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

To advance carbon neutrality policies, many countries are increasingly integrating Renewable Energy Sources (RESs) into their energy mix. However, for harnessing natural energy in power generation, RESs present challenges in output control, potentially leading to power imbalances. Such imbalances, when coupled with an excessive reliance on unpredictable RES, may necessitate the curtailment of grid-integrated renewable generation, halting production despite available capacity. This paper proposes strategies to mitigate the economic and operational efficiency impacts of increased RESs on the power grid. It focuses on the optimal operation of Battery Energy Storage Systems (BESSs) and Power to Gas (P2G) technology, which flexibly converts surplus electricity into hydrogen. By employing Mixed Integer Linear Programming (MILP) for optimization and Stochastic Programming (SP) for the management of the uncertainty of renewable energy output, this paper evaluates the anticipated benefits of BESS and P2G applications. [ABSTRACT FROM AUTHOR]

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

Carbon Capture, Utilization, and Storage (CCUS) stands as one of the effective means to reduce carbon emissions and serves as a crucial technical pillar for achieving experimental carbon neutrality. CO2-enhanced oil recovery (CO2-EOR) represents the foremost method for CO2 utilization. CO2-EOR represents a favorable technical means of efficiently developing extra-low-permeability reservoirs. Nevertheless, the process known as the direct injection of CO2 is highly susceptible to gas scrambling, which reduces the exposure time and contact area between CO2 and the extra-low-permeability oil matrix, making it challenging to utilize CO2 molecular diffusion effectively. In this paper, a comprehensive study involving the application of a CO2 nanobubble system in extra-low-permeability reservoirs is presented. A modified nano-SiO2 particle with pro-CO2 properties was designed using the Pickering emulsion template method and employed as a CO2 nanobubble stabilizer. The suitability of the CO2 nanobubbles for use in extra-low-permeability reservoirs was evaluated in terms of their temperature resistance, oil resistance, dimensional stability, interfacial properties, and wetting-reversal properties. The enhanced oil recovery (EOR) effect of the CO2 nanobubble system was evaluated through core experiments. The results indicate that the CO2 nanobubble system can suppress the phenomena of channeling and gravity overlap in the formation. Additionally, the system can alter the wettability, thereby improving interfacial activity. Furthermore, the system can reduce the interfacial tension, thus expanding the wave efficiency of the repellent phase fluids. The system can also improve the ability of CO2 to displace the crude oil or water in the pore space. The CO2 nanobubble system can take advantage of its size and high mass transfer efficiency, among other advantages. Injection of the gas into the extra-low-permeability reservoir can be used to block high-gas-capacity channels. The injected gas is forced to enter the low-permeability layer or matrix, with the results of core simulation experiments indicating a recovery rate of 66.28%. Nanobubble technology, the subject of this paper, has significant practical implications for enhancing the efficiency of CO2-EOR and geologic sequestration, as well as providing an environmentally friendly method as part of larger CCUS-EOR. [ABSTRACT FROM AUTHOR]

On the basis of "Carbon Peak and Carbon Neutral" goals, carbon sequestration projects are increasing in China. The integrity of cement sheaths, as an important factor affecting carbon sequestration projects, has also received more attention and research. When CO2 is injected into the subsurface from sequestration wells, the cement sheath may mechanically fail due to the pressure accumulated inside the casing, which leads to the sealing of the cement sheath failing. The elasticity and strength parameters of the cement sheath are considered in this paper. The critical bottom-hole injection pressures of inclined well sections under anisotropic formation stresses at different depths were calculated for actual carbon-sealing wells in the X block—the CO2 sequestration target block. The sensitivity factors of the critical bottom-hole injection pressure were also analyzed. It was found that the cement sheath damage criterion was tensile damage. The Young's modulus and tensile strength of the cement sheath are the main factors affecting the mechanical failure of the cement sheath, with Poisson's ratio having the second highest influence. An increase in the Young's modulus, Poisson's ratio, and tensile strength of the cement sheath can help to improve the mechanical stability of cement sheaths in CO2 sequestration wells. This model can be used for the design and evaluation of cement in carbon sequestration wells. [ABSTRACT FROM AUTHOR]

Through extensive data research and analysis, this paper comprehensively summarizes the status and key insights of global carbon dioxide capture and storage (CCS) development. It aims to gain a comprehensive understanding of the relevant policies, technologies, and security measures adopted by major countries in their CCS development processes. Furthermore, it explores the existing status and limitations of China's offshore development efforts, while providing valuable recommendations for enhancing China's offshore CCS initiatives, as well as serving as a reference for other nations worldwide. Offshore CCS plays a crucial role for China to achieve the development target of carbon peak and carbon neutrality, due to its energy structure and industrial distribution. While China possesses significant offshore CCS potential, achieving commercialization still requires substantial efforts. To facilitate the process and draw insights from successful experiences in other countries, this paper illustrates the characteristics and generalizes the experience of offshore CCS industry practices in America, Europe and Japan, respectively. Furthermore, it is recommended that a new round of investigation into offshore CCS potential be conducted, while promoting integrated collaboration between geological surveying and marine scientific research. Additionally, further research on industrial policies and green financial strategies should be undertaken. [ABSTRACT FROM AUTHOR]

With the shortage of fossil energy and the increasingly serious environmental problems, renewable energy based on wind and solar power generation has been gradually developed. For the problem of wind power uncertainty and the low-carbon economic optimization problem of an integrated energy system with power to gas (P2G) and carbon capture and storage (CCS), this paper proposes an economic optimization scheduling strategy of an integrated energy system considering wind power uncertainty and P2G-CCS technology. Firstly, the mathematical model of the park integrated energy system with P2G-CCS technology is established. Secondly, to address the wind power uncertainty problem, Latin hypercube sampling (LHS) is used to generate a large number of wind power scenarios, and the fast antecedent elimination technique is used to reduce the scenarios. Then, to establish a mixed integer linear programming model, the branch and bound algorithm is employed to develop an economic optimal scheduling model with the lowest operating cost of the system as the optimization objective, taking into account the ladder-type carbon trading mechanism, and the sensitivity of the scale parameters of P2G-CCS construction is analyzed. Finally, the scheduling scheme is introduced into a typical industrial park model for simulation. The simulation result shows that the consideration of the wind uncertainty problem can further reduce the system's operating cost, and the introduction of P2G-CCS can effectively help the park's integrated energy system to reduce carbon emissions and solve the problem of wind and solar power consumption. Moreover, it can more effectively reduce the system's operating costs and improve the economic benefits of the park. [ABSTRACT FROM AUTHOR]

