Revenue Trends in US Natural Resources

INFO 511 - Fall 2024: Group Final Project

Author

Affiliation

Indecision Scientists

College of Information Science, University of Arizona

Abstract

As a culminating project to showcase skills from INFO 511: Fundamentals of Data Science, the Indecision Scientists team aimed to examine revenue trends from renewable and non-renewable resources on federal and Native American lands in the United States over the past two decades. This exploration of long-term trends, analysis by land type, and comparisons across revenue sources provides a relevant perspective on the economics of sustainable energy and reveal whether renewable resource revenue, particularly from geothermal energy, has grown over time relative to non-renewables such as oil and gas.

Introduction

Impacts to economic growth and energy sources in recent years as consequences of climate change have become increasingly pressing concerns for many countries, their governing bodies, and their citizens (Mehdi & Montassar, 2020, p. 196). The added historical context of decline in economic welfare associated with the depletion of natural non-renewable resources has also called attention to the particular relevance of assessing the state of domestic natural resources both renewable and non-renewable within the United States (Terreaux, 2022, p. 2). Additionally, the potential for foreign countries to enact restrictions on the export of their own natural resources means that many countries including the U.S. must increase its reliance on its own domestic resources and subsequent revenue streams, especially if the natural resources are non-renewable (Charlier & Guillou, 2014, p. 321).

With the aforementioned considerations in mind, our team was interested in providing a relevant exploratory perspective on revenue trends in U.S. sustainable and non-renewable energy and other natural resources. In addition to analyses of economic factors, we also wanted to examine potential relationships and insights with the commodity types, resource land types, and geographic region.

Research Questions

1. In what ways have revenue patterns from renewable versus non-renewable resource extraction (e.g., geothermal, oil, and gas) evolved over the past two decades?

2. How does the interaction between resource type and land category (onshore versus offshore) influence these revenue trends across different regions?

Hypotheses

1. Revenue from renewable resources has increased over time

   • \(H_0\) : Renewable resource revenue has decreased or stayed stagnant over time
   • \(H_1\) : Renewable resource revenue has increased over time

2. Offshore lands generate higher revenue from non-renewable resources

   • \(H_0\) : Offshore lands generate lower or the same amount of revenue from non-renewable resources compared to renewable resources
   • \(H_1\) : Offshore lands generate higher revenue from non-renewable resources

3. More revenue from renewable resources comes from onshore lands

   • \(H_0\) : Less or the same amount of revenue from renewable resources comes from onshore lands compared to offshore lands
   • \(H_1\) : More revenue from renewable resources comes from onshore lands

4. Revenue from non-renewable resources is high but fluctuates

   • \(H_0\) : Revenue from non-renewable resources is either 1) high and steady or 2) low
   • \(H_1\) : Revenue from non-renewable resources is high but fluctuates

Data

The U.S. Natural Resources Revenue (2003-2013) (Badole, 2024) dataset was sourced from Kaggle with 48,413 observations across 12 variables. The dataset is a compilation of information collected and managed by the U.S. Department of the Interior’s Office of Natural Resources Revenue in collaboration with the U.S. Geological Survey, Bureau of Ocean Energy Management, U.S. Census Bureau, and Energy Information Administration. The dataset comprises attributes of natural resources in the United States such as federal lands, waters, and indigenous lands along with the revenue generated from their extraction.

Data included key variables such as:

• Calendar Year - Calendar year when the revenue was recorded (e.g., 2005, 2010, etc.)
• Land Category - Type of land (i.e., onshore or offshore)
• State - U.S. state where the natural resource is located
• Commodity - Resource extracted (e.g., oil, gas, coal, or specific minerals)
• Revenue - Revenue generated from specific natural resource in U.S. dollars

Data Wrangling & Cleaning

• We found data to be clean, as it was produced by US Department of Natural Resources.

• Observations with missing data for key variables were removed.

• The Offshore Region variable was removed for most analytics.

• A geographic region variable was created based on CDC standards (U.S. Centers for Disease Control and Prevention, 2024).

• Aggregation was conducted on revenue, and stratified by state and year.

Exploratory Data Analysis

• We focused on revenue as a response variable.

• Findings showed differentiation in revenue by geographical region of the United States. There were major outliers within states.

• To distinguish major outliers that were difficult to discern in a choropleth based on overall minimum and maximum revenues by state, a revised choropleth utilized logarithmic scaling.

• There were also states with no reported revenue from 2003-2023 from USNR.

• There were more than 90 commodities found within the data; a graph of the top 5 was found to be most useful for visualizing where most of the revenue over the past 20 years comes from.

Text(0.5, 1.0, 'Top 5 Commodity Revenues Across all of the US')

Methodology

The analysis conducted for this project is primarily exploratory. The main goal is to identify and visualize trends in natural resource revenues over time, understand differences between renewable and non-renewable resources, and explore regional variations.

