Monitoring GHG Emission from ABC's Participants Using GIS Technology

Running head: MONITORING GHG EMISSION USING GIS TECHNOLOGY

Monitoring GHG Emission from ABC's Participants Using GIS

Technology

Ahmed Alhazmi

Client Organization Name: ABC (A Better City)

Technology Geographic Information System Project Implementation

GPH945

Salem State University

Spring 2015

MONITORING GHG EMISSION USING GIS TECHNOLOGY2

Executive summary

As a graduate student in the Department of Geography at

Salem Stata University, it is a part of Technology Geographic

Information System Project Implementation Course (GPH945) for

everyone to undergo and explore GIS implementation plan with non-

profit organization. This report is an attempt to know how the

theories of monitoring GHG emissions using GIS-based approach can

be applied to practical live situation, which was ABC (A Better

City), especially working with the challenge of sustainability

program coordinators to implement GIS techniques in gathering,

managing, and tracking the GHG emissions progress from ABC’s

participants.

In the first part of project report, previous literature of

reviews has been explored to know the manner of implementing GIS-

based approach in such organization’s activity. Besides that,

brief description about client organization and current sequences


of work frame of monitoring GHG emissions reduction from client

organization participants’ facilities were declared.

Successively, project’s goal, Project’s objectives, Project’s

benefits, and Project’s workflow (including need-to-know

questions) were identified for second phase.

In the second part of project report, system requirements

have been described, including all needed software, hardware,

institutional, and output requirement limitations. Also, all

required steps in making project’s information products have been

stated to create interactive vector basemap or operational layers

in interactive maps. Error tolerance consideration also has been

identified in that part.

In the third part of project report, Database schema

specification, Data categorization, Attributes able

specification, Data source steps, Data acquisition constraints,

and Future data preparation to analysis stage are stated in

sequenced manner. This part defined all needed steps to structure

the GHG emission inventory to be ready to Data Entry and Results

Analysis stage.


In the fourth part of project report, analysis stage has

been investigated to link Need-to Know Questions to available

data of all four categories (Participants, Energy End-uses, GHG

emissions, Actions). This stage is essential to investigate the

declared benefits of GIS-based approach in deep in the first

part.

In the fifth part of project report, detailed description of

Cost-Benefits Analysis steps has been declared, starting with

modeling cost consideration to showing the estimated benefits of

the applied GIS-based approach in the challenge of sustainability

program.

My focus was on how to reach the targets of ABC’s

coordinators, which was GHG reduction attempts and communicate

them about GIS course and its advantages. In last decades, rapid

growth in urbanization was observed in Boston, MA, increasing the

possibilities in releasing more emissions especially from big and

small business sector’s facilities.

The outcomes of implementing GIS-based approach in the

challenge of sustainability program at ABC shows observable


benefits during the time span of GHG reduction plan of ABC’s

program. An example of successful story of a participant for the

first year, and projected cost-benefit growth for that

participant has been contemplated over project period, in attempt

to empower the application of GIS-based approach.

TABLE OF CONTENTS

Section Description page

1.0 Background and Problem Statement 4

1.1 Background 4

1.2 Literature review 6

1.3 Project client 8

1.4 Project goal 9

1.5 Project objectives 9

1.6 Project benefits 9

1.7 Project workflow 10

2.0 System Requirements 12

2.1 Introduction 12


2.2 Need to know questions 12

2.3 Information Product Descriptions 13

2.4 Software Requirements 17

2.5 Hardware Requirements 18

2.6 Institutional Requirements 18

3.0 Data Acquisition 19

3.1 Introduction 19

3.2 Database Schema Specification: 19

3.3 Data Source Steps 24

3.4 Data Acquisition Constraints: 25

3.5 Future Data Preparation 25

4.0 Analysis Plan 26

4.1 Introduction 26

4.2 Analysis Plan Consideration 26

4.3 WP Diagram 29

5.0 Cost-Benefit Analysis 31

5.1 Introduction 31

5.2 Cost Model 32

5.3 Benefits 34

5.4 Compare benefits and cost to the participants

(clients)

36

5.5 Risk Analysis 37

5.6 Costs-Benefits Analysis Summary 39

References 40


Appendix 42

Monitoring GHG Emission from ABC's Participants Using GIS

Technology


1. Background and Problem Statement

1.1 Background:

Since the start of the industrial revolution around 1750,

human activates have been adding substantial amounts of carbon

dioxide (CO2) and other greenhouse gases (such as methane,

nitrous oxide, and fluorinated gases) to the atmosphere.

Greenhouse gases trap heat in the atmosphere, which makes the

Earth warmer, threat human being and creators on earth, and most

importantly causing environmental disasters such as tropical

storms, floods and droughts. Generally, and according to The

United States Environmental Protection Agency (EPA) (8/20/2014),

“The six main sources are responsible for releasing GHG are:

Electricity production (32%), Transportation (28%), Industry

(20%), Commercial and Residential (10%), Agriculture (10%), Land

Use and Forestry (offset of 15%)”. The GHG emission from

residential/commercial buildings and electrical productions are

more detectable in terms of data inventories availabilities than

other sources. Puliafito et al. (2015) declared that, “estimating

emissions from mobile sources is complex because vehicular


traffic statistics are randomly detected both temporally and

spatially, so mobile sources inventories estimation is considered

a difficult mission.” (p.304). Therefore, in residential and

commercial (real estate buildings) case, we can largely rely on

readily the available data about fuel and electricity consumption

and usage rates to determine emissions.

