Finance
Landscape of Climate Finance in Ethiopia – CPI
Macroeconomic reforms and escalating climate shocks are placing climate finance at the center of Ethiopia’s development trajectory. The country contributes 0.4% of global emissions but faces high climate risks, particularly due to its reliance on rain-fed agriculture and hydropower. At the same time, high inflation, foreign-exchange shortages, rising debt service obligations, and a recent sovereign default have constrained fiscal space and raised the cost of capital. Ethiopia must therefore rapidly scale up climate investment in line with its Nationally Determined Contribution (NDC 3.0), while navigating macroeconomic constraints and the declining predictability of international concessional and donor finance.
Ethiopia’s climate policy framework is increasingly investment-oriented, moving from ambition to action. Building on the Climate Resilient Green Economy (CRGE) Strategy (2011) and earlier NDCs, the country’s NDC 3.0 (2025–2035) shifts from high-level ambition toward defined sectoral pathways and financing needs. Parallel reforms signaling growing institutional readiness include greening the financial sector under the National Bank of Ethiopia, developing a national green taxonomy, capital market reforms linked to the Ethiopian Securities Exchange, and emerging carbon market frameworks. However, coordination challenges, fragmented mandates, and limited project preparation capacity continue to constrain delivery.
Tracking how climate finance is mobilized and deployed is critical to inform policy decisions, guiding development partner strategies, and identify opportunities to crowd in domestic and private capital. This second iteration of the Landscape of Climate Finance in Ethiopia provides an updated baseline of project-level climate finance commitments for 2019 to 2023, with a focus on the biennial average for 2022 and 2023. It tracks flows across mitigation, adaptation, and dual-benefit activities, mapping finance from domestic and international sources, through public and private actors, to instruments and end-use sectors.
This assessment draws on publicly available and proprietary datasets compiled on a best-effort basis. Data gaps remain material, especially for domestic public spending, given the absence of systematized climate budget tagging, and for certain private sector investments that are not consistently disclosed. As a result, some flows, particularly domestic public spending and difficult-to-track private investments, are likely underestimated.
Key findings
- Ethiopia’s climate finance has gradually increased but must rise by at least fourfold to meet identified needs. Tracked flows averaged USD 2.3 billion annually in 2022/23, equivalent to approximately 1.7% of GDP. This is an 11% increase from the annual average of USD 2.1 billion in 2020/21 but still well below the estimated USD 10.6 billion annual requirement under the NDC 3.0 (2025–2035).
- Ethiopia’s heavy reliance on international public sources exposes its climate agenda to the constraints of external concessional finance. In 2022/23, 93% of tracked flows originated from international public sources. Public actors committed approximately USD 2.2 billion annually, primarily through grants (80%) and concessional debt (14%). Multilateral development finance institutions and donor governments were the largest providers. This concentration underscores the urgency of mobilizing broader and more sustainable domestic and private funding sources.
- Ethiopia’s shallow capital markets and regulatory uncertainty have limited private climate finance. Private actors contributed USD 113 million annually in 2022/23, representing less than 5% of total flows. This is insufficient to signal a functioning market or provide any buffer against public finance volatility. Private flows were concentrated in agriculture, forestry, and other land use (AFOLU) and small-scale energy activities. Investments were influenced by guarantee-backed transactions and philanthropic grants. Macroeconomic risk, currency constraints, shallow capital markets, and regulatory uncertainty continue to deter private participation at scale.
- Adaptation finance accounts for the majority of Ethiopia’s climate flows, reflecting the country’s high vulnerability to drought, hydrological variability, and disaster risk. Adaptation represented 59% of tracked climate finance in 2022/23 (USD 1.4 billion annually), a slight rise from 56% in 2019/20. This finance was overwhelmingly grant-based (92%) and internationally sourced. While they exceed mitigation in volume, adaptation flows remain far below the estimated USD 4 billion annual need.
- Mitigation finance remains insufficient relative to emissions structure and targets and costed needs. These flows averaged approximately USD 500 million annually, compared to the estimated USD 6.6 billion requirement under NDC 3.0. Finance was concentrated in the energy sector and largely concessional in nature. Mitigation flows declined relative to 2020/21 due to project cycle effects. The AFOLU sector, a large source of emissions, received a small share of mitigation finance, highlighting a structural imbalance between emissions sources and investment patterns.
- Cross-sectoral and resilience-oriented programs feature prominently across both mitigation and adaptation. In 2022/23, adaptation investment averaged USD 644 million, mitigation investment USD 77 million, and dual-benefit projects received USD 306 million. These flows targeted initiatives such as disaster-risk management, food security, institutional capacity building, and policy support. This reflects Ethiopia’s integrated CRGE vision and climate–development nexus and requires strong coordination, monitoring, and financial management systems.
