diff --git a/initial_view/secondtake.ipynb b/initial_view/secondtake.ipynb
index 2f3fbf06..bc35512c 100644
--- a/initial_view/secondtake.ipynb
+++ b/initial_view/secondtake.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -68,7 +68,7 @@
        "      dtype='object', length=127)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -126,19 +126,12 @@
     "gdf = gpd.read_file(shapefile_path)\n",
     "\n",
     "# Print the head of the GeoDataFrame\n",
-    "print(gdf.head())import geopandas as gpd\n",
-    "\n",
-    "# Load the shapefile\n",
-    "shapefile_path = '/home/dadams/Repos/california_equity_git/california_enviroscreen/calif_enviroscreen_shape/CES4 Final Shapefile.shp'\n",
-    "gdf = gpd.read_file(shapefile_path)\n",
-    "\n",
-    "# Print the head of the GeoDataFrame\n",
     "print(gdf.head())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -177,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -296,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -375,7 +368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -558,7 +551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -605,7 +598,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -653,6 +646,985 @@
     "plt.tight_layout()\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "1. **Project Volume Evolution**\n",
+    "- Growth phase: 2014-2019 (from ~0 to 5000+ projects)\n",
+    "- Plateau: 2019-2022 (~4000-5000 projects)\n",
+    "- Sharp decline: 2023-2024 (down to ~500 projects)\n",
+    "\n",
+    "2. **Average Project Size**\n",
+    "- Relatively stable 2014-2023 ($0.1-0.3M per project)\n",
+    "- Dramatic increase in 2024 (to ~$1.3M per project)\n",
+    "- Suggests shift to fewer but larger projects\n",
+    "\n",
+    "3. **Total GGRF Funding**\n",
+    "- Steady increase: 2014-2020 (reaching ~$1.1B)\n",
+    "- Recent decline: 2020-2024 (down to ~$0.67B)\n",
+    "- More stable pattern than project counts\n",
+    "\n",
+    "4. **Partnership Trends**\n",
+    "- Generally stable at 1.0-1.1 partners until 2020\n",
+    "- Spike in 2020 (~1.24 partners)\n",
+    "- New peak in 2024 (~1.32 partners)\n",
+    "- Suggests increasing regional collaboration\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Program Scale Distribution:\n",
+      "size_category\n",
+      "Small     21\n",
+      "Large     11\n",
+      "Medium     5\n",
+      "Mega       1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Geographic Concentration:\n",
+      "Top 5 counties account for 39.3% of funding\n",
+      "\n",
+      "Program Categories by Total Funding (Billions $):\n",
+      "size_category\n",
+      "Large     2.19\n",
+      "Small     2.00\n",
+      "Mega      1.19\n",
+      "Medium    0.78\n",
+      "Name: total_funding, dtype: float64\n",
+      "\n",
+      "Largest Programs (by total funding):\n",
+      "                                                    total_funding  \\\n",
+      "Program Name                                                        \n",
+      "Affordable Housing and Sustainable Communities ...     1192203125   \n",
+      "Low Carbon Transit Operations Program                   775906434   \n",
+      "Transit and Intercity Rail Capital Program              771556000   \n",
+      "Fire Prevention Program                                 596274123   \n",
+      "Community Air Protection                                529523228   \n",
+      "\n",
+      "                                                    project_count  \\\n",
+      "Program Name                                                        \n",
+      "Affordable Housing and Sustainable Communities ...             93   \n",
+      "Low Carbon Transit Operations Program                         766   \n",
+      "Transit and Intercity Rail Capital Program                    135   \n",
+      "Fire Prevention Program                                       600   \n",
+      "Community Air Protection                                     5187   \n",
+      "\n",
+      "                                                    avg_funding  dac_rate  \\\n",
+      "Program Name                                                                \n",
+      "Affordable Housing and Sustainable Communities ...  12819388.44      0.16   \n",
+      "Low Carbon Transit Operations Program                1012932.68      0.09   \n",
+      "Transit and Intercity Rail Capital Program           5715229.63      0.19   \n",
+      "Fire Prevention Program                               993790.20      0.00   \n",
+      "Community Air Protection                              102086.61      0.00   \n",
+      "\n",
+      "                                                    ghg_per_dollar  \n",
+      "Program Name                                                        \n",
+      "Affordable Housing and Sustainable Communities ...            0.00  \n",
+      "Low Carbon Transit Operations Program                         0.01  \n",
+      "Transit and Intercity Rail Capital Program                    0.01  \n",
+      "Fire Prevention Program                                       0.00  \n",
+      "Community Air Protection                                      0.00  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# 1. Program Scale Analysis\n",
+    "program_scale = data_filtered.groupby('Program Name').agg({\n",
+    "    'Total Program GGRFFunding': ['count', 'sum', 'mean'],\n",
+    "    'Is Benefit Disadvantaged Communities': 'mean',\n",
+    "    'Total Project GHGReductions': 'sum'\n",
+    "}).round(2)\n",
+    "\n",
+    "# Flatten column names\n",
+    "program_scale.columns = ['project_count', 'total_funding', 'avg_funding', \n",
+    "                        'dac_rate', 'total_ghg']\n",
+    "\n",
+    "# Calculate GHG efficiency\n",
+    "program_scale['ghg_per_dollar'] = program_scale['total_ghg'] / program_scale['total_funding']\n",
+    "\n",
+    "# Categorize programs by size\n",
+    "def categorize_program_size(mean_funding):\n",
+    "    if mean_funding > 10e6:  # 10M\n",
+    "        return 'Mega'\n",
+    "    elif mean_funding > 1e6:  # 1M\n",
+    "        return 'Large'\n",
+    "    elif mean_funding > 500e3:  # 500K\n",
+    "        return 'Medium'\n",
+    "    else:\n",
+    "        return 'Small'\n",
+    "\n",
+    "program_scale['size_category'] = program_scale['avg_funding'].apply(categorize_program_size)\n",
+    "\n",
+    "print(\"Program Scale Distribution:\")\n",
+    "print(program_scale['size_category'].value_counts())\n",
+    "\n",
+    "# 2. Geographic Analysis\n",
+    "geographic_dist = data_filtered.groupby('County').agg({\n",
+    "    'Total Program GGRFFunding': ['count', 'sum'],\n",
+    "    'Is Benefit Disadvantaged Communities': 'mean',\n",
+    "    'Total Project GHGReductions': 'sum'\n",
+    "})\n",
+    "\n",
+    "geographic_dist.columns = ['project_count', 'total_funding', 'dac_rate', 'total_ghg']\n",
