Table2_Deforestation perspectives of dry temperate forests: main drivers and possible strategies.XLSX

Deforestation is the accelerating factor of climate change in developing countries. The German Watch Report 2020 had rated Pakistan number seventh most affected country due to adverse impacts of climate change. The problem of deforestation poses an existential danger to the forest-depleted country. It is of utmost importance to predict the main drivers to control deforestation. This study was conducted from October 2021 to August 2022 in dry temperate forests of the Chilas to investigate the current condition, causes of deforestation, and predicted the main drivers by using a binary regression model. Stratified random sampling techniques and fixed area plot method were used and taken ground measurements during field inventory to access current situation of deforestation. While a non-probability quota sampling technique and semi-structured questionnaire were utilized for the determination of main drivers of deforestation through respondent’s survey. The forest inventory result showed that most trees fall in immature and sub-mature (mainly in 10–20 and 20–30 cm) diameter classes while the binary logistic regression model predicted dominating four primary drivers (unsustainable fuel wood extraction, unsustainable timber extraction and urban crawling and rural expansion/habituation, and free and uncontrolled livestock grazing) and one secondary driver (wood for energy needs). To address the underlying causes of deforestation, the government must supply alternate energy sources, as well as other economic possibilities to reduce dependency on forests.

森林砍伐是发展中国家气候变化的加速诱因。2020年德国观察（German Watch）发布的报告将巴基斯坦列为受气候变化不利影响最严重的第七大国家。森林砍伐问题对这个森林资源匮乏的国家构成了生存层面的威胁。探明主要驱动因素以管控森林砍伐，至关重要。本研究于2021年10月至2022年8月在奇特拉斯（Chilas）地区的干旱温带森林（dry temperate forests）开展，旨在调查当地森林砍伐的现状与成因，并通过二元回归模型（binary regression model）预测主要驱动因素。研究采用分层随机抽样（stratified random sampling）技术与固定面积样地（fixed area plot）法，在野外调查中开展地面实测以评估当前森林砍伐现状；同时采用非概率配额抽样（non-probability quota sampling）技术与半结构化问卷（semi-structured questionnaire），通过受访者调查确定森林砍伐的主要驱动因素。森林清查结果显示，多数树木处于幼龄林与中龄林阶段，对应胸径级主要为10–20 cm与20–30 cm；二元逻辑回归模型（binary logistic regression model）筛选出4个核心驱动因素：不可持续薪柴采伐、不可持续木材采伐、城市蔓延与农村扩张/定居，以及自由无管控的牲畜放牧，另有1个次要驱动因素：用于能源需求的木材采伐。为解决森林砍伐的根源性问题，政府应提供替代能源及其他经济发展机遇，以降低民众对森林资源的依赖。