Search

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]

The construction industry's heavy reliance on conventional cement production contributes significantly to global carbon dioxide emissions, prompting the need for eco-friendly building materials. Geopolymers, synthesized from natural materials like clay and fly ash, offer a sustainable alternative to traditional cement. This paper investigates the development of phosphoric acid-activated geopolymers with sanitary ware waste (SWW) as aggregates, aiming to create high-performance, environmentally friendly building materials. Various particle sizes of SWW were incorporated into the geopolymer matrix, revealing a strong relationship between particle size and compressive strength. The inclusion of SWW improved compressive strength, with a notable 83 % increase at 30 % SWW content. Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) analyses highlighted the formation of new bonds in the geopolymeric gel, while scanning electron microscopy (SEM) showed microcracks and good adhesion between SWW particles and the geopolymer matrix. Thermal resistance tests demonstrated that SWW-reinforced geopolymers exhibited improved resistance to high temperatures compared to the reference geopolymer. XRD analysis of thermally treated geopolymers revealed the transformation of amorphous phases into stable crystalline phases. SEM/EDX mapping confirmed the presence of silica-rich phases, contributing to the materials' mechanical properties and thermal resistance. This study underscores the potential of SWW-based geopolymers as sustainable, high-performance building materials. [ABSTRACT FROM AUTHOR]

The ever-increasing challenges related to water security and climate mitigation underscore the pressing need for forward-thinking systems planning tools to drive urban sustainability transitions. In this study, we propose a novel city-level multi-objective planning framework that explores the nexus between carbon trading and water management, with the aim of facilitating water-efficient urban industrial restructuring. The framework integrates cap-and-trade mechanisms into a two-layered optimization model, creating an integrated approach to optimizing carbon emissions and generating economic opportunities for improving water efficiency. To illustrate the potential application of this framework, we conducted a case study focusing on Dongguan City, a water-stressed industrial metropolis in southern China. The results illustrate potential synergies between water planning strategies and carbon trading schemes, which could be harnessed to enable targeted reductions in water usage and carbon emissions. Furthermore, our findings identify the textile, apparel, and paper products manufacturing industries as primary candidates for strategic production scale reduction, emphasizing the importance of sustained support for the development of the computer and electronic manufacturing sector as a catalyst for urban sustainability transitions. By pioneering this new nexus-based perspective, our study offers valuable insights into long-term strategic planning for a low-carbon and resource-efficient urban economy. • Synergizing carbon trading and water management within a novel integrated planning framework. • Implementing resource-efficiency-driven optimization for water- and carbon-intensive manufacturing sectors. • Gaining insights into the specific roles of key manufacturing sectors in water management and carbon trading. • Introducing carbon trading systems may help in promoting water-efficient industrial restructuring. [Display omitted] [ABSTRACT FROM AUTHOR]

The widespread use of digital technology has driven a huge change in the way payments are made. Based on the micro-data of the China Family Panel Studies (CFPS), this paper explores the relationship between mobile payment and carbon emissions from the consumption side. With the exogenous impact of Alipay's "Collecting Five Blessings" campaign, this paper finds that mobile payment significantly increases household consumption carbon emissions, but the marginal effect is gradually decreasing. Mechanism analysis shows that mobile payment mainly realizes the above impact through promoting the increase of consumption expenditure and upgrading of consumption structure. Further analysis indicates that carbon reduction policies introduced by the Chinese government in the personal consumption sector can mitigate the carbon emissions growth associated with mobile payments. Still, there is a need to strengthen policy measures further to effectively address the issue of rising carbon emissions from household consumption. [ABSTRACT FROM AUTHOR]

Hydrogen Direct Reduction of Iron (HDRI) combined with renewable electricity is an attractive option for low-carbon steel production. In this paper, we present a novel and computationally efficient techno-economic power-to-x-plant optimization model which is then applied to an HDRI plant located in Finland. Plant dimensioning was carried out in several current and future power market and regulatory scenarios. We predict a production cost of 373 €/t for hot briquetted iron, and 351 €/t for a future scenario of 2025–2030. When the recently introduced EU rules for renewable fuels of non-biological origin are applied, the production costs increased by 30–46 €/t. The rules also have a significant increasing effect on the required hydrogen storage. The flexibility of the direct reduction shaft emerged as an important parameter affecting the required hydrogen storage as well as total production cost. The results of this paper hold significance for the optimal design of future low-carbon steel plants. • Computationally efficient optimization model for Power-to-X operations and dimensions. • Hydrogen Direct Reduction Ironmaking (HDRI) plant using wholesales and wind power. • Significant amounts of intermittent wind power can be absorbed by the HDRI plant. • HDRI/HBI production costs analyzed for EU regulatory scenarios on green hydrogen. • Emission reductions over 96% compared to CO 2 emissions of fossil steelmaking. [ABSTRACT FROM AUTHOR]

As the regulations related to greenhouse gases (GHG) emissions from fuel become stricter, fundamental changes are being effectuated to achieve the Paris Agreement agenda. Since the aviation sector contributes to 2.5 % of the global CO 2 emission, it is imperative to tackle this conundrum imminently by reducing the CO 2 emission from commercial flights, which are the main contributors to CO 2 emission in the aviation sector. Sustainable aviation fuel (SAF) has garnered tremendous attention in achieving carbon neutrality in the aviation sector. SAF is a suitable alternative since little to no modification of the aircraft is required for SAF usage. There are many challenges when it comes to implementing the use of SAF, including the large-scale production of SAF and the cost associated with the production of SAF. Therefore, this paper aims to provide an overview of various factors related to the global implementation of SAF, considering the latest supporting policy frameworks and from the perspective of feedstock. It examines how existing SAF pathways contribute to large-scale production and explores the role of emerging technologies—from potential feedstocks to the latest advancements. The paper delves into several emerging technologies, including hydrothermal liquefaction, aqueous phase reforming, pyrolysis, and photofermentation, discussing their potential in SAF production and the challenges they present. Furthermore, this paper analyses the life cycle assessment (LCA) and the techno-economic analysis (TEA) of different feedstocks and processes for SAF production. [Display omitted] • Existing policies associated with SAF growth and their limitations are reviewed. • Emerging technologies and challenges of sustainable aviation are explored. • Various feedstocks and ASTM-certified processes for SAF production are discussed. • Techno-economic and environmental aspects related to SAF are summarised. [ABSTRACT FROM AUTHOR]

The main challenge for future sustainable societies is dealing with the high portion of released CO2 throughout smart power transmission systems; consequently, a comprehensive carbon-based framework is required that directly calculates stakeholders' contribution to the network's CO2 emissions. In this case, this paper proposes a mechanism driven by the carbon-tracing (CTR) process to tackle carbon emissions in smart transmission networks. Using the CTR mechanism enables the digitalized independent system operator (ISO) to allocate the total emissions among participants using the real-time data received from smart grid infrastructures. Furthermore, a carbon-based nodal pricing mechanism for gas and power systems is proposed, which reflects the received data from CTR analysis. To reduce carbon emissions in the smart power transmission network, a carbon-based demand response (DR) procedure is suggested, under which consumers will receive higher incentives for reducing their consumption, especially in high-carbon-intensity buses. In addition, in this paper, for effective management of carbon pollution, the uncertainties of wind and loads and leading risks to carbon and operation costs have been modeled. Hence, the conditional value-at-risk (CVaR) method is utilized for modeling the risks related to uncertainties. It can be seen that applying the proposed CVaR-based risk assessment framework and carbon-incentive-based DR programs led to a decrease in daily total carbon and operation costs by 0.66% and 1.02%, respectively. • Distribution of carbon in power systems, aiding stockholders in carbon management. • Introducing environmental impacts to pricing promoting eco-conscious consumption. • Utilizing CVaR to manage risk, leading to significant carbon and cost variation. • Managing client consumption based on distributed carbon flow intensity in network. [ABSTRACT FROM AUTHOR]

Compared to cast-in-situ construction, prefabricated construction reduces the generation of construction waste; however, construction waste cannot be entirely eliminated through prefabrication and can be generated at each stage of its life cycle. This paper generates a system dynamics model, comprising two causal-loop diagrams and one stock-flow diagram, to simulate the effectiveness of policies on decarbonizing prefabricated concrete building waste. The model can predict the annual and total construction waste, along with the associated carbon emissions, under different policy scenarios in China by 2030. This paper focuses on prefabricated concrete buildings in China, with specific attention to the wastage of concrete, bricks, mortar, and metal. In single-factor scenario simulations, increasing landfilling fee is the most effective policy for waste decarbonization, potentially reducing carbon emissions by 31%. Increasing prefabrication rate and enhancing regulation degree at the design phase of prefabrication can directly reduce prefabricated building waste generation, subsequently resulting in an 18% and 7% carbon emissions reduction. Multi-factor scenario simulations demonstrate that the simultaneous application of all four policies is most effective, achieving a 44% reduction in carbon emissions. This research establishes a theoretical framework for prefabricated concrete building waste management and provides the Chinese government with practical insights on optimizing policies for waste decarbonization in the dynamic landscape of prefabrication. • This paper develops the prefabricated building waste generation and disposal systems. • The model predicts the construction waste and associated carbon emissions by 2030. • Raising landfill fee is an effective policy for decarbonizing construction waste by 31.06%. • Prefabrication rate reaching 100% can reduce 18.04% of carbon emissions from waste. • Applying all policies is most effective, achieving a 44.34% carbon emissions reduction. [ABSTRACT FROM AUTHOR]

