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Introduction: As a means of environmental regulation, the low-carbon city pilot policy aims to address climate change and alleviate environmental resource pressures in economic development. The development of low-carbon cities often requires changes to the existing industrial and energy structures, which will have a significant impact on the regional economy. Methods: The study is based on panel data from 273 prefecture level cities in China from 2012 to 2022, and uses the Analytic Hierarchy Process and Entropy Comprehensive Evaluation Method to construct an evaluation index system for urban circular benefits. Finally, the multiple time point differences model and mediation effect model were used to evaluate the impact and influencing factors of policies on urban economy. Results: The results indicate that at the 1% level, the explanatory variable coefficient significantly improves the level of urban circular economy development after adding control variables. The construction of low-carbon pilot cities has improved the performance of circular economy development by about 6.42%. The mediating effect of industrial structure rationalization is significantly positive at the 1% significance level, indicating that the construction of low-carbon pilot cities has greatly promoted the coordinated development of industrial relations within China's low-carbon development regions. Discussion: It can be seen that the low-carbon city pilot policy has a significant promoting effect on China's economic development and the development of urban circular economy. Therefore, it is recommended to further expand the pilot scope of low-carbon cities and fully leverage their promoting role. This article is based on panel data from 273 prefecture level cities from 2012 to 2012, and uses a multi time difference model to analyze the impact of low-carbon city pilot policies on the development of urban circular economy. The research results indicate that the construction of low-carbon pilot cities has significantly promoted the coordinated development of intra regional industrial relationships. [ABSTRACT FROM AUTHOR]

Land use is one of the main sources of carbon emissions, driven by numerous human economic and social activities. This study focuses on the Dongjiang River Basin to investigate how land use optimization can better contribute to low-carbon development. This study first optimizes the land use structure in the basin under two low-carbon development scenarios, maximizing carbon sinks and minimizing carbon emissions. A CAMarkov model was employed to simulate the land use spatial pattern in the river basin for 2025. We then compared and analyzed the low-carbon effect of future land use in the basin under the general development scenario and the two low-carbon scenarios, and proposed countermeasures to regulate land use in the Dongjiang River Basin under a low-carbon orientation. The results showed that (1) under the natural development scenario, the proportion of cultivated land in the Dongjiang River Basin increased from 15.66% in 2020 to 18.66%, construction land from 3.76% to 7.71%, and forest land decreased from 74.36% to 65.01%. Major changes were concentrated in the southwestern and central regions of the basin, where rapid economic development was driving a significant increase in construction and cultivated land, leading to a reduction in forest area. Additionally, in some northern and southeastern areas of the basin, forest land has been converted to grassland, cultivated land, and construction areas. (2) Under the two low-carbon development scenarios, the proportion of forest land in the Dongjiang River Basin was significantly higher than that under the natural development scenario, mainly due to changes in construction and cultivated land. Compared to the natural development scenario, the proportion of cultivated land under the two low-carbon scenarios decreased by approximately 4%-5%, construction land decreased by about 4%, and forest land increased by over 10%. It is evident that the carbon sink maximization scenario achieved a better low-carbon effect than the carbon emission minimization scenario, primarily because of the larger proportion of forest land. (3) The Dongjiang River Basin should enhance the utilization efficiency of cultivated and construction land, converting underutilized and unutilized land into areas with high carbon sequestration capacity, such as forests, or into land with lower carbon emissions to promote sustainable economic development. (4) Adding the location information of the administrative centers to the CA-Markov model can effectively improve the simulation accuracy of the model. This demonstrates that integrating key geographical factors, such as regional administrative centers, can significantly improve the accuracy of land-use simulations, offering valuable methodological insights for similar studies, particularly in predicting complex regional land-use changes. This study provides critical insights into land-use changes and their low-carbon effects under different scenarios, providing scientific evidence for policymakers in planning and policy formulation. [ABSTRACT FROM AUTHOR]

In the era of digital transformation, artificial intelligence (AI) technology—one of the swiftest growing emerging technologies—when integrated with urban planning, can introduce innovative approaches for low-carbon city development and foster the attainment of dual carbon objectives: carbon neutrality and peak carbon emissions. Current research predominantly investigates the influence and alterations of emerging technologies on urban elements, yet it overlooks a comprehensive examination of the applicable procedures of these technologies and their potential synergy with urban planning. Consequently, this study employs a systematic literature review to delve into the application of AI in sectors such as architecture, transportation, land use, and green space development. It categorizes the specific impact processes into monitoring, identification, simulation, and prediction. By offering an exhaustive analysis of urban planning's content and methodology, this paper elucidates the role of AI technology in the creation of low-carbon cities. The study found that: (1) Due to the varying degrees of application and integration with professional technologies in different fields, the current research focuses more on architecture, land use, and transportation. (2) Combining the four steps of urban planning, artificial intelligence can be divided into monitoring, recognition, simulation, and prediction types, each with its own characteristics. (3) Overall, AI technology is mainly applied in the identification and simulation of architecture, transportation, and land use. (4) There is still room for improvement in the application of AI technology in waste emissions and other algorithms. [ABSTRACT FROM AUTHOR]

Limestone calcined clay cement (LC3), enhanced through reactions with volcanic ash and the interaction between limestone and clay, significantly improves the performance of cementitious materials. It has the potential to cut CO2 emissions by up to 30% and energy consumption in cement manufacture by 15% to 20%, providing a promising prospect for the large-scale production of low-carbon cement with a lower environmental effect. To effectively manufacture LC3 concrete, this study utilized limestone (15%), calcined clay (30%), and gypsum (5%) as supplementary cementitious materials (SCMs), replacing 50% of ordinary Portland cement (OPC). However, in regions abundant in sulfate, sulfate attack can cause interior cracking of concrete, reducing the longevity of the building. To address this issue, microcapsules containing microcrystalline wax, ceresine wax, and nano-CaCO3 encapsulated in epoxy resin were prepared and successfully incorporated into LC3 concrete. Sulfate resistance tests were conducted through sulfate dry–wet cycles, comparing samples with and without microcapsules. The findings revealed that the initial mechanical and permeability properties of LC3 concrete did not significantly differ from OPC concrete. LC3 concrete with added microcapsules (SP4) exhibited enhanced resistance to sulfate attack, reducing mass loss and compressive strength degradation. SEM images displayed a mesh-like structure of repair products in SP4. After 14 days of self-repair, SP4 exhibited a 44.2% harmful pore ratio, 98.1% compressive strength retention, 88.7% chloride ion diffusion coefficient retention, 91.12 mV maximum amplitude, and 9.14 mV maximum frequency amplitude. The experimental results indicate that the presence of microcapsules enhances the sulfate attack self-healing performance of LC3 concrete. [ABSTRACT FROM AUTHOR]

