Evaluating Global Warming Potentials with Historical Temperature: an Application of ACC2 Inversion

Global Warming Potentials (GWPs) are evaluated as historical temperature proxies by applying them to convert historical CH4 and N2O emissions to equivalent CO2 emissions. Our GWP analysis is based on the inverse estimation for the Aggregated Carbon Cycle, Atmospheric Cycle, and Climate Model (ACC2). It was found that, for both CH4 and N2O, indices higher than the Kyoto GWPs (100-year time horizon) would reproduce better the historical temperature. The CH4 GWP provides a best fit to the historical temperature with the time horizon of 44 years. However, the N2O GWP does not approximate the historical temperature with any time horizon. We introduce a new exchange metric, TEMperature Proxy index (TEMP) that is defined so that it provides a best fit to the temperature projection of a given period. By comparing the GWPs and TEMPs, we found that the inability of the N2O GWP to reproduce the historical temperature is caused by the fact that the GWP calculation methodology in IPCC gives coarse treatments to the background system dynamics and uncertain parameter estimations. Furthermore, our TEMP calculations demonstrate that indices have to be progressively updated upon the acquisition of new measurements and/or the advancement of our understanding on the Earth system processes

Setting priorities for land management to mitigate climate change

<p>Abstract</p> <p>Background</p> <p>No consensus has been reached how to measure the effectiveness of climate change mitigation in the land-use sector and how to prioritize land use accordingly. We used the long-term cumulative and average sectorial C stocks in biomass, soil and products, C stock changes, the substitution of fossil energy and of energy-intensive products, and net present value (NPV) as evaluation criteria for the effectiveness of a hectare of productive land to mitigate climate change and produce economic returns. We evaluated land management options using real-life data of Thuringia, a region representative for central-western European conditions, and input from life cycle assessment, with a carbon-tracking model. We focused on solid biomass use for energy production.</p> <p>Results</p> <p>In forestry, the traditional timber production was most economically viable and most climate-friendly due to an assumed recycling rate of 80% of wood products for bioenergy. Intensification towards "pure bioenergy production" would reduce the average sectorial C stocks and the C substitution and would turn NPV negative. In the forest conservation (non-use) option, the sectorial C stocks increased by 52% against timber production, which was not compensated by foregone wood products and C substitution. Among the cropland options wheat for food with straw use for energy, whole cereals for energy, and short rotation coppice for bioenergy the latter was most climate-friendly. However, specific subsidies or incentives for perennials would be needed to favour this option.</p> <p>Conclusions</p> <p>When using the harvested products as materials prior to energy use there is no climate argument to support intensification by switching from sawn-wood timber production towards energy-wood in forestry systems. A legal framework would be needed to ensure that harvested products are first used for raw materials prior to energy use. Only an effective recycling of biomaterials frees land for long-term sustained C sequestration by conservation. Reuse cascades avoid additional emissions from shifting production or intensification.</p

Risk hedging strategies under energy system and climate policy uncertainties

The future development of the energy sector is rife with uncertainties. They concern virtually the entire energy chain, from resource extraction to conversion technologies, energy demand, and the stringency of future environmental policies. Investment decisions today need thus not only to be cost-effective from the present perspective, but have to take into account also the imputed future risks of above uncertainties. This chapter introduces a newly developed modeling decision framework with endogenous representation of above uncertainties. We employ modeling techniques from finance and in particular modern portfolio theory to a systems engineering model of the global energy system and implement several alternative representations of risk. We aim to identify salient characteristics of least-cost risk hedging strategies that are adapted to considerably reduce future risks and are hence robust against a wide range of future uncertainties. These lead to significant changes in response to energy system and carbon price uncertainties, in particular (i) higher short- to medium-term investments into advanced technologies, (ii) pronounced emissions reductions, and (iii) diversification of the technology portfolio. From a methodological perspective, we find that there are strong interactions and synergies between different types of uncertainties. Cost-effective risk hedging strategies thus need to take a holistic view and comprehensively account for all uncertainties jointly. With respect to costs, relatively modest risk premiums (or hedging investments) can significantly reduce the vulnerability of the energy system against the associated uncertainties. The extent of early investments, diversification, and emissions reductions, however, depends on the risk premium that decision makers are willing to pay to respond to prevailing uncertainties and remains thus one of the key policy variables