Though the literature on inclusive growth is rich, further well-founded studies are required on the issue of sustainable inclusive growth. This paper seeks to summarize the role of environmental degradation in inclusive growth based on carbon dioxide emissions, and its interaction with factors such as social inclusion, digital inclusion, and institutions. One of the findings derived from the generalized method of moments (GMM) model is that sustainable inclusive growth can be achieved in all three income groups from the global data while focusing on institutional quality, digital inclusion, and social inclusion. Simultaneously, the harmful effects of carbon dioxide emissions can be circumscribed. The major recommendations of this study are that efforts to achieve sustainable inclusive growth should combine mutually reinforcing policies, namely: (i) promoting environmentally focused sustainable inclusive growth with socio-digital inclusivity; (ii) ensuring a strong institutional playing field for achieving inclusive growth; and (iii) strengthening macroeconomic policies, which means controlling inflation, and enhancing trade openness and literacy levels.

For decades, renewable energy consumption has been presented as the ultimate solution to reduce pollutant gas emissions. However, their production, marketing, and, above all, consumption are not entirely climate neutral, so society needs new practices, such as circular energy, to achieve a more efficient energy transition (Khan et al., 2021a; Khan et al., 2021d). This study aims at exploring the effects of energy intensity, renewable energy consumption and forestation on CO2 emissions for developed and developing countries. Owing to cross-country income disparity, the data were categorized into four distinct groups: lower, lower-middle, upper-middle, and high-income over the period of 1980–2018. The Pedroni and Fisher-Johnson estimators of pooled data are employed for determining the plausible presence of a long-run co-integrating relationship between model variables. For all four country groups, Pedroni cointegration testing results reveal a significant long-run association between CO2 emission and its determinants. However, the Fisher-Johansen estimator yield mixed results, with low- and lower-middle income country groups outperforming the other two groups. However, the magnitude of this long-run association is less substantial, particularly in case of renewable energy consumption and forestation. For all country groups, energy intensity turns out to be the most influential long-run determinant of CO2 emissions, holding a positive coefficient value ranging from 0.30 to 1.31. Renewable energy consumption is the second most important long-run determinant of CO2 emissions. In case of forestation, the series imparts (statistically) significant long-run effects only for lower-middle-income countries. The overall results back the idea of curtailing the existing levels of energy intensity and encouraging the exploration and the use of renewable energy sources as a policy tool against controlling the prevalent situation of carbon-based emissions in our subject group of countries.

In conventional Econometrics, the unit root and cointegration analysis are the only ways to circumvent the spurious regression which may arise from missing variable (lag values) rather than the nonstationarity process in time series data. We propose the Ghouse equation solution of autoregressive distributed lag mechanism which does not require additional work in unit root testing and bound testing. This advantage makes the proposed methodology more efficient compared to the existing cointegration procedures. The earlier tests weaken their position in comparison to it, as they had numerous linked testing procedures which further increase the size of the test and/or reduce the test power. The simplification of the Ghouse equation does not attain any such type of error, which makes it a more powerful test as compared to widely cited exiting testing methods in econometrics and statistics literature.