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Environmental benefits and household costs of clean heating options in northern China

Nature Sustainability volume 5, pages 329–338 (2022)Cite this article

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Abstract

The Chinese government accelerated the clean residential heating transition in northern China as part of a successful effort to improve regional air quality. Meanwhile, China has committed to carbon neutrality by 2060, making strategic choices for long-term decarbonization of the residential sector necessary. However, the synergies and trade-offs for health and carbon of alternative heating options and associated costs have not been systematically considered. Here we investigate air-quality–health–carbon interdependencies as well as household costs of using electricity (heat pumps or resistance heaters), gas or clean coal for residential heating for individual provinces across northern China. We find substantial air-quality and health benefits, varied carbon emissions and increased heating costs across clean heating options. With the 2015 power mix, gas heaters offer the largest health–carbon co-benefits, while resistance heaters lead to health–carbon trade-offs. As the power grid decarbonizes, by 2030 heat pumps achieve the largest health–carbon synergies of the options we analysed. Despite high capital costs, heat pumps generally have the lowest operating costs and thus are competitive for long-term use. With increased subsidies on the purchase of heat pumps, the government can facilitate further air-quality improvements and carbon mitigation in the clean heating transition.

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Fig. 1: Changes in upstream and downstream residential heating emissions in northern China in 2015.
Fig. 2: Simulated surface PM2.5 concentrations and estimated health benefits.
Fig. 3: Unsubsidized and subsidized UCC and AOC of various heating options for typical urban and rural households in each province across northern China.
Fig. 4: Benefits and costs of each clean heating option.
Fig. 5: Emission ratio of operating AAHP and NGH under various conditions.

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Data availability

The Multi-Resolution Emission Inventory of China (MEIC) and the MIX Asia emission inventory are available from the MEIC website (http://meicmodel.org, registration required). WRF-Chem outputs and data generated in this study are publicly available on the Princeton archive at https://doi.org/10.34770/wz62-f790. Data for cost analyses are collected from governmental documents, newspapers, reports, previous literature and conversations with local residents and heating device suppliers, and are listed in the Supplementary Information. Hourly surface PM2.5 measurements across mainland China are available at http://106.37.208.233:20035, and processed monthly mean PM2.5 concentrations are provided as additional supplementary file. Surface meteorological observations are available at https://www.ncei.noaa.gov/products/land-based-station/integrated-surface-database. Source codes of the WRF-Chem model utilized in this study are available at https://github.com/wrf-model/WRF/releases/tag/V3.6.1. Source data are provided with this paper.

References

  1. Lelieveld, J. et al. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 525, 367–371 (2015).

    Article  CAS  Google Scholar 

  2. Mallapaty, S. How China could be carbon neutral by mid-century. Nature 586, 482–483 (2020).

    Article  CAS  Google Scholar 

  3. Shen, G. et al. Impacts of air pollutants from rural Chinese households under the rapid residential energy transition. Nat. Commun. 10, 3405 (2019).

    Article  CAS  Google Scholar 

  4. Liu, J. et al. Decadal changes in anthropogenic source contribution of PM2.5 pollution and related health impacts in China, 1990–2015. Atmos. Chem. Phys. 20, 7783–7799 (2020).

    Article  CAS  Google Scholar 

  5. Yun, X. et al. Residential solid fuel emissions contribute significantly to air pollution and associated health impacts in China. Sci. Adv. 6, eaba7621 (2020).

    Article  CAS  Google Scholar 

  6. Zheng, B. et al. Trends in China’s anthropogenic emissions since 2010 as the consequence of clean air actions. Atmos. Chem. Phys. 18, 14095–14111 (2018).

    Article  CAS  Google Scholar 

  7. Liu, J. et al. Air pollutant emissions from Chinese households: a major and underappreciated ambient pollution source. Proc. Natl Acad. Sci. USA 113, 7756–7761 (2016).

    Article  CAS  Google Scholar 

  8. Huang, R.-J. et al. High secondary aerosol contribution to particulate pollution during haze events in China. Nature 514, 218–222 (2014).

    Article  CAS  Google Scholar 

  9. Zhang, L. et al. Source attribution of particulate matter pollution over north China with the adjoint method. Environ. Res. Lett. 10, 084011 (2015).

