Search

In order to improve the quality of solid waste utilization, this study simultaneously used recycled coarse aggregate and recycled fine aggregate to prepare recycled aggregate concrete, with fly ash partially replacing cement as a binder. After the particle gradation of recycled aggregate was artificially adjusted into continuous gradation, the effects of accelerated carbonation on the performance and microstructure of recycled concrete were studied. The microstructural change was analyzed using mercury intrusion porosimetry and scanning electron microscopy–energy dispersive spectroscopy. Additionally, the environmental benefits of the recycled concrete were evaluated based on carbon emissions using the life cycle assessment method. The experimental results indicate that accelerated carbonation can increase the compressive strength of recycled concrete by up to 13%, and its microstructure becomes more compact after carbonation. The carbon emissions are reduced by more than 13% after using 20% fly ash, contributing to sustainable development. Additionally, the optimal replacement rate of recycled fine aggregate should be controlled to under 15% when both recycled coarse and fine aggregates are used. [ABSTRACT FROM AUTHOR]

In recent years, self-healing, ultra-thin overlay has been recognized as an advanced technology and gradually applied in asphalt pavement maintenance, but its sustainability has not been well addressed quantitatively regarding practical maintenance projects. This study utilizes steel fiber as a media-induction material for self-healing, ultra-thin overlay and verifies its integrative benefits in terms of carbon emissions and economic costs from a six-year life-cycle perspective. The system framework and research boundary were developed to include the material extraction, on-site construction, later maintenance, and demolition phases. Meanwhile, carbon emissions and economic cost inventories were established through investigations of the test section of a maintenance project. The results indicated that self-healing, ultra-thin overlay could have benefits, with a reduction of 59.43% carbon emissions and 73.15% economic costs in the six-year life cycle, during which the material extraction phase generated over 50% of the carbon emissions and economic costs in self-healing, ultra-thin overlay due to the addition of steel fiber. Comparatively, the later maintenance phase caused the most environmental and financial impacts, with over half of the carbon emissions and costs. The obtained results could act as significant reference material for the sustainable maintenance implementation of asphalt pavement. [ABSTRACT FROM AUTHOR]

This study presents a bi-objective optimization model for the Green Vehicle-Routing Problem in cold chain logistics, with a focus on symmetric distance matrices, aiming to minimize total costs, including carbon emissions, while maximizing customer satisfaction. To address this complex challenge, we developed a Stage-Specific Multi-Objective Five-Element Cycle Optimization algorithm (MOFECO-SS), which dynamically adjusts optimization strategies across different stages of the process, thereby enhancing overall efficiency. Extensive comparative analyses with existing algorithms demonstrate that MOFECO-SS consistently outperforms in solving the multi-objective optimization model, particularly in reducing total costs and carbon emissions while maintaining high levels of customer satisfaction. The symmetric nature of the distance matrix further aids in achieving balanced and optimized route planning. The results highlight that MOFECO-SS offers decision-makers flexible route planning options that balance cost efficiency with environmental sustainability, ultimately improving the effectiveness of cold chain logistics operations. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources and distributed energy storage systems increasingly complicates the operation of distribution networks, while stringent carbon reduction targets demand low-carbon operational strategies. To address these complexities, this paper introduces a two-stage model for reconfiguring distribution networks and ensuring low-carbon dispatch. Initially, second-order cone programming is employed to minimize losses in the network. Subsequently, the outputs of renewable energy and energy storage systems are optimized using the mantis search algorithm (MSA) to achieve low-carbon dispatch, with the network's carbon potential as the evaluation metric. The proposed model demonstrates a significant reduction in average active power loss by 34.85%, a decrease in daily carbon emissions by 509.97 kg, and a reduction in carbon emission costs by 17.24%, thereby markedly enhancing the economic and social benefits of grid operations. [ABSTRACT FROM AUTHOR]

Land use change has always been a significant factor affecting global carbon emissions. Dissecting the characteristics of territorial space evolution and its impact on carbon emissions is crucial for developing low-carbon-oriented territorial space optimization and governance strategies. This paper calculates the carbon emissions associated with territorial spaces in Qingdao from 2000 to 2020, utilizing land use data alongside various statistical data. Based on the accounting results, the evolution characteristics of territorial spaces and their corresponding carbon emissions, as well as the carbon transition dynamics resulting from space transfer, are analyzed. A carbon transition decomposition formula is then proposed to quantify the differential and spatially heterogeneous impacts of changes in space types and socio-economic development on emissions. The results indicate that: (1) the evolution of territorial spaces in Qingdao during 2000–2020 is characterized by an expansion of living space and a contraction of production and ecological spaces; (2) net carbon emissions rose from 313.98 × 104 tons to 1068.58 × 104 tons, with urban production space contributing the most (69.96% in 2020) due to its significantly high emission density. The spatial distribution of carbon emissions exhibited a stable "northwest–southeast" pattern, with increased dispersion and weakened directionality; (3) the transformation of territorial spaces promoted carbon emissions in Qingdao, with the conversion of urban production space to other uses yielding the most favorable carbon transitions, while the expropriation of agricultural production spaces for urban production and residents' living has resulted in the most detrimental carbon transitions; (4) socio-economic development shapes the overarching pattern of regional emission density changes, whereas space transfers account for local variations. This paper also identifies priorities for spatial optimization and key sectors for emission reduction. The findings contribute to a deeper understanding of the carbon emission consequences of territorial space transformation in Qingdao, thereby providing valuable insights for regional spatial planning and optimization aimed at promoting low-carbon development. [ABSTRACT FROM AUTHOR]

The carbon emissions of new energy vehicles (NEVs)have transited from the use stage to the production stage, indicating that the environmental impact of NEVs in the manufacturing stage cannot be ignored. To reduce carbon emissions and maintain profits, this study proposes a fuzzy multi-objective optimization model to achieve a sustainable production planning scheme for NEVs. The proposed model not only considers the maximum profits of automobile enterprises but also the minimum target of carbon emissions in the production process, to coordinate the optimal production quantity. The results show that the output of NEVs in different price ranges has different proportions. The market share of blade electric vehicles is the highest, accounting for 39% of the NEV market, and the proportion of plug-in hybrid and blade electric vehicles is increasing. The sensitivity analysis further reflects the impact of government subsidy "recession" and body lightweight on the output, carbon emissions, and annual profits of NEVs in China. Accordingly, this paper provides policy implications for achieving a sustainable production planning scheme for NEVs in China. [ABSTRACT FROM AUTHOR]

Comprehensively clarifying the influencing factors of carbon emissions is crucial to realizing carbon emission reduction targets in China. To address this issue, this paper develops a four-level carbon emission influencing factor system from six perspectives: population, economy, energy, water resources, main pollutants, and afforestation. To analyze how these factors affect carbon emissions, we propose an improved partial least squares structural equation model (PLS-SEM) based on a random forest (RF), named RF-PLS-SEM. In addition, the entropy weight method (EWM) is employed to evaluate the low-carbon development level according to the results of the RF-PLS-SEM. This paper takes Shandong Province as an example for empirical analysis. The results demonstrate that the improved model significantly improves accuracy from 0.8141 to 0.9220. Moreover, water resources and afforestation have relatively small impacts on carbon emissions. Primary and tertiary industries are negative influencing factors that inhibit the growth of carbon emissions, whereas total energy consumption, the volume of wastewater discharged and of common industrial solid waste are positive and direct influencing factors, and population density is indirect. In particular, this paper explores the important role of fisheries in reducing carbon emissions and discusses the relationship between population aging and carbon emissions. In terms of the level of low-carbon development, the assessment system of carbon emission is constructed from four dimensions, namely, population, economy, energy, and main pollutants, showing weak, basic, and sustainable stages of low-carbon development during the 1997–2012, 2013–2020, and 2021–2022 periods, respectively. [ABSTRACT FROM AUTHOR]

