林业有害生物防治压力区域差异及影响因素分析

doi:10.3969/j.issn.1000-2006.201811036

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南京林业大学学报（自然科学版） ›› 2020, Vol. 44 ›› Issue (1): 111-118.doi: 10.3969/j.issn.1000-2006.201811036

林业有害生物防治压力区域差异及影响因素分析

才琪¹(), 才玉石², 李岩¹, 侯一蕾¹, 温亚利¹^,^*()

1.北京林业大学经济管理学院,北京 100083
2.国家林业和草原局森林和草原病虫害防治总站,辽宁沈阳 110034

收稿日期:2018-11-23 修回日期:2019-03-28 出版日期:2020-02-08 发布日期:2020-02-02
通讯作者: 温亚利
作者简介:才琪( caiqilinda1010@126.com),博士。
基金资助:
国家林业局林业软科学项目(2017-R04)

The spatially differentiated factors of forest pest control pressure

CAI Qi¹(), CAI Yushi², LI Yan¹, HOU Yilei¹, WEN Yali¹^,^*()

1. School of Economics and Management, Beijing Forestry University, Beijing 100083,China
2. General Station of Forest and Grassland Pest Management, National Forestry and Grassland Administration, Shenyang 110034,China

Received:2018-11-23 Revised:2019-03-28 Online:2020-02-08 Published:2020-02-02
Contact: WEN Yali

摘要/Abstract

摘要：

【目的】林业有害生物防治面临区域差异化的压力,加重了森林资源的经济与生态损失。测度我国林业有害生物防治压力及其影响因素,探索防治压力的区域分布特征,从而针对性地提出优化措施。【方法】分析林业有害生物防治压力现状,利用聚类分析法将各省份防治压力分成3类,结合面板校正标准误模型评价产生防治压力的原因及影响因素,并与防治率及无公害防治率一同呈现于地图,进行区域差异特征比较。【结果】我国林业有害生物危害的总体防治压力较重,减灾机制只能在一半程度上缓解灾害暴发的扩散趋势,投入资金的使用效率严重不足,从业人员及机构数量尚不能满足区域化防治压力需求,且管理机制存在一定的“马太效应”,“胡线”西部生态化防治意识较弱,目前采取的措施不足以缓解较大的防治压力。【结论】针对区域发展需求完善基础设施建设,提高从业人员专业化水平,调动社会力量构建社会化防治组织,缓解区域差异及政府防治压力,保障森林资源和国家生态安全。

关键词: 林业有害生物, 防治压力, 区域差异, 面板校正标准误模型

Abstract:

【Objective】 As China is facing spatially differentiated forest pest control pressure, the economic and ecological losses of forest resources would increase. In this paper, we assessed the control pressure and its influencing factors, summarized the spatial features, and proposed advices to improve the outcomes. 【Method】 We first analyzed the current control pressure and divided the natural control pressure for each province into three degrees through the Cluster Analysis method; then, we examined the reasons and factors driving the spatial differentiation of the control pressure based on the panel calibration standard error model (PCSE). We depicted the final results on the map of China and compared them with the control rate and ecological control rate.【Result】 The results showed that China forest pest control is under huge pressure, as the recent control system could only recover half of the damage with low efficiency of the input fund utilization; in addition, the number of control workers and institutions are far from satisfying the regional control necessity. Moreover, according to the “Matthew Effect”, the ecological control awareness in the western part of “HU Huanyong” scored weaker than in the eastern part, while the control measures hardly satisfied the increased control pressure in the west. 【Conclusion】 Firstly, the government needs to provide the infrastructure according to regional development needs, and improve the professional skills of control workers. Then, socialized control organizations should be established by mobilizing social forces to diminish the regional differences and solitary governmental control pressure, while also reinforcing the ecological balance of natural resources.

Key words: forest pest disaster, control pressure, spatially difference, panel calibration standard error model (PCSE)

中图分类号:

S763

才琪,才玉石,李岩,等. 林业有害生物防治压力区域差异及影响因素分析[J]. 南京林业大学学报（自然科学版）, 2020, 44(1): 111-118.

