ECONOMIC ANALYSIS OF THE EFFECTS OF THE PHILIPPINE CLEAN AIR

ACT ON SECTORAL PRODUCTION USING AN AUGMENTED INPUT-

OUTPUT MODEL

HERYKA CERBO ASILO

SUBMITTED TO THE FACULTY OF THE DEPARTMENT OF ECONOMICS

COLLEGE OF ECONOMICS AND MANAGEMENT

UNIVERSITY OF THE PHILIPPINES LOS BAÑOS

IN PARTIAL FULFILLMENT OF THE

REQUIREMENTS FOR

THE DEGREE OF

BACHELOR OF SCIENCE IN ECONOMICS

APRIL 2012

iii

BIOGRAPHICAL SKETCH

Heryka, the youngest of four daughters to Architect Eric and Helen Asilo, was

born on the 1st of December 1992. She took up her secondary education in Los Baños

National High School as part of the pilot section. She graduated in 2008 with the

knowledge that she passed the UPCAT under the Bachelor of Science in Economics.

She was accepted to the University of the Philippines Los Baños in 2008, and

completed her first semester in the University as a College Scholar.

During her sophomore year, she decided to join the prestigious organization

exclusively for BS Economics majors, the UPLB Economics Society. The UPLB

Economics Society or ECONSOC is an active member organization of the Junior

Philippine Economics Society (JPES). During the Academic Year 2011-2012, she was

elected as the Publicity Committee Head of the said Organization.

She is creative and artistic. She is interested in interior design, scrapbooking, and

photography, and hopes to pursue more of her interests after graduation.

iv

ACKNOWLEDGEMENTS

My thesis manuscript, and therefore my graduation, would not be possible if not for these

wonderful people. Thank you so much for everything!

To my adviser – Dr. Asa Jose Sajise, thank you for your ideas, patience, and

understanding. Thank you for your guidance throughout this whole process.

To my reader – Prof. Agham Cuevas, thank you for all your valuable inputs for this

manuscript.

To the DE Faculty, especially those who have been my professors in the past, thank you

for teaching me everything that I know about Economics. Special thanks to Sir Harvey for all the

help. Also to Tita Lorns and Tita Nel at the department, thank you po!

To my co-advisees – Kuya Tiano, Daisy, Mayeen, and Gladz, thanks for everything!

Congratulations to us! Yey!

To my brods and sisses in UPLB Economics Society, thank you for the bonding

moments and successful activities. To my OPEC batchmates – Dan, Amara, Ven, Donna,

Rousey, and Gladz, thank you for being the BEST batchmates ever. To my beloved Pubcom

and Execom, for making my life extra busy… and extra SPECIAL this year! To my inaanak

Madz, for joining Econsoc (haha), and for your graduation surprise. Thank you all Soc and I love

you!

To all Econsoc SENIORS, thank you for our tambayan moments. Thank you for making

me feel that I am not alone in this endeavor. Special thanks to Gladz and Marco for our NSCB-

NSO fieldtrip!

To my bestfriends in Econsoc – Bis Tricia, Bestfriend April, Amara and Gerald,

thanks for the foodtrips, chismisan, and everything else!

To my high school barkada Wakoko_Czyguyz – Bebbin, Gio, Marlon, King, Jessie,

PJ, Pius, Allan, Jordan, Roy, Paolo, Anna, Mariz, especially to Loraine, Jclyn, and Zaren,

thank you for inspiring me not to give up! Thank you for being true friends. I love you and I miss you!

To the people who I must thank but I am somehow forgetting to mention, SORRY and THANK YOU SO MUCH for whatever it is. Haha.

To my loving Family – Daddy, Ate Toy, Ate Bok, Ate Leng, my nieces Boinky and

Jam, my nephew Yuri, and most especially to my mom, for the constant reminders to drink

water, eat breakfast and drink vitamins; for waking up in the middle of the night to check up on

me; for the simple things you do to cheer me up; and for many other things that remind me that I

am blessed to have you as my family.

And above all else, thank You for blessing my life with these wonderful plans and

people. Thank You for Your guidance because I wouldn’t have done all these without You. Thank You, Lord!

v

ABSTRACT

ASILO, HERYKA C. 2012. Economic Analysis of the Effects of the Philippine Clean

Air Act on Sectoral Production Using an Augmented Input-Output Model. College

of Economics and Management, University of the Philippines Los Baños

(Undergraduate Thesis).

Thesis Adviser: Dr. Asa Jose U. Sajise

The main objective of the study was to analyze the effects of the Philippine Clean

Air Act on sectoral production with the use of an augmented Input-Output model. The

study proceeded in two stages. First, a regression analysis was conducted to attribute the

changes in fuel consumption to the implementation of the Philippine Clean Air Act. The

results of this regression were then used as basis for simulating changes in the final

demand due to the Philippine Clean Air Act.

The results of the simulations showed that the Household Sector was the sector

with the highest decrease in sectoral gross output while, the sector of Waterworks and

Supply was the one with the lowest decrease in sectoral gross output.

The calculation of the changes in impact variables show that the change in final

demand caused the highest percentage changes in the air pollutants SOx and NOx relative

to the other impact variables, equal to -0.01480689 and -0.00963582, respectively.

The environmental impact variable multipliers were also computed to determine

the effect on the impact variables of a peso increase in each sector’s final demand. Again,

the Household Sector exhibited the highest residual multiplier for air pollution related

variables such as particulate matter (PM), VOC, and CO. It also had the highest air

pollution damage multiplier, equal to almost 0.01 which means that a peso increase in the

final demand is associated with 0.01 pesos in air pollution damages. With this, it can be

said that regulations aimed at reducing pollution levels should also focus on the

household sector since it can contribute significantly to pollution.

vi

TABLE OF CONTENTS

TITLE PAGE i

APPROVAL PAGE ii

BIOGRAPHICAL SKETCH iii

ACKNOWLEDGEMENTS iv

ABSTRACT v

TABLE OF CONTENTS vi

LIST OF TABLES viii

LIST OF APPENDICES ix

CHAPTER 1. INTRODUCTION 1

1.1 Background of the Study 1

1.2 Statement of the Problem 4

1.3 Objectives of the Study 5

1.4 Significance of the Study 5

CHAPTER 2. REVIEW OF LITERATURE 6

2.1 Environmental Regulations and the Economy 6

2.2 Environmental Regulations and the Manufacturing Sector 7

2.3 Environmental Regulations and the Agricultural Sector 8

2.4 Input-Output Models and Pollution 9

CHAPTER 3. THEORETICAL/ CONCEPTUAL FRAMEWORK 11

3.1 Efficient Level of Pollution 11

3.2 Environmental Regulations 12

CHAPTER 4. METHODOLOGY 16

4.1 The Model 16

4.2 Modifications and Extensions 18

4.3 The Household Sector 21

4.4 Impact Variables 22

4.5 Economic Policy Simulations 24

vii

CHAPTER 5. RESULTS AND DISCUSSION 26

5.1 Regression Analysis 26

5.2 Input-Output Modeling 28

SUMMARY AND CONCLUSION 36

RECOMMENDATION 39

REFERENCES 40

APPENDICES 42

viii

LIST OF TABLES

TABLE NO. TITLE PAGE

1 Results of the Regression Analysis 27

2 Actual and Simulated Sectoral Petroleum 29

Consumption, 1990

3 Vector of Changes in Final Demand (ΔY) 30

and Sectoral Gross Output (ΔX)

4 Vector of Changes in Impact Variables (Δv) 33

ix

LIST OF APPENDICES

APPENDIX

TABLE NO. TITLE PAGE

A.1 Correlation Matrix 43

A.2 ENRA-Modified IO Table, Philippines, 1990 44

(In ‘000 pesos)

A.3 Leontief Inverse Matrix, ENRA-Modified IO Table, 46

Philippines, 1990

A.4 Matrix of Impact Coefficients 48

A.5 Environmental Impact Variable Multipliers (Δv) 50

Obtained Using the ENRA-Modified A Matrix

ECONOMIC ANALYSIS OF THE EFFECTS OF

THE PHILIPPINE CLEAN AIR ACT ON SECTORAL PRODUCTION

USING AN AUGMENTED INPUT-OUTPUT MODEL1

1A thesis manuscript submitted to the Faculty of the Department of Economics,

College of Economics and Management, in partial fulfilment of the requirements for

graduation for the degree of Bachelor of Science in Economics, under the supervision

of Dr. Asa Jose U. Sajise.

HERYKA CERBO ASILO

CHAPTER I

INTRODUCTION

1.1 Background of the Study

Industrialization and urbanization throughout the world may have brought

positive effects to the economic status of many countries, but another effect of this

phenomenon is often taken for granted, and that is its negative effect on the

environment, pollution. Rapid urbanization and motorization is said to have been

the cause of air pollution problems in Asian countries (Hirota, 2010). Although the

Philippines is still perceived to be an agricultural country, it is not exempted from

the worldwide phenomenon of industrialization, and consequently air pollution.

2

Sources of air pollution can be classified into stationary, mobile, and area

sources. In the Philippines, among the three classifications, mobile sources

contribute 65% of total emissions based on the 2006 Philippine National Emission

Inventory, with Carbon Monoxide having the biggest pollution load contribution of

50% (Environmental Management Bureau, 2009). According to the Asian

Development Bank (2006), this was relatively due to the threefold increase in

number of road vehicles from 1.6 million to more than 5 million from 1990 to

2005, with gasoline-fuelled vehicles comprising 72% of total fleet.

The national total suspended particulate (TSP), another criteria air pollutant,

was observed to be decreasing from 144 to 97 microgram per normal cubic meter

from years 2003 to 2007. However, TSP geometric mean concentrations are still

above the 90 microgram per normal cubic meter annual mean TSP guideline value

(Environmental Management Bureau, 2009).

The quality of air in the Philippines, especially in urban areas, has been

declining in recent years. The Philippine government, in taking action towards

solving this serious problem, implemented Republic Act 8749 or the Philippine

Clean Air Act of 1999. According to the Act's Declaration of Principles, “The State

shall promote and protect the global environment to attain sustainable development

while recognizing the primary responsibility of local government units to deal with

environmental problems.”

Under the General Provisions of the Philippine Clean Air Act, the

Department of Environment and Natural Resources (DENR) shall monitor the air

3

quality in the Philippines and prepare an annual National Air Quality Status Report.

This report would include but will not be limited to the following information:

“a) Extent of pollution in the country, per type of pollutant and per type of source, based on reports of

the Department’s monitoring stations;

b) Analysis and evaluation of the current state, trends

and projections of air pollution at the various levels

provided herein;

c) Identification of critical areas, activities, or projects

which will need closer monitoring or regulation;

d) Recommendations for necessary executive and

legislative action; and

e) Other pertinent qualitative and quantitative

information concerning the extent of air pollution and

the air quality performance rating of industries in the country.”

The Act also sets “enforceable emission limitations” for identified criteria

pollutants, and tasks the Department of Environment and Natural Resources to

designate non-attainment areas. Non-attainment areas, as defined in the Act, are

areas “where specific pollutants have already exceeded ambient standards.” DENR

will manage these areas and make sure that no new sources of the exceeded air

pollutant will be established without a corresponding elimination of existing

sources. Similar provisions regarding attainment and non-attainment areas can be

found in the Clean Air Act of the US. According to Becker (2001), local regulation

(or the designation of a country as a non-attainment or attainment area) affects the

compliance costs of manufacturing plants in the US.

Provisions of the Philippine Clean Air Act may sometimes be specific to the

different types of pollution source, such as the provision on attainment and non-

4

attainment areas which mainly apply to stationary sources or factories. Their overall

effect on pollution levels and economic activity, however, are not contained in

specific regions or areas. The emissions from sources, whether they may be mobile

or stationary, naturally spread in the atmosphere. With this, isolating the effect of

the air pollution emissions on polluting firms or sectors only would be difficult and

unnecessary.

Economic relationships between different sectors should also be taken into

account since an output of one sector may be another’s input for production. A

change in demand for goods produced in one sector affects not only the production

of that sector but also the production of all other sectors providing inputs to that

sector (Burkander, 2008). Burkander further discusses that given that pollution is an

externality from production, an increase in demand for goods in one sector will also

cause increased pollution in the related sectors.

1.2 Statement of the Problem

Recognizing that sectors of an economy are interrelated with one another, not

only through their production but also the pollution they generate, this study asks

how an environmental policy such as the Philippine Clean Air Act would affect

sectors involved in significant pollution-generating activities and also those which

are indirect contributors to air pollution. This analysis seeks to answer how the

provisions of the said Act affect the overall economic performance of the different

sectors, and ultimately, the Philippine economy as a whole.

5

1.3 Objectives of the Study

This study aims to analyze the effects of the Philippine Clean Air Act on

sectoral production and on the economy as a whole with the use of an augmented

Input-Output model. Specifically, this analysis intends to:

1. Determine the change in production of different sectors brought about

by the provisions of the Clean Air Act;

2. Identify the relationship between pollution and performance of the

Philippine economy and its different sectors, and;

1.4 Significance of the Study

This study can offer new insights on the effects of the Philippine Clean Air

Act, and probably of similar environmental regulations, on the economy. In

addition, this analysis will explore not only the overall effect of the Clean Air Act

but also its effect on the individual economic sectors and their relationships with

one another.

