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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]

Abstract After high speed development on economics during the past decades, China have accumulated a lot of air environment problems. A lot of studies based on data envelopment analysis (DEA) have been done to measure China's air problem by using monthly or annual data. As the development of real-time air quality monitoring, more valuable daily data should be utilized to evaluate air problem. The present paper constructs a set of DEA models for calculating air quality, in which the integral and zero-sum gain constraints are considered to match the characteristics of daily air monitoring data. An empirical analysis have been done to measure the air quality of 31 main cities in China by using the daily Air Quality Index. There are two main finds as follows. Firstly, all the 31 major cities in China need to do more work to improve air quality, while there are significant differences between the performance of different cities. Secondly, the air quality of Chinese cities has changed over month, the best air quality appeared in August and September while the worst occurred in December and January. Also, we calculate the target values for improving the air quality of Chinese cities. Highlights • Daily Air Quality Index data is used to evaluate air quality in China. • A set of DEA approaches are construct by inserting integral and zero-sum gain constraints. • The air quality of Chinese cities have significant tendency of variation during different months. • Specific values are provided for Chinese cities to improve their air quality. [ABSTRACT FROM AUTHOR]

In Africa, energy plays an important role in the processes of economic and sustainable development. However, inefficiency such as mismanagement of resources constrains productivity. Prior energy efficiency studies in Africa have failed to provide the paths through which energy efficiency improvement can be achieved. The current study aims to assess energy efficiency improvement among 25 selected countries in Africa. First, the dynamic slack-based measure (DSBM) data envelopment analysis (DEA) model is applied to gauge the efficiency measurement. Further, the Malmquist productivity index (MPI) is employed to investigate the energy efficiency improvement during 2006–2014. Empirically, the results from the dynamic slack-based measure (DSBM) model show that energy efficiency in Africa is generally low. Also, the findings from the MPI suggest there is no significant improvement in energy efficiency in Africa. Based on the estimated results, some energy efficiency improvement strategies are further proposed for sample countries in Africa. [ABSTRACT FROM AUTHOR]

An increasing amount of resources is being utilized to combat the pollution problem in China. Measuring and improving the efficiency of this resource allocation is an effective way to promote pollution treatment at the present technical level. This paper utilizes a two-stage data envelopment analysis (DEA) model to attain allocation results based on the zero-sum gain and fixed-sum gain assumptions. We divide all the inputs into discretionary and non-discretionary inputs and construct two models for allocating resources based on the environmental efficiency scores. The new models are applied to calculate the performance and resource allocation scheme of 30 Chinese provinces from 2011 to 2015. Most provinces are inefficient at controlling pollution emission or treating pollution while using the given level of input resources. Based on the production possibility set of each province in 2015, the processing capacity of wastewater and gas could be increased by 1% and 3%, respectively, by reallocating resources for pollution treatment among different provinces. Environmental efficiency scores and resource allocation plans based on various assumptions are obtained in this paper to enlighten the corresponding decision-makers. Image 1 • The paper constructs two resource allocation models based on the DEA approach. • This research considers the allocation of public pollution treatment resources between different sectors. • Two strategies for resource allocation are composed to deal with different conditions relating to resources. • We apply the new models to measure the performance of 30 regions in China. [ABSTRACT FROM AUTHOR]

Abstract: During the fast developing process of the Chinese economy, China consumes lots of energy and environmental pollution caused by energy consumption currently urgently needs solving. So, paying more attention to energy saving and emission reduction is very important. Therefore, to reasonably allocate resources and reduce emissions in China’s different regions has practical significance. So this paper firstly considers the model construction method for resource allocation considering undesirable outputs between different decision making units based on the DEA framework under the CRS and VRS assumption. We have proposed some resource allocation models as a multiple objective linear problem which considers the input reduction, desirable output reduction and undesirable output reduction. Then, to demonstrate the practical application of the resource allocation model, numerical examples are used to illustrate the approach. In addition, we explore the resource allocation problem of China’s regions in 2006–2010. The empirical results show that the resource allocation models dealing with undesirable outputs have effective configuration results, that is, the reduction proportion for desirable output is smaller than the reduction proportion for undesirable output. From the resource allocation results in China’s regions, we find that the eastern area has more emission reduction than the central and western areas corresponding to the same energy consumption reduction, and the energy and environmental policy should focus more on the central and western areas. All of these conclusions will give some policy recommendations on saving energy and reducing pollution emissions. [Copyright &y& Elsevier]