Research on the trend prediction and structure transfer of embodied carbon in China’s export trade

doi:10.12006/j.issn.1673-1719.2023.185

Abstract

Abstract:

Based on the Lasso method of feature selection, the core indicators affecting the total amount and intensity of embodied carbon emissions (CO₂) in China’s export trade are determined respectively, and the BO-BiLSTM model was constructed to predict the trend of total amount change and intensity evolution. At the same time, the Markov chain was used to further explore the structural transfer phenomenon of embodied carbon emissions in China’s export trade. The results are as follow. (1) From 2021 to 2035, the total amount of carbon emissions embodied in China’s export trade shows a trend of gradient reduction. It is expected to reach 1.98 Gt in 2030 and further decrease to 1.83 Gt in 2035. The expansion of export trade scale and the improvement of international economic and trade situation are the key influencing factors. (2) From 2021 to 2035, the embodied carbon emission intensity in China’s export trade has maintained a steady and declining trend. It is expected to drop to 0.91 t per CNY 10000 in 2030, which is 67% lower than that in 2005. The change of export trade structure and the increase of environmental regulation intensity are important driving factors. (3) From 2021 to 2035, the structure of embodied carbon emissions in China’s export trade still focuses on knowledge-intensive manufacturing industry, which has great potential for emission reductions, while capital-intensive service industry and capital-intensive manufacturing industry have the characteristics of long cycle of carbon emission reductions.

Key words: Export trade embodied carbon, Input-output model, Machine learning prediction

HU Jian-Bo, MAI Jun-Nan. Research on the trend prediction and structure transfer of embodied carbon in China’s export trade[J]. Climate Change Research, 2024, 20(1): 62-74.

Figures/Tables 7

Table 1 Time series input-output table of the department of the unified merger results

Table 2 Descriptive statistics of the input characteristics for prediction of China’s total carbon emissions embodied in export trade

Table 3 Descriptive statistics of the input characteristics for prediction of China’s carbon emission intensity embodied export trade

Fig. 1 Prediction results of total embodied carbon emissions in China’s export trade

Fig. 2 Prediction results of embodied carbon emission intensity of China’s export trade

Table 4 The average transition probability matrix of China’s export trade embodied carbon emissions structure

Fig. 3 Prediction results of embodied carbon emissions structure in China’s export trade

