Digitalization means green? Linking the digital economy to environmental performance in the tourism industry

Abstract

Considering that carbon emissions are one of the main causes of climate change, it is increasingly urgent to reduce CO₂ emissions from tourism. This study aims to scientifically assess whether the digital economy can mitigate the negative environmental effects of tourism. Based on a two-way fixed effects model for a global sample of 100 countries from 2003 to 2020, this study highlights the tourism-CO₂ emissions nexus is negatively moderated by the digital economy. Specifically, while tourism development results in increased CO₂ emissions, our research shows that the digital economy can mitigate this negative impact. Furthermore, heterogeneity analyses reveal that this moderating effect is particularly pronounced in high-income countries. This study provides valuable insights for policymakers in fostering the growth of the digital economy in support of sustainable tourism development.

Keywords

tourism the digital economy environmental sustainability

Introduction

Among the most rapidly growing industries in the world, tourism serves as a catalyst for resource exchange across nations (Yang and Fik, 2014). From an economic perspective, tourism plays a pivotal role in driving vertical and horizontal growth within tourism-related sectors. Moreover, it serves as a driver for regional development and wealth accumulation, making significant contributions to local investment, entrepreneurial activities, trade expansion, foreign exchange inflows, and employment (Akinboade and Braimoh, 2010; Aratuo et al., 2019; Chang et al., 2012; Zhang and Zhang, 2021). Nevertheless, as environmental concerns increasingly pervade society, tourism is no longer perceived as a “green” industry (Liu and Yin, 2022). And they become less environmentally friendly as tourism activities expand, especially when tourists seek exceptional tourism experiences (Priskin, 2003). According to the Glasgow Declaration, carbon emissions from tourism increased by over 60% between 2005 and 2016. The figure is projected to increase by more than 25% by 2030 compared to 2016. The adverse environmental consequences of tourism, especially in terms of CO₂ emissions, have emerged as a significant concern for global sustainable development (Mishra et al., 2022; Yang et al., 2023; Zha et al., 2019).

Against the backdrop of ongoing discussions regarding the environmental ramifications of tourism, numerous academic inquiries have been conducted to unravel how tourism affects CO₂ emissions. However, the results of these studies are highly inconsistent, with some scholars arguing for a positive correlation between tourism development and CO₂ emissions (Eyuboglu and Uzar, 2020; Yorucu, 2016), while others advocating for a negative association (Lee and Brahmasrene, 2013; Shaheen et al., 2019). The disparities observed in these studies can be attributed to methodological variations and inconsistencies in the data utilized (Zhang, 2022). Yet another plausible explanation for this discrepancy seems to lie in the varying degrees of the digital economy. The digital economy has emerged as a distinct and transformative economic paradigm, revolutionizing traditional modes of production and exchange. As elucidated during the G20 Leaders’ Summit in Hangzhou in 2016, the digital economy encompasses a vast array of economic activities that are centered on data resources, which serve as a pivotal factor in production processes. These activities harness the capabilities of modern information networks, serving as the conduit for seamless data exchange, and rely on the innovative applications of information and communication technologies as the driving forces for enhancing operational efficiency and optimizing the overall economy. In general, the digital economy is considered to be low carbon. It has been recognized that the digital economy can reduce CO₂ emissions and improve carbon efficiency (Wang et al., 2022). In fact, the widespread use of digital technology in the travel industry has become a popular trend. With the emergence of digital services such as online booking, virtual reality tours, and smart tour guides, the evolution of digital technology has changed individuals’ travel patterns and travel experiences (Pencarelli, 2020). While these digital advancements enhance the tourism experience, they also exert a certain influence on the reduction of CO₂ emissions. Therefore, from this perspective, the digital economy appears to play an important role in reducing CO₂ emissions in the tourism sector, and also appears to provide direction for the transformation of the tourism industry from resource-intensive to environmentally friendly. Consequently, a comprehensive understanding of the intricate role played by the digital economy on the influence of tourism on CO₂ emissions is crucial for informed policymaking and sustainable tourism development.

In light of this, this paper examines whether the digital economy decreases the adverse effects of tourism on CO₂ emissions based on 100 countries’ samples from 2003 to 2020. The summary of this paper’s additional work on prior studies is as follows. First, this study uncovers the influence of tourism on CO₂ emissions depending on the digital economy. To achieve this objective, we have constructed a comprehensive analytical framework that encompasses tourism development, the digital economy, and CO₂ emissions. Through the establishment of this framework, we delve deeply into the moderating influence of the digital economy on tourism-CO₂ emissions, offering a plausible explanation for the inconsistent findings of prior research. Next, the research idea of this paper is novel and reveals the marginal impact of tourism on environmental degradation at different levels of the digital economy. Although the basic regression confirms the existence of the moderating effect, the calculation of the marginal effect enables the comparison of the magnitude of tourism’s role in influencing CO₂ emissions at varying digital economy levels, thus facilitating more targeted and effective responses by countries at diverse stages of digital economy development. Third, a multidimensional indicator was used to measure the digital economy and further examine how its sub-indicators (infrastructure, social impact, digital trade and social support) moderated the impact of tourism pressure on CO₂ emissions. Finally, our findings provide valuable insights for policymakers to develop strategies to reduce CO₂ emissions and provide clear direction for government officials. The findings provide policymakers with insightful information on how to effectively promote low-carbon tourism and address climate issues, contributing to the achievement of net-zero emissions.

