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Although there are opportunities to reduce electrical energy demand in unit processes of mechanical pulp-based paper and paperboard production, this may not be financially beneficial. This is generally because energy optimization opportunities connected to reduced refiner electricity demand in mechanical pulping systems also results in less steam available for the drying of the paper. As modern high consistency refiner systems produce approximately one ton of steam for each MWh of electricity when producing one ton of pulp, a reduction in electric energy demand leads to increased fuel demand in steam boilers to compensate for the steam shortage. In this study, we investigated what the financial and environmental situation would look like if we were to expand the system border from a paper mill to a larger system consisting of a mechanical pulp-based paper or paperboard mill, a district heating system with an incineration boiler and a chemical pulp mill. Mechanical pulp production has a wood to product yield of >90%, a high electric energy demand to separate woodchips to pulp and is a net producer of heat and steam while chemical pulp-based production has a wood to product yield of 50%, a low electric energy demand and is a net heat and electricity producer due to the combustion of dissolved wood polymers. The aim of this research is to create useful and robust models of how to use excess heat from certain industry sites to cover the steam shortage in other industry sites by means of utilizing and optimizing the district heating systems connecting these sites. For this purpose, we used a simulation tool which dynamically allows us to evaluate different scenarios. Our results shows that there is great potential to reduce both carbon dioxide emissions and production costs for industry sites and society by means of these tools. [ABSTRACT FROM AUTHOR]

The concept of a Positive Energy District (PED) has become a vital component of the efforts to accelerate the transition to zero carbon emissions and climate-neutral living environments. Research is shifting its focus from energy-efficient single buildings to districts, where the aim is to achieve a positive energy balance across a given time period. Various innovation projects, programs, and activities have produced abundant insights into how to implement and operate PEDs. However, there is still no agreed way of determining what constitutes a PED for the purpose of identifying and evaluating its various elements. This paper thus sets out to create a process for characterizing PEDs. First, nineteen different elements of a PED were identified. Then, two AI techniques, machine learning (ML) and natural language processing (NLP), were introduced and examined to determine their potential for modeling, extracting, and mapping the elements of a PED. Lastly, state-of-the-art research papers were reviewed to identify any contribution they can make to the determination of the effectiveness of the ML and NLP models. The results suggest that both ML and NLP possess significant potential for modeling most of the identified elements in various areas, such as optimization, control, design, and stakeholder mapping. This potential is realized through the utilization of vast amounts of data, enabling these models to generate accurate and useful insights for PED planning and implementation. Several practical strategies have been identified to enhance the characterization of PEDs. These include a clear definition and quantification of the elements, the utilization of urban-scale energy modeling techniques, and the development of user-friendly interfaces capable of presenting model insights in an accessible manner. Thus, developing a holistic approach that integrates existing and novel techniques for PED characterization is essential to achieve sustainable and resilient urban environments. [ABSTRACT FROM AUTHOR]

Foundation industries, encompassing metals, ceramics, cement, paper, chemicals, and glass, play a vital role in driving industrial economies. Despite their pivotal role, a comprehensive understanding of shared processes and their impact on resource utilisation remains elusive. This study employs a novel approach, leveraging an adapted Dependency Structure Matrix (DSM), to unveil the core processes commonly utilised among these industries. These processes are then evaluated based on their influence on energy consumption and CO2 emission. The investigation revealed 18 common processes categorised by their processing principles, their expected outcomes, and the equipment used. Remarkably, these processes emerge as significant contributors to both energy consumption and CO2 emissions. Notably, pyroprocessing emerged as a prevalent practice in five out of the six sectors, while the production of dried products and crushers and mills were the most frequently encountered outcomes and equipment used, respectively. This paper discusses the implications of these findings for foundation industries, emphasising potential areas for enhancing manufacturing operations to reduce environmental damage and facilitate knowledge transfer among the various sectors. Furthermore, the study identifies shared abatement options that can be collectively implemented across industries to achieve more substantial reductions in environmental footprint. By identifying and prioritising the most impactful processes in foundation industries, this study provides a strategic footing for advancing sustainable and efficient manufacturing practices within these critical sectors. [ABSTRACT FROM AUTHOR]

There is a consensus on the need to reduce the emissions of carbon compounds. The increase in global greenhouse gas (GHG) emissions in the maritime industry poses a serious challenge to environmental sustainability, climate change, and the operating costs of ships. This article shows how hybrid versus diesel propulsion technology for ships can help reduce carbon dioxide (CO2) emissions and fuel consumption, and how these changes can be achieved. The need to reduce exhaust emissions and the increasing need for the shipping industry to seek alternative fuels means that existing regulations for marine engines and engine emissions are being updated almost constantly and new regulations are being formulated. The cost implications of the new regulations may lead to an increase in emissions as engines with lower fuel consumption are chosen, i.e., larger marine engines. Alternative approaches are needed to reduce CO2 emissions and fuel consumption, which could ultimately lead to hybrid propulsion for ships. This paper examines the current state of greenhouse gas emissions in shipping by analyzing the CO2 emissions and operating costs of two ships of the same type with similar technical and technological characteristics and different propulsion systems to gain insight into the problem. This paper compares the reductions in CO2 emissions, fuel consumption, and fuel costs for two suction hopper dredgers with standard diesel and hybrid propulsion. The technical characteristics, CO2 emissions, fuel consumption, and price of the two ships were analyzed to determine the advantages and disadvantages of each propulsion system. The novelty of this study is that two suction hopper dredgers from the same company with similar technical–technological characteristics but different propulsion systems were used for the case study and a mathematical procedure for calculating CO2 and other greenhouse gasses was presented in comparison, all to determine to what extent and in what way the hybrid propulsion system of a ship can contribute to reductions in CO2 emissions and fuel costs at the ship and company levels compared to a standard diesel propulsion system. This comparative analysis shows how much lower CO2 emissions, fuel consumption, and fuel cost savings can be expected when using a hybrid propulsion system compared to a standard diesel propulsion system. Finally, a conclusion is drawn on the efficiency and environmental compatibility of the two systems. [ABSTRACT FROM AUTHOR]

The introduction of fifth-generation (5G) mobile networks leads to an increase in energy consumption and higher operational costs for mobile network operators (MNOs). Consequently, the optimization of 5G networks' energy efficiency is crucial, both in terms of reducing MNO costs and in terms of the negative environmental impact. However, many aspects of the 5G mobile network technology itself have been standardized, including the 5G network slicing concept. This enables the creation of multiple independent logical 5G networks within the same physical infrastructure. Since the only necessary resources in 5G networks need to be used for the realization of a specific 5G network slice, the question of whether the implementation of 5G network slicing can contribute to the improvement of 5G and future sixth-generation networks' energy efficiency arises. To tackle this question, this review paper analyzes 5G network slicing and the energy demand of different network slicing use cases and mobile virtual network operator realizations based on network slicing. The paper also overviews standardized key performance indicators for the assessment of 5G network slices' energy efficiency and discusses energy efficiency in 5G network slicing lifecycle management. In particular, to show how efficient network slicing can optimize the energy consumption of 5G networks, versatile 5G network slicing use case scenarios, approaches, and resource allocation concepts in the space, time, and frequency domains have been discussed, including artificial intelligence-based implementations of network slicing. The results of the comprehensive discussion indicate that the different implementations and approaches to network slicing pave the way for possible further reductions in 5G MNO energy costs and carbon dioxide emissions in the future. [ABSTRACT FROM AUTHOR]

