Towards context-independent indicators for an unbiased assessment of environmental sustainability in higher education: An application to Italian universities.

Higher education institutes (HEIs) are important drivers for the development and implementation of best practices for environmental sustainability. However, reliable indicators are needed to objectively evaluate the environmental performance of HEIs and their policies. The present paper aims at identifying suitable indicators for unbiased comparisons among different HEIs and for the identification of temporal trends in terms of environmental sustainability performance. At this aim, sustainability reports made publicly available by 24 Italian HEIs over a 10-year period were considered. Normalization of sustainability variables such as the annual electrical and thermal energy consumptions, related greenhouse gas emissions, and water consumption, against context-specific factors such as the number of users of each university, latitude, illuminance, heating degree days (HDDs) and cooling degree days allowed identifying the actual possible disturbance of the same variables. HDDs were found to positively affect the thermal energy consumption and the related CO2 emissions. Based on this, a novel indicator was formulated where the actual value of thermal energy consumption and the related CO2 emissions are divided not only by the number of users but also by the HDDs of the HEIs' locations. Indeed, this is a remarkable finding that, prior to confirmation with data from world HEIs, could be implemented in world university green ranking systems for improved and less biased sustainability assessments.

Carbon Dots: A Review with Focus on Sustainability.

Carbon dots (CDs) are an emerging class of nanomaterials with attractive optical properties, which promise to enable a variety of applications. An important and timely question is whether CDs can become a functional and sustainable alternative to incumbent optical nanomaterials, notably inorganic quantum dots. Herein, the current CD literature is comprehensively reviewed as regards to their synthesis and function, with a focus on sustainability aspects. The study quantifies why it is attractive that CDs can be synthesized with biomass as the sole starting material and be free from toxic and precious metals and critical raw materials. It further describes and analyzes employed pretreatment, chemical-conversion, purification, and processing procedures, and highlights current issues with the usage of solvents, the energy and material efficiency, and the safety and waste management. It is specially shown that many reported synthesis and processing methods are concerningly wasteful with the utilization of non-sustainable solvents and energy. It is finally recommended that future studies should explicitly consider and discuss the environmental influence of the selected starting material, solvents, and generated byproducts, and that quantitative information on the required amounts of solvents, consumables, and energy should be provided to enable an evaluation of the presented methods in an upscaled sustainability context.

Complex artificial intelligence models for energy sustainability in educational buildings.

Energy consumption of constructed educational facilities significantly impacts economic, social and environment sustainable development. It contributes to approximately 37% of the carbon dioxide emissions associated with energy use and procedures. This paper aims to introduce a study that investigates several artificial intelligence-based models to predict the energy consumption of the most important educational buildings; schools. These models include decision trees, K-nearest neighbors, gradient boosting, and long-term memory networks. The research also investigates the relationship between the input parameters and the yearly energy usage of educational buildings. It has been discovered that the school sizes and AC capacities are the most impact variable associated with higher energy consumption. While 'Type of School' is less direct or weaker correlation with 'Annual Consumption'. The four developed models were evaluated and compared in training and testing stages. The Decision Tree model demonstrates strong performance on the training data with an average prediction error of about 3.58%. The K-Nearest Neighbors model has significantly higher errors, with RMSE on training data as high as 38,429.4, which may be indicative of overfitting. In contrast, Gradient Boosting can almost perfectly predict the variations within the training dataset. The performance metrics suggest that some models manage this variability better than others, with Gradient Boosting and LSTM standing out in terms of their ability to handle diverse data ranges, from the minimum consumption of approximately 99,274.95 to the maximum of 683,191.8. This research underscores the importance of sustainable educational buildings not only as physical learning spaces but also as dynamic environments that contribute to informal educational processes. Sustainable buildings serve as real-world examples of environmental stewardship, teaching students about energy efficiency and sustainability through their design and operation. By incorporating advanced AI-driven tools to optimize energy consumption, educational facilities can become interactive learning hubs that encourage students to engage with concepts of sustainability in their everyday surroundings.

