基于倾向得分匹配方法的中国自然保护区缓解人类活动压力评估

doi:10.11821/dlxb202103013

地理学报 ›› 2021, Vol. 76 ›› Issue (3): 680-693.doi: 10.11821/dlxb202103013

基于倾向得分匹配方法的中国自然保护区缓解人类活动压力评估

张涵¹(), 黎夏²(), 石洪¹, 刘晓娟¹

1.中山大学地理科学与规划学院广东省城市化与地理环境空间模拟重点实验室,广州 510275
2.华东师范大学地理科学学院地理信息科学教育部重点实验室,上海 200241

收稿日期:2020-03-22 修回日期:2020-12-20 出版日期:2021-03-25 发布日期:2021-05-25
通讯作者: 黎夏(1962-), 男, 广西梧州人, 教授, 研究方向为元胞自动机、地理模拟系统、全球土地利用变化模拟模型。E-mail: lixia@geo.ecnu.edu.cn
作者简介:张涵(1996-), 女, 云南巍山人, 硕士生, 研究方向为自然保护区有效性评估。E-mail: zhangh575@mail2.sysu.edu.cn
基金资助:
国家自然科学基金项目(41531176);国家重点研发计划(2017YFA0604402)

An assessment of the effectiveness of China's nature reserves for mitigating anthropogenic pressures based on propensity score matching

ZHANG Han¹(), LI Xia²(), SHI Hong¹, LIU Xiaojuan¹

1. Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
2. Key Lab of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China

Received:2020-03-22 Revised:2020-12-20 Published:2021-03-25 Online:2021-05-25
Supported by:
National Natural Science Foundation of China(41531176);National Key R&D Program of China(2017YFA0604402)

摘要/Abstract

摘要：

科学地评估自然保护区缓解人类活动压力的效果,对于有效的自然保护至关重要。目前中国国家尺度下的这方面研究,仅将保护区内、外的人类活动压力进行对比,其缺陷是样本选择性偏差会导致评估结果的不合理。本文选择了倾向得分匹配（Propensity Score Matching）方法来克服保护区评价中的样本选择性偏差问题。首先耦合多源数据构建了2013年、2015年、2017年的人类活动压力指数,然后采用倾向得分匹配方法对自然保护区内、外的随机点进行匹配,使两个对比组的观测变量尽可能相似。最后通过相对有效性指标和面板模型从不同层面评估了中国680个自然保护区在2013—2017年间缓解人类活动压力的效果。研究结果表明：① 2013—2017年,全国86.72%的自然保护区内人类活动压力指数呈现上升趋势,其面积占保护区总面积的43.80%。② 69.85%的自然保护区在缓解人类活动压力方面的效果较好。其中,除了海洋海岸、野生植物、野生动物类的保护区以外,其余保护区类型都表现出较好的缓解人类活动压力的效果,且保护区级别越高,保护效果越好。③ 中国自然保护区建设在2013—2017年间能缓解22.90%的人类活动压力,且保护区缓解人类活动压力的能力存在区域性差别。本文研究结果可为中国自然保护区监测、评估和管理提供更科学的参考依据。

关键词: 自然保护区, 人类活动压力, 倾向得分匹配, 有效性评估, 面板模型

Abstract:

Assessing the effectiveness of nature reserves for mitigating anthropogenic pressures is essential for effective nature protection. The current research at national scale in China only compares anthropogenic pressures inside nature reserves to that outside, but fails to solve the problem of sample selection deviation, which will lead to bias in assessment results. In order to solve this problem, we adopted propensity score matching method to improve the accuracy of the results in the evaluation of nature reserves. The multi-source data were firstly integrated to build Anthropogenic Pressure Index in 2013, 2015, and 2017, then the propensity score matching method was applied to match the random points inside and outside the nature reserves, to make sure that the observed variables of the two comparison groups were as similar as possible. Finally, we used relative effectiveness indicator and panel model to assess the effectiveness of 680 nature reserves in China for mitigating anthropogenic pressures from 2013 to 2017 at different levels. The results show that: (1) From 2013 to 2017, 86.72% of China's nature reserves experienced a positive trend of anthropogenic pressures, which accounted for 43.80% of the total area. (2) Some 69.85% of nature reserves performed well in mitigating anthropogenic pressures. Except for marine coasts, wild plants, and wild animals, the types of nature reserves were under sound protection, and the higher the level of nature reserves, the better the protection effectiveness. (3) From 2013 to 2017, anthropogenic pressures were mitigated by 22.90% by the construction of China's nature reserves, and there were regional differences regarding the degree of mitigation. The results can provide a more scientific reference for future monitoring, assessment and management of nature reserves in China.

