Carbon emissions and policies in China's building and construction industry: Evidence from 1994 to 2012

The production of construction projects is carbon-intensive and interrelated to multiple other industries that provide related materials and services. Thus, the calculations of carbon emissions are relatively complex, and the consideration of other factors becomes necessary, especially in China, which has a massive land area and regions with greatly uneven development. To improve the accuracy of the calculations and illustrate the impacts of the various factors at the provincial level in the construction industry, this study separated carbon emissions into two categories, the direct category and the indirect category. The features of carbon emissions in this industry across 30 provinces in China were analysed, and the logarithmic mean Divisia index (LMDI) model was employed to decompose the major factors, including direct energy proportion, unit value energy consumption, value creation effect, indirect carbon intensity, and scale effect of output. It was concluded that carbon emissions increased, whereas carbon intensity decreased dramatically, and indirect emissions accounted for 90% to 95% of the total emissions from the majority of the provinces between 2005 and 2014. The carbon intensities were high in the underdeveloped western and central regions, especially in Shanxi, Inner-Mongolia and Qinghai, whereas they were low in the well-developed eastern and southern regions, represented by Beijing, Shanghai, Zhejiang and Guangdong. The value creation effect and indirect carbon intensity had significant negative effects on carbon emissions, whereas the scale effect of output was the primary factor creating emissions. The factors of direct energy proportion and unit value energy consumption had relatively limited, albeit varying, effects. Accordingly, this study reveals that the evolving trends of these factors vary in different provinces; therefore, overall, our research results and insights support government policy and decision maker’s decisions to minimize the carbon emissions in the construction industry.

With the rapid development of the global economy, the amount of China’s carbon emission has been increasing consistently in a high speed, causing huge environment problems. The construction industry, as the leading pillar of the national economic and social development, accounts for a large proportion of the total carbon emissions in China. Several calculation methods have been used to calculate carbon emissions. However, the main influencing factors need to be found to reduce carbon emissions. In this paper, the Logarithmic Mean Divisia Index (LMDI) technique is used to decompose the energy-induced carbon emissions of the construction industry into four factors: construction areas, construction investment efficiency (output value per unit), energy intensity, and carbon intensity. Based on IPCC carbon emission factors and data from Chinese Energy Statistical Yearbooks and Chinese Construction Industry Statistical Yearbooks, the factors of energy-induced carbon emissions in China were decomposed with LMDI method and Kaya equation. Proper countermeasures are proposed to reduce the energy-induced carbon emissions of the construction industry in China.

With the rapid development of Chinese economy and increasing improved living standards, the amount of carbon emissions in China has been increasing consistently in a high speed, which consists of the largest percentage of the world’s total carbon emissions in recent years. The construction industry, playing an important role in the Chinese economy, accounts for a large proportion of the total carbon emissions in China. In this paper, the carbon emissions from construction industry in China in 2009 are analyzed by adopting Multi Regional Input-output (MRIO) Model and the World Input-Output Database (WIOD). Results show that, according to the data in 2009, the construction industry is the largest carbon emitter among all industries in China, responsible for the emissions of 2,121,649.31 kt CO2, accounting for 66.54 % of Chinese total carbon emissions. This emission value is contributed by other economic sectors and activities, and it has been found that the industrial sector “Electricity, Gas and Water Supply” is the largest contributor to the carbon emissions of Chinese construction industry, with an amount of 984,830.85 kt CO2, accounting for 46.42 % of the total carbon emissions of Chinese construction industry. Furthermore the carbon emissions in the construction industry comprise 71,418.19 kt CO2 (3.37 %) of direct carbon emissions and 2,050,231.12 kt CO2 (96.63 %) of indirect carbon emissions. The carbon emissions of domestic goods, exports and imports within construction industry are 2,129,974.07, 8663.33 and 338.58 kt CO2, respectively, consisting of 100.39, 0.41 and 0.02 % of the total carbon emissions of Chinese construction industry. The results can help identify critical areas where policymakers can formulate effective policy measures for carbon emissions reduction in Chinese construction industry.

Analysis of the non-equilibrium and evolutionary driving forces of carbon emissions in China's construction industry

