Ru Yu

The advancement of artificial intelligence technology is profoundly reshaping firms’ production models and operational methods. When driven by artificial intelligence, new quality productive forces (NQPF) are exhibiting greater development potential and green efficacy. Utilizing data on Chinese listed firms from 2013 to 2022, this study employs vertical relationship theory to empirically examine the impact of NQPF in downstream firms on carbon emissions in upstream firms, as well as the transmission mechanisms. The empirical findings show that the NQPF of downstream firms significantly curbs the carbon emissions of upstream firms; in other words, a 1% increase in the NQPF index of downstream firms leads to a 0.187% decrease in the carbon emissions of upstream firms. Among the sub-indicators of NQPF, technological productive forces demonstrate the most pronounced effects on emission reduction. Mechanism analysis reveals that the NQPF of downstream firms effectively enhances supply chain resilience by optimizing supply-demand matching, stabilizing supply-demand relationships, and improving supply quality, which collectively incentivize upstream firms to reduce carbon emissions. Furthermore, heterogeneity analysis indicates that this backward spillover effect is more pronounced when the upstream suppliers are state-owned, technology-driven, or labor-intensive. These findings provide valuable empirical insights and policy suggestions for optimizing the development path of NQPF, promoting the establishment of green supply chains, and reducing carbon emissions in firms.

Aiming to address the complexity and challenges of predicting pure electric vehicle (EV) sales, this paper integrates a time series model, support vector machine and combined model to forecast EV sales in China. Firstly, a seasonal autoregressive integrated moving average (SARIMA) model was constructed using historical EV sales data, and the model was trained on sales statistics to obtain forecasting results. Secondly, variables that were highly correlated with sales were analyzed using gray relational analysis (GRA) and utilized as input parameters for the support vector regression (SVR) model, which was constructed to optimize sales predictions for EVs. Finally, a combined model incorporating different algorithms was verified against market sales data to explore the optimal sales prediction approach. The results indicate that the SARIMA-GRA-SVR model with the squared prediction error and inverse method achieved the best predictive performance, with MAPE, MAE and RMSE values of 12%, 1.45 and 2.08, respectively. This empirical study validates the effectiveness and superiority of the SARIMA-GRA-SVR model in forecasting EV sales.