+    "\n",
+    "# Calculate concentration metrics\n",
+    "total_funding = geographic_dist['total_funding'].sum()\n",
+    "top_5_counties = geographic_dist['total_funding'].nlargest(5)\n",
+    "concentration = (top_5_counties.sum() / total_funding) * 100\n",
+    "\n",
+    "print(\"\\nGeographic Concentration:\")\n",
+    "print(f\"Top 5 counties account for {concentration:.1f}% of funding\")\n",
+    "\n",
+    "# 3. Print key findings\n",
+    "print(\"\\nProgram Categories by Total Funding (Billions $):\")\n",
+    "size_summary = program_scale.groupby('size_category')['total_funding'].sum().sort_values(ascending=False)/1e9\n",
+    "print(size_summary.round(2))\n",
+    "\n",
+    "# Show largest programs\n",
+    "print(\"\\nLargest Programs (by total funding):\")\n",
+    "print(program_scale.nlargest(5, 'total_funding')[\n",
+    "    ['total_funding', 'project_count', 'avg_funding', 'dac_rate', 'ghg_per_dollar']\n",
+    "].round(2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "1. **Program Scale Distribution**\n",
+    "- Most programs (21) are \"Small\" scale\n",
+    "- 11 \"Large\" programs\n",
+    "- Only 1 \"Mega\" program (Affordable Housing at $1.19B)\n",
+    "- More balanced distribution than when including transportation subsidies\n",
+    "\n",
+    "1. **Funding Allocation**\n",
+    "- Large programs: $2.19B total\n",
+    "- Small programs: $2.00B total\n",
+    "- Mega programs: $1.19B total\n",
+    "- Medium programs: $0.78B total\n",
+    "- Total GGRF funding: ~$6.16B\n",
+    "\n",
+    "1. **Top Programs by Funding**\n",
+    "- Affordable Housing: $1.19B (93 projects)\n",
+    "- Low Carbon Transit: $776M (766 projects)\n",
+    "- Transit/Rail Capital: $772M (135 projects)\n",
+    "- Fire Prevention: $596M (600 projects)\n",
+    "- Community Air Protection: $530M (5,187 projects)\n",
+    "\n",
+    "1. **Program Characteristics**\n",
+    "- Wide range in project counts (93 to 5,187)\n",
+    "- Average project sizes vary significantly:\n",
+    "  * Affordable Housing: $12.8M/project\n",
+    "  * Transit/Rail: $5.7M/project\n",
+    "  * Community Air Protection: $102K/project\n",
+    "\n",
+    "1. **Geographic Distribution**\n",
+    "- Less concentrated than before\n",
+    "- Top 5 counties: 39.3% of funding (vs. previous 75.6%)\n",
+    "- Suggests more equitable geographic distribution\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1. DAC Benefits by Program Size:\n",
+      "              dac_rate             total_funding project_count\n",
+      "                  mean   min   max           sum           sum\n",
+      "size_category                                                 \n",
+      "Large            0.087  0.00  0.67    2185763970          1314\n",
+      "Medium           0.036  0.00  0.18     779274523           831\n",
+      "Mega             0.160  0.16  0.16    1192203125            93\n",
+      "Small            0.084  0.00  0.92    2003043460         28913\n",
+      "\n",
+      "2. Regional Distribution:\n",
+      "                total_funding  project_count  dac_rate  total_ghg\n",
+      "region                                                           \n",
+      "Central Valley     1387932197          14496     0.104   25320641\n",
+      "Other              2289435192           9195     0.101   24839824\n",
+      "Urban              2482917689           7460     0.161   15727243\n",
+      "\n",
+      "4. Multi-County vs Single-County Projects:\n",
+      "                Multi-County  Single-County\n",
+      "project_count   7.520000e+02   3.039900e+04\n",
+      "total_funding   5.210746e+08   5.639210e+09\n",
+      "avg_funding     6.929184e+05   1.855064e+05\n",
+      "dac_rate        1.090000e-01   2.730000e-01\n",
+      "ghg_per_dollar  1.200000e-02   1.100000e-02\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1. DAC Benefits by Program Size\n",
+    "dac_by_size = program_scale.groupby('size_category').agg({\n",
+    "Would you like to:\n",
+    "1. Analyze DAC benefit patterns by program size?\n",
+    "2. Look deeper into geographic distribution patterns?\n",
+    "3. Examine the relationship between project size and GHG efficiency?\n",
+    "4. Investigate multi-county collaboration patterns?\n",
+    "   'dac_rate': ['mean', 'min', 'max'],\n",
+    "   'total_funding': 'sum',\n",
+    "   'project_count': 'sum'\n",
+    "}).round(3)\n",
+    "\n",
+    "# 2. Geographic Analysis\n",
+    "# Add region classification\n",
+    "def classify_region(county):\n",
+    "   if isinstance(county, str):  # Handle multi-county cases\n",
+    "       counties = county.split(',')\n",
+    "       county = counties[0].strip()\n",
+    "   \n",
+    "   urban_counties = ['Los Angeles', 'San Francisco', 'Alameda', 'San Diego', 'Orange']\n",
+    "   central_valley = ['Fresno', 'Kern', 'Kings', 'Madera', 'Merced', 'San Joaquin', 'Stanislaus', 'Tulare']\n",
+    "   \n",
+    "   if county in urban_counties:\n",
+    "       return 'Urban'\n",
+    "   elif county in central_valley:\n",
+    "       return 'Central Valley'\n",
+    "   else:\n",
+    "       return 'Other'\n",
+    "\n",
+    "geographic_dist['region'] = geographic_dist.index.map(classify_region)\n",
+    "regional_metrics = geographic_dist.groupby('region').agg({\n",
+    "   'total_funding': 'sum',\n",
+    "   'project_count': 'sum',\n",
+    "   'dac_rate': 'mean',\n",
+    "   'total_ghg': 'sum'\n",
+    "}).round(3)\n",
+    "\n",
+    "# 3. Project Size vs GHG Efficiency\n",
+    "# Create scatter plot\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "plt.scatter(program_scale['avg_funding']/1e6, \n",
+    "          program_scale['ghg_per_dollar'],\n",
+    "          alpha=0.6)\n",
+    "plt.xlabel('Average Project Size (Millions $)')\n",
+    "plt.ylabel('GHG Reduction per Dollar')\n",
+    "plt.title('Project Size vs. GHG Efficiency')\n",
+    "\n",
+    "# 4. Multi-county Analysis\n",
+    "multi_county_data = data_filtered[data_filtered['County'].str.contains(',', na=False)]\n",
+    "single_county_data = data_filtered[~data_filtered['County'].str.contains(',', na=False)]\n",
+    "\n",
+    "multi_vs_single = pd.DataFrame({\n",
+    "   'Multi-County': {\n",
+    "       'project_count': len(multi_county_data),\n",
+    "       'total_funding': multi_county_data['Total Program GGRFFunding'].sum(),\n",
+    "       'avg_funding': multi_county_data['Total Program GGRFFunding'].mean(),\n",
+    "       'dac_rate': multi_county_data['Is Benefit Disadvantaged Communities'].mean(),\n",
+    "       'ghg_per_dollar': (multi_county_data['Total Project GHGReductions'].sum() / \n",
+    "                         multi_county_data['Total Program GGRFFunding'].sum())\n",
+    "   },\n",
+    "   'Single-County': {\n",
+    "       'project_count': len(single_county_data),\n",
+    "       'total_funding': single_county_data['Total Program GGRFFunding'].sum(),\n",
