中国秸秆优质化能源开发利用特征及影响因素

doi:10.18402/resci.2019.10.03

摘要/Abstract

摘要：

为考察中国秸秆优质化能源开发利用特征,本文在分析中国2009—2016年秸秆优质化能源产量年际变化、区域分布及结构特征的基础上,采用增强回归树方法定量分析了秸秆优质化能源开发利用水平的省际差异及其影响因素。结果表明：中国秸秆优质化能源产量从2009年的119.63万t标准煤上升到2016年的508.51万t标准煤,以生物气化和固化成型为主,其中生物气化比重逐渐下降,固化成型比重迅速上升。全国秸秆优质化能源开发利用水平均值从2009年的7.54 kg标准煤/hm ²上升到2016年的30.51 kg标准煤/hm ²。供给、需求和环境因素对其省际差异的贡献率分别为37.10%、35.60%和22.30%。秸秆优质化能源开发利用水平总体上随着粮食作物播种面积比例、农村居民人均纯收入、农村人均用电量和农村能源财政投入的上升而上升,随着人均耕地面积和机耕面积比例的上升而下降,随着饲草需求强度的上升呈先上升后下降趋势。当前中国秸秆优质化能源开发利用很大程度上依赖于农村经济发展水平的提高和行政力量的推动。西北、东北和华中等地区应加强对秸秆优质化能源开发利用的财政和科技支持力度。

关键词: 秸秆, 优质化能源, 省际差异, 影响因素, 增强回归树, 中国

Abstract:

In order to investigate the characteristics of China’s straw-based high quality energy development and utilization, this study calculated the straw-based high quality energy in China from 2009 to 2016, and analyzed its yearly changes, regional distribution, and structural characteristics. The straw-based high quality energy productivity per unit sown area was used to reflect the development and utilization level of straw-based high quality energy. The boosting regression tree method was used to analyze the influencing factors of provincial differences of straw-based high quality energy productivity, and the contribution percentages were calculated. The results show that the straw-based high quality energy productivity increased from 1.1963 million ton of coal equivalent (tce) in 2009 to 5.3443 million tce in 2014, and the annual growth rate was 34.9%. The biogas and straw pelletized fuel are the two dominant energy types. The bio-gasification rate decreased, mainly distributed in the midwest and three provinces in southern China. The percentage of pelletized fuel rapidly increased and currently mainly distributed in the eastern region and the three provinces in central China. The development and utilization level increased from 7.54 kg of coal equivalent (kgce) per hm ² in 2009 to 32.30 kgce/hm ² in 2014. The differences between the seven regions and intraregional differences among the provinces are relatively large, and on the whole have a tendency to expand. The boosting regression tree analysis results show that this method can better fit the relationship between the development and utilization level and its influencing factors. The contribution rate of supply factors, demand factors, and environmental factors to provincial differences of the development and utilization level of straw-based high quality energy were 37.10%, 35.60%, and 22.30%. The development and utilization level of straw-based high quality energy was positively related to the proportion of grain cropping area, per capita net income of rural residents, rural per capita consumption of energy, and financial investment and negatively related to per capita arable land and the areal proportion of tractor use, and showed a “V” type relationship with forage demand intensity. The per capita net income of rural residents and rural financial investment contributed most significantly to the development and utilization of straw-based high quality energy. The development & utilization of high-quality straw-based energy depends to a large extent on the improvement of rural economic development level and the promotion of administrative power. In the northwest, northeast and central China, financial and technology support should be strengthened to improve the development & utilization level of high-quality straw-based energy.

Key words: straw, high-quality energy, provincial differences, influencing factors, boosting regression tree, China

王明新,叶倩,王迪. 中国秸秆优质化能源开发利用特征及影响因素[J]. 资源科学, 2019, 41(10): 1791-1800.

WANG Mingxin,YE Qian,WANG Di. Development and utilization characteristics and influencing factors of straw-based high quality energy in China[J]. Resources Science, 2019, 41(10): 1791-1800.

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地区	2009	2010	2011	2012	2013	2014	2015	2016
北京	430.42	433.48	407.12	302.48	330.67	406.66	1194.40	115.26
天津	31.18	54.81	48.69	30.37	27.08	382.52	31.00	85.46
河北	5.27	11.54	15.10	16.76	24.72	75.24	50.80	68.92
山西	8.24	8.80	9.45	9.81	8.14	7.45	7.77	7.88
内蒙古	1.04	1.84	2.88	3.64	4.21	3.26	2.18	4.40
辽宁	27.11	64.42	59.46	61.27	65.11	64.98	82.66	75.39
吉林	0.26	0.38	1.23	1.03	1.18	1.14	1.13	1.13
黑龙江	5.34	13.84	30.97	6.54	33.50	29.97	14.67	13.35
上海	0.00	9.43	16.21	17.66	35.54	47.87	57.24	137.04
江苏	14.38	46.26	82.58	108.94	121.96	111.82	97.35	98.44
浙江	28.40	75.46	51.68	69.88	90.47	99.24	116.43	106.10
安徽	2.04	2.19	9.66	17.09	23.36	20.71	32.94	43.54
福建	19.37	14.99	35.66	36.81	36.37	34.62	34.28	34.33
江西	5.47	10.85	9.63	11.50	13.68	14.96	15.59	16.44
山东	18.27	12.52	43.79	63.19	64.90	66.24	45.10	28.90
河南	4.12	13.95	13.95	18.71	14.31	22.81	25.00	31.66
湖北	2.70	5.33	25.16	30.57	29.20	38.56	41.37	41.08
湖南	3.14	4.51	14.34	14.53	23.00	28.45	17.46	25.11
广东	8.88	12.41	14.13	28.99	28.61	32.25	48.07	45.82
广西	2.53	3.09	4.02	4.19	3.69	3.01	2.54	4.15
海南	58.51	60.34	106.42	87.77	93.71	96.77	113.89	120.72
重庆	2.66	4.29	6.25	7.00	6.79	8.15	11.20	14.39
四川	7.66	10.14	20.31	23.71	27.10	28.76	30.47	41.71
贵州	2.75	1.89	3.96	4.46	4.97	5.53	6.52	8.59
云南	0.32	0.28	0.23	0.28	0.36	0.40	0.45	0.84
西藏	0.00	0.00	0.00	4.22	0.73	0.73	1.49	3.99
陕西	0.75	1.29	2.33	3.69	3.59	2.74	3.70	5.52
甘肃	1.88	2.79	2.98	3.15	3.26	3.85	4.47	7.53
青海	1.71	3.92	6.06	1.30	2.59	2.60	2.87	2.86
宁夏	1.98	2.19	3.99	7.40	9.31	9.64	9.83	9.75
新疆	0.62	1.15	2.20	2.74	3.51	4.20	6.08	6.84
全国	7.54	12.98	21.00	23.55	27.66	32.30	29.39	30.51

	2009	2010	2011	2012	2013	2014	2015	2016
华北	11.72	15.26	16.37	15.15	18.10	47.29	34.36	34.46
东北	8.16	20.17	29.25	15.78	31.58	29.24	24.13	21.59
华东	12.36	20.79	38.33	52.37	58.74	57.14	52.25	50.34
华中	3.49	9.14	16.99	20.66	20.61	28.48	27.07	32.18
华南	9.26	11.08	15.60	20.29	20.25	22.03	29.06	29.34
西南	4.04	5.03	9.61	11.03	12.26	13.13	14.41	19.61
西北	1.14	1.79	2.69	3.45	3.86	4.04	5.15	6.72
全国	7.54	12.98	21.00	23.55	27.66	32.30	29.39	30.51