Low carbon operation of power systems is a key way to achieve the goal of energy power carbon peaking and carbon neutrality. In order to promote the low carbon transition of energy and power and the coordinated and optimized operation of distributed energy sources in virtual power plants (VPP), this paper proposes a framework for collaborative utilization of pumped storage–carbon capture–power-to-gas (P2G) technologies. It also constructs a multi-time scale low carbon economic dispatch model for VPP to minimize the internal resource operation cost of VPP in each time period. During the intraday scheduling stage, the day-ahead scheduling results as the planned output and the energy flow is then dynamically corrected at a short-term resolution in the framework. This allows for the exploration of the low-carbon potential of each aggregation unit within the virtual power plant. The results of the simulation indicate that the strategy and model proposed in this paper can effectively encourage the consumption of renewable energy sources, promote the low-carbon operation of power system power, and serve as a valuable reference for the low-carbon economic operation of the power system. [ABSTRACT FROM AUTHOR]

In order to improve the consumption of renewable energy and reduce the carbon emissions of integrated energy systems (IESs), this paper proposes an optimal operation strategy for an integrated energy system considering the coordination of electricity and hydrogen in the context of carbon trading. The strategy makes full use of the traditional power-to-gas hydrogen production process and establishes a coupling model comprising cogeneration and carbon capture equipment, an electrolytic cell, a methane reactor, and a hydrogen fuel cell. Taking a minimum daily operating cost and minimal carbon emissions from the system as objective functions, a mixed-integer nonlinear optimal scheduling model is established. This paper designs examples based on MATLAB R2021b and uses the GUROBI solver to solve them. The results show that compared with the traditional two-stage operation process, the optimization method can reduce the daily operation cost of an IES by 26.01% and its carbon emissions by 90.32%. The results show that the operation mode of electro-hydrogen synergy can significantly reduce the carbon emissions of the system and realize a two-way flow of electro-hydrogen energy. At the same time, the addition of carbon capture equipment and the realization of carbon recycling prove the scheduling strategy's ability to achieve a low-carbon economy of the scheduling strategy. [ABSTRACT FROM AUTHOR]

Solid-state hydrogen storage technology has emerged as a disruptive solution to the "last mile" challenge in large-scale hydrogen energy applications, garnering significant global research attention. This paper systematically reviews the Chinese research progress in solid-state hydrogen storage material systems, thermodynamic mechanisms, and system integration. It also quantitatively assesses the market potential of solid-state hydrogen storage across four major application scenarios: on-board hydrogen storage, hydrogen refueling stations, backup power supplies, and power grid peak shaving. Furthermore, it analyzes the bottlenecks and challenges in industrialization related to key materials, testing standards, and innovation platforms. While acknowledging that the cost and performance of solid-state hydrogen storage are not yet fully competitive, the paper highlights its unique advantages of high safety, energy density, and potentially lower costs, showing promise in new energy vehicles and distributed energy fields. Breakthroughs in new hydrogen storage materials like magnesium-based and vanadium-based materials, coupled with improved standards, specifications, and innovation mechanisms, are expected to propel solid-state hydrogen storage into a mainstream technology within 10–15 years, with a market scale exceeding USD 14.3 billion. To accelerate the leapfrog development of China's solid-state hydrogen storage industry, increased investment in basic research, focused efforts on key core technologies, and streamlining the industry chain from materials to systems are recommended. This includes addressing challenges in passenger vehicles, commercial vehicles, and hydrogen refueling stations, and building a collaborative innovation ecosystem involving government, industry, academia, research, finance, and intermediary entities to support the achievement of carbon peak and neutrality goals and foster a clean, low-carbon, safe, and efficient modern energy system. [ABSTRACT FROM AUTHOR]

In this paper, we develop a two-level supply chain model with low carbon emissions for defective deteriorating items under learning in fuzzy environment by using the double inspection process. Carbon emissions are a major issue for the environment and human life when they come from many sources like different kinds of factories, firms, and industries. The burning of diesel and petrol during the supply of items through transportation is also responsible for carbon emissions. When any company, firm, or industry supplies their items through a supply chain by using of transportation in the regular mode, then a lot of carbon units are emitted from the burning of petrol and diesel, etc., which affects the supply chain. Carbon emissions can be controlled by using different kinds of policies issued by the government of a country, and lots of companies have implemented these policies to control carbon emissions. When a seller delivers a demanded lot size to the buyer, as per demand, and the lot size has some defective items, as per consideration, the demand rate is uncertain in nature. The buyer inspects the received whole lot and divides it into two categories of defective and no defective deteriorating items, as well as immediately selling at different price. The fuzzy concept nullifies the uncertain nature of the demand rate. This paper covers two models, assuming two conditions of quality screening under learning in fuzzy environment: (i) the buyer shows the quality screening and (ii) the quality inspection becomes the seller's responsibility. The carbon footprint from the transporting and warehousing the deteriorating items is also assumed. The aim of this study is to minimize the whole inventory cost for supply chains with respect to lot size and the number of orders per production cycle. Jointly optimizing the delivery lot size and number of orders per production cycle will minimize the whole fuzzy inventory cost for the supply chain and also reduce the carbon emissions. We take two numerical approaches with authentic data (from the literature reviews) for the justification of the proposed model 1 and model 2. Sensitivity observations, managerial insights, applications of these proposed models, and future scope are also included in this paper, which is more beneficial for firms, the industrial sector, and especially for online markets. The impact of the most effective parameters, like learning effect, fuzzy parameter, carbon emissions parameter, and inventory cost are shown in this study and had a positive effect on the total inventory cost for the supply chain. [ABSTRACT FROM AUTHOR]

With the development of global clean energy and the implementation of carbon emission reduction policies, the direct purchase of electricity by large consumers has been increasingly promoted as a special form of electricity trading. Therefore, on the basis of the completion of low-carbon emission reduction targets in each country, how to rationalize the electricity purchase by large consumers in the electricity market so as to reduce their electricity purchase costs has become the main target of attention in each country. Currently, there are fewer studies in existing research on the direct electricity purchase strategy of large consumers under the consideration of the weight of consumption responsibility and risk. Based on this, this paper constructs a dual-game model for direct electricity purchase by large consumers based on the Stackelberg game and non-cooperative game theory. The concept of value at risk is further introduced, and the optimal strategy of direct electricity purchase by large consumers is proposed. The results of this study show that when market players make decisions on the purchase and sale of electricity, power suppliers will increase their biddings to obtain the highest returns, and large consumers can reduce the transaction costs by combining the medium- and long-term market with the spot market to purchase electricity. In the choice of electricity purchase market, with the increasing risk factor, large consumers shift from the risky spot market to the less risky medium- and long-term market and option market. This paper provides a reference for the issues of power suppliers' contract bidding and large consumers' electricity purchase strategy in the medium- and long-term contract transactions. [ABSTRACT FROM AUTHOR]

