Carbon Emission Model Research Articles

An energy trade-off management strategy for hybrid ships based on event-triggered model predictive control

This paper addresses the energy management problem of hybrid ships by proposing an event-triggered model predictive control (ET-MPC) method. The novelty in this work lies in the establishment of an event-triggered mechanism and a state prediction model for energy management of hybrid ships. First, torque models of the internal combustion engine (ICE) and electric machine (EM) are developed using a data-driven approach, followed by the construction of fuel consumption and carbon emission models. Second, an event-triggered mechanism, dependent on state prediction error, is introduced and updated at each time step based on the system’s current state. Additionally, a cubature Kalman filter (CKF) is employed to estimate and correct the state prediction error, minimizing inaccuracies. A trade-off coefficient is incorporated to optimize the balance between fuel consumption and carbon emissions. The ET-MPC method results in a 0.68% difference in fuel consumption and 3.43% increase emissions compared to the traditional MPC method. However, ET-MPC significantly reduces computational overhead by 56.66. The ET-MPC method effectively allocates the ship’s energy according to the varying trade-off coefficient, achieving optimal energy management under different constraints.

Research on the Carbon Emission Prediction and Reduction Strategies for the Civil Aviation Industry in China: A System Dynamics Approach

With the continuous growth in the volume of global air transportation, the carbon emissions of the civil aviation industry have received increasing attention. Carbon emission reduction in civil aviation is an inevitable requirement for achieving sustainable social development. This article aims to use system dynamics (SD) methods to establish a carbon emission model for the civil aviation industry that includes economic, demographic, technological, policy, and behavioral factors; analyze the key factors that affect carbon emissions; and explore effective emission reduction strategies. Researchers have found that SD-based carbon emission prediction has a high accuracy and is suitable for predicting carbon emissions in civil aviation. Through different scenario simulations, it has been found that any single emission reduction measure will struggle to effectively contribute to the expected carbon reductions in China’s civil aviation. Simultaneously adopting measures such as improving fuel efficiency, adopting clean energy, and using new-power aircraft is an effective way to reduce carbon emissions from civil aviation. In addition, policy intervention and technological innovation are equally crucial for achieving long-term emission reduction goals. The research results not only provide a scientific basis for the sustainable development of the aviation industry but also provide a reference for policymakers to formulate comprehensive emission reduction strategies.

Estimation Model and Spatio-Temporal Analysis of Carbon Emissions from Energy Consumption with NPP-VIIRS-like Nighttime Light Images: A Case Study in the Pearl River Delta Urban Agglomeration of China

Urbanization is growing at a rapid pace, and this is being reflected in the rising energy consumption from fossil fuels, which is contributing significantly to greenhouse gas impacts and carbon emissions (CE). Aiming at the problems of the time delay, inconsistency, uneven spatial coverage scale, and low precision of the current regional carbon emissions from energy consumption accounting statistics, this study builds a precise model for estimating the carbon emissions from regional energy consumption and analyzes the spatio-temporal characteristics. Firstly, in order to estimate the carbon emissions resulting from energy consumption, a fixed effects model was built using data on province energy consumption and NPP-VIIRS-like nighttime lighting data. Secondly, the PRD urban agglomeration was selected as the case study area to estimate the carbon emissions from 2012 to 2020 and predict the carbon emissions from 2021 to 2023. Then, their multi-scale spatial and temporal distribution characteristics were analyzed through trends and hotspots. Lastly, the influence factors of CE from 2012 to 2020 were examined with the OLS, GWR, GTWR, and MGWR models, as well as a ridge regression to enhance the MGWR model. The findings indicate that, from 2012 to 2020, the carbon emissions in the PRD urban agglomeration were characterized by the non-equilibrium feature of “high in the middle and low at both ends”; from 2021 to 2023, the central and eastern regions saw the majority of its high carbon emission areas, the east saw the region with the highest rate of growth, the east and the periphery of the high value area were home to the area of medium values, while the southern, central, and northern regions were home to the low value areas; carbon emissions were positively impacted by population, economics, land area, and energy, and they were negatively impacted by science, technology, and environmental factors. This study could provide technical support for the long-term time-series monitoring and remote sensing inversion of the carbon emissions from energy consumption in large-scale, complex urban agglomerations.

