A hybrid Bayesian approach for assessment of industry 4.0 technologies towards achieving decarbonization in manufacturing industry

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 degrees 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.