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• Published: 29 March 2024

Studying tourism development and its impact on carbon emissions

• Xiaochun Zhao 1 ,
• Taiwei Li 1 &
• Xin Duan 1  

Scientific Reports volume  14 , Article number:  7463 ( 2024 ) Cite this article

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• Environmental impact
• Sustainability

Analyzing the influence of tourism on carbon emission has significant implications for promoting the sustainable development of tourism. Based on the panel data of 31 tourist cities in China from 2005 to 2022, this study utilizes a structural equation model to explore the carbon reduction effect of tourism development and its influencing mechanism. The results show that: (1) The overall carbon emission efficiency of tourism cities first decreased and then increased, rised to a peak of 0.923 in 2022. (2) Tourism development has a significant positive impact on carbon emission efficiency, and there are three influence paths: tourism → environmental regulation → carbon emission efficiency, tourism → environmental regulation → industrial structure → carbon emission efficiency, and tourism → industrial structure → carbon emission efficiency. (3) The influence of tourism development on carbon emission efficiency mainly depends on the direct effect, and the development of tourism also indirectly affect the industrial structure. Environmental regulation also mainly depends on the direct effect on carbon emission efficiency. (4) Foreign direct investment lead to the reduction of carbon emission efficiency in both direct and indirect aspects.

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Introduction.

Global climate change has become one of the major challenges of humanity, bringing a series of harms, including an increase in extreme weather events such as heatwaves, droughts, floods, and hurricanes. According to the report of United Nations Intergovernmental Panel on Climate Change (IPCC), by 2080, the average global temperature will increase by more than 1 °C 1 . Global warming is not merely a natural phenomenon, but also a result of human activity. In various sectors of the economy, the tourism industry has experienced rapid growth. According to data from the World Tourism Organization, the tourism industry accounts for 10.4% of the global Gross Domestic Product (GDP) and provides 313 million job opportunities 2 . However, the rapid development of the tourism industry has also resulted in intensified impacts on the environment. Tourism industry has become one of the main sources of global carbon dioxide emissions, accounting for 5% of the total global carbon emissions. China is one of the largest tourism markets in the world, and the tourism industry plays a vital role in China's economy. In China's 14th Five-Year Plan, the concept of green and low-carbon development is emphasized, highlighting the need for environmentally friendly tourism and carbon emission reduction. Balancing tourism industry development with carbon emission reduction is a major challenge for the tourism industry. Existing studies on tourism and carbon emissions mainly focus on the carbon emission efficiency of tourism development itself and the impact of tourism on carbon emissions. However, these studies fail to analyze the mechanism behind tourism's impact on carbon emission efficiency. While some studies have analyzed the impact mechanism of tourism development and carbon emissions 3 , they primarily focused on the impact of tourism on carbon emission intensity rather than carbon efficiency. Carbon intensity is typically measured as a ratio of carbon emissions to GDP. In contrast, carbon efficiency provides a more comprehensive assessment of a city's environmental performance and sustainability. Comprehensive analyzing the mechanism of the influence of tourism development on carbon emission efficiency is essential for formulating environmental protection policies to promote the green development of tourism. Therefore, this paper aims to study the influence mechanism of tourism on carbon emission efficiency s of tourism development. Using 31 tourist cities in China as research samples, the paper adopts the entropy weight method and the Slacks-Based Measure (SBM), introduces the Structural Equation Model (SEM), and uses panel data of 31 tourist cities to analyze the influence of tourism industry on carbon emission. The findings of this study are hoped to provide inspiration for the transformation of tourism cities.

The remainder of this study is divided into four sections. The first section is the literature review, which examines the carbon emission efficiency of the tourism industry itself and the impact of tourism development on carbon emission efficiency from a tourism research perspective. The second section is the research design, where the paper utilizes the entropy weight method and the super-efficiency SBM model to measure the development level of the tourism industry and carbon emission efficiency, respectively. This section also constructs a structural equation model to explore the mechanism of the impact of tourism development on carbon emission efficiency. The third section presents the research results and the last section concludes this study and provides suggestions based on research findings.

Literature review

Tourism plays a vital role in economic growth by creating jobs 4 . Scholars have conducted extensive research and achieved significant academic results. The current research on the tourism industry and carbon emission efficiency primarily revolves around two aspects.

Firstly, scholars focus on the carbon emission efficiency of the tourism industry. For example, Gössling et al. 5 analyzed the economic benefits and environmental effect of tourism, evaluating the ecological efficiency of the tourism industry by using carbon dioxide emissions and economic benefits. Osorio et al. 6 compared the carbon emission efficiency of the Spanish tourism industry before and after the pandemic of COVID-19, and found that the carbon emission efficiency in 2020 improved compared before COVID-19 pandemic. Ghaderi et al. 7 conducted research on the carbon emission efficiency of tourism industry in the Middle East and North Africa, this study indicated that tourist arrivals can reduce carbon emissions, while energy consumption and trade openness are contributors to carbon emissions.

Secondly, scholars focus on the impact of tourism on carbon emission. However, the consensus among scholars has not yet been reached on whether the tourism industry promotes carbon emission. Some scholars have analyzed the impact of tourism activities on carbon emissions in Mediterranean countries and concluded that tourism revenue does not have direct impact on carbon emissions 8 . Voumik et al. 9 studied tourism industry in 40 Asian countries and found that while tourism helps slow down the deterioration of environment, factors such as population growth, energy use, and economic development still contribute to increasing carbon emission, which is consistent with the conclusions of Guo et al. 10 . Erdoğan et al. 11 focused on the impact of international tourism on carbon emissions and found that international tourism leads to the increase of carbon emissions, but eco-friendly innovation in the transportation sector can mitigate the negative impact on the environment. Ahmad et al. 12 revealed an inverted U-shaped curve in the impact of international tourism development on carbon emissions in China, with the negative impact of technological innovation being strongest in highly developed provinces and weakest in moderately developed provinces. Ghosh et al. 13 found tourism industry can alleviate environmental degradation, policy direction that promote tourism, renewable energy, economic growth and urbanization have a significant effect on the environment, which is consistent with the conclusion of Zikirya et al. 14 . Rahman et al. 15 shifted their focus to Malaysia and found a positive correlation between the number of tourists and carbon emissions.

In summary, existing studies primarily focus on the carbon emission efficiency of tourism and the impact of tourism on carbon emission. However, there is a lack of focus on how tourism affects carbon emission efficiency. This study aims to address this gap by taking 31 tourist cities in China as research samples. This study constructs an indicator system to assess tourism industry and carbon emission efficiency. Furthermore, this study introduces a structural equation model to analyze the mechanisms about how tourism industry affects carbon emission efficiency, to provide inspiration for promoting the green development of tourism.

Research Design

Analyzing the influence mechanism of tourism on carbon emission.

The tourism industry is considered a smokeless and green industry, due to its significant advantages in resource utilization and environmental protection. The development of the tourism industry can not only promote the growth of employment rate in the destination, but also increase the income of tourist destinations. Compared with the secondary industry, the tourism industry is more environmentally friendly in terms of resource consumption and pollution emission. Especially in tourism cities, the proportion of tourism economy in GDP is larger, tourism has a bigger impact on the green development of the tourism city. Consequently, the influence mechanism of tourism on carbon emissions is analyzed as follows:

Firstly, a good natural ecological environment is a fundamental requirement for the development of the tourism industry. Tourism cities typically implement strict governance measures on the local environment and ecology. The development of tourism can incentive the local government to introduce more stringent environmental policies, thereby improving the ecological environment 16 . Additionally, stricter environmental regulations often impact carbon emission efficiency 17 , 18 . Simultaneously, intensified environmental regulations can limit the development space of heavily polluting industries and influence the industrial structure of the destination, ultimately affecting carbon emission efficiency 19 .

Secondly, the development of tourism also affects the industrial structure of cities 20 . The growth of tourism promotes the rise of related industries. Numerous supporting industries, such as hotels, catering, transportation, tour guides, and others, are needed to meet the demands of tourists and create numerous employment opportunities. Consequently, tourism development can attract individuals to switch from other industries to the tourism sector, which in turn impacts the industrial structure and has a significant effect on carbon emissions 21 .

Finally, foreign direct investment is an important factor that affects carbon emission efficiency 22 . This study draws on the conclusion of Bakhsh et al. 23 , which suggests that including foreign direct investment in analysis can improves the overall fit of the structural equation model. On one hand, foreign direct investment can bring advanced production technology, thereby directly improving carbon emission efficiency 24 . On the other hand, foreign investment also leads to pollution transfer, negatively impacting the environment and reducing carbon emission efficiency 25 . At the same time, foreign direct investment can indirectly affect carbon emission efficiency by influencing the local industrial structure. Moreover, the advanced technologies brought about by foreign direct investment also have an impact on technological innovation, thereby indirectly affecting carbon emissions.

Based on above analysis, this study builds a structural equation model about the influence of tourism on carbon emission (see Fig. 1 ).

The influence mechanism of tourism on carbon emission.

Research method

• Structural equation model

The Structural Equation Model (SEM), first proposed by Jöreskog 26 , is used to study complex relationships among different variables, including multiple causal relationships. When examining the impact of tourism on carbon emissions, it is important to consider that this impact is not a single direct effect. Instead, there are complex internal mechanisms, including indirect effects and interactions among variables 27 . Therefore, this study chooses to employ SEM to analyze the internal mechanism of how tourism affects carbon emission efficiency.

Entropy weight method

In this study, the entropy weight method is utilized to calculate the Tourism Development (TD) level. The entropy weight method is a quantitative approach based on the concept of entropy in information theory. It helps determine the weight of indicators by calculating the entropy and difference coefficient of each index. This calculation process reflects the importance of each index in the overall assessment. By multiplying and summing the standardized index with the entropy weight, the assessment results can be obtained 28 . The specific calculation process is as follows:

Firstly, the raw data needs to be standardized, see formula ( 1 ) and formula ( 2 ) for details.

Positive indicator:

Negative indicator:

Among them, \({ }x_{ij} { }\) represents the data of the indicator, \(i{ }\) represents city. \(j{ }\) represents index, \(r_{ij}^{ + }\) and \(r_{ij}^{ - }\) represents standardized data.

Secondly, calculate the weight of \(j\) index by using formula ( 3 ).

Thirdly, calculate the entropy of \(j\) by using formula ( 4 ).

Fourthly, calculate information entropy redundancy by using formula ( 5 ).

Fifthly, calculate index weight by using formula ( 6 ).

Finally, calculate the assess results by using formula ( 7 ).

Non-expected output super efficiency SBM model

Tone 29 proposed a super-efficient model based on the traditional SBM model, which combines the advantages of both the traditional SBM model and the super efficiency model. This model not only considers the influence of unexpected output, but also solves the problem that the traditional SBM model cannot evaluate the Decision-Making Unit (DMU) with the efficiency value of 1 on the front plane. By recalculating the DMUs with an efficiency value of 1, the model enables the comparison of effective DMUs. The specific formulas are as follows:

Designing index system for tourism and carbon emission efficiency, variable explaining and data source

Designing index system and variable interpretation.

This study utilizing the entropy weight method to calculate the Tourism Development level(TD). To evaluate the development level of tourism, this paper designs the index system of tourism development (see Table 1 ). Firstly, the number of tourists is an important indicator that represents the development of tourism, as it reflects the scale of tourism and market demand 30 . Secondly, tourism income is a crucial index for measuring the economic benefit of tourism, as it represents the economic benefit and profit level of tourism. Tourism income directly impacts the sustainable development of tourism and related industries. Finally, the proportion of tourism revenue to GDP is an essential indicator for measuring the contribution and impact of tourism on the overall economy. On the basis of previous studies, this study constructs the evaluation index system of tourism development level.

The essence of Carbon Emission Efficiency (CEE) is the result of the joint action of capital, labor, energy, and other inputs and outputs in economic activities. Therefore, adopting a multi-input and multi-output perspective, this study uses MATLAB software to measure the carbon emission efficiency of 31 tourist cities. Acknowledging that efficiency values are influenced by both inputs and outputs, this study selects five indicators: labor input, capital input, energy input, expected output, and undesirable output to measure carbon emission efficiency (see Table 2 ). Firstly, the total number of employees in enterprises and public institutions reflects the economic scale of state-owned enterprises and public institutions, while the total number of urban private self-employed employees highlights the scale of the development of the private and individual economy 31 . Therefore, the sum of the total number of employees in enterprises and public institutions and the total number of private and individual employees in cities and towns is chosen as the representative of labor input, which fully reflects the employment scale and labor supply of a country or region. Secondly, electricity is widely used as an energy source in cities, and its consumption largely reflects a city's energy consumption 32 . The total electricity consumption of the city is selected to represent the energy input. Thirdly, investments in fixed assets reflects the investment of a country or region in capital goods such as production equipment and buildings over a certain period, and it is an important measure of capital formation 33 . The capital stock of the city is calculated based on the investment in fixed assets to represent the capital input. Fourthly, GDP is the sum of all the market value created by all the residents of a country or region in a certain period, and it is the most important macroeconomic indicator for measuring the overall economic performance of a country or region 34 . Fifthly, undesirable outputs usually denote by-products or negative effects that occur during the production process, which are not the desired outcomes of manufacturing activities. Carbon dioxide emissions are selected as the undesirable outputs. Finally, this paper takes 2005 as the base period to calculate the capital stock and GDP, to enhance the comparability of data between different years.

Variables involved in structural equation model

Based on the existing research and data availability, proxy variables for the structural equation model are set up (see Table 3 ). (1) Tourism, calculated by entropy weight method, reflects the development level of urban tourism; (2) Carbon emission efficiency, calculated by the non-expected output super efficiency SBM, reflects the carbon emission and resource utilization efficiency of the city; (3) Environmental regulation. Currently, there are three quantitative methods for environmental regulation, which are single index method 35 , scoring method 36 and comprehensive index method 37 . This paper uses the proportion of investment in environmental pollution control in GDP(Gross Domestic Product) as a proxy variable for environmental regulation. (4) Industrial structure, the proportion of the output value of the tertiary industry and the output value of the secondary industry are used as the proxy variable,(5) Foreign direct investment, some scholars believe that foreign direct investment has a negative impact on the environment, supporting the pollution paradise hypothesis, while other scholars believe that foreign direct investment has an improving effect on the environment, supporting the pollution halo hypothesis. Because of fact that the stock of foreign investment can more accurately reflect the impact of foreign investment on environmental pollution, this paper adopts the proportion of foreign direct investment in regional GDP as a proxy variable by referring to the practice of Afi et al. 38 . (6) Urban innovation, referring to the research of Cheng et al. 39 , China's urban innovation index is adopted as a proxy variable. The index is mainly based on two parts of data, namely patent data of the State Intellectual Property Office and enterprise registered capital data of the State Administration for Industry and Commerce, including innovation output and patent value.

