research paper on climate finance

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Article Contents

1. an inconvenient void, 2. launching a call for new research on climate finance, 3. contributions to the climate finance research program, 4. where does climate finance research go from here, 5. one final remark, acknowledgement, climate finance.

• Article contents
• Figures & tables
• Supplementary Data

Harrison Hong, G Andrew Karolyi, José A Scheinkman, Climate Finance, The Review of Financial Studies , Volume 33, Issue 3, March 2020, Pages 1011–1023, https://doi.org/10.1093/rfs/hhz146

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Climate finance is the study of local and global financing of public and private investment that seeks to support mitigation of and adaptation to climate change. In 2017, the Review of Financial Studies launched a competition among scholars to develop research proposals on the topic with the goal of publishing this special volume. We describe the competition, how the nine projects featured in this volume came to be published, and frame their findings within what we view as a broader climate finance research program.

Climate finance is defined by the United Nations Framework Convention on Climate Change (UNFCCC) to be “local, national, or transnational financing—drawn from public, private, and alternative sources of financing—that seeks to support mitigation and adaptation actions that will address climate change.” It is about investments that governments, corporations, and households have to undertake to transition the world’s economy to a low-carbon path, to reduce greenhouse gas concentrations levels, and to build resilience of countries to climate change. The European Commission estimates that energy and infrastructure investments, mainly from the private sector, would have to rise to 2.8% of European Union gross domestic product (GDP) from 2% today (or an additional |${\$}$| 376 billion annually) to reduce EU net greenhouse gas emissions to zero by midcentury. 1 Estimates for decarbonizing power generation in the United States are comparable. Absent mitigation and adaptation, a major scientific report issued by thirteen U.S. federal agencies in November 2018 predicted that the potential damage from the consequences of climate change would knock as much as 10% off the size of the U.S. economy by century’s end. 2

These large estimates only coarsely encapsulate the significant risks of global warming for firm profits, capital markets, and household wealth. Many sectors, ranging from energy, food, and insurance to real estate, are directly impacted by risks generated by a potential price on carbon, adverse shocks to agricultural productivity, or exposures to rising sea levels, to name a few. Underlying these concerns are difficult questions regarding the distribution of damages from global warming, how society should price and mitigate risks from global emissions, and whether capital markets—to the extent that they can assess and price these exposures—can raise such large sums and potentially help households and institutions hedge climate change risks.

Answers to these questions in turn depend on expectations that agents in the economy hold. A case in point is that ongoing worries by some in the private sector about wavering commitments to the Paris Climate Agreement goals have prompted efforts to mobilize, such as the Climate Finance Leadership Initiative and the newly launched Principles for Responsible Banking. 3 Such sustainable investing initiatives have the potential to influence the cost of capital for high carbon emissions companies even in the case of explicit carbon taxes. Raising the trillions necessary over the coming decades to address global warming will also no doubt rely on financial innovations.

Even though questions such as pricing and hedging of risks, the formation of expectations, and financial innovations are natural ones for financial economists to tackle, little research has been published to date in our top finance journals. The award of 2018’s Nobel Prize in Economics to William Nordhaus for his work on integrated assessment models for climate change going back to the mid-1970s (for example, Nordhaus 1977 ) reminds us of the missed research opportunity for financial economists to assess the risks associated with climate change as a global externality ( Nordhaus 2019 ).

The current volume represents the effort of the Review to remedy this dearth of research on climate finance. It was a problem that the editorial team of the Review recognized back in 2016. To this end, the editors, with the support of the Society for Financial Studies, launched a competition in 2017 using a novel editorial protocol—a Registered Reports (RR) format—that drew 106 submissions from scholars around the world on the topic. This competition paralleled in structure another one focused on the similarly under-researched topic of fintech that was published in the Review in May 2019 (see Goldstein, Jiang, and Karolyi 2019 ).

The idea behind RRs, a peer-reviewed editorial protocol developed in the cognitive sciences by the journal Cortex , is simple. Authors submit for review a research plan that designs an experiment, outlines the data to be collected, and describes potential interpretations of what findings may come; expert reviewers judge the proposed plan on its merit and not on the basis of the findings. Further, editors offer in-principle acceptance of the submission before final results are known. While the primary goal of RRs is to eliminate disincentives to publish negative or non-results and to mitigate publication bias, the editors of the Review believed this would be the ideal editorial protocol for a topic as controversial as that of climate change and global warming. 4 Not only would it allow for the truth about the potential economic consequences of climate change for financial markets to come to the fore, but it might provide the incentives needed to draw out hesitant scholars who might be inclined to take on new research on climate finance.

We, as the editors of this special volume, needed to secure additional support beyond that of the Society to make this competition work. Major financial support came from Norges Bank Investment Management for the preliminary project proposal workshop at Columbia University (November 2017) and for a conference co-hosted by Imperial College Business School in London (October 2018) at which the findings would be unveiled. We also received additional support from the Program for Economic Research at Columbia University, the Brevan Howard Centre at Imperial University, and two of the Prince of Wales’s charities, Accounting for Sustainability (A4S) and the University of Cambridge Institute for Sustainability Leadership (CISL).

The editorial team assembled a twenty-three-person scientific council of reviewers to help review the proposals among the first pass of 106 submissions and the finalists who were to be invited to the Columbia University workshop, at which a number of them served as discussants. 5 We also drew on dozens of additional anonymous reviewers who participated in one or more of the three rounds of reviews that each of the ultimately nine successful proposals survived.

In the remainder of this editorial, we will outline how the research competition evolved and what we received, and then lay out the findings on climate finance that this special volume represents within what we view going forward as a broader climate finance research agenda.

Our call for proposals was launched on January 31, 2017. We purposefully allowed a short five-month window for proposals to come forward with a deadline of July 30, 2017, in order that the competitive process would feature bona-fide proposals true to the goals of the RR process.

In the call for proposals, we asked for research on a number of questions, including: (i) linking trends in global temperatures to firm and industry cash flows; (ii) modeling valuations of vulnerable sectors to a warming climate; (iii) agency, governance, and general incentive problems that might distort corporate climate risk management; (iv) the risks of stranded assets for energy- and carbon-intensive firms and industries; (v) the building of hedge portfolios of climate state variables; and (vi) understanding a firm’s climate risk exposures based on sparse corporate disclosures or environmental ratings by public/private agencies.

Our expectations about what might come forward were modest given that there were so few available working papers dealing with such questions. Remarkably, we received 106 proposals from 281 scholars affiliated with 183 different universities, government agencies, banks, and private firms from around the world. Many of those scholars are from outside North America. Figure 1 shows that only 46% (129) of the scholars on the proposal teams are domiciled in the United States and only 5% (14) are from Canada. Figure 2 demonstrates that it was younger scholars that revealed an appetite for climate finance research; 63% of the contributing scholars were either assistant professors (141) or Ph.D. students (23).

Composition of authors among RFS climate finance proposals submitted by geographic location

This figure reports the country of domicile of affiliated academic institution for the 409 authors among the 106 RFS climate finance proposals received by July 30, 2017, to the open call issued on January 31, 2017.

Composition of authors among RFS climate finance proposals by academic rank

This figure reports the rank of the 281 authors among the 106 RFS climate finance proposals received by July 30, 2017, to the open call issued on January 31, 2017.

Figure 3 shows that the largest number of proposals fell into what we called the category of “Carbon,” which focused on carbon trading, adaptation, and stranded assets. This was not unexpected, as climate economics as a line of inquiry had already delved into this area. 6 The next largest category of submissions was under “Disclosure,” which examines mandated disclosures, the study of attitudes and actions of institutional investors and analysts; followed by “Investing,” which focuses on investing strategies, priced risk factors, and informational efficiency; “Uncertainty,” which examines modeling uncertainty, long-run risks, learning, beliefs, and ambiguity; and finally, “Weather,” which emphasized extreme weather risk.

Topic areas featured among the RFS climate finance Registered Reports proposals

This figure reports the topic areas among eight major topic areas among the 106 climate finance proposals received by July 30, 2017, to the open call issued on January 31, 2017.

The final nine papers that make up this special issue on climate finance reflect the diversity of the topics addressed in the submissions. They ask questions and use tools that have been emphasized by financial economists and show how finance can contribute to the discussion on the impact of climate change. We frame these contributions along the lines of what we view as a reasonable climate finance research agenda going forward.

3.1 Uncertain social cost of carbon

There has been considerable work and controversy surrounding the choice of discount rates in the climate economics literature on the social cost of carbon. Gollier (2013) provides a useful review. Financial economics has emphasized the importance of the Arrow-Debreu insight that discounting of payoffs should be state-contingent. Barnett, Brock, and Hansen (2019) combine asset pricing methods and decision theory to estimate the social cost of carbon. They point out that this estimation must face both the uncertain impact of climate on human welfare and the uncertainty concerning models that describe the transmission mechanism of human activity to climate.

Barnett, Brock, and Hansen (2019) nest influential models in the literature, such as those of Nordhaus (1977) and Weitzman (2009) . They show that the interaction of these two sources of uncertainty is multiplicative, such that the impact on the social cost of carbon is substantial when each component is important. They also show that the presence of ambiguity aversion and aversion to model misspecification can substantially increase the estimates of the social cost of carbon. Their methodology shows how one can effectively integrate simplified models developed by physical scientists with tractable models of the economic damage caused by climate change.

3.2 Hedging climate risks

While Barnett, Brock, and Hansen (2019) demonstrate how asset pricing approaches can inform even developed topics in academic research on climate change, Engle et al. (2019) show how traditional portfolio approaches can raise new topics such as how to hedge climate risks. They use another standard tool of asset pricing, namely that of mimicking portfolios, to build portfolios that are hedged against innovations in climate change news. They start by constructing a series of innovations on views on climate change using textual analysis of newspapers and then use the mimicking portfolio approach to build climate-change-news hedge portfolios. Engle et al. (2019) then show that the mimicking portfolios they construct can successfully hedge the innovations in climate change news using a number of out-of-sample performance tests. To the extent climate change news is a good proxy for underlying climate change risks, their mimicking portfolio approach can serve as a hedge against climate change risks.

This work harkens back to a large finance literature on economic tracking portfolios and macroeconomic variables such as GDP. An interesting avenue for future research would then focus on the extent to which temperature or other climate variables such as droughts can be economically tracked using the returns of stock portfolios.

3.3 Efficiency of capital markets and climate change

Given the macroeconomic impact of climate change, asset prices should be particularly sensitive to the exposure of their cash flows to climate risks. Murfin and Spiegel (2019) use a data set of recent residential real estate transactions matched with property level elevation and tidal data to ask whether house prices reflect differential sea level rise risk. In particular, they are able to separate the impact of sea level rise as distinct from the hedonic value of property elevation by using the pace of land subsidence and rebound as a source of variation in the expected pace of regional relative sea level rise. In contrast to the work of Bernstein, Gustafson, and Lewis (2019) or Baldauf, Garlappi, and Yannelis (2019) , their findings indicate limited price effects, perhaps due to optimism about sea level rise or the possibilities of mitigation and bailouts.

Baldauf, Garlappi, and Yannelis (2019) use transactions data to measure the effect of flooding projections of individual homes and local measures of beliefs about climate change on house prices. They document that houses projected to be underwater in believer counties sell at a discount when compared with houses in denier counties, suggesting substantial difference-in-beliefs across locations. In contrast to Murfin and Spiegel (2019) , Baldauf, Garlappi, and Yannelis (2019) emphasize the importance in beliefs about climate risk to how or if it is priced. Also, the National Oceanic and Atmospheric Administration (NOAA) data they use ignores land rebound and subsidence but accounts for local and regional tidal variability and variation in property risk due to protective land topography between a property and the nearest shoreline.

These two papers directly address an important and wide-ranging debate on the efficiency of capital markets in pricing risks associated with climate change. And whereas regulatory discussions on market efficiency have mostly focused on the “stranded asset issue” (or energy corporations’ exposures to potential carbon taxes), these papers point to more general vulnerabilities of other asset classes to climate change risks such as agricultural output from droughts ( Hong, Li, and Xu 2019 ) or even aggregate stock market performance ( Bansal, Kiku, and Ochoa 2016 ).

3.4 Beliefs and climate change risks

Beliefs play a crucial role in financing new technologies for climate mitigation and in determining prices of assets that are sensitive to climate change. Hence, characterizing the beliefs of investors and of key insiders of corporations such as CEOs is crucial to the efficient markets debate when it comes to pricing of climate change risks.

Three papers in this issue deal directly with the impact of beliefs. Choi, Gao, and Jiang (2019) seek to measure the impact of abnormally high local temperature on beliefs about climate change. They document that Google search volume for “global warming” in different cities around the world increases when the local temperature is abnormally high, indicating that attention to the topic increases at those times. In addition, by examining trading volume and returns in the stock markets of those cities during these events, retail, but not institutional, investors sell carbon-intensive stocks, and carbon-intensive firms underperform firms with low emissions.

Although institutional investors do not react to abnormal local temperature, Alok, Kumar, and Wermers (2019) document that professional money managers overreact to large climatic disasters that happen close to them, underweighting disaster-zone stocks to a much greater degree than distant mutual fund managers. They also document that this overreaction can be costly to fund investor performance.

Krueger, Sautner, and Starks (2019) survey global institutional investors on perceptions related to climate risk. They find that although institutional investors rank climate risk down the ranks relative to financial, legal, and operational risks, they still regard them to be important. The investors also believe these risks already affect firms they invest in. In addition, many investors report that they do take actions related to climate risks. Risk management and engagement are considered preferable to divestment. Krueger, Sautner, and Starks (2019) also found that these institutional investors believe that equity valuations in some sectors do not fully reflect climate risks.

3.5 Damage functions

A crucial input for analysis of climate change risks is the causal impact of higher temperatures on economic activity, what is called the distribution of damages. A large economics literature finds that years with abnormally higher temperatures (due to exogenous weather fluctuations) lead to lower economic activity in less developed countries. The literature argues that one can use these estimates to extrapolate the likely impact of 2 |$^{o}$| Celsius increases on output holding fixed any adaptations ( Dell, Jones, and Olken 2014 ; Schenkler and Roberts 2009). This analysis is subject to two caveats: (i) adaption in the long run should lessen the shock; (ii) climate scientists emphasize that short-term temperature variations are not a reliable gauge of long-run climate change, particularly if there are tipping points in damage functions.

Addoum, Ng, and Ortiz-Bobea (2019) extend this literature to account for the effects of temperature on establishment sales by building a detailed panel of temperature exposures for economic establishments across the United States to estimate how location-specific temporary temperature shocks affect establishment-level sales and productivity. They find that the population average effects on sales and productivity of these shocks are close to zero, indicating that establishments are able to accommodate these temporary shocks. This conclusion is subject to a caveat that long-run changes in temperature can and do lead to a rise in extreme weather risks. So extrapolating from short-term temperature changes is also potentially problematic for this reason. This study suggests that companies in developed countries are less likely to be affected by extreme temperature, at least on average.

3.6 Short-termism and corporate emissions

Given that corporations and insiders (CEOs) ultimately need to make investments to address climate change, two traditional corporate finance issues loom large: agency problems associated with corporate short-termism and financing constraints. Even absent the issues of externalities, agency and financial constraint consideration can result in a less than first-best level of investment to address climate change.

To this end, Shive and Forster (2019) use data on greenhouse gas emissions to show that independent private firms are less likely to pollute or incur Environmental Protection Agency (EPA) penalties than public firms. These results suggest that ownership structure, and hence agency and horizon considerations, may affect the climate change impact of firms. It would be interesting to further link emissions to real innovations as measured by patents geared toward climate change risks.

The taxonomy that we have laid out above is not a bad place to start as far as advancing the climate finance agenda. However, there are several other areas not covered by our set of nine papers that we think researchers should also tackle.

We see an important open question about the modeling and sharing of extreme weather risks. Extreme weather risks, such as the impact of Hurricane Sandy in 2012 on New York City property prices or the impact of the 2018 California wildfires on the Pacific Gas & Electric bankruptcy, are a reminder that climate change risks need not be confined to long horizons. Scientists predict that climate change will lead to more frequent and ever more extreme weather risks. Remote sensing (primarily through satellite or drone imaging) and machine learning can potentially help companies and society manage these risks.

Financial economists can use these tools to characterize the loss distributions using insurance data for floods or fires. These distributions in turn can depend on locational decisions by households and firms, technology decisions (such as by means of fire suppression or building codes) in terms of mitigating the damage of disasters, and the underlying weather processes themselves. Modeling these interactions and how they contribute to tail risks would be valuable for managers and policy makers.

Beyond modeling these loss distributions, the insurance and mortgage industries play critical roles in facilitating risk-sharing and extending credit in the aftermath of extreme weather events. Recent work by Ouazad and Kahn (2019) shows that mortgages in areas adversely affected by hurricanes are more likely to be securitized. This evidence is suggestive of the important role played by the finance industry in helping households manage climate risks.

A second major research push should focus on divestment, stranded assets, and the consequences for financial stability. Energy companies have become the new “sin stocks” facing divestment campaigns and lawsuits from shareholders alleging misleading disclosures regarding the costs of climate change. These divestment and legal campaigns are similar to what tobacco companies faced a generation ago and have the potential to influence the cost of capital of these companies (see, among others, Heinkel, Kraus, and Zechner 2001 ; Hong and Kacperczyk 2009 ). Recent work by Bolton, and Kacperczyk (2019) documents that institutions might indeed already be screening out companies of high carbon exposure based on similar considerations.

Such a divestment scenario in conjunction with lawsuits and lobbying for potential regulation would lead to significant stranded asset risk. Given the importance of the energy sector, this could be just the kind of systemic or financial stability type of event raised by outgoing Bank of England governor Mark Carney (2015) . Modeling this stranded asset risk requires integrating analyses of lobbying and regulation into otherwise standard asset pricing or corporate finance frameworks and would be extremely interesting.

A third research initiative is on the impact of climate change on municipal finance. Cities are increasingly affected by severe weather events. Even abnormal precipitation can cost millions to clean up and stretch already limited city budgets. Ratings agencies are considering incorporating climate change resilience measures into municipal bond ratings. How to measure resilience of municipalities and the impact of this resilience on municipal bond prices would be valuable. There is some preliminary evidence that municipal bonds have begun responding to some of these potential risks ( Painter Forthcoming ).

