我国农村能源碳排放时空格局、影响因素及空间溢出效应

doi:10.3864/j.issn.0578-1752.2023.13.009

Abstract

Abstract:

【Objective】In the context of the “dual carbon” strategy, clarifying the current characteristics, spatial-temporal pattern and influencing factors of rural energy carbon emissions can provide important support for effectively promoting rural low-carbon development. 【Method】Carbon emission factor method is used to measure rural energy carbon emissions in China effectively, and analyze its temporal and spatial characteristics. Then, the autocorrelation model is used to explore its spatial correlation pattern. Finally, the introduction of STIRPAT extended model is used to analyze the main factors affecting its intensity changes and the spatial spillover effect. 【Result】China's total rural energy carbon emissions are in a continuous upward trend, with an increase of 77.55% in 2019 compared with 2005, which is mainly attributed to the increase in rural residents' domestic energy consumption. Rural energy carbon emission intensity has increased slightly during the investigation period. Although there are some inter-annual fluctuations, the overall fluctuations are small. In 2019, there were significant inter-provincial differences in rural energy carbon emissions, with Hebei leading the way and Ningxia at the bottom. Compared with 2005, only 5 provinces were in a downward trend. In 2019, Beijing ranked first in rural energy carbon emission intensity, while Hainan ranked last, with the latter even less than one tenth of the former. Since 2008, China's rural energy carbon emissions have shown obvious and stable spatial dependence, as well as local spatial clustering, with a small and relatively stable number of high-high concentration provinces and a lager and growing number of low-low concentration provinces. Among the social factors, the increase of rural affluence can lead to an increase of rural energy carbon emission intensity, while agricultural technology progress and rural labor force structure variables have a dampening effect, with only rural affluence showing a spatial spillover effect in a negative direction. Among the economic factors, the increase in the rural financial agglomeration and the improvement of agricultural development level both lead to the increase of rural energy carbon emission intensity, and both have spatial spillover effects, with the former positive and the latter negative. While agricultural financial investment does not have a direct effect but shows a negative spatial spillover effect. Among the industry-level factors, the increase of agricultural industry agglomeration leads to the increase of rural energy carbon emission intensity, but at the same time, it also presents a negative spatial spillover effect. 【Conclusion】The total amount and intensity of rural energy carbon emissions in China are on the rise, with significant inter-provincial differences. China's rural energy carbon emissions show obvious spatial dependence and spatial heterogeneity. Rural energy carbon emissions are affected by a combination of social, economic and industrial factors.

Key words: rural energy carbon emissions, agricultural carbon emission, spatial-temporal pattern, influencing factors, spillover effect

TIAN Yun, YIN Minhao, ZHANG Huijie. Spatial-Temporal Pattern, Influencing Factors and Spatial Spillover Effect of Rural Energy Carbon Emissions in China[J].Scientia Agricultura Sinica, 2023, 56(13): 2547-2562.

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URL: https://www.chinaagrisci.com/EN/10.3864/j.issn.0578-1752.2023.13.009

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Figures/Tables 8

Table 1

Table 2

Table 3

Comparison of total amount and intensity of rural energy carbon emissions in China’s 30 provinces"

