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This paper aims to investigate the influence of air and rail transportation on pollution in Turkey from 1970 to 2020.

Fourier Autoregressive Distributive Lags (ADL) and Fourier Fractional ADL cointegration tests (Banerjee et al., 2017; Ilkay et al., 2021) are employed to analyze the relationship be-tween the variables. Cointegration tests that take into account soft transitions under structural changes are implemented. Structural change issues are crucial for this topic since the changes in countries’ environmental policies and transportation habits are shaped by the decisions taken in relation to environmental regulations. Finally, for robustness purposes, we tested the estimated equation with a completely different methodology. Thus, a Machine Learning (ML) analysis is conducted, through a Ridge Regression (RR).

The findings obtained by applying Fourier Autoregressive Distributive Lags (FADL) and Fourier Fractional ADL cointegration tests, which can control for structural changes, reveal the existence of a long-term relationship between the variables. In addition, FMOLS estimates emphasize that economic growth and air transport can lead to increased pollution in the long run, while rail transport reduces it. Moreover, the statistically significant trigonometric terms indicate the existence of a smooth structural change among the variables. Robustness checks are performed through a Machine Learning (ML) analysis, which roughly confirms the previous results.

To our knowledge, existing research in Turkey focuses mainly on road transport, while the impact of rail and air transport on pollution has not yet been investigated. As such, this study will be a significant addition to the academic literature.

The purpose of this paper is to show the effect of Turkey's geopolitical risk on the number of international tourist arrivals to the country. When Turkish economy in 2019 is analyzed, it is seen that the share of tourism in national income is 11%. For this reason, national economy is significantly affected by changing of the number of international tourist arrivals. Security problems are an important variable affecting tourist arrivals.

The paper focused on secondary data for the period 2000–2019 for macroeconomic variables. Accordingly, the number of international tourist arrivals was added as a dependent variable, geopolitical risk as an independent variable, gross domestic product (GDP) and economic freedom index as control variables and inflations as an external variable to the model. The residual augmented least squares–the autoregressive distributive lag (RALS-ADL) cointegration test and the dynamic ordinary least squares (DOLS) coefficient estimator were used. It allows for more robust results to be obtained when the residues do not have a normal distribution.

The RALS-ADL cointegration test result shows that there is a cointegration relationship between variables at a 1% significance level. Moreover, the DOLS coefficient estimator results indicate that an increase in economic freedom and GDP increase the number of international tourists, whereas an increase in the Geopolitical Risk Index and inflation decreases the number of international tourism arrival. It can be said that tourists consider the security and economic stability of the host country when making tourism decisions.

Turkey is one of the most risky developing countries, as well as one of the most popular travel destinations. When the literature is examined, it has been found that studies for Turkey usually determine the relationship between the variables for a short period of time. However, to ensure sustainable growth and environment of confidence, the long-run relationship between variables should be determined so that policymakers can make more impactful decisions. Therefore, the aim of this study is to make a literature contribution, taking into account the long-term effects. In addition, unlike other studies, this study fills the gap in literature using the RALS-ADL cointegration test, which produces robust estimators.

This research aims to inspect the existence of the “environmental Kuznets curve” (EKC) in the presence of foreign direct investment (FDI), financial development (FD) and urbanization throughout 1972–2018 for Pakistan.

For time series analysis, Phillips and Perron (PP) and Augmented Dickey–Fuller (ADF) unit root tests are used to confirm the level of integration. For robustness, Kim and Perron (2009)’s structural break unit root test is employed, which identifies the order of integration in the presence of structural break years. Further, combined cointegration analysis is performed to confirm the existence of a long-run association between underlying variables. Furthermore, autoregressive distributed lag (ARDL) analysis is employed for the robustness of the cointegration approach.

The cointegration analysis confirms the existence of a long-run association among variables. The authors find a positive and significant impact of urbanization, FD and foreign development on environmental degradation in the long run. Similarly, only FDI increases environmental degradation in the short run. In addition, the authors find an inverted U-shape relationship between economic growth and environmental quality which, further, confirms the presence of EKC in Pakistan.

This research contributes to applied economics in many ways: the combined effect of urbanization, FD, FDI and economic growth on carbon dioxide (CO₂) emission is checked simultaneously. To avoid ambiguity, this study constructs the FD index through the principal component analysis (PCA). Moreover, the role of structural breaks has been considered through the analysis. Novel Bayer-Hanck combined cointegration analysis is employed to detect the existence of long-run relationships among underlying variables.

Introduction: India has the 15th-largest domestic natural gas consumption (NGC), critical to sustainable economic growth. Promoting natural gas will have a crucial impact on production in all industries.

