Application of a Panel Data Quantile-Regression Model to the Dynamics of Carbon Sequestration in Pinus kesiya var. langbianensis Natural Forests

Chang Liu,Yao Fu,Chengcheng Zhang,Cairong Yue,Guanglong Ou

doi:10.3390/f13010012

Abstract

Even though studies on forest carbon storage are relatively mature, dynamic changes in carbon sequestration have been insufficiently researched. Therefore, we used panel data from 81 Pinus kesiya var. langbianensis forest sample plots measured on three occasions to build an ordinary regression model and a quantile-regression model to estimate carbon sequestration over time. In the models, the average carbon reserve of the natural forests was taken as the dependent variable and the average diameter at breast height (DBH), crown density, and altitude as independent variables. The effects of the DBH and crown density on the average carbon storage differed considerably among different age groups and with time, while the effect of altitude had a relatively insignificant influence. Compared with the ordinary model, the quantile-regression model was more accurate in residual and predictive analyses and removed large errors generated by the ordinary model in fitting for young-aged and over-mature forests. We are the first to introduce panel-data-based modeling to forestry research, and it appears to provide a new solution to better grasp change laws for forest carbon sequestration.

Highlights

Forests are the largest terrestrial ecosystems and play an important role in the global carbon cycle [1,2,3,4]
Many studies have focused on forest biomass and carbon storage [5,6,7,8,9], and accurate estimation of forest carbon storage has become an important part of global climate change and carbon cycle research [10,11]
If there is no significant difference between individuals in terms of time and crosssections, it is a mixed-regression model, which can be estimated by the ordinary least square (OLS) method

Summary

Introduction

Forests are the largest terrestrial ecosystems and play an important role in the global carbon cycle [1,2,3,4]. Many studies have focused on forest biomass and carbon storage [5,6,7,8,9], and accurate estimation of forest carbon storage has become an important part of global climate change and carbon cycle research [10,11]. The traditional sample inventory method, vorticity correlation method, and model estimation method all have certain limitations for estimating forest carbon storage [12]. The environment has an effect on them [14] These data are often not well-correlated or follow spatially non-normal distributions [15]. A large number of spatial models have been applied [9,16,17], such as the GWR (Geographically weighted regression model), GWRK (Geographically weighted regression kriging model) [18], LMM (Linear mixed model), SEM (Spatial error model), and SLM (Spatial lag model), etc

Methods

Results

Discussion

Conclusion