考虑空间溢出效应的森林质量与经济增长关系EKC检验

doi:10.11707/j.1001-7488.20201217

Abstract

Abstract:

Objective: In-depth exploration of the relationship between forest quality(FQ)and economic growth from a spatial perspective, and analysis of the differences between forest areas under regional heterogeneity conditions, and prediction of the time path of future evolution in different regions. Method: Drawing on the analytical framework of environmental Kuznets curve(EKC), the spatial econometric model is established between forest quality and economic growth based on interprovincial panel data from 2003 to 2016 in China. Result: 1) The promotion of economic growth to forest quality improvement is a long-term process, and the EKC hypothesis is validated by the U-shaped variation between forest quality and economic growth at present stage. 2) There are significant differences between different forest regions. The northeast China and north China are characterized by inverted N-type, northwest China is characterized by U-type while southwest China is linear of negative, however, there is no EKC relationship in south and southeast China. At present, the forest quality in northeast China and north China has not crossed the second inflection point. Conclusion: The econometric test can provide a more differentiated explanation consider heterogeneity conditions of different forest areas. So the existence of spatial spillover effects makes it necessary for neighboring regions to establish a complete cooperation mechanism for forest management and protection, forestry industry guarantee and factor flow mechanism, and in combination with their own endowment conditions to seek a equilibrium point for the coordinated development of forest quality improvement, ecological protection and economic growth.

Key words: forest quality, economic growth, spatial spillover effect, non-linear relationship, regional heterogeneity

CLC Number:

F307.2

Mengyang Hou,Yuanjie Deng,Shunbo Yao,Guangquan Liu. EKC Test of the Relationship between Forest Quality and Economic Growth Considering Spatial Spillover Effects[J]. Scientia Silvae Sinicae, 2020, 56(12): 145-156.

Figures/Tables 6

Table 1

Table 2

Fig.1

Table 3

Econometric analysis of the relationship between forest quality and economic growth"

变量 Variables	SLM		SEM		SDM		OLS-FE
变量 Variables	模型1 Model 1	模型2 Model 2	模型3 Model 3	模型4 Model 4	模型5 Model 5	模型6 Model 6	模型7 Model 7	模型8 Model 8
lnpGDP	-2.302 (-0.36)	-1.718 ^*** (-3.45)	-1.672 (-0.25)	-1.469 ^** (-2.51)	0.325 (0.05)	-0.842 (-1.34)	-0.127 (-0.02)	-2.629 ^*** (-5.05)
(lnpGDP)²	0.149 (0.23)	0.085 ^*** (3.37)	0.099 (0.14)	0.078 ^*** (2.60)	-0.074 (-0.10)	0.045 (1.46)	-0.121 (-0.17)	0.135 ^*** (4.97)
(lnpGDP)³	-0.003 (-0.10)		-0.001 (-0.03)		0.004 (0.17)		0.009 (0.35)
lnURBAN	0.341 ^*** (3.51)	0.341 ^*** (3.51)	0.296 ^*** (2.97)	0.296 ^*** (2.97)	0.337 ^*** (3.37)	0.338 ^*** (3.38)	0.444 ^*** (4.13)	0.446 ^*** (4.16)
lnFAIF	0.007 (0.93)	0.007 (0.92)	0.008 (1.00)	0.008 (1.00)	0.015 ^* (1.74)	0.014 ^* (1.75)	0.007 (0.84)	0.007 4 (0.86)
lnTY	0.043 ^*** (2.67)	0.042 ^*** (2.72)	0.039 ^** (2.38)	0.039 ^** (2.42)	0.047 ^*** (2.83)	0.047 ^*** (2.89)	0.048 ^*** (2.66)	0.049 ^*** (2.83)
lnPIPF	-0.177 ^*** (-4.42)	-0.176 ^*** (-4.48)	-0.190 ^*** (-4.39)	-0.190 ^*** (-4.43)	-0.138 ^*** (-2.88)	-0.139 ^*** (-3.01)	-0.239 ^*** (-5.46)	-0.242 ^*** (-5.67)
lnPD	-0.418 ^* (-1.91)	-0.426 ^** (-2.09)	-0.369 (-1.51)	-0.371 (-1.59)	-0.162 (-0.46)	-0.160 (-0.46)	-0.560 ^** (-2.29)	-0.529 ^** (-2.33)
lnPRE	-0.007 (-0.15)	-0.007 (-0.16)	0.025 (0.44)	0.025 (0.44)	0.048 (0.76)	0.047 (0.75)	-0.006 (-0.12)	-0.005 (-0.09)
lnTEM	-0.009 (-0.28)	-0.009 (-0.28)	-0.023 (-0.67)	-0.022 (-0.67)	-0.016 (-0.51)	-0.017 (-0.52)	0.012 (0.35)	0.013 (0.35)
C							11.70 (0.51)	19.68^*** (5.82)
ρ/λ	0.370 ^*** (6.78)	0.369 ^*** (6.79)	0.379 ^*** (5.83)	0.379 ^*** (5.84)	0.223 ^*** (3.31)	0.223 ^*** (3.32)
R²	0.565	0.566	0.512	0.513	0.428	0.427	0.331	0.330
lgL	220.539	220.534	213.846	213.845	235.236	235.217
AIC	-417.078	-419.068	-403.691	-405.691	-426.472	-430.434
Sigma²	0.021 ^*** (14.54)	0.021 ^*** (14.54)	0.021 ^*** (14.45)	0.021 ^*** (14.45)	0.020 ^*** (14.64)	0.020 ^*** (14.64)

