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Abstract Confronted with the concurrent challenges of economic advancement and environmental management, this study explores whether implementing Intellectual Property Demonstration Policies (IPDP) can be a covert force in enhancing carbon emission efficiency. Utilizing panel data from 280 prefecture-level cities in China over the period 2007–2019, we employ a quasi-natural experimental design, incorporating multiple-period difference-in-differences models, mediation effect models, and spatial Durbin difference-in-differences models to assess the impacts of IPDP on carbon emission efficiency, its mechanisms of action, and its spatial spillover effects. The regression results of the multi-period difference-in-differences model reveal a statistically significant enhancement in carbon emission efficiency due to IPDP, with an impact coefficient of 0.044. Through heterogeneity tests, it is observed that the influence of IPDP on carbon emission efficiency varies based on regional characteristics, carbon emission levels, and the extent of marketization. The mediation effect model demonstrates that IPDP enhances carbon emission efficiency by fostering green technological innovation and facilitating the transformation of industrial structures. Furthermore, the spatial Durbin difference-in-differences model illustrates that IPDP positively influences the carbon emission efficiency of neighboring regions, indicating favorable spatial spillover effects. Notably, the indirect effect coefficients in the geographical distance matrix, economic distance matrix, and economic-geographical nested matrix are calculated as 0.673, 0.250, and 0.386, respectively. These findings offer compelling theoretical and empirical support for strengthening the intellectual property framework to optimize its environmental impact.

Abstract Breast cancer is one of the most common cancer types which is described as the leading cause of cancer death in women. After competitive endogenous RNA (ceRNA) hypothesis was proposed, this triple regulatory network has been observed in various cancers, and increasing evidences reveal that ceRNA network plays a significant role in the migration, invasion, proliferation of cancer cells. In the current study, our target is to construct a CD24-associated ceRNA network, and to further identify key prognostic biomarkers in breast cancer. Using the transcriptom profiles from TCGA database, we performed a comprehensive analysis between CD24high tumor samples and CD24low tumor samples, and identified 132 DElncRNAs, 602 DEmRNAs and 26 DEmiRNAs. Through comprehensive analysis, RP1-228H13.5/miR-135a-5p/BEND3 and SIM2 were identified as key CD24-associated biomarkers, which exhibited highly significance with overall survival, immune microenvironment as well as clinical features. To sum up the above, the current study constructed a CD24-associated ceRNA network, and RP1-228H13.5/miR-135a-5p/BEND3 and SIM2 axis worked as a potential therapeutic target and a predictor for BRCA diagnosis and prognosis.