High Carbon Vulnerability Jobs

MARY ALLEN | CLAUDIA KLEFFMANN | ANTONIA MEDINA ABELL GSAPP | FALL 2019

High Carbon Vulnerability in NYS

GEOGRAPHIC INFORMATION SYSTEMS | LEAH MESTERLIN | CARSTEN RODIN

TABLE OF CONTENTS 1. Background 2. The Question 3. Methodology 4. Analysis 5. Conclusions 6. Sources 7. Appendix

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1 BACKGROUND (1) it is the duty of the Federal Government to create a Green New Deal (A) to achieve net-zero greenhouse gas emissions through a fair and just transition for all communities and workers; (B) to create millions of good, high-wage jobs and ensure prosperity and economic security for all people of the United States;(...)

(E) to promote justice and equity by stopping current, preventing future, and repairing historic oppression of indigenous peoples, communities of color, migrant communities, deindustrialized communities, depopulated rural communities, the poor, low-income workers,women, the elderly, the unhoused, people with disabilities, and youth (referred to in this resolution as ‘‘frontline and vulnerable communities’’)”

THE GREEN NEW DEAL In February 2019, the Green New Deal resolution was passed in the House of Representatives. Its goal is to re-structure the US economy in a way that echoes the New Deal, proposing a series of measures that should respond to climate change by decarbonizing the economy, bringing overdue justice to the most vulnerable people. But who are these vulnerable people?

WHAT ARE FRONTLINE VULNERABLE COMMUNITIES? The interpretation of frontline communities remains largely undefined. The resolution names them as: “ indigenous peoples, communities of color, migrant communities, deindustrialized communities, depopulated rural communities, the poor, low-income workers, women, the elderly, the unhoused, people with disabilities, and youth.”This broad list leaves much to interpretation.

What happens to people who suddenly hold what are now called “high-carbon jobs”?

Do they become vulnerable too?

WHAT ARE HIGH CARBON JOBS? High-Carbon jobs are not described in the Green New Deal. However, the emphasis on decarbonizing and creating a “just transition”implies that some jobs will be lost in this process, as we move, for example, from fossil fuel sourced energy to cleaner, renewable sources. This made us wonder, are these jobs located and measured? Who are the people holding them and are they vulnerable in other aspects as well? And finally, Does there need to be a new index to measure socio-economic vulnerability for communities that depend on high carbon industries, as we transition to a carbonless economy?

SOURCE: (House Resolution 109 (“Green New Deal”), page 5, lines 1-17)

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2 THE QUESTION

[1] WHERE ARE THERE CONCENTRATIONS OF HIGH CARBON JOBS WITHIN NEW YORK STATE? [2] ADDITIONALLY, HOW VULNERABLE ARE COMMUNITIES DUE TO THEIR CONCENTRATION OF HIGH CARBON JOBS IN COMPARISON TO THE CURRENT SOCIAL VULNERABILITY INDEX?

SCOPE AND LIMITATIONS The scope of this analysis focuses on high carbon job vulnerability across New York State. New York State was chosen as the area of study due to the passing of the Climate Leadership and Protection Act. At this time this is the “most comprehensive and aggressive climate change legislation in the nation.” It intends “to address and mitigate the effects of climate change by drastically cutting greenhouse gases, diverting the state’s energy reliance to renewable sources, and creating green jobs to promote environmental justice across New York State.”

The passing of this bill makes the argument that New York State should have the most comprehensive understanding of their greenhouse gas emissions, and sources of high carbon jobs.

Although, limitations of this study include lack of a formal definition of “high carbon jobs” that is widely accepted. Additionally, limitations included limited data on greenhouse gas emissions. The only readily available data on emissions was obtained by the EPA greenhouse emission report We acknowledge the lack of comprehensive data collection, due to the EPA report containing only the highest emitting companies, and therefore excludes the total emissions caused from a higher density of lower polluting companies.