Big data is a pivotal factor in propelling the digital economy forward and emerges as a novel driver in realizing the goals of carbon peaking and carbon neutrality. This study focuses on a quasi-natural experiment, namely national big data comprehensive pilot zones (NBD-CPZs), and employs a multi-period difference-in-differences (DID) model to identify the influence of big data on carbon emissions. The findings of this study are as follows. Overall, big data significantly reduces carbon emissions within the pilot zones. Mechanism analysis shows that big data reduces urban carbon emissions by promoting green innovation, optimizing energy structure, mitigating capital mismatch and improving public awareness of environmental protection. Heterogeneity analysis shows that the carbon reduction effect of big data are more pronounced in cities with high levels of digital economy, non-resource-based cities, cities with strong intellectual property rights protection and the Guizhou Province. Spatial effect analysis indicates that within a radius of 400–500 km, the NBD-CPZ increases urban carbon emissions, signifying a significant siphoning effect; within a radius of 500–900 km, the NBD-CPZ reduces urban carbon emissions, signifying a significant spillover effect, and beyond a distance of 900 km, the spatial effect of the NBD-CPZ is not significant. Based on the above conclusions, this study puts forward several policy recommendations to effectively exert the carbon emission reduction effect of big data. [ABSTRACT FROM AUTHOR]

This paper investigates the development of low-carbon buildings and carbon emission control in Jiangsu Province, China, utilizing a mathematical model. Through correlation analysis and principal component analysis, the carbon emissions of the entire life cycle of residential buildings are evaluated, and a Grey Prediction Model is established. The study shows that the annual carbon emission from air conditioners is 370.92 kg, given an annual electricity consumption of 1324.71 kW and a carbon emission of 0.28 kg per kWh. It identifies the key carbon emission indicators, including precipitation, temperature, energy consumption, building area, construction materials, water, natural gas, and waste. Principal component analysis ranks building area as the most significant factor. Using the GM (1,1) model, the carbon emissions of Jiangsu Province in 2024 were predicted to be 1.5576 million tons by historical data. Emission reduction suggestions are proposed, such as constructing thicker walls, increasing green spaces, reducing construction waste, and promoting balanced economic development. Moreover, prioritizing insulation materials in building design can reduce winter energy consumption since energy consumption is higher in winter than in summer. This research supports China's goals of achieving a carbon peak by 2030 and carbon neutrality by 2060 while encouraging low-carbon technological innovation and improving people's living standards. This study also emphasizes the importance of locally tailored strategies for effective emissions reduction. [ABSTRACT FROM AUTHOR]

The integration of numerous distributed energy sources into the power system offers exciting opportunities to enhance the resilience of distribution networks. It is worth noting that the renewable-to-ammonia system has the potential to alleviate the multi-temporal and spatial imbalance of the power system. Therefore, this paper proposes a mathematical model for a renewable-to-ammonia system, taking into account the material balance and power balance of each unit. Based on this, this paper further explores the optimization scheduling method for flexible ammonia loads in distribution networks. A relaxation method for branch flow models in distribution networks based on second-order cone programming is proposed. An optimization scheduling model for flexible ammonia loads in distribution networks is constructed to minimize network loss. Moreover, considering the environmental advantages of the renewable-to-ammonia system, this paper compares the changes in hydrogen production technologies under different carbon emission constraints. Finally, a case study of the IEEE 33-node system is adopted to verify the effectiveness of the proposed model and method. It indicates that the renewable-to-ammonia system has environmental benefits and can reduce network loss to a certain extent. [ABSTRACT FROM AUTHOR]

The monitoring of carbon emissions is increasingly becoming a sustainability issue worldwide. Despite being largely unnoticed, the toxic gas carbon monoxide (CO) is ubiquitous in mechanized tunnel driving, but the individual sources, release and enrichment mechanisms are often unknown. In this study, the generation of CO from organic matter containing sedimentary rocks was investigated during mechanized tunnel driving and by reacting claystone and sandstone with 10 mM NaCl solutions for 2 months at 70 °C and 140 °C. The mineralogical and geochemical evolution of the solids and fluids was assessed by CO measurements and the XRD, DTA, TOC, IC and ICP-OES methods. The CO concentration in the atmosphere reached up to 1920 ppm (100 ppm on average) during tunnel driving, which is more than three times higher than the legal daily average dose for tunnellers, thus requiring occupational safety operations. Mineral-specific dissolution processes and the rapid decomposition of labile organic matter upon thermal alteration contributed to the liberation of CO and also carbon dioxide (CO2) from the host rocks. In mechanized tunnel driving, frictional heat and 'cold' combustion with temperatures reaching 50–70 °C at the drill head is an important mechanism for increased CO and CO2 generation, especially during drilling in sedimentary rocks containing significant amounts of OM and when the ventilation of the tunnel atmosphere and air mixing are limited. Under such conditions, human health damage due to CO exposure (HHDCO) can be 30 times higher compared to tunnel outlets, where CO is emitted from traffic. [ABSTRACT FROM AUTHOR]

Long-term excessive use of fertilizers and intensive cultivation not only decreases soil organic carbon (SOC) and productivity, but also increases greenhouse gas emissions, which is detrimental to sustainable agricultural development. The purpose of this paper is to identify organic amendments suitable for winter wheat growth in the North China Plain by studying the effects of organic amendments on the economic benefits, carbon emissions, and carbon sequestration for winter wheat fields and to provide a theoretical basis for the wide application of organic amendments in agricultural fields. The two nitrogen rates were N0 (0 kg ha−1) and N240 (240 kg ha−1), and the four organic amendments were straw, manure, mushroom residue (M R), and biochar. The results showed that, compared to N0, N240 significantly increased the yield by 244.1–318.4% and the organic carbon storage by 16.7–30.5%, respectively, but increased the carbon emissions by 29.3–45.5%. In addition, soil carbon stocks increased with all three types of organic amendments compared to the straw amendment, with the biochar treatment being the largest, increasing carbon storage by 13.3–33.6%. In terms of yield and economic benefits, compared to the straw amendment, the manure and biochar amendments increased winter wheat yields by 0.0–1.5% and 4.0–13.3%, respectively, and M R slightly decreased wheat yield; only the economic benefit of the M R amendment was greater than that of the straw amendment, with an increase in economic benefit of 1.3% and 8.2% in the 2021–2022 and 2022–2023 seasons, respectively. Furthermore, according to the net ecosystem productivity (NEP), N0 was the source of CO2, while N240 was a sink of CO2. The TOPSIS results showed that N240 with a mushroom residue amendment could be recommended for increasing soil carbon stocks and economic benefits for winter wheat in the NCP and similar regions. Low-cost M R can increase farmer motivation and improve soil organic carbon, making a big step forward in the spread of organic materials on farmland. [ABSTRACT FROM AUTHOR]

Aiming at the difficulty of measuring various costs and time-consuming elements in multimodal transport, this paper constructs a green vehicle comprehensive multimodal transport model which incorporates transportation, transit, quality damage, fuel consumption, and carbon emission costs and proposes a hybrid embedded time window to calculate the time penalty cost in order to reflect the actual transport characteristics. Furthermore, in order to better solve the model, a hybrid sand cat swarm optimization (HSCSO) algorithm is proposed by introducing Logistic–Tent chaotic mapping and an adaptive lens opposition-based learning strategy to enhance the global search capability, and inspired by the swarm intelligence scheme, a momentum–bellicose strategy and an equilibrium crossover pool are introduced to improve the search efficiency and convergence ability. Through testing nine benchmark functions, the HSCSO algorithm exhibits superior convergence accuracy and speed in dealing with complex multi-dimensional problems. Based on the excellent global performance, the HSCSO algorithm was utilized for multimodal vehicle transportation in East China, and a path with a lower comprehensive cost was successfully planned, which proved the effectiveness of the HSCSO algorithm in green intermodal transport path planning. [ABSTRACT FROM AUTHOR]

