Analyzing driving forces of China’s carbon emissions from 1997 to 2040 and the potential emission reduction path: through decomposition and scenario analysis

Song, Ce; Zhao, Tao; Wang, Juan

doi:10.1007/s10098-021-02240-7

Cited by 10 publications

(6 citation statements)

References 54 publications

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“…It has practical value for the future assignment of regional carbon emission responsibility and the establishment of a corresponding assignment system. The results shown in the above table prove that the carbon emission factor is strongly correlated with three direct influencing factors, the URICFPG, the PFPG, and the CRFPG, which is consistent with the internal experts and the studies of [28][29][30][31][32][33]. Therefore, regression analysis was performed using the time series of the above impact factors.…”

Section: Discussionsupporting

confidence: 79%

“…The scenario analysis-based method can fully use current economic and energy policies and build a carbon emission calculation model based on direct influencing factors involved in resource endowments and the whole process, including energy production, consumption, and demand. Therefore, it is more accurate and it can be combined with the above methods [29,30]. For example, ZHANG et al ( 2021) combined the LMDI model and scenario analysis, firstly decomposed China's carbon emissions exponentially, identified influencing factors, and then conducted scenario analysis on carbon intensity targets in 2020 and 2030, thus helping policy-makers evaluate the effectiveness of current policies in detail [31].…”

Section: Carbon Emission Calculation Methods Based On Direct Variablesmentioning

confidence: 99%

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A Calculation Model of Carbon Emissions Based on Multi-Scenario Simulation Analysis of Electricity Consumption

et al. 2022

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In order to reach the peak of carbon emission in China by 2030 and to meet the low-carbon conversion of energy and the growing demand for electricity, this study aims to propose a more accurate and scientific method to calculate the carbon emissions of the entire power industry chain. This paper analyzes the historical actual operation data of the energy and power industry from 2000 to 2020, and originally proposes a carbon emission calculation model based on a multi-scenario simulation analysis of electricity consumption. This paper is an original study from the perspective of the whole industry chain of electricity production, transmission, and consumption. Firstly, a carbon emission model of the power system is established based on the carbon emission composition and transmission mechanism of the whole power industrial chain, which consists of calculation models for carbon emissions from overall electricity demand and carbon emissions from electricity network losses. Secondly, the concept of carbon emission coefficient is proposed, and the key parameters of the carbon emission coefficient of the power system are obtained through the econometric model. On this basis, the carbon emission coefficient is obtained by regression fitting of multiple key parameters according to historical data. Finally, electricity consumption per unit output value (ECPUOV) and per capita electricity consumption (PCEC) are used to predict electricity consumption in the next 15 years. This paper also makes a quantitative analysis of the relationship between CO2 emissions from the power system and electricity consumption. This paper takes G province, which ranks first in total energy consumption and economic aggregate in China, as an example and calculates its CO2 emissions and achievement of peak CO2 emissions by multi-scenario analysis. The case study results show that the low carbon scenario(LC) is the best route for G province to peak CO2 emissions from energy consumption. The method proposed in this paper can set an achievable goal of 2030 carbon peaking for the government and industry policymakers, and find a feasible implementation path.

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Section: Discussionsupporting

confidence: 79%

Section: Carbon Emission Calculation Methods Based On Direct Variablesmentioning

confidence: 99%

A Calculation Model of Carbon Emissions Based on Multi-Scenario Simulation Analysis of Electricity Consumption

et al. 2022

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“…According to the research report Challenges of Maintaining Global Warming Below 2 • C by the Tyndall Center for Climate Change Research, China, as the world's largest energy consumer, ranks first in the world in terms of total carbon emissions [8], and its carbon emissions amounted to 37.2 billion tons in 2018 [9]. The Chinese government has realized that 2 of 21 China should participate extensively in international climate cooperation and undertake its corresponding international obligations [10,11]. Hence, at the 75th United Nations General Assembly (UNGA), the Chinese government proposed China's commitment and strategic goals to reach peak carbon emissions by 2030 and achieve carbon neutrality by 2060 [12].…”

