Reduce Carbon Emissions Research Articles

A review of load forecasting technology based on artificial intelligence

Abstract. Driven by the "dual carbon" goals and the construction of new-type power systems, the importance of power load forecasting technology has become increasingly prominent. Accurate power load forecasting can not only ensure the safe and stable operation of the power system, but also promote the efficient use of clean energy and reduce carbon emissions. Traditional power load forecasting methods have become insufficient in dealing with the complexity and nonlinear growth of power systems, and the development of artificial intelligence technology has brought new possibilities for power load forecasting. Power load forecasting methods based on machine learning and deep learning have gradually become a hot topic of research due to their powerful nonlinear mapping capabilities and advantages in processing big data. These methods not only perform well in short-term forecasting, but also show potential in medium- and long-term forecasting. This paper discusses the application of different artificial intelligence technologies in power load forecasting by sorting out the development status of related technologies, and provides a reference direction for future research.

Mix proportion design and carbon emission assessment of high strength geopolymer concrete based on ternary solid waste

To achieve dual optimization of the mechanical properties and environmental impacts of geopolymer concrete (GPC), this study proposes a high-strength geopolymer concrete (HSGPC) without coarse aggregate. The mix proportion of HSGPC was optimized using the response surface methodology, targeting compressive strength and splitting tensile strength to determine the optimal mix. Additionally, the carbon emission impact of HSGPC was assessed and compared with ordinary Portland cement concrete, ultra-high-performance concrete, and reactive powder concrete. The results indicate that the optimal mix proportion for HSGPC includes 15% fly ash content, 10.30% silica fume content, alkali activator ratio of 2.5, and a NaOH molar concentration of 10 M. Simultaneously, the carbon emissions of HSGPC are reduced by about 30% compared to ordinary Portland cement concrete. Compared to ultra-high-performance concrete and reactive powder concrete of the same strength, the production of HSGPC respectively reduces carbon emissions by 59.87% and 68.24%. This study not only provides valuable technical support for the practical application of GPC in engineering but also holds significant implications for promoting sustainable development in the construction industry.

Heterogeneous Catalytic Materials for Carbon dioxide Transformation: Efficient Carboxylation Cyclization to Cyclic Carbonates and Oxazolidinones

The exacerbation of environmental issues is increasingly attributed to the anthropogenic emissions of carbon dioxide (CO2) . CO2 is a cost‐effective and readily available C1 source. The conversion of CO2 into value‐added organic chemicals, such as cyclic carbonate and 2‐oxazolidinone, through carboxylation cyclization reactions shows promise in reducing carbon emissions and combating climate change. The use of catalysts can facilitate this process, with heterogeneous catalysts emerging as favorable due to their advantages of easy recovery and structural stability. This paper provides a comprehensive review of heterogeneous catalysts that have been utilized for the carboxylation cyclization of CO2 to afford cyclic carbonates and oxazolidinones in recent years. The catalysts include metal organic frameworks, covalent organic frameworks, super‐crosslinked polymers, conjugated microporous polymers, porous ionic organic polymers, and composite materials. We give a detailed synthesis route for each type of catalysts. Furthermore, the characteristics of the catalysts, such as BET specific surface area, CO2 adsorption capacity, and catalytic performance, is summarized and analyzed to offer insights for improving heterogeneous catalysts for CO2 conversion via carboxylation cyclization reactions in future research.

Mathematical Modeling and Indirect Carbon Emission Reduction Analysis of Urban Wastewater Treatment Systems Under Different Temperature Conditions

