Update app.py

app.py

@@ -20,6 +20,7 @@ st.markdown("""
         text-align: center;
         margin-bottom: 20px;
         border: 1px solid #e0e0e0;
+        box-shadow: 0 4px 6px rgba(0,0,0,0.1);
     }
     .big-number {
         font-size: 3em;
@@ -30,132 +31,154 @@ st.markdown("""
         font-size: 1.2em;
         color: #555;
     }
+    .stPlotlyChart {
+        display: flex;
+        justify-content: center;
+    }
 </style>
 """, unsafe_allow_html=True)
 
 # --- Title ---
 st.title("🌍 The Climate Cost of the AI Race ⛽️")
 st.markdown("""
-**What will the US emissions of AI be in 2030?** Use the sliders to model the variables, focused on natural gas deployment. 
+**What will the US emissions of AI be in 2030?** Model the variables below, focused on the efficiency of Natural Gas deployment.
 """)
 
 st.divider()
 
-# Initialize variables to ensure scope safety
-ai_demand_gw = 100
-gas_share = 90
-ocgt_share = 50
-capacity_factor = 0.90
-ccgt_eff = 0.60
-ocgt_eff = 0.33
-base_ef = 486
-us_baseline_mmt = 6343
-
-# --- Explanation ---
-st.info(f"**Current Scenario:** {gas_share}% of AI power met by Gas, with {ocgt_share}% of that using Open Cycle turbines.")
-
 # --- Sidebar Inputs ---
 st.sidebar.header("⚙️ Scenario Settings")
 
-# Mode Toggle
-mode = st.sidebar.radio("Mode", ["Simple", "Advanced"], horizontal=True)
-
-if mode == "Simple":
-    st.sidebar.subheader("Demand Assumptions")
-    ai_demand_gw = st.sidebar.number_input("AI Power Demand in 2030 (GW)", value=100, key="adv_gw")
-    gas_share = st.sidebar.slider("Gas Share (%)", 0, 100, 90, key="adv_gas")
-    capacity_factor = st.sidebar.slider("Capacity Factor", 0.0, 1.0, 0.90, key="adv_cf")
-    
-    st.sidebar.subheader("Natural Gas Power Proportion")
-    gas_share = st.sidebar.slider(
-        "Percent of AI Power met by Natural Gas (%)", 
-        min_value=0, max_value=100, value=90, step=5,
-        key="simple_gas_share",
-        help="How much of the AI load is powered by gas vs clean energy and/or other strategies like demand response."
-    )
-    
-    st.sidebar.subheader("2. Turbine Technology")
-    ocgt_share = st.sidebar.slider(
-        "Percent Open Cycle (Speed Scenario) (%)", 
-        min_value=0, max_value=100, value=50, step=10,
-        key="simple_ocgt_share",
-        help="0% = All Combined Cycle. 100% = All Open Cycle."
-    )
-    
-else: # Advanced Mode
-    st.sidebar.subheader("Technical Specs")
-    ocgt_share = st.sidebar.slider("Open Cycle Share (%)", 0, 100, 50, key="adv_ocgt")
-    
-    ccgt_eff = st.number_input("CCGT Efficiency", value=0.60, key="adv_ccgt_eff")
-    ocgt_eff = st.number_input("OCGT Efficiency", value=0.33, key="adv_ocgt_eff")
-    base_ef = st.number_input("CCGT Emissions Factor (gCO2e/kWh)", value=486, key="adv_base_ef")
-    us_baseline_mmt = st.number_input("US Baseline Emissions (Million tCO2e)", value=6343, key="adv_base_mmt")
+# 1. AI Power Demand
+ai_demand_gw = st.sidebar.number_input(
+    "AI Power Demand in 2030 (GW)", 
+    value=100, 
+    step=10,
+    help="Projected total power load required by AI data centers in 2030."
+)
+
+# 2. Gas Share
+gas_share = st.sidebar.slider(
+    "Natural Gas Proportion (%)", 
+    min_value=0, max_value=100, value=90, step=5,
+    help="The percentage of AI power demand that will be met specifically by Natural Gas generation (vs. Renewables/Nuclear)."
+)
+
+# 3. Turbine Efficiency (The new logic)
+# Range 35% (Aeroderivative) to 60% (H-Class CCGT)
+turbine_eff_percent = st.sidebar.slider(
+    "Gas Turbine Average Efficiency (%)", 
+    min_value=35, max_value=60, value=45, step=1,
+    help="Thermal efficiency of the gas fleet. 35-40% = Peakers/Aeroderivative. 50-60% = Modern Combined Cycle (CCGT)."
+)
+
+# Constants
+CAPACITY_FACTOR = 0.90  # Fixed as requested
+US_BASELINE_MMT = 6343  # EPA Gross GHG Inventory Proxy
+# Base reference: 486 gCO2e/kWh at 60% efficiency
+# Constant for Carbon Content of Fuel derived from this: 486 * 0.60 = 291.6
+FUEL_CARBON_CONSTANT = 486 * 0.60 
 
