Dynamic evolution analysis of the factors driving the growth of energy-related CO2 emissions in China: An input-output analysis.

Excessive carbon emissions from energy consumption seriously restrict China's sustainable development and eco-environmental protection. Although the carbon emissions from the construction industry are less than that of the power, transportation, and manufacturing sectors, the carbon emissions released by the construction industry cannot be ignored due to its extensive development trend of high energy consumption and low efficiency. Based on this, this paper studies energy-related carbon emissions and emissions reduction of China's construction industry from 2007 to 2017 by adopting the input-output analysis method, energy consumption method, and structural decomposition model. The results show that within the sample range: (1) The optimization of the construction industry energy consumption structure has a significant reduction effect on the growth of energy carbon emissions from the construction industry in China, and the reduction effect has shown an increasing trend over time. However, it should be noted that in this sample range, the optimization of energy consumption structure in the construction industry is mainly reflected in the decrease of the proportion of high-carbon energy consumption such as raw coal, while low-carbon energy such as natural gas has not played a significant role. Therefore, the future energy optimization space of China's construction industry is still huge. (2) Energy intensity effect and input structure effect have a positive inhibitory effect on carbon emission growth of the construction industry, and the inhibitory effect of energy intensity effect is stronger than that of input structure effect. It shows that in the sample range, the generalized technological progress and energy efficiency of the construction industry have been better optimized and improved. (3) Except for 2015-2017, the final demand effect in other intervals has a positive effect on the growth of carbon emissions in the construction industry, and the secondary and tertiary industries play a major role in the final demand effect. It shows that the total demand for the construction industry in various industries still maintains a growth trend. This paper provides a theoretical analysis basis and practical guidance for China's construction industry to carry out more accurate and efficient emission reduction from the supply-side energy varieties and demand-side industry level, and further enriches the existing research on carbon emissions of the construction industry from the perspective of input-output analysis.

Energy-related CO2 emissions and structural emissions’ reduction in China’s agriculture: An input–output perspective

Economic development has largely contributed to the increment of CO2 emission. This study uses spatial econometric models to investigate the relationship between economic growth and carbon emission in China with data of 30 provinces of China during the period of 2000 to 2012. Results show that the relationship between carbon emission and economic growth in China during the recent decade has the development tendency toward an inverse U-shaped curve, approximately confirming the carbon emission’s Kuznets curve hypothesis in China. There exists a significant spatial correlation between carbon emission and economic growth, implying that carbon emission in a province may be influenced by economic growth in adjacent provinces. When economic growth reaches 279.91 million Yuan/km2 GDP (at a comparable price in 2000), the contradiction between economic growth and carbon emission begins to be gradually alleviated. These findings provide new insights and valuable information for reducing carbon emissions in China.

China is committed to achieving the goals of "peak carbon and carbon neutrality," and the carbon dioxide emissions generated in the energy utilization process mainly come from industrial and energy systems. This paper used structural decomposition analysis (SDA) and input-output analysis to study the structural emission reductions in China's industrial and energy systems in 2007-2015. The results revealed that the final demand effect was the main factor promoting the growth of energy-related CO2 emissions and that the energy intensity effect played a weak role in promoting the growth of energy-related CO2 emissions. However, the energy structure effect and input structure effect reduced energy-related carbon emission growth. We found that for energy systems, the emission reduction effects of blast furnace gas, raw coal, refinery dry gas, and natural gas were obvious, while those of crude oil, gasoline, fuel oil, and kerosene were not obvious. For industrial systems, the tertiary industry played a major role in the final demand effect, followed by the secondary industry, and the primary industry in turn. This paper provides a theoretical basis and practical guidance for the carbon peak and carbon neutrality goals of China's energy systems and industrial systems.

This manuscript develops a logarithmic mean Divisia index I (LMDI) decomposition method based on energy and CO2 allocation Sankey diagrams to analyze the contributions of various influencing factors to the growth of energy-related CO2 emissions on a national level. Compared with previous methods, we can further consider the influences of energy supply efficiency. Two key parameters, the primary energy quantity converted factor (KPEQ) and the primary carbon dioxide emission factor (KC), were introduced to calculate the equilibrium data for the whole process of energy unitization and related CO2 emissions. The data were used to map energy and CO2 allocation Sankey diagrams. Based on these parameters, we built an LMDI method with a higher technical resolution and applied it to decompose the growth of energy-related CO2 emissions in China from 2004 to 2014. The results indicate that GDP growth per capita is the main factor driving the growth of CO2 emissions while the reduction of energy intensity, the improvement of energy supply efficiency, and the introduction of non-fossil fuels in heat and electricity generation slowed the growth of CO2 emissions.