In the context of achieving the two-carbon target, this study utilized a wastewater treatment plant in Shenyang City as a case study to accurately calculate indirect emissions related to energy and chemical consumption within the energy-intensive wastewater treatment industry. Sumo software was employed for precise mathematical modeling. Considering the operational characteristics of wastewater treatment plants in cold regions, this study innovatively divided the annual operation cycle into two periods, namely normal temperature and low temperature, and determined the optimal operational parameters under a low-carbon mode. The results indicate that precise regulation of dissolved oxygen concentration to 0.5–1.5 mg/L (normal temperature period) and 1–2 mg/L (low temperature period) can significantly reduce carbon emissions related to electricity consumption by 13,781.9 t CO2-eq. From the perspective of chemical consumption, adjusting the dosage of polyaluminum chloride (PAC) to 75% and sodium acetate to 70% during the normal temperature period can lead to a reduction in indirect carbon emissions of 1614.4 t CO2-eq compared to the same period last year. During the low-temperature period, by reducing the dosage of polyaluminum chloride to 80% and sodium acetate to 75%, the indirect carbon emissions can be reduced by 1557.3 t CO2-eq compared to the corresponding period last year. After optimization, USD 1.49 million can be saved. This study simulated the operation conditions of cold-region urban wastewater treatment plants at different times to effectively control carbon emissions resulting from energy and chemical consumption in wastewater treatment. This result can provide innovative ideas for energy saving and carbon reduction in cold-region wastewater treatment plants.

Factors Influencing Consumer Upcycling Behavior—A Study Based on an Integrated Model of the Theory of Planned Behavior and the Technology Acceptance Model

In the context of the global climate change debate, changing consumer awareness and guiding them towards sustainable lifestyles should be key considerations. This study investigates the factors influencing consumers’ upcycling behaviors by conducting research and collecting 336 valid questionnaires at the “Eco Blet” sustainable design exhibition and workshop held at Livat Beijing (Ingka Centres Beijing). The survey results revealed that consumers’ upcycling behavior is significantly influenced by subjective norms, perceived behavioral control, perceived usefulness, and attitudes. In addition, individuals with higher levels of education and those with annual incomes of less than 100,000 RMB and more than 400,000 RMB are more likely to engage in upcycling. The empirical analysis of consumer upcycling behavior is conducted using structural equation modeling (SEM), and the theory of planned behavior (TPB) and technology acceptance model (TAM) are integrated into a comprehensive framework. This research provides both theoretical support and practical insights for promoting sustainable consumer behaviors, contributing significantly to carbon emission reduction.

Digital Economy and Urban Carbon Emissions: A Quasi-Natural Experiment of the National Big Data Comprehensive Pilot Zone in China

In this study, we assess the impact of digital economy development on urban carbon emissions using a difference-in-differences (DID) approach based on the quasi-natural experiment of the national big data comprehensive pilot zone (NBDCPZ) policy and panel data from 168 prefecture-level cities in China from 2006 to 2019. We find that the establishment of NBDCPZ has a significant carbon emission reduction effect. This finding is robust to various sensitivity tests. Further mechanism validation illustrates that the construction of NBDCPZ promotes the regional digital development level and reduces urban carbon emissions through three channels: improving energy efficiency to optimize energy consumption, increasing green total factor productivity, and promoting the clustering of high-tech industries. The heterogeneity analysis concludes that the carbon emission reduction effect of NBDCPZ policy varies with different regional locations and different city characteristics. Moreover, using spatial models, we find that the positive impact is characterized by spatial spillover effect. The findings provide detailed empirical evidence to formulate urban development strategies for enabling carbon reduction of the digital economy. JEL Classification: O30, Q53, Q55, Q58, R10

Blue Carbon as a Potent Strategy in the Pathway to Achieve Net Zero

Climate change and its various impacts, including extreme weather events, have recently been recognized as urgent threats to the ecosystems. The Blue Economy, as defined by the World Bank, involves the use of ocean resources to promote sustainable economic propriety, livelihood improvement and job creation while ensuring health of ocean ecosystems. Notably, investing in blue carbon, which encompasses ecosystems like mangroves, tidal and salt marshes, seagrasses can serve as a powerful tool in combatting climate change. Investing in innovative solutions to reduce carbon emissions and funding initiatives to create and expand carbon sinks worldwide are essential components of this endeavour. While technological innovations are a focus, nature-based solutions, particularly the preservation and reclamation of blue carbon ecosystems, can offer an expedient, sustainable and less expensive approach. This research article aims to establish the significance of investing in blue carbon initiatives. In this line, this study proposes that implementing blue carbon initiatives under the voluntary carbon markets would be a key strategy towards achieving the net-zero pathway.

