Carbon Emission Prediction Research Articles

Analysis of influencing factors and prediction of carbon emissions of typical urban agglomerations in China: a case study of Beijing-Tianjin-Hebei region.

Achieving the "double carbon" goal is a major task and challenge facing China. The emission reduction actions in typical urban agglomerations are of great significance. Based on the consideration of the impact of regional coordinated development, this study analyzed influencing factors and conducted prediction of carbon emissions from terminal energy consumption in the Beijing-Tianjin-Hebei (BTH) region. Firstly, the factors affecting carbon emissions were screened through the STIRPAT model. Then, the paper designs different scenarios and finally uses the genetic algorithm extreme learning machine (GA-ELM) algorithm to predict the carbon emissions of the BTH region, with and without considering the impact of the coordinated development strategy. The research shows that the increase in energy intensity and the improvement of energy consumption structure have the largest promotion effect on carbon emission reduction. At the same time, the significant role of the coordinated development strategy in promoting regional carbon emission reduction was verified. Therefore, the BTH region should adhere to the path of coordinated development, innovate low-carbon technology, and deepen the concept of green consumption to promote the realization of regional carbon emission reduction goals.

Carbon Emission Scenario Prediction and Peak Path Selection in China

Due to the emission of carbon dioxide and other greenhouse gases, the global climate is warming. As the world’s biggest emitter of carbon emissions, China faces a more severe challenge in reducing carbon emissions than developed countries. A reasonable prediction of the carbon peak in China will help the government to formulate effective emission reduction paths. This paper analyzes the changes in carbon emissions in China from 2004 to 2020, uses the STIRPAT model and scenario analysis method to predict carbon emissions from 2021 to 2030, and then calculates the carbon efficiency during carbon peaking to select the most effective carbon peak path for China. The results show that China’s carbon emissions increased year by year from 2004 to 2020. Under the baseline scenario, China is unlikely to reach its carbon peak before 2030. Under the regulatory scenarios, China can reach its carbon peak before 2030. The peak values from high to low are seen with the rapid development-weak carbon control scenario, rapid development-intensified carbon control scenario, slow development-weak carbon control scenario and slow development-intensified carbon control scenario, respectively. Correspondingly, China will peak its carbon emissions in 2029, 2028, 2028 and 2028, respectively, according to these scenarios. The carbon efficiency under the rapid development-weak carbon control scenario is the highest, which means that accelerating the growth rate of population, GDP and urbanization while moderately carrying out the transformation of industrial structure and energy structure is an effective way to achieve the goal of “carbon peak by 2030”.

Aggregational differentiation of soil-respired CO2 and its δ13C variation across land-use types

Land-use type can affect CO2 emission fluxes, but whether it has effects on the δ13C of respired CO2 (δ13CO2), and whether these effects vary with the heterogeneity of the soil microenvironment remain unknown. In this study, long-term soil incubation experiments and soil fractionation were combined to determine the effects of land-use type (upland, paddy and woodland) on soil-respired CO2 and δ13CO2 with different aggregate fractions under three temperature conditions. This study extended the thermal adaptation of soil respiration to the soil microenvironmental scale, i.e., the scale of smaller aggregates than bulk soil. An exponential decay function (R = a × e−bt) was fitted to the temporal soil respiration data, separately for each combination of soil aggregate, land-use type and incubation temperature. At each incubation stage, the woodland exhibited the highest average soil respiration rates. The exponential decay trend of δ13CO2 with time was influenced by the land-use type and incubation temperature; the shift from enriched to depleted δ13CO2 values first appeared in woodland. Interestingly, carbon isotopic discrimination between the substrate and respired CO2 (Δ13C, δ13CO2 – δ13CSOC) increased with aggregate size only in woodland, especially at 30 °C. Overall, aggregational differentiation of soil-respired CO2 and its δ13C was found only in the woodland, and warming intensified these differences, suggesting that it would be difficult to generalize the integrative effects of warming on soil respiration and its isotopic fractionation at the microscale from a single ecosystem study. Our findings highlight that land-use types affect the feedback of soil carbon pool to future global warming at the soil microenvironmental scale (aggregate), and are of great significance for improving carbon emission prediction in terrestrial ecosystems.

