Carbon peak and new energy vehicle market prediction system based on multimodal machine learning

To address global climate change, China has prioritized new energy vehicles (NEVs) as a strategic pillar for achieving carbon peaking and neutrality goals. This study investigates the NEV industry in the Yangtze River Delta (YRD), analyzing its development dynamics and emission reduction potential through multi-dimensional data analytics. Using the entropy weight-variation coefficient method, we established an evaluation framework for NEV development across 30 Chinese cities, highlighting the YRD's dominance in technological innovation (e.g., battery efficiency improvements) and industrial chain integration (EV production clusters). Time-series modeling via SPSS forecasts sustained growth in regional NEV ownership: 2.056 million (2023), 2.51 million (2024), and 3.01 million (2025), reflecting a 21.8% compound annual growth rate. A hybrid Random Forest-GBDT model reveals intensifying market competition, with NEV penetration rates increasing by 14.8% annually, projected to surpass 40% by 2026. Integrating the STIRPAT model with ridge regression, our scenario simulations identify differentiated carbon peaking timelines across YRD provinces: Shanghai (2024–2028, peak emissions: 181.72–225.52 Mt), Zhejiang (2025–2029, 37.71–43.99 Mt), Anhui (2027–2030, 58.45–67.10 Mt), and Jiangsu (2030–2033, 89.04–97.25 Mt). Carbon neutrality pathways are projected under three scenarios: accelerated transition (2035), balanced approach (2040), and delayed action (2045), contingent on NEV adoption rates and charging infrastructure expansion (currently 8.2 chargers/km² in urban centers). The analysis underscores the YRD's critical role in China's decarbonization strategy through its NEV industrial ecosystem, while emphasizing the need for region-specific policies to address development disparities and grid capacity constraints.