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Background: Health care systems contribute significantly to greenhouse gas emissions. One source of these emissions is single-use products. Examination table paper does not confer protection against microbial contamination and thus can be omitted while following infection control standards. The objective is to eliminate the routine use of examination table paper in outpatient oncology clinics at BC Cancer. Methods: A quality improvement approach was used. Examination tables continued to be disinfected using wipes between patients, but table paper was not used. Plan-do-studyact cycles were performed at four regional cancer centres. Results: Pre-intervention, the cancer centres used 19 to 69 rolls of paper monthly. Postintervention, usage declined to 0 to 2 rolls monthly. This was associated with annual cost savings of $3974 and a reduction of 32 501 kg of carbon dioxide emissions. Conclusions: The use of examination table paper can be eliminated in outpatient clinics, resulting in both cost savings and a reduction in carbon dioxide emissions. [ABSTRACT FROM AUTHOR]

The text provides a summary of various articles related to air quality, climate change, and environmental initiatives. It mentions a report from the World Meteorological Organization (WMO) highlighting record-breaking climate change indicators in 2023, the use of pulp and paper waste to reduce carbon emissions, the development of national indoor quality standards for public buildings, the establishment of a hydrogen hub in the Pilbara region of Australia, and the U.S. Environmental Protection Agency's new tailpipe rules to reduce pollution from cars and light trucks. These articles offer insights into the current state of the environment and efforts to address climate change. [Extracted from the article]

Every ophthalmic surgical supply, including intraocular lenses (IOLs), IOL cartridges, and ophthalmic viscosurgical device syringes, is packaged with instructions for use (IFU). These pamphlets are printed in multiple languages and, in the case of an IOL, significantly increase the size and weight of the packaging. To eliminate this significant and unnecessary source of waste, we recommend that manufacturers move to Quick Response codes that link to online electronic IFU (e-IFU) as a sensible alternative. In addition to reducing carbon emissions and manufacturing costs, e-IFU can be updated more easily and accessed by surgeons in the clinic, where IOL models and powers are selected. Varying and inconsistent IFU requirements between different countries are a barrier to wider adoption of e-IFU by the ophthalmic surgical industry. Regulatory agencies in every country should allow and encourage e-IFU. This position paper has been endorsed by the 3 major societies that sponsor EyeSustain, a consortium of global societies dedicated to advancing sustainability in ophthalmology. [ABSTRACT FROM AUTHOR]

Reducing examination table paper (ETP) use may help curb carbon emissions from health care. Six participants applied Glo Germ (DMA International) to their hands before a common physical examination (abdominal, cardiorespiratory, hip and knee) both with and without ETP. After each exam, UV light was shined on the exam table and photographs were taken. The number of hand touches on ETP-covered areas and uncovered areas were tallied and compared using t tests. Despite covering more surface area, participants touched areas without ETP significantly more than ETP-covered areas (P <.05). Despite its continued use, patients do not have much hand contact with ETP during common clinical examinations. [ABSTRACT FROM AUTHOR]

This study investigates the impact of incorporating lime mud (LM) as a filler material to develop sustainable, cementless ultra-high performance concrete (UHPC) by replacing silica powder (SP) and silica sand (SS) at varying percentages. Flowability analysis reveals that LM, with its distinctive particle size, influences flow characteristics differently at different replacement levels. Compressive strength was improved by 5.4% with 30% SP replacement, showcasing the role of LM in nucleation, as well as its particle size advantage. The heat of hydration analysis indicates that LM likely influences the early curing stages, enhancing reactivity and nucleation. TGA analysis also confirms that LM plays a role in hydration reaction, influencing weight loss and hydration peaks. The mercury intrusion porosimeter results show the impact of LM on pore distribution, with 30% for SP and 50% for SS replacements, favoring denser matrices. Life cycle assessment (LCA) demonstrates reduced CO 2 emissions and favorable environmental performance, highlighting the sustainability potential of LM replacements. Overall, the incorporation of LM in UHPC shows promise in terms of enhancing its mechanical properties and environmental sustainability. [Display omitted] • Lime mud (LM) in UHPC boosts material strength, leading to denser, more effective concrete. • LM enhances hydration, flowability, and refines pore structure. • High LM ratios in UHPC increase sustainability and reduce CO 2 emissions. • LM's use in UHPC significantly advances its mechanical properties and eco-friendliness. [ABSTRACT FROM AUTHOR]

