A Review of Building Carbon Emission Accounting and Prediction Models

Abstract

As an industry that consumes a quarter of social energy and emits a third of greenhouse gases, the construction industry has an important responsibility to achieve carbon peaking and carbon neutrality. Based on Web of Science, Science-Direct, and CNKI, the accounting and prediction models of carbon emissions from buildings are reviewed. The carbon emission factor method, mass balance method, and actual measurement method are analyzed. The top-down and bottom-up carbon emission accounting models and their subdivision models are introduced and analyzed. Individual building carbon emission assessments generally adopt a bottom-up physical model, while urban carbon emission assessments generally adopt a top-down economic input-output model. Most of the current studies on building carbon emission prediction models follow the path of “exploring influencing factors then putting forward prediction models based on influencing factors”. The studies on driving factors of carbon emission mainly use the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the Logarithmic Mean Divisia Index (LMDI) model, the grey correlation degree model, and other models. The prediction model is realized by the regression model, the system dynamics model, and other mathematical models, as well as the Artificial Neural Network (ANN) model, the Support Vector Machine (SVM) model, and other machine learning models. At present, the research on carbon emission models of individual buildings mainly focuses on the prediction of operational energy consumption, and the research models for the other stages should become a focus in future research.

Abnormal CO2 and H2O Diffusion in CALF-20(Zn) Metal–Organic Framework: Fundamental Understanding of CO2 Capture

“…However, carbon capture processes represent an expensive brick in carbon management chains such as CCS (carbon capture and storage) or CCU (carbon capture and utilization), still limiting their massive deployment worldwide. Significant research and development is thus devoted to finding economic alternatives to the canonical amine scrubbing technique, where the thermal regeneration step tends to be highly energy consuming . The physisorption technique is currently of interest, although it equally suffers from its own disadvantages, such as insufficient CO 2 product purity.…”

“…Significant research and development is thus devoted to finding economic alternatives to the canonical amine scrubbing technique, where the thermal regeneration step tends to be highly energy consuming. 3 The physisorption technique is currently of interest, although it equally suffers from its own disadvantages, such as insufficient CO 2 product purity. To overcome such a limitation, research relates to intensification by developing rapid capture processes.…”

A Review of Building Carbon Emission Accounting Methods under Low-Carbon Building Background

“…In summary, carbon emission accounting methods can be divided into the IPCC inventory method, input-output analysis, life cycle assessment and the carbon emission estimation. The emission factor estimation is the main method recommended by the IPCC [88] and is currently the most widely used method [2]. The four calculators are compared and analyzed, as shown in Table 3.…”

Application of improved graph convolutional networks in daily-ahead carbon emission prediction

“…In Ye et al (2023), the carbon emission of the industry zone was forecasted quickly via the combination of integrating autoregressive integrated moving average (ARIMA) and support vector regression (SVR). Another important part of the prediction area considering carbon emission is carbon emission peak (CEP) prediction (Gao et al, 2023). In Wu et al (2018), stochastic impacts by regression on population, affluence, and technology (STIRPAT) is employed to predict the CEP for Qingdao, China.…”