Research sample and data source

This study selects Chinese tourism cities as research samples to explore the influence of tourism on carbon emission efficiency. This study refers to the research of Zhang et al. 40 and Huang et al. 41 , a total of 31 tourism cities were selected as research samples. These cities include Beijing, Tianjin, Shenyang, Dalian, Shanghai, Nanjing, Wuxi, Suzhou, Hangzhou, Ningbo, Xiamen, Jinan, Qingdao, Guangzhou, Shenzhen, Zhuhai, Zhongshan, Guilin, Haikou, Wenzhou, Changchun, Harbin, Huangshan, Wuhan, Changsha, Luoyang, Zhangjiajie, Chongqing, Chengdu, Kunming and Xi 'an.

The study period was from 2005 to 2022. The data in this study were obtained from China Urban Statistical Yearbook (2006–2022), China Energy Statistical Yearbook (2006–2022), statistical yearbooks and statistical bulletins of provinces and cities.

Research results

Evaluation results of carbon emission efficiency and tourism development in tourist cities.

This study measured the carbon emission efficiency of 31 tourist cities from 2005 to 2022 and revealed its evolution characteristics. The calculation results are shown in Table 4 .

According to Table 4 , a clear upward trend is evident in the tourism development level of 31 tourist cities from 2005 to 2019, with the level increasing from 0.125 in 2005 to 0.499 in 2019, thereby reaching its peak. From 2020 to 2022, due to the impact of COVID-19, the number of tourists decreased, and the development level of tourism dropped significantly. From 2005 to 2022, the carbon emission efficiency of 31 tourism cities generally showed a fluctuating upward trend. The overall efficiency decreased year by year from 2005 to 2011, reaching its lowest at 0.707. But then it began to fluctuate and rise and reached a peak of 0.923 in 2022.

Analyzing the influence of tourism development on carbon emission efficiency and its influencing mechanism

Analysis the influence of tourism industry on carbon emission and its influencing mechanism based on all samples.

Based on the structural equation model, the required variables were introduced into the STATA software. The parameters of the constructed model were then estimated using the maximum likelihood estimation method, yielding the estimated results for standardized estimation coefficients, standard errors, Z-values, and P-values. The specific results are shown in Table 5 and Fig.  2 .

Structural equation model estimation results.

For all the tourist city samples, the structural equation model was estimated using the STATA software through the maximum likelihood estimation method. The estimated results included standardized estimation coefficients, standard errors, Z-values, and P-values. In terms of the overall fit of the model, the comparative fit index (CFI) is 0.902, slightly greater than 0.9, and the standardized residual root mean square (SRMR) is 0.07, slightly higher than 0.05 but less than 0.08 threshold, indicating that the overall fit of the model is good.

Logarithmic likelihood: − 8623.74;Likelihood ratio test of saturation model: chi-square(4) = 71.00, Prob > chi-square = 0.0000; The index of fit degree:RMSEA:0.109;AIC:14,115.030; BIC:16,023.930; CFI:0.902;SRMR: 0.070.

Table 5 and Fig.  2 demonstrate the mechanism of tourism's influence on carbon emission efficiency as follows: Firstly, a 1% increase in tourism development level leads to a direct increase of 0.1148% carbon emission efficiency, which passes the 1% significance level test. This indicates that the development of urban tourism significantly promotes the improvement in carbon emission efficiency. This indicates that tourism development can improve carbon emission efficiency, which is consistent with the study conducted by Si et al. 42 . On one hand, tourism stimulates local economic development,on the other hand, it consumes resources and emits less pollution compared to other industries. This implies that tourism development directly affects carbon emission efficiency and there is a mechanism of tourism → carbon emission efficiency. Secondly, the sustainable development of tourism imposes stricter demands on the ecological environment quality. As a result, the development of tourism prompts governments to introduce more rigorous environmental policies. The greater the intensity of urban environmental regulation, the more significant its impact on carbon emission efficiency. Each 1% increase in the level of tourism development would directly increase the intensity of environmental regulation by 0.1280%. There is a direct between environmental regulation and emission efficiency. Every 1% increase in environmental regulation, there is a corresponding 0.8% increase in carbon emission efficiency. This finding supports the conclusion that environmental regulation plays an effective role in reducing carbon emissions 43 . This finding also suggests that there is a mechanism of tourism → environmental regulation → carbon emission efficiency. Thirdly, empirical results reveal an influence effect of tourism → environmental regulation → industrial structure → carbon emission efficiency. Each 1% increase in environmental regulation would change the industrial structure by 0.1524%, and each 1% increase in industrial structure would increase carbon emission efficiency by 0.2048%. This suggests that the tourism industry impacts the local industrial structure by strengthening environmental regulations, thereby driving the improvement of carbon emission efficiency. Fourth, a 1% increase in tourism development level changes the industrial structure by 0.7597%, indicating that tourism development has a significant impact on the local industrial structure. Additionally, the estimated coefficient of industrial structure on carbon emission efficiency is 0.0664, meaning that the transformation of industrial structure promotes the improvement of carbon emission efficiency. In other words, tourism influences local carbon emission efficiency by influencing the industrial structure. There is a mechanism of tourism → industrial structure → carbon emission efficiency.

The mechanisms through which foreign direct investment influences carbon emission efficiency can be summarized in three aspects. Firstly, foreign direct investment has negative impacts on carbon emission efficiency. This indicates in tourist cities, FDI may intensify local energy consumption and production activities and becomes a refuge for heavily polluting enterprises. These findings are in line with the research conducted by Wang et al. 44 . Secondly, foreign direct investment significantly and positively affects the local industrial structure, indicating that the production technology brought by foreign direct investment has changed the industrial structure of the city. The results reveal an influence path of foreign direct investment → industrial structure. Empirical findings demonstrate that the industrial structure impacts carbon emission efficiency, resulting in a path of foreign direct investment → industrial structure → carbon emission efficiency. Thirdly, the impact of foreign direct investment on the innovation ability of cities did not pass the significance test (P = 0.603), indicating that there is no influence path of foreign direct investment → urban innovation → carbon emission efficiency.

Further analysis based on effect decomposition

Based on the above estimation results, this study further decomposed the direct, indirect, and total effects of each factor affecting carbon emission efficiency, the results are shown in Table 6 .

As shown in Table 6 , the total effect of tourism on carbon emission efficiency is 0.0624, with a direct effect of 0.1148, accounting for 54.36% of the total effect. The direct effect passed the significance test but the indirect effect failed. This indicates that the influence of tourism on carbon emission mainly stems from the direct impact of tourism development on carbon emission efficiency, rather than the indirect effect. This empirical result aligns with the current reality in China. Cities can achieve the goal of reducing carbon emissions by focusing on green tourism and low-carbon tourism, promoting the use of environmentally friendly transportation modes in tourism, and improving the energy efficiency of tourism facilities. Furthermore, the estimates results reveal other important factors and pathways influencing carbon efficiency. Firstly, a higher intensity of environmental regulations can directly improve carbon emission efficiency. Every 1% increase in environmental regulation intensity would directly increase carbon emission efficiency by 0.2048%. Secondly, the direct effect of foreign direct investment on carbon emission efficiency is − 0.1379, and the indirect effect is − 0.0098, indicating that foreign direct investment has a negative impact on carbon emission efficiency in both direct and indirect aspects. Foreign investors may transfer polluting enterprises to tourist cities, resulting in increased carbon emissions and decreased carbon emission efficiency. Finally, changes in industrial structure have a positively effect on carbon emission efficiency. Every 1% change in industrial structure will reduce carbon emission efficiency by 0.0664%.

Conclusion and discussion

Based on panel data from 31 tourist cities between 2005 and 2022, this study utilizes a structural equation model to analyze the influence of tourism on carbon emissions. The research findings indicate the following:

The carbon emission efficiency of tourism cities first decreased and then increased, reaching a peak of 0.923 in 2022. Second, tourism has a significant positive effect on carbon efficiency in the estimation of all samples. This influence can be summarized into three paths: tourism development → environmental regulation → carbon emission efficiency; Tourism development → environmental regulation → industrial structure → carbon emission efficiency; Tourism development → industrial structure → carbon emission efficiency. Thirdly, the influence of local tourism development on carbon emission efficiency mainly depends on the direct effect, which is consistent with the reality of China, and the development of tourism will also indirectly affect the local industrial structure. Environmental regulation also mainly depends on the direct effect on carbon emission efficiency, and foreign direct investment will lead to the reduction of carbon emission efficiency in both direct and indirect aspects.

Based on these research findings, this study proposes several suggestions: Firstly, tourism affects carbon emission efficiency through environmental regulation and industrial structure. To strengthen environmental regulation, local governments should increase supervision over enterprises, improve environmental standards, and take strict actions against environmental violations. These measures can enhance carbon emission efficiency and accelerate urban green transformation. Secondly, considering the negative impact of foreign direct investment on carbon emission efficiency, local governments should carefully evaluate potential environmental problems when dealing with foreign investments. Preferably, eco-friendly foreign direct investments should be prioritized. Thirdly, the influence of tourism on carbon emission efficiency mainly depends on the direct effect. Therefore, in the process of tourism development, the goal of improving carbon emission efficiency should be integrated to promote the development of tourism in the direction of eco-tourism and green tourism.

The content of this study is to analyze the influence of tourism on carbon efficiency, using 31 tourist cities as case studies. It introduces mechanism that explains how tourism development impacts carbon emission efficiency through considerations of environmental regulation and industrial structure. Nonetheless, it is important to acknowledge that this study also has certain limitations when compared to previous research. Firstly although this study considers the impact of environmental regulations on carbon emission efficiency, it did not conduct an in-depth analysis of different dimensions of environmental regulations. It is worth noting that the intensity and enforcement of environmental regulations may have significant differences in their impact on carbon emission efficiency as highlighted by Lin et al. 45 . Therefore, it is suggested that future studies incorporate the intensity and enforcement of environmental regulations into the model. By doing so, a more accurate assessment can be made regarding their impact on carbon efficiency. Secondly, this study suggests a pathway for tourism development to have an impact on carbon emission efficiency by influencing industrial structure. However, it does not delve deeply into the specific methods of adjusting industrial structure. Ahmad et al. 12 have demonstrated that tourism's alternative impact on traditional manufacturing and high-carbon industries is a crucial approach to reducing carbon emissions. Future studies can potentially further analyze the contribution of tourism to the low-carbon transformation of industrial structure. Thirdly, this study suggests that FDI has a negative impact on carbon emission efficiency, but it does not fully discuss its potential positive effects. Zhang et al. 47 have found that foreign direct investment can introduce advanced environmental protection technology and management experience, thereby improving the city's carbon emission efficiency. Therefore, future studies should how to achieve a positive impact on carbon emission efficiency through policy guidance and optimization of FDI structure.

Data availability

The data presented in this study are available on request from the corresponding authors.

Bunting, E. L., Fullman, T., Kiker, G. & Southworth, J. Utilization of the SAVANNA model to analyze future patterns of vegetation cover in Kruger National Park under changing climate. Ecol. Model. 342 , 147–160 (2016).

Article   Google Scholar  

Figini, P. & Patuelli, R. Estimating the economic impact of tourism in the European Union: Review and computation. J. Travel Res. 61 (6), 1409–1423 (2022).

Tong, Y., Zhang, R. & He, B. The carbon emission reduction effect of tourism economy and its formation mechanism: An empirical study of China’s 92 tourism-dependent cities. Int. J. Environ. Res. Public Health 19 (3), 1824 (2022).

Article   PubMed   PubMed Central   Google Scholar  

Irfan, M., Ullah, S., Razzaq, A., Cai, J. & Adebayo, T. S. Unleashing the dynamic impact of tourism industry on energy consumption, economic output, and environmental quality in China: A way forward towards environmental sustainability. J. Clean. Prod. 387 , 135778 (2023).

Article   CAS   Google Scholar  

Gössling, S. et al. The eco-efficiency of tourism. Ecol. Econ. 54 (4), 417–434 (2005).

Osorio, P., Cadarso, M. Á., Tobarra, M. Á. & García-Alaminos, Á. Carbon footprint of tourism in Spain: Covid-19 impact and a look forward to recovery. Struct. Change Econ. Dyn. 65 , 303–318 (2023).

Ghaderi, Z., Saboori, B. & Khoshkam, M. Revisiting the environmental Kuznets curve hypothesis in the MENA region: the roles of international tourist arrivals, energy consumption and trade openness. Sustainability 15 (3), 2553 (2023).

Yıldırım, D. Ç., Ektiren, B. & Erdoğan, F. The threshold effect of tourism, growth, and population interactions on environmental pollution in Mediterranean countries. Tour. Manag. Perspect. 47 , 101118 (2023).

Google Scholar  

Voumik, L. C., Islam, M. A. & Nafi, S. M. Does tourism have an impact on carbon emissions in Asia? An application of fresh panel methodology. Environ. Dev. Sustain. 514 , 1–19 (2023).

Guo, Y., Zhao, L. & Zhang, C. Energy resources, tourism development and growth-emission nexus in developing countries. Resour. Policy 81 , 103407 (2023).

Erdoğan, S., Gedikli, A., Cevik, E. I. & Erdoğan, F. Eco-friendly technologies, international tourism and carbon emissions: Evidence from the most visited countries. Technol. Forecast. Soc. Change 180 , 121705 (2022).

Ahmad, M., Zhu, X. & Wu, Y. The criticality of international tourism and technological innovation for carbon neutrality across regional development levels. Technol. Forecast. Soc. Change 182 , 121848 (2022).

Ghosh, S., Balsalobre-Lorente, D., Doğan, B., Paiano, A. & Talbi, B. Modelling an empirical framework of the implications of tourism and economic complexity on environmental sustainability in G7 economies. J. Clean Prod. 376 , 134281 (2022).

Zikirya, B., Wang, J. & Zhou, C. The relationship between CO 2 emissions, air pollution, and tourism flows in China: A panel data analysis of Chinese Provinces. Sustainability 13 (20), 11408 (2021).

Rahman, A. R. A., Shaari, M. S., Masnan, F. & Esquivias, M. A. The impacts of energy use, tourism and foreign workers on CO 2 emissions in Malaysia. Sustainability 14 (4), 2461 (2022).

