基于STIRPAT-GWR模型的重庆市土地利用碳排放动态演进及影响因素

黄怀玉; 唐园清; 龚直文; 陈小娟

doi:10.12153/j.issn.1674-991X.20230873

基于STIRPAT-GWR模型的重庆市土地利用碳排放动态演进及影响因素

doi: 10.12153/j.issn.1674-991X.20230873

西北农林科技大学经济管理学院

基金项目: 陕西省自然科学基础研究计划一般项目（2023-JC-YB-192）；陕西林业科技创新项目（SXLK2023-05-1）

详细信息

作者简介:
黄怀玉（1999—），女，博士研究生，主要从事资源经济与环境管理研究，hhy2021@nwafu.edu.cn

通讯作者:
龚直文(1980—)，男，副教授，主要从事林业经济理论与政策研究，gongzhiwen@nwafu.edu.cn

中图分类号: X22；F299.23
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- 文章访问数: 109
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- 被引次数: 0
出版历程
- 收稿日期: 2023-12-06
- 录用日期: 2024-04-18
- 修回日期: 2024-02-08

Dynamic evolution and influencing factors of land use carbon emissions in Chongqing based on STIRPAT-GWR model

College of Economics and Management, Northwest A&F University

摘要

摘要:
探究重庆市土地利用碳排放时空分异及影响因素，可为进一步优化土地利用结构、落实差异化的降碳减污政策提供参考。基于2000年、2010年、2020年3期土地覆盖数据揭示重庆市碳排放的区域差异及时空动态特征，综合运用STIRPAT模型和地理加权回归（GWR）模型，探究社会经济因素对碳排放的空间异质性影响。结果表明：重庆市净碳排放量在2000—2020年总计增长3 723.14×10⁴ t，其时序变化可划分为急剧增加阶段和缓慢增加阶段；土地利用碳汇与碳源仍存在收支不平衡问题。净碳排放总体呈现“中心高、两翼低”的分布格局，净碳排放增量在主城都市区的增长幅度最为剧烈，在渝东南各区县均呈现微度增长态势，渝东北各区县的增长量存在明显的空间差异性。土地利用碳排放各影响因素的空间分布格局差异较大，碳排放强度和人均GDP是关键主导因素，其他依次为城镇人口规模、地方财政一般预算支出、产业结构，碳排放强度在渝东北地区的影响强度较大，城镇人口规模在主城都市区的正向促进作用较大。
- 土地利用碳排放 /
- 时空动态 /
- 影响因素 /
- 地理加权回归模型 /
- 重庆
Abstract:
Exploring the spatiotemporal patterns and influencing factors of land use carbon emissions in Chongqing can provide a scientific reference for further optimizing land use structure and implementing differentiated carbon and pollution reduction policies. Based on three periods of land cover data from 2000 to 2020, the regional differences and spatiotemporal dynamic characteristics of carbon emissions in Chongqing were revealed. The impact of socio-economic factors on spatial heterogeneity of carbon emissions was explored by integrating the STIRPAT model and the geographically weighted regression (GWP) model. The results showed that the net carbon emissions of Chongqing increased by a total of 37.2314 million tons from 2000 to 2020. Its temporal changes could be divided into a sharp increase stage and a slow increase stage, and there was still an imbalance between land use carbon sinks and carbon sources. The overall distribution pattern of net carbon emissions in Chongqing was characterized by a pattern of "high in the center and low on both wings". The growth of net carbon emissions in the main urban areas was the most severe, while it showed a slight increase in all districts and counties of the southeast in Chongqing. There was a significant spatial difference in the growth of net carbon emissions in the northeast. The factors influencing land use carbon emissions presented a strong spatial heterogeneity. The carbon emission intensity and per capita GDP were the key leading factors, followed by urban population size, general budget expenditure of local finance, and industrial structure. The intensity of carbon emissions had a more significant impact in the northeast of Chongqing, and the size of the urban population had a greater positive effect in the main metropolitan areas.
- land use carbon emissions /
- spatiotemporal patterns /
- influencing factors /
- geographically weighted regression (GWR) model /
- Chongqing

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图 1 研究区域概况

注：地图底图的审图号为GS(2019)1822号。全文同。

Figure 1. Overview of the study area

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图 2 2000—2020年重庆市净碳排放及其增量时空格局

Figure 2. Spatiotemporal pattern of net carbon emissions and their increment in Chongqing from 2000 to 2020

