Review of Economics and Development Studies, Vol. 8 (1) 2022, 55 - 71

On the Nonlinear Impact of Tourism on Environment Quality in South Asia:
Evidence from the NARDL Approach

Waheed Ahmad a, Zubair Tanveer b, Muhammad Tariq Majeed c

a PhD Scholar, Department of Economics and Statistics (HSM) UMT Lahore, Pakistan.
Email: f2021330006@umt.edu.pk
b PhD Scholar, Department of Economics and Statistics (HSM) UMT Lahore, Pakistan.
Email: f2021330003@umt.edu.pk
c Professor, Department of Economics, Quaid I Azam University, Islamabad, Pakistan.

Email: m.tarq.majeed@gmail.com

ARTICLE DETAILS ABSTRACT
History:
Accepted 05 March 2022
Available Online March 2022

The current study inspects the nonlinear effects of tourism (TOR),
energy use, and output growth on carbon emissions in the selected
South Asian (SA) countries, namely Pakistan, Nepal, Sri Lanka, and
India. The empirical results are obtained by implementing the recently
developed nonlinear autoregressive distributive lag (NARDL) technique
covering the data spanning from 1990 to 2019. The empirical findings
suggest the nonlinear effect of TOR on carbon emissions in the long run.
Further, the results revealed that positive shocks in TOR have a positive

and significant effect on carbon emissions in the SA region. In contrast,

negative shocks in TOR mitigate carbon emissions in all SA economies
except Nepal. Moreover, the results demonstrate that energy use and
output growth also have a meaningful impact on carbon emissions.
Based on the findings, the directions for future research and policy

implications are proposed.

open access article under the Creative Commons Attribution-
NonCommercial 4.0

Keywords:
Carbon Emissions, Tourism,

Energy Use, Output Growth,
South Asian Economies

JEL Classification:
K32, L83, O13

DOI: 10.47067/reads.v8i1.434

Corresponding author’s email address: f2021330006@umt.edu.pk

1. Introduction
In the contemporary world, creating and managing a sustainable travel and tourism industry

has become a critical challenge for policymakers around the globe. On the one hand, the tourism sector
serves as an important element of inclusive growth and economic development by establishing a link
between each sector of an economy and supporting economic broadening. Besides, the tourism industry
contributes to economic growth by generating employment, enhancing income, improving ways of life,
offsetting trade deficit by improving export function, and evolving the overall financial system (Chon et
al. 2013; UNWTO 2017; Balsalobre-Lorente et al. 2018; Sinha et al. 2017). Furthermore, the influx of
international tourists raises income levels and improves the electricity and transportation industries by
expanding social, and economic goods and services (Akadiri et al. 2020).

Review of Economics and Development Studies, Vol. 8 (1) 2022, 55 - 71

On the other hand, the expansion of tourist activities has been closely connected to energy use
and carbon dioxide emissions, having environmental repercussions (Sharif et al., 2020; Ozturk and
Acaravci 2010; Khan et al., 2020). The remarkable rise in global tourist activities has fueled public

debate about tourism's meaningful impact on global warming through carbon emissions (𝐶𝑂2 𝑒𝑚).
Although tourism is highly vulnerable to environmental alteration, it contributed 5 percent of total
greenhouse gas (GHG) emissions in 2008. In contrast, transportation accounts for nearly 75 percent of
total GHG emissions (United Nations World Tourism Organization). Furthermore, the tourism
hospitality industry consumes a substantial amount of energy and accounts for approximately 20
percent of 𝐶𝑂2 𝑒𝑚. The UNWTO symposium proclaimed that carbon dioxide emissions from the
hospitality industry can be lowered up to 30–40 percent by introducing innovative technologies and
techniques in the tourism industry.

In an earlier study, Bach and Gößling (1996) investigated the empirical relationship between
tourism development and environmental quality for the first time. They demonstrated that the tourism
industry contributed substantially to environmental deterioration by escalating 𝐶𝑂2 𝑒𝑚 growth. Goudie

and Viles (2013) also concluded the same outcome. Furthermore, with increasing tourism activities,
water is excessively used, natural resources are overexploited, and the volume of wastage material at

natural sites is swelling. In this respect, Chan et al. (2018) and Latif et al. (2018) reported that an
increased tourist flow may lead to soil erosion, the polluted area of land pollution, water contamination,
air pollution, and eventually demolishing the world's natural beauty. Tourism boosts the ratio of
𝐶𝑂2 𝑒𝑚 because of the increase in the consumption of electricity and transportation and building of
relatively more houses (Nepal et al. 2019).

Contrary to this, some researchers advocate the proposition that the tourism industry has a
positive impact on environmental quality. They argue that the tourism industry provides essential
services and encourages innovation and energy proficiency for the sustainable development of a nation.

As a result, touristry is considered favorable for environmental protection (Akadiri et al. 2020; Imran et
al. 2014; Dogan and Aslan 2017; Gössling and Hall 2006; Paramati et al. 2018). Moreover, if the leaders
of the world's economies adopt eco-friendly policies, the tourism industry may positively impact
environmental quality (Ahmad et al., 2019). However, the existing empirical literature has not yet
thoroughly investigated whether the tourist industry has an asymmetric/non-linear relationship with
environmental quality.

