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Sci Data
Scientific Data
2052-4463
Nature Publishing Group UK London

39237577
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10.1038/s41597-024-03613-5
Data Descriptor
GCoM datasets: a collection of climate and energy action plans with mitigation, adaptation and energy access commitments
http://orcid.org/0000-0002-2006-3821
Franco Camilo camilo.francodelosrios@eea.europa.eu

1
Melica Giulia 1
Treville Aldo 1
Baldi Marta Giulia 2
Palermo Valentina 1
http://orcid.org/0000-0002-2797-7941
Bertoldi Paolo 1
http://orcid.org/0000-0001-5484-5744
Pisoni Enrico 1
Monforti-Ferrario Fabio 1
http://orcid.org/0000-0001-5946-3139
Crippa Monica 1
1 grid.434554.7 0000 0004 1758 4137 European Commission - Joint Research Centre (JRC), Ispra, 21027 Italy
2 European Dynamics, Luxembourg, Luxembourg
5 9 2024
5 9 2024
2024
11 96910 11 2023
5 7 2024
© European Union 2024
2024
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This paper presents a collection of datasets holding information on the energy and climate action plans of 6,850 municipalities, taking part in the transnational initiative of the Global Covenant of Mayors (GCoM). This collection includes commitments for reducing net GHG emissions by at least 20% by 2020, 55% by 2030 and becoming climate neutral by 2050. The signatories commit to addressing any of the three pillars of the initiative, namely climate change mitigation, adaptation and energy access. Following two previous releases, the third release of the GCoM collection is introduced, with closing date September 2022. The datasets include information on the action plans and monitoring reports as they are self-reported by signatories, undergoing a quality-harnessing procedure before publication. Additionally, an external comparison is developed with the Emissions Database for Global Atmospheric Research (EDGAR v7), controlling for comparable sources and activity sectors, ensuring the usability of the GCoM datasets for relevant research on local policies and their effects on reducing the impact of climate change.

Subject terms

Climate-change mitigation
Governance
issue-copyright-statement© Springer Nature Limited 2024
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pmcBackground & Summary

The Covenant of Mayors (CoM) initiative was launched by the European Commission (EC) in 2008 with an initial target for participating cities to reduce Greenhouse Gas (GHG) emissions in their territories by at least 20% by 2020. Later on, in 2014, based on the experience of the CoM and recognizing the vulnerability of urban areas to suffer the inevitable impacts of climate change, the EC launched a similar voluntary initiative (called Mayors Adapt) with a focus on climate adaptation in cities. Later, in 2015, the Mayors Adapt merged with the CoM, also setting a new target aligned with the EU overarching target of 40% GHG emission reduction by 2030. Nowadays, after joining forces with the Compact of Mayors in 2017, the Global Covenant of Mayors (GCoM) is currently the world’s largest coalition of cities and local governments voluntarily committed to fighting climate change. In Europe, cities commit to becoming climate neutral by 2050 (with an interim target which is recommended to be set at−55% GHG emissions by 2030), including actions on climate change adaptation, and more recently (launched in 2022), on energy poverty and energy access.

Cities and local authorities joining the GCoM commit to take the lead and enhance the transparency of local climate and energy policies. This is supported by setting realistic and ambitious quantified emission reduction targets; measuring the level of their GHG emissions in a reference base year according to a standardized methodological approach1; assessing climate risks and vulnerabilities, as well as energy access and energy poverty in their territories; defining a strategy and concrete actions to mitigate and adapt to climate change, and to increase energy access for the most vulnerable population groups; approving and making their action plan publicly available; aiming at regularly reporting on the implementation of their action plan; and sharing their vision, outcomes, expertise, and knowledge with other local and regional authorities within the EU and globally through direct collaboration and peer-to-peer exchange.

GCoM signatories come from all parts of the world, setting their commitment and reporting their Climate Action Plans (CAP) through two reporting platforms, MyCovenant (http://mycovenant.eumayors.eu/) and CDP-ICLEI Track (https://www.cdp.net/en/cities). Cities choose on which platform to report their CAP. In this paper we analyze only data coming from the first platform (covered by the Data Policy agreement2), which contains more than 90% of the GCoM signatories. By adhering to this initiative under the Data Policy agreement, signatories agree on considering all Covenant Data as open data (published and made available for re-use for both commercial and non-commercial purposes), timely, comprehensive, accessible, usable, comparable and inter-operable. Additionally, signatories bear responsibility for the lawfulness of sharing Covenant Data and they ensure that Covenant Data is of good quality, accurate and regularly updated. Nonetheless, the agreement also specifies that the EC-Joint Research Centre (JRC) may, in order to further improve overall quality, i) remove errors or irregularities, and ii) add new parts or functionalities2.

The CAP covers the geographical area under the jurisdiction of the local authority, including actions by both public and private sectors, aiming at translating the local vision for mitigation and adaptation to climate change, and also for alleviating energy poverty and advancing in energy access. GCoM signatories develop their CAP following the available methodological guidance1, presenting the measures to be implemented to achieve their climate mitigation, adaptation and energy access ambitions.

With regards to the mitigation pillar, the CAP contains a Baseline GHG Emissions Inventory (BEI), following a methodology that should be consistent with the IPCC framework3; a GHG emission reduction target, which should be in line with the Nationally Determined Contribution to the United Nations Framework Convention on Climate Change, mainly for target years 2020, 2030 and/or 2050; and a clear outline of the actions that the local authority intends to take in order to reduce its GHG emissions. The reduction target is measured against the BEI, and the progress made by the signatory is monitored through a Monitoring Emissions Inventory (MEI), every 4 years following the submission of the BEI. It is important to highlight that the BEI is not meant to be an exhaustive inventory of all emission sources occurring under the jurisdiction of the signatory, but it rather focuses on the energy consumption and on the sectors upon which the local authority has a potential influence.

Concerning the adaptation pillar, the CAP includes the assessment of climate risks and vulnerabilities within the territory, adaptation goals and a set of actions to increase the resilience of the local authority sectors and vulnerable groups. As for the energy poverty pillar (officially launched only recently, in 2022, being not yet mandatory to report on), it includes policies for increasing the level of energy access and/or reduce energy poverty within the boundaries of the jurisdiction, considering measurable indicators for the key attributes of secure energy, sustainable energy and affordable energy.

