(2019,0.0121),(2020,0.0121),(2021,0.0121),(2022,0.0121),(2023,0.0121),(2024,0.0121),(2025,0.0121),(2026,0.0121),(2027,0.0121),(2028,0.0121),(2029,0.0121),(2030,0.0121)) | Dmnl |
| 43 | Population | Births per year = Total population × Birth rate | 10,000 persons |
| 44 | Population | Total population = INTEGER (Births per year − Deaths per year,126743) | 10,000 persons |
| 45 | Employment | Total employed population = 0.487541 × Total population + 10700.22 | 10,000 persons |
| 46 | Employment | The proportion of employment in tertiary industry = Employment population in tertiary industry/Total employed population | Dmnl |
| 47 | Employment | Employment population in secondary industry = WITH LOOKUP (Time, ([(2000,0) − (2030,30000)],(2000,16219),(2001,16234),(2002,15682),(2003,15927),(2004,16709),(2005,17766),(2006,18894),(2007,20186),(2008,20553),(2009,21080),(2010,21842),(2011,22544),(2012,23241),(2012,23241),(2013,23170),(2013,23170),(2014,23099),(2014,23099),(2015,22693),(2016,22690),(2017,22690),(2018,22690),(2019,22690),(2020,22690),(2021,22690),(2022,22690),(2023,22690),(2024,22690),(2025,22690),(2026,22690),(2027,22690),(2028,22690),(2029,22690),(2030,22690)) | 10,000 persons |
| 48 | Employment | Structure deviation of tertiary industry = The proportion of GDP in tertiary industry/The proportion of employment in tertiary industry − 1 | Dmnl |
| 49 | Environment | Investment in environmental pollution control as a percentage of GDP = WITH LOOKUP (Time,(2000,0) − (2030,0.1)],(2000,0.0101),(2001,0.01),(2002,0.0112),(2003,0.0118),(2004,0.0118),(2005,0.0127),(2006,0.0117),(2007,0.0125),(2008,0.0155),(2009,0.0151),(2010,0.0184),(2011,0.0145),(2012,0.0153),(2013,0.0152),(2014,0.0149),(2015,0.0128),(2016,0.0128),(2017,0.0128),(2018,0.0128),(2019,0.0128),(2020,0.0128),(2021,0.0128),(2022,0.0128),(2023,0.0128),(2024,0.0128),(2025,0.0128),(2026,0.0128),(2027,0.0128),(2028,0.0128),(2029,0.0128),(2030,0.0128)) | Dmnl |
| 50 | Environment | Investment in the treatment of environment pollution = GDP × Investment in Environmental Pollution Control as a Percentage of GDP | 100 million yuan |
| 51 | Environment | Major pollutant emissions = 13.61567 × Total energy consumption + 1622315 | 10,000 tons |
| 52 | Environment | Pollution emissions per unit of energy consumption = Major pollutant emissions/Total energy consumption | 10,000 tons /10,000 tce |
Table A2.
Results of a behavior pattern test.