For these reasons, it is important for the local

environmental bodies and related organizations to take advantage

to use tools to monitor GHG emissions for the purposes of

planning and implementing more sustainable action, strategies,

and future practices. GIS applications have proven their

capabilities in monitoring, managing and even predicting the

environmental hazards. GIS power in modeling GHG emission lies in

processing the inputs data, and then visualizing the obtained

results in maps, tables, charts, or diagrams format. Many

governmental agencies have created tools to create interactive

maps to visualize the distribution of greenhouse gases emissions

using GIS technology. For instance, in October 2014, the Office

of Environmental Health Hazard Assessment, a branch of California


with assistance with EPA developed screening tool, called

CalEnviroScreen (Fig.1 Appendix A). According to The Office of

Environmental Health Hazard Assessment (OEHHA) (11/10/14),

“CalEnviroScreen is a screening methodology that can be used to

help to identify where are the most vulnerable communities with

the highest exposure to multiple environmental hazards, including

polluted air and water, waste facilities and contaminated soil.”

Another live example of using GIS in detecting GHG emission

is when California passed its Global Warming Solutions Act, which

established plans, incentives, techniques, and regulations to

reduce GHG emissions in 2006, which requires the California Air

Resource Board (ARB) to set regional GHG targets for emissions:

seven percent reduction target for 2020 and a 16 percent

reduction target for 2035 (Fig.2 Appendix A). According to U.S.

Department of Transportation John A. Volpe National

Transportation Systems Center (Volpe Center), in Cambridge,

Massachusetts report (August, 2011), “In order to achieve their

goals in GHG reduction plan, Sacramento Area Council of

Government (SACOG) is currently developing a land use and

transportation plan to achieve its GHG emissions targets”. The


report also stated that, “Previously, SACOG's land use and

transportation model used zones as the primary unit of

measurement, which did not provide enough details to assess the

land use impact on travel behavior.” Therefore, SACOG developed a

GIS parcel-based travel model to better capture GHG emission

distribution in the city and quantify the transportation-related

GHG emissions associated with each buffered parcel, which

provides a more accurate vision of the interaction between land

use and transportation.

1.2 Literature of reviews:

Multiple studies conducted in exploring the use of GIS

technology in predicting and estimating the amounts and

distribution of GHG emission. Van Hoesen & Letendre (2013) stated

that, “GIS-based approach used to develop reducing energy

consumption attempts in older buildings in a small rural town in

Vermont, to increase the efficiency of those buildings, and thus

help to address and map the locations of buildings that have the

higher GHG emissions rates.” (p. 630). Another study was done by

Asdrubali et al. (2013) on implementing GIS-based tool to support


local policy making—application in the municipality of Spoleto,

Italy. According to Asdrubali et al. (2013) “GIS-based tool

helped decision makers in the city to have a real time spatial

monitoring of GHG emissions of the city” (p.590).

Approaches to modelling GHG emissions using GIS

significantly differ depending on research’s objectives. However,

Boychuk & Bun (2014) mentioned that, “Creating a spatial

inventory of GHG emission for an area consist two steps:

performing an inventory for each grid cell, and for each category

of GHG activity using the ‘bottom-up’ approach, and then

summarizing that inventory results for all activity subsectors.”

(p. 563). Therefore, to build a spatial database of a particular

gas emission activity from a specific facility, it is preferable

to calculate GHG emission amount per unit area of that facility,

taking into account plotting the geographical locations of these

emissions activates over a period of time. Bun et al. also added

that, “For each activity sector selected, the used GIS modules

use a number of digital maps, which help to geographically


allocate the amount of fuel used in these selected individual

grid cells.” (p.2 33)

In addition, Boychuk & Bun (2014) also pointed out that, “To

establish a spatial analysis, it is important to categorize all

emission sources into three groups: line, area, and large-scale

point sources.” (p. 563). Boychuk & Bun (2014) in their study on

Western Ukraine classified “most of industrial facilitates such

as power stations and refineries as a large-scale point source

due to the huge amount emissions relative to area scale whereas

residential and commercial facility which are occupied with high

population as area source.” (pp. 565-566). All transportation

infrastructures which serve mobile sources (cars, trains, buses,

truck, ships, and airplanes) are considered line sources in GHG

emission.

However, due to the huge input data and the diversity in GHG

emission strategies, the rate of uncertainty will be high and

affect the results’ credibility. Thanks to GIS technology, the

inventory of GHG emission data have referenced spatially and

geocoded in order to decrease the uncertainty of these data.


1.3 Project client:

ABC (A Better City) organization has a long experience in

working with their participants to monitor and manage the GHG

emission that released from their facilities. One of the goals of

ABC organization in the Challenge of Sustainability Program, that

was launched in 2009, is to achieve or exceed a cumulative average of 2.5%

greenhouse gas reduction per year and assist commercial real estate sector in meeting

the “25% by 2020” goal of the Climate Action Plans of Massachusetts, and the cities of

Boston and Cambridge. They work hard to draw attention new

participants and spread their business in the whole city. In

addition, they offer to their participants a list of 1500

sustainable actions to implement in order to contribute in GHG

emissions.

The philosophy of this huge program lies in benchmarking

facilities’ environmental impact with the created sustainability

scorecard by program’s decision-makers; engaging participants in

monthly events in order to keep their data updated and share the

results with them; sharing success stories to highlight

achievements and motivate others participants; and reducing CO2


and greenhouse gas emissions. According to the program

coordinator: Garrett Sprague, “The very first meeting with a new

participant starts with identifying the facilities’ energy end-

uses statistics, and what are already done in term of

sustainability actions from that facility, and then discuss with

that participant’s representative the available actions as

starting point to achieve GHG emission reduction goal.” Users of

this program are mostly buildings owners, including hotels and

restaurants owners, and even small local businesses. The

challenge of sustainability program at ABC has a strategy to know

the updated GHG emissions from their participants’ facilities,

and if a GIS-based approach had been implemented in their

operations, that could help the programs coordinators to monitor

the distribution of GHG emissions spatially.

