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+2cf1d33b-57a5-4698-a42c-3cb427816dea.csv
+TRI_tracker_NYC.csv
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diff --git a/MiniProject/MINI_PROJ.Rmd b/MiniProject/MINI_PROJ.Rmd
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+---
+title: "Renewable energy pathways strengthening energy price stability during fuel shock periods"
+author: "Olivia Ngai"
+institute: "Data 400"
+date: "2026/2/17"
+output:
+  xaringan::moon_reader:
+    css: ninjutsu
+    lib_dir: libs
+    nature:
+      highlightStyle: github
+      highlightLines: true
+      countIncrementalSlides: false
+--- 
+class: inverse, bottom, center
+background-image: url(https://www.azom.com/images/Article_Images/ImageForArticle_21371_16457084545525256.jpg)
+
+<div style="color: white;">
+
+### Research Question: Do states with more wind and solar sources show better electricity price stability for consumers during fuel supply shock periods such as during the pandemic, geopolitical conflicts and recessions?
+
+???
+
+Image credit: (https://www.azom.com/images/Article_Images/ImageForArticle_21371_16457084545525256.jpg)
+
+</div>
+---
+# Data Retrieval
+**1. U.S. Energy Information Administration (API request)**
+- *Electricity sales to Ultimate Customers monthly by state from January 2018 to December 2023.*
+- EIA limits requests to 5000 rows at a time I had to fetch data in batches of 5000.
+--
+```{r eval=FALSE, tidy=FALSE}
+price_df = fetch_eia_data_paginated(API_KEY, start="2018-01", end="2023-12")
+```
+--
+
+**2. U.S. Energy Information Administration Historical State Data (Excel)**
+- *Monthly Data from Electric Power Monthly Net Generation by State by Type of Producer by Energy Source (2001 – Present)*
+- Created a function that reads an Excel sheet and puts the data in a DataFrame.
+- Looped through each of the desired sheets and created a combined DataFrame with observations from 2018-2023.
+--
+```{r eval=FALSE, tidy=FALSE}
+all_dfs = []
+sheets = ["2018_Final", "2019_Final", "2020_Final", "2021_Final", "2022_Final", "2023_Final"]
+for sheet_name in sheets:
+        df = read_eia_generation_sheet(FILE, sheet_name)
+```
+---
+#### Electricity sales to Ultimate Customers monthly by state: `price_df`
+<img src="/Users/oliviangai/Desktop/MiniProject/images/Screenshot 2026-02-16 at 8.48.01 PM.png" 
+     alt="Screenshot" 
+     width="800px"
+     style="display: block; margin: auto;">
+---
+#### Monthly Data from Electric Power Monthly Net Generation by State by Type of Producer by Energy Source: `electricity_df`
+<img src="/Users/oliviangai/Desktop/MiniProject/images/Screenshot 2026-02-16 at 8.23.35 PM.png" 
+     alt="Screenshot" 
+     width="800px"
+     style="display: block; margin: auto;">
+     
+---
+# Tractable Data
+### Tidy Data
+--
+
+1. `price_df`
+- Filtered for rows with "residential" in `sectorName` (population of interest is consumers)
+- Removed the `period` column and standardized the date into year-month tabular format
+- Removed regional observations (North East Central, North East Western, etc.) to only include states and US-Total
+--
+
+2. `electricity_df`
+- Filtered for rows with "Total Electric Power Industry" in `TYPE_OF_PRODUCER`
+- Standardized the date into year-month tabular format
+- Pivoted `ENERGY_SOURCE` into separate columns, using `GENERATION_MWH` as the values
+---
+# Merged DataFrame
+- Left joined `electricity_df` and `price_df` on `STATE` and `YEAR_MONTH`
+
+<img src="/Users/oliviangai/Desktop/MiniProject/images/Screenshot 2026-02-16 at 10.50.13 PM.png" 
+     alt="Screenshot" 
+     width="800px"
+     style="display: block; margin: auto;">
+---
+### EDA
+```{r eval=FALSE, tidy=FALSE}
+merged_df['SOLAR_PERCENT'] = (merged_df['Solar Thermal and Photovoltaic'] / merged_df['Total']) * 100
+merged_df['WIND_PERCENT'] = (merged_df['Wind'] / merged_df['Total']) * 100
+```
+<img src="/Users/oliviangai/Desktop/MiniProject/images/Screenshot 2026-02-16 at 11.23.56 PM.png" 
+     alt="Screenshot" 
+     width="800px"
+     style="display: block; margin: auto;">
+---
+```{r eval=require('DT'), tidy=FALSE}
+my_data <- read.csv("/Users/oliviangai/Desktop/MiniProject/annual_renewable_pct.csv")
+DT::datatable(my_data, fillContainer = FALSE, options = list(pageLength = 8, scrollX = TRUE))
+```
+---
+<img src="/Users/oliviangai/Desktop/MiniProject/images/Screenshot 2026-02-17 at 11.29.54 AM.png" 
+     alt="Screenshot" 
+     width="800px"
+     style="display: block; margin: auto;">
+--
+```{r eval=FALSE, tidy=FALSE}
+shock_periods = {
+    'Pre-Shock Baseline': ('2018-01', '2019-12'),
+    'COVID-19 Pandemic': ('2020-03', '2021-06'),
+    'Ukraine War/Energy Crisis': ('2022-02', '2023-12')}
+```
+--
+- electricity prices dip in the winter with the lowest price usually being in December
+---
+### Implications for Stakeholders
+
+- **For Policymakers:** It may be evidence that renewables stabilize prices which strengthens the case for growing Renewable Portfolio Standards and treating clean energy as consumer protection policy during economic crises.
+
+- **For Utilities and Energy Companies:** These findings would inform resource planning toward renewables not just for emissions reductions, but as independence from volatile fossil fuel markets.
+
+- **For Consumers:** Understanding which states have more stable prices helps individuals and businesses make informed decisions about where to live or operate and which utilities company they may choose to go with.
+
+- **For Environmental Advocates:** Adds "price stability" to the list of renewable benefits.
+
+- **For Investors:** Renewable-heavy utilities may be viewed as lower-risk investments during fuel supply shocks.
+
+---
+### Ethical, Legal, and Societal Implications
+
+- **Regulatory Impact:** Findings could strengthen defenses of state Renewable Portfolio Standards against legal challenges.
+
+- **Energy Justice Concerns:** If low-income communities and marginalized groups are concentrated in states with volatile prices, they bear disproportionate burden during energy crises.
+
+- **Consumer Protection:** Evidence could inform legal entities of unfair rate practices of utility companies during emergencies and support consumer advocacy for more stable pricing.