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2 SOCIAL VULNERABILITY INDEX (SVI) WHAT IS IT? The SVI is an index that weights a variety of subjects with the objective of predicting how self-reliant these communities will be in case of disaster, natural or man-made.

WHAT DOES IT MEASURE?

Source: SVI 2016 Documentation, page 3. Available in: https://svi.cdc. gov/data-and-tools-download.html.

Accessed on November 19, 2019.

WHY DOES IT MATTER?

SVI: SOCIAL VULNERABILITY INDEX NYS BY CENSUS TRACT [A HIGHER SCORE INDICATES MORE VULNERABILITY]

MORE VULNERABLE

0.75 - 1.0

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08

0.25 - 0.5 0.00 - 0.25

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HYPOTHESIS THE SOCIAL VULNERABILITY INDEX CAN SHOW AN ACCURATE DESCRIPTION OF HIGH-CARBON VULNERABILITY.?

0.5 - 0.75

12.5

Planners and officials ofter use the SVI as a base to begin identifying vulnerable communities, this report ask if the SVI is the best starting point towards determining the frontline vulnerable communities that the Green New Deal asks to define.

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Miles 100

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3 METHODOLOGY

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ANALYSIS

4.1 NEW YORK STATE HIGHEST POLLUTERS Identify the highest polluters in New York State using the EPA Greenhouse Gas Emissions Report.

4.2 NEW VULNERABILITY INDEX Construct new High Carbon Vulnerability Index to identify frontline communities for high carbon job loss.

4.3 THE COMPARISON Compare the High Carbon Vulnerability Index to the Social Vulnerability Index.

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200,000

400,000

600,000

800,000

1,000,000

4

1,200,000

ONONDAGA NIAGARA NASSAU MONROE WEST. WARREN OSWEGO SUFFOLK SENECA BRONX DUTC. ROCK. STEUBEN SCHU CHEMUNG ALLEGANY ONEIDA JEFF ORLEAN CATT MONT HERK SCHO CHAU CORT MAID

EMISSIONS PER COUNTY HIGH

4.1 NEW YORK STATE HIGHEST POLLUTERS

LOW

EMITTING COMPANY

The Greenhouse Gas Reporting Program, requires annual reporting of greenhouse gas data from the highest emitters per county, This representation was used as a decision layer to determine New York States highest emitting counties Onondaga, Nassau, and Niagara. Combined these counties account for nearly 30% of emissions reported in the EPA GHGRP.

29.5% OF TOTAL EMISSIONS

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MILES N 0

20

40

80 15

HIGHEST EMITTING COUNTIES

Data obtained by the EPA Greenhouse Gas Report show Onondaga, Nassau, and Niagara county as the highest polluting counties in New York State.

Combined these counties account for nearly 30% of emissions reported in the EPA GHGRP. COVANTA HUNTINGTON

Legend Industry Lat-Long Locations

ANHEUSER BUSCH BALDWINSVILLE BREWERY CO2 Emissions by Industry Number of Employees by Industry

When zooming into these counties the largest source of emissions is from energy production facilities. These industries were taken into consideration when developing a new high carbon vulnerability index.

SVI -999.000000 -998.999999 - 0.250000 0.250001 - 0.500200

WESTROCK - SOLVAY, LLC

0.500201 - 0.750200 0.750201 - 1.000000 NIAGARA MOHAWK

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POWER CORP.

SYRACUSE UNIVERSITY STEAM STATION

KEYSPAN GAS CORP. LI JEWISH MEDICAL CENTER COVANTA HEMPSTEAD

ONONDAGA RESOURCE RECOVERY

COVANTA BABYLON, INC.