Prediction of Carbon Emission of the Transportation Sector in Jiangsu Province-Regression Prediction Model Based on GA-SVM

To meet the twin carbon goals of “carbon peak” and “carbon neutrality”, it is crucial to make scientific predictions about carbon emissions in the transportation sector. The following eight factors were chosen as effect indicators: population size, GDP per capita, civil vehicle ownership, passenger and freight turnover, urbanization rate, industry structure, and carbon emission intensity. Based on the pertinent data from 2002 to 2020, a support vector machine model, improved by a genetic algorithm (GA-SVM), was created to predict the carbon peak time under three distinct scenarios. The penalty factor c and kernel function parameter g of the support vector machine model were each optimized using a genetic algorithm, a particle swarm algorithm, and a whale optimization algorithm. The results indicate that the genetic algorithm vector machine prediction model outperforms the particle swarm algorithm vector machine model and the whale optimization vector machine. As a result, the model integrating the support vector machine and genetic algorithm can more precisely predict carbon emissions and the peak time for carbon in Jiangsu province.

Digital twin with Machine learning for predictive monitoring of CO2 equivalent from existing buildings

The revolution of the industry 4.0 presents a new era of digital transformation for the construction industry, advancing towards the concept of digital twins, while on the other hand it faces the global challenge of reducing carbon emissions from operational assets. The current research gap in the application of digital twins for achieving net zero were reviewed in this study, highlighting its potentials for enhancement in the built environment, and emphasizing the need for demonstration of a use-case analysis for its adoption by the industry. This research presents a digital twin solution to automate the monitoring and controlling of equivalent carbon dioxide (eCO2) emissions from existing assets through the integration of IoT, BIM, and artificial intelligence across a comprehensive solution, further validating its workability through a real-life use case analysis. The study revealed the significance of BIM and IoT, as essential components of a digital twin to visualise critical spatial information for enhanced facility management specifically for monitoring of indoor air quality of spaces, while also coalescing an AI-supported system to predict carbon emissions from the collected data through integration of machine learning features across the digital twin. The output of the entire solution is displayed as an interactive dashboard for observing trends and patterns, enabling stakeholders to implement effective data-driven retrofitting strategies. This research is a fundamental initiation for implementing digital twins to monitor emissions from existing assets, a step towards achieving the net zero targets.

Influence analysis and low carbon evaluation of 3D sand printing process parameters on efficiency, resource consumption, and carbon emission

The 3D sand printing (3DSP) technology provides a richer realization path for the sustainable development of the manufacturing industry. It has the advantages of one-time molding, reducing design constraints and machining amount, and easy control of casting dimensional accuracy. Therefore, studying the impact of process parameters of the printing process on printing efficiency, resource consumption, and carbon emissions is the basis for the sustainable development of 3DSP technology. In this paper, starting from the main influencing parameters such as printing layer thickness, recoater speed, printing angle, and single printing quantity, a relationship model between printing parameters and carbon emission sources is constructed. A total factor carbon emission prediction model of 3DSP process including the impact of capital and labor is established. Build an influence relationship with printing parameters as independent variables and carbon emissions as dependent variables. Taking the sand casting industry as an example to verify the above model, the experimental results show that the thickness of the printing layer has the greatest impact on carbon emissions. When the layer thickness is 0.36 mm, the speed of the recoater is 0.22 m/s, the printing angle is (0°, 0°, 90°), and the single print quantity is 84, the total carbon emission is the lowest and 28.77% less compared to the parameter with the highest carbon emission. The average relative error of the predictive model is 1.929%. The results of this study can provide some new ideas for sustainable development of additive manufacturing technology.

What is the pathway that determines the diurnal lag time between soil respiration and soil temperature?

