Seeking for the rational basis of the median model: the optimal combination of multi-model ensemble results

International audienceIn this paper we present an approach for the statistical analysis of multi-model ensemble results. The models considered here are operational long-range transport and dispersion models, also used for the real-time simulation of pollutant dispersion or the accidental release of radioactive nuclides. We first introduce the theoretical basis (with its roots sinking into the Bayes theorem) and then apply this approach to the analysis of model results obtained during the ETEX-1 exercise. We recover some interesting results, supporting the heuristic approach called "median model", originally introduced in Galmarini et al. (2004a, b). This approach also provides a way to systematically reduce (and quantify) model uncertainties, thus supporting the decision-making process and/or regulatory-purpose activities in a very effective manner

Tax autonomy, lobbying, and welfare

What degree of tax autonomy should be granted to a taxing authority? Although the policy maker aims at maximizing social welfare, her tax policy may be distorted by the lobbying activity of taxpayers. In this political environment we characterize the conditions under which social welfare can be increased by restricting the set of tax instruments available to the policy maker, i.e. the degree of tax autonomy. We show that full tax autonomy is more costly, in terms both of welfare distortions and lobbying effort, when the lobbies are asymmetric in size, while minimal tax autonomy is more costly when the tax bases are asymmetric across different groups

Modeling the impact of sub-grid scale emission variability on upper-air concentration

International audienceThe long standing issue of sub-grid emission heterogeneity and its influence to upper air concentration is addressed here and a subgrid model proposed. The founding concept of the approach is the assumption that average emission act as source terms of average concentration, emission fluctuations are source for the concentration variance. The model is based on the derivation of the sub-grid contribution of emission and the use of the concentration variance equation to transport it in the atmospheric boundary layer. The model has been implemented in an existing mesoscale model and the results compared with Large-Eddy Simulation data for ad-hoc simulation devised to test specifically the parametrization. The results show an excellent agreement of the models. For the first time a time evolving error bar reproducing the sub-grid scale heterogeneity of the emissions and the way in which it affects the concentration has been shown. The concentration variance is presented as an extra attribute to better define the mean concentrations in a Reynolds-average model. The model has applications from meso to global scale and that go beyond air quality

The runaway taxpayer

In order to analyse the determinants of tax evasion, the existing literature on individual tax compliance typically takes a prior-to-audit point of view. This paper focuses on a post-audit, post-detection -so far unexplored- framework, by investigating what happens after tax evasion has been discovered and noncompliant taxpayers are asked to pay their debts. We fi rst develop a two-period dynamic model of individual choice, considering an individual that has been already audited and detected as tax evader, who knows that Tax Authorities are looking for her to cash the due amount. We derive the optimal decision of running away in order to avoid paying the bill, and show that the experience of a prior tax notice reduces the probability to behave as a scofflaw. We then exploit information on post-audit, post-detection tax compliance provided by an Italian collection agency for the period 2004-2007 to empirically test the effectiveness of the prior notice against scofflaws. The evidence from alternative logit model speci cations supports our theoretical prediction: experiencing a tax notice reduces the probability of running away by about 10%. However, this may prove to be insufficient to discourage some individuals to runaway in order to avoid paying their dues

Drivers in CO2 emissions variation: A decomposition analysis for 33 world countries

A decomposition analysis of energy related CO2 emissions is carried out for 33 world countries. The data pertain to the period 1995–2007. The methodology used is the Index Decomposition Analysis that allows to investigate the contribution of the following factors: (i) changes in abatement technologies, fuel quality and fuel switching; (ii) changes in the structure and efficiency of the energy system; (iii) relative ranking of a country in terms of the total GDP (Gross Domestic Product) generation and (iv) changes of the country specific total economic activity. The WIOD (World Input Output Database) has been used together with Organization for the Economic Co-operation and Development (OECD) data on GDP. Results show that economic growth has been the main driving factor of energy related CO2 emissions increase. However, in fast developing countries like India and China, an important contribution has also been the increasing role that these economies are playing in the global economic panorama. Improvements on energy efficiency have been the main element contributing to reduce the overall CO2 emission increase in all the countries considered in this study

