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The pulp and paper industry is a high energy consuming and polluting sector, and carbon emissions emitted from this sector are worthy of attention. This article, based upon an analysis of China’s Pulp and Paper Industry (CPPI), provides estimates of each of the following: carbon emissions from energy consumption, pre-treatment sector, combustion of condensed black liquor, and methane emitted from incomplete aerobic digestion during sewage treatment of CPPI. During the study period (2005–2012), total CO 2 emissions ranged from 126.0 Mt to 155.4 Mt. Energy consumption was estimated to be the largest source of carbon emissions, however, due to the application of the local emission inventory rather than the IPCC inventory, energy consumption decreased by 4.7%, a lower percentage than was calculated in a previous study. According to this study’s estimation, the emissions caused by the recovery of biomass energy contributed 26–29% of the total CO 2 emissions. CH 4 generated from sewage treatment accounted for 9–11% of the total carbon emissions. The CO 2 intensity dropped during the study period, which reflected the improvement of energy efficiency in the pulp and paper industry. The outcome of this study provides not only detailed information about CPPI’s carbon emissions, but also a calculation framework for studying carbon emissions from pulp and paper sector in the other regions. It suggests that the local carbon emissions inventory should be used for estimating carbon emissions and to reduce the fossil fuel energy, increase energy recovery from biomass, and that promoting cleaner production is essential to achieve a low carbon development of the pulp and paper industry. [ABSTRACT FROM AUTHOR]

Low-quality materials contained in separately collected (SC) packaging waste generate environmental and economic burdens associated to both sorting operations of Material Recovery Facilities (MRFs) and landfilling of non-recyclable materials. In this study, based on quantification of both impurities and recyclable fractions, the quality of SC municipal packaging waste streams was determined by compositional analysis of the waste entering a MRF throughout a year. Foreign materials (% w/w) found in the monitored SC glass, paper plus cardboard and lightweight packaging waste, were on average 5.0, 14.7 and 23.0, respectively, while landfilled materials (% w/w) after MRF sorting operations were on average 4.3, 10.3 and 22.0, respectively. A Life Cycle Assessment study was conducted to determine the environmental impact of SC packaging waste streams quality of three different scenarios: current situation (CS), better (BS) and ideal (IS) scenario with reduced presence and absence, respectively, of improper materials in the three examined SC packaging waste streams. Emission of 32.1 kg CO 2 eq/t waste was calculated for the current situation with a reduction by 66.1% and 98.0% in CO 2 eq emission for BS and IS scenario, respectively. Similar emission reductions were calculated for ozone depletion, ionizing radiations, particle matter formation, fossil fuel depletion and primary energy demand impact categories. The environmental life cycle costing study demonstrated that total savings of € 35.2 and € 66.6 for each ton of SC packaging waste stream entering the MRF could be obtained for BS and IS scenario, respectively. [Display omitted] • Impurities in packaging waste streams generate environmental and economic burdens. • 23.0% of impurities were found in separately collected lightweight packaging waste. • Plant sorting operations generate 32.1 kg CO 2 eq for each ton of packaging waste. • € 35.2/ton of sorted packaging waste could be saved by reducing impurities content. [ABSTRACT FROM AUTHOR]

• Offering a novel fuzzy robust approach for closed-loop supply chain network design. • Examining the hybrid uncertainties and flexibility of constraints in the problem. • Considering economic, responsibility, and environmental subjects in modeling. • Using the interactive fuzzy programming approach to solve the multi-objective model. • Introducing a new application for stone paper closed-loop supply chain network design. Nowadays, the production of stone paper, in addition to its widespread utilization in various fields, does not require water consumption, cutting down trees, and stone paper products are easily recyclable and recoverable. Due to the importance of developing and using stone paper and paying attention to environmental matters, Closed-Loop Supply Chain Network Design (CLSCND) is very important for stone paper products. Moreover, due to epistemic, randomness uncertainties, the uncertainties in Objective Function (OF), and the flexible constraints for CLSCND in the real world, this paper introduces a novel Mixed Robust Stochastic, Possibilistic, and Flexible Programming (MRSPFP) approach based on credibility theory. The different attitudes of the Decision-Makers (DMs) are addressed by a more flexible measurement of the optimistic and pessimistic parameters using the criterion of credibility. In the study, a comprehensive procedure is proposed for stone paper CLSCND, to minimize costs, increase responsiveness by minimizing transit time between different facilities, and regarding environmental concerns into account through minimizing carbon emissions. The model is solved utilizing an interactive fuzzy programming solution procedure and the Best-Worst Method (BWM). The results of sensitivity analysis, the effect of changing the problem parameters, and the performance of the proposed model are investigated and compared. The results show that the MRSPFP model has a better performance compared to other previous models. The MRSPFP model performs better in strategic decisions that require high investment costs due to the minimization of the absolute deviation of OF from its mean. Also, the applied results of the study show that CLSCND has a good capability and potential for sustainable development in the field of stone paper. [ABSTRACT FROM AUTHOR]

Partly inspired by the concept of masterbatch in plastics manufacturing, we developed a strategy to improve the technical viability, the economic feasibility, and the environmental performance of lignocellulosic micro-/nanofibers. Instead of investing strenuous efforts in dewatering and/or drying nanocellulose suspensions to maximize their consistency without significantly hampering redispersion, which still stands as an unresolved challenge, we used them in high proportions (up to 50 wt%) to reinforce a high-yield pulp. The suspension can be dewatered-pressed (0.70–1.75 MPa) to obtain wet laps of never-dried reinforced pulps, or dried to consistencies over 90% to produce dry boards, but at the cost of lower strengthening capability. In any case, both wet and dry reinforced boards succeeded in enhancing the breaking length of a recycled pulp by up to 62% and 56%, respectively. In light of these results, we proposed a tentative upscaling and subsequent assessment of the process, evaluating different scenarios. From the technical point of view, reinforced pulp boards are much easier to handle and to deliver than dilute aqueous suspensions. In economic terms, savings in transportation costs are worth remarking. Last but not least, this strategy fulfills at least three goals associated with the concept of cleaner production: water recovery (internal recycling), lower CO 2 emissions (especially if transportation takes place by road), and avoiding hazardous chemicals such as hypochlorite. [Display omitted] • The masterbatch strategy in plastics manufacturing is applied in papermaking. • Micro-/nanocellulose-enriched pulp boards were produced by dewatering-pressing and/or heating. • Reinforced pulp boards were used as an effective strengthening agent for packaging paper. • This novel approach to distribute lignocellulosic micro-/nanofibers minimizes volume and cost of transportation. • Water recycling is enhanced and CO 2 emissions in transport/delivery are lowered. [ABSTRACT FROM AUTHOR]