Sheppard, V. A. & Fennell, D. A. Progress in tourism public sector policy: Toward an ethic for non-human animals. Tour. Manag. 73 , 134–142 (2019).

Hashmi, R. & Alam, K. Dynamic relationship among environmental regulation, innovation, CO 2 emissions, population, and economic growth in OECD countries: A panel investigation. J. Clean. Prod. 231 , 1100–1109 (2019).

Zameer, H., Shahbaz, M. & Kontoleon, A. From Covid-19 to conflict: Does environmental regulation and green innovation improve industrial sector decarbonization efforts and environmental management?. J. Environ. Manag. 345 , 118567 (2023).

Ouyang, Y., Ye, F. & Tan, K. The effect of strategic synergy between local and neighborhood environmental regulations on green innovation efficiency: The perspective of industrial transfer. J. Clean. Prod. 380 , 134933 (2022).

Kronenberg, K. & Fuchs, M. Aligning tourism’s socio-economic impact with the United Nations’ sustainable development goals. Tour. Manag. Perspect. 39 , 100831 (2021).

Wang, S., Wang, H., Zhang, L. & Dang, J. Provincial carbon emissions efficiency and its influencing factors in China. Sustainability 11 (8), 2355 (2019).

Shaari, M. S., Lee, W. C., Ridzuan, A. R., Lau, E. & Masnan, F. The impacts of energy consumption by sector and foreign direct investment on CO 2 emissions in Malaysia. Sustainability 14 (23), 16028 (2022).

Bakhsh, K., Rose, S., Ali, M. F., Ahmad, N. & Shahbaz, M. Economic growth, CO 2 emissions, renewable waste and FDI relation in Pakistan: New evidences from 3SLS. J. Environ. Manag. 196 , 627–632 (2017).

Amara, D. B., Qiao, J. & Zada, M. How to reconcile the climate change issue with economic growth? Spatial dual mediating effects of carbon emissions and foreign investment. J. Clean. Prod. 411 , 137285 (2023).

Petrović, P. & Lobanov, M. M. Energy intensity and foreign direct investment nexus: Advanced panel data analysis. Appl. Energy 311 , 118669 (2022).

Jöreskog, K. G. A general method for estimating a linear structural equation system. ETS Res. Bull. Ser. 1970 (2), i–41 (1970).

Zyphur, M. J., Bonner, C. V. & Tay, L. Structural equation modeling in organizational research: The state of our science and some proposals for its future. Annu. Rev. Organ. Psychol. Organ. Behav. 10 , 495–517 (2023).

Wang, M., Su, M. M., Gan, C. & Yu, Z. A coordination analysis on tourism development and resident well-being in the Yangtze River Delta Urban Agglomeration, China. J. Clean Prod. 421 , 138361 (2023).

Tone, K. A slacks-based measure of efficiency in data envelopment analysis. Eur. J. Oper. Res. 130 (3), 498–509 (2001).

Article   MathSciNet   Google Scholar  

Khan, N. U., Alim, W., Begum, A., Han, H. & Mohamed, A. Examining factors that influence the international tourism in pakistan and its nexus with economic growth: Evidence from ARDL approach. Sustainability. 14 (15), 9763 (2022).

Kou, J. & Xu, X. Does internet infrastructure improve or reduce carbon emission performance? A dual perspective based on local government intervention and market segmentation. J. Clean. Prod. 379 , 134789 (2022).

Chai, J., Tian, L. & Jia, R. New energy demonstration city, spatial spillover and carbon emission efficiency: Evidence from China’s quasi-natural experiment. Energy Policy 173 , 113389 (2023).

Gao, S., Sun, D. & Wang, S. Do development zones increase carbon emission performance of China’s cities?. Sci. Total Environ. 863 , 160784 (2023).

Article   ADS   CAS   PubMed   Google Scholar  

Lan, B. et al. CO 2 emission reduction pathways of iron and steel industry in Shandong based on CO 2 emission equity and efficiency. Resour. Policy 81 , 103406 (2023).

Ahmed, Z., Ahmad, M., Rjoub, H., Kalugina, O. A. & Hussain, N. Economic growth, renewable energy consumption, and ecological footprint: Exploring the role of environmental regulations and democracy in sustainable development. Sustain. Dev. 30 (4), 595–605 (2022).

Albrizio, S., Kozluk, T. & Zipperer, V. Environmental policies and productivity growth: Evidence across industries and firms. J. Environ. Econ. Manag. 81 , 209–226 (2017).

Dechezleprêtre, A. & Sato, M. The impacts of environmental regulations on competitiveness. Rev. Env. Econ. Policy 11 (2), 183–206 (2017).

Afi, H., Boubaker, S. & Omri, A. Do foreign investment and economic freedom matter for behavioral entrepreneurship? Comparing opportunity versus necessity entrepreneurs. Technol. Forecast. Soc. Change 181 , 121761 (2022).

Cheng, J. et al. Land marketization and urban innovation capability: Evidence from China. Habitat Int. 122 , 102540 (2022).

Zhang, J., Yu, Z., Miao, C., Li, Y. & Qiao, S. Cultural tourism weakens seasonality: Empirical analysis of Chinese tourism cities. Land. 11 (2), 308 (2022).

Huang, C. et al. Coupling relationship and interactive response between intensive land use and tourism industry development in China’s major tourist cities. Land 10 (7), 697 (2021).

Si, X. & Tang, Z. Assessment of low-carbon tourism development from multi-aspect analysis: A case study of the Yellow River Basin, China. Sci Rep. 14 (1), 4600 (2024).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Wang, H. & Guo, J. Research on the impact mechanism of multiple environmental regulations on carbon emissions under the perspective of carbon peaking pressure: A case study of China’s coastal regions. Ocean Coast. Manag. 249 , 106985 (2024).

Wang, Q. & Zhang, Q. Foreign direct investment and carbon emission efficiency: The role of direct and indirect channels. Sustainability 14 (20), 13484 (2022).

Lin, B. & Zhang, A. Can government environmental regulation promote low-carbon development in heavy polluting industries? Evidence from China’s new environmental protection law. Environ. Impact Assess. Rev. 99 , 106991 (2023).

Ahmad, N. & Ma, X. How does tourism development affect environmental pollution?. Tour. Econ. 28 (6), 1453–1479 (2022).

Zhang, W., Li, G., Uddin, M. K. & Guo, S. Environmental regulation, foreign investment behavior, and carbon emissions for 30 provinces in China. J. Clean. Prod. 248 , 119208 (2020).

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Research Article

Dynamic linkages between tourism development, renewable energy and high-quality economic development: Evidence from spatial Durbin model

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation

Affiliations School of Economics and Trade, Henan University of Technology, Henan Zhengzhou, China, School of Business, Macau University of Science and Technology, Macau, China

Roles Funding acquisition, Investigation, Methodology, Project administration

Affiliation School of Economics and Trade, Henan University of Technology, Henan Zhengzhou, China

Roles Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

• HaoYu Li, 
• ZhongYe Sun, 
• Yang ChuanYu

• Published: February 14, 2024
• https://doi.org/10.1371/journal.pone.0295448
• Reader Comments

There has been a shift in focus toward environmentally and economically sustainable forms of economic growth known as High-quality economic development (HQED). However, this study analyzes the impact of tourism development (TD) and renewable energy consumption on HQED in 30 provinces of China, while covering the time period from 2007 to 2021. TD and HQED has been measured with help of Global Moran Index. This study has used dynamic spatial Durbin model (SDM) to measure the dynamic impact of TD index and renewable energy consumption on HQED along with green finance, foreign direct investment and investment in education. The findings from empirical analysis shows that TD has negative impact on HQED and in more developed regions, the relationship is positive, while in the less developed western part of China, the U-shape has been reversed. Central and northeastern China have a U-shaped connection, while it has been noticed the interaction term of TD and renewable energy endorses HQED. In addition, renewable energy consumption, green finance and increase in education investment have positive and significant impact on HQED while foreign direct investment has negative impact on HQED in China. Therefore, in the light of this study policymakers should focus on the quality of tourism industry, green finance for renewable energy supply and enhancing education investment in China to attain the goal of HQED.

Citation: Li H, Sun Z, ChuanYu Y (2024) Dynamic linkages between tourism development, renewable energy and high-quality economic development: Evidence from spatial Durbin model. PLoS ONE 19(2): e0295448. https://doi.org/10.1371/journal.pone.0295448

Editor: Magdalena Radulescu, University of Pitesti, Romania; Institute of Doctoral and Post-doctoral Studies, University Lucian Blaga of Sibiu, ROMANIA

Received: July 15, 2023; Accepted: November 21, 2023; Published: February 14, 2024

Copyright: © 2024 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data is available without any restriction. The data is available at the site of world development indicator (WDI) [ https://databank.worldbank.org/source/world-development-indicators ] of world bank. The data can be provided by all the authors.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The concept of sustainable development, as articulated by the United Nations World Commission on Development and Environment, refers to a form of development that effectively addresses the current societal demands while ensuring the preservation of resources and opportunities for future generations to fulfill their own requirements. There is a consensus among all nations that the pursuit of sustainable development is the most effective means of promoting economic progress and ensuring environmental protection at a global level. Typically, sustainable development is categorized into two main components, namely "external response" and "internal response" [ 1 ]. It is imperative to consider the human-nature relationship from an external response perspective, as the sustenance and progress of humanity are inherently intertwined with the availability of natural resources and ecological services. Additionally, the challenges and pressures posed by natural evolutionary processes further underscore the significance of this relationship. The concept of "internal response" encompasses several crucial elements for sustained growth, which is considered a significant milestone in human civilization. These elements include the promotion of social order, organization, logical cognition, social harmony, and the ability to effectively navigate diverse social interactions. The acquisition of these skills is crucial for achieving comprehensive sustainable progress [ 2 ].

Economic transition in China from rapid growth to high-quality development necessitates a shift from an extensive phase of high-speed growth, which heavily relies on increased natural resource consumption, to a phase of high-quality development that relies on technological advancements and the enhancement of workforce quality [ 3 ]. Green development has gained significant attention and recognition in the current economic and social landscape due to its alignment with ecological priorities and sustainable practices. It has emerged as a crucial strategy in response to the new normal of the economy. The idea of green growth has become the central theme of national economic and social progress in this new era. Enhancing the efficiency of green development has been identified as a pivotal factor in promoting HQED. The concept of "high-quality development" has recently emerged within the framework of China’s economic development. The existing scholarly investigations predominantly center on the comprehensive socioeconomic advancement in the context of HQED. For instance, [ 4 ] has extensively engaged in theoretical deliberations concerning the HQED of China’s economy, focusing on the five development concepts and the primary social contradictions, respectively. There exists a limited body of study that specifically focuses on the high-quality advancement of tourism. Simultaneously, due to the absence of uniformity among scholars about the nuances associated with quality tourism development, there exists significant variation in the selection of indicators and their outvomes when assessing the level of such growth. Hence, it is vital to establish a scientific assessment framework for the promotion of high-quality tourism, which entails comprehensively understanding the notion of high-quality tourism development. Additionally, it is crucial to gauge the disparity between the current state of tourism development and the desired HQED across various regions in China.

In addition, It has been determined that clean renewable energy sources are a significant part of the manufacturing procedure. REC has a significant part in economic growth alongside other aspects of production. Consequently, as economies have grown, so too has the significance of RE use. Cleaner than fossil fuels, renewable energy is predicted to considerably impact CE, as stated by Ohajionu et al. [ 5 ]. The various forms of atmospheric air pollutants originating from fossil fuel combustion could limit both the sustainable expansion of the global economy and the reduction of the world’s carrying capacity. Because of the problems produced by fossil fuels, economies around the world have begun looking for renewable energy sources to replace them [ 6 ]. Green energy and renewable resources have been offered by scientists and environmentalists as a solution to the dual problems of fossil fuel-related environmental damage and insufficient supplies to meet rising energy demand.

Green finance (GF) often encompasses investments made through green credit cards, which involve the allocation of funds towards environmental initiatives and other ecological objectives, such as sustainable promotion. Additionally, this product might be considered a financial innovation that generates both economic and ecological benefits [ 7 ]. The GF is providing financial support to activities that offer substantial assistance while simultaneously enhancing the natural environment. In recent times, there has been a notable global focus on this concept, with China emerging as the foremost market for green bonds [ 8 ]. The significant integration of wind energy into China’s eco-energy system can be attributed to the extensive commercialization in this sector. The expansion of renewable energy sources is of paramount importance in addressing the issues posed by climate change in China.

Further, this study examined the effects of FDI to ascertain if they support the Pollution Haven or Pollution Halo theory in the instance of OECD countries, as there is no apparent harmony in the role of FDI from an ecological viewpoint. Foreign direct investment (FDI) is crucial in encouraging growth and development in host nations by bridging the saving-investment gap. Environmental degradation in developing countries may result from foreign direct investment (the pollution haven hypothesis posits that developed countries will locate their polluting industries in developing nations because of lax environmental regulations there) [ 9 ]. However, some argue that this progress could compromise environmental quality due to pollutants generated by development projects. Some studies believe that FDI can increase environmental quality (pollution halo hypothesis) and productivity by drawing in new, efficient, and green technologies. Green foreign direct investment is credited with helping countries like China drastically cut their carbon output. In addition to attracting innovative technology that could reduce environmental pollution, FDI has been suggested to serve as a growth booster, employment generator, and source for host countries. Similarly, Koçak et al. [ 10 ] found that FDI in developing nations has dramatically improved environmental quality, crediting the pollution halo hypothesis.

The following are the novel contribution of the study: (1) This study explores the impact of tourism development index on the HQED index (HQED index has been measured by using Moran’s I index method) and also explores the impact of renewable energy consumption on HQED, while taking the data of 30 provinces of China, (2) This study investigates the mediating role of renewable energy consumption between TD index and HQED, (3) The impact of interaction term of TD and REC has also been measured, (4) Along with TD, the impact of green finance, FDI and investment in education projects also has been measured while using Dynamic spatial Durbin model (SDM). This research has the potential to fill a gap in the literature and contribute significantly to advancing knowledge in this area.

This article will include the following sections: The second section reviews the relevant literature. Data, empirical model, and methodology are all described in Section 3. The empirical data and their interpretation are illustrated in Section 4. Section 5 wraps up the analysis and makes some helpful policy suggestions.

2. Literature review

2.1. research on hqed.

