Geographic Analysis of US Green Sector Industry Concentration

Transcription

1 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.

2 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

3 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 Task 3: Environmental Impacts and Indicators Modified IMPLAN Model Energy conversion costs Production coefficients Water Quality Air Quality Rural Jobs Rural Economic Output Rural Value- Added Labor Income Disaggregate Total Employment Multiplier Effects Business Establishment Distribution Sectorspecific Geographic (Countylevel) Sedimentation Nutrient Loading Congestion, Traffic Safety Emissions

4 Facility locator model Inputs Crop Yields Land Use Data Transport Networks Suitability Layers Cost of Production of Traditional Crops Source: Wilson (2009) Cost of Production of Biofeedstock

5 Suitability Analysis Candidate facilities to be filtered according to industry requirements Source: Wilson (2009)

6 Facility locator results Cost-minimizing biorefinery location and feedstock supply Source: Wilson (2009)

7 Multiple Facilities

8 Renewable fuel industries and supporting sectors Biodiesel Fast pyrolysis Gasification Cellulosic Direct fire Members identified using expenditure budgets generate by NREL

9 Phase I Objective Quantify industry concentration Combine measures with facility locator model This presentation focuses on global firm concentration indices

10 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

11 Framework for Global Firm Concentration Index Expected probability of location: x j 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 p j and x j ) g = 0, no excess concentration; g = 1, all firms located in one region.

12 Spatial Aspatial Concentration indices EG (1997) Employment GFW (2007) Plant

13 Bandwidth selection (GFW, 2011)

14 Spatial global index Aspatial/spatial global concentration (gc) Aspatial global index

15 Results: overview of concentration estimates and bootstrapped 95% C.I. s (disaggregated sectors)

16 gc Concentration index (gc) distributions Combined sectors Employment Concentration 2002 Establishment Concentration 2006 Employment Concentration 2006 Establishment Concentration

17 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).

18 Coefficient of Variation Concentration, variability of expected costs savings 8 6 Biodiesel: gc = Residential solar: gc = Higher CV corresponds with potential for higher costs savings Share of Employment in All Renewable Energy Industries (x j ) Biodiesel Residential Solar

19 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?

20 References Wilson, B. Modeling Cellulosic Ethanol Plant Location Using GIS. M.S. Thesis, The University of Tennessee, Knoxville, Ellison, G. and E. L. Glaeser Geographic Concentration in US Manufacturing Industries: A Dartboard Approach. Journal of Political Economy 105(5): Guimarães, P., O. Figueiredo, and D. Woodward Measuring the Localization of Economic Activity: A Parametric Approach. Journal of Regional Science 47(4): Guimarães, P., O. Figueiredo, and D. Woodward Accounting for Neighboring Effects In Measures of Spatial Concentration. Journal of Regional Science 51(4):

21 Data used in this example (Register, 2012) 2006: Employment : IMPLAN Establishments: US CBP Data for all 3078 county divisions in the contiguous US