Furthermore, results of the study can possibly recommend policy

implications for the improvement of the Clean Air Act, considering its impact on the

economic status of the country.

CHAPTER II

REVIEW OF RELATED LITERATURE

2.1 Environmental Regulations and the Economy

The economy and the environment are very much interrelated such that

economic activity may cause certain externalities, such as pollution, that may affect

the environment. Consequently, environmental regulations implemented to address

externalities can also affect the performance of the economy. Having identified this

relationship, studies have been done to analyze the impact on environmental

regulations on different aspects and sectors of the economy.

Millimet, Roy, and Sengupta (2009) did a literature review on the effects of

environmental regulations on economic activity, specifically market structure,

which they defined as the “degree of market concentration that depends on the

number of firms in the industry and the distribution of market shares (and the

related size distribution of firms).” The firm’s production cost is the main way

through which environmental regulations can influence market structure, and the

greater these costs are, the lesser the profitability of firms; therefore, influencing the

entry and exit of firms (Millimet, et al., 2009). Katsoulacos and Xepapadeas (1996),

with the assumption that there is a linear demand function and a cost function that is

“additively separable in outputs and emissions,” found in their study that the

equilibrium number of firms is negatively related to the unit emission tax (as cited

7

by Millimet, et al., 2009). The same results were obtained by Shaffer and Lee in

1995 and 1999, respectively (as cited by Millimet, et al., 2009).

2.2 Environmental Regulations and the Manufacturing Sector

Environmental regulations generally aim to improve environmental quality or

prevent degradation of natural resources. These regulations often entail economic

costs, but these costs are seldom considered in formulating these regulations and

their provisions. Governments usually focus only on the benefits of environmental

regulations to society, as in the case of the Clean Air Act of the US. Becker, in his

2001 study, analyzed the effect of the US Clean Air Act on air pollution abatement

capital expenditures and operating costs of manufacturing plants. The study used

data from the Pollution Abatement Costs and Expenditures (PACE) Survey from

years 1979-1988. Becker concluded that manufacturing plants that emit high levels

of criteria air pollutants, as defined by the US Clean Air Act, have significantly

higher air pollution abatement costs. Results of the study also showed that local

regulation, or the designation of non-attainment and attainment areas, further affects

air pollution abatement costs, in such a way that manufacturing plants, with high

emissions of an air pollutant, located in designated non-attainment areas for that air

pollutant, had higher air pollution abatement expenditures. Another study conducted

by Greenstone (2002) looks at the impacts of environmental regulations on

industrial activity, specifically on growth of employment, capital stock and

shipments, of polluting sectors. According to the results of the study, environmental

regulations restrict the activity of manufacturing plants. “I find that in the first 15

years after the Amendments became law (1972- 1987), nonattainment counties

8

(relative to attainment ones) lost approximately 590,000 jobs, $37 billion in capital

stock, and $75 billion (1987$) of output in pollution intensive industries

(Greenstone, 2002).” This decline in activity of manufacturing plants may be

substantial in non-attainment areas; however, this is not very significant compared

to the entire manufacturing sector.

Other studies, however, claim that environmental regulations do not harm the

economy. According to Michael Porter (1991), “Strict environmental regulations do

not inevitably hinder competitive advantage against foreign rivals; indeed, they

often enhance it (as cited by Ambec, et al., 2010).” This is popularly called the

Porter Hypothesis. Porter and his co-author van der Linde (1995) further state that

more stringent yet properly designed environmental regulations, especially those

using market-based instruments, can “trigger innovation that may partially or more

fully offset the costs of complying with them” in some cases (as cited by Ambec, et

al., 2010). Jaffe et al. (1995), in examining the effect of environmental regulation on

the competitiveness of the manufacturing sector of the US, found similar results.

Their study concluded that there is relatively little evidence to prove that

environmental regulations can weaken the competitiveness of the US manufacturing

industry.

2.3 Environmental Regulations and the Agricultural Sector

The effect of environmental regulations on economic activity is observed not

only in manufacturing sectors but also in commercial agriculture, which is a major

9

polluting sector in the US. In his 2009 study, Sneeringer analyzed the effects of

environmental regulations on firm location, "the externality costs of legislation

aimed at economic growth,” and hog production’s effects on air pollution." The

findings of the study show that the legislation in North Carolina caused "an

additional 11% increase per year in hog production in North Carolina relative to the

rest of the US." This also resulted in an annual increase in ambient air pollution of

10% per county. Given that parallel changes in ambient air quality occurred together

with trends in hog production, the study therefore concluded that the hog production

was the cause of air pollution in North Carolina. The observed trend was that a

200% increase in hog production caused a 92% increase in ambient air pollution.

This increase in ambient air pollution can produce significant public health effects,

and if quantified, these effects cost North Carolina at least 20% of revenue from its

hog production sector.

2.4 Input-Output Models and Pollution

The input-output model was created by Wassily Leontief. The input-output

models are used to analyze the mechanism “by which inputs in one industry produce

outputs for consumption or for input into another industry (The Concise

Encyclopedia of Economics, 2008).” According to Leontief (1970), the input -output

analysis can be used to measure changes in pollution (as cited by Burkander, 2008).

In 2006, Alcantara and Padilla conducted an input-output analysis aimed to

establish which sectors of production were to blame for the CO2 emissions in

Spanish economy. The focus of the study was to identify the effect of an increase in

10

the value-added of the different sectors on total CO2 emissions, and also to

distinguish the sectors which cause an increase in CO2 emissions due to an increase

in income. The following sectors were found to be the “key” sectors in CO2

emissions: “electricity and gas, land transport, manufacture of basic metals,

manufacture of non-metallic mineral products, manufacture of chemicals,

manufacture of coke, refined petroleum products and nuclear fuel, wholesale and

retail trade, and agriculture (Alcantara and Padilla, 2006).”

Burkander, in his 2008 study, used input-output analysis in finding which

sectors are the highest contributors of lead and sulfuric acid pollution in the US. The

changes in lead and sulfuric acid caused by a 100-million-dollar-change in demand

for each of the 133 sectors were estimated in the study. Results show that the “Other

Electrical Equipment and Components” sector produced the largest contribution to

lead pollution, while the “Electric Power Generation, Transmission and

Distribution” sector produced the largest contribution to sulfuric acid pollution. The

study concluded that an increase in demand in the sector of electrical equipment and

components causes not only a large amount of lead pollution but also a much greater

indirect pollution from other industries, especially in the sectors which produce

inputs for that sector. However, for the sector of electric power generation, the

resulting indirect pollution was not as much.

CHAPTER III

THEORETICAL/ CONCEPTUAL FRAMEWORK

3.1 Efficient Level of Pollution

Pollution is a negative externality produced during economic activities.

Although it is an unintended damage, the efficient level of pollution is not equal to

zero. This efficient level is the amount of pollution wherein the costs imposed by

pollution is equal to the benefits derived from the economic activity causing the

pollution. The cost of pollution is referred to as environmental damage, and the

benefit foregone when pollution is reduced is the abatement cost. Environmental

damage includes all the costs associated with pollution. Some examples would be

the health costs to individuals and the damage to biodiversity. On the other hand,

abatement cost may include the reduction in the pollution-generating activity, the

use of pollution abatement technologies, or the combination of the two.

The efficient level of emissions e* is that which gives the maximum social

surplus, such that

MAC (e*) = MD (e*)

where MAC is the marginal abatement cost, MD is the marginal damage. The

marginal abatement cost curve is downward sloping due to the fact that foregoing

the pollution-generating activity or using abatement technologies becomes more

difficult and costly. The marginal damage curve is upward sloping because it

12

reflects the presence of threshold tolerances in the environment. The value of MD is

zero at the emission level below the assimilative capacity of the environment,

denoted by eA.

Figure 1. Efficient Level of Pollution

3.2 Environmental Regulations

Environmental regulations aimed at managing pollution uses two

classifications of instruments: the command-and-control instruments and the

market-based instruments. The command-and-control instruments, the more

traditional approach in regulation, sets standards for how much emissions the firm

can emit and usually, the method or process on how to achieve these standards

(Austin, 1999). According to Stavins and Whitehead (1992), command-and-control

instruments can be classified into two broad types, the technology-based and

13

performance-based (as cited by Austin, 1999). The technology-based instruments

indicates the methods and equipment that firms should use to meet the standards,

while the performance-based instruments set overall target for each firm, or plant,

but gives the firm the discretion on how to achieve the target. Austin (1999) further

discusses that command-and-control instruments are usually based on “end-of-pipe”

solutions with little consideration on how reduction in emissions can be done

through changes in the production process or product design. These kinds of

regulations give little incentive for firms to pursue these changes, since there is no

reward for achieving the target but there is a risk that standards will be raised to

reflect the change in technology.

The alternative instrument used in environmental regulations is the market-

based instruments or economic instruments. These instruments, according to

economists, can create a system for reduction in pollution that can achieve the same

level of environmental protection for lower overall cost. Also, market -based

instruments of environmental regulation give firms more freedom on compliance.

Austin (1999) enumerated the several different types of economic

instruments. They are as follows:

“A short taxonomy of Economic instruments

1. Charges, fees or taxes

These are prices paid for discharges of pollutants to the

environment, based on the quantity and/or quality of the

pollutant(s). To be most effective the charge is levied directly on the quantity of pollution (‘emissions tax or

charge’), though if this is difficult to measure or monitor, it

may be necessary to levy a charge on a proxy for the

14

emissions, typically on the resource that causes the pollution

(‘product tax or charge’). Product charges occur at different usage points. They have been levied on products either as

they are manufactured (e.g. fertilizers), consumed (e.g.

pesticides) or disposed of (e.g. batteries) (Barde, 1997).

How effective product charges are depends on how well ‘linked’ the input, or product, is to the eventual stream of

pollution. In the case of taxing carbon fuels as a proxy for

carbon dioxide emissions, the ‘linkage’ is very strong as virtually all the carbon contained in fuels is released during

combustion. Taxing the fuel is thus little different to taxing

the emissions. On the other hand, taxing pesticides as a proxy for release of certain chemicals into water systems is

less well linked as the degree of chemical infiltration will

depend on a mixture of variables relating to soil and slope

conditions, the timing of applications etc.

2. Tradable Permits

These are similar to charges and taxes except that they

operate by fixing an aggregate quantity of emissions rather than charging a price for each unit of emissions. Instead of

being charged for releases, one needs to hold a ‘permit’ to

emit or discharge. By controlling the total number of

permits, one is effectively controlling the aggregate pollution quantity.

3. Charge-Permit Hybrids

It is possible to blend the quantity-based permit approach

with a price-based charge or tax approach to try to harness

their different strengths while avoiding their weaknesses. A good example is RFF’s proposal to use a hybrid mechanism

to control CO2 emissions in the U.S. (RFF, 1998). This

would consist primarily of a permit program that would require domestic energy producers (and importers) to obtain

permits equivalent to the volume of carbon dioxide

eventually released by the fuels they sell. However, by setting the overall permit quantity, one has no idea what

price permits will sell for – this will only be revealed as

businesses and consumers begin to reduce their CO2

emissions. In order to guard against excessively high permit prices that might arise – the very prospect of which may

prevent the program being implemented in the first place –

the second aspect of the proposal would be for the government to release an unlimited number of permits at $25

per ton of carbon should the market price of permits reach

that level. This effectively sets up a charge system of $25 per ton, capping the possible market price.

15

A system like this attempts to control on the basis of

quantity, which is the most desirable goal, while creating an ‘escape valve’ should costs rise too high. Even if the escape

valve is utilised, the program amounts to the institution of a

charge on carbon.

4. Deposit-refund schemes

Under these schemes, a surcharge is levied on a product at the point of payment. When pollution is avoided by returning

the product, or its polluting components, to a specified

collection stream the surcharge is refunded. These economic instruments have been used most often for drinks containers,

batteries and packaging (OECD, 1997).

5. Subsidies

Where taxes or charges can be used as a penalty on

discharges, subsidies can be used to reward the reduction of discharges in a similar manner. The financial incentive is

effectively the same, though the flow of funds is in a

different direction. A subsidy program will involve a transfer of funds from the government to the industry, while a charge

program would be a revenue source for the government.

Subsidies may be relatively explicit in the form of grants and

soft loans, or be somewhat indirect, such as in adjusted depreciation schedules. (Barde, 1997).”

CHAPTER IV

METHODOLOGY

The study used an augmented 41-sector input-output model that incorporates

environmental coefficients developed by Orbeta (1999) in the study entitled

Development of Environmental Impact Multipliers in the Philippines. The study

conducted by Orbeta in 1999 followed the modifications and extensions done by

Mendoza (1996) for ENRAP III and expanded the coverage of sectors and

transactions table used. The following discussion of the details of the I-O model was

taken from Orbeta (1999).

