摘要
基于中国31个省份2015年林业投入产出数据,在考虑省域空间联系因素的情况下,建立空间计量经济模型对中国林业要素产出弹性进行测度。研究结果表明:中国31个省份的林业产出存在显著的空间相关性,与传统的估计方法相比,空间计量模型能够揭示中国省域林业产出的空间相关性,能更准确地测度林业生产要素的产出弹性;在考虑省域空间联系因素之后发现,劳动力投入对林业产出的弹性为0.404,资本投入对林业产出的弹性为0.682,说明资本投入对林业产出的贡献大于林业劳动力投入的贡献。最终提出制定区域林业生产激励政策、建立区域统筹协调发展的林业生产合作机制等政策建议。
⑴ Background——Collective forest rights reform,a major revolution of rural management system,has endowed farmers with partial forestry rights. So farmers are able to gain revenue by using their own forest resources. Forestry industry as an important part of China's national economy can produce huge ecological benefits and social benefits for China. In recent years,China's forestry industry has been developing rapidly.⑵ Methods——Based on the input-output data of China's 31 provinces in 2015,this paper analyzed the spatial distributions and interdependent correlation of the production of forestry by Global Moran I index and LISA cluster map. Base on the test about spatial relationships,spatial error model and spatial error model were established to measure the output elasticity of China's forestry production factors under the consideration of spatial linkage factors in the province.⑶ Results——Global Moran I index of China's forestry output is 0. 074 and the spatial autocorrelation is significant. So China's provincial forestry output exists significant spatial correlation. Compared with the traditional estimation methods,spatial econometric model can reveal the spatial correlation in forestry output and can more accurately measure the output elasticity of forestry production factors. The results of different models show that the two-factor model is superior to the three-factor model in measuring forestry output elasticity. After considering provincial spatial factors,estimated the output elasticity of forestry labor input is 0. 404,the output elasticity of capital investment is 0. 682,and the land to forestry output has no significant influence. Meanwhile,by comparing the output elasticity of the two elements in different models,it found that the output elasticity of forestry labor output is the smallest in OLS estimation,and the results obtained by this method have the tendency of underestimating the elasticity of forestry labor output. In the spatial error model,forestry labor output has the greatest elasticity( 0. 404),which is a more reasonable estimate. Similarly,the elasticity of forestry capital output is 0. 782. The result of the spatial error model is more reasonable,and the elasticity of forestry capital output is 0. 682.⑷ Conclusions and Discussion——This paper got 4 conclusions as follows. Firstly,China's provincial forestry output exists significant spatial correlation. Secondly,labor factor and capital factor are the main determinants for the province-level forestry production in China. Thirdly,the results of spatial error model shows that spatial error spillover effect is existed in provincial output of forest. Fourthly,spatial error model is more reasonable than traditional model. Research on the province-level forestry production should not ignore the spatial effect. Finally,this paper put forward some policy recommendations for the establishment of regional forestry production incentive policies,the establishment of regional coordinated development of forestry production cooperation mechanism. Through the spatial correlation analysis and the establishment of spatial measurement model,the paper measured output elasticity of forestry production factor and drew the conclusion of the study. This paper had important research significance because it put spatial factor into measurement of output elasticity firstly. In meanwhile,the study also had some shortcomings and improvements.
出处
《林业经济问题》
北大核心
2017年第4期74-78,共5页
Issues of Forestry Economics
基金
浙江工商大学研究生科研创新重点资助项目
浙江省一流学科A类(浙江工商大学统计学)资助项目
关键词
林业经济
空间计量
产出弹性
空间误差模型
forestry economy
spatial measurement
output elasticity
spatial error model
