中国林业全要素生产率空间关联网络结构及其影响因素
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摘要
基于2005-2016年中国省域面板数据,本文运用DEA-Malmquist模型测度31个省域的林业全要素生产率,采用VAR Granger因果检验方法对省域间的空间溢出关系进行识别,在社会网络分析框架下对网络结构特征及其影响效应进行分析。研究发现中国省域林业全要素生产率的空间溢出效应普遍存在,省域间空间溢出呈现多重叠加现象;关联网络呈多核心发展趋势,跨省效率传递具有明显的"梯度"溢出特征;省域间市场化落差有助于林业市场边界的拓展,相似的创新水平和金融发展水平能够促进省域之间的林业生产技术交流与合作和林业增长的"马太效应"。因此,应更加重视林业生产效率的空间非均衡格局,通过宏观调控和市场体系建设,因地制宜制定林业发展政策,以发挥各地区的功能和优势。
Based on the DEA-Malmquist model and provincial panel dataset(2005-2016), this paper measured the provincial forestry total factor productivity(TFP) in China, and identified the spatial spillover relationships between provinces by the VAR Granger causality test. On this basis, in the framework of social network analysis, the structural characteristics of spatial correlation network and its effects are analyzed. The results showed that, the spatial spillover effect of forestry TFP was commonly existed in China, and there was multiple superposition in the inter-provincial spatial forestry TFP spillovers;the correlation network presented a trend of multi-core development, and the trans-provincial efficiency spillover showed an obvious gradient feature; the gap between provinces contributed to the expansion of forestry market′s boundaries, besides, similar innovation level and financial development level can promote technology exchange and cooperation between provinces,and the "Matthew effect" of forestry growth. Therefore, more attention should be paid to the spatial disequilibrium pattern of China′s forestry production efficiency. Through macro-control and market system construction, favorable conditions should be created for the cross-regional flow and efficient allocation of production factors, and forestry development policies should be formulated and implemented according to local conditions to give play to the functions and advantages of each region.
Based on the DEA-Malmquist model and provincial panel dataset(2005-2016), this paper measured the provincial forestry total factor productivity(TFP) in China, and identified the spatial spillover relationships between provinces by the VAR Granger causality test. On this basis, in the framework of social network analysis, the structural characteristics of spatial correlation network and its effects are analyzed. The results showed that, the spatial spillover effect of forestry TFP was commonly existed in China, and there was multiple superposition in the inter-provincial spatial forestry TFP spillovers;the correlation network presented a trend of multi-core development, and the trans-provincial efficiency spillover showed an obvious gradient feature; the gap between provinces contributed to the expansion of forestry market′s boundaries, besides, similar innovation level and financial development level can promote technology exchange and cooperation between provinces,and the "Matthew effect" of forestry growth. Therefore, more attention should be paid to the spatial disequilibrium pattern of China′s forestry production efficiency. Through macro-control and market system construction, favorable conditions should be created for the cross-regional flow and efficient allocation of production factors, and forestry development policies should be formulated and implemented according to local conditions to give play to the functions and advantages of each region.
引文
[1] 史常亮,揭昌亮,石峰,等.中国林业技术效率与全要素生产率增长分解——基于SFA-Malmquist方法的估计[J].林业科学,2017,53(12):126-135.
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[5] 刘华军,何礼伟.中国省际经济增长的空间关联网络结构——基于非线性Granger因果检验方法的再考察[J].财经研究,2016(2):97-107.
[6] 刘军.整体网分析讲义[M].上海:格致出版社,2009.
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[8] 于伟,张鹏.我国省域农村教育与农业现代化的耦合协调发展[J].华南农业大学学报(社会科学版),2015(1):16-24.
[9] 黄安胜,刘振滨,许佳贤,等.多重目标下的中国林业全要素生产率及其时空差异[J].林业科学,2015,51(9):117-125.
[10] 张红丽,康茜.中国林业全要素生产率时空演变及动力源泉探析[J].江苏农业科学,2017,45(12):261-265.
[11] 董娅楠,缪东玲,程宝栋.FDI对中国林业全要素生产率的影响分析——基于DEA-Malmquist指数法[J].林业经济,2018(4):39-45.
[2] 徐锡志.中国的区域关联与经济增长的空间溢出效应研究[J].中国乡镇企业会计,2015(9):12-13.
[3] 李国平,吴爱芝,孙铁山.中国区域空间结构研究的回顾及展望[J].经济地理,2012,32(4):8-13.
[4] F?re R,Grosskopf S,Norris M,et al.Productivity Growth,Technical Progress,and Efficiency Change in Industrialized Countries[J].American Economic Association,1994,84(1):66-83.
[5] 刘华军,何礼伟.中国省际经济增长的空间关联网络结构——基于非线性Granger因果检验方法的再考察[J].财经研究,2016(2):97-107.
[6] 刘军.整体网分析讲义[M].上海:格致出版社,2009.
[7] 刘华军,刘传明,孙亚男.中国能源消费的空间关联网络结构特征及其效应研究[J].中国工业经济,2015(5):83-95.
[8] 于伟,张鹏.我国省域农村教育与农业现代化的耦合协调发展[J].华南农业大学学报(社会科学版),2015(1):16-24.
[9] 黄安胜,刘振滨,许佳贤,等.多重目标下的中国林业全要素生产率及其时空差异[J].林业科学,2015,51(9):117-125.
[10] 张红丽,康茜.中国林业全要素生产率时空演变及动力源泉探析[J].江苏农业科学,2017,45(12):261-265.
[11] 董娅楠,缪东玲,程宝栋.FDI对中国林业全要素生产率的影响分析——基于DEA-Malmquist指数法[J].林业经济,2018(4):39-45.
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