+
+- **Causation vs. Correlation:** This study may reveal some correlation between the long term stability and reduced volatility of incorporating renewable energy into the grid, but it would not prove causation. Other factors (energy efficiency, demand response, market structure) also influence stability—avoiding oversimplified claims that renewables alone guarantee price protection.
diff --git a/MiniProject/MINI_PROJ.html b/MiniProject/MINI_PROJ.html
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+++ b/MiniProject/MINI_PROJ.html
@@ -0,0 +1,304 @@
+<!DOCTYPE html>
+<html lang="" xml:lang="">
+  <head>
+    <title>Renewable energy pathways strengthening energy price stability during fuel shock periods</title>
+    <meta charset="utf-8" />
+    <meta name="author" content="Olivia Ngai" />
+    <script src="libs/header-attrs-2.29/header-attrs.js"></script>
+    <link href="libs/remark-css-0.0.1/ninjutsu.css" rel="stylesheet" />
+    <link href="libs/htmltools-fill-0.5.8.1/fill.css" rel="stylesheet" />
+    <script src="libs/htmlwidgets-1.6.4/htmlwidgets.js"></script>
+    <link href="libs/datatables-css-0.0.0/datatables-crosstalk.css" rel="stylesheet" />
+    <script src="libs/datatables-binding-0.34.0/datatables.js"></script>
+    <script src="libs/jquery-3.6.0/jquery-3.6.0.min.js"></script>
+    <link href="libs/dt-core-1.13.6/css/jquery.dataTables.min.css" rel="stylesheet" />
+    <link href="libs/dt-core-1.13.6/css/jquery.dataTables.extra.css" rel="stylesheet" />
+    <script src="libs/dt-core-1.13.6/js/jquery.dataTables.min.js"></script>
+    <link href="libs/crosstalk-1.2.2/css/crosstalk.min.css" rel="stylesheet" />
+    <script src="libs/crosstalk-1.2.2/js/crosstalk.min.js"></script>
+  </head>
+  <body>
+    <textarea id="source">
+class: center, middle, inverse, title-slide
+
+.title[
+# Renewable energy pathways strengthening energy price stability during fuel shock periods
+]
+.author[
+### Olivia Ngai
+]
+.institute[
+### Data 400
+]
+.date[
+### 2026/2/17
+]
+
+---
+
+class: inverse, bottom, center
+background-image: url(https://www.azom.com/images/Article_Images/ImageForArticle_21371_16457084545525256.jpg)
+
+&lt;div style="color: white;"&gt;
+
+### Research Question: Do states with more wind and solar sources show better electricity price stability for consumers during fuel supply shock periods such as during the pandemic, geopolitical conflicts and recessions?
+
+???
+
+Image credit: (https://www.azom.com/images/Article_Images/ImageForArticle_21371_16457084545525256.jpg)
+
+&lt;/div&gt;
+---
+# Data Retrieval
+**1. U.S. Energy Information Administration (API request)**
+- *Electricity sales to Ultimate Customers monthly by state from January 2018 to December 2023.*
+- EIA limits requests to 5000 rows at a time I had to fetch data in batches of 5000.
+--
+
+``` r
+price_df = fetch_eia_data_paginated(API_KEY, start="2018-01", end="2023-12")
+```
+--
+
+**2. U.S. Energy Information Administration Historical State Data (Excel)**
+- *Monthly Data from Electric Power Monthly Net Generation by State by Type of Producer by Energy Source (2001 – Present)*
+- Created a function that reads an Excel sheet and puts the data in a DataFrame.
+- Looped through each of the desired sheets and created a combined DataFrame with observations from 2018-2023.
+--
+
+``` r
+all_dfs = []
+sheets = ["2018_Final", "2019_Final", "2020_Final", "2021_Final", "2022_Final", "2023_Final"]
+for sheet_name in sheets:
+        df = read_eia_generation_sheet(FILE, sheet_name)
+```
+---
+#### Electricity sales to Ultimate Customers monthly by state: `price_df`
+&lt;img src="/Users/oliviangai/Desktop/MiniProject/images/Screenshot 2026-02-16 at 8.48.01 PM.png" 
+     alt="Screenshot" 
+     width="800px"
+     style="display: block; margin: auto;"&gt;
+---
+#### Monthly Data from Electric Power Monthly Net Generation by State by Type of Producer by Energy Source: `electricity_df`
+&lt;img src="/Users/oliviangai/Desktop/MiniProject/images/Screenshot 2026-02-16 at 8.23.35 PM.png" 
+     alt="Screenshot" 
+     width="800px"
+     style="display: block; margin: auto;"&gt;
+     
+---
+# Tractable Data
+### Tidy Data
+--
+
+1. `price_df`
+- Filtered for rows with "residential" in `sectorName` (population of interest is consumers)
+- Removed the `period` column and standardized the date into year-month tabular format
+- Removed regional observations (North East Central, North East Western, etc.) to only include states and US-Total
+--
+
+2. `electricity_df`
+- Filtered for rows with "Total Electric Power Industry" in `TYPE_OF_PRODUCER`
+- Standardized the date into year-month tabular format
+- Pivoted `ENERGY_SOURCE` into separate columns, using `GENERATION_MWH` as the values
+---
+# Merged DataFrame
+- Left joined `electricity_df` and `price_df` on `STATE` and `YEAR_MONTH`
+
+&lt;img src="/Users/oliviangai/Desktop/MiniProject/images/Screenshot 2026-02-16 at 10.50.13 PM.png" 
+     alt="Screenshot" 
+     width="800px"
+     style="display: block; margin: auto;"&gt;
+---
+### EDA
+
+``` r
+merged_df['SOLAR_PERCENT'] = (merged_df['Solar Thermal and Photovoltaic'] / merged_df['Total']) * 100
+merged_df['WIND_PERCENT'] = (merged_df['Wind'] / merged_df['Total']) * 100
+```
+&lt;img src="/Users/oliviangai/Desktop/MiniProject/images/Screenshot 2026-02-16 at 11.23.56 PM.png" 
+     alt="Screenshot" 
+     width="800px"
+     style="display: block; margin: auto;"&gt;
+---
+
+``` r
+my_data &lt;- read.csv("/Users/oliviangai/Desktop/MiniProject/annual_renewable_pct.csv")
+DT::datatable(my_data, fillContainer = FALSE, options = list(pageLength = 8, scrollX = TRUE))
+```
+
+<div class="datatables html-widget html-fill-item" id="htmlwidget-c1c8aac0bb42535af302" style="width:100%;height:auto;"></div>
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+---
+&lt;img src="/Users/oliviangai/Desktop/MiniProject/images/Screenshot 2026-02-17 at 11.29.54 AM.png" 
+     alt="Screenshot" 
+     width="800px"
+     style="display: block; margin: auto;"&gt;
+--
+
+``` r
+shock_periods = {
+    'Pre-Shock Baseline': ('2018-01', '2019-12'),
+    'COVID-19 Pandemic': ('2020-03', '2021-06'),
+    'Ukraine War/Energy Crisis': ('2022-02', '2023-12')}
+```
+--
+- electricity prices dip in the winter with the lowest price usually being in December
+---
+### Implications for Stakeholders
+
+- **For Policymakers:** It may be evidence that renewables stabilize prices which strengthens the case for growing Renewable Portfolio Standards and treating clean energy as consumer protection policy during economic crises.
