Purpose: 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.Design/methodology/approach: 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.Findings: 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.Originality/value: 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.

This study aims to examine the relationships between aggregated and disaggregated energy use in the industrial sector, carbon emissions and industrial output in China. The study utilizes annual frequency data for the period of 1984-2015. The unit root properties of data are tested using augmented Dickey–Fuller and Phillips and Perron unit root tests. Furthermore, the Zivot–Andrew structural breaks unit root test is used to detect the structural breaks steaming into series. The autoregressive distributed lag bound test and newly developed Bayer–Hanck combined cointegration are used to check the existence of a cointegration relationship between underlying variables. Last, the direction of causality is determined applying vector error correction model (VECM) Granger causality. The results confirm the existence of a long-run relationship in the presence of structural breaks. The authors conclude that aggregated and disaggregated energy consumption in the industrial sector increases CO₂ emission in both long and short run. The VECM Granger causality analysis indicates the bidirectional relationships between CO₂ emission, industrial growth and aggregated and disaggregated (coal, oil and natural gas) energy consumption. Based on the empirical results mentioned above, the study proposes the recommendation that China should focus on the use of natural gas in the industrial sector instead of coal and oil consumption. The most potent reasons for such a transformation are twofold: natural gas is much more environment-friendly, thus being a much lesser polluting source of energy, and, most significantly, such a change would have no adverse impact upon the output level. This study contributes to the existing literature on estimating CO₂ emission by using aggregated and disaggregated energy consumption in case of China. Notwithstanding, it also adds to the existing applied literature by using newly developed combined cointegration to confirm and substantiate the cointegration relationship between the underlying variables. Moreover, this study incorporates the role of structural breaks while investigating CO₂ emission function, which helps in providing more valuable policy suggestions.

This article aims to examine the relationship among financial development, trade balance, exchange rate and inflation by using time series data from 1972 to 2014 for Pakistan. We have tested the unit root properties of variables by using augmented Dickey–Fuller (ADF), Phillips–Perron and breakpoint unit root tests. The autoregressive distributive lag (ARDL) approach is applied to examine the cointegration between variables due to mixed orders of integration between series I(0)/ I(1). The ARDL findings suggested that long-run relationship exists among financial development, trade balance, exchange rate and inflation. Error correction model (ECM) is applied to analyze short-run relationship. The lagged value of the error correction term (ECM_t−1) is negative and significant at 1 per cent level of significance. The value of ECM_t−1 is −0.91 which states that digression from the short run towards long run is corrected by almost 91 per cent by every year. Financial development, exchange rate and inflation have significant impact on trade balance in the long run. But in the short run, only exchange rate and inflation have statistically significant impact on trade balance. Diagnostic statistics have confirmed the characteristics of model in the short run as well as in the long run. The causal relationship between variables are examined by vector error correction model (VECM) Granger causality and robustness of causal analysis is tested by variance decomposition approach (VDA). The results of VECM have predicted that unidirectional causality from financial development to trade balance exist in the long run. The results of VDA explained that 19 per cent of trade balance is explained by shocks stimulating financial development. Government should enhance financial development by managing lending interest rates to improve trade balance.