Particularly, in the South Asian region, there is still a paucity of literature on the relationship
between tourism development and environmental deterioration. As a result, the purpose of this
research is to bridge that gap by concentrating on the non-linear/asymmetric and moderating
influence of tourism on the ecological system in terms of environmental quality. The particular goals of
the study to investigate the non-linear/asymmetric relation between tourism and environmental quality

in South Asian (SA) economies.

In the recent decades, South Asian economies have emerged as an attractive tourist place owing
to its natural beauty, cultural diversity, rich historical background, and price competitiveness. This
region is home to tourism-based countries like Bhutan, Maldives, Nepal, and Sri Lanka that attract
tourists from all over the world. According to, the World Economic Forum’s Travel and Tourism
Competitiveness Index (2019) this region has been ranked as “the most improved region since 2017.”
The present study explores the nonlinear hidden effects of tourism on carbon emissions in the selected
countries over the period 1990-2019 by employing the recently developed nonlinear autoregressive
distributive lag (NARDL) technique.

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The present study contributes to the existing literature in the following ways: First, it
investigates a novel concept namely the non-linear/asymmetric relationship between tourism and
environmental quality instead of preceding literature that exclusively investigated linear relationships

between these two variables. Second, this study provides an asymmetric analysis of each South Asia
economy in a comparative setting. That is, the present research portrays a comparative picture across
South Asian economies by providing a comprehensive and updated analysis about individual economies
using a fresh data. In this manner, the present study fills the existing research gap on this subject
matter in the South Asian region as well as at the individual country level. Third, to unveil the causal
associations between the series, this study utilizes the asymmetric causal relationship. This framework
helps to explore the nonlinear hidden causal effects of the selected variables. Fourth, the NARDL
application is used to get the short-and-long-run empirical results for the relevant policy implications.
The empirical outcome suggests that the partial sum of positive shocks in tourism adversely affects the
environmental quality. These empirical findings highlight the importance of ecotourism or sustainable
tourism in achieving long-term development.

The remaining study is structured as follows: Next section provides the literature review. The
discussion on the methodological framework is provided in Section 3 and the description of the data is

provided in section 4. Section 5 provides the discussion on empirical findings and finally, section 6
concludes the study and offers some suitable policy implications.

2. Literature Review

Finding environmentally friendly explanations for economic growth is at the heart of sustainable
development. In this context, the well-known environmental Kuznets hypothesis states that economic

growth hurts ecological quality during an initial stage of economic development. However, once a
country reaches a certain level of income, the quality of the environment strengthens (Dasgupta et al.,
2002). Several studies investigated the impact of various industries on the ecological system like

Plainspoken, Olasehinde-Williams, & Alao (2019) analyzed the role of agriculture in Africa, Raza, Shah,
& Sharif (2019) examined the time-frequency relationship of transport-based energy consumption in
America, Haiti, (2015) studied Industrial growth in developing world. Sharif, Afshar, & Nisha (2017)
inspected the role of tourism on CO2 emission in Pakistan.

Travel for pleasure is considered one of the key aspects of ecological deprivation since it involves
massive energy levels for different events such as “food supplying, transportation, housing, and the
management of trip-related attractions” (Saenz-de-Miera, & Rosselló,2014). Saenz-de-Miera and
Rossello (2014) also identified that a rise in tourist stock added the greenhouse gas emissions in Spain
and adversely affected the air concentration level with a 0.45% magnitude in PM10 concentration levels
through the entrance of each vacationer. Solarin (2014) examined the cointegration and causality of the
macroeconomic factors using a multivariate framework that included GDP, energy utilization, financial

inclusion, and urbanization. Remarkably, their findings revealed that tourist inflows increased pollution
but did not adequately improve the GDP. Moreover, Katircioglu (2014) found that tourist entrances had
a substantial negative impact on CO2 emission both in the short term and long-term and also confirmed
the Environmental Kuznets Curve hypothesis in Singapore taking the data of energy resources,
urbanization, CO2 Emission, and industrialization with the help of Granger Causality test. A similar
investigation was carried out by Lee and Brahmasrene (2013) in European Union Countries to analyze
long-run equilibrium relationship through panel cointegration techniques and fixed-effects models and
found a negatively significant impact of FDI and tourism on CO2 emissions from 1988 to 2009.

Furthermore, Ozturk, Al-Mulali, and Saboori (2016) used panel data of 144 countries over

Review of Economics and Development Studies, Vol. 8 (1) 2022, 55 - 71

twenty years and employed the system panel generalized method of moments (GMM) and time series
GMM to investigate the environmental Kuznets curve (EKC) hypothesis. The researchers took the data
on ecological footprint, trade openness, energy consumption, GDP growth from tourism, and

urbanization and found an indirect link between tourism growth and the ecological footprint among
upper-middle and high-income countries. Another study conducted by Habibullah et al. (2016) took the
data of international tourist arrivals in 141 countries and investigated their impact on biodiversity using
numbers of threatened mammals, fishes, birds, and plants species as the proxy of biodiversity damage
and population growth, Per capita Income, crop production, and protected areas as control variables.
The results of the robust standard error estimator discovered biodiversity was negatively affected by
tourist arrival and positively affect by the GDP per capita with the suggestions to protect the
biodiversity to sustain the businesses of tourism. Moreover, France, America, Spain, Italy, the United
Kingdom, Mexico, Germany, Thailand, Austria, and China being top tourist nations were selected by
Katircioglu, Gokmenoglu, & Eren (2018) taking panel data from 1995–2014 to analyze the tourism-
induced environmental Kuznets curve. The panel regression analysis supported that Tourism
development exerted an adverse impact on the levels of ecological footprints and improved impact on

the levels of environmental quality confirming the inverted U-shape of the environmental Kuznets
curve.