Assessing the environmental impacts of clean energy investment in Pakistan using a dynamic autoregressive distributed lag model.

In this study, the authors projected the impacts of clean energy investment on environmental degradation by applying a novel and dynamic Autoregressive Distributed Lag (DARDL) model for Pakistan from 1990 to 2022. Most researchers have used ecological footprint or CO2 emissions indicators to look at how clean energy investment affects environmental degradation, which primarily represents contamination induced by humans' consumption patterns and does not consider the impact of the supply side. Against this background, the study scrutinized the dynamic interaction between clean energy investment and environmental sustainability using the load capacity factor (LCF) as an ecological indicator in Pakistan, including economic growth, population density, trade openness, urbanization, and industrialization in the analysis. The long-run estimates from DARDL indicate that a 1 percent upsurge in clean energy investment mitigates environmental degradation by approximately 0.42 percent on average, controlling for other factors. Further, the study also revealed that a 1 percent increase in clean energy investment diminishes dirty energy consumption by approximately 0.45 percent. The validity of the findings is confirmed using alternate methods, i.e., KRLS. The study recommends that Pakistan prioritize investment in clean energy projects to promote environmental sustainability and enforce environmental regulations to reduce the adverse externalities associated with dirty energy activities.

Sustainable radiology departments: A European survey to explore radiographers' perceptions of environmental and energy sustainability issues.

INTRODUCTION: The environmental impact of radiology and radiotherapy activities is influenced by the energy consumption of equipment, the life cycle of consumables, waste generation, and CO2 emissions caused by staff travel. This study aims to investigate radiographers' perception and knowledge of environmental sustainability issues. METHODS: An online survey was created and distributed to European radiographers and therapeutic radiographers. The survey questions (n = 43) include demographic data; questions on their perceptions and actions regarding environmental sustainability in healthcare, energy consumption, emissions from staff travel, waste generation from radiological procedures; the role of radiographers in addressing sustainability issues within their departments. RESULTS: A total of 253 responses were collected from 27 European countries. About their perception on sustainability issues, most participants considered environmental sustainability in healthcare as very important. According to 63.6% (n = 161) of respondents, the energy consumption of radiological equipment is the major source of environmental footprints from radiology activities. Additionally, 44.7% (n = 113) believe that conducting diagnostic examinations remotely could reduce environmental footprints from staff commuting About their actions at workplace, over 70% (n = 192) reported turning off devices after use. Attention to waste recycling is high, but limited to paper, plastic and glass. Contrast agents recycling procedures are implemented by 13% (n = 33). The absence or unawareness of environmental sustainability procedures in the workplace was reported by 66% (n = 167). Radiographers could play an active role in environmental sustainability programs for 243 (96.1%) participants. CONCLUSION: This study provides a comprehensive overview of European radiographers' knowledge and perceptions concerning environmental sustainability issues. While radiographers recognize the importance of a green radiology department, significant gaps remain in their understanding of eco-friendly initiatives in radiology units' activities. IMPLICATION FOR PRACTICE: Enhancing radiographers' skills with sustainability expertise could promote a greener culture within radiology departments.

Joint Optimization of Age of Information and Energy Consumption in NR-V2X System Based on Deep Reinforcement Learning.

