Energy Consumption Intensity Research Articles

The Impact of Industrial Added Value on Energy Consumption and Carbon Dioxide Emissions: A Case Study of China

China, as the world’s largest energy consumer, is faced with the growing pressure of carbon emission reduction. Promoting the sustainable transformation of the economy and society has become a major concern for all sectors of society. This paper assesses the potential impacts of industrial added value on energy consumption and carbon dioxide (CO2) emissions from 2010 to 2020 in China using a multi-objective, linear programming model. The results show that when the industrial energy consumption is 3695.17 Mtce and the industrial CO2 emission is 9364.16 Mt, the goal of energy saving and emission reduction can be achieved. This corresponds to an annual average growth rate limit of industrial added value of 7.8%. In addition, we find that when the annual average growth rate of industrial added value is greater than 9.9%, changes in the annual average growth rate of industrial added value have no impact on economic development. However, industrial energy consumption intensity and industrial carbon emission intensity decrease with the increase in the annual average growth rate of sub-sectors. This study proposes that energy conservation, emission reduction, and a realization of the sustainable transformation of industry scheme need to be achieved by the Chinese government in order to to continue to promote the development of several sectors with low industrial energy consumption intensity and low industrial carbon emission intensity through policy implications.

Identifikasi Aliran Energi Listrik pada Mesin CNC Machining Center

<p><em>CNC machine tools have been widely used in the process and manufacturing industries, which have the potential to increase the intensity of electrical energy consumption so that energy efficiency is needed. Energy efficiency is a necessity for the sustainability of the process and manufacturing industrial sector which will be related to energy costs and environmental factors. In order to carry out energy efficiency, it is necessary to identify the consumption of electrical energy by tracing and mapping the electrical energy flow to the components in a CNC machining center machine, so opportunities for energy savings can be known. This research was conducted by collecting data by monitoring energy consumption in stand-by, setting and cutting machine conditions. Cutting tests are carried out to determine the energy consumption of each machine component that affects energy use. After that it can be seen the energy flow pattern by classifying the energy user components into primary and secondary components. Based on the analysis of electrical energy consumption data during the cutting process, it is known that the significat energy users are the spindle motor, the axis feed motor (X, Y, Z) and the coolant motor.</em></p>

Multiantenna Clustering Collaboration for WPCNs Based on Nonlinear EH

This article considers a wireless-powered communication network (WPCN) composed of a multiantenna hybrid access point (HAP) based on nonlinear energy harvesting (EH). To improve some distant WDs’ throughput performance, one of them is allowed to be selected as a cluster head (CH) to help transfer information from other cluster members (CMs). Nevertheless, the proposed clustering collaboration’s performance is essentially restricted by the CH’s energy-intensive consumption (EC), which requires to transfer every WDs’ information, covering its own. In order to figure out the question, the HAP’s energy beamforming (EB) capability with multiple antennas is utilized that can concentrate greater transmission power into the CH to equilibrate its EC to assist other WDs. To be specific, each WD’s throughput performance is firstly derived under the proposed approach. A high-efficiency optimization algorithm for addressing cooperative optimization problem is put forward. In addition, the simulations are carried out in the actual network environment, and the results demonstrate that our proposed clustering collaboration with multiple antennas can validly enhance the WPCN’s throughput fairness based on nonlinear EH.

A review of on-device machine learning for IoT: An energy perspective

Recently, there has been a substantial interest in on-device Machine Learning (ML) models to provide intelligence for the Internet of Things (IoT) applications such as image classification, human activity recognition, and anomaly detection. Traditionally, ML models are deployed in the cloud or centralized servers to take advantage of their abundant computational resources. However, sharing data with the cloud and third parties degrades privacy and may cause propagation delay in the network due to a large amount of transmitted data impacting the performance of real-time applications. To this end, deploying ML models on-device (i.e., on IoT devices), in which data does not need to be transmitted, becomes imperative. However, deploying and running ML models on already resource-constrained IoT devices is challenging and requires intense energy consumption. Numerous works have been proposed in the literature to address this issue. Although there are considerable works that discuss energy-aware ML approaches for on-device implementation, there remains a gap in the literature on a comprehensive review of this subject. In this paper, we provide a review of existing studies focusing on-device ML models for IoT applications in terms of energy consumption. One of the key contributions of this study is to introduce a taxonomy to define approaches for employing energy-aware on-device ML models on IoT devices in the literature. Based on our review in this paper, our key findings are provided and the open issues that can be investigated further by other researchers are discussed. We believe that this study will be a reference for practitioners and researchers who want to employ energy-aware on-device ML models for IoT applications.

