Carbon Markets and Economic Signals : A Bibliometric Review of Global Research Trends and Emerging Themes
Abstract
This study examines the global research landscape of carbon markets in connection with broader economic signals, drawing on 75 Scopus-indexed publications retrieved through a structured bibliometric search. Using VOSviewer as the principal analytical tool, the study constructs keyword co-occurrence networks to identify dominant research themes, thematic clusters, the temporal evolution of scholarly attention, and the relative concentration of research effort across the field. The network visualization reveals four interconnected clusters spanning carbon price dynamics and forecasting, carbon trading and commerce, emissions trading and environmental economics, and the energy-policy nexus surrounding electricity and power markets. The overlay analysis indicates that themes related to machine learning, forecasting, and China-centred carbon trading represent the most recently active research frontier, while emissions trading, environmental economics, and carbon sequestration reflect a comparatively earlier, established stream of inquiry. Density mapping confirms that carbon, carbon markets, and commerce constitute the most intensively researched nodes, functioning as the intellectual core that links price-formation mechanisms with macro-financial and policy signals. Citation analysis further identifies the publications that have most shaped the field's understanding of how energy prices, geopolitical risk, and financial market dynamics transmit into carbon price behaviour. Collectively, these findings depict carbon markets as an economically embedded research domain in which price discovery, risk spillovers, and forecasting innovation are increasingly studied as interconnected economic signals rather than isolated environmental phenomena.
Keywords
References
- Balcilar, M., Elsayed, A. H., Khalfaoui, R., & Hammoudeh, S. (2025). Technological innovations fuel carbon prices and transform environmental management across Europe. Journal of Environmental Management, 373. https://doi.org/10.1016/j.jenvman.2024.123663
- Chen, F., Chen, Z., & Zhang, X. (2024). Belated stock returns for green innovation under carbon emissions trading market. Journal of Corporate Finance, 85. https://doi.org/10.1016/j.jcorpfin.2024.102558
- Duan, K., Ren, X., Shi, Y., Mishra, T., & Yan, C. (2021). The marginal impacts of energy prices on carbon price variations: Evidence from a quantile-on-quantile approach. Energy Economics, 95. https://doi.org/10.1016/j.eneco.2021.105131
- Elsayed, A. H., Khalfaoui, R., Zhang, D., & Urquhart, A. (2025). AI and carbon pricing in turbulent times: Navigating market dynamics for a sustainable future. International Review of Financial Analysis, 107. https://doi.org/10.1016/j.irfa.2025.104632
- Jiang, L. L., Cheong, W. S., & Ng, J. S. L. (2026). Multi-step ahead carbon credit price forecasting using time series foundation models. Applied Soft Computing, 198. https://doi.org/10.1016/j.asoc.2026.115290
- Katoch, R., & Khan, S. (2026). Co-movements of NFTs, DeFi tokens and carbon ETFs: nonlinear dynamics and sustainable portfolio implications. Managerial Finance, 1–24. https://doi.org/10.1108/MF-09-2025-0740
- Li, W., Kang, J., & Wei, Y. (2026). Unstructured news data and carbon price forecasting: a topic-sentiment approach for China’s emission allowance market. Applied Economics Letters. https://doi.org/10.1080/13504851.2026.2654786
- Liu, J., Lee, Y.-H., Li, B.-A., & Yang, J. J. (2025). Home bias and herding in carbon market trading. Finance Research Letters, 78. https://doi.org/10.1016/j.frl.2025.107099
- Liu, S., Zhou, P., Wang, M., & Xu, A. (2025). An agent-based approach to modeling power firms’ emission reduction strategies and market dynamics. Applied Energy, 400. https://doi.org/10.1016/j.apenergy.2025.126590
- Lyu, C., Do, H. X., Nepal, R., & Jamasb, T. (2024). Volatility spillovers and carbon price in the Nordic wholesale electricity markets. Energy Economics, 134. https://doi.org/10.1016/j.eneco.2024.107559
- Maneejuk, P., Huang, W., & Yamaka, W. (2025). Asymmetric volatility spillover effects from energy, agriculture, green bond, and financial market uncertainty on carbon market during major market crisis. Energy Economics, 145. https://doi.org/10.1016/j.eneco.2025.108430
- Paraschiv, F., Schmid, H., Schmitz, M., Dünwald, V., & Groos, E. (2024). The Interplay Between China’s Regulated and Voluntary Carbon Markets and Its Influence on Renewable Energy Development—A Literature Review. Energies, 17(22). https://doi.org/10.3390/en17225587
- Redmond, L., & Convery, F. (2015). The global carbon market-mechanism landscape: pre and post 2020 perspectives. Climate Policy, 15(5), 647–669. https://doi.org/10.1080/14693062.2014.965126
- Ren, X., Li, Y., Qi, Y., & Duan, K. (2022). Asymmetric effects of decomposed oil-price shocks on the EU carbon market dynamics. Energy, 254. https://doi.org/10.1016/j.energy.2022.124172
- Tudor, C., Girlovan, A., Sova, R., Sierra, J., & Stancu, G. R. (2025). From Policy to Prices: How Carbon Markets Transmit Shocks Across Energy and Labor Systems. Energies, 18(15). https://doi.org/10.3390/en18154125
- Wang, H., & Lyu, K. (2025). Environmental tone and carbon market behavior: Understanding market dynamics through corporate environmental attitudes in China. Structural Change and Economic Dynamics, 74, 792–813. https://doi.org/10.1016/j.strueco.2025.06.007
- Wang, X., Lu, F., Safi, A., & Li, X. (2025). Unraveling the dynamics of carbon price volatility: A comprehensive analysis of impacts from climate policy, fossil fuel and renewable energy shocks. Energy Strategy Reviews, 62. https://doi.org/10.1016/j.esr.2025.101966
- Wang, Y., Qin, L., Wang, Q., Chen, Y., Yang, Q., Xing, L., & Ba, S. (2023). A novel deep learning carbon price short-term prediction model with dual-stage attention mechanism. Applied Energy, 347. https://doi.org/10.1016/j.apenergy.2023.121380
- Yan, K., & Lin, B. (2026). Carbon–agriculture market connectedness under the EU ETS: Evidence on sectoral heterogeneity and market states. Energy Economics, 154. https://doi.org/10.1016/j.eneco.2026.109160
- Yi, Y. (2025). Forecasting regional carbon prices in china with a hybrid model based on quadratic decomposition and comprehensive feature screening. PLOS ONE, 20(6 June). https://doi.org/10.1371/journal.pone.0326926
- Zhang, J.-H. (2026). Dynamic connectedness among the carbon, financial, energy, and commodity markets and geopolitical uncertainty: A TVP-VAR-DY time-frequency spillover network approach. Energy Reports, 15. https://doi.org/10.1016/j.egyr.2026.109340
- Zhang, T., & Zou, S. (2025). Study on the Nonlinear Volatility Correlation Characteristics Between China’s Carbon and Energy Markets. Risks, 13(10). https://doi.org/10.3390/risks13100205
How to Cite
Copyright & License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.








