[Objective] To address the issues of insufficient wind power integration and the high energy consumption and pollution associated with coal-fired captive power plants， conducting generation rights trading between wind power and captive power plants represents a feasible solution. Although extensive research exists on generation rights trading involving wind power and captive power plants， studies on decision-making behavior that involves refined modeling of uncertainties across multiple time scales remain limited. To bridge this gap， this paper proposes a generation rights trading method for wind farms and captive power plants based on multi-time-scale coupling. [Methods] First， the costs and benefits of each market entity before and after participating in generation rights trading are analyzed to calculate the profit margin of such trading. Second， based on multi-time-scale wind curtailment forecasts， the Copula function is used to compute the conditional probability density distribution model of actual wind curtailment. This enables an analysis of the decision-making behavior of wind farms under multi-time-scale conditions and facilitates the implementation of generation rights trading. Finally， operational data from a provincial power system are used in a case study to validate the rationality of the proposed method. [Results] Compared with the traditional generation rights trading model， the multi-time-scale coupled generation rights trading model increases wind power integration by 3.7% and improves economic benefits by 3.3%. [Conclusions] The proposed multi-time-scale coupling model promotes wind power integration more effectively than traditional generation rights trading， while further exploring potential profit margins and maximizing the overall social benefits of generation rights trading.

[Objective] To address the practical challenges confronting China’s power and green finance sectors， which are characterized by prominent data barriers， irregular information disclosure， inconsistent standards and data collection constraints， and to resolve the resulting obstacles to low-carbon energy transition， this study proposes a cross-sectoral data sharing and information disclosure collaborative solution that is both theoretically grounded and practically applicable. [Methods] Based on institutional economics and technological innovation theory， and by integrating case studies regarding integrated development of power and green finance sectors from Chongqing and other regions， this study systematically analyzes the current status， core bottlenecks and underlying contradictions of cross-sectoral data sharing and information disclosure from the perspectives of incomplete data collection scenarios and inefficient information utilization， while clarifying the rights and responsibilities of participating entities. A trinity collaborative mechanism framework integrating "organizational structure， operational rules and technical support" is established. Specifically， it proposes a “top-level coordination + tiered implementation” cross-sectoral data sharing system and a “mandatory disclosure + voluntary supplementation” information disclosure model. Furthermore， the operational pathways for cost allocation and benefit distribution are elaborated in detail， the compatibility verification of multi-scenario technical solutions is implemented， and a comprehensive set of safeguarding measures is provided to ensure the mechanism’s operability. [Results] The proposed solution effectively improves the efficiency of data sharing and the quality of information disclosure. [Conclusions] The proposed collaborative mechanism and operational pathway effectively unblock the data transmission chain connecting electricity， carbon and finance. This provides a supporting pathway with both theoretical value and practical feasibility for promoting the low-carbon energy transition， and holds significant importance for addressing industry development challenges and improving the governance system in related fields.