Determining the Factors Affecting the Acceptance of

Filipinos on the Use of Renewable Energies: A

Pro-Environmental Planned Behavior Model

by

Alyssa P. Bayola
Sebastian Luis D. Bugayong
Keithzi Rhaz A. Cantona

A Research Paper Submitted to the Mapúa Senior High School Department

in Partial Fulfillment of the Requirements for

Research Project 4 (RES04)

Mapúa University
May 2023
 APPROVAL SHEET

This is to certify that we have supervised the preparation of and read the research paper prepared
by Alyssa P. Bayola, Sebastian Luis D. Bugayong, and Keithzi Rhaz A. Cantona entitled
Determining the Factors Affecting the Acceptance of Filipinos on the Use of Renewable
Energies: A Pro-Environmental Planned Behavior Model of SHS Research and that the said
research paper has been submitted for final approval by the Oral Examination Committee.

Edgar M. Requiron Jr. Ma. Janice J.

Thesis Coordinator Gumasing

As members of the Oral Examination Committee, we certify that we have examined this research
paper and hereby recommend that it be accepted as fulfillment of the research requirement for
the Senior High School – Science, Technology, Engineering, and Mathematics (STEM).

Jenalyn Shigella Yandug Edgar M. Requiron Jr.

Panel Member Proofreader

Committee Chairman

Josephine D. German
This research paper is hereby approved and accepted by the Mapúa Senior High School as
fulfillment of the research requirement for the Senior High School – Science, Technology,
Engineering, and Mathematics (STEM).

Charlotte M. Monteiro
Principal

ii
 ACKNOWLEDGEMENT

The journey in writing this research has been long and fulfilling, and it was made
possible by our support system and the people who generously provided vital and accurate
information. Their extensive understanding of their areas of expertise was a tremendous help.
Accordingly, we would like to extend our sincerest gratitude to them.

We thank Mr. Edgar M. Requiron, Jr. for guiding us as our instructor in RES02 and
RES04. We sincerely appreciate the support given when faced with a problem and the
continuous provision of answers to our questions for the duration of both courses. His time and
effort spent will not go unnoticed.

To our thesis adviser, Dr. Ma. Janice J. Gumasing, we are immensely grateful for the
consistent overseeing of the study. Her mastery of sustainability studies significantly eased its
creation. We are forever indebted to her kind consideration and positive outlook during nerve-
wracking circumstances.

Finally, we want to thank our loved ones who have been by our side throughout the
study. Their moral support equipped us with enough belief to continue despite rough roads. With
them, we were motivated to set a clear goal.

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 TABLE OF CONTENTS

TITLE PAGE i

APPROVAL PAGE ii

ACKNOWLEDGEMENT iii

TABLE OF CONTENTS iv

LIST OF TABLES vi

LIST OF FIGURES vii

ABSTRACT viii

INTRODUCTION 1

Background of the Study 1

Review of Related Literature 5

Synthesis and Research Gap 9

Research Objectives 10

Significance of the Study 11

Scope and Delimitation 12

Possible Outcomes 12

THEORETICAL FRAMEWORK 13

METHODOLOGY 17

Research Design 17

Setting 18

Participants and Sampling Technique 18

Data Gathering Tools 18

Research Procedures 21

iv
 Data Analysis 22

Ethical Considerations 23

RESULTS AND DISCUSSION 24

Results 24

Discussion 31

CONCLUSION 37

Practical Implication 38

Theoretical Implication 38

Limitations and Future Research Studies 39

REFERENCES 40

v
 LIST OF TABLES

TABLE 1: Construct and Measurement Items 19

TABLE 2: Reliability and convergent validity result 26
TABLE 3: Total direct, indirect and total effects 27
TABLE 4: Hypothesis Test 28
TABLE 5: Discriminant Validity: Fornell-Larcker Criterion 29
TABLE 6: Discriminant Validity: Heterotrait-Monotrait Ratio 29
TABLE 7: Model Fit 31

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 LIST OF FIGURES

FIGURE 1: Pro-Environmental Planned Behavior (PEPB) Model 14

FIGURE 2: Initial SEM Model 25
FIGURE 3: Final SEM Model 30

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 ABSTRACT

Renewable Energies (RE) or Renewable Energy Sources (RES) are taken from

inexhaustible supplies such as the sun, bodies of water, wind, tidal waves, geysers, and biomass.

Due to their sustainable and cost-efficient natures, such sources of energy trump unreplenishable

ones such as fossil fuels, crude oils, and coals, which cause irreversible ecological adversities.

This study sought to determine the factors influencing the acceptance of Filipinos from the

National Capital Region (NCR) on RE utilization. The assessment model used was the extended

Theory of Planned Behavior (TPB), which is the Pro-Environmental Planned Behavior (PEPB)

Model. A questionnaire survey was disseminated to get the required information to ascertain the

variables affecting the behavioral intention of Filipinos in accepting RE or RES. In examining

the results, Structural Equation Modeling (SEM) was utilized with Partial Least Squares SEM

(PLS-SEM) to identify other existing relationships. Findings revealed that Perceived Authority

Support (PAS) is the most significant variable affecting Perceived Environmental Concern

(PEC), inducing an indirect effect on the Behavioral Intention (BI) of Filipinos. The factor with

the highest significance affecting BI is the Subjective Norm (SN). Accordingly, such results

suggest that the government implements incentive-based strategies while taking into account the

impact of SN by gaining the public’s favor on the transition from traditional energy sources to

green alternatives. In addition, educational programs and campaigns may be administered to

spread awareness and fill in information gaps among Filipino citizens.

Keywords: Renewable energies, Pro-Environmental Planned Behavior Model, Acceptance of

Filipinos, Behavioral Intention

viii
 INTRODUCTION

Background of the Study

Climate change is defined as a long-term transformation in weather involving

temperature, precipitation, and occurrences of typhoons, measured at an average of 30 years

(Dietz et al., 2020). It has been a running global issue of the 21st century, bringing

environmental, social, and economic challenges. In the ecological aspect, Dietz et al. (2020)

discussed some of the consequences among many, which are sea-level rise and ocean

acidification due to a shift in the average temperature. People’s health has also been at risk

wherein, for instance, high-temperature and heatwave exposure heightens the possibility of

heatstroke-induced mortalities, even worsening cardiovascular and respiratory diseases (Watts et

al., 2021). Furthermore, Watts et al. (2021) also reported great financial losses due to natural

disasters, nearing a five-time greater loss in nations with low-income economies. Thus, the onset

of climate change's unfavorable effects encompasses several aspects globally.

Accounting for the lion's share of climate crises is the ongoing and widespread use of

conventional non-renewable energy sources such as fossil fuels, crude oils, coals, and more

unreplenishable supplies. Continued operations give way to extreme ecological concerns,

particularly the rise of carbon emissions due to burning fossil fuels, which would produce a

negative ripple effect. Consequently, it may be impacting people's health due to the density of air

pollutants, leading to an economic complication out of the expensive investments to curb such

consequences. Furthermore, claims of the negative impacts of non-renewables have been verified

by Watts et al. (2021), as shutting down coal power plants led to improved air quality, thus

decreasing premature mortality because of polluted air. Accordingly, increasing awareness of

the

1
limitations of non-renewable energy sources pressed alternative energy sources development that

will not deplete the Earth of its finite resources.

Multitudinous studies have been conducted regarding Renewable Energy (RE) as it is

taken from inexhaustible sources such as hydropower, wind, solar, biomass, and geothermal.

Such energy forms offer sustainability, benefiting environmental, economic, and social aspects.

In an ecological context, transitioning to Renewable Energy Sources (RES) will considerably

lower the resulting emissions from fossil fuel burning. Furthermore, RES is economically

beneficial due to its relatively lower prices in several countries worldwide, as the impending

scarcity of non-renewable energies will spike their costs. Lastly, in a social context, people’s

health will generally improve, and possible job opportunities may arise, thus boosting

employment and the economy on a national level eventually.

Kamran (2018) pointed out that the global situation of RE includes consumers,

researchers, and policymakers who have been attracted to progressing toward green alternative

energy sources; such attention is due to the increase in energy demand and the necessity to cut

conventional-fossil-fuel-generated pollutants. Consequently, a sharp upward trend has been

observed in RE markets, in which there occurred a rapid implementation of well-established

technologies (e.g., hydro, wind, solar photovoltaic) (Ellabban et al., 2014). Moreover, Ellabban

et al. (2014) expect growth in RES of 2.7 times between 2010 and 2035. As for investments,

Ellabban et al. (2014) recorded a 12% decline from 2011 to 2012 out of uncertainty over support

policies across the United States and Europe; despite this, developing RE continues to be a

global priority.

Brahim (2014) discussed that the Philippines being a net energy importer intensifies its

vulnerability and the accompanying risks of disruptions to the oil supply, instability of the oil

2
price, and systems of geopolitics shaping countries that export energy. Along with the rapid

growth rates of carbon emissions affecting the country, the uncertainty of reliance on non-

renewable energies propelled its search for alternative energy sources that were feasible (Brahim,

2014). With an abundant source of hydro and geothermal heat, it has implemented grid-

connected RE, according to Roxas and Santiago (2016). Although the country seems to

have its fair share of RE-based projects, implementations seem to be only limited to a few areas;

therefore, the expansion of green sustainable technologies is a requisite to achieving a

nationwide shift from fossil-fueled energies to RES.

Several studies discussed the significant influence of socio-demographic factors,

specifically age and level of educational attainment, in determining a consumer’s Willingness To

Pay (WTP) and overall acceptance of RE. In detail, Bertsch et al. (2016) and Makki and Mosly

(2020) reported an inverse proportionality between age and the likeliness to transition to

sustainable energies, whereas the degree of education attained is directly proportional to

consumers’ inclination to utilize RES (Avicenna and Febriani, 2021; Sardianou & Genoudi,

2013). Such weight of socio-demographic factors may be due to information dissemination and

consumption, as comparatively younger generations have greater ease in accessing material.

