Analysis of corporate environmental reports using statistical techniques and data mining

Measuring the effectiveness of corporate environmental reports, it being highly qualitative and less regulated, is often considered as a daunting task. The task becomes more complex if comparisons are to be performed. This study is undertaken to overcome the physical verification problems by implementing data mining technique. It further explores on the effectiveness by performing exploratory analysis and structural equation model to bring out the significant linkages between the selected 10 variables. Samples of five hundred and thirty nine reports across various countries are used from an international directory to perform the statistical analysis like: One way ANOVA (Analysis of Variance), MDA (Multivariate Discriminant Analysis) and SEM (Structural Equation Modeling). The results indicate the significant differences among the various types of industries in their environmental reporting, and the exploratory factors like stakeholder, organization strategy and industrial oriented factors, proved significant. The major accomplishment is that the findings correlate with the conceptual frame work of GRI

GREY DEEP NEURAL NETWORK-BASED DATA ANALYSIS FOR FINANCIAL REPORTS IN TEXT MINING APPLICATIONS

 The proposes the epic Gray Deep Neural Network Model (GDNNM), Multi-Layer Perception (MLP) Neural Network (NN) and computer integration, Model Identification Failure Prediction (MIFP) schemes. Data analysis for financial they can approximate both GDNNM and non-linear individual frame elements as a class. Based on the neural network model, unlike previous discrimination proof strategies, GDNNM subordinates frame elements to acquire an independent direct characteristic. This model has a good relationship with the project structure but is difficult to fit. The PGDM program is installed online financial data as a common sample criteria to get the remaining amount between the frame release and the GDNNM release. Early Diagnosis of Problem detection is important when building a structure, as it can save a considerable amount of space and time. With the progress of intelligent assembly, the lack of information-based search becomes an interesting issue. There are so many sources Text mining is a wide range of information testing used in semi-primary and non-basic information inquiries. This type of data is expected to cause problems in the financial information industry and problems in text mining for basic non-information testing. Besides, the checkpoints have been application research in the field of currency data, past research, auditing and control

Analiza znaczeniowa praktyk raportowania zrównoważonego rozwoju: perspektywa światowa

This study examines the sustainability reports (SRs)of 200 firms in both developed and emerging economies in order to identify the words most frequently used in disclosing sustainability practices within the Triple Bottom Line (TBL) approach to reporting (which emphasizes economic, environmental, and social dimensions). Its aim is to evaluate these sustainability reports under the umbrella of the GRI framework. It adopts a semi-automated Text-Mining (TM) technique to evaluate the corporate SRs of select firms from the top ten economies by GDP at current prices. Based on the GRI Standards guidelines, a total of 208 keywords were identified for analysis. The disclosures were then awarded points based on the appearance of these keywords so that the appearance of one resulted in the awarding of a score of one; if a keyword did not appear then the report was scored a zero for that word. Furthermore, a wordcloud was also generated in order to better understand the inclination of reporting language towards various TBL reporting categories. This analysis of the SRs of 200 firms from the top ten economies of the world sheds light on the differences in reporting practices and priorities as they relate to various aspects of the GRI Standards guidelines. The results indicate that SR practices have grown rapidly in the last half decade of the period selected for study (2013-2017) as compared to the first half (2008-2012). Canada ranked highest for its disclosure practices in this analysis followed by the UK, Germany, US, Japan, France, Italy, Brazil, India, and China. This study found that all included countries improved their sustainability performance over the period 2008-2017.W niniejszym artykule przeanalizowano raporty dotyczące zrównoważonego rozwoju (SR) z 200 firm, zarówno w gospodarkach rozwiniętych, jak i wschodzących, w celu zidentyfikowania słów najczęściej używanych przy ujawnianiu praktyk zrównoważonego rozwoju w ramach podejścia do raportowania treaple bottom line (TB, które kładzie nacisk na ekonomię, środowisko i wymiary społeczne. Celem jest ocena raportów dotyczących zrównoważonego rozwoju w ramach GRI. Przyjęto półautomatyczną technikę Text-Mining (TM) do oceny korporacyjnych praktyk na rzecz zrównoważonego rozwoju (SR) wybranych firm z dziesięciu największych gospodarek według PKB w cenach bieżących. W oparciu o wytyczne standardów GRI do analizy wytypowano łącznie 208 słów kluczowych. Przyznano im następnie punkty w oparciu o częstotliwość ich występowania, tak że pojawienie się jednorazowe skutkowało przyznaniem jednej punktacji; jeśli słowo kluczowe nie pojawiło się, raport był oceniany jako zero dla tego słowa. Ponadto utworzono chmurę słów, aby lepiej zrozumieć skłonność języka raportowania do różnych kategorii raportów TBL. Ta analiza rekomendacji 200 firm z dziesięciu największych gospodarek świata rzuca światło na różnice w praktykach i priorytetach raportowania, które odnoszą się do różnych aspektów wytycznych GRI. Wyniki wskazują, że praktyki zrównoważonego rozwoju (SR) gwałtownie wzrosły w ostatniej połowie dekady wybranej do badania (2013-2017), w porównaniu z pierwszą połową (2008-2012). W tej analizie Kanada zajęła najwyższe miejsce pod względem praktyk ujawniania informacji, a następnie Wielka Brytania, Niemcy, Stany Zjednoczone, Japonia, Francja, Włochy, Brazylia, Indie i Chiny. Badanie wykazało, że wszystkie uwzględnione kraje poprawiły swoje wyniki w zakresie zrównoważonego rozwoju w latach 2008–2017

