Researchers from UC San Diego have identified genetically enhanced crops of plants featuring enlarged roots as a viable and effective pathway for tackling excess atmospheric CO2 emissions.
In a paper published in the journal Environmental Research Letters, the research team provides an analysis on the timeframe needed for the adoption of these modified crops and the carbon removal effect they could introduce.
According to the analysis, carbon-enhanced crops would take 13 years from their first adoption to annually remove 0.9–1.2 gigatons of CO2, a staggering amount that is nearly sevenfold bigger than the carbon offsets currently supplied on the global market.
During the study, the researchers from UC San Diego’s Scripps Institution of Oceanography and School of Global Policy and Strategy (GPS) looked at previous instances in which new carbon removal technologies were rolled out on the market, along with the time needed for them to be widely accepted and the obstacles that had to be overcome along the way.
Comparing those examples to the field of crop modification, the study team notes that this approach has already managed to advance faster than other carbon removal innovations.
This progress is due to a better knowledge base and marketing within the agriculture sector, as well as the boom of the farming industry, which contributed to depleted soils that are now ready to absorb new volumes of CO2.
While other carbon removal technologies usually set out to build a completely new industry, taking years of testing and trials to mature, the method of genetically enhanced crops builds on an existing foundation that, according to the research, positions it in the lead of the decarbonization field.
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Besides evaluating the requirements for carbon dioxide removal via genetically enhanced crops and its potential impact, the study also proposes a framework to assess the speed and scaling extent of other emerging and highly uncertain technologies.
As the research team explained, this framework can be generalized across different CDR approaches, highlighting the areas of uncertainty in their scale-up potential.
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