A GIS MULTICRITERIA ANALYSIS FOR SITING A SUN/WIND POWERED PLASTIC REPROCESSING FACILITY IN THE CONTRA COSTA COUNTY, CALIFORNIA
RECYCLING MARKET DEVELOPMENT ZONE
A THESIS PRESENTED TO THE DEPARTMENT OF HUMANITIES AND SOCIAL SCIENCES
IN CANDIDACY FOR THE DEGREE OF MASTER OF SCIENCE
By DAVID B. JAQUET
NORTHWEST MISSOURI STATE UNIVERSITY MARYVILLE, MISSOURI
August, 2013
A GIS MULTICRITERIA ANALYSIS
A GIS Multicriteria Analysis for Siting a Sun/Wind Powered Plastic Reprocessing
Facility in the Contra Costa County, California Recycling Market Development Zone
David B. Jaquet
Northwest Missouri State University
THESIS APPROVED
Thesis Advisor, Dr. Patricia Drews Date Dr. Gregory Haddock Dr. Yi-Hwa Wu Dean of Graduate School Date
iii
A GIS MULTICRITERIA ANALYSIS FOR SITING A SUN/WIND POWERED PLASTIC REPROCESSING FACILITY IN THE CONTRA COSTA COUNTY, CALIFORNIA
RECYCLING MARKET DEVELOPMENT ZONE
Abstract
The purpose of this research is to create a model for siting a wind and/or solar
powered recycled plastics material transformations plant within the Contra Costa,
California Recycling Market Development Zone (RMDZ). The RMDZ Program is a
California-wide program that combines recycling with economic development to fuel
new businesses, expand existing ones, create jobs, and divert waste from landfills. A
major emphasis of the RMDZ program is finding ways to use recycled materials to create
new products. The zones cover roughly 71,790 square miles from the Oregon border to
San Diego. I am specifically interested in the RMDZ of Contra Costa County.
I conducted a GIS-based multicriteria sensitivity analysis using a weighted
overlay technique to examine the impact of various variables under different
percentages of influence for siting an optimal industrial location. Since the RMDZ
program is an initiative with strong support and enthusiasm, there are many incentives
and an ample amount of information for contacting industry experts and administrators.
This research has two parts. First, it discusses the geographic, environmental,
and economic issues pertaining to recycling and the use of renewable energies, and also
the ‘green’ business development potentials. Second, I conducted a multicriteria
iv
sensitivity GIS analysis using a weighted overlay technique that a ‘green’ entrepreneur
can apply to create a recycled plastics material transformations business.
The multicriteria sensitivity GIS analysis model shows that changes in the
weighted overlay analysis results are relative to changes in siting criteria importance,
identifies criteria that are especially sensitive to their given importance, and allows
visualizing the spatial dimension of those changes more intuitively at the jurisdiction
and parcel levels. The model found the city of Richmond to be the jurisdiction most
suitable across all siting criteria at each percentage of influence except for the ‘Tax Rate’
criterion where Contra Costa County unincorporated areas are the most suitable
locations at 76% percentage influence.
v
Table of Contents
Abstract ............................................................................................................................... iii
Table of Contents ................................................................................................................. v
List of Figures ...................................................................................................................... vi
List of Tables ...................................................................................................................... vii
Acknowledgments............................................................................................................. viii
List of Abbreviations ........................................................................................................... ix
Chapter 1: Introduction ...................................................................................................... 1
1.1 Research Background ........................................................................................... 1
1.2 Significance ........................................................................................................... 3
1.3 Rationale .............................................................................................................. 7
1.4 Research Objectives ........................................................................................... 14
Chapter 2: Literature Review ............................................................................................ 15
2.1 Recycling and GIS .................................................................................................... 15
2.2 Solar and Wind Power ............................................................................................. 18
2.3 Weighted Multicriteria Overlay Sensitivity Analysis ............................................... 21
Chapter 3: Methodology ................................................................................................... 23
3.1 Study Area ............................................................................................................... 23
3.2 Spatial Analysis ........................................................................................................ 26
3.2.1 Part 1 ................................................................................................................. 27
3.2.2 Part 2 ................................................................................................................. 45
Chapter 4: Analysis Results ............................................................................................... 53
Chapter 5: Conclusion ....................................................................................................... 75
Appendix ........................................................................................................................... 79
References ........................................................................................................................ 80
vi
List of Figures
Figure 1: California Recycling Market Development Zones (RMDZ) ................................ 13 Figure 2: Contra Costa County, California Recycling Market Development Zone ............ 24 Figure 3: Contra Costa County, California RMDZ jurisdictions ......................................... 25 Figure 4: Creation of criteria raster flowchart .................................................................. 30 Figure 5: Creation of tax area and rate raster flowchart .................................................. 32 Figure 6: Contra Costa County, California RMDZ reclassed wind power potential .......... 34 Figure 7: Contra Costa County, California RMDZ reclassed solar power potential .......... 35 Figure 8: Contra Costa County, California RMDZ reclassed tonnage ............................... 36 Figure 9: Contra Costa County, California RMDZ reclassed income ................................. 38 Figure 10: Contra Costa County, California RMDZ reclassed tax areas ............................ 39 Figure 11: Contra Costa County, California RMDZ reclassed distance from ports ........... 41 Figure 12: Contra Costa County, California RMDZ reclassed distance from major roads 42 Figure 13: Wind power suitability analysis for industrial parcels flowchart .................... 47 Figure 14: Solar power suitability analysis for industrial parcels flowchart ..................... 49 Figure 15: Wind power potential at 16% influence .......................................................... 57 Figure 16: Wind power potential at 52% influence .......................................................... 58 Figure 17: Wind power potential at 76% influence .......................................................... 59 Figure 18: Solar power potential at 16% influence .......................................................... 63 Figure 19: Solar power potential at 52% influence .......................................................... 64 Figure 20: Solar power potential at 76% influence .......................................................... 65 Figure 21: Richmond industrial parcels suitable for wind power ..................................... 71 Figure 22: Richmond industrial parcels for solar power ................................................... 73
vii
List of Tables
Table 1: 1999 average impacts statewide for additional disposal or diversion ................. 4 Table 2: Average economic impacts of additional waste disposal and diversion in 1999 . 5 Table 3: Economic impacts of diversion sectors in the Bay Area region (B) ...................... 6 Table 4: Data sources ........................................................................................................ 28 Table 5: Example with 16% influence for wind potential and a ‘1 to 6 to 1’ evaluation scale .................................................................................................................................. 44 Table 6: Non-industrial zoning codes and meaning ......................................................... 51 Table 7: M-2 Light Industrial setback standards ............................................................... 51 Table 8: M-3 Heavy Industrial setback standards ............................................................. 51 Table 9: M-4 Marine Industrial setback standards ........................................................... 52 Table 10: Wind power potential results by jurisdiction (percentage of cells).................. 55 Table 11: Wind power potential results by jurisdiction (number of cells) ....................... 56 Table 12: Solar power potential results by jurisdiction (percentage of cells) .................. 61 Table 13: Solar power potential results by jurisdiction (number of cells) ....................... 62 Table 14: Tax rates results by area ................................................................................... 66 Table 15: Tonnage of recycled plastics results by area .................................................... 67 Table 16: Income results by area ...................................................................................... 68 Table 17: Distance from major roads results by area ....................................................... 69 Table 18: Distance from ports results by area .................................................................. 70
viii
Acknowledgments
I would like to thank my thesis committee members Dr. Patty Drews, Dr. Gregory
Haddock, and Dr. Yi-Hwa Wu for their guidance and support along the way. In addition I
would like to give credit to the GIS community for the direct and indirect positive
influence it had on this project.
I dedicate this work to my wife Karina, and my sons Trystan and Mael. Their
presence, encouragement and patience have been indispensable throughout my
studies.
ix
List of Abbreviations
Abbreviation Meaning AJAX Asynchronous JavaScript and XML: Web-
based technology combining JavaScript for client-side scripting and XML for data transfer
API Application Programming Interface CIWMB California Integrated Waste Management
Board EPA Environmental Protection Agency IWMA Integrated Waste Management Act JTR Jobs Through Recycling KW Kilowatt MRF Materials Recovery Facility MSW Municipal Solid Waste PHP Hypertext Preprocessor: Server-side
scripting language PRF Plastics Reprocessing Facility PV Photovoltaic RCP Recycled-Content Products directory REAP Recycling Education, Awareness, and
Participation index RMDZ Recycling Market Development Zone WRAP Waste Reduction Awards Program
1
Chapter 1: Introduction
1.1 Research Background
Waste management represents one of the top priorities in the area of
environmental management and protection. Given that local, state, and federal
governments actively try to promote innovative ways of increasing economic growth, an
increase in consumption of industrial goods and natural resources is understandable.
For this reason, waste management should be given considerable attention. As a result
of an increased consumption of natural resources and industrial goods, an increase in
the amount of waste and environmental degradation may also result. It is imperative
that a balance be found between these realities and that the reuse of discarded
materials and resources is promoted (Pešić et al., 2012).
Municipal solid waste (MSW), commonly known as trash or garbage, consists of
discarded everyday use items such as paper, yard trimmings, food scraps, plastics,
metals, glass, wood, rubber, textile, paint, and batteries. Several MSW management
practices, such as source reduction, recycling, and composting, prevent or divert
materials from the waste stream (U.S. Environmental Protection Agency 2008b). Source
reduction or waste prevention is the practice of designing, manufacturing, purchasing,
or using materials (such as products and packaging) in ways that reduce MSW; this also
includes the reuse of products and materials (U.S. Environmental Protection Agency
2008d). Recycling involves a series of activities that includes collecting recyclable
materials that would otherwise be considered waste, sorting and processing recyclables
into raw materials such as fibers, plastics, and glass, and manufacturing raw materials
2
into new products (U.S. Environmental Protection Agency 2008c). Composting is the
controlled biological decomposition of organic matter, such as food and yard wastes,
into humus, a soil-like material which can be used for gardening and landscaping (U.S.
Environmental Protection Agency 2008a). There are other practices for disposing of
waste. Landfills are engineered areas where waste is placed into the land, usually with
liner systems and other safeguards to prevent groundwater contamination. Combustion
involves facilities that burn MSW at high temperature, reducing waste volume and
generating electricity (U.S. Environmental Protection Agency 2008b). The EPA ranks the
most environmentally sound strategies for MSW with source reduction as the most
preferred method, followed by recycling, composting, and remanufacturing, and lastly
disposing in combustion facilities and landfills. The creation of waste can never be fully
eliminated, and recycling is one of the practices that is most realistic for actively
engaging the business sector and public in minimizing the magnitude of waste in our
environment (California Integrated Waste Management Board 2007).
