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Carbon Emission Reduction Effects of the Smart City Pilot Policy in China

Sustainability 2023, 15(6), 5085; https://doi.org/10.3390/su15065085

by Long Qian, Xiaolin Xu, Yunjie Zhou, Ying Sun^* and Duoliang Ma

Reviewer 1:

Lucian Belascu

Reviewer 2:

Muhammad Omair

Reviewer 3: Anonymous

Sustainability 2023, 15(6), 5085; https://doi.org/10.3390/su15065085

Submission received: 18 January 2023 / Revised: 21 February 2023 / Accepted: 27 February 2023 / Published: 13 March 2023

Round 1

Reviewer 1 Report

The paper discusses the benefits of smart cities for carbonization, using data from a pilot project in China. Overall, the paper is well written, but several issues need to be tackled before publication, which I list below.

Introduction:

- The Introduction is too brief and fails to make the connection between smart cities and carbon emissions. Moreover, the research gap is not properly presented so the formulation of the research question for the paper is not clear.

- Other missing points in the Introduction: paper contributions and paper structure.

Literature review

- It is quite well written, but the authors mention only papers that focus on China and are mostly written by Chinese authors. There is literature outside China, as there are smart cities elsewhere than China, and the authors need to make reference to these as well.

Materials and methods

- The FDI variable is not presented. Also, it is not clear how the authors introduce industrial structure upgrading in the models, as I haven’t seen any variable linked to this.

- Which specifications were used to estimate the dynamic model? Are the models reliable since no tests for this were presented?

Author Response

RESPONSES TO THE REVIEWER’S COMMENTS ON THE SUBMISSION

Carbon emission reduction effects of smart city pilot policy in China

(Manuscript ID#: sustainability-2198579)

We are very grateful to the anonymous reviewer for offering constructive comments on our submission and giving us the opportunity to revise and resubmit this manuscript. We greatly appreciate your overall endorsement of our research and supportive comments and suggestions to improve the content and presentation of our article. Please find below our point-to-point response to your comments. Specifically, we first reproduce the your comments (in italic) and, then, respond and explain how their comments are addressed in the revised manuscript. All changes are highlighted in yellow in the main text.

RESPONSES TO REVIEWER #1

Comments and Suggestions for Authors：The paper discusses the benefits of smart cities for carbonization, using data from a pilot project in China. Overall, the paper is well written, but several issues need to be tackled before publication, which I list below.

Response: We are grateful for your overall endorsement of our research and recognition that “the paper is well written”. We are extremely grateful for the constructive comments that you have put forward to help us improve the quality and presentation of the paper. Please find below our point-to-point response to your thoughtful comments.

Introduction: The Introduction is too brief and fails to make the connection between smart cities and carbon emissions. Moreover, the research gap is not properly presented so the formulation of the research question for the paper is not clear. Other missing points in the Introduction: paper contributions and paper structure.

Response: Thank you for your excellent suggestions. Following your advice, we have added the connection between smart cities and carbon emissions, and make the formulation of the research question more clear. The Introduction is on Page 2-4 in the revised manuscript. Other missing points in the Introduction: paper contributions and paper structure have been added in the penultimate paragraph and the last paragraph of the Introduction in the revised manuscript.

Introduction

The global warming caused by the tremendous increase of carbon emissions has become a major environmental problem for all countries in the world, which not only destroys the balance of the ecosystem, but also endangers human life and health. As the largest developing country and the carbon dioxide emitter in the world, China has always assumed the responsibility in the global environmental governance and actively transformed into a low-carbon economy. In September 2020, China promised to achieve carbon peak and carbon neutrality in 2030 and 2060 respectively. In this context, the carbon emissions becomes a particularly urgent problem to be tackled.

The key to energy conservation and emission reduction lies in the cities, where human economic activities are concentrated and have accounted for nearly 85% of the total amount of carbon emissions [1-3]. The rapid urbanization process has also been regarded as a significant burden on the urban environment [4]. Currently, the development model of Chinese cities is in urgent need of shifting from gross regional product (GDP) oriented to low-carbon oriented. In this regard, China has been actively carrying out various policy pilots and practices for sustainable development.

