In the context of the escalating global environmental, social, and governance (ESG) risks, ESG performance has increasingly become a crucial reference for companies when formulating business strategies. This paper, drawing on the information asymmetry and signaling theories, delves into the impact of divergences in ESG ratings among various institutions on corporate financing constraints. By taking the sample of Chinese A-share listed companies during the period from 2018 to 2022, and using an index of ESG rating divergence, we test the theoretical hypotheses. The empirical results indicate that ESG rating divergence exacerbates financing constraints, which still hold after conducting various robustness tests. From the perspective of analysts, this phenomenon occurs because the divergences in ESG ratings lead to increased prediction biases, thereby intensifying the corporate financing constraints. Nevertheless, compliant ESG disclosure can mitigate the adverse effects of ESG rating divergences on a company's financing constraints. Moreover, within enterprises with higher ESG ratings and greater profitability, the influence of ESG rating divergences on financing constraints is less pronounced. Further analysis shows that the effect of ESG rating divergence mainly stems from domestic rating divergence, domestic and international rating divergence, and environmental rating divergence. Additionally, the impact of the inter-institutional difference, potential for ESG development, and the impact on Alpha have also been analyzed. This study significantly enriches the theoretical framework of ESG rating divergences, provides new empirical evidence on the effects of ESG ratings divergences on corporate financing activities, and offers recommendations for investors to better grasp ESG information, as well as for both enterprises and governments to alleviate financing constraints.

Dispersive solid-phase extraction (DSPE) cleanup combined with accelerated solvent extraction (ASE) is described here as a new approach for the extraction of carbamate pesticides in Radix Glycyrrhizae samples prior to UPLC-MS-MS. In the DSPE-ASE method, 15 carbamate pesticides were extracted from Radix Glycyrrhizae samples with acetonitrile by the ASE method at 60°C with a 5 min heating time and two static cycles. Cleanup of a 1 mL aliquot of the extract by the DSPE method used 20 mg PSA (primary secondary amine), 50 mg Al₂O₃-N, and 20 mg GCB (graphitized carbon black) (as cleanup sorbents) under the determined optimum conditions. The linearity of the method was in the range of 10 to 200 ng/mL with correlation coefficients (r²) of more than 0.996. The limits of detection were approximately 0.2 to 5.0 µg/kg. The method was successfully used for the analysis of target pesticides in Radix Glycyrrhizae samples. The recoveries of the carbamate pesticides at the spiking levels of 50, 100, and 200 µg/kg ranged from 79.7% to 99.3% with relative standard deviations lower than 10%. This multi-residue analytical method allows for a rapid, efficient, sensitive and reliable determination of target pesticides in Radix Glycyrrhizae and other medicinal herbs.

A method combining SPE with HPLC/electrospray ionization-MS/MS was developed for simultaneous determination of 30 synthetic food additives, including synthetic colorants, preservatives, and sweeteners in soft drinks. All targets were efficiently separated using the optimized chromatographic and MS conditions and parameters in a single run within 18 min. The LOD of the analytes ranged from 0.01 to 20 μg/kg, and the method was validated with recoveries in the 80.8 to 106.4% range. This multisynthetic additive method was found to be accurate and reliable and will be useful to ensure the safety of food products, such as the labeling and proper use of synthetic food additives in soft drinks.

Based on the theories of welfare economics, this paper analyzed the mechanism of agricultural insurance (AI) affecting agricultural economic growth (AEG), theoretically, and carried out an empirical analysis by using the random effects model and thirteen years of panel data, which included the annual data of 11 cities in Zhejiang Province, China, from 2007 to 2019. The gross output value of agriculture, forestry, animal husbandry, and fishery (GOVA) of 11 cities in Zhejiang Province is selected as the explained variable, agricultural insurance premium income (AIPI) as an explanatory variable. We selected area of waterlogging removal (AWR), rural electricity consumption (REC), total power of agricultural machinery (TPAM), and crop-sown area (CSA) as control variables. The study shows that: (1) the AIPI has a significant positive impact on the growth of GOVA. When other conditions remain unchanged, a 1% increase in AIPI increases the GOVA by 0.166%, accordingly; (2) The control variables of REC, TPAM, and CSA are statistically significant for the growth of the GOVA. The elasticity coefficient of REC is 0.325, the elastic coefficient of the TPAM is 0.287, and the elasticity coefficient of CSA is −0.281.