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More Power Generation, More Wheat Losses? Evidence from Wheat Productivity in North China

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Abstract

The adverse effects of thermal power plants on crop yield have not received adequate attention. Thus, this study aims to evaluate these effects systematically to fill the gap by utilizing county-level wheat yield data from North China spanning from 2005 to 2016. Our findings indicate that the presence of an additional upwind thermal power plant is associated with a 1.4% decline in wheat yield. Notably, these yield losses are more pronounced in regions characterized by lenient environmental regulations or a high density of large-scale thermal power plants. Reduced wheat yield due to thermal power plants results in a decline in social welfare. Furthermore, we confirm that air pollution emitted from thermal power plants is the primary driver behind the decline in wheat yield.

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Notes

  1. The data are available at https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html (BP Statistical Review of World Energy 2021).

  2. The WHO guideline standards: PM2.5 (10 μg/m3) and PM10 (20 μg/m3).

  3. The air pollutants data are available at http://www.zhb.gov.cn (Ministry of Environmental Protection of the People’s Republic of China, China Environmental Status Bulletin (2016–2018)).

  4. The data are available at http://www.fao.org/faostat/en/#data/QC (Food and Agricultural Organization of the United Nations, FAOSTAT 2019).

  5. For winter wheat, the tillering stage is from October to December, whereas the heading stage is from March to April. For spring wheat, the tillering stage is from March to April, whereas the heading stage is from April to May.

  6. The wind data are vectorial, comprising two components: direction and speed. We mark wind directions based on daily wind directions and speeds, using the vector decomposition method proposed by Grange (2014).

  7. The National Energy Administration is a central government authority that regulates energy and electricity fields. It grants approval for projects involving thermal power plant construction.

  8. Table 1 presents that the sample has a similar average number of thermal power plants across different directions, and downwind thermal power plants outnumber upwind ones in terms of the maximum number. However, we still need to determine the distribution of thermal power plants by using statistical methods.

  9. Detailed methods and results are reported in Appendix S.1 and Fig. A4.

  10. Detailed methods and results are reported in Appendix S.2, Table A2, and Figs. A5 and A6.

  11. Relevant results using installed capacity of thermal power plants as the key independent variable are available upon request.

  12. Weight factor is a function of the inverse of the distance of a thermal power plant to the county geographic center.

  13. Frequency is the number of times environmental vocabularies appear in government work reports, and relative frequency is the proportion of environmental vocabulary to the total vocabulary in government work reports.

  14. Fly ash is non-inhalable dust emitted from thermal power plants.

  15. Black carbon is the most abundant and harmful pollutant in PM2.5,

  16. The hypothesis of parallel trend assumption is verified in Appendix Fig. A8.

  17. Relevant results are available upon request.

  18. In addition, we used data on the installed capacity of thermal power plants to validate the precision of our wheat loss estimates. Our findings indicate that for each incremental 100 MW increase in the installed capacity of upwind thermal power plants during the sample period, wheat yield declined by 0.90%. Based on this specification, the implied output loss of wheat attributable to thermal power plants is about 0.59 million tons annually.

  19. In our welfare analysis, we assume wheat production is consistent except in North China and unaffected by thermal power plants.

  20. In addition, we used data on the installed capacity of thermal power plants to calculate the welfare implications with same procedures. Our findings indicate that consumer surplus for wheat purchasers declines by CNY 3.7 billion annually. In the meantime, a corresponding increase in producer surplus for wheat producers of CNY 2.1 billion is observed annually. These changes imply a net decrease of CNY 1.6 billion in total surplus annually. Furthermore, the implied loss per 100 MW increase in installed capacity of upwind thermal power plants is CNY 0.2 billion in total surplus.

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Funding

Fujin Yi gratefully acknowledges the financial support from the Joint Agricultrual Research Project between National Natural Science Foundation of China (NSFC) and the Bill & Melinda Gates Foundation (BMGF) (Grant: 72261147758), the National Social Science Foundation of China (Grant: 22VRC178), and the Leading Talents Project of Philosophy and Social Science Foundation of Zhejiang Province (Grant: 24YJRC01ZD).

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Authors and Affiliations

  1. China Academy for Rural Development, and School of Public Affairs, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, Zhejiang, China

    Fujin Yi

  2. College of Economics and Management, Nanjing Agricultural University, 1 Weigang, Nanjing, 210095, Jiangsu, China

    Sihan Lyu

  3. School of Accounting, Nanjing University of Finance and Economics, 3 Wenyuan Road, Nanjing, 210003, Jiangsu, China

    Lu Yang

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  1. Fujin Yi
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  3. Lu Yang
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Contributions

SL: Methodology, Software, Data curation, Writing-original draft, Visualization. FY: Conceptualization, Methodology, Data curation, Writing-original draft, Writing-review & editing. LY: Methodology, Writing-review & editing. Fujin Yi and Sihan Lyu share the senior authorship.

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Correspondence to Fujin Yi or Sihan Lyu.

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Yi, F., Lyu, S. & Yang, L. More Power Generation, More Wheat Losses? Evidence from Wheat Productivity in North China. Environ Resource Econ 87, 907–931 (2024). https://doi.org/10.1007/s10640-024-00841-6

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