Section: Introductionmentioning

confidence: 99%

The Carbon Emission Reduction Effect of Tourism Economy and Its Formation Mechanism: An Empirical Study of China’s 92 Tourism-Dependent Cities

Tong

Zhang

2022

IJERPH

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The tourism economy is regarded as an effective way to realize regional sustainable development. Hence, it is of great significance to explore whether and how tourism economy can alleviate regional carbon emission intensity. To this end, a structural equation model (SEM) reflecting the multiple pathways of the carbon emission reduction effect of tourism economy was constructed based on 92 tourism-dependent cities in China, and the existence and formation mechanism of the carbon emission reduction effect of tourism economy were empirically tested. The main findings are as follows: (1) The tourism economy has a significant carbon emission reduction effect in China. Although the direct impact of tourism economy on carbon emission intensity is significantly positive, the indirect impact is significantly negative and stronger than the direct impact. (2) The carbon emission reduction effect of tourism economy presents multiple pathways characteristics. There are single intermediary pathways such as Tourism Economy → Environmental Regulation → Carbon Emission Intensity, Tourism Economy → Opening-Up → Carbon Emission Intensity, and dual intermediary pathways such as Tourism Economy → Opening-Up → Industrial Development → Carbon Emission Intensity, Tourism Economy → Opening-Up → Innovation Capacity → Carbon Emission Intensity. (3) The formation mechanism of the carbon emission reduction effect of tourism economy presents obvious spatial heterogeneity.

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“…Regional Similarities and Differences of the HCEs in Urban and Rural Areas 5.2.1. Emissions Characteristics Industrial structure and resource endowments vary greatly among provinces in China because Of the huge territory [42]. Results of Liu [13] indicate that HCEs continued to grow from 1992 to 2007 for China.…”

Section: Urban-rural Disparity Of the Hces Influencing Factormentioning

confidence: 99%

Direct and Indirect Carbon Emission from Household Consumption Based on LMDI and SDA Model: A Decomposition and Comparison Analysis

et al. 2022

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Household consumption has become an important field of carbon dioxide emissions. Urban–rural disparity in the household carbon emissions (HCEs) of residents and their influencing factors are relevant to HCE reduction. Taking Fujian as the study area, the LMDI and SDA models were used to analyze the effects of influencing factors for the direct household carbon emissions (DHCEs) and indirect carbon emissions (IHCEs) of urban and rural residents from 2006 to 2018. The HCEs continue to rise, approximately 65% from the IHCEs in 2017, and urban areas occupied 67% in 2018. The gap between urban and rural per capita HCEs is narrowing. In 2017, approximately 75% of urban per capita HCEs came from the IHCEs, while the per capita DHCEs’ occupation exceeded the IHCEs in rural areas. Per capita consumption expenditure has the largest positive effect on the DHCEs and IHCEs in urban and rural areas. With the urbanization process, the inhibition effect of rural DHCEs is larger than the positive effect of the urban DHCEs, while the positive impact on urban areas is more substantial than on rural areas in the IHCEs. Combined with regional differences, urban and rural areas should take “common but differentiated” emission reduction responsibilities.

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Analyzing driving forces of China’s carbon emissions from 1997 to 2040 and the potential emission reduction path: through decomposition and scenario analysis

Cited by 10 publications

References 54 publications

A Calculation Model of Carbon Emissions Based on Multi-Scenario Simulation Analysis of Electricity Consumption

A Calculation Model of Carbon Emissions Based on Multi-Scenario Simulation Analysis of Electricity Consumption

The Carbon Emission Reduction Effect of Tourism Economy and Its Formation Mechanism: An Empirical Study of China’s 92 Tourism-Dependent Cities

Direct and Indirect Carbon Emission from Household Consumption Based on LMDI and SDA Model: A Decomposition and Comparison Analysis

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