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

摘要/Abstract

摘要：

【目的】基于当前“双碳”战略的大背景，厘清农村能源碳排放现状特征、时空格局及其影响因素，为有效推进农村低碳发展提供重要支撑。【方法】利用碳排放因子法对我国农村能源碳排放进行有效测度并分析其时空特征；而后运用自相关模型对其空间关联格局进行探讨；最后通过STIRPAT扩展模型的引入剖析影响其强度变化的主要因素并分析空间溢出效应。【结果】我国农村能源碳排放总量整体处于持续上升态势，2019年比2005年增加了77.55%，从成因来看主要归结于农村居民生活能源消费量的增加；农村能源碳排放强度在考察期内略有上升，虽存在一定年际起伏但总体波动较小。2019年农村能源碳排放量存在明显的省际差异且以河北居首宁夏最末，相比2005年仅有5个省（市、区）整体处于下降趋势；2019年农村能源碳排放强度以北京居首而海南处于最后一位，后者甚至不及前者1/10。2008年以来我国农村能源碳排放既表现出明显且稳定的空间依赖性，同时也存在局部空间聚类现象，其中高-高集聚型省份数量较少且相对稳定，而低-低集聚型省份数量较多,且处于增长态势。在社会层面因素中，农村富裕程度提升会导致农村能源碳排放强度增加,而农业技术进步与农村劳动力结构变量却能起到抑制作用，其中仅农村富裕程度表现出了空间溢出效应且作用方向为负。在经济层面因素中，农村金融集聚度与农业发展水平的提升均导致了农村能源碳排放强度的增加，同时二者都具有空间溢出效应且前者作用方向为正后者为负；而农业财政投资虽不存在直接效应但却表现出了负向的空间溢出效应。在产业层面因素中，农业产业集聚度提升会导致农村能源碳排放强度增加，但同时却也呈现出了负向的空间溢出效应。【结论】我国农村能源碳排放总量、强度整体均呈上升趋势，同时省际差异明显；我国农村能源碳排放表现出了明显的空间依赖性与空间异质性；农村能源碳排放受社会、经济以及产业等三个层面因素的共同影响。

关键词: 农村能源碳排放, 农业碳排放, 时空格局, 影响因素, 溢出效应

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

田云, 尹忞昊, 张蕙杰. 我国农村能源碳排放时空格局、影响因素及空间溢出效应[J]. 中国农业科学, 2023, 56(13): 2547-2562.

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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链接本文: https://www.chinaagrisci.com/CN/10.3864/j.issn.0578-1752.2023.13.009

https://www.chinaagrisci.com/CN/Y2023/V56/I13/2547

图/表 8

表1

表2

表3

我国30个省（市、区）农村能源碳排放总量及强度比较"

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

表3

表4

表5

2005—2019年我国农村能源碳排放强度的局部空间聚类情况"

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

表5

表6

表7

表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