metadata: authors: 0: author: Kalantzis,Fotios
1: author: Schweiger,Helena
2: author: Dominguez,Sofia
count: World
docna: 0: docna: Green Investment by Firms : Finance or
            Climate Driven ?
docty: Policy Research Working Paper
abstracts: cdata!: There is limited research on the
            determinants of firms’ green investment strategies in
            developing regions despite their importance to meet global
            climate change targets. Understanding how changes in firm
            climate investment affect environmental performance is
            essential for policy makers and firms alike. Based on unique
            data from the joint European Bank for Reconstruction and
            Development–European Investment Bank–World Bank Group
            Enterprise Surveys, this paper empirically examines the role
            of access to finance and green management practices in
            firms’ green investment strategies. Based on logistic
            regressions, the econometric analysis finds a positive
            influence of green management practices on the number of
            mitigation measures implemented. By contrast, firms that are
            financially constrained are less likely to pursue many
            mitigation measures. Finally, the results do not show
            significant differences in the impact of financial
            constraints on the type of green investment, but indicate
            that better green management practices lead to a higher
            likelihood of investing in both capital- and
            non-capital-intensive green measures.
display_title: Green Investment by Firms : Finance
            or Climate Driven
listing_relative_url: /research/2022/06/33814398/green-investment-firms-finance-or-climate-driven
topicv3: Finance and Growth m1327404 1081,Mitigation Technology m1327590 1952,Firm Behavior and Jobs m1504204 2873
publishtoextweb_dt: 2022-06-09T00:00:00Z
dois: 10.1596/1813-9450-10064
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: NA
1: author_key: 812249
2: author_key: NA
model_extractions: Policy Research Working Paper 10064

**Unlocking SUStainable Private Sector growth in the Middle eaSt and north africa**

_Background Paper_ Green Investment by Firms Finance or Climate Driven?

_Fotios Kalantzis Helena Schweiger Sofia Dominguez_ Middle East and North Africa Region Office of the Chief Economist June 2022
Policy Research Working Paper 10064

# **Abstract**

There is limited research on the determinants of firms’ green investment strategies in developing regions despite their importance to meet global climate change targets. Understanding how changes in firm climate investment affect environmental performance is essential for policy makers and firms alike. Based on unique data from the joint European Bank for Reconstruction and Development–European Investment Bank–World Bank Group Enterprise Surveys, this paper empirically examines the role of access to finance and green management practices in firms’ green investment strategies. Based on logistic regressions, the econometric analysis finds a positive influence of green management practices on the number of mitigation measures implemented. By contrast, firms that are financially constrained are less likely to pursue many mitigation measures. Finally, the results do not show significant differences in the impact of financial constraints on the type of green investment, but indicate that better green management practices lead to a higher likelihood of investing in both capital- and non-capital-intensive green measures.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at f.kalantzis@eib.org and schweigh@ebrd.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Green Investment by Firms: Finance or Climate Driven?**

Fotios Kalantzis, Helena Schweiger and Sofia Dominguez<sup>1</sup> Keywords: finance constraints, green management practices, mitigation strategies JEL classification: D22, L23, G32, L20 1 Kalantzis: Senior Economist, European Investment Bank. Email: <u>f.kalantzis@eib.org. Schweiger: Associate Director,</u> Lead Research Economist, European Bank for Reconstruction and Development. Email: <u>schweigh@ebrd.com.</u> Dominguez: Associate Economic Affairs Officer, United Nations Conference on Trade and Development (current affiliation). Email: <u>sofia.dominguezdelgado@unctad.org (Sofia contributed to this research during her traineeship at</u> the European Investment Bank). This paper was commissioned by the World Bank Middle East and North Africa Chief Economist office as part of the activity “Assessing the MENA formal Private Sector.” The activity is task managed by Asif M. Islam. The study is a background paper for the Chapeau report titled “Unlocking Sustainable Private Sector Growth in the Middle East and North Africa: Evidence from the Enterprise Survey.” The opinions expressed in this paper do not represent the views of the World Bank Group, its Board of Directors or the Governments they represent. The opinions also do not reflect the views of the European Bank for Reconstruction and Development (EBRD), the European Investment Bank (EIB), and the United Nations Conference on Trade and Development (UNCTAD). All errors and omissions are the authors’ responsibility. We are thankful to Ralph De Haas, Sergei Guriev and Simon Savšek for comments. We would also like to thank the participants of two workshops hosted by the World Bank, EBRD, and EIB for their feedback.
# **I. Introduction**

Climate change is one of today’s most pressing global challenges. Despite ongoing activities to mitigate its worst effects, a further increase in the Earth’s surface temperature is inevitable. This will lead to adverse economic impacts due to changes in frequencies of extreme weather events and long-term shifts in weather patterns (IPCC, 2021). While many developed countries have already started with their decarbonization efforts and are working out the best mitigation strategies for reducing GHG emissions, most developing countries are lagging and their economic growth is still reliant on GHG emissions.

Recent trends in clean energy spending point to a widening gap between advanced economies and the developing world even though emissions reductions are far more cost-effective in the latter (IEA, 2021). Emerging markets and developing economies account for over two-thirds of the global population and approximately 70% of the global energy demand, but only one-fifth of global clean energy investment. By contrast, emissions in advanced economies are declining, despite an anticipated 4% rebound in 2021 owing to massive investment in clean technologies and the energy transformation of all economic sectors. This turns the attention to current measures taken in developing countries to decarbonize their activities, highlighting the need for these economies to step up their climate action.

Collective efforts across countries and actors of the economy are deemed necessary to tackle the worst effects of climate change. These direct climate change effects are expected to significantly impact the natural environment and society at different spatial and time levels. Within society, firms are important entities by which adaptation to climate change effects occur. How affected firms play their part in this climate transition, to a large extent, determines the nature and scale of impacts and possibly firms’ survival in the long run.

Existing research provides insight into firms’ mitigation efforts to respond to climate change, primarily in developed economies (see, for example, Kalantzis and Revoltella, 2019; Fleitera et al, 2012, Thollander et al, 2007). However, there is limited research in the developing countries’ context. Hence, while the existing literature investigates the determinants of climate-related investments in advanced economies located in the European Union and the United States, little is known about the factors influencing firms’ behavior in emerging economies, which adopt comparatively less green profiles. In addition, while some determinants of firms’ strategies of adapting to climate change and its policy-related impacts, such as their characteristics or green management practices, have been proposed (Fankhauser et al, 1999; Bleda and Shackley, 2008), they have not been investigated comprehensively. Yet, knowledge about these determinants is particularly important for policy makers to support favorable conditions for firms’ green investment strategies. Our work is closest to that by De Haas et al (2022), who show that both financial frictions and managerial constraints slow down firm investment in more energy efficient and less polluting technologies.