The remainder of the study is structured as follows. Section 2 discusses why the digital economy is critical to tourism’s impact on CO₂ emissions. Section 3 represents a detailed description of the data utilized and the identification strategy employed. Section 4 reports and discusses the results. The study is concluded with a brief conclusion and some suggestions for further research.

Literature review

Tourism and CO₂ emissions

With the rapid development of tourism, the pressure of tourism on the environment has gradually gained attention (Dogan and Aslan, 2017; Eyuboglu and Uzar, 2020; Yorucu, 2016). Significant positive associations have been tested and validated by several scholars. For example, Eyuboglu and Uzar (2020) stated that a 1% increase in tourism results in a 0.099% increase in CO₂ emissions, and Yorucu (2016) highlighted that increasing numbers of foreign tourists exacerbate environmental deterioration through high CO₂ emissions. There are several reasons to explain this relationship. Firstly, both tourist attractions and places of accommodation release significant amounts of carbon dioxide during the production of tourism products (Kelly and Williams, 2007). Second, travellers are responsible for a considerable amount of CO₂ emissions when visiting tourist attractions, using hotel services, purchasing food and beverages, and engaging in a variety of recreational activities (Becken and Patterson, 2006; Dwyer et al. 2010; Tang and Ge, 2018). Third, in addition to the CO₂ emissions directly generated by tourism production and consumption, complementary tourism-dependent sectors such as transport and some manufacturing industries also contribute significantly to CO₂ emissions (Gunter and Wöber, 2021).

On the contrary, some studies have shown that tourism development can contribute to emission reductions. For example, Lee and Brahmasrene (2013) examined EU countries from 1988 to 2009 and concluded that tourism plays a key role in reducing CO₂ emissions. Besides, Shaheen et al. (2019) also provide evidence that international tourist arrivals are associated with reductions in CO₂ emissions. There are various reasons responsible for this reduction. First, rising tourism demand has prompted local governments and businesses to upgrade infrastructure and optimise public transport networks to improve accessibility and quality (Paramati et al., 2018; Villanthenkodath et al., 2022). In this way, tourists are encouraged to use greener modes of transport, which reduces their carbon footprint. Next, sustainable low-carbon tourism models can reduce energy consumption, waste emissions and CO₂ emissions by promoting ecological awareness (Pan et al., 2018) and green tourism products and services (Ma et al., 2021). Finally, tourism development often coincides with investments in environmental protection. Firms and destinations tend to invest in environmentally friendly projects to enhance tourism quality and attract tourists (Ben Jebli and Hadhri, 2018; Dogan and Aslan, 2017; Imran et al., 2014) and reduce CO₂ emissions.

Additionally, it is noteworthy that the tourism industry’s contribution to CO₂ emissions does not necessarily adhere to a uniform pattern, instead exhibiting a complex nonlinear relationship. This nonlinearity was initially posited by Grossman and Krueger in 1991, who introduced the concept of the Environmental Kuznets Curve (EKC). According to this theory, as per capita income rises, pollution initially worsens, reaching a peak before gradually declining as income levels continue to increase. Ghosh (2020) empirically investigated the environmental impacts of tourism using the EKC framework, providing further evidence to support this theory. The study confirmed the validity of the tourism-induced EKC hypothesis and verified the non-linear effect.

While a wide range of studies have focussed on this relationship, it is certain that the available empirical evidence on the tourism- CO₂ emissions nexus remains mixed and controversial. Some evidence suggests that the disagreement present in current studies could be attributed to disparities in methodology or sample data (Zhang, 2022). Nevertheless, it is important to recognize that the absence of consideration for the digital economy may also contribute to this discrepancy. The digital economy can propel the advancement of regional science and technology is widely acknowledged, as it serves as a fundamental catalyst for low-carbon growth by streamlining production processes and enhancing energy efficiency (Miao et al., 2017). However, the extent of its moderating impact has not been adequately explored in prior research. Hence, it is imperative to conduct further investigations to shed more light on this matter.

Why does the digital economy matter?

This section endeavors to delve into the intricate mechanisms through which the digital economy exerts its influence on tourism’s impact on CO₂ emissions. We propose a bifurcated perspective, encompassing both active and passive dimensions, to comprehensively assess this phenomenon. Proactively, the digital economy promotes environmental sustainability within the tourism sector through technological advancements (Wang et al., 2022; Zhang et al., 2022). Firstly, the digital economy has acted as a catalyst for technological innovation in the field of energy efficiency (Chen et al., 2021). This includes the deployment of intelligent energy-saving control systems, efficient lighting systems and smart heating systems (Hu, 2023). These advances not only improve the energy efficiency of tourist attractions and hotels, but also help reduce CO₂ emissions. Secondly, the digital economy has facilitated the emergence of a sharing tourism economy, providing technical support for a model that relies on shared goods rather than individual ownership, which is known for its environmental efficiency (Gössling and Michael Hall, 2019). This includes the use of electric tour buses, shared bikes and public transport options within the tourism industry, thereby reducing the carbon footprint of visitors and staff. In addition, the digital economy facilitates smart tourism management by integrating cutting-edge technologies such as drones and artificial intelligence (Wang et al., 2020; Xiang, 2018). These technologies increase the efficiency of co-operation in managing attractions, hotels and transport, thus saving human and energy resources.

