The Carbon Dioxide Emissions Differences Impacts Between Urban and Rural Areas

Yifan Li

doi:10.62051/ijgem.v4n2.40

Authors

Yifan Li

DOI:

https://doi.org/10.62051/ijgem.v4n2.40

Keywords:

Urban emission, Rural population, Digital economy

Abstract

In China, the patterns of carbon emissions exhibit a clear divergence between urban and rural areas. While cities experience a surge in carbon emissions, rural regions maintain more stable levels, albeit with modest increases. These contrasting trends contribute to widening inter-provincial disparities in urban areas, whereas rural inequalities remain relatively unchanged. Over time, the overall inequality in carbon emissions across the country has undergone distinct phases, transitioning from a period of escalation to gradual reduction, eventually reaching a point of stabilization. Market-driven factors, alongside governmental actions, are crucial in shaping this inequality, with urban and rural areas responding differently to policy interventions. The digital economy has emerged as a pivotal force in mitigating disparities, producing significant positive spillover effects across regions. The relationship between economic development and carbon emissions inequality follows a U-shaped pattern, indicating that early stages of growth might aggravate inequality before improvement occurs. On the other hand, while optimizing industrial structures can address urban-rural disparities, the spatial spillover impact of these changes remains limited. Governmental efforts to regulate carbon emissions have been less effective in reducing inequality and, in some cases, may inadvertently contribute to further divergence. Meanwhile, openness to international markets has a more beneficial influence, whereas the role of population density in this dynamic proves to be multifaceted and difficult to predict.

Downloads

Download data is not yet available.

References

[1] Belaïd, F., Boubaker, S., and Kafrouni, R. (2020). Carbon emissions, income inequality and environmental degradation: the case of Mediterranean countries. Eur. J. Comp. Economics 17, 73–102. doi: 10.25428/1824-2979/202001-73-102.

[2] Blanchard, C. L., Tanenbaum, S., and Hidy, G. M. (2013). Source attribution of air pollutant concentrations and trends in the Southeastern Aerosol Research and Characterization (SEARCH) network. Environ. Sci. Technol. 47, 13536–13545. doi: 10.1021/es402876s

[3] Chen, L., Xu, L., and Yang, Z. (2019). Inequality of industrial carbon emissions of the urban agglomeration and its peripheral cities: A case in the Pearl River Delta, China. Renewable Sustain. Energy Rev. 109, 438–447. doi: 10.1016/j.rser.2019.04.010.

[4] Christen, A., Coops, N., Crawford, B., Kellett, R., Liss, K., Olchovski, I., et al. (2011). Validation of modeled carbon-dioxide emissions from an urban neighborhood with direct eddy-covariance measurements. Atmospheric Environ. 45, 6057–6069. doi: 10.1016/j.atmosenv.2011.07.040.

[5] Crawford, B., and Christen, A. (2015). Spatial source attribution of measured urban eddy covariance CO2 ﬂuxes. Theor. Appl. Climatol 119, 733–755. doi: 10.1007/s00704- 014-1124-0.

[6] Liu, W., and Li, H. (2014). China’s coal subsidy reform and its impact on carbon dioxide emission reduction. Economic Res. J. 49(08), 146–157. doi: CNKI: SUN: JJYJ.0.2014-08-012.

[7] Luo, G., Baleentis, T., and Zeng, S. (2023). Per capita CO2 emission inequality of China’s urban and rural residential energy consumption: A Kaya-Theil decomposition. J. Environ. Manage. 331, 117265. doi: 10.1016/j.jenvman.2023.117265.

[8] Qi, Y., Bai, T., and Tang, Y. (2022). Central environmental protection inspection and green technology innovation: empirical analysis based on the mechanism and spatial spillover effects. Environ. Sci. pollut. Res. 29, 86616–86633. doi: 10.1007/s11356-022- 21833-3.

[9] Sun, Z.-Q., and Sun, T. (2020). The impact of multi-dimensional urbanization on China’s carbon emissions Based on the spatial spillover effect. Polish J. Environ. Stud. 29, 3317–3327. doi: 10.15244/pjoes/114508.