CAI Qi, CAI Yushi, LI Yan, HOU Yilei, WEN Yali. The spatially differentiated factors of forest pest control pressure[J].Journal of Nanjing Forestry University (Natural Science Edition), 2020, 44(1): 111-118.DOI: 10.3969/j.issn.1000-2006.201811036.

图/表 6

图1

图2

图3

表1

表2

各地区防治压力对平均防治压力的偏离( ∂ I * )及防治现状 "

东部地区 eastern region	防治压力偏差 control pressure deviation	防治率/ % control rate	无公害防治率/% un- pollution control rate	中部地区 midland	防治压力偏差 control pressure deviation	防治率/ % control rate	无公害防治率/% un- pollution control rate	西部地区 western region	防治压力偏差 control pressure deviation	防治率/ % control rate	无公害防治率/% un- pollution control rate
北京 Beijing	-2.101^***	99.78	99.65	江西 Jiangxi	-1.507^***	68.57	92.68	广西 Guangxi	-2.472^***	19.42	89.91
天津 Tianjin	-2.025^***	99.33	97.04	山西 Shanxi	-1.751^***	58.88	80.8	重庆 Chongqing	-1.446^***	64.57	99.17
河北 Hebei	-1.044^***	85.88	88.91	安徽 Anhui	-1.185^***	83.78	89.02	内蒙古 Inner Mongolia	-1.095^***	51.53	91.68
辽宁 Liaoning	-0.895^***	85.35	96.92	河南 Henan	-1.02^***	84.6	85.54	四川 Sichuan	-0.941^***	70	91.36
上海 Shanghai	-2.647^***	98.00	81.49	湖北 Hubei	-1.326^**	71.43	92.77	贵州 Guizhou	-1.372^***	82.64	96.17
江苏 Jiangsu	-1.762^***	92.11	84.31	湖南 Hunan	-1.546^***	77.45	93.32	云南 Yunnan	-1.058^***	92.87	81.26
浙江 Zhejiang	-1.833^***	91.87	89.81	吉林 Jilin	-1.523^***	78.26	72.6	西藏 Tibet	-1.325^***	70.78	84.56
福建 Fujian	-1.614^***	93.34	98.84	黑龙江 Heilongjiang	-1.096^***	82.58	95.05	陕西 shaanxi	-1.326^***	75.16	88.83
山东 Shandong	-1.005^***	97.34	96.39					甘肃 Gansu	-1.412^***	58.24	80.55
广东 Guangdong	-2.283^***	46.14	88.91					青海 Qinghai	-1.495^***	68.62	97.35
海南 Hainan	-3.131^***	32.12	81.72					宁夏 Ningxia	-1.66^***	37.49	80.55
								新疆 Xinjiang	-1.014^***	61.39	87.51
平均值 mean value	-1.85	83.75	91.27	平均值 mean value	-1.37	75.69	87.72	平均值 mean value	-1.37	62.73	89.08