Key words: nature reserves, anthropogenic pressures, propensity score matching, effectiveness evaluation, panel model

张涵, 黎夏, 石洪, 刘晓娟. 基于倾向得分匹配方法的中国自然保护区缓解人类活动压力评估[J]. 地理学报, 2021, 76(3): 680-693.

ZHANG Han, LI Xia, SHI Hong, LIU Xiaojuan. An assessment of the effectiveness of China's nature reserves for mitigating anthropogenic pressures based on propensity score matching[J]. Acta Geographica Sinica, 2021, 76(3): 680-693.

图/表 12

图1

表1

表2

图2

图3

表3

图4

表4

表5

表6

表7

图5

参考文献 44

[1]	Xu W H, Xiao Y, Zhang J J, et al. Strengthening protected areas for biodiversity and ecosystem services in China. PNAS, 2017,114(7):1601-1606. doi: 10.1073/pnas.1620503114 pmid: 28137858
[2]	Bacon E, Gannon P, Stephen S, et al. Aichi Biodiversity Target 11 in the like-minded megadiverse countries. Journal for Nature Conservation, 2019,51:125723. DOI: 10.1016/j.jnc.2019.125723. doi: 10.1016/j.jnc.2019.125723
[3]	UNEP-WCMC, IUCN, NGS. Protected Planet Live Report 2021. 2021.
[4]	Jones K R, Venter O, Fuller R A, et al. One-third of global protected land is under intense human pressure. Science, 2018,360(6390):788-791. doi: 10.1126/science.aap9565 pmid: 29773750
[5]	Venter O, Sanderson E W, Magrach A, et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nature Communications, 2016,7:12558. DOI: 10.1038/ncomms12558. doi: 10.1038/ncomms12558 pmid: 27552116
[6]	Veldhuis M P, Ritchie M E, Ogutu J O, et al. Cross-boundary human impacts compromise the Serengeti-Mara ecosystem. Science, 2019,363(6434):1424-1428. doi: 10.1126/science.aav0564 pmid: 30923217
[7]	Joshi A R, Dinerstein E, Wikramanayake E, et al. Tracking changes and preventing loss in critical tiger habitat. Science Advances, 2016,2(4):e1501675. DOI: 10.1126/sciadv.1501675. doi: 10.1126/sciadv.1501675 pmid: 27051881
[8]	He C Y, Liu Z F, Tian J, et al. Urban expansion dynamics and natural habitat loss in China: A multiscale landscape perspective. Global Change Biology, 2014,20(9):2886-2902. doi: 10.1111/gcb.12553
[9]	Di Marco M, Venter O, Possingham H P, et al. Changes in human footprint drive changes in species extinction risk. Nature Communications, 2018,9(1):4621. DOI: 10.1038/s41467-018-07049-5. doi: 10.1038/s41467-018-07049-5 pmid: 30397204
[10]	Harris N C, Mills K L, Harissou Y, et al. First camera survey in Burkina Faso and Niger reveals human pressures on mammal communities within the largest protected area complex in West Africa. Conservation Letters, 2019,12(5):12667. DOI: 10.1111/conl.12667.
[11]	Abukari H, Mwalyosi R B. Comparing pressures on national parks in Ghana and Tanzania: The case of Mole and Tarangire National Parks. Global Ecology and Conservation, 2018,15:e00405. DOI: 10.1016/j.gecco.2018.e00405. doi: 10.1016/j.gecco.2018.e00405
[12]	Geldmann J, Joppa L N, Burgess N D. Mapping change in human pressure globally on land and within protected areas. Conservation Biology, 2014,28(6):1604-1616. doi: 10.1111/cobi.12332
[13]	Venter O, Sanderson E W, Magrach A, et al. Global terrestrial human footprint maps for 1993 and 2009. Scientific Data, 2016,3:160067. DOI: 10.1038/sdata.2016.67. doi: 10.1038/sdata.2016.67 pmid: 27552448
[14]	Hellwig N, Walz A, Markovic D. Climatic and socioeconomic effects on land cover changes across Europe: Does protected area designation matter? PLOS ONE, 2019,14(7):e0219374. DOI: 10.1371/journal.pone.0219374. doi: 10.1371/journal.pone.0219374 pmid: 31314769
