Науковий вісник НГУ

Assessment of the efficiency of functioning of the environmental management system of enterprises

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Content №5 2024

Last Updated on 29 October 2024

Published on 30 November -0001

Hits: 805

Authors:

O.V.Barabash*, orcid.org/0000-0001-5206-2922, National Transport University, Kyiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

A.V.Pavlychenko, orcid.org/0000-0003-4652-9180, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

G.O.Waigang, orcid.org/0000-0002-2082-2322, National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Y.Yu.Vozniuk, orcid.org/0009-0003-3050-5333, National Transport University, Kyiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2024, (5): 107 - 115

https://doi.org/10.33271/nvngu/2024-5/107

Abstract:

Purpose. A multifactor model is developed to assess the efficiency level of an enterprise’s environmental management system, depending on the effectiveness of organizational environmental measures.

Methodology. The authors’ method for determining a comprehensive criterion for monitoring the effectiveness of the environmental management system, which characterizes the efficiency of operation and determines the level of environmental safety of enterprises, is proposed and used. To solve the tasks, a complex research method was also used, which included the analysis and generalization of literary and patent sources, and analytical, experimental research using computer and mathematical modeling methods.

Findings. According to the studies and calculations of the generalized environmental quality indicator – 0.64, 0.66 and 0.66, the largest negative impact on the environment is caused by the activities of enterprises in the Pecherskyi, Podilskyi, and Solomianskyi districts of Kyiv, respectively. The obtained data testify to the relationship between the effectiveness of the implemented environmental measures (saving and rational use of resources, application of environmental technologies, advanced training, and environmental competence of employees) and the level of efficiency of the environmental management system of enterprises.

Originality. As a result of the studies conducted using actual data, a system of indicators of the generalized environmental quality indicator has been proposed for the first time, which allows determining the environmental efficiency and effectiveness of the implemented environmental measures to assess the effectiveness of the functioning of the implemented environmental management system of the enterprise.

Practical value. Based on the research results, a technique is proposed to assess the effectiveness of the environmental management system of enterprises by determining a generalized environmental quality indicator in terms of reducing the negative impact of the enterprise’s activities on the environment. Such an assessment system will help the management of the enterprise to promptly introduce corrective actions to improve the efficiency of the environmental management system and increase the level of environmental safety.

Keywords: environmental management system, environmental quality, environmental protection measures, environmental safety, enterprise

References.

1. Barabash, O. V. (2019). Ecological hazard assessment of the atmospheric air at the urban ecosystem by the state of the deposit environment. Proceedings of the National Aviation University, 81(4), 57-63. https://doi.org/10.18372/2306-1472.81.14602.

2. Gilbert, N. A., Amaral, B. R., Smith, O. M., Williams, P. J., Cey­zyk, S., Ayebare, S., …, & Zipkin, E. F. (2024). A Century of Statistical Ecology. Ecology, 105(6), e4283. https://doi.org/10.1002/ecy.4283.

3. Arroyo-Esquivel, J., Klausmeier, Ch. A., & Litchman, E. (2024). Using neural ordinary differential equations to predict complex ecological dynamics from population density data. Journal of the Royal Society Interface. http://doi.org/10.1098/rsif.2023.0604.

4. Barabash, O., & Weigang, G. (2021). Mathematical Modeling of the Summarizing Index for the Biosystems Status as a Tool to Control the Functioning of the Environmental Management System at Business Entities. Mathematical Modeling and Simulation of Systems (MODS’2020), 1265, 56-66. https://doi.org/10.1007/978-3-030-58124-4_6.

5. Kramer, L., Schulze, T., Klüver, N., Altenburger, R., Hackermüller, J., Krauss, M., & Busch, W. (2024). Curated mode-of-action data and effect concentrations for chemicals relevant for the aquatic environment. Scientific Data, 11(60). https://doi.org/10.1038/s41597-023-02904-7.

6. Yazdi, H., Shu, Q., Rötzer, T., Petzold, F., & Ludwig, F. (2024). A multilayered urban tree dataset of point clouds, quantitative structure and graph models. Scientific Data, 11(28). https://doi.org/10.1038/s41597-023-02873-x.

