China’s poorest counties are transitioning towards low-carbon poverty alleviation

Abstract

Low-carbon development in poverty-stricken areas is crucial for the Sustainable Development Goals. However, the relationship between poverty alleviation and carbon emissions in impoverished regions remains unclear. This study calculated land-use carbon emissions of 505 counties in China’s contiguous poor areas, which suffer from extreme poverty and are the most difficult to lift out of poverty, established evaluation frameworks for carbon emissions efficiency and multidimensional poverty alleviation, and employed an improved coupling coordination model to assess the relationship. Results showed that all counties achieved remarkable multidimensional poverty alleviation from 2000 to 2020 while the speed varied among regions. After initial deterioration, emissions efficiency exhibited significant improvements during 2010–2020. This reversal was mainly driven by reduced carbon intensity and carbon emissions per unit of construction land. Analysis revealed 505 counties’ transformation from an uncoordinated “low development-low carbon” state toward a sustainable “high development-low carbon” state. These findings evidence that poverty alleviation development and low-carbon target can be compatible, offering valuable insights for sustainable development in global underdeveloped regions.

Keywords

Multidimensional poverty alleviation / Contiguous poor areas / Coupling coordination / Low-carbon development / Sustainable development / Carbon emissions

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

CRediT authorship contribution statement

Jinhai Li: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Formal analysis, Data curation, Conceptualization. Feifei Lin: Writing – review & editing, Visualization, Validation, Software. Peng Cheng: Writing – review & editing, Validation, Resources. Xuesong Kong: Writing – review & editing, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

Acknowledgements

This Research was supported by the National Natural Science Foundation of China (Grants No. 42293270 and 42571248).

References

[1] Alkire, S., Jahan, S., 2018. The New Global MPI 2018: Aligning with the Sustainable Development Goals. Oxford Poverty and Human Development Initiative Working Papers. University of Oxford, Oxford.

[2] Alkire, S., Kanagaratnam, U., Nogales, R., Suppa, N., 2022. Revising the global multidimensional poverty index: empirical insights and robustness. Rev. Income Wealth 68 (S2), S347-S384. doi: 10.1111/roiw.12573.

[3] Angelsen, A., Jagger, P., Babigumira, R., Belcher, B., Hogarth, N.J., Bauch, S., Börner, J., Smith-Hall, C., Wunder, S., 2014. Environmental income and rural livelihoods: a global-comparative analysis. World Dev. 64, S12-S28. doi: 10.1016/j.worlddev.2014.03.006.

[4] Angulo, R., 2016. From Multidimensional Poverty Measurement to Multisector Public Policy for Poverty reduction: Lessons from the Colombian Case. Oxford Poverty and Human Development Initiative Working Papers. University of Oxford, Oxford.

[5] Bai, Y.P., Wang, Y.W., Xuan, X., Weng, C.Y., Huang, X.K., Deng, X.Z., 2024. Teleconnections, driving forces and scenario simulation of agricultural land, water use and carbon emissions in China’s trade. Resour. Conserv. Recycl. 203, 107433. doi: 10.1016/j.resconrec.2024.107433.

[6] Bergstrom, K., 2022. The role of income inequality for poverty reduction. World Bank Econ. Rev. 36 (3), 583-604. doi: 10.1093/wber/lhab026.

[7] Bruckner, B., Hubacek, K., Shan, Y.L., Zhong, H.L., Feng, K.S., 2022. Impacts of poverty alleviation on national and global carbon emissions. Nat. Sustain. 5 (4), 311-320. doi: 10.1038/s41893-021-00842-z.

[8] Cai, A.Z., Guo, R., Zhang, Y.H., Wu, J., 2024. Status and trends of carbon emissions research at the county level in China. Adv. Clim. Change Res. 15 (2), 338-352. doi: 10.1016/j.accre.2024.03.003.

[9] Cárdenas-Mamani, Ú., Kahhat, R., Vázquez-Rowe, I., 2022. District-level analysis for household-related energy consumption and greenhouse gas emissions: a case study in Lima, Peru. Sust. Cities Soc. 77, 103572. doi: 10.1016/j.scs.2021.103572.