High-quality development theory suggestions for improving the tourism industry’s growth are made. According to the 19th National Congress report by the Communist Party of China, " China’s economy has transitioned from a phase characterized by rapid growth to a phase focused on achieving high-quality development. Currently, China finds itself in a crucial moment where it must undertake the transformation of its development mode, optimize its economic structure, and shift the driving forces behind its growth." There are currently two competing definitions of high-quality tourism business development in academic circles. When assessing the progress of the tourism industry across the board, it is essential to focus on quality and quantity [ 11 ]. Another method, quality evaluation of a tourism industry subsector or a tourism activity link, emphasizes diversity and micro-level analysis [ 12 ]. As for the former, it relies heavily on studies conducted within China. This study uses econometric and geographical statistics to compile the panel data for an all-inclusive score and distribution features. The primary sources are Raza.S.A. [ 13 ] and Balsalobre-Lorente et al. [ 14 ]. The latter is predicated on studies conducted in other countries and relies heavily on interviews and comparative experimental approaches. This article plans to use the first research topic to investigate the growth of the tourism business while maintaining a high-quality standard.

Academic research on this topic is still in its infancy because the HQED of China’s tourism industry has only recently begun. The present research focuses mainly on constructing the index system and quantifying the HQED index. However, neither a definition of high-quality expansion of the tourism business nor an elaboration of the HQED mechanism of tourism can be found in the available literature. Many academics have used the five new development philosophies of novelty, coordination, green, openness, and sharing to develop a robust evaluation system for tourism’s progress. The ’new growth vision’ was deemed scientific, detailed, and rational by Balsalobre-Lorente et al. [ 15 ], giving it significant reference relevance in constructing an HQED index system. HQED is defined by Sinha et al. [ 16 ] from the perspectives of the economy, coordination, innovation, openness, greenness, and inclusivity, and these six dimensions were used to determine how the index variables of an HQED assessment technique were chosen. Doğan et al. [ 17 ] defined a high-quality TD in light of the new development vision as a multifaceted endeavour involving, among other things, innovation, greenness, coordination, openness, cultural tourism resources and inclusivity. Additionally, some academics assess the area tourism industry’s high-quality development level from the angle of production efficiency. Index variables are often collected from tourism resource capability, scenic sites, hotels, total tourism income, travel agencies, and the total number of tourists [ 18 ].

2.2. Renewable energy consumption impact on HQED

Energy consumption and logistics have been thoroughly studied in the literature [ 19 ]. It has been stated that the logistics sector relies excessively on energy consumption, which negatively impacts human health and the planet’s longevity. The use of clean energy and the promotion of green products are two ways in which green logistics can boost environmental and financial outcomes. Using renewable energy in logistics operations has dramatically enhanced environmental performance. It is because the logistics sector is the largest emitter of hazardous gases. Tian et al. [ 20 ] found that increased trade between environmentally conscious nations was positively correlated with each nation’s economic health while using renewable energy. According to Melese et al. [ 21 ], governments can boost environmental sustainability and economic growth by switching to renewable energy and buying eco-friendly products. Policymakers are urged by Melese et al. [ 21 ] to adopt clean energy and logistics to reduce environmental impacts and boost economic growth.

Rybchenko et al. [ 22 ] demonstrate a positive correlation between REC and long-term economic growth. A clear correlation between economic growth, logistics, and energy use was also found by Ridderstaat et al. [ 23 ]. Sun et al. [ 24 ] argue that countries implementing green tourism must have access to clean energy to promote sustainability. As a result of resource scarcity and environmental challenges, Lee et al. [ 25 ] argue that using renewable energy is inevitable for such countries, as such energy is compatible with economic development.

2.3. Tourism development impact on HQED

Sørensen and Grindsted [ 26 ] discuss a thorough literature assessment on tourism indicators and high-quality economic development (HQED) economic growth. Both positive and negative effects on HQED can be attributed to tourism, and vice versa, as well as a bidirectional causality and a neutral relationship. There is little agreement on the connections between tourism and HQED indicators, leaving much room for debate. Ridderstaat et al. [ 23 ], using data for Spain from 1975 to 1997, do ground-breaking work on the association between TD and economic development using a trivariate model. The long-term dynamic association between tourism and HQED was the clincher of his findings.

Similarly, Abbas et al. [ 1 ] researched Aruba and found evidence supporting the tourism push growth concept. In addition, Zheng et al. [ 27 ] used Johansson cointegration to examine the link between tourism and HQED in Mauritania between 1950 and 1999, and VECM confirmed that the former positively influenced the latter. Also, using Spanish data, Wei and Lihua [ 28 ] found evidence of a cointegration link. Pakistan’s tourism and high-quality economic development (HQED) have been investigated from both directions. Using data from Turkey between 1963 and 2006, Dong and Li [ 29 ] recognized a unidirectional causal relationship between tourism and HQED. They advocated for the promotion of tourism as a long-term strategic industry. Similarly, Le and Nguyen [ 30 ] for Sri Lanka, Alam and Ali [ 4 ] for Romania, and Wirawan and Gultom [ 31 ] for Lebanon are noteworthy for their support of the presence of tourism lead HQED.

2.4. Renewable energy, tourism development and HQED

Since renewable energy sources dramatically reduce harmful emissions, several governments have begun developing them to attain sustainable development goals. Adopting renewable or "green" energy is costly and requires public education [ 32 ] because these sources are still in their infancy compared to fossil fuels. Since environmental performance is favourable for tourist arrivals, developing renewable energy sources plays a crucial role in nations that rely heavily on tourism. Many scientists currently focus on finding ways to combine renewable energy with travel. Energy consumption and pollution negatively impact tourism, according to the literature; as a result, authors recommend using renewable energy sources and switching to green products to boost the industry [ 33 ]. Benefits to the environment and the economy have resulted from the United Kingdom’s green energy initiative, which is realized primarily by the country’s tourism sector [ 34 ].

In addition, Yang et al. [ 35 ] suggest that governments should actively encourage renewable energy and ecotourism, which are crucial for countries still building their tourism industries. Therefore, increasing green energy utilization to enhance eco-friendly tourism is recommended in the literature. Sustainable development, it is said, relies heavily on the tourism industry. As a result, research into the link between REC and ecotourism is essential. Examining the link between green energy and green product growth in tourism is equally crucial for long-term sustainability. It is suggested in the available literature that green consumption behaviours and renewable energy sources make significant contributions to environmental sustainability, which in turn benefits the tourism industry.

However, by analyzing data from 140 nations and six regions worldwide between 1995 and 2009, Yuping et al. [ 36 ] disproved the tourism-led HQED hypothesis. Sheraz et al. [ 37 ] investigated the HQED-induced tourism theory for Malaysia. Mohsin et al. [ 38 ] found a similar pattern of bidirectional causality between tourism and financial development for nine Caribbean nations.

3. Data and model specification

Panel data for 30 provinces between 2007 and 2021 are used for this analysis. China Statistical Yearbook and Energy Statistical Yearbook are the sources for these numbers. Explained variables, exploratory variables, and control factors have all been identified.

3.1. Explained variable

Quality economic growth that does not compromise environmental sustainability. There is a variety of academic literature discussing the meaning and assessment of markers of successful economic development. While researchers and their chosen measuring indicators may have varied perspectives on the association of HQED [ 39 ], the article argues that the terms "innovation," "coordination," "green," "open," and "sharing" capture the essence of the concept. Therefore, this study focuses on coordination, greenness, openness, and sharing—to create ecologically sustainable, high-quality economic growth. To impartially evaluate, we employ the entropy technique. The entire evaluation index system and its weighting are displayed in Table 1 .

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https://doi.org/10.1371/journal.pone.0295448.t001

Please take note that the values in riigh side are weights; the average RMB exchange rate for the entire year in China is used to calculate FDI and foreign trade indicators; real GDP in growth variations is analyzed with 2002 as the constant base period, and its growth rate variations are found via HP filtering.

3.2. Explanatery variables

3.2.1. renewable energy consumption (rec)..

Consumption of Renewable energy is a clean energy source that includes solar, hydro, wind, tidal, geothermal, and biomass energies, among others [ 40 ]. Carbon-free REC plays a crucial role in China’s energy infrastructure. It is a powerful force in advancing our energy system, the environment, climate change, and the cause of sustainable development. The amount of renewable energy is determined by the amount of electricity generated from renewable sources divided by the population. It measures the amount of power used on an individual basis.

3.2.2. Tourism development (TD).

The tourism industry is linked to sustainable practices. Maintaining harmony between expanding the tourist industry, protecting the environment, and fostering robust economic growth is essential. Thus, reducing carbon emissions from the tourism sector is the primary objective. Tourism growth is one attempt to address the problem of global warming. Revenue from tourism as a percentage of provincial GDP has been used to estimate growth in the industry.

3.3. Control variables

The research in this article uses green financing (GF), FDI, and EDUI as proxies for other economic factors. Green finance refers to investments in environmentally and economically beneficial enterprises. It was determined by comparing each region’s environmental protection rate to its economic growth rate. The ratio of foreign direct investment to regional GDP is used as a proxy for FDI. Investment in Education as a Percentage of Regional Expenditures (EDUI) Education spending is quantified by its GDP ratio.

3.4. Model settings

3.4.1. theoretical framework..

The IPAT identity transformation approach is frequently employed in the analysis of the ecological consequences of human activities. The IPAT framework was first introduced by Guo B et al. [ 41 ] around the early 1970s. Subsequently, it has emerged as a pivotal conceptual framework for discerning the primary factors contributing to expeditious ecological transformations. The IPAT paradigm, as outlined by Hailiang Z et al. [ 42 ], identifies Affluence (A), Technology (T), and Population (P) as the major determinants influencing environmental quality. The value of the IPAT equation rests in its ability to identify the key drivers of environmental quality with minimal effort. Moreover, it establishes a quantitative relationship between the causal factors and outcomes.

The IPAT framework is a flexible and concise tool that facilitates the identification of the underlying factors contributing to environmental change. However, it is not exempt from certain limitations. The IPAT model, for example, fails to account for the possibility that the primary environmental factors may not consistently exhibit linear or proportionate impacts. Huang C et al. [ 43 ] introduced an additional theoretical framework known as STIRPAT, an acronym for "Stochastic Impacts through Regression on Population, Affluence, and Technology." The STIRPAT model is an enhanced iteration of the IPAT identity, encompassing all the benefits of IPAT while not being limited by them. The subsequent rendition presents the basic form of the STIRPAT model, which can be utilized to empirically examine the hypotheses.

By applying the natural logarithm to both sides of Eq ( 1 ), we can convert it into its logarithmic-linear representation.

The coefficients b, c, and d, which correspond to the variables P, A, and T, are represented as symbols in Eq ( 2 ). On the other hand, the constant is designated by C, and the random error term of the STIRPAT model is represented by ε. Furthermore, the subscript i is used to represent the variable values (P, A, and T) for various cross-sectional elements. According to Hunjra et al. [ 44 ], the environment can be significantly influenced by population and income, which are considered as crucial components in the aforementioned model. Nevertheless, the utilization of this technology is not limited to a select few components. The interpretation of technology (T) inside the STIRPAT model might vary depending on several parameters, as discussed by Iftikhar et al. [ 45 ].

3.4.2. Moran’s I index.

A geographical autocorrelation test for HQED can help determine if collaborative innovation has a spatial spillover effect on such growth. In order to undertake spatial econometric research, the dependent variable must have geographic effects. In order to determine if factors have spatial effects, Moran’s I index test is now commonly utilized. This paper employs Moran’s I index, first employed by Chen [ 8 ], to assess spatial correlation on a global scale to examine the degree to which geographically adjacent areas share a typical pattern of development.

Where n shows number of cities, wij is a weighting element in W and HQEDi, and HQEDj is a measure of City I is and City J’s quality of economic development. The standard deviation of HQED, or S2, is twice the average. Moran’s I index can take on negative or positive values between 1 and 1[−1, 1]. There is a strong positive association between places with high-quality economic development space and values closer to 1 than 0. A low value for Moran’s I index indicates that HQED is dispersed randomly across the map, with no discernible pattern. If the value is negative (i.e., less than 0), then regions with high-quality economic development are negatively correlated.

3.4.3. Spatial measurement model.

A spatial econometric model can be constructed after the variable has been tested using Moran’s I index. Exogenous connections between independent variables, endogenous connections between dependent variables, and connections between random perturbation terms are the three forms of interactions in the spatial econometric model [ 46 ]. This paper uses the spatial Durbin model (SDM) with endogenous interface to explore the direct and indirect effects (or interregional and intraregional effects) that collaborative innovation amongst different urban agglomerations has on HQED in neighbouring places and themselves. Variable logarithms are used in regression analysis to reduce heteroscedasticity and collinearity.

This means that the direction and magnitude of the spillover effect of HQED in native and nearby areas can be measured by calculating the spatial autocorrelation coefficient, denoted by the Greek letter λ. Collaborative innovation is represented by β1, while the elastic coefficient of the control variable is denoted by δn; the elastic coefficients of the spatial interaction terms of the independent variables and the control variable are denoted by ρ1, θn, and ϕi and εit denote the unnoticed individual effects and random error terms, respectively.

Ctrl nit represents a series of control variables. In addition to renewable energy consumption and tourism development, many factors will still affect China’s economic development. This paper includes several control variables like green finance, foreign direct investment (FDI) and educational investment (EDUI).

Control variables are denoted by the notation Ctrl nit . Numerous factors, including renewable energy consumption and growth in the tourism industry, will determine China’s economic growth rate. Green financing, FDI, and EDUI are only a few control factors factored into this paper’s analysis.

3.4.4. The mediating effect model.

Regression analysis will always involve a mediating effect, as such an impact may be inherent in the theoretical process by which variables exert their influenc [ 47 ]. Both direct and indirect effects on HQED through increased usage of renewable energy are possible due to increased tourism growth. Renewable energy usage acts as a moderator in this relationship.

The above equation describes the connections between them. The intermediate effect is calculated as ab/c = ab/(ab + c’), where c’ is the total impact coefficient of TD X on HQED Y, and ab is the coefficient of TD affecting HQED via the intermediate variable M of REC.

These are the stages of testing: The first stage is to determine the overall impact of X (more regional tourism) on Y (HQED). The second step is to assess whether the regression coefficient c is statistically significant independently. The mediation effect test is accepted if the regression coefficients a and b in Eqs ( 5 ) and ( 6 ) are statistically significant; otherwise, the Sobel test must be conducted. Finally, the regression coefficient c’ is tested for significance; if it is, the mediating effect is computed. The Sobel test is the fourth procedure. If the test is successful, the intermediary test is also successful; otherwise, it is unsuccessful. Furthermore, if c’ is adequate, it indicates a moderate mediation effect; failure to do so indicates a substantial mediation impact.

