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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]

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]

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]

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]

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]

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]

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]

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]

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]

A supply chain's ability to avoid an unexpected disruption or quickly return to a normal situation defines supply chain resilience. Prior works have emphasized the integration of supply chain resilience and sustainable development goals (sustainable supply chain resilience), which is the focus of this paper. This paper proposes a four-level supply chain consisting of suppliers, intermodal hubs, manufacturers, and distributors along with a heterogeneous fleet of vehicles. Two types of manufacturers are included: large and small. The small manufacturers are moveable. To contribute to sustainable logistics problems, the objectives are cost, carbon emissions, and job opportunities when the appropriate supply chain members and flow of materials are optimized. To contribute to supply chain resilience, this paper presents a new robust approach and a new multiobjective algorithm to capture uncertain parameters (e.g., supply cost and employee availability). A numerical example based on empirical data shows the applicability of the proposed model. • This paper proposes sustainable supply chain resilience. • A heterogeneous fleet of vehicles and manufacturers are considered. • A new robust approach is introduced. • A new multiobjective algorithm is proposed. • Datasets along with algorithms codes are available for journal readers. [ABSTRACT FROM AUTHOR]

Graphene oxide is an excellent substrate for pre-concentrating and recovering uranium in water. However, the need for scalable and affordable methods for producing graphene oxide has hindered its field applications. Besides, a sustainable strategy is required to recycle spent adsorbents and prevent them from entering the waste stream. In these contexts, this paper describes the application of graphene oxide aerogel (GOA) produced from graphite through a single-step combustion route for pre-concentrating and recovering uranium in water. The method allows GOA production in bulk quantity at 8–10 times less cost than prevalent Modified Hummer's graphene oxide (HGO). The paper also demonstrates using spent GOA as an active admixture in mortar to improve its mechanical properties. The physicochemical properties of the GOA and mechanistic details of uranium surface bindings were studied thoroughly. GOA showed enhanced uranium adsorption capacity (278 mg/g) compared to HGO due to abundant hydroxyl, carboxyl, and epoxy groups on GOA. However, the presence of HCO 3 − in water reduced uranium uptake due to highly water-soluble uranium-carbonate complexes such as (UO 2) 2 CO 3 (OH) 3 − , UO 2 (CO 3) 3 4 − , and UO 2 (CO 3) 2 2 − . The addition of the spent GOA improved the cement mortar's compressive strength by 15 % compared to the pristine cube and arrested the leaching of residual uranium. The GOA addition and associated enhancement in compressive strength help to save cement and lower CO 2 emissions. In short, the present work addresses the critical challenges of using GOA in pre-concentrating and recovering aqueous uranium, such as bulk production, production cost, and spent materials disposal. [Display omitted] • Affordable and scalable production of graphene oxide aerogel. • A reusable substrate for aqueous uranium pre-concentration and recovery. • The mechanism of removal of uranium by graphene oxide aerogel. • The use of spent graphene oxide aerogel as a valuable cement admixture. • Compressive strength of spent graphene aerogel-modified mortar and uranium leaching. [ABSTRACT FROM AUTHOR]

Recently, increasing pressure on industries to innovate and adopt sustainable techniques has sparked investigations into machining strategies that have minimal carbon emissions, minimal energy consumption by machine tools, and low machining costs. The primary aim of this paper is to identify the machining conditions that can fulfill the criteria of clean production and sustainable chip forming for low-alloy steel which is extensively used in the many industrial field. To achieve this goal, experiments were done under both dry and Minimum Quantity Lubrication (MQL) conditions, aided by ultrasonic vibration assisted hybrid machining conditions at various cutting speeds (100 and 140 m/min) and uncut chip thicknesses (0.1, 0.14, and 0.18 mm). The study was conducted in two stages. In the first part, machining experiments were performed afterward, cutting forces, cutting temperature, chip morphology, surface roughness, and texture were examined. The second part of this study evaluates the sustainability of various machining parameters and cutting environments. The analysis of economic feasibility or rather the total machining costs were estimated by evaluating the costs incurred due to investment of machine tools, lubricant/coolant delivery systems and ultrasonic vibration system costs, waste processing and management costs, cutting tool and labor costs, cost of cutting fluid and energy consumed. On the other hand, environmental viability was evaluated by estimating the carbon emissions (CE) due to the power consumed, material utilization (i.e., the cutting tool and cutting fluid), and the waste processing and management (i.e., disposal of cutting tool, cutting fluid and chip recycling). Results indicate that both dry and MQL ultrasonic-assisted machining processes resulted in improved chip breakability, and produced short-comma chips. Additionally, it reduced cutting force by 28% and the cutting temperature was lowered by 30 %. Besides, Minimum Quantity Lubrication + Ultrasonic Vibration Assisted Machining (MQL + UVAM) hybrid method prevented surface defects and reduced average surface roughness by 24%–35% compared to dry conditions. While utilizing the MQL + UVAM method, the overall machining cost decreased in the range of about 20–30 % as compared to dry and dry + UVAM conditions. From an environmental standpoint, the MQL + UVAM hybrid machining process was found to be a significant contribution to the overall CE due to use of cutting fluids and increased machine tool utilization. This paper contributed to the understanding of sustainability and cleaner production to mitigate CO 2 emissions by using the MQL + UVAM hybrid method. [ABSTRACT FROM AUTHOR]

