Carbon Emissions in China's Construction Industry: Calculations, Factors and Regions.

Transportation is an important part of social and economic development and is also a typical high-energy and high-emissions industry. Achieving low-carbon development in the transportation industry is a much-needed requirement and the only way to achieve high-quality development. Therefore, based on the relevant data of 30 provinces in China from 2010 to 2018, this research uses the static panel model, panel threshold model and spatial Durbin model to conduct an empirical study on the impact and mechanism of digital innovation on carbon emissions in the transportation industry, and draws the following conclusions. (1) Carbon emissions in the transportation industry have dynamic and continuous adjustment characteristics. (2) There is a significant inverted U-shape non-linear relationship between the level of digital innovation and carbon emissions in the industry. In regions with a low level of digital innovation, the application of digital technology increases carbon emissions in this industry, but as the level of digital innovation continues to increase its application suppresses carbon emissions, showing an effect of carbon emission reduction. (3) The impact of digital innovation on carbon emissions in the transportation industry has a spatial spillover effect, and its level in one province significantly impacts carbon emissions in other provinces’ transportation industry through the spatial spillover effect. Therefore, it is recommended to further strengthen the exchange and cooperation of digital innovation in the transportation industry between regions, improve the scale of digitalization in this industry, and accelerate its green transformation through digital innovation, thus promoting the green, low-carbon, and sustainable development of China’s economy.

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.

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

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

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.

China's energy chemical industry accounts for about 12.01% of the national carbon emissions, while the heterogeneous carbon emission characteristics exhibited by the subsectors have not been reliably investigated. Based on the energy consumption data of the energy chemical industry subsectors in 30 Chinese provinces from 2006 to 2019, this study systematically identified the carbon emission contributions of high-emission subsectors, examined the evolutionary changes and correlation characteristics of carbon emissions from different perspectives, and further explored the carbon emission drivers. According to the survey, coal mining and washing (CMW) and petroleum processing, coking, and nuclear fuel processing (PCN) were high-emission sectors of the energy chemical industry, with annual emissions of more than 150 million tons, accounting for about 72.98% of the energy chemical industry. In addition, the number of high-emission areas in China's energy chemical industries has gradually increased, and the spatial disequilibrium of carbon emissions in industrial sectors has gradually deepened. The development of upstream industries had a strong correlation with carbon emissions, and the upstream industry sector still has not achieved carbon decoupling. The decomposition of the driving effects of carbon emissions showed that the economic output effect is the largest contributor to the growth of carbon emissions in the energy chemical industry, while energy restructuring and energy intensity reduction help reduce carbon emissions, but there is heterogeneity in the driving effects of subsectors.

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.

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.

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.

Under the "double carbon" goal， the green and high-quality development of the construction industry in China has an important impact on the realization of carbon peak. At the provincial scale， the IPCC coefficient method was used to estimate the carbon emissions of the construction industry from 2001 to 2020 in each province. Based on the STIRPAT model， the WOA-BP neural network model was used to simulate the carbon emissions of the construction industry and its spatio-temporal evolution from 2021 to 2050 in different scenarios. The research showed that： ① From 2001 to 2020， the per capita carbon emissions of China's construction industry will gradually increase， with high per capita carbon emissions in eastern and central provinces and low per capita carbon emissions in western and northern provinces. ② Population was the most important factor affecting the carbon emissions of the construction industry in each province from 2021 to 2050， and the influence was different among provinces. ③ From 2021 to 2050， the peak time of carbon emissions from the construction industry was different under different scenarios， and the peak time was the earliest under constrained scenarios. Seventeen provinces in the constrained scenario， five provinces in the normal equilibrium scenario， and zero provinces in the relaxed radical scenario were projected to achieve carbon peak before 2030. The north and southeast coastal areas reached the peak earlier， followed by Central China， Southwest late， and Shaanxi and Liaoning late. The western development strategy would delay the carbon peak of the western construction industry， so it is necessary to strengthen the carbon emission intensity constraint.

The objective of this study is to identify the spatiotemporal change law and the leading factors of industrial carbon emission decoupling. Based on the industrial carbon emission level of the Yangtze River Delta urban agglomeration (YRDUA) from 2006 to 2020, the spatiotemporal heterogeneity was explored with the help of the spatial Markov chain, the Tapio decoupling model was used to analyze its decoupling state from the industrial economy, and its driving factors were decomposed using the Kaya identity and logarithmic mean Divisia index (LMDI) model. The results show that (1) in 51.9% of the YRDUA's cities, the industrial carbon emission situation was stable, the emission reduction observation area (medium carbon) occupied a dominant position, and the emission reduction key area (relatively high carbon) gradually decreased. (2) Industrial carbon emissions had spatial overflow and path dependency characteristics, and the probability of carbon emission type transfer maintaining the original state reached 80.0%. From 2006 to 2011, the average probability of the downward migration of high-carbon cities was 5.0%. From 2011 to 2020, the average probability of the upward transfer of low-carbon cities was 9.4%. (3) The negative decoupling rate of carbon emissions in the YRDUA experienced a transition from 3.7% to 44.4% and then back to 7.4%, showing spatial imbalance. Unsatisfactory decoupling cities were concentrated along the Yangtze River and in coastal areas. (4) The promoting efficiency of energy intensity, carbon emission coefficient, and employment structure was gradually strengthened, and the carbon-increasing effect of labor input was gradually weakened. (5) The decoupling mode of heavy difficult cities is dominated by the three-factor balanced type, which is jointly affected by industrial production, labor input, and carbon emission coefficient. The findings in this study can provide inspiration for industrial carbon emission reduction in megalopolises.