Passively, the application of the digital economy strengthens the government’s environmental governance capacity, while incentivising tourism businesses and tourists to adopt low-carbon behaviours (Eom and Lee, 2022). Contemporary countries have incorporated environmental governance into government performance assessments, reflecting an increased awareness of sustainability (Li et al., 2020). By utilising innovative management strategies, service processes and operational procedures, the digital economy has improved the environmental governance capacity of local governments, enabling them to effectively shift to more sustainable practices. It motivates tourism businesses and tourists to adopt low-carbon behaviours, effectively reducing tourism-related CO2 emissions. Based on the above analyses, we propose the first hypothesis.

Hypothesis 1

The relationship between tourism development and CO₂ emissions is influenced by the digital economy.

Various economic development levels have led to different levels of technological sophistication, regulatory maturity, and quality of human capital. Consequently, the moderating effects of DE can differ depending on income level. Indeed, economic advancement is intricately linked to technological progress, enabling the integration and application of digital technologies (Yousefi, 2011). By optimizing resource allocation and enhancing energy efficiency within the tourism sector, digital technologies can effectively harness their immense potential for mitigating CO₂ emissions. Furthermore, as the economy matures, so do policies and regulations (Goel and Nelson, 2016), fostering a conducive milieu for the digital economy to reduce CO₂ emissions by promoting technological innovations, supporting infrastructure development, and facilitating industrial transformation and upgrading. Additionally, economies with higher development levels prioritize investments in education (Ranis et al., 2000), training, and talent nurturing. By cultivating tourism professionals equipped with high-tech proficiency and environmental consciousness, these nations are able to capitalize on the digital economy’s negative regulatory impact on CO₂ emissions during tourism development, thereby promoting a more sustainable and environmentally friendly industry. Thus, we propose the second hypothesis.

Hypothesis 2

The moderating effect of the digital economy is particularly pronounced in high-income countries.

Methodology and data

Data

Our research focuses on examining the relationship between tourism, the digital economy and CO₂ emissions, and the relevant data collected from the World Development Indicators (WDI) and the UN E-Government Survey (UN EGOV). The WDI collects data for tourism (including tourism expenditure (TE), revenue (TR), and arrivals (TA)), CO₂ emissions, and the digital economy (including Fixed broadband subscriptions, Fixed telephone subscriptions et al.). Complementing this, the UN EGOV provides e-government development indicators (including the Telecommunication Infrastructure Index, Online Service Index, and E-Participation Index), aimed at quantifying a subset of the digital economy sub-indicators. Detailed explanations regarding the measurement of these variables are presented in Section 3.2.

To ensure the robustness and accuracy of our empirical results, we rigorously screen the initial sample data. This entailed the elimination of a substantial number of samples exhibiting incomplete or missing vital variables. These encompassed nations with significant gaps in data on gross tourism income or inadequate information on the digital economy, among other crucial indicators. Furthermore, to address minor data gaps for individual years, which accounted for less than 1% of the overall sample size, we employed the linear interpolation method¹. Additionally, the data are converted into logarithmic form except for the digital economy. As a result of this meticulous screening and processing, the study culminated in the acquisition of a well-balanced panel dataset encompassing 100 countries spanning 2003 to 2020, yielding a total sample size of 1800 observations, providing a robust and comprehensive foundation for our empirical analysis. The countries included in this study are listed in Appendix A (Figure A1).

Variables

The dependent variable in this paper is CO₂ emissions, which are measured using carbon dioxide emissions (metric tons per capita). This measurement is among the most commonly used indicators of environmental performance (Lee et al., 2013). The key independent variable is tourism development. Given that tourism performance can able to represent the countries’ tourism development, we use the number of foreign tourist arrivals to measure it (Adebayo et al., 2023; Ivlevs, 2017; Karabulut et al., 2020).

The moderating variable is the digital economy. The academic literature on measuring the digital economy across countries currently has a significant gap. There are two main reasons for this. First of all, the digital economy, as an emerging economic model, lacks a significant amount of relevant data. Furthermore, measuring it is complicated by the lack of widely accepted and standardized definitions. Against this backdrop, we employed the indicator system established by Shahbaz et al. (2022). The article contains numerous references to previous research works, including Pan et al. (2022), Wu et al. (2021) and Lin and Zhou (2021). In addition, this indicator system incorporates the definition of the digital economy as outlined by the G20. This ensures that the indicator system used in this study is not only scientifically rigorous but also consistent with global standards. The index is quantified through four high-level indices, comprising infrastructure, social impact, digital trade, and social support. Table 1 provides a detailed overview of the sub-indexes and the indicator measurement system. Given that the entropy weight method is suitable for evaluating different research objects over multiple periods, it was used to calculate the digital economy, and the detailed calculation process is outlined in Appendix B.

Table 1.

The sub-indexes names and data sources of the digital economy index.

