Purpose The purpose of this paper is to analyze the effect of economic growth, international trade and energy consumption on the global carbon dioxide (CO2) emissions, in the case of top CO2 emitters, namely, USA, Japan, Canada, Iran, Saudi Arabia, UK, Australia, Italy, France and Spain using the annual data from 1971 to 2013. Design/methodology/approach For this purpose, the time series, data technique is applied. Unit root test with structural break and the bounds testing approach for cointegration in the presence of structural break is tested. Finally, a vector error correction model for the Granger causality test is applied to detect the direction of causality. The authors have used the techniques that will help in examining the structural break in the time series data. Findings The results reveal that their exists a long-run relationship between CO2 emissions and its determinants in the USA, Canada, Iran, Saudi Arabia, the UK, Australia, Italy, France and Spain, energy consumption is the main determinant of carbon dioxide (CO2) emissions in the long run and for direction of causality, the authors found bidirectional causality in the long run between energy consumption and CO2 emissions in the USA, Canada, Iran, Saudi Arabia and the UK, and Granger causality running in opposite direction in the case of Australia from CO2 emissions to energy consumption was analyzed. In terms of growth-trade-pollution nexus (USA, Canada, Iran and France) hold one-way causality running from economic growth and trade openness to CO2 emissions (IV) the environmental Kuznets curve hypothesis is validated only for the USA. Robust policy implications can be derived from this study. First, without harming the economy, these countries can reduce the use of energy consumption for lower pollution. Second, the amount of trade should be decreased to lower the emissions because the authors find that an increase in trade does Granger cause to CO2 emissions in the long run. Originality/value There has been no study that investigated the relationship between CO2 emissions, real income, consumption of energy and international trade in the environmental Kuznets relation for the top CO2 emitter’s countries over the period of 1971–2013. The authors did a comparative study of the empirical finding among these nations.

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Purpose This paper aims to measure the state-level energy efficiency in Indian paper industry and simultaneously explain inter-state variation in efficiency by inefficiency effect model. Three variables, labor productivity, capital intensity and structure of paper industry, are included in inefficiency effect model to assess the likely impact on energy efficiency. Design/methodology/approach Sub-vector input distance function is derived through neo-classical production function which provides measures to estimate energy efficiency. Assuming a translog production function specification, energy efficiency is estimated by using Battese and Coelli (1995) stochastic frontier analysis (SFA). The authors also estimated four other SFA models, and energy efficiency from all the models is compared for robustness checking. Findings The results show the existence of a vast potential to improve energy efficiency. Inefficiency effect model reported a positive impact of structure of the industry and capital intensity on energy efficiency performance, while labor productivity does not have any significant impact on energy efficiency. There exists considerable energy efficiency variation among states. Uttarakhand, Punjab and Orissa are the best performing states while Rajasthan, Jharkhand and Goa have worst energy efficiency performance based on average efficiency. The ranks assigned to states according to inefficiency effects model are found contrary to the simple measure of energy efficiency, i.e. energy intensity. Thus, energy intensity may not always be a good proxy for underlying energy efficiency and need to be compared with a comprehensive possible measure. Originality/value To the best of the authors’ knowledge, this is the first study which measures energy efficiency of Indian paper industry through stochastic frontier model using region-level data. Instead of relying on traditional energy efficiency indicators (energy-output ratio), total-factor energy efficiency approach is used to conduct the empirical exercise. Deviations from the frontier because of factors beyond the scope of producers are also incorporated into analysis to portray the magnitude of random factors in influencing the efficiency performance.