Participants trust in the program has increased due to the

benefits that could gained from implementing sustainable actions

in their facilities, including energy efficiency, reduce

environmental impact and decrease operational costs, and success

to have Leadership in Energy & Environmental Design (LEED)


certificate. In attempt to encourage their participants, each

year challenged participants and other stakeholders come together

to honor and celebrate their accomplishments at the Annual

Challenge for Sustainability Awards for achieving environmental

goals and excellence in the following categories: People, Energy,

Water, Waste, Purchasing, Cleaning & Toxins and Transportation.

Participants’ feelings toward the program accomplishments have

been increased since it launched in 2009.

1.4 Project goal:

The goal of this project is to allocate ABC organization’s

participants and their progress in GHG emission reduction within

the Challenge for Sustainability program spatially. Once the GHG

emissions forecast are complete, and the reduction target is

established, mitigation actions to reduce the facility’s GHG

emissions must be developed and adopted.

1.5 Objectives:

To determine name and location of potential participant

facility that release GHG emission.

To determine the GHG emissions statistics from a

participant’s facility.


To provide a monthly scorecard to participant’s facility

about its progress in reduction GHG emission.

To provide list of actions that contribute in GHG emission

reduction.

To provide an interactive map to ABC organization to

visualize the locations of participants facilities

spatially.

To provide a scorecard to ABC organization to know the

energy end-uses in each facility.

To provide a charts to ABC organization to highlight on the

participant’s location that needs more focus due to high GHG

emissions.

1.6 Project Benefits:

The benefits of implementing a GIS-based approach in ABC

organization especially the challenge of sustainability program

lie in the following:

Managing participants data in an organized manner and easy

to reach any time.

Showing the GHG emissions statistics spatially.


Identifying the locations of participants who have high

rates of GHG emissions.

1.7 Project workflow:

1.7.1 Goal:

Estimate GHG emission from ABC’s (A Better City) participants

and Monitor their progress in GHG emissions plan spatially.

1.7.2 Objectives: To determine name and location of potential participant

facility that release GHG emission.

To determine the GHG emissions statistics from a

participant’s facility.

To provide to monthly scorecard to participant’s facility

about its progress in reduction GHG emission.

To provide list of actions contribute in GHG emission

reduction.

To help ABC organization to visualize the locations of

participants facilities spatially.

To help ABC organization to know the energy end-uses in each

facility.


To help ABC organization to highlight on the participant’s

location that needs more focus due to high GHG emissions.

1.7.3 Need-to-Know Questions: What are energy end-uses in the residential and commercial

sectors?

Information Categories:

- Types of energy end-use and their percentages in each

facility

- Location of participant facility

Who are the organization’s participants?

- List of participants

- Participants facilities classification

- History of participants with ABC organization

- Location of participant facility

How Emissions are reduced?

- List of Actions to reduce emissions

- cost of applied action

- Time of implementing the action

- Availability of action to apply


- Quality control on that action in a facility

Where are the most participants’ facilities that release

high GHG emissions?

- List of these facilities

- Locations

- Types of GHG emission coming from a facility

What are activates contribute to emissions?

- List of activities

- Reasons of these activities

1.7.4 Workflow Functions

Name of Entity: Participants

Entity Definition: Information of participants who release GHG emission

Source of Data: ABC organization

Spatial Type: Point

Attributes: participants ID, Name, Street, City, State, Zip, Telephone, Email, Type of participants

Name of Entity: Energy end-uses

Entity Definition: types of energy end-uses in a participant’s facility

Source of Data: participants

Spatial Type: text


Attributes: participles ID, Name, Street, City, State, Zip, Telephone, Email, energy end-use Type, % of energy end-uses in a facility

Name of Entity: GHG emissions

Entity Definition: Information on types GHG emission released from a participant’s facility

Source of Data: ABC organization + participles

Spatial Type: text

Attributes: emission ID, name, quantity, % reduction of that emission monthly or annually

Name of Entity: Actions

Entity Definition: Information on an applied action in a facility

Source of Data: ABC organization + participants

Spatial Type: text

Attributes: action ID, Name, Price, Service life

2. System Requirements

2.1 Introduction:


This section involves identification of all needed

components to create the IPD of Monitor GHG emission from

ABC’s (A Better City) participant’s project. By doing that,

the information product will be having more clarity, and

then we can start to acquire project data. In other words,

system design methodology includes guidelines for

identifying project requirements, making appropriate

software selection, using properly configured data sources,

and providing sufficient hardware to meet user productivity

needs.

2.2 Need-to-Know:

o What are energy end-uses in the residential and commercial

sectors?

o Who are the organization’s participants?

o How Emissions are reduced?

o Where are the most participants’ facilities that release

high GHG emissions?

o What are activates contribute to emissions?

