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Clean Technologies and Environmental Policy

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16.11.2023 | Original Paper

Measurement, influencing factors and prediction on carbon emission performance of countries along the Belt and Road

verfasst von: Jing Li, Yanping Zhou, Huiying Chen

Erschienen in: Clean Technologies and Environmental Policy | Ausgabe 3/2024

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Abstract

One of the most important issues in combating global climate change is reducing carbon emissions in the Belt and Road (B&R) countries. This research utilizes the super slacks-based measure to evaluate the carbon emission performance (CEP) of countries along the B&R, and the Tobit model is used to study the variables that affect CEP in the countries along the route on this foundation. In addition, the future CEP of the countries along the B&R is predicted using the random forest method with the economic development scenario. The results show that from 2000 to 2018, the average CEP of 51 countries along the B&R reached 99.1% of the ideal level, with 0.9% still needing to be improved. The economic development level and foreign direct investment play a significant impact in advancing the CEP of the countries along the B&R. Trade openness, industrial structure, urbanization rate, and idle rate of capital significantly inhibit the CEP of the countries along the B&R. The estimated value of CEP of nations along the route by 2030 shows an overall upward trend, regardless of whether it is the pre-setting of benchmark, high-speed, or low-carbon development scenario.

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Vorheriger Artikel Determining the impact of economic indicators on water, energy and food nexus for sustainable resource security

Nächster Artikel Process simulation of staging pyrolysis and gasification of biomass in a dual fluidized bed system

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Ang BW, Liu N (2006) A cross-country analysis of aggregate energy and carbon intensities. Energy Policy 34(15):2398–2404. https://doi.org/10.1016/j.enpol.2005.04.007CrossRef

Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324CrossRef

Chen X, Lan T, Shi X, Tong C (2020) A semi-supervised linear–nonlinear least-square learning network for prediction of carbon efficiency in iron ore sintering process. Control Eng Pract 100:104454. https://doi.org/10.1016/j.conengprac.2020.104454CrossRef

Cheng C, Ge C (2020) Green development assessment for countries along the belt and road. J Environ Manag 263:110344. https://doi.org/10.1016/j.jenvman.2020.110344CrossRef

Ding LL, Lei L, Zhao X, Calin AC (2020) Modelling energy and carbon emission performance: a constrained performance index measure. Energy 197:117274. https://doi.org/10.1016/j.energy.2020.117274CrossRef

Du Q, Lu C, Zou PX, Li Y, Li J, Cui X (2021) Estimating transportation carbon efficiency (TCE) across the belt and road initiative countries: an integrated approach of modified three-stage epsilon-based measurement model. Environ Impact Assess Rev 90:106634. https://doi.org/10.1016/j.eiar.2021.106634CrossRef

Fang G, Gao Z, Tian L, Fu M (2022) What drives urban carbon emission efficiency?-Spatial analysis based on nighttime light data. Appl Energy 312:118772CrossRef

Gao Y (2019) Research on low carbon green development performance of countries along the Belt and Road. Soft Science 33(08):78–84. https://doi.org/10.13956/j.ss.1001-8409.2019.08.14(inChinese)CrossRef

Hall RE, Jones CI (1999) Why do some countries produce so much more output per worker than others? Q J Econ 114(1):83–116CrossRef

He P, Zhang J, Li W (2021) The role of agricultural green production technologies in improving low-carbon efficiency in China: necessary but not effective. J Environ Manag 293:112837. https://doi.org/10.1016/j.jenvman.2021.112837CrossRef

Hu X, Si T, Liu C (2017) Total factor carbon emission performance measurement and development. J Clean Prod 142:2804–2815. https://doi.org/10.1016/j.jclepro.2016.10.188CrossRef

Hu J, Wu M, Chen X, Cao W, Pedrycz W (2019) Multi-model ensemble prediction model for carbon efficiency with application to iron ore sintering process. Control Eng Pract 88:141–151. https://doi.org/10.1016/j.conengprac.2019.05.009CrossRef

Hu J, Wu M, Chen L, Cao W, Pedrycz W (2022) Real-time dynamic prediction model of carbon efficiency with working condition identification in sintering process. J Process Control 111:97–105. https://doi.org/10.1016/j.jprocont.2022.02.002CrossRef

Jiang Q, Rahman ZU, Zhang X, Guo Z, Xie Q (2022) An assessment of the impact of natural resources, energy, institutional quality, and financial development on CO₂ emissions: evidence from the B&R nations. Resour Policy 76:102716. https://doi.org/10.1016/j.resourpol.2022.102716CrossRef