Under the new development model, the digital economy has become a new engine to promote the green development of the economy and realize the goal of "double carbon". Based on the panel data of 30 provinces in China from 2010 to 2020, this paper empirically investigates the impact of the development of the digital economy on energy and carbon emissions using a series of econometric models such as baseline regression, a mechanism test, and the spatial Durbin model, etc. Common prosperity plays an intermediary role between digital economy development and carbon emissions; digital economic development optimizes resource allocation, effectively solves the problem of uneven resource distribution, and reduces energy and carbon emissions while achieving common prosperity. In addition, green innovation, industrial structure, urbanization level, R&D intensity, and the degree of marketization also have different degrees of influence on energy and carbon emissions. Therefore, the government should accelerate the construction of new digital infrastructure and implement the digital economy development strategy according to local conditions, so as to promote the digital economy to produce a more significant carbon emission reduction effect. [ABSTRACT FROM AUTHOR]

In order to improve the consumption of renewable energy and reduce the carbon emissions of integrated energy systems (IESs), this paper proposes an optimal operation strategy for an integrated energy system considering the coordination of electricity and hydrogen in the context of carbon trading. The strategy makes full use of the traditional power-to-gas hydrogen production process and establishes a coupling model comprising cogeneration and carbon capture equipment, an electrolytic cell, a methane reactor, and a hydrogen fuel cell. Taking a minimum daily operating cost and minimal carbon emissions from the system as objective functions, a mixed-integer nonlinear optimal scheduling model is established. This paper designs examples based on MATLAB R2021b and uses the GUROBI solver to solve them. The results show that compared with the traditional two-stage operation process, the optimization method can reduce the daily operation cost of an IES by 26.01% and its carbon emissions by 90.32%. The results show that the operation mode of electro-hydrogen synergy can significantly reduce the carbon emissions of the system and realize a two-way flow of electro-hydrogen energy. At the same time, the addition of carbon capture equipment and the realization of carbon recycling prove the scheduling strategy's ability to achieve a low-carbon economy of the scheduling strategy. [ABSTRACT FROM AUTHOR]

Reducing greenhouse gas emissions is a crucial challenge in urban areas characterized by high energy consumption and reduced exposure to nature. In this context, the urban green system could play a pivotal role. In the literature, scholars have analyzed both the ability of species-specific and layout-specific green infrastructure to increase carbon sequestration and the best location sites for new green infrastructure to increase the provision of overall ecosystem services. There is a lack of studies helping green urban planners and designers choose where and which green infrastructure to implement based on vegetation species-specific performance and the local carbon emissions of city components. This paper uses tree inventory data from a medium-sized city in central Italy (Perugia) to develop a spatial analysis of urban park performance in carbon sequestration. Then, the method evaluates the carbon emission of a public city building to generate a spatialized balance between building demand and tree supply to support local decisions about the best locations for new green infrastructure and the choice between species. The paper contributes to GIS-based tools that vary the recommended location sites and species for new green infrastructure based on the demanded ecosystem service. [ABSTRACT FROM AUTHOR]

Vehicle-to-grid (V2G) technology has received a lot of attention as a smart interconnection solution between electric vehicles and the grid. This paper analyzes the relevant research progress and hotpots of V2G by using CiteSpace 6.1.R6 software to construct a visualization graph, which includes keyword co-occurrence, clustering, and burstiness, and further systematically summarizes the main trends and key results of V2G research. First, the connection between electric vehicles and the grid is outlined and the potential advantages of V2G technology are emphasized, such as energy management, load balancing, and environmental sustainability. The important topics of V2G, including renewable energy consumption, power dispatch, regulation and optimization of the grid, and the smart grid, are discussed. This paper also emphasizes the positive impacts of V2G technologies on the grid, including reduced carbon emissions, improved grid reliability, and the support for renewable energy integration. Current and future challenges for V2G research, such as standardization, policy support, and business models, are also considered. This review provides a comprehensive perspective for scholars and practitioners in V2G research and contributes to a better understanding of the current status and future trends of V2G technology. [ABSTRACT FROM AUTHOR]

Mining production, being one of the most energy-intensive industries globally, consumes substantial amounts of fossil fuels and contributes to extensive carbon emissions worldwide. The trend toward electrification and advanced developments in battery technology have shifted attention from diesel power to battery alternatives. These alternatives are appealing, as they contribute to decarbonisation efforts when compared to conventional diesel trucks. This paper presents a comprehensive review of recent technological advancements in powertrains for Mining Haulage Truck (MHT). It also compares these configurations based on mining system-level considerations to assess their future potential. The evaluated configurations include Diesel-Electric Truck (DET), Trolley Assist Truck (TAT), Battery-only Truck (BOT), Battery Trolley with Dynamic charging truck (BT-D), and Battery Trolley with Stationary charging truck (BT-S). According to the analysis, the energy demand for on-board diesel or battery power (excluding trolley power) in these alternative options is as follows: DET—681 kWh, BOT—645 kWh, TAT—511 kWh, BT-S—471 kWh, and BT-D—466 kWh. The paper also illustrates the theory of battery size design based on the current battery technology, battery material selection, battery package design, and battery size selection methods. In the case of tailored battery size selection, BOT, BT-D, and BT-S configurations require LiFePO4 (LFP) battery masses of 25 tonnes, 18 tonnes, and 18 tonnes, respectively. Based on a techno-economic assessment of battery MHT alternatives with a future perspective, it has been determined that BT-D requires the lowest amount of on-board battery energy. Furthermore, over a span of 20 years, BT-S has demonstrated the lowest on-board battery cost. [ABSTRACT FROM AUTHOR]

Cities play a pivotal role in achieving worldwide carbon neutrality due to their significant contribution to global energy consumption and carbon emissions. Therefore, planning effective strategies and guiding evidence-based policymaking at the city scale becomes even more crucial. Composite indices serve as a valuable tool for monitoring urban energy transition trends. This paper aims to present a novel approach, robust and flexible even under conditions of data scarcity, for tracking the energy transition trend of a city by means of a composite index (UETI). The Turin case study is introduced to test the applicability of the proposed approach. Additionally, to demonstrate the robustness of the composite index framework, the paper includes the findings of correlation and sensitivity analyses. This study reveals a significant improvement in Turin's environmental and energy domains, while the socio-economic domain shows more modest improvement. Furthermore, the study highlights the need to address the shortage of urban data to enhance the accuracy and reliability of metric-based frameworks and to extend the assessment to a larger sample of cities. [ABSTRACT FROM AUTHOR]