References 33

[1]	Sun L C, Wang J W. Inter-industrial carbon emission transfers in China: economic effect and optimization strategy[J]. Ecological Economics, 2017, 132 (2): 55-62 doi: 10.1016/j.ecolecon.2016.10.005 URL
[2]	Su B, Thomson E. China’s carbon emissions embodied in (normal and processing) exports and their driving forces[J]. Energy Economics, 2016, 59 (9): 414-422 doi: 10.1016/j.eneco.2016.09.006 URL
[3]	胡剑波, 王楷文, 张智勇. 基于CiteSpace的我国贸易与碳排放问题研究文献计量分析[J]. 杭州师范大学学报(社会科学版), 2022, 44 (1): 91-102.
	Hu J B, Wang K W, Zhang Z Y. Bibliometric analysis of the issue between trade and carbon emissions in China based on CiteSpace[J]. Journal of Hangzhou Normal University (Humanities and Social Sciences), 2022, 44 (1): 91-102 (in Chinese)
[4]	彭水军, 张文城, 卫瑞. 碳排放的国家责任核算方案[J]. 经济研究, 2016, 51 (3): 137-150.
	Peng S J, Zhang W C, Wei R. National carbon emission responsibility[J]. Economic Research Journal, 2016, 51 (3): 137-150 (in Chinese)
[5]	Wu R, Ma T, Schroder E. The contribution of trade to production: based carbon dioxide emissions[J]. Structural Change and Economic Dynamics, 2022, 60 (5): 391-406 doi: 10.1016/j.strueco.2021.12.005 URL
[6]	彭水军, 张文城, 孙传旺. 中国生产侧和消费侧碳排放量测算及影响因素研究[J]. 经济研究, 2015, 50 (1): 168-182.
	Peng S J, Zhang W C, Sun C W. China’s production-based and consumption-based carbon emissions and their determinants[J]. Economic Research Journal, 2015, 50 (1): 168-182 (in Chinese)
[7]	Rocco M V, Golinucci N, Ronco S M, et al. Fighting carbon leakage through consumption-based carbon emissions policies: empirical analysis based on the world trade model with bilateral trades[J]. Applied Energy, 2020, 274 (9): 115301 doi: 10.1016/j.apenergy.2020.115301 URL
[8]	张同斌, 孟令蝶, 孙静. 碳排放共同责任的测度优化与国际比较研究[J]. 财贸研究, 2018, 29 (10): 19-31.
	Zhang T B, Meng L D, Sun J. Common responsibility for carbon emissions: measure optimization and international comparison[J]. Finance and Trade Research, 2018, 29 (10): 19-31 (in Chinese)
[9]	Jakob M, Ward H, Steckel J C. Sharing responsibility for trade-related emissions based on economic benefits[J]. Global Environmental Change, 2021, 66 (3): 102207 doi: 10.1016/j.gloenvcha.2020.102207 URL
[10]	Mo S, Wang T. Spatial-temporal evolution and peak prediction of embodied carbon emissions in China’s interregional trade[J]. Frontiers in Public Health, 2022, 10 (11): 1010521 doi: 10.3389/fpubh.2022.1010521 URL
[11]	王勇, 王恩东, 毕莹. 不同情景下碳排放达峰对中国经济的影响: 基于CGE模型的分析[J]. 资源科学, 2017, 39 (10): 1896-1908. doi: 10.18402/resci.2017.10.09
	Wang Y, Wang E D, Bi Y. Impact of a peak in carbon emissions on China’s economy in different situations: analysis based on CGE model[J]. Resources Science, 2017, 39 (10): 1896-1908 (in Chinese)
[12]	Lu C, Li W, Gao S B. Driving determinants and prospective prediction simulations on carbon emissions peak for China’s heavy chemical industry[J]. Journal of Cleaner Production, 2020, 251 (4): 119642 doi: 10.1016/j.jclepro.2019.119642 URL
[13]	胡剑波, 罗志鹏, 李峰. “碳达峰”目标下中国碳排放强度预测: 基于LSTM和ARIMA-BP模型的分析[J]. 财经科学, 2022 (2): 89-101.
	Hu J B, Luo Z P, Li F. Prediction of China’s carbon emission intensity under the goal of carbon peak: analysis based on LSTM and ARIMA-BP model[J]. Finance & Economics, 2022 (2): 89-101 (in Chinese)
[14]	Yuan C Q, Yang Y J, Liu S F, et al. An investigation into the relationship between China’s economic development and carbon dioxide emissions[J]. Climate and Development, 2017, 9 (1): 66-79 doi: 10.1080/17565529.2015.1067182 URL
[15]	董聪, 董秀成, 蒋庆哲, 等. 《巴黎协定》背景下中国碳排放情景预测: 基于BP神经网络模型[J]. 生态经济, 2018, 34 (2): 18-23.
	Dong C, Dong X C, Jiang Q Z, et al. Scenarios prediction of the carbon dioxide emissions in China under the background of the Paris Agreement: based on BP neural network[J]. Ecological Economy, 2018, 34 (2): 18-23 (in Chinese)
[16]	魏本勇, 方修琦, 王媛, 等. 基于投入产出分析的中国国际贸易碳排放研究[J]. 北京师范大学学报(自然科学版), 2009, 45 (4): 413-419.
	Wei B Y, Fang X Q, Wang Y, et al. Estimation of carbon emissions embodied in international trade for China: an input-output analysis[J]. Journal of Beijing Normal University (Natural Science), 2009, 45 (4): 413-419 (in Chinese)
