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Results

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Limitations

Future Work

Some ideas for future related studies include:

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  • 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
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  • 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
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  • 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
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  • 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

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  • 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

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  • 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

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References

- Include some exploratory data analysis. Focus on the EDA for the response variable and a few other interesting variables and relationships. - Incorporate appropriate visualizations and summary statistics -## Methodology (\*\****Lucas and/or Maria can complete***\*\*) - -- Brief description of your analysis process. -- Explain the reasoning for the types of analyses you do, exploratory, inferential, or modeling. - - If you’ve chosen to do inference, make sure to include a justification for why that inferential approach is appropriate. - - If you’ve chosen to do modeling, describe the model(s) you’re fitting, predictor variables considered for the model including any interactions. -- Show how you arrived at the final model by describing the model selection process, interactions considered, variable transformations (if needed), assessment of conditions and diagnostics, and any other relevant considerations that were part of the model fitting process. - - Address any concerns over appropriateness of analyses chosen +```{python} +import pandas as pd +import seaborn as sns +from sklearn.preprocessing import StandardScaler, OneHotEncoder +import numpy as np +import matplotlib.pyplot as plt -## Results - -```{python} -import pandas as pd -import matplotlib.pyplot as plt -import seaborn as sns - -# Importing data -data = pd.read_csv('./data/us_natResources_revenue.csv') - -# Create Resource Type Column (Renewable vs Non-Renewable) -renewable_resources = ['Geothermal', 'Solar', 'Wind'] -data['Resource Type'] = data['Commodity'].apply(lambda x: 'Renewable' if x in renewable_resources else 'Non-Renewable') - - # Map States to Regions -state_to_region = { - "Connecticut": "New England", "Maine": "New England", "Massachusetts": "New England", "New Hampshire": "New England", - "Rhode Island": "New England", "Vermont": "New England", "New Jersey": "Middle Atlantic", "New York": "Middle Atlantic", - "Pennsylvania": "Middle Atlantic", "Illinois": "East North Central", "Indiana": "East North Central", "Michigan": "East North Central", - "Ohio": "East North Central", "Wisconsin": "East North Central", "Iowa": "West North Central", "Kansas": "West North Central", - "Minnesota": "West North Central", "Missouri": "West North Central", "Nebraska": "West North Central", "North Dakota": "West North Central", - "South Dakota": "West North Central", "Delaware": "South Atlantic", "District of Columbia": "South Atlantic", "Florida": "South Atlantic", - "Georgia": "South Atlantic", "Maryland": "South Atlantic", "North Carolina": "South Atlantic", "South Carolina": "South Atlantic", - "Virginia": "South Atlantic", "West Virginia": "South Atlantic", "Alabama": "East South Central", "Kentucky": "East South Central", - "Mississippi": "East South Central", "Tennessee": "East South Central", "Arkansas": "West South Central", "Louisiana": "West South Central", - "Oklahoma": "West South Central", "Texas": "West South Central", "Arizona": "Mountain", "Colorado": "Mountain", "Idaho": "Mountain", - "Montana": "Mountain", "Nevada": "Mountain", "New