基于VAR模型的广东省土地利用碳排放影响因素研究

张余; 姜博; 赵映慧

doi:10.19303/j.issn.1008-0384.2022.008.016

基于VAR模型的广东省土地利用碳排放影响因素研究

doi: 10.19303/j.issn.1008-0384.2022.008.016

东北农业大学公共管理与法学院，黑龙江　哈尔滨　150000

基金项目: 东北农业大学国土空间规划与管理学科团队项目（54940512）；黑龙江省自然科学基金（G2018003）

详细信息

作者简介:
张余（1996−），女，硕士研究生，研究方向：土地利用与区域经济（E-mail：Zy245656558@163.com）

通讯作者:
姜博（1979−）男，博士，教授，研究方向：城市与区域发展（E-mail：jiangbo_1979@163.com）

中图分类号: X 24;F 301.24;F 127
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- 文章访问数: 314
- HTML全文浏览量: 92
- PDF下载量: 19
- 被引次数: 0
出版历程
- 收稿日期: 2021-10-23
- 录用日期: 2021-10-23
- 修回日期: 2022-03-30
- 网络出版日期: 2022-05-21
- 刊出日期: 2022-08-28

VAR Analysis on Factors Affecting Carbon Emissions from Land Use in Guangdong

School of Public Administration and Law, Northeast Agricultural University, Harbin, Heilongjiang　150030, China

摘要

摘要: 目的分析广东省土地利用碳排放的影响因素，为广东省碳排放管理提供一定的科学依据。方法基于广东省1996—2017年的土地利用现状及社会经济统计数据，构建VAR（向量自回归模型），通过格兰杰因果分析、脉冲响应和方差分解，对广东省土地利用碳排放影响因素进行定量分析。结果近20年来，广东省土地利用碳排放整体上呈上升趋势；人均GDP，土地利用强度和能源强度与土地利用碳排放呈正相关，土地利用结构和产业结构与土地利用碳排放呈负相关；影响因子贡献率由大到小分别为：土地利用强度＞产业结构＞能源强度＞人均GDP＞土地利用结构。结论调整产业结构，推动能源结构转变，优化土地利用结构是目前广东省实现碳减排的关键。
- 土地利用强度 /
- 碳排放 /
- 经济增长 /
- VAR模型 /
- 广东省
Abstract: Objective Factors that affected the carbon emissions from land use in Guangdong Province were analyzed for improved environmental management. Methods Based on the 1996-2017 statistics, a vector autoregression (VAR) model was constructed for the carbon emissions in Guangdong. Using Granger causality analysis, impulse response, and variance decomposition, the impact of land uses on the carbon emission in the province was quantitatively analyzed. Results In the past two decades, the overall carbon emission from varied land uses in the province was on an upward trend. The per capita GDP, land use intensity, and energy intensity positively, but the structures of land use and industry negatively, correlated with the carbon emission. The contribution rates of those factor ranked by magnitude were land use intensity＞industrial structure＞energy intensity＞per capita GDP＞land use structure. Conclusion It appeared that fostering transformation on energy structure and optimizing structure of land uses were the key maneuvers the province needs to consider in order to realize substantial reduction on carbon emission.
- Land use intensity /
- carbon emissions /
- economic growth /
- VAR /
- Guangdong Province

HTML全文

图 1 1996—2017年广东省土地利用碳排放

左侧坐标轴表示碳源及碳排放总量，右侧坐标轴表示碳汇。

Figure 1. Carbon emissions from land use in Guangdong from 1996 to 2017

The left axis represents carbon sources and total carbon emissions, and the right axis represents carbon sinks.

