As 2021 advances, the COVID-19 pandemic continues to create havoc around the world. While some countries have managed to largely control the spread of the virus to date, many more are facing successive waves of COVID-19 outbreaks, with significant impacts on their economies and societies. Nevertheless, the rollout of vaccines has begun, albeit unevenly around the world, and attention is increasingly turning to shaping the economic recovery from the crisis.

During the early stages of the pandemic, many countries were quick to publicly commit to a “green recovery” through the national and sub-national stimulus packages that they were preparing to address the social and economic impacts of the crisis. A wide range of other stakeholders also called on governments to implement a green recovery, from multilateral institutions to the private sector and civil society. The OECD has been a key voice advocating for a strong sustainable inclusive and resilient recovery, including through the Ministerial Council Meeting in 2020 and a number of policy briefs beginning in April 2020 (OECD, 2020[1]) (OECD, 2020[2]).

The first spending commitments consisted mostly of emergency rescue funding, to shore up health systems, avoid firm failures and minimise widespread job losses. As 2020 progressed, this initial rescue funding started to evolve into longer-term recovery measures in an increasing number of countries. Exploring the likely environmental implications of these more forward-looking stimulus measures will be essential to understand whether the very significant sums being allocated will in reality deliver on the promise of a green recovery, thereby setting the stage for countries to “build back better” after the crisis.

To help inform and support this process, the OECD has developed a database that identifies and tracks the environmental dimensions of announced recovery measures. The aim of the OECD Green Recovery Database is to provide governments and the general public with a clear overview of announced recovery measures that are likely to have significant environmental implications, whether positively or negatively. The database tracks measures announced by OECD Member countries and beyond (also including an OECD Accession country and Key Partner countries – 43 countries in total plus the EU).1 It was first compiled in the summer of 2020 and preliminary findings were fed into a policy brief prepared for an OECD Ministerial Roundtable on Green Recovery on 14 September 2020 (OECD, 2020[1]). The data was updated in late 2020 and provided to governments for review and corrections in early 2021, before being cleared by Member countries for release.2

The database complements other OECD work related to the environmental dimensions of recovery from COVID-19. In April 2020, the OECD published an Environment Working Paper compiling lessons from past green stimulus measures (Agrawala, Dussaux and Monti, 2020[2]) focusing on the environmental policy lessons that emerged from the 2008 global financial crisis (GFC). It highlighted the lack of data and ex-post analysis of the impacts and effectiveness of green policy measures introduced in the aftermath of the GFC. Additionally, the OECD-IEA Secretariat to the Climate Change Expert Group (CCXG) produced a report on the implications of countries’ recovery measures for the achievement of the Paris Agreement’s mitigation goal (Buckle et al., 2020[2]). The report provides a framework for assessing recovery measures in terms of their impact on climate mitigation and broader well-being outcomes, through three stylised recovery pathways: “rebound” (bounce back to an emissions-intensive economy), “decoupling” (decoupling of CO2 emissions from GDP growth) and “wider well-being” (focus on wider well-being benefits in addition to reduced emissions).

This brief provides an overview of the OECD Green Recovery Database, presents the key findings from the data, and draws some conclusions for policy.3 The Annex contains a discussion of methods and approaches for categorising environmental impacts of policy measures related to COVID-19 economic recovery.

The database focuses on measures related to COVID-19 recovery efforts that are likely to have a clear positive or negative environmental impact across one or more environmental dimensions. This means that the data cover not just measures targeted at environmental improvements, but also capture more general policy measures that may have negative environmental consequences. The database does not, however, aim to comprehensively cover all COVID-19 related spending measures that do not have clear environmental implications.

Several other tracking initiatives have emerged since mid-2020, each taking a slightly different approach to documenting recovery measures (see Box 1, and further discussion in the Annex). The OECD Green Recovery Database is complementary to these efforts, none of which has the same scope (for example in terms of countries) nor method (for example in how environmental impacts are classified and tagged).

The OECD Green Recovery Database currently contains around 680 measures with environmental relevance, spread over 43 countries and the EU (a further 170 measures are classified as indeterminate for environmental impact and are not included in the analysis). The mean number of environmentally relevant measures captured per country is 15, but there is a high level of heterogeneity across countries, ranging from one to nearly 70 measures (with a median of 12). While some prominent sub-national measures are included, coverage focuses on national-level measures, which may skew results for some countries.