The intersection of green marketing and sustainable supply chain practices in FMCG SMEs

As sustainability increasingly shapes consumer behavior and corporate strategies, Fast-Moving Consumer Goods (FMCG) small and medium-sized enterprises (SMEs) are adopting green marketing and sustainable supply chain practices to gain a competitive edge. This review explores the intersection of these two critical areas, demonstrating how their integration can drive both business growth and environmental responsibility. Green marketing, which focuses on promoting eco-friendly products and sustainable business practices, has become a key tool for FMCG SMEs to differentiate themselves in a crowded marketplace. By adopting strategies such as eco-friendly packaging, ethical sourcing, and transparency in communicating sustainability efforts, SMEs can build stronger consumer trust, foster brand loyalty, and meet the growing demand for environmentally conscious products. Simultaneously, sustainable supply chain practices ensure that the production, distribution, and logistics processes align with environmental and social goals. These practices include reducing carbon emissions, minimizing waste, and working closely with suppliers to ensure ethical sourcing of materials. The synergy between green marketing and sustainable supply chains creates a feedback loop where operational sustainability supports marketing efforts, and consumer demand for green products encourages further sustainability improvements. This review also examines the challenges FMCG SMEs face in aligning these strategies, such as cost barriers, regulatory compliance, and supply chain complexity. Through case studies and industry examples, it highlights how innovative technology and stakeholder collaboration can help overcome these challenges. Ultimately, this study underscores that integrating green marketing and sustainable supply chain practices is not only essential for addressing environmental concerns but also for enhancing competitive positioning, customer satisfaction, and long-term market success in the FMCG sector. Recommendations for SMEs on how to effectively align their marketing and supply chain sustainability efforts are also provided. Keywords: Green Marketing, Supply Chain, FMCG SMEs, Review.

Evaluation of municipal solid waste (MSW) generation rates: A case study in the district of Cumba, Northern Peru

The objective of this research was to evaluate the unit rates of MSW generation in Cumba in the years 2016 and 2022. The calculations were based on the weights of the MSW disposed in the dump located 5 km from the city of Cumba since 2012. The GPC, physical composition, density, humidity were determined in the years 2016 and 2022, studied according to the methodology and group classification of Peruvian regulations. The results show that 5.45 Tn/day−1 are generated in 2016, 4.37 Tn/day−1 in 2022; according to its physical composition, 82% RO, 14% MICVC and 4% MISVC in 2016; 77% RO, 16% MICVC, 7% MISVC in 2022; density 137.90 kg/m−3 in 2016 and 172.69 kg/m−3 in 2022; humidity 67.67% in 2016 and 63.43% in 2022. It was also found that in 100.00% there is no solid waste treatment; Everything generated in homes, businesses and streets is evacuated to the final disposal site, which is a dump. In 2022, Cumba acquired 10 hectares to have adequate sanitary infrastructure and begin the closure and recovery of its current dump. This study will contribute to providing accurate data on MSW generation that allows the local government to promote the optimization of collection routes and schedules, resulting in cost savings and reduction of carbon emissions in the Amazon Region. Therefore, it is necessary to raise awareness at all levels of society through various means of communication and education, so that the risks of spreading health risks can be minimized by improving MSW management.