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图 2 VAR模型的AR根检验

Figure 2. AR root test of VAR model

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图 3 地均土地利用碳排放对人均GDP的脉冲响应

Figure 3. Impulse response of LNLCE to LNGDP

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图 4 地均土地利用碳排放对土地利用强度的脉冲响应

Figure 4. Impulse response of LNLCE to LNLA

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图 5 地均土地利用碳排放对土地利用结构的脉冲响应

Figure 5. Impulse response of LNLCE to LNLUS

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图 6 地均土地利用碳排放对产业结构的脉冲响应

Figure 6. Impulse response of LNLCE to LNIS

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图 7 地均土地利用碳排放对能源强度的脉冲响应

Figure 7. Impulse response of LNLCE to LNEGDP

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图 8 地均土地利用碳排放的方差分解

Figure 8. Variance decomposition of LNLCE

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表 1 能源标准煤折算系数与碳排放系数

Table 1. Conversion coefficients of energy standard coal and carbon emission

能源种类 Energy type	标准煤折算系数 Standard coal conversion factor/tec	碳排放系数 Carbon emission factor/（tc·tec⁻¹）
原煤 Raw coal/t	0.7143	0.7559
焦煤 Coking coal/t	0.9714	0.8550
原油 Crude oil/t	1.4286	0.5857
汽油 Gas oil/t	1.4714	0.5538
煤油 Kerosene/t	1.4714	0.5714
柴油 Diesel/t	1.4571	0.5921
燃料油 Fuel oil/t	1.4286	0.6185
液化石油气 LPG/t	1.7143	0.5042
炼厂干气 Refinery dry gas/t	1.5714	0.4602
其他石油制品 Other petroleum products/t	1.2000	0.5857
天然气 Natural gas/(10³•m³)⁻¹	12.1430	0.4483
能源标准煤折算系数与碳排放系数分别参考《中国能源统计年鉴》和IPCC《国家温室气体排放清单指南》（2006年）。
Conversion calculations according to China Energy Statistical Yearbook and IPCC Guidelines for National Greenhouse Gas Emission Inventory (2006).

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表 2 变量土地利用强度、经济发展与土地碳排放描述性统计

Table 2. Descriptive statistics of variable land use intensity, economic development, and land carbon emissions

变量 Variable	均值 Mean	最大值 Max	最小值 Min	标准差 Standard deviation
地均碳排放 LCE/（t·hm⁻²）	8672.39	9209.81	7818.10	509.91
人均GDP GDP/yuan	27442.80	55028.41	9157.00	14948.34
能源强度 EGDP/（t standard coal/10 000 yuan）	2.13	2.94	1.51	0.42
产业结构 IS	0.43	0.52	0.34	0.05
土地利用结构 LUS	0.09	0.11	0.06	0.01
土地利用强度 LA	213.96	219.81	190.29	6.99

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表 3 单位根检验结果

Table 3. Unit root test results

序列 Sequence	ADF检验 ADF test	1%临界值 1% threshold	5%临界值 5% threshold	10%临界值 10% threshold	结果 Result
LnLCE	−1.852 3	−3.7880	−3.0124	−2.6461	非平稳
LnGDP	−1.544 1	−4.4983	−3.6584	−3.2690	非平稳
LnLA	−3.198 0	−4.4983	−3.6584	−3.2690	非平稳
LnLUS	−1.701 2	−4.4983	−3.6584	−3.2690	非平稳
LnIS	−3.016 4	−4.4983	−3.6584	−3.2690	非平稳
LnEGDP	−2.813 4	−4.5716	−3.6908	−3.2869	非平稳
∆LnLCE	−3.894 1	−4.4983	−3.6584	−3.2690	**平稳
∆LnGDP	−4.385 9	−4.6679	−3.7332	−3.3103	**平稳
∆LnLA	−12.009 0	−4.4983	−3.6584	−3.2690	**平稳
∆LnLUS	−5.721 8	−4.4983	−3.6584	−3.2690	**平稳
∆LnIS	−2.052 8	−2.6857	−1.9591	−1.6075	**平稳
∆LnEGDP	−6.370 4	−4.4983	−3.6584	−3.2690	**平稳
表示1%显著水平；表示5%显著水平；*表示10%显著水平。 at 1% significant level; at 5% significant level; * at 10% significant level.

