SPATIAL CLUSTERING AND MACHINE LEARNING TO OPTIMIZE CARBON TAX DESIGN ACROSS ECONOMIC-ENVIRONMENTAL JURISDICTIONS
DOI:
https://doi.org/10.61261/taxpedia.v3i2.89Keywords:
carbon tax, spatial clustering, economic-environmental performance, machine learning, climate policyAbstract
This study focuses on designing a carbon tax policy based on spatial clustering and machine learning to identify optimal jurisdictions based on environmental-economic performance. The research aims to identify spatial patterns of environmental-economic performance across 38 countries, cluster countries based on similarity profiles using data-driven clustering methods, model the relationship between carbon prices/taxes, economic indicators, and environmental indicators, and recommend optimal carbon tax ranges for each jurisdictional cluster. Adopting a quantitative approach, this study utilizes secondary data from 38 countries, encompassing variables such as carbon prices/taxes, GDP, carbon emissions, energy consumption, industrial contribution to GDP, Environmental Performance Index (EPI), and climate change scores. The analysis employs Ward's hierarchical clustering method and evaluates silhouette coefficients to assess clustering validity. The results classify countries into five distinct clusters with varying environmental-economic characteristics. Developed nations with high environmental performance (e.g., Sweden, Norway, Denmark) are recommended to implement high carbon taxes (USD 100–150 per ton CO₂), while developing countries with high emission intensity (e.g., Indonesia, Kazakhstan) are advised to adopt low initial rates (<USD 15 per ton CO₂). Transitional economies are suggested to implement intermediate rates (USD 20–60 per ton CO₂). This study underscores the necessity of carbon tax policy differentiation based on economic capacity and environmental performance, as well as the importance of international cooperation in technology transfer and energy transition financing. The theoretical contribution lies in integrating Pigouvian Tax frameworks, spatial approaches, and machine learning to develop a more adaptive environmental fiscal policy design.
References
Adam, S., Delestre, I., Levell, P., & Miller, H. (2022). Tax policies to reduce carbon emissions. Fiscal Studies. https://doi.org/10.1111/1475-5890.12308
Afanasyev, M., & Kudrov, A. (2020). Estimates Of Economic Complexity In The Structure Of The Regional Economy. Montenegrin Journal of Economics, 16, 43–54. https://doi.org/10.14254/1800-5845/2020.16-4.4
Anselin, L. (1988). Spatial econometrics: methods and models (Vol. 4). Springer Science & Business Media.
Barrage, L. (2019). Optimal Dynamic Carbon Taxes in a Climate–Economy Model with Distortionary Fiscal Policy. The Review of Economic Studies. https://doi.org/10.1093/restud/rdz055
Bathelt, H., & Storper, M. (2023). Related Variety and Regional Development: A Critique. Economic Geography, 99, 441–470. https://doi.org/10.1080/00130095.2023.2235050
Bu, J., Liu, W., Pan, Z., & Ling, K. (2020). Comparative Study of Hydrochemical Classification Based on Different Hierarchical Cluster Analysis Methods. International Journal of Environmental Research and Public Health, 17. https://doi.org/10.3390/ijerph17249515
Cao, H., Han, L., Liu, M., & Li, L. (2025). Spatial differentiation of carbon emissions from energy consumption based on machine learning algorithm: A case study during 2015–2020 in Shaanxi, China. Journal of Environmental Sciences, 149, 358–373. https://doi.org/https://doi.org/10.1016/j.jes.2023.08.007
Carhart, M., Litterman, B., Munnings, C., & Vitali, O. (2022). Measuring comprehensive carbon prices of national climate policies. Climate Policy, 22, 198–207. https://doi.org/10.1080/14693062.2021.2014298
Chan, N., & Sayre, S. (2023). Spatial Microsimulation of Carbon Tax Incidence: An Application to Washington State. Journal of the Association of Environmental and Resource Economists, 11, 959–997. https://doi.org/10.1086/727476
Chan, Y. (2020). Optimal emissions tax rates under habit formation and social comparisons. Energy Policy, 146, 111809. https://doi.org/10.1016/j.enpol.2020.111809
Chan, Y. T. (2020). Collaborative optimal carbon tax rate under economic and energy price shocks: A dynamic stochastic general equilibrium model approach. Journal of Cleaner Production, 256, 120452. https://doi.org/https://doi.org/10.1016/j.jclepro.2020.120452
Chen, T.-L., Cui, X., & Zou, Y. (2024). Carbon Emission Controls and Optimal Carbon Related Regulation in Oligopoly: Examination of Policy Effects on Competitiveness and Entry Deterrence. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2024.144292
Chen, X., Yang, H., Wang, X., & Choi, T. (2020). Optimal carbon tax design for achieving low carbon supply chains. Annals of Operations Research, 1–28. https://doi.org/10.1007/s10479-020-03621-9