Recycling is one of the segments of waste management, but also a business
sector that is increasingly growing. This is because recycling, besides the undeniable
environmental effects, contributes to achieving savings for enterprises (in terms of
cost), and is considered the logical next step in relation to the continuous reduction of
resource consumption (Pešić et al., 2012).
Research shows that convenience is a key factor for encouraging individuals to
recycle (Field and Macey 2007). To analyze this degree of convenience, spatial,
demographic, and socio-economic factors are important variables that can be cross-
3
analyzed to help determine if there are patterns that might predict recycling behavior
and ultimately rates of recycling (Clarke and Maantay 2005).
However, diverting household and business recyclable materials from the waste
stream is the first of three steps in the entire recycling process. The second step involves
companies using these recycled materials to manufacture new products. The third step
closes the recycling loop and involves consumers purchasing products made from
recycled materials. Different recycled materials including glass, metals, organics, paper,
plastics, tires, and electronics can potentially be reprocessed into their raw form and
remanufactured into new products (California Integrated Waste Management Board
2007).
1.2 Significance
According to a 2001 economic impact study on waste disposal and diversion in
California by Goldman and Ogishi (2001), diverting solid waste has a significantly higher
impact on the economy than disposing of it. Table 1 indicates that in 1999 the statewide
economic impacts from disposal and diversion rates were 17-20 percent higher than the
impacts if all the waste had been disposed only. Specifically, the California waste
disposal sectors would have generated a total output impact (all sales in all sectors of
the economy) of $18.08 billion to the economy if all waste generation were disposed
(Goldman and Ogishi 2001). Additionally, the disposal sectors would have generated a
value-added impact (the increase in the value of goods and services sold by all sectors of
the economy minus the costs of inputs (excluding labor) of $8.99 billion and created
154,200 jobs (Goldman and Ogishi 2001). In comparison, both disposal and diversion
4
sectors operating at the 1999 rate of diversion would have generated a total output
impact of $21.20 billion, produced a value-added impact of $10.74 billion, and created
179,300 jobs (Goldman and Ogishi 2001).
Table 1: 1999 average impacts statewide for additional disposal or diversion
Disposed Diverted Additional Gain from Diversion
(Difference)
Total Sales ($/ton) $119 $254 $135
Output Impact ($/ton)
$289 $564 $275
Total Income Impact ($/ton)
$108 $209 $101
Value-added Impact ($/ton)
$144 $290 $146
Jobs Impact (Jobs/1,000 tons)
2.46 4.73 2.27
(Goldman and Ogishi 2001, p. vii)
5
Table 2 indicates that some of the highest average economic impacts from
diversion are in the Central Valley, Southern California, and San Francisco Bay Area
regions. Table 3 breaks down the economic impacts of diversion sectors for the San
Francisco Bay Area region. These regions have more agricultural, business, and industrial
infrastructure relative to the other regions, and a high percentage of the output
generated by the waste industries are re-spent in the same regions. Also, relatively
more recycling manufacturers are located in these areas, and they create more value-
added impact and jobs within the regions. (Goldman and Ogishi 2001).
Table 2: Average economic impacts of additional waste disposal and diversion in 1999
Region Total Sales 1999
($/ton)
Impacts on Regional Economy Output ($/ton)
Total Income ($/ton)
Value Added ($/ton)
Number of Jobs (Per
1,000 tons) All California Disposed
Diverted 119 289 108 144 2.46 254 564 209 290 4.73
Northern Region (A)
Disposed Diverted
115 260 94 125 2.62 186 388 143 199 3.90
Bay Area Region (B) Disposed Diverted
118 275 106 140 2.22 224 476 184 254 3.78
Central Coast Region (C)
Disposed Diverted
115 250 94 123 2.30 189 387 152 203 3.61
Central Valley Region (D)
Disposed 105 241 88 118 2.23 Diverted 276 587 222 303 5.49
Southern Region (E) Disposed 123 287 108 142 2.46 Diverted 265 557 200 278 4.62
Eastern Region (F) Disposed Diverted
131 241 87 114 2.42 55 85 31 51 0.92
(Goldman and Ogishi 2001, p. 60)
6
Table 3: Economic impacts of diversion sectors in the Bay Area region (B)
Estimated Final Sales,
1999 (in $1,000)1
Impact on
Output (in $1,000)
Total Income (in $1,000)
Value Added (in
$1,000)
Number of Jobs (in $1,000)
Recycling Collection and MRFs
315,178 713,326 312,608 403,921 5.8
Yardwaste Collection and Compost Facility
220,178 513,066 195,814 259,450 4.1
Recyclers 147,352 277,949 107,730 176,480 2.7 Collection and Transformation Facility - - - - - Recycling Manufacturers
Paper, Cardboard-related 24,432 47,625 13,690 21,775 0.3 Plastics related 7,955 16,000 5,167 7,637 0.1 Glass related 82,865 164,162 57,255 83,918 1.3 Iron and Steel related 155,220 304,074 100,458 142,048 2.1 Nonferrous metals 49,230 94,940 30,123 43,574 0.1
Regional Total 1,002,409 2,131,143 822,845 1,138,803 16.9 California Total 4,581,547 10,154,797 3,757,638 5,221,667 85.2
1. Estimated final sales are calculated by adjusting for the costs of recycling and yardwaste collection and recyclable feedstock sales to avoid double counting.
(Goldman and Ogishi 2001, p. 52)
Ultimately, the economic effects provided through recycling can be observed at
three levels. At the first level there are direct effects, which are reflected in the creation
of new business and providing new jobs, increased sales, and consequently increased
revenues. The second level consists of indirect effects, which include economic benefits
7
for other enterprises, from which the ones that deal with waste recycling have been
purchasing waste which will be processed and used for manufacturing. At the third level
there are induced effects, like an increase of the purchasing power of the population,
due to increased employment, which, because of earnings in the recycling industry,
have been buying products and services from other industries (Pešić et al., 2012).
1.3 Rationale
A 2003-2004 California waste characterization study found that while plastic
waste makes up 9.5 percent of the disposed waste stream in California, only 5 percent
of plastic is recycled statewide (California Integrated Waste Management Board 2008f).
Plastic has characteristics that make it a preferable material for packaging and
manufacturing, i.e., light weight, durable, and less expensive; specially designed plastics
have also been an integral part of the communication and electronics industry such as
for the manufacturing of chips or printed circuit boards, or housing for computers. They
are also integral components in the preparation and delivery of alternative energy
systems such as fuel cells, batteries and solar power (Subramanian 2000). The
pervasiveness and assorted mixture of plastic also makes it a challenge to collect and
recycle as it contributes to an increasing volume in the solid waste stream. Moreover
plastic materials when released in the environment can be a visual nuisance and
harmful to wildlife. Plastic debris does not degrade in the environment; instead it tends
to accumulate, creating long-term negative environmental impacts (California
Integrated Waste Management Board 2008f). Reusing plastic containers is one of the
most effective and inexpensive ways to reduce the negative environment impact of
8
packaging. It can be done either by refilling and reusing the plastic container or by
reprocessing and manufacturing plastic products into the same product or a new
product.
There are three methodologies of recycled plastic reprocessing. Primary
reprocessing entails remanufacturing the recovered product back into the same product
(Ecology Center Plastics Task Force 1996). Secondary reprocessing refers to the physical
reprocessing (e.g. grinding and melting) and reformation of post-consumer plastic
packaging materials (U.S. Department of Health and Human Services Food and Drug
Administration Center For Food Safety and Applied Nutrition Guidelines for Industry
2006). Tertiary reprocessing occurs when plastics are broken down into base chemicals
that could be reconstituted into virgin-grade material (Ecology Center Plastics Task
Force 1996).
A plastics reprocessing facility (PRF) assumes the secondary reprocessing
methodology; it is the most common type of plastic reprocessing in the USA. Secondary
reprocessing decreases the use of virgin material. For example, if there is a market for a
jacket filled with polyester fiber, and that jacket’s filling is made from post-consumer
bottles, then the bottles are diverted from landfill and the virgin resources that
otherwise would have been used to make the fiber are conserved. The principal
products made by secondary reprocessing include textiles, panels, pallets, and plastic
lumber (Ecology Center Plastics Task Force 1996). The Integrated Waste Management
Board of California provides many incentives to promote recycling based businesses:
9
• Recycling business loans, at below market rates, to manufacturers within forty Board-designated recycling market development zones (RMDZ) (California Integrated Waste Management Board 2005).
• Recycling business development assistance, including the development of business and marketing plans, market research, and technology evaluation (California Integrated Waste Management Board 2005).
• Special case studies, such as the award-winning Jobs Through Recycling (JTR) 98 project, which demonstrated the environmental and economic benefits of establishing regional markets for locally generated waste (California Integrated Waste Management Board 2005).
• Free product marketing through RecycleStore.com and a Recycled-Content Products (RCP) Directory. RecycleStore.com showcases innovative recycled-content products, and provides a way for manufacturers to promote their products to consumers, worldwide. The RCP Directory lists thousands of recycled products and provides information on the companies that reprocess, manufacture, and distribute these products (California Integrated Waste Management Board 2005).
• Business resource efficiency and waste reduction services, including a variety of resources such as fact sheets, case studies, training, an information exchange database, and a Waste Reduction Awards Program (“WRAP”), which provides an opportunity for California businesses to gain public recognition for outstanding efforts to reduce waste (California Integrated Waste Management Board 2005).
• CalMAX is a free service to help some businesses find markets for non-hazardous materials that were traditionally disposed, while helping others find less expensive manufacturing feedstock (California Integrated Waste Management Board 2005). Moreover, Contra Costa County offers streamlined permitting, and land is
available at lower cost than many other San Francisco Bay Area locations. Contra Costa
County has an extensive network of highways, railways and waterways. Businesses have
10
ready access to the San Francisco Bay Area’s huge market and beyond (California
Integrated Waste Management Board 2008g).
