Firms’ green governance
While greenhouse gas emissions in the EBRD regions have fallen since the 1990s, there remains ample scope to make firms’ production processes more energy efficient. The quality of firms’ green management – the way they address environmental issues and monitor energy usage and pollution – varies widely both between and within countries. In the EBRD regions and comparator economies, there is a lack of green leaders and the majority of firms continue to perform poorly in terms of green credentials. Foreign firms, exporters and listed companies generally perform best in this area. Financing constraints can hinder green investment, limiting firms’ ability to reduce emissions. However, for many firms it is not insufficient funding that prevents investment in this area – it is the low priority that managers assign to such investment.
Introduction
The EBRD regions have seen a substantial reduction in carbon dioxide (CO2) emissions from energy usage in the period since 1990 – the baseline year for the emission cuts agreed in the Kyoto Protocol. However, this reduction partly reflects the collapse in output at the beginning of the transition from central planning to market economies. What is more, since the early 2000s emissions have started to rise again. Many countries in the EBRD regions are still among the world’s most carbon-intensive economies.
Green management
Measuring green management practices
Nowadays, the ability to handle environmental, social and governance (ESG) issues in a proactive manner is part and parcel of effective firm management. However, information on firms’ ESG practices is often only available for listed companies, particularly when it comes to the quality of green management. In the EBRD regions, relatively few firms are listed, with many stock markets remaining underdeveloped. Consequently, few firms disclose ESG information. To help fill that gap, the most recent round of Enterprise Surveys carried out by the EBRD, the EIB and the World Bank Group (which was still in the process of being conducted as this Transition Report went to print) included a special Green Economy module with the aim of systematically collecting information on firms’ green management practices and other aspects of firm behaviour relating to climate change.
International patterns in terms of green management
The quality of firms’ green management can be quantified on the basis of their answers to several specific questions in the Enterprise Surveys (see Box 4.1). This exercise shows that the quality of firms’ green management, averaged at country level, is positively correlated with the average quality of general management practices (that is to say, firms’ general approach to operations, monitoring, targets and incentives; see Chapter 3). This positive correlation is, however, relatively modest, with a coefficient of 0.23.
Distribution of green management scores
Although there are substantial differences across countries in terms of the average quality of green management, most of the variation (92 per cent) is found within economies, even after accounting for cross-country differences in sectoral composition. As with general management scores, there are firms with low and high green management scores in every economy (see Chart 4.3). Importantly, however, green management scores are much less evenly distributed than general management scores. Namely, there is a large mass of firms with green management scores that are just below average (that is to say, slightly to the left of zero) and a long thin tail of firms with good green management scores. This pattern is also evident within each individual country.
Differences in the quality of green management across sectors
There are several factors that may explain the large differences in green management scores across firms within a given country, as shown by the green line in Chart 4.3. The analysis below looks first at internal factors – firm-level characteristics such as size and ownership structure – before turning to external factors, such as customer pressure, losses due to extreme weather, or pollution caused by other firms.
Larger and older firms have better green management practices
It is perhaps not too surprising that firms which have at least 100 employees and are at least five years old tend, on average, to have higher green management scores (see Chart 4.5). As firms grow, they may eventually reach a size at which they are obliged to monitor their emissions. They may also face increasing pressure from consumers to reduce their impact on the environment. For instance, providers of takeaway coffee and food have experienced growing pressure to switch to recyclable cups and containers. For young small and medium-sized enterprises (SMEs), emphasising their environmental credentials could also prove to be a unique selling point.
Foreign-owned and listed firms have better green management practices, as do exporters
When it comes to the impact that foreign ownership has on the environment, the results of existing studies are mixed. In general, foreign ownership often improves firm-level productivity by transferring cutting-edge technology, management practices and knowledge to acquired firms and encouraging product and process innovation. Indeed, multinationals tend to use more advanced technology and production methods than their domestic counterparts, which can improve environmental outcomes.5 This has sometimes been referred to as the “pollution halo effect”. At the same time, however, firms in polluting industries may also relocate to countries with less stringent environmental regulations (termed “pollution havens”) in response to costly regulations in their home countries, increasing pollution levels both in their host countries and globally.6
Customer pressure can lead to improved green management practices
External factors – such as customer pressure and environmental regulations, as well as firms’ own experiences of pollution and extreme weather events – can also prompt firms to reduce their environmental impact. About one in seven firms in the EBRD regions and the Czech Republic report that at least some of their customers require environmental certificates or adherence to certain environmental standards as a precondition for doing business. In every region, green management scores tend, on average, to be much higher for firms that have experienced such customer pressure than for those that have not. Indeed, in the regression analysis, the improvement in green management that is associated with facing customer pressure is almost four times the size of that associated with foreign ownership.