+    "       'avg_funding': single_county_data['Total Program GGRFFunding'].mean(),\n",
+    "       'dac_rate': single_county_data['Is Benefit Disadvantaged Communities'].mean(),\n",
+    "       'ghg_per_dollar': (single_county_data['Total Project GHGReductions'].sum() / \n",
+    "                         single_county_data['Total Program GGRFFunding'].sum())\n",
+    "   }\n",
+    "})\n",
+    "\n",
+    "print(\"1. DAC Benefits by Program Size:\")\n",
+    "print(dac_by_size)\n",
+    "\n",
+    "print(\"\\n2. Regional Distribution:\")\n",
+    "print(regional_metrics)\n",
+    "\n",
+    "print(\"\\n4. Multi-County vs Single-County Projects:\")\n",
+    "print(multi_vs_single.round(3))\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "1. **Efficiency Sweet Spot**\n",
+    "- Highest GHG efficiency (0.12-0.13 reduction per dollar) occurs in projects around $2M\n",
+    "- Secondary peak (0.06) in smaller projects under $1M\n",
+    "- Suggests optimal scale for GHG reduction isn't necessarily larger projects\n",
+    "\n",
+    "1. **Size-Efficiency Relationship**\n",
+    "- No clear linear relationship\n",
+    "- Most projects cluster in lower ranges (both size and efficiency)\n",
+    "- Largest projects ($12M+) show relatively low GHG efficiency\n",
+    "- Suggests diminishing returns as projects scale up\n",
+    "\n",
+    "1. **Distribution Pattern**\n",
+    "- Dense cluster of projects under $2M with varying efficiency\n",
+    "- Sparse distribution in higher project sizes\n",
+    "- Few projects achieve both large scale and high efficiency\n",
+    "\n",
+    "Let's identify:\n",
+    "1. What programs occupy that sweet spot around $2M with high efficiency?\n",
+    "2. What characteristics do those efficient projects share?\n",
+    "3. Are there geographic patterns in the more efficient projects?\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1. Most GHG-Efficient Programs:\n",
+      "                                                    total_funding  \\\n",
+      "Program Name                                                        \n",
+      "Sustainable Agricultural Lands Conservation Pro...      122424176   \n",
+      "Climate Smart Agriculture                               338161549   \n",
+      "Fluorinated Gases Emission Reduction Incentives           1000001   \n",
+      "Low-Carbon Fuels Production Program                      12500000   \n",
+      "Food Production Investment Program                      117791478   \n",
+      "Wetlands and Watershed Restoration                       16171301   \n",
+      "Forest Health Program                                   207621340   \n",
+      "Waste Diversion                                         118571417   \n",
+      "Renewable Energy for Agriculture Program                  9500000   \n",
+      "Woodsmoke Reduction Program                               7895909   \n",
+      "\n",
+      "                                                    avg_funding  \\\n",
+      "Program Name                                                      \n",
+      "Sustainable Agricultural Lands Conservation Pro...   1275251.83   \n",
+      "Climate Smart Agriculture                             257941.68   \n",
+      "Fluorinated Gases Emission Reduction Incentives        66666.73   \n",
+      "Low-Carbon Fuels Production Program                  3125000.00   \n",
+      "Food Production Investment Program                   2103419.25   \n",
+      "Wetlands and Watershed Restoration                   2021412.62   \n",
+      "Forest Health Program                                1701814.26   \n",
+      "Waste Diversion                                       463169.60   \n",
+      "Renewable Energy for Agriculture Program              211111.11   \n",
+      "Woodsmoke Reduction Program                             8233.48   \n",
+      "\n",
+      "                                                    ghg_per_dollar  dac_rate  \n",
+      "Program Name                                                                  \n",
+      "Sustainable Agricultural Lands Conservation Pro...           0.123      0.01  \n",
+      "Climate Smart Agriculture                                    0.062      0.05  \n",
+      "Fluorinated Gases Emission Reduction Incentives              0.037      0.00  \n",
+      "Low-Carbon Fuels Production Program                          0.036      0.00  \n",
+      "Food Production Investment Program                           0.025      0.00  \n",
+      "Wetlands and Watershed Restoration                           0.025      0.00  \n",
+      "Forest Health Program                                        0.023      0.00  \n",
+      "Waste Diversion                                              0.016      0.09  \n",
+      "Renewable Energy for Agriculture Program                     0.013      0.00  \n",
+      "Woodsmoke Reduction Program                                  0.013      0.00  \n",
+      "\n",
+      "2. Characteristics of High-Efficiency Programs:\n",
+      "\n",
+      "Size Distribution:\n",
+      "size_category\n",
+      "Large    5\n",
+      "Small    5\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "3. Geographic Distribution of High-Efficiency Projects:\n",
+      "                    Total Program GGRFFunding           \\\n",
+      "                                        count      sum   \n",
+      "County                                                   \n",
+      "Sierra                                      5   485667   \n",
+      "Mono                                       10  2946879   \n",
+      "Lassen                                     28  5510913   \n",
+      "Calaveras                                  32  9678108   \n",
+      "Los Angeles, Solano                         1   212629   \n",
+      "\n",
+      "                    Total Project GHGReductions ghg_per_dollar  \n",
+      "                                            sum                 \n",
+      "County                                                          \n",
+      "Sierra                                   730033       1.503155  \n",
+      "Mono                                    1570341       0.532883  \n",
+      "Lassen                                  2089977       0.379243  \n",
+      "Calaveras                               1857223       0.191899  \n",
+      "Los Angeles, Solano                       23680       0.111368  \n"
+     ]
+    },
+    {
+     "data": {