Eq ( 5 ) can naturally map to Eq ( 6 ) in the mediation effect test model, while Eqs ( 5 ) and ( 6 ) map to Eqs. The entire test effect model presented in this study consists of Eqs ( 4 )–( 7 ).

3.5. Stationarity test

The present investigation employs three distinct unit root tests, including LLC, IPS and the Fisher-ADF test method, to investigate the correct stationary order of the under-examined variables. Khan A et al. [ 48 ] argued that using a battery of unit root tests would be beneficial because each test has slightly different statistical properties. Moreover, non-stationarity for all concerned variables is the null hypothesis (H0) of all of the aforementioned unit root tests.

LLC unit root testing is preferred because it requires homogeneity of slope on autoregressive parameters, which indicates the lack or existence of non-stationary difficulties, even when the constant and drift are free to vary across different series. In addition, the stationarity of the variables is analyzed using the Fisher-ADF test method and the IPS stationary test.

4. Results and discussion

4.1 descriptive statistics.

Here, we provide a more formal presentation of the discussion of the empirical results. Initial empirical work here records the variables in question and shows that they follow a normal distribution ( Table 2 ). The aggregate statistics indicate a significant shift between the minimum and maximum values over the period under review. During the time frame under study, we find that the average and maximum values of tourist arrivals are the highest, followed by income and that the average and maximum values of carbon dioxide are the lowest.

https://doi.org/10.1371/journal.pone.0295448.t002

4.2 Corelation matrix and multicollinearity test

In Table 3 , the results of the correlation matrix has been presented. When there is multicollinearity, the regression output is skewed. Therefore, the explanatory factors must be tested to determine the presence of multicollinearity. In order to examine multicollinearity, this research uses the variance inflation factor (VIF). As shown in Table 4 .

https://doi.org/10.1371/journal.pone.0295448.t003

https://doi.org/10.1371/journal.pone.0295448.t004

4.3. Stationarity test

To minimize the impact of this issue, the study performs a unit root test before estimating the model. In addition, this work use IPS to simultaneously examine variable stationarity, hence lessening the unintended mistake problem and ensuring correctness. Table 5 displays the results of the unit root test using the t-test, the LLC test, and the Fisher-ADF test; all variables significantly pass the stationarity test. As a result, there is no need to worry about pseudo-regression because it is known that all variables are stationary series.

https://doi.org/10.1371/journal.pone.0295448.t005

4.4. Spatial autocorrelation test

We employ the globally applicable Moran’s I index, which has been extensively utilized in the literature on spatial studies, to investigate the potential for spatial autocorrelation in HQED. The worldwide Moran’s I index characterizes the breadth of the available spatial connection across all spatial units. Moran’s I index values on a global scale can be found between -1, and Spatial clustering among the sample countries is indicated by a positive value of Moran’s I index, with a more significant value indicating a more robust correlation (i.e. more positively correlated). If the value is negative, then there is spatial dispersion among the sample countries, and a stronger relationship (i.e. more negatively associated) exists. If the number is 0, the HQED is spread evenly throughout all provinces.

Table 6 displays Moran’s I index values and related P-value for HQED from 2007 to 2021, calculated with the trade-intensity-based spatial weight matrix. Positive and statistically significant Moran’s I value for HQED were found across all time intervals, indicating that HQED did not have a uniform distribution across the study area but rather a positive dependency among their locations. This finding suggests that countries with high HQED (resp. low HQED) tend to cluster together. Therefore, the existence of spatial autocorrelation lends credence to the importance of including spatial factors in econometric analysis.

https://doi.org/10.1371/journal.pone.0295448.t006

4.5. Determination of spatial measurement model

The LM test is used to narrow down the options before settling on an SDM, SEM, or SAR model. The emperical outcomes rejected the null hypothesis at many stages of testing, leading to the selection of the SDM model. Second, the result was statistically significant according to the Hausman test. Therefore we built a fixed-effects model. NEXT, the SDM model’s potential for simplifying a SAR or SEM is evaluated using Wald and LR tests. Since the outcomes have considerably passed multiple testing levels, building an SDM model is preferable. Ultimately, it was decided that an SDM model with fixed time-point effects would be the best fit for the data in this investigation (See Table 7 ).

https://doi.org/10.1371/journal.pone.0295448.t007

A statistical test is performed on the regression findings of HQED and tourism development to establish the sort of spatial interaction impact model that should be utilized, and an outcomes are showed in Table 8 . To begin, the significant p-values from the Lagrange multiplier (LM) test of the model indicate that the null hypothesis of no spatial errors or spatial lag effects should be accepted. The Hausman test is then used to choose between the fixed-effects and random-effects models. If the Hausman test statistic has a p-value of 0, then the fixed-effects model is selected; otherwise, the random-effects model is selected. In addition, the initial hypothesis was rejected by a significant p-value test, leading to the selection of the fixed-effects model, the selection of the time fixed in both ways as the baseline analysis model, and the utilization of the maximum likelihood approach for parameter estimation. Finally, the spatial Durbin model (SDM) cannot be simplified to be used as a spatial error model (SEM) or spatial lag model (SAR) due to the combined outcomes of the likelihood ratio (LR) test and the Wald test, representing that the SDM setting in this study is rational.

https://doi.org/10.1371/journal.pone.0295448.t008

According to the results given in Table 7 , it is clear that the SDM technique cannot be condensed to the SLM technique since the Wald test for the spatial autoregressive component (lag term) rejects the null hypothesis (H0: ρ = 0) and the Wald test for the SEM rejects the null hypothesis (H0: ρ+βθ = 0). In conclusion, the LM, Wald, LR, and Hausman tests were all passed, and the fixed-effects SDM was chosen for the empirical testing and analysis in this study.

4.6. Regression results and analysis

4.6.1. spatial doberman model test results..

REC can boost HQED, but only marginally so due to a lack of a spatial spillover effect and a limited elastic coefficient. Because RE is primarily utilized to lower carbon emissions, its influence coefficient on HQED is 0.0005, which is statistically significant at the 5% level. The emission mechanism encourages HQED because cutting carbon emissions from power plants is one way to boost renewable energy’s share of the energy market. However, the impact coefficient for renewable energy consumption is low, suggesting that the sector as a whole isn’t seeing much of a boost. This is due to several factors. Subsidies result in deadweight loss, which reduces economic output, and can have a crowding-out impact on other government spending or put an undue financial strain on electricity providers or consumers. There are challenges to developing new renewable energy technologies and making the energy shift. The positive effect will naturally grow over time as the rate of innovation in RE technology rises, the price of renewable energy decreases, and learning-by-doing effects and the dynamic economies of scale brought about by the rise in REC increase. One of the novel aspects of this research is that it is the first to observe the influence of renewable energy on HQED from a spatial perspective (See Table 9 ).

https://doi.org/10.1371/journal.pone.0295448.t009

There are no published studies comparing the spatial impact of RE on HQED to existing knowledge. Many researchers focus on the relationship between REC and economic growth, with varying findings [ 49 ]. Ma et al. [ 50 ] found a negative association between them, Zhong and Zheng [ 51 ] found a positive one. According to research by Shi et al. [ 52 ], they do not have any kind of meaningful connection. However, no one has looked at the two of them together and assessed their connection from a spatial metrology standpoint. One of the novel aspects of this study is its ability to address gaps in the literature and enhance the state of the subject.

The growth of tourism has a negative influence on HQED, and this has a spatial spillover effect as well. Carbon emissions are the primary factor in establishing this inverse correlation. Carbon emissions impact HQED, and the tourism industry contributes to those emissions. Regression analyses show that tourism expansion has stifled economic growth. The primary driver for the HQED is the requirement for energy to support the hospitality, lodging, transportation, travel, retail, and entertainment sectors that form the backbone of the tourism industry. Because of climate change, long-held beliefs like "tourism is a low-energy-consumption and low-pollution industry" are being challenged. While low-carbon tourism does not now account for a sizable share of the tourism market, it may play a role in reducing the sector’s overall carbon footprint in the future. Thus, the goal of energy emission reduction is to advocate for and promote low-carbon tourism in order to lessen the reliance of tourist’s economic growth on energy use and resource and ecological occupation. To encourage sustainable growth, the tourism industry must transition to low- or zero-carbon energy sources in essential sectors like transportation, lodging, food service, and sightseeing. The current model for tourism economic development is dependent on resources and energy, but with the help of cutting-edge skills and the strict execution of tourism energy-saving and ecological security access standards in accordance with industrial regulations, this is beginning to change.

The W TD indicator has a statistically significant -coefficient. The growth of tourism in this province has been shown to have a negative impact on the HQED of cities and provinces in its vicinity. It’s possible that this is the case because tourism development necessitates the crossing of multiple regions, the use of non-renewable energy, and carbon emissions, all of which have the potential to negatively affect and debilitate the areas immediately adjacent to the point of intersection (HQED).

The negative coefficient of the interaction term between tourism development and renewable energy and the spatial effect indicates that both of these factors can have a negative impact on HQED. This could be due to the fact that the rise in carbon emission intensity is mirrored in the expansion of the tourism industry. Since RE is still in its beginning and has little promotion effect, its combination works against HQED because it increases carbon emissions. The corresponding value for W REC_TD is 0.4437. Because the surrounding areas are easily impacted by the tourism industry’s carbon emissions due to the region’s high energy usage.

The influence of the tourism sector on carbon emissions has been researched extensively; however, the link between the tourism industry and HQED has been discussed far less. While some researchers, like Zhong and Zheng [ 51 ], and Chen and Huo [ 8 ], argue that expanding the tourism industry will lead to more greenhouse gas emissions, others, like Zhang et al. [ 53 ] and Wang and Jia [ 54 ], argue in the opposite direction. As a study and examination of the direct impact of tourism development on HQED, this paper enriches and improves the existing research field, introduces some novel theoretical concepts, and reflects the relationship between the two.

4.6.2. Results of the mediation effect test.

Stata measurement software is used to estimate Eqs ( 4 ), ( 5 ), and ( 6 ) to confirm the mediating influence of REC on the process of TD and HQED. In Table 10 , you can see the final findings.

https://doi.org/10.1371/journal.pone.0295448.t010

The use of renewable energy sources serves as a moderating factor. Model (4)’s c value is 0.2324, which is statistically significant at the 1% test level, further demonstrating that growth in tourism discourages HQED. Model (5) predicts that increasing tourism’s share of the economy will boost renewable energy use by 10%, with a value of an of 0.0321. Model (6) shows that the inhibitory impact is greatly diminished when the size of the coefficient c’ is reduced to 0.0957. At the 1% significance level, the coefficient of renewable energy consumption (b) is 0.2094. Evidence suggests that REC mediates the effect of tourism expansion on HQED, with a resulting increase in utility equal to 0.13210.2094/0.223512.38%. Possible causes include the fact that solar and wind energy technology advancements have laid the groundwork for low-carbon tourism, drastically lowering carbon emissions, and fostering HQED.

According to findings from studies on the mediating impact, the REC is a key factor in the development of high-quality economies as a whole. The threshold effect of renewable energy consumption and the direct relationship between the two are the primary foci of research. For instance, the mediating influence of renewable energy use has not been investigated by authors such as Zhou et al. [ 55 ]. This document presents the most recent data available to support efforts to improve regional economic development. It helps get us closer to our regional targets for sustainable development.

Furthermore, the Robust least squares method is employed in order to address influential observations and outliers present in the dataset. The robust least squares method exhibits greater statistical power compared to the ordinary least squares (OLS) approach. In Table 11 , it is evident that the findings remain consistent with the results observed by spatial Durban model (SDM).

(Dependent variable: HQED).

https://doi.org/10.1371/journal.pone.0295448.t011

5. Conclusions and recommendations

Using data from 30 provinces across China between 2007 and 2021, this study applies the spatial Durbin model (SDM), and the robustness test to examine the spatial association between TD, clean energy consumption on HQED in China. The results shows that tourism development and foreign direct investment have negative impact on the HQED, while renewable energy, green finance, investment in education projects have positive impact on HQED. It has been noticed that renewable energy consumption pay mediating role between TD and HQED and combined impact of TD and renewable energy consumption have positive impact on HQED. On Following are the most important takeaways:

• Using renewable sources of energy China can achieve high-quality economic growth. Green jobs have been created due to investments in renewable energy, while emissions of greenhouse gases have been reduced. China’s reliance on imported fuel has lessened because of the country’s increased use of renewable energy. Last but not least, renewable energy generation has reduced air pollution, raising living standards in many urban areas. All things considered, renewable energy has been a big factor in China’s economic growth and will continue to play a significant role in the country’s long-term economic success.
• Overall, China’s tourism industry has been booming in recent years, which has contributed to higher standards of economic growth. This improvement has manifested itself in more job openings and tax money for the government, both of which have contributed to the expansion of the economy. In addition, it has facilitated the influx of foreign tourists, raising the country’s profile abroad and giving it a competitive edge. While China’s tourism industry shows great promise, it is important to keep a close eye on its consequences due to the country’s many pressing problems, such as overpopulation, environmental degradation, and the risk of unsustainable growth. Government regulations should be crafted to safeguard the local population and ecosystem while allowing this development to continue indefinitely. High-quality economic growth in China stands to benefit greatly from well-managed tourism expansion.
• In addition, it is imperative to give precedence to the comprehensive and synchronized expansion of green finance, while concurrently fostering the advancement of green finance in all provinces across China. Furthermore, it assumes a significant function in the control of governmental affairs through the establishment of a compendium pertaining to the expansion of the environmentally conscious sector, mitigating disparities in information availability, and actively advocating for the advancement of environmentally sustainable financial instruments and investments. In order to promote investments in low-carbon initiatives, it is recommended to establish rules and regulations mandating financial institutions and corporations to disclose their carbon intensity, carbon footprint, and high-carbon assets. This measure aims to incentivize the allocation of resources towards environmentally sustainable projects. Furthermore, it is imperative to uphold market-oriented reforms in order to stimulate private sector investment in environmentally friendly industries, enhance market competitiveness, and optimize the allocation of resources. Simultaneously, there persists a necessity for novel green financial regulation to avert the adverse consequences stemming from the excessive advancement of green financing.
• The quality of China’s economic growth can be greatly enhanced by green finance and expenditures in education. The country can better prioritize environmental sustainability if more resources are allocated to green activities. China’s economic growth can now prioritize both quantity and quality thanks to educational investments that produce a more knowledgeable and competitive workforce. Access to better education has a direct impact on economic growth, and green finance and investments in education have the potential to help reduce educational gaps between rural and urban areas and expand educational opportunities for all. Last but not least, these kinds of investments can have a beneficial effect on the economy, the environment, and the creation of new jobs. China’s high-quality economic development may undoubtedly benefit from green finance and expenditures in education.

