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

Abstract

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

CLC Number:

F307.2

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.

Figures/Tables 6

Table 1

Fig.1

Table 2

Table 3

Local Moran indexes and classifications"

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

Table 3

Table 4

Panel regression results of forestry economic growth"

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^***

Table 4

Table 5

References 0

	才琪, 陈绍志, 赵荣. 中央林业投资与林业经济增长的互动关系. 林业科学, 2015. 519, 126- 133. doi: 10.3969/j.issn.1006-1126.2015.02.007
	Cai Q , Chen S Z , Zhao R . Interaction relationship between central forestry investment and forestry economic growth in China. Scientia Silvae Sinicae, 2015. 519, 126- 133. doi: 10.3969/j.issn.1006-1126.2015.02.007
	曹玉昆, 黄显乔, 朱震锋. 基于偏离-份额法的国有林区产业结构分析. 林业经济问题, 2018. 382, 1- 6, 98.
	Cao Y K , Huang X Q , Zhu Z F . Analysis of industrial structure in SFR based on the shift share method. Issues of Forestry Economics, 2018. 382, 1- 6, 98.
	程鹏飞, 李婕, 张红丽, 等. 林业产业集聚、林业经济增长及其空间溢出效应——基于2004-2015年省际面板数据的空间计量分析. 安徽农业大学学报, 2018. 456, 1063- 1070.
	Cheng P F , Li J , Zhang H L , et al. Forest industry agglomeration, forest economic growth and its space spillover effect——Based on the spatial analysis of the marginal panel data from 2004 to 2015. Journal of Anhui Agricultural University, 2018. 456, 1063- 1070.
	胡晨沛. 中国省域林业生产要素产出弹性测度——基于空间计量经济模型的实证研究. 林业经济问题, 2017. 374, 74- 78, 109.
	Hu C P . Measuring output elasticity of China's provincial forestry production factors:an empirical study based on spatial econometric model. Issues of Forestry Economics, 2017. 374, 74- 78, 109.
	孔凡斌, 廖文梅. 中国林业市场化进程的林业经济增长效应. 中国农村经济, 2013. 299, 87- 96.
	Kong F B , Liao W M . The economic growth effect of forestry in the process of forestry marketization in China. Chinese Rural Economy, 2013. 299, 87- 96.
	李研, 张大红. 要素投入对林业经济增长影响的实证分析. 统计与决策, 2018. 3415, 133- 135.
	Li Y , Zhang D H . Empirical analysis of the impact of factors input on forestry economic growth. Statistics & Decision, 2018. 3415, 133- 135.
	刘铁铎, 张越杰. 东北三省林业产业经济增长与结构优化研究. 社会科学战线, 2018. 416, 256- 260.
	Liu T D , Zhang Y J . Study on economic growth and structural optimization of forestry industry in northeast China. Social Science Front, 2018. 416, 256- 260.
	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. 统计与决策, 2017. 3314, 93- 97.
	Luo X F , Wang Z J , Li Z L , et al. A comparative analysis of forestry industrial structure changes and its economic contribution. Statistics & Decision, 2017. 3314, 93- 97.
	潘启英, 杨卫东. 基于2000-2014年数据的黑龙江省森工林区林业经济增长实证研究. 中国林业经济, 2018. 256, 9- 11, 18.
	Pan Q Y , Yang W D . Empirical study on forestry economic growth of forest industry region in Heilongjiang province based on data from 2000 to 2014. China Forestry Economics, 2018. 256, 9- 11, 18.
	谭益民, 张双全. 森林旅游业与经济增长的关系研究. 中南林业科技大学学报, 2012. 3211, 136- 138.
	Tan Y M , Zhang S Q . Studies on correlation between forest tourism and economic growth. Journal of Central South University of Forestry & Technology, 2012. 3211, 136- 138.
	汪浩. 林业产业集聚与经济增长的关系研究. 统计与决策, 2011. 273, 140- 141.
	Wang H . Study on the relationship between forestry industry agglomeration and economic growth. Statistics & Decision, 2011. 273, 140- 141.
