Interprovincial differences in the historical peak situation of building carbon emissions in China: Causes and enlightenments

Li, Rui; Yu, Yanhui; Cai, Weiguang; Liu, Qiqi; Yuan, Liangxi; Zhou, Haonan

doi:10.1016/j.jenvman.2023.117347

Cited by 37 publications

(7 citation statements)

References 40 publications

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“…Based on the above analyses and research, the main research objectives of this paper are as follows: (1) To analyze the energy consumption of office buildings in the Turpan area (cold region) using parametric energy consumption software, and to propose strategies for energy consumption reduction and renovation. (2) Establish an energy consumption assessment model for office buildings in the Turpan area using the entropy-based TOPSIS method.…”

Section: At the Window Performance Levelmentioning

confidence: 99%

“…The China Building Energy Consumption Research Report, 2020, points out that China's public building area is 129 × 10 8 m 2 , with a building operating energy consumption of 3.83 × 10 8 tce and a unit area energy consumption of 29.73 kgce/m 2 ; the residential building area is 54.5 billion m 2 , with a building operating energy consumption of 6.17 × 108 tce and a unit area energy consumption of 11.32 kgce/m 2 . The construction industry sector is one of the most prominent energy-consuming sectors in China [1]. The energy consumption generated by office buildings in China has been found to account for 30% of the energy consumption of all commercial buildings [2].…”

Section: Introductionmentioning

confidence: 99%

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Energy Performance Analysis and Study of an Office Building in an Extremely Hot and Cold Region

Liu,

Wang,

Huang

et al. 2024

Sustainability

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China is committed to reaching peak carbon by 2030 and carbon neutrality by 2060. The goals of reducing energy consumption and building a “beautiful China” are being urgently pursued in China. The building studied in this paper is located in the city of Turpan, where the problem of excessive energy use among buildings is significant due to the region’s hot summers and cold winters. Additionally, the fact that the office building studied in this paper has an east–west orientation is significant: the building’s main façade is oriented to the west, comprising a large area of single-layer glass curtain wall. Based on this, this paper proposes optimization strategies from two perspectives of renovation and new construction. Four design options are proposed at the retrofit level: glazed circular curtain wall; glazed enclosed curtain wall; west-facing double-glazed curtain wall circulation combined with south-facing light from the east; recycling of windows on the inside of the exterior glass curtain wall. These suggestions focus on retrofitting the glass curtain wall on the west elevation of the building. Two design options are proposed at the new-build level: west-facing south-oriented light and west-facing north-oriented light. These suggestions were primarily built around the idea of changing the orientation of the windows on the west elevation. The results show that the optimal solution is to implement the west-facing double-glazed curtain wall circulation combined with south-facing light from the east. This program shows a 64.14% reduction in heating energy consumption, a 77.12% reduction in cooling energy consumption, and a 69.67% reduction in total energy consumption. The above research has improved the deficiencies in the performance-based energy efficiency retrofit of office buildings in the region and provided new ideas and suggestions for policymakers and designers to build energy-efficiency retrofits in the early stages.

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Section: At the Window Performance Levelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Performance Analysis and Study of an Office Building in an Extremely Hot and Cold Region

Liu,

Wang,

Huang

et al. 2024

Sustainability

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“…Some scholars analyzed the main factors affecting CO 2 emissions of different industries in China, including power industry, transportation industry, logistics industry (Quan et al, 2020;Liu et al, 2021;He et al, 2022). Many researches evaluated the peak situation of CO 2 emissions of different sectors in China, including building, transporting, industrial, agricultural and so forth (Chen et al, 2020;Huo et al, 2021;Li et al, 2023a). For instance, Lu et al (2020) employed an improved PSO (Particle swarm optimization) algorithm optimized BP neural network model to predict carbon emissions for heavy chemical industry and its sub-sectors from 2017 to 2035.…”

Section: Introductionmentioning

confidence: 99%

Driving factors analysis and scenario prediction of CO2 emissions in power industries of key provinces along the Yellow River based on LMDI and BP neural network

Wu,

Sun,

Cui

et al. 2024

Front. Ecol. Evol.

