Carbon Emission Monitoring Research Articles

Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion

Global biomass burning represents a significant source of carbon emissions, exerting a substantial influence on the global carbon cycle and climate change. As global carbon emissions become increasingly concerning, accurately quantifying the carbon emissions from biomass burning has emerged as a pivotal and challenging area of scientific research. This paper presents a comprehensive review of the primary monitoring techniques for carbon emissions from biomass burning, encompassing both bottom-up and top-down approaches. It examines the current status and limitations of these techniques in practice. The bottom-up method primarily employs terrestrial ecosystem models, emission inventory methods, and fire radiation power (FRP) techniques, which rely on the integration of fire activity data and emission factors to estimate carbon emissions. The top-down method employs atmospheric observation data and atmospheric chemical transport models to invert carbon emission fluxes. Both methods continue to face significant challenges, such as limited satellite resolution affecting data accuracy, uncertainties in emission factors in regions lacking ground validation, and difficulties in model optimization due to the complexity of atmospheric processes. In light of these considerations, this paper explores the prospective evolution of carbon emission monitoring technology for biomass burning, with a particular emphasis on the significance of high-precision estimation methodologies, technological advancements in satellite remote sensing, and the optimization of global emission inventories. This study aims to provide a forward-looking perspective on the evolution of carbon emission monitoring from biomass burning, offering a valuable reference point for related scientific research and policy formulation.

Spatiotemporal distribution of pCO2 and CO2 flux and the regulatory factors: From the perspective of a subtropical canyon-shaped reservoir, southwest China

Reservoir carbon cycling is a critical component of the global carbon cycle, particularly in subtropical canyon-shaped reservoirs where unique geomorphological features and hydrological regulation under monsoon climate provide a distinct perspective for carbon cycle research. This study takes the Zipingpu Reservoir (ZPPR) in southwestern China as an example and reveals significant seasonal variations in partial pressure of carbon dioxide (pCO2) and carbon dioxide flux at the water-air interface (FCO2) through annual monitoring. The average pCO2 in ZPPR is 486 μatm, and the average FCO2 is 0.093 g C m-2d-1, with approximately 42.4 % of FCO2 measurements being negative, indicating the reservoir's role can act as a carbon sink. The study also estimated the gas transfer coefficient (K), with an average value of 1.888 md-1. Peak FCO2 during spring and summer is three times higher than in other seasons, consistent with the main influencing factors of inflow dynamics, metabolic processes, and reservoir operation strategies. Inflow and operational activities, particularly during spring and summer, drive CO2 emissions in the downstream reach and upstream tributary, while biological activity facilitates CO2 uptake in the lacustrine area and forebay tributary, with enhanced absorption occurring in the autumn. Concurrently, high-intensity FCO2 emissions from the upstream river section during summer underscore the importance of carbon emission monitoring and management during critical periods. The findings not only enhance the understanding of FCO2 assessment accuracy but also provide a framework for evaluating and optimizing carbon dynamics management strategies in canyon-shaped reservoirs, contributing valuable insights to global carbon cycle research.

Mechanism of Carbon Monoxide (CO) Generation and Potential Human Health Hazard during Mechanized Tunnel Driving in Organic-Rich Rocks: Field and Laboratory Study

The monitoring of carbon emissions is increasingly becoming a sustainability issue worldwide. Despite being largely unnoticed, the toxic gas carbon monoxide (CO) is ubiquitous in mechanized tunnel driving, but the individual sources, release and enrichment mechanisms are often unknown. In this study, the generation of CO from organic matter containing sedimentary rocks was investigated during mechanized tunnel driving and by reacting claystone and sandstone with 10 mM NaCl solutions for 2 months at 70 °C and 140 °C. The mineralogical and geochemical evolution of the solids and fluids was assessed by CO measurements and the XRD, DTA, TOC, IC and ICP-OES methods. The CO concentration in the atmosphere reached up to 1920 ppm (100 ppm on average) during tunnel driving, which is more than three times higher than the legal daily average dose for tunnellers, thus requiring occupational safety operations. Mineral-specific dissolution processes and the rapid decomposition of labile organic matter upon thermal alteration contributed to the liberation of CO and also carbon dioxide (CO2) from the host rocks. In mechanized tunnel driving, frictional heat and ‘cold’ combustion with temperatures reaching 50–70 °C at the drill head is an important mechanism for increased CO and CO2 generation, especially during drilling in sedimentary rocks containing significant amounts of OM and when the ventilation of the tunnel atmosphere and air mixing are limited. Under such conditions, human health damage due to CO exposure (HHDCO) can be 30 times higher compared to tunnel outlets, where CO is emitted from traffic.