A fourth research agenda should focus on the impediments to corporate and financial innovation related to climate change. Despite numerous papers in climate economics, there is surprisingly little on corporate adaptation to climate change via innovations. The few notable exceptions include Miao and Popp (2014) , who address how droughts lead to more patent activity geared toward drought-resistant crops. Given the rich literatures in corporate finance on the determinants of corporate innovation and patent activity, a focus on the subset of patents associated with adaptation to climate change and the determinants or impediments to such adaptations would be valuable. To fund these corporate innovations, there will no doubt also have to be financial innovations. Some of these innovations, such as green bonds, are gradually emerging ( Flammer 2018 ; Baker, Bergstresser, Serafeim, and Wurgler 2018 ), and the nature and impact of such innovations will be worthy topics of future study.

Even though we financial economists are late to the game, we hope that this climate finance issue illustrates that there are many important questions where financial economists are naturally suited given their toolkit and interests. We are confident that the engagement of the broader academic finance community on these issues will no doubt lead to valuable contributions to improving the usefulness of the finance field to help society address perhaps unprecedented risks from climate change in the upcoming years.

This editorial is written for a special issue of the Review of Financial Studies focused on climate finance. The authors served as the editors of the special issue of papers, which was curated using a Registered Reports editorial format. The papers were presented at a workshop event in November 2017 at Columbia University and at a conference hosted by Imperial College London in October 2018. The presentations by the authors and the comments from plenary discussions at the workshop and conference were valuable in shaping the views shared in this editorial. We thank Norges Bank Investment Management (NBIM) and the Norwegian Finance Initiative (NFI) for substantial financial support and the invaluable advice of Wilhelm Mohn and Carine Smith Ihenacho of NBIM without which the workshop and conference events could not have happened. We thank the Program in Economic Research at Columbia University, Stephanie Cohen, and Sophia Johnson for help with the workshop. We thank Franklin Allen, Jaswinder Gil, and the Imperial College Business School’s Brevan Howard Centre for Financial Analysis for help with the London conference. Comments on this editorial are gratefully acknowledged from Jawad Addoum, Shashwat Alok, Darwin Choi, Jessica Fries, Lorenzo Garlappi, Itay Goldstein, Matt Linn, Justin Murfin, Sophie Shive, Matt Spiegel, Alexis Wegerich, and Zacharias Sautner. We finally thank the members of the Scientific Review Committee who agreed to help with this initiative as well as Charlie Donovan, Jessica Fries, Hannah Brockfield, Andrew Voysey, Nina Seega, and Yazid Sharaiha. Editorial assistance was gratefully received from Managing Editor Jaclyn Einstein and Dawoon Kim. Of course, all errors in the editorial remain the responsibility of the authors.

1 See “EU’s 2050 Climate Plan Sees Benefits of Up to 2% of GDP,” Euractiv , November 28, 2018. See also the joint letter to Commissioner Miguel Arias Canete from ministers from ten EU countries charting a “credible and detailed path” to net-zero emissions by 2050.

2 See “U.S. Climate Report Warns of Damaged Environment and Shrinking Economy,” New York Times , November 23, 2018.

3 According to the International Energy Agency, spending on renewable power such as wind, solar and biomass projects slipped 1% in real terms to |${\$}$| 304 billion in 2018, the lowest level since 2014 (“Falling Renewables Investment Stalls Paris Climate Goals,” Financial Times , May 14, 2019). Former mayor Michael Bloomberg, the UN Secretary-General’s Special Envoy for Climate Action, together with former SEC chairperson Mary Schapiro, announced the Climate Summit for the 74th UN General Assembly in September 2019. The UN Environment Program’s Finance Initiative launches the Principles for Responsible Banking at the same UN General Assembly.

4 The concept was outlined on May 3, 2013, in an editorial by Chambers and Della Sala (2013) , entitled “Journal Cortex Launches Registered Reports.” Further details are in Chambers et al. (2014) . Our editors are grateful to Chris Chambers for his advice early in planning in 2016, as well as helpful discussions with Brian Nosek of the Center for Open Science, which tracks all publications across disciplines using RR formats, and Rob Bloomfield and Christian Leuz of the Journal of Accounting Research , which published its own RR special volume in 2018.

5 We want to thank our committee including Ravi Bansal, Patrick Bolton, Francesca Cornelli, Magnus Dalhquist, Robert Engle, Xavier Gabaix, Stefano Giglio, Michael Goldtsein, Valentin Haddad, Lars Hansen, Leonid Kogan, Ralph Kojien, Per Krusell, Robert Litterman, Christopher Sims, David Sraer, Heather Tookes, Rossen Valkanov, Jessica Wachter, Jiangmin Xu, Jialin Yu, and Motohiro Yogo.

6 According to the Social Sciences Research Network (SSRN), there are 772 working papers posted (as of July 20, 2019) that fall under the topic area of “carbon trading” and another 45 under “stranded assets,” and the vast majority of those were posted before 2019.

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Finance for climate action: scaling up investment for climate and development

Humanity is at a crossroads – a moment of great risk and great opportunity. One path leads to attractive growth and development; the other to great difficulties and destruction. As shown by each successive report from the Intergovernmental Panel on Climate Change, climate change is occurring at a faster pace than previously anticipated, the impacts and damage are greater than foreseen, and the time for remedial action is rapidly narrowing.

This report of the Independent High-Level Expert Group on Climate Finance is intended to provide a framework for finance for climate action, covering the overall needs for the comprehensive approach embodied in the Paris Agreement and UNFCCC. All the elements are necessary and urgent and most of the actions must start now; it is the science and the world’s perilous condition that set the urgency and timing.

The logic of this paper follows from the logic of delivering on the goals of the Paris Agreement and the Glasgow Pact. The first part focuses on the purpose and necessary investment and actions, drawing on earlier work on the analysis of investments. The second part is about the scale and nature of the different forms of finance that are necessary and how they complement each other. The final part is on how the framework and the key elements described can be taken forward through our systems for international collaboration.

Main messages

• Acting on climate is about transforming our economies, particularly our energy systems, through investing in net zero, adaptation, resilience and natural capital. Achieving this transformation will not be easy. It requires strong investment and innovation, and the right scale of finance of the right kind and at the right time.
• The failure to deliver the climate finance commitment of $100 billion per year by 2020 made by developed countries at successive COPs has eroded trust. The world needs a breakthrough and a new roadmap on climate finance that can mobilise the $1 trillion per year in external finance that will be needed by 2030 for emerging markets and developing countries (EMDCs) other than China.
• A major, rapid and sustained investment push is needed to drive a strong and sustainable recovery out of current and recent crises, transform economic growth, and to deliver on shared development and climate goals.
• The key investment priorities must encompass transformation of the energy system, respond to the growing vulnerability of developing countries to climate change, and restore the damage to natural capital and biodiversity.
• Country/sector platforms driven by countries can bring together key stakeholders around a purposeful strategy, scaling up investments, tackling obstacles or binding constraints, ensuring a just transition and mobilising finance, especially private finance.
• The scale of the investments needed in EMDCs over the next five years and beyond will require a debt and financing strategy that tackles festering debt difficulties, especially those of poor and vulnerable countries, and that leads to a major expansion of both domestic and international finance, public and private, concessional and non-concessional. 

This report was prepared by the Independent High-Level Expert Group on Climate Finance, co-chaired by Dr Vera Songwe and Professor Lord Nicholas Stern, at the request of the Egyptian Presidency of COP27, the UK Presidency of COP26 and the UN Climate Change High Level Champions for COP26 and COP27 .

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• Published: 17 June 2022

Integrating sustainability into climate finance by quantifying the co-benefits and market impact of carbon projects

• Jiehong Lou   ORCID: orcid.org/0000-0003-4606-7756 1 ,
• Nathan Hultman   ORCID: orcid.org/0000-0003-0483-2210 1 ,
• Anand Patwardhan 1 &
• Yueming Lucy Qiu   ORCID: orcid.org/0000-0001-9233-4996 1  

Communications Earth & Environment volume  3 , Article number:  137 ( 2022 ) Cite this article

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• Climate-change mitigation
• Interdisciplinary studies
• Sustainability

High-quality development rooted in low-carbon growth, new jobs, energy security, and environmental quality will be a critical part of achieving multiple sustainable development goals (SDGs). Doing this will require the dramatic scaling up of new climate finance while maximizing co-benefits across multiple outcomes, including for local communities. We developed a comprehensive methodology to identify different levels of local co-benefits, followed by an econometric analysis to assess how the market values co-benefits through the clean development mechanism. We find that projects with a likelihood of delivering the highest co-benefits received a 30.4% higher price compared to projects with the lowest co-benefits. Project quality indicators such as the Gold Standard, in conveying higher likelihood of co-benefits, conferred a significant price premium between 6.6% and 29%. Our methodology of aligning co-benefits with SDGs and the results of co-benefits valued by the markets indicate approaches to bolstering social and political support for climate finance.

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Introduction

We live in a world that is affected by climate change, that has finite resources, and that calls for global efforts to achieve a sustainable low-carbon future, where carbon benefits should be aligned with broader development goals 1 . Those diverse economic and development benefits created from climate actions, such as improving air quality, empowering women, improving farmers’ livelihoods, or creating local jobs, often are termed “co-benefits.” In many development contexts, and in many specific communities, these co-benefits are concrete and near-term, and are often seen as more directly valuable than carbon benefits. The co-benefits approach, therefore, could motivate action on climate change 2 and incentivize political support 3 by engaging a broader range of stakeholders 4 . In the context of sustainable development goals (SDGs), climate action is more than just one of the 17 SDGs, it has been shown to have strong synergies and trade-offs with other SDGs 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 . Clearly, a comprehensive understanding of the co-benefits aligned with SDGs presents potential to achieve climate change mitigation and non-climate objectives.

On the path to a low-carbon, sustainable growth transition, climate finance is a crucial aspect of achieving both climate and sustainable development goals—particularly through enabling large-scale investments in reducing greenhouse gas emissions and adapting to the adverse effects of climate change 12 , 13 . However, how the climate finance market values co-benefits remains poorly understood. This can bias policies 14 , 15 or otherwise limit the mobilization of climate finance, especially private finance 16 , potentially reducing the real co-benefits delivered to local communities.

Current research on integrating sustainability criteria or co-benefits into sustainable investing has faced several challenges. First, while it is fairly simple to calculate the cost of projects, the co-benefits are much harder to measure or estimate because these benefits are often intangible or non-monetary (such as health co-benefits). Second, standards to measure these bottom-up or distributed co-benefits are undefined and inconsistent. Third, there is a lack of globally comprehensive reporting and assessments for the different co-benefits that map on to the different SDGs. Due to inadequate co-benefit data disclosure standards and performance metrics, these scattered and inconsistent approaches further prevent researchers from assessing the presence, extent, and determinants of co-benefits 17 .

While the challenge of leveraging much larger amounts of climate finance is broadly recognized, only partial answers have been provided by previous research. Some qualitative research seeks to identify co-benefits of climate finance projects by a multiple-dimension–multiple-indicator methodology, ranging from simple project checklists 18 , 19 , to a more complicated method of extracting co-benefit-related information and building up a profile of co-benefits for each project for comparison 20 , 21 , 22 , 23 , 24 , 25 , to the most complicated method of multi-attributive assessment with a combination of indicators of qualitative, semi-quantitative, and quantitative natures 26 , 27 , 28 , 29 , 30 , 31 . These methodologies help elucidate the benefits but largely are blind to interactions between projects and market actors, particularly how much market actors value co-benefits. Another research strand evaluates co-benefits quantitatively but limits the scope to specific, easily measured and comparable categories, such as environmental indicators (e.g., CO 2 , SO 2 , etc.) or socioeconomic indicators (e.g., income, employment, etc.) 32 , 33 , 34 , 35 .

While research has expanded quickly—particularly on developing co-benefit indicators and specific, measurable outcomes—there remains less understanding of the extent to which the presence of co-benefits, especially at the local level, is valued by investors. To address this gap, we first develop an analytical framework to categorize SDGs and local co-benefits (Fig.  1 ). We test this framework using econometric analysis of how co-benefits are valued by market actors in an application of climate finance: using historical experience with a similar, real-world experiment, the clean development mechanism (CDM). As the major international carbon offset mechanism under the Kyoto Protocol 36 , the CDM was designed to lead to significant emission reductions that would both lower the cost of climate mitigation in developed countries and contribute to sustainable development in the host countries. It therefore provides a helpful historical experience that can illuminate connections between investor preferences and policy goals to support development outcomes and emissions reductions, with its nearly 8000 projects across 105 host countries, each of which generated tradable quantities of emissions reductions called certified emissions reductions (CERs). The link between the CDM and local co-benefits has been studied at some length via case studies and other qualitative approaches but assessments based on empirical data have been sparse 37 . To carry out this research, we also refine and improve data on the CDM from an existing database, by adding Emission Reduction Purchase Agreement (ERPA) dates and buyers’ sectoral information and profit status for each project. Our dataset provides the most comprehensive listing of buyers and sellers in the CDM market.

Images from: UNFCCC COP23 and United Nations.

We first establish five broad goals of co-benefits as indicated in the upper layer. Each of these five broad goals is associated with one or more SDG goals (second layer). We then produce an SDG-interaction score for each SDG, based on the specific project. Interaction components can be either positive and negative, which leads to aggregate positive and negative scores. We support the scores with evidence from the literature and confidence level assigned by the authors.

By focusing on local co-benefits, this research highlights the importance of valuing co-benefits where projects are located, and how these projects deliver impacts on local communities. Accordingly, for this paper we ask two questions: (1) do potential co-benefits from CDM projects encourage buyers to pay more as reflected in the credit price? and (2) do CDM projects with external certification deliver a price premium based on their guaranteed co-benefits? To answer the question of whether co-benefits encourage a premium, we conduct an econometric analysis of CER prices for 2259 projects for the co-benefits based on a new SDG and co-benefits analytical framework. We find that a project with a likelihood of delivering the highest co-benefits received a 30.4% higher credit price compared to projects with the lowest co-benefits. To answer the second question of whether externally certified projects deliver a price premium, we add an investigation of the so-called the Gold Standard (GS) certification for CDM credits, which focuses on sustainable development benefits. We then perform another econometric analysis of a group of 2195 regular CDM projects and 64 “Gold Standard”-certified CDM projects through a combined technique of exact matching, propensity score matching, and regression adjustment. Our results show that project quality indicators such as the Gold Standard, by conveying higher likelihood of local co-benefits, conferred a significant price premium in the range of 6.6–29%. This paper adds to our understanding of the link between investors and co-benefits from climate or carbon benefits via the CDM, which is essential for unlocking potential climate finance from the private sector. The compelling evidence from our analysis illustrates the crucial importance of rooting co-benefits with the carbon benefits. It further adds to the discussion of the importance of co-benefits in mobilizing broader stakeholder engagement—two important components of which are the local communities and climate finance investors assessed in this paper.

Assessing co-benefit valuation through an SDG co-benefit framework

The first approach we take is to assess market price premiums of co-benefits, as reflected in the CER prices for projects with different kinds of co-benefits. To do this, we develop a two-layer framework for categorizing the overall local co-benefits of carbon projects (Fig.  1 and Supplementary Fig.  1 ). The first layer captures the five categories of local co-benefits, while the second layer captures broader SDG dimensions. For this research, we focus on project components that only have a local focus on local co-benefits. We adopted a methodology of analyzing SDG benefits from McCollum (2018) 38 and the IPCC special report on Global Warming of 1.5 °C 39 . We then assess the co-benefits of projects in a more quantified way. We used a systematic literature review to assess and score SDG targets and then linked those to the potential CDM co-benefits 37 . Detailed steps and methods of this framework are summarized in the “Methods” section and also the Supplementary Note  1 .

Using this framework, we grouped 2195 projects into eight levels of co-benefits (Fig.  2 ) and sorted the level of co-benefits delivered into ranked categories. The framework was derived from an extensive structured literature review as described in Hultman, Lou, and Hutton (2020) 37 . A detailed table of this SDG framework with all the supporting literature can be found in Supplementary Table  1 .

Images from: United Nations.

This figure illustrates different levels of potential local impacts from CDM projects as drawn from the literature. Green shaded colors indicate limited, medium, and high positive impacts. The yellow color points to the projects which might have both positive and negative impacts on the local communities. The red color indicates potential negative impacts. The higher score the co-benefit gets in the final score columns, the larger value of the co-benefit.

We then conducted the regression analysis by performing four specifications (Table  1 ). Across the four models, coefficients of co-benefits show an increasing trend. In both model 3 and model 4, the coefficients of co-benefits are all statistically significant at a 95% confidence level, except for the co-benefit 5 category, which includes energy efficiency (EE), households, and small hydro projects. There is a clear increasing pattern in all models except for co-benefit 6 in model 1 and co-benefit 7 in model 4. The overall trend is consistent across the four models. Our preferred model, model 3 indicates that after controlling for projects’ features and sellers’ background, projects with a likelihood of delivering more co-benefits receive higher CER prices. For example, projects in co-benefits level 2 are likely to have an average $1.53/tCO 2 e (11%) price premium compared to projects in co-benefits level 1. Additionally, we plot the point estimates and 95th percentile confidence intervals of co-benefits of model 3 and model 4 to show the trends visually in Fig.  3 . The overall results support our initial hypothesis that customers value climate finance projects with high co-benefits more and this is reflected in the market price.

a Point estimates from linear model. b Point estimates from log-linear model. Base case in both models is co-benefit 1. The black circles represent the point estimates of each co-benefit compared to base case, which are obtained from running the regression. The orange vertical bars represent the 95% confidence intervals of the estimations.

Impact of certified premium CDM projects on perception of co-benefits and price

In addition to allowing an evaluation of standard CERs, the historical experience of the CDM provides another approach to evaluate the link between anticipated benefits of projects and the overall market price. This approach is based on the presence of a small subset of CDM projects that sought and acquired a third-party quality label called the “Gold Standard”. Unlike the regular CDM, which makes no claim to the specific projects or co-benefits that the CDM generates, these independent labels or other indicators can potentially send a signal to CER purchasers that a labeled project has higher co-benefits, and this might then stimulate higher market prices for the CER prices 26 . This certification standard provides an add-on methodology to evaluate the quality of the project across several dimensions, including specific safeguards and requirements for project type. The standard establishes a methodology that certifies projects that not only achieve the goal of emission reductions but also can deliver on at least two SDGs that are important to ensuring that the benefits are delivered to local communities 40 , 41 .