Optimal low-carbon scheduling of integrated energy systems considering stepped carbon trading and source-load side resources

Under the dual-carbon goal of achieving carbon peaking and carbon neutrality, the Integrated Energy System (IES) enhances the power sector's environmental sustainability by integrating multiple energy sources. To enhance the low-carbon utilization of IES energy, this paper introduces an economic optimization model that incorporates stepwise carbon trading and both source and load-side resources. Initially, the model integrates a Concentrated Solar Power Plant (CSPP) with a Combined Heating and Power (CHP) unit for bidirectional electricity-heat conversion, and incorporates Power to Gas (P2G) and Carbon Capture System (CCS) for carbon recycling within the IES. On the demand side, demand response optimizes the electricity-heat load curve. Subsequently, a stepped carbon trading mechanism guides the system in controlling carbon emissions and constructing a carbon emission model. Finally, the model minimizes the sum of energy purchase costs, carbon transaction costs, demand response compensation costs, wind curtailment penalties, and equipment maintenance costs to optimize the IES economically. By establishing five scenarios for comparative analysis, incorporating CSPP, P2G-CCS, demand response, and a stepped carbon trading mechanism reduces system costs, carbon emissions, and abandonment rates by 21.1 %, 30.87 %, and 23.78 %, respectively, validating the model's effectiveness in promoting a low-carbon economy.

Carbon emission calculation and uncertainty analysis of asphalt concrete during the production stage

Asphalt concrete is the main material consumed by road engineering. At present, there is no uniform standard for determining the carbon emissions of different types of asphalt concrete, and the calculation process is uncertain. In this study, a carbon emission calculation method that combines theory with practice is proposed. First, a theoretical carbon emission model based on the heat balance equation and an actual carbon emission model based on actual energy data are established, and the key links in the carbon emissions of asphalt concrete are determined. Second, the carbon emission correction factors of five different types of asphalt concrete are obtained to estimate the actual carbon emissions more accurately. Finally, the uncertainty of the parameters is evaluated and characterized via the DQI-Monte Carlo simulation method. The results show that the carbon emissions from large to small are modified asphalt concrete (MAC) > ordinary asphalt concrete (AC) > stone matrix asphalt (SMA) > recycled asphalt concrete (ZAC) > warm mix asphalt concrete (WAC). The aggregate heating stage is the key link in the carbon emission of asphalt concrete. Controlling the aggregate heating temperature and moisture content is the most direct and efficient energy savings and emission reduction measure.

An intelligent mix design system for sustainable concrete containing multi-source recycled aggregate

The source complexity of recycled aggregate (RA) generally leads to significant fluctuations in the aggregate quality, which brings difficulties in the mix design of the recycled aggregate concrete (RAC). In this study, machine-learning models were developed to predict the cubic compressive strength by collecting mix proportions data including aggregate properties. Then a carbon emission model considering carbon sequestration was established. Based on these models, optimization algorithms were employed to obtain the optimal mix proportions. The results indicate that the backpropagation neural network model optimized by genetic algorithms (GABP) demonstrates the best predictive capability. Furthermore, the GABP model exhibits the highest sensitivity towards cement strength and water absorption of recycled coarse aggregate (RCA). In addition, single-objective optimization achieves a carbon reduction of 61.8–68.4 %. In dual-objective optimization, when the optimized mix proportions exhibit a low level of carbon intensity, the cubic compressive strength of RAC exceeds 50 MPa. This suggests that more efficient and valuable utilization of RA in concrete can be achieved after mix proportion optimization.

A Study of Carbon Emissions during the Operational Period of an Integrated Expressway Construction Station

An integrated construction station for an expressway is characterized by complex carbon emission sources and high carbon emission intensity. Conducting carbon emissions accounting makes possible a comprehensive understanding of these characteristics, enabling targeted and guided carbon reduction efforts, which is crucial for advancing the low-carbon development of expressway construction. This paper, based on an in-depth analysis of the carbon emission structure during the operational period of an integrated expressway construction station identifies, as calculation boundaries, eight categories: residential areas, station transportation, mixing stations, precast beams, steel bar yards, artificial carbon emissions, chemical reactions during construction, and construction conditions. The study adopts a “bottom-up” approach to carbon emission measurement and constructs a carbon emission model for the production and operational period of the integrated construction station, based on the carbon emission factor method. Using the SG-2 section of the Fengqiu to Xiuwu stretch of the Changxiu Expressway as an engineering case, carbon emissions accounting for each component of the integrated construction station’s operational period was conducted, and the results were compared with the station’s monitoring system. High-precision characterization and calculation of total carbon emissions, as well as emissions from each process and piece of equipment during the operational period, were achieved. The results indicate that: (1) the relative error between the overall calculation results and actual monitoring is 3.6%, verifying the model’s accuracy; (2) the monthly carbon emissions of the integrated construction station during the operational period reached 72.15 tons; (3) there is a significant difference in carbon emissions among the different processes, with the highest emissions coming from transportation and residential areas, accounting for 43.4% and 23.7%, respectively. Therefore, electrification of transportation equipment could significantly reduce the overall carbon emissions of the integrated construction station.

