Reducing Deforestation and Trading Emissions: Economic Implications for the post-Kyoto Carbon Market

This paper quantitatively assesses the economic implications of crediting carbon abatement from reduced deforestation for the emissions market in 2020 by linking a numerical equilibrium model of the global carbon market with a dynamic partial equilibrium model of the forestry sector. We find that integrating avoided deforestation in international emissions trading considerably decreases the costs of post-Kyoto climate policy – even when accounting for conventional abatement options of developing countries under the CDM. At the same time, tropical rainforest regions receive substantial net revenues from exporting carbon-offset credits to the industrialized world. Moreover, reduced deforestation can increase environmental effectiveness by enabling industrialized countries to tighten their carbon constraints without increasing mitigation costs. Regarding uncertainties of this future carbon abatement option, we find both forestry transaction costs and deforestation baselines to play an important role for the post-Kyoto carbon market. --Climate Change,Kyoto Protocol,Emissions Trading,Deforestation

Special Issue On Estimation Of Baselines And Leakage In CarbonMitigation Forestry Projects

There is a growing acceptance that the environmentalbenefits of forests extend beyond traditional ecological benefits andinclude the mitigation of climate change. Interest in forestry mitigationactivities has led to the inclusion of forestry practices at the projectlevel in international agreements. Climate change activities place newdemands on participating institutions to set baselines, establishadditionality, determine leakage, ensure permanence, and monitor andverify a project's greenhouse gas benefits. These issues are common toboth forestry and other types of mitigation projects. They demandempirical evidence to establish conditions under which such projects canprovide sustained long term global benefits. This Special Issue reportson papers that experiment with a range of approaches based on empiricalevidence for the setting of baselines and estimation of leakage inprojects in developing Asia and Latin America

Empirical support for global integrated assessment modeling: Productivity trends and technological change in developing countries' agriculture and electric power sectors

Integrated assessment (IA) modeling of climate policy is increasingly global in nature, with models incorporating regional disaggregation. The existing empirical basis for IA modeling, however, largely arises from research on industrialized economies. Given the growing importance of developing countries in determining long-term global energy and carbon emissions trends, filling this gap with improved statistical information on developing countries' energy and carbon-emissions characteristics is an important priority for enhancing IA modeling. Earlier research at LBNL on this topic has focused on assembling and analyzing statistical data on productivity trends and technological change in the energy-intensive manufacturing sectors of five developing countries, India, Brazil, Mexico, Indonesia, and South Korea. The proposed work will extend this analysis to the agriculture and electric power sectors in India, South Korea, and two other developing countries. They will also examine the impact of alternative model specifications on estimates of productivity growth and technological change for each of the three sectors, and estimate the contribution of various capital inputs--imported vs. indigenous, rigid vs. malleable-- in contributing to productivity growth and technological change. The project has already produced a data resource on the manufacturing sector which is being shared with IA modelers. This will be extended to the agriculture and electric power sectors, which would also be made accessible to IA modeling groups seeking to enhance the empirical descriptions of developing country characteristics. The project will entail basic statistical and econometric analysis of productivity and energy trends in these developing country sectors, with parameter estimates also made available to modeling groups. The parameter estimates will be developed using alternative model specifications that could be directly utilized by the existing IAMs for the manufacturing, agriculture, and electric power sectors

Empirical support for global integrated assessment modeling: Productivity trends and technological change in developing countries' agriculture and electric power sectors

Integrated assessment (IA) modeling of climate policy is increasingly global in nature, with models incorporating regional disaggregation. The existing empirical basis for IA modeling, however, largely arises from research on industrialized economies. Given the growing importance of developing countries in determining long-term global energy and carbon emissions trends, filling this gap with improved statistical information on developing countries' energy and carbon-emissions characteristics is an important priority for enhancing IA modeling. Earlier research at LBNL on this topic has focused on assembling and analyzing statistical data on productivity trends and technological change in the energy-intensive manufacturing sectors of five developing countries, India, Brazil, Mexico, Indonesia, and South Korea. The proposed work will extend this analysis to the agriculture and electric power sectors in India, South Korea, and two other developing countries. They will also examine the impact of alternative model specifications on estimates of productivity growth and technological change for each of the three sectors, and estimate the contribution of various capital inputs--imported vs. indigenous, rigid vs. malleable-- in contributing to productivity growth and technological change. The project has already produced a data resource on the manufacturing sector which is being shared with IA modelers. This will be extended to the agriculture and electric power sectors, which would also be made accessible to IA modeling groups seeking to enhance the empirical descriptions of developing country characteristics. The project will entail basic statistical and econometric analysis of productivity and energy trends in these developing country sectors, with parameter estimates also made available to modeling groups. The parameter estimates will be developed using alternative model specifications that could be directly utilized by the existing IAMs for the manufacturing, agriculture, and electric power sectors