The Evaluation of Enterprise Carbon Trading Audit Based on DSR Model and GHG Accounting

INTRODUCTION: With the continuous development of social economy, environmental issues are becoming increasingly apparent. How to maintain the coordination of environmental and economic development is an urgent issue to be addressed. This study takes carbon trading as an example, starting with enterprise CAE, proposes a CAE index system that combines DSR model and GHG accounting, and hopes to urge enterprises to reduce carbon emissions during development.OBJECTIVES: Carbon audit is an important way to evaluate the low carbon development of enterprises. To effectively realize the evaluation of enterprise carbon audit, the study takes cement enterprises for instance and proposes a carbon audit evaluation (CAE) standard.METHODS: Firstly, it adopts the greenhouse gas (GHG) accounting method to monitor the carbon release of cement companies, and secondly, the main factors are analyzed based on the data obtained from the monitoring, and then the driving-force state-response (DSR) index system is constructed to realize the evaluation of carbon trading audit of cement enterprises.RESULTS: The results show that the accuracy of the adopted carbon emission monitoring method is 99.3%, which is significantly higher than the other methods, and the error is only 0.1 after stabilization. Finally, after the CAE, we can see that the CAE method proposed by the study is reasonable and feasible with the actual situation with a fit of 0.954, which means that the evaluation method proposed by the study is reasonably practicable.CONCLUSION: It is necessary to combine the DSR model with the GHG accounting approach to conduct the enterprise CAE, which can improve the enterprise CAE system and is of great value to the improvement of the efficiency of the enterprise CAE.

A distribution network carbon metering method and user carbon emission characterization method considering the influence of harmonics

Abstract This paper presents a novel carbon metering method for distribution grids that fully considers the impact of harmonics on the grid. Harmonics are sinusoidal waves with frequencies that are integer multiples of the fundamental frequency, and the presence of harmonics can lead to power losses, damage to equipment and degradation of power quality, thus increasing carbon emissions from the grid. Currently available carbon measurement methods often do not take this into account, so a more accurate method is needed to assess the carbon emissions of distribution grids. This new method, based on the carbon flow theory of trending power distribution, combines harmonic energy correction and node carbon potential correction, and is able to accurately calculate the carbon emissions as well as the harmonic carbon correction for each node and branch in the distribution network. In order to evaluate the carbon behavior characteristics of users more comprehensively, the study also adopts the DTW-K-Means clustering technique to group distribution network users. This innovative approach provides an effective technical means and theoretical basis for the monitoring, assessment and management of distribution network carbon emissions. This innovative method provides effective technical means and theoretical basis for distribution network carbon emission monitoring, assessment and management, not only considering the impact of harmonics on power metering, but also more accurately portraying the carbon behavior of users, which is expected to provide strong support for the reduction of grid carbon emissions.

A near real-time carbon accounting framework for the decarbonization of maritime transport

The recently proposed marine greenhouse gas (GHG) emission pricing mechanism has pressured shipping companies and regulators to adopt effective methods for the real-time monitoring of carbon emissions. This study proposes a Near Real-Time (NRT) carbon accounting framework that leverages machine learning models to enable carbon emission tracking at a 15-minute time interval. The framework incorporates critical factors, such as ship navigation characteristics, weather, and sea conditions to achieve accurate carbon accounting. We validate the framework’s efficacy through a case study of four mega-container ships of varying sizes and navigation scenarios. Our results show a maximum cumulative error of 5.83% for all ship navigation scenarios, even without critical data, and during the most extended voyages of their respective services. The proposed framework provides a new perspective on the decarbonization application of ship energy efficiency prediction research. By integrating it with a cloud-computing platform, shipping companies can enhance their voyage planning and route adjustment to optimize operational efficiency and reduce carbon footprints. Using this framework, international maritime transport regulators can develop an early warning system for carbon emissions to coordinate and improve environmental sustainability practices in the shipping industry.

Carbon emission warning method for machine tool manufacturing process based on dynamic adaptive EWMA control chart.

