Calculating carbon emissions and selecting carbon peak scheme for infrastructure construction in Liaoning Province, China

Zhao, Yu; Duan, Xiaoyang; Yu, Miao

doi:10.1016/j.jclepro.2023.138396

Cited by 13 publications

(5 citation statements)

References 28 publications

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“…This is carried out by calculating the carbon emissions of the construction industry through the consumption of building materials and energy, while the recycling coefficient of building materials is utilized to adjust indirect carbon emissions [15]. The IPCC database provides emission factors and calculation models for carbon emissions from the energy, industry and construction sectors, assisting in the estimation and comparison of carbon emissions from different sectors and industries [16]. This method, due to its flexible data selection, finds extensive application and is routinely used to calculate carbon emissions from various industries and regions [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive Modeling and Validation of Carbon Emissions from China’s Coastal Construction Industry: A BO-XGBoost Ensemble Approach

Hou,

Liu

2024

Sustainability

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The extensive carbon emissions produced throughout the life cycle of buildings have significant impacts on environmental sustainability. Addressing the Carbon Emissions from China’s Construction Industry (CECI), this study uses panel data from seven coastal areas (2005–2020) and the Bayesian Optimization Extreme Gradient Boosting (BO-XGBoost) model to accurately predict carbon emissions. Initially, the carbon emission coefficient method is utilized to calculate the CECI. Subsequently, adopting the concept of a fixed-effects model to transform provincial differences into influencing factors, we employ a method combining Spearman rank correlation coefficients to filter out these influencing factors. Finally, the performance of the prediction model is validated using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared (R2) and Mean Absolute Percentage Error (MAPE). The results indicate that the total CECI for the seven provinces and cities increased from 3.1 billion tons in 2005 to 17.2 billion tons in 2020, with Shandong Province having the highest CECI and Hainan Province having the lowest. The total population, Gross Domestic Product (GDP) and floor space of the buildings completed passed the significance test, among a total of eight factors. These factors can be considered explanatory variables for the CECI prediction model. The BO-XGBoost algorithm demonstrates outstanding predictive performance, achieving an R2 of 0.91. The proposed model enables potential decisions to quantitatively target the prominent factors contributing to the CECI. Its application can guide policymakers and decision makers toward implementing effective strategies for reducing carbon emissions, thereby fostering sustainable development in the construction industry.

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Section: Literature Reviewmentioning

confidence: 99%

Predictive Modeling and Validation of Carbon Emissions from China’s Coastal Construction Industry: A BO-XGBoost Ensemble Approach

Hou,

Liu

2024

Sustainability

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“…The Chinese government has made carbon peaking an important strategic goal and proposed to achieve it by 2030 [10]. Therefore, Yibin needs to achieve the carbon peaking target before 2030.…”

Section: Analysis Of Carbon Peak Targetsmentioning

confidence: 99%

“…It signifies a shift in the trajectory of carbon emissions from increasing to decreasing, marking a transition in economic development from a high-energy, high-emission model to a cleaner and more sustainable one [8,9]. In 2021, China made a commitment to achieve carbon peaking by 2030 [10].…”

Section: Introductionmentioning

confidence: 99%

“…The industrial output value accounted for 48.15% of the total GDP, surpassing cities in northeast China like Shenyang City (32.8%) and Dalian City (44.0%) [11,12]. Yibin is emerging as a new industrial city in the inland region of China [10,12]. As Yibin is located at the edge of the Sichuan basin, the wind speed is light, coupled with the Yangtze River passing through the city, the humidity in the main urban area is enormous, resulting in weak diffusion capacity in the urban area and low atmospheric environmental capacity, making it difficult to absorb the large amount of pollutants emitted in the city [13,14].…”

Section: Introductionmentioning

confidence: 99%

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Coordination Relationship of Carbon Emissions and Air Pollutants under Governance Measures in a Typical Industrial City in China

