Government Agency Sustainability¶

Motivation:¶

Problem¶

In the face of the climate crisis, investment in renewable energy and sustainability efforts are on the rise. However, its hard to know what impact investment in these areas will have on future greenhouse gas emissions.The agencies of the U.S. government have differing investment levels in energy efficiency and renewable energy. For example, in 2022 President Biden pushed for a new electric fleet of trucks for the USPS but the Post Office continued buying gas-powered trucks (New York Times).

Solution¶

Different government agencies have differing greenhouse emissions from their operations.This project seeks to identify/predict and a relationship between renwable energy investment in the different US government sectors and their greenhouse gas emissions.

Impact¶

This project will help different government agencies predict how impactful their spending in renwable energy is on actual greenhouse gas emissions are. If successful, this might help motivate new spending plans for government sectors.

Dataset:¶

Dataset 1: "Scope 1 & 2 GHG Emissions from Standard Operations, FY 2008 and FY 2021 (Metric Tons of Carbon Dioxide Equivalent)" from the US Department of Energy¶

Variables

  • Agency
  • Stationary Combustion Emissions
  • Vehicles and Equipment Emissions
  • FAST Data (fleets) Emissions
  • Fugitive Emissions & Incinerators Emissions
  • Idustrial Process Emissions
  • Purchased Electricity Emissions
  • Steam and Hot Water Emissions
  • Chilled Water Emissions
  • Other Emissions
  • Adjustments from RE use
  • Total Scope 1 & 2
  • % Change FY 2008-2021

Dataset 2: "Investment in Energy Efficiency and Renewable Energy in FY 2021 (in Adjusted Constant FY 2021 Dollars)" from the US Department of Energy¶

Variables

  • Agency
  • Direct Obligations
  • ESPC
  • UESC
  • Total investment
In [20]:
import pandas as pd 

df_ghg = pd.read_excel("AnnualEnergyData_20230225_015914_7353174.xlsx", header=4,) 
df_invest = pd.read_excel("AnnualEnergyData_20230225_015649_4294941.xlsx", header=4)
In [21]:
df_ghg.head()
Out[21]:
Agency Stationary\nCombustion Vehicles and \nEquipment FAST Data\n(fleets) Fugitive\nEmissions &\nIncinerators Industrial\nProcess\nEmissions Purchased Electricity Steam and\n Hot Water Chilled\n Water Other Adjustments from\nRE use Total Scope\n1 & 2 % Change\nFY 2008-2021
0 Postal Service 326450.71728 2217.052613 1769845.476297 106325.151208 9.638192 1640060.41017 21160.051073 0 0 -62425.216678 3803643.280155 -0.280325
1 Energy 443211.645112 51158.407855 42919.051334 183368.812451 185686.996449 1687030.749615 7183.602601 1413.672341 2455.994632 -213376.62996 2391052.302432 -0.490826
2 Veterans Affairs 853775.367548 5449.383584 48450.02151 35246.657283 0 1369370.219536 116045.092888 12519.769696 0 -209517.386314 2231339.125731 -0.246951
3 GSA 307316.942612 0 588.660744 33673.351284 0 767871.583073 82869.610609 10930.154773 35384.439592 -127614.590434 1111020.152255 -0.510703
4 Justice 323504.455472 1839.340912 3011.229425 14115.221936 0 527431.871933 15830.25941 171.209347 86.830063 -37826.047149 848164.371349 -0.472139
In [22]:
df_invest.head()
Out[22]:
Unnamed: 0 Direct Obligations\n(Thou. $) ESPC\n(Thou. $) UESC\n(Thou. $) Total Investment\n(Thou. $)
0 Veterans Affairs 3793.369 14012.53588 189697.007859 207502.912739
1 GSA 3259.94 91156 0 94415.94
2 DHS 7113.6032 36105.687 0 43219.2902
3 NASA 7663.818 0 27826.744 35490.562
4 HHS 7006.84 0 14613.734 21620.574
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