As autonomous driving may be the most important application scenario of the next generation, the development of wireless access technologies enabling reliable and low-latency vehicle communication becomes crucial. To address this, 3GPP has developed Vehicle-to-Everything (V2X) specifications based on 5G New Radio (NR) technology, where Mode 2 Side-Link (SL) communication resembles Mode 4 in LTE-V2X, allowing direct communication between vehicles. This supplements SL communication in LTE-V2X and represents the latest advancements in cellular V2X (C-V2X) with the improved performance of NR-V2X. However, in NR-V2X Mode 2, resource collisions still occur and thus degrade the age of information (AOI). Therefore, an interference cancellation method is employed to mitigate this impact by combining NR-V2X with Non-Orthogonal multiple access (NOMA) technology. In NR-V2X, when vehicles select smaller resource reservation intervals (RRIs), higher-frequency transmissions use more energy to reduce AoI. Hence, it is important to jointly considerAoI and communication energy consumption based on NR-V2X communication. Then, we formulate such an optimization problem and employ the Deep Reinforcement Learning (DRL) algorithm to compute the optimal transmission RRI and transmission power for each transmitting vehicle to reduce the energy consumption of each transmitting vehicle and the AoI of each receiving vehicle. Extensive simulations demonstrate the performance of our proposed algorithm.

Analysis of Energy and Material Consumption for the Manufacturing of an Aeronautical Tooling: An Experimental Comparison between Pure Machining and Big Area Additive Manufacturing.

Additive manufacturing (AM) has been fully incorporated into both the academic and the industrial world. This technology has been shown to lower costs and environmental impacts. Moreover, AM-based technologies, such as wire arc additive manufacturing (WAAM), have been proven suitable for the manufacturing of large products with significant mechanical requirements. This study examines the manufacture of two aeronautical toolings: first, using conventional techniques, and second, using a big area additive manufacturing (BAAM) process, specifically WAAM technology, followed by second-stage hybrid machining. Both toolings can be considered interchangeable in terms of design and performance. Energy and material consumption were analysed and compared throughout both tooling procedures. The results show the important optimisation of both procedures in manufacturing WAAM tooling, encompassing the additive process and second-stage hybrid machining. Nevertheless, the time required for WAAM tooling manufacturing increased significantly compared to conventional manufacturing tooling. Moreover, based on metrology data from the AM process, a theoretical study was conducted to assess different design optimisations for WAAM tooling manufacturing and determine their influence on material and energy consumption. These theoretical results improve those already obtained regarding energy and raw material savings.

Attojoule Hexagonal Boron Nitride-Based Memristor for High-Performance Neuromorphic Computing.

In next-generation neuromorphic computing applications, the primary challenge lies in achieving energy-efficient and reliable memristors while minimizing their energy consumption to a level comparable to that of biological synapses. In this work, hexagonal boron nitride (h-BN)-based metal-insulator-semiconductor (MIS) memristors operating is presented at the attojoule-level tailored for high-performance artificial neural networks. The memristors benefit from a wafer-scale uniform h-BN resistive switching medium grown directly on a highly doped Si wafer using metal-organic chemical vapor deposition (MOCVD), resulting in outstanding reliability and low variability. Notably, the h-BN-based memristors exhibit exceptionally low energy consumption of attojoule levels, coupled with fast switching speed. The switching mechanisms are systematically substantiated by electrical and nano-structural analysis, confirming that the h-BN layer facilitates the resistive switching with extremely low high resistance states (HRS) and the native SiOx on Si contributes to suppressing excessive current, enabling attojoule-level energy consumption. Furthermore, the formation of atomic-scale conductive filaments leads to remarkably fast response times within the nanosecond range, and allows for the attainment of multi-resistance states, making these memristors well-suited for next-generation neuromorphic applications. The h-BN-based MIS memristors hold the potential to revolutionize energy consumption limitations in neuromorphic devices, bridging the gap between artificial and biological synapses.

Non-chemical weed management: Harnessing flame weeding for effective weed control.

