| In the context of sustainable development,the extensive economic growth model is exerting increasing pressure on environmental protection.How to achieve energy saving goals and maximize economic benefits has become an important issue facing the Chinese government.Reducing energy intensity is considered to be an effective measure to resolve the contradiction between energy,economy and environment.As one of the important sectors of the national economy development,the construction industry has a high degree of industrial relevance,and has problems such as high energy consumption and high emission.The unbalanced development level of construction industry among regions leads to significant differences in energy intensity.Meanwhile,the energy intensity of a certain region is not only related to the time factor,but also may be affected by the neighboring regions.Therefore,in order to formulate targeted energy saving policies,alleviate the constraints of energy consumption on economic development and environmental protection,it is of great significance to clarify the spatial and temporal distribution characteristics and critical factors of energy intensity in the construction industry.Taking the construction industry as the research object,this paper considers the basic national conditions of regional differences in China,and applies the theory of spatial effects throughout the study.The purpose is to accurately describe the distribution characteristics and evolution rules of energy intensity in the industry,and identify the factors influencing the change in energy intensity.Firstly,based on the input-output model,the complete energy consumption and energy intensity of the construction industry are accurately calculated,and the characteristics of the two over time series are discussed from the national,regional and provincial levels.Then,the spatial autocorrelation analysis method is used to explore the basic spatial characteristics of energy intensity in the construction industry,furthermore,the standard deviation ellipse analysis method is employed to describe the evolution of its spatial distribution pattern.Finally,the static and dynamic spatial panel data models are adopted to analyze the influencing factors of the energy intensity in the construction industry,and reveal the heterogeneity of the driving mechanism of each factor.The results show that:(1)The total energy consumption of China’s construction industry showed an overall increasing trend from 2005 to 2017,while the energy intensity declined year by year.Meanwhile,the energy intensity of the construction industry presents significant differences,which is characterized by an uneven spatial distribution.(2)The energy intensity of the construction industry shows a positive spatial correlation,the provinces with high value are mainly clustered in the west and the provinces with low value are located in the east.(3)The standard deviation ellipse of energy intensity in the construction industry is characterized by a distribution pattern of northeast-southwest,which rotates in a clockwise direction.The center of gravity is located in the north of the central region of China,and has a significant shift to the northwest throughout the study period.(4)The spatial spillover effect of energy intensity in the construction industry cannot be ignored.There are obvious interactions among regions,and the energy intensity has a strong path dependence.(5)In terms of the whole country,the urbanization level,economic development,economic openness and industrial structure are the key factors affecting the energy intensity of the construction industry.The effect of economic development and industrial structure is no longer significant after the first order lag term is added.(6)As far as the eastern,central and western regions are concerned,the energy intensity of the construction industry in the east is closely related to the urbanization level,economic development and the development level of the construction industry.The energy intensity of the central and western regions is affected by economic openness,industrial structure and the construction industry development. |
