This system provides the user with a facility to select a state and county combination to determine if the selected county is part of an Office of Management and Budget (OMB) defined Core Based Statistical Area (CBSA). The system has been updated with OMB area definitions published for FY 2009.

The 1990 Census Block Statistics portion of the Archive of Census Related Products (ACRP) contains population and housing data from the U.S. Census Bureau's 1990 Summary Tape File (STF1B). The population data includes total population, age, race, and hispanic origin, while the housing data comprises number of housing Units, tenure, room density, mean contract rent, mean value, and mean number of rooms. Additional data includes land area, water area, centroids, Metropolitan Statistical Area (MSA) codes, place codes, and special area codes. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

County boundaries for the BES Metropolitan Study Area (MSA) derived from year 2000 GDT census data. This is the universal MSA boundary for all BES research. The MSA consists of the following 5 counties: Baltimore City, Baltimore County, Anne Arrundel, Carroll, Harford, and Howard.

   This is part of a collection of Baltimore Ecosystem Study metadata records that point to a geodatabase.


   The geodatabase itself is available online at beslter.org or lternet.edu. It is considerably large. Upon request, it can be shipped to you on media, such as a flash drive.


   The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders.


   Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

Release Date: 2015-02-27...Table Name.Island Areas: Industry Series: Inventories by Stage of Fabrication by Manufacturing Industry for Puerto Rico and Metropolitan Areas: End of 2011 and 2012.....Key Table Information.Refer to Methodology for additional information......Universe.The universe includes all establishments with payroll at any time during 2012, and classified in NAICS sectors 31-33. Data for 2012 are based on the 2012 NAICS Manual......Geography Coverage.The data are shown at the following geographic levels for Puerto Rico:..State-equivalent (ST - Puerto Rico).Combined Statistical Area (CSA).Metropolitan Statistical Area (MSA)..Note: The "Not in metropolitan or micropolitan area, Puerto Rico" category includes Culebra, Las Marías, Maricao, and Vieques municipios which are not part of any CSA or MSA......Industry Coverage.The data are shown for 2- and 3- NAICS code levels and for selected 4- and 5- digit NAICS code levels. The data for combined and metropolitan areas are shown at the 2- and selected 3- digit NAICS code levels.......Data Items and Other Identifying Records.This file contains data on:. . Number of establishments. End-of-year inventories. End-of-year finished goods inventories. End-of-year work-in-process inventories. End-of-year materials and supplies inventories. .Data are shown for 2012 and 2011, ending and beginning inventories by stage of fabrication......Sort Order.Data are presented in ascending NAICS code and levels by year sequence......FTP Download.Download the entire table athttps://www2.census.gov/econ2012/IA/sector00/IA1200IPRM13.zip....Contact Information.U.S. Census Bureau, Economy-Wide Statistics Division.Island Areas and Business Owners Branch.Tel: (301)763-3314.csd.ia@census.gov...Note: Data for 2012 are based on the 2012 NAICS..Note: The level of geographic detail covered varies for Puerto Rico manufacturing. Refer to geography help for a detailed list of the geographies. Note that tables IA1200IPRM02 and IA1200IPRM05 include different geographic levels (combined statistical areas (CSA), metropolitan and micropolitan statistical areas (MSA), and municipios.) Tables IA1200IPRM12 - IA1200IPRM14 present data at the CSAs and MSAs level..Note: The "Not in metropolitan or micropolitan area, Puerto Rico" category includes Culebra, Las Marías, Maricao, and Vieques municipios which are not part of any CSA or MSA..Note: Includes only establishments with payroll. Data based on the 2012 Economic Census of Island Areas. Figures may not add to total due to rounding. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Methodology..Symbols:D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableFor a complete list of all economic programs symbols, see the Symbols Glossary.Source: U.S. Census Bureau, 2012 Economic Census of Island Areas.Note: The data in this file are based on the 2012 Economic Census of Island Areas. To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only. For the full technical documentation, see Methodology link in above headnote.

The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

The Inflation Reduction Act of 2022 (IRA) became law on August 8, 2022. Under the law, new qualifying renewable and/or carbon-free electricity generation projects constructed in certain areas of the US, called energy communities, are eligible for bonus worth an additional 10% to the value of the production tax credit or a 10 percentage point increase in the value of the investment tax credit. The IRA does not explicitly map or list these specific communities. Instead, eligible communities are defined by a series of qualifications:

1. a brownfield site,
2. a metropolitan statistical area (MSA) or non-metropolitan statistical area with either (a) 0.17% or greater employment or (b) 25% or greater local tax revenues related to the extraction, processing, transport, or storage of coal, oil, or natural gas; and an unemployment rate at or above the national average for the previous year, or
3. a census tract containing or adjacent to (a) a coal mine closed after December 31, 1999 or (b) a coal-fired electric generating unit retired after December 31, 2009.

