A study of the pathway to peak carbon in China

• Autorzy: Wang Liangmin , Xue Weixian

Identyfikatory

DOI

10.37190/epe240104

• Języki publikacji: EN

Abstrakty

EN

The achievement of the peak carbon target is a complex and comprehensive project that involves various aspects such as the economy, society, and ecological environment. At the same time as reaching the carbon peak, how to balance economic and social development has become an important issue. This study uses the environmental Kuznets curve (EKC) model to predict China’s carbon peaking situation. Three key parameters, namely carbon peaking, economy, and society, are selected, and relevant decision variables are established. A multi-objective planning model is developed to facilitate the coordinated development of carbon peaking, economy, and society, which is solved using a sequential algorithm. The results show that: China’s carbon emissions were 6928.905 million t in 2020 and are expected to reach the carbon peak in 2030. At the peak, the per capita gross domestic product (GDP) is estimated to be 16 281.95 $, corresponding to a per capita CO₂ emission of 9.66 t. During China’s carbon peak, the GDP is projected to be 23 249.58 billion $, with an arable land area of 121 747 510 ha and sulfur dioxide emissions of 180.64 million t, meeting the expected target values. However, certain indicators such as the ratio of three industries, energy consumption, rural residents’ per capita disposable income, and water consumption fall short of expected. Based on these findings, relevant countermeasures have been proposed for the realization path and key breakthroughs for China’s carbon peak.

Słowa kluczowe

EN

sulfur dioxide; ecological environment; economic development; energy utilization; China

PL

dwutlenek siarki; środowisko ekologiczne; rozwój ekonomiczny; wykorzystanie energii; Chiny

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Environment Protection Engineering
• Rocznik: 2024
• Tom: Vol. 50, nr 1
• Strony: 71--89
• Opis fizyczny: Bibliogr. 27 poz., rys., tab.

Twórcy

autor

Wang Liangmin

• School of Economics and Management, Xi’an University of Technology, Xi’an 710054, China

• https://orcid.org/0009-0008-8534-5732

autor

Xue Weixian

• School of Economics and Management, Xi’an University of Technology, Xi’an 710054, China