In the context of global carbon neutrality, ports face significant electricity demand for cargo handling and pressure to reduce carbon emissions. The abundant wind energy resources in port areas make wind power highly promising for port applications. The optimal selection of site and turbine types for wind power systems can effectively reduce emissions in ports, achieving sustainability and improving economic benefits. The practical implementation of wind energy systems considering practical constraints holds significant research significance. Taking Ningbo-Zhoushan Port as an example, this paper analyzes the wind energy resources in the port area and provides an overview of wind power system construction sites. Based on the actual conditions of the port area, this paper comprehensively reviews the site selection of wind turbines from the perspectives of wind resources, specific climates, and noise impacts. With the consideration of engineering preferences, this paper selects performance indicators based on the four mainstream turbine models and proposes a comprehensive weight determination method using the entropy weight method and analytic hierarchy process (AHP) to determine the weights of the indicators. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is then employed to score and compare four turbine plans, enabling the turbine selection process to consider both engineering preferences and objectivity, thereby enhancing the accuracy and reliability of wind turbine planning and achieving significant ecological and economic benefits through benefit analysis. [ABSTRACT FROM AUTHOR]

Cities play a pivotal role in achieving worldwide carbon neutrality due to their significant contribution to global energy consumption and carbon emissions. Therefore, planning effective strategies and guiding evidence-based policymaking at the city scale becomes even more crucial. Composite indices serve as a valuable tool for monitoring urban energy transition trends. This paper aims to present a novel approach, robust and flexible even under conditions of data scarcity, for tracking the energy transition trend of a city by means of a composite index (UETI). The Turin case study is introduced to test the applicability of the proposed approach. Additionally, to demonstrate the robustness of the composite index framework, the paper includes the findings of correlation and sensitivity analyses. This study reveals a significant improvement in Turin's environmental and energy domains, while the socio-economic domain shows more modest improvement. Furthermore, the study highlights the need to address the shortage of urban data to enhance the accuracy and reliability of metric-based frameworks and to extend the assessment to a larger sample of cities. [ABSTRACT FROM AUTHOR]

In order to achieve the ambitious goal of "carbon neutrality", countries around the world are striving to develop clean energy. Against this background, this paper takes China and Italy as representatives of developing and developed countries to summarize the energy structure composition and development overview of the two countries. The paper analyzes the serious challenges facing the future energy development of both countries and investigates the possibilities of energy cooperation between the two countries, taking into account their respective advantages in energy development. By comparing the policies issued by the two governments to encourage clean energy development, this paper analyzes the severe challenges faced by the two countries' energy development in the future and combines their respective energy development advantages to look forward to the possibility of energy cooperation between the two countries in the future. This lays the foundation for China and Italy to build an "Energy Road" after the "Silk Road". [ABSTRACT FROM AUTHOR]

The sustainable implementation of resources and the transition to low-carbon construction is on the world's daily agenda. This paper describes the development of criteria for the efficient management of material resources in low-carbon construction. Net income, gross profit, balance sheets, and equity are proposed as indicators that serve as criteria for assessing the efficient use of resources. Nonlinear regression models were the methodological basis for the establishment of cause-and-effect relationships of the volume of construction of transport structures. It was established that since construction companies do not purchase materials for storage, the cost of materials for use in low-carbon technological construction work is directly proportional to the cost of construction. The volume of production in the construction industry is determined by construction costs. More efficient use of low-carbon materials reduces cost and construction waste. In this paper, we have established the relationship between the indicator of efficiency for the functioning of the construction enterprises in Ukraine and the use of low-carbon materials for the construction of transport structures. The practical value of the obtained results for low-carbon construction management lies in proving the relationship between the performance indicators of the construction enterprise (net profit, gross profit, output volume, balance sheets, and equity capital) and the use of low-carbon material resources. Our results form the basis for future research into the use of a cost-based model for low-carbon economy transition in municipalities and regions. [ABSTRACT FROM AUTHOR]

With the objectives of achieving "peak carbon" and "carbon neutrality", accurately quantifying the carbon emissions of road transportation becomes crucial. It is challenging to accurately describe the energy consumption of vehicles at both temporal and spatial scales from a macro perspective. Therefore, focusing on the quantitative model of vehicle micro energy consumption and road meso energy consumption, this paper reviewed and summarized the energy consumption model of road traffic in terms of data collection, quantification accuracy, and scope of application. Based on this analysis, this paper identifies the challenges of the current road traffic energy consumption model. Finally, we look forward to future research directions for studying quantitative models of energy consumption from road transportation. [ABSTRACT FROM AUTHOR]

As global climate change intensifies, nations around the world are implementing policies aimed at reducing emissions, with carbon-trading mechanisms emerging as a key market-based tool. China has launched carbon-trading markets in several cities, achieving significant trading volumes. Carbon-trading mechanisms encompass cap-and-trade markets and voluntary markets, influenced by various factors, including policy changes, economic conditions, energy prices, and climate fluctuations. The complexity of these factors, coupled with the nonlinear and non-stationary nature of carbon prices, makes forecasting a substantial challenge. This paper proposes a dynamic weight hybrid forecasting model based on a dual sliding window approach, effectively integrating multiple forecasting models such as LSTM, Random Forests, and LASSO. This model facilitates a thorough analysis of the influences of policy, market dynamics, technological advancements, and climatic conditions on carbon pricing. It serves as a potent tool for predicting carbon market price fluctuations and offers valuable decision support to stakeholders in the carbon market, ultimately aiding in the global efforts towards emission reduction and achieving sustainable development goals. [ABSTRACT FROM AUTHOR]

In order to support a low-carbon economy and manage market competition, location–inventory–routing logistics management must play a crucial role to minimize carbon emissions while maximizing customer satisfaction. This paper proposes a bi-objective mixed-integer nonlinear programming model with time window constraints that satisfies the normal distribution of stochastic customer demand. The proposed model aims to find Pareto optimal solutions for total cost minimization and customer satisfaction maximization. An improved non-dominated sorting genetic algorithm II (IMNSGA-II) with an elite strategy is developed to solve the model. The model considers cost factors, ensuring that out-of-stock inventory is not allowed. Factors such as a carbon trading mechanism and random variables to address customer needs are also included. An entropy weight method is used to derive the total cost, which is comprised of fixed costs, transportation costs, inventory costs, punishment costs, and the weight of carbon emissions costs. The IMNSGA-II produces the Pareto optimal solution set, and an entropy–TOPSIS method is used to generate an objective ranking of the solution set for decision-makers. Additionally, a sensitivity analysis is performed to evaluate the influence of carbon pricing on carbon emissions and customer satisfaction. [ABSTRACT FROM AUTHOR]

The traditional centralized optimization method encounters challenges in representing the interaction among multi-agents and cannot consider the interests of each agent. In traditional low-carbon scheduling, the fixed carbon quota trading price can easily cause arbitrage behavior of the trading subject, and the carbon reduction effect is poor. This paper proposes a two-layer dynamic community integrated energy system (CIES) low-carbon collaborative optimization operation method. Firstly, a multi-agent stage feedback carbon trading model is proposed, which calculates carbon trading costs in stages and introduces feedback factors to reduce carbon emissions indirectly. Secondly, a two-layer CIES low-carbon optimal scheduling model is constructed. The upper energy seller (ES) sets energy prices. The lower layer is the combined cooling, heating, and power (CCHP) system and load aggregator (LA), which is responsible for energy output and consumption. The energy supply and consumption are determined according to the ES energy price strategy, which reversely affects the energy quotation. Then, the non-dominated sorting genetic algorithm embedded with quadratic programming is utilized to solve the established scheduling model, which reduces the difficulty and improves the solving efficiency. Finally, the simulation results under the actual CIES example show that compared with the traditional centralized scheduling method, the total carbon emission of the proposed method is reduced by 16.34%, which can improve the income of each subject and make the energy supply lower carbon economy. [ABSTRACT FROM AUTHOR]