[10] Chen, J.D., Gao, M., Cheng, S.L., Hou, W.X., Song, M., Liu, X., Liu, Y., 2022. Global 1 km × 1 km gridded revised real gross domestic product and electricity consumption during 1992-2019 based on calibrated nighttime light data. Sci. Data 9 (1), 202. doi: 10.1038/s41597-022-01322-5.

[11] Chen, N., Qin, F., Zhai, Y.X., Cao, H.P., Zhang, R., Cao, F.P., 2020. Evaluation of coordinated development of forestry management efficiency and forest ecological security: a spatiotemporal empirical study based on China’s provinces. J. Clean. Prod. 260, 121042. doi: 10.1016/j.jclepro.2020.121042.

[12] Chen, X.L., Xie, Q.Y., Cao, X., Li, Q.L., 2024. Examining the effectiveness of China’s energy poverty alleviation policies: a text analysis on inter-provincial panel data. Energy Policy 186, 113978. doi: 10.1016/j.enpol.2023.113978.

[13] Chen, Z.Q., Yu, B.L., Yang, C.S., Zhou, Y.Y., Yao, S.J., Qian, X.J., Wang, C.X., Wu, B., Wu, J.P., 2021. An extended time series (2000-2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration. Earth Syst. Sci. Data 13 (3), 889-906. doi: 10.5194/essd-13-889-2021.

[14] Chen, Z.N., Zhang, Z.K., Feng, T., Liu, D.Y., 2023. What drives the temporal dynamics and spatial differences of urban and rural household emissions in China? Energy Econ. 125, 106849. doi: 10.1016/j.eneco.2023.106849.

[15] Cheng, X., Liu, Y.T., Yu, Z.Y., Gao, J.Y., Dai, Y., Chen, J., Liu, Y., Wang, C.F., Shuai, C.M., Li, W.J., Xie, Z.J., 2024. Adding a basis for sustainable poverty monitoring: the indicator systems and multi-source data of multi-dimensional poverty measurement. Environ. Dev. 49, 100966. doi: 10.1016/j.envdev.2024.100966.

[16] CONEVAL, 2010. Methodology for multidimensional poverty measurement in Mexico. Consejo Nacional de Evaluación de la Política de Desarrollo Social (CONEVAL).

[17] Diakoulaki, D., Mavrotas, G., Papayannakis, L., 1995. Determining objective weights in multiple criteria problems: the critic method. Comput. Oper. Res. 22 (7), 763-770. doi: 10.1016/0305-0548(94)00059-H.

[18] Fu, R., Jin, G., Chen, J.Y., Ye, Y.Y., 2021. The effects of poverty alleviation investment on carbon emissions in China based on the multiregional input-output model. Technol. Forecast. Soc. Change 162, 120344. doi: 10.1016/j.techfore.2020.120344.

[19] Hahn, M.B., Riederer, A.M., Foster, S.O., 2009. The livelihood vulnerability index: a pragmatic approach to assessing risks from climate variability and change-a case study in Mozambique. Glob. Environ. Change 19 (1), 74-88. doi: 10.1016/j.gloenvcha.2008.11.002.

[20] He, H.J., 2016. Child labour and academic achievement: evidence from Gansu province in China. China Econ. Rev. 38, 130-150. doi: 10.1016/j.chieco.2015.12.008.

[21] Hossain, M., Mendiratta, V., Savastano, S., 2024. Agricultural and rural development interventions and poverty reduction: global evidence from 16 impact assessment studies. Glob. Food Secur. 43, 100806. doi: 10.1016/j.gfs.2024.100806.

[22] Hubacek, K., Baiocchi, G., Feng, K.S., Patwardhan, A., 2017. Poverty eradication in a carbon constrained world. Nat. Commun. 8 (1), 912. doi: 10.1038/s41467-017-00919-4.

[23] Intergovernmental Panel on Climate Change , 2006. Guidelines for national greenhouse gas inventories. https://www.ipcc-nggip.iges.or.jp/public/2006gl/chinese/index.html. (accessed 26 August 2024).

[24] Intergovernmental Panel on Climate Change . Climate change 2023: synthesis report. https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_LongerReport.pdf.

[25] Ivanova, D., Barrett, J., Wiedenhofer, D., Macura, B., Callaghan, M., Creutzig, F., 2020. Quantifying the potential for climate change mitigation of consumption options. Environ. Res. Lett. 15 (9), 093001. doi: 10.1088/1748-9326/ab8589.

