Analysis of China’s Manufacturing Industry Carbon Lock-In and Its Influencing Factors

Round 1

Reviewer 1 Report

This paper studies carbon emission from the high-carbon manufacturing industry of 30 provinces in China based on the IPCC carbon emissions coefficient method and the energy consumption data.

The paper is well written; however, I have the following suggestions to improve this work.

 

Please improve English writing, there are several problems. The abstract is a bit long, please rewrite the abstract and try to be as concise as possible. Please state the novelty of this work. The introduction and literature seem to be integrated, however, the combined intro plus literature review is very short, please add more papers related to your research. Please add relevant papers from “sustainability” journal

Author Response

Response to Reviewer Comments

Dear Editors and Reviewers:

Thank you for your letter and for the reviewers’ concerning our manuscript entitled “Spatial-temporal Variation of Carbon Emissions in the Chinese High-carbon Manufacturing Industry”(ID: Sustainability-658232).Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red in the respond. The main corrections in the paper and responds to the reviewer’s comments are as flowing:

Responds to the reviewer’s comments:

 

（1）Please improve English writing, there are several problems.

Thank you for pointing this out. When the article is revised, the English has been polished. If further polishing is needed after the modification, we will continue to improve.

 

（2）The abstract is a bit long, please rewrite the abstract and try to be as concise as possible.

Thank you for pointing this out. We have modified the abstract and the modification is as follows:

 

Abstract: There are industry lock-in and regional lock-in phenomena in China's manufacturing industry carbon emissions. However, the existing researches often focus on global carbon emissions, which is not conducive to finding the main problems of manufacturing industry carbon emissions. Therefore, in order to provide support for the green development of China's manufacturing industry in accordance with local conditions, this study focuses on identifying the industry lock-in and regional lock-in of China's manufacturing industry, and exploring the regional influencing factors of carbon lock-in. This paper was based on IPCC carbon emissions coefficient method and the energy consumption data from 2000 to 2016 to count the carbon emissions of manufacturing industry of 30 provinces in China (except Hong Kong Macao, Taiwan and Tibet). On this basis, the paper used spatial-temporal geographical weighted regression model to analysis the regional influence factors of high-carbon manufacturing industry. Results demonstrate that: China's high-carbon manufacturing industry is mainly concentrated in ferrous metal processing industry, nonmetallic mineral manufacturing industry and other sectors. In addition, the carbon emissions of high-carbon manufacturing industry are mainly concentrate in Bohai Bay and North China Plain. The industrial structure and economic scale are the main reasons for the carbon regional lock-in of high-carbon manufacturing industry, and the strength of the lock-in has continued to increase. Resource endowment is a stable factor of carbon lock-in in high-carbon regions. Technological progress helps to unlock carbon lock-in, while foreign investment has led to a stronger lock-in in carbon emissions. The biggest contribution of this study is to find out the regional factors that influence the carbon lock-in of China's manufacturing industry, which points out the direction for the low-carbon transformation of local manufacturing industry

 

 

 

（3）Please state the novelty of this work.

Thank you for pointing this out, We have stated the innovation of this work in the abstract and introduction, the statement is as follows:

The biggest innovation of this paper is to find out the industry lock-in and regional lock-in of China's manufacturing industry. In addition, based on the regional heterogeneity, this paper analyzes the regional influencing factors of carbon emissions, and puts the time factor into the analysis framework, Therefore, the temporal-spatial evolution characteristics of carbon emissions influencing factors are also well described. At the same time, the key factors for the regional lock-in of China's manufacturing industry carbon emissions are analyzed.

There are three main contributions: (1) find out the regional influencing factors of carbon emissions, and provide decision-making reference for making carbon emissions reduction measures according to local conditions. (2) Analyzed the temporal-spatial evolution characteristics of carbon emissions influencing factors. (3) Find out the key factors of industry lock-in and regional lock-in in carbon emissions of China's manufacturing industry, in order to provide help for China's manufacturing industry to break the carbon lock-in and realize low-carbon transformation and upgrading.