地区 Region	2005				2019				变化率Ⅰ Change rateⅠ (%)	变化率Ⅱ Change rate Ⅱ (%)
地区 Region	农业生产 Agricultural production (×10⁴t)	居民生活 Resident living (×10⁴t)	合计 Total (×10⁴t)	强度 Intensity (t/10⁴yuan)	农业生产 Agricultural production (×10⁴t)	居民生活 Resident living (×10⁴t)	合计 Total (×10⁴t)	强度 Intensity (t/10⁴yuan)	变化率Ⅰ Change rateⅠ (%)	变化率Ⅱ Change rate Ⅱ (%)
北京Beijing	134.30	524.52	658.83	7.11	34.30	503.27	537.57	6.89	-18.41	-3.07
天津Tianjin	103.71	198.26	301.97	3.13	130.59	435.05	565.64	4.10	87.31	30.76
河北Hebei	344.84	2184.03	2528.87	2.30	375.61	5204.48	5580.09	3.08	120.66	34.16
山西Shanxi	470.97	1084.32	1555.29	7.47	448.80	1400.36	1849.16	5.24	18.90	-29.87
内蒙古Inner Mongolia	455.92	764.76	1220.67	2.56	540.04	1153.32	1693.37	2.02	38.72	-21.07
辽宁Liaoning	501.67	1003.87	1505.53	2.07	559.95	1682.08	2242.03	1.83	48.92	-11.82
吉林Jilin	397.99	483.74	881.73	1.67	300.19	932.72	1232.91	1.34	39.83	-19.77
黑龙江Heilongjiang	559.83	402.23	962.05	1.90	1316.11	1009.16	2325.27	2.16	141.70	13.74
上海Shanghai	167.78	257.61	425.39	5.68	89.99	196.66	286.65	5.83	-32.62	2.59
江苏Jiangsu	523.74	1056.44	1580.18	1.47	807.18	3340.40	4147.59	2.51	162.48	70.94
浙江Zhejiang	626.80	1115.30	1742.09	2.17	736.38	2895.58	3631.95	3.23	108.48	48.96
安徽Anhui	251.22	607.94	859.16	1.05	378.58	2171.44	2550.02	1.74	196.80	66.78
福建Fujian	568.03	919.66	1487.69	1.97	314.56	2089.74	2404.30	1.89	61.61	-3.97
江西Jiangxi	346.68	555.81	902.49	1.45	253.74	1649.70	1903.44	1.68	110.91	16.24
山东Shandong	1395.39	2142.25	3537.64	2.21	632.47	3937.84	4570.31	1.68	29.19	-23.68
河南Henan	569.15	2119.78	2688.93	1.78	710.33	4137.29	4847.61	1.78	80.28	-0.03
湖北Hubei	586.63	998.25	1584.88	1.89	765.50	2394.04	3159.53	2.05	99.36	8.31
湖南Hunan	836.50	1308.06	2144.55	2.22	1148.37	3296.54	4444.91	2.68	107.27	20.77
广东Guangdong	583.01	1708.40	2291.41	2.05	592.18	4936.43	5528.61	2.95	141.28	43.86
广西Guangxi	110.60	350.32	460.92	0.66	239.60	1345.78	1585.38	1.17	243.96	77.14
海南Hainan	83.68	47.86	131.53	0.45	128.40	274.22	402.62	0.62	206.10	39.30
重庆Chongqing	516.53	545.55	1062.08	3.21	216.22	801.47	1017.69	1.67	-4.18	-48.02
四川Sichuan	343.76	1635.67	1979.43	1.62	423.90	2486.51	2910.40	1.45	47.03	-10.77
贵州Guizhou	596.98	1980.85	2577.83	7.95	510.12	1877.51	2387.62	3.47	-7.38	-56.27
云南Yunnan	444.04	789.31	1233.35	2.26	520.67	1827.06	2347.73	1.97	90.35	-12.94
陕西Shaanxi	136.22	311.63	447.85	1.27	226.44	1484.46	1710.90	2.37	282.03	86.43
甘肃Gansu	198.20	699.18	897.39	3.45	228.52	1042.95	1271.47	2.29	41.69	-33.66
青海Qinghai	17.06	130.69	147.74	3.07	33.95	230.79	264.73	2.84	79.18	-7.47
宁夏Ningxia	31.88	210.07	241.95	4.25	45.80	185.56	231.35	1.93	-4.38	-54.59
新疆Xinjiang	431.26	711.48	1142.74	3.02	658.32	1279.08	1937.41	2.33	69.54	-22.75

Table 3

Table 4

Table 5

Local spatial clustering of rural energy carbon emission intensity in China from 2005 to 2019"