+
+- **For Utilities and Energy Companies:** These findings would inform resource planning toward renewables not just for emissions reductions, but as independence from volatile fossil fuel markets.
+
+- **For Consumers:** Understanding which states have more stable prices helps individuals and businesses make informed decisions about where to live or operate and which utilities company they may choose to go with.
+
+- **For Environmental Advocates:** Adds "price stability" to the list of renewable benefits.
+
+- **For Investors:** Renewable-heavy utilities may be viewed as lower-risk investments during fuel supply shocks.
+
+---
+### Ethical, Legal, and Societal Implications
+
+- **Regulatory Impact:** Findings could strengthen defenses of state Renewable Portfolio Standards against legal challenges.
+
+- **Energy Justice Concerns:** If low-income communities and marginalized groups are concentrated in states with volatile prices, they bear disproportionate burden during energy crises.
+
+- **Consumer Protection:** Evidence could inform legal entities of unfair rate practices of utility companies during emergencies and support consumer advocacy for more stable pricing.
+
+- **Causation vs. Correlation:** This study may reveal some correlation between the long term stability and reduced volatility of incorporating renewable energy into the grid, but it would not prove causation. Other factors (energy efficiency, demand response, market structure) also influence stability—avoiding oversimplified claims that renewables alone guarantee price protection.
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diff --git a/MiniProject/annual_renewable_pct.csv b/MiniProject/annual_renewable_pct.csv
new file mode 100644
index 0000000..c8e62a4
--- /dev/null
+++ b/MiniProject/annual_renewable_pct.csv
@@ -0,0 +1,313 @@
+STATE,YEAR,WIND_PERCENT,SOLAR_PERCENT,WIND_SOLAR_PERCENT
+AK,2018,2.48,0.0,2.48
+AK,2019,2.36,0.0,2.36
+AK,2020,2.06,0.0,2.06
+AK,2021,2.01,0.0,2.01
+AK,2022,2.1,0.0,2.1
+AK,2023,1.68,0.0,1.68
+AL,2018,0.0,0.25,0.25
+AL,2019,0.0,0.27,0.27
+AL,2020,0.0,0.27,0.27
+AL,2021,0.0,0.35,0.35
+AL,2022,0.0,0.62,0.62
+AL,2023,0.0,0.86,0.86
+AR,2018,0.0,0.3,0.3
+AR,2019,0.0,0.33,0.33
+AR,2020,0.0,0.52,0.52
+AR,2021,0.0,0.76,0.76
+AR,2022,0.0,1.13,1.13
+AR,2023,0.0,1.29,1.29
+AZ,2018,0.5,4.62,5.12
+AZ,2019,0.5,4.65,5.16
+AZ,2020,0.62,5.4,6.02
+AZ,2021,1.52,6.2,7.72
+AZ,2022,1.57,6.82,8.38
+AZ,2023,1.59,6.7,8.28
+CA,2018,7.06,13.71,20.77
+CA,2019,6.68,13.74,20.42
+CA,2020,7.01,15.57,22.58
+CA,2021,7.7,17.59,25.28
+CA,2022,7.22,19.02,26.25
+CA,2023,6.48,18.81,25.29
+CO,2018,17.75,1.93,19.68
+CO,2019,19.42,2.19,21.61
+CO,2020,24.86,2.81,27.67
+CO,2021,26.91,3.03,29.94
+CO,2022,29.51,4.16,33.67
+CO,2023,28.33,6.28,34.62
+CT,2018,0.03,0.26,0.29
+CT,2019,0.03,0.35,0.38
+CT,2020,0.03,0.53,0.56
+CT,2021,0.03,0.6,0.63
+CT,2022,0.03,0.99,1.02
+CT,2023,0.03,1.08,1.11
+DC,2018,0.0,0.0,0.0
+DC,2019,0.0,5.13,5.13
+DC,2020,0.0,6.57,6.57
+DC,2021,0.0,8.61,8.61
+DC,2022,0.0,13.8,13.8
+DC,2023,0.0,13.93,13.93
+DE,2018,0.1,0.84,0.94
+DE,2019,0.12,1.13,1.25
+DE,2020,0.13,1.16,1.29
+DE,2021,0.16,1.6,1.76
+DE,2022,0.11,1.35,1.47
+DE,2023,0.11,3.59,3.7
+FL,2018,0.0,1.0,1.0
+FL,2019,0.0,1.6,1.6
+FL,2020,0.0,2.59,2.59
+FL,2021,0.0,3.69,3.69
+FL,2022,0.0,4.39,4.39
+FL,2023,0.0,5.43,5.43
+GA,2018,0.0,1.56,1.56
+GA,2019,0.0,1.68,1.68
+GA,2020,0.0,3.17,3.17
+GA,2021,0.0,3.95,3.95
+GA,2022,0.0,5.55,5.55
+GA,2023,0.0,5.84,5.84
+HI,2018,6.21,1.88,8.09
+HI,2019,5.49,2.73,8.22
+HI,2020,6.55,5.36,11.91
+HI,2021,7.15,5.52,12.66
+HI,2022,6.71,5.93,12.63
+HI,2023,6.93,7.07,14.01
+IA,2018,33.71,0.02,33.73
+IA,2019,41.51,0.03,41.53
+IA,2020,58.1,0.04,58.13
+IA,2021,55.4,0.34,55.73
+IA,2022,62.61,0.54,63.15
+IA,2023,58.94,0.76,59.69
+ID,2018,14.73,3.03,17.76
+ID,2019,14.05,3.01,17.06
+ID,2020,15.74,3.22,18.96
+ID,2021,16.12,3.27,19.39
+ID,2022,15.1,3.31,18.41
+ID,2023,13.74,4.67,18.41
+IL,2018,6.41,0.04,6.44
+IL,2019,7.9,0.03,7.94
+IL,2020,9.52,0.05,9.57
+IL,2021,10.79,0.28,11.07
+IL,2022,12.81,0.84,13.65
+IL,2023,12.46,1.09,13.55
+IN,2018,4.83,0.26,5.09
+IN,2019,6.13,0.32,6.45
+IN,2020,7.22,0.4,7.63
+IN,2021,8.86,0.63,9.49
+IN,2022,10.29,1.12,11.41
+IN,2023,10.02,2.06,12.08
+KS,2018,36.96,0.02,36.97
+KS,2019,42.0,0.02,42.02
+KS,2020,44.28,0.1,44.39
+KS,2021,45.75,0.11,45.85
+KS,2022,48.51,0.12,48.63
+KS,2023,47.0,0.14,47.14
+KY,2018,0.0,0.05,0.05
+KY,2019,0.0,0.06,0.06
+KY,2020,0.0,0.07,0.07
+KY,2021,0.0,0.07,0.07
+KY,2022,0.0,0.07,0.07
+KY,2023,0.0,0.25,0.25
+LA,2018,0.0,0.0,0.0
+LA,2019,0.0,0.0,0.0
+LA,2020,0.0,0.04,0.04
+LA,2021,0.0,0.15,0.15
+LA,2022,0.0,0.19,0.19
+LA,2023,0.0,0.3,0.3
+MA,2018,0.88,3.72,4.6
+MA,2019,1.04,5.67,6.71
+MA,2020,1.56,8.78,10.34
+MA,2021,1.19,8.76,9.95
+MA,2022,1.15,9.58,10.73
+MA,2023,0.96,10.38,11.34
+MD,2018,1.32,0.9,2.22
+MD,2019,1.35,1.26,2.6
+MD,2020,1.58,1.46,3.04
+MD,2021,1.4,1.66,3.06
+MD,2022,1.39,1.92,3.32
