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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.

Intro -- Acknowledgement -- Abstract -- Zusammenfassung -- Contents -- List of Figures -- List of Tables -- List of Algorithms -- Acronyms -- Glossary -- 1 Introduction -- 1.1 Controlling traffic lights -- 1.2 Data acquisition methods -- 1.3 Airborne sensors -- 1.4 Self-Organizing Traffic Lights -- 1.5 Traffic optimization goals -- 1.6 Greenhouse gas -- 1.7 Vehicles' emissions -- 1.8 Hypothesis and the research questions -- 1.9 Organization of the thesis -- 2 Comparison of travel time es-timations using intelligent in-frastructure and floating car data -- 2.1 Introduction -- 2.2 State of the art -- 2.3 Simulation environment -- 2.4 Methods -- 2.5 Measurements -- 2.6 Results -- 2.7 Summary -- 3 Integration of cellular automata traffic simulation with carbon dioxide emission model -- 3.1 Traffic simulators -- 3.2 Greenhouse gases and carbon dioxide emis-sion -- 3.3 CO2 emission model -- 3.4 Microscopic traffic simulator -- 3.5 Cellular automata traffic simulator -- 3.6 Integration of cellular automata traffic sim-ulation with emission model -- 3.6.1 Data acquisition -- 3.6.2 Data preparation -- 3.6.3 Vehicles' acceleration rate -- 3.6.4 Data analysis -- 3.6.5 Distribution of the acceleration -- 3.6.6 Deceleration -- 3.6.7 Adaptation of Rule 184 -- 3.7 Rule 184-based CO2 traffic emission model -- 3.7.1 Implementation note -- 3.8 NaSch-based CO2 traffic emission model -- 3.9 Critics, discussion and future work -- 4 The impact of dynamic route guidance system driven by travel time measurements on carbon dioxide emission -- 4.1 Introduction -- 4.2 Dynamic route guidance system architec-ture -- 4.2.1 Shortest Paths Algorithm -- 4.2.2 Cost function -- 4.2.3 Path with minimal cost -- 4.3 Methodology and measurements -- 4.4 Simulation -- 4.4.1 Virtual cities -- 4.5 Test scenarios and results -- 4.5.1 First scenario -- 4.5.2 Second scenario.

Introduction / Badi H. Baltagi, Thomas B. Fomby, R. Carter Hill -- Testing for common cyclical features in nonstationary panel data models / Alain Hecq, Franz C. Palm, Jean-Pierre Urbain -- The local power of some unit root tests for panel data / J(c)·org Breitung -- On the estimation and inference of a cointegrated regression in panel data / Chihwa Kao, Min-Hsien Chiang -- Testing for unit roots in panels in the presence of structural change with an application to OECD unemployment / Christian J. Murray, David H. Papell -- Panel data limit theory and asymptotic analysis of a panel regression with near integrated regressors / Heikki Kauppi -- Stationarity tests in heterogeneous panels / Yong Yin, Shaowen Wu -- Instrumental variable estimation of semiparametric dynamic panel data models : Monte Carlo results on several new and existing estimators / M. Douglas Berg, Qi Li, Aman Ullah -- Small sample performance of dynamic panel data estimators in estimating the growth-convergence equation : a Monte Carlo study / Nazrul Islam -- Estimation in dynamic panel data models : improving on the performance of the standard GMM estimator / Richard Blundell, Stephen Bond, Frank Windmeijer -- Nonstationary panels, cointegration in panels and dynamic panels : a survey / Badi H. Baltagi, Chihwa Kao -- Fully modified OLS for heterogeneous cointegrated panels / Peter Pedroni

The implementation of activities aimed to mitigate global greenhouse gas emissions is more cost-efficient in developing countries than in most of the industrialized world. Thus it has been a major, but contentious topic in the climate negotiations to allow crediting of emissions reduction in developing countries towards domestic emission targets of industrial countries. The Kyoto Protocol instituted a Clean Development Mechanism (CDM) that is to assure that the interests of all parties from industrialized and developing countries are equally represented. Many issues concerning the structure of the CDM remain to be decided. Crediting critically depends on these decisions. Credits should accrue only after verification. A crucial issue that influences all decisions on creation and distribution of credits is whether they are tradable. Concerning credit creation, it would be advisable not to set quotas on the share of CDM credited toward Annex-B targets as they give no dynamic incentive for innovation. To reach the latter goal, crediting should be gradually reduced in the long run. Crediting should also be related to externalities and thus be differentiated according to project categories. In a fund model, the reduction of credits could be evenly spread over all investors. In a clearinghouse model it would have to be related to each project. Uncertainties should not be covered through discounting but through a compulsory insurance. Credit sharing leads to higher costs for the investors and a lower demand for CDM projects. Free negotiation of the credit sharing ratio will lead to a competition between host countries. In case of tradability, host countries could set up projects with own funds to earn credits they can sell. Such a de facto extension of emissions trading could work against the goal of inducing developing countries to voluntarily adopt emission targets. This could be promoted by making credits non-tradable but allowing banking against future targets.

