新疆农业碳排放的时空差异与空间溢出效应分析

doi:10.12118/j.issn.1000-6060.2023.344

Abstract

Abstract:

This paper evaluates the total amount and intensity of agricultural carbon emissions across 13 prefectures in Xinjiang, China from 2007 to 2020. It employs the Gini coefficient decomposition method to examine regional disparities in the intensity of agricultural carbon emissions in Xinjiang and utilizes the spatial Durbin model to assess the spatial spillover effects and driving factors of these emissions. The findings indicate that the evolution of total agricultural carbon emissions in Xinjiang during the study period can be categorized into three phases: a rapid increase, a continuous decline, and a steady rise, with straw burning being the predominant source of emissions, followed by livestock farming. The intensity of agricultural carbon emissions exhibited a pronounced downward trend throughout the period. Spatial disparities in the intensity of emissions at the beginning and end of the study period were substantial, characterized by lower levels in the north and higher levels in the south. Based on emission composition, Xinjiang can be classified into five distinct regional types. The Gini coefficients for the intensity of agricultural carbon emissions in Xinjiang as a whole, and specifically in its northern and southern parts, demonstrated a fluctuating downward trend, with inter-regional differences accounting for the majority of the overall disparities. Furthermore, there was a significant spatial agglomeration of emission intensity during the study period, with increasing spatial linkages among cities and towns over time. The primary influences on agricultural carbon emissions in Xinjiang were market and governmental factors. At the market level, factors such as industrial agglomeration, agricultural industry structure, agricultural development level, and planting structure were significantly positively correlated with emission intensity, accompanied by notable inter-regional spillover effects. From a governmental perspective, the level of environmental governance and the extent of regional disasters exhibited a significant negative correlation with the intensity of agricultural carbon emissions.

Key words: agricultural carbon emission intensity, regional differences, spatial spillover effect, spatial Durbin model, Xinjiang

XIA Wenhao, HUO Yu, LU Yuan, WANG Chaoyi. Spatialtemporal differences and spatial spillover effects of agricultural carbon emissions in Xinjiang[J].Arid Land Geography, 2024, 47(6): 1084-1096.

Figures/Tables 9

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变量	符号	样本量	均值	标准差	最小值	最大值
农业碳排放强度	lnEI	182	1.36	0.60	0.24	3.32
农业产业集聚	lnALQ	182	1.12	0.59	0.04	3.11
农业产业结构	lnAIS	182	0.94	0.03	0.84	0.98
农业发展水平	lnAGDP	182	3.41	2.81	0.40	16.32
种植结构	lnPS	182	0.42	0.19	0.03	0.79
农村居民人均纯收入	lnRI	182	0.92	0.52	0.08	2.28
水土流失治理面积	lnSEC	182	64.89	61.90	1.40	301.40
灾害发生面积	lnDA	182	96.43	129.70	2.18	606.90

年份	农资利用		水稻种植		秸秆燃烧		禽畜养殖		总量 /10⁴ t	强度 /10⁴t·元^-1
年份	排放量/10⁴ t	比重/%	排放量/10⁴ t	比重/%	排放量/10⁴ t	比重/%	排放量/10⁴ t	比重/%	总量 /10⁴ t	强度 /10⁴t·元^-1
2007	391.50	18.96	27.88	1.35	952.07	46.11	693.31	33.58	2064.76	1.92
2008	444.27	21.23	30.41	1.45	1067.06	50.99	550.98	26.33	2092.72	1.75
2009	471.66	20.93	34.00	1.51	1191.67	52.87	556.47	24.69	2253.80	1.71
2010	493.20	21.00	32.30	1.38	1272.55	54.19	550.08	23.43	2348.13	1.25
2011	531.78	21.12	34.62	1.38	1399.41	55.59	551.58	21.91	2517.40	1.27
2012	546.71	20.03	37.02	1.36	1540.76	56.45	604.82	22.16	2729.31	1.18
2013	581.86	20.09	38.85	1.34	1657.09	57.23	617.85	21.34	2895.65	1.13
2014	671.71	22.02	36.30	1.19	1700.00	55.73	642.59	21.06	3050.59	1.10
2015	687.56	21.64	32.01	1.01	1803.01	56.75	654.69	20.61	3177.27	1.12
2016	707.84	23.86	33.10	1.12	1578.06	53.19	648.06	21.84	2967.07	0.99
2017	690.01	23.27	29.41	0.99	1593.03	53.72	652.94	22.02	2965.39	0.96
2018	661.03	22.28	35.42	1.19	1656.88	55.85	613.38	20.68	2966.71	0.81
2019	639.68	21.01	26.10	0.86	1673.16	54.96	705.37	23.17	3044.30	0.78
2020	611.87	19.76	21.51	0.69	1730.67	55.90	731.98	23.64	3096.03	1.10
累计增速/%	56.29		-22.85		81.78		5.58		49.95	-42.71
平均增速/%	4.33		-1.76		6.29		0.43		3.84	-3.29

