The Climate Action Monitor 2023

Annex I. Data gaps, methodology and limitations

Chapter 1: How far are countries from achieving national and global mitigation objectives?

The availability of accurate, complete, and timely data is fundamental to support countries in developing and implementing their climate change policies, and critical for achieving the Paris Agreement long-term temperature goal. This information provides insights regarding the countries’ GHG emission trends and can help policy makers to monitor their performance.

However, despite considerable efforts, data on GHG emissions remains limited and insufficient. Official country level data is usually based on emission inventories reported to the UNFCCC. These inventory data are compiled using territory-based and production-based principles following the IPCC guidelines. The territory-based principle does not include emissions from international transport and production-based principle does not include emission from imports of goods to satisfy consumption demand. The approaches underestimate the true carbon footprint of an economy. In addition, data quality varies considerably across countries. Often inventories use a combination of the three tiers of the IPCC guidelines to compile data for a single sector resulting in considerable data quality differences across countries as well as within a country across sectors.1

Furthermore, countries can use varying types of emissions factors that have different degrees of precision, for example, industrial plant specific, IPCC default, country specific data, and models. Moreover some countries do not report annually. These are mainly non-OECD countries. Therefore, GHG emissions data and associated indicators are characterised by gaps, lack of timeliness and granularity as well as varying quality. While recognising these caveats, for the analysis presented above, IPAC has used official data when possible. However, in some cases, such as for aggregates, it was necessary to make estimations. When no other data was available, Climate Watch data (Climate Watch, 2023[6]) was used, particularly for global comparisons and to compare IPAC or OECD totals with global emissions.

The data sources and approach used in this publication are summarised below:

• GHG emissions data from national inventories is currently available for all OECD countries that report annually to UNFCCC for the period 1990-2021.

• Data for other OECD countries (formerly referred to as “non-Annex 1”) is obtained through the OECD GHG emissions questionnaire. However, the time coverage is not complete, for example, Colombia covers data up to 2018, Costa Rica up to 2017 and Mexico up to 2019. There are also gaps for Israel before 2002.

• For OECD partner countries many gaps remain, for example official emissions data for 2020 is not available on the UNFCCC GHG emission data interface. Major gaps are also present for large emitters, such as China and India. China has provided official data for only five years (1994, 2005, 2010, 2012 and 2014), while India has presented data only for four years (1994, 2000, 2010 and 2016). There are also important gaps for Peru (for the 1990-2010 period), Saudi Arabia (presenting only four years between 1990 and 2012), South Africa (for the 1990-2000 period), and Indonesia (for the 1990-2000 period).2

• In this report, when official data was not available, estimated data are used to compile country aggregates.

Annex Table I.1. GHG emissions data availability per year, country level

Countries	Official data
Annex I OECD countries	1990 to 2021
Chile	1990-2020
Colombia	1990-2018
Costa Rica	1990-2017
Israel	1996, 2000, 2002-2020
Korea	1990-2020
Mexico	1990-2019
OECD partner countries	Complete official data only for 2010
China (P.R. of)	1994, 2005, 2010, 2012, 2014
India	1994, 2000, 2010, 2016
Argentina	1990-2018
Peru	2008-2019
Saudi Arabia	1990, 2000, 2010, 2012
South Africa	1990, 1994, 2000-2017
Brazil	1990-2016
Bulgaria	1990-2021
Croatia	1990-2021
Indonesia	1990-1994, 2000-2014, 2019
Malta	1990-2021
Romania	1990-2021

Source: UNFCCC, GHG emissions inventory, BURs and (OECD, 2023[7])

Chapter 2: What are the trends in climate-related hazards and disasters?

The OECD set of indicators is based on historical observational data -collected and recorded- that goes back as far as 1979. This time-period is relatively short for analysing climate change events, nevertheless, the data, while limited, still shows the exposure of climate-related hazards to the population, croplands, forests, and urban areas.3 These 43 years of data illustrate that climate change impacts are already visible by analysing even a short period of historical data (for a full discussion see (Maes et al., 2022[20])). A limitation is that these indicators reflect what has happened, not what will happen (Box 6). Nevertheless, the data set can support countries to understand the evolution and potential impact of climate-related hazards to guide policy choices.