Emissions trading mechanisms have been proposed, and in some cases implemented, as a tool to reduce pollution. We explore the similarities between emission-trading mechanisms and monetary mechanisms. Both attempt to implement desirable allocations under various frictions, including risk and private information. In addition, implementation relies on the issue and trading of objects whose value is at least partially determined by expectations, namely money and permits, respectively. We use insights from dynamic mechanism design in monetary economics to derive properties of dynamic emissions trading mechanisms. At the optimum, the price of permits must increase over time. Efficient tax policies are state-contingent, and there is an equivalence between state-contingent taxes and emissions trading. Restrictions resulting from the money-like feature of permits can break this equivalence when there is endogenous progress in clean technologies. These restrictions must be taken into consideration in actual policy implementation.

'Against all odds, the CDM has shown that market mechanisms for greenhouse gas reduction in developing countries can work. Nevertheless, as Paula Castro explains convincingly, the CDM is no "magic bullet". Advanced developing countries need to be "weaned off" the CDM in order to take up commitments, while the monetary incentive from emission credit sales is insufficient to put least developed countries on a low-emission pathway. However, experience from the CDM remains critical in designing new market mechanisms.' - Axel Michaelowa, University of Zurich, Switzerland. In this groundbreaking book, Paula Castro presents the first systematic categorization of positive and negative incentives generated by the Clean Development Mechanism (CDM) for climate change mitigation in the Global South. To reduce the cost of meeting their greenhouse gas emission reduction commitments under the Kyoto Protocol, industrialized countries may rely on the CDM, a market instrument that allows them to count emission reductions from projects in developing countries as their own. Presented in four core empirical chapters, the book critically reviews whether and how the CDM creates incentives or disincentives for developing country action towards reducing greenhouse gas emissions, and draws lessons for the future international climate change regime. Recommendations and discussion on the reform of the CDM invoke debate on the future of this policy in developing countries, which is vital material for both policymakers and international institutions introducing similar instruments. Students and researchers working on topics related to environmental politics, climate policy, environmental economics and environmental science will also find this resource invaluable

Dynamic factor models and dynamic stochastic general equilibrium (DSGE) models are widely used for empirical research in macroeconomics. The empirical factor literature argues that the co-movement of large panels of macroeconomic and financial data can be captured by relatively few common unobserved factors. Similarly, the dynamics in DSGE models are often governed by a handful of state variables and exogenous processes such as preference and/or technology shocks. Boivin and Giannoni(2006) combine a DSGE and a factor model into a data-rich DSGE model, in which DSGE states are factors and facto

This paper proposes the transformed maximum likelihood estimator for short dynamic panel data models with interactive fixed effects, and provides an extension of Hsiao et al. (2002) that allows for a multifactor error structure. This is an important extension since it retains the advantages of the transformed likelihood approach, whilst at the same time allows for observed factors (fixed or random). Small sample results obtained from Monte Carlo simulations show that the transformed ML estimator performs well in finite samples and outperforms the GMM estimators proposed in the literature in almost all cases considered.

Panel Data and Structural Labour Market Models is the latest volume in a series of four, reporting on the original work of an international group of scholars with research interests in the performance of the labour markets that condition the dynamic labour market experiences of individual workers. The book contains papers focusing on theoretical and empirical modelling of the labour market covering both wage equilibrium models and models for labour market transition. Contributions range from the theoretical or econometric through empirical structural methods and exploratory data analysis based on employer and employee level data. Academic libraries, labour economists, labour and industrial relations research institutes and statistical agencies will find this a particularly useful piece of work

This book assesses the structure of projects under the Clean Development Mechanism (CDM) of the Kyoto Protocol. It explains why, instead of the expected bilateral structure where a company from an industrialized country invests in a project in a developing country and receives the emission reduction credits in return, a unilateral structure prevails whereby a company from a developing country finances the emission reduction project itself and sells the emission reduction credits. The book arrives at three fundamental, interconnected, conclusions: CDM is logically a unilaterally driven investment activity; CDM investment is an irrelevant compliance instrument for companies from industrialised countries and that this state of affairs is unlikely to change post 2012; and CDM thrives in less equal and less ambitious post-2012 climate regimes. Unique in its analysis of corporate views on investment in CDM projects, this book will find widespread appeal amongst climate policy analysts, company representatives involved in developing CDM acquisition strategies and climate policymakers. It will also be of interest to anyone involved in the study of climate change, emissions reduction and trading and carbon markets