Furthermore, linked to a consumer’s attained education level, the depth of comprehension

also affects public acceptance. Stigka et al. (2014) discussed that the extent of comprehension is

in direct proportion to one's willingness to embrace RE-based systems. Through an 'adjust

knowledge' variable that respondents with high claimed levels of knowledge about RES have a

higher tendency to patronize RE-based technologies than those in the opposition because of their

limited grasp of the benefits that come along with RES implementation (Lloyd & Nakamura,

3
2022). A reason for this may be due to an innate biological response to the fear of the unknown,

ultimately distrusting the newly presented idea.

Regarding economic parameters, a household's ability to afford RES and financial

policies affect the WTP of consumers. Going in-depth with the latter, a study by Sardianou and

Genoudi (2013) reported that diminishing taxes is the most potent financial measure to

encourage the patronization of RES to buyers in the residential sector. Additionally, Sardianou

and Genoudi (2013) inferred that government subsidizing RES consumers is less effective,

which opposes the implication of Makki and Mosly (2020) that incentives significantly

encourage consumers to use RES.

As for households' financial status, thorough research reported that support for RE

expansion and willingness to implement it in homes positively correlate with RES affordability

(Lloyd & Nakamura, 2022). The increase in the cost of electricity and monthly private income

rises accordingly with the probability of household usage of RE (Sardianou & Genoudi, 2013),

which is possible because of the perceived lower cost of RE technologies. Although RES offers

favorable outcomes, the still-existing skepticism toward such techonology leads to fewer

purchasing of sustainable technology to prevent the spending of a considerable portion of their

savings or income.

With the objective of determining factors affecting social acceptance of RE, the outcomes

of this study will identify the necessary courses of action to control the determinants that will

considerably drive the public to embrace energies that are non-depleting in a human timeframe.

Given that the measures taken have proven to be effective, positive environmental, social, and

economic results are expected: alleviation and delay of climate crisis effects, health

improvement, increased employment opportunities, and low-cost development and

4
implementation, both on a national and local scale. Furthermore, future research may utilize the

results from this study to rigidify existing observations on determinants of higher willingness to

adopt RE.

Review of Related Literature

Several literature studies have been conducted to analyze and determine the factors that

may affect public consumers' use of renewable energies or their complete switch to them. In

addition, each paper gives further understanding as it may address other variables that show

significance to the initial topic. The following studies tackle the factors affecting the public in

adopting renewable energy, public acceptance of renewable energy, consumers' behavior toward

renewable energy, Theory of Planned Behavior, and the Pro-Environmental Planned Behavior

Model. Consequently, the insights gained from each study will prove relevant due to their

potential to aid researchers and consumers in the improvement of renewable energy use as

troubles regarding climate change effects and rising expenses (e.g., electricity) continue to occur.

Factors affecting the public's adoption of renewable energy

With the varying opinions of the public on the discussion of RE, several studies have

been conducted to determine and analyze the factors that influence their acceptance of

transitioning to RE. In such a way, three significant factors were discovered: knowledge about

renewable energies, consumers’ socio-demographics, and economic parameters. In the studies by

Avicenna and Febriani (2021) and Sardianou and Genoudi (2013), it was found that consumers’

education level affects their acceptance of RE. Consumers with a broader knowledge of the

benefits of RE and the adverse effects of fossil-fueled energy sources tend to have a higher

acceptance rate than those without such knowledge (Lloyd & Nakamura, 2022; Avicenna &

Febriani, 2021). Supported by the study by Wall et al. (2021), findings suggest a clear

5
correlation

6
between a person's willingness to embrace such RE and their perceptions of their efficacy, care

for the environment, awareness of RE, and ideas about its advantages. Furthermore, there is an

inverse proportionality between age and the likelihood of transitioning to sustainable energies;

the older the buyer is, the less likely they are to support RES generally (Bertsch et al., 2016;

Makki & Mosly, 2020). Such weight of socio-demographic factors may be because of

information dissemination and consumption, as comparatively younger generations have greater

ease in accessing material.

Additionally, Lloyd and Nakamura (2022) reported through an 'adjust knowledge'

variable that respondents with high claimed levels of knowledge about RES have a higher

tendency to patronize RE-based technologies than those in the opposition because of their

limited grasp of the benefits that come along with RES implementation.The fear of people seeing

or trying new things that are heavily different from the perceived usual may be the cause of the

skepticism on such new concepts. Thus, as discussed by Stigka et al. (2014), the extent of

comprehension is in direct proportion to one's willingness to embrace RE-based systems.

Regarding economic parameters, a household's ability to afford RES and financial

policies affect the WTP of consumers. In order to go in-depth with the latter, a study by

Sardianou and Genoudi (2013) reported that diminishing taxes is the most potent financial

measure to encourage the patronization of RES to buyers in the residential sector. In addition,

subsidies in RE usage follow shortly in terms of popularity and effectiveness (Sardianou &

Genoudi, 2013), which counters the implication of Makki and Mosly (2020) that there is a

necessity for RES consumers to receive government subsidies and incentives to encourage RE

utilization. Regardless, both measures in the financial context influence public acceptance;

therefore, there will be an observation done on both in this study set in the Philippines. As

7
regards households' financial status, thorough research reported that support for RE expansion

and willingness to implement such in homes are positively correlated with RES affordability

(Lloyd & Nakamura, 2022). Furthermore, Sardianou and Genoudi (2013) added that the

probability of using RE in households rises due to an increase in electricity costs and monthly

private salaries. Such results are expected because individuals avoid spending a sizable

proportion of their income or savings on the promising yet uncertain benefits of sustainable

modern technologies.

Public acceptance of renewable energy

Given these factors, contrasting opinions from the public are observed in the studies by

Bertsch et al. (2016) and Stigka et al. (2014). Currently, a general awareness exists supporting

the modification of non-renewable energies into RE (Avicenna & Febriani, 2021). However,

observations of Stigka et al. (2014) prove that people from rural areas are unwilling to pay for

RES due to adverse effects experienced in their community after RE projects. In contrast, the

higher acceptance of urban people is attributed to their broader knowledge regarding the benefits

of RE, such as air pollution reduction, wildlife preservation, and job creation (Sitgka et al.,

2014). Parallel with the study of Bersch et al. (2016), which implies how renewable energy is

accepted nationally. However, on a local level, a decline in consumers’ acceptance has been

observed as a result of factors including the proximity of energy infrastructure to them. These

factors will be taken into consideration by the researchers, although not as primary determinants.

Consumers’ behavior toward renewable energy

Thereupon, the Willingness to Pay (WTP) and behavior of consumers are examined by

Ma et al. (2015) and Trudel (2018). Ma et al. (2015) discussed how electricity consumption and

consumer education affect the WTP. Transitioning to solar and wind-generated electricity is

8
favorable for consumers, as they have a broader knowledge of these RES. Contrary to the

conclusion of Trudel (2018), consumers prefer low-impact RE behaviors, like the 3Rs, to high-

impact behaviors, such as using solar and other sustainable energy sources.

Theory of Planned Behavior

In an effort to address environmental issues, several researchers produced ecologically

friendly technologies. Some studies about waste treatment technology (Sarkis & Weinrach,

2001; Fukumori et al., 2002), recycling technology (Merrild et al., 2008; Cherubini & Ulgiati,

2010), or alternatives to non-renewable raw materials (Mintie, 2006; Wilke & Vorlop, 2004)

involved eco-friendly products. Thus, consumers need to recognize environmentally sustainable

products employing RES. They will be able to take part in the effort to assist the environment as

a result.

The Theory of Planned Behavior (TPB) has been extensively employed to predict

intention and behavior (Teo & Lee, 2010). TPB has been used to examine how people behave

environmentally in various countries. A study by Gao et al. (2016) determined the link between

consumer perceptions and eco-friendly behavior. They discovered a significant positive

association between the two variables. Research by Scalco et al. (2017) also determines the

sustainable consumer purchasing trends for organic food and finds that the most important factor

influencing a person's intention to purchase is their attitude. Similarly, in order to find the

pertinent predictor of customer preference for automobiles or other environmentally friendly

modes of transportation, Lanzini and Khan (2017) conducted a study. They discovered that

environmental factors play a significant role in influencing how people intend to behave when

choosing their mode of transportation. Hence, previous studies have found that TPB can be used

to determine customers' intentions to use environmentally friendly products.

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Pro-Environmental Planned Behavior Model

Conner (2015) argues that it is essential to expand the TPB in order to emphasize its

contributions. There have been various attempts to expand the TPB to include additional possibly

important behavioral factors in order to improve prediction (Bagot et al., 2015; Donald et al.,

2014; Kashif & De Run, 2015). The aim of the extended TPB's environmental-related topics was

to support environmental sustainability (Han & Kim, 2010; Chen & Tung, 2014). Thus, the

PEPB, or extended TPB, will be employed in this study. The TPB model is continued by PEPB

(Steg & Vlek, 2019; Turaga, 2010). TPB solely takes into account how each individual perceives

the context, as reflected relatively in perceived behavioral control. Perceived Authority Support

(PAS) and Perceived Environmental Concern (PEC) are two additional elements that the PEPB

adds. Persada (2016) proposed this model to forecast the Environmental Impact Assessment

(EIA) procedure. These two elements were added to TPB to create PEPB. Therefore, this study

aims to explore factors that affect Filipinos’ intentions to use renewable energy sources using the

PEPB model using six variables, namely: Perceived Authority Support (PAS), Perceived

Environmental Concern (PEC), Attitude (AT), Subjective Norms (SN), Perceived Behavior

Control (PBC), and Behavior Intention (BI). The behavioral theory was used explicitly in this

study's analysis. Due to its comprehensive approach, which is frequently used in research on

green energy technology, the implementation of this model is, therefore, acceptable because it

makes it easier for researchers to understand the limitations to the advancement of RE.

Synthesis and Research Gap

The research studies gathered defined the factors that make up the aspects that

conceivably hinder or nurture the transition of consumers to using and supporting products and

processes generated from renewable energies. A consistent similarity in factors and deductions

1
throughout the studies can be perceived, which then displayed implications for the niche topic. It

implied the inverse proportionality between age and the likelihood of transitioning to renewable

energies, the positive relationship between familiarity with RES and inclination to use RE-based

technologies, the influence of economic context on public acceptance, the electricity

consumption's impact on consumers' WTP, and the necessary consumers' recognition on

environmentally sustainable products.