In 1989 the State of California enacted the Integrated Waste Management Act
(IWMA) which set a waste diversion requirement of 25 percent for 1995 and 50 percent
for 2000 (California Integrated Waste Management Board 2008a). A new integrated
waste management hierarchy was installed in order of priority: (1) source reduction, (2)
recycling and composting, and (3) environmentally safe transformation and land
disposal (California Integrated Waste Management Board 2008a). In actuality, by 2000
the diversion rate reached 42 percent, lower than the set milestone, though still a 5
percent increase from the previous year (California Integrated Waste Management
Board 2008d). Working with this momentum, in November 2001 the California
Integrated Waste Management Board (CIWMB) ratified to promote a "Zero-Waste
California" where the public, industry, and government would strive to reduce, reuse, or
recycle all municipal solid waste materials back into nature or the marketplace in a
manner that protects human health and the environment (O’Connell 2002). Since 2001,
California’s diversion rate of waste has steadily increased from 42 percent to 54 percent
in 2006, when over 92.2 million tons of waste was generated, 42.2 million tons were
disposed, and 50.1 million tons were diverted (California Integrated Waste Management
Board 2008c).
Within the framework of the “Zero Waste California” vision, in December 2004
California implemented the Governor’s “Green Building” Executive order. A green
building, also known as a sustainable building, is a structure that is designed, built,
11
renovated, operated, or reused from an ecological and resource-efficient perspective.
Some of the objectives of green building design include protecting occupant health,
improving employee productivity, using energy, water, and other resources more
efficiently, and reducing the overall impact to the environment (California Integrated
Waste Management Board 2008e). The plan has the purpose of taking measures for
reducing state building electricity usage by retrofitting, building and operating the most
energy and resource efficient buildings by using prescribed cost-effective measures. The
goal is to reduce grid-based fossil fuel energy purchases for state-owned buildings by 20
percent by 2015 (Office of the Governor, Governor of the State of California. 2004). The
executive order also encourages the private commercial sector to set the same goal
(The California Energy Commission 2004). Implementing technologies and systems that
use renewable sources of energy such as solar and wind can allow energy and monetary
savings goals to be met. Some utilities such as Pacific Gas and Electric (PG&E) have net
metering programs which can increase monetary savings. Net metering programs allow
grid-tied utility customers who generate electricity in excess of their consumption to
credit that amount for later use. For example, when a wind turbine or solar panels
produce more electricity than is consumed on-site, excess electricity is sent to the grid.
For net metered systems, the utility acts like a giant battery. When wind or solar power
becomes unavailable, the site can use the energy credits from the utility (Wang 2008).
The California Integrated Waste Management Board continuously works to
ensure the success of a "zero waste California" vision with the Recycling Market
Development Zone (RMDZ) program. First established in 1993, it combines recycling
12
with economic development to create new businesses, expand existing ones, create
jobs, and divert waste from landfills. This program provides attractive loans, technical
assistance, and free product marketing to businesses that use materials from the waste
stream to manufacture their products and are located within an RMDZ. The thirty-three
RMDZs cover roughly 71,790 square miles of California from the Oregon border to San
Diego (Figure 1) (California Integrated Waste Management Board 2008h).
13
Figure 1: California Recycling Market Development Zones (RMDZ)
14
The RMDZ of special interest for this project is Contra Costa County, which is
located in the East Bay region of the San Francisco Bay area and follows the industrial
shoreline of the County. Target materials for recycling business potential include
construction and demolition debris, tires, plastics, green waste, textiles, and electronic
waste (Contra Costa County 2008).
1.4 Research Objectives
This thesis project uses a GIS-based weighted multicriteria overlay sensitivity
analysis to locate potential sites for placing a solar/wind powered PRF, specifically in the
RMDZ of Contra Costa County, California. By obtaining the necessary spatial datasets,
and using spatial parameters and weighted analysis criteria for industrial zoning areas
throughout the RMDZ of Contra Costa County, California, potential sites can be more
accurately identified to help quick start a successful green business venture.
15
Chapter 2: Literature Review
2.1 Recycling and GIS
GIS has become an increasingly important instrument for modeling and analyzing
potential locations for recycling facilities (Field and Macey 2007). Lober (1995 cited Field
and Macey 2007) developed a GIS model to produce a map of attitudes of opposition
based on the distances between residences and potential recycling centers. An example
of a complex application of GIS for recycling is the sanitation department of a city using
GIS to study recycling rates and behavior and analyze what can be done to improve
recycling education, awareness and participation. A 2005 study (Clarke and Maantay
2005) in New York City, New York examined the possible reasons for the disparity in
recycling diversion rates throughout the city’s neighborhoods, from a low of 9% to a
high of 31% of the total waste generated. The study specifically sought to determine
which demographic or socio-economic variables might help explain this disparity in
recycling rates and ultimately to develop a one-number descriptive index for each of
New York city’s 59 sanitation districts that, at the time, took into account the recycling
behavior and variables that predict recycling behavior. This recycling education,
awareness, and participation (REAP) index could then be used to help inform decision-
and policy-making about strategies for increasing recycling education, awareness, and
participation.
Bishop et al. (2001) presented a method to quantify the relationship between
the demand and supply of suitable land for waste disposal over time. Based on
projections of population growth, urban sprawl and waste generation, the method can
16
allow policy and decision-makers to measure the dimension of the problem of shortage
of land into the future. The procedure can provide information to guide the design and
schedule of programs to reduce and recover waste and can potentially lead to a better
use of the land resource. There is a variety of environmental, transportation, economic,
political, and social factors to consider when planning the location of a recycling center.
A GIS can be used to identify areas that are more or less desirable for recycling points by
analyzing the most cost-effective routes, demographics data, industry and commercial
centers (Lober 1995 cited Field and Macey 2007). In addition, a GIS can be used to
examine where certain types of materials are likely to be generated and in what
volumes, so that recycling collection frequency and optimal routes can be planned in
advance (Stinnet 1996 cited Field and Macey 2007).
GIS offers government agencies the opportunity to offer a wide array of geo-
referenced information to the public and other interested parties. A government agency
at any level of jurisdiction can offer a web-based GIS service to show information not
only from one agency but from numerous agencies that are sharing information. For
example, a private citizen can simply use a city’s web-based GIS to locate the closest city
recycling centers or depots and the type(s) of material they recycle (Petker et al. 2000).
On April 18, 2000 the California Integrated Waste Management Board (CIWMB)
launched the web-based ‘California Waste Stream Profiles’ GIS application in order to
assist decision makers and other interested parties obtain high-level information on
California waste streams. The data comes from numerous database sources under the
following categories: jurisdictions, counties, facilities, materials, legislative districts, and
17
schools; and is displayed in text, images, GIS maps and layers, tables, lists, charts, graphs
and also links to other related web resources. Specifically, the GIS allows a user to locate
a local jurisdiction or specific waste facility on a map and also see details such as roads,
jurisdiction boundaries, landfill sites, tire sites, used oil collection facilities,
transfer/processing sites, transformation sites, demographics, tribal lands, schools and
school districts, and RMDZs and their participating businesses. The GIS mapping
technology involves ESRI MapObjects and MapObjects Internet Map Server within the
Visual Basic development environment. The mapping solution is designed to display
spatial information regarding the selected jurisdiction/site and other spatial features
being viewed within the profiles system (Petker et al. 2000), a web-based tool that
displays summary information on solid waste issues. The profiles display information on
education, state agency recycling efforts, landfills, recycling centers, composting,
transfer stations, used oil, and recycling plastics (California Environmental Protection
Agency 2000).
An earlier study by Benjamin (1994) is of special interest for this project. It
examined the use of GIS to determine suitable locations for a plastics recycling
manufacturing facility in Massachusetts using four siting criteria: supply of plastic
recyclables to be used for processing and manufacturing, site availability for
commercial/industrial purposes, access to major transportation routes, and potential
costs based on commercial property tax rates and wage rates. Benjamin (1994) also
discusses how additional siting criteria can be analyzed using a GIS to answer different
and specific business questions. In this thesis project solar and wind power potential are
18
analyzed as additional criteria for siting a plastic reprocessing facility (PRF) in the Contra
Costa, California RMDZ.
Additional metering and financial incentives from utilities and government
agencies can be provided to a business that uses renewable sources of energy such as
solar and wind for electrical power. Energy efficiency efforts and initiatives to reduce
pollution and demand on grid-based resources in order to save resources and money
are increasingly becoming priorities of government agencies (Nielsen et al., 2002).
2.2 Solar and Wind Power
Renewable energy obtained from sources such as solar and wind is essentially
inexhaustible. While fossil fuels are being depleted, renewable energy technologies
provide a sustainable source of energy. Solar and wind power are abundant resources
and the technologies are well established. Additionally, implementing a renewable
energy system can reduce energy bills. Some utilities such as Pacific Gas and Electric
(PG&E) have net metering programs which can increase monetary savings. Net metering
programs allow grid-tied utility customers who generate electricity in excess of their
consumption to credit that amount for later use. For example, when a wind turbine or
solar panels produce more electricity than is consumed on-site, excess electricity is sent
to the grid. For net metered systems, the utility acts like a giant battery. When wind or
solar power becomes unavailable, the site can use the energy credits from the utility
(Wang 2008). Financial programs also provide incentives. The CaliforniaFIRST Program
(the Program) is a Property Assessed Clean Energy (PACE) finance program for non-
residential properties. The Program allows property owners to finance the installation of
19
energy improvements on commercial and industrial buildings and pay the amount back
as a line item on their property tax bill. The Program solves many of the financial hurdles
facing property owners wanting to install energy improvements: competitive rates,
longer payback terms, customized financing for each property, and decreased utility bills
from reduced electricity (CaliforniaFIRST 2012).
