Digitalization and carbon emissions: How does digital city construction affect china's carbon emission reduction?

Yang, Zhen; Gao, Weijun; Han, Qing; Qi, Liyan; Cui, Yajie; Chen, Yuqing

doi:10.1016/j.scs.2022.104201

Cited by 113 publications

(45 citation statements)

References 57 publications

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“…China’s CD&GDD and the four major economic zones have their own distinct development laws. In this study, we found that the level of the digitalization and green development of Chinese cities showed an obvious upward trend, which is consistent with the studies of existing scholars [ 62 , 63 ]. In contrast to previous studies, we expanded on the features and rules of the coupling coordination relations between the two and discovered fresh results.…”

Section: Discussionsupporting

confidence: 90%

“…Data are the most influential factors in production, transforming it into competitiveness, service and creativity. With the help of digital governance means, cities accelerate their industrial agglomeration and inter-regional factor circulation, thus, improving the utilization efficiency of urban resources, forming a circular economic system and reducing pollutant emissions [ 62 ], thereby making the urban development greener [ 63 ]. Digital governance has broken the spatial barriers to cooperation between cities [ 64 ].…”

Section: Theoretical Analysismentioning

confidence: 99%

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Coupling Coordination Measurement and Evaluation of Urban Digitalization and Green Development in China

Guo

Diao

2022

IJERPH

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The coordinated promotion of urban digitalization and green development is an inevitable requirement for sustainable development in the digital age. Based on the coupling mechanism of urban digitalization and green development, in this study, we took 282 cities at the prefecture level and above in China from 2011 to 2019 as the research object, and we constructed the evaluation index system and calculated the coupling coordination degree (CD&GDD) of the two through the coupling coordination degree model. We further used the Dagum Gini coefficient, kernel density estimation, Markov chain and Moran’s I to assess the spatial effects of the regional differences, dynamic evolution trends and degree of coupling coordination. The results show the following: (1) The level of urban digitalization and green development show a fluctuating upward trend, and the interaction between the two is obvious. (2) Although the CD&GDD of most cities is continuously improving, it is still at a low level. There are large differences in the levels between the regions. (3) The inter-regional differences are the main source of the large overall differences in the CD&GDD in China, and these are mainly composed of the hypervariable density and net differences between the regions. (4) The phenomenon of “club convergence” exists in the CD&GDD. (5) The coupling coordination relationship between cities has a substantial spatial effect, and the spatial effect has obvious regional heterogeneity. The results and conclusions provide a reference for developing countries to promote green and low-carbon urban development.

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Section: Discussionsupporting

confidence: 90%

Section: Theoretical Analysismentioning

confidence: 99%

Coupling Coordination Measurement and Evaluation of Urban Digitalization and Green Development in China

Guo

Diao

2022

IJERPH

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“…Liu et al (2022) found the increase in information and communication technology can increase carbon emissions. In addition, some scholars found the impact of the digital economy on carbon emissions presents an inverted U-shaped non-linear relationship (Yang Z et al, 2022), which accords with the hypothesis of the environmental Kuznets curve (EKC) .…”

Section: Literature Reviewmentioning

confidence: 56%

The impact of the digital economy on carbon emission intensity: Evidence from China

Xiao-chun

Jiang²,

Zhao³

2023

Front. Environ. Sci.

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Evaluating the impact of the digital economy on carbon emission intensity has great significance in promoting sustainable development. Based on the panel data of 30 provinces in China from 2013 to 2019, the level of the digital economy is estimated by using entropy weight and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model. The panel vector auto-regressive (PVAR) model is used to analyze the impact of the digital economy on carbon emission intensity. The results show that, first, the comprehensive development level of the digital economy in China is not high, and it tends to rise slowly from 0.208 in 2013 to 0.221 in 2019. Second, the carbon emission intensity of China shows a downward trend from 0.720 in 2013 to 0.607 in 2019. There are significant differences in carbon emission intensity among different regions, and a decreasing trend is seen from the western region to the eastern region. Third, there is a long-term equilibrium relationship between the digital economy and carbon emission intensity. Fourth, the digital economy has a long-term negative effect on carbon emission intensity, but carbon emission intensity has no negative effect on the digital economy. According to the research conclusion, this study puts forward some suggestions.

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“…China’s innovation input index was 219.0 in 2021, while its innovation output index was 353.6. Environmental protection and low-carbon development should receive attention in some provinces at the late-industrialization stage, and technical innovation should be improved in some provinces at the late-industrialization stage [ 44 ]. Enterprises and research institutes should increase capital investment in R&D and improve the efficiency of transforming technical achievements [ 12 ].…”

Section: Discussionmentioning

confidence: 99%

Differentiation Analysis on Carbon Emission Efficiency and Its Factors at Different Industrialization Stages: Evidence from Mainland China

Wei

Wang

2022

IJERPH

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Industrial production is currently the main source of global carbon emissions. There are obvious differences in regional carbon emission efficiencies (CEE) at different industrial stages. We investigate CEE and explore its factors in mainland China at different industrialization stages from 2008-2020 using the super-SBM model with an undesirable output and the STIRPAT model. There is significant spatial heterogeneity in regional CEE, with gaps gradually widening. CEE’s spatial heterogeneity in mid-industrialized provinces is narrowing, while in late-industrialized and post-industrialized provinces, it is widening. CEE’s factors also differ in provinces at different industrialization stages. At the mid-industrialization stage, the industrial structure (IS) is the dominant factor, while population urbanization (PU) is dominant at the late-industrialization stage, and both PU and IS are dominant at the post-industrialization stage. Based on CEE’s characteristics at different industrialization stages, we propose suggestions for green development.

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Digitalization and carbon emissions: How does digital city construction affect china's carbon emission reduction?

Cited by 113 publications

References 57 publications

Coupling Coordination Measurement and Evaluation of Urban Digitalization and Green Development in China

Coupling Coordination Measurement and Evaluation of Urban Digitalization and Green Development in China

The impact of the digital economy on carbon emission intensity: Evidence from China

Differentiation Analysis on Carbon Emission Efficiency and Its Factors at Different Industrialization Stages: Evidence from Mainland China

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