Primary indexes	Secondary indexes	Units	Data sources
Infrastructure	Fixed broadband subscriptions	per 100 people	WDI
	Fixed telephone subscriptions	per 100 people	WDI
	Mobile cellular subscriptions	per 100 people	WDI
	Telecommunication Infrastructure index		UN EGOV
Social impact	Individuals using the internet	% Of population	WDI
	Online service index		UN EGOV
	E-Participation index		UN EGOV
	Medium and high-tech manufacturing value added	% Of manufacturing value added	WDI
Digital trade	ICT goods exports	% Of total goods exports	WDI
Digital trade	ICT goods imports	% Of total goods imports	WDI
Social support	Per capita value added of service industry	$US/person	WDI

In addition, to avoid the omitted variable bias, we also include some control variables in our model, namely economic growth, population density, urbanization level, and industrial development (Hocaoglu and Karanfil, 2011).

Economic growth

Carbon dioxide emissions are largely influenced by economic growth. Energy is a key driver of economic development and national prosperity, necessitating the use of fossil fuels. This in turn inevitably leads to environmental degradation in the form of increased CO₂ emissions (Hu et al., 2021). Nonetheless, the significant expansion of the tertiary sector and the emergence of green industries in recent years have brought a degree of ambiguity to this relationship. Therefore, it is possible that economic growth could have a mitigating effect on CO₂ emissions (Zhang et al., 2014).

Population density

The impact of population density on CO₂ emissions is multifaceted and can yield both favorable and adverse outcomes (Wang and Su., 2019). On the one hand, a concentrated population can foster agglomeration economies, affording nations the advantage of enhanced productivity and consequently, a potential reduction in pollution levels. This is attributed to the efficient utilization of resources and the streamlining of the production processes within densely populated areas. Conversely, an escalation in population density can also lead to agglomeration diseconomies, characterized by intensified competition and transport congestion. Such conditions have the propensity to exacerbate CO₂ emissions, as they often necessitate higher amounts of energy consumption and subsequent environmental degradation.

Urbanization level

Urbanization is a leading cause of CO₂ emissions worldwide. On the one hand, the large influx of rural populations into the cities leads to an escalated demand for housing and public amenities as urbanization advances, which necessitates the consumption of substantial amounts of fossil fuels and exacerbates CO₂ emissions (Sun et al., 2014). On the other hand, urbanization contributes to the utilization efficiency of urban infrastructure, which is an important way to reduce CO₂ emissions (Li and Lin, 2015). Thus, it is still unclear how urbanization influences CO₂ emissions.

Industrial development

The industrial sector holds a pivotal role in influencing CO₂ emissions. The preponderance of energy sources utilized within this sector comprises fossil fuels. The combustion of these fossil fuels during industrial operations results in the emission of significant quantities of CO₂. The study uses the share of industrial value added in GDP to represent industrial development.

Table 2 contains the data sources, definitions, and descriptive statistics for the variables.

Table 2.

Descriptive statistics.

Variables	Description	N	Mean	sd	min	max	Source
Interest variables
lnCO₂	(Logarithm of) carbon dioxide emissions (metric tons per capita)	1800	1.055	1.300	−2.970	3.243	WDI
lnTA	(Logarithm of) the number of foreign tourist arrivals (person)	1800	15.276	1.601	10.779	19.199	WDI
DE	An aggregate indicator of digital economy using the entropy value method	1800	0.326	0.167	0.018	0.742	Our elaborationon
Control variables
lnGDP	(Logarithm of) the national per capita GDP ($US/person)	1800	11.348	2.284	6.992	18.991	WDI
lnURB	(Logarithm of) urban population (person)	1800	4.064	0.424	2.700	4.605	WDI
lnPOP	(Logarithm of) population density (people per sq. km of land area)	1800	4.233	1.342	0.844	8.983	WDI
lnInd	(Logarithm of) the fraction of industrial value added to GDP	1800	3.218	0.345	1.893	4.222	WDI

Model

Ehrlich and Holdren introduced the IPAT model as a framework for assessing the environmental consequences of human activities in 1971 (Ehrlich and Holdren, 1971). This model proposes that the environmental impact is jointly determined by the effect of three key factors: population (P), affluence (A), and technology (T). However, it is worth noting that, as an accounting equation, the IPAT model is inappropriate for hypothesis testing due to its deterministic nature. To surmount these constraints, Dietz and Rosa (1994) expanded the deterministic framework into a stochastic variant known as the STIRPAT model. The standardized representation of the STIRPAT model can be expressed as

I_{i t} = a P_{i t}^{b} A_{i t}^{c} T_{i t}^{d} e_{i t}

(1)

Introducing a natural logarithmic transformation of the parameters can mitigate the potential effects of heteroscedasticity. Therefore, we transform the STIRPAT model into natural logarithmic form

\ln I_{i t} = a + b l n P_{i t} + c l n A_{i t} + d l n T_{i t} + e_{i t}

(2)

The underlying hypothesis articulated in this research posits that the digital economy can promote the decoupling of tourism and CO₂ emissions. In particular, we propose that while tourism development may result in increased CO₂ emissions, a higher level of the digital economy tends to mitigate the negative impact of tourism. In order to empirically examine our hypothesis, we enhance model (2) by incorporating tourism development and the digital economy. Consequently, the augmented model is defined as follows

\ln I_{i t} = a + b l n P_{i t} + c l n A_{i t} + d l n T_{i t} + {γ_{1} \ln {T A}_{i t} + γ_{2} {D E}_{i t} + γ_{3} \ln {T A}_{i t} \times {D E}_{i t} + μ}_{i} + δ_{t} + e_{i t}

(3)

where

I

is per capita CO₂ emissions,

A

is measured by per capita GDP,

P

stands for population density and urbanization rate (Zhang and Zhou, 2016), and

T

is proxied by the share of industrial value added in GDP. Additionally,

T A

represents tourism development,

D E

denotes DE, and

T A \times D E

is the interaction term.