Transitioning to low-carbon energy systems is crucial for sustainable development, particularly in oil-rich developing countries (ORDCs) that face intertwined economic and environmental challenges. This review uses the PRISMA methodology to systematically assess the current state and prospects of low-carbon thermal electricity generation and utilization technologies in ORDCs. The study emphasizes clean thermal technologies such as biogas, biofuels, biomass, hydrogen, and geothermal energy, focusing on solutions that are technically feasible, economically viable, and efficient in combustion processes. These nations face significant challenges, including heavy reliance on fossil fuels, transmission losses, and financial constraints, making energy diversification urgent. The global shift towards renewable energy and the need to mitigate climate change presents an opportunity to adopt low-carbon solutions that align with Sustainable Development Goals related to energy access, economic growth, and climate action. This review aims to (1) evaluate the current state of low-carbon thermal electricity technologies, (2) analyze the technical and economic challenges related to combustion processes and energy efficiency, and (3) provide recommendations for research and policy initiatives to advance the transition toward sustainable thermal energy systems in ORDCs. The review highlights practical approaches for diversifying energy sources in these nations, focusing on overcoming existing barriers and supporting the implementation of clean thermal technologies. [ABSTRACT FROM AUTHOR]

Fighting climate change has become a major task worldwide. One of the key energy sectors to emit greenhouse gases is transportation. Therefore, long term strategies all over the world have been set up to reduce on-road combustion emissions. In this context, the road freight sector faces significant challenges in decarbonization, driven by its limited availability of low-emission fuels and commercialized zero-emission vehicles compared with its high energy demand. In this work, we develop the Mobility and Energy Transportation Analysis (META) Model, a python-based optimization model to quantify the impact of transportation projected policies on freight transport by projecting conventional and alternative fuel technologies market acceptance as well as greenhouse gas (GHG) emissions. Along with introducing e-fuels as an alternative refueling option for conventional vehicles, META investigates the market opportunities of Mobile Carbon Capture (MCC) until 2050. To accurately assess this technology, a techno-economic analysis is essential to compare MCC abatement cost to alternative decarbonization technologies such as electric trucks. The novelty of this work comes from the detailed cost categories taken into consideration in the analysis, including intangible costs associated with heavy-duty technologies, such as recharging/refueling time, cargo capacity limitations, and consumer acceptance towards emerging technologies across different regions. Based on the study results, the competitive total cost of ownership (TCO) and marginal abatement cost (MAC) values of MCC make it an economically promising alternative option to decarbonize the freight transport sector. Both in the KSA and EU, MCC options could reach greater than 50% market shares of all ICE vehicle sales, equivalent to a combined 35% of all new sales shares by 2035. [ABSTRACT FROM AUTHOR]

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.)

Land use is one of the main sources of carbon emissions, driven by numerous human economic and social activities. This study focuses on the Dongjiang River Basin to investigate how land use optimization can better contribute to low-carbon development. This study first optimizes the land use structure in the basin under two low-carbon development scenarios, maximizing carbon sinks and minimizing carbon emissions. A CA-Markov model was employed to simulate the land use spatial pattern in the river basin for 2025. We then compared and analyzed the low-carbon effect of future land use in the basin under the general development scenario and the two low-carbon scenarios, and proposed countermeasures to regulate land use in the Dongjiang River Basin under a low-carbon orientation. The results showed that (1) under the natural development scenario, the proportion of cultivated land in the Dongjiang River Basin increased from 15.66% in 2020 to 18.66%, construction land from 3.76% to 7.71%, and forest land decreased from 74.36% to 65.01%. Major changes were concentrated in the southwestern and central regions of the basin, where rapid economic development was driving a significant increase in construction and cultivated land, leading to a reduction in forest area. Additionally, in some northern and southeastern areas of the basin, forest land has been converted to grassland, cultivated land, and construction areas. (2) Under the two low-carbon development scenarios, the proportion of forest land in the Dongjiang River Basin was significantly higher than that under the natural development scenario, mainly due to changes in construction and cultivated land. Compared to the natural development scenario, the proportion of cultivated land under the two low-carbon scenarios decreased by approximately 4%-5%, construction land decreased by about 4%, and forest land increased by over 10%. It is evident that the carbon sink maximization scenario achieved a better low-carbon effect than the carbon emission minimization scenario, primarily because of the larger proportion of forest land. (3) The Dongjiang River Basin should enhance the utilization efficiency of cultivated and construction land, converting underutilized and unutilized land into areas with high carbon sequestration capacity, such as forests, or into land with lower carbon emissions to promote sustainable economic development. (4) Adding the location information of the administrative centers to the CA-Markov model can effectively improve the simulation accuracy of the model. This demonstrates that integrating key geographical factors, such as regional administrative centers, can significantly improve the accuracy of land-use simulations, offering valuable methodological insights for similar studies, particularly in predicting complex regional land-use changes. This study provides critical insights into land-use changes and their low-carbon effects under different scenarios, providing scientific evidence for policymakers in planning and policy formulation.

