At this stage, we move from identification to evaluation. We seek to answer the question: What is the scale, feasibility, and funding need of an impact strategy? We think this step is often the most important, since it gives us adequate granularity to more confidently compare the impact potential of one strategy over another. Subsequent steps of identifying specific organizations and funding opportunities are also essential, but may be relatively less important than generally prioritizing one impact strategy over another.
With so many different strategies to investigate, we run the risk of conducting overly shallow dives on many topics and accidentally deprioritizing an impactful opportunity. On the other hand, we also risk conducting overly deep dives on just a few topics, limiting our capacity to investigate other impactful opportunities. To balance these risks, we use three broad criteria to assess the promisingness of an approach: scale (how big a problem is it?), feasibility (how hard is it to address?), and funding need (how much would more donations help?).11
For each of these indicators, we initially use a combination of metrics and heuristics to assign low/medium/high ratings for these indicators (see research prioritization dashboard). We then use these ratings to prioritize impact strategies for additional research, which no longer relies on these qualitative rankings. See below for additional high-level commentary on each indicator.
Scale: How big a problem is it?
What do we want to know? At a high level, we want to determine how much a specific problem is contributing to climate change, or how much an impact strategy could reduce it. As a rough proxy for this, we estimate how much an approach could theoretically avoid greenhouse gas emissions, remove atmospheric greenhouse gasses, and/or reduce radiative forcing. Though these are intermediate effects, we use them because they are relatively easy to measure and communicate. However, we also use heuristics (see below) to prioritize strategies we think are especially likely to improve well-being, and sometimes consider ways to estimate well-being more directly.
Why does scale matter? All else equal, the bigger the scale, the more we want to look into it. We can also think about scale as a hedge on the other indicators. If feasibility or funding need (see below) turn out to be lower-than-expected, bigger scale is a way to still ensure high impact relative to a smaller-scale problem with similar feasibility and funding need.
How do we assess scale?
- Metrics: We estimate the percentage of future expected greenhouse gas emissions that could be avoided or removed by a certain strategy, or how much it would generally reduce radiative forcing through 2100.12
- Heuristics: Is this problem expected to grow or shrink? Is this problem concentrated in a country or region where this problem is generally expected to grow or shrink? Do we expect this opportunity to scale rapidly enough to significantly affect climate change within the next 75 years? Is this problem or opportunity relevant across a wide range of climate scenarios, including scenarios with relatively high suffering (roughly defined as > 4°C)?13
How do we use our assessment to decide whether to prioritize further research? Scale matters, but we don’t think bigger is always better. For example, we think some strategies and sectors with high scale already receive substantial funding and may benefit less from a Giving Green recommendation compared to other causes we could fund. Therefore, we think scale is primarily useful for deprioritizing research into topics that seem less promising because they are not a big or likely problem or opportunity. Based on the metrics and heuristics above, we assign a simple low/medium/high qualitative ranking.14 For strategies with a low rating, we only prioritize further research if we are especially optimistic about feasibility and funding need.
What are key uncertainties or limitations to this approach? We are especially uncertain as to the value to place on reduced climate change over different time periods (see Key uncertainties).15
Example: We estimated that industry accounts for around 29% of global greenhouse gas emissions.16 “Industry” is a broad term that may not be fully targeted by some impact strategies. Nevertheless, we ranked its scale as high, since we believe impact strategies in this area could generally affect 5% or more of future expected greenhouse gas emissions through 2100. Some topics are also cross-cutting (e.g., clean energy) or have a highly uncertain future (e.g., specific carbon removal technologies).
Feasibility: How hard is the problem to address?
What do we want to know? At a high level, we want to determine an impact strategy’s likelihood of achieving scale relative to the counterfactual. Feasibility assesses how much a funding opportunity can actually contribute to solving a problem.
Why does feasibility matter? Focusing resources on a problem is not useful unless it is solvable. Feasibility takes our scale assessment and fact-checks it based on practical real-life constraints. All else equal, we prioritize opportunities where there is a clear place for philanthropy to add to a strategy’s success, in order to increase the odds that aspirational impact becomes a reality.
How do we assess feasibility?
- Metrics: We think it is unlikely we can estimate actual likelihood of success with much certainty, and therefore rely primarily on heuristics for this assessment.
