## 4 Results and Discussion
\begin{table}
\begin{tabular}{l c c c} \hline Dependent Variable: REN & & & \\ \hline Method: Generalized linear model (Newton–Raphson/Marquardt steps) & & & \\ \hline Sample: 2010 2022 & & & \\ \hline Family: Normal & & & \\ \hline Link: Identity & & & \\ \hline Dispersion computed using Pearson Chi-Square & & & \\ \hline Convergence achieved after 0 iterations & & & \\ \hline Coefficient covariance computed using observed Hessian & & & \\ \hline Variable & Coefficient & Std. Error & z-Statistic & Prob. \\ \hline AFFORD & \(-\)0.215804 & 0.062879 & \(-\)3.432035 & 0.0006 \\ \hline INFR & 0.282854 & 0.068234 & 4.145334 & 0.0000 \\ \hline USAD & \(-\)0.142603 & 0.044065 & \(-\)3.236194 & 0.0012 \\ \hline C & 6.506090 & 6.803710 & 0.956256 & 0.3389 \\ \hline Mean dependent var & 17.35287 & S.D. dependent var & & 4.783833 \\ \hline Sum squared resid & 734.8027 & Root MSE & & 3.759098 \\ \hline Log-likelihood & \(-\)142.7232 & [PERSON] info criterion & 5.643201 \\ \hline Schwarz criterion & 5.793297 & Hannan–Quinn criter. & & 5.700744 \\ \hline Deviance & 734.8027 & Deviance statistic & & 15.30839 \\ \hline Restr. deviance & 1167.138 & LR statistic & & 28.24172 \\ \hline Prob (LR statistic) & 0.000003 & Pearson SSR & & 734.8027 \\ \hline Pearson statistic & 15.30839 & Dispersion & & 15.30839 \\ \hline \end{tabular} Source: Own processing, 2024.  
\end{table} 
Table 6: GLM estimations for M2. In the case of the first research question, Q1, the results from Model M1 indicate a statistically significant negative relationship between improved digital infrastructure and greenhouse gas emissions. This suggests that enhancements in digital infrastructure contribute to a reduction in emissions, likely through more efficient energy use, improved resource management, and the integration of low-carbon technologies. For the second research question, Q2, Model M2 results indicate a negative and statistically significant relationship between the affordability of digital technologies and the use of renewable energy. This implies that as digital technologies become more affordable, the share of renewable energy may decrease. This could suggest that affordability drives broader digital adoption that, paradoxically, increases reliance on nonrenewable energy due to the higher overall energy demand. For Q3, in Model M1, user adoption of digital technologies is positively correlated with greenhouse gas emissions. This indicates that higher user adoption might be associated with activities that increase energy consumption and emissions, potentially due to the higher energy demands of widespread digital usage. Conversely, in Model M2, user adoption shows a negative correlation with renewable energy adoption, suggesting that increased digital usage may not align with renewable energy use, possibly due to the current energy mix in emerging economies. Based on the results from Models M1 and M2, advanced digital infrastructure contributes to enhancing environmental sustainability in nuanced ways. Model M1 demonstrates a statistically significant negative relationship between digital infrastructure and greenhouse gas emissions. This suggests that improvements in digital infrastructure lead to a reduction in emissions, likely due to more efficient energy use, better resource management, and the integration of cleaner technologies. However, the results from Model M2 present a more complex picture. While digital infrastructure positively correlates with the adoption of renewable energy, the relationship between user adoption and renewable energy is negative. This implies that while advanced infrastructure enables the deployment of renewable energy, other factors, such as how digital technologies are used, might counterbalance these gains by increasing overall energy demand and reliance on non-renewable sources. The results of this study align with and expand upon several theoretical perspectives on digital transformation and environmental sustainability.