Based on an extended economic model and space econometrics, this essay analyzed the spatial distributions and interdependent relationships of the production of forestry in China; also the input-output elasticity of forestry production were calculated. Results figure out there exists significant spatial correlation in forestry production in China. Spatial distribution is mainly manifested as spatial agglomeration. The output elasticity of labor force is equal to 0.6649, and that of capital is equal to 0.8412. The contribution of land is significantly negative. Labor and capital are the main determinants for the province-level forestry production in China. Thus, research on the province-level forestry production should not ignore the spatial effect. The policy-making process should take into consideration the effects between provinces on the production of forestry. This study provides some scientific technical support for forestry production.
The reform of collective forest rights is another major revolution of the rural management system after land reform in China [
This paper consists of four parts, the first part gives a brief description of relative research, the second part specifies the materials and methods used in this paper, the third part gives out results and discussion, and the last part is the conclusion part. Through this paper, we try to prove that the forestry production in one province influences that in another province.
The forestry production in China is found with severe spatial differences and is largely correlated with the differences and fluidity of regional forestry resource [
Under the circumstance of zero correlation, Moran’
The global Moran’s index can partially represent the space autocorrelation. However, owing to the repeated computation or mutual cancellation during computations, we used a local Moran’
When
Moran scatter diagram shows the 2D scatter plot that visualizes
LISA (Local Indictors of Spatial Association) analysis is used to figure out the spatial differences in production. When LISA passes the significance test, there is local positive spatial autocorrelation, or this region is surrounded by regions with similar performance, called spatial agglomeration. When this region and its nearby regions are all found with large observed data, it is called a high-high region, and otherwise, it is called a low-low region.
The selection of spatial weight
According to traditional economics, the economic growth mainly depends on two endogenous factors: labor and capital, but it is affected by technological progress, an exogenous factor. In this model, the land element is considered as an internal factor of economic growth. In other words, the output level
The basic model of forestry production does not involve space correlations. Taking spatial effects into account means the regional forestry production is affected not only by the local investment level, but also by the spillover effect from other nearby forestry regions. In this way, SLM is determined:
SEM takes into account the variables that may be ignored in the decision model, such as human capital, research level, and climate change. The space error model is used to measure the roles that may be played by the spatially interacting errors. SEM is expressed as
This study was targeted at 31 provinces or autonomous regions or municipality cities of Mainland China in 2013. The data were cited from
To study the interferences of weight indices on the space effect, we used three space weight matrices, and through stepwise distance increment, we tested the attenuation effect of distance (Table
Global Moran’s index for space autocorrelation for forestry productions in different regions.
Moran’s |
Mean | SD |
| |
---|---|---|---|---|
|
0.3685 | −0.0249 | 0.1112 | 0.0040 |
|
0.0880 | −0.0247 | 0.0780 | 0.0700 |
|
−0.0309 | −0.0318 | 0.0696 | 0.4660 |
|
0.3685 | −0.0297 | 0.1041 | 0.0030 |
|
0.0880 | −0.0324 | 0.0791 | 0.0780 |
|
−0.0309 | −0.0288 | 0.0712 | 0.4930 |
|
0.1031 | −0.0233 | 0.1989 | 0.2410 |
|
0.0950 | −0.0266 | 0.1454 | 0.1880 |
|
0.2133 | −0.0284 | 0.1166 | 0.0290 |
Table
Local Moran’s index based on
The first quadrant involves Shandong, Anhui, Hubei, Zhejiang, Jiangxi, Hunan, Fujian, Guangxi, and Guangdong, which are all high-output provinces surrounded by high-output provinces. The second quadrant involves Shanghai, Guizhou, Henan, Yunnan, and Chongqing, which are all low-output provinces surrounded by high-output provinces. The third quadrant involves Chongqing, Hebei, Jilin, Heilongjiang, Xinjiang, Shaanxi, Shanxi, Inner Mongolia, and Tibet, which are all low-output provinces surrounded by low-output provinces. The fourth quadrant involves Liaoning and Sichuan, which are both high-output provinces surrounded by low-output provinces. Clearly, the spatial differences of forestry outputs are very significant among all provinces in China, and the typical characteristics of positive local correlations and accumulation are very significant.
