Integrated inventory-based carbon accounting for energy-induced emissions in Chongming eco-island of Shanghai, China

With accelerating urbanization, building sector has been becoming more important source of China’s total carbon emission. In this paper, we try to calculate the life-cycle carbon emission, analyze influencing factors of carbon emission, and assess the delinking index of carbon emission in China’s building sector. The results show: (i) Total carbon emission in China’s building industry increase from 984.69 million tons of CO2 in 2005 to 3753.98 million tons of CO2 in 2013. The average annual growth rate is 18.21% per year. Indirect carbon emission from building material consumption accounted to 96–99% of total carbon emission. (ii) The indirect emission intensity effect was leading contributor to change of carbon emission. The following was economic output effects, which always contributed to increase in carbon emission. Energy intensity effect and energy structure effect took negligible role to offset carbon emission. (iii) Delinking index show the status between carbon emission and economic output in China’s building industry during 2005–2006 and 2007–2008 was weak decoupling; during 2006–2007 and during 2008–2010 was expansive decoupling; and during 2010–2013 was expansive negative decoupling.

Transportation is an important source of carbon emissions in China. Reduction in carbon emissions in the transportation sector plays a key role in the success of China’s energy conservation and emissions reduction. This paper, for the first time, analyzes the drivers of carbon emissions in China’s transportation sector from 2000 to 2015 using the Generalized Divisia Index Method (GDIM). Based on this analysis, we use the improved Tapio model to estimate the decoupling elasticity between the development of China’s transportation industry and carbon emissions. The results show that: (1) the added value of transportation, energy consumption and per capita carbon emissions in transportation have always been major contributors to China’s carbon emissions from transportation. Energy carbon emission intensity is a key factor in reducing carbon emissions in transportation. The carbon intensity of the added value and the energy intensity have a continuous effect on carbon emissions in transportation; (2) compared with the increasing factors, the decreasing factors have a limited effect on inhibiting the increase in carbon emissions in China’s transportation industry; (3) compared with the total carbon emissions decoupling state, the per capita decoupling state can more accurately reflect the relationship between transportation and carbon emissions in China. The state of decoupling between the development of the transportation industry and carbon emissions in China is relatively poor, with a worsening trend after a short period of improvement; (4) the decoupling of transportation and carbon emissions has made energy-saving elasticity more important than the per capita emissions reduction elasticity effect. Based on the conclusions of this study, this paper puts forward some policy suggestions for reducing carbon emissions in the transportation industry.

Decoupling economic growth from carbon dioxide emissions in China: A sectoral factor decomposition analysis

This study analyzed the spatiotemporal differences and driving factors of carbon emission in China’s prefecture-level cities for the period 2003–2019. In doing so, we investigated the spatiotemporal differences of carbon emission using spatial correlation analysis, standard deviation ellipse, and Dagum Gini coefficient and identified the main drivers using the geographical detector model. The results demonstrated that 1) on the whole, carbon emission between 2003 and 2019 was still high, with an average of 100.97 Mt. Temporally, carbon emission in national China increased by 12% and the western region enjoyed the fastest growth rate (15.50%), followed by the central (14.20%) and eastern region (12.17%), while the northeastern region was the slowest (11.10%). Spatially, the carbon emission was characterized by a spatial distribution of “higher in the east and lower in the midwest,” spreading along the “northeast–southwest” direction. 2) The carbon emission portrayed a strong positive spatial correlation with an imbalance polarization trend of “east-hot and west-cold”. 3) The overall differences of carbon emission appeared in a slow downward trend during the study period, and the interregional difference was the largest contributor. 4) Transportation infrastructure, economic development level, informatization level, population density, and trade openness were the dominant determinants affecting carbon emission, while the impacts significantly varied by region. In addition, interactions between any two factors exerted greater influence on carbon emission than any one alone. The findings from this study provide novel insights into the spatiotemporal differences of carbon emission in urban China, revealing the potential driving factors, and thus differentiated and targeted policies should be formulated to curb climate change.

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.

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 carbon emissions of sectors and households enabled by primary inputs have practical significance in reality. Considering the mutual effect between the industrial sector and the household, this paper firstly constructed an environmentally extended semi-closed Ghosh input–output model with an endogenized household sector to analyze the relationship between carbon emissions and the Chinese economy from the supply-side perspective. The structural decomposition analysis and the hypothetical extraction method were remodified to identify the supply-side driving effects of the changes in carbon emissions and investigate the net carbon linkage. The results show that the electricity, gas, and water supply sector was the key sector with the highest carbon emission intensity enabled by primary inputs. The household sector had an above 93% indirect effect of the enabled intensity, with its enabled intensity dropping significantly by more than 55% from 2007 to 2017. The operating surplus and mixed income caused 3214.67 Gt (34.17%) of the enabled emissions in 2017. The supply-side economic activity, measured by the value added per capita, was the main factor of the carbon emission growth, mainly attributed to the development of the manufacturing sector and the electricity, gas, and water supply sector. The emission intensity and allocation structure both brought a decrease in carbon emissions. The electricity, gas, and water supply sector and the manufacturing sector were the major sources of the supply-induced cross-sectoral input emissions, while the commercial and service sector and the household sector were the top source of supply-induced cross-sectoral output emissions. This paper sheds light on the policies of the carbon emission abatement and the adjustment of the allocation structure from the perspective of supply.

