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Background: Health care systems contribute significantly to greenhouse gas emissions. One source of these emissions is single-use products. Examination table paper does not confer protection against microbial contamination and thus can be omitted while following infection control standards. The objective is to eliminate the routine use of examination table paper in outpatient oncology clinics at BC Cancer. Methods: A quality improvement approach was used. Examination tables continued to be disinfected using wipes between patients, but table paper was not used. Plan-do-studyact cycles were performed at four regional cancer centres. Results: Pre-intervention, the cancer centres used 19 to 69 rolls of paper monthly. Postintervention, usage declined to 0 to 2 rolls monthly. This was associated with annual cost savings of $3974 and a reduction of 32 501 kg of carbon dioxide emissions. Conclusions: The use of examination table paper can be eliminated in outpatient clinics, resulting in both cost savings and a reduction in carbon dioxide emissions. [ABSTRACT FROM AUTHOR]

Every ophthalmic surgical supply, including intraocular lenses (IOLs), IOL cartridges, and ophthalmic viscosurgical device syringes, is packaged with instructions for use (IFU). These pamphlets are printed in multiple languages and, in the case of an IOL, significantly increase the size and weight of the packaging. To eliminate this significant and unnecessary source of waste, we recommend that manufacturers move to Quick Response codes that link to online electronic IFU (e-IFU) as a sensible alternative. In addition to reducing carbon emissions and manufacturing costs, e-IFU can be updated more easily and accessed by surgeons in the clinic, where IOL models and powers are selected. Varying and inconsistent IFU requirements between different countries are a barrier to wider adoption of e-IFU by the ophthalmic surgical industry. Regulatory agencies in every country should allow and encourage e-IFU. This position paper has been endorsed by the 3 major societies that sponsor EyeSustain, a consortium of global societies dedicated to advancing sustainability in ophthalmology. [ABSTRACT FROM AUTHOR]

Reducing examination table paper (ETP) use may help curb carbon emissions from health care. Six participants applied Glo Germ (DMA International) to their hands before a common physical examination (abdominal, cardiorespiratory, hip and knee) both with and without ETP. After each exam, UV light was shined on the exam table and photographs were taken. The number of hand touches on ETP-covered areas and uncovered areas were tallied and compared using t tests. Despite covering more surface area, participants touched areas without ETP significantly more than ETP-covered areas (P <.05). Despite its continued use, patients do not have much hand contact with ETP during common clinical examinations. [ABSTRACT FROM AUTHOR]

The text provides a summary of various articles related to air quality, climate change, and environmental initiatives. It mentions a report from the World Meteorological Organization (WMO) highlighting record-breaking climate change indicators in 2023, the use of pulp and paper waste to reduce carbon emissions, the development of national indoor quality standards for public buildings, the establishment of a hydrogen hub in the Pilbara region of Australia, and the U.S. Environmental Protection Agency's new tailpipe rules to reduce pollution from cars and light trucks. These articles offer insights into the current state of the environment and efforts to address climate change. [Extracted from the article]

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

This study investigates the impact of incorporating lime mud (LM) as a filler material to develop sustainable, cementless ultra-high performance concrete (UHPC) by replacing silica powder (SP) and silica sand (SS) at varying percentages. Flowability analysis reveals that LM, with its distinctive particle size, influences flow characteristics differently at different replacement levels. Compressive strength was improved by 5.4% with 30% SP replacement, showcasing the role of LM in nucleation, as well as its particle size advantage. The heat of hydration analysis indicates that LM likely influences the early curing stages, enhancing reactivity and nucleation. TGA analysis also confirms that LM plays a role in hydration reaction, influencing weight loss and hydration peaks. The mercury intrusion porosimeter results show the impact of LM on pore distribution, with 30% for SP and 50% for SS replacements, favoring denser matrices. Life cycle assessment (LCA) demonstrates reduced CO 2 emissions and favorable environmental performance, highlighting the sustainability potential of LM replacements. Overall, the incorporation of LM in UHPC shows promise in terms of enhancing its mechanical properties and environmental sustainability. [Display omitted] • Lime mud (LM) in UHPC boosts material strength, leading to denser, more effective concrete. • LM enhances hydration, flowability, and refines pore structure. • High LM ratios in UHPC increase sustainability and reduce CO 2 emissions. • LM's use in UHPC significantly advances its mechanical properties and eco-friendliness. [ABSTRACT FROM AUTHOR]

Corporate carbon emissions accounting has become a research focus for researchers and government departments. Based on corporate production processes, this study proposes and establishes a comprehensive system for corporate carbon emissions. Taking Stora Enso (Guangxi) Company as an example, this paper provides a thorough analysis of the production and operational status of paper-making companies. The production process is divided into five stages: material preparation, pulping, pulp board production, papermaking, and alkali recycling. Based on these production processes, it proposes and constructs a corporate carbon emissions accounting system from an accounting perspective, which specifically includes accounting objectives, information quality characteristics, accounting recognition, accounting measurement, accounting treatments, and information disclosure. The findings show that the carbon emissions accounting system targeted at production processes can clearly awaken corporate carbon emissions responsibilities, further improve the corporate accounting system, and provide effective technical support for the government to implement environmental regulations and formulate carbon reduction policies. [ABSTRACT FROM AUTHOR]

This paper analyses carbon emissions and value-added embodied in trade between two large developed countries, South Korea and the United States, during 2000-2014. Using multi-regional input-output (MRIO) tables, our analysis reveals that carbon emissions and value-added embodied in exports grew by 19% and 101% for South Korea but shrank by 43% and 7% for the United States. As a result, South Korea experienced a 40% increase in net carbon exports and 243% increase in net valueadded exports. At the industry level, the primary drivers of changes in carbon exports were electricity and basic materials. The majority of industries in witnessed improvements in carbon intensities suggesting improved environmental efficiency. While both countries achieved a decoupling of carbon emissions from value-added exports, substantial year-to-year and sectoral variations were observed. Finally, structural decomposition analysis indicates that domestic supply-side factors played a role in decreasing emissions whereas foreign demand-side factors contributed to emissions increases. In line with the main findings, various implications for policy and future research are discussed. [ABSTRACT FROM AUTHOR]

This paper explores the creation, development, and dissemination of prefabricated concrete rural houses in Jiangsu Province in East China from 1961 to the 1980s, an example of the technological transition provoked by the depletion of forest and timber in China. Through archival research, fieldwork and interviews, the paper examines the two waves of design and dissemination of prefabricated concrete rural houses between 1961 and 1965 and their subsequent 'vernacularisation' in the 1970s and the 1980s. This research provides a twofold insight into the current scholarly debates surrounding built heritage and global climate change. On one hand, it addresses a historical context of concrete overuse in contemporary China, a matter of critical importance in relation to carbon emissions and global climate change. On the other hand, it offers a counter-argument to today's call for reintroducing timber structures in many places, as evidenced by the case of East China, where the widespread use of materials like concrete was primarily a consequence of the ecological crisis following decades of deforestation and timber resource depletion. In addition, the 'vernacularisation' of concrete structures manifested by this case still provides lessons for today's efforts to popularise more eco-friendly construction materials and technologies, especially in rural areas, and the prefabricated concrete houses possess potential heritage values as trackers of ecological changes. [ABSTRACT FROM AUTHOR]

