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Mendeley reader count is good evidence of the early impact of scientific output since it appears before citations. This paper aims to scope and compare Mendeley readers of Library and Information Science (LIS) articles published from India and China. Mendeley readership data for the highly cited 1,000 articles in Web of Science are extracted using Webometric Analyst for both countries and are analysed using Excel and SPSS. The findings reveal that LIS articles that are published from China got more readers as compared to LIS articles published from India with an excess of 97 readers per paper on Mendeley. The occupational status of readers tells that PhD students are the top readers for both the countries’ publications, followed by masters students. Discipline-wise readership shows that readers were spread across 29 different fields, with the highest readers from business, management and accounting, followed by computer science for both countries’ publications. Location-wise readership depicts that the top engaged readers are from the United States for both the countries’ publications. Finally, the study reports a positive association between citations and Mendeley bookmarks, justifying that Mendeley readership can be used to measure the early research impact of LIS scholarship in both countries. [ABSTRACT FROM AUTHOR]

With the progress of science and technology and the continuous deepening of audit information construction, audit has gradually separated from the traditional paper vouchers and inspection audit methods, and changed into the application of artificial intelligence, system information technology methods such as cloud technology and mobile Internet. block chain technology, as an advanced information technology, is highly valued by financial accounting experts and scholars due to its powerful data analysis methods and independent distributed architecture systems, which will also cause breakthrough changes to the audit industry. In terms of China's development, block chain technology has been successively used in commodity traceability, financing mode and other fields. In addition, it has become the backbone of the rapid technological, organizational and efficient changes of China's social system. [ABSTRACT FROM AUTHOR]

Driven by the dual effects of artificial intelligence and accounting transformation, it has become one of the research hotspots of accounting intelligence in China to replace accountants with computers to make professional judgments and automatically evaluate accounting. On the basis of summarizing and analyzing the research status in the field of intelligent accounting, the realization path of intelligent accounting in water companies is put forward in this paper. By introducing the improved Apriori learning algorithm based on Boolean mapping matrix, intelligent data mining is carried out for evaluation of water accounting. With the help of the improved attribute inductive learning algorithm, the corresponding relationship between the original water consumption attribute and the water right transaction is mined and output. In addition, with the help of forward reasoning in inference engine technology, the computer intelligent data evaluation function is realized. Finally, the functional architecture of accounting system of the water company is designed, which provides reference for the development and application of intelligent accounting system, and explores a new path for the integration of accounting and intelligent algorithms. [ABSTRACT FROM AUTHOR]

Carbon emission trading, a market-incentive environmental regulation, plays a critical role in leading China towards the carbon peaking and carbon neutrality. To improve the national carbon emission trading in China, we analyze the effects of the policy in pilot provinces and pilot A-share listed firms from 2008 to 2016. Particularly, to explore the pilot policy's environmental and financial impacts, this paper applies a difference-in-difference method. After the robustness check, the results confirm the carbon emission trading pilot's significantly positive impact at both firm- and province-level regarding carbon emission reduction. Meanwhile, corporate innovation seems to partially mediate the relationship between the policy and corporate carbon emission reduction. Plus, the policy promotes firms' market-based financial performance but such impact on accounting-based financial performance is insignificant. Ultimately, the operational mechanism of the policy is proved to be effective. In this paper, industry- and firm-level carbon emissions databases are established via environmental extended input-output table, which fill the research gap of China's firm-level carbon emissions. To ensure the reliability of results, the list of covered firms is sorted out manually in accordance with the disclosed data of seven carbon trading markets. Our results are meaningful for avoid productivity reduction and mechanism re-design in the context of the national carbon emission trading launched in 2021. Specifically, encouraging firms to further improve their productivity and competitiveness by innovation could eventually achieve a win-win situation between economic performance and environmental merits in the long run. [ABSTRACT FROM AUTHOR]

The United Nations' expanded program for Reducing Emissions from Deforestation and Forest Degradation (REDD+) aims to mobilize capital from developed countries in order to reduce emissions from these sources while enhancing the removal of greenhouse gases (GHGs) by forests. To achieve this goal, an agreement between the Parties on reference levels (RLs) is critical. RLs have profound implications for the effectiveness of the program, its cost efficiency, and the distribution of REDD+ financing among countries. In this paper, we introduce a methodological framework for setting RLs for REDD+ applications in tropical forests in Xishuangbanna, China, by coupling the Good Practice Guidance on Land Use, Land Use Change, and Forestry of the Intergovernmental Panel on Climate Change and land use scenario modeling. We used two methods to verify the accuracy for the reliability of land classification. Firstly the accuracy reached 84.43%, 85.35%, and 82.68% in 1990, 2000, and 2010, respectively, based on high spatial resolution image by building a hybrid matrix. Then especially, the 2010 Globeland30 data was used as the standard to verify the forest land accuracy and the extraction accuracy reached 86.92% and 83.66% for area and location, respectively. Based on the historical land use maps, we identified that rubber plantations are the main contributor to forest loss in the region. Furthermore, in the business-as-usual scenario for the RLs, Xishuangbanna will lose 158,535 ha (158,535 × 104 m2) of forest area in next 20 years, resulting in approximately 0.23 million t (0.23 × 109 kg) CO2e emissions per year. Our framework can potentially increase the effectiveness of the REDD+ program in Xishuangbanna by accounting for a wider range of forest-controlled GHGs. [ABSTRACT FROM AUTHOR]