When considering environmental impacts, the database covers a range of environmental dimensions, beyond the focus on energy and climate that has been at the heart of several other recovery-tracking exercises. In addition to implications for climate change mitigation, the database also seeks to capture measures with impacts on pollution (air, plastics), water, biodiversity, waste management and climate change adaptation.

The database focusses on policies related to economic recovery from the COVID-19 crisis. This includes some emergency measures taken during the immediate economic rescue phase initiated soon after the pandemic struck, where they have clear environmental implications (for example, unconditional bail-outs of environmentally damaging firms). However, the bulk of the measures covered form part of stimulus packages intended to trigger the economic recovery from the COVID-19-induced recession. In some cases, it can be difficult to determine whether a particular policy, fund or investment is exclusively related to COVID-19 recovery, or whether it existed prior to the crisis and has been expanded or accelerated as part of efforts related to COVID-19 recovery. Where identified, such measures have been included given that they represent policy that has been heavily influenced by the COVID-19 crisis.

In the database, policy measures are described according to a number of different characteristics (Box 2). This includes an estimation of the likely environmental impacts of measures. Categorising the environmental impacts of policy measures is challenging for a number of reasons. An outline of these challenges and an overview of the approach taken for this exercise are laid out in the Annex to this document. In summary, the categorisation used here draws on existing and emerging classification systems for environmental effects, such as the EU Taxonomy for Sustainable Activities, and OECD assessments of those methods already published (e.g. (OECD, 2020[4])). Nevertheless, the categorisation is a high-level assessment and is intended to be used for aggregate analysis. It is not intended as a definitive judgement or assessment of the likely impacts of individual policy measures.

This section presents a brief overview of the aggregate findings from the database, based on data gathered up to 5 March 2021.

Total funding allocated to country-specific measures with a likely positive environmental impact is estimated at around USD 336 billion (Figure 1). By contrast, measures marked as having negative or “mixed” environmental impacts total around USD 334 billion. This result suggests that funding for environmentally positive measures, while impressive, is nonetheless almost matched by funding allocated to negative and mixed measures. These findings are broadly in line with other tracking efforts published by other organisations, albeit with different focus and scope. Nevertheless the results should be interpreted with caution (see Box 3).

It is also important to see this result in the context of wider COVID-19 recovery spending. The spending tagged as environmentally positive accounts for only around 17% of recovery spending, using an estimate of the total drawn from data from (O’Callaghan and Murdock, 2021[5]). Mixed and negative measures together account for another 17% of the total. Nearly two thirds of the total is therefore not so far categorised as directly impacting on the environment positively or negatively. This does not mean that such measures are environmentally benign. On the contrary, the small percentage of measures tagged as “green” implies that total stimulus packages overall still lean heavily towards investments in business as usual type activities, rather than the transformational investments required. The weight of green measures is even smaller if taken as a percentage of total COVID-19 spending: environmentally positive measures account for barely more than 2% of all rescue and recovery funding combined. However, much of the emergency rescue funding was short-term, designed to shore up health systems and prevent job losses and firm failures during the darkest days of the health and economic crises, so it is not unexpected that such measures lacked an environment focus.

An important additional factor is EU-wide funding announced through the Next Generation EU programme. This is shown separately given its magnitude relative to the total of other measures, and is shown as a hatched bar because there is a potential risk for double-counting in the database with national measures already announced. As 30% of both the Recovery and Resilience Facility and the React EU facility are earmarked for climate-related investments, 30% of the total has been classed as positive in the hatched green bar in Figure 1 (USD 233 billion). The remaining 70% of the overall Next Generation EU envelope has been classified as indeterminate and so is not shown. Internationally, further recovery packages and plans are being announced all the time – such as the American Jobs Plan proposed by President Biden in March 2021, not included in these numbers – and these will continue to shape the balance of funding relating to environmental impacts.