In order to control the urban carbon emissions, the Chinese government has issued many environmental and regional policies to alleviate the contradiction between economic growth and carbon emissions, such as low carbon city pilot policy, carbon emission trading pilot policy, low carbon industrial-park pilot policy, regional collaborative governance policy, environmental supervision policy, innovative pilot city policy, etc. At the same time, scholars have conducted a more in-depth discussion on the carbon emission control effect of these environmental policies (Khanna et al., 2014 [5]; Clarkson et al., 2015 [6]; Scott, Carter, 2019 [7]; Thi et al., 2022 [8]; Liu et al., 2022 [9]; Liu et al., 2022 [10]). Unfortunately, the existing research has not fully paid attention to the possible positive impact of the smart city construction, which is a regional policy, on urban carbon emission reduction, and this provides a valuable opportunity for this study.

Smart city is a new urban development model that combines the Internet and modern information technology with urbanization to promote urban development from factor-driven, investment-driven to innovation-driven. It focuses on the quality of development, adheres to the concept of ecological urban design and construction, relies on the Internet of Things, big data, blockchain, cloud computing and other information technologies, and emphasizes the comprehensive and sustainable development of economy, society and environment through people-oriented. The smart city was first defined as the application of information and communications technologies [11] and was regarded as the sustainable city [12]. In the last several years, the smart city has been considered as an essential path to sustainability development [13].

In order to better promote the smart city construction, the Ministry of Housing and Urban Rural Development of China announced the official launch of the national smart city pilot project in December 2012. This policy aims to deeply apply the Internet of Things, cloud computing and other information technologies, realize the application of interconnection of everything and intelligent integration, improve the cities' sustainable innovation capability and accelerate the construction of smart cities.

Smart city pilot policy is a new model to accelerate the socioeconomic sustainable development [14]. To this day, China has launched three batches of smart city pilot projects. However, can the smart city pilot policy alleviate high carbon emissions in China? How does the pilot policy affect the carbon emission reduction? With carbon peak and carbon neutrality goal listed as a China's priority, the impact of smart city pilot policy on the carbon emissions is worth studying.

In this context, this paper takes the China's smart city pilot policy as a quasi-natural experiment to measure smart cities construction, examines the effects of smart city construction on carbon emission reduction through the difference in difference (DID) method, as well as explores the influence mechanism, further investigates the heterogeneity influence. The scientific answers to these questions will help comprehensively evaluate the implementation effect of China's smart city pilot policy, improve the environmental governance capacity of smart city, and facilitate high-quality urban development.

The scientific novelty of this paper consists in the following three points: First, the smart city construction, which is a new urban development model, is taken as the starting point of this study, and the impact of smart city construction on carbon emissions is deeply discussed, which boosts the theoretical research of new urbanization. Second, in terms of carbon emission control, the evaluation of the policy implementation effect of smart city construction enriches the research on the relationship between the digital economy and the environment, expands the influence factors of carbon emissions control, extends the new realization path of the carbon peak and carbon neutrality goal, and ascends the theoretical and practical research of the digital economy. Third, in terms of research method, the implementation of China's smart city pilot policy is regarded as a quasi-natural experiment in the smart city construction and the new urbanization construction, which could avoid the measurement error and possible endogenous problems caused by urbanization only measured by indicators, which could identify the causal effect of smart city construction, expand the application space of the difference in difference method, and promote the in-depth application of policy evaluation methods more accurately. Fourth, this paper deeply explores the impact mechanism of smart city construction on carbon emission reduction, clarifies the critical reasons for the positive impact, better transforms the smart city construction into environmental governance advantages, and enriches the theoretical relationship between smart city construction and carbon emission reduction.

This paper is organized as follows. Section 2 presents an overview of smart city and carbon emissions. Section 3 describes the policy backgrounds and theoretical analysis. Section 4 is the research design. Section 5 presents the empirical research results. Section 6 introduces the heterogeneity analysis. Finally, Section 7 addresses the conclusions and policy recommendations.

Literature review: It is quite well written, but the authors mention only papers that focus on China and are mostly written by Chinese authors. There is literature outside China, as there are smart cities elsewhere than China, and the authors need to make reference to these as well.

Response: Many thanks for this suggestion, which help to improve quality of the paper. According to your advice, we have added some literature that focus on the outside of China and are not written by Chinese authors (Please refer to Page 4-6).

