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%----------------------------------------------------------------------------------------
% PACKAGES AND THEMES
%----------------------------------------------------------------------------------------
%\documentclass[aspectratio=169,xcolor=dvipsnames]{beamer} \usetheme{SimpleDarkBlue} \usepackage{hyperref}
\documentclass[aspectratio=169,xcolor=table]{beamer} \usetheme{SimpleDarkBlue} \usepackage{hyperref}
\usepackage{graphicx} % Allows including images
\usepackage{chronology}
\usepackage{adjustbox}
\usepackage{framed}
\usepackage{tikz}
\usetikzlibrary{arrows,positioning,decorations.pathreplacing,shapes,shapes.geometric}
\usepackage{inputenc}
\usepackage{adjustbox}
\usepackage{bm}
\usepackage{amssymb}
\usepackage{booktabs} % Allows the use of \toprule, \midrule and \bottomrule in tables
\usepackage{textpos}
\usepackage[table]{xcolor}
%----------------------------------------------------------------------------------------
% TITLE PAGE
%----------------------------------------------------------------------------------------
%\title[Bitcoin Mining, a Coproduct of Oil Production]{Bitcoin Mining, a Coproduct of Oil Production} % The short title appears at the bottom of every slide, the full title is only on the title page
\title[Will Bitcoin Mining Create the Next Shale Boom?]{Will Bitcoin Mining Create the Next Shale Boom?} % The short title appears at the bottom of every slide, the full title is only on the title page
\subtitle{An Estimation of Long Run Regional Welfare Effects}
\author[Alex Gebben] {Alexander Gebben}
\institute[Colorado School of Mines] % Your institution as it will appear on the bottom of every slide, may be shorthand to save space
{
Department of Mineral and Energy Economics \\
Colorado School of Mines
\vskip 3pt
}
\date{November 3, 2022}
% \date{\today} % Date, can be changed to a custom date
%----------------------------------------------------------------------------------------
% PRESENTATION SLIDES
%----------------------------------------------------------------------------------------
\begin{document}
%------------------------------------------------
\section{Introduction \& Overview}
\input{./Sections/Intro.tex}
\input{./Sections/About_me.tex}
\input{./Sections/Other_Research.tex}
\section{Background}
\input{./Sections/Background.tex}
\input{./Sections/Key_Questions.tex}
\input{./Sections/Diagram.tex}
\input{./Sections/Mechanism.tex}
\section{Bitcoin Mining}
\input{./Sections/Bitcoin_Intro.tex}
\input{./Sections/Bitcoin_Incentives.tex}
\input{./Sections/Mining_Structure.tex}
\input{./Sections/Mining_Model.tex}
\input{./Sections/Mining_elast.tex}
%\input{./Sections/Reg_elas.tex}
\section{Oil Market}
\input{./Sections/Oil_Intro.tex}
\input{./Sections/Oil_Structure.tex}
\input{./Sections/Oil_Assumptions.tex}
\input{./Sections/Oil_Econometrics.tex}
\input{./Sections/Data.tex}
\section{Results}
\input{./Sections/Maps.tex}
\input{./Sections/Subsidy_Chart.tex}
\input{./Sections/Results.tex}
\section{Conclusion}
\input{./Sections/Conclusion.tex}
\input{./Sections/Questions.tex}
\section{Bibliography}
\input{./Sections/bib.tex}
\end{document}

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library(tidyverse)
library(lubridate)
library(tidyquant)
library(stargazer)
BTC <- tq_get("BTC-USD")
hash <- read_csv("BCHAIN-HRATE.csv") %>% rename(date=Date,hash=Value)
df <- hash %>% inner_join(BTC)
df$hash_dif <- c(NA,diff(df$hash))
df$hash_dif <- ifelse(df$hash_dif==0,0.0001,df$hash_dif)
df <- df %>% mutate(p_dif = close-open)
df$p <- (df$close+df$open)/2
df$p_dif <- ifelse(df$p_dif==0,0.0001,df$p_dif)
df$p_dif2 <- c(NA,diff(df$p))
df$down <- ifelse(0>df$p_dif2,1,0)
#lag_dis <- 30*12
df <- df %>% arrange(date)
lag_dis <- 30
df$pw <- df$p-lag(df$p,lag_dis)
df$pw <- ifelse(df$pw==0,0.0001,df$pw)
df$down <- ifelse(df$pw<0,1,0)
df$hw <- df$hash-lag(df$hash,lag_dis)
df$hw <- ifelse(df$hw==0,0.0001,df$hw)
library(lubridate)
df$month <- as.factor((month.abb[month(df$date)]))
df$year <- as.factor(year(df$date))
model1 <- (lm(log(hw)~lag(hash,360)+year+month+log(pw),data=df))
summary(model1)
acf(model1$resid)
pacf(model1$resid)
model1 <- (lm(log(hw)~as.factor(year(date))+as.factor(month(date))+log(pw),data=df))
summary(lm(hw~as.factor(year(date))+as.factor(month(date))+lag(hash,lag_dis)+log(pw),data=df))
summary(lm(hash~as.factor(year(date))+as.factor(month(date))+(hash,lag_dis)+log(pw),data=df))
stargazer
summary(model1)
rm(model1)
model1 <- (lm(log(hw)~as.factor(year(date))+as.factor(month(date))+log(pw),data=filter(df,year(df$date)>2012)))
summary(model1)
pacf(model1$resid)
acf(model1$resid)
plot(model1$resid)