Box 6. Developing forward-looking indicators for climate-related hazards

Impacts from climate-related hazards are expected to increase in the future, as climate change is projected to increase both the frequency and intensity of climate-related hazards (IPCC, 2021[70]). Understanding these hazards helps make a stronger case for pursuing ambitious mitigation policies. It also supports both disaster risk management and adaptation policies as it is crucial to know which countries and regions are particularly prone to experience climate-related hazards, and how this is projected to evolve under different climate scenarios. Developing forward-looking indicators will therefore be essential to guide policy makers to project future impacts. For this reason, the OECD is building on past work to assess the future exposure of people and assets to the climate-related hazards.

In this upcoming OECD paper would use climate model output data from multi-model ensembles to develop a set of indicators that provides predictions of the impact of climate-related hazards up to 2100. This would include indicators of climate-related hazards and exposures for three hazard types (extreme temperature, drought and sea level change) and two exposure variables (cropland and population density). This paper is expected to be delivered in Q3/Q4 2023.

Existing information on disaster events and their related costs has limitations due to inconsistent reporting by national governments and in international databases, in addition to complexities and challenges associated to the collection of accurate and representative data. The loss databases are essential to assess policy and monitor progress, but it is hardly ever mandated by national or supra‑national legislations. There are several supra-national framework directives, but they remain vague when it comes to recording losses from disasters, although their implementation would hugely benefit from the availability of such information.

No single database has complete coverage of losses from disaster events, underscoring the importance of strengthening common frameworks for disaster and loss accounting databases. For example, the United Nations Disaster Risk Reduction (UNDRR) DesInventar-Sendai database provides a common platform for countries to collect loss data on a national level; however, only 10 OECD and OECD partner countries use this database to date (UNDRR, n.d.[71]).4 The quantification of economic losses in particular faces challenges in harmonisation. Although definitions exist for calculating basic measurements of economic losses, such as affected buildings, agricultural assets and civil infrastructure, this is not consistently done for all disaster events across countries.5 The threshold employed in a database to determine whether an extreme weather event is recorded is also significant and can generate different results and comparability issues.6

Finally, methods for calculating losses exist in the context of estimating damages in the immediate aftermath of a disaster to anticipate the level of support required by the international community such as the Post-Disaster Needs Assessment (PDNA). To mainstream and standardise the PDNA method, the United Nations, the World Bank and the European Commission have jointly developed methodological guidelines. Physical damages and economic losses are evaluated using the Damage and Loss Assessment (DALA) and human recovery needs are investigated through the Human Recovery Needs Assessment (HRNA) and a Recovery Framework. However, there is no central database to collect the results of PDNAs that had been conducted, except for the countries covered in DesInventar.

Chapter 3: How has countries’ climate action to meet emission targets progressed?

Tracking and monitoring countries’ climate mitigation policies is essential to assess progress towards targets and commitments. However, extensive, consistent, and internationally harmonised data on climate actions and policies do not exist to date.

The Climate Actions and Policies Measurement Framework (CAPMF) aims to fill this gap. It is an internationally harmonised climate policy database developed by the OECD, based on a structured policy typology that tracks a common set of policies with common definitions and harmonised policy attributes on an annual basis. The CAPMF is complementary to other international policy tracking tools such as the reporting frameworks to the UNFCCC.

The CAPMF tracks 56 climate actions and policies, which cover 75% of policies listed in the 2022 IPCC report, from 1990-2022 for 50 countries and the European Union. These countries are jointly responsible for over 63% of global GHG emissions. For each policy, the CAPMF measures policy stringency, defined as the degree to which policies incentivise emissions reductions. The CAPMF comprises climate-positive instruments (e.g. carbon taxes) as well as reform of climate-negative measures (e.g. reform of fossil fuel subsidies). The CAPMF also includes some climate-relevant policies such as air pollution standards, i.e. policies whose primary intent is not mitigation, but which have a material effect on emissions. While the focus of the CAPMF is on national climate action, it still includes key sub-national policies such as sub‑national emissions trading schemes and renewable portfolio standards.

