Published
North America
The Climate Urgency Gap
A U.S. state-level analysis of the gap between climate belief, emotional urgency, and support for clean energy action
69% of Americans believe climate change is happening. Only 63% are worried about it.
The 6 percentage point national gap between knowing and feeling is the central finding. It is consistent across almost every state. Belief is not the bottleneck. Emotional connection to the issue is. The data infrastructure that translates physical climate reality into felt urgency is failing at the last mile.
The gap is widest in the states with the most to lose.
Maine, Utah, Florida, Montana, and Idaho all show gaps above 7.5 percentage points between belief and worry. Florida is already experiencing accelerating sea level rise and intensifying hurricane seasons. The states where the belief-worry gap is largest are not the states least exposed to climate risk. They are often among the most exposed.
Despite the worry gap, support for clean energy action is high everywhere.
Across all 50 states, support for funding renewable energy ranges from 71% to 84%. Even the states with the largest gaps between knowing and feeling show strong majority support for solar, wind, and CO2 regulation. The policy window is open. The urgency to use it is not keeping pace.
Summary
No short summary yet.
Most Americans know climate change is real. Far fewer feel urgency about it. And yet, despite that gap, support for clean energy policy across every U.S. state remains remarkably high. This analysis uses Yale's 2025 Climate Opinion Maps to ask a question the headline surveys rarely surface: is the perception gap a belief problem, a worry problem, or something else entirely?
The answer matters because the data infrastructure that communicates climate progress to the public is not working. The transition is accelerating. Coal is falling. Renewables crossed the 30% threshold in Europe. Solar is on track to become the world's largest electricity source within six years. And yet pessimism about collective action is rising, not falling, among the demographic groups most likely to support the policies that make those milestones possible.
Charts & blocks
This scatter plot places every U.S. state as a labelled dot. The x-axis shows the percentage of state residents who believe climate change is happening. The y-axis shows the percentage who support government funding of renewable energy research. Each dot is sized by the percentage who are worried about climate change, and coloured by the gap between belief and worry: cyan dots have a small gap, meaning belief and worry are well aligned; orange and deep orange dots have a large gap, meaning many more people believe than feel urgency.
Methodology
Data source: Yale Program on Climate Change Communication, Climate Opinion Maps 2025 edition, covering all 50 U.S. states plus the District of Columbia. The dataset is based on a multilevel regression and poststratification model applied to national survey data, producing state-level estimates of climate opinion across more than 30 variables. Analysis was conducted on Panthaion using the 2025 cross-section of state-level estimates. The belief-worry gap is calculated as the difference between the percentage of state residents who believe climate change is happening and the percentage who say they are worried about it. Dot size in the scatter plot encodes the percentage who are worried. Colour encodes the belief-worry gap from cyan (small gap) to deep orange (large gap).
Datasets used
Yale Climate Opinion Maps 2025
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Provenance
May 29, 2026
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Code
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.io as pio
from IPython.display import Image, display
yale_raw = df.copy()
states = yale_raw[yale_raw['GeoType'] == 'state'].copy()