There are great examples of recycling-based companies that have implemented
renewable energy technologies to supply their electricity needs. In 2005 Middlebury
College in Middlebury, Vermont installed a 10KW (kilowatt) wind turbine which provides
electricity to a local material recovery center, powering lights and machinery. The
Middlebury turbine has been providing approximately 25% of the electrical needs of the
recycling facility. The wind turbine produces more than 8,000 kilowatt annually—
approximately equivalent to the annual energy consumption of a home powered
entirely by electricity. The idea for the wind turbine project originated in a Middlebury
College environmental studies class, and with funding from an environmental council
grant, students investigated potential campus locations and funding sources for the
wind turbine. Middlebury received a $22,500 grant from the United States Department
of Energy and partnered with the Vermont Department of Public Service (Middlebury
College News Room 2005). In the San Francisco Bay Area, the San Francisco, California
based resource recovery company Recology partnered in 2007 with the San Francisco
Public Utilities Commission and installed solar panels on the roof of the Recycle Central
plant at Pier 96. These solar panels are capable of producing 30% of the recycling
facility’s electricity, generating more than 380,000 KWh annually. Recology also uses
20
clean wind power to supply electricity to the Metro Central Transfer Station in Portland,
Oregon (Recology 2013). In 2009 Michigan-based recycling company Padnos installed
solar panels at its recycling facility making it the state’s largest solar-installation. The
business case was made for Padnos to use solar energy when the state of Michigan
passed a renewable energy portfolio standard, including a 2015 deadline for public
utilities to tap 10 percent of their supply from renewable energy sources; the utility
company supplying Padnos launched a kilowatt buyback-program for electricity
generated from solar power, paying above market rates on an eight-year contract, 37.5
cents per kilowatt generated; and available state property tax breaks for renewable
energy installations (Bauer 2009). Also in 2009 GreenWaste of San Jose, California and
Foster City, California installed a solar power system to provide electricity to its
materials recovery center (MRF) to process and recover residential and commercial
recyclable materials, yard trimmings, and solid waste. The GreenWaste MRF solar power
system is one of the largest commercial solar power installations in the city of San Jose.
The 300 KW (DC) –rated solar arrays are expected to produce approximately 408,000
kilowatt-hours of zero-emission solar electricity annually, enough to power
approximately 40-50 areas homes. GreenWaste is already considered one of the most
innovative processing facilities in the world, capable of sorting, recovering and recycling
85 percent of household waste. This 85-percent diversion rate translates into huge
volumes of waste that are not buried into landfills but instead are transformed into
usable products. GreenWaste is able to further reduce its carbon footprint, air pollution
and dependence on fossil fuel-based grid electricity by utilizing clean, renewable energy
21
to power its operations (Hansen and Bass 2009). In 2011 Marglen Industries of Rome,
Georgia installed a 95.2 KW solar energy photovoltaic (PV) system on the rooftop of
their plastic bottle recycling plant. The plant produces a post-consumer recycled PET
resin that is used in the manufacturing of sustainable food-grade packaging. The plant
also produces a polyester fiber that is used in the manufacturing of sustainable flooring
and other textile products. The amount of electricity generated by the PV system will
offset energy demands for ten average American homes (PR Newswire 2011).
2.3 Weighted Multicriteria Overlay Sensitivity Analysis
A weighted overlay sensitivity analysis is a powerful technique used for model
building whereby the variations in input criteria are evaluated by analyzing their effects
on the output variations of the model (Crosetto et al. 2000). A sensitivity analysis is a
prerequisite where there is a lack of literature on the definitive importance of individual
criteria to determine weights (Malczewski 1999). Potentially, the input criteria of an
analysis will represent different types of data and have different scales of measure;
therefore they have to be set on a common evaluation scale before the weighted
overlay (Jiang and Eastman 2000). A case in point is this thesis research where a
solar/wind powered plastics reprocessing facility business model with different siting
criteria: tonnage of recycled plastics, distance to ports, distance to major roads,
industrial property tax rates, per capita income, solar power potential, and wind power
potential data and the uncertain degree of importance or weight these criteria should
have for determining potential locations. Their variation in weights will illustrate the
impact of small changes to these input criteria on evaluation outcomes (Crosetto et al.,
22
2000). The change in the output suitability classification relative to changes in input
criteria weights identify criteria that are especially sensitive to weight variations and
allow visualizing the spatial dimension of change dynamics (Chen et al., 2009). Similarly,
Issa and Al Shehhi (2012) discuss a GIS-based multicriteria evaluation system for
selecting landfill sites in Abu Dhabi, United Arab Emirates. The study examines eight
ranked and weighted criteria for identifying potential landfill sites: proximity to urban
areas, proximity to wells, water table depth, geology and topography, proximity to
touristic and archeological sites, distance from roads network, distance from drainage
networks, and land slope. A map was produced for each suitability criterion and a final
composite map was also produced by overlaying the individual maps. Key points are
that criteria are assigned on a common evaluation scale, and weights are assigned to
criteria to express relative importance. To be meaningful and consistent, the weights
add up to 100%. The weights were determined by taking into account the possibility of
modifying the natural conditions of the sites by appropriate engineering interventions,
so as to increase their suitability (Delgado et al. 2008 cited Issa and Al Shehhi 2012).
Likewise, criteria that are of less importance to the conditions of the United Arab
Emirates and its climate were given less weights (Issa and Al Shehhi 2012). However the
Issa and Al Shehhi study differs from this thesis research because the sensitivity of the
model by assigning different weights to each criterion to visualize changes in overlay
outcomes is not investigated.
23
Chapter 3: Methodology
3.1 Study Area
Contra Costa County is one of the nine counties that comprise the San Francisco
Bay Area. It is located in the East Bay, has a total area of 802.15 square miles
(2,078 km²) and a 2010 census population of 1,049,025 (U.S. Census Bureau 2010).
Starting in the City of El Cerrito, the Contra Costa RMDZ heads north, following
the shorelines of the San Francisco and San Pablo Bays, encompassing the cities of
Richmond, San Pablo, Pinole, and Hercules. The zone then heads east at the Carquinez
Straits to include the cities of Martinez, Concord, Pittsburg, Antioch, Oakley and
Brentwood going up into the Sacramento Delta waterways. All unincorporated areas
and communities in-between these cities are also part of the Contra Costa RMDZ (such
as El Sobrante, Rodeo, Crockett, Port Costa, Pacheco, Bay Point and Byron) (Figures 2
and 3) (Contra Costa County 2008).
24
Figure 2: Contra Costa County, California Recycling Market Development Zone
25
Figure 3: Contra Costa County, California RMDZ jurisdictions
26
Contra Costa has an extensive network of highways, railways and waterways.
Interstate 80 provides east/west access from San Francisco to Sacramento and across
the county. Interstate 680 connects Contra Costa to the Silicon Valley. State Highway 4
runs east through the County’s growth corridor’ and connects to Interstate 5. The
completion of the Richmond Parkway connects Interstates 80 and 580, which makes a
significant portion of industrial and commercial land in west Contra Costa County easily
accessible. Four hub airports are located within a 60-mile radius from most locations
(Oakland, San Francisco, San Jose and Sacramento), and seven ports are located nearby,
including Richmond, California’s third largest in annual tonnage, and Oakland, which
accounts for nearly 5 percent of U.S. exports. Major railways are the high-speed rail
Denver & Rio Grande Western (San Joaquin Valley to Oregon), Union Pacific (Oakland to
Chicago) and Burlington Northern Santa Fe (Richmond to San Joaquin Valley) (Figure 2)
(California Integrated Waste Management Board 2008g).
3.2 Spatial Analysis
Contra Costa County does not have uniform county level development
standards/requirements on industrial development; unincorporated (county) areas and
incorporated jurisdictions each have their own development standards/requirements.
To identify the industrial parcels at the jurisdiction level with the most potential for
developing a solar and/or wind powered PRF, the spatial analysis consists of two parts.
Part 1 consists of a weighted multicriteria overlay sensitivity analysis to identify the
preferred jurisdiction encompassing the industrial parcels for siting a solar or wind
powered PRF. Part 2 consists of a suitability analysis using the selected jurisdiction’s
27
zoning setback standards to identify the industrial parcels that could use solar/wind
power within the jurisdiction.
3.2.1 Part 1
I performed a sensitivity analysis on multiple evaluation criteria by using the
‘weighted overlay’ technique where different and dissimilar input raster criteria are
analyzed on a common evaluation scale of suitability. I chose a sensitivity analysis
because of a lack of literature on the definitive importance of individual criteria to
determine weights (Malczewski 1999). Moreover, different business models will require
different criterial emphases for evaluating and determining potential locations. The
variation in weights on input criteria illustrate the impact of small changes to these
input criteria on evaluation outcomes (Crosetto et al., 2000). The changes in the output
suitability classification relative to changes in input criteria weights identify criteria that
are especially sensitive to weight variations, and allow visualizing the spatial dimension
of change dynamics (Chen et al., 2009). I ran the model by assigning different weights to
criteria important for siting a solar/wind powered PRF. Criteria are included in Table 4
with their data sources: 2000 census per capita income (in dollars as a proxy for
wages/labor costs) in jurisdictions, 2008 tonnage of recycled plastics for jurisdictions,
2008 industrial property tax rates of the industrial zoning districts’ tax areas within the
jurisdictions, distance to ports, distance to major transportation routes, wind resource
potential, and solar photovoltaic (PV) potential.
28
Table 4: Data sources
Data Source Data Name Data Type
Bay Area Census Contra Costa RMDZ cities 2000 Census data - Per capita income (dollars)
Non-spatial
California Environmental Protection Agency, Integrated Waste Management Board
Contra Costa Recycling Market Development Zone
Polygons
City of Antioch 2008 recycled plastics tonnage Non-spatial City of Brentwood City of Concord Contra Costa county unincorporated City of Martinez City of Oakley City of Pittsburg West Contra Costa Integrated Waste Management Authority
El Cerrito Hercules Pinole Richmond San Pablo
Contra Costa County Auditor - Controller
Contra Costa County Property Tax Publications: Detail of Tax Rates 2008 - 2009
Non-spatial
Contra Costa County Mapping Information Center
City Limits Polygons Tax Rate Areas Parcels Water bodies Zoning General Plan
City of Richmond Port Facilities Port addresses Non-spatial The California Spatial Information Library (CaSIL)
Tiger 2000 Transportation Layer
Local Roads Lines State Highways US Highways
National Renewable Energy Laboratory (NREL)
Wind potential: California High Resolution
Polygons
Solar Photovoltaics (PV) potential: Lower 48 States Low Resolution
29
These criteria are based on Benjamin (1994), who used criteria of industrial
property tax rates, wages, and distance to ports and access to major transportation
routes in siting a recycling processing or manufacturer facility. Waterways can be an
important criterion when considering export opportunities, particularly to Asia
(California Integrated Waste Management Board 1996). This research analyzed the
locations that provide the best potential for solar photovoltaic energy based on the
annual average daily total solar resource and for wind energy based on the annual
average wind resource potential at a 50 meter height. Specific criteria of minimum
amounts of energy required are not used because such criteria would vary by size of PRF
and business plan for a specific type of PRF, which would dictate relative proportions of
solar and wind energy required.