Firms that are exposed to extreme weather or pollution have better green management practices
Firms with direct, first-hand experience of environmental and climate change-related problems – for example, firms that have suffered monetary losses due to extreme weather events or have been negatively affected by pollution produced by nearby firms – may be more inclined to enhance their green credentials. Data from the Enterprise Surveys reveal that about 10 per cent of all firms in the EBRD regions and the Czech Republic have experienced monetary losses due to extreme weather events over the last three years. For instance, Moldova, North Macedonia and Romania all experienced severe flooding in 2016, and heatwaves and droughts have become a common occurrence in many countries during the summer months. Similarly, severe hailstorms have occurred in Croatia, Poland, Romania and Slovenia.
Environmental regulations also affect the quality of green management
Another important external factor is environmental regulations, which can be proxied by energy taxes or levies (see also Box 4.3 on energy efficiency standards). Where energy is expensive, firms have an incentive to use less of it. The resulting positive impact on the environment is especially large where energy is generated using fossil fuels. The estimates in Table 4.1 suggest that firms which are subject to an energy tax or levy have substantially better green management practices than firms which are not. That effect is about twice the size of the impact of being under foreign ownership or listed on a stock exchange. In fact, a formal comparison of the sizes of all the estimates reported in Table 4.1 reveals that the two most important drivers of green management scores are both external factors: customer pressure and being subject to an energy tax or levy.
Source: Enterprise Surveys and authors’ calculations.
Source: Enterprise Surveys and authors’ calculations.
Note: “PIMS” means Portugal, Italy, Malta and Spain.
Source: Enterprise Surveys and authors’ calculations.
Note: Cross-country differences in the sectoral composition of the sample are controlled for. Density is calculated by dividing the number of values that fall into each class by the number of observations in the set and the width of the class.
Source: Enterprise Surveys and authors’ calculations.
Note: Sectors are based on ISIC Rev. 3.1. Clean sectors include food, beverages and tobacco (15-16), textiles, textile products, leather and footwear (17-19), wood (20), fabricated metal products, machinery and equipment (28-33), transport equipment (34-35) and construction (45). Emission-intensive sectors include paper and paper products (21), printing and publishing (22), coke and petroleum (23), chemical products (24), rubber and plastic products (25), non-metallic mineral products (26), basic metals (27), land transport (60), water transport (61) and air transport (62). Wholesale and retail (50-52), hotels and restaurants (55), supporting and auxiliary transport activities (63), post and telecommunications (64) and IT (72) cannot be classified as either clean or emission-intensive owing to data availability issues.
Source: Enterprise Surveys and authors’ calculations.
Note: SMEs have fewer than 100 employees; young firms are less than five years old.
Dependent variable | Green management score | |
---|---|---|
(1) | (2) | |
Old SME (indicator) | -0.079* | -0.095** |
(0.044) | (0.041) | |
Large young firm (indicator) | 0.149 | 0.074 |
(0.119) | (0.113) | |
Large old firm (indicator) | 0.214*** | 0.138*** |
(0.046) | (0.041) | |
25% or more foreign-owned (indicator) | 0.236*** | 0.219*** |
(0.053) | (0.044) | |
Direct exporter (indicator) | 0.187*** | 0.139*** |
(0.037) | (0.031) | |
Listed (indicator) | 0.212*** | 0.191*** |
(0.054) | (0.047) | |
Sole proprietorship (indicator) | -0.108** | -0.070* |
(0.041) | (0.040) | |
Financial reports audited (indicator) | 0.390*** | 0.262*** |
(0.028) | (0.024) | |
General management score (z-score) | 0.172*** | 0.128*** |
(0.014) | (0.012) | |
Customer pressure (indicator) | 0.853*** | |
(0.040) | ||
Monetary losses due to extreme weather (indicator) | 0.167*** | |
(0.049) | ||
Monetary losses due to pollution caused by others (indicator) | 0.335*** | |
(0.110) | ||
Energy tax/levy (indicator) | 0.454*** | |
(0.036) | ||
Observations | 7,362 | 7,294 |
R2 | 0.220 | 0.342 |
SOURCE: Enterprise Surveys and authors’ calculations.