[15]	Guetté A, Godet L, Juigner M, et al. Worldwide increase in Artificial Light At Night around protected areas and within biodiversity hotspots. Biological Conservation, 2018,223:97-103. doi: 10.1016/j.biocon.2018.04.018
[16]	Durán A P, Rauch J, Gaston K J. Global spatial coincidence between protected areas and metal mining activities. Biological Conservation, 2013,160:272-278. doi: 10.1016/j.biocon.2013.02.003
[17]	Radeloff V C, Stewart S I, Hawbaker T J, et al. Housing growth in and near United States protected areas limits their conservation value. PNAS, 2010,107(2):940-945. doi: 10.1073/pnas.0911131107 pmid: 20080780
[18]	Qiu C, Hu J M, Yang F L, et al. Human pressures on natural reserves in Yunnan Province and management implications. Scientific Reports, 2018,8:3260. DOI: 10.1038/s41598-018-21654-w. doi: 10.1038/s41598-018-21654-w pmid: 29459749
[19]	Zhao Guanghua, Tian Yu, Tang Zhiyao, et al. Distribution of terrestrial national nature reserves in relation to human activities and natural environments in China. Biodiversity Science, 2013,21(6):658-665. doi: 10.3724/SP.J.1003.2013.08048
	[ 赵广华, 田瑜, 唐志尧, 等. 中国国家级陆地自然保护区分布及其与人类活动和自然环境的关系. 生物多样性, 2013,21(6):658-665.]
[20]	Joppa L N, Pfaff A. High and far: Biases in the location of protected areas. PLOS ONE, 2009,4(12):e8273. DOI: 10.1371/journal.pone.0008273. doi: 10.1371/journal.pone.0008273 pmid: 20011603
[21]	Joppa L, Pfaff A. Reassessing the forest impacts of protection The challenge of nonrandom location and a corrective method. Annals of the New York Academy of Sciences. 2010,1185:135-149. doi: 10.1111/j.1749-6632.2009.05162.x pmid: 20146766
[22]	Geldmann J, Manica A, Burgess N D, et al. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. PNAS, 2019,116(46):23209-23215. doi: 10.1073/pnas.1908221116 pmid: 31659036
[23]	Rosenbaum P R, Rubin D B. The central role of the propensity score in observational studies for causal effects. Biometrika, 1983,70(1):41-55. doi: 10.1093/biomet/70.1.41
[24]	Xu Haigen, Ding Hui, Ouyang Zhiyun, et al. Assessing China's progress toward the 2020 Global Biodiversity Targets. Acta Ecologica Sinica, 2016,36(13):3847-3858.
	[ 徐海根, 丁晖, 欧阳志云, 等. 中国实施2020年全球生物多样性目标的进展. 生态学报, 2016,36(13):3847-3858.]
[25]	Shu Song, Yu Bailang, Wu Jianping, et al. Methods for deriving urban built-up area using night-light data: Assessment and application. Remote Sensing Technology and Application, 2011,26(2):169-176.
	[ 舒松, 余柏蒗, 吴健平, 等. 基于夜间灯光数据的城市建成区提取方法评价与应用. 遥感技术与应用, 2011,26(2):169-176.]
[26]	Dai Z X, Hu Y F, Zhao G H. The suitability of different nighttime light data for GDP estimation at different spatial scales and regional levels. Sustainability, 2017,9(2):305. DOI: 10.3390/su9020305. doi: 10.3390/su9020305
[27]	Zhao N Z, Hsu F C, Cao G F, et al. Improving accuracy of economic estimations with VIIRS DNB image products. International Journal of Remote Sensing, 2017,38(21):5899-5918. doi: 10.1080/01431161.2017.1331060
[28]	Zhao X, Li D R, Li X, et al. Spatial and seasonal patterns of night-time lights in global ocean derived from VIIRS DNB images. International Journal of Remote Sensing, 2018,39(22):8151-8181. doi: 10.1080/01431161.2018.1482022
