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Coupling Agricultural Carbon Emission Efficiency and Economic Growth: Evidence from Jiangxi Province, China

Sustainability 2025, 17(9), 4246; https://doi.org/10.3390/su17094246

by Lulu Yang^1,†, Xieqihua Liu^2,†, Xiaolan Kang³, Yuxia Zhu², Chaobao Wu³, Bin Liu⁴

and Wen Li^5,*

Reviewer 1: Anonymous

Reviewer 2:

Ammar Bany Ata

Reviewer 3: Anonymous

Sustainability 2025, 17(9), 4246; https://doi.org/10.3390/su17094246

Submission received: 11 March 2025 / Revised: 27 April 2025 / Accepted: 29 April 2025 / Published: 7 May 2025

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Dear authors,

Thank you for this piece of research. Please see comments below

Comments for author File: Comments.pdf

Comments on the Quality of English Language

Some improvements are needed.

Author Response

Response to Reviewer 1

Dear reviewers,

Thank you for your professional review comments, which are greatly appreciated by our team. We have revised and improved the content of the article according to your proposed revisions and hope to improve the quality of the paper.

Below is the description of the revisions and responses:

Question 1: Introduction：Line 56: ‘engaging in international climate governance’. Engaging in review of governance? Workshops ? other? It is just a language issue. But please be clear. Line 61: ‘significant ecological barrier’. What do you mean by that? Line 76: ‘super efficiency’. Do you mean super efficient? Isn’t it a bit bold ? Is it

justified?

Question 2：Literature Review：The information provided here is OK, but more bibliographic resources would be

beneficial. Also please see comments on methodology, to be altered in correlation to

that.

Answers 1-2: We thank the reviewers for their professional revisions. We have checked the content of the article and revised and improved the introduction on this basis. First, the inappropriate expressions were modified. Second, the introduction and literature review chapters were merged. Thirdly, on this basis, according to the content of the thesis, some new literature was added to improve the theoretical support.

The following is the revised introduction:

Introduction

The global challenge of climate change, particularly global warming, has reached a level that cannot be ignored. According to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report, the average temperature of our planet has risen by 1.1°C over the past century^[1]. Increased emissions of greenhouse gases are leading to more severe weather phenomena, posing significant risks to both human societies and natural ecosystems^[2]. The concept of "carbon neutrality" emerged in 2018, when the IPCC recommended that net-zero global greenhouse gas emissions be achieved by mid-century as a strategy to effectively combat global climate change^[3]. Given that China is the leading carbon emitter, it is actively engaging in international climate governance and has set the goal of "actively and steadily promoting carbon peaking and carbon neutrality"^[4], responding to mounting pressures to decrease emissions. In this context, researchers are focusing on the interrelated development of the ecological environment and regional economies.

1.1 Literature review

Existing studies provide a foundational framework for understanding the relationship between ACE and AEG. Early scholars defined carbon emissions and carbon emission efficiency through Kaya equation^[5] and energy intensity indicators^[6], and then the research was gradually extended to the field of agriculture by integrating them into a comprehensive indicator that considers economic growth, resource consumption and agricultural carbon emissions^[7]. In the field of research methodology, scholars have adopted different methods to measure the efficiency of agricultural carbon emissions. Charnes first proposed the data envelopment analysis model to assess the relative efficiency of decision-making units^[8]. However, the DEA model was unable to solve the slackness of inputs and outputs, which led to a large bias in the efficiency measurement^[9]. In contrast, the super-efficient SBM model has been widely used to analyze the spatio-temporal divergence of ACE in different regions of China by integrating the non-expected outputs and slack optimization^[10-13]. In terms of influencing factors, studies have shown that labor force size has an inhibitory effect on agricultural carbon emission efficiency, while optimization of agricultural industrial structure and urbanization process can significantly enhance efficiency^[14].Ye pointed out that agro-industrial agglomeration has an inverted U-shape nonlinear effect on agro-environmental efficiency^[15]. In addition, urbanization process^[16], AEG^[17-18], agricultural tax and fee levels^[19], and digital finance^[20] have all been shown to be dynamically associated with ACE. It is noteworthy that some studies have pointed out that the evolution of ACE shows significant provincial differences^[21]. Therefore, in the exploration of ACE, regional characteristics and spatial correlation should be taken into account^[22].

Academics usually measure AEG by the total output value of agriculture, forestry, animal husbandry and fishery, but it is difficult to reflect the internal differences of the industry and the characteristics of regional resource endowment, which can easily lead to the bias of economic level measurement^[3]. For this reason, this paper draws on the research results of Tan^[23], and adopts per capita agricultural value added (agricultural value added/agricultural employees) as an indicator for measuring AEG, to comprehensively reflect the actual AEG level of the agricultural industry in each region. In the study of the relationship between agricultural carbon emissions and economic growth, scholars have explored the relationship between agricultural carbon emissions and economic growth based on the EKC model^[24], the Tapio decoupling model^[25], the ARDL-ECM model^[26], and the coupled coordination model^[3], i.e., the short-term expansion of the agricultural economy may push up the carbon emissions, whereas the long-term technological innovation and structural optimization significantly inhibit the growth of carbon emissions. Meanwhile, the spatial distribution of ACE gradually shifted from dispersion to agglomeration, and its relationship with the agricultural economy transitioned from weak decoupling to negative decoupling^[27]. In addition, the utilization of renewable energy and the promotion of low-carbon technologies can not only alleviate the inhibitory effect of carbon emissions on the agricultural economy^[28-29], but also promote sustainable development by enhancing the competitiveness of agricultural exports^[30]. Thus, it is necessary to improve low-carbon production technologies, increase renewable energy consumption, and improve agricultural production and ecological conditions to realize the goal of sustainable development in the agricultural sector^[31].

The relationship between ACE and AEG has been widely discussed in the existing literature, providing an important basis for understanding the transformation and upgrading of modern agriculture in the context of the new era. However, there are still some limitations in the study of ACE and AEG. First, current research focuses more on the relationship between agricultural carbon emissions and economic growth, while relatively little attention is paid to ACE. ACE not only focuses on the total amount of carbon emissions, but also considers economic output and environmental factors, which helps to reveal more accurately the impact of agricultural carbon emissions on agricultural economic development. Secondly, current research focuses on large-scale studies, such as national and regional studies, which may overlook local or micro-level correlations due to differences in agricultural management practices in different regions.

1.2 Research Problems

Jiangxi Province is an important ecological reserve and agricultural province in China, with rich natural resources and ecological advantages, and its agricultural structure and carbon emissions also have significant uniqueness, which provides an ideal research sample for analyzing the mechanism of low-carbon transition in agriculture. The rice sowing area in Jiangxi Province ranks among the top three in the country, and methane emissions from rice cultivation account for more than 50% of the total agricultural greenhouse gases^[25] , while compared with Hunan Province, which is also a major rice-producing area, Jiangxi Province has a higher fertilizer application per unit of production, resulting in lower carbon emission efficiency^[32] , and this “high-carbon lock” phenomenon is closely related to the crude production model. This “high carbon lock-in” phenomenon is closely related to the rough production mode. Despite the implementation of the “river chief system”^[33] and the ecological compensation mechanism^[34], the shrinking of wetland area^[35] and agricultural surface pollution^[36] still constrain the green development of Jiangxi Province^[37]. In addition, there are significant differences in the agricultural structures of the 11 district cities in the province, leading to prominent spatial heterogeneity in ACE^[38]. Therefore, clarifying the synergistic law of ACE and AEG in Jiangxi Province is both an urgent need for regional low-carbon development and an important supplement to improve small- and medium-scale studies.

This paper aims to address the following scientific questions: first, what is the spatio-temporal divergence pattern of ACE in Jiangxi Province? Second, does the coupled and coordinated relationship between ACE and AEG in Jiangxi Province show dynamic evolution? Third, what are the main sources of spatial differences in the coupled coordination of ACE and AEG in Jiangxi Province? Fourth, what are the external factors limiting the coordinated development between the two systems?

1.3 Research objectives

This study takes Jiangxi Province as the research object and takes 2008-2022 as the research period to explore the coupling and coordination relationship between ACE and AEG in Jiangxi Province. The specific research contents are as follows: First, quantify the spatial and temporal differentiation characteristics of ACE by using the super-efficiency SBM model; Second, explore the coupling and coordination level and spatial and temporal characteristics between ACE and AEG by means of the coupling and coordination model; Third, combine with the Dagum Gini coefficient to further decompose the spatial differences, and explore the sources of differences in the level of coupling and coordination; Fourth, identify the coupling and coordination relationship between ACE and AEG by means of Tobit model. Fourth, the Tobit model is used to identify the external factors affecting the coupled and coordinated development of the two, and then propose a balanced development path for regional low-carbon agriculture.