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7dqwhyRg8eHCG15Jey86dOw1JxogRI+web968uREeHm5Xc2bCwsKMRx55xDhx4oRx4sQJY8uWLUbbtm0NSUa3bt0MwzCM/fv3G5IMPz8/4/jx43bPr1KlihEUFGScOnXKtmzLli2Gm5ubER8fb1v2+OOPGwUKFDCOHDliW7Z7924jX758xo2/RiUZbm5uxt9//52h3gsXLth9f/nyZSMqKspo0KBBhm14enraHYOjRo0yJBkhISFGcnKybXlCQoLd8bpp0yZDkjF9+vSs3jrbMZB+7JixWq22n3lwcLDRrl0748svvzQOHjyYYd304/z6uq934sQJ47777jMqV65spKSkGIZhGCtXrjQkGZMmTbJbd8GCBZkuv1FsbKxRvXp1u2Xr1q2zO0YdfU8y06FDB8PHx8d2jO3Zs8f44IMPDIvFYtx///2GYRjGd999Z7i5uRkrV660e+7IkSMNScaqVatsy7I6PjL77A8ZMsSQZEycONG27PLly0ZsbKzh6+trOxayOs7Tf9ZdunSxLbt69apRsmRJw2KxGB9++KFt+ZkzZwxvb2+735VXr141UlNT7bZ55swZIzg42Hj++edty9JrKFq0qHH69Gnb8h9//NGQZPz000+2ZfXq1TMKFiyY4Ti6/jPfuXNno1ixYsbJkyft1mnbtq3h7++f4fMEAEBeoC+5hr6EviS3+xLD+N/xn9nXoEGDbOtVqVLFKFasmHH27FnbskWLFhmSjLCwMNuyG4/bdOk/q3HjxmWo83o+Pj52n+V0CQkJhqenp93+jx8/buTLl8/o379/lq8xISHByJ8/v914OjU11ShUqJDd2Nvf3z/D59AR6b8DlixZYpw4ccI4fPiwMWXKFKNo0aKGt7e38d///jdbn8ORI0dm2Fd6L3W9zZs3G5KMF154wW557969DUnGsmXLbMvSf94LFiywWzf95xYVFWVcvnzZtrxdu3aGxWIxmjRpYrd+bGys3c/dMDJ+BgzDMBo3bmyUKlXKbpmjv3P79etnSDJmzpyZYbvpvz+c6Rtxe2I6F9zRvvzySy1evDjD1/3333/T5y5YsECxsbF2Z1EWKVJE7du3z3T9ihUrqm7durbvAwMDVa5cOe3bty9H9c6fP9/h59/Mpk2btH//fvXo0SPDPIWZXSaZbubMmbJarWrdurVOnjxp+woJCVGZMmUyTNvg6+trdzaGh4eHatWqZfde/PDDDwoICFC3bt0y7C+9lrJlyyomJkaTJk2yPXb69GnNnz9f7du3z7LmdIsWLVJgYKACAwMVHR2t6dOn67nnntNHH31kt16rVq3sztA4evSoNm/erI4dO9qdbXL//ferUaNGmjdvnqRrZ64sWbJELVq0sJv7OjIy0nZ2y43i4uIynT/y+rnbzpw5o6SkJNWtWzfTS+4efvhhu0sNY2JibK+jYMGCGZanv/fpZ3QsXLgwy0si33nnHRmGcdOzPSwWixYuXKiBAweqcOHC+v777/Xqq68qLCxMbdq0sZsTPStpaWlq166dzp07p1mzZsnHx0eSNH36dPn7+6tRo0Z2x1716tXl6+ub4di7UZs2bbRhwwbbmRPStbM0PD09bZfkOvqemDl//rztGIuMjNTbb7+t2NhY21kS06dPV4UKFVS+fHm715B+SeSNr8Hs+MjMvHnzFBISYjcvYf78+dW9e3elpKTol19+sVv/xuP8ei+88ILt3+7u7qpRo4YMw1Dnzp1tywsVKpTh95q7u7ttHkOr1arTp0/r6tWrqlGjRqbHbps2bVS4cGHb9+m/N9O3eeLECf366696/vnndd9999k9N/0zbxiGfvjhBz3++OMyDMPufW3cuLGSkpKyfakqAAC5gb7EHn0JfYmr+5Lr95PZZy19vJz+fnbo0MHuitNGjRq59P5DWYmPj1dqaqrdFDlTp07V1atXb3pVQ5s2bXTlyhXNnDnTtmzRokU6e/as2rRpY1tWqFAhrV27Vv/880+2amzYsKECAwMVGhqqtm3bytfXV7NmzVKJEiWc/hx6enqqU6dODu03/Xju1auX3fL0K17mzp1rtzwiIsJ29v2N4uPj7a5ajomJkWEYev755+3Wi4mJ0eHDh3X16lXbsus/A+lX5sTFxWnfvn0Zpvx05HfuDz/8oOjo6AxXH0j/+z3jbN+I2w/TueCOVqtWrUxviFO4cOFML/G63sGDBzO9jOrG6SfS3Rj2pO8nfc7ltLQ0nThxwu7xIkWK2N1Ew6xeV0kPEqOiopx63u7du2UYhsqUKZPp4zdOp1GyZMkMA8nChQvrzz//tKulXLlyN53+Iz4+Xl27dtXBgwcVFham6dOn68qVK3ruueccqj0mJkYDBw6UxWJRgQIFVKFChQwDdenaf77XS59H+/rLwdJVqFBBCxcu1Pnz55WcnKyLFy9melyYHSs37ivdzz//rIEDB2rz5s12c55lNii/8XhLHwCGhoZmujz9OIyIiFCvXr00ePBgTZo0SXXr1lXz5s1tc2Fnh6enp/7zn//oP//5j44ePapffvlFQ4cO1bRp05Q/f35NnDjxptvo06ePli1bprlz56p06dK25bt371ZSUpLd9C7XO378eJbbffrpp9WrVy9NnTpVb7/9tgzD0PTp09WkSRP5+flJyvl74uXlpZ9++sn2XkRERNhdCrp7925t377dNLy+8TWYHR+ZOXjwoMqUKSM3N/u/eVeoUMH2uKPbzuyY8vLyUkBAQIblp06dsls2YcIEffbZZ9qxY4fdZemZ7e/G/aQH6unHaPpgM6vfUydOnNDZs2c1evRojR49OtN1bnZsAABwK9GX2KMvoS/Jjb5EkgICAtSwYUPTx9Pfz8yOoXLlyt2SEzHKly+vmjVratKkSbYTViZNmqQHHnjA9GeVLjo6WuXLl9fUqVNtz506daoCAgJsYaskffzxx+rQoYNCQ0NVvXp1PfbYY4qPj1epUqUcqvHLL79U2bJllS9fPgUHB6tcuXK2nsPZz2GJEiUcvnnowYMH5ebmluF9CAkJUaFChXLc30iZH5tWq1VJSUm26WpWrVql/v37a/Xq1Rn+yJOUlGR3jN7sd6507fdMq1atTGuVnO8bcfshRAcc5O7unulywzAkSYcPH87wC97Red3ymtVqlcVi0fz58zN9nb6+vnbf3+y9cEbbtm3Vs2dPTZo0SW+//bYmTpyoGjVqZDqIzMzNBlHpbuUdvDPb18qVK9W8eXPVq1dPX331lYoVK6b8+fNr3Lhxmjx5cob1zd5jR977zz77TB07dtSPP/6oRYsWqXv37ho0aJDWrFmT43kAixUrprZt26pVq1aqVKmSpk2bpvHjx2fZlMyePVsfffSR3nvvPT366KN2j1mtVgUFBdmd9XM9swFGuuLFi6tu3bqaNm2a3n77ba1Zs0aHDh3KcMZPTt4Td3f3LI8xq9WqypUra/DgwZk+fuMgLjePxay2ndmx48jxNHHiRHXs2FEtWrTQ66+/rqCgILm7u2vQoEF2VwA4s82bsVqtkqRnn302w3yQ6Rw5sw8AgLsRfcn/0Jc4v6+7pS9xBbMrDNLS0nK87fj4eL322mv673//q9TUVK1Zs0bDhw936Llt2rTR+++/r5MnT6pgwYKaM2eO2rVrZ9dztW7dWnXr1tWsWbO0aNEiffLJJ/roo480c+ZM0ysTrpfVH9Kc/Rxm55h25OqOm207u8fm3r179fDDD6t8+fIaPHiwQkND5eHhoXnz5unzzz+39SKObs9RzvaNuP0QouOeFRYWpj179mRYntkyR4SEhGjx4sV2y6Kjo7O1rexKP8N369atDg3grn+eYRiKiIhQ2bJlXVbL2rVrdeXKFdMbQ0rXzopp2rSpJk2apPbt22vVqlUaMmSIS2rISvoNM3fu3JnhsR07diggIEA+Pj7y8vKSl5dXjo+VH374QV5eXlq4cKHdTaLGjRuXjepvrnLlyqpcubL69Omj33//XXXq1NHIkSM1cOBAl2w/f/78uv/++7V7927bpX2Z2bVrlzp06KAWLVro7bffzvB46dKltWTJEtWpUyfbDUWbNm30yiuvaOfOnZo6daoKFCigxx9/PMN6ufWelC5dWlu2bNHDDz/s8GDQUWFhYfrzzz9ltVrtzkbfsWOH7fHcNmPGDJUqVUozZ860e339+/fP1vbSz47ZunWr6TqBgYEqWLCg0tLSnPpdBgDAnYi+xP559CX/Q1/ivPT3c/fu3Rkeu/E9Tr9i8sYpKm88G9pMVmP/tm3bqlevXvr+++918eJF5c+f3246lqy0adNGAwYM0A8//KDg4GAlJyerbdu2GdYrVqyYXnnlFb3yyis6fvy4qlWrpvfff9+hED0rufE5TBcWFiar1ardu3fbrq6VpMTERJ09e/aW9Dc//fSTUlNTNWfOHLuzzHMynUrp0qWz7G/S18mtvhG3BnOi457VuHFjrV69Wps3b7YtO336tOkZsTfj5eWlhg0b2n1dPy/wrVCtWjVFRERoyJAhGQYCWf2V9Mknn5S7u7sGDBiQYT3DMDJM7eCIVq1a6eTJk5n+tf3GfTz33HPatm2bXn/9dbm7u2c6QHC1YsWKqUqVKpowYYLde7V161YtWrRIjz32mKT/nYU8e/Zsu/nm9uzZ49S8ke7u7rJYLHZnNRw4cMDld+BOTk62m+tNujZwdXNzs7tU8+TJk9qxY8dN5wjfvXu3Dh06lGH52bNntXr1ahUuXNj0bPGUlBS1bNlSJUqU0IQJEzIdKLRu3VppaWl67733Mjx29epVh+Zcb9Wqldzd3fX9999r+vTpatasmW3Odcnx9yS7WrdurSNHjujrr7/O8NjFixd1/vz5bG/7scce07FjxzR16lTbsqtXr2rYsGHy9fVVXFxctrftqPQzL67/3K5du1arV6/O1vYCAwNVr149jR07NsOxlb4Pd3d3tWrVSj/88EOmg9EbL1EHAOBORl/yP/QlZ23L6Uuy5/r38/q5rRcvXqxt27bZrRsWFiZ3d3f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",
+      "text/plain": [
+       "<Figure size 1500x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's identify the most GHG-efficient projects and analyze their characteristics\n",
+    "\n",
+    "# Add efficiency metric to original program data\n",
+    "program_scale['efficiency_tier'] = pd.qcut(program_scale['ghg_per_dollar'], \n",
+    "                                        q=4, \n",
+    "                                        labels=['Low', 'Medium-Low', 'Medium-High', 'High'])\n",
+    "\n",
+    "print(\"1. Most GHG-Efficient Programs:\")\n",
+    "high_efficiency = program_scale[program_scale['efficiency_tier'] == 'High'].sort_values('ghg_per_dollar', ascending=False)\n",
+    "print(high_efficiency[['total_funding', 'avg_funding', 'ghg_per_dollar', 'dac_rate']].round(3))\n",
+    "\n",
+    "# Analyze characteristics of high-efficiency programs\n",
+    "print(\"\\n2. Characteristics of High-Efficiency Programs:\")\n",
+    "print(\"\\nSize Distribution:\")\n",
+    "print(high_efficiency['size_category'].value_counts())\n",
+    "\n",
+    "# Geographic analysis of high-efficiency programs\n",
+    "print(\"\\n3. Geographic Distribution of High-Efficiency Projects:\")\n",
+    "# Filter original data for these programs\n",
+    "high_eff_projects = data_filtered[\n",
+    "   data_filtered['Program Name'].isin(high_efficiency.index)\n",
+    "]\n",
+    "geographic_dist = high_eff_projects.groupby('County').agg({\n",
+    "   'Total Program GGRFFunding': ['count', 'sum'],\n",
+    "   'Total Project GHGReductions': 'sum'\n",
+    "}).round(2)\n",
+    "\n",
+    "# Calculate efficiency by region\n",
+    "geographic_dist['ghg_per_dollar'] = (\n",
+    "   geographic_dist[('Total Project GHGReductions', 'sum')] / \n",
+    "   geographic_dist[('Total Program GGRFFunding', 'sum')]\n",
+    ")\n",
+    "\n",
+    "print(geographic_dist.nlargest(5, 'ghg_per_dollar'))\n",
+    "\n",
+    "# Create visualization of efficiency patterns\n",
+    "plt.figure(figsize=(15, 8))\n",
+    "\n",
+    "plt.subplot(1, 2, 1)\n",
+    "# Size vs Efficiency for high performers\n",
+    "plt.scatter(high_efficiency['avg_funding']/1e6, \n",
+    "          high_efficiency['ghg_per_dollar'],\n",
+    "          alpha=0.6,\n",