As the largest energy consumer, China's control of carbon emissions from energy consumption plays a pivotal role in world climate governance. However, few studies have been conducted to explore the emission reduction pathways that promote a high level of synergy between China's economic growth and the " carbon peaking and carbon neutrality " goal from the perspective of energy consumption. Based on the measurement of energy consumption carbon emissions, this paper reveals the spatial and temporal distribution and evolution trends of carbon emissions in China at the national-provincial level. The multi-dimensional socio-economic factors such as R&D and urbanization are taken into account, and the LMDI model is used to decompose the driving effects of energy consumption carbon emissions at the national-provincial levels. Further, this paper combines the Tapio decoupling index with the LMDI model to decompose the decoupling states of China year by year and at the provincial level in four periods to explore the reasons for the change of carbon decoupling states. The results show that: (1) China's energy consumption carbon emissions grew at a high rate before 2013, and slowed down after that. There are significant differences in the scale and growth rate of carbon emissions among provinces, which can be classified into four types accordingly. (2) The R&D scale effect, urbanization effect, and population scale effect are the factors driving the growth of China's carbon emissions; while the energy structure effect, energy consumption industry structure effect, energy intensity effect, and R&D efficiency effect inhibit the growth of China's carbon emissions. (3) Weak decoupling is the most dominant decoupling state in China from 2003 to 2020, and the decoupling state varies significantly among provinces. According to the conclusions, this paper proposes targeted policy recommendations based on China's energy endowment.

In recent years, the environmental problems caused by excessive carbon emissions from energy sources have become increasingly serious, which not only aggravates the climate change caused by the greenhouse effect but also seriously restricts the sustainable development of Chinese economy. An attempt is made in this paper to use energy consumption method and input-output method to study the carbon emission structure of China's energy system and industry in 2015 from two perspectives, namely China's energy supply side and energy demand side, by taking into account the two factors of energy invest in gross capital formation and export. The results show that neglecting these two factors will lead to underestimation of intermediate use carbon emissions and overestimation of final use carbon emissions. On energy supply side, the carbon emission structure of China's energy system is still dominated by high-carbon energy (raw coal, coke, diesel, and fuel oil, etc.), accounting for more than 70% of total energy carbon emissions; on the contrary, the natural gas such as clean energy accounts for only 3.45% of total energy carbon emissions, indicating that the energy consumption structure optimization and emission reduction gap of China's energy supply side are still substantial. On energy demand side, the final use (direct consumption by residents and government) produces less carbon emissions, while the intermediate use (production by enterprises) produces more than 90% of the total energy carbon emissions. Fossil energy, power sector, heavy industry, chemical industry, and transportation belong to industries with larger carbon emissions and lower carbon emission efficiency, while agriculture, construction, light industry, and service belong to industries with fewer carbon emissions and higher carbon emission efficiency. This means that the optimization of industrial structure is conducive to slowing down the growth of energy carbon emissions on the demand side.