Compared with the established literature, the innovations are reflected in two aspects: (1) revealing the coupling and coordination law between ACE and AEG in 11 cities in Jiangxi Province, making up for the shortcomings of the existing research on the provincial scale, filling the gap of its spatial heterogeneity research, and providing a more detailed empirical basis for understanding the regional characteristics of the relationship between ACE and AEG; (2) this study introduces the Dagum Gini coefficient decomposition method into the study of the coupling and coordination relationship between ACE and AEG in order to clarify the three major sources of spatial differences. Dagum Gini coefficient decomposition method into the study of the coupled and coordinated relationship between ACE and AEG in order to clarify the three major sources of spatial differences, which not only provides a scientific tool for in-depth analysis of the spatial differentiation of the coupled and coordinated relationship between the regions, but also provides an important methodological reference for the future study of coordinated development of the region, which has strong theoretical and practical value.

Figure 1 Research roadmap

Methodology and data sources

Question 3:The mathematical models are significant, but prior to that a short introduction on what is agricultural carbon efficiency/ carbon in agriculture would be necessary. This needs to be described in the literature review with a very short introduction in the methodology, so the mathematical formulas do not be introduced out of the blue.

Question 4: Lines 197-200: This could be higher up on the methodology. Almost as an introduction to the formulas.

Answer 3-4: Thanks to the professional opinions of the reviewers, we have made a short introduction to carbon efficiency in accordance with your suggestions, and at the same time, we have merged the construction of its indicators into this introductory process, so that it is easy for the readers to read and understand the sources and contents of the formulae.

The following is the revised 2.2 Agricultural carbon efficiency

2.2 Agricultural carbon efficiency

ACE is a comprehensive indicator of agricultural carbon emissions and agricultural economic growth^[39], and its measurement needs to consider the dual attributes of desired outputs (economic value) and non-desired outputs (environmental costs). Traditional DEA models may have limitations in dealing with non-desired outputs, making it difficult to effectively identify inefficiencies due to non-desired outputs. And although the GML index can capture the dynamic characteristics of productivity, it is unable to decompose the components of efficiency over a specific time period^[40]. In contrast, the SBM-DEA model can consider both desired and non-desired outputs, effectively solving the problem of sorting and juxtaposition and ensuring the scientific and accurate assessment results^[41].

Based on the economic growth theory and drawing on Wang's study^[42], this study, combined with the characteristics of agricultural development in Jiangxi Province, utilizes the super-efficient SBM model to construct an ACE evaluation system covering production inputs, desired outputs, and non-desired outputs. The construction of the specific index system contains three dimensions: (1) labor, land, water resources, fertilizers, pesticides and other core elements are selected at the production input end, reflecting the characteristics of the allocation of agricultural production factors; (2) the desired output characterizes the economic efficiency in terms of the total agricultural output value; and (3) the non-desired output adopts the agricultural carbon emission to characterize the environmental load. The specific indicator system is shown in Table 1.

The expression is:

&nbsp (1)

t. ;

&nbsp (2)

Where is the ACE; , and stand for input factors, desired output, and non-desired output, respectively; , and for input factor slack variables, desired output, and non-desired output, respectively; is the intensity variable; m, for the number of input indicators, desired output indicators, and non-desired output indicators, respectively; and is the vector of weights.

This paper builds an agricultural carbon emission evaluation system from three aspects of agricultural production inputs, desired outputs, and non-desired output elements, based on the economic growth theory and prior research^[43], as well as the features of agricultural development in Jiangxi Province. This essay makes the case that three factors, such as labor input, land input, and agricultural input, ought to be included in agricultural input indicators. Table 1 displays the evaluation index system:

Table 1. Selection and Explanation of ACE Variables in Jiangxi Province

Level 1 title	standard	variable	unit (of measure)
Input metrics	labor input	People working in agriculture	ten thousand people
	land input	Crop sown area	thousand hectares
	water input	Effective irrigated area	thousand hectares
	Fertilizer inputs	Fertilizer usage	tones
	Pesticide inputs	Pesticide usage	tones
	Agricultural film inputs	Agricultural plastic film use	tones
Expected outputs	Gross agricultural output	Gross agricultural output	trillion yuan
Non-expected outputs	Agricultural carbon emissions	Carbon emissions from agricultural inputs	tones

Question 5: 3.3 seems like a literature review. Consider moving it either to the introduction or literature. Or if it stays at the methodology, then mention why is this significant for your methods?

Answer 5: Thank you for the reviewer's professional review, we have merged the content of 3.3 into the second part of the literature review according to your comments, and the following is the presentation of the revised part of the literature review.

Question 6: Lines 300-305: I think this works almost as an introduction for the methodology and I would put it in the beginning of the section. Imagine someone who is not familiar with your work or the mathematical formulas. They need to understand what you are doing and why.

Answer 6: Thanks to the reviewers' suggestions, we have adjusted this part to the introduction of the methodology of the 2.5tobit model according to your comments, so as to facilitate readers to understand the reasons for the selection of indicators and the conditions of model selection.

The following is the revised introduction of the 2.5tobit model:

2.5Tobit model

Tobit regression analysis is a statistical model used to analyze binary dependent variables, which can flexibly deal with various types of independent variables and has good interpretability for outliers and non-normally distributed data^[49]. In contrast, the least squares method to explore the relationship between the coupled coordination degree of the two systems and the external factors is prone to bias in the parameter estimates^[50]. Therefore, in this paper, the Tobit model is chosen to solve the problem of restricted dependent variables to explore the external factors affecting the coupled and coordinated development of ACE and AEG. The explanatory variable of this study is the measured coupling coordination level of ACE and AEG, which is a restricted variable. The coupling coordination level is not only affected by economic factors, but also by agricultural production conditions and social factors, and plays an increasingly important role in modern agricultural production. Based on the existing research results^[42] and the availability of data, this paper summarizes the factors affecting the level of coupled coordination into six aspects, including government input, education level, agricultural industry structure, energy use, living standard and urbanization level. The formula for calculation is:

&nbsp (18)

The dependent variable's size for the ith variable, the explanatory variable for the ith variable, a vector of intercept terms, a vector of unknown parameters, and an independent, normally distributed random error term are all included in this.

Table 3. Selection and Explanation of Indicator Variables of Factors Influencing the Coupling Coordination Degree of ACE and AEG in Jiangxi Province.

variable	norm	Description of indicators	unit (of measure)
explained variable	degree of coupling coordination	ACE and AEG Coupling Harmonization Degree	-
explanatory variable	government input	Expenditure on agriculture, forestry, and water/general expenditure budget of local finances	%
	educational level	Ratio of effective irrigated area to sown crop area	%
	industrial structure	Gross value of agricultural output/gross value of agricultural, forestry, livestock, and fishery output	%
	energy use	Rural electricity consumption	kW·h
	living standards	Per capita disposable income of rural residents	Yuan
	urbanization level (of a city or town)	Urban residents as a proportion of total population	%

Results and analysis

Question 7：Line 370: in 2022 there ‘are’ five cities. Perhaps there were five cities ? We are in 2025 now.

Question 8：Line 418: Better in what? And also, what coordination? Please add more details here. The Figure 4 is quite interesting. Please try to describe the carbon emission efficiency and explain how this has changed and what it has been affected. At the moment, it only describes coordination and it is not that clear.

Answers 7-8: Thanks to the reviewer's comments, we have revised and improved the content of 3.2 Coupling Coordination Analysis according to your suggestions, and supplemented the data letter information to support the presentation of results.