NOTE: Estimated using ordinary least squares. All regressions include country, sector, locality, accuracy and truthfulness fixed effects. Old firms are at least five years old; large firms have at least 100 employees. Omitted size category: young SME (firm with fewer than 100 employees). Standard errors clustered at four-digit industry level are reported in parentheses, and *, ** and *** denote statistical significance at the 10, 5 and 1 per cent levels respectively.
Source: Enterprise Surveys and authors’ calculations.
Source: Enterprise Surveys and authors’ calculations.
Green investment
Evidence on green investment
In addition to improving their green management practices, firms can also invest in measures that directly reduce their environmental impact. In the Enterprise Surveys, firms are asked about various types of green investment. Some of these reduce firms’ environmental impact as a by-product of achieving other objectives. For instance, as innovation proceeds, new vintages of assets such as machines and vehicles tend to be more energy efficient than the outdated models they replace. Thus, investment in new assets may also lead to improvements in energy efficiency. Improvements to heating and cooling systems, machinery and equipment upgrades, vehicle upgrades and improvements to lighting systems all fall into this category. In the analysis that follows, these four types of investment are referred to as “mixed” green investment.
Factors explaining differences in green investment
Firms in emission-intensive sectors are more likely to be aware of the need to reduce their impact on the environment and thus more likely to engage in green investment. Indeed, Chart 4.9 shows that levels of pure and mixed green investment are typically higher for firms in sectors with above-median CO2 emissions per unit of value added. However, that difference is only statistically significant for pure green investment.
Why do so many firms refrain from investing in energy efficiency?
Despite the potential environmental and efficiency benefits of investment aimed at reducing firms’ impact on the environment, there are many firms that refrain from implementing such measures. In order to better understand the rationale behind these decisions, the Enterprise Surveys ask firms that have decided not to adopt one specific type of pure green investment – energy efficiency measures – about their reasons for forgoing such measures.
Source: Enterprise Surveys and authors’ calculations.
Note: The lines show the 5th and 95th percentiles of the distribution.
Source: Enterprise Surveys and authors’ calculations.
Note: For details of clean and emission-intensive sectors, see the note accompanying Chart 4.4.
Source: Enterprise Surveys and authors’ calculations.
Source: Enterprise Surveys and authors’ calculations.
Note: * and *** denote statistical significance at the 10 and 1 per cent levels respectively, based on t-tests for differences in sample means. SMEs have fewer than 100 employees; large firms have 100 or more.
Access to credit, the quality of green management and green investment
Bearing in mind that a lack of financial resources is the second most common reason cited by firms that have not adopted energy efficiency measures, this section provides more structured analysis of the relationship between firms’ ability to access bank credit, their green management credentials and their propensity to undertake green investment. In the analysis that follows, a firm is regarded as credit-constrained if its survey answers indicate that it needed credit in the past year but was either rejected by a bank when it applied for credit or was discouraged from applying in the first place.
Credit constraints, green management, firms’ performance and energy consumption
This subsection looks at the impact that credit constraints and the quality of green management have on firms’ performance. Financial constraints and green management practices both appear to matter, but in different ways (see Table 4.4). As expected, credit constraints have a negative impact on both sales per worker (a measure of labour productivity; see column 1) and overall sales (see column 2). When firms are financially constrained and cannot invest as much as they would like, their capital-to-labour ratio may be lower than that of similar firms in the same country and sector. Indeed, the analysis above showed that such firms tend to reduce their investment in fixed assets. Output per worker is likely to be correspondingly lower, and this may, in turn, negatively affect total sales.
