a gis multicriteria analysis for siting a sun/wind …

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

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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

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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.

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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

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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

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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

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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.

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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

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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

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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-

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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

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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)

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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)

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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

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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

http://www.ciwmb.ca.gov/Recycle/



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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:

http://www.ecologycenter.org/ptf/report1996/report1996_05.html




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• 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,










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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



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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).


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Figure 1: California Recycling Market Development Zones (RMDZ)

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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.

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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.


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.


4) Select parcels containing Richmond 76% influence 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


M-3 heavy Industrial




52

Table 9: M-4 Marine Industrial setback standards


M-4 Marine Industrial


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)


16% 52% 76%


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


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


59


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)


16% 52% 76%


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


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)


16% 52% 76%


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


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


65


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


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


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


16% 52% 76%


5 5 6

Juris

dict

ions

Antioch 0 0 0

Brentwood 0 1.5% 0

Concord 0 0 0


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


16% 52% 76%


4 5 5

Juris

dict

ions

Antioch 10.4% 0 0

Brentwood 0.4% 0 0

Concord 0 0 0


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


16% 52% 76%


5 5 6

Juris

dict

ions

Antioch 0 9% 0

Brentwood 0 0.8% 0

Concord 0 0 0


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


16% 52% 76%


5 5 6

Juris

dict

ions

Antioch 0 0 0

Brentwood 0 0 0

Concord 0 0 0


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)


Neighborhood Council Districts


MySQL Open-source relational database system




80

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