- Heuristics: In general, how strong is the theory of change? Are there important and weak parts of the theory of change that suggest low feasibility? In general, how complicated or simple is the impact strategy? Are there organizations working on this, past examples of success, promising track records, or other pieces of evidence that suggest high feasibility? For topics without substantial precedent, is there any forward-looking or theoretical analysis that makes a strong case for future success? Does philanthropy have an important role to play in this strategy, or is it more easily supported by government or private sector stakeholders?
How do we use our assessment to decide whether to prioritize further research? Based heuristics above, we assign a low/medium/high qualitative ranking.17 For strategies with a low rating, we only prioritize further research if we are especially optimistic about scale and funding need.
What are key uncertainties or limitations to this approach? As part of our initial assessment, we think this is the indicator we are most likely to get wrong, since feasibility assessments often require a deep understanding of specific nuances. If we misunderstand these nuances, we think there is a relatively high risk of creating false negatives.18 The main way we try to mitigate this risk is by soliciting external input. In later stages, we also develop a more formal theory of change (see Theory of change) that allows us to more methodically estimate feasibility.
Example: We struggled to easily and confidently the feasibility of decarbonizing heavy industry. This was partially due to highly varying impact strategies, ranging from corporate pressure campaigns to tweaking international trade regulation.19 However, we also heard varying opinions from reports we read and key stakeholders we spoke with. On one hand, economic incentives seem to give credence to the “hard-to-decarbonize industry” nickname.20 On the other hand, some specific impact strategies we assessed seemed highly plausible. At this early stage in our analysis, we classified this overall strategy as having medium feasibility.
Funding need: How much would more donations help?
What do we want to know? At a high level, we want to understand (a) whether climate philanthropy funding opportunities exist and (b) how much an impact strategy is constrained by philanthropic funding.
Why does funding need matter? There are thousands of non-profit organizations with highly effective approaches. However, some of these approaches are relatively well-established and well-funded, and additional donations may be unlikely to have a major effect on future impact. On the other hand, there are also highly effective (or highly promising) impact opportunities that are not well-funded, have large growth potential, and/or could engage in higher-risk activities with more funding certainty. All else equal, we want to prioritize approaches with relatively higher funding needs.
How do we assess funding need?
- Metric(s): Conditional on available data, we assess current philanthropic, private sector, and public sector funding to roughly understand (a) which source(s) provide funding and (b) how these funding amounts vary in size. For broader impact strategies (e.g., clean energy), we also estimate its percentage of philanthropic funding to generally understand whether it has been a major focus of philanthropic funding.21 We also consider year-over-year changes in philanthropic spending to assess whether funding has grown or shrunk over time.
- Heuristics: Is there a clear role or gap that climate philanthropy is well-placed to fill? Are there specific phases of an impact strategy (e.g., RD&D) that may benefit from philanthropic funding?22 Is this a low-interest idea that might signal funding need?23 On the other hand, are there governments, corporations, donors, and/or organizations already allocating substantial money/resources to this? How is philanthropic funding distributed among different geographies?24
How do we use our assessment to decide whether to prioritize further research? If we cannot identify any organizations working on an impact strategy, we deprioritize further research. (In the future we may look to help seed new organizations for promising strategies that have no one working on them.) For those remaining, we assign a qualitative low/medium/high ranking based on the metrics and heuristics above.25 In particular, we examine relatively well-funded impact strategies to consider whether they may have relatively lower funding needs. For any topic for which we believe the funding need is low, we deprioritize further research.
What are key uncertainties or limitations to this approach? Trying to determine funding need based on funding trends can be difficult and counterintuitive. For example, ClimateWorks notes that carbon removal efforts received a “sizable” funding increase in 2021.26 This could imply that funding need is now lower since there is more philanthropic funding. However, increased philanthropic funding could also indicate even higher funding need, perhaps because philanthropic efforts play a complementary role to increased US government support for carbon removal.27 Assessing broader funding trends can also hide niche funding opportunities with high funding needs. For example, it is our general impression that US policy advocacy work has relatively low funding need. In 2022, we nonetheless recommended Evergreen Collaborative in part because of the timely and specific funding opportunity to assist specific states to take better advantage of Inflation Reduction Act funding.28 Because of this, we rely more heavily on heuristics than metrics, and generally have a high bar for deprioritizing impact strategies based on funding need.