Combined heat and power is an inevitable trend in the development of waste-to-power industry. Many waste incineration power generation projects that have already put into operation in China are waiting for transition and upgrading to combined heat and power projects. From a growth option perspective, this paper considers the irreversible process and uncertainty in economic benefits, introduces carbon emission trading mechanism and develops the growth option theory to design the expansion decision making model of waste-to-energy combined heat and power projects, so as to obtain the project value and find the optimal expansion timing. In this paper, a case study is carried out on the expansion decision making of a waste-to-energy combined heat and power project in Shandong Province in China. And two scenarios with specific and flexible expansion times are developed from European and American options separately, as European options require investors to expand at a certain time but American options not. The results indicate that 1) real option approach successfully improves the project value through considering uncertainty, and American option can achieve the optimal expansion time, 2) under the carbon emission trading mechanism, the carbon price affects the expansion index, which has a positive effect on the project value, 3) the linear nature of the income and cost expansion index can help investors make judgments on expansion investments. The effectiveness of the case study implies the practicality of the expansion option method for similar projects to reach dual carbon targets in cost and benefit analysis. • Real option approach is applied in energy transition in WTECHP projects. • European and American options are proposed for certain and flexible decision times. • Expansion time and triggers of WTECHP are incorporated into life cycle management. • CCER is introduced as an environmental element to evaluate expansion decision making. [ABSTRACT FROM AUTHOR]

As a significant source of carbon emissions, road logistics operators are under increasing pressure from governments and customers to find ways to decarbonize as part of broader global efforts to reach net zero. Decarbonization of road logistics relies on the widespread diffusion of existing and emerging technological transport solutions. These include alternative fuel vehicles, battery technologies, renewable energy and charging solutions. All promise to reduce carbon emissions, but their implementation remains challenging, expensive, and risky. As early adopters of alternative fuel vehicles have reported, transitioning from fossil fuels poses challenges that must be prepared for and understood to minimize the obstacles to genuine emissions reductions in road logistics. A considerable volume of academic research has been published advocating emission reduction solutions and providing detailed analyses of their potential positive environmental impacts. However, to our knowledge, no frameworks integrate these analyses with practical insights into current and anticipated challenges for road logistics operators transitioning from fossil fuels to a decarbonized future. This paper analyses contemporary evidence and insights on current and future issues and presents an integrated framework for road freight logistics decarbonization challenges. The framework primarily focuses on decarbonization through alternative fuels and analyses three categories of challenges from socio-technical, economic, and environmental perspectives. The three categories of challenges relate to alternative fuel vehicles and fleets, alternative fuel distribution infrastructure and renewable energy generation. The underlying principle of the framework is to provide a practical tool that supports a more informed view of both the advantages and challenges of transitioning to alternative fuels. This research contributes to the body of knowledge by providing a framework to help direct future research into tackling these logistics decarbonization challenges. The insights presented in this paper may also inform practitioners by sensitizing them to potential roadblocks in the transition away from fossil fuels. • Framework integrating road freight logistics decarbonization challenges. • Analyse alternative fuels, alternative fuel distribution and renewable energy. • Three perspectives on decarbonization: socio-technical, economic, and environmental. • Highlight logistics services provider strategies to minimize risk and uncertainty. [ABSTRACT FROM AUTHOR]

The support of digital technology is urgently needed to promote low-carbon development and green economic and social transformation and to realize the goal of "carbon neutrality" early. This paper utilizes the "Golden Tax III" project for the digital transformation of China's tax collection and administration and empirically confirms that digital tax collection and administration significantly reduces carbon emissions based on the panel data of 30 provinces (municipalities directly under the central government and autonomous regions) in China from 2010 to 2020, i.e., digital technology plays a positive role in carbon emission reduction. The mechanism test shows that digital technology indirectly affects carbon emissions through industrial structure, technological innovation, and tax structure. Heterogeneity analysis shows that digital technology has a significant inhibitory effect on carbon emissions in the northern region, non-Yangtze River region, provincial administrative areas with heavy local government debt, and a large proportion of tax revenue from the secondary industry and tax revenue from state-owned enterprises. Under the requirement of high-quality and green development of the economy, this paper is of great theoretical and practical significance for using digital technology to ensure the governance functions of tax, help low-carbon development, and reach the goal of "carbon neutrality" at an early date. • This study assesses the impact of digital technology on carbon emissions from the government's perspective. • This study enriches the mechanism by which digital technology affects carbon emissions. • This study provides suggestions to promote the digitization of government affairs and carbon emission reduction. [ABSTRACT FROM AUTHOR]