With global climate change posing a major threat to human society, a growing number of countries have taken "carbon-neutral" as a national strategy and proposed a vision of carbon-free future. As an important supplement to traditional fossil energy, renewable energy is the main force to reduce the use of high-carbon energy and carbon dioxide emissions, which will become the trend of social development in the future. Finding the optimal renewable energy source is of particular significance for achieving the net zero emissions. However, the existing evaluation methods of renewable energy sources have obvious shortcomings. In terms of weight calculation methods, such as the randomness of the subjective method is strong and the index weights do not reflect the small changes of the evaluation matrix, which affect the reliability and accuracy of the evaluation result. The existing ranking methods can only achieve the complete ranking of the different objects, but cannot classify the renewable energy technical alternatives into different grades. Given this background, this paper proposes a novel evaluation method for renewable energy plans based on improved sparrow search algorithm and projection pursuit model. Firstly, this paper improves the traditional sparrow search algorithm from three aspects: population initialization, population update and population variation. Then, the projection pursuit model is constructed, and the improved sparrow search algorithm is applied to optimize the projection target to find the optimal projection direction, so as to determine the weight values of each evaluation index. Finally, the weighted rank-sum ratio method is used to select the best renewable energy technical plan, which can not only realize the complete ranking of different plans, but also classify the technical plans into different levels. Based on the actual renewable energy development data from a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some existing evaluation methods of renewable energy technical plans. [ABSTRACT FROM AUTHOR]

The focus of this paper is on achieving a win-win situation regarding the economic, environmental, and social impacts of the cold chain logistics terminal distribution system. This paper proposes three multi-objective models to investigate the above effects by incorporating soft time windows, heterogeneous fleets, and path flexibility, with defining the objectives of examining logistics costs, fuel consumption, carbon emissions, quality damage to perishable commodities, and customer satisfaction using six evaluation functions. To solve the proposed models, an efficient optimization framework is developed by combining domain operators with versatile multi-objective evolutionary algorithms (MOEA) to obtain Pareto solutions. Extensive experiments are conducted to test the validity of the concerned model and algorithms. The results demonstrate that: (1) the proposed algorithm is effective in solving the proposed model; (2) the proposed multi-path strategy can effectively improve the performance of cold-chain logistics systems compared to single-path strategies; (3) evaluation functions that assess customer satisfaction greatly affect the performance of cold-chain logistics systems; and (4) the trade-off relationship between the objectives should be investigated to define the model. The paper also provides valuable managerial insights for improving the efficiency and sustainability of cold-chain logistics operations. [ABSTRACT FROM AUTHOR]

This research investigates the complex interplay between carbon dioxide emissions, population dynamics, migration patterns, foreign direct investment, and gross domestic product in Vietnam. The paper uses the unrestricted fixed and random panel data method. The data is collected from the World Bank and Vietnam General Office of Statistics. This study employs a multidimensional analytical framework in the country's rapid economic growth context, combining quantitative and qualitative methods to unravel the nuanced relationships within this nexus. The main finding of this study reveals significant correlations between carbon dioxide emissions, population, migration, FDI- foreign direct investment inflows, and GDP- gross domestic product growth, highlighting the pivotal role of these factors in shaping Vietnam's economic landscape. The study draws on comprehensive datasets spanning multiple years. The research reveals significant correlations, notably the positive link between foreign direct investment inflows and gross domestic product growth. Simultaneously, the study sheds light on the environmental challenges associated with urbanization and the socioeconomic impacts of internal migration. The findings of this paper contribute to the theoretical understanding of these interconnected factors, emphasizing the need for an integrated policy approach. Policy recommendations include sustainable urban development strategies, sector-specific foreign direct investment initiatives, and inclusive migration policies. Additionally, fostering green technologies, community engagement, and international collaborations are proposed to advance Vietnam's sustainable development goals. This research is a foundation for policymakers, scholars, and stakeholders, offering actionable insights to guide Vietnam toward a balanced and resilient future. The study's methodology, findings, and policy implications contribute to the academic discourse on sustainable development and provide remarkable guidance for navigating the complexities of economic growth and environmental stewardship in emerging economies. [ABSTRACT FROM AUTHOR]

[Display omitted] • Measure carbon emission inequalities (CEIs) of urban agglomerations under rapid urbanization. • Identify the key factors determining CEIs within the three urban agglomerations. • Account for the differences between adjacent or distant urban agglomerations. • The declining CEI is mainly due to changes in industrial structure and economic development. • CEI in the upper reaches of the YREB increases due to the changes in energy intensity. Increasing countries are articulating ambitious goals of carbon neutrality. However, large inequalities in regional emissions within a country may hinder progress toward a carbon–neutral future, as the unequal distribution of reduction responsibilities among regions could impair just transition and exacerbate uneven development, which necessitates an in-depth understanding of the mechanism of multi-scale carbon inequalities within country, region, and city. Yet, the evolution of carbon inequalities within urban agglomerations and the differences between adjacent or distant urban agglomerations have not been well understood, especially in countries undergoing rapid urbanization. Using the data of 89 cities in China's Yangtze River Economic Belt (YREB) during 2006–2021, this paper quantifies carbon emissions inequality (CEI) at different scales in a systematic regional-urban agglomeration-city hierarchical structure. Then, under the integrated mean logarithmic deviation-logarithmic mean Divisia index (MLD-LMDI) decomposition framework, multi-scale CEIs are perfectly decomposed into six interrelated drivers, i.e., industrial emission structure, energy emission intensity, industrial energy mix, energy intensity, industrial structure, and economic development. The results show that economic development, energy intensity, and industrial energy mix disparities are the main determinants accounting for CEIs at different scales. The decreasing CEI in YREB is mainly due to the changes in industrial structure and economic development, while the energy intensity effect partially hinders the mitigation of CEI. In the upper reaches of the YREB, the energy intensity effect accounts for over 94% growth of CEI during 2006–2021, while the decline in CEIs in middle and lower reaches is primarily caused by the effects of industrial energy mix and industrial structure, respectively. Further spatial decomposition analysis reveals more refined city-level heterogeneous effects and emphasizes the prioritized emission reduction direction for each city. This paper offers implications for reducing carbon inequality and insights into coordinated carbon emissions mitigation at the regional level for a carbon–neutral future. [ABSTRACT FROM AUTHOR]

In recent years, there has been a notable surge in focus on the mitigation of carbon emissions, mostly motivated by the urgent concern about global warming and its adverse impacts on the Earth's climate. Power-to-gas (PtG) systems have emerged as a promising technological solution in the pursuit of mitigating carbon emissions. This paper presents a mathematical modeling framework that aims to accurately represent the power-to-gas system integrated with wind power plants while taking into account the technological constraints of the system in practical scenarios. To analyze the proposed system under the stresses, worst situations caused by existing uncertain parameters, especially wind power, is modeled using a risk modeling technique called downside risk constraints (DRC). The DRC endeavors to mitigate the likelihood of situations resulting in significant stresses by incorporating an expected downside risk into the proposed framework. This paper will provide a number of conservative tactics to be employed in the decision-making process under the realizations of uncertainties. These tactics, including strategic operational control strategies, provide a structured approach for decision-making under uncertain conditions. By incorporating downside risk constraints, the framework enables conservative decision-making that prioritizes mitigating potential losses and minimizing the operational risk within scheduling horizon of proposed PtG facility. The findings demonstrate that the implementation of PtG technology has the potential to yield a substantial reduction in external energy consumption, namely by 39.1 %. Additionally, it has been seen that PtG may effectively mitigate carbon emissions, resulting in a decrease of 38.9 %. The utilization of PtG technology and gas storage will result in a substantial reduction in wind curtailment, ultimately leading to a near elimination of curtailment. Further study is conducted on the model in order to explore the benefits of carbon and methane storage, as supported by the results obtained. [ABSTRACT FROM AUTHOR]

