Tourism has become the world’s third-largest export industry after fuels and chemicals, and ahead of food and automotive products. From last few years, there has been a great surge in international tourism, culminates to 7% share of World’s total exports in 2016. To this end, the study attempts to examine the relationship between inbound tourism, financial development and economic growth by using the panel data over the period 1995–2015 for five BRICS (Brazil, Russia, India, China and South Africa) countries. The results of panel ARDL cointegration test indicate that tourism, financial development and economic growth are cointegrated in the long run. Further, the Granger causality analysis demonstrates that the causality between inbound tourism and economic growth is bi-directional, thus validates the ‘feedback-hypothesis’ in BRICS countries. The study suggests that BRICS countries should promote favorable tourism policies to push up the economic growth and in turn economic growth will positively contribute to international tourism.

Purpose The genesis of Environmental Kuznets curve (EKC) of “grow now clean later” has led to a substantial deterioration of local as well as the global environment. India has not been spared of this malaise and accounts for the third-largest carbon dioxide emitter in the world. Thus, the present study revisits the curvilinear relationship between economic growth and environmental pollution in case of India over the period of 1971-2014. Design/methodology/approach Dickey–Fuller generalised least square (DF-GLS) test developed by Elliott et al. is used to ensure that none of the variables is I(2). The study applies the autoregressive distributed lag (ARDL) bounds estimation technique to test for the existence of cointegration among variables and estimate long-run and short-run parameters. The study also applies the Bai–Perron structural break test with unknown break date to determine the threshold point. The study further uses the vector error correction model (VECM) Granger causality test to check the direction of causality between variables. Findings The ARDL bounds estimation technique confirms the cointegration among variables. The long-run coefficients of energy consumption, economic growth and financial development are found to have an adverse impact on environmental quality. The results also validate the existence of conventional EKC hypothesis. Bai–Perron structural break test, along with t-test and scatter graph, shows that inverted U-shaped relationship between environmental pollution and economic growth holds true. The VECM-based causality results support “growth hypothesis” both in the long run and short run. Research limitations/implications This study refrained from considering a variety of variables, as the main intention of the study is to investigate whether any threshold or turnaround point exists for India. The future studies should consider a new set of variables (e.g. population, corruption index, social indicators, political scenario, energy research and development expenditures, foreign capital inflows, public investment towards alternate energy exploration, etc.) in the estimation of EKC hypothesis. Practical implications The results validate the existence of conventional EKC hypothesis. Thereby the study argues that instead of being a threat to environmental quality, economic growth is observed to generate a sustainable environment to live in. Further, bi-directional causality is found between carbon emissions and economic growth. Thus, any effort to mitigate CO2 or environment conservation policy will impede economic growth. Consequently, controlling primary energy consumption and supply and replacing it with renewable and clean energy could be desirable for climate change mitigation. Originality/value The data set has been refined so that the EKC estimation issues raised by Stern (2004) are addressed. In particular, statistical properties of the data set such as serial correlation, presence of a stochastic or deterministic trend, has been adequately taken care of to remove any spurious correlation. Finally, various control variables have been included to provide consideration to issues of model adequacy, such as the possibility of omitted variables bias. To the authors’ best knowledge, there is no India-specific study which has taken care of data-related issues, as suggested by Stern, in the estimation of a curvilinear relationship between environmental degradation and economic growth in India. Further, this is the first study which has used Bai–Perron structural break test with unknown break date to identify the threshold point while estimating EKC in India.

PurposeThe main objective of the present study is to figure out the effect of agricultural development on environmental pollution in the Indian context over the period 1970 to 2018. The study also tests the applicability of pollution haven hypothesis.Design/methodology/approachTo begin with, the authors test the stationarity of the variables by using the DF-GLS and KPSS tests. To examine the relationship between agricultural development and carbon emissions, the study applies nonlinear autoregressive distributed lag cointegration test developed by Shin et al. (2014). The study also applies Wald test to test the asymmetry between agriculture and environmental pollution.FindingsThe findings of this study indicate that agricultural development in India is good for carbon mitigation in the long run whereas energy consumption degrades the environment. The findings document the existence of an asymmetric association between agricultural development and environmental pollution. Furthermore, the results did not find any presence of pollution haven hypothesis for India.Originality/valueThis is the only empirical work that assesses the contribution of agricultural sector to carbon mitigation in the Indian context. The novelty of the study is further ensured by the very nature that it is the first study that examines the effect of agricultural sector on environment in an asymmetric configuration.

No abstract

The present study empirically examines the factors accounting for inflation in India in an open economy framework by utilizing the bounds testing approach to cointegration for the 2006: Q3-2019: Q4 period. The findings reveal the existence of a long-run relationship with the household survey-based inflation expectation, real output, narrow money aggregate and interest rate as important determinants of inflation. The study concludes that inflation is well explained by a combination of structural and monetary factors. Notably, the significance of inflation expectation as an important explanatory variable corroborates the utilization of inflation forecast by the RBI as an intermediate target in the flexible inflation targeting framework. In this backdrop, it is imperative for RBI to conduct a high frequency inflation expectations survey of households to account for frequent information updation on the part of certain groups of households.