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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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://18.104.22.168:20035, 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.
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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.
The authors declare no competing interests.
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.
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.
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.
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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