The criticality of tourism development, economic complexity, and country security on ecological footprint

The green response of financial inclusion, infrastructure development and renewable energy to the environmental sustainability: A newly evidence from OECD economies

Recently, economic environmental degradation is being considered a leading chellenge in forefront of policy analysts. Thus, the present study introduces core environmental determinants such as infrastructure development, finacail inclusion, gross domestic product, population, and renewable energy consumption. Financial inclusion (FI) is crucial for attaining a environment. The present study selects the Organization for Economic Co-operation and Development (OECD) over period of 2004-2022. The results show that financial inclusin, infrastructure development(ID), and renewable energy (RE) play a vital influence in decreasing carbon emissions. The OECD nations should surge their investment in renewable energy and infrastructure development. Furthermore, to ensure long-term environmental sustainability, it is imperative to broaden the scope of FI. Thus, the inclusion of green infrastructure is essential in order to shift from the utilization of fossil fuels to RE sources. Similarly, policymakers should incorporate FI into climate actions at the local, national, and regional levels. However, it is crucial to promote the economic shift towards RE sources in order to mitigate the environmental impact from humn and economic activities. This study is conducive to the execution of the United Nations (UN) Sustainable Development Goals (SDG).

Toward maritime sustainability in GCC countries: What role do economic freedom and human capital play?

Gulf Cooperation Council (GCC) members have recently experienced rapid environmental degradation. Although there has been a boom in studies investigating the causes of environmental degradation, little is known about the drivers of maritime sustainability. This study examines the impacts of economic freedom and human capital on the fishing grounds footprint in GCC countries between 2000 and 2021. To account for potential heterogeneity and nonnormal distribution of the data, the study implements the Method of Moments Quantile Regression (MMQR). The empirical investigation suggests interesting findings. First, the analysis confirms the Marine Environmental Kuznets Curve across GCC countries, with a turning point of $38,177 per capita. In addition, the population has long-term detrimental effects on the fishing grounds footprint. Economic freedom and financial development have also deteriorated maritime sustainability, but only for low and medium quantiles. These factors are neutral for high levels of maritime degradation. Furthermore, improved human capital contributes to maritime sustainability in the long-run. Finally, the adverse repercussions of economic freedom are reduced by improved human capital and environmental awareness.

How do income inequality, poverty and industry 4.0 affect environmental pollution in South Asia: New insights from quantile regression

While many factors have been studied as potential causes of environmental degradation, the impact of poverty and inequality has been largely overlooked in the research. The Sustainable Development Goals are aligned with the intersection of poverty, inequality, and the environment. In addition, most previous research has used carbon dioxide (CO2) emissions as a surrogate for pollution. These gaps are filled by this study, which uses ecological footprint (a comprehensive measure of pollution) and CO2 emissions to examine the effects of income disparity and poverty on environmental pollution in 13 nations. Dynamic panel Quantile regression methods are used in this study because of their resilience to various econometric problems that can crop up during the estimate process. The empirical results reveal that the whole panel's carbon emissions and ecological footprint rise when income disparity and poverty exist. When the panel is subdivided, however, we see that income inequality reduces carbon emissions and environmental footprint for the wealthy but has the opposite effect on the middle class. While high-income households see no impact from poverty on their carbon emissions, middle-income households see an increase in both. Overall, the results of this study suggest that income disparity and poverty are major factors in ecological degradation. Therefore, initiatives to reduce environmental degradation should pay sufficient attention to poverty and inequality to achieve ecological sustainability.

Economic complexity, greenfield investments, and energy innovation: policy implications for sustainable development goals in newly industrialised economies

The crucial role of environmental assessment quality has been recognised by environmental and sustainable development goals in addressing climate change challenges. By focusing on the key identifier of environmental assessment, progress can be made towards overcoming climate change issues effectively. The current study considers environmental commitments under COP28 to study the role of economic complexity, greenfield investments, and energy innovation in environmental degradation in newly industrialised economies from 1995 to 2021. We employ novel panel estimations from CS-ARDL, CS-DL, AMG, and CCEMG to confirm that economic growth and greenfield investments degrade environmental quality. On the other hand, energy innovation and urbanisation improve environmental sustainability. Lastly, we confirm the EKC hypothesis for economic complexity as well. Given the reported empirical findings, the study suggests policymakers must focus on economic complexity to transform industrial sectors' economic potential. Furthermore, foreign investment projects must be linked with environmental goals to increase renewable energy capacity.

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

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.

Does tourism development contribute to the green water-use efficiency of the Yellow River Basin in China?