Various econometric models of panel and time-series data have been used to investigate the

dynamic relationship between trade openness and carbon dioxide emissions. Shahbaz et al. (2018)
investigated the variables that contribute to CO2 emission in Japan from 1970 to 2014 employing the
ARDL model and identified that energy consumption, economic growth, and Globalization significantly
contributed to carbon emissions. Similarly, a panel data analysis for 36 Sub-Saharan African countries

from 1990 to 2013 was conducted by Twerefou et al. (2017) using the system General Method of
Moments estimation technique and found that the effects of globalization were outweighed by the
positive effects of income on environmental quality and sustainability. However, some studies identified

that globalization deteriorated the harmful effects of greenhouse emissions like Lv and Xu (218) did a
panel data analysis in 15 developing countries over the period from 1970 to 2012 to examine the
influence of economic globalization on CO2 emissions and found improvements in environmental
situations by reducing CO2 emissions in results of globalization. Similarly, a spatial panel data
econometric model for 83 countries was estimated by You and Lv (2018) to investigate the spatial
impacts of economic globalization on CO2 emissions. The results supported an inverted-U shaped
Environmental Kuznets Curve for CO2 emissions and concluded that a relatively high globalized
country had a relatively encouraging impact on CO2 emissions.

Shakouri et al. (2017) examined the long-run relationship between CO2 emission, energy
utilization, economic growth, and tourism by estimating Environmental Kuznets Curve hypothesis in
twelve Asia-Pacific countries from 1995 to 2013. The results indicate an increase in the foreign tourist

enduring the climate degradation in these countries. Another study (Dogan & Aslan 2017) estimated
cross-sectional dependence through a heterogeneous panel model on the data of real income, tourism,
energy utilization, and greenhouse gas emissions of European Union nations. The long-run relationship
among these variables was confirmed by the LM bootstrap panel cointegration test and although
tourism and real income diminished the level of CO2 emission the energy consumption contributed to
climate degradation.

Similarly, the influence of the expenditure on foreign tourist transport, foreign direct
investment, urban population, energy consumption, and trade openness on per capita income and CO2
emission from 1995 to 2013 in eleven emerging economies was investigated by Zaman, Moemen, and

Review of Economics and Development Studies, Vol. 8 (1) 2022, 55 - 71

Islam (2017) employing panel econometric techniques and variance decomposition analysis. Their
results indicated that CO2 emission had a significant positive relationship with income and tourism.
Moreover, a new approach called wavelet transform framework that decomposes data into different

time frequencies used by Raza et al. (2017) and calculated covariance, correlation, continuous power
spectrum, Granger causality, and coherence spectrum to empirically study the impact of tourism
development on the degradation of the environment in America taking monthly data between 1996 and
2015. The results revealed that CO2 emissions increased both in the short run and long run due to the
development of the tourism industry. Likewise, Sharif, Afshar, & Nisha (2017) employed three
cointegration methods to examine the time series data of tourist inflows growth and carbon dioxide
(CO2) emission over 40 years and found a statistically significant impact of tourism on CO2 emission
through unidirectional causality running from tourist inflows towards climate degradation.

3. Methodology

This study investigates the asymmetric effect of tourism on carbon pollution for the period 1990-
2019 in the context of Pakistan, India, Nepal, and Sri Lanka by employing the NARDL estimation

technique and incorporating other control variables such as energy and GDP. This technique is the
extended version of the ARDL model, which is helpful to inspect the asymmetric and nonlinear linkage

between tourism and carbon pollution. The model for this analysis is as follows,

𝐶𝑂2,𝑡 = 𝛽𝑜 + 𝛽1 𝑇𝑂𝑅𝑡 + 𝛽2 𝐸𝑁𝑡 + 𝛽3 𝐺𝐷𝑃𝑡 + є𝑡 (1)

Here, the equation one states that carbon dioxide emissions (𝐶𝑂2) is dependent on international

tourism (TOR), energy use kilowatt-hour (kwh) per capita (EN), gross domestic product growth annual
(GDP), and the error term. Thus, the equation (1) shows a long-run model irrespective of using
estimation technique, it offers a long-run estimate. To find the short-run and long-run estimates this
study restructured the equation (1) into a framework of error correction is as follows below equation

(2)

𝛥𝐶𝑂2,𝑡 = 𝛽𝑜 + ∑ ɵ𝑘
𝑝
𝑘=1 𝛥𝐶02,𝑡−𝑘 + ∑ 𝜋𝑘

𝑝
𝑘=0 𝛥𝑇𝑂𝑅𝑡−𝑘 + ∑ 𝛿𝑘

𝑝
𝑘=0 𝛥𝐸𝑁𝑡−𝑘 + ∑ 𝜆𝑘

𝑝
𝑘=0 𝛥𝐺𝐷𝑃𝑡−𝑘 +

ɑ1𝐶𝑂2,𝑡−1+ ɑ2𝑇𝑂𝑅𝑡−1 + ɑ3𝐸𝑁𝑡−1 + ɑ4𝐺𝐷𝑃𝑡−1 + є𝑡 (2)