A major goal of the "14th Five-Year Plan" phase is to promote the green transformation of industrial enterprises to address the 'dual carbon' challenge. Utilizing the China Industrial Enterprises Database and the Polluting Enterprises Database, this paper calculates the carbon emissions of Chinese industrial enterprises from 2001 to 2010 at the micro level. It presents an analysis of the heterogeneity of carbon emission efficiency (TPI) in industrial enterprises, as well as the factors influencing corporate TPI. This study finds that enterprises within a subdivided industry exhibit heterogeneous levels of TPI, with carbon emissions largely affected by the structure of energy consumption. The researchers suggest accelerating the transition of industrial enterprises to green technology and argue that carbon emission policies should shift from controlling direct total targets to strengthening market-oriented policy tools. Carbon reduction targets should be more stringent for enterprises with lower TPI, considering the heterogeneity among enterprises. To meet the challenges of emission reduction, industrial enterprises are encouraged to actively reform their energy consumption structure. Government policies should aim to reduce clean energy costs and encourage the use of clean energy by industrial enterprises. [ABSTRACT FROM AUTHOR]

In response to the urgent need to address climate change and reduce carbon emissions, there has been a growing interest in innovative approaches that integrate AI and CDR technology. This article provides a comprehensive review of the current state of research in this field and aims to highlight its potential implications with a clear focus on the integration of AI and CDR. Specifically, this paper outlines four main approaches for integrating AI and CDR: accurate carbon emissions assessment, optimized energy system configuration, real-time monitoring and scheduling of CDR facilities, and mutual benefits with mechanisms. By leveraging AI, researchers can demonstrate the positive impact of AI and CDR integration on the environment, economy, and energy efficiency. This paper also offers insights into future research directions and areas of focus to improve efficiency, reduce environmental impact, and enhance economic viability in the integration of AI and CDR technology. It suggests improving modeling and optimization techniques, enhancing data collection and integration capabilities, enabling robust decision-making and risk assessment, fostering interdisciplinary collaboration for appropriate policy and governance frameworks, and identifying promising opportunities for energy system optimization. Additionally, this paper explores further advancements in this field and discusses how they can pave the way for practical applications of AI and CDR technology in real-world scenarios. [ABSTRACT FROM AUTHOR]

As an important means of farmland policy, whether land transfer can promote agricultural green transformation is worthy of further study; however, related research is relatively rare. Based on the inter-provincial panel data from 2005 to 2020, this paper examines the influence of land transfer on agricultural green transformation and its underlying mechanism by using a two-way fixed effect model and an intermediary effect model. This study reveals significant findings as follows: (1) Land transfer substantially promotes agricultural green transformation. (2) Energy consumption is a major contributor to the growth of agricultural carbon emissions; however, land transfer can mitigate this by reducing energy consumption. (3) Land transfer can promote agricultural green transformation by fostering agricultural technology progress. (4) Further analysis reveals that land transfer in economically developed areas and the southeastern side of the "Hu-Huanyong Line" significantly enhances agricultural green transformation. Based on these findings, this paper suggests promoting land transfer while considering regional differences. Additionally, attention should be directed towards reducing energy consumption and encouraging agricultural technology's progress. [ABSTRACT FROM AUTHOR]

Under the influence of the dual policies of sustainable economic development and the national dual-carbon target, the establishment of an environmental protection department for the treatment of heavily polluting industries is imminent, and the country has launched pollution control policies and regulations to restrict the emission rights of heavily polluting industries. Therefore, this paper focuses on whether the restriction of emission rights in key industries has reduced carbon emissions. To achieve this, this paper uses panel data of prefecture-level cities in China from 2006 to 2019 and adopts a two-way fixed-effects DID model to systematically analyze the impact of the key pollution industry governance policies launched by the Ministry of Environmental Protection on CO2 emissions in 2017. And further analyze the role of variables such as green technology innovation patents and energy efficiency using this model, while parallel trend tests and placebo tests, and related policies are used to ensure the robustness of the regression results. This paper reveals that: (1) The heavy pollution industry governance policy implemented in 2017 can effectively reduce CO2 emissions in the cities of the treated group, and the effect is more significant in the year of policy implementation; (2) Green utility patents and energy-use efficiency are the effective mediating mechanisms to reduce CO2 emissions; (3) Over time, the effect of heavy pollution industry governance policy on CO2 emissions gradually decreases; (4) The reliability of the baseline regression results of this paper is proved by the use of parallel trend tests, placebo tests, and tests excluding the influence factors such as relevant policies in the same period. Therefore, the key polluting industries treatment policy launched by China's Ministry of Environmental Protection in 2017 under the recent dual-carbon policy development goals formulated by China, can effectively reduce carbon emissions; however, in the future economic development process, the government should give more consideration to the continuity of the policy impact and its coherence on economic development when implementing the policy. [ABSTRACT FROM AUTHOR]

From the perspective of energy consumption optimization, this paper studies the impact of green finance on carbon emissions in China. Firstly, based on the theoretical perspective, this paper explores the mechanism and path by which green finance influences carbon emissions, and analyzes the role of energy consumption in this process. Then, this paper utilizes the STIRPAT model, chain multiple mediation effect model and panel threshold model to empirically analyze the influence of green finance on carbon emissions, using provincial data from China from 2005 to 2019. The results are as follows: (1) Green finance significantly reduces carbon emissions. After accounting for potential endogeneity, this conclusion is still valid. The heterogeneity test reveals that the inhibitory effect is more remarkable in northern regions, high-carbon emission regions and energy-rich regions. (2) The results of the bootstrap test reveal that at the national level, green finance decreases carbon emissions through three paths: green technological innovation, ecological evolution of the industrial structure and green technological innovation facilitating ecological evolution of the industrial structure. Furthermore, in energy-rich regions, green finance significantly inhibits carbon emissions through all three paths, while in energy-poor regions, green finance reduces carbon emissions only through green technological innovation. (3) There is a nonlinear relationship between green finance and carbon emissions. Specifically, regardless of energy intensity or energy consumption structure, only when it is below the threshold can green finance significantly inhibit carbon emissions. Thus, realizing energy consumption optimization is an effective way to ensure the carbon emission reduction effect of green finance. [ABSTRACT FROM AUTHOR]

With the objectives of achieving "peak carbon" and "carbon neutrality", accurately quantifying the carbon emissions of road transportation becomes crucial. It is challenging to accurately describe the energy consumption of vehicles at both temporal and spatial scales from a macro perspective. Therefore, focusing on the quantitative model of vehicle micro energy consumption and road meso energy consumption, this paper reviewed and summarized the energy consumption model of road traffic in terms of data collection, quantification accuracy, and scope of application. Based on this analysis, this paper identifies the challenges of the current road traffic energy consumption model. Finally, we look forward to future research directions for studying quantitative models of energy consumption from road transportation. [ABSTRACT FROM AUTHOR]

The ageing of coal-fired power stations in South Africa has led to regular power outages. Therefore, the country will need to urgently increase its electricity capacity to meet further energy demand from growing urbanization and population growth. This challenge has increased interest in alternative energy sources, such as renewable energy (RE). South Africa is gradually implementing appropriate renewable energy practices, reducing carbon emissions, cleansing the air, and assuring a more sustainable future. This paper summarizes the availability, current state, and future potential of renewable energy choices in South Africa. This paper also evaluates specific policy measures and government actions aimed at eliminating barriers and increasing renewable energy deployment in the future. It also considers the South African network's specialized techno-economic analysis. The findings show that South Africa is still heavily reliant on coal, with 78% of the current installed capacity coming from coal power in 2022, compared to 9.3% for solar and wind energy. [ABSTRACT FROM AUTHOR]