Dogan, A., & Birant, D. (2021). K-centroid link: a novel hierarchical clustering linkage method. Applied Intelligence, 52, 5537–5560. https://doi.org/10.1007/s10489-021-02624-8
Gao, X. (2021). Urban green economic development indicators based on spatial clustering algorithm and blockchain. J. Intell. Fuzzy Syst., 40, 7049–7060. https://doi.org/10.3233/jifs-189535
Ghazouani, A., Xia, W., Jebli, M. Ben, & Shahzad, U. (2020). Exploring the Role of Carbon Taxation Policies on CO2 Emissions: Contextual Evidence from Tax Implementation and Non-Implementation European Countries. Sustainability. https://doi.org/10.3390/su12208680
Gong, M., Zhang, Y., Li, J., & Chen, L. (2024). Dynamic spatial–temporal model for carbon emission forecasting. Journal of Cleaner Production, 463, 142581. https://doi.org/https://doi.org/10.1016/j.jclepro.2024.142581
Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31, 685–695. https://doi.org/10.1007/s12525-021-00475-2
Jemeļjanova, M., Kmoch, A., & Uuemaa, E. (2024). Adapting machine learning for environmental spatial data - A review. Ecological Informatics, 81, 102634. https://doi.org/https://doi.org/10.1016/j.ecoinf.2024.102634
Kim, C., Do, T. N., & Kim, J. (2024). Spatially explicit supply chain for nationwide CO2-to-fuel infrastructure: Data-driven optimization with Gaussian mixture model -based region screening and clustering. Journal of Cleaner Production, 471, 143390. https://doi.org/https://doi.org/10.1016/j.jclepro.2024.143390
Kim, K., Yang, E., & Choi, Y. (2024). Heterogeneous effects of carbon tax, subsidy and foreign carbon emissions on low- and high-carbon firms. Economic Analysis and Policy. https://doi.org/10.1016/j.eap.2024.06.011
Korir, E. K. (2024). Comparative clustering and visualization of socioeconomic and health indicators: A case of Kenya. Socio-Economic Planning Sciences. https://doi.org/10.1016/j.seps.2024.101961
Kudal, P., Patnaik, A., Dawar, S., Satankar, R., & Dawar, P. (2023). Segmentation of OECD countries on the basis of selected global environmental indicators using k-means non-hierarchical clustering. Environmental Science and Pollution Research, 1–12. https://doi.org/10.1007/s11356-023-26679-x
Li, L., Zhang, S., Cao, X., & Zhang, Y. (2021). Assessing economic and environmental performance of multi-energy sharing communities considering different carbon emission responsibilities under carbon tax policy. Journal of Cleaner Production, 328, 129466. https://doi.org/https://doi.org/10.1016/j.jclepro.2021.129466
Li, R., Song, X., Zhao, A., Zhang, X., Li, J., Yu, Z., & Sun, H. (2025). Balancing Growth and Emission Reduction: Evaluating Carbon Tax’s Impact on Sustainable Development in China. Sustainability. https://doi.org/10.3390/su17104517
Li, W., Yang, G., Li, X., Sun, T., & Wang, J. (2019). Cluster analysis of the relationship between carbon dioxide emissions and economic growth. Journal of Cleaner Production, 225, 459–471. https://doi.org/https://doi.org/10.1016/j.jclepro.2019.03.220
Li, Y., & Song, J. (2021). A comparative study of carbon tax and fuel tax based on panel spatial econometric model. Environmental Science and Pollution Research, 29, 15931–15945. https://doi.org/10.1007/s11356-021-16650-z
Liu, X., & Yang, X. (2021). Impact of China’s environmental decentralization on carbon emissions from energy consumption: an empirical study based on the dynamic spatial econometric model. Environmental Science and Pollution Research International, 29, 72140–72158. https://doi.org/10.1007/s11356-022-18806-x
Mardani, A., Liao, H., Nilashi, M., Alrasheedi, M., & Cavallaro, F. (2020). A multi-stage method to predict carbon dioxide emissions using dimensionality reduction, clustering, and machine learning techniques. Journal of Cleaner Production, 275, 122942. https://doi.org/https://doi.org/10.1016/j.jclepro.2020.122942
Misiuk, B., & Brown, C. J. (2023). Improved environmental mapping and validation using bagging models with spatially clustered data. Ecological Informatics, 77, 102181. https://doi.org/https://doi.org/10.1016/j.ecoinf.2023.102181
Nadirgil, O. (2023). Carbon price prediction using multiple hybrid machine learning models optimized by genetic algorithm. Journal of Environmental Management, 342, 118061. https://doi.org/10.1016/j.jenvman.2023.118061
Nong, D., Simshauser, P., & Nguyen, D. (2021). Greenhouse gas emissions vs CO2 emissions: Comparative analysis of a global carbon tax. Applied Energy, 298, 117223. https://doi.org/10.1016/J.APENERGY.2021.117223
Noubissi, E., Nkengfack, H., Pondie, T. M., & Ngounou, B. A. (2023). Economic impact of the carbon tax: Evaluation of the reduction in CO2 emissions. Natural Resources Forum. https://doi.org/10.1111/1477-8947.12348
Pan, J., Cross, J., Zou, X., & Zhang, B. (2024). To tax or to trade? A global review of carbon emissions reduction strategies. Energy Strategy Reviews. https://doi.org/10.1016/j.esr.2024.101508
Pigou, A. C. (1920). The Economics of Welfare. The Economic Journal, London, Ma, 1–983.
