Meta Regression Analysis of Pathway of Peak Carbon Emissions in China
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摘要: 尽早实现碳达峰、碳中和是中国推动经济社会全面绿色低碳转型的内在需求，开展碳达峰路径研究对中国合理制定2030年碳达峰目标和措施具有重大现实意义.该文筛选发表于2015—2020年间的18篇文献，采用Meta回归分析方法研究中国碳达峰路径及主要影响因素.结果表明：①多数文献预测中国将于2030年或2030年前实现碳达峰，平均预测峰值水平约10.9 Gt CO2；碳达峰时煤炭占比平均值为51.9%，非化石能源占比平均值为22.4%，经济年增长率平均值为5.4%，碳排放强度下降率平均值为54.0%.②该文筛选的样本对碳达峰路径预测结果与中国2030年前碳达峰目标一致，文献发表时间越晚预测的达峰时间越早且峰值越高.③除碳达峰时碳排放强度下降率（peakCEI）外，其余变量均对碳达峰峰值具有显著性；除文献类型（PTY）、影响因子（IF）、碳达峰时煤炭占比分类（yblcoal）、碳达峰时非化石能源占比（pnf）外，其余变量均对碳达峰时间具有显著性.未来中国应从基于成本效益的最优达峰路径、完善温室气体清单核算方法、大力推动清洁能源技术进步、提高经济发展质量等方面开展深入研究.Abstract: Achieving peak in carbon emissions and carbon neutrality as soon as possible is an internal need for promoting a comprehensive green and low-carbon transformation in China's economic society. Studying the pathway of carbon peaking is of great practical significance for China to reasonably formulate the targets and measures for carbon peaking in 2030. This research selects 18 papers published between 2015 and 2020, and uses Meta regression analysis to study the pathways for peaking carbon emissions and their main influencing factors. The results show: (1) Most papers predict that China will achieve the peak in carbon emissions in 2030 or before 2030, with an average predicted peak carbon emissions of about 10.9 Gt CO2. The average proportion of coal at the peak of carbon emissions would be 51.87%, and the average proportion of non-fossil energy would be 22.4%. The average annual economic growth rate would be 5.39%, and the average reduction rate of carbon emissions intensity would be 54.04%. (2) The results of the samples selected in the research, which predict the pathway of the carbon peaking, are consistent with carbon peaking target of China before 2030. The later the paper is published, the earlier the predicted carbon peak time and the higher the carbon peak. (3) The regression results show that all variables are significant to the peak CO2 emissions, except the variable of the decline rate of carbon emission intensity at the peak of carbon emissions (peakCEI). And all variables are significant to the time of carbon emissions peaking except the variable of paper type (PTY), impact factor (IF), the classification of the proportion of coal at the peak of carbon emissions (yblcoal) and the proportion of coal at the peak of carbon emissions (pnf). In the future, China should conduct in-deep research in these areas, including studying the optimal pathway to the carbon peak based on cost-benefit theory, improving the accounting methods of GHG inventories, vigorously promoting the progress of clean energy technology, and improving the quality of economic development, etc.
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表 1 筛选获取的文献与样本数据列表
Table 1. List of selected paper and its sample data
