Effects of digital economy on carbon emission intensity in Chinese cities: A life-cycle theory and the application of non-linear spatial panel smooth transition threshold model

Bai, Ling; Guo, Tianran; Xu, Wei; Liu, Yaobin; Kuang, Ming; Jiang, Lei

doi:10.1016/j.enpol.2023.113792

Cited by 59 publications

(15 citation statements)

References 53 publications

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“…Digital transformation not only poses a technological challenge, but also plays an irreplaceable role in greening the transition and promoting green development [20]. The existing literature has examined the digital transformation on carbon emissions, green development [21][22][23][24][25][26][27], etc., but there is a lack of research on its ability to cope with shocks from a resilience perspective. Some scholars have pointed out that it is particularly important to analyze urban greening and coping with the impact of environmental pollution from a resilience perspective [28].…”

Section: Introductionmentioning

confidence: 99%

“…Second, there is the impact of the digital economy on carbon emissions. Most of the existing scholars agree that the development of the digital economy will reduce carbon emissions, and they believe that the development of the digital economy has an inverted U-shaped nonlinear characteristic on urban carbon emission reduction [26,27]. According to their mechanism test, they believe that upgrading the industrial structure [30], green technology innovation [31], and energy structure improvement [32,33] play a mediating role.…”

Section: Introductionmentioning

confidence: 99%

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A Study on the Impact of Digital Transformation on Green Resilience in China

Wang,

Song,

Zhang

2024

Sustainability

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Enhancing green resilience is an important element in realizing environmental protection and green development, and with the continuous development of digital technology, digital transformation has become a new driving force for enhancing green resilience. Based on the panel data of 31 provinces in China from 2013 to 2021, this study examines the impact of digital transformation on green resilience. The results show that digital transformation can significantly enhance green resilience, and this conclusion is still valid after considering a series of robustness tests and endogenous problems; the heterogeneity analysis shows that in the eastern and central regions, the enhancement of green resilience by digital transformation is still significant, while in the western region, digital transformation has curbed green resilience but is not significant; both resource-based and non-resource-based provinces’ digital transformation has enhanced green resilience, and the enhancement is more significant in resource-based provinces; in different levels of green resilience, digital transformation has a stronger impact on provinces with high levels of green resilience, showing the “Matthew effect”; the mediation effect results show that digital transformation can enhance green resilience by attracting government investment, fostering industrial integration and increasing public environmental concern; the threshold results show that digital transformation contributes to green resilience across the sample. However, with the development of digital platforms, the growth rate of digital transformation on the promotion of green resilience will first increase and then gradually decrease and eventually rebound, showing an “N-shaped” relationship.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Study on the Impact of Digital Transformation on Green Resilience in China

Wang,

Song,

Zhang

2024

Sustainability

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“…Compared to unweighted networks, weighted networks offer a richer expression in aspects like node centrality measurement and dynamic characteristics. For example, the multiscale self-similarity of weighted networks [34], the nonlinear relationship between degree and strength [35,36], among others. Moreover, network weights also have varying degrees of impact on dynamic processes such as disease transmission, percolation, and diffusion [37,38].…”

Section: Introductionmentioning

confidence: 99%

Feature selection and interpretability analysis of compound faults in rolling bearings based on the causal feature weighted network

Yu,

Li,

et al. 2024

Meas. Sci. Technol.

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Feature selection is a crucial step in fault diagnosis. When rolling bearings are susceptible to compound faults, causal relationships are hidden within the signal features. Complex network analysis methods provide a tool for causal relationship modeling and feature importance assessment. Existing studies mainly focus on unweighted networks, overlooking the impact of the strength of causal relationships on feature selection. To address this issue, we propose a compound fault feature selection method based on the causal feature weighted network. First, we construct a weighted network using the incremental association Markov blanket discovery algorithm and Pearson correlation coefficient. Then, we quantify the importance of features by treating node strength as a centrality index and rank them to partition the feature subset. Finally, the optimal feature subset is obtained through a neural network with the accuracy of compound fault diagnosis as the threshold. Analysis of public datasets and comparative experiments demonstrate the advantages of our method. Compared to existing research, our method not only effectively reduces the number of optimal feature subsets to 11 but also improves the accuracy of compound fault diagnosis to 95.2%. Furthermore, we employ the SHapley Additive exPlanations to interpret the contribution of each feature in the optimal subset to the accuracy of compound fault diagnosis. This provides reference from both physical and network perspectives to feature selection and compound fault diagnosis in rolling bearings in practical working conditions.

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“…High levels of primary energy consumption imply a faster depletion of limited natural energy resources, posing not only a resource depletion issue but also an energy security concern. Furthermore, as an indicator reflecting a nation or region’s sustainable economic development, in 2019, China’s carbon intensity was 48.1% lower than in 2005, but it still exceeded the global average during the period from 1990 to 2019 3 . This underscores the necessity of controlling carbon emission intensity.…”

Section: Introductionmentioning

confidence: 99%

Assessment of green technology innovation on energy-environmental efficiency in China under the influence of environmental regulation considering spatial effects

Li,

Xu,

Huang

et al. 2023

Sci Rep

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Enhancing energy-environmental efficiency (EEE) is crucial for achieving energy conservation and emission reduction goals. Investigating the mechanism through which green technology innovation (GTI) affects EEE and understanding the role of environmental regulation (ER) in this process provides a theoretical basis for efficient utilization of GTI and ER. This study employs a Dynamic Spatial Durbin Model and utilizes panel data from 2003 to 2017 for 30 Chinese provinces to examine the impact of GTI on EEE in the presence of ER. The empirical results reveal: (1) GTI has a U-shaped impact on EEE, primarily driven by SubGI. (2) GTI’s influence on EEE is predominantly reflected in PTE, also stemming from SubGI. (3) The interaction term between ER and GTI is 0.0022, while the GTI coefficient is − 0.0741, and the GTI quadratic term coefficient is 0.0007, all statistically significant. This implies that ER mitigates the negative impact of GTI on EEE while strengthening its positive effect. These findings provide empirical evidence and policy insights for more effectively utilizing GTI and ER to enhance EEE and achieve energy conservation and emissions reduction goals.

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Effects of digital economy on carbon emission intensity in Chinese cities: A life-cycle theory and the application of non-linear spatial panel smooth transition threshold model

Cited by 59 publications

References 53 publications

A Study on the Impact of Digital Transformation on Green Resilience in China

A Study on the Impact of Digital Transformation on Green Resilience in China

Feature selection and interpretability analysis of compound faults in rolling bearings based on the causal feature weighted network

Assessment of green technology innovation on energy-environmental efficiency in China under the influence of environmental regulation considering spatial effects

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