30
3.2.1.1 Create criteria rasters for sensitivity analysis
Figures 4 and 5 illustrate the steps for processing the source data to create the criteria
rasters for the sensitivity analysis. Figure 4 illustrates the steps for six of the criteria,
while Figure 5 illustrates the steps to create the property tax and rate raster. A detailed
description of these steps follows.
Figure 4: Creation of criteria raster flowchart
31
Steps from Figure 4
1.1) Add city, jurisdiction, recycled plastic tonnage, income fields and data to
Contra Costa RMDZ region feature class.
1.2) Create recycled plastics tonnage, and income layers.
1.3) Convert Contra Costa RMDZ tonnage and income feature classes to
rasters.
1.4) Clip wind potential and solar potential feature classes using Contra Costa
RMDZ region feature class.
1.5) Convert Contra Costa RMDZ solar potential and Contra Costa RMDZ wind
potential feature classes to rasters.
1.6) Calculate Euclidean distance on Contra Costa tiger 2000 major roads to
obtain a raster for distance from major roads.
1.7) Geocode Contra Costa port terminal addresses.
1.8) Calculate Euclidean distance on Contra Costa port terminal points to
obtain a raster for distance from port terminals.
32
Figure 5: Creation of tax area and rate raster flowchart
33
Steps from Figure 5 2.1) Clip Contra Costa tax area dataset with each city’s RMDZ industrial
landuse zoning areas.
2.2) Create an Industrial zone tax rate map by ‘Union’ all Contra Costa RMDZ
tax area datasets and use resulting dataset to ‘clip’ from the Contra Costa
tax area dataset.
2.3) Import Contra Costa county tax area codes and rates data into an Excel
table, and convert to a dBase table.
2.4) Create ‘Joins’ between 2008-2009 tax rate dBase table and each city’s
RMDZ industrial landuse zoning areas dataset to get tax rates for each
industrial zone’s tax area. The Tax Area field is the common field for the
‘Join’.
2.5) Convert the resulting RMDZ cities’ tax area/rate feature class to a raster.
3.2.1.2 Sensitivity Analysis
1) Reclassification
High values for wind potential, solar potential, and tons of recycled
plastic represent the most desirable locations. The raster datasets for the wind
potential (Figure 6), solar potential (Figure 7), and tons of recycled plastic criteria
(Figure 8) were reclassified into 6 classes with values from 1 to 6, with a value of
6 representing the most desirable locations. Solar potential was reclassified
using Equal Intervals, while Natural Breaks (Jenks) was used for wind potential
34
and tons of recycled plastic so that class boundaries would conform to large gaps
in the data.
Figure 6: Contra Costa County, California RMDZ reclassed wind power potential
35
Figure 7: Contra Costa County, California RMDZ reclassed solar power potential
36
Figure 8: Contra Costa County, California RMDZ reclassed tonnage
37
Low values for income and property tax rates represent the most
desirable locations. The raster datasets for the income (Figure 9) and property
tax rates (Figure 10) criteria were reclassified into 6 classes with values from 1 to
6, with a value of 6 representing the most desirable locations. Income was
reclassified using Equal Intervals while Natural Breaks (Jenks) was used for
property tax rates so that class boundaries would conform to large gaps in the
data.
38
Figure 9: Contra Costa County, California RMDZ reclassed income
39
Figure 10: Contra Costa County, California RMDZ reclassed tax areas
40
Low values for distance from ports and distance from major roads
represent the most desirable locations. The raster datasets for the distance
from ports (Figure 11) and distance from major roads (Figure 12) criteria were
reclassified into 6 classes with values from 1 to 6, with a value of 6 representing
the most desirable locations. Distance from ports and distance from major roads
were reclassified using Equal Intervals.
41
Figure 11: Contra Costa County, California RMDZ reclassed distance from ports
42
Figure 12: Contra Costa County, California RMDZ reclassed distance from major roads
43
2) Sensitivity
2.1) Weighted overlay analyses of the reclassified input criteria using
influence percentage weights (16%, 52%, and 76%) were performed to
analyze changes in potential locations.
These weights were chosen so that the sum of all the weights equaled
100% within the context of seven input criteria. 16% is as close to equal
influence as possible with the other criteria at 14%. With 52% and 76%
for the weighted criterion, the influence of the other criteria changes to
8% and 4%, respectively, which is cutting the influence of the other
criteria in half (or as close to it as possible with seven input criteria).
These specific percentage influence parameters simulate potential
influences that a criterion is given by an analyst as a weight against other
criterion to emphasize its importance for determining an optimal location
for a specific type of recycled plastic manufacturer. Table 5 illustrates an
example with 16% influence for the wind potential criteria on a ‘1 to 6 to
1’ evaluation scale. Field value is the reclassified field and value of the
input criteria raster used for weighing (steps 1.2 and 1.3) and scale values
are the scaled values for the criterion specified in the ArcGIS’ weighted
overlay evaluation scale setting ‘1 to 6 to 1’.
44
Table 5: Example with 16% influence for wind potential and a ‘1 to 6 to 1’ evaluation scale
Input Raster % Influence Field Value Scale Value
Wind potential 16 1-6 1-6
Solar potential 14 1-6 1-6
Tax rate 14 1-6 1-6
Income 14 1-6 1-6
Recycled plastics tonnage
14 1-6 1-6
Distance to major roads
14 1-6 1-6
Distance to ports 14 1-6 1-6
2.2) Results of the weighted overlays are rasters of the most suitable cells at
each percentage of influence for each criterion. I also needed to find out
which jurisdiction has the highest percentage of the most suitable cells
for each criterion. I used the following steps to calculate the percentages
of the most suitable cells (at each percent of influence) that lie in each
jurisdiction or city. In other words, of all the suitable cells for a given
criterion at a percent of influence, I calculated the percentage of these
cells that lie in each jurisdiction/city.
The raster of each criterion’s most suitable location at each
percent of influence was overlaid with the city/jurisdiction raster, e.g.
45
("ovrlay_wnd76" == 3) & ("city_rstr" == 12), where 3 is a suitability cell
value of a weighted overlay result with a 76% influence for wind and 12 is
the jurisdiction’s/city’s identification value. To find out which jurisdiction
has the highest percentage of the most suitable cells for each criterion, I
divided the number of resulting cells from the map algebra overlays by
the total number of cells of a criterion’s most suitable locations at a
percent of influence for each jurisdiction.
3.2.2 Part 2
The results of Spatial Analysis Part 1, more specifically the resulting jurisdiction
from the sensitivity analysis, were analyzed through the jurisdiction’s industrial zoning
setback standards for placing a PRF. Setbacks are the minimum distance requirements
that a building or structure can be placed from a property line, structure, or space and
can vary depending on the zoning (City of Richmond, California 2011). Figure 13 for wind
power and Figure 14 for solar power illustrate the suitability analysis for the industrial
parcels with the optimal locations for wind and solar power in the City of Richmond
using the 76% percent influence solar potential and 76% percent influence wind
potential rasters. The industrial parcels were identified and assigned their zoning code
along with their abutting non-industrial zoning areas as described in Table 6 according
to the jurisdiction’s zoning map. Multiple ring buffers were then calculated around the
non-industrial zoning areas abutting industrial zoning parcels to simulate the application
of setback standards to buffer between non-industrial zoning areas and industrial zoning
parcels. The buffers around the non-industrial zoning areas were calculated according to
46
setback standards described in Tables 7, 8, and 9. Side and rear buffer distances are
calculated as multiple ring buffers since classifying a parcel side as a ‘left or right’ or
‘rear’ is beyond the scope of this project. The resulting buffered non-industrial zoning
areas illustrate the limits of where a PRF can potentially be developed on industrial
zoning parcels to prevent encroachment on non-industrial zoning areas per zoning
ordinances.
47
Figure 13: Wind power suitability analysis for industrial parcels flowchart
48
The steps illustrated in Figure 13 to find suitable industrial parcels for a wind-powered
PRF in Richmond are as follows:
1) Convert Richmond 76% influence wind raster to a feature.
2) Select optimal location attribute (grid code= 1) from Richmond 76%
influence wind vector.
3) Create layer from selected features.
4) Select parcels containing Richmond 76% influence features.
5) Create layer from selected features.
6) Add zoning field and enter industrial parcels’ zoning code: M-1, M-2, M-3,
and M-4.
7) Clip Richmond industrial parcels features using Richmond features.
8) Select parcels for each industrial zoning code.
9) Create layers from selection.
10) Using Contra Costa RMDZ industrial parcels vector, select non-industrial
zoning areas that abutter with Richmond industrial parcels.
11) Enter zoning code of non-industrial abutting zoning areas.
12) Create layers from selected non-industrial abutting zoning areas features.
13) Create multiple ring buffers according to industrial zoning standards.
49
Figure 14: Solar power suitability analysis for industrial parcels flowchart
50
The steps illustrated in Figure 14 to find suitable industrial parcels for a solar-powered
PRF in Richmond are as follows:
1) Convert Richmond 76% influence solar raster to a feature.
2) Select optimal location attribute (grid code= 1) from Richmond 76%
influence wind vector.
3) Create layer from selected features.
4) Select parcels containing Richmond 76% influence features.
5) Create layer from selected features.
6) Add zoning field and enter industrial parcels’ zoning code: M-1, M-2, M-3,
and M-4.
7) Clip Richmond industrial parcels features using Richmond features.
8) Select parcels for each industrial zoning code.
9) Create layers from selection.
10) Using Contra Costa RMDZ industrial parcels vector, select non-industrial
zoning areas that abutter with Richmond industrial parcels.
11) Enter zoning code of non-industrial abutting zoning areas.
12) Create layer from selected non-industrial abutting zoning areas features.
13) Create multiple ring buffers according to industrial zoning standards.
51
Table 6: Non-industrial zoning codes and meaning
Abbreviation Meaning CC Coastline Commercial CRR Community and Regional Recreational District PA Planned Area
Table 7: M-2 Light Industrial setback standards
Zoning Front Sides Rear
M-2 Light Industrial
Minor street: 10 ft.; Collector street: 25 ft. (N/A)
10ft; 15 ft. only when abutting residential, public park, recreational trail or recreational right-of-way or shoreline.
None; 15 ft. only when abutting residential, public park, recreational trail or recreational right-of-way or shoreline.
Table 8: M-3 Heavy Industrial setback standards
Zoning Front Sides Rear
M-3 heavy Industrial
Minor street: 10 ft.; Collector street: 25 ft. (N/A)
None; 10 ft. only when abutting residential, public park, recreational trail or recreational right-of-way or shoreline.
