Submit to this Journal Review for this Journal Propose a Special Issue

Article Menu

Share Help Cite Discuss in SciProfiles

Open AccessArticle

Peer-Review Record

Synergistic Effect of Carbon Trading Scheme on Carbon Dioxide and Atmospheric Pollutants

Sustainability 2021, 13(10), 5403; https://doi.org/10.3390/su13105403

by Zhiguo Li, Jie Wang^* and Shuai Che

Reviewer 1: Anonymous

Reviewer 2: Anonymous

Sustainability 2021, 13(10), 5403; https://doi.org/10.3390/su13105403

Submission received: 7 April 2021 / Revised: 28 April 2021 / Accepted: 30 April 2021 / Published: 12 May 2021

Round 1

Reviewer 1 Report

The text needs to be revised by a mother tongue excerpt because it fails in several parts.
The abstract needs to be revised because it is a little bit confused.
Line 30-42: the concept expressed in that part are confused. It is not clear if the CO2 trading is in a pilot form or it is developed. Only in a latter part this concept becomes clear.
Figure 1 presents a typo
The presented literature review is appropriate designed but the discussion of the reported works is poor. The literature lack is not clearly presented as well as the way that the authors want to use to cover it.
The method is clear to me but, it is not clear the assumption used in the results elaboration as well the available data.

Author Response

Point 1: The text needs to be revised by a mother tongue excerpt because it fails in several parts.

Response 1: We follow this advice and polish most sentences in the text, to reduce grammatical errors and inappropriate expressions and make the language more fluent. The description and analysis in several parts have been made major changes, including the introduction, data and Empirical results and discussion.The revised contents are as follow:

Line 53-58:

this paper conducts decomposition analysis with the combination method of IPAT and Logarithmic Mean Divisia Index (IPAT-LMDI) model and further undertakes quantitative analysis (as shown in Fig.1). Moreover, difference-in-difference (DID) and propensity score matching difference-in-difference (PSM-DID) methods are uesd to verify the synergic emission reduction effect of ETS pilots in China, covering the selected period from 2007 to 2016.

Line 180-186:

This paper aims to analyze synergic emission reduction effect between CO2 and atmospheric pollutants with IPAT-LMDI method. Furthermore, DID and PSM-DID methods are used to conducts quantitative analysis on synergic emission reduction effect driven by ETS. To show the synergic emission reduction more clearly, this paper decomposes synergic emission reduction effect into direct and indirect parts. At last, the concrete formation mechanism is discussed further from the perspective of economic development, industry structure and energy efficiency.

Line 301-314:

Comprehensive least generalized square method (C-FGLS) solves heteroscedasticity and synchronous correlation effectively. Table 4 provides regression results for Formula (4) evaluated using C-FGLS. The results verify the robust synergic emission reduction relationship between CO2 and atmospheric pollutants, among which SO2, Dust and NOX pass significant tests within the 5% or 10% confidence interval, while the PM2.5 fails.

Line 328-333:

Two-stage policy effect assessments were conducted to analyze the synergic emission reduction effect of ETS. As the Table 5 shown, the ETS works significantly in CO2 emission reduction. Such results are consistent with most of the latest research. Similarly, SO2 emission and PM2.5 are also curbed by ETS, but the the PM2.5 fails in significant test. Therefore, the impact on atmospheric pollutants driven by ETS is mainly manifested as SO2 emission reduction currently.

Line 337-350:

According to Formula (6), the synergic emission reduction effect is decompose into direct synergy, economy synergy, industry synergy and efficiency synergy. Direct synergy is obtained through the multiplication of total CO2 emission and synergic coefficient in Formula (4). However, indirect synergic requires the coefficient of interactive item listed in Formula (5), as showed in Table 6.

The interaction items of efficiency and industry are negative, revealing that carbon emission reduction hindered atmospheric pollutant emission reduction through energy efficiency and industrial structure. On the one hand, rebound effect caused by the improvement of energy efficiency leads to an increase in energy consumption, which weakens ultimately the SO2 emission reduction effect. On the other hand, energy investment is the main driver for industrial development, similarly contributing to the decline in SO2 emission reduction. However, the coefficients of economy is positive. CO2 emission reduction contributes to the green economic development model and promotes SO2 emission reduction.