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

Solar energy absorption and storage of integrated ceramic materials is both the absorption of sunlight and storage of sunlight into thermal energy functional materials. In this paper, the effect of Fe 2 O 3 on the solar absorptivity, thermal storage properties, sintering temperature, and physical properties of mullite-based thermal storage ceramics was investigated. The results show that the absorptivity and thermal storage density of the sample AF4 (Bauxite:50 wt%, Clay:50 wt%, Fe 2 O 3 :11 wt%) after sintering at 1420 °C are 87.2% and 1116.5 kJ kg−1, respectively; and the unit volume of the AF4 sample can provide the power grid with 918.01 kW h of electricity, save 112.91 kg of coal, and reduce 361.33 kg of CO 2 emission. This study systematically elucidated the mechanism of the enhancement of sample absorption caused by Fe3+ doping-induced changes in substrate color, reduction in material forbidden bandwidth, and lattice anharmonic vibration effect, in addition to comprehensively evaluating the feasibility of the optimal AF4 sample for use in solar energy absorbing and storing integrated ceramic 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]

This research stems from the urgent need to address the complex challenges faced in multimodal transportation within the cargo sector, particularly concerning the reduction of transportation costs and CO 2 emissions. Existing transportation systems often struggle to efficiently balance economic and environmental concerns, necessitating innovative solutions. In this paper, we have introduced a novel mathematical model that integrates Singular Value Decomposition (SVD), Genetic Algorithms (GA), and the Adaptive Neural Fuzzy Inference System (ANFIS). The synergy of GA and the ANFIS forms a dynamic learning system that continually refines solutions by adapting to historical decisions and aligning with supply-demand dynamics. This integrated approach aims to optimize cost allocation and resource utilization while simultaneously minimizing CO 2 emissions. Deploying the ANFIS+GA+SVD model in multimodal scenarios yields impressive results, with a minimal average percentage error (MAPE) of 0.28% for transportation costs and a remarkable coefficient of determination R2 of 0.992 for the Truck + Ship scenario. Similarly, a MAPE of 0.11% for CO 2 emissions, supported by an impressive R2 of 0.989, underscores the model's accuracy. Observations of people's transportation choices in response to emission price changes reveal a significant decrease in truck usage, plummeting from 84% to below 50%. Alternative transportation methods, especially in the 3S-3 M-3 C scenario, show a substantial surge, with railways leading at a remarkable 30% increase in utilization. The combined quantity and cost comparison, utilizing the ANFIS+GA+SVD model, emphasizes the suitability of the Truck + Ship scenario for Iraq's transportation network, reinforcing the case for sustainable and efficient multimodal transportation systems. [ABSTRACT FROM AUTHOR]

A new integrated energy system (IES) framework is created in order to encourage the consumption of renewable energy, which is represented by wind and solar energy, and lower carbon emissions. The connection between the units in the composite system is examined in this research. In-depth analysis is done on how energy is transferred between electricity, heat, gas, and hydrogen. The system model and constraints are used to build an objective function with the lowest total operating cost. The calculation of carbon trading includes the ladder carbon trading method. And set up 6 cases for analysis, which verifies the effectiveness of the participation of the concentrated solar power plant (CSPP) in the heat supply and power to hydrogen system (P2HS) in reducing the total operating cost of the system, reducing wind curtailment and light curtailment, and reducing carbon emissions. Under the model considered in this paper, reduces the total operating cost reduces by 27.04% when the concentrated solar power plant is involved in the supply of thermal load. And the carbon emission is reduced by 14.529%. Compared with the traditional power to gas, considers the power to hydrogen system in this paper, which reduces the total operating cost by 4.79%. [ABSTRACT FROM AUTHOR]

Mitigating carbon emissions and waste, along with economic objectives, are the major concerns for businesses and industries today. Current studies have underlined the requirement to shift the company from entrepreneur to ecopreneur with three goals, i.e., social, economic, and environmental sustainability. This paper investigates an economic order quantity model for deteriorating items with partial backordering on integrating the factors that affect the environment and society. Carbon emissions linked to operational activities of inventory can be reduced with the help of investment in green technology. Deterioration is controlled here with the help of investment in preservation technology. Dumping of waste without the proper mechanism has a hazardous effect on society. So, the cost is also included in this study to adopt the appropriate means to dispose of the wastage. Here, imprecise demand with selling price and advertisement cost-dependent demand is considered. This paper provides some theoretical results related to the concavity of the objective function and solution algorithm to obtain the optimal values of decision variables. A numerical analysis is presented to validate the model. Concavity is demonstrated graphically by considering the decision variables in a pair. Results indicate that as the capital investment in preservation technology rises from 3 to 8 units, around 83% and 90% declines in deterioration rate and disposable cost are observed, respectively. Due to the investment in preservation technology, the disposable cost reduces and profit increases to a certain level. A decline of 4% in carbon footprints is observed due to enhancing the investment in green technology by 33%. In the end, sensitivity analysis has been performed and managerial insights have been provided. [ABSTRACT FROM AUTHOR]

This paper presents research performed on set-based multi-criteria optimization for the preliminary design of slab frame bridges. As a result of immense CO2 emissions emerging from concrete production, there is a need for optimization methods decreasing the volume of concrete without affecting the function of the structure. Previous research suggests a general correlation between CO2 footprint- and cost reduction, due to minimized material use. However, the aspect of buildability may conflict with lessened material, as optimized designs might simultaneously be less buildable. This research aimed to develop an optimization method with respect to the investment cost and environmental impact while also considering buildability cost aspects. The optimization algorithm shows the possibilities of reducing the environmental impact by up to 13.7% for a slightly increased cost of 2.3%. Thus, by implementing optimization procedures in the early stages of the planning process the holistic cost effects related to green solutions can be presented, favoring the choice of sustainable designs amongst clients during tendering procedures. [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 sustainability issue in the transportation sector brings the electric vehicle (EV) as a new promising solution for reducing carbon emissions. However, the EV adoption faces issues regarding range, recharging time, and high initial investment. To enhance the adoption, supporting infrastructures should be planned. Therefore, forecasting the growth of EV adoption becomes important to help industries and government in strategic decision making. This paper provides a literature review about forecasting methods in EV ownership, which includes studies from 2011 to March 2023. This will contribute to highlight the current methods and stimulating more developments of forecasting methods related to EV ownership. [ABSTRACT FROM AUTHOR]