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图 3 GWR模型影响因素回归系数估计值空间分布

Figure 3. Spatial distribution of regression coefficients of influencing factors in GWR model

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表 1 折标煤系数及碳排放转换系数

Table 1. Conversion coefficient of standard coal and carbon emission

能源类型	折标准煤系数	碳排放转换系数
煤炭	0.714 3	0.755 9
焦炭	0.971 4	0.855 0
汽油	1.471 4	0.553 8
煤油	1.471 4	0.571 4
柴油	1.457 1	0.592 1
天然气	1.330 0	0.448 3
电力	0.404 0	0.793 5

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表 2 影响因素的描述性统计

Table 2. Descriptive statistics of the influencting factors

变量维度	变量指标	变量对数	最小值	最大值	均值	标准差	方差膨胀因子	容差
环境压力(I)	土地利用净碳排放	$ \mathrm{l}\mathrm{n}\ E $	1.49	5.72	4.54	1.03
人口规模(P)	城镇人口数量	$ \mathrm{l}\mathrm{n}\mathrm{\ U}\mathrm{P} $	1.96	4.74	3.63	0.59	4.98	0.20
富裕度(A)	人均GDP	$ \mathrm{l}\mathrm{n}\mathrm{\ R}\mathrm{G}\mathrm{D}\mathrm{P} $	10.24	12.34	11.13	0.42	2.31	0.43
富裕度(A)	政府一般预算支出	$ \mathrm{l}\mathrm{n}\mathrm{\ G}\mathrm{B}\mathrm{E} $	12.78	14.29	13.58	0.30	3.01	0.33
技术水平(T)	碳排放强度	$ \mathrm{l}\mathrm{n\ }\mathrm{C}\mathrm{E}\mathrm{I} $	−2.95	−1.01	−1.69	0.41	1.75	0.57
产业结构(S)	第二产业增加值	$ \mathrm{l}\mathrm{n\ }\mathrm{I}\mathrm{S} $	11.50	15.73	14.45	0.96	7.10	0.14

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表 3 2000—2020年重庆市土地利用碳排放与碳吸收

Table 3. Land use carbon emissions and absorption in Chongqing from 2000 to 2020 10⁴ t　

年份	碳排放			碳吸收					净碳排放量
年份	耕地	建设用地	合计	林地	草地	水域	未利用地	合计	净碳排放量
2000	189.49	1 348.98	1 538.47	−205.71	−1.34	−0.23	0.00	−207.28	1 331.19
2010	189.82	4 458.37	4 648.19	−211.73	−1.07	−0.25	0.00	−213.05	4 435.14
2020	180.21	5 087.33	5 267.54	−211.85	−1.04	−0.32	0.00	−213.21	5 054.33

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表 4 基于STIRPAT模型的OLS估计结果

Table 4. OLS estimation results based on STIRPAT model

变量	回归系数	标准误差	t	P
截距项	−10.688***	1.251	−8.546	0.000
$ \mathrm{ln}\mathrm{\ RGDP} $	0.707***	0.059	11.971	0.000
$ \mathrm{ln\ UP} $	0.566***	0.062	9.146	0.000
$ \mathrm{ln\ CEI} $	0.967***	0.054	18.069	0.000
$ \mathrm{ln\ GBE} $	0.343***	0.094	3.629	0.001
$ \mathrm{ln\ IS} $	0.159***	0.046	3.451	0.002
注：***表示在1%水平上显著。

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表 5 OLS模型与GWR模型的拟合效果比较

Table 5. Comparison of fitting effects between OLS and GWR model

模型	拟合优度R²	调整后的R²	AICc	残差平方和
OLS模型	0.992	0.990	−47.931	0.322
GWR模型	0.993	0.991	−56.819	0.278

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表 6 GWR模型参数估计值描述性统计

Table 6. Descriptive statistics of GWR model parameter estimates

影响因素	平均值	最小值	最大值	下四分位值	中位值	上四分位值
常数	−10.774	−11.744	−10.058	−11.089	−10.655	−10.388
$ \mathrm{l}\mathrm{n}\mathrm{\ R}\mathrm{G}\mathrm{D}\mathrm{P} $	0.710	0.702	0.717	0.707	0.711	0.714
ln UP	0.568	0.494	0.615	0.541	0.576	0.594
ln CEI	0.950	0.929	0.996	0.936	0.943	0.959
ln GBE	0.341	0.289	0.421	0.311	0.331	0.364
ln IS	0.161	0.152	0.177	0.156	0.159	0.165

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