This paper investigates the performance and fuel-saving effect of a velocity control algorithm on modern 50 cc scooters (Euro 5). The European Parliament has adopted major CO 2 emission reductions by 2030. But modern combustion-powered scooters are inefficiently restricted and emit unnecessary amounts of CO 2. Replacing the original restriction method with the system presented in this paper, the engine's operating point is being improved significantly. A Throttle-by-Wire-System senses the rider's throttle command and manipulates the throttle valve. A redundant wheel speed sensor measures the precise vehicle velocity using the Hall effect. The entire system is managed by a central ECU, executing the actual velocity control, fail-safe functions, power supply and handling inputs/outputs. For velocity control, an adaptive PI-controller has been simulated, virtually tuned and implemented, limiting the max. velocity regulated by legal constraints (45 km/h). In this way, the environmentally harmful restrictors used today can be bypassed. By implementing a human–machine interface, including a virtual dashboard, the system is capable of interfacing with the rider. For evaluation purposes a measurement box has been developed, logging vehicle orientation, system/control variables and engine parameters. A Peugeot Kisbee 50 4T (Euro 5) is serving as test vehicle. Finally, the system has been evaluated regarding performance and fuel efficiency both through simulation and road testing. Fuel savings of 13.6% in real-world test scenarios were achieved while maintaining vehicle performance. • Efficiency is increased and the throttle valve opening is reduced by 50%. • The controlled air supply minimizes the fuel injected by up to 25%. • Significant improvements of fuel consumption and CO 2 emissions by 14%. • Consistent performance is demonstrated by using energy more effectively. [ABSTRACT FROM AUTHOR]

The carbon dioxide (CO 2) emissions have reached their highest levels since measurements began in recent years. This is widely regarded as one of the primary factors contributing to the increasing frequency of extreme climate events. Consequently, accurate and reliable CO 2 emission data becomes the benchmark in shaping policies and setting targets aimed at reducing emissions, which are essential for carbon emission-related research. Among all industries, the electric power sector takes the highest proportion of CO 2 emissions, making the carbon emission accounting for this sector a crucial component of overall carbon emissions accounting. Therefore, the objective of this paper is to provide a comprehensive review of the applied technologies and research progress in electricity carbon emissions accounting. The paper begins by introducing the fundamental concept of electricity carbon emission accounting. Electricity carbon emissions are categorized into two types: direct carbon emissions from electricity production and indirect carbon emissions from electricity consumption. Then, the current applied approaches for accounting for these two types of emissions are reviewed separately. After analyzing the shortages of existing applied carbon emission accounting approaches, the paper proceeds to review the research on the improved approaches of direct and indirect carbon emission accounting. Finally, the paper outlines the future development prospects of carbon emission accounting technology. • Application status of carbon emission accounting approaches in the electric power industry is analyzed. • Direct carbon emission accounting approaches based on big data technologies are introduced. • Research exploration in electricity indirect carbon emission accounting is reviewed and compared. [ABSTRACT FROM AUTHOR]

The exploitation of fossil fuels in various sectors, such as power and heat generation, and the transportation sector has been the primary source of greenhouse gas (GHG) emissions, which are the main contributors to global warming. Qatar's oil and gas sector notably contributes to CO 2 emissions, accounting for half of the total emissions. Globally, it is essential to transition into cleaner fossil fuel production to achieve carbon neutrality on a global scale. In this paper, we focus on clean hydrogen, considering carbon capture to make hydrogen a viable low carbon energy alternative for the transition to clean energy. This paper systematically reviews emerging technologies in carbon capture and conversion (CCC). First, the road map stated by the Intergovernmental Panel on Climate Change (IPCC) to reach carbon neutrality is discussed along with pathways to decarbonize the energy sector in Qatar. Next, emerging CO 2 removal technologies, including physical absorption using ionic liquids, chemical looping, and cryogenics, are explored and analyzed regarding their advancement and limitations, CO 2 purity, scalability, and prospects. The advantages, limitations, and efficiency of the CO 2 conversion technology to value-added products are grouped into chemical (plasma catalysis, electrochemical, and photochemical) and biological (photosynthetic and non-photosynthetic). The paper concludes by analyzing pathways to decarbonize the energy sector in Qatar via coupling CCC technologies for low-carbon hydrogen, highlighting the challenges and research gaps. [ABSTRACT FROM AUTHOR]

Energy development in industrial parks promotes both industrial and inter-park economies. However, as the number of industrial parks increases and the economic level of the parks rises, energy consumption is also increasing rapidly. The rapid expansion of inter-park trading markets is accompanied by a surge in carbon emissions, making industrial parks increasingly significant contributors to carbon emissions within the industrial sector. In response to this environmental challenge, this paper proposes an integrated trading approach within industrial parks that considers carbon constraints. By implementing a spot trading method for surplus industrial energy, the goal is to mitigate the carbon emission pressure within these parks. Firstly, the paper conducts an analysis of the characteristics of combined heat and power units in industrial parks. It introduces an integrated analysis method within the target region, consolidating information from various park units to enhance flexibility in energy utilization. Secondly, an energy storage model is established. This model efficiently leverages energy storage capacity to balance fluctuations in energy supply and demand within industrial parks, thereby alleviating carbon emission pressure. Finally, the study and analysis of an industrial park in Liaoning Province were conducted using the Yalmip + Gurobi commercial software on the MATLAB platform. Compared with the traditional industrial park, the carbon dioxide emission of the industrial park is reduced by 91.49t, and the energy cost is saved by 11.2 million RMB. Numerical results from instances indicate that this method surpasses traditional trading approaches, aligning with the rigorous standards of scientific research. • An energy storage model is proposed to address the shortage of energy storage in waste heat trading in industrial parks. • A coordinated scheduling program is presented to reduce energy scheduling imbalances between multiple campuses. • The carbon-constrained energy storage device is integrated to improve the decarbonization capacity. [ABSTRACT FROM AUTHOR]

Blending hydrogen into the existing natural gas pipeline network is regarded as the most potential transportation mode on the utility-scale. However, the impact of hydrogen on the operation performance of the pipeline remained unclear. This paper analyzes the economic and environmental performance of natural gas pipeline under different hydrogen blending ratios and different hydraulic boundary conditions. In order to reduce the operation costs and carbon emissions in each condition, this paper establish a non-linear optimization model to get the new operation plan of the pipeline system. Further, in order to characterize different boundary conditions of the system, four scenarios, namely, the baseline scenario (BS), the setpoint of flow rate scenario (SF), the setpoint of pressure (SP), and the setpoint of heat flow rate (SH) are proposed in this paper. Two studied cases demonstrated that: (1) the original compressor for natural gas can adapt to a hydrogen blending without change the type of compressor. (2) The optimization model has significant potential in reducing the economic cost and carbon emissions of the system with an average decrease of 11.48%. (3) For every 1% of hydrogen added, the annual operating cost of the system is reduced by $0.73 million (3.29%) in SF, 0.017 million (0.08%) in SP, or increased by $1.67 million (7.50%) in SH. Moreover, the annual carbon emission is reduced by 0.38 kt (3.53%) in SF, 0.047 kt (0.44%) in SP, or increased by 0.76 kt (7.14%) in SH. However, when the hydrogen mixing ratio is more than 8%, the contrary trend may occur. (4) The maximum blending ratio at different points of the multi-source pipeline network are physically interdependent. The paper proposed an optimization model for the 2-E analysis of the pipeline and can provide theoretical guidance for the further application of this transportation mode. • Hydraulic impact of hydrogen on natural gas pipeline is estimated. • Factors that constrain further hydrogen blending are analyzed. • A power optimization model is proposed for the given hydrogen blending ratio. • Pareto set of hydrogen blending are obtained for multi-source pipeline. [ABSTRACT FROM AUTHOR]

With the increasing severity of global warming, the goal of carbon sequestration and emission reduction in various industries has become an important responsibility for sustainable development. As an important part of carbon sink, the improvement of net carbon efficiency in the plantations industry will make a great contribution to the realization of the "double carbon" target in agriculture. However, there are large spatial and temporal differences in the net carbon efficiency of the plantations sector in various provinces, and there is still a large scope for increasing carbon sink and reducing carbon emission. In this paper, we have analyzed spatial and temporal trends in the net carbon sink efficiency of the plantations' production processes in 31 provinces in China (excluding Hong Kong, Macao and Taiwan) from 2000 to 2020. On this basis, we have analyzed the influencing factors of net carbon efficiency in the plantations' production process through an extended Kaya constant equation. Through the geographically and temporally weighted regression model, we explore the spatio-temporal heterogeneity of the influencing factors, and further analyze the driving and restraining factors of net carbon efficiency in plantations' production process, in order to propose appropriate countermeasures. The study shows that: (1) The net carbon efficiency of plantations sector in China increase between 2000 and 2020, and the development of net carbon efficiency is uneven across regions. (2) The geographic center of net carbon efficiency of China's plantations industry shift from the northeast to the southwest and is consistent with the migration direction of agricultural inputs. (3) The influencing factors of net carbon efficiency in the plantations sector show large regional differences, and can be specifically divided into four major types of zones. In this regard, this paper provides more appropriate countermeasure suggestions for increasing carbon sink and reducing carbon emission in each zone, with a view to achieving the goal of double carbon in agriculture with practical significance. • Net carbon efficiency is a new objective indicator to measure the carbon sink level of cropland. • Cultivated land carbon sink capacity is closely related to agricultural inputs. • Regional differences in the factors influencing net carbon efficiency. [ABSTRACT FROM AUTHOR]

