Achieving the Paris climate goals critically depends on reducing non-CO2 greenhouse gases

Achieving the stringent global climate target of the Paris agreement, limiting global temperature change to well-below 2 or even 1.5 degrees, requires unprecedented emission reductions of CO2, as well as non-CO2 greenhouse gases, such as methane, nitrous oxide and fluorinated gases. Most attention in climate policy research has been paid to CO2, due to its large share in overall emissions, but non-CO2 emissions will play an increasingly more important role, as they cannot be fully brought to zero. However, the exact level of remaining non-CO2 emissions  is highly uncertain. In an article in Nature Communications, researchers from PBL, IIASA, PIK and UU show how the uncertainty in non-CO2 reductions determines the remaining CO2 budget, climate policy costs and the feasibility of the Paris goals. The study finds that under pessimistic non-CO2 mitigation assumptions, limiting temperature change to below 1.5 degrees is not possible.

Moreover, in an SSP3-scenario with very high emissions, pessimistic non-CO2 mitigation assumptions might even keep the 2-degree target out of reach. CO2 emission reductions also need to compensate for the level of non-CO2 reductions, under a given climate target. In a 2-degree scenario, the difference between the optimistic and pessimistic non-CO2 mitigation assumptions leads to a difference of 240 Gt CO2 in the CO2. This makes non-CO2 a substantially influential factor, considering the (default) remaining CO2 budgets of roughly 1000 Gt and 400 Gt in a 2-degree and 1.5-degree case, respectively. Similarly, climate policy costs highly depend on the available non-CO2 mitigation potential, illustrated by 30-40% higher costs under pessimistic mitigation assumptions.

The study’s uncertainty analysis is based on a literature review of case studies of mitigation measures, such as reducing methane leakage in the oil and gas industry or the use of fertilizer with less nitrous oxide emissions. Based on the literature study, the authors have determined ranges for relevant factors that determine the emission reduction potential, such as: relative reductions when a measure can be applied, the applicability of a measure, technological progress and costs. For the agricultural sources (livestock, fertilizer, rice production) these input parameters have been varied in a Monte Carlo analysis (i.e. creating a large number of estimates by randomly varying parameter values) to determine the lower and upper bounds of the overall relative reduction potential per emissions source. The study is performed with the most detail for the agricultural sectors, as agricultural non-CO2 emission sources are the hardest to abate (and thus most relevant in future climate scenarios) while the mitigation potential is the most uncertain.

The coordinating author, Mathijs Harmsen from PBL, explains that “the findings are especially relevant now, since at the COP26 in Glasgow in 2021, the world agreed upon the Global Methane Pledge, stating that nations should “curb emissions of potent non-CO2 gases, such as methane” and achieve a 30% reduction in global methane emissions in 2030 compared to the 2020 level. However, relatively little is known about the feasibility of the GMP and potentially more ambitious future goals. There is  surprisingly little literature dedicated to the future mitigation potential of non-CO2 greenhouse gases. Model-based scenario studies are often based on old data and with a ‘middle-of-the-road’ estimate of non-CO2 mitigation potentials. This means that much of the uncertainty and potentially large consequences for climate policy feasibility remain hidden or are underestimated. We hope to remedy that with this paper”. Detlef van Vuuren, also a co-author, adds that the paper also contributes by showing how uncertainty in non-CO2 emission has direct implications on how fast CO2 and thus fossil fuels need to be reduced.

One of the recommendations of the study is to conduct more studies on mitigation measures. This would maximize learning and thus increase reduction and/or reduce costs. It would also stimulate early action, limiting short-term climate change and avoiding any barriers in future upscaling of measures. Also, more case studies would also help to understand the limitations of non-CO2 greenhouse gas mitigation, narrowing down the large uncertainty range and contributing to more effective policy strategies.


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