The paper addresses the overlooked interaction between power-to-gas (P2G) devices and carbon capture and storage (CCS) equipment, along with the stepwise carbon trading mechanism in the context of current multi-park integrated energy microgrids (IEMGs). Additionally, it covers the economic and coordinated low-carbon operation issues in multi-park IEMGs under the carbon trading system. It proposes a multi-park IEMG low-carbon operation strategy based on the synchronous Alternating Direction Method of Multipliers (ADMM) algorithm. The algorithm first enables the distribution of cost relationships among multi-park IEMGs. Then, using a method that combines a CCS device with a P2G unit in line with the tiered carbon trading scheme, it expands on the model of single IEMGs managing thermal, electrical, and refrigeration energy. Finally, the comparison of simulation cases proves that the proposed strategy significantly reduces the external energy dependence while keeping the total cost of the users unchanged, and the cost of interaction with the external grid is reduced by 56.64%, the gas cost is reduced by 27.78%, and the carbon emission cost is reduced by 29.54% by joining the stepped carbon trading mechanism. [ABSTRACT FROM AUTHOR]

In the quest to achieve "double carbon" goals, the urgency to develop an efficient Integrated Energy System (IES) is paramount. This study introduces a novel approach to IES by refining the conventional Power-to-Gas (P2G) system. The inability of current P2G systems to operate independently has led to the incorporation of hydrogen fuel cells and the detailed investigation of P2G's dual-phase operation, enhancing the integration of renewable energy sources. Additionally, this paper introduces a carbon trading mechanism with a refined penalty–reward scale and a detailed pricing tier for carbon emissions, compelling energy suppliers to reduce their carbon footprint, thereby accelerating the reduction in system-wide emissions. Furthermore, this research proposes a flexible adjustment mechanism for the heat-to-power ratio in cogeneration, significantly enhancing energy utilization efficiency and further promoting conservation and emission reductions. The proposed optimization model in this study focuses on minimizing the total costs, including those associated with carbon trading and renewable energy integration, within the combined P2G-Hydrogen Fuel Cell (HFC) cogeneration system. Employing a bacterial foraging optimization algorithm tailored to this model's characteristics, the study establishes six operational modes for comparative analysis and validation. The results demonstrate a 19.1% reduction in total operating costs and a 22.2% decrease in carbon emissions, confirming the system's efficacy, low carbon footprint, and economic viability. [ABSTRACT FROM AUTHOR]

As global warming intensifies, the world is increasingly concerned about carbon emissions. As an important industry that affects carbon emissions, the air transportation industry takes on the important task of energy saving and emission reduction. For this reason, major airlines have designed or will design different kinds of new-energy aircraft; however, each aircraft has a different scope of application according to its energy source. Biofuels have an obvious carbon emission reduction effect in the whole life cycle, which can offset the drawback of the high pollutant emission of traditional fossil fuels in the preparation and combustion stages. At the same time, a battery has zero emissions in the operating condition, while the low energy density also makes it more applicable to short-range navigation in small aircraft. In this paper, the development direction of a biofuel–electric hybrid aircraft is proposed based on the current development of green aviation, combining the characteristics of biofuel and electric aircraft. [ABSTRACT FROM AUTHOR]

Carbon storage services play an important role in maintaining ecosystem stability. Land use/cover change (LUCC) is the main factor leading to changes in ecosystem carbon storage. Understanding the impact of LUCC on regional carbon storage changes is crucial for protecting regional ecosystems and promoting sustainable socio-economic development. This paper selects Shanxi province as the study area and explores the spatial and temporal evolution characteristics of carbon storage in Shanxi province based on the InVEST model and univariate spatial autocorrelation. The results show that the total carbon storage in Shanxi Province in 2000, 2010, and 2020 is 513.51 × 104 t C, 513.46 × 104 t C, and 509.29 × 104 t C, respectively. High carbon storage areas are distributed in forest and grassland land types, while low carbon storage areas are widely distributed in building land in urban metropolitan areas. Shanxi Province is mainly dominated by farmland, which has decreased by 3448.60 km2 in the past 20 years. Grassland has decreased by 1588.31 km2 and the area of building land has increased by 4205.73 km2. Due to the influence of carbon conversion among different land use types, the total carbon storage loss of Shanxi Province in the past 20 years was 4.21 × 104 t C. The transfer of farmland resulted in an increase in carbon stock of 14.46 × 104 t C. The transfer of grassland resulted in an increase of 17.15 × 104 t C, while the transfer of forest resulted in a decrease of 41.44 × 104 t C. The increase in land use types with low carbon density and the decrease in land use types with high carbon density led to the decrease in carbon storage in Shanxi Province. Furthermore, social factors were more likely to influence the carbon storage than natural factors, and the influence of social factors was often negative. On this basis, regional development countermeasures were proposed for the current situation of carbon storage in Shanxi Province and provide a scientific basis for Shanxi Province to achieve the carbon neutrality target. [ABSTRACT FROM AUTHOR]

This paper studied the characteristics of the carbon emission of an industrial wastewater treatment plant (IWWTP) and a domestic WWTP (DWWTP) located in a high-tech industrial park of Shaanxi Province, China. The results showed that the total carbon emissions of the IWWTP and DWWTP were 10.13 kg/t and 1.84 kg/t in 2020, respectively. Indirect carbon emissions play a dominant role in the total carbon emission of the IWWTP, which accounts for about 97.6% of the total amount. The direct carbon emissions of greenhouse gases (e.g., CH4, N2O) account for about 62% of the total carbon emissions in the DWWTP, followed by the indirect carbon emissions generated by electricity consumption (31.06%) and chemical consumption (6.94%). Additionally, the centralized recycling and re-utilization of the wastewater could achieve 1.06 kg/t and 1.16 kg/t of carbon emission reduction in the IWWTP and DWWTP, respectively, while the carbon emissions of the DWWTP are inversely proportional to the treatment capacity. Therefore, it is of great value to collect and utilize intensively the recycled water to achieve the goal of regional carbon emission reduction and carbon neutralization of WWTPs in industrial parks. [ABSTRACT FROM AUTHOR]

To address the challenges posed by global climate change, developing green energy systems characterized by informatization, digitalization, and intelligence is crucial for achieving carbon neutrality. This article is a research report type paper on water source heat pump (WSHP) energy stations, aiming to use digital twin technology and other information technologies to resolve conflicts between clean energy development and efficient energy utilization. The primary objective of this study is to identify and analyze issues in traditional energy station operations and management systems. Based on this analysis, specific technical solutions are proposed, including pathways for technological research, methodologies, and content. The results provide a comprehensive theoretical framework for the intelligent transformation of energy station systems and essential technical support for the WSHP energy station project in the Hankou Binjiang International Business District. The findings have significant implications for the widespread adoption of WSHP energy stations and the achievement of national carbon neutrality goals. [ABSTRACT FROM AUTHOR]