2.3 Information Product Descriptions:


a. Title or name of IP:

Map based on locations of participants’ facilitates which

release GHG; map of GHG emission distribution in amount

per unit area

b. Synopsis:

The information product will be an interactive online map

showing ABC (A Better City) participants. Points will

identify the location of these participants with

probability classify the participants in an included

legend. When a participant is selected, a table

containing attributes about that participant, listing all

needed information associated with that selected

participant, and any associated web links will open in

different webpage. Another interactive map will be GHG

emission distribution from all participants with ABC

organization. When a participant facility is selected, a

table, or chart will pop-up, showing GHG emission

statistics.

c. List of output requirements:


Interactive online maps of participants and GHG emission,

lists, tables, or chart of GHG emission quantities from

each participant facility, lists of Energy end-uses, and

lists of applied actions to reduce GHG emissions.

d. Map output requirements:

o Title-GHG emissions from ABC’s participants.

o Vector Layers of participants or GHG emissions, showing

fully information are related to these participants and

GHG emissions statistics.

o Legend- explains participant’s information. Another

legend shows GHG emissions statistics.

o Scale bar

o North arrow

o Pop-up windows with attribute tables about

participants, or GHG emissions.

e. Document retrieval requirements:

Text files (tables) describing participants or GHG emissions.

Web links or chart may be associated with these tables.

f. Steps required to make the product:


In order to create the IP, the participants and GHG

emissions data tables are required. The base map

containing vector data (step d.) and map elements are

needed. The next table shows the steps of monitoring GHG

emissions from ABC organization’s participants.

Data source Steps to make productCreate interactive vector basemap

Digital survey maps showing participantsdistribution in Boston

Digital map showing GHG emissions comingfrom ABC organization’s participants

1. Participants locations, addresses, zip codes, contact information, GHG emission statistics2. Identify energy-end uses from a participant facility.3. Lists of actions have been done to reduce GHG emissions.4. Maps are updated monthly.

Create operational layers in the interactive mapsABC organization reports about their participants

Scorecard about GHG emission reduction

Create pop-up windows :Participants informationEnergy end usesDate of actions applicationGHG emissions chartActions have been done (updated yearly)% of GHG emission reduction (updated monthly)


g. Processing complexity in the project is high due to the

four functions will be used (participants, energy end-uses, GHG

emission, actions)

h. Display complexity is light for this project.

i. Frequency of use:

Ideally the IP will be updated on a monthly basis

(possibly yearly especially for some data such as actions have

been done to reduce GHG emissions in a facility)

j. Logical linkages:

1) Point feature layer between participants and GHG

emissions tables will be used.

2) Point feature layer between participants and Actions

list will be used

3) Point feature layer between participants and Energy

end-uses tables will be used

k. Error tolerance:


Data of energy end-uses and GHG emissions reduction must be

accurate to the month. For example, a topological error could be

considered for incorrect address of participant by 0 %.

Generally, the following table shows some possible errors and

their impact on the benefits of application.

Error type Specific error Tolerance NoteReferential

Energy use intensity valueElectricity EUIFuel EUITotal EUI calculated in kBtu

± 0.5 KWh/fs/yr

± 0.5 kBtu/fs/yr

1 kWh = 3.414 kBtu where Energy intensiveness is simply energy demand per unit area of the building's floorplan, usually in square meters orsquare feet.

Topological

Incorrect address of facility

0 %

Relative None NoneAbsolute None None

ReferentialPossible error Energy use intensity valueResults of error Wrong estimating from

participantImpact of error Affect the GHG emission and

energy end-uses recordsConcerns for error tolerance Must consider this tolerance in

calculating the energy-end uses errors from facility to reduce wrong calculation in the application


The UML class diagramming (Entity-Relationship (ER) diagram):

2.4 Software Requirements:


2.4.1 Checklist of software function capabilities:

Question Question code

Data processing

Software function capability

What are energy end-uses in participant’s facilities?

A RecordCategorizeSource

EstimateList

Who are the organization’sparticipants?

B Identify List

How Emissions are reduced? C Find solution

Action

Where are the most participants’ facilities that release high GHG emissions?

D RecordList

Allocate


E RecordList

Estimate emission

2.4.2 Matrices of questions and entity classes, andquestions and software functions:


2.5 Hardware Requirements:

Medium server with sufficient storage to handle the files,

layers, and data of every class of entities starting from

participants facilitates data, energy end-uses, GHG

emissions, and actions data. For Data processing storage

requirement, backup storage should be available in order to

ensure data stability and existence any time.

2.6 Institutional Requirements:


Most Commercial real estate and business leaders in Greater

Boston are involved in the challenge of sustainability

program from ABC organization. A possible elite group of

businesses, property owners and governmental and non-

governmental institutions such as BARR FOUNDATION, CITY OF

BOSTON, and GREENOVATE BOSTON provide access to knowledge,

resources, and/or perspectives to support the development of

the project. Other organizations which concerned about

sustainability such as GREEN PLUS, Union of Concerned

Scientists, and EPA could also be involved in this

application.


3. Data Acquisition

3.1 Introduction:

This stage involves identifying Database schema specification,

Data categorization, Attributes able specification, Data source

steps, Data acquisition constraints, and Future data preparation

to analysis stage. After identifying the systems requirements in

the previous stage, analyst or ABC coordinator starts

manipulating the data and asking some questions in order to

assure that input data are precise and accurate and readily to

run in the system. These questions will be discussed in next

steps. All data of this project will be tables containing all

required data for four categories (Participants, Energy end-uses,

GHG emissions, Actions).

3.2 Database Schema Specification:

3.2.1 Logical schema: database structure in this project will

be in table’s format of all data categories (will described in

next section). The name and formatting of project database

will be combination of shapefiles, text files, and xls files.


The next image describes project schema and the relations

among all four categories.