We can thus compare GS projects to regular CDM projects to estimate the actual price premium a buyer is willing to pay for a Gold-Standard-labeled CDM project. To do this, we conducted a propensity matching and exact matching analysis with five alternate models (Table  2 ). These five models represent different matching techniques, which are explained in detail in the “Methods” section. Across the five models, coefficients of the treatment effect are all statistically significant at a 90% confidence level. The difference between the CER prices of Gold Standard and regular CDM shows consistent trends in all the five models. Model 1 indicates that statistically controlling for differences in projects’ features and sellers’ background, Gold Standard projects received a price premium of $1.90/tCO 2 e (10.3% of CER price increase due to the Gold Standard Certification in Supplementary Table  2 ). Results of models 2 and 3 indicate that when matched on their propensity to receive Gold Standard, projects with Gold Standard displayed a higher price premium. Compared to matched projects without certification, the price premium is from $4.21/tCO 2 e (29%) to $2.58/tCO 2 e (14%). However, due to the poorly matched results from model 2, estimates from model 2 might overestimate the impact of Gold Standard. Model 4 displays an estimate of the effect of Gold Standard for CDM projects that, within each credit buyer’s country, were predicted to have statistically similar propensities of obtaining Gold Standard certification. The price premium from model 4 is $2.33/tCO 2 e (11.2% of CER price), close to the results from model 3. Model 5 presents an estimate of the price premium where each credit buyer’s country and project location (country level) should be exactly matched, were predicted to have statistically similar propensities of obtaining certification. The price premium from model 5 is $1.13/tCO 2 e (6.6%), which is also expected. In model 5, due to our limited number of projects in the treatment group, eventually, we only have two locations of projects left in the model: China and Vietnam.

Heterogenous effects analysis

One potentially influential factor in co-benefit valuation is whether the company or the geographical location of buyers matters to their relative priority for the quality of sustainable development versus simply low-cost credits 42 . We used information on 218 companies across 21 countries to study the credit buyers’ behavior by country and company. Supplementary Fig.  8 presents the scale of purchasing CDM projects and the average CER prices aggregated by country, showing country-level variation from the average global carbon price.

Table  3 shows the results of all company-level regression models. One set of results with statistical significance relates to the location of the projects. For example, credit buyers paid higher prices for CERs generated in Africa compared to those based in the other regions. Results indicate that a 10% increase in the proportion of projects that are located in Asia is associated with a $0.42/tCO 2 e (a 1.9%) decrease in the CER prices compared if the projects are located in Africa. In terms of credit buyers’ industry or for-profit and not-for-profit status, we do not find a price difference among them. This indicates that in the compliance carbon markets, prices of CERs do not differ based on buyers’ profit status or sectors.

We further evaluate the buyers’ preference for the Gold Standard certification in the compliance carbon markets through the hedonic price method. We classify the credit buyers either through their types of sector (Fig.  4 a), or their types of profit status (Fig.  4 b), and show the resulting point estimates, with more detailed results presented in Supplementary Tables  3 and 4 . Figure 4a shows that the results of the analysis comparing the Gold Standard CDM projects and regular CDM projects are statistically significant in the following industries: industrial and material, carbon-related (including carbon assets management, carbon consulting management, etc.), and government and foundation. If these buyers operate in the industrial and material sector, the results indicate that the price premium of Gold Standard CDM projects paid by credit buyers is $6.50/tCO 2 e or 32% more, compared to those regular CDM projects. For credit buyers focused on carbon-related asset management, the price premium was $2.90/tCO 2 e or 14% more if the projects obtained Gold Standard certification. Buyers from government entities and foundations are willing to pay $1.60/tCO 2 e or 15% more if the projects are certified by the Gold Standard. The rest of the coefficient estimates of interest are not statistically significant. We find no price difference between Gold Standard certified projects and regular CDM projects in other industries.

a Coefficients of interaction between buyer’s sector and treatment. b Coefficients of interaction between buyer’s profit status and treatment. It shows that the treatment effects differ across buyer’s sectors. The red bars represent the point estimates of each interaction, which are obtained from running hedonic models. The black vertical bars represent the 95% confidence intervals of the estimations.

Figure 4b presents the results of analyzing the different preferences over Gold Standard-certified CDM projects and regular CDM projects based on broader buyers’ status as for-profit and not-for-profit. The results from the for-profit entities have no statistical significance, while the results from not-for-profit (government and multilateral development banks (MDBs)) entities continue to have statistical significance. The price premium for Gold Standard CDM projects is $2.30/tCO 2 e or 19% more from government entities and is $0.60/tCO 2 e or 7% more from MDB, respectively. The rest of the coefficient estimates of interest are not statistically significant. The results deliver an important message that non-for-profit organizations value co-benefits more, and they are pushing the assessment of the co-benefits in the local communities by purchasing the Gold Standard-certified CERs with a price premium.

Overall, our results demonstrate that a project with a likelihood of delivering the highest co-benefits received a 30.4% higher credit price compared to projects with the lowest co-benefits. We also find that project quality indicators such as the Gold Standard, in conveying higher likelihood of local co-benefits, conferred a significant price premium in the range of 6.6–29%. Our results show that organizations supported by the public funding are willing to pay more for projects with higher co-benefits for the CERs with a Gold Standard certification in the compliance carbon markets. However, we do not see that the private sector is willing to pay more in general. When we break the entire private sector down further in detail, we observe certain sectors, such as industrial and material, and carbon-related services (including carbon assets management, carbon consulting management) are willing to pay more for projects with higher co-benefits for the CERs with a Gold Standard certification. Their willingness to pay is even higher than for public entities. One possible explanation of carbon-related asset management paying more for Gold Standard projects is that they are the experts in the carbon market and have deeper awareness of the add-on value that the Gold Standard provides to the projects. We highlight the potential implications of communicating the value of co-benefits to a broader set of stakeholders, including investors, local communities, project developers, and policymakers, to fully capitalize on potentially positive impacts. Additionally, our results show that investors are willing to pay more for projects in certain locations (e.g., African countries) or that have certain project-related attributes (e.g., small wind, small solar, and small biomass projects).

As countries scale up climate finance, it is clear from the historical experience with CDM that an expanded global financial flow to support projects that reduce greenhouse gas emissions can create tension in providing financial return while still delivering co-benefits and carbon benefits. And while co-benefits are difficult to monetize, they represent the real impact on local communities and are a critical component of broader national development strategies. From a policy perspective, it is also essential to set a framework that can place a high strategic value on delivering local co-benefits to receiving communities through climate investment. In this framework, public funding can play an enabling role at the early stage, mobilize for-profit entities to engage in climate finance, optimize the value of climate investment, and support delivery of real and lasting sustainability. Our results highlight the importance of effective communication of co-benefits, aligning with SDGs, among the entire society.

While CDM experience was heterogeneous in its ability to deliver community co-benefits, those benefits were more likely to manifest in projects following general best practices for finance, including significant community consultation and engagement in the planning and implementation process. Discussions on any new system focused on sustainable development or climate finance goals should include mechanisms that support local co-benefits. Additionally, the new system should provide safeguards of preventing undesired effects derived from climate actions. To do this, the international climate community can establish safeguards in three channels. First, learn lessons from extensive experience in the Reducing Emissions from Deforestation and forest Degradation (REDD+), which provides an improved understanding of safeguards associated with its implementation 43 . Policy should seek to promote safeguards, such as effective participation of local communities 44 ; add-on incentive mechanisms 45 ; the necessary level of social and political support by linking the co-benefits further to SDGs 3 , to avoid negative impacts from climate action. Second, given that socioeconomic conditions are recognized to co-improve SDG indicators with climate policies 9 , policymakers should make a joint effort to implement both and in doing so, should focus their implementing policies centrally on the reporting and transparency needed to evaluate progress on this goal. Finally, national or subnational prioritization for projects should generate co-benefits that are closely related to households, that have a direct positive impact on them, and that enables wider societal and systems transformation.

Our research utilized variations in the co-benefit valuation through the SDG co-benefit framework to establish a correlation between co-benefit valuation and carbon prices for the regular CDM projects, and a causal connection between project quality indicators, which guaranteed higher co-benefits delivered, and carbon prices for the CDM Gold Standard projects. This approach fills the sparse comprehensive quantitative analysis gap based on empirical data in the co-benefit literature 38 . More importantly, our model provides a way to better link co-benefits and local communities robustly and in a way that is suitable for understanding policy priorities in a broader setting. Second, our results could be further elucidated and complemented by qualitative analyses that focus on local knowledge and communication in places where projects are located. One of our findings is that certain regions gain more attention from investors due to co-benefits. Therefore, a future qualitative analysis would add depth and further context to how this attention is experienced and what could be gained by adjusting or enhancing this aspect. Finally, it contributes to the research on preventing undesired effects derived from climate action. The SDG co-benefit framework developed here can relatively easily be applied in other contexts of sustainable infrastructure investment. It allows for evaluating co-benefits either at the project level or aggregated levels such as region or country. The framework could be enhanced in other ways through project evaluation reports or interviews. This and similar assessment frameworks can assist the sustainable finance area by providing some guidelines for including these considerations in the new sustainable development era.

Our paper provides strong evidence that the carbon market values co-benefits through investors’ willingness to pay more for projects with higher co-benefits. Nevertheless, several limitations are worth noting. First, the SDG interaction scores are based on the authors’ judgment supported by a large literature review. While we have sought to include transparency on these elements (for example, through the confidence scores), this could be approached differently with a structured expert elicitation or other techniques. Second, our assessment of co-benefit valuation through the SDG co-benefit framework is based on the project technology types. Further extension of this current study on assessing the link between co-benefits and financing can be developing more fine-grained resolution based on individual project level to capture the variations of co-benefits of each project. Third, our sample focused on a specific mechanism, CDM, a compliance carbon market, and there is a need to test this approach across other mechanisms and settings (e.g., the voluntary carbon market, sustainable development mechanism). Finally, we did not assess CDM afforestation/reforestation (CDM-AR) projects due to their significantly different characteristics. However, we believe the results from this study might provide some perspective on enhancing the social and financial viability of CDM-AR-type projects, including implementation of Article 6 of the Paris Agreement. The evidence of investors’ willingness to pay more for projects with higher co-benefits could also support appropriate AR projects given that such projects can potentially offer multiple economic, social, and environmental benefits. A sectoral approach, such as through a new sustainable development mechanism, would potentially enable the host countries to scale up AR projects to achieve both emission reductions and sustainable development benefits.

Build up interactive SDG co-benefit framework

From our previous systematic literature review 37 , we find that a great deal of variation in co-benefits existed not only among project types but also within project type. In this section, we take one step further to assess the co-benefits of these projects in a more quantified way by drawing up studies of scoring exercise at the level of the SDG targets to better understanding the interaction between the CDM project technologies and the SDG dimensions. The two primary studies on this topic are 38 and the Intergovernmental Panel on Climate Change (IPCC) special report Global Warming of 1.5 °C 39 . Both studies have conducted thorough research on the potential SDG targets from the deployment of mitigation options. Our paper adopts the structure of integrating the SDG targets into the mitigation options from both studies, while adding another layer of five co-benefit criteria on top of this structure. Thus, the final structure of the assessment is presented in Supplementary Fig.  1 .

Under each SDG target, we assign an SDG-interaction score from this specific SDG target and the project. The SDG-interaction score is a seven-point scale score. Interaction between outcomes of the CDM projects and the SDG targets can be positive and/or negative. For the positive interaction, we have “high impact”, “medium impact”, and “limited impact” scales, and for negative interaction, we have “minor damage”, “medium damage” and “massive damage”. Additionally, we present the validity of the results in the literature by examining the quantity, quality, and consistency of the literature into four scales, limited, medium, robust, and extensive. Eventually, we assign the current level of confidence (“low”, “medium”, “high”) to each SDG interaction based on the previous two aspects. This bottom-up direction of assessing the SDG interaction scores eventually can be aggregated at the level of co-benefit criteria. Our implication assumption is that the SDG goals are weighted equally, despite those countries may have different focus areas on sustainable development based on their national development priorities.

Our data of the interactive SDG co-benefit framework is based on 84 academic peer-reviewed and grey studies conducted on the topic of carbon finance and community co-benefits from a systematic literature review search. The primary data source for economic models is the UNEP DTU CDM/JI Pipeline Analysis and Database (CDM/JI Pipeline). Additional information, such as ERPA dates, is extracted by Python from CDM documents in PDF format on the UNFCCC CDM projects site. To check the accuracy of the ERPA dates extracted by the computer, we adopted two methods to validate the data (see Supplementary Note  2 : Accuracy of the ERPA Dates). We include 2259 CDM projects in our paper. The dataset covers 20 project types and two project sizes 46 . We present the statistical summaries of the data in Supplementary Table  5 . We also plot the distribution of CER prices in Supplementary Fig.  9 . Within these 2259 CDM projects, 1655 are regular CDM projects, and 64 are Gold Standard CDM projects. Detailed results segregated by project types and sizes are listed in Supplementary Table  6 .

The underlying assumption of our analysis is that carbon prices (including any additional premiums from non-carbon sustainable development benefits) will compensate for the opportunity costs in the project, including transaction and implementation costs. For most of the CDM projects, additional revenue from carbon prices will help the CDM projects pass the additionality requirement. Because CDM projects need to demonstrate that projects are not viable unless carbon benefits are considered (i.e. with a non-zero carbon price). This assumption is valid for most CDM projects due to the additionality requirement they must pass, particularly when using financial additionality to meet the additionality requirement. During this additionality requirement, it is not supposed to include monetized co-benefits, which is precisely why projects with co-benefits are valued more highly by investors. However, CDM-AR projects suffer from particularly high opportunity costs, transaction costs, and implementation costs 3 , 47 , 48 , 49 , 50 . For example, the World Bank BioCarbon Fund, the major credit buyer holding17 out of the 66 CDM-AR projects 51 , reported that the transaction costs of CDM-AR projects exceeded $1 per tCO 2 e, higher than any other CDM project type 52 . As a result, the World Bank adopted a default price in financial analysis of CDM-AR projects 51 . Also, in the case of CDM-AR projects these costs are likely to be higher since they involve land and property rights and more complex implementation arrangements 53 . In addition, the high opportunity costs of land and labor are well discussed in the CDM-AR literature 54 , 55 , 56 , 57 . These challenges can complicate evaluation of the co-benefits of the CDM-AR projects and lead to the failure of these projects 3 , 54 . Therefore, in our analysis, we did not include CDM-AR projects due to these characteristics as well as the small number of registered projects (a total of 66 registered projects). As a result, we believe that the assumption of our analysis is reasonable.

Credit buyers

The CDM mechanism creates CERs as an important share of the global carbon markets. Like the regular markets, the demand side of the CERs is from carbon credit buyers. CDM credit buyers can be categorized into three groups, the first group called compliance buyers who are seeking to buy offsets for compliance in the EU ETS and other regional schemes; the second group called sovereign buyers, mainly Annex I parties, who are obtaining CERs directly to meet their quantified emission limitation and reductions obligations (QELRO) commitments under the Kyoto Protocol; the last group contains MDBs and carbon funds 58 . We further divide credit buyers into different categories by using two classification systems. First, credit buyers (company level) are classified into 14 industries by their primary business activities using the Bloomberg Industry Classification Systems (BICS). Second, credit buyers are also categorized into five statuses based on their profit status, e.g., local private companies, global private companies, government entities, MDBs, and foundations.

Definition of co-benefits (non-carbon benefits)

Although the idea of co-benefits has attracted increasing attention from governments, NGOs, financial institutions, and academic research in recent years, there is no consensus on a concrete definition or agreed list of what counts as a co-benefit 59 . The IPCC considers co-benefits as “the positive effects that a policy or measure aimed at one objective might have on other objectives, irrespective of the net effect on overall social welfare” 60 . In this paper, we focus on a smaller subset of co-benefits, particularly on co-benefits to local communities as a result of CDM mitigation actions (carbon projects) that are targeted at addressing global climate change. Thus, we adopted and adjusted the co-benefits description from the World Bank the Community Development Carbon Fund (CDCF) 2013 report on key community outcomes, where five broad areas are listed. These five areas capture the complex dimensions of co-benefits. The co-benefits of this paper cover the following five areas: Enhanced local infrastructure (e.g., roads, health clinics, schools, water, parks, community centers, etc.); access to cleaner and affordable energy for heating and/or cooking; improved income and employment; improved access to electricity and/or energy efficient lighting; and improved natural resource and environmental services (e.g., reduced pollution, natural resource conservation, forest protection, biodiversity).

Definitions of other terms

Climate finance is defined as the public and private financial flows used to support mitigation and adaptation action to address climate change 61 , 62 There are currently two types of carbon markets for carbon offsets: compliance and voluntary markets. The market settings are different for the two markets. In the compliance (mandatory) market, buyers are primarily motivated to purchase offsets that can provide a more economic sense to reduce emissions to fulfill their lawful requirements, such as in a cap-and-trade regime 63 . The voluntary carbon market grew later compared to the compliance carbon market. It picked up in the late 2000s and kept a relatively stable trend until 2017. While in the voluntary markets, buyers (for example, companies) are primarily motivated by their social responsibility and concerns about climate change to reduce their emissions 64 , 65 . Multi-national, private, for-profit companies make the bulk of voluntary offset purchases by volume. Official Development Assistance (ODA) is defined as the aid from government entities to developing countries with a target to promote economic development and welfare 66 . Carbon benefits of CDM projects are defined as the anthropogenic emissions of greenhouse gases by sources being reduced below those that would have occurred in the absence of the registered CDM project activity 46 .

Empirical strategies

Our main model is expressed in the following regression equation:

where i indicates projects, and t indicates years when the credit purchase agreement was signed. In all models, the dependent variable Y it is the CER price for each project. The variables of interest are Co-benefit 1–8 , with their coefficients β 1–8 indicate the effect of different levels of co-benefits on the CDM projects. We also control for a group of other variables listed in Supplementary Table  7 , e.g., project location fixed effects ( γ i ), credit buyer fixed effects ( δ i ), project type fixed effects ( φ i ), year fixed effects ( ω t ), and project size dummy ( θ i ). Finally, the error term captures unobserved factors affecting our dependent variable that changes over the year.

Hedonic model

The hedonic model is expressed in the following regression equation at credit buyers’ company level, where CER prices can be explained as a function of credit buyers and project characteristics 67 , 68 .

where i indicates companies. In all models, the dependent variable P CER i is the average CER price paid by company i . We also control for a group of variables such as, numprojects i is the number of offset projects under management; location i is a categorical variable indicating the country where the credit buyer i is located, GS i is the proportion of projects that have Gold Standard certification. We also control for investment portfolio in terms of project regions. Thus, asicapacific i is the proportion of projects that company i invests in Asia and Pacific region, africa i is the proportion of projects that company i invests in Africa, the same to the latinamerica i , centraleuropean i , middleeast i . Finally, ε i is an error term assumed to be normally distributed.

