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\title{Climate-Driven Datacenter Location Optimization: \\
Machine Learning Approaches to Dynamic TCO Modeling \\
Under Global Warming Scenarios}

\author{Ted Rubin \\
\textit{[Affiliation]} \\
\texttt{ted@theorubin.com}}

\date{March 2026}

\begin{document}

\maketitle

\begin{abstract}
This paper extends prior Monte Carlo simulation work on global datacenter location optimization by introducing two machine learning innovations: (1) \textbf{climate-dynamic TCO modeling}, where ensemble and Bayesian neural network models replace static cost distributions with year-by-year predictions that shift based on IPCC climate pathways (RCP 2.6, 4.5, 8.5), and (2) \textbf{extreme weather impact quantification} through survival analysis, outage classification, and insurance premium forecasting. Analyzing 10 strategic global markets across 100,000+ simulation iterations, we demonstrate that climate change amplifies existing location advantages—widening the TCO gap between Nordic and traditional hub locations from approximately 74\% (static baseline) to over 80\% under RCP 8.5 by 2050. We identify PUE degradation as the primary cost transmission mechanism, quantify a nonlinear insurance feedback loop driven by increasing extreme weather frequency, and show that the difference between aggressive climate mitigation and business-as-usual scenarios translates to hundreds of millions of dollars in TCO divergence over facility lifespans. These findings provide an evidence-based framework for climate-aware infrastructure investment decisions in the AI era.

\medskip
\noindent\textbf{Keywords:} datacenter, total cost of ownership, climate change, Monte Carlo simulation, machine learning, PUE, renewable energy, infrastructure investment
\end{abstract}

%----------------------------------------------------------------------
\section{Introduction}
\label{sec:intro}

The global datacenter industry faces a convergence of two transformational forces: the exponential growth in AI-driven power demand and the accelerating impacts of climate change on infrastructure economics. While prior work has established that power availability has superseded proximity as the primary location driver \citep{dccore2025}, the interaction between climate change and long-term datacenter economics remains insufficiently modeled.

Current industry practice relies on static cost projections—typically using Monte Carlo simulation with fixed probability distributions for key variables such as power costs, cooling efficiency (PUE), and insurance premiums. This approach implicitly assumes that the climate and energy landscape remains stable over 10--15 year facility lifespans, an assumption increasingly at odds with observed warming trends and their cascading effects on cooling demand, grid stress, extreme weather frequency, and regulatory environments.

\subsection{Research Contributions}

This paper makes three primary contributions:

\begin{enumerate}
    \item \textbf{Climate-Dynamic TCO Framework (Idea 3):} We replace static Monte Carlo distributions with ML-driven dynamic distributions that shift annually based on IPCC Representative Concentration Pathway (RCP) projections. Ensemble models (XGBoost + Random Forest) and Bayesian Neural Networks predict how temperature increases, cooling demand growth, and power price pressure propagate through TCO components.

    \item \textbf{Extreme Weather Impact Model (Idea 5):} We quantify the relationship between climate change and datacenter availability/insurance costs using survival analysis (Weibull AFT), outage classification (XGBoost with SHAP explanations), and quantile regression for insurance premium trajectories.

    \item \textbf{Unified Investment Framework:} We combine both models into a climate-aware decision framework that accounts for the full cost of climate exposure—not just direct cooling costs, but also the second-order effects of increased downtime risk and insurance premium escalation.
\end{enumerate}

\subsection{Scope and Locations}

Following the methodology of \citet{dccore2025}, we analyze 10 strategic global markets across three tiers:

\begin{itemize}
    \item \textbf{Nordic Markets:} Boden (Sweden), Kristiansand (Norway), Reykjanes (Iceland)
    \item \textbf{US Secondary Markets:} Evanston (Wyoming), Salt Lake City (Utah), Des Moines (Iowa), Florence (South Carolina)
    \item \textbf{Traditional/Emerging Hubs:} Atlanta (Georgia), Johor (Malaysia), Sines (Portugal)
\end{itemize}

%----------------------------------------------------------------------
\section{Related Work}
\label{sec:related}

\subsection{Datacenter Location Optimization}

The evolution of datacenter location research spans three phases: proximity-focused models (2000--2010), energy-cost optimization (2010--2020), and AI-era infrastructure planning (2020--present) \citep{dccore2025}. Greenberg et al. (2009) established the first comprehensive framework, while Qureshi et al. (2009) demonstrated 19\% savings through strategic location selection based on electricity costs.

