## 4 Results and Discussion
Conversely, the correlation between greenhouse gas emissions and user adoption is moderately positive, indicating that higher emissions correspond with higher user adoption rates. The relationship between green \begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**LLC**} & \multicolumn{2}{c}{**IPS**} \\ \hline  & **Statistic** & **Probability** & **Statistic** & **Probability** \\ \hline InGHG & \(-3.3149\) & \(0.0005\) & \(-1.9863\) & \(0.0235\) \\ REN & \(-3.6247\) & \(0.0001\) & \(-1.6173\) & \(0.0529\) \\ AFFORD & \(0.5406\) & \(0.7056\) & \(1.0675\) & \(0.8571\) \\ INFR & \(-122.0910\) & \(0.0000\) & \(-77.5371\) & \(0.0000\) \\ USAD & \(-3.4510\) & \(0.0003\) & \(-2.5603\) & \(0.0052\) \\ \hline \end{tabular} Source: Own processing, 2024.  
\end{table} 
Table 3: Unit root test for the data in the model. house gas emissions and infrastructure is weak and statistically insignificant, implying that infrastructure does not have a significant linear effect on emissions within this model. In Model M2, the emphasis is placed on renewable energy, which exhibits moderate correlations with both infrastructure and affordability, yet has a minimal impact on user adoption. Specifically, renewable energy is positively correlated with infrastructure, suggesting that better infrastructure supports increased use of renewable energy. Similarly, there is a negative correlation between renewable energy and affordability, implying that higher renewable energy usage may be associated with lower affordability levels. However, the correlation between renewable energy and user adoption is very weak and not statistically significant, indicating that renewable energy does not substantially influence user adoption rates. The estimation results from Table 5 reveal the influence of digital transformation indicators on greenhouse gas emissions. The negative and statistically significant coefficient for digital infrastructure (INFR) (\(-0.0323\), \(p\)-value = 0.0025) confirms that improvements in digital infrastructure contribute to a reduction in GHG emissions. This finding supports the notion that enhanced digital connectivity facilitates energy-efficient practices, smart grid optimization, and reduced reliance on carbon-intensive activities. The results align with previous studies suggesting that technological advancements can drive emissions reductions through increased efficiency in industrial processes and urban management [22; 23]. Conversely, the positive and statistically significant coefficient for user adoption (0.0219, \(p\)-value = 0.0015) indicates that higher rates of digital technology adoption are associated with increased GHG emissions. This suggests that while digitalization enables efficiency gains, it may also lead to higher electricity demand, especially in regions where the energy mix is dominated by fossil fuels. This result highlights a potential trade-off between digital expansion and environmental sustainability, reinforcing the need for parallel investments in renewable energy infrastructure to offset increased energy consumption. The negative coefficient for affordability (\(-0.0294\), \(p\)-value = 0.0028) further supports this interpretation. As digital technologies become more affordable, their widespread adoption appears to drive energy demand, which, in turn, affects emissions levels. However, affordability may also enable the adoption of energy-efficient digital solutions, thus contributing to lower emissions in the long run. These findings suggest that policies promoting energy efficiency within the digital sector are crucial for ensuring that digital transformation leads to net-positive environmental outcomes.