Energy Consumption Perspective Research Articles

Will it get there? A deep learning model for predicting next-trip state of charge in Urban Green Freight Delivery with electric vehicles

To enhance urban freight efficiency and green development, China has implemented the Urban Green Freight Delivery (UGFD) project, which includes optimizing vehicle traffic control policies and increasing the number of new energy vehicles (NEV). However, range anxiety is a significant challenge for freight drivers performing delivery tasks with electric vehicles (a major component of NEV). We constructed a prediction model for the state of charge (SOC), or battery remaining energy percentage when UGFD vehicles reach the next trip point, aiming to alleviate this issue. The model consists of three modules: (1) a vehicle SOC context prediction module, (2) a vehicle energy consumption prediction module, and (3) a multi-perspective SOC prediction value fusion module. Specifically, in the SOC context prediction module, historical SOC sequences, vehicle status (loading/unloading, charging), and time intervals between SOC points are used to accurately describe context change trends, and directly predict the vehicle SOC at the next trip point. The energy consumption prediction module combines community-level and grid-level geographical location information for the vehicle stops using weather, vehicle parameters, etc., to model the spatial dynamic correlation of energy consumption. The vehicle SOC at the next trip point is the difference between the current vehicle SOC and the predicted energy consumption. The multi-perspective SOC prediction value fusion module is a combination of the predicted values from the context and energy consumption perspectives, resulting in the final vehicle SOC prediction value. Taking Suzhou, China as an example, the results show that the mean absolute error, root mean square error, and symmetric mean absolute percentage error for the constructed model are 23.67%, 10.39%, and 20.03% less, respectively, than for the baseline models focusing on SOC short-term time series prediction. The research results can provide scientific evidence for formulating refined energy management, charging station layout, and freight delivery optimization approaches.

Research on Rock Energy Constitutive Model Based on Functional Principle

The essence of rock fracture can be broadly categorized into four processes: energy input, energy accumulation, energy dissipation, and energy release. From the perspective of energy consumption, the failure of rock materials must be accompanied by energy dissipation. Dissipated energy serves as the internal driving force behind rock damage and progressive failure. Given that the process of rock loading and deformation involves energy accumulation and dissipation, the rock constitutive model theory is expanded by incorporating energy principles. By introducing the dynamic energy correction coefficient, according to the law of the conservation of energy, the total energy exerted by external loads on rocks is equal to the energy dissipated through the dynamic energy inside the rocks. A new type of energy constitutive model is established through the functional principle and momentum principle. To validate the model’s accuracy, a triaxial compression test was conducted on sandstone to examine the stress–strain behavior of the rock during the failure process. A sensitivity analysis of the parameters introduced into the model was conducted by comparing the model results, which helped to clarify the innate laws of significance of these parameters. The results indicated that the energy model more accurately captures the non-linear mechanical behavior of sandstone under high-stress loading conditions. The model curve fits the test data to a high degree. The fitting curve was basically consistent with the changing trend of the test curve, and the correlation coefficients were all above 0.90. Compared with other models, the model based on the energy principle not only accurately reflects the rock’s stress–strain curve, but also reflects the energy change law of rock. This has reference value for the safety analysis of rock mass engineering under loading conditions and aids in the development of anchoring and support schemes. The research results can fill in the blanks that exist in the energy method in terms of rock deformation and failure and provide a theoretical basis for deep rock engineering. Moreover, this research can further improve and extend the rock mechanics research system based on energy.

Analysis of the Measurement of Transportation Carbon Emissions and the Emission Reduction Path in the Yangtze River Economic Belt under the Background of “Dual Carbon” Goals

Carbon emissions from the Yangtze River Economic Belt are an important element of China’s carbon emission endeavor, and a study of its emission reduction pathway can provide a reference for the country’s overall management of carbon emission reduction. From the perspective of energy consumption, this paper uses the carbon emission factor method to estimate the carbon emissions of the transportation industry in the Yangtze River Economic Belt during 2006–2020, based on the extended STIRPAT model, considering the influence of seven factors, i.e., population size, urbanization rate, GDP per capita, transportation added value, energy structure, energy intensity, and transportation intensity, on carbon emissions. Based on these factors, a scenario analysis, combined with a forecasting model, is used to predict the peak carbon performance of the transportation industry under different development scenarios. The results show that the overall carbon emissions of transportation in the YEB from 2006 to 2020 show a fluctuating upward trend, and the downstream carbon emissions are significantly higher than those in other regions. The main factors influencing carbon emissions from transportation in different upstream, midstream, and downstream regions vary, with both population and economic factors contributing to carbon emissions, while technical factors affect them differently. There are significant differences in the peak carbon performance of transportation under different development scenarios, and the government should take effective measures to work towards achieving the goals of the low-carbon or enhanced low-carbon scenarios.

