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https://doi.org/10.56556/gssr.v1i2.408 

                                                                  

 

Global Scientific Research   53 
 

Toward environmental sustainability: Nexus between tourism, economic growth, 
energy use and carbon emissions in Singapore 
 

Asif Raihan1*, Dewan Ahmed Muhtasim2, Sadia Farhana3, Md Ahsan Ul Hasan2, Omar Faruk2, Arindrajit Paul2 
 
1Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia 
2Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia 
3 School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia 
 

*Corresponding author: asifraihan666@gmail.com, ORCID ID: 0000-0001-9757-9730 

Received: 09 December, 2022, Accepted: 25 December, 2022, Published: 27 December, 2022 

 

 

Abstract 

Singapore is a renowned tourist destination; however, the country's rapid economic growth has led to rising energy 

consumption and carbon emissions. This study aims to examine the factors that contribute to carbon dioxide (CO2) emissions 

in Singapore, including tourism, economic growth, and energy use. The dynamic ordinary least squares (DOLS) approach 

was used to analyze time series data from 1990 to 2020. The results of the empirical study revealed that the tourist coefficient 

is positive and significant. A 0.50% increase in CO2 emissions relates to a 1% increase in tourism activities over time, 

according to the findings. In addition, the result indicates that the economy's long-run growth coefficient is significantly 

negative. This shows that a 1% economic growth will reduce CO2 emissions by 0.03% in the long run. Furthermore, a positive 

and statistically significant correlation for energy consumption suggests that a long-term increase of 1% in energy 

consumption is associated with an increase of 0.88% in CO2 emissions. To promote the emergence of sustainable development 

and a low-carbon economy, this article proposed policy recommendations addressing the reduction of emissions and the 

promotion of ecologically responsible and sustainable tourism while boosting the utilization of renewable energy 

technologies. 

 

Keywords: CO2 emissions; Sustainability; Tourism; Low-carbon economy; Renewable energy 

 

Introduction 

 

Most greenhouse gases (GHGs) in the atmosphere are CO2, 

and humans are responsible for most of these emissions. 

Human activities that contribute to climate change include 

using fossil fuels for energy and cutting down trees (Raihan 

et al., 2018; Jaafar et al., 2020; Raihan et al., 2021a; Raihan 

et al., 2021b; Isfat & Raihan, 2022). CO2 emissions are 

expected to have devastating effects on the global climate 

system and every area of human existence. Due to 

atmospheric carbon dioxide sensitivity, the global climate 

system is quite sensitive. This is because climate change is 

expected to be one of humanity's biggest issues in the next 

decades (Raihan et al., 2019; Raihan et al., 2022a; Islam et 

al., 2022). Thus, reducing carbon dioxide emissions and 

enhancing the environment are two of the most critical 

global issues that must be tackled immediately to promote 

sustainable growth and mitigate climate change (Raihan & 

Said, 2022; Raihan & Tuspekova, 2022a). As a small, 

lowland city-state with one of the world's most open 

economies, Singapore is especially vulnerable to climate 

change's negative effects (Raihan & Tuspekova, 2022b). 

Singapore has ratified several international conventions to 

limit its carbon emissions and mitigate climate change. The 

Kyoto Protocol and Paris Agreement are examples of these 

conventions. As a result, understanding the main elements 

that affect CO2 concentrations is crucial to improving 

Singapore's environment. Tourism, energy security, 

economic growth, and environmental sustainability are all 

crucial in Southeast Asian countries like Singapore. If 

modern growth methods cannot be separated from natural 

resources, the ecosystem will degrade (Raihan et al., 

2022b).  

Sustainable tourism is garnering global attention due to the 

direct, indirect, and induced economic effects of the tourist 

industry’s expansion. Singapore, one of Asia's most 

industrialized nations, relies heavily on tourism (Katirciolu, 

2014). Singapore's economy also benefits from tourism. 

More than three times the country's population visited 

Singapore in 2019. However, the tourism industry's key 

players' decisions are affected by its fragility. According to 

the UNWTO, worldwide travel will peak in 2030. Because 

of this, global revenue is expected to reach $2 billion every 

year (Raihan et al., 2022c). Tourism boosts economies 

https://doi.org/10.56556/gssr.v1i2.408
mailto:asifraihan666@gmail.com


Global Sustainability Research 

Global Scientific Research   54 
 

worldwide (Voumik et al., 2022a). However, tourism 

increases the need for energy to power transportation, 

accommodation, and support facilities, as well as food 

production and tourist site management, all of which can 

harm the environment (Raihan & Tuspekova, 2022c). The 

UNWTO estimates that tourism accounts for 5% of global 

emissions. Seventy-five percent of tourism emissions come 

from transportation, while twenty percent come from 

accommodations (Raihan & Tuspekova, 2022d). As a result, 

when economy and transportation activities are included, 

the country's energy usage illuminates the link between 

tourism growth and CO2 emissions (Raihan & Tuspekova, 

2022e). Tourism's importance in economic growth, 

particularly in Singapore, has been ignored in emission 

models due to the idea that tourism drives economic growth. 

