Carbon Incentive Mechanisms and Land-Use Implications for Canadian Agriculture

This research examines effects of various factors on participation in agricultural tree plantations for economic, environmental, social and carbon-uptake purposes. Using survey data from 2000 mail surveys of Canadian farmers, a discrete choice random utility analysis is used to determine probability of farmers' participation and the corresponding mean willingness to accept a tree-planting program. Estimation results show that the required compensation for accepting a tree-planting program is higher than the compensation suggested by a normative approachEnvironmental Economics and Policy, Land Economics/Use,

Learning from the machine: interpreting machine learning algorithms for point- and extended- source classification

We investigate star-galaxy classification for astronomical surveys in the context of four methods enabling the interpretation of black-box machine learning systems. The first is outputting and exploring the decision boundaries as given by decision tree based methods, which enables the visualization of the classification categories. Secondly, we investigate how the Mutual Information based Transductive Feature Selection (MINT) algorithm can be used to perform feature pre-selection. If one would like to provide only a small number of input features to a machine learning classification algorithm, feature pre-selection provides a method to determine which of the many possible input properties should be selected. Third is the use of the tree-interpreter package to enable popular decision tree based ensemble methods to be opened, visualized, and understood. This is done by additional analysis of the tree based model, determining not only which features are important to the model, but how important a feature is for a particular classification given its value. Lastly, we use decision boundaries from the model to revise an already existing method of classification, essentially asking the tree based method where decision boundaries are best placed and defining a new classification method. We showcase these techniques by applying them to the problem of star-galaxy separation using data from the Sloan Digital Sky Survey (hereafter SDSS). We use the output of MINT and the ensemble methods to demonstrate how more complex decision boundaries improve star-galaxy classification accuracy over the standard SDSS frames approach (reducing misclassifications by up to $\approx33\%$). We then show how tree-interpreter can be used to explore how relevant each photometric feature is when making a classification on an object by object basis.Comment: 12 pages, 8 figures, 8 table

Does Inclusion of Landowners’ Non-Market Values Lower Costs of Creating Carbon Forest Sinks?

This research examines effects of various factors on farmer participation in agricultural tree plantations for economic, environmental, social and carbon-uptake purposes. Using data from a survey of Canadian agricultural landowners, a discrete choice random utility model is used to determine the probability of farmers’ participation and corresponding mean willingness to accept (WTA) compensation for a tree-planting program. WTA includes positive and negative nonmarket benefits to landowners from planting trees. Estimates of WTA are less than foregone agricultural rents, but average costs of creating carbon credits still exceed their projected value under a CO2-emissions trading scheme.Willingness to accept compensation for tree planting, afforestation, climate change

Are Agricultural Values a Reliable Guide in Determining Landowners’ Decisions to Create Carbon Forest Sinks?

This research examines the effects of various factors on farmer participation in agricultural tree plantations for economic, environmental, social and carbon-uptake purposes, and potential costs of sequestering carbon through afforestation in western Canada. Using data from a survey of landowners, a discrete choice random utility model is used to determine the probability of landowners’ participation and corresponding mean willingness to accept (WTA) compensation for a tree-planting program. WTA includes positive and negative benefits to landowners from planting trees, benefits not captured by foregone returns from agricultural activities on marginal land. Estimates of WTA are less than foregone returns, but even so average costs of creating carbon credits still exceed their projected value under a CO2-emissions trading scheme.Willingness to accept compensation for tree planting, afforestation, climate change

On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data

With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly-observed variables based on a small number of time-series measurements. In this paper, we introduce a methodology for variable-star classification, drawing from modern machine-learning techniques. We describe how to homogenize the information gleaned from light curves by selection and computation of real-numbered metrics ("feature"), detail methods to robustly estimate periodic light-curve features, introduce tree-ensemble methods for accurate variable star classification, and show how to rigorously evaluate the classification results using cross validation. On a 25-class data set of 1542 well-studied variable stars, we achieve a 22.8% overall classification error using the random forest classifier; this represents a 24% improvement over the best previous classifier on these data. This methodology is effective for identifying samples of specific science classes: for pulsational variables used in Milky Way tomography we obtain a discovery efficiency of 98.2% and for eclipsing systems we find an efficiency of 99.1%, both at 95% purity. We show that the random forest (RF) classifier is superior to other machine-learned methods in terms of accuracy, speed, and relative immunity to features with no useful class information; the RF classifier can also be used to estimate the importance of each feature in classification. Additionally, we present the first astronomical use of hierarchical classification methods to incorporate a known class taxonomy in the classifier, which further reduces the catastrophic error rate to 7.8%. Excluding low-amplitude sources, our overall error rate improves to 14%, with a catastrophic error rate of 3.5%.Comment: 23 pages, 9 figure