Under the goal of "carbon peaking" and "carbon neutrality" in China, it is significant to explore whether emission reduction commitments contribute to carbon risk premium. Therefore, based on a sample of Chinese listed companies that publicly disclosed questionnaires in the CDP from 2009 to 2021, this paper empirically examines the relationship between emission reduction commitments and carbon risk premium and their formation mechanism. The results show that emission reduction commitments can effectively hedge the carbon risk premium, which pass several robustness tests. Specifically, the more comprehensive and ambitious the commitment is, the greater the suppression of the carbon risk premium. The mechanism test shows that disclosing emission reduction commitment information can reduce carbon risk by improving investor confidence and attracting political resources. Differences in enterprise emission levels, regional environmental regulation intensity and green financial development result in different risk-hedging effects of emission reduction commitments. Moreover, the greater the substantial pressure from stakeholders, the greater the tendency for firms to set up emission reduction commitments. Finally, the heterogeneity analysis reveals that confidence level of commitments has a significant impact on risk premium. Specifically, enterprises with SBTi validation, higher CDP ratings and higher target completion are more helpful in curbing carbon risk premium. Our findings contribute to corporate commitment to climate change and complement the carbon risk premium hypothesis. [Display omitted] The possible highlights of this study are as follows. • With the help of forward-looking information on emission reduction commitments, we re-examine the conclusion of the carbon risk premium and find that emission reduction commitments can effectively hedge the risk premium, enriching and developing the carbon risk premium hypothesis • This paper brings commitment levels and carbon emission levels into a unified analytical framework to study the correspondence between emission reduction commitments made under pressure to reduce emissions and the carbon risk premium, providing micro evidence for solving the mystery of the carbon risk premium. • Measure emission reduction commitment indicators from various angles, making the data on emission reduction commitments more objective and scientific. [ABSTRACT FROM AUTHOR]

Existing studies on emission accounting in global value chains (GVCs) have not focused on the relational nature of GVC activities manifested as the interaction of intermediate goods between enterprises. This paper proposes a relational GVC emissions accounting framework based on an inter-country input-output (ICIO) model distinguishing between domestic-owned enterprises (DOEs) and foreign-invested enterprises (FIEs), which is classified as trade-based, investment-based and hybrid-based emissions. Using AMNE-ICIO tables from 2005 to 2016, this paper traces emissions and carbon intensity in relational GVC activities at the global, economic, and industry levels. We find the following: (1) Investment-based emissions accounted for about 94.15% of the increase in relational GVC emissions, of which more than half comes from the interactive activities between DOEs and FIEs. (2) For the United States, high-income economies, and high-tech manufacturing, investment-based emissions were higher than trade-based emissions, and the reverse was true for upper-middle and lower-middle income economies and China. (3) Although relational GVC carbon intensity was high, the trend of decline was significant. (4) Compared to trade activities, investment-based activities were less carbon intensity in China and upper-middle income economies due to the interaction of production between FIEs and DOEs. The findings add new insights into the coordination of global emissions reductions and the allocation of emissions responsibility. • Emphasize the interdependence between enterprises in cross-border production. • Combine GVC linkages with carbon emissions and carbon intensity. • Investment-based emissions are the main factor of relational GVC emissions growth. • The carbon intensity of relational GVC decreased significantly. • Developing countries can access green spillover through investment-based GVC links. [ABSTRACT FROM AUTHOR]

This paper aims to study the significance of China's Industrial Structure Rationalization (ISR) in reducing carbon emissions. To this end, this study uses panel data from 30 Chinese province from 2000 to 2019 to measure the Industrial Structure Rationalization of each province by calculating the Thiel index, base on which a nonlinear autoregressive lagged distribution (NARDL-PMG) model is established to explore the impacts of industrial structure rationalization on carbon emissions. The results show that: (1) There still exist significant regional differences between Eastern and Central-Western regions in industrial structure as illustrated by the ISR index. The industrial structure in Eastern region is more rational; (2) In the long run, the carbon emission in China will drop when the industrial structure becomes more rational; (3) However, in the short run, the carbon emission in China will increase when the industrial structure becomes more rational. Besides the findings above, the major difference between this paper and the extant researches is that this paper pays attention to the relationship between industrial structure rationalization and environmental quality by investigating the asymmetric influences of industrial structure rationalization index's positive and negative shocks on carbon emission, which lays solid foundations for government aiming to set more detailed policies. • Focus on the impact of rationalization of industrial structure on CO2 emissions. • Focused on the linear relationship between industrial structure and carbon emissions. • Attempts to analyze three regional pairs from the East, Central and West regions. • Provide a reference for developing countries to achieve sustainable development. [ABSTRACT FROM AUTHOR]

The climate crisis is recognized as one of the world's most serious concerns, posing a threat to environmental, social, and economic growth. However, previous work has failed to address the dynamic effects through which economic growth may affect green growth and climate crisis. In this research, we take a closer look at the impact of carbon emissions, eco-innovation adoption, and international collaboration on economic growth and the influence of economic growth on green growth. This paper used the generalized method of moments (GMM) for 54 African countries from 2010 to 2019, allied with an imbalanced panel of 540 observations. This paper finds a positive relationship between carbon emissions, eco-innovation international cooperation, and economic growth with p-values lower than 0.047. Our study discovers economic growth's robust and positive impact on green growth with a p-value of 0.009. This research finds a three-pronged mediating effect of economic growth between its driving factors and green growth. Lastly, this paper furnishes new visions into the argument over economic growth versus green growth. It informs policymakers of the need to pay attention to the growing effect of eco-innovation driving factors on attaining sustainable and greener growth and climate crisis mitigation. [ABSTRACT FROM AUTHOR]