Under this approach, where cities voluntarily self-report on their action plans and advances in their implementation, assuring a fair level of data quality becomes a key challenge. In this sense, cities might be reporting biased estimations or evident errors with respect to the GCoM reporting framework1, or might be missing information or suffering of lack of coherence4.

Therefore, a quality harnessing procedure was developed in order to publish a structured collection of datasets holding relevant information on the CAP and monitoring reports (previous in-house JRC-reports5,6 outline the statistical procedures described here in the Technical validation for harnessing the quality of the GCoM data sets7–9, being the first time this collection and its curating methods are described in peer-reviewed scientific literature). Such a procedure also aimed at facilitating its potential reuse, allowing to assess the policy impact for mitigation, adaptation and energy access ambitions inside the GCoM initiative6,10,11, but also, across different energy and climate initiatives and compatible databases (such as CDP-ICLEI Track12).

It should be noted that a previous GCoM data set has been published13, considering only European and Southern Mediterranean signatories, which made use of the data gathered at MyCovenant at the time (2019). Such a data set followed a different curation process than the one presented here, and was used as benchmark for developing the comparison with the EDGAR database in the Technical validation.

Focusing on the third release for the GCoM datasets9 (with closing date September 2022), Fig. 1 shows a European close-up on the location of the different municipalities and cities with active action plans. The red circles correspond with signatories having presented a BEI, and the yellow ones with signatories having also presented a MEI. In total, there are 6,850 signatories with a BEI, and 2,279 having also reported at least one MEI. The complete summary of GCoM signatories by geographical area is presented in Table 1.Fig. 1 European close-up of active municipalities and cities committing under the GCoM, having reported a BEI and MEI (on top of the BEI), including Azores and Canary Islands.

Table 1 Summary of the GCoM signatories with a submitted action plan, by geographical area and their associated baseline energy consumption and emissions per capita.

Area	No. BEI	Population (mil.)	TWh/year	MtCO2-eq	MWh/year per capita	tCO2-eq per capita	
Europe - EU27	6,501	166.66	3,101.48	900.07	18.6	5.4	
Eastern Europe	216	17.9	223.3	71.55	12.47	3.99	
Europe - EFTA	15	1.88	46.88	7.11	24.95	3.78	
Rest of Europe	92	38.57	530.76	169.2	13.76	4.38	
South Mediterranean	22	3.27	22.28	8.13	6.81	2.48	
Rest of the world	4	1.47	18.88	6.86	12.84	4.66	
Total	6,850	229.76	3,943.6	1,162.93	17.16	5.06	
Notice that Eastern Europe here refers to Eastern Europe outside the EU27.

Methods

Recollection of data

The GCoM collection of action plans and monitoring reports is the result of the ongoing efforts of signatory cities in planning, implementing and self-reporting their advances towards climate change mitigation, adaptation and energy access. All Covenant Data, including the local climate and energy strategy, the amount of final energy consumption and energy production and associated emissions by energy carrier and by sector, the main climate vulnerabilities, hazards, the impacts and risks assessment, the climate change mitigation and adaptation actions, together with stakeholders and estimated impacts, falls under the Data Policy agreement2. Therefore, after a quality scrutiny, Covenant Data may be published and regularly updated as an open dataset, on the public websites of Regional Covenants, as well as on the European Union Open Data Portal (https://data.europa.eu/euodp/en/homeor the website of the European Commission (https://ec.europa.eu/jrc/).

The present third release of the GCoM dataset collection, with closing date September 2022, follows the previous first7 (with closing date May 2021), and second8 (closing date March 2022) GCoM releases, following the FAIR guiding principles for scientific data management and stewardship14, being easily accessible, inter-operable and reusable. All previous releases have been followed by their respective scientific and technical assessment reports5,6,15,16.

Data extraction

The GCoM datasets include data that is submitted via the password protected MyCovenant web application (https://mycovenant.eumayors.eu/). The web application points to a externally managed database, which is copied into the JRC servers on a daily basis (accessed through the European Commission Authentication System login credentials). Then, a series of SQL-scripts can be executed, feeding different data tables to the overall harnessing process articulating the GCoM datasets.

In this way, the extraction of the GCoM data is carried out using SQL queries tailored to retrieve the necessary data from the JRC servers, connecting data points across the 260 tables that constitute the whole database structure. The complexity of the data extraction reflects the one of the database structure, which has undergone, and continues to undergo changes throughout the life of the project to accommodate renewed ambitions and methodological developments.

Firstly, the complete list of signatories is analysed, identifying the ones to be included in the datasets. Namely, they are published signatories, which are compliant with the GCoM reporting requirements, and on-hold signatories, which are temporarily suspended, due to non-compliance with mandatory requirements. This process leaves out signatories that never concluded the registration process or are not yet formally confirmed as GCoM signatories. Additionally, some administrative information, such as the population, the date of adhesion, the commitments subscribed to, or the type of adhesion (whether individual or in a group with other signatories), is retrieved in this phase.

Secondly, the action plans and monitoring reports with a “submitted” or “resubmitted” status are retrieved for those published or on-hold signatories (identified in the previous phase). A preliminary validation process looks at the completion of some key mandatory fields, thereby excluding entire action plans and monitoring reports that do not fulfil the required standards. For example, excluding a CAP with an emission reduction target outside the 20%−100% range (the minimum percentage reduction of GCoM signatories should be of at least 20% by 2020), or with incomplete data, i.e., lacking a baseline emission inventory or an explicit reduction target. Lastly, a manual check is performed to analyse case by case and attribute the action plan or monitoring report to their corresponding commitment.

Data Records

The present third release of the GCoM dataset collection has been published in the JRC data repository, as well as in the European open data portal9 (CC-BY public access, under the European Commission Reuse and Copyright Notice https://data.jrc.ec.europa.eu/licence/com_reuse). It consists of three Excel files (spreadsheets), one (df1) presenting the whole set of GCoM signatories (either submitting through MyCovenant or CDP-ICLEI Track), and a second (df2) and a third one (df3), holding all the information related to the action plans and monitoring reports, respectively, that have been submitted through MyCovenant.

The data is organized for publication into three main datasets: df 1 - Signatories, df 2 - Action Plans and df 3 - Monitoring Reports. The first file, df 1 - Signatories, contains the identification and description of the GCoM signatories. It includes the key IDs: organisation_id, which allows relating this df1 file with the other GCoM df2 and df3 files, and the gcom_id, which allows identifying a city in the GCoM eco-system. Table 2 contains the general description of the information contained in this file.Table 2 Contents of df1-Signatories.

