Structural factors influencing energy carbon emissions in China’s service industry: an input–output perspective

Yu, Jinghan; Yu, Yang; Jiang, Tangyang

doi:10.1007/s11356-022-19287-8

Cited by 19 publications

(9 citation statements)

References 36 publications

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“…The study differs from the other research reports and their achievements devoted to low carbon emissions in the development characteristics and regional reduction potential of carbon emissions and carbon sinks in the developed areas in China [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 94 , 95 , 96 ]. For instance, the total carbon emissions increased sharply, from 76.11 to 140.19 TgC yr −1 at the provincial level, with an average annual growth rate of 10.52%, while the vegetation carbon sinks declined slightly, from 54.52 to 53.20 TgC yr −1 in Guangdong Province [ 17 ].…”

Section: Discussioncontrasting

confidence: 71%

“…On September 21, 2021, Chinese President Xi Jinping advised at the 76th United Nations General Assembly to achieve the carbon peaking by 2030 and its carbon neutrality by 2060 [ 13 , 14 , 15 ]. Under the overall requirements of Chinese goals of dual carbon and carbon neutrality, understanding the overall carbon emission level and carbon sink potential is the national basis and core element of promoting low-carbon development in the China [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. Similarly, under the regional “14th Five-Year Plan”, the provincial government is devoted to reduce its carbon emission to 65% of that in 2005 by 2030 in Anhui Province.…”

Section: Introductionmentioning

confidence: 99%

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The Carbon Emission Characteristics and Reduction Potential in Developing Areas: Case Study from Anhui Province, China

Zhang

Jiang

Jin

et al. 2022

IJERPH

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Global warming and world-wide climate change caused by increasing carbon emissions have attracted a widespread public attention, while anthropogenic activities account for most of these problems generated in the social economy. In order to comprehensively measure the levels of carbon emissions and carbon sinks in Anhui Province, the study adopted some specific carbon accounting methods to analyze and explore datasets from the following suggested five carbon emission sources of energy consumption, food consumption, cultivated land, ruminants and waste, and three carbon sink sources of forest, grassland and crops to compile the carbon emission inventory in Anhui Province. Based on the compiled carbon emission inventory, carbon emissions and carbon sink capacity were calculated from 2000 to 2019 in Anhui Province, China. Combined with ridge regression and scenario analysis, the STIRPAT model was used to evaluate and predict the regional carbon emission from 2020 to 2040 to explore the provincial low-carbon development pathways, and carbon emissions of various industrial sectors were systematically compared and analyzed. Results showed that carbon emissions increased rapidly from 2000 to 2019 and regional energy consumption was the primary source of carbon emissions in Anhui Province. There were significant differences found in the increasing carbon emissions among various industries. The consumption proportion of coal in the provincial energy consumption continued to decline, while the consumption of oil and electricity proceeded to increase. Furthermore, there were significant differences among different urban and rural energy structures, and the carbon emissions from waste incineration were increasing. Additionally, there is an inverted “U”-shape curve of correlation between carbon emission and economic development in line with the environmental Kuznets curve, whereas it indicated a “positive U”-shaped curve of correlation between carbon emission and urbanization rate. The local government should strengthen environmental governance, actively promote industrial transformation, and increase the proportion of clean energy in the energy production and consumption structures in Anhui Province. These also suggested a great potential of emission reduction with carbon sink in Anhui Province.

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Section: Discussioncontrasting

confidence: 71%

Section: Introductionmentioning

confidence: 99%

The Carbon Emission Characteristics and Reduction Potential in Developing Areas: Case Study from Anhui Province, China

Zhang

Jiang

Jin

et al. 2022

IJERPH

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“…These incidents reduced consumers' trust in Chinese dairy products and brought setbacks to the development of the Chinese dairy industry. To solve this problem, the government introduced the "Plan for the Rectification and Revitalization of the Dairy Industry" [33], strengthened the supervision of dairy product quality, and improved relevant laws and regulations. With the strong support of the government, the dairy industry gradually recovered.…”

Section: Analysis Of the Temporal Evolution Characteristics Of Gtfp I...mentioning

confidence: 99%

Temporal and Spatial Evolution Characteristics and Influencing Factors Analysis of Green Production in China’s Dairy Industry: Based on the Perspective of Green Total Factor Productivity