 # --- Calculations ---
-# 1. Calculate weighted emission factor
-ocgt_ef_calc = base_ef * (ccgt_eff / ocgt_eff)
-weighted_ef = ((ocgt_share/100) * ocgt_ef_calc) + ((1 - (ocgt_share/100)) * base_ef)
+
+# 1. Calculate Emissions Factor based on selected efficiency
+# Formula: EF_output = Constant_Fuel_Carbon / Efficiency
+if turbine_eff_percent > 0:
+    calculated_ef = FUEL_CARBON_CONSTANT / (turbine_eff_percent / 100)
+else:
+    calculated_ef = 0
 
 # 2. Calculate Total Energy
 gas_gw_capacity = ai_demand_gw * (gas_share/100)
-total_gwh = gas_gw_capacity * capacity_factor * 8760
+total_gwh = gas_gw_capacity * CAPACITY_FACTOR * 8760
 
 # 3. Calculate Emissions (MMT CO2e)
-ai_emissions_mmt = (total_gwh * weighted_ef) / 1_000_000 
+# (GWh * 1,000,000 -> kWh) * (gCO2 / 1,000,000 -> Tonnes) / 1,000,000 -> Million Tonnes
+# Simplifies to (GWh * EF) / 1,000,000
+ai_emissions_mmt = (total_gwh * calculated_ef) / 1_000_000 
 
-percent_increase = (ai_emissions_mmt / us_baseline_mmt) * 100
+percent_increase = (ai_emissions_mmt / US_BASELINE_MMT) * 100
 
 # --- Dashboard Layout ---
 
-# 1. The Big Number
-col1, col2, col3 = st.columns([1, 2, 1])
-with col2:
+# 1. The Big Number (Centered)
+c1, c2, c3 = st.columns([1, 2, 1])
+with c2:
     st.markdown(f"""
     <div class="metric-card">
-        <div class="sub-text">Projected US Emissions Increase in 2030</div>
+        <div class="sub-text">Projected US Emissions Increase</div>
         <div class="big-number">+{percent_increase:.2f}%</div>
         <div class="sub-text">({ai_emissions_mmt:.1f} Million tCO2e)</div>
+        <div style="font-size: 0.9em; color: #888; margin-top: 10px;">
+            Based on {turbine_eff_percent}% Efficiency & {gas_share}% Gas Share
+        </div>
     </div>
     """, unsafe_allow_html=True)
 