    Article  CAS  Google Scholar 

  10. Archer-Nicholls, S. et al. The regional impacts of cooking and heating emissions on ambient air quality and disease burden in China. Environ. Sci. Technol. 50, 9416–9423 (2016).

    Article  CAS  Google Scholar 

  11. Zhao, B. et al. Change in household fuels dominates the decrease in PM2.5 exposure and premature mortality in China in 2005–2015. Proc. Natl Acad. Sci. USA 115, 12401–12406 (2018).

    Article  CAS  Google Scholar 

  12. Tao, S. et al. Quantifying the rural residential energy transition in China from 1992 to 2012 through a representative national survey. Nat. Energy 3, 567–573 (2018).

    Article  Google Scholar 

  13. Li, J. et al. Transition from non-commercial to commercial energy in rural China: insights from the accessibility and affordability. Energy Policy 127, 392–403 (2019).

    Article  Google Scholar 

  14. Clean Winter Heating Plan for Northern China (2017–2021) (National Development and Reform Commission, 2017); https://www.ndrc.gov.cn/xxgk/zcfb/tz/201712/t20171220_962623.html?code=&state=123

  15. Press Briefing on How the Energy Industry in China is Fighting Against Poverty (State Council Information Office, 2020); http://www.nea.gov.cn/2020-10/19/c_139451999.htm

  16. Meng, W. et al. Energy and air pollution benefits of household fuel policies in northern China. Proc. Natl Acad. Sci. USA 116, 16773–16780 (2019).

    Article  CAS  Google Scholar 

  17. Barrington-Leigh, C. et al. An evaluation of air quality, home heating and well-being under Beijing’s programme to eliminate household coal use. Nat. Energy 4, 416–423 (2019).

    Article  Google Scholar 

  18. Wang, J. et al. Exploring the trade-offs between electric heating policy and carbon mitigation in China. Nat. Commun. 11, 6054 (2020).

    Article  CAS  Google Scholar 

  19. Liu, H. & Mauzerall, D. L. Costs of clean heating in China: evidence from rural households in the Beijing–Tianjin–Hebei region. Energy Econ. 90, 104844 (2020).

    Article  Google Scholar 

  20. Qin, Y. et al. Air quality–carbon–water synergies and trade-offs in China’s natural gas industry. Nat. Sustain. 1, 505–511 (2018).

    Article  Google Scholar 

  21. Li, Q. et al. Semi-coke briquettes: towards reducing emissions of primary PM2.5, particulate carbon, and carbon monoxide from household coal combustion in China. Sci. Rep. 6, 19306 (2016).

    Article  CAS  Google Scholar 

  22. Li, Q. et al. Improving the energy efficiency of stoves to reduce pollutant emissions from household solid fuel combustion in China. Environ. Sci. Technol. Lett. 3, 369–374 (2016).

    Article  CAS  Google Scholar 

  23. Interim Measures on the Administration of Coal Reduction and Replacement in the Key Areas (National Development and Reform Commission, 2015); http://news.bjx.com.cn/html/20150114/581798.shtml

  24. Clean Winter Heating Plan for Beijing Rural Villages in 2018 (Beijing Municipal Government, 2018); http://www.beijing.gov.cn/zhengce/zhengcefagui/201905/t20190522_61224.html

  25. National Bureau of Statistics of China China Electric Power Statistics 2016 (China Statistics Press, 2016).

  26. World Energy Outlook 2020 (IEA, 2020); https://webstore.iea.org/world-energy-outlook-2020

  27. Wu, R. et al. Air quality and health benefits of China’s emission control policies on coal-fired power plants during 2005–2020. Environ. Res. Lett. 14, 094016 (2019).

    Article  CAS  Google Scholar 

  28. Work Plan on the Implementation of Ultra-Low Emission and Energy-Saving Transformation of Coal-Fired Power Plants (Ministry of Ecology and Environment, 2015); http://mee.gov.cn/gkml/hbb/bwj/201512/W020151215366215476108.pdf

  29. Wang, X., Dickinson, R. E., Su, L., Zhou, C. & Wang, K. PM2.5 pollution in China and how it has been exacerbated by terrain and meteorological conditions. Bull. Am. Meteorol. Soc. 99, 105–120 (2018).

    Article  Google Scholar 

  30. Burnett, R. et al. Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter. Proc. Natl Acad. Sci. USA 115, 9592–9597 (2018).

