A Review of Building Carbon Emission Accounting and Prediction Models
Abstract
:1. Introduction
2. Methodology
2.1. Research Scope
2.2. Criteria for Data Screening
- Is the object of study a single building or regional complex, rather than a single component within the building?
- Does the study explicitly use a carbon emission calculation model or prediction model?
- Did the study conduct a case study of the model used and analyze the advantages and disadvantages?
2.3. Carbon Emission Accounting Methods
3. Carbon Emission Accounting Model
3.1. Carbon Emission Accounting Methods
3.2. Building Carbon Emission Calculation Methods
3.2.1. The PB Method
Author | PB Method | I-O Method | Hybrid Method | Microscale | Macroscopic Scale | Location | Life Cycle Stage | Building Type | Boundary Information |
---|---|---|---|---|---|---|---|---|---|
Ooteghem and Xu [37] | YES | YES | Toronto | The whole life cycle | Single-story retail building | The maintenance process includes production of replacement materials, transportation and waste disposal | |||
Gerilla et al. [38] | YES | YES | Saga | The whole life cycle | Residential building | Operational energy consumption includes heating, lighting and so on | |||
Peng [10] | YES | YES | Nanjing, China | The whole life cycle | Office building | Operating energy consumption includes air conditioning, lighting, elevators, office and other equipment | |||
Shao et al. [39] | YES | YES | Beijing, China | Construction and operation | Office building | The construction phase input list includes: materials, equipment, energy and manpower | |||
Acquaye and Duffy [40] | YES | YES | Ireland | - | - | - | |||
Biswas [41] | YES | YES | Australia | From cradle to use | Teaching building | Operation energy consumption includes lighting, computer, office, kitchen heating, air conditioning, fans, etc. | |||
Wang et al. [42] | YES | YES | China | - | - | - | |||
Yu et al. [43] | YES | YES | China | Material preparation, construction and demolition | Bamboo structure single-story house model | Including felling, pruning, stranding, winch loading, transportation, storage, processing and all information related to bamboo handling | |||
Mao et al. [28] | YES | YES | China | construction stage | Semi-prefabricated building | - | |||
Cuéllar-Franca and Azapagic [44] | YES | YES | UK | The whole life cycle | Hypothetical residential building | The operational phase includes water consumption and maintenance carbon emissions | |||
Dong et al. [45] | YES | YES | Beijing, China | - | - | - | |||
Monahan and Powell [46] | YES | YES | Norfolk, England | From cradle to scene | Residential building | Construction includes the production of building materials, transportation and the sorting and transportation of waste at the construction site | |||
You et al. [47] | YES | YES | China | The whole life cycle | Residential building | Operating energy consumption includes heating, cooling, hot water preparation, lighting, | |||
Chang et al. [48] | YES | YES | China | - | - | - | |||
Yan et al. [49] | YES | YES | Hong Kong, China | Construction stage | Commercial building | Construction includes manufacturing and transportation of building materials, energy consumption of construction equipment and energy consumption of processing resources | |||
Nässén et al. [50] | YES | YES | Sweden | - | - | - | |||
Li et al. [51] | YES | YES | Nanjing, China | The whole life cycle | Residential building | Electricity and natural gas are considered as energy consumption in operation |
3.2.2. EI-O Method
3.2.3. Hybrid Method
3.3. The Division of the Whole Life Cycle Framework of the Building
4. Building Carbon Emission Prediction Model
4.1. Mathematical Model
4.1.1. The Factor Decomposition Model Based on Kaya Identity
- 1.
- IPAT model
- 2.