地州市	2007年				2020年				总量变动率/%	强度变动率/%
地州市	总量/10⁴ t	排名	强度/10⁴t·元^-1	排名	总量/10⁴ t	排名	强度/10⁴t·元^-1	排名	总量变动率/%	强度变动率/%
乌鲁木齐市	26.57	11	1.40	9	27.78	12	0.66	12	4.55	-53.86
昌吉州	166.72	5	1.39	10	221.68	5	0.90	7	32.97	-35.25
吐鲁番市	17.25	13	0.65	13	23.04	13	0.24	13	33.57	-63.08
哈密市	20.64	12	1.30	11	37.17	11	0.69	11	80.10	-46.92
伊犁州直	562.40	1	2.90	2	455.80	1	1.39	2	-18.95	-52.07
塔城地区	186.09	4	2.31	5	360.48	4	1.33	3	93.71	-42.42
阿勒泰地区	88.99	8	2.85	3	154.78	7	1.32	4	73.93	-53.68
博州	55.79	9	2.09	7	108.78	9	1.16	6	94.98	-44.50
巴州	117.76	7	1.29	12	189.96	6	0.71	10	61.31	-44.96
阿克苏地区	233.52	3	2.21	6	395.17	3	0.83	8	69.22	-62.44
克州	38.38	10	3.32	1	55.55	10	1.26	5	44.74	-62.05
喀什地区	333.38	2	1.93	8	438.87	2	0.76	9	31.64	-60.62
和田地区	133.99	6	2.59	4	113.35	8	1.46	1	-15.40	-43.63

类型	地州市	农资利用		水稻种植		秸秆燃烧		禽畜养殖
类型	地州市	数量/10⁴ t	比重/%	数量/10⁴ t	比重/%	数量/10⁴ t	比重/%	数量/10⁴ t	比重/%
禽畜养殖主导型	乌鲁木齐市	7.91	28.48	3.84	13.83	2.61	9.40	13.41	48.29
	阿勒泰地区	28.55	18.45	0.07	0.05	50.85	32.85	75.31	48.66
	克州	7.40	13.32	0.45	0.81	19.28	34.71	28.42	51.16
秸秆燃烧主导型	昌吉州	60.68	27.37	0.12	0.05	117.11	52.83	43.78	19.75
	塔城地区	74.29	20.61	0.06	0.02	228.74	63.45	57.39	15.92
	博州	25.54	23.48	0.06	0.06	63.65	58.51	19.54	17.96
	巴州	56.35	29.66	0.28	0.15	86.61	45.59	46.72	24.59
	阿克苏地区	111.03	28.10	6.62	1.68	201.93	51.10	75.60	19.13
	喀什地区	126.63	28.85	1.25	0.28	237.51	54.12	73.48	16.74
秸秆燃烧-禽畜养殖主导型	和田地区	24.62	21.72	1.87	1.65	51.68	45.59	35.18	31.04
秸秆燃烧-禽畜养殖主导型	伊犁州直	68.85	15.11	6.89	1.51	216.62	47.53	163.44	35.86
农资利用主导型	吐鲁番市	10.21	44.30	0.00	0.00	4.75	20.61	8.09	35.10
均衡型	哈密市	9.81	26.39	0.00	0.00	11.55	31.07	15.81	42.53