The aggregation of monetary value or volume of funding allocated to different recovery measures provides a key indication of the overall environmental impact of recovery programmes, but it is not the whole story. Not all recovery measures have been announced with indications of the total funding allocated, and where funding has been announced, the data vary considerably in availability and quality. For some measures, precise numbers have been made available (for example a total funding envelope available for grants or subsidies for a particular sector). For others, numbers are harder to discern, such as tax breaks without ceilings or budget estimation announced, or loan guarantees where it is not possible to evaluate in advance how much of the potential guarantee pot will be exercised. Some announcements are also not clear about the timeframe of the funding window (e.g. over 1, 3 or 5 years). Additionally, some types of measure do not have funding allocation at all, but are nonetheless important, such as regulatory changes. Overall, around 80% of measures in the database have a monetary value attached, excluding regulatory changes.

Another reason for caution is that the database likely under-reports the level of funding allocated towards measures with potentially harmful environmental outcomes, for the reasons explained in Box 3. Also, even where large sums have been allocated towards environmentally positive measures, this does not necessarily mean that the overall packages will be aligned with longer-term climate change plans, such as targets for net-zero emissions, or other important environmental objectives. A more detailed analysis comparing likely emissions implications of each measure relative to net-zero scenarios would be necessary to draw conclusions in that regard.

The most common type of measure recorded is grant or loan, including interest-free loans, making up around 38% of the 680 measures that have positive, mixed or negative environmental impacts. The next most common measure is tax reduction or other subsidies (16% of the total), with regulatory changes next at around 11%. There are few measures dedicated to skills and training, with only 11 such measures listed as having environmental impacts (less than 2% of the total). Skills-related policies may however be present but hidden within other measure types.

More than 60% of the measures are sector-specific, with a further 24% listed as being economy wide, and 13% specific to cities or sub-national regions (reflective of the main focus of the database being on national-level measures). Of sectoral policies, by far the most common sectors targeted are energy and surface transport (comprising around 20% and 16% of the total respectively, with no other individual sector exceeding 5% of measures).

Table 1 focuses on only those measures classified as having positive environmental impacts, displaying a “heatmap” of the total number of measures by sector and by type of measure. While this analysis provides a useful indication of the distribution of different types of measures and their sectoral targets, it does not account for the weight or importance of different measures. While energy and ground transport are the most prevalent individual sectors, the “other or multiple” category is the largest as it contains all economy-wide measures as well as those covering more than one sector.

Source: OECD Green Recovery Database

Examining funding totals (again, where available) by sector highlights that both energy and surface transport – the largest individual sectors by both number of measures and funding – show greater allocation to environmentally positive measures than to mixed or negative measures (Figure 2). These sectors are often in the spotlight because they account for a high proportion of GHG emissions in many countries. In addition, energy and surface transport sectors are often good candidates to have “shovel-ready” projects in the pipeline – for example renewable electricity projects and electric vehicle infrastructure – that can be rolled out or accelerated as a quick response to the economic crisis. Again, however, the proportion of environmentally positive measures does not imply that the measures announced – and overall packages – are sufficient to drive the needed transformation towards long-term objectives on climate change and other environmental objectives.

On the other hand, measures for key sectors like aviation and industry show a clear weighting towards mixed and negative categories. However, the important “multiple or other” category, which captures economy-wide measures as well as those covering multiple or unspecified sectors, encouragingly does lean towards positive measures.

The database also aims to capture which environmental dimensions are likely to be most affected by each measure; for example if a measure targets positive improvements in a particular dimension (such as reducing GHG emissions, i.e. climate mitigation) or if a measure with expected negative environmental impacts is likely to particularly affect one or more environmental factors.

Climate change mitigation is by far the most common environmental dimension impacted by the recovery measures tracked (Figure 3). Nearly 90% of funding allocated is for measures tagged as having clear implications for GHG emissions, roughly evenly split between measures that reduce emissions and those likely to increase emissions (though the proportion is slightly lower when counting the number of measures involved, at around 75% of total measures). The next most common dimension impacted is air pollution (with around a third of total funding, again evenly split), and also accounting for around a third of the number of measures counted. The strong number for air pollution is largely because of the synergy with climate measures, meaning that many measures are categorised as being positive (or negative) for both climate and air pollution simultaneously.

In contrast, other environmental dimensions feature much less strongly. For example, measures impacting biodiversity account for less than 10% of the funding allocated, despite biodiversity being regularly mentioned in government priorities. Within that 10%, less than half is for measures judged to be actively tackling biodiversity loss. In terms of numbers of measures, a slightly higher proportion are tagged for biodiversity (around 15%), suggesting that on average biodiversity measures are smaller than other environmentally positive measures in monetary terms, or that funding is not reported. Water only accounts for around 8% of measures in both funding and measures (though it is possible that water-based measures are hidden in other broader measures). Other important dimensions such as waste and recycling, and climate change adaptation, have so far also received a very small proportion of total funding and are targeted by a small number of measures.

