How to Effectively Control Energy Consumption Growth in China’s 29 Provinces: A Paradigm of Multi-Regional Analysis Based on EAALMDI Method
Abstract
:1. Introduction
- (1)
- As enabled by the new paradigm, we have achieved the first application of EAA-LMDI to a multi-regional analysis. This paradigm provides a high-resolution way to formulate differentiated policies to control the energy consumption growth of multiple regions.
- (2)
- With high technical resolution, this paper reveals the similarities and differences in energy consumption, the growth of this consumption in China’s provinces, and the reasons behind this growth. For the first time, energy allocation Sankey diagrams of 29 provinces in China are mapped and the LMDI decomposition results that consider the KPECR are presented.
- (3)
- The case study of China indicates a new policy-making model. When making regional energy consumption control targets, this model for determining the targets based on the characteristics of the main energy flow and the driving factors of energy consumption growth will be more effective than the commonly used current model, which directly determines the targets involving energy consumption and its growth.
2. Literature Review
2.1. Multi-Regional Input–Output Methods
2.2. Econometric Methods
2.3. Decomposition Analysis Method
2.4. EAA-LMDI Method
3. Methodology and Data
3.1. EAA-LMDI Method
3.1.1. Energy Allocation Analysis and Primary Energy Consumption Responsibility
3.1.2. EAA-LMDI Calculation
3.2. Standard Paradigm for Comparative Analysis of Multi-Regional Energy Consumption
3.3. Data Sources
4. Results and Discussion
4.1. Energy Allocation Sankey Diagram of Each Province in 2016
4.2. LMDI Decomposition of Energy Consumption Growth in the Economic Sector
4.2.1. The General Impact of Each Driving Factor
4.2.2. The Special Cases of Each Driving Factor
4.3. LMDI Decomposition of Energy Consumption Growth in the Residential Sector
4.4. Policy Implication Analysis
4.4.1. Formulation and Completion of Existing Policy Objectives
- (1)
- Simultaneously achieved economic growth and energy intensity reduction targets—15 provinces: Tianjin, Jilin, Zhejiang, Anhui, Fujian, Shandong, Henan, Hubei, Hunan, Guangdong, Guangxi, Chongqing, Guizhou, Yunnan, and Qinghai. According to the decomposition results of LMDI, the decrease in energy intensity in these regions largely offset the increase in energy consumption brought about by economic growth, thus inhibiting the growth of energy consumption.
- (2)
- Achieved energy intensity reduction targets but failed to meet economic targets—15 provinces: Beijing, Hebei, Inner Mongolia, Liaoning, Heilongjiang, Shanghai, Jiangsu, Jiangxi, Hainan, Sichuan, Shaanxi, Gansu, and Ningxia (Ningxia’s energy intensity did not include the coal chemical project in eastern Ningxia). Judging from the decomposition results of LMDI, these regions put more effort into the adjustment of industrial structure, which may have affected the economic growth to a certain extent. In addition, in our calculations, Liaoning and Heilongjiang did not meet their energy intensity reduction targets. After inspection, we found that, from 2015 to 2016, Liaoning’s GDP decreased by 22.4%, and Heilongjiang’s GDP remained basically unchanged, which may be related to changes in the caliber of data statistics. The underlying data issues make our calculations somewhat different from the energy intensity data published by the country.
- (3)
- Unmet energy intensity reduction targets: Shanxi and Xinjiang. Both regions are important energy bases for China. While undertaking energy production tasks, many high-energy-consuming industries have also grown up in these two regions, resulting in high energy intensity. Combined with the results of LMDI decomposition, Xinjiang and Shanxi’s economic sector’s energy consumption growth ranks in the top two in the country, and changes in energy intensity resulted in more energy consumption, which is also consistent with the completion of the target.
