Reduced emissions from deforestation and forest degradation (REDD): a climate change mitigation strategy on a critical track

<p>Abstract</p> <p>Background</p> <p>Following recent discussions, there is hope that a mechanism for reduction of emissions from deforestation and forest degradation (REDD) will be agreed by the Parties of the UNFCCC at their 15th meeting in Copenhagen in 2009 as an eligible action to prevent climate changes and global warming in post-2012 commitment periods. Countries introducing a REDD-regime in order to generate benefits need to implement sound monitoring and reporting systems and specify the associated uncertainties. The principle of conservativeness addresses the problem of estimation errors and requests the reporting of reliable minimum estimates (RME). Here the potential to generate benefits from applying a REDD-regime is proposed with reference to sampling and non-sampling errors that influence the reliability of estimated activity data and emission factors.</p> <p>Results</p> <p>A framework for calculating carbon benefits by including assessment errors is developed. Theoretical, sample based considerations as well as a simulation study for five selected countries with low to high deforestation and degradation rates show that even small assessment errors (5% and less) may outweigh successful efforts to reduce deforestation and degradation.</p> <p>Conclusion</p> <p>The generation of benefits from REDD is possible only in situations where assessment errors are carefully controlled.</p

Estimates of live-tree carbon stores in the Pacific Northwest are sensitive to model selection

<p>Abstract</p> <p>Background</p> <p>Estimates of live-tree carbon stores are influenced by numerous uncertainties. One of them is model-selection uncertainty: one has to choose among multiple empirical equations and conversion factors that can be plausibly justified as locally applicable to calculate the carbon store from inventory measurements such as tree height and diameter at breast height (DBH). Here we quantify the model-selection uncertainty for the five most numerous tree species in six counties of northwest Oregon, USA.</p> <p>Results</p> <p>The results of our study demonstrate that model-selection error may introduce 20 to 40% uncertainty into a live-tree carbon estimate, possibly making this form of error the largest source of uncertainty in estimation of live-tree carbon stores. The effect of model selection could be even greater if models are applied beyond the height and DBH ranges for which they were developed.</p> <p>Conclusions</p> <p>Model-selection uncertainty is potentially large enough that it could limit the ability to track forest carbon with the precision and accuracy required by carbon accounting protocols. Without local validation based on detailed measurements of usually destructively sampled trees, it is very difficult to choose the best model when there are several available. Our analysis suggests that considering tree form in equation selection may better match trees to existing equations and that substantial gaps exist, in terms of both species and diameter ranges, that are ripe for new model-building effort.</p

Bank Competition, Lending Relationships and Firm Default Risk : An Investigation of Italian SMEs

Building on the literature relating to bank market structure and on the research analysing costs and benefits of concentrated credit relationships, this article aims to empirically assess whether, and to what extent, local credit market concentration impacts on SME default riskiness – making this impact conditional on the closeness of lending relationships. Controlling for inertia, unobserved heterogeneity and endogeneity, we find that bank market concentration positively and significantly affects SME default riskiness when these firms borrow heavily from their main bank and have few credit relationships with other intermediaries. Furthermore, according to our evidence, the influence of credit market concentration on firm default probability is greater as the duration of (close) credit relationships lengthens.Peer reviewe