Singapore has become one of the world's wealthiest nations 

due to its economy's rapid growth in recent decades. In 

2019, Singapore's GDP per capita was USD 61174, ranking 

third worldwide (World Bank, 2022). However, whether 

unfettered economic growth harms the environment and 

generates enough cash to pay for environmental protection 

affects environmental development and sustainability plans. 

Environmental development and sustainability plans will be 

rescinded if unfettered economic growth harms the 

environment (Raihan et al., 2022d). Conversely, the 

environment could be improving due to the economy's 

ongoing replacement of polluting technologies with cleaner 

ones (Raihan & Tuspekova, 2022f). Because of this, 

assessing if Singapore's economic growth is linked to 

environmental sustainability is one of the most important 

things to undertake. Singapore's amazing economic growth 

is also linked to an increase in energy consumption and 

tourism. Due to its various energy resources, fast 

industrialization, and rising tourism Singapore's economy is 

one of the world's strongest. Singapore's growing energy 

needs can only be fulfilled by burning a range of fossil fuels 

(Mehmood, 2021). Singapore's rising dependence on fossil 

fuels for energy has increased CO2 emissions and degraded 

the environment. Due to rising energy needs, As a result, 

there is widespread concern about rising emission intensity, 

especially in the energy sector. Thus, climate change has 

exacerbated Singapore's energy conservation debate. 

Because of this, it is more important than ever to understand 

how tourism, energy consumption, economic growth, and 

CO2 emissions are linked. 

However, despite the fact that the interplay between CO2 

emissions and their causes has recently been a prominent 

issue of discussion among researchers worldwide, only a 

small amount of research has been done in Singapore. To 

fill this research gap, the present study used econometric 

approaches to examine the dynamic effects of tourism, 

economic growth, and energy consumption on Singapore's 

CO2 emissions. This study contributes to both the existing 

literature and Singapore's policymaking process. To be 

more explicit, the current study fills a research gap in the 

academic literature by conducting a comprehensive 

econometric analysis of CO2 and its determinants in 

Singapore. The findings may give readers from diverse 

countries new ideas on environmental dynamics and 

sustainable management. Time series statistical features and 

long-term correlations among determinants are shown in 

this study. The study's findings would provide decision-

makers with more complete and relevant statistics to 

establish successful policies in sustainable and 

environmentally friendly tourism, a green and low-carbon 

economy, and renewable energy development. These 

strategies may lessen climate change and carbon dioxide 

emissions. This research also aids in analyzing 

environmental laws and creating new ones. This will help 

Singapore prepare for global warming's effects. The 

guidance may also reinforce policies and action plans to 

mitigate climate change, ensuring long-term sustainable 

development and environmental quality. 

The rest of the article is structured as follows. The 

Introduction is followed by the section Literature Review, 

where relevant research studies have been discussed. The 

third section is the Methodology section, followed by the 

Results and Discussion section. Subsequently, the last 

section presents the Conclusion, policy recommendations, 

limitations of the study, and future research directions. 

 

Literature Review 

 

The association between economic progress, energy usage, 

and pollution has been thoroughly documented in empirical 

investigations. A variety of research including numerous 

countries, factors, and methodologies were considered. 

Raihan et al. (2022e) revealed the positive effects of 

economic growth and energy use on CO2 emissions In 

Bangladesh utilizing the DOLS, FMOLS, and CCR 

methods using the data over 1972-2018. Odugbesan and 

Adebayo (2020) found the positive impacts of economic 

growth and energy consumption on CO2 emissions in 

Nigeria by utilizing the yearly data spanning from 1981 to 

2016 employing ARDL, FMOLS, and DOLS techniques. 

Adebayo and Kalmaz (2021) used ARDL, FMOLS, and 

DOLS methods to uncover a positive interaction between 

economic growth and energy use on CO2 emissions in Egypt 

by using the data from 1971 to 2014. By employing the 

ARDL approach, Nondo and Kahsai (2020) revealed the 

positive effects of economic growth and energy intensity on 

CO2 emissions in South Africa from 1970 to 2016. Liu and 

Bae (2018) revealed the positive effects of economic growth 

and energy consumption on CO2 emissions in China from 

1970 to 2015 applying the ARDL method. By using time 

series data over 1985-2013 for 20 African countries, 

Raheem and Ogebe (2017) found that economic growth and 

energy use increases CO2 emissions. By utilizing FMOLS 

and DOLS estimators using the data from 1971-2014, Vo et 

al. (2019) revealed that the level of CO2 emissions is 

positively associated with economic growth and energy use 



Global Sustainability Research 

Global Scientific Research   55 
 

in five ASEAN nations (Indonesia, Myanmar, Malaysia, the 

Philippines, and Thailand). 