None; 15 ft. only when abutting residential, public park, recreational trail or recreational right-of-way or shoreline.
52
Table 9: M-4 Marine Industrial setback standards
Zoning Front Sides Rear
M-4 Marine Industrial
Minor street: 10 ft.; Collector street: 25 ft. (N/A)
10 ft. None; 15 ft. only when abutting residential, public park, recreational trail or recreational right-of-way or shoreline.
53
Chapter 4: Analysis Results
For part 1 of the spatial analysis the city of Richmond is the area most suitable
across all input variables at each percentage influence except for the ‘Tax Rate’ input
variable where Contra Costa County unincorporated areas are the most suitable
locations at 76% influence, the city of Antioch has the most suitable locations at 52%
influence, and the city of Richmond has the most suitable locations at 16% influence.
This result is expected since Richmond has the highest property tax rate and Contra
Costa County unincorporated areas have the lowest property tax rate. The weighted
overlay results showed variability in the highest suitability cell values on the reclassed
suitability scale of 1 to 6 at different percentages of influence for each input criteria. For
instance, the weighted overlay analyzing wind power potential resulted with outputs
where 4 is the highest suitability cell value at 16% influence and 3 is the highest
suitability cell value at 52% and 76% influence. The weighted overlay analyzing solar
power potential resulted with outputs where 4 is the highest suitability cell value at 16%
influence and 5 is the highest suitability cell value at 52% and 76% influence. These
weighted overlay results show that while Richmond is the jurisdiction with the most
wind and/or solar power potential across all three percentages of influence, the solar
power potential is superior to wind power potential by 33% (3/6 versus 5/6 on a
suitability scale of 1-6) when analyzed with a weight or influence of 52% and 76%, and is
potentially a better candidate as an alternative source of electrical power.
54
Wind power potential
Tables 10 and 11 and Figures 15, 16, and 17 show that Richmond has the most wind
power potential across all percentages of influence with its percentage and number of
the highest suitable cell values. In Figures 15, 16, and 17 the optimal locations refer to
those areas that contain most of the highest suitability cell values at a percentage of
influence. Non-optimal locations refer to those areas that do not contain any of the
highest suitability cell values.
55
Table 10: Wind power potential results by jurisdiction (percentage of cells)
Percentage Influence
16% 52% 76%
Suitability cell values from weighted overlay on a scale 1 to 6
3 4 2 3 2 3
Juris
dict
ions
Antioch .007% 10.4% .008% 9.4% 4.83% 4.1%
Brentwood 0.5% 0.4% 0.1% 0 0.5% 0
Concord 7.1% 0 7.8% 0 4.26% 0
Contra Costa County unincorporated areas
74.3% 24% 71.4% 31.8% 52.8% 37.8%
El Cerrito 2.3% 0 2.5% 0 1.4% 0
Hercules 0.7% 0 0.7% 0 0.4% 0
Martinez 13.9% 0 15.3% 0 8.4% 0
Oakley 0 0 0 0 0 0
Pinole 0.7% 0 0.7% 0 0.4% 0
Pittsburg 0 0 0 0 0 0
Richmond .001% 65.1% 0 58.6% 27% 58.1%
San Pablo 0.3% 0 0.3% 0 0.2% 0
56
Table 11: Wind power potential results by jurisdiction (number of cells)
Percentage Influence
16% 52% 76%
Suitability cell values from weighted overlay on a scale 1 to 6
3 4 2 3 2 3
Juris
dict
ions
Antioch 25 28197 25 28197 25965 2257
Brentwood 1638 1145 2783 0 2783 0
Concord 22897 0 22897 0 22897 0
Contra Costa County unincorporated areas
239355 64955 208997 95313 283571 20739
El Cerrito 7391 0 7391 0 7391 0
Hercules 2148 0 2148 0 2148 0
Martinez 45010 0 45010 0 45010 0
Oakley 0 0 0 0 0 0
Pinole 2253 0 2253 0 2253 0
Pittsburg 0 0 0 0 0 0
Richmond 5 175526 0 175531 143652 31879
San Pablo 1143 0 1143 0 1143 0
Total number of cells for the suitability cell values
321865 269823 292647 299041 536813 54875
57
Figure 15: Wind power potential at 16% influence
58
Figure 16: Wind power potential at 52% influence
59
Figure 17: Wind power potential at 76% influence
60
Solar power potential
Tables 12 and 13 and Figures 18, 19, and 20 show that Richmond has the most
solar power potential across all percentages of influence with its percentage and
number of the highest suitable cell values. In Figures 18, 19, and 20 the optimal
locations refer to those areas that contain most of the highest suitability cell
values at a percentage of influence. Non-optimal locations refer to those areas
that do not contain any of the highest suitability cell values.
61
Table 12: Solar power potential results by jurisdiction (percentage of cells)
Percentage Influence
16% 52% 76%
Suitability cell values from weighted overlay on a scale 1 to 6
3 4 2 3 5 2 3 5
Juris
dict
ions
Antioch .007% 10.4% 0 5.4% 0 1.7% 4% 0
Brentwood 0.5% 0.4% 0 0.5% 0 0.3% 2.5% 0
Concord 7.1% 0 46.6% .002% 0 4.3% 0 0
Contra Costa County unincorporated areas
74.3% 24.1% 0 58.3% 4.9% 53% 57.3% 4.9%
El Cerrito 2.3% 0 15% 0 0 1.4% 0 0
Hercules 6.6% 0 0 0.4% 0 4.1% 0 0
Martinez 13.9% 0 35.9% 5.2% 0 8.6% 0 0
Oakley 0 0 0 0 0 0 0 0
Pinole 0.7% 0 0 0.4% 0 0.4% 0 0
Pittsburg 0 0 0 0 0 0 0 0
Richmond .001% 65.1% .01% 29.5% 95.1% 29.3% 0 95.1%
San Pablo 0.3% 0 2.3% 0 0 0.2% 0 0
62
Table 13: Solar power potential results by jurisdiction (number of cells)
Percentage Influence
16% 52% 76%
Suitability cell values from weighted overlay on a scale 1 to 6
3 4 2 3 5 2 3 5
Juris
dict
ions
Antioch 25 28197 0 28222 0 9337 18885 0
Brentwood 1638 1145 0 2783 0 1598 1185 0
Concord 22897 0 22885 12 0 22897 0 0
Contra Costa County unincorporated areas
239317 64955 0 303162 1148 276213 26949 1148
El Cerrito 7391 0 7391 0 0 7391 0 0
Hercules 2148 0 0 2148 0 2148 0 0
Martinez 45010 0 17618 27392 0 45010 0 0
Oakley 0 0 0 0 0 0 0 0
Pinole 2253 0 0 2253 0 2253 0 0
Pittsburg 0 0 0 0 0 0 0 0
Richmond 5 175526 5 153161 22365 153166 0 22365
San Pablo 1143 0 1143 0 0 1143 0 0
Total number of cells for the suitability cell values
321827 269823 49042 519133 23513 521156 47019 23513
63
Figure 18: Solar power potential at 16% influence
64
Figure 19: Solar power potential at 52% influence
65
Figure 20: Solar power potential at 76% influence
Tax Rate
Table 14 shows that Richmond has the most suitable locations for industrial
property tax rates at 16%, Antioch has the most suitable locations at 52%, and
66
Contra Costa County unincorporated areas have the most suitable locations at
76%.
Table 14: Tax rates results by area
Percentage Influence
16% 52% 76%
Highest suitability cell value from weighted overlay on a scale 1 to 6
4 5 5
Juris
dict
ions
Antioch 9.6% 58.5 22.6
Brentwood 0.4% 0 0
Concord 0 0 0
Contra Costa County unincorporated areas
30.4% 41.5% 77.3%
El Cerrito 0 0 0
Hercules 0 0 0
Martinez 0 0 0
Oakley 0 0 0
Pinole 0 0 0
Pittsburg 0 0 0
Richmond 59.7% 0 0
San Pablo 0 0 0
67
Tonnage of recycled plastics
Table 15 shows that Richmond has the most suitable locations for tonnage of
recycled plastics across all percentages of influence with its percentage of the
highest suitable cell values.
Table 15: Tonnage of recycled plastics results by area
Percentage Influence
16% 52% 76%
Highest suitability cell value from weighted overlay on a scale 1 to 6
5 5 6
Juris
dict
ions
Antioch 0 0 0
Brentwood 0 1.5% 0
Concord 0 0 0
Contra Costa County unincorporated areas
0 0 0
El Cerrito 0 0 0
Hercules 0 0 0
Martinez 0 0 0
Oakley 0 0 0
Pinole 0 0 0
Pittsburg 0 0 0
Richmond 100% 98.5% 100%
San Pablo 0 0 0
68
Income
Table 16 shows that Richmond has the most suitable locations for income across
all percentages of influence with its percentage of the highest suitable cell
values.
Table 16: Income results by area
Percentage Influence
16% 52% 76%
Highest suitability cell value from weighted overlay on a scale 1 to 6
4 5 5
Juris
dict
ions
Antioch 10.4% 0 0
Brentwood 0.4% 0 0
Concord 0 0 0
Contra Costa County unincorporated areas
24% 0 0
El Cerrito 0 0 0
Hercules 0 0 0
Martinez 0 0 0
Oakley 0 0 0
Pinole 0 0 0
Pittsburg 0 0 0
Richmond 65% 95.1% 99.4%
San Pablo 0.4% 5% 0.6%
69
Distance from major roads
Table 17 shows that Richmond has the most suitable locations with the shortest
distances from major roads across all percentages of influence with its
percentage of the highest suitable cell values.
Table 17: Distance from major roads results by area
Percentage Influence
16% 52% 76%
Highest suitability cell value from weighted overlay on a scale 1 to 6
5 5 6
Juris
dict
ions
Antioch 0 9% 0
Brentwood 0 0.8% 0
Concord 0 0 0
Contra Costa County unincorporated areas
0 35.6% 0
El Cerrito 0 0 0
Hercules 0 0 0
Martinez 0 0 0
Oakley 0 0 0
Pinole 0 0 0
Pittsburg 0 0 0
Richmond 100% 54.4% 100%
San Pablo 0 0.4% 0
70
Distance from ports
Table 18 shows that Richmond has the most suitable locations with the shortest
distances from ports across all percentages of influence with its percentage of
the highest suitable cell values.