Low-carbon development in the field of buildings is an important means to achieve the goal of "carbon peaking and carbon neutrality". In public buildings, the operation of science and technology museum buildings (TMB) has high carbon emissions, and the application of green building technology for energy saving and carbon reduction has great potential. In this paper, typical TMB in cold areas are selected and built, and energy consumption is simulated by Designbuilder. The calculation boundaries and carbon emission factors of carbon emissions are set according to current standards, and the carbon reduction potential of green science and technology museum buildings (GTMB) under different levels is compared. The results show that compared with the benchmark building based on GB 55015-2021, the carbon emission of the GTMB is significantly reduced, and the carbon emission reduction rates of silver, gold, and platinum TMB are 7.9%, 13.4%, and 29.6%, respectively. Based on the existing optimization design of passive measures such as natural ventilation and natural lighting, the GTMB should pay more attention to the realization of its optimal control strategy and automatic control. [ABSTRACT FROM AUTHOR]

The construction industry is a significant contributor to carbon emissions in China. To effectively meet the "dual carbon" targets, several provincial regions within the country started to implement policies promoting prefabricated buildings. This study examines data from 18 provinces in China over the period from 2012 to 2021, treating the introduction of prefabricated building policies as a quasi-natural experiment. Utilizing the difference-in-differences methodology, this research assesses the impact of these policies on the carbon emission performance of China's construction sector and evaluates the robustness of the findings. The results indicate that the prefabricated building policies positively influenced the carbon emission efficiency of the construction industry. Specifically, these policies enhance carbon emission efficiency by increasing labor productivity, optimizing the allocation of mechanical resources, and improving the utilization rate of building materials. Additionally, the effectiveness of these policies is positively correlated with the level of regional technological innovation, environmental protection efforts, and the advancement of energy structure optimization. The study concludes with several policy recommendations aimed at further enhancing the effectiveness of prefabricated building policies. [ABSTRACT FROM AUTHOR]

Amidst the global pursuit of environmental sustainability, the concurrent trends of decarbonization and intelligentization have emerged as critical strategic priorities. However, the interplay between these phenomena, particularly within the logistics sector, remains an underexplored area. This study investigates the complex dynamics between logistics intelligentization and decarbonization across 29 provinces in China from 2006 to 2019, providing a fresh perspective on a globally relevant issue. By employing Differential GMM, Systematic GMM, and instrumental variable-based GMM models, we evaluate the impact of logistics intelligence on carbon emissions while effectively addressing endogeneity issues inherent in the empirical analysis. Our findings reveal that the advancement of intelligent logistics correlates positively with increased carbon emissions, highlighting a significant incompatibility between decarbonization objectives and intelligentization efforts within the logistics sector. Additionally, we identify mediating pathways—specifically scale, structure, and technology effects—and moderating mechanisms that influence this relationship. These insights underscore the need for policymakers to establish environmental standards for intelligent technology adoption and to promote initiatives that reconcile intelligentization with sustainability goals. Ultimately, our study provides critical guidance for promoting sustainable and smart industrial growth in the logistics sector. [ABSTRACT FROM AUTHOR]

China's tourism industry has rapidly grown over the past two decades, significantly contributing to the national economy but also increasing carbon emissions. This has raised concerns about environmental sustainability, especially given China's commitments to peak carbon emissions by 2030 and achieve carbon neutrality by 2060. This study analyzes the changes in carbon emissions, carbon carrying capacity, and the Tourism Carbon Pressure Index (TCPI) of China's tourism industry from 2000 to 2022, aiming to support the green transition of the sector. Examining data from 31 provinces, we observed a significant growth trend in carbon emissions, with a temporary decline during the COVID-19 pandemic in 2020, followed by a rebound in 2021 and 2022. The findings reveal that carbon emissions and carrying capacity are relatively stable in the eastern regions, but more pronounced in central and western regions, particularly the west. The TCPI is higher in the east, indicating greater environmental pressure, while it is rapidly increasing in central and western regions. To achieve sustainable tourism development, we propose promoting green transportation, eco-friendly hotels, and green scenic spots, improving resource utilization efficiency, enhancing policy support and technological innovation, and adopting regional development strategies. These measures will help reduce tourism-related carbon emissions and contribute to national carbon neutrality goals. This study provides a comprehensive analysis and actionable insights to guide the sustainable transformation of China's tourism industry. [ABSTRACT FROM AUTHOR]

As climate change has become a common challenge to global sustainable development, China has also proposed carbon peaking and carbon neutrality goals to cope with it. To achieve the dual-carbon goal, it has released a series of specific measures, like controlling both the amount and intensity of carbon emissions. It has also put in place a "1+N" policy framework for carbon peak and carbon neutrality, among which the industrial structure adjustment and technological progress are the most direct and effective ways to achieve climate-friendly sustainable development. So, it is of great benefit to examine the industrial structure adjustment and corresponding carbon emissions effect for the formulation of reasonable industrial adjustment policies. Based on the provincial panel data of China from 2005 to 2019, this paper adopts the panel threshold model to investigate the influence of industrial structure adjustment on carbon emissions at different levels of green innovation. Its findings show that there exists a nonlinear relationship between the industrial structure adjustment and carbon emissions and the influence of the former on the latter has the threshold effect of green innovation. Specifically, when green innovation capacity falls below a certain threshold value, the industry structure adjustment has no significant correlation with carbon emissions; when the threshold value is exceeded, changing industrial structure can dramatically reduce carbon emissions. According to the findings, it is suggested that in the process of attaining the dual-carbon goal, the government should highly promote industrial restructuring and technological advancement, especially supporting low-carbon and green technological innovation and ensuring the continuity and consistency of green innovation policy to enhance the carbon emission reduction effect of industrial optimization. [ABSTRACT FROM AUTHOR]

Reducing carbon emissions from planting (PCE) is crucial for achieving the "dual carbon" objective within the agricultural sector. This paper aims to investigate the impact of agricultural productive services (APSs) on carbon emissions in the planting industry, providing novel insights into reducing carbon emissions in this field. Based on the panel data of 30 provinces and regions in China from 2005 to 2021, this study measured the PCE and the level of APSs. The internal relationship between the APSs and PCE is subsequently examined empirically using various statistical models, including the fixed effect model, instrumental variable model, mediating effect model, and threshold effect model. The findings suggest that the PCE experienced an initial increase, followed by a subsequent decrease between 2005 and 2021, with the peak observed in 2015. APSs has a significant inhibitory effect on PCE, which remains significant even after conducting various robustness tests. However, the carbon emission reduction effect of APSs varies across different crop-producing areas. The study also revealed that APSs can inhibit PCE by facilitating land transfer. Additionally, the inhibitory effect of APSs on PCE is influenced by the scale of land management and exhibits a U-shaped nonlinear relationship. To effectively reduce PCE and achieve sustainable agricultural development, policymakers should actively promote the integration of APSs with modern agriculture and form moderate-scale operations by facilitating land transfer to maximize APSs' carbon emission reduction effect. [ABSTRACT FROM AUTHOR]