+MD,2023,1.39,2.61,4.0
+ME,2018,20.91,0.11,21.02
+ME,2019,23.55,0.07,23.63
+ME,2020,24.26,0.29,24.55
+ME,2021,23.29,1.46,24.75
+ME,2022,21.22,3.46,24.68
+ME,2023,19.02,4.16,23.18
+MI,2018,4.83,0.1,4.93
+MI,2019,5.09,0.12,5.21
+MI,2020,6.4,0.15,6.55
+MI,2021,6.76,0.37,7.13
+MI,2022,8.05,0.73,8.79
+MI,2023,6.99,1.06,8.05
+MN,2018,17.54,1.7,19.24
+MN,2019,18.67,2.11,20.78
+MN,2020,21.3,2.97,24.27
+MN,2021,21.32,3.27,24.59
+MN,2022,26.2,3.31,29.51
+MN,2023,25.81,3.67,29.48
+MO,2018,3.41,0.11,3.52
+MO,2019,3.76,0.13,3.89
+MO,2020,4.78,0.14,4.92
+MO,2021,8.82,0.15,8.97
+MO,2022,9.75,0.2,9.94
+MO,2023,10.7,0.27,10.97
+MS,2018,0.0,0.51,0.51
+MS,2019,0.0,0.49,0.49
+MS,2020,0.0,0.66,0.66
+MS,2021,0.0,0.64,0.64
+MS,2022,0.0,0.77,0.77
+MS,2023,0.0,0.87,0.87
+MT,2018,7.65,0.12,7.77
+MT,2019,8.72,0.11,8.82
+MT,2020,13.26,0.14,13.4
+MT,2021,13.92,0.14,14.06
+MT,2022,14.72,0.12,14.84
+MT,2023,16.77,0.92,17.69
+NC,2018,0.42,4.59,5.01
+NC,2019,0.42,5.72,6.14
+NC,2020,0.46,6.79,7.25
+NC,2021,0.41,7.87,8.28
+NC,2022,0.42,8.64,9.07
+NC,2023,0.44,9.17,9.61
+ND,2018,25.19,0.0,25.19
+ND,2019,27.42,0.0,27.42
+ND,2020,32.48,0.0,32.48
+ND,2021,34.76,0.0,34.76
+ND,2022,36.85,0.0,36.85
+ND,2023,34.46,0.0,34.46
+NE,2018,15.14,0.07,15.21
+NE,2019,19.6,0.09,19.69
+NE,2020,25.15,0.15,25.29
+NE,2021,25.94,0.16,26.1
+NE,2022,31.82,0.18,32.0
+NE,2023,30.47,0.2,30.67
+NH,2018,2.57,0.0,2.57
+NH,2019,2.45,0.0,2.45
+NH,2020,3.55,0.03,3.58
+NH,2021,3.1,0.02,3.12
+NH,2022,2.63,0.02,2.65
+NH,2023,2.63,0.02,2.66
+NJ,2018,0.03,1.31,1.34
+NJ,2019,0.03,1.65,1.68
+NJ,2020,0.04,2.15,2.19
+NJ,2021,0.03,2.25,2.29
+NJ,2022,0.04,2.35,2.38
+NJ,2023,0.03,2.41,2.44
+NM,2018,19.52,4.17,23.69
+NM,2019,20.02,3.93,23.95
+NM,2020,21.69,5.19,26.88
+NM,2021,30.43,5.09,35.51
+NM,2022,35.43,4.87,40.3
+NM,2023,38.49,6.78,45.27
+NV,2018,0.83,11.9,12.73
+NV,2019,0.85,12.07,12.92
+NV,2020,0.83,13.67,14.51
+NV,2021,0.85,15.65,16.5
+NV,2022,0.76,21.22,21.98
+NV,2023,0.71,22.93,23.64
+NY,2018,3.09,0.22,3.32
+NY,2019,3.48,0.4,3.88
+NY,2020,3.59,0.66,4.25
+NY,2021,3.39,0.93,4.32
+NY,2022,3.74,1.43,5.18
+NY,2023,3.97,1.82,5.79
+OH,2018,1.39,0.1,1.49
+OH,2019,1.73,0.12,1.85
+OH,2020,1.93,0.14,2.07
+OH,2021,2.1,0.53,2.63
+OH,2022,2.41,0.69,3.09
+OH,2023,2.2,1.02,3.23
+OK,2018,32.58,0.07,32.65
+OK,2019,34.86,0.07,34.93
+OK,2020,36.84,0.08,36.91
+OK,2021,41.33,0.09,41.43
+OK,2022,45.62,0.1,45.71
+OK,2023,42.78,0.09,42.87
+OR,2018,11.59,0.91,12.49
+OR,2019,10.66,1.09,11.76
+OR,2020,13.83,1.73,15.56
+OR,2021,15.51,2.47,17.98
+OR,2022,13.58,2.69,16.27
+OR,2023,14.51,3.12,17.62
+PA,2018,1.69,0.03,1.71
+PA,2019,1.45,0.04,1.48
+PA,2020,1.69,0.06,1.76
+PA,2021,1.47,0.09,1.56
+PA,2022,1.52,0.1,1.62
+PA,2023,1.41,0.15,1.56
+RI,2018,2.17,0.37,2.53
+RI,2019,3.09,0.73,3.82
+RI,2020,2.63,2.17,4.8
+RI,2021,2.26,3.14,5.4
+RI,2022,3.0,4.58,7.58
+RI,2023,1.82,4.01,5.83
+SC,2018,0.0,0.52,0.52
+SC,2019,0.0,0.86,0.86
+SC,2020,0.0,1.76,1.76
+SC,2021,0.0,2.33,2.33
+SC,2022,0.0,2.44,2.44
+SC,2023,0.0,2.74,2.74
+SD,2018,22.99,0.01,23.0
+SD,2019,19.66,0.01,19.67
+SD,2020,40.21,0.01,40.22
+SD,2021,54.18,0.01,54.19
+SD,2022,57.81,0.01,57.82
+SD,2023,54.4,0.25,54.65
+TN,2018,0.05,0.21,0.26
+TN,2019,0.05,0.4,0.44
+TN,2020,0.05,0.42,0.47
+TN,2021,0.04,0.44,0.47
+TN,2022,0.02,0.88,0.9
+TN,2023,0.02,1.25,1.27
+TX,2018,16.24,0.67,16.91
+TX,2019,17.69,0.9,18.59
+TX,2020,19.91,1.76,21.68
+TX,2021,21.32,3.05,24.37
+TX,2022,22.39,4.23,26.62
+TX,2023,22.66,5.07,27.73
+US-Total,2018,6.64,1.53,8.16
+US-Total,2019,7.26,1.74,9.0
+US-Total,2020,8.6,2.23,10.83
+US-Total,2021,9.42,2.81,12.23
+US-Total,2022,10.5,3.41,13.9
+US-Total,2023,10.3,3.95,14.25
+UT,2018,2.11,5.92,8.03
+UT,2019,2.13,5.8,7.93
+UT,2020,2.24,7.16,9.4
+UT,2021,1.99,8.22,10.21
+UT,2022,1.88,9.93,11.81
+UT,2023,2.09,12.15,14.23
+VA,2018,0.0,0.82,0.82
+VA,2019,0.0,1.0,1.0
+VA,2020,0.0,1.35,1.35
+VA,2021,0.05,3.53,3.59
+VA,2022,0.06,5.28,5.34
+VA,2023,0.05,6.08,6.14
+VT,2018,17.17,5.39,22.56
+VT,2019,16.87,7.16,24.03
+VT,2020,18.97,9.74,28.71
+VT,2021,16.11,8.48,24.59
+VT,2022,19.02,10.51,29.54
+VT,2023,13.85,8.02,21.87
+WA,2018,6.72,0.0,6.72
+WA,2019,6.26,0.04,6.3
+WA,2020,8.03,0.04,8.07
+WA,2021,8.55,0.05,8.59
+WA,2022,7.07,0.07,7.15
+WA,2023,7.42,0.36,7.78
+WI,2018,2.53,0.06,2.59
+WI,2019,3.03,0.06,3.09
+WI,2020,2.99,0.15,3.15
+WI,2021,2.6,0.57,3.17
+WI,2022,3.11,1.34,4.45
+WI,2023,2.96,2.05,5.01
+WV,2018,2.76,0.0,2.76
+WV,2019,2.65,0.0,2.65
+WV,2020,3.57,0.0,3.57
+WV,2021,2.7,0.0,2.7
+WV,2022,3.91,0.0,3.91
+WV,2023,4.11,0.0,4.11
+WY,2018,8.97,0.0,8.98
+WY,2019,10.09,0.45,10.54
+WY,2020,12.85,0.41,13.26
+WY,2021,19.49,0.42,19.91
+WY,2022,21.33,0.41,21.74
+WY,2023,20.37,0.42,20.79
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diff --git a/Olivia_Ngai_Idea1.md b/Olivia_Ngai_Idea1.md
new file mode 100644
index 0000000..c289551
--- /dev/null
+++ b/Olivia_Ngai_Idea1.md
@@ -0,0 +1,73 @@
+# Idea #1