- STIRPAT model
Author | Name | Characteristics |
---|---|---|
Nie et al. [69] | IPAT model | Reducing environmental change to the product of three interrelated driving forces, population, affluence, and technology. The disadvantage is that it does not allow hypothesis testing for missing items in the formula, and its extension model is commonly used, such as STIRPAT, etc. |
Gu et al. [74] | LMDI model | Complete decomposition, no unexplained residuals, the results are more accurate, the widest range of use. |
Zhou et al. [75] | MNR model | Regression analysis is simple and convenient, and suitable for preliminary analysis, but the equation assumptions are strict, “pseudo-regression” phenomena often appear, and large data samples are required. Often combined with STIRPAT, LMDI, and other factor decomposition models. |
Sim [76] | System dynamics | It can effectively deal with nonlinear, complex and high-order practical problems, and can reflect the relationship between internal and external factors of the research object. It is especially good at dealing with long-term periodic and nonlinear complex system problems. |
Wang and Gong [77] | Grey relational degree model | The degree of correlation between factors is judged according to the degree of similarity of the development trend of each factor, and there is no limit to whether there is any rule in the sample. |
Li [78] | Grey GM (1,1) model | It requires less information, has high accuracy, is easy to check, and is very effective in dealing with small sample prediction problems. |
Song and Zhang [79] | BP neural network | The nonlinear mapping ability is strong, it can approximate any continuous function, and it has the characteristics of adaptive learning and robust fault tolerance. However, the convergence rate of this model is slow and it may exhibit non-convergence and local minimum problems. |
Hao and Gao [80] | NSGA-II-BP neural network | This algorithm can optimize the weight and threshold of the BP neural network, so as to improve the convergence speed of the latter. |
Heydari et al. [81] | GWO-GRNNW | Grey wolf optimization mimics the hunting behavior and social leadership of the grey wolf. Different types of wolves assume different leadership levels, which can improve the spatial search efficiency. |
Xu and Song [82] | FCS-SVM | The FCS algorithm avoids human influence when selecting kernel function type, kernel parameter, and penalty parameter in the SVM algorithm. |
Wei et al. [83] | FOA-LSSVM | FOA is an intelligent optimization algorithm based on Drosophila foraging behavior. Combined with LSSVM, FOA can solve complex nonlinear mapping problems well. |
- 3.
- The LMDI model
4.1.2. The Regression Model
4.1.3. System Dynamics Model
4.1.4. Grey System Theory
4.2. Machine Learning Models
4.2.1. BP Neural Network
4.2.2. The Support Vector Machine Model
5. Conclusions and Perspectives
- Carbon emission accounting methods can be divided into the carbon emission factor method, the mass balance method, and the measurement method. The carbon emission factor method is the main method recommended by IPCC, and is also the most widely used method at present. Although many countries and organizations have provided rich carbon emission factor databases for inquiry, the accuracy of carbon emission factor calculations using these databases is not good due to the poor timeliness or wide application range of the data contained in the databases. Therefore, supplementing and updating the carbon emission factor database is important work in the calculation of carbon emissions in various industries.
- Building carbon emission models are divided into the PB method, EI-O method, and hybrid method. Starting from the energy and material list of the building, the PB method calculates the carbon emissions of the whole building in detail, with high calculation accuracy but high cost. The EI-O method analyzes the carbon emissions of the whole building industry from a macro perspective, and is suitable for the analysis of carbon emissions at the city level. The hybrid method combines the advantages of the first two methods, and is the most widely used method at present.
- Research on the driving factors of carbon emission is mainly carried out using the STIPAT model, LMDI model, grey correlation degree, and other models. The main feature of these models is that the contribution rate of each factor to carbon emissions can be clearly obtained, and these models are suitable for carbon emission analysis at the level of industry and society.
- The prediction model is realized through mathematical models such as the regression model and the system dynamics model, as well as machine learning models such as the neural network model and the support vector machine model. In terms of application, various regression models, support vector machine models and their improved models are the research hotspots. However, in addition to the regression model, which is often used to predict the carbon emission of individual buildings, other commonly used prediction models mostly focus on the carbon emission prediction at the city or provincial level.
- For the renewal and expansion of carbon emission factors, society should label the vast majority of products with carbon emission factors, similar to the “net content” label for every commodity. This work can be conducted by the manufacturer, so that the timeliness and details of the carbon emission factors can be addressed at the same time. The government should introduce corresponding compulsory measures and provide preferential policies to improve the enthusiasm of manufacturers.