Example: We used ClimateWorks data to assess that industry efforts received a relatively small amount (~3%) of climate philanthropy funding.29 However, there were also some signs that well-funded climate philanthropists (e.g., Jeff Bezos) may be ramping up funding for this impact strategy, which might reduce overall funding need.30 In tandem with increasing focus and funding from the climate philanthropy community, we also became aware of some new early-stage funding opportunities that might have high funding need.31 We also assessed the degree to which private sector incentives and spending might render philanthropic funding moot, but generally did not find these arguments to be overly compelling. Given all these considerations, we ranked industry decarbonization as having medium funding need.
Shallow dives and deep dives
For impact strategies we prioritize based on the criteria above, we subsequently evaluate them at two depths: shallow dives and deep dives.32 These dives continue to evaluate opportunities based on scale, feasibility, and funding need, but move beyond the qualitative low/medium/high ranking we use to make early-stage research prioritizations. For example, instead of assessing general funding need, we attempt to determine how a marginal donation would actually be used, as well as what would happen in the absence of that donation. Additionally, we consider major co-benefits and adverse effects that may affect our prioritization decisionmaking.33
We conduct many shallow dives on different impact strategies and, for the subset we continue to prioritize, deep dives to reassess in substantially more detail. While both focus on the same fundamental criteria, we start with a quicker analysis and only dig deeper if we think a strategy has a high probability of leading to a top recommendation.34
At this stage of our analysis, we also formally introduce two analytical tools to help guide our decision-making: theories of change and cost-effectiveness analyses.
Theory of change
We use theories of change to help us map out and assess an impact strategy’s pathway to reducing suffering due to climate change. For a given theory of change, we focus especially on assumptions that are important for a theory of change to become reality. For each assumption, we rank whether we have low, medium, or high certainty in our assessment.
Theories of change may not always be amenable to easy measurement or quantification, or supported by a robust evidence base. All else equal, we think strategies that have lengthier and/or less certain theories of change are less likely to be successfully implemented, since there are more and/or larger opportunities for an important node or link to fail.
Cost-effectiveness analysis
We use cost-effectiveness analyses (CEAs) as an input into our comparison of the cost-effectiveness of different strategies and organizations. CEAs often go hand-in-hand with theories of change, as we often consider CEAs to be a “quantified theory of change.” However, many of the opportunities we view as most promising also have highly uncertain inputs.35 Because of this, our CEAs often primarily serve as a way to (a) identify or confirm especially important parameters that most directly determine how much a donation might reduce climate change and (b) assess whether it is plausible that a donation could be highly cost-effective.36
For example, our decarbonizing heavy industry CEA (explanation, model) estimates the cost-effectiveness of advocacy efforts to secure a federal US government commitment to switch to lower-carbon cement procurement and its potential subsequent effects on global cement. Since this CEA includes highly subjective guess parameters, its estimates are highly uncertain and should not be taken literally. Instead, it helped us think through what parameters and assumptions were necessary in order for nonprofit advocacy to reduce greenhouse gas emissions, and whether these efforts plausibly have high cost-effectiveness.
We also sometimes use CEAs to compare roughly similar climate impact strategies to determine which to prioritize. By constructing CEAs for each strategy, we can identify (a) which parameters vary and (b) whether one opportunity appears substantially more cost-effective than the other. Though we may have low confidence in our specific cost-effectiveness estimates, we may have high confidence in our assessment of the relative cost-effectiveness of one opportunity over another, and prioritize accordingly. For instance, we found this approach useful when comparing advocacy organizations in the US in the lead-up to climate legislation in 2022.37
Example: As we initially refined our investigation, we roughly mapped out nine different strategies to decarbonize heavy industry.38 Though there are many different industry types and decarbonization impact strategies, our general impression was that heavy industry can be decarbonized if there is (1) adequate demand for low-carbon products, (2) a supportive regulatory framework, and (3) transition assistance that facilitates a switch to low-carbon production. To further refine our thinking, we built a simple theory of change and constructed a CEA as a plausibility check of this strategy’s cost-effectiveness.39
11 We developed these indicators by generally speaking with different climate researchers and philanthropists about their assessment/funding criteria, as well as reviewing publicly available frameworks such as Holden Karnofsky’s 2013 Importance, Tractability, and Neglectedness (ITN) framework, MacAskill et al 2022’s Significance, Persistence, and Contingency framework, and GiveWell’s research criteria. We do not adopt these specific terminologies because (1) they have formal and varied formulas that we do not use and (2) we think our terminology may be more easily interpretable by a broader audience.