4.1 The Model

Consider an economy with n sectors of production, let

X = [Xi] where Xi is the gross output of sector i,

= n x 1 vector of gross output;

A = [aij] where aij is the Leontief IO technical coefficient and

aij ≡ zij/Xj where zij is the monetary value of the input flow

from sector i to sector j,

= n x n Leontief IO coefficient matrix; and

17

Y = [Yi] where Yi is the total final demand for sector i,

= n x 1 vector of final demands.

The gross output vector X can then be expressed as

X = AX + Y, (1)

that is, each sector’s gross output should equa l the sum of the intermediate demand

and final demand for its products. By matrix manipulation, the gross output vector

X can be rewritten as

X = (I – A)-1

Y (2)

where (I - A)-1

is often referred to as the Leontief inverse. The multiplicative effect s

in the economy of an exogenous change in one or more components of final demand

can be obtained using

Δ X = (I - A)-1

Δ Y (3)

where Δ Y denotes the changes in final demands and Δ X denotes the changes in the

sectoral gross output.

The change in sectoral gross output may not be the only measure of the

economic effects of changes in exogenous final demands. Suppose there are other

impact variables of interest, such as labor income and employment, which can be

measured either in monetary or physical flow units. Furthermore, environmental and

natural resources can be included as impact variables as well. Let

18

V = [vkj] where vkj is the impact coefficient defined as the amount

of impact variable k associated with a peso worth of sector j’s

output,

= impact coefficient matrix; and

Δ v = vector of impact effects.

Then, the changes in the impact variables due to changes in final demands are given

by

Δ v = V (I - A)-1

Δ Y, or (4)

Δ v = V Δ X (5)

4.2 Modifications and Extensions

The following are the adjustments for household production, environmental

inputs and outputs, and natural resource depreciation:

a. Incorporating income from nonmarketed, nature based household

production in upland agriculture and fuelwood gathering

The conventional IO table will be adjusted to incorporate the nonmarketed

household production by accounting the value of production as pure labor income in

the input side. On the output side, it would become a positive adjustment in gross

output through an increase in personal consumption expenditure (PCE).

19

b. Incorporating natural resource and environmental variables

The following are the adjustments done outside the conventional IO table:

i. Natural resource depletion for forests, fisheries, minerals and soil

Natural resource depletion is taken into account as another input similar to

physical capital depreciation. The natural resource depletion value for the

agriculture sector is based on the estimated quantity of upland soils eroded. For the

forestry sector, it is based on the change in stock of forest resources – dipterocarps,

plantation products, mangrove resources, pine and rattan – and the quantity of

upland soils eroded – both from grassland and woodland. For the mining sector, it is

based on the quantity of copper and gold extracted. For the fishery sector, it is based

on the change in quantity of small pelagic catch.

The estimates of natural resource depletion corresponding to physical can be

recorded positively or negatively. Because resource depreciation is assumed to be a

nonmarketed good, recording it positively yields a value of output if the natural

resource is priced. Recording it negatively, on the other hand, yields a value

reflecting the net value of production to the economy. To be consistent with the

ENRAP aggregate national income and product accounts, the natural resource

depreciation is entered negatively. On the output side, negative adjustments are

made on the relevant sectors’ values of total output corrected for household

production. The highest resource depreciation estimates were found in the forestry

and fisheries sectors, both renewable resource sectors.

ii. Environmental (air and water) waste disposal services

20

The air and water waste disposal services are entered negatively as

environmental inputs. ENRAP computed these values based on the pollution

abatement cost that would be incurred if pollution were to be reduced 90%. These

are the attributed economic values of the residuals or pollutants as “inputs” to or

“negative outputs” of the production process. Since pollutants are being generated

and firms are not incurring pollution abatement cost, the environment acts as an

unpriced input to production. The values of environmental services for air are based

on the cost of reducing particulate matter (PM) and lead (Pb). For water, on the

other hand, environmental services are valued based on pollution abatement costs

for biochemical oxygen demand 5 (BOD5) and suspended solids (SS).

iii. Environmental (air and water) damages

ENRAP based the value for environmental damages on the health effects and

productivity losses due to pollution. The values are attributed to the various

production sectors as generators of pollution. These being undesirable outputs or

economic bads, the values are entered as negative adjustments in the value of the

sector’s total output.

iv. Net environmental benefits

The net environmental benefit (NEB), which is introduced as an accounting

balancing entry like the operating surplus concept for produced assets, is defined as

the difference between the absolute values of environmental services (ES) and

environmental damages (ED). Hence, it can be expressed as

NEB = │ES│- │ED│

21

that is, the savings in pollution abatement cost of the firm by polluting minus the

resulting pollution damages. A positive NEB suggests that the net social benefit of

polluting is positive for the sector, or that the value of damages prevented by the

pollution reduction is less than the pollution abatement cost.

A positive value for the NEB is not unexpected because zero or near zero

pollution levels, assumed in the calculation of the environmental waste disposal

services, is not socially optimal. The socially efficient level of pollution is that

which equates the marginal abatement cost and the marginal damages prevented.

Conversely, a negative value for NEB implies that the net social benefit of some

incremental pollution abatement for the given sector is positive.

v. Direct nature services

The value of direct nature services, as estimated by ENRAP, consists of

those for diving activities, forest recreation and coastal beach services. On the input

side, the NEB would now be given by

NEB = │ES│- │ED│ + direct nature services

On the output side, the value of gross output for the other services sector,

which includes the direct nature sectors, is adjusted positively given that direct

nature services are desirable outputs.

4.3 The Household Sector

The IO model can be closed with respect to the household. To endogenize the

sector, it must be moved from the final demand column to the technically

22

interrelated table. This also accounts for the dependence of the household

consumption on labor income which in turn depends on the gross output of each of

the sectors. Correspondingly, the labor services row (compensation of employees) is

moved up inside the technically interrelated table. The household consumption

expenditure (labor PCE), assumed to be financed by labor income, is considered to

be a constant proportion of total PCE. The possibility that the demand for a good

can be sensitive to income is overlooked. This arises due to the fact that, implicitly,

the analysis assumes a single representative consumer and equity considerations are

ignored.

In environmental accounting, endogenizing the household sector is

acceptable if it emits significant levels of pollution. The 41 x 41 technology matrix,

which has been adjusted for the endogenized household sector, has an extra row for

labor income and an extra column for household consumption expenditure, which is

financed by labor income.

4.4 Impact Variables

The impact effects of the policy were calculated for the following variables:

a. gross sectoral outputs;

b. labor income;

c. natural resource depreciation

d. environmental waste disposal services: air and water;

e. environmental damages;

23

f. natural resource depletion:

upland soils (metric tons [mt]): agriculture, grassland, woodland

fisheries (mt): small pelagic

forestry (cubic meters [cu m]): dipterocarp, plantation, mangrove,

pine, rattan (lineal meters)

minerals: copper (mt), gold (ounces)

g. pollutants or residuals:

air: PM - particulate matter

SOx - sulfur oxides

NOx - nitrogen oxides

VOC - volatile organic compounds

CO - carbon monoxide

water: BOD5 - biochemical oxygen demand 5

SS - suspended solids

TDS - total dissolved solids

Oil - oil

N - nitrates

P - phosphates

The first five sets of variables are determined in monetary units while the last

two sets in physical terms. The assumption in this study is that each sector generates

24

pollutants (residuals) in fixed proportions to its output and that the environmental

waste disposal service and damage values are linearly related to the amount of

pollutants generated, and ultimately to output. The study does not consider the

accumulation of residuals and the assimilative capacity of the environment. Also,

the study ignores the possibility that there could be nonlinear relationships between

pollution abatement costs and damages on one hand, and the levels of pollutants on

the other. The impact coefficients may then be used to study changes in pollution

levels, abatement costs or environmental services, and damages, under alternative

output assumptions.

4.5 Economic Policy Simulations

The study was done in two phases. First, the relevant resource and

environmental effects of the provisions of the Philippine Clean Air Act was

examined. Simulations were conducted using equation (4), i.e.

∆ v = V (I - A)-1

∆ Y (4)

where the effect of the provisions of the Philippine Clean Air Act was modeled as

exogenous changes in finals demands, ∆ Y, using the augmented 41 x 41 matrix A.

Next, the calculation of gross output, labor income and environmental impact

multipliers was done. The multipliers give the change in the impact variables per

one peso increase in final demand from sector, and are computed using equation (4)

but with the matrix of final demand changes equal to an identity matrix.

According to the Environmental Management Bureau (2009), mobile sources

contribute 65% of total emissions based on the 2006 Philippine National Emission

25

Inventory. Hence, a change in fuel consumption due to the provisions of t he

Philippine Clean Air Act was considered because one of the main mechanisms

through which the provisions of the Act will have an effect on ambient air quality

standards is through fuel prices.

An ordinary least squares regression analysis was conducted to attribute the

change in fuel consumption to the implementation of the Clean Air Act. The

regression equation is given by

DFCPC = α + β1TRP + β2RPI + β3GDPPC + β4PCAA + μ

where DFCPC is the Road Sector Diesel Fuel Consumption Per Capita (kt of oil

equivalent), TRP is the Total Refinery Production (in thousand barrels), RPI is the

Retail Price Index for Mineral Fuels, Lubricants and Related Materials (1978=100),

GDPPC is the Gross Domestic Product Per Capita (in current Philippine pesos),

PCAA is the intercept dummy variable for the implementation of the Philippine

Clean Air Act, with the value of PCAA=1 if data is from 1999, the year of the

implementation of the Act, to present and PCAA=0 if otherwise.

CHAPTER V

RESULTS AND DISCUSSION

5.1 Regression Analysis

An ordinary least squares regression analysis was conducted to attribute the

change in fuel consumption of the different sector due to the Philippine Clean Air

Act. Given that mobile sources are the leading source of total emissions in the

Philippines, one mechanism through which the Clean Air Act may affect the

economy is through fuel consumption. Refinery production, price of fuel, per capita

income, and regulatory factors (i.e., the provisions of the Clean Air Act) were

considered to be the other variables affecting fuel consumption. The regression

equation is given by

DFCPC = α + β1TRP + β2RPI + β3GDPPC + β4PCAA + μ

where DFCPC is the Road Sector Diesel Fuel Consumption Per Capita (kt of oil

equivalent), TRP is the Total Refinery Production (in thousand barrels), RPI is the

Retail Price Index for Mineral Fuels, Lubricants and Related Materials (1978=100),

GDPPC is the Gross Domestic Product Per Capita (in current Philippine pesos),

PCAA is the intercept dummy variable for the implementation of the Philippine

Clean Air Act, with the value of PCAA=1 if data is from 1999, t he year of the

implementation of the Act, to present and PCAA=0 if otherwise.

27

Time-series data from 1979 to 2008 were gathered from the World Bank

Databank and the National Statistical Coordination Board.

The data was tested for the problems of heteroscedasticity, autocorrelation,

and multicollinearity. Using the Cook-Weisberg test, it was detected to have no

heteroscedasticity with the Prob > chi2 = 0.9898. Testing for autocorrelation, the

Durbin-Watson d-statistic equal to 1.541069 was compared to the cr itical values of

dL and dU for α = 5, k = 3, and N = 30 which are equal to 1.214 and 1.650,

respectively. The value of the d-statistic falls in the inconclusive region.

The problem of multicollinearity was also detected by generating the

pairwise correlation matrix (See Appendix A.1.). The variable RPI was discovered

to cause the problem, and one way to treat this problem is by dropping the variable

causing the multicollinearity; however, the variable for the price of fuel is an

economically relevant variable affecting fuel consumption. Therefore, it was

chosen not to be dropped from the regression analysis.

Table 1. Results of the Regression Analysis

DFCPC Coefficient Std. Err. t P > |t| [95% Conf. Interval]

TRP 1.32 e-07 3.37 e-08 3.93 0.001 6.29 e-08 2.02 e-07

RPI -0.000016 5.70 e-06 -2.80 0.010 -0.0000277 -4.23 e-06

GDPPC 5.00 e-07 1.50 e-07 3.32 0.003 1.90 e-07 8.10 e-07

PCAA -0.0005953 0.0026019 -0.23 0.821 -0.0059541 0.0047634

_cons 0.298428 0.0029579 10.09 0.000 0.23751 0.0359347

R2

= 0.8701; Adj. R2

= 0.8493

Results of the regression analysis show that among the four explanatory

variables, all, except the dummy variable PCAA, were found to be significant. The

28

variables TRP and GDPPC were significant at 1%, while the variable RPI was

significant at 5%. The RPI and the intercept dummy PCAA have negative

regression coefficients, while the TRP and the GDPPC have positive coefficients.

The value of the R2

is high, which is expected since the data used were time-series.

The R2 of the model was equal to 0.8701, while the Adj. R

2 was 0.8493. This value

implies that 87% of the variation in the Road Sector Diesel Fuel Consumption Per

Capita can be explained by the explanatory variables of Total Refinery Production,

Retail Price Index, Gross Domestic Product Per Capita, and the Philippine Clean

Air Act.

