Abstract
Improving carbon emission efficiency is crucial for realizing carbon neutralization. Many critical influencing factors of carbon emission efficiency were identified by previous studies, but they ignored the impact of carbon capture, utilization, and storage (CCUS) technology, which is considered in this study. By employing the panel fixed effect, the moderating effect, and the panel threshold regression models, this study investigates the influence of CCUS technology on carbon emission efficiency and how that impact fluctuates when digital economy is incorporated. Data for China’s 30 provinces from 2011 to 2019 is adopted. The results suggest that improving CCUS technology significantly promotes carbon emission efficiency and the promotion effect is positively moderated by digital economy. Considering the level of CCUS technology or digital economy, the effect of CCUS technology on carbon emission efficiency is nonlinear and has significant double-threshold effects. Only when CCUS technology reaches a certain threshold, can it has a significantly positive impact on carbon emission efficiency and that effect has an increasing trend in marginal utility. Meanwhile, with the deepening of digital economy, the relationship between CCUS technology and carbon emission efficiency shows an S-shaped curve trend. Those findings, first combining CCUS technology, digital economy, and carbon emission efficiency together, reflect the significance of advancing CCUS technology and adjusting the development of digital economy for achieving sustainable low-carbon development.
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This research was supported by the Philosophy and Social Science Program [grant number: 22Q083] and Soft Science Research Program of Hubei Province [grant number: 2023EDA064].
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[LJ]: writing—original draft, writing—review and editing, funding acquisition. [LZ]: writing—original draft, writing—review and editing, data curation, funding acquisition. [FZ]: data curation, formal analysis, writing—review.
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Appendix. The specific calculation steps of the improved entropy method
Appendix. The specific calculation steps of the improved entropy method
Assuming that there are n indexes, \(\theta\) years, and m provinces, then Xtij represents index j of province i in year t, where j = 1, 2, …, n; t = 1,2,…, \(\theta\); i = 1,2,…,m. The specific calculation steps of the improved entropy method are as follows:
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Step 1: Index standardization
As the indexes measuring the digitalization degree in this study are all positive index, the standardization of the indexes can be obtained by the following Eq. (7):
where Xtij represents index j of province i in year t, where j = 1, 2, …, n; t = 1,2,…, \(\theta\); i = 1,2,…,m; \({\mathrm{X}}_{\mathrm{tij}}^{\mathrm{^{\prime}}}\) is the standardization value of Xtij; \(\mathrm{max}\left\{{X}_{\mathrm{tij}}\right\}\) is the maximum value; \(\mathrm{min}\left\{{X}_{\mathrm{tij}}\right\}\) is the minimum value. To facilitate subsequent calculation, when \({\mathrm{X}}_{\mathrm{tij}}^{\mathrm{^{\prime}}}=0\), we replace the value of \({\mathrm{X}}_{\mathrm{tij}}^{\mathrm{^{\prime}}}\) by 0.0000000001.
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Step 2: Calculate the specific weight of Ptij (8):
$${P}_{\mathrm{tij}}=\frac{{X}_{\mathrm{tij}}^{\mathrm{^{\prime}}}}{\sum\limits_{t=1}^{\theta }\sum\limits_{i=1}^{m}{X}_{\mathrm{tij}}^{\mathrm{^{\prime}}}}$$(8) -
Step 3: Calculate the entropy value of ej ( 9 and 10):
$$k=1/\mathrm{ln}\;\left(\theta m\right)$$(9)$${e}_{j}=-k\sum\limits_{i=1}^{m}{P}_{\mathrm{tij}}\;\mathrm{ln}\;\left({P}_{\mathrm{tij}}\right)$$(10) -
Step 4: Calculate the weight of Wj (11):
$${W}_{j}=\frac{1-{e}_{j}}{\sum\limits_{j=1}^{n}1-{e}_{j}}$$(11) -
Step 5: Calculate the comprehensive level (Lti) (12)
$${L}_{\mathrm{ti}}=\sum_{j=1}^{n}{W}_{j}{X}_{\mathrm{tij}}^{\mathrm{^{\prime}}}$$(12)
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Zhang, L., Jiang, L. & Zhang, F. CCUS technology, digital economy, and carbon emission efficiency: Evidence from China’s provincial panel data. Environ Sci Pollut Res 30, 86395–86411 (2023). https://doi.org/10.1007/s11356-023-28312-3
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DOI: https://doi.org/10.1007/s11356-023-28312-3