Tourism, urbanization and natural resources rents matter for environmental sustainability: The leading role of AI and ICT on sustainable development goals in the digital era

In the era of development, the world is facing severe challenges, and environmental degradation is one of them. However, the globe has tried to introduce several initiatives to fight for environmental sustainability, such as the Sustainable Development Goals. The leading role of the proposed goals is to balance development and environmental anxiety. Therefore, to these issues, artificial intelligence and technological advancements play a vital role in the natural resource economy in the digital age. Policy analysts are always looking for solutions and have come up with several viable remedies to this problem. Consequently, information & communication technology (ICT) plays a significant role in sustainability in the digital era. However, under the theme of natural resource sustainability, the effectiveness of ICT has a significant impact on sustainability. Accordingly, the current study investigates the long-run effect of income per capita, tourism, natural resources rents, urbanization, and ICT on environmental sustainability in 36 OECD economies from 2000 to 2018. The current research employs an Augmented Mean Group (AMG) and two-step GMM to investigate the study's objectives. Results show the positive contribution of urbanization, natural resources, and tourism to CO2 emissions, while ICT reduces emissions. Besides, an inverted EKC curve is also validated for selected economies. In addition, the moderate effect of ICT on urbanization, natural resources, and tourism shows a significant decline in CO2 emissions. In light of the findings, this study recommends several crucial measures for environmental sustainability.

Jan 23, 2024 - 18:35 Updated: Jan 24, 2024 - 18:10

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Tourism, urbanization and natural resources rents matter for environmental sustainability: The leading role of AI and ICT on sustainable development goals in the digital era

1. introduction

Over time, there has been a rising trend in environmental degradation (ED), and the globe is curious to find the best solutions (Abbas et al., 2023; Jiakui et al., 2023). However, rapid progress in human & development activities is also being caused for ED (Long and Ji, 2019; Sinha et al., 2020). Likewise, such activities at the cost of ED have become a leading hurdle in sustainability, and ED has remained a key focus of Sustainable Development Goals (SDGs). In addition, advanced and emerging economies are not without problems (Micah et al., 2023; Su et al., 2022). They have encountered many difficulties in achieving the desired peak of SDG 13 (Zafar et al., 2022; Zhuang et al., 2022). The literature blames the industry-led growth hypothesis as the main cause of environmental degradation (Gao et al., 2021). Because of industrialization, rapid economic expansion has led to an increase in ED, which ultimately threatens humankind (Alharthi et al., 2021; Haseeb et al., 2019; Usman and Radulescu, 2022). Consequently, countries are working on cutting the emissions that account for the vast majority of environmental issues (Sinha et al., 2020; Wegener and Amin, 2019). Identifying the factors contributing to the emission is crucial to develop policies to mitigate them. However, ensuring rapid progress to compete with other nations makes countries greedy. Therefore, countries become highly dependent on natural resource depletion (Iorember et al., 2022; Liu et al., 2021). A sustainable expansion cannot be achieved without the prudesnt use of natural resources under the prism of sustainability (Huang et al., 2020) (see Fig. 4).

Major ecological issues like water scarcity, climate change, and deforestation are caused by the unsustainable use of natural resources (Dong et al., 2017; Sinha and Sengupta, 2019). It is a common view that the generation of CO₂ through social activities or gatherings is a significant sign of potential impending global warming (Haque et al., 2019). Using petroleum for transportation, energy production, domestic and industrial uses, and other economic operations causes ED (Aeknarajindawat, 2020; Hussain et al., 2021; Li et al., 2022; Zhang et al., 2022). This is the so-called reindustrialization period, characterized by increasing economic development, industrial expansion, and rising energy demand (Abbasi et al., 2021; Hafeez et al., 2023; Raza Abbasi et al., 2021). Positive financial outcomes are associated with enhancing social & economic indicators, such as stimulating investment in cleaner tools and raising environmental awareness (Moradi et al., 2021; Yu et al., 2022). Economic expansion is the driving force behind industrial expansion, which boosts resource extraction and increases agricultural production (Balsalobre-Lorente et al., 2018). All of these economic activities deplete natural resources and produce harmful trash. Mining, deforestation, and agriculture all contribute to natural resources extractions and impact sustainability (Hajko et al., 2018).

Nowadays, artificial intelligence and digitalization are performing well in engaging environmental sustainability. Over the last three decades, information & communication technology has attained significant importance in daily activities (Chavanne et al., 2015). The environmental consequences of this rapid rise in ICT use remain unknown, while it is expected to boost productivity and energy efficiency. According to several researchers, ICT benefits reduce greenhouse gas emissions [(Chien et al., 2021; Nguyen et al., 2020; Shah et al., 2022)]. Others come to the opposite conclusion, claiming that the usage of ICTs puts pressure on energy utilization (Moyer and Hughes, 2012) by the resulting rise in electricity usage, one of the significant factors contributing to global carbon emissions (Hamdi et al., 2014). One key idea is that ICT can manage long-term growth and excessive energy usage in economic and daily activities (Ishida, 2015; Rohman, 2013). As a result, ICT may overcome the well-known environment-growth contradiction via two critical pathways. For starters, ICT can improve power distribution and storage while increasing grid reliability and efficiency. Furthermore, ICT may lower transaction costs, stimulate knowledge growth, and provide network effects and spillovers that boost productivity (Huang and Khan, 2022).

By having a long debate concerning environmental issues and countries' initiatives to minimize them, it is crucial to add a brief contribution by this study to existing literature. Firstly, this study focuses on economic envelopment and its significant progress in environmental sustainability. Therefore, in the existing literature, numerous empirical studies have used this factor to validate the environmental-related theories, but they voluntarily ignored the crucial role of development in other sectors. Simply put, it is understood that when economies make significant progress, they must boost their other development sectors to clean & green sectors. Therefore, it would be the first look inside the wall and tries to discuss the significance of development in selected OECD economies. At this time, it is supposed that the development level in OECD economies raises environmental deterioration, but it tries to pus-up all other sectors of the economy. Therefore, this empirical investigation tries to offer an accurate picture of the development level and its penetration into environmental quality. Secondly, for the social factors, urbanization (UB) is used as another ED determinant. In the development era, UB has received the special attention of policymakers and played a significant role in societies & modern economic development. Simply put, UB is a symbol of modernization. Since the last five decades, the World urbanization ratio has increased from 1.38 billion to 4.3 billion and it is expected to rise to approximately 6.7 billion (Nations et al., 2019). Over time, an unprecedented increase in UB has caused half of ED since the industrial revolution. However, massive urbanization from rural to urban areas and their human & economic activities across the different sectors may bring environmental deterioration. Therefore, it would cause enormous distortion at the initial development level, ultimately declining sustainability. However, on behalf of the outcome, this study provides the actual association of UB with environmental damages in OECD economies and tries to suggest some concurrent policy implications for sustainable urbanization.