However statistically insignificant, the coefficient of the dummy variable

PCAA equal to -0.0005953 will be used to simulate the change in fuel consumption

of the 41 sectors in the input-output table.

5.2 Input-Output Modelling

Using the data from the 1990 ENRA-Modified Input-Output Table of the

Philippines and the coefficient of the variable PCAA from the regression analysis,

the petroleum consumption of the 41 sectors was simulated to illustrate the change

in fuel consumption due to the implementation of the Philippine Clean Air Act.

Table 2 shows the actual and simulated petroleum consumption of the different

sectors, and the difference between the two which gives the change in final demand

for petroleum products (ΔY).

29

Table 2. 1990 Actual and Simulated Petroleum Consumption (In ‘000 pesos)

Sector Actual Petroleum

Consumption

Simulated Petroleum

Consumption ΔY

1 Palay and corn production 79,775 79727.50994 -47.49006

2 Veg. , fruits & nuts (exc. coconut) prod. 178,301 178194.8574 -106.14259

3 Coconut 102,246 102185.133 -60.86704

4 Sugarcane 157,787 157693.0694 -93.93060

5 Other agri. crops 60,989 60952.69325 -36.30675

6 Livestock, poultry & other animal prod. 240,689 240545.7178 -143.28216

7 Agricultural services 170,439 170337.5377 -101.46234

8 Fishery 4,701,329 4698530.299 -2798.70115

9 Forestry 909,789 909247.4026 -541.59739

10 Metallic ore mining 1,006,046 1005447.101 -598.89918

11 Non-metallic mining & quarrying 130,329 130251.4151 -77.58485

12 Food manufacturing 6,413,554 6409736.011 -3817.98870

13 Beverage manufacturing 450,349 450080.9072 -268.09276

14 Tobacco manufacturing 68,447 68406.2535 -40.74650

15 Textile manufacturing 574,424 574082.0454 -341.95461

16 Wearing apparel, leather & leather products 487,370 487079.8686 -290.13136

17 Mfr. of wood & wood products incl. fur & fixtures 1,060,297 1059665.805 -631.19480

18 Mfr. of paper & paper prod. 295,584 295408.0388 -175.96116

19 Printing, publishing & allied products 50,853 50822.72721 -30.27279

20 Mfr. of chemicals & plastic products 1,257,851 1257102.201 -748.79870

21 Petroleum refineries & misc. prod of petrol & coal 574,353 574011.0877 -341.91234

22 Mfr. of rubber products 160,473 160377.4704 -95.52958

23 Mfr. of glass & glass products 296,839 296662.2917 -176.70826

24 Mfr. of cement 2,147,063 2145784.853 -1278.14660

25 Mfr. of other non-metallic mineral products 285,350 285180.1311 -169.86886

26 Basic metal industries 837,807 837308.2535 -498.74651

27 Mfr. of fab. metal prod., mach. & eqpt. (exc. electrical) 201,142 201022.2602 -119.73983

28 Manufacture of electrical machinery, etc. 124,512 124437.878 -74.12199

29 Other manufacturing industries 205,606 205483.6027 -122.39725

30 Electricity and gas 9,070,447 9065047.363 -5399.63710

31 Waterworks and supply 42,371 42345.77654 -25.22346

32 Construction 3,145,428 3143555.527 -1872.47329

33 Wholesale and retail trade 18,133,126 18122331.35 -10794.64991

34 Transportation and storage services 1,725,482 1724454.821 -1027.17943

35 Communication 64,506 64467.59958 -38.40042

36 Financing, insurance, real estate and bus. services 1,044,326 1043704.313 -621.68727

37 Public admin. & defense 1,714,032 1713011.637 -1020.36325

38 Education services 409,193 408949.4074 -243.59259

39 Med., dental, other health & veterinary services 244,766 244620.2908 -145.70920

40 Other community, social & personal services 1,254,971 1254223.916 -747.08424

HH Household Sector 2,637,105 2635535.131 -1569.86861

30

The resulting vector of changes in final demand for petroleum products (ΔY) was

then multiplied to the Leontief Inverse Matrix of the ENRA-Modified 41 x 41 IO Table

(See Appendix A.3.) in order to calculate the ΔX, following the equation,

Δ X = (I - A)-1

Δ Y

where (I - A)-1

is the Leontief inverse, Δ Y denotes the changes in final demand,

and Δ X denotes the changes in the sectoral gross output.

Table 3. Vector of Changes in Final Demand (ΔY) and Sectoral Gross Output (ΔX)

Sector ΔY ΔX

1 Palay and corn production -47.49006 -2516.7067

2 Veg. , fruits & nuts (exc. coconut) prod. -106.14259 -1189.8827

3 Coconut -60.86704 -554.5475

4 Sugarcane -93.93060 -369.5231

5 Other agri. crops -36.30675 -989.7860

6 Livestock, poultry & other animal prod. -143.28216 -2561.5617

7 Agricultural services -101.46234 -446.5745

8 Fishery -2798.70115 -4481.2900

9 Forestry -541.59739 -1199.5414

10 Metallic ore mining -598.89918 -1650.2331

11 Non-metallic mining & quarrying -77.58485 -5288.9082

12 Food manufacturing -3817.98870 -12060.8601

13 Beverage manufacturing -268.09276 -1029.4307

14 Tobacco manufacturing -40.74650 -425.7064

15 Textile manufacturing -341.95461 -2553.2346

16 Wearing apparel, leather & leather products -290.13136 -835.7086

17 Mfr. of wood & wood products incl. fur & fixtures -631.19480 -1209.7328

18 Mfr. of paper & paper prod. -175.96116 -1400.6790

19 Printing, publishing & allied products -30.27279 -292.4028

20 Mfr. of chemicals & plastic products -748.79870 -6511.4753

21 Petroleum refineries & misc. prod of petrol & coal -341.91234 -6967.0432

22 Mfr. of rubber products -95.52958 -1331.7163

23 Mfr. of glass & glass products -176.70826 -454.6617

24 Mfr. of cement -1278.14660 -1501.6499

25 Mfr. of other non-metallic mineral products -169.86886 -393.3161

26 Basic metal industries -498.74651 -2854.4887

31

27 Mfr. of fab. metal prod., mach. & eqpt. (exc.

electrical) -119.73983 -1853.8497

28 Manufacture of electrical machinery, etc. -74.12199 -1617.8959

29 Other manufacturing industries -122.39725 -2778.9282

30 Electricity and gas -5399.63710 -6827.1854

31 Waterworks and supply -25.22346 -159.3837

32 Construction -1872.47329 -2178.7730

33 Wholesale and retail trade -10794.64991 -14166.3664

34 Transportation and storage services -1027.17943 -6101.9440

35 Communication -38.40042 -311.2127

36 Financing, insurance, real estate and bus. services -621.68727 -5198.9741

37 Public admin. & defense -1020.36325 -1032.5785

38 Education services -243.59259 -516.0922

39 Med., dental, other health & veterinary services -145.70920 -484.9800

40 Other community, social & personal services -747.08424 -3085.2105

HH Household Sector -1569.86861 -14361.6461

Table 3 shows the vector of changes in final demand ΔY and the resulting

vector of changes in sectoral gross output ΔX. The Household Sector, followed by

the sectors of Wholesale and Retail Trade, Food Manufacturing, Petroleum

Refineries and Miscellaneous Production of Petrol and Coal, and Electricity and

Gas, was the sector with the highest negative change in sectoral gross output due to

the simulated decrease in final demand for fuel. On the other hand, the sectors of

Waterworks and Supply, Printing, Publishing and Allied Products, Communication,

Sugarcane Production, and Manufacturing of Other Non-metallic Mineral Products

were those with the lowest negative changes in sectoral gross output due to the

simulated decrease in final demand for petroleum products.

The Household sector consumes fuel for most of its daily activities. The

sector’s fuel consumption is mainly for cooking, lighting, heating water for bathing

and washing garments, and air conditioning of rooms (Shukla, 2009). This goes to

32

show that the household is very dependent on fuel, thus explaining the high sectoral

output change due to the simulated change in fuel consumption; however, the

dependency of this sector on petroleum products does not manifest in the IO table

(See Appendix A.2.). The IO table shows that the largest amounts of input needed

for the production of the household sector are from the sector of Food

Manufacturing, Financing, Insurance, Real Estate and Business Services, and

Transportation; therefore, it can be said that it is through these indirect channels

that the effect of changes in the market for petroleum products is transmitted.

For the wholesale and retail trade sector, the distribution and transportation

of the goods is the main cause for the consumption of fuel. The IO table shows that

the highest amount of input needed by this sector is from the Petroleum, Refineries

& Miscellaneous Production of Petrol and Coal. The same goes for the Electricity

and Gas sector which uses petroleum products as its main input, as shown in the IO

table.

The change in final demand affects not only sectoral gross output but also

other impact variables such as labor income, environment and natural resources.

Given

V = [vkj] where vkj is the impact coefficient defined as the amount

of impact variable k associated with a peso worth of sector j’s

output,

= impact coefficient matrix (See Appendix A.4); and

Δ v = vector of impact effects.

33

Then, the changes in the impact variables due to changes in final demands are

given by

Δ v = V (I - A)-1

Δ Y, or

Δ v = V Δ X

Table 4. Vector of Changes in Impact Variables (Δv)

Impact Variable Δv Base year

values % changes

NR (Physical)

Agriculture 17356.51987 -457000000 -0.0037979 Grassland 101.4812024 -1562000 -0.0064969

Woodland 1754.809114 -27000000 -0.0064993 Small Pelagics 1.792516 -20322 -0.0088206

Dipterocarps 131.5896916 -2025400 -0.006497 Plantation -479.4566976 7376800 -0.0064995

Mangroves 5.87775286 -91300 -0.0064378 Pine 7.67706496 -117710 -0.006522

Rattan 13543.78204 -208383160 -0.0064995 Copper 15.67721445 -180460 -0.0086874

Gold 38.94550116 -445900 -0.0087341

Residuals PM -117.3442683 2047255 -0.0057318

SOx -58.44233439 394697 -0.0148069 NOx -31.28722643 324697 -0.0096358

VOC -165.6812181 3406047 -0.0048643 CO -546.1878439 10711421 -0.0050991

BOD5 -369.7289672 8177958 -0.004521 SS -30202.15784 515195570 -0.0058623

TDS -63.24080054 1501230 -0.0042126 OIL -0.74321694 63500 -0.0011704

N -121.9423193 2357059 -0.0051735 P -7.103119872 153101 -0.0046395

Labor Income (CE) -12799.81397 309,895,416 -0.0041304

Environmental Variables

NR Depn 432.2871402 -5945463 -0.0072709 EWDS (Air) 531.3945406 -6611402 -0.0080375

EWDS (Water) 1625.673564 -30547134 -0.0053219 Air Damages 102.0834138 -1762158 -0.0057931

Water Damages 84.63666197 -1476354 -0.0057328

34

Table 4 shows the changes in the impact variab les (Δv), computed by

multiplying the V matrix and the vector of changes in sectoral gross output (ΔX).

Also shown in the table are the percentage changes in the impact variables relative

to the base year values.

The change in final demand for fuel was found to cause the highest

percentage changes in the air pollutants SOx and NOx relative to the other impact

variables. SOx and NOx had percentage changes equal to -0.01480689 and -

0.00963582, respectively. Also, the waste disposal services for air had a higher

percentage change compared to that of the waste disposal services for water, but

the percentage changes of air and water damages are almost the same.

The environmental impact variable multipliers were also computed, and are

presented in Appendix A.5. The Fishery sector has the highest natural resource

depreciation multiplier equal to 0.0928. This means that a peso increase in the final

demand for products of the fishery sector leads to a depreciation of about 0.09

pesos. The sectors of Forestry, Manufacturing of Wood and Wood Products, and

Metallic Ore Mining also exhibited high natural resource depreciation multipliers.

For the air pollutant PM, the Household Sector, Basic Metal Industries,

Non-metallic Mining and Quarrying, and Public Administration and Defense show

the highest residual multipliers. On the other hand, the sectors of Electricity and

Gas, Manufacturing of Cement, and Manufacturing of Paper and Paper Products

were the sectors with the highest multipliers for the pollutant SO x. Manufacturing

of Cement and Non-metallic Mining and Quarrying had the highest multipliers for

35

NOx. For both the pollutants VOC and CO, the Household Sector and the Public

Administration and Defense exhibited the highest residual multipliers.

The sector of Non-metallic Mining and Quarrying had the highest waste

disposal service multiplier for air equal to 0.05. This multiplier means that the

sector produces air pollutants but utilizes disposal services to reduce the harmful

effects of the pollutants before it is released into the environment. For the water

waste disposal services, Forestry exhibited the highest impact multiplier of 0.48.

