One of the dual objectives of the Clean Development Mechanism (CDM) of the Kyoto Protocol is to promote sustainable development in the host countries. With different CDM indicators for 58 CDM host countries over 2005-10, this paper empirically assesses whether CDM project development fulfils this objective of sustainable development. Using a unique dynamic panel data method based on long-differences of the model, this research provides evidence in support of significant contribution to sustainable development of CDM projects in the host countries. It sheds light on the role of CDM projects in the process of sustainable development with clear policy implications for developing countries and the wider world.
The Clean Development Mechanism (CDM) is an offset mechanism designed to reduce the overall cost of implementing a given global target for greenhouse gas (GHG) emissions in industrialized "Annex B" countries of the Kyoto Protocol. This paper discusses various ways in which CDM projects do not imply full offset of emissions, thus leading to an overall increase in global GHG emissions when considering the Annex-B emissions increase allowed by the offsets. The authors focus on two ways in which this may occur: baseline manipulation; and leakage. Baseline manipulation may result when agents that carry out CDM projects have incentives to increase their initial (or baseline) emissions in order to optimize the value of CDM credits. Leakage occurs because reductions in emissions under a CDM project may affect market equilibrium in local and/or global energy and product markets, and thereby increase emissions elsewhere. Remedies against these problems are discussed. Such remedies are more obvious for the baseline problem (where one is simply to choose an exogenous baseline independent of the project) than for the leakage problem (which is difficult to prevent, and where a prediction of the effect must rely on information about overall market equilibrium effects).
This paper examines the cost of producing emission reduction credits under the Clean Development Mechanism. Using project-specific data, cost functions are estimated using alternative functional forms. The results show that, in general, the distribution of projects in the pipeline does not correspond exclusively to the cost of generating anticipated credits. Rather, investment choices appear to be influenced by location and project type considerations in a way that is consistent with variable transaction costs and investor preferences among hosts and classes of projects. This implies that comparative advantage based on the marginal cost of abatement is only one of several factors driving Clean Development Mechanism investments. This is significant since much of the conceptual and applied numerical literature concerning greenhouse gas mitigation policies relies on presumptions about relative abatement costs. The authors also find that Clean Development Mechanism projects generally exhibit constant or increasing returns to scale. In contrast, they find variations among classes of projects concerning economies of time.
This study investigates the existence of environmental Kuznets curve (EKC) for carbon dioxide (CO2) emissions and its relationship with economic growth, energy consumption and globalization over the period of 1990-2010. We apply a dynamic panel data (GMM-system estimator) using the data of selected 18 countries. This estimator permits to solve the problems of serial correlation, heteroskedasticity and endogeneity for some explanatory variables. The environmental consequences of economic growth are according to environmental Kuznets (EKC) hypothesis. Globalization seems to be a main engine that provides a way to enhance production intensively by utilizing abundant domestic resources efficiently. The energy consumption has positive impact on CO2 emissions. Urbanization improves environmental quality by lowering CO2 emissions, i.e an inverted-U shaped relationship between urbanization and CO2 emissions.
“Esemplare fuori commercio per il deposito legale agli effetti della legge 15 aprile 2004 n. 106” Quaderno riprodotto al Dipartimento di Scienze Economiche, Matematiche e Statistiche nel mese di settembre 2008 e depositato ai sensi di legge Authors only are responsible for the content of this preprint.
Panel data are a very valuable resource for finding empirical solutions to political science puzzles. Yet numerous published studies in political science that use panel data to estimate models with dynamics have failed to take into account important estimation issues, which calls into question the inferences we can make from these analyses. The failure to account explicitly for unobserved individual effects in dynamic panel data induces bias and inconsistency in cross-sectional estimators. The purpose of this paper is to review dynamic panel data estimators that eliminate these problems. I first show how the problems with cross-sectional estimators arise in dynamic models for panel data. I then show how to correct for these problems using generalized method of moments estimators. Finally, I demonstrate the usefulness of these methods with replications of analyses in the debate over the dynamics of party identification.
Many experts believe that low-cost mitigation opportunities in agriculture are abundant and comparable in scale to those found in the energy sector. They are mostly located in developing countries and have to do with how land is used. By investing in projects under the Clean Development Mechanism (CDM), countries can tap these opportunities to meet their own Kyoto Protocol obligations. The CDM has been successful in financing some types of agricultural projects, including projects that capture methane or use agricultural by-products as an energy source. But agricultural land-use projects are scarce under the CDM. This represents a missed opportunity to promote sustainable rural development since land-use projects that sequester carbon in soils can help reverse declining soil fertility, a root cause of stagnant agricultural productivity. This paper reviews the process leading to current CDM implementation rules and describes how the rules, in combination with challenging features of land-use projects, raise transaction costs and lower demand for land-use credits. Procedures by which developed countries assess their own mitigation performance are discussed as a way of redressing current constraints on CDM investments. Nevertheless, even with improvements to the CDM, an under-investment in agricultural land-use projects is likely, since there are hurdles to capturing associated ancillary benefits privately. Alternative approaches outside the CDM are discussed, including those that build on recent decisions taken by governments in Copenhagen and Cancun.
The Clean Development Mechanism (CDM) allows emission reduction (or emission removal) projects in developing countries to earn Certified Emission Reduction (CER) credits, each equivalent to one tonne of CO2. These CERs can be traded and sold, and used by industrialized countries to meet a part of their emission reduction targets under the Kyoto Protocol. The mechanism stimulates sustainable development and emission reductions, while giving industrialized countries some flexibility in how they meet their emission reduction limitation targets. Accepted projects must qualify through a rigorous and public registration and issuance process designed to ensure real, measurable and verifiable emission reductions that are additional to what would have occurred without the project. Between November 2004 and May 2009, the mechanism has registered 1,653 projects and is anticipated to currently produce CERs amounting to 303 106 tonnes of CO2 equivalent yearly. The mechanism is extremely interesting since it is the first global, environmental investment and credit scheme of its kind, providing a standardized emissions offset instrument. However the geographical distribution of the CDM projects is revealing very large differences in between developing countries since China, India, Brazil and South-Korea totalise 82 % of the CERs while Africa only represents 3,3% of the total.