ObjectivesTraditional electric energy storage has limitations in scale, duration, and environmental impact.Moreover, the renewable energy absorption capacity in the microgrid is low, and low-carbon and economy cannot be taken into account in planning. In order to solve the above problems, based on the basic working principle of hydrogen energy storage, hydrogen energy storage was incorporated into the microgrid instead of traditional electric energy storage, and a low-carbon and economic synergy bi-level optimization configuration model of microgrid with hydrogen energy storage was established.MethodsThe upper-level planning model aimed at minimizing the comprehensive equivalent annual value of the microgrid, based on the joint operation of electricity and hydrogen. The carbon trading mechanism was introduced to plan the capacity of various power generation equipments in the microgrid, which can enhance the low-carbon of the system. The lower-level operation model aimed to minimize the sum of the absolute values of the difference between the new energy output and the load demand. The model also encouraged users to adopt diversified demand-side response behaviors with the goal of accurately tracking the new energy output curve, and feeded back the user’s energy consumption behavior to the upper-level model to optimize the load curve. On the basis of improving the absorption capacity of new energy, the system economy is further improved, deeply exploring the synergy between the low-carbon and economic characteristics of microgrids.ResultsThe simulation results of microgrid in a certain industrial park show that the proposed method yields a planning scheme with excellent low-carbon and economy. Compared with the traditional planning method, the low-carbon and economy are improved by 53.6% and 37.1%, respectively.ConclusionsThe model presented in this paper not only enhances the capacity for new energy absorption but also further improves the system economic performance. It achieves a synergistic enhancement of the microgrid low-carbon and economy.

We consider a low-carbon tourism supply chain (LTSC) network composed of two low-carbon scenic spots (LSSs) and two travel agencies (TAs). The LSSs as leaders and the TAs as followers play Stackelberg games in the vertical direction. Meanwhile, the competing LSSs play Nash game to determine emission reduction efforts and ticket prices, and the competing TAs play Nash game to conduct retail prices. We establish four kinds of differential game models with different LTSC network structures, derive the equilibrium decision-makings of the LSSs and TAs, and explore the influences of competition or cooperation at vertical and horizontal level of the LTSC network members on the equilibrium solutions of the LSSs and TAs, low-carbon level of tourism products and performance of the LTSC network. Then, we find that the low-carbon preference of tourists improves the low-carbon level of tourism products, while the competition intensity between the LSSs leads to the decrease of the low-carbon level of tourism products. In addition, the results indicate that horizontal or vertical cooperations among the LTSC network members are not always conducive to the performance of the whole LTSC network. Only when the sensitivity of tourist demand to retail price meets certain conditions, these cooperations can develop tourism products with higher low-carbon level and bring higher revenues. [ABSTRACT FROM AUTHOR]

[Display omitted] • A universal strategy for the recycling of hazardous industrial organic waste is proposed. • The conversion of hard-to-recover waste to activated carbon serves as a case study. • Conversion mechanisms validate the feasibility of low-carbon conversion concepts. • Prepared activated carbon exhibits excellent multiphase adsorption capacity. • This strategy is proven to possess low-carbon and economic benefits. The management of large volumes of hazardous organic waste poses remarkable challenges due to costs and environmental pollution in industrial processes. Developing a universal strategy for recycling such waste is crucial for environmental protection. Herein, a reaction-based strategy has been proposed to recover and recycle the heavy phenolic slag wastes (HPSW) in a high-valued way. After analyzing the chemical structure of HPSW, the polycondensation method has been used to convert HPSW into an activated carbon adhesive (ACA) through reaction between phenolic hydroxyl groups and formaldehyde. The newly prepared activated carbon sample (AC-12, at optimal conditions) exhibited a high iodine value of 1320.80 mg·g−1 and a specific surface area of 1368.8 m2·g−1. It demonstrates high efficiency in adsorbing organic pollutants from both gaseous (p -xylene vapor) and liquid phases (methylene blue), with an equilibration time under 15 min, achieving the standard of high-end commercial ACs. This environmentally friendly and cost-effective method offers innovative approaches for the resourceful recycling and treatment of various types of organic waste, facilitating the efficient utilization and disposal of hazardous organic solid wastes in industries. [ABSTRACT FROM AUTHOR]

Abstract The growing urgency for low-carbon lifestyles necessitates developing effective strategies to promote sustainable consumer choices. This study investigates key dimensions of information quality that shape consumer behavior within digital marketing to achieve this goal. Employing a mixed-methods approach that integrates grounded theory and machine learning, this study identifies three core dimensions of low-carbon information quality: matching quality, presentation quality, and interpretability quality. These dimensions underscore the importance of aligning information with consumer needs, ensuring clear and accurate presentation, and fostering transparency for trustworthiness. A Random Forest algorithm-based evaluation model is constructed to assess low-carbon information quality, demonstrating its effectiveness in identifying high-quality, sustainable content. This research provides a practical tool for digital marketers to enhance their strategies, raise consumer awareness of sustainable options, and ultimately contribute to the growth of the low-carbon consumption market.

Abstract With the continuous growth of low-carbon economy and people’s increasing preference for low-carbon products, the building carbon emission reduction (CER) has been highly concerned by the government. Therefore, in this study, we establish a game model through differential game to analyze the impact of government policies on building’s CER. We get: (1) Compared with the carbon tax policy, the government policy combining carbon tax and cost subsidy is more conducive to reducing carbon emissions; (2) For the three government policies, when the cost subsidy rate is lower than a fixed value, the government policy combining carbon tax and cost subsidy is the optimal strategy for building’s CER. When the cost subsidy rate is greater than a fixed value, the cost subsidy policy is the best strategy for building’s CER.

The growing urgency for low-carbon lifestyles necessitates developing effective strategies to promote sustainable consumer choices. This study investigates key dimensions of information quality that shape consumer behavior within digital marketing to achieve this goal. Employing a mixed-methods approach that integrates grounded theory and machine learning, this study identifies three core dimensions of low-carbon information quality: matching quality, presentation quality, and interpretability quality. These dimensions underscore the importance of aligning information with consumer needs, ensuring clear and accurate presentation, and fostering transparency for trustworthiness. A Random Forest algorithm-based evaluation model is constructed to assess low-carbon information quality, demonstrating its effectiveness in identifying high-quality, sustainable content. This research provides a practical tool for digital marketers to enhance their strategies, raise consumer awareness of sustainable options, and ultimately contribute to the growth of the low-carbon consumption market. [ABSTRACT FROM AUTHOR]

