Artificial Intelligence Technology and Regional Carbon Emission Performance: Does Energy Transition or Industrial Transformation Matter?

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

I carefully reviewed this article (sustainability-3429962). By empirically analyzing panel data from 278 Chinese cities from 2009-2019, the authors explore the dual impacts of AI technologies on carbon emission performance, and find that the current application of AI technologies not only increases the scale of carbon emissions and reduces their efficiency, but also has the potential to reduce emissions through energy transformation. The article is distinctive, but I suggest the following revision before publication. Since the article does not have specific line numbers, my comments may be generalized.

1. An obvious flaw is that the iThenticate report of this article has a high repetition rate of 32%, which seriously affects the originality of the article and reduces the innovation of the article, and it must be down-weighted.

2. Although the introduction briefly describes the research background of artificial intelligence technology and carbon emission performance, it is still insufficient, and should expand the synthesis of AI, new technology, carbon emission, and so on. I suggest the authors to refer to the following references to enhance the quality of the introduction. - How Do Varying Socio-Economic Driving Forces Affect China’s Carbon Emissions? New Evidence from a Multiscale Geographically Weighted Regression Model；Forecasting carbon emissions in Chinese coastal cities based on a gated recurrent unit model；Dynamic integrated simulation of carbon emission reduction potential in China's building sector；Dynamic simulation of street-level carbon emissions in megacities: A case study of Wuhan City, China (2015–2030)

3. Have the authors considered the possible limitations of the STIRPAT model in dealing with complex nonlinear relationships and interactions? It may be difficult for the authors to adequately capture the complex dynamics of the relationship between AI technology and carbon emission performance using the model, and clarification and explanation should be required.

4. The article utilizes a number of econometric models, but the analysis of mediating and moderating effects is not in-depth enough, and when exploring the specific mechanisms of mediating and moderating variables, it lacks in-depth exploration of changes in the intensity and direction of their effects in different contexts.

5. I believe that the mediating and moderating effects in different contexts should be refined in the analysis of the results, and the economic mechanisms and environmental factors behind them should be explored, so as to provide more targeted suggestions for policy formulation, rather than generalizations.

6. Generally speaking, carbon emission performance cannot be affected by only one-sided factors, but should be comprehensive. Therefore, the article may have overlooked some factors, such as the macroeconomic situation, international trade, and consumer behavior of the population, etc. The article mainly focuses on the impacts of AI technology and investment in the energy sector, and fails to adequately consider the combined effects of these other factors.

 

Author Response

Responses to the comments of Reviewer #1

 

First of all, we would like to express our sincere gratitude to the reviewers for their hard work and review. We have given highest priority to the valuable comments made by the reviewers and we will try best to revise each potential issue. Thank you again for your review of this paper. Below we respond to specific questions point by point:

 

Comment 1.

An obvious flaw is that the iThenticate report of this article has a high repetition rate of 32%, which seriously affects the originality of the article and reduces the innovation of the article, and it must be down-weighted.

Response 1:

Thanks to the comments of the reviewers, the repetition rate of the manuscript is mainly due to the variable expression of the article and the fixed econometric expression of the tables in the manuscript (e.g. fixed effect, R², ○, and ╳). To mitigate the potential threat of manuscript duplication, it is common practice to submit manuscript text, tables, figs, etc., separately.

In addition, repetitive words are also the cause of the increase in manuscript repetition rate, so we have modified the expression of repetitive words in the whole paper (e.g. carbon emission efficiency to CEE, carbon emission scale to CES, and so on).

We have rechecked it in the Turnitin system.

 

 

Comment 2.

Although the introduction briefly describes the research background of artificial intelligence technology and carbon emission performance, it is still insufficient, and should expand the synthesis of AI, new technology, carbon emission, and so on. I suggest the authors to refer to the following references to enhance the quality of the introduction. - How Do Varying Socio-Economic Driving Forces Affect China’s Carbon Emissions? New Evidence from a Multiscale Geographically Weighted Regression Model；Forecasting carbon emissions in Chinese coastal cities based on a gated recurrent unit model；Dynamic integrated simulation of carbon emission reduction potential in China's building sector；Dynamic simulation of street-level carbon emissions in megacities: A case study of Wuhan City, China (2015–2030)

Response 2:

Thank you to the reviewer for your suggestions. In the introduction to the manuscript, we have added important documents related to the content (Dynamic simulation of street-level carbon emissions in megacities: A case study of Wuhan City, China (2015–2030); How Do Varying Socio-Economic Driving Forces Affect China’s Carbon Emissions? New Evidence from a Multiscale Geographically Weighted Regression Model).

In addition, we rearrange the logic of the introduction to highlight the research topic. Specifically, the first part of the introduction addresses the importance of reducing the scale of carbon emissions and improving their efficiency. The second part of the introduction addresses the potential and dilemma of technological innovation (especially AI technological innovation) in carbon emission control. The third part of the introduction describes the potential ways that AI technologies can influence the scale and efficiency of carbon emissions at the macro level (energy transition and industrial transformation).

Specific modification:

In the second paragraph of the introduction, we added “Deploying AI-enabled hardware, such as servers and associated equipment, in specific industries can also result in a substantial carbon footprint”.

In the second paragraph of the introduction, we added “Consequently, there remains considerable uncertainty regarding the direction of AI technology's impact on carbon emission performance”.

In the second paragraph of the introduction, we added “An increasing body of research is shifting its focus from carbon emissions alone to carbon efficiency, as reducing emissions should not come at the expense of output”.

 

 

Comment 3.

Have the authors considered the possible limitations of the STIRPAT model in dealing with complex nonlinear relationships and interactions? It may be difficult for the authors to adequately capture the complex dynamics of the relationship between AI technology and carbon emission performance using the model, and clarification and explanation should be required.

Response 3:

Thanks to the reviewer's comments, STIRPAT model may indeed have some shortcomings in examining the impact of AI technology on carbon emissions with nonlinear relationships or interactions. We construct a quadratic term for AI techniques (lnAIT_2) to investigate whether such nonlinear effects exist.

Table R1_1. Empirical results of nonlinear relationship.