Thirdly, the tourism industry (TR) significantly contributes to an economy's strength. Similarly, under the theme of sustainability, most of the studies have focused on the descriptive analysis of the tourism-sustainability association. Furthermore, the real picture of a long-term association between tourism and the environmental situation is not yet clear, and existing studies are gladly ignored. Also, they have failed to propose appropriate implications to reach the desired level of sustainable-tourism; therefore, this study tries to offer a way to consider its role in environmental deterioration on the basis of empirical estimation. In recent years, few studies have employed different models, such as the computable general equilibrium model and decomposition approach to demonstrate the critical role of TR in ED (Liu et al., 2011; Zhang and Zhang, 2018), but they ignored the STIRPAT model to investigate the mentioned association. On average empirical studies tried to keep attention to determinants, which can variate tourism-related emissions. However, it is very shocking if economies adopt emission-reduction policies, which may increase tourism-economic loss (Wang and Wang, 2018). Therefore, the mentioned policies or outcomes may have biases and create a hurdle to follow-up sustainable tourism. Consequently, the present study tries to construct a bridge by providing a detail tourism-environment framework.

Fourthly, an immense use of natural resources (NRs) causes unsustainable daily production and consumption activities. However, it is common think that the rising global temperature is a result of natural resource extraction. Therefore, it is an essential question that natural resource utilization harms the environment in selected economies. An irregular utilization of NRs may cause additional exhaustion under the rebound effect. Similarly, a massive fertilizer ratio is used for agricultural and fishery purposes, and these factors are also causing ED (Caglar and Mert, 2022). Approximately 1 billion formers are directly linked with resource extraction and significantly disturb sustainability (Taghizadeh-Hesary et al., 2022). Thus, it is crucial to investigate whether NRs utilization causes environmental harm and treat human kind. This study tries to build supportive views concerning the association of NRs with emissions in specified economies.

Fifthly, ICT has been considered a core factor in modernization for the last three decades. Therefore, various empirical researchers have used this factor to deal with environmental sustainability but have not reached a single opinion. Simply put, most studies have investigated ICT's role in sustainability via focusing on the energy-environment model. However, there found a significant rise in ED by ICT via the usage of massive energy in ICT-linked devices (Avom et al., 2020; Lee and Brahmasrene, 2014). In contrast, some studies show a significant contribution to sustainability via efficient energy use (Chatti, 2021; Wang and Xu, 2021). On behalf of these views, it is hard to conclude whether the response of ICT in sustainability is positive or negative. Besides, this study uses the STIRPAT model to increase the attractiveness of ICT in the environment-development mode. However, ICTs related studies have not tried to consider ICT as a core variable for environment, they employed as an explanatory variable. Therefore, this study tries to fill-up mentioned curious gap by a special intension toward the ICT and uses it as a best proxy for technology under the STIRPAT model.

Moreover, this study tries to contribute to ICT-environment literature by offering an intelligent concept of moderate effect. In a nutshell, the current empirical study attempts to investigate the moderate role of ICT on natural resources, urbanization and tourism in selected OECD economies. Undoubtedly, ICT performs well in urbanized areas, the tourism sector, and efficient utilization of NRs; therefore, the current study believes that the rapid involvement of ICT in specified sectors may bring sustainability. However, the proposed study's gap can be analyzed with the help of some estimation techniques, and this study uses advanced econometric methods. In these, the CADF and CIPS unit root tests, CSDs test and Co-integration test are implied for initial screening. Similarly, the AMG tests and the two-step technique of the Generalized Method of Moments (GMM) are used to measure long-term association among selected variables.

The rest of the essay is organized as follows. An overview of earlier research literature is included in Section 2. Section 3 explains the theoretical approach and econometric methodology. Section 4 presents the results, discussion, and conclusion. Section 5 gives policy recommendations.

2. Summary of the past studies

It is essential to summarize the past studies; therefore, the current research also focuses on theoretical and empirical literature. In theoretical literature, this study demonstrates the evolution of Information & communication technology (ICT), natural resources (NRs) and the tourism industry and their linger-on connection with environmental sustainability (ES).

In theoretical literature, ICT has been debated as a core social and economic progress factor. However, in Sustainable Development Goals, their targets, such as poverty, education, hunger, growth, energy etc., are directly linked with ICT. However, there are numerous theories, i.e. modernization, social development, and development theories by (Grossman and Krueger, 1991; Hakkio and Rush, 1991). In the modern era, social development defines life scenarios such as health, culture, education and fashion (Houghton, 2010). On the other hand, development theories refer to the employment sector, living standards, and rapid countries' progress (Kirikkaleli et al., 2022). More interestingly, ICT in both theories performs like an origin, and its contribution to social and economic progress is valuable. Since the last three decades, the rapid growth has brought severe challenges regarding the environment. Countries have damaged the domestic climate and caused global issues such as ozone layer, globe temperature, and greenhouse gases for development. With such leading global problems, policymakers rely on ICT and consider it one of the core factors for rapid growth at minimum environmental damage. Therefore, various studies show a positive sign for its role in emissions reduction and fast green growth (Matthews, 2003; Takase and Murota, 2004; Yi and Thomas, 2007). Consequently, the connection of ICT with environmental sustainability has been an attractive debate arena among policymakers, who considered it a lost resort in green growth and sustainability. However, various methods have been used to investigate ICT's role in environmental deterioration via life cycle assessment, Enablement method and partial footprint (Bieser and Hilty, 2018). Besides the theoretical literature, numerous empirical studies also have been shown on this association and summarized in Table 1.

Table 1. Summary of the past studies.