| Variable | Total Energy Consumption | Raw Coal Consumption in the Secondary Industry | Crude oil Consumption in the Secondary Industry | Investment in the Treatment of Environment Pollution | ||||||||
| Unit | 10000 tce | 10000 tce | 10000 tce | 100 million yuan | ||||||||
| Year | Simulation Value | Statistical Data | Deviation | Simulation Value | Statistical Data | Deviation | Simulation Value | Statistical Data | Deviation | Simulation Value | Statistical Data | Deviation |
| 2000 | 150948 | 140993 | 6.59% | 78530.6 | 74466.31 | 5.18% | 16562.7 | 15312.67 | 7.55% | 1012.83 | 1014.9 | −0.20% |
| 2001 | 161846 | 148264 | 8.39% | 85915.5 | 79039.68 | 8.00% | 16813.3 | 15472.36 | 7.98% | 1086.03 | 1084.541 | 0.14% |
| 2002 | 175353 | 161935 | 7.65% | 95391.8 | 86439.30 | 9.38% | 17150.4 | 16708.70 | 2.58% | 1327.03 | 1331.77 | −0.36% |
| 2003 | 194054 | 189269 | 2.47% | 109844 | 104821.98 | 4.57% | 17705.5 | 17982.76 | −1.57% | 1537.93 | 1545.259 | −0.48% |
| 2004 | 211681 | 220738 | −4.28% | 122412 | 126164.23 | −3.07% | 18238 | 19979.27 | −9.55% | 1693.25 | 1695.182 | −0.11% |
| 2005 | 233654 | 250835 | −7.35% | 138495 | 150119.20 | −8.39% | 19005.2 | 19299.72 | −1.55% | 2030.15 | 2040.028 | −0.49% |
| 2006 | 257287 | 275134 | −6.94% | 154396 | 166705.73 | −7.97% | 19884.5 | 20549.21 | −3.34% | 2107.81 | 2109.241 | −0.07% |
| 2007 | 281530 | 299271 | −6.30% | 168184 | 184709.24 | −9.83% | 20773.4 | 21491.25 | −3.46% | 2571.71 | 2582.762 | −0.43% |
| 2008 | 299656 | 306455 | −2.27% | 178656 | 189942.49 | −6.32% | 21548.2 | 21110.99 | 2.03% | 3498.24 | 3491.141 | 0.20% |
| 2009 | 316187 | 321336 | −1.63% | 186153 | 201183.74 | −8.07% | 22167.5 | 22692.05 | −2.37% | 3728.31 | 3723.37 | 0.13% |
| 2010 | 337881 | 343601 | −1.69% | 197944 | 204946.71 | −3.54% | 23275 | 27064.43 | −16.28% | 5024.67 | 5040.05 | −0.31% |
| 2011 | 357172 | 370163 | −3.64% | 207083 | 223614.55 | −7.98% | 24271.5 | 26498.75 | −9.18% | 4335.82 | 4355.176 | −0.45% |
| 2012 | 372495 | 381515 | −2.42% | 212133 | 224590.46 | −5.87% | 24885 | 26132.19 | −5.01% | 4936.45 | 4934.669 | 0.04% |
| 2013 | 388097 | 394794 | −1.73% | 216477 | 228222.25 | −5.43% | 25454.5 | 26381.25 | −3.64% | 5286.71 | 5285.642 | 0.02% |
| 2014 | 403797 | 400299 | 0.87% | 221054 | 226571.00 | −2.50% | 26101.2 | 27454.14 | −5.18% | 5560.68 | 5554.47 | 0.11% |
| 2015 | 418015 | 402164 | 3.79% | 222346 | 220300.92 | 0.92% | 26293.4 | 28609.74 | −8.81% | 5106.56 | 5104.19 | 0.05% |