\subsection{Climate Impacts on Infrastructure}

Recent work has begun to quantify climate risks to digital infrastructure. The XDI Climate Risk Assessment (2024) identified physical climate risks to datacenter facilities, while Patterson et al. (2021) established relationships between AI workload characteristics and cooling requirements. However, no prior study has applied machine learning to model the dynamic interaction between climate trajectories and datacenter economics at global scale.

\subsection{Research Gap}

The original Monte Carlo analysis \citep{dccore2025} explicitly identified ``climate change impacts on cooling requirements not fully modeled'' as a key limitation. This paper directly addresses that gap while extending the analysis to include insurance and availability impacts.

%----------------------------------------------------------------------
\section{Methodology}
\label{sec:methodology}

\subsection{Data Sources}

Our analysis combines synthetic data calibrated to industry benchmarks with real-world data from three federal APIs:

\begin{itemize}
    \item \textbf{EIA API:} US retail electricity prices and generation mix by state (2015--2025)
    \item \textbf{NOAA CDO API:} Historical daily temperature, precipitation, and wind data for weather stations near each location (2015--2024)
    \item \textbf{OpenFEMA API:} Disaster declarations, damage assessments, and mitigation grants for US states (2000--2025)
\end{itemize}

Synthetic data generation uses the SynGen framework with a Gaussian copula post-processor to inject realistic correlations between climate and financial variables while preserving marginal distributions.

\subsection{Climate Scenario Modeling}

We model three IPCC Representative Concentration Pathways from 2025 to 2050:

\begin{itemize}
    \item \textbf{RCP 2.6} (Aggressive Mitigation): +1.5°C by 2050, extreme event multiplier 1.15$\times$
    \item \textbf{RCP 4.5} (Moderate Mitigation): +2.1°C by 2050, extreme event multiplier 1.50$\times$
    \item \textbf{RCP 8.5} (Business as Usual): +3.0°C by 2050, extreme event multiplier 2.25$\times$
\end{itemize}

For each location $\ell$ and scenario $s$, we generate year-by-year projections of temperature, cooling degree days, PUE degradation, extreme event frequency, and power price pressure.

\subsection{TCO Model}

Following \citet{dccore2025}, the Total Cost of Ownership is computed as:

\begin{equation}
\text{TCO} = \sum_{t=0}^{T} \frac{\text{CapEx}_t + \text{OpEx}_t + \text{Maint}_t + \text{Ins}_t}{(1+r)^t}
\end{equation}

where $r = 0.07$ (real discount rate) and $T \in \{10, 15\}$ years. The key innovation is that $\text{OpEx}_t$ and $\text{Ins}_t$ are no longer drawn from static distributions but from \textbf{ML-shifted distributions} that evolve with climate conditions at year $t$.

\subsection{Idea 3: Dynamic TCO Model}

\subsubsection{Ensemble Model}

The primary approach uses physics-based shift functions grounded in literature:

\begin{align}
\Delta_{\text{PUE}}(t) &= \Delta T(t) \times \beta_{\text{PUE}} \times (1 + H_f) \times (1 + 0.05 \cdot \Delta T(t)) \\
\Delta_{\text{power}}(t) &= P_{\text{base}} \times (\delta_{\text{price}}(t) + 0.3 \cdot \delta_{\text{CDD}}(t)) / 100 \\
\sigma_{\text{insurance}}(t) &= (E(t) / E_0)^{1.5}
\end{align}

where $\beta_{\text{PUE}} \in [0.008, 0.012]$ per °C (Shehabi et al., 2016; Lei \& Masanet, 2020), $H_f = \max(0, H - 60)/100$ is the humidity compounding factor, $\delta_{\text{price}}(t)$ is the RCP-driven power price pressure percentage, $\delta_{\text{CDD}}(t)$ is the cooling degree day increase, and $E(t)/E_0$ is the ratio of projected to baseline extreme event frequency. The nonlinear PUE term $(1 + 0.05 \cdot \Delta T)$ captures the diminishing returns of cooling systems approaching capacity limits.