 

(4) The introduction and literature seem to be integrated, however, the combined intro plus literature review is very short, please add more papers related to your research. Please add relevant papers from “sustainability” journal

Thank you for pointing this out, I have revised the introduction of the paper, the modification is as follows:

Introduction

China is undergoing a rapid industrialization and the rigid demands for energy consumption because of economic growth will maintain a high growth rate. Carbon emissions in energy consumption may bring great pressure on the sustainable development of economy, society and environment. Manufacturing industry is the main driving force of industrial economic growth in China. It produces strong negative feedback to the environment while bringing high economic benefits. The proportion of manufacturing industry carbon emissions has exceeded 66.7% of total industrial carbon emissions since 2000 [1]. Therefore, how to achieve carbon emissions reduction of manufacturing industry has become an urgent problem to be solved by academia. However, the carbon emissions of China’s manufacturing industry has a significant industry concentration and lock-in. From 2000 to 2016, the ferrous metal processing industry, nonmetallic mineral manufacturing industry, petroleum smelting industry, chemical raw material product industry and non-ferrous metal metallurgic processing industry account for more than 81.59% of carbon emissions in the whole manufacturing industry [1]. Therefore, it is of great practical significance to clarify the causes of carbon lock-in in these high-carbon manufacturing industry and analyze the factors of carbon emissions regional lock-in.

Many studies on content and method of manufacturing industry carbon emissions have been reported. Most of the existing research focuses on economically developed countries or regions. For example, Hammond et al. [2] used the LMDI method to decompose and analyze the influencing factors of manufacturing industry carbon emissions in the UK, and believed that energy intensity was the primary factor that led to the decline in carbon emissions. Kopidou et al. [3] also used the LMDI method to explore the impact of fuel mixing and economic growth on manufacturing industry emissions in Europe. It is found that economic growth and resource intensity were the main driving forces of carbon emissions growth, and the optimization of the energy structure has a limited effect on carbon emissions reduction. Diakoulaki et al. [4] decomposed the carbon increment of 14 European Union countries from 1990 to 2003 and found that the output effect and energy intensity were the main factors affecting the carbon emission growth. Clara et al. [5] took Germany and Colombia as examples, and found that although the total energy consumption of manufacturing industry in the two countries increased, the energy intensity showed a downward trend. In addition, Since China is one of the major carbon emissions countries, the academia has also done a lot of research on the carbon emissions of China's manufacturing industry. Among them, Chang et al. [6] used structural decomposition analysis (SDA) to found that the decrease of carbon intensity was conducive to the decrease of carbon emissions, but an increase in investment scale will lead to carbon emissions growth. Lee et al. [7] estimated the shadow price of carbon emissions of 30 manufacturing industries in China based on the input distance function, and believed that using capital to improve the production efficiency of coal, oil and other industries will make it easier for China to achieve the goal of "green and low-carbon development". Huw [8] analyzed the essence and consequences of China's economic rise by taking manufacturing industry as the leading role, and believed that the possibility of China's economic growth slowing down in the short term is low. Lin et al. [9] calculated the carbon emissions transfer between different industrial sectors in China based on the input-output method, and used regression analysis to found that energy consumption is the main factor leading to carbon growth, and energy-saving technology can significantly reduce energy intensity, thus reducing carbon emissions.

Through literature review, we found that researchers usually cover the whole manufacturing industry in terms of carbon emissions of manufacturing industry, and few scholars study the issue of carbon emissions industry lock-in of manufacturing industry, which will lead to the lack of pertinence of research and is not conducive to carbon unlocking in high-carbon manufacturing industry. In addition, in the analysis of carbon emissions influencing factors, researchers usually pay attention to the influencing factors of carbon emissions in the whole region. However, due to regional heterogeneous characteristics, there are significant differences in economic development level, natural resource reserves, opening-up degree and infrastructure construction in different regions. On this basis, whether there are differences in the key factors leading to carbon emissions in different regions needs to be further discussed. In addition, although related research has clarified the regional differences in manufacturing industry carbon emissions, there are still a lack of literature on the long-term temporal-spatial evolution of carbon emissions and the key factors of regional lock-in of carbon emissions.

