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| 1 |
ID:
191137
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| Summary/Abstract |
The carbon emissions (CEs) of public buildings (PBs) in the operation stage significantly impact building life cycle assessments (LCA). Energy conservation and carbon reduction during this stage is necessary for China to achieve peak CEs and neutralization. Energy consumption and CEs are analyzed using energy audit data for PBs in Xi'an. CEs of PBs in the operation stage are calculated by our prediction method with an average deviation of only 4.41%. Using the logarithmic mean Divisia index (LMDI) method to analyze factors influencing CEs, we find that economic development levels impact CEs the most in the operation stage; the impact coefficient reaches 3.81. This study discusses the contribution of various driving factors of CEs via the stochastic impacts by regression on population, affluence, and technology (STIRPAT) method and simulates seven scenarios to predict change trends of the CEs of PBs in the operation stage. The rough development scenario fails to achieve the peak, the comprehensive optimization development scenario achieves the peak in 2040, and the remaining scenarios achieve the peak in 2050. The results provide practical and theoretical support for CE research and a scientific basis for predicting the CEs of PBs in the operation stage throughout China and worldwide.
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| 2 |
ID:
137693
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| Summary/Abstract |
This study aims to estimate carbon intensity abatement potential in China at the regional level by proposing a particle swarm optimization–genetic algorithm (PSO–GA) multivariate environmental learning curve estimation method. The model uses two independent variables, namely, per capita gross domestic product (GDP) and the proportion of the tertiary industry in GDP, to construct carbon intensity learning curves (CILCs), i.e., CO2 emissions per unit of GDP, of 30 provinces in China. Instead of the traditional ordinary least squares (OLS) method, a PSO–GA intelligent optimization algorithm is used to optimize the coefficients of a learning curve. The carbon intensity abatement potentials of the 30 Chinese provinces are estimated via PSO–GA under the business-as-usual scenario. The estimation reveals the following results. (1) For most provinces, the abatement potentials from improving a unit of the proportion of the tertiary industry in GDP are higher than the potentials from raising a unit of per capita GDP. (2) The average potential of the 30 provinces in 2020 will be 37.6% based on the emission's level of 2005. The potentials of Jiangsu, Tianjin, Shandong, Beijing, and Heilongjiang are over 60%. Ningxia is the only province without intensity abatement potential. (3) The total carbon intensity in China weighted by the GDP shares of the 30 provinces will decline by 39.4% in 2020 compared with that in 2005. This intensity cannot achieve the 40%–45% carbon intensity reduction target set by the Chinese government. Additional mitigation policies should be developed to uncover the potentials of Ningxia and Inner Mongolia. In addition, the simulation accuracy of the CILCs optimized by PSO–GA is higher than that of the CILCs optimized by the traditional OLS method.
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