12 We think the future of climate change is difficult to predict, and it is our general impression that there is fairly large disagreement and uncertainty over a wide range of future climate scenarios. We use a 2100 timeline to very roughly balance two broad scenarios: (1) mitigation efforts are relatively successful, and suffering due to climate change is concentrated through 2100; (2) mitigation efforts are relatively unsuccessful, and suffering due to climate change increases over time, including beyond 2100. We are highly uncertain about the future of climate change, but broadly think scenario 1 is more likely and would cause less suffering than scenario 2. We think this based on a medium-depth review of the available literature, as well as speaking with various climate scientists and generalist forecasters. For example: “Assessment of current policies suggests that the world is on course for around 3 °C of warming above pre-industrial levels by the end of the century — still a catastrophic outcome, but a long way from 5 °C” Source: Hausfather and Peters 2020; “warming can be kept just below 2 degrees Celsius if all conditional and unconditional pledges are implemented in full and on time. Peak warming could be limited to 1.9–2.0 degrees Celsius (5%–95% range 1.4–2.8 °C) in the full implementation case—building on a probabilistic characterization of Earth system uncertainties in line with the Working Group I contribution to the Sixth Assessment Report6 of the Intergovernmental Panel on Climate Change” Source: Meinshausen et al 2022.
To compare the effects of non-CO2 greenhouse gas emissions to CO2 emissions, we use a 75-year global warming potential (GWP). This allows us to roughly estimate the amount of energy the emissions of 1 ton of a non-CO2 greenhouse gas will absorb through 2100. For additional background on GWP, see Understanding Global Warming Potentials | US EPA. We believe GWP is an imperfect approach to assessing global temperature effects, as well as suffering due to climate change. For example, see Allen et al 2018 (“Using conventional Global Warming Potentials (GWPs) to convert [short-lived climate pollutants] to ‘CO2-equivalent’ emissions misrepresents their impact on global temperature”). We use GWP75 because it is relatively simple to calculate and communicate, and because we believe our heuristics help us address two of the main flaws of GWP: GWP does not account for (1) varying potency within a given time period and (2) any potential effects beyond the GWP duration. Our heuristic, “is this problem or opportunity relevant across a wide range of climate scenarios, including scenarios with relatively high suffering?” is meant to downgrade mitigation efforts that are less likely to apply in longer-term (2100+) scenarios with relatively high suffering. In practice, we think these mitigation efforts are primarily limited to (a) single-instance reductions in near-term term climate pollutants (e.g., methane) and (b) CO2 removal with less than 100-year permanence.
13 We think the future of climate change is difficult to predict, and it is our general impression that there is fairly large disagreement and uncertainty over a wide range of future climate scenarios. For example, the IPCC outlines five Shared Socioeconomic Pathways (SSP), but does not make claims as to the likelihood of different scenarios occurring (Source: “...the developers of the SSPs make no claim as to the relative likelihood of any scenario coming to pass.” Explainer: How ‘Shared Socioeconomic Pathways’ explore future climate change - Carbon Brief). Another major uncertainty is the degree to which “tipping elements” (“conditions beyond which changes in a part of the climate system become self-perpetuating. These changes may lead to abrupt, irreversible, and dangerous impacts with serious implications for humanity” Source: McKay et al 2022) exist and/or are likely to occur. All else equal, we think that impact strategies that remain relevant across a wide range of scenarios have a greater likelihood of reducing the most suffering due to climate change. Strategies that apply to worser-case scenarios may be especially impactful, since we think reducing climate change in worser-case scenarios will reduce relatively more suffering than similar efforts in better-case scenarios. For example, we think that avoiding a 0.5 °C global temperature increase from 1.5 °C to 2.0 °C will reduce relatively less suffering than avoiding a similar temperature increase from 2.5 °C to 3.0 °C. However, given uncertainties across different climate change scenarios and what we believe to be a moderate likelihood that we do not encounter worser-case scenarios (for example, “Assessment of current policies suggests that the world is on course for around 3 °C of warming above pre-industrial levels by the end of the century — still a catastrophic outcome, but a long way from 5 °C” Source: Hausfather and Peters 2020; “ warming can be kept just below 2 degrees Celsius if all conditional and unconditional pledges are implemented in full and on time. Peak warming could be limited to 1.9–2.0 degrees Celsius (5%–95% range 1.4–2.8 °C) in the full implementation case—building on a probabilistic characterization of Earth system uncertainties in line with the Working Group I contribution to the Sixth Assessment Report6 of the Intergovernmental Panel on Climate Change” Source: Meinshausen et al 2022).