Regulatory changes, by their nature, are unlikely to have funding or a monetary value attached to them. They can nevertheless be very important in determining the overall impacts and effectiveness of stimulus packages, whether through weakened regulations changing behaviour and investment patterns, or new regulations strengthening the investment case for clean technologies.

Regulatory changes have been analhsed separately, given that they are essentially inivisible in the analysis of funding totals above. Regulatory changes make up around 73 of the 677 measures categorised as having positive, mixed or negative environmental impacts. Of those, around 45% represent regulatory changes that are assessed to have a positive environmental impact, and around 55% have been categorised as negative or mixed (Figure 4).

The regulatory changes recorded also show an interesting distribution across sectors (Figure 5). Energy and transport (including surface, aviation and maritime) show a fairly even distribution of regulatory changes with both positive and mixed/negative environmental outcomes, but with a small majority of positive measures. However industry shows a very strong balance in favour of regulatory measures that negatively impact the environment, as does the multiple/other category capturing economy-wide or unspecified sectoral focus.

The aggregate data from the Green Recovery Database can be used to provide important insights on the direction, magnitude and likely “green-ness” of the recovery and stimulus measures that countries have put in place to date. Bearing in mind the caveats that are laid out in Box 1 and the need for further detailed analysis, there are nevertheless a number of observations for policyu that are worth reflecting on at this stage.

  • The importance of transparency. Increasing the transparency around recovery and stimulus plans is key to enhancing the ability of governments to identify and monitor the expected economic and environmental impacts of the measures. To the extent that governments are still in the process of developing new recovery measures as the pandemic continues, and refining already existing recovery measures, such transparency can help to orient current and future policy decisions towards more sustainable outcomes. The increased use of “green budgeting” in OECD and other countries can help to enhance such transparency around pubic expenditures, increasingly linking recovery measures to crucial yearly budgeting exercises.

  • Green recovery measures are a relatively small component of overall stimulus packages. While the USD 336 billion of environmentally positive recovery measures (or USD 575 billion including 30% of the Next Generation EU fund) is clearly a significant investment in driving a more sustainable recovery, the amount only accounts for around 17% of total COVID-19 recovery spending. This relatively small percentage highlights that recovery measures thus far are unlikely to have the transformational effects needed to address environmental crises while building back the economy.

  • Significant funds are still allocated to measures with likely environmentally negative or mixed impacts. The current analysis points to around USD 334 billion targeted towards measures categorised as negative or mixed environmental impacts – nearly the same as that allocated to environmetnall positive measures, and this is expected to be an underestimate. Renewed attention is required to ensure that all recovery measures are focused on “building back better”; there is still scope for a better matching of the green recovery rhetoric with the reality of expenditure plans (OECD, 2020[6]).

  • Ensuring alignment across policies and sectors, and over time. There is a clear need to ensure alignment between the short-term objectives of boosting income, jobs and growth with long-term environmental commitments (for example with net-zero emissions goals and Nationally Determined Contributions (NDCs) submitted by countries under the Paris Agreement) and enhancing resilience. While further analysis is required, these results raise concerns that such alignment is missing and that measures are largely having the effect of locking in existing industrial structures or going back to business-as-usual. Similarly, the uneven spread of measures across sectors points to potential missed opportunities to drive sustainability and the needed transformation in key sectors such as agriculture, waste management and forestry.

  • The importance of ex-ante evaluation. The results highlight the importance of ex-ante evaluation of measures in terms of their expected impacts, in order to help governments understand the likely impacts of measures as well as where policy misalignments may exist. This evaluation needs to go beyond expectations for jobs and economic growth to also include not only environmental dimensions, but also a consideration of broader social well-being objectives that are an important basis for ensuring a lasting and sustainable recovery.

