In order to find out why energy-not-supplied in Norway - the most important indicator for the quality of service in the quality-regulation regime there - decreased more pronounced before the introduction of quality-regulation in 2001 than after it, we develop a dynamic quality-DEA-model and apply it to a representative sample of distribution-net operators. Our model enables us to calculate a counter-factual and thus to tentatively answer the question: What would have happened, had there been no quality-regulation? This way we find strong evidence that the quality-regulation in Norway did not have an effect on the behavior of the firms. (author's abstract) / Series: Working Papers / Research Institute for Regulatory Economics

The 2015 flood event in the Shire River basin was characterised by Malawi Government’s Department of Disaster Management (DoDMA) as the worst on record. It led to the damage in property worth millions of dollars with recovery still ongoing 3 years later. Over 150 fatalities were confirmed at the time with hundreds of others missing. The extent of the damage of the disaster was perhaps underlined by the swift adoption of the disaster management policy which was still in draft format then and the adoption of the climate change management policy a year later. In the aftermath of the disaster, as with most extreme weather events elsewhere around the world, questions were asked as to whether climate change might have had a hand in the occurrence of such an event and whether, going into a warmer climate, events of that nature of extremity will be the new normal. By using the risk-based event attribution methodology based on dedicated attribution experiments with a global climate model, and focusing on one of the sub-catchments of the Shire River basin, this study explored whether climate change from anthropogenic sources might have influenced the likelihood of such an event occurring. However, given the nature of hydrological events and the land use history of the basin, land use and cover change is another potential flood risk factor which, if overlooked, might affect conclusions with regards to the contribution of external factors to the risk of flooding. To account for both climate change and land use and land change, four sets of rainfallrunoff simulations were run using the Hydrologiska Byrans Vattenbalans-avdelning (HBV) hydrological model which has the ability to simulate the impact of land use and climate change on rainfall-runoff relationships. Each set was a combination of a climate scenario-either “factual” or “counter-factual”- and land use and cover change scenario-either factual (historical) or counterfactual (current). The climate scenarios were based on simulated rainfall and temperature from the HadAM3p model run in two modes-the “factual” and “counter-factual”- simulating the climate with atmospheric conditions closely resembling the atmosphere at the time of occurrence of the event and the climate as it would have been without human emissions of greenhouse gases. The proportion of the risk was calculated to determine how the risk of experiencing a flood of the January-April 2015 magnitude (for 1-day, 10- day, and 30-day maximum flows) changes with climate change only, land use and cover change only, as well as both climate change and land use and cover change. The results demonstrated that the probability of exceeding the 1-day maximum flow of the 2015 magnitude was lower in the factual (current) climate than in the counter-factual. However, changes in land use modify the flood risk such that, when land use change was accounted for, the extent of the reduction in the risk was lower. On the other hand, exceedance probabilities for 10-day and 30-day maximum flows were higher in the factual (current) climate. This was further heightened by changes in land use and cover. The study also established that observational uncertainties typical of the region may influence event attribution results to some extent. The results, which are based on a single attribution method and a single global climate model, do not span the method-model uncertainty range. As a consequence, the results are limited and do not constitute a fully defensible attribution statement.