PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 6, 2026Energies1 citationsOpen Access

Global Co-Evolution of Carbon Pricing Instruments, Emissions Coverage and Revenues: A Long-Run Time-Series Assessment

View Full Paper
MPMariusz Pyra

Key Points

  • The study aims to analyze the long-run relationship between carbon pricing instruments, emissions coverage, and generated revenues.
  • Utilized annual global time-series data from 1990 to 2024
  • Analyzed emissions coverage and revenues with overlapping samples from 2005 to 2023
  • Employed correlation analysis and trend modelling with robustness checks
  • Demonstrated a strong positive correlation between the number of carbon pricing mechanisms and emissions coverage
  • Noted pronounced non-linear scaling of carbon pricing revenues over time
  • Synthesis interpreted as co-movement patterns rather than causal relationships

Abstract

The expansion of carbon pricing instruments, such as carbon taxes and emissions trading systems (ETS), has been rapid over the last three decades. However, the global quantitative evidence is often presented in descriptive reports rather than in a unified empirical framework. The present study documents the long-run co-evolution between three factors: firstly, the global diffusion of carbon pricing mechanisms, secondly, the share of global greenhouse gas emissions covered by an explicit carbon price, and thirdly, global carbon-pricing revenues. The present study utilises annual global time-series data spanning the period 1990–2024 (mechanisms) and overlapping samples for coverage and revenues (2005–2024; 2006–2023). Employing correlation analysis, trend modelling and robustness checks tailored to trending series, the study offers a transparent and replicable quantitative synthesis of the data. The findings suggest a robust positive long-term correlation between the number of mechanisms in operation and emissions coverage. Revenues manifest a pronounced non-linear scaling over time; nevertheless, given the aggregate nature of the dataset, the estimates are interpreted as co-movement patterns rather than causal effects of specific instruments. The paper makes a significant contribution to the field by offering a transparent and replicable quantitative synthesis of global carbon-pricing diffusion and fiscal scaling. It is important to note, however, that the paper also explicitly states the limits of causal inference and outlines panel-data extensions for future research.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Mariusz Pyra (2026) studied this question.

synapsesocial.com/papers/69aa70d6531e4c4a9ff5af5chttps://doi.org/10.3390/en19051277
Ask AI
Helpful
Bookmark
Share
View Full Paper