Mitigating the rate of global warming is imperative to preserve the natural environment upon which humanity relies for survival; greenhouse gas emissions serve as the principal driver of climate change, rendering the promotion of urban carbon peaking and carbon neutrality a crucial initiative for effectively addressing climate change and attaining sustainable development. This study addresses the inherent uncertainties and complexities associated with carbon dioxide emission accounting by undertaking a scenario prediction analysis of peak carbon emissions in Dalian, utilizing the STIRPAT model in conjunction with a GA-BP neural network model optimized through a genetic algorithm. An analysis of the mechanisms underlying the influencing factors of carbon emissions, along with the identification of the carbon emission peak, is conducted based on carbon emission accounting derived from nighttime lighting data. The GA-BP prediction model exhibits significant advantages in addressing the nonlinear and non-stationary characteristics of carbon emissions, attributable to its robust mapping capabilities and probabilistic analysis proficiency. The findings reveal that energy intensity, tertiary industry value, resident population, and GDP are positively correlated with carbon emissions in Dalian, ranked in order of importance. In contrast, population density significantly reduces emissions. The GA-BP model predicts carbon emissions with 99.33% accuracy, confirming its excellent predictive capability. The recommended strategy for Dalian to achieve its carbon peak at the earliest is to adopt a low-carbon scenario, with a forecasted peak of 191.79 million tons by 2033.

Formulating a scientifically sound and efficient approach to allocating carbon quota aligned with the carbon peaking goal is a fundamental theoretical and practical challenge within the context of climate-oriented trading in the power sector. Given the highly irrational allocation of carbon allowances in China’s power sector, as well as the expanding role of renewable energy, it is essential to rationalize the use of green energy in the development of carbon reduction in the power sector. This study addresses the risk of “carbon transfer” within the power industry and develops a predictive model for CO₂ emission based on multiple influential factors, thereby proposing a carbon quota distribution scheme adapted to green energy growth. The proposed model employs a hybrid of the gray forecasting model-particle swarm optimization-enhanced back-propagation neural network (GM-PSO-BPNN) for forecasting and allocating the total carbon quota. Assuming consistent total volume control through 2030, carbon quota is distributed to regional power grids in proportion to actual production allocation. Results indicate that the PSO algorithm mitigates local optimization constraints of the standard BP algorithm; the prediction error of carbon emissions by the combined model is significantly smaller than that of the single model, while its identification accuracy reaches 99.46%. With the total national carbon emissions remaining unchanged in 2030, in the end, the regional grids received the following quota values: 873.29 million tons in North China, 522.69 million tons in Northwest China, 194.15 million tons in Northeast China, 1283.16 million tons in East China, 1556.40 million tons in Central China, and 1085.37 million tons in the Southern Power Grid. The power sector can refer to this carbon allowance allocation standard to control carbon emissions in order to meet the industry’s emission reduction standards.

This study focuses on the core cities of the Yangtze River Delta, constructing a carbon emission efficiency evaluation system and using the SBM-DDF model to quantitatively assess the carbon emission efficiency of each city. The results indicate that the overall carbon emission efficiency of the core cities in the Yangtze River Delta is relatively low, with significant regional differences. Cities such as Shanghai and Hangzhou perform better in carbon emission efficiency, while cities like Jiaxing and Shaoxing lag behind, facing substantial pressure for green transformation. Since the implementation of the “Dual Carbon” policy in 2020, some cities have seen improvements in carbon emission efficiency, but fluctuations remain, indicating issues with the sustainability of short-term policy effects. Based on these findings, this paper suggests policy recommendations such as enhancing green technology innovation, promoting the green upgrading of industrial structures, and fostering regional collaboration to improve carbon emission efficiency and facilitate a green low-carbon transition in the Yangtze River Delta region.