The goal of the current study was to create and assess the effectiveness of a hand-pulled ergonomically designed flame weeder. The developed weeder was tested in the field at three operating pressures (20, 30 and 40 Psi) and forward speeds (1.00, 1.25 and 1.50 km/h) to study their effects on plant damage, survival rates, weight preservation rates, weed management effectiveness, soil temperatures, and gas and energy consumption. Thereafter, at optimized values of forward speed and operating pressure, a comparative assessment of flame weeding with traditional methods (mechanical and manual weeding) was done in terms of weed control effectiveness, operational time, energy consumption, and cost of operation. Results showed that the optimal performance of the designed flame weeder was achieved when operated at a speed of 1 km/h and an operating pressure of 40 psi. The survival rate, weight preservation rate, weed control efficiency, change in soil temperature, recovery rate, plant damage, gas consumption, and energy consumption were observed to be 27.3 %, 32.5 %, 91.1 %, 40.74 °C, 8.5 %, 2.2 %, 4.05 kg/h, and 2500.24 MJ/ha, respectively, at optimized values of forward speed (1.00 km/h) and operating pressure (40 Psi). The actual field capacity, field efficiency and operating cost of the flame weeder were 0.0755 ha/h, 94.94 %, and 3620.81 ₹/ha, respectively. Hand weeding had the best level of weed control effectiveness, but it was a laborious, time-consuming process. When compared to manual weeding, flame weeding was 50.42 % cheaper and 94.82 % faster.

The impact of education expenditure on environmental innovation.

Growing environmental challenges necessitate increased focus on sustainability education. This study examines the effects of environmental education programs in China on air and water quality perception, waste reduction, and energy consumption reduction. A comparative quantitative design with 650 participants divided into four groups was employed. Data were collected using the Environmental Sustainability Assessment Survey (ESAS) instrument to assess environmental awareness and behavior changes. Statistical tests were used to identify significant differences between groups. Findings showed significant improvements in perceived air and water quality, with web-based programs demonstrating particular success. Waste reduction efforts also varied, with web-based education again proving effective. Energy consumption reduction was most evident in the corporate sector, where leadership in electric vehicles and sustainable transportation played a key role. Supportive government policies and environmental NGOs further highlighted the power of informed environmental decision-making. This study emphasizes the critical role of environmental education in addressing sustainability challenges. It empowers individuals and communities to actively engage in environmental conservation actively, fostering a harmonious relationship between humans and the environment. Our findings have global implications, highlighting education's vital role in shaping a sustainable future.

Evaluation of pulse-jet baghouse dust collectors' contribution to CO2 emissions.

Dust cleaning systems are mandatory for use almost in any manufacturing process. Their market size is expected at US$10.77 billion by 2030 growing from US$7.28 billion in 2022. Removing dust particles is the main purpose of these systems and they make an invaluable contribution to environmental safety. However, while cleaning the air from solid particles, industrial pulse-jet baghouse collectors have an additional impact on the environment that usually is not considered. An analysis of energy consumption at the manufacturing and operation stages of the baghouse dust collectors allows for the evaluation of CO2 emissions. The analysis shows that, given the current state of affairs in the industry, by 2030 manufacturing and operation of baghouse dust collectors over the world will emit 70+ million tons of carbon dioxide additionally to the levels of 2021. To reduce the CO2-related environmental impact of industrial pulse-jet baghouse collectors, among all scientific and technical measures, it is recommended to simply scale up the dust collection system, which involves replacing several low-capacity collectors with one general-capacity collector within one industrial enterprise. This allows for a reduction in energy consumption at the collector manufacturing stage from 3 to 10 times and also ensures a significant reduction in operation energy consumption of the dust collector during its service life.

GSHFA-HCP: a novel intelligent high-performance clustering protocol for agricultural IoT in fragrant pear production monitoring.