Mexico": "Mountain", "Utah": "Mountain", "Wyoming": "Mountain", "Alaska": "Pacific", - "California": "Pacific", "Hawaii": "Pacific", "Oregon": "Pacific", "Washington": "Pacific" -} - - -data['Region'] = data['State'].map(state_to_region) - -data['Land Category'] = data['Land Category'].fillna('Onshore') - -# Create all possible combinations of 'Calendar Year', 'Region', 'Resource Type', and 'Land Category' -all_combinations = pd.MultiIndex.from_product( - [data['Calendar Year'].unique(), data['Region'].unique(), data['Resource Type'].unique(), ['Onshore', 'Offshore']], - names=['Calendar Year', 'Region', 'Resource Type', 'Land Category'] -) - -# Create an empty DataFrame with this MultiIndex -timeseries_data = pd.DataFrame(index=all_combinations) - -# Merge with the original data to ensure all categories are represented -timeseries_data = timeseries_data.reset_index() -timeseries_data = pd.merge(timeseries_data, data[['Calendar Year', 'Region', 'Resource Type', 'Land Category', 'Revenue']], - on=['Calendar Year', 'Region', 'Resource Type', 'Land Category'], how='left') - -# Fill any NaN Revenue values with 0 -timeseries_data['Revenue'] = timeseries_data['Revenue'].fillna(0) - -# Group by 'Calendar Year', 'Region', 'Resource Type', and 'Land Category' and aggregate 'Revenue' -timeseries_data = timeseries_data.groupby(['Calendar Year', 'Region', 'Resource Type', 'Land Category']).sum().reset_index() - -timeseries_data['Calendar Year'] = timeseries_data['Calendar Year'].round().astype(int) -``` - -```{python} - # Visualization of Revenue Trends - plt.figure(figsize=(9.5,6)) - sns.lineplot(data=timeseries_data, x='Calendar Year', y='Revenue', hue='Resource Type', style='Resource Type', markers=True) - plt.title('Revenue Trends: Renewable vs Non-Renewable Resources Over Time') - plt.xlabel('Year') - plt.ylabel('Revenue') - plt.xticks(timeseries_data['Calendar Year'].unique(), rotation=45) - plt.legend(title='Resource Type') - plt.grid(True) - plt.show() -``` - -```{python} -pivot_data = timeseries_data.pivot_table(values='Revenue', index='Region', columns=['Resource Type', 'Land Category'], aggfunc='sum', fill_value=0) -pivot_data.plot(kind='bar', stacked=True, figsize=(8, 6), colormap='coolwarm') -plt.title('Stacked Revenue by Region, Resource Type, and Land Category') -plt.xlabel('Region') -plt.ylabel('Revenue') -plt.legend(title='Resource Type & Land Category', bbox_to_anchor=(1.05, 1), loc='upper left') -plt.grid(True) -plt.tight_layout() -plt.show() -``` - -```{python} -pivot_data = timeseries_data.pivot_table(values='Revenue', index='Region', columns=['Resource Type', 'Land Category'], aggfunc='sum', fill_value=0) -plt.figure(figsize=(9, 6)) -sns.heatmap(pivot_data, annot=True, cmap='coolwarm', fmt='.1f', linewidths=0.5) -plt.title('Revenue Heatmap by Region, Resource Type, and Land Category') -plt.xlabel('Resource Type & Land Category') -plt.ylabel('Region') -plt.tight_layout() -``` - -## Key Results - -### **1. Revenue Evolution of Resources:** +# Importing data +usnr = pd.read_csv("./data/us_natResources_revenue.csv") +usnr = usnr[list(set(usnr.columns) - set(['Offshore Region']))] + +df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv') + +us_state_to_abbrev = { + "Alabama": "AL", + "Alaska": "AK", + "Arizona": "AZ", + "Arkansas": "AR", + "California": "CA", + "Colorado": "CO", + "Connecticut": "CT", + "Delaware": "DE", + "Florida": "FL", + "Georgia": "GA", + "Hawaii": "HI", + "Idaho": "ID", + "Illinois": "IL", + "Indiana": "IN", + "Iowa": "IA", + "Kansas": "KS", + "Kentucky": "KY", + "Louisiana": "LA", + "Maine": "ME", + "Maryland": "MD", + "Massachusetts": "MA", + "Michigan": "MI", + "Minnesota": "MN", + "Mississippi": "MS", + "Missouri": "MO", + "Montana": "MT", + "Nebraska": "NE", + "Nevada": "NV", + "New Hampshire": "NH", + "New Jersey": "NJ", + "New Mexico": "NM", + "New York": "NY", + "North Carolina": "NC", + "North Dakota": "ND", + "Ohio": "OH", + "Oklahoma": "OK", + "Oregon": "OR", + "Pennsylvania": "PA", + "Rhode