AGENDA FOR DEFENSE AND PEACE ECONOMICS

This brief introduction celebrates the 20th Anniversary of the Journal, Defence and Peace Economics. Suggesting elements of an agenda for the future of this branch of economics, I raise several topics that are new and that seem to indicate that the field will expand and shift focus substantially in future years.     

GERMANY'S CURRENT AND FUTURE PLUTONIUM INVENTORY

In the past, Germany reprocessed a significant amount of its spent nuclear fuel, partly on its own territory but mostly as a customer of British and French reprocessing plants. In mid-2005, Germany stopped this practice, banning new transports of spent fuel for reprocessing—although the already-exported material would be allowed to be reprocessed and recycled in German reactors as mixed-oxide (MOX) fuel. In total, about 6,500 tonnes of heavy metal have been contracted for reprocessing, but a significant portion of this material has neither been reprocessed nor recycled as MOX fuel in German reactors. Due to the complex import-export history and the partly nontransparent information policy of the German government and utilities, a comprehensive and up-to-date plutonium balance for Germany is not publicly available. This report provides an assessment of Germany's plutonium inventory (stored domestically or abroad) based on open-source information. Special attention is paid to the issue of whether the entire inventory of separated plutonium can be completely irradiated in German nuclear reactors before the last of them are shut down in 2022. The authors conclude that Germany's stock of plutonium waiting to be recycled was about 12.2 tonnes as of 2010; this plutonium should be completely re-imported from the United Kingdom and France by 2017. Germany's MOX-consumption capacities should be sufficient to irradiate the remaining plutonium, although further delays are expected that could leave Germany with an inventory of separated (unirradiated) plutonium.     

Predicting older-donor kidneys' post-transplant renal function using pre-transplant data

This paper provides a methodology for predicting post-transplant kidney function, that is, the 1-year post-transplant estimated Glomerular Filtration Rate (eGFR-1) for each donor-candidate pair. We apply customized machine-learning algorithms to pre-transplant donor and recipient data to determine the probability of achieving an eGFR-1 of at least 30 ml/min. This threshold was chosen because there is insufficient survival benefit if the kidney fails to generate an eGFR-1 ≥ 30 ml/min. For some donor-candidate pairs, the developed algorithm provides highly accurate predictions. For others, limitations of previous transplants' data results in noisier predictions. However, because the same kidney is offered to many candidates, we identify those pairs for whom the predictions are highly accurate. Out of 6977 discarded older-donor kidneys that were a match with at least one transplanted kidney, 5282 had one or more identified candidate, who were offered that kidney, did not accept any other offer, and would have had ≥80% chance of achieving eGFR-1 ≥ 30 ml/min, had the kidney been transplanted. We also show that transplants with ≥80% chance of achieving eGFR-1 ≥ 30 ml/min and that survive 1 year have higher 10-year death-censored graft survival probabilities than all older-donor transplants that survive 1 year (73.61% vs. 70.48%, respectively).