Fuel choices for cooking in China: Analysis based on multinomial logit model
Fuel choices for cooking in China: Analysis based on multinomial logit model
复制标题
中国做饭的燃料选择：基于多项logit模型的分析
DOI：
10.1016/j.jclepro.2019.03.302
复制
发表时间：
2019-07-10
影响因子：
11.1
通讯作者：
Wu, Jingwen
中科院分区：
文献类型：
--
作者：
Liao, Hua;Chen, Tianqi;Wu, Jingwen
确保所有人都能获得负担得起、可靠、可持续的现代能源是联合国可持续发展目标(SDGs)的17项目标之一。中国实现了100%通电，而农村地区仍然广泛使用木柴等传统固体燃料。本文利用长期、大规模的微观数据集和多项Logit模型，对中国农村地区烹饪燃料选择的影响因素进行了定量研究。研究结果表明，除了先前对家庭收入的了解外，职业对烹饪燃料的转换也是至关重要的。平均而言，如果户主将职业从农业转向非农，使用柴火的可能性将减少约14%-21%。收入的影响微乎其微。收入增加10%可能导致使用柴火的可能性为0.5%。在使用有序Logit回归(OLR)和广义OLR考虑了可能的能量阶梯之后，这些结论是稳健的。在控制了收入和职业等其他因素后，我们没有发现教育程度和性别对户主和家庭成员数量的影响的具体证据。要加快燃料转换，除了增加家庭收入外，政府还应注意为农村创造更多的非农就业机会。(C)2019爱思唯尔有限公司。保留所有权利。
Ensuring household access to affordable, reliable, sustainable and modern energy for all the people is one of the 17 United Nations Sustainable Development Goals (SDGs). China has achieved 100% electricity access, while the traditional solid fuels such as firewood are still widely used in its rural area. This paper, using a long-term and large micro dataset and multinomial logit model, investigates quantitatively the determinants of cooking fuel choice in rural China. The results show that in addition to the previous knowledge on household income, occupation is crucial to the cooking fuel transition. In average, if the head of household changes its occupation from farm to non-farm, the possibility of using firewood would reduce by around 14-21%. The impact of income is slightly small. A 10% increase in income may result in 0.5% of possibility of firewood use. These conclusions are robust after considering the possible energy ladders using ordered logit regressions (OLR) and generalized OLR. After controlling other factors such as income and occupation, we have not found concrete evidence on the influences of education and gender of the household head and household member numbers. To accelerate the fuel transition, in addition to increase the household income, the government should pay attention to create more non-farm work opportunities for the rural. (C) 2019 Elsevier Ltd. All rights reserved.