# The Non-Linear Impact of Industry 4.0 on Carbon Emissions in China's Logistics Sector
## 3 Results
### Main Findings
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Variable** & **Coefficient** & \begin{tabular}{c} **Standard** \\ **Error** \\ \end{tabular} & \(t\)**-Value** & \(p\)**-Value** \\ \hline OLS Model & \begin{tabular}{c} Industry 4.0 \\ Index (ind4) \\ \end{tabular} & 2.383 ** & 1.027 & 2.32 & 0.02 \\ \hline Fixed Effects & \
\begin{tabular}{c} Industry 4.0 \\ Index (ind4) \\ \end{tabular} & 1.788 * & 0.926 & 1.93 & 0.054 \\ \hline Random &
\begin{tabular}{c} Industry 4.0 \\ Index (ind4) \\ \end{tabular} & 4.291 *** & 0.837 & 5.13 & 0 \\ \hline \end{tabular} Explanation: This table illustrates the repercussions of Industry 4.0 implementation on carbon emissions for different types of players. In fact, it shows the power and importance of the connection, thus setting the mood of this article. * represents significance at the 10\% level (\(p<0.10\)), meaning there is a 90\% confidence that the result is not due to random chance. ** represents significance at the 5\% level (\(p<0.05\)), meaning there is a 95\% confidence that the result is not due to random chance. *** represents significance at the 1\% level (\(p<0.01\)), meaning there is a 99\% confidence that the result is not due to random chance. *** indicates statistical significance at the 1\% level (\(p<0.01\)), meaning there is a 99\% confidence that the result is not due to random chance. \\ \hline \end{tabular}
\end{table} 
Table 2: Regression Results—Impact of Industry 4.0 on Carbon Emissions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mediator** & **Indirect Effect** & **Standard Error** & \(t\)**-Value** & \(p\)**-Value** \\ \hline Production Efficiency & 0.674 ** & 0.235 & 2.87 & 0.004 \\ \hline Business Output Scale & 2.306 *** & 0.507 & 4.55 & 0 \\ \hline Information Integration & 0.985 * & 0.432 & 2.28 & 0.023 \\ \hline \end{tabular} Explanation: This table reveals that, along with higher productivity efficiency, the scaling of business output and broader information integration are the key drivers through which Industry 4.0 can reduce its carbon footprint. It gives an idea of the mechanisms behind the impact both from positive and negative perspectives. * Indicates statistical significance at the 10\% level (\(p<0.10\)), meaning there is a 90\% confidence that the result is not due to random chance. ** indicates statistical significance at the 5\% level (\(p<0.05\)), meaning there is a 95\% confidence that the result is not due to random chance. ** indicates statistical significance at the 1\% level (\(p<0.01\)), meaning there is a 99\% confidence that the result is not due to random chance. ** indicates statistical significance at the 1\% level (\(p<0.01\)), meaning there is a 99\% confidence that the result is not due to random chance. \\ \hline \end{tabular}
\end{table} 
Table 3: Mediation Analysis—The Roles of Production Efficiency, Business Output Scale, and Information Integration. Figure 2: Dynamic Panel Regression Results—Long-Term Impact of Industry 4.0. Explanation: This radar chart shows the long-term effects of Industry 4.0 on air pollution in different industries and regions with the help of a dynamic panel regression. The chart indicates that the influence of Industry 4.0 on reducing carbon emissions increased over time, with the coefficients growing from 2011 to 2019. This suggests that the long-term effects of Industry 4.0 become more significant, contributing increasingly to carbon emission reductions (Figure 2). Detailed statistical results are presented in Table 2, showing the impacts of different Industry 4.0 technologies on carbon emissions across various provinces. The results indicate that the IoT and AI had the most significant positive impact on reducing emissions, while automation technologies initially increased emissions due to high energy consumption. These findings are supported by robustness checks and sensitivity analyses, confirming the validity of the results.