Based on the carbon emissions trading pilot policy in China, this paper takes Chinese A‐share listed companies from 2012 to 2020 as a sample, and analyzes the role of company's cost transfer capability (ECTC) in the mechanism of market‐incentivized environmental regulation policy (MERP) influencing company green transformation. This paper finds the following four primary results. First, ECTC significantly strengthens the green transformation driving effect of MERP in general. However, results based on the division of ECTC by percentiles show that MERP‐induced green transformation is more pronounced in the top 25% of companies, which distinguishes them from companies in the top 25%–50%, whose cost transfer capacity hampers MERP's contribution to the green transformation. Second, the level of regional economic development, emission regulation, and business reputation have heterogeneous influences on the moderating effect of ECTC. Third, the results based on the moderated mediation model show that ECTC can influence the green transition driving effect of MERP by moderating the two paths of risk‐taking ability and financing constraints of companies. Finally, there is a diffusion effect on the green transformation driven by MERP through cost transfer among companies in non‐pilot industries and non‐pilot regions. [ABSTRACT FROM AUTHOR]

This research paper introduces a novel approach by combining a Backpropagation (BP) neural network with a non-angular and non-radial directional distance function to construct a BPNN-DDF model. This innovative model evaluates, decomposes, and analyzes China's agricultural sector's carbon emission rate across nine key subregions between 2010 and 2021. The key findings of this study are that China's agricultural carbon emission rate is decreasing, primarily due to technological advancements rather than technological efficiency. Subregions with robust economies and stable climates exhibit higher carbon emission efficiency, whereas those with underdeveloped economies, low agricultural technology, and volatile climates show relatively lower efficiency. The Dagum Gini coefficient analysis reveals a widening disparity in carbon emission rates among agricultural subregions, escalating from 0.174 in 2010 to 0.425 in 2021, indicating a growing gap between subregions that demands immediate attention. The kernel density distribution demonstrates an overall upward trend in China's carbon emission efficiency but also highlights an increasing divergence among subregions, particularly between the South China Area, the Huang-Huai-Hai Plain, and other regions. Therefore, this paper posits that strategies focusing on technological progress, sustainable agricultural development, regional development initiatives, and addressing inter-subregional imbalances will be crucial pathways for China's future low-carbon agricultural development. [ABSTRACT FROM AUTHOR]

This paper analyzes the theoretical mechanism by which urbanization and consumption structure upgrading exert an impact on carbon emissions in the context of promoting higher quality and efficiency in urbanization, advancing the transition and upgrading of consumption structure, and achieving carbon peaking and carbon neutrality (“dual carbon”) goals. By establishing an extended STIRPAT model and an intermediary model, this paper takes nine prefecture-level cities in Fujian Province from 2011 to 2021 as samples to investigate the effects of urbanization and consumption structure upgrading on carbon emission reduction in Fujian Province, revealing the interrelationship and transmission mechanism among the three. The results of this study show that: (i) The relationship between urbanization and carbon emissions in Fujian Province from 2011 to 2021 shows an inverted U-shaped curve, i.e. urbanization accelerated carbon emissions in the early stage and inhibited carbon emissions in the later stage; (ii) the regional heterogeneity analysis shows that under the current urbanization background, the upgrading of consumption structure in western and southern Fujian can significantly reduce carbon emissions, while the relationship between consumption structure upgrading and carbon emission reduction in eastern Fujian is not significant; (iii) the upgrading of consumption structure can help achieve and promote carbon emission reduction through increasing the proportions of the tertiary industry and immaterial consumption and enhancing the level of culture and education, while exerting little effect on carbon emission reduction through consumption in transportation, communication and health care. [ABSTRACT FROM AUTHOR]

With the establishment of the "double carbon" goal, various industries are actively exploring ways to reduce carbon emissions. Cloud data centers, represented by cloud computing, often have the problem of mismatch between load requests and resource supply, resulting in excessive carbon emissions. Based on this, this paper proposes a complete method for cloud computing carbon emission prediction. Firstly, the convolutional neural network and bidirectional long-term and short-term memory neural network (CNN-BiLSTM) combined model are used to predict the cloud computing load. The real-time prediction power is obtained by real-time prediction load of cloud computing, and then the carbon emission prediction is obtained by power calculation. Develop a dynamic server carbon emission prediction model, so that the server carbon emission can change with the change of CPU utilization, so as to achieve the purpose of low carbon emission reduction. In this paper, Google cluster data is used to predict the load. The experimental results show that the CNN-BiLSTM combined model has good prediction effect. Compared with the multi-layer feed forward neural network model (BP), long short-term memory network model (LSTM), bidirectional long short-term memory network model (BiLSTM), modal decomposition and convolution long time series neural network model (CEEMDAN-ConvLSTM), the MSE index decreased by 52 % , 50 % , 34 % and 45 % respectively. [ABSTRACT FROM AUTHOR]

Hydrogen as a clean energy to deal with carbon emissions has been favored by many scholars, while the problems of low efficiency and high energy consumption in the process of liquefaction storage and transportation have become one of the important factors restricting the development of it. The combination of heat exchanger and ortho-para (O–P) hydrogen converter has attracted considerable attention as a high efficiency and low energy consumption technology. In this paper, based on the catalyst-filled spiral wound heat exchanger (CSWHE) and coupled with ortho-para hydrogen conversion, the flow and heat transfer characteristics of gaseous hydrogen are studied. Firstly, low-temperature O–P hydrogen catalytic conversion experiments and flow characteristics experiments were carried out by filling catalyst in the heat exchange tube. and an equation for calculating pressure drop in the section filled catalyst was proposed. Secondly, a new numerical model was established validated by the experiments. The influence of catalyst on heat transfer and the effects of velocity on the heat transfer performance and O–P hydrogen conversion efficiency were studied by numerical simulation. Finally, this paper analyzed the significant factor affecting the para hydrogen content at outlet: reaction time. And an equation for calculating para hydrogen content at outlet with reaction time was proposed. This study is expected to provide a reference for the design of CSWHE. • An isothermal reactor is designed and employed to low-temperature ortho and para hydrogen catalytic conversion experiments. • A new pressure drop calculation equation for porous region is proposed by modifying Ergun equation. • A new numerical model is established to study the coupling mechanism of flow and heat transfer. • The key factor affecting the catalytic conversion of ortho and para hydrogen is pointed out: reaction time. [ABSTRACT FROM AUTHOR]