Field	Type	Short description	
organisation_id	string	Identification code for the GCoM signatory	
organisation_name	string	Name of the GCoM signatory	
coordinator_name	string	Name of the GCoM Coordinator	
supporter_name	string	Name of the GCoM supporter	
country_code	string	Country ISO 3166 code	
country_name	string	English name of the country	
population_adhesion	integer	Population in the year of adhesion	
organisation_longitude	integer	Geographical longitude	
organisation_latitude	integer	Geographical latitude	
group_organisation_id	string	Identification code of the GCoM group (if applicable)	
group_name	string	Name of the GCoM group (if applicable)	
commitment_flag	boolean	Identifies signatories of the particular GCoM initiative	
adhesion_type	string	If signatory joins as individual or as group	
signatory_status	string	Status of the signatory	
mayor	string	Name of the Mayor	
date_of_adhesion	date	Date of adhesion to the GCoM	
action plan expected	boolean	If action plan is expected	
group_profile	boolean	If group of local authorities	
gcom_id	string	Identification code inside the GCoM platform	
nuts3_code	string	Nomenclature of Territorial Units for Statistics (NUTS3) 2016 - Eurostat	

The second file, df 2 - Action Plans, comprises the information related to submitted and resubmitted plans, spanning from mitigation, adaptation and energy access commitments, to energy and emissions inventories, actions overviews and local policies. It allows identifying each city by their corresponding organisation_id, relating it with the df1 file, and also has an action_plan_id, which allows relating all the tables in this dataset (df2). Tables 3, 4, 5 contains the general description of the information contained in this file.Table 3 Contents of df2-Action plans.

Dataframe	Field	Type	Short description	
2 - Action plans	organisation_id	string	Identification code for GCoM signatory	
organisation_name	string	Name of the GCoM signatory	
action_plan_id	string	Identification code of the action plan	
approval_date	date	Date of approval by the municipal council	
action_plan_commitment	boolean	If valid commitment for the target year	
action_plan_pillar	boolean	If valid commitment for the pillar	
2A_1 - Mitigation Commitments	action_plan_id	string	Identification code of the action plan	
co2_target	float	Reduction percentage from BEI	
base_year	date	Year of BEI accounting	
target_year	date	Target year for the commitment	
reduction_type	string	Absolute or per capita reduction	
population_estimate	integer	Estimated population for the target year	
impact_year_of_actions	date	Year when actions are reported	
2A_2 - Mitigation Actions Overview	action_plan_id	string	Identification code of the action plan	
commitment	string	Type of commitment	
action_sector	string	energy activity sector	
action_number	integer	Number of mitigation actions	
estimates_co2_reduction	float	Reported estimated GHG emissions reduction	
estimates_energy_savings	float	Reported estimated energy savings	
estimates_energy_production	float	Reported energy to be produced	
2B_1 - Adaptation Commitments	action_plan_id	string	Identification code of the action plan	
goal_id	string	Identification code of the goal	
goal	string	Adaptation goal	
unit	string	Unit for adaptation target measurement	
base_year	date	Year of the baseline indicator	
base_value	integer	Value of the indicator at the base year	
target_year	date	Target year for the commitment	
target_value	integer	Value of the indicator at the target year	
climate_hazard	string	Main targeted climate hazard	
2B_2 - Adaptation Actions Overview	action_plan_id	string	Identification code of the action plan	
action_sector	string	Activity sector of the proposed action	
action_number	integer	Number of proposed actions	

Table 4 Contents of df2-Action plans (continuation).

Dataframe	Field	Type	Short description	
2C - Actions details	action_plan_id	string	Identification code of the action plan	
action_id	string	Identification code of the action	
mitigation_flag	boolean	If the action addresses mitigation	
adaptation_flag	boolean	If the action addresses adaptation	
energy_poverty_flag	boolean	If the action addresses energy poverty	
action_title	string	Title of the action	
key_action_flag	boolean	If the action is a “key action"	
action_origin	string	Initiator of the action	
responsible_body	string	Body responsible for the action	
short_description	string	Description of the nature of the action and scope	
timeframe_start	date	Year when the action starts	
timeframe_end	date	Year when the action ends	
action_implementation	string	Status of the action	
action_stakeholders	string	Relevant stakeholders	
financing_sources	string	Source(s) of funding	
mitigation_sector	string	Sector addressed by the action	
mitigation_area	string	Area within the selected sector	
mitigation_instrument	string	Type of policy instrument	
impact_co2_reduction	float	Reported estimated emissions reduction	
impact_energy_savings	float	Reported estimated energy savings	
impact_renewable_production	float	Reported estimated energy to be produced	
adaptation_hazards	string	Climate hazard(s) addressed by action	
adaptation_sectors	string	Adaptation sector(s) addressed by the action	
energy_poverty_details	string	Area addressed by the action	
vulnerability_group_targeted	string	Vulnerable population groups(s)	
2D_1 - Mitigation inventories Energy consumption & Emissions	action_plan_id	string	Identification code of the action plan	
emission_inventory_id	string	Identification code of the inventory	
baseline_flag	boolean	If baseline emission inventory	
inventory_year	date	Year of the inventory	
population	integer	Number of inhabitants in the inventory year	
sector	string	Activity sector	
carrier	string	Type of energy carrier used	
activity_reporting_unit	string	Unit for energy measurement: MWh/year	
energy_measure	float	Energy consumption for the sector and carrier	
emission_factor_type	string	Approach for measuring the emission inventory	
emission_reporting_unit	string	Units for the emission inventories	
emission_measure	float	Amount of emissions for the sector and carrier	
2D_2 - Mitigation inventories Energy supply	action_plan_id	string	Identification code of the action plan	
emission_inventory_id	string	Identification code of the inventory	
baseline_flag	boolean	If year of the BEI	
inventory_year	date	Year of the inventory	
supply_type_id	string	Identification code of the supply type	
energy_supply_type	string	Energy supply sector	
energy_carrier_id	string	Identification code of the energy carrier	
energy_carrier	string	Energy carrier	
energy_output	float	Amount of produced energy	
emission_measure	float	Amount of emissions of the energy produced	

Table 5 Contents of df2-Action plans (continuation).

