Abstract Transportation network companies (TNCs), such as Uber and Lyft, have pledged to fully electrify their ridesourcing vehicle fleets by 2030 in the United States. In this paper, Aniruddh Mohan, Matthew Bruchon, Jeremy Michalek, and Parth Vaishnav introduce AgentX, a novel agent-based model built in Julia for simulating ridesourcing services with high geospatial and temporal resolution. The
AbstractSarah Meier, Robert J.R. Elliott and Eric Strobl estimate the impact of wildfires on the growth rate of gross domestic product (GDP) and employment of regional economies in Southern Europe from 2011 to 2018. To this end the authors match Eurostat economic data with geospatial burned area perimeters based on satellite imagery for 233 Nomenclature of Territorial Units for Statistics (NUTS)
Abstract Purpose: This study aims to provide and illustrate the application of a framework for conducting techno-economic analyses (TEA) of early-stage designs for net-zero water and energy, single-family homes that meet affordable housing criteria in diverse locations. Design/methodology/approach: The framework is developed and applied in a case example of a TEA of four designs for achieving net
AbstractUsing machine learning methods in a quasi-experimental setting, Marica Valente studies the heterogeneous effects of introducing waste prices – unit prices on household unsorted waste disposal – on waste demands and municipal costs. Using a unique panel of Italian municipalities with large variation in prices and observables, she shows that waste demands are nonlinear. The author finds
Abstract[A meta-analysis by Kent Kovacs, Grant West, David J. Nowak and Robert G. Haight] uses 21 hedonic property value studies and 157 unique observations to study the influence of tree cover on the value of homes in the United States. The authors construct elasticity estimates of the percentage change in home value for a 1% change in the percentage of tree cover around a home. Cluster weighted
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