In response to the challenges of low wind power consumption and high pollution emissions from thermal power, the implementation of wind-thermal power generation rights trading is a proactive attempt to reduce wind power curtailment and promote its consumption. This study first regards the alternating bidding process between the two parties as a dynamic game, using the Rubinstein bargaining game model to determine the incremental profit allocation and optimal bidding for both parties in power generation rights trading. Secondly, an energy conservation and emission reduction model is constructed to analyze the benefits from the perspectives of standard coal consumption saving and the carbon emission reduction caused by power generation rights trading. Finally, a combined trading revenue model is established to analyze the final profit of both parties involved in the trading. The results show that the combined trading of wind-thermal power generation rights, incorporating carbon trading and green certificate trading, can effectively promote coal consumption savings in thermal power units and reduce the carbon emissions of the power industry. Moreover, it significantly increases the final profit for both parties, stimulating the enthusiasm of generators for participating in power generation rights trading, and ultimately promoting wind power consumption. [ABSTRACT FROM AUTHOR]

The high carbon emissions and pollution of China's thermal power industry chain have exacerbated environmental and climate degradation. Therefore, accelerating the green transformation process is of great significance in promoting the sustainable development of enterprises. This study selected 30 listed thermal power enterprises in China as research objects, analyzed their data from 2018 to 2022, set targeted input–output indicators for different stages, and used a two-stage dynamic data envelopment analysis (DEA) model to evaluate and measure the efficiency of the green transformation of Chinese thermal power enterprises. In addition, this study also uses the logarithmic mean Divisia index (LMDI) method to analyze the driving effects of green transformation. The results indicate that in terms of overall efficiency, there is a significant difference in the overall performance of these 30 thermal power enterprises, with a large difference in average efficiency values. Efficiency values are related to enterprise size. In terms of stage efficiency, the average efficiency value of thermal power enterprises in the profit stage was significantly higher than that in the transformation stage, and the profitability of Chinese thermal power enterprises was better. In terms of sub-indicator efficiency, the efficiency of each indicator shows a "U"-shaped trend, and there is a certain correlation between the operating costs and revenue of thermal power enterprises, the market value of green transformation, and related indicators. In addition, the most important factor affecting the efficiency of green transformation is the sewage cost they face, whereas their operational capabilities have the least impact on their green transformation. In this regard, thermal power enterprises should increase their investment in the research and development of key technologies for thermal power transformation and continuously optimize their energy structure. The government will increase financial support for thermal power green transformation enterprises and correspondingly increase emission costs. [ABSTRACT FROM AUTHOR]

Abandoned cropland is a widespread issue globally, with the impacts of utilizing abandoned cropland, such as grain production and carbon emissions, raising increasing concern. However, existing studies have largely overlooked the potential spillover effects on other regions through grain flows when increasing grain production in one region by utilizing abandoned cropland. Therefore, this study aimed to comprehensively estimate the impacts of using winter fallow fields (a typical seasonal abandoned cropland) on grain production and carbon emissions, particularly its neglected spillover impact. Focusing on Zhejiang province, this study used remote sensing techniques to identify winter fallow fields in 2018 and then assessed the impact of using those winter fallow fields on grain production based on grain yield data from the FAO, as well as its local and spillover impacts on carbon emissions based on the Greenhouse Gas Emission Factor method and a transportation carbon emission model. The results indicate the following: (1) The winter fallow fields in Zhejiang cover 5,161,000 hectares, accounting for 40.8% of the total cropland, with a notable prevalence in Jiaxing, Huzhou, Jinhua, and Quzhou. (2) Using winter fallow fields would increase grain production by 1,870,000 tons. (3) At the same time, local carbon emissions would rise by 261,000 tons if using winter fallow fields, but this would be paired with a reduction of 668,000 tons of carbon emissions from other regions (that is, a spillover impact), reflecting a net reduction (−447,000 tons) in overall emissions. In conclusion, using winter fallow fields can achieve a 'win–win' effect, increasing grain production while reducing carbon emissions. This study highlights that the spillover effects of using winter fallow fields on carbon emissions significantly surpass the localized impact, underscoring a critical aspect that has been traditionally undervalued, which should be paid more attention when policymakers formulate and implement cropland use policies. This study not only contributes to the academic discourse on sustainable land management but also serves as a practical guide for policymakers seeking to optimize agricultural productivity while curtailing the carbon footprint, thereby advancing towards a more secure and environmentally responsible food system. [ABSTRACT FROM AUTHOR]

The purpose of this research is to compare clustering methods and pick up the optimal clustered approach for rural building energy consumption data. Research undertaken so far has mainly focused on solving specific issues when employing the clustered method. This paper concerns Yushan island resident's time-series electricity usage data as a database for analysis. Fourteen algorithms in five categories were used for cluster analysis of the basic data sets. The result shows that Km_Euclidean and Km_shape present better clustering effects and fitting performance on continuous data than other algorithms, with a high accuracy rate of 67.05% and 65.09%. Km_DTW is applicable to intermittent curves instead of continuous data with a low precision rate of 35.29% for line curves. The final conclusion indicates that the K-means algorithm with Euclidean distance calculation and the k-shape algorithm are the two best clustering algorithms for building time-series energy curves. The deep learning algorithm can not cluster time-series-building electricity usage data under default parameters in high precision. [ABSTRACT FROM AUTHOR]

Achieving the dual goals of "carbon neutrality and carbon peaking" will necessitate not only improving urban production and lifestyles to reduce carbon emissions but also minimizing the adverse effects of urban building elements on the diffusion of carbon emissions. This can facilitate the rapid flow of carbon emissions to high-carbon sink areas, thereby accelerating urban carbon circulation. This study calculated the carbon emissions of Hangzhou, utilized the WRF/Hysplit coupling method to simulate the city's carbon emission diffusion status in 2020, and explored the impact of construction land layouts on carbon emission diffusion in terms of building height and building location. The results indicate the following: (1) The main sources of carbon emissions are located within the city, displaying a multi-center spatial distribution. The primary corridor for carbon emission diffusion is on the east side, where the underlying building density is relatively low. (2) As the building height increases from 24 to 36 m, the obstructive effect on carbon emission diffusion rapidly intensifies; however, this increase slows considerably once the building height exceeds 54 m. (3) The impact of buildings on carbon emission diffusion is the greatest when the distance between construction land and a carbon source area is 0 km. When this distance is 2 km, the obstructive effect of buildings significantly improves, depending on their heights. Beyond 7 km, the building height has almost no impact on carbon emission diffusion. The findings of this study may provide valuable suggestions for optimizing building heights in newly developed areas on the outskirts of cities, aiding in the effective design of construction land layouts to help China achieve its carbon neutrality and carbon peaking goals. [ABSTRACT FROM AUTHOR]