It is crucial to examine whether carbon information disclosure (CID) can truly enhance corporate value (CV), which motivates companies to reduce carbon emissions endogenously. Given that the dynamic impact of CID on CV evolves over time, this paper continuously investigates this time-varying relationship within the functional framework. Based on the continuous curves smoothed from 65 companies in the Shanghai Stock Exchange Social Responsibility Index, we construct an evaluation index using functional entropy weight to continuously measure the level of CID. Furthermore, to quantify the time-varying effect of CID on CV, we use functional regression to visualize its dynamics in symbol, strength, and statistical significance from 2009 to 2021. Finally, the real-time moderating effects of nature of ownership and industry attributes are also examined, revealing how the impact of CID on CV varies across different companies and periods. The results suggest the following: (1) CID adversely affected CV initially but favored later. (2) The initial negative impact and the subsequent positive impact of CID on CV are stronger in non-state-owned companies than in state-owned companies. (3) These impacts are also stronger in low-polluting companies than in high-polluting companies. In summary, this paper expands the research toolset for exploring the dynamic effect of CID on CV and offers practical implications for companies to make informed disclosure decisions. • Via FDA, the impact of carbon information disclosure (CID) on corporate value is dynamically quantified and visualized. • A Functional CID index constructed using functional entropy weights evaluates CID development continuously. • CID originally plays a negative impact on corporate value, but a positive one in the later stage. • Initially, CID has a stronger promoting impact on corporate value in state-owned and low-polluting firms, but weakens later. [ABSTRACT FROM AUTHOR]

The increasing urban logistics transportation activities hugely impact the economy and environment. This paper investigates the combined impact of ambient temperature, path flexibility, and hybrid fleet on the economy and environment in urban logistics. The proposed problem is a variant of the location-routing problem (LRP) named dual-mode energy-conserving LRP (DMECLRP) that handles the combination of three types of decisions: the location of depots, the design of the distribution routes, and the location of charging stations (CSs) and battery swapping stations (BSSs). The objective is to minimize the total cost, including the daily fixed costs of operating depots, the cost of renting vehicles and depreciation, driver compensation, and the routing costs, where the latter can be defined concerning the cost of the consumed energy and CO 2 emissions. Due to the NP-hardness of the problem, this paper presents a Q-learning-based hyper-heuristic (QLHH) algorithm to address the DMECLRP. The QLHH employs a Q-learning approach to select appropriate heuristics through its search process and simulated annealing to determine the acceptance of solutions. Results show that the proposed algorithm is effective, providing competitive results for LRP benchmark instances within reasonable computing time, and the proposed model can effectively reduce logistics costs, energy consumption, and CO 2 emissions. Extensive analyses are carried out to empirically assess the effect of ambient temperature, path flexibility, and hybrid fleet on key performance indicators, including energy consumption, carbon emissions, and operational costs. Several managerial insights are provided. • Impacts of ambient temperature on electric vehicle in logistics network. • A strategy of path flexibility and a hybrid fleet of electric and fuel vehicles. • A model for the energy-conserving logistics network considering carbon emissions. • A Q-learning-based hyperheuristic combined with simulated annealing acceptance. [ABSTRACT FROM AUTHOR]

Big data industry development and carbon dioxide emissions reduction are the crucial driver and objective functions of economic growth, respectively. Whether the two types of corresponding policies can achieve the "win-win" effect is the key to green development transformation in the future. However, little attention has been paid to the influence of big data industry development on carbon dioxide emissions. Based on the quasi-natural experiment in China's national big data comprehensive experimental area, the paper identifies the causal effect between big data industry development and carbon dioxide emissions. Specifically, the panel data of 282 cities in China spanning from 2010 to 2019 are used in regression analysis. The study reveals that the big data industry development has a substantial impact in reducing carbon dioxide emissions. Meanwhile, the findings are confirmed by a set of rigorous robustness checks. The heterogeneity analyses suggest that the effect of inhibiting carbon dioxide emissions due to the big data industry development is more significant in eastern & central China, and large-sized cities. In addition, the big data industry reduces carbon dioxide emissions through the effects of industrial structure optimization, technological innovation, and resource allocation. Our findings contribute to the nexus between big data industry development and carbon dioxide emissions. In light of these findings, the paper recommends that governments integrate economic growth and environmental protection policies by promoting big data industry development. • The paper provides a novel insight into the CO 2 emission from the perspective of big data industry. • Based on quasi-natural experiment, the paper has identified the causal effect between the big data industry development and CO 2 emissions with the Difference-in-Difference (DID) methodology. • Big data industry development effectively stimulate the reduction of CO 2 emissions. [ABSTRACT FROM AUTHOR]

