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Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: With the accelerating process of globalization, trade activities have had profound impacts on both the environment and economic development. Methods: This paper comprehensively evaluates the synergistic effect of bilateral trade on "pollution reduction" and "carbon mitigation," as well as "economic growth," utilizing panel data from China and RCEP countries spanning the period from 1997 to 2020. Results: The empirical results reveal that bilateral trade significantly propels economic growth in RCEP countries and exhibits a positive "pollution reduction" effect, whereas its "carbon reduction" effect remains uncertain. To delve into the underlying reasons for bilateral trade's failure to effectively mitigate carbon emissions, this paper undertakes a mechanism test along two opposing paths. Notably, the influence of bilateral trade on economic growth and "pollution reduction" in RCEP countries varies regionally. Specifically, bilateral trade is more efficacious in fostering economic growth in ASEAN countries. Additionally, bilateral trade enhances environmental quality in ASEAN countries, yet it deteriorates in non-ASEAN countries. Furthermore, this paper examines the intricate relationship between bilateral trade, economic growth, and "pollution reduction" through the application of the PVAR model. It concludes that optimizing environmental quality in RCEP countries is conducive to both local economic growth and bilateral trade. Conclusion: This study not only bears significant implications for understanding the complex interplay between economic growth, environmental quality, trade liberalization, and environmental policies, but also provides invaluable guidance for policy formulation and implementation aimed at achieving green transformation and fostering sustainable economic development. [ABSTRACT FROM AUTHOR]

Introduction: With the accelerating process of globalization, trade activities have had profound impacts on both the environment and economic development. Methods: This paper comprehensively evaluates the synergistic effect of bilateral trade on "pollution reduction" and "carbon mitigation," as well as "economic growth," utilizing panel data from China and RCEP countries spanning the period from 1997 to 2020. Results: The empirical results reveal that bilateral trade significantly propels economic growth in RCEP countries and exhibits a positive "pollution reduction" effect, whereas its "carbon reduction" effect remains uncertain. To delve into the underlying reasons for bilateral trade's failure to effectively mitigate carbon emissions, this paper undertakes a mechanism test along two opposing paths. Notably, the influence of bilateral trade on economic growth and "pollution reduction" in RCEP countries varies regionally. Specifically, bilateral trade is more efficacious in fostering economic growth in ASEAN countries. Additionally, bilateral trade enhances environmental quality in ASEAN countries, yet it deteriorates in non-ASEAN countries. Furthermore, this paper examines the intricate relationship between bilateral trade, economic growth, and "pollution reduction" through the application of the PVAR model. It concludes that optimizing environmental quality in RCEP countries is conducive to both local economic growth and bilateral trade. Conclusion: This study not only bears significant implications for understanding the complex interplay between economic growth, environmental quality, trade liberalization, and environmental policies, but also provides invaluable guidance for policy formulation and implementation aimed at achieving green transformation and fostering sustainable economic development. [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]

Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Resources Development & Market is the property of Sichuan Resource Development & Market Magazine Co., Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

This systematic study on the international research trends in carbon neutrality underscores its critical role in combating global warming and advancing sustainable development. By leveraging the "Web of Science Core Collection" databases and employing CiteSpace software for visual analysis, we examined 2223 research papers to track the influence and trends of key countries, institutions, and authors. Our results reveal a significant increase in publication volume, indicating a robust development potential for carbon neutrality research. The study also identifies environmental science, environmental research, and energy and fuel science as central interdisciplinary hubs, highlighting the importance of cross-disciplinary collaboration. Notably, China leads in publication output but has room for improvement in citation impact, suggesting a need for enhanced research quality and international visibility. The study's findings are instrumental for guiding future research directions, policy-making, and interdisciplinary cooperation, particularly in the fields of environmental science and energy, to accelerate progress towards carbon neutrality and global climate governance. [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]

Carbon reduction is an important issue of global concern today, and with the construction industry accounting for a significant portion of global carbon emissions, carbon reduction research for the civil engineering industry is becoming increasingly important. The current research on carbon emission of buildings mainly focus on energy consumption, choice of building materials, and energy‐saving technologies. While different construction programs can affect the construction sequence and have a significant impact on carbon emissions, especially for ultra‐high‐rise steel structures. In this paper, carbon emission is introduced as the optimization objective, and a nodal optimization procedure based on carbon emission is given to explore the optimization scheme of ultra‐high‐rise steel structure construction based on carbon emission. Calculations using the carbon emission factor method were carried out to analyze the differences in carbon emissions between different construction schemes and to explore the impact of the method of carbon emission optimization. Results show that the combined lifting scheme can save 463.4 kg carbon emissions and reduce the number of lifts by 780 times. [ABSTRACT FROM AUTHOR]