Variables	lnCES		lnCEE
	(1)	(2)	(3)	(4)
lnAIT	0.3566*** (3.17)	0.0157 (0.60)	0.0660** (2.43)	0.0273 (1.10)
lnAIT_2	-0.1424*** (-2.63)	-0.0041 (-0.33)	-0.0360*** (-2.71)	-0.0177** (-2.29)
Control variables	○	○	○	○
City fixed effect	╳	○	╳	○
Year fixed effect	╳	○	╳	○
Adj R2	0.271	0.977	0.299	0.783
Observations	3006	3006	3006	3006

The results in Table R1_1 show that the nonlinear relationship between AI technology and carbon emission performance appears to be unstable after taking fixed effects into account. Therefore, we still believe that AI technology has not played a role in reducing carbon emissions at this stage.

 

 

Comment 4.

The article utilizes a number of econometric models, but the analysis of mediating and moderating effects is not in-depth enough, and when exploring the specific mechanisms of mediating and moderating variables, it lacks in-depth exploration of changes in the intensity and direction of their effects in different contexts.

Response 4:

In response to the reviewers' comments, we have revised the mechanism analysis section to deepen our exploration of various research contexts. Specifically, we divide the sample in two ways: first, by treating the Yangtze River Economic Belt as a special economic zone, and second, by using the Heihe-Tengchong line (Southeast) as a geographical indicator of economic intensity. This approach allows us to explore both the intensity and direction of the mechanism more comprehensively.

The table added in the mechanism analysis section is as follows:

Table 11b. Mechanism analysis of energy transition in the Yangtze River Economic Belt.

Variables	esc	lnCES	lnCEE	ei	lnCES	lnCEE
	(1)	(2)	(3)	(4)	(5)	(6)
lnAIT	-0.0042 (-1.15)	0.0091 (1.47)	-0.0080** (-2.20)	-0.0541*** (-2.77)	0.0089 (1.44)	-0.0052 (-1.49)
esc		0.0118 (0.33)	0.0457 (1.30)
ei					-0.0030 (-0.29)	0.0547*** (7.96)
Control variables	○	○	○	○	○	○
City fixed effect	○	○	○	○	○	○
Year fixed effect	○	○	○	○	○	○
Adj R2	0.805	0.979	0.799	0.753	0.979	0.814
Observations	1185	1185	1185	1185	1185	1185

Notes: *, **, *** indicate significance level at 10%,5% an 1%, respectively. The values in parentheses are t-statistic using the clustering robust standard error.

Table 11c. Mechanism analysis of energy transition in the Heihe-Tengchong line (Southeast).

Variables	esc	lnCES	lnCEE	ei	lnCES	lnCEE
	(1)	(2)	(3)	(4)	(5)	(6)
lnAIT	-0.0061** (-2.12)	0.0078** (2.32)	-0.0087*** (-4.12)	-0.0273* (-1.94)	0.0078** (2.34)	-0.0081*** (-3.85)
esc		-0.0325 (-1.55)	0.0570*** (2.74)
ei					-0.0065 (-1.31)	0.0370*** (8.81)
Control variables	○	○	○	○	○	○
City fixed effect	○	○	○	○	○	○
Year fixed effect	○	○	○	○	○	○
Adj R2	0.765	0.979	0.781	0.757	0.979	0.788
Observations	2787	2787	2787	2787	2787	2787

Notes: *, **, *** indicate significance level at 10%,5% an 1%, respectively. The values in parentheses are t-statistic using the clustering robust standard error.

Table 12b. Mechanism analysis of industrial transformation in the Yangtze River Economic Belt.

Variables	ind_up	lnCES	lnCEE	ind_agg	lnCES	lnCEE
	(1)	(2)	(3)	(4)	(5)	(6)
lnAIT	-0.0102** (-2.34)	0.0077 (1.21)	-0.0099*** (-2.81)	0.0013 (0.19)	0.0079 (1.25)	-0.0106*** (-3.00)
ind_up		-0.0350 (-0.64)	0.0780 (1.36)
ind_agg					0.0391 (1.20)	0.0002 (0.01)
Control variables	○	○	○	○	○	○
City fixed effect	○	○	○	○	○	○
Year fixed effect	○	○	○	○	○	○
Adj R2	0.914	0.979	0.797	0.455	0.979	0.795
Observations	1148	1148	1148	1149	1149	1149

Notes: *, **, *** indicate significance level at 10%,5% an 1%, respectively. The values in parentheses are t-statistic using the clustering robust standard error.

Table 12c. Mechanism analysis of industrial transformation in the Heihe-Tengchong line (Southeast).

Variables	ind_up	lnCES	lnCEE	ind_agg	lnCES	lnCEE
	(1)	(2)	(3)	(4)	(5)	(6)
lnAIT	-0.0006 (-0.20)	0.0080** (2.38)	-0.0095*** (-4.51)	0.0445 (1.16)	0.0081** (2.38)	-0.0092*** (-4.42)
ind_up		-0.0076 (-0.31)	0.0317 (1.15)
ind_agg					-0.0021 (-1.45)	-0.0063*** (-5.98)
Control variables	○	○	○	○	○	○
City fixed effect	○	○	○	○	○	○
Year fixed effect	○	○	○	○	○	○
Adj R2	0.926	0.979	0.780	0.784	0.979	0.782
Observations	2724	2724	2724	2725	2725	2725

Notes: *, **, *** indicate significance level at 10%,5% an 1%, respectively. The values in parentheses are t-statistic using the clustering robust standard error.

In addition, we have further explained the new table:

In the second paragraph of the mechanism analysis section, we added “Table 11b and Table 11c discuss the mechanism of energy transition in the context of typical geographical locations (Yangtze River Economic Belt, Heihe-Tengchong line). In terms of the Yangtze River Economic Belt, the mechanism of energy transition is not established, which mean that the Yangtze River Economic Belt is not the most widely applied AI technology in China. However, the results for the Heihe-Tengchong line are largely consistent with those presented in Table 11a. It is evident that the southeast side of the Heihe-Tengchong line encompasses over 90% of the observed prefecture-level cities, which are clearly the most significant sources of carbon emissions in China.”

In the third paragraph of the mechanism analysis section, we added “Table 12b and Table 12c show similar results. In other words, even when accounting for certain special industries and economically developed regions, AI technology remains unable to influence carbon emissions through industrial transformation. From a macro perspective, the penetration of artificial intelligence technology in the process of industrial transformation remains insufficient. Of course, AI technology is already widely employed in specific industries (such as information transmission, software, and information technology services). The impact of AI technologies on the carbon emission performance of specific industries may be observed with micro-level firm data.”

 

 

Comment 5.

I believe that the mediating and moderating effects in different contexts should be refined in the analysis of the results, and the economic mechanisms and environmental factors behind them should be explored, so as to provide more targeted suggestions for policy formulation, rather than generalizations.