Soil respiration (Rs) is a critical component of the global carbon cycle in terrestrial ecosystems and is considered a function of soil temperature (Ts); however, Rs often shows a hysteresis effect with Ts. The mechanism causing this hysteresis pattern remains controversial, hindering our ability to accurately simulate and predict Rs in response to climate change. Here, a two-year field experiment was conducted to investigate the factors and pathways influencing the diurnal lag time between Rs and Ts (LTTs−Rs); we used an automatic chamber continuous measurement of Rs combined with 3 levels of precipitation manipulation: −30 % (P − 30), +0% (CK), +30 % (P + 30) of ambient precipitation in rain-fed winter wheat (Triticum aestivum L.) and alfalfa (Medicago sativa L.) agroecosystems on the Loess Plateau, China. The results indicated that the monthly average diurnal dynamic peak time of Rs always occurred prior to that of Ts. The LTTs−Rs ranged from 1 h to 8 h in winter wheat under the three precipitation treatments over two years. The range was similar in alfalfa under the CK and P + 30 treatments; however, in alfalfa, the value ranged from 1 h to 10 h under the P − 30 treatment. The LTTs−Rs value is determined by the direct pathway of photosynthetic product transport (the lag time between Rs and photosynthetically active radiation (PAR), LTRs−PAR) and the indirect pathway of heat transfer (the lag time between Ts and PAR, LTTs−PAR); that is, the LTTs−Rs is equal to the sum of the LTRs−PAR and LTTs−PAR. Moreover, the LTTs−PAR is equal to the sum of the lag time between air temperature (Ta) and PAR (LTTa−PAR) and the lag time between Ta and Ts (LTTs−Ta) in both winter wheat and alfalfa under the three precipitation treatments. Soil volume water content (VWC) affects LTTs−Rs by influencing the process (i.e., the transport process of heat and photosynthate) that comprises the LTTs−Rs. This is the first attempt to quantify the pathway processes that determine the LTTs−Rs and clarify the mechanism of the LTTs−Rs response to VWC. The results have important implications for our understanding of the LTTs−Rs production mechanisms and the accurate prediction of soil carbon emissions by future models.

Census-based urban building energy modeling to evaluate the effectiveness of retrofit programs

Housing retrofits are essential for meeting societal decarbonization goals, alongside addressing energy insecurity, improving public health, and creating new jobs. Yet, despite their multiple benefits and comprehensive government efforts to incentivize retrofits, adoption rates across the world remain low, usually less than 1% per year. Barriers to adoption among homeowners include lack of knowledge of what combination of energy retrofitting upgrades are most cost effective for their situation given available incentive programs. Similarly, cities lack urban-level analysis tools to optimize uptake of and predict carbon emissions reduction from existing incentive programs. To address the latter gap, we present a census-based Urban Building Energy Modeling framework that combines a technical energy saving potential analysis with a socioeconomic model that includes occupant demographics, local building regulations, and incentive eligibility criteria. We use the framework to evaluate the effectiveness of retrofit programs in two Boston neighborhoods with median incomes of $110,00 and $42,000. Results reveal that for the higher income, neighborhood predicted and actual adoption rates between 2014 and 2017 are comparable. In the lower income neighborhood, the proportion of households that would financially benefit from incentive offerings is higher. However, current participation rates do not reflect this difference suggesting that many viable projects do not happen for reasons that are not yet captured by the model. Urban planners, energy policy designers, and community advocates seeking to plan and evaluate energy incentive programs can use this framework to understand the breakdown of opportunities and barriers for different socio-demographic groups and geographic locations.

Scenario Prediction of Carbon Emission Peak of Urban Residential Buildings in China’s Coastal Region: A Case of Fujian Province

With the acceleration of China’s urbanization process, the importance of energy conservation and emission reduction in the building sector has become increasingly prominent. The effective control of carbon emissions in coastal provinces has a decisive impact on achieving the carbon emissions peak target nationwide. Based on the analysis of the influencing factors, this study establishes an urban residential buildings carbon emission prediction model by combining the IPAT model and the ridge regression model. In addition, the prediction model is combined with scenario analysis to simulate the evolution of carbon emission trends of urban residential buildings in Fujian Province from 2018 to 2050 under different scenarios. The results show that total population, urban living area, residents’ consumption expenditure, urbanization rate, per capita GDP, and energy structure are key factors affecting carbon emissions from urban residential buildings in coastal cities. Only under the ultra-low carbon model scenario can Fujian’s urban residential buildings achieve the carbon peak goal in 2027 (13.4748 million tons of CO2), which requires a reduction of 59.67% compared to that under the baseline model scenario. This study can provide an effective reference for energy conservation and emission reduction work of the regional scale and even the national scale.

