要素投入驱动、产业结构升级与林业经济增长的空间效应解析——以黑龙江省重点国有林区为例

doi:10.11707/j.1001-7488.20200614

摘要/Abstract

摘要：

目的: 验证林业经济增长过程中是否存在显著的空间关联效应、空间集聚效应和空间溢出效应，探讨林业经济增长与要素投入、产业结构升级存在的内在关系。方法: 以黑龙江省重点国有林区40个林业局为例，运用全局Moran指数验证林业经济增长是否存在空间自关联效应，是否与劳动力、资本、森林管护、产业结构之间存在空间交叉关联效应；运用局部Moran指数检验林业经济增长是否存在空间集聚效应；运用空间回归模型检验林业经济增长是否存在空间溢出效应，并探讨空间溢出效应的表现形式。结果: 劳动力投入增长与林业总产值增长的全局Moran指数均值为负值，其他全局Moran指数均值为正值，并且整体上在0.10显著性水平上通过检验；局部Moran指数在0.10显著性水平上只有绥棱等8个林业局存在高增长集聚特征；空间回归模型显示劳动力投入存在显著的负向空间溢出效应，林业总产值、资本投入、森林管护、产业结构升级存在显著的正向空间溢出效应，并且空间杜宾固定效应模型的溢出效应均大于本地效应。结论: 要素投入驱动、产业结构升级与林业经济增长之间存在显著的空间关联效应，开始形成空间集聚效应；要素投入驱动与产业结构升级的边际效应主要通过空间溢出效应表现出来。黑龙江省重点国有林区林业经济增长的空间溢出效应本质上是知识溢出效应，具体表现在3个方面：生产内容与经营模式的同化、要素配置结构的趋同、产业结构升级的同步。

关键词: 林业经济增长, 要素投入, 产业结构升级, 空间效应

Abstract:

Objective: The aims of this study are to verify whether there are significant spatial correlation effects, spatial agglomeration effects and spatial spillover effects in the process of forestry economic growth, and to analyze the inner relationship between factor inputs, industrial structure upgrading and forestry economic growth. Methods: By taking 40 key state-owned forestry bureaus in Heilongjiang Province as an example, global Moran index was used to verify whether there is spatial auto-correlation effect for forestry economic growth, and whether there are spatial cross-correlation effects with labor, capital, forest management and industrial structure; Local Moran indices were used to test the existence of spatial agglomeration effects for forestry economic growth; Spatial regression models were used to test whether there are spatial spillover effects for forestry economic growth, and the manifestations were discussed. Results: The results showed that global Moran index mean value for labor input growth and forestry gross output growth is negative while the others are positive at 0.10 significance level in general; only 8 forestry bureaus such as Suiling have high growth agglomeration characteristics for local Moran indices at 0.10 significant level; Labor input has significant negative spatial spillover effect, while forestry gross output, capital investment, forest management and industrial structure upgrading have significant positive effects, and the spillover effects are greater than local effects for fixed effect of SDM(spatial Durbin model). Conclusion: Our results demonstrated that there are significant spatial correlation effects between factor inputs, industrial structure upgrading and forestry economic growth, while the spatial agglomeration effects are gradually forming; The marginal effects of factor inputs driving and industrial structure upgrading are mainly manifested through spatial spillover effects. The spatial spillover effects of forestry economic growth in Heilongjiang key state-owned forest areas are essentially knowledge spillover effects, which are embodied in three aspects:assimilation of production contents and management models, convergence of element allocation structure, and synchronization of industrial structure upgrading.

Key words: forestry economic growth, factor inputs, industrial structure upgrading, spatial effects

中图分类号:

F307.2

张滨,吕洁华. 要素投入驱动、产业结构升级与林业经济增长的空间效应解析——以黑龙江省重点国有林区为例[J]. 林业科学, 2020, 56(6): 142-151.

Bin Zhang,Jiehua Lü. Spatial Effects Analysis for Factor Inputs Driving, Industrial Structure Upgrading and Forestry Economic Growth: A Case Study of Heilongjiang Key State-Owned Forest Region[J]. Scientia Silvae Sinicae, 2020, 56(6): 142-151.

