期刊
JOURNAL OF CLEANER PRODUCTION
卷 142, 期 -, 页码 809-818出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2016.02.100
关键词
Natural resource; Resource allocation; Big data; Data envelopment analysis
资金
- National Natural Science Funds of China [71222106, 71571173, 71110107024, 7150198]
- Research Fund for the Doctoral Program of Higher Education of China [20133402110028]
- Foundation for the Author of National Excellent Doctoral Dissertation of P. R. China [201279]
- Top-Notch Young Talents Program of China
China's economic reform and opening policy has resulted in China's rapid growth, especially in terms of economy. The growth, however, is mainly based on huge consumption of natural resources, and that consumption should be reduced by significant amounts to protect global ecosystems. Emerged in 1980s, big data has developed very quickly in recent years, bringing new perspectives and opportunities for all kinds of academic fields, especially in DEA since it is a research highly based on data. In this paper, we propose a SBM-DEA model based on natural resource input orientation to evaluate the efficiency of natural resource utilization for 26 provincial regions in mainland China from 2005 to 2012. A DEA-based approach is developed to allocate a reduced total of natural resources among the 26 provincial regions. Drawing support from the developments of big data, we use each provincial region's previously observed production data to characterize its own production technology, which allows a better analysis of how the production will change after allocations of natural resources are reduced. Our empirical study shows that most provincial regions perform well in terms of efficiency in utilizing natural resources, but that is based on huge consumption of natural resources. From an area perspective, the eastern area of China achieved the highest average efficiency value, followed by the central and western areas. Our study also indicates that the most effective allocation of the natural resources requires about half of the 26 provincial regions to reduce their natural resource consumption while the remaining ones can maintain their original consumption. (C) 2016 Published by Elsevier Ltd.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据