Driving Factors of Energy Consumption in the Developed Regions of Developing Countries: A Case of Zhejiang Province, China
Abstract
:1. Introduction
2. Data and Method
2.1. Data
2.2. Method
2.2.1. Divisia Model
2.2.2. Divisia Method of Relative Quantities in the Industrial Sector
2.2.3. Divisia Method of Absolute Quantity in the Industrial Sector
3. Results and Discussion
3.1. Total Energy Consumption
3.2. Decompositon of Energy Consumption
3.3. Industrial Decomposition Results
4. Conclusions and Recommendations
4.1. Conclusions
- (1).
- The growth effect of per capita GDP on energy consumption is still the dominant factor compared with other elements. The economic effect from 2010 to 2019 more significantly impacted energy consumption, reaching the highest value of 14 million tce in 2015.
- (2).
- The effect of population size on energy consumption showed a low-level increasing trend, and the annual increase in energy consumption is between 1 and 2 million tce. Energy structure had a relatively small impact on energy consumption. Energy intensity annually reduced energy consumption between 5 and 15 million tce.
- (3).
- The industrial sector’s structural and intensity effects were decomposed, and the values were primarily negative. The intensity effect on energy-intensive industries was principally adverse, such as the petrochemical, metal smelting, calendering, textile printing and papermaking, and power and heating industries. Industries with adverse structural effects mainly include the petrochemical, textile printing, and paper industries. The intensity effect on the general purpose, special purpose, and transportation equipment manufacturing industry increased energy consumption.
4.2. Policy Implications
- (1).
- According to the decomposition results for energy consumption, GDP per capita is still the most critical contributing factor. The government must control energy consumption and reduce energy consumption per unit of GDP and carbon emission intensity.
- (2).
- The government took the opportunity of “carbon peaking” and “carbon neutrality” to promote the transformation of the energy structure and carry out the goal of carbon dioxide peaking in the energy sector.
- (3).
- Technology effects have a significant negative driving effect on energy consumption Enterprises should enhance the level of intelligence and efficiency of dispatch management and emergency response. Zhejiang should accelerate the establishment of an international oil and gas trading center and resource allocation base.
Author Contributions
Finanzierung
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Author | Country/Region | Sector | Forschung |
---|---|---|---|
Malla (2009) | 7 countries | Electricity | CO2 |
Zhang (2010) | Some cities in China | Traffic | Energy |
Fernandez (2014) | European Union | All sectors | Energy |
Andres (2015) | Spanien | Traffic | Energy |
Chong (2015) | China | Coal | Energy |
Lin (2016) | China | Chemical | CO2 |
Shao (2016) | Shanghai | All sectors | CO2 |
Mousavi (2017) | Iran | All sectors | CO2 |
Kim (2017) | Südkorea | Fertigungsindustrie | CO2 |
Moutinho (2018) | European Union | All sectors | CO2 |
Xia (2020) | 138 countries | All sectors | CO2 |
Variable | Symbol | Meaning |
---|---|---|
energy structure | S | different types of consumption/energy consumption |
energy intensity | I | energy consumption/GDP |
economic effect | A | GDP/population |
population | P | population |
Year | Factors (million tce) | Total (million tce) | |||
---|---|---|---|---|---|
Energy Structure | Energy Intensity | Economic Effect | Population | ||
2010–2011 | 0 | −5.32 | 13.73 | 1.21 | 9.62 |
2011–2012 | 0 | −11.32 | 13.13 | 0.68 | 2.49 |
2012–2013 | 0 | −8.83 | 13.41 | 10.5 | 5.64 |
2013–2014 | 0 | −11.85 | 12.46 | 12.5 | 1.86 |
2014–2015 | 0.01 | −6.94 | 14.22 | 0.56 | 7.84 |
2015–2016 | 0 | −7.95 | 13.07 | 1.53 | 6.66 |
2016–2017 | 0.01 | −7.96 | 13.54 | 1.96 | 7.54 |
2017–2018 | 0.01 | −8.19 | 12.82 | 1.81 | 6.45 |
2018–2019 | 0 | −7.31 | 12.77 | 1.71 | 7.18 |
Year | ||||
---|---|---|---|---|
2010–2011 | 0.00 | −0.55 | 1.43 | 0.13 |
2011–2012 | 0.00 | −4.55 | 5.28 | 0.27 |
2012–2013 | 0.00 | −1.57 | 2.38 | 0.19 |
2013–2014 | 0.00 | −6.37 | 6.70 | 0.67 |
2014–2015 | 0.00 | −0.89 | 1.81 | 0.07 |
2015–2016 | 0.00 | −1.19 | 1.96 | 0.23 |
2016–2017 | 0.00 | −1.06 | 1.80 | 0.26 |
2017–2018 | 0.00 | −1.27 | 1.99 | 0.28 |
2018–2019 | 0.00 | −1.02 | 1.78 | 0.24 |
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Qing, G.; Luo, Y.; Huang, W.; Wang, W.; Yue, Z.; Wang, J.; Li, Q.; Jiang, S.; Sun, S. Driving Factors of Energy Consumption in the Developed Regions of Developing Countries: A Case of Zhejiang Province, China. Atmosphere 2021, 12, 1196. https://doi.org/10.3390/atmos12091196
Qing G, Luo Y, Huang W, Wang W, Yue Z, Wang J, Li Q, Jiang S, Sun S. Driving Factors of Energy Consumption in the Developed Regions of Developing Countries: A Case of Zhejiang Province, China. Atmosphere. 2021; 12(9):1196. https://doi.org/10.3390/atmos12091196
Chicago/Turabian StyleQing, Ganghua, Yifan Luo, Weiwei Huang, Wanjue Wang, Zijing Yue, Jie Wang, Qingyi Li, Shuhan Jiang, and Shien Sun. 2021. "Driving Factors of Energy Consumption in the Developed Regions of Developing Countries: A Case of Zhejiang Province, China" Atmosphere 12, no. 9: 1196. https://doi.org/10.3390/atmos12091196