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

With the growing concern for the environment, sustainable development centred on a low-carbon economy has become a unifying pursuit for the energy industry. Integrated energy systems (IES) that combine multiple energy sources such as electricity, heat and gas are essential to facilitate the consumption of renewable energy and the reduction of carbon emission. In this paper, gas turbine (GT), carbon capture and storage (CCS) and power-to-gas (P2G) device are introduced to construct a new carbon capture coupling device model, GT-CCS-P2G (GCCP), which is applied to the integrated electrical and gas system (IEGS). Multi-agent soft actor critic (MASAC) applies historical trajectory representations, parameter spatial techniques and deep densification frameworks to reinforcement learning for reducing the detrimental effects of time-series data on the decisional procedure. The energy scheduling problem of IEGS is redefined as a Markov game, which is addressed by adopting a low carbon economic control framework based on MASAC with minimum operating cost and minimum carbon emission as the optimization objectives. To validate the rationality and effectiveness of the proposed low-carbon economy scheduling model of IEGS based on MASAC, this paper simulates and analyses in integrated PJM-5 node system and seven nodes natural gas system. [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]

In the United States, coal, natural gas, and oil stand as primary contributors to carbon emissions. The transition toward carbon-free energy sources has significantly reduced the demand for conventional fossil fuels while also significantly altering its labor market environment. This concurrently opens a good number of job prospects in low-carbon energy production sectors. Consequently, navigating this labor force transition requires informed decisions. This research aims to explore the economic impacts of this transition using the state of Wyoming as a case study, a state at the forefront of moving from fossil fuel reliance to embracing low-carbon energy sources. Through an in-depth analysis, including autoregressive integrated moving average (ARIMA) modeling, this paper explored the retraining needs of workers in each sector, the associated costs, and the current state of Wyoming's workforce. The results of the analysis showed that the coal mine sector and oil and gas industries face significant workforce migration pressures with a relatively higher number of workers who need to be migrated, whereas coal power plants are less affected. With Wyoming found to be at the "take-off" phase, ready to accelerate into a new energy paradigm, a relatively modest investment ranging from $3,060 to $7,201, per worker, can facilitate this transition. This research provides critical insights into managing workforce transitions, informing strategic decisions for a sustainable shift from fossil fuels energy production to low-carbon energy production. This research could assist government agencies in the establishment of workforce transition initiatives in the energy production industries and beyond. This research potentially offers a practical framework for understanding the intricacies of workforce transitioning that could be utilized for other sectors such as the architecture, engineering, and construction (AEC) sector. [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]

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]

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]

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]

China has recently implemented a dual-carbon strategy to combat climate change and other environmental issues and is committed to modernizing it sustainably. This paper supports these goals and explores how the digital economy and green finance intersect and impact carbon emissions. Using panel data from 30 Chinese provinces over the period 2011-2021, this paper finds that the digital economy and green finance can together reduce carbon emissions, and conducts several robustness tests supporting this conclusion. A heterogeneity analysis shows that these synergistic effects are more important in regions with low levels of social consumption Meanwhile, in the spatial dimension, the synergistic effect of the local digital economy and green finance adversely impacts the level of carbon emissions in surrounding areas. The findings of this paper provide insights for policymakers in guiding capital flow and implementing carbon-reduction policies while fostering the growth of China's digital economy and environmental sustainability. [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]

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]

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]

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]

Laser welding has been widely employed for aluminum alloy body-in-white. However, owing to the low utilisation efficiency of energy and material, the carbon emission of laser welding is serious. In this paper, the mathematical modelling of carbon emissions and process parameters optimisation for laser welding cells are studied. The carbon emission characteristics of the laser welding system are analysed, and the carbon emission model of the laser welding cell considering both welding features and welding transfers is established. Besides, the process parameters optimisation model of laser welding cell is developed. To obtain optimal welding sequence, a combinatorial algorithm based on Culture Algorithm and Ant Colony Algorithm is proposed. Based on the optimal welding sequence, the optimal transfer speed is obtained through mathematical theory. Furthermore, a case study is performed to verify the feasibility and reliability of the process parameters optimisation model. Additionally, compared with the Ant Colony Algorithm, the proposed solution algorithm has better comprehensive performance in terms of convergence speed and optimisation accuracy. This study lays a theoretical foundation for carbon emission modelling of the laser welding cell, and can support the automobile enterprise to make processing parameters selection of the laser welding line in the design stage. [ABSTRACT FROM AUTHOR]