| Variable | Raw Coal Consumption | Crude Oil Consumption | Natural Gas Consumption | Other Energy Consumption | ||||||||
| Unit | 10000 tce | 10000 tce | 10000 tce | 10000 tce | ||||||||
| Year | Simulation Value | Statistical Data | Deviation | Simulation Value | Statistical Data | Deviation | Simulation Value | Statistical Data | Deviation | Simulation Value | Statistical Data | Deviation |
| 2000 | 108310 | 100810 | 6.92% | 35074.1 | 32287.4 | 7.95% | 2107.36 | 3242.8 | −53.88% | 5315.34 | 4652.8 | 12.47% |
| 2001 | 116668 | 106009 | 9.14% | 36478.3 | 32914.6 | 9.77% | 2771.66 | 3706.6 | −33.73% | 5790.77 | 5634.0 | 2.71% |
| 2002 | 127222 | 116269 | 8.61% | 38074.9 | 35625.7 | 6.43% | 3544.64 | 3886.4 | −9.64% | 6379.74 | 6153.5 | 3.55% |
| 2003 | 142561 | 138545 | 2.82% | 39743.2 | 39557.2 | 0.47% | 4437.31 | 4542.5 | −2.37% | 7182.67 | 6624.4 | 7.77% |
| 2004 | 156778 | 161580 | −3.06% | 41525.2 | 45913.5 | −10.57% | 5281.74 | 5297.7 | −0.30% | 7988.14 | 7946.6 | 0.52% |
| 2005 | 174017 | 189130 | −8.68% | 43876.8 | 46655.3 | −6.33% | 6548.55 | 6270.9 | 4.24% | 9105.06 | 8779.2 | 3.58% |
| 2006 | 191662 | 207726 | −8.38% | 46939.1 | 50074.4 | −6.68% | 8134.89 | 7703.8 | 5.30% | 10449.6 | 9629.7 | 7.85% |
| 2007 | 208376 | 226249 | −8.58% | 51004.8 | 52671.7 | −3.27% | 10086.9 | 9277.4 | 8.03% | 11978.8 | 11073.0 | 7.56% |
| 2008 | 220996 | 229841 | −4.00% | 53893.5 | 53323.2 | 1.06% | 11502.5 | 10725.9 | 6.75% | 13199.1 | 12564.7 | 4.81% |
| 2009 | 230965 | 240681 | −4.21% | 57536.9 | 55269.8 | 3.94% | 13225.2 | 11889.4 | 10.10% | 14403.7 | 13496.1 | 6.30% |
| 2010 | 245236 | 249798 | −1.86% | 61349.4 | 62879.0 | −2.49% | 15162.5 | 14431.2 | 4.82% | 16091.2 | 16492.8 | −2.50% |
| 2011 | 257127 | 271700 | −5.67% | 65250.8 | 65148.7 | 0.16% | 17080.9 | 17767.8 | −4.02% | 17692 | 15546.8 | 12.12% |
| 2012 | 265322 | 275454 | −3.82% | 69290.7 | 68291.2 | 1.44% | 18918.2 | 19457.3 | −2.85% | 18961.9 | 18312.7 | 3.42% |
| 2013 | 273113 | 281488 | −3.07% | 73788.8 | 71062.9 | 3.69% | 20941.4 | 22108.5 | −5.57% | 20269.8 | 20134.5 | 0.67% |
| 2014 | 280937 | 279409 | 0.54% | 78253.9 | 74055.3 | 5.37% | 22994.5 | 24017.9 | −4.45% | 21642.8 | 22817.0 | −5.43% |
| 2015 | 286430 | 273874 | 4.38% | 83614.6 | 78824.1 | 5.73% | 25265.6 | 24934.2 | 1.31% | 22749.2 | 24532.0 | −7.84% |
| Variable | Death Rate | Total Population | Total Employed Population | Major Pollutant Emissions | ||||||||
| Unit | Dmnl | 10000 persons | 10000 persons | 10000 tons | ||||||||