The influence of tourism on environmental degradation has 

been a frequent issue of controversy in recent years. Raihan 

and Tuspekova (2022c) revealed the positive effect of 

economic growth and tourism on CO2 emissions in India by 

applying the DOLS, FMOLS, and CCR methods utilizing 

the data from 1990 to 2020. By utilizing the ARDL model 

for Pakistan using time series data from 1981 to 2017, Ali 

et al. (2020) found that economic growth, energy use, and 

tourism positively influence CO2 emissions. Raihan and 

Tuspekova (2022d) revealed the positive effect of economic 

growth and tourism on CO2 emissions in Turkey by 

applying the DOLS, FMOLS, and CCR methods utilizing 

the data from 1990 to 2020. By employing the DOLS, 

FMOLS, and CCR approaches using time series data 

covering 1990-2019, Raihan et al. (2022c) reported that 

economic growth and tourism increase CO2 emissions in 

Argentina. Ng et al. (2015) found positive impacts of 

economic growth, energy use, and tourism on CO2 

emissions by employing the ARDL technique for Malaysia 

using the data over 1981-2011. By employing DOLS, 

FMOLS, and CCR techniques utilizing the data from 1990 

to 2019 for Brazil, Raihan and Tuspekova (2022e) revealed 

that economic growth, energy use, and tourism have a 

positive impact on CO2 emissions.  

Furthermore, Ahmad et al. (2019) reported the positive 

effects of economic growth, energy use, and tourism on CO2 

emissions in Indonesia and the Philippines by applying the 

FMOLS technique utilizing the data over 1995-2014. 

Selvanathan et al. (2021) utilized the ARDL methodology 

to discover a positive effect of economic growth, energy 

use, and tourism on CO2 emissions in South Asian countries 

using data over the period of 1990-2014. By applying 

DOLS, FMOLS, and CCR estimators using the yearly data 

spanning between 1990 and 2019, Raihan and Tuspekova 

(2022g) found that economic growth, energy use, and 

tourism influence CO2 emissions positively in Mexico. In 

addition, Raihan and Tuspekova (2022h) used DOLS, 

FMOLS, and CCR methods to uncover a positive 

interaction between economic growth and energy use on 

CO2 emissions in Kazakhstan by using the data from 1996 

to 2020. By employing the DOLS technique using yearly 

data between 1995 and 2010, Dogan et al. (2017) reported 

that economic growth, energy use, and tourism trigger CO2 

emissions in OECD countries. By employing the DOLS, 

FMOLS, and CCR approaches using time series data 

covering 1990-2019, Raihan and Tuspekova (2022i) 

reported that economic growth and energy use increase CO2 

emissions in Nepal. In addition, Zaman et al. (2016) 

reported that economic growth and tourism positively 

influence CO2 emissions in the panel of three diversified 

World regions including East Asia & Pacific, the European 

Union, and High-income OECD and Non-OECD countries.  

However, the review of empirical literature indicates that 

there is a scarcity of research on the relationship between 

CO2 emissions and its determinants in the case of Singapore, 

although it has become a hot topic among current 

researchers worldwide. Therefore, the present study 

attempts to fill up the literature gap by investigating the 

dynamic impacts of economic growth, energy use, and 

tourism on CO2 emissions in Singapore. 

 

Methodology  

 

Data 

 

This study used the DOLS method of cointegration (Stock 

& Watson, 1993) because it may represent a continuous 

response variable as a function of one or more predictor 

components. It can be used to investigate experimental, 

economic, and environmental data and predict complex 

system behavior. Singapore is a popular tourist destination, 

and tourism increases energy demand, CO2 emissions, and 

economic growth. Therefore, this study examined tourism, 

economic expansion, and energy consumption to determine 

how each of these factors affects CO2 emissions. Time 

series data on Singapore from 1990 to 2020 were obtained 

from the World Development Indicator (WDI) dataset 

(World Bank, 2022). To assure a normal distribution, the 

variables are logarithmically converted for estimation. After 

logarithmically transforming the variables, organizing the 

features into a bell curve with more conventional 

proportions improves model fit. This lets us depict data 

more accurately. Table 1 lists variables, measuring units, 

logarithmic forms, and data sources. In addition, Figure 1 

shows the study variables' annual trends. 