Since reservoirs with permeability less than 10 mD are characterized by high injection difficulty, high-pressure drop loss, and low pore throat mobilization during the water drive process, CO2 is often used for development in actual production to reduce the injection difficulty and carbon emission simultaneously. However, microfractures are usually developed in low-permeability reservoirs, which further reduces the injection difficulty of the driving medium. At the same time, this makes the injected gas flow very fast, while the gas utilization rate is low, resulting in a low degree of recovery. This paper conducted a series of studies on the displacement effect of CO2-soluble foaming systems in low-permeability fractured reservoirs (the permeability of the core matrix is about 0.25 mD). For the two CO2-soluble blowing agents CG-1 and CG-2, the effects of the CO2 phase state, water content, and oil content on static foaming performance were first investigated; then, a more effective blowing agent was preferred for the replacement experiments according to the foaming results; and finally, the effects of the blowing agents on sealing and improving the recovery degree of a fully open fractured core were investigated at different injection rates and concentrations, and the injection parameters were optimized. The results show that CG-1 still has good foaming performance under low water volume and various oil contents and can be used in subsequent fractured core replacement experiments. After selecting the injection rate and concentration, the blowing agent can be used in subsequent fractured cores under injection conditions of 0.6 mL/min and 2.80%. In injection conditions, the foaming agent can achieve an 83.7% blocking rate and improve the extraction degree by 12.02%. The research content of this paper can provide data support for the application effect of a CO2-soluble blowing agent in a fractured core. [ABSTRACT FROM AUTHOR]

This paper proposes a semiparametric spatial lag model and develops a Bayesian estimation method for this model. In the estimation of the model, the paper combines Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm, random walk Metropolis sampler, and Gibbs sampling techniques to sample all the parameters. The paper conducts numerical simulations to validate the proposed Bayesian estimation theory using a numerical example. The simulation results demonstrate satisfactory estimation performance of the parameter part and the fitting performance of the nonparametric function under different spatial weight matrix settings. Furthermore, the paper applies the constructed model and its estimation method to an empirical study on the relationship between economic growth and carbon emissions in China, illustrating the practical application value of the theoretical results. [ABSTRACT FROM AUTHOR]

Accurately predicting the trajectory of carbon emissions is vital for achieving a sustainable shift toward a green and low-carbon future. Hence, this paper created a novel model to examine the driver analysis and integrated prediction for Chinese carbon emission, a large carbon-emitting country. The logarithmic mean divisia index (LMDI) approach initially served to decompose the drivers of carbon emissions, analyzing the annual and staged contributions of these factors. Given the non-stationarity and non-linear characteristics in the data sequence of carbon emissions, a decomposition–integration prediction model was proposed. The model employed the empirical mode decomposition (EMD) model to decompose each set of data into a series of components. The various carbon emission components were anticipated using the long short-term memory (LSTM) model based on the deconstructed impacting factors. The aggregate of these predicted components constituted the overall forecast for carbon emissions. The result indicates that the EMD-LSTM model greatly decreased prediction errors over the other comparable models. This paper makes up for the gap in existing research by providing further analysis based on the LMDI method. Additionally, it innovatively incorporates the EMD method into the carbon emission study, and the proposed EMD-LSTM prediction model effectively addresses the volatility characteristics of carbon emissions and demonstrates excellent predictive performance in carbon emission prediction. [ABSTRACT FROM AUTHOR]

Transitioning to clean cooking fuels is not only part of achieving SDG7 but also makes a significant contribution to mitigating climate change by reducing carbon emissions. Research projects and pilots across a number of countries in Africa and South Asia have been exploring the suitability and energy performance of different cooking appliances and fuels. The paper presents the first statistical analysis across these multiple datasets to determine the range of energy required to cook dishes using different technologies and fuels. The paper draws out distinctions between African and Asian dishes, notably the impact of energy-intensive dishes prepared mostly in Africa. The paper demonstrates that the standard efficiency-based approaches to comparing the performance of stoves are not appropriate to modern electric cooking devices, so a novel alternative approach based on specific energy consumption and termed energy ratios is developed. Charcoal stoves are shown to use 15 times as much energy as electric pressure cookers (EPCs) to cook African dishes, and a detailed review of how the EPC works explains why this should be. Energy ratios provide a basis for estimating carbon emission reductions associated with transitioning to modern cooking fuels and also for estimating household cooking costs. Fuel and electricity prices from studies show that the cost of cooking with an EPC can be only 20% of the cost of cooking with charcoal, which highlights the potential for modern, energy-efficient electric cooking devices to defy the conventional wisdom of the energy ladder. [ABSTRACT FROM AUTHOR]

This paper investigates the connection between climate change and energy security in Europe and provides empirical evidence that these issues are the two faces of the same coin. Using a panel of 39 European countries during the period 1980–2020, the empirical analysis presented in this paper indicates that increasing the share of nuclear, renewables, and other nonhydrocarbon energy and improving energy efficiency could lead to a significant reduction in carbon emissions and improve energy security throughout Europe. Accordingly, policies and reforms aimed at shifting away from hydrocarbons and increasing energy efficiency in distribution and consumption are key to mitigating climate change, reducing energy dependence, and minimizing exposure to energy price volatility. [ABSTRACT FROM AUTHOR]

Currently, collaborative distribution models have not reached the optimal state of carbon emissions. The cost of additional low-carbon expenditures and the problem of carbon data verification have led to the lack of motivation for reducing emissions among collaborative distribution enterprises. Therefore, how to incentivize them to adopt the low-carbon model is crucial for achieving low-carbon goal. By relying on a government-led digital platform, this paper designs a dual low-carbon incentive strategy to encourage enterprise-alliance to adopt a low-carbon distribution model. In this paper, we first construct an evolutionary game model of the government, enterprise-alliance and end-users; then we explore the conditions of the three-party equilibrium evolution strategy by solving the model and analyzing the stability; and finally, we conduct simulation validation and results analysis with the help of MATLAB. In summary, we found that government punishment is more effective at regulating enterprise-alliance than reward. End-users' behavior is affected by the costs they need to bear, and they no longer support enterprise-alliance to carry out collaborative low-carbon distribution above a certain threshold. [ABSTRACT FROM AUTHOR]