The equation shows the ARDL model developed by Pesaran et al. (2001). Besides, the coefficients

with symbols Δ inspects the short-run turns out while the coefficients attached with -, have been
normalized on demonstrating the long-run outcomes. This estimation technique has been very suitable
because it inspects both long-run and short-run findings only in one equation. Further, this technique is
also used whether the order of integration I (1), I (0) or mixed order as this technique may take care of
the integration variable properties.

Next, the primary aim of the investigation is to examine the asymmetric effect of TOR on carbon
emissions for selected South Asian economies such as Pakistan, India, Nepal, and Sri Lanka. To achieve
this objective, Shin et al. (2014) proposed method is used it disaggregates the variables into the positive
or negative components. Thus, the study follows the same procedure and disaggregates the variable
into a positive ( 𝑇𝑂𝑅+) part and negative ( 𝑇𝑂𝑅−) part.

𝑇𝑂𝑅𝑡

+ = ∑ 𝛥𝑇𝑂𝑅𝑡
+𝑡

𝑛=1 = ∑ 𝑚𝑎𝑥
𝑡
𝑛=1 (𝛥𝑇𝑂𝑅𝑡

+,0)= (3)

𝑇𝑂𝑅𝑡
− = ∑ 𝛥𝑇𝑂𝑅𝑡

−𝑡
𝑛=1 = ∑ 𝑚𝑖𝑛

𝑡
𝑛=1 (𝛥𝑇𝑂𝑅𝑡

−,0)= (4)

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After disaggregating the variables into positive or negative dynamics then this shock put in
equation (2) is placed with the standard variables and this equation has taken as a nonlinear form of
the ARDL model is represented as follows in equation (5)

𝛥𝐶𝑂2,𝑡 = 𝛽𝑜 + ∑ ɵ𝑘

𝑝
𝑘=1 𝛥𝐶02,𝑡−𝑘 + ∑ 𝜋𝑘

𝑝
𝑘=0 𝛥𝑇𝑂𝑅𝑡−𝑘

+ + ∑ ꝭ
𝑘

𝑝
𝑘=0 𝛥𝑇𝑂𝑅𝑡−𝑘

− + ∑ 𝛿𝑘
𝑝
𝑘=0 𝛥𝐸𝑁𝑡−𝑘 +

∑ 𝜆𝑘
𝑝
𝑘=0 𝛥𝐺𝐷𝑃𝑡−𝑘 + ɑ1𝐶𝑂2,𝑡−1+ ɑ2𝑇𝑂𝑅𝑡−𝑘

+ + ɑ3𝑇𝑂𝑅𝑡−𝑘
− + ɑ4𝐸𝑁𝑡−1 + ɑ5𝐺𝐷𝑃𝑡−1 + є𝑡 (5)

Here, equation (5) demonstrates the non-linear ARDL model developed by Shin et al. (2014). It is

similar to the traditional ARDL. Besides, non-linear ARDL has various advantages over the outdated
cointegration estimation models: for instance, firstly, it gives efficient and reliable results even in the
case the sample size is small. Secondly, the stationarity test is optional because it is applicable whether
the order of integration is I (1), I (0), or both except I (2). Lastly, its finding is effective even in the case
of the concerned variable order is I (1), 1(0), or both. After the estimation of equation (5), this study
applied various diagnostic tests, for instance, the Wald test to examine the presence of asymmetric
impact of variables both in the short-run and long-run. Besides. In the short tun, the Wald test validates

the existence of asymmetric impact as ∑ ≠𝑘 ∑ 𝜋𝑘 while the long-run asymmetric effect has been

confirmed by the Wald test in the case of
ɑ2

ɑ1
⁄ ≠

ɑ3
−

ɑ1
⁄ .

4. Data

For empirical analysis, this study used annual data set over the period 1990-2019 in the context
of concerned South Asian economies, for instance, Pakistan, India, Nepal, and, Sri Lanka. These South
Asian economies have been selected based on the reason for a well-organized and experienced tourism
sector and revealed a substantial positive effect on carbon dioxide emissions. Moreover, data of the
selected variables have been taken from World Development Indicators. In our analysis, carbon dioxide
emissions are a dependent variable while tourism is an independent variable. We also incorporate two
control variables namely energy use and GDP. Besides, the variables description details and symbols are

offered in table 1. The descriptive statistics demonstrate that India has the highest mean value of carbon
emissions per capita 1.211 annually on the contrary Nepal has the lowest carbon emissions value is
0.152. However, has the top rank in emitting carbon polluting in the South Asian region. Further, the
tourism and energy consumption average values for India are 6.639, and 2.662 while the average values
lowest for Nepal are 5.696, and 1.873. The mean value of GDP growth per capita is 1.661 lowest for
Pakistan and the highest in the case of India is 4.621.

