Claver Gatete is Executive Secretary of the UN Economic Commission for Africa. Jason Veysey is Energy Modeling Program Director and Senior Scientist at the Stockholm Environment Institute. Lisa Sachs is Director of the Columbia Center on Sustainable Investment at Columbia University.
The case for global energy transition has rarely been clearer. The closure of the Strait of Hormuz earlier this year exposed the cost of unplanned, fossil-dependent systems, while the falling cost of renewables, the rising penetration of electric vehicles, and the growing value of demand flexibility have made the direction of travel obvious. The benefits of a clean, secure, integrated system are no longer in dispute. What remains unclear is how to build it.
Countries around the world have called for faster renewable energy deployment and alternative energy arrangements. A secure, affordable, resilient, decarbonised system requires specific investments in specific places in a specific sequence, optimised across sectors and borders. But very few governments have the analytical foundation to translate those imperatives into investment.
The two instruments that are supposed to determine investment priorities for decarbonisation – Nationally Determined Contributions (NDCs) and country platforms – cannot answer the most basic question facing any country undertaking an energy transition: what should the energy system look like?
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To close this gap, every country needs a bankable, economy-wide optimisation model for its energy system. A model is not a plan, but it can help answer the critical question of what the future energy system should look like. It shows how optimal scenarios vary as assumptions and policies are adjusted, calculates investment requirements and sequencing, and quantifies how system costs are affected by assumptions, policies, and exogenous variables like trade policy and financing terms.
Tool for efficient investment
Optimisation is a simplified way of simulating an energy system, but it can be an extremely powerful tool for moving energy planning from reactive (how do we manage the disparate actions in the energy system?) to intentional (what energy system underpins our national objectives?). A model can show how optimal scenarios vary as assumptions and policies are adjusted, and how investment requirements are quantified and sequenced.
Optimisation models can treat the energy system and the sectors it serves as an integrated whole, optimising across sectors and projects in ways that can be mutually reinforcing. If considered independently, growth in industrial demand, transport electrification, and digital infrastructure can add stress to the energy system. But an optimised plan can arrange these and other changes in an efficient, synergistic way.
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New load can be added where low-cost power is available; industrial customers can ensure the viability of investments in energy supply; electric vehicle charging policy can smooth load curves and reduce costs for all consumers.
Additionally, optimisation modeling can also change the financeability of investments. Taken alone, each project faces uncertainty about the rest of the system, which raises the cost of capital and causes projects to stall or unwind after contracts are signed. A coherent, optimised plan makes visible the coordination that private capital would otherwise have to bet on: identified offtake, sequenced and committed transmission, contracted power supply, and so on.
What COP31 and COP32 should do
The upcoming COPs in Turkey and Ethiopia can shift the center of gravity of international climate cooperation from fragmented commitments to planning. Three moves are urgently needed.
First, optimised, economy-wide, long-term energy system planning must be the foundation on which any meaningful NDC, country platform, or finance commitment rests. NDCs are typically drafted by environment or single-line ministries, with limited cross-sectoral input from ministries of energy, finance, and planning. They contain targets, derived from sectoral strategies or national commitments, not from an analytically grounded picture of what the energy system should look like and what investments would make it work. Country platforms are generally a portfolio of investments assembled from existing project pipelines, rather than derived from a system-level analysis of what an optimised, decarbonised energy system would require.
Second, recognise regions as a key planning unit. Modern integrated energy systems are inherently regional. Renewable endowments are unevenly distributed; balancing variable supply across borders lowers aggregate cost, reduces redundant backup capacity, and unlocks economies of scale no individual nation can achieve. Many energy investments in Southeast Asia, East Africa, Southern Africa and Central Asia may only be financeable in a regional context. Assessing domestic infrastructure without regional optimisation perpetuates the perception that decarbonisation is more expensive than it is.
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Third, finance the planning capacity. A coordinated commitment by multilateral development banks, bilateral donors, and philanthropic partners to help every region and its constituent countries develop and maintain their own modelling capability, with open-source tools and regional analytical hubs, would close the most consequential gap in the current architecture. The cost is small relative to current spending on country platforms, failed project preparation, and misallocated infrastructure investment.