In the current context of fossil energy depletion and the requirement for low carbon emissions, the efficient use of energy and a diversified energy mix have become increasingly important. The use of cleaner and low-carbon alternative fuels is rapidly growing. However, corrosion remains a key issue limiting the large-scale application of alternative fuels. This paper describes the corrosion characteristics of different alternative fuels. Also, the mechanisms of fuel corrosion, corrosion measurement methods, and the future of corrosion inhibitor applications are discussed. Corrosion causes changes in material weight and surface morphology and generates electrical signals as it progresses. Although there are well-established methods to characterize corrosion by recognizing these changes, there is still a need to identify a universal standard signal for their detailed description. For the foreseeable future, corrosion in fuels will continue to be mitigated by the addition of corrosion inhibitors. The research on corrosion inhibitors focuses on green corrosion inhibitors and multifunctional corrosion inhibitors. However, further analysis is needed to understand the active ingredients in green corrosion inhibitors, and the mechanism of corrosion inhibition for different types of corrosion inhibitors needs further research. [ABSTRACT FROM AUTHOR]

This paper proposes a temperature-adaptive radiative cooling (TARC) coating with simple preparation, cost effectiveness, and large-scale application based on a thermochromic powder. To determine the energy efficiency of the proposed TARC coating, the heat transfer on the surface of the TARC coating was analyzed. Then, a typical two-story residential building with a roof area of 258.43 m2 was modeled using EnergyPlus. Finally, the energy-saving potential and carbon emission reduction resulting from the application of the proposed TARC roof in buildings under different climates in China were discussed. The results showed that the average solar reflectivity under visible light wavelengths (0.38–0.78 μm) decreases from 0.71 to 0.37 when the TARC coating changes from cooling mode to heating mode. Furthermore, energy consumption can be reduced by approximately 17.8–43.0 MJ/m2 and 2.0–32.6 MJ/m2 for buildings with TARC roofs compared to those with asphalt shingle roofs and passive daytime radiative cooling (PDRC) roofs, respectively. This also leads to reductions in carbon emissions of 9.4–38.0 kgCO2/m2 and 1.0–28.9 kgCO2/m2 for the buildings located in the selected cities. To enhance building energy efficiency, TARC roofs and PDRC roofs are more suitable for use on buildings located in zones with high heating demands and high cooling demands, respectively. [ABSTRACT FROM AUTHOR]

From 2035 onward, the registration of new conventional internal combustion engine vehicles will be prohibited in the European Union. This shift is driven by steadily rising fuel prices and growing concerns over carbon dioxide emissions. Electric vehicles (EVs) are becoming increasingly popular across Europe, and many manufacturers now offer modified models, making pure internal combustion versions unavailable for certain types. Additionally, the comparatively lower operational costs of EVs for end users further bolster their appeal. In the European Union, new directives have been established to define innovative approaches to energy use in Member States, known as energy communities. This article provides a comprehensive overview of the architecture of energy communities, electric vehicles, and the V2X technologies currently on the market. It highlights the evolution of electric vehicle adoption in the EU, contextualizing it within broader energy trends and presenting future challenges and development opportunities related to energy communities. The paper details the diversification of electricity sources among Member States and the share of generated electricity that is utilized for transport. [ABSTRACT FROM AUTHOR]

In order to reduce carbon emissions, which are currently a problem in the shipping and offshore plant sectors, the international community is strengthening regulations such as the Energy Efficiency Design Index (EEDI) and Energy Efficiency Existing Ship Index (EEXI). To cope with this, eco-friendly fuel propulsion technology is being developed, and the development of an ammonia fuel supply system is in progress. Among them, fuel valve train (FVT) technology was researched for the final supply and cutoff of fuel and purging through nitrogen for ammonia engines. In this paper, we analyzed the change in ammonia supply due to FVT opening and the change in nitrogen supply due to closure. In addition, a plan to minimize risk factors was presented by applying a control method to remove residual fuel in FVT. According to the presented FVT model, the difference in the flow rate of supplied fuel was as much as 17.8 kg/s. Additionally, by opening the gas bleed valve at intervals during the closing process and purging about 0.28 kg of nitrogen, the internal fuel could be completely discharged. This is expected to have an impact on improving the marine environment through the application of eco-friendly fuels and the development of fuel supply system technology. [ABSTRACT FROM AUTHOR]

From powering our homes to driving our economies, energy lies at the heart of humanity's complex challenges in the modern era. This paper reviews the evolution of smart energy systems, examining their technological advancements and societal implications while proposing a future design framework emphasizing four key pillars: holistic resource optimization, adaptive intelligence, environmental harmony, and human-centered design. While they offer numerous benefits, such as enhanced energy efficiency and reduced carbon emissions, smart energy systems also face challenges. These include cybersecurity risks, the complexity of integrating diverse energy sources seamlessly, high upfront costs, and potential compatibility issues arising from evolving technologies. Overcoming these challenges will be crucial for unleashing the full potential of smart energy systems and facilitating their global adoption. Abundant opportunities for further research and development exist in this domain, awaiting exploration and advancement. [ABSTRACT FROM AUTHOR]

Polymers are extensively used in various industries due to their versatility, durability and cost-effectiveness. To ensure functionality and longevity, polymer parts must have sufficient strength to endure external forces without deformation or breakage. Traditional approaches to increasing part strength involve adding more material; however, balancing strength to weight relationships is challenging. This paper explorers the viability of manufacturing lightweight components using a microcellular foaming polymer. Microcellular foaming has emerged as a helpful tool to achieve an optimal strength-to-weight ratio; offering advantages such as lightweight, improved mechanical properties, reduced material usage, better insulation and improved cost-effectiveness. It can also contribute to improved fuel efficiency and reduced carbon emissions, making them environmentally favourable. The combination of additive manufacturing (AM) and microcellular foaming has opened new possibilities for design innovation. This text highlights the challenges and efforts in incorporating foaming techniques into 3D printing processes, specifically fused filament fabrication (FFF). This study reveals that microcellular polymers are a viable option when balancing part strength and weight. The experiments completed during the formulation of this paper demonstrated that lightweight LW-PLA parts were significantly lighter than standard PLA parts and that a design of experiments approach can be used to optimise strength properties and provide insights into optimising manufacturability. Microcellular polymers present an opportunity for lighter and stronger 3D printed parts, offering potential energy and material savings for sustainable manufacturing practices. [ABSTRACT FROM AUTHOR]

In response to the challenges of global warming and the development of A low-carbon economy, the integrated electricity and natural gas energy system (IEGES) is known as an important structure for future energy supply; thus, its planning and design must take low-carbon and environmental protection factors into account. Regarding carbon emissions as an optimization criterion, this paper built life-cycle carbon emission models of IEGES components. Then, taking the capacities of the energy resources, storage and conversion units of IEGES as the optimization variables, a multi-objective optimization configuration model was established considering the annual investment operation cost and the life-cycle carbon emissions. The multi-objective model was transformed into a single-objective one by an ε-constraint approach and the polynomial fitting method was employed to obtain the value of ε for obtaining uniformly distributed Pareto sets. Based on the fuzzy entropy weight method and the fuzzy affiliation degree approach, the obtained Pareto sets were ranked and the solution with the highest ranking value was selected as the optimal solution for the original problem. Finally, the configuration schemes were analyzed from the perspectives of economy, carbon emission and renewable energy utilization, and the effectiveness and rationality of the proposed optimization method were verified through MATLAB simulation. [ABSTRACT FROM AUTHOR]

