Factors and structural paths of the changes in carbon emissions in China's provincial construction industries

Based on 2002–2010 comparable price input-output tables, this paper first calculates the carbon emissions of China’s industrial sectors with three components by input-output subsystems; next, we decompose the three components into effect of carbon emission intensity, effect of social technology, and effect of final demand separately by structure decomposition analysis; at last, we analyze the contribution of every effect to the total emissions by sectors, thus finding the key sectors and key factors which induce the changes of carbon emissions in China’s industrial sectors. Our results show that in the latest 8 years five departments have gotten the greatest increase in the changes of carbon emissions compare with other departments and the effect of final demand is the key factor leading to the increase of industrial total carbon emissions. The decomposed effects show a decrease in carbon emission due to the changes of carbon emission intensity between 2002 and 2010 compensated by an increase in carbon emissions caused by the rise in final demand of industrial sectors. And social technological changes on the reduction of carbon emissions did not play a very good effect and need further improvement.

The construction industry contributes considerably to China’s carbon emissions. To reduce the construction carbon emissions (CCEs) in China, understanding the spatiotemporal variations in provincial CCEs becomes essential. However, existing literature fails to consider the variations in emission distribution especially by structural paths. Thus, this study aims to comprehensively explore the spatiotemporal variations in China’s provincial CCEs from 2012 to 2017 using the multi-regional input-output analysis, structural path analysis, and Moran index. The results show that Jiangsu, Hebei, and Zhejiang were among the largest five CCEs in 2012 and 2017, whereas Fujian and Shanghai were among the lowest five intensities. Nonlocal contributions constituted over 30% in most regions with increasing proportions during the period. The nonmetallic mineral products industry (c13), metal smelting and pressing industry (c14), and electricity industry (c24) dominated the provincial CCEs. Local c13, c14, and c24 contributed substantially in most regions, while Hebei c14 and Henan c13 were the important nonlocal regional industries in numerous regions. The consumption of local c13 and c14 by the construction industry (c27), namely “Local c13→c27” and “local c14→c27”, were generally among the top ten structural paths of CCEs in most regions, while “Hebei c14→c27” and “Henan c13→c27” were the critical nonlocal paths in many regions. The intensities of provincial CCEs showed a significant positive global spatial autocorrelation in 2012 and 2017, where the central and western regions generally belonged to the High-High cluster. The findings could help policymakers appropriately implement region-specific measures for mitigating China’s CCEs.

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.

This paper expanded the Logarithmic Mean Divisia Index (LMDI) model through the introduction of urbanization, residents’ consumption, and other factors, and decomposed carbon emission changes in China into carbon emission factor effect, energy intensity effect, consumption inhibitory factor effect, urbanization effect, residents’ consumption effect, and population scale effect, and then explored contribution rates and action mechanisms of the above six factors on change in carbon emissions in China. Then, the effect of population structure change on carbon emission was analyzed by taking 2003–2012 as a sample period, and combining this with the panel data of 30 provinces in China. Results showed that in 2003–2012, total carbon emission increased by 4.2117 billion tons in China. The consumption inhibitory factor effect, urbanization effect, residents’ consumption effect, and population scale effect promoted the increase in carbon emissions, and their contribution ratios were 27.44%, 12.700%, 74.96%, and 5.90%, respectively. However, the influence of carbon emission factor effect (−2.54%) and energy intensity effect (−18.46%) on carbon emissions were negative. Population urbanization has become the main population factor which affects carbon emission in China. The “Eastern aggregation” phenomenon caused the population scale effect in the eastern area to be significantly higher than in the central and western regions, but the contribution rate of its energy intensity effect (−11.10 million tons) was significantly smaller than in the central (−21.61 million tons) and western regions (−13.29 million tons), and the carbon emission factor effect in the central area (−3.33 million tons) was significantly higher than that in the eastern (−2.00 million tons) and western regions (−1.08 million tons). During the sample period, the change in population age structure, population education structure, and population occupation structure relieved growth of carbon emissions in China, but the effects of change of population, urban and rural structure, regional economic level, and population size generated increases in carbon emissions. Finally, the change of population sex structure had no significant influence on changes in carbon emissions.