An existing gap found in previous studies regarding the consumers' behavior toward the

utilization of RE was the use of different models, excluding the Pro-Environmental Planned

Behavior (PEPB), to interpret the results. Such a gap will be aided by using PEPB in this study;

however, results will be limited to the interpretation of the model—it will not represent all the

possible determinants that influence the acceptance of Filipinos toward shifting to RE when a

different model is implemented. The framework created for this study contributes to consumer

behavior modeling by validating the relevant role of PEPB variables on consumer acceptance of

renewable energy sources.

Research Objectives

The gathered information and findings of each study are motivated by an overarching

goal of overcoming externalities (e.g., environmental). Comparably, the general objective of the

study is to explore the variables that affect Filipino consumers' behavior intentions regarding

their acceptance of Renewable Energy Sources (RES).

The current study aimed (1) to determine the socio-demographic, financial, and

knowledge-based factors affecting Filipinos' acceptance of RES and (2) to identify factors

affecting the intention of Filipino consumers to use renewable energy sources using the PEPB

model. The probable factors may be manipulated to encourage the public to transition from

1
standard fossil-fueled technologies to green, sustainable alternatives. Once the masses are

educated about the advantageous effects of the latter, there will be a higher demand for such,

which will lead to a mass green switch as large corporations will supply the need for sustainable

energy sources. Consequently, such costs will drop to affordable prices per the law of supply and

demand, which, in turn, will heighten the public's willingness to pay. Moreover, establishing the

difference, if there are any, between the PEPB model and other models may serve as an essential

reference for future studies. Such an outcome could reveal superior results for the former, a

minimal difference, or none at all.

Significance of the Study

The emission of greenhouse gasses is the apparent detrimental impact brought by the use

of non-renewable energies; this production of carbon dioxide and methane contributes to the

effects of the occurring climate change. Contrary to the beneficial and sustainable effects of

renewable energies, non-renewable energies like fossil fuels continue to damage the Earth due to

their limited quantity and massive production of greenhouse gas emissions. Renewable energies

can suspend and even aid the harmful effects of climate change; therefore, using this kind of

energy will benefit various variables. The current study's findings about the factors impacting the

Filipinos' inclination to use renewable energies will prove to be beneficial to the following:

Filipino public. The study's findings may result in the improvement of renewable energy

use, thus, making more industries interested in using it. It will ultimately help the masses

financially due to RE's affordability and sustainability because fewer materials are used in each

production of a considerable amount of power. Moreover, they will know that renewable

energies are safer for their well-being and the planet.

1
 Manufacturing industries. The study's findings may give them knowledge of what the

target market wants concerning renewable energy technologies. Understanding these trends will

be of help as it can lead to switching in an efficient process and production manner as well as an

increase in profit. They may also obtain the public's favor as they make environmentally

sustainable products, possibly increasing their profit.

Future researchers. The study's findings will function as substantial reference and

essential information on matters concerning the niche. They may gain useful knowledge in

confronting the apparent continuous concern of the adverse effects of climate change.

Scope and Delimitation

Conducting this study determined the factors influencing Filipino acceptance of

transitioning to renewable energies. As energy derived from fossil fuels has adverse effects on

the environment, one should consider using sustainable energy. The study focused on the general

public's perception of its intention to use green power sources. The questionnaire was distributed

publicly to residents within the National Capital Region (NCR) to gather enough data to interpret

the results. Since the scope of the study is limited to one region, the results of the study cannot be

used to represent the Philippines as a whole. This study only focuses on the perception among

Filipinos concerning renewable energy sources and does not represent the pure intention to

transition toward RE.

Possible Outcomes

The study's findings will provide additional information and a point of view on Filipino

consumers' willingness to use renewable energy products or processes, potentially substantiating

or broadening previous studies' findings. The outcome will serve as a fundamental basis for

influential individuals and manufacturers to design a suitable course of action to increase the

1
number of renewable energy consumers. Consequently, the growth in the target market and

improvement in the use of renewable energies may lead to consumer expansion and more

companies leaning towards creating products that are beneficial to the environment.

THEORETICAL FRAMEWORK

The present study's theoretical framework is based on Pro-Environmental Planned

Behavior (PEPB) developed by Persada (2016) to predict Filipinos' acceptance of renewable

energy sources. Consumers' intention to behave pro-environmentally is the primary predictor of

such behavior, and various researchers have explored this link (Clement et al., 2014; Synodinos

& Bevan-Dye, 2014; Macovei, 2015). According to prior studies, the intention construct is the

motivating factor that influences a particular behavior and has a significant, direct, and favorable

impact on the behavior itself.

Numerous studies have attempted to identify the factors that impact pro-environmental

behavior as awareness of the interconnectedness between human conduct and the environment

has grown. Li et al. (2016) divided the factors that influence pro-environmental behavior into

two groups: individual factors, which include demographic and psychological factors, and

external factors, which include things like social norms, cost, and convenience. Early research

mainly focused on demographic and environmental factors to understand the mechanisms behind

pro-environmental conduct. However, recent studies revealed behavioral factors to be the most

accurate predictors of pro-environmental behavior (Sidique et al., 2019; Graham-Rowe et al.,

2015; Botetzagias et al., 2015). The behavioral factor that is most frequently used is the attitude,

or the degree to which a person's assessment of behavior is positive or negative.

By using the PEPB model, this study explores the variables that affect Filipinos' behavior

intentions regarding the acceptance of renewable energy sources. As shown in Figure 1, the

1
suggested model identifies six variables: Perceived Authority Support (PAS), Perceived

Environmental Concern (PEC), Attitude (AT), Subjective Norms (SN), Perceived Behavior

Control (PBC), and Behavior Intention (BI).

Figure 1. Pro-Environmental Planned Behavior (PEPB) Model

Determinants of Behavioral Intention to Use Renewable Energy Sources

Perceived Authority Support (PAS) can be viewed as a person's perspective of the rules,

facilities, resources, and assistance offered by the agency or by the government authority that can

assist people in engaging in certain behaviors (Persada, 2016; Lin et al., 2017). The government

that established the rules governing the usage of renewable energy sources is the authority figure

in this study. The government's initiative to promote the use of RE programs is anticipated to

have an impact on customer demand for RES. Customers will be more inclined to employ

1
renewable energy sources if the government runs the RE program flawlessly. It has been

demonstrated that the PAS has a favorable impact on the AT, SN, PBC, and PEC's participation

in environmental impact assessments (Persada, 2016) and green product purchasing behavior

(Puspita, 2017). As a result, this study suggests that the PAS affects the PEC, AT, SN, and PBC

favorably when using RE sources.

H1: Perceived Authority Support (PAS) has a significant positive influence on Perceived

Environmental Concern (PEC)

H2: Perceived Authority Support (PAS) has a significant positive influence on Attitude (AT)

H3: Perceived Authority Support (PAS) has a significant positive influence on Subjective Norm

(SN)

H4: Perceived Authority Support (PAS) has a significant positive influence on Perceived

Behavioral Control (PBC)

Perceived Environmental Concern (PEC) can be thought of as an evaluation of a person's

view of environmental effects (Fransson & Gärling, 1999; Persada, 2016). The PEC in this study

represents customers' attitudes toward Renewable Energy Sources (RES). It was anticipated that

how consumers felt while using the RES would affect their choice. Customers will undoubtedly

use RES if they believe it to be beneficial to do so. It has been demonstrated that the PEC has a

favorable impact on the AT, SN, and PBC's engagement in environmental impact assessments

(Persada, 2016) and green product purchasing behavior (Puspita, 2017). Accordingly, this study

suggests that PEC positively impacts how AT, SN, and PBC are used when using RES.

H5: Perceived Environmental Concern (PEC) has a significant positive influence on Attitude

(AT)

1
H6: Perceived Environmental Concern (PEC) has a significant positive influence on Subjective

Norm (SN)

H7: Perceived Environmental Concern (PEC) has a significant positive influence on Perceived

Behavioral Control (PBC)

Behavioral Intention (BI) is a depiction component that might characterize people's

attempts to engage in a specific behavior (Azjen, 1991). There will be a specific action resulting

from the intention (Nadlifatin et al., 2016). When someone expresses a positive desire to engage

in certain conduct, that is what “Attitude” (AT) means. The AT in this study is the positive

consumer perception of using Renewable Energy Sources (RES). It was anticipated that

consumer perceptions of RES would affect their choice of that product. Thus, it has been

established that the AT benefits the BI for participation in environmental impact assessments

(Persada, 2016) and green product purchasing behavior (Puspita, 2017).

H8: Attitude (AT) has a significant positive influence on Behavioral Intention (BI) to use

renewable energy sources

Perceived social pressure, known as the “Subjective Norm” (SN), significantly impacts

whether or not someone chooses to act in a certain way. The social pressure the study's

participants felt to use RES is referred to as the SN. The preference of customers to use RES was

anticipated to be influenced by societal pressure (Lin et al., 2017). The public will use RES if

there is strong public support. Thus, it was suggested that SN affects the BI favorably when it

comes to accepting RES.

H9: Subjective Norm (SN) has a significant positive influence on Behavioral Intention (BI) to

use renewable energy sources

1
 The notion of how easy or difficult it is for a person to carry out a particular behavior is

known as Perceived Behavioral Control (PBC). The study's participants perceive the ability of

the consumer to regulate how they use the RES as PBC. It was anticipated that consumers'

perceptions of their ability to use the RES would affect their propensity to do so (Lin et al.,

2017). Thus, it was suggested by this study that PBC has a favorable impact on how BI evaluates

RES.

H10: Perceived Behavioral Control (PBC) has a significant positive influence on Behavioral

Intention (BI) to use renewable energy sources

METHODOLOGY

This chapter of the research paper discusses the methodology employed to conduct the

study and achieve its objectives. This section is split into seven (7) parts, each discussing

information necessary to the present study. These parts include the study's research design,

setting, participants sampling technique, data gathering tools, research procedures, data analysis,

and ethical considerations.