Based on the unique data from the joint European Bank for Reconstruction and Development (EBRD)European Investment Bank (EIB)-World Bank Group (WBG) Enterprise Surveys (ES), this paper empirically examines such determinants in a sample of almost 18,000 firms in 30 countries in Europe and Central Asia (ECA) and the Middle East and North Africa (MENA) regions. In this respect, the scope of firms’ green investment strategies (measured by the total number of mitigation measures) and measures that firms take to follow specific strategic directions of their business model (such as to protect the affected business) are analyzed. We consider the case of 10 climate-related measures, namely: heating and cooling improvements, type of on-site energy generation, machinery and equipment upgrades, energy 2
management, waste management, water management, air pollution control measures, upgrades of vehicles, improvements to lighting systems and other pollution control measures to investigate our testing hypotheses.

The surveys also give us access to information on firms’ credit constraints and green management practices as a proxy for awareness of possible climate change effects. In terms of the latter, we collect data on firms’ strategic objectives concerning the environment and climate change; whether there is a manager with an explicit mandate to deal with environmental issues; and how the firm sets and monitors targets (if any) related to energy and water usage, CO2 emissions, and other pollutants.

We include covariates that might affect the relationship between organizational constraints and firms’ green investment strategies. We follow an instrumentation strategy to isolate plausibly exogenous components of firm-level constraints, introduced by De Haas et al (2022). We first construct jack-knife instruments that reflect the financial constraints experienced by other firms in the vicinity, except those in the same 2-digit industry, and green managerial practices experienced by neighboring firms in higher size deciles. Then, we combine our firm-level data with geo-coded information on bank branches that surround each individual firm. This allows us to create proxies for exogenous differences in local credit conditions in the aftermath of the global financial crisis. We then use these instruments to allow for a causal interpretation of the observed relationship between firms’ financial and managerial constraints and their green investment strategies. Our logistic IV regression results suggest that financially constrained firms pursue fewer mitigation measures (particularly in ECA), while more climate-aware firms (those with better green management practices) pursue more mitigation measures. We do not, however, find much evidence of differential impact on types of green investment, with the exception of credit constrained firms being less likely to invest in both capital- and non-capital intensive measures, while firms with better green management practices are more likely to do so.

The structure of the paper is as follows: section II provides context and presents an analysis of the efforts to reduce emissions in our sample regions, namely ECA and MENA. Section III describes our data set and the variables in our econometric analysis, while section IV develops a general framework of firms’ climaterelated investment strategies and derives hypotheses for econometric analysis. In section V, we describe the methodology, while empirical results are presented in section VI. The final section discusses these results and concludes by developing recommendations for policy makers.

# **II. Progress in reducing emissions**

The adoption of the Paris Agreement at the United Nations Climate Change Conference of the Parties in 2015 (COP 21) was one of the biggest climate change milestones in history. The overarching aim of the Paris Agreement is to reduce greenhouse gas emissions and ensure that global temperature increases this century remain well below 2°C relative to pre-industrial levels, while ideally pursuing a scenario where the temperature increase remains below 1.5°C. As such, the Paris Agreement calls for very aggressive reductions in emissions – particularly carbon emissions, which account for more than three-quarters of all greenhouse gas emissions worldwide.

Recent negotiations at COP26 suggest that current efforts are insufficient to meet the Paris agreement goals, which means that pressure remains. If global warming is to be limited to 1.5 degrees, the CO2 3
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labor, and business-government relations. It also collects basic information on the firm, including age, size, and geographic coordinates.

Notably, the most recent Enterprise Surveys included a new Green Economy module. This unique module gathered information on key aspects of firm behavior related to the environment and climate change, including green management practices and green investments. In most economies, the response rate for the Green Economy module was over 95%. We thus have a representative snapshot—stratified by sector, firm size, and region—of firms’ green credentials in each of these countries.

We obtain data on our dependent variables, number and type of climate mitigation measures, as well as green management practices, credit constraints and firm-level controls from the Enterprise Surveys. We discuss these in more detail in the next subsection.

# _Bank-level data: Banking Environment and Performance Survey and Bureau van Dijk’s ORBIS database_

The geographical coordinates of 137,407 branches, operated by 1,788 banks across the countries in our sample, were collected by specialized consultants as part of the second round of the EBRD Banking Environment and Performance Survey (BEPS II). Data collection took place by contacting banks or by downloading data from bank websites. All information was double-checked with the banks as well as with the SNL Financial database. The 1,788 banks represented more than 95% of all bank assets in these 30 countries in 2013, so we have a near complete bank branch footprint. For each branch, we know the bank it belongs to. We merge this information with bank balance sheet information from Bureau van Dijk’s (BvD) ORBIS database. We download information about each bank’s pre-financial crisis assets in 2007.

# Variables

# _Dependent variable: Climate mitigation measures_

The Enterprise Surveys asked firms whether they adopted any of the ten different, potentially green, measures over the three years prior to the survey. The list included: machinery and equipment upgrades; vehicle upgrades; more climate-friendly energy generation on-site; heating and cooling improvements; waste minimization, recycling and waste management; energy management; water management; air pollution control measures; improvements to lighting systems; and other pollution control measures.

Some of these measures, such as machinery and equipment upgrades, vehicle upgrades, on-site generation of green energy and waste minimization, recycling and waste management, are capitalintensive and relate to production processes. In other words, they require large amounts of investment. Others, such as heating and cooling improvements, energy management, measures controlling air pollution, water management, lighting improvements and measures controlling other pollution, are – in comparison – less capital-intensive.

In our sample, 70% of firms have adopted at least one such measure. At the same time, 30% of respondent firms have not adopted any of them, whether capital-intensive or not, over the three years prior to the survey, while 46.1% of firms adopted both capital- and non-capital-intensive measures. These patterns vary across regions and economies within regions. In ECA, 26.9% of firms made no green investments, 10
<!-- Start of picture text --> WW L_—___<£_£<br>=<br>(OO TTT lL<br>0 20 40 60 80 100<br>Percentage of firms<br>@ No green investment @ One type @ Two types<br>@ Three types @ Four types @ Five types<br>@ Six types @ Seven types @ Eight types<br>@ Nine types @ Ten types<br><!-- End of picture text --> <!-- Start of picture text --> Ord lsDDD _<br>CA<br>MENA EE<br>0 20 40 60 80 100<br>Percentage of firms<br>@ No green investment<br>@ Capital-intensive green investment only<br><!-- End of picture text -->

- ® Non-capital-intensive green investment

- @ Capital- and non-capital-intensive green investment
Note: Data represent unweighted averages across countries in our sample. Capital-intensive green investments are investment in more climate-friendly energy generation on site, machinery and equipment upgrades, vehicle upgrades and investment in waste minimization, recycling and waste management. ECA – Europe and Central Asia; MENA – Middle East and North Africa.

Source: Enterprise Surveys and authors’ calculations.

Based on the percentage of firms making a certain number of different green investments in Figure 7, we consider two categorical groupings: one with four categories and one with six categories. Four category version of the number of different green investments variable has the following categories: no green investments; one green investment; two to four different green investments; and five or more different green investments. Six-category version of the same variable has the following categories: no green investment; one green investment; two different green investments; three different green investments; four different green investments; and five or more different green investments.