Study on the Relationship between Energy Structure Change and Economic Structure in China

Since 1978, China's economic growth rate has attracted international attention. While China's economy has maintained rapid growth, energy consumption has shown an opposite trend, namely, the growth rate of energy consumption has slowed down, and the energy consumption intensity of the national economy has continued to decline. The tertiary industry is underdeveloped, and the producer service industry is stagnant, with outstanding problems. It is worth noting that the proportion of China's industrial added value in GDP(Gross Domestic Product) exceeded the peak value of developed countries during the Wang Yehua period. Under the new normal of economic growth shift and tight resource and environment constraints, structural contradictions will become more prominent in the coming period. In order to practice the low-carbon economic model, the government puts forward the requirements of ensuring China's energy and economic security and accelerating the transformation of energy revolution and economic development mode. However, structural adjustment is not a simple superposition of policies and measures. This paper studies the relationship between energy structure change and economic structure in China. Structural adjustment is not simply the superposition of policies and measures. We must also with comprehensive consideration of energy supply security and use security, so as to provide decision-making basis for ensuring energy security and carrying out national macro-control.

The carbon emission reduction effect and mechanism analysis of digital economy: Evidence of prefecture-level cities in China

ABSTRACT This study investigates the impact and mechanisms of carbon emission reduction associated with digital economy development, utilizing data from 265 prefecture-level cities in China during the period of 2003 to 2019. The comprehensive index of digital economy development at the prefecture level is evaluated using the entropy method, followed by empirical analysis employing the dual fixed effect model. The findings confirm a significant improvement in urban carbon emission reduction resulting from the digital economy. Mechanism analysis reveals that the digital economy enhances the carbon emission reduction effect in cities by promoting urban green innovation and reducing energy consumption intensity. Additionally, heterogeneity analysis demonstrates that the carbon reduction effect of digital economy development is influenced by factors such as city location, population size, and development type. By shedding light on the relationship between the digital economy and carbon emission reduction, this study offers valuable insights into the mechanisms through which the digital economy contributes to sustainable urban development.

Electrochemical Nitrate Reduction: Ammonia Synthesis and the Beyond.

Natural nitrogen cycle has been severely disrupted by anthropogenic activities. The overuse of N-containing fertilizers induces the increase of nitrate level in surface and ground waters, and substantial emission of nitrogen oxides causes heavy air pollution. Nitrogen gas, as the main component of air, has been used for mass ammonia production for over a century, providing enough nutrition for agriculture to support world population increase. In the last decade, researchers have made great efforts to develop ammonia processes under ambient conditions to combat the intensive energy consumption and high carbon emission associated with the Haber-Bosch process. Among different techniques, electrochemical nitrate reduction reaction (NO3RR) can achieve nitrate removal and ammonia generation simultaneously using renewable electricity as the power, and there is an exponential growth of studies in this research direction. Here, a timely and comprehensive review on the important progresses of electrochemical NO3RR, covering the rational design of electrocatalysts, emerging CN coupling reactions, and advanced energy conversion and storage systems is provided. Moreover, future perspectives are proposed to accelerate the industrialized NH3 production and green synthesis of chemicals, leading to a sustainable nitrogen cycle via prosperous N-based electrochemistry.

Will “Dual Control of the Amount and Intensity of Energy Consumption (DCEC)” Policy Increase Urban Green Competitiveness?

