Project Proposal - Advancements in Renewable Energy in the USA

Key Drivers of the Growth of Renewable Energy Capacity in the USA from 2000 to 2023

Authors

Pingfan Hu

Abbey Kollar

Published

September 24, 2023

1. Abstract

The renewable energy capacity in the USA has dramatically increased throughout the years. This project studies the renewable energy capacity development from 2000 to 2023, both nation-wide and state-wide. The study focuses on three variables: energy prices, renewable energy promotional policies, and research fundings. The data sources mainly come from governmental websites and peer reviewed journals. The anticipated results section evaluates the relationships between the above three explanatory variables and the renewable energy capacity. The results of these variables and comparisons are illustrated in tables and charts using R programming. A summarized conclusion will be presented in the end.

2. Research Question

How have energy prices, renewable energy promotional policies, and research funding affected the growth of renewable energy capacity in the USA from 2000 to 2023?

3. Data Sources

Data sources can be varied based on future research, but the team has initialized the research and had valuable findings from some official websites:

The EIA (US Energy Information Administration) and the EERE (office of Energy Efficiency & Renewable Energy) are both governmental platforms for renewable energy data collection, evaluation, and sharing. Their data are mainly in four formats: APIs, raw csv files, pre-generated line graphs, and well-formatted reports.

Below is the breakdown of each data source.

3.1 The EIA Electricity Data Browser

Validity: Pre-processed by EIA, originated from EIA API.

Description: This data browser is generated by EIA (2023) (US Energy Information Administration). In this browser, the data can be selected as net generation, consumption, or retail; and the reported data components can be selected based on our purposes. The 1.1a Net generation by renewable sources: total - all sectors report is a good starting point. It contains data of different renewable energy sources from 2000 to 2023.

3.2 The EIA Open Data API

Validity: Original data, established and managed by EIA.

Description: The EIA Open Data (2023) is the API version of the EIA data browser, which is also the most original data for all forms of EIA data reports. It is a handy tool to generate energy data based on our preferences. In most cases, the EIA Electricity Data Browser will be sufficient, but the team might make use of this API for an unusual facet of data selection.

3.3 The EERE Publication and Product Library

Validity: Literature reference, generated by the US Dept of Energy.

Description: The EERE Library (2023) is published by EERE (Energy Efficiency & Renewable Energy). It is an online library containing energy-related publications by US Dept of Energy. For example, the report of Clean cities Coalitions is a well-structured publication of clean energy adoption in different regions of the USA.

3.4 The EERE Clean Cities Coalition

Validity: Pre-processed by EERE, originated from NETL regional managers.

Description: The Clean Cities Coalition (2023) is a funding program initiated by US Dept of Energy for more than 2 decades. This coalition is a collection of government funding that encourages renewable energy development. The annual total of project awards are summarized and can be illustrated to show tendency.

3.5 The EERE AFDC (back-up resource)

Validity: Pre-processed by State Energy Data System, originated from EIA API.

Description: The team has been looking for the number of renewable energy policies. The alternative fuels can be treated as a sub-group. The AFDC (2023) (Alternative Fuels Data Center) is a sophisticated data center of “Alternative Fuels” for vehicles, also provided by the EERE. It contains Biodiesel, Electricity, Ethanol, Hydrogen, Natural Gas, Propane, and Renewable Diesel. This website collects the number of alternative fuel policies for each state, out of which the tendency can be illustrated by a proper plot.

Summary: The above references contain original data, pre-processed data, and literature resources. The team will further process the data to fit with the necessity of data analysis and illustration.

4. Anticipated Results

4.1 Variables and Distributions

The variables we expect to find in the data include:

Capacity of renewable energy technologies (GW),
Price of the renewable energy technologies,
Price of fossil fuels (coal, natural gas, oil),
Number of implemented policies related to renewable energy promotion, and
Amount of funding (USD) for renewable energy research.