Research Design

This study used a causal research design. Studies on causality are considered to help

understand phenomena using conditional logic. This research assessed how a specific change

would affect accepted beliefs and norms. Most social scientists try to find explanations for what

happened that are in line with tests of hypotheses. When the change in one thing—an

independent variable—causes or, on average, leads to the change in another thing—a dependent

variable—it is said to have a causal effect. In this study, the factors of the PEPB model, such as

Perceived Authority Support (PAS), Perceived Environmental Concern (PEC), Attitude (AT),

Subjective Norm (SN), Perceived Behavioral Control (PBC) are seen as independent variables

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that affect the Behavioral Intention (BI) to use renewable energy sources, which is the study's

dependent variable.

Setting

Due to the unknown population of possible users of renewable energy sources, a

convenience sampling technique was employed for data collection in this study. The target

respondents are users in the National Capital Region (NCR).

Participants and Sampling Technique

The non-probability sampling method was utilized in this research, specifically

convenience sampling using an online survey. The target respondents are residents of the

National Capital Region (NCR). The expected minimum number of respondents is 300, as

suggested by the study of Yamane (1967), where the level margin of error was set at 10.

Data Gathering Tools

The online survey was self-administered and distributed via a Google form. The

questionnaire was distributed in multiple cross-sectional designs, and the link to the survey was

sent to the target respondents for two months.

The survey consists of 30-item questions and is presented in the English language. The

respondent's demographics were determined in the first section of the questionnaire using 5-item

questions, including age, gender, civil status, area of residence, and monthly income.

The questionnaire's second part is comprised of the indicators based on the PEPB model:

Perceived Authority Support (PAS), Perceived Environmental Concern (PEC), Attitude (AT),

Subjective Norm (SN), and Perceived Behavioral Control (PBC). This measured the users'

perceived intention to use the RES. The survey consists of item questions where all answers were

1
on a 5-point Likert scale, which ranges from "strongly disagree" to "strongly agree."

Additionally, six (6) latent variables were used in the survey. The summary of measures and

constructs is shown in Table 1. The items for the constructs were adopted from existing studies.

Table 1. Construct and Measurement Items

Items Measure Supporting References

Perceived Authority Support

PAS1 I believe that producers and consumers have German et. al., 2022b; Lin et.
the option to participate in the Environmental al., 2017; Persada et al., 2015
Impact Assessment (EIA) process using
government-provided methodologies.

PAS2 I believe that producers and consumers have

the option to take part in a
government-established environmental
program, such as the Environmental Impact
Assessment (EIA) procedure.

PAS3 The government supports the law allowing

citizen-consumers to participate in the
Environmental Impact Assessment (EIA)
procedure.

Perceived Environmental Concern

PEC1 I strongly believe that producers and German et. al., 2022b; Lin et.
consumers should be involved in the al., 2017; Persada et al., 2015
Environmental Impact Assessment (EIA)
process because I am very concerned about
the state of the environment around the globe
and what it will imply for my future.

PLA2 Because of the enormous environmental

abuse committed by humanity, producers and
consumers should take part in the

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 Environmental Impact Assessment (EIA)
procedure.

PLA3 It concerns me that producers and consumers

should participate in the Environmental
Impact Assessment (EIA) process, as human
interference with the natural world frequently
results in disastrous outcomes.

Attitude

AT1 I usually think about using renewable energy German et. al., 2022b; Soorani,
sources due to climate change. & Ahmadvand, 2019; Ong et.
al., 2021
AT2 Using renewable energy sources is a good
idea for our society.

AT3 Using renewable energy sources will benefit

our society, especially our environment.

AT4 I think using renewable energy sources is

valuable, especially for our environment.

AT5 I want to be safe; that is why I prefer to use

renewable energy sources.

Subjective Norm

SN1 People who are important to me think I

should use renewable energy sources.
German et. al., 2022b;
SN2 People who are important to me approve of Peña-García et al., 2020;
my usage of renewable energy sources. Carfora et al. (2019);
SN3 People who are important to me want me to
use renewable energy sources.

SN4 I feel under social pressure to use renewable

energy sources.

SN5 I usually think about using renewable energy

sources

Perceived Behavioral Control

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 PBC1 I believe the use of renewable energy sources
improves our society. German et. al., 2022b; Soorani,
& Ahmadvand, 2019; Ong et.
PPBC2 Using renewable energy sources is entirely al., 2021
under my control.

PPBC3 I have the resources, knowledge, and skills to

use renewable energy sources.

PBC4 I have the capability to choose the renewable

energy sources I want to utilize

Behavioral Intention

BI1 I intend to use renewable energy sources. German et. al., 2022b; Kwak
et. al., 2021; Ong et. al., 2021
BI2 I intend to encourage others to use renewable
energy sources.

BI3 I predict that our society will predominantly

support the use of renewable energy sources.

BI4 I intend to explain the positive aspects of

using renewable energy sources.

BI5 I recommend that other people should use

renewable energy sources.

Research Procedures

The researchers created an online survey for the research's data gathering through the

platform Google Forms. The questions from relevant existing studies served as a guide and were

modified for the current questionnaire's use to ensure timeliness, accuracy, and applicability.

After checking and assuring the validity of the questionnaire, the researchers acquired a written

consent form for conducting the procedure. The survey's respondents were approached by the

researchers, receiving the survey link and the mentioned consent form through various online

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platforms, such as Facebook Messenger, Microsoft Teams, and via email (Outlook and Gmail).

As the questionnaire was set up online, the researchers were able to track and manage the

gathered data more efficiently. The data gathering of the study targeted Filipinos residing in the

National Capital Region (NCR) as its respondents and aimed to have a minimum number of 300

respondents. The procedure is expected to be finished within two months after its start.

Moreover, after succeeding with the initial measures necessary and getting the data, the

researchers utilized Partial Least Squares SEM (PLS-SEM) to analyze the relationship between

the results obtained from the survey.

Data Analysis

The collected data from the survey were analyzed using multivariate analysis. In this

study, the SEM that was utilized with maximum likelihood estimation is a variance-based Partial

Least Squares SEM (PLS-SEM). PLS-SEM is a means for studying the relationships between

abstract ideas (Hair et al., 2012). It deals with complex constructs with higher levels of

abstraction and higher claims for construct reliability and validity, which makes it great for

prediction (Dash & Paul, 2021) and useful in this study. Its main goal is to explain the variation

in the dependent constructs as much as possible. The data quality is also judged based on the

properties of the measurement model. According to Ouellette and Wood (1998), PLS-SEM

differs from previous modeling approaches as it considers the direct and indirect effects on

presumptive causal links and is progressively seen in scientific investigations and studies. Also,

PLS-SEM is the best method for coming up with new theories and making predictions, while

CB-SEM is better for testing and proving theories that already exist (Hair et al., 2012).

Several fit indices were utilized to justify the study's model fit using PLS-SEM, like

Standardized Root Mean Square Residual (SRMR), Normal Fit Index (NFI), and Chi-square. For

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SRMS, a value of under 0.08 is considered a good fit (Hu & Bentler, 1998). According to

Baumgartner and Homburg (1996), for NFI, a value of 0.80 and over indicates an acceptable fit,

whereas for Chi-square, a value under 5.0 indicates a well-fitting model.

Ethical Considerations

The questionnaire was briefly discussed with each respondent, and written consent was

obtained. Following the Data Privacy Act or Republic Act No. 10173 in the Philippines, the

respondents were asked to sign a consent form that says the responses and information they give

will only be used for academic and research purposes. Before collecting data, the researchers

also asked the Mapúa University Research Ethics Committee for permission.

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 RESULTS AND DISCUSSION

Results

The visual representation of a model in determining the factors affecting the acceptance

of Filipinos on using renewable energies is illustrated in Figure 2. This model comprises six (6)

latent variables and twenty-five (25) indicators. The model's factor loading and its indicators'

reliability and validity are presented in Table 2; this reliability analysis is necessary before

conducting structural equation modeling (SEM). In an analysis of behavioral intentions models,

it is expected to use Cronbach's alpha (α) and further justify the analysis for reliability and

validity with Factor Loading (FL), Composite Reliability (CR), and Average Variance Extracted

(AVE). On the first hand, Cronbach's alpha, FL, and CR need to exceed a value of 0.7 (Alarcón

et al., 2015; Field, 2006). According to Hair (2016), determining each component's function in

defining a factor is made possible by Factor Loading. By taking into account the FL perspective,

CR is a measure of the overall reliability of a group of items that make up the latent variables.

On the other hand, the value for AVE should exceed a value of 0.5 (Bradley, 1978). The AVE

gauges the degree of variance caused by measurement error to the amount of variance collected

by the construct. Consequently, Table 2 shows that 2 out of 25 measures were rejected, i.e.,

Subjective Norm 4 and Perceived Behavioral Control 2, as they did not meet the necessary value

for FL. However, the remaining 23 have exceeded the mentioned values and can be considered

to have validity and reliability. Ultimately, this analysis is done to evaluate the internal

consistency of each and if the constructs and measures correlate.

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Figure 2. Initial SEM Model

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Table 2. Reliability and convergent validity result

Construct Items Mean S.D. FL (≥0.7) α (≥0.7) CR (≥0.7) AVE (≥0.5)

Perceived PAS1 4.31 0.76 0.886

Authority
Support (PAS) PAS2 4.28 0.77 0.899 0.807 0.822 0.887
PAS3 3.97 0.93 0.762

Perceived PEC1 4.53 072 0.902

Environmental
Concern (PEC) PEC2 4.57 0.59 0.895 0.745 0.819 0.854
PEC3 4.02 1.13 0.720

Attitude (AT) AT1 4.27 0.89 0.700

AT2 4.67 0.57 0.856

0.858 0.856 0.899
AT3 4.68 0.59 0.842

AT4 4.71 0.52 0.827

AT5 4.42 0.76 0.767

Subjective SN1 4.00 0.93 0.781

Norm (SN)
SN2 4.02 0.86 0.814
0.802 0.863 0.857
SN3 3.91 0.86 0.803

SN4 2.95 1.24 0.606

SN5 3.96 0.98 0.765

Perceived PBC1 4.52 0.72 0.795

Behavioral
Control (PBC) PBC2 3.25 1.20 0.617
0.766 0.891 0.822
PBC3 3.39 1.01 0.726

PBC4 3.59 1.15 0.783

Behavioral BI1 4.11 0.89 0.844

Intention (BI)
BI2 4.15 0.84 0.807
0.881 0.885 0.913
BI3 3.99 0.89 0.755

BI4 3.96 0.89 0.863

BI5 4.30 0.85 0.845

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 The results of the performed PLS-SEM to test the suggested hypotheses using the Pro-

Environmental Planned Behavior Model are presented in Table 3. The results implied that the

Perceived Authority Support (β = 0.450, p = 0.001) significantly influences Perceived

Environmental Concern and Subjective Norm (β = 0.420, p = 0.001) to Behavioral Intention.