Urban green transformation is an important part of global low-carbon development. Coercive government policies are critical to achieving success, but, as of now, there is no unified concept of “green competitiveness”. To address climate change, it is necessary to clearly define core concepts and apply measures. For this study, “urban green competitiveness” refers to the continuous improvement of total factor productivity by cities while fully emphasizing resource limitations and environmental issues to achieve economic growth. This article focuses on a Chinese policy of “dual control of the amount and intensity of energy consumption” (DCEC), which has not been fully studied. The DCEC policy incorporates a quasi-natural experiment to assess whether urban green competitiveness has been affected. This paper builds on the multi-period DID model and explores the effect based on city-level data from 2003 to 2019. The conclusion shows that DCEC policy can address climate change by restricting energy consumption and intensity. Success is achieved through the promotion of local green patent innovation and by intensifying environmental regulation. The policy should start from a long-term perspective by promoting structural and technological changes in the economy, rather than short-term quick success and instant benefits.

Energy Transitions in Yunnan Province Based on Production Function Theory

Yunnan is rich in renewable energy resources. An understanding of its energy structure and developmental trajectories would assist in enabling the design of suitable decarbonizing pathways and how to fit into the national 30–60 agenda. Drawing from endogenous growth theory and time series analysis, our study employs comparative functions and scenario assessments to predict the changes in the key economic indicators, such as GDP, industrial structure shifts, population, and urbanization rates, during the low-carbon transition. We further show energy structure patterns and intensity trends using regression-modeling and data-fitting methods. Based on our analyses, we project that by 2035, Yunnan’s GDP will grow to CNY 5.4761 trillion, with secondary and tertiary industries contributing 88.8%. The population is estimated to grow to 52.08 million with an urbanization rate of 70%. Moreover, fossil fuel energy consumption is forecasted to diminish to 38.7%, and energy consumption intensity is projected to be reduced to 0.38 tons of standard coal per CNY 10,000. If these metrics follow the inherent endogenous growth trend, Yunnan’s emissions are forecasted to peak at around 220 million tons of CO2 by 2030. These findings not only provide a data foundation for Yunnan’s low-carbon development goals but also illuminate pathways for regions rich in renewable resources to transition towards sustainable growth, emphasizing the harmony between advancement and environmental stewardship.

Digital Economy and Intelligent Manufacturing Coupling Coordination: Evidence from China

The digital economy uses its own digital information advantages to reduce the intensity of energy consumption brought by economic growth. Intelligent manufacturing achieves cost reduction and efficiency through the integration of manufacturing and intelligence as well as digitalization and information technology. The two have become a new engine for sustainable economic development at present, and they can promote and influence each other. However, there is a lack of research on the relationship between them. In this regard, this study aims to build a coupling coordination model of digital economy and intelligent manufacturing and to make an empirical analysis using the data of Chinese provincial administrative regions in order to provide a theoretical reference for promoting sustainable economic development. The research finds that (1) the digital economy and intelligent manufacturing are mainly cross-coupled from four aspects: infrastructure, technological innovation, product optimization and organizational change. The development level and speed of the former are significantly higher than those of the latter, and the gap does not decrease with time. The two have a strong correlation, but there is no high-quality coupling coordination. (2) The main obstacle factors to the digital economy lie in the imperfect supporting facilities, the short board of technological innovation and the lack of technological application capacity. Intelligent manufacturing lacks intelligent application and technological innovation. (3) Influencing factors such as opening to the outside world, economic development, high-level talent input, industrial structure and innovation emphasis have different effects on their coupling and coordinated development in different regions. (4) The spatial correlation test shows that the coupling coordination degree of each region is spatially positively correlated. This research helps to promote the coupling and coordinated development of the digital economy and intelligent manufacturing.

Spatial heterogeneity of factors affecting GHG emission intensity in urban water supply and wastewater treatment systems in China

