Assurance of the Sustainability Reports from the Chemical Industry Practices and trends

Chersan, Ionela-Corina; Ignat, Gabriela; Ungureanu, George; Sandu, Ion; Costuleanu, Carmen Luiza; Simeanu, C.; Vintu, Catalin Razvan

doi:10.37358/rc.18.3.6165

Cited by 5 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Forecasting is a decision-making approach in which future occasions are in basic historical information predictions (Al-Janabi et al, 2015). These prediction models use various mathematical or statistical approaches (Gavrila, 2017) and take into account different parameters like NOx and NO 2 (Chersan et al, 2018). The modernistic models are on the basis of neural network and Bayesian network models (Chersan et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…These prediction models use various mathematical or statistical approaches (Gavrila, 2017) and take into account different parameters like NOx and NO 2 (Chersan et al, 2018). The modernistic models are on the basis of neural network and Bayesian network models (Chersan et al, 2018). The neural network applications may vary; however, the forecasting besides procedure control is considered the most elaborate technique.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms

Yafouz

Ahmed

Zaini

et al. 2021

Engineering Applications of Computational Fluid Mechanics

View full text Add to dashboard Cite

To accurately predict tropospheric ozone concentration(O 3 ), it is needed to investigate the variety of artificial intelligence techniques' performance, such as machine learning, deep learning and hybrid models. This research aims to effectively predict the hourly ozone trend via fewer input variables. This ozone prediction attempt is performed on diversity data of air pollutants (NO 2 , NO x , CO, SO 2 ) and meteorological parameters (wind-speed and humidity). The historical datasets are collected from 3 sites in Malaysia. The study's methodology progressed in two paths: standalone and hybrid models where hourly-averaged datasets are applied based on 5-time horizon analysis scenario, with different inputs' combinations. For evaluation, all models are tested throughout 5-performance indicator and illustrated on Modified Taylor diagram. Sensitivity analysis of input variables is quantified. Additionally, uncertainty analysis is conducted to assess their confidence level associated with Willmott Index. Based on R 2 , results indicated that XGBoost has higher accuracy compared to MLP and SVR; meanwhile, LSTM and CNN outweighs XGBoost. In terms of robustness and accuracy, the proposed hybrid model possesses superlative performance compared to all above-mentioned techniques. The proposed model achieved exceptional results as the highest R 2 , the highest 95% confidence degree, and narrower confidence interval width, are 93.48%, 98.16%, and 0.0014195, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms

Yafouz

Ahmed

Zaini

et al. 2021

Engineering Applications of Computational Fluid Mechanics

View full text Add to dashboard Cite

show abstract

“…The top sustainability focuses of the industry were found to be health and safety, human rights, reducing GHG emissions, conserving energy, and community investment. These days, all companies, regardless of size, report on sustainability matters and inform stakeholders regarding their actions and goals [28]. The chemical industry, more than others, is faced with pollution and hazardous substances.…”

Section: Literature Reviewmentioning

confidence: 99%

Sustainability Material Topics and Materiality Analysis in the Chemical Industry

Papafloratos,

Markidis,

Kotzaivazoglou

et al. 2023

Sustainability

View full text Add to dashboard Cite

Chemicals are an essential ingredient for many industries. The chemical industry has a direct environmental and social impact and massive potential to contribute towards a more sustainable world. Environmental and social awareness and pressure from society are increasing, customers expect from companies to show increased corporate social responsibility (CSR), and there are increasing legal requirements for companies to disclose information on their social, economic, and environmental performance through CSR reporting. This paper explores the most material sustainability topics in the chemical industry. We reviewed 25 sustainability reports of some of the biggest companies in the chemical industry—in terms of annual sales. We studied the reports, created a database, and then analyzed the findings. We identified the topics that were most commonly reported as being material by the chemical industry. This enabled us to identify trends, differences, or similarities within the data. The industry’s main environmental impacts are pollution and energy consumption, which is directly related to carbon emissions. Other material issues include health and safety, and water and waste management. Overall, it became apparent that the chemical industry recognizes the importance that the materiality assessment plays, but the content and detail of the sustainability reports can be improved.

show abstract

“…Unlike banks and financial firms, environmentally sensitive industries such as oil, gas and coal exploration and distribution have larger environmental footprints, and now they report environmental, social and governance (ESG) issues. During the past decade, many have urged an introduction of external audit to enhance such reports' credibility (Chersan et al, 2018). Audit committees and auditors now have added responsibilities regarding the disclosure of ESG aspects (Arif et al, 2021).…”

Section: Hypotheses Development 31 Industry Features and Kams Disclosurementioning

confidence: 99%

Disclosure of key audit matters (KAMs) in financial reporting: evidence from an emerging economy

Rahaman

Hossain

Bhuiyan

2022

JAEE

View full text Add to dashboard Cite

PurposeThe new audit regulation for disclosure of key audit matters (KAMs) in financial reporting has been introduced in both developed and developing countries. This study investigates the influence of three distinctive sets of variables, namely industry features, firm characteristics and auditor attributes, on the extent, pattern and level of disclosure of KAMs by companies listed in Bangladesh, an emerging economy.Design/methodology/approachThe study uses qualitative and quantitative research approaches to investigate the pattern of disclosure of KAMs and their determinants. With a sample of 447 firm-year observations from companies listed on the Dhaka Stock Exchange over 2018–2020, the study reveals industry-level, firm-level and auditor-specific characteristics that affect KAMs' communication in the new audit reporting model.FindingsThe findings suggest that significant differences exist between firms in the number and types of KAMs reported and the extent of their disclosure. The study findings also observed variations both within and across different industry sectors. Highly regulated firms disclose a greater number of KAMs, while environmentally sensitive firms are found to provide a greater detail of the issues presented as KAMs. Further, both firm size and age positively impact the number of KAMs disclosed and the extent of the disclosure provided. Big-4-affiliated auditors do not issue a significantly higher number of KAMs but deliver extensive details to their KAMs description, compared to non-Big-4 auditors. In addition, while auditors, in general, tend to issue boilerplate KAMs, Big-4 associates are found to disclose more new KAMs. However, audit fees and auditor rotation do not influence KAMs disclosure.Research limitations/implicationsThis study is based on two years of publicly available data. However, future studies could consider in-depth interviews to explore the motivation behind KAMs' disclosure in Bangladesh and other developing countries with similar cultural and contextual values.Practical implicationsThese findings have substantial policy considerations for improving firms' audit quality and, thus, their financial reporting quality, with implications for national and international standard-setters, regulators and other stakeholders.Originality/valueThis study is one of the earliest endeavours to investigate KAMs in a context of an emerging country, such as Bangladesh, which adopted KAMs' disclosure in 2018.

show abstract

Assurance of the Sustainability Reports from the Chemical Industry Practices and trends

Cited by 5 publications

References 0 publications

Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms

Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms

Sustainability Material Topics and Materiality Analysis in the Chemical Industry

Disclosure of key audit matters (KAMs) in financial reporting: evidence from an emerging economy

Contact Info

Product

Resources

About