# _Independent variables: Green management practices_

The Green Economy Module asked firms about their green management practices in four areas. The first area concerns whether firms have strategic objectives related to the environment and climate change. The second area looks at whether firms employ a manager with an explicit mandate to deal with green issues. Conditional on the existence of such an environmental manager, additional information was collected on whom they report to, and whether their performance is evaluated against how well the establishment performs on energy consumption, CO2 emissions or other pollution or environmental targets. The third area covered by the Green Economy Module asks whether firms have clear and attainable environmental targets. Lastly, the fourth area looks at whether firms actively and frequently monitor their energy and water usage, CO2 emissions and other pollutants in order to reduce their environmental footprint.

We normalize the scores for each question such that they have a mean of 0 and a standard deviation of 1 in the sample. We then aggregate them to average z-scores for each of the four areas of green management. Lastly, we create an overall green management z-score as a normalized unweighted average of the four categories. A z-score above zero indicates that a firm’s green management practices are better than the sample average.

# _Independent variables: Credit constraints_

To identify credit-constrained enterprises, we combine answers to various survey questions and first distinguish between firms with and without a demand for credit. Among the former, we then identify those that were credit constrained as those that were either discouraged from applying for a loan or were 12
rejected when they applied. Non-credit constrained firms are those that either had no need for credit or whose demand for credit was satisfied.<sup>4</sup>

# _Firm covariates_

Firm-level control variables include the log of firm age (and its square), percentage of employees with a university degree, and dummy variables for whether the firm is publicly listed, a sole proprietorship, an exporter, and whether an external auditor reviews its financial statements. In the main paper, we present estimates without firm covariates; estimates with firm covariates included can be found in the Appendix.

# _Local credit market conditions controls_

To control for local credit market conditions, we use detailed data about the banking sectors in our sample countries. We connect the firm and branch data by drawing circles with a radius of 15km around the coordinates of each firm and then linking the firm to all branches inside that circle. For each firm, we first measure the number of bank branches within a 15km radius. Next, we calculate the branch-weighted average asset size of banks with branches within this radius. This allows us to control for the number and the size of banks that make up the local credit market around each firm.

# Descriptive statistics

Table 1 presents summary statistics (mean, median, standard deviation, minimum, maximum) for the explanatory variables (Figures 7 and 8 show the relative frequencies for the outcome variables). It shows that almost a quarter of firms are credit constrained (24.6%). The standardized green management variable is by construction close to zero on average, but varies between -1.91 and 6.98. Close to a fifth of the firms in our sample are exporters, and 6% of them are publicly listed. Among the firms, 19.7% are sole proprietorships and 39.4% had their annual financial statements checked and certified by an external auditor. On average, 30.9% of the firms’ employees had a university degree. The table also shows that all firms in our sample have at least one bank branch within a 15km radius. The change in local branchweighted average Tier 1 ratio between 2007 and 2014, one of our instrumental variables, was on average 1.7 percentage points.

> 4 We start by using the question: “Did the establishment apply for any loans or lines of credit in the last fiscal year?” For firms that answered “No”, we move to the question: “What was the main reason the establishment did not apply for any line of credit or loan in the last fiscal year?” Firms that answered “Yes”, were asked: “In the last fiscal year, did this establishment apply for any new loans or new credit lines that were rejected?” We classify firms that applied for credit and received a loan as unconstrained while we classify firms as credit constrained if they were either rejected or discouraged from applying due to “Interest rates are not favorable”; “Collateral requirements are too high”; “Size of loan and maturity are insufficient”; or “Did not think it would be approved”.

13
**_Table 1: Summary statistics_**

||N|Mean|Median|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Credit constrained|17,902|0.246|0.000|0.431|0.000|1.000|
|Green management|17,902|-0.019|-0.394|0.973|-1.908|6.980|
|Firm age (log)|17,902|2.719|2.833|0.727|0.000|5.263|
|Firm age (log squared)|17,902|7.920|8.027|3.722|0.000|27.700|
|Exporter|17,902|0.193|0.000|0.394|0.000|1.000|
|Percentage of employees with a<br>university degree|17,902|30.850|22.220|27.310|0.000|100.000|
|Publicly listed|17,902|0.060|0.000|0.238|0.000|1.000|
|Sole proprietorship|17,902|0.197|0.000|0.398|0.000|1.000|
|Audited|17,902|0.394|0.000|0.489|0.000|1.000|
|No. bank branches|17,902|247.600|87.000|417.100|1.000|3649.000|
|Local banks' average asset size in<br>2007 (log)|17,902|15.220|15.670|1.614|10.250|18.450|
|Leave-one-out mean credit<br>constraints|17,902|0.238|0.182|0.203|0.000|1.000|
|Change in local average Tier 1 ratio<br>(\% points)|17,902|1.702|1.529|7.684|-35.880|44.600|
|Leave-one-out mean green<br>management practices|17,902|0.193|0.000|0.697|-1.317|6.980|
|No data on leave-one-out mean<br>credit constraints|17,902|0.007|0.000|0.081|0.000|1.000|
|No data on leave-one-out mean<br>green management|17,902|0.118|0.000|0.323|0.000|1.000|

Source: Authors’ calculations based on Enterprise Surveys and BEPS II.

# **IV. Firms’ mitigation strategies**

Scope and strategic preferences of mitigation The number and quality of green measures that a firm adopts communicates its green investment strategy. The scope of firms’ transition to a low-carbon future describes the degree of adjustment required (Smit and Wandel, 2006) or the extent to which the firm reduces its environmental impact by, for example, pursuing several climate mitigation measures simultaneously. However, the mitigation measures pursued show different strategic preferences that reflect climate objectives and the level of ambition. A firm has a choice to implement multiple of these strategic measures at the same time, possibly with different intensities. Therefore, a mitigation strategy can be conceptualized as the combination of mitigation measures with possibly distinct strategic preferences that a company has. A first fundamental distinction between mitigation measures that a firm seeks to implement to reduce its energy cost and, in parallel, its carbon footprint was made by Thollander et al (2007).

Based on the work of Thollander et al (2007), we develop a framework by which all mitigation measures can be assigned to one strategic preference. For such a framework, two different aspects are considered in terms of how the mitigation measures can affect firms’ carbon performance in line with Kalantzis and 14
Revoltella (2019). Mitigation measures that help firms reduce their carbon footprint relate directly or indirectly to the company’s production model. Measures that are considered supportive, including energy management systems, improvement to lighting and air control measures, are less capital intensive and part of the “low-hanging fruit” strategies. By contrast, mitigation measures linked to the production line, such as more climate-friendly energy generation, machinery and equipment, and vehicle upgrades, are more capital intensive and usually are pursued by firms where the energy cost is an important input in their production processes. We express the measures along these two strategic preferences as measures to improve support processes and measures to improve production processes.

Hypotheses on determinants of firms’ mitigation measures In theory, measures targeted at production processes have high scope for emissions reductions, as they are related to producing their products or services. In energy-intensive firms, energy efficiency and mitigation measures are given higher priority in the investment budget because of the potential returns and cost savings that these measures entail. As a strategic investment, they are expected to be more prevalent in energy-intensive industries compared to non-energy intensive sectors. Nevertheless, empirical studies find that mitigation strategies concerning support processes have higher returns than those in production processes. Energy efficiency potentials are higher for production processes if suitable management practices relating to support processes are adopted by the firm (Backlund et al, 2012; Waide and Brunner, 2011; EC, 2006). As an example, Waide and Brunner (2011) illustrate that supporting management practices are vital in disseminating energy efficiency measures that would otherwise be concentrated on one piece of machinery and yield limited returns in the automotive industry. According to Backlund et al (2012) and Waide and Brunner (2011), while improvements in the technical potential of production processes are important to decarbonize activities, the returns of supporting processes that apply such technologies to their context and systems have greater potential.