Table 3

Table 4

Quantitative results of the relationship between FQ and pGDP in different forest regions"

变量 Variables	东北地区-SLM Northeast China-SLM		华北地区-SLM North China-SLM		西北地区-SEM Northwest China-SEM		西南地区-OLS Southwest China-OLS		华南及东南地区-SLM South and southeast China-SLM
	固定效应FE		固定效应FE		随机效应RE		固定效应FE		随机效应RE
	模型1 Model 1	模型2 Model 2	模型3 Model 3	模型4 Model 4	模型5 Model 5	模型6 Model 6	模型7 Model 7	模型8 Model 8	模型9 Model 9	模型10 Model 10
lnpGDP	-59.543 ^** (-2.39)	-2.411 ^** (-1.97)	-8.632 ^** (-2.58)	-1.083 (-1.01)	-56.00^* (-1.75)	-3.616 ^** (-2.19)	1.867 (1.15)	-0.21^** (-2.68)	-0.757 (-0.87)	0.066 (0.89)
(lnpGDP)²	5.859 ^** (2.34)	0.120 ^* (1.93)	0.826 ^** (2.55)	0.065 (1.18)	5.703 ^* (1.70)	0.205 ^** (2.53)	-0.113 (-1.28)		0.043 (0.95)
(lnpGDP)³	-0.191 ^** (-2.29)		-0.026 ^** (-2.51)		-0.192 (-1.64)
lnURBAN	0.667 ^*** (3.64)	0.712 ^*** (3.73)	0.303 (1.13)	0.288 (1.08)	-1.652 ^** (-3.08)	-1.463 ^** (-2.84)	-0.053 (-0.32)	-0.071 4 (-0.42)	0.159 (0.68)	0.181 (0.78)
lnFAIF	0.019 (0.84)	0.029 (1.26)	0.004 (0.43)	0.003 (0.27)	0.046 (1.47)	0.054 (1.63)	0.023 (1.27)	0.014 (0.84)	0.024 (1.39)	0.018 (1.14)
lnTY	0.044 ^* (1.78)	0.052 ^** (2.03)	0.064 ^** (2.57)	0.065 ^*** (2.60)	0.039 (1.31)	0.031 (1.02)	-0.130 ^** (-2.28)	-0.180 ^** (-2.60)	-0.052 (-1.28)	-0.043 (-1.09)
lnPIPF	-0.488 ^*** (-6.42)	-0.455 ^*** (-5.83)	-0.363 ^*** (-5.69)	-0.360 ^*** (-5.65)	-0.061 (-0.66)	-0.060 (-0.63)	-0.57^*** (-3.87)	-0.50^*** (-3.64)	-0.138 ^** (-2.19)	-0.153 ^** (-2.50)
lnPD	-5.380 ^*** (-3.12)	-6.752 ^*** (-3.98)	-0.501 ^* (-1.80)	-0.611 ^* (-1.91)	0.209 (0.93)	0.253 (1.03)	-0.103 (-0.11)	-0.941 (-1.42)	-0.362 ^** (-2.23)	-0.331 ^** (-2.11)
lnPRE	-0.033 5 (-0.87)	-0.034 8 (-0.86)	-0.111 (-1.50)	-0.109 (-1.47)	-0.012 1 (-0.11)	0.014 (0.12)	0.003 (0.02)	-0.005 (-0.03)	0.076 (0.78)	0.092 (0.97)
lnTEM	-0.008 (-0.26)	-0.005 (-0.16)	0.158 (0.81)	0.137 (0.72)	0.036 (0.79)	0.032 (0.67)	-0.047 (-1.03)	-0.042 (-0.92)	0.226 (0.54)	0.211 (0.50)
C					19.13^* (1.87)	24.25^*** (3.26)	3.987 (0.38)	16.40^*** (4.05)	7.534 (1.52)	3.348 (1.48)
ρ	0.270 ^* (1.93)	0.260 ^* (1.81)	0.543 ^*** (7.16)	0.538 ^*** (7.11)	0.757 ^*** (7.40)	0.682 ^*** (6.20)			0.331 ^*** (3.54)	0.342 ^*** (3.71)
R²	0.692	0.686	0.761	0.766	0.699	0.682	0.523	0.530	0.488	0.489
lgL	88.061	85.548	88.299	88.172	37.912	36.794			38.001	37.550
AIC	-152.122	-149.095	-152.599	-154.345	-47.825	-47.588			-50.000	-51.101
Sigma²	0.002 ^*** (5.20)	0.003 ^*** (5.21)	0.006 ^*** (6.23)	0.006 ^*** (6.24)	0.010 ^*** (5.04)	0.011 ^*** (5.14)			0.027 ^*** (8.24)	0.027 ^*** (8.24)