Table 18: Distance from ports results by area
Percentage Influence
16% 52% 76%
Highest suitability cell value from weighted overlay on a scale 1 to 6
5 5 6
Juris
dict
ions
Antioch 0 0 0
Brentwood 0 0 0
Concord 0 0 0
Contra Costa County unincorporated areas
0 0 0
El Cerrito 0 0 0
Hercules 0 0 0
Martinez 0 0 0
Oakley 0 0 0
Pinole 0 0 0
Pittsburg 0 0 0
Richmond 100% 99.4% 100%
San Pablo 0 0.6% 0
71
In part 2 of the spatial analysis, the wind power potential suitability analysis
resulted in industrial parcels of zones M-2, M-3, and M-4 as illustrated in Figure 21.
Figure 21: Richmond industrial parcels suitable for wind power
72
Industrial M-2 Light Industrial zoned parcels of North Richmond are abutted by
non-industrial CRR (Community and Regional Recreational District) zoned parcels. Per
the setback standards a 15ft distance buffer was calculated on the CRR parcels.
Industrial M-2 Light Industrial zoned parcels of the Marina Bay district of South
Richmond are abutted by non-industrial PA (Planned Area) zoned parcels but a buffer
was not calculated on the PA parcels because that particular zone does not require
setback standards. Industrial M-2 Light Industrial zoned parcels of South Richmond are
abutted by non-industrial CRR zoned parcels, and per the setback standards a 15ft
distance buffer was calculated on the CRR parcels. Industrial M-3 Heavy Industrial zoned
parcels of Point Richmond (Pt. San Pablo) in South Richmond are abutted by non-
industrial CRR zoned parcels, and per the setback standards a 15ft distance buffer was
calculated on the CRR parcels. Industrial M-4 Marine Industrial zoned parcels of Point
Richmond District of South Richmond and Marina Bay Districts of South Richmond are
not abutted by non-industrial zoned parcels so there are no setback buffers. Industrial
M-4 Marine Industrial zoned parcels of the Point Richmond District of South Richmond
are abutted by non-industrial CRR, PA, and CC (Coastline Commercial) zoned parcels. Per
the setback standards a 15ft distance buffer was calculated on the CRR and CC parcels
but not around the PA parcels because that particular zone does not require setback
standards.
The solar power potential suitability analysis resulted in a smaller geographical
area for locating a PRF than did the wind power potential suitability analysis. Industrial
parcels of zones M-2, M-3, and M-4 were concentrated only in the Point Richmond (Pt.
73
San Pablo) District in South Richmond, South Richmond, and North Richmond areas as
illustrated in Figure 22.
Figure 22: Richmond industrial parcels for solar power
74
Industrial M-2 Light Industrial zoned parcels of North Richmond are not abutted
by non-industrial zoned parcels so no setback buffers were necessary. Industrial M-3
Heavy Industrial zoned parcels of South Richmond and Point Richmond are abutted by
non-industrial CRR and PA zoned parcels. Per the setback standards 10ft and 15ft
distance buffers were calculated on the CRR parcels but not around the PA parcels
because that particular zone does not require setback standards. Industrial M-4 Marine
Industrial zoned parcels of Point Richmond District of South Richmond are not abutted
by non-industrial zoned parcels so there are no setback buffers.
The solar and wind power potential suitability analyses provide realistic results
for placing a PRF to the degree to which the spatial and non-spatial data used for the
analyses is accurate and/or current. The results should be interpreted within the context
of a snapshot in time rather than real-time as parcel availability, zoning designations of
parcels, and zoning ordinances can be subject to change in a jurisdiction with changing
economic and political conditions. Because of the relevance of the criteria and zoning
setback information used for the analyses, the industrial parcels located through the
model are a practical starting point towards developing a solar/wind powered PRF.
75
Chapter 5: Conclusion
The research objective of this thesis was to develop a GIS-based weighted
multicriteria overlay sensitivity analysis model to locate potential sites for siting a
solar/wind powered PRF in the RMDZ of Contra Costa County, California. The model
achieves a more specific purpose than what the CIWMB has implemented because it
allows a PRF entrepreneur to develop their business model by factoring solar and/or
wind power as alternatives for sources of electrical power, thus refining the cost/benefit
analysis when analyzed with other siting criteria. The changes in the weighted overlay
analysis results relative to changes in siting criteria importance identify criteria that are
especially sensitive to their given importance, and allow visualizing the spatial
dimension of those changes more intuitively at the jurisdiction and parcel levels.
The model found the city of Richmond to be the jurisdiction most suitable across
all siting criteria at each percentage of influence except for the ‘Tax rate’ criterion where
Contra Costa County unincorporated areas are the most suitable locations at 76%
percentage influence, the city of Antioch has the most suitable locations at 52%
influence, and the city of Richmond has the most suitable locations at 16% influence.
The ‘Tax rate’ criterion analysis result shows that even though the City of Richmond is
the only jurisdiction with ports, the ‘Distance to ports’ criterion is not necessarily the
driving factor for determining the optimal jurisdiction since other jurisdictions are found
more suitable when ‘Tax rate’ is given more influence. This effect would be particularly
relevant for a business model that favors low property taxes over other siting criteria.
The weighted overlay results also show that while Richmond is the jurisdiction with the
76
most wind and/or solar power potential across all three percentages of influence, the
solar power potential is superior to wind power potential by 33% and is potentially a
better candidate as an alternative source of electrical power.
Despite the model’s success in identifying potential sites, there were quite a few
challenges encountered throughout the data gathering process. There was difficulty
obtaining data and information from some jurisdictions’ departmental contacts where
spatial information was either nonexistent in spatial file formats, or some contacts were
unwilling to share the spatial datasets for policy reasons. This issue was addressed
through detailed comparison and analysis between available non-spatial datasets (.pdfs
and images) and available proxy spatial datasets. Also, obtaining recycled plastics data
from recycling business owners or representatives was impossible due to the highly
competitive nature of the industry. The solution was to obtain the recycled plastics
tonnage data from the jurisdictions directly.
This research and model have limitations that are worth noting. A solar/wind
powered PRF siting process can require evaluating many factors and criteria and
processing much spatial data and information. Most importantly, any GIS analysis is
obviously limited by the data availability and accuracy. Seven different criteria used as
thematic layers were considered in the analysis, but there are certainly other factors
that could also be considered for further research, such as detailed electrical power
requirements to develop a PRF of a specific size and corresponding wind and solar
power system requirements, up-to-date information on industrial space availability, and
a building codes analysis which would require a detailed PRF business plan.
77
Ultimately, further research should be directed at enhancing the usability of the
model through its development within a distributed systems architecture and modern
web-based framework and hosting in a scalable cloud computing environment.
Boroushaki and Malczewski (2009) discuss a conceptual framework for an open
collaborative WebGIS-Multicriteria Decision Analysis. The framework integrates two
major components of spatial decision-making and planning, deliberation and analysis, in
an integrated fashion. The deliberative component of the framework comprises a
collaborative environment where participants can communicate and debate for spatial
planning, while the analytical component consists of multicriteria procedures and
algorithms for producing a compromise solution between an individual participant’s
preference for combining weighted criteria maps and group preference from aggregated
participant preferences. The system architecture consists of free open-source web-
based technologies: AJAX for client-side scripting and data transfer, PHP for server-side
scripting, MySQL for a database, and Google Maps server and API for spatial data and
GIS functionality. A cloud computing environment for hosting service such as Amazon
Web Services would allow the computing resources to be scalable, depending on
utilization and traffic, for the storage, processing, and delivery of GIS data and
information. This combination of technologies allows for a richer end-user experience
with asynchronous exchanges between browser and backend systems, thus obviating
the need to reload the full page to display results. The integration of deliberative and
analytical elements in a webpage is made possible with a set of interactive features that
resemble those of a desktop GIS. Most importantly, mapping and spatial data become
78
more accessible to the public and experts and as a result make spatial decisions more
participatory and potentially more cost-effective for entrepreneurs.
79
Appendix
Resource Name URL City of Richmond Zoning
http://www.ci.richmond.ca.us/DocumentView.aspx?DID=3624
Policing Sectors (Districts)
http://www.ci.richmond.ca.us/DocumentView.aspx?DID=403
Neighborhood Council Districts
http://www.ci.richmond.ca.us/DocumentView.aspx?DID=400
MySQL Open-source relational database system
80
References
Bauer, J., 2009. Michigan's largest solar-panel installation goes on line this week at Padnos Iron & Metal in Wyoming [online]. Available from: http://www.mlive.com/business/west-michigan/index.ssf/2009/12/michigans_largest_solar-panel.html [Accessed 10 January 2013].
Benjamin, C., 1994. A new chapter for recycling: using geographic information systems (GIS) to improve the market development and transportation of recycled materials. School of the Environment: Duke University. [Online]. Available at: http://www.epa.gov/nscep/ [Accessed 06 October 2009]. Bishop I., Evans D., and Leao S., 2001. Assessing the demand of solid waste disposal in urban region by urban dynamics modelling in a GIS environment. Resources, Conservation and Recycling, [Online]. 33(4), Available at: http://ncgia.ucsb.edu/projects/gig/v2/About/references/Porto_Alegre_Brazil/leao_2001.pdf (Project Gigalopolis: Urban and Land Cover Modeling) [Accessed 07 October 2009]. Boroushaki, S., and Malczewski J., 2009. ParcitipatoryGIS.com: A WebGIS-based Collaborative Multicriteria Decision Analysis. URISA Journal, Vol (22), 1-23. [Online] Available at: http://www.urisa.org/files/Boroushaki%20-%20ParcitipatoryGIS%20com%20(2).pdf [Accessed 03 March 2013]. California Environmental Protection Agency, 2000. 2000 Accomplishments and Priorities. [Online] (Updated 19 November 2003) Available at: http://www.calepa.ca.gov/publications/Reports/AP2000A/CIWMB.htm [Accessed 28 January 2013]. CaliforniaFIRST [Online], 2012. Available from: https://californiafirst.org/property_owners_faq [Accessed 30 January 2013]. California Integrated Waste Management Board, 1996. Market status report: secondary material export markets. [Online] (Updated 08 April 2009) Available at: http://www.ciwmb.ca.gov/Markets/StatusRpts/Exports.htm [Accessed 01 November 2007]. California Integrated Waste Management Board, 2005. Recycling: good for the environment, good for the economy. [Online] (Revised November 2005). Available at: http://www.ciwmb.ca.gov/Publications/Economics/41004002.pdf [Accessed 24 October 2008].