Predictive modeling is important for assessing and reducing energy consumption and CO2 emissions of light-duty vehicles (LDVs). However, LDV emission datasets have not been fully analyzed, and the rich features of the data pose challenges in prediction. This study aims to conduct a comprehensive analysis of the CO2 emission data for LDVs and investigate key prediction model characteristics for the data. Vehicle features in the data are analyzed for their correlations and impact on emissions and fuel consumption. Linear and non-linear models with feature selection are assessed for accuracy and consistency in prediction. The main behaviors of the predictive models are analyzed with respect to vehicle data. The results show that the linear models can achieve good prediction performance comparable to that of nonlinear models and provide superior interpretability and reliability. The non-linear generalized additive models exhibit enhanced accuracy but display varying performance with model and parameter choices. The results verify the strong impact of fuel consumption and powertrain attributes on emissions and their substantial influence on the prediction models. The paper uncovers crucial relationships between vehicle features and CO2 emissions from LDVs. These findings provide insights for model and parameter selections for effective and reliable prediction of vehicle emissions and fuel consumption. [ABSTRACT FROM AUTHOR]

Artificial lighting comprises nearly one-third of the total electrical load of buildings, resulting in significant carbon emissions. Reducing the carbon emissions caused by artificial lighting is one of the ways to achieve low-carbon buildings. To meet the demand for high-efficiency, energy-saving, and comfortable lighting, light pipes are increasingly used in buildings. This paper reviews the research and development of light pipes and integrated technology. Sky conditions as a dynamic factor always affect the performance of light pipes. The combination of light pipes and an artificial lighting system can effectively solve this problem. A light pipe can be integrated with a ventilation stack to achieve the ventilation and cooling or heating of a building. A lighting-heating coupled light guide can improve the energy efficiency and sustainability in buildings, such as where antimony tin oxide nanofluid is introduced to absorb additional heat and then provide domestic hot water. The application of a photocatalyst to light pipes can realize air purification and self-cleaning. The use of light pipes does not consume electricity and can reduce the time spent using artificial lighting, thus allowing for power savings. From a whole life cycle perspective, the use of light pipes can be a balance of cost and benefit. In conclusion, such information could be useful for engineers, researchers, and designers to assess the suitability of applying integrated light pipes in different building types and examine the potential of energy and cost savings. [ABSTRACT FROM AUTHOR]

The maritime Internet of Things (MIoT), a maritime version of the Internet of Things (IoT), is envisioned as a promising solution that can provide ubiquitous connectivity over land and sea. Due to the rapid development of maritime activities and the maritime economy, there is a growing demand for computing-intensive and latency-sensitive maritime applications requiring various energy consumption, communication, and computation resources, posing a significant challenge to MIoT devices due to their limited computational ability and battery capacity. Mobile Edge Computing (MEC), which can handle computation tasks at the network's edge more efficiently and with less latency, is emerging as a paradigm for fulfilling the ever-increasing demands of MIoT applications. However, the exponential increase in the number of MIoT devices has increased the system's energy consumption, resulting in increased greenhouse gas emissions and a negative impact on the environment. As a result, it is vital for MIoT networks to take traditional energy usage minimization into account. The integration of renewable energy-harvesting capabilities into base stations or MIoT devices possesses the potential to reduce grid energy consumption and carbon emissions. However, making an effective decision regarding task offloading and resource allocation is crucial for maximizing the utilization of the system's potential resources and minimizing carbon emissions. In this paper, we first propose a green MEC-enabled maritime IoT network architecture to flexibly provide computing-intensive and latency-sensitive applications for MIoT users. Based on the architecture, we formulate the joint task offloading and resource allocation problem by optimizing the total system execution efficiency (including the total size of completed tasks, task execution latency, and the system's carbon emissions) and then propose a deep-deterministic-policy-gradient-based joint optimization strategy to solve the problem, eventually obtaining an effective resolution through continuous action space learning in the changing environment. Finally, simulation results confirm that our proposal can yield good performance in system execution efficiency compared to other benchmarks; that is, it can significantly reduce the system's carbon emissions and tasks' delay and improve the total size of completed tasks. [ABSTRACT FROM AUTHOR]

To achieve the goal of carbon peaking, it is crucial to reduce both carbon emissions and energy consumption during the operational stage of residential buildings. This paper proposed a method for assessing carbon emissions and energy consumption for an energy system utilized in a rural residence. First, an equivalent thermal resistance-capacitance model for a rural residence was established. The parameters of thermal resistance and capacitance were optimized based on the data collected from an operating air source heat pump heating system. On this basis, the energy consumption was derived, and it was compared with real consumption. Then, a method of estimating house energy consumption index per unit area under specified weather conditions was proposed. Finally, the carbon emissions of three heating types—heating driven by air source heat pump, gas boiler, and coal boiler—were compared. Results showed that the derived energy consumption index per unit area was 46.77 W/m2. The carbon emissions of the air source heat pump were 1406.1 kgCO2. [ABSTRACT FROM AUTHOR]

Background: In container terminals, optimizing the scheduling of external trucks and yard cranes is crucial as it directly impacts the truck turnaround time, which is one of the most critical performance measures. Furthermore, proper scheduling of external trucks contributes to reducing CO 2 emissions. Methods: This paper proposes a new approach based on a mixed integer programming model to schedule external trucks and yard cranes with the objective of minimizing CO 2 emissions and reducing truck turnaround time, the gap between trucking companies' preferred arrival time and appointed time, and the energy consumption of yard cranes. The proposed approach combines data analysis and operations research techniques. Specifically, it employs a K-means clustering algorithm to reduce the number of necessary truck trips for container handling. Additionally, a two-stage mathematical model is applied. The first stage employs a bi-objective mathematical model to plan the arrival of external trucks at the terminal gates. The second stage involves a mathematical model that schedules yard cranes' movements between different yard blocks. Results: The results show that implementing this methodology in a hypothetical case study may lead to a substantial daily reduction of approximately 31% in CO 2 emissions. Additionally, the results provide valuable insights into the trade-off between satisfying the trucking companies' preferred arrival time and the total turnaround time. Conclusions: The integration of data clustering with mathematical modeling demonstrates a notable reduction in emissions, underscoring the viability of this strategy in promoting sustainability in port-related activities. [ABSTRACT FROM AUTHOR]

Despite the serious threat of global warming caused by carbon emissions, commercial refrigeration systems that generate cooling are unable to adjust their usage time. Therefore, it is essential to enhance the energy efficiency of refrigeration systems themselves. Recently developed refrigeration systems offer improved energy consumption efficiency as they utilize inverter-type outdoor units that can communicate with indoor units. However, traditional legacy refrigeration systems still suffer from poor energy efficiency because the existing indoor units cannot communicate with inverter-type outdoor units. Hence, this paper introduces a heuristic energy efficiency improvement algorithm for commercial refrigeration systems using legacy indoor units when an inverter-type outdoor unit is installed. In particular, to reduce the computational complexity of the process of selecting the optimal temperature difference and target low-pressure variables that can enhance energy efficiency, the algorithm employs the Monte Carlo method. Finally, the performance of the proposed heuristic energy efficiency improvement algorithm was evaluated in a laboratory environment to confirm its applicability. [ABSTRACT FROM AUTHOR]