- Institutional reform momentum is building, but delivery constraints persist. Ethiopia has implemented several climate-related reforms, including fuel subsidy reform, electric mobility incentives, financial sector greening initiatives, carbon market readiness efforts, and capital market development. These reforms can help to mobilize domestic and private capital. Yet fragmented governance structures, limited project preparation capacity, incomplete climate finance tracking systems, and constrained fiscal space continue to limit the scale and predictability of flows.
Recommendations
Strengthening governance, institutional capacity, and monitoring systems can help align climate finance mandates, build investable pipelines, and improve investor confidence. Strategic use of concessional finance, supportive regulation, and appropriate financial instruments can help mobilize private capital over time. This report highlights six priority actions for scaling Ethiopia’s climate finance:
- Strengthen climate finance governance to accelerate implementation. Enhance the role of the Climate Resilient Green Economy (CRGE) strategy as an inter-ministerial coordination mechanism with clear mandates and decision rights. This should link NDC planning to budget allocation, including climate budget tagging, and be aligned with public financial management processes. TCRGE efforts can serve as a central platform for screening and prioritizing NDC-aligned projects, coordinating technical assistance, and structuring blended finance/PPP transactions.
- Build capacity for project preparation as well as institutional and subnational delivery to convert policy ambition into implementable pipelines. Improve technical capacity for feasibility studies, financial structuring, safeguards, risk allocation, and results-based planning across line ministries and subnational institutions, and establish standardized project preparation tools and targeted support for high-priority sectors, particularly AFOLU.
- Strengthen climate finance tracking, transparency, and data credibility. Climate budget tagging could be extended to regional and local levels, as well as to climate-aligned sectors such as energy, AFOLU, transport, water and wastewater, buildings and infrastructure and industry. Embedding tagging in budget execution and reporting can reconcile climate-relevant expenditures with actual spending and outputs.
- Optimize scarce public resources through catalytic de-risking and innovative fiscal instruments. Ethiopia must meet its NDC3.0 USD 2.4 billion annual domestic public finance target amid fiscal constraints, including rising debt servicing (13% of revenue), declining tax-to-GDP ratio (7.5%), and volatile donor finance. The country can strategically use its CRGE Facility and national funds to provide guarantees or first-loss capital to crowd in private flows. Aggregation mechanisms (SPVs, Platform-based structures, financial intermediary aggregation) can also help accelerate a shift from small, planning-oriented grants to scalable investments. Debt-for-climate swaps may be another viable source.
- Unlock international and institutional capital through stronger enabling frameworks and domestic markets. High country risk, regulatory gaps, and weak monitoring limit private investment. Momentum is building through initiatives such as Ethiopia’s National Carbon Market Strategy, the establishment of the Ethiopian Securities Exchange, and the NBE’s Greening Financial Systems program. Next steps could include frameworks and regulations for carbon markets, green bonds, and other climate-aligned instruments to reduce uncertainty, enable transactions, and scale local-currency finance. Carbon markets offer a near-term opportunity to mobilize private capital, given the country’s land restoration and reforestation programs.
- Scale finance for sectors that are hard to abate or prioritized under the NDC 3.0. The limited climate finance flowing to industry represents a missed opportunity, given the sector’s importance in shaping Ethiopia’s long-term emissions trajectory and development ambition. Costed pipelines for carbon-intensive sectors, blended finance, and technical assistance for project preparation, standards, and technology deployment can help direct more capital to NDC 3.0 mitigation priorities, including industrial energy efficiency, fuel switching, and low-carbon technologies.
New research shows the finance sector leading regulatory authorities in adopting artificial intelligence (AI).
First home buyer Jessica Ricci was just trying to save a little extra money through her superannuation in a federal government scheme intended to help people like her. But an error from tax authorities has left her paying more tax than the top income bracket on some super contributions – ironically having the exact opposite of the intended effect of the policy.
As a result, she’s lost out on an extra $2,250 in savings that was supposed to go to her house deposit. While the ATO pushed back over who was at fault for the mix-up, her case has highlighted an increasingly problematic blindspot when it comes taxpayers getting the short end of the stick when dealing with tax authorities.
“I’m definitely feeling a little bit helpless,” she told Yahoo Finance. “There’s not a clear path to rectify this.”