图/表 6

表1

图1

表2

表3

局部Moran指数及其分类①"

林业局Bureaus	ΔY/Y-ΔY/Y	ΔY/Y-ΔL/L	ΔY/Y-ΔK/K	ΔY/Y-lnF	ΔY/Y-ISU
大海林Dahailin	-0.053 3^d	-0.086 4^d	0.075 0^a	0.015 7	-0.008 8
柴河Chaihe	-0.075 1^c	0.088 8^b	-0.041 3^c	-0.014 1	0.044 2^b
东京城Dongjingcheng	0.020 3	-0.377 9^d	-0.000 4^d	0.164 8^a	0.031 7
穆棱Muling	0.002 8^a	0.002 6	0.016 9^a	0.006 0^a	0.000 8
绥阳Suiyang	0.045 0	-0.454 1^d	0.688 5^a	-0.086 9	0.304 0^a
海林Hailin	-0.000 5	0.019 4^b	0.008 1^b	-0.012 5^c	0.006 0^b
林口Linkou	0.232 6^a	-0.995 9^d	-0.147 5	0.404 3^a	-0.516 8^d
八面通Bamiantong	0.043 5^a	-0.094 7^d	0.025 1^a	0.166 9^a	-0.055 7^d
桦南Huanan	0.001 0	-0.007 1^d	-0.004 3^d	-0.006 7^d	0.005 7^a
双鸭山Shuangyashan	0.001 0	0.288 3^a	-0.086 1^d	0.020 9	0.085 4^a
鹤立Heli	-0.160 2^d	0.156 7	-0.007 4	0.148 5	-0.165 5^d
鹤北Hebei	0.030 6^b	-0.036 7^c	0.023 1^b	-0.003 2	0.078 9^b
东方红Dongfanghong	-0.028 0^c	0.018 8	0.064 3	0.114 4	-0.130 1^c
迎春Yingchun	-0.028 0	-0.119 2^d	0.035 8^a	0.184 4^a	0.082 8
清河Qinghe	-0.034 1	0.071 1^b	0.007 8	-0.163 6^c	-0.055 4
双丰Shuangfeng	-0.073 5^c	-0.091 8	-0.007 0	0.011 6	-0.102 0
铁力Tieli	-1.103 4^d	1.601 2^a	-1.513 1^d	-3.982 6^d	1.195 0^a
桃山Taoshan	-0.046 6^c	-0.020 5^c	0.009 3	0.022 7^b	0.038 0^b
朗乡Langxiang	0.034 7^b	0.005 0	0.005 1	0.101 0^b	-0.016 2
南岔Nancha	0.254 7^b	-0.071 5	0.215 5^b	0.455 0^b	0.381 6^b
金山屯Jinshantun	0.312 0^b	-0.008 9	0.149 0^b	-0.282 8^c	0.249 2^b
美溪Meixi	0.582 7^b	-0.583 8^c	0.332 5^b	0.386 2^b	0.500 0^b
乌马河Wumahe	-0.338 4^c	-0.758 4^c	0.330 5^b	0.372 8^b	0.036 4
翠峦Cuiluan	-0.133 3^c	-0.132 0^c	0.046 4^b	0.041 3^b	-0.083 4^c
友好Youhao	0.151 3^b	-0.241 4^c	0.046 1	0.129 8^b	-0.150 7^c
上甘岭Shangganling	0.011 3	-0.098 0^c	0.008 5	-0.048 5^c	-0.047 7^c
五营Wuying	0.130 1^b	-0.364 7^c	0.115 5	0.006 6	0.095 4^b
红星Hongxing	0.109 3^b	-0.416 3^c	-0.125 5^c	-0.112 4	-0.139 7^c
新青Xinqing	-0.014 5^d	0.006 7	0.002 0	-0.000 5	0.004 0
汤旺河Tangwanghe	-0.196 5^d	0.658 6^a	-0.003 9	-0.238 3^d	0.130 7
乌伊岭Wuyiling	0.019 3^a	0.047 2^a	-0.085 8^d	-0.002 3	0.010 7
山河屯Shanhetun	-0.250 8^d	-0.258 0^d	-0.057 5	0.015 1	-0.010 7
苇河Weihe	-0.449 9^c	1.750 9^b	1.803 5^b	-1.119 4^c	-0.653 3
亚布力Yabuli	-0.084 6^d	-0.181 4^d	0.015 4	0.007 8	-0.071 2^d
方正Fangzheng	0.208 8^a	-0.541 5^d	-0.065 9	0.425 7^a	-0.152 7^d
兴隆Xinglong	-0.113 4^d	-0.118 4^d	0.013 2	-0.321 2^d	-0.375 0^d
绥棱Suiling	0.139 3^a	0.154 3^a	-0.054 5^d	0.109 1^a	0.100 6^a
通北Tongbei	0.014 1^a	0.021 2	-0.031 8^d	0.108 3^a	0.033 8^a
沾河Zhanhe	-0.036 7^d	0.129 2^a	-0.056 3^d	0.010 1	0.060 5^a
带岭Dailing	-0.065 4^c	-0.160 9^c	0.003 6	0.070 9^b	0.024 7
正Positive	20	16	24	25	23
负Negative	20	24	16	15	17
a	7	6	5	8	7
b	8	4	8	8	8
c	8	10	2	5	5
d	9	11	8	4	6