Author	Region	Span	Technique	Outcome
Digitalization-Environment Nexus
(Zhou et al., 2019)	China	1995–2015	Data Treatment	ICT $↓$ ED
(Raheem et al., 2020)	G7	1990–2014	PMG, MG, DFE	ICT $↑$ ED
(Godil et al., 2020)	Pakistan	1995–2018	QARDL	ICT $↓$ ED
(Avom et al., 2020)	21 SSA economies	1996–2014	FGLS, FE & RE	ICT $↑$ ED
(Haini, 2021)	ASEAN economies	1996–2019	PFE	ICT $↓$ ED
(Khanal, 2021)	Taiwan	2004–2018	PMG	ICT $↓$ electricity demand
(Chatti, 2021)	43 countries	2002–2014	TS GMM	ICT $↓$ ED
(Usman et al., 2021)	9 Asian economies	1990–2018	NARDL, ARDL	ICT $↓$ ED except for UAE
(Appiah-Otoo et al., 2022)	ICT countries	2000–2018	IV-GMM	ICT $↓$ ED
(Wang et al., 2022)	41 China's Cities	2003–2016	S-GMM, D-GMM, PTM	Variation in behavior of ICT to ED
(Khan et al., 2022)	Morocco	1985–2020	D-ARDL	ICT $↓$ ED
(Xu et al., 2022)	31 China Provinces	2001–2018	SDM	ICT $↓$ ED
NRs-Environment Nexus
(Nassani et al., 2019)	Low, high and middle income economies	1975–2017	RLS	NRs $↑$ ED
(Bekun et al., 2019)	16 EU economies	1996–2014	PMG	NRs $↑$ ED
(Wang et al., 2020)	G7 economies	1996–2017	CS-ARDL	NRs $↑$ ED
(Kwakwa et al., 2020)	Ghana	1971–2013	ARDL	NRs $↑$ ED
(Loganathan et al., 2020)	Malaysia	1970–2018	BQE	Ambiguous
(Dogan et al., 2020)	Developed economies	2001–2017	PQ-FE	NRs $↑$ FD
(Balsalobre-Lorente et al., 2021)	European economies	1990–2017	CS-ARDL	NRs $\cap$ ED
(Xiaoman et al., 2021)	MENA economies	1980–2018	CUP-FM & CUP-BC	NRs $↓$ ED
(Tufail et al., 2021)	OECD	1990–2018	PARDL	NRs $↓$ ED
(Nwani and Adams, 2021)	93 nations	1995–2013	AMG	NRs $↑$ ED
(Dogan et al., 2021)	18 developing countries	1985–2018	PQR	GPR $↓$ NRs
(Li et al., 2022)	South East Asia	1996–2019	CS-ARDL	NRs $↑$ ED

(Hussain et al., 2022)	China	1961–2016	ARDL	NRs $↑$ ED
(Ling et al., 2022)	China	1980–2017	NARDL	NRs $↑$ ED
(Yu-Ke et al., 2022)	G-20 economies	1995–2018	PMG	NRs $↑$ ED
(Dogan et al., 2022)		2020–2021	TVGC	NRs price $↑$ $↓$ SM
(Gyamfi, 2022)	SSA economies	1990–2018	DK-OLS, AMG, S-GMM	NRs $↑$ ED
(Ibrahim et al., 2022)	5 African economies	1990–2019	CS-ARDL. AMF & CCE-MG	NRs $↑$ ED
(Usman and Balsalobre-Lorente, 2022)	NIC economies	1990–2019	AMG	NRs $↓$ ED
(Usman et al., 2023)	Mercosur countries	1990–2018	DK Approach	NRs $↓$ ED
Tourism-Environment Nexus
(Bi and Zeng, 2019)	China	2003–2016	Spatial dependence model	TRs $\cap$ ED
(Shaheen et al., 2019)	10 tourism-induced economies	1995–2016	PGC	TRs $\leftrightarrow$ ED
(Akadiri et al., 2020)	16 small Island	1995–2014	BSPGC	TRs $\leftrightarrow$ ED
(Chishti et al., 2020)	5 South Asian	1980–2018	NARDL	TRs $↑$ ED
(Yıldırım et al., 2021)	15 Mediterranean	2001–2017	PTHM	TRs $↓$ ED
(Chen et al., 2021)	31 China's Provinces	2000–2020	RE & FE	TRs $↑$ ED
(Huang et al., 2021)	30 Provinces of China	2005–2016	SRM	TRs $↓$ ED
(Erdoğan et al., 2022)	Most Visited economies	1995–2018	PQR	TRs $↑$ ED
(Tong et al., 2022)	92 tourism based China's cities	2005–2016	SEM	TRs $↓$ ED
(Shah et al., 2022)	Top 8 Asian Tourist economies	1995–2017	AMG & CS-ARDL	TRs $↑$ ED
(Katircioglu and Katircioglu, 2022)	Malta	1990–2018	ARDL	TRs $↑$ ED
(Ahmad et al., 2022)	G7	2000–2019	DOLS & FMOLS	TRs $↑$ ED
(Ghosh et al., 2022)	G7	1990–2018	FMOLS & DOLS	TRs $↑$ ED

Note: ↑: Increase, ↓: Decrease, ∩: Inverted U-shaped, ↔: Bi-directional association, PMG: Pooled Mean Group, MG: Mean Group, DFE: Dynamic Fixed Effect, QARDL: Quantile Auto-regressive Distributive Lag Model, FGLS: Feasible Generalized Least Square, FE: Fixed Effect, RE: Random Effect, PFE: Panel Fixed Effect, TS-GMM: Two-Stage Generalized Method of Moments, NARDL: Non-Auto-Regressive Distributive Lag model, S-GMM: System GMM, D-GMM: Dynamic GMM, PTM: Panel Threshold model, D-ARDL: Dynamic ARDL, SDM: Spatial Durbin model, RLS: Robust least square, BQE: Bootstrap quantile estimates, CUP-FM: Continuously Updated Fully Modified, DK-OLS: Driscoll-kraay OLS, PGC: Panel granger causality, BSPGC: Bootstrap Panel granger causality test, SRM: Spatial regression model, DOLS: Dynamic Ordinary Least Square, FMOLS: Fully Modified Ordinary Least Square, ED: Environmental Degradation, TRs: Tourism, NRs: Natural resources, ICT: Information & Communication Technology, TVGC: Time varying Granger Causality test, SM: Stock Market.

Similarly, natural resource utilization (NRs) has a leading role in economic progress and environmental sustainability. In the empirical literature, the connection of NRs has been debated well. However, the top economists have different opinions regarding NRs in the home countries. These economists, i.e., Adam Smith and David Ricardo, pointed out that NRs significantly contribute to economic development. Similarly, in 1970, numerous post-war economies supported this view (Viner, 1952). Late (Rostow, 1959) also validated that NRs perform well to escape from the under-developed trap to take-off position. However, later on, economists also offered their support to leading economists (Nankani, 1985). Besides its association with the economy's growth, in recent decades, the think of researchers have been changed, and now this factor is utilized mainly with environmental sustainability. In the current literature, its association has been divided into direct and indirect effects. The direct effect refers to the availability of NRs at a lower price, which has made resources based on the behavior of regions. Therefore, multinational or domestic companies try to locate a suitable place with an ambitious lower risk and minimum costs. Consequently, the massive use of NRs at lower prices causes environmental degradation (ED). On the other hand, indirect effects refer to significant progress in industrial structure development but at the cost of environmental deterioration (Sun and Ye, 2012). The industrial structure becomes dominated on behalf of natural resource abundance, which shrinks modern development. Furthermore, in recent years, numerous studies have tried NRs association with sustainability; some of these are summarized in Table 1.