Based on the energy consumption data of the logistics industry in 30 provinces and cities in China, this paper uses the carbon emission accounting method of IPCC to estimate the total carbon emissions of the logistics industry in China from 2010 to 2019, and introduces the carbon emission Theil index. The Logarithmic Mean Divisia Index (LMDI) model is used to measure the regional differences in carbon emissions of China's logistics industry and decompose the influencing factors. The research results show that the total carbon emissions of China's logistics industry have increased significantly, and the total carbon emissions and growth rates of the eastern region are significantly higher than those of the central and western regions. On the whole, the inter-provincial differences in carbon emissions of China's logistics industry have not changed substantially and have shown a downward trend. Intra-regional differences are the main reason for the overall difference in carbon emissions in China's logistics industry, and inter-regional differences have little impact on the overall difference. Among the three major regions, the eastern region has the largest inter-provincial carbon emissions difference, followed closely by the central region, with the western region having the smallest difference. Furthermore, the difference in carbon emissions between the eastern, central, and western regions is expanding, the results of the LMDI model show that the economic development effect is the most important positive effect of the logistics industry's carbon emissions in terms of the national average. The population scale effect is second, and the energy intensity effect is the main negative effect, followed by the energy structure effect. In terms of regional differences, the energy intensity effect is the most obvious in the eastern region, and the population scale effect has a relatively greater

The textile industry is one of the pillar industries in the Yangtze River Delta Region and its green and low-carbon transformation is important for supporting the high-quality development of the Yangtze River Delta. Considering the demonstration zone of green and integrated ecological development of the Yangtze River Delta as an example, this integrated study was conducted on the carbon emission inventory of the textile industry, the driving factors of carbon emissions in the industry, and decoupling effects. Based on the emission factor method, the carbon emissions of Scope 1 and 2 of the textile industry in the demonstration zone were estimated. The carbon emission efficiency of the industry was analyzed using the super efficiency slack-based measure （SBM） model with unexpected outputs. Combining the LMDI factor decomposition method and Tapio decoupling analysis, the driving factors of carbon emissions in the textile industry in the demonstration zone and the decoupling situation between emissions and economic development were identified. The results indicated： ① Between 2014 and 2021, the carbon emissions of the textile industry in the demonstration zone showed a fluctuating trend, reaching the highest value in 2019 at 9.19 million tons of CO2 equivalent. Wujiang District was the primary emission area, with electricity, heat, and coal consumption emissions being the top three emission sources. ② The overall carbon emission efficiency of the textile industry in the demonstration zone showed an upward trend； however, significant differences were present in carbon emission efficiency between regions, with Jiashan County having considerable room for improvement in carbon emission efficiency. ③ Between 2014 and 2021, the driving factors of carbon emissions in the textile industry in various regions of the demonstration zone showed significant changes, with the level of economic development being a positive driving factor affecting carbon emissions. ④ In terms of the decoupling status between carbon emissions and economic development, the overall textile industry in the demonstration zone showed a transition from negative decoupling to decoupling status between 2014 and 2016. The research results provide a scientific basis for the future balanced development of the green and low-carbon transformation of the textile industry and the high-quality development of the economy in the demonstration zone.

Global warming caused by excessive emissions of CO2 and other greenhouse gases is one of the greatest challenges for mankind in the 21st century. China is the world’s largest carbon emitter and its transportation industry is one of the fastest growing sectors for carbon emissions. However, China is a vast country with different levels of carbon emissions in the transportation industry. Therefore, it is helpful for the Chinese government to formulate a reasonable policy of regional carbon emissions control by studying the factors influencing the carbon emissions of the Chinese transportation industry at the regional level. Based on data from 1997 to 2017, this paper adopts the logarithmic mean divisia index (LMDI) decomposition method to analyze the influencing degree of several major factors on the carbon emissions of transportation industry in different regions, puts forward some suggestions according to local conditions, and provides references for the carbon reduction of Chinese transportation industry. The results show that (1) in 2017, the total carbon emissions of the Chinese transportation industry were 714.58 million tons, being 5.59 times of those in 1997, with an average annual growth rate of 9.89%. Among them, the carbon emissions on the Eastern Coast were rising linearly and higher than those in other regions. The carbon emissions in the Great Northwest were always lower than those in other regions, with only 38.75 million tons in 2017. (2) Economic output effect is the most important factor to promote the carbon emissions of transportation industry in various regions. Among them, the contribution values of economic output effect to carbon emissions on the Eastern Coast, the Southern Coast and the Great Northwest continued to rise, while the contribution values of economic output effect to carbon emissions in the other five regions decreased in the fourth stage. (3) The population size effect promoted the carbon emissions of the transportation industry in various regions, but the population size effect of the Northeast had a significant inhibitory influence on the carbon emissions in the fourth stage. (4) The regional energy intensity effect in most stages inhibited carbon emissions of the transportation industry. Among them, the energy intensity effects of the North Coast and the Southern Coast in the two stages had obvious inhibitory influences on carbon emissions of the transportation industry, but the contribution values of the energy intensity effect in the Great Northwest and the Northeast were positive in the fourth stage. (5) Except for the Great Southwest, the industry-scale effects of other regions had inhibited the carbon emissions of transportation industry in all regions. (6) The influences of the carbon emissions coefficient effect on carbon emissions in different regions were not significant and their inhibitory effects were relatively small.