With the development of energy technology, hydrogen is gradually becoming widely used. China is also rapidly developing its own hydrogen energy industry in an effort to accomplish the "dual carbon target'. Hydrogen from methane is one of the mainstream sources, of which hydrogen from methane cracking is the decomposition of methane into hydrogen and solid carbon, with the solid carbon also having a high economic value. In particular, this technology does not emit carbon dioxide, which is very friendly to the environment, but it cannot avoid larger energy consumption. In contrast, hydrogen production from methane cracking driven by solar energy not only combines the characteristics of no CO 2 emission, but also greatly reduces the consumption of primary energy, which is the future development trend of hydrogen production from methane. This paper provides a brief overview of the various technological pathways for methane to hydrogen production in the context of China's actual development, focusing on the current status of the development and application of solar methane cracking to hydrogen production technologies at home and abroad. Among them, this paper mainly discusses the characteristics of solar reactors of different sizes and structures, and summarizes the results of related optimization studies. Meanwhile, the application of catalysts in solar reactors and the problem of carbon deposition are analyzed, and the practical application of solar methane cracking hydrogen production system is outlined. Finally, the future development of solar methane cracking hydrogen production technology is prospected from the national situation of China. [Display omitted] • Solar methane cracking for hydrogen can reduce emissions and energy consumption. • The current state of research on solar reactors is summarized. • Solar methane cracking requires higher performing catalysts. • Providing insights into the future development of solar methane cracking in China. [ABSTRACT FROM AUTHOR]

In order to reduce pollution caused by ship emissions, the International Maritime Organization (IMO) implemented sulfur emission control areas (SECAs). In comparison to ordinary vessels, cruise ships with dual attributes of transportation and tourism generate a greater amount of marine pollution, which poses a significant threat to the marine environment in both berthing ports and the sailing area. In light of the fierce competition of the cruise tourism market, cruise lines are looking for strategies, such as designing more attractive cruise routes, to maintain their core competencies under the emission control policy. In order to achieve this goal, this paper presents a mixed-integer non-linear programming (MINP) model with two objectives and is derived from the traditional route optimization problem. The primary objective is to optimize the route and speed of a cruise liner, while simultaneously enhancing route competitiveness and minimizing carbon emissions both within and outside the SECAs. Subsequently, the multi-objective particle swarm optimization (MOPSO) algorithm was used to reach the objective, and simulations were carried out to verify the effectiveness of the model and method. The results show that speed and sailing route optimization can affect carbon emissions. This paper has a certain application value and guiding significance for cruise line decision makers that will be beneficial for the environment. [ABSTRACT FROM AUTHOR]

The power sector is one of the major CO2-emitting industries in China. It is also the first key emissions control industry included in China's national carbon trading market established in 2021. Therefore, based on the data of 30 provinces and cities in China from 2015 to 2022, this study analyzes the impact of the national carbon market policy on the carbon emissions of the power industry based on the generalized Difference-in-Difference (DID) model. Based on the method of text analysis, this paper constructs the carbon market policy intensity index of each region and incorporates it into the DID model. Empirical analysis finds that the national carbon market policy can significantly inhibit the carbon emissions of the power industry. However, the heterogeneity analysis of 30 provinces and cities shows that the national carbon market policy has a significant impact on the regions that already have a carbon pilot, but not on the non-pilot regions. Therefore, this paper demonstrates the effectiveness of the national carbon market policy in the power industry emissions control, and also exposes the heterogeneity between regions, providing an important empirical basis for the inclusion of other energy-intensive industries. [ABSTRACT FROM AUTHOR]

Global climate change, driven by increasing carbon emissions, has posed a significant challenge to both human survival and development, becoming a major issue for the sustainable progress of human society. This paper examines the characteristics and differences in the spatio-temporal distribution of carbon emissions and sinks across Wuhan Region that is stratified into three spatial levels: Hubei Province, the Wuhan Metropolitan Area, and Wuhan City. On a finer spatial scale within Wuhan, it uncovers the spatial correlations and disparities between carbon emissions and sinks. Accordingly, the paper proposes strategies and suggestions for reducing carbon emissions and enhancing carbon sinks at the provincial, metropolitan, and municipal levels. These findings are valuable for sustainable regional development and construction of low-carbon eco-cities. [ABSTRACT FROM AUTHOR]

In the context of global carbon emission reduction, solar photovoltaic (PV) technology is experiencing rapid development. Using high-resolution remote sensing images to accurately obtain PV information over a large region, including location and size, has the advantages of high statistical efficiency and timely data update for the PV energy management. Due to the intra-class diversity of PV panels and the intricate variability in their deployment environments, existing semantic segmentation methods often have problems such as under-segmentation and mis-segmentation. To alleviate these problems, this paper proposes an improved DeepLabv3+ semantic segmentation network to more accurately extract PV panels from high-resolution remote sensing images. With the aim of alleviating under-segmentation, a multi-level context aggregation module is developed. This module can enhance the model's ability to learn the characteristics of PV panels and their surrounding environment by aggregating rich contextual information from multi-scale and semantic levels. To alleviate the problem of mis-segmentation, a hybrid attention module is introduced. This module sequentially and adaptively adjusts the weight distribution in both the channel and spatial dimensions, thus enabling the model to focus more on the feature information and spatial positions of PV objects. Experiments conducted on a self-constructed Beijing PV segmentation dataset show that the method in this paper has advantages of completeness and accuracy in extracting PV panels compared to the baseline model and current mainstream semantic segmentation network. In addition, the results of experiments on extracting PV panels in real region show that our model also has good stability and generalization capability. [ABSTRACT FROM AUTHOR]

The electric power sector is the primary contributor to carbon emissions in China. Considering the context of dual carbon goals, this paper examines carbon emissions within China's electricity sector. The research utilizes the LMDI approach for methodological rigor. The results show that the cumulative contribution of economies scale, power consumption factors and energy structure are 114.91%, 85.17% and 0.94%, which contribute to the increase of carbon emissions, the cumulative contribution of power generation efficiency and ratio of power dissipation to generation factor are -19.15% and -0.01%, which promotes the carbon reduction. The decomposition analysis highlights the significant influence of economic scale on carbon emissions in the electricity industry, among the seven factors investigated. Meanwhile, STIRPAT model, Logistic model and GM(1,1) model are used to predict carbon emissions, the average relative error between actual carbon emissions and the predicted values are 0.23%, 8.72% and 7.05%, which indicates that STIRPAT model is more suitable for medium- to long-term predictions. Based on these findings, the paper proposes practical suggestions to reduce carbon emissions and achieve the dual carbon goals of the power industry. [ABSTRACT FROM AUTHOR]