Policy analysts consider the tourism industry a booster dose for economies; however, in the existing theoretical literature, two different approaches have been introduced, e.g., production and consumption accounting approaches (Turner et al., 2012). However, the production approach describes rising emissions within tourism boundaries (Munday et al., 2013). In addition, this approach also includes emissions via tourist exit regions. More interestingly, this approach has some limitations, such as not capturing the ED by visitors' imported goods and intermediary products used by tourism industries (Sun, 2014). Similarly, the consumption approach refers to rising emissions by tourists under the jurisdiction (Munday et al., 2013). It includes ED by domestic and international tourists and neglects the inbound tourist emissions (Sun, 2014). Finally, its coefficient does not explain the considerable variation between tourism and emissions. Alongside numerous empirical studies have tried to investigate the long-term association of tourism with emissions, some of which are reported in Table 1. More interestingly, Table 1 divides into three sub-sections such as 1) Digitalization-Environment Nexus, 2) NRs-Environment Nexus, and 3) Tourism-Environment Nexus.

However, having a long-debate regarding the association of leading factors of environmental degradation (ED), the existing studies have numerous flaws, and this empirical research tries to fill-out. Firstly, in recent years various studies demonstrated the connection of ICT, tourism, and natural resources with ED, and they found a very ambiguous outcome that varies from region to region. Therefore, the current study selects the 36 OECD economies to demonstrate the present situation of mentioned environmental variables and would be able to guide some attractive policies for policy analysts. Secondly, previous studies have not focused on green technology, especially in OECD countries. Therefore, this study utilizes a theoretical model known as the STIRPAT model to infer the leading role of ICT in sustainability. In addition, most existing studies used the ICT as an explanatory variable and have not tried to investigate its indirect role in sustainable tourism and natural resource utilization. Thus the current study also tries to minimize this flaw by providing solid empirical evidence via ICT's moderate role in the tourism and natural resources sectors and their role in sustainability. Besides, the summarized literature by researchers has used advanced techniques, but at the same time, they have not cared for panel data issues. This study also tries to cover all matters expected in panel data via advanced econometric techniques such as AMG and two-step GMM estimators.

3. data and methodology

3.1. Variables discussion

In the growing world, countries try to compete with other nations at their development level. Since the last three decades, there has been an increasing rate of environmental deterioration due to a significant rise in income per capita. Therefore, it is a common view that with a significant increase in income, people demand massive energy-intensive products or utilizes massive energy in their human and economic activities. Thus the present study also uses the income per capita as a proxy of development and tries to investigate the key role of OECD development toward environmental sustainability. Similarly, social and economic indicators have a crucial role in sustainability. Therefore, urbanization has a strong correlation with rising income. Simply put, a significant rise in development may cause urbanization. However, this factor also has a significant impact on environmental quality. Therefore, the existing literature has shown its two effects on the environment by keeping all other things constant. Firstly, it changes the resource use pattern by significantly transitioning to modern fuels. Secondly, to maximize the efficient use of natural resources (NRs) and economies of scale, it provides a suitable place rather than a rural one. Therefore, economic activities in disperse areas are less efficient than in compact cities. Thus the designing of cities is a core hurdle to reaching sustainability. Moreover, rapid urbanization can influence sustainability through the massive use of pollution-intensive products.

Similarly, natural resources are also being added to the development-environment model because most economies try to use their revenue and make significant global progress. Therefore, economies have focused on NRs extraction at the cost of environmental damage. When economies prefer quantity over quality, they demand NRs exploitation with less environmental laws and low industrial standards that may cause ED (Sarkodie and Strezov, 2019). Moreover, the increasing desire for development is also interlinked with NRs exploitation and causes of environmental pressure (Sarkodie and Strezov, 2018). Undoubtedly, economic activities have remained the root cause of pollution via production and consumption patterns, but nowadays, tourism activities are considered the major cause of ED (Saenz-de-Miera and Rosselló, 2014). Therefore, most empirical studies tried to put all environmental issues on shove of tourism (Başarir and Çakir, 2015). In such clogging environmental conditions, only information & communication technology (ICT) is being considered as a last resort for policy analysts. By having this view, it can be supposed that ICT may not contribute to environmental damage/deterioration. Therefore, it is expected that the utilization of ICT and its related equipment or services would increase by a handsome ratio and significantly reduce the level of ED. Thus on such interesting views, this study considers the given determinants of the environment.

In order to choose the environmental determinants, this study considers income per capita (GDPC), urbanization, ICT, tourism and natural resource rents in 36 OECD economies for 2000–2018. Therefore, the data symbols, measurements, and sources are described in Table 2. Likewise, the box plot for each selected variable is given in Fig. 1.

Table 2. Data and variable description.

Variables	Definition	Sources
CO2	Carbon emission (Kt)	WDI
GDPC	GDP per capita (current US$)	WDI
UB	Urbanization (% of total population)	WDI
ICT	Information & communication technology (internet users; % of the total population)	WDI
NR	Natural resources (% of GDP)	WDI
TR	Tourism (number of arrivals)	WDI

Data Source: https://data.worldbank.org/

However, the current selection of study's variables has been made on behalf of the Sustainable Development Goals (SDGs). Because this study is directly associated with SDG goals: climate change: Goal 13, Decent Work and Economic Growth: SDG 8; Sustainable Cities and Communities: Goal 11, Responsible Production & Consumption: Goal 12; and Partnership for Goals: SDG 17. Furthermore, these have been converted into targets under the highlighted Goals, such as Target: 13.2.2, Target: 8.1.1, Target: 8.9.1, Target: 11.3.2, Target: 12.2, and Target: 17.8.1, respectively. However, the conceptual framework is given below under the SDGs Fig. 2. See Fig. 2, Fig. 3 below.