This paper studies the impact of digital economy development on carbon emissions, which is a hot topic in the field of environmental economics. This topic is within the scope of the Frontiers in Environmental Science and is particularly in line with the theme of Environmental Economics and Management section in the journal. Firstly, this paper constructs the Digital Economy Development Index and its variable indicators to measure the development of the digital economy. Secondly, this paper uses urban panel data to reveal the impact of digital economy development on carbon emissions, and further adopts IV method to deal with endogeneity issues. Thirdly, this paper performs the heterogeneity analyses depending on the characteristics of the city. The results can provide a basis for formulating differentiated policies. Fourthly, this paper further explores the mechanism by which the digital economy affects carbon peaking. The results show that the digital economy may influence carbon peaking through upgrading of the industrial structure, technical innovation, and energy consumption. [ABSTRACT FROM AUTHOR]

Carbon emissions from the Yangtze River Economic Belt are an important element of China's carbon emission endeavor, and a study of its emission reduction pathway can provide a reference for the country's overall management of carbon emission reduction. From the perspective of energy consumption, this paper uses the carbon emission factor method to estimate the carbon emissions of the transportation industry in the Yangtze River Economic Belt during 2006–2020, based on the extended STIRPAT model, considering the influence of seven factors, i.e., population size, urbanization rate, GDP per capita, transportation added value, energy structure, energy intensity, and transportation intensity, on carbon emissions. Based on these factors, a scenario analysis, combined with a forecasting model, is used to predict the peak carbon performance of the transportation industry under different development scenarios. The results show that the overall carbon emissions of transportation in the YEB from 2006 to 2020 show a fluctuating upward trend, and the downstream carbon emissions are significantly higher than those in other regions. The main factors influencing carbon emissions from transportation in different upstream, midstream, and downstream regions vary, with both population and economic factors contributing to carbon emissions, while technical factors affect them differently. There are significant differences in the peak carbon performance of transportation under different development scenarios, and the government should take effective measures to work towards achieving the goals of the low-carbon or enhanced low-carbon scenarios. [ABSTRACT FROM AUTHOR]

Carbon emission issues are becoming increasingly severe, and the carbon emissions in shrinking cities, primarily characterized by population loss, are often overlooked and insufficiently studied. This paper focuses on the carbon emissions from county-level administrative units in China's three northeastern provinces from 2001 to 2017. The study scientifically identified shrinking cities and measured the differences in carbon emission characteristics between growing and shrinking cities using the Theil index. Ultimately, the paper constructs a panel spatial econometric model to analyze the factors influencing them and explore their spatial effects. (1) The total carbon emissions in the Three Northeastern Provinces exhibited an inverted U-shaped trend, increasing from 734.21 million tons in 2001 to 1731.73 million tons in 2017, with the Mann–Kendall trend test showing a significant increase; spatially, this manifests as a significant positive spatial autocorrelation. (2) The region has 138 shrinking cities, accounting for over 50%; regarding carbon emission characteristics, the Theil index has consistently remained above 0.18, indicating significant differences between the carbon emissions of growing and shrinking cities. (3) The panel spatial econometric model results show that the influencing factors of carbon emissions in shrinking cities have unique directions, intensities, and spatial effects. In shrinking cities, aside from localized GDP effects and per-capita GDP acting as a suppressant, the population size has a pronounced inhibitory effect on local and surrounding carbon emissions. The analysis reveals significant differences in the carbon emission patterns and mechanisms between growing and shrinking cities; based on these results, the paper proposes differentiated carbon control strategies. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

