CCUS technology, digital economy, and carbon emission efficiency: Evidence from China’s provincial panel data

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.

Appendix. The specific calculation steps of the improved entropy method

Assuming that there are n indexes, $\theta$ years, and m provinces, then X_tij 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:

• 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):

$${X}_{\mathrm{tij}}^{^{\prime}}=\frac{{X}_{\mathrm{tij}}-\mathrm{min}\left\{{X}_{\mathrm{tij}}\right\}}{\mathrm{max}\left\{{X}_{\mathrm{tij}}\right\}-\mathrm{min}\left\{{X}_{\mathrm{tij}}\right\}}$$

(7)

where X_tij 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 X_tij; $\mathrm{max}\left\{{X}_{\mathrm{tij}}\right\}$ 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.

• Step 2: Calculate the specific weight of P_tij (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 e_j ( 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 W_j (11):

  $${W}_{j}=\frac{1-{e}_{j}}{\sum\limits_{j=1}^{n}1-{e}_{j}}$$

  (11)

• Step 5: Calculate the comprehensive level (L_ti) (12)

  $${L}_{\mathrm{ti}}=\sum_{j=1}^{n}{W}_{j}{X}_{\mathrm{tij}}^{\mathrm{^{\prime}}}$$

  (12)