Driving factors of the changes in the carbon emissions in the Chinese construction industry

Driving factors of carbon emissions in China: A joint decomposition approach based on meta-frontier

China’s petrochemical industries are playing an important role in China’s economic development. However, the industries consume large amounts of energy and have become primary sources of carbon emission. In this paper, the change in carbon emissions from China’s petrochemical industries between 2000 and 2010 was quantitatively analyzed with the Log-Mean Divisia Index method, which was decomposed into economic output effect, industrial structural effect and technical effect. The results show that economic output effect is the most important factor driving carbon emission growth in China’s petrochemical industries; industrial structural effect has certain decrement effect on carbon emissions; adjustment of industrial structure by developing low-carbon emission industrial sectors may be a better choice for reducing carbon emissions; and the impact of technical effect varies considerably without showing any clear decrement effect trend over the period of year 2000–2010. The biggest challenge is how to make use of these factors to balance the relationship between economic development and carbon emissions. This study will promote a more comprehensive understanding of the inter-relationships of economic development, industrial structural shift, technical effect and carbon emissions in China’s petrochemical industries and is helpful for exploration of relevant strategies to reduce carbon emissions.

Examining the relationships between carbon emissions and land supply in China

The adverse effects of global climate change on human production and life are becoming increasingly prominent. Responding to climate change has become a severe challenge faced by human society, and the reduction in greenhouse gas emissions has gradually become a common action by all countries. Therefore, analyzing carbon emissions through scientific methods has become an important foundation for responding to the national "dual carbon" strategy. This study used provincial-level carbon emission statistics, combined with nighttime light data and population data, and assigned carbon emissions to the grid scale. It also analyzed the temporal and spatial characteristics and evolution characteristics of carbon emissions in China in 2000, 2005, 2010, 2015, and 2018, as well as the correlation between carbon emissions and the economy. The results showed that:① from 2000 to 2018, the total CO2 emissions in China continued to grow, but the growth rate slowed over time. The average annual growth rate of carbon emissions dropped from 9.9% in 2000-2010 to 7.4% in 2010-2018. From the perspective of spatial distribution, carbon-free areas were mainly distributed in the northwest uninhabited area and northeast forest and mountainous areas, low-carbon emissions were mainly distributed in the vast small and medium-sized cities and towns, and high-carbon emissions were concentrated in northern, central, eastern coastal, and western provincial capitals and urban agglomerations. ② Carbon emissions had high-value or low-value agglomerations at prefecture-level cities; this agglomeration tended to stabilize as a whole and had strengthened after 2005. Low-low agglomeration areas were mainly distributed in the western contiguous areas and Hainan Island. With economic and social development, low-low agglomeration areas began to fragment and reduce in size; high-high agglomeration areas were mainly distributed in the Beijing-Tianjin-Hebei urban agglomeration, Taiyuan urban agglomeration, Yangtze River Delta urban agglomerations, and Pearl River Delta urban agglomerations, and the scale was gradually strengthened and consolidated; high-low and low-high agglomeration areas mainly appeared in neighboring cities with large differences in economic development levels. ③ Carbon emissions in most parts of China were relatively stable. The areas where carbon emissions had changed were mainly distributed in the peripheral areas of provincial capitals and key cities, and there was a circle structure with no changes in the central urban area and changes in carbon emissions in the peripheral areas. ④ The overall process of urban development in China from 2000 to 2018 followed a shift from "low emission-low income" to "high emission-low income" to "high emission-high income" and finally to "low emission-high income." The growth rate of carbon emissions in China is slowing down. Under the background of the "dual carbon" strategy, different regions face different carbon emission reduction tasks and pressures due to different carbon emission situations. Therefore, the differentiated carbon emissions policy should be implemented by regions and industries.