Using a panel data set from 2007 to 2019, we empirically evaluate the impact of carbon capture, utilization, and storage (CCUS) technology innovation on green total factor productivity (GTFP). The findings show that (1) CCUS technology innovation significantly improves GTFP. (2) CCUS technology innovation significantly contributes to GTFP by promoting industrial structure upgrading and carbon emissions efficiency. (3) Environmental regulation plays a positive moderating role in the nexus between CCUS technology innovation and GTFP. The findings of this paper provide guidance for China to achieve green energy transition and build a green energy system. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to explore the pathways of carbon transfer in 200 US corporations along with the motivations that drive such transfers. The particular focus is on each firm's embeddedness in the global value chain (GVC) and the influence of environmental law, operational costs and corporate social responsibility (CSR). The insights gleaned bridge a gap in the literature surrounding GVCs and corporate carbon transfer. Design/methodology/approach: The methodology comprised a two-step research approach. First, the authors used a two-sided fixed regression to analyse the relationship between each firm's embeddedness in the GVC and its carbon transfers. The sample consisted of 217 US firms. Next, the authors examined the influence of environmental law, operational costs and CSR on carbon transfers using a quantitative comparison analysis. These results were interpreted through the theoretical frameworks of the GVC and legitimacy theory. Findings: The empirical results indicate positive relationships between carbon transfers and GVC embeddedness in terms of both a firm's position and its degree. From the quantitative comparison, the authors find that the pressure of environmental law and operational costs motivate these transfers through the value chain. Furthermore, CSR does not help to mitigate transfers. Practical implications: The findings offer insights for policymakers, industry and academia to understand that, with globalised production and greater value creation, transferring carbon to different parts of the GVC – largely to developing countries – will only become more common. The underdeveloped nature of environmental technology in these countries means that global emissions will likely rise instead of fall, further exacerbating global warming. Transferring carbon is not conducive to a sustainable global economy. Hence, firms should be closely regulated and given economic incentives to reduce emissions, not simply shunt them off to the developing world. Social implications: Carbon transfer is a major obstacle to effectively reducing carbon emissions. The responsibilities of carbon transfer via GVCs are difficult to define despite firms being a major consideration in such transfers. Understanding how and why corporations engage in carbon transfers can facilitate global cooperation among communities. This knowledge could pave the way to establishing a global carbon transfer monitoring network aimed at preventing corporate carbon transfer and, instead, encouraging emissions reduction. Originality/value: This study extends the literature by investigating carbon transfers and the GVC at the firm level. The authors used two-step research approach including panel data and quantitative comparison analysis to address this important question. The authors are the primary study to explore the motivation and pathways by which firms transfer carbon through the GVC. [ABSTRACT FROM AUTHOR]

This paper analyses carbon emissions and value-added embodied in trade between two large developed countries, South Korea and the United States, during 2000-2014. Using multi-regional input-output (MRIO) tables, our analysis reveals that carbon emissions and value-added embodied in exports grew by 19% and 101% for South Korea but shrank by 43% and 7% for the United States. As a result, South Korea experienced a 40% increase in net carbon exports and 243% increase in net valueadded exports. At the industry level, the primary drivers of changes in carbon exports were electricity and basic materials. The majority of industries in witnessed improvements in carbon intensities suggesting improved environmental efficiency. While both countries achieved a decoupling of carbon emissions from value-added exports, substantial year-to-year and sectoral variations were observed. Finally, structural decomposition analysis indicates that domestic supply-side factors played a role in decreasing emissions whereas foreign demand-side factors contributed to emissions increases. In line with the main findings, various implications for policy and future research are discussed. [ABSTRACT FROM AUTHOR]

Copyright of Transactions 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.)

This article, titled "Comments by Phouphet Kyophilavong on Toward a Green Energy System: How Does Carbon Capture, Utilization, and Storage Technology Innovation Promote Green Total Factor Productivity?" explores the impact of carbon capture, utilization, and storage (CCUS) technology innovation on green total factor productivity (GTFP). The author raises questions about the calculation of GTFP and the advantage of using the Global Malmquist-Luenberger (GML) index. They also inquire about the variables and calculation of the CCUS technology innovation index and suggest conducting tests for normality, nonlinearity, and parameter stability. Additionally, the author recommends including more literature on mediating and moderating roles and addressing multicollinearity problems. [Extracted from the article]

