森林资源与经济增长的EKC关系检验——基于省际面板数据的实证研究

doi:10.11707/j.1001-7488.20191212

Abstract

Abstract:

Objectve: The aim of this study was to test the interactive relationships about environmental Kuznets curve (EKC) between forest resources and economic growth, and to explore the path to maintain the coordinated development of forest resources and economic growth. Method: Based on the theory of EKC, and regarded the per capita forest stock volume(pfsv) as the dependent variable, the constant price per capita GDP(pgdp) as independent variable and social economic factors as control variables, using the 1996-2015 provincial panel data in China, the relationships between the amount change of forest resources and economic growth were tested. In order to select the most appropriate regression model, the general EKC model and the extended EKC model with the control variables are constructed by using the panel unit root and cointegration test method, and find the time path of inflection point of each province. Result: 1) Two types of EKC model support the pfsv and pgdp present inverted N-EKC relationship, per capita forest resources along with the increase of pgdp showed the evolution process about "decline→rise→decline", but the extended EKC model fits better, and two inflection point will reach when the pgdp reached 639.79 yuan and 33 560.58 yuan, and most provinces are in the ascent phase of the EKC curve. 2) Different control variables have different influences on amount change of forest resources, the technical progress and government investment in environmental protection are positively related with forest resources growth, and can maintain the growth of forest resources effectively, but the positive correlation between industrial structure and forest resources is not significant, while the population size is negatively related to the growth of forest resources, and population growth will encroach on the growth of the forest resources. 3) Most provinces crossed the minimum inflection point to enter the uplift channel before 1990, and the time reach the maximum inflection point between different provinces is different, which is relevant with the level of economic development on the geographical spatial, and presented as the earliest in the east, followed by the middle, the latest of the west. Conclusion: It is in the rising stage of inverted N-EKC relationships between forest resources and economic growth at present. The forest resources increase with economic growth, but the growth of forest resources will reach saturation, the per capita forest resources growth will tend to be steady. Various measures can be adopted to continue to optimize the industrial structure, increase forestry investment and introduce private capital so as to avoid declining when per capita forest resources reach the maximum inflection point, and we must guide the transition to forest quality after forest quantity reach the maximum inflection point.

Key words: forest resources, economic growth, the extended EKC model, the inflection point, panel data

CLC Number:

F307.2

Mengyang Hou,Shunbo Yao. Testing of the EKC Relationship between Amount of Forest Resources and Economic Growth: An Empirical Study Based on Provincial Panel Data[J]. Scientia Silvae Sinicae, 2019, 55(12): 113-122.

Figures/Tables 7

Table 1

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Fig.1

Table 6

The time of each province reaches the inflection point and the prediction of pgdp"

地区 Region	极小值出现时间 The time of MinIP	当前所处阶段Stage	人均GDP年均增长率 The average annual growth of pgdp(%)	极大值出现时间 The time of MaxIP	极大值出现所需时间 The time required of MaxIP	极大值出现时的人均GDP The pgdp when the MaxIP appears/yuan
全国(不包括港澳台) National(excluding Hong Kong, Macao and Taiwan)	1985	上升Rising	8.42	2033	18	34 697.26
北京Beijing	1986年之前Before 1986	上升Rising	7.18	2025	10	35 628.00
天津Tianjin	1986年之前Before 1986	上升Rising	9.39	2017	2	33 827.26
河北Hebei	1987	上升Rising	9.68	2029	14	35 353.38
山西Shanxi	1986	上升Rising	8.44	2034	19	35 481.08
内蒙古Inner Mongolia	1986	上升Rising	11.12	2023	8	36 257.77
辽宁Liaoning	1986年之前Before 1986	上升Rising	8.98	2024	9	33 930.97
吉林Jilin	1986年之前Before 1986	上升Rising	9.58	2028	13	36 429.70
黑龙江Heilongjiang	1986年之前Before 1986	上升Rising	8.41	2031	16	35 087.32
上海Shanghai	1986年之前Before 1986	下降Declining	7.57	2014	已出现Already	34 055.14
江苏Jiangsu	1986年之前Before 1986	上升Rising	11.08	2019	4	34 004.98
浙江Zhejiang	1986年之前Before 1986	上升Rising	10.16	2023	8	35 405.54
安徽Anhui	1986年之前Before 1986	上升Rising	9.00	2034	19	36 260.18
福建Fujian	1987	上升Rising	11.08	2024	9	34 763.71
江西Jiangxi	1990	上升Rising	9.25	2033	18	35 808.36
山东Shandong	1986年之前Before 1986	上升Rising	10.73	2024	9	34 305.76
河南Henan	1991	上升Rising	10.03	2030	15	34 373.58
湖北Hubei	1986年之前Before 1986	上升Rising	9.71	2027	12	33 764.49
湖南Hunan	1992	上升Rising	9.32	2033	18	35 602.92
广东Guangdong	1986年之前Before 1986	上升Rising	10.19	2024	9	35 856.99
广西Guangxi	1994	上升Rising	9.76	2035	20	36 604.63
海南Hainan	1986年之前Before 1986	上升Rising	9.26	2030	15	34 705.63
重庆Chongqing	1990	上升Rising	10.87	2026	11	36 651.09
四川Sichuan	1991	上升Rising	9.57	2031	16	33 697.19
贵州Guizhou	1996	上升Rising	8.34	2043	28	35 533.81
云南Yunnan	1992	上升Rising	8.78	2038	23	34 907.46
西藏Tibet	1988	上升Rising	8.87	2032	17	35 401.71
陕西Shaanxi	1988	上升Rising	10.02	2028	13	34 048.65
甘肃Gansu	1988	上升Rising	9.10	2032	17	33 769.16
青海Qinghai	1986年之前Before 1986	上升Rising	8.05	2036	21	34 006.32
宁夏Ningxia	1986年之前Before 1986	上升Rising	7.88	2037	22	33 758.31
新疆Xinjiang	1986年之前Before 1986	上升Rising	7.76	2038	23	36 060.39