Table 1. Explanation and Definition of the Selected Variables

Variables Symbol Definition Data Source

International tourism REM International tourism, number of arrival

person

WDI

Electric power consumption EN Electric power consumption (kwh per
capita)

WDI

Gross domestic product GDP GDP per capita growth (annual %) WDI

Carbon dioxide emissions 𝐶𝑂2 𝐶𝑂2 emissions (metric tons per capita) WDI

Review of Economics and Development Studies, Vol. 8 (1) 2022, 55 - 71

Table 2. Descriptive Statistics

CO2 TOR EN GDP

Pakistan

Mean 0.829 5.815 2.601 1.661

Std. dev. o.128 0.195 0.069 1.799

India

Mean 1.211 6.639 2.662 4.621

Std. dev. 0.374 0.312 0.152 1.984

Nepal

Mean 0.152 5.697 1.873 3.083

Std. dev. 0.077 0.161 0.208 1.865

Sri Lanka

Mean 0.602 5.801 2.506 4.327

Std. dev. 0.241 0.277 0.193 2.115

5. Empirical results
5.1 Outcomes of Unit Root Analysis

The first step toward the empirical analysis is to check the order of integration of the selected
variables. Therefore, to examine the stationarity of the variables, the study used the ADF test and
Phillip-Perron Test. The results obtained from these tests are presented in Table 3. As described earlier,
four variables have been taken for the empirical analysis of four countries. According to the results of
both tests, CO2 emissions and the number of arrivals of international tourists to Pakistan are stationary
at first difference while energy consumption and GDP per capita are stationary at level. In the case of
India, the variable of CO2 emissions and energy consumption have integration order one whereas GDP
per capita is stationary at order zero. However, the ADF test advocates that the number of international

tourists’ arrival series is I (0) but the Phillip-Perron test is in favor of I(1). All selected variables of Nepal
are stationary at first difference except GDP per capita which has I(0). Similar to Pakistan’s data, CO2
emissions and the number of arrivals of international tourists in Sri Lanka are stationary at first
difference while energy consumption and GDP per capita are stationary at level.

Table 3. Results of the unit root tests

Country
Variables

ADF Test Phillip-Perron Test

I (0) I (1) Decision I (0) I (1) Decision

Pakistan CO2 -0.015 -1.178*** I (1) -0.015 -0.094** I (1)

TOT -0.018 -1.092*** I (1) -0.018 -1.092*** I (1)

EN -0.093**

I (0) -0.093*

I (0)

GDP -0.0.643***

I (0) -1.256***

I (0)

India CO2 0.005 -0.959*** I (1) 0.007 -0.959*** I (1)

TOR -0.182**

I (0) -0.009 -1.006*** I (1)

EN -0.019 -0.810*** I (1) -0.019 -0.810*** I (1)

GDP -0.739***

I (0) -0.739***

I (0)

Nepal CO2 -0.041 -1.578*** I (1) -0.100 -1.578*** I (1)

TOR -0.094 -1007*** I (1) -0.094 -1.007*** I (1)

EN -0.017 -1.074*** I (1) -0.017 -1.074*** I (1)

GDP -0.957***

I (0) -0.957***

I (0)

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Sri Lanka CO2 0.028 -1.228*** I (1) -0.028 -1.225*** I (1)

TOR -0.046 -0.695*** I (1) -0.002 -0.695*** I (1)

EN -0.090***

I (0) -0.032**

I (0)

GDP -0.759***

I (0) -0.759***

I (0)

Note: *, **, and *** show 10%, 5%, and 1% level of significance.

4.2 Asymmetric effect of tourism on CO2 emissions based on NARDL

Outcomes of NARDL for Pakistan
The asymmetric effects of tourism on CO2 emissions are examined with the help of the non-

linear ARDL technique. For Pakistan, the results of NARDL are given in Table 4. The short-run estimates
of NARDL depict that an increase in the number of international tourists raises CO2 emissions.
Similarly, energy consumption and GDP per capita also have a direct relationship with CO2 emissions.
Moreover, a positive shock in the number of tourists arrival has statistically insignificant positive effects
on carbon emissions whereas a negative shock has a statistically significant positive effect on CO2
emission which reveals that tourism considerably contributes to greenhouse gas emissions in Pakistan.

Panel B of Table 4 shows the long-run estimation of NARDL for Pakistan. A positive shock in
tourist arrival has a substantial positive effect on CO2 emissions and a partial sum of negative change in
international travelers in Pakistan declines CO2 emissions. In other words, a one percent increase
(decrease) in international tourists in Pakistan 0.077 percent (-0.130) increase in carbon emission. The
diagnostics of NARDL given in panel C show that the overall model is statistically significant as F-stats
are highly significant and adjusted R-square about 98 percent. Moreover, the Wald tests for short-run
and long-run coefficients of tourism variables are also statistically significant. Furthermore, the value of

the error correction term is not only negative but also statistically reliable states that a shock in the
short run will annually converge to its long-run equilibrium with the speed of 0.73 percent.

