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The aim of this research is to examine the link between CO 2 emissions, hydroelectricity consumption, urbanisation and real GDP in China and India during the period of 1965–2013. The long-run cointegration is investigated by the autoregressive distributed lag (ARDL) bounds testing approach, which is augmented with structural breaks. We employ the ARDL cointegration test to establish long run relationship in the variables. Furthermore, we use the ARDL to show that real GDP and urbanisation have long-run positive impact on emission, while hydroelectricity consumption exerts long-run negative impact on emission in both countries. The results support the existence of environmental Kuznets curve (EKC) hypothesis in China and India. Besides, the paper assesses the causal link between the variables by using Granger causality procedures and the results show that there is long-run bidirectional relationship between the variables in both countries. [ABSTRACT FROM AUTHOR]

The aim of this paper is to test whether income inequality contributes to environmental degradation in China and India, where both countries are giving a great concern about the implications of their unsustainable energy consumption to the environment not only to themselves but also to the global environmental sustainability. By applying the bounds test approach to cointegration we found in both countries a long-run but not a statistically significant relationship between income inequality and CO 2 emissions both in the long-run and in the short-run. The robustness of our cointegration results is also ascertained by using three different long-run tests. Furthermore, applying the variance decomposition analysis to examine the dynamic causal relationship between the Gini coefficient of income distribution and CO 2 emissions, we found that income inequality was the least important factor in determining CO 2 emissions and that there was no causality running in any direction between CO 2 emissions and income inequality in both countries. Still income and energy consumption are the major determinants of CO 2 emissions in both countries. Nevertheless, even though the evidence is not statistically significant, in the case of China it indicates that reducing income inequality can have a positive impact on environmental quality, while in India redistributing income to improve environmental degradation may not be effective. [ABSTRACT FROM AUTHOR]

China and India accounted for two-thirds of the world׳s rise in energy use between 2000 and 2012. This paper aimed to calculate the P (population)– A (affluence)– T (technology) effects of energy use in China and India – the world׳s two most populous and largest developing countries. To this end, a combination of the IPAT method and the logarithmic mean divisia index technique, and annual time-series data on population, energy consumption, and gross domestic product during 1970–2012 are used. In China, a 12.53-fold growth of energy use emissions from 1970 to 2012 is driven by a combination of rapid growth in individual income and slow growth in population, with offset by technological advancement since 1980. The accelerating rise in energy use since 2000 is a result of accelerating growth rates in individual income and a reversal of earlier declining in energy intensity (technological advancement). Unlike China, the long-term rise in energy use exceeded the long-term rise in individual income in India. In addition, a strong trend of decline in energy intensity has not yet occurred in India. Thus, a 7.39-folds growth of Indian energy use for 1970–2012 was a result of relatively rapid increase in population and relatively slow increase in income, without effective offset by technological advancement. It suggests that market-oriented economic and energy reforms need to send the correct price signal to promote energy-efficient technologies thus improving energy efficiency, which is the key to a sustainable energy future in China and India. [ABSTRACT FROM AUTHOR]

Abstract: This paper addresses the impact of both economic growth and financial development on environmental degradation using a panel cointegration technique for the period between 1980 and 2007, except for Russia (1992–2007). In long-run equilibrium, CO2 emissions appear to be energy consumption elastic and FDI inelastic, and the results seem to support the Environmental Kuznets Curve (EKC) hypothesis. The causality results indicate that there exists strong bidirectional causality between emissions and FDI and unidirectional strong causality running from output to FDI. The evidence seems to support the pollution haven and both the halo and scale effects. Therefore, in attracting FDI, developing countries should strictly examine the qualifications for foreign investment or to promote environmental protection through the coordinated know-how and technological transfer with foreign companies to avoid environmental damage. Additionally, there exists strong output-emissions and output-energy consumption bidirectional causality, while there is unidirectional strong causality running from energy consumption to emissions. Overall, the method of managing both energy demand and FDI and increasing both investment in the energy supply and energy efficiency to reduce CO2 emissions and without compromising the country’s competitiveness can be adopted by energy-dependent BRIC countries. [ABSTRACT FROM AUTHOR]

Accurately forecasting energy demand can better predict future changes in energy demand. This study is aimed to develop more accurate clean energy prediction model from two perspectives of the modeling mechanisms and improvements on the model structure. For this purpose, a novel Riccati–Bernoulli differential equation is established through social practice theory, supply–demand relationship, and the preference of consumption in the energy economy. Considering the limited information in observation data, this differential equation is then transformed into a grey Riccati–Bernoulli model (GRBM(1,1)) according to the differential information principle. With the response function solved on the basis of polynomial equation theory and the power exponent optimized combining the modified flower pollination algorithm, the main process of GRBM(1,1) can be summarized. The four validation examples are provided for confirming the effectiveness and reliability of the new model by comparing with other existing models. Finally, the proposed model is employed to estimate and forecast the clean energy consumption in China and India. The results show that the proposed model demonstrates better estimation in all cases and efficiency in short-term clean energy consumption forecasting. Therefore, by using this optimum model, China's preference coefficient in total energy consumption is 0.7103, lower than that of India (0.8799), and China's preference coefficient in clean energy consumption is 0.9665, higher than that of Indian (0.7155), which means China has less interest to increase total energy consumption but more interest in popularizing the clean energy. • This paper proposed a novel grey Riccati–Bernoulli model for energy consumption prediction. • A specific solution algorithm for this Riccati–Bernoulli equation is discussed in this research. • We compared the proposed novel grey model with other 12 exiting prediction models. • The novel model also reveals China's effort in popularizing the use of clean energy. [ABSTRACT FROM AUTHOR]

Following India's efforts to mitigate the emission of greenhouse gases, there has been a transition from fossil fuels to clean energy sources, such as solar and wind, in the energy portfolio. The renewable energy sector is widely acknowledged as a fertile ground for employment creation, as it is labour-intensive. Against this backdrop, the main objective of this paper is to provide an empirical explanation of how the growth of renewable energy sources, particularly solar photovoltaic (PV) technology, affects employment creation in India. The findings reveal that, despite a substantial investment in India's solar PV technology, the number of jobs created by the sector declined marginally from 164000 in 2017–114000 in 2018. India's estimated employment elasticity is just 0.004, implying that the level of employment does not show a significant positive response to changes in installed capacity. Almost 60 per cent of the global solar PV workforce is in China, which has become the leading country both in terms of installed capacity of global solar PV technology and manufacturing of solar PV cells and modules. The wide disparity in solar PV employment between India and China is explained by the former's excessive dependence on China for solar PV cells. Alongside climate conservation, India should, therefore, place more emphasis on revitalising the domestic solar manufacturing industry to bring about efficient use of the nation's untapped resources. [ABSTRACT FROM AUTHOR]