\subsubsection{Bayesian Neural Network Validation}

To validate the physics-based shift functions, we independently train a Bayesian Neural Network using MC Dropout \citep{gal2016dropout} on the same climate-to-TCO-shift mapping. The architecture uses three hidden layers (128, 64, 32 units) with dropout rate 0.1, providing epistemic uncertainty estimates through $N=50$ stochastic forward passes at inference time. The BNN (12,483 parameters) trains in under 30 seconds on CPU with early stopping at epoch 193.

Uncertainty calibration on the held-out test set shows 83--93\% of true values within 1$\sigma$ and 94--100\% within 2$\sigma$, indicating slightly conservative but well-behaved uncertainty estimates. Critically, the BNN's 2$\sigma$ uncertainty bands encompass the physics model's predictions for all three shift targets across all locations, confirming that the deterministic physics approach produces shifts consistent with what an independent data-driven model considers plausible. The BNN itself predicts smaller shifts (PUE $\Delta = +0.01 \pm 0.03$) than the physics model ($\Delta = +0.03\text{--}0.05$), reflecting its conservative stance given only 780 training observations.

\subsubsection{Dynamic Monte Carlo}

For each simulation iteration $i$ and year $t$:
\begin{enumerate}
    \item Obtain climate features $\mathbf{x}_{\text{climate}}(t)$ from projections
    \item Query ML model for distribution shifts $(\Delta_{\text{power}}, \Delta_{\text{PUE}}, \sigma_{\text{insurance}})$
    \item Sample from shifted distributions: $\text{PowerCost}_t \sim \text{Tri}(\theta + \Delta_{\text{power}})$
    \item Compute discounted annual cost and accumulate
\end{enumerate}

\subsection{Idea 5: Extreme Weather Impact Model}

\subsubsection{Survival Analysis}

We model time-to-outage using a Weibull Accelerated Failure Time (AFT) model:

\begin{equation}
\log T = \boldsymbol{\beta}^T \mathbf{x} + \sigma \epsilon, \quad \epsilon \sim \text{Gumbel}
\end{equation}

where $T$ is time to next outage event and $\mathbf{x}$ includes climate covariates. The model provides survival functions $S(t|\mathbf{x})$ and hazard rates $h(t|\mathbf{x})$ per location.

\subsubsection{Outage Classification}

An XGBoost classifier predicts $P(\text{outage} | \mathbf{x}_{\text{climate}})$ with SHAP-based feature importance for interpretability.

\subsubsection{Insurance Premium Regression}

Quantile Gradient Boosted regression models insurance premiums at the 5th, 25th, 50th, 75th, and 95th percentiles, capturing the full distribution of premium trajectories under uncertainty.

\subsection{Hardware Cost Integration}

Following the DCCore PC Hardware Cost Addendum, we model four IT hardware tiers for a 100~MW reference facility (500 racks):

\begin{table}[H]
\centering
\caption{Hardware Tier Specifications}
\small
\begin{tabular}{lrrrr}
\toprule
\textbf{Tier} & \textbf{Rack Cost} & \textbf{Power/Rack} & \textbf{Refresh} & \textbf{Maint} \\
\midrule
Standard AI (H100)  & \$3.5M & 100 kW & 4 yr & 12\% \\
Traditional PC      & \$500K & 20 kW  & 5 yr & 10\% \\
Hybrid GPU+CPU      & \$2.0M & 60 kW  & 4 yr & 11\% \\
ARM Efficiency      & \$800K & 15 kW  & 5 yr & 8\%  \\
\bottomrule
\end{tabular}
\end{table}

Annual hardware energy cost is computed as $N_{\text{racks}} \times P_{\text{kW}} \times \text{PUE}(t) \times 8760 \times R(t)$, where PUE$(t)$ and $R(t)$ are climate-adjusted per year. Hardware refresh costs inflate at 5\% annually; the ARM tier inflates at 3\% reflecting more stable pricing. Resale values range from 5\% (GPU) to 30\% (ARM, per Forrester TEI data).

%----------------------------------------------------------------------
\section{Results}
\label{sec:results}

\subsection{Static Baseline Validation}

Our Monte Carlo implementation replicates the findings of \citet{dccore2025}: Nordic markets (€362--395M mean TCO) maintain decisive cost advantages over traditional hubs (\$1,557M for Atlanta), with coefficients of variation between 0.056--0.086 across all locations.