Research on the green development path of prefabricated building industry based on intelligent technology

PurposeIn order to make the construction industry develop in the direction of greening, this paper analyzes whether the application of intelligent technology in prefabricated buildings can achieve carbon emission reduction, starting from the problems of weak technology and insufficient encouragement policies in the prefabricated building industry. It also designs dynamic and adjustable incentives for the smart transformation of prefabricated buildings and makes recommendations to facilitate the transformation of assembly manufacturers into “smart factories”.Design/methodology/approachThis paper takes the intelligent technology for carbon reduction, energy efficiency and policy design in the prefabricated buildings industry as the starting point. Based on in-depth expert interviews and questionnaire survey data, a linear multiple regression model is used to establish an association network of intelligent technology in the production and transportation, construction, operation and maintenance, demolition and scrapping stages. On this basis, an evolutionary game theory is used to construct a smart transformation and carbon reduction utility game model between the government and manufacturers, and relevant suggestions for smart empowerment of green construction development technology combinations and policy settings are proposed.FindingsAn assembly manufacturing plant with smart empowerment is an important way to achieve green and sustainable development in the construction industry. Among them, BIM and IoT have made a greater impact on carbon emission reduction of prefabricated buildings in all stages of the whole life cycle. The government’s proposed energy efficiency incentives and environmental tax amount will effectively increase companies' motivation for smart transformation of prefabricated buildings. However, when the environmental tax amount is low, the government should strengthen the regulation of the industry in order to increase the speed of smart transformation of assembly manufacturers. Therefore, a reasonable setting of the environmental tax rate and energy-saving incentives and flexible adjustment of the regulatory efforts can maximize the functional utility of the government in the process of smart transformation.Research limitations/implicationsThis paper focuses on the impact of intelligent technologies on the overall carbon emissions of the industry and provides an evolutionary analysis of the strategic game between the government and assembly manufacturers, the main players in the smart transformation process of prefabricated buildings. However, smart technologies for different categories of assembly manufacturing plants and strategic options for a wider range of stakeholders have not been examined in depth.Originality/valueDifferent from existing research, this study focuses on exploring the strategic game between the government and assembly manufacturers in the smart transformation of prefabricated buildings. It provides an innovative explanation of the connection between intelligent technology and carbon emissions. The study develops an evolutionary game model for both parties, addressing the research gap on the combined effects of policy incentives and intelligent technology on carbon reduction and efficiency improvement in the prefabricated buildings industry. This research not only offers practical reference for the government in designing incentive mechanisms and establishing regulatory systems but also provides feasible practical guidance for the smart transformation and carbon reduction efforts of assembly manufacturing plants.

ESG Performance and Enterprise Competitive Advantage Construction: A Case Study of Walmart

This essay opens with an in-depth discussion of the growing importance of ESG concepts in modern corporate management and highlights their significant role in promoting long-term corporate value creation. With the increasing global focus on sustainability, ESG concepts have become an integral part of corporate strategic planning and operational decision-making. Next, this article selects the global retail giant Walmart as a case study and uses SWOT analysis and other management tools to analyse in detail how Walmart has effectively constructed and consolidated its competitive advantages in the highly competitive market through the implementation of an active ESG strategy. The article specifically describes Walmart's practices on several ESG dimensions, including but not limited to the implementation of environmental protection measures, active support to the community, the enhancement of employee welfare, and the optimisation of corporate governance structure. In terms of environmental protection, Walmart has significantly reduced the environmental impact of its operations through measures such as reducing carbon emissions, improving energy efficiency and promoting sustainable products. In terms of community support, the company has strengthened its ties with local communities and enhanced its brand image through various community programmes and charitable activities. Employee well-being has been achieved through improved working conditions and career development opportunities, which have not only increased employee satisfaction but also enhanced the company's attractiveness. The article concludes by summarising the important contribution of Walmart's ESG practices to the company's sustainability and long-term competitive advantage and suggests possible future strategic directions and recommendations for Walmart in ESG based on current market trends and company performance. These recommendations are intended to provide valuable references and insights for Walmart and other companies in their future strategic planning and decision-making processes, especially in terms of how to balance economic efficiency and social responsibility to achieve long-term sustainable development. However, due to the limitation of data and the depth of research on Walmart ESG, the article still has limitations.