表3

表4

林业经济增长的面板回归结果①"

变量Variables	普通面板回归模型OLS			空间自回归模型SAR			空间误差模型SEM			空间杜宾模型SDM
变量Variables	混合Mixed	固定Fixed	随机Random	混合Mixed	固定Fixed	随机Random	混合Mixed	固定Fixed	随机Random	混合Mixed	固定Fixed	随机Random
Constant	3.905 8^***(5.936 3)	-2.595 5 (-1.337 3)	3.351 6^***(3.090 8)	1.104 6 (1.555 5)	-1.751 5 (-1.176 4)	—	4.843 6^***(8.344 4)	4.473 0^***(2.970 5)	—	6.113 0^***(4.656 6)	-5.793 6^**(-2.086 2)	—
lnL	0.171 9^***(3.226 3)	-0.301 3^***(-5.427 5)	-0.247 7^***(-4.787 7)	0.212 8^***(4.303 4)	-0.137 6^***(-3.202 7)	-0.335 4^***(-5.900 0)	0.227 2^***(4.375 7)	-0.078 6^*(-1.658 2)	-0.007 0(-0.156 8)	0.225 3^***(4.394 8)	-0.051 4(-1.147 4)	-0.119 7^**(-2.181 0)
lnK	0.431 0^***(15.172 8)	0.251 3^***(14.673 0)	0.267 6^***(15.766 1)	0.335 8^***(11.398 2)	0.118 8^***(8.121 6)	0.317 4^***(14.814 2)	0.423 5^***(12.381 5)	0.153 2^***(7.073 9)	0.166 7^***(7.673 8)	0.272 3^***(6.650 0)	0.075 4^***(3.414 7)	0.112 1^***(3.983 6)
lnF	0.043 1 (1.081 6)	0.954 1^***(6.344 7)	0.413 1^***(5.130 7)	0.073 7^**(1.973 1)	0.488 6^***(4.210 2)	0.878 9^***(16.059 1)	0.021 5(0.578 2)	0.338 1^***(2.830 2)	0.645 8^***(16.853 6)	0.001 1(0.028 7)	0.385 3^***(3.295 3)	0.439 6^***(4.443 6)
ISU	0.556 6^***(4.480 6)	1.161 2^***(13.839 2)	1.192 2^***(14.560 9)	0.240 7^**(2.082 3)	0.529 1^***(7.328 2)	1.486 5^***(13.946 2)	-0.059 9(-0.429 9)	0.760 6^***(7.131 9)	0.805 0^***(7.694 1)	-0.614 6^***(-4.096 8)	0.287 5^***(2.582 1)	0.283 4^**(2.008 4)
W*lnY				0.322 0^***(8.562 8)	0.530 0^***(18.328 4)	-0.236 1^***(-5.983 1)				0.343 0^***(8.658 7)	0.431 0^***(11.973 1)	-0.236 1^***(-5.044 3)
spat.aut.							0.424 0^***(11.384 1)	0.630 0^***(23.056 7)	0.620 4^***(21.808 2)
teta						0.124 3^***(6.366 7)			4.722 5^***(4.693 3)			0.113 8^***(6.359 9)
W*lnL										-0.270 7^***(-3.757 5)	-0.296 0^***(-4.386 0)	-0.586 8^***(-7.509 8)
W*lnK										-0.020 7(-0.429 4)	0.066 7^***(2.623 0)	0.212 2^***(6.548 7)
W*lnF										-0.163 7^**(-2.428 1)	0.650 0^***(3.490 2)	0.711 4^***(6.291 6)
W*ISU										1.229 4^***(6.827 3)	0.218 6^*(1.671 7)	1.074 2^***(6.570 4)
R²	0.402 9	0.834 5	0.657 2	0.482 7	0.902 0	0.764 9	0.360 4	0.759 7	0.876 4	0.571 3	0.905 8	0.835 7
Hausman test			76.031 2^***			834.740 9^***			8.544 8			536.686 9^***
log likelihood	-326.796 4	58.084 4	—	-296.353 3	176.993 3	-47.214 8	-291.492 0	113.945 5	13.223 9	-241.672 8	202.487 9	60.316 7
LM-SAR				14.868 0^***	23.595 5^***	90.688 6^***	1 259.643 9^***	602.937 1^***	1 178.770 4^***
LM-SEM				2.695 3	5.361 9^**	123.560 3^***	127.674 1^***	362.618 9^***	0.274 5
R-LM-SAR				38.432 0^***	52.673 6^***	24.570 4^***	3 917.972 5^***	240.461 6^***	1 463.112 0^***
R-LM-SEM				26.259 3^***	34.440 0^***	57.442 0^***	2 786.002 7^***	0.143 3	284.616 0^***
LR-SAR										0.566 8	1.341 6	-284.597 3
LR-SEM										6.414 5	30.328 2^***	-255.442 3
Wald-SAR										51.176 2^***	50.860 2^***	193.647 6^***
Wald-SEM										46.529 4^***	141.874 5^***	143.734 8^***

