Review on Quantitative Assessment of Corporate Governance

Recently a lot of different institutions and research centers around the world gradually launch their corporate governance assessment system, making each interested party to get to know the corporate governance level and making reasonable decisions. This essay is based upon corporate governance and assessment connotation, summarizes and concludes the research progress of domestic and overseas corporate governance quantitative assessment, finding that corporate governance quantitative assessment mainly relies on composite indicator assessment, but there is still big difference in the starting point, targeted object and assessment indicator of assessment indicator system, so some people question the validity and applicability of this assessment indicator system. Based on this, this essay points out that the existent literature review has some problems such as assessment subject is vague, missing out some key indicators, weighting subjectively and failing to abide the principle of “ substance over form”, thus bringing up corresponding suggestion.Recently a lot of different institutions and research centers around the world gradually launch their corporate governance assessment system, making each interested party to get to know the corporate governance level and making reasonable decisions. This essay is based upon corporate governance and assessment connotation, summarizes and concludes the research progress of domestic and overseas corporate governance quantitative assessment, finding that corporate governance quantitative assessment mainly relies on composite indicator assessment, but there is still big difference in the starting point, targeted object and assessment indicator of assessment indicator system, so some people question the validity and applicability of this assessment indicator system. Based on this, this essay points out that the existent literature review has some problems such as assessment subject is vague, missing out some key indicators, weighting subjectively and failing to abide the principle of “ substance over form”, thus bringing up corresponding suggestion

Study of the Influence of Corporate Governance Level on Investors’ Confidence

Stock market investment has the Sheep-Flock Effect, so investors’ confidence relates to the stability and healthy development of the stock market. The functional mechanism of investors’ confidence is complicated with many influential factors. This paper selects the factor of corporate governance level to investigate and study the great effect of corporate governance level evaluation on maintaining and increasing investors’ confidence from the perspective of investors. In this paper, the method to measure investors’ confidence and corporate governance level is improved, and the data of A-share companies listed in Shanghai Stock Exchange of China in 2011-2013 is selected as the sample to analyze the panel data. The results show that, the higher the corporate governance level is, the stronger investors’ confidence is; investors’ confidence is also influenced by the macro level of the market and the nature of various industries is different, so significances of influences of corporate governance level in different industries on investors’ confidence are not the same. At the same time, the empirical results show that investors’ confidence has a positive lag effect

Spatial distribution of soil organic carbon stock in Moso bamboo forests in subtropical China

Moso bamboo (Phyllostachys heterocycla (Carr.) Mitford cv. Pubescens) is an important timber substitute in China. Site specific stand management requires an accurate estimate of soil organic carbon (SOC) stock for maintaining stand productivity and understanding global carbon cycling. This study compared ordinary kriging (OK) and inverse distance weighting (IDW) approaches to study the spatial distribution of SOC stock within 0–60cm using 111 soil samples in Moso bamboo forests in subtropical China. Similar spatial patterns but different spatial distribution ranges of SOC stock from OK and IDW highlighted the necessity to apply different approaches to obtain accurate and consistent results of SOC stock distribution. Different spatial patterns of SOC stock suggested the use of different fertilization treatments in Moso bamboo forests across the study area. SOC pool within 0–60cm was 6.46 and 6.22 Tg for OK and IDW; results which were lower than that of conventional approach (CA, 7.41 Tg). CA is not recommended unless coordinates of the sampling locations are missing and the spatial patterns of SOC stock are not required. OK is recommended for the uneven distribution of sampling locations. Our results can improve methodology selection for investigating spatial distribution of SOC stock in Moso bamboo forestsThis study was funded by Demonstration Project of Bamboo Forest of State Forest Administration ([2015]13), National Scientific Plan for Rural Area (Grant No. 2015BAD04B0203)S

Ecosystem Carbon Stock Loss after Land Use Change in Subtropical Forests in China

Converting secondary natural forests (SFs) to Chinese fir plantations (CFPs) represents one of the most important (8.9 million ha) land use changes in subtropical China. This study estimated both biomass and soil C stocks in a SF and a CFP that was converted from a SF, to quantify the effects of land use change on ecosystem C stock. After the forest conversion, biomass C in the CFP (73 Mg¨ ha´1 ) was significantly lower than that of the SF (114 Mg¨ ha´1 ). Soil organic C content and stock decreased with increasing soil depth, and the soil C stock in the 0–10 cm layer accounted for more than one third of the total soil C stock over 0–50 cm, emphasizing the importance of management of the top soil to reduce the soil C loss. Total ecosystem C stock of the SF and the CFP was 318 and 200 Mg¨ ha´1 , respectively, 64% of which was soil C for both stands (205 Mg¨ ha´1 for the SF and 127 Mg¨ ha´1 for the CFP). This indicates that land use change from the SF to the CFP significantly decreased ecosystem C stock and highlights the importance of managing soil C

Ecosystem Carbon Stock Loss after Land Use Change in Subtropical Forests in China

Converting secondary natural forests (SFs) to Chinese fir plantations (CFPs) represents one of the most important (8.9 million ha) land use changes in subtropical China. This study estimated both biomass and soil C stocks in a SF and a CFP that was converted from a SF, to quantify the effects of land use change on ecosystem C stock. After the forest conversion, biomass C in the CFP (73 Mg¨ ha´1 ) was significantly lower than that of the SF (114 Mg¨ ha´1 ). Soil organic C content and stock decreased with increasing soil depth, and the soil C stock in the 0–10 cm layer accounted for more than one third of the total soil C stock over 0–50 cm, emphasizing the importance of management of the top soil to reduce the soil C loss. Total ecosystem C stock of the SF and the CFP was 318 and 200 Mg¨ ha´1 , respectively, 64% of which was soil C for both stands (205 Mg¨ ha´1 for the SF and 127 Mg¨ ha´1 for the CFP). This indicates that land use change from the SF to the CFP significantly decreased ecosystem C stock and highlights the importance of managing soil C

One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems

The purpose of sequential recommendation is to utilize the interaction history of a user and predict the next item that the user is most likely to interact with. While data sparsity and cold start are two challenges that most recommender systems are still facing, many efforts are devoted to utilizing data from other domains, called cross-domain methods. However, general cross-domain methods explore the relationship between two domains by designing complex model architecture, making it difficult to scale to multiple domains and utilize more data. Moreover, existing recommendation systems use IDs to represent item, which carry less transferable signals in cross-domain scenarios, and user cross-domain behaviors are also sparse, making it challenging to learn item relationship from different domains. These problems hinder the application of multi-domain methods to sequential recommendation. Recently, large language models (LLMs) exhibit outstanding performance in world knowledge learning from text corpora and general-purpose question answering. Inspired by these successes, we propose a simple but effective framework for domain-agnostic recommendation by exploiting the pre-trained LLMs (namely LLM-Rec). We mix the user's behavior across different domains, and then concatenate the title information of these items into a sentence and model the user's behaviors with a pre-trained language model. We expect that by mixing the user's behaviors across different domains, we can exploit the common knowledge encoded in the pre-trained language model to alleviate the problems of data sparsity and cold start problems. Furthermore, we are curious about whether the latest technical advances in nature language processing (NLP) can transfer to the recommendation scenarios.Comment: 10 pages, 7 figures, 6 table