Recently, there has been an increasing effort to promote energy efficiency, using renewable energies and electric mobility to achieve a more sustainable future and even carbon neutrality by 2050. This paper aims to understand if combining these technologies leads to a win–win solution. For that, the system's characteristics that will be used for the simulation were defined as a residential community consumption scenario with and without electric vehicles charging overnight. The simulation was completed in software, and eight scenarios were tested: high population density/low population density with/without electric mobility and hourly tariff/simple tariff. After these scenarios had been tested, the conclusion was that the low population density and hourly tariff without and with electric mobility were the best two cases economically (in terms of levelized cost of energy, net present costs, and savings) and environmentally, and the worst was high population density with hourly tariff and electric mobility. Other scenarios were then tested, including changes in the load curve, namely a commercial load curve, and changes in the load curve of electric vehicle chargers, mainly daytime charging. The conclusion was that even though the initial hypothesis did not lead to a win–win solution, with changes in the hypothesis, the integration of electric mobility in energy communities might lead to that. [ABSTRACT FROM AUTHOR]

Enhancing energy efficiency within the building sector is imperative to curbing energy losses, given that this sector alone contributes to over 34% of global energy consumption. Employing a building management system, along with its regular updates, presents a strategic avenue to decrease energy usage, enhance building energy efficiency, and more. Tailored control strategies, aligned with the unique characteristics and usage patterns of each building, are essential for achieving energy savings. This article presents an evaluation of HVAC system efficiency in office buildings, utilizing a data-driven approach coupled with simulations conducted in building performance simulation software. The research explores the control strategy of an office building equipped with a constant air volume HVAC system, featuring a regularly controlled air handling unit. The objective is to boost energy efficiency while striking a balance between occupant comfort and energy consumption. The findings indicate that by analyzing measured data and adjusting the configurable parameters, the energy consumption of buildings can be significantly reduced. The close monitoring of indoor parameters by building operators and making corresponding adjustments to the HVAC system can yield energy savings of up to 16%. Leveraging these insights, this paper suggests integrating data-driven and dynamic simulation methods into building management system models to optimize HVAC systems, enhance energy efficiency, and advance ambitious carbon neutrality objectives. [ABSTRACT FROM AUTHOR]

This study investigates the competitive issues of the newsvendor problem with product substitution under the carbon cap-and-trade system. Building on existing research, this paper introduces the carbon cap-and-trade system under uncertain market demand and considers that the original equipment manufacturer (OEM) can choose to procure raw materials from the contract manufacturer (CM), with both final products being substitutable. Furthermore, we explore the different substitution relationships between OEM and CM products under both pure competitive and co-opetitive modes. For this problem, decision models are developed for various scenarios, and optimal solutions satisfying given conditions are provided. We find that in one-way substitution, under pure competition, an increase in the OEM's (or CM's) green investment and substitution rate only leads to an increase in OEM's (or CM's) yields, while an increase in the OEM's (or CM's) green investment does not necessarily reduce CM's (or OEM's) yields. In the co-opetitive mode, an increase in the substitution rate and green investments of both manufacturers may lead to an increase in the yields of both manufacturers. Furthermore, an increase in carbon trading prices does not necessarily inhibit the manufacturer's yields. Moreover, we find that under the same competition mode, under certain conditions, two-way substitution between OEM and CM can bring better profits to both manufacturers and the entire supply chain. When the two modes are in the same substitution scenario, and the CM cannot substitute for the OEM, the optimal decisions and total supply chain profits of the two modes are equal. Finally, through numerical analysis, we find that neither mode is necessarily optimal when CM can substitute for OEM. Additionally, it is observed that when the same mode is in different substitution scenarios, total supply chain profits may be enhanced in the presence of product substitution. [ABSTRACT FROM AUTHOR]

With the challenge posed by global warming, accurately estimating and managing carbon emissions becomes a key step for businesses, especially power generation companies, to reduce their environmental impact. Optuna–LightGBM–XGBoost, a novel power and carbon emission relationship model that aims to improve the efficiency of carbon emission monitoring and estimation for power generation companies, is proposed in this paper. Deeply exploring the intrinsic link between power production data and carbon emissions, this model paves a new path for "measuring carbon through electricity", in contrast to the emission factor method commonly used in China. Unit data from power generation companies are processed into structured tabular data, and a parallel processing framework is constructed with LightGBM and XGBoost, and optimized with the Optuna algorithm. The multilayer perceptron (MLP) is used to fuse features to enhance prediction accuracy by capturing characters that the individual models cannot detect. Simulation results show that Optuna–LightGBM–XGBoost can achieve better performance compared to existing methods. The mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) of the model are 0.652, 0.939, 0.136, and 0.994, respectively. This not only helps governments and enterprises to develop more scientific and reasonable emission reduction strategies and policies, but also lays a solid foundation for achieving global carbon neutrality goals. [ABSTRACT FROM AUTHOR]

Offshore wind energy entering the grid in coastal areas creates issues with the safe and stable operation of power systems. To control the carbon emission of power systems and increase the proportion of offshore wind consumption, a microgrid optimization model considering offshore wind power and carbon trading is proposed in this paper. To avoid the defect of Particle Swarm Optimization (PSO) falling into the local optimum prematurely, the PSO algorithm is improved by dynamically decreasing inertia weights and chaos factors. Combined with the powerful optimization capability of the genetic algorithm (GA), the improved PSO-GA algorithm is used to solve the model. The simulation results show that the improved algorithm iterates 11 times before the parameters reach the optimal value, with high convergence accuracy. The proposed approach can increase the proportion of offshore wind consumption and ensure the optimal economic performance of the system while reducing the carbon emission. [ABSTRACT FROM AUTHOR]

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

As a world-class urban agglomeration, the Yangtze River Delta urban agglomeration is significant for China's carbon neutrality strategy when calculating the carbon budget and dividing carbon compensation zones. This paper focused on 129 county-level cities in the core area of the Yangtze River Delta urban agglomeration, calculating the carbon budget for 2000–2020 using night-time light data and analyzing the evolution of spatiotemporal patterns. On this basis, a carbon compensation zoning model was constructed using the K-means algorithm; 129 cities were divided into different carbon compensation zones after combining this model with the main functional zones. The results showed that (1) the carbon emissions increased, with overall stabilization after 2012. The distribution of high carbon emission areas evolved from a "core-periphery" pattern into a "Z-shaped" pattern. Ecological carbon sinks showed a continuous decline, with the southern sinks performing better than the northern ones. (2) The carbon budget of the study area showed a deficit, gradually widening and exhibiting an unbalanced spatial distribution characterized by a "high in the south and low in the north" pattern. (3) Eleven types of carbon compensation zones were designated after overlaying the main functional zones. Low-carbon development suggestions were proposed for each zone type. [ABSTRACT FROM AUTHOR]