In our study, treatment is if a project receives a Gold Standard certification. The control group includes all the regular CDM projects. The rationale behind matching is to identify (based on the available covariates) a control group of projects with similar characteristics to a treated group of projects for comparison. Thus, the selection of covariates should be those variables that are thought to be related to the outcome (CER prices), but not the treatment 69 . Our strategy is to perform a propensity score matching at the level of five continuous variables. Beyond that, we also conduct the exact matching using two scenarios. Scenario 1 performs exact matching at the buyers’ country level, and Scenario 2 conducts exact matching at both buyers’ country and project location level. After finding good matches for the treatment group, the model will be adjusted by running a regression to control for the fixed effect from contract year, project type, project location, and buyers’ location.

Python and Stata are used jointly for data analysis. Supplementary Fig.  10 shows that there is overlap in the range of propensity scores across the treatment and comparison group, which we called the “common support” 69 , 70 . Assessing the common support condition ensures that any combination of characteristics observed in the treatment group can also be observed among the control group. Additionally, diagnostic tests for balancing of covariates are shown in Supplementary Fig.  11 . We can see that matching did a quite good job at balancing the covariates across the treatment and control group, with all (except one) p -values from both the KS-test and the grouped permutation of the Chi-Square distance after matching to be >0.05.

Our model is expressed in the following regression equation:

where i indicates projects, and t indicates years. In all models, the dependent variable Y it is the CER price for each project. The variable of interest is Treat it , with its coefficient β 1 indicates the effect of Gold Standard on CDM projects. We also control for a group of continuous covariates listed in Supplementary Table  7 , project location fixed effects ( γ i ), credit buyer fixed effects ( δ i ), project type fixed effects ( φ i ), and year fixed effects ( ω t ). Finally, the error term captures unobserved factors affecting our dependent variable that changes over the year.

Matching techniques

Model 1 in Table  2 conducted OLS regression using the nine covariates that used to estimate the propensity to receive the treatment. That is, model 1 displays the difference in being Gold Standard CDM projects and regular CDM projects by controlling for the nine covariates. Model 2 through model 5 show results of estimates by using different matching techniques. Models 2 and 3 only used propensity score matching, while models 4 and 5 used the combined exact matching and propensity score matching technique. The difference between models 2 and 3 is the number of covariates used to obtain the results. In model 2, we perform the propensity score technique for all nine covariates, including both continuous and categorical covariates. In model 3, we only conduct the propensity score with the five continuous covariates. The models of interest are models 4 and model 5. In model 4, we perform the exact matching at the credit buyers’ country level, in order to obtain the impact of Gold Standard on projects within the same country of buyers. In model 5, we restricted our model further to conduct exact matching on both credit buyers’ country level and also the projects’ location level. Model 5 is the most restricted model among these five models. We lost some observations due to model restriction in model 5, and we only obtained 21 projects in the treatment group.

Balancing test

We adopted the standardized differences (SD) technique, which is the standardized difference of means, to assess the differences between multiple variables of the treatment and control groups 71 in Supplementary Table  8 . If there is no big difference between these two groups, we can conclude that there is adequate balance between these two groups of observations. Before matching Supplementary Table 8 (a), the treated and untreated groups are unbalanced. When we do propensity score matching at both categorical and continuous covariates level Supplementary Table 8 (b), we still did not get balanced groups. However, in the last test Supplementary Table 8 (c), when we only conduct propensity score matching at the continuous covariates level, we get balanced groups.

Robustness checks for regular CDM projects

Many factors can influence the CER prices as indicated in Supplementary Table  7 . One of the many factors is the 2008 financial crisis, which is the main cause of the price drop of CERs in that year. The price decreased by about 50% 72 . Thus, we dropped the 461 projects with a signed ERPA date of 2008, because we think that the year 2008 would have an impact on the CER prices. We re-ran the analysis with the remaining 1744 projects. We get very similar results (results are presented in Supplementary Table  9 ) across all four models compared to the results in Fig.  3 and all coefficient estimates of variables of interest deliver a similar increasing trend.

Robustness checks for regular Gold-Standard CDM projects

We conducted two robustness checks for our matching analysis. First, we replaced the credit buyer’s country information with the indicators representing the health of a country’s economy, such as GDP per capita, employment rate, government expenditure, and inflation rate. We get very similar results (results are presented in Supplementary Table  10 ) across all five models compared to the results in Table  1 . All coefficient estimates of Gold Standard treatment are statistically significant. This indicates that our models are quite robust. Second, we conducted a “placebo” test by randomly selecting 50% of the data from our control group and artificially assigning them into the treatment group. By doing that, we created a “fake” treatment group, that is, a group that we know was not affected by the Gold Standard. we estimated the models by using the “fake” treatment, and the results are presented in Supplementary Table  11 . All the coefficients of treatment effect are not statistically significant. Since we do not find that there is a difference in the absence of the real treatment, Gold Standard certificates, we successfully reject this falsification. This result increases the credibility of our research design.

Data availability

The primary data source is the UNEP DTU CDM/JI Pipeline Analysis and Database (CDM/JI Pipeline) at https://www.cdmpipeline.org/ . Additional information, such as ERPA dates, is extracted by Python from CDM documents in PDF format on the UNFCCC CDM projects site ( https://cdm.unfccc.int/Projects/projsearch.html ). ERPA dates are available on the GitHub from https://github.com/Jiehonglou/Integrating-Sustainable-Development-Goals-into-Climate-Finance-Projects .

Code availability

All data and models are processed in Stata 14.0 and Python. The figures are produced in Python and R. All custom code is available on GitHub from https://github.com/Jiehonglou/Integrating-Sustainable-Development-Goals-into-Climate-Finance-Projects . SDG icon statement: we thank the United Nations SDG Permissions grant the permission of using the United Nations Sustainable Development Goals icons ( https://www.un.org/sustainabledevelopment/ ). The content of this publication has not been approved by the United Nations and does not reflect the views of the United Nations or its officials or Member States.

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Acknowledgements

We thank Dr. Steve Hutton from the World Bank IEG (Independent Evaluation Group) for his vision, guidance, and provide the opportunity to work on the initial co-benefit concept of the carbon finance. We thank the seminar participants at the Center for Global Sustainability of the University of Maryland for their suggestions and recommendations during the preparation of this draft.

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J.L. designed the original study and conducted the entire analysis. N.H. provided the guidance on the initial co-benefits project with the methodology design. N.H. and A.P. contributed to the design of the study and provided guidance during the entire writing. Y.L.Q. encouraged J.L. to investigate the robustness of the empirical analysis.

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Lou, J., Hultman, N., Patwardhan, A. et al. Integrating sustainability into climate finance by quantifying the co-benefits and market impact of carbon projects. Commun Earth Environ 3 , 137 (2022). https://doi.org/10.1038/s43247-022-00468-9

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Improving the Effectiveness of Climate Finance: Key Lessons

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Jessica brown , jb jessica brown barbara buchner , bb barbara buchner miriam chaum , mc miriam chaum angela falconer , af angela falconer chris faris , cf chris faris katherine sierra , katherine sierra former brookings expert chiara trabacchi , and ct chiara trabacchi gernot wagner gernot wagner senior lecturer in discipline of economics - columbia business school @gernotwagner.

November 23, 2011

EXECUTIVE SUMMARY

Flows of finance to developing countries to support climate mitigation and adaptation efforts are growing in speed and scale, toward the target formalized in the Cancún Agreements to increase flows from developed to developing countries to $100 billion a year by 2020. Ensuring that this money is well spent, and hence maximizing its impact and effectiveness, will of course be critical for achieving outcomes and maintaining support. However, the tools and methods that are now being used to estimate, measure, monitor and disseminate the impact of public climate finance will not be sufficient to support this expansion. With many international institutions and bilateral agencies boosting their climate portfolios, as well as the creation of the Green Climate Fund, the time is ripe to examine current practices to improve the effectiveness of climate finance.

This paper presents an overview of existing practices by summarizing the findings from an extensive survey of various institutions, drawing on the lessons learned from development finance, the public and private activities of international financial institutions and experience with market-based instruments. The paper mainly focuses on mitigation, and it seeks to discern lessons for policymakers by addressing two key questions: What makes climate finance effective? and what tools, methods or systems might improve the effectiveness of climate finance?

What Makes Climate Finance Effective? Lessons from existing practices suggest that climate finance will be more effective when:

• It promotes clear objectives that are shared among key stakeholders.
• It supports activities that have a powerful transformative or demonstration effect.
• It ensures the most effective balance between public and private capital.
• The actions it funds incorporate a results-based approach.
• It considers cost-effectiveness—that is, actions with a larger “climate return on investment” per dollar allocated—as one of its guiding principles.
• It supports actions that are nationally owned and aligned with local and national priorities.
• Funding is predictable, coordinated and less fragmented.
• It is administered transparently, with flows and results shared to promote accountability and support effective prioritization, and is supported by strong “real-time” systems to measure progress, draw early lessons, and allow modification.

What Tools, Methods or Systems Might Improve the Effectiveness of Climate Finance? With regard to the methods and systems that might improve the effectiveness of climate finance, this study suggests that the following tools need to be developed, refined and applied:

• robust and credible ex ante and ex post estimates of the scale and cost of abatement likely to result from a particular intervention, addressing the demonstration potential and other transformative impacts;
• a common “climate effectiveness” methodology and metric, with tools, methods and systems that allow some comparison between funding proposals, institutions and activities;
• real-time evaluation of operations to enable prompt learning and corrective actions to be undertaken within the lifetime of a particular intervention, incorporating independent verification;
• systematic postaction reviews of climate activities, with lessons incorporated into the design of future actions;
• tools, methods and systems that strike a balance between the rigor of measurement systems and the related transaction and administrative costs;
• processes to estimate in advance the potential climate impact of all interventions, not just those within a “climate portfolio”;
• tools to promote transparency and the coordination of donor funding.

It will be necessary to build capacity across the relevant actors to include these additional elements required to provide more accurate and harmonized information on the effectiveness of climate finance. Current approaches provide a set of ready field experiments; exploring this knowledge will allow lessons to be learned from ongoing practices to scale up finance for a transition toward low-carbon, climate-resilient development.

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• Investors are optimistic about climate-related finance flows being poised to grow in the next two years, especially in Latin America and Asia.
• Subsidies and incentives are the strongest tailwinds for climate finance, but slow and complex rollout has left open questions for investors.
• Commercial investors and concessionary investors want to work together more and say that increased de-risking, such as blended finance, can help accelerate the speed and scale of deployment.

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Sustainable Finance and Investing

/ article, what investors’ attitudes reveal about the future of climate finance.

By  Wendy Woods ,  Vinay Shandal ,  Veronica Chau ,  Naomi Desai ,  Shaun Knowles ,  Maria Kozloski ,  Lily Han , and  Alex Bashian

Key Takeaways

The field of climate finance has come a long way in recent years, but questions remain about the market for investment in climate mitigation, adaptation, and resilience—even as demand for capital continues to grow.

To find out about the state of the market today, BCG and The Rockefeller Foundation surveyed more than 100 climate finance leaders. These included representatives of private equity, venture capital, multibillion-dollar asset owners, banks, philanthropic organizations, multilateral development banks (MDBs), development finance institutions, and government bodies. To deepen our insights, we also conducted extensive one-on-one interviews with additional practitioners. (See “About Our Research.”)

About Our Research

Overall, what we learned is encouraging. Investors from across the public and private sectors believe climate finance opportunities exist across every major theme and market. They believe those opportunities cut across regions. And, notably, despite market fluctuations, they believe the investment pipeline is growing. However, our research also showed that long-standing deployment challenges continue. These issues, combined with recent macroeconomic headwinds that have increased project risk, are making some investors wary.

Fortunately, new solutions are emerging. We see heightened interest in collaboration among commercial and concessionary investors. Today, the challenge is how to mobilize most effectively at scale.

Investors Predict Growth for Climate Finance, Especially in Latin America and Asia

Climate, transition, and other related finance flows 1 1 We define climate-related finance to include “climate finance” (finance that supports greenhouse gas abatement and sequestration and protects human and ecological systems from the harmful effects of climate change); “transition finance” (finance that supports the decarbonization of emissions intensive and hard-to-abate sectors) and “other financing flows” (finance that has positive climate impact but does not meet the definition for climate or transition finance). Notes: 1 We define climate-related finance to include “climate finance” (finance that supports greenhouse gas abatement and sequestration and protects human and ecological systems from the harmful effects of climate change); “transition finance” (finance that supports the decarbonization of emissions intensive and hard-to-abate sectors) and “other financing flows” (finance that has positive climate impact but does not meet the definition for climate or transition finance). are poised to grow over the next two years, say investors—and in some places significantly. (See Exhibit 1.) The rising tide of flows will lift most regions, and no area is forecast to decline. But investors are particularly excited about Latin America and Asia. Finance flows in these regions have lagged others over the last five years. The Climate Policy Initiative reports that climate finance in Latin America, East Asia, and South Asia have risen by at most 16% annually since 2017, compared to the 23% and 29% seen by North America and Western Europe respectively. 2 2 “Global Landscape of Climate Finance 2023,” Climate Policy Initiative, November 2023. Notes: 2 “Global Landscape of Climate Finance 2023,” Climate Policy Initiative, November 2023. Now, investors say these areas are about to “take off.” The head of climate at a private equity fund that is focused on a number of developing markets reinforced this: “What the space looked like five years ago is completely different than what it is today. And what it will be five years from now will be completely different as well. So, we’re bullish.”

While the upward trend is encouraging, the survey suggests growth will come in steps rather than leaps, with investors saying they expect finance flows to be only “slightly higher” than today’s levels, and not “significantly higher.” That tempered rise could be a problem, given the Climate Policy Initiative estimates that suggest flows will need to increase at least five-fold by 2030 to generate the $6 trillion to $12 trillion in finance needed each year. 3 3 “Global Landscape of Climate Finance 2023,” Climate Policy Initiative, November 2023. Notes: 3 “Global Landscape of Climate Finance 2023,” Climate Policy Initiative, November 2023.

Power Generation Leads, but All Mitigation Themes Have Strong Investor Support

Investors are pursuing all climate mitigation themes. (See Exhibit 2.) Power generation has particularly strong investor support. More respondents said they were actively originating (defined as seeking investment opportunities) in power generation than in any other theme. In terms of specific technologies, solar stands out, with 72% of power-focused respondents looking to originate in this area. Smart grid technologies and utility-scale storage came second and third (60% and 57%, respectively).

Industrial and building decarbonization and transportation are also gaining widespread interest, particularly in advanced economies. North America leads the way in each of these sectors (more than 60% of respondents who invest in this theme are actively originating in these themes in the US and Canada), with Europe close behind. At the sub-theme level, we see the highest levels of active origination in building efficiency, heating and cooling and in the circular economy. In transportation, electric vehicles and charging infrastructure predominate.

Nature-Based Solutions May Be Breaking Through Commercial Viability Concerns

Nature-based solutions (NbS) play a fundamental role in addressing climate change and other environmental crises but have suffered from lingering concerns over their commercial viability, owing in part to a lack of confidence in carbon markets. Our survey suggests these mindsets are shifting, and we see interest across regions. Over 60% of those surveyed who focus on NbS said they are actively originating in North America and nearly half (48%) are doing so in Europe and in Latin America.

Looking ahead, the fact that COP30 will be held in Brazil in 2025 could spur continued interest in NbS both globally and in Latin America. One senior asset manager said that “COP30 will be groundbreaking, especially for land restoration and reforestation as well as for carbon markets.”

Among the most active areas for origination are those at the intersection of NbS and agriculture, specifically conservation and regenerative agriculture (56% of interested investors), carbon markets and services (48%), and climate-resilient agriculture (49%). (See Exhibit 3.) In addition to tailwinds from their carbon and biodiversity benefits, these themes generate new revenue streams and gain lift from corporate commitments for regenerative supply chains. Agriculture technology has gained traction, given its parallels to other areas of climate tech. It is high growth, asset light, and fits into the investment scope of many VC investors. Over 40% of interested investors say they are originating in reforestation (the restoration of existing forests) and afforestation (the planting of new forests).

Adaptation and Resilience Is Gaining Momentum, but More Growth Is Needed

Although the overall A&R funding gap is still significant, some developing economies lead the way in attracting the interest of A&R-focused investors. For example, 50% of respondents focused on this theme are actively originating in India, 45% are doing so in the developing economies of Asia, and 42% in sub-Saharan Africa. However, Europe and North America are seeing comparable levels of active origination (42% and 40%, respectively). This suggests that adaptation and resilience is no longer just a developing economy story, and that safeguarding critical assets and enabling supply chain resilience are universal needs.

That said, some key regions continue to lag. Only 25% of the investors focused on adaptation and resilience are actively originating in Latin America and just 28% are doing so in the Middle East and North Africa. This low degree of engagement is concerning. As we reported last year in What Gets Measured Gets Financed , the Middle East and Africa and the Americas have the largest adaptation and resilience financing gaps of any region globally, with shortfalls in the range of $100 billion or more annually.

Globally within adaptation and resilience, water quality and supply, food security, and industry resilience are key focal areas. Other categories, such as data management and operations, safeguarding health systems, and safeguarding biodiversity have higher levels of opportunistic investing. (See Exhibit 4.)

Challenging Market Dynamics Could Temper Investor Optimism and Put Projects at Risk

Rising interest rates, currency volatility, and macroeconomic uncertainty are creating a vicious cycle. These financial market dynamics are driving up the cost of capital and making investors wary. Respondents almost universally said that financial market dynamics pose the greatest risk to future climate finance flows. (See Exhibit 5.) These factors in turn are exacerbating project development challenges and heightening off-taking and permitting risks. Many decarbonization initiatives are capital intensive. And projects that looked attractive when central bank rates were at 1.5% look decidedly less attractive with rates at 5% or higher.

These headwinds are already buffeting some investments. Challenges in offshore wind farms have been well publicized in recent months, with some projects cancelled due to factors such as inflation and interest rates, combined with supply chain challenges.

Foreign exchange risk is another consistent worry. The US dollar has grown stronger in recent years, and this can make it harder for climate initiatives based in other countries—particularly in developing economies—to secure financing. 4 4 The US dollar’s value is up 40% since 2011 when compared to the average for other global currencies in the US Dollar Index. Notes: 4 The US dollar’s value is up 40% since 2011 when compared to the average for other global currencies in the US Dollar Index. This is because investors may be loath to back projects if they feel currency depreciation will erode returns. The head of climate strategy at an investment firm focused on emerging markets said, “Currency is everything. Even at fund level, [we] would take currency support, since this is the hardest risk to manage.”

Recognizing and addressing implementation roadblocks like these will become imperative. With financial and economic volatility likely to remain persistent, measures that can allay the high cost of capital and enable deployment can shore up investor confidence.

Investors Want Closer Collaboration but Haven’t Yet Enabled It

Blended finance, first-of-its-kind finance, and related financial incentives top the list of actions that private investors hope concessional capital institutions will take. (See Exhibit 6.) But adapting them to the needs of key stakeholders will require collective resolve. Commercial and concessionary investors move at different speeds, have different blended finance capabilities, and focus on different sectors.