Island": "RI", + "South Carolina": "SC", + "South Dakota": "SD", + "Tennessee": "TN", + "Texas": "TX", + "Utah": "UT", + "Vermont": "VT", + "Virginia": "VA", + "Washington": "WA", + "West Virginia": "WV", + "Wisconsin": "WI", + "Wyoming": "WY", + "District of Columbia": "DC", + "American Samoa": "AS", + "Guam": "GU", + "Northern Mariana Islands": "MP", + "Puerto Rico": "PR", + "United States Minor Outlying Islands": "UM", + "Virgin Islands, U.S.": "VI", +} +``` + +```{python} +import plotly.express as px +usnr = usnr.dropna(axis=0) +usnr['state code'] = usnr['State'].map(us_state_to_abbrev) + +usnr = usnr.dropna(axis=0) + +fig = px.choropleth(usnr[['state code', 'Revenue']].groupby("state code").sum().reset_index(), + locations='state code', + color='Revenue', + locationmode='USA-states', + scope='usa', + range_color=(0, 1000000000), + color_continuous_scale="Viridis", + title="20 year historical revenue of natural resources by state") + +fig.show() +``` + +```{python} +data = usnr[['state code', 'Revenue']].groupby("state code").sum().reset_index() + +data['Revenue'] = np.log(data['Revenue']) +fig = px.choropleth(data, + locations='state code', + color='Revenue', + locationmode='USA-states', + scope='usa', + range_color=(data['Revenue'].min() - 1, data['Revenue'].max() + 1), + color_continuous_scale="Viridis", + title="20 year revenue by state, logarithmically scalled for clearer differentiation") +fig.show() +``` -- **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 - -- Bhattacharyya, S., & Collier, P. (2014). Public capital in resource rich economies: Is there a curse? *Oxford Economic Papers, 66*(1), 1-24. https://doi.org/10.1093/oep/gps073 - -- Cockburn, J., Henseler, M., Maisonnave, H., & Tiberti, L. (2018). Vulnerability and policy responses in the face of natural resource discoveries and climate change: Introduction. *Environment and Development Economics, 23*(5), 517-526. https://doi.org/10.1017/S1355770X18000347 - -- Fu, R., & Liu, J. (2023). Revenue sources of natural resources rents and its impact on sustainable development: Evidence from global data. *Resources Policy, 80*. https://doi.org/10.1016/j.resourpol.2022.103226 - -- Smělá, M. & Sejkora, J. (2022). Natural resource revenue management: Which institutional factors matter? *Review of Economic Perspectives, 22*(1), 2-23. https://doi.org/10.2478/revecp-2022-0001 +```{python} +# Using CDC geographic US regions: https://www.cdc.gov/nchs/hus/sources-definitions/geographic-region.htm +us_regions = { + "New England": [ + "Connecticut", "Maine", "Massachusetts", "New Hampshire", "Rhode Island", "Vermont" + ], + "Middle Atlantic": [ + "New Jersey", "New York", "Pennsylvania" + ], + "East North Central": [ + "Illinois", "Indiana", "Michigan", "Ohio", "Wisconsin" + ], + "West North Central": [ + "Iowa", "Kansas", "Minnesota", "Missouri", "Nebraska", "North Dakota", "South Dakota" + ], + "South Atlantic": [ + "Delaware", "District of Columbia", "Florida", "Georgia", "Maryland", + "North Carolina", "South Carolina", "Virginia", "West Virginia" + ], + "East South Central": [ + "Alabama", "Kentucky", "Mississippi", "Tennessee" + ], + "West South Central": [ + "Arkansas", "Louisiana", "Oklahoma", "Texas" + ], + "Mountain": [ + "Arizona", "Colorado", "Idaho", "Montana", "Nevada", "New Mexico", "Utah", "Wyoming" + ], + "Pacific": [ + "Alaska", "California", "Hawaii", "Oregon", "Washington" + ] +} + +timeseries_data = usnr[['Calendar Year', 'State', 'Revenue Type', 'Mineral Lease Type', 'Commodity', 'Revenue']].copy() + +state_to_region = { + "Connecticut": "New England", + "Maine": "New England", + "Massachusetts": "New England", + "New Hampshire": "New England", + "Rhode Island": "New England", + "Vermont": "New England", + "New Jersey": "Middle Atlantic", + "New York": "Middle Atlantic", + "Pennsylvania": "Middle Atlantic", + "Illinois": "East North Central", + "Indiana": "East North Central", + "Michigan": "East North Central", + "Ohio": "East North Central", + "Wisconsin": "East