Li X (2021) Research on the influencing factors of total factor energy efficiency in provinces related to the Belt and Road. Res World 03:49–57. https://doi.org/10.13778/j.cnki.11-3705/c.2021.03.007(inChinese)CrossRef

Li H, Fang K, Yang W, Wang D, Hong X (2013) Regional environmental efficiency evaluation in China: analysis based on the super-SBM model with undesirable outputs. Math Comput Model 58(5–6):1018–1031CrossRef

Li Y, Liang S, Zhao Y, Li W, Wang Y (2017) Machine learning for the prediction of L. chinensis carbon, nitrogen and phosphorus contents and understanding of mechanisms underlying grassland degradation. J Environ Manag 192:116–123. https://doi.org/10.1016/j.jenvman.2017.01.047CrossRef

Li L, Cai Y, Liu L (2019) Research on the effect of urbanization on China’s carbon emission efficiency. Sustainability 12(1):163CrossRef

Li Y, Li J, Wang W, Huang Q (2021) The impact mechanism of global value chain embedding on carbon emission efficiency—evidence and inspiration from the manufacturing industry of countries along the Belt and Road. China Popul Resour Environ 31(07):15–26

Lin B, Wu R (2020) Designing energy policy based on dynamic change in energy and carbon dioxide emission performance of China’s iron and steel industry. J Clean Prod 256:120412. https://doi.org/10.1016/j.jclepro.2020.120412CrossRef

Lin B, Zhou Y (2021) Does the Internet development affect energy and carbon emission performance? Sustain Prod Consum 28:1–10. https://doi.org/10.1016/j.spc.2021.03.016CrossRef

Liu Y, Gao Y (2022) Measurement and impactor analysis of agricultural carbon emission performance in Changjiang economic corridor. Alex Eng J 61(1):873–881. https://doi.org/10.1016/j.aej.2021.04.083CrossRef

Liu M, Yang L (2021) Spatial pattern of China’s agricultural carbon emission performance. Ecol Ind 133:108345. https://doi.org/10.1016/j.ecolind.2021.108345CrossRef

Lv Y, Liu J, Cheng J, Andreoni V (2021) The persistent and transient total factor carbon emission performance and its economic determinants: evidence from China’s province-level panel data. J Clean Prod 316:128198. https://doi.org/10.1016/j.jclepro.2021.128198CrossRef

Meng F, Wang W (2021) Heterogeneous effect of “Belt and Road” on the two-stage eco-efficiency in China’s provinces. Ecol Ind 129:107920. https://doi.org/10.1016/j.ecolind.2021.107920CrossRef

Meng M, Fu Y, Wang T, Jing K (2017) Analysis of low-carbon economy efficiency of Chinese industrial sectors based on a RAM model with undesirable outputs. Sustainability 9(3):451. https://doi.org/10.3390/su9030451CrossRef

Meng F, Su B, Wang Q (2021) Meta-frontier-based assessment on carbon emission performance considering different mitigation strategies: evidence from China’s manufacturing sectors. J Clean Prod 289:125662. https://doi.org/10.1016/j.jclepro.2020.125662CrossRef

Mielnik O, Goldemberg J (1999) Communication the evolution of the “carbonization index” in developing countries. Energy Policy 27(5):307–308. https://doi.org/10.1016/S0301-4215(99)00018-XCrossRef

Shan H (2008) Re estimation of China’s capital stock K: 1952–2006. Res Quant Econ Tech Econ 10:17–31

Shen C, Feng R, Yu B, Liao Z (2018) Industrial CO₂ emissions efficiency and its determinants in China: analyzing differences across regions and industry sectors. Pol J Environ Stud 27(3):1239–1253. https://doi.org/10.15244/pjoes/76794CrossRef

Stretesky PB, Lynch MJ (2009) A cross-national study of the association between per capita carbon dioxide emissions and exports to the United States. Soc Sci Res 38(1):239–250. https://doi.org/10.1016/j.ssresearch.2008.08.004CrossRef

Su X, Li Y, Fang K, Long Y (2022) Does China’s direct investment in “Belt and Road Initiative” countries decrease their carbon dioxide emissions? J Clean Prod 339:130543. https://doi.org/10.1016/j.jclepro.2022.130543CrossRef