• Purpose: To gain insights into the dataset, uncover patterns, and generate hypotheses about the data.
• Approach:
  • Visualized revenue trends over time, categorized by renewable vs. non-renewable resources and onshore vs. offshore land categories.
  • Examined regional differences by mapping states to broader regions.
  • Investigated missing data and ensured all categories were represented to avoid bias in visualizations.
• Reasoning:
  • Exploratory analysis is the most appropriate choice when the primary aim is to summarize and visualize data rather than make predictions or test hypotheses.
  • The dataset provides rich information about revenues, resource types, and regions, making EDA ideal for understanding underlying trends without needing to build predictive or inferential models.
  • Given the categorical nature of variables (e.g., resource types, regions), EDA and visualizations help summarize these categories effectively.
  • Line plots, grouped bar charts, and other visual tools were employed to identify trends, relationships, and anomalies in the data.

Results

Key Results

1. Revenue Evolution of Resources:

• Non-Renewable Resources:

  • Exhibit a clear upward trend in revenue over time, especially post-2020, indicating a potential rise in demand or price fluctuations.
  • Sharp fluctuations reflect significant market volatility, which is typical for sectors dependent on external factors such as global demand or geopolitical issues.

• Renewable Resources:

  • Revenue remains flat with only minimal growth over the years.
  • This pattern suggests slower adoption or lower revenue generation in comparison to non-renewables, highlighting a lag in renewable resource development or market integration.

2. Onshore vs. Offshore Contributions:

• Onshore resources, particularly in the non-renewable sector, have been the dominant drivers of revenue across most regions.
• Offshore resources contribute minimal revenue, particularly in renewable sectors, indicating that offshore extraction (for both non-renewables and renewables) has not reached the same level of economic significance.

Discussion

Overall Findings

Our analysis explored how revenue patterns from renewable versus non-renewable resource extraction have evolved over the past two decades and examined how the interaction between resource type (renewable vs. non-renewable) and land category (onshore vs. offshore) influences these trends across different regions.

Key Insights

• Non-renewable onshore resources continue to lead in terms of revenue generation, reflecting significant growth but also volatility due to market fluctuations.
• Offshore resources, including both renewable and non-renewable, show little revenue impact, with offshore renewable resources contributing especially low values.

Limitations

Greater granularity of our analyses extending to Indigenous-owned natural resources was not achievable due to national-level data as opposed to state- and county-level data being reported for Indigenous-owned resources to protect sensitive and private information. The original data also grouped resources as being sourced from either onshore or offshore land types; drilling down further to sub-categorize these land types could reveal greater insights than what these two major groups showed. Additonally, given the collaboration among the five government and non-profit organizations to create the dataset for this study, it was surprising that data for state-owned natural resources could not be incorporated as well. Having data for both federally-owned and state-owned resources would provide even more observations from which potential trends could be identified.

Regarding the validity and reliability of the data, it was not clear how exactly the organizations collected the data. The author of the Kaggle dataset cited these organizations as original sources, but because there were no specific details about the way data was captured, we are limited in our ability to confirm the validity of their methodology and reliability of their measurements. Finally, in terms of analyses, given the large imbalance of non-renewable resource observations compared to renewable resource data, statistical testing for significance in differences between the groups may not be valid without power analyses to confirm that sample requirements for both groups could be met.

Future Work

Some ideas for future related studies include:

• Considering long-term sustainability by exploring transitions to alternate revenue streams for high producing states of non-renewable resources to prevent lapses in economic growth and resource utilization

• Conducting regressive time-series analyses for future economic planning to benefit institutions that could be impacted by resource extraction-adjacent factors such as employment and infrastructure requirements

• Assessing revenue trends in natural resources by assessing sources by U.S.-specific land biomes such as forest, desert, and grassland in addition to geographic region instead of onshore/offshore land types; more granular categorizations could potentially reveal instances of Simpson’s paradox

References

• Badole, S. (2024). U.S. Natural Resources Revenue (2003-2023). Kaggle. https://www.kaggle.com/datasets/saurabhbadole/u-s-natural-resources-revenue-2003-2023

• Charlier, C., & Guillou, S. (2014). Distortion effects of export quota policy: An analysis of the China-Raw Materials dispute. China Economic Review, 31, 320-338. https://doi.org/10.1016/j.chieco.2014.10.004

• Mehdi, B. J., & Montassar, K. (2020). The interdependence between CO2 emissions, economic growth, renewable and non-renewable energies, and service development: Evidence from 65 countries. Climatic Change, 162(2), 193-212. https://doi.org/10.1007/s10584-020-02773-8

• Terreaux, J.-P. (2022). The rise and fall of La Graufesenque: The fate of development based on a non-renewable resource. Ecological Economics, 196. https://doi.org/10.1016/j.ecolecon.2022.107412

• U.S. Centers for Disease Control and Prevention. (2024, July). Geographic division or region. National Center for Health Statistics. https://www.cdc.gov/nchs/hus/sources-definitions/geographic-region.htm