Liu,

Liu

2023

Sustainability

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Accelerating the green development of the dairy industry is an important work to promote the construction of ecological civilization and ensure the safe supply of dairy products. Existing studies lack a comprehensive analysis of the green development characteristics of China’s dairy industry. Based on the input–output system, the study measured and analyzed the green total factor productivity of China’s dairy industry in 29 provinces (cities, autonomous regions, and municipalities) since the 10th Five-Year Plan period, using the super-efficiency EBM model and the GML index based on non-directional and variable scale returns. Accelerating the green development of the dairy industry is an important work to promote the construction of ecological civilization and ensure the national nutrition intake. The existing studies lack a comprehensive understanding of the green development characteristics of China’s dairy industry. Therefore, this paper constructs an input–output system, measures and analyzes the green total factor productivity of the dairy industry in 29 provinces (cities, autonomous regions and municipalities directly under the Central Government), since the “15th Five-Year Plan” period based on the non-oriented super-efficiency EBM model and GML index with variable returns to scale. On this basis, the dynamic evolution of regional differences was explored using Kernel density estimation and the Dagum Gini coefficient, and the influencing factors of green total factor productivity in China’s dairy industry were analyzed using a two-way fixed effects model. The results show that from 2001 to 2020, the green total factor productivity of China’s dairy industry showed an overall upward trend, and presented a gradient pattern of “Northeast–East–Central–West” in turn, with green technical efficiency being the main driving force for promoting green total factor productivity in China and various regions. The gap in green total factor productivity between provinces and cities is gradually narrowing, and the polarization phenomenon is weakening. Super variation density is the main source of regional differences, and the difference between the West and the East is the largest, while the difference between the Central and the Northeast is the smallest. As for the influencing factors, industry agglomeration, economic development level, and environmental planning level have a significant positive promoting effect on the green total factor productivity of China’s dairy industry, while the level of population urbanization has a significant inhibitory effect on it. In order to promote the green and sustainable development of China’s dairy industry and promote the coordinated development of regional green, it is necessary to accelerate the efficiency of green technology while promoting the innovation of green technology, accelerate the integrated development of industry and formulate relevant policies according to local conditions to promote the coordinated development of green technology between regions.

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“…In fact, the service industry is not an emission-free sector. The energy consumption structure of the service industry mainly includes electricity, coal and natural gas, and the industrial scale is expanding globally (Yu et al, 2022). This indicates that the service sector has become a major sector of energy consumption and carbon emissions, and more attention should be given to energy restructuring and energy saving in the service sector in the future.…”

Section: Sectoral Carbon Footprintmentioning

confidence: 99%

Carbon footprint and embodied carbon emission transfer network obtained using the multi–regional input–output model and social network analysis method: A case of the Hanjiang River basin, China

et al. 2022

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China is the largest carbon emitter in the world; thus, reducing carbon emissions while maintaining economic growth has become an important issue. Within the context of carbon neutrality strategies, calculation of the carbon footprint and embodied carbon transfer can help policymakers formulate reasonable carbon reduction plans. The multi–regional input–output (MRIO) model can clarify carbon flow pathways between regions, and social network analysis (SNA) can comprehensively evaluate the different positions of individual sectors. Combining these two approaches, the specific characteristics of carbon emissions in complex production and trade relationships can be analyzed. China has become the world’s top total carbon emitter, and the Hanjiang River basin (HJRB) constitutes an important economic link between the developed and less developed regions of China. Studying carbon emissions in the HJRB can provide a reference for other, similar regions and is vital for the realization of China’s carbon emission reduction targets. This paper examines the carbon footprint and embodied carbon emission transfer among three provinces and 12 sectors in the HJRB during different periods and identifies the key industries in the carbon transfer process. The results indicate that (1) the total carbon footprint in the HJRB exhibits an increasing trend. Energy-based Shaanxi Province exhibits the highest growth rate of the carbon footprint, agriculture-based Henan Province shows a decreasing trend, and consumption-based Hubei Province displays the lowest carbon footprint intensity. (2) There are differences in the carbon emission coefficient and final consumption rate among various sectors; construction, metal processing and metal and non-metallic products, processing and manufacturing of petroleum, coking, nuclear fuel, chemical products, and other services are the sectors accounting for a high proportion of emissions. (3) The more obvious the supply relationship is, the higher the flow of embodied carbon emission transfer between sectors. (4) Energy-based regions transfer large amounts of fossil energy, electricity, steel and coal resources to developed regions and simultaneously assume more of the carbon reduction pressure imposed on developed regions. (5) The key industries within the embodied carbon emission transfer network notably control the carbon emissions of other industries and can provide breakthroughs to achieve challenging carbon emission reduction targets.

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Structural factors influencing energy carbon emissions in China’s service industry: an input–output perspective

Cited by 19 publications

References 36 publications

The Carbon Emission Characteristics and Reduction Potential in Developing Areas: Case Study from Anhui Province, China

The Carbon Emission Characteristics and Reduction Potential in Developing Areas: Case Study from Anhui Province, China

Temporal and Spatial Evolution Characteristics and Influencing Factors Analysis of Green Production in China’s Dairy Industry: Based on the Perspective of Green Total Factor Productivity

Carbon footprint and embodied carbon emission transfer network obtained using the multi–regional input–output model and social network analysis method: A case of the Hanjiang River basin, China

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