To achieve ambitious United Kingdom decarbonization targets, consumers will need to engage with energy services more so than they have done to date. This engagement could be active or delegated, where in the latter consumers pass responsibility for engagement to a third party in return for ceding some control over decisions. To date, insight into the barriers to consumer adoption of future business models has been limited. To address this gap this study explored benefits, risks and enabling conditions using two extreme consumer-centric business models, third Party Control and Shared Economy. The approach yielded information from stakeholders on what would have to be true for one of the business models to dominate the market. The results show substantial agreement across the expert groups on five key issues that will need to be addressed in the near-term to enable energy business model innovation in the United Kingdom market. These are: 1) Create space to enable business model innovation; 2) Ensure smart devices and data are interoperable and secure; 3) Improve the service standards of energy businesses; 4) Ensure business models work for consumers in all situations; and 5) Implement targeted carbon regulation. [ABSTRACT FROM AUTHOR]

Highlights: Data analysis revealed that an appropriate amount of BPG can enhance the mechanical properties of cementitious materials, with compressive strength increasing by an average of 7–30%. The mechanical properties and underlying mechanisms of BPG-based cementitious materials were summarized. This study analyzed the mechanisms of the conversion and utilization of elements of impurities in BPG, as well as the solidification mechanisms of hazardous elements. The mechanisms of synergistic utilization between BPG and silica–alumina-based solid waste, as well as alkaline solid waste, are summarized, showcasing innovative waste management strategies. A sustainable solution for promoting the collaborative low-carbon development of various industrial solid wastes using BPG is proposed. Industrial byproduct gypsum (BPG) is a secondary product that is mainly composed of calcium sulfate discharged during industrial production. BPG primarily consists of desulfurized gypsum, phosphogypsum, and titanium gypsum, which account for 88% of the total BPG in China. The large-scale utilization of these three types of solid waste is crucial for the safe disposal of BPG. BPG contains various impurities and harmful elements, limiting its applications. The continuous accumulation of BPG poses a serious threat to the safety of the environment. Based on a literature review (2021–2023), it was found that 52% of BPG is used in the preparation of cementitious materials, and the addition of BPG results in an average improvement of 7–30% in the mechanical properties of cementitious materials. Moreover, BPG has a positive impact on the immobilization of hazardous elements in raw materials. Therefore, the utilization of BPG in cementitious materials is beneficial for its large-scale disposal. This study primarily reviews the effects and mechanisms of BPG on the mechanical properties of cementitious materials and the solidification of hazardous elements. Most importantly, the review reveals that BPG positively influences the hydration activity of silica–alumina-based solid waste (such as steel slag and blast furnace slag) and alkaline solid waste (such as carbide slag and red mud). This improves the proportion of solid waste in cement and reduces production costs and carbon emissions. Finally, this article summarizes and proposes the application of BPG in cementitious materials. The application of BPG + silica–alumina solid waste + alkaline solid-waste-based cementitious materials is expected to realize a new type of green ecological chain for the joint utilization of multiple industrial solid wastes and to promote the low-carbon sustainable development of industrial clusters. [ABSTRACT FROM AUTHOR]

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie 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 Natural Gas & Oil is the property of Editorial Department of Natural Gas & Oil 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.)

Under the background of "carbon peak and carbon neutrality", the interconnection of multiple regional integrated energy systems has become an important way to coordinate multiple energy sources and save energy and reduce emissions. This paper proposes a low-carbon economic dispatch model for multi-regional integrated energy systems considering the joint demand response mechanism; first, the concept and model of joint demand response are proposed, and on this basis, the energy conversion model between multiple energy forms in the multi-regional integrated energy system is studied; secondly, green certificate trading and carbon trading mechanisms are introduced, and green certificate trading and carbon trading are combined through the green certificate emission reduction mechanism; finally, a low-carbon economic dispatch model is proposed with the goal of minimizing the sum of system energy purchase cost, green certificate trading cost, carbon trading cost and joint demand response compensation cost. The example results show that the introduction of joint demand response mechanism and green certificatecarbon trading mechanism in the multi-regional integrated energy system reduces the total system cost and carbon emissions by 14.03% and 26.04% respectively. [ABSTRACT FROM AUTHOR]

Green design refers to a design principle and method that comprehensively considers energy and resource conservation, emission reduction, and environmental impact during the product manufacturing and operation process in the product design stage. It strives to reduce greenhouse gas emissions throughout the entire product lifecycle. In the era of information networks, green design is supported by big data, AI network collaborative design, network detection and monitoring, etc. It involves selecting energy-saving processes, green and low-carbon materials, and optimizing product geometry design and surface treatment to achieve efficient resource utilization and minimize waste. For example, selecting environmentally friendly materials through networked intelligence, planning collaborative processing and efficient logistics support, and using reusable packaging are all essential elements of green design. Additionally, green design must support the full lifecycle management and maintenance of products, as well as waste recycling and disposal to promote the circular use of resources. The strategy of green design is crucial for building a green and low-carbon society because it helps reduce the damage to the natural ecological environment caused by human activities and promote harmony between human beings and nature. [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]

With the continuous growth of low-carbon economy and people's increasing preference for low-carbon products, the building carbon emission reduction (CER) has been highly concerned by the government. Therefore, in this study, we establish a game model through differential game to analyze the impact of government policies on building's CER. We get: (1) Compared with the carbon tax policy, the government policy combining carbon tax and cost subsidy is more conducive to reducing carbon emissions; (2) For the three government policies, when the cost subsidy rate is lower than a fixed value, the government policy combining carbon tax and cost subsidy is the optimal strategy for building's CER. When the cost subsidy rate is greater than a fixed value, the cost subsidy policy is the best strategy for building's CER. [ABSTRACT FROM AUTHOR]

In Europe, buildings account for 40% of the energy consumption and produce 36% of CO2 emissions. Renovation could be a great tool to decarbonize the building stock since it allows for a decrease in the operational energy required for buildings and is less material-consuming than new construction. Further benefits are brought by the usage of bio-based insulation materials that can drastically reduce embodied emissions and transform structures into factual carbon sinks. This study focuses on a particular kind of biogenic material, mycelium-wood composites, consisting of organic matter bound by the root structure of fungal organisms. This innovative insulation material was compared with traditional ones for the renovation of the building stock, with a focus on vertical components like walls in the Helsinki metropolitan area. To characterize mycelium-wood composites, density and carbon content information were gathered from the samples realized in the Politecnico di Milano MaBa.SAPERLab, while the production processes were included in a SimaPro model to obtain the GWP value. Different scenarios were then defined by two variables: the renovation rate of the building stock and the market penetration of myceliumwood composites. For each scenario, the overall GWP and CO2 stored values were calculated. Results show the great potential of the innovative material that grants carbon storage in the building stock that could even surpass the amount stored in the 32,500 ha of forest in the area. However, this possibility is heavily influenced by factors independent of the type of insulation used that should be further investigated. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power is the property of Electric Power 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.)