The following is the content of 3.2 after modification：

3.2 Analysis of the coupled harmonization of ACE and AEG

As can be seen from Figure 4, in 2022, the coupling coordination degrees of Xinyu and Nanchang are 0.986 and 0.916, respectively, which are at the level of high-quality coordination, indicating that the efficiency of their ACE and AEG has achieved synergistic development, and the economic development and regional ecology have achieved simultaneous improvement. Specifically, Xinyu City has improved the efficiency of its agricultural carbon emissions by optimizing its agricultural industrial structure. Nanchang, on the other hand, realized a win-win situation between AEG and ecological protection by strengthening agricultural science and technology innovation and green agricultural development. Jingdezhen, Fuzhou, and Yingtan follow in the third, fourth, and fifth places, with coupling coordination degrees of 0.882, 0.848, and 0.811, respectively. jingdezhen enhances the efficiency of agricultural carbon emissions through the development of eco-tourism and green agriculture, fuzhou city through the development of three-dimensional agriculture, and yingtan city through the promotion of low-carbon planting technology and other diversified strategies. Meanwhile, Jiujiang, Ganzhou, Yichun, and Pingxiang have coupling harmonization degrees between 0.76 and 0.8, with greater progress during the examination period. Among them, Jiujiang City improves the ACE by promoting planting methods with low farm inputs; Ganzhou City develops specialty and ecological agriculture to achieve coordinated economic and ecological development; Yichun City promotes organic agriculture and ecological planting techniques to enhance agricultural output and reduce carbon emissions; and Pingxiang City strengthens the construction of agricultural infrastructure and the promotion of water-saving irrigation techniques to improve the sustainability of agricultural production. Together, these initiatives have contributed to the green transformation and sustainable development of agriculture in the region.

Comparatively speaking, the coupling coordination degree of Shangrao and Ji'an needs to be improved, and their coupling coordination degree is still in the primary coordination stage, with a coupling coordination degree of 0.66 and 0.657 respectively. Compared with 2008, the coupling coordination degree of the 11 cities in the region has increased significantly and realized a hierarchical leap. Among them, Jinjiang has the largest improvement, with an increase of 87.421% in coupling coordination degree, followed by Shangrao, Ganzhou and Yingtan, with increases of 80.909%, 78.742% and 78.052% respectively. Xinyu, Jingdezhen and Nanchang saw relatively small increases of 60.953%, 64.505% and 65.502% respectively. In terms of tier leaping, Yingtan realized a four-level leap, while all other municipalities except Ji'an realized a three-level leap, and Ji'an realized a two-level leap. According to the evaluation results of the coupling coordination level division, it can be seen that as of 2022, six cities, Nanchang, Jingdezhen, Pingxiang, Xinyu, Yingtan, and Fuzhou, have achieved high-quality coupling coordination, and all of them are economically lagging; a total of five cities, Jiujiang, Ganzhou, Ji'an, Yichun, and Shangrao, are well coordinated, and all of them are economically lagging. The coupling coordination of each district city in Jiangxi Province in 2022 reaches good coordination and above, and the overall situation is much better than that in 2008. By reviewing the economic development data and carbon emission data of the cities in Jiangxi Province, it can be seen that the root causes of the failure to synchronize the development of the cities vary: in 2008, the lower degree of coupling coordination of the cities in Jiangxi Province can be attributed to the lagging level of the development of the agricultural economy, while the increase in the degree of coupling coordination in 2022 is more limited by the ACE.

Figure 5. Spatial Distribution of ACE and AEG Coupling and Harmonized Development in Various Cities in Jiangxi Province.

Figure 5 demonstrates a significant upward trend in Jiangxi's level of coupled coordinated development from 2008 to 2022. The coordination level in the study period is shown in Table 2.Specifically, it seems that the coupling coordination degree of five cities, Yichun, Yingtan, Ganzhou, Shangrao, and Jiujiang, is 0.185, 0.178, 0.169, 0.126, 0.100 respectively in 2008, which is lower than 0.200, and is in the stage of serious dislocation, and that the coupling coordination degree of Xinyu, Nanchang, Jingdezhen, Pingxiang, Fuzhou, and Ji'an is 0.385 respectively, 0.316, 0.301, 0.252, 0.235, and 0.213 respectively, which is in the intermediate dissonance stage. From the perspective of regional distribution, most of the cities with a low level of coordinated development are in hilly areas. The level of economic development in these areas is relatively lagging behind, and due to the limitations of natural resources, the scale and mechanization of agricultural operations are insufficient, resulting in their coupling coordination level being in a dysfunctional state.In 2012, the coupling coordination degree grade of many cities improved, among which Xinyu's coupling coordination degree improved to 0.643, the grade jumped from primary dysfunctional to intermediate coordination, and Nanchang, Jingdezhen, Pingxiang, and Fuzhou were upgraded from primary dysfunctional to intermediate coordination, which is worthy of attention. to intermediate coordination, it is noteworthy that the coupling coordination degree of Jiujiang, Yingtan, Ganzhou, Ji'an, Yichun, Shangrao, a total of six municipalities, is lower than 0.400, and is still in the stage of primary dysfunction. The lagging of agricultural surface pollution management due to high agricultural inputs in these areas is the main constraint.In 2018, the coupling coordination degree of all the cities is greater than 0.400, and the coordination level is also raised to endangered dislocation and above, among which, Nanchang, Jingdezhen, Pingxiang, Xinyu, Yingtan, and Fuzhou are in the intermediate coordination stage, while Jiujiang, Ganzhou, Ji'an, Yichun, and Shangrao are in the endangered dislocation stage. This indicates that the transformation and development of green and low-carbon industries in Jiangxi Province has achieved certain results, and the scale operation of agriculture and per capita efficiency output have been improved, which in turn enhances the ACE and the level of AEG. In 2022, the coupling coordination degree of Nanchang, Xinyu, Jingdezhen, Yingtan, Fuzhou, Pingxiang, are 0.916, 0.986, 0.848, 0.811, 0.882, 0.800, respectively. Further upgraded to the high-quality coordination stage, and the coupling coordination degrees of Jiujiang, Ganzhou, Yichun, Ji'an, and Shangrao were 0.795, 0.795, 0.760, 0.657, 0.660, all in the medium coordination stage. The effectiveness of this stage mainly stems from the government's emphasis on improving the quality and efficiency of the agricultural industry, as well as the strong support of relevant complementary policies, which promotes the synchronization of the ACE and AEG.

Question 9：4.3.1 I think it is important to start by stating what is the Gini coefficient and why it is relevant to this research. It is good that this section is technical, but it would be necessary to have a description. Line 581: Remove one of the words ‘fifth’

Answer 9: Thanks to the comments of the reviewers, we have explained the Gini coefficient model in 2.4 Methodology and explained the applicability of this model, and we have also checked the expressions in the whole text and deleted and modified the inappropriate expressions.

The following is the presentation of the revised 2.4 content:

2.4 Dagum Gini coefficient decomposition method

The Dagum Gini coefficient decomposition is a statistical method that decomposes overall income inequality into within-group differences, between-group differences and hyper variance density. Among them, the within-group disparity reflects the differences within the same group, the between-group disparity reflects the average differences between different groups, and the hyper variance density reflects the differences in the overlapping parts of the groups. Compared with Theil's index, which was mostly used in earlier studies^[47], Dagum's Gini coefficient decomposition method overcomes the limitations of the subgroup overlap problem by introducing an index of the degree of net difference between subgroups^[48], and is able to analyze the differences in the intra-region, inter-region, and hyper variance densities of the Yangtze River Economic Belt in a clearer way. In order to examine the spatial variations in the coupled coordination degree of ACE and AEG, as well as its causes, this research uses the Dagum Gini coefficient decomposition approach. The following is the formula:

Conclusions and Recommendations

Question 10：The conclusions need to be stronger. At the moment they read like a repetition of the discussion. Why are your findings relevant? How are they different? What is the contribution to knowledge?

Answer 10:Thank you to the expert reviewers for their professional evaluation. After revising the paper, we have modified and improved the conclusion section of the article, and pointed out our research findings and conclusions.

The following is the revised research summary：

This study systematically reveals the spatio-temporal evolution law of the coupling and coordination of ACE and economic growth in Jiangxi Province from 2008 to 2022 and its driving mechanism, and the main conclusions are as follows:

First, the low-carbon transformation of Jiangxi Province's agriculture is remarkable. The overall carbon emission efficiency shows an upward trend, growing from 0.172 in 2008 to 0.624 in 2022, with a growth rate of 72.433%. This shows that Jiangxi Province has achieved remarkable results in agricultural low-carbon transformation, but there is still a certain distance from the optimal production frontier, and there is a large potential for emission reduction.

Second, the degree of coordination between ACE and economic growth in Jiangxi Province increased significantly from 0.190 to 0.730 during the study period, realizing the leap from “serious dissonance” to “intermediate coordination”. The gradual narrowing of inter-regional differences indicates that Jiangxi Province has achieved success in the low-carbon transformation of agriculture, but the problem of unbalanced development still exists. The North, Northeast and West regions have higher levels of coupling and coordination due to their technological and scale advantages, while the Central and South regions have lower coupling and coordination efficiencies due to their traditional cropping patterns and topographical constraints.