In order to study the influence of urban green space landscape pattern on urban carbon emissions， nighttime lighting data， socioeconomic development data， and land use remote sensing data from 2000 to 2020 are used as the basis of analysis， and the three major coastal economically developed regions in China-Bohai Rim， Yangtze River Delta （YRD）， and Pearl River Delta （PRD） （nearly 100 cities in total） are used as the study area to analyze the spatial and temporal evolution characteristics of urban carbon emissions， as well as the influence of urban green space landscape pattern and its spatial and temporal changes. We also explored the influence of 10 urban green space landscape pattern indices on urban carbon emissions by using the random forest model and the Lasso regression model and further analyzed the four factors （number of patches， density of patches， dispersion of patches， and complexity of the shape of patches） that had a greater influence by using the spatio-temporal geographically weighted regression model， to explore the results of the spatial and temporal evolution of the influence of the urban green space landscape pattern on carbon emissions. The main findings of this study are as follows： ① Carbon emissions in the three study areas showed a slow growth trend， with the Bohai Rim showing a relatively fast growth rate. Carbon emissions were spatially aggregated in the selected study areas， with the majority of cities in the "high and high" agglomeration and the "low and low" agglomeration regions. There was spatial aggregation of carbon emissions in the selected study areas， with the majority of cities in "high and high" agglomeration and "low and low" agglomeration. The land-averaged carbon emissions in the three study areas were dispersed in all directions， with the economically strong cities as the core， and the overall carbon emission level was dispersed from the center to the surroundings. Additionally， along the rivers and coastal areas， carbon emissions were higher due to the concentration of ports， industrial zones， and cities. ② Landscape occupied by patches， number of patches， and density of patches had a significant negative correlation with urban carbon emissions， which indicates that the higher the number， density， and proportion of the landscape occupied by urban green space patches， the more it could hinder the growth of carbon emissions. On the contrary， the shape index and patch fragmentation index had a positive correlation with urban carbon emissions， indicating that the higher the shape complexity of urban green space patches and the higher the fragmentation degree of patches， the more it promoted the growth of urban carbon emissions. In addition， the aggregation index also showed a significant negative correlation with urban carbon emissions， which indicates that the higher the degree of aggregation of patches， the more it could inhibit the growth of carbon emissions. ③ The correlation between the green space landscape pattern index and carbon emissions showed significant spatial and temporal differences， with large changes around 2010. In the Bohai Rim Region， the influence of the urban landscape pattern index on carbon emissions remained relatively stable， and its influence over time generally showed a decline. In the YRD Region， the shape complexity and dispersion of urban green space had a greater impact on carbon emissions than the number of patches and patch density factors. However， on the contrary， in the PRD Region， the impacts of the number of urban green spaces and density index were increasing. In addition， the spatial influence changes on all showed the clustering of regression coefficients. The impact of urban green space on carbon emissions varied greatly across locations and time， suggesting that policy makers cannot rely on a one-size-fits-all approach to urban green space planning. In the Bohai Rim Region， it is more important to balance the distribution of urban green space with other land uses to maintain stability； in the YRD Region， highly fragmented and overly complex green space patch planning should be reduced； and in the PRD Region， priority should be given to increasing the amount and distribution density of urban green space.

Estimation and decomposition analysis of carbon emissions from the entire production cycle for Chinese household consumption

Identifying the carbon emission characteristics, driving factors, and decoupling status of the industrial subsectors is important for developing effective policy measures. This allows for implementing industrial emission reduction that, eventually, decouple carbon emission and economic growth. Such an analysis is especially important for the case of China on its way towards sustainable development and increasing global interrelationships. However, the literature still lacks comprehensive analysis, especially, at the industry level. This study uses the Logarithmic Mean Divisia Index and decoupling indicator to analyze how different factors contribute to CO2 emissions in 28 industries in China during 2002–2017. The results reveal that the growth of industrial CO2 emissions has been positive but decreasing. The highest CO2 emission change is observed for production and supply of electric and heat power, processing of petroleum, coking, and nuclear fuel, and smelting and pressing of metals. These sectors also show high carbon intensity levels. The economic output (scale) effect and population effect comprise the two major factors promoting the CO2 emission. The energy intensity effect is the key inhibiting factor of the industrial energy-related CO2 emission in China. The suppressive effects of energy and industrial structure have been continuously increasing. The economic growth and CO2 emission has been gradually decoupling in the case of the 28 sectors analyzed. Manufacture of cloths, leather, fur, feather, and related products as well as production and supply of gas exhibit a relatively stable strong decoupling. Based on the decoupling analysis, this study shows that energy intensity has induced the decoupling, whereas the opposite effect has occurred due to economic growth, and the other factors showed little effect on CO2 emission decoupling.

Refined assessment and decomposition analysis of carbon emissions in high-energy intensive industrial sectors in China

Carbon emissions from land use change have become one of the main sources of regional carbon emissions. In order to explore the changes, 87 districts and counties in Gansu Province are taken as research objects. Based on the remote sensing data and statistical data of land use, the carbon emission coefficient method was used to investigate the spatial characteristics of land use carbon emission of each district and county in Gansu Province in recent 20years from the perspective of carbon ecological support coefficient and per capita carbon footprint. The main results are as follows: (1) the growth of land use carbon emissions in Gansu Province from 2000 to 2020 was significant, but the growth of carbon emissions after 2010 was fast, and the growth of carbon sinks was relatively slow. (2) The ecological support coefficient of carbon emissions at county level in Gansu Province showed a trend of high in the south and low in the north, high in the east and low in the west, and this trend became more and more obvious with the passage of time. (3) Based on carbon emission, county population, and carbon ecological support capacity, the per capita carbon footprint of each county in Gansu Province was analyzed. The results showed that the per capita carbon footprint in Gansu Province was increasing, indicating that the gap between carbon emission and carbon absorption in each county was widening. By the above result, the author divides the counties of Gansu Province into three regions, low-carbon maintenance area, green development area, and ecological optimization area, and puts forward development suggestions for different regions, respectively. Therefore, this paper can also provide a theoretical reference for the formulation of carbon neutral planning measures in inland northwest China.