Based on the whole life cycle perspective, the carbon emissions of the provincial construction industry in China from 2011 to 2019 were calculated from the production, construction, operation, and demolition stages of building materials. A spatial correlation network matrix of the carbon emissions in the construction industry was constructed by using the modified gravity model, and the structural characteristics of the correlation network were described by introducing social network analysis. Through the quadratic assignment program, the spatial correlation matrix of carbon emissions in the construction industry and its influencing factors were regressed and analyzed. The conclusions were as follows:① the spatial correlation network of carbon emissions in China's construction industry clearly existed. The network density and network correlation numbers were gradually rising, and the network tightness and stability were gradually improving. ② Shanghai, Tianjin, Beijing, and Jiangsu had a higher degree centrality and closeness centrality, which are the core and dominant positions of the spatial correlation network of carbon emissions in the construction industry. Zhejiang replaced Shanghai in the top four from 2013 to 2018, and the betweenness centrality of each province had unbalanced characteristics. ③ Beijing, Tianjin, Jiangsu, Inner Mongolia, Shanghai, and Shandong were "net beneficiaries" blocks, receiving the carbon emissions from other regions. Four provinces, Guangdong, Chongqing, Fujian, and Shandong, belonged to the "broker" sector, achieving a dynamic balance between the production and consumption sides of building carbon emissions. The remaining 20 provinces played a "net spillovers" role, actively sending carbon emissions from the construction industry to other provinces. The correlation between blocks was much greater than the correlation relationship within the blocks. ④ Industrial structure, urban population, spatial adjacency, consumption level, and construction industry process structure had a significant influence on the spatial correlation of carbon emissions in the construction industry. The greater the inter-provincial differences in industrial structure, urban population, spatial adjacency, and consumption level, the greater the similarity of inter-provincial construction industry process structure, and the stronger the spatial correlation and spatial spillover of the construction industry carbon emissions. Finally, according to the evolution characteristics and influencing factors of the spatial correlation network of building carbon emissions, relevant countermeasures and suggestions were provided for the collaborative carbon reduction development of the construction industry region.

Analyzing the influence factors of the carbon emissions from China's building and construction industry from 2000 to 2015

The building sector contributes significantly to carbon emissions, impeding China's progress toward its 2030 carbon emissions peak target due to the limited utilization of renewable energy sources. This study aims to forecast the peak and timing of carbon emissions in China's construction industry to chart a low-carbon roadmap for the sector's future. Initially, an extended logarithmic mean divisia index (LMDI) decomposition model, based on the Kaya identity, is proposed to gauge the contribution levels of driving factors affecting building carbon intensity. Subsequently, a hybrid prediction model (IGA-BP) is constructed, employing an optimized two-hidden-layer neural network via a genetic algorithm, to forecast building carbon emissions and intensity. Additionally, four scenarios are outlined, each defining pathways to simulate emissions peak, carbon peak timing, and intensity within the Chinese building sector from 2020 to 2050. The research findings reveal: (1) The final emission factor of buildings primarily drives the surge in building carbon intensity, while the industrial structure stands as the most significant limiting factor. (2) Compared to alternative models, the proposed hybrid prediction model more effectively captures the evolution pattern of carbon emissions. (3) The prediction results indicate that China's building carbon intensity has reached its peak. Pathway 12 closely aligns with the sector's carbon emissions peak, projecting a peak value of 5.609 billion tons in 2029. To attain this pathway, China needs to develop more precise and feasible emission reduction strategies for its buildings. Overall, the research outcomes furnish robust references for decision-making in future efforts aimed at reducing building emissions.

In order to clarify the current situation of research on carbon emissions of China's construction industry and point out the research direction for the related research of carbon emissions in China and similar economies in the world, this study uses the systematic literature review method to screen the articles related to carbon emissions of China's construction industry from 2014 to 2021, and 134 papers were kept. These articles were analyzed from three aspects: carbon emission stages, research scope and research method. This study finds that: 1) in terms carbon emission stages, the amounts of articles on the whole life cycle of Chinese construction industry is the largest, accounting for 47.01%; The second is the articles on the materialization stage, accounting for 17.91%. 2) In terms of research scope, the research on individual projects and the research from the industry level accounted for 61.94% and 38.06% of the total amount of articles respectively. Among the articles on individual projects, the articles on buildings were the most, accounting for 67.47% of such articles. Among the articles on the industry level, the articles on carbon emissions of national construction industry were the most, accounting for 60.78% of such articles. 3) In terms of research method, the quantitative method used in micro-level research is mainly the carbon emission coefficient method, and the analysis method is mainly the life cycle assessment method; the quantitative method used in macro-level research is mainly the input-output method, and the analysis method is mainly decomposition analysis methods.

The changes in the carbon emissions in China's provincial construction industries are of high complexity. It is essential to understand the changes in the construction carbon emissions (CCEs) in China on the provincial scale. This study evaluates the factors and structural paths of the changes in provincial CCEs in China between 2012 and 2017 using the structural path decomposition analysis. The results show that the emission intensity effect and production structure effect contributed greatly to the reduction of CCEs across various regions, while the final demand effect had contrary impacts. The local nonmetallic mineral products industry (c13), metal smelting and pressing industry (c14), and electricity industry (c24) generally contributed significantly to the emission intensity effect, production structure effect, and final demand effect across most regions. The consumption of local c13, c14, and c24 by the construction industry (c27), namely “local c13→c27”, “local c14→c27”, and “local c24→c27” were generally the important structural paths of the CCEs changes across various regions. Nonlocal industries such as Hebei c14 and nonlocal structural paths such as “Hebei c14→c27” contributed substantially to the CCEs changes in many regions such as Beijing. The emission intensity effect, first-order production structure effect, and final demand effect typically dominated the effects of the critical structural paths of the CCEs changes across various regions. This study can help policymakers better understand the changes in China's provincial CCEs to formulate region-specific emission reduction measures and provide a comprehensive reference for related research.