[10] Ueyama, M., and Ando, T. (2016). Diurnal, weekly, seasonal, and spatial variabilities in carbon dioxide ﬂux in different urban landscapes in Sakai, Japan. Atmospheric Chem. Phys. 16, 14727–14740. doi: 10.5194/acp-16-14727-2016.

[11] Wu, R., and Xie, Z. (2020). Identifying the impacts of income inequality on CO2 emissions: Empirical evidences from OECD countries and non-OECD countries. J. cleaner production 277, 123858. doi: 10.1016/j.jclepro.2020.123858.

[12] Wu, R., Tan, Z., and Lin, B. (2023). Does carbon emission trading scheme really improve the CO2 emission efﬁciency? Evidence from China's iron and steel industry. Energy 277, 127743. doi: 10.1016/j.energy.2023.127743.

[13] Xu, C. (2023). Towards balanced low-carbon development: Driver and complex network of urban-rural energy-carbon performance gap in China. Appl. Energy 333, 120663. doi: 10.1016/j.apenergy.2023.120663

[14] Xu, X. (2023). Identifying the impact of industrial agglomeration on China’s carbon emissions based on the spatial econometric analysis. J. Environ. Public Health, 2023. doi: 10.1155/2023/4354068.

[15] Zhang, S., Kharrazi, A., Yu, Y., Ren, H., Hong, L., and Ma, T. (2021). What causes spatial carbon inequality? Evidence from China’s Yangtze River economic belt. Ecol.Indic. 121, 107129. doi: 10.1016/j.ecolind.2020.107129.

[16] Zhang, Z., Zhang, T., and Feng, D. (2022). Regional differences, dynamic evolution, and convergence of carbon emission intensity in China. J. Quantitative Technological Economics 39 (04), 67–87. doi: 10.13653/j.cnki.jqte.2022.04.001.

[17] Buckley J D. Carbon-carbon-an overview [J]. American Ceramic Society Bulletin, 1988, 67.

[18] Zhang J, Lin G, Vaidya U, et al. Past, present and future prospective of global carbon fibre composite developments and applications[J]. Composites Part B: Engineering, 2023, 250: 110463.

[19] Liu H, Zhong X, Pan Q, et al. A review of carbon dots in synthesis strategy [J]. Coordination Chemistry Reviews, 2024, 498: 215468.

[20] Caineng Z O U, Songtao W U, Zhi Y, et al. Progress, challenge and significance of building a carbon industry system in the context of carbon neutrality strategy [J]. Petroleum Exploration and Development, 2023, 50(1): 210-228.

[21] Peng L, Searchinger T D, Zionts J, et al. The carbon costs of global wood harvests [J]. Nature, 2023, 620(7972): 110-115.

[22] Luo J, Zhuo W, Liu S, et al. The optimization of carbon emission prediction in low carbon energy economy under big data [J]. IEEE Access, 2024.

[23] Zhao R, Huang X, Xue J, et al. A practical simulation of carbon sink calculation for urban buildings: a case study of Zhengzhou in China [J]. Sustainable Cities and Society, 2023, 99: 104980.

[24] Zhan J, Wang C, Wang H, et al. Pathways to achieve carbon emission peak and carbon neutrality by 2060: A case study in the Beijing-Tianjin-Hebei region, China [J]. Renewable and Sustainable Energy Reviews, 2024, 189: 113955.

[25] Mo L, Zohner C M, Reich P B, et al. Integrated global assessment of the natural forest carbon potential [J]. Nature, 2023, 624(7990): 92-101.

[26] Guo Y, Luo L, Liu T, et al. A review of low-carbon technologies and projects for the global cement industry [J]. Journal of Environmental Sciences, 2024, 136: 682-697.

[27] Smith S, Geden O, Gidden M, et al. The state of carbon dioxide removal [J]. 2024.

[28] Xiao Y, Ma D, Zhang F, et al. Spatiotemporal differentiation of carbon emission efficiency and influencing factors: From the perspective of 136 countries [J]. Science of the Total Environment, 2023, 879: 163032.