The agriculture Internet of Things (IoT) has been widely applied in assisting pear farmers with pest and disease prediction, as well as precise crop management, by providing real-time monitoring and alerting capabilities. To enhance the effectiveness of agriculture IoT monitoring applications, clustering protocols are utilized in the data transmission of agricultural wireless sensor networks (AWSNs). However, the selection of cluster heads is a NP-hard problem, which cannot be solved effectively by conventional algorithms. Based on this, This paper proposes a novel AWSNs clustering model that comprehensively considers multiple factors, including node energy, node degree, average distance and delay. Furthermore, a novel high-performance cluster protocol based on Gaussian mutation and sine cosine firefly algorithm (GSHFA-HCP) is proposed to meet the practical requirements of different scenarios. The innovative Gaussian mutation strategy and sine-cosine hybrid strategy are introduced to optimize the clustering scheme effectively. Additionally, an efficient inter-cluster data transmission mechanism is designed based on distance between nodes, residual energy, and load. The experimental results show that compared with other four popular schemes, the proposed GSHFA-HCP protocol has significant performance improvement in reducing network energy consumption, extending network life and reducing transmission delay. In comparison with other protocols, GSHFA-HCP achieves optimization rates of 63.69%, 17.2%, 19.56%, and 35.78% for network lifespan, throughput, transmission delay, and packet loss rate, respectively.

Mixing due to Solution Switch Limits the Performance of Electrosorption for Desalination.

Electrosorption (ES) is a research frontier in electrochemical separation, with proven potential applications in desalination, wastewater treatment, and selective resource extraction. However, due to the limited adsorption capacity of film electrodes, ES requires short circuiting or circuit reversal, accompanied by a solution switch between the feed solution and receiving solution, to sustain desalination over many charge-discharge cycles. In previously reported studies, solution switches have been commonly ignored to simplify experimental procedures, and their impacts on separation performance are thus not well understood. This study aims to provide a quantitative analysis of the impacts of mixing due to a solution switch on the performance of ES-based desalination. A numerical model of ES has been employed to evaluate the adverse effects of the solution switch on the desalination performance in three commonly used operation modes. The analysis reveals that the impacts of mixing due to solution-switch are more severe with a larger concentration difference between the desalinated water and the brine and provides insights into the effectiveness of increasing electrode loading or specific capacity in mitigating the detrimental impacts of mixing. Even with state-of-the-art systems, producing freshwater from seawater or even brackish water with medium-to-high salinity is practically challenging due to the presence of solution switch.

Exploring urban compactness impact on carbon emissions from energy consumption: A township-level case study of Hangzhou, China.

Given that cities are the major contributors to carbon emissions, studying urban compactness (UC) and its impact on carbon emissions from energy consumption (CEECs) is crucial. This study calculated Hangzhou's township-level urban UC and CEECs using a hybrid subjective-objective weighted regression model on integrated panel datasets. By employing a geographically weighted regression (GWR) model, the spatio-temporal heterogeneity of the UC-CEEC relationship from 2006 to 2019 was uncovered. The results indicated an overall increase in UC, with significant variations across different counties. CEECs were higher in the central region, shifting eastward due to distinct urban development levels and policies. Moreover, the effects of various UC factors exhibited significant spatiotemporal inconsistency, with the impact intensity gradually diminishing. Additionally, the explanatory power of these factors declined and diversified over time. These findings emphasize the need for a comprehensive understanding of the relationship between UC and CEECs within the complex metropolitan environment and the importance of regulating their coordinated development. The research not only offers a more scientific approach to managing the growth of county-level cities and supporting balanced urbanization but also presents policy recommendations.

Ultrasound-assisted extraction of bioactive compounds from longan seeds powder: Kinetic modelling and process optimization.