类型Type	2005	2010	2015	2019
高-高集聚 High-high agglomeration	北京Beijing、天津Tianjin、甘肃Gansu、青海Qinghai、宁夏Ningxia、新疆Xinjiang	北京Beijing、天津Tianjin、山西Shanxi、内蒙古Inner Mongolia、甘肃Gansu、青海Qinghai、宁夏Ningxia	北京Beijing、天津Tianjin、河北Hebei、山西Shanxi、内蒙古Inner Mongolia、浙江Zhejiang	北京Beijing、天津Tianjin、河北Hebei、山西Shanxi、上海Shanghai、浙江Zhejiang
低-低集聚 Low-low agglomeration	辽宁Liaoning、吉林Jilin、黑龙江Heilongjiang、江苏Jiangsu、安徽Anhui、福建Fujian、江西Jiangxi、湖北Hubei、湖南Hunan、广东Guangdong、广西Guangxi、海南Hainan	辽宁Liaoning、吉林Jilin、黑龙江Heilongjiang、江苏Jiangsu、安徽Anhui、福建Fujian、江西Jiangxi、湖北Hubei、湖南Hunan、广东Guangdong、广西Guangxi、海南Hainan、云南Yunnan	辽宁Liaoning、吉林Jilin、黑龙江Heilongjiang、江苏Jiangsu、安徽Anhui、福建Fujian、江西Jiangxi、湖北Hubei、湖南Hunan、广西Guangxi、海南Hainan、重庆Chongqing、四川Sichuan、云南Yunnan、陕西Shaanxi	辽宁Liaoning、吉林Jilin、黑龙江Heilongjiang、江苏Jiangsu、安徽Anhui、福建Fujian、江西Jiangxi、湖北Hubei、广西Guangxi、海南Hainan、重庆Chongqing、四川Sichuan、云南Yunnan、陕西Shaanxi、甘肃Gansu、新疆Xinjiang
高-低集聚 High-low agglomeration	山西Shanxi、上海Shanghai、重庆Chongqing、贵州Guizhou	上海Shanghai、重庆Chongqing、贵州Guizhou	上海Shanghai、广东Guangdong、贵州Guizhou、青海Qinghai	湖南Hunan、广东Guangdong、贵州Guizhou、青海Qinghai
低-高集聚 Low-high agglomeration	河北Hebei、内蒙古Inner Mongolia、浙江Zhejiang、山东Shandong、河南Henan、四川Sichuan、云南Yunnan、陕西Shaanxi	河北Hebei、浙江Zhejiang、山东Shandong、河南Henan、四川Sichuan、陕西Shaanxi、新疆Xinjiang	山东Shandong、河南Henan、甘肃Gansu、宁夏Ningxia、新疆Xinjiang	内蒙古Inner Mongolia、山东Shandong、河南Henan、宁夏Ningxia

Table 5

Table 6

Table 7

Table 8

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变量名称 Variable name	样本数 Sample number	均值 Mean value	标准差 Standard deviation	最小值 Minimum value	最大值 Maximum value
农村能源碳排放强度 Rural energy carbon emission intensity (t/10⁴yuan)	450	2.749	1.696	0.419	10.607
农村富裕程度 Rural wealth (×10⁴ yuan)	450	0.320	0.129	0.147	0.839
农业技术进步 Agricultural technology progress	450	1.019	0.040	0.012	0.190
农村劳动力结构 Rural labor force structure	450	0.649	0.227	0.127	1.000
农村金融集聚 Rural financial agglomeration	450	1.167	1.333	0.159	5.792
农业发展水平 Agricultural development level (×10⁴ yuan/person)	450	1.045	0.522	0.217	2.903
农业财政投资 Agriculture fiscal investment	450	0.103	0.036	0.012	0.190
农业产业集聚Agricultural industrial agglomeration	450	1.014	0.409	0.089	1.829
农业产业结构Agricultural industrial structure	450	0.825	0.103	0.538	0.964

年份 Year	农业生产 Agricultural production (×10⁴t)	居民生活 Resident living (×10⁴t)	合计 Total (×10⁴t)	强度 Intensity (t/10⁴yuan)
2005	12334.34	26847.81	39182.16	2.13
2006	12656.51	28274.48	40930.99	2.12
2007	12732.59	30399.30	43131.89	2.14
2008	11916.75	32331.41	44248.16	2.09
2009	12228.52	33703.07	45931.60	2.08
2010	12771.94	36287.31	49059.25	2.13
2011	13813.10	39867.43	53680.53	2.23
2012	14900.85	42531.69	57432.54	2.27
2013	14014.79	44749.21	58764.01	2.23
2014	14341.31	46097.02	60438.33	2.21
2015	14878.24	48980.14	63858.39	2.25
2016	15191.01	48451.03	63642.04	2.17
2017	15685.77	52983.72	68669.49	2.25
2018	13555.81	55314.80	68870.62	2.18
2019	13366.81	56201.47	69568.28	2.14
增速Growth rate	0.58%·a^-1	5.42%·a^-1	4.19%·a^-1	0.03%·a^-1

年份 Year	Moran’s I	P值 P value	Z统计值 Z statistics	年份 Year	Moran’s I	P值 P value	Z统计值 Z statistics
2005	-0.005	0.369	0.335	2013	0.196	0.005	2.594
2006	-0.000	0.348	0.390	2014	0.196	0.005	2.585
2007	0.035	0.213	0.796	2015	0.204	0.003	2.702
2008	0.105	0.053	1.612	2016	0.212	0.003	2.792
2009	0.110	0.045	1.696	2017	0.157	0.015	2.158
2010	0.130	0.024	1.979	2018	0.208	0.003	2.747
2011	0.123	0.028	1.914	2019	0.193	0.005	2.599
2012	0.139	0.025	1.967	—	—	—	—

