As the “carbon neutrality” strategy is implemented, green technology R&D, a core competitive strength for sustainable enterprise development, is an essential pathway for China’s transformation and green growth. Green technology, a breakthrough over traditional production technologies, involves lengthy and costly R&D processes with high risks typically beyond the reach of a single enterprise. It requires the heterogeneous functions of enterprises, universities, and research institutions to complement each other’s advantages and establish an “industry–university–research” collaborative innovation alliance for green technologies. This paper constructs differential game models for non-cooperative and cooperative green technology R&D involving a green manufacturer and a research institution. We solve and compare the profits for both parties under these scenarios, apply a time-consistent payment distribution mechanism to allocate cooperative profits, and ensure that neither party deviates from the optimal cooperative trajectory over a prolonged period, achieving Pareto improvement and enhancing social welfare.

Immersive virtual reality (IVR) has shown positive effects on second language (L2) learning, yet the mechanisms underlying these effects remain unclear. Based on the Cognitive Affective Model of Immersive Learning (CAMIL), the present study used structural equation modeling to examine how affective (situational interest, intrinsic motivation, emotions) and cognitive variables (cognitive load, embodied learning, self-efficacy) contribute to receptive L2 vocabulary learning through IVR. A group of native Chinese speakers was asked to learn Korean words in an IVR environment. They were then administered a lexical recognition task that measured learning outcomes. The results revealed two sequences of associations through which IVR affected vocabulary recognition performance: a cognitive path that intersected with presence and self-efficacy; and an affective path that intersected with presence and emotions. Situational interest and intrinsic motivation also demonstrated a sequence of associations with receptive L2 vocabulary recognition. Although further independent confirmation is needed, these findings put forth theoretically meaningful patterns regarding the cognitive-affective mechanisms of IVR-based L2 learning.

The rapid growth of the sharing economy has improved resource utilization in car-sharing, yet it has also sharpened market competition and diversified user demand. A persistent obstacle is the low coordination efficiency between asset-heavy operating companies and traffic-driven platforms, whose misaligned objectives waste social resources. This paper uses differential game theory to analyze their dynamic coordination strategies and benefit allocation mechanisms. The Nerlove–Arrow model captures the evolution of brand goodwill, while the company’s decisions on station layout, vehicle dispatch, and pricing, together with the platform’s advertising investment, form the core decision variables in a two-party game framework linking the asset side and the traffic side. Compared with the non-cooperative Nash equilibrium, the cooperative mode removes the double marginalization effect, strengthens the investment incentives of both parties, and raises the system’s steady-state goodwill and total profit, achieving a Pareto improvement. To ground the cooperative framework in rigorous theory, we supply a verification theorem confirming that the linear candidate value functions satisfy the Hamilton–Jacobi–Bellman equations over the entire admissible state space. A formal proof of instantaneous rationality ensures that neither party falls into a cooperation trap on the horizon $[0,T]$, and the asymptotic stability of the steady-state goodwill trajectory is established. We further endogenize the revenue-sharing coefficient through a generalized Nash bargaining model that admits asymmetric bargaining structures, and introduce a Stackelberg leadership benchmark as a third comparative regime. Sensitivity analyses with respect to the discount rate and user heterogeneity confirm the robustness of the findings. A dedicated discussion section bridges the gap between idealized parameterization and data-driven calibration, describing practical pathways via A/B testing, user churn metrics, and econometric estimation of demand parameters. The results offer a scientific decision-making reference for strategic cooperation in the car-sharing industry.