These variables will likely be on a per year basis. Each of these variables will be broken down further by specific technology (ie. solar, hydropower, biomass, geothermal, biomass). We would expect the following distributions either on the national level or state level, depending on the data available and our research objectives.

Below is the breakdown of each variable.

Variable 1: Capacity of renewable energy technologies

Data Source: EIA data browser or API

Description: This variable is unimodal and either tightly-group or widely-spread depending on the technology. Since capacity is mostly dependent on capital infrastructure, which is very costly, it is not easy to quickly increase or decrease capacity. Therefore, it is expected that the capacity for these technologies is growing and yet to plateau (so unimodal). However, some of these technologies are expected to be growing at a faster rate than others so the faster growing technologies (like solar) will be more wide-spread in capacity than the slower growing technologies.

Variable 2: Price of renewable energy technologies

Data Source: EIA data browser or API

Description: This variable is multimodal and either tightly-group or widely-spread depending on the technology. The prices of these technologies are expected to fluctuate per year in this time period, likely due to different governments emphasis on renewables, thus creating a multimodal distribution. However, even with this variation from year to year, we expect the overall trend in the price of renewables to be decreasing since the technology has become more accepted and efficient. The tightness of the grouping will depend on the capital cost elasticity of the technology (solar panels are more elastic but nuclear reactors are not due to high initial capital costs). In other words, some of the technologies will show a greater change in price than others.

Variable 3: Price of fossil fuels (coal, natural gas, oil)

Data Source: EIA data browser or API

Description: This variable is multimodal and either tightly-group or widely-spread depending on the fuel. The prices of these fuels are expected to fluctuate per year in this time period, likely due to different governments emphasis on renewables/fossil fuels, thus creating a multimodal distribution. However, even with this variation from year to year, we expect the overall trend in the price of coal and oil to be relatively steady since they have been long term sources of energy in the US and have not seen any major development. The price of natural gas, due to its large increase in supply in recent years due to fracking, is expected to have an overall decreasing cost. Using that same logic, coal and oil prices are expected to be tightly-spread, while natural gas prices are more wide-spread.

Variable 4: Number of implemented policies related to renewable energy promotion

Date Source: The EERE AFDC (AFDC only counts the policies for alternative fuels of vehicles, which is not 100% matching to the teams goal, but by far this is the most feasible source. If the team has reached a better data source, this one will be replaced.)

Description: This variable is multimodal and widely-spread. The number of implemented policies on a federal and state level will vary greatly by administration as they have different renewable energy targets.

Variable 5: Amount of funding (USD) for renewable energy research

Data Source: The EERE Clean Cities Coalition

Description: This variable is multimodal and widely-spread. As with the number of implemented policies, the amount of funding for renewables on a federal level will vary greatly by administration as they have different renewable energy targets.

Summary: As mentioned previously, these variables will be either on the nation level or state level. The expected distributions will vary widely depending on the state due to differences in resource availability, policy, and existing infrastructure.

4.2 Relationships

The relationships we expect to find among these variables are:

The price of renewable energy is negatively correlated with renewable energy capacity.
The price of fossil fuels is positively correlated with renewable energy capacity.
The number of promotional policies is positively correlated with renewable energy capacity.
The amount of funding for renewable research is positively correlated with renewable energy capacity.

4.3 Charts

The charts we would use to help visualize these relationships are:

A set of correlated illustrations of scatter plot & bar chart for the capacity of renewable energy, prices of fossil fuels and renewables, number of promotional policies, and amount of renewable research fundings versus time. This will show us the general distributions for these variables and overall trends both nation-wide and state-wide. We will also be able to see if there is a time-lag when a change in one variable manifests a change in capacity.
Three maps of the United States of capacity changes of renewable energy sources by state in 2000, 2010, and 2020. This will allow us to visualize the differences in renewable energy capacity among states. If some states are growing in capacity while others are decreasing capacity, this is not a balanced growth of renewables in the nation. Ideally, we want to see that the entirety of the United States is moving forward with renewables.

