Methodology

Our data is freely available with attribution under a Creative Commons BY 4.0 License. Please cite as follows:

For media:

Net Zero Tracker. Energy and Climate Intelligence Unit, Data-Driven EnviroLab, NewClimate Institute, Oxford Net Zero. 2023.

For academic publications:

John Lang, Camilla Hyslop, Zhi Yi Yeo, Richard Black, Peter Chalkley, Thomas Hale, Frederic Hans, Nick Hay, Niklas Höhne, Angel Hsu, Takeshi Kuramochi, Silke Mooldijk, Steve Smith. Net Zero Tracker. Energy and Climate Intelligence Unit, Data-Driven EnviroLab, NewClimate Institute, Oxford Net Zero. 2023.

The Net Zero Tracker has been built on co-collaboration and people power. We want to hear from you. To alert us of inaccuracies in the data or provide information on entities that helps contribute to a fuller picture, please use our custom form:

If you have any questions, please email updates@zerotracker.net.

The Net Zero Tracker collects information on targets for net zero emissions (and similar aims) pledged by countries, cities, states/regions/provinces (hereafter 'regions' for short), and companies.

It includes two tiers of data collection:

Tier 1:

Defined populations for which we include all actors as designated below, whether they include a target or not.

1. All UNFCCC member states, other regions and territories

2. All cities with population greater than 500,000

3. All regions in the top 25-emitting countries

4. All of the 2,000 largest public companies

Tier 2:

Other actors included if we find a relevant target.

In aggregate, the data allows for complete claims about the population of entities in Tier 1 (e.g. 'X large cities have Y'), and “at least” claims about the global population (e.g. 'at least Z of all companies in the world have Y').

Entities are analysed systematically by a team of volunteer data coders who have undergone a training session and follow a codebook. We rely on publicly available sources such as entities’ websites or published documentation, press releases, or news articles.

All Tier 1 entities are scanned manually at regular intervals. In parallel, regular web-scraping collects information to pass for manual coding.

Entities are coded for the targets and policies they have in effect on the day they are analysed. If a policy is announced on a certain day, but does not go into effect until a later date, we record the date it goes into effect.

The following target names are considered in scope:

• Net zero

• Zero emissions

• Zero carbon

• Climate neutral

• Climate positive

• Carbon neutral(ity)

• GHG neutral(ity)

• Carbon negative

• Net negative

• 1.5°C target

• Science-based target

If references to any of these are found for an entity then it is taken forward for further analysis against the other indicators in the tracker.

Please refer to our 'Codebook' for further details on what data we collect:

All entities are analysed by a second coder to verify data accuracy. This ensures that all entities are double checked. In a previous version of the tracker, double checked entities found identical data acquisition results in 94% of cases. This high intercoder reliability rate builds confidence in the accuracy of the coding process. Spot checks are also undertaken to verify the accuracy of data entries for specific major actors, in order to confirm that any subsequent, important updates pertaining to net zero targets are accounted for.

Our dataset is limited by several factors.

First, it is not globally comprehensive. While we have included all countries in our analysis, we limited ourselves to states, regions, and provinces of the top 25 emitting countries; cities with a population over 500,000; and the 2,000 largest publicly-listed companies by sales. Private companies, smaller public companies, regions in lower-emitting countries and smaller cities have little to no coverage. Despite these exclusions, the data captures a globally significant range of actors that account for the vast bulk of global emissions.

Second, we only include data in the public domain. This may not reflect the most complete and current information held by individual entities.

Third, there are potential gaps in our analysis of net zero targets in some languages resulting from limits to translation. We mitigate this risk by assigning the coding for non-English actors to fluent speakers where possible, and then by translating non-English documents. For some languages however we are unable to enlist fluent speakers. Key concepts that are used to describe net zero commitments (e.g. ‘offsetting’ and ‘coverage’) may not be discussed by non-English speakers in the same way or using the same terminology. Where languages do not use Roman script, we cannot rely on accurate translation from algorithms such as Google Translate. While many such actors are less likely to have net zero targets at this point, certain gaps in the analysis may remain due to this constraint.

We’re in the business of reflecting publicly available information, not making value judgments. However, where we use ‘traffic light’ indicators on the homepage — so in the entity cards (pop ups) and companies table — it's important to explain how we categorise these. It's also important to note that an entity with many 'green lights' does not necessarily mean that the entity’s target is of high quality — there are multiple factors that contribute to the integrity of a net zero target. If you have any questions or concerns about this form of presentation, we encourage you to provide us with feedback.