Carpooling has gained significant attention as an effective solution to mitigate traffic congestion, reduce carbon emissions, and optimize transportation resources. Traditional carpooling platforms typically rely on centralized intermediaries to match drivers with passengers, leading to concerns regarding privacy, trust, and operational inefficiencies. To address these challenges, this paper proposes a novel approach to peer-to-peer carpooling utilizing blockchain technology.The application of blockchain in carpooling enables the establishment of a decentralized platform that facilitates direct interactions between drivers and passengers. By leveraging the immutability, transparency, and security features of blockchain, this peer-to-peer carpooling system enhances trust, privacy, and efficiency in ride-sharing arrangements.The proposed system utilizes smart contracts, a self-executing and tamper-proof code stored on the blockchain, to automate various carpooling processes such as ride requests, matching, payment settlements, and reviews. These smart contracts ensure that transactions are securely recorded and executed without the need for intermediaries, reducing costs and potential conflicts.Additionally, the use of blockchain technology allows for the implementation of reputation systems, where participants can build their reputation based on past interactions. This reputation mechanism enhances trust among users, fostering a safe and reliable carpooling environment. In conclusion, this paper presents an innovative approach to peer-to-peer carpooling using blockchain technology. By leveraging the decentralized nature of blockchain, smart contracts, and reputation systems, this system offers a secure, efficient, and trust-based platform for carpooling, contributing to the development of sustainable and intelligent transportation systems. [ABSTRACT FROM AUTHOR]

With the establishment of the "double carbon" goal, various industries are actively exploring ways to reduce carbon emissions. Cloud data centers, represented by cloud computing, often have the problem of mismatch between load requests and resource supply, resulting in excessive carbon emissions. Based on this, this paper proposes a complete method for cloud computing carbon emission prediction. Firstly, the convolutional neural network and bidirectional long-term and short-term memory neural network (CNN-BiLSTM) combined model are used to predict the cloud computing load. The real-time prediction power is obtained by real-time prediction load of cloud computing, and then the carbon emission prediction is obtained by power calculation. Develop a dynamic server carbon emission prediction model, so that the server carbon emission can change with the change of CPU utilization, so as to achieve the purpose of low carbon emission reduction. In this paper, Google cluster data is used to predict the load. The experimental results show that the CNN-BiLSTM combined model has good prediction effect. Compared with the multi-layer feed forward neural network model (BP), long short-term memory network model (LSTM), bidirectional long short-term memory network model (BiLSTM), modal decomposition and convolution long time series neural network model (CEEMDAN-ConvLSTM), the MSE index decreased by 52 % , 50 % , 34 % and 45 % respectively. [ABSTRACT FROM AUTHOR]

In the process of promoting energy green transformation, the optimization of regional integrated energy system faces many challenges such as cooperative management, energy saving and emission reduction, as well as uncertainty of new energy output. Therefore, this paper proposes a multi-agent game operation method of regional integrated energy system based on carbon emission flow. First, this paper establishes a carbon emission flow calculation model for each subject, and proposes a comprehensive tariff model based on the carbon emission flow, which discounts the carbon emissions from the power supply side to the power consumption side. Secondly, considering the interests of each subject, this paper establishes the decision- making model of each subject. And the new energy uncertainty, the cost of energy preference of prosumers, and the thermal inertia of buildings are considered in the decision model. Finally, the model is solved using differential evolution algorithm and solver. The case study verifies that the comprehensive electricity pricing model based on carbon emission flow developed in this paper can play a role in balancing economy and low carbon. [ABSTRACT FROM AUTHOR]

Based on panel data for 30 provinces in China from 2000 to 2020, this paper uses entropy method to construct the integrated development indicators of energy Internet, and explores the impact of the development of the energy Internet on reducing carbon emissions in China and its internal transmission mechanism. The research results show that the development of energy Internet can significantly reduce regional carbon dioxide emissions, and increasing the use of renewable energy is an important mechanism for the comprehensive development of energy Internet to curb carbon emissions. Heterogeneity analysis shows that the emission reduction effect brought by the development of energy Internet is more remarkable in the regions with lower carbon emissions and western China. Furthermore, this paper finds that in the setting of the development of energy Internet, the development of renewable energy can promote natural gas consumption and advance energy conversion efficiency. The government's emphasis on regional technology development has an external strengthening effect on carbon emission reduction. The research in this paper expands and diversifies carbon reduction pathways and provides reference for more developing countries to utilize the development of energy internet to achieve carbon reduction. [ABSTRACT FROM AUTHOR]