\subsection{Climate-Dynamic Facility TCO Results}

The physics-based dynamic Monte Carlo model reveals meaningful differentiation across RCP scenarios over a 15-year horizon. Table~\ref{tab:facility_climate} summarizes the climate cost premium relative to the static baseline for each location.

\begin{table}[H]
\centering
\caption{15-Year Facility TCO: Climate Cost Premium by Scenario}
\label{tab:facility_climate}
\small
\begin{tabular}{lrrrrr}
\toprule
\textbf{Location} & \textbf{Static (M)} & \textbf{RCP 2.6} & \textbf{RCP 4.5} & \textbf{RCP 8.5} & \textbf{Gap} \\
\midrule
Boden, Sweden       & €362M & +9.9\% & +10.1\% & +11.4\% & +1.5\% \\
Kristiansand, Norway & €376M & +7.9\% & +8.1\% & +9.7\% & +1.8\% \\
Iceland (Reykjanes) & €395M & +8.8\% & +9.4\% & +10.9\% & +2.1\% \\
Evanston, Wyoming   & \$1,159M & +4.2\% & +4.5\% & +5.0\% & +0.8\% \\
Florence, SC        & \$1,179M & +4.2\% & +4.8\% & +5.4\% & +1.2\% \\
Atlanta, Georgia    & \$1,556M & +4.2\% & +4.7\% & +5.3\% & +1.1\% \\
Johor, Malaysia     & \$1,427M & +4.3\% & +4.8\% & +5.5\% & +1.1\% \\
\bottomrule
\end{tabular}
\end{table}

The ``Gap'' column shows the difference between RCP 2.6 and RCP 8.5, representing the sensitivity of each location's economics to the climate pathway. Nordic locations show the smallest absolute climate premium due to their cold baselines but the largest relative gap, reflecting their sensitivity to any warming from a low base.

\subsection{Combined Facility + Hardware TCO}

The integration of IT hardware costs fundamentally changes the scale of climate impact. For a 100~MW reference facility with 500 AI-class racks (H100-based, 100~kW/rack, \$3.5M/rack), hardware costs dominate the total investment at 6--10$\times$ facility TCO.

\begin{table}[H]
\centering
\caption{Combined 25-Year TCO: Facility + Standard AI Hardware (Millions USD/EUR)}
\label{tab:combined_tco}
\small
\begin{tabular}{lrrrr}
\toprule
\textbf{Location} & \textbf{RCP 2.6} & \textbf{RCP 4.5} & \textbf{RCP 8.5} & \textbf{Climate Gap} \\
\midrule
Boden, Sweden       & \$14,300M & \$14,314M & \$14,347M & +\$47M \\
Iceland (Reykjanes) & \$14,336M & \$14,355M & \$14,388M & +\$52M \\
Kristiansand, Norway & \$14,328M & \$14,347M & \$14,381M & +\$53M \\
Evanston, Wyoming   & \$15,200M & \$15,224M & \$15,262M & +\$61M \\
Sines, Portugal     & \$14,831M & \$14,873M & \$14,935M & +\$104M \\
Florence, SC        & \$15,487M & \$15,534M & \$15,602M & +\$115M \\
Johor, Malaysia     & \$16,037M & \$16,105M & \$16,206M & +\$170M \\
Atlanta, Georgia    & \$16,220M & \$16,292M & \$16,398M & +\$179M \\
\bottomrule
\end{tabular}
\end{table}

The combined Boden--Atlanta gap is \$2.1B over 25 years, with climate change adding an additional \$132M differential under RCP 8.5 (\$47M at Boden vs.\ \$179M at Atlanta). Hardware energy alone accounts for \$456M of additional cost at Atlanta under RCP 8.5 versus only \$99M at Boden—a 4.6$\times$ climate exposure ratio. The compounding effect is striking: the facility-only climate gap grows from \$7.6M at 10 years to \$37.1M at 25 years for Atlanta, a 4.9$\times$ amplification.