[17]	赫永达, 文红, 孙传旺. “十四五”期间我国碳排放总量及其结构预测: 基于混频数据ADL-MIDAS模型[J]. 经济问题, 2021 (4): 31-40.
	He Y D, Wen H, Sun C W. Forecasting China’s total carbon emission and its structure in the 14th Five-Year Plan: based on mixed-frequency ADL-MIDAS model[J]. on Economic Problems, 2021 (4): 31-40 (in Chinese)
[18]	Kulshrestha A, Krishnaswamy V, Sharma M. Bayesian BiLSTM approach for tourism demand forecasting[J]. Annals of Tourism Research, 2020, 83 (8): 102925 doi: 10.1016/j.annals.2020.102925 URL
[19]	Ghahramani Z. Probabilistic machine learning and artificial intelligence[J]. Nature, 2015, 521 (5): 452-459 doi: 10.1038/nature14541
[20]	崔佳旭, 杨博. 贝叶斯优化方法和应用综述[J]. 软件学报, 2018, 29 (10): 3068-3090.
	Cui J X, Yang B. Survey on Bayesian optimization methodology and applications[J]. Journal of Software, 2018, 29 (10): 3068-3090 (in Chinese)
[21]	张红霞, 夏明, 苏汝劼, 等. 中国时间序列投入产出表的编制: 1981—2018[J]. 统计研究, 2021, 38 (11): 3-23.
	Zhang H X, Xia M, Su R J, et al. The compilation of the time series input-output tables in China: 1981-2018[J]. Statistical Research, 2021, 38 (11): 3-23 (in Chinese)
[22]	樊茂清, 黄薇. 基于全球价值链分解的中国贸易产业结构演进研究[J]. 世界经济, 2014, 37 (2): 50-70.
	Fan M Q, Huang W. Research on the evolution of China’s trade industrial structure based on the decomposition of global value chain[J]. The Journal of World Economy, 2014, 37 (2): 50-70 (in Chinese)
[23]	廖华, 向福洲. 中国“十四五”能源需求预测与展望[J]. 北京理工大学学报(社会科学版), 2021, 23 (2): 1-8.
	Liao H, Xiang F Z. Forecast and prospect of energy demand in China’s 14th Five-Year’ Plan period[J]. Journal of Beijing Institute of Technology (Social Sciences Edition), 2021, 23 (2): 1-8 (in Chinese)
[24]	王利宁. 2060年世界与中国能源展望(2021版)[R]. 北京: 中国石油集团经济技术研究院, 2021.
	Wang L N. World and China energy outlook 2060 (2021 edition)[R]. Beijing: China Petroleum Group Economic and Technological Research Institute, 2021 (in Chinese)
[25]	刘尚希. 中国财政政策报告 (2021)[M]. 北京: 社会科学文献出版社, 2021.
	Liu S X. Annual report of fiscal policy of China (2021)[M]. Beijing: Social Sciences Academic Press, 2021 (in Chinese)
[26]	国务院. 中华人民共和国国民经济和社会发展第十四个五年规划和2035年远景目标纲要[M]. 北京: 人民出版社, 2021.
	The State Council. Outline of the 14th Five-Year Plan for national economic and social development and vision 2035 of the People’s Republic of China[M]. Beijing: People’s Publishing House, 2021 (in Chinese)
[27]	李平, 娄峰, 王宏伟. 2016—2035年中国经济总量及其结构分析预测[J]. 中国工程科学, 2017, 19 (1): 13-20.
	Li P, Lou F, Wang H W. Analysis and forecast of China’s total economy and its structure from 2016-2035[J]. Strategic Study of CAE, 2017, 19 (1): 13-20 (in Chinese)
[28]	《经济研究》智库经济形势分析课题组. 2021年中国外贸形势分析与2022年展望[R]. 北京: 中国社会科学院经济研究所, 2022.
	Economic Research Journal Think Tank Economic Situation Analysis Research Group. Analysis of China’s foreign trade situation in 2021 and outlook for 2022[R]. Beijing: Institute of Economics, Chinese Academy of Social Sciences, 2022 (in Chinese)
[29]	商务部. “十四五” 对外贸易高质量发展规划[EB/OL]. 2021 [2022-12-01]. https://www.gov.cn/zhengce.
	Ministry of Commerce. 14th Five-Year foreign trade high-quality development plan[EB/OL]. 2021 [2022-12-01]. https://www.gov.cn/zhengce (in Chinese)
[30]	赵忠秀, 王苒, 闫云凤. 贸易隐含碳与污染天堂假说: 环境库兹涅茨曲线成因的再解释[J]. 国际贸易问题, 2013 (7): 93-101.
	Zhao Z X, Wang R, Yan Y F. Embodied carbon emission and pollution heaven hypothesis: re-explaining environmental Kuzbets curve[J]. Journal of International Trade, 2013 (7): 93-101 (in Chinese)
[31]	王丽丽, 王媛, 毛国柱, 等. 中国国际贸易隐含碳SDA分析[J]. 资源科学, 2012, 34 (12): 2382-2389.
	Wang L L, Wang Y, Mao G Z, et al. Structure decomposition analysis of embodied carbon emissions for China’s international trade[J]. Resources Science, 2012, 34 (12): 2382-2389 (in Chinese)
[32]	张杰, 陈志远, 刘元春. 中国出口国内附加值的测算与变化机制[J]. 经济研究, 2013, 48 (10): 124-137.
	Zhang J, Chen Z Y, Liu Y C. Measuring the domestic value added in China’s exports and the mechanism of change[J]. Economic Research Journal, 2013, 48 (10): 124-137 (in Chinese)
[33]	张庆君. 人民币升值能否促进我国出口商品结构的改善[J]. 国际贸易问题, 2010 (6): 112-120.
	Zhang Q J. Can appreciation of RMB promote improvement of Chinese export commodity structure[J]. Journal of International Trade, 2010 (6): 112-120 (in Chinese)