Investigating the extraction of bioactive compounds represents a hopeful direction for maximizing the value of longan fruit byproducts. This study explored the influence of ultrasonic-assisted extraction (UAE) parameters-specifically ultrasonic power ratios, temperatures, and exposure times-utilizing water as a green solvent on several properties of the longan seeds extract (LSE). These properties encompassed the energy consumption of the UAE process (EC), extraction yield (EY), total phenolic contents (TPC), total flavonoid contents (TFC), and antioxidant activity (DPPH). Additionally, the study sought to optimize the conditions of UAE process and examine its thermodynamic properties. A three-level, three-factor full factorial design was utilized to assess the effects of different factors on LSE properties. Results indicated that EC, EY, TPC, TFC, and DPPH were significantly influenced by power ratios, temperatures, and exposure time. Moreover, the proposed models effectively characterized the variations in different properties during the extraction process. The optimized extraction conditions, aimed at minimizing EC while maximizing EY, TPC, TFC, and DPPH radical scavenging activity, were demonstrated as an ultrasonic power ratio of 44.4 %, a temperature of 60 °C, and an extraction time of 17.7 min. Optimization led to 563 kJ for EC, 7.85 % for EY, 47.21 mg GAE/mL for TPC, 96.8 mg QE/mL for TFC, and 50.15 % for DPPH radical scavenging activity. The results emphasized that the UAE process exhibited characteristics of endothermicity and spontaneity. The results provide valuable insights that could inform the enhancement of extraction processes, potentially benefiting industrial utilization and pharmaceutical formulations.

The driving forces behind renewable energy consumption: A comprehensive analysis of key determinants and environmental concerns in ASEAN countries.

Renewable energy consumption is a crucial solution to addressing pressing environmental issues, particularly climate change and air pollution. Investigating the factors that drive its adoption is highly significant, as it provides policymakers and stakeholders with valuable insights to accelerate the transition to renewable energy sources. Through this approach, we can minimise the negative consequences of our reliance on fossil fuels, thereby protecting the integrity of the environment. Therefore, the primary goal of this study is to thoroughly investigate the main factors that influence renewable energy consumption and environmental change in six specifically chosen ASEAN countries. The stationarity of the 1990-2019 data was tested using panel data techniques such as Levin, Lin, and Chu (LLC), Im Pesaran (IPS), and the Shin W-stat test. According to the stationarity tests, after the first order, all variables exhibit stationarity. Additionally, Pedroni's co-integration test result confirmed that there was a long-term relationship among the variables. Different methods, such as dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS), and pooled ordinary least squares (POLS), are used for cointegration estimating. The results suggest that there is a positive co-integration between renewable energy use and GDP in six ASEAN countries, indicating a long-term relationship. The positive relationship between GDP and renewable energy use suggests that economic growth is the primary driving force behind ASEAN's renewable energy adoption. However, factors like carbon emissions, population density, and foreign direct investment (FDI) negatively impact the demand for renewable energy. The limited availability of renewable energy in certain ASEAN countries may discourage foreign direct investment (FDI) due to the inverse relationship between FDI and renewable energy use. The studies also revealed that carbon emissions, which contribute to environmental pollution, do not motivate industries to invest in renewable energy. This finding would challenge the Environmental Kuznets Curve (EKC) hypothesis. According to the EKC, there is a significant transition towards renewable energy as a response to environmental degradation. However, it is worth noting that several ASEAN countries have experienced economic growth while also experiencing higher levels of carbon emissions. Given that economic expansion might not be environmentally beneficial, this research has implications for ASEAN energy policies. The ASEAN region faces a challenge in investing in renewable energy due to the excessive dependence on fossil fuels. Therefore, an in-depth evaluation of the main factor behind ASEAN's environmental concerns, which promotes the adoption of renewable energy, can greatly influence policy decisions, particularly in attaining net zero emissions. Policymakers can utilise this comprehensive analysis to establish informed objectives for policies related to renewable energy and develop strategic plans, i.e. reforming fuel subsidies. The goal is to encourage the development of environmentally friendly and sustainable energy plans for the future in the ASEAN region.

Receive wireless sensor data through IoT gateway using web client based on border gateway protocol.