Buildings are energy- and resource-hungry: their construction and use account for around 39% of global carbon dioxide emissions; they consume around 40% of all the energy produced; they are responsible for over 35% of the EU's total waste generation; and account for about 50% of all extracted (fossil) materials. Therefore, they present a significant challenge to meeting national and international Net Zero targets of reducing greenhouse emissions and fossil resource use. The CircularB Project, is at the heart of this issue, which will underpin synergies of multi-scale circular perspectives (from materials, to components, to assets and built environments), digital transformation solutions, data-driven and complexity science, stakeholder behavioral science, and interdisciplinary capabilities towards achievable, affordable and marketable circular solutions for both new and existing buildings, for sustainable urban design, and for circular built environments across Europe. This paper contributes to the project by deriving new insights into the stakeholders' influences, inter-relationships, and obstacles in the implementation of circular economy concepts on existing building stocks in Europe, which represent over 90% of whole building assets. In order to identify and derive the insights, our study is rigorously based on (i) a robust critical literature review of key documentations such as articles, standards, policy reports, strategic roadmaps and white papers; and (ii) interviews with relevant stakeholders and decision makers. Uniquely, our work spans across all scales of CE implementation from materials, to products and components, to existing building stocks, and to living built environments. The findings point out the current challenges and obstacles required to be tackled. Inadequacies of financial incentives and governmental enforcement (via policy, legislation, or directive) are commonly found to be the most critical obstacles found throughout Europe. Circular economy is the global challenge and not just a single country can resolve the climate issue without the cooperation of other countries. The insights thus highlight the essential need for harmonized actions and tactical/pragmatic policies promoted and regulated by the European Commission, national and local governments who can dominate the influence, promote inter-relationship, and overcome the barriers towards circular economy much more effectively. [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]

A global transition to low-carbon, resource-efficient economies is occurring in response to risks posed by climate change and environmental degradation. Hydrogen is proposed as a zero-carbon substitute for natural gas with the potential to reduce carbon emissions in homes and businesses. Trades, including gasfitting, will be affected by the transition to a low-carbon economy and require upskilling to develop competencies to work with hydrogen to ensure safety and support domestic uptake. Understanding the training needs of trades practitioners is essential to the successful transition to a zero-carbon economy. This paper presents findings from a qualitative study of Australian gasfitters, exploring what motivates gasfitters to participate in training, what informs their ongoing learning and what their preferences are for hydrogen training. Most gasfitters are either self-employed or work in small businesses, and, as such, this context presents both challenges and opportunities to consider in upskilling strategies. Drawing on interview findings and literature on small business learning, the paper presents considerations to inform training approaches and policies in the transition to hydrogen and also, more broadly, for upskilling other small trade businesses similarly affected by a shift to low-carbon and green technologies. [ABSTRACT FROM AUTHOR]

The environmental issues within the transport industry need to be given priority attention. Optimizing the transport structure and promoting low-carbon transport are essential measures for achieving carbon emission reduction. However, the rail-water multimodal transport scale is currently limited. The government has to intervene in the market through incentive policies, so how to expand the revenue of carriers and enhance the volume has become a major dilemma. Our paper constructs a transport system that includes road, water, and railway, and examines the impact of incentive policies on separate stakeholders. It is discovered that incentive policies can increase the revenue of carriers and simultaneously attract more shippers to elect rail-water multimodal transport. Subsidy policy can hasten the structural transformation of inland transport, making railway transport volume rise. Unfortunately, although the dry port policy may enhance the revenue of railway carriers, it leads to an apparent decline in the revenue of road-water multimodal transport participants. However, the subsidy policy can create a win-win scenario for multiple carriers while achieving carbon emission reduction, but to the detriment of social welfare. Additionally, the paper compares the interactions between diverse incentive policies and pivotal factors such as transport time. The conclusions of our research serve strategic recommendations for maximizing carriers revenues and provide support for government decision-making. • The impact of incentive policies on the rail-water multimodal transport is inspected. • Subsidy policy can make carriers better off, but may have adverse effects on social welfare. • Subsidy policy expands carrier volume and stimulates transport structure transformation. • Although dry port is conducive to social welfare, it hurts the revenue of carriers. • The dry port is beneficial for achieving long-distance carbon emissions reduction. [ABSTRACT FROM AUTHOR]

Urbanization and the release of carbon emission are two significant features of contemporary economic and social progress in China. However, there are few studies that consider how urbanization policy goals affect carbon emission. This paper aims to study the nonlinear effects of urbanization policy goals on carbon emission in 13 cities of the Beijing-Tianjin-Hebei region. In this paper, a multi-dimensional spatial disturbance grey model is proposed. Based on matrix perturbation theory, this paper explores the spatial perturbation bounds of the novel model and verifies that the spatial property of the model is consistent with the viewpoint of the First Law of Geography. Then, the novel model is used to predict the urban carbon emission under different urbanization policy goal scenarios. The results showed that the novel model has spatial regional correlation in spatial region prediction. With the development of urbanization level, carbon emissions show a downward trend in more than 50% of cities from 2022 to 2025. In addition, the improvement of urbanization policy goals will promote the carbon peak time of most cities. The carbon emission intensity will exhibit an inverted U-shaped trend distribution in space. The research results are conducive to providing scientific guidance for the formulation of new-type urbanization policies and helping to achieve the dual carbon goals. • A multi-dimensional spatial disturbance grey model is proposed. • The novel grey model is consistent with the viewpoint of the First Law of Geography. • The spatial distribution of carbon intensity showed an inverted U-shaped pattern. [ABSTRACT FROM AUTHOR]

In recent years, the problem of global warming caused by the massive emission of carbon dioxide has attracted the attention of all countries in the world. As the world's largest carbon dioxide emitter. China is under great pressure to conserve energy and reduce emissions. How to effectively control carbon dioxide emissions has become one of the important issues that needs to be solved in China's economic and social development. The digital economy is characterized by technological innovation, environmental friendliness and green development providing new opportunities for carbon emission reduction. However, the existing literature has not reached a consensus on the study of the impact of China's digital economy development on carbon emissions, and the study of the internal mechanism of China's digital economy development on carbon emissions is not deep enough, and the study of the spatial spillover effect of China's digital economy development on carbon emissions is relatively few. Based on the extended Cobb-Douglas production function, this paper analyzes the impact of digital economy development on carbon emissions by using Chinese urban panel data. The results show that the development of the digital economy can significantly reduce the level of carbon emissions, and this result is still valid after a series of robustness tests. In terms of its mechanism, digital economy can reduce carbon emissions by strengthening environmental regulation, improving human capital level and promoting industrial agglomeration. After further considering the spatial correlation characteristics of the emission reduction effect of digital economy, it is found that the development of digital economy can not only promote carbon emission reduction in the region, but also help reduce carbon emissions in neighboring regions. Heterogeneity test shows that the development of digital economy significantly reduces carbon emissions in central cities and high-innovation cities, but the carbon emission reduction effect of digital economy in peripheral cities is not significant, and the development of digital economy in low- innovation cities significantly increases carbon emissions. The development of digital economy in eastern and central and western cities has significantly reduced carbon emissions, and the carbon emission reduction effect is decreasing from the central and western regions to the eastern regions. The possible marginal contributions of this paper are as follows: first, a theoretical model is built to analyze and empirically test the impact of digital economy on carbon emission by incorporating digital elements into Cobb-Douglas production function and introducing carbon emission equation, which deepens the understanding of the relationship between digital economy development and carbon emission. Second, three intermediary variables, i. e. industrial agglomeration, environmental regulation and human capital are included, which enriches the mechanism of the impact of digital economy development on carbon emissions from a new perspective based on multiple paths. A multidimensional index evaluation system is constructed to measure intermediary variables, which can reflect the characteristics of intermediary variables more completely and comprehensively than a single indicator measurement. Third, two spatial weight matrices of adjacency and inverse distance were constructed, and bidirectional fixed effect spatial Durbin model was adopted to test the spatial spillover effect of digital economy development on carbon emissions from a more subtle urban scale. Three intermediary variables of industrial agglomeration, environmental regulation and human capital were included in the model successively, and the spatial spillover effect of different intermediary variables on carbon emissions was investigated. It provides an empirical basis for the national planning of carbon emission reduction policies. The research in this paper deepens the understanding of the relationship between digital economy development and carbon emission to a certain extent, reveals the internal logic of China's digital economy development affecting carbon emission, clarifies the path and method of carbon emission reduction, and provides theoretical support for government departments to formulate more targeted policies on digital economy development and carbon emission reduction under the constraint of "dual carbon" goal. This will help promote the development of digital economy and carbon emission reduction, and better achieve China's carbon emission reduction targets. [ABSTRACT FROM AUTHOR]