  • Focusing on the just transition. Stimulus measures often have a central objective of creating jobs in the near term to help ease unemployment caused by the economic crisis. Recovery measures aimed at achieving environmental objectives, including tackling climate change, also need to have a strong focus on creating lasting, quality jobs. This requires measures to support, retrain and relocate workers in industries likely to be negatively affected, to ensure a “just transition”. The relatively few measures focused on skills training and on innovation point to an opportunity to direct more attention to measures that can drive transformation and adjustment.

Assessing the environmental implications of recovery-related policies and measures is challenging and necessarily imprecise, especially at the level of aggregation used in the OECD Green Recovery Database. Several factors complicate the exercise of categorising likely environmental implications:

  • Measures that are beneficial for one environmental dimension may be harmful for other dimensions, either immediately or over time. This can become increasingly complex as more environmental dimensions are considered (e.g. beyond climate and air pollution issues to consider also water, biodiversity etc.).

  • Initial information available on measures (such as title and descriptions) may be insufficient to gauge either the full sectoral scope of the measure (which sectors or infrastructure types will be affected) or the environmental implications across different dimensions (positive or negative or mixed).

  • Even where a measure has clearly defined sectoral scope, such as subsidies or grants for a particular energy generation technology, different interpretations can exist as to how environmentally favourable a particular technology is across different environmental dimensions.

  • There is necessarily an element of counterfactual required when assessing the environmental impacts of a particular measure: estimating what would occur in the absence of the measure to find out whether the measure is more or less impactful on the environmental dimensions considered. Carrying out such analysis for every relevant measure would be prohibitively time-consuming.

For this exercise, each measure has been assessed at a high level and tagged as having positive, mixed, negative or indeterminate environmental implications. These categories are summarised in Table 2.

The classification of each measure has been carried out on a bottom-up line-by-line basis. The approach has been informed by existing detailed environmental classification methods, such as those described in the next section, and draws on the analysis of such methods carried out under previous work (e.g. (OECD, 2020[3])). However, in many case recovery-related measures are broad and not sufficiently specific, for example, to use the precise activity-level technology-based classification used in sustainable finance taxonomies such as that in the EU. Nonetheless, principles from those taxonomy approaches have been used, such as for example the cross-examination of different environmental dimensions introduced by the “do no significant harm” principle of the EU sustainable finance taxonomy. In this database, if another environmentally positive measure appears likely to negatively impact another environmental dimension, it is categorised as “Mixed”. The same category is used for broad measures that may have a wide-range of environmental impacts, such as a broad infrastructure programme, as described in Table 2.

While COVID-19 response measures have some notable characteristics, the general challenge of categorising the environmental impacts of policies, projects and investments is not new. Several existing exercises have informed the tagging carried out in this database, and some these are briefly summarised here. Nevertheless there is no globally agreed definition of “what is environmentally sustainable”, as the question can be asked at various levels; for example, recent development of sustainable finance taxonomies are at the activity level; green bonds are specific financial products, and green budgeting relates to public budgets.

A key area of development in recent years has been taxonomies aiming to influence sustainable finance decisions, by providing clear guidance over which projects or existing activities can be labelled as “sustainable”. The OECD has carried out detailed analysis of progress and prospects for different taxonomy approaches around the world (OECD, 2020[4]). A prominent example is the EU taxonomy of sustainable economic activities, currently under development, which aims at providing clear positive lists and criteria for what can be considered a sustainable economic activity in the EU. Once fully developed, the taxonomy will cover six environmental objectives (climate change mitigation and adaptation, water and marine resources, circular economy, pollution prevention and ecosystem protection). Recognising that no individual economic activity is independent of the wider system in which it operates, activities need to demonstrate that, as well as making a substantial contribution towards one of the objectives, they also need to demonstrate no significant harm to any of the other five objectives.

A major growth area in green finance in recent years has been through specialised debt instruments such as green bonds. To improve standardisation, a number of market initiatives have developed standards and guidelines for determining what projects and use-of-proceeds can qualify for a bond to be considered green. For example, the Climate Bonds Standard and Certification Scheme has been developed by the Climate Bonds Initiative and used internationally.

Different approaches to taxonomies and green bond standards are being explored around the world. For example, at the national level, the People’s Bank of China issued the first iteration of its Green Bond Endorsed Project Catalogue in 2015. In Japan, the Ministry of the Environment launched the nation’s green bond guidelines in 2017. A comparison of these parallel approaches to providing clear definitions for sustainability was recently carried out as the basis for OECD empirical analysis on institutional investment (OECD, 2020[3]).