发表时间 文献来源 模型设定 碳达峰时间 CO2峰值/(108 t) 样本情景名称 数据来源 2019年 中国环境科学 情景分析+多属性决策模型 2030年 116.18 能源成本+CO2排放成本最小 文献[30] 2023年 108.72 政策约束 2030年 112.82 能源成本+CO2排放成本最小 2022年 109.43 政策约束 2023年 113.65 无约束 2024年 111.74 能源成本+CO2排放成本最小 2015年 中国人口·资源与环境 能源系统跨时段优化和碳排放模型 2030年 93.50 达峰情景 文献[31] 2015年 中国人口·资源与环境 IAMC模型+情景分析 2020年 100.50 深绿(DGS) 文献[32] 2025年 105.30 浅绿(LGS) 2030年 109.20 浅蓝(LBS) 2040年 119.70 深蓝(DBS) 2010年 中国环境科学 MARKAL-MACRO+情景分析 2036年 107.53 能源结构优化 文献[33] 2031年 94.72 气候变化约束 2016年 中国人口·资源与环境 能源系统优化模型+情景分析 2030年 104.00 达峰情景PS1 文献[34] 2020年 Renewable and Sustainable Energy Reviews 情景分析+EKC 2030年 106.90 计划能源结构(PE) 文献[13] 2025年 103.70 低碳能源结构(LE) 2020年 Science of the Total Environment 情景分析+投入产出优化模型 2030年 124.10 — 文献[1] 2020年 Ecological Indicators 情景分析+STIRPAT模型 2028年 117.70 — 文献[35] 2019年 Applied Energy 情景分析+LEAP 2025年 95.90 RFS 文献[36] 2019年 Journal of Cleaner Production 情景分析+IPAT模型 2030年 105.70 A1+B2+C2 文献[37] 2019年 Energy Policy NARX+情景分析 2029年 100.80 低速发展 文献[38] 2031年 107.80 中速发展 2035年 116.30 高速发展 2017年 Journal of Cleaner Production IMEC+投入产出最优模型 2026年 112.00 — 文献[39] 2017年 Resources, Conservation and Recycling 情景分析+CGE模型 2034年 112.00 资源约束情景 文献[40] 2030年 102.00 低碳情景 2016年 Energy Policy 情景分析+Bottom up+ FAIR/TIMER 2030年 125.00 当前政策情景 文献[41] 2030年 119.00 INDC情景 2030年 115.50 强化政策情景 2016年 Energy Economics 情景分析+C-GEM 2030年 101.58 深度努力(AE) 文献[42] 2040年 121.02 持续努力(CE) 2015年 Energy Policy 情景分析+Kaya 2030年 94.72 SA21 文献[23] 2030年 94.39 SB11 2015年 Advances in Climate Change Research Kaya+情景分析 2030年 117.00 — 文献[25] 2015年 Advances in Climate Change Research IAMC模型+情景分析 2030年 120.00 LBS-2030 文献[26] 2025年 116.00 LGS-2025 
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表 2 变量描述性统计结果
Table 2. Results of variable descriptive statistics
变量名称 样本量/组 平均值 标准差 最小值 最大值 peakCO2 36 109.30 8.82 93.50 125.00 time 36 -0.78 4.40 -10.00 10.00 PT 36 1.83 2.52 -5.00 5.00 PTY 36 0.61 0.49 0.00 1.00 IF 36 4.88 2.57 1.99 12.11 yblcoal 36 1.75 0.65 0.00 3.00 peakcoal 36 51.87 5.86 36.92 62.30 pnf 36 22.40 6.50 15.40 44.09 nf 36 22.82 5.17 15.60 39.00 nonfossil 36 0.36 0.12 0.13 0.63 CEI 36 2.84 0.70 0.84 4.30 peakCEI 36 54.04 16.27 16.63 96.04 yblCEIG 36 0.44 0.50 0.00 1.00 GDP 36 5.39 1.02 2.50 7.50 GDPY 36 2.83 1.03 0.00 4.00
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表 3 样本数据异质性检验结果
Table 3. Heterogeneity test results of sample data
项目 异质性检验 统计值 自由度 P值 peakCO2 Q检验 23.69 35 0.927 I2 0.00 time Q检验 16.85 35 0.996 I2 0.00
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表 4 MRA模型估计结果
Table 4. The estimation results of MRA model
项目 Y1 Y2 样本特征 PT 0.67*(0.35) -0.32***(0.11) PTY 9.42***(2.21) 1.13(0.83) IF -1.92***(0.45) -0.15(0.17) 样本变量 yblcoal 9.17***(2.84) 1.26(0.82) peakcoal 1.19***(0.32) 0.31***(0.09) pnf -1.27***(0.37) 0.49(0.46) nf -1.68***(0.36) -0.60***(0.15) nonfossil -5.02***(0.82) -8.46***(2.54) CEI -1.38**(0.59) -0.74**(0.28) peakCEI 0.16(0.18) -0.12**(0.04) yblCEIG -5.88**(2.16) -3.49***(1.24) GDP -4.33**(1.81) -1.71***(0.47) GDPY 4.86**(1.95) 1.97***(0.66) 常数项 15.83***(1.75) 20.62***(4.40) 样本量/组 36 36 R2 0.89 0.95 注：括号内数值为标准误；***、**、*分别为0.01、0.05和0.10的显著性水平. 下同.
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表 5 稳健性检验结果
Table 5. Result of robustness test
项目 Y1 Y2 样本特征 PT 0.78***(0.22) -0.26**(0.11) PTY 7.93***(2.35) 1.22(0.72) IF -1.62***(0.50) -0.15(0.21) 样本变量 yblcoal 5.23*(2.76) 0.16(0.58) peakcoal 0.90**(-0.42) 0.33***(0.11) pnf -1.01**(0.43) 0.54***(0.12) nf -1.26***(0.44) -0.63***(0.12) nonfossil -4.70***(-1.03) -8.48***(2.63) CEI -0.20(1.85) -0.34(0.29) peakCEI 0.24**(0.11) -0.11***(0.04) yblCEIG -6.24**(2.87) -2.90**(1.26) GDP -4.68**(-1.90) -2.23***(0.45) GDPY 2.13*(1.23) 1.62***(0.42) 常数项 14.42***(2.83) 14.73**(5.43) 样本量/组 36 36 R2 0.86 0.96
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