+#### _Renewable energy pathways strengthening energy price stability during fuel shock periods_
+Olivia Ngai
+#### **Research Question:** 
+Do states with more wind and solar sources show better electricity price stability for consumers during fuel supply shock periods such as during the pandemic, geopolitical conflicts and recessions?
+
+#### **Why:** 
+Current analysis of electricity data on a state by state basis reveals that there is no statistically significant relationship between increased renewable energy sources changing consumer electricity prices. While the Biden administration argued energy prices would go down with the growth of the renewable energy sector, the Trump administration has been touting the exact opposite. As it currently stands, the numbers do not support either claims. Changes in electricity costs align more with other dynamic factors such as global pandemics and lockdowns, disturbances in supply chains, recession periods and inflation. 
+
+I hypothesize that states with a larger proportions of renewable energy infrastructure such as wind and solar will have less intense fluctuations in electricity pricing due to a lower dependence on fossil fuels whose prices are coupled with the global market. Energy independence is a pressing issue, especially during times of global conflict where supply chains are disturbed and diplomatic relations are tense. 
+#### **Who are the stakeholders?**
+The stakeholder who might be interested in these findings include consumers who pay for electricity for their homes. Policy makers who could use this information to advocate for investments in the renewable energy sector. Industrial companies that require large amounts of electricity to continue their operations. The long term benefits of having stable electricity costs mean that during times of economic hardship, there would be one less financial burden for consumers and coorporations to stress over.
+
+#### **Ethical, legal and societal concerns?** 
+There are a lot of confounding variables at play with this research question, so while this study/analysis may provide some association or insight into the longterm benefits of incorporating renewable energy into the grid, it would not prove causation. This study would only be to prove the longterm benefits of energy independence but it does not advocate for reckless placement or over placement ("green sprawl") of renewables such as wind or solar. The climatic and environmental benefits of renewable energy are great, but they must be strategically placed in order to avoid unintended environmental or ecological impacts.
+
+#### **Data Retrieval:**
+U.S. Energy Information Administration (API request)
+Electricity sales to Ultimate Customers monthly by state from January 2001 to November 2025
+https://www.eia.gov/opendata/browser/electricity/retail-sales?frequency=monthly&data=price;&start=2001-01&end=2025-11&sortColumn=period;&sortDirection=desc;
+U.S. Energy Information Administration Historical State Data (Excel)
+Monthly Data from Electric Power Monthly
+Net Generation by State by Type of Producer by Energy Source (2001 – Present)
+https://www.eia.gov/electricity/data/state/
+
+#### **Model Specifications**
+
+State-level panel using OLS model:
+Volatility~it~ = β~0~ + β~1~RenewShare~it~ + β~2~Shock~t~ + β~3~(RenewShare~it~×Shock~t~) + X~it~γ + α~i~ + δ~t~ + ε~it~
+
+***i*** indexes U.S. states and ***t*** indexes months.
+
+##### **Response Variable:**
+
+**Electricity Price Volatility (Volatility~it~)**
+- I will run the model on two different time frames: 12 month and 24 month. These windows will be defined by the rolling standard deviation of the log of retail electricity prices (cents per kWh) for each state. This measure captures within-state price changes over time, especially during fuel supply shock periods.
+- The longer window will allow me to see pre-shock, shock, and recovery dynamics in a single measure. The shorter window will capture more spikes and nuanced dynamics in detail.
+- In addition this measure captures price stability rather than price levels.
+- Higher values will indicate greater month-to-month price fluctuations faced by consumers.
+
+##### **Explanatory Variables:**
+
+**Renewable Energy Share (RenewShare~it~)** 
+The proportion of total in-state electricity generation coming from wind and solar sources:
+
+RenewShare~it~= $$\frac{Wind~it~+Solar~it~}{Total Generation~it~}$$
+
+**Fuel Supply Shock Indicator (Shock~t~)**
+A binary indicator equal to 1 during periods characterized by major fuel supply disruptions, and 0 otherwise.
+
+**Shock period of focus:** The COVID-19 pandemic (2020–2021)
+
+**Interaction Term**
+Renewable Share × Fuel Shock (RenewShare~it~×Shock~t~)
+
+This interaction term measures whether states with higher renewable energy sources experience smaller increases in electricity price volatility during fuel shock periods.