To enhance the utilization of clean energy sources, such as wind power and photovoltaic within virtual power plants, and mitigate carbon emissions, this paper proposes a virtual power plant participation in the electricity carbon joint market optimization operation model based on ensemble empirical mode decomposition–improved bat algorithm (IBA)–Markov chain new energy output prediction. First, complementary set empirical mode decomposition is performed on historical data to construct a Markov chain based wind power and photovoltaic prediction model optimized by IBA. Second, this prediction model is used to predict the daily generation power of wind power and photovoltaic power. Finally, with the optimization goals of maximizing the benefits and minimizing the carbon costs of virtual power plants, a virtual power plant system participating in the electricity carbon joint market model based on wind power and photovoltaic output prediction results is constructed. At the same time, demand response factors are introduced and solved using the NSGA-II algorithm. Taking a certain park as an example for simulation analysis, the research results show that the combined effect of carbon market and demand response can achieve 99.82% of new energy consumption in virtual power plants without significantly reducing profits, basically achieving complete new energy consumption, demonstrating the effectiveness of the proposed model in this paper. [ABSTRACT FROM AUTHOR]

The incorporation of net-zero technology into preexisting energy networks is crucial for facilitating the shift toward an ecologically conscious and sustainable energy infrastructure. The primary objective of this integration is to effectively decrease carbon footprints and to provide a comprehensive understanding of the current approaches and trends related to the design and management frameworks of integrated energy networks. The initial section of this study establishes the foundation for a comprehensive examination of the particular challenges associated with decarbonization in the strategic and operational aspects of integrated energy networks. The subsequent analysis proceeds to elucidate the fundamental framework and technological architecture upon which these energy networks are constructed. This provides significant insights into the operational complexity and efficacy of the system. In addition, the paper provides a concise examination of prominent frameworks and alternative approaches that tackle the issue of low-carbon design and administration. The degree of accuracy facilitates individuals when selecting systems that align with the specific requirements of unique circumstances. Furthermore, this study provides explicit suggestions for future research based on an examination of the distinct attributes and framework of integrated energy networks. The anticipated outcome of implementing these recommendations is to enable the advancement of sustainable development and expedite the shift toward energy infrastructure with reduced carbon emissions. This will make a significant contribution to the collaborative endeavor of mitigating climate change and fostering a sustainable energy future. This study further elucidates the significant contribution of integrated energy networks in addressing climate change and enhancing energy efficiency. It achieves this by synthesizing a complete range of concepts sourced from many academic papers, industry reports, and case studies. This statement offers an examination of the multifaceted technological, legislative, and planning factors that contribute to the attainment of net-zero objectives. [ABSTRACT FROM AUTHOR]

Digitalization and greening are key themes in global development, the manufacturing industry, as a major sector of energy consumption and carbon emissions, is in urgent need of transformation to realize energy conservation and emission reduction, digital transformation provides an opportunity for its green development. However, whether digital transformation can restrain the carbon emission intensity (CEI) of enterprises is still controversial. Based on the data of China's A‐share manufacturing listed companies from 2013 to 2020, this paper uses fixed effect model and intermediary effect model to explore the relationship between digital transformation and enterprise CEI and the influence path. the results show that: (1) Digital transformation has significantly reduced the CEI of manufacturing enterprises. (2) Mechanism analysis indicates that green technological innovation and resource allocation efficiency play a chain mediating role in this relationship. (3) Heterogeneity analysis shows that the carbon emission reduction effect of digital transformation is more significant when enterprises belong to an energy‐intensive manufacturing industry or face strong environmental regulations and industry competition. The analysis of this paper not only deepens enterprises' understanding of digital transformation and green development but also provides a policy basis for China to advocate the coordinated development of the digital economy and green economy. [ABSTRACT FROM AUTHOR]

With China's rapid industrialization and urbanization in the process of socio-economic development, the extensive use of energy has resulted in a large amount of CO2 emissions, which puts great pressure on China's carbon emission reduction task. Through multivariate socio-economic data, this paper proposes an extraction and screening method of multivariate variables based on land-use types, and the downscaled spatial decomposition of carbon emissions at different scales was carried out by using the spatial lag model (SLM). This paper makes up for the shortcomings of previous studies, such as an insufficient modeling scale, simple modeling variables, limited spatio-temporal span of spatial decomposition, and no consideration of geographical correlation. Based on the results of the spatial decomposition of carbon emissions, this paper explores the spatial and temporal dynamics of carbon emissions at different scales. The results showed that SLM is capable of downscaling the spatialization of carbon emissions with high precision, and the continuity of the decomposition results at the provincial scale is stronger, while the differences of the decomposition results at the municipal scale are more obvious within the municipal units. In terms of the spatial and temporal dynamics of CO2 emissions, carbon emissions at both scales showed a significant positive correlation. The dominant spatial correlation types are "Low–Low" at the provincial level, and "Low–Low" and "High–High" at the municipal level. The smaller spatial scope is more helpful to show the geographic dependence and geographic differences of China's carbon emissions. The findings of this paper will help deepen the understanding of the spatial and temporal changes of carbon emissions in China. They will provide a scientific basis for the formulation of feasible carbon emission reduction policies. [ABSTRACT FROM AUTHOR]

Many regions or countries seek to transition to a low-carbon economy, but there is ambiguity about how carbon emissions change with economic growth. This paper captures characteristics of carbon emissions in China using the Tapio Method. To detect driving factors of carbon emissions in economic growth, we adopt the Log-Mean Divisia Index decomposition method. The results of the paper are as follows: (1) Economic growth in most regions of China has outpaced the growth rate of carbon emissions, showing the characteristics of decoupling. (2) Metropolitan cities have a greater impact on the level of decoupling in neighboring provinces and municipalities. (3) The decoupling of carbon emissions from economic growth is caused by declining energy intensity in China. (4) The energy mix and industrial structure have little impact on the growth rate of carbon emissions. In addition, some policy implications are summarized according to the findings of this paper. [ABSTRACT FROM AUTHOR]