yale = states.pivot(index='GeoName', columns='Variable', values='x2025').reset_index()
yale.columns.name = None
for col in yale.columns[1:]:
yale[col] = pd.to_numeric(yale[col], errors='coerce')
yale = yale[yale['GeoName'] != 'District of Columbia'].copy()
yale['gap'] = yale['happening'] - yale['worried']
abbr = {
'Alabama':'AL','Alaska':'AK','Arizona':'AZ','Arkansas':'AR','California':'CA',
'Colorado':'CO','Connecticut':'CT','Delaware':'DE','Florida':'FL','Georgia':'GA',
'Hawaii':'HI','Idaho':'ID','Illinois':'IL','Indiana':'IN','Iowa':'IA',
'Kansas':'KS','Kentucky':'KY','Louisiana':'LA','Maine':'ME','Maryland':'MD',
'Massachusetts':'MA','Michigan':'MI','Minnesota':'MN','Mississippi':'MS',
'Missouri':'MO','Montana':'MT','Nebraska':'NE','Nevada':'NV','New Hampshire':'NH',
'New Jersey':'NJ','New Mexico':'NM','New York':'NY','North Carolina':'NC',
'North Dakota':'ND','Ohio':'OH','Oklahoma':'OK','Oregon':'OR','Pennsylvania':'PA',
'Rhode Island':'RI','South Carolina':'SC','South Dakota':'SD','Tennessee':'TN',
'Texas':'TX','Utah':'UT','Vermont':'VT','Virginia':'VA','Washington':'WA',
'West Virginia':'WV','Wisconsin':'WI','Wyoming':'WY'
}
yale['abbr'] = yale['GeoName'].map(abbr)
# Light mode colours — all contrast against white
ORANGE = '#C2410C' # darker orange for readability on white
ORANGE_MID = '#EA580C'
CYAN = '#0369A1' # dark teal/blue instead of light cyan
DARK = '#0F172A'
CARD_BG = '#FFF7ED' # warm off-white card
CARD_BORDER = '#F97316'
GRID = '#E2E8F0'
MUTED = '#475569'
TREND = '#94A3B8'
WHITE = '#FFFFFF'
colorscale = [
[0.0, '#0369A1'], # deep blue — small gap, well aligned
[0.4, '#0891B2'], # teal
[0.65, '#F97316'], # orange
[0.85, '#EA580C'], # deep orange
[1
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Code
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.io as pio
from IPython.display import Image, display
yale_raw = df.copy()
states = yale_raw[yale_raw['GeoType'] == 'state'].copy()
yale = states.pivot(index='GeoName', columns='Variable', values='x2025').reset_index()
yale.columns.name = None
for col in yale.columns[1:]:
yale[col] = pd.to_numeric(yale[col], errors='coerce')
yale = yale[yale['GeoName'] != 'District of Columbia'].copy()
yale['gap'] = yale['happening'] - yale['worried']
abbr = {
'Alabama':'AL','Alaska':'AK','Arizona':'AZ','Arkansas':'AR','California':'CA',
'Colorado':'CO','Connecticut':'CT','Delaware':'DE','Florida':'FL','Georgia':'GA',
'Hawaii':'HI','Idaho':'ID','Illinois':'IL','Indiana':'IN','Iowa':'IA',
'Kansas':'KS','Kentucky':'KY','Louisiana':'LA','Maine':'ME','Maryland':'MD',
'Massachusetts':'MA','Michigan':'MI','Minnesota':'MN','Mississippi':'MS',
'Missouri':'MO','Montana':'MT','Nebraska':'NE','Nevada':'NV','New Hampshire':'NH',
'New Jersey':'NJ','New Mexico':'NM','New York':'NY','North Carolina':'NC',
'North Dakota':'ND','Ohio':'OH','Oklahoma':'OK','Oregon':'OR','Pennsylvania':'PA',
'Rhode Island':'RI','South Carolina':'SC','South Dakota':'SD','Tennessee':'TN',
'Texas':'TX','Utah':'UT','Vermont':'VT','Virginia':'VA','Washington':'WA',
'West Virginia':'WV','Wisconsin':'WI','Wyoming':'WY'
}
yale['abbr'] = yale['GeoName'].map(abbr)
ORANGE = '#F97316'
CYAN = '#67E8F9'
DARK = '#0F172A'
BG = '#0D1B2A'
CARD = '#1E2D3D'
WHITE = '#FFFFFF'
MUTED = '#94A3B8'
DIVIDER = '#2A3F55'
z = np.polyfit(yale['happening'].dropna(), yale['fundrenewables'].dropna(), 1)
p = np.poly1d(z)
x_line = np.linspace(yale['happening'].min(), yale['happening'].max(), 100)
colorscale = [
[0.0, CYAN],
[0.4, '#67E8F9'],
[0.65, '#FB923C'],
[0.85, ORANGE],
[1.0, '#C2410C'],
]
fig = go.Figure()
fig.add_trace(go.Scatter(
x=x_line, y=p(x_line),
mode='lines',
line=dict(color=MUTED, dash='dash',
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Code
import plotly.graph_objects as go
import numpy as np
# Editorial palette (FT / Bloomberg style)
NAVY = "#0B1F3A"
TEXT = "#1F2937"
SUBTEXT = "#6B7280"