Dataframe	Field	Type	Short description	
2E_1 - Inventories Adaptation R& VA_1	action_plan_id	string	Identification code of the action plan	
climate_hazard	string	Relevant climate hazard	
other_input	string	Relevant climate hazard not included previously	
probability_of_hazard	string	Level of probability of occurrence of the climate hazard	
impact_of_hazard	string	Level of impact of the climate hazard in the present	
expected_intensity	string	Expected change in hazard intensity	
expected_change	string	Expected change in hazard frequency	
timeframe	string	Timeframe for expected changes	
2E_2 - Inventories Adaptation R& VA_2	action_plan_id	string	Identification code of the action plan	
climate_hazard	string	Relevant climate hazard	
vulnerable_sector	string	Relevant vulnerable sectors	
vulnerable_sector_level	string	Current level of vulnerability	
2E_3 - Inventories Adaptation R& VA_3	action_plan_id	string	Identification code of the action plan	
climate_hazard	string	Relevant climate hazard	
vulnerable_sector	string	Relevant vulnerable sectors	
adaptive_capacity_factor	string	Adaptive capacity factors for each sector	
adaptive_capacity_level	string	Current adaptive capacity level of the capacity factor	
2E_4 - Inventories Adaptation R& VA_4	action_plan_id	string	Identification code of the action plan	
climate_hazards	string	Relevant climate hazard	
vulnerable_population_groups	string	Relevant vulnerable population group(s)	
2E_6 - Inventories Adaptation R& VA scoreboard	action_plan_id	string	Identification code of the action plan	
cycle_steps	string	Adaptation cycle steps	
actions	string	Actions for the Adaptation cycle steps	
self_check_status	string	Self-check status in the adaptation cycle	
2F_2 - Energy poverty Actions overview	action_plan_id	string	Identification code of the action plan	
macro_areas	string	Macro area(s) addressed by the action plan	
action_number	integer	Number of actions for energy poverty	

Lastly, the third file df 3 - Monitoring Reports, holds the main information from the monitoring reports. It allows identifying each city by their corresponding organisation_id, relating it with the df1 and df2 files, each action plan by the corresponding action_plan_id, relating it directly to the df2 file, and it also has a monitoring_report_id, which allows relating all the tables in this dataset (df3). Table 6 contains the general description of the information contained in this file.Table 6 Metadata of df3-Monitoring reports.

Dataframe	Field	Type	Short description	
3 - Monitoring reports	organisation_id	string	Identification code for GCoM signatory	
organisation_name	string	Name of the GCoM signatory	
action_plan_id	string	Identification code of the action plan	
monitoring_report_id	string	Identification code of the monitoring report	
monitoring_report_type	string	If an emissions inventory is included ("full")	
submission_timestamp	date	Date of submission of the monitoring report	
action_plan_commitment	boolean	If valid commitment for the target year	
action_plan_pillar	boolean	If valid commitment for the pillar	
3A - Mitigation Actions Overview	monitoring_report_id	string	Identification code of the monitoring report	
action_sector	string	Energy activity sector	
action_number	integer	Number of mitigation actions	
mitigation_status	float	Percentage of ongoing/completed/postponed actions	
3B_1 - Adaptation Commitments	monitoring_report_id	string	Identification code of the monitoring report	
monitoring_goal_id	string	Identification code of the goal in the monitoring report	
goal_id	string	Identification code of the goal	
goal	string	Adaptation goal	
unit	string	Unit for adaptation target measurement	
base_year	date	Year of the baseline indicator	
base_value	integer	Value of the indicator at the base year	
target_year	date	Target year for the commitment	
target_value	integer	Value of the indicator at the target year	
climate_hazard	string	Main targeted climate hazard	
progress	string	Progress made towards the adaptation target	
3B_2 - Adaptation Actions Overview	monitoring_report_id	string	Identification code of the monitoring report	
adaptation_action_sector	string	Activity sector of the proposed action	
adaptation_action_number	integer	Number of proposed actions on adaptation	
adaptation_status	float	Percentage of ongoing/completed/postponed actions	
3D_1 - Mitigation inventories Energy consumption & Emissions	monitoring_report_id	string	Identification code of the monitoring report	
emission_inventory_id	string	Identification code of the inventory	
baseline_flag	boolean	If baseline emission inventory	
inventory_year	date	Year of the inventory	
population	integer	Number of inhabitants in the inventory year	
sector	string	Activity sector	
carrier	string	Type of energy carrier used	
activity_reporting_unit	string	Unit for energy measurement: MWh/year	
energy_measure	float	Energy consumption for the sector and carrier	
emission_factor_type	string	Approach for measuring the emission inventory	
emission_reporting_unit	string	Units for the emission inventories	
emission_measure	float	Amount of emissions for the sector and carrier	
3D_2 - Mitigation inventories Energy supply	monitoring_report_id	string	Identification code of the monitoring report	
emission_inventory_id	string	Identification code of the inventory	
baseline_flag	boolean	If year of the BEI	
inventory_year	date	Year of the inventory	
supply_type_id	string	Identification code of the supply type	
energy_supply_type	string	Energy supply sector	
energy_carrier_id	string	Identification code of the energy carrier	
energy_carrier	string	Energy carrier	
energy_output	float	Amount of produced energy	
emission_measure	float	Amount of emissions of the energy produced	

Technical Validation

A quality-harnessing computational procedure is developed, aiming at enhancing the internal coherence and contents reliability of the datasets that are published. The work-flow for such a procedure is presented in Fig. 2, structuring the information into three datasets.Fig. 2 Flowchart for the GCoM data extraction and quality-harnessing process, as developed by the JRC, prior to the publication of the datasets. DF1, DF2 and DF3 appear in capital letters to distinguish the initial (pre-processed) versions from the final open data sets: df1, df2 and df3.

Additionally, a comparison is developed with the Emissions Database for Global Atmospheric Research (EDGAR v7), assessing the usability of the GCoM datasets for relevant research on local policies and their effects on reducing the impact of climate change.

Energy consumption validation

Following the data extraction and the preliminary identification of valid action plans and monitoring reports (as described above), the validation process is extended by evaluating the activity on energy consumption. Hence, a general methodology is developed for screening outlier energy activity observations with respect to national per capita references17,18. It should be noted here that, for the purpose of this process, labeling an observation as an outlier does not entail its direct elimination, but rather, that it deserves a close-up examination to judge if it can be justified, if it is an evident mistake, or if it has to be discarded.