In recent years, the issues related to carbon emissions and environment have attracted extensive attentions. Considering four scenarios (the energy conversion, energy capital savings and loans, energy exports and cement production carbon emissions), this paper adopts the energy consumption method and input-output method to analyze China’s carbon emissions structure on the supply-side and demand-side of energy, and finally provides policy recommendations for China’s structural emission reduction. The results show that, if the four influencing factors were not considered, the measurement of carbon emissions from the final demand was 44.91% higher than the baseline scenario, 12.36% lower than the baseline scenario from intermediate demand, and 10.23% lower than the baseline scenario from the total. For China’s carbon emissions structure on the supply-side of energy, the carbon emissions from high-carbon energy, represented by raw coal, accounted for 66.805% of the total energy-related carbon emissions, while the carbon emissions from low-carbon energy, represented by natural gas, only accounted for 2.485%. For China’s carbon emissions structure on the demand-side of energy, the carbon emissions from intermediate demand (enterprise production) accounted for more than 95% of total energy-related carbon emissions, while the carbon emissions from final demand (residents and government use) accounted for less than 5%. For each specific industry in intermediate demand for energy, the heavy industry, electric power, fossil energy, and chemical industry have high carbon emissions and low carbon emissions efficiency. However, the agriculture, construction, light industry, and service are the opposite. Finally, we provide policy recommendations for improving the accuracy of carbon emissions measurement and carbon emissions efficiency.

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.

China's provincial carbon emission driving factors analysis and scenario forecasting

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.

China's manufacturing sector has experienced increasing robot adoption and capital‐embodied technological progress, accompanied by massive energy consumption and carbon emissions. The robot adoption brings technological and environmental risks in the manufacturing sector. Based on the data of 28 manufacturing sub‐sectors, this study uses the logarithmic mean Divisia index method to investigate the contributions of robot adoption, labor, capital, and energy factors to the changes in carbon emissions in China's manufacturing sector. Furthermore, we conduct the scenario analysis and Monte Carlo simulation to project the future trajectories of carbon emissions in China's manufacturing sector under the different scenarios until 2035. Results show that during 2006–2019, both scale effect and technical effect driven by robots contributed to carbon emission reduction. Robot scale was the dominant contributor to the carbon emission increase, followed by capital automation. On the contrary, the workforce structure and energy‐robot structure played dominant roles in carbon emission reduction. Labor productivity, capital deepening, and the carbon intensity of energy exerted marginal effects on carbon emissions. During 2020–2035, carbon emissions will increase consistently from 62.4 million tons (Mt) to 72.6 and 228.2 Mt under the business‐as‐usual scenario and higher‐emission scenario, respectively, while they will have obvious inflection points under other three scenarios. Carbon emissions will peak at 65.3 Mt in 2023 and have the largest mitigation potential in the lower‐emission scenario. Finally, several policy suggestions are raised for China to build a manufacturing system with the coordinated development of intelligence and low carbon.

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.

The power industry is the industry with the most direct uses of fossil fuels in China and is one of China’s main carbon industries. A comprehensive and accurate analysis of the impacts of carbon emissions by the power industry can reveal the potential for carbon emissions reductions in the power industry to achieve China’s emissions reduction targets. The main contribution of this paper is the use of a Generalized Divisia Index Model for the first time to factorize the change of carbon emissions in China’s power industry from 2000 to 2015, and gives full consideration to the influence of the economy, population, and energy consumption on the carbon emissions. At the same time, the Monte Carlo method is first used to predict the carbon emissions of the power industry from 2017 to 2030 under three different scenarios. The results show that the output scale is the most important factor leading to an increase in carbon emissions in China’s power industry from 2000 to 2015, followed by the energy consumption scale and population size. Energy intensity levels have always promoted carbon emissions reduction in the power industry, where energy intensity and carbon intensity effects of energy consumption have great potential to mitigate carbon levels. By setting the main factors affecting carbon emissions in the future three scenarios, this paper predicts the carbon emissions of China’s power industry from 2017 to 2030. Under the baseline scenario, the maximum probability range of the potential annual growth rate of carbon emissions by the power industry in China from 2017 to 2030 is 1.9–2.2%. Under the low carbon scenario and technological breakthrough scenario, carbon emissions in China’s power industry continue to decline from 2017 to 2030. The maximum probability range of the potential annual drop rate are measured at 1.6–2.1% and 1.9–2.4%, respectively. The results of this study show that China’s power industry still has great potential to reduce carbon emissions. In the future, the development of carbon emissions reduction in the power industry should focus on the innovation and development of energy saving and emissions reduction technology on the premise of further optimizing the energy structure and adhering to the low-carbon road.

There are disagreements regarding the accuracy of estimation and spatial distribution of carbon emissions in China. It is of great significance to estimate a more detailed carbon emission inventory for China and analyze the carbon emission characteristics of different regions. This study comprehensively estimated carbon dioxide and methane emissions (and their spatial distributions) across eight carbon-emitting sectors in 360 prefecture-level cities in China in 2020. The results indicated that total carbon emissions in China amounted to 146.00 × 108 t, with carbon dioxide and methane accounting for 95.87% and 4.13%, respectively. The industrial sector was the main source of carbon emissions, accounting for 75.42% of the total. The North China Plain, the Northeast Plain, and the Sichuan Basin were identified as the carbon emission hotspot areas with the most intensive carbon emission densities. Among the clustered four carbon emission zones based on carbon emission density and economic carbon intensity, the High Carbon Emission Density and High Economic Carbon Intensity zones accounted for 41.73% of total carbon emissions. To achieve carbon neutrality, it is essential to devise emission reduction strategies for specific areas by thoroughly considering spatially explicit variation at the prefecture level, with a focus on primary carbon-emitting cities and sectors.