3.2.2 Physical schema:

3.2.2. a Data category name:

o Participant - this file contains information about ABC

organization participants and all required data such as ID,

Name, Street, City, State, Zip, Telephone, Email, and

participant type. ABC categories their participants as the

following: T=Tenant, B=Building Owner, I=Institution,


TO=Tenant and Owner, R=Retail, H=Hotel, RS=Restaurant. 105

participants

Source of the data: the Challenge for Sustainability program

coordinator at ABC

Spatial object type: point

o Energy end-uses - this file contains a list of energy uses

in a particular participant, including participles ID, Name,

Street, City, State, Zip, Telephone, Email, energy end-use

Type (space heating, water heating, air conditioning,

appliances, lighting, electronics), % of energy end-uses in

a facility.

Source of the data: participant’s facility coordinator

Spatial object type: text

o GHG emission - this file contains a list of all GHG

emission that released from a particular participant,

including , participant, ID, emission ID, name, quantity,

unit (CO2 / kWh, metric tons CO2/gallon of gasoline), , %

reduction (monthly or annually)

Source of the data: participant’s facility coordinator



o Actions - this file contains information about all possible

actions to apply in a particular participant’s facility,

including Action ID, Name, Price, Service life. 1,800

sustainable actions.

Source of the data: ABC’s coordinator


3.2.2 b Attributes table’s specification:

Participants table:

Attributes field

name

Data

type

Example

participles ID Integer P001

Name text Ashmont Grill

participant type text Restaurant

Street text 555 Talbot Ave

City text Dorchester

State text MA

Zipcode Integer 02124

Telephone Integer (617) 825-4300

Email text [email protected]


Energy end-uses table:

Attributes field

name

Data type Example

participles ID Integer P001

GHG emission ID text Ashmont Grill

Name text Restaurant

Street text 555 Talbot Ave

City text Dorchester

State text MA

Zipcode Integer 02124

Telephone Integer (617) 825-4300

Email text [email protected]

Energy end-use

Type

Text Cooking appliances

Quantity/month Long

integer

500 kWh

% of energy end-

uses in a

facility

Integer 20


GHG emission table:

Attributes field

name

Data

type

Example

Participles ID Integer P001

Emission ID text E01

Name text Carbon dioxide (CO2)

Participant type text Restaurant

Quantity per

month

Long

Integer

0.23

Unit text tons CO2 / kWh

% of reduction

per month

Integer 2.5

Action table:

Attributes field

name

Data

type

Example

Participles ID Integer P001

Action ID text A01


Name text Green roofing

Cost Integer $5-25 per square foot

Service life Integer 50 ears

3.3 Data Source Steps:

In order to accomplish my project goal which is monitoring GHG

emissions from ABC’S participants, I believe the following steps

should have been taken in consideration:

1. Make an initial interview with participant’s coordinator

in order to gather all basic information about

participant and the agreement to be involved in the

challenge of sustainability program at ABC organization,

and this step will take just one meeting to collect all

data about this data category.

2. Estimating the energy end-uses of participant’s facility,

and this requires assistance from the participant


coordinator to feed the ABC organization with scorecard

of GHG emissions. Categorization of energy end-uses to

the following aspects: kilowatt- hour of electricity,

therms of natural gas. Most of big participants such as

hotels and business buildings have their energy

consumption’ consultant, and that shorten the time period

of getting GHG emissions data. Some small participants

such as restaurants need more time to get from them GHG

emissions data, but in over all this step has range of

time about 10-15 days.

3. Estimating GHG emission that releases from a

participant’s facility depends on the scorecard of energy

uses. Once these scorecards are on the table, processing

of finding the amount of GHG emissions will be run and

inserted in the system. EPA provides Greenhouse Gas

Equivalencies Calculator tool in tons and pounds. By

following such approach, analyst decreases the time of

estimating GHG emissions from small participants’

facilities, provided that energy uses data which comes

from participant coordinator should have a higher


accuracy and precision. This step just takes a regular

processing time (several hours).

4. Finally, we have all data about GHG emissions as tables

to be inserted in GIS software to run the distribution of

emissions spatially. After that, set of actions will be

offered to participant on order to reduce the GHG

emission, increase the energy efficiency, and decline the

operational cost. No specific time in that final stage

because of the continuous monitoring of GHG emissions of

participants’ facilities.

3.4 Data Acquisition Constraints:

In case of this kind of projects, few constraints should be taken

in consideration, so data can be acquired in a timelier manner in

the future. However, here are some possible constraints could be

shown on the surface:

In most cases, the project team conducts only one

interview with each participant coordinator; brief

follow-up interviews will be held on in the future


depending on the accuracy of received data of energy uses

and GHG emissions.

Low effectiveness of initial applied actions to reduce

GHG emissions; in order to acquire data of actions,

monthly feedback from participants of cumulative of

percentage of energy uses reduction should be sent to the

program coordinator.

3.5 Future Data Preparation:

In order to prepare project data for analysis stage, analyst or

program coordinator should check data quality in terms of

accuracy and precision of locations, attributes (all data related

to data categories: Participants, Energy end-uses, GHG emissions,

Actions). Also, the analyst should manipulate input data in terms

of logical consistency (does the data follow normal rules of

logic?), completeness (is the data complete or lacking in

coverage?), data currency (is data up to date?), and

accessibility (is the data readily accessible?)

4. Analysis Plan:

4.1 Introduction:


ABC organization especially the challenge of sustainability

program took the initiative to monitor the GHG emissions that

released from some business building including commercial real

estate, hotels, restaurants, and even non-profit institutions.

Then, they have tried to estimate the amount of these released

emissions and apply sustainable actions to reduce them. There is

no change in project goal but some questions are added to the

need-to-know questions in order cover all data categories.

