Search

Since the "double carbon" target was proposed in 2020, emissions reduction has become the focus of the Chinese government's work. Industrial development is a crucial factor contributing to the increase in carbon emissions. Therefore, implementing carbon reduction in the industrial sector is an important part of China's goal of "double carbon." In this context, this paper uses a three‐stage data envelopment analysis model to calculate the green total factor productivity (GTFP) of 18 industry sectors in Shanxi Province. The results show that: (1) the external environment has influence on the GTFP of energy revolution reform pilot area industrial sectors, and the efficiency value of two industries is overestimated before excluding environmental factors; (2) the industry difference of GTFP is significant, among which pharmaceutical manufacturing industry has the greatest promoting effect on GTFP, and ferrous metal mining and dressing industry has the greatest inhibiting effect on GTFP; (3) there is a serious redundancy in R&D investment of energy revolution reform pilot area in China, and only six industries belong to zero redundancy. (4) To reflect the GTFP of various industrial sectors more clearly, this paper divides four industries: "Double‐high" industry, "High‐low" industry, "Low‐high" industry, and "Double‐low" industry. [ABSTRACT FROM AUTHOR]

According to the Paris Agreement signed on 12 December 2015, forests have great impact on controlling CO2 emissions. This study uses the nonoriented dynamic slack‐based data envelopment analysis model to evaluate the impact of a forestry area on the annual and overall energy efficiency in 28 EU countries during 2009‐2016. We found that annual and overall energy efficiency improved slightly and the energy efficiency in each country also changed when the forestry area was used as the output variable. Although the total energy consumption has declined slowly, CO2 emissions have not declined effectively. Thus, it will be challenging for the EU to meet its target of cutting CO2 emissions by 30% by 2030. All countries should take additional measures, such as energetically increasing forestry areas, optimally distributing the labor force among industries, and gradually replacing fossil fuels with renewable energy to improve their energy efficiency. [ABSTRACT FROM AUTHOR]

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]

Mankind is constantly pursuing economic growth and social development, and while these bring convenience to people, they involve large amounts of greenhouse gas emissions under the heavy use of energy, which produces quite serious air pollution, which not only affects people's health but also leads to ecological environmental damage. Nowadays, the impact of the environment and the negative impacts of the world's economies while promoting economic growth are the most important issues for economies to achieve balanced development. This study uses the dynamic slack‐based data envelopment analysis (DEA) model to assess the environmental energy efficiency of high‐income economies (including China) and explore the negative impacts on the environment, to obtain a basis for energy‐saving emission reduction methods or configurations by using 48 high‐income economies (including China) from 2010 to 2014. The interperiod (carryover) variable is gross domestic product (GDP). The empirical results show that economies with high energy efficiency have a large consumption of energy and are unable to reduce carbon dioxide emissions. We also find that there exists a difference in the results depending on whether GDP is considered as the carryover variable. European economies are more efficient in energy consumption than others, and Asian economies are the most inefficient. In order to pursue GDP growth, economies need to consider reducing their energy consumption and CO2 emissions and improve their energy usage efficiency, further implementing sustainability. [ABSTRACT FROM AUTHOR]