Evaluation of Self-Development Ability and Study of Its Obstacle Factors for State-Owned Forest Farms: Applying the SEM–PPM

Jiangdi Bai,Pan Tan,Wenhui Chen,Junchang Liu

doi:10.3390/su13063119

Abstract

State-owned forest farms (SOFFs) help maintain forest ecological security and hold an important strategic position in China’s development. In this study we used 1305 sample data from 16 provinces, a structural equation model (SEM), and a projection pursuit model (PPM) to evaluate the self-development abilities of SOFFs, and used the obstacle model to analyze factors hindering these abilities in various provinces, at different development levels, and with different subordination relationships. The results show that (1) the self-development abilities of SOFFs remain weak, and there are many more provinces with low than with high levels; (2) the subordination relationship significantly affects the self-development ability, which is the highest for municipal SOFFs; and (3) social services, people’s livelihood security, management ability, and forest resources are the main constraints for SOFF’s self-development abilities, and people’s livelihood security has the greatest influence for SOFFs with high self-development abilities, while social services are the most important for those with low self-development abilities.

Highlights

0.5, and the square root of the average variance extracted (AVE) value is larger than the coefficient of the correlation between latent variables, indicating discriminate validity
We ran projection pursuit model (PPM) using this score and MATLAB 2018, in which the parameters for the real coding accelerating genetic algorithm (RAGA) algorithm were set with reference to Su et al (2018): the population size was 400, the crossover probability was 0.8, the mutation probability was 0.2, the number of optimization variables was 7, the random number required for the variation direction was 10, and the acceleration was 7
The weak self-development ability of SOFFs is closely related to the positive externality of forestry production

Summary

Methods

The preliminarily constructed evaluation index system was only a framework obtained by a qualitative method, which is subjective to some extent [25] Both objective (such as the entropy weight, gray correlation, and principal component analysis methods) and subjective (such as the analytic hierarchy process and fuzzy comprehensive evaluation method) weighting methods currently exist. In the former, the weight is determined according to the data characteristics of indicators or the data relationships existing among indicators (such as correlations); in the latter, the weight is determined according to the subjective judgment of expert experience. The relationships between the sub-dimensions and total dimensions are not simple and linear but non-linear, and they can be solved through PPM [26]

Results

Discussion

Conclusion