4.4.2. Suggestions on Scientific Formulation of China’s Multi-Regional “Dual Control” Policy Targets
5. Limitations and Uncertainties
- (1)
- This paradigm is based on the EAA-LMDI method. Although the method has the advantages of high resolution, some factors that need to be considered in policy are not included (resource availability is not considered, policy is not quantitative enough, and no fossil energy proportion factor is not introduced in LMDI).
- (2)
- The method to decide “taxonomy of main energy flow characteristics” is not accurate enough. In China, the problem of coal is prominent, but other countries may not have a similar situation. Therefore, the determination and analysis of this characteristic value may need further improvement.
- (3)
- The resolution of end-use consumption is not high enough. Compared with the continuous growth of China’s energy consumption, energy consumption has remained stable in many countries and regions. Policymakers are more concerned with the distribution of energy consumption in the end-use sector, and need to analyze and discuss a higher resolution of end-use energy consumption.
- (4)
- A few provinces are not included. For example, Guangdong is not included in the research because of data errors, and Tibet lacks sufficient energy data. In the future, we can consider how to deal with an incorrect energy balance table and strengthen the research on Tibet.
- (5)
- The policy analysis section is relatively rough and needs further improvement.
6. Conclusions and Recommendations for Future Research
- (1)
- According to the shape of coal flow, China’s provinces can be divided into four categories. The shape of coal flow in most provinces is similar to “π”, and provinces with coal flow shape similar to “┵” and “Z” should be priority regions for energy consumption control.
- (2)
- The most important driving factor to promote the growth of energy consumption is GDP per capita, followed by population and end-use energy structure. The main factors that restrain the growth of energy consumption are the economic structure and end-use energy intensity, followed by energy supply efficiency. Compared with the improvement of energy supply efficiency, the decrease in end-use energy intensity has a more obvious effect on reducing energy consumption. The adjustment of the end-use energy structure (mainly by replacing coal with electricity) and the improvement of residents’ living standards lead to more energy consumption.
- (3)
- The energy intensity of the secondary industry in some resource-rich areas and old industrial bases has increased, resulting in a significant increase in energy consumption. Fewer generation hours in some provinces affects the efficiency of electricity conversion, resulting in more energy consumption.
- (4)
- The formulation of China’s regional energy policy targets during the 12th and 13th Five-Year-Plan periods did not fully consider the differences in energy consumption among regions. For the formulation of the multiregional “dual control” target, the development targets of the main driving factors can be determined first, and then the “dual control” policy targets are determined by the targets of the driving factors.
- (1)
- Strengthen the linkage between energy policy-making and other policy-making. Energy consumption is deeply affected by population, economic development, industrial structure and other factors. When formulating policies on population, economy and industry, the impact on energy consumption should be fully considered. Meanwhile, it is also necessary to verify whether the formulated energy policy targets can meet the development needs of the main driving factors. Furthermore, the adjustment of the policy targets is necessary when faced with conflict.
- (2)
- The taxonomy of main energy flow characteristics can be used as a reference to determine the priority of controlling regional energy consumption. The regions with a higher proportion of coal production and consumption, such as the eight areas belonging to categories (2) and (3) in Section 4.1, should be the priority regions of energy consumption control and adopt more stringent energy-saving policies. The regions with a small quantity of coal production and consumption, such as the six areas belonging to category (4) in Section 4.1, can, relative to the regions with higher coal use, loosen the control of total energy consumption and encourage the development of non-fossil energy.