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State,Flaring
Alaska,55219
California,1506
Colorado,22787
Louisiana,37907
Montana,29822
New_Mexico,200339
North_Dakota,924350
Texas,1242937
Utah,2078
Wyoming,148226
1 State Flaring
2 Alaska 55219
3 California 1506
4 Colorado 22787
5 Louisiana 37907
6 Montana 29822
7 New_Mexico 200339
8 North_Dakota 924350
9 Texas 1242937
10 Utah 2078
11 Wyoming 148226

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library(tidyverse)
library(readxl)
library(lubridate)
df <- read_excel("NG_PROD_SUM_A_EPG0_VGV_MMCF_A.xls",sheet=2,col_names=T,skip=2)
colnames(df) <-c("Date","National","Alaska","Alaska_onshore","Alaska_State","Arkansas","California","Calif_onshore","California_State","Federal_Offshore_Calif","Colorado","Offshore_Gulf_of_Mexico","Kansas","Louisiana","Louisiana_onshore","Louisiana_State","Montana","New_Mexico","North_Dakota","Ohio","Oklahoma","Pennsylvania","Texas","Texas_onshore","Texas_State_Offshore","Utah","West_Virginia","Wyoming")
df$Year <- year(df$Date)
df <- df %>% filter(Year>2013) %>% summarize(Alaska=sum(Alaska,na.rm=TRUE),Arkansas=sum(Arkansas,na.rm=TRUE),California=sum(California,na.rm=TRUE),Colorado=sum(Colorado,na.rm=TRUE),Kansas=sum(Kansas,na.rm=TRUE),Louisiana=sum(Louisiana,na.rm=TRUE),Montana=sum(Montana,na.rm=TRUE),New_Mexico=sum(New_Mexico,na.rm=TRUE),North_Dakota=sum(North_Dakota,na.rm=TRUE),Ohio=sum(Ohio,na.rm=TRUE),Oklahoma=sum(Oklahoma,na.rm=TRUE),Pennsylvania=sum(Pennsylvania,na.rm=TRUE),Texas=sum(Texas,na.rm=TRUE),Utah=sum(Utah,na.rm=TRUE),West_Virginia=sum(West_Virginia,na.rm=TRUE),Wyoming=sum(Wyoming,na.rm=TRUE))
temp1 <- colnames(df)
temp2 <- df %>% as.data.frame %>% t %>% c
graph_dat <- cbind(temp1,temp2)
colnames(graph_dat) <- c("State","Flaring")
graph_dat <- graph_dat %>% as_tibble %>% mutate(Flaring=as.numeric(Flaring)) %>% filter(Flaring>0)
#ggplot(data=graph_dat,aes(x="",y=Flaring,fill=State)) + geom_bar(stat="identity",width=1)+coord_polar("y",start=0)
ggplot(data=graph_dat,aes(x="",y=Flaring,fill=State))+geom_col(color="black") +coord_polar(theta="y")

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library(tidyverse)
library(readxl)
library(ggthemes)
library(lubridate)
#devtools::install_github("Mikata-Project/ggthemr")
#library(ggthemr)
df <- read_excel("NG_PROD_SUM_A_EPG0_VGV_MMCF_A.xls",sheet=2,col_names=T,skip=2)
colnames(df) <-c("Date","National","Alaska","Alaska_onshore","Alaska_State","Arkansas","California","Calif_onshore","California_State","Federal_Offshore_Calif","Colorado","Offshore_Gulf_of_Mexico","Kansas","Louisiana","Louisiana_onshore","Louisiana_State","Montana","New_Mexico","North_Dakota","Ohio","Oklahoma","Pennsylvania","Texas","Texas_onshore","Texas_State_Offshore","Utah","West_Virginia","Wyoming")
df$Year <- year(df$Date)
Alaska <- df %>% mutate(State="Alaska",volume=Alaska/National) %>% select(Year,State,volume)
Arkansas <- df %>% mutate(State="Arkansas",volume=Arkansas/National) %>% select(Year,State,volume)
California <- df %>% mutate(State="California",volume=California/National) %>% select(Year,State,volume)
Colorado <- df %>% mutate(State="Colorado",volume=Colorado/National) %>% select(Year,State,volume)
Kansas <- df %>% mutate(State="Kansas",volume=Kansas/National) %>% select(Year,State,volume)
Louisiana <- df %>% mutate(State="Louisiana",volume=Louisiana/National) %>% select(Year,State,volume)
Montana <- df %>% mutate(State="Montana",volume=Montana/National) %>% select(Year,State,volume)
New_Mexico <- df %>% mutate(State="New Mexico",volume=New_Mexico/National) %>% select(Year,State,volume)
North_Dakota <- df %>% mutate(State="North Dakota",volume=North_Dakota/National) %>% select(Year,State,volume)
Ohio <- df %>% mutate(State="Ohio",volume=Ohio/National) %>% select(Year,State,volume)
Oklahoma <- df %>% mutate(State="Oklahoma",volume=Oklahoma/National) %>% select(Year,State,volume)
Pennsylvania <- df %>% mutate(State="Pennsylvania",volume=Pennsylvania/National) %>% select(Year,State,volume)
Texas <- df %>% mutate(State="Texas",volume=Texas/National) %>% select(Year,State,volume)
Utah <- df %>% mutate(State="Utah",volume=Utah/National) %>% select(Year,State,volume)
West_Virginia <- df %>% mutate(State="West Virginia",volume=West_Virginia/National) %>% select(Year,State,volume)
Wyoming <- df %>% mutate(State="Wyoming",volume=Wyoming/National) %>% select(Year,State,volume)
######
df <- rbind(Alaska,Arkansas,California,Colorado,Kansas,Louisiana,Montana,New_Mexico,North_Dakota,Ohio,Oklahoma,Pennsylvania,Texas,Utah,West_Virginia,Wyoming) %>% filter(!is.na(volume))
Other <- df %>% group_by(Year) %>% summarize(State="Other",volume=1-sum(volume,na.rm=TRUE))
graph_dat <- full_join(Other,df)
graph_dat$volume <- 100*graph_dat$volume
stacked <- ggplot(data=graph_dat,aes(x=Year,y=volume,fill=State)) + geom_area(postion='stacked') + scale_x_continuous(n.breaks = 10)+ylab(NULL)
ggsave(stacked,file="flared.jpeg")