The water supply and treatment in urban water systems (UWS) consume massive energy, quantitative estimate of the intensity of their accompanying GHG (Greenhouse Gas) emissions is important to clarify the major source of the GHG for UWS. Herein, on the city scale, the intensity of energy consumption and GHG emissions of the water supply and wastewater treatment systems were calculated, based on the operating data of 5972 water or wastewater treatment plants in China. Results showed that the water supply system consumed 38.6 billion kWh of electrical energy and accounted for 26.8 Mt CO2-eq GHG emissions, accordingly of 14.4 billion kWh and 21.9 Mt CO2-eq for the wastewater treatment system. For wastewater treatment, significant spatial autocorrelation was observed for the intensities of energy consumption and GHG emission, and the clustering areas highly matched with the topography, especially the N2O and CH4 emissions high-high clustering in the Loess Plateau and Inner Mongolia Plateau. Screened by multi-scale geographically weighted regression, the annual precipitation was found to be the main factor influencing the intensities, with a higher regression coefficient than social factors (e.g., GDP and population density). And a clear spatial heterogeneity was observed with the regression coefficient distributed higher in the northwest than in the southeast, with a gap of 2.3–7.5 times. This study provides systematic estimation results of energy consumption and GHG emissions intensity in urban water supply and wastewater treatment systems on the city scale in China, quantifies regional differences in energy consumption and GHG emission intensity of UWS, and revealed the inequality of UWS in regions with different basic conditions while facing low-carbon transformation. Our findings provide a reference for the formulation of regionally targeted UWS low-carbon transition policies.

Industry 4.0 and energy in manufacturing sectors in China

Digitalisation in manufacturing (or “industry 4.0”) is expected to improve energy efficiency and thus reduce energy intensity in manufacturing, but studies show that it may also increase energy consumption. In this article, we investigate to what extent the degree of industry 4.0 is linked to energy consumption and energy intensity in ten Chinese manufacturing sectors between 2006 and 2019. We approximate the degree of industry 4.0 by combining data on a) patent intensity of industry 4.0-related technologies and b) industrial robot intensity. Our results indicate that there is no significant overall relationship between the degree of industry 4.0 and energy consumption or energy intensity, in contrast to some earlier studies in the Chinese context which find energy intensity reducing effects of digitalisation. We argue that industry 4.0 in China might have fewer energy related benefits than expected by politics and industry. Growth-inducing effects and outsourcing of energy-intensive manufacturing tasks, for instance, may counteract efficiency-related savings. To decarbonise manufacturing in line with China's proclaimed objective of carbon neutrality by 2060, policy makers and industry should identify specific opportunities and take seriously risks of industry 4.0. The focus should be on reducing absolute energy consumption as opposed to energy intensity, which may disguise digital rebound effects; and on integrating renewable energies, particularly in the most energy-intensive sectors (metals, chemicals, non-metallic minerals).

Trends of sustainable recycling technology for lithium‐ion batteries: Metal recovery from conventional metallurgical processes to innovative direct recycling

AbstractThe remarkable market growth of lithium‐ion batteries (LIBs) for various applications has been witnessed in the past two decades. However, as a retirement wave of more and more LIBs approaches, the disposed end‐of‐life batteries represent a growing hazard to ecosystem and human health. Recycling valuable metals from those spent LIBs still remain challenging because the current conventional metallurgical recycling processes involve the emission of toxic gas and waste chemicals as well as intensive energy consumption. These processes are not considered as green recycling approaches that contradict with the principles of carbon neutrality and circular economy embraced by the world. Hence, implementing green and sustainable recycling technologies of spent LIBs, especially for cathode materials, is an urgent need. This review provides a comprehensive understanding and critical evaluation of traditional pyrometallurgy, hydrometallurgy, and state‐of‐the‐art direct recycling for recovering valuable metal materials from the spent LIB cathode. The fundamentals, methods, efficiencies, and feasibility of the three recycling approaches are also assessed. In addition, the recent progress for green innovation of hydrometallurgical and direct recycling processes as well as the potential research tendency for spent LIBs are discussed.

Influence and sensitivity evaluation of window thermal parameters variations on economic benefits of insulation materials for building exterior walls–a case study for traditional dwelling in China

The energy consumption intensity of rural buildings in China shows an increasing trend. Adding insulation materials for exterior walls can lower the building energy demand (ED), and evaluating its economic benefits (EB) can provide valuable references for decision-makers. Although many studies have evaluated the EB of wall insulation, the effect of window thermal parameters is rarely discussed. The variations of window thermal parameters can change the ED and the investment cost of the wall insulation, then change the final EB of the wall insulation. In this work, the impacts of four window thermal parameters (U-value, solar heat gain coefficient, window-to-wall ratio, air changes per hour) on the ED and EB of wall insulation are investigated. Then they are ordered according to the global sensitivity analysis results. Meanwhile, the effects of building orientations on these results also be discussed. Results show: (1) The optimum insulation thickness is 50 mm for traditional dwellings when considering the window system; (2) The EB of wall insulation is most sensitive to the air changes per hour, followed by window-to-wall ratio, U-value, and solar heat gain coefficient; meanwhile, the variation of building orientation cannot change this sensitivity order; (3) The insulation costs cannot be balanced out when the air changes per hour exceeds 1.3 h−1 and insulation is not thicker than 5 mm.