δ_{t}

μ_{i}

denote the time-specific effects and the country-specific effects, respectively.

e_{i t}

is the idiosyncratic error term. According to our expectations, the sign of the interaction term coefficient should be negative (

γ_{3} < 0

The marginal effect of tourism development on CO₂ emissions can be calculated by examining the following partial derivative in equation (4)

\frac{\partial {l n C O}_{2 i t}}{\partial \ln {T A}_{i t}} = γ_{1} + γ_{3} {D E}_{i t}

(4)

Results and discussion

Basic results

Before regression analysis, we conducted a variance inflation factor (VIF) test to prevent spurious regressions stemming from multicollinearity among the various factors that influence CO₂ emissions. Notably, all the explanatory variables exhibited VIF values below 10 (mean VIF is 1.55), indicating no significant multicollinearity issues in our analysis. Then, drawing from the preceding theoretical analysis, the moderating role of the digital economy on tourism affecting CO₂ emissions is examined. The results are presented in Table 3 in columns (1)-(4). Column (1) displays the regression outcomes for the random effects (RE) model, while columns (2) to (4) present the results for the fixed effects (FE) model. It is worth noting that columns (2) and (3) control for individual fixed effects and time fixed effects, respectively, and column (4) contains both individual fixed effects and time fixed effects. In order to determine the appropriate model, a Hausman test was conducted, which showed the rejection of the null hypothesis (p = .000). Consequently, we concluded that the estimation results from the FE model are superior to those from the RE model. Therefore, our subsequent discussion will primarily focus on the results presented in column (4). The results indicate the digital economy moderates the tourism-CO₂ emissions nexus (

γ_{3}

= -0.262, p < .01). This result confirms Hypothesis 1.

Table 3.

The results of baseline.

	(1)	(2)	(3)	(4)
lnTA	0.192*** (5.554)	0.150*** (4.596)	0.192*** (5.388)	0.144*** (4.481)
DE	4.371*** (4.144)	3.732*** (3.584)	5.749*** (5.696)	4.641*** (4.811)
$lnTA \times$ DE	−0.301*** (−4.629)	−0.280*** (−4.459)	−0.317*** (−5.200)	−0.262*** (−4.546)
lnGDP	0.155*** (2.880)	0.523*** (4.817)	0.235*** (4.076)	0.678*** (5.733)
lnURB	1.210*** (4.081)	0.690** (2.235)	1.347*** (4.197)	0.754** (2.338)
lnPOP	−0.070 (−0.714)	0.002 (0.012)	0.149 (1.484)	0.517*** (3.143)
lnIND	0.278*** (3.503)	0.216*** (2.846)	0.184** (2.250)	0.081 (1.019)
Constant	−9.034*** (−6.501)	−10.457*** (−7.006)	−11.271*** (−7.288)	−14.257*** (−8.335)
Country fixed effects	NO	Yes	NO	YES
Year fixed effects	NO	NO	YES	YES
Observations	1800	1800	1800	1800
R-squared		0.427	0.484	0.539

Note: (1) Robust t-statistics in parentheses. (2) ***: p < .01, **: p < .05, *: p < .1.

While the aforementioned findings provide valuable insights, they fail to explicitly articulate the net bias impact of tourism development on CO₂ emissions or the statistical significance of the relationship between lnTA and lnCO₂ across varying levels of the digital economy. Therefore, it is essential to proceed with the computation of the marginal effect of lnTA on lnCO₂ across different levels of the digital economy. Utilizing the interaction term model (as outlined in column 4 of Table 3), we have estimated the slope of lnTA over lnCO₂, while maintaining a constant digital economy ranging from 0.01 (the minimum) to 0.75 (the maximum). Our analysis reveals that lnTA exerts a negative influence on lnCO₂ when the digital economy attains a value of 0.6. Conversely, when the digital economy falls below 0.3, the impact of lnTA on lnCO₂ is positive and statistically noteworthy. The precise numerical outcomes are detailed in Table 4. Additionally, Figure 1 offers a graphical representation of Table 4, visually depicting the marginal effect of lnTA on lnCO₂ across the entire spectrum of observed digital economy levels.

Table 4.

Average marginal effects of TA on CO₂ emissions at different levels of digital economy.

DE	d (lnCO₂)/d (lnTA)	t	p-Value	95 % conf. Interval
0.01	0.142***	4.451	0.000	[0.079,0.205]
0.1	0.118***	4.102	0.000	[0.061,0.176]
0.2	0.092***	3.507	0.001	[0.040,0.144]
0.3	0.066***	2.661	0.009	[0.017,0.115]
0.4	0.040	1.618	0.109	[-0.009,0.089]
0.5	0.014	0.531	0.597	[-0.037,0.065]
0.6	−0.012	−0.446	0.657	[-0.068,0.043]
0.7	−0.039	−1.237	0.219	[-0.101,0.023]
0.75	−0.052	−1.563	0.121	[-0.117,0.014]

Note: (1) ***: p < .01, **: p < .05, *: p < .1.