As the demand for natural gas grows fast, efficient peak-shaving technology is of great necessity, especially in areas where natural gas resources are scarce. This paper aims to present a novel natural gas peak-shaving process with gas hydrates as the medium to address the imbalance between supply and demand in natural gas, especially for the LNG-sourced natural gas, where the cold energy from LNG gasification can be efficiently utilized. Firstly, the commercial process simulation software Aspen HYSYS V10 is adopted to design the whole process, including the production, storage, and dissociation of NGH; afterwards, energy consumption analysis and economic accounting are conducted; finally, a case study is carried out to show the efficiency of the LNG-sourced natural gas peak-shaving. The results of the case study show that the economic efficiency can be improved maximumly by 18.51% compared to the traditional operation model of LNG receiving terminal. This indicates the feasibility of providing a flexible natural gas peak-shaving process with gas hydrates as the medium, in order to meet the demand for long-term and short-term peak-shaving of natural gas. • The LNG-sourced natural gas peak-shaving process was designed. • The energy consumption of main equipment in the process was evaluated. • The model of the economic evaluation of peak-shaving process was build. • The case study was conducted based on the LNG import by China from Qatar. [ABSTRACT FROM AUTHOR]

Evaluating the influence of anthropogenic-emission changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emission changes. However, the ability of these widely used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using simulations from a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic-emission changes in the US (2011 to 2017) and China (2013 to 2017) on PM 2.5 and O3 , we show that widely used regression methods do not perform well in correcting for meteorological variability and identifying long-term trends in ambient pollution related to changes in emissions. The estimation errors, characterized as the differences between meteorology-corrected trends and emission-driven trends under constant meteorology scenarios, can be reduced by 30 %–42 % using a random forest model that incorporates both local- and regional-scale meteorological features. We further design a correction method based on GEOS-Chem simulations with constant-emission input and quantify the degree to which anthropogenic emissions and meteorological influences are inseparable, due to their process-based interactions. We conclude by providing recommendations for evaluating the impacts of anthropogenic-emission changes on air quality using statistical approaches. [ABSTRACT FROM AUTHOR]

With the continuous development of China's market economy, market competition has become increasingly fierce. In this context, enterprises will face more and more problems. Among them, the financial crisis is undoubtedly the most significant and the biggest problem that threatens the survival and development of enterprises, and it is an unavoidable problem for all enterprises. Irregular accounting information disclosure is an important factor that leads to the financial crisis of enterprises. Because accounting information contains a lot of important financial information within the enterprise, if the information is not disclosed in compliance with the norm, it will cause immeasurable financial crisis to the enterprise. Therefore, it is of great significance to establish an effective financial crisis beforehand warning system and avoid the noncompliant disclosure of accounting information in order to avoid and control the occurrence of financial crisis in enterprises that threaten the survival and development of enterprises. At present, artificial neural network technology is constantly developing and improving with the development of science and technology, and it has been proven that it has remarkable performance for handling nonlinear data, which provides new ideas and technical support for enterprises' financial crisis beforehand warning. This article is aimed at studying accounting information disclosure and financial crisis beforehand warning based on an artificial neural network. Based on the BP learning algorithm of the artificial neural network, an accounting information disclosure test and financial crisis beforehand warning model were constructed, and with the help of this model, the accounting information disclosure test and financial crisis beforehand warning experiment with H company as an example were carried out, and the conclusions were drawn: the accounting information disclosure test and financial crisis beforehand warning model based on the artificial neural network BP learning algorithm reduce the company' s financial loss by 8% by reducing the company' s noncompliant accounting information disclosure rate, and the accuracy rate of the company' s financial crisis beforehand warning is also increased by 35%. [ABSTRACT FROM AUTHOR]

Ecological compensation is an important part of ecological civilization which has gained widespread attention in academia in recent years. This study uses the knowledge graph tool CiteSpace to identify and analyze the literature related to ecological compensation research published in academic journals in the CNKI database, in order to provide an objective and scientific reference for the research in this field. The results show that the domestic research on ecological compensation began to appear around 2000, and the first research boom was ushered in starting in 2009. Most studies have explored ecological compensation from the connotation of ecological compensation, compensation subjects and objects, compensation standards, compensation methods, compensation supervision and evaluation of compensation effects, etc., and the articles on ecological compensation standards are relatively more abundant. At the same time, ecological compensation research is often linked to the realization of ecosystem service value and ecological product value. In the future, researchers should focus on establishing and improving the ecological compensation standard accounting system, exploring the market-based ecological compensation mechanism, attaching importance to the ecological compensation fund distribution mechanism, and strengthening research related to ecological compensation effect assessment. Ecological compensation theory should be developed in the direction of deepening and providing new research perspectives for helping China to deal with the relationship between conservation and development. [ABSTRACT FROM AUTHOR]

Abstract: With the integration of China''s economy and international markets, accounting computerization, which conducts accounting and accounting control by taking advantage of computer, has become a major component sector of accounting modernization and the main content of accounting reform. The popularization of accounting computerization is beyond question. Only this popularization can meet the requirement of knowledge economy for accounting information. It is the need to deepen accounting reform, to further enhance the level of accounting work and to achieve China''s modernization of science and technology as well. This paper discusses problems and relevant solutions in the popularization process of accounting computerization so as to carry out this popularization better. [Copyright &y& Elsevier]