Simplifying the analysis, the initial step for identifying outlier energy activity involves segregating electricity from other carriers. For the national consumption references, the industry sector is omitted for both categories, taking only the commercial and public services, road, and residential sectors. Therefore, the energy consumption declared by signatories is aggregated and transformed into per capita terms, and it is labelled as outlier if it is greater than a clustering-based maximum threshold (as explained below), or less than a minimum threshold set up to 0.01 (MWh/year per capita).

To determine the maximum threshold for each category (electricity and other carriers), signatories are grouped together based only on their per capita energy consumption national references. For doing so, the k-medoids technique is used19 (aiming at a more robust grouping under the presence of outlier observations), developing a heuristic search in the same fashion as in the k-means, but using a real observation as cluster centroid instead of an average. Partitions of 2 to 5 clusters are explored, and an optimal partition is identified according to the overall satisfaction of various statistical indices measuring the density and separation between clusters20. As a result, the best partition consists of three clusters with maximum thresholds of 2.6, 3.3 and 7.3 (MWh/year per capita), for electricity, and with maximum thresholds of 14, 18.7 and 25 (MWh/year per capita), for other carriers.

Following the completion of the outlier screening process, a more thorough analysis is conducted on the subset of inventories initially identified. Some outliers might be rare but plausible, and if an evident error is detected, it can be corrected. For example, if the city reports in kWh instead of MWh, or in activity per capita instead of absolute activity. Only if the reported values appear to be incomplete or to make no proper sense, then the inventory is removed. After such an analysis, the emission inventories from 7 signatories were removed.

In this way, before this analysis, there were 6,883 signatories with a BEI, corresponding with 10,478 inventories (counting all the available BEIs and MEIs). Then, 26 signatories were removed due to their nationalities from Russia and Belarus (banned due to the Russian invasion to Ukraine), leaving a total of 10,447 inventories. In consequence, leaving out the 7 signatories mentioned above, 6,850 signatories are left, holding a total of 10,391 inventories.

Additionally, internal coherence is checked by focusing on action plans with a monitoring history, which is available for arbitrary years counting from their corresponding base year. Here, the reported energy consumption should follow a reasonable trend, and if a particular MEI falls out of such a trend, then the inventory is eliminated as long as no evident correction can be identified. Lastly, advanced outlier detection techniques are applied, namely the Isolation Forest21 and the Local Outlier Factor22 algorithms, over the remaining set of inventories. This is done in order to refine the search for outlier values, paying special attention to the inventories with higher outlier values. Here again, if atypical values are identified and no reasonable explanation or correction is found for them, then the inventory is taken out from the GCoM datasets. After this internal coherence process, we have the same number of signatories (6,850), but less inventories (excluding outlier MEIs), for a total of 10,372 inventories.

Emission factors

After removing evident mistakes from the energy consumption activity, the emissions have to be estimated by multiplying the energy activity (MWh/year) and the corresponding emission factors (i.e., for each energy carrier being reported, there is an associated emission factor). This entails a second validation phase, now on the emission factors used. Emission factors could be made available by cities when reporting their own estimation of the emissions, but this is not always the case, as sometimes signatories fail to report the explicit emission factor that they used. For the case where the emission factors are reported, they have to be assessed and validated. As for the case of missing emission factors, an imputation process had to be applied.

Therefore, emission factors are verified using carrier-specific references sourced from the JRC repository23,24. Only in case the emission factors provided by the cities are absent, significantly divergent from the carrier-specific reference (as it will be explained below), or negative, then an appropriate reference is utilized instead. The assessment on the difference between the reported values and the references considers separately emission factors for electricity with national or local origin, and for all other energy carriers.

For national electricity factors, outliers are identified if they exceed the threshold of being 50% off from their national yearly references, or if their value is greater than the national historical maximum. In the case of local electricity emission factors, outliers consist in values 2 times bigger than the national historical maximum. The higher tolerance for local than for national electricity emission factors lies in the fact that there is more uncertainty on the exact mix of energy sources that is being used locally. And for all other energy carriers, outlier emission factors are identified if they surpass the threshold of being 20% higher.

In total, 12.6% of all signatories presented some emission factors that had to be revised. In consequence, 10.9% of all inventories had some emission factor, associated to a particular energy carrier, which had to be replaced by its respective reference. Concerning missing emission factors, not being reported by signatories, the percentage was higher. In total, 36.8% of all signatories were missing some information regarding their emission factors, entailing that 30% of all inventories had some emission factor, associated to a particular energy carrier, which had to be filled in by their respective reference.

Energy production

A separate procedure was implemented, following internal consistency rules (as explained below), for validating the reported energy supply values for local electricity and heat/cold energy production, as well as for certified green electricity sales and purchases.

Initially, an implicit emission factor was calculated for local heat/cold energy and locally distributed electricity production by comparing the reported supply and emissions, categorizing them by renewable and fossil sources. Therefore, emissions were validated only if that implicit emission factor was less than 2 and greater or equal than 0.1 (tCO2-eq/MWh) for fossil sources (0 for renewable). Here again, as with local electricity emission factors and the high level of uncertainty on the specific mix of energy sources being implemented locally, those thresholds were established as broad upper and lower bounds according to the existing body of literature on emission factor references23,24.

Secondly, considering renewable energy only for locally distributed electricity production, the reported energy supply was compared with energy consumption, and only if the energy produced was far greater than the declared consumption (exceeding it in more than 150 times), it was taken out from the validated dataset.

Lastly, in the case of certified green electricity acquisitions, the reported purchases were cross-referenced with electricity consumption. Only those purchases were validated and retained for publication if the energy procured did not exceed 1.05 times the electricity consumption. Hence, a small 5% tolerance was allowed on the deviation of the declared purchases with respect to what the city actually used.

Risks and Vulnerability Assessment and adaptation goals

The Climate Risk and Vulnerability Assessment (RVA) and the adaptation goals are also subject of a revision and validation, verifying that the data reported into the platform complies with completeness, consistency, and coherence standards.

Concerning the RVA, it is noted that the adaptation pillar of the GCoM was initially introduced as a separate initiative25, and later merged with the mitigation pillar in 2015, and therefore, signatories that were initially committed to mitigation were allowed to report information on adaptation on an optional basis. Thus, data on adaptation was not consistently collected across all signatories, and a separate deadline was established for signatories committed to adaptation, giving two additional years to report information on it. As a result, data on the RVA was collected through the platform using different templates over time, resulting in potentially incomplete and inconsistent records.

