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In view of the climate emergency, it is necessary to model and forecast road transport emissions for implementing more effective climate policy. This paper fills an important research gap by quantifying the challenges, barriers and remedies of achieving road transport emission reduction and air quality improvement in developing countries. It contributes to the debate on top-down and bottom-up approaches to reconcile carbon and other transport related emissions. China and India are chosen as case studies. Using various official statistics and proxy variables, we estimate and compare the road transport carbon emissions of the two countries systematically from 2009 to 2020. Insights from detailed mode-specific estimations of the bottom-up approach are valuable for targeted policies and measures. An analysis of the major components of road transport carbon emissions points to strategies of promoting electric vehicles, reducing transport demand, accelerating modal shift to low-carbon modes, and cleaner electricity to achieve climate policy. [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]