The results get from the study endorsed the concept of tourism development to enhance economic development, but using clean energy is necessary; otherwise, tourism development hurts high-quality economic development. The government and policymakers should enhance renewable energy consumption by using green finance. The combined effect of tourism development, renewable energy consumption and green finance on the HQED is significant. Moreover, the Foreign development investment has a positive impact on HQED in China; it causes the entry of new technology, which could help reduce carbon emissions. It could be helpful to attain the project of green transformation of China and HQED.

In this study, the factors affecting the HQED have been measured in the case of China; there is a gap in the literature regarding panel data, so in further studies, emerging countries can be considered for the study. In the present study, tourism development and renewable energy consumption are the main variables. Still, many other important variables have yet to be addressed, like financial development, non-renewable energy consumption, technological innovation, etc.

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Dynamic linkages between tourism development, renewable energy and high-quality economic development: Evidence from spatial Durbin model

1 School of Economics and Trade, Henan University of Technology, Henan Zhengzhou, China

2 School of Business, Macau University of Science and Technology, Macau, China

ZhongYe Sun

Yang chuanyu, associated data.

The data is available without any restriction. The data is available at the site of world development indicator (WDI) [ https://databank.worldbank.org/source/world-development-indicators ] of world bank. The data can be provided by all the authors.

There has been a shift in focus toward environmentally and economically sustainable forms of economic growth known as High-quality economic development (HQED). However, this study analyzes the impact of tourism development (TD) and renewable energy consumption on HQED in 30 provinces of China, while covering the time period from 2007 to 2021. TD and HQED has been measured with help of Global Moran Index. This study has used dynamic spatial Durbin model (SDM) to measure the dynamic impact of TD index and renewable energy consumption on HQED along with green finance, foreign direct investment and investment in education. The findings from empirical analysis shows that TD has negative impact on HQED and in more developed regions, the relationship is positive, while in the less developed western part of China, the U-shape has been reversed. Central and northeastern China have a U-shaped connection, while it has been noticed the interaction term of TD and renewable energy endorses HQED. In addition, renewable energy consumption, green finance and increase in education investment have positive and significant impact on HQED while foreign direct investment has negative impact on HQED in China. Therefore, in the light of this study policymakers should focus on the quality of tourism industry, green finance for renewable energy supply and enhancing education investment in China to attain the goal of HQED.

1. Introduction

The concept of sustainable development, as articulated by the United Nations World Commission on Development and Environment, refers to a form of development that effectively addresses the current societal demands while ensuring the preservation of resources and opportunities for future generations to fulfill their own requirements. There is a consensus among all nations that the pursuit of sustainable development is the most effective means of promoting economic progress and ensuring environmental protection at a global level. Typically, sustainable development is categorized into two main components, namely "external response" and "internal response" [ 1 ]. It is imperative to consider the human-nature relationship from an external response perspective, as the sustenance and progress of humanity are inherently intertwined with the availability of natural resources and ecological services. Additionally, the challenges and pressures posed by natural evolutionary processes further underscore the significance of this relationship. The concept of "internal response" encompasses several crucial elements for sustained growth, which is considered a significant milestone in human civilization. These elements include the promotion of social order, organization, logical cognition, social harmony, and the ability to effectively navigate diverse social interactions. The acquisition of these skills is crucial for achieving comprehensive sustainable progress [ 2 ].

Economic transition in China from rapid growth to high-quality development necessitates a shift from an extensive phase of high-speed growth, which heavily relies on increased natural resource consumption, to a phase of high-quality development that relies on technological advancements and the enhancement of workforce quality [ 3 ]. Green development has gained significant attention and recognition in the current economic and social landscape due to its alignment with ecological priorities and sustainable practices. It has emerged as a crucial strategy in response to the new normal of the economy. The idea of green growth has become the central theme of national economic and social progress in this new era. Enhancing the efficiency of green development has been identified as a pivotal factor in promoting HQED. The concept of "high-quality development" has recently emerged within the framework of China’s economic development. The existing scholarly investigations predominantly center on the comprehensive socioeconomic advancement in the context of HQED. For instance, [ 4 ] has extensively engaged in theoretical deliberations concerning the HQED of China’s economy, focusing on the five development concepts and the primary social contradictions, respectively. There exists a limited body of study that specifically focuses on the high-quality advancement of tourism. Simultaneously, due to the absence of uniformity among scholars about the nuances associated with quality tourism development, there exists significant variation in the selection of indicators and their outvomes when assessing the level of such growth. Hence, it is vital to establish a scientific assessment framework for the promotion of high-quality tourism, which entails comprehensively understanding the notion of high-quality tourism development. Additionally, it is crucial to gauge the disparity between the current state of tourism development and the desired HQED across various regions in China.

In addition, It has been determined that clean renewable energy sources are a significant part of the manufacturing procedure. REC has a significant part in economic growth alongside other aspects of production. Consequently, as economies have grown, so too has the significance of RE use. Cleaner than fossil fuels, renewable energy is predicted to considerably impact CE, as stated by Ohajionu et al. [ 5 ]. The various forms of atmospheric air pollutants originating from fossil fuel combustion could limit both the sustainable expansion of the global economy and the reduction of the world’s carrying capacity. Because of the problems produced by fossil fuels, economies around the world have begun looking for renewable energy sources to replace them [ 6 ]. Green energy and renewable resources have been offered by scientists and environmentalists as a solution to the dual problems of fossil fuel-related environmental damage and insufficient supplies to meet rising energy demand.

Green finance (GF) often encompasses investments made through green credit cards, which involve the allocation of funds towards environmental initiatives and other ecological objectives, such as sustainable promotion. Additionally, this product might be considered a financial innovation that generates both economic and ecological benefits [ 7 ]. The GF is providing financial support to activities that offer substantial assistance while simultaneously enhancing the natural environment. In recent times, there has been a notable global focus on this concept, with China emerging as the foremost market for green bonds [ 8 ]. The significant integration of wind energy into China’s eco-energy system can be attributed to the extensive commercialization in this sector. The expansion of renewable energy sources is of paramount importance in addressing the issues posed by climate change in China.

Further, this study examined the effects of FDI to ascertain if they support the Pollution Haven or Pollution Halo theory in the instance of OECD countries, as there is no apparent harmony in the role of FDI from an ecological viewpoint. Foreign direct investment (FDI) is crucial in encouraging growth and development in host nations by bridging the saving-investment gap. Environmental degradation in developing countries may result from foreign direct investment (the pollution haven hypothesis posits that developed countries will locate their polluting industries in developing nations because of lax environmental regulations there) [ 9 ]. However, some argue that this progress could compromise environmental quality due to pollutants generated by development projects. Some studies believe that FDI can increase environmental quality (pollution halo hypothesis) and productivity by drawing in new, efficient, and green technologies. Green foreign direct investment is credited with helping countries like China drastically cut their carbon output. In addition to attracting innovative technology that could reduce environmental pollution, FDI has been suggested to serve as a growth booster, employment generator, and source for host countries. Similarly, Koçak et al. [ 10 ] found that FDI in developing nations has dramatically improved environmental quality, crediting the pollution halo hypothesis.

The following are the novel contribution of the study: (1) This study explores the impact of tourism development index on the HQED index (HQED index has been measured by using Moran’s I index method) and also explores the impact of renewable energy consumption on HQED, while taking the data of 30 provinces of China, (2) This study investigates the mediating role of renewable energy consumption between TD index and HQED, (3) The impact of interaction term of TD and REC has also been measured, (4) Along with TD, the impact of green finance, FDI and investment in education projects also has been measured while using Dynamic spatial Durbin model (SDM). This research has the potential to fill a gap in the literature and contribute significantly to advancing knowledge in this area.

This article will include the following sections: The second section reviews the relevant literature. Data, empirical model, and methodology are all described in Section 3. The empirical data and their interpretation are illustrated in Section 4. Section 5 wraps up the analysis and makes some helpful policy suggestions.

2. Literature review

2.1. research on hqed.

High-quality development theory suggestions for improving the tourism industry’s growth are made. According to the 19th National Congress report by the Communist Party of China, " China’s economy has transitioned from a phase characterized by rapid growth to a phase focused on achieving high-quality development. Currently, China finds itself in a crucial moment where it must undertake the transformation of its development mode, optimize its economic structure, and shift the driving forces behind its growth." There are currently two competing definitions of high-quality tourism business development in academic circles. When assessing the progress of the tourism industry across the board, it is essential to focus on quality and quantity [ 11 ]. Another method, quality evaluation of a tourism industry subsector or a tourism activity link, emphasizes diversity and micro-level analysis [ 12 ]. As for the former, it relies heavily on studies conducted within China. This study uses econometric and geographical statistics to compile the panel data for an all-inclusive score and distribution features. The primary sources are Raza.S.A. [ 13 ] and Balsalobre-Lorente et al. [ 14 ]. The latter is predicated on studies conducted in other countries and relies heavily on interviews and comparative experimental approaches. This article plans to use the first research topic to investigate the growth of the tourism business while maintaining a high-quality standard.

Academic research on this topic is still in its infancy because the HQED of China’s tourism industry has only recently begun. The present research focuses mainly on constructing the index system and quantifying the HQED index. However, neither a definition of high-quality expansion of the tourism business nor an elaboration of the HQED mechanism of tourism can be found in the available literature. Many academics have used the five new development philosophies of novelty, coordination, green, openness, and sharing to develop a robust evaluation system for tourism’s progress. The ’new growth vision’ was deemed scientific, detailed, and rational by Balsalobre-Lorente et al. [ 15 ], giving it significant reference relevance in constructing an HQED index system. HQED is defined by Sinha et al. [ 16 ] from the perspectives of the economy, coordination, innovation, openness, greenness, and inclusivity, and these six dimensions were used to determine how the index variables of an HQED assessment technique were chosen. Doğan et al. [ 17 ] defined a high-quality TD in light of the new development vision as a multifaceted endeavour involving, among other things, innovation, greenness, coordination, openness, cultural tourism resources and inclusivity. Additionally, some academics assess the area tourism industry’s high-quality development level from the angle of production efficiency. Index variables are often collected from tourism resource capability, scenic sites, hotels, total tourism income, travel agencies, and the total number of tourists [ 18 ].

2.2. Renewable energy consumption impact on HQED

Energy consumption and logistics have been thoroughly studied in the literature [ 19 ]. It has been stated that the logistics sector relies excessively on energy consumption, which negatively impacts human health and the planet’s longevity. The use of clean energy and the promotion of green products are two ways in which green logistics can boost environmental and financial outcomes. Using renewable energy in logistics operations has dramatically enhanced environmental performance. It is because the logistics sector is the largest emitter of hazardous gases. Tian et al. [ 20 ] found that increased trade between environmentally conscious nations was positively correlated with each nation’s economic health while using renewable energy. According to Melese et al. [ 21 ], governments can boost environmental sustainability and economic growth by switching to renewable energy and buying eco-friendly products. Policymakers are urged by Melese et al. [ 21 ] to adopt clean energy and logistics to reduce environmental impacts and boost economic growth.

Rybchenko et al. [ 22 ] demonstrate a positive correlation between REC and long-term economic growth. A clear correlation between economic growth, logistics, and energy use was also found by Ridderstaat et al. [ 23 ]. Sun et al. [ 24 ] argue that countries implementing green tourism must have access to clean energy to promote sustainability. As a result of resource scarcity and environmental challenges, Lee et al. [ 25 ] argue that using renewable energy is inevitable for such countries, as such energy is compatible with economic development.

2.3. Tourism development impact on HQED

Sørensen and Grindsted [ 26 ] discuss a thorough literature assessment on tourism indicators and high-quality economic development (HQED) economic growth. Both positive and negative effects on HQED can be attributed to tourism, and vice versa, as well as a bidirectional causality and a neutral relationship. There is little agreement on the connections between tourism and HQED indicators, leaving much room for debate. Ridderstaat et al. [ 23 ], using data for Spain from 1975 to 1997, do ground-breaking work on the association between TD and economic development using a trivariate model. The long-term dynamic association between tourism and HQED was the clincher of his findings.

Similarly, Abbas et al. [ 1 ] researched Aruba and found evidence supporting the tourism push growth concept. In addition, Zheng et al. [ 27 ] used Johansson cointegration to examine the link between tourism and HQED in Mauritania between 1950 and 1999, and VECM confirmed that the former positively influenced the latter. Also, using Spanish data, Wei and Lihua [ 28 ] found evidence of a cointegration link. Pakistan’s tourism and high-quality economic development (HQED) have been investigated from both directions. Using data from Turkey between 1963 and 2006, Dong and Li [ 29 ] recognized a unidirectional causal relationship between tourism and HQED. They advocated for the promotion of tourism as a long-term strategic industry. Similarly, Le and Nguyen [ 30 ] for Sri Lanka, Alam and Ali [ 4 ] for Romania, and Wirawan and Gultom [ 31 ] for Lebanon are noteworthy for their support of the presence of tourism lead HQED.

2.4. Renewable energy, tourism development and HQED

Since renewable energy sources dramatically reduce harmful emissions, several governments have begun developing them to attain sustainable development goals. Adopting renewable or "green" energy is costly and requires public education [ 32 ] because these sources are still in their infancy compared to fossil fuels. Since environmental performance is favourable for tourist arrivals, developing renewable energy sources plays a crucial role in nations that rely heavily on tourism. Many scientists currently focus on finding ways to combine renewable energy with travel. Energy consumption and pollution negatively impact tourism, according to the literature; as a result, authors recommend using renewable energy sources and switching to green products to boost the industry [ 33 ]. Benefits to the environment and the economy have resulted from the United Kingdom’s green energy initiative, which is realized primarily by the country’s tourism sector [ 34 ].

In addition, Yang et al. [ 35 ] suggest that governments should actively encourage renewable energy and ecotourism, which are crucial for countries still building their tourism industries. Therefore, increasing green energy utilization to enhance eco-friendly tourism is recommended in the literature. Sustainable development, it is said, relies heavily on the tourism industry. As a result, research into the link between REC and ecotourism is essential. Examining the link between green energy and green product growth in tourism is equally crucial for long-term sustainability. It is suggested in the available literature that green consumption behaviours and renewable energy sources make significant contributions to environmental sustainability, which in turn benefits the tourism industry.