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\begin{frame}{About Me}
\begin{columns}[c] % The "c" option specifies centered vertical alignment while the "t" option is used for top vertical alignment
\column{.45\textwidth} % Left column and width
\textbf{Experience}
\begin{itemize}
\item PhD candidate in Mineral and\newline Energy Economics % at Colorado School of Mines
\item Mineral and Energy Economics, M.S.
\item Petroleum Engineering, B.S.
\item Worked as a data analyst for oil companies
\end{itemize}
\textbf{Personal}
\begin{itemize}
\item{Coding and Linux}
\item{Fly fishing}
\item{Camping}
\end{itemize}
\column{.5\textwidth} % Right column and width
\includegraphics[width=\columnwidth,height=\textheight,keepaspectratio]{./img/fish.jpg}
\tiny Greenback trout caught at Rocky Mountain National Park
\end{columns}
\end{frame}

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%------------------------------------------------
\begin{frame}{Background}
\begin{columns}[c] % The "c" option specifies centered vertical alignment while the "t" option is used for top vertical alignment
\column{.45\textwidth} % Left column and width
\textbf{Natural Gas Flaring}
\begin{itemize}
\item By-product of oil production
\item Remote and high gas/oil ratio (GOR) reservoirs
\item Pipelines are expensive
\item Free disposal
\end{itemize}
\column{.5\textwidth} % Right column and width
\includegraphics[width=\columnwidth,height=0.43\textheight,keepaspectratio]{./img/flaring.jpg}
\end{columns}
\begin{columns}[c] % The "c" option specifies centered vertical alignment while the "t" option is used for top vertical alignment
\pause
\column{.45\textwidth} % Left column and width
\textbf{Bitcoin Mining}
\begin{itemize}
\item Bitcoin miners use electricity to earn profit
\item No need to ship to market
\item Paying oil producers for waste gas
\item Contracts are about \(\frac{1}{3}\) wholesale price
\end{itemize}
\column{.5\textwidth} % Right column and width
\includegraphics[width=\columnwidth,height=0.43\textheight,keepaspectratio]{./img/Miner.jpg}
\end{columns}
\end{frame}
%------------------------------------------------
\begin{frame}{Relevance to Wyoming}
\begin{columns}[c] % The "c" option specifies centered vertical alignment while the "t" option is used for top vertical alignment
\column{.45\textwidth} % Left column and width
\textbf{Factors of the Wyoming Market}
\begin{itemize}
\item Increasing flaring rates
\item Regulatory concerns about emissions
\item Most Bitcoin miner friendly regulations in the U.S.
\item Cyclical low temperatures
\item On grid prices
\item Major basins with different GOR
\end{itemize}
\column{.5\textwidth} % Right column and width
Percentage of U.S. Flaring by State
\includegraphics[width=\columnwidth,height=0.85\textheight,keepaspectratio]{./img/flared.jpeg}
\end{columns}
\end{frame}

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%------------------------------------------------
\begin{frame}{Bitcoin Mining Rewards}
Two sources of income for Bitcoin miners, a block reward and a user fee.
\newline
\begin{center}
\textbf{Constant Miner Reward}
\begin{formal}
``The steady addition of a constant amount of new coins is analogous to gold miners expending resources to add gold to circulation. In our case, it is CPU time and electricity that is expended.''
\begin{list}{$-$}
\item{Satoshi Nakamoto}
\end{list}
\end{formal}
\end{center}
%Current reward is 6.5 Bitcoin
\begin{center}
\textbf{Variable User Fees}
\begin{formal}
%The incentive can also be funded with transaction fees. If the output value of a transaction is less than its input value, the difference is a transaction fee that is added to the incentive value of the block containing the transaction. Once a predetermined number of coins have entered circulation, the incentive can transition entirely to transaction fees and be completely inflation free.
``The incentive can also be funded with transaction fees\ldots Once a predetermined number of coins have entered circulation, the incentive can transition entirely to transaction fees.''
\begin{list}{$-$}
\item{Satoshi Nakamoto}
\end{list}
\end{formal}
\end{center}
%Currently ranges from 1\% to 2\% of reward, but has spiked to 25\%
\end{frame}

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\begin{frame}[plain]{Understanding the Bitcoin Mining Market}
\begin{center}
\includegraphics[width=\textwidth,height=0.85\textheight,keepaspectratio]{./img/Bitcoin_miners.jpg}
\end{center}
\end{frame}