Figure 1.

Marginal effects of lnTA on lnCO₂ at various levels of the digital economy. Notes: the middle line illustrates the estimated marginal effects and the dashed lines represent the 95% confidence intervals.

In terms of control variables, the results show that there is a significant positive correlation between economic growth, urbanisation and population density, which is consistent with the studies of Sun et al. (2014) and Xiang (2018). It’s worth noting that industrial development does not exhibit a significant impact on CO₂ emissions, thereby necessitating further empirical exploration to elucidate the intricate relationship between these variables.

Robustness check

A variety of additional analyses are conducted in this section in order to ensure the validity of our findings. These include alternate tourism and CO₂ emission measurements, alternative model specifications, and an examination of the influence of potential outliers.

Replacement of key indicator variables

This study employs international tourism arrivals as a metric for national tourism development. While this indicator is prevalent in numerous academic works, this measure may not be standardized enough. Therefore, Inspired by the study of Lv (2020), we also constructed a composite tourism index utilizing Principal Component Analysis, which is a weighted index that considers three indicators of tourism development (TE, TR, and TA). Overall, the results are comparable to earlier findings (see column 1 in Table 5). In addition, we used CO₂ emissions intensity (kg per 2017 PPP $ of GDP) (Yang and Yu, 2017) as a proxy for CO₂ emissions. The results obtained using this proxy remain consistent with our previous findings, further validating the robustness of our analysis (see column 2 in Table 5).

Table 5.

Robustness analysis: an alternative measure of tourism development and carbon emission.

	(1) lnCO₂	(2) CO₂ide
lnTA		0.045*** (4.480)
TD	0.124* (1.955)
DE	0.457* (1.858)	1.027*** (3.485)
$lnTA \times$ DE		−0.063*** (−3.433)
TD $\times$ DE	−0.278*** (−3.442)
lnGDP	0.695*** (4.960)	−0.119*** (−2.826)
lnURB	0.974*** (2.740)	0.149* (1.776)
lnPOP	0.450** (2.348)	0.106** (2.474)
lnIND	0.095 (1.053)	0.019 (1.006)
Constant	−12.933*** (−5.637)	−0.198 (−0.417)
R-squared	0.518	0.450

Note: (1) Robust t-statistics in parentheses. (2) ***: p < .01, **: p < .05, *: p < .1.

Results from the dynamic panel model

It is possible that tourism and CO₂ emissions are related in a bidirectional manner. This would result in the unbiased construction of uncorrelated explanatory variables and residual terms, thereby introducing biases in regression coefficients and hindering the accurate estimation of causal effects. To ensure the reliability of our results, this paper uses GMM estimation to mitigate endogeneity concerns, and Table 6 presents the results obtained. Notably, none of the AR (2) statistics exhibit significance, suggesting that the model does not suffer impaired serial correlation. Furthermore, the Husman test conducted to assess the validity of the instrumental variables employed indicates that the overall choice of these variables is statistically sound.

Table 6.

Robustness analysis: GMM estimations of dynamic panel data models.

	(1) DIF-GMM	(2) SYS-GMM
L.lnCO₂	0.658*** (6.250)	0.899*** (20.699)
lnTA	0.021 (1.142)	0.035** (2.147)
DE	0.828 (1.475)	1.399** (2.505)
$lnTA \times$ DE	−0.053* (−1.663)	−0.073** (−2.425)
lnGDP	0.510*** (3.524)	0.019 (1.166)
lnURB	0.059 (0.372)	0.090 (1.240)
lnPOP	0.238** (2.459)	−0.007 (−0.915)
lnIND	0.107 (1.354)	0.037 (0.808)
Constant		−1.135* (−1.845)
AR (1) (p-value)	0.000	0.000
AR (2) (p-value)	0.227	0.281
Hansen test (p-value)	0.185	0.111

Note: (1) Robust t-statistics in parentheses. (2) ***: p < .01, **: p < .05, *: p < .1.

An examination of the influence of potential outliers

To prevent extreme outliers from biasing the benchmark regression results, this study conducted a rigorous outlier analysis. Specifically, we eliminated data values falling below the 1st percentile and above the 99th percentile then regressed the pruned dataset following Xu et al. (2022) and Dong et al. (2022). The empirical results show that the estimated coefficient of the interaction terms is −0.37 and passes the stringent significance test at the 1% level (Table 7). This robust result underscores the reliability of our analysis, even after accounting for potential outliers.

Table 7.

Robustness analysis: An examination of the influence of potential outliers.

	lnCO₂
lnTA	0.164*** (4.290)
DE	5.501*** (4.273)
$lnTA \times$ DE	−0.370*** (−4.681)
lnGDP	0.319*** (4.496)
lnURB	1.321*** (3.789)
lnPOP	0.679*** (4.667)
lnIND	0.372*** (3.849)
Constant	−4.974*** (−2.739)
Country fixed effects	YES
Year fixed effects	YES
Observations	1597
R-squared	0.546

Note: (1) Robust t-statistics in parentheses. (2) ***: p < .01, **: p < .05, *: p < .1.