Literature review

Present researches on carbon emissions mostly concentrate on the economic, social and industrial levels. At the economic and social level, foreign direct investment [15, 16], carbon emission trading market [17], urbanization [18, 19] have influences on carbon emissions. The industrial structure [20], industrial agglomeration [21] are the important factors at the industrial level. In these present literature, most research paid attention to the traditional urbanization influence on carbon footprint (environmental pollution), which could be roughly segmented into the following three classes. First, some believe that urbanization has aggravated carbon emissions (environmental pollution). Liu and Bea [22] found that for every 1% increase in urbanization, carbon emissions will increase by 1% through autoregressive distributed lag (ARDL) and the vector error correction model (VECM). Urbanization has brought “urban diseases”, such as environmental deterioration [23]. Furthermore, Sarwar and Alsagaf [24] proposed that the urbanization increased carbon dioxide emissions and meanwhile, per capita income increase can effectively control carbon emissions caused by urbanization. Second, some believe that urbanization can restrain carbon dioxide emissions. Muñoz et al. [25] divided more than 8000 families in Austria into three different levels of urbanization: urban areas, semi-urban areas and rural areas, founding that urban residents have the lowest carbon footprint. Based on the analysis of provincial panel data in China from 2008 to 2017, Zhang et al. [26] found that the urbanization rate is negatively related to carbon emissions, and the improvement of technology level is conducive to reducing carbon emissions. Third, the others insist that there is nonlinear relationship between urbanization and carbon emissions. Feng et al. [27] further found that in the early stage of the urbanization, there was no significant relationship between carbon emissions and urbanization by using the threshold regression model; while during the middle stage, the urbanization inhibited CO₂ emissions; and during the later stage, the urbanization promoted carbon emissions. Using the STIPART model, Chikaraishi et al. [28] proposed that when a country's economy mainly depends on the tertiary industry, the urban carbon emissions will decrease with the improvement of urbanization. On the contrary, when the secondary industry becomes the leading industry, the process of urbanization will aggravate carbon emissions. In general, the above research mainly explored the influences of traditional urbanization development mode on carbon emissions.

The research on the relationship between new urbanization and carbon emissions have gradually increased recently, such as decarbonization concepts [29], nearly or net-zero energy [30], carbon neutral [31]. In the last decade, the research on the smart city construction has begun to emerge and gradually attracted attention. Sarah Giest [32] made the case study of the cities of Copenhagen, London, Malmö, Oxford and Vienna. AL-Dabbagh R [33] mainly addressed the advanced measures and technologies adopted by Dubai to build a smart city. Contreras et al. [34] took the London Environment Strategy (LES) as the case scenario, and pointed out that only smart mobility and smart regulation programs could improve the emissions trend. Bracco et al. [35] introduced the demonstration activities of University of Genoa at Savona Campus aiming to reduce the carbon footprint for the case study.

There are a certain number of research focused on particular fields of smart city. Some studies focus on specific aspects of smart city construction, such as transportation energy consumption, garbage classification, residential construction, civic responsibility, consumer behavior, etc. Ruggieri et al. [36] analyzed how energy efficiency and power transportation in smart cities , proposing that the habits and behavior of citizens are also critical factors of environmental problems. Hoang et al. [37] introduced the main components and functions of renewable energy in smart cities, and designed how to integrate it into the energy system of smart cities. Zawieska et al. [38] investigated the impact of the implementation of smart city concept on reducing carbon emissions generated by transportation, analyzed the additional impact of smart city as a liquidity determinant on carbon dioxide emissions. Vaidya et al. [39] designed and implemented an intelligent electric vehicle charging management system using charging strategy, exploring the construction and development of smart cities from the perspective of building and improving the charging management of smart electric vehicles. Ruggieri et al. [40] analyzed the electric mobility through the overview of six European smart cities, including Olso, London, Hamburg, Milan, Florence and Bologna, and pointed out that the electric mobility is an important path to decarbonization. Oralhan et al. [41] analyzed the optimization of garbage collection in smart cities based on the Internet of Things technology to reduce environmental pollution. Kylili et al. [42] qualitatively described the potential contribution of zero energy buildings to European smart city construction. Caponio et al. [43] proposed a simulation model based on System Dynamics and validated the effectiveness of the model in simulating and improving the local energy planning policies in the residential building sector in a smart city. Preston et al. [44] took two projects from Nottingham in the UK and found that citizen should take the most primary responsibility in smart city construction for carbon emission reduction.

