## 4 Results and Discussion  
Conducting stationarity tests before applying an econometric model is crucial for avoiding spurious regressions and ensuring the validity of the results. Stationarity of a time series, which implies a constant mean, variance, and autocovariance over time, is essential because using nonstationary series can lead to apparently significant relationships between variables that are, in reality, coincidental and do not reflect a true connection. Therefore, stationarity tests help confirm that the estimated econometric relationships are robust and reliable. In our study, we employed two distinct unit root tests to assess the stationarity of the panel data: the Levin, Lin, and Chu (LLC) test [54] and the Im, Pesaran, and Shin (IPS) test [55]. The LLC test is designed to evaluate the presence of a unit root under the assumption of a common unit root process across all cross-sectional units within the panel. This means that the LLC test assumes a homogeneous autoregressive parameter across the entities, making it suitable for panels where the cross-sectional units share similar dynamics over time. On the other hand, the IPS test is more flexible, as it allows for heterogeneity in the autoregressive coefficients across different cross-sectional units. This test accommodates individual unit root processes, recognizing that each entity in the panel may have its own unique time-series properties. By applying both the LLC and IPS tests (Table 3), we aim to capture a comprehensive picture of the stationarity properties of our data, accounting for both common trends and individual variations within the panel. This dual approach enhances the robustness of our analysis and ensures that our econometric modeling is based on reliable and properly diagnosed data. Considering that not all variables employed in the analysis exhibit stationarity, the use of the GLM is not only justified but also advantageous for obtaining consistent and robust results. Unlike traditional time series models, which often require strict stationarity assumptions to avoid spurious relationships, the GLM framework is designed to handle a broader range of data structures. GLM does not rely on the time-dependent properties of the data in the same way that models such as ARIMA or VAR do, making it particularly suitable for analyzing datasets where variables may be integrated at different orders or exhibit nonstationary behavior. By utilizing appropriate link functions and error distributions, GLM allows for flexibility in modeling complex relationships between variables, thereby mitigating potential biases or inefficiencies that might arise from nonstationary data. This capability ensures that the estimations remain reliable and that the inferences drawn from the model are valid, even in the presence of nonstationarity among the variables. The correlation matrix between variables presented in Table 4 is a statistical tool that displays the correlation coefficients between all pairs of variables within a dataset, thereby providing a comprehensive view of the linear relationships among them. These coefficients range from \(-1\) to \(1\), indicating, respectively, a perfect negative correlation, a perfect positive correlation, or the absence of a linear relationship. The matrix is symmetric with respect to the main diagonal, and the elements along this diagonal are always \(1\), as each variable is perfectly correlated with itself. Analyzing the correlation matrix allows for the identification of the strength and direction of relationships between variables, which is crucial for understanding interdependencies and for subsequent statistical modeling. In Model M1, the analysis of the correlation between greenhouse gas emissions and other variables reveals a nuanced set of relationships characterized by a combination of moderate positive and negative associations. Specifically, the correlation between greenhouse gas emissions and affordability is moderately negative, suggesting that increased emissions are associated with reduced affordability.