Artificial intelligence technology is constantly developing and changing. Due to the continuous development of technology and production of carbon emissions, various problems have occurred, forcing various regions to start promoting low‐carbon work. What kind of method can effectively and quickly improve the performance of carbon emissions has become one of the current research topics that has attracted much attention. In response to this problem, it is of great significance to study in the field of carbon emission performance improvement methods. With the in‐depth research on carbon emissions, the research on the intervention of water conservancy policies in carbon emissions has gradually been carried out, and its functional effects are of great significance to solve the problem of improving carbon emissions performance. This paper has aimed to study the impact and mechanism of water conservancy policy on carbon emission performance in the context of artificial intelligence. Through the analysis and research of the water conservancy policy under artificial intelligence, the double difference model (DID) is used to evaluate it, so that it can show the impact and mechanism on carbon emission performance, so as to solve the problem of improving carbon emission performance. This paper has analysed water policy, carbon emission performance and DID model under artificial intelligence. Its influence and mechanism have been experimentally analysed, and related theoretical formulas have been used to explain. The results have shown that the water conservancy policy has a significant and continuous driving effect on the reduction of carbon emission intensity, and its interaction coefficient under the control variable is −0.934, which is significantly negative. Further analysis of variables has shown that the energy intensity coefficient is 0.714, and the secondary industry coefficient is 0.924, which are both positive and significant. Water conservancy policies mainly reduce carbon intensity through the improvement of energy efficiency and industrial structure upgrading. It can be seen that water conservancy policies under artificial intelligence can meet the needs of improving carbon emission performance, and the level of economic growth and green development has been greatly improved. [ABSTRACT FROM AUTHOR]

This paper uses the biomass of livestock, poultry manure and straw to study the resource utilization and integration technology by division and classification. The SPSS regression analysis and integration were applied to analyze the economic benefits of the base period (2018) and the audit period (2020). The analysis gives an opportunity in rural areas to reduce chemical fertilizers, pesticides, electricity and coal cost in planting, and this paper opens a novel window for utilizing renewable energy resources and reducing carbon dioxide emissions. [ABSTRACT FROM AUTHOR]

Extensive uncertainty can affect the efficiency and fairness of consensus in the consensus reaching process (CRP), but few scholars have studied consensus modeling that focuses on fairness and efficiency in uncertain environments. Additionally, the weight of the decision maker (DM) in the CRP is influenced by multiple factors. Therefore, this paper proposes robust consensus models (EFCMs) focusing on fairness and efficiency under uncertain costs to address these issues. Firstly, this paper constructs multiple uncertainty sets to describe the uncertainty of the unit adjustment cost. Secondly, the fair utility level and opinion adjustment distance are used to measure the fairness and efficiency of reaching consensus, respectively. Furthermore, this paper uses a data-driven method based on the KDE method combined with trust propagation in social networks to determine the DMs' weights jointly. Finally, this paper also applies the proposed models to the carbon emission reduction negotiation process between the government and enterprises. The experimental results and sensitivity analysis show that the consensus cost budget and the DMs' jealous preference behavior particularly affects the efficiency of reaching consensus, which provides a theoretical basis for solving practical decision making problems. [ABSTRACT FROM AUTHOR]

In this paper, we develop a two-level supply chain model with low carbon emissions for defective deteriorating items under learning in fuzzy environment by using the double inspection process. Carbon emissions are a major issue for the environment and human life when they come from many sources like different kinds of factories, firms, and industries. The burning of diesel and petrol during the supply of items through transportation is also responsible for carbon emissions. When any company, firm, or industry supplies their items through a supply chain by using of transportation in the regular mode, then a lot of carbon units are emitted from the burning of petrol and diesel, etc., which affects the supply chain. Carbon emissions can be controlled by using different kinds of policies issued by the government of a country, and lots of companies have implemented these policies to control carbon emissions. When a seller delivers a demanded lot size to the buyer, as per demand, and the lot size has some defective items, as per consideration, the demand rate is uncertain in nature. The buyer inspects the received whole lot and divides it into two categories of defective and no defective deteriorating items, as well as immediately selling at different price. The fuzzy concept nullifies the uncertain nature of the demand rate. This paper covers two models, assuming two conditions of quality screening under learning in fuzzy environment: (i) the buyer shows the quality screening and (ii) the quality inspection becomes the seller's responsibility. The carbon footprint from the transporting and warehousing the deteriorating items is also assumed. The aim of this study is to minimize the whole inventory cost for supply chains with respect to lot size and the number of orders per production cycle. Jointly optimizing the delivery lot size and number of orders per production cycle will minimize the whole fuzzy inventory cost for the supply chain and also reduce the carbon emissions. We take two numerical approaches with authentic data (from the literature reviews) for the justification of the proposed model 1 and model 2. Sensitivity observations, managerial insights, applications of these proposed models, and future scope are also included in this paper, which is more beneficial for firms, the industrial sector, and especially for online markets. The impact of the most effective parameters, like learning effect, fuzzy parameter, carbon emissions parameter, and inventory cost are shown in this study and had a positive effect on the total inventory cost for the supply chain. [ABSTRACT FROM AUTHOR]

COVID-19 has represented a watershed in people's lifestyle. One important consequence has been the change in how people buy groceries online/offline that represent the most frequent shopping-related trip any household performs. Since this has substantially different transport and environmental implications, it is crucial to investigate what are the COVID-19-induced variations. This paper estimates both travel and environmental impacts grocery channel choice implies by comparing, in a diachronic fashion, the pre and during COVID-19 situation in Norway, considering alternative scenarios describing different service configurations for each channel considered. Stated preferences are used to estimate market shares for grocery purchase distinguishing between different channels: in-store, home delivery and click&pick. The robust comparative analysis between the two time periods is guaranteed by the use of the same methodological approach in the same geographical context. Results show that total emissions are lower during the pandemic and adopting a free delivery strategy not only increases home delivery market shares but also produces the lowest level of CO2 emissions regardless of COVID-19. The results obtained are particularly relevant in the "new normal" the world is experiencing both from a public policymaking perspective, when promoting transport-related interventions to curb emissions, and from a private standpoint, when customising market strategies to increase sales and profits. • COVID-19 has represented a watershed in people's lifestyle. • Investigating changes in how people buy groceries online/offline is important. • Stated preferences are used to estimate grocery market shares for different channels. • The paper estimates travel and environmental impacts pre and during COVID-19 via scenario analysis. • Results are particularly relevant for both the public and private sector in the "new normal" conditions. [ABSTRACT FROM AUTHOR]