Low-carbon energy scheduling for integrated energy systems considering offshore wind power hydrogen production and dynamic hydrogen doping strategy

This paper proposes a low-carbon energy scheduling model for integrated energy systems (IES) considering offshore wind power hydrogen production and dynamic hydrogen doping strategy. The offshore wind electrolysis system, hydrogen mixed gas turbine (HMGT), carbon capture, utilization and storage, and laddered carbon trading are introduced to reflect the values of hydrogen blending in carbon emission reduction for IES. A carbon emission and combustion model for HMGT based on the system thermodynamics and chemical reaction kinetics is presented. The dynamic hydrogen doping strategy is leveraged to consider the impact of gas composition variations on HMGT operating and gas system. Then, an energy balance-based quasi-steady-state model is proposed to deal with the different calorific value of the gas mixture. In order to effectively solve the resulting highly nonlinear and nonconvex problem arising from the varying hydrogen blending ratio, the nonlinear hydrogen production efficiency, and the convex–concave energy balance-based quasi-steady-state model, a tractable solution formulation is proposed. Numerical results illustrate the effectiveness of the proposed model.

Study on the life cycle model of residential building carbon emission based on principal component analysis method

Study on the life cycle model of residential building carbon emission based on principal component analysis method

Exploring the dynamics and trends of carbon emission spatiotemporal patterns in the Chengdu–Chongqing Economic Zone, China, from 2000 to 2020

Analysis of the spatial–temporal pattern and trend of carbon emissions provides an important scientific basis for the development of a low-carbon economy. Based on the corrected NPP-VIIRS and DMSP/OLS nighttime light data, a carbon emission model for the Chengdu–Chongqing Economic Zone (CCEZ) in China is constructed. Furthermore, the article establishes an integrated qualitative and quantitative research system. The qualitative results show that at the city and county scales, the high carbon emission areas and counties are mainly distributed in Chengdu and Chongqing, while the low carbon emission areas are concentrated in the marginal cities of the CCEZ and the counties with low levels of industrialization around the Sichuan Basin. The high-carbon emission zone tended to expand to the north, and the low-carbon emission zone tended to expand to the south. At the grid scale, the carbon emissions of the CCEZ fluctuated and increased from 2000 to 2020, forming a trend connected with those of the central city, with high carbon emissions at the core and radiating outward expansion. Quantitative analysis revealed that carbon emissions at the county and grid scales exhibited a significant positive global spatial correlation, and the overall correlation degree exhibited an increasing trend.

Roof Shape Design for Ice Rinks in Cold Regions under Carbon Reduction Targets

In the midst of today’s energy crisis, carbon emissions from ice rinks in cold regions present a significant environmental challenge. The shape of an ice rink’s roof significantly influences these emissions. This study developed a methodology to quantify the carbon emissions of ice rinks and explained how their roof shapes impact emissions during the operational phase. Roof shapes were divided into the following three categories: flat, curved, and combined torsion shell. Carbon emission modeling was established and calibrated using the Ladybug + Honeybee platform, followed by regression analyses on the slope and curvature of each roof type. The findings indicate a robust correlation between the carbon emissions of an ice rink and the slope and curvature of its roof. Roof shape influences approximately 2% of carbon emissions during the operational phase of an ice rink. Among the various roof shapes, the curved dome roof demonstrates the most effective overall carbon savings, at a rate of 0.93% compared to the flat roof. Selecting an appropriate roof shape has significant carbon-saving potential for ice rinks. The findings of this study may serve as a valuable reference for the formulation of energy-saving design standards in cold regions.

Low-carbon development path based on carbon emission accounting and carbon emission performance evaluation: a case study of Chinese coal production enterprises.