Consistent or inconsistent? The club convergence effect and matching degree analysis on energy consumption intensity and production density: Evidence from the data of 2000–2020 in China

Clarifying the regional differences in coal production and consumption is important for the adjustment of the energy structure. To understand the spatial and temporal variability of coal production and consumption, this study examined coal consumption and production across 30 Chinese provinces over the period of 2000–2020 using the methodology introduced by Phillips and Sul. Then, we used the kernel density estimation method to analyze the dynamic changes. Finally, the Gini coefficient and coefficient of variation methods were used to quantify the unevenness in the production and consumption of coal. The analytical results strongly support the existence of convergence clubs. We find five convergent clubs for coal consumption and six convergent clubs for coal production. There are significant differences in the geographic distribution of convergence clubs for coal consumption and coal production. In addition, the difference in coal consumption levels between provinces widened, while the difference in coal production narrowed during the study period. The unevenness of the regional distribution and matching of coal production and consumption increased. Overall, the regional unevenness of coal production and consumption should be given sufficient attention. This study provides a reference on how to differentiate and adjust the production and consumption of coal in each region.

Assessing the high-quality development strategy of mineral resource enterprises

Purpose The purpose of this paper is to explore the high-quality development (HQD) strategy of Chinese mineral resource enterprises, which is important for Chinese mineral resource enterprises to improve the efficiency and benefit of resource utilization, reduce the intensity of resource and energy consumption and gradually form resource-saving and environment-friendly enterprises. Design/methodology/approach This study establishes an evaluation index system with four dimensions: economy, environment, society and management innovation. The entropy value method assigns weights to them and then uses the system dynamics (SD) model for case simulation. Findings The results of the SD simulation conclude that the fulfillment of social responsibility and the implementation of management innovation can accelerate the realization of HQD of mineral resource enterprises; profitability plays a crucial role in economic indicators; the improvement of energy-saving volume has the most significant impact on environmental benefits; the social contribution is the key element to measure social indicators; and the sales rate of core products has the most significant impact on the benefits of management innovation. Originality/value Based on the few studies on the evaluation of the development strategy of mineral resource enterprises, this study establishes an evaluation index system that considers the interactions between indicators, combines the entropy value method with SD and uses the SD model to comprehensively and systematically analyze the impact and degree of each factor on the HQD of mineral resource enterprises.

Study on the impact of industrial digitization on carbon emissions: evidence from China’s Logistics Industry

Digitization of the industrial sector is critical to combat climate change and reduce carbon emissions. The application of digital technology in traditional high-carbon emission industries can improve energy use efficiency and provide an essential technological path for carbon reduction. This study uses a coupled coordination degree model to measure the degree of integration between China’s logistics industry and the digital economy. An environmental Kurtzwarts curve (EKC) panel and a non-linear mediation model was then constructed to analyse the impact of digitization of China’s logistics industry on carbon emissions and its driving mechanisms using panel data from 30 regions in China from 2011 to 2020. The empirical results show that: (1) The level of integration between the logistics industry and digital economy led to an evident characteristic of ‘high in the east and low in the west.’ However, the regional differences are gradually decreasing over time. (2) The influence of the integration between the logistics industry and digital economy on carbon emission is in an inverted U-shape when the integration degree of the two exceeds the inflection point value of 0.76 to play the role of carbon At present, only in Guangdong, the degree of integration crosses the inflection point and plays an environment-friendly digital role. (3) As a crucial external environmental condition affecting carbon emissions, the degree of integration between logistics and digital economy will play an early role in carbon emission reduction under environmental regulation. (4) The degree of integration of the logistics industry and digital economy achieves carbon emission reduction through strengthening energy use efficiency and technological progress, in which the mediating effect of energy consumption intensity accounts for 23.05% of the total effect, ranging from 18.82% to 31.68%; the mediating effect of technological progress accounts for 13.25% of the total effect, ranging from 12.67% to 14.40%.

