Recurrent neural network strategies for decoupling energy consumption and greenhouse gas emissions in Hungary’s industrial sector

Abstract

This study addresses the critical challenge facing Hungary’s industrial sector by focusing on the need to decouple economic growth from greenhouse gas (GHG) emissions to meet EU climate targets while maintaining industrial productivity. Although Hungary has achieved significant emission reductions, its industrial sector remains heavily reliant on carbon-intensive energy sources, underscoring the need for advanced analytical approaches to identify effective decoupling strategies. To address this gap, the study utilizes a Recurrent Neural Network (RNN), which is effective for modeling complex, non-linear, and temporal relationships, to analyze the interactions among industrial energy consumption, economic performance, and GHG emissions from 1995 to 2020. The results indicate that reducing coal and heat consumption by 2.5 petajoules yields significant GHG emission decreases of 4.4 percent and 4.3 percent, respectively, while a similar reduction in renewables and waste leads to a 3.5 percent drop in emissions. A 2.5 petajoule reduction in natural gas consumption results in just over a 1 percent decrease in GHG emissions, highlighting its lower emissions intensity and role as a viable transitional fuel. These findings provide critical insights for designing targeted policy interventions prioritizing coal and heat reduction and scaling up low-emission renewables to meet Hungary’s climate commitments. The methodological contribution of using RNN offers a scalable and replicable framework for other countries aiming to balance industrial productivity with sustainable development objectives.