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%------------------------------------------------
\begin{frame}{Bullet Points}
\begin{itemize}
\item Lorem ipsum dolor sit amet, consectetur adipiscing elit
\item Aliquam blandit faucibus nisi, sit amet dapibus enim tempus eu
\item Nulla commodo, erat quis gravida posuere, elit lacus lobortis est, quis porttitor odio mauris at libero
\item Nam cursus est eget velit posuere pellentesque
\item Vestibulum faucibus velit a augue condimentum quis convallis nulla gravida
\end{itemize}
\end{frame}

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%------------------------------------------------
\begin{frame}{Multiple Columns}
\begin{columns}[c] % The "c" option specifies centered vertical alignment while the "t" option is used for top vertical alignment
\column{.45\textwidth} % Left column and width
\textbf{Heading}
\begin{enumerate}
\item Statement
\item Explanation
\item Example
\end{enumerate}
\column{.5\textwidth} % Right column and width
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer lectus nisl, ultricies in feugiat rutrum, porttitor sit amet augue. Aliquam ut tortor mauris. Sed volutpat ante purus, quis accumsan dolor.
\end{columns}
\end{frame}

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%------------------------------------------------
\begin{frame}{Conclusion}
Bitcoin miners are starting to use flared natural gas as an affordable source of electricity. This will have long run impact on the oil market.
\begin{itemize}
% \item{Bitcoin mining can become a coproduct to oil production}
\item{The price of Bitcoin affects the price paid for the natural gas}
\item{A subsidy effect will persist even when Bitcoin prices are low}
\item{The profitability depends on geology}
\item{Bitcoin mining will disproportionately affect states like Wyoming and North Dakota}
\end{itemize}
\end{frame}

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%------------------------------------------------
\begin{frame}{Data}
\begin{enumerate}
\item{Enverus}
\begin{itemize}
\item{U.S. well data}
\item{Gas hookup company}
\item{SPUD date}
\end{itemize}
\item{Energy Information Administrations}
\begin{itemize}
\item{Population weighted cooling degree days (CDD)}
\item{Population weighted heating degree days (HDD)}
\item{Storage Levels}
\item{Price data}
\end{itemize}
\item{National Oceanic and Atmospheric Administration}
\begin{itemize}
\item{Disaster events}
\end{itemize}
\end{enumerate}
\end{frame}

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\begin{frame}{Market Linkage}
\tikzstyle{market} = [rectangle, rounded corners, text centered,draw=black,fill=orange!30,minimum width=3cm,minimum height=1cm]
\tikzstyle{inputmarket} = [rectangle, rounded corners, text centered,draw=black,fill=green!30,minimum width=3cm,minimum height=1cm]
\tikzstyle{output} = [rectangle, rounded corners, text centered,draw=black,fill=blue!30,minimum width=3cm,minimum height=1cm]
\tikzstyle{both} = [rectangle, text centered,draw=black,fill=red!30,minimum width=3cm,minimum height=1cm]
\tikzstyle{input} = [rectangle, text centered,draw=black,fill=blue!30]
\tikzstyle{arrow} = [thick,dash dot, ->,>=stealth]
\tikzstyle{arrowsolid} = [thick, ->,>=stealth]
\begin{tikzpicture}[node distance=2cm]
\node (Bitcoin)[market] {Bitcoin Mining};
\node (Oil)[market,left of=Bitcoin,xshift=10cm] {Drilling Wells};
\node (BTCP)[inputmarket,above of=Bitcoin] {Bitcoin Price};
\node (OILP)[inputmarket,above of=Oil,xshift=4cm] {Oil Price};
\node (GASP)[inputmarket,above of=Oil,xshift=.5cm] {Gas Price};
\node (BGASP)[inputmarket, left of=GASP,xshift=-2cm] {Bitcoin Price for Gas};
\node (Gas)[output,below of=Oil,xshift=-3.5cm] {Gas};
\node (Oil_out)[output,below of=Oil] {Oil};
\node (BTCOUT)[output,below of=Bitcoin] {Mining Hash};
\node (Elect)[both,below of=Gas] {Electricity Price};
\draw [arrow] (BGASP) -- (Oil);
\draw [arrow] (BTCP) -- (Bitcoin);
\draw [arrow] (OILP) -- (Oil);
\draw [arrow] (GASP) -- (Oil);
\draw [arrow] (Gas) -- (Elect);
\draw [arrow] (Elect) -- (Bitcoin);
\draw [arrow] (Bitcoin) -- (BGASP);
\draw [arrowsolid] (Bitcoin) -- (BTCOUT);
\draw [arrowsolid] (Oil) -- (Oil_out);
\draw [arrowsolid] (Oil) -- (Gas);
\end{tikzpicture}
\end{frame}

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%------------------------------------------------
\begin{frame}
\Huge{\centerline{The End}}
\end{frame}

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\begin{frame}[plain]{Micro Data Regression}
\begin{center}
\includegraphics[width=\textwidth,height=0.90\textheight,keepaspectratio]{./Micro/Micro_regression.pdf}
\end{center}
\end{frame}

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%------------------------------------------------
\begin{frame}{Figure}
Uncomment the code on this slide to include your own image from the same directory as the template .TeX file.
%\begin{figure}
%\includegraphics[width=0.8\linewidth]{test}
%\end{figure}
\end{frame}