Response 5:

Thanks to the reviewer's comments, we have also refined the analysis of the moderating effect and revised the policy recommendations accordingly. The new tables and analyses are as follows:

Table 13b. Expansion analysis of energy industry investment in the Yangtze River Economic Belt.

Variables	lnCES	lnCEE	lnCES	lnCEE
	(1)	(2)	(3)	(4)
lnAIT	0.0082 (1.34)	-0.0098*** (-2.77)	0.0051 (0.81)	-0.0110*** (-2.86)
lnAIT×eii	0.0041** (2.00)	0.0077*** (4.26)
lnAIT×seii			0.0048** (2.46)	0.0034** (2.02)
Control variables	○	○	○	○
City fixed effect	○	○	○	○
Year fixed effect	○	○	○	○
Adj R2	0.979	0.803	0.979	0.799
Observations	1185	1185	1185	1185

Notes: *, **, *** indicate significance level at 10%,5% an 1%, respectively. The values in parentheses are t-statistic using the clustering robust standard error.

Table 13c. Expansion analysis of energy industry investment in the Heihe-Tengchong line (Southeast).

Variables	lnCES	lnCEE	lnCES	lnCEE
	(1)	(2)	(3)	(4)
lnAIT	0.0074** (2.22)	-0.0109*** (-5.15)	0.0062* (1.79)	-0.0101*** (-4.32)
lnAIT×eii	0.0013 (1.34)	0.0043*** (4.19)
lnAIT×seii			0.0020* (1.89)	0.0012 (1.01)
Control variables	○	○	○	○
City fixed effect	○	○	○	○
Year fixed effect	○	○	○	○
Adj R2	0.978	0.782	0.979	0.780
Observations	2809	2809	2809	2809

Notes: *, **, *** indicate significance level at 10%,5% an 1%, respectively. The values in parentheses are t-statistic using the clustering robust standard error.

In the second paragraph of the Expansion analysis section, we added “The results in Table 13c are consistent with those in Table 13a, indicating that AI technology plays a more significant role in enhancing carbon emission efficiency. However, the results presented in Table 13b indicate that for the Yangtze River Economic Belt, a specific economic zone with significant environmental relevance, the moderating effect of energy industry investment is more pronounced (0.0041 > 0.0020, 0.0077 > 0.0042). Strengthening the application of AI technology in the energy sector within this region can facilitate the realization of its emission reduction potential.”

In the 4th paragraph of the Conclusion and policy implications section, we added “We advocate reinforcing the application of AI technology in the energy sector and environmental protection, developing AI technologies with distinctive characteristics specific to the energy sector, such as intelligent exploration and renewable energy integration.”

In the 5th paragraph of the Conclusion and policy implications section, we added “Companies in the energy sector should not only focus on the productivity impacts of AI technology but also prioritize its carbon reduction effects.”

 

 

Comment 6.

Generally speaking, carbon emission performance cannot be affected by only one-sided factors, but should be comprehensive. Therefore, the article may have overlooked some factors, such as the macroeconomic situation, international trade, and consumer behavior of the population, etc. The article mainly focuses on the impacts of AI technology and investment in the energy sector, and fails to adequately consider the combined effects of these other factors.

Response 6:

In addition to technical factors (AI technology), we consider other factors that may affect carbon performance and include them as control variables in the baseline model. Includes: economic affluence (gdp), population (pop), industrial structure (is), foreign direct investment (fdi), financial development (fd), government governance (gov), urbanization (urb), research & development investment (rd), and environmental regulation (er). Due to manuscript size constraints, we do not show the influence of other factors in the baseline regression. Table R1_2 shows empirical results including other influencing factors:

Table R1_2. Empirical results of baseline regression with other influencing factors.

Variables	lnCES		lnCEE
	(1)	(2)	(3)	(4)
lnAIT	0.0624*** (5.46)	0.0073** (2.01)	-0.0085** (-2.53)	-0.0090*** (-4.02)
gdp	0.0089 (0.28)	-0.0020 (-0.17)	0.1807*** (13.06)	0.0584** (2.04)
pop	0.1959*** (5.47)	0.0339 (0.98)	0.0301*** (5.19)	0.1228*** (3.38)
is	-1.1842*** (-9.29)	-0.0269 (-0.52)	-0.2730*** (-9.82)	0.1541** (2.18)
fdi	6.4576* (1.67)	1.0410 (1.21)	-1.3498* (-1.69)	-1.7082** (-2.02)
fd	-0.1819*** (-6.60)	0.0118** (2.25)	-0.0413*** (-7.77)	-0.0080 (-1.29)
gov	-0.0514 (-0.24)	0.0210 (0.45)	0.2176*** (6.10)	-0.1907** (-2.39)
urb	0.6453*** (4.91)	0.0209 (0.59)	-0.0910*** (-2.94)	-0.0888** (-2.51)
rd	-21.1788*** (-3.62)	-0.0541 (-0.09)	-0.2504 (-0.25)	-0.2455 (-0.45)
er	11.0321** (1.97)	0.1295 (0.09)	-12.5206*** (-8.50)	-3.4260*** (-3.03)
City fixed effect	╳	○	╳	○
Year fixed effect	╳	○	╳	○
Adj R2	0.270	0.977	0.298	0.783
Observations	3006	3006	3006	3006

The coefficients of variable lnAIT in Table R1_2 are consistent with those of variable lnAIT in Panel A in Table 2 of the manuscript. Considering that the topic of the manuscript is to explore the impact of artificial intelligence technology on carbon emission performance, other potential influencing factors are not explained.

 

 

Finally, we look forward to hearing from you regarding our submission. We would be glad to respond to any further questions and comments that you may have.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Artificial intelligence technology and carbon emission performance are very important research topics in the field of environmental sustainability.  The authors attempted to examine the relationship between artificial intelligence technology and carbon emission performance and included other influencing factors. But, there are some debatable issues with this research.

1. As the conclusion mentioned "AI technology not only increases the carbon emission scale, but also has an undesirable impact on carbon emission efficiency, which indicates that the use of AI technology currently is not necessarily improve carbon emission performance".  the authors' findings were negative conclusions and did not find any contribution of AI to carbon emission performance. In other words, the author's research conclusion proves that the conjecture is not meaningful. It is suggested that the authors must address the relationship between research logic and research findings.

2. It is suggested that the authors can supplement the importance of the research, especilly if  AI doesn't have positive effect on carbon emission efficiency.

