Energy system transformations and carbon emission mitigation for China to achieve global 2 °C climate target

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.

With continuous increase of carbon emission, China is facing more and more pressure from international community. However, what is the driving force of China's carbon emission growth? Whether China's carbon emission should be borne by the Chinese people? Based on carbon accounting resulted from energy consumption, this paper quantitativly analyze the effect of exports on carbon emission using China's 28 manufacturing industrial panel data from 2001 to 2007. The results show that: There is a close link between China's exports and carbon emission, and the increasing commodity exports are an important factor for China's carbon emission in recent years, so the developed countries should bear some responsibility for China's carbon emission. Finally, according to the results of this study, this paper proposes some recommendations to reduce carbon emission.

In the context of the "dual carbon" goals， estimating carbon emissions and simulating peak scenarios has significant importance. However， accurately identifying the factors influencing carbon emissions and scientifically predicting the peak carbon emission time still present considerable challenges. This study focused on 30 provinces in China （data were not available for Hong Kong， Macau， Taiwan， and Tibet， China） and utilized the IPCC carbon emission coefficient method to estimate the total carbon emissions from 2000 to 2020， while also depicting their spatial and temporal distribution characteristics. Furthermore， various machine learning algorithms were employed to construct and select the optimal carbon emission estimation model， quantifying the main factors influencing carbon emissions in China. Finally， considering different development trends across the country and regions， scenario analysis and the carbon emission estimation model were used to predict changes in carbon emissions nationwide and in various regions over the next 15 years. The results follow： ① From 2000 to 2020， China's carbon emissions exhibited a "rapid then slow" growth pattern through time， with a spatial distribution characterized as "higher in the east and lower in the west， higher in the north and lower in the south." ② The main factors affecting China's carbon emissions include total energy consumption， energy structure， resident population， urbanization level， and the advancing industrial structure. ③ Under baseline， low-carbon， and high-carbon scenarios， carbon emissions nationwide and in various regions are projected to show an "increase then decrease" trend from 2021 to 2035. Notably， except for the northwest region under the high-carbon scenario， all regions are expected to achieve their carbon peak targets by 2030 under the three scenarios. This study provides valuable insights for formulating and implementing comprehensive control strategies for regional carbon peak emissions.

The increase in carbon emissions has had great negative impacts on the healthy developments of the human environment and economic society. However, it is unclear how specific socio-economic factors are driving carbon emissions. Based on the multiscale geographically weighted regression (MGWR) model, this paper analyzes the impact mechanism of China's carbon emission data during 2010-2017. The results show that (1) during the study period, China's carbon emissions have obvious positive correlations in the spatial distribution, and the spatial autocorrelation of carbon emissions on the time scale has a further strengthening trend. (2) Compared with the results of the geographically weighted regression (GWR) model, the MGWR model is more robust, and the results are more realistic and reliable. The impacts of energy intensity, proportion of green coverage in built-up areas, and industrial structure on provincial carbon emissions are close to the global scale, and their spatial heterogeneity is weak. Other factors have spatially heterogeneous impacts on carbon emissions with different scale effects. (3) Except for proportion of green coverage in built-up areas, the industrial structure and trade openness have insignificant impacts on carbon emissions, but other variables have significant impacts. The total population, urbanization rate, energy intensity, and energy structure have positive impacts on carbon emissions, while the GDP per capita and foreign direct investment have negative impacts on it. This study shows that the main socio-economic factors have different degrees of impacts on carbon emissions with different scale, and we can refer to it to formulate more scientific measures to reduce carbon emissions.

Research on the impact of COVID-19 on the spatiotemporal distribution of carbon dioxide emissions in China

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.

Spatial and temporal evolution patterns and spatial spillover effects of carbon emissions in China in the context of digital economy.