7. Yin, J., Ibrahim, S., Mohd, N. N. A., Zhong, C., & Mao, X. (2024). Can green finance and environmental regulations promote carbon emission reduction? Evidence from China. Environmental Science and Pollution Research, 31, 2836-2850. https://doi.org/10.1007/s11356-023-31231-y.

8. Mohan, J., Kaswan, M. S., & Rathi, R. (2024). An analysis of green lean six sigma deployment in MSMEs: a systematic literature review and conceptual implementation framework. TQM Journal. https://doi.org/10.1108/TQM-06-2023-0197.

9. Barkley, L. V., Short, C. J., & Chivers, C.-A. (2024). Exploring the potential of long-term agreements for achieving landscape-scale environmental recovery. Wiley Interdisciplinary Reviews: Energy and Environment, 13(1). https://doi.org/10.1002/wene.501.

10. Xiajie, Z., Chenxi, L., Lijuan, C., Wei, L., Xinsheng, Z., Jinzhi, W., Yinru, L., & Jing, L. (2023). Coupled patterns of natural and anthropogenic resources in typical ecosystems in coastal areas of China. Environmental Research, 239(2). https://doi.org/10.1016/j.envres.2023.117411.

11. Kaur, N., Sharma, R., & Mehta, K. (2024). 9 Emerging Green: Exploring Strategic Factors for SMEs’ Adoption of Green Technology and Innovation in India. Sustainability, Green Management, and Performance of SMEs, 165-186. https://doi.org/10.1515/9783111170022-009.

12. Khan, A., Naveed, M., Aayanifard, Z., & Rabnawaz, M. (2022). Efficient chemical recycling of waste polyethylene terephthalate. Resources, Conservation and Recycling, 187. https://doi.org/10.1016/j.resconrec.2022.106639.

13. Naiel, B., Fawzy, M., Mahmoud, A. E. D., & Halmy, M. W. A. (2024). Sustainable fabrication of dimorphic plant-derived ZnO nanoparticles and exploration of their biomedical and environmental potentialities. Scientific Reports, 14, 13459. https://doi.org/10.1038/s41598-024-63459-0.

14. Karltorp, K., & Maltais, A. (2024). Financing green industrial transitions: A Swedish case study. Energy and Climate Change, 5, 100138. https://doi.org/10.1016/j.egycc.2024.100138.

15. Barabash, O. V., Lozova, T. M., & Kozlova, T. A. (2018). Assessment of the urban environment quality in Kyiv. Acta Carpatica, 27, 5-11.

16. Li, C., & Huang, M. (2023). Environmental Sustainability in the Age of Big Data: Opportunities and Challenges for Business and Industry. Environmental Science and Pollution Research, 30, 119001-119015. https://doi.org/10.1007/s11356-023-30301-5.

17. Niu, Y., Han, Y., Li, Y., Zhang, M., & Li, H. (2024). Low-carbon regulation method for greenhouse light environment based on multi-objective optimization. Expert Systems with Applications, 252. https://doi.org/10.1016/j.eswa.2024.124228.

18. Wu, B., Gu, Q., Liu, Z., & Liu, J. (2023). Clustered institutional investors, shared ESG preferences and low-carbon innovation in family firm. Technological Forecasting and Social Change, 194. https://doi.org/10.1016/j.techfore.2023.122676.

19. Barabash, O., Weigang, G., Dychko, A., Belokon, K., & Zhelnovach, G. (2021). Modeling a Set of Management Approaches for the Effective Operation of the Environmental Management System at the Business Entities. Ecological Engineering & Environmental Technology, 22(6),1-10. https://doi.org/10.12912/27197050/141895.

20. Hsieh, Y. L., & Yeh, S. C. (2024). The trends of major issues connecting climate change and the sustainable development goals. Discover Sustainability, 5(31). https://doi.org/10.1007/s43621-024-00183-9.

21. Claire, J. L., Asif, R., Muhammad, I., & Adeel, L. (2023). Green innovation, environmental governance and green investment in China: Exploring the intrinsic mechanisms under the framework of COP26. Technological Forecasting and Social Change, 194. https://doi.org/10.1016/j.techfore.2023.122708.

• < Prev
• Next >