Credit constraints and greenhouse gas emissions
If credit constraints prevent firms from undertaking some green investment projects – especially those of a mixed nature (see Table 4.2) – one might expect that, perhaps with some lag, they could also hamper firms’ ability to reduce the emission of greenhouse gases and other pollutants. In order to investigate that question, this subsection examines changes in the levels of greenhouse gas emissions and other air pollutants produced by 1,819 industrial facilities in 10 eastern European countries (Bulgaria, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, the Slovak Republic and Slovenia) in the period 2007-17. The green dots in Chart 4.12 show the locations of those various facilities. For each facility, the European Pollutant Release and Transfer Register (E-PRTR) provides data on annual emissions of greenhouse gases, ammonia, carbon monoxide, sulphur oxides and other noxious air pollutants.
First stage | Second stage | |||||
---|---|---|---|---|---|---|
Dependent variable | Credit-constrained | Green management (z-score) | Investment in fixed assets | Investment in fixed assets excluding pure green investment | Mixed green investment (z-score) | Pure green investment (z-score) |
(1) | (2) | (3) | (4) | (5) | (6) | |
Local banks’ dependence on wholesale funding | 0.004*** | 0.002 | ||||
(0.001) | (0.001) | |||||
Monetary losses due to extreme weather | -0.065** | 0.401*** | ||||
(0.026) | (0.058) | |||||
Monetary losses due to external pollution | 0.111** | 0.529*** | ||||
(0.044) | (0.127) | |||||
Credit-constrained | -0.801*** | -0.246* | -0.781*** | -0.311 | ||
(0.194) | (0.139) | (0.286) | (0.347) | |||
Green management | 0.213*** | -0.022 | 0.565*** | 0.734*** | ||
(0.053) | (0.029) | (0.089) | (0.085) | |||
Observations | 4,646 | 4,646 | 4,646 | 4,646 | 4,602 | 4,646 |
R2 | 0.574 | 0.201 | 0.322 | 0.109 | 0.140 | 0.346 |
F-statistic | 62.21 | 23.49 |
SOURCE: Enterprise Surveys, Banking Environment and Performance Survey II (BEPS II), Bureau Van Dijk’s Orbis database and authors’ calculations.
NOTE: This table shows the results of instrumental variables regressions explaining the impact that credit constraints and the quality of green management have on green investment at firm level. Columns 1 and 2 show the first-stage regressions, where the dependent variable is credit-constrained (column 1) or green management (column 2). The dependent variables in the second stage are: a dummy indicating whether the firm has invested in any fixed assets in the past year (column 3); a dummy indicating whether the firm has invested in fixed assets other than pure green investment (column 4); the z-score for mixed green investment over the past three years (column 5); and the z-score for pure green investment over the past three years (column 6). The first-stage instruments are a branch-weighted measure of average dependence on wholesale funding across all banks within 5 km of the firm and dummies indicating whether the firm has recently experienced monetary losses due to extreme weather events or pollution caused by other firms. The mixed green investment score is a z-score based on the following types of investment: improvements to heating and cooling systems; machinery and equipment upgrades; vehicle upgrades; and improvements to lighting systems. The pure green investment score is a z-score based on the following types of investment: energy management; waste minimisation, recycling and waste management; water management; on-site generation of green energy; measures controlling air pollution; other pollution control measures; and energy efficiency measures. All regressions include firm-level controls (indicators for exporter status, listed firm, sole proprietorship and audited financial reports, as well as the log of firm age), as well as country, sector, locality, accuracy and truthfulness fixed effects. Standard errors clustered at four-digit industry level are shown in parentheses, and *, ** and *** denote statistical significance at the 10, 5 and 1 per cent levels respectively.
Dependent variable | Mixed green investment | Pure green investment | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Improved heating/cooling system | Machinery upgrade | Vehicle upgrade | Improved lighting | Generation of green energy | Energy management | Waste and recycling | Measures controlling air pollution | Water management | Other pollution control measures | Energy efficiency measures | |
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | |
Credit-constrained | -0.264 | -0.463*** | -0.269* | -0.153 | -0.147 | 0.033 | -0.480*** | -0.035 | -0.263* | 0.310* | -0.073 |
(0.182) | (0.144) | (0.139) | (0.186) | (0.115) | (0.168) | (0.181) | (0.126) | (0.135) | (0.181) | (0.168) | |
Green management | 0.243*** | 0.221*** | 0.194*** | 0.186*** | 0.139*** | 0.211*** | 0.198*** | 0.206*** | 0.231*** | 0.214*** | 0.237*** |
(0.045) | (0.045) | (0.038) | (0.044) | (0.033) | (0.041) | (0.044) | (0.041) | (0.039) | (0.048) | (0.050) | |
Observations | 4,511 | 4,542 | 4,526 | 4,547 | 4,418 | 4,535 | 4,484 | 4,396 | 4,460 | 4,464 | 4,646 |
R2 | 0.407 | 0.496 | 0.418 | 0.559 | 0.215 | 0.480 | 0.402 | 0.359 | 0.329 | 0.138 | 0.505 |
SOURCE: Enterprise Surveys, BEPS II, Bureau Van Dijk’s Orbis database and authors’ calculations.