Table 4: NARDL coefficient estimates in Pakistan

Variables Coefficient Std. Error t-Statistic

Panel A: Short-run estimates
ΔTORt 0.091*** 0.020 -4.550

ΔTOR𝑡
+ 0.136 0.088 -1.539

ΔTOR𝑡
− 0.095*** 0.030 -3.166

ΔTORt−1

ΔENt 0.348*** 0.082 4.243
ΔGDPt 0.006*** 0.001 4.800

ΔGDPt-1 0.002 0.002 0.975

Panel B: Long-run estimates

TOR𝑡
+

0.077*** 0.026 2.961
𝑇𝑂𝑅𝑡

−
-0.130*** 0.060 -2.167

𝐸𝑁t 1.826*** 0.404 4.518
𝐺𝐷𝑃t 0.009*** 0.003 2.812

C -3.917*** 1.022 -3.830

Panel C: Diagnostic tests

F-test 10.803***

ECM -0.730*** 0.143 -5.094

LM test 0.0762

RESET 1.505

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Adj-R2 0.986

CUSUM S

CUSUM squares S

WALD SR-TOR 2.876**

WALD LR-TOR 3.984***

Note: *, **, and *** show 10%, 5%, and 1% level of significant, respectively.

4.3 Outcomes of NARDL for India

The estimates for the asymmetric impact of the tourism industry on environmental degradation
in India based on NARDL are presented in Table 5. Similar to the estimates of Pakistan, CO2 emissions
increase with an increase in international tourists in the country in the short run. As for as the long run
results are concerned, a partial sum of positive shocks raises, and a partial sum of negative shocks
reduces the CO2 emission. Moreover, the magnitude of positive shock is greater than negative shock
revealing that a one percent increase (decrease) in international travelers in India would cause to surge
of 0.458 percent (-0.283) fatal gas. Similarly, a one percent increase in energy consumption and GDP

per capita also rise CO2 emissions by 0.035 and 0.011 percent respectively.

F-stat of the model advocates the significance of the model with a high value of adjusted R-

square. Furthermore, the Wald test describes that short-run and long-run estimations for tourism are
statistically reliable. Besides, the estimated value of ECM is also statistically negative and significant
with 0.232 magnitude of the speed of convergence towards long-run equilibrium.

Table 5: NARDL coefficient estimates in India
Coefficient Std. Error t-Statistic

Panel A: Short-run estimates
ΔTORt 0.106*** 0.141 -2.439

ΔTOR𝑡
+

ΔTOR𝑡
− 0.077 0.061 1.256

ΔTORt

ΔTORt−1

ΔENt 0.002 0.001 1.532
ΔGDPt -0.001 0.001 -1.332

ΔGDPt-1 0.002 0.002 0.975

Panel B: Long-run estimates
𝑇𝑂𝑅t 0.458*** 0.190 2.410
𝑇𝑂𝑅𝑡

−
-0.283*** -0.032 -8.611

𝐸𝑁t 0.035** 0.019 1.856
𝐺𝐷𝑃t 0.011* 0.006 1.833

C -0.196 0.220 -0.891

Panel C: Diagnostic tests

F-test 6.205***

ECM -0.232*** -0.093 -2.475

LM test 1.350

RESET 0.085

Adj-R2 0.992

CUSUM S

CUSUM squares S

Review of Economics and Development Studies, Vol. 8 (1) 2022, 55 - 71

WALD SR-TOR 3.384**

WALD LR-TOR 7.386***

Note: *, **, and *** show 10%, 5%, and 1% level of significant, respectively.

4.4 Outcomes of NARDL for Nepal

Estimates of the NARDL model for Nepal are given in Table 6. International tourists and
degradation of the environment are directly related in the short run with a magnitude of 0.093.
Moreover, a negative shock in tourism causes to increase in carbon emissions in the short run.
Furthermore, GDP per capita and energy consumption are significant sources of greenhouse gas
emissions in the short-run as well as in long run. Similarly, a partial sum of both positive and negative
shocks contributes to polluting the environment in long run.

As for as the significance of the model is concerned F-test is highly significant and Wald tests for
short-run and long-run tourism coefficients are acknowledged that the estimates coefficients are
different from zero. Besides, the value of the adjusted R-square is very high. Moreover, the estimated

value of the error correction term is not only negative but also statistically significant depicting the

model converges towards its long-run equilibrium with the annual speed of 0.44 percent if any shock
occurs during the short run.

Table 6: NARDL coefficient estimates in Nepal

Variables Coefficient Std. Error t-Statistic

Panel A: Short-run estimates
ΔTORt 0.093*** 0.029 3.206

ΔTOR𝑡
+

ΔTOR𝑡
− 0.357*** 0.093 3.09

ΔTORt−1 -0.528*** 0.176 -2.991
ΔENt 0.036*** 0.013 2.769
ΔGDPt 0.008*** 0.003 2.34

ΔGDPt-1 0.008*** 0.002 2.849

Panel B: Long-run estimates

ΔTOR𝑡
+

0.212* 0.110 1.927
TOR𝑡

− 0.370* 0.194 1.909
𝐸𝑁t 0.081*** 0.017 4.764
𝐺𝐷𝑃t 0.060* 0.033 1.793

C -0.196 0.220 -0.891

Panel C: Diagnostic tests

F-test 7.169***

ECM -.0.440*** 0.183 -2.400

LM test 1.374

RESET 0.724

Adj-R2 0.943

CUSUM S

CUSUM squares S

WALD SR-URB 10.890**

WALD LR-URB 15.032***

Note: *, **, and *** show 10%, 5%, and 1% level of significant, respectively.