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%------------------------------------------------
\begin{frame}[fragile] % Need to use the fragile option when verbatim is used in the slide
\frametitle{Citation}
An example of the \verb|\cite| command to cite within the presentation:\\~
This statement requires citation \cite{p1}.
\end{frame}

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%------------------------------------------------
\begin{frame}{Blocks of Highlighted Text}
In this slide, some important text will be \alert{highlighted} because it's important. Please, don't abuse it.
\begin{block}{Block}
Sample text
\end{block}
\begin{alertblock}{Alertblock}
Sample text in red box
\end{alertblock}
\begin{examples}
Sample text in green box. The title of the block is ``Examples".
\end{examples}
\end{frame}

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\begin{frame}[plain]
% Print the title page as the first slide
\titlepage
\begin{center}
{\includegraphics[height=1cm]{./img/Mines.jpg}}
\end{center}
\end{frame}

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%------------------------------------------------
\begin{frame}{Key Questions}
\textbf{What are the long term regional welfare changes that can be expected as Bitcoin mining becomes a coproduct of oil?}
\begin{enumerate}
\item{How much extra revenue will oil producers receive?}
\item{How will levels of drilling change?}
\item{How will the location of of drilling change?}
\end{enumerate}
\end{frame}
\begin{frame}{Strategy}
\begin{itemize}
\item{Identify the incentives}
\begin{itemize}
\item{Microeconomics model of Bitcoin mining}
\item{Upstream oil production model}
\end{itemize}
\pause
\item{Find regional variation of the incentives}
\begin{itemize}
\item{Collect well attribute data}
\item{Estimate of gas produced in oil wells}
\item{Identify wells that do not have a gas pipeline}
\end{itemize}
\pause
\item{Identify responses of oil producers in each region}
\begin{itemize}
\item{Estimate response to oil and gas price changes}
\item{Econometric estimates at the state-basin level of elasticity of supply}
\item{Forecast new wells \& oil production}
\end{itemize}
\pause
\item{\emph{Future Work:} Estimate other welfare changes }
\begin{itemize}
\item{Engineering based pollution estimates}
\item{Local externality counterfactual}
\end{itemize}
\end{itemize}
%\textbf{Results in counterfactual oil production by geologic basin and state, in key metrics.}
\end{frame}

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%------------------------------------------------
\begin{frame}{Realignment of Well Location}
\begin{columns}[c] % The "c" option specifies centered vertical alignment while the "t" option is used for top vertical alignment
\column{.5\textwidth}
\begin{center}
\textbf{Wells Drilled Since 2013}
\end{center}
\includegraphics[width=\textwidth,height=0.8\paperheight,keepaspectratio]{./img/wells.jpeg}
\column{.5\textwidth}
\begin{center}
\textbf{Subsidy Distribution in 2021}
\end{center}
\includegraphics[width=\textwidth,height=0.8\paperheight,keepaspectratio]{./img/subsidy.jpeg}
\end{columns}
\end{frame}

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\begin{frame}{Mechanisms}
When Bitcoin miners pay for flared gas, the change in income is equivalent to a subsidy on oil production.
\newline
\newline
\textbf{Direct Effects}
\begin{enumerate}
\item{More drilling}
\item{Different location of wells}
\item{Bitcoin miners clean the gas}
\end{enumerate}
\end{frame}
\begin{frame}{Outcomes}
\begin{columns}[c] % The "c" option specifies centered vertical alignment while the "t" option is used for top vertical alignment
\column{.5\textwidth} % Left column and width
\textbf{Certain Outcomes}
\begin{itemize}
\item{Increase in number of wells}
\item{Increase in profits}
\item{Decrease in price of oil}
\item{Non-decreasing price of gas}
\end{itemize}
\column{.5\textwidth} % Left column and width
\textbf{Uncertain Outcomes}
\begin{itemize}
\item{More pollution: More wells drilled}
\item{Less pollution: Gas is cleaned}
\item{Localized benefits: New location of wells}
\item{Localized costs: New location of wells}
\end{itemize}
\end{columns}
\end{frame}

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\begin{frame}{Bitcoin Mining Market Structure}
\begin{center}
% \includegraphics[width=\textwidth,height=0.8\textheight,keepaspectratio]{./img/SDMining.png}
\begin{adjustbox}{width=\textwidth,height=0.85\textheight,keepaspectratio}
\begin{tikzpicture}[domain=0:4]
\def\qone{4}
\draw[very thin,color=gray] (-0.1,-0.1) grid (11.9,6.7);
\draw[- >,thick] (-0.2,0) -- (12,0) node[right] {$Hash$};
\draw[- >,blue,thick] (5.2,0.6) parabola (9,5.5) node[left] {Supply} ;
\draw[blue,thick] (0,0.3) -- (5.2,0.6) ;
\fill[black] (5.2,0.6) circle (2pt);
\draw (5.2,1) node[above] {Functional shift};
\draw[red] (6,3.5) node[left] {Total Revenue (\$)};
\draw[red] (5.5,3) node[left] {\(y=m\cdot x \)};
\draw[cyan] (8,1) node[above] {MR=m};
\draw[cyan] (10.5,2) node[above] {\(MR=m\cdot\)(1+x-k)};
\draw[- >,dashed,thick] (9.4,0) -- (9.4,5.8) node[above] {Capital Constraint (k)};
\draw[- >,thick] (0,-0.2) -- (0,6.9) node[above] {$\frac{\$}{BTC}$};
\draw[- >,thick, color=red] (0,0)--(11.5,5.5) ;
\draw[thick, color=cyan] (0,1)--(9.4,1) ;
\draw[- >,thick, color=cyan] (9.4,1)--(11,2.0909) ;
\end{tikzpicture}
\end{adjustbox}
\end{center}
\end{frame}