With the increasing global concern over climate change, reducing greenhouse gas emissions has become a universal goal for governments and enterprises. For oversize and heavyweight cargo (OHC) transportation, multimodal transportation has become widely adopted. However, this mode inevitably generates carbon emissions, making research into effective emission reduction strategies essential for achieving low-carbon economic development. This study investigates the optimization of multimodal transportation paths for OHC (OMTP-OHC), considering various direct carbon pricing policies and develops models for these paths under the ordinary scenario—defined as scenarios without any carbon pricing policies—and two carbon pricing policy scenarios, namely the emission trading scheme (ETS) policy and the carbon tax policy, to identify the most cost-effective solutions. An enhanced genetic algorithm incorporating elite strategy and catastrophe theory is employed to solve the models under the three scenarios. Subsequently, we examine the impact of ETS policy price fluctuations, carbon quota factors, and different carbon tax levels on decision-making through a case study, confirming the feasibility of the proposed model and algorithm. The findings indicate that the proposed algorithm effectively addresses this problem. Moreover, the algorithm demonstrates a small impact of ETS policy price fluctuations on outcomes and a slightly low sensitivity to carbon quota factors. This may be attributed to the relatively low ETS policy prices and the characteristics of OHC, where transportation and modification costs are significantly higher than carbon emission costs. Additionally, a comparative analysis of the two carbon pricing policies demonstrates the varying intensities of emission reductions in multimodal transportation, with the ranking of carbon emission reduction intensity as follows: upper-intermediate level of carbon tax > intermediate level of carbon tax > lower-intermediate level of carbon tax = ETS policy > the ordinary scenario. The emission reduction at the lower-intermediate carbon tax level (USD 8.40/t) matches that of the ETS policy at 30%, with a 49.59% greater reduction at the intermediate level (USD 50.48/t) compared to the ordinary scenario, and a 70.07% reduction at the upper-intermediate level (USD 91.14/t). The model and algorithm proposed in this study can provide scientific and technical support to realize the low-carbonization of the multimodal transportation for OHC. The findings of this study also provide scientific evidence for understanding the situation of multimodal transportation for OHC under China's ETS policy and its performance under different carbon tax levels in China and other regions. This also contributes to achieving the goal of low-carbon economic development. [ABSTRACT FROM AUTHOR]

The advent of new digital technologies has catalyzed a disruptive technological revolution, fostering significant industrial changes and advancing the green transformation of the economy and society. This paper investigates the influence of digitization on the green transformation of agribusiness firms, focusing on agriculture-related companies listed on the Shanghai and Shenzhen A-share markets from 2013 to 2021. Employing fixed-effect and mediated-effect models, the study examines the mechanisms through which digitization impacts these enterprises. The findings indicate that the relationship between digitization and green transformation in agribusiness is non-linear; a certain threshold of digitization must be achieved before it positively affects green transformation. The effect of digitization on green transformation varies according to the nature of business ownership, company size, supply chain flexibility, and regional environmental regulations. The study reveals that digitization influences green transformation through several mechanisms that promote economies of scale, technological innovation, and structural adjustments. While economies of scale derived from digitization do not directly support green transformation, they facilitate technological innovation and structural adjustments that enhance green initiatives in agribusiness. [ABSTRACT FROM AUTHOR]

As biomass power generation projects progress, the supply chain of biomass resources has become a key link for the green development of biomass power facilities. The role of biomass recycling logistics in the sustainable development of biomass resources and power generation endeavors has grown increasingly significant. To realize environmentally friendly and economical straw logistics, it is imperative to establish an efficient, economic and sustainable straw recycling logistics network. Therefore, based on the weighting theory and top-down method, this study proposes a planning model aiming at minimum total cost and minimum carbon emission to locate the logistics network. The immune algorithm is applied to solve and analyze the proposed examples. It is concluded that under the condition that the total cost is reasonable, reducing transportation cost. The verification model can enhance the environmental and economic performance of straw logistics networks. From the perspective of theory and practice, we provide ideas for the optimization of relevant biomass logistics networks and promote the realization of low-carbon sustainable development of logistics networks. [ABSTRACT FROM AUTHOR]

In the face of contemporary challenges, such as economic instability, environmental degradation, and the urgent global warming crisis, the imperative of sustainability and energy efficiency has reached unparalleled significance. Sustainability encompasses not only the natural environment, but also extends to our immediate surroundings, including the built structures and the communities they serve. Embracing this comprehensive perspective, we embarked on a mission to conceive and construct a model house that harnesses state-of-the-art energy-efficient technologies. Our goal was to seamlessly integrate these features not only to meet our sustainability objectives, but also to mitigate environmental threats.This model embodies a harmonious fusion of indigenous resources, employing locally sourced stone and employing traditional construction techniques. Through this approach, we achieved significant reductions in carbon emissions and established a framework for passive cooling and heating systems. Moreover, the design is intrinsically attuned to its contextual surroundings, preserving the diverse tapestry of regional architectural styles. This study stands as a testament to the potential of innovative design and technology in shaping a sustainable future. The research employs a multi-dimensional approach, encompassing strategies of architectural design with a traditional planning approach, sustainable material selection, energy efficiency, and life cycle assessment across a diverse set of case studies. Building energy analysis is conducted through the application of BIM (Ecotect), providing insights into how BIM can adapt and thrive in various environments. Key findings underscore that thermal performance, minimizing energy loads, and reducing carbon emissions are pivotal aspects in designating a building as both green and energy efficient. [ABSTRACT FROM AUTHOR]

Due to the recent abnormalities in global temperature and increasing carbon emissions, the world is working to reduce carbon emissions. In particular, the construction sector accounts for about 37% of all carbon emissions, so it is one of the areas where sustainable reduction efforts must be made. Therefore, in this paper, an optimal design process was performed by evaluating carbon emissions as the objective function, a choice which differed from the objective function of the existing research used in the optimal design of truss structures. The metaheuristics algorithm used for the process was the advanced crow search algorithm. The levels of carbon emissions generated when the material of a truss structure consisted of a customary material (steel) were compared to scenarios in which timber was used, and a construction scenario centered on the Republic of Korea was established for comparison. The structures used as examples were 10-, 17-, 22-, and 120-bar truss structures. As a result, it was confirmed that truss structures using timber had fewer carbon emissions than structures using steel. In addition, it was confirmed that, even in the same timber structures, domestic timber had fewer carbon emissions than imported timber. These results confirmed that in order to achieve carbon neutrality in the construction field, carbon emissions must be considered in advance, in the design stage. [ABSTRACT FROM AUTHOR]

To reduce the impact of the cold chain logistics center layout on economic benefits, operating efficiency and carbon emissions, a layout optimization method is proposed based on the improved dung beetle algorithm. Firstly, based on the analysis of the relationship between logistics and non-logistics, a multi-objective optimization model is established to minimize the total logistics cost, maximize the adjacency correlation and minimize the carbon emissions; secondly, based on the standard Dung Beetle Optimization (DBO) algorithm, in order to further improve the global exploration ability of the algorithm, Chebyshev chaotic mapping and an adaptive Gaussian–Cauchy hybrid mutation disturbance strategy are introduced to improve the DBO (IDBO) algorithm; finally, taking an actual cold chain logistics center as an example, the DBO algorithm and the improved DBO algorithm are applied to optimize its layout, respectively. The results show that the total logistics cost after optimization of the IDBO algorithm is reduced by 25.54% compared with the original layout, the adjacency correlation is improved by 29.93%, and the carbon emission is reduced by 6.75%, verifying the effectiveness of the proposed method and providing a reference for the layout design of cold chain logistics centers. [ABSTRACT FROM AUTHOR]

This paper examines the impact of family ownership and supervisory board characteristics on carbon emission disclosure. It uses balanced panel data and a matched-pair design of 124 non-financial firms listed on the Indonesia Stock Exchange from 2017 to 2019. This study finds that family firms and larger boards improve, while female board members harm carbon emission performance. Further analyses reveal non-linear relationships between family ownership and carbon performance. When control rights are limited, family firms prioritize controlling managers and improving carbon quality. Conversely, they prioritize personal objectives over environmental concerns when there are high control rights, resulting in decreased carbon emission performance. Additionally, family board members generate more carbon information, indicating the family owners effectively utilize their position on the supervisory boards to influence the company's carbon emission performance. Finally, the study reports that more faculty member boards seem to hurt carbon emission reduction efforts. This result suggests that the diversity of their professional experiences does not affect the environmental effectiveness of supervisory boards. Our findings highlight the importance of understanding SEW principles and their connection to families in comprehending Indonesian corporate carbon emissions disclosures. The findings of this study enrich the worldwide literature by exploring the potential benefits of family business environmental performance. This study also adds to the literature on corporate governance, especially the role played by supervisory boards. Our findings align with the resource dependence theory, emphasizing the central function of supervisory boards as a monitoring tool. This study is constrained by its reliance on carbon emission data extracted from the annual reports of public firms, with a particular emphasis on pre-COVID-19 data. Future research should focus on sustainability reports and explore the time frame encompassing COVID-19 (2020–2022 datasets) to determine any differences in the findings. [ABSTRACT FROM AUTHOR]