Undeniably, the smart city has caught great attention all over the world and previous research has probed the influence of smart city construction on urban innovation capacity [45, 46], high-quality economic development [47] and other aspects. In addition to the above research, some research paid close attention to the relationship between the smart city and the environment. Li et al. [48] found that the smart city construction has significantly optimized the air quality in pilot areas, and the policy has a spatial spillover effect. Feng [49] put forward that the smart city infrastructures had effect on city haze pollution and there were spatial heterogeneities at national, regional, and city administrative rank levels. Chu et al. [50] pointed out that the smart city pilot policy significantly reduced urban pollutant through the classic land allocation decision-making theoretical model. Wang et al. [51] put forward that smart city construction improved green total factor productivity through technological innovation, industrial structure upgrading and resource optimization. Jiang et al. [52] analyzed the effect of the first batch of pilot smart city in China on the GTFP and green technology progress.

Some studies have focused the quantitative relationship between smart cities and the carbon emissions. Yu and Zhang [53] put forward that that the pilot policy of low-carbon cities improved the carbon emission efficiency by 1.7% and every 1% increase in carbon emission efficiency will reduce carbon dioxide emissions by 8.37 million tons, using the difference in difference (DID) and the spatial difference in difference (SDID) models. Cavada et al. [54] explored the relationship between the smart cities and the low carbon emissions through smart city case study of Copenhagen and Singapore. Yigitcanlar et al. [55] took the UK smart cities as the data sample, and used the panel data analysis method to analyze the carbon emissions of 15 cities in the UK from 2005 to 2013, and pointed out the nonlinear relationship between city smartness and the carbon emission emissions.

To sum up, the relevant research on urbanization is relatively rich at present, but there are still the following research gaps. First, research on the traditional urbanization is relatively abundant, while insufficient on the new urbanization, especially on the smart city. Second, the limited research on the new urbanization mainly examined the economic and social impact of the new urbanization construction, and paid less attention to the environmental impact, especially the relationship between smart city construction and carbon emission reduction, which is an important embodiment of the new urbanization construction. Third, previous studies used various indicators to measure urbanization, which is likely to cause measurement errors and endogenous problems.

In view of this, this paper uses China's urban panel data, takes the pilot policy of smart city construction as a quasi-natural experiment to measure the construction of new urbanization, and uses the difference in difference method to deeply investigate the impact of smart city construction on urban carbon emissions, and further examines the dynamic impact effect, impact mechanism and heterogeneity of the policy.

3.Materials and methods: The FDI variable is not presented. Also, it is not clear how the authors introduce industrial structure upgrading in the models, as I haven’t seen any variable linked to this.

Response: Thanks for the constructive comments that you have put forward to help us improve the quality of this paper. The revised part is as follows:

4.1.3. Mediating Effect Model

If the smart city construction significantly reduces the urban carbon emissions, how does this effect realize is a problem worth exploring. Therefore, it is necessary to examine the specific paths of carbon emissions reduction from three aspects: technology innovation, foreign direct investment and industrial structure upgrading. Referring to Baron and Kenny (1986)'s research idea [61], the intermediary effect model is built on the basis of the benchmark model (1).

(3)

(4)

Model (3) and model (4) constitute the intermediary effect model. In model (3), represents the intermediary variable, that is technology innovation (), foreign direct investment (), industrial structure upgrading () respectively. The detailed definitions of intermediary variables are in the Section of 4.2.4 Intermediary Variable. The other variables have the same meaning as those in the benchmark model (1). First, regress the model (3). If the estimation coefficient of is significantly positive, smart city construction has a significant positive impact on the intermediary variables. Second, regress the model (4). If the estimation coefficient of intermediary variable is significant, and the estimation coefficient of is non-significant or significant, but the coefficient value is lower than that of the benchmark model (1), the smart city building reduces the urban CO₂ emissions through the intermediary variables. If at least one of the regression coefficients of in model (3) and in model (4) is not significant, it is necessary to use the bootstrap method to judge the significance. If the upper and lower limits of the bootstrap confidence interval do not contain 0, the intermediary effect is significant.

4.Which specifications were used to estimate the dynamic model? Are the models reliable since no tests for this were presented?

Response: In the 5.3. Parallel Trend and Dynamic Effect Test of the article, the dynamic model is used to estimate the dynamic impact of the smart city construction on urban carbon emissions. Figure 3 shows the estimated results of the dynamic effect model.