Another area of recent developments regarding definitions of sustainable activities is in the area of public budgeting. Some governments have increasingly committed to improve the “greenness” or environmental impacts of their annual public budgets. The OECD has been supporting countries to deliver on that ambition through the Paris Collaborative on Green Budgeting, a coordinating platform for data and best-practice sharing, and facilitating alignment between national and international budgeting exercises (OECD, 2020[5]). A recent notable example of green budgeting in practice was France’s 2021 budget announced in September 2020. The draft budget was accompanied by a detailed assessment of the environmental implications of the spending measures identified, across all six of the environmental objectives cited by the EU Sustainable Finance Taxonomy (Republique Francaise, 2020[6]). This analysis aimed to classify where spending measures are positive, negative or mixed, in a similar approach to that employed for this database.

France has also carried out assessment of the environmental implications of its announced recovery package, France Relance, including an initial assessment at the time of the launch of the package in September. More recently, the government’s independent advisory council, Haut Conseil pour le Climat, published a detailed analysis in late 2020 (Haut Conseil pour le Climat, 2020[7]). Focusing on the climate dimensions, that analysis seeks to evaluate measures not only against whether they will reduce emissions relative to the status quo, but also to assess to what extent they are aligned with a trajectory towards net-zero emissions by 2050, in line with the country’s climate change commitment.

A the international level, the OECD-led Research Collaborative on Tracking Finance for Climate Action conducts work to test data and methods for providing policymakers with a practical approach to analysing economy-wide investments and financing with respect to national or international mitigation objectives and reference points. Such focus is relevant in the context of recovery from the COVID-19 crisis, and complements the numerous initiatives relating to climate alignment that take financial assets as a starting point to measuring climate alignment. Pilot studies conducted to date focused on the manufacturing industries in Norway, the transport sector in Latvia, and the buildings sector in the UK. Next steps underway include further sectoral pilots at international level, the design of a framework to guide policy makers, or the development of potential more timely and less resource intensive indicators.

Other recovery tracking initiatives

Finally, several non-government organisations and academic institutions have developed various tools to track and evaluate stimulus measures. Each has its own focus and approach, bringing unique insights, and none duplicates the particular added value of the OECD Green Recovery Database. The “Greenness of Stimulus Index” developed by Vivid Economics, supported by the Finance for Biodiversity Initiative, has developed a methodology to provide a single index score per country, rating the “greenness” of the overall stimulus package (Vivid Economics, 2020[8]). The calculation takes into account both the volume of stimulus funding flowing into environmentally relevant sectors, combined with a factor assessing whether the measures themselves are more or less impactful on the environment relative to a set of generic archetype stimulus policy measures. The version of the index released in late 2020 finds that while around USD 4 trillion of stimulus funding has been targeted at environmentally important sectors (energy, transport, industry, agriculture, waste), in most countries surveyed the index shows a net negative impact on the environment.

Another important tracking initiative is the Energy Policy Tracker, launched by a consortium of NGOs and universities (Energy Policy Tracker, 2020[10]). The tracker aims to provide a comprehensive view of energy policy developments in covered countries (including, and in some cases beyond, COVID-19 recovery measures), and classifies the measures as to whether they relate to clean or fossil energy, and whether they are conditional or not on environmental considerations. While the classification into clean and fossil energy avoids a discussion about what qualifies as environmentally positive or sustainable, it nevertheless requires a clear definition of “clean”. In some cases, the analysis introduces an “other” category for cases where categorisation is not clear. The April 2021 version of the tracker reports that 41% of energy stimulus funding is targeted towards fossil fuels (for 30 major economies) totalling USD 313 billion.

In March 2021, the Global Recovery Observatory was launched.4 This is a collaboration led by Oxford University and with the support of the Green Fiscal Policy Network, including UNEP and the IMF. The Observatory seeks to comprehensively track all COVID-19-related spending, not just those with environmental implications, across (currently) around 50 countries. Measures are assessed not only for environmental impact (covering greenhouse gas emissions, air pollution, natural capital) but also social impact (wealth inequality, quality of life, rural livelihood) and economic impact (multiplier, speed of implementation). To do this, measures are first mapped to 40 exhaustive and mutually exclusive archetypes, as well as 158 sub-archetypes (O’Callaghan and Murdock, 2021[5]) .