3.2. An extension of the STIRPAT model

In order to demonstrate the role of socio, economic and technical activities in environmental sustainability, the literature has introduced the $� � � �$ model (Enrlich and Holdren, 1971).(1) $� = � � �$

However, in Eq. (1) $�, �, �,$ and $�$ refer to the environment, population, economic growth and technology. However, later on, researchers felt the proposed model was incomplete, and there was some need to add (York et al., 2003). Therefore, to solve this issue (Dietz and Rosa, 1997), have tried to introduce the STIRPAT model, which can be expressed as,(2) $�_{� �} = � �_{� �}^{�} �_{� �}^{�} �_{� �}^{�} �_{� �}$

Similarly, Eq. (2) shows the population, affluence, and technology with their slopes b, c, and d. However, α is the intercept, and i & t refers to the number of cross sections and time period. However, the transform model form is given as,(3) $I n (�_{� �}) = � + � � � (�_{� �}) + � � � (�_{� �}) + � � � (�_{� �}) + �_{� �}$

Eq. (3) shows the linear association among the selected environmental determinants, and α, b, c, and d are equal to one (Wang et al., 2013). However, it is recommended that T can be crumbled into different environmental factors (York et al., 2003). Therefore, in the light of existing studies, this study decomposes T into technology (ICT), natural resources and tourism activities in specified economies. The extended model can be written as,(4) $� � � � 2_{� �} = � + � (� � � �_{� �}) + � (� � � � � �_{� �}) + �_{1} (� � � � �_{� �}) + �_{2} (� � � � �_{� �}) + �_{3} (� � � �_{� �}) + �_{� �}$

In the given Eq. (4) $� � � � 2$ , $� � � �$ , $� � � � � �$ , $� � � � �$ , $� � � � �$ and $� � � �$ shows the natural log of emissions, urbanization, income per capita, technology (ICT), natural resources, and tourism. Moreover, “ $�$ ” & “ $�$ ” refers to the number of cross sections and time period. However, according to the model, as people's income increases, they demand more economical and human activities without any care for their sustainability. Thus, this study supposes that the slope of GDPC would be positive ( $�$ > 0). Similarly, a significant rise in income would cause urbanization. However, this phenomenon happened due to better opportunities concerning jobs, education and the health sector, but daily movement from one place to another with massive energy use causes ED. It imagines that its slope would be positive ( $�$ > 0). Furthermore, information & communication technology (TEC) is performing well in daily activities and tries to minimize social distance due to access in hand. Nowadays, in numerous economies, not even a single sector that not utilizes its services. Therefore, this study considers it would cause a decline in emissions, and its slope would be negative ( $�_{1}$ < 0). However, in the era of development, countries are trying to make rapid development at any cost; therefore, utilization of natural resources has become a leading source for economies. Massive extraction of NRs may cause environmental deterioration, and its slope world be positive ( $�_{2}$ > 0). Finally, the tourism industry is also working well to support the economy's progress, but at the cost of ED. The slope of tourism would be positive ( $�_{3}$ > 0).

Similarly, another leading objective of this study is to investigate technology's moderate role and try to add a new way of thinking for forthcoming studies. This study investigates the role of ICT in urbanization, natural resources and tourism sectors because all of these sectors are directly linked with online services. Firstly, the present study investigates the moderate role of ICT in the urbanized sector and its association with carbon emissions. It is a common belief of policy analysts that the massive involvement of ICT in human life brings a significant decline in travel. Therefore, the current study supposes that irrelevant activities may decline in the urbanized sector due to ICT facilities and cause emission reduction. Thus, its slope would be negative [ $�_{4 *}$ < 0; ${� \times � �}_{� �}$ ].(5) $� � � � 2_{� �} = � + � (� � � �_{� �}) + � (� � � � � �_{� �}) + �_{1} (� � � � �_{� �}) + �_{2} (� � � � �_{� �}) + �_{3} (� � � �_{� �}) + �_{4 *} (� � � � {� \times � �}_{� �}) + �_{� �}$

Similarly, the moderate effect of ICT on NRs also may cause of reduction in ED, and its slope would be negative [ $�_{4 * *}$ < 0; ${� \times � � �}_{� �}$ ].(6) $� � � � 2_{� �} = � + � (� � � �_{� �}) + � (� � � � � �_{� �}) + �_{1} (� � � � �_{� �}) + �_{2} (� � � � �_{� �}) + �_{3} (� � � �_{� �}) + �_{4 * *} (� � � � {� \times � � �}_{� �}) + �_{� �}$

However, the moderate effect of ICT on tourism activities also brings a sustainable environment, and its slope would be negative as well [ $�_{4 * * *}$ < 0; ${� \times � �}_{� �}$ ].(7) $� � � � 2_{� �} = � + � (� � � �_{� �}) + � (� � � � � �_{� �}) + �_{1} (� � � � �_{� �}) + �_{2} (� � � � �_{� �}) + �_{3} (� � � �_{� �}) + �_{4 * * *} (� � � � {� \times � �}_{� �}) + �_{� �}$

3.3. Empirical methodology

This study follows the recently updated techniques to deal with data problems in the empirical estimation strategy. Due to globalization, it is necessary to check out the CSD in the panel data; therefore, the current study employs three different CSDs, namely Frees, Freidman, and Pesaran proposed by (Frees, 1995; Friedman, 1937; Pesaran, 2004). Under the CSD, the first-generation test becomes inappropriate; therefore, the current study employs the CADF and CIPS unit root test as suggested by (Pesaran, 2007). However, to investigate the long-run association among selected variables, we use the Westerlund co-integration test proposed by (Westerlund, 2007). However, under specified advantages, the most appropriate test is suggested by literature known as the Augmented Mean Group (AMG) proposed by (Bond and Eberhardt, 2013). More interestingly, the AMG method applies to data integration and cross-section dependence. Also, this test allows the slope of homogeneity, and results by AMG estimator may be robust if there is no co-integration among selected variables. However, this test can cover the expected endogeneity problems. However, the general form of AMG estimator can be written as well,(6) $Δ l n C O 2_{i t} = � Δ �_{i t} + \sum_{� = 2}^{�} ς_{�} (Δ �_{�}) + �_{i t}$ (7) $A M G = \frac{1}{�} \sum_{� = 1}^{�} � {}_{I}$

Similarly, there may be a problem of endogeneity in the panel data; therefore, the present study employs the two-step GMM estimator. It is more appropriate when cross-sections are greater than the time period. However, two-step GMM (TS GMM) is superior to one-step GMM. Thus, finally, the (Blundell and Bond, 2000) two-step process GMM is employed to check out the robustness of the AMG estimator. However, in the recent years these techniques have been applied by (Khan et al., 2019; Zhang et al., 2021).

4. Results and discussion

Table 3 describes the descriptive statistics findings, and there is no significant difference between the Mean & Median values. Simply put, there is no chance of an outlier in panel data.

Table 3. Descriptive statistics results.

