Suchergebnisse

Der Clean Development Mechanism (CDM) des Kyoto-Protokolls hat eine zweifache Zielsetzung. Erstens soll damit den Industrieländern erlaubt werden, in emissionssparende Projekte in Entwicklungsländern zu investieren und sich die so erzielte Emissionsreduktion auf ihre Kyoto-Ziele anzurechnen. Denn dort sind Emissionsreduktionen oft kostengünstiger zu erzielen als in Industrieländern. Zweitens sollen die CDM-Projekte zur nachhaltigen Entwicklung der Gastgeberländer beitragen. Inzwischen sind weltweit über 2.000 CDM-Projekte genehmigt und weitere Tausende sind beantragt. Der Mechanismus hat damit eine Dimension erreicht, in der er wesentliche Wirkungen auslösen kann - zum Positiven oder zum Negativen. Laut Kyoto-Protokoll sind die beiden oben genannten Ziele gleichrangig. Es wird jedoch nur der Aspekt der erzielten Emissionsreduktionen durch ein internationales Verfahren reguliert und überwacht. Die Entscheidung, ob ein Projekt zur nachhaltigen Entwicklung beiträgt, obliegt allein den Gastgeberländern, die bisher jegliche Vorschläge, auch für diese Frage ein internationales Verfahren einzurichten, abgelehnt hatten. Jedes Entwicklungsland hat daher ganz eigene Kriterien für die Bewertung der Nachhaltigkeit seiner Projekte entwickelt, doch meist ohne die erforderlichen Kapazitäten, um Projekte wirklich zu prüfen. Die hier vorgestellte Diplomarbeit von Bea Wittger untersucht diese Problematik am Beispiel Brasilien. Brasilien beheimatet weltweit die drittmeisten CDM-Projekte und hat den Ruf, ein besonders gründliches Genehmigungsverfahren durchzuführen. Doch auch in der brasilianischen Zivilgesellschaft wird teils harsche Kritik am CDM geübt. Die Diplomarbeit wurde an der Universität zu Köln vorgelegt und von Wolfgang Sterk betreut. Sie nimmt eine detaillierte Analyse der brasilianischen Genehmigungsverfahren und -praxis vor, erstens anhand ihrer inhärenten Konsistenz und Stringenz und zweitens anhand von zwei Projektbeispielen. ; The Clean Development Mechanism (CDM) of the Kyoto Protocol has two targets: Firstly to enable the industrialised countries to invest in emission saving projects in developing countries thus influencing the achievement account of the Kyoto targets at home. Secondly the CDM-projects are to contribute to the sustainable development of the host countries. However each developing country has developed its own individual system of assessing the sustainability of its projects, mostly without the capacity needed to effectively survey the projects. The here presented diploma thesis written by Bea Wittger analyses this problem taking Brazil as an example. Focusing on two project examples and taking into account the inherent consistency and stringency the analysis explores in detail the Brazilian approval procedures and practices.

AbstractThe current study uses the Generalised Method of Moments to examine the dynamic relationship between access to microfinance and poverty reduction. The panel‐data set is constructed from the Vietnam Household Living Standard Surveys during 2002–2008. The results show that access to microfinance in the previous period significantly improves per adult‐equivalent income and consumption of the households in the current period. However, previous participation in microfinance programs is not sufficient to help households escape from poverty. In addition, both private and government bank loans significantly help improve household per adult‐equivalent income, consumption, and poverty rates. The empirical results imply that a larger loan size, diversified loans for different consumption purposes, and improvement in infrastructure and facilities, especially in rural areas, are believed to help reduce poverty.

AbstractThis paper discusses the role of the Clean Development Mechanism (CDM) in the market for carbon quotas and countries' commitments to reduce their carbon emission levels. We show that the CDM contributes to an efficient funding of clean technology investments in least developed countries. However, the CDM is not neutral on the global level of carbon emissions as it entices countries to raise their emission caps. The CDM may also make inappropriate the inclusion of any country that takes no emission abatement commitment. It can even make inefficient a country's decision to commit to an emission target. The implications of the presence of non‐additional projects are also analyzed.

