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The carbon emissions trading system (CETS) is a helpful policy instrument for separating carbon emissions from economic expansion, and it significantly impacts energy efficiency (EE). This study uses 30 Chinese provinces from 2007 to 2020 as its research samples, and classifies energy efficiency into single-factor energy efficiency (SFE) and total-factor energy efficiency (TFE), using the difference-in-differences model to examine the effect and mechanism of the CETS on EE. As an additional tool to assess the efficacy of the CETS, the corresponding evolution of the rebound effect of energy-related carbon emissions (RECE) is also calculated. This study shows that the CETS can significantly improve EE in China's pilot provinces. The influence mechanism indicates that the effect of the CETS on EE is influenced by the level of government governance, green innovation, and industrial structure optimization. Further study finds that after the CETS was carried out, the RECE in pilot provinces was higher than that in non-pilot provinces, and 31.4% of carbon emissions reduced by EE improvement rebounded. Therefore, the CETS has yet to realize its full carbon reduction potential. The study offers specific policy proposals for the enhancement of China's CETS in light of the aforementioned findings. [ABSTRACT FROM AUTHOR]

Ecological degradation is a major challenge for all nations. The problem is particularly worrying for South Africa, which has recently suffered from various ecological catastrophes. Thus, the empirical study evaluates the nexus between CO2 emissions and financial development, renewable energy, economic growth and environmental-related technologies in South Africa utilizing data between 1980 and 2020. We employed autoregressive distributed lag (ARDL) and time-varying causality to evaluate these connections. The results from the ARDL show that financial development and environmental-related technologies lessen CO2 emissions while economic progress intensifies CO2 emissions. Surprisingly, renewable energy does not mitigate CO2 emissions. Furthermore, the time-varying causality shows that all the independent variables can forecast CO2 emissions at different sub-periods. Finally, our results are resilient to various policy ramifications useful in reducing CO2 emissions and associated adverse ecological consequences. [ABSTRACT FROM AUTHOR]

The population has a significant influence on economic growth, energy consumption, and climate change. Many scholars and organizations have published projections for China's future population due to its substantial population amounts. However, these projections have not been evaluated or analyzed, which may lead confusion to extensional studies based on these datasets. This manuscript compares several China's projection datasets at multiscale and analyzes the impacting factors affecting projection accuracy. The results indicate that the slow of actual population growth rates from 2017 is earlier than most datasets projected. Therefore, the turning point of population decline probably comes rapidly before these datasets expected during 2024 and 2034. Furthermore, the projections do not reveal the population decline from 2010 in the Northeast provinces such as Jilin and Heilongjiang, and underrate the population increase in the southern provinces such as Guangdong and Chongqing. According to the results of regression models, the rate of population changes and the number of migrations people play a significant role in projection accuracy. These findings provide meaningful guidance for scholars to understand the uncertainty of those projection datasets. Moreover, for researchers performing population projections, our discoveries provide insights to increase the projection accuracy. [ABSTRACT FROM AUTHOR]