Search

With the advancement of urbanization, urban agglomerations have become a main carrier of China's development. As the ultimate stage of urban agglomeration, the realization of urban agglomeration integration (UAI) plays a dominant role in regional prosperity. Appropriate evaluation of UAI could monitor the integration level and guide its coordinated development. However, little attention has been paid to UAI evaluation research. To this end, this study proposes a comprehensive evaluation model to investigate UAI via the entropy method blended with TOPSIS. Furthermore, utilizing grey relational analysis (GRA), critical dimensions of UAI are also identified to provide concrete evidence. Finally, the proposed model is applied to evaluate the UAI of the Pearl River Delta (PRD), and the main results are as follows: (1) The development trend of the overall UAI in the PRD has increased in fluctuation during the research period, achieving a "good" degree; (2) Except for the policy measures integration (0.882), the integration values for other dimensions are in the range of 0.6 to 0.8 in 2020, reflecting a fairly high level; (3) The infrastructure integration has a major effect on UAI. These findings are favorable in providing references for understanding the current situation of UAI in China, which are of considerable practical significance. [ABSTRACT FROM AUTHOR]

Service provider portfolio selection (SPPS) can be a major challenge for organizations to achieve project success. Hence, organizations need to decide on which service provider portfolio (SPP) is appropriate for project management (PM). However, there has been limited research on how to select a SPP in PM. To address this research gap, we establish a novel model for SPPS based on a BP neural network integrated with entropy-AHP from the perspective of the comprehensive economic benefit. This model employs a BP neural network due to its robustness and memory and nonlinear mapping abilities. Furthermore, we implement the proposed model for a construction project to verify the effectiveness. Our results indicate that the model performs well with a prediction accuracy of 97%. Moreover, the model is confirmed to be robust as it still achieves high prediction accuracy when the input data are disturbed randomly. [ABSTRACT FROM AUTHOR]

China has set ambitious targets to reduce its carbon intensity by 2020. Improved energy efficiency is an important strategy to achieve this goal. However, the rebound effect may act as a major obstacle to fully realizing the potential for energy savings. As one of three major energy consumption sectors in China, the construction industry is vital to reducing carbon emissions. This paper established a static computable general equilibrium (CGE) model to study the rebound effect of different energy sources used in the construction industry. The main energy sources used in the construction industry are coal, oil, natural gas, and electricity, and the conclusions show that the largest rebound effect was found for improvements in natural gas efficiency, with an average of 99.20%, while the lowest was for improvements in electricity efficiency, with an average of 83.47%. Moreover, the rebound effect of the primary energy sources (coal, oil, natural gas) was greater than the rebound effect of the secondary energy source (electricity). Our conclusions indicate that improving the energy efficiency in the construction industry will have a positive impact on GDP and on the mitigation of carbon emissions, and the presence of the rebound effect is significant, especially with regard to electricity sources. The implications of the results are that policymakers should primarily focus on improving the efficiency of electricity. In addition, this paper suggests that the rebound effect can be reduced by removing fossil fuel subsidies and imposing a carbon tax. [ABSTRACT FROM AUTHOR]

Climate change caused by carbon emissions continuously threatens sustainable development. Due to China’s immense territory, there are remarkable regional differences in carbon emissions. The construction industry, which has strong internal industrial differences, further leads to carbon emission disparity in China. Policymakers should consider spatial effects and attempt to eliminate carbon emission inequality to promote the sustainable development of the construction industry and realize emission reduction targets. Based on the classic Markov chain and spatial Markov chain, this paper investigates the club convergence and spatial distribution dynamics of China’s carbon intensity in the construction industry from 2005 to 2014. The results show that the provincial carbon intensity in the construction industry is characterized by “convergence clubs” during the research period, and very low-level and very high-level convergence clubs have strong stability. Moreover, the carbon intensity class transitions of provinces tend to be consistent with that of their neighbors. Furthermore, the transition of carbon intensity types is highly influenced by their regional backgrounds. The provinces with high carbon emissions have a negative influence on their neighbors, whereas the provinces with low carbon emissions have a positive influence. These analyses provide a spatial interpretation to the “club convergence” of carbon intensity. [ABSTRACT FROM AUTHOR]

Indirect carbon emissions account for a large ratio of the total carbon emissions in processes to make the final products, and this implies indirect carbon emission flow across industries. Understanding these flows is crucial for allocating a carbon allowance for each industry. By combining input-output analysis and complex network theory, this study establishes an indirect carbon emission flow network (ICEFN) for 41 industries from 2005 to 2014 to investigate the interrelationships among different industries. The results show that the ICEFN was consistent with a small-world nature based on an analysis of the average path lengths and the clustering coefficients. Moreover, key industries in the ICEFN were identified using complex network theory on the basis of degree centrality and betweenness centrality. Furthermore, the 41 industries of the ICEFN were divided into four industrial subgroups that are related closely to one another. Finally, possible policy implications were provided based on the knowledge of the structure of the ICEFN and its trend. [ABSTRACT FROM AUTHOR]

A synergistic perspective to selecting the right project portfolio is the key to realizing strategic organizational objectives. Based on the theory of the composite system, this paper identifies the factors that affect the science of project portfolio selection (PPS) and investigates the synergistic relationship among the organizational strategic objective subsystem, the project portfolio subsystem and the environment subsystem, which collectively constitute the composite system of project portfolio selection. Utilizing the concept of order parameters, an order parameter system is built for each subsystem to reflect the synergistic relationships in the composite system of PPS. The PPS model is constructed based on the degree of synergy in the composite system by describing the order degree of the subsystem and the synergy degree of the composite system, which helps to quantify the synergistic relationships among these subsystems. The effectiveness and feasibility of the proposed model are demonstrated and validated by a case study. [ABSTRACT FROM AUTHOR]