 

 

Table 1. Logarithmic representations, units, and data sources of the variables 

Variables Description 
Logarithmic 

forms 
Units Sources 

C CO2 emissions LC Kilotons WDI 

T International tourism LT Number of tourist arrivals WDI 

Y Economic growth LY Constant Singapore dollar WDI 

E Energy use LE Kg of oil equivalent per capita WDI 



Global Sustainability Research 

Global Scientific Research   56 
 

  

  
(a) CO2 emissions     (b) Tourism  

 

  
(c) Economic growth     (d) Energy use  

 

Figure 1. Singapore's yearly trends for the variables studied 

Source: World Bank (2022) 

 

Econometric strategies 

 

In theory, tourism contributes to pollution and higher CO2 

emissions, which in turn are linked to rising energy use and 

a growing economy. This research aimed to estimate the 

impacts of tourism, economic growth, and energy 

consumption on CO2 emissions by plugging relevant data 

into the following Equation (1) generated within the 

Marshallian demand function (Friedman, 1949) at time t: 

 

Ct = ƒ (Tt; Yt; Et)     

      (1) 

 

Moreover, Equation (2) depicts the empirical model: 

 

Ct= τ0 + τ1Tt + τ2Yt + τ3Et   

      (2) 

 

Further, it is possible to use Equation (2) as the 

econometric model in Equation (3): 

 

Ct= τ0 + τ1Tt + τ2Yt + τ3Et + εt   

      (3) 

 

where τ0 and εt stand for intercept and error term, 

respectively. In addition, τ1, τ2, and τ3 denote the 

coefficients.  

Furthermore, the logarithmic arrangement of Equation (3) 

can be expressed in Equation (4) as follows: 

 

LCt= τ0 + τ1LTt + τ2LYt + τ3LEt + εt  

      (4) 

 

Figure 2 depicts a flow diagram of the analytical 

procedures employed in the study to investigate the 

nexus between tourism, economic growth, energy 

consumption, and CO2 emissions in Singapore. 

 

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Global Sustainability Research 

Global Scientific Research   57 
 

 

Figure 2. The flow chart of the analysis 

 

Avoiding an incorrect regression requires testing the unit 

root (Raihan & Voumik, 2022a). First, differentiate the 

regression variables, then estimate the desired equation 

using stationary processes. This ensures the experiment's 

variables won't change (Raihan & Tuspekova, 2022h). In 

the empirical literature, the sequence of integration must be 

understood before determining cointegration. When 

defining the integration order of a series, several unit root 

tests are needed because their power depends on the sample 

size (Raihan & Tuspekova, 2022i). The current study used 

Dickey and Fuller's (1979) Augmented Dickey-Fuller 

(ADF) analysis, the DF-GLS test proposed by Elliott et al. 

(1996), and Phillips and Perron's (1998) P-P unit root test. 

These tests were aimed to find the autoregressive unit root 

(Raihan & Voumik, 2022b). In this study, the unit root test 

confirmed that no variable surpassed the order of integration 

and supported the DOLS methodology as an alternative to 

cointegration methods. 

This study used DOLS to analyze time series data that 

includes initial difference term leads and lags. It also 

considers variables that explain outcomes. This is done to 

preserve endogeneity and compute standard deviations 

using a serial correlation-free covariance matrix of errors.  

This ensures data accuracy. The DOLS method ensures that 

the predicted standard deviations were calculated correctly 

(Raihan and Tuspekova 2022j). The DOLS approach shows 

that the incorrect term has been orthogonalized by 

considering the words before and after each term. This can 

be achieved by comparing leading and trailing terms. The 

DOLS test can assess statistical significance as the DOLS 

estimator’s standard deviations follow a normal asymptotic 

distribution (Raihan and Tuspekova 2022k). The DOLS 

method incorporates individual variables into the 

cointegrated framework in mixed-order integration. This 

can be done by estimating the dependent variable's value 

with respect to the explanatory components' levels, leads, 

and lags (Raihan et al., 2022e). The DOLS estimation's main 

strength is its cointegrated outline, which includes 

individual integration variables of a mixed order (Raihan et 

al., 2022f). In the DOLS technique of estimating, one of the 

I (1) components was regressed with the other variables, 

some of which were also I(1) variables with leads (p) and 

lags (-p) of the original difference, while others were I(0) 

variables with a constant term. This was done to determine 

which variable best described the relationship between the 

two sets of variables. Each aspect's value was carefully 



Global Sustainability Research 

Global Scientific Research   58 
 

evaluated. These features were studied and contrasted 

before being grouped and summarized. By pooling 

explanatory component leads and lags, this estimate 

overcomes small sample bias, endogeneity, and 

autocorrelation (Begum et al., 2020). After determining that 

the variables under inquiry cointegrate, Equation (5) was 

used to estimate the DOLS long-run coefficient. 

 

ΔLCt = τ0 + τ1LCt−1 + τ2LTt−1 + τ3LYt−1 + τ4LEt−1

+ ∑ γ1

q

i=1

ΔLCt−i + ∑ γ2

q

i=1

ΔLTt−i

+ ∑ γ3

q

i=1

ΔLYt−i + ∑ γ4

q

i=1

ΔLEt−i+ εt 

(5) 

where Δ is the first difference and q is the optimum lag 

length in the above Equation (5). 

 

Results and Discussion 

 

Skewness, kurtosis, probability, and Jarque-Bera normality 

tests are shown in Table 2. The kurtosis statistic was used to 

identify whether the series had light or heavy tails compared 

to the normal distribution. The empirical evidence shows 

that each series is platykurtic since every value is smaller 

than 3, the critical number. Skewness scores around zero 

suggest that all variables have met the normality premise. 