Purpose: This research gives an overview of NGC and gross domestic product (GDP) in India from 1990 to 2021 and investigates the association and nature of causality between NGC and GDP in India.

Methodology: For the years 1990 through 2021, we used annual statistics from the NGC and the GDP of India. Both research variables data have been taken from the World Bank Indicator.

Findings: There is no causality and correlation between natural gas and GDP in India.

Practical Implications: Based on the research, the Government of India can create different policies for substituting natural gas for other energy sources to have a healthier impact on a sustainable environment in the short and long term. In the future, researchers can work on environmental degradation and GDP.

This study examines the contribution of urban development to Vietnam's economic progress over the period 1986–2020. The study uses an augmented Solow framework, where urbanization is included as a shift variable in addition to capital per worker and accounts for structural breaks. It examines the cointegration and the long-run and short-run effects of urban development from four different cases of cointegration (constant, restricted constant, restricted trend and trend options), with each case accounting for the effect of a significant structural break. Also, the study provides causality nexus to check the presence of urbanization-led growth hypothesis. From the results, we note a long-run positive effect of urbanization on economic growth, but no short-run effects. In all cases, a 1% increase in population results in 0.8–0.9 increase of output, hence supporting a positive and momentous effect of urbanization over the long-term. We note a significant positive effect of a single structural break period (1991), which is a period of major shift towards economic progress. We note bidirectional causality between capital and output and a unidirectional causality from output and capital to urbanization. The former suggests a mutually reinforcing effect of capital accumulation and economic growth, and the latter indicates that urban development necessitates economic growth and capital accumulation. The findings derived from this study provide further impetus for urban development and urban policies and consider urbanization as a critical source of economic growth for Vietnam.

This study aims to examine the relationship between the environment and tourism flows, as well as the economic variables of the 32 states of Mexico for the period 1999–2019 based on data availability. The related literature studying tourism and environmental impacts is scarce at a national level, with most of them being local case studies. Some international studies find that if the relationship exists, it is weak or nonexistent, using CO₂ as a proxy in most cases.

The present study uses panel data and cointegration panel methodologies, while also using geographic information systems to observe the distribution of variables at a state level between tourism and environmental variables.

The findings of the study are as follows: state gross domestic product, the inertia of environmental variables (i.e. volume of water treatment and solid waste), occupied rooms (proxy variable for tourism activity) and average temperature have an impact on the contemporary evolution of environmental variables; national and international tourist variables have no impact on the environment; the panels are integrated in such a way that there is a long-term equilibrium between states and some environmental care variables; and no conclusive evidence is found regarding the impact of tourism activity on the considered environmental variables.

The main limitations and areas of opportunity of the work refer to the amount of data available over time and the precision of the measurement of the variables. The availability, temporality and frequency of the data are also limitations of the research. An example of this is the nonexistence of CO₂ emissions at the state level. Additionally, studying other countries and regions for which there are limitations of data and applied studies is also a challenge.

The results are important for economies (in growth) and societies whose economic growth depends on tourism flows and have done little to reverse the damage that tourism has on the environment.

The models can contribute to study the relation between tourism and environmental variables and could be extended to regions, states and provinces for decision-making on actions to be taken for the present and future.

The originality of the research is innovative for the region: Mexico, Central and Latin America. There are no works that have studied these problems with this methodology and these variables. In terms of originality, the classic models of panel data and cointegration of panel data are useful and easily replicable for others to use for different countries. The results are relevant because there is apparently no relationship between tourism and some environmental variables in the short run, but there exists a weak and strong long-run relation between some of them.

本研究采用面板数据和协整面板模型方法, 同时利用地理信息系统(gis)观察州一级层面旅游和环境方面的变量分布。

本研究根据数据可用性, 研究了墨西哥32个州1999–2019年期间环境与旅游流量及经济变量之间的关系。在国家层面上研究旅游与环境影响的相关文献很少, 而且大多是地方的个案研究。一些国际研究发现, 即使有这种关系, 大多数案例中使用二氧化碳作为替代变量, 这种关系也是很弱或不存在。

i)国家国内生产总值, 环境变量的惯性(即水处理量和固体废物量), 占用的房间(旅游活动的代理变量)和平均温度对环境变量的现有演化有影响。ii)国内和国际旅游变量对环境没有影响。iii)面板数据以这样一种方式集成, 即国家和一些环境变量之间存在一种长期平衡。iv)关于旅游活动对所考虑的环境变量的影响没有确凿的证据。

这项工作的主要局限和机会领域是指随着时间的推移可获得的数据量和变量测量的精度。数据的可用性、时效性和频率也是本研究的局限性。这方面的一个例子是在州一级不存在二氧化碳排放。此外, 由于数据和应用研究的局限, 研究其他国家和地区也是一个挑战。