This includes supporting regional institutions such as the ASEAN Centre for Energy, the African Energy Commission, regional power pools, and the Latin American and Caribbean Energy Organization to determine what optimised regional systems require. Country-by-country pledging, repeated at every COP, will not deliver what meaningfully integrated systems can.
The 2026 energy crisis made the cost of unplanned, fossil-dependent systems newly visible. That window of clarity will close. The international community should seize the moment to build the planning foundation that has been missing for thirty years, rather than commissioning another round of NDCs or pledges, striving for outcomes neither was designed to deliver.
The post Every country needs a model to help optimise its energy transition appeared first on Climate Home News.
Every country needs a model to help optimise its energy transition
The world is currently experiencing what is expected to become the strongest El Niño on record – dubbed a “super El Niño” by many.
El Niño is the warm phase of a recurring climate pattern in the tropical Pacific that releases heat from the ocean into the atmosphere.
This temporarily raises global temperatures and reshapes rainfall and extreme weather around the world – impacting the lives of billions of people.
The current El Niño event began in June and is expected to last into 2027.
El Niño is part of a wider climate pattern called the El Niño-Southern Oscillation (ENSO) cycle.
The ENSO cycle also has a cool phase, known as La Niña, as well as a “neutral” phase. El Niño and La Niña events typically last between nine and 12 months, but can go on longer.
Below, Carbon Brief explains how the ENSO cycle works, its impacts on extreme weather and global temperatures and why this El Niño event is projected to be the most intense since records began.
The post Explainer: How the ‘super El Niño’ will reshape the world’s weather appeared first on Carbon Brief.
https://interactive.carbonbrief.org/el-nino-explainer/index.html
The second-largest reservoir in the US reached a record-low water height on Saturday – just days after the country’s largest reservoir broke its own record.
Both Lake Mead and Lake Powell are located on the Colorado River.
They provide water for populations across seven US states in the south-western US, with around 40 million people getting some or all of their municipal water from the Colorado River.
The river also provides water for around 5.5m acres (22,258 square kilometres) of farmland across Colorado, Arizona, California and the other states in the river basin.
Experts tell Carbon Brief that climate change, population growth and over-consumption are all contributing to the current record-low levels of the reservoirs.
Record lows
At full capacity, Lakes Mead and Powell can hold a combined 68 cubic kilometres of water – enough to supply all household consumption in the contiguous US for nearly 1.5 years. However, the water level in both reservoirs has been declining for decades.
The chart below shows the water level of Lake Mead, in metres above mean sea level. The reservoir, which began to fill in 1935 following the construction of the Hoover Dam, has a “full pool” maximum capacity of 347.60 metres. The water level in Lake Mead reached a record low of 317.11 metres on 7 August.
The following chart shows the water level of Lake Powell, in metres above mean sea level. Lake Powell’s full-pool level is 1,127.76 metres.
While the reservoir reached its maximum capacity several times in the 1980s, it has not done so since. On 15 August, the water level in Lake Powell was recorded at a new record-low of 1,072.87 metres.
Both reservoirs have continued to decline in the days since breaking their respective records. The downward trend will largely continue in both lakes until next spring, when the snowpack in the mountains of the Upper Colorado River Basin begins to melt, says Dr Jack Schmidt, a senior research scientist at Utah State University’s Center for Colorado River Studies. He tells Carbon Brief:
“The big dilemma of the moment is that we’re only in the middle of August, and we have no assurance of what the coming winter will be. The only thing we can be sure of is that we will be depleting overall total basin reservoir storage from now until, roughly, early April.”
Compounding factors
The record lows across the two reservoirs are the result of several compounding factors, experts tell Carbon Brief.
Since the turn of the 20th century, the amount of water flowing along the Upper Colorado River has declined by about 20%. Research suggests that half of this decline can be attributed to human-induced climate change.
Most of the river’s streamflow comes from the snowpack of the Upper Colorado River Basin, which stretches across five western US states but is primarily located in Colorado and Utah.
This region has been gripped by a historic “megadrought” for more than a quarter of a century. Nearly half of the megadrought’s intensity over 2000-18 is attributable to climate change, according to a 2020 study.
At the same time, the increasing population in the US south-west has put added pressure on the Colorado River’s water supply. The number of people obtaining some or all of their water from the Colorado system has grown by 15 million (around 60%) since 1992.