In order to foster a more sustainable and eco-friendly trajectory for the construction industry, while concurrently mitigating environmental pollution and energy inefficiency, it is imperative to cultivate an environmentally conscious building and urban environment. Under the background of Carbon Peak and Carbon Neutrality, the green building rating system has become a research hotspot in the field of green building. This paper systematically summarizes the research progress of the GBRS in weight setting, indicator setting, and the evaluation process, and creatively proposes the following three directions for future research: (1) Weight determination methods based on machine learning or deep learning models, and reasonable weight allocation by mixing multiple evaluation methods. (2) Setting dynamic evaluation indicators, strengthening interdisciplinary research and regional consideration, and introducing a life cycle assessment to solve the problem of setting indicators in the existing evaluation system. (3) Combine building information modeling with GBRS to realize the automation and intelligence of evaluation and improve the comprehensiveness and accuracy of evaluation. [ABSTRACT FROM AUTHOR]

Sewage sludge and red mud, as common industrial waste, have become a research hotspot in the field of achieving carbon peaking and carbon neutrality, reducing carbon emissions, and solving environmental problems. However, their treatment and disposal have always been a difficult problem in the environmental field. Utilizing these two materials for thermal energy storage can not only improve energy utilization efficiency but also further reduce carbon emissions during their treatment process, providing a new approach for sustainable development in the industrial sector. This article summarizes the research progress for the resource recovery of sewage sludge and red mud for direct thermal energy recovery and composite phase change energy storage. After proper treatment, sludge and red mud can be directly used as energy storage materials. In addition, sludge and red mud can be combined with phase change materials to prepare composite materials with an excellent energy storage performance. This composite has broad application prospects in fields such as solar energy utilization and building energy efficiency. However, there are still some challenges and issues in this resource recovery and utilization, such as potential environmental pollution during the treatment process, the long-term stability of energy storage materials, and cost-effectiveness, which require further research and resolution. The purpose of this paper is to evaluate the potential of sewage sludge and red mud as energy storage materials, to explore their feasibility and advantages in practical applications, and to reveal the research progress, technical challenges, and future development directions of these two materials in the field of thermal energy storage. [ABSTRACT FROM AUTHOR]

Assessments of the hosting capacity of electricity distribution networks are of paramount importance, as they facilitate the seamless integration of rooftop photovoltaic systems into the grid, accelerating the transition towards a more carbon neutral and sustainable system. This paper employs a deep reinforcement learning-based approach to evaluate the real-time hosting capacity of low voltage distribution networks in a model-free manner. The proposed approach only requires real-time customer voltage data and solar irradiation data to provide a fast and accurate estimate of real-time hosting capacity at each customer connection point. This study addresses the imperative for accurate electrical models, which are frequently unavailable, in evaluating the hosting capacity of electricity distribution networks. To meet this challenge, the proposed approach utilizes a deep neural network-based, data-driven model of a low-voltage electricity distribution network. This proposed methodology incorporates model-free elements, enhancing its adaptability and robustness. In addition, a comparative analysis between model-based and model-free hosting capacity assessment methods is presented, highlighting their respective strengths and weaknesses. The utilization of the proposed hosting capacity estimation model enables distribution network service providers to make well-informed decisions regarding grid planning, leading to cost minimization. [ABSTRACT FROM AUTHOR]

With a significant share of renewable power generation integrated into the power supply, it is crucial to timely adjust the regulating peak load for coal-fired power plants equipped with CO2 capture to ensure the stable operation of the multi-energy supply system. In this paper, the effects of varying boiler loads on the techno-economic performance of the retrofitted power plant were studied. Furthermore, the potential for reducing the cost of CO2 capture was investigated, and early opportunities for demonstration were discussed. Results showed that when the boiler load decreased from 100% turbine heat acceptance condition to 50% turbine heat acceptance condition, the cost of CO2 capture increased from 37.0 $/t CO2 to 57.0 $/t CO2, cost contribution of energy penalty and extra capital investment also increased from 20.6 $/t-CO2 to 25.7 $/t-CO2, and from 16.4 $/t-CO2 to 31.3 $/t-CO2, respectively. Furthermore, by improving separation efficiency from 0.15 to 0.5, a 25% to 30% reduction in CO2 capture cost can be achieved. The cost of CO2 capture could decrease by 42.2–50.5% when the cumulative capacity reaches 250 GW under the high investment learning rate scenario. According to the distribution of coal prices and renewable energy sources in China, the early demonstration projects of multi-energy supply systems should prioritize the northern region. The results of this work can provide informative references for making roadmaps and policies for CO2 emission reduction toward carbon neutrality. [ABSTRACT FROM AUTHOR]

The use of renewable energy sources to achieve carbon neutrality is increasing. However, the uncertainty and volatility of renewable resources are causing problems in power systems. Flexible and low-carbon resources such as Energy Storage Systems (ESSs) are essential for solving the problems of power systems and achieving greenhouse gas reduction goals. However, ESSs are not being installed because of Korea's fuel-based electricity market. To address this issue, this paper presents a method for determining the optimal investment timing of Battery Energy Storage Systems (BESSs) using the Least Squares Monte Carlo (LSMC) method. A case study is conducted considering the System Marginal Price (SMP) and Capacity Payment (CP), which are electricity rates in Korea. Revenue is calculated through the arbitrage of a 10 MW/40 MWh lithium-ion BESS, and linear programming optimization is performed for ESS scheduling to maximize revenue. The ESS revenue with uncertainty is modeled as a stochastic process using Geometric Brownian Motion (GBM), and the optimal time to invest in an ESS is determined using an LSMC simulation considering investment costs. The proposed method can be used as a decision-making tool for ESS investors to provide information on facility investments in arbitrage situations. [ABSTRACT FROM AUTHOR]

Intensive economic and human activities present challenges to the carbon storage capacity of terrestrial ecosystems, particularly in arid regions that are sensitive to climate change and ecologically fragile. Therefore, accurately estimating and simulating future changes in carbon stocks on the northern slope economic belt of Tianshan Mountains (NSEBTM) holds great significance for maintaining ecosystem stability, achieving high-quality development of the economic belt, and realizing the goal of "carbon neutrality" by 2050. This study examines the spatiotemporal evolution characteristics of the NSEBTM carbon stocks in arid regions from 1990 to 2050, utilizing a combination of multi-source data and integrating the Patch-generating Land use Simulation (PLUS) and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models. Additionally, an attribution analysis of carbon stock changes is conducted by leveraging land use data. The findings demonstrate that (1) the NSEBTM predominantly consists of underutilized land, accounting for more than 60% of the total land area in the NSEBTM. Unused land, grassland, and water bodies exhibit a declining trend over time, while other forms of land use demonstrate an increasing trend. (2) Grassland serves as the primary reservoir for carbon storage in the NSEBTM, with grassland degradation being the leading cause of carbon loss amounting to 102.35 t over the past three decades. (3) Under the ecological conservation scenario for 2050 compared to the natural development scenario, there was a net increase in carbon storage by 12.34 t; however, under the economic development scenario compared to the natural development scenario, there was a decrease in carbon storage by 25.88 t. By quantitatively evaluating the land use change in the NSEBTM and its impact on carbon storage in the past and projected for the next 30 years, this paper provides scientific references and precise data support for the territorial and spatial decision making of the NSEBTM, thereby facilitating the achievement of "carbon neutrality" goals. [ABSTRACT FROM AUTHOR]