研究结果对经济增长依赖旅游业流量的经济体和社会具有重要意义, 这些经济体和社会对扭转旅游业对环境的破坏方面做得还不够。

这些模型有助于研究旅游业与环境变量之间的关系, 并可推广到地区、州和省, 以制定当前和未来的行动决策。

这项研究的原创性对该地区(墨西哥、中美洲和拉丁美洲)来说是具有创新性的。没有人用这种方法和这些变量研究过这些问题。就原创性而言, 面板数据和面板数据协整的经典模型是有用的且易于复制, 可供其他国家使用。 研究结果具有一定的相关性, 因为旅游业与部分环境变量在短期内不存在明显的相关性, 但在它们中的一些变量在长期内存在着或强或弱的相关性。

Se examina la relación entre medio ambiente y flujos turísticos, así como variables económicas de los 32 estados de México para el período 1999-2019 basado en la disponibilidad de datos. La literatura relacionada que estudia el turismo y los impactos ambientales es escasa a nivel nacional, siendo la mayoría de ellos estudios de casos locales. Estudios internacionales encuentran que, si la relación existe, es débil o inexistente, utilizando el CO2 como un indicador en la mayoría de los casos.

Se utilizaron metodologías de datos de panel y cointegración de panel, además sistemas de información geográfica para observar la distribución de variables a nivel estatal.

i) El Producto Interno Bruto Estatal, la inercia de las variables ambientales (es decir, volumen de tratamiento de agua y residuos sólidos), habitaciones ocupadas (proxy de la actividad turística) y temperatura promedio tienen un impacto en la evolución contemporánea de las variables ambientales, ii) las variables turísticas nacionales e internacionales no tienen un impacto en el medio ambiente, iii) los paneles están integrados de tal manera que existe un equilibrio a largo plazo entre turismo, crecimiento económico y algunas variables ambientales, y iv) no se encuentra evidencia concluyente con respecto al impacto de la actividad turística en las variables ambientales consideradas.

Las principales limitaciones y áreas de oportunidad del trabajo se refieren a la cantidad de datos disponibles en el tiempo y a la precisión de la medición de las variables. La disponibilidad, temporalidad y frecuencia de los datos también son limitaciones de la investigación. Un ejemplo de ello es la inexistencia de emisiones de CO2 a nivel estatal. Además, el estudio de otros países y regiones para los que existen limitaciones de datos y estudios aplicados también es un reto.

Los resultados son importantes para las economías (en crecimiento) y las sociedades cuyo crecimiento económico depende de los flujos turísticos y que han hecho poco por invertir los daños que el turismo produce en el medio ambiente.

Los modelos pueden contribuir a estudiar la relación entre el turismo y las variables medioambientales y podrían extenderse a regiones, estados y provincias para la toma de decisiones sobre las acciones a emprender para el presente y el futuro.

El artículo proporciona un análisis innovador y exploratorio hacia una perspectiva futura que agrega valor al turismo y la planificación para la sostenibilidad. La relación entre turismo y medio ambiente se ha estudiado durante varios años. La UNTWO ha abordado las consecuencias del turismo en el medio ambiente, particularmente, más basura, mayor consumo de agua, emisiones de CO2 y otros aspectos. Pocos trabajos estudian la relación entre estas variables.

La originalidad de la investigación es innovadora para la región: México, América Central y América Latina. No existen trabajos que hayan estudiado estos problemas con esta metodología y estas variables.

En términos de originalidad, los modelos clásicos de datos de panel y cointegración de datos de panel son útiles y fácilmente replicables para que otros los utilicen en diferentes países.

Los resultados son relevantes porque aparentemente no hay una relación entre el turismo y algunas variables ambientales a corto plazo, existe una relación débil y fuerte a largo plazo entre algunas de ellas.

Wheat, which is one of the major staple food grain crops in India, continues to depict occasional fluctuation in the prices though Union government has adopted administered price policy for wheat by intervening in its procurement at assured prices and distribution. Such fluctuations in prices are usually attributed to inefficient functioning of the agricultural markets. Since spatially separated markets also play an important role to determine efficiency of the agricultural markets, the study has used market integration as one of the tools to analyze the price transmission across the spatially separated markets to identify causes of price fluctuations and suggest ways to stabilize wheat prices.

