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The 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.

To 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.

The 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.

This 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.

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.

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.

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.

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.

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.

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.

This study aims to examine food waste behaviours among university students in Indonesia by adapting the theory of planned behaviour (TPB) alongside the theory of domains of knowledge.

A total of 518 students at five Indonesian universities participated in an online survey, answering questions about their food waste behaviours based on the TPB and the domains of knowledge frameworks. Among them, 250 students had taken a course that covered the food waste topic, while 268 students had not taken the course. The data were later analysed by using partial least squares-structural equation modelling and measurement invariance across the composite models.

The findings reveal that environmental awareness, attitude towards food waste behaviours, subjective norms and different types of knowledge which are procedural knowledge, declarative knowledge, personal and social knowledge significantly influence food waste behaviour. Students who attended a course related to the food waste topic were more likely to align their intentions to reduce food waste with their reduction behaviour.

While previous research has highlighted the importance of the TPB in understanding food waste intentions and behaviour, studies also show that students’ knowledge about food waste plays a crucial role in shaping these intentions. This study builds on these insights by combining the TPB with the domains of knowledge theory, specifically looking at how participating in food waste-related courses influences food waste behaviours among Indonesian students.

This study examines how tourism contributes to employment.

Using various econometric techniques for panel data, the study estimates the contribution of tourism to employment in a sample of 148 economies from 2002 to 2017. The analysis is also carried out for three sub-samples according to income levels.

This study has three significant contributions: Firstly, it shows that investment and consumption in the tourism sector have positive benefits for employment. Furthermore, the improvement of institutional quality boosts these positive gains. Secondly, there is a U-inverted relationship between the income level and total contributions of tourism to employment. The development of the tourism industry would therefore follow the pattern suggested by the Kuznets curve hypothesis. Thirdly, the positive effects of tourism investment and consumption in tourism are evidenced in all three sub-samples. In contrast, the effects of institutions seem to be weaker in higher-income economies (implying that there is a larger space for low-income economies to use institutional reform to boost the development and contribution of tourism in their economies). Finally, institutional quality appears to enhance the contribution of tourism to employment.

The study highlights the importance of the tourism industry in enhancing employment.