Hydrogen pressure-based comparative and applicability analysis of different innovative Claude cycles for large-scale hydrogen liquefaction

The Claude cycle is the preferred option for large-scale hydrogen liquefaction. However, the thermophysical properties of hydrogen vary substantially around the critical temperature and are strongly affected by hydrogen pressure. This leads to the fact that the classic Claude cycle is not universal for hydrogen at different pressures. Three innovative Claude cycles have been proposed. The applicability of three hydrogen liquefaction cycles is evaluated from specific energy consumption (SEC) perspective. The classic Claude cycle (Cycle 1) is suitable for the liquefaction of hydrogen with pressure above 3500 kPa, and the SEC for the liquefaction of hydrogen at 3500 kPa is 5.02 kWh/kgLH2. The Claude cycle (Cycle 2) with two cross-arranged refrigeration cycles is suitable for hydrogen with pressure between 2200 and 3500 kPa, and the SEC is 4.98 kWh/kgLH2 for the liquefaction of hydrogen at 2500 kPa. The Claude cycle (Cycle 3) with a split-flow refrigeration cycle is suitable for hydrogen with pressure less than 2200 kPa, and the SEC for the liquefaction of hydrogen at 1500 kPa is 5.27 kWh/kgLH2. Furthermore, the reasons for the SEC and applicability of three cycles being affected by hydrogen pressure are investigated by means of parametric analysis, heat exchanger performance analysis, and exergy analysis.

Dynamic mechanism of CO2 capture from flue gas via hydrate-based method induced by using IB group transition metal functionalized carbon nanotubes

Capturing CO2 emitted from industrial power plants is the most efficient method for reducing global carbon emissions at present. In this work, the enhanced dynamic mechanism of CO2 capture from flue gas has been investigated through IB group transition metal functionalized carbon nanotubes (CNTs) as kinetic accelerators via the hydrate-based approach in combination with theoretical calculations and experiments. The computational results revealed that the both the hydrophilic oxygen-containing group and Cu modified CNTs not only enhanced the CO2 adsorption ability but also exhibited high selectivity towards CO2. Importantly, experiments demonstrated that the multi-walled carbon nanotubes (MWCNTs) based accelerators significantly enhanced the dynamic performance of the hydrate formation, substantially increasing gas consumption and CO2 separation efficiency. Particularly, the theoretical selected Cu@FMWCNTs mixed with THF accelerators exhibited superior kinetic indexes during the hydrate formation even under relative low pressure at 1.5 MPa with the largest CO2 separation efficiency of 76.49%, which showed Cu@FMWCNTs to be a potential kinetic accelerator for CO2 capture. This study could provide valuable findings for nanomaterial design of novel hydrate accelerators and facilitate the development of the hydrate-based technology for CO2 capture from the economic perspective of low energy consumption.

Energy-Efficient, Cluster-Based Routing Protocol for Wireless Sensor Networks Using Fuzzy Logic and Quantum Annealing Algorithm.

The main limitation of wireless sensor networks (WSNs) lies in their reliance on battery power. Therefore, the primary focus of the current research is to determine how to transmit data in a rational and efficient way while simultaneously extending the network's lifespan. In this paper, a hybrid of a fuzzy logic system and a quantum annealing algorithm-based clustering and routing protocol (FQA) is proposed to improve the stability of the network and minimize energy consumption. The protocol uses a fuzzy inference system (FIS) to select appropriate cluster heads (CHs). In the routing phase, we used the quantum annealing algorithm to select the optimal route from the CHs and the base station (BS). Furthermore, we defined an energy threshold to filter candidate CHs in order to save computation time. Unlike with periodic clustering, we adopted an on-demand re-clustering mechanism to perform global maintenance of the network, thereby effectively reducing the computation and overhead. The FQA was compared with FRNSEER, BOA-ACO, OAFS-IMFO, and FC-RBAT in different scenarios from the perspective of energy consumption, alive nodes, network lifetime, and throughput. According to the simulation results, the FQA outperformed all the other methods in all scenarios.