The energy-related carbon emissions of China’s manufacturing increased rapidly, from 36988.97 × 104 tC in 1996 to 74923.45 × 104 tC in 2012. To explore the factors to the change of the energy-related carbon emissions from manufacturing sector and the decoupling relationship between energy-related carbon emissions and economic growth, the empirical research was carried out based on the LMDI method and Tapio decoupling model. We found that the production scale contributed the most to the increase of the total carbon emissions, while the energy intensity was the most inhibiting factor. And the effects of the intrastructure and fuel mix on the change of carbon emissions were relatively weak. At a disaggregative level within manufacturing sector, EI subsector had a greater impact on the change of the total carbon emissions, with much more potentiality of energy conservation and emission reduction. Weak decoupling of manufacturing sector carbon emissions from GDP could be observed in the manufacturing sector and EI subsector, while strong decoupling state appeared in NEI subsector. Several advices were put forward, such as adjusting the fuel structure and optimizing the intrastructure and continuing to improve the energy intensity to realize the manufacturing sustainable development in low carbon pattern.

The construction industry contributes significantly to CO2 emissions in China. Understanding the changes in construction CO2 emissions is important for mitigating the emissions. This study examined the structural paths of changes in construction CO2 emissions in China during 2002–2017 by using structural path decomposition analysis. The results demonstrate that construction CO2 emissions increased considerably during the periods of 2002–2007, 2007–2012, and 2012–2017. The final demand effect contributed most to the emission increases, followed by the production structure effect and energy intensity effect. The critical paths contributing to the production structure effect were also identified. “Non-metallic mineral products industry→construction industry” was the critical path to the emission increases. On this path, the final demand effect and energy intensity effect were the main drivers. This study’s findings can help policymakers better understand the dynamics of construction CO2 emissions and thus formulate effective policies to reduce the emissions.

Investigating factors affecting carbon emission in China and the USA: A perspective of stratified heterogeneity

The Hu-Bao-O-Yu urban area is a major source of carbon emissions in China. It is also a major source of energy exports and high-end chemicals in China. Reaching peak carbon emissions early is especially important for meeting the national peak goal. For urban areas that rely on natural resources, we need to make it clearer how carbon emissions and economic growth affect each other and slowly break the strong link between the two. Therefore, in this paper, based on the data on carbon emissions, the decoupling state and the driving mechanism of carbon emissions in the Hu-Bao-O-Yu City group are researched by using the Tapio decoupling model and GDIM method. A new decoupling index model is constructed by combining GDIM and the traditional decoupling model. The main findings are as follows: (1) The Hu-Bao-O-Yu urban agglomeration, Ordos City, Baotou City and Yulin City have significant growth trends in annual carbon emissions, with Yulin City being the most important carbon source for the Hu-Bao-O-Yu urban agglomeration and its economic contribution to carbon emissions of the whole urban agglomeration is the most efficient. (2) The decoupling of Hu-Bao-O-Yu, Huhhot City, Baotou City, and Ordos City is dominated by expansionary negative decoupling, whereas Yulin City has strong negative decoupling. (3) The Hu-Bao-O-Yu urban cluster mainly affects the carbon intensity of fixed asset investments and output carbon intensity, which is a key part of the carbon separation process. The energy scale and structure also play a part in this process over time. (4) Changes in GDP per capita are a bigger part of changes in carbon emissions in the Hu-Bao-O-Yu urban agglomeration. These changes also determine the direction for changes in carbon emissions in the Hu-Bao-O-Yu urban agglomeration. In the future, the Hu-Bao-O-Yu urban agglomeration needs to coordinate its economic growth. Ordos and Yulin need to speed up the optimisation and transformation of their energy structures. Baotou needs to push for the low-carbon transformation of its industries. Huhhot needs to do more research on carbon sequestration technology and spend more on environmental protection. This will make the Hu-Bao-O-Yu urban agglomeration a resource-saving urban agglomeration and improve its ability to reduce emissions.

Based on the Environmental Kuznets Curve (EKC), this paper empirically analyzes the impact of green finance development on industrial carbon emissions in China by using the panel data of Chinese mainland province. It is found that the development of green finance has significantly suppressed the industrial carbon emissions in China. Heterogeneity test shows that the inhibition effect on carbon emission in central China is the most obvious, and the inhibition effect on carbon emission in eastern and western regions decreases in turn. Technological progress significantly inhibits carbon emissions, especially in central China, followed by the western region and finally the eastern region. It is suggested to improve the green and low-carbon financing system, support the optimization of energy consumption structure and guide substantive technological progress, so as to promote the realization of carbon emission reduction targets.

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.