Studying urban land use and its impact on carbon emissions is crucial for achieving China's dual carbon goals. This research utilized the Shared Socio-economic Pathways (SSPs) scenarios 126, 245, and 585 from the Sixth International Coupled Model Intercomparison Project (CMIP6), along with a coupled System Dynamics (SD) and Patch-generating Land Use Simulation (PLUS) model and a carbon emission coefficient method to simulate and predict Xi'an's land use carbon emissions from 2020 to 2040. The results indicate the following: (1) Cultivated and forest lands are the predominant land use types in Xi'an, with cultivated and grassland areas projected to decline under all three SSP scenarios by 2040. The most significant expansion of construction land, primarily at the expense of farmland, is projected under the SSP585 scenario, with an increase of 515.92 km2 by 2040. (2) Land use carbon emissions increased from 414.15 × 104 t in 2000 to 2376.10 × 104 t in 2020, with construction land being the primary source of emissions and forest land serving as the main carbon sink. However, the carbon sink capacity remained low at only 21.38 × 104 t in 2020. (3) Carbon emissions are expected to continue increasing under all scenarios through 2030 and 2040, though at a decreasing rate. The SSP126 scenario predicts the lowest emissions, reaching 9186.00 × 104 t by 2040, while SSP585 predicts the highest at 14,935.00 × 104 t. The findings of this study provide theoretical support for future low-carbon and high-quality urban development strategies. [ABSTRACT FROM AUTHOR]

Global warming caused by massive carbon dioxide emissions can lead to a chain of ecological disasters. As one of the main sources of carbon emissions, transportation is of great significance, and the evaluation of its connections with carbon emissions is necessary to achieve "carbon neutrality". Taking Beijing as an example, this study evaluated traffic efficiency (TE) by utilizing principal component analysis and fuzzy comprehensive evaluation. Using the Tapio decoupling model and coupling coordination degree model, the corresponding relationship between urban low carbon level (LCL) and TE was explored. The results showed the following: (1) The total carbon emission (CE) level exhibited fluctuating variation from increasing to decreasing. The carbon emission intensity (CEI) continued to slow down, and the rapid growth of population density played a key role in low-carbon development. (2) The traffic operations continually showed a positive trend in development. TE increased from a step-like to a slow shape, until it declined in 2020 due to the pandemic. (3) TE and LCL both developed from low coordination to an extreme level of coordination. Per capita carbon emission (CEP) and TE presented an inverted U-shaped curve; meanwhile, with increases in TE, the decline in CEI slowed. In addition, the weak decoupling of TE changed to become strong, due to CE and CEP, and maintained a strong decoupling state from CEI. (4) There is a necessity for the rational planning of land use for transportation infrastructure, the encouragement of a combination of public and private transportation, and the strengthening of the maintenance of the relative infrastructure and the management of traffic behaviors to attain a win–win situation. The results provide a reference for optimizing the traffic structure to achieve "carbon neutrality". [ABSTRACT FROM AUTHOR]

The scientific quantification of the ecological effects of carbon emissions, the reduction of ecological risk (ER), and the evaluation of the interaction effect between carbon emissions and ER are the pivotal measures for ensuring the longevity and continuity of sustainability. However, a dearth of comprehensive and macro-level evaluations exist pertaining to the ER and carbon emissions within the entirety of the Yangtze River Economic Belt (YREB). We have constructed four distinct simulated scenarios within the YREB, which include natural development (ND), cultivated land protection (CLP), ecological conservation (EC), and low carbon (LC) scenarios. Based on the consideration of future uncertainty, we predicted LUCEs and ERI under different scenarios, and analyzed the spatial interaction effects of LUCEs and ERI from the dual perspectives of the spatial spillover effect and spatial coupling effect. The results showed that under the four outlined scenarios, encompassing diverse parameters, conversion possibilities, and areas subject to restrictions, the land utilization patterns of the YREB in 2030 have unveiled pronounced disparities. The LUCEs in the YREB showed significant spatial heterogeneity under the four scenarios; the maximum value was 6.65 under the CLP scenario and the minimum value was 4.65 under the LC scenario. The ER has the highest value under the ND scenario and the lowest value under LC scenario. Construction land is the largest contributor to increased LUCEs, and forest land is the form of terrestrial utilization that reduces the impact of LUCEs. In different scenarios, LUCEs have a significant negative spillover effect on ER, while the bidirectional spatial coupling effect between LUCEs and ERI presents significant differences. Under the LC scenario, land with a strong carbon sequestration capacity increased significantly, the fragmentation of water bodies was alleviated, and the CCD was the highest. This study offers scientific counsel for the sustainable development of various regions within the YREB, thereby fostering the achievement of a harmonious coexistence between the ecological milieu and economic development. [ABSTRACT FROM AUTHOR]

Regional integration plays a pivotal role in the socio-economic advancement of various global regions and is closely linked with the expansion of construction land. This expansion is a major contributor to urban carbon emissions. Utilizing a geographical regression discontinuity design (GRDD), this paper estimates the impact of urban construction land expansion on carbon emissions and explores the underlying mechanisms within the regional integration process of the Yangtze River Delta (YRD), China. The findings reveal that urban construction land expansion significantly influences carbon emissions, displaying an inverted "U"-shaped pattern. Furthermore, this expansion affects carbon emissions through the transformation of industrial structures, shifts in consumption patterns, and enhancements in scientific and technological investments. Our findings span the performance of the Yangtze River Delta from its early development stages to a relatively mature phase. This paper also partially reveals how the Yangtze River Delta, with both megacities and large- to medium-sized cities, manages urban construction land expansion during the integration process and strives for low-carbon emissions reduction. These results can provide green growth recommendations that balance socio-economic development, low-carbon emissions, and social equity not only for other urban agglomerations in China but also for similar regions in other developing countries by altering construction land utilization patterns. [ABSTRACT FROM AUTHOR]

The mounting impacts of climate change on infrastructure demand proactive adaptation strategies to ensure long-term resilience. This study investigates the effects of predicted future global warming on asphalt binder performance grade (PG) selection in the United States using a time series method. Leveraging Long-Term Pavement Performance (LTPP) data and Superpave protocol model, the research forecasts temperature changes for the period up to 2060 and calculates the corresponding PG values for different states. The results reveal significant temperature increases across the majority of states, necessitating adjustments in PG selection to accommodate changing climate conditions. The findings indicate significant increases in average 7-day maximum temperatures across the United States by 2060, with 38 out of 50 states likely to experience rising trends. Oregon, Utah, and Idaho are anticipated to face the largest temperature increases. Concurrently, the low air temperature has risen in 33 states, with notable increases in Maine, North Carolina, and Virginia. The widening gap predicted between required high and low PG poses challenges, as some necessary binders cannot be produced or substituted with other grades. The study highlights the challenge of meeting future PG requirements with available binders, emphasizing the need to consider energy consumption and CO2 emissions when using modifiers to achieve the desired PG properties. [ABSTRACT FROM AUTHOR]