NOTE: This table shows the results of second-stage instrumental variables regressions explaining the impact that credit constraints and the quality of green management have on the probability of a firm undertaking mixed green investment (columns 1 to 4) or pure green investment (columns 5 to 11). Standard errors clustered at four-digit industry level are shown in parentheses, and *, ** and *** denote statistical significance at the 10, 5 and 1 per cent levels respectively. For more details, see the note accompanying Table 4.2.
Source: E-PRTR.
Note: Based on the locations of 1,819 industrial facilities in Bulgaria, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, the Slovak Republic and Slovenia in the period 2007-17, as recorded in the E-PRTR.
Dependent variable | Labour productivity (log) | Sales (log) | Electricity intensity of sales (kWh/US$; log) |
---|---|---|---|
(1) | (2) | (3) | |
Credit-constrained | -2.036*** | -1.014** | -0.053 |
(0.726) | (0.488) | (0.203) | |
Green management | 0.325 | 0.030 | -0.091* |
(0.208) | (0.140) | (0.049) | |
Observations | 4,060 | 4,043 | 1,887 |
R2 | 0.982 | 0.986 | 0.422 |
SOURCE: Enterprise Surveys, BEPS II, Bureau Van Dijk’s Orbis database and authors’ calculations.
NOTE: This table shows the results of instrumental variables regressions explaining the impact that credit constraints and the quality of green management have on firm-level labour productivity (column 1), sales (column 2) and the electricity intensity of sales (column 3). Labour productivity is defined as the ratio of sales to employment and is winsorised at 1 per cent. The electricity intensity of sales is defined as the ratio of the amount of electricity consumed in kWh to sales and is winsorised at 5 per cent. Standard errors clustered at four-digit industry level are shown in parentheses, and *, ** and *** denote statistical significance at the 10, 5 and 1 per cent levels respectively. For more details, see the note accompanying Table 4.2.
Dependent variable | Log of total emissions of air pollutants + 1 | Log of total greenhouse gas emissions + 1 | ||
---|---|---|---|---|
(1) | (2) | (3) | (4) | |
Local banks’ dependence on wholesale funding | -0.043** | -0.044** | -0.029 | -0.030 |
(0.022) | (0.022) | (0.032) | (0.032) | |
Post-2007 | -0.797** | -0.796** | -1.360 | -1.360 |
(0.336) | (0.336) | (0.900) | (0.900) | |
Post-2007* Local banks’ dependence on wholesale funding | 0.012*** | 0.012*** | 0.026** | 0.026** |
(0.004) | (0.004) | (0.012) | (0.012) | |
Observations | 3,638 | 3,638 | 3,638 | 3,638 |
R2 | 0.435 | 0.436 | 0.408 | 0.408 |
SOURCE: E-PRTR, BEPS II, Bureau Van Dijk’s Orbis database and authors’ calculations.
NOTE: This table shows the results of difference-in-difference regressions explaining the impact that local credit constraints have on total air pollution (columns 1 and 2) and total greenhouse gas emissions (columns 3 and 4) at the level of industrial facilities. If raw data on total air pollution and greenhouse gas emissions are missing, they are assumed to be zero. Local banks’ dependence on wholesale funding measures the average dependence on wholesale funding of all bank branches located within 15 km of the industrial facility – or, in the case of multi-facility firms, the parent company – in 2007. Post-2007 is a dummy variable that is 0 in 2007 and 1 thereafter. All regressions control for the latitude and longitude of the facility, country and sector fixed effects, and (in columns 2 and 4) whether the facility is owned by a private company, the state, a financial institution/bank, or an individual or family. Standard errors clustered by parent company are shown in parentheses, and *, ** and *** denote statistical significance at the 10, 5 and 1 per cent levels respectively.