Battery-supercapacitor (SC) hybrid energy storage systems (HESS) are today known as an effective means to extend the service life of batteries that are prone to early failures, mainly caused by current-related stress. While this holds true in most cases, the overall economic costs and benefits of this architecture are either overlooked or incomplete in previous research works. This paper introduces a life cycle cost optimization model for cost-effective upgrade of battery-alone energy storage systems (BESS) into battery-SC HESS. The case study in this paper shows that the presence of SC can result in up to 1.95% reduction in LCC over the remaining five years of the plant's lifespan. As side benefits, 5.2% decrease in energy consumption and CO 2 equivalent emissions associated to the manufacturing and operation of the HESS components. The proposed model can therefore inform about the relevance of a BESS retrofit in both brownfield and greenfield projects. • Life-cycle cost model for battery-supercapacitor hybrid energy storage systems. • Optimal supercapacitor sizing for cost-effective retrofit of existing battery ESS. • Lower life cycle cost compared to conventional battery-supercapacitor HESS. • Increase in battery lifespan and decrease in asset replacement. • Environmental improvement compared to alternative supercapacitor sizing methods. [ABSTRACT FROM AUTHOR]

China's industry is facing a carbon lock-in (CLI) dilemma. Based on the complexity and dynamics of CLI, exploring diversified unlocking paths is an important way to achieve emission reduction goals. This paper innovatively applies system dynamics (SD) to the study of CLI prediction and unlocking path, and systematically constructs an industrial CLI SD model, trying to break the positive feedback mechanism of CLI through multiple causality and feedback chains. This paper simulates and predicts the changing trend of the CLI level of China's industry and 8 major subsectors from 2020 to 2050. At the same time, the random forest algorithm is used to select the regulatory variables of policy analysis. Compared with the previous research methods, the accuracy and objectivity of the selection of regulatory variables are improved, so as to test the effectiveness of the unlocking path under each scenario. The results show that (1) the whole industry faces a serious CLI problem and is still in a deep lock-in state. From the perspective of subsectors, the light, electromechanical, and textile industries have achieved unlocking; the oil and extractive industries are in a moderately lock-in state; the chemical, steel, and power industries are in a deep lock-in state; and the CLI situation is serious. (2) The results of the random forest algorithm show that R&D investment, the proportion of fixed asset investments in the energy industry, car sales, energy structure, and carbon emission industry structure are the most significant factors affecting CLI in each subsystem. (3) Compared with the existing economic development model, the technological, institutional, social, and comprehensive unlocking scenarios can all enable industry to achieve carbon peak and carbon unlocking in advance. However, the effect of each scenario is as follows: comprehensive unlocking scenario > technological unlocking scenario > institutional unlocking scenario > social unlocking scenario. (4) The unlocking times of the oil, chemical, steel, and power industries are relatively late in each scenario. To realize carbon unlocking in industry, these four areas should be focused on in the future. This study helps policymakers formulate reasonable policies to accelerate industrial carbon unlocking and promote the implementation of the "dual carbon" strategy. • The whole industry faces a serious carbon lock-in problem. • The technological, institutional, social and comprehensive unlocking scenarios can all achieve carbon unlocking in advance. • The comprehensive unlocking scenario has the best effect, and the social unlocking scenario has the worst effect. • The oil, chemical, steel and power industries should be focused on in the future. [ABSTRACT FROM AUTHOR]

The failure of the global food system to produce food in a sustainable way needs to change urgently, and a solid push to harmonize low-carbon food production with stable supply security is the right direction in which modern agriculture must evolve, but it is more difficult for large food-producing countries, and finding the right practices or systems to help policymakers or producers address this dilemma is critical. This requires political will, increased financing and top-level design. Digital inclusive finance (DIFI) embodies the financial concept of a community of human destiny, and it is worth examining whether this more inclusive and comprehensive financial service can shoulder the "responsibility" of "ensuring food security and reducing carbon emissions" in agriculture. This paper takes China's grain production system as an example, measures the coordinated development level of food production ensuring supply and reducing carbon emissions, and then analyzes the overall and regional characteristics of it. It is found that the coordinated development trend of China's food production ensuring supply and reducing carbon emissions is relatively stable during 2011–2020, fluctuating slightly above and below 0.9, and the central region has the highest level of coordinated development. The empirical results show that DIFI has led to a 4.3 percentage point increase in the level of coordinated development of food production ensuring supply and reducing carbon emissions during the decade, showing a stronger spatial spillover effect on neighboring regions than the local effect. At the same time, in the research of regional division in China, the impact of DIFI on the level of coordinated development of food production ensuring supply and reducing carbon emissions varies across regions, with typical uneven characteristics, and the promotion effect is more significant in the central and northern regions of China. Overall, this paper provides a simple tool for policy makers or producers to understand the balance between guaranteed output and low-carbon development of agricultural production activities. This paper will provide a methodological basis for assessing and standardizing low-carbon agricultural practices, innovating financial development models and systems, and promoting sustainable agriculture. [Display omitted] • Measuring coordinated development level of ensuring food supply and low-carbon. • Digital inclusive finance had increased the coordinated development level by 4.3%. • Digital inclusive finance also had a significant spatial spillover effect. • The role of digital inclusive finance showed a typical non-equilibrium feature. • The study's results could help to sustainable agriculture in agricultural country. [ABSTRACT FROM AUTHOR]