Low Jarque-Bera probabilities indicate normal parameters. 

 

Table 2. Summary statistics of the variables 

Variables LC LT LY LE 

Mean 10.59614 16.04240 26.21635 8.539070 

Median  10.56695 15.93525 26.22507 8.541262 

Maximum 10.80243 16.76604 26.89910 8.905262 

Minimum 10.27402 14.82420 25.30307 8.238199 

Std. Dev. 0.129226 0.432733 0.497200 0.124795 

Skewness -0.584899 -0.224998 -0.201045 0.456229 

Kurtosis 2.138226 2.290868 1.825116 2.865745 

Jarque-Bera 1.792233 0.370839 1.991788 2.571713 

Probability 0.408152 0.830756 0.369393 0.6061676 

Sum 328.4804 497.3144 812.7069 264.7112 

Table 3 shows the linearity of the variable connection. All 

parameters appear to be strongly correlated. This shows that 

when the first variable increases, so do the other variable. 

This inquiry used unit root tests to assess whether the 

variables were stationary based on correlation analysis. 

 

 

Table 3. The results of the correlation analysis 

 LC LT LY LE 

LC 1.000000 0.829646 0.882937 0.916525 

LT 0.829646 1.000000 0.864545 0.927679 

LY 0.882937 0.864545 1.000000 0.912535 

LE 0.916525 0.927679 0.912535 1.000000 

The autoregressive unit root was found using ADF, DF-

GLS, and P-P techniques. Table 4 shows ADF, DF-GLS, 

and P-P unit root test results. Three unit root tests showed 

that variables were not stationary at the levels but became 

stationary at the first difference. Thus, the difference-

oriented DOLS methodology is suitable for data analysis. 

 

 

Table 4. The findings of unit root testing 

Logarithmic form of the variables LC LT LY LE 

ADF 
Log levels -2.3288 -1.8033 -2.4978 -2.0176 

Log first difference -4.8421*** -2.7904** -3.8153*** -5.9125*** 

DF-GLS 
Log levels -0.7277 -1.7846 -0.3732 -2.1862 

Log first difference -4.7079*** -2.6944* -3.9132*** -6.0523*** 

P-P 
Log levels -2.3158 -1.8033 -2.4979 -2.3458 

Log first difference -4.8182*** -2.8369** -3.7357*** -5.7726*** 

***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. 

 



Global Sustainability Research 

Global Scientific Research   59 
 

Table 5 shows DOLS estimates. The long-run coefficient of 

LT, which is positive and statistically significant at the 1% 

level, shows that a 1% increase in tourism leads to a 0.50% 

increase in CO2 emissions over time. This study found a 

strong correlation between Singapore's carbon dioxide 

emissions and tourist numbers. Tourism increases 

Singapore's air pollution, which worsens the environment. 

The study found that more visitors visiting Singapore 

increases energy usage and climate change. Since the 

research study's conclusions match those of other 

Singaporean studies, this discovery is not surprising. Zhang 

and Liu (2019) found that Singapore's carbon dioxide 

emissions increased with foreign tourists. In addition, 

research from other countries, such as Ng et al. (2015), 

Zaman et al. (2016), Dogan et al. (2017), Ahmad et al. 

(2019), Ali et al. (2020), Selvanathan et al. (2021), and 

Raihan et al. (2022c), supports the current study's 

conclusion that tourism and CO2 emissions in Singapore are 

positively correlated. However, if tourism is not planned 

and controlled, it may harm the environment, which is vital 

to the tourism industry. Tsai et al. (2014) found that hotels 

with better service levels emit more CO2 per guest. 

International tourism and travel spending increase carbon 

dioxide emissions in wealthy and developing nations 

(Zaman et al., 2016). Dogan et al. (2017) also revealed that 

tourism increases CO2 emissions through various modes of 

transportation, touristic infrastructure development, and 

local government and commercial services. The travel and 

tourist business is a major contributor to the degrading 

environment. Tourism generates greenhouse gas emissions 

in transportation, energy, and heat production (Ng et al., 

2015). Tourism harms the biophysical and sociocultural 

environment. Tourism releases smoke, sulfur dioxide, 

nitrogen oxides, and other hazardous pollutants into the 

atmosphere, deteriorating the environment. Tourist 

activities may damage the ecosystem, making the site less 

appealing. Waste mismanagement may turn a beautiful spot 

into the trash. Tourism also causes noise pollution from 

vehicles. The growth of the airline sector, hotel occupancy, 

and motorized boat use have all contributed to this issue 

(Raihan & Tuspekova, 2022b). Sustainable tourism is 

necessary to reduce tourism's negative impacts on society, 

the environment, the climate, and the economy.  

