The impact and mechanism analysis of Clean Development Mechanism on the synergistic effects of pollution mitigation and carbon reduction.

The establishment of the Clean Development Mechanism (CDM) has greatly improved China's carbon emission trading system. However, due to the unbalanced development of CDM in China, the effects and mechanism of CDM on reducing pollution and carbon are still unclear. In order to explore the effects and mechanism of CDM on the synergistic effects of pollution mitigation and carbon reduction, we first set up a theoretical analysis framework. Utilizing panel data from 254 prefecture-level cities across China spanning from 2004 to 2021, we employ a synergy degree model of composite system to evaluate the synergistic effects of pollution mitigation and carbon reduction. By treating CDM as a quasi-natural experimental research subject, we construct a multi-period difference-in-difference model to assess the CDM projects' effects. Our findings indicate a positive association between CDM projects and the synergistic effects of pollution mitigation and carbon reduction. Heterogeneity analysis reveals that CDM projects located in the western region, areas with lower levels of economic development, non-resource cities, non-old industrial bases, and projects with Certified Emission Reductions issued exhibit the most pronounced synergistic effects. Specially, dynamic policy effect analysis shows that only non-resource cities and non-old industrial bases exhibit enhanced policy implementation regarding CDM. Mechanism analysis demonstrates that CDM primarily enhances synergistic effects through improved energy efficiency, technological innovation and energy transition. These findings enrich empirical investigations concerning market-driven emission reduction policy in China, shedding light on pivotal pathways for synergistic control of pollution mitigation and carbon reduction and offering valuable policy insights for comprehensive economic and social green transformation in China.

Computer-Aided Multiphoton Microscopy Diagnosis of 5 Different Primary Architecture Subtypes of Meningiomas.

Meningiomas rank among the most common intracranial tumors, and surgery stands as the primary treatment modality for meningiomas. The precise subtyping and diagnosis of meningiomas, both before and during surgery, play a pivotal role in enabling neurosurgeons choose the optimal surgical program. In this study, we utilized multiphoton microscopy (MPM) based on 2-photon excited fluorescence and second-harmonic generation to identify 5 common meningioma subtypes. The morphological features of these subtypes were depicted using the MPM multichannel mode. Additionally, we developed 2 distinct programs to quantify collagen content and blood vessel density. Furthermore, the lambda mode of the MPM characterized architectural and spectral features, from which 3 quantitative indicators were extracted. Moreover, we employed machine learning to differentiate meningioma subtypes automatically, achieving high classification accuracy. These findings demonstrate the potential of MPM as a noninvasive diagnostic tool for meningioma subtyping and diagnosis, offering improved accuracy and resolution compared with traditional methods.