Investigation on Accelerated Initiation of Oblique Detonation Wave Induced by Laser-Heating Hot-Spot

A reliable initiation of oblique detonation is critical in oblique detonation engines, especially for oblique detonation engines under extreme conditions such as a high altitude and low Mach number, which may lead to excessive length of the induction zone and even the phenomenon of extinction. In this paper, surface ignition was applied to the initiation of oblique detonation, and a high-temperature region was set on the wedge to simulate the presence of a hot-spot induced by the laser heating. The two-dimensional multi-component Navier–Stokes equations considering a detailed H2 combustion mechanism are solved, and the oblique detonation wave accelerated by a hot-spot is studied. In this paper, hot-spots in the induction zone on the wedge, are introduced to explore the possibility of hot-spot initiation, providing a potential method for initiation control. Results show that these methods can effectively promote the accelerated initiation of the oblique detonation. Furthermore, the hot-spot temperature, size and position are varied to analyze their effects on the initiation position. Increasing the temperature and size of the hot-spot both can accelerate initiation, but from the perspective of energy consumption, a small hot-spot at a high temperature is preferable for accelerating ODW initiation than a large hot-spot at a low temperature. The initiated position of the oblique detonation is sensitive to the position of the hot-spots; if a 2000 K hotspot is at the beginning of the wedge, then the ODW’s initiation distance will be reduced to about 30% of that without hotspot acceleration.

Innovating for cleaner skies: A study on the impact of China's national innovation demonstration zones on urban air quality from the perspective of energy consumption

In pursuit of innovation-driven green development, effectively leveraging innovation policies for environmental betterment has emerged as a pivotal challenge in China's sustainable development trajectory. Investigating 287 Chinese cities from 2010 to 2020, this study adopts the pilot initiative of National Innovation Demonstration Zones (NIDZs), a cornerstone of China's regional innovation strategy, as a quasi-natural experimental setup. Utilizing a double machine learning method, we probe into the implications of implementation National Innovation Demonstration Zones pilot policy on urban air quality, its underlying mechanisms, and the regional heterogeneity in policy outcomes. The empirical evidence underscores that implementation National Innovation Demonstration Zones pilot policy significantly bolster urban air quality, a result that persists endogenous controls and rigorous robustness checks. Mechanistically, setting up the National Innovation Demonstration Zone can significantly curb the deterioration of urban air quality by reducing the total energy consumption of the whole society, including the reduction of total electricity consumption, the use of manufactured gas and natural gas, and the consumption of liquefied petroleum gas. Regionally, the Southeast demonstrates enhanced efficacy in mitigating air quality degradation and PM2.5 concentration, whereas the Northwest sees pronounced reductions in urban carbon dioxide emissions following the implementation National Innovation Demonstration Zones pilot policy.

Insights into Bitcoin and energy nexus. A Bitcoin price prediction in bull and bear markets using a complex meta model and SQL analytical functions

Cryptocurrencies are in the center of attention of investors, public authorities and researchers, but the interest has shifted from purely financial aspects regarding the way of trading, lack of regulation and supervision of transactions, volatility, correlation with other assets to aspects related to sustainability taking in account the high energy consumption generated by the mining process and the impact on environmental pollution. Bitcoin was chosen for the research considering the dominance that this financial asset has on the cryptocurrency market and its position as alpha currency.The article focuses on the relationship between Bitcoin transactions and energy consumption, for period 1st January 2019—31st of May 2022, this interval having significant price movements. The authors made a prediction of the Bitcoin price using a complex meta-model and SQL analytical functions. The analysis is based on 15 fundamental variables in order to forecast the price: Bitcoin data (prices and volume), electricity price and traded quantity on day-ahead market (DAM), gas price and traded quantity on DAM, inflation in EU, EU-ETS emissions certificates and oil prices. The study reveals the importance of the relationship Bitcoin—energy—carbon emissions, elements that capture the impact of the mining process on the environment from the perspective of energy consumption. Investors on the Bitcoin market must be aware not only of the importance of financial aspects on the price of cryptocurrencies (inflation, demand, offer), but also of other elements related to the evolution of energy prices (electricity, oil, gas, renewable energy) and the evolution of emissions certificates prices. Considering the promotion of the principles of sustainable development on the capital market, portfolio investors have become increasingly attentive to the social and environmental performance of financial assets. This study aims to make financial market players aware of the non-financial implications of their transactions. In addition, the energy transition and the reconfiguration of the energy mix are elements of impact on the cryptocurrency market through the technical levers involved in the mining process.