 

 

Table 5. DOLS outcomes: dependent variable LCO2 

Variables Coefficient Standard Error t-Statistic p-value 

LT 0.498110*** 0.314185 1.585401 0.0013 

LY -0.035536* 0.060398 -0.585401 0.0719 

LE 0.880200*** 0.329043 2.675034 0.0025 

C 3.729782 1.843548 2.023155 0.1153 

R2 0.923172    

Adjusted R2 0.902524    

Standard error of the estimate 0.057280    

Long run variance 0.007186    

Mean of the dependent variable 10.59614    

F-statistic 1129.36    

Prob (F-statistic) 0.000000    

Root mean square error (RMSE) 0.021297    

Mean Absolute Error (MAE) 0.018543    

***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. 

 

 

Singapore is used as a case study to see if economic 

production and environmental pollution are related. Despite 

LY's negative long-run coefficient, this discovery is 

statistically significant at 10%. This means that Singapore's 

CO2 emissions will reduce by 0.03% for every 1% increase 

in economic development. This study found that economic 

growth did not harm the environment. The result shows that 

an expanding economy negatively impacts CO2 emissions 

over time. The result showed that Singapore's ability to 

sustainably manage its environment increases with 

economic development. The study result is supported by 

Katirciolu (2014), Mehmood (2021), and Mansoor (2021)'s 

conclusions that Singapore's GDP and CO2 emissions are 

inversely related. Studies from other countries also 

supported our findings. For example, Zaman et al. (2016), 

Ali et al. (2020), Ng et al. (2015), Ahmad et al. (2019), 

Selvanathan et al. (2021), Dogan et al. (2017), Raheem and 

Ogebe (2017), Liu and Bae (2018), Vo et al. (2019), Raihan 

et al. (2022e), Odugbesan and Adebayo (2020), Adebayo 

and Kalmaz (2021), Nondo and Kahsai (2020). Singapore's 

economy will rise which could improve air quality, as 

predicted by (Mehmood 2021). Even while economic 

expansion may improve human existence, it is crucial to 

determine if and how it may be made sustainable (Raihan et 

al 2022g). Development activities satisfy more societal 

demands as economic growth rises. These actions increase 

pollution, waste, and environmental damage (Raihan and 



Global Sustainability Research 

Global Scientific Research   60 
 

Tuspekova, 2022b). Thus, economic activities appear to 

protect and improve the environment rather than endanger 

it. However, not all economic growth is destructive to the 

environment or incompatible with environmental 

preservation. As income rises, people will be able to donate 

more to causes like environmental protection and pollution 

reduction. Tech-driven economic expansion boosts 

productivity and reduces pollution. 

This research focuses on Singapore's high energy usage and 

environmental degradation. This study confirmed that fossil 

fuels constitute Singapore's main energy source, which 

increases CO2 emissions over time. The anticipated long-

run coefficient of LE is positive and statistically significant 

at the 1% level, indicating that a 1% increase in energy 

consumption in Singapore increases CO2 emissions by 

0.8%. As energy consumption rises, the environment will 

deteriorate. According to DOLS's extrapolations, greater 

tourism in Singapore boosts the economy but worsens the 

environment due to higher energy usage. This study's 

finding is consistent with Singapore's study by Katirciolu 

(2014). The positive association between energy use and 

CO2 emissions supports previous studies that found many 

nations largely rely on coal, natural gas, and oil, which 

increases CO2 emissions and environmental deterioration. 

For example, Dogan et al. (2017), Raihan et al. (2022a), 

Raheem and Ogebe (2017), Liu and Bae (2018), Vo et al. 

(2019), Raihan et al. (2022e), Odugbesan and Adebayo 

(2020), Adebayo and Kalmaz (2021), Nondo and Kahsai 

(2020), Ali et al. (2020), Ng et al. (2015), Ahmad et al. 

(2019), Selvanathan et al. (2021), and Raihan et al. (2022f). 

The present study’s result shows that Singapore's 

environmental quality is worsening as energy demand rises. 

Despite this, 86% of Singapore's primary energy comes 

from petroleum and other liquids, and 13% from natural gas. 

Thus, building a renewable energy infrastructure that can 

replace fossil fuels is the most important policy. Renewable 

energy sources are needed to ensure sustainable 

development and mitigate climate change's negative effects 

(Raihan et al 2022h). Renewable energy boosts the 

economy and lowers carbon emissions. It is eco-friendly 

and other benefits include increased energy availability and 

energy security (Voumik et al., 2022b).  

As global environmental consciousness rises, Singapore 

must switch to renewable energy sources to enable the use 

of environmentally friendly energy sources and the creation 

of an eco-friendly ecosystem. Renewable energy accounts 

for less than 1% of Singapore's primary energy demand. 

