内蒙古沿黄河流域七盟市碳排放影响因素与情景分析

于晓燕; 沈碧霞; 吴雨彤; 高雅娴; 魏光普

doi:10.12153/j.issn.1674-991X.20230678

内蒙古沿黄河流域七盟市碳排放影响因素与情景分析

doi: 10.12153/j.issn.1674-991X.20230678

于晓燕^{1, 2,},
沈碧霞^1, ,,
吴雨彤¹,
高雅娴¹,
魏光普^{1, 2}

1.
内蒙古科技大学
2.
内蒙古科技大学产业信息化与产业创新研究中心

基金项目: 内蒙古自治区自然科学基金项目（2022LHMS07004）；内蒙古自治区高校人文社会科学重点研究基地基金资助项目

详细信息

作者简介:
于晓燕（1985—），女，副教授，博士，研究方向为资源环境管理与可持续发展，yuxiaoyan2005@163.com

通讯作者:
沈碧霞（1997—)，女，硕士研究生，研究方向为资源环境管理与可持续发展，1123047977@qq.com

中图分类号: X321
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- 文章访问数: 100
- HTML全文浏览量: 80
- PDF下载量: 45
- 被引次数: 0
出版历程
- 收稿日期: 2023-09-19
- 录用日期: 2024-03-06
- 修回日期: 2023-12-05

Influencing factors and scenario analysis of carbon emissions in seven cities along the Yellow River basin in Inner Mongolia

YU Xiaoyan^{1, 2
,},
SHEN Bixia^{1
, ,},
WU Yutong¹,
GAO Yaxian¹,
WEI Guangpu^{1, 2}

1.
Inner Mongolia University of Science and Technology
2.
Research Center of Industrial Informationization and Innovation in Inner Mongolia University of Science and Technology

摘要

摘要:
“双碳”目标下，内蒙古沿黄河流域七盟市经济高质量发展是实现该地区高质量发展的关键途径。为探讨碳排放影响因素并预测碳排放峰值，选取内蒙古沿黄河流域七盟市2005—2022年面板数据，运用岭回归和扩展STIRPAT模型探究人口规模、城镇化率和人均GDP等6个因素以及各因素间的交互作用对七盟市碳排放的影响，并基于情景分析法预测七盟市2023—2035年碳排放变化趋势和达峰量。结果表明：2005—2022年七盟市碳排放量总体呈现波动上升趋势；人口规模、城镇化率、人均GDP的增加导致碳排放量增加，而能源强度和碳排放强度的降低可以有效减缓碳排放量增加；城镇化率和人均GDP的交互作用会导致七盟市碳排放量增加，而人口规模和人均GDP的交互作用、能源强度和产业结构的交互作用能有效抑制该地区碳排放量增加；6种不同预测情景下七盟市的碳排放量变化趋势差异较大，高碳情景和基准情景模拟下2030年碳排放量均未达到峰值，而其他4种情景碳排放量均在2030年出现峰值，且此4种情景模拟下碳减排的效果依次为全面低碳情景>能源强度降低情景>碳排放强度降低情景>产业结构优化情景。因此，全面实现产业结构优化、清洁能源开发和绿色工业技术突破性发展是内蒙古沿黄河流域七盟市实现经济与节能减排协同发展目标的最优策略。
- 黄河流域 /
- 碳排放 /
- 情景分析法 /
- STIRPAT模型
Abstract:
Under the carbon peaking and carbon neutrality goals, the high-quality economic development of seven cities along the Yellow River basin in Inner Mongolia is a key way to realize the high-quality development of the region. In order to explore the influencing factors of carbon emissions and predict the peak of carbon emissions, the panel data of seven cities along the Yellow River basin in Inner Mongolia from 2005 to 2022 was selected, and the Ridge regression and the extended STIRPAT model were used to study the six influencing factors, including population size, urbanization rate, and GDP per capita, as well as the interaction of them, on the carbon emissions of the seven cities. Based on scenario analysis, the trends and peak levels of carbon emissions of the seven cities from 2023 to 2035 were predicted. The results show that: from 2005 to 2022, the carbon emissions of seven cities showed a fluctuating upward trend; the increase in population size, urbanization rate, and GDP per capita can lead to an increase in carbon emissions, while the reduction in energy intensity and carbon intensity can slow the increase in carbon emissions. The interaction of urbanization rate with GDP per capita can lead to a further increase in carbon emissions of the seven cities, while the interaction of population size and GDP per capita, and the interaction of energy intensity and industrial structure can effectively curb the increase in carbon emissions in the region; the trend of changes in carbon emissions of the seven cities have bigger differences under six different forecast scenarios, under both the high carbon and benchmark scenarios, carbon emissions will not peak by 2030, while the other four scenarios will all peak carbon emissions by 2030. The order of carbon emission reduction effectiveness under these scenarios is as follows: comprehensive low-carbon scenario, energy intensity reduction scenario, carbon emission intensity reduction scenario, and industrial structure optimization scenario. Therefore, the comprehensive optimization of industrial structure, the development of clean energy, and the breakthrough of green industrial technology are the optimal strategies for achieving the synergistic development goals of economy, energy conservation and emission reduction in the seven cities along the Yellow River basin in the Inner Mongolia Autonomous Region.
- Yellow River basin /
- carbon emission /
- scenario analysis /
- STIRPAT model

