Author(s)

Stanley Sia Chong Soon

Affiliation(s)

Faculty of Business and Economics, University of Malaya, Kuala Lumpur, Malaya.

Corresponding Author

Stanley Sia Chong Soon

ABSTRACT

Climate change represents one of the most significant global challenges, necessitating urgent action to reduce greenhouse gas emissions. Among various policy measures, carbon emission taxes have emerged as a critical tool for incentivizing reductions in carbon footprints by imposing financial charges on carbon-intensive fuels. This paper explores data-driven forecasting methods for carbon emission taxes, emphasizing their potential to enhance policy effectiveness. Accurate forecasting is essential for policymakers to understand the economic and environmental impacts of carbon taxes, enabling informed decisions that align with climate goals. Utilizing advanced data analytics and modeling techniques, this study investigates the trajectories of carbon emissions and potential tax revenues under diverse scenarios, highlighting the complexities of global carbon markets and the need for adaptive policy frameworks. This research contributes significantly to the discourse on sustainability by offering a robust framework for forecasting carbon emission taxes. The insights gained not only enhance the understanding of carbon emissions dynamics but also support the development of more effective and equitable carbon pricing mechanisms. As the global community continues to confront climate change, the findings of this study provide essential guidance for policymakers, businesses, and environmental advocates striving for a sustainable future. Future research should focus on refining these forecasting methods and exploring the long-term implications of carbon taxation on sustainable development.

KEYWORDS

Carbon emission taxes; Data-driven forecasting; Sustainability

CITE THIS PAPER

Stanley Sia Chong Soon. Forecasting for carbon emission taxes through a data-driven path to sustainability. World Journal of Information and Knowledge Management. 2024, 2(3): 1-10. DOI: https://doi.org/10.61784/wjikm3019.

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