Urban building energy modeling (UBEM) has attracted wide attention to the requirement for global carbon emission reduction. This paper presents a UBEM tool, AutoBPS-Param, to generate building energy models (BEMs) with parameterized geometry and detailed thermal zones, especially for complex building types, considering the shading effect from surrounding buildings simultaneously. Three building number scales and four scenarios were analyzed in the hotel-related buildings in Changsha, China. For the prototype modeling of Scenario 1, eighteen prototype building energy models for six building types in three vintages were created, and their simulation results were aggregated based on their representative floor areas. For AutoBPS-Param of Scenario 4, the method created one EnergyPlus (Version: 9.3.0) model for each building. The geometry of the prototype model was scaled and modified based on the target building's length, width, and number of stories. The surrounding buildings were also added to the AutoBPS-Param simulation to better capture the urban dynamic impact. The results showed that the annual electricity and natural gas energy use intensity (EUI) of the pre-2005 HotelOffice prototype model was 172.25 and 140.45 kWh/m2. In contrast, with the AutoBPS-Param method, the annual electricity EUIs of 71 HotelOffice buildings constructed before 2005 ranged from 159.51 to 213.58 kWh/m2 with an average of 173.14 kWh/m2, and the annual gas EUIs ranged from 68.02 to 229.12 kWh/m2 with an average of 108.89 kWh/m2. The proposed method can better capture the diversity of urban building energy consumption. [ABSTRACT FROM AUTHOR]

Modern marine propulsion systems must be reliable, energy efficient, environmentally friendly, and economical. Efforts to reduce fuel costs and carbon dioxide (CO2) emissions per nautical mile have a significant impact on the choice of propulsion system. Considering that there is no alternative for maritime transport, various technical and technological solutions are being considered that aim to improve efficiency and reduce the negative impact on the environment. One of the ways to achieve this goal is slow steaming, which reduces fuel consumption and CO2 emissions. The designed speed of the vessel has a significant impact on the efficiency of slow steaming. Slow steaming is particularly suitable for large container ships with a design speed of more than 20 knots. In this paper, the effects of slow steaming are analyzed using the example of a container ship with diesel-engine propulsion. Propulsion systems with low-speed and medium-speed marine diesel engines with mechanical power transmission are investigated. Data on the required engine power and propeller speed were used for the study, obtained from calculations during testing of the ship's hull model. The effects of speed reduction on specific fuel consumption and emission reduction were analyzed using numerical models of two-stroke and four-stroke diesel engines. The models were calibrated and validated using data provided by the engine manufacturers. The paper analyses four different cases where one or two low-speed diesel engines, or three or four medium-speed diesel engines, are used for propulsion. The analysis concludes that slow steaming can effectively reduce fuel consumption and CO2 emissions, but the choice of the optimal propulsion system is highly dependent on maritime market conditions in maritime transportation. The choice of propulsion system affects the potential of slow steaming. [ABSTRACT FROM AUTHOR]

With the gradual depletion of fossil energy sources and the improvement in environmental protection attention, efficient use of energy and reduction in carbon emissions have become urgent issues. The integrated electricity and heating energy system (IEHS) is a significant solution to reduce the proportion of fossil fuel and carbon emissions. In this paper, a stochastic optimization model of the IEHS considering the uncertainty of wind power (WP) output and carbon capture power plants (CCPs) is proposed. The WP output in the IEHS is represented by stochastic scenarios, and the scenarios are reduced by fast scenario reduction to obtain typical scenarios. Then, the conventional thermal power plants are modified with CCPs, and the CCPs are equipped with flue gas bypass systems and solution storage to form the integrated and flexible operation mode of CCPs. Furthermore, based on the different load demand responses (DRs) in the IEHS, the optimization model of the IEHS with a CCP is constructed. Finally, the results show that with the proposed optimization model and shunt-type CCP, the integrated operation approach allows for a better reduction in carbon capture costs and carbon emissions. [ABSTRACT FROM AUTHOR]

Big cities affected by intense mobility, traffic and pollution are adopting electrification-based solutions for the reduction of the CO 2 emissions of combustion engines. An interesting field in which the transition toward electrification can achieve important benefits is the area of instant deliveries. Instant deliveries deal with the mobility related to commercial trades between suppliers and customers. In this respect, optimal solutions can be considered during route planning based on the minimization of several metrics, such as distance, energy and road slope, among others. To this end, this paper presents an optimal solution to the instant deliveries problem in which the result is the optimal route, in the city under study, that minimizes energy consumption based on road slope and total distance traveled, and that gives higher priority to routes that include cycling infrastructure that the city can provide. The paper uses electric bikes since they are easily transportable and are highly versatile for instant deliveries. The results obtained were compared to a previous version of the optimal algorithm already published by the authors which minimizes the Haversine and Euclidian distances only. It was found that the shortest distance travelled between customers does not necessarily imply the least energy consumption. The latter, in combination with an energy consumption estimation approach, represent the original contribution of the work. [ABSTRACT FROM AUTHOR]

Under the global goal of "carbon peaking and carbon neutrality", new power system is facing a new energy development trend of increasing the capacity of new energy and reducing the capacity and quantity of coal power. Furthermore, new power systems are also moving towards new models such as the Energy Internet and energy storage diversification. This paper combines the needs of new power systems' "source-load interaction" and a differentiated analysis of the specific carbon emission characteristics of each subject of the Energy Internet "source-network-load-storage". This paper analyzes the energy efficiency coefficient and carbon emission factors of each link of "source-grid-load-storage". Therefore, the paper proposes a carbon control model for the Energy Internet. This paper focuses on the characteristics of coal power with the largest carbon reduction. Therefore, this article constructs a multi-dimensional low-carbonization evaluation index system for coal-fired power plants. In addition, this article designs a fusion weight calculation method for subjective and objective indicators based on CRITIC-G1. Additionally, this paper uses the ideal solution–gray relational method to evaluate the specified samples. Finally, this article verifies the effectiveness and feasibility of the implementation of the low-carbonization evaluation index system for coal-fired power plants. [ABSTRACT FROM AUTHOR]

This paper addresses issues related to modeling energy consumption and emissions using microscopic traffic simulations. This paper develops a method in which a traffic model is used to calculate the energy needed to travel through selected types of intersections. This paper focuses on energy consumption and derived values of calculated energy, which can be, for example, carbon dioxide emissions. The authors present a review of the scientific literature on the study of factors affecting energy consumption and emissions and methods to estimate them in traffic. The authors implemented an energy consumption model into a microsimulation software module to estimate results as a function of varying traffic volumes at selected types of intersections and for selected traffic organization scenarios. The results of the study show the lowest energy consumption and the lowest emissions when road solutions are selected that contribute to reducing vehicle travel times on the urban street network at higher average vehicle speeds. In addition, the positive impact of the share of electric vehicles in the traffic flow on the reduction of energy consumption and emissivity was estimated. [ABSTRACT FROM AUTHOR]