ONONDAGA COUNTY

TGP STATION 241 LAFAYETTE

at-Long Locations

sions by Industry Employees by Industry

±

00

99 - 0.250000

Industry

0.500200

CO2 Emissions

0.750200

by Industry

0

1.000000

2

4

8

12

NASSAU COUNTY

Miles 16

# Employees

by Industry

SVI 0 - 0.25

Esri, HERE, Garmin, (c) OpenStreetMap contributors, and the GIS user community

NIAGARA COUNTY

0.25 - 0.50

ONONDAGA COUNTY

0.50 - 0.75 0.75 - 1.00

EMISSIONS: 669,321 EMPLOYEES: 10,295

MILES

N

0

2

4

8

EMISSIONS: 637,777 EMPLOYEES: 202

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ENERGY PRODUCTION

THE SVI FOOD MANUFACTURING SOURCES OF EMISSIONS

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NASSAU COUNTY

EDUCATION

ENERGY PRODUCTION

MEDICAL FACILITIES

SOURCES OF EMISSIONS

Taking an initial look, it would appear that the Social Vulnerability Index has some relationship to the emitter sources. The further findings contrast this initial assumption.

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WEIGHTED INDUSTRIES BY GHG EMISSIONS CO2 EMISSIONS BY SECTOR (WORLD) X

Source: Our World in Data

1

Wholesale Trade

X

2

Transportation, Warehousing & Utilities

X

1

Retail Trade

X

1

60%

Public Administration

X

1

Professional, Scientific, Management, Administrative & Waste Management Services

Energy

Agriculture, Land Use & Forestry

Transport

Industry

X Residential & Commercial

1

8%

16%

7%

10% 1% Waste

Other sources

1.5%

X

Other Services, except Public Administration

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Manufacturing

X

1

Information

X

1

Finance and Insurance, Real Estate Rental & Leasing

X

1

Educational Services, Health care & Social Assistance

X

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NEW SOCIAL VULNERABILITY JOB DENSITY & GHG EMISSIONS BY INDUSTRY HIGH 79

X

1

Arts, Entertainment, Recreation, Accommodation & Food Services

X

7

Agriculture, Forestry, Fishing, Hunting & Mining

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MORE JOBS & GHGE

Construction

4.2 HIGH CARBON & JOB DENSITY For this phase we wanted to identify the job types and density within New York State, for which we worked with Census Data. As a result we obtained different layers for the different categories of occupation and displayed them individually showing the job density.

To assign a weight for the ranking model that would reflect how much each industry contributes to the overall GHG emissions, we assigned them to the different categories shown in the pie chart: Energy, Agriculture, Land Use & Forestry, Transport, Industry, Residential & Commercial, Waste and Other Sources.

Yet, according to the descriptions for each of these categories, we had to place some of the Census Labels in more than one category since they include many types of industry.

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With this methodology we established a specific weight for each layer that we would then rank with GIS geoprocessing.

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WHAT DOES HIGH VULNERABILITY LOOK LIKE?

JOB IN HIGH CARBON EMITTING INDUSTRY

4

UNEMPLOYMENT

$

+ LOW EDUCATION LEVEL

LOW HOUSEHOLD INCOME

OLDER POPULATION

POVERTY

HIGHER CHANCES OF LOSING A JOB + LESS CAPACITY TO ADAPT

CARBON VULNERABILITY JOB DENSITY & GHG EMISSIONS BY INDUSTRY

LEAST VULNERABLE 20

MORE VULNERABLE

MOST VULNERABLE

4.2 CARBON VULNERABLE COMMUNITIES As a next step it was important to add more layers to our ranking model to consider additional factors that can make certain communities more vulnerable than others. Factors like Age, Education level or English Proficiency could make some more resilient to change, if in the proper range. We used the Social Vulnerability Index as a reference to choose which factors are relevant, yet we used those that were more related to a person’s possibility of changing their job.

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TES MORE

SVI:

SOCIAL VULNERABILITY INDEX

After obtaining the final weighted version of our Carbon Vulnerability Map, we made a side-by-side comparison with the SVI map.