North Central", + "Iowa": "West North Central", + "Kansas": "West North Central", + "Minnesota": "West North Central", + "Missouri": "West North Central", + "Nebraska": "West North Central", + "North Dakota": "West North Central", + "South Dakota": "West North Central", + "Delaware": "South Atlantic", + "District of Columbia": "South Atlantic", + "Florida": "South Atlantic", + "Georgia": "South Atlantic", + "Maryland": "South Atlantic", + "North Carolina": "South Atlantic", + "South Carolina": "South Atlantic", + "Virginia": "South Atlantic", + "West Virginia": "South Atlantic", + "Alabama": "East South Central", + "Kentucky": "East South Central", + "Mississippi": "East South Central", + "Tennessee": "East South Central", + "Arkansas": "West South Central", + "Louisiana": "West South Central", + "Oklahoma": "West South Central", + "Texas": "West South Central", + "Arizona": "Mountain", + "Colorado": "Mountain", + "Idaho": "Mountain", + "Montana": "Mountain", + "Nevada": "Mountain", + "New Mexico": "Mountain", + "Utah": "Mountain", + "Wyoming": "Mountain", + "Alaska": "Pacific", + "California": "Pacific", + "Hawaii": "Pacific", + "Oregon": "Pacific", + "Washington": "Pacific" +} +``` + +## Methodology (\*\****Lucas and/or Maria can complete***\*\*) + +- Brief description of your analysis process. +- Explain the reasoning for the types of analyses you do, exploratory, inferential, or modeling. + - If you’ve chosen to do inference, make sure to include a justification for why that inferential approach is appropriate. + - If you’ve chosen to do modeling, describe the model(s) you’re fitting, predictor variables considered for the model including any interactions. +- Show how you arrived at the final model by describing the model selection process, interactions considered, variable transformations (if needed), assessment of conditions and diagnostics, and any other relevant considerations that were part of the model fitting process. + - Address any concerns over appropriateness of analyses chosen + +## Results + +```{python} +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns + +# Importing data +data = pd.read_csv('./data/us_natResources_revenue.csv') + +# Create Resource Type Column (Renewable vs Non-Renewable) +renewable_resources = ['Geothermal', 'Solar', 'Wind'] +data['Resource Type'] = data['Commodity'].apply(lambda x: 'Renewable' if x in renewable_resources else 'Non-Renewable') + + # Map States to Regions +state_to_region = { + "Connecticut": "New England", "Maine": "New England", "Massachusetts": "New England", "New Hampshire": "New England", + "Rhode Island": "New England", "Vermont": "New England", "New Jersey": "Middle Atlantic", "New York": "Middle Atlantic", + "Pennsylvania": "Middle Atlantic", "Illinois": "East North Central", "Indiana": "East North Central", "Michigan": "East North Central", + "Ohio": "East North Central", "Wisconsin": "East North Central", "Iowa": "West North Central", "Kansas": "West North Central", + "Minnesota": "West North Central", "Missouri": "West North Central", "Nebraska": "West North Central", "North Dakota": "West North Central", + "South Dakota": "West North Central", "Delaware": "South Atlantic", "District of Columbia": "South Atlantic", "Florida": "South Atlantic", + "Georgia": "South Atlantic", "Maryland": "South Atlantic", "North Carolina": "South Atlantic", "South Carolina": "South Atlantic", + "Virginia": "South Atlantic", "West Virginia": "South Atlantic", "Alabama": "East South Central", "Kentucky": "East South Central", + "Mississippi": "East South Central", "Tennessee": "East South Central", "Arkansas": "West South Central", "Louisiana": "West South Central", + "Oklahoma": "West South Central", "Texas": "West South Central", "Arizona": "Mountain", "Colorado": "Mountain", "Idaho": "Mountain", + "Montana": "Mountain", "Nevada": "Mountain", "New Mexico": "Mountain", "Utah": "Mountain", "Wyoming": "Mountain", "Alaska": "Pacific", + "California": "Pacific", "Hawaii": "Pacific", "Oregon": "Pacific", "Washington": "Pacific" +} + + +data['Region'] = data['State'].map(state_to_region) + +data['Land