BG = "#FAFAF7"
GRID = "#E5E7EB"
BLUE = "#3B82F6"
ORANGE = "#F97316"
LIGHT_BLUE = "#BFDBFE"
colorscale = [
[0.0, "#60A5FA"],
[0.45, "#93C5FD"],
[0.65, "#FDBA74"],
[0.85, "#FB923C"],
[1.0, "#EA580C"],
]
fig = go.Figure()
# Trend line
fig.add_trace(go.Scatter(
x=x_line,
y=p(x_line),
mode='lines',
line=dict(color="#9CA3AF", dash='dash', width=2),
hoverinfo='skip',
showlegend=False
))
# Background states (de-emphasized)
fig.add_trace(go.Scatter(
x=yale['happening'],
y=yale['fundrenewables'],
mode='markers',
marker=dict(
size=yale['worried']/3.5,
color="#D1D5DB",
opacity=0.45,
line=dict(width=0)
),
hoverinfo='skip',
showlegend=False
))
# Highlight only key states
highlight = ['TX','FL','CA','NY','MA','NJ','CT','ME','LA','ND','WV']
plot = yale[yale['abbr'].isin(highlight)]
fig.add_trace(go.Scatter(
x=plot['happening'],
y=plot['fundrenewables'],
mode='markers+text',
text=plot['abbr'],
textposition='middle center',
textfont=dict(size=13, color="white", family="Arial Black"),
marker=dict(
size=plot['worried']/2.6,
color=plot['gap'],
colorscale=colorscale,
line=dict(color="white", width=2),
opacity=0.95,
colorbar=dict(
title="Belief–worry gap",
thickness=14,
outlinewidth=0,
tickfont=dict(size=11, color=SUBTEXT),
titlefont=dict(size=12, color=TEXT),
x=1.03
)
),
hovertemplate=(
"<b>%{text}</b><br>"
"Belief: %{x:.1f}%<br>"
"Support: %{y:.1f}%<extra></extra>"
)
))
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notebook_saved
1 month, 1 week ago
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Saved notebook to project: "Analyze with Python — Yale Climate Opinion Maps 2025 - Panthaion".
No extra payload on this event.
JA
cell_execution
1 month, 1 week ago
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Code
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.io as pio
from IPython.display import Image, display
yale_raw = df.copy()
states = yale_raw[yale_raw['GeoType'] == 'state'].copy()
yale = states.pivot(index='GeoName', columns='Variable', values='x2025').reset_index()
yale.columns.name = None
for col in yale.columns[1:]:
yale[col] = pd.to_numeric(yale[col], errors='coerce')
yale = yale[yale['GeoName'] != 'District of Columbia'].copy()
yale['gap'] = yale['happening'] - yale['worried']
abbr = {
'Alabama':'AL','Alaska':'AK','Arizona':'AZ','Arkansas':'AR','California':'CA',
'Colorado':'CO','Connecticut':'CT','Delaware':'DE','Florida':'FL','Georgia':'GA',
'Hawaii':'HI','Idaho':'ID','Illinois':'IL','Indiana':'IN','Iowa':'IA',
'Kansas':'KS','Kentucky':'KY','Louisiana':'LA','Maine':'ME','Maryland':'MD',
'Massachusetts':'MA','Michigan':'MI','Minnesota':'MN','Mississippi':'MS',
'Missouri':'MO','Montana':'MT','Nebraska':'NE','Nevada':'NV','New Hampshire':'NH',
'New Jersey':'NJ','New Mexico':'NM','New York':'NY','North Carolina':'NC',
'North Dakota':'ND','Ohio':'OH','Oklahoma':'OK','Oregon':'OR','Pennsylvania':'PA',
'Rhode Island':'RI','South Carolina':'SC','South Dakota':'SD','Tennessee':'TN',
'Texas':'TX','Utah':'UT','Vermont':'VT','Virginia':'VA','Washington':'WA',
'West Virginia':'WV','Wisconsin':'WI','Wyoming':'WY'
}
yale['abbr'] = yale['GeoName'].map(abbr)
ORANGE = '#F97316'
CYAN = '#67E8F9'
DARK = '#0F172A'
BG = '#0D1B2A'
CARD = '#1E2D3D'
WHITE = '#FFFFFF'
MUTED = '#94A3B8'
DIVIDER = '#2A3F55'
z = np.polyfit(yale['happening'].dropna(), yale['fundrenewables'].dropna(), 1)
p = np.poly1d(z)
x_line = np.linspace(yale['happening'].min(), yale['happening'].max(), 100)
colorscale = [
[0.0, CYAN],
[0.4, '#67E8F9'],
[0.65, '#FB923C'],
[0.85, ORANGE],
[1.0, '#C2410C'],
]
fig = go.Figure()
# Trend line
fig.add_trace(go.Scatter(
x=x_line, y=p(x_line),
mode='lines',
line=dict(color=MUTED,
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notebook_saved
1 month, 1 week ago
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Saved notebook to project: "Analyze with Python — Yale Climate Opinion Maps 2025 - Panthaion".