Machine tools constitute the backbone of the industrial sector, representing the largest global inventory of equipment. The carbon emissions resulting from the production of each machine tool merit attention. Effective management of carbon emissions in the machine tool manufacturing process is crucial. This paper introduces a novel method for early carbon emission warnings in the machine tool manufacturing process, utilizing an adaptive dynamic exponentially weighted moving average (EWMA) approach. This method addresses the challenges in identifying and monitoring abnormal carbon emissions, emerging from uncertainties and dynamic correlations. Utilizing dynamic sampling techniques and adaptive principles, this method constructs an adaptive dynamic EWMA control chart. The EWMA control chart incorporates a multi-objective optimization design model, concentrating on carbon emissions in the machine tool manufacturing process, and incorporates statistical, economic, and environmental objectives. To mitigate slow convergence rates and enhance optimization accuracy in complex control chart multi-objective optimization algorithms, this study proposes an enhanced Harris hawks optimization (HHO) algorithm as the solving algorithm. Finally, the application of this method is demonstrated through the monitoring of carbon emissions in the manufacturing process of a five-axis machine tool (EOC), as a case study. The results validate the method's rapid responsiveness to abnormal carbon emissions, providing alerts. This further confirms the efficacy and feasibility of the proposed approach. Ultimately, this approach offers a viable strategy for fostering environmentally conscious and high-quality growth in the machine tool industry.

Balancing growth and sustainability: The impact of Greenfield investment on trade adjusted carbon emissions

In the last two decades, the surge in carbon emissions has escalated environmental damage and is a major concern globally. Recognized as a significant threat to humanity, unchecked environmental degradation can potentially hinder the achievement of sustainable development. As a result, accurate monitoring of carbon emissions becomes imperative for formulating effective climate policies. Taking into consideration, this study has taken the newly developed consumption-based carbon emissions measure to study the pollution haven hypothesis and examine the link between Greenfield Investment (GFI) inflows to host nations and their environmental impact for 85 developing countries from 1990 to 2020. The results show a positive correlation between Greenfield investment and Consumption-based Carbon Dioxide Emissions (CCO2) in sampled nations. Similarly, energy usage and export damage the environment because developing countries rely on conventional and old methods of energy usage. The results were further analyzed for low, lower middle, and upper middle income countries as well. The subsample outcome shows that Greenfield investment has a more damaged environment in low income countries as compared to lower middle and upper middle income countries. These insights underscore the urgency for developing countries to adopt environmentally conscious policies to attract international investors. It also emphasizes the need for stringent regulations aimed at curbing environmental pollution and complying with the Sustainable Development Goals (SDGs). Similarly, low and lower middle income countries to attract Greenfield investment, may also focus more on strict environmental pollution policies. Industries must be shifted from conventional energy methods to renewable energy sources. Sustainable Development Goals; 7, 12, and 13 can be achieved by host countries, alluring investors to invest in terms of Greenfield in renewable energy resources, which would be used in automobile transportation, to shift industries from conventional energy resources to renewable energy resources. The same Greenfield investment would also be used in bringing efficient machinery for more production in industries with minimal environmental pollution.

ANALYSIS OF THE IMPACT OF SHARDING ON THE SCALABILITY AND EFFICIENCY OF BLOCKCHAIN TECHNOLOGIES FOR THE CREATION OF INFORMATION-ANALYTICAL SYSTEMS FOR ENVIRONMENTAL MONITORING OF EMISSIONS INTO THE ENVIRONMENT

This study examines the impact of sharding on the scalability and efficiency of blockchain systems, specifically in the development of a complex of intelligent information and communication systems for environmental monitoring of emissions into the environment for decision-making in the context of carbon neutrality. Utilizing the Ikarus Network infrastructure, sharding was implemented on masternodes as a key technology to optimize transaction processing. Sharding enables the blockchain to be divided into multiple parallel chains, significantly increasing throughput and reducing the load on individual nodes. The results demonstrate a 70% increase in transaction processing speed, allowing the system to handle up to 5000 transactions per second, compared to the previous 3000 transactions per second. Network throughput increased by 50%, ensuring more efficient load distribution and stable operation even with high data volumes. Statistical analysis using ANOVA confirmed significant improvements in transaction processing speed, confirmation time, and resource usage post-sharding implementation. The F-value for transaction processing speed was 4567 with a P-value of 0.0001, indicating substantial improvements. Visual data analysis further confirmed these results, showing noticeable performance enhancements in the blockchain system. Distribution charts and histograms of transaction processing speed and confirmation time revealed an increase in the average number of transactions per second and greater system stability post-sharding. Sharding not only increased throughput but also enhanced system security by decentralizing data among shards, complicating potential cyberattacks. The study aimed to determine how sharding can improve the scalability and efficiency of blockchain systems. These improvements position the Ikarus Network as a promising solution for scalable and secure blockchain-based applications, especially for tasks related to carbon emission monitoring and management. These findings can underpin further study and the development of more efficient blockchain technologies.