Singapore needs a comprehensive renewable energy policy 

to transition to a low-carbon economy. However, the 

Singaporean government is investigating several ways to 

cut carbon emissions. The Singapore Carbon Pricing Act 

started operationally in 2019. This statute required carbon 

pricing. The Singapore Energy Market Authority (EMA) 

recently launched "4 Switches." This effort promotes clean 

power production. The Energy Management Agency 

(EMA) will research energy storage technologies for 

variable energy flows and potential technical solutions 

including carbon capture and storage. Regional electricity 

systems are another EMA focus. Singapore approved its 

"Green Plan" to mitigate climate change. The plan aims to 

add more than 321 acres of parkland, prohibit new diesel car 

and taxi registrations starting in 2025, establish 60,000 

vehicle charging outlets nationwide by 2030, with two-

thirds in public parking spaces and one-third on private 

property, and increase solar irradiation. Singapore launched 

a nationwide 111-acre floating solar panel farm in July 

2021. The world's most powerful floating solar farm 

generates 60 megawatts of power. Singapore's five water 

treatment plants provide enough hydropower. The National 

Water Agency of Singapore expects the Sembcorp 

Industries-owned solar farm in Singapore to reduce carbon 

emissions by 32,000 metric tons. 

It's crucial to note that the projected coefficients' signs are 

constant conceptually and practically. The current 

investigation uses many diagnostic methods to determine if 

the predicted model accurately represents reality. This study 

examines whether the calculated model matches reality. The 

revised regression model fits the data well, with an R2 value 

of 0.92 and an adjusted R2 of 0.90. These figures show that 

the independent factors might account for 90% of the 

dependent variable's change. The F-statistic supports the 

computed DOLS regression from both the dependent and 

independent variables. Given the F statistic's 0.0000 p-

value, the model's variables' linear relationship is 

statistically significant. Third, the root mean square error 

and mean absolute error helped evaluate the model's 

predictions. The RMSE and MAE figures were near to 0 and 

did not indicate a negative value supporting the model’s 

fitness. To verify cointegration analysis, this study tested for 

normality, heteroscedasticity, and serial correlation. Table 6 

summarizes the diagnostic test results. Residuals follow a 

normal distribution, according to four studies. The model 

also suggests no autocorrelation or heteroscedasticity. To 

determine model stability, the cumulative sum of recursive 

residuals (CUSUM) and the cumulative sum of squares of 

residuals (CUSUMQ) tests were applied. Figure 3 shows 

CUSUM and CUSUMQ statistics plots after applying a 5% 

significance threshold to the comparison. This figure shows 

confidence levels as red lines and residual values as blue 

lines. 

 

 

 

 

 

 

 

 

 

 



Global Sustainability Research 

Global Scientific Research   61 
 

Table 6. The outcomes of diagnostic tests 

Diagnostic tests Coefficient p-value Decision 

Jarque-Bera test 0.735637 0.5300 Residuals are normally distributed 

Breusch-Godfrey LM test 0.725496 0.6102 No serial correlation exits 

Breusch-Pagan-Godfrey test 0.562482 0.7169 No heteroscedasticity exists 

 

 

    

 
Figure 3. The plots of CUSUM and CUSUMQ 

 

Conclusions and Policy Implications 

 

This study examined the relationship between economic 

growth, tourism, energy consumption, and CO2 emissions in 

Singapore using time series data from 1990 to 2020. The 

ADF, DF-GLS, and P-P unit root tests were utilized in order 

to determine the integration order of the series. The findings 

from the DOLS estimation revealed that tourism and energy 

use raise CO2 emissions, which degrade Singapore's 

environment, but economic expansion decreases CO2 

emissions in the long run. This article proposes 

environmental policy concepts to ensure the long-term 

viability of the environment by implementing strict 

regulatory policy tools to stop environmental deterioration. 

The outcomes offer recommendations to governments 

seeking to achieve environmental sustainability while 

minimizing climate change's effects and adapting to them. 

The analysis found that Singapore's government might help 

markets by creating a rigorous regulatory framework that 

produces long-term value for emission reduction and 

continuously supports innovative solutions that reduce 

carbon dependence. Singapore may continue to establish 

rules like a high carbon tax, carbon capture and storage, and 

emission trading schemes to minimize carbon dioxide 

emissions from fossil fuel combustion in industrial 

processes and power generation. Decoupling regionally 

involves changes in centralized nations' policies, conduct, 

and scientific and technological advancement. This will 

allow modernization based on technology that can meet 

growing demand while preserving natural capital. The 

government's main responsibility is funding research and 

development to reduce production's resource use and energy 

efficiency. For Singapore's stronger economic growth to 

reduce environmental challenges, the economy must 

convert to renewable energy. Legislators may help 

corporations create innovative technology and sustainable 

energy. Institutional alignment also promotes sustainable 

economic growth and the use of renewable and alternative 

energy in all economic activities. In addition, environmental 

regulations must be meticulously followed.  

The study suggests using more renewable or clean energy 

sources to optimize Singapore's energy consumption 

structure performance. The latest study supported this 

notion. Conventional energy, which still dominates 

Singapore's energy use, is the main source of its high carbon 

dioxide emissions. Singapore may transition to renewable 

energy to decrease its environmental impact. Due to the 

limited area for renewable energy generation, practically all 

of Singapore's energy is imported. Singapore can meet all 

its renewable energy needs since solar energy is so 

abundant. Singapore has very strong sun radiation. 

