Does Smart City Construction Decrease Urban Carbon Emission Intensity? Evidence from a Difference-in-Difference Estimation in China
Round 1
Reviewer 1 Report
Abstract, what is the point of originality? Practical, academic and policy implications?
Abstract, it states “some feasible policy suggestions for low-carbon urban development were
proposed.” Please state what may be the most important suggestion here instead. Is an incorrect word?
Abstract is the first impression of the whole paper. Yet, there are a lot of errors / grammatical problems in this section which needs copy edit.
Line 17, influencing mechanism
Line 30, delete In this background
Lines 30—36, missing citations.
Lines 42-44, polish the sentences as. A few questions arise; can’t traditional urban development mode eliminate the climatic curse; can promoting urban technological innovation and development by reforms in urban governance mode create conditions…can should not follow by a verb with ing and there should be a subject before that.
For u-shaped study related to carbon study, please also refer to Achieving Carbon Neutrality – The Role of Heterogeneous Environmental Regulations on Urban Green Innovation (for page 3 of 17 in the document)
Lines 110-111, please add citation for Smart city construction, which has been implemented by the Government of China in 2012
Line 117, incorrect word “marginal”
Line 156, can is not needed
Line 157 can affect should be repaced by reduces?..
Line 177, Policy refers to the pilot policy of a smart. Is this a dummy variable or?
Table 1, please add the source of the data, what is variable policy? Why there is a mean for policy variables?
4.3.1. Placebo Test paragraph of description should be put before the figure.
Lines 338-340, would pilot smart cities affect the model results?
5.2, how does the result compare with existing literatures? Like the paper we mentioned above?
Conclusion, there are many types of infrastructures related to smart cities, would that affect the conclusion?
Tables 4 and 5, why only fixed effects are studied but not random effects?
Figures 1 and 2, missing citation, why the figures 2 only show 2015, 2019, 2011,2007, how about the other years?
Polish English
Author Response
Point 1: Abstract, what is the point of originality? Practical, academic and policy implications?Abstract, it states “some feasible policy suggestions for low-carbon urban development were proposed.” Please state what may be the most important suggestion here instead. Is an incorrect word?Abstract is the first impression of the whole paper. Yet, there are a lot of errors / grammatical problems in this section which needs copy edit.
Line 17, influencing mechanism
Response 1: According to the comments of the reviewer, this paper has made major modifications to the abstract, and the modified abstract is as follows:
Abstract: Climatic changes and environmental pollution caused by traditional urban development models have increased due to accelerated urbanisation and industrialisation. As a new model of urban development, smart city construction relies on digital technology reform to achieve intelligent urban governance, which is crucial to reducing carbon emission intensity and achieving regional green development. This paper constructs a multi-period DID model based on panel data from 283 cities from 2007 to 2019 to explore the impact of smart city construction on urban carbon emission intensity. This study found that smart city construction decreased urban carbon emis-sion intensity significantly. According to heterogeneity analysis, the integration of smart city developments could decrease carbon emission intensity in northern China’s cities and re-source-based cities significantly, but had an insignificant influence on carbon emission intensity in southern China’s cities and non-resource-based cities. Based on an analysis of the influencing mechanisms, smart city construction can decrease urban carbon emission intensity by stimulating green innovation vitality, upgrading industrial structures, and decreasing energy consumption. These research conclusions can provide directions for urban transformation and low-carbon de-velopment, as well as a case study and experience for countries that have not yet established smart city construction.
Point 2: Line 30, delete In this background
Response 2: Thank you very much for pointing out the syntax error in this article. "In this background" has been deleted. MDPI has been entrusted to edit the full text.
Point 3: Lines 30—36, missing citations.
Response 3: According to the opinions of expert, the following citations are added.
King, L.C.; van den Bergh, J. Potential carbon leakage under the Paris Agreement. Clim. Change 2021, 165, doi:10.1007/s10584-021-03082-4.
He, R.; Luo, L.; Shamsuddin, A.; Tang, Q.L. Corporate carbon accounting: a literature review of carbon accounting research from the Kyoto Protocol to the Paris Agreement. Accounting and Finance 2022, 62, 261-298, doi:10.1111/acfi.12789.