"Carbon Peaking" and "Carbon Neutralization" is the strategic direction and key period in the current stage of ecologic civilization construction. Green finance refers to making the financial industry a guide and supervisor of other industries. Green finance will help other industries save energy and reduce emissions to achieve "Carbon Peaking" and "Carbon Neutralization" at an early date. This paper takes Zhejiang, Jiangxi, Guangdong, Guizhou and Xinjiang provinces, which were approved as green finance reform pilot zones in 2017, as examples, and uses synthetic control method to study the constraint effect of the local carbon emissions in pilot zones. The results show that Guizhou has the best emission constraint effect. Jiangxi Province has a certain degree of emission constraint effect. Zhejiang, Guangdong and Xinjiang have no emission constraint effect. The following placebo test results show that the experiment is stable and robust. By combining the actual pilot zones policy schemes and experimental results of the five provinces, this paper concludes that the main reason for the differences in emission constraint effects of the five pilot zones are the differences in the policy selection and the different characteristics of the provinces. Based on this result, relevant policy suggestions are put forward. [ABSTRACT FROM AUTHOR]

Carbon trading price is the core component of the carbon emission trading market. Accurate carbon trading price prediction is of great significance for forming an effective carbon trading market and achieving the "dual carbon" goal. Based on the carbon trading price data in China Emissions Exchange, this paper predicts the carbon trading price through the proposed hybrid model of SSA+LSTM. After comparing with the classical methods based on econometrics, machine learning and deep learning, it is found that the prediction model proposed in this paper achieves the optimal values of RMSE and MAE, and is a relatively effective carbon trading price prediction model. [ABSTRACT FROM AUTHOR]

Discrete manufacturing systems (MSs) are prevalent across various sectors such as electronic production, food processing, and apparel manufacturing. However, their operation raises critical concerns regarding significant energy consumption and carbon emissions. Implementing local electricity and carbon allowance sharing among MSs presents a potential solution to these issues, which is explored in this paper. The main contribution of this paper lies in achieving distributed electricity and carbon allowance sharing among MSs while addressing challenges related to algorithmic and physical feasibility. Firstly, a local electricity and carbon allowance sharing problem for MSs is formulated, which involves numerous binary decision variables due to the operation of discrete manufacturing facilities and energy storage, rendering the problem challenging to solve in a distributed manner. To tackle this challenge, we propose an alternating optimization procedure (AOP)-based distributed method to solve the problem while ensuring algorithmic feasibility. Secondly, the second-order cone relaxation program (SOCP)-based power flow model is identified cannot guarantee the exactness of the distribution system model when conducting local electricity sharing. We tackle this challenge by employing a convex-concave procedure (CCP)-based feasibility recovery procedure (FRP) to recover the exactness of the SOCP relaxation, thereby ensuring physical feasibility. The numerical results demonstrate that conducting local electricity and carbon allowance sharing can effectively reduce energy costs and carbon emissions for MSs. Moreover, compared with the alternating direction method of multipliers (ADMM), the proposed distributed method can guarantee both algorithmic and physical feasibility when solving the problem. • Enabling electricity and carbon allowance sharing among manufacturing systems. • A local electricity and carbon allowance sharing problem is formulated. • An alternating optimization procedure-based distributed method is proposed. • A convex-concave procedure-based feasibility recovery procedure is introduced. • Guaranteeing algorithmic and physical feasibility when solving the problem. [ABSTRACT FROM AUTHOR]

The promotion of sustainable tourism to advance the United Nations Sustainable Development Goals has garnered considerable attention. This paper aims to present a comprehensive analytical framework with data envelopment analysis (DEA) based index systems for examining the interaction between energy and the economic growth of tourism, with a specific focus on tourism destinations within the Yangtze River Delta region of China. The significance of energy in enhancing tourism economy efficiency is established by treating energy input as separable and disposable, while non-energy inputs are considered quasi-fixed. Subsequently, a quasi-fixed energy input directional distance function within the DEA framework has been developed to assess tourism economy efficiency. Furthermore, this paper explores the relationships among environmental pollution, tourism carbon emissions, and tourism economy growth using coupling coordination and decoupling models, respectively. The results indicate that, although improving, there remains a gap for tourism destinations to achieve integrated development between environmental and economic systems. The decoupling type of tourism destinations transitions from expansive negative decoupling to strong decoupling, and the relationship between tourism economic development and tourism carbon emissions tends to be coordinated. This paper provides an empirical study on the measurement of tourism economy efficiency and the relationship between carbon emissions, environmental pollution, and tourism performance. It advances understanding towards implementing sustainable and integrated regional development strategies. • We propose an adjusted quasi-fixed energy input directional distance function model. • We focus on the key role of energy and its related elements on tourism economy growth. • Integrated development remains gap between and environment and economy systems. • Decoupling type changes from expansive negative decoupling to strong decoupling. • Tourism carbon emission and tourism economic growth nexus tends to be coordinated. [ABSTRACT FROM AUTHOR]

China's carbon emissions from households have increased with the growth of the consumption scale. To accomplish the ambitious goals of reducing emissions, carbon emission reduction on the household side should not be neglected. Based on the 2012–2018 CFPS database, This paper examines the relationship between household carbon emissions(HCEs) and the digital economy by analyzing data on household consumption expenditure and carbon emission coefficients. Utilize instrumental variables and the "Broadband China" natural experiment to conduct robustness tests, and further clarify how the digital economy influences direct and indirect household carbon emissions. The study finds that (1) Direct HCEs witness an increase while indirect HCEs experience a decrease due to the digital economy. (2) The digital economy increases direct HCEs by expanding the consumption scale, but surprisingly, the digital economy has not made a significant impact on enhancing residents' environmental awareness. (3) When analyzing indirect HCEs, we introduced a macro perspective, and it is found that the digital economy results in a decline in indirect HCEs primarily due to a reduction in the energy intensity of the whole industry. This paper provides new evidence on the digital economy and its impact on carbon emissions at the household level, which is instructive for understanding the interaction between the production and consumption sides in achieving carbon neutrality and how to promote emission reduction on the consumption side. • Household carbon emissions are measured from direct and indirect perspectives. • The impact of digital economy on household carbon emissions is heterogeneous. • Digital economy increases household carbon emissions through consumption scale. • Digital economy has no significant impact on environmental awareness. • Digital economy reduces indirect household carbon emissions through energy intensity. [ABSTRACT FROM AUTHOR]