Rahmati, R., Neghabi, H., Bashiri, M., & Salari, M. (2023). Stochastic green profit-maximizing hub location problem. Journal of the Operational Research Society, 75, 99–121. https://doi.org/10.1080/01605682.2023.2175734
Randriamihamison, N., Vialaneix, N., & Neuvial, P. (2020). Applicability and Interpretability of Ward’s Hierarchical Agglomerative Clustering With or Without Contiguity Constraints. Journal of Classification, 38, 363–389. https://doi.org/10.1007/s00357-020-09377-y
Timilsina, G. (2022). Carbon Taxes. Journal of Economic Literature. https://doi.org/10.1257/jel.20211560
Tobler, W. R. (1970). Spectral analysis of spatial series. Library Photographic Service, U. of California.
Tu, Q., & Wang, Y. (2022). ANALYSIS OF THE SYNERGISTIC EFFECT OF CARBON TAXES AND CLEAN ENERGY SUBSIDIES: AN ENTERPRISE-HETEROGENEITY E-DSGE MODEL APPROACH. Climate Change Economics. https://doi.org/10.1142/s2010007822400127
Uddin, S., Ong, S., & Lu, H. (2022). Machine learning in project analytics: a data-driven framework and case study. Scientific Reports, 12. https://doi.org/10.1038/s41598-022-19728-x
Wang, G., Han, Q., & de vries, B. (2021). The multi-objective spatial optimization of urban land use based on low-carbon city planning. Ecological Indicators, 125, 107540. https://doi.org/https://doi.org/10.1016/j.ecolind.2021.107540
Wang, J., & Zhuang, Z. (2022). A novel cluster based multi-index nonlinear ensemble framework for carbon price forecasting. Environment, Development and Sustainability, 25, 6225–6247. https://doi.org/10.1007/s10668-022-02299-2
Wang, Q., Alexander, W., Pegg, J., Qu, H., & Chen, M. (2020). HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models. IEEE Transactions on Visualization and Computer Graphics, 27, 1417–1426. https://doi.org/10.1109/TVCG.2020.3030449
Wang, X., Cai, Y., Liu, G., Zhang, M., Bai, Y., & Zhang, F. (2022). Carbon emission accounting and spatial distribution of industrial entities in Beijing - Combining nighttime light data and urban functional areas. Ecol. Informatics, 70, 101759. https://doi.org/10.1016/j.ecoinf.2022.101759
Xu, X., & Li, S. (2022). Neighbor-Companion or Neighbor-Beggar? Estimating the Spatial Spillover Effects of Fiscal Decentralization on China’s Carbon Emissions Based on Spatial Econometric Analysis. Sustainability. https://doi.org/10.3390/su14169884
Yu, Q., Rahman, R. A., & Wu, Y. (2024). Carbon price prediction in China based on ensemble empirical mode decomposition and machine learning algorithms. Environmental Science and Pollution Research International. https://doi.org/10.1007/s11356-024-35316-0
Zhong, J., & Pei, J. (2022). Beggar thy neighbor? On the competitiveness and welfare impacts of the EU’s proposed carbon border adjustment mechanism. Energy Policy. https://doi.org/10.1016/j.enpol.2022.112802
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