| Year | Simulation Value | Statistical Data | Deviation | Simulation Value | Statistical Data | Deviation | Simulation Value | Statistical Data | Deviation | Simulation Value | Statistical Data | Deviation |
| 2000 | 0.0056 | 0.0065 | −14.21% | 126743 | 126743 | 0.00% | 72492.6 | 72085 | 0.56% | 3.67 × 106 | − | − |
| 2001 | 0.0059 | 0.0064 | −8.49% | 127801 | 127627 | 0.14% | 73008.4 | 72797 | 0.29% | 3.82 × 106 | − | − |
| 2002 | 0.0062 | 0.0064 | −3.95% | 128756 | 128453 | 0.24% | 73474 | 73280 | 0.26% | 4.01 × 106 | − | − |
| 2003 | 0.0064 | 0.0064 | 0.12% | 129622 | 129227 | 0.30% | 73896.2 | 73736 | 0.22% | 4.26 × 106 | − | − |
| 2004 | 0.0065 | 0.0064 | 0.70% | 130398 | 129988 | 0.31% | 74274.6 | 74264 | 0.01% | 4.50 × 106 | 4824094 | −7.14% |
| 2005 | 0.0065 | 0.0065 | −0.39% | 131158 | 130756 | 0.31% | 74645.1 | 74647 | 0.00% | 4.80 × 106 | 5245089 | −9.23% |
| 2006 | 0.0068 | 0.0068 | −0.60% | 131933 | 131448 | 0.37% | 75022.9 | 74978 | 0.06% | 5.12 × 106 | 5144802 | −0.40% |
| 2007 | 0.0068 | 0.0069 | −1.33% | 132636 | 132129 | 0.38% | 75365.7 | 75321 | 0.06% | 5.45 × 106 | 5568494 | −2.08% |
| 2008 | 0.0069 | 0.0071 | −2.02% | 133333 | 132802 | 0.40% | 75705.5 | 75564 | 0.19% | 5.70 × 106 | 5716801 | −0.26% |
| 2009 | 0.0070 | 0.0071 | −0.79% | 134023 | 133450 | 0.43% | 76041.9 | 75828 | 0.28% | 5.93 × 106 | 5890877 | 0.62% |
| 2010 | 0.0071 | 0.0071 | 0.12% | 134689 | 134091 | 0.44% | 76366.6 | 76105 | 0.34% | 6.22 × 106 | 6172562 | 0.82% |
| 2011 | 0.0071 | 0.0071 | −0.48% | 135333 | 134735 | 0.44% | 76680.6 | 76420 | 0.34% | 6.49 × 106 | 6591922 | −1.62% |
| 2012 | 0.0070 | 0.0072 | −1.57% | 135981 | 135404 | 0.42% | 76996.5 | 76704 | 0.38% | 6.70 × 106 | 6847612 | −2.27% |
| 2013 | 0.0071 | 0.0072 | −0.81% | 136669 | 136072 | 0.44% | 77331.9 | 76977 | 0.46% | 6.91 × 106 | 6954433 | −0.67% |
| 2014 | 0.0070 | 0.0072 | −2.56% | 137352 | 136782 | 0.41% | 77664.9 | 77253 | 0.53% | 7.12 × 106 | 7161751 | −0.55% |
| 2015 | 0.0071 | 0.0071 | −0.65% | 138096 | 137462 | 0.46% | 78027.7 | 77451 | 0.74% | 7.32 × 106 | 7353227 | −0.51% |
References
- The 13th Five Year Plan for the National Economic and Social Development of the People’s Republic of China. In Standing Committee of the National People’s Congress; Central Compilation and Translation Press: Beijing, China, 2016.
- Decisiveness, Building a Well-Off Society in an All-Round Way and Seizing The Great Victory Of Socialism with Chinese Characteristics. In The New Era-Report on the 19th National Congress of the Communist Party of China[R]; People’s Publishing House: Beijing, China, 2017.
