Spatio-temporal interaction heterogeneity and driving factors of carbon emissions from the construction industry in China

Labor linkages and flow paths of industry in China

As economic power increases and market patterns adapt, labor becomes an increasingly significant factor of production. However, there is a dearth of discourse regarding the structural changes that have occurred in the correlation of the labor force across industries, as well as a visual representation of the labor force's movement across industries. To quantify and analyze the correlation effect with greater precision, it is necessary to establish an input-output model as the foundation of analysis, comparing the changes in the total output of the economic system prior to and subsequent to the exclusion using the vertical integration algorithm. By decomposing the path structure, the average propagation distance of the labor force population's demand for each industry can be determined. By employing labor force population data from the corresponding years and China's input-output tables published by the National Bureau of Statistics of China (NBS) from 2005 to 2020, this study conducts a quantitative analysis of the correlation effect between labor force population and the trend of its transfer across 19 industries. The findings indicate that the correlation effect between labor force and population is most pronounced in the manufacturing sector. Furthermore, the construction sector faces an especially critical requirement for labor force personnel from other industries. The article culminates with a recommendation that the government enhance its macro-control endeavors to address labor market risk shocks and take an active stance in response to labor market fluctuations.

Spatial-temporal evolution of carbon emissions and spatial-temporal heterogeneity of influencing factors in the Bohai Rim Region, China

The total change in carbon emissions in the Bohai Rim Region (BRR) plays a guiding role in the policy formulation of carbon emission reduction in northern China. Taking the 43 cities in the BRR as an example, the spatial-temporal evolution of carbon emissions in the BRR was analyzed using kernel density estimation (KDE), map visualization, and standard deviation ellipses, and the spatial autocorrelation model was used to explore the spatial clustering of carbon emissions. On this basis, the spatial-temporal heterogeneity of the factors influencing carbon emissions is explained using a Geodetector. The results are as follows: (i) During the study period, the carbon emissions in the BRR were on the rise, the share of carbon emissions in the Beijing-Tianjin-Hebei region (BTHR) and Liaoning Province was decreasing, and the contribution of Shandong Province was gradually enhanced. The spatial distribution of carbon emissions shows a geographical pattern of "middle-high and low-outside." (ii) Carbon emissions from different regions show the characteristics of BTHR > Shandong Province > Liaoning Province. The high-value carbon emission area continues to move from the northwest of Beijing-Tianjin-Hebei to the southeast. (iii) Municipal carbon emissions showed a significant positive spatial correlation in the later part of the study. The high-high aggregation area is in Tianjin, and the low-low aggregation area is in Liaoning Province. (iv) The level of transport development contributes to carbon emissions with the highest growth rate, followed by industrial structure. There are also regional differences in the dominant influences on municipal carbon emission differences. Population size, urbanization, and economic development level are the core influencing factors of carbon emissions in the BTHR, Shandong Province, and Liaoning Province, respectively. In addition, the explanatory power of the interaction between the level of economic development and other factors on carbon emissions is at a high level.

The mechanism of China's renewable energy utilization impact on carbon emission intensity: Evidence from the perspective of intermediary transmission

Renewable energy (RE) plays a crucial role in global energy transformation, and a thorough study of the potential impact of RE on regional carbon emissions is of great significance. This is particularly relevant to China, which needs to clarify its path to carbon reduction. Using the sample data of 30 provinces in China from 2000 to 2021, this paper uses the Granger causality test to verify the causal relationship between carbon emission intensity (CEI) and other factors. It builds a mediation effect model on this basis to explore the direct impact effect and indirect transmission path of renewable energy utilization (REU) on CEI. The results show that REU has a one-way causal relationship with CEI. REU can directly and indirectly reduce CEI by improving social wealth and changing the direction of energy investment. In addition, REU indirectly increases CEI through the transmission paths of investment in the energy industry - social affluence and industrial level-social affluence. The CEI is indirectly reduced through the conduction paths of (social affluence-Urbanization rate), (Investment in the energy industry-Urbanization rate), (Industrial level-Urbanization rate), and (Industrial level-Investment in the energy industry). These conclusions will assist policymakers in exploring targeted pathways for low-carbon power development, providing a reference for strategic and sustainable carbon reduction policies.

Empirics on linking industrial agglomeration, energy consumption, residential construction sector growth, and environmental sustainability

We looked at the long-term and short-term diversified relationships between industrial agglomeration, aggregate energy consumption, residential construction sector growth, and air pollution in China's 30 provincial units from 2004 through 2020. We contributed to the existing knowledge by calculating a holistic air pollution index (API) and applying advanced methods. We also augmented the Kaya identity by including industrial agglomeration and residential construction sector growth in the baseline framework. Based on empirical results: First, we revealed long-term stability among our covariates through panel cointegration analysis. Second, we uncovered a positive bilateral relationship between residential construction sector growth and industrial agglomeration in the long and short term. Third, we unfolded a unilateral positive correlation emerging from aggregate energy consumption to API, displaying the greatest influence in the east zone of China. Fourth, we observed a unilateral positive connection stemming from industrial agglomeration and residential construction sector growth to aggregate energy consumption and API in the long- and short-term dimensions. Finally, the linking nature was homogeneously prevailing across the long term and short term; however, the long-term impact size outweighed that of the short term. Given our empirical results, useful policy insights are discussed to provide the readers with a take-home message for substantiating sustainable development goals.