• We incorporate spatial factors affecting carbon emissions as drivers into the RF-CA model. • We change the constraints in the CA model for land use change prediction. • We predicted the land use change of Shanghai in 2025 under a low-carbon scenario. As global climate change intensifies, climate protection is important for the sustainable development of human society. In the process of urbanization and industrialization, carbon dioxide emissions are an important factor contributing to global warming. Therefore, modelling projections of future urban land use under low-carbon scenarios are essential for sustainability policy development. Few studies have focused on the impact of carbon emissions on urban land use change in Shanghai, as current research has primarily concentrated on developing methods for urban land use modelling. This paper utilizes a cellular automata (CA) simulation model based on the Random Forest (RF) algorithm to select various spatial variables of carbon emissions as the driving factors that affect urban land use changes. These variables include traffic location factors, economic development factors, electricity consumption, and population density. In this study, remote sensing imagery of urban nighttime lighting is also used to construct a simulation model of land use types in Shanghai. The model is then used to analyze the contribution of carbon emission constraints to urban land use changes. Actual historical land use data from 2013 and 2019 are used for validation, and the prediction model is used to predict land use outcomes under different low-carbon scenarios in 2025. The model is validated by simulating multiple intra-city land use maps for 2019 (kappa = 0.88, OA=92.71 %). The method of out-of-bag error from the random forest is used to evaluate the significance of carbon emission constraints. Using the validated model, the constraints in the CA model are changed to predict the land use simulation results of Shanghai in 2025 under different low-carbon scenarios. In terms of significance, factors such as distance to power plants, distance to major roads, real GDP, and population density can all have a significant impact on changes in urban land use. By selecting the low-carbon scenario with the most appropriate thresholds for each driver, it is possible to obtain the land use simulation results of Shanghai in 2025 under the optimal low-carbon scenario, while ensuring the high accuracy of the RF-CA model and simultaneously reducing the impact of factors on the city's overall carbon emissions. This paper provides a scientific base for urban planners and scholars to thoughtfully design urban land use while cutting down on carbon emissions. Furthermore, it can aid government agencies in establishing associated planning approaches. [ABSTRACT FROM AUTHOR]

Industry is nowadays one of the most energy-demanding sectors representing a major contributor of global greenhouse gas emissions. The simultaneous need for electricity and high-temperature heat is what makes some industrial processes difficult to decarbonize via current commercially available technologies. As the demand for materials and goods is expected to grow in the upcoming years, it is crucial to define which strategies and technologies will serve as the cornerstone of sustainable development. This study addresses the imperative need for emission reduction of energy-intensive sectors by proposing a novel hydrogen-based cogeneration system in the framework of the paper and pulp industry, with the aim of providing general insights relevant to a broader spectrum of similar applications. The comparative analysis presented in this work focuses on three cogeneration options aimed at satisfying the paper mill energy needs: a conventional natural gas-fuelled gas turbine, a Solid Oxide Fuel Cell (SOFC) fed with grey hydrogen produced via steam methane reforming, and a SOFC operating using green hydrogen produced on-site. The latter involves an integrated multi-energy system with photovoltaic panels, electrolyzers, compressors, and storage tanks. Indeed, the SOFC potential of supplying electricity and high-temperature heat in the form of pressurized steam for industrial applications has not been well investigated yet and represents one of the main objectives of this work. Building on the real consumption profiles of a paper mill facility, techno-economic analyses are carried out for many system configurations, varying components size and layout to assess their performance with respect to CO 2 emissions and two key economic parameters, the Levelized Cost of Hydrogen (LCOH) and the net present cost. An in-house-developed flexible simulation framework is presented and expanded for the purposes of this study, including a detailed model that accounts for design and off-design performance of a SOFC cogeneration unit. Results demonstrate that integrating a SOFC with a heat recovery steam generator result in a 75% reduction in the mass flow of generable pressurized steam in comparison to a gas turbine. Additionally, in the cost-optimal scenario, CO 2 emissions are 25% lower than the conventional gas turbine-based configuration, achieving complete independence from the electricity grid and an LCOH of 5.81€/kg without considering revenues from electricity sold. [Display omitted] • Green H 2 production, compression, storage and use in energy-intensive industries. • Solid oxide fuel cell modelling and validation for heat and power generation. • Comparison of gas turbine and solid oxide fuel cell cogeneration performance. • Optimal LCOH of 5.81 €/kg is identified for a PV-to-electrolyzer ratio of 2. • Sensitivity analysis on natural gas purchase price and electricity selling price. [ABSTRACT FROM AUTHOR]

Harmonizing the cleanliness and economics of energy is an effective means of integrating economic growth with carbon peaking goals, and realizing sustainable development in China. However, due to the existence of the energy trilemma, a worthwhile research question is how to fulfill the carbon peaking commitment while guaranteeing China's stable national development. Based on this, this paper not only discusses the nonlinear relationship between clean energy consumption transition and economic growth through threshold regression, but also simulates several scenarios of energy transition dynamics with different growth rates by using Oracle Crystal Ball software to forecast the future trend of China's carbon emissions up to 2030, aiming to find a transition scenario that can make the peak of carbon emissions appear without significant economic burden, so as to put forward countermeasures and suggestions to avoid falling into the energy trilemma in the process of fulfilling the commitment of carbon peaking. This paper finds that: firstly, there is an inverted U-shaped non-linear relationship between clean energy consumption transition and economic growth, with the inflection point of the inverted U-shape being 0.595; and secondly, only when energy transition exceeds 0.535 in 2030, the peak of carbon emissions (about 12,922.28 Mt) will occur. In summary, this paper argues that China should steadily advance energy transition, avoiding either too fast or too slow a pace (bring clean energy consumption transition to within the 0.535–0.595 range by 2030), in order to secure economic growth while honoring its commitment to peak carbon. Therefore, China should prudently design a coal exit strategy to avoid falling into the energy trilemma when promoting energy transition. Moreover, if China wants to further fulfill its commitment to carbon neutrality by 2060, it needs to continue to deepen the construction of green financial markets in order to better balance the relationship between energy cleanliness and economy. [ABSTRACT FROM AUTHOR]

