The human cytochrome P450 2D6 (CYP2D6) gene copy number variation, involving CYP2D6 gene deletion (CYP2D6*5) and duplication or multiduplication (CYP2D6*×N), can result in reduced or increased metabolism of many clinically used drugs. The identification of CYP2D6*5 and CYP2D6*×N and the investigation of their allelic distributions in ethnic populations can be important in determining the right drug and dosage for each patient. The CYP2D6*5 and CYP2D6 genes, and CYP2D6 gene duplication were identified by 2 modified long PCR, respectively. To determine duplicated alleles, a novel long PCR was developed to amplify the entire duplicated CYP2D6 gene which was used as template for subsequent PCR amplification. A total of 363 unrelated Eastern Han Chinese individuals were analyzed for CYP2D6 gene copy number variation. The frequency of CYP2D6*5 and CYP2D6*×N were 4.82% (n=35) and 0.69% (n=5) in the Eastern Han Chinese population, respectively. Of the 5 duplicated alleles, 3 were CYP2D6*1×N and 2 were CYP2D6*10×N. One individual was a carrier of both CYP2D6*5 and CYP2D6*1×N. Taken together, the CYP2D6 gene rearrangements were present in 10.74% of subjects. Allelic distributions of the CYP2D6 gene copy number variation differ among Chinese from different regions, indicating ethnic variety in Chinese. Long PCR are convenient, cost effective, specific and semiquantitative for the detection of the CYP2D6 gene copy number variation, and amplification of the entire duplicated CYP2D6 gene is necessary for the accurate identification of duplicated alleles.

Based on a comprehensive evaluation index system for regional economic resilience and port logistics development, this study employs multiple methodologies including coupling coordination degree model, kernel density estimation, gravity center model, spatial autocorrelation model, and geographic detector model to explore the spatial–temporal evolution patterns and driving factors of coupling coordination between regional economic resilience and port logistics in the Guangxi Beibu Gulf Economic Zone from 2012 to 2022. The results indicate that: (1) The coupling coordination degree between the two systems showed an upward trend during the study period, although with stage-specific bipolar differentiation that weakened in the later stages. (2) The spatial distribution pattern of coupling coordination evolved from a “single-core” driven by Nanning to a “dual-core” led by Nanning and Yulin, forming a distinct concentric layer structure; the gravity center of coupling coordination exhibited a “southeast–northwest” dynamic migration pattern. (3) Spatial autocorrelation analysis revealed significant positive spatial dependence of coupling coordination within the study area, with spatial agglomeration values showing a “core–transition–depression” differentiation pattern. (4) Information technology level emerged as the dominant driving factor, forming a “technology–finance–infrastructure” ternary collaborative driving model with financial development level and logistics infrastructure level, which became the main force promoting the coordinated development of the coupled systems.

Developing the green economy is a fundamental requirement for China’s new development stage, aimed at applying new concepts, building new patterns, and achieving high-quality growth. Guangxi, as a vital engine of China’s green economy, strongly supports this process. This study establishes a green economy evaluation system for Guangxi using the DPSR model, applies the entropy weight-TOPSIS and obstacle degree models to assess its development from 2012 to 2022, and identifies key obstacles. It further analyzes the spatial–temporal heterogeneity of nine major factors through OLS and GTWR models. Findings reveal that Guangxi’s green economy is progressing with a “dual-core” pattern centered on Nanning and Liuzhou, faces significant pressures mainly from the pressure and state dimensions, and experiences varying impacts from key factors across regions and over time. These insights offer theoretical foundations and policy recommendations to improve Guangxi’s green economic development.

