## 3 Results and Analysis
### The Impact Path Test of Urbanization on Carbon Emissions  
From Table 6, we can see the following. (1) The total benefits and direct effects of urbanization on carbon emissions. From the estimation results of Column1, Column4, and Column7 in Table 6, the urbanization level (\(lnL\)) coefficient is statistically significant at the 5% significance level, indicating that the total effect of \(lnU\) on carbon emissions (\(lnY\)) is significant. From the results of Column3, Column6, and \(Column9\), the \(lnU\) coefficients are statistically significant at the 5% significance level, indicating that the direct effect of \(lnU\) on \(lnY\) is significant. Moreover, \(lnU\) has a positive impact on \(lnY\), different from previous studies [32], indicating that the scale effect of urbanization on carbon emissions has not yet appeared. This is mainly because the overall urbanization development of the Yangtze River Delta is still in the primary stage of urbanization development. Urbanization has promoted large scale infrastructure construction and housing construction in the region, resulting in a large amount of energy consumption. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**The Adjacent Spatial Weight Matrix**} & \multicolumn{2}{c}{**The Economic Distance Weight Matrix**} \\ \hline
2008 & \(-0.009\) & \(0.014\) \\  & [0.110] & [0] *** \\
2009 & \(-0.009\) & \(0.014\) \\  & [0.110] & [0] *** \\
2010 & \(-0.010\) & \(0.014\) \\  & [0.150] & [0] *** \\
2011 & \(-0.009\) & \(0.014\) \\  & [0.008] *** & [0] *** \\
2012 & \(-0.009\) & \(0.013\) \\  & [0.131] & [0] *** \\
2013 & \(-0.009\) & \(0.013\) \\  & [0.160] & [0] *** \\
2014 & \(-0.009\) & \(0.013\) \\  & [0.160] & [0] *** \\
2015 & \(0.003\) & \(0.013\) \\  & [0.130] & [0] *** \\ \hline \hline \end{tabular} Note: Inside of the [ ] is the p-value; *, **, and *** indicate statistically significant differences at the 10%, 5%, and 1% levels, respectively.  
\end{table} 
Table 5: [PERSON]’s value of carbon emissions from the county scale under the two-weight matrix. On the other hand, urban roads have higher road hardening rates, sparse vegetation, and a lower \"carbon sink effect\". (2) The impact of urbanization on the proportion of secondary and tertiary industries and the level of economic development. According to the results that are shown in Column 2, Column 5, and Column 8 in Table 6, the urbanization level (\(lnU\)) coefficient is statistically significant at the 5% significance level. Moreover, \(InU\) has a positive impact on \(lnY\), different from previous studies [41], it shows that the development of urbanization has promoted improvements in the economic development level and in the development of secondary and tertiary industries. The partial regression coefficients of the urbanization level (InU) for the secondary industry ratio (\(\rm{InC_{1}}\)) and the tertiary industry ratio (\(\rm{InC_{2}}\)) are 0.035 and 0.028, respectively, and they are statistically significant at the 5% significance level (Column 5 and Column 8). It shows that the development of urbanization has promoted the development of the secondary and tertiary industries. According to the results of Column2 and Column5, for every 1% increase in urbanization level (InU) in the Yangtze River Delta region, the proportion of secondary (\(\rm{InC1}\)) and tertiary (\(\rm{InC2}\)) industries increased by 0.035% and 0.028%, respectively. Urbanization plays a greater role in promoting the secondary industry than its contribution to the tertiary industry. Therefore, in the process of urbanization, the industrial structure should be optimized to expand the role of urbanization in driving the development of the tertiary industry. At present, although the role of urbanization in the Yangtze River Delta region for the tertiary industry has been highlighted, its level of action needs to be further improved.