Graphene transparent electrode (GTE) has been attracting much attention due to fascinating physical properties. However, the extensive deployment of copper foil within GTE production has imparted substantial environmental burden. This paper is a cradle-to-gate life cycle assessment (LCA) study to investigate the environmental impacts of roll-to-roll produced chemical vapor deposition (CVD) GTE and the environmental potential of recycling copper foil for cleaner production. Four production scenarios are developed to promote the lab-to-fab progress, including lab scenario, industry baseline scenario, industry recycling scenario and microwave plasma chemical CVD scenario. The functional unit is set as 1 m2 of the GTE production and the life cycle inventories of different scenarios are explored. Results show that the copper foil is a major contributor in baseline scenario in the category of primary energy consumption and global warming. The impacts of GTE production in industry recycling scenario vary from 0.01 to 0.18 of the values in industry baseline scenario. Therefore, copper foil recycling shows environmental potential for GTE production. If all building integrated photovoltaics transition to employing perovskite solar cells with GTE produced in copper recycling scenario, the potential reduction in CO 2 emissions is estimated at 141.2 million kilograms per year. The findings serve as a roadmap for the industry, highlighting key areas where improvements can be made to upscale production while minimizing environmental impact. This paper provides insights into the major environmental contributors in the GTE production, guiding the upscaling routes for cleaner GTE production in the future. [ABSTRACT FROM AUTHOR]

Global environmental problems are worsening. Among various regulatory measures, market-oriented approaches are most favored by governments worldwide. Using provincial panel data from 2004 to 2020, a multi-period DID model was hereby constructed to empirically study the pollution reduction effects of the pilot policy for carbon emissions trading in China. Findings of this paper could be listed as follows: (1) The pilot policy has achieved notable reductions in waste gas and solid waste, although its effect on wastewater pollutants has been less pronounced; (2) Pollution reduction in the pilot regions can be achieved by optimizing the energy consumption structure; (3) Significant variations in pollution reduction effects are observed between the Northern and Southern pilot regions, with notably stronger policy effects in the north across various pollutants compared to the south. Overall, this paper offers pertinent policy recommendations to aid the Chinese government in enhancing its carbon market. [ABSTRACT FROM AUTHOR]

The pressure of the global sustainability problem is growing faster than ever after the social and economic havoc wreaked by the pandemic, as well as the present time nearing to the 2050 net zero carbon goal. In 2021 the steel industry had a turnover of around €125 billion and was directly responsible for providing at least 300000 jobs. In 2022, it was responsible for roughly 5% of CO 2 emissions in the EU and 7% globally, and was responsible for at least 20278 injuries and 90 deaths. Environmental solutions for mitigating negative consequences of the steel industry exist such as hydrogen-based steelmaking, carbon capture, utilisation and storage and electrolysis, whilst safety regulations and personal protective equipment have been used to improve safety. However, little has been done to combat the problem using the most powerful emerging technology of the fourth industrial revolution, artificial intelligence. In particular, computer vision has already shown great competence in a range of applications related to steelmaking, but without seriously considering sustainability, resulting in limited awareness of the potential benefits computer vision can bring to industry. A lack of this awareness leads to missed opportunities for sustainable development. This paper aims to address the gap in research that discusses computer vision capabilities for enhancing the sustainability of steel production by providing a literature review covering recent advances in computer vision, as well as an industry 4.0 approach for integrating computer vision systems with steelworks. Research presented here successfully exposes the untapped potential of computer vision in the steel industry and paves the way for future developments by exhibiting a blueprint for simultaneously elevating sustainability and technological advancement. [ABSTRACT FROM AUTHOR]

• Integration of parabolic trough collector to oil refinery is investigated. • MATLAB simulation was done to define solar field design points & and heat exchanger. • Natural gas savings, reduction in CO 2 emissions & and payback period are presented. • Gas savings of 0.5 MMMBtu and prevention of 30 ktons of CO 2 is predicted annually. • System capital cost breakeven is achieved during 6th year of operation. Crude oil heating is considered an energy-intensive process in the oil industry that requires a huge amount of heat to process the crude oil. There is scarcity of a thorough research that deals with the techno-economic viability of introducing renewable energy solutions to the refinery industry including its environmental benefits. Therefore, a renewable energy solution i.e. a parabolic trough system is reviewed and examined to support minimizing the burning of natural gas in crude heaters and relying on thermal heat from sun radiation to increase crude oil temperature prior to going into the fractionation column. The system is designed to support refinery operation during day time whereas system design and analysis were done from thermal and financial points of view. Furthermore, benefits such as natural gas savings, reduction in CO 2 emissions, and total payback period are presented in the paper to reflect the feasibility of constructing such a solution. Moreover, a MATLAB simulation was carried out to define the design points for the solar field and related heat exchanger components. This is to assure that the system can operate during winter and summer seasons given that the direct normal irradiance (DNI) is typically variant throughout the year. It has been concluded that integrating a parabolic trough collector into the operation of an oil refinery, i.e. crude oil heater, can potentially result in natural gas savings of 555,515 MMBtu and can prevent 30,020 tons of CO 2 emissions annually. Moreover, the system is anticipated to result in cost savings of approximately 1.65 M $ per year. [ABSTRACT FROM AUTHOR]

Load prediction is crucial to unit commitment and scheduling in electricity systems; however, load prediction models consume considerable electricity. Most conventional prediction methods are based on deep learning (DL) models, such as long short-term memory and gated recurrent units. The nonlinear activation functions and backpropagation mechanisms used in these methods result in high computational complexity and correspondingly high energy consumption. This paper proposes a prediction model based on a green learning (GL) framework aimed at reducing energy consumption. The proposed GL model replaces the activation functions conventionally used for feature extraction with a hybrid scheme combining categorical and numerical features, such as seasonal climate features and multi-autoregression terms. The proposed GL model also eliminates the backpropagation methods used to optimize hyperparameters by employing seasonal centroids and the quantile auto-regression forest algorithm for classification/regression. In a case study, the proposed GL model significantly outperformed conventional DL models in terms of energy consumption without compromising accuracy. The energy savings of the proposed GL model are equivalent to the annual electricity consumption of 211 ∼ 565 households, increasing with model penetration. This paper demonstrates the importance of energy-saving models in mitigating the linkage effect of industrial carbon emissions. It also explores policy implications for the implementation of GL models. [ABSTRACT FROM AUTHOR]