	魏素豪, 宗刚. 我国林业产业集聚的空间计量分析. 统计与决策, 2017. 338, 130- 133.
	Wei S H , Zong G . Spatial econometric analysis of forestry industry agglomeration in China. Statistics & Decision, 2017. 338, 130- 133.
	魏肖杰, 张敏新. 中国林业产业集聚影响因素作用机制的实证研究——基于空间杜宾模型. 资源开发与市场, 2018. 3412, 1731- 1737. doi: 10.3969/j.issn.1005-8141.2018.12.016
	Wei X J , Zhang M X . Empirical study on mechanism of factors influencing Chinese forestry industrial agglomeration——Based on spatial Durbin model SDM. Resource Development & Market, 2018. 3412, 1731- 1737. doi: 10.3969/j.issn.1005-8141.2018.12.016
	徐德云. 产业结构升级形态决定、测度的一个理论解释及验证. 财政研究, 2008. 291, 46- 49.
	Xu D Y . A theoretical explanation and verification of industrial structure upgrading form decision and measurement. Public Finance Research, 2008. 291, 46- 49.
	赵晓光, 田博林. 黑龙江省国有林区经济差异的空间统计分析. 林业经济问题, 2016. 363, 203- 208.
	Zhao X G , Tian B L . Exploratory spatial data analysis of the forestry regional economic disparities in the reemployment of Heilongjiang province. Issues of Forestry Economics, 2016. 363, 203- 208.
	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. 浙江林业科技, 2017. 375, 54- 60.
	Zhao X G , Zhao J H . Spatial panel analysis on factors influencing forestry economic development in key state-owned forested areas in Heilongjiang province. Journal of Zhejiang Forestry Science and Technology, 2017. 375, 54- 60.
	钟艳, 李湘玲, 史常亮. 东北地区林业产业结构变动对林业经济增长的贡献. 资源开发与市场, 2011. 2711, 1006- 1009. doi: 10.3969/j.issn.1005-8141.2011.11.014
	Zhong Y , Li X L , Shi C L . Contribution of change in forestry industrial structure to economic growth in northeast China. Resource Development & Market, 2011. 2711, 1006- 1009. doi: 10.3969/j.issn.1005-8141.2011.11.014
	Anselin L . Local indicators of spatial association-LISA. Geographical Analysis, 1995. 272, 93- 115.
	Elhorst J P . Spatial econometrics from cross-sectional data to spatial panels. New York: Springer. 2014.
	Karlsson M , Wolf A . Using an optimization model to evaluate the economic benefits of industrial symbiosis in the forest industry. Journal of Cleaner Production, 2008. 1614, 1536- 1544.
	Kies U , Klein D , Schulte A . Germany's forest cluster:exploratory spatial data analysis of regional agglomerations and structural change in wood-based employment. Ultramicroscopy, 2010. 601, 137- 152.
	Klein D , Kies U , Schulte A . Regional employment trends of wood-based industries in Germany's forest cluster:a comparative shift-share analysis of post-reunification development. European Journal of Forest Research, 2009. 1283, 205- 219.
	LeSage J P , Pace R K . Introduction to spatial econometrics. Boca Raton: CRC Press. 2009.
	Moran P A P . Notes on continuous stochastic phenomena. Biometrika, 1950. 37, 17- 23. doi: 10.1093/biomet/37.1-2.17
	Sandberg D , Vasiri M , Trischler J , et al. The role of the wood mechanical industry in the Swedish forest industry cluster. Scandinavian Journal of Forest Research, 2014. 294, 352- 359.

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)

Spatial Effects Analysis for Factor Inputs Driving, Industrial Structure Upgrading and Forestry Economic Growth: A Case Study of Heilongjiang Key State-Owned Forest Region

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