Sustainability pertains to the interplay between environmental, economic, and social systems at varying scales. In transportation systems, vehicles use different fuels to fulfill their energy requirements. However these fuels emit green house gases that pollute the environment. Taking into account this fact, the present paper delves into the investigation of a multi-objective multi-item fixed cost solid transportation problem with various driving styles. Also, three types of fuels are incorporated in the proposed model. The primary focus of this paper is to maximize profit by reducing breakage of the items along with reducing carbon emission and time of transportation. In real-life situations, the parameters of multi-objective multi-item fixed cost solid transportation problem may not be tackled crisply. Hence triangular intuitionistic fuzzy numbers are introduced in the proposed model. The score function is employed to convert intuitionistic fuzzy problem to deterministic one. Thereafter, a methodology is developed to solve the proposed model involving different driving styles and is extended to consider the optimistic, pessimistic, and mixed view of the decision maker. Comparisons are made among the compromise solutions that are derived from different approaches and are shown by various charts and graphs. A case study is included to illustrate the applicability and validity of the proposed methodology. • A sustainable multi-objective 4DTP with multiple items and fixed charge is modeled. • Three types of fuels: diesel, petrol and CNG are incorporated in the proposed model. • Driving style is included to analyze the fuel consumption and breakability of items. • The decision-maker's optimistic, pessimistic, and mixed viewpoints are discussed. [ABSTRACT FROM AUTHOR]

Carbon emission policy aims to restrict carbon emissions from energy-intensive companies. It encourages target companies to take green innovation but brings fulfillment pressure. Policy prospects for a low-carbon economy are widely discussed in current studies while insufficient attention has been paid to the effect of the policy on the operating activities of target companies. Using the data of Chinese A-share listed companies from 2007 to 2020, this paper explores the effect of carbon emission policy on the financial performance of target companies. The main empirical results prove the significant positive effect of the policy on the financial performance of target companies and reject the interference of earnings management and survivor bias. Further analysis indicates that target companies significantly reduce the selling, general and administrative (SG&A) expenses and non-operating expenses. The trade of carbon quotas between target companies is allowed and it is regarded as non-operating activity. Results of non-operating revenues and expenses prove that target companies tend to actively reduce carbon emissions but the action does not exceed the policy expectation. The increasing efficiency of asset utilization represents that target companies have taken relevant measures to deal with overcapacity. This paper confirms the positive policy responses of target companies. Policy implications are proposed from the perspective of coverage expansion and market incentive mechanisms to realize the full potential of target companies for carbon emission reduction. [ABSTRACT FROM AUTHOR]

The global energy landscape is experiencing substantial transformations, and the energy policies of various countries are also evolving under the influence of economic development and climate change mitigation. The Policies and Measures Database of the International Energy Agency assembles over 7000 pieces of information on energy policy from around the world and classifies them into four categories based on their objectives: addressing climate change policy, renewable energy policy, energy efficiency policy, and building energy efficiency policy. This paper aims to analyze the status quo of global energy policy development from these four aspects and examine the effectiveness of both aggregate and individual instruments utilizing an econometric model based on panel data analysis. Moreover, this paper analyzes the energy policy paths of six leading countries and compares the effectiveness of their policies. The results revealed that global climate policy plays a significant role in reducing carbon emission, while the performance of policies varies among different countries. Based on these findings, this paper provides reference for policymakers to formulate energy policies aligned with global and domestic requirements. [Display omitted] • Characterizing a full picture of global climate policy inventories. • Different policy instruments have varying performances in reducing carbon dioxide emissions. • The policy effectiveness varies across countries, better in developing countries. [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]

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]

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]

Energy-intensive industries are one of the key greenhouse gas emitters, and decarbonization of such industries is a top priority to promote global sustainability. As one of the top energy-intensive industries, ceramic manufacturing industries are urged to boost energy efficiency in ceramic structure production. In this paper, we present a rapid and low carbon footprint strategy for freeform manufacturing spatial ceramic composites. Our approach integrates layer-by-layer deposition, photothermal in-situ solidification, and self-sustaining ceramization, enabling consolidation using only 1920 J energy. Compared to state-of-the-art processes, energy consumption can be reduced by over 100-fold, resulting in an estimated 1000-fold decrease in CO 2 emissions. This energy-efficient process also demonstrates the rapid fabrication of ceramic gyroid heat exchangers, capable of withstanding extreme environments. • Design ceramic powders-mixed feedstocks for both printability and capability of self-sustaining consolidation. • Identify the process window for low porosity and low energy consumption. • Consolidation at high temperatures with neglectable external energy. • Reduce energy consumption by over 100-fold and CO 2 emissions by 1000-fold. • Manufacturing heat exchanger with heat transfer coefficient improved by 13-fold. [ABSTRACT FROM AUTHOR]