Third, the Dagum Gini coefficient decomposition reveals that inter-regional differences are the main source of overall differences, with the contribution rate increasing from 31.49% to 37.43% and peaking at 62.20% in 2021. The Gini coefficients of northern, northeastern, and western of Jiangxi show a decreasing trend, indicating a gradual increase in regional coordination, but the differences in central and southern of Jiangxi still need to be further reduced. This finding fills the gap in the study of low-carbon transformation of agriculture in small- and medium-scale regions and reveals the necessity of fine-grained governance within the province.

Fourthly, government investment, the agricultural industrial structure, the level of urbanization, and educational attainment all exert a significantly positive impact on enhancing the degree of coupling coordination. To foster the coupled and coordinated development of agricultural carbon emission efficiency and economic growth, it is imperative to adopt a multifaceted approach and facilitate the formulation of a comprehensive and effective sustainable development strategy.

This study not only provides a regional case for understanding the synergistic relationship between low-carbon transformation of agriculture and economic growth, but also probes deeply into the actual development of Jiangxi Province by combining the super-efficient SBM-DEA model with Dagum's Gini coefficient decomposition method, and the results of the study provide references to theoretical research and practical applications for promoting agricultural modernization and optimizing regional development.

Question 11：The section 5.2 is quite good though. Try to enhance further and align it with the research scope.

Answer 11:Thank you to the reviewers for their comments. Based on the feedback, we have revised and improved the suggestions section of the paper.

Here are the modifications made to the suggestions:

First, strengthen inter-regional cooperation and establish a “cross-regional ecological compensation and technology sharing” mechanism to promote the balanced development of low-carbon agriculture. According to the resource endowment and economic development level of each region, Jiangxi Province should formulate low-carbon agriculture development plan according to local conditions. Set up a low-carbon agriculture demonstration zone in Poyang Lake Plain in northern of Jiangxi, promote technologies such as precision fertilization by drones and intelligent mechanization, and promote information sharing and experience sharing. At the same time, break the development barriers between regions, set up a province-wide technology transfer platform, and share the rice water-saving irrigation technology in northern of Jiangxi to central and southern of Jiangxi, and northeast of Jiangxi, to improve agricultural productivity and ecological benefits.

Second, to speed up the growth of agricultural modernization, spend more on agricultural research and technology innovation. The government should encourage the deeper integration of agricultural research institutes and production methods, boost funding for agricultural science and technology innovation, and quicken the adoption and dissemination of agricultural scientific and technology advancements. Pilot bio-pesticides and green manure rotations in northeast of Jiangxi, use the Yichun Agricultural Science and Technology Park platform to deploy soil moisture sensors and early warning systems for pests and diseases, and other digital agricultural technologies, and popularize straw biogas cogeneration and livestock and poultry manure nano-film fermentation waste recycling technologies in central and southern of Jiangxi, to achieve low-carbon development of the agricultural industry.

Third, encourage the expansion of the agriculture industry chain and optimize the industrial structure. Jiangxi Province should keep modifying the agricultural industrial structure and encourage the growth of agriculture toward high value-added and low energy consumption. The agricultural industry chain can be expanded to realize the double enhancement of economic and ecological benefits through the development of the vegetable industry and the breeding of distinctive livestock and poultry in northeast Jiangxi, the large-scale rice cultivation in north Jiangxi, the selenium-rich agriculture in west Jiangxi, and the ecological and recycling agriculture in central and south Jiangxi.

Fourth, increase the degree of urbanization and reduce the conflict between rural residents and land. Jiangxi Province should keep pushing for urbanization, create a more harmonious urban-land interaction, resolve the conflict between rural residents and land, and encourage the transition from small-scale to intensive farming. At the same time, relying on the characteristics of the agricultural town, to attract agricultural product processing enterprises, cold chain logistics, as well as e-commerce platform stationed in the extension of the agricultural industry chain and the formation of industrial clusters, to improve the efficiency of resource utilization.

These are the main contents of our revision, and on behalf of our team, I would like to express our gratitude to all the reviewing experts for their professional opinions. It is the hard work of the reviewing experts that gives us the opportunity to continuously improve the quality of the paper, and we are also very grateful to all of you for giving us another opportunity to improve the content of the article, thank you!

Wen Li （corresponding author）

Lecturer, School of Humanities and Public Administration, Jiangxi Agricultural University

E-mail: liwen13870963721@163.com

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Major Comments

Research Gap and Originality
- Comment: The manuscript highlights the importance of coupling agricultural carbon emission efficiency (ACE) and agricultural economic growth (AEG), using Jiangxi Province as a case study. However, the paper does not explicitly clarify how the study advances previous empirical work on similar topics (e.g., the broad literature on environmental efficiency, low-carbon transformations, or decoupling analysis). Clarifying how this study’s approach is novel (or how it improves on prior methods) would make the contribution clearer.
Justification of Focus on Jiangxi Province
- Comment: The manuscript focuses on Jiangxi Province due to its agricultural significance and carbon intensity. However, the justification could be strengthened by comparing Jiangxi Province to other provinces with similar structural characteristics. Is there something especially unique about Jiangxi that makes it a compelling “laboratory” for this research? Explain in more detail.
Clarity of Research Objectives
- Comment: The four “scientific concerns” (lines 70–75 in the partial text) are a good start, but the statement of these objectives is slightly scattered across several paragraphs. Consolidate them in a dedicated paragraph or bullet points to make the research aims and questions easier to identify.
Integration of Literature Review
- Comment: The Literature Review section at times feels like a broad overview of carbon emission measurement and data envelopment analysis but does not thoroughly connect the references back to the authors’ conceptual framework. Consider reorganizing to show how each cited study relates to (a) your methods, (b) your study region, or (c) your coupling-coordination perspective. Linking them more tightly would better situate the work in the existing literature.
Methodological Rigor in the Super-Efficiency SBM-DEA Model
- Comment: The paper uses the super-efficiency SBM-DEA approach to estimate ACE. While the choice is valid, there is no in-depth discussion of how potential multicollinearity or sensitivity (e.g., choice of inputs/outputs) might affect the efficiency scores. The authors should discuss and justify why these specific inputs and outputs were chosen and whether any robustness checks have been conducted.
Selection of Inputs and Outputs
- Comment: The paper emphasizes using labor, land, water, fertilizer, pesticide, and plastic film as inputs, and “gross agricultural output” as the desired output, while “carbon emissions” serve as a non-desired output. However, it is unclear why certain potential inputs (e.g., agricultural machinery usage, livestock feed inputs) were excluded. Provide a thorough rationale or, if relevant, mention data limitations to justify these choices.
Carbon Emission Calculation
- Comment: The manuscript provides only a short overview of carbon emission sources, referencing multiple prior works. However, the paper should include more detail on the formula(s) or references for calculating agricultural carbon emissions from fertilizers, pesticides, irrigation, etc. This adds transparency for readers who want to replicate or confirm the approach.
Coupling Coordination Formula Explanation
- Comment: While the text presents the coupling coordination model’s final formula, some readers may be unfamiliar with how “coupling degree” and “coordination degree” are derived. Consider adding a short, plain-language explanation (perhaps in an appendix) to clarify why the model is well suited to measure synergy/coordination between two systems (ACE and AEG).
Justification of Tobit Model
- Comment: The authors justify using a Tobit model due to truncated data (coupling coordination degree ranges from 0 to 1). While that is appropriate, the paper provides little discussion of the potential for alternative bounded models (e.g., Beta regression) or the sensitivity of results to the Tobit specification. A short methodological note clarifying these points would strengthen the argument.
Comparisons with Other Regions or Times

Comment: The results show interesting trends for Jiangxi Province over time, but the manuscript never references how these trends compare with national-level or neighboring provinces’ patterns. A short comparison would help underscore whether the patterns in Jiangxi are unique or align with broader Chinese trends in agricultural carbon efficiency and economic growth.

Methodology and Analysis Comments

Time Horizon and Structural Breaks

Comment: The manuscript analyzes data from 2008 to 2022 but does not discuss important policy or structural changes (e.g., major shifts in China’s agricultural subsidies or carbon policies) that could cause structural breaks. If such events occurred, it may be worth testing whether these breakpoints significantly affect the results.

Spatial Autocorrelation

Comment: The analysis is done city by city within the province, yet there may be spatial interdependencies—pollution or technology adoption could diffuse across neighboring cities. Consider if Moran’s I or other spatial correlation tests should be performed to see whether results are spatially clustered.