While we have expectations for the general correlation between these variables, the underlying question is how these variables affected the growth of renewable energy capacity. Therefore, by visualizing the data we will be able to see how the growth or diminishing of one variable affects capacity both in terms of how fast the change occurred and the amount of change (ie. how efficient the changes in these variables are for changing the growth of renewables).

5. Attribution

This proposal is accomplished as a team by Pingfan Hu and Abbey Kollar. The topic was picked together with proper conversations and debates, including the scope, the variables, and the relationships.

Attribution breakdown:

2. Research Question was generated by both members.
Pingfan was responsible for the sections of 1. Abstract and 3. Data Sources.
Abbey worked on the section of 4. Anticipated Results.

References

AFDC. 2023. “Alternative Fuels Data Center Home Page.” https://afdc.energy.gov/.

Clean Cities Coalition. 2023. “Clean Cities Coalition Network - Projects & Funding.” https://cleancities.energy.gov/annual-project-funding/.

EERE Library. 2023. “EERE Digital Library.” https://www1.eere.energy.gov/library/default.aspx.

EIA. 2023. “Electricity Data Browser.” https://www.eia.gov/electricity/data/browser/.

EIA Open Data. 2023. “EIA Open Data.” https://www.eia.gov/opendata/.

--- title: "Project Proposal - Advancements in Renewable Energy in the USA" subtitle: "Key Drivers of the Growth of Renewable Energy Capacity in the USA from 2000 to 2023" date: "Sept 24, 2023" bibliography: references.bib author: - "Pingfan Hu" - "Abbey Kollar" --- ## 1. Abstract The renewable energy capacity in the USA has dramatically increased throughout the years. This project studies the renewable energy capacity development from 2000 to 2023, both nation-wide and state-wide. The study focuses on three variables: energy prices, renewable energy promotional policies, and research fundings. The data sources mainly come from governmental websites and peer reviewed journals. The anticipated results section evaluates the relationships between the above three explanatory variables and the renewable energy capacity. The results of these variables and comparisons are illustrated in tables and charts using R programming. A summarized conclusion will be presented in the end. ## 2. Research Question How have energy prices, renewable energy promotional policies, and research funding affected the growth of renewable energy capacity in the USA from 2000 to 2023? ## 3. Data Sources Data sources can be varied based on future research, but the team has initialized the research and had valuable findings from some official websites: 1. [The EIA Electricity Data Browser](https://www.eia.gov/electricity/data/browser/) 2. [The EIA Open Data API](https://www.eia.gov/opendata/) 3. [The EERE Publication and Product library](https://www1.eere.energy.gov/library/default.aspx) 4. [The EERE Clean Cities Coalition](https://cleancities.energy.gov/annual-project-funding/) 5. [The EERE AFDC](https://afdc.energy.gov/) The **EIA** (US Energy Information Administration) and the **EERE** (office of Energy Efficiency & Renewable Energy) are both governmental platforms for renewable energy data collection, evaluation, and sharing. Their data are mainly in four formats: APIs, raw csv files, pre-generated line graphs, and well-formatted reports. Below is the breakdown of each data source. ### 3.1 [The EIA Electricity Data Browser](https://www.eia.gov/electricity/data/browser/) **Validity**: Pre-processed by EIA, originated from [EIA API](https://www.eia.gov/opendata/). **Description**: This data browser is generated by @EIADataBrowser2023 (US Energy Information Administration). In this browser, the data can be selected as net generation, consumption, or retail; and the reported data components can be selected based on our purposes. The 1.1a Net generation by renewable sources: total - all sectors report is a good starting point. It contains data of different renewable energy sources from 2000 to 2023. ### 3.2 [The EIA Open Data API](https://www.eia.gov/opendata/) **Validity**: Original data, established and managed by [EIA](https://www.eia.gov). **Description**: The @EIAOpenData2023 is the API version of the EIA data browser, which is also the most original data for all forms of EIA data reports. It is a handy tool to generate energy data based on our preferences. In most cases, the EIA Electricity Data Browser will be sufficient, but the team might make use of this API for an unusual facet of data selection. ### 