The transport sector is a major energy-intensive and significant contributor to CO 2 emissions. With heavy-duty internal combustion engine vehicles (HD-ICEVs) set to remain a primary mode of transport for goods and passengers, the European Union plans to implement CO 2 emission rights starting in 2027 to address this issue. To mitigate CO 2 emissions, on-board carbon capture technologies can be an innovative solution, but a comprehensive analysis from several perspectives must be carried out. To tackle the existing knowledge gap on this subject, this paper presents the techno-economic assessment of a CO 2 capture and storage system hybridised with an organic Rankine cycle (CCS-ORC) integrated into an HD-ICEV whose technical ability has been previously demonstrated. In this paper, the capture installation based on temperature swing adsorption is designed for two engines with different displacement volumes, at varying carbon capture rates (CCR), and using three different sorbents. The size of the thermal devices is estimated as the determinant factor for the required space and the installation costs. The results show that the heat exchangers can achieve a minimum area density of 100 m2/m3, while the most significant temperature swing adsorption device obtained is scarcely 0.26 m3. The carbon abatement cost (CAC) obtained for the CCS-ORC system is less than 35 €/tCO 2 at 100 % of CCR, and with an engine size greater than 18 and 21 L, the CAC of the CCS-ORC system is zero at 100 and 70 % of CCR, respectively. Furthermore, with a CO 2 emissions right price above 71 €/tCO 2 , the projected payback of the initial investment of the CCS-ORC system is achieved in the lifespan of the HD-ICEV for all sorbents evaluated at 100 % of CCR. • Thermal study for dimensioning the CCS-ORC system operating in HD-ICEV. • Techno-economic assessment of a novel CCS-ORC system for operation in HD-ICEV. • CAC less than 35 €/tCO 2 for a carbon capture rate of 100 %. • CCS-ORC system would be profitable with a CO 2 tax above of 80 €/tCO 2. [ABSTRACT FROM AUTHOR]

In response to the threat of climate change and to accelerate the process of carbon neutrality, China has implemented energy-saving targets that take into account the level of regional economic development. This approach does reduce overall carbon dioxide (CO 2) emissions but is accompanied by significant carbon leakage. However, carbon leakage between cities has shown a downward trend since 2015. One possible reason is that the construction of new-energy model cities (NEMC), which began in 2014, has facilitated the promotion and application of renewable energy. Renewable energy, characterized by its wide distribution and "zero carbon" features, ensures a stable energy supply while reducing CO 2 emissions. Its promotion and application have effectively mitigated the carbon leakage caused by energy-saving targets. To verify this conjecture, this paper utilizes data from 298 cities during the period of 2005–2021, establishes a fixed-effect model and a moderating effect model, and systematically examines the carbon leakage effect of energy-saving targets and the moderating effect of NEMC. The findings reveal that regions with strict energy-saving targets can increase CO 2 emissions in regions with lax energy-saving targets through industrial transfer, while NEMC can attenuate the carbon leakage effect of these targets. Further analysis indicates that the mechanism of carbon leakage between regions is significantly influenced by geographic, economic, and energy linkages. In particular, cities with strict energy-saving targets tend to choose cities with close geographic proximity and strong energy links as their first choice for industrial transfer, leading to carbon leakage in these cities, while NEMC can effectively reduce carbon leakage through the path of industrial transfer. It is worth noting, however, that mere economic links between cities with lax energy-saving targets and cities with strict energy-saving targets do not lead to industrial transfer; only close economic links become important areas for industrial transfer, leading to carbon leakage. This paper addresses the critical issue of inter-city carbon leakage, which is of great significance in promoting the process of carbon neutrality. • Energy-saving targets will lead to carbon leakage. • New-energy model cities will weaken carbon leakage from energy-saving targets. • Geographic and energy factors of industrial transfer are the main paths. [ABSTRACT FROM AUTHOR]

Concrete is essential in global construction but significantly contributes to carbon emissions, primarily due to cement production. The cement industry is adopting low-carbon practices, such as using supplementary cementitious materials (SCMs) to reduce emissions. Concrete prepared with calcined clay can achieve a carbon reduction effect of approximately 30 % compared to ordinary cement. Current studies highlight the reuse of kaolin clay, the predominant constituent of engineering sediment in Shenzhen, China, providing raw materials for sustainable low-carbon and eco-friendly concrete. However, high substitution rates of calcined clay can compromise concrete strength, posing a challenge for the application in load-bearing structures. This study proposed a novel low-carbon eco-friendly concrete (i.e., using calcined clay as supplementary cementitious materials) filled into FRP tubes (i.e., Low-Carbon Eco-Concrete-Filled FRP Tubes, termed LCE-CFFTs) aiming to enhance substitution rates of calcined clay and to improve compressive strength and ductility. This paper provides a detailed description of the preparation method, chemical reaction process, chemical composition, and microstructure of calcined clay. Experimental tests investigated the axial compressive performance of LCE-CFFTs, considering calcined clay substitution rates and FRP thickness as key parameters. The experimental work and theoretical analysis indicated that: the inclusion of 10 % and 40 % calcined clay led to a reduction in carbon emissions, resulting in a decrement of 5.8 % and 23.3 %, respectively; confinement provided by FRP tubes mitigated the reduction in axial compressive strength resulting from calcined clay inclusion, particularly evident at higher substitution rates (40 %); the effect of FRP tube confinement in improving peak stress and axial performance of LCE-CFFTs became more pronounced with higher calcined clay substitution rates; to more accurately predict the axial compressive behavior of LCE-CFFTs of this study, it is essential to consider the biaxial stress-strain state of filament-wound FRP tubes. • This paper studied novel low-carbon eco-concrete filled into FRP tubes (i.e., LCE-CFFTs) under axial compression. • Axial compression tests indicated that calcined clay substitution rates had minimal impact on failure modes of LCE-CFFTs. • FRP tube confinement mitigated the reduction in axial compressive strength resulting from calcined clay inclusion. • The effectiveness of FRP confinement for LCE-CFFTs became more pronounced with higher rates of calcined clay substitution. [ABSTRACT FROM AUTHOR]