The activities of the forestry sector, such as refinishing and restoring wood, and

wood preservation, generate hazardous wastes like “waste solvents, paints... waste

chemicals, sludge and wastewater (British Columbia, 2008).” Hence, more costs

incurred to dispose of and treat the sector’s waste water. Among the sectors, the

household has the highest air pollution damage multiplier, equal to almost 0.01;

meaning a peso increase in the final demand is associated with 0.01 pesos in air

pollution damages. This may be brought about by the household’s dependency on

fuel for many of its activities, and combustion of fuel is the main source o f air

pollutants. While for water damages, the sector of Other Agricultural Crops had the

highest multiplier with 0.02. This multiplier suggests that a large amount of

wastewater is generated in the production of agricultural crops and this affects the

supply of potable water. This in turn causes damages on the health and productivity

losses.

CHAPTER VI

SUMMARY AND CONCLUSION

The main objective of the study is to analyze the effects of the Philippine

Clean Air Act on sectoral production with the use of an augmented Input-Output

model. Specifically, this analysis intends to determine the change in the production

of different sectors brought about by the provisions of the Clean Air Act, identify

the relationship between pollution and performance of the economy and its

different sectors, and provide policy implications based on the results of this study.

Regression analysis was performed to attribute changes in fuel consumption

to the implementation of the Philippine Clean Air Act. The Clean Air Act was

introduced into the regression model as an intercept dummy. Refinery production,

price of fuel, and per capita income were the other explanatory variables tested in

the regression.

The dummy variable for the Philippine Clean Air Act has a negative

coefficient of -0.0005953, and was found to be insignificant. Despite this, the

regression coefficient of the dummy variable PCAA was used to simulate the

change in petroleum consumption of the 41 sectors in the 1990 IO table used by

Orbeta (1999). This obtains the vector of changes in final demand (ΔY). The vector

of changes in final demand was multiplied with the Leontief Inverse Matrix of the

41 x 41 IO table for the Philippines to determine the vector of changes in sectoral

gross output (ΔX).

37

The Household Sector, Wholesale and Retail Trade, Food Manufacturing,

Petroleum Refineries and Miscellaneous Production of Petrol and Coal, and

Electricity and Gas were the five sectors with the highest decreases in sectoral

gross output due to the simulated change in final demand. While the sectors of

Waterworks and Supply, Printing, Publishing and Allied Products, Communication,

Sugarcane Production, and Manufacturing of Other Non-metallic Mineral Products

were those with the lowest decreases in sectoral gross output.

Other than the changes in sectoral output due to changes in final demand,

the changes in impact variables were also computed using the impact coefficient

matrix used by Orbeta (1999). The change in final demand for fuel was found to

cause the highest percentage changes in the air pollutants SOx and NOx relative to

the other impact variables. The pollutants SOx and NOx had percentage changes

equal to -0.01480689 and -0.00963582, respectively. Also, the waste disposal

services for air had a higher percentage change compared to that of the waste

disposal services for water, but the percentage changes of air and water damages

are almost the same.

The environmental impact variable multipliers were calculated to determine

the effect on the impact variables of a peso increase in each sector’s final demand.

The Fishery sector has the highest depreciation multiplier equal to 0.0928. This

means that a peso increase in the final demand for products of the fishery sector

leads to a natural resource depreciation of about 0.09 pesos.

38

For the air pollutant PM, the Household Sector exhibits the highest residual

multiplier, while the sector of Electricity and Gas had the highest multiplier for the

pollutant SOx. On the other hand, Manufacturing of Cement and Non-metallic

Mining had the highest multiplier for NOx. For both the pollutants VOC and CO,

the Household Sector and the Public Administration and Defense generated the

highest residual multipliers. While for water damages, the sector of Other

Agricultural Crops had the highest multiplier with 0.02.

Among the sectors, the household has the highest air pollution damage

multiplier, equal to almost 0.01 which means that a peso increase in the final

demand is associated with 0.01 pesos in air pollution damages. With this, it can be

said that regulations aimed at reducing pollution levels should also focus on the

household sector since it can contribute significantly to pollution.

CHAPTER VII

RECOMMENDATION

Future studies on the effects of the Philippine Clean Air Act are

recommended to focus on possible mechanisms, other than fuel consumption,

through which the policy can affect the economy. Such mechanisms, whether

directly or indirectly, can cause changes in the output of the different sectors of the

economy, and can therefore be the basis for future research.

REFERENCES

Alcantara, V. and Padilla, E. (2006). An input-output analysis of the “key” sectors

in CO2 emissions from a production perspective: An application to the

Spanish economy. Retrieved from http://dep-economia-

aplicada.uab.cat/repec/doc/wpdea0601.pdf.

Ambec, S., Cohen, M.A., Elgie, S., and Lanoie, P. (2010). The Porter Hypothesis at

20: Can environmental regulation enhance innovation and competitiveness?

(Chairs’ Paper). Retrieved from

http://www.isc.hbs.edu/pdf/PorterHypothesis_at20_Montreal.pdf.

Asian Development Bank. (2006). Country synthesis report on urban air quality

management: Philippines (Discussion Draft) . Manila, Philippines.

Austin, D. (1999). Economic instruments fir pollution control and prevention: A

brief overview. Retrieved from http://pdf.wri.org/incentives_austin.pdf.

Becker, R.A. (2001). Air pollution abatement costs under the Clean Air Act:

Evidence from the PACE survey. Center for Economic Studies, US Bureau of

the Census.

British Columbia Environment Industry Association (2008). Forestry & Forest-Based

Product Manufacturers Fact Sheet. Retrieved from

http://www.hazwastebc.com/documents/bceia_009.pdf.

Burkander, P. (2008). Modeling pollution using input-output analysis. Retrieved

from http://people.emich.edu/aross15/project-

guides/sample_project_leontief_pollution.pdf.

Environmental Economics and Policy. (n.d.). Economic efficiency and the

environment. Retrieved from

http://web.uvic.ca/~ehutchin/531/reading/ch4.PDF.

Environmental Management Bureau. (2009). National air quality status report.

Quezon City, Philippines: Department of Environment and Natural

Resources.

Greenstone, M. (2002). The impacts of environmental regulations on industrial

activity: evidence from the 1970 and 1977 Clean Air Act Amendments and

the Census of Manufactures. J. Polit. Econ. 110:1175–219

Hirota, K. (2010). Comparative studies on vehicle related policies for air pollution

reduction in ten Asian countries. Sustainability, 2, 145-162.

41

Jaffe A.B., Peterson S.R., Portney P.R., and Stavins R.N. (1995). Environmental

regulation and the competitiveness of U.S. manufacturing: What does the

evidence tell us? J. Econ. Lit. 33:132–63.

Mendoza, M.N.F. (1996). Input-output modeling. In the Philippine Environmental

and Natural Resources Accounting Project (ENRAP Phase III): Technical

Appendices. DENR and USAID, Philippines.

Millimet, D.L., Santanu, R., and Sengupta, A. (2009). Environmental regulations

and economic activity: Influence on market structure . Retrieved from

http://people.smu.edu/asengupt/arre.pdf.

National Statistical Coordination Board (NSCB). (2010). Philippine Statistical Yearbook.

Makati City, Philippines.

National Statistics Office (NSO). (2009). Philippine Yearbook. Sta. Mesa, Manila,

Philippines.

Orbeta, E.M. (1999). Development of environmental impact multipliers in the

Philippines. Retrieved from http://web.idrc.ca/en/ev-64656-201-1-

DO_TOPIC.html.

Shukla, R. (2009). Pattern of domestic fuel consumption. Retrieved from

http://economictimes.indiatimes.com/opinion/view-point/pattern-of-domestic-

fuel-consumption/articleshow/5161846.cms.

Sneeringer, S. (2009). Effects of environmental regulation on economic activity and

pollution in commercial agriculture. Retrieved from

http://ageconsearch.umn.edu/bitstream/46591/2/SAEAPaperSneeringer.pdf.

Thaiprasert, N., and Hicks, M.J. (2011). The effect of higher fuel prices on Indiana’s

economy (Policy Brief). Center for Business and Economic Research. Ball

State University.

The Concise Encyclopedia of Economics. (2008). Wassily Leontief. Retrieved from

http://www.econlib.org/library/Enc/bios/Leontief.html.

The World Bank Group. (2012). Philippines. Retrieved from

http://data.worldbank.org/country/philippines.

APPENDICES

43

APPENDIX A.1

Correlation Matrix

DFCPC TRP RPI GDPPC PCAA

DFCPC 1.0000

TRP 0.7995* 1.0000

0.0000

RPI 0.4503* 0.1056 1.0000

0.0125 0.5785

GDPPC 0.6612* 0.3187 0.9512* 1.0000

0.0001 0.0861 0.0000

PCAA 0.5863* 0.2338 0.7908* 0.8719* 1.0000

0.0007 0.2137 0.0000 0.0000

APPENDIX A.2

ENRA-Modified IO Table, Philippines, 1990

(In ‘000 pesos)

44

(APPENDIX A.2 cont’d.)

45

APPENDIX A.3

Leontief Inverse Matrix, ENRA-Modified IO Table, Philippines, 1990

Sector 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

1 1.1627 0.0453 0.0300 0.0481 0.0394 0.1105 0.0394 0.0332 0.0520 0.0431 0.0368 0.2629 0.0943 0.0283 0.0531 0.0448 0.0378 0.0353 0.0431 0.0917 0.0271 0.0544

2 0.0296 1.1369 0.0205 0.0304 0.0249 0.0318 0.0268 0.0196 0.0363 0.0228 0.0208 0.0503 0.0250 0.0170 0.0271 0.0247 0.0240 0.0205 0.0240 0.0289 0.0154 0.0241

3 0.0092 0.0093 1.0060 0.0101 0.0083 0.0134 0.0080 0.0066 0.0105 0.0099 0.0081 0.0472 0.0176 0.0059 0.0122 0.0100 0.0218 0.0076 0.0095 0.0239 0.0060 0.0131

4 0.0050 0.0050 0.0033 1.0053 0.0044 0.0078 0.0044 0.0037 0.0058 0.0047 0.0040 0.0299 0.0133 0.0031 0.0058 0.0049 0.0042 0.0039 0.0047 0.0098 0.0030 0.0059

5 0.0163 0.0165 0.0110 0.0173 1.1260 0.0217 0.0177 0.0126 0.0189 0.0158 0.0158 0.0660 0.0261 0.1438 0.1054 0.0638 0.0237 0.0227 0.0206 0.0281 0.0114 0.1548

6 0.0482 0.0487 0.0328 0.0510 0.0416 1.2988 0.0430 0.0348 0.0573 0.0426 0.0374 0.2247 0.0843 0.0295 0.0519 0.0460 0.0402 0.0363 0.0436 0.0776 0.0276 0.0507

7 0.0535 0.0627 0.0642 0.0794 0.0250 0.0579 1.0102 0.0046 0.0100 0.0058 0.0051 0.0284 0.0110 0.0063 0.0086 0.0070 0.0069 0.0053 0.0061 0.0110 0.0038 0.0094

8 0.0312 0.0315 0.0217 0.0324 0.0265 0.0329 0.0286 1.1684 0.0381 0.0248 0.0234 0.0728 0.0324 0.0186 0.0295 0.0267 0.0259 0.0225 0.0262 0.0331 0.0173 0.0267

9 0.0065 0.0072 0.0049 0.0071 0.0060 0.0068 0.0072 0.0054 1.1058 0.0094 0.0094 0.0080 0.0058 0.0213 0.0085 0.0074 0.2386 0.0897 0.0404 0.0070 0.0066 0.0072

10 0.0073 0.0074 0.0048 0.0087 0.0087 0.0070 0.0088 0.0189 0.0119 1.0244 0.0271 0.0093 0.0189 0.0122 0.0122 0.0128 0.0228 0.0323 0.0201 0.0135 0.0184 0.0141

11 0.0346 0.0380 0.0268 0.0552 0.0372 0.0382 0.0447 0.0932 0.0751 0.0906 1.0587 0.0530 0.0562 0.0428 0.0885 0.0720 0.0845 0.0870 0.0715 0.1220 0.7001 0.0817

12 0.2158 0.2187 0.1452 0.2315 0.1894 0.3405 0.1910 0.1617 0.2525 0.2046 0.1757 1.3211 0.4707 0.1366 0.2515 0.2131 0.1825 0.1697 0.2060 0.4229 0.1296 0.2554

13 0.0176 0.0176 0.0123 0.0179 0.0145 0.0146 0.0160 0.0113 0.0219 0.0127 0.0117 0.0135 1.1098 0.0098 0.0150 0.0140 0.0141 0.0118 0.0136 0.0130 0.0087 0.0127

14 0.0119 0.0119 0.0083 0.0121 0.0098 0.0099 0.0108 0.0077 0.0148 0.0085 0.0078 0.0090 0.0063 1.2944 0.0100 0.0094 0.0095 0.0079 0.0091 0.0079 0.0058 0.0083

15 0.0339 0.0346 0.0231 0.0356 0.0331 0.0301 0.0306 0.0431 0.0414 0.0334 0.0387 0.0346 0.0236 0.0307 1.7821 0.9769 0.0863 0.0336 0.0421 0.0432 0.0275 0.3131