    Article  CAS  Google Scholar 

  31. Zhang, Q. et al. Drivers of improved PM2.5 air quality in China from 2013 to 2017. Proc. Natl Acad. Sci. USA 116, 24463–24469 (2019).

    Article  CAS  Google Scholar 

  32. Data from “Residents’ Incomes and Consumption Expenditures in 2019” (National Bureau of Statistics of China, 2019); http://www.stats.gov.cn/tjsj/zxfb/202001/t20200117_1723396.html

  33. Notification on Providing Financial Supports from the Central Government to Clean Heating in Pilot Cities of Northern China (Ministry of Finance, 2017); http://www.gov.cn/xinwen/2017-05/20/content_5195490.htm

  34. Notification on Expanding Financial Supports from the Central Government to Clean Heating in Pilot Cities of Northern China (Ministry of Finance, 2018); http://www.gov.cn/xinwen/2018-08/24/content_5316392.htm?cid=303

  35. Notification on Releasing Budget for Air Pollution Prevention and Control in 2019 (Ministry of Finance, 2019); http://www.mof.gov.cn/zhuantihuigu/cczqzyzfglbf/zxzyzf_7788/dqwrfzzj/201907/t20190701_3288324.htm

  36. Knobloch, F. et al. Net emission reductions from electric cars and heat pumps in 59 world regions over time. Nat. Sustain. 3, 437–447 (2020).

    Article  Google Scholar 

  37. Electric heaters were upgraded during the “coal-to-electricity” process. Beijing has prohibited the use of resistance heaters without thermal storage. Beijing Morning Post (31 March 2017); http://m.haiwainet.cn/middle/352345/2017/0331/content_30830515_1.html

  38. Wang, Z., Sun, Q., Wang, B. & Zhang, B. Purchasing intentions of Chinese consumers on energy-efficient appliances: is the energy efficiency label effective? J. Clean. Prod. 238, 117896 (2019).

    Article  Google Scholar 

  39. Qin, Y. et al. Environmental consequences of potential strategies for China to prepare for natural gas import disruptions. Environ. Sci. Technol. (in the press).

  40. Wang, S. et al. Natural gas shortages during the “coal-to-gas” transition in China have caused a large redistribution of air pollution in winter 2017. Proc. Natl Acad. Sci. USA 117, 31018–31025 (2020).

    Article  CAS  Google Scholar 

  41. Notification on Collecting Opinions to Solve the Problems in the Process of Promoting Clean Heating from Coal to Gas and Coal to Electricity (National Energy Administration, 2019); http://www.gov.cn/hudong/2019-07/04/content_5405952.htm

  42. Li, M. et al. Persistent growth of anthropogenic non-methane volatile organic compound (NMVOC) emissions in China during 1990–2017: drivers, speciation and ozone formation potential. Atmos. Chem. Phys. 19, 8897–8913 (2019).

    Article  CAS  Google Scholar 

  43. Peng, L. et al. Underreported coal in statistics: a survey-based solid fuel consumption and emission inventory for the rural residential sector in China. Appl. Energy 235, 1169–1182 (2019).

    Article  CAS  Google Scholar 

  44. Qin, Y., Edwards, R., Tong, F. & Mauzerall, D. L. Can switching from coal to shale gas bring net carbon reductions to China? Environ. Sci. Technol. 51, 2554–2562 (2017).

    Article  CAS  Google Scholar 

  45. Yang, X. Current situations and technical routes of rural clean heating. In Proc. 14th Session of Building Energy Efficiency Academic Week in Tsinghua University: Clean Heating Forum, Beijing (2018); http://www.ne01.com/news/8001.html

  46. Stohl, A. et al. Evaluating the climate and air quality impacts of short-lived pollutants. Atmos. Chem. Phys. 15, 10529–10566 (2015).

    Article  CAS  Google Scholar 

  47. Qin, Y. et al. Air quality, health, and climate implications of China’s synthetic natural gas development. Proc. Natl Acad. Sci. USA 114, 4887–4892 (2017).