The RVA dataset includes information on climate hazards (type, current impact and probability, expected magnitude and frequency), vulnerable sectors (and their vulnerability level), adaptive capacity factors, and population groups. To ensure that the data is minimally coherent, incoherent RVA reports that are submitted by signatories are filtered out, as well as old (reported before 2020) or redundant information, being submitted elsewhere on the platform. Lastly, climate hazards are cleaned from invalid text entries. Figure 3 presents the close up on active signatories holding an RVA and planning on adaptation actions.Fig. 3 Close-up of the active GCoM signatories with an RVA and adaptation actions (on top of the RVA).

On the other hand, regarding adaptation goals, they started to be collected since in the platform since 2020, as a text entry related to its base and target years. In 2022, the adaptation goal collection was enhanced with additional data fields, which included the collection of a baseline value, a target value, and the main climate hazard being addressed.

The process to clean the adaptation goals involves filtering out incoherent adaptation goals reported by signatories without an adaptation commitment or without a CAP covering the adaptation pillar, and removing any invalid text entries related to mitigation, emissions reduction, and the progress towards the target or the monitoring of the adaptation goal. The summary of this process, regarding the cleaning of the adaptation goals, is presented in Table 7. Overall, 65% of all the submitted goals were kept in the datasets. Because adaptation plans may comprise a range of goals, from several to none, there is no direct relationship between the number of plans submitted and the number of goals published.Table 7 Summary of the number of entries for published, excluded and total submitted adaptation goals per EU country.

Country	Published (Valid and accepted)	Excluded (Invalid or irrelevant)	Excluded (Mitigation not adaptation)	Excluded (Unpublished plans)	Total (Submitted goals)	
Austria	2	0	0	0	2	
Belgium	395	0	4	100	499	
Bulgaria	1	0	0	5	6	
Croatia	24	0	14	11	49	
Cyprus	16	0	0	0	16	
Czech Republic	2	0	0	5	7	
Finland	15	0	0	0	15	
France	45	1	2	1	49	
Germany	8	0	1	3	12	
Greece	35	2	13	19	69	
Hungary	77	1	2	11	91	
Ireland	15	0	0	0	15	
Italy	475	8	73	287	843	
Latvia	2	0	0	2	4	
Luxembourg	1	0	0	0	1	
Netherlands	0	0	0	1	1	
Poland	2	0	1	1	4	
Portugal	60	0	15	15	90	
Romania	5	0	4	36	45	
Slovakia	7	0	1	0	8	
Slovenia	1	0	0	3	4	
Spain	888	5	191	295	1379	
Sweden	3	0	0	3	6	
Total	2079	17	321	798	3215	
Note that the number of goals may vary, with multiple or no entries available within individual plans.

Local policies and their estimated impact

The GCoM datasets contain all the individual policies reported in MyCovenant by the local governments, which can be described in English but also in local languages, making it a multi-lingual dataset. The quality-harnessing procedure focused on the aggregate estimates for the impact of those policies at sector level, but also on the individual policies.

Firstly, at sector level, the validation process involved an initial screening to identify any clear inconsistencies between the commitments and mitigation measures. In this way, the estimated emissions to be reduced by the target year, as reported by signatories by sector, were aggregated into a total figure, and compared with the total baseline emissions reduction, as declared in their commitment. Then, in case the estimates by sector entailed a percentage reduction of 2 times greater than the absolute target percentage reduction declared in the respective commitment (taking into account that different actions could be redundant on their expected achievements), or exceeded the total baseline emissions, then the estimated emissions reduction was discarded. Similarly, the estimated impact on energy savings was verified, such that values that were greater than 1.2 times the energy consumption were excluded.

Further analysis was developed by sector as well as on an individual basis for all actions, computing an implicit factor between the estimates of CO2 reduction and the sum of energy savings and renewable energy production. Here, following the same logic as with local heat/cold energy and locally distributed electricity production, if such a factor was greater than 2 or less than 0.01 (tCO2-eq/MWh), then the action-sector was excluded from the validated dataset, or for individual actions, the corresponding estimates were discarded.

Comparison with EDGAR database

The EDGAR database v7 is used to compare with the CO2 emissions data available in the GCoM (a recent comparison of the emissions inventories reported by ICLEI C40 cities and EDGAR gridded datasets can be found in26). Each one, EDGAR and GCoM, follows a different methodology, being there some disagreement in terms of spatial/geographical coverage, emissions sources, emissions allocation or the type of emissions considered.

In general terms, the GCoM datasets contain emissions data that are accounted from locally available information, which is then aggregated at municipal or city level, regarding the local administrative boundaries. Meanwhile, EDGAR27 uses national activity data and reference emission factors for accounting GHG emissions at global scale, which are then spatially distributed by sector, with respect to the human population, on a 0.1 × 0.1 degree resolution.

Therefore, controlling for the divergences between both approaches, the comparison between the GCoM and EDGAR data is performed on direct CO2 emissions, taking only the buildings (IPCC codes 1A4 and 1A5) and road transport (IPCC code 1a3b) sectors. Focus is placed on a sample of European cities (enabling the geographical matching between the GCoM cities identified by a LAU code, and the EDGAR grid-based emissions), taking the GCoM annual inventories for every year after 1999, for all of the available cities having a LAU code and population above 50,000 inhabitants, and matching them with the corresponding geographical polygon of the EDGAR grid.

To develop this analysis, emissions are extracted from the EDGAR database for each one of the cities (polygons) included in this study, combining different LAUs if needed to have the most complete match based on the LAU(s) and the EDGAR gridded data. Greater uncertainty should be associated to small areas, as such a match is more difficult to implement given the resolution of the EDGAR grid. Hence, the sample of cities to compare is composed of medium to big cities, although there are some small polygons in the sample, where 54 cities have surface of less than 100 km2.

In total, there are 322 different cities that could be matched with sufficient confidence between GCoM and EDGAR. For emissions in the buildings and road transport sectors, there are 587 and 582 observations, respectively, which include different yearly inventories for the same city. The difference between sectors is due to the fact that not all cities consistently report for all sectors throughout their emissions accounting.

Tables 8 –9 present the results for this comparison, for both datasets for the buildings and road transport sectors, respectively. Firstly, this study replicates a previous study presented back on 2019, based on a previous sample of the Covenant data13, which can be taken as a benchmark on the reliability of the GCoM data with respect to EDGAR. In that paper13, the analysis presented a correlation (CORR) coefficient, the normalized root mean squared error (NRMSE) and the bias estimation between the Covenant and the EDGAR data, taken for the same sectors and by size of the city in terms of population. Here, we extend that analysis by including the Mean Absolute Percentage Error (MAPE), which can be understood as the estimated bias in absolute value, and consider the data by country.Table 8 Summary of the general statistics for the GCoM and EDGAR comparison.