3. It is recommended that the authors should add more discussions of the reasons for the conclusion of why AI can not contribute to carbon emission efficiency.

4. It is recommended that the authors add more comparative analysis with existing studies.

Author Response

Responses to the comments of Reviewer #2

 

First of all, we would like to express our sincere gratitude to the reviewers for their hard work and review. We have given highest priority to the valuable comments made by the reviewers and we will try best to revise each potential issue. Thank you again for your review of this paper. Below we respond to specific questions point by point:

 

Comment 1.

As the conclusion mentioned "AI technology not only increases the carbon emission scale, but also has an undesirable impact on carbon emission efficiency, which indicates that the use of AI technology currently is not necessarily improve carbon emission performance". the authors' findings were negative conclusions and did not find any contribution of AI to carbon emission performance. In other words, the author's research conclusion proves that the conjecture is not meaningful. It is suggested that the authors must address the relationship between research logic and research findings.

Response 1:

Thanks to the reviewer's comments, we sorted out the logic of the research hypothesis once again.

This paper holds that the impact of AI technology on carbon emission performance may be a double-edged sword. On the one hand, AI technology may improve production efficiency and thus reduce carbon emission; on the other hand, the application of AI technology itself will consume energy and thus aggravate carbon emission. First, we use econometric models to observe whether current AI technologies are beneficial or detrimental to carbon emission performance, which is one of the objectives of this paper. Second, we discussed the mechanism of AI technology affecting carbon emission performance from the perspective of energy transformation and industrial transformation, and found that the mechanism of energy transformation is established, and AI technology has the potential to optimize carbon emission performance through energy transformation, which is another goal of this paper. Third, we further examine whether the impact of AI technology on carbon emission performance is moderated by energy investment.

 

 

Comment 2.

It is suggested that the authors can supplement the importance of the research, especilly if AI doesn't have positive effect on carbon emission efficiency.

Response 2:

Thank you to the reviewer for your suggestions. In the introduction to the manuscript, we rearrange the logic of the introduction to highlight the research topic. Specifically, the first part of the introduction addresses the importance of reducing the scale of carbon emissions and improving their efficiency. The second part of the introduction addresses the potential and dilemma of technological innovation (especially AI technological innovation) in carbon emission control. The third part of the introduction describes the potential ways that AI technologies can influence the scale and efficiency of carbon emissions at the macro level (energy transition and industrial transformation).

Specific modification:

In the second paragraph of the introduction, we added “Deploying AI-enabled hardware, such as servers and associated equipment, in specific industries can also result in a substantial carbon footprint”.

In the second paragraph of the introduction, we added “Consequently, there remains considerable uncertainty regarding the direction of AI technology's impact on carbon emission performance”.

In the second paragraph of the introduction, we added “An increasing body of research is shifting its focus from carbon emissions alone to carbon efficiency, as reducing emissions should not come at the expense of output”.

 

 

Comment 3.

It is recommended that the authors should add more discussions of the reasons for the conclusion of why AI can not contribute to carbon emission efficiency.

In response to the reviewers' comments, we have re-evaluated the potential reasons why AI technology may not be conducive to carbon emission efficiency in the benchmark regression analysis and have incorporated these insights into the manuscript. The main findings can be summarized as follows:

(1) Technology implementation costs: Although AI can offer long-term energy-saving benefits, the initial investment costs may be substantial, encompassing software, hardware, and human resources. This high cost may render it unaffordable for some small and medium-sized enterprises (SMEs), thereby limiting the widespread adoption of AI technology.

(2) The carbon footprint of AI technology: The training process of certain AI models may require substantial computational resources, leading to significant carbon emissions. This could potentially offset the positive impact of these technologies on emission reduction.

(3) Lack of supportive policies and regulations: Policy frameworks in many countries and regions are not yet sufficiently developed to adequately support the use of AI in reducing emissions.

 

 

Comment 4.

It is recommended that the authors add more comparative analysis with existing studies.

In response to the reviewer's comments, we have incorporated additional literature pertinent to the topic of this paper and made the following revisions:

In the first paragraph of the introduction, we add “In response to the challenges posed by climate change risks, optimizing carbon emission performance has emerged as a global consensus. At present, the way to optimize carbon emission performance is mainly to reduce carbon emission scale (CES) or improve carbon emission efficiency (CEE) (Yu and Zhang, 2021; Zhang et al., 2022b)”

• Zhang, W., Liu, X., Wang, D., Zhou, J., 2022b. Digital economy and carbon emission performance: Evidence at China’s city level. Energy Policy 165, 112927.
• Yu, Y., Zhang, N., 2021. Low-carbon city pilot and carbon emission efficiency: Quasi-experimental evidence from China. Energy Econ. 96, 105125.

In the second paragraph of the introduction, we add “Intuitively, one might assume that AI technologies generally create a positive impression, leading to the subconscious belief that they are already contributing positively to optimizing carbon performance.” and “Deploying AI-enabled hardware, such as servers and associated equipment, in specific industries can also result in a substantial carbon footprint. Consequently, there remains considerable uncertainty regarding the direction of AI technology's impact on carbon emission performance” and “An increasing body of research is shifting its focus from carbon emissions alone to carbon efficiency, as reducing emissions should not come at the expense of output (Liu et al., 2024).”

• Liu, Z., Zhong, J., Liu, Y., Liang, Y., Li, Z., 2024. Dynamic simulation of street-level carbon emissions in megacities: a case study of wuhan city, China (2015–2030). Sustainable Cities and Society, 115.

 

 

Finally, we look forward to hearing from you regarding our submission. We would be glad to respond to any further questions and comments that you may have.

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The paper addresses an important topic on the use of AI in the field of carbon emissions. The paper needs to be shortened, and the findings presented in a more concise manner. The references need to include recent publications in the field which can be accessed by doing a google search on the title of this paper. The economic ramifications to industry should be addressed as a part of the overall research findings. 

Author Response

Responses to the comments of Reviewer #3

First of all, we would like to express our sincere gratitude to the reviewers for their hard work and review. We have given highest priority to the valuable comments made by the reviewers and we will try best to revise each potential issue. Thank you again for your review of this paper. Below we respond to specific questions point by point:

 

Comment 1. The paper addresses an important topic on the use of AI in the field of carbon emissions. The paper needs to be shortened, and the findings presented in a more concise manner. The references need to include recent publications in the field which can be accessed by doing a google search on the title of this paper. The economic ramifications to industry should be addressed as a part of the overall research findings. 

 

Response 1:

Thank the reviewers for their suggestions.