Hunan province energy consumption carbon emissions based on the industrial structure was analyzed with carbon emissions factor method in 2000-2012. Results show that Hunan province’s carbon emissions have a rapid growth in 2000-2012. Since 2007 the growth of carbon intensity is slowly, and there is an emergence of signs of decline. Recently the correlation between the growth of GDP and carbon emissions in Hunan Province becomes weakening, but carbon intensity is still higher. Industry occupies a dominant position in the energy consumption carbon emissions. Since 2007 the proportion of industrial carbon emissions is decreased form 79.41% to 72.30% in 2012, there is an obvious decline. Recently, the growth rate of industrial carbon emissions is relative lower. The growth of carbon emissions from the construction industry and the tertiary industry is the most obvious. Relevant policies should be formulated as soon as possible, to promote the level of construction technology, control energy consumption and carbon emissions per unit of output.

The power industry is a major source of carbon emissions in China. In order to better explore the driving factors of carbon emissions in China’s power industry and assist the Chinese government in formulating emission reduction strategies for the power industry, this study applies the improved production-theoretical decomposition analysis (PDA) method to analyze the carbon emission drivers of China’s power industry. This study investigates the impact of energy intensity, per capita GDP, population density, power generation structure, and environmental climate on carbon emissions in China’s power industry in 30 provinces from 2005 to 2020. It was found that the carbon emission ratios of the power sector in all provinces and cities are basically greater than 1, which indicates that carbon emissions in most of the power sectors in the country are still increasing as of 2020. Overall, the effects of potential thermal fuel carbon emission efficiency, potential thermal energy consumption efficiency, the carbon emission efficiency of thermal power generation, economic scale, population density, and annual rainfall change are mostly greater than 1 and will promote the growth of carbon emissions in the power sector. Moreover, the effects of thermal power generation energy efficiency technology, thermal power generation emission reduction technology, power generation structure, and power generation per unit GDP are mostly less than 1 and will inhibit the growth of carbon emissions in the power sector. However, each of these drivers does not have the same degree of influence and impact effect for each province and city. Based on the research results, some policy recommendations are proposed.

The farming-pastoral ecotone has an important strategic place in the energy supply and ecological layout of China. Thus, exploring the spatial and temporal variation characteristics of carbon emissions in this region will help to deeply understand the information on the historical carbon emissions in China's energy production bases and provide data references for the formulation of differentiated emission reduction policies and the promotion of regional energy-saving and carbon-reducing measures, which is of great significance for the realization of low-carbon economic development. This study constructed a spatialization model of carbon emissions based on land use, night lighting, and provincial energy consumption data； explored the spatiotemporal changes and aggregation characteristics of carbon emissions in the farming-pastoral ecotone from 1995 to 2020 using the global Moran's index and hotspot analysis； and then combined it with the slack-based measure model to calculate the carbon emission efficiency and emission reduction potential of each city from 2010 to 2020 and classify cities to propose a differentiated emission reduction path. The results showed that, firstly, the estimated results at the prefectural city level of the carbon emission spatialization model constructed in this study with multi-source data could reach an R2 of 0.92 for a linear fit. Secondly, the total carbon emissions in the farming-pastoral ecotone increased from 176.29 million tons in 1995 to 1 014.51 million tons in 2020. However, the carbon emission intensity and growth rate both decreased, which was related to adjusting the energy structure and improving energy efficiency. Regarding spatial distribution, the cities with high carbon emissions over time were Datong, Baotou, and Yulin in order. Thirdly, the carbon emissions in the study area showed a significant global spatial positive correlation at the county level, with the hot spots mainly located at the junction of Shanxi, Shaanxi, and Inner Mongolia, while the cold spots were extended from Yanan City to Qingyang and Guyuan City after 2010. Finally, based on the differences in carbon emission efficiency and reduction potential, cities could be classified into four types： "high-efficiency and high potential," "low-efficiency and high potential," "high-efficiency and low potential," and "low-efficiency and low potential" to implement targeted emission reduction strategies.