\subsection{ARM Efficiency as Climate Adaptation}

Replacing Standard AI racks with ARM-based infrastructure (Apple silicon equivalent, 15~kW/rack, \$800K/rack) reduces hardware energy consumption by 85\% across all locations. Under RCP 8.5:

\begin{itemize}
    \item Boden combined TCO: \$9,339M (AI) $\rightarrow$ \$1,796M (ARM) — \$7.5B savings
    \item Atlanta combined TCO: \$11,059M (AI) $\rightarrow$ \$3,103M (ARM) — \$8.0B savings
    \item The ARM advantage is \$400M larger at Atlanta than Boden, demonstrating that ARM efficiency partially offsets climate-vulnerable locations
\end{itemize}

While ARM hardware lacks the compute density for primary AI training workloads, it represents a viable strategy for inference, caching, and edge tiers—particularly in climate-exposed locations where the energy penalty of x86/GPU hardware is amplified by PUE degradation.

\subsection{Extreme Weather and Insurance Impact}

Using real FEMA disaster declaration data (69,769 records, 1953--2026) for the five US locations, the Idea 5 models produce significantly stronger results than synthetic-only training:

\textbf{Survival Model (Weibull AFT):} Concordance index of 0.944 (up from 0.57 with synthetic data), confirming that extreme event frequency is the dominant predictor of outage risk. Georgia averages 148.7 disaster declarations per year versus Wyoming's 9.0—a 16$\times$ disparity that directly translates to outage hazard.

\textbf{Outage Classifier (XGBoost):} AUROC of 1.00, with feature importance dominated by event severity (64.7\%) and extreme event frequency (18.3\%). The classifier confirms that climate-driven event intensity, not just frequency, determines outage outcomes.

\textbf{Insurance Premium Model (Quantile GBM):} RMSE of \$0.71M with prediction intervals. Under RCP 8.5, annual premiums at climate-exposed locations (Atlanta, Florence, Johor) show nonlinear acceleration after 2035, driven by the event\_ratio$^{1.5}$ scaling validated by Swiss Re catastrophe models.

\subsection{Bayesian Uncertainty Validation}

The BNN independently validates the physics model's predictions. Table~\ref{tab:bnn_calibration} summarizes the uncertainty calibration results.

\begin{table}[H]
\centering
\caption{BNN Uncertainty Calibration (MC Dropout, 50 samples)}
\label{tab:bnn_calibration}
\small
\begin{tabular}{lrrrr}
\toprule
\textbf{Target} & \textbf{RMSE} & \textbf{Mean $\sigma$} & \textbf{Within 1$\sigma$} & \textbf{Within 2$\sigma$} \\
\midrule
Power cost shift  & 0.421 & 0.907 & 83.1\% & 94.1\% \\
PUE shift         & 0.010 & 0.026 & 93.2\% & 100.0\% \\
Insurance scale   & 0.163 & 0.212 & 78.0\% & 98.3\% \\
\bottomrule
\end{tabular}
\end{table}

The 2$\sigma$ coverage rates (94--100\%) closely match the theoretical 95\% target, confirming the BNN produces well-calibrated uncertainty estimates. The physics model's PUE predictions (+0.03--0.05) fall within the BNN's 2$\sigma$ bands (+0.01 $\pm$ 0.06), validating the physics approach through an independent methodology.

\subsection{Unified Investment Framework}

Combining all three contributions—dynamic facility TCO, hardware cost integration, and extreme weather/insurance modeling—produces a complete climate-aware investment framework. The key insight is that \textbf{climate change does not increase costs uniformly}; it amplifies existing location advantages through three compounding channels:

\begin{enumerate}
    \item \textbf{PUE degradation} (facility + hardware energy): +0.008--0.012 PUE per °C, applied to both facility cooling and 500$\times$100~kW racks
    \item \textbf{Power price escalation}: Grid stress from concurrent cooling demand raises marginal electricity costs, with climate-constrained grids (low reliability score) experiencing higher volatility
    \item \textbf{Insurance feedback loop}: More extreme events $\rightarrow$ higher premiums $\rightarrow$ higher TCO, with nonlinear scaling that penalizes already-vulnerable locations disproportionately
\end{enumerate}

%----------------------------------------------------------------------
\section{Discussion}
\label{sec:discussion}

\subsection{Implications for Investment Strategy}

Our results strengthen the case for Nordic-first datacenter deployment, with the added dimension that climate resilience amplifies existing cost advantages over time. The dynamic TCO model reveals that the ``decision window'' for optimal location selection narrows under aggressive warming scenarios—facilities built in climate-vulnerable locations today face compounding cost disadvantages that were invisible to static models.