The chemical industry is one of the fundamental industries of economic development. Coordinating the relationship between economic growth and carbon emissions (EGCE) is crucial in realizing the carbon neutrality target. The Tapio model and decomposition models are used to assess the degree of decoupling within the Chinese chemical industry and identify pivotal factors impeding progress. The energy efficiency decomposition model explores the crux of failure to achieve strong decoupling. This framework further decomposed the energy intensity to uncover how technical efficiency, technological progress, and input substitution changes affect energy intensity and CO 2 emission. The paper found that the EGCE of the chemical industry were stable in a weak decoupling mode from 2009 to 2019. The reliance on technological progress to reduce energy intensity and emissions is insufficient (accounting for only 18.2%), far below the cumulative contribution from economic growth (accounting for 57.76%), which has become the crux of the strong decoupling failure. Technological advancements drive energy substitution for labour, resulting in 244.41 Mt of cumulative carbon emissions. The improper factor input allocation hinders the desired reduction in energy intensity and carbon emissions. A series of policy implications are proposed based on the insights derived from the decomposition analysis to foster sustainable practices in the chemical industry. [Display omitted] • This study focuses on the decoupling in chemical industry. • The decoupling between economic growth and carbon emissions (EGCE) is explored. • The reasons for the failure of strong decoupling from technical perspective are tested. • The decoupling between EGCE of chemical industry was in a weak decoupling mode. • Technological progress accelerated the substitution of energy for labour. [ABSTRACT FROM AUTHOR]

The energy demand and CO 2 emissions of the steel processing industry are a global challenge. During conventional steel processing, the treatment of iron ore and steel in a molten state heavily contributes to this problem. This paper provides an in-depth investigation of the benefits and technical requirements of an alternative processing pathway with minimal energy and CO 2 burdens. Our proposed method, scrap metal consolidation (SMC) by rolling , is adapted from roll bonding, a scalable metal bonding technique, commonly used for niche composite applications to achieve material properties unattainable by monolithic alloy design. SMC transforms steel scrap into hot rolled steel in solid state without melting. Based on pre-published high-fidelity industrial data, we determined that processing hot rolled steel from scrap in the solid state would consume 94% less energy compared to the primary steel processing route with 94% less CO 2 burden. Compared to conventional recycling methods, the energy savings of SMC would be 86%, with an 84% decrease in CO 2 emissions. The proposed method is described in detail, and the process windows for AISI 1008 mild steel and SS304 stainless steel were determined in terms of rolling temperature and reduction using a lab-scale rolling mill at a temperature range of 700–1100 °C. The formability of the consolidated mild steel is also evaluated via the hemisphere punch test, a standard industrial test for assessing the formability of sheet metals. While the fracture height of consolidated specimens is in the 9.27–10.62 mm range, the monolithic sample has a fracture height of 10.34 mm. The test results show that the consolidated sheets have comparable formability to monolithic specimens. These investigations altogether demonstrate that SMC-by-rolling is a feasible and environmentally sustainable alternative for conventional steelmaking or recycling processes. [Display omitted] • Solid-state consolidation of steel scrap uses 94% less energy than primary processing. • Compared to recycling, scrap metal consolidation saves 84% of process CO 2 emissions. • Process boundaries for mild and stainless steel follow a reverse S-curve trend. • Test results exhibit comparable formability for roll-bonded and monolithic samples. • Most sheet metal forming operations don't cause opening stress, reducing failure risk. [ABSTRACT FROM AUTHOR]

Abstract: A number of empirical methods have been developed to study China's sectoral carbon emissions (CSCE). Measuring these emissions is important for climate change mitigation. While several articles have reviewed specific methods, few attempts conduct a systematic analysis of all the major research methods. In total 807 papers were published on CSCE research between 1997 and 2017. The primary source of literature for this analysis was taken from the Web of Science database. Based on a bibliometric analysis using knowledge mapping with the software CiteSpace, the review identified five common families of methods: 1) environmentally-extended input-output analysis (EE-IOA), 2) index decomposition analysis (IDA), 3) econometrics, 4) carbon emission control efficiency evaluation and 5) simulation. The research revealed the main trends in each family of methods and has visualized this research into ten research clusters. In addition, the paper provides a direct comparison of all methods. The research results can help scholars quickly identify and compare different methods for addressing specific research questions. [ABSTRACT FROM AUTHOR]

Abstract With the development of China's economy, the use of fossil energy has become more and more, resulting in increasing carbon emissions. CO 2 emissions have caused global warming, threatening humans and creatures on Earth. In order to effectively suppress the growth of carbon emissions, it is necessary to analyze the influencing factors of carbon emissions and apply them to predict carbon emissions. This paper presents sixteen potential influencing factors and uses grey relational analysis to identify the factors that have a strong correlation with carbon emissions. The principal component analysis (PCA) is used to extract the four principal components, which reduce the redundancy of the input data. The long short-term memory (LSTM) method is established to predict carbon emissions in China. We use back propagation neural network (BPNN) and Gaussian process regression (GPR) to compare LSTM method. The simulation results show that the prediction accuracy of carbon emissions based on LSTM is better than that of BPNN and GPR, indicating the effectiveness of PCA and LSTM in prediction of carbon emissions. Finally, this paper provides the theoretical basis for China to reduce carbon emissions by studying prediction of carbon emissions. Highlights • Grey correlation analysis is applied to identify factors associated with carbon emissions. • Principal components analysis is used to simplify input variables and obtain the main factors affecting carbon emissions. • The long short-term memory model is used for the first time to predict carbon emissions. • The direction of reducing carbon emissions in China is given by analyzing the prediction results. [ABSTRACT FROM AUTHOR]

