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• Decoupling of GDP from all energy sources under multiple principles are examined. • World economy achieved weak energy decoupling in most years during 2000–2011. • World economy coupled with coal use and tended to couple with cleaner energy use. • None of studied economies achieved long-term GDP-embodied energy decoupling. • Decoupling delusions occurred when only considering direct and total energy use. Decoupling indicators are widely used to understand links between economic growth and energy use. However, traditional decoupling analyses mostly focus on domestic energy consumption (i.e., the production-based principle) and neglect off-site energy use across global supply chains to satisfy an economy's final consumption (i.e., the consumption-based principle). Moreover, analyses for total primary energy conceal an economy's preference for different energy sources. Therefore, this paper evaluates decoupling states of GDP from all types of primary energy use under consumption-based principle, for world economy and eight typical economies during 2000–2011. Regarding total primary energy, world economy witnessed weak decoupling in most years, and most economies studied (e.g., USA, Japan and China) achieved decoupling initially but performed negative decoupling finally. For EU, USA, Japan, Russia and India, production-based decoupling performances were generally better than consumption-based ones. Decoupling phenomena detected under production-based principle even became coupling or negative decoupling under consumption-based principle in some cases. As for each energy source, world economy decoupled from oil use, but still coupled with coal use, and gradually showed a trend to couple with natural gas and renewables use. Different energy sources showed distinct decoupling degrees from GDP, affected by individual embodied energy requirement structure. This paper uncovers potential energy decoupling delusions to deepen the understanding of relationships between energy use and economic growth. [ABSTRACT FROM AUTHOR]

Literature has highlighted the existence of a gap in clean fuel usage between the social groups in most emerging nations across the world, which is detrimental to global efforts to achieve Sustainable Development Goal (SDG) 7 (access to clean energy) and SDG 10 (reducing inequalities) and just energy transition with recognition justice. This paper, assesses the clean fuel gap across social groups in India using three rounds of representative data (2004, 2014, and 2018) by applying the logit model, exogenous switching treatment effect regression (ESTER), and the Blinder–Oaxaca Decomposition Model. Such an analysis is instrumental in identifying the clean energy usage gap and its causes, thereby providing insight to policymakers to design policies to achieve just energy transition and carbon neutrality by 2060. The results show a substantive clean fuel gap between social groups in India, which is a cause of concern for just energy transition. Clean energy utilization gaps among social groups are alarming across geographical locations and consumption quartiles, highlighting the importance of recognition justice for just energy transition. The results show that in rural areas, only 19.6 % of the General Caste households and 14.5 % of the disadvantaged social groups (Scheduled Tribes, Scheduled Castes, and Other Backward Class) in quartile one use clean energy, while in urban areas, the shares are 60.4 % and 46.3 %, respectively. To achieve a just distribution of the benefits of energy transition, energy policies should focus on disadvantaged social groups and simultaneously target General Caste households from a lower economic status and those residing in rural areas, indicating the need for targeting the neglected regions. [Display omitted] • Clean energy use has steadily increased among all social groups in India, indicating some improvement in energy justice. • Clean energy access gap among social groups has declined, but this gap still persists. • Scheduled Castes and Scheduled Tribes are the most disadvantaged social groups in terms of clean energy. • All social groups from low-income families have limited access to clean energy. • All social groups from households with low education have limited access to clean energy. [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]