Carbon emission accounting is the basic premise of effective carbon emission reduction and management. This study aimed to establish the carbon emission model and performance evaluation framework of coal mine production enterprises and clarify the low-carbon development path of enterprises. In this study, we took a typical coal production enterprise (K enterprise) in the Shanxi province of China as the research object. We also estimated the carbon emissions of the enterprise mainly according to the Chinese Carbon Emission Accounting Standard (GB/T 32151.11-2018). The triangular model was used to construct the carbon performance evaluation framework. On this basis, we suggested the enterprise's low-carbon development path. The results showed that (1) the carbon emission of K enterprise in 2021 was 36,875.38 tCO2eq; the carbon emission intensity of each ton of coal produced was 0.089 tCO2eq. The critical carbon emissions were electricity consumption and methane fugitive emissions during production. (2) The evaluation indicators for carbon emission performance revealed an imbalance in K enterprise's economic, energy, and environmental development in 2021. The work on energy saving and consumption reduction was relatively weak. (3) Countermeasures for low-carbon development, including a carbon emission ledger, were proposed based on carbon emission accounting and performance evaluation results. This study can help typical underground coal production enterprises in Shanxi province obtain more accurate carbon emission data, providing practical guidance and reference for the same underground coal production enterprises to improve the carbon emission control effect.

The calculation and distribution of CAV carbon emissions on urban transportation systems: A comparative analysis of renewable and non-renewable energy sources

When powered by electricity, Connected and Autonomous Vehicles (CAVs), an emerging mode of transportation, possess the capacity to reduce exhaust emissions greatly. However, accurately measuring carbon emissions in urban transportation remains a challenge, especially considering emissions from electricity generation and gasoline consumption. This paper proposes an innovative method for calculating CAVs' carbon emissions distribution, utilizing both renewable and non-renewable energy. The study employs SUMO, an agent-based simulation platform, to develop an intelligent driver model and cooperative adaptive cruise control modules, tracking vehicle movement behavior across various vehicle types, including Gasoline Vehicles (GVs), Electric Vehicles (EVs), Human-Driven Vehicles (HDVs), and CAVs. Subsequently, a lifecycle electric carbon emission model is constructed, integrating the energy consumption model of EVs with carbon emission factors of renewable and non-renewable energy. Visualization models are then developed to clarify the carbon emission distribution within the traffic network. A case study conducted in Suzhou, China validates the model, analyzing the spatiotemporal distribution of carbon emissions. Results show EVs can reduce carbon emissions by 70%–90 % compared to GVs on urban roads during rush hour, while CAVs can further reduce emissions by 35%–50 % compared to HDVs. Additionally, carbon emissions from non-renewable energy sources were found to exceed those from renewable sources.

Research on Carbon Emission Peaks in Large Energy Production Region in China —Based on the Open STIRPAT Model

<p>The interdependence between different regions within a country has an important impact on the prediction of regional carbon emission peaks. In this paper, taking Shanxi Province, a large energy producing region in China, as an example, we select the driving factors to construct an open STIRPAT model from both Shanxi and national levels, and use scenario analysis to predict the carbon emissions of Shanxi Province from 2021 to 2040. The study shows that： (1) In the Open STIRPAT model, the year and amount of peak carbon emissions in Shanxi province are mainly determined by the national scenarios. If the baseline scenario is chosen for the whole country, then no matter which scenario is chosen for Shanxi, it will not be able to realize the carbon peak in 2030. Under the scenario combination of low carbon-low carbon, Shanxi will realize the carbon peak in 2027. Under the scenario combination of low carbon-energy saving and energy saving-low carbon, Shanxi will realize the carbon peak in 2028. (2) The peak carbon emissions in Shanxi are mainly affected by the national scenario setting and Shanxi's emission reduction strategy. Controlling per capita GDP and energy consumption of 10,000 yuan GDP is an effective emission reduction strategy. (3) Compared with the open STIRPAT model, the closed STIRPAT model delays the year of peak carbon in Shanxi and overestimates carbon emissions by 20%-44%. Therefore, Shanxi should pay attention to the development of energy saving at the national level in formulating its carbon emission strategy, take it as an important constraint for Shanxi's peak carbon policy, and formulate a flexible carbon emission peaking program.</p>