So, as far as China's manufacturing industry is concerned, is there industry lock-in in its carbon emissions? What is the mechanism that leads to lock-in? In addition, in the case of carbon lock-in in high-carbon industry, is there regional lock-in in manufacturing industry carbon emissions? What would be the key factors that lead to the regional lock-in? In order to answer these questions, this paper was based on IPCC carbon emissions coefficient method and the energy consumption data from 2000 to 2016 to count the carbon emissions of 30 provinces in China. Based on the regional perspective, GTWR model is used to study the regional factors of carbon emissions. This paper aims to provide decision-making reference for China's manufacturing industry to break the carbon lock-in and realize low-carbon transformation.

 

Special thanks to you for your valuable comments.

Author Response File: Author Response.docx

Reviewer 2 Report

The papers contribution is to calculate emissions in high-carbon manufacturing industries at the regional (provincial) level in China and to analyse their distribution and evolution by means of a spatial-temporal regression  model.

The main problem that I have with this paper is that the main findings of the paper do not warrant a publication in a journal of the level of Sustainability. The exercise presented in the paper is in effect mainly descriptive, and the regression does not add much understanding to the underlying reasons of the increase in emissions than from what is known from anecdotal knowledge, which can be summarised by the world industrialization. It offers little concrete policy advice as to what can be done to limit emissions, other than very general remarks relating to the adoption of cleaner technology.

With regard to the regression, I believe a more subtle choice of regressors could help in improving the paper. Right now, the variables are too general to offer much insight other than a description of the evolution of emissions. For example, what is the nature of the technological R&D expenses? Is it in clean technologies, or fossil-fuel related technologies? Similarly, are the investments (foreign or fixed asset investments) in capital related to clean or dirty technologies?

The results of the regression should also be presented in a standard table.

Furthermore, the paper requires a thorough proofreading by a native speaker. There are many basic grammatical errors such as: “…is conducive to reduce…”. “carbon emissions was”,

Author Response

Response to Reviewer Comments

Dear Editors and Reviewers:

Thank you for your letter and for the reviewers’ concerning our manuscript entitled “Spatial-temporal Variation of Carbon Emissions in the Chinese High-carbon Manufacturing Industry”(ID: Sustainability-658232).Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red in the respond. The main corrections in the paper and responds to the reviewer’s comments are as flowing:

Responds to the reviewer’s comments:

According to the valuable opinions of the expert, We have made great changes to the article. First of all, we change the title of the article to “Analysis of China's manufacturing industry carbon lock-in and its influencing factors”, Second, we have revised and improved it according to the following suggestions of expert

（1）The exercise presented in the paper is in effect mainly descriptive, and the regression does not add much understanding to the underlying reasons of the increase in emissions than from what is known from anecdotal knowledge, which can be summarised by the world industrialization.

Thank you for pointing this out. When doing regression analysis, the main purposes of this article are:

Analyze regional factors affecting in carbon emissions of manufacturing industry. (Due to regional heterogeneous characteristics, there are significant differences in economic development level, natural resource reserves, opening-up degree and infrastructure construction in different regions. On this basis, using spatial-temporal geographical weighted regression (GTWR) is to explore how the key factors that lead to carbon emissions in different regions will differ (especially in the locked regions of carbon emissions)). Find out the key factors of regional lock-in in carbon emissions of China's manufacturing industry.（Due to there is a phenomenon of regional lock-in in carbon emissions of manufacturing industry. Thus, it is necessary to incorporate the time factor into the regional spatial analysis model, due to the GTWR includes time factor and it can be used to analyze the key factors that lead to the regional lock-in of carbon emissions during the study period.） Analyzed the temporal-spatial evolution characteristics of carbon emissions influencing factors.（Due to the GTWR includes time factor, the fitting coefficient of influencing factors to carbon emissions in different years is comparable, which can be used to analyze the change of the effect intensity of influencing factors on carbon emissions in the same region.）

Therefore, in order to achieve the above three goals, we chose GTWR for regression analysis. In addition, the article has modified and supplemented the reasons for the regional differences of carbon emissions influencing factors and the reasons for the spatial-temporal differences of influencing factors. Hoping that experts can give guidance and suggestions.