    Article  CAS  Google Scholar 

  48. National Bureau of Statistics of China China Statistical Yearbook 2016 (China Statistics Press, 2016).

  49. National Bureau of Statistics of China China Energy Statistical Yearbook 2016 (China Statistics Press, 2016).

  50. Grell, G. A. et al. Fully coupled “online” chemistry within the WRF model. Atmos. Environ. 39, 6957–6975 (2005).

    Article  CAS  Google Scholar 

  51. NCEP FNL Operational Model Global Tropospheric Analyses (National Center for Atmospheric Research, 2015); https://doi.org/10.5065/D6M043C6

  52. Lamarque, J. F. et al. CAM-Chem: description and evaluation of interactive atmospheric chemistry in the Community Earth System Model. Geosci. Model. Dev. 5, 369–411 (2012).

    Article  Google Scholar 

  53. Guenther, A. et al. Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature). Atmos. Chem. Phys. 6, 3181–3210 (2006).

    Article  CAS  Google Scholar 

  54. Li, M. et al. MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP. Atmos. Chem. Phys. 17, 935–963 (2017).

    Article  CAS  Google Scholar 

  55. Zhang, L. et al. Agricultural ammonia emissions in China: reconciling bottom-up and top-down estimates. Atmos. Chem. Phys. 18, 339–355 (2018).

    Article  CAS  Google Scholar 

  56. Chen, D. et al. Simulations of sulfate–nitrate–ammonium (SNA) aerosols during the extreme haze events over northern China in October 2014. Atmos. Chem. Phys. 16, 10707–10724 (2016).

    Article  CAS  Google Scholar 

  57. Zhou, M. et al. The impact of aerosol–radiation interactions on the effectiveness of emission control measures. Environ. Res. Lett. 14, 024002 (2019).

    Article  CAS  Google Scholar 

  58. CIESIN Gridded Population of the World, Version 4 (GPWv4): Population Count Adjusted to Match 2015 Revision of UN WPP Country Totals (SEDAC, 2016).

  59. Building Energy Research Center at Tsinghua University China Building Energy Conservation Annual Report 2011 (China Building Industry Press, 2011).

  60. Zheng, X. et al. Characteristics of residential energy consumption in China: findings from a household survey. Energy Policy 75, 126–135 (2014).

    Article  Google Scholar 

  61. Zhang, W. et al. An analysis of the costs of energy saving and CO2 mitigation in rural households in China. J. Clean. Prod. 165, 734–745 (2017).

    Article  Google Scholar 

  62. Rout, A., Sahoo, S. S. & Thomas, S. Risk modeling of domestic solar water heater using Monte Carlo simulation for east-coastal region of India. Energy 145, 548–556 (2018).

    Article  Google Scholar 

  63. A Report of Analyzing and Forecasting China’s Electricity Supply and Demand During 2020 and 2021 (China Electricity Council, 2021); https://www.cec.org.cn/detail/index.html?3-293198

Download references

Acknowledgements

We thank J. Yang for early scoping analysis, Y. Guo, M. Li and Y. Zheng for assistance on health calculations, M. Li for assistance on preparing residential VOC emission inventories, Q. Kong, K. Liu, G. Zhang, X. Yang, J. Chen, S. Shi, C. Xie, R. Han, R. Liu and D. Li for data collection. We thank the Ma Huateng Foundation grant to the Princeton School of Public and International Affairs at Princeton University for supporting M.Z. and H.L., the National Natural Science Foundation of China no. 41922037 for supporting L.Z. and M.Z., as well as no. 72173095 supporting H.L., and the China Scholarship Council Liujinxuan (2019) no. 110 for supporting M.Z. and Liujinfa (2017) no. 5047 for supporting H.L.

Author information

Author notes

  1. These authors contributed equally: Mi Zhou, Hongxun Liu.

Authors and Affiliations

  1. Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA

    Mi Zhou, Hongxun Liu, Liqun Peng & Denise L. Mauzerall

  2. Laboratory for Climate and Ocean–Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China

    Mi Zhou & Lin Zhang

  3. School of Economics and Finance, Xi’an Jiaotong University, Shaanxi, China

    Hongxun Liu

  4. College of Environmental Sciences and Engineering, Peking University, Beijing, China

    Yue Qin

  5. Institute of Urban Meteorology, China Meteorological Administration, Beijing, China

    Dan Chen

  6. Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA

    Denise L. Mauzerall

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Contributions

D.L.M. designed the research. M.Z. and H.L. performed the research. H.L., L.Z., L.P. and Y.Q. contributed data for scenario setups. M.Z., L.Z. and D.C. contributed air-quality model improvements. M.Z., H.L. and D.L.M. analysed data. M.Z., H.L., and D.L.M. wrote the paper with feedback from all other authors. M.Z. and H.L. contributed equally to this work.