However, by analyzing data from 140 nations and six regions worldwide between 1995 and 2009, Yuping et al. [ 36 ] disproved the tourism-led HQED hypothesis. Sheraz et al. [ 37 ] investigated the HQED-induced tourism theory for Malaysia. Mohsin et al. [ 38 ] found a similar pattern of bidirectional causality between tourism and financial development for nine Caribbean nations.

3. Data and model specification

Panel data for 30 provinces between 2007 and 2021 are used for this analysis. China Statistical Yearbook and Energy Statistical Yearbook are the sources for these numbers. Explained variables, exploratory variables, and control factors have all been identified.

3.1. Explained variable

Quality economic growth that does not compromise environmental sustainability. There is a variety of academic literature discussing the meaning and assessment of markers of successful economic development. While researchers and their chosen measuring indicators may have varied perspectives on the association of HQED [ 39 ], the article argues that the terms "innovation," "coordination," "green," "open," and "sharing" capture the essence of the concept. Therefore, this study focuses on coordination, greenness, openness, and sharing—to create ecologically sustainable, high-quality economic growth. To impartially evaluate, we employ the entropy technique. The entire evaluation index system and its weighting are displayed in Table 1 .

Please take note that the values in riigh side are weights; the average RMB exchange rate for the entire year in China is used to calculate FDI and foreign trade indicators; real GDP in growth variations is analyzed with 2002 as the constant base period, and its growth rate variations are found via HP filtering.

3.2. Explanatery variables

3.2.1. renewable energy consumption (rec).

Consumption of Renewable energy is a clean energy source that includes solar, hydro, wind, tidal, geothermal, and biomass energies, among others [ 40 ]. Carbon-free REC plays a crucial role in China’s energy infrastructure. It is a powerful force in advancing our energy system, the environment, climate change, and the cause of sustainable development. The amount of renewable energy is determined by the amount of electricity generated from renewable sources divided by the population. It measures the amount of power used on an individual basis.

3.2.2. Tourism development (TD)

The tourism industry is linked to sustainable practices. Maintaining harmony between expanding the tourist industry, protecting the environment, and fostering robust economic growth is essential. Thus, reducing carbon emissions from the tourism sector is the primary objective. Tourism growth is one attempt to address the problem of global warming. Revenue from tourism as a percentage of provincial GDP has been used to estimate growth in the industry.

3.3. Control variables

The research in this article uses green financing (GF), FDI, and EDUI as proxies for other economic factors. Green finance refers to investments in environmentally and economically beneficial enterprises. It was determined by comparing each region’s environmental protection rate to its economic growth rate. The ratio of foreign direct investment to regional GDP is used as a proxy for FDI. Investment in Education as a Percentage of Regional Expenditures (EDUI) Education spending is quantified by its GDP ratio.

3.4. Model settings

3.4.1. theoretical framework.

The IPAT identity transformation approach is frequently employed in the analysis of the ecological consequences of human activities. The IPAT framework was first introduced by Guo B et al. [ 41 ] around the early 1970s. Subsequently, it has emerged as a pivotal conceptual framework for discerning the primary factors contributing to expeditious ecological transformations. The IPAT paradigm, as outlined by Hailiang Z et al. [ 42 ], identifies Affluence (A), Technology (T), and Population (P) as the major determinants influencing environmental quality. The value of the IPAT equation rests in its ability to identify the key drivers of environmental quality with minimal effort. Moreover, it establishes a quantitative relationship between the causal factors and outcomes.

The IPAT framework is a flexible and concise tool that facilitates the identification of the underlying factors contributing to environmental change. However, it is not exempt from certain limitations. The IPAT model, for example, fails to account for the possibility that the primary environmental factors may not consistently exhibit linear or proportionate impacts. Huang C et al. [ 43 ] introduced an additional theoretical framework known as STIRPAT, an acronym for "Stochastic Impacts through Regression on Population, Affluence, and Technology." The STIRPAT model is an enhanced iteration of the IPAT identity, encompassing all the benefits of IPAT while not being limited by them. The subsequent rendition presents the basic form of the STIRPAT model, which can be utilized to empirically examine the hypotheses.

By applying the natural logarithm to both sides of Eq ( 1 ), we can convert it into its logarithmic-linear representation.

The coefficients b, c, and d, which correspond to the variables P, A, and T, are represented as symbols in Eq ( 2 ). On the other hand, the constant is designated by C, and the random error term of the STIRPAT model is represented by ε. Furthermore, the subscript i is used to represent the variable values (P, A, and T) for various cross-sectional elements. According to Hunjra et al. [ 44 ], the environment can be significantly influenced by population and income, which are considered as crucial components in the aforementioned model. Nevertheless, the utilization of this technology is not limited to a select few components. The interpretation of technology (T) inside the STIRPAT model might vary depending on several parameters, as discussed by Iftikhar et al. [ 45 ].

3.4.2. Moran’s I index

A geographical autocorrelation test for HQED can help determine if collaborative innovation has a spatial spillover effect on such growth. In order to undertake spatial econometric research, the dependent variable must have geographic effects. In order to determine if factors have spatial effects, Moran’s I index test is now commonly utilized. This paper employs Moran’s I index, first employed by Chen [ 8 ], to assess spatial correlation on a global scale to examine the degree to which geographically adjacent areas share a typical pattern of development.

Where n shows number of cities, wij is a weighting element in W and HQEDi, and HQEDj is a measure of City I is and City J’s quality of economic development. The standard deviation of HQED, or S2, is twice the average. Moran’s I index can take on negative or positive values between 1 and 1[−1, 1]. There is a strong positive association between places with high-quality economic development space and values closer to 1 than 0. A low value for Moran’s I index indicates that HQED is dispersed randomly across the map, with no discernible pattern. If the value is negative (i.e., less than 0), then regions with high-quality economic development are negatively correlated.

3.4.3. Spatial measurement model

A spatial econometric model can be constructed after the variable has been tested using Moran’s I index. Exogenous connections between independent variables, endogenous connections between dependent variables, and connections between random perturbation terms are the three forms of interactions in the spatial econometric model [ 46 ]. This paper uses the spatial Durbin model (SDM) with endogenous interface to explore the direct and indirect effects (or interregional and intraregional effects) that collaborative innovation amongst different urban agglomerations has on HQED in neighbouring places and themselves. Variable logarithms are used in regression analysis to reduce heteroscedasticity and collinearity.

This means that the direction and magnitude of the spillover effect of HQED in native and nearby areas can be measured by calculating the spatial autocorrelation coefficient, denoted by the Greek letter λ. Collaborative innovation is represented by β1, while the elastic coefficient of the control variable is denoted by δn; the elastic coefficients of the spatial interaction terms of the independent variables and the control variable are denoted by ρ1, θn, and ϕi and εit denote the unnoticed individual effects and random error terms, respectively.

Ctrl nit represents a series of control variables. In addition to renewable energy consumption and tourism development, many factors will still affect China’s economic development. This paper includes several control variables like green finance, foreign direct investment (FDI) and educational investment (EDUI).

Control variables are denoted by the notation Ctrl nit . Numerous factors, including renewable energy consumption and growth in the tourism industry, will determine China’s economic growth rate. Green financing, FDI, and EDUI are only a few control factors factored into this paper’s analysis.

3.4.4. The mediating effect model

Regression analysis will always involve a mediating effect, as such an impact may be inherent in the theoretical process by which variables exert their influenc [ 47 ]. Both direct and indirect effects on HQED through increased usage of renewable energy are possible due to increased tourism growth. Renewable energy usage acts as a moderator in this relationship.

The above equation describes the connections between them. The intermediate effect is calculated as ab/c = ab/(ab + c’), where c’ is the total impact coefficient of TD X on HQED Y, and ab is the coefficient of TD affecting HQED via the intermediate variable M of REC.

These are the stages of testing: The first stage is to determine the overall impact of X (more regional tourism) on Y (HQED). The second step is to assess whether the regression coefficient c is statistically significant independently. The mediation effect test is accepted if the regression coefficients a and b in Eqs ( 5 ) and ( 6 ) are statistically significant; otherwise, the Sobel test must be conducted. Finally, the regression coefficient c’ is tested for significance; if it is, the mediating effect is computed. The Sobel test is the fourth procedure. If the test is successful, the intermediary test is also successful; otherwise, it is unsuccessful. Furthermore, if c’ is adequate, it indicates a moderate mediation effect; failure to do so indicates a substantial mediation impact.

The foregoing analysis informs the following configuration of the model:

Eq ( 5 ) can naturally map to Eq ( 6 ) in the mediation effect test model, while Eqs ( 5 ) and ( 6 ) map to Eqs. The entire test effect model presented in this study consists of Eqs ( 4 )–( 7 ).

3.5. Stationarity test

The present investigation employs three distinct unit root tests, including LLC, IPS and the Fisher-ADF test method, to investigate the correct stationary order of the under-examined variables. Khan A et al. [ 48 ] argued that using a battery of unit root tests would be beneficial because each test has slightly different statistical properties. Moreover, non-stationarity for all concerned variables is the null hypothesis (H0) of all of the aforementioned unit root tests.

LLC unit root testing is preferred because it requires homogeneity of slope on autoregressive parameters, which indicates the lack or existence of non-stationary difficulties, even when the constant and drift are free to vary across different series. In addition, the stationarity of the variables is analyzed using the Fisher-ADF test method and the IPS stationary test.

4. Results and discussion

4.1 descriptive statistics.

Here, we provide a more formal presentation of the discussion of the empirical results. Initial empirical work here records the variables in question and shows that they follow a normal distribution ( Table 2 ). The aggregate statistics indicate a significant shift between the minimum and maximum values over the period under review. During the time frame under study, we find that the average and maximum values of tourist arrivals are the highest, followed by income and that the average and maximum values of carbon dioxide are the lowest.

4.2 Corelation matrix and multicollinearity test

In Table 3 , the results of the correlation matrix has been presented. When there is multicollinearity, the regression output is skewed. Therefore, the explanatory factors must be tested to determine the presence of multicollinearity. In order to examine multicollinearity, this research uses the variance inflation factor (VIF). As shown in Table 4 .

All VIFs are less than 10, indicating no multicollinearity; this allows for regression analysis.

4.3. Stationarity test

To minimize the impact of this issue, the study performs a unit root test before estimating the model. In addition, this work use IPS to simultaneously examine variable stationarity, hence lessening the unintended mistake problem and ensuring correctness. Table 5 displays the results of the unit root test using the t-test, the LLC test, and the Fisher-ADF test; all variables significantly pass the stationarity test. As a result, there is no need to worry about pseudo-regression because it is known that all variables are stationary series.

** means p < 0.05

*** means p < 0.01.

4.4. Spatial autocorrelation test

We employ the globally applicable Moran’s I index, which has been extensively utilized in the literature on spatial studies, to investigate the potential for spatial autocorrelation in HQED. The worldwide Moran’s I index characterizes the breadth of the available spatial connection across all spatial units. Moran’s I index values on a global scale can be found between -1, and Spatial clustering among the sample countries is indicated by a positive value of Moran’s I index, with a more significant value indicating a more robust correlation (i.e. more positively correlated). If the value is negative, then there is spatial dispersion among the sample countries, and a stronger relationship (i.e. more negatively associated) exists. If the number is 0, the HQED is spread evenly throughout all provinces.

Table 6 displays Moran’s I index values and related P-value for HQED from 2007 to 2021, calculated with the trade-intensity-based spatial weight matrix. Positive and statistically significant Moran’s I value for HQED were found across all time intervals, indicating that HQED did not have a uniform distribution across the study area but rather a positive dependency among their locations. This finding suggests that countries with high HQED (resp. low HQED) tend to cluster together. Therefore, the existence of spatial autocorrelation lends credence to the importance of including spatial factors in econometric analysis.

*** means significant at the 1% level, respectively.

4.5. Determination of spatial measurement model

The LM test is used to narrow down the options before settling on an SDM, SEM, or SAR model. The emperical outcomes rejected the null hypothesis at many stages of testing, leading to the selection of the SDM model. Second, the result was statistically significant according to the Hausman test. Therefore we built a fixed-effects model. NEXT, the SDM model’s potential for simplifying a SAR or SEM is evaluated using Wald and LR tests. Since the outcomes have considerably passed multiple testing levels, building an SDM model is preferable. Ultimately, it was decided that an SDM model with fixed time-point effects would be the best fit for the data in this investigation (See Table 7 ).

A statistical test is performed on the regression findings of HQED and tourism development to establish the sort of spatial interaction impact model that should be utilized, and an outcomes are showed in Table 8 . To begin, the significant p-values from the Lagrange multiplier (LM) test of the model indicate that the null hypothesis of no spatial errors or spatial lag effects should be accepted. The Hausman test is then used to choose between the fixed-effects and random-effects models. If the Hausman test statistic has a p-value of 0, then the fixed-effects model is selected; otherwise, the random-effects model is selected. In addition, the initial hypothesis was rejected by a significant p-value test, leading to the selection of the fixed-effects model, the selection of the time fixed in both ways as the baseline analysis model, and the utilization of the maximum likelihood approach for parameter estimation. Finally, the spatial Durbin model (SDM) cannot be simplified to be used as a spatial error model (SEM) or spatial lag model (SAR) due to the combined outcomes of the likelihood ratio (LR) test and the Wald test, representing that the SDM setting in this study is rational.

According to the results given in Table 7 , it is clear that the SDM technique cannot be condensed to the SLM technique since the Wald test for the spatial autoregressive component (lag term) rejects the null hypothesis (H0: ρ = 0) and the Wald test for the SEM rejects the null hypothesis (H0: ρ+βθ = 0). In conclusion, the LM, Wald, LR, and Hausman tests were all passed, and the fixed-effects SDM was chosen for the empirical testing and analysis in this study.

4.6. Regression results and analysis

4.6.1. spatial doberman model test results.

REC can boost HQED, but only marginally so due to a lack of a spatial spillover effect and a limited elastic coefficient. Because RE is primarily utilized to lower carbon emissions, its influence coefficient on HQED is 0.0005, which is statistically significant at the 5% level. The emission mechanism encourages HQED because cutting carbon emissions from power plants is one way to boost renewable energy’s share of the energy market. However, the impact coefficient for renewable energy consumption is low, suggesting that the sector as a whole isn’t seeing much of a boost. This is due to several factors. Subsidies result in deadweight loss, which reduces economic output, and can have a crowding-out impact on other government spending or put an undue financial strain on electricity providers or consumers. There are challenges to developing new renewable energy technologies and making the energy shift. The positive effect will naturally grow over time as the rate of innovation in RE technology rises, the price of renewable energy decreases, and learning-by-doing effects and the dynamic economies of scale brought about by the rise in REC increase. One of the novel aspects of this research is that it is the first to observe the influence of renewable energy on HQED from a spatial perspective (See Table 9 ).