The construction of public buildings involves a significant amount of carbon emissions, particularly during the materialization phase of underground construction, which is characterized by a rapid and intense release of carbon emissions. In response to this issue, this study aims to develop a Building Information Modeling (BIM)-based method for assessing the factors influencing carbon emissions during the materialization phase of underground construction in public buildings. To achieve this goal, a process-based quantitative and qualitative method was established to evaluate carbon emissions at various stages, including raw material extraction and processing, building material/equipment production, and on-site processing of components, as well as construction and installation. The proposed method may serve as a valuable tool for selecting design schemes in the early stages of building design. By analyzing 125 actual cases in Xi'an, China and utilizing a carbon emission calculation model, accurate carbon emission data was obtained. This study's findings indicate a correlation between the embodied carbon emissions and the area of underground space in the cases analyzed. Specifically, larger underground spaces were associated with higher embodied carbon emissions in underground engineering. Furthermore, this study categorized the cases into two groups based on the presence or absence of underground space, resulting in the identification of 16 factors for cases without underground space and 19 factors for cases with underground space. These findings provide valuable insights for understanding and mitigating the impact of carbon emissions during the materialization phase of underground construction in public buildings. [ABSTRACT FROM AUTHOR]

Hospital buildings provide healthcare services at the costs of significant amounts of energy consumption and carbon emissions, further exacerbating the environmental load. Because of the limited research on the life cycle carbon emissions of Chinese hospitals, this study conducted a detailed carbon-accounting and comparative study. Firstly, BIM and LCA were used to quantify the carbon emissions of the inpatient building in each stage of the life cycle. Secondly, the differences in carbon emissions by stage were compared on the basis of 20 cases of public buildings. The results show that the whole-life carbon emissions of the inpatient building was 10,459.94 kgCO2/m2. The proportion of operational carbon emissions was 94.68%, with HVAC (52.57%), equipment (27.85%), and lighting (10.11%) being the main sources. Embodied carbon emissions accounted for 4.54%, and HRB400 steel and C30 concrete were the main sources of carbon emissions. Hospitals are second only to emporiums in terms of operational carbon intensity, being 1.71 and 1.41 times that of schools and office buildings, with inpatient buildings being 3 and 1.7 times that of medical complexes and outpatient buildings, respectively. The future sustainable development of hospital buildings should promote efficient building performance and good environmental quality, both in terms of energy efficiency and carbon reduction. [ABSTRACT FROM AUTHOR]

Chinese cities are pursuing an energy transition to decouple energy-related carbon emissions (ERCEs) from economic growth. Despite numerous studies focusing on the factors influencing carbon emissions, few have quantitatively analyzed their respective contribution rates, thus leaving a gap in effectively guiding policies. This study took 16 cities in the Yangtze River Delta (YRD) as the study area. The decoupling between ERCEs and economic growth was analyzed during 2000–2020, and the contribution rates of different factors were explored. The results showed that the total ERCEs increased from 413.40 million to 1265.86 million tons during 2000–2020, increasing by over three times. Coal and oil were the dominant energy sources in most cities, but natural gas consumption increased from 0.15% to 5.96%. Moreover, 14 cities showed a decoupling status, indicating a certain win–win situation between economic growth and ERCE reduction. Economic growth greatly increased ERCEs, with its contribution rate ranging from 114.65% to 493.27% during 2000–2020. On the contrary, energy structure and energy intensity both contributed to reducing ERCEs in most cities, and their maximum contribution rates reached −32.29% and −449.13%, respectively, which were the main forces for the win–win situation. Finally, carbon reduction proposals are put forward, which provide theoretical support for achieving the "Double Carbon" goal in the YRD. [ABSTRACT FROM AUTHOR]

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

Diverse demands have promoted the rapid development of the cold chain logistics industry. In the paper, a novel approach for calculating the comprehensive carbon emission cost was proposed and the front warehouse mode was analyzed under the background of energy conservation and emission reduction. To solve the two-echelon low-carbon location-routing problem (2E-LCLRP), a mathematical model considering operating cost, total transportation cost, fixed cost, refrigeration cost, cargo damage cost, and comprehensive carbon emission cost was proposed to determine the minimum total cost. A hybrid ant colony optimization (HACO) algorithm based on an elbow rule and an improved ant colony optimization (IACO) algorithm was proposed to solve the 2E-LCLRP. According to the elbow rule, the optimal number of front warehouses was determined and an IACO algorithm was then designed to optimize vehicle routes. An adaptive hybrid selection strategy and an optimized pheromone update mechanism were integrated into the HACO algorithm to accelerate convergence and obtain global optimal solutions. The proposed model and algorithm were verified through the case study of the 2E-LCLRP in Nanjing, China. The HACO algorithm outperformed the original ant colony optimization (ACO) algorithm in terms of convergence rate and solution quality. This study provides significant insights for enhancing heuristic algorithms as well as valuable research methods. Furthermore, the results can help cold chain logistics companies in balancing economic costs and environmental benefits and address cold chain distribution of agricultural products. [ABSTRACT FROM AUTHOR]

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

The reactive power optimization of an active distribution network can effectively deal with the problem of voltage overflows at some nodes caused by the integration of a high proportion of distributed sources into the distribution network. Aiming to address the limitations in previous studies of dynamic reactive power optimization using the cluster partitioning method, a three-stage dynamic reactive power optimization decoupling strategy for active distribution networks considering carbon emissions is proposed in this paper. First, a carbon emission index is proposed based on the carbon emission intensity, and a dynamic reactive power optimization mathematical model of an active distribution network is established with the minimum active power network loss, voltage deviation, and carbon emissions as the satisfaction objective functions. Second, in order to satisfy the requirement for the all-day motion times of discrete devices, a three-stage dynamic reactive power optimization decoupling strategy based on the partitioning around a medoids clustering algorithm is proposed. Finally, taking the improved IEEE33 and PG&E69-node distribution network systems as examples, the proposed linear decreasing mutation particle swarm optimization algorithm was used to solve the mathematical model. The results show that all the indicators of the proposed strategy and algorithm throughout the day are lower than those of other methods, which verifies the effectiveness of the proposed strategy and algorithm. [ABSTRACT FROM AUTHOR]

Decarbonization is gaining priority from the macro to the micro level. However, achieving this is a critical challenge, as industries are still immature. This study explores the practices used to calculate and reduce embodied carbon (EC) in New Zealand (NZ) commercial construction projects. In the Paris Agreement, NZ pledged to reduce its net GHG emissions to 50 percent below the gross 2005 levels by 2030. The built environment generates approximately 40% of global greenhouse gas (GHG) emissions, with 11% being generated by manufacturing materials. EC represents carbon dioxide (CO2) emitted into the atmosphere throughout the extraction, fabrication, transportation, and assembly of building materials. A survey questionnaire was distributed to stakeholders in commercial construction via the New Zealand Institute of Quantity Surveyors (NZIQS) open forum. Twenty-seven valid responses were analyzed. The survey tested and expanded on the interview findings. Calculating and reducing EC are not mandatory in NZ. Most industry professionals had yet to experience EC calculation in projects. Clients most commonly drive EC reduction in public projects with calculations that are often conducted during the concept or detailed design stages. The challenges in measuring and lowering EC include a lack of client willingness to fund EC calculation, lack of knowledge and experience, lack of previous cost data, lack of EC materials, and lack of fit-for-purpose EC calculation tools. These findings may inform NZ government policy initiatives supporting EC reduction to meet their 2050 target. [ABSTRACT FROM AUTHOR]