One of the significant topics in the field of the Internet of Things (IoT) pertains to the interaction and information sharing among people. The utilization of the Border Gateway Protocol (BGP) stack enhances the integration of web protocols and sensor networks, leading to greater accessibility. However, the BGP protocol stack introduces substantial overhead to messages transmitted at each layer, resulting in increased data overhead and energy consumption in networks by several orders of magnitude. This paper proposes a method to reduce the overhead on small and medium-sized packets. In multi-temporal networks utilizing BGP, scheduling and aggregating BGP packets at sensor nodes help achieve specific objectives. Various research methodologies and measures are employed to facilitate this, including request classification, BGP response prioritization within the network, determination of maximum acceptable delay, and overall network management. Synchronization and temporal integration of received messages at sensor nodes are performed, considering the maximum allowable delay for each message and the availability of the destination to process the accumulated messages. The evaluation results of the proposed method demonstrate a significant reduction in energy consumption and network traffic, particularly in monitoring applications within multi-stage networks. The protocol stack used is derived from the BGP standard.

Simultaneous Emerging Contaminant Removal and H2O2 Generation Through Electron Transfer Carrier Effect of Bi─O─Ce Bond Bridge Without External Energy Consumption.

Conventional advanced oxidation processes (AOPs) require significant external energy consumption to eliminate emerging contaminants (ECs) with stable structures. Herein, a catalyst consisting of nanocube BiCeO particles (BCO-NCs) prepared by an impregnation-hydrothermal process is reported for the first time, which is used for removing ECs without light/electricity or any other external energy input in water and simultaneous in situ generation of H2O2. A series of characterizations and experiments reveal that dual reaction centers (DRC) which are similar to the valence band/conducting band structure are formed on the surface of BCO-NCs. Under natural conditions without any external energy consumption, the BCO-NCs self-purification system can remove more than 80% of ECs within 30 min, and complete removal of ECs within 30 min in the presence of abundant electron acceptors, the corresponding second-order kinetic constant is increased to 3.62 times. It is found that O2 can capture electrons from ECs through the Bi─O─Ce bond bridge during the reaction process, leading to the in situ production of H2O2. This work will be a key advance in reducing energy consumption for deep wastewater treatment and generating important chemical raw materials.

Construction and Application of Energy Footprint Model for Digital Twin Workshop Oriented to Low-Carbon Operation.

To address the difficulty of accurately characterizing the fluctuations in equipment energy consumption and the dynamic evolution of whole energy consumption in low-carbon workshops, a low-carbon-operation-oriented construction method of the energy footprint model (EFM) for a digital twin workshop (DTW) is proposed. With a focus on considering the fluctuations in equipment energy consumption and the correlation between multiple pieces of equipment at the workshop production process level (CBMEatWPPL), the EFM of a DTW is obtained to characterize the dynamic evolution of whole energy consumption in the workshop. Taking a production unit as a case, on the one hand, an EFM of the production unit is constructed, which achieved the characterization and visualization of the fluctuations in equipment energy consumption and the dynamic evolution of whole energy consumption in the production unit; on the other hand, based on the EFM, an objective function of workshop energy consumption is established, which is combined with the tool life, robot motion stability, and production time to formulate a multi-objective optimization function. The bee colony algorithm is adopted to solve the multi-objective optimization function, achieving collaborative optimization of cross-equipment process parameters and effectively reducing energy consumption in the production unit. The effectiveness of the proposed method and constructed EFM is demonstrated from the above two aspects.

Energy and emissions: Comparing short and long fruit cold chains.

This paper evaluates GHG emissions and energy usage in "short" and "long" cold chains for oranges, table grapes, and apples transported from South Africa to a retail store in Scotland. Novel formulae assess energy usage and emissions based on cold chain duration. "Short" chains show carbon footprints between 0.87 and 1.28 kg CO2e/kg of saleable fruit, contrasting starkly with extended cold chains. Extending storage durations increases emissions; a one-month extension results in 24-27 % emissions for oranges and grapes and 16 % for apples. Six months of CA storage of apples increases emissions by 96 % compared to "short" cold chains. Energy consumption follows a similar trend as emissions. This research informs policymakers and consumers, emphasising the need for sustainable and "short" cold chains. This is also the first paper that comprehensively assesses both the energy requirements and emissions outputs in a fruit supply chain based on the combined transport and storage duration of the cold chain from tree to retail markets.