Carbon peak time and peak level of relevant provinces in the Yellow River Basin under stable economic growth

WANG, Liangdong; Chen, Yulong; Ma, Xiaoyan; DU, Mengna

doi:10.18402/resci.2021.11.15

Cited by 2 publications

(3 citation statements)

References 5 publications

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“…Hence, technical progress has played an extremely key role and has an important function in enhancing carbon emission reduction. It is essential to strengthen scientific and technological research and development (R&D) investments and to introduce advanced production technology in order to improve local production technology and energy efficiency [43].…”

Section: Carbon Emission Efficiency Of 11 Prefecture-level Citiesmentioning

confidence: 99%

“…The results show that the primary source of energy consumption of Shanxi Province has come from the burning of coal, and its economic development has been dependent on the resource industry. In order for energy conservation and emission reduction to be achieved, it is necessary for the development of emerging industries to be accelerated, for the transformation and upgrading of traditional industries to be promoted, and for the optimization of energy structures to be encouraged [43,44]. The Malmquis index of the super-efficiency SBM-DEA model was used to measure the carbon emission efficiency of Shanxi Province and its 11 prefecture-level cities.…”

Section: Conclusion and Suggestionsmentioning

confidence: 99%

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Carbon Emission Efficiency and Low-Carbon Optimization in Shanxi Province under “Dual Carbon” Background

Guo

Wang

et al. 2022

Energies

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As an important energy base in China, Shanxi Province, in its economic development, has depended heavily on coal resources. However, enormous coal consumption produces a large amount of carbon dioxide and an aggravated ecological problem. In this paper, the super efficiency slack-based measure and data envelopment analysis (SBM-DEA) model and Malmquist index were used to calculate the energy consumption and carbon emission efficiency of 11 prefecture-level cities in Shanxi in the period from 2000 to 2020. The results were as follows: (1) The primary form of energy consumption in Shanxi Province is the burning of coal, and the carbon emission efficiencies of the 11 prefecture-level cities are quite different. The technical efficiency, technological progress, and total factor productivity of Taiyuan and Shuozhou were found to be greater than 1 compared to other cities in Shanxi. (2) On the whole, although the carbon emission rate of Shanxi Province has slowed down, it still faces the problems of a large total emission base, high carbon emission intensity, and low efficiency of the unit carbon emission output. Industrial structure is the key factor restricting improvements in the efficiency of carbon emissions in Shanxi Province. (3) From the spatial and temporal evolution of the carbon emission efficiency in Shanxi Province, we can see that the carbon emission efficiency of the whole province gradually dispersed and improved from the central and northern regions to the southern regions during the period of 2000–2020. In order to achieve a low-carbon economy, Shanxi Province must optimize its industrial structure, accelerate research and development in low-carbon technologies, adjust the energy structure, and promote the construction of an energy supply system with clean low-carbon energy as the main source.

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Section: Carbon Emission Efficiency Of 11 Prefecture-level Citiesmentioning

confidence: 99%

Section: Conclusion and Suggestionsmentioning

confidence: 99%

Carbon Emission Efficiency and Low-Carbon Optimization in Shanxi Province under “Dual Carbon” Background

Guo

Wang

et al. 2022

Energies

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“…Murshed et al [28] explored that the use of nuclear energy would slow down carbon emissions, while socioeconomic growth would increase them. At present, the research on carbon emissions in the YRB mainly focuses on the following aspects: (1) Study based on different types of carbon emissions, such as energy carbon emissions, land use carbon emissions, tourism carbon emissions, and so on [29,30]; (2) Study based on spatiotemporal variation and driving factors in the view of different spatial scales, such as on the perspective of prefecture-level administrative divisions, in the view of watershed geographic differentiation, and on the analysis of county carbon emissions [10,31,32]; (3) Study on the control methods of carbon emissions, such as ecological compensation research, carbon peak levels, carbon emission reduction paths, and carbon decoupling effects [3,[33][34][35]. Various methods were used in the above approaches, such as the Gini coefficient, Tapio index, spatial panel model, carbon ecological compensation model, and others [3,[32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variations of Carbon Emissions and Their Driving Factors in the Yellow River Basin

Wang

Sun

et al. 2022

IJERPH

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The Yellow River Basin (YRB) is a significant area of economic development and ecological protection in China. Scientifically clarifying the spatiotemporal patterns of carbon emissions and their driving factors is of great significance. Using the methods of spatial autocorrelation analysis, hot-spot analysis, and a geodetector, the analysis framework of spatiotemporal differentiation and the driving factors of carbon emissions in the YRB was constructed in this paper from three aspects: natural environment, social economy, and regional policy. Three main results were found: (1) The carbon emissions in the YRB increased gradually from 2000 to 2020, and the growth rates of carbon emissions in the different river reaches were upper reaches > middle reaches > lower reaches. (2) Carbon emissions have an obvious spatial clustering character from 2000–2020, when hot spots were concentrated in the transition area from the Inner Mongolia Plateau to the Loess Plateau. The cold spots of carbon emissions tended to be concentrated in the junction area of Qinghai, Gansu, and Shaanxi. (3) From 2000 to 2020, the driving factors of spatial differentiation of carbon emissions in the YRB and its different reaches tended to be diversified, so the impacts of socioeconomic factors increased, while the impacts of natural environmental factors decreased. The influence of the interactions of each driving factor showed double factor enhancement and nonlinear enhancement. This study will provide a scientific reference for green and low-carbon development, emphasizing the need to pay more attention to environmental protection, develop the green economy vigorously, and promote the economic cycle, so as to achieve green development and reduce carbon emissions.

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Carbon peak time and peak level of relevant provinces in the Yellow River Basin under stable economic growth

Cited by 2 publications

References 5 publications

Carbon Emission Efficiency and Low-Carbon Optimization in Shanxi Province under “Dual Carbon” Background

Carbon Emission Efficiency and Low-Carbon Optimization in Shanxi Province under “Dual Carbon” Background

Spatiotemporal Variations of Carbon Emissions and Their Driving Factors in the Yellow River Basin

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