Despite calling out for more catalytic investment, uptake of existing blended finance opportunities has been somewhat muted, with only about 38% of commercial investors participating in these transactions. Convergence’s State of Blended Finance report found that deal volume fell by 55% between 2012 and 2022, taking the number to a ten-year low in 2022.

When we asked why, a leading European asset manager told us, “Blended finance is far too limited and slow to deploy.” The head of strategic initiatives at a North American investment manager added, “We need to build capability to interact and engage with concessionary capital, and we need concessionary finance players to move with the speed of the market.” And a senior leader from a large global pension plan said that “concessional finance is not prevalent enough, and nor is blended finance.”

Commercial investors want concessionary finance to focus most on industrial decarbonization , adaptation and resilience, and NbS and agriculture. According to the latest data from the Climate Policy Initiative, these finance flows are limited, with the majority of public funding going toward energy, transport, building decarbonization, and infrastructure. A senior leader at an Asian MDB told us, “There is a desire for concessional investors to de-risk what commercial investors are doing. But this is not where we should be using our concessional finance. We should use it to supplement those initiatives where there isn’t yet enough money to get something going.”

Commercial investors also want to see a wider variety of risk-absorbing plays. The head of climate strategy referenced earlier said, “There are high upfront costs for clean products like solar panels or electric vehicles, so funding at the consumer level rather than at the fund level would be more impactful for us.”

But the challenges are not one-sided. Leaders of some MDBs and other concessionary institutions are reliant on capital flows from donors, many of them nation states, but these flows have been shrinking as governments address an increasing number of geopolitical priorities.

There is reason to be optimistic though. Many MDBs are aware of these challenges and the need to reframe how they operate. As one leading MDB executive said, “We have completely flipped [our] mandate. We are still a development institution, but we look at everything through a climate lens and a private sector lens. That’s the shift that every MDB needs to go through.”

Concessionary Investors Are Three Times More Likely Than Commercial Investors to Say Pipeline Is a Challenge

Lack of available pipeline for climate finance initiatives used to be a common complaint among private investors. But things have changed. Only 13% of respondents said that sourcing was a top-three challenge. (See Exhibit 7.)

It’s a different story for concessionary investors. Nearly 40% of those surveyed said that they struggle to find suitable projects to finance. One possible explanation is that concessionary investors are more focused on the overall challenge of mitigating and adapting to climate change and perceive a shortage of investable projects across a broader context than their specific pipeline. Another is that many concessionary financiers focus on emerging markets, which typically have more challenging project environments and result in slower-moving pipelines. Our data reflects this, with respondents from concessionary capital providers more likely to be focused on investing in emerging and developing markets than commercial players.

There is a disconnect between what concessional players are offering and what commercial investors are asking for. Adapting their offerings and finding better ways to collaborate could help commercial and concessionary investors expand their pipeline and lay the groundwork for a more diverse array of partnerships with the private sector.

Subsidies and Incentives Provide Clear Tailwinds, but Implementation Drags

A proliferation of new laws and policy initiatives are creating unprecedented momentum. They include the Inflation Reduction Act (IRA) and Infrastructure Investment and Jobs Act in the US and the Green Deal in the European Union. They also include subsidies, tax credits, and funding programs in other regions. Almost half (49%) of investors said that these measures play a larger role in mobilizing climate finance than any other lever. And 77% ranked it in their top three most powerful growth enabler. (See Exhibit 8.)

But it’s one thing to enact these policies and another to implement them, and despite the enthusiasm, many investors are concerned that the pace of implementation is too slow. According to respondents, deal flow has grown by one to two times across most themes as a result of favorable policies, which is less than most had hoped for. (The notable exception is transport, which respondents say has seen flow rise by three or four times.) As one investor we spoke to said, “Implementation of the IRA has been slower and more complicated than anticipated.”

Bottlenecks include delayed implementation as governments scale up their administrative operations to execute these policies, complex rules and tax codes that require additional guidance, lengthy review processes, and multiple approval stages.

Additionally, there are fears that the policies enacted by advanced economies are not allowing the energy transition to happen equitably and could be siphoning flows away from other markets that lack similarly muscular fiscal incentives—leaving climate finance projects in developing economies particularly vulnerable. A senior leader in one development finance institution told us, “In the midst of global macroeconomic concerns and rising costs, these schemes have resulted in investor retrenchment. Financiers would rather concentrate in markets where these subsidy programs exist than continuing investing in EMDEs.”

The field of climate finance has come a long way. While climate finance is far from mature, respondents of our survey made clear that they believe the economic business case for investment exists, that opportunities are growing across markets, and span multiple themes. This is exciting, but we are still at the proverbial tip of the iceberg. To close the climate finance gap, public and private sector investors will need to find ever-more innovative and collaborative ways to fund vital measures. Our research suggests that the opportunity is there. It is now time to enable it.

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The impact of carbon pricing, climate financing, and financial literacy on COVID-19 cases: go-for-green healthcare policies

Haroon ur rashid khan.

1 Faculty of Business, University of Wollongong in Dubai, Dubai, United Arab Emirates

Bushra Usman

2 School of Management, Forman Christian College (A Chartered University), Lahore, Pakistan

Khalid Zaman

3 Department of Economics, The University of Haripur, Haripur, Khyber Pakhtunkhwa Pakistan

Abdelmohsen A. Nassani

4 Department of Management, College of Business Administration, King Saud University, P.O. Box 71115, Riyadh, 11587 Saudi Arabia

Mohamed Haffar

5 Department of Management, Birmingham Business School, University of Birmingham, Birmingham, UK

Gulnaz Muneer

6 Institute of Management Sciences, Bahauddin Zakariya University, Multan, Pakistan

Associated Data

The data is freely available at Worldometer ( 2021 ) at https://www.worldometers.info/coronavirus/ and World Development Indicators published by World Bank ( 2021 ) at https://databank.worldbank.org/source/world-development-indicators .

Climate finance and carbon pricing are regarded as sustainable policy mechanisms for mitigating negative environmental externalities via the development of green financing projects and the imposition of taxes on carbon pollution generation. Financial literacy indicates that it is beneficial to invest in cleaner technology to advance the environmental sustainability goal. The current wave of the COVID-19 epidemic has had a detrimental effect on the world economies’ health and income. The pandemic crisis dwarfs previous global financial crises in terms of scope and severity, collapsing global financial markets. The study’s primary contribution is constructing a climate funding index (CFI) based on four critical factors: inbound foreign direct investment, renewable energy usage, research and development spending, and carbon damages. In a cross-sectional panel of 43 nations, the research evaluates the effect of climate funding, financial literacy, and carbon pricing in lowering exposure to coronavirus cases. The study utilized Newton–Raphson and Marquardt steps to estimate the current parameter estimates while evaluating the COVID-19 prediction model with level regressors using the robust least squares regression model (S-estimator). Additionally, the innovation accounting matrix predicts estimations over a specific period. The findings indicate that climate finance significantly reduces coronavirus exposure by introducing green financing initiatives that benefit human health, which eventually strengthens the immune system’s ability to fight infectious illnesses. Financial literacy and carbon pricing, on the other hand, are ineffectual in controlling coronavirus infections due to rising economic activity and densely inhabited areas that enable the transmission of coronavirus cases across countries. Similar findings were obtained using the alternative regression apparatus. The COVID-19 predicted variable was used as a “response variable,” and climate financing was shown to have a favorable impact on containing coronavirus exposure. As shown by the innovation accounting matrix, carbon pricing would drastically decrease coronavirus cases’ exposure over a time horizon. The study concludes that climate finance and carbon pricing were critical in improving air quality indicators, which improved countries’ health and wealth, allowing them to reduce coronavirus infections via sustainable healthcare reforms.

Introduction

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus strain that causes coronavirus disease (COVID-19). The virus first identified in the city of Wuhan, China, exacerbates the infection rate in a shorter time all over the world. The WHO declared a healthcare emergency on 30th January 2020, while it declared a global pandemic on 11th March 2020 (Hua and Shaw 2020 ). The COVID-19 pandemic confined its impact on deteriorating human health while its negative effect on economic and financial activities (Anser et al. 2021a , b ). The US economy has a highest COVID-19 infected cases, followed by India, Brazil, Russia, and the UK, with a value of 28,381,220; 10,937,320; 9,921,981; 4,099,323; and 4,058,468, respectively (Worldometer 2021 ). Besides that, the US economy spent an enormous amount of R&D activities, i.e., 2.840% of GDP, limiting carbon emissions up to 0.834% of GNI in the current year. The Indian economy spent huge money on renewable energy consumption, i.e., 36.021% of total energy consumption, which attracts inbound FDI up to 1.764% of GDP. Brazil’s economy spent 1.264% of its R&D expenditures to maintain its per capita income up to US$11,121.74, fueled by high green energy consumption, i.e., 43.790%, and inbound FDI, i.e., 3.995% of GDP. The Russian economy confined its population density up to 8.822 people per square km of land area, which maintains an inflation rate of 4.470%. Finally, UK reduced carbon damages up to 0.443% of GNI, which helps to reduce average inflation of 1.738% that enables a country to improve its per capita income up to US$43,711.71 in the current years. Climate financing is considered a vital factor to improve the environmental quality level. The four main factors remain actively visible in the earlier studies that can be combined to form climate financing index (CFI), i.e., FDI inflows (Zubair et al. 2020 ), R&D expenditures (Fragkiadakis et al. 2020 ), renewable energy consumption (Anser et al. 2020a ), and carbon damages (Hong et al. 2020 ). The study’s main contribution is to amalgamate the stated factors to form a relative weighted index across countries. Figure  1 shows information about the climate financing index that has a range between − 1.784 and 4.657. The data illustration spikes show that 13 countries have a positive spike and have a positive value while the remaining 30 countries have a negative index value. The positive value shows that the countries spent an enormous amount to protect the natural environment through green financing. In contrast, the negative values show that countries keep struggling to use environmental sustainability policies to devote an adequate sum of money to climate protection. The study assumes that climate financing is helpful to contain coronavirus cases through smart and sustainable financing. The greening projects improve environmental air quality while it improves individual’s health that developed the resistance against any contagious disease, which reduces healthcare sufferings. The green healthcare policies are desirable to mitigate carbon pollution and coronavirus cases with a caution to use sustainable financing.

Trend values of COVID-19 cases, R&D expenditures, carbon pricing, and climate financing index.

Source: Worldometer ( 2021 ), World Bank ( 2021 ), and author’s estimate. Note: Dark blue region shows the high intensity of COVID-19 cases and greater climate financing while light blue region shows a less number of COVID-19 cases and lower climate financing

The sizeable research is available on the impact of environmental pollutants on COVID-19 cases, while little work on climate financing and its positive impact on the environmental quality reduce coronavirus cases globally. Hepburn et al. ( 2020 ) argued that during the COVID-19 pandemic, different fiscal packages introduce subsidizing financial and economic activities that create cleaner production and healthcare sustainability agendas globally. The climate change agenda is highly prioritized in developed countries, while rural support programs are vital for low- and middle-income countries to restore economic activities in pandemic crises. Obergassel et al. ( 2020 ) concluded that climate governance played a vital role in improving environmental quality that is equally important to tackle pandemic crisis during the exacerbation of high infected cases, which deprived the global economies. The world introduced different recovery packages to minimize coronavirus cases; however, these packages affected the Paris agreement on carbon control, which faced the commercialized market’s forefront challenges. Fuentes et al. ( 2020 ) discussed the vulnerability of coronavirus cases and climate change together. Both have a different scale of disasters and covered a wide range of international territories that need uniform economic and environmental solutions to tackle them. Barbier and Burgees ( 2020 ) proposed a sustainable policy framework after COVID-19 to achieve different sustainable development goals, including promotion of clean energy investment and reduced fossil fuel dependency; improved pure drinking water, water supply, sanitation facilities, and make a wastewater infrastructure; and introduced a carbon pricing mechanisms to fund climate solutions. Brown and Susskind ( 2020 ) emphasized the need to integrate global economies to increase their cooperation to reduce coronavirus cases by improving through providing healthcare facilities. Mintz-Woo et al. ( 2020 ) argued that introducing carbon pricing in the COVID-19 pandemic is the optimal solution to combat environmental issues and reducing healthcare sufferings. Malliet et al. ( 2020 ) found that strict lockdown substantially decreases the economic output of 5% of France’s GDP. However, its positive effect associated with air quality improvement is far greater than the country’s losses. Although it is foresighted that both the effects are temporary, once the pandemic issue has settled down, the country’s economic growth exacerbates that negatively affects the country’s environmental sustainability agenda. The greater need is to devise a sustainable environmental policy that helps both in the short- and long-term reduce coronavirus cases and tackle environmental issues, which lead to the countries toward green transformation. Khurshid and Khan ( 2021 ) simulated the impacts of the COVID-19 pandemic on Pakistan’s energy and environment and found that COVID-19 will negatively affect its economic growth while quarantine situation and energy consumption improves environmental quality in the coming years. Thus, the need to sustain a country’s economic growth through sustainable environmental policies helps a country move forward toward green development. Fujiki ( 2021 ) concluded that financial literacy improves financial services during a pandemic crisis, as peoples were more aware of the dealing of payment in cashless mode and increasing demand for non-face-to-face financial activities. Thus, it helps to reduce the incidence of coronavirus cases in commercialized activities.

Bhattacharyya ( 2022 ) stressed the need to regulate green financing choices to address challenges of the energy transition, climate sensitivity, and sustainable development. Financial investors and regulators must tighten financial disclosure requirements to pave the way for green projects. Newell ( 2022 ) favors public–private financial distributions that reduce social inequalities, including poverty and income inequality; additionally, they contribute to the achievement of the healthcare sustainability agenda, which has accelerated in recent years to the COVID-19 pandemic. Thus, effective financial management and community engagement are necessary to reap the benefits of long-term investment that contributes to reducing social inequalities. Gholipour et al. ( 2022 ) conducted a study of a broad panel of high- and low-income countries to assess the effect of green finance in lowering GHG emissions from 2012 to 2018. The findings indicate that green construction finance is a feasible policy tool for enhancing environmental quality, with a stronger positive effect in developing nations. Thus, measures aimed at sustaining green property finance should be implemented to accelerate the transition to cleaner development. Mavlutova et al. ( 2022 ) examined the potential for sustained financial well-being in Latvia. They found that financial development and literacy were critical in mitigating socioeconomic, environmental, and governance challenges that predominated during the COVID-19 pandemic. Green and clean funding enables the allocation of additional resources to enhance people’s livelihoods by creating a social comfort zone and resolving economic concerns, contributing to the achievement of the healthcare sustainability goal. Martínez et al. ( 2022 ) discovered that healthcare personnel are disproportionately affected by the COVID-19 pandemic. They were exposed to the coronavirus at a higher rate than the general population. The findings were found from a large sample of Spanish healthcare professionals who observed increased healthcare issues, such as poor health and psychosocial dangers. The increased requirement for healthcare worker prevention ensures that they work productively and take care of their health via concrete policy measures and psychotherapy. Adetunji et al. ( 2022 ) stated that information and communication technology would aid in monitoring, delivering, and managing sustainable healthcare services that contribute to the global reduction of coronavirus infections.

The study’s contribution is to construct a comprehensive climate financing index, including four vital socio-environmental factors, including inbound FDI, green energy consumption, R&D expenditures, and carbon damages. The earlier literature mostly used the stated factors in relation with the climate financing as regressors that impact the carbon emissions; however, they are not making a weighted contribution of the stated factors to form an index (Nawaz et al. 2021 ; Sarkodie et al. 2020 ; Muhammad et al. 2021 ). Another contribution of the study is to use carbon pricing as a mediator that supports the climate financing agenda to reduce negative environmental externalities that ultimately improve the countries’ health and wealth. About the COVID-19 factor, the study believes that this is the first study that used both the sustainable policy instruments in infectious healthcare modeling to reduce contagious diseases likely. The earlier studies used different financing coping factors to reduce the incidence of coronavirus cases globally; for instance, Yoshino et al. ( 2021 ) used portfolio investment to reduce coronavirus cases by investing in sustainable development goals. Klioutchnikov and Kliuchnikov ( 2021 ) considered renewable energy and energy efficiency investment opportunities to create a healthy living environment that tackles COVID-19 cases. Smith et al. ( 2021 ) used carbon pricing to mitigate fossil fuel combustion that needs to be more focused on the pandemic crisis for sustainable growth. Finally, the study used financial literacy in the healthcare modeling framework that supports climate financing and carbon pricing objectives to improve environmental and healthcare infrastructure.

Based on the study’s contribution, the study formulated the following research questions to help move forward for achieving healthcare sustainability. First, does climate financing helps to reduce the exposure of COVID-19 cases through achieving healthcare sustainability? This question implies that climate financing has an indirect impact on reducing the exposure of coronavirus cases. It helps mitigate negative environmental externalities by initiating green financing projects, which improve air quality indicators and human health that support reducing contagious diseases. Second, to what extent carbon pricing improves environmental and healthcare quality? This question argued that taxes imposition on dirty production is desirable for mitigating carbon emissions and achieving energy efficiency leading to sustainable healthcare financing. Finally, does financial literacy helps to increase knowledge spillover to reduce coronavirus cases? The advancement in cleaner technologies is not limited to achieving green development agendas while improving economic efficiency and healthcare sustainability to reduce the exposure of coronavirus cases globally.

The study’s contribution and research questions help to make the research objectives of the study, i.e.,

• i) To examine the role of climate financing in reducing the exposure of coronavirus cases across countries,
• ii) To determine the impact of carbon pricing and financial literacy on reducing COVID-19 cases, and
• iii) To analyze the inter-temporal relationship between climate financing and coronavirus cases over a time horizon.

These objectives need to be analyzed using statistical techniques to get sound parameter inferences, which helps to propose sustainable long-term policies for achieving healthcare sustainability.

Materials and methods

The study used COVID-19 cases as a response variable, while climate financing, carbon pricing, financial literacy, the country’s per capita income, and population density served as regressors. The COVID-19 cases are taken from Worldometer ( 2021 ), while the stated variables’ remaining data are taken from World Bank ( 2021 ) database. The study constructs a relative weighted index for climate financing by combing four key factors, including inbound FDI (% of GDP), renewable energy consumption (% of total energy demand), R&D expenditures (% of GDP), and carbon damages (% of GNI). The formulation of climate financing index (denoted by CFI) is made by principal component analysis (PCA) to get eigenvalues and eigenvectors loadings, which form a unique CFI data containing all the properties of the used variables to represent the index value efficiently. Table ​ Table1 1 shows the construction of CFI by PCA matrix.

