Carbonomics returns
Hydrogen headwinds, a fragmenting world order, and new energy frontiers
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In late 2019, Goldman Sachs (GS) published their first estimate of the marginal abatement cost (MAC) curve for carbon. The Carbonomics cost curve as the bank calls it, shows the reduction potential for anthropogenic greenhouse gas emissions based on over 100 different technologies.
The Carbonomics cost curve takes account of the cost of technologies available at commercial scale, and assumes economies of scale for technologies currently in the pilot phase. The cost curve is applied globally, across power generation, industry and industrial waste, transport, buildings, and agriculture. GS update their estimate each year, taking account of changes in the cost of carbon abatement technologies (e.g., solar, green hydrogen, etc.), and the relative cost of energy.
The most recent update, published in March 2025, a couple of weeks before ‘Liberation Day’, is shown in the chart below. With one eye on the potential for tariffs to disrupt trade, the analysts at GS also examined what a retreat from globalisation might mean for the cost of decarbonisation.
Before we get to that, lets look at how things have evolved over the past few years. The cost of decarbonising the first 50% of the Carbonomics cost curve has declined by 7% between 2023 and 2025 to $0.88 trillion, according to GS. The drop in cost has been driven by advances in utility scale batteries, and ongoing deflation in the cost of solar power panels.
At the other end of the curve, EV battery cost deflation, EV economies of scale, and the increased use of biofuels in transportation (i.e., renewable diesel and Sustainable Aviation Fuel, or SAF) has led a decrease in the implied cost of switching away from internal combustion engine vehicles.
It’s not all good news. The middle of the curve, including the majority of the hard-to-abate industrial sectors, has become more costly to abate since 2023. For example, decarbonisation pathways dependent on green hydrogen (gH2) are facing much higher costs as earlier production forecasts prove too optimistic, and the rate of adoption turns out to be much slower than anticipated.
