Comprehensively understanding how the policy influences the sustainability transition of the automotive industry is a critical focus in recent years. This paper focuses on the dual-credit policy, an underexplored but critical policy in China, and examines how it would influence the transition of automotive industry. To achieve this goal, an SD model is developed to simulate the effect of policy parameters on the policy objectives by considering the interaction of production, technology and market. The findings reveal that this policy can in general promote the sustainable development of the automotive industry by greatly decreasing its overall energy consumption. However, its impact on the market share of new energy vehicles is limited. Based on the results, some policy suggestions are proposed so as to improve the effectiveness of the dual-credit policy.

Electric carsharing (ECS) is a potential option to address the problem of unsustainability in the transportation sector. The business-to-consumer model of ECS, which is one of several different electric carsharing models, has gained much popularity in recent years. Generating sufficient revenue to cover costs is a critical factor for ECS companies to maintain healthy development. This study makes an economic analysis, on the basis of life-cycle cost and monetary revenue associated with the operation of ECS, of two Chinese ECS companies: EVCARD and LCCS. Based on data gathered by field investigation, this study aims to determine the break-even moment for each company’s main vehicle models by means of the net present value method. The results show that EVCARD achieved an earlier break-even moment than LCCS. The break-even moment of Chery eQ of EVCARD was the shortest of all the vehicle models, at only 181.3 min. Moreover, a sensitivity analysis was conducted to portray how different cost-related and revenue-related factors influence the break-even moment. Our findings indicate that a wide difference exists in terms of the influence of different factors on the break-even moment. Among these, the manufacturer’s suggested retail price is the most influential variable, followed by the unit rental price. The reaction of the break-even moment to the market price of a charging pile and the non-rental revenue per vehicle—especially the latter—was found to be negligible in the sensitivity analysis.

In order to fully examine how driving behavior affects vehicle energy consumption and further cut down on excessive energy use while driving, an energy-saving evaluation method for driving behavior based on the SHAP (Shapley Additive exPlanations) scorecard model is proposed. First, to categorize short trip segments, a LightGBM model is built, which performs better and has high interpretability. The SHAP method is then used to statistically assess the categorization findings in order to ascertain how various driving events and driving behavior characteristics affect energy consumption. After that, the data are converted into scores using a scorecard model, which is then used to examine the evaluation outcomes of each sample and individual driver. Lastly, by checking the distribution of scores and the pertinent factors for various scores, the scorecard model’s correctness and reasonableness in evaluating energy-saving driving practices of short-trip segments are confirmed.

The security of new energy vehicle (NEV) industry chains has become a strategic issue for industrial competitiveness, the energy transition, and economic security. This study examines how digital economy capabilities jointly support NEV industry chain security across 30 provincial-level administrative regions in China. Drawing on Organizational Information Processing Theory and Dynamic Capability Theory, we conceptualize artificial intelligence capability (AIC), big data analytics capability (BDA), cloud computing infrastructure (CCI), and blockchain application level (BCL) as complementary information-processing and reconfiguration capabilities. We combine Necessary Condition Analysis (NCA), fuzzy-set Qualitative Comparative Analysis (fsQCA), and Random Forest/SHAP analysis. The revised results show that AIC is a practically necessary condition for supply chain resilience, BDA is a necessary condition for achieving a high cybersecurity level, and BCL is a dimension-specific necessary condition for data security. Four sufficient configurational paths—technology-driven, data-driven, infrastructure-driven, and security-synergistic—lead to high comprehensive NEV industry chain security. Robustness checks using alternative calibration anchors and consistency thresholds show that the core configurations are stable. A revised machine learning specification using only digital economy predictors confirms the high relative importance of AIC. It also shows that the marginal contribution of AIC tends to flatten beyond the upper-middle range. The findings provide a configurational and regionally differentiated perspective on digital economy empowerment while avoiding overgeneralization beyond the Chinese provincial context.