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%------------------------------------------------
\begin{frame}{Bitcoin Mining}
\begin{columns}[t] % The "c" option specifies centered vertical alignment while the "t" option is used for top vertical alignment
\column{.45\textwidth} % Left column and width
\textbf{Rewards and Fees}
\begin{enumerate}
\item{Awarded by ``lottery''}
\item{Lottery completes \(\approx \) every 10 minutes}
\item{Risk pools smooth returns}
\end{enumerate}
\column{.5\textwidth} % Right column and width
\textbf{Costs}
\begin{enumerate}
\item{Fixed cost: Specialized computers }
\item{Marginal cost: Electricity}
\end{enumerate}
\end{columns}
\mbox{}
\newline
\newline
\textbf{Model of Single Period Returns}
\newline
\(Revenue=\text{Total Reward}\cdot \frac{\text{Tickets Owned}}{\text{All Tickets}}\)
\newline
\(Costs=\text{Electricity Used}\cdot\text{Price of Electricity}\)
\newline
\(\text{Tickets Owned}=f(\text{Computer Output},\text{Electricity Used})\)
\newline
\(\text{Total Reward}=\text{Mining Reward} + \text{User Fees}\)
\end{frame}

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\begin{frame}{Bitcoin Mining Market Structure}
\begin{block}{Revenue}
\begin{equation}
R_{i,t}=\eta_{t}\cdot\frac{q_{i}}{Q^{-i}_{t}}
\end{equation}
\end{block}
Where \(R_{i,t}\) is the revenue of miner \(i\in I\), at time t. \(q_{i}\) is the hash power of a miner i, and \(Q^{-i}_{t}\) is the hash of all other miners. The miner reward is \(\eta\)
\begin{block}{Revenue change from t to t+1}
\begin{align}
% R_{i,t+1}-R_{i,t}=\eta \cdot\frac{q_{i}}{Q^{-i}_{t+1}-Q^{-i}_{t}}
% \\
\%\Delta R=\%\Delta P_{btc}(1-\epsilon)
\end{align}
\end{block}
If the Bitcoin price elasticity of hash \(\epsilon\) is greater than one (elastic), than a decrease in price will \alert{increase profits} for the low marginal cost producers, like the miners using flared gas.
\end{frame}

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\begin{frame}{Oil and Gas Production: Assumptions }
\textbf{Assumptions}
\begin{enumerate}
\item{Engineer probabilistically knows the production path of each well }
\item{Marginal production choice is to drill a new well in a particular location}
\item{Future production is modeled by a best fit Arps model}
\item{Constant discount rate of 4.5\%}
\end{enumerate}
\end{frame}

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\begin{frame}{Oil and Gas Production: Econometric Model }
\begin{block}{Estimated model}
\begin{equation}
Q_{O,b,t}=\beta_{1} Q_{O,b,t-1}+\beta_{2} P_{WTI,t-1}+\beta_{3} P_{HH,t-1}+\beta_{4}\theta_{t}+\gamma_{t,b}+\epsilon_{b,t}
\end{equation}
\end{block}
Where \(Q_{O,b,t}\) is the net present oil produced in a basin state pair b, at time t, \(P_{WTI}\) is the West Texas Intermediate futures price, \(P_{HH}\) is the futures price of the Henry Hub spot market, \(\theta_{t}\) is a month dummy, and \(\gamma_{b,t}\) is a variable that represents the amount of monetary damage from natural disasters.\footnote{(Gilbert and Gavin, 2020)}\newline
\textbf{Three-stage least squares is used with instruments of:}
\begin{enumerate}
\item{Oil refinery shocks}
\item{Natural gas storage shocks}
\item{Regional population weighted cooling and heating degree days}
\item{Sum of the standard deviation of each instrument over the preceding 12 months}
\end{enumerate}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}{Oil and Gas Production: Model Sequence of Events}
\begin{center}
\begin{tikzpicture}
\draw [thick,decorate,decoration={brace,amplitude=6pt,raise=0pt}] (0.25,0.15) -- (3.25,0.15);
1.25
\node[align=center] at (2.4,0.55) {t};
\node[align=center] at (0,0.95) {\(Y_{t-1}\)};
\draw [thick,->] (0,.75) -- (0,0.15);
\node[align=center] at (2.25,.95) {\(Q_{energy}\)};
\draw [thick,->] (2.25,.75) -- (2.25,0.15);
\draw [thick,->] (2.25,-0.75) -- (2.25,-0.15);
\node[align=center] at (2.25,-0.95) {\(\theta_{oil}\)};
\node[align=center] at (3.15,0.55) {t+1};
\draw [thick,decorate,decoration={brace,amplitude=6pt,raise=0pt}] (2.5,0.15) -- (3.75,0.15);
\draw [thick,->] (4,0.85) -- (4,0.15);
\node[align=center] at (6.25,0.6) {Infectious period};
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\node[align=center] at (9.15,0.5) {Immune\\stage};
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\node[align=center] at (6.25,-0.85) {Symptomatic\\period};
\node[align=center] at (4.6,-1.3) {Onset of\\symptoms};
\end{tikzpicture}
\end{center}
\end{frame}