Leveraging Text Mining for Trend Analysis and Comparison of Sustainability Reports: Evidence from Fortune 500 Companies

In the recent upsurge in environmental concerns, business sustainability has become more prominent than ever. Organizations worldwide are expected to function sustainably, causing the least negative impact on the environment and promoting harmony among the firm, environment, and society. Most firms report their actions related to sustainability in corporate social responsibility (CSR) reports. This research aims to understand and analyze contemporary trends in CSR reports by Fortune 500 companies using text mining. It compares how the focus of sustainability reports varies across countries and industries along key dimensions of sustainability (i.e., environmental, economic, social, and government). Findings from the study suggest variations in the focus of sustainability reports based on various factors, such as country of origin and company size, sector, and tenure, on the Fortune 500 list. Thus, it helps to gain a deeper understanding of the company’s motivations for focusing on various dimensions of corporate sustainability

Intelligent CALL

This chapter describes the provision of corrective feedback in Tutorial CALL, sketching the challenges in the research and development of computational parsers and grammars. The automatic evaluation and assessment of free-form learner texts paying attention to linguistic accuracy, rhetorical structures, textual complexity, and written fluency is at the centre of attention in the section on Automatic Writing Evaluation. Reading and Incidental Vocabulary Learning Aids looks at the advantages of lexical glosses, or look-up information in electronic dictionaries for reading material aimed at language learners. The conclusion looks at the role of ICALL in the context of general trends in CALL

Can we trust ESG Ratings? Some insights based on a bibliometric analysis of ESG data quality and rating reliability

The aim of this research is to investigate the quality and reliability of ESG data provided by companies, as well as the accuracy and objectivity of ESG ratings produced by sus- tainability rating agencies (SRAs). Since SRAs use companies’ non-financial information as input data when formulating their ESG ratings, these two topics appear to be strictly interconnected. Drawing on the Shanon and Weaver (1949) model of communication, we have addressed these issues by means of a systematic literature review combined with a bibliometric anal- ysis. In our investigation we run: i) the co-citation analysis to detect the seminal papers; ii) a keyword co-occurrence analysis to explore how the main features of the academic debate have unfolded in the last five years; iii) a keyword co-occurrence analysis to obtain a network visualisation map to explore how the research broad scope was articulated in different clusters (i.e., themes of research). Among the clusters that emerged from the mapping, we have decided to delve into the streams of research we consider most relevant and deal with: the relationships between ESG and Artificial Intelligence (AI). Namely, we deem that AI may allow us to process massive amounts of data that contain crucial infor- mation for ESG investing. However, even if computer algorithms are able to analyse all information available efficiently, and in a timely manner, managers and investors should be aware of their opportunities and criticisms, while scholars should list propositions for advancing the research on these topics

Defining Next Generation Supply Chain Sustainability

The importance of understanding supply chain sustainability is being realized by increasingly more people, including corporate managers, investors, policy makers, customers and other stakeholders. A lot of practitioners and academic researchers have addressed this issue in past few years. However, most of their studies lack systematic thinking and are not quantifiable. Thus, a systematic and quantifiable model which incorporates economic, environmental and social factors is needed. In our study, a systematic and quantifiable risk assessment model based on the concept of “Triple Bottom Line” is developed in order to solve supply chain sustainability problem from risk assessment perspectiveMaster of ScienceNatural Resources and EnvironmentUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/110983/1/276-Defining Next Generation Supply Chain Sustainability_2015.pd