Source: E-PRTR, BEPS II, Bureau Van Dijk’s Orbis database and authors’ calculations.
Note: These coefficients are estimated by using a difference-in-difference regression to explain the impact that local credit constraints have on the logarithm of greenhouse gas emissions (in kilograms of CO2) in every year after 2007 (the base year). The lines show the 95 per cent confidence interval. See also the note accompanying Table 4.5.
Conclusion
Greenhouse gas emissions in the EBRD regions have fallen substantially since the 1990s, but if the regions’ economies are to fulfil their commitments under the Paris Agreement, those improvements will need to continue. This, in turn, will require further improvements to the green credentials of the regions’ firms. While some firms in the EBRD regions (as well as comparator countries) have excellent green management practices, most continue to perform poorly in this regard. Firms with weaker green management practices may be aware of the importance of monitoring their impact on the environment, but lack the organisational structures necessary to set and achieve targets in this area.
Credit constraints hamper investment by firms, including investment with environmental benefits. However, when it comes to pure green investment (such as improvements in energy management, the generation of green energy and controls on air pollution), access to finance is not the main constraint. The empirical analysis in this chapter shows that whether a firm undertakes such investment projects – many of which have uncertain outcomes and involve large externalities – depends primarily on the strength of the firm’s green management practices.
Indeed, many firms refrain from undertaking pure green investment for the simple reason that managers believe it to be a low priority relative to other types of investment. While firms may, in principle, want to reduce their environmental impact, they often face more pressing matters in the short term. In the face of financial and time constraints, managers may prioritise non-green investment, even where green investment would have a positive, albeit small, net present value.
In line with that interpretation, this chapter also shows that firms tend to bump green management and investment up their priority list when environmental issues suddenly become more important to them in the wake of exposure to adverse weather events or external pollution, as well as in response to customer pressure. This suggests that behavioural barriers could also be preventing the adoption of better green management practices. Experience of negative environmental effects may focus minds and make firms more aware of such opportunities.
Thus, improving the availability of credit is just one element of the broad policy mix that is necessary to stimulate green investment and improve firms’ green management practices. Governments may also have to compel firms to produce in a more energy efficient manner using environmental standards or other regulations (see Box 4.3) or via subsidies that are contingent on the use of specific green technologies. Targeted green credit lines can also encourage firms to prioritise green investment (see Box 4.4 for details of the EBRD’s Green Economy Transition approach). However, an important precondition for the success of such interventions is effective enforcement of regulations in a corruption-free environment.11 Lastly, firms are also known to improve their green credentials in response to pressure from their customers. With this in mind, voluntary environmental standards may help to leverage the power of peer pressure and consumer awareness in order to further reduce firms’ environmental footprints.
Box 4.1. Measuring green management practices and green investment
The most recent round of Enterprise Surveys conducted by the EBRD, the EIB and the World Bank Group included a special Green Economy module, which sought to gather information on key aspects of firm behaviour relating to climate change (including green management practices). In most economies, the response rate for the Green Economy module was in excess of 95 per cent.
Box 4.2. Corporate climate governance
As discussed elsewhere in this chapter, the management of environmental risks and the fostering of better environmental performance can have a positive impact on a firm’s financial outcomes. The strength of this relationship depends, among other things, on the type of industry in question, the firm’s location, and the quality of governance in the country where the firm is located.12
Box 4.3. Energy efficiency standards and green transition
In the absence of improvements in energy efficiency, global energy usage would have increased by 65 per cent between 2000 and 2017, instead of the 33 per cent that was actually recorded, according to the International Energy Agency (IEA).17 Investment in energy efficiency can lower energy bills and prevent premature deaths associated with air pollution. However, despite these benefits, many efficiency savings remain untapped. The IEA estimates that two-thirds of the cost-effective energy efficiency measures that are available today may not be implemented by 2040.
Box 4.4. The Green Economy Transition approach
High levels of carbon intensity and climate vulnerability remain key issues for many economies in the EBRD regions. The desire to help firms move towards lower-carbon production structures and create more climate-resilient economies lies at the heart of the EBRD’s Green Economy Transition (GET) approach, which is closely aligned with the United Nations’ Sustainable Development Goals and the Paris Agreement.
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