+    "          s=100)\n",
+    "plt.xlabel('Average Project Size (Millions $)')\n",
+    "plt.ylabel('GHG Reduction per Dollar')\n",
+    "plt.title('High-Efficiency Programs: Size vs Performance')\n",
+    "\n",
+    "# Add program labels\n",
+    "for idx, row in high_efficiency.iterrows():\n",
+    "   plt.annotate(idx[:20] + '...' if len(idx) > 20 else idx,\n",
+    "               (row['avg_funding']/1e6, row['ghg_per_dollar']),\n",
+    "               xytext=(5, 5), textcoords='offset points')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "# DAC Rate vs Efficiency\n",
+    "plt.scatter(high_efficiency['dac_rate'], \n",
+    "          high_efficiency['ghg_per_dollar'],\n",
+    "          alpha=0.6,\n",
+    "          s=100)\n",
+    "plt.xlabel('DAC Benefit Rate')\n",
+    "plt.ylabel('GHG Reduction per Dollar')\n",
+    "plt.title('High-Efficiency Programs: Equity vs Performance')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Urban/Rural Analysis Summary:\n",
+      "                Project Count  Total Funding (B)  Avg Project Size (M)  \\\n",
+      "region_type                                                              \n",
+      "Central Valley          14496              1.388                 0.096   \n",
+      "Rural                    8474              2.183                 0.258   \n",
+      "Urban                    8181              2.589                 0.316   \n",
+      "\n",
+      "                GHG Efficiency  DAC Rate  \n",
+      "region_type                               \n",
+      "Central Valley           0.018     0.181  \n",
+      "Rural                    0.011     0.233  \n",
+      "Urban                    0.007     0.462  \n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Top Programs by Region (Funding in Millions $):\n",
+      "region_type                                         Central Valley   Rural  \\\n",
+      "Program Name                                                                 \n",
+      "Transit and Intercity Rail Capital Program                   18.28   86.89   \n",
+      "Affordable Housing and Sustainable Communities ...          205.61  321.80   \n",
+      "Low Carbon Transit Operations Program                        52.88  178.45   \n",
+      "Community Air Protection                                    144.75  145.80   \n",
+      "Transformative Climate Communities                           69.17   62.67   \n",
+      "Urban Greening Program                                       18.91   58.50   \n",
+      "Fire Prevention Program                                      51.09  493.55   \n",
+      "Waste Diversion                                              22.71   50.54   \n",
+      "Low-Income Weatherization Program                            62.87   57.02   \n",
+      "Urban and Community Forestry Program                          7.55   13.80   \n",
+      "\n",
+      "region_type                                          Urban  \n",
+      "Program Name                                                \n",
+      "Transit and Intercity Rail Capital Program          666.38  \n",
+      "Affordable Housing and Sustainable Communities ...  664.79  \n",
+      "Low Carbon Transit Operations Program               544.58  \n",
+      "Community Air Protection                            238.97  \n",
+      "Transformative Climate Communities                   70.89  \n",
+      "Urban Greening Program                               61.36  \n",
+      "Fire Prevention Program                              51.63  \n",
+      "Waste Diversion                                      45.32  \n",
+      "Low-Income Weatherization Program                    39.08  \n",
+      "Urban and Community Forestry Program                 37.84  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define urban/rural classification\n",
+    "def classify_urban_rural(county):\n",
+    "   if isinstance(county, str):  # Handle multi-county cases\n",
+    "       counties = county.split(',')\n",
+    "       county = counties[0].strip()\n",
+    "   \n",
+    "   urban_counties = ['Los Angeles', 'San Francisco', 'Alameda', 'San Diego', 'Orange', 'Santa Clara']\n",
+    "   central_valley = ['Fresno', 'Kern', 'Kings', 'Madera', 'Merced', 'San Joaquin', 'Stanislaus', 'Tulare']\n",
+    "   \n",
+    "   if county in urban_counties:\n",
+    "       return 'Urban'\n",
+    "   elif county in central_valley:\n",
+    "       return 'Central Valley'\n",
+    "   else:\n",
+    "       return 'Rural'\n",
+    "\n",
+    "# Add classification to data\n",
+    "data_filtered['region_type'] = data_filtered['County'].map(classify_urban_rural)\n",
+    "\n",
+    "# Analyze by region type\n",
+    "region_analysis = data_filtered.groupby('region_type').agg({\n",
+    "   'Total Program GGRFFunding': ['count', 'sum', 'mean'],\n",
+    "   'Total Project GHGReductions': ['sum', 'mean'],\n",
+    "   'Is Benefit Disadvantaged Communities': 'mean'\n",
+    "}).round(3)\n",
+    "\n",
+    "# Calculate GHG efficiency by region\n",
+    "ghg_efficiency = (region_analysis[('Total Project GHGReductions', 'sum')] / \n",
+    "                region_analysis[('Total Program GGRFFunding', 'sum')])\n",
+    "\n",
+    "print(\"Urban/Rural Analysis Summary:\")\n",
+    "summary_df = pd.DataFrame({\n",
+    "   'Project Count': region_analysis[('Total Program GGRFFunding', 'count')],\n",
+    "   'Total Funding (B)': region_analysis[('Total Program GGRFFunding', 'sum')]/1e9,\n",
+    "   'Avg Project Size (M)': region_analysis[('Total Program GGRFFunding', 'mean')]/1e6,\n",
+    "   'GHG Efficiency': ghg_efficiency,\n",
+    "   'DAC Rate': region_analysis[('Is Benefit Disadvantaged Communities', 'mean')]\n",
+    "})\n",
+    "print(summary_df.round(3))\n",
+    "\n",
+    "# Visualize key metrics\n",
+    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 12))\n",
+    "\n",
+    "# 1. Total Funding by Region\n",
+    "ax1.bar(summary_df.index, summary_df['Total Funding (B)'], color='skyblue')\n",
+    "ax1.set_title('Total Funding by Region (Billions $)')\n",
+    "ax1.set_ylabel('Funding (Billions $)')\n",
+    "\n",
+    "# 2. Average Project Size\n",
+    "ax2.bar(summary_df.index, summary_df['Avg Project Size (M)'], color='skyblue')\n",
+    "ax2.set_title('Average Project Size by Region (Millions $)')\n",
+    "ax2.set_ylabel('Average Size (Millions $)')\n",
+    "\n",
+    "# 3. GHG Efficiency\n",
+    "ax3.bar(summary_df.index, summary_df['GHG Efficiency'], color='skyblue')\n",
+    "ax3.set_title('GHG Reduction Efficiency by Region')\n",
+    "ax3.set_ylabel('GHG Reduction per Dollar')\n",
+    "\n",
+    "# 4. DAC Benefit Rate\n",
+    "ax4.bar(summary_df.index, summary_df['DAC Rate'], color='skyblue')\n",
+    "ax4.set_title('DAC Benefit Rate by Region')\n",
+    "ax4.set_ylabel('Proportion Benefiting DACs')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Look at program distribution by region\n",
