Abstract
Due to the growing pollution concerns in China, there have been major efforts to design interrelated environmental protection policies. As the policy of reducing coal consumption is implemented, China's natural gas consumption is expected to commensurately rise. Such a trend may lead to the significant impacts on China's energy security and the global gas market. This paper empirically investigates the relationship between the natural gas consumption and influencing factors in a comprehensive LMDI-STIRPAT-PLSR framework. It is found that fossil energy structure and non-clean energy structure are the most important factors followed by urbanization, per capita GDP, industrialization, and industrial energy intensity. And then, these factors from LMDI decomposition are innovatively divided into economic development and cleaning indicators when using scenario analysis to forecast China's natural gas consumption. The prediction results suggest that economic development with high clean energy system would make a significant increase in gas consumption, a commensurate increase in demand even the system of economic-energy under scenario of low economic development and low clean. As China would depend on gas imports to meet this demand, there may be a serious supply shortage by 2020. Therefore, the government should consider the future energy security issue in making economic and environmental policies.
| Original language | English |
|---|---|
| Pages (from-to) | 215-225 |
| Journal | Energy Policy |
| Volume | 119 |
| Online published | 1 May 2018 |
| DOIs | |
| Publication status | Published - Aug 2018 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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SDG 8 Decent Work and Economic Growth
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SDG 9 Industry, Innovation, and Infrastructure
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SDG 11 Sustainable Cities and Communities
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SDG 13 Climate Action
Research Keywords
- LMDI decomposition
- Natural gas consumption
- PLS regression
- Scenario analysis
- STIRPAT model
ESI Highly Cited Papers
- Highly Cited Paper 2020
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