Analysis of Distribution Characteristics and Driving Factors of Forestry Enterprises in China Using Geospatial Technology and Models

被引:0
|
作者
Ma, Qiang [1 ]
Ni, Honghong [2 ,3 ]
Su, Xiangxiang [1 ]
Nian, Ying [1 ]
Li, Jun [1 ]
Wang, Weiqiang [1 ]
Sheng, Yali [1 ]
Zhu, Xueqing [1 ]
Liu, Jiale [1 ]
Li, Weizhong [3 ]
Liu, Jikai [1 ,4 ]
Li, Xinwei [1 ,4 ,5 ]
机构
[1] Anhui Sci & Technol Univ, Coll Resource & Environm, Chuzhou 233100, Peoples R China
[2] Anhui Sci & Technol Univ, Off Acad Res, Chuzhou 233100, Peoples R China
[3] Northwest A&F Univ, Coll Forestry, Yangling 712100, Peoples R China
[4] Anhui Engn Res Ctr Smart Crop Planting & Proc Tech, Chuzhou 233100, Peoples R China
[5] Anhui Prov Agr Waste Fertilizer Utilizat & Cultiva, Chuzhou 233100, Peoples R China
来源
FORESTS | 2025年 / 16卷 / 02期
关键词
sustainable development; forestry enterprises; spatial agglomeration; spatio-temporal evolution; spatial heterogeneity; MGWR model; CAPITAL STRUCTURE; MANAGEMENT; CLUSTERS; INDUSTRY; PROGRESS;
D O I
10.3390/f16020364
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Forestry enterprises play a pivotal role in economic development, ecological civilization construction, and sustainable development. This study employs GIS-based spatial analysis to examine the distribution patterns and interrelationships of forestry enterprises, investigating their key determinants and spatial heterogeneity. The findings provide valuable insights for policymakers aiming to optimize industrial structures and enhance national ecological security. This research develops a comprehensive evaluation index system to assess the factors influencing forestry industry development in China. Nine factors are considered: human resources, economic development, industrial structure, technological support, trade development, financial environment, natural conditions, urbanization, and transportation. Using panel data from 367 cities in 2020, the Multiscale Geographically Weighted Regression (MGWR) method quantifies the influence of these factors and their spatial variations. The results show the following. (1) Forestry enterprises in China exhibit persistent spatial clustering. The eastern regions have a notably higher concentration than the western regions, and new enterprises are increasingly concentrated in a few hotspot cities in the east. (2) The spatial center of forestry enterprises has steadily moved southeast. Initially, the distribution was balanced in the eastern regions, but it has become highly concentrated in the southeastern coastal areas. (3) Regarding spatial autocorrelation, regions within the northwest cold spot cluster have been disappearing entirely. The northeast and southwest hotspot clusters have shrunk significantly, while the southeast hotspot cluster has remained large. (4) Permanent population size and green land area are the most strongly positively correlated with forestry enterprise distribution. Patent authorizations, orchard area, and forest land area also show positive effects. In contrast, road density and total import/export volume are negatively correlated with the number of forestry enterprises. This aligns with the structure of China's forestry industry, which relies more on natural resources and market demand than on economic development level or financial environment. (5) The factors influencing forestry enterprise distribution show significant spatial variation, driven by regional factors such as resources, economy, and population. These factors ultimately determine the spatiotemporal distribution of forestry enterprises. This study provides data-driven insights to optimize the distribution of forestry industries and formulate more effective ecological protection policies.
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页数:28
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