The distribution of space autocorrelation patterns among the 31 provinces is as follows (Table
Local Moran’s index based on space weight matrix
Province |
|
|
---|---|---|
Beijing | 0.3633 | 0.27 |
Tianjing | 0.3745 | 0.23 |
Hebei | 0.0130 | 0.48 |
Shanxi | 0.2943 | 0.29 |
Neimenggu | 0.3060 | 0.14 |
Liaoning | −0.0611 | 0.39 |
Jilin | 0.0189 | 0.31 |
Heilongjiang | 0.0534 | 0.41 |
Shanghai | −0.0002 | 0.03 |
Jiangsu | −0.0382 | 0.04 |
Zhejiang | 0.0251 | 0.06 |
Anhui | 1.2997 | 0.01 |
Fujian | −0.1454 | 0.01 |
Jiangxi | 0.7301 | 0.02 |
Shandong | 0.6203 | 0.11 |
Henan | 0.3067 | 0.24 |
Hubei | −0.2512 | 0.49 |
Hunan | 0.3949 | 0.1 |
Guangdong | 2.3471 | 0.04 |
Guangxi | 0.6460 | 0.07 |
Hainan | 0.0000 | 0.01 |
Chongqing | −0.9785 | 0.46 |
Sichuan | 0.3683 | 0.01 |
Guizhou | 0.0096 | 0.31 |
Yunnan | 0.2826 | 0.45 |
Xizang | 1.0362 | 0.22 |
Shaanxi | 0.4459 | 0.15 |
Gansu | 0.4282 | 0.09 |
Qinghai | 0.1428 | 0.04 |
Ningxia | 0.2303 | 0.09 |
Xinjiang | 0.4537 | 0.01 |
Note:
Then, a space econometric model was used to estimate the elasticity coefficients of labor, land, and capital inputs to province-level forestry production (Table
Space correlation OLS test based on space weight matrix
Test | Trivariate function | Bivariate function | ||
---|---|---|---|---|
Index |
|
Index |
| |
Moran’ |
4.8671 | 0.0000 | 4.7938 | 0 |
Lagrange multiplier (lag) | 0.335 | 0.5628 | 0.4905 | 0.4837 |
Robust LM (lag) | 0.2234 | 0.6365 | 0.1971 | 0.6571 |
Lagrange multiplier (error) | 14.9747 | 0.0001 | 16.3942 | 0 |
Robust LM (error) | 14.8631 | 0.0001 | 16.1007 | 0 |
Lagrange multiplier (SARMA) | 15.1981 | 0.0005 | 16.5912 | 0.0002 |
To identify the effects and contributions of different input elements to forestry production, we built an SEM using three elements that decide agricultural production (Table
Different regression models based on space weight matrix
Trivariate function | Bivariate function | |||||
---|---|---|---|---|---|---|
OLS | SLM | SEM | OLS | SLM | SEM | |
Constant | 0.3768 | 0.3559 | 0.4777 | 0.5702 | 0.5369 | 0.6657 |
(0.7390) | (0.7328) | (0.5558) | (0.6242) | (0.6219) | (0.4199) | |
|
0.8443 |
0.8350 |
0.9091 |
0.3968 | 0.4006 | 0.6492 |
(0.0297) | (0.0151) | (0.0016) | (0.1531) | (0.1174) | (0.0058) | |
|
−0.4543 |
−0.4418 |
−0.3293 | |||
(0.0955) | (0.0718) | (0.1372) | ||||
|
0.7377 |
0.6947 |
0.6332 |
0.8412 |
0.7830 |
0.6603 |
(0.0025) | (0.0014) | (0.0004) | (0.0007) | (0.0004) | (0.0003) | |
|
0.0395 | 0.6540 |
0.0503 | 0.6672 | ||
(0.5711) | (0.0000) | (0.4881) | 0.0000 | |||
|
−49.1486 | −48.9847 | −42.96 | −50.7651 | −50.5221 | −44.0254 |
|
0.8701 | 0.8714 | 0.9215 | 0.8562 | 0.8585 | 0.9167 |
AIC | 106.30 | 107.97 | 93.93 | 107.53 | 109.04 | 94.0509 |
SC | 112.16 | 115.30 | 99.79 | 111.93 | 114.91 | 98.4481 |
Note:
Results show neither the estimations nor the significance levels of output elasticity are always the same for any of the three elements, in both OLS and space economic models. The output elasticity of forestry labor from the bivariate SEM is 0.6492 (
As shown in Table
The trivariate model also involves the land input, but the forestry production measured from this variable is not very significant (
It should be noted that the model used here is based on space economic theory. This method indicates that the space effect should not be ignored. However, the estimation results only reveal the state-level standard but do not take into account the regional differences. In the future, more accurate and precise economic models are needed. Another thing that may be a limit for this paper is that, as to limitation of the data and observations, the result may vary according to different situations, which needs future study.
Here we used the sectional data of province-level forestry input-output in 2013 in China. Then, the Moran’
The global and local Moran’
From the perspective of policy implications, due to the existence of space error spillover effect in forest products and the remaining forestry output, the forestry production behaviors in nearby provinces will affect the agricultural production behaviors of the tested province. The time “incentive” effect in decision-making for regional agricultural production will influence the production game competition among nearby provinces and finally impact the input scales and allocation efficiency of province-level forestry production elements. Thus, the formulation of relevant forestry policies should not ignore the transverse cross-effect among provincial forestry production, and should take into account the interactions of forestry production among nearby provinces. A regional coordination mechanism should be established to coordinate the rational flow of forestry production elements, to improve the space complementarity and space allocation efficiency of elements, and to promote the regional forestry production ability.
The authors declare that there is no conflict of interests regarding the publication of this article.
This research was supported by the National Natural Science Foundation of China for Distinguished Young Scholars (Grant no. 71225005), the Key Project in the National Science & Technology Pillar Program of China (Grant no. 2013BACO3B00), and the funding support from the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (Grant no. 2012ZD008).