In addition, capital spending for production processes is higher than investments in support processes. In their study, Kalantzis and Revoltella (2019) examine the impact of energy audits, a green management practice, on production versus support processes, measured by machinery equipment and the quality of buildings, respectively. They find that not only are energy efficiency measures easier to implement for support processes, but they also stand at a lower price point compared to production processes. Investments in support processes can also be less disruptive to a firms’ operation than those that focus on machinery and equipment and that may involve obtaining further certifications and licenses for quality control purposes. Overall, there are push factors for firms to incline their efforts towards energy savings on support processes rather than production due to associated costs, lead times, and potential returns. We stipulate two key hypotheses:

**Hypothesis 1.** Better green management practices overcome the information barriers to mitigation measures and induce firms to pursue, at least, “low-hanging fruit” (that is, non-capital-intensive) measures.

**Hypothesis 2.** The higher a firm’s awareness of possible climate change effects, as reflected by better green management practices, the more mitigation measures it will pursue.

15
Asymmetric information poses a challenge to mitigation strategies, as information constraints hinder a firm’s awareness and willingness to implement measures to address climate-related concerns. EIB (2020) finds a positive correlation between access to information about climate needs and investment in climaterelated measures. Indeed, firms that make efforts to improve information through green management practices, such as the employment of dedicated climate staff members, energy audits and climate targets, are more likely to invest in climate. All these practices can help alleviate information asymmetries by disseminating knowledge on the impact of climate-related risks and potential solutions to mitigate such. As outlined by Kalantzis and Revoltella (2019), this knowledge exchange can also support energy planning and the design of energy-saving strategies and associated measures. While there is a consensus that access to information is important for mitigation strategies, there are differences in the magnitude of effect reported by different studies. Kalantzis and Revoltella (2019) examine the effect of energy audits on firms’ energy efficiency investments and report that firms that conduct energy audits are more likely to invest in energy efficiency measures than those who do not. Meanwhile, Thollander et al (2007) find that while information campaigns lead to increased awareness regarding the importance of energy efficiency, they translate to a marginally small rise in the implementation of energy efficiency measures. In light of the above, we expect awareness of climate change, as reflected by better green management, to be positively correlated with the number of mitigation measures. We also expect that better green management practices overcome the information barriers to mitigation and induce firms to pursue, at least, “low-hanging fruit” measures. As such, we investigate two additional hypotheses:

**Hypothesis 3.** Credit constraints matter more for capital-intensive mitigation measures - in other words, climate measures in production processes.

**Hypothesis 4.** The more finance constrained a firm is, the fewer the mitigation measures it will pursue.

An additional barrier to mitigation strategies are financial constraints. Mitigation and energy efficiency investments can be costly and require sufficient financial capital for firms to undertake, especially if they are capital-intensive. This is in line with existing literature, which asserts that financial constraints are negatively correlated with climate-related mitigation measures (e.g. Götz, 2018, Fleitera et al, 2012). Fleitera et al (2012) find that the adoption of energy efficiency measures is mainly driven by profitability and that a significant limiting constraint is a firm ́s financial resources. Götz (2018) and Xu and Kim (2021) also find that financial constraints are negatively correlated with firms’ pollution abatement measures. Instead, financially constrained firms increase the likelihood of a firm polluting by 3% – 3.5% (Xu and Kim, 2021). Based on the discussion, we expect that the more finance constrained a firm is, the fewer the mitigation measures it will pursue.

# **V. Methodology**

Logistic regression estimation To capture corporate strategies to tackle climate change and policy-related impacts, we have taken into account the characterization framework of energy-efficiency investments with selected attributes based on Trianni et al (2014). As firms’ business decisions are influenced by a wide range of factors, including market demand, competition and environmental regulations, it is difficult to distinguish between mitigation measures that are only driven by climate change impacts and those that are the results of other 16
( o+B3,jCreditConstrained;+P, jGreenMgmt;+y;X;+Hr+0s5+é;) (B0+B1,4CreditConstrained; +B .GreenMgmt;+yv},X;+Ur+9s+é)
of different green investments among M categories of green investment is within the range of the cutpoints estimated for the outcome:<sup>< ββ0 + ββ1CCPPC</sup> P , (2) ii �<sup>κκmm−1CCCCCPPCCCCii+ ββ2GGPPCCCGGGCCii</sup> � where is firm (YY = GG) = P _i_ ’s choice of the number of green investments ii rr ss ii _m_ , and mm 1 2 are the cut-points +γγ<sup>′</sup> XX + μμ + θθ + εε ≤κκ ( ). ii MM−1 YY κκ , κκ , ... , κκ We are interested not only in the link between credit constraints, green management practices and the GG= 1,2, ... , GG We are interested not only in the link between credit constraints, green management practices and the type of green investment in our sample of (mostly) developing countries in Europe and Central Asia as well as MENA, but also in whether – and how – this link might differ depending on the region, given the differences in their progress in reducing emissions as well as policy choices regarding primary energy supply and fossil fuel subsidies, among other things. We thus estimate versions of equations (1) and (2), where we allow the estimated coefficients on our main explanatory variables of interest as well as all firmlevel covariates, the number of branches, and the average amount of 2007 assets held by banks owning those branches to vary by region. The parameters in all models are estimated by using maximum likelihood, and standard errors are clustered on locality.

# Logistic regression instrumental variables estimation

Past (green) investments could influence credit constraints or green management practices. If the firm has successfully completed an investment project recently, banks might view its new green investment project, either capital- or non-capital-intensive, more favorably. Alternatively, they might consider that the firm could overstretch and hence take a less favorable view. The banks might also view new green investment projects with several different measures less favorably. On the side of green management practices, investment in several measures, such as air pollution control, energy management, machinery and equipment upgrades, could facilitate the adoption of green management practices such as environmental monitoring or target setting. To deal with such concerns, we use an IV strategy, in line with the one used in De Haas et al (2022). Specifically, we assume that a firm’s local environment provides a source of exogenous variation that affects the firm’s decision about the type and number of green investments only via financing or the quality of green management.

When it comes to credit constraints, the international evidence shows that due to agency costs, small and medium-sized enterprises (majority of our sample) can only access nearby banks (Degryse and Ongena, 2005, Agarwal and Hauswald, 2010).<sup>5</sup> We can use the variation in those banks’ regulatory (Tier 1) capital ratio as a plausibly exogenous driver of firms’ financing constraints. Tier 1 capital ratio relates a bank’s core equity capital (shareholders’ equity and disclosed reserves) to its risk-weighted assets. During and after the global financial crisis, as well as after the 2011 regulatory stress tests by the European Banking Authority, many banks had to improve their Tier 1 capital ratio within a short period of time. Because raising additional equity was costly due to the difficult situation in the global capital markets, many banks deleveraged by shrinking their risk-weighted assets, including through cuts in lending (Gropp et al, 2019).

