One of the headline outcomes to emerge from COP30 was a new target to “at least triple” finance for climate adaptation in developing countries by 2035.
Vulnerable nations stress that they urgently need to strengthen their infrastructure as climate hazards intensify, but they struggle to attract funding for these efforts.
The new goal, which builds on a previous target agreed four years ago to double adaptation finance by 2025, was a central demand for many developing countries at the UN climate summit in Belém.
Yet, throughout the two-week negotiations, developed-country parties opposed new targets that would give them more financial obligations.
As a result of this opposition, the final target is less ambitious than the idea originally floated by developing countries, resulting in less pressure on developed countries to provide public funds.
This article looks at precisely what the final COP30 outcome does – and does not – say about tripling adaptation finance, as well as the implications for developing countries.
- 1) The final COP30 decision delayed the ‘tripling’ target by five years and added uncertainty
- 2) The new target is looser than the previous ‘doubling’ goal for adaptation finance
- 3) The target also falls far short of developing countries’ adaptation needs
1. The final COP30 decision delayed the ‘tripling’ target by five years and added uncertainty
At COP26 in Glasgow in 2021, a target was agreed for developed nations to double the amount of adaptation finance they would provide to developing countries by 2025.
This target has been broadly interpreted as approximately $40bn by 2025, using the agreed baseline of $18.8bn in 2019.
As of 2022, the latest year for which official data is available, annual adaptation finance from developed countries had reached $28.9bn. (Final confirmation of whether the target has been met will not come until 2027, due to the delay in climate-finance reporting.)
With the “doubling” target set to expire this year, some developing countries came to COP30 with the aim of agreeing on a new target.
The least-developed countries (LDCs) group called for “a tripling of grant-based adaptation finance by 2030 to at least $120bn”. They were backed by small-island states, the African group and some Latin American countries.
This proposal was included in the first draft of the “global mutirão“, the key overarching decision text produced by the COP30 presidency.
However, the text that ultimately emerged pushed the “tripling” deadline back to 2035. As the chart below shows, this delayed target could mean far less adaptation finance in the short term, due to developed countries taking longer to ramp up their contributions.
Lina Yassin, an adaptation advisor to the LDCs, tells Carbon Brief that this goal is “fundamentally out of step” with the obligation for developed countries to achieve a “balance” between adaptation and mitigation finance.
(This obligation is set out in the Paris Agreement, but, in practice, developed countries provide far more finance for mitigation initiatives, such as clean-energy projects. Adaptation finance has been around a third of the total in recent years and this would still be the case if the overall $300bn climate-finance and tripling adaptation finance targets are both met.)
The final text also removed a mention of 2025 as the baseline year, adding uncertainty as to what precisely the 2035 target means.
“The [LDCs] wanted a clear number, tied to a clear baseline year, that you can actually track and hold providers accountable for,” Yassin explains.
The text does allude to the “doubling” target agreed at COP26 in Glasgow, which some analysts say is an indicator of what the baseline should be.
“It is obviously deliberately vaguely written, but we think the reference to the Glasgow pledge means they should triple that pledge,” Gaia Larsen, director for climate finance access at the World Resources Institute (WRI), tells Carbon Brief.
2. The new target is looser than the previous ‘doubling’ goal for adaptation finance
The “doubling” target set at COP26 was based on adaptation finance “provided” by developed countries.
This means it exclusively comes as publicly funded grants and loans from many EU member states, the US, Japan and a handful of other nations, including finance they raise via multilateral development banks (MDBs) and funds.
The LDCs’ original proposal for the “tripling” goal was even more specific. It called for “grant-based finance”, meaning any loans would not be included.
Amid widespread cuts to aid budgets, notably in the US, developed countries have been unwilling to commit to new targets based solely on them providing public finance.
Instead, they stressed at COP30 that any new pledges should align with the “new collective quantified goal” (NCQG) to raise $300bn by 2035, which was agreed last year. This is reflected in the final decision, which says the tripling target is “in the context of” the NCQG.
Unlike the COP26 goal, the NCQG covers finance from a variety of sources, including “mobilised” private finance and voluntary contributions from wealthier developing countries.