The coordination between economic development as well as water resources conservation in ecologically fragile areas is the basis for achieving sustainable development in developing countries. Nonetheless, the existing literature pays little attention to such an issue. The purpose of this quantitative study is to explore the causal relationship between tourism development and green water-use efficiency in the Yellow River Basin (YRB) of China. The findings are as follows: (1) Tourism development can significantly enhance the green water-use efficiency in the YRB; For every 1% increase in tourism revenue, the green water-use efficiency will increase by 4.38%. (2) Tourism affects the green water-use efficiency by increasing the intensity of water pollution and decreasing the intensity of water use; For every 1% increase in tourism revenue, the green water-use efficiency will decrease by 0.2% and increase by 0.9% respectively by increasing the intensity of water pollution and decreasing the intensity of water use. (3) Strengthening environmental regulation and improving service facilities will further enhance the positive impact of tourism development; An increase of one standard deviation in the intensity of environmental regulation or one standard deviation in the level of service facilities will increase the impact of tourism on green water-use efficiency by 1.1% or 1.7%, respectively. The aforementioned findings provide enlightenment for effectively promoting the coordination between economic development and water resources protection in ecologically fragile areas of developing countries.

Estimation of ecological footprint based on tourism development indicators using neural networks and multivariate regression

The ecological footprint has attracted a lot of attention in the top tourism destination countries, and this issue may be worrying. This study aims to estimate the ecological footprint, using such indicators as economic growth, natural resources, human capital, and the number of tourists in top tourism destination countries. For this purpose, artificial neural network models and multivariate regression were used for a period of 24 years (1995-2019). The results of the study showed a significant positive correlation between economic growth and ecological footprint. Multivariate regression estimation (R = 0.75) is weaker than neural network models (R = 96.3). Regarding predicting the ecological footprint, neural network models have better performance in comparison with the multivariate regression statistical methods. Accordingly, one can say that for planning ecological footprint, deeper look at neural networks can be more effective in predicting top tourism destination countries.

Study on the Spatial-Temporal Pattern and Driving Mechanism of Tourism Eco-Security in the Yellow River Basin

Tourism eco-security evaluation is an effective tool for facilitating the coordinated and sustainable economic and environmental development of tourist destinations. Based on system theory, this study established a comprehensive evaluation index system for the DPSIR model, applying the entropy-TOPSIS method, spatial autocorrelation, spatial econometric model and geo-detector to investigate the spatial and temporal evolution and drivers of tourism eco-security of the Yellow River basin. The results showed that the tourism eco-security of the Yellow River basin steadily and significantly increased from 2003 to 2020, reaching a peak in 2019, while there was a low level of overall tourism eco-security and improvement possibility. The results show a spatial evolution pattern of expansion from provincial capital cities to nearby prefecture-level cities from the middle and lower reaches to the middle and upper reaches, with significant spatial clustering and spillover effects. Factors affecting the tourism eco-security of the Yellow River basin vary in and between regional basins. Because there are many influencing factors, the key factors were further identified by spatial effect decomposition. The results of this study have important theoretical and practical value in promoting the coordinated and sustainable development of the tourism economy and ecological environment in the Yellow River basin.

Does financial development and renewable energy consumption impact on environmental quality: A new look at China’s economy

Environmental problems such as climate change have brought to light the necessity of implementing more stringent environmental regulations and expanding the use of renewable energy sources in order to protect the environment and maintain a green ecosystem. As a result, this study aims to investigate the impact of China’s financial development and consumption of renewable energy on the country’s environmental quality from 2009 to 2019. Following the application of the ARDL method, this research begins by employing the NARDL (non-linear autoregressive distributive lag) model in order to analyze the asymmetry in the data that results from the presence of either positive or negative aspects of financial development. The results of the NARDL bound test indicate that the variables are long-term co-integrated. This enables the application of the ARDL methodology. The ARDL bound test findings show a positive relationship that exists over the long-term between financial development, trade openness, renewable energy consumption, economic growth, and CO₂ emissions. In addition, the error correction model (ECM) provides evidence that there is, at least in the short run, a connection between CO₂ emissions, financial development, economic growth, and energy consumption. Furthermore, according to a dynamic multiplier graph, the positive aspect of financial development has a greater influence on carbon emissions for a longer time than the shocks associated with a less favorable financial development. According to the findings, there does not appear to be any asymmetry between CO₂ emissions and financial development, which supports the idea that both the positive and negative aspects of financial development have an equally significant impact.

The impact of air quality on international tourism arrivals: a global panel data analysis

Using data from World Development Indicators (WDI), this research constructs panel data of 99 countries from 1996 to 2018 and employs a spatial econometric model to analyze the impact of air quality on international tourism arrivals. Evidence shows that Moran's I values are significantly positive, indicating a strong positive spatial dependence in each country and that poor air quality does have a negative impact on the number of tourist arrivals. The results of grouped data illustrate that middle-income countries, low-income countries, high concentrations of PM_2.5, and countries with less numbers of tourists have negative effects on tourist arrivals in neighboring countries. The contrary groups, however, have positive effects on tourist arrivals - that is, the influence of air quality on the number of tourist arrivals exhibits heterogeneity. In addition, tests of the interaction term show that countries with higher R&D intensity have better air quality and thus attract more tourists. Therefore, countries with poor air quality should improve the environment through international cooperation and undertake technology transfer, thus ultimately increasing the number of tourists.