Statistics	$� � � � 2$	$� � � � � �$	$� � � �$	$� � � � �$	$� � � � �$	$� � � �$
Mean	4.9003	4.3958	1.8788	1.0702	−0.4879	7.0291
Median	4.8136	4.4593	1.8956	1.0368	−0.4123	6.9228
Maximum	6.7616	5.0922	1.9912	1.9766	1.2360	8.3263
Minimum	3.2695	3.3582	1.7054	−0.1023	−3.7068	5.4983
Std. Dev.	0.7591	0.3389	0.0673	0.3239	0.9178	0.6257
Probability	0.000	0.000	0.000	0.000	0.000	0.002

Table 4 exhibits the outcomes regarding correlation across the selected variables. The results describe all explanatory variables positively correlated with emissions at 1%, except UB, which is at 5% significance. However, to validate that the data is free from multicollinearity, this study uses the VIF test.

Table 4. Correlation probability results.

Variables	$� � � � 2$	$� � � � � �$	$� � � �$	$� � � � �$	$� � � �$	$� � � �$	VIF	1/VIF
$� � � � 2$	1						1.37	0.7310
$� � � � � �$	0.1488*	1					1.25	0.7970
$� � � �$	0.0993**	0.3927*	1				1.24	0.8074
$� � � � �$	0.1891*	0.0286**	−0.1410*	1			1.13	0.8886
$� � � � �$	0.1389*	−0.3302*	−0.1480*	0.2599*	1		1.06	0.9413
$� � � �$	0.7185*	0.0456*	−0.1542*	0.1286*	0.1139**	1
Mean VIF							1.21

Note: * and ** show the significance level at 1% & 5%, respectively.

The results of the cross-sectional dependence are summarized in Table 5. The estimated outcomes meet the desired expectations and validate the presence of CSD. Also, this study uses the test for homogeneity and obtains the robust results.

Table 5. Cross-sectional dependence.

Tests	Value	P-value
CSDs test
Pesaran's test	17.561	0.000
Frees' test	11.577	0.000
Friedman's test	114.689	0.000
The slope of homogeneity test.
	Value.	P. Value.
$Δ$	16.098	0.000
$Δ$ Adj.	19.715	0.000

Similarly, Table 6 describes the outcomes of advanced econometric techniques i.e., CADF & CIPS. Therefore, the present study uses the second-generation unit root tests and shows the most reliable results.

Table 6. CADF and CIPS test.

Variables	CADF UR test		CIPS UR test
Empty Cell	Level.	1st difference.	Level.	1st Difference.
$� � � � 2$	−3.353*	−5.179	−3.159*	−4.431
$� � � � � �$	−1.857	−2.068**	−1.997	−3.904*
$� � � �$	−1.188	−3.660*	−1.999	−2.777**
$� � � � �$	−3.527*	−3.881	−3.990*	−3.649
$� � � � �$	−1.969	−4.926*	−1.063	−3.987*
$� � � �$	−2.029	−4.830*	−2.029	−3.178*

Note: * and ** show the significance level at 1% and 5%, respectively.

Table 7 shows the results of long-term co-integration among selected variables and the robust outcomes.

Table 7. Westerlund cointegration.

Statistics	Value	Z-value	P-value	Robust P-value
$�_{�}$	−3.260	−1.601	0.055	0.000
$�_{�}$	−2.794	10.724	1.000	1.000
$�_{�}$	−18.214	−1.505	0.066	0.000
$�_{�}$	−3.298	8.236	1.000	0.000

4.1. A deep look with comparison and discussion

Similarly, this study tries to demonstrate the long-run association among mentioned variables and uses the two most reliable estimators, AMG and two-step GMM (Table 8). Under the AMG estimator, the income per capita (development) slope shows a positive link with environmental degradation (ED). It is very shocking news for the OECD economies that they made their development at the cost of ED. However, there may be quite a few logics behind the scene, and it would not be better to blame increasing development. Undoubtedly, the rising development may indirectly affect the environmental quality. Firstly, as income rises, the populace demands better facilities to make their lives comfortable. Many other sectors are involved in these facilities, such as transport, energy, infrastructure, and society's development. In order to ensure these basic facilities, higher authorities try to consume their income resources on development. In this process, massive construction activities and causes of environmental deterioration are seen. Secondly, an increase in income may cause to variate the demand side, and domestic consumers raise their demand for products. Therefore, at this time, the production sector is at its toddler phase and to meet the excess demand, the production side uses emissions-intensive inputs that cause ED. Furthermore, income encourages buying energy-intensive equipment, which utilizes energy at its optimum point and damages sustainability. However, the industrial sector also causes environmental pollution and further deteriorates environmental quality. The noteworthy point is that people have no environmental awareness, and there is a need to start a campaign and guide the people to secure their environmental quality. However, this finding supports existing views by (Dogan and Inglesi-Lotz, 2020; Katircioglu et al., 2020).

Table 8. AMG results.

Variables	Model 1	Model 2	Model 3	Model 4
$� � � � � �$	0.1145*	0.1309*	0.1138*	0.3154*
$� � � �$	1.2536*	5.5585**	1.0720*	1.1790**
$� � � � �$	−0.0499**	−10.451*	−0.0489**	−0.3548*
$� � � � �$	0.0175*	0.0097**	0.1095**	0.1523**
$� � � �$	0.0650**	0.0665**	0.0682*	0.1620**
$\ln (� � � \times � �)$	–	−5.2395*	–	–
$\ln (� � � \times � � �)$	–	–	−0.1194*	–
$\ln (� � � \times � �)$	–	–	–	−0.2159*
Cons.	6.2457**	14.785*	5.7461*	5.7461*

Note: * and ** show the significance level at 1% & 5%, respectively.

Similarly, urbanization (UB) also significantly contributes to environmental damage in OECD economies. This outcome shows the direct link of UB with sustainability that due to rapid & no-organized society's development in urban areas causes environmental deterioration. However, there has been continuous migration from rural to urban areas by having a common belief in better health, education and earnings opportunities. Likewise, urbanized regions cannot absorb an unprecedented migration. Due to low city capacity this phenomenon creates pressure on other facilities such as sanitation, clean water, and clean air due to low city capacity, which further degrades the environment. Furthermore, there has been a massive distortion of trees, and UB has become a significant cause of ED. In addition, there has been a substantial increase in energy demand due to the extensive use of transport services in urban areas and the ultimate increase in pollution. However, the urban populous try to use electric equipment for their home activities such as cooking, washing, and cooling, which are also energy-intensive and would be caused by ED. This finding supports the existing outcomes by (Adebayo et al., 2021; Shah et al., 2020; Sun et al., 2022).