Potential Endogeneity in Tobit Model

Comment: Some explanatory variables (e.g., government expenditure, industrial structure) might be endogenous to ACE or simultaneously determined with agricultural development. The authors should at least acknowledge potential endogeneity and discuss how it may or may not bias the Tobit results.

Robustness Checks

Comment: The paper lacks an explicit section on robustness checks. For instance, testing how the results change when removing outliers or using alternative definitions of key variables (e.g., substituting “energy use” with “rural energy consumption per capita” vs. total consumption). Consider including at least one table showing the sensitivity of results.

Data Quality and Sources

Comment: While the authors mention that data comes from the Statistical Yearbook and official bulletins, it would be beneficial to include a brief discussion of any known biases or data reliability issues. For example, local-level statistics in certain regions can be incomplete or delayed. Indicate if any interpolation or smoothing was necessary.

Definition of “Industrial Structure”

Comment: The variable “industrial structure” is measured as the ratio of “gross value of agricultural output” to “gross value of agriculture, forestry, livestock, and fishery output.” This might be somewhat confusing, because both the numerator and denominator are agricultural in scope. Clarify the logic behind using that ratio, or consider labeling it differently, as it might not fully capture “industrial structure optimization.”

Interpretation of the Negative Impact of Rural Living Standards

Comment: The Tobit regression suggests that rising rural living standards impede the coupling coordination. That is an interesting result that diverges from some prior literature. Add more in-depth discussion or plausible mechanisms—e.g., changes in consumption habits that increase emissions? Or do higher rural incomes shift land usage? This is a notable finding that warrants more careful interpretation.

Calculating Regional Gini Coefficients

Comment: Dagum Gini coefficient decomposition is employed to analyze sources of disparity. Provide a quick example or short table to illustrate how the decomposed Gini is interpreted. This will help readers who are less familiar with Dagum’s approach.

Choice of Decomposition Method

Comment: The authors reference that Dagum’s Gini is superior to other decomposition methods (e.g., Theil index, mean log deviation) for addressing negative contributions and overlap. A short paragraph summarizing these advantages would be helpful so that readers understand why this approach is especially relevant to measuring coordination disparities.

Consistency between Figures and Tables

Comment: Ensure the data in the text (e.g., Gini values or coordination degree results) precisely match the numerical values in the tables and figures. Any rounding or averaging differences should be explained, or the same rounding rules used consistently throughout.

Minor and Editorial Comments

Title Specificity

Comment: The current title highlights “Research on the Relationship and Coordination of Agricultural Carbon Emission Efficiency and Agricultural Economic Growth—Using Jiangxi Province as an Example.” Consider slightly shortening it or making it more direct, e.g., “Coupling Agricultural Carbon Emission Efficiency and Economic Growth: Evidence from Jiangxi Province, China.”

Keywords

Comment: The keywords list overlaps somewhat (e.g., “Agricultural Carbon Efficiency” vs. “Low-Carbon Agricultural Transformation”). Merge or refine to boost discoverability. Possibly add “Data Envelopment Analysis (DEA)” or “SBM” if those are central to your methodology.

Abstract Clarity

Comment: The abstract is reasonably comprehensive but could more explicitly mention the key numerical findings—e.g., the final coupling coordination degree, the largest factor behind its improvement, or the leading city. This will make the abstract’s results more memorable.

Abbreviations

Comment: The text uses several acronyms (ACE, AEG, AC, etc.). Make sure each acronym is fully spelled out and defined at first mention in both the abstract and main text. Also, avoid introducing acronyms not used repeatedly, to keep the manuscript concise.

Reference Formatting

Comment: The references (e.g., [1], [2], [3]) appear consistent, but check that each cited work in the text matches a complete entry in the reference list (and vice versa). Also ensure correct formatting for the journal’s style (e.g., MDPI’s Sustainability typically requires certain referencing formats).

Citation Updates

Comment: Some references to “Tan, 2024; Huang, 2024” in the partial text appear to be placeholders or references to future works. Verify that these references are correct, properly published, and correspond to real sources. Otherwise, remove or replace them.

Section Headings

Comment: The standard structure (Introduction, Literature Review, Methodology, Results, Discussion, Conclusions) is present. However, some overlap occurs between the Literature Review and Introduction. Merge or reorganize to avoid redundancy.

Figure Quality

Comment: While the partial text shows some references to figures (e.g., Figure 1, Figure 2), confirm that the resolution, labeling, and titles on each figure meet publication standards. Each figure should be self-explanatory with a descriptive caption.

Table Formatting

Comment: For the main methodological or results tables, ensure the columns are adequately labeled. For example, clarify column headings for Gini decomposition, coupling coordination classification, etc. Also, confirm that each table has a short “Table X. Title” format above it.

Precision in Numerical Results

Comment: Results for ACE or coupling degrees are reported to three decimal places in some places and four decimal places in others. Decide on a consistent style (e.g., three decimals throughout) to increase readability.

Policy Context

Comment: The text sometimes references government initiatives like “Zero Growth of Fertilizers” or “River Chief System.” Cite official policy documents or local government guidelines when referencing them, so that readers can see the official basis for your statements.

Stylistic Consistency

Comment: In some parts of the partial text, the word “carbon emission efficiency” and “agricultural carbon emission efficiency” are used interchangeably. Similarly, the terms “coordination degree” or “coupling coordination degree” are also used. Standardize these terms to reduce confusion.

English Grammar and Syntax

Comment: Overall, the writing is understandable, but there are occasional grammar slips (e.g., use of “dissonance” vs. “disorder,” inconsistent singular vs. plural references). A careful read-through or light language editing would help ensure clarity.

Pagination or Section Numbering

Comment: MDPI journals often require a particular referencing style for sections and subsections. Verify that the headings (1., 2., 2.1., etc.) conform to the required style, and make sure references to tables and figures match their final numbering in the revised version.

Clarity on Unit of Measurement

Comment: For variables like “fertilizer usage,” “pesticide usage,” and “rural electricity consumption,” ensure the units are unambiguously stated in the text and in relevant tables. Sometimes you mention “tons,” “kW·h,” or “thousand hectares,” but ensure it is consistent.

Completeness of Conclusion

Comment: The conclusion currently lists four main findings. Consider adding a short reflection on how policymakers could use these findings to guide targeted intervention in carbon reduction or how local governments might implement your recommendations.

Limitations Section

Comment: The manuscript would benefit from a short “Limitations and Future Research” subsection after Discussion or Conclusion. Mention issues like data availability, potential unobserved variables, or the risk of model misspecification, and propose ways future research could address them.

Expanding on Policy Recommendations

Comment: The manuscript closes with “recommendations,” but they are fairly generic (e.g., “strengthen regional cooperation,” “innovate in agricultural technology,” etc.). Provide more specific or practical measures, such as exact types of technologies, or how local policy might reduce carbon emissions effectively without hurting farmers’ incomes.

Comparative Statements

Comment: The text makes comparative statements, e.g., “ACE in north and west Jiangxi is higher than in central and southern Jiangxi.” It would help to quantify these statements (e.g., “X% higher”) and provide references to relevant figures or tables each time a comparison is stated.

Terminology for Agricultural Inputs

Comment: The term “agricultural inputs” is used broadly (fertilizers, pesticides, films, etc.). Consider discussing any dynamic changes in the composition of these inputs (for instance, whether new biological or organic fertilizers are being adopted) to explain changes in ACE over the study period.

Role of Technology Adoption

Comment: The manuscript briefly suggests that adopting more advanced agricultural technology can cut carbon emissions while boosting yields. However, no indicator of technology adoption (e.g., mechanization rates, R&D investment) is included in the models. At least mention this limitation in the Discussion.

Organization of the Discussion

Comment: The Discussion section repeatedly references “some scholars” or “previous studies” without systematically comparing your results to theirs. Try adding a paragraph specifically contrasting your main findings with those from the literature review to highlight novel insights or resolve discrepancies.

Highlighting Non-Linear Effects

Comment: The text mentions that some studies found an inverted U-shaped (EKC-type) relationship for carbon and economic growth. Your results do not explicitly test for non-linearity. If feasible, you could test that or at least address whether your data might fit such patterns.

Robustness to Data Outliers

Comment: Some city-level data points (e.g., very high or low fertilizer usage in a certain year) could skew the efficiency estimates. Discuss whether any data smoothing or outlier diagnosis was carried out prior to the DEA. If not, mention that as a possible limitation.