According to the structure of the article proposed by the reviewers and the problems of recent literature citations, we made some adjustments. In the introduction to the manuscript, we have added important documents related to the content (Dynamic simulation of street-level carbon emissions in megacities: A case study of Wuhan City, China (2015–2030); How Do Varying Socio-Economic Driving Forces Affect China’s Carbon Emissions? New Evidence from a Multiscale Geographically Weighted Regression Model).

In addition, we rearrange the logic of the introduction to highlight the research topic. Specifically, the first part of the introduction addresses the importance of reducing the scale of carbon emissions and improving their efficiency. The second part of the introduction addresses the potential and dilemma of technological innovation (especially AI technological innovation) in carbon emission control. The third part of the introduction describes the potential ways that AI technologies can influence the scale and efficiency of carbon emissions at the macro level (energy transition and industrial transformation).

Specific modification:

In the second paragraph of the introduction, we added “Deploying AI-enabled hardware, such as servers and associated equipment, in specific industries can also result in a substantial carbon footprint”.

In the second paragraph of the introduction, we added “Consequently, there remains considerable uncertainty regarding the direction of AI technology's impact on carbon emission performance”.

In the second paragraph of the introduction, we added “An increasing body of research is shifting its focus from carbon emissions alone to carbon efficiency, as reducing emissions should not come at the expense of output”.

Next, for the economic ramifications to industry, we read the recent relevant literature, and in the literature review section, we added the impact of AI technology on other aspects of the economy, with the specific modifications as follows：As an important power for a new wave of enterprise development and the evolution of industry, AI technology is widely employed in energy, medical care, transportation, finance, and other sectors. On the one hand, AI technology plays a huge role in stimulating green innovation, developing circular economy and promoting total factor productivity of firms; On the other hand, AI technology causes potential carbon emission.

- Can artificial intelligence technology improve companies' capacity for green innovation? Evidence from listed companies in China

- How Artificial Intelligence Applications Affect the Total Factor Productivity of the Service Industry: Firm-level Evidence from China

- Unveiling the effects of artificial intelligence and green technology convergence on carbon emissions: An explainable machine learning-based approach

 

Finally, we look forward to hearing from you regarding our submission. We would be glad to respond to any further questions and comments that you may have.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

The paper is interesting and can provide a very useful contribution through its main finding: that AI technologies do not currently contribute to improving sustainability performance (e.g. caron emissions). There is room for improvement in this paper. The suggestions below aim to enhance its readability and scholarly impact.

Regarding the structure, the paper is well-organized and clear up to Section 4; from Section 5 onward, it becomes overly detailed and, unfortunately, harder to follow. To address this, it is suggested to move more analyses and intermediate considerations to the Appendix (as already done with some elements), focusing the attention on fewer (but most critical) elements leading to your main findings into the main text. I would also recommend slightly rephrasing the abstract to emphasize – as indeed it does – that policy recommendations will be proposed. Always in the abstract, it is further suggested to replace "The debate is aided by this paper’s" with more direct formulations, e.g., "This paper contributes...".

From a content perspective, the statement “the demand for energy in economic growth is rigid” (p. 7) is unclear: how, during growth phases, is energy demand rigid? What does “rigid” mean in this context?

Another point of doubt arises from this statement: “However, empirical results show that AI technology not only fails to reduce the carbon emission scale, but also inhibits carbon emission efficiency. This indicates that the negative impact of AI technology on carbon emission performance is greater than its positive impact” (p. 10). Because we are talking about AI systems, how are the dynamic features offered by the type of technology considered here? Is it true that as the capacity to compute information (machine learning) increases, energy efficiency still does not improve? It can be interesting to consider/challenge this aspect (even very briefly).

I find Section 6 particularly interesting and, therefore, should be given more space/attention within the text (once some analyses are moved to the appendix). This sentence caught my attention: “voluntary environmental policies should be utilized to stimulate innovation in the energy sector by firms with advantages in AI technology” (p. 20). The term “voluntary” could help link the paper’s findings to other future directions in the debate that has recently gained momentum. For instance, the creation of energy communities based entirely on voluntary agreements between private citizens, or in more hybrid forms, e.g., with industry or public administrations. It is largely assumed that AI can improve energy performance at the community level, particularly in optimizing demand management (e.g., predicting and learning from consumption habits). As for supply, the relationship is, of course, non-linear, but it is also assumed here that AI can suggest better/more efficient resource allocation. The generalization of such improvements depends also on factors such as scalability (of information, algorithms, impacts) and adaptability to local specificities. There are many operational uncertainties that this paper obviously cannot resolve but could at least mention. In this regard, I suggest reviewing these recent papers, which I believe are worth considering in your discussion:

https://doi.org/10.1016/j.erss.2024.103828; https://doi.org/10.3390/en17081848

Minor comments:

Check the formatting of citations and confirm whether footnotes are allowed (from my past experience with other MDPIs, it doesn’t seem to be permitted).

Regarding acronyms, please use capital letters consistently (e.g., Energy Structure Cleanness: ESC; Energy Intensity: EI). Check also the use of colours other than black and bold text within the manuscript.

 

Finally, please keep in mind to provide a bit more context for international readers: what is meant by prefecture-level cities? If possible, include some information about the scale, e.g., the average population size of these contexts.

Comments on the Quality of English Language

The English seem correct and fluent; if any sections were moved/modified, some further check may be necessary to ensure the internal coherence of the text.

Author Response

Responses to the comments of Reviewer #4

First of all, we would like to express our sincere gratitude to the reviewers for their hard work and review. We have given highest priority to the valuable comments made by the reviewers and we will try best to revise each potential issue. Thank you again for your review of this paper. Below we respond to specific questions point by point:

 

Comment 1

Regarding the structure, the paper is well-organized and clear up to Section 4; from Section 5 onward, it becomes overly detailed and, unfortunately, harder to follow. To address this, it is suggested to move more analyses and intermediate considerations to the Appendix (as already done with some elements), focusing the attention on fewer (but most critical) elements leading to your main findings into the main text. I would also recommend slightly rephrasing the abstract to emphasize – as indeed it does – that policy recommendations will be proposed. Always in the abstract, it is further suggested to replace "The debate is aided by this paper’s" with more direct formulations, e.g., "This paper contributes...".

Response 1:

Thank the reviewers for their suggestions.

In the Section 5, the baseline regression, endogeneity test, robustness test, mechanism analysis, expansion analysis and heterogeneity analysis are carried out. Based on your comments, we have moved some of the robustness tests to the Appendix and added more analysis in the benchmark regression, mechanism test, and extended analysis sections.