> 5 The median Belgian SME borrower in Degryse and Ongena (2005) and the median US SME borrower in Agarwal and Hauswald (2010) were located 2.5km and 4.2km, respectively, from the lending bank branch.

18
The first credit constraints instrument captures the fact that the intensity of deleveraging varied significantly across banks, even those within the same country. Firms that were surrounded by branches of banks that had to boost their Tier 1 capital ratio more during the crisis, found it more difficult to access bank credit; therefore, we expect a positive relationship between the increase in local banks’ average Tier 1 capital ratio and the likelihood that nearby firms were credit constrained. To create the instrument , we combine the information on the geographic coordinates of both firms and the bank branches that surround them, and measures the change in average Tier 1 capital ration over the period ΔTTC PP1 2007 (just before the global financial crisis) to 2014 (after both the global financial crisis and the ΔTTC PP1 subsequent Eurozone crisis) for all banks in a firm’s vicinity, where the latter is defined as a circle with a 15km radius.

The second credit constraints instrument is motivated by credit constraints being determined by local credit market conditions. This instrument is an average credit constraints indicator of firms within a 15km radius, excluding those from a firm’s own sector .<sup>6</sup> This is similar to the “leave-one-out” strategy pursued in “jack-knife” approaches (Angrist et al, 1999), and is calculated as follows:<sup>7</sup> CC(CC) 1 C C C C C = ∑ C C C C , (3) iissii jj∈[ss(jj)≠ss(ii)&dd(ii,jj)≤15kkmm] jj where CCPPC is the distance between firms CCPPC 1OO NN and . CCPPC CCPPC CC The green management instrument is motivated by green management being determined by the local CC(CC, jj) CC jj The green management instrument is motivated by green management being determined by the local diffusion of management practices. It is unlikely, though, that firms learn from every other firm in their vicinity; they are more likely to learn from larger firms only. We divide the firms in deciles based on the number of permanent, full-time employees and then calculate average green management z-score of firms within a 15km radius in deciles above the firm’s own decile. This implies that firms in the top decile in such a radius do not have any firms in the vicinity based on which we can calculate such average green management z-score; in such cases, we set this average to 0 and define an indicator variable equal to 1. The green management instrument is thus calculated as follows:

> 6 Sectors used can be found in Table A.3 in the Appendix.

> 7 Similar approaches have been used in a number of other studies, including Fisman and Svensson (2007), Aterido et al (2011), and Commander and Svejnar (2011).

19
( ot By, ;CreditConstrained +Ur+9s+e;) (Bo+B1,¢CreditConstrain Xj+Ur+9s5+é;) =m) =Pr ( )
Credit constraints can hinder green investment. Panel A of Table 2 also indicates that being credit constrained is associated with a 2.2 percentage points higher probability of making no green investment and a 3.3 percentage points lower probability of making both capital- and non-capital-intensive green investment. Average marginal effects of being credit constrained show no statistically significant impact on either capital-intensive or non-capital-intensive investments only. While credit constraints affect whether firms make a green investment, contrary to our hypothesis 3, there is no difference between the impact credit constraints have on capital-intensive versus non-capital investments in the overall sample. This could be because we do not know the actual capital intensity of each green investment the company reported. Investment in green energy generation on site, for example, could mean that the firm bought one solar panel and installed it on its roof (non-capital-intensive green investment) but it could also mean that the firm built a hydroelectric power plant (capital-intensive green investment).

**_Table 2: Multinomial logistic regression for type of green investment (average marginal effects)_**

||No green<br>investment|Capital-intensive<br>green<br>investment only|Non-capital<br>intensive green<br>investment only|Both capital- and<br>non-capital-<br>intensive green<br>investment|
|---|---|---|---|---|
|||_Panel A: O_<br>|_verall sample_<br>||
|Credit constraints|0.022*|0.001|0.009|-0.033***|
||(0.009)|(0.007)|(0.006)|(0.009)|
|Green management|-0.213***|-0.035***|0.018***|0.230***|
||(0.011)|(0.007)|(0.004)|(0.007)|
|||_Panel B: Allow all coef_|_ficients to vary by reg_|_ion_<br>|
|Credit constraints ECA|0.027**|-0.009|0.013|-0.031**|
||(0.009)|(0.007)|(0.007)|(0.011)|
|Credit constraints MENA|0.002|0.038|-0.006|-0.035*|
||(0.026)|(0.020)|(0.017)|(0.015)|
|Green management ECA|-0.194***|-0.038***|0.004|0.228***|
||(0.011)|(0.006)|(0.004)|(0.007)|
|Green management MENA|-0.281***|-0.072*|0.097***|0.256***|
||(0.036)|(0.032)|(0.011)|(0.018)|
|Observations||17|,902||
|Clusters||3|,022||

Note: This table shows the average marginal effect estimates of credit constraints and green management on the type of green investment, based on multinomial logistic regression with the type of green investment as a dependent variable. Regression controls for locality-level credit market controls (log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius) ; population size class; and region and sector fixed effects. In Panel B, estimated coefficients for all firm-level covariates as well as log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius are allowed to vary by region. Locality-clustered standard errors in parentheses. ***, **, * - statistically significant at 1%, 5%, and 10%, respectively.

Source: Enterprise Surveys and authors’ calculations.

The magnitude of the effect of credit constraints and green management quality on the type of green investment varies by region. Estimates in Panel B indicate that while credit constraints matter for some 21
types of green investment by firms in ECA, they are marginally relevant only for making both capital and non-capital intensive green investment in MENA. In MENA, green management quality plays a more important role for any type of green investment. A unit increase in the quality of green management in the MENA region is associated with a 28.1 percentage points decrease in the likelihood of making no green investment and a 7.2 percentage points decrease in the likelihood of making capital-intensive green investments only. It is associated with a 9.7 percentage points increase in the likelihood of making noncapital-intensive green investments only and 25.6 percentage points increase in making both capital- and non-capital-intensive green investments. The magnitude of these associations is larger in MENA than in ECA, and the magnitude differences are statistically significantly different from each other at a 5% level of significance. The estimates broadly confirm our hypothesis 1, but not hypothesis 3.

# _Number of different green investments_

Credit constraints and green management quality also affect the number of different green investments a firm makes. Table 3 presents the average marginal effect estimates of the association between credit constraints, green management quality, and the number of different green investments, based on the ordered logit regressions with the number of green investments as a dependent variable. We combine the number of different green investments into the following four categories: no green investments; one green investment; two to four different green investments; and five or more different green investments.<sup>8</sup> All regressions include locality-level credit market controls, and region and sector fixed effects.<sup>9</sup> Overall, firms are more likely to invest in more green measures if they have higher quality green management practices and experience fewer financial constraints. Average marginal effect estimates in Panel A indicate that a unit increase in the quality of green management practices is associated with a 19.6 and 2.4 percentage points lower probability of making no or only one green investment, as well as a 7.6 and 14.5 percentage points higher probability of making two to four, or five or more different green investments, respectively. In other words, the better the quality of green management, the more likely the firm is to make a higher number of green investments, congruent with hypothesis 2. Better quality green management practices can provide better access to information about climate needs and investment in climate-related measures, necessary to address asymmetric information and prompt firms to adopt a mitigation strategy. Similar to Kalantzis and Revoltella (2019), the magnitude of the effect indicated in this study is rather substantial (almost 15 percentage points) between such management practices and a firm adopting over five green investments.