Anomaly Detection and Identification Method for Shield Tunneling Based on Energy Consumption Perspective

Anomaly Detection and Identification Method for Shield Tunneling Based on Energy Consumption Perspective

An environmental perspective of energy consumption, overpopulation, and human capital barriers in South Asia

Prior literature is substantive in highlighting the nexus between pollutant and socio-economic predictors; however, the role of human interaction has not been sufficiently explored. Thus, the present study examines the validity of the environmental Kuznets Curve (EKC) hypothesis in the presence of energy consumption, overpopulation, and human capital index in five South Asian countries. It employs fixed effects, random effects, and dynamic panel causality techniques with a set of panel data from 1972 to 2021. The baseline results validate the existence of the EKC hypothesis in the recipient panel. Nevertheless, the findings reveal that energy consumption and population density have positive effects, while human capital has negative impacts on CO2 emissions. Furthermore, the study observes that energy consumption and per capita GDP have a significant causal link with CO2 emissions, whereas CO2 emissions are evident to have causality with population density and human capital index. The results are robust and suggest that the consolidation of an effective regulatory framework and technological improvements are substantial measures to improve environmental quality in South Asia. Moreover, allocating sufficient resources to uplift contemporary educational and health status would be imperative to improving environmental quality as aspired to by the Paris Agreement.

Dynamic Voltage and Frequency Scaling as a Method for Reducing Energy Consumption in Ultra-Low-Power Embedded Systems

Dynamic voltage and frequency scaling (DVFS) is a technique used to optimize energy consumption in ultra-low-power embedded systems. To ensure sufficient computational capacity, the system must scale up its performance settings. The objective is to conserve energy in times of reduced computational demand and/or when battery power is used. Fast Fourier Transform (FFT), Cyclic Redundancy Check 32 (CRC32), Secure Hash Algorithm 256 (SHA256), and Message-Digest Algorithm 5 (MD5) are focused functions that demand computational power to achieve energy-efficient performance. Selected operations are analyzed from the energy consumption perspective. In this manner, the energy required to perform a specific function is observed, thereby mitigating the influence of the instruction set or system architecture. For stable operating voltage scaling, an exponential model for voltage calculation is presented. Statistical significance tests are conducted to validate and support the findings. Results show that the proposed optimization technique reduces energy consumption for ultra-low-power applications from 27.74% to up to 47.74%.

Facile preparation of low temperature carbon dots with long-wavelength emission and their sensing applications for crystal violet

Crystal violet (CV) is one of the main components of common fungicides in daily life, which has inhibitory effect on gram-positive bacteria. However, CV remains in the environment for a long time and have potential risk of disease. Therefore, it is necessary to develop effective methods for detecting CV. Low-temperature carbon dots (LT-CDs) are studied to provide a new idea for the development of CDs green preparation technology from the perspective of low energy consumption. In this experiment, LT-CDs with long-wavelength emission were prepared based on the oxidation, cross-linking polymerization and Schiff base reaction using o-phenylenediamine and hydroquinone as carbon source at low temperature, and were characterized by various techniques. It was found that LT-CDs could be used as a fluorescent probe for quantitative detection of CV based on the inner filter effect, and the practicability of the method was verified by real samples.

Carbon emission scenario prediction for highway construction projects

In order to carry out the research on the measurement and prediction of carbon emissions from highway projects, the prediction index system is constructed by selecting seven influencing factors, namely, urbanization rate, industrial structure, energy intensity, energy structure, key low-carbon technologies, highway construction planning, and green financial support. The carbon emission coefficient method is used to measure the carbon emissions of highway projects from the perspective of energy consumption, and finally, a carbon emission scenario prediction model based on the scenario analysis method and the LSTM algorithm of the recurrent neural network is constructed to put forward a new way of predicting the carbon emissions of highway projects. Taking Hunan province as a case study object, the carbon emission trends in the next 15 years are predicted under three scenarios: low carbon, baseline, and high carbon. The results show that the highway project in Hunan province is expected to peak in 2031 under the low-carbon scenario, in 2037 under the baseline scenario, and around 2044 under the high-carbon scenario. It is also found that energy saving and emission reduction measures have a certain lag in the emission reduction effect of highway projects. Finally, based on this, suggestions and countermeasures for carbon emission reduction of highway development in Hunan province are proposed.