Furthermore, Perceived Behavioral Control, Attitude, and Subjective Norm are significantly

influenced by Perceived Environmental Concern with (β = 0.399, p = 0.005), (β = 0.337, p =

0.016), and (β = 0.315, p = 0.004), respectively. Other notable relationships are Perceived

Behavioral Control (β = 0.378, p 0.001) to Behavioral Intention and Attitude and Subjective

Norm with Perceived Authority Support (β = 0.395, p = 0.001) and (β = 0.226, p = 0.033). These

relationships of factors are within the cutoff value of 0.05 in p-value, contrary to the Perceived

Authority Support to Perceived Behavioral Control and Attitude to Behavioral Intention, as they

have a p-value of 0.076 and 0.075, respectively. As such, it is rejected due to its insignificant

relation.

Table 3. Total direct, indirect and total effects

No Relationship Direct Effect p-value Indirect Effect p-value Total effect p-value

1 PAS→PEC 0.450 <0.001 - - 0.450 <0.001

2 PAS→AT 0.395 <0.001 - - 0.395 <0.001

3 PAS→SN 0.226 0.033 - - 0.226 0.033

4 PAS→PBC 0.214 0.076 - - 0.214 0.076

5 PEC→AT 0.337 0.016 - - 0.337 0.016

6 PEC→SN 0.315 0.004 - - 0.315 0.004

7 PEC→PBC 0.399 0.005 - - 0.399 0.005

8 AT→BI 0.131 0.075 - - 0.131 0.075

9 SN→BI 0.420 < 0.001 - - 0.420 <0.001

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 10 PBC→BI 0.378 < 0.001 - - 0.378 <0.001

11 PEC→BI - - 0.328 <0.001 0.328 <0.001

12 PAS→BI - - 0.228 0.009 0.228 0.009

Table 4. Hypothesis Test

No Relationship Beta coefficient p-value Result Significance Hypothesis

1 PAS→PEC 0.450 <0.001 Positive Significant Accept

2 PAS→AT 0.395 <0.001 Positive Significant Accept

3 PAS→SN 0.226 0.033 Positive Significant Accept

4 PAS→PBC 0.214 0.076 Positive Not Significant Reject

5 PEC→AT 0.337 0.016 Positive Significant Accept

6 PEC→SN 0.315 0.004 Positive Significant Accept

7 PEC→PBC 0.399 0.005 Positive Significant Accept

8 AT→BI 0.131 0.075 Positive Not Significant Reject

9 SN→BI 0.420 <0.001 Positive Significant Accept

10 PBC→BI 0.378 <0.001 Positive Significant Accept

In order to determine the significant correlation between the given factors and evaluate

the measurement model, the study used the discriminant validity of the Fornell–Larcker criterion

and the Heterotrait–Monotrait ratio of correlation, as suggested by Henseler (2015). When the

Heterotrait-Monotrait ratio for assigned constructs is less than 0.85 when utilizing variance-

based SEM and when assigned constructs have a higher value than all other construct loadings

for Fornell-Larcker, discriminant validity has been proven (Hair et al., 2012). In addition, the

highest value per column is placed on top of its respective column. As seen in Table 5 and Table

6, all of the data fit the expected range. Each is well within 0.85; thus, all the values

2
have acceptable reliability and convergent validity. This proves that the overall results of the

constructs are satisfactory.

Table 5. Discriminant Validity: Fornell-Larcker Criterion

AT BI PBC PAS PEC SN

AT

BI 0.537

PBC 0.338 0.684

PAS 0.470 0.464 0.365

PEC 0.526 0.486 0.309 0.557

SN 0.478 0.721 0.351 0.243 0.396

Table 6. Discriminant Validity: Heterotrait-Monotrait Ratio

AT BI PBC PAS PEC SN

AT 0.800

BI 0.475 0.824

PBC 0.412 0.650 0.753

PAS 0.395 0.384 0.314 0.841

PEC 0.447 0.413 0.415 0.450 0.816

SN 0.448 0.675 0.518 0.226 0.353 0.743

In Figure 3, it illustrates the final SEM model of the study, which is a Pro-Environmental

Behavioral Model (PEPB). The solid lines indicate the significant positive relationship between

one construct and the other, respectively, whereas the broken lines show that the specific

correlation between the two is insignificant. Furthermore, the two rejected measures were

removed from the final model, which is why the Subjective Norm only has four (4) measures,

3
removing SN4, and Perceived Behavioral Control is now three (3), removing PBC2. Hair et al.

2
(2012) implied that with the � score at 0.20 or higher in a paper, it is deemed high due to it

defining behavioral intentions and usage behavior. Consequently, most of the correlations are

high due to it being well over 0.20, aside from those rejected.

Figure 3. Final SEM Model

Subsequently, the model fit analysis is done to perceive the PEPB model's validity. It is

composed of and uses Standardized Root Mean Square Residual (SRMR), Chi-square, and

Normal Fit Index (NFI). Table 7 presents that the study's SRMR parameter estimates is at 0.062,

Chi-square at 4.03, and NFI at 0.921. Each model fit's parameter estimates are within the

3
suggested cutoff value, values of which were mentioned previously, making the model

considered valid.

Table 7. Model Fit

Model Fit for SEM Parameter Estimates Minimum cut-off Recommended by

SRMR 0.062 < 0.08 Hu & Bentler (1999)

(Adjusted) Chi-square/dF 4.03 < 5.0 Hooper (2008)

Normal Fit Index (NFI) 0.921 > 0.80 Baumgartner (1996)

Discussion

With the persistence of climate change, which brings social, economic, and

environmental concerns, the search for alternative, sustainable energy sources has risen in the

21st century. In order to measure whether proposed alternatives will be widely used to achieve

the foreseen positive outcome, it is essential to understand the motivations and behavioral

intentions of consumers' willingness to use such. Thus, the aim of the present study was to

determine the significant factors affecting Filipino acceptance of the use of renewable energies

using Structural Equation Modeling (SEM). During the analysis, several latent factors were used,

such as Perceived Authority Support (PAS), Perceived Environmental Concern (PEC), Attitude

(AT), Subjective Norms (SN), Perceived Behavior Control (PBC), and Behavior Intention (BI).

As seen in the results, Perceived Authority Support (PAS) holds the highest significance

and direct influence on Perceived Environmental Concern (PEC) (β = 0.450, p = <0.001), which

validates H1. Thus, the extent of authority support is perceived to affect Filipinos' environmental

concerns positively. PAS refers to the knowledge of resources, procedures, rules, and tasks that

can be accomplished with the assistance of authorities or the government (German et al., 2022a).

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This suggests that authorities hold an important role in fostering environmental concern among

Filipino citizens. The study by Mohanty et al. (2021) showed that government initiatives have a

significant effect not only on a consumer's behavior intention but also on their behavior that is

good for the environment. Thus, if the government wants to increase PAS, the results of this

study will provide information on how sustainability and environmental concerns can be

included in spreading and keeping an eye on PAS for the good of the people. Such a claim shows

that green policies increase awareness of existing environmental issues (Chin et al., 2018).

Moreover, to further the growth of public concern for the environment and encourage pro-

environmental behavior, authorities must introduce policies with benefits (Nanggong &

Rahmatia, 2019).

It was also revealed that PAS directly affects Attitude (AT) (β = 0.395, p = <0.001) and

subjective norm (β = 0.226, p = 0.033), thereby supporting H2 & H3. This proves that the

perception of Filipinos about authority support has a significant contribution to their attitude and

subjective norm. This means that Filipinos value the impact and implementation of

environmental protection efforts by the government. Lin et al. (2017) found that support from the

government has a positive and significant effect on the attitude (AT) of consumers toward using

renewable sources. This means that the government has a significant effect on its citizens. AT is

referred to as an inclination to support renewable energies. OECD (2011) proves how

government policies offering incentives influence the attitude and actions of consumers

favorably. For instance, providing incentives for metering and billing urges households to use

efficient water appliances, which leads to a 20 percent reduction in water consumption. Thus, in

order for this strategy to be effective, policymakers must consider low-income households that

are prone to the negative effects of the abrupt increase in charges. They may opt for financial

3
provisions that are provided directly to these households or have tax reductions while still

keeping in mind the vitality of broadening the masses' knowledge of the benefits of

environmental-related charges (OECD, 2011).

In addition, how Filipinos see authority support influences social pressure to use eco-

friendly energy sources and an increase in Filipino citizens' interest and willingness to use such

alternatives. This may be done by utilizing public figures and community chiefs (Chin et al.,

2018). This would attract mass attention, leading to the perception that everyone is consuming

these services, thus heightening the patronization of renewable energies. In order to extend the

effectivity of this relationship, the various effects of different policies must be monitored to see

whether measures result in reduced participation or increased motivation to take into account the

social factor of green behavior (e.g., recycling, zero-waste practice, and composting) (OECD,

2011).

On the contrary, PAS was found to have an indirect effect on the perceived behavioral

control (PBC) (β = 0.214, p = 0.076), therefore, rejecting H4. According to Brahim (2014), the

Philippines has an existing public inadequacy of knowledge of the benefits of renewable energy

projects and a limited market for efficient and effective renewable energy sources. Thus, the

insignificant relationship of the aforementioned indicators may be attributed to how the

Philippine government has yet to offer concrete alternatives for sustainable energy sources,

which leads to a lack of understanding of the difficulty or ease of adopting eco-friendly

measures. Regardless, as high awareness constitutes the willingness of consumers to utilize

renewable energy sources, it is recommended that the Philippine government conducts

informational training programs to aid this gap.