The digital economy plays an important role in reducing the global warming process. This paper explores the spatiotemporal heterogeneity impacts of the digital economy on provincial carbon emissions and its underlying mechanisms. Initially, this paper examines the inhibitory impact of the digital economy on carbon emissions, alongside the mediating role of economic agglomeration, at a theoretical level. Subsequently, it empirically explores the quantitative associations among the digital economy, economic agglomeration, and carbon emissions by utilizing Chinese provincial panel data spanning from 2000 to 2021. This investigation employed static and dynamic spatial Durbin models as well as mediation models to analyze the interrelationships. The results firstly revealed that the digital economy notably diminishes carbon emissions, with economic agglomeration playing a significant mediating role. This conclusion remained consistent even after substituting the explanatory variables and weight matrix, modifying the sample period, and conducting other robustness tests. Secondly, the impact of the digital economy on carbon emissions exhibited spatial spillovers. Compared with the impact on the local area, the impact on neighboring provinces was found to be weaker. Thirdly, carbon emissions showed a significant "snowball" effect in the time dimension. This paper emphasizes the important role of digital technology in curbing carbon emissions, and it provides some policy insights for studying the digital economy, economic agglomeration, and carbon emissions. Furthermore, it offers valuable insight and suggestions to reduce carbon emissions and realize the goal of the "dual-carbon" strategy. [ABSTRACT FROM AUTHOR]

The land use, land-use change and forestry (LULUCF) sector, as a source and a sink of greenhouse gas (GHG) emissions, is critical for achieving carbon neutrality. Many academic journals have published papers on land use carbon emission or sink (LUCES), but LUCES reviews are relatively rare, which poses great challenges in accurately understanding the research progress and future prospects. This work analyzes the research characteristics, hotspots and future perspectives of LUCES research by using a bibliometric analysis (such as DDA, VOSviewer, CiteSpace software) and a review based on the data (6115 scientific papers) during 1991–2023 from the Web of Science (WoS) platform. We found that (1) over the past 33 years, it first presented a steady growth, then fluctuating growth, and finally a rapid growth trend in the yearly number of publications in LUCES research. The USA (17.31%), China (14.96%), and the UK (7.37%) occupy a dominant position in this research field. (2) The related LUCES research is interdisciplinary, which mainly cover science and technology, meteorology and atmospheric sciences, geology, and environmental sciences and ecology disciplines. (3) The research hotspot analysis on LUCES shows that these articles mostly covered the follow three aspects: ecosystem services, climate change, and carbon neutrality. (4) A review of the past LUCES literature suggests that it is mainly focused on exploring the forefront issues in terms of the definition and boundaries, evaluation method and influencing factors, etc. This work suggests that further research could explore the main scientific problems on quantification of land-based carbon neutrality, quantitative analysis of the impact mechanisms, as well as interdisciplinary research and collaborative governance needed for carbon neutrality. [ABSTRACT FROM AUTHOR]

Though economic growths of most of the nations have seen exponential rise due to industrialization, it has also caused proportional increase in their carbon emissions. This paper exploits this proportionate relationship of carbon emission with GDP to predict the per-capita GDP of those nations whose GDP values are missing in the world bank database. The reason behind the same was, those countries were either war-torn or politically isolated/unstable. To achieve the objective of predicting the missing GDP values of those countries from their carbon emissions, this paper exploits the non-linear relationship among the carbon emissions from solid fuels, liquid fuels, and gaseous fuels. It is so because even the differential utilization of these fuels impact economy differently. Use of traditional solid fuel for cooking points toward energy poverty, and access to clean cooking gas indicates higher living standard. However, the available data from the war-torn or isolated countries are very little, and hence insufficient for building a robust predictive machine learning model. So, this paper employs multi-source unsupervised transfer learning to precisely estimate the missing per-capita GDP of those nations. It suitably enlarges the training domains for the prediction models to be more robust. We empirically evaluate the proposed methodology for different regression techniques to estimate the missing GDP values of eleven different nations belonging to diverse strata of economies viz. developed economies, developing, and/or least developing economies. Proposed methodology profoundly improves the prediction preciseness of these regression techniques in estimating the missing per-capita GDP of the considered nations. [ABSTRACT FROM AUTHOR]