[26] Ji, Q., Yang, J.P., Chu, Y.N., Chen, H.J., Guo, X.Y., 2024. Inequality of rural residents’ income in China since the targeted poverty alleviation strategy: new trends, causes, and policy implications. Res. Cold Arid Reg. 16 (4), 201-213. doi: 10.1016/j.rcar.2024.09.002.

[27] Jia, L.R., Liu, Y.S., Liu, J.L., Li, J.T., 2018. Study on the poverty causes and aid demands of poor rural households in the concentrated poverty-stricken areas in China. Hum. Geogr. 33 (1), 85-93. doi: 10.13959/j.issn.1003-2398.2018.01.011, (in Chinese).

[28] Jiang, L., Bai, L., Wu, Y.M., 2017. Coupling and coordinating degrees of provincial economy, resources and environment in China. J. Nat. Resour. 32 (5), 788-799. doi: 10.11849/zrzyxb.20160512, (in Chinese).

[29] Jin, G., Fu, R., Li, Z.H., Wu, F., Zhang, F., 2018. CO ₂ emissions and poverty alleviation in China: an empirical study based on municipal panel data . J. Clean. Prod. 202, 883-891. doi: 10.1016/j.jclepro.2018.08.221.

[30] Jin, G., Guo, B.S., Deng, X.Z., 2020. Is there a decoupling relationship between CO ₂ emission reduction and poverty alleviation in China? Technol. Forecast. Soc. Change 151, 119856. doi: 10.1016/j.techfore.2019.119856.

[31] Koçak, E., Ulucak, R., Dedeoğlu, M., Ulucak, Z.Ş., 2019. Is there a trade-off between sustainable society targets in sub-Saharan Africa? Sust. Cities Soc. 51, 101705. doi: 10.1016/j.scs.2019.101705.

[32] Kong, X.S., Jiang, X.J., Cheng, P., 2023. Correlation analysis between multi-dimensional poverty reduction and construction land expansion in eleven concentrated and contiguous poverty-stricken areas in China. China Land Sci. 37 (5), 125-136. doi: 10.11994/zgtdkx.20230510.105200, (in Chinese).

[33] Leyk, S., Maclaurin, G.J., Hunter, L.M., Nawrotzki, R., Twine, W., Collinson, M., Erasmus, B., 2012. Spatially and temporally varying associations between temporary outmigration and natural resource availability in resource-dependent rural communities in South Africa: a modeling framework. Appl. Geogr. 34, 559-568. doi: 10.1016/j.apgeog.2012.02.009.

[34] Li, L.X., Hu, T., Yang, G.Y., He, W., Zhang, H.Y., 2024. High-resolution comprehensive regional development mapping using multisource geographic data. Sust. Cities Soc. 113, 105670. doi: 10.1016/j.scs.2024.105670.

[35] Li, N., Yuan, R., Zheng, S.L., 2023a. Trade-offs between poverty alleviation and household energy intensity in China. Environ. Impact Assess. Rev. 98, 106957. doi: 10.1016/j.eiar.2022.106957.

[36] Li, N., Yuan, R., Zheng, S.L., 2023b. Impacts of targeted poverty alleviation on urban-rural residential energy consumption inequality: evidence from 178 Chinese prefecture-level cities. Urban Clim. 50, 101583. doi: 10.1016/j.uclim.2023.101583.

[37] Liu, Z., Guan, D.B., Wei, W., Davis, S.J., Ciais, P., Bai, J., Peng, S.S., Zhang, Q., Hubacek, K., Marland, G., Andres, R.J., Crawford-Brown, D., Lin, J.T., Zhao, H.Y., Hong, C.P., Boden, T.A., Feng, K.S., Peters, G.P., Xi, F.M., Liu, J.G., Li, Y., Zhao, Y., Zeng, N., He, K.B., 2015. Reduced carbon emission estimates from fossil fuel combustion and cement production in China. Nature 524 (7565), 335-338. doi: 10.1038/nature14677.

[38] Lv, Q., Liu, H.B., Wang, J.T., Liu, H., Shang, Y., 2020. Multiscale analysis on spatiotemporal dynamics of energy consumption CO ₂ emissions in China: utilizing the integrated of DMSP-OLS and NPP-VIIRS nighttime light datasets . Sci. Total Environ. 703, 134394. doi: 10.1016/j.scitotenv.2019.134394.