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 A significant direct influence was also observed between Perceived Environmental

Concern (PEC) and Attitude (AT) (β = 0.337, p = 0.016), which leads to the acceptance of H5.

Given that there is a general outlook that sufficient knowledge of the environmental

consequences of human activities is linked to a higher likelihood of adopting certain behaviors

toward a more environmentally friendly society (Mufidah et al., 2018), it is intuitive to connect

raised environmental consciousness to a more positive attitude. Furthermore, the study by

Nanggong and Rahmatia (2019) about customer behavior on technology adoption supported

such, showing that consumers who are ecologically aware are more inclined to make use of

digital e-ticketing instead of its traditional counterpart. With that, the essential role of education

is implied to widen the public use of renewable energy sources and induce a gradual general

adoption of sustainable practices.

Perceived Environmental Concern (PEC) was also observed to have a direct effect on the

Subjective Norm (SN) (β = 0.315, p = 0.004) and Perceived Behavioral Control (PBC) (β =

0.399, p = 0.004), thereby accepting H6 and H7. The results suggest that environmental concern

has a significant effect on subjective norms. Because of this, a strong understanding of the

environment is linked to a solid social push to self-impose green practices. This significance is

higher than the results of the study by Chin et al. (2018) (β = 0.22), which were significantly less

than the other presented correlations. This may be due to the study's specifications on skincare,

which cater to a smaller population in comparison to the scope of this study. In the present study,

it was also shown that environmental concern among consumers contributes to their perception

of accepting the idea of using renewable energy sources. According to the study by German et al.

(2022a), this is due to consumers' awareness of the environmental issues that make them support

pro-environment products. Good environmental knowledge among citizens is one factor that

3
affects their behavior toward the environment (Lin, 2017). Since citizens are aware of the

problems present in the environment, it affects their acceptance of renewable energy sources to

lessen the adverse effect of non-renewable energy on the environment.

The correlations of AT, SN, PBC, and BI are the primary constructs of the Theory of

Planned Behavior. Many studies have found strong and positive relationships between these

variables; however, inconsistencies with the result may still appear as the AT and BI in this study

do not show a positive direct effect (β = 0.131, p = 0.075). It can be seen that AT has a direct

effect on BI among all correlations, making it insignificant, rejecting H8. This means that the

consumers' perception of the acceptance of renewable energy does not affect their choice of BI

for a particular product. Similar to the results of the study by Best and Mayerl (2019), which

assessed the consistency of attitude and behavioral intentions to protect the environment among

different countries. It was found that the environmental attitude was insignificant to the

environmental intention. Compared to the countries with high GDP, poor or third-world

countries are most likely to show an insignificant relationship between the two variables. This

concerns the national wealth acts and the individual socioeconomic resources that influence their

attitude. Since the Philippines is still a third-world country, there is a low indicator of the attitude

toward environmental intentions.

Similarly, the other indicators, such as SN (β = 0.420, p <0.001) and PBC (β = 0.378,

p<0.001), appeared to have a significant direct effect on the BI among consumers, thereby

accepting H9 and H10. This finding is similar to that of the study by Abeysekera et al. (2022),

which analyzed the factors influencing green purchase intention and behavior in a Philippine

setting using the theory of planned behavior. Findings showed that SN and PBC significantly

affect consumers' green purchase intention. Considering the culture in the country, wherein

3
subjective norms are rampant, then it is one of the reasons why SN has a significant direct effect

on purchase intention. As long as other people encourage them to do so, they will more likely get

persuaded. Meanwhile, the PBC of consumers includes external factors such as the accessibility

of green products, thus making it also a significant factor in their purchase intention. Another

study by Eugenio et al. (2022) states that students' subjective norms and perceived behavioral

control significantly affect their environmental sustainability. However, there is a greater

engagement between students with higher Higher Education Institutions (HEIs) as they are aware

of the importance of a sustainable society.

It was also found that PEC (β = 0.328, p = <0.001) and PAS (β = 0.228, p = 0.009)

indirectly affect the behavioral intention of consumers. It can be seen in Figure 2 that PEC and

PAS have a significant direct effect on the SN; then SN has a significant direct effect on the BI.

Therefore, since PEC and PAS directly affect SN, it indirectly affects the behavioral intention

(BI) among consumers. Multiple factors contribute to the relationship between PEC and PAS to

SN, which made the results significant. However, one of the contributing factors that made SN

significant to the BI of consumers is the culture present in the country, wherein subjective norms

are observed almost everywhere. This implies that authority support and knowledge about

environmental problems significantly affect, indirectly, the intention of consumers to use

renewable energy sources.

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 CONCLUSION

This study utilized the PEPB model to analyze the factors contributing to Filipino

citizens' acceptance of using Renewable Energy Sources (RES) in the National Capital Region.

The PEPB model has six factors which are Perceived Authority Support (PAS), Perceived

Environmental Concern (PEC), Attitude (AT), Subjective Norm (SN), Perceived Behavioral

Control (PBC), and Behavioral Intention (BI). The results indicated that PAS has the highest

significance to the PEC of Filipino citizens, which produces a positive indirect effect on the

individual's BI. Another factor that appears to have a high significance is the SN, which is the

strongest factor affecting citizens' BI. In contrast, AT appears to be the weakest factor, making it

insignificant.

The results implied that out of the ten hypotheses made, eight hypotheses were accepted,

H1, H2, H3, H5, H6, H7, H9, and H10, and only two were rejected, H4 and H8. This signifies

that Filipinos in the NCR have a high acceptance of switching to renewable energy sources as

they are concerned with the environmental effects that contribute to climate change. The

authority support from the government impacts the environmental concern, attitudes, and

subjective norms of consumers. In such a way, consumers tend to accept renewable energy

whenever there is social pressure from the government. However, due to the relatively low

authority support, the PBC of consumers is also low, which suggests that the government shall

provide programs that educate its citizens about renewable energy sources in order to aid the

inadequate awareness of the advantages of renewable energy sources. Consumers have a higher

acceptance when they know about sustainable energy's benefits. Moreover, different factors

affect their BI: the PBC and SN for the direct effect and PAS and PEC for the indirect effect.

This states that Philippine culture has rampant subjective norms that its citizens follow. As long

3
as the products are accessible, citizens will switch to that product as suggested by the people.

However, the country's socioeconomic status impacts the AT of consumers, making AT an

insignificant factor in their BI. The perception of consumers regarding sustainable energy does

not affect their BI due to their limited socioeconomic resources.

Practical Implication

The Philippine government and its assistance play an integral role in the advancement of

sustainability and environmental actions among Filipinos. Authorities may use the study's results

as a fundamental basis for actions to be done in the renewable energy field because it explores

the positive impact of government support on citizens' attitudes and willingness to use such

energies. In order to attain this desired sustainability, the government must consider the potential

impact of increased charges on its citizens while also gaining the public's opinion in favor of this

transition, as the subjective norm heavily influences it. Subjective norms, along with peers and

authorities, play a significant role in shaping consumer behavior; therefore, promoting the

advantages of switching to renewable energies and raising awareness of its benefits on

environmental concerns may prompt Filipinos to adopt it. They may have informational

campaigns or programs that can aid in filling this gap and boosting awareness among citizens.

The public's awareness of such environmental aspects is linked to their willingness to adopt

green practices. While putting into practice strategies and programs aimed at encouraging

renewable energy sources and minimizing the damaging effects of non-renewable energies on

the environment, policymakers must take these considerations into account.

Theoretical Implication

Understanding the factors that influence consumers' behavior toward renewable energies

still proves relevant due to prevailing environmental issues in the Philippines and the rest of the

3
world. Similar to prior results of other studies, the researched factors have shown significant

correlations with one another, except for perceived authority support to perceived behavioral

control and attitude to behavioral intention. The main difference the current study provides from

previous research is its extensive analysis of a larger scope. This is evident when compared to a

study by Chin et al. (2018), whose results showed less significance between measures, as they

only cater to skincare products with a smaller population, contrary to the current study that

analyzes the acceptance of renewable energies as a whole. Moreover, the results provide more

insights into the inconsistencies of other studies with attitude and behavioral intention (Best &

Mayerl, 2019; Eugenio et al., 2021; Lin et al., 2017; Mufidah et al., 2018), which may then serve

as a basis for future research on the same niche. However, various environmental attitudes and

behavior toward the topic from different populations are still present and should be taken into

account. With the Pro-Environmental Planned Behavior Model, the study presented relevant

results and additional insights on the relations of government initiatives, public awareness, and

knowledge of environmental issues to consumers' behavior toward adopting renewable energies.

Limitations and Future Research Studies

There are limits to this study that can be investigated more in the future. The first

limitation is that the model used in this study is limited to the PEPB model's pre-determined

factors. Other models can be explored to test the variation of results between different models.

Another area for improvement is the scope of the study in terms of location, as it was limited to

Filipino citizens residing in NCR, an urbanized area. Future studies can be conducted in a

different region or among the country's rural places. Lastly, the sampling method in this study

utilized the convenience sampling method. Future studies can use different probability sampling

methods to gather data which can produce different results due to the diversity of respondents.