This article analyzes a growing group of fixed T dynamic panel data estimators with a multifactor error structure. We use a unified notational approach to describe these estimators and discuss their properties in terms of deviations from an underlying set of basic assumptions. Furthermore, we consider the extendability of these estimators to practical situations that may frequently arise, such as their ability to accommodate unbalanced panels and common observed factors. Using a large-scale simulation exercise, we consider scenarios that remain largely unexplored in the literature, albeit being of great empirical relevance. In particular, we examine (i) the effect of the presence of weakly exogenous covariates, (ii) the effect of changing the magnitude of the correlation between the factor loadings of the dependent variable and those of the covariates, (iii) the impact of the number of moment conditions on bias and size for GMM estimators, and finally (iv) the effect of sample size. We apply each of these estimators to a crime application using a panel data set of local government authorities in New South Wales, Australia; we find that the results bear substantially different policy implications relative to those potentially derived from standard dynamic panel GMM estimators. Thus, our study may serve as a useful guide to practitioners who wish to allow for multiplicative sources of unobserved heterogeneity in their model.

Cover -- Title -- Copyright -- Contents -- Tables, Figures, and Examples -- Foreword -- Acknowledgments -- Abbreviations -- Executive Summary -- Introduction -- Purpose -- Target Audience -- Latest Information -- MODULE 1: THE CLEAN DEVELOPMENT MECHANISM -- 1.1 Introduction to Module -- 1.2 The Clean Development Mechanism -- 1.3 Clean Development Mechanism Projects -- 1.4 Clean Development Mechanism Project Cycle -- 1.5 Postregistration Changes -- 1.6 Selling Certified Emission Reductions and the Emission Reduction Purchase Agreement -- MODULE 2: MONITORING AND REPORTING -- 2.1 Introduction to Module -- 2.2 Monitoring, Reporting, and Verification Definitions and Principles -- 2.3 The Monitoring Framework -- 2.4 The Monitoring Process -- 2.5 Managing Required Changes to the Monitoring Plan -- 2.6 Monitoring Issues -- MODULE 3: VERIFICATION -- 3.1 Introduction to Module -- 3.2 Definition of Verification -- 3.3 Timing of Verification -- 3.4 How to Select and Work with a Designated Operational Entity -- 3.5 Designated Operational Entity Activities -- 3.6 Clarific ation Requests, Corrective Action Requests, and Forward Action Requests -- 3.7 Results of the Verification and Request for Issuance -- 3.8 Preliminary Verification -- MODULE 4: POSTREGISTRATION CHANGES -- 4.1 Introduction to Module -- 4.2 Classification of Project Changes under the Clean Development Mechanism -- 4.3 When to Notify the Executive Board of Temporary or Permanent Changes -- 4.4 Procedure for Obtaining Approval for Temporary and Permanent Changes -- MODULE 5: PROGRAMME OF ACTIVITIES -- 5.1 Introduction to Module -- 5.2 Programme of Activities Explained -- 5.3 Monitoring Process for a Programme of Activities -- 5.4 Revisions and Deviations to the Programme of Activities Monitoring Plan -- 5.5 Verification Process for the Programme of Activities.

The recent introduction of a low-emission zone and enlarged car-free zones in three Belgian cities led to the development of information systems processing data about vehicles, persons, companies and policy impact effects. The collection, analysis and use of these data brought up certain problems that needed to be solved. This case study research aims to categorize these problems using the sequential problem/solution-model. Furthermore, the research results help to explore some focus areas for a future data governance model in the public sector.

This article analyzes a growing group of fixed T dynamic panel data estimators with a multifactor error structure. We use a unified notational approach to describe these estimators and discuss their properties in terms of deviations from an underlying set of basic assumptions. Furthermore, we consider the extendability of these estimators to practical situations that may frequently arise, such as their ability to accommodate unbalanced panels and common observed factors. Using a large-scale simulation exercise, we consider scenarios that remain largely unexplored in the literature, albeit being of great empirical relevance. In particular, we examine (i) the effect of the presence of weakly exogenous covariates, (ii) the effect of changing the magnitude of the correlation between the factor loadings of the dependent variable and those of the covariates, (iii) the impact of the number of moment conditions on bias and size for GMM estimators, and finally (iv) the effect of sample size. We apply each of these estimators to a crime application using a panel data set of local government authorities in New South Wales, Australia; we find that the results bear substantially different policy implications relative to those potentially derived from standard dynamic panel GMM estimators. Thus, our study may serve as a useful guide to practitioners who wish to allow for multiplicative sources of unobserved heterogeneity in their model.