Wang,

Ma,

Wang

et al. 2023

Sustainability

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Coordinating and controlling carbon and atmospheric pollutant emissions in industrial cities poses challenges, making it difficult to formulate effective environmental governance strategies in China. This study used the Community Multiscale Air Quality (CMAQ) and Long-range Energy Alternatives Planning (LEAP) models, with a typical industrial city in the Sichuan Basin as the case study. Five emission reduction scenarios, one integration scenario, and one baseline scenario were set to quantitatively analyze the synergistic effect between carbon emissions and atmospheric pollutant emissions. The results indicate a high synergy between sulfur dioxide and greenhouse gases. For every one-point decrease in the Air Quality Composite Index (AQCI), the Industrial Restructuring Scenario (IR), Other Source Management Scenario (OSM), Transportation Energy Efficiency Improvement Scenario (TEEI), Industrial Energy Efficiency Improvement Scenario (IEEI), and Transportation Restructuring (TR) scenarios would require a reduction in carbon emissions by 56,492.79 kilotons, 39,850.45 kilotons, 34,027.5 kilotons, 22,356.58 kilotons, and 3243.33 kilotons, respectively. The results indicate that governance measures, such as improving transportation structure and upgrading industrial technologies, provide stronger support for simultaneous carbon emissions reductions and air quality improvement.

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“…Domestic and foreign scholars mainly use the displacement and trajectory of the center of gravity to analyze the spatial mobility and temporal change trend of regional elements [1,2]. The research on land use carbon emissions predominantly centers on carbon emission accounting [3][4][5], carbon emission influencing factors analysis [6][7][8][9][10], and land use lowcarbon optimization [11][12][13]. Land use carbon emissions can be classified into direct carbon emissions and indirect carbon emissions.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Land Use Gravity Center Change and Carbon Emission Impact in Chengdu Plain of China from 2006 to 2022

Wu,

Pu,

Dong

et al. 2024

Land

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As the economic center and major grain-producing area in Southwest China, the calculation of the carbon budget and the protection of cultivated land in the Chengdu Plain are of vital significance for China to achieve a carbon peak strategy and ensure food security. For the purpose of clarifying the trend of land use focus and carbon emissions in the Chengdu Plain, the carbon peak level of land use in 33 counties in the Chengdu Plain was explored. Based on the gravity center model and IPCC carbon emission coefficient method, the changing trend of land use gravity center and carbon emission in Chengdu Plain from 2006 to 2022 was clarified. PLS regression model and LMDI model were used to explore the main influencing factors of the carbon emission of cropland and the carbon emission of building land. PLUS model was used to simulate future land use patterns and carbon emissions. (1) The center of gravity of cropland, building land, water, and other and unused land shifted to the northeast by 4.23 km, 5.46 km, 8.44 km, and 31.58 km, respectively, and that of forest and grass shifted to the southeast by 11.12 km and 3.41 km, respectively. For major food crops, the centers of gravity of rice and maize moved northeastward by 15.47 km and 7.52 km, respectively, while wheat moved southwestward by 17.77 km. (2) From 2006 to 2022, carbon emissions from land use in the 33 counties of the Chengdu Plain are all on the rise, with a total increase of 13.552 million tons, and carbon sinks in the 31 counties continue to decline, with a total decrease of 0.691 million tons. (3) Under the natural scenario, carbon sink scenario, and carbon reduction scenario, the carbon emissions from land use decrease by 0.5391 million tons, 3.4728 million tons, and 4.5265 million tons from 2022, respectively. Among the 33 counties in the Chengdu Plain, 11 counties did not achieve carbon peak under the natural scenario, 5 counties did not achieve carbon peak under the carbon sink scenario, and all the counties achieved carbon peak under the carbon sink scenario. During the study period, there was a serious loss of cropland in the Chengdu Plain, mainly to building land in the central part of the Chengdu Plain and to forests within the Longmen Mountain, Longquan Mountain, and Leshan City, and there is a need to strengthen cropland protection in this region in the future. Under the natural scenario, carbon sink scenario, and carbon reduction scenario, land use in the Chengdu Plain region can achieve carbon peak, and the carbon reduction model will be more helpful for the counties to achieve carbon peak.

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Calculating carbon emissions and selecting carbon peak scheme for infrastructure construction in Liaoning Province, China

Cited by 13 publications

References 28 publications

Predictive Modeling and Validation of Carbon Emissions from China’s Coastal Construction Industry: A BO-XGBoost Ensemble Approach

Predictive Modeling and Validation of Carbon Emissions from China’s Coastal Construction Industry: A BO-XGBoost Ensemble Approach

Coordination Relationship of Carbon Emissions and Air Pollutants under Governance Measures in a Typical Industrial City in China

Analysis of Land Use Gravity Center Change and Carbon Emission Impact in Chengdu Plain of China from 2006 to 2022

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