Point 4: Lines 42-44, polish the sentences as. A few questions arise; can’t traditional urban development mode eliminate the climatic curse; can promoting urban technological innovation and development by reforms in urban governance mode create conditions…can should not follow by a verb with ing and there should be a subject before that.
Response 4: Thank you very much for pointing out the syntax errors in this article. This article has made adjustments to this part. This part is revised as follows: “Is the traditional urban development model unable to eliminate the climate effects?”
Point 5: For u-shaped study related to carbon study, please also refer to Achieving Carbon Neutrality – The Role of Heterogeneous Environmental Regulations on Urban Green Innovation (for page 3 of 17 in the document)
Response 5: Thanks for the literature provided by the reviewer. Achieving Carbon Neutrality – The Role of Heterogeneous Environmental Regulations on Urban Green Innovation is a representative article on the study of u-shaped study related to carbon study, which has greatly helped the revision of this article. On the basis of this article, we reorganized the literature review, and the specific modifications are as follows:
Carbon emission intensity refers to the CO2 emission volume per unit of gross domestic product (GDP). It reflects the mutual relationship between pollutants and economic growth and is an important index for evaluating sustainable economic de-velopment [6]. The existing literature on the influencing factors of carbon emission in-tensity is relatively extensive, listing as factors environmental regulation [7,8], fiscal and taxation system [9,10], urbanisation [11,12], FDI [13,14], international trade [15,16], etc. The impact of urbanisation on carbon emissions is significant. However, the existing research on the relationship between the promotion or inhibition of urbanisation and carbon emissions is still inconclusive. On the one hand, urbanisation can promote in-dustrialisation and increase the energy consumption demand, leading to extensive carbon emissions [17,18]; furthermore, with the increase in population density, urban management faces greater pressure [19]. On the other hand, with the development of urbanisation, the scale effect of public goods and the accumulation effect of human capital can improve the efficiency of energy use and create conditions for technological advances, thereby reducing the intensity of carbon emissions [20,21].
Some scholars have ignored the differences in urbanisation brought about by dif-ferent urban development models and the key role of new urban development models in reducing carbon emission intensity [22,23]. The traditional urban development model blindly pursues the development speed of urbanisation, ignoring the development quality. The high investment and high energy consumption development model sig-nificantly increased environmental pollution, especially that in the form of carbon emissions [24]. Is the traditional urban development model unable to eliminate the climate effects? In the era of intelligence and digitalisation, the implementation of a new urban governance model could be the key for countries around the world to fulfil their environmental protection responsibilities and establish a new competitive advantage [25,26]. With the emerging disadvantages of the traditional urban development model, an increasing number of countries have begun to explore new models. Scholars have also begun to study the economic and social effects of these new urban development models, such as smart cities, confirming the positive impact of smart cities on envi-ronmental pollution control [27], enterprise technology innovation [28], energy effi-ciency improvement [29], and advancement of various industrial structures [30]. However, few scholars have investigated the impact of smart-city-based urban devel-opment models on urban carbon emission intensity.
Point 6: Lines 110-111, please add citation for Smart city construction, which has been implemented by the Government of China in 2012
Response 6: According to the comments of reviewer, the following citation has been added to this article for “Smart city construction, which has been implemented by the Government of China in 2012.”
Wang, J.X.; Deng, K. Impact and mechanism analysis of smart city policy on urban innovation: Evidence from China. Economic Analysis and Policy 2022, 73, 574-587, doi:10.1016/j.eap.2021.12.006.
Point 7: Line 117, incorrect word “marginal”
Line 156, can is not needed
Line 157 can affect should be repaced by reduces?..
Response 7: Thank you very much for the comments of the reviewers. The above syntax errors have been corrected and MDPI has been entrusted to edit the full text.
Point 8: Line 177, Policy refers to the pilot policy of a smart. Is this a dummy variable or?
Response 8: Policy is a dummy variable. The definition of policy in this article is not clear enough and has been modified to “Policy, a dummy variable, refers to the pilot policy of a smart city and is the intersection between the policy dummy variable (Treat) and time dummy variable (Post).”
Point 9: Table 1, please add the source of the data, what is variable policy? Why there is a mean for policy variables?