Energy efficient dry-storage systems in the semiconductor manufacturing industry

The semiconductor industry is a rapidly-changing one and many new electronic devices are being developed every year. As components get smaller and thinner, humidity-related problems increase since moisture can easily penetrate to critical areas. There are various techniques to prevent moisture ingress during manufacturing and assembly. One of the best methods is to store semiconductor devices in dry storage enclosures (also known as Dry Boxes) between consequent processes and operations. However, since they are typically supplied with Compressed Dry Air (CDA), Dry Boxes tend to be energy intensive. By using an industrial setup at a manufacturing facility in Malta as a case study, an empirical assessment was carried out to investigate the use of Dry Boxes from an energy consumption perspective. The energy performance of Dry Boxes using CDA was compared to that of Dry Boxes using a desiccant-rotor technology. The relative humidity and air temperature inside the Dry Box were monitored in order to assess the behaviour of both technologies with time. An analysis of the Dry Box conditions was conducted to better understand the sources of any existing losses and to identify whether scope for improvement, if any, existed. The study showed that even if the drying technology on the supply side is not changed, improvements on the demand side can have an effective reduction in resource consumption. Using the desiccant rotor instead of CDA to dehumidify the Dry Box did not decrease the energy consumption. However, it was positively concluded that by reducing the infiltration rate and the unoccupied internal volume of the Dry Box, the CDA consumption and hence the energy and carbon footprint of the process decreased by 26%, contributing towards the sustainability goals of the manufacturing industry.

A Survey on Extraterrestrial Habitation Structures with a Focus on Energy-Saving 3D Printing Techniques

In the past two decades, various space agencies have shown great enthusiasm for constructing habitable structures on lunar and Martian surfaces. Consequently, several extraterrestrial structures have been proposed by different researchers. Nevertheless, only a small number of those structures are energy-efficient and cost-effective. In this research, a comprehensive review of the proposed extraterrestrial structures has been conducted. The objective is to evaluate different habitat construction techniques from technical, economic, and energy-consumption perspectives. To carry this out, different proposed structures are elaborated, and their advantages and limitations are discussed. The primary focus is on the 3D printing technique, which has demonstrated significant potential in automated manufacturing tasks. From the conducted research, it was found that the combination of 3D-printed components along with an internal breathable inflatable module is the most promising technique for habitat development on the Moon and Mars. Moreover, the microwave sintering method was identified as the most energy-saving and reliable approach for melting the on-site regolith for use in the 3D printing process. This survey has applied a multidisciplinary approach to evaluate the most energy-saving planetary construction techniques that are economically crucial for different private or government-funded space agencies.

Energy-efficient data collection over underwater MI-assisted acoustic cooperative MIMO WSNs

Underwater magnetic induction (MI)-assisted acoustic cooperative multiple-input-multiple-output (MIMO) has been recently proposed as a promising technique for underwater wireless sensor networks (UWSNs). For the more, the energy utilization of energy-constrained sensor nodes is one of the key issues in UWSNs, and it relates to the network lifetime. In this paper, we present an energy-efficient data collection for underwater MI-assisted acoustic cooperative MIMO wireless sensor networks (WSNs), including the formation of cooperative MIMO and relay link establishment. Firstly, the cooperative MIMO is formed by considering its expected transmission range and the energy balance of nodes with it. Particularly, from the perspective of the node's energy consumption, the expected cooperative MIMO size and the selection of master node (MN) are proposed. Sequentially, to improve the coverage of the networks and prolong the network lifetime, relay links are established by relay selection algorithm that using matching theory. Finally, the simulation results show that the proposed data collection improves its efficiency, reduces the energy consumption of the master node, improves the networks' coverage, and extends the network lifetime.

Reuse of engineering waste soil and recycled fine aggregate to manufacture eco-friendly unfired clay bricks: Experimental assessment, data-driven modeling and environmental friendliness evaluation