No extra payload on this event.
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Code
# ── 7. SUMMARY ────────────────────────────────────────────────
print("=" * 65)
print("WHEN THE DATA DOES NOT REACH THE PEOPLE — KEY FINDINGS")
print("=" * 65)
print("BELIEF (2025 national):")
print(" Happening: " + str(nat_happening) + "% (was " + str(happening_2018) + "% in 2018)")
print(" Human-caused: " + str(nat_human) + "%")
print(" Scientific consensus: " + str(nat_consensus) + "%")
print(" Are worried: " + str(nat_worried) + "% (was " + str(worried_2018) + "% in 2018)")
print("\nACTION SUPPORT (2025 national):")
print(" Fund renewables: " + str(nat_fundren) + "% (was " + str(fundren_2018) + "% in 2018)")
print(" Support solar: " + str(nat_suppsolar) + "%")
print(" Support wind: " + str(nat_suppwind) + "%")
print(" Regulate CO2: " + str(nat_co2) + "%")
print("\nSTATE VARIATION:")
print(" Highest belief: " + yale.nlargest(1,'happening')['GeoName'].values[0] + " (" + str(round(yale['happening'].max(),1)) + "%)")
print(" Lowest belief: " + yale.nsmallest(1,'happening')['GeoName'].values[0] + " (" + str(round(yale['happening'].min(),1)) + "%)")
print(" Range: " + str(round(yale['happening'].max() - yale['happening'].min(),1)) + " percentage points")
print("\nSource: Yale Climate Opinion Maps 2025 | Analysed on Panthaion")
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# ── 6. CHART 4: Action support 2018 vs 2025 ──────────────────
action_vars = {
'fundrenewables': 'Fund renewables',
'suppsolar': 'Support solar',
'suppwind': 'Support wind',
'generaterenewable': 'Require utilities\nto use renewables',
'co2limits': 'Regulate CO2',
'regulate': 'Regulate\nglobal warming',
}
labels, vals_25, vals_18 = [], [], []
for var, label in action_vars.items():
v25 = get_nat(var, 2025)
v18 = get_nat(var, 2018)
if v25 and v18:
labels.append(label)
vals_25.append(v25)
vals_18.append(v18)
fig4 = go.Figure()
fig4.add_trace(go.Bar(x=labels, y=vals_18, name='2018', marker_color=MUTED, opacity=0.7))
fig4.add_trace(go.Bar(x=labels, y=vals_25, name='2025', marker_color=ORANGE))
fig4.update_layout(
title=dict(
text='U.S. Support for Climate Action: 2018 vs 2025<br>'
'<sup>Despite growing pessimism, support for specific actions remains high | Yale Climate Opinion Maps</sup>',
font=dict(size=15, color=DARK)
),
barmode='group',
xaxis=dict(tickfont=dict(size=11)),
yaxis=dict(title='% of U.S. adult population', gridcolor='#F1F5F9', range=[0,100]),
plot_bgcolor='white',
paper_bgcolor='white',
legend=dict(font=dict(size=12)),
height=480,
width=900
)
display(Image(pio.to_image(fig4, format='png', width=900, height=480, scale=2)))
print("Chart 4 done")
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# ── 5. CHART 3: Belief vs renewable support scatter ──────────
fig3 = go.Figure()
fig3.add_trace(go.Scatter(
x=yale['happening'],
y=yale['fundrenewables'],
mode='markers+text',
text=yale['GeoName'].str[:2],
textposition='top center',
textfont=dict(size=8, color=DARK),
marker=dict(
size=yale['worried'] / 4,
color=yale['belief_worry_gap'],
colorscale=[[0, GREEN],[0.5, ORANGE],[1, RED]],
colorbar=dict(title='Belief-worry gap', thickness=12),
opacity=0.8,
line=dict(color='white', width=1)
)
))
z = np.polyfit(yale['happening'].dropna(), yale['fundrenewables'].dropna(), 1)
p = np.poly1d(z)
x_line = np.linspace(yale['happening'].min(), yale['happening'].max(), 100)
fig3.add_trace(go.Scatter(
x=x_line, y=p(x_line),
mode='lines',
