Browsing by Author "Ramtiyal, Bharti"

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Citation - WoS: 10
Citation - Scopus: 16
A hybrid Bayesian approach for assessment of industry 4.0 technologies towards achieving decarbonization in manufacturing industry
(Elsevier Ltd, 2024) Devesh Kumar; Gunjan Soni; Fauzia Jabeen; Neeraj Kumar Tiwari; Gorkem Sariyer; Bharti Ramtiyal; Ramtiyal, Bharti; Jabeen, Fauzia; Soni, Gunjan; Kumar Tiwari, Neeraj; Sariyer, Gorkem; Kumar, Devesh; Tiwari, Neeraj Kumar
Since the 1st Industrial Revolution the Earth's atmosphere has warmed due to human activities like deforestation burning fossil fuels for energy generation and livestock raising. Without preventative measures the Earth's atmosphere would warm by 2 °C before the next Industrial Revolution. Thus it has become crucial to move toward a low-carbon economy. Reaching carbon neutrality means cutting our carbon footprint to zero. Innovative research methods and technologies can play a significant role in supporting the economy in its carbon reduction efforts. Industry 4.0 (I4.0) technologies hold great potential for decarbonizing the economy. However there is a need to explore and utilize this potential effectively. This study aims to address this by developing a methodology that identifies relevant attributes and critical measures from existing literature mapping them with I4.0 technologies. Using a MCDM approach each measure is prioritized based on importance. To better understand the interrelationships between these attributes and I4.0 technologies the Bayesian Network (BN) method is employed. This approach enables the exploration of dependencies and influences among variables. By implementing this four-stage strategy economies can make informed decisions and prioritize actions contributing to carbon neutrality while leveraging the benefits of I4.0 technologies. This approach offers a comprehensive framework for guiding economies on their path towards carbon neutrality considering the potential of I4.0 technologies and the importance of various attributes identified through literature. © 2024 Elsevier B.V. All rights reserved.
Citation - WoS: 5
Citation - Scopus: 6
Discovering Hidden Associations among Environmental Disclosure Themes Using Data Mining Approaches
(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Ece Acar; Gorkem Sariyer; Vipul Jain; Bharti Ramtiyal; Sarıyer, Görkem; Ramtiyal, Bharti; Jain, Vipul; Acar, Ece
Environmental concerns play a crucial role in sustainability and public opinion on supply chains. This is why how and to what extent the firms experience environmental-related actions and inform their stakeholders which is under discussion by most researchers. This paper aims to leverage data mining and its capabilities by applying association rule mining to the environmental disclosure context. With the aim of extracting hidden relationships between environmental disclosure themes for BIST 100 firms serving the Turkish supply chain this research implements a novel association rule mining approach and uses the Apriori algorithm. With this purpose the environmental information of BIST 100 firms was collected manually from sustainability reports, the raw data were processed, and the following seven themes identified the representing firms’ disclosure items: environmental management climate change energy management emissions management water management waste management and biodiversity management. The results indicate various hidden relations between the sector and disclosures allowing us to generate sector-based rules between environmental disclosure themes. © 2023 Elsevier B.V. All rights reserved.
Machine Learning-Driven Clustering Based Environmental, Social, and Governance Performance Prediction Model
(Springer Science and Business Media B.V., 2026) Sarioglu, Mert; Sariyer, Gorkem; Ramtiyal, Bharti