Corresponding authors

Correspondence to Lin Zhang or Denise L. Mauzerall.

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The authors declare no competing interests.

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Peer review information Nature Sustainability thanks Hongbo Duan, Hongyou Lu and Haiwang Zhong for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Mean surface PM2.5 concentration changes during the heating season.

Surface PM2.5 concentration changes averaged over three months (January, March, and November) in 2015. PM2.5 concentration changes in the CCIS_high scenario (CCIS_high minus BASE, a), and the further changes in CCIS_low (CCIS_low minus CCIS_high, b), NGH (NGH minus CCIS_high, c), AAHP_2015 (AAHP_2015 minus CCIS_high, d), RH_2015 (RH_2015 minus CCIS_high, e), AAHP_2030 (AAHP_2030 minus CCIS_high, f), and RH_2030 (RH_2030 minus CCIS_high, g). Note the inset numbers are the absolute changes (percentage changes in parentheses) of population-weighted mean PM2.5 concentrations for China (Black) and northern China (Orange). Dashed lines denote the boundaries of northern China identified in the Clean Heating Plan. White areas inside China denote grids where changes are either not statistically significant at 99% confidence level (alpha = 0.01) or smaller than 0.1 μg/m3.

Extended Data Fig. 2 Unsubsidized and subsidized total annual costs (TAC) for typical urban and rural households in each province across northern China.

Unsubsidized and subsidized total annualized costs (TAC) for typical urban and rural households in each province across northern China. TAC equals annual operating costs (AOC) plus annualized capital costs (ACC). ACC was determined by upfront capital costs (UCC) and lifespan of each heating device, as well as discount rate, which is 8% in this paper. See Table 1 for definition of heating option acronyms. The subsidies we use were in effect during 2018-2020. For provinces having different subsidies across their subordinate cities/counties, we use the population-weighted average subsidies to calculate the subsidized UCC and AOC at provincial level. Subsidies for UCC and AOC in each province are listed in Supplementary Tables 3 and 4. See location of each province in Supplementary Figure 1. DCTS and DCIS were not subsidized in our calculation.

Source data

Extended Data Fig. 3 Payback time for households if coal stoves are replaced with AAHP rather than NGH.

Payback time if households switch to heat pumps rather than gas heaters, household heating demand, and averaged temperature during heating season across northern China. a, Required time (payback time, in years) for the total heating costs of gas heaters to exceed those of heat pumps for households in each province. Total heating costs are UCC plus the total operating costs over time. Life span for gas heaters is 8 years while that for heat pumps is 15 years. Note that in Qinghai and Inner Mongolia, AOC of NGH are lower than AOC of AAHP because AAHP are inefficient in these cold regions and gas price there are low. Therefore, the total heating costs of gas heaters are always lower than those of heat pumps in Qinghai and Inner Mongolia. For each color interval, the lower bound is included while the upper bound is excluded. b, Annual heating demand for households in each province. c, The spatial pattern of 2-m temperature averaged for the winter heating season from 2010 to 2019. Here, we use monthly mean temperature in January, February, March, November, and December to represent the average conditions during the heating season in each year. The reanalysis data is from the European Centre for Medium-Range Weather Forecasts (https://www.ecmwf.int/). Dashed lines denote the boundaries of northern China identified in the Clean Heating Plan.

Supplementary information

Supplementary Information

Supplementary Discussion, Figs. 1–5 and Tables 1–12.

Reporting Summary

Supplementary Data 1

Monthly mean surface PM2.5 concentrations from observations and WRF-Chem outputs, which were used to plot Supplementary Information Fig. 3.

Source data

Source Data Fig. 1

Data for plotting Fig. 1a–d.

Source Data Fig. 2

Data for plotting Fig. 2e,f.

Source Data Fig. 3

Data for plotting Fig. 3a–h.

Source Data Extended Data Fig. 2

Data for plotting Extended Data Fig. 2a–d.

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Zhou, M., Liu, H., Peng, L. et al. Environmental benefits and household costs of clean heating options in northern China. Nat Sustain 5, 329–338 (2022). https://doi.org/10.1038/s41893-021-00837-w

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