Assuming $120bn as the 2035 objective, WRI has estimated what its composition could be, based on the looser accounting allowed under the new adaptation-finance goal.
As the chart below shows, the institute estimates that more than a quarter of the target could be met by these new sources, with the rest coming from developed-country governments.
WRI assumes that MDBs will play a “critical role” in meeting the 2035 target, amid calls for them to triple their overall finance. More MDB funding would also automatically be counted, as the new adaptation goal includes MDB funds that are attributable to developing countries, as set out in the NCQG.
The WRI analysis also assumes a big increase in the amount of private finance for adaptation that is “mobilised” by public spending, scaling up significantly to $18bn by 2035.
Traditionally, it has been difficult to raise private investment for adaptation initiatives, as they provide less return on investment than clean-energy projects.
3. The target also falls far short of developing countries’ adaptation needs
The UN Environment Programme’s (UNEP) recent “adaptation gap” report estimates that developing countries’ adaptation investment requirements – based on modelled costs – will likely hit $310bn each year by 2035.
Developing countries have self-reported even higher financial “needs” in their nationally determined contributions (NDCs) and national adaptation plans (NAPs) submitted to the UN.
When added together, UNEP concludes these needs amount to $365bn each year for developing countries between 2023 and 2035.
(According to NRDC, most of this discrepancy comes from middle-income countries reporting significantly higher needs than the UNEP-modelled costs.)
As the chart below shows, the new COP30 target would not cover more than a third of these estimated needs by 2035.
Both domestic spending and private-sector investment that is independent of developed-country involvement are expected to play a role in meeting developing countries’ adaptation needs.
Nevertheless, UNEP states that the overarching climate-finance goals set by countries are “clearly insufficient” to close the adaptation-finance “gap”.
Even in a scenario based on the LDCs’ original proposal of tripling adaptation finance to $120bn by 2030, the UNEP report concluded that a “significant” gap would have remained.
The post Analysis: Why COP30’s ‘tripling adaptation finance’ target is less ambitious than it seems appeared first on Carbon Brief.
Analysis: Why COP30’s ‘tripling adaptation finance’ target is less ambitious than it seems
David Sathuluri is a Research Associate and Dr. Marco Tedesco is a Lamont Research Professor at the Lamont-Doherty Earth Observatory of Columbia University.
As climate scientists warn that we are approaching irreversible tipping points in the Earth’s climate system, paradoxically the very technologies being deployed to detect these tipping points – often based on AI – are exacerbating the problem, via acceleration of the associated energy consumption.
The UK’s much-celebrated £81-million ($109-million) Forecasting Tipping Points programme involving 27 teams, led by the Advanced Research + Invention Agency (ARIA), represents a contemporary faith in technological salvation – yet it embodies a profound contradiction. The ARIA programme explicitly aims to “harness the laws of physics and artificial intelligence to pick up subtle early warning signs of tipping” through advanced modelling.
We are deploying massive computational infrastructure to warn us of climate collapse while these same systems consume the energy and water resources needed to prevent or mitigate it. We are simultaneously investing in computationally intensive AI systems to monitor whether we will cross irreversible climate tipping points, even as these same AI systems could fuel that transition.
The computational cost of monitoring
Training a single large language model like GPT-3 consumed approximately 1,287 megawatt-hours of electricity, resulting in 552 metric tons of carbon dioxide – equivalent to driving 123 gasoline-powered cars for a year, according to a recent study.
GPT-4 required roughly 50 times more electricity. As the computational power needed for AI continues to double approximately every 100 days, the energy footprint of these systems is not static but is exponentially accelerating.
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And the environmental consequences of AI models extend far beyond electricity usage. Besides massive amounts of electricity (much of which is still fossil-fuel-based), such systems require advanced cooling that consumes enormous quantities of water, and sophisticated infrastructure that must be manufactured, transported, and deployed globally.