*p<0.05

**p<0.10

***p<0.01

There are no published studies comparing the spatial impact of RE on HQED to existing knowledge. Many researchers focus on the relationship between REC and economic growth, with varying findings [ 49 ]. Ma et al. [ 50 ] found a negative association between them, Zhong and Zheng [ 51 ] found a positive one. According to research by Shi et al. [ 52 ], they do not have any kind of meaningful connection. However, no one has looked at the two of them together and assessed their connection from a spatial metrology standpoint. One of the novel aspects of this study is its ability to address gaps in the literature and enhance the state of the subject.

The growth of tourism has a negative influence on HQED, and this has a spatial spillover effect as well. Carbon emissions are the primary factor in establishing this inverse correlation. Carbon emissions impact HQED, and the tourism industry contributes to those emissions. Regression analyses show that tourism expansion has stifled economic growth. The primary driver for the HQED is the requirement for energy to support the hospitality, lodging, transportation, travel, retail, and entertainment sectors that form the backbone of the tourism industry. Because of climate change, long-held beliefs like "tourism is a low-energy-consumption and low-pollution industry" are being challenged. While low-carbon tourism does not now account for a sizable share of the tourism market, it may play a role in reducing the sector’s overall carbon footprint in the future. Thus, the goal of energy emission reduction is to advocate for and promote low-carbon tourism in order to lessen the reliance of tourist’s economic growth on energy use and resource and ecological occupation. To encourage sustainable growth, the tourism industry must transition to low- or zero-carbon energy sources in essential sectors like transportation, lodging, food service, and sightseeing. The current model for tourism economic development is dependent on resources and energy, but with the help of cutting-edge skills and the strict execution of tourism energy-saving and ecological security access standards in accordance with industrial regulations, this is beginning to change.

The W TD indicator has a statistically significant -coefficient. The growth of tourism in this province has been shown to have a negative impact on the HQED of cities and provinces in its vicinity. It’s possible that this is the case because tourism development necessitates the crossing of multiple regions, the use of non-renewable energy, and carbon emissions, all of which have the potential to negatively affect and debilitate the areas immediately adjacent to the point of intersection (HQED).

The negative coefficient of the interaction term between tourism development and renewable energy and the spatial effect indicates that both of these factors can have a negative impact on HQED. This could be due to the fact that the rise in carbon emission intensity is mirrored in the expansion of the tourism industry. Since RE is still in its beginning and has little promotion effect, its combination works against HQED because it increases carbon emissions. The corresponding value for W REC_TD is 0.4437. Because the surrounding areas are easily impacted by the tourism industry’s carbon emissions due to the region’s high energy usage.

The influence of the tourism sector on carbon emissions has been researched extensively; however, the link between the tourism industry and HQED has been discussed far less. While some researchers, like Zhong and Zheng [ 51 ], and Chen and Huo [ 8 ], argue that expanding the tourism industry will lead to more greenhouse gas emissions, others, like Zhang et al. [ 53 ] and Wang and Jia [ 54 ], argue in the opposite direction. As a study and examination of the direct impact of tourism development on HQED, this paper enriches and improves the existing research field, introduces some novel theoretical concepts, and reflects the relationship between the two.

4.6.2. Results of the mediation effect test

Stata measurement software is used to estimate Eqs ( 4 ), ( 5 ), and ( 6 ) to confirm the mediating influence of REC on the process of TD and HQED. In Table 10 , you can see the final findings.

Table 10 : Test of mediation effect of renewable energy consumption; ab/c = model (4), model (5), and model (6). Model (4) represents the influence of tourist development on HQED; model (5) represents the effect of tourism development on REC.

The use of renewable energy sources serves as a moderating factor. Model (4)’s c value is 0.2324, which is statistically significant at the 1% test level, further demonstrating that growth in tourism discourages HQED. Model (5) predicts that increasing tourism’s share of the economy will boost renewable energy use by 10%, with a value of an of 0.0321. Model (6) shows that the inhibitory impact is greatly diminished when the size of the coefficient c’ is reduced to 0.0957. At the 1% significance level, the coefficient of renewable energy consumption (b) is 0.2094. Evidence suggests that REC mediates the effect of tourism expansion on HQED, with a resulting increase in utility equal to 0.13210.2094/0.223512.38%. Possible causes include the fact that solar and wind energy technology advancements have laid the groundwork for low-carbon tourism, drastically lowering carbon emissions, and fostering HQED.

According to findings from studies on the mediating impact, the REC is a key factor in the development of high-quality economies as a whole. The threshold effect of renewable energy consumption and the direct relationship between the two are the primary foci of research. For instance, the mediating influence of renewable energy use has not been investigated by authors such as Zhou et al. [ 55 ]. This document presents the most recent data available to support efforts to improve regional economic development. It helps get us closer to our regional targets for sustainable development.

Furthermore, the Robust least squares method is employed in order to address influential observations and outliers present in the dataset. The robust least squares method exhibits greater statistical power compared to the ordinary least squares (OLS) approach. In Table 11 , it is evident that the findings remain consistent with the results observed by spatial Durban model (SDM).

(Dependent variable: HQED).

5. Conclusions and recommendations

Using data from 30 provinces across China between 2007 and 2021, this study applies the spatial Durbin model (SDM), and the robustness test to examine the spatial association between TD, clean energy consumption on HQED in China. The results shows that tourism development and foreign direct investment have negative impact on the HQED, while renewable energy, green finance, investment in education projects have positive impact on HQED. It has been noticed that renewable energy consumption pay mediating role between TD and HQED and combined impact of TD and renewable energy consumption have positive impact on HQED. On Following are the most important takeaways:

• Using renewable sources of energy China can achieve high-quality economic growth. Green jobs have been created due to investments in renewable energy, while emissions of greenhouse gases have been reduced. China’s reliance on imported fuel has lessened because of the country’s increased use of renewable energy. Last but not least, renewable energy generation has reduced air pollution, raising living standards in many urban areas. All things considered, renewable energy has been a big factor in China’s economic growth and will continue to play a significant role in the country’s long-term economic success.
• Overall, China’s tourism industry has been booming in recent years, which has contributed to higher standards of economic growth. This improvement has manifested itself in more job openings and tax money for the government, both of which have contributed to the expansion of the economy. In addition, it has facilitated the influx of foreign tourists, raising the country’s profile abroad and giving it a competitive edge. While China’s tourism industry shows great promise, it is important to keep a close eye on its consequences due to the country’s many pressing problems, such as overpopulation, environmental degradation, and the risk of unsustainable growth. Government regulations should be crafted to safeguard the local population and ecosystem while allowing this development to continue indefinitely. High-quality economic growth in China stands to benefit greatly from well-managed tourism expansion.
• In addition, it is imperative to give precedence to the comprehensive and synchronized expansion of green finance, while concurrently fostering the advancement of green finance in all provinces across China. Furthermore, it assumes a significant function in the control of governmental affairs through the establishment of a compendium pertaining to the expansion of the environmentally conscious sector, mitigating disparities in information availability, and actively advocating for the advancement of environmentally sustainable financial instruments and investments. In order to promote investments in low-carbon initiatives, it is recommended to establish rules and regulations mandating financial institutions and corporations to disclose their carbon intensity, carbon footprint, and high-carbon assets. This measure aims to incentivize the allocation of resources towards environmentally sustainable projects. Furthermore, it is imperative to uphold market-oriented reforms in order to stimulate private sector investment in environmentally friendly industries, enhance market competitiveness, and optimize the allocation of resources. Simultaneously, there persists a necessity for novel green financial regulation to avert the adverse consequences stemming from the excessive advancement of green financing.
• The quality of China’s economic growth can be greatly enhanced by green finance and expenditures in education. The country can better prioritize environmental sustainability if more resources are allocated to green activities. China’s economic growth can now prioritize both quantity and quality thanks to educational investments that produce a more knowledgeable and competitive workforce. Access to better education has a direct impact on economic growth, and green finance and investments in education have the potential to help reduce educational gaps between rural and urban areas and expand educational opportunities for all. Last but not least, these kinds of investments can have a beneficial effect on the economy, the environment, and the creation of new jobs. China’s high-quality economic development may undoubtedly benefit from green finance and expenditures in education.

The results get from the study endorsed the concept of tourism development to enhance economic development, but using clean energy is necessary; otherwise, tourism development hurts high-quality economic development. The government and policymakers should enhance renewable energy consumption by using green finance. The combined effect of tourism development, renewable energy consumption and green finance on the HQED is significant. Moreover, the Foreign development investment has a positive impact on HQED in China; it causes the entry of new technology, which could help reduce carbon emissions. It could be helpful to attain the project of green transformation of China and HQED.

In this study, the factors affecting the HQED have been measured in the case of China; there is a gap in the literature regarding panel data, so in further studies, emerging countries can be considered for the study. In the present study, tourism development and renewable energy consumption are the main variables. Still, many other important variables have yet to be addressed, like financial development, non-renewable energy consumption, technological innovation, etc.

Funding Statement

The author(s) received no specific funding for this work.

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Renewable Energy Matters for Tourism Industry in BRICS Plus Turkey Countries

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• Elma Satrovic 4 ,
• Adnan Muslija 5 &
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The scope of this paper is to investigate whether or not the renewable energy influences the tourism industry in the case of BRICS plus Turkey countries. Due to the fact that the primary energy source in these countries is fossil fuels, this has risen up a serious concern on environmental issues. The motivation to select these countries lies in the fact that all of these six have recorded an exponential economic growth in the last few decades. As a consequence, the standard of living has been increased as well as the energy consumption. Thus, the annual panel data are collected in the period between 1995 and 2015 in the case of BRICS countries plus Turkey to explore the link of interest. We have employed the panel VAR methodology. The most important findings suggest a response of tourism industry to renewable energy to be significant and negative. However, this negative relationship holds true in the short-run while the long-run impact tends to be positive. These results can be of great importance for policy makers, thus this paper summarizes in detail the policy implications.

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Zhang S, Liu X (2019) The roles of international tourism and renewable energy in environment: new evidence from Asian countries. Renew Energy 139:385–394. https://doi.org/10.1016/j.renene.2019.02.046

Article   Google Scholar  

http://www.hurriyetdailynews.com/erdogan-suggests-adding-t-to-brics135160

Gössling S (2013) National emissions from tourism: an overlooked policy challenge? Energy Policy 59:433–442. https://doi.org/10.1016/j.enpol.2013.03.058.

Nepal R, al Irsyad MI, Nepal SK (2019) Tourist arrivals, energy consumption and pollutant emissions in a developing economy–implications for sustainable tourism. Tourism Manag 72:145–154. https://doi.org/10.1016/j.tourman.2018.08.025

Katircioglu ST (2014) International tourism, energy consumption, and environmental pollution: the case of Turkey. Renew Sust Energ Rev 36:180–118. https://doi.org/10.1016/j.rser.2014.04.058

Katircioglu ST, Feridun M, Kilinc C (2014) Estimating tourism-induced energy consumption and CO 2 emissions: the case of Cyprus. Renew Sust Energ Rev 29:634–640. https://doi.org/10.1016/j.rser.2013.09.004

Blanke J, Chiesa T (2011) The travel and tourism competitiveness index 2011: assessing industry drivers in the wake of the crisis. In: Blanke J, Chiesa T (eds) The travel and tourism competitiveness report 2011: beyond the downturn. World Economic Forum, Geneva

Google Scholar  

Day J, Cai L (2012) Environmental and energy-related challenges to sustainable tourism in the United States and China. Int J Sust Dev World 19:379–388. https://doi.org/10.1080/13504509.2012.675600

Pereira MG, Camacho CF, Freitas MA, Silva NF (2012) The renewable energy market in Brazil: current status and potential. Renew Sust Energ Rev 16:3786–3802. https://doi.org/10.1016/j.rser.2012.03.024

Overland I, Kjernet H (2009) Russian renewable energy the potential for international cooperation. Ashgate, Farnham

Pillai IR, Banerjee R (2009) Renewable energy in India: status and potential. Energy 34:970–980. https://doi.org/10.1016/j.energy.2008.10.016

Liu W, Lund H, Mathiesen BV, Zhang X (2011) Potential of renewable energy systems in China. Appl Energy 88:518–525. https://doi.org/10.1016/j.apenergy.2010.07.014.

Pegels A (2010) Renewable energy in South Africa: potentials, barriers and options for support. Energy Policy 38:4945–4954. https://doi.org/10.1016/j.enpol.2010.03.077.

Vardar A, Taskin O (2014) Renewable energy sources and Turkey. Int J Energy Power Eng 3:245–249. https://doi.org/10.11648/j.ijepe.20140305.14

Satrovic E (2018) The human development relies on renewable energy: evidence from Turkey. In: 3rd International Energy & Engineering, Book of Proceedings, Gaziantep, Turkey, pp 19–27. https://docs.wixstatic.com/ugd/315b3d_a4d7493ae65e4815be5ae3ab05f83d3f.pdf . Accessed 21 Nov 2018

Abrigo M, Love I (2016) Estimation of panel vector auto regression in Stata: a package of programs. Working Paper No. 16-2.University of Hawai

Canova F, Pappa P (2007) Price differentials in monetary unions: the role of fiscal shocks. Econ J 117:713–737. https://doi.org/10.1111/j.1468-0297.2007.02047.x

Canova F, Perez Forero F (2012) Estimating over identified, non-recursive, time varying coefficients, structural VARs. UPF working paper

Satrovic E, Muslija A (2018) Causality relationship between foreign direct ınvestment and tourism. Int J Econ Adm Stud 22:65–76. https://doi.org/10.18092/ulikidince.418132.

Satrovic E (2018) Merits of life insurance. GRIN Verlag, Munich

Andrews DWK, Lu B (2001) Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models. J Econ 101:123–164. https://doi.org/10.1016/S0304-4076(00)00077-4

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Satrovic, E., Muslija, A., Ozelturkay, E.Y. (2020). Renewable Energy Matters for Tourism Industry in BRICS Plus Turkey Countries. In: Kavoura, A., Kefallonitis, E., Theodoridis, P. (eds) Strategic Innovative Marketing and Tourism. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-36126-6_17

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