line=dict(color=MUTED, dash='dash', width=1.5),
showlegend=False
))
fig3.update_layout(
title=dict(
text='Belief vs Renewable Support by State (2025)<br>'
'<sup>Dot size = % worried | Color = gap between belief and worry | Yale Climate Opinion Maps</sup>',
font=dict(size=15, color=DARK)
),
xaxis=dict(title='% who believe climate change is happening', gridcolor='#F1F5F9'),
yaxis=dict(title='% who support funding renewable energy', gridcolor='#F1F5F9'),
plot_bgcolor='white',
paper_bgcolor='white',
height=560,
width=900
)
display(Image(pio.to_image(fig3, format='png', width=900, height=560, scale=2)))
print("Chart 3 done")
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# ── 4. CHART 2: Trend 2018-2025 ──────────────────────────────
trend_vars = {
'happening': 'Believe it is happening',
'worried': 'Are worried',
'human': 'Believe human-caused',
'fundrenewables': 'Support funding renewables',
'suppsolar': 'Support solar energy',
}
years = list(range(2018, 2026))
colors_trend = [CYAN, ORANGE, GREEN, PURPLE, '#FBBF24']
fig2 = go.Figure()
for (var, label), color in zip(trend_vars.items(), colors_trend):
y_vals = [get_nat(var, yr) for yr in years]
fig2.add_trace(go.Scatter(
x=[str(y) for y in years],
y=y_vals,
name=label,
line=dict(color=color, width=2.5),
mode='lines+markers',
marker=dict(size=7)
))
fig2.update_layout(
title=dict(
text='U.S. Climate Opinion Trends 2018 to 2025<br>'
'<sup>National averages | Yale Climate Opinion Maps</sup>',
font=dict(size=16, color=DARK)
),
xaxis=dict(title='Year', gridcolor='#F1F5F9'),
yaxis=dict(title='% of U.S. adult population', gridcolor='#F1F5F9', range=[40,100]),
plot_bgcolor='white',
paper_bgcolor='white',
legend=dict(font=dict(size=11)),
height=500,
width=900
)
display(Image(pio.to_image(fig2, format='png', width=900, height=500, scale=2)))
print("Chart 2 done")
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# ── 3. CHART 1: Belief vs worry by state ─────────────────────
df_sorted = yale.sort_values('happening', ascending=True).copy()
fig1 = go.Figure()
fig1.add_trace(go.Bar(
y=df_sorted['GeoName'],
x=df_sorted['happening'],
name='Believe it is happening',
orientation='h',
marker_color=CYAN,
opacity=0.85
))
fig1.add_trace(go.Bar(
y=df_sorted['GeoName'],
x=df_sorted['worried'],
name='Are worried about it',
orientation='h',
marker_color=ORANGE,
opacity=0.85
))
fig1.update_layout(
title=dict(
text='Climate Belief vs Climate Worry by U.S. State (2025)<br>'
'<sup>The gap between knowing and caring | Yale Climate Opinion Maps 2025</sup>',
font=dict(size=16, color=DARK)
),
barmode='overlay',
xaxis=dict(title='% of state population', gridcolor='#F1F5F9', range=[0,100]),
yaxis=dict(tickfont=dict(size=9)),
plot_bgcolor='white',
paper_bgcolor='white',
legend=dict(orientation='h', yanchor='bottom', y=1.02),
height=900,
width=900,
margin=dict(l=160, r=40)
)
display(Image(pio.to_image(fig1, format='png', width=900, height=900, scale=2)))
print("Chart 1 done")
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# ── 2. NATIONAL TRENDS ────────────────────────────────────────
nat = yale_raw[yale_raw['GeoType'] == 'national'].copy()
def get_nat(var, yr):
row = nat[nat['Variable'] == var]
if len(row) > 0:
val = row[f'x{yr}'].values[0]
return float(val) if pd.notna(val) else None
return None
nat_happening = get_nat('happening', 2025)
nat_worried = get_nat('worried', 2025)
nat_human = get_nat('human', 2025)
nat_consensus = get_nat('consensus', 2025)