In response to the growing emphasis on green packing, e-commerce businesses actively pursue sustainable solutions that balance cost reduction with environmental conservation. Traditional research on three-dimensional bin packing has concentrated on maximizing spatial utilization under fixed box dimensions. However, in the practical context of e-commerce, packing boxes exhibit a wide range of sizes within a single warehouse. Customizing packing to prevent material and space wastage has become crucial for sustainable development. This paper innovates traditional three-dimensional packing research by introducing open dimensions instead of fixed ones. It addresses challenges in e-commerce packing, such as multiple orders and diverse bin shapes, to formulate a mixed-integer programming model. Focused on the carbon emission lifecycle's usage phase for packing boxes, the model aims to minimize associated costs while ensuring that items' weight, the center of gravity, sufficient support, and non-overlapping placement are maintained during the packing process. It also incorporates one-dimensional and two-dimensional fixed bin sizes. To solve this model, the paper proposes an improved hybrid genetic algorithm for stack sequence optimization. This algorithm systematically optimizes the dimensions for each box type. Building upon this, the study introduces an algorithm capable of estimating packing material consumption and the percentage reduction in carbon emissions during the packing process, given the known variability in packing box utilization. These two algorithms are integrated into an open-dimension packing box warehouse management ecological closed loop system designed for the B2B e-commerce context. In experiments with the proposed model and algorithms, the results demonstrate an average packing rate improvement of 10%–12% compared to alternative methods. Additionally, packing material consumption and carbon emissions are reduced by approximately 16.56%–19.85%, confirming the effectiveness of the proposed approach. • Open dimension model boosts green e-commerce packing efficiency. • Stacking algorithm lifts space use up to 12%. • Material and emissions cut 16%–20% on space gains. • Eco warehouse system enables box reuse and material recycle. [ABSTRACT FROM AUTHOR]

Trade-induced carbon transfer has emerged as a significant contributor to global carbon emissions, with international carbon competitiveness playing a pivotal role in this transfer. Based on a sample of 43 major economies from 2000 to 2014, this paper empirically examines the influence of digital transformation on the international carbon competitiveness of the manufacturing sector. The results reveal a substantial enhancement in manufacturing's international carbon competitiveness facilitated by digital transformation. Three primary mechanisms drive this enhancement: the scale effect, advancement of the forward global value chains position, and an improved energy structure. Notably, both domestic and foreign sources of digital transformation bolster the international carbon competitiveness of domestic manufacturing. Furthermore, the enabling of segmented digital industry strengthens this competitiveness, especially in publishing, programming, and broadcasting activities. This paper enriches the research on the environmental implications of digital transformation and offers insights for policymakers aiming to boost international carbon competitiveness and promote greener industrial growth. [ABSTRACT FROM AUTHOR]

Under dual constraints of green production responsibility and recycling responsibility, the optimal contract between upstream and downstream firms is crucial to achieve low-carbon development, but information asymmetry poses a great challenge to the above contract design. Based on Stackelberg game theory, this paper develops a contract model in a supply chain with information asymmetry under dual environmental responsibility constraints, in which the producer invests in green technology while the retailer exerts green marketing efforts. The paper aims to explore how the producer designs optimal contracts under the different information structures to improve environmental performance while ensuring profitability. Results indicate that: (i) in the full information scenario, the two-part optimal contract makes the decentralized supply chain to achieve the same channel profit and carbon emissions as the centralized scenario. (ii) In the information asymmetry scenario, the producer should design differentiated contract configurations for different types of retailers to eliminate double marginalization. That is, the producer designs low wholesale price and high side payment for the high-type retailer, which enables the producer to earn high profit with low carbon emissions and makes the retailer to obtain additional information profit. While under some conditions, the producer considers the possibility of terminating cooperation with the retailer of low-type, as such a cutoff policy not only guarantees the profitability of the channel members but also improves environmental performance. Furthermore, the impact of some key factors on firms' decisions, profits, and equilibrium carbon emissions is summarized to guide management decisions. [ABSTRACT FROM AUTHOR]

This study aims to explore the direct and indirect impacts of the Carbon Emission Trading System (CETS) on the green transition development (GTD) of Chinese cities. This paper constructs a GTD evaluation system from the perspective of prevention and governance at the source, in terms of carbon reduction, pollution reduction, green expansion and growth, and measures the GTD performance of 281 cities based on the EW-TOPSIS model. It is found that the average increase in GTD of the sample cities from 2006 to 2020 is 3.40 %, the GTD progress of the 281 cities has a considerable positive spatial autocorrelation, with the GTD progress of the CETS pilot cities being significantly bigger than that of the non-pilot cities. Further this paper adopts the spatial difference model (S-DID) to scientifically identify the influence mechanism and heterogeneity of the direct and spatial effects of CETS on city GTD, and the results show that CETS has a positive effect on the GTD of both local and neighboring cities. Propensity score matching (PSM) is used to further corroborate these findings. The study also shows that CETS improves local GTD by promoting green innovation, rationalizing industrial structures, and enhancing energy efficiency in cities. Nevertheless, the ensuing siphon effect hinders nearby cities' levels of energy efficiency, industrial structure rationalization, and green innovation. The policy effects of CETS are more significant in large cities, eastern cities, old industrial areas, and non-resource-based cities. CETS can work in synergy with smart city construction, low-carbon city pilot policies, and emission trading policies to accelerate the process of GTD. [ABSTRACT FROM AUTHOR]

The dynamic nature of the supply chain due to personalised demand and technological advancements has made supplier selection a challenging issue in supply chain management. This research paper aims to tackle the supplier selection problem in a multi-period, multi-sourced, and multi-product environment by creating a process that places the manufacturer at the centre. Firstly, this paper has constructed a multi-period supplier dynamic preference model that takes into account environmental protection, to minimise the total cost and carbon emissions. Secondly, the Taguchi method is extended to the supplier selection problem in the field of quality engineering. The parameter optimisation process is then designed based on the Taguchi method, aiming to optimise the main key parameters that affect supplier selection and determine the optimisation level combinations upon which the model is solved. Finally, the model's feasibility and evolution are verified through examples of its application. The paper's methodology enhances the model's relevant parameters' robustness, augments decision-making efficiency, and offers suppliers guidance to boost their performance and substantially cut the manufacturer's total procurement expenditure and carbon dioxide emissions. [Display omitted] • Propose a supplier selection process under multi-cycle multi-parameter variation. • Research results can provide decision-making tools for suppliers and manufacturers. • Decision makers should balance short-term benefits with sustainability goals. • Companies need to continually adapt business models to face up to the challenges. [ABSTRACT FROM AUTHOR]

China has committed to peak carbon emissions by 2030 and become carbon neutral by 2060. Hebei, as a major energy province in China, has more challenging carbon peaking and carbon neutrality targets. Based on the extended STIRPAT model, this paper evaluates the time to peak carbon and post-peak carbon trend changes in Hebei province under different scenarios from 2020 to 2060. Based on the support vector machine model with particle swarm optimization, this paper evaluates the carbon neutral pathways under different scenarios in Hebei province from 2020 to 2060 in terms of forests, croplands, wetlands, grasslands and Carbon Capture, Utilization and Storage (CCUS). The results show that a monotonically flat trend without inflection point, a single-inflection-point plateau-period trend and a multi-inflection-point inverted U-wave trend occur after carbon peaks. Hebei Province can achieve carbon neutrality by 2060 under the policy scenario and the enhanced scenario, and the enhanced scenario is better. Comparing with existing studies, the innovation of this paper is to design different trends of changes after carbon peak, combining carbon absorption and carbon emission, and providing more diversified development paths for carbon neutral strategies through a more integrated perspective and multi-scenario comprehensive analysis. Therefore, In the future, Hebei Province should establish a platform for carbon emissions monitoring and carbon footprint assessment, and set up an early warning and assessment mechanism for carbon peaking, so as to prevent a rebound trend in carbon emissions after peaking. At the same time, it should introduce and develop carbon capture and conversion technologies and promote the low-carbon transformation of the industrial and energy sectors, thereby forcing the early realization of peak carbon. Pay attention to the role of forestry resources in carbon neutrality, increase carbon sink resources such as forests and wetlands, promote the development of CCUS, and enhance the implementation of the policy. [ABSTRACT FROM AUTHOR]

This paper studies the biomass–coal co-firing (BCC) production planning problem under the carbon emission quota allocation (CEQA) mechanism. However, carbon emission parameters are uncertain due to many factors, such as the type of power units and coal quality. To address this challenge issue, this paper proposes a new globalized robust bi-level optimization model, where the uncertain parameters are characterized by a pair of uncertainty sets. In the proposed model, the upper government as the leader decides the CEQA mechanism, while the lower power plants as the followers develop the production planning according to the given CEQA mechanism. Moreover, based on the Lagrange duality theory and Karush–Kuhn–Tucker (KKT) conditions, the proposed model is equivalently converted into a computationally tractable single-level model. Finally, a practical case study in Shandong Province demonstrates that compared with the nominal bi-level and robust bi-level models, the proposed optimization model can not only effectively resist parameter uncertainty, but also be less conservative. [Display omitted] • A bi-level GRO model for BCC production planning problem under CEQA mechanism is proposed. • The uncertain carbon emission parameters are characterized by a pair of uncertainty sets. • The established globalized robust counterpart is transformed equivalently into a finite convex system. • The effectiveness of the developed GRO approach is verified via a case study. [ABSTRACT FROM AUTHOR]