[39] Malerba, D., 2020. The trade-off between poverty reduction and carbon emissions, and the role of economic growth and inequality: an empirical cross-country analysis using a novel indicator. Soc. Indic. Res. 150 (2), 587-615. doi: 10.1007/s11205-020-02332-9.

[40] Marinho, E., Campelo, G., França, J., Araujo, J., 2017. Impact of infrastructure expenses in strategic sectors for Brazilian poverty. Economia 18 (2), 244-259. doi: 10.1016/j.econ.2017.01.002.

[41] Meng, L.N., Graus, W., Worrell, E., Huang, B., 2014. Estimating CO ₂ (carbon dioxide) emissions at urban scales by DMSP/OLS (Defense Meteorological Satellite Program’s Operational Linescan System) nighttime light imagery: methodological challenges and a case study for China . Energy 71, 468-478. doi: 10.1016/j.energy.2014.04.103.

[42] Multidimensional Poverty Peer Network , 2019. Multidimensional poverty in vietnam: sustainable poverty reduction 2016-2020. https://www.mppn.org/multidimensional-poverty-viet-nam/. (accessed 9 April 2025).

[43] National Development and Reform Commission , 2020. Ecological poverty alleviation goals and tasks fully accomplished, helping over 20 million impoverished people shake off poverty and increase income. https://www.ndrc.gov.cn/fggz/jyysr/jysrsbxf/202012/t20201224_1260211.html. (accessed 20 April 2025).

[44] Ning, J., 2021. A Miracle on Earth: A Statistical Monitoring Report on China’s Poverty Alleviation. China Statistical Press, Beijing (in Chinese).

[45] O’Neill, D.W., Fanning, A.L., Lamb, W.F., Steinberger, J.K., 2018. A good life for all within planetary boundaries. Nat. Sustain. 1 (2), 88-95. doi: 10.1038/s41893-018-0021-4.

[46] Paudel, J., 2021. Why are people energy poor? Evidence from ethnic fractionalization. Energy Econ. 102, 105519. doi: 10.1016/j.eneco.2021.105519.

[47] Paudel, J., 2022. Deadly tornadoes and racial disparities in energy consumption: implications for energy poverty. Energy Econ. 114, 106316. doi: 10.1016/j.eneco.2022.106316.

[48] Pereira, M.G., Freitas, M.A.V., da Silva, N.F., 2010. Rural electrification and energy poverty: empirical evidences from Brazil. Renew. Sustain. Energy Rev. 14 (4), 1229-1240. doi: 10.1016/j.rser.2009.12.013.

[49] Proville, J., Zavala-Araiza, D., Wagner, G., 2017. Night-time lights: a global, long term look at links to socio-economic trends. PLoS One 12 (3), e0174610. doi: 10.1371/journal.pone.0174610.

[50] Ren, Y.S., Jiang, Y., Narayan, S., Ma, C.-Q., Yang, X.-G., 2022. Marketisation and rural energy poverty: evidence from provincial panel data in China. Energy Econ. 111, 106073. doi: 10.1016/j.eneco.2022.106073.

[51] Shan, Y.L., Guan, D.B., Zheng, H.R., Ou, J.M., Li, Y., Meng, J., Mi, Z.F., Liu, Z., Zhang, Q., 2018. China CO ₂ emission accounts 1997-2015 . Sci. Data 5 (1), 170201. doi: 10.1038/sdata.2017.201.

[52] Shan, Y.L., Liu, J.H., Liu, Z., Xu, X., Shao, S., Wang, P., Guan, D.B., 2016. New provincial CO ₂ emission inventories in China based on apparent energy consumption data and updated emission factors . Appl. Energy 184, 742-750. doi: 10.1016/j.apenergy.2016.03.073.

[53] Shannon, C.E., 1948. A mathematical theory of communication. Bell Syst. Tech. J. 27 (3), 379-423. doi: 10.1002/j.1538-7305.1948.tb01338.x.