• https://orcid.org/0009-0000-9180-7034

Bibliografia

• [1] LEVITT C.J., PEDERSEN M.S., SORENSEN A., Examining the efforts of a small, open economy to reduce carbon emissions. The case of Denmark, Ecol. Econ., 2015, 119, 94–106. DOI: 10.1016/j.ecolecon. 2015.08.002.
• [2] LIANG S., LIN X., LIU X., The Pathway to China’s Carbon Neutrality Based on an Endogenous Technology CGE Model, Int. J. Environ. Res. Publ. Health, 2022, 19 (10), 6251. DOI: 10.3390 /ijerph 19106251.
• [3] ZHENG Y., XIAO J.Z., HUANG F., TANG J., How do resource dependence and technological progress affect carbon emissions reduction effect of industrial structure transformation? Empirical research based on the rebound effect in China, Environ. Sci. Poll. Res. Int., 2023, 30 (34), 81823–81838. DOI: 10.1007/s11356-022-20193-2.
• [4] ZHANG X.L., HUANG X.D., ZHANG D., GENG Y., TIAN L., FAN Y., CHEN W., Energy economy transition path and policy research under carbon neutral target, Manage. World, 2022, 38 (1), 35–66. DOI: 10.19744/j.cnki.11-1235/f.2022.0005.
• [5] NYAMBUU U., SEMMLER W., Climate change and the transition to a low carbon economy. Carbon targets and the carbon budget, Econ. Model, 2020, 84, 367–376. DOI: 10.1016/j.econmod.2019.04.026.
• [6] SHAO S., ZHANG X., ZHAO X., Empirical decomposition and peak path of carbon emission in China’s manufacturing industry. Generalized Diels–Alder Index decomposition and dynamic scenario analysis, China Ind. Econ., 2017, 3, 44–63. DOI: 10.19581/j.cnki.ciejournal.2017.03.003.
• [7] PANG J., LI H., LU C., LU C., CHEN X., Regional Differences and Dynamic Evolution of Carbon Emission Intensity of Agriculture Production in China, Int. J. Env. Res. Public. Health, 2020, 17, 7541. DOI: 10.3390/ijerph17207541.
• [8] GAO P., YUE S., CHEN H., Carbon emission efficiency of China’s industry sectors: From the perspec-tive of embodied carbon emissions, J. Clean Prod., 2021, 283, 124655. DOI: 10.1016/j.jclepro.2020. 124655.
• [9] NUNES R.A.O., ALVIM-FERRAZ M.C.M., MARTINS F.G., CALDERAY-CAYETANO F., DURÁN-GRADOS V., MORENO-GUTIÉRREZ J., JALKANEN J.-P., HANNUNIEMI H., SOUSA S.I.V., Shipping emissions in the Ibe-rian Peninsula and the impacts on air quality, Atmos. Chem. Phys., 2020, 20 (15), 9473–9489. DOI: 10.5194/acp-20-9473-2020.
• [10] STAFFELL I., Measuring the progress and impacts of decarbonising British electricity, Energy Pol., 2017, 102, 463–475. DOI: 10.1016/j.enpol.2016.12.037.
• [11] GLEUE M., UNTERBERG J., LÖSCHEL A., GRÜNEWALD P., Does demand-side flexibility reduce emis-sions? Exploring the social acceptability of demand management in Germany and Great Britain, En. Res. Soc. Sci., 2021, 82, 102290. DOI: 10.1016/j.erss.2021.102290.
• [12] CHURCHILL S.A, INEKWE J., IVANOVSKI K., RUSSELL S., The environmental Kuznets curve across Australian states and territories, Energ. Econ., 2020, 90, 104869. DOI: 10.1016/j.eneco.2020.104869.
• [13] JIANG H.Q., LI Y.X., CHEN M.M., SHAO X.X., Prediction and realization of carbon peak in Zhejiang Province under the vision of carbon neutrality, Areal Res. Dev., 2022, 41 (4), 157–161, 168. DOI: 10.3969/j.issn.1003-2363.2022.04.026.
• [14] FONT A., FULLER G.W., Did policies to abate atmospheric emissions from traffic have a positive effect in London? Environ. Poll., 2016, 218, 463–474. DOI: 10.1016/j.envpol.2016.07.026.
• [15] PILLAI D., BUCHWITZ M., GERBIG C., KOCH T., REUTER M., BOVENSMANN H., MARSHALL J., BURROWS J.P., Tracking city CO2 emissions from space using a high-resolution inverse modelling approach: a case study for Berlin, Germany, Atmos. Chem. Phys., 2016, 16 (15), 9591–9610. DOI: 10.5194/acp-16-9591-2016.
• [16] SUN S., XIE Y., LI Y., Analysis of dynamic evolution and spatial-temporal heterogeneity of carbon emissions at county level along “the belt and road”. A case study of Northwest China, Int. J. Env. Res. Public. Health, 2022, 19 (20), 13405. DOI: 10.3390/ijerph192013405.
• [17] WU Y., XU B., When will China’s carbon emissions peak? Evidence from judgment criteria and emis-sions reduction paths, En. Rep., 2022, 8, 8722–8735. DOI: 10.1016/j.egyr.2022.06.069.
• [18] SU B., ANG B.W., Multi-region comparisons of emission performance: The structural decomposition analysis approach, Ecol. Indic., 2016, 67, 78–87. DOI: 10.1016/j.ecolind.2016.02.020.
• [19] ORTEGA-RUIZ G., MENA-NIETO A., GOLPE A.A., GARCÍA-RAMOS J.E., CO2 emissions and causal relationships in the six largest world emitters, Renew. Sust. En. Rev., 2022, 162, 112435. DOI: 10.1016 /j.rser.2022.112435.
• [20] ALGIERI B., FÜG O., LOMBARDO R., The Italian Journey: Carbon dioxide emissions, the role of tourism and other economic and climate drivers, J. Clean Prod., 2022, 375, 134144. DOI: 10.1016/j.jclepro. 2022.13144.
• [21] LEE M.H., LEE J.S., LEE J.Y., KIM Y.H., PARK Y.S., LEE K.M., Uncertainty Analysis of a GHG Emission Model Output Using the Block Bootstrap and Monte Carlo Simulation, Sustainability, 2017, 9, 1522. DOI: 10.3390/su9091522.
• [22] TIAN X., YU Z., SARKIS J., GENG Y., Environmental and Resource Impacts from an Aggressive Region-alized Carbon Peak Policy, Environ. Sci. Technol., 2022, 56 (18), 12838–12851. DOI: 10.1021 /acs.est.2c02884.
• [23] LIN H.X., ZHOU Z.Q., CHEN S., JIANG P., Clustering and assessing carbon peak statuses of typical cities in underdeveloped Western China, Appl. Energ., 2023, 329, 10. DOI: 10.1016/j.apenergy.2022.120299.
• [24] CAVIGLIA-HARRIS J.L., CHAMBERS D., KAHN J.R., Taking the U out of Kuznets. A comprehensive analysis of the EKC and environmental degradation, Ecol. Econ., 2009, 68 (4), 1149–1159. DOI: 10.1016 /j.ecolecon.2008.08.006.
• [25] SHAFIK N., BANDYOPADHYAY S., Economic growth and environmental quality: time series and cross- -country evidence, Policy Res. Working Paper, S, 1992.
• [26] HSU G.J.Y., CHOU F.Y., Integrated planning for mitigating CO2 emissions in Taiwan: a multi-objective programming approach, En. Pol., 2000, 28 (8), 519–523. DOI: 10.1016/S0301-4215(00)00006-9.
• [27] CHEN X.K., YANG C.H., ZHU K.F., WANG H., LI X., YIN J., Forecast analysis and policy suggestions of China’s economic growth in 2023, Bull. Chin. Acad. Sci., 2023, 38 (1), 81–90. DOI:10.16418 /j.issn.1000-3045.20221226001.