Response 9: Table 1 is the descriptive statistics of the main variables in this paper, and the source of the data have been described in 3.3. The approval data for the smart city were collected from the official website of the Ministry of Housing and Urban-Rural Development of the People’s Republic of China (MOHURD) and organised manually. Light data were obtained from CEADs database of NASA. Other city-level data were obtained from the China City Statistical Yearbook for the period of 2008–2020. Policy is a dummy variable, the pilot policy of a smart city and is the intersection between the policy dummy variable (Treat) and time dummy variable (Post).
Point 10: Placebo Test paragraph of description should be put before the figure.
Response 10: According to the comments of reviewer, we have put Placebo Test paragraph of description before the figure.
Point 11: Lines 338-340, would pilot smart cities affect the model results?
Response 11: In smart city construction, some cities may be included in the pilot regions for a county or a district (Pudong New District of Shanghai, Shangcheng District of Hang-zhou, and Boye County of Baoding City were included as the pilot smart cities). These samples may affect the benchmark regression results. These samples are excluded from the robustness test. The results showed that the coefficient of the core explanatory variable was still negative significant at the level of 5%, and the value of the coefficient did not change significantly. Therefore, these samples will not have a significant impact on the benchmark regression results of this paper. In other words, the benchmark regression conclusion in this paper is robust.
Point 12: 5.2, how does the result compare with existing literatures? Like the paper we mentioned above?
Response 12: This paper first analyzes the mechanism of smart city construction affecting urban carbon emission intensity at the theoretical level, and then verifies it through empirical research. The study found that smart city construction can reduce urban carbon emission intensity through green innovation effect, industrial upgrading effect and energy efficiency improvement effect. Research has shown that green technology innovation, industrial structure upgrading and energy efficiency improvement are all important means to reduce urban carbon emission intensity. The conclusion of this study is consistent. In addition, the research conclusion of this paper also shows that smart city construction has played a positive role in green technology innovation, industrial structure upgrading and energy efficiency improvement.
Point 13: Conclusion, there are many types of infrastructures related to smart cities, would that affect the conclusion?
Response 13: Thank the reviewers for their comments. There are indeed other policies that may affect the research conclusion, which is an omission of this paper. It is found that the pilot policy of "broadband China" strategy implemented since 2014 and the pilot policy of innovative cities implemented since 2012 are relatively relevant to this study. This paper further introduces the dummy variables of broadband China strategy and pilot policy of innovative cities in the robustness test. The results show that the coefficient of the core explanatory variable Policy is still significantly negative, which indicates that after excluding the impact of other policies, Smart city construction can still significantly reduce urban carbon emission intensity. See 4.3.8 in the text for details.
Point 14: Tables 4 and 5, why only fixed effects are studied but not random effects?
Response 14: The DID model itself is a fixed effect model and is not suitable for the random effect model. Besides, we found that the value of P is 0.0642 through hausman test, strongly rejecting the original hypothesis. So using the fixed effect model is more reasonable.
Point 15: Figures 1 and 2, missing citation, why the figures 2 only show 2015, 2019, 2011,2007, how about the other years?
Response 15: The data sources of Figure 1 and Figure 2 have been pointed out above in this paper. See lines 195-202 of the article. In Figure 2, this paper only shows the data of 2007, 2011, 2015 and 2019, mainly considering the problem of article layout. If all the data of 13 years are shown in the article, it will occupy a large space. Therefore, this paper shows the distribution of carbon emission intensity of Chinese cities in the first, fifth, ninth and thirteenth years of the sample period. The four selected years are evenly distributed, which can reflect the overall change of carbon emission intensity to a certain extent, and the data of other years also basically meet the rules described in the paper.
Point 16: Polish English
Response 16: MDPI has been entrusted to edit the full text.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
I found this article to be very informative on the application of smart cities and their reflection on decreasing urban carbon emission intensity. Smart cities contribute to knowledge in a critical area. This manuscript has some minor issues that can lead to a better version.
The title is well-written and accurately reflects the content of the article. However, I would recommend adding a subtitle that indicates which methods were used.
Rewrite the abstract to reflect the research context and challenges. The rest of the findings are in the report. It was suggested that the main results should be written as sentences, not as numbered text.
The introduction is somehow well-written. Minor revisions are required to add to the novelty or value of the present study. The literature needs to be discussed in the introduction. Several research questions are mentioned in the introduction, but the introduction does not include a few sentences that define the research problem. Several research questions are also missing from the introduction, but the introduction should include a few sentences that define the research problem. A recent reference should be cited in the first paragraph of the introduction. The methods should also be mentioned briefly in the introduction. In this section, IBM's strategy can be said briefly and described in detail in the methods section.