Singapore may invest in innovation, research, and test beds 

to improve solar power system efficiency and explore 

creative ways to integrate them into urban areas. Singapore 

may examine regional power networks and create low-

carbon solutions like low-carbon hydrogen utilization and 

carbon capture and storage to boost energy security and 

explore new energy supply possibilities. Singapore is 

constantly researching new energy sources to promote 

energy diversity and security. This would be part of 

nationwide efforts to provide reliable electricity availability. 

Singapore wants to continue research into nuclear energy 

and create the necessary capacities to understand nuclear 

science and technology, even though some technologies, 

like nuclear power, may become obsolete. Singapore can 

enhance its energy education programs. Tax incentives, 

economic subsidies, and government acquisitions can 

encourage greener energy consumption. There is a 

possibility that the government will enlist the assistance of 

the media in order to further its push for "low-carbon 

behaviors and consumption patterns" and its "green lifestyle 

notion." 

-16

-12

-8

-4

0

4

8

12

16

94 96 98 00 02 04 06 08 10 12 14 16 18 20

CUSUM 5% Significance

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

94 96 98 00 02 04 06 08 10 12 14 16 18 20

CUSUM of Squares 5% Significance



Global Sustainability Research 

Global Scientific Research   62 
 

The government of Singapore is able to put in place a system 

that will hold individuals, tourists, and other interested 

parties accountable for the damage they cause to the natural 

environment of the country's most popular tourist 

destinations. This will allow Singapore to continue to attract 

tourists to its most beautiful and unique areas. Not only 

would tourists have an overall more positive experience if 

all parties involved in the tourism industry were encouraged 

to embrace sustainability and environmental responsibility 

in their business practices, but they would also benefit from 

an increase in their level of education as a result of this. This 

is due to the fact that visitors would enjoy a more satisfying 

experience all around. In addition to educational pamphlets 

and booklets, it is likely that as many public service 

announcements incorporating easily digestible infographics 

should be delivered to the general population. People would 

be encouraged to appreciate the benefits of energy savings 

and environmental sustainability, as well as to engage in 

green behaviors while on vacation if these announcements 

were made, coupled with information on the efforts being 

made by the authorities and ongoing green development. In 

addition, the dissemination of this knowledge would 

motivate individuals to engage in environmentally 

responsible actions while they are at work. It is of the utmost 

importance that advancements in transportation technology, 

such as the employment of high-speed trains and airplanes 

that consume less fuel, be supported. Because of the rise in 

tourists, there is now a chance to cut carbon dioxide 

emissions by updating public transportation, investing in 

energy efficiency, and enhancing waste management. In 

order to safeguard the environment, the government might 

also decide to implement and strictly enforce environmental 

levies in regions that have a high visitor density and are 

important tourist destinations. Additionally, the government 

should make it less difficult for businesses in the tourism 

industry to adopt environmentally friendly and low-carbon 

technology in addition to alternative sources of energy for 

transportation, logistics, hotels, and other activities. This 

would result in a decrease in the total amount of carbon 

dioxide emissions and would also prevent excessive 

consumption of the earth's natural resources. There is a 

chance that Singapore would tighten the environmental 

regulations that it already has in place and shine a light on 

other nations whose tourism industries are contributing to 

the deterioration of the environment. The concept of 

"sustainable tourism" can be used to apply to a wide range 

of distinct sorts of tourism. Some examples of these types 

of tourism include cultural tourism, ecotourism, tourism that 

is centered on enjoyment and adventure, and educational 

tourism. It is vital that the governments in the Southeast 

Asian region collaborate and coordinate their efforts in 

order to put proactive measures in place to ensure that 

tourism is ecologically responsible. 

However, despite providing a lot of factual data on 

Singapore, the analysis contains several shortcomings that 

need to be addressed in future research. Our study's major 

flaw was the unavailability of tourism data beyond the study 

period. This limited the prediction power of the econometric 

methodologies. Using econometric models or micro-

disaggregated data, other nations can conduct more 

research. The study suggests studying the dynamic effects 

of socioeconomic and environmental variables on 

environmental pollution in developing nations with rapid 

economic growth to balance ecologically sustainable 

development with emission reduction. This will help to 

balance environmentally sustainable development and 

emission reduction. Future research could also consider 

other development aspects not examined in this study. For 

example, urbanization, industrialization, trade openness, 

financial sector development, direct foreign investments, 

institutional quality, globalization, technical innovation, 

and others. In order to compare country findings to panel 

forecasts, a future study may apply more complicated 

econometric methods. These comparisons with this study's 

findings may illuminate the relevant literature. 

 

Acknowledgment: The authors declare no conflict of 

interest 

 

Conflict of interest: The authors declare no conflict of 

interest 

 

Funding: none 

 

 

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