In order to explore the possible application of engineering waste soil (EWS) and recycled fine aggregate (RFA) in cement-based unfired clay bricks (CUCB), this paper utilizes the orthogonal experiment to investigate the combined utilization of EWS and RFA to fabricate eco-friendly CUCB. The study employs an L9 (34) orthogonal table with four factors and three levels for designing mix proportions. The factors include the ratios of water-to-cement (w/c), cement-to-EWS (c/e), RFA-to-EWS (r/e) and additive content. CUCB were tested for density, compressive strength, and flexural strength; and the impact of each factor on these properties was analyzed. The test results reveal that the eco-friendly CUCB exhibit lightweight characteristics and desirable mechanical properties. The ratio of flexural strength-to-compressive strength for eco-friendly CUCB ranges from 0.23 to 0.28, indicating that eco-friendly CUCB have good toughness. Data-driven models were developed to construct the relationships between target properties (i.e., density, compressive strength, and flexural strength) and four factors. The optimal mix proportion for physical and mechanical properties was determined to be w/c= 0.55, c/e = 0.3, r/e = 0.4 and additive content= 10%, with predicted compressive and flexural strengths of 19.68 MPa and 5.19 MPa, respectively. In addition, Scanning Electron Microscope (SEM) was performed to figure out the strength enhancement mechanism of eco-friendly CUCB with varying mix proportions. Environmental friendliness evaluation shows that the optimal mix pro-portion is more environmentally friendly to fabricate CUCB from the perspective of strength-normalized carbon footprint and energy consumption. Using large quantity of cement cannot increase the compressive strength and only acts as filler in the microstructure of eco-friendly CUCB.

Neural energy computations based on Hodgkin-Huxley models bridge abnormal neuronal activities and energy consumption patterns of major depressive disorder

Limited by the current experimental techniques and neurodynamical models, the dysregulation mechanisms of decision-making related neural circuits in major depressive disorder (MDD) are still not clear. In this paper, we proposed a neural coding methodology using energy to further investigate it, which has been proven to strongly complement the neurodynamical methodology. We augmented the previous neural energy calculation method, and applied it to our VTA-NAc-mPFC neurodynamical H–H models. We particularly focused on the peak power and energy consumption of abnormal ion channel (ionic) currents under different concentrations of dopamine input, and investigated the abnormal energy consumption patterns for the MDD group. The results revealed that the energy consumption of medium spiny neurons (MSNs) in the NAc region were lower in the MDD group than that of the normal control group despite having the same firing frequencies, peak action potentials, and average membrane potentials in both groups. Dopamine concentration was also positively correlated with the energy consumption of the pyramidal neurons, but the patterns of different interneuron types were distinct. Additionally, the ratio of mPFC's energy consumption to total energy consumption of the whole network in MDD group was lower than that in normal control group, revealing that the mPFC region in MDD group encoded less neural information, which matched the energy consumption patterns of BOLD-fMRI results. It was also in line with the behavioral characteristics that MDD patients demonstrated in the form of reward insensitivity during decision-making tasks. In conclusion, the model in this paper was the first neural network energy computational model for MDD, which showed success in explaining its dynamical mechanisms with an energy consumption perspective. To build on this, we demonstrated that energy consumption levels can be used as a potential indicator for MDD, which also showed a promising pipeline to use an energy methodology for studying other neuropsychiatric disorders.

Specific energy consumption based comparison of distributed additive and conventional manufacturing: From cradle to gate partial life cycle analysis

The additive manufacturing (AM) paradigm, especially in the distributed manufacturing setting, is a novel paradigm that can bring production of components closer to the consumers, at potentially lower economic and environmental costs. In this paper, we present a comparative analysis of AM with conventional manufacturing (CM) from the energy consumption perspective to assess under what conditions AM is better—a key gap in the area. Leveraging an energy consumption survey between CM and AM supply chains, this paper analyzes the significance of transportation in a cradle-to-gate partial life cycle analysis. Though the decentralization of AM has been discussed in energy consumption assessments, it has not been discussed in terms of how the distributed components and their transportation would affect the two supply chains' scenarios. This gap motivated this research to conduct energy efficiency assessment of AM and its comparison with CM in a distributed supply chain. Our analysis showed that transportation contributed 3x to 4.5x increase in the overall energy impact in CM compared to corresponding transportation in the AM setting. In effect, from our analysis, for every three CM parts, the transportation energy consumption approximately equaled the energy needed to manufacture one similar part in AM. Our analysis leverages the solid-to-envelope ratio “α”, defined as the volume of solid material within a part's envelope. Based on our analysis, AM would be the preferred production method for α values falling within the range of 0.08–0.22 (8–22% of raw material efficiency in CM). Hence part geometry, where AM has greater potential for energy savings by reduction in the amount of material waste, is a good indicator for using AM over CM for energy efficiency. We also discuss several future directions for better assessment of AM versus CM under different settings and upcoming innovations such as Industry 4.0.