When a machine fails, the conversation almost always turns to money. And the numbers are staggering: according to Siemens' True Cost of Downtime 2024 report, unplanned downtime now costs the world's 500 largest companies around $1.4 trillion a year, equivalent to 11% of their revenues, up from $864 billion (8%) in 2019–2020 [1]. In the automotive sector, a single idle production line in a major plant can cost up to $2.3 million per hour [1].
But there is a second ledger that rarely appears in maintenance reports, one measured not in dollars but in tonnes of CO₂. Every failing bearing, leaking air line and emergency shutdown carries an emissions bill of its own. And in 2026, that bill is becoming very real. Industry remains the largest emitting sector in the world, responsible for around 37% of global CO₂ emissions from fuel combustion once electricity and heat are allocated to end users [2], while global energy-related CO₂ emissions reached a new record of nearly 38.4 billion tonnes in 2025 [3]. At the same time, carbon is acquiring a price tag for industrial exporters, particularly in Türkiye.
As Mirleon AI, a startup developing AI-MATE, a multi-agent predictive maintenance platform, we want to look at machine failure through this second lens: what does unreliability cost the climate, and why is maintenance quietly becoming one of the most practical decarbonisation levers available to industry?
Degradation Burns Energy Long Before Anything Breaks
The most underestimated part of the carbon cost of failure is the part that happens before failure. Machines rarely go from healthy to broken in an instant; they degrade. And degraded machines consume more energy to do the same work.
Consider the scale involved. Electric motor systems accounted for 53% of global electricity consumption in 2023, and in industry their share climbs to 72% [4]. Pumps, fans, compressors and conveyors are the heartbeat of every factory, which means that even small efficiency losses across a motor fleet translate into large volumes of wasted electricity and, by extension, avoidable emissions.
Mechanical faults are a major source of these losses. Shaft misalignment is one of the most common defects in rotating machinery, experimental research shows that energy consumption rises markedly with the degree of misalignment. Studies in this field report that even misalignment within tolerable limits can meaningfully increase consumption, with periodic realignment delivering reductions of up to 13% in energy use [5]. The damage does not stop at the electricity meter: research from the University of Tennessee's Maintenance and Reliability Center found that an offset misalignment as small as 5 mils (about 0.13 mm) can, in some cases, cost up to half of a bearing's expected life [6].
Compressed air offers an even more striking example. The U.S. Department of Energy notes that leaks often waste as much as 20–30% of a compressor's output [7]. A typical plant that has not been well maintained will likely lose around 20% of its compressed air production capacity to leaks, whereas proactive detection and repair can bring that figure below 10% [8]. These losses make no noise on the production floor, trigger no alarms and never show up as "downtime", yet they run 24 hours a day.
This is the paradox of the hidden carbon cost: the machine is still running, output is still on target, and nobody is looking.
The Emissions Spike of a Stoppage
When degradation finally turns into failure, emissions do not simply stop along with production. In many cases, they spike.
Thermal processes are the clearest example. Furnaces, kilns, melting lines and curing ovens cannot simply be switched off and on. During an unplanned stop, they are often held at temperature while producing nothing, or they cool down and must be reheated from a lower baseline. Either way, energy is consumed with zero output, and the first production after a restart is frequently off-spec. Restarts are also getting longer: reporting on the Siemens findings notes that the average plant now needs 81 minutes to restart after a stoppage, compared with 49 minutes in 2019 [9].
In process industries, the picture can be even more visible. Refinery and petrochemical engineers distinguish between planned flaring during scheduled shutdowns and unplanned flaring caused by equipment malfunctions or emergency shutdowns [10]. Each unplanned event means valuable gas is burned simply to keep the plant safe.
Then there is scrap. Every product that is rejected during a failure or a turbulent restart carries the full embodied carbon of its raw materials and processing, with none of the value. In steel, for instance, every tonne produced in 2024 was associated on average with 2.18 tonnes of CO₂e across scopes 1, 2 and 3, and the sector as a whole accounts for 7–8% of global anthropogenic greenhouse gas emissions [11]. Scrapping material at that carbon intensity is an environmental loss, not just a financial one.
The Carbon Footprint of the Emergency Response
The emissions story continues after the machine stops. Reactive maintenance is, by definition, a rush job, and rushing is carbon-intensive.
Supply chains are a key pressure point. In a 2024 industry survey, 72% of maintenance and operations professionals linked rising downtime costs to the rising cost of parts and shipping [9]. When a critical spare part is not on the shelf, it is often flown in. The difference is dramatic: air freight emits roughly 1,054 grams of CO₂ per tonne-kilometre, while maritime transport emits around 19 [12]. An emergency air shipment of a heavy gearbox or motor can therefore carry a transport footprint dozens of times larger than the same part delivered on a planned schedule.