4
 REFERENCES

Abeysekera, I., Manalang, L., David, R., & Grace Guiao, B. (2022). Accounting for

environmental awareness on green purchase intention and behaviour: Evidence from the

Philippines. Sustainability, 14(19). 10.3390/su141912565

Alarcón, D., Sánchez, J. A., & De Olavide, U. (2015, October). Assessing convergent and

discriminant validity in the ADHD-R IV rating scale: User-written commands for

Average Variance Extracted (AVE), Composite Reliability (CR), and Heterotrait-

Monotrait ratio of correlations (HTMT). In Spanish STATA meeting, 39,. 1–39.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision

Processes, 50(2), 179–211. 10.1016/0749-5978(91)90020-T

Avicenna, F., & Febriani, N. S. (2021). Consumer behavior as a potential acceptance of

renewable energy source in Indonesia. Jurnal Komunikasi Ikatan Sarjana Komunikasi

Indonesia, 6(1), 86–95. 10.25008/jkiski.v6i1.525

Bagot, K. L., Masser, B. M., & White, K. M. (2015). Using an extended theory of planned

behavior to predict a change in the type of blood product donated. Annals of Behavioral

Medicine, 49(4), 510–521. 10.1007/s12160-014-9677-9

Baumgartner, H., & Homburg, C. (1996). Applications of structural equation modeling in

marketing and consumer research: A review. International Journal of Research in

Marketing, 13(2), 139–161. 10.1016/0167-8116(95)00038-0

4
Bellis, M. (2019). Introduction to green technology. Making the case for sustainable resources.

ThoughtCo. https://www.thoughtco.com/introduction-to-green-technology-1991836

Bertsch, V., Hall, M., Weinhardt, C., & Fichtner, W. (2016). Public acceptance and preferences

related to renewable energy and grid expansion policy: Empirical insights for Germany.

Energy, 114, 465–477. 10.1016/j.energy.2016.08.022

Best, H. & Mayerl, J. (2019). Attitudes and behavioral intentions to protect the environment:

How consistent is the structure of environmental concern in cross-national comparison?

International Journal of Sociology, 49(1), 27–52. 10.1080/00207659.2018.1560980

Botetzagias, I., Dima, A.-F., & Malesios, C. (2015). Extending the theory of planned behavior in

the context of recycling: The role of moral norms and of demographic predictors.

Resources, Conservation and Recycling, 95, 58–67. 10.1016/j.resconrec.2014.12.004

Bradley, J. V. (1978). Robustness? British Journal of Mathematical and Statistical Psychology,

31(2), 144-152.

Brahim, S. P. (2014). Renewable Energy and Energy Security in the Philippines. Energy

Procedia, 52, 480–486. 10.1016/j.egypro.2014.07.101

Carfora, V., Cavallo, C., Caso, D., Del Giudice, T., De Devitiis, B., Viscecchia, R., Nardone, G.,

& Cicia, G. (2019). Explaining consumer purchase behavior for organic milk: Including

trust and green self-identity within the theory of planned behavior. Food Quality and

Preference, 76, 1–9. https://doi.org/10.1016/j.foodqual.2019.03.006

4
Chen, C., Xu, X., & Arpan, L. (2017). Between the technology acceptance model and sustainable

energy technology acceptance model: Investigating smart meter acceptance in the United

States. Energy Research & Social Science, 25, 93-104. 10.1016/j.erss.2016.12.011

Chen, M.-F., & Tung, P.-J. (2014). Developing an extended theory of planned behavior model to

predict consumers’ intention to visit green hotels. International Journal of Hospitality

Management, 36, 221–230. 10.1016/j.ijhm.2013.09.006

Cherubini, F., & Ulgiati, S. (2010). Crop residues as raw materials for biorefinery systems – A

LCA case study. Applied Energy, 87(1), 47–57. 10.1016/j.apenergy.2009.08.024

Chin, J., Jiang, B. C., Mufidah, I., Persada, S. F., & Noer, B. A. (2018). The Investigation of

Consumers’ Behavior Intention in Using Green Skincare Products: A Pro-Environmental

Behavior Model Approach. Sustainability, 10(11), 3922. 10.3390/su10113922

Ciolkosz, D. (2019). What is renewable energy? Penn State Extension.

https://extension.psu.edu/what-is-renewable-energy

Clement, C. A., Henning, J. B., & Osbaldiston, R. (2014). Integrating Factors that Predict Energy

Conservation: The Theory of Planned Behavior and Beliefs about Climate Change.

Journal of Sustainable Development, 7 (6), 46-69, 10.5539/jsd.v7n6p46

Conner, M. (2015). Extending not retiring the theory of planned behaviour: a commentary on

Sniehotta, Presseau and Araújo-Soares. Health Psychology Review, 9(2), 141–145.

10.1080/17437199.2014.899060

4
Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and

technology forecasting. Technological Forecasting and Social Change, 173, 121092.

10.1016/j.techfore.2021.121092

Dietz, T., Shwom, R. L., & Whitley, C. T. (2020, July 30). Climate Change and Society. Annual

Review of Sociology, 46(1), 135–158. 10.1146/annurev-soc-121919-054614

Donald, I. J., Cooper, S. R., & Conchie, S. M. (2014). An extended theory of planned behaviour

model of the psychological factors affecting commuters’ transport mode use. Journal of

Environmental Psychology, 40, 39–48. 10.1016/j.jenvp.2014.03.003

Effects of non-renewable resources on the environment (2019) Greentumble.

https://greentumble.com/harmful-effects-of-non-renewable-resources-on-the-environment

Ek, Å., Runefors, M., & Borell, J. (2014). Relationships between safety culture aspects – A work

process to enable interpretation. Marine Policy, 44, 179–186.

10.1016/j.marpol.2013.08.024

Eugenio, T., Carreira, P., Miettinen, N., & Lourenco, I. M. E. C. (2021). Understanding students’

future intention to engage in sustainability accounting: The case of Malaysia and the

Philippines. Journal of Accounting in Emerging Economies, 12(4), 695–715.

10.1108/jaee-10-2020-0277

Field, A. (2006). Discovering statistics using SPSS statistics (2nd ed.). SAGE Publications.

4
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable

variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

10.2307/3151312

Fransson, N., & Gärling, T. (1999). Environmental concern: Conceptual definitions,

measurement methods, and research findings. Journal of Environmental Psychology,

19(4), 369–382. 10.1006/jevp.1999.0141

Fukumori, K.; Matsushita, M.; Okamoto, H.; Sato, N.; Suzuki, Y.; Takeuchi, K. (2002).

Recycling technology of tire rubber. JSAE Rev. 23(2), 259–264. 10.1016/S0389-

4304(02)00173-X

Gao, Y. L., Mattila, A. S., & Lee, S. (2016). A meta-analysis of behavioral intentions for

environment-friendly initiatives in hospitality research. International Journal of

Hospitality Management, 54, 107–115. 10.1016/j.ijhm.2016.01.010

German, J. D., Ong, A. K. S., Perwira Redi, A. A. N., & Robas, K. P. E. (2022a). Predicting

factors affecting the intention to use a 3PL during the COVID-19 pandemic: A machine

learning ensemble approach. Heliyon, 8(11). 10.1016/j.heliyon.2022.e11382

German, J. D., Redi, A. A. N. P., Prasetyo, Y. T., Persada, S. F., Ong, A. K. S., Young, M. N., &

Nadlifatin, R. (2022b). Choosing a package carrier during COVID-19 pandemic: An

integration of pro-environmental planned behavior (PEPB) theory and service quality

(SERVQUAL). Journal of Cleaner Production, 346, 131123.

10.1016/j.jclepro.2022.131123

4
González, A., Sandoval, H., Acosta, P., & Henao, F. (2016). On the acceptance and sustainability

of renewable energy projects—A systems thinking perspective. Sustainability, 8(11),

1171. 10.3390/su8111171

Graham-Rowe, E., Jessop, D. C., & Sparks, P. (2015). Predicting household food waste

reduction using an extended theory of planned behaviour. Resources, Conservation and

Recycling, 101, 194–202. 10.1016/j.resconrec.2015.05.020

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate

data analysis (6th ed.). Upper Saddle River, NJ: Pearson University Press.

Hair, J.F., Hult, G.T.M., Ringle, C.M. and Sarstedt, M. (2016). A primer on partial least squares

structural equation modeling (PLS-SEM). Sage Publications Inc., Thousand Oaks, CA.

Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial

least squares structural equation modeling in marketing research. Journal of the Academy

of Marketing Science, 40(3), 414–433. 10.1007/s11747-011-0261-6

Han, H., & Kim, Y. (2010). An investigation of green hotel customers’ decision formation:

Developing an extended model of the theory of planned behavior. International Journal

of Hospitality Management, 29(4), 659–668. 10.1016/j.ijhm.2010.01.001

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant

validity in variance-based structural equation modeling. Journal of the Academy of

Marketing Science, 43(1), 115–135. /10.1007/s11747-014-0403-8

4
Hu, L.-t., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to

underparameterized model misspecification. Psychological Methods, 3(4), 424–453.

10.1037/1082-989X.3.4.424

Kåberger, T. (2018). Progress of renewable electricity replacing fossil fuels. Global Energy

Interconnection, 1(1), 48-52. 10.14171/j.2096-5117.gei.2018.01.006

Kardooni, R., Yusoff, S. B., & Kari, F. B. (2016). Renewable energy technology acceptance in

Peninsular Malaysia. Energy Policy, 88, 1-10. 10.1016/j.enpol.2015.10.005

Kashif, M., & De Run, E. C. (2015). Money donations intentions among Muslim donors: an

extended theory of planned behavior model. International Journal of Nonprofit and

Voluntary Sector Marketing, 20(1), 84–96. 10.1002/nvsm.1519

Kwak, S.-Y., Cho, W.-S., Seok, G.-A., & Yoo, S.-G. (2020). Intention to use sustainable green

logistics platforms. Sustainability, 12(8), 3502. 10.3390/su12083502

Lanzini, P., & Khan, S. A. (2017). Shedding light on the psychological and behavioral

determinants of travel mode choice: A meta-analysis. Transportation Research Part F:

Traffic Psychology and Behaviour, 48, 13–27. 10.1016/j.trf.2017.04.020

Li, D.; Zhao, L.; Ma, S.; Shao, S.; Zhang, L. (2019). What influences an individual’s pro-

environmental behavior? A literature review. Resour. Conserv. Recycl, 146, 28–34,

10.1016/j.resconrec.2019.03.024

Lin, S.-C., Nadlifatin, R., Amna, A., Persada, S., & Razif, M. (2017). Investigating citizen

behavior intention on mandatory and voluntary pro-environmental programs through a

4
 pro-environmental planned behavior model. Sustainability, 9(7), 1289.

10.3390/su9071289

Lloyd, S., & Nakamura, T. (2022). Public perceptions of renewable energy in the Philippines.

