Predictive Modeling to Mitigate Greenhouse Gas Emissions in Taiwan’s Diversified Energy Landscape

Document Type

Review

Publication Date

2-1-2026

Abstract

Taiwan’s energy landscape, characterized by a reliance on coal, natural gas, nuclear power, renewable energy, and fuel oil, reflects a strategic diversification aligned with global sustainability goals. In light of escalating greenhouse gas (GHG) emissions, which primarily originate from fossil fuel consumption, electricity production has been identified as the principal contributor, accounting for over 50% of annual emissions. To address this critical issue, our study employs state-of-the-art deep learning methodologies, specifically artificial neural networks (ANN) and bidirectional long short-term memory (BiLSTM) networks, to predict GHG emissions based on the composition and evolution of Taiwan’s power generation mix. Anchored in data from 2013 to 2021, this period corresponds with the implementation of Taiwan’s Carbon Footprint Verification initiative, which reflects a growing commitment to environmental accountability. Our comprehensive analysis evaluates the predictive performance of these models using robust statistical metrics, including RMSE, MBE, rRMSE, R2, and MAPE. Notably, the performance indicators achieved with the ANN model are compelling. The R2 value of 0.9851 reflects strong predictive correlation, the RMSE of 0.0392 indicates high precision, and the MBE of -0.0378 reveals minimal bias. In addition, the exceptionally low rRMSE and MAPE values, recorded at 0.3157% and 0.3062% respectively, further validate the model’s forecasting accuracy. This work not only highlights the increasing trend of GHG emissions but also demonstrates the effectiveness of predictive models as powerful analytical tools within the energy sector. These models support informed policy-making and strategic planning, encouraging cleaner energy production in Taiwan while providing a replicable framework for GHG emission estimation in other geopolitical contexts.

Publication Title

Environmental Modeling and Assessment

ISSN

14202026

DOI

10.1007/s10666-025-10075-5

Volume

31

Issue

1

First Page

23

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