Accounting and finance are intricately intertwined with the global quest for environmental sustainability by applying accounting and finance tools for carbon reduction initiatives. Clean energy financial investment is one of the many alternative tools through which accounting contributes to carbon reduction. Accordingly, this paper analysed the impact of separate and integrated clean energy investment alternatives on carbon reduction. Data on clean energy financial investment and carbon emission per capita were collected from the International Energy Agency (IEA) and Our World in Data archives, respectively. Data was analysed by using multiple pooled OLS to evaluate the impact of individual clean energy financial investments on carbon reduction and the impact of integrating the various clean energy financial investment alternatives on carbon reduction separately. Findings show that individual clean energy financial investments may not separately offer desired carbon reduction, hence, albeit some negative coefficients, individual clean investments showed no significant impact on carbon reduction. However, furthering the test by pooling all the clean energy financial investment alternatives shows a significant negative effect of clean energy financial investment on carbon reduction at a P-value of 0.05. This shows that an integration of different alternatives of clean energy financial investment may offer an enhanced reduction of carbon emission, which outweighs the effect of relying on a single clean energy investment alternative. The findings offer significant insight for policy makers' future strategies towards a combination of multiple clean energy financial investments. Furthermore, the findings from this paper are a further testament that accounting and finance are connected with the global quest for environmental sustainability through the application of accounting and financial investment tools in conducting clean energy financial investment. [ABSTRACT FROM AUTHOR]

The foundry industry has gradually become an important concern for carbon emissions. The composite patternless casting, which combines sand mould 3D printing and sand mould milling technologies, is an effective way to explore the potential of energy saving and emission reduction in the foundry industry. However, the technical advantages of composite patternless casting in terms of energy saving and emission reduction have not been fully studied. To address this issue, this paper combines the composite patternless casting with the modular division method, and proposes a influence modelling and analysis method for carbon emissions and carbon efficiency ratio based on the process characteristics stage. The model and method are applied to the patternless casting process of a cylinder block. And the modular division schemes of the mould were proposed. The results show that through different modular division schemes, carbon emissions can be effectively reduced by 8.39%, the carbon efficiency ratio can be increased by up to 30%. This method is helpful to describe the carbon emission influence of the patternless casting forming process, and analyse the energy saving and emission reduction potential and carbon efficiency of the process. [ABSTRACT FROM AUTHOR]

This paper demonstrates the enhanced resilience performance of steel structures with viscoelastic friction dampers (VEFDs) based on numerical simulations of building responses. Velocity-dependent dampers, which are widely used to increase seismic resilience, may increase the axial force of the column under strong earthquake conditions because the generated force depends on the interstory velocity. This often leads to plastic hinges being placed on the columns of the structure, which can lead to structural collapse via weak-layer failure. In addition, while viscoelastic dampers are effective in reducing story drift, peak acceleration, and peak velocity, the proposed hybrid VEFD offers the additional benefit of reducing base shear via the friction damper. Simulation results for 10- and 20-story buildings with the novel VEFDs show that the proposed dampers can control drift and plastic deformation in structural members. Nonlinear dynamic analysis of 20 far-fault seismic ground motion records conducted using OpenSees also reveals lower peak absolute floor acceleration and velocity. Overall, the results suggest that the proposed VEFD has excellent potential for use in the performance-based seismic design of structures because it can reduce both structural and nonstructural damage. The results verify the damper's effectiveness in controlling story drift without a significant increase in the base shear. Collapse probability assessment also demonstrates the collapse resistance of moment-resisting frames when used in conjunction with VEFDs. [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]

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Copyright of Power Generation Technology is the property of Power Generation Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

In order to alleviate the high pollution and high energy consumption of the calcium carbide method for acetylene production, and to promote the partial oxidation of methane process, while improving the utilization rate of domestic coal-bed methane (CBM), this paper proposes a process for producing acetylene using partial oxidation of CBM as a raw material. This process is combined with the ethanolamine (MEA) CO2 absorption process, and the model is built using Aspen Plus software. Full simulation, including sensitivity analysis, economic analysis, and carbon emission analysis of the process is performed. The results show that this process can meet an annual production of 3x104 t of acetylene with a purity greater than 99%, and the cost of acetylene is 12 423.73 CNY/t. It can also produce 1.86x104 t/a of hydrogen with a purity of 99.99%, demonstrating good overall economic benefits. The carbon emission analysis shows that this process emits 3 048.66 kg of CO2 per ton of acetylene produced, which is only 46.5% of 6 560 kg produced by the mainstream calcium carbide method. [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]

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]

Copyright of Resources Development & Market is the property of Sichuan Resource Development & Market Magazine Co., Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