14 Though this ranking is qualitative to allow for high degrees of uncertainty in our initial assessments, we think scale is generally a criteria for which we can ultimately have a relatively quantifiable metric. We generally consider scale to be low if we believe the problem or opportunity could affect less than 2% of future expected greenhouse gas emissions through 2100, medium for 2% to 5%, and high for greater than 5%.
15 Time frame is important when thinking about scale because (1) different greenhouse gases have different atmospheric lifetimes, (2) different interventions have varying levels of permanence, and (3) time frame impacts whether we prioritize mature ready-to-scale technologies over emerging technologies. For example, prioritizing a longer time frame may favor strategies that focus on permanently removing longer-lived climate pollutants (e.g., some direct air capture projects) over single-instance avoidance of near-term (sometimes referred to as “short-lived”) climate pollutant emissions (e.g., refrigerant destruction). See section Key uncertainties, Defining and estimating suffering due to climate change, for additional commentary.
16 Industry greenhouse gas emissions: ~29% (2016). See Emissions by sector - Our World in Data, “Global greenhouse gas emissions by sector” figure, industry + energy use in industry
17 We describe our assessment as low/medium/high to increase readability and avoid false precision. Since these terms can be interpreted differently, we use rough heuristics to define them as percentage likelihoods we think a strategy can, on average, be successful. Low = 0-60%, medium = 60-80%, high = 80-100%.
18 We are more concerned about false negatives than false positives during our initial assessment. Namely, we can catch false positives at later stages in our analysis whereas false negatives drop out of consideration, meaning we’ve lost the ability to rectify our mistake.
19 See Decarbonizing Heavy Industry | Giving Green for additional detail
20 “Energy-intensive industries (EIIs) produce basic materials, such as steel, petrochemicals, aluminum, cement, and fertilizers, that are responsible for around 22 percent of global CO2 emissions (Bataille 2019).” Unlocking the “Hard to Abate” Sectors | World Resources Institute.
21 For example, industry accounts for 29% of global greenhouse gas emissions and receives around 3% of climate philanthropic funding from foundations. Industry greenhouse gas emissions: ~29% (2016). See Emissions by sector - Our World in Data, “Global greenhouse gas emissions by sector” figure, industry + energy use in industry. Philanthropic funding focused on industry: ~3% (annual average, 2017-2021). See Climateworks Funding Trends 2022, figure 4. Calculation: $55M / $1.7B = 3%. This might suggest that industry has a relatively high philanthropic funding need, since its percentage of philanthropic funding is lower than that of its emissions. This is a very rough heuristic, as there could be many other factors that mean this is not the case (e.g., limited philanthropic funding opportunities).
22 For example, see Climate Tech’s Four Valleys of Death and Why We Must Build a Bridge.
23 An approach might be low-interest due to relatively low scale or feasibility. It may also be low-interest due to a limited evidence base, minimal track record, political sensitivity, or lack of private sector incentives.
24 For example, see Climateworks Funding Trends 2022, figure 4. From 2017-2021, Indonesia received an annual average of $25M in philanthropic funding, around 2% of country-specific funding (calculation: $25M / ($1.7B - $505M) = 2%. However, it is the world’s fifth largest emitter, equivalent to ~4% of global greenhouse gas emissions (see WRI: Indonesia Climate Change Data 2019). This might suggest that Indonesia has a relatively high philanthropic funding need, since its percentage of philanthropic funding is lower than that of its emissions. This is a very rough heuristic, as there could be many other factors that mean this is not the case (e.g., limited philanthropic funding opportunities).
25 We describe our assessment as low/medium/high to increase readability and avoid false precision. Since these terms can be interpreted differently, we use rough heuristics to define them as percentage likelihoods a climate philanthropy funding need is within the range we would consider for a top recommendation. Low = 0-60%, medium = 60-80%, high = 80-100%.
26 “2021 saw sizable [philanthropic] funding increases for forests and carbon dioxide removal.” Climateworks Funding Trends 2022.