Specific changes are as follows:

1. Nonlinearity is considered in the baseline regression section; we construct a quadratic term for AI techniques (lnAIT_2) to investigate whether such nonlinear effects exist.

Table R1_1. Empirical results of nonlinear relationship.

Variables	lnCES		lnCEE
	(1)	(2)	(3)	(4)
lnAIT	0.3566*** (3.17)	0.0157 (0.60)	0.0660** (2.43)	0.0273 (1.10)
lnAIT_2	-0.1424*** (-2.63)	-0.0041 (-0.33)	-0.0360*** (-2.71)	-0.0177** (-2.29)
Control variables	○	○	○	○
City fixed effect	╳	○	╳	○
Year fixed effect	╳	○	╳	○
Adj R2	0.271	0.977	0.299	0.783
Observations	3006	3006	3006	3006

The results in Table R1_1 show that the nonlinear relationship between AI technology and carbon emission performance appears to be unstable after taking fixed effects into account. Therefore, we still believe that AI technology has not played a role in reducing carbon emissions at this stage.

 

2. In mechanism analysis, we have revised the mechanism analysis section to deepen our exploration of various research contexts. Specifically, we divide the sample in two ways: first, by treating the Yangtze River Economic Belt as a special economic zone, and second, by using the Heihe-Tengchong line (Southeast) as a geographical indicator of economic intensity. This approach allows us to explore both the intensity and direction of the mechanism more comprehensively.

The table added in the mechanism analysis section is as follows:

Table 11b. Mechanism analysis of energy transition in the Yangtze River Economic Belt.

Variables	esc	lnCES	lnCEE	ei	lnCES	lnCEE
	(1)	(2)	(3)	(4)	(5)	(6)
lnAIT	-0.0042 (-1.15)	0.0091 (1.47)	-0.0080** (-2.20)	-0.0541*** (-2.77)	0.0089 (1.44)	-0.0052 (-1.49)
esc		0.0118 (0.33)	0.0457 (1.30)
ei					-0.0030 (-0.29)	0.0547*** (7.96)
Control variables	○	○	○	○	○	○
City fixed effect	○	○	○	○	○	○
Year fixed effect	○	○	○	○	○	○
Adj R2	0.805	0.979	0.799	0.753	0.979	0.814
Observations	1185	1185	1185	1185	1185	1185

Notes: *, **, *** indicate significance level at 10%,5% an 1%, respectively. The values in parentheses are t-statistic using the clustering robust standard error.

Table 11c. Mechanism analysis of energy transition in the Heihe-Tengchong line (Southeast).

Variables	esc	lnCES	lnCEE	ei	lnCES	lnCEE
	(1)	(2)	(3)	(4)	(5)	(6)
lnAIT	-0.0061** (-2.12)	0.0078** (2.32)	-0.0087*** (-4.12)	-0.0273* (-1.94)	0.0078** (2.34)	-0.0081*** (-3.85)
esc		-0.0325 (-1.55)	0.0570*** (2.74)
ei					-0.0065 (-1.31)	0.0370*** (8.81)
Control variables	○	○	○	○	○	○
City fixed effect	○	○	○	○	○	○
Year fixed effect	○	○	○	○	○	○
Adj R2	0.765	0.979	0.781	0.757	0.979	0.788
Observations	2787	2787	2787	2787	2787	2787

Notes: *, **, *** indicate significance level at 10%,5% an 1%, respectively. The values in parentheses are t-statistic using the clustering robust standard error.

Table 12b. Mechanism analysis of industrial transformation in the Yangtze River Economic Belt.

Variables	ind_up	lnCES	lnCEE	ind_agg	lnCES	lnCEE
	(1)	(2)	(3)	(4)	(5)	(6)
lnAIT	-0.0102** (-2.34)	0.0077 (1.21)	-0.0099*** (-2.81)	0.0013 (0.19)	0.0079 (1.25)	-0.0106*** (-3.00)
ind_up		-0.0350 (-0.64)	0.0780 (1.36)
ind_agg					0.0391 (1.20)	0.0002 (0.01)
Control variables	○	○	○	○	○	○
City fixed effect	○	○	○	○	○	○
Year fixed effect	○	○	○	○	○	○
Adj R2	0.914	0.979	0.797	0.455	0.979	0.795
Observations	1148	1148	1148	1149	1149	1149

Notes: *, **, *** indicate significance level at 10%,5% an 1%, respectively. The values in parentheses are t-statistic using the clustering robust standard error.

Table 12c. Mechanism analysis of industrial transformation in the Heihe-Tengchong line (Southeast).

Variables	ind_up	lnCES	lnCEE	ind_agg	lnCES	lnCEE
	(1)	(2)	(3)	(4)	(5)	(6)
lnAIT	-0.0006 (-0.20)	0.0080** (2.38)	-0.0095*** (-4.51)	0.0445 (1.16)	0.0081** (2.38)	-0.0092*** (-4.42)
ind_up		-0.0076 (-0.31)	0.0317 (1.15)
ind_agg					-0.0021 (-1.45)	-0.0063*** (-5.98)
Control variables	○	○	○	○	○	○
City fixed effect	○	○	○	○	○	○
Year fixed effect	○	○	○	○	○	○
Adj R2	0.926	0.979	0.780	0.784	0.979	0.782
Observations	2724	2724	2724	2725	2725	2725

Notes: *, **, *** indicate significance level at 10%,5% an 1%, respectively. The values in parentheses are t-statistic using the clustering robust standard error.

In addition, we have further explained the new table:

In the second paragraph of the mechanism analysis section, we added “Table 11b and Table 11c discuss the mechanism of energy transition in the context of typical geographical locations (Yangtze River Economic Belt, Heihe-Tengchong line). In terms of the Yangtze River Economic Belt, the mechanism of energy transition is not established, which mean that the Yangtze River Economic Belt is not the most widely applied AI technology in China. However, the results for the Heihe-Tengchong line are largely consistent with those presented in Table 11a. It is evident that the southeast side of the Heihe-Tengchong line encompasses over 90% of the observed prefecture-level cities, which are clearly the most significant sources of carbon emissions in China.”

