The OECD Green Recovery Database: Examining the environmental implications of COVID-19 recovery policies

 Introduction and background

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.

 Scope and method of the OECD Green Recovery Database

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.

 Results

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

 Breakdown based on monetary value of measures

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.

 

Figure 1. Total funding allocated by environmental categorisation

Note: NGEU-30 = Next Generation EU, the EU recovery fund. The funding indicated here represents 30% of the total NGEU allocation, i.e the proportion that will be earmarked for climate-related investments (including the Recovery and Resilience Facility and ReactEU). The remaining 70% of the total has been marked as “indeterminate” and so does not feature in the analysis. Note that the NGEU funding is available to all EU27 countries, some of which are neither OECD members nor accession/key partners and so are otherwise not covered in the database.

Source: OECD Green Recovery Database

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.

 

Box 3. Caveats for interpreting the results of the database

There are a number of reasons why these results should be interpreted with caution. These caveats include the following:

• Only about 80% of measures have funding totals allocated, and those that do are not necessarily comparable (e.g. due to timeframe differences, inclusion of loan guarantees, etc);

• Measures vary in their form and status, ranging from broad early-stage announcements to very specific proposals with implementation plans. While this calls for caution when carrying out comparisons, it also highlights the scope to still influence those plans and proposals that are still at an early stage.

• There is a likely bias towards reporting of environmentally positive measures compared to negative measures both in terms of total numbers of measures (as the “green” measures are often more visible) and in terms of funding (because funding is reported for a higher proportion of positive measures (85%) than for negative measures (70%), meaning that negative measures contribute less to reported funding totals).

• The identification of which measures are likely to have clear positive and negative environmental effects is challenging and necessarily requires on an element of expert judgement (see Annex).

• Some measures may be part of pre-existing or accelerated policy initiatives, even though they have been announced and promoted as part of COVID-19 recovery packages;

• Given that a large number of measures are cross-cutting among economic sectors, some of which cannot at this point be easily split into component sectors, there is a natural bias towards categorisations in the “multiple or other” sector category.

• Potential overlaps between supranational measures and individual national policies, in particular for the EU. Care has been taken to avoid double-counting EU and national funding allocations where possible, but there is still likely to be some overlap in these numbers at this stage.

• There are differences in comprehensiveness of coverage between countries, with OECD members generally having more in-depth coverage than Key Partner countries. In addition, coverage of sub-national measures is variable across countries and is not intended to be comprehensive. A full sub-national study would be a useful additional exercise.

 Breakdown by sectors

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.

 

Figure 2. Funding totals per sector and environmental agencies

Note: “Multiple or other” category includes economy-wide or non-specific measures.

Source: OECD Green Recovery Database

 Assessment by environmental dimensions

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.

 

Figure 3. Proportion of total funding allocated to measures impacting different environmental dimensions

Source: OECD Green Recovery Database

 Regulatory changes

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).

 

Figure 4. Total number of regulatory changes with estimated environmental categories

Source: OECD Green Recovery Database

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.

 

Figure 5. Number of regulatory measures by sector

Source: OECD Green Recovery Database

 Policy insights

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.