In this paper, a fully distributed strategy for the economic dispatch problem (EDP) in the smart grid is proposed. The economic dispatch model considers both traditional thermal generators and wind turbines (WTs), integrating generation costs, carbon trading expenses, and the expected costs associated with the unpredictability of wind power. The EDP is transformed into an equivalent optimization problem with only an equality constraint and thus can be solved by an alternating-direction method of multipliers (ADMM). Then, to tackle this problem in a distributed manner, the outer-layer framework of the proposed strategy adopts a parallel ADMM, where different variables can be calculated simultaneously. And the inner-layer framework adopts a finite-step consensus algorithm. Convergence to the optimal solution is achieved within a finite number of communication iterations, which depends on the scale of the communication network. In addition, leveraging local and neighbor information, a distributed algorithm is designed to compute the eigenvalues of the Laplacian matrix essential for the finite-step algorithm. Finally, several numerical examples are presented to verify the correctness and effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

The liberalization of energy retail markets empowered consumers with the ability to be part of new emerging entities, such as Citizen Energy Communities. With the increasing penetration of decentralized variable generation, communities have the advantage of incentive local carbon neutrality and sustainability. Local generation reduces transport grid usage and costs to consumers. Furthermore, worldwide legislation incentives energy communities by providing them discounts to other fee parts of the tariff apart from wholesale prices. This paper presents a model of strategic behavior, investment, and trading of energy communities. The model comprises the investment in local renewable generation, the design of competitive tariffs, and strategic bidding on wholesale markets. Consumers have an optimization model that selects the retail tariff that minimizes their costs with energy. These models are tested using data from Portuguese consumers and the Iberian electricity market. Results from the study indicate that inflexible consumers may reduce their costs by 29% by being part of the community. Furthermore, they have the potential to reduce their costs above 50% when using demand–response, adapting themselves to local production and wholesale prices. [ABSTRACT FROM AUTHOR]

Frequency response performance in power systems is becoming vulnerable due to the transition toward the higher penetration of renewable energy such as achieving carbon neutrality. In particular, reducing power system inertia energy as the asynchronous generation increases could result in violating the frequency stability constraint when a disturbance occurs in the power systems. In order to control the rapidly fluctuating frequency of the power system with low inertia, it is necessary to introduce fast frequency response resources such as a Battery Energy Storage System (BESS). This paper proposes a method to calculate the required capacity of BESS for compensating the frequency control performance of the power system using inertia energy. For calculating the required capacity of BESS, the inertia energy in the critical power system, where frequency control performance marginally satisfies frequency stability constraint, should be calculated. Also, the inertia energy in the evaluated power system having deficit inertia energy should be calculated. By comparing power systems that respond with different dynamics when the same disturbance occurs, the proposed calculation corresponds to the ratio of inertia energy deficiency based on critical power system inertia energy within the power imbalance. Through various case studies employing Korean power systems, the effectiveness of the inertia energy-based calculation method for the required BESS is verified by the fact that the BESS integrated power system marginally satisfies the frequency stability constraint. In these study cases, it is found that the instant response of BESS is very effective for compensating the frequency control performance of the low inertia power system. By applying the proposed method, it is also found that about 840 MW of BESS can achieve carbon neutrality in the Korean power system. [ABSTRACT FROM AUTHOR]

Featured Application: Repowering of existing vessels with hybrid power system for electricity production and storage with focus on reduced power requirement, increased efficiency, and eliminated greenhouse gas emissions are the applications. As marine traffic is contributing to pollution, and most vessels have predictable routes with repetitive load profiles, to reduce their impact on environment, hybrid systems with proton exchange membrane fuel cells (PEMFC-s) and battery pack are a promising replacement. For this purpose, the new approach takes into consideration an alternative to diesel propulsion with the additional benefit of carbon neutrality and increase of system efficiency. Additionally, in the developed numerical model, control of the PEMFC–battery hybrid energy system with balance of plant is incorporated with repowering existing vessels that have two diesel engines with 300 kWe. The goal of this paper is to develop a numerical model that analyzes and determines an equivalent hybrid ship propulsion system for a known traveling route. The developed numerical model consists of an interconnected system with the PEMFC stack and a battery pack as power sources. The numerical model was developed and optimized to meet the minimal required power demand for a successful route, which has variable loads and sees ships sail daily six times along the same route—in total 54 nautical miles. The results showed that the equivalent hybrid power system consists of a 300 kWe PEMFC stack and battery pack with 424 kWh battery and state of charge varying between 20 and 87%. To power this new hybrid power system, a hydrogen tank of 7200 L holding 284.7 kg at pressure of 700 bar is required, compared to previous system that consumed 1524 kg of diesel and generated 4886 kg of CO2. [ABSTRACT FROM AUTHOR]

In response to the challenges of global warming and the development of A low-carbon economy, the integrated electricity and natural gas energy system (IEGES) is known as an important structure for future energy supply; thus, its planning and design must take low-carbon and environmental protection factors into account. Regarding carbon emissions as an optimization criterion, this paper built life-cycle carbon emission models of IEGES components. Then, taking the capacities of the energy resources, storage and conversion units of IEGES as the optimization variables, a multi-objective optimization configuration model was established considering the annual investment operation cost and the life-cycle carbon emissions. The multi-objective model was transformed into a single-objective one by an ε-constraint approach and the polynomial fitting method was employed to obtain the value of ε for obtaining uniformly distributed Pareto sets. Based on the fuzzy entropy weight method and the fuzzy affiliation degree approach, the obtained Pareto sets were ranked and the solution with the highest ranking value was selected as the optimal solution for the original problem. Finally, the configuration schemes were analyzed from the perspectives of economy, carbon emission and renewable energy utilization, and the effectiveness and rationality of the proposed optimization method were verified through MATLAB simulation. [ABSTRACT FROM AUTHOR]

In 2023, China's crude steel production amount reached 1.019 billion tons, and the energy consumption of China's steel industry amount reached 561 million tons of coal. China's steel industry, with its dominant reliance on coal for energy and the primary use of blast furnaces and converters in production processes, as well as its massive output, has become the main field for achieving China's "carbon peaking" and "carbon neutrality" goals. Firstly, this article summarizes the current production status of the steel industry and the situation of carbon emissions in the steel industry. Secondly, it discusses the dual-carbon policies based on the national and steel industry levels and outlines the future directions for China's steel industry. Subsequently, it analyzes the current state of research and application of mature and emerging low-carbon technology in China's steel industry and details the low-carbon plans of China's steel companies using the low-carbon technology roadmaps of two representative steel companies as examples. Finally, the article gives policy suggestions for the further carbon reduction of China's steel industry. The purpose of this paper is to show the efforts and contributions of China's steel industry to the early realization of its "carbon peaking" and "carbon neutrality" goals. [ABSTRACT FROM AUTHOR]