Mapping socioeconomic well-being across EU regions

Purpose – The purpose of this paper is to use multidimensional approach to provide a well-being description across European regions. Design/methodology/approach – By considering the set of socioeconomic indicators provided by Eurostat for the EU 266 NUTS-2 regions, three main analyses have been performed for the year 2009: first, the “ideal point” technique has been used to identify: the best EU performances; the number and type of indicators that needs to be improved in every European regions. Second, a map of well-being has been elaborated to provide a picture summarizing the regional values in comparison to the European average. Third, Gini coefficient has been calculated to identify the indicators performing the largest inequalities across EU. The method presented in this paper is suitable to be complemented with subjective ranking of values and preference, making the proposed methodology useful to investigate well-being in a national, regional or individual scale. Findings – By providing a multidimensional description of well-being across the 266 EU regions, the present paper identifies and maps the existing differences on socioeconomic performance. Originality/value – The results provided can be useful to design policies oriented to reduce inequalities and to promote socioeconomic and environmental convergences across European regions. As far as the authors know, this is the first paper that provides a map of regional socioeconomic well-being across Europe

Trace gas/aerosol boundary concentrations and their impacts on continental-scale AQMEII modeling domains

Copyright 2011 Elsevier B.V., All rights reserved.Over twenty modeling groups are participating in the Air Quality Model Evaluation International Initiative (AQMEII) in which a variety of mesoscale photochemical and aerosol air quality modeling systems are being applied to continental-scale domains in North America and Europe for 2006 full-year simulations for model inter-comparisons and evaluations. To better understand the reasons for differences in model results among these participating groups, each group was asked to use the same source of emissions and boundary concentration data for their simulations. This paper describes the development and application of the boundary concentration data for this AQMEII modeling exercise. The European project known as GEMS (Global and regional Earth-system Monitoring using Satellite and in-situ data) has produced global-scale re-analyses of air quality for several years, including 2006 (http://gems.ecmwf.int). The GEMS trace gas and aerosol data were made available at 3-hourly intervals on a regular latitude/longitude grid of approximately 1.9° resolution within 2 "cut-outs" from the global model domain. One cut-out was centered over North America and the other over Europe, covering sufficient spatial domain for each modeling group to extract the necessary time- and space-varying (horizontal and vertical) concentrations for their mesoscale model boundaries. Examples of the impact of these boundary concentrations on the AQMEII continental simulations are presented to quantify the sensitivity of the simulations to boundary concentrations. In addition, some participating groups were not able to use the GEMS data and instead relied upon other sources for their boundary concentration specifications. These are noted, and the contrasting impacts of other data sources for boundary data are presented. How one specifies four-dimensional boundary concentrations for mesoscale air quality simulations can have a profound impact on the model results, and hence, this aspect of data preparation must be performed with considerable care.Peer reviewedFinal Accepted Versio

Examination of the Community Multiscale Air Quality (CMAQ) model performance over the North American and European domains

Copyright 2011 Elsevier B.V., All rights reserved.The CMAQ modeling system has been used to simulate the air quality for North America and Europe for the entire year of 2006 as part of the Air Quality Model Evaluation International Initiative (AQMEII). The operational model performance of tropospheric ozone (O), fine particulate matter (PM) and total particulate matter (PM) for the two continents has been assessed. The model underestimates daytime (8am-8pm LST) O mixing ratios by 13% in the winter for North America, primarily due to an underestimation of daytime O mixing ratios in the middle and lower troposphere from the lateral boundary conditions. The model overestimates winter daytime O mixing ratios in Europe by an average of 8.4%. The model underestimates daytime O by 4-5% in the spring for both continents, while in the summer daytime O is overestimated by 9.8% for North America and slightly underestimated by 1.6% for Europe. The model overestimates daytime O in the fall for both continents, grossly overestimating daytime O by over 30% for Europe. The performance for PM varies both seasonally and geographically for the two continents. For North American, PM is overestimated in the winter and fall, with an average Normalized Mean Bias (NMB) greater than -30%, while performance in the summer is relatively good, with an average NMB of -4.6%. For Europe, PM is underestimated throughout the entire year, with the NMB ranging from -24% in the fall to -55% in the winter. PM is underestimated throughout the year for both North America and Europe, with remarkably similar performance for both continents. The domain average NMB for PM ranges between -45% and -65% for the two continents, with the largest underestimation occurring in the summer for North American and the winter for Europe.Peer reviewedSubmitted Versio