Revolutionizing Textile Manufacturing: Sustainable and Profitable Production by Integrating Industry 4.0, Activity-Based Costing, and the Theory of Constraints

The textile industry, a cornerstone of daily life and a highly globalized sector, faces significant environmental challenges due to its high water and energy consumption and extensive chemical usage. This study proposes a comprehensive green production planning and control model integrating Industry 4.0 concepts, activity-based costing (ABC), and the theory of constraints (TOC). The model utilizes mathematical programming to optimize product mix, maximize profitability, and minimize environmental impact. It leverages real-time sensing technologies and ERP systems to facilitate waste recovery, reduce carbon emissions, and achieve energy savings. Various carbon emission cost models, including continuous and discontinuous tax functions, are explored to balance corporate profitability with environmental sustainability. The findings demonstrate the model’s potential in optimizing resource utilization, reducing the environmental footprint, and enhancing profitability.

Artificial Intelligence in Logistics Optimization with Sustainable Criteria: A Review

In recent years, the integration of artificial intelligence (AI) into logistics optimization has gained significant attention, particularly concerning sustainability criteria. This article provides an overview of the diverse AI models and algorithms employed in logistics optimization, with a focus on sustainable practices. The discussion covers several techniques, including generative models, machine learning methods, metaheuristic algorithms, and their synergistic combinations with traditional optimization and simulation methods. By employing AI capabilities, logistics stakeholders can enhance decision-making processes, optimize resource utilization, and minimize environmental impacts. Moreover, this paper identifies and analyzes prominent challenges within sustainable logistics, such as reducing carbon emissions, minimizing waste generation, and optimizing transportation routes while considering ecological factors. Furthermore, the paper explores emerging trends in AI-driven logistics optimization, such as the integration of real-time data analytics, blockchain technology, and autonomous systems, which hold immense potential for enhancing efficiency and sustainability. Finally, the paper outlines future research directions, emphasizing the need for further exploration of hybrid AI approaches, robust optimization frameworks, and scalable solutions that accommodate dynamic and uncertain logistics environments.

Modeling of Carbon Footprint Emissions in Sugarcane Production Using System Dynamics

Objective: This study proposes a model aimed at estimating and reducing carbon emissions in sugarcane cultivation and juice extraction, designed for potential application in real-world systems. Method: The model grounded in a hypothetical case study of a 60-hectare sugarcane plantation in a warm climate with a 7-month growth cycle and a three-year simulation period, focuses on estimate and evaluate mitigation scenarios to decrease emissions from fuel and electricity usage in activities such as plowing, sowing, harvesting, and irrigation. Utilizing Vensim PLE, a System Dynamics model that combine event and dynamic simulation. Results and Discussion: Utilizing Vensim PLE, a System Dynamics model that combine event and dynamic simulation estimated a 3060.81 tons CO2 equivalent carbon footprint. Two scenarios aimed at footprint reduction were tested: substituting electrical energy with solar power reduced the footprint by 86% to 450.092 tons, demonstrating clean energy's efficacy. Conversely, replacing an industrial mill with the traditional trapiche technique for juice extraction yielded a mere 1% reduction, indicating its ineffectiveness for real-world application. Research Implications: This study emphasizes the development of a simulation model based on system dynamics to estimate the carbon footprint emissions in sugar cane production considering its cultivation and juice extraction. The results support that through simulations, it is possible to determine the factors that influence the generation of carbon dioxide, offering a broader vision to establish and implement mitigation measures such as adopting clean energies and reducing fuel consumption. Originality/Value: This research contributes to the understanding of the application of system dynamics by using software such as Vensim PLE to estimate the carbon footprint emissions in sugar cane production, considering its cultivation and juice extraction. The results found through simulations suggest the implementation of mitigation measures such as the adoption of clean energies and the reduction of fuel consumption.