+    "print(\"\\nTop Programs by Region (Funding in Millions $):\")\n",
+    "program_by_region = pd.crosstab(\n",
+    "   data_filtered['Program Name'], \n",
+    "   data_filtered['region_type'], \n",
+    "   values=data_filtered['Total Program GGRFFunding'],\n",
+    "   aggfunc='sum'\n",
+    ")/1e6\n",
+    "\n",
+    "print(program_by_region.sort_values('Urban', ascending=False).head(10).round(2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "1. **GHG Efficiency Gap**\n",
+    "- Central Valley is ~3x more efficient than Urban areas (0.0175 vs 0.0065 GHG reduction per dollar)\n",
+    "- Rural areas also outperform Urban (0.010 vs 0.0065)\n",
+    "- Suggests climate dollars go further in non-urban areas\n",
+    "\n",
+    "1. **Inverse DAC Pattern**\n",
+    "- Urban areas have highest DAC benefit rate (~0.45)\n",
+    "- Rural areas at ~0.23 DAC rate\n",
+    "- Central Valley lowest at ~0.18\n",
+    "\n",
+    "1. **Investment Distribution**\n",
+    "- Urban areas get most funding ($2.5B)\n",
+    "- Rural second ($2.2B)\n",
+    "- Central Valley least ($1.4B)\n",
+    "- Average project sizes follow same pattern\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Program Types and Efficiency by Region:\n",
+      "\n",
+      "Urban Most Efficient Programs:\n",
+      "                                        Program Name  \\\n",
+      "100                      Woodsmoke Reduction Program   \n",
+      "29   Fluorinated Gases Emission Reduction Incentives   \n",
+      "61          Renewable Energy for Agriculture Program   \n",
+      "89                                   Waste Diversion   \n",
+      "32                Food Production Investment Program   \n",
+      "\n",
+      "     Total Program GGRFFunding  efficiency  \n",
+      "100                        808      0.1027  \n",
+      "29                      530239      0.0380  \n",
+      "61                       42551      0.0290  \n",
+      "89                    45316021      0.0203  \n",
+      "32                    13221462      0.0129  \n",
+      "\n",
+      "Rural Most Efficient Programs:\n",
+      "                                         Program Name  \\\n",
+      "69  Sustainable Agricultural Lands Conservation Pr...   \n",
+      "49                Low-Carbon Fuels Production Program   \n",
+      "28    Fluorinated Gases Emission Reduction Incentives   \n",
+      "31                 Food Production Investment Program   \n",
+      "37                              Forest Health Program   \n",
+      "\n",
+      "    Total Program GGRFFunding  efficiency  \n",
+      "69                  108577417      0.1273  \n",
+      "49                    5000000      0.0905  \n",
+      "28                     469762      0.0352  \n",
+      "31                   32710900      0.0298  \n",
+      "37                  163034942      0.0285  \n",
+      "\n",
+      "Central Valley Most Efficient Programs:\n",
+      "                                         Program Name  \\\n",
+      "68  Sustainable Agricultural Lands Conservation Pr...   \n",
+      "14                          Climate Smart Agriculture   \n",
+      "30                 Food Production Investment Program   \n",
+      "87                                    Waste Diversion   \n",
+      "59           Renewable Energy for Agriculture Program   \n",
+      "\n",
+      "    Total Program GGRFFunding  efficiency  \n",
+      "68                    9744397      0.1253  \n",
+      "14                  274120208      0.0751  \n",
+      "30                   71859116      0.0255  \n",
+      "87                   22712521      0.0252  \n",
+      "59                    3761222      0.0180  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate efficiency ratios and examine program types\n",
+    "print(\"Program Types and Efficiency by Region:\")\n",
+    "program_efficiency = data_filtered.groupby(['Program Name', 'region_type']).agg({\n",
+    "    'Total Program GGRFFunding': 'sum',\n",
+    "    'Total Project GHGReductions': 'sum'\n",
+    "}).reset_index()\n",
+    "\n",
+    "program_efficiency['efficiency'] = (program_efficiency['Total Project GHGReductions'] / \n",
+    "                                  program_efficiency['Total Program GGRFFunding'])\n",
+    "\n",
+    "# Show top efficient programs by region\n",
+    "for region in ['Urban', 'Rural', 'Central Valley']:\n",
+    "    print(f\"\\n{region} Most Efficient Programs:\")\n",
+    "    region_data = program_efficiency[program_efficiency['region_type'] == region]\n",
+    "    print(region_data.nlargest(5, 'efficiency')[\n",
+    "        ['Program Name', 'Total Program GGRFFunding', 'efficiency']\n",
+    "    ].round(4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This breakdown is really revealing about what works where:\n",
+    "\n",
+    "1. **Urban Efficiency Leaders**\n",
+    "- Woodsmoke Reduction (0.1027)\n",
+    "- Fluorinated Gases (0.0380)\n",
+    "- Renewable Energy for Agriculture (0.0290)\n",
+    "- But notice: Top performers have relatively small funding amounts (except Waste Diversion at $45M)\n",
+    "\n",
+    "2. **Rural Efficiency Champions**\n",
+    "- Sustainable Agricultural Lands (0.1273, $108M)\n",
+    "- Low-Carbon Fuels (0.0905)\n",
+    "- Larger programs achieving high efficiency\n",
+    "- Strong agriculture and land-use focus\n",
+    "\n",
+    "3. **Central Valley Success Stories**\n",
+    "- Sustainable Agricultural Lands (0.1253)\n",
+    "- Climate Smart Agriculture (0.0751, $274M)\n",
+    "- High efficiency with substantial funding amounts\n",
+    "\n",
+    "**Key Patterns:**\n",
+    "1. Agricultural and land-use programs dominate efficiency in rural/CV\n",
+    "2. Urban areas achieve efficiency through smaller, targeted programs\n",
+    "3. Central Valley shows ability to maintain efficiency at scale\n",
+    "4. Similar program types (e.g., Food Production) perform differently by region\n",
+    "\n",
+    "Would you like to:\n",
+    "1. Analyze the characteristics of the most efficient programs in each region?\n",
+    "2. Look at how program size relates to efficiency within each region?\n",
+    "3. Examine whether these patterns change over time?\n",
+    "4. Investigate if multi-county collaborations affect these regional differences?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Multi-County vs Single-County by Region:\n",
+      "                               Total Program GGRFFunding              \\\n",
+      "                                                   count         sum   \n",
+      "region_type    is_multi_county                                         \n",
+      "Central Valley False                               14312  1314145988   \n",
+      "               True                                  184    73786209   \n",
+      "Rural          False                                8194  2060718991   \n",