[29]	Sanderson E W, Jaiteh M, Levy M A, et al. The human footprint and the last of the wild. Bioscience, 2002,52(10):891-904. doi: 10.1641/0006-3568(2002)052[0891:THFATL]2.0.CO;2
[30]	Bowker J N, de Vos A, Ament J M, et al. Effectiveness of Africa's tropical protected areas for maintaining forest cover. Conservation Biology, 2017,31(3):559-569. doi: 10.1111/cobi.12851 pmid: 27696505
[31]	Kukkonen M O, Tammi I. Systematic reassessment of Laos' protected area network. Biological Conservation, 2019,229:142-151. doi: 10.1016/j.biocon.2018.11.012
[32]	Shi H, Li X, Liu X P, et al. Global protected areas boost the carbon sequestration capacity: Evidences from econometric causal analysis. Science of the Total Environment, 2020,715:137001. DOI: 10.1016/j.scitotenv.2020.137001. doi: 10.1016/j.scitotenv.2020.137001
[33]	Chen Qiang. Advanced Econometrics and Stata Applications. 2nd ed. Beijing. Higher Education Press, 2014: 542-545.
	[ 陈强. 高级计量经济学及Stata应用. 2版. 北京:高等教育出版社, 2014: 542-545.]
[34]	Rosenbaum P R, Rubin D B. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 1985,39(1):33-38.
[35]	Li Ben, Wu Lihua. Development zone and firms' growth: Research on heterogeneity and mechanism. China Industrial Economics, 2018(4):79-97.
	[ 李贲, 吴利华. 开发区设立与企业成长: 异质性与机制研究. 中国工业经济, 2018(4):79-97.]
[36]	He Siyuan, Su Yang, Min Qingwen. Boundary, zoning, and land use management of the China national parks: Learning from nature reserves and scenic areas. Acta Ecologica Sinica, 2019,39(4):1318-1329.
	[ 何思源, 苏杨, 闵庆文. 中国国家公园的边界、分区和土地利用管理: 来自自然保护区和风景名胜区的启示. 生态学报, 2019,39(4):1318-1329.]
[37]	Levin N, Ali S, Crandall D, et al. World Heritage in danger: Big data and remote sensing can help protect sites in conflict zones. Global Environmental Change, 2019,55:97-104. doi: 10.1016/j.gloenvcha.2019.02.001
[38]	Bennett M M, Smith L C. Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics. Remote Sensing of Environment, 2017,192:176-197. doi: 10.1016/j.rse.2017.01.005
[39]	Cao Wei, Huang Lin, Xiao Tong, et al. Effects of human activities on the ecosystems of China's National Nature Reserves. Acta Ecologica Sinica, 2019,39(4):1338-1350.
	[ 曹巍, 黄麟, 肖桐, 等. 人类活动对中国国家级自然保护区生态系统的影响. 生态学报, 2019,39(4):1338-1350.]
[40]	Gray C L, Hill S L L, Newbold T, et al. Local biodiversity is higher inside than outside terrestrial protected areas worldwide. Nature Communications, 2016,7:12306. DOI: 10.1038/ncomms12306. doi: 10.1038/ncomms12306 pmid: 27465407
[41]	Geldmann J, Barnes M, Coad L, et al. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biological Conservation, 2013,161:230-238. doi: 10.1016/j.biocon.2013.02.018
[42]	Wu R D, Zhang S, Yu D W, et al. Effectiveness of China's nature reserves in representing ecological diversity. Frontiers in Ecology and the Environment, 2011,9(7):383-389. doi: 10.1890/100093
[43]	Andam K S, Ferraro P J, Pfaff A, et al. Measuring the effectiveness of protected area networks in reducing deforestation. PNAS, 2008,105(42):16089-16094. doi: 10.1073/pnas.0800437105 pmid: 18854414
[44]	Dähler N B, Holderegger R, Flora I, et al. Effectiveness of Swiss protected areas in maintaining populations of rare vascular plants. Journal for Nature Conservation, 2019,52:125749. DOI: 10.1016/j.jnc.2019.125749. doi: 10.1016/j.jnc.2019.125749