+
+A negative and statistically significant coefficient on this interaction indicates that renewable energy acts as a stabilizing force during periods of fuel market disruption.
+
+
+**Control Variables (X~it~)**
+
+The following variables include time-varying state-level controls to account for alternative drivers of electricity price volatility:
+
+**Natural gas generation share**: captures the exposure to fuel prices closely linked to global markets.
+**Coal generation share**: controls for legacy fossil fuel dependence.
+**Electricity demand (log of total electricity sales)**: accounts for demand-driven price pressures.
+**Inflation (CPI)**: controls for general price level changes affecting utility costs.
+
+**Fixed Effects (α~i~ + δ~t~)**
+The fixed effects control for unobserved factors that could bias the estimated relationship between renewable energy and electricity price volatility. The state fixed effect ensures each state has its own baseline level of price volatility and the time fixed effects accounts for shocks common to all states in a given month.
+
+
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+<h1 class="code-line" data-line-start=0 data-line-end=1 ><a id="Idea_1_0"></a>Idea #1</h1>
+<h4 class="code-line" data-line-start=1 data-line-end=2 ><a id="_Renewable_energy_pathways_strengthening_energy_price_stability_during_fuel_shock_periods__1"></a><em>Renewable energy pathways strengthening energy price stability during fuel shock periods</em></h4>
+<p class="has-line-data" data-line-start="2" data-line-end="3">Olivia Ngai</p>
+<h4 class="code-line" data-line-start=3 data-line-end=4 ><a id="Research_Question_3"></a><strong>Research Question:</strong></h4>
+<p class="has-line-data" data-line-start="4" data-line-end="5">Do states with more wind and solar sources show better electricity price stability for consumers during fuel supply shock periods such as during the pandemic, geopolitical conflicts and recessions?</p>
+<h4 class="code-line" data-line-start=6 data-line-end=7 ><a id="Why_6"></a><strong>Why:</strong></h4>
+<p class="has-line-data" data-line-start="7" data-line-end="8">Current analysis of electricity data on a state by state basis reveals that there is no statistically significant relationship between increased renewable energy sources changing consumer electricity prices. While the Biden administration argued energy prices would go down with the growth of the renewable energy sector, the Trump administration has been touting the exact opposite. As it currently stands, the numbers do not support either claims. Changes in electricity costs align more with other dynamic factors such as global pandemics and lockdowns, disturbances in supply chains, recession periods and inflation.</p>
+<p class="has-line-data" data-line-start="9" data-line-end="10">I hypothesize that states with a larger proportions of renewable energy infrastructure such as wind and solar will have less intense fluctuations in electricity pricing due to a lower dependence on fossil fuels whose prices are coupled with the global market. Energy independence is a pressing issue, especially during times of global conflict where supply chains are disturbed and diplomatic relations are tense.</p>
+<h4 class="code-line" data-line-start=10 data-line-end=11 ><a id="Who_are_the_stakeholders_10"></a><strong>Who are the stakeholders?</strong></h4>
+<p class="has-line-data" data-line-start="11" data-line-end="12">The stakeholder who might be interested in these findings include consumers who pay for electricity for their homes. Policy makers who could use this information to advocate for investments in the renewable energy sector. Industrial companies that require large amounts of electricity to continue their operations. The long term benefits of having stable electricity costs mean that during times of economic hardship, there would be one less financial burden for consumers and coorporations to stress over.</p>
+<h4 class="code-line" data-line-start=13 data-line-end=14 ><a id="Ethical_legal_and_societal_concerns_13"></a><strong>Ethical, legal and societal concerns?</strong></h4>
+<p class="has-line-data" data-line-start="14" data-line-end="15">There are a lot of confounding variables at play with this research question, so while this study/analysis may provide some association or insight into the longterm benefits of incorporating renewable energy into the grid, it would not prove causation. This study would only be to prove the longterm benefits of energy independence but it does not advocate for reckless placement or over placement (“green sprawl”) of renewables such as wind or solar. The climatic and environmental benefits of renewable energy are great, but they must be strategically placed in order to avoid unintended environmental or ecological impacts.</p>
+<h4 class="code-line" data-line-start=16 data-line-end=17 ><a id="Data_Retrieval_16"></a><strong>Data Retrieval:</strong></h4>
+<p class="has-line-data" data-line-start="17" data-line-end="24">U.S. Energy Information Administration (API request)<br>
+Electricity sales to Ultimate Customers monthly by state from January 2001 to November 2025<br>
+<a href="https://www.eia.gov/opendata/browser/electricity/retail-sales?frequency=monthly&amp;data=price;&amp;start=2001-01&amp;end=2025-11&amp;sortColumn=period;&amp;sortDirection=desc;">https://www.eia.gov/opendata/browser/electricity/retail-sales?frequency=monthly&amp;data=price;&amp;start=2001-01&amp;end=2025-11&amp;sortColumn=period;&amp;sortDirection=desc;</a><br>
+U.S. Energy Information Administration Historical State Data (Excel)<br>
+Monthly Data from Electric Power Monthly<br>
+Net Generation by State by Type of Producer by Energy Source (2001 – Present)<br>
+<a href="https://www.eia.gov/electricity/data/state/">https://www.eia.gov/electricity/data/state/</a></p>
+<h4 class="code-line" data-line-start=25 data-line-end=26 ><a id="Model_Specifications_25"></a><strong>Model Specifications</strong></h4>
+<p class="has-line-data" data-line-start="27" data-line-end="29">State-level panel using OLS model:<br>
+Volatility<sub>it</sub> = β<sub>0</sub> + β<sub>1</sub>RenewShare<sub>it</sub> + β<sub>2</sub>Shock<sub>t</sub> + β<sub>3</sub>(RenewShare<sub>it</sub>×Shock<sub>t</sub>) + X<sub>it</sub>γ + α<sub>i</sub> + δ<sub>t</sub> + ε<sub>it</sub></p>
+<p class="has-line-data" data-line-start="30" data-line-end="31"><strong><em>i</em></strong> indexes U.S. states and <strong><em>t</em></strong> indexes months.</p>
+<h5 class="code-line" data-line-start=32 data-line-end=33 ><a id="Response_Variable_32"></a><strong>Response Variable:</strong></h5>
+<p class="has-line-data" data-line-start="34" data-line-end="35"><strong>Electricity Price Volatility (Volatility<sub>it</sub>)</strong></p>
+<ul>
+<li class="has-line-data" data-line-start="35" data-line-end="36">I will run the model on two different time frames: 12 month and 24 month. These windows will be defined by the rolling standard deviation of the log of retail electricity prices (cents per kWh) for each state. This measure captures within-state price changes over time, especially during fuel supply shock periods.</li>