Sustainability, 14(16), 9906. 10.3390/su14169906

Macovei, O. I. (2015). Determinants of Consumers’ Pro-Environmental Behavior–Toward an

Integrated Model. Journal of Danubian Studies and Research, 5 (2), 261-275.

Ma, C., Rogers, A. A., Kragt, M. E., Zhang, F., Polyakov, M., Gibson, F., Chalak, M., Pandit, R.,

& Tapsuwan, S. (2015). Consumers’ willingness to pay for renewable energy: A meta-

regression analysis. Resource and Energy Economics, 42, 93–109.

10.1016/j.reseneeco.2015.07.003

Makki, A. A., & Mosly, I. (2020). Factors affecting public willingness to adopt renewable

energy technologies: An exploratory analysis. Sustainability, 12(3), 845.

10.3390/su12030845

Merrild, H. K., Damgaard, A., & Christensen, T. H. (2008). Life cycle assessment of waste paper

management: The importance of technology data and system boundaries in assessing

recycling and incineration. Resources, Conservation and Recycling, 52(12), 1391-1398.

doi.org/10.1016/j.resconrec.2008.08.004

Mintie, A. (2006). Using stone and ceramic tile for a healthy building environment. Stone

World. https://www.stoneworld.com/articles/84629-using-stone-and-ceramic-tile-for-a-

healthy-b uilding-environment

Mohanty, P. K., Patro, A., Harindranath, R. M., Senthil Kumar, N., Panda, D. K., & Dubey, R.

4
(2021). Perceived government initiatives: Scale development, validation and impact on

4
 consumers’ pro-environmental behaviour. Energy Policy, 158.

10.1016/j.enpol.2021.112534

Mufidah, I., Jiang, B. C., Lin, S., Chin, J., Rachmaniati, Y. P., & Persada, S. F. (2018).

Understanding the Consumers’ Behavior Intention in Using Green Ecolabel Product

through Pro-Environmental Planned Behavior Model in Developing and Developed

Regions: Lessons Learned from Taiwan and Indonesia. Sustainability, 10(5), 1423.

10.3390/su10051423

Nanggong, A., & Rahmatia, R. (2019). Perceived Benefit, Environmental Concern and

Sustainable Customer Behavior on Technology Adoption. The Asian Journal of

Technology Management, 12(1), 31–47. 10.12695/ajtm.2019.12.1.3

Nadlifatin, R., Lin, S.-C., Rachmaniati, Y., Persada, S., & Razif, M. (2016). A pro-

environmental reasoned action model for measuring citizens’ intentions regarding

ecolabel product usage. Sustainability, 8(11), 1165. 10.3390/su8111165

Nelson, G. C., Valin, H., Sands, R. D., Havlík, P., Ahammad, H., Deryng, D., Elliott, J.,

Fujimori, S., Hasegawa, T., Heyhoe, E., Kyle, P., Von Lampe, M., Lotze-Campen, H.,

Mason d’Croz, D., van Meijl, H., van der Mensbrugghe, D., Müller, C., Popp, A.,

Robertson, R.,

. . . Willenbockel, D. (2013, December 16). Climate change effects on agriculture:

Economic responses to biophysical shocks. Proceedings of the National Academy of

Sciences, 111(9), 3274–3279. 10.1073/pnas.1222465110

Ntanos, S., Kyriakopoulos, G., Chalikias, M., Arabatzis, G., & Skordoulis, M. (2018). Public

perceptions and willingness to pay for renewable energy: A case study from Greece.

5
Sustainability, 10(3), 687. 10.3390/su10030687

5
OECD (2011). Greening household behavior: The role of public policy. OECD Publishing.

10/1787/9789264096875-en

Ong, A. K. S., Prasetyo, Y. T., Salazar, J. M. L. D., Erfe, J. J. C., Abella, A. A., Young, M. N.,

Chuenyindee, T., Nadlifatin, R., & Ngurah Perwira Redi, A. A. (2021). Investigating the

acceptance of the reopening Bataan nuclear power plant: Integrating Protection

motivation theory and extended theory of planned behavior. Nuclear Engineering and

Technology, 54(3). 10.1016/j.net.2021.08.032

Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple

processes by which past behavior predicts future behavior. Psychological Bulletin,

124(1), 54–74. 10.1037/0033-2909.124.1.54

Peña-García, N., Gil-Saura, I., Rodríguez-Orejuela, A., & Siqueira-Junior, J. R. (2020). Purchase

intention and purchase behavior online: A cross-cultural approach. Heliyon, 6(6), e04284.

10.1016/j.heliyon.2020.e04284

Persada, S. F. (2016). Pro environmental planned behavior model to explore the citizens’

participation intention in environmental impact assessment: An evidence case in

Indonesia. National Taiwan University of Science and Technology: Taipei, Taiwan.

Persada, S. F., Lin, S.C., Nadlifatin, R., & Razif M. (2015). Investigating the citizens' intention

level in environmental impact assessment participation through an extended theory of

planned behavior model. Global NEST Journal, 17(4), 847-857. 10.30955/gnj.001703

5
Puspita, G.C. (2017). Investigating the Intention of Female Consumers for Purchasing Green

Skincare by Using the Pro-Environmental Planned Behavior Model; Institut Teknology

Sepuluh Nopember: Surabaya, Indonesia.

Roxas, F., & Santiago, A. (2016, June). Alternative framework for renewable energy planning in

the Philippines. Renewable and Sustainable Energy Reviews, 59, 1396–1404.

10.1016/j.rser.2016.01.084

Sardianou, E., & Genoudi, P. (2013). Which factors affect the willingness of consumers to adopt

renewable energies? Renewable Energy, 57, 1–4. 10.1016/j.renene.2013.01.031

Sarkis, J., & Weinrach, J. (2001). Using data envelopment analysis to evaluate environmentally

conscious waste treatment technology. Journal of Cleaner Production, 9(5), 417–427.

10.1016/s0959-6526(00)00084-6

Sidique, S. F., Lupi, F., & Joshi, S. V. (2010). The effects of behavior and attitudes on drop-off

recycling activities. Resources, Conservation and Recycling, 54(3), 163–170.

10.1016/j.resconrec.2009.07.012

Scalco, A., Noventa, S., Sartori, R., & Ceschi, A. (2017). Predicting organic food consumption:

A meta-analytic structural equation model based on the theory of planned behavior.

Appetite, 112, 235–248. 10.1016/j.appet.2017.02.007

Shaftel, H., Callery, S., Jackson, R., & Bailey, D. (2022). The effects of climate change. NASA.

https://climate.nasa.gov/effects/

5
Soorani, F., & Ahmadvand, M. (2019). Determinants of consumers’ food management behavior:

Applying and extending the theory of planned behavior. Waste Management, 98, 151–

159. https://doi.org/10.1016/j.wasman.2019.08.025

Steg, L., & Vlek, C. (2009). Encouraging pro-environmental behaviour: An integrative review

and research agenda. Journal of Environmental Psychology, 29(3), 309–317.

10.1016/j.jenvp.2008.10.004

Stigka, E. K., Paravantis, J. A., & Mihalakakou, G. K. (2014). Social acceptance of renewable

energy sources: A review of contingent valuation applications. Renewable and

Sustainable Energy Reviews, 32, 100-106. 10.1016/j.rser.2013.12.026

Synodinos, C. & Bevan-Dye, Al. (2014). Determining African Generation Y Students’

Likelihood of Engaging in Pro-environmental Purchasing Behaviour. Mediterranean

Journal of Social Sciences, 5 (21), 101-110.

Teo, T., & Lee, C. B. (2010). Examining the efficacy of the Theory of Planned Behavior (TPB)

to understand pre-service teachers’ intention to use technology. Curriculum, technology

& transformation for an unknown future. Proceedings ascilite Sydney. 968–972.

http://ascilite.org.au/conferences/sydney10/procs/Teo-concise.pdf

Tierney, S., & Bird, L. (2020). Setting the record straight about renewable energy. World

Resources Institute.

https://www.wri.org/insights/setting-record-straight-about-renewable-energy#:~:text=Ren

ewables%20generate%20more%20energy%20than

5
Trudel, R. (2018). Sustainable consumer behavior. Consumer Psychology Review, 2(1).

10.1002/arcp.1045

Turaga, R. M. R., Howarth, R. B., & Borsuk, M. E. (2010). Pro-environmental behavior. Annals

of the New York Academy of Sciences, 1185(1), 211–224. 10.1111/j.1749-

6632.2009.05163.x

Wall, W. P., Khalid, B., Urbański, M., & Kot, M. (2021). Factors influencing consumer’s

adoption of renewable energy. Energies, 14(17), 5420. 10.3390/en14175420

Wang, C., Padmanabhan, P., & Huang, C. (2022). The impacts of the 1997 Asian financial crisis

and the 2008 global financial crisis on renewable energy consumption and carbon dioxide

emissions for developed and developing countries. Heliyon, 8(2), e08931.

10.1016/j.heliyon.2022.e08931

Watts, N., Amann, M., Arnell, N., Ayeb-Karlsson, S., Beagley, J., Belesova, K., Boykoff, M.,

Byass, P., Cai, W., Campbell-Lendrum, D., Capstick, S., Chambers, J., Coleman, S.,

Dalin, C., Daly, M., Dasandi, N., Dasgupta, S., Davies, M., Di Napoli, C., . . . Costello,

A. (2021c, January). The 2020 report of The Lancet Countdown on health and climate

change: responding to converging crises. The Lancet, 397(10269), 129–170.

10.1016/s0140-6736(20)32290-x

Why is renewable energy important? (2019). REN21.

https://www.ren21.net/why-is-renewable-energy-important/#:~:text=Renewable%20energ

y%20is%20no%20exception

5
Willke, Th., & Vorlop, K.-D. (2004). Industrial bioconversion of renewable resources as an

alternative to conventional chemistry. Applied Microbiology and Biotechnology, 66(2),

131–142. 10.1007/s00253-004-1733-0

Yamane, T. (1967). Statistics: An introductory analysis. New York Harper and Row CO. USA,

213, 25.