中国林业产业发展的空间影响机制——基于31个省域面板数据的空间计量分析
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摘要
文中利用2006—2015年中国31个省域面板数据,对林业产业发展进行空间自相关性检验和空间面板计量模型分析,结果显示:中国林业产业发展具有显著的空间相关性,绝大多数省域处于H-H地区和L-L地区,通过空间杜宾模型也证明林业产业发展存在显著的空间溢出效应;在林业产业发展的空间影响机制中,林业产业的财政投入、林业系统年末从业人员、各省域造林面积、林业职工劳动力成本、林业产业已有规模水平以及第二产业产值比重和第三产业产值比重均对中国林业经济发展具有显著的促进效应,同时效应分解结果表明中国林业产业发展的空间溢出效应主要表现在其直接效应方面。
Based on the panel data of 31 provinces in China from 2006 to 2015, this paper conducts spatial autocorrelation test and spatial panel measurement model analysis for forestry industry development.The results show that there is significant spatial correlation in the forestry industry development in China, and most provinces are in HH area and LL area.The spatial Dubin model also proves that there is significant spatial spillover effect in the development of forestry industry.In the spatial influence mechanism of forestry industry development, the financial input, employees of forestry sector at the end of year, the afforested area of the provinces, the forest labor costs, the scale of forestry industry, and the proportion of the secondary and tertiary industries in output value all have significant promoting effects on the development of China's forestry economy.And the results of the effect decomposition indicate the spillover effect of China's forestry industry development is mainly manifested in its direct effect.
Based on the panel data of 31 provinces in China from 2006 to 2015, this paper conducts spatial autocorrelation test and spatial panel measurement model analysis for forestry industry development.The results show that there is significant spatial correlation in the forestry industry development in China, and most provinces are in HH area and LL area.The spatial Dubin model also proves that there is significant spatial spillover effect in the development of forestry industry.In the spatial influence mechanism of forestry industry development, the financial input, employees of forestry sector at the end of year, the afforested area of the provinces, the forest labor costs, the scale of forestry industry, and the proportion of the secondary and tertiary industries in output value all have significant promoting effects on the development of China's forestry economy.And the results of the effect decomposition indicate the spillover effect of China's forestry industry development is mainly manifested in its direct effect.
引文
[1]郑宇梅,高纯一,雷光春.林业产业集聚水平与生态效率实证分析:基于中国15个省域面板数据的检验[J].经济地理,2017,37(10):136-142.
[2]罗小锋,王泽均,李兆亮,等.林业产业结构变动及其经济贡献的比较分析[J].统计与决策,2017(14):93-97.
[3]赵丹.西南地区林业产业结构分析[J].数学的实践与认识,2017,47(21):17-23.
[4]刘洵,马贵珍,麦强盛.云南林业产业集聚与区域经济增长研究[J].西南林业大学学报,2017,37(1):216-220.
[5]蒋业恒,陈勇,张曦.中国林业产业发展与变化分析:基于世界投入产出表的实证研究[J].林业经济,2018(1):44-49,55.
[6]熊立春,王凤婷,程宝栋.中国林业产业结构优化及其影响因素分析[J].农业现代化研究,2018,39(3):378-386.
[7]魏素豪,宗刚.我国林业产业集聚的空间计量分析[J].统计与决策,2017(8):130-133.
[8]赵晓光,赵佳欢.黑龙江省国有重点林区林业经济发展影响因素的空间面板实证分析[J].浙江林业科技,2017,37(5):54-60.
[9]严莹莹.空间计量模型及其在我国的应用研究[D].武汉:华中科技大学,2012.
[10]邵建利,宋宁,刘凯杰.基于我国第三产业区位分布的空间效应研究[J].统计与决策,2016(5):108-113.
[11]ZHANG C,LUO L,XU W,et al.Use of Local Moran’s I and GIS to identify pollution hot spots of Pb in urban soils of Galway,Ireland[J].Science of The Total Environment,2008,398(1/2/3):212-221.