+      "               True                                  280   122681272   \n",
+      "Urban          False                                7893  2264345480   \n",
+      "               True                                  288   324607138   \n",
+      "\n",
+      "                                            Total Project GHGReductions  \\\n",
+      "                                       mean                         sum   \n",
+      "region_type    is_multi_county                                            \n",
+      "Central Valley False              91821.268                    24949448   \n",
+      "               True              401012.005                      371193   \n",
+      "Rural          False             251491.212                    22645122   \n",
+      "               True              438147.400                      906509   \n",
+      "Urban          False             286880.208                    12069861   \n",
+      "               True             1127108.118                     4945575   \n",
+      "\n",
+      "                               Is Benefit Disadvantaged Communities  \\\n",
+      "                                                               mean   \n",
+      "region_type    is_multi_county                                        \n",
+      "Central Valley False                                          0.182   \n",
+      "               True                                           0.103   \n",
+      "Rural          False                                          0.238   \n",
+      "               True                                           0.086   \n",
+      "Urban          False                                          0.474   \n",
+      "               True                                           0.135   \n",
+      "\n",
+      "                               ghg_per_dollar  \n",
+      "                                               \n",
+      "region_type    is_multi_county                 \n",
+      "Central Valley False                 0.018985  \n",
+      "               True                  0.005031  \n",
+      "Rural          False                 0.010989  \n",
+      "               True                  0.007389  \n",
+      "Urban          False                 0.005330  \n",
+      "               True                  0.015236  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Analyze multi-county projects by region type\n",
+    "data_filtered['is_multi_county'] = data_filtered['County'].str.contains(',', na=False)\n",
+    "\n",
+    "regional_collab = data_filtered.groupby(['region_type', 'is_multi_county']).agg({\n",
+    "    'Total Program GGRFFunding': ['count', 'sum', 'mean'],\n",
+    "    'Total Project GHGReductions': 'sum',\n",
+    "    'Is Benefit Disadvantaged Communities': 'mean'\n",
+    "}).round(3)\n",
+    "\n",
+    "# Calculate efficiency\n",
+    "regional_collab['ghg_per_dollar'] = (\n",
+    "    regional_collab[('Total Project GHGReductions', 'sum')] / \n",
+    "    regional_collab[('Total Program GGRFFunding', 'sum')]\n",
+    ")\n",
+    "\n",
+    "print(\"Multi-County vs Single-County by Region:\")\n",
+    "print(regional_collab)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This intersection of multi-county and regional patterns reveals:\n",
+    "\n",
+    "1. **Project Size Patterns**\n",
+    "- Average project size is consistently larger in multi-county projects across all regions:\n",
+    "  * Urban: $1.13M vs $287K\n",
+    "  * Rural: $438K vs $251K\n",
+    "  * Central Valley: $401K vs $92K\n",
+    "\n",
+    "1. **GHG Efficiency Varies By Region**\n",
+    "- Single-county efficiency:\n",
+    "  * Central Valley leads (0.019)\n",
+    "  * Rural second (0.011)\n",
+    "  * Urban lowest (0.005)\n",
+    "- Multi-county efficiency shows different pattern:\n",
+    "  * Urban leads (0.015)\n",
+    "  * Rural and Central Valley lower (0.007 and 0.005)\n",
+    "\n",
+    "1. **DAC Benefits Trade-off**\n",
+    "- Single-county DAC rates:\n",
+    "  * Urban highest (47.4%)\n",
+    "  * Rural (23.8%)\n",
+    "  * Central Valley (18.2%)\n",
+    "- Multi-county shows lower DAC rates across all regions:\n",
+    "  * Urban (13.5%)\n",
+    "  * Central Valley (10.3%)\n",
+    "  * Rural lowest (8.6%)\n",
+    "\n",
+    "Key Findings:\n",
+    "1. Multi-county collaborations most successful in urban areas for GHG efficiency\n",
+    "2. Single-county projects better at reaching DACs across all regions\n",
+    "3. Central Valley efficiency advantage disappears in multi-county projects\n",
+    "4. Urban areas seem to benefit most from regional coordination\n",
+    "\n",
+    "This suggests different collaboration strategies might be needed for different regions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Tract', 'ZIP', 'County', 'ApproxLoc', 'TotPop19', 'CIscore',\n",
+       "       'CIscoreP', 'Ozone', 'OzoneP', 'PM2_5', 'PM2_5_P', 'DieselPM',\n",
+       "       'DieselPM_P', 'Pesticide', 'PesticideP', 'Tox_Rel', 'Tox_Rel_P',\n",
+       "       'Traffic', 'TrafficP', 'DrinkWat', 'DrinkWatP', 'Lead', 'Lead_P',\n",
+       "       'Cleanup', 'CleanupP', 'GWThreat', 'GWThreatP', 'HazWaste', 'HazWasteP',\n",
+       "       'ImpWatBod', 'ImpWatBodP', 'SolWaste', 'SolWasteP', 'PollBurd',\n",
+       "       'PolBurdSc', 'PolBurdP', 'Asthma', 'AsthmaP', 'LowBirtWt', 'LowBirWP',\n",
+       "       'Cardiovas', 'CardiovasP', 'Educatn', 'EducatP', 'Ling_Isol',\n",
+       "       'Ling_IsolP', 'Poverty', 'PovertyP', 'Unempl', 'UnemplP', 'HousBurd',\n",
+       "       'HousBurdP', 'PopChar', 'PopCharSc', 'PopCharP', 'Child_10',\n",
+       "       'Pop_10_64', 'Elderly65', 'Hispanic', 'White', 'AfricanAm', 'NativeAm',\n",
+       "       'OtherMult', 'Shape_Leng', 'Shape_Area', 'AAPI', 'geometry'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "MergeError",
