Energy Use Efficiency Past-to-Future Evaluation: An International Comparison

Abstract

Despite the many benefits that energy consumption brings to the economy, consuming energy also leads nations to expend more resources on environmental pollution. Therefore, energy efficiency has been proposed as a solution to improve national economic competitiveness and sustainability. However, the growth in energy demand is accelerating while policy efforts to boost energy efficiency are slowing. To solve this problem, the efficiency gains in countries where energy consumption efficiency is of the greatest concern such as China, India, the United States, and Europe, especially, emerging economies, is central. Additionally, governments must take greater policy actions. Therefore, this paper studied 25 countries from Asia, the Americas, and Europe to develop a method combining the grey method (GM) and data envelopment analysis (DEA) slack-based measure model (SMB) to measure and forecast the energy efficiency, so that detailed energy efficiency evaluation can be made from the past to the future; moreover, this method can be extended to more countries around the world. The results of this study reveal that European countries have a higher energy efficiency than countries in Americas (except the United States) and Asian countries. Our findings also show that an excess of total energy consumption is the main reason causing the energy inefficiency in most countries. This study contributes to policymaking and strategy makers by sharing the understanding of the status of energy efficiency and providing insights for the future.

A DEA Resampling Past-Present-Future Comparative Analysis of the Food and Beverage Industry: The Case Study on Thailand vs. Vietnam

“…However, in the literature, DEA models are only applied to analyze a firm's performance in the past and present without fully evaluating a firm's efficiency by explicitly considering its inputs and outputs including the past-present-future time span. One of the most popular models used to forecast the efficiency is the Grey model GM (1,1) thanks to its computational efficiency [33]. Chiou et al [34] applied the Grey model GM (1,1) in forecasting the demand for planned spare parts in the navy of Taiwan.…”

Demographic efficiency drivers in the Chinese energy production chain: A hybrid neural multi‐activity network data envelopment analysis

“…However, the traditional black box DEA model does not consider the inner structure (Liu & Wang, 2015). In the previous literature, scholars have taken different methods, including traditional DEA, Grey method and Slack‐based DEA, two‐stage double bootstrap DEA, and multiplicative network DEA to measure energy efficiency for different countries/regions (Jebali et al, 2017; Moon & Min, 2017; Ouyang & Yang, 2020; Wang et al, 2019).…”

“…The previous studies show that China has a lower level of energy efficiency than the one of the European countries (Wang et al, 2019). Using a sample of 71 countries over the period 1990–2014, Sun et al (2019) find that governmental institutional backing and green innovation have a strong and positive influence on energy efficiency.…”

“…Similarly, the national and local governments, which are the key stakeholders of the energy industry, need to increase the sustainability of energy consumption (Burandt et al, 2019). From the international perspective, energy efficiency connects with economic competitiveness and sustainability in the world, which makes the study of energy production chains very relevant and necessary (Wang et al, 2019).…”

Evaluation of the Operating Efficiency of a Hybrid Wind–Hydro Powerplant

“…Therefore, each energy conversion technology has its own efficiency. However, the evaluation of energy efficiency in power systems has been based on economic metrics [17]. In [18], Maheshwari and Ramakumar described a procedure to design an optimal size microgrid that supplies energy to a rural area, but do not analyze nor define its operating efficiency.…”