An in-depth investigation into the effect of embedment in global value chain (GVC) on energy conservation and emissions reduction is of great significance for scientifically assessing the environmental impact of GVC participation, and promoting high-quality development in China. This paper incorporates GVC embedment, energy consumption and carbon emissions into the same analysis framework for the first time. Based on the WIOD database, this paper theoretically and empirically examines the impact and mechanism of global value chain embeddedness on carbon emission reduction from two dimensions: energy consumption intensity and energy consumption structure. The study found that GVC embedment significantly reduced the industry's carbon emission intensity; developing economies' embedment in GVC helped reduce their carbon emission intensity, while the effect was not obvious in developed economies. GVC embedment had a significant inhibitory effect on the carbon emissions in both upstream and downstream industries, but not conducive to carbon reduction of low-tech manufacturing. The mechanism test shows that the GVC embedment not only exhibits the dual effects of energy conservation and emissions reduction, but also has a significant impact on carbon emissions by reducing the energy consumption intensity and improving the energy consumption structure. [ABSTRACT FROM AUTHOR]

Energy shortage and excessive carbon emissions have become the main constraints to the sustainable development of China's logistics industry. Improving the efficiency of the logistics industry is crucial to promoting regional ecological environment protection and sustainable development of the logistics industry. In this paper, the efficiency of China's logistics industry in 2013–2019 is evaluated by using the non-oriented EBM model considering the unexpected output, and then the influencing factors of the efficiency of the logistics industry in the central region are analyzed by using the panel Tobit model. The results show that the logistics industry in the central region is at a low efficiency level. Factors, such as the level of economic development, industrial structure and technical level have a significant positive impact on the efficiency of the logistics industry in the central region, while factors such as government support and logistics infrastructure have a significant negative impact on the efficiency of the logistics industry in the central region. Finally, the paper puts forward some targeted recommendations for the sustainable development of the logistics industry in the central region. [ABSTRACT FROM AUTHOR]

With the rapid development of urbanization in China, urban energy consumption increases rapidly, leading to energy shortages and environmental pollution, of which building operational energy consumption carbon emissions (BECCE) account for a large proportion. It has a vital impact on global warming and urban green and sustainable development. Chengdu city in Sichuan Province is taken as the research area in this paper. First, basic information and power data on four types of single buildings, including large-sized buildings, small- and medium-sized buildings, government agencies, and residential buildings, are collected. Second, the characteristics of the four types of buildings are extracted, and the calculation model of BECCE ("electricity-carbon" model) based on particle swarm optimization algorithm-support vector machine (PSO-SVM) is constructed, and the model is trained and verified using the method of five-fold cross-validation. Then, according to the mean absolute error (MAE), root mean square error (RMSE), and R² evaluation indicators, the constructed "electricity-carbon" model is compared and evaluated. Finally, the generalization ability of the "electricity-carbon" model is verified. The research results show that (1) the "electricity-carbon" model constructed in this paper has a high accuracy rate, and the fitting ability of the PSO-SVM model is significantly better than that of the support vector regression (SVR) model; (2) in the testing stage, the fitting situation of large buildings is the best, and MAE, RMSE, and R² are 858.7, 1108.6, and 0.91, respectively; and (3) the spatial distribution map of regional BECCE can be quickly obtained using the "electricity-carbon" model constructed in this paper. The "electricity-carbon" model constructed in this paper can provide a scientific reference for building emission reduction. [ABSTRACT FROM AUTHOR]

As a new generation of transportation, electric vehicles play an important role in carbon-peak targets. The development of electric vehicles needs the support of a charging network, and improper planning of charging stations will result in a waste of resources. In order to expand the charging network of electric vehicles and give full play to the low-carbon and efficient characteristics of electric vehicles, this paper proposed a charging station planning method that considers the characteristics of carbon emission trends. This paper combined the long short-term memory (LSTM) network with the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model to predict the carbon emission trend and quantified the correlation between the construction speed of a charging station and the evolution characteristics of carbon emission by Pearson's correlation coefficient. A multi-stage charging station planning model was established, which captures the dynamic characteristics of the charging demand of the transportation network and determines the station deployment scheme with economic and low-carbon benefits on the spatiotemporal scale. The Pareto frontier was solved by using the elitist non-dominated sorting genetic algorithm. The model and solution algorithm were verified by the actual road network in a certain area of Shanghai. The results showed that the proposed scheme can meet the charging demand of regional electric vehicles in the future, improve the utilization rate of charging facilities, and reduce the carbon emission of transportation networks. [ABSTRACT FROM AUTHOR]