The increasing environmental concerns due to emissions from the shipping industry have accelerated the interest in developing sustainable energy sources and alternatives to traditional hydrocarbon fuel sources to reduce carbon emissions. Predominantly, a hybrid power system is used via a combination of alternative energy sources with hydrocarbon fuel due to the relatively small energy efficiency of the former as compared to the latter. For such a hybrid system to operate efficiently, the power management on the multiple power sources has to be optimised and the power requirements for different vessel types with varying loading operation profiles have to be understood. This can be achieved by using energy management systems (EMS) or power management systems (PMS) and control methods for hybrid marine power systems. This review paper focuses on the different EMSs and control strategies adopted to optimise power management as well as reduce fuel consumption and thus the carbon emission for hybrid vessel systems. This paper first presents the different commonly used hybrid propulsion systems, i.e., diesel–mechanical, diesel–electric, fully electric and other hybrid systems. Then, a comprehensive review of the different EMSs and control method strategies is carried out, followed by a comparison of the alternative energy sources to diesel power. Finally, the gaps, challenges and future works for hybrid systems are discussed. [ABSTRACT FROM AUTHOR]

The global issue of climate change has accelerated the international commitment to net-zero carbon emission development. Decarbonizing the building sector has been put on several governments' sustainable development agendas. To provide a reference for decarbonizing the building sector, this paper summarizes the U.S. experience in zero-carbon buildings (ZCBs) from the aspects of policies, codes, and standards at the federal and local levels and those of professional societies. Based on the definition and boundaries of ZCBs, this paper introduces policies on building energy efficiency, electrification, on-site renewable energy deployment, and "buy clean", illustrating highlights in building phases, energy systems, materials production, and fiscal incentives. The synergic efforts and coordination between federal and local levels and with professional societies are also introduced. Successful experiences in policy and standard implementation are summarized, including the systemic work of multilevel governance, clearly defined goals and stringent policies, constant upgrades of codes and standards, transparency in reporting and information sharing, and increased financial and investment opportunities. This paper provides concrete recommendations for developing zero-carbon building policies. [ABSTRACT FROM AUTHOR]

Because they are environmentally friendly and safe, automated guided vehicles (AGVs) are increasingly used in newly constructed automated container terminals. However, their scheduling strategy is constrained by their limited battery capacity. When batteries reach their charging threshold, the AGVs need to be returned to battery-swapping stations. Moreover, the movement speed of AGVs has a significant impact on their energy consumption and operating times. Therefore, in this paper, a speed control strategy that considers the traffic environment of the terminal is proposed from the perspective of energy conservation and emission reduction. In addition, the charging capacity of the terminal is discretized to model its limited handling capacity to avoid congestion in the battery-swapping stations. To minimize the costs of delays and carbon emissions of AGV operations, a mixed integer programming model is established. It optimizes the efficiency and carbon emissions of the operations by assigning and prioritizing container transportation and AGV battery-swapping tasks. An improved genetic algorithm-based approach is designed where a better initial solution is obtained through a greedy strategy, while simulated annealing is adopted for population selection to prevent the algorithm from falling into local optima. Furthermore, an adaptive adjustment strategy for crossover and mutation probabilities is adopted to improve the algorithm's convergence. Finally, a series of numerical experiments is conducted to verify the efficiency of the proposed method. The experimental results indicate that considering the variability of AGV speed can more accurately characterize their energy consumption, and increasing the number of AGVs and enhancing the battery-swapping capacity can effectively reduce the costs of delays and carbon emissions. [ABSTRACT FROM AUTHOR]

The escalating issue of global climate change necessitates urgent measures to reduce carbon emissions globally. Within this context, the construction industry emerges as a critical sector to address given its high energy consumption, substantial CO2 emissions, and low utilization rate. Therefore, it is pivotal to foster energy conservation and reduce emissions in this sector. To this end, this paper delineates two primary objectives: (1) identifying optimal research methodologies and index parameters for evaluating carbon emission efficiency in the construction industry, and (2) assessing the variance in carbon emission efficiency at disparate stages and regions. Leveraging the Malmquist index, we scrutinize the carbon emission data from 30 Chinese provinces spanning from 2010 to 2019. Our findings indicate a geographical dichotomy in China's construction industry's carbon emission efficiency—lower in the west and higher in the east. Additionally, this study delves into the distinguishing features of emission efficiency alterations across regions, the main influencing factors, and avenues for enhancement. Subsequently, it proposes policy recommendations tailored to the unique attributes of various regions and the overarching framework. [ABSTRACT FROM AUTHOR]

Smart energy networks provide an effective means to accommodate high penetrations of variable renewable energy sources like solar and wind, which are key for the deep decarbonisation of energy production. However, given the variability of the renewables as well as the energy demand, it is imperative to develop effective control and energy storage schemes to manage the variable energy generation and achieve desired system economics and environmental goals. In this paper, we introduce a hybrid energy storage system composed of battery and hydrogen energy storage to handle the uncertainties related to electricity prices, renewable energy production, and consumption. We aim to improve renewable energy utilisation and minimise energy costs and carbon emissions while ensuring energy reliability and stability within the network. To achieve this, we propose a multi-agent deep deterministic policy gradient approach, which is a deep reinforcement learning-based control strategy to optimise the scheduling of the hybrid energy storage system and energy demand in real time. The proposed approach is model-free and does not require explicit knowledge and rigorous mathematical models of the smart energy network environment. Simulation results based on real-world data show that (i) integration and optimised operation of the hybrid energy storage system and energy demand reduce carbon emissions by 78.69%, improve cost savings by 23.5%, and improve renewable energy utilisation by over 13.2% compared to other baseline models; and (ii) the proposed algorithm outperforms the state-of-the-art self-learning algorithms like the deep-Q network. [ABSTRACT FROM AUTHOR]

This study intends to shed light on the environmental impacts of energy decisions in South Korea by analyzing the correlation between energy consumption patterns and environmental indicators such as carbon dioxide emissions. In 2021, global CO2 emissions increased by 6%—to the highest ever level of 36.3 billion tons—according to the International Energy Agency (IEA). This increase in CO2 emissions is a big problem for all countries around the world. The aim of this article is an analyze the impact of fossil fuels, renewable energy, and nuclear energy on South Korea's environment based on the STIRPAT (stochastic impact by regression on population, affluence, and technology) model. Exploring the intricate nexus between economic and energy indicators and environmental outcomes, this study employs the STIRPAT model to analyze the influence of GDP, population dynamics, fossil fuels, renewable energy, and nuclear energy on South Korea's environment. The yearly data from 1972 to 2021 are analyzed in this paper using an autoregressive distributed lag (ARDL) model. The reliability of this study is also examined by employing FMOLS (fully modified ordinary least squares) and CCR (canonical cointegrating regression) estimators. This study confirms the findings of previous research by showing that the rising South Korea GDP and population can lead to higher CO2 emissions and that a strategy switching to renewable energy can cut down on CO2 emissions in Korea, as it exhibits a coefficient of −0.085 *. The robustness results of FMOLS and CCR's findings support baseline ARDL findings. [ABSTRACT FROM AUTHOR]