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表 4 最佳滞后阶数的选择

Table 4. Selection of optimal lag order

滞后阶数 Lag order	LOGL	LR	FPE	AIC	SC	HQ
0	220.9553	NA	0.0000	−21.4955	−21.1968	−21.4372
1	384.6752	212.835 8	0.0000	−34.2675	−32.1765	−33.8593
2	470.3642	59.9823*	1.6e-24*	−39.2364*	−35.3531*	−38.4784*

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表 5 Johansen协整检验结果

Table 5. Johansen cointegration test results

假定协整数量 Assumed cointegration number	迹统计量 Trace statistics	5%临界值 5% threshold	最大特征值统计量 Largest eigenvalue statistic	5%临界值 5% threshold
无 Nothing	245.6617	103.8473	78.0065	40.9568
最多一个 At most one	167.6552	76.9728	54.9164	34.8059
最多二个 Up to two	112.7388	54.0790	43.41 9	28.5881
最多三个 Up to three	69.3269	35.1928	35.9915	22.2996
最多四个 Up to four	33.3354	20.2618	23.1432	15.8921
最多五个 Up to five	10.1923	9.1645	10.1923	9.1645

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表 6 格兰杰因果检验结果

Table 6. Granger Causality test results

变量关系 Variable relationship	原假设 Null hypothesis	F统计量 F statistic	Prob	结论 Conclusion
LnLCE与各影响因子间的因果关系检验 The causal relationship test between LnLCE and various influencing factors	LnGDP不是LnLCE的格兰杰原因 LnGDP is not the Granger reason for LnLCE	1.3502	0.2604	Accept
	LnLCE不是LnGDP的格兰杰原因 LnLCE is not the Granger reason for LnGDP	13.8457	0.0016	***Reject
	LnLA不是LnLCE的格兰杰原因 LnLA is not the Granger reason for LnLCE	20.3356	0.0003	***Reject
	LnLCE不是LnLA的格兰杰原因 LnLCE is not the Granger reason for LnLA	5.03462	0.0377	***Reject
	LnLUS不是LnLCE的格兰杰原因 LnLUS is not the Granger reason for LnLCE	11.1064	0.0037	***Reject
	LnLCE不是LnLUS的格兰杰原因 LnLCE is not the Granger reason for LnLUS	9.36186	0.0067	***Reject
	LnIS不是LnLCE的格兰杰原因 LnIS is not the Granger reason for LnLCE	3.53485	0.0552	***Reject
	LnLCE不是LnIS的格兰杰原因 LnLCE is not the Granger reason for LnIS	3.0075	0.0797	***Reject
	LnEGDP不是LnLCE的格兰杰原因 LnEGDP is not the Granger reason for LnLCE	0.0396	0.8444	Accept
	LnLCE不是LnEGDP的格兰杰原因 LnLCE is not the Granger reason for LnEGDP	0.9078	0.3533	Accept
LnGDP、LnEGDP对其他因子的因果关系检验 The causal relationship test of LnGDP and LnEGDP to other factors	LnGDP不是LnLA的格兰杰原因 LnGDP is not the Granger reason for LnLA	0.2407	0.7891	Accept
	LnGDP不是LnLUS的格兰杰原因 LnGDP is not the Granger reason for LnLUS	13.5481	0.0004	***Reject
	LnGDP不是LnIS的格兰杰原因 LnGDP is not the Granger reason for LnIS	5.8825	0.0130	***Reject
	LnEGDP不是LnLA的格兰杰原因 LnEGDP is not the Granger reason for LnLA	9.9247	0.0055	***Reject
	LnEGDP不是LnLUS的格兰杰原因 LnEGDP is not the Granger reason for LnLUS	5.3968	0.0321	***Reject
	LnEGDP不是LnIS的格兰杰原因 LnEGDP is not the Granger reason for LnIS	6.5393	0.0198	***Reject
表示1%显著水平；表示5%显著水平；*表示10%显著水平。 means 1% significant level; means 5% significant level; * means 10% significant level.

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