Renewable energy, forest cover, export diversification, and ecological footprint: a machine learning application in moderating eco-innovations on agriculture in the BRICS-T economies.

The United Nations Climate Change Conference (COP26) recommended that the member nations enhance their technological progression and structural transformation to mitigate the problems of climate change. The BRICS-T countries consisting of Brazil, Russia, India, China, South Africa, and Turkey agreed to implement COP26's policy suggestions. These countries accounted for 40% of global greenhouse gas emissions in 2017, thus posing severe threats to the global environment. The current study explores the role of renewable energy, forest depletion, eco-innovations, and export diversification in impacting the ecological footprint for those BRICS-T countries. We further examine the moderating effect of eco-innovations on agriculture on the BRICS-T nations. The study contributes to the existing literature by providing newer empirical insights on how eco-innovations and export diversification, along with renewable energy, forest cover, and agriculture, affecting the ecological footprint in the BRICS-T nations. It utilizes novel empirical methods like parametric and non-parametric techniques to derive the short-run and long-run empirical results. The empirical findings based on the augmented mean group and the kernel regularized least square methods document that economic growth, agriculture value added, and forest depletion increase the ecological footprint. In contrast, renewable energy and eco-innovations decrease the level of ecological footprint. In the long run, a 1% rise in GDP leads to a rise in the ecological footprint by 0.64% using the augmented mean group (AMG) estimation. The mean marginal effects are - 0.27%, 0.29%, and 0.17% for renewable energy; agriculture and forest cover, respectively, using the kernel-based regularized least square methods. The study suggests that policies designed for controlling the ecological footprints focus on the use of energy efficient technologies, particularly in the agricultural sector.

The impact of economic complexity on energy demand in OECD countries.

This paper investigates the role of economic complexity on energy demand using the panel dataset of 25 Organization for Economic Co-operation and Development (OECD) countries from 1978 to 2016. Both real per capita income level and economy-wide real energy price index are critical determinants in energy demand modeling. The battery of the cross-sectional dependency test proposed by Pesaran (2004 and 2007) is used, signaling the presence of cross-sectional dependency in the dataset. Thus, the Westerlund (2007) cointegration test is also used, revealing the long-run relationship between the series. Moreover, the results from using the Augmented Mean Group (AMG) estimations illustrate that real per capita income level positively affects energy demand while real energy price and economic complexity negatively influence on it. From a policy perspective, we suggest increasing technological innovation (i.e., higher economic complexity) will reduce the energy demand. The reduction of massive energy usage may be beneficial for the natural environment's health in the OECD countries.

What matters for environmental quality in the Next Eleven Countries: economic growth or income inequality?

This study uses panel data for the period 1971-2013 to explore the implications of growth, wealth disparities, and per capita energy consumption on carbon emissions in a sample of Next Eleven (N-11) countries. It uses the first-generation (Pedroni and Kao) and second-generation (Westerlund) cointegration techniques to highlight a long-run interplay between the selected variables in carbon emission functions for all the N-11 countries. It also analyzes the long-run interactions among the series. Contrastingly, it also shows that economic growth, income inequalities, and per capita energy consumption accelerate CO2 emissions. Besides examining the effects of wealth disparity square, the study also uses the environmental Kuznets curve hypothesis in the context of the N-11 countries and discusses the policy implications of its findings.

Is skewed income distribution good for environmental quality? A comparative analysis among selected BRICS countries.

A large number of studies have examined the linkage between income inequality and environmental quality at the individual country levels. This study attempts to examine the linkage between the two factors for the individual BRICS economies from a comparative perspective, which is scarce in the literature. It examines the selected countries (Brazil, India, China and South Africa) by endogenising the patterns of primary energy consumption (coal use and petroleum use), total primary energy consumption, economic growth, and urbanisation as key determining factors in CO2 emission function. The long-run results based on ARDL bounds testing revealed that income inequality leads to increase in CO2 emissions for Brazil, India and China, while the same factor leads to reduction in CO2 emissions for South Africa. However, it observes that while coal use increases CO2 emissions for India, China and South Africa, it has no effect for Brazil. In contrast, the use of petroleum products contributes to CO2 emissions in Brazil, while the use of the same surprisingly results in reduction of carbon emissions in South Africa, India and China. The findings suggest that given the significance of income inequality in environmental pollution, the policy makers in these emerging economies have to take into consideration the role of income inequality, while designing the energy policy to achieve environmental sustainability.