Wastewater treatment plants are the major energy consumers and significant sources of greenhouse gas emissions, among which biological nitrogen removal of wastewater is an important contributor to carbon emissions. However, traditional heterotrophic denitrification still has the problems of excessive residual sludge and the requirement of external carbon sources. Consequently, the development of innovative low-carbon nitrate removal technologies is necessary. This review outlines the key roles of sulfur autotrophic denitrification and hydrogen autotrophic denitrification in low-carbon wastewater treatment. The discovered nitrate/nitrite dependent anaerobic methane oxidation enables sustainable methane emission reduction and nitrogen removal by utilizing available methane in situ. Photosynthetic microorganisms exhibited a promising potential to achieve carbon-negative nitrate removal. Specifically, the algal-bacterial symbiosis system and photogranules offer effective and prospective low-carbon options for nitrogen removal. Then, the emerging nitrate removal technology of photoelectrotrophic denitrification and the underlying photoelectron transfer mechanisms are discussed. Finally, we summarize and prospect these technologies, highlighting that solar-driven biological nitrogen removal technology is a promising area for future sustainable wastewater treatment. This review has important guiding significance for the design of low-carbon wastewater treatment systems. [ABSTRACT FROM AUTHOR]

Copyright of Smart Rail Transit is the property of Smart Rail Transit 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 study of low-carbon behavior is significant for coping with climate change, resource conservation, environmental protection, sustainable development, and social behavior. Many factors affect low-carbon behaviors. Through investigation and analysis, the five factors of educational measures, low-carbon knowledge, economic measures, low-carbon awareness, and herd mentality were found to significantly influence low-carbon behavior. College students were selected as the research subjects using structural equation modelling methods. This work aims to study the impact of these five factors on low-carbon behavior. The results show that (1) low-carbon awareness has the most direct and significant effect on low-carbon behavior; (2) economic measures and low-carbon knowledge have a direct positive impact on low-carbon behavior; and (3) educational measures and herd mentality show only indirect effects. The theoretical model framework proposed in this study provides a reference for research on the low-carbon behaviors of other groups. In addition, some suggestions are proposed to promote the low-carbon behavior of college students: (1) establish a good low-carbon campus culture; (2) formulate reasonable low-carbon economic incentive measures; and (3) strengthen the publicity and popularization of low-carbon education and knowledge.

India's traditional building systems and knowledge involving natural materials provide fine examples of low-carbon, place-adaptive architectural solutions as well as embodying simplicity and holistic sustainability. They utilize locally available materials prepared and applied so as to build structures and dwellings that are resilient to climate conditions and able to provide comfort and livability. Culturally, this knowledge is transferred orally from generation to generation and continuously recreated. Unfortunately these knowledge systems and practices have become endangered owing to a rupture in their continuity for reasons such as a preference for and incentivization of easier conventional solutions like cement and steel. With a focus on natural plasters used traditionally, this paper highlights some of these diverse plasters along with their natural additives.

In the context of green grid, it is crucial to enhance the low-carbon transformation of integrated energy systems. To improve the degree of low-carbon in these systems, this paper proposes an optimal allocation method of oxygen enriched combustion-carbon capture low-carbon integrated energy system considering the uncertainty of carbon-source-load. Firstly, this paper provides an analysis of uncertainties of carbon-source-load and uses budget uncertainty sets to model the three uncertain parameters. Secondly, in the integrated energy system, the by-product O2 of hydrogen production by electrocution and the oxygen storage device are used to provide oxygen-enriched combustion conditions for the gas turbine to reduce the CO2 generated in the combustion process. At the same time, the carbon capture device is used to collect CO2, and the oxygen enriched combustion-carbon capture integrated energy system is formed. Finally, an improved multi-populations quantum-behaved particle swarm optimization algorithm is proposed to solve the proposed model. This method considers the uncertainty of carbon emission intensity, which makes the optimal allocation of integrated energy system more inclined to low-carbon rather than economic. In addition, in terms of model structure, this method integrates oxygen enriched combustion-carbon capture into the integrated energy system to form a low-carbon model, which directly improves the low-carbon performance of the system. The simulation results show that the carbon emission of the proposed method is reduced by 38.8 % compared with other methods.

IntroductionAs a means of environmental regulation, the low-carbon city pilot policy aims to address climate change and alleviate environmental resource pressures in economic development. The development of low-carbon cities often requires changes to the existing industrial and energy structures, which will have a significant impact on the regional economy.MethodsThe study is based on panel data from 273 prefecture level cities in China from 2012 to 2022, and uses the Analytic Hierarchy Process and Entropy Comprehensive Evaluation Method to construct an evaluation index system for urban circular benefits. Finally, the multiple time point differences model and mediation effect model were used to evaluate the impact and influencing factors of policies on urban economy.ResultsThe results indicate that at the 1% level, the explanatory variable coefficient significantly improves the level of urban circular economy development after adding control variables. The construction of low-carbon pilot cities has improved the performance of circular economy development by about 6.42%. The mediating effect of industrial structure rationalization is significantly positive at the 1% significance level, indicating that the construction of low-carbon pilot cities has greatly promoted the coordinated development of industrial relations within China’s low-carbon development regions.DiscussionIt can be seen that the low-carbon city pilot policy has a significant promoting effect on China’s economic development and the development of urban circular economy. Therefore, it is recommended to further expand the pilot scope of low-carbon cities and fully leverage their promoting role.

