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This paper investigates the nexus between CO2 emissions (CO2E), GDP, energy use (ENU), and population growth (PG) in India from 1980–2018 by comparing the “vector error correction” model (VECM) and “auto regressive distributed lag” (ARDL). We applied the unit root test, Johansen multi-variate cointegration, and performed a Variance decomposition analysis using the Cholesky approach. The VECM and ARDL-bound testing approaches to cointegration suggest a long-term equilibrium nexus between GDP, energy use, population growth and CO2E. The empirical outcomes show the existence of a long-term equilibrium nexus between the variables. The Granger causality results show that short-term bi-directional causality exists between GDP and ENU, while a uni-directional causality between CO2E and GDP, CO2E and ENU, CO2E and PG, and PG and ENU. Evidence from variance decomposition indicates that 58.4% of the future fluctuations in CO2E are due to changes in ENU, 2.8% of the future fluctuations are due to changes in GDP, and 0.43% of the future fluctuations are due to changes in PG. Finally, the ARDL test results indicate that a 1% increase in PG will lead to a 1.4% increase in CO2E. Our paper addresses some important policy implications.

Research on forecasting the seasonality and growth trend of natural gas (NG) production and consumption will help organize an analysis base for NG inspection and development, social issues, and allow industrials elements to operate effectively and reduce economic issues. In this situation, we handle a comparison structure on the application of different models in monthly NG production and consumption forecasting using the cross-correlation function and then analyze the association between exogenous variables. Moreover, the SARIMA-X model is tested for US monthly NG production and consumption prediction via the proposed method for the first time in the literature review in this study. The performance of that model has been compared with SARIMA (p, d, q) * (P, D, Q)s. The results from RMSE and MAPE indicate that the superiority of the best model. By applying this method, the US monthly NG production and consumption is forecast until 2025. The success of the proposed method allows the use of seasonality patterns. If this seasonal approach continues, the United States’ NG production (16%) and consumption (24%) are expected to increase by 2025. The results of this study provide effective information for decision-makers on NG production and consumption to be credible and to determine energy planning and future sustainable energy policies.

This paper applies a novel Bootstrap Autoregressive Distributed Lag (BARDL) approach to investigate the relationship between green innovation (GI), economic growth (GDP), drama and film (D&F) industry, and environmental sustainability in India for the 1995 to 2020 period. The data has been checked for its stationarity by applying the Zivot and Andrews (ZA) unit root test, and the cointegration test results suggest a long-run equilibrium relationship between the variables. The empirical finding of long-run estimates reveals that 1% augments of GI, GDP, and D&F industry increase CO2 emissions by −0.079, 0.566%, and 0.143%, respectively. Furthermore, the main results indicate that GDP and the D&F industry have statistically significant positive effects on CO2 emissions, and GI has statistically significant negative effects on CO2 emissions. The GI leads to lower environmental damage by reducing carbon emissions. Regarding causal relationships, bidirectional causality is found between D&F and CO2 emissions, GI and CO2 emissions. In addition, a unidirectional causality is also revealed from GDP to CO2 emissions. Based on the finding of this study, policy implications are suggested for India.

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