In this article, a comprehensive analysis is conducted on the influencing factors of carbon emissions in the transportation sector during the evolution of electrification in Beijing. We also considered the impact of indirect carbon emissions caused by carbon emissions from public and private cars and electricity consumption on overall carbon emissions. Based on the LMDI decomposition theory, nine influencing factors are separated, including development level, energy intensity, vehicle structure, number of private cars, and private transportation energy consumption. The analysis results show that, from 2010 to 2021, the carbon emissions from transportation in Beijing increased from 922.98 x 104t to 1490.6 x 104t. The private road carbon emission accounts for about 77.97%, and has become a decisive factor affecting road carbon emissions. In the public domain the contribution values of the development level and energy intensity are 25.20% and -38.71%, respectively. And they are the two most critical factors affecting carbon emissions on public road carbon emissions. In the private domain, the contribution values of the number of private cars, energy consumption of private transportation, and vehicle structure are 60.17%, 47.86%, and -12.99%, respectively. They are the three key factors affecting the private road carbon emissions. There is a significant difference in the proportion of indirect carbon emissions from electricity in the public and private domain. Indirect carbon emissions from electricity account for about 13.6% of road carbon emissions in the public sector, and about 0.9% in the private sector. The results in this paper provide useful references for decisionmaking in the adjustment of transportation energy structure and the promotion of electrified transportation in Beijing and other cities. [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]

The development of electric vehicles (EVs) exhibits rapid and remarkable progress nowadays, serving as a crucial route to accomplish the target of mitigating greenhouse gas emissions. As an integral part of the thermal management system oriented toward electric vehicles, the heat pump air conditioning system for electric vehicles is the result of a comprehensive choice that trades off the cooling and heating performance, environmental performance and economic cost. Particularly, different regions around the world suffer varying cooling and heating challenges due to the complicated climatic characteristics. Thus the most suitable refrigerant and system cycle structure may differ. This paper focuses on evaluating both the refrigerants and cycle structures to screen the most suitable choice. According to the climate conditions of different cities, the annual energy consumption, life cycle climate performance, and economic cost of the basic system (Base), two-stage compression system (TSC,IC), and vapor injection (VI) system with CO2, R134a, and R1234yf refrigerants respectively, are quantitatively analyzed and evaluated. Subsequently, through comparative analysis, a comprehensive selection map for heat pump systems in electric vehicles worldwide is developed and the most suitable heat pump air conditioning system for each cites is determined. The results can provide a selection reference and decision-making for the air conditioning system of electric vehicles from regional considerations. It was found that the CO2 HPACVI was recommended for cold regions to meet both environmental and economic requirements. In warm region, the R1234yf HPACBase system was recommended to be used. For regions transitioning from cold to warm climates, the R1234yf HPACVI system was suggested. In hot region, the R1234yf AC system was recommended. [ABSTRACT FROM AUTHOR]

Dynamic pricing is a crucial revenue management technique when product status changes over time. It can become more prominent if applied in conjunction with an inventory management strategy for deteriorating items. This paper presents a framework for dynamic pricing in a continuous time horizon for a dual-channel supply chain consisting of a manufacturer and a retailer. The manufacturer produces a deteriorating product and sells through direct and indirect retail channels, and both channels compete with each other over retail prices. The freshness of the product varies with time, and thus both channels invest in preservation technology, which further leads to carbon emissions. We derive an analytical solution to the dual-channel pricing model. Furthermore, the case of single-channel pricing has been investigated to study and compare its pricing strategy with the dual-channel pricing strategy. Lastly, a numerical illustration is presented to study the impact of key parameters on optimal decisions through sensitivity analysis. We provide managerial recommendations in various scenarios like an increase in competition, the shift of consumer preference between channels and fluctuation in demand. [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]

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]

The carbon emissions generated during the rapid development of the manufacturing industry have caused severe environmental pollution. Research on parallel machine scheduling problems considering carbon emission constraints has started to emerge in recent years to promote sustainable development and achieve low carbon emissions in production. In this paper, we consider an unrelated parallel machine scheduling problem with various carbon emission constraints in different time intervals. A mixed integer linear programming model with the objective of minimising the makespan is formulated to describe the problem accurately. Since the high complexity of our problem, we propose an efficient tabu search algorithm with a dedicated linked list structure to address this problem. Firstly, we generate four initial sequences according to the proposed heuristic rules and apply a greedy insertion decoding method to obtain the scheduling scheme. Then, we employ the 2-swap neighbourhood search strategy to exploit the promising solution space. The proposed model and algorithm are tested on extensive instances generated randomly. Computational results validate the correctness of the model and the effectiveness of the tabu search algorithm. [ABSTRACT FROM AUTHOR]