Avoiding emissions versus creating sinks-Effectiveness and attractiveness to climate finance.

The perception of greater impact via new sinks, as opposed to through avoided emissions, has already led some large investors to focus on sink-related projects. This is a flawed perception when applied universally and carries a risk that effective routes to mitigation through avoiding emissions are side-lined. In reality, both emissions avoidance and emissions removal are needed, and both can be a cost-effective means of delivering mitigation.

Agricultural methane emissions and the potential formitigation.

Agriculture is the largest anthropogenic source of methane (CH4), emitting 145 Tg CH4 y-1 to the atmosphere in 2017. The main sources are enteric fermentation, manure management, rice cultivation and residue burning. There is significant potential to reduce CH4 from these sources, with bottom-up mitigation potentials of approximately 10.6, 10, 2 and 1 Tg CH4 y-1 from rice management, enteric fermentation, manure management and residue burning. Other system-wide studies have assumed even higher potentials of 4.8-47.2 Tg CH4 y-1 from reduced enteric fermentation, and 4-36 Tg CH4 y-1 from improved rice management. Biogas (a methane-rich gas mixture generated from the anaerobic decomposition of organic matter and used for energy) also has the potential to reduce unabated CH4 emissions from animal manures and human waste. In addition to these supply side measures, interventions on the demand-side (shift to a plant-based diet and a reduction in total food loss and waste by 2050) would also significantly reduce methane emissions, perhaps in the order of greater than 50 Tg CH4 y-1. While there is a pressing need to reduce emissions of long-lived greenhouse gases (CO2 and N2O) due to their persistence in the atmosphere, despite CH4 being a short-lived greenhouse gas, the urgency of reducing warming means we must reduce any GHG emissions we can as soon as possible. Because of this, mitigation actions should focus on reducing emissions of all the three main anthropogenic greenhouse gases, including CH4. This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part1)'.

Quantification of biophysical adaptation benefits from Climate-Smart Agriculture using a Bayesian Belief Network.

The need for smallholder farmers to adapt their practices to a changing climate is well recognised, particularly in Africa. The cost of adapting to climate change in Africa is estimated to be $20 to $30 billion per year, but the total amount pledged to finance adaptation falls significantly short of this requirement. The difficulty of assessing and monitoring when adaptation is achieved is one of the key barriers to the disbursement of performance-based adaptation finance. To demonstrate the potential of Bayesian Belief Networks for describing the impacts of specific activities on climate change resilience, we developed a simple model that incorporates climate projections, local environmental data, information from peer-reviewed literature and expert opinion to account for the adaptation benefits derived from Climate-Smart Agriculture activities in Malawi. This novel approach allows assessment of vulnerability to climate change under different land use activities and can be used to identify appropriate adaptation strategies and to quantify biophysical adaptation benefits from activities that are implemented. We suggest that multiple-indicator Bayesian Belief Network approaches can provide insights into adaptation planning for a wide range of applications and, if further explored, could be part of a set of important catalysts for the expansion of adaptation finance.

Microorganisms and climate change: terrestrial feedbacks and mitigation options.

Microbial processes have a central role in the global fluxes of the key biogenic greenhouse gases (carbon dioxide, methane and nitrous oxide) and are likely to respond rapidly to climate change. Whether changes in microbial processes lead to a net positive or negative feedback for greenhouse gas emissions is unclear. To improve the prediction of climate models, it is important to understand the mechanisms by which microorganisms regulate terrestrial greenhouse gas flux. This involves consideration of the complex interactions that occur between microorganisms and other biotic and abiotic factors. The potential to mitigate climate change by reducing greenhouse gas emissions through managing terrestrial microbial processes is a tantalizing prospect for the future.