The scale of multi-microgrid (MMG) and hydrogen fuel cell vehicles (HFCVs) is increasing dramatically with the increase in the new energy penetration ratio, and developing an integrated energy system containing a multi-microgrid for hydrogen fuel vehicles brings great challenges to power grid operation. Focusing on the difficulties of the access of multiple microgrids for the low-carbon and economic operation of the system, this paper proposes an optimal interconnected heterogeneous multi-microgrid power–heat–carbon scheduling strategy for hydrogen-fueled vehicles. Firstly, an HFCV model is established, and then an optimal scheduling model is constructed for the cooperative trading of power–heat–carbon in a multi-microgrid, on the basis of which the low-carbon economic operation of the multi-microgrid is realized. The results of the case study show that the scheduling strategy in this paper reduces carbon emissions by about 7.12% and costs by about 3.41% compared with the independent operation of the multi-microgrid. The degrees of interaction of each multi-microgrid are also analyzed under different HFCV penetration rates. [ABSTRACT FROM AUTHOR]

This paper aims to update our understanding of the carbon cycle in the Himalayas, the most intense collisional orogeny globally, by providing new insight into its impact on Cenozoic climate cooling through the use of isotopic variations in both organic and inorganic carbon and an isotopic mass balance model. Our results from 20 selected hot springs show that the relative contributions of dissolved carbon from the mantle, metamorphic decarbonization, aqueous dissolution, and soil organic matter are approximately 2%, 82%, 6%, and 10%, respectively. Approximately 87% ± 5% of CO2 generated in the deep crust precipitates as calcite, while approximately 5.5% ± 1% of this carbon is converted to biomass through microbial chemosynthesis at depths less than 2 km. Our random forests approach yielded a metamorphic carbon flux from the entire Himalayan orogenic belt of approximately 2.7 ∼ 4.5 × 1012 mol/yr. The minor CO2 released into the atmosphere (2.5 ∼ 4.2 × 1011 mol/yr) is comparable to the carbon consumption driven by Himalayan weathering. This paper provides new insights into deep carbon cycling, notably that approximately 93% of deeply sourced carbon is trapped in the shallow crust, rendering orogenic processes carbon neutral and possibly acting as one of the major triggers of long‐term climate cooling in the Cenozoic. Plain Language Summary: Large‐scale deep metamorphic decarbonization and CO2 release into the atmosphere during continental collision orogenesis worldwide have had significant impacts on Cenozoic global climate change. However, the carbon sources and sinks and the mechanisms underlying their impacts on climate change are not fully understood. This paper reports the chemistry of aqueous solutions, carbon isotopic compositions of dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC), and helium and carbon isotopic compositions of free gases from hot springs in southern Tibet. The carbon isotope fractionation model between gas and aqueous solutions revealed that the carbon released from depth underwent six different stages. Based on the random forests machine learning algorithm, we estimated the metamorphic carbon flux across the Himalayan orogenic belt. Furthermore, only a small fraction (7%) of deep carbon is eventually released into the atmosphere, and this volume is the same magnitude as the amount of carbon absorbed by silicate weathering on the Tibetan Plateau. This paper emphasizes that the carbon sequestration process in the upper crust may have resulted in nearly carbon‐neutral geological bodies in the Himalayan orogenic belt, thus playing a critical role in Cenozoic climate cooling. Key Points: Approximately 87% of deeply sourced carbon precipitates as calcite, while approximately 5.5% is sequestered as biomass in the shallow crustThe carbon flux across the Himalayan orogenic belt was estimated by the random forests approachCrustal thickening has attenuated long‐term deep carbon emissions from the Himalayas [ABSTRACT FROM AUTHOR]

In response to the inherent requirements of low-carbon land spatial planning in Jiangxi Province and the lack of existing research, this paper explored the mechanism of spatial form elements of Poyang Lake urban agglomeration on urban carbon emissions. Based on generalized linear regression and geographically weighted regression models, this paper analyzed the spatiotemporal distribution characteristics of carbon emissions, the spatiotemporal relationship between urban form index and carbon emissions, and the spatial differentiation of the intensity of dominant factors from 63 county-level administrative units in the Poyang Lake city group from 2005 to 2020. The results showed that: ① The carbon emissions of urban agglomerations around Poyang Lake are generally increasing, and the spatial distribution of carbon emissions is characterized by high-value concentration in the middle and low-value agglomeration in pieces; ② The main driving factor for the spatial heterogeneity of carbon emissions was the expansion of built-up area; ③ Improving urban compactness and optimizing urban form could effectively reduce urban carbon emissions. The results showcased the correlation between urban spatial landscape pattern and the spatiotemporal distribution of carbon emissions, which could make the lowcarbon land spatial planning in the Poyang Lake city group more reasonable and practical. [ABSTRACT FROM AUTHOR]

Due to the serious problems with energy efficiency, carbon emissions, and thermal comfort of rural residences in northern China, an optimization of active and passive heating technologies for rural residences is necessary. In this paper, an optimization for rural residences in northern China is conducted with four objectives: the whole life cycle carbon emission; the annual energy consumption through heating, ventilation, and air conditioning systems; the annual cost; and thermal comfort. In addition, the optimization model with active–passive heating technology synergy is resolved by NSGA-II genetic algorithm. The active and passive design variables, including the type of air source heat pump, orientation, the type and thickness of envelope insulation, the layer of window glass, the window-to-wall area ratio, as well as sunspace parameters are preferred to obtain the optimal solution. The results indicate that the optimal solution obtained by the ideal point method gives the most outstanding performance. Compared with the prototype, the optimized carbon emissions in severe cold and cold regions decreased by 56.1% and 54.6%, respectively. The annual energy consumption decreased by 59.7% and 62.2%. Finally, the roof insulation thickness is the most sensitive design variable in Pareto-optimal solution sets. This paper offers significant guidance in the application of the optimization method of active–passive technology synergy to the energy-saving design of buildings. [ABSTRACT FROM AUTHOR]

Low or even zero carbon dioxide emissions will be an essential requirement for energy supplies in the near future. Besides transport and electricity generation, industry is another large carbon emitter. Hydrogen produced by renewable energy provides a flexible way of utilizing that energy. Hydrogen, as an energy carrier, could be stored in a large capacity compared to electricity. In Sweden, hydrogen will be used to replace coal for steel production. This paper discusses how the need for electricity to produce hydrogen will affect the electricity supply and power flow in the Swedish power grid, and whether it will result in increased emissions in other regions. Data of the Swedish system will be used to study the feasibility of implementing the hydrogen system from the power system viewpoint, and discuss the electricity price and emission issues caused by the hydrogen production in different scenarios. This paper concludes that the Swedish power grid is feasible for accommodating the additional electricity capacity requirement of producing green hydrogen for the steel industry. The obtained results could be references for decision makers, investors, and power system operators. [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]