Land use change is an important factor to consider in carbon emissions estimates, and quantification of changes in carbon flows and stocks in the “Land Use, Land Use Change and Forestry” sector is largely less reliable than emissions’ assessments from stationary sources and transport. The purpose of the research is to test the methodology for regional assessment of carbon stocks of ecosystems and their transformation under the land use/land cover change (based on the example of the Yaroslavl region). We used available European Space Agency spatial land cover data and publications on carbon density of major reservoirs of terrestrial ecosystems. Calculations were carried out in InVEST, ArcGIS, MS Excel. It was revealed that for 1992–2019 changes in land cover affected 6.8% of the region’s area. About 337.4 million tons of carbon were accumulated in the ecosystems of the Yaroslavl region, the dynamics of reserves is positive, which is explained by an increase in the areas of forests and wetlands. About 73% of the total carbon reserves are accumulated in forest ecosystems, about 20% – in arable lands, pastures and meadows, 5% – in swamps. Trends in land use change and carbon stock dynamics within land cover classes/ecosystem types should be taken into account for developing regional-level recommendations for ecosystem management to reduce emissions and increase carbon sequestration.

With the rapid development of commercial complexes in recent years, energy consumption and carbon emissions of buildings are growing continuously. In this context, the low-carbon planning of commercial complexes is studied including shopping, restaurants, offices, and accommodation. In addition, a low-carbon planning model for building considering ladder carbon reward and punishment with the introduction of the time-sharing carbon measurement model of superior network power purchase and an integrated demand response (IDR) considering the differentiated predicted mean vote (PMV) of each functional area of the building is established. First, the time-sharing carbon measurement model of superior network power purchase is introduced to evaluate the equivalent carbon emissions of the building. Then, a ladder carbon reward and punishment model is built to measure the carbon emissions of the building. Based on the characteristics of the commercial complex planned in this paper, the IDR considering load time shifting and energy use reduction, end-use energy equipment substitution, and energy use type conversion at the superior energy end is constructed. Afterwards, a low-carbon planning model for building is established to determine the equipment type and capacity considering the PMV for each functional area of the building. Especially, the objective function is to optimize the total annual planning cost of the building by taking into account carbon reward and punishment costs of the building. A distributionally robust optimization model based on Kullback-Leibler divergence is proposed to cope with the output volatility of the connected distributed photovoltaic and photovoltaic curtain wall. Finally, the effects of the ladder carbon reward and punishment mechanism of the time-sharing carbon measurement model of superior network power purchase, the IDR of the differentiated PMV of each functional area of the building, and the volatility of the connected photovoltaic output on the building planning are analyzed to verify the effectiveness of the planning model proposed for energy saving and emission reduction in the building.

Energy security and the “dual carbon” goals are impacting the global energy industry and supply chains, presenting some urgent needs for the secure, efficient, and green low-carbon transformation of energy and power systems in China. To ensure energy security, the coal-fired power plant will remain an important support for electricity supply security and the integration of renewable energy for a considerable period into the future in China. To achieve carbon neutrality, the energy system is gradually shifting from primarily relying on fossil fuels to primarily relying on renewable energy. However, the intermittency, randomness, and volatility of renewable energy make power system regulation more challenging, highlighting the issues of system balance and security. In this context, the zero-carbon fuels such as ammonia play an indispensable role in dealing with the intermittency of renewable energy. They can serve as carriers for long-term and large-scale energy storage to facilitate the large-scale integration of renewable energy and can also be flexibly used directly in power equipment such as boilers to replace fossil fuels. However, due to the differences in physical and chemical properties between zero-carbon fuels and fossil fuels, some challenges arise in the widespread utilization of ammonia fuel, such as poor flame stability and the generation of nitrogen oxides during combustion. Therefore, based on the feasibility, economy and necessity of ammoniac-coal co-combustion, this paper comprehensively reviews the chemical reaction kinetics, combustion characteristics (ignition, steady combustion) and pollutants characteristics (NOx, fly ash particles and soot) of ammoniac-coal co-combustion, and discusses the scaling laws of burner based on the dimensionless number. The efficient and stable combustion control strategy of existing industrial grade ammonia-coal burners is discussed in detail. The C—N fuel separation, air staged and their joint control technology can effectively reduce NOx emissions. In the future, artificial intelligence, big data and digital twin and other information technologies are integrated. It is expected to provide a scientific support and path reference for the research and development of the next generation of new green power generation system oriented to the dual carbon strategy from the source.

【Objective】This paper discusses full lifecycle energy efficiency of optical communication systems in the purpose of energy conservation and carbon reduction. It aims to establish a universal model for description of energy efficiency, which covers all stages of system lifecycle, and it is not limited to specific technologies or network structures. It also analyzes the key influencing factors for improvement of energy efficiency of optical communication systems.【Methods】This paper explores the useful work and energy consumption of optical communication systems, to establish a universal model for energy efficiency. In the expression of useful work, in addition to the commonly used factor data rate, distance factor is introduced as an important factor. The paper recommends that the value generated by data transmissions which meet specific quality requirements and maintaining network connectivity in specific scenario applications should be considered in the description of the useful efficacy. The description method is named as comprehensive useful efficacy. In the discussion of energy consumption, the system lifecycle is decomposed into four stages: manufacturing, installation and construction, operation and maintenance, and waste recycling. Main influencing factors of energy efficiency improvement are discussed based on the established model.【Results】Main results of the research include: (1) Distance factor can be one of the main factors for evaluating the useful work of a system, and distance factors affect energy consumption at various stages of the system's lifecycle; (2) For new scenarios and applications, setting weights for data transmissions which meet specific quality requirements and assigning values for maintaining connectivities status in description of the system useful work could reflect the value brought by the energy cost of the system to meet the needs of scenario applications; (3) Energy consumption factors of each stage of the life cycle should be considered in evaluation of energy consumption of the full lifecycle, and the energy consumption of each stage should be converted into unit time based on the system operating life for accumulation; (4) Optimized network topology and routing design, reduction of line losses, as well as evolution of optical transmission technology and equipment/facility energy saving technologies, are important means to reduce energy consumptions throughout the entire lifecycle of the system and improve the energy efficiency.【Conclusion】This paper discusses the comprehensive useful work and full life cycle energy consumption of optical communication systems, and establishes a universal model to describe the full life cycle energy efficiency of the system. This model takes distance as an important influencing factor in the expression of the useful efficiency and also energy consumption of the system, which corresponds the value of the optical communication system based on communication distance with the energy consumption in the life-cycle caused by distances. It also suggests considering the value generated by data transmissions which meet specific quality requirements and maintaining network connectivity in the evaluation of the useful efficiency. This model could comprehensively evaluate the energy efficiency of optical communication systems, for improvements of system energy efficiency, and could adapt and promote applications of optical communication systems in new scenarios.