This paper presents a quantitative assessment of the value of hydroelectric power plants (HPPs) in power systems with a significant penetration of variable renewable energy sources (VRESs). Through a capacity expansion planning (CEP) model that incorporates a detailed representation of HPP operating principles, the study investigates the construction and application of HPP rule curves essential for seasonal operation. A comparative analysis is also conducted between the proposed rule curve formulation and alternative modeling techniques from the literature. The CEP model optimizes installed capacities per technology to achieve predefined VRES penetration targets, considering hourly granularity and separate rule curves for each HPP. A case study involving twelve reservoir hydropower stations and two open-loop pumped hydro stations is examined, accounting for standalone plants and cascaded hydro systems across six river basins. The study evaluates the additional generation and storage required to replace the hydropower fleet under high VRES penetration levels, assessing the resulting increases in total system cost emanating from introducing such new investments. Furthermore, the study approximates the storage capabilities of HPPs and investigates the impact of simplified HPP modeling on system operation and investment decisions. Overall, the findings underscore the importance of reevaluating hydro rule curves for future high VRES penetration conditions and highlight the significance of HPPs in the energy transition towards carbon neutrality. [ABSTRACT FROM AUTHOR]

Wave energy, as a significant renewable and clean energy source with vast global reserves, exhibits no greenhouse gas or other pollution during real-sea operational conditions. However, throughout the entire lifecycle, wave energy convertors can produce additional CO2 emissions due to the use of raw materials and emissions during transportation. Based on laboratory test data from a wave energy convertor model, this study ensures consistency between the model and the actual sea-deployed wave energy convertors in terms of performance, materials, and geometric shapes using similarity criteria. Carbon emission factors from China, the European Union, Brazil, and Japan are selected to predict the carbon emissions of wave energy convertors in real-sea conditions. The research indicates: (1) The predicted carbon emission coefficient for unit electricity generation ( E F co 2 ) of wave energy is 0.008–0.057 kg CO2/kWh; when the traditional steel production mode is adopted, the E F co 2 in this paper is 0.014–0.059 kg CO2/kWh, similar to existing research conclusions for the emission factor of CO2 for wave energy convertor (0.012–0.050 kg CO2/kWh). The predicted data on carbon emissions in the lifecycle of wave energy convertors aligns closely with actual operational data. (2) The main source of carbon emissions in the life cycle of a wave energy converter, excluding the recycling of manufacturing metal materials, is the manufacturing stage, which accounts for 90% of the total carbon emissions. When the recycling of manufacturing metal materials is considered, the carbon emissions in the manufacturing stage are reduced, and the carbon emissions in the transport stage are increased, from about 7% to about 20%. (3) Under the most ideal conditions, the carbon payback period for a wave energy convertor ranges from 0.28 to 2.06 years, and the carbon reduction during the design lifespan (20 years) varies from 238.33 t CO2 (minimum) to 261.80 t CO2 (maximum). [ABSTRACT FROM AUTHOR]

Several northern European countries have announced ambitious plans to become carbon neutral already before the year 2050. Recent research has, however, highlighted how potential bottlenecks in raw material and resource availability could significantly delay or hinder wind and solar photovoltaic (PV) expansion and continued biomass usage in parts of Europe. To address this issue, this paper assesses how exposed the national energy and climate plans (NECPs) of Finland, Estonia, Germany, Sweden, and Denmark are to resource limitations and techno-economic risks by reviewing and analysing 2030 NECP targets compared to statistical energy use data in these countries. The results indicate that the NECPs of Denmark and Germany are particularly exposed to risks related to global raw material availability, as Denmark plans to rapidly grow the share of wind and solar PV in electricity generation to 81% and 13% by 2030, respectively, followed by Germany, which outlines a 39% and 16% share of wind and solar PV in its national climate strategy. The NECPs of Finland and Germany are also shown to be vulnerable to limitations in biomass availability, as there is a significant disparity between the projected biomass usage and legally binding European Union (EU) targets for land use, land use change, and forestry (LULUCF) sector emissions in 2030 in these countries. [ABSTRACT FROM AUTHOR]

Performance assessment of low carbon cities (LCCs) attracts great interest and attention from academia, industry, and government as an effective measure to promote urban low-carbon development. However, the efforts of LCCs could be misinterpreted without consideration of the fact that cities with different endowments face different challenges and assume different responsibilities, thus it is important that this fact is considered in assessing LCCs' performance. This study develops the previous study by the research team, "dual perspective diagnosis method for assessing LCC performance", by introducing a correction factor to take into account the impacts of local endowments and mitigate the 'one-size-fits-all' phenomenon when comparing LCC assessments between cities. The empirical case study presented in the paper indicates that the adoption of a correction factor has improved the accuracy of the assessment results by demonstrating actual management efforts in developing LCC performance. It is proposed that applying the correction factor can help to achieve more accurate assessments of the status of low-carbon city practice, based on which more effective low-carbon policies can be designed and implemented towards achieving carbon peaking and carbon neutrality goals. [ABSTRACT FROM AUTHOR]

Carbon dioxide, the leading contributor to anthropogenic climate change, is released mainly via fossil fuel combustion, mostly for energy generation. Carbon capture technologies are employed for reducing the emissions from existing huge point sources, along with capturing them from direct air, to reduce the existing concentration. This paper provides a quantitative analysis of the various subtypes of carbon capture technologies with the aim of providing an assessment of each from technological, social, geo-political, economic, and environmental perspectives. Since the emissions intensity and quantity, along with the social–political–economic conditions, vary in different geographic regions, prioritising and finding the right type of technology is critical for achieving ambitious net-zero targets. Four main types of carbon capture technology were analysed (adsorption, absorption, membrane, and cryogenic) under four scenarios depending on the jurisdiction. The Technique for Order of Preference by Similarity to Ideal Solution (also known as the TOPSIS method) was used to establish a quantitative ranking of each, where weightages were allocated according to the emissions status and economics of each depending on the jurisdiction. Furthermore, forecasting the trends for technology types vis à vis carbon neutral targets between 2040 and 2050 was carried out by applying regression analysis on existing data and the emissions footprint of major contributing countries. The study found the membrane score to be the highest in the TOPSIS analysis in three of the four scenarios analysed. However, absorption remains the most popular for post-combustion capture despite having the highest energy penalty per ton of CO2 capture. Overall, capture rates are well short of projections for carbon neutrality; the methodology put forward for prioritising and aligning appropriate technologies and the region-by-region analysis will help highlight to technocrats, governments, and policymakers the state of the art and how to best utilise them to mitigate carbon emissions—critical in achieving the net-zero goals set at various international agreements on climate change. [ABSTRACT FROM AUTHOR]