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IntroductionPower industry is one of the largest sources of CO2 emissions in China. The Yellow River Basin plays a supportive role in guaranteeing the effective supply of electricity nationwide, with numerous power generation bases. Understanding the drivers and peak of CO2 emissions of power industry in the Yellow River Basin is vital for China to fulfill its commitment to reach carbon emissions peak by 2030.MethodsThe Logarithmic Mean Divisia Index (LMDI) model was employed to explore the drivers to the change of CO2 emissions in power industries of three study areas, including Inner Mongolia Autonomous Regions, Shanxi Province, and Shandong Province in the Yellow River Basin. And Back Propagation (BP) neural network was combined with scenario analysis to empirically predict the trend of the amount of CO2 emitted by power industry (CEPI) from provincial perspective.ResultsCEPI in Inner Mongolia under the scenarios of a low degree of CO2 emissions promotion with a medium degree of CO2 emissions inhibition (LM) and a low degree of CO2 emissions promotion with a high degree of CO2 emissions inhibition (LH) scenario can reach a peak as early as 2030, with the peak value of 628.32 and 638.12 million tonnes, respectively. Moreover, in Shanxi, only CEPI under a low degree of CO2 emissions promotion scenarios (LL, LM, LH) can achieve the peak in 2025 ahead of schedule, with amounts of 319.32, 308.07, and 292.45 million tonnes. Regarding Shandong, CEPI under scenarios of a low degree of CO2 emissions promotion with a high degree of CO2 emissions inhibition (LH) and a medium degree of CO2 emissions promotion with a high degree of CO2 emissions inhibition (MH) could achieve the earliest peak time in 2025, with a peak of 434.6 and 439.36 million tonnes, respectively.DiscussionThe earliest peak time of CEPI in Shandong Province and Shanxi Province is 2025, but the peak of CEPI in Shanxi is smaller than that of Shandong. The peak time of CEPI in Inner Mongolia is relatively late, in 2030, and the peak is larger than that of the other two provinces. The per capita GDP is the most positive driving factor that contributes to the CEPI. Shandong has a strong economy, and its per capita GDP is much higher than Shanxi’s. Therefore, even under the same peak time, the CEPI in Shandong is much higher than that of Shanxi. Inner Mongolia is extensive and sparsely populated, which makes its per capita GDP rank among the top in China. In addition, Inner Mongolia’s coal-based power generation structure and high power generation also contribute to its late CO2 peak time and large CO2 peak.

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“…According to the existing studies, the calculation of building energy consumption [12,13] mainly focuses on: Wholesale, Retail Trade, Accommodation and Catering Services; Others; Residential Life Consumptions. In the study of the factors influencing carbon emissions of buildings within China and its provinces [14][15][16], the methods mainly used by researchers are EKC curve analysis, the Kaya equation [17][18][19][20], and the LMDI (Logarithmic Mean Divisia Index) model. For the prediction of carbon emission, the frequently used methods are the STIRPAT model, LEAP model, and scenario prediction.…”

Section: Introductionmentioning

confidence: 99%

A Simulation Study on Peak Carbon Emission of Public Buildings—In the Case of Henan Province, China

Yangwen

Gong

et al. 2023

Sustainability

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With the continuous development of the social economy, carbon emissions from various buildings are increasing. As the most important category of building carbon emissions, the rapid peaking of public buildings is an important part of achieving carbon peak and carbon neutrality. This paper is based on the industrial background of the energy consumption structure of Henan Province, a central province in the developing country of China. Firstly, the energy consumption intensity of buildings and public buildings in Henan Province from 2010 to 2020 was calculated according to the energy balance sheet. The Kaya–LMDI decomposition method was also used to analyse the carbon emissions of public buildings, determining the impact of each influencing parameter on public buildings. Secondly, the scenario prediction model Monte Carlo was run 100,000 times to set the stochastic parameters of the variables in the model to predict the time of carbon peak and carbon emissions. The analysis results indicated that: ① Carbon emissions in Henan Province have exhibited a steady growth trend, increasing from 1533 t in 2010 to 6561 t in 2020; ② The primary factors influencing carbon emissions of public buildings in Henan Province were urbanization rate, public floor area per capita, and energy intensity per unit of public floor area; and ③ Carbon emissions of public buildings in Henan Province followed an inverted U-shaped distribution and are expected to peak at approximately 7423 t by the year 2035. The research method in this paper can guide the simulation study of peak carbon emission prediction in Henan Province based on the influencing parameters of carbon emission from different types of buildings. Moreover, the results of this paper can provide a reference for a more precise study of building carbon reduction in similar regions of developing countries.

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Interprovincial differences in the historical peak situation of building carbon emissions in China: Causes and enlightenments

Cited by 37 publications

References 40 publications

Energy Performance Analysis and Study of an Office Building in an Extremely Hot and Cold Region

Energy Performance Analysis and Study of an Office Building in an Extremely Hot and Cold Region

Driving factors analysis and scenario prediction of CO2 emissions in power industries of key provinces along the Yellow River based on LMDI and BP neural network

A Simulation Study on Peak Carbon Emission of Public Buildings—In the Case of Henan Province, China

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