The water-energy nexus in climate-vulnerable regions
A single data center can consume up to 5 million gallons of drinking water per day – sufficient to supply thousands of households or farms. In the Phoenix area of the US alone, more than 58 data centers consume an estimated 170 million gallons of drinking water daily for cooling.
The geographical distribution of this infrastructure matters profoundly as data centers requiring high rates of mechanical cooling are disproportionately located in water-stressed and socioeconomically vulnerable regions, particularly in Asia-Pacific and Africa.
At the same time, we are deploying AI-intensive early warning systems to monitor climate tipping points in regions like Greenland, the Arctic, and the Atlantic circulation system – regions already experiencing catastrophic climate impacts. They represent thresholds that, once crossed, could trigger irreversible changes within decades, scientists have warned.
Yet computational models and AI-driven early warning systems operate according to different temporal logics. They promise to provide warnings that enable future action, but they consume energy – and therefore contribute to emissions – in the present.
This is not merely a technical problem to be solved with renewable energy deployment; it reflects a fundamental misalignment between the urgency of climate tipping points and the gradualist assumptions embedded in technological solutions.
The carbon budget concept reveals that there is a cumulative effect on how emissions impact on temperature rise, with significant lags between atmospheric concentration and temperature impact. Every megawatt-hour consumed by AI systems training on climate models today directly reduces the available carbon budget for tomorrow – including the carbon budget available for the energy transition itself.
The governance void
The deeper issue is that governance frameworks for AI development have completely decoupled from carbon budgets and tipping point timescales. UK AI regulation focuses on how much computing power AI systems use, but it does not require developers to ask: is this AI’s carbon footprint small enough to fit within our carbon budget for preventing climate tipping points?
There is no mechanism requiring that AI infrastructure deployment decisions account for the specific carbon budgets associated with preventing different categories of tipping points.
Meanwhile, the energy transition itself – renewable capacity expansion, grid modernization, electrification of transport – requires computation and data management. If we allow unconstrained AI expansion, we risk the perverse outcome in which computing infrastructure consumes the surplus renewable energy that could otherwise accelerate decarbonization, rather than enabling it.
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What would it mean to resolve the paradox?
Resolving this paradox requires, for example, moving beyond the assumption that technological solutions can be determined in isolation from carbon constraints. It demands several interventions:
First, any AI-driven climate monitoring system must operate within an explicitly defined carbon budget that directly reflects the tipping-point timescale it aims to detect. If we are attempting to provide warnings about tipping points that could be triggered within 10-20 years, the AI system’s carbon footprint must be evaluated against a corresponding carbon budget for that period.
Second, governance frameworks for AI development must explicitly incorporate climate-tipping point science, establishing threshold restrictions on computational intensity in relation to carbon budgets and renewable energy availability. This is not primarily a “sustainability” question; it is a justice and efficacy question.
Third, alternative models must be prioritized over the current trajectory toward ever-larger models. These should include approaches that integrate human expertise with AI in time-sensitive scenarios, carbon-aware model training, and using specialized processors matched to specific computational tasks rather than relying on universal energy-intensive systems.
The deeper critique
The fundamental issue is that the energy-system tipping point paradox reflects a broader crisis in how wealthy nations approach climate governance. We have faith that innovation and science can solve fundamental contradictions, rather than confronting the structural need to constrain certain forms of energy consumption and wealth accumulation. We would rather invest £81 million in computational systems to detect tipping points than make the political decisions required to prevent them.
The positive tipping point for energy transition exists – renewable energy is now cheaper than fossil fuels, and deployment rates are accelerating. What we lack is not technological capacity but political will to rapidly decarbonize, as well as community participation.
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Deploying energy-intensive AI systems to monitor tipping points while simultaneously failing to deploy available renewable energy represents a kind of technological distraction from the actual political choices required.
The paradox is thus also a warning: in the time remaining before irreversible tipping points are triggered, we must choose between building ever-more sophisticated systems to monitor climate collapse or deploying available resources – capital, energy, expertise, political attention – toward allaying the threat.
The post Using energy-hungry AI to detect climate tipping points is a paradox appeared first on Climate Home News.
Using energy-hungry AI to detect climate tipping points is a paradox
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