Climate change is arguably the most important global societal challenge. Developing ‘low-carbon societies’, i.e. reducing greenhouse gas (GHG) emissions and adapting to a changing climate, is becoming a policy goal across the globe. Agriculture plays an important role in this transformation. The sector is highly vulnerable to climate variability, and is a significant source of emissions. At the same time, it has potential for reducing GHG emissions and also provides opportunity for carbon sequestration in soils and crop biomass. Policy support for mitigating GHG emissions is being informed by scientific evidence on the effectiveness and costs of mitigation opportunities. This information is frequently depicted in marginal abatement cost curves (MACCs), an assessment tool which can help to visualise the hierarchy of technical measures and their cumulative level of abatement. Similarly to other assessment tools, MACCs’ suitability to provide information has certain limitations. Furthermore, different derivations of MACCs are appropriate to answer different questions. In order to draw both informative and reliable conclusions for policy decisions, the characteristics of the MACCs and the resulting limitations have to be presented clearly. This dissertation seeks to answer the general question whether the agricultural MACCs can be improved so that they provide more comprehensive and tailored information to policy makers. In particular five limitations of the MACCs are discussed: the lack of representation of wider effects, the issue of cost-effectiveness of policy instruments and the inclusion of transaction costs, the uncertainty in the MACCs, the boundaries and the heterogeneity of the analysis. Theoretical frameworks are developed and case study examples are provided for these limitations, and the frameworks are assessed in terms whether they achieve the goal of providing more comprehensive information to policy makers than a conventional MACC. Furthermore, the dissertation summarises the available methodologies and applications in agriculture to enhance the MACCs and provides guidelines for researchers and policy makers about the choice of methods and the communication of the results in order to improve the use of MACCs in the policy process.

Microgrid can provide stable, clean, and sustainable electricity supply for remote places since it can operate on renewable energy sources and work isolated from the utility grid. This thesis evaluates the life cycle greenhouse gas (GHG) emissions of the microgrid system which is located at the ‘North-five Islands’ of Changshan archipelago in China. The existing electricity generation technologies of the microgrid system are wind turbine, PV system and diesel generators with the capacity of 2 MW, 300 kW and 2046 kW, respectively. The total demand of electricity (362.2 GWh) will be supplied by the wind turbine, PV system and diesel generators with 32.03%, 2.36% and 65.62%, respectively, if the microgrid system is required to supply the electricity demand for the ‘North-five Islands’ area alone under the islanded mode during 20 years lifespan. The thesis uses the Life Cycle Assessment (LCA) to evaluate the life cycle GHG emissions of the microgrid system. The life cycle stages of this study include: raw material extraction, manufacturing, transportation and operation. In order to assess the environmental benefits of the microgrid system, three electricity supply options – ‘microgrid electricity supply option’, ‘grid extension electricity supply option’, and ‘conventional fossil diesel generators electricity supply option’ are designed to evaluate the life cycle GHG emissions for supplying 20 years electricity demand (362.2 GWh) of the ‘Northfive Islands’. The results show that the life cycle GHG emissions of the ‘microgrid electricity supply option’ are 223.19 million kgCO2eq. Compared to the ‘grid extension electricity supply option’ and ‘conventional fossil diesel generators electricity supply option’, the net savings of the GHG emissions are 70.56 and 112.18 million kgCO2eq, respectively. It mainly results from the differences of the electricity supply methods of the three electricity supply options. For the ‘microgrid electricity supply option’ itself, the operation stage takes the most responsibility of the life cycle GHG emissions with 97.6%. The raw material extraction, manufacturing and transportation stages account for 1.93%, 0.44% and 0.026%, respectively. For the system components of the microgrid system, the wind turbine, PV system, diesel generators, energy storage system, and cables account for 0.34%, 0.18%, 97.75%, 0.60%, and 1.12%, respectively, of the microgrid system’s life cycle GHG emissions. The thesis conducts the sensitivity analysis of diesel burn rate efficiency (L/kWh) of the microgrid system’s diesel generators due to a large quantity (60.84 million L) of diesel consumption by the diesel generators during 20 years operation time. According to the results of the sensitivity analysis, the diesel burn rate efficiency can directly impact the diesel consumption of the diesel generators, and consequently has a significant impact on the life cycle GHG emissions of the ‘North-five Islands’ microgrid system. Since the diesel burn rate efficiency represents the amount of diesel consumption, this results highlight the significance of any factors that affect the diesel consumption (e.g. quantity of diesel, temperature, altitude, etc.), in affecting the life cycle GHG emissions of the ‘North-five Islands’ microgrid system. In addition, the thesis performers the sensitivity analysis of renewable energy (wind and solar energy in specific) fraction of the studied microgrid system because of the huge potential of available renewable energy (63.65 MW of wind turbines) nearby the microgrid system. The results of the sensitivity analysis show that the life cycle GHG emissions of the microgrid system decrease linearly with the increase of wind and solar energy fraction. Particularly, the life cycle GHG emissions of the microgrid system decrease 1.46% (3.26 million kgCO2eq) and 1.37% (3.05 million kgCO2eq) with an increase of 1% in wind and solar energy, respectively.