Line 353-360:

Furthermore, the synergic emission reduction effect of ETS pilots and several representative regions were shown in Fig 2. Overall, the direct synergy accounts for relatively high proportion , which is consistent with the emission reduction of CO2. The indirect synergy accounted for low proportion, corresponding to the locked energy efficiency, economic development and industrial structure, among which efficiency synergy dominates in SO2 synergic emission reduction, especially in Beijing, Tianjin and Shanghai. Therefore, this paper maintain focus on the direct synergy of SO2 driven by ETS.

Line 425-441:

Based on the decomposition results, Formula (5) and (9) are applied to test how ETS achieves synergic emission reduction between CO2 and SO2. As shown in Table 6, economic development, enrtgy efficiency and industrial structure prove to be potential channels for synergy effect. Meanwhile, synergic emission reduction effect of CO2 and SO2 has been enhanced dramatically under multiple channels, which emphasizes the importance of multi-dimensional synergic emission reduction.

Further, this study investigates how the ETS achieves the emission reduction of CO2 through the same channels. As illustrated in Table 10, the synergic emission reduction effect is difficult to achieve with single channel. Under the background of ETS, The economic development channel hinders the CO2 emission reduction significantly, and energy efficiency and industrial structure both potentially restrained that either. However, under the function together with above three channels, ETS promotes the emission reduction of CO2. Especially energy efficiency and industry structure channels, their inhibitory effect on emission reduction of CO2 transforms into promotion effect and the effect intensity increases substantially. Therefore, the emission reduction of CO2 driven by ETS depends on the combined effects of energy efficiency and industrial structure.

Line 446-463:

Line:467-483:

First, government should emphasize the coordinated management of CO2 and atmospheric pollutants, through integrating high-carbon industries into comprehensive emission reduction system and innovating synergic technology applied in environmental management. Given the current state of synergic emission reduction, to avoid the dilemma of broken treatment, introducing encironmental policies to strengthen the coordination of regional environmental protection work.

Second, national carbon trading market need to promoted vigorously and ETS should be continually improved. For example, the assessment criterion is no longer confined to carbon emission reduction, while also covers atmospheric pollutants emission reduction.

Finally, to improve carbon trading policy, improving energy efficiency and optimizing industry structure should be taken into account. On the one hand, the government should actively promote low-carbon technological innovation of enterprises, but also pay attention to avoid the excessive carbon emission caused by economic expansion. On the other hand, the government needs to promote the transformation of high-carbon industries, investment of green-oriented technology and improvement of energy efficiency within the framework of synergic emission reduction.

Point 2: The abstract needs to be revised because it is a little bit confused.

Response 2: We follow this advice and make adjustments on abstract to summary our paper more clearly. We try to more specifically introduce the research objectives, research methods and specific research conclusions in abstract. Then, we further put forward specific suggestions to promote the synergic effect of carbon trading scheme on carbon dioxide and atmospheric pollutants. The revised abstract is as follow:

Line 7-23:

To estimate the synergic emission reduction effect resulting from carbon emissions trading scheme (ETS) pilots launched in 2013, this paper estimated the synergic emission reduction relationship between carbon dioxide (CO2) and atmospheric pollutants, consisting of sulfur dioxide (SO2), nitrogen oxides (NOX), dust pollutants (Dust) and particulate matter 2.5 (PM2.5). Using the extended Logarithmic Mean Divisia Index (LMDI) method and the IPAT equation, this paper decomposed the synergic emission reduction effect into the direct and indirect parts driven by energy efficiency, economic development and industrial structure. Moreover, this paper quantified the synergic emission reduction effect of ETS pilots with difference-in-differences method (DID) and Propensity Score Matching difference-in-differences method (PSM-DID). The results show that, from 2013 to 2016, CO2 and atmospheric pollutants achieved emission reduction synergically through ETS, among which the synergic emission reduction effect between CO2 and SO2 was most significant. Compared with direct part, the indirect part accounted for smaller proportion in synergic emission reduction effect. The combined action of energy efficiency and industrial structure had potential positive influence on synergic emission reduction effect of ETS. Consequently, it suggests that government need to develop domestic carbon market further, improve the energy efficiency and optimize the industrial structure to promote synergic emission reduction.

Point 3: Line 30-42: the concept expressed in that part are confused. It is not clear if the CO2 trading is in a pilot form or it is developed. Only in a latter part this concept becomes clear.