Implications Beyond Agriculture

Comment: The link between agricultural emissions and broader environmental impacts (like water pollution, biodiversity loss) is hinted at but not detailed. You could briefly address whether lower carbon emissions also bring other co-benefits, such as improved soil quality, to show broader relevance.

Use of Sub-Indices

Comment: When presenting the super-efficiency SBM approach, consider including an explanation or formula in an appendix to show exactly how slack-based measures of undesired output are incorporated. This will improve reproducibility.

Section on Theoretical Framework

Comment: The manuscript jumps quickly from a general background to methodology. If possible, include a short conceptual diagram or theoretical framework that visually represents how inputs, outputs, and coupling coordination interact. This helps readers follow the logic flow.

Policy Context for Urbanization

Comment: The results find that urbanization positively affects coupling coordination. Delve into how changes in land use policy, urban expansion, or the migration of the rural workforce might be contributing. That can further clarify the mechanism behind your regression finding.

Data Availability Statement

Comment: Journals often require a statement about data sharing or data availability. Indicate if the municipal data is publicly available (e.g., from statistical yearbooks), or if it can be obtained from the authors upon reasonable request.

Potential for Future Extensions

Comment: In your concluding section, outline how future research could incorporate more comprehensive environmental indicators (e.g., water usage, biodiversity indices) or extend the analysis to examine cross-provincial interactions. This demonstrates the broader potential impact and fosters follow-up studies.

Summary

The manuscript addresses an important topic at the intersection of agricultural economics and environmental sustainability by measuring and analyzing the coupling between agricultural carbon emission efficiency and agricultural economic growth in Jiangxi Province.
Strengths of the paper include the adoption of an advanced DEA framework (super-efficiency SBM) and the use of a coupling coordination model to illustrate complex relationships.
Areas needing improvement include clearer justification for the chosen variables, a deeper examination of the negative effect of rural living standards, more explicit discussion of data limitations, and stronger policy recommendations.

Comments on the Quality of English Language

Overall, the manuscript’s English is understandable and conveys the main points of the research effectively. However, there are a few areas where minor improvements can strengthen clarity and consistency:

Grammar and Syntax:
- Some sentences would benefit from more concise phrasing.
- Occasional grammar slips (e.g., subject–verb agreement and singular/plural forms) appear in a few paragraphs.
Word Choice and Terminology:
- Key terms (e.g., “carbon emission efficiency,” “agricultural inputs,” “coordination degree”) should be used consistently throughout the text.
- Where possible, use simpler or more direct vocabulary to avoid ambiguity (for example, replace “dissonance” with “imbalance” or “misalignment” if it fits better in context).
Overall Flow and Readability:
- A brief, thorough proofreading or professional language editing pass could unify writing style across sections, making the paper read more cohesively.
- Check that each paragraph opens with a clear topic statement and transitions naturally to the next.

Author Response

Letters of response to reviewer 2

Dear reviewers,

Thank you very much for reviewing our manuscript again and giving us very insightful suggestions. We have carefully read your revision letter, we do not do well enough in these aspects, we also in the original text on the basis of the situation, according to your revision comments, the content of the article to modify and improve, I hope to further improve the quality of the paper, and strive to meet the requirements for publication, thank you again for your hard work, our team is grateful for this.

The following are the specific modifications:

Major Comments

1 Research Gap and Originality

Comment: The manuscript highlights the importance of coupling agricultural carbon emission efficiency (ACE) and agricultural economic growth (AEG), using Jiangxi Province as a case study. However, the paper does not explicitly clarify how the study advances previous empirical work on similar topics (e.g., the broad literature on environmental efficiency, low-carbon transformations, or decoupling analysis). Clarifying how this study’s approach is novel (or how it improves on prior methods) would make the contribution clearer.

Response 1: Thanks to the reviewer's suggestion, we have rewritten the introduction content to summarize the content of the introduction and the literature review, and to address the research innovations.

The following are the revised potential innovations:

2 Justification of Focus on Jiangxi Province

Comment: The manuscript focuses on Jiangxi Province due to its agricultural significance and carbon intensity. However, the justification could be strengthened by comparing Jiangxi Province to other provinces with similar structural characteristics. Is there something especially unique about Jiangxi that makes it a compelling “laboratory” for this research? Explain in more detail.

Response 2: Thanks to the reviewers' suggestions, we have addressed the reasons for choosing Jiangxi Province as the study area. Below are the changes made to this section:

1.2 Research Problems

Jiangxi Province is an important ecological reserve and agricultural province in China, with rich natural resources and ecological advantages, and its agricultural structure and carbon emissions also have significant uniqueness, which provides an ideal research sample for analyzing the mechanism of low-carbon transition in agriculture. The rice sowing area in Jiangxi Province ranks among the top three in the country, and methane emissions from rice cultivation account for more than 50% of the total agricultural greenhouse gases[25] , while compared with Hunan Province, which is also a major rice-producing area, Jiangxi Province has a higher fertilizer application per unit of production, resulting in lower carbon emission efficiency[32] , and this “high-carbon lock” phenomenon is closely related to the crude production model. This “high carbon lock-in” phenomenon is closely related to the rough production mode. Despite the implementation of the “river chief system”[33] and the ecological compensation mechanism[34], the shrinking of wetland area[35] and agricultural surface pollution[36] still constrain the green development of Jiangxi Province[37]. In addition, there are significant differences in the agricultural structures of the 11 district cities in the province, leading to prominent spatial heterogeneity in ACE[38]. Therefore, clarifying the synergistic law of ACE and AEG in Jiangxi Province is both an urgent need for regional low-carbon development and an important supplement to improve small- and medium-scale studies.

3 Clarity of Research Objectives

Comment: The four “scientific concerns” (lines 70–75 in the partial text) are a good start, but the statement of these objectives is slightly scattered across several paragraphs. Consolidate them in a dedicated paragraph or bullet points to make the research aims and questions easier to identify.

Response 3: Thanks to the reviewers' comments, we have revised the research questions and study content, and added a research roadmap for readers' convenience.

The following are the modifications:

1.3 Research objectives

Figure 1 Research roadmap

4 Integration of Literature Review

Comment: The Literature Review section at times feels like a broad overview of carbon emission measurement and data envelopment analysis but does not thoroughly connect the references back to the authors’ conceptual framework. Consider reorganizing to show how each cited study relates to (a) your methods, (b) your study region, or (c) your coupling-coordination perspective. Linking them more tightly would better situate the work in the existing literature.

Response 4: Thank you for the professional review comments from the reviewers, we have integrated the literature review and introduction according to your suggestions, and deleted some old, anachronistic literature and added some new literature to support our research theory.

The following is the content of the revised literature review:

Introduction

The global challenge of climate change, particularly global warming, has reached a level that cannot be ignored. According to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report, the average temperature of our planet has risen by 1.1°C over the past century[1]. Increased emissions of greenhouse gases are leading to more severe weather phenomena, posing significant risks to both human societies and natural ecosystems[2]. The concept of "carbon neutrality" emerged in 2018, when the IPCC recommended that net-zero global greenhouse gas emissions be achieved by mid-century as a strategy to effectively combat global climate change[3]. Given that China is the leading carbon emitter, it is actively engaging in international climate governance and has set the goal of "actively and steadily promoting carbon peaking and carbon neutrality"[4], responding to mounting pressures to decrease emissions. In this context, researchers are focusing on the interrelated development of the ecological environment and regional economies.

1.1 Literature review

Existing studies provide a foundational framework for understanding the relationship between ACE and AEG. Early scholars defined carbon emissions and carbon emission efficiency through Kaya equation[5] and energy intensity indicators[6], and then the research was gradually extended to the field of agriculture by integrating them into a comprehensive indicator that considers economic growth, resource consumption and agricultural carbon emissions[7]. In the field of research methodology, scholars have adopted different methods to measure the efficiency of agricultural carbon emissions. Charnes first proposed the data envelopment analysis model to assess the relative efficiency of decision-making units[8]. However, the DEA model was unable to solve the slackness of inputs and outputs, which led to a large bias in the efficiency measurement[9]. In contrast, the super-efficient SBM model has been widely used to analyze the spatio-temporal divergence of ACE in different regions of China by integrating the non-expected outputs and slack optimization[10-13]. In terms of influencing factors, studies have shown that labor force size has an inhibitory effect on agricultural carbon emission efficiency, while optimization of agricultural industrial structure and urbanization process can significantly enhance efficiency[14].Ye pointed out that agro-industrial agglomeration has an inverted U-shape nonlinear effect on agro-environmental efficiency[15]. In addition, urbanization process[16], AEG[17-18], agricultural tax and fee levels[19], and digital finance[20] have all been shown to be dynamically associated with ACE. It is noteworthy that some studies have pointed out that the evolution of ACE shows significant provincial differences[21]. Therefore, in the exploration of ACE, regional characteristics and spatial correlation should be taken into account[22].