4.2 Analysis Plan Considerations

The goal is to monitor GHG emission from ABC’s (A Better City)

participants spatially and answering the following updated need-

to-know questions according on the data requirements in the

analysis stage:

What are statistics of energy end-uses of every participant’s

facility?

To answer this question, three processing tasks should be

applied: record, categorize, and identify the source of data

about energy uses in each facility. Scorecards will contains all

information such as type and quantity for each energy end-use in


a particular facility as mentioned in the previous table in data

category section. This question will be assigned to participant’s

coordinator to achieve desired results.

Who are the organization’s participants?

To answer this question, analyst should identify and list all

general information about participants because this data category

considered essential in terms of commination between program

coordinator and each facility one. This question will be assigned

to ABC’s coordinator to achieve desired results.

What are the estimated GHG emissions from each facility?

How these emissions are reduced?

To answer these questions, analyst should gather all data about

GHG emission from each facility in order to apply action later.

Then, he identifies suitable approaches to reduce these GHG

emissions by discussing that with each participant’s coordinator.

That requires few meetings to assign the appropriate actions.

These questions will be assigned to ABC’s coordinator to achieve

desired results.

Where are the most participants’ facilities that release high GHG

emissions?


To answer this question, analyst shows map allocate the

distribution of the higher GHG emission on an interactive after

identifying and listing these participants’ facilities of

concern. This question is very important in terms of future

analysis in different aspects such as showing the EJ communities

or areas of Boston and other surrounding cities. This question

will be assigned to both ABC’s coordinator and participant one to

achieve desired results.


What is\are the suitable actions to apply?

These questions also are fundamental in determining the

appropriate actions to reduce GHG emission, decline the

operational cost, and increase energy efficiency in each business

participant in the program. Finding a suitable action to apply is

assigned to ABC’s coordinator to achieve the desired results.

The following conceptual diagram describes GIS data analysis

tasks for each updated need-to-know question:



4.3 WP Diagram:


5. Cost-Benefit Analysis:

5.1 Introduction:

In order to understand the benefits of GIS implementation in

estimating and monitoring GHG emissions, it is preferable to

identify the current situation of the operational system in the

department that one attends to implement a GIS-based approach in.

In my project case, the challenge of sustainability department in

ABC organization use a basic technique (excel sheets) in

gathering, managing, estimating, and monitoring and GHG emissions

from their participants’ facilities. Using GIS-based approach in

estimating GHG emissions from participants of ABC organization

enables to create an emissions baseline, monitor progress, assess

the relative contributions of emission sources, communicate with

these participants’ coordinators, and create an informed

mitigation strategy based on the available information.

Understanding the scope of GHG emissions can help to inform the

development of a climate action plan or the implementation of

climate policies. Generally, an inventory is usually considered

the first step should be taken in order to establish entities


structure of GHG emissions. An inventory can help ABC’s

coordinator to:

Identify the participant’s facility , sources, and

activities within their jurisdiction that are

responsible for greenhouse gas emissions

Understand emission trends from each facility

Quantify the benefits of activities that reduce

emissions

Establish a basis for sustainable actions plan

Track progress in reducing emissions

Set goals and targets for future reductions (as ABC’s

goal: Since 2009, Challenge for Sustainability

participants has reduced CO2 emissions by 18% moving the

City closer to achieving its "25% by 2020" goal.)

5.2 Cost Model:

Based on the timeline of the challenge of sustainability

program in ABC, the primary strategy will continue until 2020

to achieve the ultimate of GHG reduction (25%) in each

facility has been participated in the program. Accordingly,


the cost matrix will consider the following timeline: 2015

(current year) to 2020.

5.2.1 Hardware and Software (One-Time

Costs):

o Workstation: (low-end): there is need for two

workstations since there are two coordinators are

working on the data analysis in the program (the

department already had the space and equipment)

o Servers: the department already has data server,

cloud hosting services, and there is need for map

server and applications server in order to

integrate all spatial, quantitative, and progress

in GHG reduction data of participants and link them

with other servers (ESRI Interactive Mapping

Software - ArcGIS Server which is considered a

highly interactive, customizable, designed for both

interactive maps and interactive processing (do

your GIS tasks online)). Cost: $10,000.

o Input devices: scanners, GPS cards, USB thumb

drive, Digital cameras ($ 4500 as a total)


o Output devices: laser printers, plotters ($250-900,

$850-4500)

o Software licensees: Annual User License of ArcGIS,

Google earth pro ($2,250 , $399/Year)

o Maintenance cost: workstations, computers (Already

had contact)

5.2.2 Data:

Conversion and development of GHG emission, energy end-uses,

and applied actions data are assumed to be under the staffing

time.

5.2.3 Staffing and Training:

In order to run GIS-based model in challenge of

sustainability program in ABC, a Senior GIS Analyst should be

hired. The approximate cost of hiring a person has the

experience in GIS modeling is $48,959 / Per Year

(Payscale.com). However, focusing only on salary on the new

guy in the department may be a mistake, so it is desirable to

think of the benefits have the highest long-run payoff for

that employee. Annie Mueller, (February 23, 201) stated

that, “according to Joe Hadzima, a columnist for the Boston


Business Journal and lecturer at MIT's Sloan School of

Management, The salary plus benefits usually totals in the

1.25 to 1.4 times base salary range.” Therefore, in the case

of hiring that GIS analyst, the total cost will be $ 68,542

(Fig.4 Appendix A)

Training for two coordinators who already worked for the

program could be done by many choices either by taking online

courses or a specific course in site configuration

administration from ESRI. Cost: 2 days (16 hours) for $1,070

USD/per person. Since the program’s coordinates are taking

training on doing GIS work in first year, there is no need to

hire a Senior GIS Analyst anymore in the following years.