- Most of the predictions of building carbon emissions in the literature focus on the macro level, and less attention is paid to predicting the carbon emissions of individual buildings, especially for prediction models in the building design stage. As the main model for predicting the embodied carbon emissions of single buildings, the accuracy of the regression model requires a large amount of measured data as the analysis basis, but it is difficult to obtain complete and effective measured data, which requires further accumulation by researchers.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
LMDI | Logarithmic Mean Divisia Index |
GRNN | Generalized Regression Neural Network |
SVM | Support Vector Machine |
GWO | Grey Wolf Optimizer |
BP neural network | Back Propagation neural network |
LEAP | Long-range Energy Alternatives Planning System |
FCS | Fuzzy Cuckoo Search |
MNR | Multivariate nonlinear regression |
NSGA-II | Non-dominated Sorting Genetic Algorithm-II |
LSTM | Long-Short Term Memory |
FOA-LSSVM | Fruit fly Optimization Algorithm-Least Squares Support Vector |
Machine | |
CPCD | China Products Carbon footprint factors database |
IPCC | International Panel on Climate Change |
EFDB | Emission Factor Database |
EI-O | Economic Input-output |
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Method | Input | Advantages | Limitations | Applicable Scale | Applications |
---|---|---|---|---|---|
Emission factor method | Activity level, Carbon emission factor. | Simple, clear, and easy to understand. A mature database of formulas, activity data and emission factors. There are plenty of application examples for reference. | Subject to technical level, production status and technological process, etc. The emission factors are regional and uncertain. | Macroscopic scale; mesoscale; microscale. | It is suitable for industries with stable changes in socio-economic emission sources or where the other two methods are not suitable. |
Mass balance method | The amount of input material and its carbon content; the amount of output material and its carbon content. | The research is systematic and comprehensive. Strong science, high implementation effectiveness. Captures the differences between various types of facilities and equipment. | Need a comprehensive understanding of the production process, chemical reaction; adverse reactions and management, etc.; heavy workload; data demand is high. | Macroscopic scale; Mesoscale. | Suitable for industries with good data foundations. Examples include the chemical and steel industries. |
Measurement method | Flow rate, concentration; unit conversion factor. | Fewer intermediate links; accurate results. | Large consumption of manpower and material resources, high cost; data are difficult to obtain; poor representativeness or required representativeness of test samples. | Microscale. | This method is suitable for small areas and simple emission sources, such as industrial chimneys or small areas of natural emission sources with the ability to obtain first-hand detection data. |
Model | Research Method | Classification | Characteristic | Advantage |
---|---|---|---|---|
Bottom-up | PB method | Physical model | The energy consumption intensity of individual buildings is simulated, and then the energy consumption intensity of the region is estimated. | Strong detail, high precision. |
Statistical model | Based on the regression analysis method, the carbon emissions of individual buildings are used to calculate the regional carbon emissions. | Energy saving, savemanpower, high efficiency. | ||
Up-bottom | EI-O method | Economic model | The relationship between carbon emissions and the economy is demonstrated based on GDP and other variables. | Emphasize macroeconomic factors. |
Technical model | It also includes factors such as energy mix and technological progress. | The boundary truncation error is overcome. | ||
Hybrid | Hybrid method | Hybrid model | It has the advantages of the PB method and the IO method. | The details are strong and economic considerations are taken into account. |
Author | Name | Characteristic |
---|---|---|
Zha et al. [97] | Divisia method | Compared with other factor decomposition methods, it has the unique advantages of zero residual error and uniform polymerization. |
Feng and Wang [98] | Tapio decoupling model | It is often used to analyze the strength of the link between regional economic development and carbon emissions, which can be divided into three categories: “decoupling”, “linking” and “negative decoupling”. |
Zhang [99] | LEAP model | The model is an ensemble model covering all sectors of energy consumption, production and energy use, which can be used to analyze urban energy demand and carbon emissions. |
Ma et al. [100] | K-means clustering and logistic model | The K-means algorithm is easy to implement, simple, and has a fast clustering speed. The logistic algorithm is simple in calculation and has obvious economic significance. It can describe the growth of S-shaped curves. |
Mansoor et al. [101] | LSTM | It has a strong approximation ability to nonlinear and non-stationary time series and is more accurate than BP neural network. |
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Gao, H.; Wang, X.; Wu, K.; Zheng, Y.; Wang, Q.; Shi, W.; He, M. A Review of Building Carbon Emission Accounting and Prediction Models. Buildings 2023, 13, 1617. https://doi.org/10.3390/buildings13071617
Gao H, Wang X, Wu K, Zheng Y, Wang Q, Shi W, He M. A Review of Building Carbon Emission Accounting and Prediction Models. Buildings. 2023; 13(7):1617. https://doi.org/10.3390/buildings13071617
Chicago/Turabian StyleGao, Huan, Xinke Wang, Kang Wu, Yarong Zheng, Qize Wang, Wei Shi, and Meng He. 2023. "A Review of Building Carbon Emission Accounting and Prediction Models" Buildings 13, no. 7: 1617. https://doi.org/10.3390/buildings13071617