Abstract In the era of big data, traditional analysis methods are insufficient to meet the growing demand for green and low-carbon travel orders in shared bicycle systems. To address this issue, a new order demand forecasting model, named the “convolutional neural network (CNN)”—“long short-term memory (LSTM)” model (CNN-LSTM), is proposed by integrating CNN and LSTM techniques. The research further validates the spatiotemporal prediction performance of this model. The experimental results demonstrate that LSTM exhibits favorable prediction performance in terms of time feature analysis, as evidenced by the overlapping of the true value (TV) and predicted value (PV) curves. Notably, LSTM achieves an impressively low mean squared error (MSE) value of 0.0063, which is significantly lower compared to CNN (0.0082) and XGBoost (0.0074). Upon incorporating date and weather characteristics, the predictive performance improves significantly, achieving an outstanding MSE value of 0.0043. However, when it comes to spatial feature analysis, the LSTM algorithm alone proves inadequate, obtaining a MSE value of 0.0084. Thus, by employing the CNN-LSTM combination model, a lower MSE value of 0.0066 is achieved, outperforming the comparison algorithms. Overall, the CNN-LSTM model exhibits strong predictive capabilities regarding the temporal and spatial requirements of shared bicycles. This model plays a key role in accurately forecasting order demands, facilitating urban transportation planning and management, as well as guiding the planning and location of non-motorized vehicle stops.

Copyright of Study on Optical Communications / Guangtongxin Yanjiu is the property of Study on Optical Communications 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.)

Although the tourism sector has made efforts to promote low-carbon tourism as one of the ways to address the challenge of climate change, understanding of the current low-carbon tourist segments is still limited and fragmented. This study analyzes low-carbon tourist segments in Thailand's island destinations using cluster analysis, identifying three distinct types: Low-carbon Supporters, Pleasure Seekers, and Destination Protectors, each varying in their perceived importance of low-carbon tourism dimensions. Using Important-Performance Map Analysis (IPMA), the research highlights key items significant to each cluster. It contributes theoretically and offers practical marketing strategies to promote low-carbon destinations effectively to these segments. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University 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.)

In an effort to achieve carbon neutrality, governments are developing technologies to reduce carbon emissions. China and Russia have approved policy documents containing recommendations for transforming the domestic energy balance into a low-carbon model. Interstate projects are an important tool for scientific innovation and technological decarbonization. The article analyzes the terms of cooperation, characteristics and technologies of projects to reduce carbon emissions in the field of nuclear energy, natural gas, construction, carbon capture, utilization and storage (CCUS). Presenting the experience of the PRC, the authors propose the development of a low-carbon model of the state based on a system for managing energy projects, changing the structure of the energy balance, using information technologies in the construction and operation of infrastructure facilities, improving education and training systems, and using dynamic monitoring models. [ABSTRACT FROM AUTHOR]

We studied various fund investing options in the circular economy sector. We found that most circular economy mutual funds and exchange-traded funds charge higher fees and take higher risks than their benchmarks. However, they appear to have underperformed their benchmarks during their short existence so far. Most of these funds are rated as sustainable and low-carbon funds. Investors keen on circular economy startups may consider private equity/venture capital funds, but most of these funds are exclusive to institutional and accredited investors. [ABSTRACT FROM AUTHOR]

Strain-hardening cementitious composites (SHCC) have been widely studied due to their high toughness and durability. The high cement content increases a huge burden on the cost and CO2 emissions of SHCC. To reduce the environmental impacts of SHCC, this study first develops low-carbon and lightweight SHCC by using high-volume recycled concrete powder (RCP) as supplementary cementitious materials and cenosphere waste as lightweight aggregate. The influence of RCP content (0%, 15%, 30%, and 45% by mass) on the hydration mechanism, mechanical properties, and sustainability of low-carbon and lightweight SHCC was investigated. The results showed that the incorporation of RCP in the SHCC matrix resulted in a decrease in hydration heat with increasing RCP content. The filling and pozzolanic effects of RCP were significantly lower than those of cement. The increased porosity and the presence of the interface transition zone due to RCP incorporation led to reduced compactness of the SHCC, which consequently led to decreased compressive strength and fracture toughness of the cement matrix. While the fracture toughness of the cement matrix was reduced, the SHCC still exhibited remarkable bending toughness and tensile ductility. The developed low-carbon and lightweight SHCC containing 45% RCP showed a density of 1482.5 kg/m3, a tensile strength of 3.94 MPa, and a tensile strain capacity of 6.80%, which successfully pushed the performance of low-carbon and lightweight SHCC. The replacement of cement with high-volume RCP in the low-carbon and lightweight SHCC resulted in a significant reduction in embodied carbon compared to conventional SHCC. Therefore, SHCC combines the advantages of lightweight, low-carbon, and highly ductile, making it a promising material for widespread utilization in concrete structures. [ABSTRACT FROM AUTHOR]