In contrast, being credit constrained is associated with a 3.1 and 0.4 percentage points higher probability of making either no green investments or one green investment, respectively. It is also associated with a 1.2 and 2.3 percentage points lower probability of making two to four, or five or more different green investments, respectively. These results align with hypothesis 4, whereby the more credit constrained the firm, the fewer the mitigation measures it will pursue. As suggested in the literature, financial constraints can hinder green investment as it limits the capability of the firm to undertake the necessary capital

> 8 Table A.7 shows that the estimates are robust to using six categories of the number of green investments: no green investment; one green investment; two different geen investments; three different green investments; four different green investments; and five or more different green investments. The estimates confirm hypotheses 2 and 4 for the overall sample and ECA, as well as hypothesis 2 for MENA.

> 9 The estimates are broadly robust to the inclusion of firm-level controls; see Table A.6.

22
requirements associated with the mitigation measures.

**_Table 3: Ordered logistic regression for the number of different green investments (average marginal effects)_**

|Variables|No green<br>investment|One|Two to four|Five or more|
|---|---|---|---|---|
|||_Panel A: Ov_<br>|_erall sample_<br>||
|Credit constraints|0.031***<br>(0.006)|0.004***<br>(0.001)|-0.012***<br>(0.002)|-0.023***<br>(0.004)|
|Green management|-0.196***<br>(0.004)|-0.024***<br>(0.001)|0.076***<br>(0.002)|0.144***<br>(0.002)|
||_Pa_<br>|_nel B: Allow all coeff _<br>|_icients to vary by regi_<br>|_on_<br>|
|Credit constraints ECA|0.035***<br>(0.006)|0.006***<br>(0.001)|-0.012***<br>(0.002)|-0.028***<br>(0.005)|
|Credit constraints MENA|0.009|-0.001|-0.005|-0.003|
||(0.017)|(0.002)|(0.009)|(0.006)|
|Green management ECA|-0.182***|-0.035***|0.061***|0.156***|
||(0.004)|(0.001)|(0.002)|(0.003)|
|Green management MENA|-0.263***|0.027***|0.142***|0.093***|
||(0.011)|(0.003)|(0.006)|(0.004)|
|Observations||17,|902||
|Clusters||3,0|22||

Note: This table shows the average marginal effect estimates of credit constraints and green management on the number of green investments, based on ordered logistic regression with the categories based on the number of green investments as a dependent variable. Regression controls for the locality-level credit market controls (log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius); population size class; and region and sector fixed effects. In Panel B, estimated coefficients for all firm-level covariates as well as log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius are allowed to vary by region. Locality-clustered standard errors in parentheses. ***, **, * - statistically significant at 1%, 5%, and 10%, respectively.

Source: Enterprise Surveys and authors’ calculations.

In MENA, green management quality appears to be more important for the firms’ decision to make green investments (and the number of different green investments) than credit constraints; the estimates are consistent with our hypothesis 2, but not with hypothesis 4. Estimates in Panel B indicate that compared with firms in ECA, firms in MENA appear to be less sensitive to credit constraints when deciding on the number of different green investments to make – none of the estimated average marginal effects is statistically significant. In both regions, the number of green investments firms make seems to depend more on the quality of green management practices. In the ECA region, the pattern looks similar to the one in Panel A. A unit increase in the quality of green management practices increases in magnitude with the number of different green investments. It is associated with an 18.2 and 3.5 percentage points decrease in the likelihood of making no or one green investments, respectively, and a 6.1 and 15.6 23
percentage points increase in the likelihood of making two to four, or five or more different green investments, respectively. On the other hand, in the MENA region, a unit increase in the quality of green management practices is associated with a 26.3 percentage points decrease in the likelihood of making no green investments. Meanwhile, it is associated with a 2.7 percentage points increase in the likelihood of making one green investment, 14.2 percentage points increase in the likelihood of making two to four different green investments, and a 9.3 percentage points increase in the likelihood of making five or more different green investments. In other words, it appears that the decision of how many different types of green investments to make depends on some other factor besides the quality of green management practices, which serves as more of a threshold.

Logistic regression instrumental variables estimation As discussed in the methodology section, a firm’s investment decisions can influence its credit constraints and green management practices. In this subsection, we take a logistic IV regression, with three instruments for our two variables of interest.

Table 4 shows the results of the first stage. We regress each firm’s credit constraint indicator and green management score against the three instruments in columns 1 and 2, respectively. In Panel A, coefficients are not allowed to vary by region, while in Panel B they do so. The estimates confirm that firms are more likely to be credit constrained in the overall sample as well as in ECA and MENA separately if firms in their vicinity from other sectors are also credit constrained. In MENA, firms are more likely to be credit constrained if the banks in their vicinity had to increase their Tier 1 capital ratio between 2007 and 2014, and had to deleverage and reduce their risky assets. In column 2, the green management score is positively correlated with the local green management quality overall, and much more strongly for MENA than for ECA.

**Table 4: First stage IV regressions**

||Credit constrained<br>(indicator)|Green management<br>(z-score)|
|---|---|---|
||[1]|[2]|
||_Panel A: Ov_<br>|_erall sample_<br>|
|Local credit constraints|0.268***<br>(0.033)|0.046<br>(0.081)|
|Change in local average Tier 1 ratio (% points)|0.002<br>(0.001)|0.000<br>(0.003)|
|Local green management|-0.015*|0.241***|
||(0.006)|(0.039)|
||_Panel B: Allow all coeffi_|_cients to vary by region_|
|Local credit constraints|0.259***|0.108|
||(0.034)|(0.084)|
|Local credit constraints x MENA|0.314**|-0.396|
||(0.102)|(0.214)|

24
|Change in local average Tier 1 ratio (% points)|0.002<br>(0.001)|-0.001<br>(0.003)|
|---|---|---|
|Change in local average Tier 1 ratio (% points) x MENA|0.031*|0.090|
||(0.014)|(0.055)|
|Local green management|-0.009|0.233***|
||(0.006)|(0.041)|
|Local green management x MENA|-0.057**|0.304***|
||(0.020)|(0.075)|
|MENA|0.951|0.153|
||(1.053)|(2.036)|
|Observations|17,902|17,902|
|Clusters|3,022|3,022|

Note: This table shows the first stage regressions corresponding to Tables 5 and 6. Regression controls for localitylevel credit market controls (log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius); an indicator for no firms in other sectors in a 15km radius with data on credit constraints; an indicator for no firms in a 15km radius in higher deciles with data on green management; population size class; and region and sector fixed effects. Locality-clustered bootstrapped standard errors in parentheses. ***, **, * - statistically significant at 1%, 5%, and 10%, respectively.

Source: Enterprise Surveys and authors’ calculations.