NYS BY CENSUS TRACT

Observing them together at the State scale, we tested our hypothesis and realized that the location of vulnerable places

MORE VULNERABLE

0.75 - 1.0

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THE COMPARISON

0.5 - 0.75 0.25 - 0.5

HCVI:

HIGH-CARBON JOBS VULNERABILITY INDEX

highlighted by our index is not the same as the most vulnerable areas in the SVI map. To the contrary, the most vulnerable census tracts in our map don’t seem to be anywhere near the SVI areas. Based on this comparison, we expect the final result to have very little overlap.

NYS BY CENSUS TRACT

MOST VULNERABLE

0.00 - 0.25

20

08

LEAST VULNERABLE

0

120

Miles

ONONDAGA COUNTY

BY CENSUS TRACT

MORE VULNERABLE

4.3

ONONDAGA COUNTY

BY CENSUS TRACT

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52 + SOCIAL VULNERABILITY INDEX: HIGHEST VALUES

CARBON VULNERABILITY INDEX: HIGHEST VALUES

CONCLUSIONS The New York State analysis did not produce overlapping results of the Social Vulnerability Index and the High Carbon Vulnerability Index. The resulting HCVI and the SVI only intersected high vulnerability in 4% of the area of study. Therefore the Social Vulnerability Index cannot be used to reliably predict areas vulnerable to high carbon job loss. This finding suggests a need for further investigation towards defining frontline communities as outlined in the Green New Deal.

OVERLAPPING BETWEEN SVI AND CVI

4% IMPLICATIONS & RECOMMENDATIONS

NEW YORK STATE: INTERSECT OPERATION

This report was limited by the ability of adequate data. The limited spatial data available on green house gas emissions forced this study to rely on census data of industry and occupation. The current categorization of industries is broad, and could be misrepresenting the high carbon job vulnerability. Due to the recent passing of legislation like the Green New Deal, and the Climate Leadership and Protection Act, more accurate information will be needed to identify vulnerability to protect frontline communities as both documents have written.

Additionally better definitions of high carbon jobs should be developed to better understand the complete definition of vulnerability in a Green New Deal Era.

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6 SOURCES IMAGES [1] Carbon Emissions

Peters, Adel. “Carbon Emissions.” FastCompany, 9 Mar. 2017, www.fastcompany.com/3068770/carbon-emissions-inthe-uk-are-now-as-low-as-they-were-in-1894.

[2] Green New Deal

Loeb. Saul. “Democratic Rep Alexandria Ocasio-Cortez Announces Green New Deal.” AFP. GettyImages. February 7 2019

[3] Frontline Communities

“Frontline of Crisis Forefront of Change.” The Leap. 2017

[4] Icons The Noun Project, December 11, 2019 [5] Scottish Power Williamson, Jonny“Technological Supremacy.” The Manufacturer October 16, 2017

DATASETS United States Environmental Protection Agency/ Greenhouse Gas Reporting Program/ Greenhouse Gas Emissions in NYS, Facilities Reporting as Emitters [2018] Excel spreadsheet. Available in: https://www.epa.gov/enviro/greenhouse-gas-customized-search Accessed on November, 2019.

Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry/ Geospatial Research, Analysis, and Services Program. Social Vulnerability Index [2016] Database New York.data-and-tools-download.html. Accessed on November 19, 2019.

REFERENCE USA Infogroup, Inc. Retrieved December 1, 2019 from ReferenceUSA database.

AMERICAN COMMUNITY SURVEY U.S. Census Bureau ; American Fact Finder; using American Fact Finder; <http://factfinder. census.gov> (9 December 2019).

OTHER RESEARCH SOCIAL VULNERABILITY INDEX:

SVI 2016 Documentation. Available in: https://svi.cdc.gov/data-and-tools-download.html. Accessed on November 19, 2019.

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7 APPENDIX HIGH CARBON INDUSTRY RANKING

HIGH CARBON VULNERABILITY RANKING

CENSUS LAYER INFORMATION

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7 ACTIVITY LOG 11/19/2019

11/29/2019

Description: This is the base layer for all of our maps. Check with data librarian if projection is correct. Confusion is between West - East - Long Island - Central. Which one is appropriate for the whole state? - Resolved

Problem geolocating addresses on map: After doing the Table Join we have a Latitude and Longitude for each address, yet we cannot create point features on the map.