Response 3: We follow this advice and introduce the background of ETS pilots in China more clearly. For example, we introduce the duration of pilots and the decvelopment of national carbon trading market in detail. Besides, we make adjustments on sentence order to adviod confused expression. The revised content are as follow:

Line 28-37:

The externality cost of carbon emission can be internalized through emission trading scheme (ETS), which contributes to carbon emission reuction. Consequently, the ETS has been widely adopted to implement emission reduction targets. As the largest carbon-emitting country, China promised that 60%-65% reduction in carbon dioxide emissions per unit gross domestic product (GDP) in 2030 compared with 2005” at the fundamental Hagen meeting. To complete the emission reduction goals, China actively launched the ETS pilots program, which lasted for 3 years, from 2013 to 2015, consisting of Beijing, Tianjin, Shanghai, Guangdong, Hubei, Chongqing and Shenzhen. Then, the ETS was peomoted widely and the national-wide carbon market has been gradually established until 2017.

Point 4: Figure 1 presents a typo

Response 4: We follow this advice and correct this word.

Fig.1. Industry

Point 5: The presented literature review is appropriate designed but the discussion of the reported works is poor. The literature lack is not clearly presented as well as the way that the authors want to use to cover it.

Response 5: We follow this advice and introduce complemental explanation and analysis on related literature. For example, We further summary the different conclusions and analyze the reason based on the research object and research cycle. And we also sort out the action mechanism about ETS how to influence carbon emission reduction. The Revised content are as follow:

Line 63-77:

Carbon trading is an important market tool in driving economic growth and carbon dioxide emission reduction, in which the emission right are thought of as commodity. A vast body of existing literature expounds the theoretical mechanism and reality carbon emission effect achieved by ETS. Generally, carbon trading is defined as a forced mechanism that restrictes pollutants emissions through high cost and technological progress. Fan et al. (2017) found that carbon trading contributes to cost savings and futher develops low-carbon technology through the reinvestment of revenue. Furthermore, some studies show that carbon trading promootes low-carbon technological innovation, Li and Wang (2021) suggested that carbon trading promotes spatial carbon emission by technical progress. Considering the complex mechanism of ETS how to work, industrial structure, energy consumption structure and economic development are all defined as conduction pathways. Wang et al. (2018) found the effectiveness of emission reduction driven by ETS is related to the local economy. Wang and Gao (2020) reported ETS can stimulate the structural adjustment of high pollution industries and eliminate backward production capacity.

Line 79-85:

Point 6: The method is clear to me but, it is not clear the assumption used in the results elaboration as well the available data

Response 6: We follow this advice and introduce the assumption used in the results elaboration and the available data in detail. For the assumption, we further explain the requirement and how to satisfy it. And for the data, we make up more detailed sources. Besides, we also describe how to structure the some variables such as the emission reduction of CO2 and atmospheric pollutants. The revised contents are as follow:

Line 289-297:

The annual time-series data, covering the selected period from 2007 to 2016, are collected from China Statistical Yearbook, China Industrial Statistical Yearbook and China Energy Statistical Yearbook. Specifically, the data of SO2, NOx and Dust is available on State Statistical Bureau official website, PM2.5 is obtained from the global PM2.5 density data(1998-2016) released by Columbia University, and carbon dioxide emission comes from the China Carbon Emissions Database (CEADs), covering the carbon emissions associated with fossil fuel combustion and cement production. On this basis, the emission reduction of CO2 and atmospheric pollutants are calculated by subtracting the current emissions from the previous period emissions.

Line 367-370:

Line 376-384:

To ensure the dominant role of ETS in the synergic emission reduction of SO2, the nonparallel trends in the dependent variables between groups need to be tested further. The parallel trends of direct synergy, economy synergy, energy efficiency synergy and industry synergy in SO2 are shown in Fig 3. The vertical dashed line represents the starting year of ETS implementation. Overall, the change trends of the left curve remain basically stable, while that on the right differed significantly. Specifically, under the ETS pilots executed, the direct synergy of treatment group surpasses that of control group gradually, with growing gaps. However, the efficiency synergy and industry synergy show reverse change trend, and economy synergy maintains stable gap consistently.

Reviewer 2 Report

The article concerns the globally important problem of emissions trading in relation to regional solutions and, according to the reviewer, is an interesting source of data that should be published.

Unfortunately there are a lot of errors in the article i.e.

- on fig. "Industryy", some shifting words to a new line is inadmissible

- uppercase and lowercase letters in tables are used inconsistently

- subscripts are required when writing chemical compound forms

- x axis descriptions in Figure 2 need to be improved (some have changed the format)

- the number of leading digits in the tables must be standardize

- the description of the legend in Figure 3 is illegible (font too small - one description is enough for all charts)

The paper requires a thorough revision in this regard.

