Spatial Correlation Network and Driving Effect of Carbon Emission Intensity in China’s Construction Industry

Building carbon peak scenario prediction in China using system dynamics model

As the issue of global climate change caused by carbon emissions is of great concern, China has proposed achieving its achieve carbon peak goal by 2030. Building carbon emissions account for approximately 50% of China's total carbon emissions. It is crucial to study the time and values of building carbon peaks. In this paper, based on a system dynamics model, logarithmic mean Divisia index model and Monte Carlo simulation, we predict the building carbon peak in China. The following conclusions are obtained: 1) in the baseline scenario, China's building carbon emissions will peak at 5,427 million tons in 2027. In the high-speed development scenario, China's building carbon emissions will peak at 6,298 million tons in 2032. In the coordinated development scenario, the green development scenario, the low-carbon development scenario, and the low-speed development scenario, the peak occurs in 2030 at 5,972 million tons, 5,991 million tons, 5,657 million tons, and 6,329 million tons, respectively. 2) According to the comprehensive simulation, China's building carbon emissions will reach the peak in 2030, with an 80% probability of reaching 5,729-6,171 million tons.

Has Urban Construction Land Achieved Low-Carbon Sustainable Development? A Case Study of North China Plain, China

The rapid expansion of urban construction land (UCL) provides a guarantee to support rapid economic development and meet the social needs of urban residents. However, urban construction land is also an important source of carbon dioxide emissions. Therefore, it is of great research value to investigate the relationship between UCL and carbon emissions in depth. Based on this, using panel data of 57 cities in the North China Plain from 2007 to 2018, the study found that there is a strong positive correlation between UCL and CO2 emissions. It can be seen that the expansion of UCL is an important source of CO2 emissions. On the basis of this research conclusion, first, this paper uses the Tapio decoupling model to analyze the decoupling relationship between UCL and carbon emissions in the North China Plain. Then, the spatial autocorrelation analysis was applied to explore the spatial correlation characteristics of the carbon emission intensity of UCL in cities in the North China Plain. Finally, using the GTWR model to analyze the influencing factors of the carbon emission intensity of UCL, the following conclusions were drawn. In 2007–2015, the decoupling relationship performed well, but it deteriorated significantly from 2015 to 2018; in addition, there was a significant positive spatial correlation of carbon emission intensity of UCL. Various influencing factors have a significant impact on the carbon emission intensity of UCL, for example, the urbanization rate, industrial structure, economic development level, and population density have a positive impact, and environmental regulations, foreign investment intensity, land use efficiency and greenery coverage have a negative impact. The research results of this paper provide a scientific basis for making decisions and optimizing pathways to achieve carbon emission reduction from UCL in the North China Plain, as well as certain reference values for other regions to achieve low-carbon development of UCL. This is significant for exploring the optimal solution of land and carbon emissions and building a harmonious human–land relationship.

Integrated Economic and Environmental Assessment-Based Optimization Design Method of Building Roof Thermal Insulation

The design of thermal insulation in roofs is very important to reduce energy consumption and decrease the environmental impacts of buildings. An integrated economic and environmental assessment-based optimization design method is presented in this paper to find the best candidate insulation design scheme for building roofs, including the determination of roof thermal insulation type and the optimum insulation thickness. In the optimization design method, a zonal method-based double-skin ventilation roof heat transfer model is developed to predict the roof energy consumption. Economic and environmental benefits due to thermal insulation are calculated by using the economic analysis model, the environmental analysis model, and roof energy consumption. Moreover, an integrated dimensionless economic and environmental assessment index is proposed to evaluate different roof thermal insulation design schemes. The optimum insulation thickness is determined by maximizing the sum of economic benefit and environmental benefit due to thermal insulation. The validation results in a real building show that the predicted data for the zonal-based double-skin ventilation roof heat transfer model agreed well with the measured data, with a maximum relative error of 8.2%. The optimum insulation thickness of extruded polystyrene (EPS), mineral wool (MW), and polyurethane (PU) was between 0.082 m and 0.171 m for the single-skin roof in a low-temperature granary in Changsha region in China. The ranking of the integrated assessment indexes of thermal insulation is EPS > MW > PU. A double-skin ventilation roof can reduce the optimum thickness of thermal insulation. The best result is obtained by EPS for the double-skin roof with a grey outer surface color for the low-temperature granary roof in Changsha region in China. The influencing factors of insulation type, roof structure, and roof outer-surface color should be considered in finding the best candidate insulation design solution for building roofs. The integrated economic and environmental assessment-based optimization design method can help designers to efficiently find the best design scheme of thermal insulation to maximize the sum of economic benefit and environmental benefit for building roofs.