Settling the metric selection debate for assessing the energy efficiency of energy-intensive industries

Industry dominates energy consumption and carbon emissions in China, and industrial energy efficiency is critical for the achievement of energy transformation and carbon emission reduction. With the rapid development of the digital economy, its impact on energy efficiency is gradually emerging, and it is necessary to clarify the influencing mechanism on industrial energy efficiency. Based on the panel data of industrial sectors in 41 cities in the Yangtze River Delta from 2011 to 2019, the main objectives of this study are to more accurately measure the industrial total factor energy efficiency in each city by using the Super-Dynamic-SBM model. It analyses the influence mechanism of the digital economy and other influencing factors on industrial total factor energy efficiency with different methods. The research results indicate that, first, the total factor energy efficiency of the industrial sector in the Yangtze River Delta urban agglomeration generally showed a steady upward trend. Second, the digital economy and environmental regulation play a significant role in promoting total factor energy efficiency. In addition, industrial energy efficiency and the digital economy show an inverted "U" shaped relationship. With the improvement of the digital economy, its marginal contribution to total factor energy efficiency gradually weakens. Finally, technological progress is an important transmission channel for the impact of the digital economy on total factor energy efficiency.

Review of policies and measures for energy efficiency in industry sector

Energy efficiency in industry is a crucial topic for Turkey, as the country has an import dependency of 80 percent in energy. Although the importance of enhancing energy efficiency in industry is widely acknowledged, there has not been any study examining the energy efficiency in Turkish industry at micro level. Employing a sound decomposition methodology on a firm-level data set of manufacturing firms, this paper documents that there was a significant decrease in the energy intensity of firms over 2005-12. In contrast, structural change across manufacturing sectors and across firms within sectors had positive but limited effects on the overall energy efficiency over the period.

Energy efficiency in industry is a crucial topic for Turkey, as the country has an import dependency of 80 percent in energy. Although the importance of enhancing energy efficiency in industry is widely acknowledged, there has not been any study examining the energy efficiency in Turkish industry at micro level. Employing a sound decomposition methodology on a firm-level data set of manufacturing firms, this paper documents that there was a significant decrease in the energy intensity of firms over 2005-12. In contrast, structural change across manufacturing sectors and across firms within sectors had positive but limited effects on the overall energy efficiency over the period.

Energy Efficiency and Emissions Reduction Potential of China’s Industrial Sector

Industrial energy efficiency and driving forces behind efficiency improvement: Evidence from the Pearl River Delta urban agglomeration in China

Industrial agglomeration effect for energy efficiency in Japanese production plants

Review of methodologies and polices for evaluation of energy efficiency in high energy-consuming industry

The industrial sector is one of the main energy consuming sectors in Thailand and accounted for 36.7% of total energy consumption in 2005. The trend of rising energy prices and tougher competition increases the demand to improve energy efficiency in Thai industry. However, the existence of various barriers often hinders the realization of even some cost-effective energy efficiency measures. In an attempt to investigate key barriers to and drivers for energy efficiency improvement in Thai industry, this study found that the most important barrier expressed by both the textile and cement industries studied as well as experts interviewed is that the management is concerned about production and other matters rather than energy efficiency. Reducing product cost by reducing energy cost is found to be the main driver for energy efficiency investment. Using a conceptual industrial energy efficiency policy framework this study shows how various energy efficiency policies can affect the process of decision-making for and investment in energy efficiency in industry.

Energy efficiency in large industrial plants. Legislative aspects

This paper applied the advocacy coalition framework to explore and explain the political processes creating policies to enhance energy efficiency of European Union (EU) industry. The paper used legislation on energy audits and energy management systems as a proxy for EU policy on energy efficiency in industry. Based on qualitative text analysis of EU policy documents, including a proposal to recast the energy efficiency directive, amendments to the proposal suggested by Member States, the Council and the European Parliament, and reports from negotiations, the paper identified four advocacy coalitions with different core beliefs, spanning from those that want few companies to implement energy audits or energy management systems, and that recommendations from audits should not be mandatory to implement, to those that advocate that many companies implement energy audits and management systems and that it should be mandatory to implement measures recommended in audits. It was further found that policy change followed an external shock, deliberative negotiations, and policy-oriented learning. The identification of core beliefs and advocacy coalitions will help policymakers and other stakeholders become more aware of their own and others’ values on energy efficiency and how these could be changed. As important was the differentiation of deep core beliefs, policy core beliefs and secondary beliefs. Which beliefs can be easily changed, which cannot?

