Integrating a multi-variable scenario with Attention-LSTM model to forecast long-term coastal beach erosion.

Beach erosion is an adverse impact of climate change and human development activities. Effective beach management necessitates integrating natural and anthropogenic factors to address future erosion trends, while most current prediction models focus only on natural factors, which may provide an incomplete and potentially inaccurate representation of erosion dynamics. This study enhances prediction methods by integrating both natural and anthropogenic factors, thereby enhancing the accuracy and reliability of erosion projections. By extracting historical shorelines through CoastSat model from 1986 to 2020, we develop multivariable scenarios with Attention-LSTM model to predict the regional impacts of natural and anthropogenic factors on erosion to sandy beaches along the typical shoreline of Shenzhen in China. Results reveal that Shenzhen's beaches experienced erosion up to 12 m over the past 35 years. Here we project a decrease in the mean erosion rate of the beaches, identifying population growth (21.0 %) as the main controlling factor before the mid-century in a range of scenarios. We find that Attention-LSTM multi-model ensemble approach can provide overall improved accuracy and reliability over a wide range of beach erosion compared to scenario prediction model of Attention-LSTM and statistical model of Digital Shoreline Analysis System (DSAS), yielding an average uncertainty of 10.99 compared to 13.29. These insights reveal policies to safeguard beaches because of the rising demand for beaches due to human factors, coupled with decreased impervious surfaces through ecological conservation, lead to mitigation for beach erosion. Accurate forecasts empower policymakers to implement effective coastal management strategies, safeguard resources, and mitigate erosion's adverse effects. Our study offers finely-tuned predictions of coastal erosion, providing crucial insights for future coastal conservation efforts and climate change adaptation along the shoreline, and serving as a foundation for further research aimed at understanding the evolving environmental impacts of beach erosion in Shenzhen.

International accounting network memberships and audit fees: Evidence from China.

This paper investigates the international accounting network memberships' impact on audit fees. We find that, firstly, the audit fees charged by the member audit firms are significantly higher; secondly, if the revenue, ranking or audit and accounting business share of the international network the audit firm join is higher, the charge is also higher. Additional results show that economic policy uncertainty will intensify this positive relationship, and member audit firms charge higher fees by improving their overseas expertise. We also find that international network memberships will reduce abnormal audit fees, and improve the quality of financial reports.

Low-carbon development pathways for resource-based cities in China under the carbon peaking and carbon neutrality goals.

Resource-based cities are important strategic bases for securing resources in China and have made great contributions to the country's economic development. Long-term extensive resource development has made resource-based cities an important region constraining China from achieving comprehensive low-carbon development. Therefore, it is of great significance to explore the low-carbon transition path of resource-based cities for their energy greening, industrial transformation, and high-quality economic development. This study compiled the CO2 emission inventory of resource-based cities in China from 2005 to 2017, explored the contribution to CO2 emissions from three perspectives (driver, industry, and city), and predicted the peak of CO2 emissions in resource-based cities. The results show that resource-based cities contribute 18.4% of the country's GDP and emit 44.4% of the country's CO2 and that economic growth and CO2 emissions have not yet been decoupled. The per capita CO2 emissions and emission intensity of resource-based cities are 1.8 times and 2.4 times higher than the national average, respectively. Economic growth and energy intensity are the biggest drivers and main inhibitors of CO2 emissions growth. Industrial restructuring has become the biggest inhibitor of CO2 emissions growth. Based on the different resource endowments, industrial structures, and socio-economic development levels of resource-based cities, we propose differentiated low-carbon transition pathways. This study can provide references for cities to develop differentiated low-carbon development paths under the "double carbon" target.

Extraction and Complexation Investigation of Palladium(II) by a Nitrilotriacetate-Derived Triamide Ligand.

Effective and selective separation and recovery of the fission product palladium from high-level liquid waste are conducive not only to reducing its hazards to the public health and environment but also to alleviate the pressure on the increasing demand for natural palladium. Herein, the Pd2+ extraction in an HNO3 solution with a nitrilotriacetate-derived triamide ligand NTAamide(n-Oct) and the complexation between them were investigated. Using n-octanol as a diluent, NTAamide(n-Oct) demonstrated an excellent selectivity, strong extractability, and high loading capacity for Pd2+ extraction. Combined with the results of single-crystal X-ray diffraction, Fourier transform infrared spectroscopy, electrospray ionization-mass spectroscopy, microcalorimetric titration, and slope analysis, the extracted complexes were determined as [PdL2](NO3)2 and [PdL2][Pd(NO3)4] (where L denotes the NTAamide ligand) in 0.10 and 3.0 mol/L HNO3 solutions, respectively. The extraction model closely depended on the solvation state of Pd2+ in the HNO3 solution. An ion-pair extraction model was proposed and discussed.