The dynamic model is reliable and the reasons are as follows.

First, the dynamic model has been widely used. From 2010 to 2022, the number of papers published under the theme of "dynamic model" in the "Web of Science database" maintained a rapid growth (Figure 1), and the number of the total amount has reached 1,011,054 according to statistics. In the past three years, more than 100,000 papers have been published annually. The authority and reliability of the dynamic model have been widely recognized in the academic community.

Fig.1 The number of published articles on the dynamic model (2010-2022)

Fig.2 The search results

Second, in this paper, the dynamic effect model is mainly used as an auxiliary extension of the benchmark model, which is an auxiliary analysis. In policy evaluation, this paper mainly tests the conclusions drawn by the benchmark model (1), which is the customary practice of the difference in difference method. For this reason, this paper uses a variety of methods placebo test, parallel trend test, PSM-DID method, excluding the impact of other relevant policies in the same period to test the robustness of the benchmark conclusions drawn by the benchmark model (1).

Author Response File: Author Response.pdf

Reviewer 2 Report

Dear Authors,

The manuscript is related to the assessment of different controlled variables for their impact on reducing carbon emissions. Hypothesis testing is used to assess the impact of various input parameters. China's pilot policy for smart construction is selected for the experiment. It an interesting to read the work related to the environmental policy of the construction industry that is of utmost importance for the reduction of carbon emissions.

1. The title of the manuscript is complicated to understand the context of the work. The word “effects” looks improper with the “assessment”. Improve it well.

2. The abstract is too lengthy, where there is no need to explain all the objectives. Just follow the standard structure of the abstract and crunch results with insights. The standard size is 250-300 words.

3. Only three keywords: Difference in difference method is a keyword. Add at least two more keywords. Urbanization may be one of the keywords.

4. I have concerns about the considered variables i.e., how can you consider these factors for China's pilot policy of carbon emission reduction? Are these not generic? A few more specifically to China are also required for the assessment.

5. A very well representation of the hypothesis testing for the impact of industrial structure upgrading, FDI, and Technology Innovation on carbon reduction. However, just need to know, the impact can be assessed by the combined effect of all on carbon emission reduction.

6. Your conclusion is similar to the Abstract. In the Abstract the structure must be different.

7. Results are well represented.

8. Conclusion is good

9. The research work is to assess the smart city construction project on the basis of carbon emissions considering controlled and other variables. This is directed to create awareness for smart construction projects to reduce carbon emissions. What do you suggest about the cost e.g., these initiatives as a control variable will also carry huge capital?

10. Too many things are part of the proposed research work, e.g., input variables, their impact, effect methods, urbanization, resources consumption, benchmarking regression, global warming, hypothesis testing, etc. I recommend adding a clear and concise contribution of your work and research gap at the ending paragraph of the Introduction and Literature review respectively. In addition, be convergent in writing.

11. There are numerous English grammatical errors in the article. Please read the article thoroughly and correct all the errors.

Author Response

RESPONSES TO THE REVIEWER’S COMMENTS ON THE SUBMISSION

Carbon emission reduction effects of smart city pilot policy in China

(Manuscript ID#: sustainability-2198579)

RESPONSES TO REVIEWER #2

Response: We are grateful for your overall suggestions of our research. We are especially thankful for the constructive comments that you have put forward to help us improve the quality of the paper. Please find below our point-to-point response to your thoughtful comments.

The title of the manuscript is complicated to understand the context of the work. The word “effects” looks improper with the “assessment”. Improve it well.

Response: We appreciate your excellent suggestion to better motivate our research. The title has been rewritten as “Carbon emission reduction effects of smart city pilot policy in China”.

The abstract is too lengthy, where there is no need to explain all the objectives. Just follow the standard structure of the abstract and crunch results with insights. The standard size is 250-300 words.

Response: Thanks to your excellent suggestions, which help us to better present the main content of our research. The author has optimized the abstract according to the idea of research questions → research importance → research methods → research results. Please refer to Page 2 for the detailed abstract.