Schmidt tells Carbon Brief:
“There’s an ultimate cause of the present water crisis, and there’s a proximate cause. The ultimate cause is a warming climate, a warming planet and a pretty clear correlation between warming conditions and decreased runoff in the Colorado River Basin.
“The proximate cause is that in this messy democratic republic of ours, big policy decisions that match the variability of the climate occur painfully slowly – with intense political negotiations – and only incrementally.”
On 31 July, the US Bureau of Reclamation, which manages water resources in the western US, released an environmental impact statement on its proposed post-2026 strategy for managing Lakes Powell and Mead. The strategy itself has not been released yet.
Schmidt notes that the statement does appear to give the Bureau flexibility to “respond to crisis” by reducing the delivery of water to several states. However, he adds:
“They acknowledge it won’t work if we just stay critically dry, and of course every climate model for the 21st century, especially with a continually warming planet, says that that’s exactly what’s going to happen.”
The post Analysis: The two largest reservoirs in the US have hit record-low levels appeared first on Carbon Brief.
Analysis: The two largest reservoirs in the US have hit record-low levels
As tech giants race to build out AI and the sprawling infrastructure it depends on, climate concerns have tended to focus on one thing: power-hungry data centres.
Their electricity use is growing so fast that by 2030, it’s projected to be nearly three times more than the combined annual consumption of Pakistan, Bangladesh and Nigeria. With the explosion in the construction of data centres driving new investment in fossil fuels, especially in the US, greenhouse gas emissions generated by data centres – now standing at less than 1% of the global total – are set to soar.
But this narrow focus on electricity has let AI’s supporters and the International Energy Agency (IEA) make a convenient case: that rising emissions can be more than offset by the technology’s green applications, like optimising renewables or boosting efficiency. That story conceals how AI’s real climate danger lies elsewhere: in the oil fields, where it’s helping fossil fuel companies extract planet-heating oil and gas faster and more cheaply.
As a senior manager at Microsoft, Holly Alpine was shocked by this blind spot. In 2024, she and her husband Will – also a Microsoft manager – quit their jobs and launched a campaign to hold Big Tech accountable for the emissions its technology enables.
Over the past two years, they have teamed up with two researchers to quantify just how deep the fossil fuel industry’s embrace of AI tools runs.
Their peer-reviewed study, published last week, found that when AI is adopted at similar rates across the fossil fuel and renewable energy sectors, the net effect is a rise in emissions of 0.47–1.8 gigatonnes of CO2 annually. That’s equivalent to Mexico’s annual emissions at the low end, and to Russia’s – the world’s fourth-largest emitter – at the high end. It is also 3.3 to 13.3 times higher than the emissions currently generated by powering AI data centres.
We spoke with Alpine about the risks of overlooking this side of the AI climate story and what can be done to shift the focus.
Q: Why has the climate conversation focused so heavily on data-centre power use when your modelling suggests that’s the smaller part of the AI emissions story?
A: It’s been quite unfortunate that it has been framed that way and that it has stuck so much because that framing is wholly incomplete, very misleading and is leading to very dangerous consequences.
It’s in the fossil fuel industry and the technology companies’ favour to frame the equation in this way because it leaves out any responsibility and accountability of the tech’s use by fossil fuel companies, which is a large part of their business. They’re some of their largest customers and they have teams of engineers and sales folks who are dedicated to the fossil fuel industry.
Simply comparing the power needed to run the technology and its [clean energy] applications is also kind of apples to oranges. On the one hand, you have real-world actual emissions and, on the other, hypothetical future avoidance of emissions as a result of potential future use cases for renewables.
What we are saying is that we need to look at both sides of the ledger for AI applications, renewables versus fossil fuels, and then also add the emissions generated by running data centers on top of it.
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Q: How do AI applications help fossil fuel companies in a way that drives up emissions?
A: It’s everything from finding more oil and gas underground by processing hundreds of terabytes of seismic and well data that would otherwise have to be done manually. These AI models can process this data extremely quickly and create high-resolution images of what is underground. It helps companies pinpoint the oil and gas reserves that are most likely to be commercially recoverable.
Fossil fuel companies can identify and develop fossil fuel deposits with a lot more certainty, allowing them to move forward with projects that would otherwise have been too risky or too slow to pursue. AI makes them viable.
