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In the quest for economic and social prosperity, countries all over the world are constantly engaged in energy consumption to propel development. The utilization of energy emits dangerous greenhouse gases which have adverse impact on human health and the entire ecosystem. Finding the trade‐off that exists between economic growth and environmental protection is essential nowadays. Previous energy efficiency studies in Africa have focused on static efficiency and ignoring dynamic implications of crossover factors in productivity. Those studies in Africa have also failed to capture energy efficiency trends and patterns over time. Therefore, the current study applies the dynamic slack‐based measure (DSBM) in a data envelopment analysis (DEA) framework to assess the dynamic energy efficiency of 25 sampled African economies from 2007 to 2014 by adopting energy stock as carryover factors. Further, the study investigates the inputs, output, and carryover factors inefficiency in the model to identify the potential areas where inefficiencies occurred. The empirical results suggest that these selected African countries are far from being energy efficient (0.519). Therefore, both adjustments and projections on the inputs, output, and carryover variables should be taken into account, to improve efficiency. Finally, useful suggestions for energy efficiency improvement are further discussed based on the empirical results. [ABSTRACT FROM AUTHOR]

Environmental regulation has been recognized as an important way to directly improve environmental performance or indirectly impact environmental performance through increasing environmental innovation. The present paper constructs an energy and carbon emission total factor productivity index using the Malmquist–Luenberger productivity index and data envelopment analysis. The proposed technique is used to measure the environmental performance of 30 Chinese provinces during the period 2010–2015. The energy and carbon emission total factor productivity measure is divided into a pure technical efficiency index and a technical progress index to provide detailed environmental performance information. Then an ordinary least squares model is adopted to analyse the impact of environmental regulation on environmental innovation operation and environmental performance by hypothesis testing. The empirical results show that environmental regulation and environmental innovation have positive direct effects on environmental performance and that market‐based environmental regulation has a positive indirect effect on environmental performance by increasing environmental innovation. [ABSTRACT FROM AUTHOR]