With China's rapid industrialization and urbanization in the process of socio-economic development, the extensive use of energy has resulted in a large amount of CO2 emissions, which puts great pressure on China's carbon emission reduction task. Through multivariate socio-economic data, this paper proposes an extraction and screening method of multivariate variables based on land-use types, and the downscaled spatial decomposition of carbon emissions at different scales was carried out by using the spatial lag model (SLM). This paper makes up for the shortcomings of previous studies, such as an insufficient modeling scale, simple modeling variables, limited spatio-temporal span of spatial decomposition, and no consideration of geographical correlation. Based on the results of the spatial decomposition of carbon emissions, this paper explores the spatial and temporal dynamics of carbon emissions at different scales. The results showed that SLM is capable of downscaling the spatialization of carbon emissions with high precision, and the continuity of the decomposition results at the provincial scale is stronger, while the differences of the decomposition results at the municipal scale are more obvious within the municipal units. In terms of the spatial and temporal dynamics of CO2 emissions, carbon emissions at both scales showed a significant positive correlation. The dominant spatial correlation types are "Low–Low" at the provincial level, and "Low–Low" and "High–High" at the municipal level. The smaller spatial scope is more helpful to show the geographic dependence and geographic differences of China's carbon emissions. The findings of this paper will help deepen the understanding of the spatial and temporal changes of carbon emissions in China. They will provide a scientific basis for the formulation of feasible carbon emission reduction policies. [ABSTRACT FROM AUTHOR]

High-energy consumption and high-emission industries contribute a lot to economic development, but their carbon emissions are also huge. In order to achieve the dual-carbon target as early as possible, it is necessary to reduce the carbon emissions of high-energy consumption and high-emission industries. This paper selected five representative factors (population, per capita gross domestic product (GDP), energy intensity, energy structure and carbon emission coefficient) and adopted the logarithmic mean divisia index (LMDI) method to decompose the driving factors of carbon emissions. Therefore, this paper uses Tapio decoupling model to analyze the decoupling relationship between the two factors with the greatest impact on carbon emissions and carbon emissions. The results show that: (i) There is a good decoupling between high-energy consumption and high-emission industries and per capita GDP, and the impact of per capita GDP on carbon emissions will gradually decrease in the future; (ii) The decoupling relationship between carbon emissions and energy intensity is poor. For some industries, the reduction of energy intensity can help reduce carbon emissions. Finally, this paper puts forward some suggestions to promote carbon emission reduction. This paper provides theoretical support for studying how to reduce carbon emissions and formulate relevant emission reduction policies in the high-energy consumption and high-emission industries. [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]

Cities are the core carriers and key positions to achieve the dual carbon goals. It is of great significance to explore whether promoting urbanization can improve carbon emission performance, which is of great significance to comprehensively promote the goal of carbon neutrality. Based on the panel data from 2006 to 2021, this paper analyzes the spatial autocorrelation of carbon emission performance per unit space and the impact mechanism of urbanization process on it. The fixed-effect model was further used to identify the influencing factors of spatial carbon emission performance. The results show that: 1) China's carbon emission performance per unit space is declining year by year. 2) There is a strong positive spatial correlation and stable path dependence on the performance of carbon emissions per unit space in each region. 3) To a certain extent, increasing the level of urbanization will reduce the carbon emission performance per unit space. 4) The urbanization process has a spatial spillover effect on the carbon emission performance per unit space of surrounding provinces, and the spatial spillover effect of industrial structure and energy consumption structure is more obvious than that of economic level, population density and urbanization rate. Based on the conclusions, this paper puts forward specific policy suggestions to reduce the carbon emission performance per unit space to help the low-carbon development of cities. [ABSTRACT FROM AUTHOR]