Category'] = data['Land Category'].fillna('Onshore') + +# Create all possible combinations of 'Calendar Year', 'Region', 'Resource Type', and 'Land Category' +all_combinations = pd.MultiIndex.from_product( + [data['Calendar Year'].unique(), data['Region'].unique(), data['Resource Type'].unique(), ['Onshore', 'Offshore']], + names=['Calendar Year', 'Region', 'Resource Type', 'Land Category'] +) + +# Create an empty DataFrame with this MultiIndex +timeseries_data = pd.DataFrame(index=all_combinations) + +# Merge with the original data to ensure all categories are represented +timeseries_data = timeseries_data.reset_index() +timeseries_data = pd.merge(timeseries_data, data[['Calendar Year', 'Region', 'Resource Type', 'Land Category', 'Revenue']], + on=['Calendar Year', 'Region', 'Resource Type', 'Land Category'], how='left') + +# Fill any NaN Revenue values with 0 +timeseries_data['Revenue'] = timeseries_data['Revenue'].fillna(0) + +# Group by 'Calendar Year', 'Region', 'Resource Type', and 'Land Category' and aggregate 'Revenue' +timeseries_data = timeseries_data.groupby(['Calendar Year', 'Region', 'Resource Type', 'Land Category']).sum().reset_index() + +timeseries_data['Calendar Year'] = timeseries_data['Calendar Year'].round().astype(int) +``` + +```{python} + # Visualization of Revenue Trends + plt.figure(figsize=(9.5,6)) + sns.lineplot(data=timeseries_data, x='Calendar Year', y='Revenue', hue='Resource Type', style='Resource Type', markers=True) + plt.title('Revenue Trends: Renewable vs Non-Renewable Resources Over Time') + plt.xlabel('Year') + plt.ylabel('Revenue') + plt.xticks(timeseries_data['Calendar Year'].unique(), rotation=45) + plt.legend(title='Resource Type') + plt.grid(True) + plt.show() +``` + +```{python} +pivot_data = timeseries_data.pivot_table(values='Revenue', index='Region', columns=['Resource Type', 'Land Category'], aggfunc='sum', fill_value=0) +pivot_data.plot(kind='bar', stacked=True, figsize=(8, 6), colormap='coolwarm') +plt.title('Stacked Revenue by Region, Resource Type, and Land Category') +plt.xlabel('Region') +plt.ylabel('Revenue') +plt.legend(title='Resource Type & Land Category', bbox_to_anchor=(1.05, 1), loc='upper left') +plt.grid(True) +plt.tight_layout() +plt.show() +``` + +```{python} +pivot_data = timeseries_data.pivot_table(values='Revenue', index='Region', columns=['Resource Type', 'Land Category'], aggfunc='sum', fill_value=0) +plt.figure(figsize=(9, 6)) +sns.heatmap(pivot_data, annot=True, cmap='coolwarm', fmt='.1f', linewidths=0.5) +plt.title('Revenue Heatmap by Region, Resource Type, and Land Category') +plt.xlabel('Resource Type & Land Category') +plt.ylabel('Region') +plt.tight_layout() +``` + +## 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 + +- Bhattacharyya, S., & Collier, P. (2014). Public capital in resource rich economies: Is there a curse? *Oxford Economic Papers, 66*(1), 1-24. https://doi.org/10.1093/oep/gps073 + +- Cockburn, J., Henseler, M., Maisonnave, H., & Tiberti, L. (2018). Vulnerability and policy responses in the face of natural resource discoveries and climate change: Introduction. *Environment and Development Economics, 23*(5), 517-526. https://doi.org/10.1017/S1355770X18000347 + +- Fu, R., & Liu, J. (2023). Revenue sources of natural resources rents and its impact on sustainable development: Evidence from global data. *Resources Policy, 80*. https://doi.org/10.1016/j.resourpol.2022.103226 + +- Smělá, M. & Sejkora, J. (2022). Natural resource revenue management: Which institutional factors matter? *Review of Economic Perspectives, 22*(1), 2-23. https://doi.org/10.2478/revecp-2022-0001 diff --git a/sitemap.xml b/sitemap.xml index 1920ea6..b2c2478 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -18,6 +18,6 @@ https://INFO-511-F24.github.io/final-project-IndecisionScientists/index.html - 2024-12-20T04:19:23.067Z + 2024-12-20T04:59:25.976Z