压力	得分	描述
城市建设用地	0, 10	所有城市建设用地像元得分均为10
人口密度	0~10	人口密度大于1000人/km²的像元得分为10,其余按公式计算
电力设施	0~10	对夜间灯光数据进行分位数分级
农田	0, 7	所有农田像元得分均为7
公路	0~10	公路像元得分为10,公路外部按距离以指数递减,一直到距离公路15 km处
铁路	0, 10	所有铁路像元得分为10

数据	数据属性	年份	数据来源
中国自然保护区矢量边界	矢量数据	-	http://www.papc.cn/
中国自然保护区名录	统计数据	2017	http://www.mee.gov.cn/
VIIRS DNB夜间灯光影像	遥感影像	2013、2015、2017	https://www.ngdc.noaa.gov/eog/viirs/ download_dnb_composites.html
中国城市建设用地面积	统计数据	2013、2015、2017	http://www.stats.gov.cn/
人口密度	栅格数据	2010、2015、2020	https://sedac.ciesin.columbia.edu/data/ collection/gpw-v4/sets/browse
农田	栅格数据	2013、2015、2017	http://maps.elie.ucl.ac.be/CCI/viewer/ index.php
公路、铁路	矢量数据	2014、2015、2017	https://download.geofabrik.de/
高程	栅格数据	-	http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/zh/
气温、降水	站点数据	2013、2015、2017	https://data.nodc.noaa.gov/cgi-bin/iso?id=gov.noaa.ncdc:C00516

变量		均值		标准偏差 (%)	标准偏差减小幅度(%)	t检测
变量		实验组	对照组	标准偏差 (%)	标准偏差减小幅度(%)	t统计量	t检验相伴概率
lnpecp	匹配前	-2.5682	-2.8677	37.5		50.42	0.000
lnpecp	匹配后	-2.5682	-2.5679	0.2	99.5	0.22	0.824
lntemp	匹配前	3.9294	3.9054	10.3		15.26	0.000
lntemp	匹配后	3.9294	3.935	-2.4	76.9	-2.55	0.011
lnslope	匹配前	0.76959	0.23972	29.9		44.54	0.000
lnslope	匹配后	0.76959	0.76637	0.2	99.4	0.22	0.829
lnelev	匹配前	6.7174	6.5833	9.1		12.84	0.000
lnelev	匹配后	6.7174	6.7039	0.9	89.9	1.07	0.286
lntjcq	匹配前	10.496	10.454	4.3		5.69	0.000
	匹配后	10.496	10.497	-0.0	99.3	-0.03	0.973
lntoroad	匹配前	10.805	10.716	7.5		10.06	0.000
	匹配后	10.805	10.797	0.7	91.0	0.76	0.445
lnlandcover	匹配前	0.73629	0.73672	-0.1		-0.10	0.918
	匹配后	0.73629	0.74132	-0.9	-1053.9	-1.07	0.284

保护区类别	保护区数目	指标为负(%)	指标为正(%)	相对有效性(平均值)
东北	115	79.13	20.87	-0.40
华北	66	68.18	31.82	-0.31
华东	90	56.67	43.33	0.43
中南	154	66.23	33.77	0.06
西北	87	74.71	25.29	-0.30
西南	158	72.15	27.85	-0.18
全国	670	69.85	30.15	-0.11

保护区类别	保护区数目	指标为负(%)	指标为正(%)	相对有效性(平均值)
草原草甸	7	85.71	14.29	-0.37
地质遗迹	21	71.43	28.57	-0.04
古生物遗迹	11	81.82	18.18	-0.02
海洋海岸	11	63.64	36.36	0.53
荒漠生态	16	81.25	18.75	-0.67
内陆湿地	81	67.90	32.10	-0.28
森林生态	304	70.72	29.28	-0.18
野生动物	179	68.72	31.28	0.04
野生植物	40	62.50	37.50	0.19

基于倾向得分匹配方法的中国自然保护区缓解人类活动压力评估

An assessment of the effectiveness of China's nature reserves for mitigating anthropogenic pressures based on propensity score matching

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 44

相关文章 11

Metrics

本文评价

保护区类别	保护区数目	指标为负(%)	指标为正(%)	相对有效性(平均值)
国家级	359	73.54	26.46	-0.29
省级	128	69.53	30.47	-0.06
地级市级	46	63.04	36.96	0.06
县级	137	62.77	37.23	0.27