In the Excel file: Latitude and Longitude column need to be in “number” Identify coordinate system of the geocoded file (Geocodio uses WGS 84) Then we inserted the point features

NYS_CivilBoundaries - State.shp projected in NAD 1983 UTM zone 18N Transverse Mercator Datum: North American 1983 Source: NYS Office of Information Technology Services GIS Program Office (GPO)

Attempt 1: Table Join Table join between zip code layer and .CSV file for EPA greenhouse emissions. Results worked but gave a lot of nulls. We had multiple zip codes repeating in the table and not adding up, which we think is the reason why results are not correct (different companies in the same zip code are not added up).

11/20/2019

SIPA - Lehman Library Meeting with Eric Glass: Nulls in first table join file are because of redundancy of zip codes.

Solution: Create new column in CSV file with FIPS codes (unique for each county) so we can attempt a table join with the same column in ArcMap. Before the table join, we created a pivot table in Excel to have the total sum of emissions for each FIPS code. Then we joined the resulting table to the county layer in Arcmap. The result allows us to display a choropleth map of the highest carbon emitting Counties (classification to be determined).

Classification by quantities leaves the nulls blank, which is what we want.

Next: ReferenceUSA (website accessed through CLIO) allows us to get the number of employees of each company and facility in the highest emitters list. This will allow us to determine a density of vulnerability, since the highest emitting companies with the highest number of employees have more jobs at risk..

We will create this map by getting the number of employees in each facility (estimations rounded up to the highest number provided) and then showing that in a NYS map as proportional symbols.

We plan to contrast that with census data (industry by occupation). Scale to be determined.

11/26/2019

Geocoding of addresses: In order to assign Latitude and Longitude to each facility address in our CSV table of GHG emitting facilities, we used “Geocodio”. We had to insert the excel file into the website and it created two new columns in the same file with LAT and LONG. Only two addresses were out of place (located in other states of the US).

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Table Join with Geocoded addresses: To do the Table Join between CO2_emissionsByCounty_Clipped with the new geocoded CSV file we had to change the name of the CSV file (EPA GHG emissions Geocoded_001 to EPA_GHGemissionsGeocoded_001) and also the Column names (in the CSV file, from COUNTY_FIPS to Counties_f).

Creating a Cloropleth map with number of employees per County: We had to create a Pivot Table in Excel to make a sum of all the employees according to the Counties FIP. Important note: there was no employee data available for some of the facilities. This means that 0 does not mean “no employees”, instead it is “no data available”.

Then we needed to create a new CSV File in order to make a table join.

Joining the new table to the CO2_emissionsByCounty_Clipped layer and defining the classification for the choropleth map as Natural Breaks with 5 classes and with slight modification of numbers for easier reading.

Additionally we created one Pie Chart for GHG emissions per county and one Bar Chart for employees per counties.

12/08/2019

Ranking GHG emissions and job density: First we had to find GHG emission data by sector that would allow us to agglomerate the Census categories for industries within the GHG emission ones. We then had to assign a weight to each category (industry type) according to the percentage it represents in the GHG emission chart.

In Arcmap we created one layer for each industry representing job density, rasterized these with “feature to raster” and then reclassified these with the new weight assigned. Finally, using raster calculator we combined all layers with their assigned weight and obtained a final map but only for job density and GHG emissions.

We repeated the same process but for the Census Data information that we wanted to use to define vulnerability and then combined all together with the raster calculator to obtain our final Carbon Vulnerability Map. Finally in order to intersect our newly generated map with the SVI map, we had to convert our raster map to a vector layer using “raster to polygon”. We intersected in both cases just the highest values in order to see if these matched, and the result was that only 4% matched.

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