Abstract: Carbon emission reduction is an important goal of China's sustainable economic development. As a new urbanization construction model, the importance of smart city construction for economic growth and innovation has been recognized by the academic community. The impact of smart cities on the environment, especially on the carbon emission reduction, has yet to be verified, which is directly related to the green and low-carbon transformation of China, the realization of the carbon peak and carbon neutrality goal and the effect of smart city pilot policies. For this reason, this paper adopts China's urban panel data, uses the difference in difference method, and takes the smart city pilot policy as a quasi-natural experiment of new urbanization construction to investigate the impact of smart city construction on urban carbon emission reduction. The results show that (1) The smart city construction has reduced the carbon emissions of pilot cities by about 4.36%, compared with non-pilot cities. (2) The dynamic impact analysis addresses that the carbon emission reduction effect of smart city construction will not be effective until the third year of the implementation of the policy, and the policy effect will gradually increase over time, and its carbon emission reduction dividend has a long-term sustainability. (3) The analysis of the influence mechanism presents that the smart city construction mainly promotes urban carbon emission reduction through three paths: improving technology innovation capacity, enhancing the attraction of foreign direct investment, and accelerating the upgrading of industrial structure. (4) Heterogeneity analysis indicates that the smart city construction has a stronger carbon emission reduction effect in the “two control zones”, non-old industrial bases and non-resource-based cities.

Only three keywords: Difference in difference method is a keyword. Add at least two more keywords. Urbanization may be one of the keywords.

Response: We are thankful for this suggestion. These new keywords have been added in this paper. Please refer to Page 2 for detailed keywords.

Keywords: smart city construction; carbon emission reduction; difference in difference method; urbanization; policy; effect

I have concerns about the considered variables i.e., how can you consider these factors for China's pilot policy of carbon emission reduction? Are these not generic? A few more specifically to China are also required for the assessment.

Response: Thank you for your thoughtful suggestions. When introducing control variables, the authors have fully referred to the practice of other relevant research. Therefore, each introduced control variable has corresponding references as the basis, which is not subjective and arbitrary. It should be noted that this paper focuses on the impact of smart city construction on urban carbon emission reduction, and pays special attention to the core explanatory variable “smart city construction ()”. The control variable is only used as an auxiliary variable for analysis, which is also a common practice of econometrics. It is hereby stated that we hope the expert will forgive us.

A very well representation of the hypothesis testing for the impact of industrial structure upgrading, FDI, and Technology Innovation on carbon reduction. However, just need to know, the impact can be assessed by the combined effect of all on carbon emission reduction.

Response: We wish to express our sincere gratitude to you for your constructive comments and suggestions. The combined effect test has been added on Page 19. We hope that this revision could meet with your approval.

5.4.4. Test of the Combined Effect

In addition, smart city construction may also affect urban carbon emission reduction through the combined effect of technological innovation, foreign direct investment and industrial structure upgrading. Therefore, this paper uses the interaction item () of technology innovation (), foreign direct investment () and industrial structure upgrading () as the explained variable, and uses the intermediary model (3) and (4) for regression. The results are shown in Table 4. Where is equal to the cross-term of the variables , and .

Table 4. Combined effect inspection.

	(1)	(2)
	0.0004^** (2.2582)	-0.0675^*** (-10.0618)
		-8.0484^*** (-9.7727)
Control variables	√	√
Time FE	√	√
City FE	√	√
_cons	0.0069^** (2.2162)	1.1639^*** (8.7464)
Observations	3285	3285
R-squared	0.0911	0.9238

Note: t-statistics in parentheses;*** p<0.01, ** p<0.05, * p<0.1; “√” is “control”; “×” is “no control”.

In Column (1) of Table 4, the estimated coefficient of is 0.0004, which passes the significance test, indicating that the smart city construction has facilitated the combined effect of technology innovation (), foreign direct investment (), industrial structure upgrading (). In Column (2) of Table 4, the estimated coefficient of is significantly negative, with the value of -0.0675, proving that the combined effect of technology innovation (), foreign direct investment (), industrial structure upgrading () partially serve as the intermediary path for smart city construction to redound the urban carbon emission reduction. The three paths have certain synergistic carbon reduction function.

Your conclusion is similar to the Abstract. In the Abstract the structure must be different.

Response: Thank you very much for your instructive suggestions. The author has optimized the abstract according to the idea of research questions → research importance → research methods → research results. Please refer to Page 2 for the detailed abstract.

Results are well represented.

Response: We are grateful for your recognition that “Results are well represented”.

Conclusion is good.

Response: We are grateful for your overall endorsement of our research and recognition that “Conclusion is good”.