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
LMDI | The logarithmic mean Divisia index I; |
EAA | Energy allocation analysis; |
GHG | Greenhouse gas; |
MRIO | Multi-regional input–output methods; |
IDA | Index decomposition analysis; |
EAA-LMDI | The logarithmic mean Divisia index I decomposition method based on energy allocation analysis; |
KPECR | The primary energy consumption responsibility conversion factor; |
SDA | Structural Decomposition Analysis; |
Subscript i | Economic subsector i involved during the LMDI decomposition of energy consumption growth in economic sector, including primary industry, secondary industry and tertiary industry/area i involved during the LMDI decomposition of residential energy consumption growth, including urban areas or rural areas; |
Subscript j | Energy type j; |
The primary energy quantity conversion factor of the regional secondary energy j after the correction of the import impact; | |
The primary energy quantity conversion factor of the external secondary energy j; | |
Energy expressed in the form of standard quantity; | |
Energy expressed in the form of primary energy consumption responsibility; | |
Quantity of energy produced locally in standard quantity; | |
Quantity of imported energy in standard quantity; | |
P | Population; |
GDP | Gross domestic product; |
GDPi | Value added within economic subsector i; |
ESQ,i | Total energy consumption of economic subsector i expressed in standard quantity form during the LMDI decomposition of energy consumption growth in economic sector/total energy consumption of area i expressed in standard quantity form during the LMDI decomposition of residential energy consumption growth; |
ESQ,ij | Energy type j consumption expressed in standard quantity form in the economic subsector i during the LMDI decomposition of energy consumption growth in economic sector/energy type j consumption expressed in standard quantity form of area i during the LMDI decomposition of residential energy consumption growth. |
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No. | Author | Research Object | Main Conclusion |
---|---|---|---|
1 | Lan et al. [23] | Energy footprint of 186 countries | When a country’s per capita GDP is increasing, that country’s total energy footprint becomes increasingly concentrated on imports or consumption. |
2 | Hong et al. [24] | Embodied energy of the Chinese construction industry | Energy flows are from resource-abundant areas in the central part of China to resource-deficient areas in the eastern coast. |
3 | Mi et al. [25] | China’s embodied carbon emissions | In the “new normal”, the strongest factors that offset CO2 emissions have shifted from efficiency gains to structural upgrading. |
4 | White et al. [26] | Water–energy–food nexus in East Asia | China’s current national export-oriented economic growth strategy in East Asia is not sustainable, as China is a net virtual exporter of nexus resources to Japan and South Korea. |
5 | Duan et al. [27] | Embodied energy in final consumption of 121 countries | The total energy consumption conditions across different countries throughout the world became more and more equal from 2000 to 2013. |
6 | Li et al. [28] | Embodied carbon in international trade | At a global level, the network density increases, indicating the widely expanding carbon leakage among economies. |
7 | Xia et al. [29] | Embodied coal in the global economy | 64.99% of the world’s coal direct supply is ultimately embodied in international trade, thereby indicating the crucial roles of trade links in global coal utilization. |
8 | Guo et al. [30] | Embodied energy of the service industry in six Asian cities | Service industries consume 17.02–46.40% of the total embodied energy in the six cities. |
9 | Chen et al. [31] | Embodied energy in international trade | At a global level, a small-world scenario has been found, implying that economies are highly connected through embodied energy transfers. |
10 | Zhang et al. [32] | China’s provincial-level embodied energy | Over a half of the national interregional transfers of embodied energy via domestic trade were induced by the three economic circles’ final demand. |
