中国出口贸易隐含碳的趋势预测及结构转移研究

doi:10.12006/j.issn.1673-1719.2023.185

摘要/Abstract

摘要：

基于特征选择的Lasso方法分别确定影响中国出口贸易隐含碳排放（CO₂）总量和强度的核心指标，并由此构建BO-BiLSTM模型对总量变动和强度演进的趋势展开预测，同时采用Markov链进一步探讨中国出口贸易隐含碳排放的结构转移现象。结果表明：(1) 2021—2035年间中国出口贸易隐含碳排放总量呈现阶梯式减少的趋向，预计2030年达到1.98 Gt，在2035年降为1.83 Gt，出口贸易规模扩大和国际经贸形势改善是关键影响因素。(2) 2021—2035年间中国出口贸易隐含碳排放强度保持稳中有降的态势，预计2030年减至0.91 t/万元，相较于2005年减少67%，出口贸易结构变迁和环境规制强度提高是重要驱动因素。(3) 2021—2035年间中国出口贸易隐含碳排放结构仍偏重知识密集型制造业，其存在较大减排潜力，而资本密集型服务业和资本密集型制造业具有减排周期较长的特点。

关键词: 出口贸易隐含碳, 投入产出模型, 机器学习预测

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

胡剑波, 麦骏南. 中国出口贸易隐含碳的趋势预测及结构转移研究[J]. 气候变化研究进展, 2024, 20(1): 62-74.

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.

图/表 7

表1 时间序列投入产出表的部门统一合并结果

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

表2 中国出口贸易隐含碳排放总量预测输入特征的描述性统计

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

表3 中国出口贸易隐含碳排放强度预测输入特征的描述性统计

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

图1 中国出口贸易隐含碳排放总量预测结果

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

图2 中国出口贸易隐含碳排放强度预测结果

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

表4 中国出口贸易隐含碳排放结构平均转移概率矩阵

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

图3 中国出口贸易隐含碳排放结构预测结果

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

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