Similarly, information & communication technology (ICT) shows an inverse association with ED in OECD economies. This significant impact on sustainability shows a crucial role to fight in against environmental deterioration. However, the existing studies have seen a profound association of ICT with development activities. A significant ICT rise and its play in economic progress compel policy analysts to consider this factor an eco-friendly indicator. In recent decades, ICT has provided a deep help to ease social, economic and physical well-being (Mack et al., 2019). Economic efficiency can be increased by using ICT services in the production process. In this way, firms can reduce their transaction cost at an increasing rate of labor efficiency (Niebel, 2018). Besides, it can help to reduce environmental problems. It has been observed that ICT performs well in managing the production process and securing sustainability. In the era of development, ICT provides symmetric information concerning online shopping, business meetings, and educational activities that ultimately reduce environmental pressure (Hilty and Ruddy, 2010). However, seeking environmental education and management ICT helps divert human behavior from a dirty to a clean environment (Gong et al., 2020). This finding is in line with the outcomes by (Appiah-Otoo et al., 2022; Haini, 2021).

Similarly, the finding of NRs shows a significant contribution toward environmental damages. It is interesting to know that most economies are highly dependent on natural resources revenue to boost their economic progress and ultimately increase ED. However, the subsidy policies on traditional energies offer massive use in the country's production and consumption process at the cost of environmental deterioration. Furthermore, emissions-intensive plants have been observed in recent years that cause ED; therefore, such economies have closed their eyes to their development. Additionally, the massive use of NR increases the bio-diversity issues (Bekun et al., 2019). Selected economies are the newly industrialized nations, and the industrialized economy's share of material extraction is increasing mainly due to developing new infrastructure. Given that industrialization is accelerating in some economies, rapid economic growth is also linked to increased resource exploitation and dependence on imported fossil fuels due to unsustainable resource use (Oberle et al., 2019). Also, this outcome is supported by (Ling et al., 2022) and in contrast with (Dogan and Aslan, 2017).

According to the coefficients, a 1% increase in tourism will increase carbon emissions. Tourism raises CO2 emissions by 0.0650%, 0.0665%, 0.0682%, and 0.1620%, respectively. This illustrates how the level of the environmental deficit for the OECD economies will rise over time due to an influence on inbound tourism. The examined economies do not have solid ecological quality improvement strategies. One of the possible causes is that the goods and services purchased in advance in OECD economies are not pollution-free. For example, shipping and transportation-related activities are closely related to the tourism industry in a country that burns more fossil fuels than average, which will significantly impact carbon emissions (Al Halbusi et al., 2022, Lebni et al., 2020, Shah et al., 2023). In addition, it has been noted that hotels and restaurants in every tourist destination significantly increase their carbon emissions every year due to the vast amount of garbage they produce and the irresponsible eco-actions of their patrons. However, this is in support of (Ghosh et al., 2022; Katircioglu and Katircioglu, 2022).

By having a long debate regarding each determinant of the environment, we found that ICT is a leading factor for sustainability. Therefore, another foremost objective of this study is to demonstrate the moderate role of ICT in urbanization, natural resources and tourism. Accordingly, the moderate role of ICT ( $\ln (I C T \times U B)$ ) shows a significant decline in emissions due to a 1% rise in this factor. This association can be explained with logic. Over time, ICT has penetrated human being lives, and most of the populace has access to advanced technologies in terms of internet use and its devices. Now the whole globe is in hand, and there is no chance of asymmetric information; therefore, the urban sector has declined its non-productive activities such as outdoor shopping, dinner, and entertainment. A significant decline in human movement from one place to another place has helped-out to reduce ED. Similarly, the moderate effect on natural resources also shows a significant decrease in emissions by 0.1194%. Due to modern digitalization, natural resources have become more efficient, and with the help of digitalization, they significantly reduce emissions. Finally, $\ln (I C T \times T R)$ diminishes in ED by 0.215% due to a 1% rise in this interaction term. Nowadays, tourism is directly associated with environmental sustainability; therefore, in the digitalization era, tourists try to find a safe place to spend their holidays. Moreover, in tourist areas, consumers try to travel only for entertainment, while ICT has significantly reduced other activities such as meals, shopping, and additional relevant information. Consequently, due to its significant involvement in daily life, ICT causes to decline in environmental pressure. In concluding remarks, ICT is the most suitable technology to support the targets settled by SDGs.

4.2. Robust check by two-step system GMM

The result of the robust check by the two-step system reveals that all variables have the same impact via sign-on CO₂ emissions. Development is significant and positive, showing that GDP per capita raises carbon emission by 0.0784%, 0.0034%, 0.0656%, and 0.0321%. The coefficients of UB show that urbanization positively impacts CO₂ emission by 4.6184%, 6.9150%, 4.5407%, and 1.2751%. The results indicate that information & communication technology negatively impacts CO2 emission. This suggests that with increasing information & communication technology, the level of CO2 emission decreases by 3.544%, 0.064%, 3.576%, and 5.228%. The outcomes indicate that natural resources have a positive impact on CO2 emissions. This suggests that due to the increase in natural resources, the level of CO2 emission also increases by 0.282%, 0.244%, 0.233%, and 0.182%. The results indicate that tourism increases CO2 emission by 1.820%, 1.708%, 1.870%, and 1.990%. The moderate effect of ICT on UB, natural resources, and tourism negatively impacts emissions. The results indicate that a 1% increase in moderate effect will decrease the level f carbon emission by 1.831%, 0.031%, and 0.052%. Table 9 exhibits two-step system GMM results.

Table 9. Two-step system GMM results.

Variables	Model 1	Model 2	Model 3	Model 4
$� � � � � �$	0.0784**	0.0034*	0.0656**	0.0321*
$� � � �$	4.6184**	6.9150**	2.5407*	1.2751*
$� � � � �$	−3.5441*	−0.0649*	−3.5764*	−5.2289*
$� � � � �$	0.2829**	0.2447*	0.2334*	0.1821**
$� � � �$	1.8205**	1.7087**	1.8707**	1.9902*
$\ln (� � � \times � �)$	–	−1.8312*	–	–
$\ln (� � � \times � � �)$	–	–	−0.0310*	–
$\ln (� � � \times � �)$	–	–	–	−0.0522*
AR (1)	−1.25 (0.210)	−1.17 (0.243)	−1.24 (0.216)	−1.88 (0.221)
AR (2)	−0.64 (0.521)	−0.55 (0.581)	−0.59 (0.556)	−0.35 (0.621)
Sargan test	2.31 (0.511)	1.22 (0.543)	2.07 (0.355)	2.61 (0.654)
Hansen test	0.90 (0.825)	0.68 (0.712)	0.69 (0.709)	0.88 (0.114)

Note: * and ** show the significance level at 1% & 5%, respectively.