The research work is to assess the smart city construction project on the basis of carbon emissions considering controlled and other variables. This is directed to create awareness for smart construction projects to reduce carbon emissions. What do you suggest about the cost e.g., these initiatives as a control variable will also carry huge capital?

Response: We are thankful for this suggestion. The paper has added relevant content about cost analysis on Page 14-15.

By the way, smart city construction needs to bear lots of costs, and the control variable introduced in this paper also needs carry huge capital. Based on this, this paper attempts to examine whether it is cost-effective to bear these costs from the perspective of economic growth. In Column (7) of Table 3, urban economic growth (measured by the natural logarithm of urban GDP) is taken as the explained variable, and the benchmark model (1) is used for regression. It is easy to see that the estimated coefficient of , the core explanatory variable, is significantly positive (0.0278), which indicates that smart city construction can not only promote urban carbon emission reduction, but also promote urban economic growth, while releasing environmental dividends and economic dividends, and promoting sound economic development. It can be seen that even if it takes a lot of costs to engage in smart city construction and control relevant variables, smart city construction can still effectively protect the environment without sacrificing economic growth, and achieve the goal of transforming economic growth and carbon emission reduction from opposition to harmony, which will ensure that the benefits generated by smart city construction are greater than the costs it bears.

Table 2. Benchmark regression results.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	-0.0669***	-0.0424^***	-0.0400^***	-0.0400^***	-0.0412^***	-0.0436^***	0.0278^***
	(-6.8858)	(-5.6456)	(-5.3837)	(-5.3827)	(-5.5745)	(-5.9207)	(4.5276)
		-0.5958^***	-0.5405^***	-0.5405^***	-0.5436^***	-0.5414^***	0.5508^***
		(-45.5585)	(-37.3606)	(-37.3544)	(-37.8055)	(-37.7839)	(56.3577)
			0.3775^***	0.3775^***	0.3689^***	0.3665^***	-0.6583^***
			(8.5028)	(8.5013)	(8.3607)	(8.3399)	(-19.4297)
				-0.0125	0.1695	0.1326	0.0371
				(-0.0635)	(0.8611)	(0.6759)	(0.2109)
					0.0712^***	0.0639^***	-0.0484^***
					(6.5862)	(5.8811)	(-3.0019)
						0.6556^***	-0.1551^*
						(5.0118)	(-1.7111)
_cons	-3.4956^***	-3.3268^***	-3.4656^***	-3.4650^***	-3.4934^***	-3.5225^***	1.0720^***
	(-396.8900)	(-406.8000)	(-190.3000)	(-174.2300)	(-172.8200)	(-168.0800)	(10.8786)
Time FE	√	√	√	√	√	√	√
City FE	√	√	√	√	√	√	√
Observations	3285	3285	3285	3285	3285	3285	3285
R-squared	0.9043	0.9065	0.9065	0.9065	0.9078	0.9085	0.9677

Note: t-statistics in parentheses;*** p<0.01, ** p<0.05, * p<0.1; “√” is “control”; “×” is “no control”.

Too many things are part of the proposed research work, e.g., input variables, their impact, effect methods, urbanization, resources consumption, benchmarking regression, global warming, hypothesis testing, etc. I recommend adding a clear and concise contribution of your work and research gap at the ending paragraph of the Introduction and Literature review respectively. In addition, be convergent in writing.

Response: Thanks to your thoughtful suggestions, the contribution of this work has been added at the ending paragraph (on Page 3-4) of the Introduction.

According to your sincere suggestion, the research gap has been added at the ending paragraph (on Page 6) of the Literature review.

There are numerous English grammatical errors in the article. Please read the article thoroughly and correct all the errors.

Response: We appreciate your suggestions for further improving its presentation. In this revision, we have made substantial changes throughout the manuscript and carefully proofread the paper to make it typo-free. Thanks to your thoughtful comments, the quality and presentation of this research have been significantly improved. We hope that this revision could meet with your approval.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments on “Carbon emission reduction effects assessment of smart city pilot policy in China”

Dear Authors,

The paper must be significantly improved. Please consider the following remarks:

Major comments:

(1) Please improve abstract part. Please answer the following questions:

a) What problem did you study and why is it important?

b) What methods did you use?

c) What were your main results?

Please explain “..of pilot areas by about 4.36%”

(2) Literature review should be improved. You should analyse references more deeply.