PCA matrix for climate financing index (CFI)

FDI shows foreign direct investment inflows, REC shows renewable energy consumption, R&D shows research and development expenditures, and CARDAM shows carbon damages.

The estimation shows four main variables used in the formulation of CFI that have a relative contribution of 1.480, 1.313, 0.806, and 0.399, respectively. Out of four eigenvalues, the first two eigenvalues surpassed the unit’s threshold that confirmed the importance of the variables in forming CFI value. The proportional variance shows that the first and second factors contribute 37.02% and 32.83%, respectively, which have a cumulative sum of 69.85%, while the remaining two factors constitute 20.01% and 0.099%. The eigenvectors loadings confirmed that the principal component-4 (PC 4) matrix has a greater sum of values that help make an efficient index of climate financing. Figure  2 shows the scree plot, eigenvalue differences, and cumulative eigenvalue proportion for ready reference.

Eigenvectors loadings. Note: Blue line shows eigenvalue estimates while the red line shows the critical region

Figure  3 shows the eigenvalue vectors loadings that confirmed the greater proportion of component matrix in the formation of orthonormal loadings. The first component has a higher share of CFI formation with a value of 37% than the second component that has a respective value of 32.8%. The rest of the variations have been found by the other two factors that have a combined response of 30.2%. These loadings help form a reliable CFI matrix for using a separate variable in the regression apparatus to get policy insights.

Factor loadings. Note: Blue line shows factor loadings. RND01 shows research and development expenditures, REC shows renewable energy consumption, CARDAM shows carbon damages, and FDI shows inbound FDI

The study constructs an index of climate financing based on the PCA matrix, which has a range value of minimum to − 1.784 and a maximum value of 4.657. The study used a cross-sectional panel of 43 countries that fall in the prescribed range of CFI. Out of 43 countries, only 13 countries have a positive CIF value, while the remaining countries exhibit a negative CFI value. Further, out of 13 countries, there are only three countries with a more than 2 CFI value (high frequency), while four countries have a value of more than one but less than the CFI value 2 (medium frequency). The rest of the six countries have CFI values greater than zero but less than one (low frequency). Table ​ Table2 2 shows the complete picture of CFI values for ready reference.

CFI trended values

Source: Author’s estimation. CFI shows climate financing index.

The other regressors include carbon pricing (denoted by CPRICE), financial literacy (denoted by FLIT), the country’s per capita income (denoted by GDPPC), and population density (dented by POPDEN) used in the study for robust inferences. The following variable’s definitions used in this study are as follows:

• i) COVID-19: The study used the number count of registered coronavirus cases on 18th February 2021 in a cross-sectional panel of 43 countries at one point on time. The study evaluated different economic and environmental factors that could impact COVID-19 cases that formulate broad-based global policies. The COVID-19 cases served as the primary regressand variable in this study that was influenced by several factors. The study used a wide variety of positive and negative factors that influenced COVID-19 cases across countries.
• ii) Climate financing index (CFI): The study constructed a relative weighted index of climate financing by PCA matrix. The main factors used in the construction of CFI are FDI inflows, renewable energy consumption, R&D expenditures, and carbon damages. The stated factor served as a regressor that likely to influence COVID-19 cases across countries. The index value corresponds to both negative and positive values based on the stated variables’ actual data. The study used the latest available data on the World Bank database of the corresponding variables for analysis.
• iii) Carbon pricing (CPRICE): Carbon pricing is considered a policy instrument for a green economy. It is a viable factor that helps to attain green resources globally. The study used carbon tax as a pricing substitute in this study to improve environmental quality. The study assumes that carbon tax should be optimal and flexible that limits dirty production globally. Thus, there is a greater need to use a uniform tax value equally applicable to all countries. The study assumed that carbon tax should impose an equal proportion of price level changes across countries. Based on this assumption, this study used the consumer price index (inflations, annual %) as a proxy for carbon pricing.
• iv) Financial literacy (FLIT): Finance literacy is essential for trading goods in the stock market. It is essential to absorb the price volatility and other exogenous shocks that possible only when an adequate base of financial knowledge has been inhabited. The broad money supply (% of GDP) gives a substantial knowledge about the liquidity of money in circulation. The study assumed that higher literacy about the transaction of money supply in the economy leads to a more absorbing financial risk capacity due to other exogenous shocks. Based on this assumption, the study used money supply as a proxy variable of financial literacy that absorbs financial crisis in the pandemic era.
• v) Economic growth (GDPPC): The continued economic growth sustains economic activities that absorb any exogenous shocks that could prevail during the pandemic era. The rise and fall in economic activities are mostly visible during the time of the COVID-19 pandemic; hence, the study used GDP per capita in constant 2020 US$ as a variable factor that influenced coronavirus cases across countries.
• vi) Population density (POPDEN): During the COVID-19 pandemic, the World Bank and other healthcare agencies mainly provoked the need to maintain the adequate distance between the individuals to avoid the exposure of coronavirus cases. The study assumed that high dense population has a greater susceptibility rate of coronavirus cases than the less dense population area. Hence, the study used population density (people per square km of land area) used as a control variable to analyze its impact on COVID-19 cases across countries.

Theoretical underpinning

Sandberg ( 2018 ) articulated the theory of sustainable finance effectively, arguing that the financial sector has failed to meet the societal perspective of the welfare economy, staying obsessed with neoclassical economic theories with a self-centered aim of increasing corporate payoffs. Climate financing is mostly being explored about worsened COVID-19 cases, which are expected to spread through environmental toxins (Shamsi et al. 2021 ). Anser et al. ( 2021a , b ) proposed a theory of healthcare signaling. Government and healthcare professionals warned the general population to avoid contagious diseases through different communication channels and trained them to prevent them via comprehensive standardized operating procedures. Consequently, it reduces societal costs and expands access to preventive treatment. Preventative interventions in healthcare, such as the logistical supply of protective equipment and improved corporate social responsibility, may help minimize sensitive COVID-19 cases (Sasmoko et al. 2021 ). Healthcare supply chain management contributes to pandemic containment by implementing sustainable business strategies (Sriyanto et al. 2021 ). By implementing a green healthcare system within the context of climate financing, we may mitigate economic and environmental complexity during the COVID-19 pandemic (Jia et al. 2021 ).

Based on the sustainable financing and healthcare theories, the study extended the scholarly work of Yu et al. ( 2021 ) and Anser et al. ( 2020b ) that comprehensively discussed the vulnerability of COVID-19 cases that leads economies into the global depression. The study included climate financing, carbon pricing, and financial literacy in the response of COVID-19 that is less explored in the exiting work, which gives new insights and directions to contain coronavirus cases across countries possibly. The following empirical equations are used to explore the interlinkages between climate financing and COVID-19 cases in a cross-sectional panel of countries, i.e.,

where COVID19 shows coronavirus cases, COVID19_F shows the forecasted value of coronavirus cases, CFI shows climate financing index, CPRICE shows carbon pricing, FLIT shows financial literacy, GDPPC shows GDP per capita, POPDEN shows population density, and ε shows error term.

Equation ( 1 ) shows that climate financing, carbon pricing, and financial literacy will likely reduce coronavirus cases, whereas continued economic growth and population density will increase the exposure of coronavirus cases in a cross-sectional panel of countries. On the other hand, Eq. ( 2 ) shows that climate financing, carbon pricing, and financial literacy assume to impact positively to minimize the incidence of future increase in coronavirus cases. On the other hand, GDP per capita and population density will likely increase more coronavirus cases in the future due to high population density and resuming economic activities across countries. Figure  4 shows the research framework of the study.

Research framework of the study.

Source: Author’s extract. ↓ shows decrease and ↑ shows an increase

Figure  4 illustrates that climate financing index, carbon tax, and knowledge spillover will likely have a positive impact on reducing coronavirus cases’ exposure. In contrast, population density and the country’s per capita income will likely increase coronavirus cases’ susceptibility due to increased commercialization and socialization activities across economies. The following tentative statements have checked the possibilities in a given situation, i.e.,

• H1: Climate financing will be likely to reduce the exposure of coronavirus cases through sustainable healthcare financing.
• H2: Carbon pricing will likely reduce the susceptibility rate of increasing the coronavirus cases through mitigating carbon emissions.
• H3: Financial literacy will likely absorb the adverse pandemic shocks through smart production.

These hypotheses need to be checked by sophisticated statistical techniques, including generalized least square (GLS) regression, robust least square (RLS) regression, and innovation accounting matrix (IAM). The GLS regression approach is more efficient than the simple least squares regression and weights regression to handle the possible correlation between the stochastic term and regressors in the specified model. The estimates of GLS are considered efficient, asymptomatically normal, consistent, and unbiased, which gives unique linear transformation for model predictions. Further, the study used the RLS regression approach to find out the impact of the climate finance index and other potential regressors on the forecasted COVID-19 cases. The RLS regression approach evaluates Eq. ( 2 ) through S-estimator. The S-estimator absorbs the possible outliers from the regressors that help get the robust parameter estimates from the regression. Finally, the study used a variance decomposition analysis (VDA) approach to analyze the inter-temporal relationship between the coronavirus cases and its possible determinants across countries. The VDA decomposition can be seen in Eqs. ( 3 ) and ( 4 ), i.e.,

Equation ( 4 ) shows the mean square error term of the respective candidate variables, i.e.,

where MSE shows mean square error.

Table ​ Table3 3 and Table ​ Table4 4 show the descriptive statistics for climate financing indicators and other potential determinants of COVID-19 cases in a cross-sectional panel of countries. Table ​ Table3 3 shows that inbound FDI has a minimum value of 0.731% of GDP and a maximum value of 28.346% of GDP with a mean value of 3.711% of GDP. The mean value of renewable energy consumption, R&D expenditures, and carbon damages is 22.851% of total energy demand, 0.881% of GDP, and 1.930% of GNI. These factors were used to construct a composite index of climate financing in the study.

Descriptive statistics for climate financing indicators

FDI shows foreign direct investment, REC shows renewable energy consumption, RND01 shows research and development expenditures, and CARDAM shows carbon damages.

Descriptive statistics of the key determinants of COVID-19 cases

COVID19 shows COVID-19 cases, CFI shows climate financing index, CPRICE shows carbon pricing, GDPPC shows GDP per capita, and POPDEN shows population density.

Table ​ Table4 4 shows that the average number count of COVID-19 cases reached 1,848,278 with a maximum of 28,381,220 and a minimum value of 19,598. The climate financing index fall is in the range of − 1.784 to 4.657. Financial literacy is checked by money supply across countries, showing an average value of 74.271% of GDP. The carbon pricing is, on average, suggested to impose 3.163% on dirty production. The average value of per capita income and population density is US$14,363.16 and 285.839 people per square km of land area, respectively.

Table ​ Table5 5 shows the GLS and RLS estimates of coefficient parameters and found that climate financing is the only statistically significant contributor that minimizes the exposure of coronavirus cases at the initial and forecast level. Other variables, including financial literacy, carbon pricing, GDP per capita, and population density, unable to contain the susceptibility of coronavirus cases across countries.

Generalized linear model and robust least squares regression estimates

* indicates 99% confidence interval. CFI shows climate financing index, CPRICE shows carbon pricing, POPDEN shows population density, and superscript “a” shows z-statistics estimated values.

The result implies that climate financing improves air quality indicators that ultimately achieve the healthcare sustainability agenda. On the other hand, carbon pricing and financial literacy are expected to join hands with climate financing to minimize the chances to spread coronavirus cases. However, due to inadequate financial literacy and ease in environmental regulations, these factors cannot explain their positive impact on achieving healthcare policy agendas. Continued economic growth leads to increased coronavirus cases due to increased commercialization activities, while the high population density area remains a risk to spread contagious disease across countries. Domínguez-Amarillo et al. ( 2020 ) argued that indoor air quality levels should be green and clean to reduce healthcare sufferings, ultimately minimizing the risk of spreading coronavirus cases. Bashir et al. ( 2020 ) concluded that environmental pollutants directly linked with the spread of the COVID-19 pandemic need a greater amount of sustainable healthcare financing to reduce coronavirus cases and environmental pollutants simultaneously. Rupani et al. ( 2020 ) confirmed the significant drop down in carbon pollution during COVID-19 due to strict measures adopted to contain coronavirus cases. Thus, it clearly shows that healthcare reforms are directly linked with environmental sustainability to minimize healthcare sufferings and improve air quality levels that need stringent environmental regulations to achieve the stated goals. Ye et al. ( 2020 ) point out that the hospital environment is probably a great source of human-to-human transmission of coronavirus cases due to contaminated hospital environment. It is essential to pay urgent attention to cleaning the environment, hospital wards and giving training to prevent infectious disease to healthcare workers and the public to prevent it from contagious diseases. Rume and Islam ( 2020 ) discussed both the positive and negative arguments of environmental sustainability and healthcare reforms during the pandemic era and argued that although the pandemic era reduces GHG emissions and restores ecological diversity by adopting strict measures of reducing coronavirus cases, however, it increases national healthcare bills that lead to the severe loss of economic output. The proper implementation for achieving healthcare sustainability is pivotal for reducing healthcare sufferings and improving economic development.

Table ​ Table6 6 shows the VDA estimates of COVID-19 cases and found that carbon pricing will exert a more significant share to influence COVID-19 cases with a variance of 9.083%, followed by per capita income, financial literacy, population density, and climate financing with a variance of 6.400%, 3.245%, 1.821%, and 1.710%, respectively, over a time horizon.

Variance decomposition analysis of COVID-19

S.E. shows standard error, COVID19 shows COVID-19 cases, CFI shows climate financing index, CPRICE shows carbon pricing, and POPDEN shows population density.

Conclusions

The purpose of this study is to explore the impact of climate finance in advancing the healthcare sustainability agenda by assisting nations in controlling coronavirus cases through carbon pricing and financial literacy in a cross-sectional panel of 43 countries. Along with carbon pricing and financial literacy, the study developed the climate finance index, which functioned as the primary explanatory factor for COVID-19 cases at the initial and projected levels. In comparison, economic growth and population density were moderators of the link between the abovementioned variables. The findings indicate that climate financing has a beneficial effect on lowering coronavirus exposure at both the initial and projected levels. Carbon pricing and financial literacy, on the other hand, are impotent to advance healthcare sustainability objectives as a result of greater socializing and commercialization activities after the global relaxation of the lockdown situation. The variance decomposition study indicates that carbon pricing would be critical in reducing carbon emissions and eventually strengthening nations’ health and wealth systems to limit worldwide coronavirus infections. According to the study’s findings, the following three policy implications are desired for reducing coronavirus exposure across countries, i.e.,

• i) Climate financing is a green effort for air pollution reduction. Carbon emissions are the primary cause of environmental degradation, which has a detrimental effect on human health. Unhealthy individuals are more prone to infection by infectious illnesses, including COVID-19. Therefore, there is a more considerable need to enhance climate finance to explore green energy sources and improve energy efficiency. Renewable energy sources are considered environmentally and humane, advancing the global sustainability agenda for healthcare.
• ii) Imposing a tax on polluting output is deemed beneficial to enhance environmental quality via carbon reduction. Carbon pricing is a regulation choice made by the government to rein in pollution levels, which ultimately improves the healthcare agenda. Coronavirus is a fatal illness likely to spread by photochemical smog and fuel combustion; thus, it is critical to improving air quality standards to avoid infectious diseases.
• iii) Financial literacy acts as a knowledge spillover, allowing for the development of policies to broaden the base of climate finance and the application of carbon taxes on polluting industries. Making the appropriate investment in cleaner production technologies is only achievable with knowledge of green finance instruments that help attain the healthcare sustainability goal. It is desirable to invest in sustainable healthcare technology in order to limit coronavirus cases on a worldwide scale.

Climate funding is critical to fulfilling the healthcare sustainability goal desired for economic growth. Economic progress is impossible without human development; hence, it is critical to invest in human health to protect from communicable illnesses that hinder people from participating in economic output.

Acknowledgements

Researchers Supporting Project number (RSP-2022/87), King Saud University, Riyadh, Saudi Arabia.

Author contribution

HURK: conceptualization, methodology, writing—reviewing and editing. BU: software, formal analysis, writing—reviewing and editing. KZ: methodology, software, formal analysis, writing—reviewing and editing. AAN: supervision, resources, writing—reviewing and editing. MH: formal analysis, resources, validation. GM: resources, visualization, formal analysis.

Data availability

Declarations.

Not applicable.

The study was conducted with equal participation by all authors.

The paper’s publication is permitted by all of the authors.

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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This paper identifies and explores a number of challenges in using international public climate finance effectively towards contributing to low-carbon and resilient growth in lower- and middle-income countries. We explore key quantitative and qualitative trends in the climate finance architecture, including predictability of disbursements, affordability and concessionality of funding, provider proliferation and project fragmentation, implementation via modalities supporting recipient ownership, and the degree to which climate-related interventions are evaluated. Our research considers these trends against globally agreed principles of development effectiveness, with the aim of improving understandings of both the common and the climate-specific challenges within development finance. Ultimately, we find that climate-related development finance faces a number of challenges relative to other official development flows, including significantly lower disbursement ratios, a higher share of finance provided through debt instruments—and a rising share of loans to lower-income countries assessed as being at high risk of debt distress, a faster pace in proliferation of providers and shrinking project sizes, and fewer efforts to systematically evaluate impacts of interventions. Each of these areas will need to be tackled by public climate finance providers to ensure that the available funding is used towards climate objectives effectively. These and other issues related to the quality of climate finance should also be considered during the design of the new quantitative climate finance target under the UNFCCC to ensure that the structure of the goal promotes accountability and increases recipients’ ability to trust in the climate finance architecture.