5. Conclusion and policy suggestions

The leading aim of this study is to summarize the core determinants of the environment in which income per capita (development), urbanization, technology, natural resources and tourism activities in 36 OECD economies from 2000 to 2018. However, this empirical research also extends a well-known STIRPAT model by including natural resources and tourism. In this empirical research, we employ the Augmented Mean Group (AMG) and two-step Generalized Method of moments (T-S GMM) to investigate the proposed study objectives. Consequently, the outcomes significantly contribute to environmental degradation by development, UB, NRs and the tourism industry. However, technology substantially contributes to the sustainability level. Similarly, the moderate role of ICT on UB, NRs and tourism secures the environmental quality; thus, technology is the best solution to reduce environmental concerns.

5.1. Policy recommendations

However, on behalf of outcomes, the current study proposes some imperative policy implications under the sustainability theme. This study shows the parallel movement of development with ED, which is very shocking news. In selected economies, rising emissions over time are considered a leading hurdle in sustainability; therefore, higher authorities should introduce carbon-caption and advanced storage methods to reduce environmental burdens. Similarly, the government successfully introduced environmental taxes, but their check & balance is absent. In order to secure the environment, consumers need to follow strict laws & regulations, and government should audit via third parties. Also, the government should start a crusade to deliver the advantages of a clean environment and how the populous can secure it. More interestingly, from the outcomes, it can be considered that the OECD economies are diverging from their basic goals and only attaining higher development. This divergence has been observed via progress in the industrial and urbanization sectors. Therefore, the government should try to focus on energy-efficient technologies in their development plans under the supervision of skilled labor.

Similarly, urbanization contributes to environmental deterioration; therefore, this study tries to guide policymakers for sustainable urbanization. Firstly, a significant rise in migration from rural to urban areas demands massive construction activities that bring environmental damage. Therefore, urban development planners must focus on green & clean urbanized sectors and try to re-shaped their urban-development plans. Moreover, the transport sector also plays a crucial role in urbanized sectors; therefore, higher authorities should focus on a green and sustainable transport system. Similarly, there is a need to introduce a common transport system that facilitates consumers rather than individual preferences. Similarly, under SDG 17, there should be a strong association between public-private partnerships to support green transport in urban areas. Consequently, the usage of public transportation in urban areas may bring a significant decline in environmental pressure. We advise policymakers in the remaining nations to start various urban environmental awareness campaigns. Additionally, the domestic sector has to promote the use of energy-efficient electric home equipment. Urban centers need innovative technology and energy-efficient hybrid vehicles because urbanization encourages increased transportation demand. Policymakers can inspire urban residents to adopt a sustainable lifestyle that includes energy conservation, recycling, and renewable energy sources.

Similarly, ICT performs well in reducing ED significantly. Therefore, this study suggests that OECD economies keep gonging with the same policy pattern to reach their sustainable development at a minimum cost of ED. Thus, these policies can work as cutting edge to secure the environment from natural resources and tourism activities. It is essential to highlight that selected economies have achieved a paramount ICT technology; thus, there is further re-consideration of its more suitable usage to secure the environment. Moreover, higher authorities should insist on producers and consumers to ensure the efficient use of digitalization. As a result, it is suggested that regulations be put in place to limit the scope of what can be imported and even actively prohibit certain types of imports. Policymakers contend that solid governance and efficient resource management are necessary to curb the exploitation of natural resources. Since, institutions must perform competently to manage the efficient use of NRs in the production process. Overall, the study's empirical findings demonstrate that attaining a sustainable environment and, consequently, sustainable development requires efficient and sustainable resource usage and a rise in the percentage of renewable energy in the energy mix. However, tourism is a significant contributor to development; thus, the selected economies should introduce sustainable strategies to secure the environment. Therefore, the inclusion of a green infrastructure budget is necessary to confirm the environment from tourism activities. Besides, there is a need to revise the NRs policies to stop the over-exploitation of natural resources and save future generations from environmental concerns.

The policies can be created with a perspective on sustainable development that is inclusive. These countries should reflect on the rise in ecotourism and nature tourism to enhance tourism. They should consider the energy consumption patterns of the tourism-related sectors growing up around the tourist sites as they do this. Policymakers must work to maintain and enhance the environmental quality in the OECD countries to encourage ecotourism. They can accomplish this by exercising control over the energy consumption patterns of those tourism-related companies. In order to gradually replace conventional fossil fuel-based energy solutions and minimize ambient air pollution, policymakers should impose clean energy usage in those locations. When this procedure is in place, the demand for renewable energy solutions may increase, leading to more job possibilities in this field. By expanding job opportunities, the tourism industry will be able to contribute to the reduction of economic disparity. This multi-stakeholder strategy is made possible by requiring clean energy solutions in well-known tourist areas in OECD countries. Moreover, those countries that unable to follow the Kyoto protocol or the Paris Agreement, they should introduce a subsidies mechanism on clean & green projects such as green transport, green energy etc. Moreover, carbon capture methods and technical innovations are highly recommended.

5.2. Study limitations

The current research tries to cover some important factors that influence the sustainability level; however, this study is not free from limitations. Firstly, the current study utilizes a sample from 2000 to 2018 due to data restrictions; thus, future studies can prolong this data up to 2022 and may compare their studies' results. Similarly, the current research uses Augmented Mean Group & T-S GMM estimators for empirical investigation; however, forthcoming studies try to use some non-linear estimation strategies to validate our study's results. Furthermore, by focusing on OECD economies, this study brings a future gap and recommends this work into different regions such as BRICS, MENA, E7, G7 and Asian economies. Another prominent gap for future studies is considering the SDGs goals in Asia. Therefore, forthcoming studies could clearly explain Asian countries' development towards the SDGs. Last but not least, the upcoming researchers should select some governance factors i.e., government stability, corruption perception index, the rule of law, in the environment-development models and try to clear their influence on sustainability.

Check original source

Tourism, urbanization and natural resources rents matter for environmental sustainability: The leading role of AI and ICT on sustainable development goals in the digital era

1. introduction

2. Summary of the past studies

3. data and methodology

3.1. Variables discussion

3.2. An extension of the STIRPAT model

3.3. Empirical methodology

4. Results and discussion

4.1. A deep look with comparison and discussion

4.2. Robust check by two-step system GMM

5. Conclusion and policy suggestions

5.1. Policy recommendations

5.2. Study limitations

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