No. | Author | Driving Factors of Energy Consumption Growth | Main Findings |
---|---|---|---|
1 | Hao et al. [6] | GDP | There is a causal relationship between energy consumption and economic development. |
2 | Li et al. [7] | Population, GDP, and urbanization | Urbanization, population, and the economy are the three main factors affecting China’s energy consumption, and the demand elasticity of the three factors changes periodically. |
3 | Akkemik et al. [8] | GDP | The two-way relationship between energy consumption and GDP in different provinces is quite different. |
4 | Wang and Shao [34] | Urbanization, carbon emissions | Urbanization intensifies the growth of energy consumption, but has a differential impact on the carbon emissions in different regions. |
5 | Wang et al. [9] | GDP, carbon emission from fossil energy, and cement industry | The carbon emissions of each province are increasing rapidly, but the law of growth operates differently in each province. |
6 | Liu et al. [35] | Urbanization | Urbanization helps to reduce the local energy consumption level, but its spillover effects increase the energy consumption level in neighboring areas. |
7 | Sheng et al. [36] | Urbanization | There is a causal relationship between urbanization and the increase in energy consumption, but it cannot be proved that there is a causal relationship between urbanization and energy efficiency. |
8 | Si et al. [37] | Energy policy | Some energy policies have a positive effect on curbing energy consumption, but other energy policies have the opposite effect. |
9 | Nan and Gao [10] | GDP | China’s current economic growth comes at the cost of huge energy consumption and environmental pollution. |
No. | Author | Driving Factors of Energy Consumption Growth | Time Period; Research Object; Main Findings |
---|---|---|---|
1 | Wang et al. [43] | Investment effect, energy intensity, economic structure, energy structure, and labor effect | 1991–2011; China. Energy intensity is the main factor to reduce energy consumption. Investment and labor promote the growth of energy consumption. |
2. | Sun et al. [44] | GDP, economic structure, and energy intensity | 2000–2010; Shenyang. The rapid growth of energy is mainly due to economic development, and energy intensity is the main restraining factor. |
3. | Wang et al. [45] | GDP, economic structure, and energy intensity | 2003–2012; Tianjin. Industrial production scale is the main factor of energy consumption growth, and energy intensity has a restraining effect on growth. |
4 | Wang et al. [46] | Population, GDP per capita, and energy intensity | 1970–2012; China. The growth in per capita income and population promotes the growth of energy consumption. The energy intensity has a restraining effect, but it weakens after 2000. |
5 | Lima et al. [47] | GDP, economic structure, and energy intensity | 1990–2012; 6 countries. The decrease in energy intensity is not enough to offset the increase in energy consumption brought about by economic development. |
6 | Wang et al. [48] | GDP, economic structure, and energy intensity | 2006–2015; Hunan. The scale effect promotes the rapid growth of energy consumption; the efficiency effect can reduce energy consumption, while the structure has no significant inhibition on the growth of energy consumption. |
7 | Lin et al. [49] | Energy structure, energy intensity, economic structure, spatial structure, investment efficiency, fixed asset investment per capita, and population proportion | 2000–2016; 30 provinces. The expansion of fixed asset investment per capita is the main factor leading to the growth in energy consumption, energy intensity and investment efficiency are the main energy-saving factors, and the decoupling state of economy and energy consumption is mainly determined by secondary industry. |
8 | Feng et al. [50] | GDP, economic structure, and energy intensity | 2005–2013; 21 cities in Guangdong. The 21 cities are in different stages of economic development and energy utilization; Guangdong Province still has a great potential to improve its energy utilization efficiency. |