Achieving corporate carbon neutrality: A multi-perspective framework

The objective of this study is to analyze the corporate disclosures related to carbon neutrality in the agri-food sector using several complementary theoretical lenses. A qualitative content analysis of climate disclosures made by 135 food and beverage companies shows numerous gaps in the information they provide about carbon emission monitoring, the setting of net-zero emission targets, carbon footprint reduction, and offsetting practices. Taken together, these seriously undermine the credibility of the data disclosed. However, the proliferation of opaque information, unclear targets, and poorly defined concepts on carbon neutrality cannot be explained solely by greenwashing practices. Interpreting the results using functionalist, critical, and postmodernist perspectives leads to different conclusions about the underlying reasons for the lack of transparency in disclosures and offers a broader perspective on corporate carbon neutralization practices. This study makes important contributions to the literature on corporate carbon disclosure and the ideology of numbers that characterizes sustainability reporting. It also responds to calls from a variety of stakeholders for more scrutiny of corporate climate performance. The practical implications of the study and avenues for future research are also discussed.

Research on Carbon Emissions Estimation in Key Industries Based on the Electricity–Energy–Carbon Model: A Case Study of Henan Province

This study focuses on the carbon emissions of key industries in Henan Province, employing techniques of seasonal adjustment, frequency transformation, and statistical modeling to construct an industry-level “Electricity–Energy–Carbon” model to aid in the high-frequency monitoring of carbon emissions in the province’s industries. Based on relevant data, this research performs high-frequency calculations of carbon emissions from energy consumption in 34 typical industries and from the production processes of 53 typical sub-categories in the industrial sector of Henan. The findings reveal the following: Firstly, industrial energy consumption in Henan accounts for over half of the total provincial energy consumption, with most months seeing proportions around 60%. Industries such as energy, non-ferrous metals, building materials, steel, chemicals, petrochemicals, and paper making contribute to over 80% of the industrial energy consumption’s carbon emissions, often nearing 90% in most months. Secondly, among the major industries, such as non-ferrous metals, chemicals, building materials, and steel, there is a dual challenge of being restricted under the “high energy consumption and high emissions” project while also being required to build key industrial bases, leading to fluctuating trends in historical annual carbon emissions data. Thirdly, six sub-categories, namely plastic products, cement, flat glass, steel, ten types of non-ferrous metals, and alumina, have significant carbon emissions in their production processes, accounting for about 72.3% of the total production-related emissions.

Monitoring high-carbon industry enterprise emission in carbon market: A multi-trusted approach using externally available big data

Carbon markets are widely recognized as effective strategies for regulating carbon emissions from the enterprises of the high-carbon industry. Accurate monitoring of emissions in these enterprises is crucial for ensuring fair and trusted carbon trading. However, traditional monitoring methods have encountered various challenges including poor accuracy, low frequency, significant hysteresis and weak credibility. Addressing these concerns, this paper introduces a novel, multi-trusted, high-frequency monitoring approach for the measurement of both direct and indirect carbon emissions of enterprises, utilizing externally available multi-source big data, vertical federated long short-term memory network with a self-attention mechanism (VF-LSTMSA) and additive homomorphic encryption. The efficacy of this model is thoroughly assessed via case studies on six hourly datasets from the power and steel sectors. Our principal findings include: (1) In the current scenario, the proposed VF-LSTMSA model demonstrates superior federated efficiency, improving with the inclusion of more federated participants. (2) The VF-LSTMSA model surpasses other prevalent AI models, achieving a Mean Absolute Percentage Error (MAPE) of less than 9% across power and steel datasets. (3) The advanced high-frequency carbon emission monitoring method exhibits errors of +0.28%, +1.29%, and −0.41% in the current scenario monitoring task, suggesting that the model effectively leverages multi-source externally available big data to precisely monitor the target enterprise's carbon emissions while safeguarding multi-party data security. These findings suggest that the proposed monitoring method can accurately track corporate hourly carbon emissions, significantly improving the carbon trading and verification processes for enterprises

The Optuna–LightGBM–XGBoost Model: A Novel Approach for Estimating Carbon Emissions Based on the Electricity–Carbon Nexus

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.

