Over the last 30 years, the number of jurisdictions that have implemented a carbon pricing mechanism has grown significantly. Today, 43 national and 32 subnational jurisdictions have such a mechanism in at least one sector. However, a standardized and centralized record of the sectoral scope and prices applied to CO₂ emissions by these mechanisms is lacking.

This dataset provides an essential contribution to filling that gap. It covers mechanisms introduced since 1990 at the national and subnational levels and is the most comprehensive attempt at providing a systematic description of carbon pricing mechanisms in terms of their sectoral (and fuel) scope and the associated price signal.

A key feature of this dataset is that it provides information structured by territorial jurisdiction, not carbon pricing mechanism. This is achieved by mapping information available for each mechanism onto jurisdictions. It should prove of interest to a wide range of parties, including academic researchers, policy analysts, and interested civil society organizations.

The description below constitutes a brief summary of the construction of the dataset. More details are available in the technical note accompanying the dataset.

The database is created by constructing a mapping of data on carbon pricing mechanisms to the national and subnational jurisdictions in which they are in force. It is constituted of two essential building blocks: (i) data on jurisdiction, sector, and fuel scope of each mechanism, and (ii) the tax rates and allowance prices at which these emissions are priced. Importantly, this data had not previously been systematically recorded using a standardized framework. As a result, dataset construction requires three steps:

1. Data collection: information on each mechanism’s institutional design, sectoral scope, and associated prices is collected from official government or secondary sources.

2. Data encoding: the information collected is structured and encoded. Coverage information (jurisdiction, sector, fuel) is recorded in a Python file. Other institutional design features and price information are recorded in ad hoc csv files.

3. Dataset compilation: the material created is used to generate the final dataset.

The compilation of the dataset happens in 5 steps, which are all contained in the db_build.py script:

1. Instantiate a dataframe containing the entire structure of the dataset; that is, the keys columns (jurisdiction, year, ipcc_cat_code, and Product), and all rows.

2. The coded scope information contained in ets_scope.py and taxes_scope.py, as well as the price information, extracted from the relevant raw csv files where they are

recorded using the ets_prices.py and tax_rates.py scripts, are used to generate the following columns: tax, ets, tax*_id, tax*_rate_excl_ex_clcu, tax*_ex_rate, tax*_curr_code, ets*_id, ets*_price, ets*_curr_code.

3. Calculate tax*_rate_incl_ex_clcu by using files containing information on price rebates.

4. The dataset includes one additional step, calculating mechanism scope values for aggregate IPCC sectors based on the value for subsectors; these take the value 1 if and

only if all subsectors are covered.

5. Finally, all variable entries for which the corresponding tax or ETS indicator value is 0 are set to “NA.”

The structure of the dataset allows for straightforward integration with other data sources that follow IPCC 2006 sectoral disaggregation. One such integration is with jurisdictions’ GHG emissions inventories, such as reported through the UNFCCC process and available through the UNFCCC data portal (https://di.unfccc.int/time_series) or estimated and compiled by institutions such as the Joint Research Centre of the European Commission and available in its Emissions Database for Global Atmospheric Research (EDGAR, https://edgar.jrc.ec.europa.eu/).