Buildings	Total	City size	
Statistics	Big	Medium	Small	
No. Cities	314 (1945)	27 (8)	284 (96)	3 (1841)	
No. Obs.	587	67	517	3	
CORR	0.83 (0.92)	0.75 (0.94)	0.84 (0.64)	1.0 (0.6)	
NRMSE	0.09 (3.3)	0.26 (0.45)	0.06 (1.37)	0.08 (1.4)	
Bias (%)	10 (35)	20 (32)	2 (48)	5 (10)	
MAPE (%)	71	66	72	38	
Emissions-GCoM (Mt)	163.4 (54.4)	84.2 (16.02)	79 (24.5)	0.19 (13.8)	
Emissions-EDGAR (Mt)	147.9 (37.6)	70.3 (12.6)	77.4 (12.5)	0.18 (12.48)	
For the Buildings sector, the number of cities (No. Cities), number of observations (No. Obs.), correlation (CORR), Normalized Root Mean Squared Error (NRMSE), Bias, Mean Absolute Percentage Error (MAPE), and the GCoM and EDGAR emissions values, expressed in million tCO2-eq/year (Mt), by size (Big, Medium and Small cities). In parenthesis, the corresponding statistics for the 2019 benchmark.

Table 9 Summary of the general statistics for the GCoM and EDGAR comparison.

Road transport	Total	City size	
Statistics	Big	Medium	Small	
No. Cities	312 (1945)	27 (8)	282 (96)	3 (1841)	
No. Obs.	582	67	512	3	
CORR	0.68 (0.66)	0.57 (0.93)	0.63 (0.37)	0.45 (0.49)	
NRMSE	0.45 (5.1)	0.75 (1.48)	1.26 (1.7)	0.52 (1.5)	
Bias (%)	106 (43)	180 (218)	71 (130)	58 (-35)	
MAPE (%)	157	196	153	102	
Emissions-GCoM (Mt)	186.15 (48.6)	82.6 (10.7)	103.3 (25.09)	0.2 (12.8)	
Emissions-EDGAR (Mt)	90.2 (34.02)	29.48 (3.36)	60.6 (10.9)	0.13 (19.75)	
For the Road transport sector, the number of cities (No. Cities), number of observations (No. Obs.), correlation (CORR), Normalized Root Mean Squared Error (NRMSE), Bias, Mean Absolute Percentage Error (MAPE), and the GCoM and EDGAR emissions values, expressed in million tCO2-eq/year (Mt), by size (Big, Medium and Small cities). In parenthesis, the corresponding statistics for the 2019 benchmark.

Focusing on Tables 8, 9 it is observed that the 2019 benchmark focused mainly in small cities, on the contrary to this study, which considers a higher number of medium and big sized cities. The overall correlation of 0.83 in the buildings sector is lower than the benchmark of 0.92, meanwhile the overall correlation is very similar to the one of the benchmark in the road transport sector, obtaining 0.68. Regarding medium sized cities, there are 284 medium cities in the buildings sector obtaining a correlation of 0.84, surpassing the benchmark of 0.64 for 96 cities. On the other hand, on the road transport sector, there is a higher correlation of 0.63 for 283 medium cities, against the 0.37 of 96 cities included in the 2019 benchmark. Additionally to the correlation statistic, which may not be the most suitable for the comparison between EDGAR and GCoM emissions, the NRMSE and the bias also throw better values for the GCoM datsets against the 2019 benchmark, having an overall lower NRMSE (0.09 against 3.3) and bias (10% against 35%).

Examining the MAPE, the average absolute deviation is 71% and 157%, respectively for the buildings and the road transport sectors. Aiming at understanding the source of such discrepancies, Table 10 presents a comparison by country. In the buildings sector, the greatest discrepancies come from 2 signatories in Ireland (277%), 4 signatories in Cyprus (222%), and 4 signatories in France (210%). Meanwhile, in the road transport sector, the greatest discrepancies come from 1 signatory in Lithuania (814%), 4 signatories in Cyprus (509%), 1 signatory in Estonia (476%) and 3 signatories in Latvia (467%). Such big outliers allow to partially explain the high absolute deviations by sector, identifying a low number of geographically located cities with atypical inventories.Table 10 Summary of the statistics between EDGAR and GCoM emissions values for the buildings and road transport sectors by country.

Country	Buildings	Road transport	
Cities (N)	COR	NRMSE	Bias (%)	MAPE (%)	Cities (N)	COR	NRMSE	Bias (%)	MAPE (%)	
Austria	1 (2)	−1	1.6	27.5	27.9	1 (2)	1	1.9	185.8	185.8	
Belgium	8 (25)	0.95	0.38	28.3	38.1	8 (25)	0.95	0.66	43.1	38.6	
Bulgaria	3 (6)	0.33	1.87	−77.1	77.4	3 (6)	0.28	0.84	−57.2	56.7	
Croatia	8 (13)	0.98	0.1	−8.5	52.1	8 (13)	0.95	0.74	270.2	329.2	
Cyprus	4 (6)	0.09	1	138.3	221.9	4 (6)	0.02	1.3	381.5	509.5	
Czech Republic	4 (10)	0.97	0.19	−23.1	39.2	4 (9)	0.96	0.17	54.6	64.1	
Denmark	10 (15)	0.16	0.63	−34.3	50.7	10 (13)	0.92	0.23	13.5	19.9	
Estonia	1 (3)	0.97	0.67	46.1	50.1	2 (4)	0.96	1.35	559.3	475.9	
Finland	2 (5)	0.98	1.14	−67.2	70.2	2 (5)	0.99	1.13	188.3	152.8	
France	4 (5)	0.77	0.53	59.8	210.3	4 (5)	0.97	0.5	115.25	204.7	
Germany	25 (52)	0.77	0.28	27.8	60.5	25 (51)	0.65	0.56	193.1	187.1	
Greece	35 (57)	0.36	0.03	2.15	97.7	35 (57)	0.02	0.19	21.3	246.7	
Hungary	10 (16)	0.98	0.3	−27.3	37	10 (16)	0.92	0.55	137.8	147.3	
Ireland	2 (3)	0.92	1.19	141.85	277.1	2 (3)	0.92	1.4	217.4	243.1	
Italy	87 (151)	0.89	0.24	31.1	68.1	87 (152)	0.5	1.1	81.6	113	
Latvia	3 (10)	−0.05	1.3	−42.1	47.5	3 (10)	−0.9	1.9	371.9	467.7	
Lithuania	1 (1)	NA	NA	−52.2	52.2	1 (1)	NA	NA	814.7	814.7	
Netherlands	11 (18)	0.85	0.25	20.8	50.8	9 (14)	0.7	0.15	13.5	63.1	
Poland	5 (15)	0.9	0.4	−32.8	35.8	5 (15)	0.87	1.06	176.8	187.4	
Portugal	23 (41)	0.5	0.05	−5.05	76.8	23 (41)	0.24	0.55	132.6	207.6	
Romania	18 (27)	0.47	0.26	39.7	107.8	19 (28)	0.2	0.99	133.6	232.7	
Slovakia	3 (5)	0.99	0.18	31.1	45.3	3 (5)	0.99	0.55	304.2	302.7	
Slovenia	3 (3)	0.99	0.36	56.6	63.1	3 (3)	0.99	0.3	48.2	69	
Spain	26 (62)	0.58	1.03	−44.7	67.7	26 (62)	0.77	1.04	60	86.8	
Sweden	17 (36)	0.63	0.65	−54.8	74.9	17 (36)	0.47	0.55	70	78.9	
Along with the number of cities included in the sample for each country, N stands for the total number of observations.