Correlation analysis of energy consumption, carbon emissions and economic growth

In today's highly advanced industrialised and modernised world, China's economy is still growing, and its demand for energy is increasing daily. It is crucial to examine the connection between energy consumption, carbon emissions, and economic growth in order to promote economic growth based on energy conservation and emission reduction. Using Dezhou City in Shandong Province as an example, the study builds a VAR model of carbon emission, energy consumption, and economic growth in Dezhou City based on simplified macroeconomic sub-models, energy sub-models, and environmental sub-models. It then determines the correlation and influence mechanism between the three using tests like ADF unit root and Granger causality. The pertinent elements affecting Dezhou's carbon emissions were then investigated using grey correlation analysis. Finally, based on the study's findings, policy suggestions are made regarding energy use, carbon emissions, and economic expansion. It is necessary not only to restrain high-energy consumption industries and fundamentally optimize the energy consumption structure, but also to find new economic growth points and improve economic growth channels, so as to optimize the industrial structure. In this process, increasing the proportion of the tertiary industry is a key measure. In addition, the government needs to advocate the citizens to adopt a low-carbon lifestyle, and the concept of low-carbon environmental protection will be deeply rooted in the hearts of the people. This study will provide suggestions and theoretical guidance for China's energy consumption and carbon emissions, and help achieve high-quality growth of China and even the world economy.

Construction and Analysis of Machine Learning Based Transportation Carbon Emission Prediction Model

Addressing the issue of carbon emissions in the transportation sector, this research constructed various predictive models using multiple machine learning algorithms based on panel data from 30 provinces in China from 2005 to 2019. The study aimed to identify the optimal machine learning algorithm and key factors influencing the carbon emissions of transportation, providing potent references for policymakers and decision-makers to reduce carbon emissions and promote the sustainable development of the transportation sector. Initially, drawing from the concept of the fixed effects model, we included the heterogeneity differences among provinces as an important factor. We further employed a combined method of Pearson's correlation coefficient and Spearman's rank correlation coefficient to screen 18 factors influencing transportation carbon emissions. We then made a preliminary selection of seven common machine learning algorithms and used the screened factors as explanatory variables for model training. The three algorithms with the best performance were further optimized and trained. Subsequently, we utilized the K-fold cross-validation method; plotted learning curves to test the performance of each predictive model; and used MSE, MAE, R2, and MAPE as evaluation indicators to determine the best predictive model. SHAP values were chosen to calculate the importance of each explanatory variable in the optimal predictive model. The results indicated that the multicollinearity among the seven factors of provincial differences, total consumption of social goods, urban green space area, freight turnover, number of private cars, transportation industry output, and permanent population was weak, and all passed the significance test. They could be used as explanatory variables in the prediction model of transportation carbon emissions. The prediction results of the Random Forest and XGBoost algorithms were both outstanding, with R2 values above 0.97 and errors below 10 %, showing no signs of overfitting or underfitting. Among them, the XGBoost algorithm performed the best, whereas the KNN algorithm performed poorly. The importance ranking of the explanatory variables was as follows:provincial differences > total consumption of social goods > number of private cars > permanent population > freight turnover > urban green space area > transportation industry output. A comprehensive analysis of relevance and importance showed that provincial differences were an indispensable variable in the prediction of transportation carbon emissions. In conclusion, this study provides a new approach to the governance of carbon emissions in the transportation industry, and the results can serve as a reference for policymakers and decision-makers. In future policy design and decision-making, the distinctive factors of each province should not be overlooked. Measures targeted at specific regions need to be formulated to promote the sustainable development of the transportation industry.

Multi-Scale Analysis of Spatial and Temporal Evolution of Carbon Emissions in Yangtze River Economic Belt and Study of Decoupling Effects

An in-depth, longitudinal examination of carbon emissions and decoupling within the Yangtze River Economic Belt, supplemented by a dynamic assessment of its evolutional trajectory, provides a scientifically grounded framework and pragmatic value for the drafting of regional carbon emission mitigation strategies. Using the Yangtze River Economic Belt as a context, this study formulates a carbon emission model spanning provincial, city, and county levels. The model serves to uncover the spatiotemporal characteristics of carbon emissions within the Yangtze River Economic Belt from a multi-scalar vantage point. The Tapio decoupling model is then invoked to examine the extent and nature of decoupling between economic advancement and carbon emissions across these disparate scales. The outcomes divulge the following: (1) At the provincial echelon, the progression of carbon emissions born from energy consumption within the Yangtze River Economic Zone presents an escalating then stabilizing trend line. The carbon emissions growth rate transitions from a swift ascension of 8.44 percent initially, subsequently tapering to a moderate increment of 0.42 percent at the period’s culmination. The trajectory of carbon decoupling at the provincial scale tends to be generally propitious. (2) At the municipal scale, the overall carbon emission level shows a gradual upward trend, and then gradually forms a pattern of centripetal aggregation and peripheral diffusion. The decoupling status during the study period is mainly weak and strong decoupling, with the number of weak decoupling showing a fluctuating change in increasing and then decreasing, while the strong decoupling shows a slow and orderly growth trend, and is mainly distributed in most of the municipalities in Jiangsu, Zhejiang, and Shanghai. (3) At the county scale, centripetal aggregation and peripheral diffusion were already present at the beginning of the study period, followed by the gradual expansion and formation of several carbon emission centers of different sizes. The temporal evolution of county-level decoupling is more significant, with weak and strong decoupling dominating the county-scale decoupling during the study period, especially in the upper and middle reaches of the Yangtze River Economic Belt, but the overall trend shows signs of gradual decoupling.