The literature review is unclear on the link between smart cities and a decrease in carbon emissions.
The discussion section needs to link the main findings with others' works. Discussing the limitation is also needed. The following article can strengthen the argument.
doi: 10.3390/su14031699
doi: 10.3390/su142114472
doi: 10.3390/su142114100
The conclusion and policy enlightenment are well written. This minor issue in this context must be linked to long-term development goals.
Author Response
Point 1: The title is well-written and accurately reflects the content of the article. However, I would recommend adding a subtitle that indicates which methods were used.
Response 1: According to the opinions of reviewer, the title of the article has been adjusted. The revised title is: "Does Smart City Construction Decrease Urban Carbon Emission Intensity? Evidence from A Difference in Difference Estimation in China"
Point 2: Rewrite the abstract to reflect the research context and challenges. The rest of the findings are in the report. It was suggested that the main results should be written as sentences, not as numbered text.
Response 2: According to the comments of the reviewer, this paper has made major modifications to the abstract, and the modified abstract is as follows:
Abstract: Climatic changes and environmental pollution caused by traditional urban development models have increased due to accelerated urbanisation and industrialisation. As a new model of urban development, smart city construction relies on digital technology reform to achieve intelligent urban governance, which is crucial to reducing carbon emission intensity and achieving regional green development. This paper constructs a multi-period DID model based on panel data from 283 cities from 2007 to 2019 to explore the impact of smart city construction on urban carbon emission intensity. This study found that smart city construction decreased urban carbon emis-sion intensity significantly. According to heterogeneity analysis, the integration of smart city developments could decrease carbon emission intensity in northern China’s cities and re-source-based cities significantly, but had an insignificant influence on carbon emission intensity in southern China’s cities and non-resource-based cities. Based on an analysis of the influencing mechanisms, smart city construction can decrease urban carbon emission intensity by stimulating green innovation vitality, upgrading industrial structures, and decreasing energy consumption. These research conclusions can provide directions for urban transformation and low-carbon de-velopment, as well as a case study and experience for countries that have not yet established smart city construction.
Point 3: The introduction is somehow well-written. Minor revisions are required to add to the novelty or value of the present study. The literature needs to be discussed in the introduction. Several research questions are mentioned in the introduction, but the introduction does not include a few sentences that define the research problem. Several research questions are also missing from the introduction, but the introduction should include a few sentences that define the research problem. A recent reference should be cited in the first paragraph of the introduction. The methods should also be mentioned briefly in the introduction. In this section, IBM's strategy can be said briefly and described in detail in the methods section.
Response 3: According to the opinions of the reviewers, this paper makes some adjustments to the introduction, mainly including the following points: (1) We added some sentences defining the research questions; (2) We discuss the literature review in the introduction; (3) We have added some recent references; (4) In the introduction, we simply explained the method used in this paper; (5) We briefly introduced IBM's strategy; (6) We commissioned MDPI to edit in English. See the revised version for details.
Point 4: The literature review is unclear on the link between smart cities and a decrease in carbon emissions.
Response 4: According to the opinions of the reviewers, we have made the following modifications to the literature review and theoretical analysis: (1) We have discussed the literature review in the introduction; (2) We adjusted the content of literature review to make it more consistent with the research content of this paper; (3) We further refined the theoretical analysis content and analyzed the impact of smart city construction on urban carbon emission intensity from the theoretical level. See the revised version for details.
Point 5: The discussion section needs to link the main findings with others' works. Discussing the limitation is also needed. The following article can strengthen the argument.
doi: 10.3390/su14031699
doi: 10.3390/su142114472
doi: 10.3390/su142114100
Response 5: Thank you very much for your comments. The article you recommended is very helpful for the modification of this article. The revised discussion section is as follows:
Unlike previously published articles on the relationship between urbanisation and carbon emissions, this paper focuses on the key role of new urban development models in reducing carbon emission intensity. This paper confirms smart city construction can effectively mitigate carbon emission pollution in the process of urbanisation. This pro-vides a decision-making basis for countries around the world to aid in the control of urban diseases. Due to the different development levels, technological conditions, and resource availability of smart cities, the effects of smart city construction may vary greatly. In the heterogeneity analysis, we only considered the influence of urban loca-tions and city types on the policy effect of smart city construction. In the next step, the interactions between city size, industrial characteristics, transportation conditions, and smart city construction policies will be further analysed. The spatial spillover effect of smart city construction would also be an interesting topic to explore. In addition, can other new urban development models, such as innovative cities, also reduce urban carbon emission intensity? This is a question worthy of further exploration.