 

（2）It offers little concrete policy advice as to what can be done to limit emissions, other than very general remarks relating to the adoption of cleaner technology.

Thank you for pointing this out, I have revised and supplemented the policies and suggestions on how to limit carbon emissions, and the revised contents are as follows:

Therefore, we find that the key to unlocking the carbon emissions lies in the rational adjustment and control of the industrial structure and economic scale of high-carbon manufacturing industry. In addition, if we want to achieve carbon unlocking in high-carbon regions, we must further optimize the use of foreign investment, develop and promote clean energy, and improve carbon-based technologies.

It is the trend to realize the carbon unlocking of manufacturing industry and take the road of green sustainable development. Firstly, attentions shall be paid to “hot” provinces in carbon emissions of manufacturing industry. Secondly, heterogeneous characteristics of carbon emissions industries and regions shall be taken into account and more attentions shall be paid to carbon emissions in ferrous metal processing industry and Eastern China and. In addition, when formulating measures of carbon emissions reduction, it is necessary to avoid policy equalization. Moreover, China is undergoing rapid industrialization and carbon emissions reduction shall not be achieved at the cost of economic benefits. Instead, provincial high-carbon manufacturing industry industrial structure shall be further adjusted reasonably (especially in North China and Northeast China). In addition, the increased technological expenditure can reduce carbon emissions in high-carbon manufacturing industry to a limited extent. Therefore, breaking technological bottlenecks and developing energy-saving and emission-reduction technologies (development and utilization of new energy) are still key breakthroughs for government and enterprises to reduce carbon emissions at present. Finally, the spatial heterogeneity and spillover effect of carbon emissions factors in the high-carbon manufacturing industry require provinces to cooperate and formulate differentiate carbon emissions reduction policies, and develop their own advantages to realize the goal of carbon emissions reduction. These are conducive to realize low-carbon green transformation and upgrading of whole industries.

 

 (3) Right now, the variables are too general to offer much insight other than a description of the evolution of emissions. For example, what is the nature of the technological R&D expenses? Is it in clean technologies, or fossil-fuel related technologies? Similarly, are the investments (foreign or fixed asset investments) in capital related to clean or dirty technologies?

Thank you for pointing this out, I have made further explanation on the selection of index and why to select these index. I have revised and analyzed the technology effect (“clean” and “dirty” technology), the specific content is in the second part of the article ("analysis framework"), in addition, I have also explained it in the “Technological effect (LnT)”. I hope experts can give guidance and suggestions

(4) The results of the regression should also be presented in a standard table.

Thank you for pointing this out, because the regression results and significance test results involve the data of 30 provinces in China (2000a and 2016a), it takes up a lot of space. Therefore, after improvement, the regression results and the significance test results of influencing factors are visualized (Figure 4a-e for details). If the experts think this modification is not appropriate, I will list the regression results and significance test results.

 

(5) Furthermore, the paper requires a thorough proofreading by a native speaker.

Thank you for pointing this out. When the article is revised, the English has been polished. If further polishing is needed after the modification, we will continue to improve.

 

Special thanks to you for your valuable comme

Reviewer 3 Report

The paper is interesting. However, there are issues with readability that can be corrected with the assistance of a good editor. I have noted a few areas in the attached edited/reviewed draft but there are many more.

Additionally, I would strongly suggest that the authors define key terms used in the paper, such as "lock-in" to ensure the reader's meaning is consistent with the intention.

Comments for author File: Comments.pdf

Author Response

Response to Reviewer Comments

Dear Editors and Reviewers:

Thank you for your letter and for the reviewers’ concerning our manuscript entitled “Analysis of China's manufacturing industry carbon lock-in and its influencing factors”(ID: Sustainability-658232).Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red in the respond. The main corrections in the paper and responds to the reviewer’s comments are as flowing:

Responds to the reviewer’s comments:

 

（1）The paper is interesting. However, there are issues with readability that can be corrected with the assistance of a good editor. I have noted a few areas in the attached edited/reviewed draft but there are many more.