Exploring potential of urban land-use management on carbon emissions—— A case of Hangzhou, China

Land-use patterns affected carbon emissions, which were influenced by urban land-use management in the long term. However, few studies linked carbon emissions with composite urban land-use management directly, leading to difficulties in reducing carbon emissions by urban land-use planning. Here, we proposed an integrated framework for carbon emissions accounting by land-use types and prediction through urban land-use management including the adjustment of urban growth mode and land-use structure. And the FLUS model and linear programming model were linked in the scenario analysis to predict carbon emissions. Finally, a decoupling model was used to evaluate the effects of urban land-use management on carbon emissions decoupling. A case study was conducted for Hangzhou from 1995 to 2015, showing the differences in carbon emissions in various scenarios for 2035. The results showed: (1) From 2010 to 2015, the carbon emissions of Hangzhou increased by 4.47 times, and it was dominated by the growth of carbon emissions for industrial land. (2) The potential of land-use structure optimization on carbon emissions reductions was from 23.80% to 28.04%, and that of compact urban growth was from 13.72% to 18.5%. (3) Decoupling relationships did not exist between carbon emissions and economic growth, and carbon emissions and urban land growth for all scenarios under urban land-use management in 2035. Our findings proposed an approach to consider practical ways to reduce carbon emissions through land-use management.

A new hybrid short-term carbon emissions prediction model for aviation industry in China

Given the China's construction of green aviation, a reliable carbon emissions prediction is essential to identify and mitigate the environmental impact of the aviation industry. In this regard, the current research aims at constructing “decomposition-prediction” to predict carbon emissions. To develop the above techniques, 1044 data points derived from the carbon emission accounts and datasets were used (70 % of the points for training and 30 % for validation). To solve the selection of mode number K and penalty factor α of variational mode decomposition (VMD) and calculate the matrix weight coefficient and kernel functions of extreme learning machine (ELM), optimized VMD and ELM is proposed. To begin with, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used to decompose the experimental data and spatial-dependence recurrence sample entropy (SdrSampEn) is used to quantify the complexity of all intrinsic mode functions (IMFs) to obtain the high and low complexity IMFs respectively. Then, high complexity IMFs are integrated, and then VMD of gray wolf optimizer algorithm (GWO) is used for decomposition to obtain IMFs, named G-IMFs. Next, low complexity IMFs are predicted by optimization ELM of slime mould algorithm (SMA) and G-IMFs are predicted by optimization ELM of particle swarm optimization algorithm (PSO). Lastly, all prediction results are reconstructed to obtain the final prediction results. To validate the superiority of the proposed method, 9 comparative models are established and their prediction effectiveness is assessed by R2 and RMSE. The results show that the proposed method has the best prediction performance for domestic aviation (R2 = 0.9978, RMSE = 0.0015) and international aviation (R2 = 0.9942, RMSE = 0.0003) respectively.

Analysis and prediction of carbon emissions from food consumption of middle-income groups: evidence from Yangtze River Economic Belt in China

Analysis and prediction of carbon emissions from food consumption of middle-income groups: evidence from Yangtze River Economic Belt in China

Early Warning of the Carbon-Neutral Pressure Caused by Urban Agglomeration Growth: Evidence from an Urban Network-Based Cellular Automata Model in the Greater Bay Area

