| Title | Multi-scale decomposition of energy-related industrial carbon emission by an extended logarithmic mean Divisia index: a case study of Jiangxi, China |
|---|---|
| ID_Doc | 15078 |
| Authors | Jia, JS; Jian, HY; Xie, DM; Gu, ZY; Chen, CD |
| Title | Multi-scale decomposition of energy-related industrial carbon emission by an extended logarithmic mean Divisia index: a case study of Jiangxi, China |
| Year | 2019 |
| Published | Energy Efficiency, 12, 8 |
| Abstract | Our objective has been to decompose the energy-related industrial carbon emissions (ERICE) from both the macroeconomic and the microeconomic scales using an extended logarithmic mean Divisia index (LMDI), which few scientists have applied, for Jiangxi, China, over the period of 1998-2015. The macroeconomic factors were output, industrial structure, energy intensity, and energy structure. The microeconomic factors were investment intensity, R&D intensity, and R&D efficiency. It was found that output, R&D intensity, and investment intensity were mainly responsible for the increase of the ERICE, and their average annual contribution rates were 33.212%, 9.537%, and 4.200%, respectively. However, considering the infeasibility of decelerating industrial activities related to these three drivers, the development pattern of a circular economy was promoted. Then, the driving effect of the energy structure was the weakest (0.017%). Nevertheless, the potential of energy structure optimization to improve energy efficiency in Jiangxi should be given sufficient attention, e.g., greatly reducing the use of coal. Inversely, the R&D efficiency, energy intensity, and industrial structure presented obvious mitigating effects on the ERICE (- 13.737%, - 11, 652%, and - 7.804%, respectively). Therefore, some regulatory policy instruments have been recommended. For example, carbon reduction liability and carbon labels related to R&D investment should be implemented to encourage industrial firms to improve their energy efficiency. Then, reducing the energy intensity unceasingly while inhibiting the possible rebound effect should serve as a long-term strategy for the local government. Last, the potential mitigation effect of industrial structure optimization should be given sufficient attention when designing related reduction policies. Particularly, the top five energy-intensive subsectors S33 (Production and Supply of Electric Power and Heat Power), S23 (Smelting and Pressing of Ferrous Metals), S17 (Processing of Petroleum, Coking, and Processing of Nuclear Fuel), S22 ( Manufacture of Nonmetallic Mineral Products), and S1 (Mining and Washing of Coal) should be given priority. |
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