• This paper proposes a bi-level Stackelberg low-carbon economic dispatch model with reward and penalty stepped carbon trading mechanism, horizontal and vertical integrated demand response, and dynamic supply and demand multi-energy pricing. • This paper also provides a multi-angle sensitivity analysis of the impact of different parameters of the stepped carbon trading mechanism on the operation of the CIES. • An innovative analysis of the impact of single and two-factor carbon interval length, carbon growth rate, and carbon base price on the CIES is presented. • The sensitivity analysis of horizontal integrated demand response is also carried out. • Through horizontal and vertical integrated demand response, the dynamic supply and demand multi-energy pricing of CIES is further improved. To solve the problem of coupled and coordinated dispatch of multi-energy between campus integrated energy system operator and users. A bi-level Stackelberg low-carbon economic dispatch model with horizontal and vertical integrated demand response and dynamic supply and demand multi-energy pricing (DSDMEP) is proposed. Firstly, the DSDMEP is established to effectively coordinate the interests of the upper CIESO and lower users. Secondly, the combined heat and power containing the power to gas and carbon capture system and the reward and penalty stepped carbon trading mechanism (SCTM) are introduced on the energy supply side to minimize carbon emissions. Then, the HVIDR is introduced to the users to increase the energy coupling and adjustable price characteristics of supply and demand. Finally, the results show that the proposed model compared to other scenarios has its carbon emissions reduced by 21.72%, the benefits of CIESO are increased by 27.86%, and the cost of the users is reduced by 6.9%. This paper also provides a multi-angle sensitivity analysis of the impact of different parameters of the SCTM on the operation of the campus integrated energy system (CIES), as well as a sensitivity analysis of the HVIDR and peak and valley electricity price difference. [ABSTRACT FROM AUTHOR]

• Constructed an operation architecture of the coupling of energy supply and demand. • Established the comprehensive demand response mechanism based on time-sharing energy prices. • Established the minimum carbon emission cost economic scheduling model as an objective function. • The scheduling method could achieve the goal of low-carbon economic dispatch. Aiming at the problems of low energy utilization and environmental pollution in China's livestock and poultry breeding industry under the carbon peaking and carbon neutralization goals, this paper proposes an optimal scheduling model for multi-energy coupling integrated energy system with ladder-type carbon trading mechanism and demand response. First, based on the construction of a reward and punishment model with ladder-type carbon trading mechanism, this paper explores the potential interaction between the coupling ability of multiple heterogeneous energy sources and carbon emissions in consideration of the impact of carbon capture equipment on carbon emissions. Second, in view of the diversity and complexity of demands for agricultural energy supply, the energy supply side equipment model and the comprehensive demand response model of electricity, heat, and gas are established, and a low-carbon economic scheduling model of integrated agricultural energy system is proposed, aiming at minimizing the total operation cost of the system. Finally, the model is solved by CPLEX, and the coupling system of a 6-node power distribution network, a 6-node gas network, and a 4-node heat supply network is used to verify the effectiveness of the proposed model and method and optimize the operation of the integrated agricultural energy system. [ABSTRACT FROM AUTHOR]

• Taking carbon in RIES as energy flow, which is equivalent to electricity-heat-gas to ensure the carbon quota requirements of the system. • Proposing a new demand-side response mechanism to meet the purpose of reducing carbon emissions. • Scheduling RIES with comprehensive consideration of economy and low carbon. • Proposing a new RIES ladder carbon price trading mechanism to match the purpose of demand side management. Effective mitigation and reduction of carbon emissions is consistently underscored in Regional Integrated Energy Systems (RIES), aligning with the goals of carbon-neutralization. However, the inherent trade-off between carbon emissions reduction and economic operation is an obvious challenge not only in power systems but especially in the coupled electricity-gas-heat energy systems. This paper develops a bi-level low-carbon optimal scheduling model, highlighting reliable operation and the balance of supply and demand of RIESs with demand-response uncertainty. In this paper we firstly redefined the carbon emission of a RIES as a carbon flow and proposed a reward and punishment carbon trading mechanism based on the ladder carbon price. Secondly, we designed an incentive-based demand-side response policy within the subsystem encompassing electricity, gas, and heat. Concurrently, the probability density distribution functions of transferable, replaceable, and interruptible loads are computed separately. Based on the above, the deviation of the load reduction and the actual load is recalculated to delimit the constraint of the demand-side response. Finally, the mechanism of carbon trading and the demand-response is leveraged to address the economic scheduling incorporating operation cost, carbon transaction cost, imbalance costs of supply and demand, and load loss. Simulations demonstrated in the paper prove that the optimal solution with demand-response uncertain may effectively increase the economic performance and reduce carbon emissions of a RIES. [ABSTRACT FROM AUTHOR]

Addressing the urgent need for accurate carbon emissions forecasting to support global emissions reduction goals, this paper introduces a novel approach for carbon emissions prediction that dynamically considers the spatial–temporal correlation of carbon emissions across forecasting targets. Traditional statistical and machine learning models have limitations, such as oversimplification and inefficiency in capturing evolving dynamics among multiple forecasting targets, alongside a focus on longer forecasting horizons like monthly or yearly. To tackle these, we propose an innovative Dynamic Spatial–Temporal Graph Convolutional Recurrent Network (DSTGCRN), a blend of graph convolutional and recurrent neural network structures customized for up-to-date multi-regional spatial–temporal predictions at a daily level. Comprehensive empirical analyses on datasets from China, the US, and the EU confirm DSTGCRN's superior performance and robustness across diverse geographical contexts. It outperforms the second-best among 10 baseline models, achieving 40.6%, 24.6%, and 38.5% improvements in MAE, RMSE, and MAPE, respectively, across various horizons. The incorporation of environmental data, including temperature and Air Quality Index, as supplementary predictors in the DSTGCRN model, has demonstrated its efficacy in the context of the China dataset. The DSTGCRN model's improved accuracy and daily multi-regional forecasting deepen emission dynamics understanding, supporting the development of informed and region-specific environmental policies that can respond promptly to changes. [ABSTRACT FROM AUTHOR]

This study delves into the nexus between the efficiency of carbon emission allocation and the efficiency of green development within the context of innovative city construction. Leveraging data from 283 prefecture-level cities and corresponding provincial-level panel data spanning the period from 2005 to 2019 in mainland China, the research employs the zero-sum gain DEA model to compute provincial carbon emission allocation efficiency and allocate allowances. Furthermore, it utilizes the all-factor non-radial directional distance function and the SBM-DEA model to gauge the green development efficiency of prefecture-level cities. The double-difference method is applied to evaluate the impact of innovative city construction on regional green development efficiency. The results indicate that an imbalance between carbon emission allocation efficiency and emission quotas detrimentally affects urban green development. However, technological progress, factor accumulation, and the institutional environment associated with innovative city building mitigate this negative impact. Heterogeneity analysis reveals a differentiated effect of innovative city building on regional green development efficiency. This paper not only explores the concept of achieving an equitable distribution of carbon emission allowances but also examines how innovative city building influences regional green development. By establishing connections between urban policies, the equitable distribution of carbon emission allowances, and regional green development, it introduces a new perspective for promoting high-quality development at the regional level. [ABSTRACT FROM AUTHOR]

The scientific formulation of procurement and inventory plans is an important topic in the steel industry, when considering both economic and environmental impacts in an uncertain market environment. Robust optimization methods offer an effective solution to this problem. This study has investigated a multi-echelon and multi-period front-end supply chain centered on a steel enterprise under uncertain purchase prices and demand. The goal is to minimize the costs associated with mining, processing, transportation, procurement, and inventory along with the corresponding carbon emission costs. To address this issue, we present a robust rolling horizon model (RRHM) that considers the inventory capacity constraints of steel enterprise and incorporates ore grade requirements. Subsequently, the RRHM was reformulated into a solvable mixed-integer linear programming model, and the relationship between the robust model and a specific deterministic model was established. Finally, the model was validated by generating instances based on real scenarios. The results demonstrate that the robust rolling horizon model proposed in this paper outperforms both the Sample Average Approximation (SAA) strategy and the (s, S) strategy in terms of cost and robustness. In addition, a sensitivity analysis of the key parameters was conducted. The findings reveal that the unit inventory, mining, and carbon emission costs have a limited impact on the total cost, highlighting the robustness of the model. These findings provide valuable insights into the green and sustainable development of the steel industry, enabling the formulation of effective strategies to mitigate the impacts of uncertainties. • Making steel raw material procurement plans amidst demand and price uncertainties. • Robust rolling horizon model is formulated, exploring its relationship with others. • The robust model exhibits certain advantages compared to SAA and (s, S) strategies. • Managerial insights for steel enterprises' procurement strategies are offered. [ABSTRACT FROM AUTHOR]

