Residential building energy conservation and avoided power plant emissions by urban and community trees in the United States

Urban trees and forests alter building energy use and associated emissions from power plants by shading buildings, cooling air temperatures and altering wind speeds around buildings. Field data on urban trees were combined with local urban/community tree and land cover maps, modeling of tree effects on building energy use and pollutant emissions, and state energy and pollutant costs to estimate tree effects on building energy use and associated pollutant emissions at the state to national level in the conterminous United States. Results reveal that trees and forests in urban/community areas in the conterminous United States annually reduce electricity use by 38.8 million MWh ($4.7 billion), heating use by 246 million MMBtus ($3.1 billion) and avoid thousands of tonnes of emissions of several pollutants valued at $3.9 billion per year. Average reduction in national residential energy use due to trees is 7.2percent. Specific designs to reduce energy use using urban trees could increase these values and further reduce energy use and improve air quality in the United States

CGIAR Research Program on Forests, Trees and Agroforestry - Plan of Work and Budget 2020

There were no significant changes in 2019 to FTA’s theory of change1. FTA plans all its work on the basis of its operational priorities. These, in turn, focusresearch towards major development demands and knowledge gaps, orienting FTA towards the implementation of the SDGs and other global commitments. Three operational priorities were added in 2020 (see list in Appendix 1) to better delineate pre-existing research areas addressing development bottlenecks needing dedicated investment and visibility: smallholder tree-crop commodities, tree seeds and seedlings delivery systems, and foresight. FTA organized in 2019, at the request of its ISC, a joint ISC-FTA workshop on impact assessment methods for the program. Based on the outcomes of this workshop FTA will, inter alia, revisit in 2020 its impact pathways and end of programme outcomes, and if need be, corresponding adjustments to the ToC of FTA and/or of its FPs will be made

CGIAR Research Program on Forests, Trees and Agroforestry - Plan of Work and Budget 2020

There were no significant changes in 2019 to FTA’s theory of change1. FTA plans all its work on the basis of its operational priorities. These, in turn, focusresearch towards major development demands and knowledge gaps, orienting FTA towards the implementation of the SDGs and other global commitments. Three operational priorities were added in 2020 (see list in Appendix 1) to better delineate pre-existing research areas addressing development bottlenecks needing dedicated investment and visibility: smallholder tree-crop commodities, tree seeds and seedlings delivery systems, and foresight. FTA organized in 2019, at the request of its ISC, a joint ISC-FTA workshop on impact assessment methods for the program. Based on the outcomes of this workshop FTA will, inter alia, revisit in 2020 its impact pathways and end of programme outcomes, and if need be, corresponding adjustments to the ToC of FTA and/or of its FPs will be made

Circular Regression Trees and Forests with an Application to Probabilistic Wind Direction Forecasting

While circular data occur in a wide range of scientific fields, the methodology for distributional modeling and probabilistic forecasting of circular response variables is rather limited. Most of the existing methods are built on the framework of generalized linear and additive models, which are often challenging to optimize and to interpret. Therefore, we suggest circular regression trees and random forests as an intuitive alternative approach that is relatively easy to fit. Building on previous ideas for trees modeling circular means, we suggest a distributional approach for both trees and forests yielding probabilistic forecasts based on the von Mises distribution. The resulting tree-based models simplify the estimation process by using the available covariates for partitioning the data into sufficiently homogeneous subgroups so that a simple von Mises distribution without further covariates can be fitted to the circular response in each subgroup. These circular regression trees are straightforward to interpret, can capture nonlinear effects and interactions, and automatically select the relevant covariates that are associated with either location and/or scale changes in the von Mises distribution. Combining an ensemble of circular regression trees to a circular regression forest yields a local adaptive likelihood estimator for the von Mises distribution that can regularize and smooth the covariate effects. The new methods are evaluated in a case study on probabilistic wind direction forecasting at two Austrian airports, considering other common approaches as a benchmark