Measuring county-level heterogeneity of CO2 emissions attributed to energy consumption: A case study in Ningxia Hui Autonomous Region, China
文献类型：期刊论文
| 作者 | Guan, Yang2,3; Kang, Lei1; Shao, Chaofeng2; Wang, Ping2; Ju, Meiting2 |
| 刊名 | JOURNAL OF CLEANER PRODUCTION
 |
| 出版日期 | 2017-01-20 |
| 卷号 | 142页码:3471-3481 |
| 关键词 | CO2 emissions Energy consumption STIRPAT model Random parameters model |
| ISSN号 | 0959-6526 |
| DOI | 10.1016/jjclepro.2016.10.120 |
| 通讯作者 | Shao, Chaofeng(shaocf@nankai.edu.cn) |
| 英文摘要 | With the implementation of the western development strategy, large areas in western China are experiencing rapid industrialization and urbanization phases. Simultaneously, increasing energy consumption linked to CO2 emissions is becoming a serious problem. The stochastic impacts by regression on population, affluence, and technology model and the random parameters model were developed to assess and quantify the strength of the relationship between CO2 emissions that are attributed to energy consumption and 'their major drivers (i.e., population, economy, and technology). The Ningxia Hui Autonomous Region, which is a typical region in western China with low energy efficiency and rapid economic growth, was selected as an example. This study determined CO2 emissions attributed to energy consumption from 1991 to 2011, and assessed the effects of the major drivers of CO2 emissions at the county level. The following results were obtained. (1) The CO2 emissions of Ningxia increased rapidly throughout the study period. (2) Increasing gross domestic product (GDP) per capita, urban employment, and the contribution to GDP of secondary industries would promote CO2 emissions. By contrast, increasing the growth of the per capita annual disposable income of rural households, the contribution to GDP of tertiary industries, and population density would restrain CO2 emissions. (3) The county-level heterognerity was found to be significant which suggests adopting a uniform policy might not be optimal. Finally, the study method, effects of CO2 emission factors, and policy suggestions could also be applied to other provinces and autonomous regions in western China because of the similar characteristics of these areas such as vast lands, rich resources, and low development level. (C) 2016 Elsevier Ltd. All rights reserved. |
| WOS关键词 | STIRPAT MODEL ; ECOLOGICAL FOOTPRINT ; IMPACT FACTORS ; POPULATION ; CITY ; DECOMPOSITION ; AFFLUENCE ; PROVINCE ; CHOICE |
| 资助项目 | National Science and Technology Pillar Program during the 12th Five-year Plan Period: Assessment and early warning platform construction for rural environment[2012BAJ24B04] |
| WOS研究方向 | Engineering ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000395726300003 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | National Science and Technology Pillar Program during the 12th Five-year Plan Period: Assessment and early warning platform construction for rural environment |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/64853]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Shao, Chaofeng |
| 作者单位 | 1.Univ Calif Berkeley, Dept Civil & Environm Engn, Berkeley, CA 94720 USA 2.Nankai Univ, Coll Environm Sci & Engn, 94 Weijin Rd, Tianjin 300071, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guan, Yang,Kang, Lei,Shao, Chaofeng,et al. Measuring county-level heterogeneity of CO2 emissions attributed to energy consumption: A case study in Ningxia Hui Autonomous Region, China[J]. JOURNAL OF CLEANER PRODUCTION,2017,142:3471-3481. |
| APA | Guan, Yang,Kang, Lei,Shao, Chaofeng,Wang, Ping,&Ju, Meiting.(2017).Measuring county-level heterogeneity of CO2 emissions attributed to energy consumption: A case study in Ningxia Hui Autonomous Region, China.JOURNAL OF CLEANER PRODUCTION,142,3471-3481. |
| MLA | Guan, Yang,et al."Measuring county-level heterogeneity of CO2 emissions attributed to energy consumption: A case study in Ningxia Hui Autonomous Region, China".JOURNAL OF CLEANER PRODUCTION 142(2017):3471-3481. |
入库方式： OAI收割
来源：地理科学与资源研究所
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