表4

表5

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变量Variables	样本量Samples	极小值Min.	极大值Max.	均值Mean	标准差SD
林业总产值Forestry gross output / 10⁴yuan	600	13 437.00	180 618.12	61 840.83	33 316.44
劳动力Labor / person	600	1 381.00	14 842.00	5 282.59	1 959.43
林业投资Forest investment / 10⁴yuan	600	2 671.49	68 680.23	13 905.42	10 274.82
森林管护面积Forest management area / hm²	600	70 597.00	751 283.00	226 426.55	119 985.06
产业结构升级Industrial structure upgrading	600	1.31	2.00	1.77	0.16

年份Year	自关联Auto-correlation	交叉关联Cross-correlation
年份Year	ΔY/Y-ΔY/Y	ΔY/Y-ΔL/L	ΔY/Y-ΔK/K	ΔY/Y-lnF	ΔY/Y-ISU
2004	-0.144 1^**	-0.120 4^*	0.136 7^**	0.109 2^**	0.150 3^***
2005	0.002 6^*	-0.112 1^**	0.072 5^**	0.168 6^***	0.134 4^***
2006	0.164 9^***	0.146 8^*	0.235 7^***	-0.056 4^***	-0.079 0
2007	0.230 0^***	-0.123 8^**	0.226 2^***	-0.122 7^**	0.081 2^*
2008	0.210 2^**	-0.353 4^***	0.138 6^***	-0.090 0	0.084 6
2009	0.319 2^***	0.171 7^**	0.302 4^***	-0.201 5^**	0.070 5
2010	0.145 0^**	-0.177 7^***	0.234 1^***	0.122 3^**	0.120 2^**
2011	0.224 7^***	-0.176 2^***	0.260 4^***	0.306 1^***	0.420 3^***
2012	0.264 9^***	-0.201 3^***	0.203 3^***	-0.069 1^*	0.299 3^**
2013	0.182 1^***	-0.508 5^***	0.162 5^***	0.148 5^*	0.327 3^***
2014	0.245 1^**	-0.111 3^**	0.323 9^***	0.194 7^**	0.329 0^***
2015	0.268 4^***	0.095 3^*	0.323 6^***	0.126 7^**	0.196 1^***
2016	0.351 8^***	-0.251 1^***	0.088 7^**	0.080 8^**	0.139 5^**
2017	0.271 8^***	-0.540 1^***	0.241 5^***	0.309 6^***	0.093 7^***
均值Mean	0.195 7	-0.161 6	0.210 7	0.073 3	0.169 1

指标Indices	本地效应Local effects	溢出效应Spillover effects	总效应Total effects
lnL	-0.112 4^**(-2.419 5)	-0.500 5^***(-4.828 1)	-0.612 9^***(-4.908 7)
lnK	0.094 7^***(4.574 3)	0.154 1^***(5.509 7)	0.248 8^***(9.757 7)
lnF	0.540 2^***(4.299 9)	1.282 4^***(4.419 9)	1.822 6^***(5.053 9)
ISU	0.346 2^***(3.302 0)	0.541 7^***(3.652 8)	0.887 9^***(6.500 1)

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