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How much bilateral and multilateral climate adaptation finance is targeting the health sector? A scoping review of official development assistance data between 2009–2019

Contributed equally to this work with: Tilly Alcayna, Devin O’Donnell, Sarina Chandaria

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected] , [email protected]

Affiliations Red Cross Red Crescent Climate Centre, The Hague, The Netherlands, Centre on Climate Change & Planetary Health, London School of Hygiene & Tropical Medicine, London, United Kingdom, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom, Health in Humanitarian Crises Centre, London School of Hygiene & Tropical Medicine, London, United Kingdom

Roles Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Red Cross Red Crescent Climate Centre, The Hague, The Netherlands

Roles Data curation, Formal analysis, Visualization, Writing – original draft, Writing – review & editing

• Tilly Alcayna, 
• Devin O’Donnell, 
• Sarina Chandaria

• Published: June 14, 2023
• https://doi.org/10.1371/journal.pgph.0001493
• Reader Comments

Climate change is adversely affecting human health. Rapid and wide-scale adaptation is urgently needed given the negative impact climate change has across the socio-environmental determinants of health. The mobilisation of climate finance is critical to accelerate adaptation towards a climate resilient health sector. However, a comprehensive understanding of how much bilateral and multilateral climate adaptation financing has been channelled to the health sector is currently missing. Here, we provide a baseline estimate of a decade’s worth of international climate adaptation finance for the health sector. We systematically searched international financial reporting databases to analyse 1) the volumes, and geographic targeting, of adaptation finance for the health sector globally between 2009–2019 and 2) the focus of health adaptation projects based on a content analysis of publicly available project documentation. We found that health was largely a co-benefit, not the principal objective, within the projects. We estimate that USD 1,431 million (4.9%) of total multilateral and bilateral adaptation has been committed to health activities across the decade. However, this is likely an overestimate of the true figure. Most health adaptation projects were in Sub-Saharan Africa, with average project funding comparable to East Asia and the Pacific and the MENA region. Fragile and conflict affected countries received 25.7% of total health adaptation financing. The paucity of health indicators as part of project monitoring and evaluation criteria and the lack of emphasis on local adaptation were particularly significant. This study contributes to the wider evidence base on global health adaptation and climate financing by quantifying adaptation funds directed towards the health sector and revealing specific gaps in financing health adaptation. We anticipate these results will support researchers in developing actionable research on health and climate finance and decision-makers in mobilizing funds to low-resource settings with high health sector adaptation needs.

Citation: Alcayna T, O’Donnell D, Chandaria S (2023) How much bilateral and multilateral climate adaptation finance is targeting the health sector? A scoping review of official development assistance data between 2009–2019. PLOS Glob Public Health 3(6): e0001493. https://doi.org/10.1371/journal.pgph.0001493

Editor: Dinesh Bhandari, Monash University, AUSTRALIA

Received: September 28, 2022; Accepted: April 26, 2023; Published: June 14, 2023

Copyright: © 2023 Alcayna et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data is publicly available from Climate Funds Update ( https://climatefundsupdate.org/ ) and OECD-DAC Databases ( https://www.oecd.org/development/climate-change.htm ).

Funding: Tilly Alcayna, Devin O'Donnell and Sarina Chandaria were funded by the The Horizon 2020 project ENBEL (Enhancing Belmont Research Action to support EU policy making on climate change and health) Consortium, with additional support by the Norwegian Red Cross. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The health sector faces a dual challenge vis-à-vis climate change. Rapid and wide scale adaptation is needed to prepare for, minimise and potentially avoid impacts which are already adversely affecting human health and health systems [ 1 – 5 ]. Simultaneously, the health sector must rapidly ‘green’ to reduce its carbon footprint, as its activities contribute 4–4.9% of annual greenhouse gas emissions globally [ 3 , 6 , 7 ]. Adaptation to climate change within the health sector aims to reduce death and illness by improving the resilience of health infrastructure to shocks and stresses, utilising early warning systems for extreme weather events or climate-sensitive infectious diseases [ 4 ], enhancing disease surveillance, developing heat health action plans [ 8 , 9 ], and expanded mental health and psychosocial health (MHPH) care [ 10 , 11 ], amongst other activities [ 12 , 13 ]. Overall, health adaptation requires a system-wide integrated and transformational approach to reduce climate risk and cultivate high adaptative capacity within health systems. Moreover, health should be integrated as a cross-cutting principle across all sectors including food systems, livelihoods, social support structures, water and sanitation, amongst others [ 1 ].

As part of United Nations Framework Convention on Climate Change (UNFCCC) commitments, countries develop and regularly update Nationally Determined Contributions (NDCs) outlining the pathway to meet carbon reduction targets (e.g., as part of mitigation efforts) and identify priority areas for adaptation. 73% (135/185 countries) of NDCs include a reference of health, with 60 countries noting climate-related health outcomes or adaptation measures and 40 countries having detailed health adaptation plans [ 14 ]. A 2021 WHO analysis of 19 National Adaptation Plans (NAPs) found that all reviewed NAPs highlighted health as a “high-priority sector vulnerable to climate change” [ 15 ] with varying consideration taken to address highlighted health risks. Countries are increasingly prioritising climate change and health through the development of specific strategies and plans such as Health National Adaptation Plans (HNAPs) [ 16 ]. However, there remains a disconnect between written priorities and the direction of financial flows [ 2 , 17 ]. The 2021 WHO Health and Climate Change Global Survey found that whilst 51% of countries reported having a national health and climate change strategy or plan in place, less than a quarter of respondents had achieved high or very high levels of implementation [ 18 ]. Insufficient financing or budget was identified as a key barrier to reaching full implementation by 70% of responding countries [ 18 ].

The UNFCCC estimates that global health adaptation will require USD 26.8 to USD 29.4 billion in funding annually by 2050 [ 19 ]. Many low income countries are unable to provide basic health services, much less the investments needed to adapt services to unfolding climate risks [ 20 ]. The World Bank estimates that by 2050 Sub-Saharan Africa will incur 80% of the global health costs from increased case burden of malaria and diarrheal disease [ 21 ]. If health system adaptation is not prioritised, an increased health burden will contribute significantly to the both the economic cost and non-economic loss and damage attributable to climate change [ 22 ].

International climate finance will play an important role—alongside domestic government spending and public-private partnerships—in funding the unmet need for health sector adaptation. Across all sectors, mitigation receives significantly more public climate financing than adaptation [ 23 ]. Global tracking shows adaptation finance as having reached just USD 46 billion in 2019/2020 [ 24 ], significantly lower than estimates in the upper range of USD 140–300 billion needed for adaptation efforts by 2030 [ 25 ]. The 2018 Adaptation Gap Report, which took a special focus on health, concludes that “there is a significant global adaptation gap in health, as efforts are well below the level required to minimise negative health outcomes” [ 26 ]. To date, funding for adaptation projects that specifically address health are a minute percentage (<0.5%) of an already limited pool of adaptation funding [ 3 , 27 ]. The mobilisation of climate adaptation finance is an important mechanism by which to address climate change impacts on health in the most vulnerable countries. Therefore, it is essential to track funding flows and establish baseline measurements in order to better coordinate the use of scarce public resources.

The Lancet Countdown tracks health financing via an indicator (‘2.2.4 Health related-adaptation funding’ in 2021) [ 28 – 32 ]. These estimates are typically cited in reports from the WHO [ 18 ] or United Nations, for example, in the United Nations Environment Program Adaptation Gap Report 2018 [ 26 ]. The indicator is composed of two elements: first, spending on adaptation for health and health-related activities; and second, health adaptation funding from global climate financing mechanisms. Estimates are derived from the kMatrix Adaptation and Resilience to Climate Change dataset (a private dataset, which track financial transactions from insurance companies, financial sector, governments relevant to climate change adaptation) and the Climate Funds Update database (a public dataset, which tracks data on multilateral climate change funds) [ 29 , 33 , 34 ]. Successive Lancet Countdown reports estimate that health adaptation funding as a percentage of total adaptation financing has been increasing from 4.6% across 2015–2016 [ 28 ], 3.8% in 2017 [ 35 ], 5% across 2017–2018 [ 36 ], 5.3% across 2018–2019 [ 30 ], 5.6% in 2019–2020 [ 32 ], and 5.6% in 2020–2021 [ 31 ]. The Lancet Countdown also estimates health-related adaptation financing (i.e. projects that could support health and health-care adaptation in other sectors, such as agriculture, water, transport etc) which tends to be higher than health specific financing (13.3%-28.5% between 2017–2022) [ 28 , 30 , 35 , 36 ]. For international climate financing mechanisms, the Lancet Countdown draws on only multilateral donor information (from Climate Funds Update) [ 28 , 34 ] or specifically only from the Green Climate Fund [ 31 ].

Here, we seek to build on these previous estimates by extending the analysis of health adaptation funding from international climate financing mechanisms to track volumes of both multilateral donor adaptation funding and bilateral donor adaptation funding from publicly available datasets. We investigate two key questions. Firstly, what are the volumes of adaptation finance targeting the health sector globally in the previous decade (2009–2019), and which countries access this money? Secondly, what is the focus of the health adaptation projects funded in the past decade (2009–2019)? We build on previous examinations of international climate adaptation finance for the health sector in three key ways. First, by analysing a longer time series of adaptation finance data (2009–2019, a full decade). Second, by including a quantitative review of bilateral climate finance data in addition to multilateral climate finance data. Third, by reviewing both volumes of financing and the content of health adaptation projects through a qualitative content analysis of publicly available project documentation. Estimating volumes of funding since the 15 th Conference of the Parties (COP15) of the UNFCCC in Copenhagen in 2009 marks an important decadal baseline, as wealthier countries committed to collectively mobilising USD 100 billion per year with equal consideration for mitigation and adaptation by 2020 during this meeting [ 37 ]. This commitment was not met in 2020 and postponed to 2023 [ 38 , 39 ].

This study contributes to the wider evidence base on health adaptation funding from international climate financing gaps by providing geographical information on the volume of health adaptation funding globally and by examining the gaps in health adaptation activities. It is widely known that funding efforts are currently well below the level required to minimise negative health outcomes [ 26 ], and yet action is slow to mobilise. Our aim is to provide further evidence to support researchers in developing actionable research on health and climate finance and decision-makers in mobilizing funds to low-resource settings with high health sector adaptation needs.

Materials and methods

Search strategy.

PRISMA-ScR guidelines [ 40 ] were adapted for this review of all adaptation projects in publicly available datasets. The two main online databases for project-level information on climate adaptation finance were searched. These were: Climate Funds Updates (CFU) which contains information on climate adaptation and mitigation projects funded by multilateral organisations [ 34 ] and the OECD-DAC ‘Rio Markers’ climate-related development finance database which contains information on climate adaptation and mitigation projects funded by bilateral donors and some multilateral organisations [ 41 ]. The search was completed from September 2021 to July 2022. 2009 to 2019 OECD-DAC data was downloaded. 2003 (the year the database was approved) to 2021 data from CFU was downloaded. These downloaded results were stored in Microsoft Excel files. To ensure consistency between the two databases only projects between 2009–2019 were analysed, to provide a time series of a decade. Projects were extracted to a separate Excel sheet for further analysis if they were tagged as ‘health’ according to the database categorisation or via a keyword search of project titles for ‘health specific’ projects (see inclusion criteria) carried out by the research team (see S1 Data ). Duplicates and irrelevant projects were removed. Funding amounts for duplicates between CFU and OECD databases were averaged. Proposals or evaluations (i.e., project documentation) of the relevant projects were then manually searched for via an internet search of donor databases. Three reviewers (TA, DO, SC) screened the full texts independently and disagreements were resolved by consensus.

Inclusion criteria

The following inclusion criteria were applied: projects must have clear climate objectives; projects must have been awarded funding between 2009 and 2019; projects must be a health-related adaptation project; and funding must have been awarded by multilateral or bilateral donors (i.e., not private). Projects were assumed to be adaptation if they were labelled as such by the databases (see S1 Text ). For example, in the OECD-DAC ‘Rio Markers’ are used for climate change mitigation or adaptation projects [ 42 ]. ‘Only ‘principal’ labelled adaptation projects in the OECD-DAC were retrieved as these are official development assistance (ODA) projects that have been explicitly designed with climate adaptation objectives. All projects on the CFU database have strict climate objectives and as such were treated as ‘principal’ equivalent. Projects were included if they were tagged by the OECD-DAC and CFU database as ‘health’. For the OECD-DAC database the following health tag purpose codes were used: 1.2 Health, 1.2.a General Health, and 1.2.b. Basic Health (12110, 12182, 12220, 12230, 12250, 12261, 12262, 12263, 12281). For CFU, the ‘health tag’ is categorized using the DAC120- tag which includes all DAC health markers (1.2 Health, 1.2.a General Health, 1.2.b Basic Health). Additionally, projects were included if they contained one of the following health specific keywords in their title or abstract ( Table 1 ). Projects were excluded for the qualitative analysis if they did not have a direct health-specific objective (i.e., the health objective was not explicitly indicated in the activity documentation), if project documentation could not be retrieved, or if the documentation was not in English (see Limitations ).

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Data cleaning

In order to avoid double-counting the same project (due it being listed on both CFU and OECD databases) and over-counting the number of projects in a given country (due to the same project listed across multiple years due to funding being released in tranches), these repeat entries were identified and consolidated. Firstly, the funding amounts for duplicates (identifying by having the same title, year, donor, recipient country, and implementing agency) between CFU and OECD were averaged. Secondly, in cases where a project (with the same title, donor, recipient country, implementing agency) had multiple entries over consecutive years with different funding amounts, the original project documents were located, and tranche funding was assumed if the total funding allocated in the project documents aligned with the sum of the entries listed in the dataset. These entries were consolidated into one entry with a sum of the total funding. For entries that had the same title, donor, recipient country, implementing agencies but different funding amounts over multiple years, but where documents were not located, they were assumed to be representing the same project with tranche funding if the funding was received in consecutive years or with no more than two years between funding receipt. These entries were consolidated into one entry with a sum of the total funding. Thirdly, for entries that had the same title, donor, recipient country, and implementing agency but had funding more than five years apart (e.g., 2014 and 2019), project documents were located and used to clarify if this was the same project (and therefore consolidated) or separate phases of a project with different proposals (and therefore left as separate entries). Where project documentation was not located, entries were left separate. Finally, for entries that had minor variations in title (e.g., Rural Water for Sudan-Darfur and Rural Water for Sudan-East) but funding was provided in the same year (e.g., 2017), original project documentation was located and checked. If these entries were clearly part of the same programme, then they were consolidated with a sum of total funding. If project documentation was not located and/or funding was received in different years alongside minor variations in title, then entries were left separate.

Quantitative data analysis

A quantitative descriptive analysis of the retrieved projects was carried out to describe the total number of health-adaptation projects within overall adaptation projects; funding trends over the past decade; geographical distribution of health-adaptation projects; average project funding per region; and who the major donors were (bilateral/multilateral) and funding amount. Summary statistics and pivot tables were generated using Excel.

Content analysis

A content analysis matrix was developed for the systematic extraction of data from retrieved multilateral project documentation (proposals and evaluations). Data were extracted for the following variables: Project title; Donor; Implementing partner; Country; Region; Country classification (high income, middle income, low income etc); Funding amount (committed, 2019 equivalent); Funding type (grant, loan); Duration; Document type (proposal, evaluation etc); Health tag or keyword; Target population; Climate shock or stressor addressed; Health focus (e.g. non communicable disease, infectious disease, health system, etc); Intended health outcome; Main program activity; Health indicator.

Following this data extraction, projects were then classified as health principal, health significant, or not health focused. Projects were tagged as health principal if the main project aim, objectives, and metrics were directly tied to the health sector (i.e., health system capacity building) or the prevalence of health conditions (i.e., reducing vector-borne disease). Projects were tagged as health significant if the main project aim, objectives, and metrics were not directly tied to the health sector, but a strong correlation was made between the activities and a health outcome. The health outcome should be both defined and measured in order for the project to be tagged as health significant . For example, livelihood and agricultural programming that showed a clear, measured relationship between the activities and a reduction in food insecurity or malnutrition would be tagged as health significant. Projects were tagged as not health focused if a connection was not made between the main program activities and a health outcome. For example, a livelihood programme that did not make the connection between the programme activities and a measured potential health outcome, irrespective of whether it could have a potential health benefit, was tagged as not health focused.

From the 10,120 multilateral and bilateral adaptation projects retrieved from the CFU and OECD databases between 2009 and 2019, 678 entries were health adaptation projects. After applying the exclusion criteria, 509 health adaptation projects remained for quantitative analysis (See Fig 1 ).

Projects were excluded at three stages throughout the analysis.

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Volumes of funding

We estimate that USD 1,431 million (4.9%) of multilateral and bilateral international adaptation finance has been committed to health activities in a decade of climate adaptation financing (2009–2019) (see Table 2 ). Multilaterals funded 52 health projects worth USD 521.8 million (1.8% of total adaptation financing or 5.7% of total multilateral adaptation funding). Bilaterals funded 457 health projects, worth USD 910.1 million (3.1% of total adaptation financing or 4.6% of total bilateral adaptation funding).

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Donor funding

Donors contributed a small amount (less than USD 100 million) of funding to health prior to 2015. In 2015, multilateral and bilateral contributions towards health adaptation increased substantially (reaching USD 400 million in 2017) (see Fig 2 ). However, this increase was not maintained and in 2018 returned to amounts on par with pre-2015 levels. This increase in funding in the years following 2015 could perhaps be linked to the adoption of the Paris Agreement at COP21 or the beginning of project funding in the same year by GCF, with the subsequent lull potentially attributable to changing political goals around climate financing by key government funders.

Source: OECD-DAC CRS database and CFU database.

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More than half (59.7%) of all health adaptation finance was provided by three donors (see Table 3 ). EU institutions contributed 26.06% (USD 373.1 million) of total funding, followed by the Green Climate Fund (GCF) (21.93%, USD 314.0 million) and the United States (11.68%, USD 167.3 million). The EU institutions and the GCF both provided the highest average amount of funding per project (USD 31.1 million and USD 26.2 million, respectively). The United States had a high number of projects compared to other donors (128 projects 2009–2019), but relatively low funding per project (USD 1.3 million).

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Overall, 99% of projects were funded by grants (100% of bilateral and 94% of multilateral). Three multilateral projects were funded partially by grants alongside loans or equity financing. Many countries also contributed financing via multilaterals such as the GCF or GEF.

Implementing agencies

Donor country based NGOs accounted for 27% (n = 139) of all implementing agencies. This was followed by donor governments (13%, n = 65), UN agencies such as UNDP, UNEP, UN Population Fund, WHO, FAO, IOM, IUCN, UNICEF (12%, n = 61), and recipient governments (11%, n = 58). The remaining implementing agencies were comprised of non-donor country-based NGOs, academic/research institutions, or multilateral banks (such as: the African Development Bank; the Asian Development Bank) amongst others.

Geographic distribution of funding

Climate adaptation financing for health has not consistently targeted countries with the highest level of health or climate vulnerability (according to the ND-GAIN Index) (see Fig 3 ). This index calculates health vulnerability as a composite indicator using “projected change in deaths from climate induced diseases, projected change in length of transmissions season of vector-borne diseases, dependency on external resources for health services, informal settlement population, medical staff, access to improved sanitation facilities” and climate vulnerability as the “propensity or predisposition of human societies to be negatively impacted by climate hazards” [ 43 ]. Certain countries in Sub-Saharan Africa (SSA) with high vulnerability to climate change and within the health sector (for example, the Democratic Republic of the Congo or Tanzania) have not received any health adaptation funding to date.