3.3 [The EERE Publication and Product Library](https://www1.eere.energy.gov/library/default.aspx) **Validity**: Literature reference, generated by the [US Dept of Energy](https://www.energy.gov). **Description**: The @EERELibrary2023 is published by EERE (Energy Efficiency & Renewable Energy). It is an online library containing energy-related publications by US Dept of Energy. For example, the [report of Clean cities Coalitions](https://afdc.energy.gov/files/u/publication/clean_cities_coalitions_overview.pdf?71e8076292) is a well-structured publication of clean energy adoption in different regions of the USA. ### 3.4 [The EERE Clean Cities Coalition](https://cleancities.energy.gov/annual-project-funding/) **Validity**: Pre-processed by EERE, originated from [NETL regional managers](https://cleancities.energy.gov/contacts/?open=regional#headingregionalManagers). **Description**: The @CleanCitiesCoalition2023 is a funding program initiated by US Dept of Energy for more than 2 decades. This coalition is a collection of government funding that encourages renewable energy development. The annual total of project awards are summarized and can be illustrated to show tendency. ### 3.5 [The EERE AFDC](https://afdc.energy.gov/) (back-up resource) **Validity**: Pre-processed by [State Energy Data System](http://www.eia.gov/state/seds/), originated from [EIA API](https://www.eia.gov/opendata/browser/seds). **Description**: The team has been looking for the number of renewable energy policies. The alternative fuels can be treated as a sub-group. The @EEREAFDC2023 (Alternative Fuels Data Center) is a sophisticated data center of "Alternative Fuels" for vehicles, also provided by the EERE. It contains Biodiesel, Electricity, Ethanol, Hydrogen, Natural Gas, Propane, and Renewable Diesel. This website collects the number of alternative fuel policies for each state, out of which the tendency can be illustrated by a proper plot. **Summary**: The above references contain original data, pre-processed data, and literature resources. The team will further process the data to fit with the necessity of data analysis and illustration. ## 4. Anticipated Results ### 4.1 Variables and Distributions The variables we expect to find in the data include: 1. Capacity of renewable energy technologies (GW), 2. Price of the renewable energy technologies, 3. Price of fossil fuels (coal, natural gas, oil), 4. Number of implemented policies related to renewable energy promotion, and 5. Amount of funding (USD) for renewable energy research. These variables will likely be on a per year basis. Each of these variables will be broken down further by specific technology (ie. solar, hydropower, biomass, geothermal, biomass). We would expect the following distributions either on the **national level** or **state level**, depending on the data available and our research objectives. Below is the breakdown of each variable. **Variable 1: Capacity of renewable energy technologies** Data Source: [EIA data browser](https://www.eia.gov/electricity/data/browser/) or [API](https://www.eia.gov/opendata/) Description: This variable is unimodal and either tightly-group or widely-spread depending on the technology. Since capacity is mostly dependent on capital infrastructure, which is very costly, it is not easy to quickly increase or decrease capacity. Therefore, it is expected that the capacity for these technologies is growing and yet to plateau (so unimodal). However, some of these technologies are expected to be growing at a faster rate than others so the faster growing technologies (like solar) will be more wide-spread in capacity than the slower growing technologies. **Variable 2: Price of renewable energy technologies** Data Source: [EIA data browser](https://www.eia.gov/electricity/data/browser/) or [API](https://www.eia.gov/opendata/) Description: This variable is multimodal and either tightly-group or widely-spread depending on the technology. The prices of these technologies are expected to fluctuate per year in this time period, likely due to different governments emphasis on renewables, thus creating a multimodal distribution. However, even with this variation from year to year, we expect the overall trend in the price of renewables to be decreasing since the technology has become more accepted and efficient. The tightness of the grouping will depend on the capital cost elasticity of the technology (solar panels are more elastic but nuclear reactors are not due to high initial capital costs). In other words, some of the technologies will show a greater change in price than