Point 6: The conclusion and policy enlightenment are well written. This minor issue in this context must be linked to long-term development goals.
Response 6: According to the comments of reviewers, we have added the following policy recommendations on long-term development goals: In general, in the context of urbanisation and industrialisation, countries around the world should learn from this case study on smart city construction and actively explore new urban development models that meet their own development needs.
Author Response File: Author Response.pdf
Reviewer 3 Report
This paper is interesting and have a high degree of completion. It may be considered for publication after revision.
1. Abstract: The research background and significance should be pointed out briefly at first.
2. Literature Review should be reorganized. This paper mainly discussed the relationship between smart city and carbon emissions; however, the second paragraph (Lines 77-104) showed the studies about the influence of urbanization on carbon emissions, and no sentence explains the relationship the urbanization and smart city. There is a suspicion of equating urbanization with smart city. Additionally, if authors discussed the urban development mode, should the relationship between compact cities and carbon emissions also be included in the literature review?
3. For the total samples of 3679, it is necessary to give the proportion of smart city samples in the paper.
4. Why this paper selected the DID method, and could the break-point regression be replaced?
5. In Eq.(2), what t0 means? Why k is from -7 to 7? There are 15 years from -7 to 7; however, the study period from 2007 to 2019 only have 13 years.
This paper is interesting and have a high degree of completion. It may be considered for publication after revision.
1. Abstract: The research background and significance should be pointed out briefly at first.
2. Literature Review should be reorganized. This paper mainly discussed the relationship between smart city and carbon emissions; however, the second paragraph (Lines 77-104) showed the studies about the influence of urbanization on carbon emissions, and no sentence explains the relationship the urbanization and smart city. There is a suspicion of equating urbanization with smart city. Additionally, if authors discussed the urban development mode, should the relationship between compact cities and carbon emissions also be included in the literature review?
3. For the total samples of 3679, it is necessary to give the proportion of smart city samples in the paper.
4. Why this paper selected the DID method, and could the break-point regression be replaced?
5. In Eq.(2), what t0 means? Why k is from -7 to 7? There are 15 years from -7 to 7; however, the study period from 2007 to 2019 only have 13 years.
Author Response
Point 1: Abstract: The research background and significance should be pointed out briefly at first.
Response 1: According to the comments of the reviewer, this paper has made major modifications to the abstract, and the modified abstract is as follows:
Abstract: Climatic changes and environmental pollution caused by traditional urban development modes have increased due to accelerated urbanisation and industrialisation. As a new model of urban development, smart city construction relies on digital technology reform to achieve urban intelligent governance, which is crucial to reduce carbon emission intensity and achieve regional green development. This paper constructs a multi-period DID model based on panel data of 283 cities from 2007 to 2019 to explore the impact of the smart city construction on urban carbon emission intensity. This study finds that smart city construction decreased urban carbon emission intensity significantly. According to heterogeneity analysis, smart city could only decrease carbon emission in-tensity in Northern China’s cities and resource-based cities significantly, but it had insignificant influences on carbon emission intensity in Southern China’s cities and non-resource cities. Based on the analysis of influencing mechanism, smart city construction can decrease urban carbon emission intensity by stimulating green innovation vitality, upgrading industrial structures, and decreasing energy consumption. These research conclusions can provide directions for urban transformation and low-carbon development, as well as a learning case and some experience for countries that have not yet established smart city.
Point 2: Literature Review should be reorganized. This paper mainly discussed the relationship between smart city and carbon emissions; however, the second paragraph (Lines 77-104) showed the studies about the influence of urbanization on carbon emissions, and no sentence explains the relationship the urbanization and smart city. There is a suspicion of equating urbanization with smart city. Additionally, if authors discussed the urban development mode, should the relationship between compact cities and carbon emissions also be included in the literature review?