Achieving the target of carbon neutrality has been an important approach for China to mitigate global climate change. Enterprises are major carbon emitters, and a well-designed early warning system is needed to ensure that their emissions align with carbon neutrality goals. Therefore, this study utilized electricity big data to construct an early warning model for enterprise carbon emissions based on carbon quota allocation. Taking key carbon-emitting enterprises in Guangxi as a case study, we aim to provide insights to support China's dual carbon goals. Firstly, we established the Carbon Quota Allocation System, enabling carbon quota allocation at the enterprise levels. Secondly, we developed the Enterprise Carbon Neutrality Index, facilitating dynamic warnings for carbon emissions among enterprises. The main conclusions are as follows: (1) In 2020, Guangdong received the highest carbon quota of 606 million tons, representing 5.72% of the national total, while Guangxi only received 2.63 billion tons. (2) Only 39.34% of enterprises in Guangxi are able to meet the carbon neutrality target, indicating significant emission reduction pressure faced by enterprises in the region. (3) Over 90% of enterprises in Guangxi receive Commendation and Encouragement warning levels, suggesting that enterprises in Guangxi are demonstrating a promising trend in emission reduction efforts. [ABSTRACT FROM AUTHOR]

Farmland ecosystems represent a vital carbon source and sink within terrestrial ecosystems. The investigation into the characteristics of carbon emissions and their influencing factors within farmland ecosystems is crucial for the realization of carbon reduction and the promotion of low-carbon development. This study leveraged the analytical prowess of CiteSpace software (version 6.1.5) to examine a comprehensive body of 2805 scholarly works related to carbon emissions within agricultural ecosystems, as documented in the Web of Science Core Database from 1991 through to 2023. Through a visual dissection of data based on national, institutional, and thematic dimensions, the study elucidated emergent focal points and evolving trajectories within this scholarly domain. The findings revealed that current scholarly discussions on carbon emissions from agricultural lands have primarily focused on three key areas: the factors that influence these emissions, the possibilities for their reduction, and the subsequent environmental impacts. Focal points of research have predominantly centered on four key themes: "greenhouse gas emissions from farmland ecosystems", "carbon emission models for farmland ecosystems", "carbon sequestration in farmland ecosystems", and "sustainable development in agricultural ecosystems". The academic perspective has gradually shifted from a broad overview of carbon emissions towards a detailed examination of the determinants of emissions and the efficiency of agricultural resource utilization. Looking forward, it is crucial to explore the mechanisms behind high-carbon agricultural practices and to establish their optimal operational thresholds. The focus of research is shifting from macro carbon emissions to the analysis of emission factors and the efficiency of agricultural input use. Future efforts should emphasize exploring the mechanisms of the environmental impacts caused by high-carbon agricultural inputs and the optimal input levels, refining emission reduction methods in agricultural ecosystems, and promoting collaboration and exchange among researchers worldwide. [ABSTRACT FROM AUTHOR]

To achieve the goals of "carbon peaking and carbon neutrality", this paper puts forward the connotation and measurement method for the carbon emission intensity of urban industrial land and conducts an empirical study with the Yangtze River Economic Belt (YREB) as an example. We defined the carbon intensity of urban industrial land as the industrial carbon emissions per unit area of land, which is a spatial mapping of urban industrial economic development and carbon spillover and a key indicator for urban and territorial spatial planning oriented towards the "dual carbon" goal. Findings: The carbon emission density of industrial land in the YREB varied greatly between cities and exhibited significant positive spatial autocorrelation. In addition, the geographical pattern and spatio-temporal evolution model of the urban industrial land carbon emission density had a very complex driving mechanism, and different factors had significant synergistic effects. Therefore, it is suggested that while striving towards the goal of "dual carbon", the government should incorporate the carbon emission density indicator of urban industrial land into the urban and territorial spatial planning system, and based on the threshold of the medium suitable density, they should design differentiated management policies according to concrete urban policies and encourage cooperation among cities to jointly promote carbon emission management of urban industrial land. In policy design, emphasis should also be placed on highlighting the interactive effects of foreign direct investment, fiscal expenditure, and the number of patent authorizations as well as constructing a combination of policies centered around them to better leverage the impacts of globalization, government intervention, and innovation. [ABSTRACT FROM AUTHOR]

Predicting carbon emissions is important in various sectors, including environmental management, economic planning, and energy policy. Traditional forecasting models typically require extensive training data to achieve high accuracy. However, carbon emission data are usually available on an annual basis, which is insufficient for effectively training conventional forecasting models. To address this challenge, this paper introduces an innovative carbon emissions prediction model that integrates Fibonacci attenuation particle swarm optimization (FAPSO) with the gated recurrent unit (GRU). The FAPSO algorithm is used to optimize the hyperparameters of the GRU, thereby alleviating the decline in prediction accuracy that conventional recurrent neural networks often face when dealing with limited training data. To evaluate the effectiveness of the FAPSO-GRU model, we tested it using carbon emission data from Hainan Province. Compared to the conventional GRU model, the FAPSO-GRU model achieved a significant reduction in the mean absolute error (42.27%), root mean square error (42.38%), and mean absolute percentage error (43.06%). Furthermore, we validated the FAPSO-GRU model with real data from Beijing, Guangdong, Hubei, Hunan, and Shanghai. The experimental results convincingly demonstrate that the proposed model provides a highly accurate solution for carbon emission prediction tasks, effectively addressing the limitations posed by limited training data. [ABSTRACT FROM AUTHOR]

Raised access floors are nowadays widely used in buildings. A novel raised access flooring product is developed by this research, with a set of sustainable features, including less environmental impact and high strength. Its floor panels are made of polyurethane (PU) reinforced with glass fibre, which is light-weight and fire-resistant, replacing the traditional floor panel materials, and the panels are supported by simplified steel stringers to reinforce the strength of the flooring product. Instead of the conventional sandwich design consisting of a core material encapsulated by outer layers, the new floor panel design adopts the reinforced PU as its sole material, which not only simplifies the structure but also reduces floor weight and costs. The sustainable advantage is further approved by the environmental life cycle assessments of the new raised flooring product in comparison to traditional ones made of cement and woodchips, with results showing that the new floor product's total environmental impact is 52% less than cement floor and 47% less than woodchip board floor. Further, the finite element analysis (FEA) was carried out, and the experimental test was conducted to verify the FEA results, indicating that the new product's strength is higher than the requirements of the raised access flooring product standards. There is no raised access flooring product made of PU reinforced with glass fibre available in the market, and, hence, the new product developed by this research is a novel contribution. [ABSTRACT FROM AUTHOR]

In this paper, we explore the integration of building information modeling (BIM) technology to assess carbon emissions, emphasizing the unique contributions to smart and sustainable approaches in prefabricated buildings and focusing on the application of digital construction strategies facilitated by BIM to evaluate carbon emissions in green prefabricated buildings, with a detailed case study on C-House at Southeast University, Nanjing, China. The research methodology involved creating a BIM model of C-House in Rhino and collecting data from the operationalization phase. This research work delves into analyzing the structural components, on-site assembling process, and evaluation of carbon emissions by using a BIM-based assessment, as well as the energy load and consumption of prefabricated components, including sustainable PV panels, to enhance building efficiency and sustainability. The findings uncover the life cycle of C-House, which spans seven stages, compared with the five stages of conventional builds. Currently in its third cycle, C-House exhibits significant reductions of 70.57% in carbon emissions during the second cycle and 43.53% in the first one. This highlights the pattern showing that the prolonged reuse of prefabricated buildings leads to decreasing emissions over time. Such results underscore the potential carbon emission reductions and environmental advantages of reusing green prefabricated buildings. Furthermore, this study provides insights into the entire life cycle of the building, from inception to occupation and post-phase performance evaluation. By employing BIM for modeling, simulation, and analysis, we offer practical insights into the application of smart technologies for sustainable construction practices, significantly contributing to the advancement of green and digital construction technologies. [ABSTRACT FROM AUTHOR]