Also in March 2021, the Green Recovery Tracker was launched by the NGO E3G, together with the Wuppertal Institute. This tracker focuses specifically on assessing recovery plans in certain EU Countries (E3G and Wuppertal Institute, 2021[11]). The tracker main assesses implications for greenhouse gas emissions, with a categorisation of measures from “very positive” to “very negative”.

References

[3] Agrawala, S., D. Dussaux and N. Monti (2020), “What policies for greening the crisis response and economic recovery?: Lessons learned from past green stimulus measures and implications for the COVID-19 crisis”, OECD Environment Working Papers, No. 164, OECD Publishing, Paris, https://dx.doi.org/10.1787/c50f186f-en.

[4] Buckle, S. et al. (2020), “Addressing the COVID-19 and climate crises: Potential economic recovery pathways and their implications for climate change mitigation, NDCs and broader socio-economic goals”, OECD/IEA Climate Change Expert Group Papers, No. 2020/04, OECD Publishing, Paris, https://dx.doi.org/10.1787/50abd39c-en.

[13] E3G and Wuppertal Institute (2021), Green Recovery Tracker, https://experience.arcgis.com/experience/f2700c9b597a4aababa4c80e732c6c5c/page/page_13/?views=view_16 (accessed on 19 January 2021).

[12] Energy Policy Tracker (2020), Energy Policy Tracker - Track funds for energy in recovery packages, https://www.energypolicytracker.org/ (accessed on 19 January 2021).

[10] Haut Conseil pour le Climat (2020), FRANCE RELANCE : QUELLE CONTRIBUTION À LA TRANSITION BASSCARBONE.

[6] O’Callaghan, B. and E. Murdock (2021), Are We Building Back Better? Evidence from 2020 and Pathways for Inclusive Green Recovery Spending - Oxford University Economic Recovery Project, United Nations Environment Programme, https://recovery.smithschool.ox.ac.uk/are-we-building-back-better-evidence-from-2020-and-pathways-for-inclusive-green-recovery-spending/ (accessed on 12 April 2021).

[1] OECD (2020), Building Back Better: A Sustainable, Resilient Recovery after Covid-19 - OECD, https://read.oecd-ilibrary.org/view/?ref=133_133639-s08q2ridhf&title=Building-back-better-_A-sustainable-resilient-recovery-after-Covid-19 (accessed on 13 April 2021).

[5] OECD (2020), Developing Sustainable Finance Definitions and Taxonomies, Green Finance and Investment, OECD Publishing, Paris, https://dx.doi.org/10.1787/134a2dbe-en.

[7] OECD (2020), Green Infrastructure in the Decade for Delivery: Assessing Institutional Investment, Green Finance and Investment, OECD Publishing, Paris, https://dx.doi.org/10.1787/f51f9256-en.

[2] OECD (2020), Making the green recovery work for jobs, income and growth, http://www.oecd.org/coronavirus/policy-responses/making-the-green-recovery-work-for-jobs-income-and-growth-a505f3e7/ (accessed on 19 January 2021).

[8] OECD (2020), OECD Green Budgeting Framework OECD Green Budgeting Framework Paris Collaborative on Green Budgeting, http://www.oecd.org/environment/green-budgeting/ (accessed on 19 January 2021).

[9] Republique Francaise (2020), Rapport sur l’impact environnemental du budget de l’État, https://www.budget.gouv.fr/files/uploads/extract/2021/PLF_2021/rapport_IEE.PDF (accessed on 19 January 2021).

[11] Vivid Economics (2020), Greenness of Stimulus Index.

Notes

← 1. In addition to the 37 OECD members, other countries are Costa Rica (Accession) and Brazil, China, India, Indonesia and South Africa (key partners).

← 2. The measures included in the database for South Africa have been independently compiled by the OECD Secretariat and have not been endorsed by the Government of South Africa.

← 3. This brief has been developed from the cover note presented to the OECD Environmental Policy Committee in February 2021 (ENV/EPOC(2021)5)

Disclaimer

This paper is published under the responsibility of the Secretary-General of the OECD. The opinions expressed and the arguments employed herein do not necessarily reflect the official views of OECD member countries.

This document, as well as any data and map included herein, are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area.

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