There has been limited progress in addressing the demand uncertainty inherent over time and the sustainability impact on food logistics. This paper aims to fill this gap, addresses the sustainability of cold food supply chains, and proposes a mixed-integer programming model to solve a multi-period inventory routing problem (IRP) under non-stationary stochastic demand, route-dependent costs, and environmental concerns. The study investigates the replenishment and routing plans to minimise the total expected cost while producing a minimum amount of CO 2 emission. Due to the uncertainty of demand, we apply the static-dynamic strategy and propose a mathematical model under the (R , S) replenishment policy to maintain flexibility in ordering decisions under a pre-determined replenishment schedule. Our numerical experiments suggest that the (R , S) policy reduces inventory costs significantly, since it solves the excess inventory issue caused by the higher buffer stock levels due to the pre-determined order quantities in the (R , Q) policy. However, the resulting CO 2 emission levels and routing costs remain similar in both models. Due to the reduced inventory costs, the (R , S) policy makes a significant improvement on the total cost. Moreover, our numerical experiments show that the difference between the cumulative ending inventory levels for the (R , Q) and (R , S) policies increasingly grows as the time horizon gets longer, and it results in increasingly larger differences in the total cost values for both policies. [ABSTRACT FROM AUTHOR]

The full exploitation of mass and energy cross-feeding relationships among subsystems of chemical industries has great potential to enhance mass-energy efficiency. This paper proposes the concept of mass-energy cross-feeding system (MECS) with pathways for mass-energy efficiency promotion. Then a mixed integer non-linear programming (MINLP) model considering quantitative regulation is formulated. Afterwards, an MECS including propane dehydrogenation, ethane cracking, synthetic urea, and synthetic HIPP and LDPE is designed to perform demonstration. Through process simulation, integration and optimization, the results show that MECS with subsystem scale factors of 53.6, 100.0, 74.6, 57.3, and 93.1 show the optimal performance in terms of atomic utilization, energy recovery, profitability, raw material cost, and carbon emission per unit of output, with results of 0.56, 0.54, 0.44, 0.696 MM$/unit, and 0.24 t/(h·unit). This work demonstrated that implementing quantitative regulation among subsystems based on cross-feeding can greatly improve comprehensive performance of chemical manufacture systems. [Display omitted] • Mass-energy cross-feeding is set as the foundamental to enhance process integration. • An efficient mass-energy cross-feeding system (MECS) is developed. • A MINLP model through quantitative regulation is formulated as optimization solution. • Case study demonstration for the ascendency of MECS on comprehensive performance. [ABSTRACT FROM AUTHOR]

• Static (3PB) and impact (Charpy) properties of closed-cell metal foams were investigated. • Foam density and loading speed significantly influence the mechanical behavior. • Mechanical properties are closely related to the topology of the fracture surface. • Correlations between the static and impact properties of metal foams have been proposed. Currently, to reduce CO 2 emissions and fuel consumption, the automotive industry seeks to replace high-density components with low-density ones. Due to their advantageous properties (high stiffness/strength-to-weight ratio and high energy absorption performances), metallic foams (MFs) are identified as ideal candidates for this purpose. This paper investigates the influence of density (0.3–1.0 g/cm3) and loading speed (1.74 mm/min – static to 174000 mm/min – impact) on the mechanical behavior of MFs. Studied MFs have closed-cells and are manufactured from aluminum alloys (AlMg1Si0.5 + 0.4 wt.% Ti) through the powder metallurgy route. For this purpose, rectangular specimens with lateral notches and surface skin were used. A 3-point bending test was used for the static properties, while the instrumented Charpy hammer was adopted for the impact behavior. Following the experimental tests, a macrostructural analysis of the fracture surfaces of the specimens was performed to highlight the influences of the topology on the MF properties. It was found that both foam density and loading speed significantly influence their mechanical behavior. Significant increases in properties were obtained with the increase in foam density and test speed. Equations have been proposed that can predict with high-accuracy the main mechanical properties of MFs. It was observed that the mechanical properties of MFs are closely related to the topology of the fracture surface, as well as to the shape of the foam cells and the skin of the specimens. [ABSTRACT FROM AUTHOR]

The escalating CO 2 emissions in recent years underlined the need for advanced Carbon Capture and Utilization (CCU) technologies. This context has spurred the exploration of novel materials for promoting CCU efficiency, among which electrospun nanofibers have emerged as a promising candidate. Electrospinning coupled with various post-treatment (such as heat-treating, in-situ growth, selective removal, etc.) offers a versatile approach to fabricate diverse nanofibers, including polymeric, carbonaceous, metallic, and composite fibers, with tunable surface morphologies and chemical properties. This paper provides a comprehensive review of electrospun nanofibers in CCU processes, including adsorption, membrane separation and absorption, electrochemical and photo-electrochemical reduction, thermal catalytic hydrogenation, and more. Each CCU technology is examined in depth to cover the recent research achievements with a specific focus on the contributions of electrospun nanofibers. Lastly, a critical discussion and a list of potential future research directions for this evolving field are provided. • Latest electrospun nanofiber-based CCU technologies are thoroughly reviewed. • Electrospinning enables creating various nanofibers, aiding multiple CCU technologies. • Provide critical discussion and future directions for this evolving research field. [ABSTRACT FROM AUTHOR]

Projecting future urban CO 2 emissions is of paramount importance in comprehending and addressing climate change, as well as evaluating the impact of various policies on future greenhouse gas emissions. While previous studies on projecting CO 2 emissions have primarily focused on large administrative scales, fine-resolution mapping of CO 2 emissions is also vital for identifying CO 2 emission hotspots and delineating emission reduction responsibilities. In this paper, we projected the future CO 2 distribution at a fine-resolution (1 km) under the shared socioeconomic pathways (SSPs) scenario framework, based on which we assessed the peak time, peak volume, spatial distribution, and CO 2 emission intensity (emissions per unit building volume) for cities in the Pearl River Delta (PRD) region. We first simulated the future three-dimensional (3D) spatial patterns of cities by using the FLUS-3D model, and then projected fine-resolution CO 2 emissions by combining the 3D information and socioeconomic factors. Results show that the total CO 2 emissions in the PRD region will reach 180–345 million tons (Mt) by 2030, and 67% of cities in the PRD region will peak CO 2 emissions before 2030 under all five SSP scenarios. Three cities (Jiangmen, Zhongshan and Foshan) will witness a continual rise in CO 2 emissions after 2030 under the SSP5 scenario. Additionally, we found that the time (2010) to reach maximum CO 2 emission intensity of different urban functional types precedes the peak time (2010−2030) of city-level CO 2 emissions. This suggests that the escalation in CO 2 emissions since 2010 will be primarily driven by urban expansion and population growth, rather than an increase in the CO 2 emission intensity of buildings. These findings provide extensive knowledge for future spatial distribution of CO 2 emissions and contribute to the construction of low-carbon cities. • Future gridded CO 2 emissions (1 km resolution) are projected under the shared socioeconomic pathways (SSPs). • The future evolution of three-dimensional urban structures is considered in CO 2 emission projection. • Most cities in the PRD will peak CO 2 emissions before 2030 under all five SSPs. • The CO 2 emission intensity of urban lands declines since 2010 for most of cities. [ABSTRACT FROM AUTHOR]

The sustainable energies take increasing proportion in the power systems due to the "net-zero emission" goal, and the future trend is to make the new type power systems operate safely and stably while maintaining low carbon and economic efficiency. This paper proposes a novel smart generation system (SGS) architecture and a smart generation system consensus (SGSC) algorithm from the perspective of automatic generation control. The SGS is based on stratified sequencing method and multi-agent theory, which describes the dynamic and optimal generation dispatch of the units as a hierarchical decentralized multiple objectives programming problem. Further, the SGSC is proposed based on distributed Newton algorithm and leaderless consensus algorithm to solve the security performance degradation caused by decentralization. All the units are configured in parallel with the designed communication topology to achieve dispatch decentralization, thus ensuring low-carbon, economical and safe operation of the new type power system. The effectiveness of the proposed algorithm is verified on a multi-layer, multi-zone smart generation system model with a high proportion of sustainable energies from macro and micro aspects. Different control methods are also used for performance comparison on the two-area load frequency control model with lower regulation costs and lower carbon emissions achievement. • A decentralized control Architecture of autonomous SGS is developed. • A novel SGSC algorithm is proposed for dynamic generation command optimization of AGC. • The proposed algorithm not only minimizes regulation costs and carbon emissions but also ensures the stability of system operations, even during communication failures. [ABSTRACT FROM AUTHOR]