Author Response

Point 1: on fig. "Industry", some shifting words to a new line is inadmissible

Response 1: We follow this advice and correct this word. Fig.1. Industry.

Point 2: uppercase and lowercase letters in tables are used inconsistently

Response 2: We follow this advice and make the uppercase and lowercase letters in tables consistent, such as Table 2, Table 4, Table 6, Table 7, Table 8, Table 9 and Table 10.

Table 2. Definition and expression of variables.

variable	Variable meaning	Variable description	Expected sign
PGDP	Economic Level	Per capita income level	+
Energy	Energy Consumption	Energy consumption per capita	+
Intensity	Carbon Intensity	CO₂ emissions per unit of output	-
Efficiency	Energy Efficiency	Energy consumption level per unit of output	+
Tech	Technology Progress	The number of patent applications per capita	-
Density	Population Density	The ratio of total population to administrative area	+
Urban	Urban Level	The proportion of urban pollutants in total pollutants	-
Industry	Industry Structure	The proportion of industrial output value in total output value	-

...

Point 3: subscripts are required when writing chemical compound forms

Response 3: We follow this advice and use subscripts when writing chemical compound forms in the text, such as CO₂, SO₂, NO_X, PM_2.5.

Line 8-11:

this paper estimated the synergic emission reduction relationship between carbon dioxide (CO₂) and atmospheric pollutants, consisting of sulfur dioxide (SO₂), nitrogen oxides (NO_X), dust pollutants (Dust) and particulate matter 2.5 (PM_2.5).

Line 200-202:

In Formula (1), k represents province. i and j refer to industry and the energy type. TPO represents sulfur dioxide (SO₂), nitrogen oxides (NO_X), dust pollutants (Dust) and particulate matter (PM_2.5) respectively.

...

Point 4: x axis descriptions in Figure 2 need to be improved (some have changed the format)

Response 4: We follow this advice and improve the descriptions of x axis in Figure 2, such as showing the title of x axis and the label of every year .

Point 5: the number of leading digits in the tables must be standardize

Response 5: We follow this advice and standardize the number of leading digits in the tables, such as Table 4, Table 6, Table 7, Table 8, Table 9 and Table 10.

Table 4. Regression results of synergic emission reduction effects.

(1)

(2)

(3)

(4)

SSO₂

SDust

SNO_X

SPM_2.5

SCO₂

0.0010^**

(1.86)

0.0011^**

(2.33)

0.0022^*

(1.52)

0.0113

(1.18)

PGDP

-1.4505^**

(-2.14)

-0.0001^*

(-1.55)

-0.0002

(-1.59)

0.0025^**

(2.27)

PGDP²

1.2644^***

(2.84)

0.0001

(1.26)

0.0001

(1.50)

-0.0014^***

(-2.59)

Efficiency

2.0437^*

(1.30)

0.0004

(1.26)

0.0005

(1.51)

0.0021

(1.05)

Density

0.0004^*

(1.11)

-0.0005^*

(-1.74)

-0.0014^**

(-1.83)

0.0016

(0.90)

Urban

-0.1051

(-0.23)

-0.0001

(-0.10)

0.0001

(0.48)

-0.0007

(-0.62)

Energy

0.0359

(0.91)

0.0367

(1.11)

0.0379

(0.52)

0.5511

(0.70)

Tech

-0.0008

(-0.07)

0.0122

(1.26)

0.0164

(0.62)

-0.0142

(-0.10)

Industry

-0.4488^*

(-1.20)

-0.0001^*

(-1.64)

-0.0001

(-1.32)

0.0001

(0.10)

Intensity

-0.0026

(-0.30)

-0.0001

(-0.41)

0.0001

(0.40)

0.0001

(0.19)

Constant

Term

0.4478

(1.18)

0.0001

(1.35)

0.0001

(0.63)

-0.0011^**

(-2.55)

Note: ***, **, * indicates statistical significance at 1%, 5% and 10% levels, respectively.

...

Point 6: the description of the legend in Figure 3 is illegible (font too small - one description is enough for all charts)

Response 6: We follow this advice and enlarge the font in Figure 3.

Round 2

Reviewer 1 Report

Thank you for answering to my comments.

Article Menu

Synergistic Effect of Carbon Trading Scheme on Carbon Dioxide and Atmospheric Pollutants

Further Information

Guidelines

MDPI Initiatives

Follow MDPI