Chenchen Song

In response to the urgent global call for “dual carbon” targets, the sustainable transformation of public museums has become a focal issue in both academic research and engineering practice. This study proposes and empirically validates an integrated management framework that unites digital twin modeling, artificial intelligence, and green energy systems for next-generation green smart museums. A unified, closed-loop platform for data-driven, adaptive management is implemented and statistically validated across distinct deployment scenarios. Empirical evaluation is conducted through the comparative analysis of three representative museum cases in China, each characterized by a distinct integration pathway: (A) advanced digital twin and AI management with moderate green energy adoption; (B) large-scale renewable energy integration with basic AI and digitalization; and (C) the comprehensive integration of all three dimensions. Multi-dimensional data on energy consumption, carbon emissions, equipment reliability, and visitor satisfaction are collected and analyzed using quantitative statistical techniques and performance indicator benchmarking. The results reveal that the holistic “triple synergy” approach in Case C delivers the most balanced and significant gains, achieving up to 36.7% reductions in energy use and 41.5% in carbon emissions, alongside the highest improvements in operational reliability and visitor satisfaction. In contrast, single-focus strategies show domain-specific advantages but also trade-offs—for example, Case B achieved high energy and carbon savings but relatively limited visitor satisfaction gains. These findings highlight that only coordinated, multi-technology integration can optimize performance across both environmental and experiential dimensions. The proposed framework provides both a theoretical foundation and practical roadmap for advancing the digital and green transformation of public cultural buildings, supporting broader carbon neutrality and sustainable development objectives.

In this study, a bending–torsional coupling vibration model of the hydro-turbine generator unit (HTGU) incorporating the gyroscopic effect is developed. Using numerical simulation grounded in the actual installation and operational parameters of a hydropower station in China, the vibration characteristics of the HTGU are analyzed under conditions with and without hydraulic excitation, as well as with and without consideration of the gyroscopic effect. Through numerical simulation, it was found that the difference in the x-direction vibration amplitude of the generator rotor between model 1 and model 2 during the start-up phase was less than 5%, indicating that the gyroscopic effect had no influence on the bending–torsional vibration of the HTGU during the start-up phase. However, after a certain period of time, the gyroscopic effect on the bending–torsional vibration of the unit gradually becomes apparent. The vibration amplitude in the x-direction of model 1 is 28% higher than that of model 2, and the amplitude difference in the y-direction reaches 19%. Furthermore, it was found that due to the effects of hydraulic excitation and gyroscopic force, the vibration characteristics of the generator rotor and the turbine runner in the x- and y-directions were different. Under hydraulic excitation, the amplitude of the y-direction vibration of the turbine runner in model 1 was 31% lower than that in model 2. The findings of this study provide a theoretical basis for the modeling and installation of hydro-turbine generator units (HTGUs).

The energy transition is not only a technological or market-driven process but also a discursive and institutional challenge. While conventional research emphasizes financial incentives and regulatory frameworks, the role of policy narrative intensity in shaping renewable energy innovation has received limited empirical attention. This study addresses this gap by analyzing 8837 provincial-level policy documents (2005–2023) from 31 regions across China. We construct a policy narrative intensity index using the PMC framework to systematically assess how institutional discourse influences the direction and intensity of renewable energy development. The results reveal that a 1% increase in policy narrative intensity corresponds to a 4.60% rise in renewable energy innovation, as measured by renewable electricity generation, with robustness confirmed through IV and IHS methods. Regional heterogeneity is also evident: executive-led regions such as Jiangxi, Shandong, and Fujian exhibit higher narrative strength and stronger renewable energy outcomes, while market-driven provinces like Shanghai and Guangdong show weaker narrative alignment. Mechanism testing demonstrates that policy narratives enhance renewable energy innovation by (1) strengthening environmental regulation enforcement (β = 0.37), (2) increasing green patent activity by 23.6%, and (3) raising public adoption of renewable energy by 17.2 percentage points. This study highlights the governing value of policy narratives as institutional public goods and reveals their crucial role in aligning administrative capacity, corporate innovation, and public engagement to drive energy transition. These insights contribute to the broader discourse on SDG7/SDG13-aligned sustainability governance.

Carbon lock-in (CLI), defined as the structural persistence of fossil-fuel-based systems, poses a significant barrier to decarbonization. As CLI continues to impede China’s progress toward carbon neutrality, understanding the role of next-generation productive forces (NGPFs) in breaking this path dependence has become increasingly urgent; however, it remains underexplored in empirical research. This study examines the impact of NGPFs on CLI using provincial panel data from 2012 to 2022. Composite indices for NGPFs and CLI are constructed using the entropy weight method. The analysis applies instrumental variable estimation (IV-GMM) to address potential endogeneity, feasible generalized least squares (FGLS) to account for heteroskedasticity, and spatial Durbin models (SDMs) to capture spatial dependence. In addition, quantile regression is used to explore distributional effects, and subsample regressions are conducted to assess regional heterogeneity. The results show that (1) a 1% increase in NGPFs leads to approximately a 0.9643% reduction in CLI, effectively mitigating CLI. (2) NGPF levels are high in Beijing, Shanghai, and Guangdong, while being constrained in Heilongjiang, Gansu, and Qinghai. Provinces like Jiangsu, Zhejiang, and Shandong are rapidly catching up. (3) Shanxi, Inner Mongolia, and Shandong struggle with high comprehensive CLI from carbon-heavy industries; in contrast, Beijing, Shanghai, and Hainan show low CLI. (4) As CLI levels increase (90th percentile), the effectiveness of NGPFs in reducing CLI gradually diminishes (−0.2724). (5) The impact of NGPFs on CLI is not significant in the Eastern region, while in the Central and Western regions, the effects are −1.1365 and −1.0137, respectively. This study offers vital insights for shaping policies that promote sustainable growth and mitigate CLI in China.

This paper conducts an in-depth study on the application of inductor-capacitor-inductor (LCL) filters in grid-connected photovoltaic (PV) inverters. First, the resonance issues associated with LCL filters are analyzed, and solutions are discussed, with a focus on the implementation of passive damping strategies. Various passive damping schemes, based on the placement of resistors (R), are compared and analyzed, ultimately selecting the capacitor branch series resistor as the optimal solution. During the design process, multiple parameters, such as total inductance, inverter-side inductance, grid-side inductance, capacitance, and damping resistors, are considered in light of their mutual constraints. Detailed analysis and optimization of these parameters are performed based on steady-state operation, current ripple, and power loss limitations. Finally, it is concluded that the passive damping solution using a series resistor in the capacitor branch meets the requirements for stable operation and efficient filtering. The optimal solutions are identified as R₁ = 0, R₂ = ∞, R₃ ≠ 0, and R₄ = ∞, providing a reliable and effective filtering solution for grid-connected PV inverter systems.