- Kraft, J.; Kraft, A. Relationship between energy and GNP. J. Energy Financ. Dev. 1978, 32, 401–403. [Google Scholar]
- Meadows, D.H.; Meadows, D.L.; Behbens, J. The Limits to Growth: A Report for the Club of Rome’s Project on the Predicament of Mand; Universe Press: New York, NY, USA, 1972. [Google Scholar]
- Smil, V.; Kuz, T. Energy/GDP relationships—The elastic snaps. Energy Policy 1977, 5, 162–164. [Google Scholar] [CrossRef]
- Soytas, U.; Sari, R. Energy consumption and GDP: Causality relationship in G-7 countries and emerging markets. Energy Econ. 2003, 25, 33–37. [Google Scholar] [CrossRef]
- Fotis, P.; Karkalakos, S.; Asteriou, D. The relationship between energy demand and real GDP growth rate: The role of price asymmetries and spatial externalities within 34 countries across the globe. Energy Econ. 2017, 66, 69–84. [Google Scholar] [CrossRef] [Green Version]
- Nachane, D.M.; Nadkarni, R.M.; Karnik, A.V. Co-Integration and Causality Testing of the Energy GDP Relationship: A Cross-Country Study. Appl. Econ. 1988, 20, 1511–1531. [Google Scholar] [CrossRef]
- Mi, Z.F.; Pan, S.Y.; Yu, H.; Wei, Y.M. Potential impacts of industrial structure on energy consumption and CO2 emission: A case study of Beijing. J. Clean. Prod. 2015, 103, 455–462. [Google Scholar] [CrossRef]
- Han, Z.Y.; Fan, Y.; Jiao, J.L.; Yan, J.S.; Wei, Y.M. Energy structure, marginal efficiency and substitution rate: An empirical study of China. Energy 2007, 32, 935–942. [Google Scholar] [CrossRef]
- Tiba, S.; Omri, A. Literature survey on the relationships between energy, environment and economic growth. Renew. Sustain. Energy Rev. 2017, 69, 1129–1146. [Google Scholar] [CrossRef]
- Zaman, K.; Abdullah, A.; Khan, A.; Nasir, M.R.B.; Hamzah, T.A.A.T.; Hussain, S. Dynamic linkages among energy consumption, environment, health and wealth in BRICS countries: Green growth key to sustainable development. Renew. Sustain. Energy Rev. 2016, 56, 1263–1271. [Google Scholar] [CrossRef]
- Feng, Y.Y.; Chen, S.Q.; Zhang, L.X. System dynamics modeling for urban energy consumption and CO2 emissions: A case study of Beijing, China. Ecol. Model. 2013, 252, 44–52. [Google Scholar] [CrossRef]
- Ansari, N.; Seifi, A. A system dynamics model for analyzing energy consumption and CO2 emission in Iranian cement industry under various production and export scenarios. Energy Policy 2013, 58, 75–89. [Google Scholar] [CrossRef]
- Shahiduzzaman, M.; Alam, K. Cointegration and causal relationships between energy consumption and output: Assessing the evidence from Australia. Energy Econ. 2012, 34, 2182–2188. [Google Scholar] [CrossRef]
- Han, S.; Zhang, B.S.; Tang, X.; Qi, S.; Meng, F.Y. Study on the driving factors in China’s Energy Intensity Change—Based on LMDI Decomposition Technique. Mod. Econ. Sci. 2016, 38, 89–98,127. [Google Scholar]
- González, P.F.; Landajo, M.; Presno, M.J. Multilevel LMDI decomposition of changes in aggregate energy consumption. A cross country analysis in the EU-27. Energy Policy 2014, 68, 576–584. [Google Scholar]
- Carmona, M.; Feria, J.; Golpe, A.; Iglesias, J. Consumption in the US reconsidered. Evidence across sources and economic sectors. Renew. Sustain. Energy Rev. 2017, 77, 1055–1068. [Google Scholar] [CrossRef]
- Paul, S.; Bhattacharya, R.N. Causality between energy consumption and economic growth in India: A note on conflicting results. Energy Econ. 2004, 26, 977–983. [Google Scholar] [CrossRef]
- Davis, M.; Ahiduzzaman, M.; Kumar, A. Mapping Canadian energy flow from primary fuel to end use. Energy Convers. Manag. 2018, 156, 178–191. [Google Scholar] [CrossRef]
- Subramanyam, V.; Paramshivan, D.; Kumar, A.; Mondal, M.A.H. Using Sankey diagrams to map energy flow from primary fuel to end use. Energy Convers. Manag. 2015, 91, 342–352. [Google Scholar] [CrossRef] [Green Version]
- Shahbaz, M.; Hoang, T.H.V.; Mahalik, M.K.; Roubaud, D. Energy Consumption, Financial Development and Economic Growth in India: New Evidence from a Nonlinear and Asymmetric Analysis. Energy Econ. 2017, 66, 199–212. [Google Scholar] [CrossRef]
- Bildirici, M.E.; Bakirtas, T. The relationship among oil, natural gas and coal consumption and economic growth in BRICTS (Brazil, Russian, India, China, Turkey and South Africa) countries. Energy 2014, 65, 134–144. [Google Scholar] [CrossRef]