We’ve seen that rig counts [number of active drilling rigs] have dropped dramatically, so they need fewer resources to get out even more fossil fuels. Their costs are decreasing, while their production is increasing.
Q: How deep do these relationships run between Big Tech and fossil fuel companies? How do they compare with equivalent relationships with renewable energy companies?
A: I have to caveat that I have not worked for Microsoft for about two years. But what we saw at the time was that the fossil fuel-dedicated teams were much larger in terms of the number of employees, the size of the contracts, and the long-standing relationships.
This is not new. Microsoft has worked with the fossil fuel industry for many years and has deep partnerships, starting with the humble machine-learning going back many years. AI is just the latest wave of technology being applied in this way.
There are also relationships between the tech companies and renewables companies [and] battery storage developers. There are definitely sustainability-related applications of the technology.
One of the recommendations that we had given the company [Microsoft] was to shift the ratio of engineering resources from fossil to low and no-carbon energy sectors within the company. When they came out with their principles for engagement with the fossil fuel industry in 2023, they committed to shifting engineering resources. But then we did not see any actual change in business practices.
Q: Tech companies are now quietly scaling back some of their climate commitments, but there was a point, not long ago, when they wanted to be seen as climate leaders. Was there ever a genuine commitment to do that, or was it just an image they were projecting?
A: It depends on how you evaluate a company for its climate impact. If all we are looking is its own operational emissions, then in that case, Microsoft was and, still is to some extent, a climate leader.
But if we evaluate a company based on what it is producing, then I would say it’s a very different story. Back in 2019, ExxonMobil said it was able to produce an extra 50,000 barrels [of oil] per day purely thanks to Microsoft technology. There was also another public and quantified deal with Chevron.
We calculated that those emissions alone from just two deals among dozens were 300% of Microsoft’s entire operational emissions, including data centres. So, how do you want to evaluate your company?
If you look at other sectors and, say, evaluate a weapons manufacturer on its violence footprint, you don’t just look at their supply chain and the violence within it to create the weapons. You look at the real-world impact of the weapons they’re manufacturing. Yet we completely left technology companies off the hook.
Q: You make some recommendations as well in the paper. They include the idea of putting some supply-side constraints on this AI-enabled productivity for fossil fuel companies. What would that look like in practice?
A: Ultimately, our goal would be to have disclosure and governance measures that limit AI’s role in increasing fossil fuel productivity. The first thing would be a recognition of “enabled emissions” even as a measurable category because, at the moment, they are not included in any emissions disclosure or accountability frameworks.
Then we should require transparency around these fossil fuel contracts and constrain some of these specific mechanisms that the research identifies.
We are not trying to have a blanket ban on AI or even a blanket ban on AI use in the fossil fuel industry. There are some great applications, like methane leak detection, for example. But we just want to align applications with climate science and ensure that any contracts that move forward have been evaluated against a 1.5C future.
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The easy thing would be for companies to voluntarily put guardrails on how their tech can be used, which is not new. There just currently are none for climate. But we do think that… policy is what needs to be implemented.
We also think that if we can change the market structure and incentives, then this kind of restriction will follow. If we look at ESG investing and how sustainable investing is defined, if we include what these companies are doing into that evaluation, then that can move capital flows.
Q: What do you think are the most promising avenues where you can shift the AI narrative and drive the change you are seeking to achieve?
A: We are now building off the study and there are various governance frameworks that we are attempting to incorporate this sort of evaluation into like the Greenhouse Gas Protocol or the Science Based Targets initiative (SBTi)
Luckily, we have seen some very promising drafts for the future of those frameworks that do include evaluations and disclosures of this work, which is really exciting.
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We also need to look at companies for impacts in order to evaluate their sustainability metrics, and there could be potential greenwashing concerns that we could address on the legal side of things.
And then [there are] different policy workstreams. In the EU, we were quite hopeful about the AI Act,and the various use cases that were classified as high risk and would go through additional scrutiny. Unfortunately, with the Omnibus passing [in July], that opportunity is a little restrained.
But now with the Cloud and AI Development Act (CADA) coming out with various European frameworks around evaluating tech’s impacts, we hope to inform those discussions with this research.
The post “Dangerous consequences” – how AI’s climate framing lets Big Tech off the hook appeared first on Climate Home News.
“Dangerous consequences” – how AI’s climate framing lets Big Tech off the hook
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