-# 2. Charts
-col_chart1, col_chart2 = st.columns(2)
-
-with col_chart1:
-    st.subheader("US Emissions Trajectory")
-    
-    # Data Setup
-    years = [2005, 2010, 2015, 2020, 2023]
-    emissions_hist = [7200, 6900, 6600, 6000, 6200] # Proxies
-    target_2030 = 7200 * 0.50
-    bau_2030 = 5800 # Optimistic decline
-    
-    fig1 = go.Figure()
-    
-    # History
-    fig1.add_trace(go.Scatter(x=years, y=emissions_hist, mode='lines', name='Historical', line=dict(color='gray')))
-    
-    # Target Path
-    fig1.add_trace(go.Scatter(x=[2023, 2030], y=[6200, target_2030], mode='lines', name='Paris Goal', line=dict(color='green', dash='dash')))
-    
-    # AI Impact Stack
-    fig1.add_trace(go.Bar(x=[2030], y=[bau_2030], name='US Baseline', marker_color='lightgray'))
-    fig1.add_trace(go.Bar(x=[2030], y=[ai_emissions_mmt], name='AI Impact', base=bau_2030, marker_color='#ff4b4b'))
-    
-    fig1.update_layout(
-        title="Can we hit the 50% reduction target?",
-        yaxis_title="Emissions (Million tCO2e)",
-        barmode='stack',
-        legend=dict(orientation="h", y=-0.2),
-        height=400
-    )
-    st.plotly_chart(fig1, use_container_width=True)
+# 2. Charts (Centered / Full Width)
+# Using columns with spacers to center the chart if it feels too wide, 
+# or just full width for better visibility.
+st.subheader("US Emissions Trajectory vs. AI Impact")
+
+# Data Setup
+years = [2005, 2010, 2015, 2020, 2023]
+emissions_hist = [7200, 6900, 6600, 6000, 6200] 
+target_2030 = 7200 * 0.50 # 50% reduction from 2005
+bau_2030 = 5800 # Optimistic business-as-usual decline
+
+fig1 = go.Figure()
+
+# History Line
+fig1.add_trace(go.Scatter(
+    x=years, y=emissions_hist, 
+    mode='lines+markers', 
+    name='Historical Emissions', 
+    line=dict(color='gray', width=2)
+))
+
+# Target Path Line
+fig1.add_trace(go.Scatter(
+    x=[2023, 2030], y=[6200, target_2030], 
+    mode='lines', 
+    name='2030 Climate Goal (50% Cut)', 
+    line=dict(color='green', dash='dash', width=2)
+))
+
+# AI Impact Stacked Bar
+fig1.add_trace(go.Bar(
+    x=[2030], y=[bau_2030], 
+    name='2030 Baseline Estimate', 
+    marker_color='lightgray'
+))
+fig1.add_trace(go.Bar(
+    x=[2030], y=[ai_emissions_mmt], 
+    name='Additional AI Gas Emissions', 
+    base=bau_2030, 
+    marker_color='#ff4b4b'
+))
+
+fig1.update_layout(
+    yaxis_title="Emissions (Million tCO2e)",
+    barmode='stack',
+    legend=dict(orientation="h", y=1.1, x=0.5, xanchor='center'),
+    height=500,
+    margin=dict(l=40, r=40, t=40, b=40),
+    hovermode="x unified"
+)
+
+# Centering the chart using container width
+st.plotly_chart(fig1, use_container_width=True)
+
 
 st.markdown("---")
 with st.expander("📚 Data Sources & Methodology"):
-    st.markdown("""
-    * **Baseline:** Based on EPA Gross GHG Inventory (~6,343 MMT).
-    * **Efficiency:** Combined Cycle (60%) vs Aeroderivative OCGT (33%).
-    * **Emission Factors:** Derived from NREL Lifecycle Analysis.
-    * **Logic:** OCGT is modeled as ~1.8x more carbon intensive per kWh than CCGT due to lower efficiency.
-    """)
+    st.markdown(f"""
+    **Methodology Updates:**
+    * **Baseline Emissions:** Compared against approx. {US_BASELINE_MMT} MMT (EPA Gross GHG Inventory).
+    * **Efficiency Logic:** Emissions Factor is calculated dynamically based on the **Electrical Efficiency** slider ({turbine_eff_percent}% currently selected).
+        * *Formula:* `EF = (Benchmark Carbon Content) / Efficiency %`
+        * *Benchmark:* Based on standard NREL data for CCGT (486 gCO2e/kWh @ 60% efficiency).
+        * Lower efficiency (e.g., 35% for Aeroderivative/Peakers) results in higher emissions per kWh.
+    * **Capacity Factor:** Fixed at **{int(CAPACITY_FACTOR*100)}%** to represent high uptime requirements for AI workloads.
+    * **Source Data:** Turbine efficiency ranges (35-60%) derived from industrial comparisons of Aeroderivative GTs vs H-Class CCGTs.
+    """)