DOI: 10.5061/dryad.547d7wmbq
title: Sectoral coverage and prices of carbon pricing mechanisms introduced since 1990

description: 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.
description: 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.”
description: 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/).