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\begin{frame}[plain]{Understanding Oil Production Decisions}
\begin{center}
\includegraphics[width=\textwidth,height=0.85\textheight,keepaspectratio]{./img/Well.jpg}
\end{center}
\end{frame}

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\begin{frame}{Oil and Gas Production }
\begin{block}{Well Profitability Before Bitcoin Mining}
\begin{equation}
\pi_{w}=\int_{t=0}^{\infty}\left[e^{-rt}\left(P_{o,t}\cdot q_{o,t}+\theta_{GL}\cdot P_{g,t}\cdot q_{g,t}-C(t)\right)\right]dt
\end{equation}
\end{block}
Where \(P_{t} \) is price of the oil (o) or gas (g) at time t, \(q_{t}\) is the volume of the product produced, C(t) is the cost function, and \(\theta_{GL}\) is a dummy that is one if a gas hookup line is attached to the well.
\begin{block}{Well Profitability Post Bitcoin Mining}
\begin{equation}
\pi_{w}=\int_{t=0}^{\infty}\left[e^{-rt}\left(P_{o,t}\cdot q_{o,t}+(\theta_{GL}\cdot P_{g,t}+\bm{\right|\theta_{GL}-1\left|\cdot P_{g,btc}})\cdot q_{g,t}-C(t)\right)\right]dt
\end{equation}
\end{block}
Allowing a Bitcoin miner to purchase gas is equivalent to a subsidy for oil production.
The size of the subsidy depends on the path of the GOR, the discount rate, decline rate of the well, and the maximum willingness to pay for gas of Bitcoin miners.
\end{frame}

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\begin{frame}{Other Research}
\begin{columns}[c] % The "c" option specifies centered vertical alignment while the "t" option is used for top vertical alignment
\column{.45\textwidth} % Left column and width
\textbf{Previous Topics of Research}
\begin{itemize}
\item Ground water with USDA grant \newline (loos et al.,2022) % \footnote{(loos et al.,2022)} %
\item Critical materials
\item Wind energy
\item CGE and electricity grids
\item Institutional economics in resource management
\end{itemize}
\column{.5\textwidth} % Right column and width
% \scalebox{.5}{\includegraphics{./img/SLV_Map.png} % scales both width and height by 0.5
\includegraphics[width=\columnwidth,height=\textheight,keepaspectratio]{./img/SLV_Map.png}
% \scalebox{.1}{\includegraphics{./img/SLV_Map.png}} % scales both width and height by 0.5
\end{columns}
\end{frame}

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\begin{frame}{Overview}
% Throughout your presentation, if you choose to use \section{} and \subsection{} commands, these will automatically be printed on this slide as an overview of your presentation
\tableofcontents
\end{frame}

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\begin{frame}[plain]{Questions}
\huge
\begin{center}
Thank you for your time
\end{center}
\end{frame}

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\begin{frame}[plain]{Regression Discontinuity}
\begin{center}
\includegraphics[width=\textwidth,height=0.90\textheight,keepaspectratio]{./img/SP_Letter1_edit.pdf}
\pause
\includegraphics[width=\textwidth,height=0.90\textheight,keepaspectratio]{./img/SP_Letter2_edit.pdf}
\end{center}
\end{frame}

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%------------------------------------------------
\begin{frame}{Elasticity Estimates}
Place Holder
\\
Regression Equation
\\
Regression Table
\end{frame}

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%------------------------------------------------
\begin{frame}{Results}
\begin{columns}[c] % The "c" option specifies centered vertical alignment while the "t" option is used for top vertical alignment
\column{.5\textwidth}
\tiny
\input{tables/reg.tex}\footnote{Year/Month fixed effects omitted}\footnote{All reported values are logged}
\column{.5\textwidth} % Left column and width
\normalsize
\tiny
\begin{block}{Long Run Effect on Oil Production}
\begin{equation}
\Delta Q_{O}= \Delta P_{WTI}\cdot\beta_{2}\left[\sum_{t=1}^{\infty}\left( \beta_{1}^{t}\right)+ 1\right]
\end{equation}
\end{block}
\normalsize
\center
%\(\beta_{1}=0.198\),\(\beta_{2}=0.143\)
\begin{equation*}
\Delta Q_{O}= 0.18\cdot \Delta P_{WTI}
\end{equation*}
\end{columns}
\end{frame}
\begin{frame}{Initial Estimates}
\include{tables/Basin_Analysis.tex}
\end{frame}

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\begin{frame}[plain]{Regression Discontinuity}
\begin{center}
\includegraphics[width=\textwidth,height=0.90\textheight,keepaspectratio]{./img/CHANGE_Disc.png}
\end{center}
\end{frame}

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%------------------------------------------------
\begin{frame}[plain]{Increasing Relevance to Wyoming Oil and Gas Production}
\begin{center}
\includegraphics[width=\textwidth,height=0.80\textheight,keepaspectratio]{./img/Subsidy_Bar_Chart.jpeg}
\footnotetext{Assumes market price is \$1.5 per MCF}
\end{center}
\end{frame}

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%------------------------------------------------
\begin{frame}{Table}
\begin{table}
\begin{tabular}{l l l}
\toprule
\textbf{Treatments} & \textbf{Response 1} & \textbf{Response 2} \\
\midrule
Treatment 1 & 0.0003262 & 0.562 \\
Treatment 2 & 0.0015681 & 0.910 \\
Treatment 3 & 0.0009271 & 0.296 \\
\bottomrule
\end{tabular}
\caption{Table caption}
\end{table}
\end{frame}