Timeline of costs of implementing GIS-based approach in the

challenge of sustainability program in ABC:

Yea

r

Cumulative

Costs

Note

201

5

$ 93,231 Startup year


201

6

$ 5,400 For output devices budget

only since the other costs

will not be repeated any

more as mentioned above

201

7

$ 5,400

201

8

$ 5,400

201

9

$ 5,400

202

0

$ 5,400

5.3 Benefits:

As mentioned in the need assessment report, the information

product of the project will be presenting Map based on

locations of participants’ facilitates of ABC organization

which release GHG; map of GHG emission distribution in

amount per unit area.

Line-item budget that will be affected by the new

information:


1. Saving: by following the implemented GIS-based approach

in detecting, measuring, and monitoring GHG emissions,

there is absolutely saved staff time in preparing and

managing the GHG emissions data that come in from

participants as well as shrinkage the meetings with

participants’ coordinators as is the case formerly.

Another benefits will be the visualizing the

distribution of participants facilities spatially, and

thus helps the program coordinator to see directly

where the area of interest across Boston area and other

towns in Massachusetts. In details,

2. Benefits to the organization: noticeable upgrade in the

operational efficiency of gathering, managing,

estimating, monitoring GHG emissions as well as the

progress in reduction in energy uses in each facility

after applying the appropriate action. Increasing on

the program’s coordinator ability in detecting where

the most participants are release more emissions are

spatially. Also, Increase in the credibility and


accuracy of collected data of energy consumption and

GHG emissions.

3. Benefits to the participants: reduction in energy uses

after applying the offered action from ABC

organization. For instance, and according to ABC,

“Between 2012 and 2013, DANA-FARBER CANCER INSTITUTE-

Shields Warren Building reduced energy consumption by

21%, which was the equivalent of saving approximately

200,000 kWh of electricity per year, which values in

dollars about 145,000 annually and paid back the cost

of applied action in 0.95 year after installing motion

sensor lighting and retro-commissioning the HVAC system

to improve operation and control.” Knowing that, there

is no similar progress in all participants since there

is variation in actions that applied and the periods

that take actually to accomplish the energy reduction

target.

4. Future benefits: the aim of program is to reduce the

released GHG emissions from participants’ facilitates ,

increase the energy efficiency, and that will


contribute in helping city planners and other

environmental agencies such as EPA in their broad goal

in GHG emission reduction plan.

5.4 Compare benefits and cost to the participants

(clients):

Let’s take the DANA-FARBER CANCER INSTITUTE- Shields Warren

Building as an example in costs-benefits analysis. Savings will

vary depending on the area size, type of lighting, and occupancy

pattern. Manufacturers claim that in some applications, savings

can approach 75%, but average estimates show that typical savings

range from 35-45% (Tabe.1 Appendix A)

However, and according to ABC, “Prior to the retro-

commissioning, the HVAC system at the Shields Warren Building

operated twenty- four hours, seven days a week. The new system

features an operating schedule configured to match building

occupancy and a control switch that allows employees working off-

peak hours to run the system on a shorter, two hour cycle.

Improved control of the HVAC system has already contributed to


energy reduction and will continue to be paramount for future

energy conservation at the facility.”

The following table shows the feature estimation of cumulative

benefits in dollar value for DANA-FARBER CANCER INSTITUTE-

Shields Warren Building over the project period:

Year Costs

(dollars)

Benefits (dollars)

2010 147,000 145,000

2011 10,000 145,000

2012 10,000 145,000

2013 10,000 145,000

2014 10,000 145,000

2015 10,000 145,000

2016 10,000 145,000

2017 10,000 145,000

2018 10,000 145,000

2019 10,000 145,000

2020 10,000 145,000

Result

s

The total costs ($) = 237,000; Discounted costs ($) =

185,991.12; Total Benefits ($) = 145,000; Discounted


Benefits ($) = 890,962.23; Benefit/Cost Ratio = 4.79

5.5 Risk Analysis:

5.5.1 Identifying the risks:

5.5.5.1.1 Technology:

GHG emission inventory and management considered is not

adapted new and many live examples are already exist such as the

Office of Environmental Health Hazard Assessment, a branch of

California with assistance with EPA when developed screening

tool, called CalEnviroScreen to capture the GHG emissions

distribution in the coastal area in California. The used software

(ArcGIS) is practical one and many governmental and private

institutions have used it due to high advantages that it has.

There are no gaps in that technology prevent from supporting the

needs of project. Generally, the proven technology has proven its

ability create more 95 % of the information product.

5.5.5.1.2 Organizational functions and interactions:


There are no functional changes will be shown if the GIS-

based approach will be implanted in the workflow of the challenge

of sustainability program. There is no interaction with other

organizations, and the output of project is considered

complementary effort to be added to the governmental GHG

emissions plan such as the huge program in EPA.

5.5.5.1.3 Constraints:

As mentioned earlier, the timing of project already

identified and there is no addition. The budget assumes to be

reasonable to be added due to the advantages of expected benefits

in the future at all levels. In general, the program’s

coordinator must assess risks based on the data collected. Here

are some Potential risks should be taken in consideration:

The ability for organization management to override internal

controls.

Inadequate, poorly controlled and/or poorly documented

mechanisms for collecting data, quantifying emissions and

preparing the GHG assertion including quality checks.