+<li class="has-line-data" data-line-start="36" data-line-end="37">The longer window will allow me to see pre-shock, shock, and recovery dynamics in a single measure. The shorter window will capture more spikes and nuanced dynamics in detail.</li>
+<li class="has-line-data" data-line-start="37" data-line-end="38">In addition this measure captures price stability rather than price levels.</li>
+<li class="has-line-data" data-line-start="38" data-line-end="40">Higher values will indicate greater month-to-month price fluctuations faced by consumers.</li>
+</ul>
+<h5 class="code-line" data-line-start=40 data-line-end=41 ><a id="Explanatory_Variables_40"></a><strong>Explanatory Variables:</strong></h5>
+<p class="has-line-data" data-line-start="42" data-line-end="44"><strong>Renewable Energy Share (RenewShare<sub>it</sub>)</strong><br>
+The proportion of total in-state electricity generation coming from wind and solar sources:</p>
+<p class="has-line-data" data-line-start="45" data-line-end="46">RenewShare<sub>it</sub>= <section><eqn><span class="katex-display"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mfrac><mrow><mi>W</mi><mi>i</mi><mi>n</mi><mi>d</mi><mtext> </mtext><mi>i</mi><mi>t</mi><mtext> </mtext><mo>+</mo><mi>S</mi><mi>o</mi><mi>l</mi><mi>a</mi><mi>r</mi><mtext> </mtext><mi>i</mi><mi>t</mi><mtext> </mtext></mrow><mrow><mi>T</mi><mi>o</mi><mi>t</mi><mi>a</mi><mi>l</mi><mi>G</mi><mi>e</mi><mi>n</mi><mi>e</mi><mi>r</mi><mi>a</mi><mi>t</mi><mi>i</mi><mi>o</mi><mi>n</mi><mtext> </mtext><mi>i</mi><mi>t</mi><mtext> </mtext></mrow></mfrac></mrow><annotation encoding="application/x-tex">\frac{Wind~it~+Solar~it~}{Total Generation~it~}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:1.37144em;"></span><span class="strut bottom" style="height:2.05744em;vertical-align:-0.686em;"></span><span class="base"><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.37144em;"><span style="top:-2.314em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord mathit" style="margin-right:0.13889em;">T</span><span class="mord mathit">o</span><span class="mord mathit">t</span><span class="mord mathit">a</span><span class="mord mathit" style="margin-right:0.01968em;">l</span><span class="mord mathit">G</span><span class="mord mathit">e</span><span class="mord mathit">n</span><span class="mord mathit">e</span><span class="mord mathit" style="margin-right:0.02778em;">r</span><span class="mord mathit">a</span><span class="mord mathit">t</span><span class="mord mathit">i</span><span class="mord mathit">o</span><span class="mord mathit">n</span><span class="mspace"> </span><span class="mord mathit">i</span><span class="mord mathit">t</span><span class="mspace"> </span></span></span><span style="top:-3.15em;"><span class="pstrut" style="height:3em;"></span><span class="stretchy" style="height:0.2em;"><svg width='400em' height='0.2em' viewBox='0 0 400000 200' preserveAspectRatio='xMinYMin slice'><path d='M0 80H400000 v40H0z M0 80H400000 v40H0z'/></svg></span></span><span style="top:-3.677em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord mathit" style="margin-right:0.13889em;">W</span><span class="mord mathit">i</span><span class="mord mathit">n</span><span class="mord mathit">d</span><span class="mspace"> </span><span class="mord mathit">i</span><span class="mord mathit">t</span><span class="mord rule" style="margin-right:0.2222222222222222em;"></span><span class="mspace"> </span><span class="mbin">+</span><span class="mord rule" style="margin-right:0.2222222222222222em;"></span><span class="mord mathit" style="margin-right:0.05764em;">S</span><span class="mord mathit">o</span><span class="mord mathit" style="margin-right:0.01968em;">l</span><span class="mord mathit">a</span><span class="mord mathit" style="margin-right:0.02778em;">r</span><span class="mspace"> </span><span class="mord mathit">i</span><span class="mord mathit">t</span><span class="mspace"> </span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.686em;"></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span></eqn></section></p>
+<p class="has-line-data" data-line-start="47" data-line-end="49"><strong>Fuel Supply Shock Indicator (Shock<sub>t</sub>)</strong><br>
+A binary indicator equal to 1 during periods characterized by major fuel supply disruptions, and 0 otherwise.</p>
+<p class="has-line-data" data-line-start="50" data-line-end="51"><strong>Shock period of focus:</strong> The COVID-19 pandemic (2020–2021)</p>
+<p class="has-line-data" data-line-start="52" data-line-end="54"><strong>Interaction Term</strong><br>
+Renewable Share × Fuel Shock (RenewShare<sub>it</sub>×Shock<sub>t</sub>)</p>
+<p class="has-line-data" data-line-start="55" data-line-end="56">This interaction term measures whether states with higher renewable energy sources experience smaller increases in electricity price volatility during fuel shock periods.</p>
+<p class="has-line-data" data-line-start="57" data-line-end="58">A negative and statistically significant coefficient on this interaction indicates that renewable energy acts as a stabilizing force during periods of fuel market disruption.</p>
+<p class="has-line-data" data-line-start="60" data-line-end="61"><strong>Control Variables (X<sub>it</sub>)</strong></p>
+<p class="has-line-data" data-line-start="62" data-line-end="63">The following variables include time-varying state-level controls to account for alternative drivers of electricity price volatility:</p>
+<p class="has-line-data" data-line-start="64" data-line-end="68"><strong>Natural gas generation share</strong>: captures the exposure to fuel prices closely linked to global markets.<br>
+<strong>Coal generation share</strong>: controls for legacy fossil fuel dependence.<br>
+<strong>Electricity demand (log of total electricity sales)</strong>: accounts for demand-driven price pressures.<br>
+<strong>Inflation (CPI)</strong>: controls for general price level changes affecting utility costs.</p>
+<p class="has-line-data" data-line-start="69" data-line-end="71"><strong>Fixed Effects (α<sub>i</sub> + δ<sub>t</sub>)</strong><br>
+The fixed effects control for unobserved factors that could bias the estimated relationship between renewable energy and electricity price volatility. The state fixed effect ensures each state has its own baseline level of price volatility and the time fixed effects accounts for shocks common to all states in a given month.</p>
+
+</body></html>
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+
+
+#### Research Question: *What is the relationship between historical hazardous toxic exposure/waste and growth of urban green space in the NYC metropolitan area? Has the presence of toxic waste led to an increase in environmental remediation efforts through urban green spaces?*
+
+
+##### TRI Toxins data
+CSV file
+##### NYC department of parks and rec GIS
+CSV file
+
+#### Model Specifications:
+##### Primary model: Panel Fixed Effects with Lagged Toxic Exposure
+This model explores if neighborhoods with higher historical toxic exposure experience more or less greening later.
+
+GreenSpacei~t~ = β~1~ToxicExposure~i,t−k~ + α~i~ + γ~t~ + ϵ~i,t~
+i = neighborhood (census tract or grid cell)
+t = year
+ToxicExposure~i,t−k~ = lagged TRI exposure (e.g., 5-year lag)
+α~i~ = neighborhood fixed effects
+γ~t~= year fixed effects
+
+##### Growth Model:
+This model measures whether polluted neighborhoods experienced faster greening growth.