HTML全文

图 1 2005—2022年内蒙古沿黄河流域七盟市碳排放量

Figure 1. Carbon emissions in seven cities along the Yellow River basin in Inner Mongolia from 2005 to 2022

下载: 全尺寸图片幻灯片

图 2 不同情景下内蒙古沿黄河流域七盟市碳排放量预测

Figure 2. Prediction of carbon emissions in seven cities along the Yellow River basin in Inner Mongolia under different scenarios

下载: 全尺寸图片幻灯片

表 1 变量解释

Table 1. Variable explanation

类型		变量	定义	符号	单位
因变量		碳排放量	当年七盟市CO₂排放总量	I	万t
自变量	单一变量	人口规模	当年总人口	P	万人
		城镇化率	当年城镇人口占总人口的比例	U	%
		人均GDP	当年GDP与总人口的比值	A	万元
		能源强度	当年能源消耗标准煤与GDP 的比值	T	t/万元
		产业结构	当年第三产业与第二产业的比值	S	%
		碳排放强度	当年CO₂排放总量与GDP 的比值	C	t/万元
	交互变量	人口规模×城镇化率	人口规模和城镇化率的交互作用	P×U
		人口规模×人均GDP	人口规模和人均GDP的交互作用	P×A
		人口规模×能源强度	人口规模和能源强度的交互作用	P×T
		人口规模×产业结构	人口规模和产业结构的交互作用	P×S
		人口规模×碳排放强度	人口规模和碳排放强度的交互作用	P×C
		城镇化率×人均GDP	城镇化率和人均GDP的交互作用	U×A
		城镇化率×能源强度	城镇化率和能源强度的交互作用	U×T
		城镇化率×产业结构	城镇化率和产业结构的交互作用	U×S
		城镇化率×碳排放强度	城镇化率和碳排放强度的交互作用	U×C
		人均GDP×能源强度	人均GDP和能源强度的交互作用	A×T
		人均GDP×产业结构	人均GDP和产业结构的交互作用	A×S
		人均GDP×碳排放强度	人均GDP和碳排放强度的交互作用	A×C
		能源强度×产业结构	能源强度和产业结构的交互作用	T×S
		能源强度×碳排放强度	能源强度和碳排放强度的交互作用	T×C
		产业结构×碳排放强度	产业结构和碳排放强度的交互作用	S×C

下载: 导出CSV

表 2 单一变量回归分析结果

Table 2. Single variable regression analysis results

变量	系数	P	R²	调整R²
常数项	9.305	0.000**	0.971	0.954
ln P	0.394	0.003**
ln U	0.851	0.000**
ln A	0.282	0.028*
ln T	0.222	0.047*
ln S	−0.105	0.112
ln C	0.286	0.017*
注：*、分别代表1%、5%的显著性水平；R²为决定系数。

下载: 导出CSV

表 3 交互变量回归分析结果

Table 3. Interactive variable regression analysis results

变量	系数	P	R²	调整R²
常数项	10.006	0.000**	0.937	0.924
ln(P×A)	−0.976	0.002**
ln(U×A)	1.410	0.000**
ln(T×S)	−0.137	0.048*
注：同表2。

下载: 导出CSV

表 4 内蒙古沿黄河流域七盟市不同情景下各影响因素年均变化速率设置

Table 4. Setting of annual average change rates of each influencing factor under different scenarios in seven cities along the Yellow River basin in Inner Mongolia %　

情景设置	年份	人口规模	城镇化率	人均GDP	能源强度	产业结构	碳排放强度
高碳情景	2023—2025	0.20	0.38	5.00	−1.70	3.00	−2.60
	2026—2030	−0.05	0.30	4.80	−1.40	2.00	−2.00
	2031—2035	−0.20	0.20	3.60	−1.00	0.30	−1.40
基准情景	2023—2025	0.20	0.38	5.00	−2.70	4.00	−3.60
	2026—2030	−0.05	0.30	4.80	−2.40	3.00	−3.00
	2031—2035	−0.20	0.20	3.60	−2.00	1.00	−2.40
全面低碳情景	2023—2025	0.20	0.38	5.00	−3.20	4.50	−4.10
	2026—2030	−0.05	0.30	4.80	−2.90	3.50	−3.50
	2031—2035	−0.20	0.20	3.60	−2.50	1.50	−2.90
能源强度降低情景	2023—2025	0.20	0.38	5.00	−3.20	4.00	−3.60
	2026—2030	−0.05	0.30	4.80	−2.90	3.00	−3.00
	2031—2035	−0.20	0.20	3.60	−2.50	1.00	−2.40
碳排放强度降低情景	2023—2025	0.20	0.38	5.00	−2.70	4.00	−4.10
	2026—2030	−0.05	0.30	4.80	−2.40	3.00	−3.50
	2031—2035	−0.20	0.20	3.60	−2.00	1.00	−2.90
产业结构优化情景	2023—2025	0.20	0.38	5.00	−2.70	4.50	−3.60
	2026—2030	−0.05	0.30	4.80	−2.40	3.50	−3.00
	2031—2035	−0.20	0.20	3.60	−2.00	1.50	−2.40

下载: 导出CSV

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