Fuel cell hybrid vehicles (FCHVs) are significant for achieving zero carbon emissions. Connected FCHVs can leverage traffic information to collaboratively optimize cruise and power allocation control, enhancing various performance aspects. For urban driving scenarios, this paper introduces a multi-strategy series control architecture for longitudinal cruise and power allocation control in connected FCHVs. However, particle swarm optimization (PSO) algorithms face challenges in high-dimensional decision and objective spaces when optimizing multiple strategies. Additionally, manually preset PSO parameters hinder particle evolution from dynamically adapting to unknown multi-objective spaces, thereby limiting the development of multiple performance metrics. To address this issue, this paper proposes a Q-learning multi-objective PSO (QMOPSO) algorithm. This algorithm tackles high-dimensional optimization challenges by improving population initialization distribution and subpopulation division, and enables particles to dynamically adjust exploration strategies, thereby maximizing multiple objective performances. The results indicate that compared to a control scheme optimized with PSO under predefined driving conditions, the multi-strategy series control framework optimized with the QMOPSO algorithm improves tracking stability by 50.20%, driving comfort by 1.77%, fuel economy by 6.10%, and reduces power source degradation by 2.04% in urban driving scenarios. Compared to PSO and multi-objective PSO algorithms, the QMOPSO algorithm demonstrates superior trade-offs. This research provides a collaborative optimization solution for FCHVs in connected environments. • A cruise and power allocation integrated control is designed for connected FCHVs. • Multi-strategy multi-objective co-optimization is performed under urban scenarios. • QMOPSO algorithm is proposed to address the PSO preset parameter adaptation flaw. • Control strategy with QMOPSO improves four different performance metrics. [ABSTRACT FROM AUTHOR]

With the increasing concentration of greenhouse gases, carbon peaking and carbon neutrality have gradually become a global issue. As the last link of e-commerce logistics, the last mile delivery should not only focus on customer satisfaction and enterprise cost, but also reduce carbon emission and promote green development. In order to solve the last mile delivery problem, this paper builds the last mile delivery model for two common delivery vehicles, considering the impact of transportation cost, carbon emission and customer satisfaction on the last mile delivery path planning. The last mile delivery is an NP-hard problem, a novel Discrete Marine Predators Algorithm(DMPA) is proposed to solve the problem, which combines neighborhood search strategy and RS hybrid operator. Six different test examples are generated based on Solomon for simulation experiments, and the experimental results verify that the proposed algorithm is obviously superior to the marine predators algorithm, simulated annealing algorithm and genetic algorithm. DMPA obtains significantly higher customer satisfaction than other algorithms. The customer satisfaction of C101 and C201 is as high as 0.99, which is close to the optimal value of 1. DMPA can achieve lower transportation costs and less carbon emissions compared with other algorithms. DMPA reduces the transportation cost by at least 61.12$ and the carbon emission by at least 1033.64 kg for C101 by minivan delivery. DMPA reduces the transportation cost by at least 28.89$ and the carbon emission by at least 210.9 kg for C101 by electric tricycle delivery. It can effectively reduce carbon emission, lower transportation cost, improve service level, and promote the integration of the dual carbon target with the internal requirements of national development. • Carbon emission, transportation cost and service level are considered in the last mile delivery. • This paper builds the problem model for two delivery vehicles. • An improved discrete marine predators algorithm is proposed. • The proposed algorithm is superior to other well-known algorithms. [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]

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]

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]

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]

Population aging has indeed emerged as a significant challenge for China. Using data from four waves of the China Family Panel Studies, this paper explores the impact of population aging on household carbon emissions and its underlying mechanisms. Results show that population aging significantly decreases per capita household carbon emissions, and this conclusion is robust with respect to sample selection, measurement of primary independent variable, and data selection. Population aging influences household carbon emissions through negative consumption effects and positive composition effects, with the former playing a dominant role. Notably, population aging increases per capita household carbon emissions in items such as medicine, heating, and healthcare, while decreasing emissions in most other items. Furthermore, empirical evidence suggests impact heterogeneity across hukou, income level, and the health status of household head. The insights presented in this paper are crucial for advancing our understanding of sustainable development. • Population aging decreases per capita household carbon emissions. • It is achieved through negative consumption effects and positive composition effects. • The consumption effects play a dominant role. • Evidence indicates impact heterogeneity across, hukou, income level, and the health status of household head. [ABSTRACT FROM AUTHOR]

The design of energy and climate policy is usually seen as an 'expert' domain, relying on technical tools such as economic analysis. However, policy requires the support of politicians and citizens alike, through the democratic process. Many policies, such as reducing carbon emissions from homes and transport, also affect people's lives directly. It is thus necessary to supplement technical analysis with methods that allow an exploration of people's views, values and behaviours. One such method is codesign, whereby groups of citizens work with policy analysts to develop strategies informed by the lived experience of citizens, as well as the specialist knowledge of analysts. There is a history of co-design approaches in public services such as healthcare, but there have been few attempts to co-design public policy measures. This paper reports on a Citizens' Panel, a representative group of people working with specialists to create co-designed policy recommendations for reducing carbon emissions from homes. The process revealed that policy co-design can provide detailed, viable proposals. In this case, panellists emphasised the necessity of leadership and support from government; tailored, consistent information; targeted regulation; and financial incentives. Finally, the wider potential role for policy co-design for energy and climate is addressed. • Co-design processes are widely used but rarely applied to the design of energy and climate policy. • Co-design processes can embed citizen perspectives in policy design. • The paper reports on an experimental panel on home energy decarbonisation. • Co-design can ensure that policies are effective and will gain support of politicians and publics. • Changes to cultures of policymaking are needed to embed such approaches. [ABSTRACT FROM AUTHOR]

Ultra-lightweight foam concrete (ULFC) is a relatively new construction material that has gained attention recently due to its unique properties. This material is not very heavy and low-density, making it easy to form into different shapes and sizes. Its excellent thermal insulation makes it popular for building walls, roofs, and floors. This review paper provides an in-depth examination of the properties and characteristics of ULFC. Its composition typically consists of cement, water, foam, additives, and the various manufacturing methods used to produce it—the mechanical properties of ULFC, including its compressive strength. The density of ULFC can vary greatly, usually falling between 100 and 600 kg/m3. The diversity of this can be affected by different factors, such as the kind and number of additives applied and the methods of foaming and thickening employed. The paper highlights the various applications of ULFC in the construction industry. Its use is as a TC material, which can help reduce heating and cooling costs; as a structural material, it can help reduce dead loads of structures. The review paper also discusses the challenges associated with ULFC production, including foam stability. Foam insulation concrete emits 302–508 kg CO 2 per cubic meter, primarily due to Portland cement, but enhances building energy efficiency, reducing household CO 2 emissions, while advanced recycled insulation materials offer superior thermal and acoustic properties with lower environmental impact. Additionally, we examine the current research on ULFC and suggest potential avenues for future investigation. This paper aims to introduce and put forward a range of cutting-edge and original recommendations and informative guidelines, with the ultimate objective of directing and shaping future research endeavors that focus on effectively exploiting and utilizing ultra-light foamed concrete. • Utilisation of Ultra-light foamed concrete for sustainable construction. • Mechanical properties and thermal insulation perspective incorporating Ultra-light foamed concrete. • The density of ULFC materials compared with normal concrete. • Microstructures properties of concrete incorporated with ULFC. • Recommendations and observations are advanced for future research in ULFC. [ABSTRACT FROM AUTHOR]