- Bilgen, S. Structure and environmental impact of global energy consumption. Renew. Sustain. Energy Rev. 2014, 38, 890–902. [Google Scholar] [CrossRef]
- Ding, Q.; Cai, W.; Wang, C. The relationships between household consumption activities and energy consumption in China—An input-output analysis from the lifestyle perspective. Appl. Energy 2017, 207, 520–532. [Google Scholar] [CrossRef]
- Feng, T.W.; Sun, L.Y.; Zhang, Y. The relationship between energy consumption structure, economic structure and energy intensity in China. Energy Policy 2009, 37, 5475–5483. [Google Scholar] [CrossRef]
- Schäfer, A.; Kyle, P.; Pietzcker, R. Exploring the use of dynamic linear panel data models for evaluating energy/economy/environment models-an application for the transportation sector. Clim. Chang. 2016, 136, 141–154. [Google Scholar] [CrossRef]
- Suzuki, S.; Nijkamp, P. An evaluation of energy-environment-economic efficiency for EU, APEC and ASEAN countries: Design of a Target-Oriented DFM model with fixed factors in Data Envelopment Analysis. Energy Policy 2016, 88, 100–112. [Google Scholar] [CrossRef]
- Taghavee, V.M.; Aloo, A.S.; Shirazi, J.K. Energy, Environment, and Economy Interactions in Iran with Cointegrated and ECM Simultaneous Model. Procedia Econ. Financ. 2016, 33, 414–424. [Google Scholar] [CrossRef]
- Liu, G.Y.; Yang, Z.F.; Fath, B.D.; Shi, L.; Ulgiati, S. Time and space model of urban pollution migration: Economy-energy-environment nexus network. Appl. Energy 2017, 186, 96–114. [Google Scholar] [CrossRef] [Green Version]
- Forrester, J.W. Industrial Dynamics. J. Oper. Res. Soc. 1997, 48, 1037–1041. [Google Scholar] [CrossRef]
- Forrester, J.W. Industrial dynamics: A major breakthrough for decision makers. Harv. Bus. Rev. 1958, 36, 37–66. [Google Scholar]
- Forrester, J.W. Industrial Dynamics; MIT Press: Cambridge, MA, USA, 1961. [Google Scholar]
- Wang, Q.F. System Dynamics, Revised Edition; Tsinghua University Press: Beijing, China, 1994. [Google Scholar]
- Qudrat Ullah, H.; Seong, B.S. How to do structural validity of a system dynamics type simulation model: The case of an energy policy model. Energy Policy 2010, 38, 2216–2224. [Google Scholar] [CrossRef]
- National Bureau of Statistics. China’s Statistical Yearbook 2015; Statistical Publishing House: Beijing, China, 2016. [Google Scholar]
- National Bureau of Statistics. China’s Energy Statistical Yearbook 2016; Statistical Publishing House: Beijing, China, 2016. [Google Scholar]
- Barlas, Y. Formal aspects of model validity and validation in system dynamics. Syst. Dyn. Rev. 1996, 12, 1–28. [Google Scholar] [CrossRef]
- Yu, Z.M. The Research on Sustainable Development of Coal Industry Based on System Dynamics; Wuhan University of Technology: Wuhan, China, 2012; Volume 11, p. 61. [Google Scholar]
- Du, J. System Dynamics Simulation of the Economy-Energy-Environment 3e System in Chengdu; Chendu University of Technology: Chengdu, China, 2014; Volume 6, p. 37. [Google Scholar]
- Forrester, J.W.; Senge, P.M. Test for building confidence in system dynamics models. In System Dynamics Group Alfred P; Sloan School of Management Massachusetts Institute of Technology Cambridge: Cambridge, MA, USA, 1979; Volume D-2926-7, pp. 1–36. [Google Scholar]
- Zhu, J.; Liu, X.M.; Chu, Z.P. Scenario Analysis on Energy Demand and CO2 Emission of Low Carbon City. China Popul. Resour. Environ. 2015, 25, 48–55. [Google Scholar]
- Kuznets, S. Modern Economic Growth: Rate, Structure, and Spread; Yale University Press: New Haven, CT, USA, 1966. [Google Scholar]
- Özokcu, S.; Özdemir, Ö. Economic growth, energy, and environmental Kuznets curve. Renew. Sustain. Energy Rev. 2017, 72, 639–647. [Google Scholar] [CrossRef]
- Pablo-Romero, M.D.P.; Jesús, J.D. Economic growth and energy consumption: The energy-environmental Kuznets curve for Latin America and the Caribbean. Renew. Sustain. Energy Rev. 2016, 60, 1343–1350. [Google Scholar] [CrossRef]
- Wang, X.Y.; Cheng, J.H.; Yi, X.H. Coordination Analysis and Countermeasures of Industrial Structure and Energy Consumption Structure; Social Science Edition; Wuhan University of Technology: Wuhan, China, 2013; Volume 26, pp. 201–208. [Google Scholar]
Figure 1.