# _Type of green investment_

The second stage average marginal effect estimates in Table 5, Panel A show that once we account for potential endogeneity of credit constraints and green management, the only significant estimates are found for both capital- and non-capital intensive green investment category. A unit increase in the quality of green management practices, equivalent to moving from the 10<sup>th</sup> to 50<sup>th</sup> percentile of the distribution of the quality of green management practices, is associated with a whopping 42.6 percentage points higher probability of making both capital- and non-capital-intensive green investment. This is almost twice the size of the estimate in Panel A of Table 2 that does not account for endogeneity. Our hypothesis 1 is thus only partly confirmed in this setting.

Overall average marginal effect estimates also indicate that being credit constrained leads to a 30.9 percentage points lower probability of making both capital- and non-capital-intensive green investment, although the estimate is statistically significant only at 10% level of significance.

Estimates in Panel B show that the overall average marginal effect estimates mask differences at the regional level. They indicate that credit constraints matter for some types of green investment by firms in ECA (at least at 10% level of significance): being credit constrained leads to a 29.7 percentage points higher probability of making no investment and a 15.2 percentage points higher probability of making noncapital-intensive investment only. However, credit constraints do not play a role in MENA firms’ decisions about the type of green investment. In MENA, green management quality plays a more important role for either no green investment or both capital- and non-capital-intensive green investment. A unit increase in the quality of green management in the MENA region leads to a 46 percentage points decrease in the likelihood of making no green investment, compared with 28.4 percentage points decrease in ECA. The 25
same increase in the quality of green management leads to a 36.2 percentage points increase in the likelihood of making both capital- and non-capital-intensive green investments in MENA, but a 41.6 percentage points increase in ECA – though the magnitude differences are not statistically different from each other at 5% level of significance. In ECA, a unit increase in the quality of green management is also associated with a 5.4 and 7.8 percentage points decrease in the likelihood of making capital-intensive and non-capital-intensive green investment, respectively. The estimates marginally confirm our hypothesis 1, but not hypothesis 3.

**_Table 5: Multinomial logistic IV regression for type of green investment (average marginal effects)_**

||No green<br>investment|Capital-intensive<br>green investment<br>only|Non-capital<br>intensive green<br>investment only|Both capital- and<br>non-capital-<br>intensive green<br>investment|
|---|---|---|---|---|
|||_Panel A: O_<br>|_verall sample_<br>||
|Credit constraints|0.268|-0.056|0.097|-0.309*|
||(0.224)|(0.073)|(0.190)|(0.134)|
|Green management|-0.351|-0.024|-0.050|0.426***|
||(0.260)|(0.079)|(0.169)|(0.057)|
|||_Panel B: Allow all coef_<br>|_ficients to vary by re_<br>|_gion_<br>|
|Credit constraints ECA|0.297*|-0.074|0.152*|-0.376**|
||(0.117)|(0.076)|(0.075)|(0.135)|
|Credit constraints MENA|0.229|0.306|-0.178|-0.357|
||(0.262)|(0.178)|(0.206)|(0.188)|
|Green management ECA|-0.284***|-0.054*|-0.078**|0.416***|
||(0.041)|(0.021)|(0.024)|(0.044)|
|Green management MENA|-0.460***|0.092|0.006|0.362***|
||(0.070)|(0.054)|(0.040)|(0.060)|
|Observations||17|,902||
|Clusters||3|,022||

Note: This table shows the average marginal effect estimates of credit constraints and green management on the type of green investment, based on multinomial IV logistic regression with the type of green investment as a dependent variable. Regression controls for locality-level credit market controls (log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius); indicators for no firms in other sectors in a 15km radius with data on credit constraints and green management; population size class; and region and sector fixed effects. In Panel B, estimated coefficients for all firm-level covariates as well as log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius are allowed to vary by region. Table 4 provides the first stage of the IV regressions. Locality-clustered bootstrapped standard errors in parentheses. ***, **, * - statistically significant at 1%, 5%, and 10%, respectively.

Source: Enterprise Surveys and authors’ calculations.

To some extent, lack of more significant results could be because we do not know the actual capital intensity of each green investment the company reported. Investment in green energy generation on site, 26
for example, could mean that the firm bought one solar panel and installed it on its roof (not particularly capital-intensive green investment) but it could also mean that the firm has built a solar power plant with thousands of solar panels (capital-intensive green investment).

# _Number of different green investments_

Turning to the number of different green investments, Table 6 presents the average marginal effect estimates of the association between credit constraints, green management quality, and the number of different green investments, based on the ordered logistic IV regressions with the number of green investments as a dependent variable.<sup>10</sup> Average marginal effect estimates in Panel A indicate that a unit increase in the quality of green management practices leads to a 35.2 and 5.7 percentage points lower probability of making no or only one green investment, as well as a 10.3, and 30.6 percentage points higher probability of making two to four, or five or more different green investments, respectively. This confirms our hypothesis 2. Better quality green management practices can provide better access to information about climate needs and investment in climate-related measures, necessary to address asymmetric information and prompt firms to adopt a mitigation strategy. In contrast, being credit constrained leads to a 26.5 and 4.3 percentage points higher probability of making either no green investments or one green investment, respectively. It also leads to a 7.8, and 23.3 percentage points lower probability of making two to four, or five or more different green investments, respectively. As was the case with the estimates not taking into account endogeneity in Table 4, these results align with hypothesis 4, whereby the more credit constrained the firm, the fewer mitigation measures it will pursue.

Panel B confirms hypotheses 2 and 4 for firms in the ECA region. For firms in the MENA region, however, the average marginal effects estimates for both credit constraints and green management are lower for the five or more different green investments than for the two to four green investments. They indicate that compared with firms in ECA, firms in MENA are much more sensitive to credit constraints if they do not make any green investment or if they make two to four different green investments (with the estimates statistically significant at 10% level of significance). Being credit constrained leads to a 20.3 percentage points decrease in the likelihood of making two to four different green investments, compared to a 6.5 percentage points decrease in ECA, but to an 18.6 percentage points decrease in the likelihood of making five or more different green investments.<sup>11</sup> In ECA, the number of green investments firms make seems to depend more on the quality of green management practices than on credit constraints. In the MENA region, they appear to be closer in terms of magnitude: a unit increase in the quality of green management practices leads to a 39.8 percentage points decrease in the likelihood of making no green investments, while being credit constrained leads to a 41.5 percentage points decrease. Meanwhile, a unit increase in the quality of green management practices leads to a 19.5 and 17.9 percentage points increase in the likelihood of making two to four green and five or more different green investments, respectively.

> 10 Table A.8 contains the equivalent estimates for the number of green investments variable with 6 categories. 11 The difference between the two average marginal effect estimates for the MENA region is not statistically significant. Estimates in Table A.8 indicate that none of the credit constraints average marginal effect estimates for MENA is statistically significant if the two to four different green investments category is split up into two, three, and four different green investments.

27
In other words, it appears that in MENA, the decision of how many different types of green investments to make depends on both credit constraints and the quality of green management practices, once the estimates account for endogeneity.