These maps and data layers contain GIS data for coal mines, coal-fired power plants, fossil energy related employment, and brownfield sites. Each record represents a point, tract or metropolitan statistical area and non-metropolitan statistical area with attributes including plant type, operating information, GEOID, etc. The input data used includes:

1. Brownfields – Source: EPA. No analysis was performed on this data layer. However, tract polygon layers have a column denoting brownfield presence (0 for no brownfield site, 1 if the tract contains a brownfield somewhere within the polygon).
2. Eligible Employment MSAs (“Final_Employment_Qualifying_MSAs”) – Source: US Census County Business Patterns. MSAs and non-MSA regions with employment over 0.17% in the fossil fuel industry (defined here as NAICS codes 211, 2121, 213, 23712, 324, 4247, and 486) and unemployment greater than or equal to 3.9% (the average national unemployment rate in 2021, according to the Bureau of Labor Statistics).

--Possibly Eligible MSAs (“FossilFuel_Employment_Qualifying_MSAs”) are MSA and non-MSA regions that meet or exceed the 0.17% employment in the fossil fuel industry threshold but do not exceed the unemployment threshold.

--Relevant columns include:

a) SUM_nhgis0: Total employment in 2020.

b) SUM_nhgis1: Total unemployment in 2020.

c) P_Unemp: Percent unemployment in 2020.

d) Q_Unemp: Boolean column indicating if the MSA or non-MSA’s unemployment rate is at or above the national average of 3.9%.

e) FF_Qual: Boolean column indicating if the MSA or non-MSA had employment in the fossil fuel industry at or above 0.17% in the past 11 years.

f) final_Qual: Boolean column indicating if an MSA or non-MSA qualifies for both unemployment rate and fossil fuel employment under the IRA.

1. Retired Power Plants – Source: EIA via HFLID. Qualifying power plants were selected by use of coal in at least one generator, and if they were retired (RET_DATE) on or after January 1, 2010. This data goes through December 2021.

--Adjacent tract data was derived by Cecelia Isaac using ESRI ArcGIS Pro.

1. Abandoned Coal Mines – Source: MSHA. Mines labeled “Abandoned”, “Abandoned and Sealed” or “NonProducing” between January 1, 2000 and September 2022.

--Adjacent tract data was derived by Cecelia Isaac using ESRI ArcGIS Pro.

5) US State Borders– Source: IPUMS NHGIS.

Also included here are polygon shapefiles for Onshore Wind and Solar Candidate Project Areas from Princeton REPEAT. These files have been updated to include columns related to the energy communities.

New columns include:

1. CoalPlantTract: Boolean column indicating if the CPA is within a tract that qualifies because of a retired coal plant.
2. CoalMineTract: Boolean column indicating if the CPA is within a tract that qualifies because of a closed coal mine.
3. FossilFuelEmp: Boolean column indicating if the CPA is within an MSA or non-MSA with greater than or equal to 0.17% employment in the fossil fuel industry.
4. UnempQualification: Boolean column indicating if the CPA is within an MSA or non-MSA with greater than or equal to 0.17% employment in the fossil fuel industry.
5. MSA_non_to: The code of the MSA or non-MSA area that contains the CPA.
6. P_Unemp: The percent unemployment of the MSA or non-MSA that contains the CPA in 2021.

