کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1731906 | 1521457 | 2015 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Decomposition and forecasting analysis of China's energy efficiency: An application of three-dimensional decomposition and small-sample hybrid models
ترجمه فارسی عنوان
تجزیه و تجزیه و تحلیل پیش بینی از بهره وری انرژی چین: استفاده از تجزیه سه بعدی و مدل های ترکیبی کوچک نمونه
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کلمات کلیدی
بهره وری انرژی، چین، تجزیه، پیش بینی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی (عمومی)
چکیده انگلیسی
The coordinated actions of the central and the provincial governments are important in improving China's energy efficiency. This paper uses a three-dimensional decomposition model to measure the contribution of each province in improving the country's energy efficiency and a small-sample hybrid model to forecast this contribution. Empirical analysis draws the following conclusions which are useful for the central government to adjust its provincial energy-related policies. (a) There are two important areas for the Chinese government to improve its energy efficiency: adjusting the provincial economic structure and controlling the number of the small-scale private industrial enterprises; (b) Except for a few outliers, the energy efficiency growth rates of the northern provinces are higher than those of the southern provinces; provinces with high growth rates tend to converge geographically; (c) With regard to the energy sustainable development level, Beijing, Tianjin, Jiangxi, and Shaanxi are the best performers and Heilongjiang, Shanxi, Shanghai, and Guizhou are the worst performers; (d) By 2020, China's energy efficiency may reach 24.75 thousand yuan per ton of standard coal; as well as (e) Three development scenarios are designed to forecast China's energy consumption in 2012-2020.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy - Volume 89, September 2015, Pages 283-293
Journal: Energy - Volume 89, September 2015, Pages 283-293
نویسندگان
Ming Meng, Wei Shang, Xiaoli Zhao, Dongxiao Niu, Wei Li,