表2

图4

参考文献 24

[1]	张继权, 刘兴朋, 严登华. 综合灾害风险管理导论[M]. 北京: 北京大学出版社, 2012.
	ZHANG J Q, LIU X P, YAN D H. Introduction to integrated disaster risk management [M]. Beijing: Peking University Press, 2012.
[2]	VALENTA V, MOSER D, KAPELLER S, et al. A new forest pest in Europe: a review of Emerald ash borer (Agrilus planipennis) invasion[J]. Journal of Applied Entomology, 2017, 141(7):507-526. DOI: 10.1111/jen.12369. doi: 10.1111/jen.12369
[3]	宋玉双, 苏宏钧, 于海英, 等. 2006—2010年我国林业有害生物灾害损失评估[J]. 中国森林病虫, 2011, 30(6):1-4, 24.
	SONG Y S, SU H J, YU H Y, et al. Evaluation of economic losses caused by forest pest disasters between 2006 and 2010 in China[J]. Forest Pest and Disease, 2011, 30(6):1-4, 24. DOI: 10.3969/j.issn.1671-0886.2011.06.001. doi: 10.3969/j.issn.1671-0886.2011.06.001
[4]	闫峻. 我国林业生物灾害管理的经济学分析与对策研究[D]. 北京:北京林业大学, 2008.
	YAN J. Economic analysis and countermeasure of forest bio-disaster management in China[D]. Beijing: Beijing Forestry University, 2008.
[5]	国务院. 森林病虫害防治条例[J]. 内蒙古林业 1990(2):2-3.
[6]	国家林业局. 中国林业统计年鉴[M]. 北京: 中国林业出版社, 2014.
[7]	乔恒, 高峻崇. 林业有害生物灾害应急管理[M]. 北京: 中国林业出版社, 2011.
	QIAO H, GAO J C. Emergency management of forest pest disasters [M]. Beijing: China Forestry Publishing House, 2011.
[8]	李亚娟, 陈田, 王婧, 等. 中国历史文化名村的时空分布特征及成因[J]. 地理研究, 2013, 32(8):1477-1485.
	LI Y J, CHEN T, WANG J, et al. Temporal-spatial distribution and formation of historic and cultural villages in China[J]. Geographical Research, 2013, 32(8):1477-1485.
[9]	孔钦钦, 郑景云, 王新歌. 1979—2014年中国气候舒适度空间格局及时空变化[J]. 资源科学, 2016, 38(6):1129-1139.
	KONG Q Q, ZHENG J Y, WANG X G. Spatial pattern and temporal variation in thermal comfort in China from 1979 to 2014[J]. Resources Science, 2016, 38(6):1129-1139. DOI: 10.18402/resci.2016.06.12. doi: 10.18402/resci.2016.06.12
[10]	杨远盛, 张晓霞, 于海艳, 等. 中国森林生物量的空间分布及其影响因素[J]. 西南林业大学学报, 2015, 35(6):45-52.
	YANG Y S, ZHANG X X, YU H Y, et al. The spatial distribution of China’s forest biomass and its influencing factors[J]. Journal of Southwest Forestry University, 2015, 35(6):45-52. DOI: 10.11929/j.issn.2095-1914.2015.06.008. doi: 10.11929/j.issn.2095-1914.2015.06.008
[11]	胡焕庸. 中国人口之分布——附统计表与密度图[J]. 地理学报 1935(2):33-74. doi: 10.11821/xb193502002
	HU H Y. The distribution of population in China, with statistics and maps[J]. Acta Geographica Sinica, 1935(2):33-74.
[12]	GAO Y Y, ZHENG J H, BU M L. Rural-urban income gap and agricultural growth in China[J]. China Agricultural Economic Review, 2014, 6(1):92-107. DOI: 10.1108/caer-02-2012-0016. doi: 10.1108/caer-02-2012-0016
[13]	HU Z C, WANG Y L, LIU Y S, et al. Spatio-temporal patterns of urban-rural development and transformation in east of the “Hu Huanyong Line”, China[J]. ISPRS International Journal of Geo-Information, 2016, 5(3):24. DOI: 10.3390/ijgi5030024. doi: 10.3390/ijgi5030024
[14]	徐玮, 冯彦, 包庆丰. 中国林业生产效率测算及区域差异分析: 基于Malmquist-DEA模型的省际面板数据[J]. 林业经济, 2015, 37(5):85-88.
	XU W, FENG Y, BAO Q F. China’s forestry production efficiency calculation and the analysis of regional difference: based on malmquist-DEA model provincial panel data[J]. Forestry Economics, 2015, 37(5):85-88. DOI: 10.13843/j.cnki.lyjj.2015.05.017. doi: 10.13843/j.cnki.lyjj.2015.05.017