Feasibility assessment of the carbon emissions peak in China's construction industry: Factor decomposition and peak forecast

Carbon emission accounting and carbon peak prediction are the prerequisites for carbon reduction in the current construction industry in China, constituting an important basis for fulfilling the responsibility of carbon reduction. To accurately depict the evolutionary trend of carbon emissions in the construction industry, the carbon emissions of the Chinese construction industry were calculated in stages, based on a full life cycle perspective. The Pearson test was used to select the factors influencing carbon emissions in the construction industry, and an extended STIRPAT model was established. The logarithmic mean Divisia index （LMDI） method was used to analyze the factors in the extended model and calculate the contribution rate of each factor influencing carbon emission. Finally, a multivariate nonlinear regression prediction model based on ASO-BP was constructed to explore the evolution of carbon emissions in the construction industry under multiple scenarios, and policy suggestions were proposed for material production, building operation, and construction. The research results showed： ① Under a small sample environment, the atom search algorithm was superior to other traditional intelligent algorithms in terms of prediction accuracy and time. ② Under multiple scenarios, the Chinese construction industry will achieve carbon peaking in 2030； however, under the current population growth scenario, the construction industry will not reach its peak until 2031, lagging behind in the carbon peaking target. ③ Population changes will lead to the postponement of carbon peaking in three stages, particularly having a considerable impact on the operational stage.

Carbon emission reduction in the construction industry is vital for realizing sustainable development, and the development of the digital economy plays an important role in this process. The impact of the digital economy on reducing carbon emissions in the construction industry is empirically explored through econometric analyses on a sample of panel data from 30 provinces in China from 2012 to 2021. The empirical results show that developing a digital economy can significantly reduce the construction industry's carbon emission intensity. Additionally, this impact has a significant spatial spillover effect and can benefit the neighboring regions. The mechanism test shows that the digital economy can reduce carbon emissions by improving the technological level of the construction industry. Moreover, the inhibiting effects of the digital economy on carbon emissions in the construction industry vary across different regions. They are more pronounced in the eastern and western regions of low coal-consuming regions. These findings offer valuable insights for policymakers to help drive the deeper integration of the digital economy with the construction industry and facilitate its transition to low-carbon development.

Digital economy's role in shaping carbon emissions in the construction field: Insights from Chinese cities

Driving force analysis of carbon emissions in China’s building industry: 2000–2015

The construction industry is the backbone of most countries, but its carbon emissions are huge and growing rapidly, constraining the achievement of global carbon-peaking and carbon-neutrality goals. China’s carbon emissions are the highest in the world, and the construction industry is the largest contributor. Due to significant differences between provinces in pressure, potential, and motivation to reduce emissions, the “one-size-fits-all” emission reduction policy has failed to achieve the desired results. This paper empirically investigates the spatial and temporal evolution of carbon emissions in China’s construction industry and their decoupling relationship with economic growth relying on GIS tools and decoupling model in an attempt to provide a basis for the formulation of differentiated construction emission reduction policies and plans in China. The study shows that, firstly, the changes in carbon emissions and carbon intensity in the provincial construction industry are becoming increasingly complex, with a variety of types emerging, such as declining, “inverted U-shaped”, growing, “U-shaped”, and smooth fluctuating patterns. Secondly, the coefficient of variation is higher than 0.65 for a long time, indicating high spatial heterogeneity. However, spatial agglomeration and correlation are low, with only a few cluster-like agglomerations formed in the Pearl River Delta, Yangtze River Delta, Bohai Bay, Northeast China, and Loess and Yunnan–Guizhou Plateau regions. Thirdly, most provinces have not reached peak carbon emissions from the construction industry, with 25% having reached peak and being in the plateau stage, respectively. Fourthly, the decoupling relationship between carbon emissions from the construction industry and economic growth, as well as their changes, is increasingly diversified, and most provinces are in a strong and weak decoupling state. Moreover, a growing number of provinces that have achieved decoupling are moving backward to re-coupling, due to the impact of economic transformation and the outbreaks of COVID-19, with the degraded regions increasingly concentrated in the northeast and northwest. Fifthly, we classify China’s 30 provinces into Leader, Intermediate, and Laggard policy zones and further propose differentiated response strategies. In conclusion, studying the trends and patterns of carbon-emission changes in the construction industry in different regions, revealing their spatial differentiation and correlation, and developing a classification management strategy for low carbonized development of the construction industry help significantly improve the reliability, efficiency, and self-adaptability of policy design and implementation.