+ΔGreenSpace~i,t~ = GreenSpace~i,t~ − GreenSpace~i,t−1~
+
+#### Implications for stakeholders:
+
+This project could involve local government and urban planning agencies, local communities and residents, environmental and public health organizations and environmental regulators.
+
+- My findings would reveal whether green space development is growing in areas that actually need it such as polluted areas as a remediation effort, or whether it is disproportionately benefiting clean neighborhoods and enhancing environmental inequalities.
+- If there is a positive relationship between polluted neighborhoods and growth of green spaces then it shows that remediation efforts are occurring and that urban planning agencies/policy are moving in the right direction
+- If there is a negative relationship then it reveals that systemic inequalities are being exacerbated and that there is a lack of investment targeting high toxic exposure communities and neighborhoods. This would provide evidence to policy makers that they should increase funding in environmentally degraded areas and lower the inequality gap regarding green spaces.
+
+#### Ethical, Legal, and Societal Implications
+
+This was touched on in the earlier section for stakeholders, but this project would highlight environmental justice and historical environmental inequalities. There is ample research that exists on highly polluting factories and industries situating themselves in low income predominantly black neighborhoods. This legacy continues into today and it has serious health implications for the residents in the area. 
+
+Asthma rates go up due to air pollution, water becomes contaminated due to hazardous waste material leaching into ground water, and contamination due to contact with the toxic chemicals. Depending on the chemicals and exposure rates, this has led to the decline in health of the surrounding communities and has highlighted environmental racism and systemic problems that need to be addressed. 
+
+While this project will not necessarily target those responsible for these wrongdoings, it will draw attention to whether there has been measurable improvement in these neighborhoods due to an increase in green spaces. 
+
+
+
+
+
+
+
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+<!DOCTYPE html><html><head><meta charset="utf-8"><title>Olivia_Ngai_Idea2.md</title><style></style></head><body id="preview">
+<h4 class="code-line" data-line-start=2 data-line-end=3 ><a id="Research_Question_What_is_the_relationship_between_historical_hazardous_toxic_exposurewaste_and_growth_of_urban_green_space_in_the_NYC_metropolitan_area_Has_the_presence_of_toxic_waste_led_to_an_increase_in_environmental_remediation_efforts_through_urban_green_spaces_2"></a>Research Question: <em>What is the relationship between historical hazardous toxic exposure/waste and growth of urban green space in the NYC metropolitan area? Has the presence of toxic waste led to an increase in environmental remediation efforts through urban green spaces?</em></h4>
+<h5 class="code-line" data-line-start=5 data-line-end=6 ><a id="TRI_Toxins_data_5"></a>TRI Toxins data</h5>
+<p class="has-line-data" data-line-start="6" data-line-end="7">CSV file</p>
+<h5 class="code-line" data-line-start=7 data-line-end=8 ><a id="NYC_department_of_parks_and_rec_GIS_7"></a>NYC department of parks and rec GIS</h5>
+<p class="has-line-data" data-line-start="8" data-line-end="9">CSV file</p>
+<h4 class="code-line" data-line-start=10 data-line-end=11 ><a id="Model_Specifications_10"></a>Model Specifications:</h4>
+<h5 class="code-line" data-line-start=11 data-line-end=12 ><a id="Primary_model_Panel_Fixed_Effects_with_Lagged_Toxic_Exposure_11"></a>Primary model: Panel Fixed Effects with Lagged Toxic Exposure</h5>
+<p class="has-line-data" data-line-start="12" data-line-end="13">This model explores if neighborhoods with higher historical toxic exposure experience more or less greening later.</p>
+<p class="has-line-data" data-line-start="14" data-line-end="20">GreenSpacei<sub>t</sub> = β<sub>1</sub>ToxicExposure<sub>i,t−k</sub> + α<sub>i</sub> + γ<sub>t</sub> + ϵ<sub>i,t</sub><br>
+i = neighborhood (census tract or grid cell)<br>
+t = year<br>
+ToxicExposure<sub>i,t−k</sub> = lagged TRI exposure (e.g., 5-year lag)<br>
+α<sub>i</sub> = neighborhood fixed effects<br>
+γ<sub>t</sub>= year fixed effects</p>
+<h5 class="code-line" data-line-start=21 data-line-end=22 ><a id="Growth_Model_21"></a>Growth Model:</h5>
+<p class="has-line-data" data-line-start="22" data-line-end="24">This model measures whether polluted neighborhoods experienced faster greening growth.<br>
+ΔGreenSpace<sub>i,t</sub> = GreenSpace<sub>i,t</sub> − GreenSpace<sub>i,t−1</sub></p>
+<h4 class="code-line" data-line-start=25 data-line-end=26 ><a id="Implications_for_stakeholders_25"></a>Implications for stakeholders:</h4>
+<p class="has-line-data" data-line-start="27" data-line-end="28">This project could involve local government and urban planning agencies, local communities and residents, environmental and public health organizations and environmental regulators.</p>
+<ul>
+<li class="has-line-data" data-line-start="29" data-line-end="30">My findings would reveal whether green space development is growing in areas that actually need it such as polluted areas as a remediation effort, or whether it is disproportionately benefiting clean neighborhoods and enhancing environmental inequalities.</li>
+<li class="has-line-data" data-line-start="30" data-line-end="31">If there is a positive relationship between polluted neighborhoods and growth of green spaces then it shows that remediation efforts are occurring and that urban planning agencies/policy are moving in the right direction</li>
+<li class="has-line-data" data-line-start="31" data-line-end="33">If there is a negative relationship then it reveals that systemic inequalities are being exacerbated and that there is a lack of investment targeting high toxic exposure communities and neighborhoods. This would provide evidence to policy makers that they should increase funding in environmentally degraded areas and lower the inequality gap regarding green spaces.</li>
+</ul>
+<h4 class="code-line" data-line-start=33 data-line-end=34 ><a id="Ethical_Legal_and_Societal_Implications_33"></a>Ethical, Legal, and Societal Implications</h4>
+<p class="has-line-data" data-line-start="35" data-line-end="36">This was touched on in the earlier section for stakeholders, but this project would highlight environmental justice and historical environmental inequalities. There is ample research that exists on highly polluting factories and industries situating themselves in low income predominantly black neighborhoods. This legacy continues into today and it has serious health implications for the residents in the area.</p>
+<p class="has-line-data" data-line-start="37" data-line-end="38">Asthma rates go up due to air pollution, water becomes contaminated due to hazardous waste material leaching into ground water, and contamination due to contact with the toxic chemicals. Depending on the chemicals and exposure rates, this has led to the decline in health of the surrounding communities and has highlighted environmental racism and systemic problems that need to be addressed.</p>
+<p class="has-line-data" data-line-start="39" data-line-end="40">While this project will not necessarily target those responsible for these wrongdoings, it will draw attention to whether there has been measurable improvement in these neighborhoods due to an increase in green spaces.</p>
+
+</body></html>
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