others. **Variable 3: Price of fossil fuels (coal, natural gas, oil)** Data Source: [EIA data browser](https://www.eia.gov/electricity/data/browser/) or [API](https://www.eia.gov/opendata/) Description: This variable is multimodal and either tightly-group or widely-spread depending on the fuel. The prices of these fuels are expected to fluctuate per year in this time period, likely due to different governments emphasis on renewables/fossil fuels, thus creating a multimodal distribution. However, even with this variation from year to year, we expect the overall trend in the price of coal and oil to be relatively steady since they have been long term sources of energy in the US and have not seen any major development. The price of natural gas, due to its large increase in supply in recent years due to fracking, is expected to have an overall decreasing cost. Using that same logic, coal and oil prices are expected to be tightly-spread, while natural gas prices are more wide-spread. **Variable 4: Number of implemented policies related to renewable energy promotion** Date Source: [The EERE AFDC](https://afdc.energy.gov/) (AFDC only counts the policies for alternative fuels of vehicles, which is not 100% matching to the teams goal, but by far this is the most feasible source. If the team has reached a better data source, this one will be replaced.) Description: This variable is multimodal and widely-spread. The number of implemented policies on a federal and state level will vary greatly by administration as they have different renewable energy targets. **Variable 5: Amount of funding (USD) for renewable energy research** Data Source: [The EERE Clean Cities Coalition](https://cleancities.energy.gov/annual-project-funding/) Description: This variable is multimodal and widely-spread. As with the number of implemented policies, the amount of funding for renewables on a federal level will vary greatly by administration as they have different renewable energy targets. **Summary**: As mentioned previously, these variables will be either on the nation level or state level. The expected distributions will vary widely depending on the state due to differences in resource availability, policy, and existing infrastructure. ### 4.2 Relationships The relationships we expect to find among these variables are: 1. The price of renewable energy is *negatively* correlated with renewable energy capacity. 2. The price of fossil fuels is *positively* correlated with renewable energy capacity. 3. The number of promotional policies is *positively* correlated with renewable energy capacity. 4. The amount of funding for renewable research is *positively* correlated with renewable energy capacity. ### 4.3 Charts The charts we would use to help visualize these relationships are: 1. A set of correlated illustrations of scatter plot & bar chart for the capacity of renewable energy, prices of fossil fuels and renewables, number of promotional policies, and amount of renewable research fundings versus time. This will show us the general distributions for these variables and overall trends both nation-wide and state-wide. We will also be able to see if there is a time-lag when a change in one variable manifests a change in capacity. 2. Three maps of the United States of capacity changes of renewable energy sources by state in 2000, 2010, and 2020. This will allow us to visualize the differences in renewable energy capacity among states. If some states are growing in capacity while others are decreasing capacity, this is not a balanced growth of renewables in the nation. Ideally, we want to see that the entirety of the United States is moving forward with renewables. While we have expectations for the general correlation between these variables, the underlying question is how these variables affected the growth of renewable energy capacity. Therefore, by visualizing the data we will be able to see how the growth or diminishing of one variable affects capacity both in terms of how fast the change occurred and the amount of change (ie. how efficient the changes in these variables are for changing the growth of renewables). ## 5. Attribution This proposal is accomplished as a team by **Pingfan Hu** and **Abbey Kollar**. The topic was picked together with proper conversations and debates, including the scope, the variables, and the relationships. Attribution breakdown: - [2. Research Question] was generated by **both** members. - **Pingfan** was responsible for the sections of [1. Abstract] and [3. Data Sources]. - **Abbey** worked on the section of [4. Anticipated Results].