Abstract Physical, Regulatory and Brand value risks due to climate change challenges are foreseen by businesses globally. This paper is oriented towards these challenges and prepared a comprehensive Greenhouse gases (GHG) footprint plan and identification of mitigation opportunities for ONGC, a fortune 500 and national oil company of India. Internationally recognized standards and guidelines like Greenhouse Gas Protocol, ISO-14064, and American Petroleum Institute compendium have been referred for quantifying carbon emissions in nearly real 400 + operation control facilities. The GHG inventory is based on Operational Control approach to identify the boundary for which emissions are to be quantified and two significant scopes of emissions viz Direct Emissions and Indirect Emissions are identified and quantified. The quantification concluded that GHG emission footprint for the year 2016–17 is in the range of 9 million tons of CO 2 equivalent. GHG emissions mainly account due to stationary combustion, fugitive emissions and wastewater treatment, which share the around 56% of total emissions. Another 24–26% of emissions are contributed due to flaring, processes by glycol dehydrator, acid gas removal units, and by electricity purchased from the grid. As a result, it has been found that ONGC emits 187 kg CO 2 e emissions per tonne throughput and also compared with its global peer organizations. At the organization level, "Key Industrial Sites" have been identified as contributing higher GHG emissions and accordingly key "hot-spots," a list of 15 GHG mitigation opportunities have been proposed in the paper. Two GHG mitigation initiatives based on solar energy and bioenergy application undertaken at ONGC has also been briefed on case studies. Highlights • Comprehensive carbon footprint plan and identification of mitigation opportunities for Oil and Natural Gas Corporation (ONGC), a fortune 500 and national oil company of India. • Guidelines like Greenhouse Gas Protocol, ISO-4064 and American Petroleum Institute compendium are referred for quantifying carbon emissions from direct and indirect sources. • Green policy, incentives and mandatory compliance. • CO2e emissions per tonne throughput and also compared with its peer global organisations. • Key hot-spots, with a list of 15 GHG mitigation opportunities. [ABSTRACT FROM AUTHOR]

Due to rigorous environmental legislations and competitive economics worldwide, a growing number of companies are devoted to recovering and remanufacturing used products. Consequently, over the past few decades, the management of manufacturing/remanufacturing systems has been receiving increasing attention from researchers and companies leaders. Most of the existing research papers in the literature considered that remanufactured products have the same quality and price as new ones. However, in practice, the market perceives new items as higher quality products rather than remanufactured items. This paper aims to bridge the gap in research on manufacturing/remanufacturing supply chain, by investigating an optimal storage and manufacturing/remanufacturing planning, while taking into consideration the difference between new and remanufactured items, random machine failures, carbon constraints and distinct random customer demands for both items types. Furthermore, to make our system more realistic, it is assumed that the return rate of used items depends on the sales in the past periods, machine repair time is stochastic and set-up time period is not negligible. In this study, two models are developed, considering carbon emissions or not, to determine the optimal values of the manufacturing/remanufacturing period lengths and serviceable stock capacities of new and remanufactured items. Numerical results are provided to illustrate the impact of set-up cost, percentage of returned used items, machine availability, carbon cap and carbon trading price on the optimal storage and production planning. Moreover, the influences of carbon cap, carbon trading price and percentage of returned used items on carbon emissions have been analyzed. The results reveal that set-up cost, percentage of returned used items and machine availability have significant impact on storage and production planning of new and remanufactured items. In addition, the results indicate that a lower carbon cap and/or a high carbon trading price, induce the producer to collect and remanufacture used items and curb carbon emissions. [ABSTRACT FROM AUTHOR]

Climate change has steadily gained attention as the global economy has grown more quickly, preventing continued high-quality economic growth. In this context, the eco-innovation concept has recently risen to the top of the list of academic research topics since it is a crucial tool that combines reduced environmental effects with a favourable socioeconomic impact. Nevertheless, the environmental situation is not improving despite the increasing eco-innovation level. From one perspective, instead of concentrating on how eco-innovation lessens carbon emissions, this research explores the indirect effect of carbon emissions on the GDP-eco-innovation nexus. From another perspective, since FDI might lessen harmful pollution, this research analyses the indirect effect of FDI on the economic growth-eco-innovation nexus. Therefore, this paper highlights the relationship between eco-innovation and economic growth (GDP), considering the dual mediating roles of carbon emissions and FDI. First, we examine the effects of GDP on eco-innovation activities in nine MENA countries from 2000 to 2019 using the geographic Durbin model based on spatial panel data. Second, this paper analysed the dual mediating effect of carbon emissions and FDI using the spatial stepwise regression model. The findings of this paper are as follows: (i) GDP positively impacts eco-innovation activities, and (ii) carbon emissions and FDI mediate the GDP-eco-innovation nexus. Therefore, GDP, FDI, and carbon emissions are significant drivers of eco-innovation. This research represents an essential step towards a better understanding of the need to support the transition to sustainability. [ABSTRACT FROM AUTHOR]