Review of Economics and Development Studies, Vol. 8 (1) 2022, 55 - 71

4.5 Outcomes of NARDL for Sri Lanka

Almost all estimated coefficients are statistically insignificant except for positive and negative

shock in short-run results presented in panel A of Table 7. Positive shock and negative shock in tourism
demonstrate a direct impact on CO2 emissions with 0.513 and 0.093 magnitudes respectively in the
short-run in Sri Lanka. A similar trend of these shocks could be observed from panel B in long run.
Nevertheless, energy consumption and GDP per capita are significant elements of environmental
degradation in long run.

Similar to the results of previous countries, the F-stats of the model and Wald tests of short-run
and long-run coefficients of tourism are statistically meaningful. Besides, adjusted R-square states that
around 94 percent of the variation in CO2 emission is explained by regressors. Moreover, estimated
ECM is negative and significant advocates the convergence model.

Table 7: NARDL coefficient estimates in Sri Lanka

Variables Coefficient Std. Error t-Statistic

Panel A: Short-run estimates
ΔTORt 0.020 0.266 0.077

ΔTOR𝑡
+ 0.513* 0.286 1.792

ΔTOR𝑡
−

-0.093*** 0.016 -5.812
ΔTORt−1

ΔENt 0.358 0.394 0.909
ΔGDPt 0.005 0.002 2.380

ΔGDPt-1

Panel B: Long-run estimates

TOR𝑡
+ 0.306*** 0.121 2.522

TOR𝑡
− -0.283** 0.061 -1.934

𝐸𝑁t 0.455*** 0.211 2.156
𝐺𝐷𝑃t 0.021** 0.009 2.131

C -0.614 1.044 -0.588

Panel C: Diagnostic tests

F-test 7.809***

ECM -0.787*** 0.211 -3.730

LM test 1.358

RESET 0.857

Adj-R2 0.942

CUSUM S

CUSUM squares S

WALD SR-URB 2.987**

WALD LR-URB 12.409***

Note: *, **, and *** show 10%, 5%, and 1% level of significant, respectively.

4.6 Asymmetric causality among four selected variables

In Pakistan, unidirectional ono-linear causality exists from tourists’ arrival to CO2 emission and
from tourism to GDP per capita. Moreover, bidirectional asymmetric causality also exists between
energy consumption and tourism. From energy consumption to CO2 emission, from GDP per capita to
CO2 emission, and from GDP per capita to energy consumption non-linear causality exist in India as

Review of Economics and Development Studies, Vol. 8 (1) 2022, 55 - 71

estimated from the test. Furthermore, bidirectional asymmetric causality exists between tourism and
CO2 emission, and energy consumption and tourism.

Non-linear causality is observed from energy consumption to tourism and energy consumption
to GDP per capita in Nepal. Similarly, tourism and CO2 emission, GDP and CO2 emission, and GDP per
capita and tourism have significant bidirectional causality. Results of asymmetric causality among the
variables of Sri Lanka demonstrate that unidirectional non-linear causality exists from energy
consumption to CO2 emission, GDP per capita to CO2 emission, and GDP per capita to energy
consumption. Besides, bidirectional asymmetric exists between tourism and CO2 emission, and energy
consumption and tourism in Sri Lanka.

Table 8: Results of Symmetric Causality Test

Null Hypothesis: Pakistan India Nepal Sri Lanka

𝑇𝑂𝑅t
+
➔CO2 Yes Yes Yes Yes

CO2➔𝑇𝑂𝑅t
+ No Yes Yes Yes

𝑇𝑂𝐸t
−

➔CO2 No Yes No Yes

CO2➔𝑇𝑂𝑅t
−

No Yes Yes Yes

EN ➔CO2 No Yes No Yes

CO2 ➔EN No No No No

GDP➔CO2 No Yes Yes Yes

CO2➔GDP No No Yes No

EN➔𝑇𝑂𝑅t
+ Yes Yes Yes Yes

TOR+➔EN Yes Yes No Yes

GDP➔ 𝑇𝑂𝑅t
+ No No Yes No

𝑇𝑂𝑅t
+
➔GDP Yes No Yes No

𝐸𝑁 ➔𝑇𝑂𝑅t
−

Yes Yes Yes Yes

𝑇𝑂𝑅t
− ➔𝐸𝑁 Yes Yes No Yes

𝐺𝐷𝑃➔𝑇𝑂𝑅t
− No No Yes No

𝑇𝑂𝑅t
−

➔𝐺𝐷𝑃 No No No No

GDP➔𝐸𝑁 No Yes No Yes

EN➔GDP No No Yes No

Review of Economics and Development Studies, Vol. 8 (1) 2022, 55 - 71

5. Results’ Discussion
The asymmetric effect of tourism on CO2 emissions in Pakistan, India, Nepal, and Sri Lanka is

investigated in the study employing a non-linear ARDL model. The short-run estimated coefficients of
the models show that number of international tourist arrival has a direct proportion to CO2 emissions
in Pakistan, India, Nepal, and Sri Lanka with the magnitude of 0.091, 0.106, 0.093, and 0.020
respectively. Moreover, long-run results reveal that partial sum of positive shock in tourist’ arrival