Carbon neutrality is becoming an important development goal for regions and countries around the world. Land-use cover/change (LUCC), especially urban growth, as a major source of carbon emissions, has been extensively studied to support carbon-neutral planning. However, studies have typically used methods of small-scale urban growth simulation to model urban agglomeration growth to assist in carbon-neutral planning, ignoring the significant characteristics of the process to achieve carbon neutrality: large-scale and long-term. This paper proposes a framework to model large-scale and long-term urban growth, which couples a quantity module and a spatial module to model the quantity and spatial allocation of urban land, respectively. This framework integrates the inertia of historical land-use change, the driving effects of the urbanization law (S-curve), and the traction of the urban agglomeration network to model the long-term quantity change of urban land. Moreover, it couples a partitioned modeling framework, spatially heterogeneous rules derived by geographically weighted regression (GWR), and quantified land-use planning orientations to build a cellular automata (CA) model to accurately allocate the urbanized cells in a large-scale spatial domain. Taking the Guangdong–Hong Kong–Macao Greater Bay Area (GHMGBA) as an example, the proposed framework is calibrated by the urban growth from 2000 to 2010 and validated by that from 2010 to 2020. The figure of merit (FoM) of the results simulated by the framework is 0.2926, and the simulated results are also assessed by some evidence, which both confirm the good performance of the framework to model large-scale and long-term urban growth. Coupling with the coefficients proposed by the Intergovernmental Panel on Climate Change (IPCC), this framework is used to project the carbon emissions caused by urban growth in the GHMGBA from 2020 to 2050. The results indicate that Guangzhou, Foshan, Huizhou, and Jiangmen are under great pressure to achieve the carbon-neutral targets in the future, while Hong Kong, Macao, Shenzhen, and Zhuhai are relatively easy to bring up to the standard. This research contributes to the ability of land-use models to simulate large-scale and long-term urban growth to predict carbon emissions and to support the carbon-neutral planning of the GHMGBA.

Design of Substation Carbon Emission Prediction Model Based on Cloud Model

In order to achieve the goal of energy conservation and emission reduction, all walks of life are taking active actions. Power system, as the main dispatching application of energy, is in full swing in the monitoring and management of carbon emissions and other related studies. The purpose of this paper is to study the design of substation carbon emission prediction model based on the cloud model. First, the basic concepts and numerical characteristics of the cloud model are introduced. Secondly, it introduces the principle of carbon emission prediction method based on cloud model and the common carbon emission prediction method. Combining the real-time substation carbon emission data simulation with the cloud model prediction, the prediction results were compared with the BP neural network, and two average error values were used to measure the prediction results. It was found that the cloud model had a higher accuracy in predicting carbon emission, which verified its feasibility and superiority.

Prediction of Carbon Emissions from Natural and Human Systems Based on Correlation Analysis and Logistic Models

With the increasing trend of global warming and the growing emphasis on carbon reduction programs at home and abroad, there is an urgent need to propose solutions to prevent the greenhouse effect. In order to solve such problems, exploring and predicting the trend of carbon dioxide emissions is essential to better establish targeted carbon reduction programs. By analyzing the relationship among carbon emissions, human and natural systems, the relationship between factors such as economic level status, resident population, road passenger traffic, and vegetation carbon sequestration capacity and carbon emissions is studied in five characteristic regions of China, and the predicted results are verified by carbon emission release.

The prediction of carbon emissions from construction land in central Yunnan urban agglomeration area based on multiple linear regression model

The prediction of carbon emissions from construction land in central Yunnan urban agglomeration area based on multiple linear regression model

Prediction of carbon emissions from energy consumption in China under the “dual carbon” goal

Prediction of carbon emissions from energy consumption in China under the “dual carbon” goal

Scenario prediction of energy consumption and carbon emissions in Lingang New Area based on LEAP model

Based on the statistical data of Lingang New Area in 2021, the LEAP-Lingang model was constructed, and the energy consumption and carbon emissions of Lingang New Area from 2022 to 2035 were predicted and studied by setting three scenarios: benchmark, policy and low carbon. The results show that under the policy scenario, the new area can maintain low energy consumption and carbon emission development, but fails to peak carbon emissions before 2035, and can basically peak carbon emissions around 2030 under low-carbon conditions. On the basis of existing policies, the new area can further promote clean energy development, accelerate regional industry and energy efficiency upgrading, and actively promote green power and green certificate trading to accelerate the process of carbon peaking.

Short term carbon emission prediction method of green building based on IPAT model

Short term carbon emission prediction method of green building based on IPAT model

基于Lasso-GA-SVR的区域物流业碳排放预测：以天津市为例

基于Lasso-GA-SVR的区域物流业碳排放预测：以天津市为例