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Jess was tipping extra money into her superannuation as part of the First Home Super Saver Scheme which has been running for years and allows eligible first home buyers to take advantage of the tax benefits of their retirement savings and then pull those extra contributions out to use for a house deposit.
As part of the scheme, individuals need to apply to the ATO, which in turn requests the related money from the person’s super fund.
Over four years, Jess contributed the maximum $50,000 amount, ensuring not to exceed the $15,000 yearly cap. She did so with the expectation of claiming the benefit at the time of her house purchase, as per the rules of the scheme.
When she went to make the claim, much of the information was auto-populated by the ATO website. And after receiving her funds, and the amount being less than expected, she soon discovered that her first contribution was wrongly classified as a concessional contribution, meaning $2,250 was, in the words of an ATO official, “retained by the ATO as withholding tax”.
She has spent months going back and forth with tax officials trying to get the money she believes should be owed to her.
“They’ve all taken the same stance, which is; ‘Well, yeah, we made a mistake, but you didn’t catch it. You said that what we provided you was fine, so it’s your fault’.
“I think it’s crazy to put the onus or the burden on the average person. I think most people would rightfully assume that pre-filled data provided by the ATO would be accurate,” she said.
Most coverage of artificial intelligence in finance focuses on what these tools can do. Less attention is paid to how they perform under scrutiny, particularly in financial modeling, where small errors can carry real consequences.
After testing Anthropic’s Claude in real-world modeling scenarios, one conclusion stands out: Claude produces outputs that look credible at first glance but contain structural flaws that only an experienced professional would catch.
That gap between appearance and reliability is where risk begins.
Where AI Performs Well
Claude handled several foundational elements of financial modeling competently. It was able to:
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Build basic revenue models
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Generate standard financial statements
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Apply consistent formatting, labels and units
The outputs appeared polished and professional. In some cases, they resembled models produced by junior analysts. That is what makes them risky.
The models looked right. The structure appeared logical. Formatting signaled credibility. For a time-constrained professional, those cues can create trust before a full audit is completed.
The Errors That Hide in Plain Sight
A closer review revealed issues that would likely go unnoticed without technical expertise:
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Broken linkages between financial statements
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Hardcoded values instead of centralized assumptions
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Non-dynamic formulas and inconsistent logic across periods
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Balance sheets that did not balance
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Timing mismatches between beginning- and end-of-period values
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Circular reference issues in areas like revolving credit
These are not edge cases. They point to a broader issue. The model may function, but it is not built on a reliable or auditable foundation.
Where Best Practices Break Down
Beyond individual errors, the models often failed to follow core financial modeling principles:
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Assumptions were not clearly separated from calculations
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Error checks were largely absent
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KPIs lacked depth and industry-specific nuance
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Formula design was inconsistent or inefficient
These gaps affect more than presentation. They determine whether a model can be trusted, adapted and audited under pressure.
The Real Risk Is Overconfidence
The key distinction is not between AI and human-built models. It is between models that are understood and those that are not. When a professional builds a model, every assumption and linkage is intentional. Even limitations are typically known. With AI-generated models, that understanding is outsourced.
This creates a different kind of risk:
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The logic behind the model may not be fully clear
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The structure may not align with internal standards
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The review process may be less rigorous because the output appears complete
In practice, credibility is inferred from how the model looks, not how it was built.
Reviewing Is Not the Same as Building
There is also a practical workflow issue. Reviewing an AI-generated model is not equivalent to building one.
When reviewing:
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You are interpreting logic you did not design
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Errors can be harder to trace
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Inconsistent structure increases audit time
In some cases, it is faster to build a clean model from scratch than to fix a flawed AI-generated one.
What This Means in Practice
Financial models support decisions involving significant capital. Even small issues can cascade:
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Misstated cash flows can distort debt capacity
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Timing errors can affect liquidity assumptions
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Weak KPIs can lead to incomplete analysis
There is also a question of accountability. Regardless of how a model is created, responsibility for its output remains with the professional using it.
Where AI Fits Today
AI tools can still be useful in financial modeling. They can help:
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Speed up repetitive components
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Generate starting points for analysis
But they are not a substitute for professional judgment. Nor are they ready to operate without close oversight. For now, their role is best defined as assistive, not authoritative.
A More Practical View of AI in Finance
The conversation around AI in finance does not need more optimism or skepticism. It needs more precision. AI can produce outputs that are visually convincing and directionally correct. In financial modeling, that is not enough.
The real risk is not that AI makes mistakes. It is those mistakes that are easy to miss, especially when the output looks finished. For financial professionals, the takeaway is simple: treat AI-generated models as drafts, not decision-ready tools.
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