For The Climate Action Monitor 2023, missing policy data in 2022 was substituted by the last observed data in the last five years. While it cannot be ruled out that the conclusions change once missing 2022 data becomes available, it seems likely that this data would reinforce the core messages of Chapter 3. This is because missing data predominantly concerns fossil fuel subsidies, for which stringency levels are expected to have decreased in 2022. In addition, other variables missing in 2022 do either change only very rarely (e.g. air pollution standards) or have approached already the highest stringency levels in most countries (e.g. financing mechanisms, energy efficiency mandates).

The primary focus of the CAPMF is to monitor the evolution and stringency of mitigation policies over time (1990 to 2022) collecting a broad range of harmonised data that is internationally comparable and suitable for a broad based quantitative and qualitative analysis. However, the CAPMF has some limitations and, hence, should be interpreted carefully (Nachtigall et al., 2022[45]).

1. The country coverage of the CAPMF is not global, it covers 50 countries plus the EU, mostly developed or emerging economies, which can help illustrate policy trends and key mitigation efforts of major emitters. It covers all countries covered by IPAC except for the United States that has not yet validated its data.

2. Despite the broad coverage of policies, the CAPMF does not capture all relevant policies due to data availability constraints. Policies included in the CAPMF may, thus, not be fully representative of mitigation approaches of some countries. Important policy gaps that are planned to be filled in the coming years include policies in the agricultural, forestry and waste sector as well as policies related to climate finance.

3. The results of the CAPMF should be interpreted in an informative, not in a normative way. An increase in policy adoption or policy stringency does not necessarily imply higher effectiveness of reducing GHG emissions, although previous work found some positive associations (Nachtigall et al., 2022[45]). The effects of increased policy adoption and policy stringency depend on factors such as emissions coverage and economic costs and likely have different impacts across countries.

Notes

← 1.  IPCC recommends using a three-tier approach to collect and organize emissions inventories. Tier 1 is less demanding and less detailed while Tier 3 is the most detailed process.

← 2.  Additional data may be available in other sources such as national statistics websites. IPAC is exploring different alternatives to fill data gaps including conducting desk research to examine different sources and developing statistical methods.

← 3.  The development of this set of indicators is informed by standards from the World Meteorological Organization, the US National Ocean and Atmospheric Administration, latest research and standard developed by well-recognised organisations and builds on international frameworks for assessing climate‑related hazards. Notwithstanding, over- or under-estimations of the actual exposure to climate-related hazards may occur and further details on these limitations can be found in the OECD Working Paper (Maes et al., 2022[20]).

← 4.  Several databases gather secondary data on disaster occurrence and their human and economic cost such as the EM-DAT database from the Centre for Research on the Epidemiology of Disasters’ Emergency, hazard-specific databases (e.g. the Dartmouth Flood Observatory), actuaries and re-insurers (e.g. MunichRe’s Natcat-SERVICE and SwissRe’s Sigma databases). A weakness of such databases is the heterogeneous nature of the secondary data it relies upon, it often has problems with comparability across countries.

← 5.  For example, EM-DAT collects data on costs that accrue directly to assets, but it does not account for costs that accrue from business disruption in areas directly affected by the disaster, while this is included in SwissRe’s Sigma database. Beyond this, more intangible losses such as the impact on health or the environment, as well as cultural heritage loss and loss of reputation, are hardly ever accounted for due to difficulties in monetisation.

← 6.  For example, the WMO World Atlas report compares human loss figures from nationally reported data in two databases (DesInventar and EM-DAT) with different thresholds across Colombia, Ecuador, Indonesia and Niger (WMO, 2021[137]). It concluded that different threshold levels between the two databases did not affect the reporting of intensive (high-intensity, low-frequency) disasters (WMO, 2021[137]). However, the two databases had varying thresholds for extensive disasters (low-intensity, high‑frequency), responsible for the majority of economic losses from disasters (68.5% between 2005‑2017) and therefore extensive disasters were sometimes included in one database but not the other (UNDRR, 2019[136]).