Investigating the nexus between carbonization and industrialization under Kaya's identity: findings from novel multivariate quantile on quantile regression approach.

Developing nations aim to industrialize and grow sustainably often ignoring the environmental consequences. However, few empirical studies have looked at the influence of industrialization-driven economic transition on carbon footprint in developing nations using a non-parametric approach. In this milieu, on the ground of Kaya's identity and the novel multivariate quantile-on-quantile regression (QQR) (extension of Sim and Zhou's (2015) bivariate QQR model), the present research studies the impact of industrial value-added (IGVA), population, energy intensity, and carbon intensity on CO2 emissions in India. This study is one of the first in the literature to evaluate the industrialization-carbonization nexus in the context of Kaya's identity for the Indian economy utilizing an innovative multivariate QQR approach, which makes a methodological and empirical addition to the literature. The outcomes of the multivariate QQR technique demonstrate that economic and environmental development requires continual long-run strategies. The empirical findings revealed that there is no authentication that India's carbon emissions increased due to its industrialization, which exhibited that IGVA has a negative and significant connection with CO2 emissions. In some quantiles, population size positively impacts CO2 emissions. On the other hand, carbonization in the Indian economy is asymmetrically affected by GDP per capita and energy and carbon intensity. The quantile Granger causality study further supported the aforementioned results. The current analysis also offers policy suggestions for environmentally friendly sustainable economic growth and to achieve the sustainable development goals (SDGs) of India.

The asymmetric influence of environmental-related technological innovation on climate change mitigation: what role do FDI and renewable energy play?

This study aims to provide a new perspective on environmental studies by examining the influence of environmental-related technological innovation, foreign direct investment, renewable energy consumption, and economic growth on the climate change index (CCI), a novel proxy for environmental quality indicators. From the econometric standpoint, this study employs the "non-linear autoregressive distributed lag" model and spectral causality over the period of 1999-2018 for India. The results show that positive shocks to economic growth have detrimental long- and short-term effects on environmental quality, whereas negative shocks have no effect. While a positive shock has an insignificant impact, a negative shock to environmental technology innovation has a long-term negative impact on environmental quality. This study provides evidence for the pollution halo hypothesis in India. Besides, a long-term negative shock to the usage of renewable energy fosters environmental degradation. Furthermore, in short-, medium-, and long-term frequency, spectral causality demonstrates unidirectional causation from CCI to environmental-related technological innovation. Bidirectional causation is demonstrated between the CCI and renewable energy consumption in the short and medium term. In addition, environmental-related technological innovation and foreign direct investment are demonstrating a bidirectional relationship in the short term. This study has advocated the Sustainable Development Goals (SDGs)-centric policy paradigm, which can assist the Indian government in achieving SDG-13 (mitigating climate change) and SDG-7 (clean energy consumption).

Re-examining the environmental Kuznets curve (EKC) for India via the multiple threshold NARDL procedure.

Irrespective of the vast array of empirical evaluations pertaining to the environmental Kuznets curve (EKC) hypothesis, both for India and other countries, previous studies, amid divergent submissions, inadvertently failed to highlight the relevant threshold that ensures significant reductions in environmental decay. Additionally, the implications of environmental-control technology on environmental quality are also lacking mostly in the context of Indian economy. Thus, this study enlists environmental-control technology and other relevant factors over the period 1980-2018 and employs the novel multiple threshold nonlinear ARDL technique, a model rarely applied in previous studies for updated empirical narratives. Accordingly, the empirical evidence rectifies that the variables converged to long-run equilibrium. Furthermore, from the tercile partial deviations, it is established that at the middle threshold (GDP2W2), pollution shrinks more significantly amid rising income, thereby validating the EKC hypothesis for India. Likewise, environmental-control technologies provided only a short-term insignificant carbon neutrality pathway, whereas they provided long-term insignificant emission increasing effects. This implies that the depth of such technology in India is inadequate to invoke cleaner environments at all times. Likewise, energy consumption and urbanization processes are significant environmental polluters, while trade openness provides insignificant long- and short-term carbon emission effects. Against this background, economic growth within the middle threshold promises a more sustainable environment amid rising national income at all times. Moreover, given its short-term outcomes, strengthening the depth of environmental-control technology is imperative to ensure a long-lasting clean environment in India.