Climate change is progressing faster than previously envisioned. Efforts to mitigate the challenges of greenhouse gas emissions by countries through the establishment of the Intergovernmental Panel on Climate Change has resulted in continuous environmental improvements in the energy efficiency and carbon emission signatures of products. In this paper, an energy–carbon emissions nexus causal model was applied using the Leontief Input–Output mathematical model for low-carbon products in future transport-manufacturing industries., The relationship between energy savings, energy efficiency, and the carbon intensity of products for the carbon emissions signature of the future transport manufacturing in South Africa was established. The interrelationship between the variables resulted in a 29% improvement in the total energy intensity of the vehicle body part products, 7.22% in the cumulative energy savings, and 16.25% in the energy efficiency. The scope that has been examined in this paper will be interesting to agencies of government, researchers, policymakers, business owners, and practicing engineers in future transport manufacturing and could serve as a fundamental guideline for future studies in these areas. [ABSTRACT FROM AUTHOR]

The goal of reaching the peak of carbon in the construction industry is urgent. However, the research on the feasibility of realizing this goal and the implementation of relevant policies in China is relatively superficial. In view of the historical data of energy consumption and building CO2 emission from 1995 to 2019, this paper establishes a BP neural network model for predicting building CO2 emissions. Moreover, the influencing factors, such as population, GDP, and total construction output, are introduced as the parameters in the model. Through the scenario analysis method explores the practical path to accomplish the peak of building CO2 emissions. When using traditional prediction methods to predict building carbon emissions, the long prediction cycle will increase the possibility of significant errors. Therefore, this paper constructs the calculation model of building carbon emission and forecasts the future carbon emission value through the BP neural network to avoid the error caused by the nonlinear relationship between influencing factors and predicted value. It will effectively predict the feasibility of the carbon peak and the carbon-neutral target set by government, and provide a useful predictive tool for adjusting the new energy structure and formulating related emission reduction policies. [ABSTRACT FROM AUTHOR]

Aiming to meet the low-carbon demands of power generation in the process of carbon peaking and carbon neutralization, this paper proposes an optimal PV-hydrogen zero carbon emission microgrid. The light–electricity–hydrogen coupling utilization mode is adopted. The hydrogen-based energy system replaces the carbon-based energy system to realize zero carbon emissions. Firstly, the mathematical models of photovoltaic, hydrogen and electric energy storage systems in a microgrid are built. Then, the optimal allocation model of the microgrid source storage capacity is established, and a scheduling strategy considering the minimum operational cost of energy storage equipment is proposed. The priority of equipment output is determined by comparing the operational costs of the hydrogen energy storage system and the electric energy storage system. Finally, the proposed scheme is compared with the scheduling scheme of the battery priority and the hydrogen energy system priority in an actual microgrid. It is verified that the scheme can ensure stable power-generating, zero carbon operation of a microgrid system while reducing the total annual power costs by 9.8% and 25.1%, respectively. [ABSTRACT FROM AUTHOR]

The relationship between information and communication technology investment (ICT), environmental impacts, and economic growth has received increasing attention in the last 20 years. However, the relationship between ICT, energy intensity, environmental impacts, and economic growth was relatively neglected. In this paper, we aimed to contribute to the environmental literature by simultaneously analyzing the relationship between ICT, energy intensity, economic growth, Carbon dioxide (CO2) emissions, and energy consumption for the period of 1990–2020 in G7 countries. We employed the Panel Quantile Auto Regressive Distributed Lag (PQARDL) method and Panel Quantile Granger Causality (PQGC) methods. According to the results of PQARDL method, energy consumption, ICT, CO2 emission, and energy intensity have effects on economic growth in the long and short run. According to the of PQGC methods allowing causality results for different quantiles, there is evidence of a bidirectional causality between ICT investment and economic growth for all quantiles and evidence of a unidirectional causality from ICT to energy consumption and from CO2 emissions to ICT investment and energy efficiency. Our results indicate that the governments of the G7 countries have placed energy efficiency and ICT investment at the center of their policies while determining their environmental and energy policies, since energy consumption is a continuous process. [ABSTRACT FROM AUTHOR]

Extended-Range Electric Vehicles (EREVs) are intended to improve the range of battery electric vehicles and thus eliminate drivers' concerns about running out of energy before reaching the desired destination. This paper gives an insight into EREV's performance operating according to the proposed control strategy over various driving cycles, including the Worldwide Harmonized Light-duty Test Cycle Class 3b (WLTC 3b), Federal Test Procedure (FTP-75), and China Light-Duty Vehicle Test Cycle (CLTC-P). Simulation runs were performed in Matlab-Simulink® for different cases of drive range, electricity mix, and vehicle mass. The control strategy goal was to aim at a specified value of battery state of charge at the targeted range value. The obtained test results included: pure electric drive range, acceleration times, EREV range tests, control strategy range errors, Range Extender (REX) utilization metric and distribution of its engagement instances, fuel consumption, total equivalent CO2 emission, powertrain efficiency, and specific energy consumption. The control strategy operated on average with a range error of −1.04% and a range mean square error of 2.13%. Fuel consumption (in range extension mode) varied between 1.37 dm3/100 km (FTP-75) and 6.85 dm3/100 km (WLTC 3b Extra-High 3). CO2eq emission was 95.3–244.2 g/km for Poland, 31.0–160.5 g/km for EU-27, and 1.2–147.6 g/km for Sweden. This paper is a valuable source of information for scientists and engineers seeking to learn the advantages and shortcomings of EREV drives with a proposed control strategy, based on various sets of results. [ABSTRACT FROM AUTHOR]

The reduction of carbon emissions in the power industry will play a vital role in global decarbonization. The power industry has three main strategies to achieve this reduction in emissions: to implement lean marketing strategies that effectively target users of power and encourage them to adopt decarbonizing technologies and services; to optimize the efficiency of these users of power; and to improve the efficiency of renewable energy sources. This paper establishes a comprehensive evaluation system of indexed data from power industry customers for the development of lean marketing strategies. This system evaluates indexes derived from customer data on renewable energy sources, carbon emissions, energy efficiency, and customer credit. It adopts the DEMATEL–CRITIC combination weight assignment and VIKOR method for system evaluation and conducts simulation experiments on customer data in a region of Northeastern China to give an example of how this method could be applied in practice to lean marketing. The results show that the evaluation system proposed in this paper can govern the lean marketing decision-making of power sales enterprises. [ABSTRACT FROM AUTHOR]

With the increasingly prominent environmental problems in the world today, the development of an integrated energy system and the introduction of a carbon-trading mechanism have become important means to realize the low carbonization of the energy industry. Based on this, this paper introduces the carbon-trading mechanism into the research on the optimal dispatch of an integrated energy system. The mechanism of integrated energy demand response participating in low-carbon economic dispatch is analyzed. The relationship between carbon emissions and carbon-trading price in carbon-trading mechanism is described. On the basis of considering the commodity attributes of the electricity and gas load and the flexible supply characteristics of the thermal load, an incentive-type comprehensive energy demand response model is established. Finally, aiming at the lowest comprehensive operating cost, a comprehensive energy system model considering the power balance and equipment constraints of the electric–gas–heat system is established, using an improved particle swarm algorithm to solve it. Simulations verify the effectiveness of the proposed method in reducing the carbon emissions and operating costs of integrated energy systems. [ABSTRACT FROM AUTHOR]