Heterogeneous tests

Income heterogeneity

Given the inherent variations in economic development levels across countries, the moderating effect of the digital economy is likely to exhibit heterogeneity. To capture this heterogeneity, we split the entire sample into four income groups: low-income (LI), lower-middle-income (LMI), upper-middle-income (UMI), and high-income (HI) using criteria established by the World Bank. Subsequent regression analyses are conducted on these subgroups, and Table 8 illustrates the results. Accordingly, the digital economy negatively moderates the effect of tourism on CO₂ emissions in the LMI, UMI group, and HI groups. However, in the LI group, the opposite trend is observed. Specifically, in these lower-income countries, the digital economy appears to exacerbate, rather than mitigate, the CO₂ emissions associated with tourism.

Table 8.

Heterogeneity analysis: Income heterogeneity.

	(1) Low-income countries	(2) Lower-middle-income countries	(3) Upper-middle-income countries	(4) High-income countries
lnTA	−0.097 (−0.880)	0.188*** (3.820)	0.133* (1.934)	0.071 (1.409)
DE	−22.375* (−2.242)	9.092*** (4.101)	2.219 (1.268)	3.317*** (3.173)
$lnTA \times$ DE	1.532* (2.422)	−0.570*** (−3.896)	−0.157 (−1.480)	−0.146** (−2.154)
lnGDP	0.623*** (7.184)	1.019*** (4.952)	0.592** (2.191)	0.469*** (3.122)
lnURB	0.777 (1.885)	1.493 (1.618)	0.109 (0.320)	0.156 (0.322)
lnPOP	3.048** (3.024)	−0.137 (−0.311)	0.358 (0.852)	0.222 (1.174)
lnIND	0.317 (1.684)	0.088 (0.512)	−0.109 (−0.764)	0.117 (0.951)
Constant	−24.793*** (−4.798)	−19.985*** (−4.729)	−8.930** (−2.225)	−6.535** (−2.231)
Observations	126	360	576	738
R-squared	0.806	0.702	0.449	0.657

Note: (1) Robust t-statistics in parentheses. (2) ***: p < .01, **: p < .05, *: p < .1.

The underlying reasons for this phenomenon are multifaceted. Firstly, high-income countries possess comparative advantages over low-income nations in terms of financial resources and scientific and technological expertise. These advantages serve as crucial external catalysts for the green transformation of tourism, enabling these countries to pursue sustainable practices more effectively. Secondly, high-income countries have a longer history of developing the digital economy, resulting in the maturation of technologies. These advancements facilitate the exploitation of data elements’ potential, thereby unlocking the CO₂ emission reduction benefits associated with the digital economy. Conversely, in low-income countries, the digital economy remains in its infancy, with digital infrastructure lagging behind. This limited capacity hinders their ability to leverage the power of the digital economy in mitigating the positive impact of tourism on CO₂ emissions.

Moderating effect of digital economy sub-indicators

Given that this study employs a composite index to encapsulate the multifaceted nature of the digital economy, it becomes imperative to delve deeper into its constituent dimensions to more accurately elucidate its moderating influence on the nexus between tourism development and CO₂ emissions. Therefore, the present investigation further disaggregates the digital economy into its core components: infrastructure, social impact, digital trade, and social support. The results are presented in Table 9. Specifically, the coefficients are significantly negative in Columns (1) and (2), indicating that digital infrastructure and social impact are pivotal in mitigating the tourism-CO₂ emissions nexus. Conversely, the coefficients in Columns (3) and (4) fail to reach statistical significance, suggesting that digital trade and social support exert negligible effects. This discrepancy can be rationalized by the fact that the digital economy’s emission-mitigating potential is primarily realized through technological innovations and enhanced regulatory frameworks. These mechanisms are intimately linked to infrastructural advancements and social impact, rather than digital trade or social support.

Table 9.

Heterogeneity analysis: Moderating Effect of Digital Economy Sub-Indicators.

	(1)	(2)	(3)	(4)
lnTA	0.141*** (4.921)	0.133*** (3.911)	0.090*** (2.672)	0.092*** (2.908)
DE₁	12.219*** (6.632)
$lnTA \times$ DE₁	−0.665*** (−5.922)
DE₂		5.542*** (3.239)
$lnTA \times$ DE₂		−0.352*** (−3.468)
DE₃			4.188 (0.672)
$lnTA \times$ DE₃			−0.101 (−0.247)
DE₄				74.102 (1.389)
$lnTA \times$ DE₄				−1.909 (−0.985)
lnGDP	0.641*** (5.628)	0.739*** (6.258)	0.739*** (6.266)	0.753*** (6.501)
lnURB	0.756** (2.469)	0.751** (2.296)	0.721** (2.233)	0.627* (1.912)
lnPOP	0.535*** (3.401)	0.505*** (3.087)	0.640*** (3.995)	0.638*** (3.977)
lnIND	0.104 (1.268)	0.065 (0.804)	0.086 (1.064)	0.105 (1.253)
Constant	−13.978*** (−8.660)	−14.531*** (−8.280)	−14.469*** (−8.377)	−14.276*** (−8.275)
R-squared	0.565	0.514	0.504	0.495

Note: (1) Robust t-statistics in parentheses. (2) ***: p < .01, **: p < .05, *: p < .1.