Table 6

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变量Variables	pfsv/ m³	ln pgdp/yuan	tech/item	is(%)	density/km^-2
平均值Mean	33.161 71	7.834 67	9 857.334	43.666 89	450.238 3
最大值Max.	882.867	10.506 99	269 944	68.46	3 850.79
最小值Min.	0.062	5.829 975	1	11.9	1.65
标准差SD	127.949 8	0.993 057	28 738.41	9.148 131	678.836 3
偏度Skewness	5.325 363	0.240 21	5.686 719	-0.764 45	3.199 831
峰度Kurtosis	30.061 63	2.229 188	40.142 4	3.984 107	13.592 82
观测量Observations	930	930	930	930	930

检验方法Method	LLC	IPS	ADF	PP	结果Result
pfsv	8.856(0.999)	15.475(1.000)	17.774(1.000)	13.874(0.861)	不平稳Not stable
Δpfsv	1.499^**(0.033)	0.576^**(0.018)	115.792^***(0.000)	105.321^***(0.001)	平稳Stable
ln pgdp	-1.173(0.121)	6.987(1.000)	26.046(1.000)	9.178(1.000)	不平稳Not stable
Δln pgdp	-6.878^***(0.000)	-7.221^***(0.000)	159.293^***(0.000)	144.405^***(0.000)	平稳Stable
(ln pgdp)²	2.756(0.999)	10.767(1.000)	12.111(0.937)	1.425(1.000)	不平稳Not stable
Δ(ln pgdp)²	-5.958^***(0.000)	-5.542^***(0.000)	129.604^***(0.000)	110.693^***(0.001)	平稳Stable
(ln pgdp)³	-7.648^***(0.000)	-2.249(0.012)	100.155(0.015)	54.625(0.736)	不平稳Not stable
Δ(ln pgdp)³	-5.472^***(0.000)	-4.143^***(0.000)	110.08^***(0.002)	83.474^**(0.035)	平稳Stable

检验方法 Method	统计形式 Statistical form	普通EKC模型 The general EKC model		扩展EKC模型 The extended EKC model
检验方法 Method	统计形式 Statistical form	统计量Statistic	P	统计量Statistic	P
Pedroni	Panel PP-statistic	-2.463 5^***	0.006 9	-1.511 3^*	0.065 4
Pedroni	Panel ADF-statistic	2.006 1^*	0.077 6	-3.254 6^***	0.000 6
Pedroni	Group PP-statistic	1.748 0^*	0.059 8	-1.789 4^**	0.036 8
Pedroni	Group ADF-statistic	-4.415 9^***	0.000 0	-3.634 8^***	0.000 1
Kao	ADF	-6.910 6^***	0.000 0	-14.988 9^***	0.000 0

类型Test type	Test Summary	Chi-Sq. statistic	Chi-Sq. d.f.	Prob.	检验结果Test results
普通EKC形式 The general EKC model	Cross-section random	0.573 2	3	0.002 5	固定效应模型 Fixed effect model
扩展EKC形式 The extended EKC model	Cross-section random	21.542 7	6	0.001 5	固定效应模型 Fixed effect model

解释变量 Explanatory variables	模型1(普通EKC模型) Model 1(general EKC model)	模型2(扩展EKC模型) Model 2(extended EKC model)
ln pgdp	-44.494 5^***(0.000)	-102.781^***(0.012)
(ln pgdp)²	5.680 2^***(0.000)	12.885^***(0.000)
(ln pgdp)³	-0.232 9^***(0.000)	-0.509^***(0.004)
density		-0.001^***(0.000)
tech		0.014^**(0.021)
is		0.025(0.114)
gov		0.551^***(0.000)
μ	166.948 0^***(0.000)	177.664 7^***(0.091)
R²	0.989 9	0.993 8
Adjusted R²	0.989 5	0.993 5
F-statistic	2 675.118	3 769.000
Prob(F-statistic)	0.000 0	0.000 0
EKC形式EKC curve	倒N形Inverted N	倒N形Inverted N

Testing of the EKC Relationship between Amount of Forest Resources and Economic Growth: An Empirical Study Based on Provincial Panel Data

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