Thank you for pointing this out. According to the expert's opinion, we will modify the grammar of the article. If there are still problems, the article will be further improved in English writing.

 

（2）Additionally, I would strongly suggest that the authors define key terms used in the paper, such as "lock-in" to ensure the reader's meaning is consistent with the intention.

 

Thank you for pointing this out. according to experts' opinions, the article revised “2.1 Carbon lock-in of high-carbon manufacturing industry"and further explain the meaning of "carbon lock-in".

The core of this study is " manufacturing industry carbon lock-in ". Among them, "carbon lock-in" is divided into industry lock-in and regional lock-in. The explanation of carbon lock-in in the industry mainly participates in Unruh's point of view. The specific contents are as follows:

"Carbon lock-in" was first proposed by Unruh, a Spanish scholar. [10], who pointed out that once the carbon-based technology trapped in fossil energy is stable, under the positive feedback of increasing returns to scale, the stakeholders develop the system around high-carbon technology, thus gradually forming a "Techno-Institutional Complex" (TIC). In addition, Unruh. [11] further pointed out that the carbon-based technology that has been locked in fossil energy, due to the path dependence formed by the interaction with the system, thus strengthening the carbon lock-in.

However, regional lock-in is based on industry lock-in, by analyzing the regional differences of high-carbon manufacturing industry carbon emissions. The regional lock-in is mainly to build the indicators of influencing factors and analyze them. (The specific contents in the ”2.2 Carbon lock-in of high-carbon regional and influencing factors”).

Special thanks to you for your valuable comments.

 

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

I would like to thank the authors for the revision of the paper. I have the following remarks.

The authors write that:” it is necessary to incorporate the time factor into the regional spatial analysis model”….. to Find out the key factors of regional lock-in in carbon emissions”.

In my view key factors behind the lock in should be analysed by different means other than a time variable – a time variable does not really say anything about the nature of a lock in. In general, and this I see as the main drawback of the paper, is that the link between the variables in the model and the lock-in effect should be stated more clearly and the nature of the lock-in described more in detail.

When writing the abstract, I would write the major contribution as one of the first sentences and not the last.

Further English and overall editing would be welcome, there are still some errors (not only grammatical, but in the equations as well) for example:

Equation (1) should be corrected,  C_j should be C_ij, or some other correction should be made, because now this equation is clearly wrong.

1. Missing ‘s’ in the word “concentrate”, as shown below:

 In addition, the carbon emissions of high-carbon manufacturing industry are mainly concentrate in Bohai Bay and North China Plain.

2. should be “find” instead of ”found” in:

used structural decomposition analysis (SDA) to found that

3. “Attention shall be paid” instead of “attentions shall be paid”.

Author Response

Response to Reviewer Comments

Dear Editors and Reviewers:

Thank you for your letter and for the reviewers’ concerning our manuscript entitled “Analysis of China's manufacturing industry carbon lock-in and its influencing factors”(ID: Sustainability-658232).Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red in the respond. The main corrections in the paper and responds to the reviewer’s comments are as flowing:

Responds to the reviewer’s comments:

 

（1）In my view key factors behind the lock in should be analysed by different means other than a time variable – a time variable does not really say anything about the nature of a lock-in.

Thank you for pointing this out. In this paper, it is not clear why GTWR model is adopted.

First of all, the GTWR model is used to explain the effect of influencing factors on carbon emissions, including time factors, so that the fitting results of regional influencing factors of carbon emissions can be comparable at different time points, so as to better find the key factors that affect the regional carbon lock-in. (However, the time factor is not used as a separate indicator to explain the regional lock-in of carbon emissions.)

Secondly, according to experts' opinions, the first part of 4.2.1 "model selection." will be modified as follows:

 (1) Model selection. The above research shows that there are significant regional differences in carbon emissions of high-carbon manufacturing industry. Regional differences should be considered in the analysis of carbon emissions factors. Therefore, the regional spatial analysis model is more suitable for this study. However, the GTWR model is based on the GWR model, and further grasps the regional differences of different influencing factors on carbon emissions in time, which can provide the analysis of carbon emissions influencing factors in different time and different regions, and also help to find out the key factors that lead to the regional lock-in of carbon emissions in the research period.