(3) Please highlight scientific novelty – Line 138 “ possible novelty”

(4) Page 10. Please show raw data

Minor comments:

(1) Line 106: “every 1% increase in carbon emission efficiency…” how calculated? Percent or percent point?

(2) Line 377. Please improve units

(3) Figure 3. Please improve the name of vertical and horizontal axis

(4) Figure 4. Please improve the name of Figure

(5) Line 770. Please avoid of using “we”

Author Response

RESPONSES TO THE REVIEWER’S COMMENTS ON THE SUBMISSION

Carbon emission reduction effects of smart city pilot policy in China

(Manuscript ID#: sustainability-2198579)

RESPONSES TO REVIEWER #3

The paper must be significantly improved.

Response: We are grateful for your overall endorsement of our research. Please find below our point-to-point response to your thoughtful comments.

Major comments:

(1) Please improve abstract part. Please answer the following questions:

a) What problem did you study and why is it important?
b) What methods did you use?
c) What were your main results?

Response: Thank you very much for your instructive suggestions. The author has optimized the abstract according to your thoughtful suggestion of research questions → research importance → research methods → research results. Please refer to Page 2 for detailed abstract.

Please explain “..of pilot areas by about 4.36%”

Response: Thanks to your thoughtful suggestions. The complete regression results of the benchmark model (1) are reported in column (6) of table 2. The estimated coefficient of the core explanatory variable is -0.0436 (the approximate value) and has passed the significance test. The explained variable in this paper is the carbon emission measured by the carbon emission intensity. Therefore, the value (-0.0436) means that the construction of smart cities has reduced the carbon emissions of pilot areas by 0.0436, which equals 4.36%. Therefore, the construction of smart cities has reduced carbon emissions in pilot areas by about 4.36%.

(2) Literature review should be improved. You should analyze references more deeply.

Response: We are grateful for your suggestion. The literature review has been improved on Page 4-6. In addition to the original overview and analysis, we have added the analysis on relevant references that are focused on the case study and qualitative research of the smart city construction in certain regions, particular fields of smart city, the relationship between the smart city construction and the environment. Furthermore, we analyzed the few research that quantitatively explore the relationship between the smart city construction and the carbon emissions. On this basis, the research gaps are summarized. The revised detailed literature review is as follows.

Literature review

(3) Please highlight scientific novelty – Line 138 “ possible novelty”

Response: We wish to express our sincere gratitude to you for your constructive comments and suggestions. The scientific novelty has been added on Page 3-4.

(4) Page 10. Please show raw data

Response: Thank you very much for your instructive suggestions. The author has uploaded raw data as an attachment to the magazine system.

Minor comments:

(1) Line 106: “every 1% increase in carbon emission efficiency…” how calculated? Percent or percent point?

Response: A lot of thanks for your kindness. The sentence “every 1% increase in carbon emission efficiency will reduce carbon dioxide emissions by 8.37 million tons, using DID and SDID models” comes from the research conclusion of literature “Yu, Y., Zhang, N. Low-carbon city pilot and carbon emission efficiency: quasi- experimental evidence from China. Energy Econ. 2021, 96, 105125”. In their research, a general nonconvex meta-frontier data envelopment analysis model was developed to calculate the carbon emission efficiency, and a unique dataset of 251 cities in China during the years 2003 to 2018 was taken as a quasi-experimental evidence through difference-in-differences (DID) and spatial DID (SDID) estimators.

From this perspective, it means every one percent point increase in carbon emission efficiency.

(2) Line 377. Please improve units

Response: Thanks a lot for your sincere suggestions. The units have been improved in the revised manuscription on Page 11, and details are as follows: the values of which are 2.1622kg/m³, 3.1013kg/m³ and 1.3023kg/(kw • h) respectively.

(3) Figure 3. Please improve the name of vertical and horizontal axis

Response: We are thankful for this suggestion. The name of vertical and horizontal axis has been improved.

Figure 3. Parallel trend test.

(4) Figure 4. Please improve the name of Figure

Response: Thank you very much for your instructive suggestions and the name of Figure 4 has been improved.

Figure 4. Placebo Test Results.

(5) Line 770. Please avoid of using “we”

Response: We wish to express our sincere gratitude to you for your constructive comments and suggestions, having helped us to significantly improve the presentation of our paper. The study has been carefully modified accordingly and all the “we” in the original manuscript have been deleted.

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