Detailed plan

• Green — Complete plan: The entity has included all four conditions of what we determine as a detailed plan in G1 of our codebook.
• Orange — Incomplete plan: The entity has included at least one condition of what we determine as a detailed plan, but not all four conditions in G1 of our codebook are satisfied.
• Red — No plan: A plan does not exist as far as we know.

Reporting mechanism

• Green — Annual reporting: Reporting occurs on an annual basis (or more regularly).
• Orange — Less than annual reporting: Reporting occurs but less frequently than annually.
• Red — No reporting mechanism: A reporting mechanism does not exist as far as we know.

Use of carbon credits (international offsets)

• Green — No: The entity has ruled out using external offset credits (hereafter ‘credits’) to meet part of its target.
• Orange — Yes, with conditions applied: The entity plans to use credits to meet part of its target, but provides at least one condition to qualify their use (as detailed in S2 of our codebook).
• Red — Not specified or Yes, without conditions applied: The entity either has not specified details about its planned use of credits (S1 of our codebook) or the entity plans to use credits without providing any conditions to their use (S2 of our codebook).

Greenhouse gas coverage (countries, regions, cities only)

• Green — Carbon dioxide and other GHGs: The entity’s target covers carbon dioxide (CO2) and at least one of a number of other greenhouse gases (GHGs), for example nitrous oxide (N2O), methane (CH4) and fluorinated gases (F-gases).
• Orange — Carbon dioxide only: The target only covers the entity’s carbon dioxide (CO2) emissions.
• Red — Not specified: The entity does not specify which gases its target covers.
  

Scope 3 coverage (companies only)

• Green — Complete Scope 3 coverage: The company claims its target covers all Scope 3 emissions, in other words its full value chain, including downstream and upstream emissions.
• Orange — Partial Scope 3 coverage: The company claims its target covers part of its Scope 3 emissions, for example its upstream (or suppliers) emissions.
• Red — No Scope 3 coverage or Not specified: The company does not include Scope 3 emissions in its target or fails to specify whether it does or not.

We source our national-level greenhouse gas emissions data from the ClimateWatch CAIT dataset (2019).

Besides those in the citation above, many other contributors have been involved in shaping, building and operating the Net Zero Tracker since its inception. They include Ria Aiyer, Kaya Axelsson, Mirte Boot, Patricia Curmi, Lyndsey Fowks, Brendan Mapes, Kelvin Xu Shiwen and Tristram Walsh.

Last but not least, we owe a debt of gratitude to the following volunteers who have put in thousands of hours collecting data on our over 4,000 entities:

• Shiemaa Ahmed

• Fatima Arif

• Wallerand Bazin

• Nina Bengtsson

• Carys Bill

• Olivia Bisel

• Amy Booth

• Barasha Borthakur

• Samuel Boyer

• Macarena Carmona Schwartzmann

• Annabel Chantry

• Abigail Chen

• Fang Wei Chua

• Mia Clement

• Judith Condor-Vidal

• Matthew Doran

• Robert Edge-Partington

• Marwan El Kilany

• Adriana Elera Tejada

• Joshua Fearnett

• Andrew Fletcher

• Lyndsey Fowks

• Julian Gonzales

• Manu Gupta

• Rachel Hart

• Jonathan Heale

• Kate Hulett

• Camille Hulot

• Amelie Hylton

• Diana Jaramillo

• Joe Kearney

• Kara Keenan-Wilson

• Barry Lee

• Maria Lemos Gonzalez

• Amy Leung

• Agnes Liddell

• Zilun Lin

• Harry Linehan-Hill

• Natasha Lutz

• Lucy Lyons

• Charlotte Maddinson

• Lucy Main

• Ebba Mark

• Esmé McMillan

• Nadia Merghani

• Michelle Midzi

• Sasha Mills

• Hettie Moorcroft

• Ella Needham-Hewavisenti

• Alexander Newton

• Joy Nkosi

• Pippa Noble

• Fergus O'Keeffe

• Lucia Palacio Sasse

• Lizeth Palencia

• Sze Ann Pang

• Zelie Pelletier Hochart

• Aiminayanate Pepple

• Jocelyn Perry

• Carol Serban

• Abigail Sheppard

• Bridget Stuart

• Elizabeth Tatham

• Daulet Teginbayev

• Nayah Thu

• Leah Tillmann-Morris

• Maria Torres Santeli Jose

• Allan Torres

• Irene Trung

• Simant Verma

• Michelle Viotti

• Jan Vlcek

• Audrey Wagner

• Elisabeth Ward

• Nicola Whittington

• Kun Yan

• Valeska Yánez

• Lina Yassin

• Anna Zhukova

Thank you.