\subsection{Policy Implications}

For governments: the renewable energy and climate advantage translate directly to economic competitiveness for datacenter attraction. Nordic governments should accelerate permitting and grid expansion. For US state governments in secondary markets: investment in grid resilience and renewable generation can partially offset climate-driven cost increases.

\subsection{Hardware Cost Implications}

The integration of IT equipment costs reveals that hardware energy—not facility operations—is the dominant channel through which climate change affects datacenter economics. For a 100~MW AI facility, the 15-year hardware energy bill ranges from \$203M (Boden, RCP~2.6) to \$1,074M (Atlanta, RCP~8.5), a 5.3$\times$ spread. This finding suggests that prior TCO analyses focusing solely on facility costs have systematically underestimated the climate sensitivity of datacenter investments.

The ARM efficiency tier demonstrates that hardware architecture choices can partially compensate for location disadvantage—an 85\% reduction in hardware energy consumption narrows the Boden--Atlanta gap from \$1.7B to approximately \$1.3B. However, the compute density trade-off (15~kW ARM vs.\ 100~kW GPU per rack) limits this strategy to inference and edge workloads.

\subsection{Limitations}

\begin{itemize}
    \item US disaster data (FEMA) is comprehensive but international locations rely on synthetic event generation calibrated to climate projections
    \item EIA retail commercial electricity prices (\$87--110/MWh) significantly exceed the paper's assumed industrial/wholesale rates (\$28--85/MWh); datacenter-specific Power Purchase Agreements would provide better calibration
    \item Climate projections carry inherent uncertainty; our RCP scenarios bracket but do not eliminate this
    \item The physics-based PUE shift model (0.008--0.012/°C) represents literature central estimates; facility-specific cooling technology could shift sensitivity significantly
    \item Hardware cost projections assume 5\% annual inflation for GPU hardware, which may underestimate or overestimate given rapid AI hardware evolution
\end{itemize}

%----------------------------------------------------------------------
\section{Conclusions and Future Work}
\label{sec:conclusions}

This paper demonstrates that incorporating climate dynamics into datacenter TCO modeling fundamentally changes the investment calculus. Static models underestimate the long-term cost advantage of climate-resilient locations and overstate the viability of traditional hubs. Our ML-driven dynamic Monte Carlo framework provides a replicable methodology for climate-aware infrastructure decision-making.

Future work should: (1) incorporate real-time grid stress data from EIA Form 930, (2) extend survival analysis with actual datacenter outage records, (3) model the feedback between datacenter electricity demand and regional grid carbon intensity, and (4) validate predictions against emerging real-world climate impacts on datacenter operations.

%----------------------------------------------------------------------
\bibliographystyle{plainnat}
\begin{thebibliography}{9}

\bibitem[DCCore(2025)]{dccore2025}
{Rubin, T.}
\newblock Global Datacenter Location Optimization: A Monte Carlo Simulation Analysis of Strategic Markets.
\newblock 2025.

\bibitem[Gal and Ghahramani(2016)]{gal2016dropout}
{Gal, Y. and Ghahramani, Z.}
\newblock Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning.
\newblock \emph{ICML}, 2016.

\bibitem[Greenberg et al.(2009)]{greenberg2009}
{Greenberg, A., Hamilton, J., Maltz, D.A., and Patel, P.}
\newblock The Cost of a Cloud: Research Problems in Data Center Networks.
\newblock \emph{ACM SIGCOMM CCR}, 39(1):68--73, 2009.

\bibitem[Patterson et al.(2021)]{patterson2021}
{Patterson, D., Gonzalez, J., Le, Q., et al.}
\newblock Carbon Emissions and Large Neural Network Training.
\newblock \emph{arXiv:2104.10350}, 2021.

\bibitem[Qureshi et al.(2009)]{qureshi2009}
{Qureshi, A., Weber, R., Balakrishnan, H., Guttag, J., and Maggs, B.}
\newblock Cutting the Electric Bill for Internet-Scale Systems.
\newblock \emph{ACM SIGCOMM}, 2009.

\bibitem[Strubell et al.(2019)]{strubell2019}
{Strubell, E., Ganesh, A., and McCallum, A.}
\newblock Energy and Policy Considerations for Deep Learning in NLP.
\newblock \emph{ACL}, 2019.

\end{thebibliography}

\end{document}
