Table 8.
Regression analysis of the drivers of land use/land cover change
| Coefficients | Std. Error | t-Stat | P-Value | |
|---|---|---|---|---|
| (Constant) | −1.80 | 0.70 | −2.57 | 0.01 |
| Population growth | 0.10 | 0.03 | 3.33 | 0.00 |
| Agricultural expansion | 0.20 | 0.05 | 4.00 | 0.00 |
| Expansion of built-up areas | 0.15 | 0.05 | 3.00 | 0.00 |
| Deforestation | 0.30 | 0.07 | 4.29 | 0.00 |
| Land degradation | 0.40 | 0.08 | 5.00 | 0.00 |
| Demand for farmland and forest products | 0.20 | 0.07 | 2.86 | 0.01 |
| Unemployment | 0.10 | 0.05 | 2.00 | 0.05 |
| Lack of alternative income source | 0.15 | 0.06 | 2.50 | 0.01 |
| Open access and limited conservation of resources | 0.20 | 0.07 | 2.86 | 0.01 |
| Natural disasters | 0.05 | 0.03 | 1.67 | 0.10 |
| Policy and planning | 0.00 | 0.03 | 0.00 | 1.00 |
| R-squared = 0.750 | ||||
| Adjusted R-squared = 0.720 |
The research result in the above table depicts multiple linear regression analysis that was employed to scrutinize the link amongst independent variables, which are factors influencing or causing changes in land use and land cover (LULC), and the dependent variable, namely LULC changes; based on community perception. The alterations in land use and land cover represent a dynamic and intricate process influenced by a multitude of interacting factors, spanning from diverse natural elements to socioeconomic changes [35]. They have profound effects on the environment and ecosystem services at different scales [107]. The regression model has a high R-squared value of 0.750, which means that 75 % of the difference in LULC changes can be elucidated by the independent (factor) variables.