 

（2）In general, and this I see as the main drawback of the paper, is that the link between the variables in the model and the lock-in effect should be stated more clearly and the nature of the lock-in described more in detail.

Thank you for pointing this out. according to experts' opinions, the article revised "3.3 index selection of carbon emissions affecting factors" and further explained the influence of influencing factors on carbon emissions regional lock-in. The specific content of the modification is as follows:

Scale effect (P): There are obvious regional differences in the impact of scale

effect on high-carbon manufacturing carbon emissions. Among them, when the high-carbon manufacturing industry is in the stage of increasing returns to economic scale, with the continuous expansion of regional output scale, the demand for energy consumption will also increase, and under the rigid condition of fossil energy consumption as the main part, carbon emissions will also increase, thus strengthening the regional lock-in of carbon emissions [28].Since the GDP index fails in the collinearity test, sales value of high-carbon manufacturing industry was chose to measure the impact of scale effect on regional carbon lock-in.

(2) Structural effect (IS): In this study, the manufacturing industries with high-carbon

lock-in (ferrous metal processing industry, non-metallic mineral manufacturing industry, etc.) are all high-energy consumption industries. However, at present, the energy consumption structure dominated by fossil fuels is difficult to achieve fundamental changes. To some extent, the high-energy consumption industry means high-carbon emissions industry. Therefore, the higher the proportion of high-carbon manufacturing industries, the more unfavorable to achieve regional carbon unlocking [29].The proportion of high-carbon manufacturing industry employment in the total manufacturing industry was applied to measure the impact of structural effect on regional carbon lock-in.

(3) Technological effect (T): The expenditure of technology cost has obvious bias.

Among them, “Green point” of technological progress caused by technological expenditure is the primary factor to reduce carbon emissions [23]. But if the enterprise profits through the development of "dirty" technology, coupled with the path dependence of technological progress, the development of "dirty" new technology will lead to the increase of carbon emissions, further leading to the carbon emissions industry and regional lock-in. In this study, internal R&D expense of the high-carbon manufacturing industry was chosen to measure influences of technological effect on carbon emissions industry and regional lock-in.

(4) Resource endowment (RE): Resource endowment is one of the important factors

that influence production layout of enterprises. The development level of coal, oil, ferrous metal, non-ferrous metal and non-metal mining industry plays a critical role in the production layout of high-carbon manufacturing industry. In terms of different regions, the higher the development level of coal, oil and other mining industries, the more unfavorable to achieve regional carbon unlocking. In this study, total production and sales volume of coal mining industry, petroleum mining industry, ferrous metal mining industry, nonferrous metal mining industry and non-metallic mining industry was chosen to measure the impact of resource endowment level of high-carbon manufacturing industry on regional carbon lock-in.

(5) Foreign direct investment (FDI): The environmental impact of foreign investment on host countries has two sides. Part of the research results show that the increase of foreign direct investment is not only conducive to improving the capacity of environmental regulation, but also conducive to the development of clean-energy through the introduction and absorption of advanced technology, so as to achieve carbon emissions reduction. However, another part of the research results believe that the increase of foreign direct investment in high-carbon manufacturing industry will lead to the increase of production. However, the energy consumption structure dominated by fossil fuels is difficult to achieve a breakthrough, which will inevitably lead to the increase of carbon emissions and further strengthen the regional carbon lock-in[24]. In this study, the amount of FDI of high-carbon manufacturing industry was chosen to measure influences of FDI on regional carbon lock-in.

 

（3）When writing the abstract, I would write the major contribution as one of the first sentences and not the last.

According to experts' opinions, the main contributions of this paper are written in front of the summary. See the red mark of "abstract" of the article for the specific content of revision.

 

(4) There are problems in English writing.

According to the expert's opinion, we will modify the grammar of the article. If there are still problems, the article will be further improved in English writing.

 

Special thanks to you for your valuable comments.