Clustering of tropical species through multivariate analysis of the bonding properties of edge-glued panels (EGP)

Tropical woods have high added value, and the use of waste for panel production is an initiative to add more value to this material, typically consisting of various species. The use of raw materials from tropical solid wood waste in the Amazon region contributes to the reduction of carbon emissions associated with the burning of this material. In this context, this study aimed to cluster species based on the bonding properties of Amazonian woods by applying multivariate analysis. The apparent density 12% and strength of finger joint and edge-glued joints of Cedrela odorata, Enterolobium schomburgkii, Erisma uncinatum and Qualea paraensis was evaluated which were joined in homogeneous and hybrid configurations with the adhesives PVA and EPI, as spreading rate of 180 g/m2. The treatments were compared by multivariate analysis of variance and discriminant analysis. The discrimination of species and combinations based on wood density (low, medium, and high) revealed that this is the most important characteristic for grouping different species that make up tropical hardwood waste for the production of EGP panels. In practice, the separation of species into groups according to wood densities can contribute to a better definition of the indications for use of edge glued panels of each density class, and assertive targeting for applications according to the strength ranges required. In the static bending, parallel tensile and shear tests all species and their combinations met the minimum requirements normative, indicating the approval of species and combinations studied for the production of EGP panels.

Measurement methods and influencing factors of carbon emissions from residents' travel

Residents' travel is a significant source of transport carbon emissions. Revealing the mechanisms behind travel-related carbon emissions can assist transportation departments in developing effective emission reduction policies. However, current research often focuses on specific aspects such as measurement accuracy or the impact of certain factors, lacking a comprehensive review and discussion. This study addresses the research gap by conducting a systematic literature review on measurement methods and influencing factors of carbon emissions from residents' travel. Through evaluating each method's applicability and limitations, we establish a systematic framework for measuring carbon emissions from residents' travel. By selecting appropriate measurement methods, detailed quantitative analysis of influencing factors is enabled, providing crucial data for subsequent emission reduction strategies. Influencing factors are categorized into four groups, including public infrastructure, road traffic facilities, land use and population, and traveler's socio-economic attributes, with a total of nine sub-factors across these categories. The relationship between each factor and carbon emissions is examined through statistical and qualitative analyses, establishing a robust quantitative analysis methodology for future research. Finally, a roadmap for reducing carbon emissions in residents' travel is presented.

An Analysis of the Global EV Market: Sustainable Pathways to Net Zero Emissions

The rising urgency to address climate change has positioned electric vehicles (EVs) as a critical solution for achieving the United Nations' net-zero carbon emission targets. As human activities increase greenhouse gas emissions, the average global temperature continues to rise, leading to severe natural disasters in many regions. This situation underscores the urgent need to limit pollutant emissions and pursue sustainable resource management. This study analyzes the global EV market's trajectory towards sustainability, focusing on the interplay of policy frameworks and consumer engagement strategies in major EV markets, including the USA, the European Union, and China. The transition to EVs must be considered in conjunction with policy frameworks and the broader social environment. Using mixed methods and statistical models, we assess how different policies influence EV adoption and their alignment with sustainability goals. As the concept of sustainable development becomes more ingrained in society, it is promising that, through sustained policy implementation and resource development, the shift from ICE vehicles to EVs will make a crucial contribution to reducing carbon emissions. The findings provide actionable insights for optimizing policies and refining marketing strategies, offering a comprehensive blueprint for guiding the global EV market towards a sustainable future.

Thermodynamic evaluation of a Ca-Cu looping post-combustion CO2 capture system integrated with thermochemical recuperation based on steam methane reforming

The calcium looping integrated with the chemical looping combustion (CaL-CLC) process is an efficient and cost-effective CO2 capture technology that avoids the energy-intensive air separation unit in the calcium looping (CaL) process. However, in these CaL-CLC and CaL system integration schemes, the carbonation heat is utilized for steam generation, resulting a significant temperature difference and considerable irreversible loss. To prevent temperature mismatch, this paper proposes a novel Ca-Cu looping post-combustion CO2 capture method with thermochemical recuperation based on steam methane reforming. Additionally, a novel system integrated with turbine exhaust heat recovery is introduced to effectively reduce carbon emissions from flue gas. Results show that the proposed system has superior performance compared to the reference system based on the Ca-Cu looping method. The specific primary energy consumption for CO2 avoidance decreased from 2.40 MJLHV/kg CO2 in the reference system to 2.02 MJLHV/kg CO2. Exergy analysis indicates that a total of 3.0 % reduction in exergy destruction can be achieved in chemical reaction processes and heat recovery processes, contributing to the superior performance of the proposed system. Furthermore, the effects of key operating parameters indicate that cascaded turbine exhaust recovery is essential for improving the thermodynamic efficiency of the proposed system. Overall, recovering the mid-temperature carbonation heat via thermochemical regeneration and integrating with exhaust heat recovery contribute to reducing SPECCA, thus providing a promising low-energy-consumption alternative for CO2 capture.

