Which Influencing Factors Cause CO2 Emissions
Differences in China’s Provincial Construction
Industry: Empirical Analysis from
a Quantile Regression Model

Wang, Jingmin; Song, Xiaojing; Chen, Keke

doi:10.15244/pjoes/105239

Cited by 16 publications

(8 citation statements)

References 14 publications

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“…Direct carbon emissions mainly include the increase of carbon emissions caused by the consumption of nine kinds of fossil energy, electric power and thermal energy. Indirect carbon emissions include the carbon emissions of five building materials industries: steel, wood, cement, glass, and aluminum [49]. It can be calculated as follows [50]:…”

Section: Carbon Emission Estimationmentioning

confidence: 99%

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

Wang¹,

Zhou²,

Zhao³

et al. 2022

Preprint

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To explore the spatial network structure characteristics and driving effects of carbon emission intensity in China's construction industry, the investigation combined the modified gravity model and social network analysis method to deeply analyze the spatially associated network structure characteristics and driving effects of carbon emission intensity in China's construction industry, based on the measurement of carbon emission data of China's construction industry from 2006 to 2017. The results show that the regional differences of carbon emission of construction industry are significant, and the carbon emission intensity of construction industry show a fluctuation trend. The overall network of carbon emission intensity shows an obvious “core-edge” state, the hierarchical network structure is gradually broken. Economically developed provinces generally play a leading role in the network, and play an intermediary role to guide other provinces to develop together with them. Among the network blocks, most of the blocks play the role of “brokers”. The block with the leading economic development has a strong influence on the other blocks. The increase of network density, the decrease of network hierarchy and network efficiency will reduce the construction carbon emission intensity.

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Section: Carbon Emission Estimationmentioning

confidence: 99%

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

Wang¹,

Zhou²,

Zhao³

et al. 2022

Preprint

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“…The extended STIRPAT model further takes into account the impact of technological progress. The existing studies have explored the factors influencing CO 2 emissions in the construction industry by constructing extended STIRPAT models and combining them with fixed effects ordinary least squares (OLS) (Zhang et al, 2021), ridge regression (Cong et al, 2015;Zou et al, 2016;Peng, 2019), panel quantile regression (Wang et al, 2020a;Li and Chen, 2020). In addition, some scholars have carried out the structural path of carbon dioxide emissions in the construction industry to discover the impact of different structural paths on carbon dioxide emissions in the construction industry (Wang et al, 2020b;Chen et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

“…In order to fully and accurately explore the main influencing factors of CO 2 emissions from the construction industry, we added several other important factors to the model (Du et al, 2017;Li B. et al, 2020;Wang et al, 2020a;Lu et al, 2020;Zhang et al, 2021) and expressed all variables in the logarithmic form to eliminate possible heteroskedasticity. The extended STIRPAT model is as follows: (10) where the CO 2 emissions from the construction industry are the dependent variable.…”

Section: Stirpat Modelmentioning

confidence: 99%

Analysis of the spatial and temporal heterogeneity of factors influencing CO2 emissions in China’s construction industry based on the geographically and temporally weighted regression model: Evidence from 30 provinces in China

Gao

2022

Front. Environ. Sci.

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With the rapid economic development in recent years, China has increased its investment in infrastructure construction, and the construction industry has become a significant contributor to China’s carbon dioxide (CO2) emissions. Therefore, carbon emission reduction in the construction industry is crucial to achieving the goal of “carbon peaking and carbon neutrality” as soon as possible. However, few studies have investigated the factors influencing CO2 emissions from the construction industry in terms of spatial and temporal differences. To address this gap, we first improve the calculation method for the construction industry’s life-cycle assessment (LCA). The geographically and temporally weighted regression (GTWR) model is then utilized to provide insight into the spatio-temporal heterogeneity of the various factors influencing CO2 emissions across other regions and times. The results show that: 1) CO2 emissions from the construction industry in China increased rapidly from 576.5 million tons (Mt) in 2004–3,230 Mt in 2012 and then gradually decreased to 1998.51 Mt in 2020; indirect CO2 emissions accounted for more than 90% of the total CO2 emissions after 2008. 2) There is a solid global positive correlation between CO2 emissions from the construction industry in China during most of the time, and the spatial distribution of CO2 emissions shows a northeast-southwest pattern, with the center of gravity gradually shifting from central China to the southwest. 3) Economic output and industrial agglomeration are positive factors for the increase of CO2 emissions from the construction industry; and urbanization level, production efficiency, and energy efficiency are inhibiting factors for the increase of CO2 emissions from the construction industry. But the contribution and trend of each influencing factor differed significantly across time and regions, showing substantial spatial and temporal heterogeneity. Our findings provide a scientific basis for the Chinese government to implement a regional carbon reduction strategy for the construction industry.

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“…In addition to the proportion of the urban population, new urbanization pursues the coordinated development of all aspects, such as society, economy, land, ecology, and culture. With the continuous acceleration of urbanization, urban–rural population migration in China is growing at a rate of about 1% per year, thereby resulting in an increasing demand for urban housing (Wang et al. , 2020).…”

Section: Introductionmentioning

confidence: 99%

Coupling coordination degree spatiotemporal characteristics and driving factors between new urbanization and construction industry: evidence from China

et al. 2022

ECAM

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PurposeThe coordinated development of the urbanization and construction industry is crucial for the sustainable development of cities. However, the coupling relationship and coordination mechanism between them remain unclear. To bridge this gap, this study attempts to explore the level of coupling coordination between new urbanization and construction industry development and investigate the critical driving factors influencing their coupling coordination degree.Design/methodology/approachBy referring to the existing literature, two index systems were established to evaluate the development level of the new urbanization and construction industry. The spatiotemporal characteristics of the coupled coordinated development of the new urbanization and construction industry in China from 2014 to 2020 were investigated using the coupling coordination model. The Markov chain and geographic detector were adopted to understand the transition probability and driving factors of the coupling coordination degree.FindingsThe results indicate that the coupling degree of China's new urbanization and construction industry is high, and the two systems exhibit obvious interaction phenomena. However, the construction industry in most provinces lags behind the new urbanization. A positive interactive relationship and coordination mechanism has not been established between the two systems. Furthermore, the coupling contribution degree of the driving factors from high to low is as follows: market size > labor resource concentration > government investment ability > economic development level > industrial structure > production efficiency > technology level. Accordingly, a driving mechanism including market, policy, economic, and production technology drivers was developed.Originality/valueThis study contributes to the existing body of knowledge by providing a set of scientific analysis methods to address the deficiency of coordination mechanism research on new urbanization and the construction industry. The results also provide a theoretical basis for decision makers to develop differentiated sustainable development policies.

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Which Influencing Factors Cause CO2 Emissions Differences in China’s Provincial Construction Industry: Empirical Analysis from a Quantile Regression Model

Cited by 16 publications

References 14 publications

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

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

Analysis of the spatial and temporal heterogeneity of factors influencing CO2 emissions in China’s construction industry based on the geographically and temporally weighted regression model: Evidence from 30 provinces in China

Coupling coordination degree spatiotemporal characteristics and driving factors between new urbanization and construction industry: evidence from China

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