Release Date: 2015-02-27...Table Name.Island Areas: Industry Series: General Statistics by Manufacturing Industry for Puerto Rico, Metropolitan Areas, and Municipios: 2012.....Key Table Information.Refer to Methodology for additional information......Universe.The universe includes all establishments with payroll at any time during 2012, and classified in NAICS sectors 31-33. Data for 2012 are based on the 2012 NAICS Manual......Geography Coverage.The data are shown at the following geographic levels for Puerto Rico:. . State-equivalent (ST - Puerto Rico). Combined Statistical Area (CSA). Metropolitan Statistical Area (MSA). County-equivalent (COUNTY - Municipio). .....Industry Coverage.The data are shown for 2- through 5-digit NAICS code levels for Puerto Rico. The data for combined and metropolitan statistical areas are shown at 2- and selected 3-digit NAICS code levels. The data for municipios are shown at the 2-digit NAICS code (31-33) level.......Data Items and Other Identifying Records.This file contains data on:. . Number of establishments. Annual payroll. First-quarter payroll. Payroll taxes and any other legally required employee benefits. Voluntarily provided benefits. Number of paid employees. Average number of production workers. Production workers wages. Other employees. Total payroll for other employees. Value added. Cost of materials. Capital expenditures. Rental payments. Value of shipments. .....Sort Order.Data are presented in ascending NAICS code and levels sequence......FTP Download.Download the entire table athttps://www2.census.gov/econ2012/IA/sector00/IA1200IPRM02.zip....Contact Information.U.S. Census Bureau, Economy-Wide Statistics Division.Island Areas and Survey of Business Owners Branch.Tel: (301)763-3314.csd.ia@census.gov...Note: Data for 2012 are based on the 2012 NAICS..Note: The level of geographic detail covered varies for Puerto Rico manufacturing. Refer to geography help for a detailed list of the geographies. Note that tables IA1200IPRM02 and IA1200IPRM05 include different geographic levels (combined statistical areas (CSA), metropolitan and micropolitan statistical areas (MSA), and municipios.) Tables IA1200IPRM12 - IA1200IPRM14 present data at the CSAs and MSAs level..Note: Includes only establishments with payroll. Data based on the 2012 Economic Census of Island Areas. Figures may not add to total due to rounding. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Methodology..Symbols:D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableFor a complete list of all economic programs symbols, see the Symbols Glossary.Source: U.S. Census Bureau, 2012 Economic Census of Island Areas.Note: The data in this file are based on the 2012 Economic Census of Island Areas. To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only. For the full technical documentation, see Methodology link in above headnote.

Abstract (en): Extracted from the 2002 Census of Governments, this dataset provides the number of general-purpose local governments in each United States Metropolitan Statistical Area (MSA). Data from Consolidated Metropolitan Statistical Areas (CMSAs) and their component Primary Metropolitan Statistical Areas (PMSAs) are included. There are nine variables in this study. They contain information on locations (city and state); Metropolitan Statistical Areas; population at each location in the year 2000; number of General-Purpose Governments at each location as well as per 100,000 people; water, land, and total area in square miles; and General-Purpose Governments per 100,000 square miles of land area. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Checked for undocumented or out-of-range codes.. Number of General Purpose Local Governments in United States Metropolitan Statistical Areas Smallest Geographic Unit: Metropolitan Statistical Areas

Release Date: 2015-07-28..Table Name..Island Areas: Industry Series: Assets, Capital Expenditures, and Depreciation by Construction Industry for Puerto Rico and Municipios: 2012....Key Table Information.Refer to Methodology for additional information......Universe.The universe includes all establishments with payroll at any time during 2012, and classified in NAICS sectors 23. Data for 2012 are based on the 2012 NAICS Manual.....GeographyCoverage.The data are shown at the following geographic levels for Puerto Rico:.. State-equivalent (ST - Puerto Rico). County-equivalent (COUNTY - Municipio)......IndustryCoverage.The data are shown for 2- through 5-digit NAICS code levels for Puerto Rico. The data for municipios are shown at the 2-digit NAICS code level. .....Data Items and Other Identifying Records.This file contains data on:. . End-of-year gross value of depreciable assets for buildings and other structures. Beginning-of-year gross value of depreciable assets for buildings and other structures. Capital expenditures for new buildings and other structures. Capital expenditures for used buildings and other structures. Gross value of depreciable assets retired for buildings and other structures . Depreciation charges on buildings and other structures. Rental payments for buildings and other structures. End-of-year gross value of depreciable assets for machinery, equipment, and vehicles. Beginning-of-year gross value of depreciable assets for machinery, equipment, and vehicles. Capital expenditures for new automobiles and trucks. Capital expenditures for new machinery, equipment, and vehicles. Capital expenditures for used machinery, equipment, and vehicles. Gross value of depreciable assets retired for machinery, equipment, and vehicles. Depreciation charges on machinery, equipment, and vehicles. Rental payments for machinery and equipment.......Sort Order.Data are presented in ascending NAICS code and geography levels sequence.....FTP Download.Download the entire table athttps://www2.census.gov/econ2012/IA/sector00/IA1200IPRC10.zip....Contact Information.U.S. Census Bureau, Economy-Wide Statistics Division.Island Areas and Survey of Business Owners Branch.Tel: (301)763-3314.csd.ia@census.gov...Note: The level of geographic detail covered varies for Puerto Rico construction. Refer to business.census.gov for a detailed list of the geographies. Note that table IA1200IPRC02 includes different geographic levels (combined statistical areas (CSA), metropolitan and micropolitan statistical areas (MSA).) Tables IA1200IPRC04 and IA1200IPRC06 - IA1200IPRC11 present data at the municipio level. The Jayuya micropolitan area and the Aguadilla-Isabela metropolitan area are not included in any CSA. The municipios of Culebra, Las Marias, Maricao, and Vieques are not included in any MSA or CSA..Note: Data for 2012 are based on the 2012 NAICS..Symbols:D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableFor a complete list of all economic programs symbols, see the Symbols Glossary.Source: U.S. Census Bureau, 2012 Economic Census of Island Areas