The study utilizes monthly wholesale prices for January, 2006 to May, 2016 for dara wheat. First, the study employs augmented Dickey and Fuller (ADF), Phillips and Perron (PP) and Kwiatkowski, Phillips, Schmidt and Shin (KPSS) tests to check stationarity in wheat prices. Second, Johansen's cointegration test is applied to assess the integration of wholesale prices between selected pairs of wheat markets to determine long-run relationship among them. Third, Granger casualty test is used to find the direction of causality between the wheat market pairs. Finally, threshold vector error correction model (TVECM) and likelihood ratio (LR) tests are employed to examine long-run adjustment of prices towards the equilibrium in selected wheat markets.

Since wheat wholesale prices for the selected markets are found to be integrated of the order one, that is [I(1)], Johansen's test of cointegration is employed and its findings reveal that the selected wheat market pairs exhibit cointegration and show a long-run price association among themselves. There exists a bi-directional causality among the wheat market pairs. Since LR test is in favor of threshold model (except for Etawah–Delhi pair), one and two threshold models were also performed accordingly. Findings show that wholesale prices of wheat in Delhi markets remain higher than the prices of all other regional markets as regional markets are found to adjust their prices towards Delhi market. Distance of the wheat markets from each other is directly associated with threshold parameters, which are analogous to the transaction costs. Geographically dispersed wheat markets incorporate high transaction and vice versa.

The study argues that there is need to improve rural infrastructure and connectivity of the agricultural markets and remove market asymmetries through unified market regulating mechanisms across the states. This will enable price adjustment process from primary wholesale markets (in production regions) to the secondary wholesale markets (in scarcity regions) quickly.

The contribution of the study in the existing literature lies in the fact that there are no empirical evidences in the context of India that use price transmission as a tool of market integration among spatially separated wheat markets using TVCEM as this model examines role of transaction costs in efficient functioning of the agricultural markets.

Sustainable development is considered a key factor in addressing environmental issues, global inequalities and poverty. This study aims to investigate the impact of stock market indicators on sustainable development across 16 emerging markets from 2003 to 2020.

The research uses causality and cointegration analyses to explore the relationships between stock market indicators and sustainable development.

Univariate causality analysis reveals a bidirectional causal relationship between the stock market turnover ratio and sustainable development, as well as a unidirectional relationship from sustainable development to stock market capitalization and total value traded. Panel-level cointegration analysis suggests that only stock market capitalization has a weak positive influence on sustainable development. However, the impact of stock market indicators on sustainable development varies significantly among countries, as revealed by country-level cointegration analysis.

While this study provides valuable insights, it is not without limitations. The findings are limited to the selected emerging markets and the specified timeframe (2003–2020). The complexity of factors influencing sustainable development suggests the need for further exploration in diverse contexts.

Understanding the nuanced relationships between stock market indicators and sustainable development can offer valuable insights for policymakers, investors and stakeholders.

This research contributes to the existing literature by examining the multifaceted connections between stock market indicators and sustainable development, focusing on country-specific causality relationships. The study highlights the reciprocal nature of this relationship, where financial market development can both influence and be influenced by a country's progress toward sustainability. This approach provides a more nuanced understanding of the complex interaction between stock market maturity and sustainability goals.

This study aims to examine whether energy poverty impacts gender inequality and CO₂ emissions in African countries with the biggest economies by gross domestic product (GDP) per capita from 1996 to 2020. Additionally, this study examines the existence of the gender Kuznets curve (GKC) and the environmental Kuznets curve (EKC) theories. Furthermore, it evaluates the connection between economic development and carbon emissions, on the one hand, and economic development and gender inequality on the other.

This study uses the augmented Dickey–Fuller and Phillip–Perron unit root tests to determine the degree of integration between the variables. It also uses the Pedroni and Fisher–Combine Johansen cointegration tests to assess a long-run relationship between the variables. The authors adopted the pooled mean group (PMG)-autoregressive distributed lag model and used the E-Views 12 software to run the analysis.

The empirical analysis approves the long-run correlation among the variables used in this study. Increased energy poverty and GDP increase CO₂ emissions, whereas income square hurts CO₂ emissions. These results are consistent with the EKC hypothesis, which proposes a non-linear relationship between CO₂ emissions and economic growth in the studied areas (similar to an inverted U shape). Long-term foreign direct investment (FDI) has a negative correlation with CO₂ emissions. On the contrary, energy poverty, GDPsq and FDI find a positive relationship with gender inequality, whereas GDP finds a negative association with gender inequality. The negative relationship between GDPsq and gender inequality establishes a “U”-shaped connection between income and gender inequality. Thus, it supports the hypothesis of the GKC. Therefore, this study proposes that decreasing energy poverty is vital for promoting a clean environment and mitigating gender inequality.

This study supports the hypothesis of the GKC. Therefore, this study proposes that decreasing energy poverty is vital for promoting a clean environment and mitigating gender inequality.

Climate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.

This study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.

The study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.

The present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.