Green logistics has become an important pathway for promoting sustainable port development under the dual pressures of carbon reduction and maritime economic transformation. However, existing studies mainly focus on mature ports in developed coastal regions, while research on emerging ports remains limited. In addition, traditional efficiency evaluations often neglect undesirable outputs and the dynamic evolution of productivity. To address these gaps, this study evaluates the green logistics efficiency of the Beibu Gulf Port (Qinzhou, Fangchenggang, and Beihai) during 2014–2023 using a Super-SBM–GML model that incorporates undesirable outputs and dynamic productivity analysis. The results show that the overall green logistics efficiency of the Beibu Gulf Port exhibits a gradual upward trend, although the efficiency level remains below the optimal frontier. Significant spatial heterogeneity exists among the three ports, with Qinzhou Port demonstrating the highest static efficiency, while Fangchenggang Port shows relatively stronger dynamic productivity growth. Furthermore, improvements in green total factor productivity are mainly driven by efficiency catch-up effects rather than substantial technological progress, indicating that green technological innovation remains a key constraint on sustainable port transformation. This study extends green logistics efficiency research from mature ports to emerging port clusters within the context of the Belt and Road Initiative (BRI) and the New International Land–Sea Trade Corridor (ILSTC). The study further proposes an “Anchor–Corridor–Network” framework to explain how technological upgrading, institutional coordination, and regional integration jointly drive the green transition of emerging ports. The findings provide practical implications for promoting low-carbon port transformation, regional logistics integration, and sustainable maritime governance in developing economies.

Digital transformation is increasingly recognized as an important pathway toward urban sustainability. However, how digital economy development affects integrated pollution–carbon pressure across spatially connected cities remains insufficiently understood. Using a balanced panel dataset of 266 Chinese prefecture-level cities from 2010 to 2023, this study constructs an integrated pollution–carbon pressure index (IPCB) and employs a two-way fixed-effects Spatial Durbin Model to examine the environmental effects of digital economy development. The results show that digital economy development is significantly associated with lower IPCB, indicating that digital transformation contributes to alleviating the combined environmental pressure generated by conventional pollutants and carbon emissions. Spatial-effect decomposition further reveals significant negative direct, indirect, and total effects, suggesting that the environmental benefits of digitalization extend beyond local administrative boundaries through spatial spillovers. Exploratory subgroup analyses reveal differing coefficient patterns across regions, urban-agglomeration status, and city-size groups; because formal cross-group coefficient-equality tests are not conducted, these patterns are interpreted as exploratory rather than as statistically confirmed heterogeneity. Moreover, energy consumption intensity and artificial intelligence attention strengthen the negative association between digital economy development and IPCB, highlighting the importance of local conditions in translating digital transformation into environmental improvements. The main negative association is supported by alternative measurement and permutation-based placebo tests, while the sample-exclusion, lagged-variable, and policy-based checks reveal some variation in local and spillover estimates. These results emphasize the role of digital connectivity, targeted digital upgrading, and technology-related information diffusion in supporting sustainable urban development.

This study empirically investigates the dynamic and interactive relationship between port logistics systems and regional economic resilience within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Utilising panel data from 11 GBA cities spanning 2013 to 2023, this paper constructs comprehensive indicator systems for both subsystems and employs the Entropy Weight Method to determine objective indicator weights. The core analysis is conducted using a Coupling Coordination Degree (CCD) model to quantify the level of synergy between the two systems. The findings reveal several key points: (1) The overall coordination level of the GBA is in a state of “mild imbalance,” with the average CCD value increasing from 0.335 in 2013 to 0.383 in 2023, indicating a gradual but slow improvement. (2) Significant spatiotemporal heterogeneity exists across the region; cities such as Macao and Guangzhou demonstrate higher levels of coordination, whereas others, including Zhaoqing and Zhongshan, lag behind. (3) The temporal evolution of the CCD reveals three distinct development archetypes among the cities: a high-coordination but vulnerable group, a rapid-riser group driven by innovation, and a steady-improver group from the periphery. This research provides a quantitative framework for understanding the complex interplay between logistics infrastructure and economic resilience, offering valuable policy insights for promoting sustainable and balanced regional development in the GBA.