• A single-manufacturer multi-retailer cold chain is investigated. • Quality degradation of the product occurs at multiple stages of the cold chain. • Quality degradation is captured by several features and the global stability index. • An algorithm is presented to determine the optimal solution. • It is found that operating a cold chain as a regular supply chain leads to a higher cost. This paper develops a model for a cold chain operated among a single product manufacturer and multiple retailers to minimize the total cost of the chain. A distinguishing feature of the cold chain is that the quality of the product degrades with time. The storage temperature and time influence the degradation rate. In this context, quality can be measured using multiple distinct indices. A global stability index is used to quantify the overall quality. The non-Arrhenius model is exclusively applied to measure the quality degradation during the transportation and at retailers' storage as the product moves in the cold chain. Optimal replenishment times for the manufacturer and retailers are decision variables and the cold chain's total cost includes the cooling cost, carbon emission cost, and loss of value in addition to the traditional supply chain costs. It is found that for a given manufacturer's replenishment time, the total cost is convex on each retailer's replenishment time. Hence, an algorithm is developed based on this property to find the optimal replenishment times for the manufacturer and retailers. This paper also investigates how specific cold chain parameters influence optimal decisions and the total cost. The analyses show that the resulting costs will be significantly higher if quality degradation is ignored in a cold chain. [ABSTRACT FROM AUTHOR]

Carbon trading is widely used as a carbon finance instrument to promote green transformation. This paper focuses on carbon trading's impact on the bond spread for high-carbon enterprises. Using variation in carbon trading access due to pilot policies in China, we find that bonds issued by high-carbon enterprises participating in the carbon trading pilots have lower spreads. Furthermore, the results suggest that carbon trading reduces bond spreads by encouraging high-carbon enterprises to improve their environmental performance. Another channel is the information asymmetry between bondholders and enterprises. More active carbon markets provide investors with more information about enterprises' motivations for reducing carbon emissions, leading to a greater reduction in bond spreads. In addition, the impact of carbon trading is more significant in local SOEs, high-innovation enterprises, and longer-term bonds. Our research helps us understand the importance of carbon trading in achieving a low-carbon economy. [ABSTRACT FROM AUTHOR]

• An improved singular spectrum decomposition is proposed. • High complexity components are secondarily decomposed to reduce its complexity. • An improved prediction model of optimization algorithm is proposed. • Intelligent weighting strategy is introduced to overcome traditional weighting problem. • A combined prediction model for carbon emission is proposed. Accurate prediction of carbon emission is critical for the development of low-carbon economy. However, most carbon emission prediction studies use a single model with low prediction accuracy, and do not consider the instability of carbon emission. Therefore, this paper proposes a combined prediction model of carbon emission. Firstly, the original data is decomposed by singular spectrum decomposition to obtain a limited amount of singular spectrum components. Secondly, high complexity components are secondarily decomposed by variational mode decomposition. Then, chameleon swarm algorithm and carnivorous plant algorithm are used to train the regularization coefficients and kernel parameters of kernel extreme learning machine and least squares support vector machine respectively, and the trained model is used to predict the decomposition components. Finally, induced ordered weighted averaging operator is used to calculate the weight of single model, and error correction is introduced to further promote the prediction accuracy. The carbon emission data of China and the United States is used to make a prediction experiment. The results indicate that the proposed model is superior to other comparative models in different indexes, which provides a new idea for carbon emission prediction. [ABSTRACT FROM AUTHOR]

The current energy structure of most economies must be upgraded to a renewable energy source (RES) to help mitigate ecological dilapidation and achieve sustainable development goals. The transition to RES has recently received attention from energy experts, international bodies and environmental scientists. However, there is contrasting evidence on what factors affect the evolution and deployment of RES, especially from the perspective of emerging African economies. Hence, the current analysis sought to explore the influence of economic development (ECD), carbon emission (CEM), human capital (HUC), financial development (FID), and the country's risk factors, such as political risk (POR) and institutional quality (INQ) on the development of RES. The research employed a panel dataset from 1990 to 2020 and used the newest estimation approach of Cross-Sectional Augmented autoregressive Distributed Lag (CS-ARDL) to establish the long-run connection among the variables. The study confirmed the prevalence of the "energy-led ECD hypothesis". In addition, the empirical findings indicated that RES deployment could be facilitated through ECD, CEM, FID and HUC. Moreover, the country's risk factors, which include POR and INQ, had an inverse connection with the deployment of RES. Lastly, there is a unidirectional causality between CEM, FID, HUC, INQ and RES, while a directional causality exists between ECD, POR and RES. The paper highlights a variety of policy angles to help with the expansion of RES in emerging regions. • The study confirmed the prevalence of the "energy-led ECD hypothesis. • RES deployment could be facilitated through ECD, CEM, FID and HUC. • POR and INQ, had an inverse connection with the deployment of RES. • A directional causality exists between ECD, POR and RES. [ABSTRACT FROM AUTHOR]

• This paper investigates the impact of "environmental protection fee to tax" policy on carbon emissions of China. • Enhancing green technological innovation and executives environmental awareness can facilitate the positive impacts of "environmental protection fee to tax" policy on carbon emissions. • The study reveals that the green awareness of enterprise executives plays a moderating role. This paper investigates the impact of "environmental protection fee to tax" policy on carbon emissions, officially implemented on January 1, 2018 in China. Using a novel dataset covering Chinese firms from 2015 to 2020, our findings reveal a significant reduction in carbon emissions attributable to the "environmental protection fee to tax" policy. Additional analysis suggests that enhancing green technological innovation and executives environmental awareness can facilitate the positive impacts of "environmental protection fee to tax" policy on carbon emissions. Notably, heterogeneity tests underscore that the policy's adverse effects are more pronounced among non-state-owned enterprises, companies located in eastern regions, and small-scale enterprises in China. [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]