nat_fundren = get_nat('fundrenewables', 2025)
nat_suppsolar = get_nat('suppsolar', 2025)
nat_suppwind = get_nat('suppwind', 2025)
nat_co2 = get_nat('co2limits', 2025)
happening_2018 = get_nat('happening', 2018)
worried_2018 = get_nat('worried', 2018)
fundren_2018 = get_nat('fundrenewables', 2018)
print("NATIONAL AVERAGES 2025")
print("Believe it is happening: " + str(nat_happening))
print("Are worried: " + str(nat_worried))
print("Believe human-caused: " + str(nat_human))
print("Believe scientists agree: " + str(nat_consensus))
print("Support funding renewables: " + str(nat_fundren))
print("Support solar: " + str(nat_suppsolar))
print("Support wind: " + str(nat_suppwind))
print("Support CO2 limits: " + str(nat_co2))
print("\n2018 comparison:")
print("Happening 2018: " + str(happening_2018))
print("Worried 2018: " + str(worried_2018))
print("Fund renewables 2018: " + str(fundren_2018))
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Code
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.io as pio
from plotly.subplots import make_subplots
from IPython.display import Image, display
import warnings
warnings.filterwarnings('ignore')
ORANGE = '#F97316'
CYAN = '#67E8F9'
DARK = '#0F172A'
RED = '#EF4444'
GREEN = '#22C55E'
PURPLE = '#A78BFA'
MUTED = '#94A3B8'
# ── 1. LOAD AND CLEAN ─────────────────────────────────────────
yale_raw = df.copy()
# Filter to state level only
states = yale_raw[yale_raw['GeoType'] == 'state'].copy()
# Pivot: one row per state, one column per variable for 2025
yale = states.pivot(index='GeoName', columns='Variable', values='x2025').reset_index()
yale.columns.name = None
# Convert all opinion columns to numeric
for col in yale.columns[1:]:
yale[col] = pd.to_numeric(yale[col], errors='coerce')
# Drop DC for cleaner state analysis
yale = yale[yale['GeoName'] != 'District of Columbia'].copy()
# Add belief-worry gap
yale['belief_worry_gap'] = yale['happening'] - yale['worried']
print("Dataset ready:", yale.shape)
print("\nTop 5 states by % who think climate change is happening:")
print(yale.nlargest(5, 'happening')[['GeoName','happening','worried','human']].to_string(index=False))
print("\nBottom 5 states:")
print(yale.nsmallest(5, 'happening')[['GeoName','happening','worried','human']].to_string(index=False))
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for name in ['df', 'df_1', 'df_2', 'df_3', 'df_4', 'df_5']:
try:
test = eval(name)
print(f"Found: {name} — shape: {test.shape} — cols: {list(test.columns[:4])}")
except:
print(f"Not found: {name}")
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Code
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.io as pio
from plotly.subplots import make_subplots
from IPython.display import Image, display
import warnings
warnings.filterwarnings('ignore')
ORANGE = '#F97316'
CYAN = '#67E8F9'
DARK = '#0F172A'
RED = '#EF4444'
GREEN = '#22C55E'
PURPLE = '#A78BFA'
MUTED = '#94A3B8'
# ── 1. LOAD AND CLEAN YALE DATA ───────────────────────────────
yale_raw = pd.read_csv('/mnt/user-data/uploads/Yale_Climate_Opinion_Maps_2018_to_2025.csv')
# Filter to state level only
states = yale_raw[yale_raw['GeoType'] == 'state'].copy()
# Pivot to wide format: one row per state, one column per variable
yale = states.pivot(index='GeoName', columns='Variable', values='x2025').reset_index()
yale.columns.name = None
# Load EIA data
eia_raw = pd.read_excel(
'/mnt/user-data/uploads/EIA-861_Annual_Electric_Power_Industry_Report.xlsx',
