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Assessing the impact of energy internet and energy misallocation on carbon emissions: new insights from China

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Abstract

With the deterioration of environmental quality caused by fossil energy use, the research on energy internet and energy misallocation is of critical relevance to achieve low-carbon sustainable development. However, we find that the relevant research that analyzes energy internet and energy misallocation on carbon emissions under the same framework is ignored. For this purpose, the generalized method of moments (GMM), panel threshold model, and spatial analysis (deviation ellipse, hotspot analysis, and geographically and temporally weighted regression (GTWR)) model were applied to investigate the impact of energy internet and energy misallocation on carbon emissions using panel data of 30 provinces in China from 2004 to 2018. The major statistical results include the following: (1) energy misallocation significantly contributes to carbon emissions, while energy internet inhibits carbon emissions. Energy internet can negatively moderate the positive effect of energy misallocation on carbon emissions. (2) The effect of energy misallocation on carbon emissions reveals an inverted “U-shaped” characteristic of first promoting and later inhibiting, but the inhibiting effect is insignificant. Moreover, the marginal effect of energy misallocation on carbon emissions decreases when the energy internet crosses the second thresholds consecutively, while the marginal effect of the energy internet on carbon emissions shows an inverted “N” shape. (3) Compared with the under-allocated regions, the promotion effect of energy misallocation on carbon emissions and the inhibitory effect of energy internet on carbon emissions are stronger in the over-allocated regions, while the energy internet has a more significant negative moderating effect on energy misallocation. (4) The gravity center of China’s carbon emissions gradually shifts to the northwest with time. The longitude of the gravity center (east–west direction) changes greatly, while the latitude of the gravity center (north–south direction) changes less. Besides, the carbon emission hotspot regions centered on Shanxi spread to the neighboring provinces, which form a high-high agglomeration region, and the cold spot region dominated by Qinghai, Guangxi, and Guangdong forms low-low agglomeration characteristics. Finally, the GTWR model shows that the impact of energy internet and energy misallocation on carbon emissions shows significant hierarchical, banded, or block-like characteristics in spatial distribution.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. See more details: https://www.noaa.gov/news/carbon-dioxide-levels-in-atmosphere-hit-record-high-in-may.

  2. See more details: http://www.creei.cn/portal/index/index.html.

  3. See more details: https://www.iea.org/reports/world-energy-statistics-2019.

  4. See more details: https://www.iea.org/data-and-statistics.

  5. Energy misallocation refers to the deviation of energy allocation from the Pareto optimal state due to market distortion. Taking natural gas as an example, the frequent inversion of natural gas prices ostensibly lowers the ex-factory price of products, but actually inhibits the willingness of manufacturers to supply, fuels the unbridled expansion of energy-intensive industries, and slows the pace of natural gas substitution for traditional energy sources.

  6. Energy internet interconnects a large number of energy nodes consisting of distributed energy harvesting devices, distributed energy storage devices, and various types of loads, such as electric power networks, oil networks, and natural gas networks, to realize a peer-to-peer exchange and sharing network of energy flow in both directions.

  7. The eastern coast of China is rich in wind resources; the northwest has a long length of sunshine; and the southwest has a high-quality clean hydropower base.

  8. If $${\widehat{\gamma }}_{{E}_{it}}=1$$, i.e., $${\tau }_{{E}_{it}}=0$$, which means that the cost of energy use in period $$t$$ in province $$i$$ is equal to the national average and the actual allocation of energy is equal to the theoretical level at effective allocation. If $${\widehat{\gamma }}_{{E}_{it}}>1$$, i.e., $${\tau }_{{E}_{it}}<0$$, which means that the cost of energy use in period $$t$$ in province $$i$$ is low when compared to the whole area, causing energy over-allocation. Conversely, if $${\widehat{\gamma }}_{{E}_{it}}<1$$, i.e., $${\tau }_{{E}_{it}}>0$$, which indicates that the energy use cost in period t in province i is high when compared to the whole country, resulting in energy under-allocation.

  9. Due to space constraints, we only analyze the general trend of carbon emission center of gravity shift.

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Acknowledgements

We thank the editor and the reviewers for their comments to improve the manuscript. We would also like to thank Abd Alwahed Dagestani from Business School of Central South University for helping to improve an earlier version of our manuscript.

Funding

The authors acknowledge financial support from the Project of National Natural Science Foundation of China (71463057), the Project of Natural Science Foundation of Xinjiang Uygur Autonomous Region (2017D01C071), and the graduate research and innovation project of Xinjiang University (XJ2021G014, XJ2021G013). The party central committee’s Xinjiang-Governance strategy theory and practice research key project (19ZJFLZ09). The usual disclaimer applies.

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Authors and Affiliations

  1. School of Economics and Management, Xinjiang University, Urumqi, 830047, China

    Xiaodong Yang, Xufeng Su, Qiying Ran, Bing Chen, Weilong Wang & Jianlong Wang

  2. School of Economics and Management, Tarim University, Alar, 843300, China

    Xufeng Su

  3. Center for Innovation Management Research of Xinjiang, Xinjiang University, Urumqi, 830047, China

    Xiaodong Yang, Qiying Ran, Bing Chen, Weilong Wang & Jianlong Wang

  4. School of Economics, Nankai University, Tianjin, 300071, China

    Siyu Ren

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Contributions

Xiaodong Yang: conceptualization, project administration, writing—review and editing, writing—original draft. Qiying Ran: formal analysis, data curation. Bing Chen: software, visualization. Weilong Wang: writing—original draft, writing—review and editing, formal analysis. Siyu Ren: methodology, data curation, writing—review and editing, validation. Xufeng Su: writing—review and editing, validation. Jianlong Wang: writing—review and editing, writing—original draft, conceptualization, methodology, funding acquisition, supervision.

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Yang, X., Su, X., Ran, Q. et al. Assessing the impact of energy internet and energy misallocation on carbon emissions: new insights from China. Environ Sci Pollut Res 29, 23436–23460 (2022). https://doi.org/10.1007/s11356-021-17217-8

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