In the context of "dual carbon", restrictions on carbon emissions have attracted widespread attention from researchers. In order to solve the issue of the insufficient exploration of the synergistic emission reduction effects of various low-carbon policies and technologies applied to multiple microgrids, we propose a multi-microgrid electricity cooperation optimization scheduling strategy based on stepped carbon trading, a hydrogen-doped natural gas system and P2G–CCS coupled operation. Firstly, a multi-energy microgrid model is developed, coupled with hydrogen-doped natural gas system and P2G–CCS, and then carbon trading and a carbon emission restriction mechanism are introduced. Based on this, a model for multi-microgrid electricity cooperation is established. Secondly, design optimization strategies for solving the model are divided into the day-ahead stage and the intraday stage. In the day-ahead stage, an improved alternating direction multiplier method is used to distribute the model to minimize the cooperative costs of multiple microgrids. In the intraday stage, based on the day-ahead scheduling results, an intraday scheduling model is established and a rolling optimization strategy to adjust the output of microgrid equipment and energy purchases is adopted, which reduces the impact of uncertainties in new energy output and load forecasting and improves the economic and low-carbon operation of multiple microgrids. Setting up different scenarios for experimental validation demonstrates the effectiveness of the introduced low-carbon policies and technologies as well as the effectiveness of their synergistic interaction. [ABSTRACT FROM AUTHOR]

Against the backdrop of increasing global demand for reducing greenhouse gas emissions, promoting the use of energy-saving and environmentally friendly products has become a crucial aspect of low-carbon economic development. Customer satisfaction plays a vital role in the promotion of these products. To address the challenges of dealing with big data in the conventional customer satisfaction analysis tool, Importance Performance Analysis (IPA), a machine learning-based method is proposed to improve IPA. Firstly, the Latent Dirichlet Allocation (LDA) model is used to capture users' opinions on different product topics. Then, the Support Vector Machine (SVM) and Random Forest (RF) algorithms are employed respectively to assess the satisfaction and importance of product attributes, enabling an objective measurement of customer satisfaction and adapting to the current trend of big data. The proposed method is applied to the analysis of water heater satisfaction on the JD platform, obtaining satisfaction levels for 10 topics. The research findings demonstrate that the improved IPA method based on SVM-RF effectively explores customer satisfaction and can provide some improvement strategies for platform managers and manufacturers. [ABSTRACT FROM AUTHOR]

With the rapid development of economic globalization and green manufacturing, traditional flexible job shop scheduling has evolved into the low-carbon heterogeneous distributed flexible job shop scheduling problem (LHDFJSP). Additionally, modern smart manufacturing processes encounter complex and diverse contingencies, necessitating the ability to address dynamic events in real-world production activities. To date, there are limited studies that comprehensively address the intricate factors associated with the LHDFJSP, including workshop heterogeneity, job insertions and transfers, and considerations of low-carbon objectives. This paper establishes a multi-objective mathematical model with the goal of minimizing the total weighted tardiness and total energy consumption. To effectively solve this problem, diverse composite scheduling rules are formulated, alongside the application of a deep reinforcement learning (DRL) framework, i.e., Rainbow deep-Q network (Rainbow DQN), to learn the optimal scheduling strategy at each decision point in a dynamic environment. To verify the effectiveness of the proposed method, this paper extends the standard dataset to adapt to the LHDFJSP. Evaluation results confirm the generalization and robustness of the presented Rainbow DQN-based method. [ABSTRACT FROM AUTHOR]

The promotion of low-carbon production is a crucial aspect of sustainable development, and carbon trading has proven to be an effective means of achieving this goal. However, the limited capital hinders small and medium-sized enterprises (SMEs) from achieving low-carbon production. Thus, this paper examines a low-carbon supply chain comprising a financially constrained manufacturer and a well-funded retailer. We propose a game-theoretical model that addresses the financial constraints of the manufacturer through buyer-backed purchase order financing (BPOF) or advance payment discount (APD) with carbon trading revenue-sharing contracts. We investigate the determination of pricing and carbon emission reduction strategies by firms utilizing diverse financing models. We find that the total amount of carbon emission reduction increases with the manufacturer's carbon trade revenue-sharing ratio. The financing rate of BPOF has a negative impact on the amount of carbon emission reduction, while the discount rate of APD will not affect either the carbon emission reduction or the revenue of the supply chain. Among the two financing strategies, APD is optimal for maximizing profit and promoting lowcarbon development throughout the supply chain. [ABSTRACT FROM AUTHOR]

Under the background of “carbon peak and carbon neutrality”, the interconnection of multiple regional integrated energy systems has become an important way to coordinate multiple energy sources and save energy and reduce emissions. This paper proposes a low-carbon economic dispatch model for multi-regional integrated energy systems considering the joint demand response mechanism; first, the concept and model of joint demand response are proposed, and on this basis, the energy conversion model between multiple energy forms in the multi-regional integrated energy system is studied; secondly, green certificate trading and carbon trading mechanisms are introduced, and green certificate trading and carbon trading are combined through the green certificate emission reduction mechanism; finally, a low-carbon economic dispatch model is proposed with the goal of minimizing the sum of system energy purchase cost, green certificate trading cost, carbon trading cost and joint demand response compensation cost. The example results show that the introduction of joint demand response mechanism and green certificate-carbon trading mechanism in the multi-regional integrated energy system reduces the total system cost and carbon emissions by 14.03% and 26.04% respectively.

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.

In Europe, buildings account for 40% of the energy consumption and produce 36% of CO2 emissions. Renovation could be a great tool to decarbonize the building stock since it allows for a decrease in the operational energy required for buildings and is less material-consuming than new construction. Further benefits are brought by the usage of bio-based insulation materials that can drastically reduce embodied emissions and transform structures into factual carbon sinks. This study focuses on a particular kind of biogenic material, mycelium-wood composites, consisting of organic matter bound by the root structure of fungal organisms. This innovative insulation material was compared with traditional ones for the renovation of the building stock, with a focus on vertical components like walls in the Helsinki metropolitan area. To characterize mycelium-wood composites, density and carbon content information were gathered from the samples realized in the Politecnico di Milano MaBa.SAPERLab, while the production processes were included in a SimaPro model to obtain the GWP value. Different scenarios were then defined by two variables: the renovation rate of the building stock and the market penetration of mycelium-wood composites. For each scenario, the overall GWP and CO2 stored values were calculated. Results show the great potential of the innovative material that grants carbon storage in the building stock that could even surpass the amount stored in the 32,500 ha of forest in the area. However, this possibility is heavily influenced by factors independent of the type of insulation used that should be further investigated.