Substantial transition to clean household energy mix in rural China

Author:

Shen Guofeng1ORCID,Xiong Rui1,Tian Yanlin12,Luo Zhihan1,Jiangtulu Bahabaike3,Meng Wenjun1,Du Wei4,Meng Jing5ORCID,Chen Yuanchen6,Xue Bing7,Wang Bin38,Duan Yonghong9,Duo Jia10111213,Fan Fenggui14,Huang Lei15,Ju Tianzhen16,Liu Fenggui1718,Li Shunxin19,Liu Xianli20,Li Yungui21,Wang Mu2223,Nan Ying24,Pan Bo25,Pan Yanfang26,Wang Lizhi27,Zeng Eddy28,Zhan Chao29,Chen Yilin30,Shen Huizhong30,Cheng Hefa1,Tao Shu130

Affiliation:

1. College of Urban and Environmental Sciences, Peking Universit y, Beijing 100871, China

2. Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC), Nanjing University of Information Science and Technolog y, Nanjing 210044, China

3. Institute of Reproductive and Child Health, Peking Universit y, Beijing 100191, China

4. Laboratory of Geographic Information Science, School of Geographic Sciences, East China Normal Universit y, Shanghai 200241, China

5. The Bartlett School of Sustainable Construction, University College Londo n, London WC1E 7HB, UK

6. College of Environment, Zhejiang University of Technolog y, Hangzhou 310014, China

7. Institute of Applied Ecology, Chinese Academy of Science s, Shenyang 110016, China

8. Department of Epidemiology and Biostatistics, School of Public Health, Peking Universit y, Beijing 100191, China

9. College of Resources and Environment, Shanxi Agricultural Universit y, Jinzhong 030801, China

10. Xinjiang Key Laboratory of Environmental Pollution and Bioremediation, Xinjiang Institute of Ecology and Geography, Chinese Academy of Scienc es, Urumqi 830011, China

11. National Engineering Technology Research Center for Desert-Oasis Ecological Construction, Xinjiang Institute of Ecology and Geography, Chinese Academy of Science s, Urumqi 830011, China

12. University s, Beijing 100049, China

13. of Chinese Academy of Science s, Beijing 100049, China

14. School of Geography and Tourism, Anhui Universit y, Wuhu 241000, China

15. School of Environment, Nanjing Universit y, Nanjing 210033, China

16. College of Geography and Environmental Science, Northwest Normal Universit y, Lanzhou 730070, China

17. College of Geographical Science, Qinghai Normal University , Xining 810008, China

18. Academy of Plateau Science and Sustainabilit y, Xining 810008, China

19. College of Chemistry, Chemical Engineering and Environment, Minnan Normal Universit y, Zhangzhou 363000, China

20. School of Environmental Science and Engineering, Hubei Polytechnic Universit y, Huangshi 435003, China

21. Department of Environmental Engineering, Southwest University of Science and Technolog y, Mianyang 621010, China

22. College of Food Scienc e, y, Linzhi 860000, China

23. Tibet Agricultural and Animal Husbandry Universit e, y, Linzhi 860000, China

24. College of Geography and Ocean Sciences, Yanbian Universit y, Yanji 133002, China

25. Faculty of Environmental Science and Engineering, Kunming University of Science and Technolog y, Kunming 650500, China

26. College of Geography and Environmental Science, Henan Universit y, Kaifeng 475001, China

27. College of Ecology and Environment, Hainan Universit y, Haikou 570228, China

28. School of Environment, Jinan Universit y, Guangzhou 510632, China

29. Institute of Coastal Research, Ludong Universit y, Yantai 264025, China

30. College of Environmental Science and Technology, Southern University of Science and Technolog y, Shenzhen 518055, China

Abstract

Abstract The household energy mix has significant impacts on human health and climate, as it contributes greatly to many health- and climate-relevant air pollutants. Compared to the well-established urban energy statistical system, the rural household energy statistical system is incomplete and is often associated with high biases. Via a nationwide investigation, this study revealed high contributions to energy supply from coal and biomass fuels in the rural household energy sector, while electricity comprised ∼20%. Stacking (the use of multiple sources of energy) is significant, and the average number of energy types was 2.8 per household. Compared to 2012, the consumption of biomass and coals in 2017 decreased by 45% and 12%, respectively, while the gas consumption amount increased by 204%. Increased gas and decreased coal consumptions were mainly in cooking, while decreased biomass was in both cooking (41%) and heating (59%). The time-sharing fraction of electricity and gases (E&G) for daily cooking grew, reaching 69% in 2017, but for space heating, traditional solid fuels were still dominant, with the national average shared fraction of E&G being only 20%. The non-uniform spatial distribution and the non-linear increase in the fraction of E&G indicated challenges to achieving universal access to modern cooking energy by 2030, particularly in less-developed rural and mountainous areas. In some non-typical heating zones, the increased share of E&G for heating was significant and largely driven by income growth, but in typical heating zones, the time-sharing fraction was <5% and was not significantly increased, except in areas with policy intervention. The intervention policy not only led to dramatic increases in the clean energy fraction for heating but also accelerated the clean cooking transition. Higher income, higher education, younger age, less energy/stove stacking and smaller family size positively impacted the clean energy transition.

Funder

National Natural Science Foundation of China

Chinese Academy of Sciences

Ministry of Science and Technology

Publisher

Oxford University Press (OUP)

Subject

Multidisciplinary

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