Selected Publications

This exploratory study analyses voluntary disclosures of greenhouse gas (GHG) emissions to the Carbon Disclosure Project (CDP) by the largest emitters in the US utilities sector. We compare the amounts that firms voluntarily disclose to the CDP to mandatory amounts that should be less than them and find that the opposite is true for some firms. Examining the comparability of the CDP amounts, we find that firms calculate them using different reporting boundaries and accounting methodologies. We analyse how the choice of boundary affects emissions and find that it results in differences in emissions amounts. We also find that most firms do not have their reported amounts verified by third parties. Our analysis indicates that the amounts that some of the largest emitters disclose to the CDP are unreliable and incomparable. Our results suggest caution in relying on voluntary GHG emissions disclosures.
Social and Environmental Accountability Journal, 2018

Recent Publications

. Reliability and Comparability of GHG Disclosures to the CDP by US Electric Utilities. Social and Environmental Accountability Journal, 2018.


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Intermediate accounting Subject to revision Contact information Prerequisites Course format Course materials Learning objectives Schedule Grading In-class participation 100 points Quizzes 400 points Exams 500 points Policies Intermediate accounting Subject to revision Contact information Instructor: Elizabeth Stanny, PhD Email: Office: Wine Center 1016 Office phone: 707-664-4287 Office hours: Mondays 10:30 to 11:30 AM and 4 to 6 PM by appointment Prerequisites BUS 230A Proficiency in spreadsheet software Course format This is a hybrid course.


Steps to participate Email me to request a company (you will be assigned one ticker at a time) You will be added to the google teamdrive You must complete and receive full credit for one ticker before requesting another Email me when you complete a ticker and I’ll get back to you within 24 hours (during the week) Instructions on how to complete the asssignment are posted on the drive Scoring 30 points for each complete and correct ticker (7 years of data) Background Reading New York Sues Exxon Mobil, Saying It Deceived Shareholders on Climate Change (NYT, Oct 24, 2018)


library(pacman) p_load(tidyverse, scales, ggthemes, RColorBrewer, plotly) energy_cons <- read_csv("") energy_cons %>% glimpse ## Observations: 8,316 ## Variables: 6 ## $ MSN <chr> "CKTCEUS", "CKTCEUS", "CKTCEUS", "CKTCEUS", "CKTCEU… ## $ YYYYMM <dbl> 197301, 197302, 197303, 197304, 197305, 197306, 197… ## $ Value <dbl> 108.289, 97.698, 97.366, 93.084, 94.346, 97.757, 10… ## $ Column_Order <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … ## $ Description <chr> "Coal, Including Coal Coke Net Imports, CO2 Emissio… ## $ Unit <chr> "Million Metric Tons of Carbon Dioxide", "Million M… energy_cons_totals <- energy_cons %>% mutate(YYYYMMDD = paste0(YYYYMM, "01"), #get into format so can use lubridate date = lubridate::ymd(YYYYMMDD), year = lubridate::year(date), description = factor(Description), description = case_when( grepl("Coal", description) ~ "Coal", grepl("Natural Gas", description) ~ "Natural Gas", grepl("Petroleum", description) ~ "Petroleum", # grepl("Jet Fuel", description) ~ "Jet Fuel", grepl("Motor Gasoline", description) ~ "Motor Gasoline", grepl("Total Energy", description) ~ "Total Energy", TRUE ~ "Other") ) %>% group_by(year, description) %>% filter(!


Finnacial Accounting Subject to revision Contact information Course format Course materials Schedule Grading Quizzes 600 points Exam 400 points Policies Finnacial Accounting Subject to revision Contact information Instructor: Elizabeth Stanny, PhD Email: Office: Wine Center 1016 Office phone: 707-664-4287 Office hours: every weekday at 1 PM online on Zoom Course format This is an online course. Course site: stanny.


library('pacman') p_load(tidyverse, readxl, purrr) # download xlsx files from moodle put in List file <- list.files(pattern = "*.xlsx") # columns (course, group) to add to each spreadsheet course <- c("fa-18-IntAcctA", "fa-18-IntAcctA", "fa-18-AdvAcct") group <- c("12PM", "2PM", "4PM") files_in <- function(file, course, group){ file %>% read_xlsx() %>% select(firstname = `First name`, lastname = `Last name`, username = `ID number`, email = `Email address`) %>% mutate(course1 = course, group1 = group) %>% select(firstname, lastname, username, course1, group1, email) } l <- list(file, course, group ) pmap_dfr(l, files_in) %>% write_csv("enroll-fa18.