Table 4

Table 5

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变量 Variables	单位 Unit	说明 Instructions	平均值 Mean	标准差 Standard deviation	最小值 Min.	最大值 Max.
森林质量 Forest quality	m³·hm^-2	单位面积森林蓄积量 Forest stock per unit area	54.62	36.78	6.23	435.67
经济增长 Economic growth	yuan·person^-1	不变价人均GDP GDP per capita in constant prices	32 480.14	22 443.72	3 701.00	116 799.00
城镇化水平Urbanization	%	常住人口城镇化率 Urbanization of the resident population	49.52	15.28	21.05	90.30
林业固定资产投资 Fixed assets investment in forestry	10⁸yuan	营林和造林固定资产投资 Fixed asset investment	33.26	97.30	0.09	950.00
林木生产 Forest production	10⁴m³	木材产量 Timber yield	325.44	429.02	0.00	2 954.80
林业劳动力 Forestry labor force	person	林业系统第一产业从业人员 Primary industry practitioners in forestry	33 051.68	50 730.52	125.00	294 239.00
人口密度 Population density	person·km^-2	单位土地面积的人口数量 Population per unit land area	402.845	577.587	1.977	3 850.79
降水量 Precipitation	mm	遥感解译的年均降水量 Average precipitation by remote sensing	953.07	528.01	131.53	2 432.60
气温 Temperature	℃	遥感解译的年均气温 Annual temperature by remote sensing	12.62	6.26	0.05	25.35

指标Index	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016
Moran’s I	0.082 1	0.179 3	0.185 9	0.208 3	0.208 7	0.221 1	0.226 8	0.224 9	0.213 3	0.200 0	0.185 1	0.168 6	0.144 6	0.141 7
Z	1.789	1.854	1.961	2.170	2.173	2.336	2.391	2.364	2.249	2.118	1.974	1.816	1.806	1.745
P	0.074	0.064	0.049	0.029	0.029	0.019	0.017	0.018	0.025	0.034	0.048	0.069	0.072	0.078

地区Regions	曲线形式 Curved form	地区 Regions	当前所处阶段 Current stage	拐点出现年份 Year of inflection point	跨过拐点所需时间 Time to cross the inflection point/a
东北地区 Northeast China	倒N形Inverted N-shape	内蒙古Inner Mongolia	下降Decline	—	—
	倒N形Inverted N-shape	辽宁Liaoning	上升Increase	2017—2018	1~2
	倒N形Inverted N-shape	吉林Jilin	上升Increase	2017—2018	1~2
	倒N形Inverted N-shape	黑龙江Heilongjiang	上升Increase	2020—2021	4~5
华北地区 North China	倒N形Inverted N-shape	北京Beijing	上升Increase	2017—2018	1~2
	倒N形Inverted N-shape	天津Tianjin	上升Increase	2017—2018	1~2
	倒N形Inverted N-shape	河北Hebei	上升Increase	2026—2027	10~11
	倒N形Inverted N-shape	山西Shanxi	上升Increase	2028—2029	12~13
	倒N形Inverted N-shape	山东Shandong	上升Increase	2021—2022	5~6
	倒N形Inverted N-shape	河南Henan	上升Increase	2025—2026	9~10

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EKC Test of the Relationship between Forest Quality and Economic Growth Considering Spatial Spillover Effects

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