2.3.2. Analysis of Drivers

Firstly, we explored the relationship between forest area in 2018 and its drivers. Data for the most recent available year were used to consider potential drivers as indicated: land use change (2018), population (2015), gross domestic product (GDP, 2015), accessibility (2010), karstification intensity (2010), drought index (2015), mean annual precipitation (2015), and slope (2009). Using them as baseline values, we then determined the influence of these factors on forest change over time.

To assess the influence of these factors on forest change over time, we conducted a correlation analysis between forest changes and the various drivers and then applied generalized linear model (GLM) regression to quantify the relative contribution of each variable. The GLM regression extends linear model regressions by expanding the distribution range of dependent variables and introducing a continuous function, and it is generally applicable

to non-normally distributed data [61]. As Formulae (8)–(10) show, the model is a function of mean μ with a linear combination x<sup>β</sup> formed from regressor x and coefficient vector *β*.

$$\mu\_i = E(Y\_I | X\_1, X\_2, \dots, X\_k), \ i = 1, \dots, n \tag{8}$$

$$\mathfrak{n}\_{i} = \mathbf{g}(\mathfrak{n}\_{i}) \tag{9}$$

$$\mathbf{g}(\mu\_i) = \boldsymbol{\eta}\_i = \beta\_0 + \beta\_1 \boldsymbol{X}\_{i1} + \beta\_2 \boldsymbol{X}\_{i2} + \beta\_3 \boldsymbol{X}\_{i3} + \dots + \beta\_k \boldsymbol{X}\_{ik} \tag{10}$$

where *X* is explanatory variables (factors driving forest change), *YI* is dependent variables (the area of forest), μ*<sup>i</sup>* is n independent samples subject to exponential distribution; η*<sup>i</sup>* represents k linear combinations of explanatory variables; g(μ*i*) refers to a function linking μ*<sup>i</sup>* and η*i*, and *k* is the number of explanatory variables.

#### **3. Results**