In the third paragraph of the mechanism analysis section, we added “Table 12b and Table 12c show similar results. In other words, even when accounting for certain special industries and economically developed regions, AI technology remains unable to influence carbon emissions through industrial transformation. From a macro perspective, the penetration of artificial intelligence technology in the process of industrial transformation remains insufficient. Of course, AI technology is already widely employed in specific industries (such as information transmission, software, and information technology services). The impact of AI technologies on the carbon emission performance of specific industries may be observed with micro-level firm data.”

 

3、In expansion analysis, we have also refined the analysis of the moderating effect and revised the policy recommendations accordingly. The new tables and analyses are as follows:

Table 13b. Expansion analysis of energy industry investment in the Yangtze River Economic Belt.

Variables	lnCES	lnCEE	lnCES	lnCEE
	(1)	(2)	(3)	(4)
lnAIT	0.0082 (1.34)	-0.0098*** (-2.77)	0.0051 (0.81)	-0.0110*** (-2.86)
lnAIT×eii	0.0041** (2.00)	0.0077*** (4.26)
lnAIT×seii			0.0048** (2.46)	0.0034** (2.02)
Control variables	○	○	○	○
City fixed effect	○	○	○	○
Year fixed effect	○	○	○	○
Adj R2	0.979	0.803	0.979	0.799
Observations	1185	1185	1185	1185

Notes: *, **, *** indicate significance level at 10%,5% an 1%, respectively. The values in parentheses are t-statistic using the clustering robust standard error.

Table 13c. Expansion analysis of energy industry investment in the Heihe-Tengchong line (Southeast).

Variables	lnCES	lnCEE	lnCES	lnCEE
	(1)	(2)	(3)	(4)
lnAIT	0.0074** (2.22)	-0.0109*** (-5.15)	0.0062* (1.79)	-0.0101*** (-4.32)
lnAIT×eii	0.0013 (1.34)	0.0043*** (4.19)
lnAIT×seii			0.0020* (1.89)	0.0012 (1.01)
Control variables	○	○	○	○
City fixed effect	○	○	○	○
Year fixed effect	○	○	○	○
Adj R2	0.978	0.782	0.979	0.780
Observations	2809	2809	2809	2809

Notes: *, **, *** indicate significance level at 10%,5% an 1%, respectively. The values in parentheses are t-statistic using the clustering robust standard error.

In the second paragraph of the Expansion analysis section, we added “The results in Table 13c are consistent with those in Table 13a, indicating that AI technology plays a more significant role in enhancing carbon emission efficiency. However, the results presented in Table 13b indicate that for the Yangtze River Economic Belt, a specific economic zone with significant environmental relevance, the moderating effect of energy industry investment is more pronounced (0.0041 > 0.0020, 0.0077 > 0.0042). Strengthening the application of AI technology in the energy sector within this region can facilitate the realization of its emission reduction potential.”

In the 4th paragraph of the Conclusion and policy implications section, we added “We advocate reinforcing the application of AI technology in the energy sector and environmental protection, developing AI technologies with distinctive characteristics specific to the energy sector, such as intelligent exploration and renewable energy integration.”

In the 5th paragraph of the Conclusion and policy implications section, we added “Companies in the energy sector should not only focus on the productivity impacts of AI technology but also prioritize its carbon reduction effects.”

Next, for the abstract, according to the suggestion of the reviewer, we have revised the original text into: This paper contributes to investigate this issue by the use of data from 278 Chinese cities from 2009 to 2019 based on the two-way fixed effects, instrumental variables (IV), spatial Durbin (SDM), mediation effect, and moderating effect model.

 

 

Comment 2

From a content perspective, the statement “the demand for energy in economic growth is rigid” (p. 7) is unclear: how, during growth phases, is energy demand rigid? What does “rigid” mean in this context?

Response 2:

Thank the reviewers for their suggestions.

The wrong use of words was our mistake and we apologize. In the process of economic growth, energy demand is rigid. The term “rigid” here means that the demand for energy is stable and the increase in the demand for energy is irreversible. Here, the simple use of the term rigid may be misleading, and we have amended it as suggested. The modification results are as follows: However, the demand for energy in economic growth is stable, and the growth in energy demand is irreversible, which reflects a characteristic of rigidity.

 

 

Comment 3

Another point of doubt arises from this statement: “However, empirical results show that AI technology not only fails to reduce the carbon emission scale, but also inhibits carbon emission efficiency. This indicates that the negative impact of AI technology on carbon emission performance is greater than its positive impact” (p. 10). Because we are talking about AI systems, how are the dynamic features offered by the type of technology considered here? Is it true that as the capacity to compute information (machine learning) increases, energy efficiency still does not improve? It can be interesting to consider/challenge this aspect (even very briefly).

Response 3:

Thank the reviewers for their suggestions.

However, empirical results show that AI technology not only fails to reduce the carbon emission scale, but also inhibits carbon emission efficiency. This indicates that the negative impact of AI technology on carbon emission performance is greater than its positive impact.” This conclusion is based on the sample data of this paper, which has certain limitations, that is, this paper does not take into account the dynamic features (nonlinear features) of AI technology.

Nonlinearity is considered in the baseline regression section; we construct a quadratic term for AI techniques (lnAIT_2) to investigate whether such nonlinear effects exist.

Table R1_1. Empirical results of nonlinear relationship.

Variables	lnCES		lnCEE
	(1)	(2)	(3)	(4)
lnAIT	0.3566*** (3.17)	0.0157 (0.60)	0.0660** (2.43)	0.0273 (1.10)
lnAIT_2	-0.1424*** (-2.63)	-0.0041 (-0.33)	-0.0360*** (-2.71)	-0.0177** (-2.29)
Control variables	○	○	○	○
City fixed effect	╳	○	╳	○
Year fixed effect	╳	○	╳	○
Adj R2	0.271	0.977	0.299	0.783
Observations	3006	3006	3006	3006

The results in Table R1_1 show that the nonlinear relationship between AI technology and carbon emission performance appears to be unstable after taking fixed effects into account. Therefore, we still believe that AI technology has not played a role in reducing carbon emissions at this stage.

Based on this, we read the relevant literature and affirm the positive role of AI technology in improving energy efficiency and carbon emission performance in the future. Therefore, we add the following statement in the original text: However, the potential of AI technology to increase carbon emission performance is undoubted due to its rapidly evolving nature. It can be predicted that AI-related technologies such as machine learning and algorithm improvement will become a powerful weapon to improve carbon emission performance.