+     "evalue": "Not allowed to merge between different levels. (1 levels on the left, 2 on the right)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mMergeError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[29], line 19\u001b[0m\n\u001b[1;32m     12\u001b[0m county_funding \u001b[38;5;241m=\u001b[39m data_filtered\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCounty\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39magg({\n\u001b[1;32m     13\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTotal Program GGRFFunding\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msum\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcount\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m     14\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTotal Project GHGReductions\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msum\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m     15\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIs Benefit Disadvantaged Communities\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     16\u001b[0m })\u001b[38;5;241m.\u001b[39mround(\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m     18\u001b[0m \u001b[38;5;66;03m# Merge the datasets\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m county_analysis \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmerge\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     20\u001b[0m \u001b[43m    \u001b[49m\u001b[43mces_county\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     21\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcounty_funding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     22\u001b[0m \u001b[43m    \u001b[49m\u001b[43mleft_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     23\u001b[0m \u001b[43m    \u001b[49m\u001b[43mright_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m     24\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCounty Environmental Burden vs Funding:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28mprint\u001b[39m(county_analysis\u001b[38;5;241m.\u001b[39msort_values(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCIscore\u001b[39m\u001b[38;5;124m'\u001b[39m, ascending\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m10\u001b[39m))\n",
+      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/reshape/merge.py:170\u001b[0m, in \u001b[0;36mmerge\u001b[0;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m    155\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _cross_merge(\n\u001b[1;32m    156\u001b[0m         left_df,\n\u001b[1;32m    157\u001b[0m         right_df,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    167\u001b[0m         copy\u001b[38;5;241m=\u001b[39mcopy,\n\u001b[1;32m    168\u001b[0m     )\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 170\u001b[0m     op \u001b[38;5;241m=\u001b[39m \u001b[43m_MergeOperation\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[43m        \u001b[49m\u001b[43mleft_df\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    172\u001b[0m \u001b[43m        \u001b[49m\u001b[43mright_df\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    173\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhow\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    174\u001b[0m \u001b[43m        \u001b[49m\u001b[43mon\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mon\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    175\u001b[0m \u001b[43m        \u001b[49m\u001b[43mleft_on\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mleft_on\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    176\u001b[0m \u001b[43m        \u001b[49m\u001b[43mright_on\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mright_on\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    177\u001b[0m \u001b[43m        \u001b[49m\u001b[43mleft_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mleft_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    178\u001b[0m \u001b[43m        \u001b[49m\u001b[43mright_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mright_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    179\u001b[0m \u001b[43m        \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    180\u001b[0m \u001b[43m        \u001b[49m\u001b[43msuffixes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msuffixes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    181\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindicator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindicator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    182\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvalidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    183\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    184\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result(copy\u001b[38;5;241m=\u001b[39mcopy)\n",
+      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/reshape/merge.py:784\u001b[0m, in \u001b[0;36m_MergeOperation.__init__\u001b[0;34m(self, left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, indicator, validate)\u001b[0m\n\u001b[1;32m    778\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _left\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m!=\u001b[39m _right\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels:\n\u001b[1;32m    779\u001b[0m     msg \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    780\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNot allowed to merge between different levels. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    781\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_left\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m levels on the left, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    782\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_right\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m on the right)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    783\u001b[0m     )\n\u001b[0;32m--> 784\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m MergeError(msg)\n\u001b[1;32m    786\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mleft_on, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mright_on \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_left_right_on(left_on, right_on)\n\u001b[1;32m    788\u001b[0m (\n\u001b[1;32m    789\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mleft_join_keys,\n\u001b[1;32m    790\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mright_join_keys,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    793\u001b[0m     right_drop,\n\u001b[1;32m    794\u001b[0m ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_merge_keys()\n",
+      "\u001b[0;31mMergeError\u001b[0m: Not allowed to merge between different levels. (1 levels on the left, 2 on the right)"
+     ]
+    }
+   ],
+   "source": [
+    "# First, summarize CES data by county\n",
+    "ces_county = gdf.groupby('County').agg({\n",
+    "    'CIscore': 'mean',\n",
+    "    'PollBurd': 'mean',\n",
+    "    'Poverty': 'mean',\n",
+    "    'TotPop19': 'sum',\n",
+    "    'Hispanic': 'mean',\n",
+    "    'White': 'mean'\n",
+    "}).round(2)\n",
+    "\n",
+    "# Compare with our funding distribution\n",
+    "county_funding = data_filtered.groupby('County').agg({\n",
+    "    'Total Program GGRFFunding': ['sum', 'count'],\n",
+    "    'Total Project GHGReductions': 'sum',\n",
+    "    'Is Benefit Disadvantaged Communities': 'mean'\n",
+    "}).round(2)\n",
+    "\n",
+    "# Merge the datasets\n",
+    "county_analysis = pd.merge(\n",
+    "    ces_county,\n",
+    "    county_funding,\n",
+    "    left_index=True,\n",
+    "    right_index=True\n",
+    ")\n",
+    "\n",
+    "print(\"County Environmental Burden vs Funding:\")\n",
+    "print(county_analysis.sort_values('CIscore', ascending=False).head(10))"
+   ]
   }
  ],
  "metadata": {