contributes positively and partial sum of negative shock in tourist’ arrival contributes negatively to CO2
emission in Pakistan, India, and Sri Lanka whereas in Nepal both shocks contribute positively to CO2
emissions. Apart from that examination of the causality revealed two-way causality between tourism
and environmental degradation exists in India, Nepal, and Sri Lanka while unidirectional causality
exists from tourism to CO2 emission in Pakistan. The outcome of the study is supported by Saenz-de-
Miera and Rossello (2014) who identified that a rise in tourist stock added the greenhouse gas

emissions in Spain and adversely affected the air concentration level and Katircioglu (2014) found that
tourist entrances had a substantial negative impact on CO2 emission both in the short term and long-
term in Singapore. Furthermore, a study in European Union Countries identified a negatively
significant impact of tourism on CO2 emissions (Lee & Brahmasrene 2013).

Energy consumption also contributes to polluting the environment. For Pakistan and Nepal, the
estimated coefficients of energy consumption are positive in sign and statistically significant both in the

short-run and long-run, however, this coefficient is only significant in long run for India and Sri Lanka,
revealing that increase in energy consumption considerably reduced environment quality of the selected
countries. Moreover, energy consumption has bidirectional causality with tourism in Pakistan and India

Pakistan India

Nepal Sri Lanka

Figure 1: Asymmetric Plots

Review of Economics and Development Studies, Vol. 8 (1) 2022, 55 - 71

whereas it has unidirectional causality from energy consumption to CO2 emission in India. In Nepal,
energy consumption has a unidirectional relationship from energy consumption to tourism and GDP
per capita. Similarly, in Sri Lanka, energy consumption depicts two-way causality with tourism and

unidirectional causality with CO2 emission and GDP per capita. These outcomes are consistent with
Shahbaz et al. (2018) identified that energy consumption significantly contributed to carbon emissions
using the ARDL approach in Japan. Moreover, Dogan and Aslan (2017) employed the LM bootstrap
panel cointegration test and confirmed that although tourism and real income diminished the level of
CO2 emission, the energy consumption contributed to climate degradation.

As ECK illustrates that being a developing country CO2 emission/environmental degradation
increases with the increase in national income. Because Pakistan, India, Nepal, and Sri Lanka are
developing nations, most of the estimated coefficients of GDP per are positive and statistically
significant advocating the EKC phenomenon.

6. Conclusion and Policy Implications

The primary objective of this study is to inspect the effect of TOR on carbon emissions for the
period 1990-2019 by employing nonlinear ARDL for the five SA economies. The empirical finding

depicts that TOR infers a nonlinear relationship with carbon emissions. The NARDL results revealed
that positive shock in TOR has a statistically significant and considerable impact on carbon emissions in
the considered SA nations, i.e., PAK, SL, NEP, and IND.

At the same time, negative shocks in TOR show a substantial and negative influence on carbon
emissions in SA economies in the long run. Thus, it highlights a 1% increase (decrease) in TOR could be

0.077% (0.130%) emissions in PAK, approximately 0.458% (0.283%) in IND, 0.212% (0.370%) in
NEP, and 0.306% (0.283%) in SL, respectively. Also, the short-run nonlinear turnout shows a
significant effect of TOR on carbon emissions. The results demonstrate that positive fluctuations in TOR

positively impact carbon emissions in PAK, IND, and NEP except for SL. On the contrary, negative
fluctuations in TOR also badly affect environmental pollution in the short run in PAK and NEP.

This paper concludes that SA countries need to use clean production technologies in their
economic growth strategy. To mitigate environmental degradation, they should increase the percentage
of renewable energy in their fuel mix relative to non-renewable energy. Because tourism harms
environmental degradation, SA countries may need to exercise sustainable tourism to reap the benefits
without jeopardizing environmental quality. According to our findings, the governments of PAK, IND,
NEP, and SL should encourage and persuade foreign investors the investment in green and clean energy
schemes to upsurge environmental quality through invention and innovation. Moreover, policymakers
of these nations may improve the long-term quality of ecological health by implementing environmental
laws.

Moreover, these countries should take advantage of developed countries' transportation and

construction incentives. Long-term transportation and infrastructure policies in Nepal and Sri Lanka
should be redesigned. In addition, well-defined transportation and construction rules in tourism regions
may be adopted to improve environmental quality. There are significant differences between developed
and South Asian countries regarding social, economic, institutional, infrastructure, human capital,
technological, and ecological awareness (Chan et al., 2018). As a result, the tourism industry in South
Asia and other developing countries should formulate a comprehensive plan to develop environment-
friendly touristry products based on the innovation produced by the developed economies.

Review of Economics and Development Studies, Vol. 8 (1) 2022, 55 - 71

Furthermore, the most effective strategy to prevent pollution in tourist zones is to use green
energy. By reducing environmental pollution, SA economies could improve their tourism sectors. The
South Asian region has the potential to import and use environment-friendly tourist products currently

being practiced in developed countries to contain greenhouse emissions. This study generated a
potential conclusion based on various quantiles that could be studied further in the future. Future
studies can focus on environmental contamination asymmetries and discrepancies in South Asian
countries, which may be assessed using macroeconomic indicators.

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