16 0.0155 0.0155 0.0109 0.0160 0.0131 0.0130 0.0143 0.0104 0.0194 0.0120 0.0109 0.0120 0.0087 0.0093 0.0169 1.0204 0.0188 0.0114 0.0128 0.0111 0.0081 0.0154

17 0.0075 0.0087 0.0069 0.0087 0.0082 0.0105 0.0108 0.0073 0.0111 0.0169 0.0137 0.0074 0.0057 0.0065 0.0087 0.0079 1.1634 0.0102 0.0090 0.0078 0.0094 0.0082

18 0.0120 0.0208 0.0083 0.0143 0.0116 0.0119 0.0113 0.0101 0.0153 0.0159 0.0228 0.0141 0.0212 0.3860 0.0306 0.0254 0.0207 1.8970 0.7796 0.0254 0.0165 0.0283

19 0.0040 0.0055 0.0028 0.0051 0.0036 0.0037 0.0038 0.0031 0.0053 0.0043 0.0038 0.0039 0.0051 0.0219 0.0100 0.0085 0.0058 0.0041 1.0235 0.0046 0.0028 0.0053

20 0.1505 0.1610 0.0709 0.2128 0.1794 0.1433 0.0999 0.0773 0.0986 0.3720 0.2524 0.1209 0.1057 0.1166 0.4950 0.3425 0.1540 0.1981 0.3073 1.5933 0.1835 0.6586

21 0.0407 0.0453 0.0346 0.0684 0.0432 0.0466 0.0589 0.1335 0.1045 0.1109 0.0673 0.0688 0.0658 0.0549 0.1019 0.0860 0.1145 0.1144 0.0861 0.0891 1.0559 0.0820

22 0.0144 0.0124 0.0083 0.0140 0.0131 0.0119 0.0104 0.0096 0.0145 0.0193 0.0196 0.0154 0.0121 0.0399 0.0182 0.0161 0.0178 0.0165 0.0242 0.0265 0.0138 1.1059

23 0.0049 0.0052 0.0027 0.0063 0.0054 0.0048 0.0037 0.0030 0.0042 0.0098 0.0069 0.0059 0.0433 0.0052 0.0127 0.0093 0.0057 0.0062 0.0084 0.0366 0.0050 0.0161

24 0.0008 0.0009 0.0006 0.0009 0.0008 0.0008 0.0008 0.0008 0.0011 0.0029 0.0025 0.0010 0.0009 0.0011 0.0012 0.0011 0.0016 0.0019 0.0015 0.0015 0.0017 0.0014

25 0.0015 0.0015 0.0010 0.0017 0.0014 0.0014 0.0015 0.0014 0.0018 0.0028 0.0044 0.0019 0.0018 0.0021 0.0023 0.0020 0.0039 0.0056 0.0035 0.0038 0.0030 0.0024

26 0.0191 0.0193 0.0123 0.0227 0.0220 0.0184 0.0233 0.0512 0.0317 0.0671 0.0770 0.0249 0.0528 0.0323 0.0308 0.0296 0.0594 0.0862 0.0536 0.0368 0.0521 0.0379

27 0.0179 0.0166 0.0106 0.0199 0.0191 0.0154 0.0179 0.0710 0.0340 0.0558 0.0767 0.0242 0.0635 0.0216 0.0242 0.0218 0.0592 0.0529 0.0342 0.0309 0.0516 0.0234

28 0.0153 0.0163 0.0109 0.0174 0.0162 0.0165 0.0204 0.0173 0.0249 0.0523 0.0317 0.0158 0.0158 0.0173 0.0259 0.0223 0.0377 0.0247 0.0256 0.0234 0.0219 0.0247

29 0.0238 0.0255 0.0167 0.0287 0.0364 0.0232 0.0267 0.0467 0.0340 0.0556 0.0366 0.0248 0.0283 0.0385 0.0535 0.0809 0.0813 0.1037 0.0640 0.0299 0.0258 0.0383

30 0.0214 0.0227 0.0158 0.0248 0.0208 0.0278 0.0438 0.0396 0.0315 0.0453 0.0274 0.0353 0.0353 0.0345 0.0812 0.0631 0.0419 0.0826 0.0579 0.0406 0.0201 0.0633

31 0.0027 0.0029 0.0025 0.0057 0.0024 0.0027 0.0203 0.0017 0.0024 0.0017 0.0022 0.0022 0.0044 0.0018 0.0020 0.0018 0.0021 0.0018 0.0020 0.0019 0.0015 0.0019

32 0.0047 0.0046 0.0031 0.0050 0.0042 0.0043 0.0047 0.0045 0.0057 0.0090 0.0092 0.0045 0.0042 0.0057 0.0061 0.0055 0.0068 0.0066 0.0066 0.0056 0.0064 0.0053

33 0.0589 0.0641 0.0409 0.0781 0.0623 0.0587 0.0585 0.0519 0.0831 0.0754 0.0781 0.0599 0.0540 0.0635 0.0745 0.0697 0.1102 0.0750 0.0783 0.0692 0.0567 0.0668

34 0.0892 0.1035 0.0608 0.1098 0.0884 0.1153 0.0831 0.0853 0.1245 0.1364 0.1082 0.1151 0.1113 0.1666 0.1557 0.1382 0.1775 0.2049 0.1997 0.1470 0.0786 0.1499

35 0.0047 0.0053 0.0035 0.0054 0.0049 0.0049 0.0048 0.0037 0.0060 0.0047 0.0054 0.0045 0.0044 0.0052 0.0058 0.0056 0.0068 0.0056 0.0067 0.0051 0.0039 0.0053

36 0.0822 0.0833 0.0564 0.0917 0.0744 0.0765 0.0853 0.0673 0.1027 0.0921 0.0954 0.0777 0.0703 0.1014 0.1065 0.0983 0.1159 0.1015 0.1046 0.0942 0.0682 0.0911

37 0.0001 0.0001 0.0000 0.0001 0.0000 0.0000 0.0001 0.0000 0.0001 0.0001 0.0001 0.0000 0.0000 0.0000 0.0001 0.0001 0.0001 0.0000 0.0001 0.0000 0.0000 0.0000

38 0.0085 0.0085 0.0060 0.0086 0.0070 0.0071 0.0078 0.0055 0.0106 0.0065 0.0059 0.0065 0.0052 0.0048 0.0073 0.0068 0.0069 0.0058 0.0067 0.0060 0.0044 0.0061

39 0.0083 0.0085 0.0068 0.0179 0.0124 0.0073 0.0076 0.0056 0.0107 0.0071 0.0068 0.0073 0.0084 0.0060 0.0082 0.0074 0.0078 0.0066 0.0079 0.0071 0.0050 0.0076

40 0.0448 0.0517 0.0338 0.0512 0.0449 0.0444 0.0523 0.0317 0.0541 0.0416 0.0715 0.0429 0.0443 0.0399 0.0473 0.0426 0.0472 0.0410 0.0478 0.0439 0.0498 0.0430

HH 0.4582 0.4581 0.3211 0.4639 0.3767 0.3798 0.4171 0.2957 0.5713 0.3257 0.2996 0.3472 0.2441 0.2533 0.3832 0.3598 0.3665 0.3041 0.3508 0.3040 0.2234 0.3189

46

(APPENDIX A.3 cont’d.)

Sector 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 HH

1 0.0319 0.0315 0.0328 0.0348 0.0343 0.0326 0.0356 0.0263 0.0280 0.0374 0.0380 0.0315 0.0192 0.0212 0.0864 0.0835 0.0771 0.0649 0.1187

2 0.0182 0.0183 0.0201 0.0198 0.0204 0.0179 0.0214 0.0172 0.0169 0.0239 0.0229 0.0205 0.0125 0.0137 0.0590 0.0578 0.0395 0.0360 0.0845

3 0.0070 0.0068 0.0070 0.0077 0.0075 0.0073 0.0079 0.0055 0.0060 0.0085 0.0079 0.0064 0.0040 0.0044 0.0173 0.0167 0.0160 0.0130 0.0234

4 0.0035 0.0035 0.0036 0.0038 0.0038 0.0036 0.0039 0.0029 0.0031 0.0041 0.0042 0.0035 0.0021 0.0024 0.0096 0.0093 0.0085 0.0072 0.0132

5 0.0126 0.0137 0.0142 0.0132 0.0139 0.0164 0.0188 0.0103 0.0100 0.0173 0.0216 0.0141 0.0087 0.0087 0.0323 0.0308 0.0265 0.0243 0.0422

6 0.0325 0.0324 0.0344 0.0354 0.0355 0.0328 0.0370 0.0282 0.0291 0.0398 0.0401 0.0340 0.0206 0.0227 0.0947 0.0918 0.0806 0.0674 0.1319

7 0.0044 0.0044 0.0047 0.0048 0.0048 0.0045 0.0051 0.0038 0.0039 0.0055 0.0055 0.0046 0.0028 0.0031 0.0126 0.0122 0.0104 0.0089 0.0175

8 0.0200 0.0208 0.0223 0.0215 0.0224 0.0200 0.0257 0.0185 0.0181 0.0257 0.0264 0.0227 0.0137 0.0155 0.0646 0.0610 0.0515 0.0683 0.0882

9 0.0070 0.0169 0.0161 0.0076 0.0074 0.0069 0.0083 0.0196 0.0087 0.0448 0.0068 0.0072 0.0041 0.0067 0.0146 0.0148 0.0089 0.0087 0.0180

10 0.0570 0.0221 0.0415 0.4918 0.2209 0.0806 0.1012 0.0142 0.0748 0.0690 0.0231 0.0162 0.0157 0.0112 0.0207 0.0176 0.0158 0.0206 0.0150

11 0.2691 0.3143 0.2270 0.1207 0.0848 0.0625 0.0626 0.2177 0.0532 0.1088 0.1726 0.0429 0.0286 0.0326 0.0815 0.0703 0.0689 0.0604 0.0716

12 0.1526 0.1508 0.1576 0.1663 0.1646 0.1556 0.1707 0.1271 0.1343 0.1804 0.1835 0.1530 0.0928 0.1024 0.4192 0.4052 0.3726 0.3152 0.5769

13 0.0104 0.1508 0.0116 0.0113 0.0118 0.0101 0.0124 0.0101 0.0098 0.0140 0.0131 0.0121 0.0073 0.0079 0.0349 0.0347 0.0205 0.0144 0.0512

14 0.0070 0.0069 0.0078 0.0075 0.0079 0.0067 0.0084 0.0068 0.0066 0.0095 0.0088 0.0082 0.0050 0.0054 0.0237 0.0236 0.0137 0.0094 0.0348

15 0.0318 0.0317 0.0388 0.0300 0.0373 0.0515 0.1163 0.0254 0.0215 0.0407 0.0529 0.0332 0.0219 0.0233 0.0765 0.0733 0.0645 0.0401 0.0910

16 0.0102 0.0100 0.0117 0.0108 0.0126 0.0274 0.0206 0.0098 0.0091 0.0139 0.0136 0.0121 0.0087 0.0106 0.0334 0.0357 0.0366 0.0142 0.0449

17 0.0086 0.0088 0.0091 0.0126 0.0120 0.0103 0.0154 0.0102 0.0062 0.0682 0.0089 0.0129 0.0047 0.0107 0.0162 0.0150 0.0102 0.0109 0.0201

18 0.0296 0.2383 0.0743 0.0150 0.0217 0.0310 0.0268 0.0137 0.0117 0.0344 0.0195 0.0232 0.0209 0.0220 0.0476 0.0593 0.0215 0.0193 0.0305

19 0.0037 0.0182 0.0084 0.0040 0.0057 0.0099 0.0049 0.0050 0.0040 0.0056 0.0068 0.0053 0.0102 0.0094 0.0308 0.0223 0.0067 0.0068 0.0110

20 0.2182 0.1879 0.1613 0.2410 0.1979 0.2486 0.1956 0.0925 0.1575 0.1458 0.1476 0.0795 0.0669 0.0714 0.1834 0.1737 0.3823 0.2079 0.1650

21 0.1405 0.3240 0.1468 0.1108 0.0855 0.0672 0.0729 0.3140 0.0607 0.1058 0.2501 0.0567 0.0363 0.0401 0.1061 0.0902 0.0778 0.0738 0.0913

22 0.0148 0.0161 0.0215 0.0167 0.0195 0.0414 0.0251 0.0108 0.0090 0.0355 0.0703 0.0287 0.0178 0.0110 0.0280 0.0240 0.0196 0.0187 0.0264

23 1.1194 0.0056 0.0186 0.0068 0.0067 0.0105 0.0109 0.0045 0.0046 0.0135 0.0055 0.0035 0.0027 0.0030 0.0075 0.0071 0.0108 0.0070 0.0080

24 0.0025 1.0061 0.2207 0.0026 0.0022 0.0029 0.0017 0.0012 0.0020 0.0679 0.0015 0.0016 0.0010 0.0055 0.0028 0.0018 0.0013 0.0012 0.0020