Usage Notes

The datasets are available in structured tables in the format of Excel spreadsheets, having their own metadata description with the name, type and short description of each field. This makes the GCoM collection of datasets easily accessible, inter-operable and re-usable (taking into account their specific attributes in order to using them properly).

An important discussion should still be addressed regarding the identification of outlier observations in the GCoM data sets. As mentioned throughout the description of the quality-harnessing procedure in the Technical validation, the thresholds that were defined for identifying outlier observations on energy consumption and production, emission factors, and local policy impacts, were the result of a detailed procedure aiming at removing or revising only evident mistakes (ensuring high quality standards while aiming at keeping as much data points as possible as they were originally submitted). Therefore, outlier observations were identified not for direct removal, but for a close up analysis. Following such an analysis, only evident mistakes were revised if their nature could be inferred. Otherwise, if the nature of a mistake could not be resolved, then it was removed. In this way, the procedure focused on ensuring the usability of the available data points, with the main purpose of harnessing the data sets from extremely low quality values. On the contrary, if a stronger approach would have been taken, then the results would have been conditioned by stronger assumptions, suggesting the removal of more data points and affecting the availability of these data for applied research. In consequence, depending on the research purpose for using these data, a more in-depth analysis could be necessary for detecting (statistical or common sense) outlier values.

Upcoming releases intend to include translated fields for individual actions and adaptation goals, offering a multi-lingual corpus of actions and goals with its respective English translation, as well as energy access and energy poverty fields. It should be mentioned that for this third release, only 0.6% of all individual actions are labelled as belonging to the (recently launched) energy access and energy poverty pillar, the majority of them, designed in combination with mitigation and/or adaptation plans. For future releases, it is expected that signatories will adhere to this pillar and present their energy access and energy poverty evaluations and actions together with their CAP.

Examining the local policies for adaptation, it is important to acknowledge that a full revision of the coherence and completeness of the adaptation actions was not performed, given that signatories only report on selected climate hazards, vulnerabilities, and actions. Hence, there might be instances where an action is missing its associated climate hazard or vulnerability, which does not invalidate the action but simply lacks an explicitly complete RVA relation. Additionally, various circumstances faced by local governments, such as lacking jurisdictional power to plan actions for certain vulnerable sectors or lacking resources to address major hazards, can make it challenging to assess the full coherence and completeness of these actions, often resulting in prioritization of solutions with limited gains. Despite these challenges, an analysis of the RVA reports submitted by signatories reveals promising progress in addressing high-risk hazards. Currently, 57% of these hazards have been effectively addressed through at least one adaptation action. Moreover, when examining the action plans of signatories that report at least one high-risk hazard, an encouraging 72% of them include matching actions to address the identified hazards.

Similarly, among all the high-vulnerable sectors reported in the RVA by signatories, 60% of them have already been addressed through at least one adaptation action. Furthermore, 85% of the signatories’ action plans that report at least one high-vulnerable sector include corresponding actions to mitigate the vulnerabilities associated with these sectors.

These findings highlight the progress made by signatories in addressing high-risk hazards and vulnerable sectors through adaptation actions. However, to ensure a more comprehensive understanding of the effectiveness of these actions, future research should focus on further evaluating the coherence and completeness, e.g., as in28, considering the inherent limitations imposed by the dataset.

The potential applications of the GCoM datasets span different research areas, ranging from multilevel governance of climate change to financing climate action, from energy and emissions to climate risks and vulnerabilities, including socio-economic aspects. The analysis of information about governance aspects, such as the presence of a regional authority (GCoM coordinator) or a city network (GCoM supporter) next to the local authority, or the decision of a signatory to join as individual or as group, can shed lights into the benefits that may stem from these approaches. The availability of emission inventory data calculated according to a common methodology for several years, for various cities, can help gain a better understanding of emission trends in cities. E.g., as it has been used in previous JRC-reports5,6, the datasets allow answering the question on how much GHG emissions have all cities in the GCoM reduced by year. Overall, these datasets contain information for assessing and estimating the impact of local policies for climate change mitigation, adaptation and energy poverty, addressing how much is actually under the control of the local governments inside a city, and what are other important factors besides those local policies, also affecting the GHG emissions inside a city.

Acknowledgements

M.G.B. is a consultant for JRC-European Commission. The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission.

Author contributions

M.G.B., G.M., C.F. and A.T. analysed the raw GCoM data, assessing the internal consistency of the action plans and monitoring reports, C.F., V.P., E.P. and F.M. conducted the GCoM comparison with the EDGAR grid-based CO2 emissions, and P.B., G.M. and M.C. supervised the study. All authors reviewed the manuscript.

Code availability

The R code used for the technical validation of the GCoM energy activity, emissions and mitigation policies, as well as its comparison with the EDGAR emissions database, is available in: https://github.com/camilofdlr/GCoM.

Competing interests

The authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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