Study on real-time estimation of carbon emissions based on the non-intrusive power load monitoring

Monitoring carbon emissions, as well as evaluating and controlling the level of carbon emissions, are important prerequisites for combating climate change and promoting sustainable development. In this paper, real-time estimation of carbon emissions based on non-intrusive electricity load monitoring is investigated, existing machine learning approaches and related issues of load identification and marginal carbon emission factors are elucidated, and existing carbon emission monitoring approaches are analyzed and summarized. The methods of real-time carbon emission measurement are summarized, and their development is anticipated. Most of the existing methods for measuring and analyzing carbon emissions in power systems are unable to meet the development needs of real-time carbon emission calculation and have the disadvantages of higher installation and maintenance costs and poorer economy of monitoring equipment. Therefore, in this paper, a real-time carbon emission monitoring scheme based on deep learning is used to estimate carbon emissions by dividing them into direct carbon emissions and indirect carbon emissions.

Analyzing carbon emissions and influencing factors in Chengdu-Chongqing urban agglomeration counties

Majority of carbon emissions originate from fossil energy consumption, thus necessitating calculation and monitoring of carbon emissions from energy consumption. In this study, we utilized energy consumption data from Sichuan Province and Chongqing Municipality for the years 2000 to 2019 to estimate their statistical carbon emissions. We then employed nighttime light data to downscale and infer the spatial distribution of carbon emissions at the county level within the Chengdu-Chongqing urban agglomeration. Furthermore, we analyzed the spatial pattern of carbon emissions at the county level using the coefficient of variation and spatial autocorrelation, and we used the Geographically and Temporally Weighted Regression (GTWR) model to analyze the influencing factors of carbon emissions at this scale. The results of this study are as follows: (1) from 2000 to 2019, the overall carbon emissions in the Chengdu-Chongqing urban agglomeration showed an increasing trend followed by a decrease, with an average annual growth rate of 4.24%. However, in recent years, it has stabilized, and 2012 was the peak year for carbon emissions in the Chengdu-Chongqing urban agglomeration; (2) carbon emissions exhibited significant spatial clustering, with high-high clustering observed in the core urban areas of Chengdu and Chongqing and low-low clustering in the southern counties of the Chengdu-Chongqing urban agglomeration; (3) factors such as GDP, population (Pop), urbanization rate (Ur), and industrialization structure (Ic) all showed a significant influence on carbon emissions; (4) the spatial heterogeneity of each influencing factor was evident.

Strategies for Mitigating Urban Residential Carbon Emissions: A System Dynamics Analysis of Kunming, China

To effectively combat environmental challenges, it is necessary to evaluate urban residential building carbon emissions and implement energy-efficient, emission-reducing strategies. The lack of a specialized carbon emission monitoring system complicates merging macro- and micro-level analyses to forecast urban residential emissions accurately. This study employs a system dynamics (SD) model to examine the influence of social, economic, energy, and environmental factors on carbon emissions in urban residences in Kunming, China. The SD model forecasts household carbon emissions from 2022 to 2030 and establishes three scenarios: a low-carbon scenario (LCS), a medium low-carbon scenario (MLCS), and a high low-carbon scenario (HLCS) to assess emission reduction potentials. It predicts emissions will climb to 4.108 million tons by 2030, significantly surpassing the 2014 baseline, with economic growth, urbanization, residential energy consumption, and housing investment as key drivers. To curb emissions, the study suggests enhancing low-carbon awareness, altering energy sources, promoting research and development investment, and expanding green areas. The scenarios indicate a 5.1% to 16.1% emission reduction by 2030 compared to the baseline. The study recommends an 8.3% to 11.4% reduction in MLCS as a practical short-term target for managing urban residential emissions, offering a valuable SD approach for optimizing carbon strategies and aiding low-carbon development.

Estimating On-Road Transportation Carbon Emissions from Open Data of Road Network and Origin-Destination Flow Data

