Research of the Efficiency of Mining and Metallurgical Enterprises Based on the Environmental, Social, and Governance Risk Rating in the Context of Digital Transformation

Pishchalkina, Ilona; Pishchalkin, Denis; Suloeva, Svetlana

doi:10.14716/ijtech.v13i7.6181

Cited by 7 publications

(4 citation statements)

References 19 publications

(19 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This fact is determined by the previously stated thesis regarding the significance of post-event risks, to which environmental risks are primarily applicable, which are in turn modeled by the emission of hazardous substances. An uncontrolled explosion, in turn, is the least significant in terms of the severity of the risk event, which indicates a high level of technical safety that minimizes chain reactions (Pishchalkina, Pishchalkin. and Suloeva, 2022).…”

Section: Resultsmentioning

confidence: 99%

Risk Modeling in the Oil and Gas Industry

Rodionov,

Gataullin,

Smirnova

et al. 2023

IJTech

View full text Add to dashboard Cite

The oil and gas industry is a sector that is prone to risks that can have severe consequences for both the environment and the economy. In this study. the aim is to develop an effective mathematical tool for risk modeling in the oil and gas industry. The research proposes a simulation modeling approach that focuses on two key risk parameters -frequency and severity. By using differentiated distributions. the unique properties of risk in the oil and gas industry can be effectively described, and an algorithm can be developed for practical applications. The findings of this study have significant implications for the oil and gas industry, policymakers, and investors. By using an effective mathematical tool for risk modeling. they can identify and manage risks more effectively, reduce the likelihood of accidents and other events that can have severe consequences. and minimize the potential impact of these events. Overall, this research provides valuable insights into the development of an effective mathematical tool for risk modeling in the oil and gas industry. By using simulation modeling and differentiated distributions. this study proposes an algorithm that can be practically applied to manage risks effectively in this important sector.

show abstract

Section: Resultsmentioning

confidence: 99%

Risk Modeling in the Oil and Gas Industry

Rodionov,

Gataullin,

Smirnova

et al. 2023

IJTech

View full text Add to dashboard Cite

show abstract

“…ESG risk ratings are crucial in corporate evaluation, affecting management and strategic investments across industries. Hassan and Meyer [72] note that ESG risk perception significantly modifies demand in the Visegrád countries' tourism sector, while Pishchalkina et al [73] find a positive correlation between high ESG ratings and operational efficiency in the mining and metallurgical sector. Gil [6] shows how environmental and social risks negatively impact financial returns, highlighting investors' trends since 2018 toward companies with strong environmental policies.…”

Section: Esg Risk Rating and Its Impact On The Creation Of Value For ...mentioning

confidence: 99%

Drivers of Value Creation and the Effect of ESG Risk Rating on Investor Perceptions through Financial Metrics

Puente De La Vega Caceres

2024

Sustainability

View full text Add to dashboard Cite

This study delves deeply into the multifaceted nature of investor value creation, focusing on financial health, growth, profitability, cash flow, and ESG (environmental, social, and governance) risk ratings. The research employs partial least squares structural equation modeling (PLS-SEM) to dissect the interactions among these variables in a sample of 482 S&P 500 firms. Data were obtained from the FINRA database (2023) and Sustainalytics ESG risk ratings (2023). The results indicate that solid financial health enhances investor value creation. While growth fosters profitability, its direct impact on value creation and cash flow appears limited. The study also uncovers that ESG risk ratings negatively moderate the relationship between cash flow and value creation. This finding suggests that higher ESG risks lead to increased operational and compliance costs, which can reduce working capital and operating cash flow. Additionally, although sustainability investments may initially incur higher costs, they generate long-term value in terms of investment cash flow. A high perception of ESG risk can also raise financing costs, negatively impacting financial cash flow. These findings offer significant contributions to both academic theory and practical applications, shedding light on the complex interplay between financial and sustainability indicators in driving value creation for investors.

show abstract

“…The former use pre-set goals and define deadlines and ways to achieve them. Exploratory forecasts, on the contrary, follow the trends in the past development of a phenomenon in order to predict its future development (Pishchalkina, Pishchalkin, and Suloeva, 2022). Economic forecasting is helpful in solving several important tasks in the context of situational response (Zhogova et al, 2020).…”

Section: Literary Reviewmentioning

confidence: 99%

The Special Aspects of Devising a Methodology for Predicting Economic Indicators in the Context of Situational Response to Digital Transformation

Eremina,

Rodionov

2023

IJTech

View full text Add to dashboard Cite

The methods currently used for detecting situational deformations in the time series of economic indicators have a number of major flaws, which encourages further research and development in this field aimed at improving the quality and stability of the regression models in the context of digital transformations. The purpose of this research is to devise a methodology for predicting economic indicators in the context of situational response to digital transformation. We examine methods for predicting economic indicators through time series analysis, following which the vector of proactive development can be determined. In order to achieve our objectives, we employed various methods, including mathematical statistics, mathematical modeling, numerical methods, and regression analysis. Our analysis of seasonality in the time series of economic indicators, given the cyclic dominance and modified series, allows us to conclude the need for their structural decomposition with proactive data rejection and modifications and considering their possible displacement. We devised our own original methodology for better processing of statistical data and improved stability of linear regression models ineffective forecasts in the context of situational response of digital transformations. The statistical tools we suggest are likely to enhance the quality of the economic forecasts obtained with the use of regression models (from 15%) due to the preliminary processing of source data and determination of the cyclic dominance of the modified series. The study shows that the methodology for predicting time series in the context of situational response offers the fastest and most accurate data analysis.

show abstract

Research of the Efficiency of Mining and Metallurgical Enterprises Based on the Environmental, Social, and Governance Risk Rating in the Context of Digital Transformation

Cited by 7 publications

References 19 publications

Risk Modeling in the Oil and Gas Industry

Risk Modeling in the Oil and Gas Industry

Drivers of Value Creation and the Effect of ESG Risk Rating on Investor Perceptions through Financial Metrics

The Special Aspects of Devising a Methodology for Predicting Economic Indicators in the Context of Situational Response to Digital Transformation

Contact Info

Product

Resources

About