Decoupling representation contrastive learning for carbon emission prediction and analysis based on time series

Carbon emissions have become particularly urgent with the acceleration of industrialization. Currently, AI-based carbon emission modeling provides essential support for solving the problem of predicting, managing, and optimizing carbon emissions. However, there are still challenges in accurately predicting carbon dioxide emissions and establishing an appropriate carbon emission regulatory model to manage carbon dioxide emissions effectively. Firstly, this paper introduces a framework for learning seasonal and trend representations called DeTF (Decoupling representation for Time and Frequency domain of time series). The framework is designed for time series data of CO2 emissions used for power generation from 1973 to 2023. Secondly, we use various data enhancement techniques to improve the robustness of the learned representations. Finally, the prediction results establish a tripartite evolutionary game relationship between the state, regulatory agencies, and enterprises. We use MSE and MAE as evaluation metrics, and our model overall outperforms current state-of-the-art end-to-end algorithms. It outperforms the best performing end-to-end prediction method by 27.08% (MSE) in the prediction results. Besides, the time series prediction results are innovatively combined with the tripartite game model to analyze the main factors determining the optimal strategy.

Research on carbon emission quantification and evaluation for prefabricated inverted arch construction in drill and blast tunnels

Drill and blast methods cannot meet the requirements of modern tunnel construction in terms of efficiency and carbon emissions. This study proposes a prefabricated inverted arch construction technology and carbon emission calculation method applicable to drill and blast tunnels. The carbon emissions during prefabricated inverted arch construction were calculated, and the carbon emission model for on-site transportation was optimized. The indirect impact of improved construction efficiency on carbon emissions was discussed, further refining the carbon emission evaluation system for drill and blast tunnels. The results show that (1) The new technology of prefabricated inverted arch significantly improves construction efficiency and has promising application prospects; (2) Carbon emissions during prefabricated inverted arch construction are 9051.67 tCO2eq less than those during cast-in-place construction, with a reduction of over 15%, mainly due to reduced cement and steel usage resulting from optimized arch structure; (3) Considering the limitations of material transportation due to tunnel excavation distance, the carbon emission model for on-site transportation is optimized, resulting in a reduction of 35%–56% in on-site transportation emissions; (4) Incorporating the indirect impact of improved construction efficiency on carbon emissions into the calculation model, this indirect effect accounts for approximately 70% of the total emission reduction achieved by the prefabricated inverted arch technology.

Study on the carbon footprint impact analysis method of integrated energy system operation in low-carbon park considering multi-domain dynamic characteristics

Low-carbon parks are an important battlefield for China to achieve the goal of peaking carbon neutrality. Accurate and efficient analysis and tracking of park carbon footprint is a necessary measure to reduce carbon and save energy in parks. Aiming at the problems of less research on the carbon footprint of low-carbon parks at home and abroad and the lack of tracking and analysis methods of dynamic influencing factors of carbon footprint, this paper focuses on the carbon footprint of typical low-carbon parks during operation and proposes a dynamic tracking method of the carbon footprint of low-carbon parks that considers the dynamic characteristics of carbon emissions in various fields. In this paper, the hierarchical impact structure of the carbon footprint of low-carbon parks was first constructed, including the impact of population, economy, construction, energy, transportation, and industry. LMDI (Logarithmic Mean Divisia Index) was used to decompose the macro-level impact of the carbon footprint of low-carbon parks. Secondly, this paper establishes a dynamic simulation model of carbon emissions of low-carbon parks in the fields of building, energy, transportation, and industry, which can realize 8760 hours of dynamic simulation calculation throughout the year. Based on dynamic simulation, this paper carries out single factor sensitivity analysis and combines the Monte Carlo simulation method to complete quantitative calculation of dynamic second-order influence weight of carbon footprint. Finally, a multi-domain impact analysis and tracking method for the carbon footprint of low-carbon parks is obtained.