9 | Li et al. [51] | Population, economic activity, and energy intensity | 2011–2015; China. Population growth and per capita GDP growth are the leading factors that stimulate energy consumption, while the increase in consumption intensity is the main factor restricting consumption growth. |
10 | Wang et al. [52] | Labor input, investment, energy intensity, energy structure, and technical level | 1990–2015; China and India. The main influence of decoupling of energy and the economy is similar in China and India. Investment effect is the biggest driving force, while energy intensity is the biggest restraining factor, and China’s energy-saving effect is better than India’s. |
11 | Wang et al. [53] | Population, GDP per capita, economic structure, energy intensity, and energy structure | 2006–2015; 30 Chinese provinces. Output scale effect and production structure effect play a leading role in the growth of energy consumption in China, while the energy intensity effect can effectively inhibit the growth of energy consumption in China. |
Driving Factors | General Impact on Energy Consumption Growth | Regions Conforming to General Impact |
---|---|---|
Population | Promotion | 26 |
GDP per capita | Promotion | 28 |
Economic structure | Inhibition | 28 |
End-use energy intensity | Inhibition | 22 |
End-use energy structure | Promotion | 23 |
Energy supply efficiency | Inhibition | 28 |
Driving Factors | General Impact on Energy Consumption Growth | Regions Conforming to General Impact |
---|---|---|
Population | Promotion | 26 |
Urban/rural structure | Promotion | 27 |
Per capita energy consumption | Promotion | 25 |
End-use energy structure | Promotion | 26 |
Energy supply efficiency | Inhibition | 28 |
Planning Objective | Actual Completion | |||
---|---|---|---|---|
Average Annual GDP Growth Rate | Reduced Energy Intensity | Average Annual GDP Growth Rate | Reduced Energy Intensity | |
Beijing | 8.0% | 17.0% | 7.5% | 21.5% |
Tianjin | 12.0% | 18.0% | 12.4% | 18.0% |
Hebei | 8.5% | 17.0% | 8.5% | 25.0% |
Shanxi | 10.0% | 16.0% | 7.9% | 14.7% |
Inner Mongolia | 12.0% | 15.0% | 10.0% | 18.8% |
Liaoning | 11.0% | 17.0% | 7.8% | 19.0% |
Jilin | 9.0% | 16.0% | 9.4% | 22.7% |
Heilongjiang | 12.0% | 16.0% | 8.3% | 18.9% |
Shanghai | 8.0% | 16.0% | 7.5% | 16.0% |
Jiangsu | 10.0% | 18.0% | 9.6% | 20.0% |
Zhejiang | 8.0% | 18.0% | 8.2% | 20.0% |
Anhui | 10.0% | 16.0% | 10.8% | 21.4% |
Fujian | 10.0% | 16.0% | 10.7% | 16.0% |
Jiangxi | 11.0% | 16.0% | 10.5% | 17.0% |
Shandong | 9.0% | 17.0% | 9.4% | 17.0% |
Henan | 9.0% | 16.0% | 9.6% | 22.5% |
Hubei | 10.0% | 16.0% | 10.8% | 22.0% |
Hunan | 10.0% | 16.0% | 10.5% | 21.0% |
Guangdong | 8.0% | 18.0% | 8.5% | 18.0% |
Guangxi | 10.0% | 15.0% | 10.1% | 16.0% |
Hainan | 13.0% | 16.0% | 9.5% | 16.0% |
Chongqing | 12.5% | 15.0% | 12.8% | 23.0% |
Sichuan | 12.0% | 16.0% | 10.8% | 24.6% |
Guizhou | 12.0% | 15.0% | 12.5% | 19.0% |
Yunnan | 10.0% | 15.0% | 11.1% | 20.7% |
Shaanxi | 12.0% | 16.0% | 11.1% | 16.4% |
Gansu | 12.5% | 15.0% | 10.55% | 21.8% |
Qinghai | 10.5% | 10.0% | 10.8% | 10.0% |
Ningxia | 12.0% | 15.0% | 9.9% | 16.0% |
Xinjiang | 10.0% | 10.0% | 10.7% | −1.7% |
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Zhao, Y.; Kong, G.; Chong, C.H.; Ma, L.; Li, Z.; Ni, W. How to Effectively Control Energy Consumption Growth in China’s 29 Provinces: A Paradigm of Multi-Regional Analysis Based on EAALMDI Method. Sustainability 2021, 13, 1093. https://0-doi-org.brum.beds.ac.uk/10.3390/su13031093
Zhao Y, Kong G, Chong CH, Ma L, Li Z, Ni W. How to Effectively Control Energy Consumption Growth in China’s 29 Provinces: A Paradigm of Multi-Regional Analysis Based on EAALMDI Method. Sustainability. 2021; 13(3):1093. https://0-doi-org.brum.beds.ac.uk/10.3390/su13031093
Chicago/Turabian StyleZhao, Yunlong, Geng Kong, Chin Hao Chong, Linwei Ma, Zheng Li, and Weidou Ni. 2021. "How to Effectively Control Energy Consumption Growth in China’s 29 Provinces: A Paradigm of Multi-Regional Analysis Based on EAALMDI Method" Sustainability 13, no. 3: 1093. https://0-doi-org.brum.beds.ac.uk/10.3390/su13031093