Academics usually measure AEG by the total output value of agriculture, forestry, animal husbandry and fishery, but it is difficult to reflect the internal differences of the industry and the characteristics of regional resource endowment, which can easily lead to the bias of economic level measurement[3]. For this reason, this paper draws on the research results of Tan[23], and adopts per capita agricultural value added (agricultural value added/agricultural employees) as an indicator for measuring AEG, to comprehensively reflect the actual AEG level of the agricultural industry in each region. In the study of the relationship between agricultural carbon emissions and economic growth, scholars have explored the relationship between agricultural carbon emissions and economic growth based on the EKC model[24], the Tapio decoupling model[25], the ARDL-ECM model[26], and the coupled coordination model[3], i.e., the short-term expansion of the agricultural economy may push up the carbon emissions, whereas the long-term technological innovation and structural optimization significantly inhibit the growth of carbon emissions. Meanwhile, the spatial distribution of ACE gradually shifted from dispersion to agglomeration, and its relationship with the agricultural economy transitioned from weak decoupling to negative decoupling[27]. In addition, the utilization of renewable energy and the promotion of low-carbon technologies can not only alleviate the inhibitory effect of carbon emissions on the agricultural economy[28-29], but also promote sustainable development by enhancing the competitiveness of agricultural exports[30]. Thus, it is necessary to improve low-carbon production technologies, increase renewable energy consumption, and improve agricultural production and ecological conditions to realize the goal of sustainable development in the agricultural sector[31].

Comment 5: The paper uses the super-efficiency SBM-DEA approach to estimate ACE. While the choice is valid, there is no in-depth discussion of how potential multicollinearity or sensitivity (e.g., choice of inputs/outputs) might affect the efficiency scores. The authors should discuss and justify why these specific inputs and outputs were chosen and whether any robustness checks have been conducted.

Comment 6: The paper emphasizes using labor, land, water, fertilizer, pesticide, and plastic film as inputs, and “gross agricultural output” as the desired output, while “carbon emissions” serve as a non-desired output. However, it is unclear why certain potential inputs (e.g., agricultural machinery usage, livestock feed inputs) were excluded. Provide a thorough rationale or, if relevant, mention data limitations to justify these choices.

Comment 7: The manuscript provides only a short overview of carbon emission sources, referencing multiple prior works. However, the paper should include more detail on the formula(s) or references for calculating agricultural carbon emissions from fertilizers, pesticides, irrigation, etc. This adds transparency for readers who want to replicate or confirm the approach.

Response 5-7: Thanks to the suggestions of the reviewers, we have revised and improved the presentation of 2.2 Methodology of Agricultural Carbon Emission Efficiency. The main modifications are (1) introducing the agricultural carbon emission efficiency; (2) comparing the SBM-DEA model with other models and explaining its suitability for this study; (3) demonstrating for the indicator selection and stating that it passed the test; and (4) explaining the principles of calculating the agricultural carbon emissions and the indicators.The following is the modified 2.2 Agricultural Carbon Emission Efficiency:

2.2 Agricultural carbon efficiency

ACE is a comprehensive indicator of agricultural carbon emissions and agricultural economic growth[39], and its measurement needs to consider the dual attributes of desired outputs (economic value) and non-desired outputs (environmental costs). Traditional DEA models may have limitations in dealing with non-desired outputs, making it difficult to effectively identify inefficiencies due to non-desired outputs. And although the GML index can capture the dynamic characteristics of productivity, it is unable to decompose the components of efficiency over a specific time period[40]. In contrast, the SBM-DEA model can consider both desired and non-desired outputs, effectively solving the problem of sorting and juxtaposition and ensuring the scientific and accurate assessment results[41].

Based on the economic growth theory and drawing on Wang's study [42], this study, combined with the characteristics of agricultural development in Jiangxi Province, utilizes the super-efficient SBM model to construct an agricultural carbon emission efficiency evaluation system covering production inputs, desired outputs and non-desired outputs. The construction of the specific index system contains three dimensions: (1) the production inputs are selected as core factors such as labor, land, water resources, fertilizers, pesticides, etc., reflecting the characteristics of the allocation of agricultural production factors, and the selection of the indicators has passed the robustness check; (2) the desired outputs characterize the economic efficiency by the total agricultural output value; (3) the non-desired outputs use the agricultural carbon emissions to characterize the environmental load. Agricultural carbon emissions are calculated as the product of the quantity of agricultural inputs (fertilizers, pesticides, agricultural films, agricultural machinery, land tilling, and effective irrigated area) and its carbon emission coefficient. The specific indicator system is shown in Table 1:

8 Coupling Coordination Formula Explanation

Comment: While the text presents the coupling coordination model’s final formula, some readers may be unfamiliar with how “coupling degree” and “coordination degree” are derived. Consider adding a short, plain-language explanation (perhaps in an appendix) to clarify why the model is well suited to measure synergy/coordination between two systems (ACE and AEG).

Response 8: Thanks to the reviewer's suggestion, we introduced the coupling coordination degree model along with the coupling degree model in the methodology of the coupling coordination model, which is convenient for readers to read separately. We also compare the model with the previous model to show its suitability for this study.

The following is the modified methodology of the coupled coordination model presentation:

2.3 Coupled Coordination Model

The coupling and coordination model is used as an analytical tool to measure the level of interaction and coordination development between two and more systems[43]. Among them, coupling degree is an indicator to measure the strength of interactions between systems, and coordination degree is a further measure of inter-systems based on coupling degree while coordination is a coordinated and cooperative, virtuous cycle relationship between systems or elements[44-45]. Compared with the traditional coupling degree model and single indicator evaluation model, it can simultaneously quantify the strength of interactions between systems and the overall level of coordinated development, thus reflecting the synergistic relationship between the economy and society and the ecosystem in a more comprehensive and dynamic way[46]. Therefore, this study adopts the coupling coordination degree model to explore the coupling relationship between ACE and AEG in Jiangxi Province. The following formula is used for calculation:

9 Justification of Tobit Model

Comment: The authors justify using a Tobit model due to truncated data (coupling coordination degree ranges from 0 to 1). While that is appropriate, the paper provides little discussion of the potential for alternative bounded models (e.g., Beta regression) or the sensitivity of results to the Tobit specification. A short methodological note clarifying these points would strengthen the argument.

Response 9: Thanks to the reviewers' professional review, we have revised the introduction of the Tobit model. Firstly, we introduce the Tobit model; secondly, we compare the Tobit model with other models to illustrate the robustness of the Tobit model. At the same time we introduce the reasons for the selection of indicators. The following is the methodological introduction of the modified Tobit model:

2.5Tobit model

Tobit regression analysis is a statistical model used to analyze binary dependent variables, which can flexibly deal with various types of independent variables and has good interpretability for outliers and non-normally distributed data[49]. In contrast, the least squares method to explore the relationship between the coupled coordination degree of the two systems and the external factors is prone to bias in the parameter estimates[50]. Therefore, in this paper, the Tobit model is chosen to solve the problem of restricted dependent variables to explore the external factors affecting the coupled and coordinated development of ACE and AEG. The explanatory variable of this study is the measured coupling coordination level of ACE and AEG, which is a restricted variable. The coupling coordination level is not only affected by economic factors, but also by agricultural production conditions and social factors, and plays an increasingly important role in modern agricultural production. Based on the existing research results[42] and the availability of data, this paper summarizes the factors affecting the level of coupled coordination into six aspects, including government input, education level, agricultural industry structure, energy use, living standard and urbanization level. The formula for calculation is:

10 Comparisons with Other Regions or Times

Comment: The results show interesting trends for Jiangxi Province over time, but the manuscript never references how these trends compare with national-level or neighboring provinces’ patterns. A short comparison would help underscore whether the patterns in Jiangxi are unique or align with broader Chinese trends in agricultural carbon efficiency and economic growth.

Response 10: Thanks to the reviewer's professional opinion, we do lack the comparison in the basin or nationwide in the presentation of results. Therefore, we have made a mention in the discussion to compare the development of Jiangxi Province with neighboring provinces and countries. The following is our revised discussion:

This study investigates the synergistic development of agro-ecology and agro-economy in Jiangxi Province by measuring the coupling relationship between ACE and AEG, the sources of regional differences and the influencing factors.

