mirМИР (Модернизация. Инновации. Развитие)MIR (Modernization. Innovation. Research)2079-46652411-796XSchool of Public Administration10.18184/2079-4665.2025.16.3.570-585mir-1998Research ArticleРАЗВИТИЕRESEARCHАнализ практик корпоративной социальной ответственности нефтегазовых компаний в России (на англ. яз.)Analysis of oil and gas companies’ corporate social responsibility practices in Russiahttps://orcid.org/0000-0002-6774-7577РытоваЕ. В.RytovaE. V.

Рытова Елена Владимировна, кандидат экономических наук, доцент Высшей инженерно-экономической школы, Институт промышленного менеджмента, экономики и торговли

Researcher ID: U-4318-2019, Scopus ID: 56958439500

Санкт-Петербург

Elena V. Rytova, Candidate of Economic Sciences; Associate Professor at the Graduate School of Industrial Economics, Institute of Industrial Management, Economics and Trade

Researcher ID: U-4318-2019, Scopus ID: 56958439500

St. Petersburg

rytova_ev@spbstu.ruhttps://orcid.org/0000-0002-1098-3915ГутманС. С.GutmanS. S.

Гутман Светлана Семеновна, кандидат экономических наук, доцент, доцент Высшей инженерно-экономической школы, Институт промышленного менеджмента, экономики и торговли

Researcher ID: H-7920-2016, Scopus ID: 56957907300

Санкт-Петербург

Svetlana S. Gutman, Candidate of Economic Sciences, Associate Professor, Associate Professor at the Graduate School of Industrial Economics, Institute of Industrial Management, Economics and Trade

Researcher ID: H-7920-2016, Scopus ID: 56957907300

St. Petersburg

SGutman@spbstu.ruhttps://orcid.org/0000-0002-9252-7410БразовскаяВ. В.BrazovskaiaV. V.

Бразовская Виктория Владимировна, кандидат экономических наук, ассистент Высшей инженерно-экономической школы, Институт промышленного менеджмента, экономики и торговли

Researcher ID: GRE-8086-2022, Scopus ID: 57222508001

Санкт-Петербург

Viktoriia V. Brazovskaia, Candidate of Economic Sciences, assistant at the Graduate School of Industrial Economics, Institute of Industrial Management, Economics and Trade

Researcher ID: GRE-8086-2022, Scopus ID: 57222508001

St. Petersburg

brazovskaya_vv@spbstu.ruhttps://orcid.org/0000-0003-4039-9274БлагойН. A.BlagoiN. A.

Благой Никита арсенович, аспирант Высшей инженерно-экономической школы, Институт промышленного менеджмента, экономики и торговли

Researcher ID: JXY-8057-2024, Scopus ID: 57215671010

Санкт-Петербург

Nikita A. Blagoi, Postgraduate student at the Graduate School of Industrial Economics, Institute of Industrial Management, Economics and Trade

Researcher ID: JXY-8057-2024, Scopus ID: 57215671010

St. Petersburg

nikita_1915@bk.ruСанкт-Петербургский политехнический университет Петра ВеликогоРоссияPeter the Great St. Petersburg Polytechnic UniversityRussian Federation202517092025163570585Copyright © Рытова Е.В., Гутман С.С., Бразовская В.В., Благой Н.A., 20252025Рытова Е.В., Гутман С.С., Бразовская В.В., Благой Н.A.Rytova E.V., Gutman S.S., Brazovskaia V.V., Blagoi N.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.mir-nayka.com/jour/article/view/1998

Целью представленной статьи является разработка подхода к выявлению и оценке лучших практик корпоративной социальной ответственности (КСО), направленных на улучшение социально-экологической эффективности предприятий и оказывающих положительное влияние на финансовые результаты и риски компаний.

Методы

Методы. Авторами применены регрессионный анализ для выявления ключевых показателей КСО, влияющих на финансовые результаты компании, и Data Envelopment Analysis (DEA) для анализа сравнительной эффективности практик КСО и выявления наиболее успешных практик с точки зрения баланса между ключевыми показателями КСО и финансовыми результатами.

Результаты работы

Результаты работы. Предложен алгоритм выявления лучших практик КСО. В частности, согласно стандарту GRI были отобраны ESG-факторы нефтегазового сектора при условии их включения в отчеты с 2012 по 2023 гг.; в качестве эндогенного показателя выбрана прибыль. С помощью корреляционно-регрессионного анализа оценено наличие связи между показателем прибыли и выбранными ESG-факторами. По результатам моделирования отобраны факторы, наиболее тесно функционально связанные с результатом, которые использованы в качестве входных переменных для анализа методом DEA, примененном для отбора компаний с наиболее эффективными практиками КСО. Проведен сравнительный анализ для выявления лучших практик корпоративной социальной ответственности. Предложены рекомендации по развитию практик КСО нефтегазовых компаний.

Выводы

Выводы. В результате проведенного исследования был разработан подход к выявлению успешных практик КСО с учетом их взаимосвязи с финансовыми результатами компаний на примере нефтегазового сектора. Сформированный алгоритм может быть использован и для компаний других отраслей при условии реализации в этих компаниях механизмов КСО и предоставления нефинансовой отчетности. Оценка эффективности КСО практик дополнена углубленным анализом конкретных мероприятий компаний-бенчмарков.  

Purpose

Purpose: to develop an approach to identifying and assessing the best corporate social responsibility (CSR) practices aimed at improving the social and environmental performance of enterprises and having a positive impact on the financial results and risks of companies.

Methods

Methods: the authors conducted a regression analysis to identify key CSR indicators affecting a company's financial results, and Data Envelopment Analysis (DEA) to analyze the comparative effectiveness of CSR practices and identify the most successful practices in terms of the balance between key CSR indicators and financial results.

Results

Results: an algorithm for identifying the best CSR practices was proposed. In particular, according to the GRI standard, ESG factors of the oil and gas sector were selected provided that they had been included in reports from 2012 to 2023; profit was chosen as an endogenous indicator; the presence of a relationship between the profit indicator and the selected ESG factors was assessed using correlation and regression analysis; based on the modelling results, the factors that are most closely functionally related to the result were selected and used as input variables for the DEA; the DEA method was used to select companies with the most effective CSR practices; a comparative analysis was conducted to identify the best practices of CSR and recommendations were suggested for oil and gas companies CSR practices development.

Conclusions and Relevance: as a result of the research, an approach was developed to identify successful CSR practices taking into account their relationship with the financial results of companies using the example of the oil and gas sector. The developed algorithm can also be used for companies in other industries, provided that these companies implement CSR and provide non-financial reporting. The assessment of CSR practices efficiency is supplemented by an in-depth analysis of specific CSR activities of benchmark companies.  

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The authors declare that there are no conflicts of interest present.