Response 2: According to the opinions of reviewers, this paper discusses the literature review in the introduction, and adjusts the content of the literature review. The adjusted contents are as follows:
Carbon emission intensity refers to the CO2 emission volume per unit of gross domestic product (GDP). It reflects the mutual relationship between pollutants and economic growth and is an important index for evaluating sustainable economic de-velopment [6]. The existing literature on the influencing factors of carbon emission in-tensity is relatively extensive, listing as factors environmental regulation [7,8], fiscal and taxation system [9,10], urbanisation [11,12], FDI [13,14], international trade [15,16], etc. The impact of urbanisation on carbon emissions is significant. However, the existing research on the relationship between the promotion or inhibition of urbanisation and carbon emissions is still inconclusive. On the one hand, urbanisation can promote in-dustrialisation and increase the energy consumption demand, leading to extensive carbon emissions [17,18]; furthermore, with the increase in population density, urban management faces greater pressure [19]. On the other hand, with the development of urbanisation, the scale effect of public goods and the accumulation effect of human capital can improve the efficiency of energy use and create conditions for technological advances, thereby reducing the intensity of carbon emissions [20,21].
Some scholars have ignored the differences in urbanisation brought about by dif-ferent urban development models and the key role of new urban development models in reducing carbon emission intensity. The traditional urban development model blindly pursues the development speed of urbanisation, ignoring the development quality. The high investment and high energy consumption development model sig-nificantly increased environmental pollution, especially that in the form of carbon emissions. Is the traditional urban development model unable to eliminate the climate effects? In the era of intelligence and digitalisation, the implementation of a new urban governance model could be the key for countries around the world to fulfil their envi-ronmental protection responsibilities and establish a new competitive advantage. With the emerging disadvantages of the traditional urban development model, an increasing number of countries have begun to explore new models. Scholars have also begun to study the economic and social effects of these new urban development models, such as smart cities, confirming the positive impact of smart cities on environmental pollution control, enterprise technology innovation, energy efficiency improvement, and ad-vancement of various industrial structures. However, few scholars have investigated the impact of smart-city-based urban development models on urban carbon emission intensity.
Point 3: For the total samples of 3679, it is necessary to give the proportion of smart city samples in the paper.
Response 3: According to the comments of the reviewers, the relevant description has been modified in this paper. The revised contents are as follows: Based on data screening, 3,679 samples from 283 cities were finally gained, including 2132 smart city samples and 1547 non smart city samples.
Point 4: Why this paper selected the DID method, and could the break-pointregression be replaced?
Response 4: DID method and break-pointregression method are both commonly used policy evaluation, but their application scopes are different. The DID method is a commonly used method to evaluate the effect of policy intervention and event handling. The characteristic of these events or policies is that they do not affect all individuals at the same time. The break-pointregression method is applicable to the study of the consequences of certain specific social science events. The characteristic of these events is that whether an individual is affected by the event depends on whether the value of an observable feature is greater than a given critical value. Smart city construction is a new urban governance model implemented in China since 2012. The pilot cities are the experimental groups and the non pilot cities are the control groups, which conforms to the scope of application of the DID method. However, there is no observable value that can determine whether the city is affected by smart city construction, so break-pointregression method is not applicable to this paper.
Point 5: In Eq.(2), what t0 means? Why k is from -7 to 7? There are 15 years from -7 to 7; however, the study period from 2007 to 2019 only have 13 years.
Response 5: Since the smart city policy is promoted in batches, the traditional DID is not applicable. Therefore, this paper uses the research of Beck et al. for reference, and uses the multi period DID to assess the impact of smart city construction on urban carbon emission intensity. At the same time, referring to the article of Du et al., the event analysis method was used for parallel trend test. In formula (2), t0 refers to the current period when the pilot policy is implemented. k is the relative time of policy implementation, i.e. k=t-t0. Taking the pilot cities selected in 2012 as an example, t0=2012, and the value range of k is - 5~7; Taking the pilot cities selected in 2014 as an example, t0=2014, and the value range of k is - 7~5. Therefore, the value range of k in this paper is - 7~7.
Author Response File: Author Response.pdf
Round 2
Reviewer 3 Report
I think it can be accepted as it is.
Author Response
Thank you very much for your hard work and recognition of our article.