High dependency on fossil fuels, low energy efficiency, poor diversification of energy sources, and a low rate of access to electricity are challenges that need to be solved in many developing countries to make their energy systems more sustainable. Cogeneration has been identified as a key strategy for increasing energy generation capacity, reducing greenhouse gas (GHG) emissions, and improving energy efficiency in industry, one of the most energy-demanding sectors worldwide. However, more studies are necessary to define approaches for implementing cogeneration, particularly in countries with tropical climates (such as Ecuador). In Ecuador, the National Plan of Energy Efficiency includes cogeneration as one of the four routes for making energy use more sustainable in the industrial sector. The objective of this paper is two-fold: (1) to identify the potential of cogeneration in the Ecuadorian industry, and (2) to show the positive impacts of cogeneration on power generation capacity, GHG emissions reduction, energy efficiency, and the economy of the country. The study uses methodologies from works in specific types of industrial processes and puts them together to evaluate the potential and analyze the impacts of cogeneration at national level. The potential of cogeneration in Ecuador is ~600 MWel, which is 12% of Ecuador’s electricity generation capacity. This potential could save ~18.6 × 106 L/month of oil-derived fuels, avoiding up to 576,800 tCO2/year, and creating around 2600 direct jobs. Cogeneration could increase energy efficiency in the Ecuadorian industry by up to 40%.

The management of waste materials, particularly non-biodegradable substances such as plastics and composites, is an increasingly pressing issue. Energy efficiency in industrial processes is crucial throughout their life cycle, including the handling of materials such as carbon dioxide (CO2), which has a significant environmental impact. This study focuses on the conversion of solid CO2 into pellets using ram extrusion, a widely used technique. The length of the die land (DL) in this process plays a critical role in determining the maximum extrusion force and the density of dry ice pellets. However, the influence of DL length on the characteristics of dry ice snow, known as compressed carbon dioxide (CCD), remains understudied. To address this research gap, the authors conducted experimental trials using a customized ram extrusion setup, varying the DL length while keeping the other parameters constant. The results demonstrate a substantial correlation between DL length and both the maximum extrusion force and dry ice pellets density. Increasing the DL length leads to a decreased extrusion force and optimized pellet density. These findings provide valuable insights for optimizing the ram extrusion process of dry ice pellets and improving waste management, energy efficiency, and product quality in industries utilizing this technique.

The aim of this paper is to assess the voluntary agreement for increasing energy efficiency in industry of Latvia and provide recommendations for more effective implementation in the future. In the first part of the paper the energy efficiency in Latvian industry is analyzed. The first chapter of the practical part is data monitoring analysis of CCFI (Climate Change Financial Instrument) project "Complex solutions to reduce greenhouse gas emissions in production facilities”. The second part consists of the evaluation of the voluntary agreement and is the outline suggestions for more effective implementation of the Latvian program to achieve the defined energy reduction targets for industry.

The growing demand for sustainable solutions and the digitalization of industrial processes have driven the adoption of photovoltaic systems and advanced decision-making technologies. In the context of Industry 4.0, where automation and artificial intelligence are fundamental, these systems stand out as a clean energy alternative, promoting savings and reducing pollutant emissions. This study aims to develop a photovoltaic energy control model that uses genetic algorithms to optimize energy efficiency in industrial environments, reducing costs and dependence on non-renewable sources. The methodology included the computational modeling of a photovoltaic system and the application of genetic algorithms to optimize parameters such as panel angle and operating hours, adapting the system in real time to variable consumption and generation conditions. The results showed that the use of genetic algorithms increased the system's efficiency by up to 20% compared to traditional methods, as well as minimizing consumption from the electricity grid at peak times. This study reinforces the importance of artificial intelligence in optimizing renewable resources, contributing to energy efficiency and sustainability in Industry 4.0.