[15]	史常亮, 朱俊峰, 揭昌亮. 中国农业全要素生产率增长地区差异及收敛性分析:基于固定效应SFA模型和面板单位根方法[J]. 经济问题探索 2016(4):134-141.
	SHI C L, ZHU J F, JIE C L. Regional differences and convergence of total factor productivity growth in China: based on SFA model and panel unit root method[J]. Inquiry into Economic Issues, 2016(4):134-141.
[16]	罗小锋, 李兆亮, 李容容, 等. 中国林业生产效率的时空差异及其影响因素研究[J]. 干旱区资源与环境, 2017, 31(3):95-100.
	LUO X F, LI Z L, L R R, et al. Temporal and regional variation of forestry production efficiency in China[J]. Journal of Arid Land Resources and Environment, 2017, 31(3):95-100.
[17]	KUMAR Ar, KUMAR Ak. Effect of abiotic and biotic factors on incidence of pests and predator in cowpea [Vigna unguiculata(L.) Walp.][J]. Legume Research-an International Journal, 2015, 38(1):121. DOI: 10.5958/0976-0571.2015.00020.x. doi: 10.5958/0976-0571.2015.00020.x
[18]	GE X Z, JIANG C, CHEN L H, et al. Predicting the potential distribution in China of Euwallacea fornicatus (Eichhoff) under current and future climate conditions[J]. Scientific Reports, 2017, 7:906. DOI: 10.1038/s41598-017-01014-w. doi: 10.1038/s41598-017-01014-w
[19]	闫峻, 刘俊昌. 论林业有害生物防治的外部经济效应[J]. 中国森林病虫, 2005, 24(6):12-14.
	YAN J, LIU J C. Economic externalities of the forest pest management[J]. Forest Pest and Disease, 2005, 24(6):12-14. DOI: 10.3969/j.issn.1671-0886.2005.06.004. doi: 10.3969/j.issn.1671-0886.2005.06.004
[20]	姜宝, 邢晓丹, 李剑. “走出去”战略下中国对欧盟逆向投资的贸易效应研究:基于FGLS和PCSE修正的面板数据模型[J]. 国际贸易问题 2015(9):167-176.
	JIANG B, XING X D, LI J. Trade effects of China’s upstream investment to EU under“Going Global”Strategy: a study based on FGLS and PCSE panel data regression estimation methods[J]. Journal of International Trade, 2015(9):167-176. DOI: 10.13510/j.cnki.jit.2015.09.015. doi: 10.13510/j.cnki.jit.2015.09.015
[21]	ZHOU Y, LI N, WU W X, et al. Socioeconomic development and the impact of natural disasters: some empirical evidences from China[J]. Natural Hazards, 2014, 74(2):541-554. DOI: 10.1007/s11069-014-1198-0. doi: 10.1007/s11069-014-1198-0
[22]	ZHANG K R, SONG C H, ZHANG Y L, et al. Natural disasters and economic development drive forest dynamics and transition in China[J]. Forest Policy and Economics, 2017, 76:56-64. DOI: 10.1016/j.forpol.2015.08.010. doi: 10.1016/j.forpol.2015.08.010
[23]	CAI Q, CAI Y S, WEN Y L. Spatially differentiated trends between forest pest-induced losses and measures for their control in China[J]. Sustainability, 2018, 11(1):73. DOI: 10.3390/su11010073. doi: 10.3390/su11010073
[24]	国家林业局森林病虫害防治总站. 林业有害生物监测预报技术[M]. 北京: 中国林业出版社, 2013.
	State of Forest Pest Control Administration (SFPCA). Forest pest monitoring and forecasting technology [M]. Beijing: China Forestry Publishing House, 2013.

影响因素 factor	系数 coefficient	标准误 SE	Z
ln F	0.532^***	0.466	11.43
ln T	0.084^***	0.021	4.06
ln L	-0.023	0.046	0.51
ln S	0.006	0.041	0.15
常数项	6.395^***	0.765	8.36
R²=0.960 1		P=0.00

林业有害生物防治压力区域差异及影响因素分析

The spatially differentiated factors of forest pest control pressure

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