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This paper explores the paradoxical impact of environmental regulations on green innovation in the manufacturing of new energy vehicles (NEVs) from a stakeholder perspective. We address the dual challenge of accelerating green innovation across various diffusion stages and refining environmental regulations for effective stakeholder engagement, including the central government, upstream suppliers, and internal operations teams. First, we utilize Stackelberg game theory to analyze the strategic interaction and behavioral rationale between local governments and NEV manufacturers at different stages of innovation diffusion, represented by specific parameter sets. Second, we examine the roles of four key stakeholders, exploring their unique impact mechanisms and potential to influence the game's Nash equilibrium. Finally, the game models' validity and primary conclusions are corroborated with real-world case studies, prominently including the ongoing shift of Chinese automakers towards NEVs. Results demonstrate that: (1) environmental regulations should be calibrated according to the diffusion stages of green innovation, and (2) misaligned stakeholder interests can lead to environmental regulations that inadvertently hinder, rather than promote, NEV manufacturers' green innovation efforts. [ABSTRACT FROM AUTHOR]

Due to the high CO 2 emissions during the coal-to-methanol process, conventional coal-to-methanol vehicles' life cycle environmental impact is significant. As the world's largest market for methanol vehicle applications, China is exploring a 'green methanol vehicles' route: using CO 2 -to-methanol or biomass-to-methanol in internal combustion engine vehicles. However, the economy, effectiveness in reducing carbon emissions, and changes in resource consumption of green methanol vehicles have yet to be investigated. This study first conducts a 'Resource-Environment-Economy' life cycle assessment on vehicles from different routes, including green methanol vehicles. Next, this study builds a system dynamics model based on the Resource-Environment-Economy assessment boundaries to predict the development trend of green methanol vehicles in the designed scenarios. CO 2 -to-methanol vehicle emits 24% less CO 2 over their life cycle than gasoline vehicle, which can be a reasonable alternative from 2028. By 2030, biomass-to-methanol vehicle has a high development potential with minimal energy and less water consumption. It suggests that green methanol vehicles be incorporated into new energy vehicles and enjoy preferential policies. [Display omitted] • Assessing resource consumption, emissions, and cost of green methanol vehicles. • Proposing a life cycle assessment-system dynamics model. • Predicting development potential of green methanol vehicles with LCA-SD model. • The results demonstrate that green methanol vehicles are promising in China. [ABSTRACT FROM AUTHOR]

To ensure energy supply, China has developed methanol vehicles since the 1980s. Although methanol vehicles are affordable and technologically mature, their life-cycle emissions are high because China uses coal to produce methanol. China has recently proposed green methanol vehicle (GMV) fueled by methane from CO 2 or biomass to reduce emissions. As an essential part of promoting alternative fuel vehicles (AFVs), a policy support scheme is necessary to increase the market share of GMV. To provide a theoretical basis for a policy support scheme before further GMV promotion, this study establishes an evolutionary game model to simulate and compare the effects of different policies on GMV deployment. The results show that by comparing the subsidy policy and the dual-credit policy, the latter is more stable and effective for GMV promotion and application; under the double-points policy, it is more favorable for GMV promotion if the government includes GMV in new energy vehicles. [Display omitted] • An evolutionary game model is constructed for the green methanol vehicle market. • Analysing the effect of subsidy policies on GMV deployment. • Analysing the effect of dual-credit policies on GMV deployment. • The result demonstrates that the effect of the dual-credit policy is more significant. [ABSTRACT FROM AUTHOR]

The ever-changing market demand accelerates the iterative upgrading of new energy vehicles, making understanding user requirements crucial for optimizing product strategies. However, the imbalance in reviews reduces the accuracy of user requirements analysis, which may mislead the purchasing intentions of potential consumers and the product development strategies of manufacturers. To address this, this study develops a BERT-TCBAD-Kano-based requirements analysis method. First, a sentiment analysis method is used to identify user preferences in online reviews. Second, the Text Classification Based on Attribute Dictionary (TCBAD) is proposed to categorize online complaints of users. Finally, user satisfaction and concern are calculated based on preference identification and complaint classification results. User requirements are prioritized based on the idea of the Kano model. Based on the online data of a new energy vehicle, 342 attribute words and 10 user requirements are extracted. The results show that the proposed method improves prediction accuracy by 30 % compared to the traditional Kano model. The method provides significant decision-making support for user-centered product development. • Improves the accuracy of user requirements analysis. • Develops a BERT-TCBAD-Kano model for user requirements analysis. • Mitigates sentiment imbalance in sentiment analysis of online reviews. • Proposes a novel method TCBAD for categorizing complaint texts. [ABSTRACT FROM AUTHOR]

• A novel tri-stage scheduling method is proposed. • An information feedback mechanism is added to the tri-stage scheduling method. • A tri-stage real-time price is formulated. • Electric vehicles are charging/discharging orderly by the real-time price. • This paper proposes a quantum adaptive sparrow search algorithm. An islanded microgrid includes a combined heating and power system as well as electric vehicles. It is a key method for increasing energy efficiency, reducing pollution, and achieving carbon neutrality. Considering the uncertainties caused by renewable energy, load, and electric vehicles, this study presents a modified tri-stage scheduling method to realise the real-time dispatch of the islanded microgrid. The novel tri-stage scheduling method incorporates an information feedback mechanism into the traditional tri-stage scheduling method to address the problem of the sub-optimal solution obtained by the traditional method, which results from the significant errors between the day-ahead forecast data and intraday forecast data. In addition, users are guided by the tri-stage real-time price to charge/discharge electric vehicles orderly to fill in the valley and shave the peak of the electric load. In this study, a quantum adaptive sparrow search algorithm is proposed based on the characteristics of the tri-stage scheduling optimisation model and the limitation that the original algorithm easily falls into a local optimum. In the case study, the superiority of the novel tri-stage scheduling method, modified algorithm, and orderly charging/discharging of electric vehicles proposed in this paper are evaluated in different seasons. In the vehicle-to-grid mode, the influence of different numbers of electric vehicles on the real-time dispatch of the islanded microgrid is analyzed and discussed. [ABSTRACT FROM AUTHOR]