81
California Integrated Waste Management Board, 2007. Recycle. [Online] (Updated 26 March 2007) Available at: http://www.ciwmb.ca.gov/Recycle/ [Accessed 24 October 2008]. California Integrated Waste Management Board, 2008a. History of California solid waste law, 1985-1989.[Online] (Updated 08 January 2008) Available at: http://www.ciwmb.ca.gov/Statutes/Legislation/CalHist/1985to1989.htm [Accessed 24 October 2008]. California Integrated Waste Management Board, 2008b. Integrated waste management act. [Online] (Updated 28 February 2008) Available at: http://www.ciwmb.ca.gov/LGCentral/Glossary/#IWMA [Accessed 24 October 2008]. California Integrated Waste Management Board, 2008c. Local government central: California’s 2006 statewide diversion rate estimate. [Online] (Updated 22 August 2008) Available at: http://www.ciwmb.ca.gov/LGCentral/Rates/Diversion/2006/Default.htm [Accessed 24 October 2008]. California Integrated Waste Management Board, 2008d. Local government central: Estimated statewide waste tonnages and rates.[Online] (Updated 09 January 2008) Available at: http://www.ciwmb.ca.gov/LGCentral/Rates/Graphs/TotalWaste.htm [Accessed 24 October 2008]. California Integrated Waste Management Board, 2008e. Sustainable (Green) building: Green building basics. [Online] (Updated 15 January 2008) Available at: http://www.ciwmb.ca.gov/GreenBuilding/Basics.htm [Accessed 24 October 2008]. California Integrated Waste Management Board, 2008f. Plastic information and resources. [Online] (Updated 28 May 2008) Available at: http://www.ciwmb.ca.gov/Plastic [Accessed 14 November 2008]. California Integrated Waste Management Board, 2008g. Recycling Market Development Zone: Contra Costa. [Online] (Updated 23 September 2008) Available at: http://www.ciwmb.ca.gov/RMDZ/ContraCosta/ [Accessed 24 October 2008]. California Integrated Waste Management Board, 2008h. Recycling Market Development Zone. [Online] (Updated 17 October 2008) Available at: http://www.ciwmb.ca.gov/RMDZ/ [Accessed 24 October 2008].
82
City of Richmond, California, 2011. Zoning ordinance. Talahassee: Municipal Code Corporation. [Online] Available at: http://www.ci.richmond.ca.us/DocumentCenter/Home/View/315 [Accessed 15 April 2013]. Chen, Y., Yu, J., Shahbaz, K., and Xevi, E., 2009. A GIS-based sensitivity analysis of multi-criteria weights, 18th World IMACS / MODSIM Congress, Cairns, Australia 13-17 July 2009.[Online] Available at: http://www.mssanz.org.au/modsim09/I2/chen_y.pdf [Accessed 21 August 2012]. Clarke, M., and Maantay, J., 2005. Optimizing recycling in all of New York City’s neighborhoods: Using GIS to develop the REAP index for improved recycling education, awareness, and participation. Resources, Conservation and Recycling, Volume 46, Issue 2, February 2006, Pages 128-148. [Online] Available at: http://www.lehman.edu/deannss/geography/publications/RCR.pdf [Accessed 02 September 2009]. Contra Costa County, 2008. Contra Costa County Recycling Market Development Zone. [Online] Available at: http://www.co.contra-costa.ca.us/depart/cd/recycle/rmdz/ [Accessed 24 October 2008].
Crosetto, M., Tarantola, S., and Saltelli, A., 2000. Sensitivity and uncertainty analysis in spatial modeling based on GIS. Agriculture Ecosystems & Environment, 81, 71-79. [Online] Available at: http://elmu.umm.ac.id/file.php/1/jurnal/A/Agriculture,%20Ecosystems%20and%20Environment/Vol81.Issue1.Oct2000/1598.pdf [Accessed 21 August 2012]. Ecology Center Plastics Task Force, 1996. Report of the Berkeley plastics task force. (Published 1996) [Online] Available at: http://ecologycenter.org/ptf/report1996/report1996_05.html [Accessed 24 October 2008]. Field, K. S., and Macey, H., 2007. Green Information Systems: GIS, geodemographics and recycling of household trash, Proceedings of the twenty-seventh Annual ESRI International User Conference, San Diego, 18th-22nd June 2007. [Online] Available at: http://proceedings.esri.com/library/userconf/proc07/papers/abstracts/a1072.html [Accessed 03 September 2009]. Goldman, G., and Ogishi, A., 2001. The Economic impact of waste disposal and diversion in California: A report to the California integrated waste management board. Department of Agricultural and Resource Economics: University of California, Berkeley. Available at: http://are.berkeley.edu/extension/EconImpWaste.pdf [Accessed 07 September 2009].
83
Hansen, E., and Bass, J., 2009. GreenWaste recovery and SolarCity complete 1.8 acre solar panel installation to power San Jose material recovery facility [online]. Reuters. Available from: http://pilot.us.reuters.com/article/2009/04/13/idUS100963+13-Apr-2009+BW20090413 [Accessed 15 January 2013]. Issa, S.M., and Al Shehhi B., 2012. A GIS-based multi-criteria evaluation system for selection of landfill sites: a case study from Abu Dhabi, United Arab Emirates. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B2, Pages 1-6. [Online] Available at: http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXIX-B2/133/2012/isprsarchives-XXXIX-B2-133-2012.pdf {Accessed 01 March 2013]. Jiang, H., and Eastman, J.R., 2000. Application of fuzzy measures in multi-criteria evaluation in GIS. Int. J. Geographical Information Science, Volume 14, Number 2, Pages 173-184. [Online] Available at: http://crs.itb.ac.id/media/jurnal/refs/critical_review/referensi/03_LainLain/ApplicationOfFuzzyMeasuresInMultiCriteriaEvaluationInGIS.pdf [Accessed 13 January 2013]. Malczewski, J., 1999. GIS and multicriteria decision analysis. [e-book] John Wiley and Sons. Available at: Google Books http://books.google.com/ [Accessed 2 December 2009]. Middlebury College News Room, 2005. Middlebury College receives $22,500 grant for wind energy system [online]. Middlebury College News Room, 11 May. Available from: http://www.bournemouth.ac.uk/library/using/guide_to_citing_internet_sourc.html [Accessed 30 January 2013]. Nielsen, J., et al., 2002. Renewable energy atlas of the west: A guide to the region’s resource potential. Land and Water Fund of the Rockies. Available at: http://www.mapcruzin.com/renewable-energy-shapefiles/atlas_final.pdf [Accessed 25 January 2013]. O'Connell, K.A., 2002. California adopts zero waste goal in strategic plan. [Online] 2002 Penton Media, Waste Age. Available at: http://wasteage.com/mag/waste_california_adopts_zero/ [Accessed 24 October 2008]. Office of the Governor, Governor of the State of California, 2004. Executive order S-20-04. (Published 2004) Available at: http://gov.ca.gov/executive-order/3360/ [Accessed 24 October 2008].
84
Pešić, M.A., Stanković, J., and Milić, V.J., 2012. Analysis of possibilities for recycling industry development - multi-criteria approach. Economics and Organization Vol. 9, No 2, 2012, pp. 241 – 255. Available through: University of Niš, Facta Universitatis website http://facta.junis.ni.ac.rs/eao/eao201202/eao201202-07.pdf [Accessed 1 March 2013]. Petker, D.L., Ralston, D., and Barnett, S., 2000. California integrated waste stream profiles: GIS application [Online] (Published 2000). Available at: http://proceedings.esri.com/library/userconf/proc00/professional/papers/pap961/p961.htm [Accessed 10 June 2009]. PR Newswire, 2011. Marglen industries chooses URE to go solar [online]. PR Newswire, 9 February. Available from: http://www.prnewswire.com/news-releases/marglen-industries-chooses-ure-to-go-solar-115640424.html [Accessed 30 January 2013]. Recology [Online], 2013. Available from: http://www.recology.com/profile/recology_commitment.htm [Accessed 30 January 2013].
State of California, Department of Conservation, Director’s Office, 2008. Biannual report of beverage container sales, returns, redemption, and recycling rates. (Published 2008) Available at: http://www.conservation.ca.gov/dor/Notices/Documents/Biannual.pdf [Accessed 24 October 2008]. Subramanian, P.M., 2000. Plastics recycling and waste management in the US. Resources, Conservation and Recycling, 28(3), pp. 253-263. Available at: http://www.sciencedirect.com/science/article/pii/S092134499900049X Accessed 24 October 2008]. The California Energy Commission, 2004. Green building initiative: state of California executive order S-20-04. (Updated August 2008) Available at: http://www.energy.ca.gov/greenbuilding/index.html [Accessed 24 October 2008]. U.S. Census Bureau, 2010. State & County QuickFacts: Contra Costa County, California. [Online] (Revised January 10 2013) Available at: http://quickfacts.census.gov/qfd/states/06/06013.html [Accessed 13 January 2013]. U.S. Department of Health and Human Services Food and Drug Administration Center For Food Safety and Applied Nutrition Guidelines for Industry, 2006. Use of recycled plastics in food packaging: chemistry considerations. (Published 2006) Available at: http://www.cfsan.fda.gov/~dms/opa2cg3b.html#recyc [Accessed 24 October 2008].
85
U.S. Environmental Protection Agency, 2008a. Composting.[Online] (Updated 7 October 2008) Available at: http://www.epa.gov/epawaste/conserve/rrr/composting/index.htm [Accessed 24 October 2008].
U.S. Environmental Protection Agency, 2008b. Municipal solid waste.[Online] (Updated 7 October 2008) Available at: http://www.epa.gov/epawaste/nonhaz/municipal/index.htm [Accessed 24 October 2008]. U.S. Environmental Protection Agency, 2008c. Recycling.[Online] (Updated 28 Aug 2008) Available at: http://www.epa.gov/epawaste/conserve/rrr/recycle.htm [Accessed 24 October 2008]. U.S. Environmental Protection Agency, 2008d. Reduce & reuse. [Online] (Updated 26 Aug 2008) Available at: http://www.epa.gov/epawaste/conserve/rrr/reduce.htm [Accessed 24 October 2008]. Wang, J., 2008. Smart energy resources guide, U.S. Environmental Protection Agency [Online] (Published 2008). Available at: http://www.epa.gov/nscep/ [Accessed 25 January 2013].