Assessing marginal abatement cost for greenhouse gas emissions from livestock production in China and Europe - accounting for uncertainties

Abstract

Climate change is probably the most challenging threat to mankind. International agreements have acknowledged the fact that anthropogenic GHG emissions must be reduced significantly to adhere to a maximum global warming of 2°C. The livestock sector plays a key role in achieving this target as it is a significant source of GHG emissions. While the livestock sector offers significant GHG reduction potential, it is currently neglected in international and national mitigation efforts. Therefore, scientific research must guide mitigation policy decisions with evidence of cost-efficient abatement potential that can be achieved through various mitigation technologies. Marginal Abatement Cost Curves (MACC) are an analytical tool for informing policy makers about the cost-effectiveness (CE) of mitigation. MACCs provide a relatively clear representation of a complicated issue based on their graphical design that prioritises various mitigation options in terms of their CE of abatement and enables assessment of total GHG reduction under a budget constraint. However, developing a MACC involves considerable data collection, depends on various interdisciplinary information sources and the methodology is subject to several limitations. These factors can result in uncertainties in marginal abatement cost (MAC) results, the assessment of which is often neglected in MACC literature. This research shows the main GHG emission sources in livestock production and possible mitigation options to reduce GHG emissions from these sources. After elaborating the MACC methodology, advantages, disadvantages and limitation of the engineering MACC are shown. This allows understanding the relevance of assessing and reporting uncertainty of MACCs. Two engineering MACCs are developed that show the CE abatement potentials available in the Chinese livestock sector and European Union 15 (EU-15) dairy sector in 2020, with emphasis on dietary mitigation options. The requirement of assessing CE of abatement for individual mitigation options is highlighted by separate derivation of technical and economic abatement potential for the EU-15 dairy sector. For the Chinese MACC, a scenario analysis (SA) and for the European MACC, a Monte Carlo (MC) simulation are utilised to show the relevance of assessing uncertainty in MACCs. To provide further evidence, the overall range of CE estimates for eight mitigation options found in relevant MACC literature is presented. This allows the generation of probability distribution functions of CE for each mitigation option with kernel density estimation (KDE). The results from this study show the significance of livestock and dairy production related GHG emissions in China and Europe, respectively. In China, baseline GHG emissions of livestock production are projected to increase significantly, while these of the EU-15 dairy production are predicted to decrease by 2020. It was found that enteric fermentation is the largest GHG emission source from dairy production and should be focus of mitigation policies. Both case studies showed mitigation options that offer abatement potential at high CE. Priorities should be given to biomass gasification, breeding techniques and feed supplements as tea saponins and probiotics for the Chinese livestock sector, and to animal selection, reduced tillage and dietary probiotics for the EU-15 dairy sector. The scenario analysis reveals that mid-term projections for the Chinese livestock sector are varying strongly, and utilising key variables from different projections has a significant impact on MAC results which changes the ranking of the mitigation options. The MC simulation shows the contribution of some model inputs to the uncertainty of abatement at negative cost and a high model output uncertainty regarding measure’s CE for most mitigation options. However, the ranking of the mitigation options remains stable. The range of MAC estimates for 8 mitigation options in the agricultural sector is high and variables like ‘study quality’ or ‘study location’ do not change this. The KDE was further used to rank the mitigations options based on their probability of being reported as cost-negative and shows that measures affecting soil N2O and carbon sequestration are reported to be more cost-efficient as compared to measures focusing on manure management. Based on these finding, the impact of study designs on MAC estimates and lack of communication uncertainty in MACC literature are discussed. Uncertainties that are underpinning MACC results can have significant impacts on CE and abatement potentials. To increase utilisation of MACCs by knowledge users, MACC research must prioritise assessment, quantification and report of uncertainties, compare results within the scientific literature and publish data and assumption of the MACC transparently.