Renewable energy sources in Latin America and the Caribbean: situation and policy proposals

Includes bibliography

Tax policy in Latin America: Assessment and guidelines for a second generation of reforms

Includes bibliography

Support area as an indicator of environmental load: Comparison between Embodied Energy, Ecological Footprint, and Emergy Accounting methods

Environmental aspects have been acknowledged as an important issue in decision making at any field during the last two decades. There are several available methodologies able to assess the environmental burden, among which the Ecological Footprint has been widely used due to its easy-to-understand final indicator. However, its theoretical base has been target of some criticisms about the inadequate representation of the sustainability concept by its final indicator. In a parallel way, efforts have been made to use the theoretical strength of the Emergy Accounting to obtain an index similar to that supplied by the Ecological Footprint. Focusing on these aspects, this work assesses the support area (SA) index for Brazilian sugarcane and American corn crop through four different approaches: Embodied Energy Analysis (SA(EE)), Ecological Footprint (SA(EF)), Renewable Empower Density (SA(R)), and Emergy Net Primary Productivity (SA(NPP)). Results indicate that the load on environment varies accordingly to the methodology considered for its calculation, in which emergy approach showed the higher values. Focusing on crops comparison, the load by producing both crops are similar with an average of 0.04 ha obtained by SA(EE), 1.86 ha by SA(EF), 4.24 ha by SA(R), and 4.32 ha by SA(NPP). Discussion indicates that support area calculated using Emergy Accounting is more eligible to represent the load on the environment due to its global scale view. Nevertheless, each methodology has its contribution depending of the study objectives, but it is important to consider the real meaning and the scope of each one. (C) 2012 Elsevier Ltd. All rights reserved.

Emergetic ternary diagrams: five examples for application in environmental accounting for decision-making

In a recent paper, "A combined tool for environmental scientists and decision makers: ternary diagrams and emergy accounting." [Giannettti BF, Barrella FA, Almeida CMVB. A combined tool for environment scientists and decision makers: ternary diagrams and emergy accounting. J Clean Prod, in press http://dx.doi.org/10.1016/j.jclepro.2004.09.002] Ternary diagrams were proposed as a graphical tool to assist emergy analysis. The graphical representation of the emergy accounting data makes it possible to compare processes and systems with and without ecosystem services, to evaluate improvements and to follow the system performance over time. The graphic tool is versatile and adaptable to represent products, processes, systems, countries, and different periods of time.The use and the versatility of ternary diagrams for assisting in performing emergy analyses are illustrated by means of five examples taken from the literature, which are presented and discussed. It is shown that emergetic ternary diagram's properties assist the assessment of the system of the system efficiency, its dependance upon renewable and non-renewable inputs and the environmental support for dilution and abatement of process emissions. With the aid of ternary diagrams, details such as the interaction between systems and between systems and the environment are recognized and evaluated. Such a tool for graphical analysis allows a transparent presentation of the results and can serve as an interface between emergy scientists and decision makers, provided the meaning of each line in the diagram is carefully explained and understood. (c) 2005 Elsevier Ltd. All rights reserved.

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.

Employment Effects of Product and Labour Market Reforms: Are There Synergies?

This article investigates the effect of product market liberalisation on employment allowing for interactions between policies and institutions in product and labour markets. Using panel data for OECD countries over the period 19802002, we present evidence that product market deregulation is more effective at the margin when labour market regulation is high. The data also suggest that product market liberalisation may promote employment-enhancing labour market reforms.

Explaining standing order reforms in the Camera dei Deputati during the liberal age

How might the continuous changes in the standing orders of the Camera dei Deputati between 1861 and 1922 be explained? To answer this question, the text investigates the political events associated with standing order reforms. Two results are emphasized. On the one hand, and contrary to common views, the study shows that the reforms were not casual or episodic, but resulted from different sets of political pressure, internal or external to the parliamentary ambit. This fact, on the other hand, draws attention to the need to go deeper into the question of the institutional evolution of the liberal parliament, chiefly with regard to relations among institutional actors.

Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)

Abstract Background An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature Sequencing (MPSS), is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error. Results We introduce a Bayesian model that accounts for the within-class variability by means of mixture distribution. We show that the previously available approaches of aggregation in pools ("pseudo-libraries") and the Beta-Binomial model, are particular cases of the mixture model. We illustrate our method with a brain tumor vs. normal comparison using SAGE data from public databases. We show examples of tags regarded as differentially expressed with high significance if the within-class variability is ignored, but clearly not so significant if one accounts for it. Conclusion Using available information about biological replicates, one can transform a list of candidate transcripts showing differential expression to a more reliable one. Our method is freely available, under GPL/GNU copyleft, through a user friendly web-based on-line tool or as R language scripts at supplemental web-site.

Esempio di NAM 2005 (National Accounting Matrix)

Modulo 2. NAM SEC riempita don i dati dei conti nazionali