[12]KANAROGLOU P S,ADAMS M D,De LUCA P F,et al.Estimation of sulfur dioxide air pollution concentrations with a spatial auto-regressive model[J].Atmospheric Environment,2013,79:421-427.
[13]黄寿峰.财政分权对中国雾霾影响的研究[J].世界经济,2017,40(2):127-152.
[14]何剑,柳开.金融创新、空间溢出与实体经济增长[J].新疆农垦经济,2018(2):48-56.
[15]ELHORST J.Spatial panel data models: handbook of applied spatial analysis[M].New York:Springer Berlin Heidelberg,2010:377-407.
① 文中展示的莫兰指数检验结果是基于空间邻接矩阵W1得出的,由地理矩阵W2得到的莫兰指数与之相差不大,限于篇幅只展示一组全局莫兰指数检验结果。
② 根据研究需要,本文将中国分为4大地区,分别是东部地区(北京、天津、河北、上海、江苏、浙江、福建、山东、广东、海南)、中部地区(山西、安徽、江西、河南、湖北、湖南)、西部地区(内蒙古、广西、重庆、四川、贵州、云南、西藏、陕西、甘肃、青海、宁夏、新疆)、东北地区(黑龙江、辽宁、吉林)。
[2]罗小锋,王泽均,李兆亮,等.林业产业结构变动及其经济贡献的比较分析[J].统计与决策,2017(14):93-97.
[3]赵丹.西南地区林业产业结构分析[J].数学的实践与认识,2017,47(21):17-23.
[4]刘洵,马贵珍,麦强盛.云南林业产业集聚与区域经济增长研究[J].西南林业大学学报,2017,37(1):216-220.
[5]蒋业恒,陈勇,张曦.中国林业产业发展与变化分析:基于世界投入产出表的实证研究[J].林业经济,2018(1):44-49,55.
[6]熊立春,王凤婷,程宝栋.中国林业产业结构优化及其影响因素分析[J].农业现代化研究,2018,39(3):378-386.
[7]魏素豪,宗刚.我国林业产业集聚的空间计量分析[J].统计与决策,2017(8):130-133.
[8]赵晓光,赵佳欢.黑龙江省国有重点林区林业经济发展影响因素的空间面板实证分析[J].浙江林业科技,2017,37(5):54-60.
[9]严莹莹.空间计量模型及其在我国的应用研究[D].武汉:华中科技大学,2012.
[10]邵建利,宋宁,刘凯杰.基于我国第三产业区位分布的空间效应研究[J].统计与决策,2016(5):108-113.
[11]ZHANG C,LUO L,XU W,et al.Use of Local Moran’s I and GIS to identify pollution hot spots of Pb in urban soils of Galway,Ireland[J].Science of The Total Environment,2008,398(1/2/3):212-221.
[12]KANAROGLOU P S,ADAMS M D,De LUCA P F,et al.Estimation of sulfur dioxide air pollution concentrations with a spatial auto-regressive model[J].Atmospheric Environment,2013,79:421-427.
[13]黄寿峰.财政分权对中国雾霾影响的研究[J].世界经济,2017,40(2):127-152.
[14]何剑,柳开.金融创新、空间溢出与实体经济增长[J].新疆农垦经济,2018(2):48-56.
[15]ELHORST J.Spatial panel data models: handbook of applied spatial analysis[M].New York:Springer Berlin Heidelberg,2010:377-407.
① 文中展示的莫兰指数检验结果是基于空间邻接矩阵W1得出的,由地理矩阵W2得到的莫兰指数与之相差不大,限于篇幅只展示一组全局莫兰指数检验结果。
② 根据研究需要,本文将中国分为4大地区,分别是东部地区(北京、天津、河北、上海、江苏、浙江、福建、山东、广东、海南)、中部地区(山西、安徽、江西、河南、湖北、湖南)、西部地区(内蒙古、广西、重庆、四川、贵州、云南、西藏、陕西、甘肃、青海、宁夏、新疆)、东北地区(黑龙江、辽宁、吉林)。