The Role of Low-Carbon Fuels and Carbon Capture in Decarbonizing the U.S. Clinker Manufacturing for Cement Production: CO2 Emissions Reduction Potentials

Low-carbon fuels, feedstocks, and energy sources can play a vital role in the decarbonization of clinker production in cement manufacturing. Fuel switching with renewable natural gas, green hydrogen, and biomass can provide a low-carbon energy source for the high-temperature process heat during the pyroprocessing steps of clinker production. However, up to 60% of CO2 emissions from clinker production are attributable to process-related CO2 emissions, which will need the simultaneous implementation of other decarbonization technologies, such as carbon capture. To evaluate the potential of fuel switching and carbon capture technologies in decarbonizing the cement industry, a study of the facility-level CO2 emissions is necessary. This study evaluates the potential for using a single low-carbon fuel as an energy source in clinker production for cement manufacturing compared to conventional clinker production (which uses a range of fuel mixes). In addition, conventional carbon capture (operated with natural gas-based steam for solvent regeneration) and electrified carbon capture configurations were designed and assessed for net-zero emission targets. Carbon emissions reductions with and without biogenic emissions credits were analyzed to ascertain their impact on the overall carbon accounting. Results show that carbon emissions intensity of cement can vary from 571 to 784 kgCO2eq/metric ton of cement without carbon capture and from 166.33 to 438.66 kgCO2eq/metric ton of cement with carbon capture. We find that when biogenic carbon credits are considered, cement production with a sustainably grown biomass as fuel source coupled with conventional carbon capture can lead to a net-negative emission cement (−271 kgCO2eq/metric ton of cement), outperforming an electrified capture design (35 kgCO2eq/metric ton of cement). The carbon accounting for the Scope 1, 2, and biogenic emissions conducted in this study is aimed at helping researchers and industry partners in the cement and concrete sector make an informed decision on the choice of fuel and decarbonization strategy to adopt.

City-wide Solar Radiation Potential Analysis by Coupling Physical Modelling and Machine Learning

Abstract. Addressing the challenges posed by climate change and meeting urban energy demands is of utmost importance in today's world. Building Integrated Photovoltaics (BIPV) emerges as a crucial solution for energy conservation and carbon emissions reduction in urban environments. However, traditional methods of assessing solar radiation on buildings using physical models are often computationally intensive and time-consuming. This paper introduces a novel hybrid approach that integrates physical model-based solar radiation calculation with machine learning techniques to analyze Solar Radiation Potential (SRP) across city-wide building infrastructure. The proposed approach precisely evaluates the SRP of representative blocks by leveraging computing-intensive physical models integrated with 3D building data. Subsequently, two machine learning models are developed to effectively predict the SRP of building roofs and facades across the entire city. To validate the efficacy of this approach, an experiment was conducted in Shenzhen, China, yielding insightful results. The findings reveal that Shenzhen has a huge potential for BIPV solar power generation, with mean annual total building roof and facade solar radiation values of 9.22*107 kwh and 2.47*108 kwh, respectively. It can be further observed that relying solely on rooftop installations is insufficient to meet electricity demand. This study not only provides an innovative alternative for city-wide SRP estimation by combining physical modeling and machine learning but also offers valuable insights for fostering low-carbon urban environments and informing data-driven and model-driven urban planning and management strategies.