年份Year	lnACI	lnRW	lnATP	lnRLS	lnRFA	lnADL	lnAFI	lnLA	lnAS
2005	0.125^**	0.412^***	0.012	0.420^***	0.154^**	0.322^***	0.005	0.340^***	0.301^***
2006	0.116^**	0.410^***	0.128^**	0.296^***	0.105^*	0.327^***	-0.004	0.332^***	0.304^***
2007	0.137^**	0.409^***	0.154^**	0.358^***	0.143^**	0.323^***	0.182^***	0.358^***	0.307^***
2008	0.190^***	0.405^***	-0.114	0.329^***	0.147^**	0.319^***	0.155^**	0.359^***	0.300^***
2009	0.202^***	0.401^***	0.031	0.321^***	0.136^**	0.315^***	0.205^***	0.363^***	0.299^***
2010	0.227^***	0.399^***	0.153^**	0.348^***	0.149^**	0.322^***	0.196^***	0.360^***	0.304^***
2011	0.218^***	0.398^***	0.049	0.349^***	0.151^**	0.317^***	0.110^*	0.365^***	0.297^***
2012	0.194^***	0.396^***	-0.049	0.345^***	0.153^**	0.310^***	0.246^***	0.388^***	0.320^***
2013	0.226^***	0.395^***	0.182^***	0.345^***	0.156^**	0.293^***	0.165^**	0.392^***	0.332^***
2014	0.209^***	0.393^***	-0.052	0.336^***	0.154^**	0.306^***	0.140^**	0.386^***	0.346^***
2015	0.217^***	0.391^***	-0.169^*	0.332^***	0.157^**	0.272^***	0.151^**	0.390^***	0.360^***
2016	0.270^***	0.389^***	0.116^**	0.321^***	0.164^**	0.245^***	0.220^***	0.390^***	0.374^***
2017	0.173^**	0.391^***	0.006	0.328^***	0.163^**	0.237^***	0.215^***	0.388^***	0.380^***
2018	0.202^***	0.390^***	0.010	0.343^***	0.168^**	0.217^***	0.266^***	0.379^***	0.375^***
2019	0.194^***	0.389^***	0.014	0.332^***	0.159^**	0.179^**	0.285^***	0.374^***	0.342^***

变量 Variable	直接效应Direct effect		间接效应Indirect effect		总效应Total effect
变量 Variable	系数Coefficient	t值t value	系数Coefficient	t值t value	系数Coefficient	t值t value
lnRW	3.626^***	5.50	-7.377^***	-4.65	-3.751^**	-2.48
lnATP	-0.749^***	-3.04	0.286	0.42	-0.462	-0.61
lnRLS	-0.238^***	-2.98	-0.015	-0.05	-0.254	-0.77
lnRFA	0.186^**	2.31	0.792^**	2.52	0.978^***	2.81
lnADL	0.230^**	2.41	-0.771^**	-2.47	-0.541	-1.58
lnAFI	0.032	0.75	-0.265^*	-1.78	-0.233	-1.40
lnLA	0.530^***	3.81	-1.937^***	-4.03	-1.407^***	-2.70
lnAS	0.288	1.12	-0.562	-0.82	-0.273	-0.36

Spatial-Temporal Pattern, Influencing Factors and Spatial Spillover Effect of Rural Energy Carbon Emissions in China

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变量 Variable	直接效应Direct effect		间接效应Indirect effect		总效应Total effect
变量 Variable	系数Coefficient	t值t value	系数Coefficient	t值t value	系数Coefficient	t值t value
lnRW	3.567^***	5.70	-12.070^***	-3.46	-8.503^**	-2.49
lnATP	-0.774^***	-3.24	0.305	0.22	-0.469	-0.32
lnRLS	-0.133	-1.51	0.792	1.25	0.659	0.97
lnRFA	0.208^***	2.60	1.502^**	2.55	1.710^***	2.78
lnADL	0.151	1.58	-1.868^***	-2.79	-1.717^**	-2.46
lnAFI	0.036	0.84	-0.427	-1.51	-0.391	-1.31
lnLA	0.328^**	2.29	-5.081^***	-4.23	-4.753^***	-3.77
lnAS	0.242	0.95	-2.080	-1.49	-1.838	-1.28