As the renewable energy transition accelerates, housing, due to its high energy demand, can play a critical role in the clean energy shift. Specifically, multifamily housing provides a unique opportunity for solar photovoltaic (PV) system adoption, given the existing competing interests between landlords and tenants which has historically slowed this transition. To address this transition gap, this project identified and ranked Metropolitan Statistical Areas (MSAs) in the United States for ZNE Capital (the client) to acquire multifamily housing to install solar PV systems. The group identified seven criteria to determine favorable markets for rooftop solar PV on multifamily housing: landlord policy favorability, real estate market potential, CO2 abatement potential, electricity generation potential, solar installation internal rate of return, climate risk avoidance, and health costs associated with primary air pollutants. A total investment favorability score is calculated based on criteria importance assigned by the user. Investment favorability scores were investigated for different preferences to demonstrate the robustness and generalizability of the framework. The data analysis and criteria calculations were conducted using RStudio, ultimately to provide reproducible code to be used for future projects. The results are presented in a ranked list from best to worst metro areas to invest in. Future studies can utilize the reproducible code to inform decisions on where to invest in solar PV on multifamily housing anywhere in the United States by changing weights within the model depending on preferences. Methods

Collecting real estate and landlord data for metropolitan statistical areas (MSAs) from federal agency databases.

Real estate metrics: Six indicator metrics were selected to represent areas with growing housing demands. The metrics included were population growth, employment growth, average annual occupancy, annual rent change, the ratios of median annual rent to median income, and median income to median home price. The population estimates and median income data was downloaded from the Census Bureau. Median rent data was downloaded from HUDuser. Median home price data was downloaded from National Association of REALTORS®. Students were provided temporary memberships to Yardi Systems Matrix to obtain multifamily occupancy rates, and this data will not be redistributed. All the real estate metrics were combined into a single dataset using CBSA codes, which each MSA has a unique 5-digit identifier. Income-to-home price and rent-to-income ratios were calculated in R Studio.

Landlord data: the minimum security deposit and eviction notice data was collected for each state and manually compiled into an Excel. Security deposit information was provided as the number of months of rent. States with no maximum deposit limit received a score of 1.0, meaning it was the most favorable. Two month's rent was scored as 0.5, and one month's rent was given a score of 0.

Using NREL's REopt web tool to 1) model solar PV system on multifamily buildings in various cities and 2) obtain data to represent energy generation, CO2 abatement potential, avoided health costs from emissions, and solar project financial criteria.

An anchor city was identified within each MSA as the city with the highest population to input into the REopt tool. Default inputs were changed based on information provided by industry experts and changes in federal funding programs. Detailed instructions of inputs were created to ensure consistency when running the model for each city. The four outputs collected from the tool include: annual energy generation from renewables (%), lifecycle total CO2 emissions, health costs associated with primary air pollutants, and internal rate of return(%). The group divided up a list of cities, input the respective data for each one, obtained the outputs, then compiled it into a Google sheet. Outputs were checked by other members to ensure accuracy.

Collecting climate risk data from FEMA's National Risk Index Map.

Climate risk data was downloaded as a CSV file. The risk score was used to represent impacts of climate variability on long-term real estate investments. Risk scores were provided at the county level. The group identified the county each city resided in, to associate the correct score to each city in R Studio

Normalizing the data

Metrics were normalized by subtracting the minimum value for the metric from each value and dividing by the difference between the maximum and minimum values. This resulted in scores between 0 and 1 that were relative to the MSAs included in the analysis.

Weighing the data

Real Estate and Landlord Criteria metrics: these two criteria contained more than one metric, so the metrics within these criteria were weighted to produce real estate and landlord scores. Weights for each criterion sum to 1, in which higher weights indicate greater importance for multifamily real estate investments. Each weight was multiplied by the respective metric, then all weighted metrics within each criterion were summed to produce the criteria score. Investment Favorability Score: seven criteria were multiplied by respective weights based on the stakeholder's preferences. Weights sum to 1 to ensure consistency throughout the project. The sum of the seven weighted criteria is the investment favorability score.