[54] Song, J., Wan, S.Q., Piao, S.L., Knapp, A.K., Classen, A.T., Vicca, S., Ciais, P., Hovenden, M.J., Leuzinger, S., Beier, C., Kardol, P., Xia, J.Y., Liu, Q., Ru, J.Y., Zhou, Z.X., Luo, Y.Q., Guo, D.L., Adam Langley, J.A., Zscheischler, J., Dukes, J.S., Tang, J.W., Chen, J.Q., Hofmockel, K.S., Kueppers, L.M., Rustad, L., Liu, L.L., Smith, M.D., Templer, P.H., Thomas, R.Q., Norby, R.J., Phillips, R.P., Niu, S.L., Fatichi, S., Wang, Y.P., Shao, P.S., Han, H.Y., Wang, D.D., Lei, L.J., Wang, J.L., Li, X.N., Zhang, Q., Li, X.M., Su, F.L., Liu, B., Yang, F., Ma, G.G., Li, G.Y., Liu, Y.C., Liu, Y.Z., Yang, Z.L., Zhang, K.S., Miao, Y., Hu, M.J., Yan, C., Zhang, A., Zhong, M.X., Hui, Y., Li, Y., Zheng, M.M., 2019. A meta-analysis of 1119 manipulative experiments on terrestrial carbon-cycling responses to global change. Nat. Ecol. Evol. 3 (9), 1309-1320. doi: 10.1038/s41559-019-0958-3.

[55] Sullivan, C., 2002. Calculating a water poverty index. World Dev. 30 (7), 1195-1210. doi: 10.1016/S0305-750X(02)00035-9.

[56] Sun, H., Liang, H.M., Chang, X.L., Cui, Q.C., Tao, Y., 2015. Land use patterns on carbon emission and spatial association in China. Econ. Geogr. 35 (3), 154-162. doi: 10.15957/j.cnki.jjdl.2015.03.023, (in Chinese).

[57] Sun, X.L., Mi, Z.F., Du, H.B., Coffman, D., 2024. Impacts of poverty eradication on carbon neutrality in China. Sci. Bull. 69 (5), 648-660. doi: 10.1016/j.scib.2023.12.039.

[58] The State Council Information Office of the People’s Republic of China, 2021. Poverty alleviation: China’s experience and contribution. http://english.scio.gov.cn/whitepapers/2021-04/06/content_77380652.htm. (accessed 26 August 2024).

[59] Tukker, A., Bulavskaya, T., Giljum, S., de Koning, A., Lutter, S., Simas, M., Stadler, K., Wood, R., 2016. Environmental and resource footprints in a global context: Europe’s structural deficit in resource endowments. Glob. Environ. Change 40, 171-181. doi: 10.1016/j.gloenvcha.2016.07.002.

[60] United Nations Development Programme , 2015. Sustainable Development Goals. https://www.undp.org/sustainable-development-goals. (accessed 26 August 2024).

[61] Wang, S., 2008. Overcoming poverty through development: a summary and assessment of China’s three decades of large-scale poverty reduction. Manage. World (11) 78-88. doi: 10.19744/j.cnki.11-1235/f.2008.11.010, (in Chinese).

[62] Wang, S.J., Kong, W., Ren, L., Zhi, D.D., Dai, B.T., 2021. Research on misuses and modification of coupling coordination degree model in China. J. Nat. Resour. 36 (3), 793-810. doi: 10.31497/zrzyxb.20210319, (in Chinese).

[63] Wang, S.G., Zeng, X.X., 2018. From regional poverty alleviation and development to precision poverty alleviation: the evolution of poverty alleviation policies in China during the 40 years of reform and opening-up and the current difficulties and countermeasures for poverty alleviation. Issues Agric. Econ. (8) 40-50. doi: 10.13246/j.cnki.iae.2018.08.005, (in Chinese).

[64] Wang, W.L., Chen, K.X., Xiao, W.W., Du, J.C., Qiao, H., 2024. Determinants of health poverty vulnerability in rural areas of western China in the post-poverty relief era: an analysis based on the Anderson behavioral model. BMC Public Health 24 (1), 459. doi: 10.1186/s12889-024-18035-6.

[65] Wang, X., Feng, H., 2020. China’s multidimensional relative poverty standard after 2020: international experience and policy orientation. China Rural Econ. (3) 2-21 (in Chinese).

[66] Wei, X.H., Li, H.J., Wang, Y., Wei, Q.F., 2024a. Impact assessment of poverty alleviation on carbon inequality: evidence from households in China. J. Clean. Prod. 450, 141843. doi: 10.1016/j.jclepro.2024.141843.