Lack of staff competency in collecting data, quantifying

emissions and preparing GHG assertion.

Lack of participant’s involvement in quantifying emissions

and preparing the GHG assertion.

Failure to identify accurately all the GHG sources come

from participants.

Misleading presentation of information in the GHG

assertion.

Inconsistent quantification methods and reporting policies.

Errors in unit conversion when consolidating input data.

Inadequate disclosure of material scientific uncertainties

and key assumptions in relation to estimates.

5.6 Costs-Benefits Analysis Summary:

As mentioned above, benefits that the organization will gain

are considered a time-saving one. The challenge of sustainability

program is under non-profits organization that receives donations

from their participants, and more participants are involved to


the program, more donations and financial support will be gained

in the future to cover all previous financial commitment and the

new ones such applying the GIS-based approach. Before applying

the GIS-based approach in GHG emissions program, the

organization’s coordinator spend more time in gathering,

managing, and monitoring all data come from participants. On the

other hand, with using GIS-based approach, the program’

coordinators many will gain benefits such as decline in time

consumption, increase the efficiency and accuracy of data and

their analysis in the future, and detecting where the areas that

the program coordinators should to focus on in terms of GHG

emission distribution over the Boston area and surrounding ones.

In this project case, it is hard to estimate exactly the benefits

that will ABC organization will have over the project life

because of the aim of the program is connected to the progress in

declining energy consumption and GHG emissions in participants’

facilities.


Summary, Conclusions, and Recommendations:

This report was an attempt to assure that GIS-based approach

can be applied in GHG emissions reduction plan for ABC’s

participants, and provides observable benefits on short time and

long time. Current situation of gathering, managing, and tracking

GHG emissions has been identified, and then compared to suggested

GIS-based approach. The results show that using GIS technology in

gathering, managing, and tracking GHG reduction in commercial

buildings and business facilities. For future analysis, I would

recommend taking in consideration using Energy End-Uses zones

interactive map as guide to them in GHG emissions reduction plan,

and link it to the data that will be analyzed. Other finding was

that estimating GHG emissions from buildings requires more

attention to data collection stage in terms QA & QC. Therefore,


Fig.5 Appendix shows a potential framework of linking GHG

observation zone management plan linked to small projects as the

Challenge of Sustainability Program at ABC.

References

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Retrieved (04/20/15) from


http://www.investopedia.com/financial-edge/0711/the-cost-of-

hiring-a-new- employee.aspx

Asdrubali, F., Presciutti, A., & Scrucca, F. (2013). Development

of a greenhouse gas accounting GIS-based tool to support

local policy making—application to an Italian municipality.

Energy Policy, 61587-594. doi:10.1016/j.enpol.2013.05.116

Boychuk, K., & Bun, R. (2014). Regional spatial inventories (cadastres) of GHG emissions in the Energy sector: Accounting for uncertainty. Climatic Change, 124(3), 561-574. doi:10.1007/s10584-013-1040-9

Bun, R., Hamal, K., Gusti, M., & Bun, A. (2010). Spatial GHG inventory at the regional level: accounting for uncertainty. Climatic Change, 103(1/2), 227-244.

doi:10.1007/s10584-010-9907-5.

Federal Highway Administration's (FHWA) Office of Planning.

Application of Geographic Information Systems (GIS) for Transportation and

Climate Change (August, 2011). Retrieved from

http://www.gis.fhwa.dot.gov/documents/Climate_Change_Report_Aug20

11.htm

Office of Environmental Health Hazard Assessment (OEHHA).

CalEnviroScreen 2.0 (11/10/14). Retrieved from

http://oehha.ca.gov/ej/ces2.html

http://oehha.ca.gov/ej/ces2.html

http://www.gis.fhwa.dot.gov/documents/Climate_Change_Report_Aug2011.htm

http://www.gis.fhwa.dot.gov/documents/Climate_Change_Report_Aug2011.htm

http://www.investopedia.com/financial-edge/0711/the-cost-of-hiring-a-new-%09employee.aspx

http://www.investopedia.com/financial-edge/0711/the-cost-of-hiring-a-new-%09employee.aspx


Payscale Human Capital. Geographic Information Systems (GIS) Analyst Salary (United States). Retrieved (04/20/15) from

http://www.payscale.com/research/US/Job=Geographic_Information_Systems_%28GIS %29_Analyst/Salary .

Puliafito, S. E., Allende, D., Pinto, S., & Castesana, P. (2015).

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spatiotemporal evolution of building-stock age in Poultney,

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Gas Emissions (8/20/2014). Retrieved from

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Appendix:


Figure 1. A map shows the distribution of GHG emission in California. Credit:OEHHA, 11/10/14


Figure 2. A map shows the correlation between land use and transportation GHG

emissions. Credit: FHWA


Figure 3. Thematic map of specific GHG emissions in the energy sector (Lviv region,Ukraine 2004; tCO2-. Credit: Bun et al. (2010)


Information Structures/Products:


Initial information product chart:


Area Type Percent (%) ReductionLocker room

65Large work room/office

55Rest room

50File room

45Small work room

40Corridors

25Small offices

22

Table 1. Possible percentage reduction in on-time for lights in a given areatype. Credit: U.S. Department of the Interior

Figure4. GIS Analyst salary average. Credit: Paysacle.com



Figure 5.Zone management framework for GHG emission Credit: InternationalInstitute for Sustainable Development (iisd

POTENTIAL FRAMEWORK FOR A GHG INVENTORY SYSTEM

Documents

Monitoring GHG Emission from ABC's Participants Using GIS Technology