[1]	彭立, 邓伟, 谭静, 林磊. 横断山区水土资源利用与经济增长的匹配关系[J]. 地理学报, 2020, 75(9): 1996-2008.
[2]	孙晶, 刘建国, 杨新军, 赵福强, 覃驭楚, 姚莹莹, 王放, 伦飞, 王洁晶, 秦波, 刘涛, 张丛林, 黄宝荣, 程叶青, 石金莲, 张劲松, 唐华俊, 杨鹏, 吴文斌. 人类世可持续发展背景下的远程耦合框架及其应用[J]. 地理学报, 2020, 75(11): 2408-2416.
[3]	杨宇,李小云,董雯,洪辉,何则,金凤君,刘毅. 中国人地关系综合评价的理论模型与实证[J]. 地理学报, 2019, 74(6): 1063-1078.
[4]	王少剑,苏泳娴,赵亚博. 中国城市能源消费碳排放的区域差异、空间溢出效应及影响因素[J]. 地理学报, 2018, 73(3): 414-428.
[5]	祝萍,黄麟,肖桐,王军邦. 中国典型自然保护区生境状况时空变化特征[J]. 地理学报, 2018, 73(1): 92-103.
[6]	李汝资, 刘耀彬, 谢德金. 中国产业结构变迁中的经济效率演进及影响因素[J]. 地理学报, 2017, 72(12): 2179-2198.
[7]	张镱锂, 胡忠俊, 祁威, 吴雪, 摆万奇, 李兰晖, 丁明军, 刘林山, 王兆锋, 郑度. 基于NPP数据和样区对比法的青藏高原自然保护区保护成效分析[J]. 地理学报, 2015, 70(7): 1027-1040.
[8]	杨飞龄, 胡金明, 武瑞东. 基于NPWP的云南植物保护优先区分析[J]. 地理学报, 2013, 68(11): 1538-1548.
[9]	范泽孟, 张轩, 李婧, 岳天祥, 刘纪远, 孙晓芳, 香宝, 匡文慧. 国家级自然保护区土地覆盖类型转换趋势[J]. 地理学报, 2012, 67(12): 1623-1633.
[10]	聂勇; 张镱锂; 刘林山; 张继平. 近30年珠穆朗玛峰国家自然保护区冰川变化的遥感监测[J]. 地理学报, 2010, 65(1): 13-28.
[11]	黄锡畴, 朱颜明, 富德义, 孟宪玺, 佘中盛. 长白山自然保护区生态环境的化学结构[J]. 地理学报, 1982, 37(1): 65-75.

变量	(1)	(2)	(1)	(2)	(3)	(4)	(5)	(6)
变量	全国	全国	东北	华北	西北	华东	西南	中南
PAS	-0.192^***	-0.229^***	-0.614^***	-0.263^***	0.252^***	-0.613^***	-0.516^***	-0.279^***
	(0.0138)	(0.0126)	(0.0292)	(0.0384)	(0.0389)	(0.0194)	(0.0194)	(0.0081)
lnelev		-0.675^***	-0.568^***	-0.620^***	-0.697^***	-0.123^***	-1.183^***	-0.173^***
		(0.0034)	(0.0169)	(0.0113)	(0.0197)	(0.0063)	(0.0087)	(0.0036)
lnslope		0.252^***	-0.0282^***	0.175^***	0.306^***	-0.0141^***	0.0852^***	-0.0654^***
		(0.0030)	(0.0094)	(0.0084)	(0.0081)	(0.0048)	(0.0063)	(0.0025)
lnpecp		-0.0262^***	0.00668^***	-0.0125^***	-0.00529^***	-0.00353	-0.0204^***	-0.0346^***
		(0.0005)	(0.0022)	(0.0013)	(0.0011)	(0.0026)	(0.0010)	(0.0016)
lntemp		-0.0878^***	-0.0284^***	-0.0660^***	-0.128^***	-0.297^***	-0.294^***	-0.164^***
		(0.0009)	(0.0014)	(0.0017)	(0.0021)	(0.0083)	(0.0058)	(0.0058)
_cons	1.383^***	6.010^***	5.145^***	5.403^***	6.030^***	4.294^***	11.38^***	3.951^***
	(0.0049)	(0.0226)	(0.0962)	(0.0771)	(0.1471)	(0.0484)	(0.0732)	(0.0332)
N	587448	577719	55552	95390	147855	56697	142963	79062
R²	0.0010	0.1677	0.1204	0.0948	0.0340	0.1580	0.2458	0.3527