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A regional analysis indicates that Sub-Saharan Africa (SSA) has received the largest proportion of funding and was the location of the highest number of health adaptation projects (61.8% of funding and 313 projects) from donors (multilateral and bilateral). However, average combined funding from multilaterals and bilaterals per project was comparable between SSA (USD 2.83 million), East Asia and Pacific region (USD 2.9 million), and Middle East and North Africa (MENA) (USD 2.8 million). Latin America and the Caribbean (LAC) received substantially higher per project funding (USD 4.1 million), whilst South Asia received substantially lower per project funding (USD 1.2 million) (see Table 4 ). Overall, 126 health adaptation projects were funded in Fragile and Conflict Affected Situations (FCS) amounting to USD 367.7 million (25.7% of total health adaptation financing). The majority of these projects (n = 112) were in SSA, followed by East Asia and the Pacific (n = 8), and LAC (n = 4) (see Table 4 ).

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Content analysis of multilateral project documentation

Of the 509 projects included in the quantitative analysis, 50 multilateral funded projects with publicly available project documentation met the inclusion criteria for content analysis. Forty percent of projects focussed on extreme weather events (such as floods, droughts, tropical cyclones, heatwaves, etc) (n = 20), whilst 54% included climate change generally (n = 27). Two projects were not specific about the climate change variables of focussed and one project focused on land salinisation. In general, most projects were focused on farmers (smallholders), agro-pastoralists, or fisherfolk (n = 16).

Health was the principal aspect of just 10 projects, focussing on infectious disease, health systems and disease surveillance. This corresponds to USD 45.9 million in funding (0.5% of multilateral adaptation finance or 0.2% of total adaptation finance). ‘Health principal’ projects sought to: strengthen the capacity of health systems; support the development of Health National Adaptation Plans or include health in National Adaptation Plans; develop health information systems and early warning systems that include health risks; improve policy and governance on climate change impacts on health; improve capacity of health workforce to deal with climate change impacts; and improve disease control and awareness. Most of these projects operated at the national level. Eight out of the ten ‘health principal’ projects were global or regional in nature. In terms of regional distribution seven were in Asia Pacific, one was unspecified, and two were in Sub-Saharan Africa. The main activities revolved around information, knowledge, and capacity building, therefore many of the proposals did not outline specific health indicators (e.g., a reduction of water-borne disease by a certain amount).

Health was a significant aspect in 25 projects (50%) wherein the proposal clearly defined a connection between the main activities and health outcomes. Health benefits of the project were both defined and measured. Ten of these ‘health significant’ projects focused on livelihood-based programming including food security or nutrition. The remaining projects included a WASH component (for water-borne diseases and water security) or generally improving wellbeing via health awareness raising. The majority of the health-relevant indicators included measures of food insecurity using established scales, number of meals, or number of people with access to WASH infrastructure or clean water.

30% of the projects (n = 15) were classified as ‘not health focused’ as a connection between health and the program aim was not made in the proposal, and health outcomes were not evaluated or measured. The program could still have a wider public health co-benefit. These projects commonly had main activities around animal health, disaster risk reduction, water infrastructure, or land management.

This scoping review re-emphasises a significant contradiction in the current climate adaptation landscape: that while donors agree that the climate crisis is a health crisis this is yet to be reflected in climate adaptation financing. Our findings contribute to previous efforts to track international climate finance for health by analysing publicly available datasets for bilateral adaptation finance in addition to multilateral adaptation finance, as the latter has solely been used for global estimates from previous studies [ 28 ]. First, we examined the volume and geographic targeting of climate adaptation finance. We estimate that globally USD 1,431 million (4.9%) of total multilateral and bilateral international adaptation finance has been committed to health activities in a decade of climate adaptation financing (2009–2019). Most health adaptation projects were in Sub-Saharan Africa, with average project funding comparable to the East Asia, Pacific and MENA region. Countries which are categorised as fragile and conflict affected situations received 25.7% of total health adaptation financing. Second, we assessed the focus of the health adaptation projects funded in the past decade (2009–2019). Within multilateral funding, the number of projects in which health was the principal focus corresponded to only about 0.5% of total multilateral adaptation financing or 0.2% of total adaptation financing. Thus, a clear gap in health adaptation financing for health persists.

Our examination of the volume of health adaptation financing offers a new estimate for bilateral funding and reveals some differences to previous estimates. We found that bilateral donors funded 457 projects, worth USD 910.1 million, and multilateral donors funded 52 health projects, worth USD 521.8 million. We were only able to compare our estimates for multilateral health adaptation funding, as previous analyses have largely focused on multilateral finance flows. We found multilateral projects tagged as health accounted for USD 521 million (5.7%) of total multilateral financing (USD 9.0 billion) whilst the Lancet Countdown 2021 found USD 711 million (13.9%) of total multilateral financing (USD 5.1 billion) went to health tagged projects [ 3 ]. This difference is likely due to methodological dissimilarities: a different time frame under analysis (2009–2019 vs 2018–2020, respectively), the use of different databases (CFU & OECD and CFU & KMatrix, respectively), and the use of different funding allocations (committed vs disbursed, respectively).

The increase in funding between 2015 and 2018 ( Fig 2 ) correlates to the Paris Climate Agreement in which health is enshrined as a right, and the establishment of the Green Climate Fund [ 44 ], which has contributed 22% of total health adaptation financing (2015–2019). EU Institutions were the top health donor and made 90% of their contributions in 2016 and 2017. The reason for the decrease in 2018 is not clear but could be tied to a lull in Green Climate Fund granting issuing. The Green Climate Fund’s initial resource mobilisation (IRM) was set to last from 2015 to 2018 [ 45 ]. Replenishment GCF-1 was completed by 2019, with programming allocation to be approved and funding disbursed between 2020 to 2023 [ 46 ].

With regards to our geographic analysis, the majority (61%) of the health adaptation projects were in Sub-Saharan Africa (SSA). This is promising as SSA has a high level of vulnerability to climate change and within the health sector. The World Bank estimates that by 2050 SSA will incur 80% of the global health costs from increased case burden of malaria and diarrheal disease [ 21 ]. For multilateral donors, 44% of projects and 48% of funding was targeting SSA. However, only two of the 10 ‘health principal’ projects were located in SSA. This indicates that although it appears that there are many health projects in SSA, these are mostly projects in which health was a co-benefit. 18 of the 20 countries with the highest health vulnerability score according to the ND-GAIN Index are in SSA. This suggests that a greater targeting of international climate finance for health should be directed towards these countries that experience the greatest need and vulnerability, including fragile and conflict affected states.

Based on the content analysis, we found that health is largely a co-benefit in projects. For example, many projects that focused on food or water security did not include a measured health indicator but mentioned expected health benefits such as increased health resilience, improved nutrition or reduced disease incidence as a potential consequential outcome of project activities. This aligns with the Lancet Countdown findings that the majority of approved ‘health related’ funding is directed at projects with potential secondary benefits for health [ 3 , 21 , 28 – 31 , 36 ]. Out of the ‘health significant’ projects, only 3 had concrete quantitative health indicators as part of their evaluation. The Green Climate Fund notes that the majority of their projects related to health comprise of mitigation projects with health co-benefits, and that studies evaluating the health co-benefits of climate policy and mitigation more broadly are becoming more prevalent [ 47 ]. However, including health indicators in the evaluation of individual projects is vital to the prioritisation of health in climate adaptation, especially if these projects are labelled as ‘health’ in databases. Secondary benefits to health are overall beneficial. However, if these outcomes are only assumed and not measured and monitored, the true benefit will remain unknown.

Additionally, if these projects are classified as relevant to health, this will lead to an overestimation of actual health-relevant projects. We found only 10 multilateral funded projects (in 2009–2019) that focused principally on health sector adaptation, worth USD 45.9 million (0.5% of total multilateral adaptation financing or 0.2% of total adaptation financing). This estimate is slightly higher than the Lancet Countdown’s estimation of USD 14.0 million (0.3% of total multilateral adaptation financing) and equivalent to a previous WHO estimate of USD 9 million (0.5% of total multilateral financing) [ 3 , 27 ]. This is also likely attributable to differences in methodology (timeframe, committed vs disbursed, definitions of health specific/health principal/health systems). Overall, across the different analyses, is it clear the volumes of adaptation finance related to health are low.

It is worth noting that analyses relying on donor self-reporting and tagging, or screening of project titles and abstracts only, may lead to an overestimation of the number of projects and volumes of financing targeting health [ 48 , 49 ] Our content analysis found only 10 projects out of the 50 projects tagged as “health” were directly tied to the health sector (i.e. health system capacity building) or the prevalence of health conditions (i.e. reducing vector-borne disease). Furthermore, for projects in which health is a co-benefit, the volume of adaptation finance does not solely target health. Project documents rarely quantified the breakdown of finance specifically directed towards health-related activities, rendering this more in-depth analysis impossible.

Significant gaps in health programming are apparent from this research. The majority of health adaptation projects in which there was a ‘principal’ health focus, were aimed at food security (with nutrition), followed by: health systems; surveillance and outbreak management; and clean drinking water supplies. Adaptation literature indicates that there is a need for more projects focussing on heat health action plans [ 8 ], mental health and psychosocial health (MHPH) [ 50 ], early warnings and early action plans [ 51 ], building climate resilient health infrastructure and systems [ 52 ], reducing global health inequity and the development of Health Action Plans as well as full implementation of health-climate strategies and plans [ 3 , 9 , 10 , 53 ]. The paucity of health indicators as part of project M&E criteria and the lack of emphasis on local adaptation were particularly significant findings. Moreover, there is growing recognition that community level health adaptation is missing from the global health adaptation financing agenda [ 54 – 56 ]. Building climate resilient and low carbon health systems was a key theme of COP26 in 2021 [ 38 ], and the breakthrough agreement on developing a new fund for loss and damage at COP27 in 2022 will have significant financial implications for highly climate vulnerable countries experiencing huge climate-linked losses and costs within the health sector [ 57 ]. The funding landscape will likely shift considerably from this baseline in the coming decade.

Historical demand for international climate finance from the health sector may be low for several reasons. Domestic government spending remains the largest source of funds for health [ 58 ], whilst international donor financing represents the smallest share of total financing for global health. However, an increasingly high proportion of development assistance for health (DAH) comes from non-governmental organisations, philanthropic organisations, public-private partnerships (such as Gavi and the Global Fund) and private corporations [ 59 ]. For example, in 2018 after the USA and the UK, the Bill & Melinda Gates Foundation was the third largest single contributor in terms of volume to development assistance for health [ 58 ]. Private sector investment, through both traditional and more innovative approaches such as blended financing (the use of development finance to mobilise additional private, commercial, or philanthropic finance), plays an important role in global health financing [ 23 , 60 , 61 ]. However, there is a significant data gap in the measuring and reporting of these private financial flows.

Low-income countries, which tend to have the highest health and climate vulnerability, rely the most on DAH whilst lower-middle income countries rely most on out-of-pocket spending and high-income countries on government and prepaid private spending [ 58 ]. Despite estimates that future health spending is projected to increase into the future, although at a slower rate of growth, global disparities in spending per capita will continue to persist [ 58 ]. Thus, while absolute levels of health spending are rising, they remain too low in many countries to finance essential health goals such as universal health coverage and climate adaptation [ 20 ].

Other reasons for low demand include the fact that climate finance has historically focused on sectors at high risk such as energy or agriculture, health ministries have historically lacked information on climate financing opportunities, and health investments do not lead to immediately obvious economic growth [ 60 ]. A 2019 survey conducted by the WHO found that a lack of information on opportunities, a lack of connection by health actors to climate change processes, and a lack of capacity to prepare country proposals were the top three challenges countries faced in accessing international climate finance for health [ 62 ]. These barriers may explain low levels of historic climate financing for health adaptation.

Limitations

Despite providing a timely and useful expansion to previous work tracking climate adaptation finance flows to health activities, some limitations must be considered when interpreting the results. First, we were limited by the data available in both CFU and OECD-DAC databases. The OECD did not provide publicly available figures of disbursed funding, therefore, to be comparable between the databases we used committed funding amounts. This means our figures are overestimates of actual money spent in-country. It is possible that looking at disbursed flows would change the distribution of funds over time. OECD did not provide project duration and therefore we did not include project duration in the analysis. Additionally, the definitions of adaptation can vary, and the projects included in the study because of their categorisation as ‘adaptation’ relied on the OECD-DAC and CFU databases internal definition of adaptation. These may not have been commensurate with one another. The keywords search had several limitations. We picked keywords that were relevant to climate adaptation, including health impacts of climate change, health systems, and climate-sensitive infectious disease. We additionally searched keywords to health-relevant terms (e.g., agriculture, livelihoods, WASH) (see Table A in S1 Text ), however, our scope and focus was on financing specific to the health sector and not to health-relevant sectors (such as agriculture). Our keywords search was restricted to English terms, therefore excluding non-English projects. CFU only had two projects that were not in English and neither project was health related. OECD had significantly more project entries not in English, particularly projects funded by French and German speaking countries. These were included if they were tagged using health sector codes. Only two projects were excluded from the content analysis for having non-English documentation. Most multilateral donors require English documentation. Although some projects would not have appeared in the keyword search, they would have been included via the sector codes and therefore included in the quantitative analysis. Additionally, some projects were missing information (such as the project title) or used broad categorizations (such as ‘global’ or ‘developing countries, unspecified’). Within the databases, the equivalent of USD 55 million was tagged as ‘global’ or ‘developing countries, unspecified’ and therefore were unaccounted for in regional calculations. Some projects are funded through partnerships between banks and receiving countries. However, only one donor is listed in the database. The amount of funding contributed by each donor could be inaccurate due to how it was entered into the database.

Furthermore, as the OECD and CFU databases track annual funding flows (meaning multi-year projects are entered multiple times), we had to make several assumptions during our data cleaning in order to analyse by project. We consolidated 153 entries under the assumption of tranche funding (see Methods ), but we could not guarantee tranche funding for all of the projects reviewed when project documentation was not available. For example, we could not guarantee that USAID funded Peace Corp projects (n = 67) were tranche funding. Therefore, our findings show that the United States funded significantly more projects (n = 128) than any other donor but had much lower average funding per project. Lastly, it was not possible to locate the majority of project documents for bilateral projects, so our content analysis was limited to available multilateral project documentation. Often, some form of documentation was available (project website, annual report) however these documents lacked necessary information including funding amounts, co-financing, project duration, and project evaluation indicators. This was true even for bilateral donors that offer project documentation on their website (i.e., of the Norwegian Agency for Development Cooperation’s (NORAD) 23 projects, only 8 appeared in their project search feature and none had programme documents). Bilateral projects were excluded from the content analysis due to difficulties in retrieving official documentation. Therefore, it is likely that there is an overestimation on the volumes of bilateral funding towards health.

The results of this study provide baseline estimates of volumes of financing and geographic targeting that can help governments, donors, and researchers identify existing shortfalls and gaps. Moving forward, future research—and, importantly, action—must focus on the solutions to reduce these gaps. Overall, volumes of international climate adaptation finance targeting the health sector are low, but international climate finance is only one source of funding for the health sector. How best to finance climate resilient and sustainable health systems will be a key area of future health economic and policy research, and links strongly with the universal health coverage (UHC) agenda. In addition, we found that the majority of projects with health as their principal focus tended to target national systems with little emphasis on local adaptation. A deeper understanding of the most effective ways of empowering local health adaptation is needed. Additionally, if health becomes a cross-cutting theme across climate adaptation programming, incorporating health indicators in projects across sectors will be vital. Identifying the most appropriate indicators as part of the monitoring and evaluation process will be a significant area of future research and can contribute to quantifying assumed health benefits. Lastly, in order to support accurate recording and research on climate finance for health, we recommend more stringent criteria for sector tags when countries are reporting projects. Ensuring accurate labelling of health projects will allow for easier checks of financial flows and can reduce the risk of overestimation. Furthermore, increased transparency from bilateral funders, including easy access to project proposal and evaluation documents will also facilitate third parties’ ability to verify and cross-check funding flows and support the expansion of climate finance research.

The mobilisation of climate adaptation finance is an important mechanism by which to address climate change impacts on health in the most vulnerable countries. As part of this, it is essential to systematically track funding flows and progress in order to establish a baseline and better direct the use of scarce public resources. This study systematically searched international financial reporting databases (OECD and CFU) to identify climate change adaptation projects related to the health sector between 2009 and 2019. Our findings show that volumes of adaptation finance related to health are low: in a decade of funding (2009–2019), USD 1,431 million (4.9%) of multilateral and bilateral international adaptation finance has been committed to health activities. Our content analysis of projects tagged as ‘health’ found that only 10 projects out of 50 potential projects were directly tied to the health sector via the main project aim, objectives, and metrics. This suggests that multilateral donors (such as GCF, GEF, Adaptation Fund) only provide <0.5% of total multilateral adaptation financing or 0.2% of total adaptation financing to health sector adaptation. Even this may be an overestimation, as publicly available project documents rarely quantified the breakdown of finance directed to different health activities. Adaptation in the health sector alone will have limited impact as health is reliant on complex interaction of environmental and social determinants. Nonetheless, it is a critical sector for adaptation as climate change’s impact on health and health systems has compounding impacts across manifold sectors. These effects are already being felt and are disproportionately affecting millions of those who have contributed the least to the problem.

Supporting information

Table A. Number of adaptation projects retrieved by health sector specific key word. Table B. Number of adaptation projects retrieved by health-relevant keywords (projects were not included in subsequent analysis). Table C. Number of health adaptation projects by donor. Table D. Health adaptation projects by channel of delivery type (i.e. implementing agency). Table E. Categorization of multilateral projects included in the qualitative content analysis. Projects could be entered more than once in this matrix if there were multiple project components.

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S1 Data. Health adaptation projects (2009–2019) dataset.

https://doi.org/10.1371/journal.pgph.0001493.s002

Acknowledgments

We would like to thank Dr. Meghan Bailey (Red Cross Climate Centre) and Fleur Monasso (Red Cross Climate Centre) for their support. In addition, we extend our thanks to Simphiwe Stewart (Red Cross Climate Centre) for the mapping support.

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Maximizing the Impact of Climate Finance: Funding Projects or Pilot Projects?

This paper contributes to the understanding of how to maximize the impact of publicly provided climate finance to leverage the private sector. Agencies seeking to promote private investment in support of climate change mitigation and adaptation may have a choice between subsidizing projects or pilot projects. Pilots are either scaled down versions of full projects or an experimental phase that generates better information about whether a full project is likely to succeed or fail. Drawing on insights about the value of experimentation for entrepreneurship and raising private capital, the theoretical model developed herein provides guidance about when subsidizing projects or pilots is more efficient.

The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research.

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