25 0.0078 0.0099 1.0384 0.0037 0.0039 0.0098 0.0028 0.0020 0.0053 0.0642 0.0024 0.0020 0.0020 0.0064 0.0035 0.0031 0.0024 0.0021 0.0035

26 0.1512 0.0617 0.1128 1.4932 0.6401 0.2367 0.1556 0.0373 0.2249 0.2026 0.0561 0.0447 0.0422 0.0284 0.0512 0.0435 0.0369 0.0469 0.0383

27 0.0379 0.0493 0.0426 0.0422 1.0867 0.0865 0.0615 0.0331 0.0199 0.1166 0.0522 0.0233 0.0280 0.0192 0.0436 0.0381 0.0295 0.0332 0.0312

28 0.0355 0.0421 0.0398 0.0379 0.0743 1.7121 0.0584 0.0545 0.0185 0.0721 0.0485 0.0196 0.1736 0.0181 0.0527 0.0334 0.0251 0.0395 0.0385

29 0.1046 0.0415 0.0870 0.0586 0.1449 0.0589 1.4103 0.0475 0.0245 0.0494 0.1215 0.0377 0.0457 0.0462 0.0953 0.0828 0.0970 0.0848 0.0584

30 0.0551 0.0923 0.0515 0.0516 0.0451 0.0453 0.0425 1.0284 0.0565 0.0348 0.0310 0.0377 0.0225 0.0232 0.0507 0.0590 0.0497 0.0548 0.0495

31 0.0015 0.0019 0.0019 0.0018 0.0018 0.0018 0.0018 0.0014 1.0013 0.0023 0.0058 0.0039 0.0039 0.0035 0.0072 0.0085 0.0048 0.0075 0.0048

32 0.0056 0.0081 0.0072 0.0075 0.0067 0.0061 0.0062 0.0067 0.0073 1.0064 0.0091 0.0085 0.0062 0.0523 0.0136 0.0112 0.0072 0.0076 0.0111

33 0.0563 0.0750 0.0857 0.0760 0.0791 0.0817 0.0797 0.0466 0.0419 0.0989 1.1325 0.1089 0.0620 0.0538 0.1266 0.1194 0.0815 0.0744 0.1369

34 0.1148 0.1626 0.1561 0.1939 0.1957 0.1996 0.1813 0.1145 0.0766 0.1567 0.1496 1.0760 0.0783 0.0590 0.1985 0.1737 0.1647 0.1185 0.2108

35 0.0045 0.0051 0.0061 0.0052 0.0059 0.0063 0.0060 0.0039 0.0036 0.0064 0.0105 0.0126 1.0291 0.0081 0.0118 0.0113 0.0078 0.0084 0.0122

36 0.0770 0.1200 0.1129 0.0941 0.0989 0.1031 0.1046 0.0855 0.1377 0.1099 0.1610 0.1545 0.1018 1.1111 0.2522 0.1873 0.1221 0.1195 0.2079

37 0.0000 0.0001 0.0001 0.0001 0.0000 0.0000 0.0001 0.0000 0.0000 0.0031 0.0001 0.0001 0.0000 0.0002 1.0041 0.0001 0.0001 0.0001 0.0002

38 0.0052 0.0051 0.0061 0.0057 0.0059 0.0052 0.0061 0.0050 0.0050 0.0069 0.0064 0.0059 0.0036 0.0040 0.0184 1.0174 0.0099 0.0068 0.0248

39 0.0057 0.0060 0.0087 0.0064 0.0066 0.0079 0.0068 0.0057 0.0062 0.0080 0.0082 0.0090 0.0043 0.0061 0.0442 0.0223 1.0120 0.0073 0.0236

40 0.0386 0.0675 0.0570 0.0394 0.0432 0.0405 0.0407 0.0368 0.0062 0.0463 0.0843 0.0461 0.0312 0.0526 0.1649 0.0933 0.0621 1.0918 0.1163

HH 0.2679 0.2660 0.2994 0.2900 0.3045 0.2581 0.3215 0.2634 0.2540 0.3646 0.3381 0.3149 0.1905 0.2061 0.9107 0.9068 0.5275 0.3606 1.3387

47

APPENDIX A.4

Matrix of Impact Coefficients

Impact Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

NR (Physical)

Agriculture (5.2285) (0.1189) (5.9500) (0.9273) (0.4185) 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Grassland 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 (0.0846) 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Woodland 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 (1.4629) 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Small Pelagics 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 (0.0004) 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Dipterocarps 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 (0.1097) 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Plantation 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.3997 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Mangroves 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 (0.0049) 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Pine 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 (0.0064) 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Rattan 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 (11.2908) 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Copper 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 (0.0095) 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Gold 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 (0.0236) 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Residuals

PM 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0025 0.0037 0.0004 0.0001 0.0000 0.0001 0.0000 0.0005 0.0026 0.0001 0.0003 0.0000

SOx 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0001 0.0009 0.0007 0.0001 0.0003 0.0001 0.0003 0.0000 0.0002 0.0010 0.0001 0.0002 0.0000

NOx 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0001 0.0003 0.0017 0.0001 0.0002 0.0001 0.0001 0.0000 0.0005 0.0003 0.0001 0.0001 0.0001

VOC 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0002 0.0015 0.0001 0.0001 0.0000 0.0001 0.0000 0.0005 0.0002 0.0001 0.0001 0.0000

CO 0.0000 0.0000 0.0000 0.0002 0.0001 0.0000 0.0001 0.0001 0.0008 0.0011 0.0091 0.0004 0.0007 0.0003 0.0005 0.0002 0.0028 0.0017 0.0008 0.0005 0.0007

BOD5 0.0088 0.0002 0.0100 0.0016 0.0007 0.0084 0.0000 0.0000 0.0847 0.0000 0.0000 0.0002 0.0020 0.0000 0.0007 0.0000 0.0000 0.0010 0.0000 0.0001 0.0000

SS 1.7488 0.0398 1.9902 0.3102 0.1400 0.0603 0.0000 0.0000 16.8156 2.2985 0.0266 0.0002 0.0021 0.0000 0.0003 0.0001 0.0000 0.0014 0.0000 0.0000 0.0000

TDS 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0039 0.0027 0.0000 0.0008 0.0002 0.0000 0.0049 0.0000 0.0000 0.0000

OIL 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

N 0.0068 0.0002 0.0077 0.0012 0.0005 0.0027 0.0000 0.0000 0.0652 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

P 0.0001 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0010 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Labor Income

CE 0.2751 0.2685 0.2109 0.2817 0.2143 0.1560 0.2806 0.1452 0.3620 0.1439 0.1388 0.0655 0.0498 0.0515 0.0911 0.0859 0.0810 0.0480 0.1114 0.0649 0.0160

Environmental Variables

NR Depn (0.0061) (0.0001) (0.0069) (0.0011) (0.0005) 0.0000 0.0000 (0.0789) (0.0375) (0.0082) 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

EWDS (Air) 0.0000 (0.0001) (0.0002) (0.0011) (0.0003) (0.0001) (0.0003) (0.0013) (0.0037) (0.0049) (0.0429) (0.0026) (0.0032) (0.0014) (0.0026) (0.0010) (0.0150) (0.0052) (0.0041) (0.0026) (0.0003)

EWDS (Water) (0.0429) (0.0010) (0.0489) (0.0076) (0.0034) (0.0039) 0.0000 0.0000 (0.4133) (0.2367) (0.0030) (0.0003) (0.0022) 0.0000 (0.0011) (0.0002) (0.0001) (0.0022) 0.0000 (0.0003) (0.0001)

Air Damages 0.0000 0.0000 0.0000 0.0000 (0.0001) 0.0000 0.0000 0.0000 (0.0001) (0.0022) (0.0032) (0.0004) (0.0001) 0.0000 (0.0001) 0.0000 (0.0004) (0.0022) (0.0001) (0.0003) 0.0000

Water Damages (0.0016) 0.0000 (0.0018) (0.0003) (0.0188) (0.0015) 0.0000 0.0000 (0.0153) 0.0000 0.0000 0.0000 (0.0004) 0.0000 (0.0001) 0.0000 0.0000 (0.0002) 0.0000 0.0000 0.0000

48

(APPENDIX A.4 cont’d.)

Impact Variable 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 HH

NR (Physical)

Agriculture 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Grassland 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Woodland 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Small Pelagics 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Dipterocarps 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Plantation 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Mangroves 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Pine 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Rattan 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Copper 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Gold 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Residuals

PM 0.0001 0.0004 0.0008 0.0003 0.0021 0.0001 0.0000 0.0000 0.0004 0.0000 0.0007 0.0000 0.0002 0.0029 0.0000 0.0001 0.0000 0.0000 0.0000 0.0047

SOx 0.0001 0.0003 0.0022 0.0002 0.0003 0.0001 0.0000 0.0000 0.0060 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001

NOx 0.0001 0.0007 0.0014 0.0003 0.0001 0.0001 0.0000 0.0000 0.0010 0.0000 0.0001 0.0000 0.0002 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000 0.0004

VOC 0.0000 0.0003 0.0002 0.0002 0.0001 0.0004 0.0000 0.0000 0.0000 0.0000 0.0001 0.0001 0.0003 0.0000 0.0000 0.0001 0.0000 0.0005 0.0000 0.0104

CO 0.0003 0.0014 0.0012 0.0014 0.0005 0.0007 0.0002 0.0001 0.0001 0.0000 0.0004 0.0001 0.0007 0.0001 0.0001 0.0004 0.0000 0.0000 0.0000 0.0319

BOD5 0.0000 0.0000 0.0000 0.0000 0.0000 0.0003 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0004 0.0134 0.0116

SS 0.0000 0.0001 0.0003 0.0000 0.0001 0.0001 0.0003 0.0000 0.0050 0.0000 0.0000 0.0004 0.0000 0.0000 0.0000 0.0127 0.0000 0.0002 0.0006 0.0052

TDS 0.0000 0.0007 0.0022 0.0000 0.0000 0.0003 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

OIL 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0006 0.0000 0.0000 0.0000 0.0000

N 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0003 0.0009

P 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0004

Labor Income

CE 0.0547 0.0747 0.0469 0.0865 0.0330 0.0741 0.0402 0.0954 0.1129 0.1194 0.1442 0.1284 0.1785 0.0851 0.0980 0.5928 0.6303 0.2875 0.1543 0.0000

Environmental Variables

NR Depn 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

EWDS (Air) (0.0013) (0.0065) (0.0057) (0.0072) (0.0028) (0.0036) (0.0012) (0.0006) (0.0007) 0.0000 (0.0021) (0.0006) (0.0029) (0.0003) (0.0002) (0.0022) (0.0001) (0.0002) (0.0003) (0.0085)

EWDS (Water) 0.0000 (0.0010) (0.0009) 0.0000 (0.0003) 0.0000 (0.0002) 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 (0.0393) 0.0000 0.0000 0.0000 (0.0357)

Air Damages 0.0000 (0.0003) (0.0007) (0.0003) (0.0018) (0.0001) (0.0000) (0.0000) (0.0004) 0.0000 (0.0006) (0.0000) (0.0002) (0.0025) (0.0000) (0.0001) 0.0000 (0.0000) 0.0000 (0.0040)

Water Damages 0.0000 0.0000 0.0000 0.0000 0.0000 (0.0001) 0.0000 0.0000 0.0000 0.0000 0.0000 (0.0000) 0.0000 0.0000 0.0000 0.0000 0.0000 (0.0001) (0.0024) (0.0021)

49

APPENDIX A.5

Environmental Impact Variable Multipliers (Δv) Obtained Using the ENRA-Modified A Matrix

Impact

Variable

One Peso Increase in Final Demand from Sector

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

NR (Physical)

Agriculture (6.1489) (0.4389) (6.1527) (1.2547) (0.7337) (0.6776) (0.2683) (0.2239) (0.3520) (0.2979) (0.2534) (1.7167) (0.6240) (0.2481) (0.4029) (0.3279) (0.3440) (0.2453) (0.2977) (0.6459) (0.1868)

Grassland (0.0005) (0.0006) (0.0004) (0.0006) (0.0005) (0.0006) (0.0006) (0.0005) (0.0936) (0.0008) (0.0008) (0.0007) (0.0005) (0.0018) (0.0007) (0.0006) (0.0202) (0.0076) (0.0034) (0.0006) (0.0006)

Woodland (0.0095) (0.0105) (0.0072) (0.0104) (0.0088) (0.0099) (0.0105) (0.0079) (1.6177) (0.0138) (0.0138) (0.0117) (0.0085) (0.0312) (0.0124) (0.0108) (0.3490) (0.1312) (0.0591) (0.0102) (0.0097)

Small Pelagics (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0005) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)

Dipterocarps (0.0007) (0.0008) (0.0005) (0.0008) (0.0007) (0.0007) (0.0008) (0.0006) (0.1213) (0.0010) (0.0010) (0.0009) (0.0006) (0.0023) (0.0009) (0.0008) (0.0262) (0.0098) (0.0044) (0.0008) (0.0007)

