geographic analysis of us green sector industry concentration
TRANSCRIPT
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Geographic Analysis of US Green
Sector Industry Concentration
D. Lane Register, Dayton M. Lambert, Burton C. English, Kimberly L. Jensen, R. Jamey Menard, and Brad Wilson, Michael D. Wilcox
Selected Paper prepared for presentation at the Applied Agricultural Economic Association Annual Meeting, Seattle, WA,
August 12-14, 2012
Portion of this research funded by USDA NIFA and the Southeast Sun Grant Center. Views expressed here are those of the authors.
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Overview
• Renewable biofuels (or so-called “green energy” or advanced biofuels) expected to be an important component of the Renewable Fuels Standard portfolio
• What impact would multiple facility sitings have on
the geographic distribution
▫ Jobs, employment, and income ▫ Emissions ▫ Water quality and land use
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Modified IMPLAN Model
●Energy conversion costs
●Production coefficients
Rural Jobs
Rural Economic
Output
Rural Value-Added
Labor Income
Task I: Technology-specific Facility Siting (Cellulosic-based Butanol, Green Diesel, Co-/Direct Firing)
Resource s •Local capacity •Industry concentration (LQ’s) •Infrastructure •Feedstock potential
Sequential, Multiple Facility Site
Selection
Impacted Region •Identify Counties •Identify Hydrologic Units
Task 2: Regional Economic Impacts
Disaggregate Total
Employment Multiplier
Effects
Task 3: Environmental Impacts and Indicators
Business Establishment Distribution
Sector-specific
Geographic (County-
level)
Water Quality
Sedimentation
Nutrient Loading
Air Quality
Congestion, Traffic Safety
Emissions
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Facility locator model
Suitability Layers Crop Yields Land Use Data Transport Networks
Inputs
Cost of Production of Traditional Crops
Cost of Production of Biofeedstock
Source: Wilson (2009)
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Suitability Analysis Candidate facilities to be “filtered” according to industry requirements
Source: Wilson (2009)
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Facility locator results Cost-minimizing biorefinery location and feedstock supply
Source: Wilson (2009)
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Multiple Facilities
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Renewable fuel industries
and supporting sectors
• Biodiesel
• Fast pyrolysis
• Gasification
• Cellulosic
• Direct fire
“Members” identified using expenditure budgets generate by NREL
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“Phase I” Objective
• Quantify industry concentration
• Combine measures with facility locator model
• This presentation focuses on global firm concentration indices
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Economic Concentration Measures • “Global” - describes the degree to which industries are
concentrated within a region. • Examples include:
▫ Florence (1934) Location Quotient ▫ Hoover’s (1937) Localization Index ▫ Gini Coefficient (Krugman 1991) ▫ Ellison and Glaeser’s (1997) Industry Concentration Index ▫ Guimarães, Figueiredo, and Woodward (2007) Industry
Concentration Index based on plant counts
Plant size tends to be larger where manufacturing firms concentrate Builds on EG index, normalizing by distribution of business
establishments
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Framework for Global Firm
Concentration Index
• Expected probability of location: xj is area j’s share of sub-sector employment E(pj) is conditional on “natural advantages” associated with location
• Variance:
• g ; degree of localization above normal economic activity
(difference between pj and xj) • g = 0, no excess concentration; g = 1, all firms located in one
region.
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Concentration indices
GFW (2007) – Plant EG (1997) – Employment
Asp
ati
al
Sp
ati
al
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Bandwidth selection (GFW, 2011)
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Aspatial/spatial
global concentration (gc)
0
0.01
0.02
0.03
0.04
0.05
0.06
0 0.01 0.02 0.03 0.04 0.05 0.06
Sp
ati
al
glo
ba
l in
de
x
Aspatial global index
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Results: overview of
concentration estimates
and bootstrapped
95% C.I.’s
(disaggregated sectors)
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Concentration index (gc) distributions Combined sectors
0.000
0.002
0.004
0.006
0.008
0.010
gc
2002 Employment Concentration 2002 Establishment Concentration
2006 Employment Concentration 2006 Establishment Concentration
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Connecting global index measures to costs
savings from external economies
(Glaeser and Ellison, 1997: J. Political Economy)
Cost elasticity: projected %-change in direct jobs in the economy divided by the projected percent change in labor income in the economy (in dollars).
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Concentration, variability of expected costs
savings
0
2
4
6
8
0 0.2 0.4 0.6 0.8 1
Coef
fici
ent
of
Var
iati
on
Share of Employment in All Renewable Energy
Industries (xj)
Biodiesel Residential Solar
Biodiesel: gc = 0.0005 Residential solar: gc = 0.01
Higher CV’corresponds with potential for higher costs savings
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Further steps
• Integrate socioeconomic and industry structure data into facility locator model
▫ Supporting industry business establishments
▫ Employment
• “If you build it, they will come”…but will they?
• So far, small, but significant levels of concentration in this analysis…how does this influence site selection parameters?
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References
• Wilson, B. Modeling Cellulosic Ethanol Plant Location Using GIS. M.S. Thesis, The University of Tennessee, Knoxville, 2009.
• Ellison, G. and E. L. Glaeser. 1997. Geographic Concentration in US Manufacturing Industries: A Dartboard Approach. Journal of Political Economy 105(5): 889-927.
• Guimarães, P., O. Figueiredo, and D. Woodward. 2007. Measuring the Localization of Economic Activity: A Parametric Approach. Journal of Regional Science 47(4):753-774.
• Guimarães, P., O. Figueiredo, and D. Woodward. 2011. Accounting for Neighboring Effects In Measures of Spatial Concentration. Journal of Regional Science 51(4):678-693.
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Data used in this example (Register, 2012)
• 2006:
▫ Employment : IMPLAN
▫ Establishments: US CBP
• Data for all 3078 county divisions in the contiguous US