The causal loop framework of E&I-SD model.
Figure 2.
The stock flow diagram of E&I-SD model.
Figure 3.
Results of the structure-oriented behavior test of the E&I-SD model.
Table 1.
The typical feedback loops in the E&I-SD model.
| Type | Typical Feedback Loops |
|---|---|
| Positive | GDP―(+) the added value of different industries―(+) the consumption of variety types of energy in different industries―(+) total consumption of different types of energy―(+)total energy consumption―(+) major pollutant emissions―(+) Investment in the treatment of environment pollution―(+) policy investment―(+) GDP |
| GDP―(+) the added value of different industries―(+)the consumption of variety types of energy in different industries―(+) total consumption of different types of energy―(+) total energy consumption―(+) major pollutant emissions―(+) technical progress―(+) policy investment―(+) GDP | |
| Negative | GDP―(+) the added value of different industries―(+) the consumption of variety types of energy in different industries―(+) total consumption of different types of energy―(+) total energy consumption―(+) major pollutant emissions―(-)total population―(-) total employed population―(-) GDP |
| The added value of different industries―(+) the consumption of variety types of energy in different industries―(+) total consumption of different types of energy―(+) total energy consumption―(-) energy security―(+) the supply of variety types of energy in different industries―(+) the added value of different industries |
Table 2.
The results of E&I-SD model in 2030.
| Indicator | Total Added Value of GDP after Regulation | Energy Consumption per Capita | GDP per Capita | Pollution Emissions per Unit of Energy Consumption |
|---|---|---|---|---|
| unit | 100 million yuan | 10,000 tce/million capita | 100 million yuan/million capita | 10,000 tons/million tce |
| 2020 | 287.536 | 3.59944 | 3.93371 | 16.7992 |
| 2025 | 639.574 | 4.27966 | 5.35941 | 16.2288 |
| 2030 | 1,106.050 | 5.13482 | 7.30722 | 15.7428 |
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Han, S.; Lin, C.; Zhang, B.; Farnoosh, A. Projections and Recommendations for Energy Structure and Industrial Structure Development in China through 2030: A System Dynamics Model. Sustainability 2019, 11, 4901. https://doi.org/10.3390/su11184901
AMA Style
Han S, Lin C, Zhang B, Farnoosh A. Projections and Recommendations for Energy Structure and Industrial Structure Development in China through 2030: A System Dynamics Model. Sustainability. 2019; 11(18):4901. https://doi.org/10.3390/su11184901
Chicago/Turabian StyleHan, Song, Changqing Lin, Baosheng Zhang, and Arash Farnoosh. 2019. "Projections and Recommendations for Energy Structure and Industrial Structure Development in China through 2030: A System Dynamics Model" Sustainability 11, no. 18: 4901. https://doi.org/10.3390/su11184901
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