Sun JW (2005) The decrease of CO₂ emission intensity is decarbonization at national and global levels. Energy Policy 33(8):975–978. https://doi.org/10.1016/j.enpol.2003.10.023CrossRef

Sun W, Huang C (2020) How does urbanization affect carbon emission efficiency? Evidence from China. J Clean Prod 272:122828CrossRef

Sun W, Huang C (2022) Predictions of carbon emission intensity based on factor analysis and an improved extreme learning machine from the perspective of carbon emission efficiency. J Clean Prod 338:130414. https://doi.org/10.1016/j.jclepro.2022.130414CrossRef

Tone K (2002) A slacks-based measure of super-efficiency in data envelopment analysis. Eur J Oper Res 143(1):32–41CrossRef

Wang Q, Zhao C (2021) Regional difference and driving factors of industrial carbon emissions performance in China. Alex Eng J 60(1):301–309. https://doi.org/10.1016/j.aej.2020.08.009CrossRef

Wang X, Shao Q, Nathwani J, Zhou Q (2019) Measuring wellbeing performance of carbon emissions using hybrid measure and meta-frontier techniques: empirical tests for G20 countries and implications for China. J Clean Prod 237:117758. https://doi.org/10.1016/j.jclepro.2019.117758CrossRef

Wang S, Gao S, Huang Y, Shi C (2020) Spatial and temporal evolution pattern and prediction of China’s urban carbon emission performance based on super efficient SBM model. J Geogr 75(06):1316–1330 (in Chinese)

Wu S, Zhang K (2021) Influence of urbanization and foreign direct investment on carbon emission efficiency: evidence from urban clusters in the Yangtze River economic belt. Sustainability 13(5):2722CrossRef

Xie B, Li S (2018) Research on the impact of trade opening and technological innovation on the carbon emission performance of china’s western manufacturing industry. Res Sci Technol Manag 38(09):84–90 (in Chinese)

Xu M, Tan R (2022) How to reduce CO₂ emissions in pharmaceutical industry of China: evidence from total-factor carbon emissions performance. J Clean Prod 337:130505. https://doi.org/10.1016/j.jclepro.2022.130505CrossRef

Xu L, Fan M, Yang L, Shao S (2021) Heterogeneous green innovations and carbon emission performance: evidence at China’s city level. Energy Econ 99:105269. https://doi.org/10.1016/j.eneco.2021.105269CrossRef

Yang J, Tang L, Mi Z, Liu S, Li L, Zheng J (2019) Carbon emissions performance in logistics at the city level. J Clean Prod 231:1258–1266. https://doi.org/10.1016/j.jclepro.2019.05.330CrossRef

Yang Y, Yuan Z, Yang S (2022) Difference in the drivers of industrial carbon emission costs determines the diverse policies in middle-income regions: a case of northwestern China. Renew Sustain Energy Rev 155:111942CrossRef

Zhang Z, Qu J, Zeng J (2008) A quantitative comparison and analysis on the assessment indicators of greenhouse gases emission. J Geog Sci 18(4):387–399. https://doi.org/10.1007/s11442-008-0387-8CrossRef

Zhang W, Liu X, Wang D, Zhou J (2022) Digital economy and carbon emission performance: evidence at China’s city level. Energy Policy 165:112927. https://doi.org/10.1016/j.enpol.2022.112927CrossRef

Zhou P, Ang BW, Han JY (2010) Total factor carbon emission performance: a Malmquist index analysis. Energy Econ 32(1):194–201. https://doi.org/10.1016/j.eneco.2009.10.003CrossRef

Zhou Z, Cao L, Zhao K, Li D, Ding C (2021) Spatio-temporal effects of multi-dimensional urbanization on carbon emission efficiency: analysis based on panel data of 283 cities in China. Int J Environ Res Public Health 18(23):12712CrossRef

Zhou A, Xin L, Li J (2022) Assessing the impact of the carbon market on the improvement of China’s energy and carbon emission performance. Energy 258:124789. https://doi.org/10.1016/j.energy.2022.124789CrossRef

Titel: Measurement, influencing factors and prediction on carbon emission performance of countries along the Belt and Road
verfasst von: Jing Li
Yanping Zhou
Huiying Chen
Publikationsdatum: 16.11.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: Clean Technologies and Environmental Policy / Ausgabe 3/2024
Print ISSN: 1618-954X
Elektronische ISSN: 1618-9558
DOI: https://doi.org/10.1007/s10098-023-02653-6

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