sheet_name='Total Electric Industry'
)
eia_raw.columns = eia_raw.iloc[1]
eia = eia_raw.iloc[2:].copy()
eia.columns = ['Year','State','Res_Rev','Res_Sales','Res_Customers','Res_Price',
'Com_Rev','Com_Sales','Com_Customers','Com_Price',
'Ind_Rev','Ind_Sales','Ind_Customers','Ind_Price',
'Trans_Rev','Trans_Sales','Trans_Customers','Trans_Price',
'Total_Rev','Total_Sales','Total_Customers','Total_Price']
eia = eia[eia['Year'] == 2024].copy()
eia['Total_Price'] = pd.to_numeric(eia['Total_Price'], errors='coerce')
# State name mapping for EIA abbreviations
state_abbr = {
'AK':'Alaska','AL':'Alabama','AR':'Arkansas','AZ':'Arizona','CA':'California',
'CO':'Colorado','CT':'Connecticut','DC':'District of Columbia','DE':'Delaware',
'FL':'Florida','GA':'Georgia','HI':'Hawaii','IA':'Iowa','ID':'Idaho',
'IL':'Illinois','IN':'Indiana','KS':'Kansas','KY':'Kentucky','LA':'Louisiana',
'MA':'Massachusetts','MD':'Maryland','ME':'Maine','MI':'Michigan','MN':'Minnesota',
'MO':'Missouri','MS':'Mississippi','MT':'Montana','NC':'North Carolina',
'ND'
JA
cell_execution
1 month, 1 week ago
▾
Notebook cell executed.
Code
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.io as pio
from plotly.subplots import make_subplots
from IPython.display import Image, display
import warnings
warnings.filterwarnings('ignore')
ORANGE = '#F97316'
CYAN = '#67E8F9'
DARK = '#0F172A'
RED = '#EF4444'
GREEN = '#22C55E'
PURPLE = '#A78BFA'
MUTED = '#94A3B8'
# ── 1. LOAD AND CLEAN YALE DATA ───────────────────────────────
yale_raw = pd.read_csv('/mnt/user-data/uploads/Yale_Climate_Opinion_Maps_2018_to_2025.csv')
# Filter to state level only
states = yale_raw[yale_raw['GeoType'] == 'state'].copy()
# Pivot to wide format: one row per state, one column per variable
yale = states.pivot(index='GeoName', columns='Variable', values='x2025').reset_index()
yale.columns.name = None
# Load EIA data
eia_raw = pd.read_excel(
'/mnt/user-data/uploads/EIA-861_Annual_Electric_Power_Industry_Report.xlsx',
sheet_name='Total Electric Industry'
)
eia_raw.columns = eia_raw.iloc[1]
eia = eia_raw.iloc[2:].copy()
eia.columns = ['Year','State','Res_Rev','Res_Sales','Res_Customers','Res_Price',
'Com_Rev','Com_Sales','Com_Customers','Com_Price',
'Ind_Rev','Ind_Sales','Ind_Customers','Ind_Price',
'Trans_Rev','Trans_Sales','Trans_Customers','Trans_Price',
'Total_Rev','Total_Sales','Total_Customers','Total_Price']
eia = eia[eia['Year'] == 2024].copy()
eia['Total_Price'] = pd.to_numeric(eia['Total_Price'], errors='coerce')
# State name mapping for EIA abbreviations
state_abbr = {
'AK':'Alaska','AL':'Alabama','AR':'Arkansas','AZ':'Arizona','CA':'California',
'CO':'Colorado','CT':'Connecticut','DC':'District of Columbia','DE':'Delaware',
'FL':'Florida','GA':'Georgia','HI':'Hawaii','IA':'Iowa','ID':'Idaho',
'IL':'Illinois','IN':'Indiana','KS':'Kansas','KY':'Kentucky','LA':'Louisiana',
'MA':'Massachusetts','MD':'Maryland','ME':'Maine','MI':'Michigan','MN':'Minnesota',
'MO':'Missouri','MS':'Mississippi','MT':'Montana','NC':'North Carolina',
'ND'
JA
notebook_created
1 month, 1 week ago
▾
Created notebook "When the Data Does Not Reach the People".
No extra payload on this event.
JA
dataset_linked
1 month, 1 week ago
▾
Dataset "Yale Climate Opinion Maps 2025" (vv1) linked.
No extra payload on this event.
JA
project_created
1 month, 1 week ago
▾
Empty project created.
No extra payload on this event.
Discussion
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