27 “In the last year, the federal government has committed to spend more than $580 billion to combat climate change through the passage of the Bipartisan Infrastructure Law (BIL) and the Inflation Reduction Act (IRA). Within this $580 billion, there is significant funding supporting development and deployment of carbon dioxide removal…” WRI: Carbon Removal in the Bipartisan Infrastructure Law and Inflation Reduction Act, 2022.
28 “Following the 2022 passage of the Inflation Reduction Act (IRA), Evergreen Collaborative is now planning to work on implementing bills and state-level policy…” Evergreen Collaborative: Deep Dive | Giving Green.
29 Philanthropic funding focused on industry: ~3% (annual average, 2017-2021). See Climateworks Funding Trends 2022, figure 4. Calculation: $55M / $1.7B = 3%. This might suggest that industry has a relatively high philanthropic funding need, since its percentage of philanthropic funding is lower than that of its emissions. This is a very rough heuristic, as there could be many other factors that mean this is not the case (e.g., limited philanthropic funding opportunities).
30 Bezos Earth Fund: The Bezos Earth Fund made its first grants to “Decarbonizing the Economy” in November 2020. “Bezos Earth Fund, Our Programs” n.d. Example of Bezos Earth Fund grant: “Rocky Mountain Institute (RMI) today announced that it has received a $10 million grant from the Bezos Earth Fund to help significantly reduce greenhouse gas (GHG) emissions in both U.S. buildings and in energy-intensive industrial and transport sectors.” "RMI Awarded $10 Million from The Bezos Earth Fund to Accelerate Decarbonization of Buildings and Industry" 2020. Microsoft Climate Innovation Fund: “We’ll focus on areas such as direct carbon removal, digital optimization, advanced energy systems, industrial materials, circular economy, water technologies, sustainable agriculture, and business strategies for nature-based markets.” "Climate Innovation Fund" n.d. Giving Green note: We note that ClimateWorks Foundation’s reported annual average spending may have increased simply because ClimateWorks learned of more foundations already donating to decarbonizing heavy industry. However, we do not think this is the case.
31 One of these opportunities eventually received a top recommendation status. See Industrious Labs: Deep Dive | Giving Green for additional details.
32 See Climate change mitigation strategies research | Giving Green for our shallow and deep dives.
33 In general, we are most focused on climate benefits and our decision making does not typically place substantial weight on co-benefits (benefits not directly related to climate). However, there are instances in which a co-benefit may (a) attract additional funding (e.g., Good Food Institute’s work may also have positive animal welfare effects) and/or (b) reduce suffering due to climate change in ways unrelated to greenhouse gases (e.g., BURN stoves allow households to reduce spending on cooking charcoal). In these cases, we think it is useful to consider and highlight co-benefits. For adverse effects, we believe there are many highly cost-effective impact strategies that do not have substantial adverse effects. Therefore, we deprioritize strategies that we think might have large and/or inequitably shared adverse effects.
34 We describe our certainty as low/medium/high to increase readability and avoid false precision. Since these terms can be interpreted differently, we use rough heuristics to define them as percentage likelihoods the assumption is, on average, correct. Low = 0-60%, medium = 60-80%, high = 80-100%.
35 We think this is most likely the case for two main reasons: (1) many climate funders explicitly or implicitly value certainty in their giving decisions, so this means less-certain funding opportunities are relatively underfunded; and (2) we think some of the most promising pathways to scale (e.g., policy influence and technology innovation) are also inherently difficult to assess due to their long and complicated causal paths.
36 We use rough benchmarks as a way to compare the cost-effectiveness of different giving opportunities. As a benchmark for our top recommendations, we consider an opportunity if its estimated cost-effectiveness is plausibly within an order of magnitude of $1/tCO2e avoided or removed (i.e., less than $10/tCO2e).
37 See Activism: Cost-Effectiveness Analysis | Giving Green for additional explanation.
38 See Decarbonizing Heavy Industry | Giving Green, Table 1. Though our initial assessment was more informal, this table encapsulates the different strategies we generally considered.
39 Theory of change: See Decarbonizing Heavy Industry | Giving Green, “Theory of change for decarbonizing heavy industry”. CEA: See Decarbonizing Heavy Industry | Giving Green, “What is the cost-effectiveness of decarbonizing heavy industry?”.
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