# Carbon Quota Allocation Prediction for Power Grids Using PSO-Optimized Neural Networks
## 5 Conclusions  
This paper, based on the implementation of national grid construction and emission reduction efforts, establishes a reasonable carbon quota allocation scheme within the top-level design framework of the carbon trading market. It employs the GM(1,1)-PSO-BPNN hybridized model to determine total carbon quota allocation for the power industry and uses the shared responsibility coefficient for emission reduction. Based on the total measurement results and assuming the total carbon emissions remain unchanged by 2030, the model redistributes carbon emissions among regional power grids. The detailed conclusions are drawn as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Area**} & **Actual Carbon Quota** & \
\begin{tabular}{c} **Forecast Carbon** \\ **Emissions** \\ **(100 Million Tons)** \\ \end{tabular} &
\begin{tabular}{c} **Difference** \\ **(100 Million Tons)** \\ \end{tabular} \\ \hline North China & 8.733 & 13.763 & \(-\)5.030 \\ Northeast China & 1.942 & 3.341 & \(-\)1.399 \\ East China & 12.832 & 8.539 & 4.293 \\ Central China & 15.564 & 9.564 & 6.001 \\ Southern China & 10.854 & 8.848 & 2.006 \\ Northwest China & 5.227 & 11.061 & \(-\)5.834 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of initial and final quotas.  
1. The PSO algorithm is integrated into the traditional BP neural network to perform a global optimization search for the initial weights and thresholds. This approach successfully helps the BP neural network escape local extrema. The performance metrics of the PSO-BPNN optimization algorithm are significantly higher than those of the traditional BP algorithm, and its overall fit surpasses that of other algorithms.
2. Given that carbon emissions are influenced by multiple factors, such as regional economic development, power structure, and technology level, rather than a single factor, the GM(1,1) algorithm is incorporated into the neural network. Testing revealed that the GM(1,1)-PSO-BPNN hybridized model achieves a prediction accuracy of 99.07% for CO\({}_{2}\) emissions in the power industry, significantly surpassing the accuracy of single learning algorithms. Thus, the combined model can be effectively used as a carbon emission prediction tool for the power sector.
3. Based on the projections of the combined model, all four quantitative indicators related to carbon emissions are expected to show an upward trend from 2024 to 2030. While China's power generation, per capita electricity consumption, and GDP are growing rapidly, population growth is slowing and gradually approaching saturation. The study on the influence of green energy on CO\({}_{2}\) emissions indicates that China's power sector can reduce its peak carbon emissions in 2030 by 133 million tons, lowering them to 5511.46 million tons during and after green energy generation.
4. Due to the complexity and variability of the internal structure of the power system, it is challenging for government departments to delineate the actual emission reduction responsibilities between power-generating and power-using regions. Therefore, shared responsibility coefficients are adopted to mitigate the risk of \"carbon transfer\" and to determine the actual emission reduction responsibility coefficients for each regional power grid. Based on model development and the adjustment of these responsibility coefficients, this paper proposes a fair and reasonable carbon quota allocated program for the power sector.