The role of new energy in carbon neutral

The costs of mitigating carbon emissions in China: findings from China MARKAL-MACRO modeling

Energy and environmental policies are important methods for the government to restrain carbon emissions growth. Identifying the potential dynamic trends of China's carbon emissions under different scenarios has important reference significance for the government's policy implementation. This paper firstly predicted China's carbon emissions from 2017 to 2040 based on three energy transition scenarios at the industrial level. Then, Logarithmic Mean Divisia Index decomposition model was applied to evaluate the driving forces of emissions changes during 1997–2040. Finally, the Spatial–Temporal Logarithmic Mean Divisia Index model was used to explore the emissions reduction potential and the potential reduction path at provincial level. The results showed that (1) as the reduction in energy intensity cannot offset the growth of industrial scale, the carbon emissions of all industries have shown an increasing trend from 1997 to 2017; (2) In the current policies scenario, China's carbon emissions cannot reach the peak before 2040. And only in the sustainable development scenario, the carbon emissions of the three industries will all reach the peaks before 2030. And the development of non-fossil energy will reduce carbon emissions by more than 30%; (3) Hebei, Shanxi, Inner Mongolia, Ningxia, and Heilongjiang are key provinces and improving energy efficiency of the secondary industry is a potential way to promote carbon emissions reduction.Graphical abstract The framework and main content of this paper.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10098-021-02240-7.

Due to the mandatory push to meet the carbon emission reduction commitments proposed in the Paris Agreement, an analysis of the peak carbon emission production times in China is required. This paper focuses on the peak production times of the total carbon emissions (TCEs) and carbon emissions intensity (CEI) in China. According to the development of China's carbon emissions and related targets in the 13th Five-Year Plan, the peak production times of TCE and CEI in different scenarios are predicted based on an influence mechanism analysis of China's carbon emissions from the perspectives of energy, economy, and society. Considering the development characteristics of China at this stage, this paper introduces several new indicators including the full-time equivalent of research and development (R&D) personnel and the investment in environmental pollution control. Based on the results of the study, several policy recommendations are put forward to fulfil China's carbon emission reduction commitments.

Due to the mandatory push to meet the carbon emission reduction commitments proposed in the Paris Agreement, an analysis of the peak carbon emission production times in China is required. This paper focuses on the peak production times of the total carbon emissions (TCEs) and carbon emissions intensity (CEI) in China. According to the development of China's carbon emissions and related targets in the 13th Five-Year Plan, the peak production times of TCE and CEI in different scenarios are predicted based on an influence mechanism analysis of China's carbon emissions from the perspectives of energy, economy, and society. Considering the development characteristics of China at this stage, this paper introduces several new indicators including the full-time equivalent of research and development (R&D) personnel and the investment in environmental pollution control. Based on the results of the study, several policy recommendations are put forward to fulfil China's carbon emission reduction commitments.

Biomass and China's carbon emissions: A missing piece of carbon decomposition

Given the escalating challenges posed by global climate change, as the world's largest carbon emitter, China is facing a huge challenge in achieving its "dual carbon" goals. Therefore, reasonable prediction of China's carbon emission intensity is crucial for formulating effective emission reduction strategies. Considering the external shocks faced by the economic system, the time breakpoint is introduced into the traditional grey prediction model. The model is optimized from two aspects： accumulation method and background value, and a new grey breakpoint model with inverse accumulation is constructed. Based on the calculation of China's carbon emissions, the carbon emission intensity from 2023 to 2030 was predicted. The following conclusions were drawn： ① By adding time breakpoints, the new model achieved accurate prediction of the future trend of the system under external shocks, further reflecting the principle of information priority in the modeling process. ② Under the external impact of the COVID-19, the growth rate of China's GDP further slowed down, and the carbon emissions showed different characteristics in the four regions. The carbon emissions in the northeast began to decline gradually, while the carbon emissions in the eastern and western regions accelerated. ③ From 2023 to 2030, China's carbon emission intensity will considerably decrease. Compared with that in 2020, the carbon emission intensity is expected to decrease by 13.2% in 2025 and by 22.6% in 2030, with the highest decline in the northeast and the lowest in the east. However, under current conditions, China still finds it difficult to fully achieve its 2025 and 2030 emission reduction targets, with the eastern and western regions facing enormous pressure to reduce carbon emissions.

Impact of Heavy Industrialization on the Carbon Emissions: An Empirical Study of China