Conclusion and policy implication

The literature has clarified the different roles of the tourism sector in CO2 emissions. Nevertheless, little attention has been paid to the main drivers behind this phenomenon. We construct an integrated analytical framework covering tourism development, the digital economy and CO₂ emissions, scrutinising the moderating role of the digital economy on the tourism-CO₂ emissions nexus. Our findings reveal that the digital economy tends to attenuate the harmful environmental consequences of tourism. We posit that this mitigating effect is attributed to several factors. Firstly, the digital economy fosters green innovation within the tourism sector, thereby promoting environmentally sustainable practices. Secondly, it enhances the environmental governance capabilities of governments, enabling more effective policies and regulations. Finally, the digital economy encourages tourism enterprises and tourists alike to adopt low-carbon behaviors, further contributing to the reduction of CO₂ emissions.

Indeed, these findings underscore the importance of the digital economy on low-carbon tourism and its potential for mitigating climate issues, providing valuable insights for policymakers in developing the digital economy to support sustainable tourism development. Firstly, this research suggests that the digital economy not only promotes the industry’s low-carbon transition but also enhances its eco-efficiency. This underscores the imperative for national governments to formulate policies that facilitate the digitalization of the tourism sector. Secondly, heterogeneity analysis reveals that higher levels of digital infrastructure and digital social impacts are associated with lower CO₂ emissions from tourism. It is therefore crucial to prioritise innovations and breakthroughs in digital technologies alongside the popularisation of digital knowledge and technologies. Furthermore, it is imperative for the State to provide support to governments at all levels in enhancing their understanding and application of digital technologies. This includes implementing more digital reforms, fostering a conducive environment for innovation, and encouraging widespread adoption of e-government initiatives. By doing so, we can harness the full potential of the digital economy in driving sustainable tourism development and mitigating the adverse effects of climate change.

While this study gives valuable insight into the intricate linkages between tourism development, the digital economy, and CO₂ emissions, it possesses certain limitations that future research endeavors should address. Firstly, this paper overlooks spatial factors, a crucial aspect that could significantly impact the analysis. Future studies must tackle this limitation by meticulously considering the spatial autocorrelation problem and examining how spatial variations influence CO₂ emissions. Moreover, the present study focuses solely on macro-level analysis, leaving a gap in micro-level understanding. With improved data availability, future research should endeavor to conduct more nuanced and granular analyses at the micro level, utilizing city-level data to gain a deeper understanding. Finally, this paper could not clarify the different effects of different types of digital technologies. Future research must delve deeper into the application of digital technologies in the tourism sector, meticulously exploring the potential heterogeneity of the moderating role of various digital technologies in the complex relationship between tourism and CO₂ emissions. By addressing these limitations in future research, the relationship between tourism, the digital economy and CO₂ emissions can be better understood.

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Supplemental Material for Digitalization means green? Linking the digital economy to environmental performance in the tourism industry by Lingling Jiang, and Zhike Lv in Tourism Economics

Footnotes

Acknowledgements

We thank especially the Editor Prof. Albert Assaf and two anonymous referees for very constructive remarks and suggestions. They make some pertinent comments on the previous version of this article. Nevertheless, any shortcomings that remain in this research paper are solely our responsibility.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is partially supported by the Hunan Natural Science Foundation of China; (2021JJ40548), Social Science Foundation of Hunan Province (No. 23YBA082), Hunan Province Degree and Graduate Teaching Reform Research Project (No. 2021JGYB075), the Postgraduate Scientific Research Innovation Project of Hunan province (No. CX20230647) and Xiangtan University Postgraduate Research Innovation project (No. XDCX2023Y027). Any shortcomings that remain in this research paper are solely our responsibility.

ORCID iD

Zhike Lv

Supplemental Material

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Note

Author biographies

Lingling Jiang is a PhD student in the School of Business at Xiangtan University. Her research interests include: Tourism economics, regional economic and human capital. Her recent work has been published in journals such as Asia Pacific Journal of Tourism Research.

Zhike Lv, PhD, is a professor in the School of Business at Xiangtan University. His research interests include: Tourism economics, regional economic and human capital. His recent work has been published in journals such as Tourism Management, Annals of Tourism Research, Journal of Travel Research, Technological Forecasting & Social Change, Current Issues in Tourism, Economics Letters, Sustainable Development, and The Social Science Journal.

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Digitalization means green? Linking the digital economy to environmental performance in the tourism industry

Abstract

Keywords

Introduction

Literature review

Tourism and CO2 emissions

Why does the digital economy matter?

Methodology and data

Data

Variables

Economic growth

Population density

Urbanization level

Industrial development

Model

Results and discussion

Basic results

Robustness check

Replacement of key indicator variables

Results from the dynamic panel model

An examination of the influence of potential outliers

Heterogeneous tests

Income heterogeneity

Moderating effect of digital economy sub-indicators

Conclusion and policy implication

Supplemental Material

Supplemental Material - Digitalization means green? Linking the digital economy to environmental performance in the tourism industry

Footnotes

Acknowledgements

Declaration of conflicting interests

Funding

ORCID iD

Supplemental Material

Note

Author biographies

References

Supplementary Material

Tourism and CO₂ emissions