[67] Wei, X.H., Zeng, C.Y., Wang, J.N., Chen, Y., 2024b. The road to green development for national-level poverty-stricken counties: does poverty alleviation help reduce carbon emissions? Energy Econ. 138, 107817. doi: 10.1016/j.eneco.2024.107817.

[68] Wei, Y., Zhong, F.L., Song, X.Y., Huang, C.L., 2023. Exploring the impact of poverty on the sustainable development goals: inhibiting synergies and magnifying trade-offs. Sust. Cities Soc. 89, 104367. doi: 10.1016/j.scs.2022.104367.

[69] Wollburg, P., Hallegatte, S., Mahler, D.G., 2023. Ending extreme poverty has a negligible impact on global greenhouse gas emissions. Nature 623 (7989), 982-986. doi: 10.1038/s41586-023-06679-0.

[70] World Bank , 2018. Piecing Together the Poverty Puzzle. World Bank Publications, Washington.

[71] World Bank , 2022. Four decades of poverty reduction in China: drivers, insights for the world, and the way ahead. https://thedocs.worldbank.org/en/doc/f692402b5b3b21154f103ee64c0c551d-0070012022/. (accessed 26 August 2024).

[72] Xiao, H.W., Ma, Z.Y., Mi, Z.F., Kelsey, J., Zheng, J.L., Yin, W.H., Yan, M., 2018. Spatiotemporal simulation of energy consumption in China’s provinces based on satellite night-time light data. Appl. Energy 231, 1070-1078. doi: 10.1016/j.apenergy.2018.09.200.

[73] Xinhua News Agency, 2022. 20th national congress of communist party of China opens in Beijing. https://english.www.gov.cn/news/topnews/202210/18/content_WS634d7cc7c6d0a757729e15b2.html. (accessed 26 August 2024).

[74] Xinhua News Agency, 2015. Decision of the CPC central committee and the state council on winning the battle against poverty alleviation. https://www.gov.cn/zhengce/2015-12/07/content_5020963.htm. (accessed 26 August 2024).

[75] Xu, Y.F., Wang, M.X., Tang, W., Li, S.N., Ai, W.Q., Xu, W.J., Xu, D., Liu, C., Sun, J.R., Cui, W.X., 2026. Territorial spatial carbon balance governance from a spatially refined perspective: incorporating sub-regional differences in spatial driving effects into zoning regulation. Environ. Impact Assess. Rev. 117, 108222. doi: 10.1016/j.eiar.2025.108222.

[76] Xu, Y.F., Zhao, X.Q., Huang, P., Pu, J.W., Ran, Y.J., Zhou, S.J., Zhao, Q.Q., Feng, Y., Tang, Y.Y., Tao, J.Y., Zhang, Y.B., 2024. A new framework for multi-level territorial spatial zoning management: integrating ecosystem services supply-demand balance and land use structure. J. Clean. Prod. 441, 141053. doi: 10.1016/j.jclepro.2024.141053.

[77] Yang, S.F., Zhu, Z.Y., Fu, W.J., Hu, S.G., 2024. Tele-connection of embodied carbon emissions from industries in China’s trade: a complex network analysis. J. Environ. Manage. 366, 121652. doi: 10.1016/j.jenvman.2024.121652.

[78] Yang, Y.K., Xiao, P.F., Feng, X.Z., Li, H.X., 2017. Accuracy assessment of seven global land cover datasets over China. ISPRS J. Photogramm. Remote Sens. 125, 156-173. doi: 10.1016/j.isprsjprs.2017.01.016.

[79] Yu, X., Tan, C., 2023. China’s process-related greenhouse gas emission dataset 1990-2020. Sci. Data 10 (1), 55. doi: 10.1038/s41597-023-01957-y.

[80] Yuan, R., Wang, J., 2021. Impacts of poverty alleviation on household GHG footprints in China. Energy Econ. 103, 105602. doi: 10.1016/j.eneco.2021.105602.

[81] Zhou, Y., Wang, H., Liu, Z., Bhattarai, N., Paudel, J., Qiu, H., 2023. Can solar photovoltaic poverty alleviation policies reduce carbon emissions and increase income in China? Environ. Sci. Technol. 57 (49), 20583-20594. doi: 10.1021/acs.est.3c06342.