Premature failure also accelerates the replacement cycle. When misalignment or poor lubrication halves the life of a bearing, the plant doesn't just face another stoppage; it also consumes another component that had to be mined, smelted, machined and shipped. Over a large asset base, shortened component lifetimes quietly inflate a company's scope 3 footprint.
Carbon Now Has a Price Tag
For many years, these hidden emissions were an environmental concern without a direct financial consequence. That era is ending.
On 1 January 2026, the EU's Carbon Border Adjustment Mechanism (CBAM) entered its definitive period, putting a price on the carbon embedded in carbon-intensive goods at the time of import into the EU [13]. Covered products include steel, aluminium, cement, fertilisers, electricity and hydrogen, and where a carbon price has already been paid in the producer country, it can be deducted from the CBAM liability [13].
Türkiye has responded quickly. The regulation establishing the national emissions trading system (TR-ETS) was published in the Official Gazette on 27 August 2026, implementing the framework of the Climate Law adopted in July 2025. The system will run a pilot phase in 2026 and 2027 before entering its first full implementation period from 2028 to 2035 [14]. Notably, Türkiye's model uses an emissions-intensity-based cap, focusing on emissions per unit of output while allowing production to grow, and the pilot phase primarily targets the sectors most exposed to CBAM, such as cement, iron and steel, aluminium and fertilisers [15].
This intensity-based design matters enormously for maintenance. When a failing machine wastes energy, holds a furnace hot with nothing to show for it, or turns good material into scrap, it increases emissions per unit of output — precisely the metric that carbon pricing systems now measure. In other words, unreliability is no longer only an operational cost; it is increasingly a compliance cost and a competitiveness risk for exporters.
Predictive Maintenance as a Decarbonisation Lever
The good news is that the same strategy that reduces the financial cost of failure also reduces its carbon cost.
The evidence on predictive maintenance is well established. According to the U.S. Department of Energy's Operations and Maintenance Best Practices guidance, a properly functioning predictive maintenance programme can deliver savings of 8% to 12% over a programme relying on preventive maintenance alone, and energy savings are among its listed benefits [16]. Measured against purely reactive maintenance, the DOE figures rise to 30–40% [17]. Siemens' research also shows that broader adoption of predictive maintenance has helped reduce the average number of monthly downtime incidents at large plants from 42 in 2019 to 25, and lost hours from 39 to 27 per month [1].
Yet the opportunity remains largely untapped. A 2025 industry report found that 67% of manufacturers still rely on reactive maintenance, fixing components only after they fail [9].
At Mirleon AI, this is exactly the gap we built AI-MATE to close. AI-MATE is a multi-agent predictive maintenance platform that monitors equipment across four sensor modalities — vibration, acoustic, magnetic and thermal — with dedicated AI agents analysing each data stream and a fusion layer combining their findings into a single confidence score. The signals that predict failure are, in many cases, the very same signals that reveal energy waste: vibration patterns expose misalignment and imbalance, acoustic signatures can point to leaks and friction, thermal data reveals overheating bearings and windings, and magnetic measurements shed light on the electrical health of motors. By running on edge hardware, the system can act close to the machine, and its LLM-based Fault Assistant translates complex findings into clear, actionable guidance for maintenance teams.
True to our human-centred approach, AI-MATE does not replace the judgement of engineers; it strengthens it. The platform tells a maintenance team what is changing, how confident the system is, and what is likely to happen next, so that people can decide when and how to intervene — before a small inefficiency becomes a large failure, and before a planned repair becomes an emergency.
Reliability Is Sustainability
For decades, reliability and sustainability were managed by different teams, measured with different metrics and discussed in different meetings. The data now suggests they are two sides of the same coin. A machine that runs smoothly uses less energy, produces less scrap, needs fewer emergency parts and lasts longer, and every one of those outcomes has a carbon dimension.
As carbon pricing moves from policy papers to balance sheets, the most sustainable kilowatt-hour may be the one a healthy machine never needed, and the greenest spare part may be the one that never had to be flown in. At Mirleon AI, we believe that making industrial assets more predictable is one of the most practical, measurable and immediately available steps towards cleaner industry.
Sources
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Siemens, The True Cost of Downtime 2024, as summarised by the Association of Electrical and Mechanical Trades (AEMT) and Inside Supply Management (ISM).
theaemt.com · ismworld.org -
International Energy Agency (IEA), Greenhouse Gas Emissions from Energy Data Explorer.
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IEA, Global Energy Review 2026 – CO₂ Emissions.
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IEA 4E Electric Motor Systems Platform (EMSA), Policy Brief #9 – Electric Motor Systems: Why Are They Important?
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