Plantation 0.0026 0.0029 0.0020 0.0028 0.0024 0.0027 0.0029 0.0022 0.4420 0.0038 0.0038 0.0032 0.0023 0.0085 0.0034 0.0030 0.0954 0.0359 0.0161 0.0028 0.0026

Mangroves (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0054) (0.0000) (0.0000) (0.0000) (0.0000) (0.0001) (0.0000) (0.0000) (0.0012) (0.0004) (0.0002) (0.0000) (0.0000)

Pine (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0071) (0.0001) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0015) (0.0006) (0.0003) (0.0000) (0.0000)

Rattan (0.0734) (0.0813) (0.0553) (0.0802) (0.0677) (0.0768) (0.0813) (0.0610) (12.4854) (0.1061) (0.1061) (0.0903) (0.0655) (0.2405) (0.0960) (0.0836) (2.6940) (1.0128) (0.4561) (0.0790) (0.0745)

Copper (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0001) (0.0001) (0.0002) (0.0001) (0.0097) (0.0003) (0.0001) (0.0002) (0.0001) (0.0001) (0.0001) (0.0002) (0.0003) (0.0002) (0.0001) (0.0002)

Gold (0.0002) (0.0002) (0.0001) (0.0002) (0.0002) (0.0002) (0.0002) (0.0004) (0.0003) (0.0242) (0.0006) (0.0002) (0.0004) (0.0003) (0.0003) (0.0003) (0.0005) (0.0008) (0.0005) (0.0003) (0.0004)

Residuals

PM 0.0026 0.0026 0.0018 0.0027 0.0022 0.0023 0.0024 0.0021 0.0034 0.0049 0.0058 0.0026 0.0020 0.0026 0.0028 0.0025 0.0031 0.0072 0.0045 0.0028 0.0040

SOx 0.0003 0.0003 0.0002 0.0003 0.0003 0.0003 0.0004 0.0005 0.0005 0.0015 0.0011 0.0005 0.0008 0.0009 0.0013 0.0009 0.0008 0.0026 0.0015 0.0008 0.0008

NOx 0.0003 0.0004 0.0002 0.0004 0.0003 0.0003 0.0004 0.0005 0.0006 0.0008 0.0021 0.0005 0.0006 0.0005 0.0007 0.0005 0.0011 0.0011 0.0008 0.0007 0.0015

VOC 0.0049 0.0049 0.0034 0.0050 0.0041 0.0041 0.0045 0.0033 0.0063 0.0039 0.0048 0.0039 0.0029 0.0029 0.0045 0.0041 0.0047 0.0038 0.0042 0.0036 0.0035

CO 0.0153 0.0154 0.0108 0.0160 0.0129 0.0130 0.0142 0.0109 0.0203 0.0130 0.0198 0.0125 0.0097 0.0100 0.0147 0.0134 0.0165 0.0144 0.0147 0.0122 0.0146

BOD5 0.0173 0.0079 0.0153 0.0094 0.0072 0.0178 0.0071 0.0051 0.1022 0.0061 0.0061 0.0104 0.0081 0.0064 0.0083 0.0071 0.0262 0.0144 0.0099 0.0068 0.0045

SS 2.1892 0.2916 2.1560 0.5664 0.3720 0.4387 0.2374 0.2156 18.7486 2.6194 0.3374 0.7474 0.3602 0.4743 0.3144 0.2710 4.1867 1.6740 0.8330 0.3747 0.2374

TDS 0.0010 0.0010 0.0007 0.0011 0.0009 0.0015 0.0009 0.0008 0.0012 0.0010 0.0009 0.0053 0.0050 0.0025 0.0026 0.0020 0.0010 0.0100 0.0047 0.0019 0.0007

OIL 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

N 0.0090 0.0017 0.0087 0.0027 0.0018 0.0052 0.0013 0.0010 0.0733 0.0014 0.0013 0.0037 0.0017 0.0020 0.0016 0.0014 0.0165 0.0066 0.0035 0.0018 0.0010

P 0.0003 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0001 0.0013 0.0001 0.0001 0.0002 0.0001 0.0001 0.0002 0.0001 0.0004 0.0002 0.0002 0.0001 0.0001

Environmental Variables

NR Depn (0.0099) (0.0033) (0.0091) (0.0044) (0.0033) (0.0037) (0.0029) (0.0928) (0.0450) (0.0111) (0.0027) (0.0081) (0.0037) (0.0027) (0.0032) (0.0029) (0.0116) (0.0057) (0.0041) (0.0037) (0.0020)

EWDS (Air) (0.0074) (0.0078) (0.0054) (0.0098) (0.0072) (0.0074) (0.0076) (0.0100) (0.0145) (0.0149) (0.0509) (0.0103) (0.0112) (0.0099) (0.0152) (0.0126) (0.0278) (0.0194) (0.0175) (0.0148) (0.0343)

EWDS (Water) (0.0718) (0.0253) (0.0655) (0.0325) (0.0245) (0.0290) (0.0227) (0.0197) (0.4838) (0.2612) (0.0267) (0.0336) (0.0240) (0.0240) (0.0262) (0.0236) (0.1208) (0.0624) (0.0388) (0.0237) (0.0191)

Air Damages (0.0022) (0.0023) (0.0016) (0.0024) (0.0020) (0.0020) (0.0021) (0.0018) (0.0030) (0.0043) (0.0051) (0.0023) (0.0018) (0.0023) (0.0025) (0.0022) (0.0027) (0.0061) (0.0039) (0.0025) (0.0035)

Water Damages (0.0034) (0.0017) (0.0030) (0.0020) (0.0223) (0.0036) (0.0016) (0.0011) (0.0188) (0.0014) (0.0014) (0.0031) (0.0020) (0.0038) (0.0034) (0.0024) (0.0052) (0.0031) (0.0022) (0.0017) (0.0010)

50

(APPENDIX A.5 cont’d.)

Impact

Variable

One Peso Increase in Final Demand from Sector

22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 HH

NR (Physical)

Agriculture (0.4355) (0.2191) (0.2163) (0.2248) (0.2392) (0.2357) (0.2262) (0.2472) (0.1793) (0.1912) (0.2600) (0.2613) (0.2144) (0.1313) (0.1445) (0.5841) (0.5643) (0.5220) (0.4378) (0.7998)

Grassland (0.0006) (0.0006) (0.0014) (0.0014) (0.0006) (0.0006) (0.0006) (0.0007) (0.0017) (0.0007) (0.0038) (0.0006) (0.0006) (0.0003) (0.0006) (0.0012) (0.0013) (0.0008) (0.0007) (0.0015)

Woodland (0.0105) (0.0102) (0.0247) (0.0236) (0.0111) (0.0108) (0.0101) (0.0121) (0.0287) (0.0127) (0.0655) (0.0099) (0.0105) (0.0060) (0.0098) (0.0214) (0.0217) (0.0130) (0.0127) (0.0263)

Small Pelagics (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)

Dipterocarps (0.0008) (0.0008) (0.0019) (0.0018) (0.0008) (0.0008) (0.0008) (0.0009) (0.0022) (0.0010) (0.0049) (0.0007) (0.0008) (0.0004) (0.0007) (0.0016) (0.0016) (0.0010) (0.0010) (0.0020)

Plantation 0.0029 0.0028 0.0068 0.0064 0.0030 0.0030 0.0028 0.0033 0.0078 0.0035 0.0179 0.0027 0.0029 0.0016 0.0027 0.0058 0.0059 0.0036 0.0035 0.0072

Mangroves (0.0000) (0.0000) (0.0001) (0.0001) (0.0000) (0.0000) (0.0000) (0.0000) (0.0001) (0.0000) (0.0002) (0.0000) (0.0000) (0.0000) (0.0000) (0.0001) (0.0001) (0.0000) (0.0000) (0.0001)

Pine (0.0000) (0.0000) (0.0001) (0.0001) (0.0000) (0.0000) (0.0000) (0.0001) (0.0001) (0.0001) (0.0003) (0.0000) (0.0000) (0.0000) (0.0000) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001)

Rattan (0.0813) (0.0790) (0.1908) (0.1818) (0.0858) (0.0836) (0.0779) (0.0937) (0.2213) (0.0982) (0.5058) (0.0768) (0.0813) (0.0463) (0.0756) (0.1648) (0.1671) (0.1005) (0.0982) (0.2032)

Copper (0.0001) (0.0005) (0.0002) (0.0004) (0.0047) (0.0021) (0.0008) (0.0010) (0.0001) (0.0007) (0.0007) (0.0002) (0.0002) (0.0001) (0.0001) (0.0002) (0.0002) (0.0002) (0.0002) (0.0001)

Gold (0.0003) (0.0013) (0.0005) (0.0010) (0.0116) (0.0052) (0.0019) (0.0024) (0.0003) (0.0018) (0.0016) (0.0005) (0.0004) (0.0004) (0.0003) (0.0005) (0.0004) (0.0004) (0.0005) (0.0004)

Residuals

PM 0.0025 0.0034 0.0042 0.0035 0.0065 0.0041 0.0025 0.0027 0.0028 0.0023 0.0038 0.0027 0.0022 0.0043 0.0014 0.0053 0.0052 0.0033 0.0024 0.0072

SOx 0.0009 0.0011 0.0034 0.0014 0.0014 0.0009 0.0007 0.0006 0.0065 0.0006 0.0008 0.0005 0.0005 0.0003 0.0003 0.0007 0.0007 0.0006 0.0006 0.0007

NOx 0.0006 0.0015 0.0023 0.0013 0.0008 0.0007 0.0005 0.0005 0.0016 0.0004 0.0007 0.0006 0.0005 0.0002 0.0002 0.0008 0.0007 0.0005 0.0004 0.0009

VOC 0.0036 0.0036 0.0036 0.0039 0.0036 0.0039 0.0030 0.0036 0.0032 0.0028 0.0043 0.0040 0.0037 0.0021 0.0023 0.0099 0.0097 0.0062 0.0040 0.0142

CO 0.0122 0.0133 0.0138 0.0142 0.0123 0.0124 0.0100 0.0118 0.0111 0.0091 0.0142 0.0132 0.0116 0.0068 0.0073 0.0311 0.0304 0.0183 0.0127 0.0443

BOD5 0.0064 0.0050 0.0067 0.0065 0.0054 0.0059 0.0049 0.0059 0.0058 0.0044 0.0096 0.0066 0.0056 0.0035 0.0042 0.0161 0.0150 0.0099 0.0210 0.0214

SS 0.3066 0.3330 0.4206 0.4516 1.3455 0.7176 0.3828 0.4609 0.4363 0.3871 1.0066 0.2644 0.2352 0.1522 0.1903 0.5145 0.4909 0.3707 0.3487 0.6222

TDS 0.0014 0.0015 0.0044 0.0015 0.0008 0.0012 0.0010 0.0010 0.0007 0.0006 0.0011 0.0009 0.0008 0.0005 0.0006 0.0021 0.0021 0.0017 0.0014 0.0026

OIL 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0006 0.0000 0.0000 0.0000 0.0000

N 0.0015 0.0011 0.0017 0.0017 0.0012 0.0012 0.0011 0.0013 0.0018 0.0011 0.0037 0.0012 0.0011 0.0007 0.0009 0.0030 0.0028 0.0020 0.0020 0.0038

P 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0002 0.0001 0.0001 0.0001 0.0001 0.0004 0.0004 0.0002 0.0002 0.0005

Environmental Variables

NR Depn (0.0030) (0.0026) (0.0027) (0.0030) (0.0063) (0.0041) (0.0028) (0.0035) (0.0025) (0.0026) (0.0046) (0.0028) (0.0024) (0.0015) (0.0017) (0.0065) (0.0062) (0.0051) (0.0064) (0.0087)

EWDS (Air) (0.0124) (0.0241) (0.0259) (0.0242) (0.0169) (0.0155) (0.0110) (0.0101) (0.0143) (0.0071) (0.0154) (0.0137) (0.0094) (0.0049) (0.0050) (0.0177) (0.0148) (0.0115) (0.0090) (0.0190)

EWDS (Water) (0.0225) (0.0301) (0.0265) (0.0304) (0.1331) (0.0690) (0.0340) (0.0416) (0.0233) (0.0325) (0.0510) (0.0235) (0.0203) (0.0136) (0.0143) (0.0888) (0.0484) (0.0315) (0.0257) (0.0666)

Air Damages (0.0021) (0.0030) (0.0037) (0.0030) (0.0056) (0.0035) (0.0022) (0.0023) (0.0024) (0.0020) (0.0033) (0.0023) (0.0019) (0.0037) (0.0012) (0.0046) (0.0045) (0.0029) (0.0021) (0.0062)

Water Damages (0.0040) (0.0011) (0.0015) (0.0014) (0.0012) (0.0013) (0.0012) (0.0014) (0.0012) (0.0010) (0.0020) (0.0016) (0.0013) (0.0008) (0.0009) (0.0035) (0.0033) (0.0023) (0.0042) (0.0046)

51