A hybrid integrated energy system (HIES) optimal scheduling model is developed by considering energy storage (ES), solar thermal (ST), wind turbine (WT), demand response (DR), and carbon capture and storage (CCS) to minimize the economic cost and pollutant gas emission; A multi-population dung beetle optimizer based on Q-learning (MODBO-QL) is designed to solve the HIES optimal scheduling model. In MODBO-QL, three subpopulations are constructed first: Global search subpopulation (GSS), Local search subpopulation (LSS), and Balanced search subpopulation (BSS). Second, the search and evolution strategies corresponding to the population characteristics are given. The GSS and LSS perform global search and local search, respectively. At the same time, the BSS is responsible for randomly exploring or exploiting the solution to increase the diversity of the population. Finally, the number of populations is dynamically and adaptively adjusted using Q-learning to enhance information exchange between different sub-populations and improve solution performance. An optimal dispatch experiment was conducted using three typical seasonal load data and renewable energy forecast data in a region of China. The experimental results show that compared with the original system, the three typical quarters' average economic costs of the dispatch results obtained by the method in this paper are reduced by 8.7%, 20.99%, and 3.97%, and carbon emissions are reduced by 617.35t, 284t, and 19.5t, respectively; MODBO-QL performs better compared to MOPSO, MOWOA, and MODBO. [ABSTRACT FROM AUTHOR]

To achieve the carbon neutrality goal, the industrial parks with heavy multi-energy consumption are gradually promoting green and low-carbon transitions. This transition is a long-term dynamic evolution process, which is affected by the strategies of different stakeholders and unpredictable events. It will lead to the industrial parks blindly promoting carbon emission reduction tasks. Therefore, it is necessary to analyze the stakeholders' strategy evolution trajectories based on the evolutionary game theory under changing scenarios to provide correct policy guidance for their carbon emission reduction process. In this paper, firstly, an evolutionary game model of carbon emission reduction in industrial parks is constructed, including an energy supplier, an industrial park operator, and the government. Secondly, the dynamic behavior characteristics of different stakeholders and the factors affecting the stability of the strategy equilibrium are analyzed thoroughly. Finally, different scenarios are formulated to simulate and analyze industrial parks' long-term dynamic evolutionary game law of carbon emission reduction. Furthermore, discussions and suggestions are provided to guide industrial parks' future carbon emission reduction transitions. [ABSTRACT FROM AUTHOR]

Concrete production and construction are responsible for a considerable share of the total carbon emissions annually. With the emerging need to cut carbon emissions from the construction sector, there has been a big interest in optimising the embodied carbon of superstructures. However, limited attention is given to optimising the embodied carbon of foundations as most designers are more interested in addressing the optimal foundations cost instead. For the first time in the open literature, this paper presents a generic genetic-algorithm-based tool to optimise the design of reinforced concrete piles. The algorithm is capable of allocating the optimal design parameters and geometry for reinforced concrete piles at any given soil conditions and required pile capacities. The algorithm is used to compare the environmental impact of different concrete pile types; solid cylindrical piles, hollow cylindrical piles, solid tapered piles and hollow tapered piles at different load capacities, the optimisation results are then compared to common industrial pile designs to quantify the possible carbon and material savings. Results show that a significant cut of more than 70% of the embodied carbon values can be achieved if the optimal pile type is used compared to common industrial design. Moreover, a finite element model built and validated over ABAQUS is used to compare the geotechnical performance of the different pile geometries. Finite element analysis results show a negligible difference between the stiffness of the different pile types when tested in the same soil conditions highlighting an outstanding optimisation efficiency of the proposed optimisation tool. [Display omitted] • A novel pile optimisation algorithm is proposed. • The embodied carbon of different pile geometries is compared. • A finite element model is presented to compare the different piles' stiffnesses. • Optimal pile geometries can save up to 70% of the carbon emissions compared to common pile designs. [ABSTRACT FROM AUTHOR]

Given the increasingly severe challenges of global climate change and sustainable development, accurate prediction of daily carbon dioxide (CO 2) emissions has become crucial. However, current research on real-time daily forecasts remains relatively scarce, often limited to annual predictions and point estimates. This paper proposes an innovative integrated model, based on quantile regression and Attention-Bidirectional Long Short-Term Memory (BILSTM), specifically designed for probabilistic prediction of daily carbon emissions. Testing the model on daily carbon emission data from major carbon-emitting countries including China, the United States, India, Russia, and Italy has demonstrated its superior performance across various metrics, including mean squared error (MSE), mean absolute error (MAE), coefficient of determination (R2), and normalized average width of prediction intervals (PINAW), compared to other benchmark models. This achievement not only highlights the model's effectiveness in predicting daily carbon emissions but also provides policymakers with a powerful tool for real-time monitoring and assessment of carbon emissions. Furthermore , the application of probabilistic forecasting methods enables decision-makers to gain a more comprehensive understanding of the potential range of future carbon emissions, providing strong support for the formulation of targeted policies. [ABSTRACT FROM AUTHOR]

The urge to reduce the dependence on natural gas for heating at the residential level has led to the deployment of different fossil fuel-free alternatives. In the Netherlands, two technologies are leading the transition: heat pumps, due to their high COP, and photovoltaic–thermal systems, due to their dual electric-thermal output. However, both represent a challenge for users and grid operators, aside from their stochastic behavior. Heat pumps alone can surpass a typical Dutch house's total energy and power consumption. Photovoltaic–thermal systems, as their only electric homologs, usually have a mismatch between generation and demand, causing energy injections to the grid. From the electric perspective, storage systems are a proven solution to reduce the energy exchange with the distribution network. This paper proposes four multi-carrier energy system configurations for a Dutch household, comprising different combinations of a photovoltaic–thermal system, a battery energy storage, a heat pump, and an underground water tank thermal energy system, providing analytical models for every component (including the thermal losses from the thermal storage to the ground), and the space heating and electrical demands. We determined the components' compatibility and evaluated the combinations considering their thermal performance, electrical performance, and equivalent CO 2 emissions. The results suggest that using a heat pump combined with a photovoltaic system and a battery provides the best trade-off. The photovoltaic–thermal system alone could not supply the thermal demand required for comfortable space heating nor reach temperatures high enough to charge the thermal storage. Combining the thermal storage with the heat pump allows a certain degree of flexibility for the heat pump activation at the cost of COPs between 0.8 and 1.38 when used to charge the thermal storage, thus increasing energy consumption and equivalent emissions considerably. [Display omitted] • Heat pumps and gas boilers have similar CO 2,eq emissions in Dutch houses. • A PV system can compensate for the CO 2,eq emissions of a heat pump in Dutch houses. • PVT cannot reach temperatures to charge a thermal storage system for space heating. • Thermal storage provides flexibility for heat pump activation but can reduce its COP. [ABSTRACT FROM AUTHOR]

Distributed photovoltaic energy storage systems (DPVES) offer a proactive means of harnessing green energy to drive the decarbonization efforts of China's manufacturing sector. Capacity planning for these systems in manufacturing enterprises requires additional consideration such as carbon price and load management. This paper proposed a triple-layer optimization model for DPVES capacity configuration in the manufacturing sector using a chemical fibre manufacturing enterprise for demonstration. Refined photovoltaic generation and energy storage lifetime models were used. Beyond the considerations of electricity prices and meteorological conditions, we further studied the influence of carbon price and user load management on system capacity configuration and associated economic feasibility. Firstly, without considering carbon, minimizing user costs requires maximizing PV capacity up to the area limit while adjusting the ES to its optimal capacity and power. The optimal DPVES annually reduces the grid electricity consumption and carbon emissions, resulting in a 12.73% annual cost reduction. When considering the costs of carbon emissions, the carbon reduction contributed by DPVES can reduce the annual costs, making the whole system more economically feasible. However, the presence of substantial carbon emissions costs diminishes the economic feasibility of the ES, leading to a reduction of 24.51% in the optimal capacity configuration. Finally, user load management can further reduce system costs because it replaces some of the functions of energy storage. This results in a decrease of over 39% in the optimal energy storage capacity and a further reduction in related costs. Additionally, we found that load management by enterprises is more effective during the low carbon price than the high carbon price, implying that companies should implement load management as soon as possible. • Established a triple-layer optimization model for capacity configuration of distributed photovoltaic energy storage systems • The annual cost can be reduced by about 12.73% through capacity and power configuration optimziation • High carbon prices may reduce the economic viability of the energy storage system, causing reduction in its optimal capacity • Load management can further reduce the energy storage capacity demand by 39%, bringing economic benefits especially in low carbon prices [ABSTRACT FROM AUTHOR]