 

 

Comment 4

I find Section 6 particularly interesting and, therefore, should be given more space/attention within the text (once some analyses are moved to the appendix). This sentence caught my attention: “voluntary environmental policies should be utilized to stimulate innovation in the energy sector by firms with advantages in AI technology” (p. 20). The term “voluntary” could help link the paper’s findings to other future directions in the debate that has recently gained momentum. For instance, the creation of energy communities based entirely on voluntary agreements between private citizens, or in more hybrid forms, e.g., with industry or public administrations. It is largely assumed that AI can improve energy performance at the community level, particularly in optimizing demand management (e.g., predicting and learning from consumption habits). As for supply, the relationship is, of course, non-linear, but it is also assumed here that AI can suggest better/more efficient resource allocation. The generalization of such improvements depends also on factors such as scalability (of information, algorithms, impacts) and adaptability to local specificities. There are many operational uncertainties that this paper obviously cannot resolve but could at least mention. In this regard, I suggest reviewing these recent papers, which I believe are worth considering in your discussion: https://doi.org/10.1016/j.erss.2024.103828; https://doi.org/10.3390/en17081848.

Response 4:

Thank the reviewers for their suggestions.

As for the word “voluntary”, we only consider governments and firms, ignoring the individual and community levels. After reading the recommended literature, we found that distributed energy communities, are an important way to improve energy efficiency and carbon emission performance. Therefore, we believe that the combination of distributed energy community and AI technology will play a greater role. And added the following statement: Encourage qualified regions that excel in AI technology and application to establish voluntary energy communities related to renewable energy, which are characterized by distributed energy resources and prosumer involvement, thereby achieving better energy supply and demand management and enhancing energy performance at the community level with help from AI technology.

In addition, we looked up the paper titled "Energy communities, distributed generation, renewable sources: Close relatives or potential friends?". For many special regions, “energy communities” is indeed the inevitable choice or the only choice. It seems that the “Jevons paradox” has hinted at the potential threat of technology-induced energy rebound and increased pollution. However, it seems that technological emission reduction is still the most efficient option at the moment, because it is not worth sacrificing economic output to reduce emissions.

 

 

Comment 5

Check the formatting of citations and confirm whether footnotes are allowed (from my past experience with other MDPIs, it doesn’t seem to be permitted).

Regarding acronyms, please use capital letters consistently (e.g., Energy Structure Cleanness: ESC; Energy Intensity: EI). Check also the use of colours other than black and bold text within the manuscript.

Response 5:

Thank the reviewers for their suggestions.

We have reloaded the template as required, so the footnote issue should be resolved. In addition, we modified the lowercase variable to uppercase variable. Thanks again.

It should be noted that the modification of variable names involves a lot, and the specific content can be observed in the new version of the manuscript.

 

 

Comment 6

Finally, please keep in mind to provide a bit more context for international readers: what is meant by prefecture-level cities? If possible, include some information about the scale, e.g., the average population size of these contexts.

Response 6:

Thank the reviewers for their suggestions.

To further reduce the dyslexia caused by unnecessary proper nouns, we have removed some non-essential words (e.g. prefecture-level).

As the second level of administrative planning in China, prefecture-level cities are similar to second-level counties in the United States. Generally, although areas with a larger population and larger economic scale are more likely to be set up as prefecture-level cities, the establishment of prefecture-level cities in China is more for the convenience of political management, so there is no fixed standard for these cities with great differences.

 

Finally, we look forward to hearing from you regarding our submission. We would be glad to respond to any further questions and comments that you may have.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have responded the review comments, and maken some revisions.

However, for the first comment, I think the author's response is inadequate. The research finding is not well compared with other existing studies.

It is suggested that the conclusion of the manuscript should have more discussion and comparisons. It would be better to have more theory to support it.

Author Response

Responses to the comments of Reviewer #2

First of all, we would like to express our sincere gratitude to the reviewers for their hard work and review. We have given highest priority to the valuable comments made by the reviewers and we will try best to revise each potential issue. Thank you again for your review of this paper. Below we respond to specific questions point by point:

 

Comment 1. The authors have responded the review comments, and maken some revisions.

However, for the first comment, I think the author's response is inadequate. The research finding is not well compared with other existing studies.

It is suggested that the conclusion of the manuscript should have more discussion and comparisons. It would be better to have more theory to support it. 

 

Response 1:

Thank the reviewers for their suggestions.

We have incorporated a new comparative analysis with existing studies in the introduction section and summarized the key themes and primary conclusions of relevant studies in tabular form. The details are as follows:

We collated recent studies on technological innovation and carbon emissions, as shown in Table 1. t is evident that research on technological innovation and carbon emissions has evolved in three key directions. First, it has expanded from green technology to encompass digital and AI technologies. Second, it now incorporates the spatial spillover effects of carbon emissions. Third, ongoing efforts are dedicated to exploring the mechanisms through which technological innovation influences carbon emissions.

Table 1. Recent studies on technological innovation and carbon emissions.

No.	Source	Research topic	Main points
(1)	(2)	(3)	(4)
1	Xia et al., (2024)	Green technology innovation, regional carbon emissions	Green technology innovation indirectly reduces regional carbon emissions by promoting energy efficiency.
2	Chen and Jin (2023)	Artificial intelligence, carbon emissions	The application of enterprise AI technology has a positive impact on carbon reduction.
3	Hou et al., (2024)	Digital-intelligence, carbon emissions	The lower level of green technology innovation will increase the carbon emission effect of digital intelligence to some extent.
4	Su et al., (2023)	CO2 emissions, technology innovations	Technological innovation has a direct and indirect impact on the increase of carbon dioxide emissions in China.
5	Liu et al., (2025)	Carbon emissions, digital technology innovation, green technology innovation	Green technology innovation has a significant positive impact on carbon emissions, while digital innovation technology has an unstable negative impact on carbon emissions.
6	Chen et al., (2022)	Digital technology, digital innovation, carbon emissions	There is a significant inverse U-shaped nonlinear relationship between digital innovation level and industrial carbon emissions.
7	Pu et al., (2024)	Digital technological innovation, carbon intensity, carbon reduction	Digital technology innovation can significantly reduce carbon intensity
8	Jiang et al., (2024)	Digital finance, low carbon development, production technology innovation	The development of digital finance can reduce carbon intensity
9	Du et al., (2022)	green technology innovation, carbon emission performance	Green technology innovation significantly improves carbon emission performance
10	Dong et al., (2024)	Green technology, innovation, carbon emission efficiency	Green technology innovation has significantly improved the carbon emission efficiency of firms

 

Finally, we look forward to hearing from you regarding our submission. We would be glad to respond to any further questions and comments that you may have.

Author Response File: Author Response.docx