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%------------------------------------------------
\begin{frame}{Theorem}
\begin{theorem}[Mass--energy equivalence]
$E = mc^2$
\end{theorem}
\end{frame}

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%------------------------------------------------
\begin{frame}{Realignment of Well Location}
\begin{columns}[c] % The "c" option specifies centered vertical alignment while the "t" option is used for top vertical alignment
\column{.5\textwidth}
\begin{center}
\textbf{Wells Drilled Since 2013}
\end{center}
\includegraphics[width=\textwidth,height=0.8\paperheight,keepaspectratio]{./img/wells.jpeg}
\column{.5\textwidth}
\begin{center}
\textbf{Subsidy Distribution in 2021}
\end{center}
\includegraphics[width=\textwidth,height=0.8\paperheight,keepaspectratio]{./img/subsidy.jpeg}
\end{columns}
\end{frame}

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%------------------------------------------------
\begin{frame}{References}
% Beamer does not support BibTeX so references must be inserted manually as below
\footnotesize{
\begin{thebibliography}{99}
\bibitem[mason]{p1} Mason, Roberts (2018)
\newblock Price Elasticity of Supply and Productivity: An Analysis of Natural Gas Wells in Wyoming
\newblock \emph{The Energy Journal} 39(S1), 79 -- 101.
%-------
\bibitem[gilbert]{p2} Gilbert, Gavin (2020)
\newblock Drill-Bit Parity: Supply-Side Links in Oil and Gas Markets
\newblock \emph{Journal of the Association of Environmental and Resource Economists} 7(S4), 619 -- 658.
%-------
\bibitem[loos]{p3} Loos, Gebben et al. (2022)
\newblock Individual to collective adaptation through incremental change in Colorado groundwater governance
\newblock \emph{Frontiers in Environmental Science} (Sec. Environmental Economics and Management ) V10
\end{thebibliography}
}
\end{frame}

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% Settings
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% Table created by stargazer v.5.2.3 by Marek Hlavac, Social Policy Institute. E-mail: marek.hlavac at gmail.com
% Date and time: Sun, Dec 04, 2022 - 07:26:07 PM
\small
\begin{table}[!ht] \centering
\label{}
\begin{tabular}{@{\extracolsep{3pt}} cc>{\columncolor{gray!40}}ccc}
\\[-1.8ex]\hline
\hline \\[-1.8ex]
\textbf{State} & {\textbf{Basin}} & \cellcolor{white!30} \(\Delta \)\textbf{Q} (\%) & \textbf{Subsidy} (\%) & \textbf{Elasticity} (\%) \\
\hline \\[-1.8ex]
\rowcolors{2}{gray!10}{gray!40}
WY & Powder river & 16.15 & 8.67 & 186.25 \\
OH & Appalachian & 15.43 & 25.18 & 61.3 \\
ND & Williston & 13.63 & 7.78 & 175.18 \\
CO & Denver-Julesburg & 6.3 & 9.16 & 68.76 \\
\hline
US & & 2.11 & 3.89 & 54.33 \\
\hline
WV & Appalachian & 1.76 & 7.95 & 22.16 \\
TX & Central Basin & 0.66 & 0.43 & 153.45 \\
TX & Gulf Coast & 0.35 & 0.23 & 154.75 \\ %orgin Gulf Coast West
TX & Midland & 0.27 & 0.44 & 60.61 \\
TX & Delaware & 0.23 & 0.22 & 103.82 \\
NM & Delaware & 0.04 & 0.06 & 64.53 \\
TX & East Texas & 0.01 & 0.01 & 105.18 \\
PA & Appalachian & 0.01 & 0.1 & 5.3 \\
OK & Anadarko & 0 & 0 & 63.7 \\
\hline \\[-1.8ex]
\end{tabular}
\end{table}
\footnote{All values in percent terms}

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% Table created by stargazer v.5.2.3 by Marek Hlavac, Social Policy Institute. E-mail: marek.hlavac at gmail.com
% Date and time: Mon, Oct 31, 2022 - 04:40:50 PM
\begin{table}[!htbp] \centering
\caption{U.S. Elasticity of Oil Supply}
\label{}
\begin{tabular}{@{\extracolsep{1pt}}lc}
\\[-1.8ex]\hline
\hline \\[-1.8ex]
& \multicolumn{1}{c}{\textit{Dependent variable:}} \\
\cline{2-2}
\\[-1.8ex] & Net Present Oil at 5\% (log) \\
\hline \\[-1.8ex]
Lag of Net Present Oil& 0.198$^{***}$ \\
& (0.060) \\
WTI Price & 0.143$^{**}$ \\
& (0.071) \\
Henry Hub Price & 0.151$^{***}$ \\
& (0.054) \\
Constant & 14.862$^{***}$ \\
& (1.145) \\
& \\
\hline \\[-1.8ex]
Observations & 295 \\
R$^{2}$ & 0.804 \\
Adjusted R$^{2}$ & 0.774 \\
Residual Std. Error & 0.173 (df = 255) \\
F Statistic & 26.828$^{***}$ (df = 39; 255) \\
\hline
\hline \\[-1.8ex]
\textit{Note: Preliminary results} & \multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\
\end{tabular}
\end{table}