**_Table 6: Ordered logistic IV regression for the number of different green investments (average marginal effects)_**

||No green<br>investment|One|Two to four|Five or more|
|---|---|---|---|---|
|||_Panel A: Ov_<br>|_erall sample_<br>||
|Credit constraints|0.265*|0.043*|-0.078*|-0.230*|
||(0.117)|(0.020)|(0.039)|(0.100)|
|Green management|-0.352***<br>(0.043)|-0.057***<br>(0.012)|0.103***<br>(0.025)|0.306***<br>(0.040)|
||_Pa_<br>|_nel B: Allow all coeff _<br>|_icients to vary by regi_<br>|_on_<br>|
|Credit constraints ECA|0.283**|0.066**|-0.065*|-0.285**|
||(0.108)|(0.025)|(0.026)|(0.108)|
|Credit constraints MENA|0.415*|-0.025|-0.203*|-0.186*|
||(0.206)|(0.014)|(0.101)|(0.092)|
|Green management ECA|-0.313***|-0.073***|0.072***|0.315***|
||(0.037)|(0.009)|(0.009)|(0.037)|
|Green management MENA|-0.398***|0.024***|0.195***|0.179***|
||(0.054)|(0.006)|(0.025)|(0.025)|
|Observations||17,|902||
|Clusters||3,|022||

Note: This table shows the average marginal effect estimates of credit constraints and green management on the number of green investments, based on ordered logistic IV regression with the categories based on the number of green investments as a dependent variable. Regression controls for the locality-level credit market controls (log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius); indicators for no firms in other sectors in a 15km radius with data on credit constraints and green management; population size class; and region and sector fixed effects. In Panel B, estimated coefficients for all firm-level covariates as well as log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius are allowed to vary by region. Table 4 provides the first stage of the IV regressions. Locality-clustered bootstrapped standard errors in parentheses. ***, **, * - statistically significant at 1%, 5%, and 10%, respectively.

Source: Enterprise Surveys and authors’ calculations.

Taken together, these estimates suggest that policy measures that ease access to bank credit specifically for green investment might not suffice to see a substantial increase in green investments. This appears to be the case particularly for MENA and may be relevant for other countries at a similar level of development and climate change awareness. Governments and development banks should also consider measures that could strengthen green management practices. These may include disseminating information on best green management practices, requirements to measure and report environmental impacts or credit lines contingent on the adoption of better green management practices by firms.

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# **VII. Conclusions**

For the analysis of firms’ investment strategies, researchers have predominantly observed and described the role of various determinants, including information barriers and financial constraints in investment strategies in developed countries (Thollander et al, 2007; Fleitera et al, 2012; Kalantzis and Revoltella, 2019). Building upon previous research, this paper develops a new framework of firms’ mitigation measures. Using unique data from the joint EBRD-EIB-WBG Enterprise Surveys, it empirically examines the determinants of the mitigation scope (i.e. the total number of mitigation measures) and the number of measures that firms take to follow certain strategic preferences of firms’ investment areas. We specifically analyze factors that other researchers had previously suggested as possible determinants for firms’ investment strategies (De Haas et al, 2022; Kalantzis and Revoltella, 2019). Methodologically, we apply an IV logistic regression model in addition to the logistic regression model in our econometric analysis.

In line with our priors, we find that better green management practices positively impact the transition strategy of firms to a sustainable future. Moreover, the magnitude of this impact increases with the number of measures implemented. This result implies that more aware firms about climate change impacts and the various existing measures that tackle them are the ones to engage the most in mitigation measures. This relationship is exponential and positive and suggests that firms with the best management practices overcome information barriers and adopt more mitigation measures.

Our econometric analysis also shows that firms’ access to finance is an important determinant for adopting measures, especially for capital-investment measures, such as those in production processes. Specifically, the more financially constrained firms are, the less likely they pursue many mitigation measures to reduce energy costs and their carbon footprint. Once again, the exponential relationship is evident, implying that the impact of financial constraints on the investment strategy of firms is higher for the lower and upper range of measures that could be implemented.

Based on the above, the key findings are that financial constraints and information barriers not only influence the transition strategy of firms, but also the _number_ of measures that they implement along the different strategic areas, namely support and production processes. The econometric results further show that the impact of these factors and their importance in firms’ transition strategies are influenced by the location where firms operate. For example, in the MENA region, green management quality appears to be more important for the firms’ decision to make green investments (and the number of different green investments to make) than credit constraints. Although access to finance is essential for investing in green, mainly because green investments compete with other core-business activities, information appears to play a greater role in the investment decision-making process and the selection of mitigation measures in this geographical setting.

This has important implications. Climate change will continue to have significant effects on business activities requiring firms in various industries and regions to adapt appropriately at a local level. Policy makers should guide this process to enable an effective and cost-efficient business model transformation because some mitigation measures will require investment decisions with long planning and amortization times (Stern, 2007). In this respect, our findings are valuable as they explain how governments can support this transformation process. Governments could increase the scope of firms’ business model 29
transformation by influencing awareness of possible climate change effects. If perceptions of climate change are not aligned across sectors, counties and various actors, the effectiveness of the policy response is jeopardized and climate action is stalled (Kalantzis et al, 2021). This could, for example, include intensively addressing the topic or providing research and information such as improved climate forecasting (Scott and McBoyle, 2007). Governments and development banks should also consider measures that could strengthen green management practices. This may include disseminating information on best green management practices, requirements to measure and report environmental impacts or credit lines contingent on firms’ adopting better green management practices.

Governments also need to understand the reasons behind limited energy transformation among less informed firms, as reflected by their green management practices for target-setting purposes. Such firms are the most static in adjusting their business models, albeit the urgency to do so. Once more information is sought, governments can determine in which situations support for transformation makes economic sense and target such efforts into specific groups. It may be, for example, that transformation is no longer economically viable for highly brown sectors, e.g. coal power plants, mining etc. After such an assessment, governments can provide financial support (e.g. tax breaks on mitigation investments, subsidies) or capability building (e.g. technical support, skills training) for targeted firms who are able to transition. This would subsequently increase the uptake of mitigation strategies to minimize climate change risks. Support can also target specific mitigation measures, such as those related to production processes that can help reduce the financial losses from more stringent climate policies at a national and international level, thereby lowering the risk of financial default.

Moreover, governments could attempt to bring firms’ adjustments in line with their desired direction at the local or regional level. If, for example, governments seek to motivate firms to build their business model around more environmentally-friendly actions rather than protecting them by maintaining environmentally harmful subsidies, they would have to lower the dependency of firms, e.g. by incentivizing new business development activities. These examples show how governments could consider the active support of firms’ mitigation efforts on a broader level and rethink the future of entire regions or industries affected by climate change.

We should note that in our study, we investigated the role of information barriers and financial constraints in the green investment strategies of firms. Nonetheless, it is important to acknowledge that other factors may influence investment decisions in this regard. Thus, future studies could further investigate the reasons for implementing green measures after accounting for unexpected behavior between variables. In particular, it would be interesting to run an instrumental variable approach – despite the difficulty in finding good instrumental variables - to observe the magnitude and direction of the causation of financial constraints and information barriers, which is needed for designing better climate policies. Another future avenue for research would be to investigate whether the impact of these two factors differs across sectors and size of firms.

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