Update app.py

app.py

@@ -146,16 +146,27 @@ gas_share = st.sidebar.slider(
 with st.sidebar.expander("More on Energy Mix"):
     st.markdown("""
     **Why Gas?**
-    The electric grid in major hubs like Texas is effectively "sold out," with wait times for connection approaching 5 years. To bypass this, AI labs are adopting "Bring Your Own Generation" (BYOG) strategies, primarily using natural gas which can be deployed in months rather than years.
+    The electric grid in major hubs like Texas is effectively "sold out," with wait times for connection approaching 5 years. To bypass this, AI labs are adopting "Bring Your Own Generation" (BYOG) strategies, primarily using natural gas which can be deployed in months rather than years. In fact, current projections suggest that **nearly a third of all new data center development will deploy behind-the-meter (BTM) gas generation** to circumvent these bottlenecks.<sup>1,2</sup>
     
+    * **Grid Dominance:** Natural gas is the backbone of the U.S. power system, accounting for approximately **43% of total utility-scale electricity generation** in 2023.<sup>3,4</sup>
+    * **The Flexibility Wildcard:** "Demand response" (or data center flexibility) could theoretically pull gigawatts "out of thin air" by matching AI training jobs to times when the grid has spare capacity.<sup>5</sup> However, many experts remain skeptical of the true magnitude of this solution, as large-scale implementation faces significant technical hurdles and pushback from major grid operators like PJM.<sup>5,6</sup>
+
     **What about Solar?**
-    While solar prices have dropped ~88% since 2009, it faces physical limits:
-    * **Land Use:** 2 GW of solar requires a land area roughly the size of Manhattan.
-    * **Uptime:** Solar requires battery backup for 24/7 reliability, adding complexity for off-grid "island" data centers.
+    While solar prices have dropped ~88% since 2009, it faces physical limits:<sup>5</sup>
+    * **Land Use:** 2 GW of solar requires a land area roughly the size of Manhattan (approx. 60 km²).<sup>5</sup>
+    * **Uptime:** Solar requires massive battery storage for 24/7 reliability, adding complexity for off-grid "island" data centers that cannot draw on spare grid capacity at night.<sup>5</sup>
       
-      [Source](https://open.substack.com/pub/semianalysis/p/how-ai-labs-are-solving-the-power)
+    ---
+    *Sources:*
+    1. [Latitude Media (Jan 2026)](https://www.latitudemedia.com/news/what-the-michigan-stargate-site-says-about-todays-ai-market)
+    2. [JLL 2026 Global Data Center Outlook](https://www.jll.com/en-us/insights/market-outlook/data-center-outlook)
+    3. [U.S. Energy Information Administration (EIA)](https://www.eia.gov/energyexplained/electricity/electricity-in-the-us.php)
+    4. [Ember Energy - U.S. Country Profile](https://ember-energy.org/countries-and-regions/united-states-of-america/)
+    5. [Epoch AI: America's AI Power Problem (Dec 2025)](https://epoch.ai/gradient-updates/is-almost-everyone-wrong-about-americas-ai-power-problem)
+    6. [Woodway Energy: Bridging the 5-Year Gap](https://www.woodwayenergy.com/off-grid-btm-gas-power-generation-data-centers/)
     """)
 
+
 st.sidebar.markdown('<div class="spacer"></div>', unsafe_allow_html=True)
 
 # 3. Turbine Efficiency
@@ -215,7 +226,6 @@ with c2:
     """, unsafe_allow_html=True)
 
     # 2. Charts
-st.subheader("US Emissions Trajectory vs. AI Impact")
 
 # Updated Data from Our World in Data (Billion tCO2e -> Million tCO2e)
 years = [2020, 2021, 2022, 2023, 2024]
@@ -232,8 +242,9 @@ fig1 = go.Figure()
 fig1.add_trace(go.Scatter(
     x=years, y=emissions_hist, 
     mode='lines+markers', 
-    name='Historical CO₂ (Fossil/Industry)', 
-    line=dict(color='gray', width=2)
+    name='Historical CO₂', 
+    line=dict(color='gray', width=4),
+    marker=dict(size=12)
 ))
 
 # Target Dot (Changed from line to single marker)
@@ -241,14 +252,14 @@ fig1.add_trace(go.Scatter(
     x=[2030], y=[target_2030], 
     mode='markers', 
     name='2030 Climate Goal (50% Cut)', 
-    marker=dict(color='green', size=12, symbol='circle')
+    marker=dict(color='green', size=15, symbol='circle')
 ))
 
 # AI Impact Stacked Bar
 fig1.add_trace(go.Bar(
     x=[2030], y=[bau_2030], 
     name='2030 Baseline Estimate', 
-    marker_color='lightgray'
+    marker_color='gray'
 ))
 fig1.add_trace(go.Bar(
     x=[2030], y=[ai_emissions_mmt], 
@@ -261,8 +272,8 @@ fig1.update_layout(
     height=650,
     font=dict(size=18), 
     title={
-        'text': "Can we hit the 50% reduction target?",
-        'font': {'size': 24},
+        'text': "US Emissions Trajectory vs. AI Impact",
+        'font': {'size': 30},
         'x': 0.5,
         'xanchor': 'center'
     },