年份	地理距离空间权重矩阵			经济距离空间权重矩阵
年份	Moran’s I	Z	P值	Moran’s I	Z	P值
2007	0.105^***	2.645	0.008	0.168^**	2.205	0.013
2008	0.112^***	2.783	0.005	0.100^***	2.418	0.008
2009	0.102^***	2.700	0.007	0.177^***	2.282	0.009
2010	0.080^**	2.274	0.023	0.074^***	2.339	0.007
2011	0.077^**	2.200	0.028	0.039^**	2.058	0.021
2012	0.092^**	2.427	0.015	0.073^**	2.267	0.012
2013	0.057^**	1.996	0.046	0.091^***	2.354	0.010
2014	0.056^**	2.043	0.041	0.054^***	2.542	0.005
2015	0.072^**	2.290	0.022	0.087^***	2.643	0.003
2016	0.064^**	2.240	0.025	0.093^***	2.675	0.003
2017	0.050^**	2.148	0.032	0.062^***	2.855	0.001
2018	0.006	1.356	0.175	0.083^***	2.253	0.008
2019	-0.012	1.083	0.279	0.003	1.458	0.135
2020	0.015	1.547	0.122	0.011	1.265	0.176

Spatialtemporal differences and spatial spillover effects of agricultural carbon emissions in Xinjiang

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检验	统计量	检验	统计量
LM(error)检验	11.67^***	LR(sdm sar)检验	110.68^***
Robust LM(error)检验	4.26^**	LR(sdm sem)检验	124.68^***
LM(lag)检验	27.77^***	Wald检验	141.51^***
Robust LM(lag)检验	20.37^***	Hausman检验	43.36^***
LR(both ind)检验	45.23^***	LR(both time)检验	333.58^***
联合显著性检验	137.51^***

变量	模型估计系数	空间矩阵估计系数	空间自相关估计系数	差异系数	直接效应	间接效应	总效应
lnALQ	0.163^*** （2.74）	1.005^*** （5.26）			0.220^*** （3.58）	1.531^*** （4.42）	1.751^*** （4.85）
lnAIS	0.159 （0.25）	-4.845^*** （-3.13）			-0.101 （-0.17）	-6.720^*** （-2.95）	-6.821^*** （-3.01）
lnAGDP	-0.014^** （-2.07）	0.022^** （1.98）			-0.012^* （-1.90）	0.026 （1.58）	0.014 （0.81）
lnPS	0.459^** （2.25）	-0.622 （-1.59）			0.432^** （2.27）	-0.671 （-1.25）	-0.238 （-0.44）
lnRI	0.097 （1.29）	-0.046 （-0.33）			0.098 （1.40）	0.010 （0.05）	0.108 （0.58）
lnSEC	-0.001^** （-2.13）	-0.001 （-1.13）			-0.001^** （-2.21）	-0.002 （-1.35）	-0.003^* （-1.75）
lnDA	0.001^** （2.21）	-0.006^*** （-6.48）			0.000 （1.02）	-0.009^*** （-4.31）	-0.008^*** （-3.76）
ρ			0.314^** （2.54）
sigma2_e				0.018^*** （9.14）
R²	0.421
Log-L	100.670

变量	模型估计系数	空间矩阵估计系数	空间自相关估计系数	差异系数	直接效应	间接效应	总效应
lnALQ	0.152^** （2.46）	0.360^** （2.28）			0.189^*** （3.11）	0.617^*** （2.86）	0.806^*** （3.65）
lnAIS	-0.274 （-0.43）	-3.276^** （-2.50）			-0.576 （-0.98）	-4.922^*** （-2.61）	-5.498^*** （-2.89）
lnAGDP	-0.016^** （-2.31）	0.008 （0.81）			-0.015^** （-2.30）	0.004 （0.25）	-0.012 （-0.77）
lnPS	0.413^* （1.95）	-1.109^*** （-3.13）			0.325 （1.64）	-1.459^*** （-2.93）	-1.134^** （-2.16）
lnRI	0.045 （0.51）	-0.003 （-0.03）			0.046 （0.57）	0.041 （0.25）	0.086 （0.63）
lnSEC	-0.001^*** （-3.95）	-0.002^** （-2.16）			-0.002^*** （-4.41）	-0.004^** （-2.44）	-0.005^*** （-3.18）
lnDA	0.001^** （2.25）	-0.004^*** （-5.79）			0.000 （1.14）	-0.005^*** （-4.21）	-0.005^*** （-3.33）
ρ			0.362^*** （3.85）
sigma2_e				0.018^*** （9.10）
R²	0.388
Log-L	97.821

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