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Carbon Sequestration in Global Forests Under Different Carbon Price Regimes

Brent Sohngen and Roger Sedjo

Year: 2006
Volume: Multi-Greenhouse Gas Mitigation and Climate Policy
Number: Special Issue #3
DOI: 10.5547/ISSN0195-6574-EJ-VolSI2006-NoSI3-6
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Abstract:
This paper examines the potential role of carbon sequestration in forests under a range of exogenously chosen carbon price paths. The price paths were chosen to simulate several different climate change policies. The results indicate that global sequestration could range from 48�147 Pg C by 2105 for carbon prices ranging from $100 to more than $800 per t C by the end of the century. The timing of sequestration is found to be sensitive to the assumed carbon price path. Low initial carbon prices ($10 - $20 per t C in 2010) followed by rapid price increases, as might occur if policy makers try to stabilize future concentrations, suggest little, if any, sequestration during the next 20 years (-0.2 to 4.5 Pg C). If policy makers develop policies that support higher initial carbon prices, ranging from $75 to $100 per t C, 17 to 23 Pg C could be sequestered in forests over the next 20 years. Overall, our results indicate that forestry is not an efficient stopgap measure for long-term policy goals, but that it is instead an important long-term partner with other mitigation options.



Global Oil Export Destination Prediction: A Machine Learning Approach

Haiying Jia, Roar Adland, and Yuchen Wang

Year: 2021
Volume: Volume 42
Number: Number 4
DOI: 10.5547/01956574.42.4.hjia
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Abstract:
We use classification methods from machine learning to predict the destination of global crude oil exports by utilising micro-level crude oil shipment data that incorporates attributes related to the contract, cargo specifications, vessel specifications and macroeconomic conditions. The results show that micro-level information about the oil shipment such as quality and cargo size dominates in the destination prediction. We contribute to the academic literature by providing the first machine learning application to oil shipment data, and by providing new knowledge on the determinants of global crude oil flows. The machine-learning models used to predict the importing country can reach an accuracy of above 71% for the major oil exporting countries based on out-of-sample tests and outperform both naïve models and discrete regression models.





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