Accounting for over 20% of the total carbon emissions, the precise estimation of on-road transportation carbon emissions is crucial for carbon emission monitoring and efficient mitigation policy formulation. However, existing estimation methods typically depend on hard-to-collect individual statistics of vehicle miles traveled to calculate emissions, thereby suffering from high data collection difficulty. To relieve this issue by utilizing the strong pattern recognition of artificial intelligence, we incorporate two sources of open data representative of the transportation demand and capacity factors, the origin-destination (OD) flow data and the road network data, to build a hierarchical heterogeneous graph learning method for on-road carbon emission estimation (HENCE). Specifically, a hierarchical graph consisting of the road network level, community level, and region level is constructed to model the multi-scale road network-based connectivity and travel connection between spatial areas. Heterogeneous graphs consisting of OD links and spatial links are further built at both the community level and region level to capture the intrinsic interactions between travel demand and road network accessibility. Extensive experiments on two large-scale real-world datasets demonstrate HENCE's effectiveness and superiority with R-squared exceeding 0.75 and outperforming baselines by 9.60% on average, validating its success in pioneering the use of artificial intelligence to empower carbon emission management and sustainability development. The implementation codes are available at this link: https://github.com/tsinghua-fib-lab/HENCE.

Research on the fine-scale spatial-temporal evolution characteristics of carbon emissions based on nighttime light data: A case study of Xi'an city

Carbon emissions have resulted in severe ecological damage, jeopardizing the very existence of humanity. In China, carbon emissions from urban areas contribute to 80% of the total carbon emissions, this significant contribution underscores the importance of addressing urban emissions. To gain insights into the spatial patterns of carbon emissions in urban areas at the fine-scale regional level, this study utilizes Xi'an as a case study, constructs a fine-scale regional carbon emission estimation model based on energy consumption and NPP-VIIRS-like NTL data, and quantifies multiscale carbon emissions from 2000 to 2021. Furthermore, global spatial autocorrelation, cold and hot spot analysis, and standard deviation ellipse analysis were used to investigate the spatial characteristics of carbon emissions at the street (township) level in Xi'an. This study revealed a sixfold increase in carbon emissions in Xi'an, rising from 51.35 million tons in 2000 to 281.95 million tons in 2021.The area affected by carbon emissions has expanded, with the centre of gravity of emission consistently shifting towards the south. The average street-level carbon emission density fluctuated and doubled compared to that in 2000. The total number of streets with low-carbon-emission-density decreased by 23%, indicating a shift away from low-carbon-emission-density streets. Furthermore, the global Moran's I index consistently remained within the range of 0.85–0.95, indicating significant spatial clustering, with cold spots expanding into surrounding districts and counties. Additionally, this study accurately identified carbon emissions within the city, offering the potential for the development of more precise carbon reduction strategies. Moreover, this approach can provide valuable support for the dynamic monitoring of carbon emissions in developing cities worldwide.

Carbon emission monitoring method and empirical study of expressway construction period

In order to improve the ability of carbon emission management in expressway construction, the precision management of carbon emission is realized. In this paper, the construction period carbon emission monitoring method is proposed, the carbon emission monitoring scope is defined, and the carbon emission monitoring content and method are determined. On this basis, taking the Zhangjinggao Yangtze River Bridge project as an example, analyze the monitoring data of construction carbon emissions. The results of the study showed that from July to December, the total carbon emission from construction was 668.30t. Among them, the carbon emissions of electricity, machinery and equipment, labor and construction water accounted for 47.02%, 40.68%, 11.99% and 0.31% of the total carbon emissions, respectively. In addition, the unit time carbon emission of the excavator is 12.12kg/h; The unit time carbon emission of the truck crane is 1.35kg/h.

Analysis of Carbon Footprint Reduction in Supply chains using Blockchains

Integrating carbon footprint reduction into contemporary supply chain networks necessitates strong cybersecurity and data integrity protocols to safeguard communication infrastructures against threats. This study examines the use of blockchain technology as a cybersecurity and traceability framework in supply chain systems, using simulated data that represents logistical information, shipping quantities, carbon emissions, and blockchain transactions. An investigation indicates varied carbon-emitting mechanisms, with air freight exhibiting the greatest emissions and rail transit the lowest. Transportation nodes in Supply varying emission rates, with urban distribution centres generating more emissions than rural logistics hubs, underscoring disparities in carbon footprints across different areas. Moreover, risks such data breaches, illegal access, and cyberattacks underscore the fragility of centralized supply chain systems. Blockchain transactions exemplify secure and transparent exchanges throughout all of the supply chain, highlighting blockchain’s capacity to provide safe and verifiable monitoring of carbon emissions inside the network. This research Supply the potential of blockchain technology to diminish carbon footprints in supply chains, emphasizing its ability to enhance data security, authenticate carbon emission assertions, and augment traceability. The findings highlight the need for customized cybersecurity frameworks for Supply demonstrate blockchain’s capacity to improve the transparency, security, and sustainability of global supply chain operations.