Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach
Source abstract
Abstract
This study introduces a novel application of a temporal convolutional network (TCN) for projecting carbon tax prices, addressing the critical need for accurate forecasting in climate policy. Utilizing data from the World Carbon Pricing Database, we demonstrate that the TCN significantly outperformed traditional time series models in capturing the complex dynamics of carbon pricing. Our model achieved a 31.4% improvement in mean absolute error over ARIMA baselines, with an MAE of 2.43 compared to 3.54 for ARIMA. The TCN model also showed superior performance across different time horizons, demonstrating a 30.0% lower MAE for 1-year projections, and enhanced adaptability to policy changes, with only a 39.8% increase in prediction error after major shifts, compared to ARIMA’s 95.6%. These results underscore the potential of deep learning for enhancing the precision of carbon price projections, thereby supporting more informed and effective climate policy decisions. Our findings have significant implications for policymakers and stakeholders in the realm of carbon pricing and climate change mitigation strategies, offering a powerful tool for navigating the complex landscape of environmental economics.
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Research context
OpenAlex topics: Atmospheric and Environmental Gas Dynamics · Energy Load and Power Forecasting · Energy, Environment, and Transportation Policies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
SC Johnson Graduate School of Management, Cornell University, Ithaca, NY 10022, USA
McCallum Graduate School of Business, Bentley University, Waltham, MA 02452, USA
The Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, TX 75080, USA
David A. Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Department of Computer Science, University of Bristol, Bristol BS8 1QU, UK
References (25)
Show source-provided references
1. Environmental Defense Fund (2021). The World’s Carbon Markets: A Case Study Guide to Emissions Trading, Environmental Defense Fund. EDF Report.
2. Carbon Pricing Leadership Coalition (2017). Report of the High-Level Commission on Carbon Prices, World Bank.
3. International Monetary Fund (2019). Fiscal Policies for Paris Climate Strategies—From Principle to Practice, International Monetary Fund. IMF Policy Paper.
4. Revisiting the social cost of carbon10.1073/pnas.1609244114 ↗
5. The promise and problems of pricing carbon: Theory and experience
6. The impact of a carbon tax on manufacturing: Evidence from microdata10.1016/j.jpubeco.2014.04.016 ↗
7. Carbon pricing in climate policy: Seven reasons, complementary instruments, and political economy considerations10.1002/wcc.462 ↗
8. Overcoming public resistance to carbon taxes10.1002/wcc.531 ↗
9. Does the EU ETS cause carbon leakage in European manufacturing?10.1016/j.jeem.2018.11.004 ↗
10. The general equilibrium impacts of carbon tax policy in China: A multi-model comparison10.1016/j.eneco.2021.105284 ↗
11. Effects of a carbon tax in the United States on agricultural markets and carbon emissions from land-use change10.1016/j.landusepol.2021.105320 ↗
12. How does a carbon tax affect Britain’s power generation composition?10.1016/j.apenergy.2021.117117 ↗
13. Assessment of the potential impacts of a carbon tax in Chile using dynamic CGE model10.1016/j.jclepro.2023.136694 ↗
14. Exchange rate prediction using machine learning techniques: An empirical study on the European carbon market
15. Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model10.1016/j.energy.2012.01.037 ↗
16. Bai, S., Kolter, J.Z., and Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv.
17. Borovykh, A., Bohte, S., and Oosterlee, C.W. (2017). Conditional time series forecasting with convolutional neural networks. arXiv.
18. Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting10.1016/j.energy.2019.01.075 ↗
19. World Carbon Pricing Database (2020, January 01). CO2 Emissions Data for Countries. Available online: https://github.com/g-dolphin/WorldCarbonPricingDatabase.
20. Box, G.E.P., Jenkins, G.M., Reinsel, G.C., and Ljung, G.M. (2015). Time Series Analysis: Forecasting and Control, John Wiley & Sons. [5th ed.].
21. Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology
22. Carbon price prediction based on improved empirical mode decomposition and long short-term memory
23. Can today’s and tomorrow’s world uniformly gain from carbon taxation?10.1016/j.euroecorev.2024.104819 ↗
24. Kyriakopoulos, G.L., Streimikiene, D., and Baležentis, T. (2022). Addressing challenges of low-carbon energy transition. Energies, 15.10.3390/en15155718 ↗
25. An efficient energy planning model optimizing cost, emission, and social impact with different carbon tax scenarios10.1016/j.apenergy.2022.119792 ↗