Analisis Audit Energi di Gedung Teknik Elektro Universitas Diponegoro

An energy audit is a work that aims to identify energy consumption in buildings that have been built so that they can find opportunities for energy savings in these buildings and buildings and improve energy efficiency and conservation in these buildings. The Electrical Engineering Building is one of the buildings at Diponegoro University that has been built for a long time, and to find out the savings opportunities in the building, an energy audit is needed. The energy audit was carried out during the pandemic era where lectures were still being held online and hybrid. Based on the results of the energy audit that has been carried out, it was found that the Energy Consumption Intensity (IKE) in the Electrical Engineering Building in 2021 was 8.59 while in 2022 it was 12.61. This is still in the efficient category according to Regulation of the Minister of energy and mineral resources No. 13/2012. Opportunities for savings can be implemented starting from no cost, low cost, to high cost to increase the efficiency of energy use in the Diponegoro University Electrical Engineering Building, so that energy use can be very efficient.

Characterizing cooling load in multi-area airport terminal buildings: Clustering and uncertainty analysis for energy flexibility

An airport terminal building is a complex public transportation facility with an extremely high energy consumption intensity. Its numerous indoor areas with various functions have disparate characteristics of cooling loads, which poses a great challenge to energy-efficient design and operation of its air-conditioning system. In this paper, we investigated the characteristics of cooling loads in two typical multi-area airport terminal buildings by simulation. Clustering analysis was utilized to categorize the areas based on their distinct cooling load characteristics. Uncertainty analysis and Bayesian calibration were employed to reveal and compare the impact of primary parameters on these cooling loads. The indoor area of an airport terminal building can be classified into three categories according to their different characteristics of cooling loads. The result shows that outdoor temperature (Tout), outdoor relative humidity (RHout), and air change rate(α) significantly contribute to the variances among the cooling loads in different areas. The peak cooling loads of the three categories were identified as 182.8 W/m2, 76.1 W/m2, and 58.3 W/m2, respectively. The Bayesian calibration result demonstrates that the differences of the main parameters (i.e., Tin and RHin) between operation and design are the key factors causing the difference of cooling load between measurement and design. The potential for flexible cooling load adjustment across different area categories was also discussed, contributing to further quantification of the energy flexibility potential of airport terminal buildings. The unique value of this paper is manifested in the new approach it provides for understanding and managing cooling loads, contributing to more energy-efficient terminal design and operation.

Spatial heterogeneity and scenario simulation of carbon budget on provincial scale in China

BackgroundConducting an extensive study on the spatial heterogeneity of the overall carbon budget and its influencing factors and the decoupling status of carbon emissions from economic development, by undertaking simulation projections under different carbon emission scenarios is crucial for China to achieve its targets to peak carbon emissions by 2030 and to achieve carbon neutrality by 2060. There are large disparities in carbon emissions from energy consumption, the extent of land used for carbon absorption, and the status of decoupling of emissions from economic development, among various regions of China.ResultsBased on night light data and land use data, we investigated carbon budget through model estimation, decoupling analysis, and scenario simulation. The results show that the carbon deficit had a continuous upward trend from 2000 to 2018, and there was a significant positive spatial correlation. The overall status of decoupling first improved and then deteriorated. Altogether, energy consumption intensity, population density of built-up land, and built-up land area influenced the decoupling of carbon emissions from economic development. There are significant scenarios of carbon emissions from energy consumption for the study area during the forecast period, only in the low-carbon scenario will the study area reach the expected carbon emissions peak ahead of schedule in 2027; the peak carbon emissions will be 6479.27 million tons.ConclusionsChina’s provincial-scale carbon emissions show a positive correlation with economic development within the study period. It is necessary to optimize the economic structure, transforming the economic development mode, and formulating policies to control the expansion of built-up land. Efforts must be made to improve technology and promote industrial restructuring, to effectively reduce energy consumption intensity.