Because transport in the port hinterlands heavily depends on roads with negative externalities, much social interest has been in modal shifts (MSs) to rail and shipping with lower environmental impact. Currently, leading industry sectors to ambitiously embrace technological innovations to decarbonize—consequently, in line with these changes, the significance of MSs should change; however, no previous studies have comprehensively presented the overall CO 2 emissions and transport mode shares for future regional cargo transport. This paper presents a scenario analysis of CO 2 emission reductions in the Japanese domestic hinterland of international maritime container transport based on future scenarios of technological innovation and change in shipper behavior. The simulation results using the global logistics intermodal network simulation model showed that up to 87% CO 2 reduction is possible, and the primary driver for this is technological innovation. Moreover, the significance of MSs fluctuates depending on the advancement of technological innovation, suggesting that MSs hold a degree of import as a decarbonization measure during the transitional phase toward carbon neutrality (CN) as technological innovation advances. The results of this study, which fluctuated considerably by scenario and timing, have policy implications with respect to the path to be followed toward CN, including whether to promote MSs. • Scenario analysis of CO 2 emission reductions in Japanese maritime transportation. • Up to 87% CO 2 reduction possible using technological innovation. • Significance of modal shifts fluctuated with the degree of technological innovation. • Policy implications suggesting that the path toward carbon neutrality is crucial. [ABSTRACT FROM AUTHOR]

Green finance is a financial innovation that directs capital allocation towards green industries. How will this affect regional carbon emissions? Based on the data on green finance and carbon emissions of 30 provinces in China from 2008 to 2019, this paper employs a multivariate linear regression model and a spatial econometric model to empirically study the impact of green finance on carbon emissions. The results find that (1) green finance significantly reduces provincial carbon emissions, specifically through promoting a cleaner energy structure and an advanced industrial structure, as well as improving the quality of green innovation. (2) Heterogeneity exists between regions. In eastern regions and areas characterized by high levels of marketization and minimal fiscal pressure, the role of green finance in promoting provincial carbon emission reduction is more pronounced. (3) Spatial econometric analysis found that green finance has a significant spatial spillover effect on carbon reduction in adjacent regions. Specifically, the flow of green resources and green ideas between provinces facilitates the formation of regional radiation, while green competition generates a siphon effect on neighboring regions. These findings provide some meaningful policy implications to achieve China's dual-carbon goal by promoting green finance. • Green finance has significantly reduced China's provincial carbon emissions. • The upgrading of energy structure and industrial structure is a positive channel to reduce carbon emissions. • Green innovation strengthens the carbon reduction effect of green finance, but reveals the potential "greenwashing". • Green finance has a significant spatial spillover effect on carbon reduction in adjacent regions. • Policymakers should develop flexible, dynamic and coordinated green finance development policies. [ABSTRACT FROM AUTHOR]

Deploying negative emission technologies has become crucial for limiting the global temperature rise to approximately 1.5 °C above preindustrial levels. DAC technologies are being explored as one of the prospective options. These technologies have been thoroughly investigated as a potential project to capture CO 2 emissions and provide purified air, natural gas, or fuel oil. An applied approach, on the other hand, was not taken into consideration while evaluating the influence that this technology has on emissions. For this reason, British Columbia provides a substantial chance to examine emissions that were produced after the DAC actions were put into place in 2015. In this study, the difference-in-differences methodology is employed for the very first time to compare the emissions that are produced by the transport sectors in British Columbia with those emitted by other provinces in Canada. The role that GDP and population play in the release of emissions is also taken into consideration in this paper. Based on the research results, it can be observed that the implementation of DAC initiatives has yielded notable effects. Evidence shows that the DAC effort has led to an average reduction of 0.08 in logarithmic CO 2 emissions in the transport sector. By accounting for GDP and population, the empirical results indicate that DAC technology reduced CO 2 emissions in British Columbia compared to provinces without DAC facilities. DAC initiatives are expected to become increasingly prevalent between the mid-2030s and 2040s. Overall policy implications suggest that there is a need for DAC technologies to collaborate with alternative mitigation technologies, or alternative technologies should collaborate with DAC technologies that are more efficient to achieve the targeted goals in a short time. • The study offers empirical evidence of DAC technology's impact, providing insights beyond theoretical conjectures. • Based on the research results, it can be seen that the implementation of DAC facilities has yielded notable effects. • DAC technology in British Columbia has led to a significant and negative impact on CO 2 emissions compared to other provinces without DAC facilities. • The study highlights the differential effectiveness of DAC across transport sectors, pointing toward a need for tailored implementation strategies. [ABSTRACT FROM AUTHOR]

This paper delves into an econometric analysis to uncover the differences between Nordic European Countries - NEC (Norway, Denmark, Finland, Sweden) and South-East European Countries - SEEC (Greece, Bulgaria, Romania, Hungary). The focus is on exploring the interplay among electricity generation from Renewable Energy Sources (RES), CO 2 emissions, urban population and GDP per capita in these two European regions. The study employs cross-sectional dependence, first and second-generation panel unit root tests, Pedroni panel and Westerlund panel cointegration tests, ARDL, Dumitrescu-Hurlin causality test to establish the relation among variables. The findings reveal that in both regions, CO 2 emissions are significantly influenced by GDP per capita, urbanization and RES generation. Furthermore, urbanization plays an important role. When examining causality in SEEC, three bidirectional relationships are observed: urbanization and CO 2 , urbanization and GDP, urbanization and generation from RES, and two unidirectional causalities: CO 2 and GDP, generation from RES and CO 2. In contrast, for the NEC, there are two bidirectional causalities: urbanization and CO 2 , urbanization and generation from RES, and two unidirectional causalities: GDP and CO 2 , generation from RES and CO 2. These results highlight the nuanced and varied nature of environmental and economic dynamics across various European regions. • Relationship between CO 2 emissions, GDP per capita and electricity production from RES. • Investigating two groups of EU countries located in the North and SE Europe. • Estimations with Fully Modified and Dynamic Ordinary Least Squares. • Causality and cointegration between variables. [ABSTRACT FROM AUTHOR]

Sensitivity analysis has been widely used in building energy analysis to identify key variables influencing building energy and carbon emissions. However, most previous studies only focus on the single time scale energy use of buildings when conducting sensitivity analysis. The multiple time-scale sensitivity analysis would lead to increased complexity and challenges in building energy analysis. This paper proposes a new systematical procedure for global variance-based sensitivity analysis of multiple time-scale energy use of buildings. A case study of an office building is used to demonstrate the application of this approach using the Bayesian adaptive spline surface (BASS) models at three different time scales (annual, monthly, and daily). The results indicate that the procedure proposed here can provide fast and reliable sensitivity results for the multiple time-scale building energy assessment. The BASS learning models have the advantages of high predictive performance and the fast computation of sensitivity indicators in a closed form without Monte Carlo integrals. The application of principal component analysis can deal with the highly correlated multi-output energy use at a smaller time scale, which can further reduce computational costs. Moreover, the sensitivity analysis at the different time scales can provide new insights into energy characteristics due to the intrinsic variations of weather conditions. This systematical procedure can provide useful guidance for the multiple time-scale model calibration and multi-step ahead predictions of buildings. Moreover, the method proposed here can be extended to the sensitivity analysis of energy use in different time scales (sub-hourly, hourly, or daily) for other energy systems (such as PV, solar thermal, and wind). • Bayesian adaptive spline surfaces (BASS) in sensitivity analysis of building energy. • Multiple time-scale sensitivity analysis of buildings using BASS models. • Building energy assessment using principal component analysis and BASS. • Reduce computational time for sensitivity analysis of building energy using BASS. [ABSTRACT FROM AUTHOR]

• Integration of blockchain characteristics into the collaborative carbon emission reduction game model of both the supply and demand sides of electricity is proposed to encompass source control and terminal suppression. • Blockchain is utilized to trace electricity types within the electricity market and to quantify the carbon emissions generated by the demand side of electricity. • By incorporating blockchain, users can actively engage in individual-level carbon trading to control and reduce carbon emissions. • Blockchain facilitates enhanced carbon emission reduction efforts by the power supply side. The task of carbon emission reduction is severe in the power industry in China under the national goal of "carbon peaking and carbon neutrality". The current carbon reduction is mainly based on supply side, but the effect is limited. To solve this problem, this paper proposed a coordinated supply–demand carbon emission reduction strategy based on blockchain technology. Based on the carbon emission reduction of complementary thermal power and renewable energy under the carbon trading mechanism of power supply side, users are made to participate in the individual level carbon trading mechanism with the help of blockchain technology, and the carbon emission reduction in power demand side is guided through the market mechanism, thus forming a supply–demand collaborative carbon emission reduction strategy of source side control and terminal inhibition. By analyzing the decision changes of both the supply and demand sides of electricity before and after the introduction of blockchain, quantifying the influence of blockchain on electricity quantity, electricity price and users' utility in turn, and establishing the personal carbon trading mechanism supported by blockchain, a game model of two-side interaction between supply and demand was constructed. The simulation results show that the collaborative carbon emission reduction strategy based on blockchain gives full play to the potential of carbon reduction in power demand side, and the personal carbon trading mechanism can better inhibit terminal carbon emissions, which is conducive to the deep carbon emission reduction of the power industry. [ABSTRACT FROM AUTHOR]