The results of this study show that the coupling of ACE and AEG in Jiangxi Province shows an upward trend, from “serious dissonance” to “intermediate coordination”, and the regional differences are gradually narrowed. This is consistent with the overall direction of the national agricultural low-carbon transition[3], indicating that Jiangxi Province has achieved success in agricultural low-carbon transition. For example, optimizing agricultural irrigation methods not only reduces energy consumption and lowers carbon emissions, but also improves soil quality and enhances the ecological environment[52]. Therefore, low-carbon transition in agriculture is not only a necessary measure to cope with climate change, but also an important path to realize the overall health of agroecosystems.

In terms of regional differences, the level of coupling harmonization is higher in northern of Jiangxi, northeastern of Jiangxi and western of Jiangxi, while it is relatively lower in central-southern of Jiangxi. This regional imbalance is consistent with the influence of regional resource endowment and economic development level on ACE[19]. In addition, this study found that inter-regional differences are the main source of overall differences, i.e., differences in the level of economic and ecological development between regions are important factors constraining the coupling and harmonization of ACE and AEG[32].

This study reveals the source of regional differences through Dagum's Gini coefficient decomposition and finds that inter-regional differences are the main contributing factor. This is consistent with Li's findings that inter-regional differences in economic status are key to the coordinated regional development[53]. Through further analysis, this study also found that the contribution rate of hypervariable density showed a decreasing trend, while the contribution rate of intra-regional differences remained relatively stable, which is consistent with the overall direction of high-quality development of agriculture in the Yangtze River Economic Belt[54]. This indicates that the coordinated development between regions is gradually improving, but further efforts are still needed to reduce the intra-regional differences.

In terms of influencing factors, this study found that government inputs, optimization of agricultural industrial structure, urbanization level and educational level have a significant positive effect on the improvement of coupling coordination, a result similar to the actual situation in Hebei Province[55], indicating that in the process of agricultural modernization and low-carbon transition, the above indicators are key factors in promoting the coordinated development of regional economy and ecology.

It is worth noting that the influence of rural residents' living standards on the coordination level is not statistically significant. This finding deviates from the emphasis placed in existing studies on the positive impacts of enhanced living standards among rural residents on the ecological environment[56]. This discrepancy may arise from the unique characteristics of the agricultural sector, wherein agricultural carbon emission efficiency is predominantly influenced by agricultural technology and the structure of agricultural production. Although an elevation in living standards may foster advancements in agricultural technology and facilitate the transformation of agricultural production methods, thereby exerting an influence on agricultural carbon emissions, this effect is indirect and subject to various constraints. Consequently, future research endeavors should delve deeper into the underlying mechanisms through which living standards impact agricultural carbon emissions, elucidate potential limitations, and subsequently identify viable solutions.

Methodology and Analysis Comments

11 Time Horizon and Structural Breaks

Comment: The manuscript analyzes data from 2008 to 2022 but does not discuss important policy or structural changes (e.g., major shifts in China’s agricultural subsidies or carbon policies) that could cause structural breaks. If such events occurred, it may be worth testing whether these breakpoints significantly affect the results.

Response 11: Thank you for the reviewer's suggestion, we very much agree with your point of view, and our research this time focuses on the coupling relationship between the two and the influencing factors. The policy perspective analysis is indeed very intriguing, and we will use the did model to analyze the impact of government policies on agricultural carbon emission efficiency and economic growth in our further research.

12 Spatial Autocorrelation

Comment: The analysis is done city by city within the province, yet there may be spatial interdependencies—pollution or technology adoption could diffuse across neighboring cities. Consider if Moran’s I or other spatial correlation tests should be performed to see whether results are spatially clustered.

Response 12: Thank you for the reviewer's suggestion, we agree with you. The spatial model is indeed a valuable extension. However, due to the division of our province into four regions, it would be less mature to do spatial modeling. Our future research will go deeper to the county level and will use the model in future studies to further improve the study.

13 Potential Endogeneity in Tobit Model

Comment: Some explanatory variables (e.g., government expenditure, industrial structure) might be endogenous to ACE or simultaneously determined with agricultural development. The authors should at least acknowledge potential endogeneity and discuss how it may or may not bias the Tobit results.

Response 13: We have added the following content:

Potential Endogeneity. Given the potential endogeneity issues among government expenditure, industrial structure, and agricultural carbon emissions, our research team lagged the data of government expenditure and industrial structure by one period and employed the fixed - effects model for estimation. Table 10 reports the results of endogeneity estimation. The results reveal that the impact of the one - period - lagged data on agricultural carbon emissions is generally consistent with that of the current - period data, indicating the absence of endogeneity problems among government expenditure, industrial structure, and agricultural carbon emissions.

Table 10. Results of endogeneity analysis

Projects	ACE
Projects	(1)	(2)	(3)
Government input	2.263^***	2.224^***	0.425^**
	(0.384)	(0.419)	(0.189)
Industrial structure	-1.936^***	-1.994^***	0.406^**
	(0.200)	(0.227)	(0.164)
L.Government input		0.231	0.459^**
		(0.393)	(0.187)
L.Industrial structure			0.180^**
			(0.075)
Other variables	Controled	Controled	Controled
Year fixed effects	Yes	Yes	Yes
Province fixed effects	Yes	Yes	Yes
_cons	0.376	0.311	-0.540^***
	(0.229)	(0.254)	(0.156)
N	165	150	150
adj. R²	0.983	0.983	0.983

Standard errors in parentheses ^* p < 0.1, ^** p < 0.05, ^*** p < 0.01

14 Robustness Checks

Comment: The paper lacks an explicit section on robustness checks. For instance, testing how the results change when removing outliers or using alternative definitions of key variables (e.g., substituting “energy use” with “rural energy consumption per capita” vs. total consumption). Consider including at least one table showing the sensitivity of results.

Response 14: We have added the following content:

3.4.2 Robustness Checks

Elimination of Extreme Values. In order to mitigate the potential distortion caused by outliers on model estimation, a two - tailed truncation procedure is applied to the independent variables at the 1%, 5%, and 10% thresholds in this research. The outcomes of the analysis conducted after the elimination of extreme values are presented in Table 8. Notably, the estimated results following the removal of extreme values exhibit a high degree of consistency with the benchmark regression results. This congruence serves as strong evidence that the model estimation results obtained in this study are robust.

Table 8. Analysis results after removing outliers

Projects	Two - tailed truncation
	1%	5%	10%
	(1)	(2)	(3)
Government input	0.511^**	0.527^***	0.419^**
	(0.205)	(0.202)	(0.203)
Industrial structure	0.575^***	0.602^***	0.546^***
	(0.125)	(0.129)	(0.132)
Living Standards	0.002	0.001	0.001
	(0.002)	(0.002)	(0.002)
Energy use	0.000	0.000	-0.000
	(0.000)	(0.000)	(0.000)
Urbanization Level	0.006^***	0.007^***	0.006^***
	(0.002)	(0.002)	(0.002)
Educational level	0.133^***	0.154^***	0.160^***
	(0.041)	(0.042)	(0.045)
Year fixed effects	Yes	Yes	Yes
Province fixed effects	Yes	Yes	Yes
_cons	-0.389^***	-0.490^***	-0.378^***
	(0.129)	(0.141)	(0.142)
N	163	143	134
adj. R²	0.983	0.980	0.980

Standard errors in parentheses ^* p < 0.1, ^** p < 0.05, ^*** p < 0.01

Model Replacement Approach. To further ascertain the robustness of the benchmark regression findings, this study utilizes quantile regression. Specifically, quantile points at the 25th, 50th, 75th, and 90th percentiles are chosen, and the quantile regression model is applied to re - estimate the outcomes. The estimated results of the quantile regression are reported in Table 9.The findings demonstrate a high degree of similarity between the quantile regression results and the benchmark regression results. This similarity strongly suggests that the model estimation results in this study exhibit robustness.

Table9. Analysis of quantile regression results

Projects	Q25	Q50	Q75	Q90
Projects	(1)	(2)	(3)	(4)
Government input	0.094	0.737^**	0.864^***	0.749^***
	(0.200)	(0.312)	(0.182)	(0.144)
Industrial structure	0.697^***	0.611^***	0.294^***	0.437^***
	(0.118)	(0.185)	(0.108)	(0.085)
Living Standards	-0.001	-0.000	0.001	0.006^***
	(0.002)	(0.003)	(0.002)	(0.002)
Energy use	0.000	-0.000	0.000	-0.000
	(0.000)	(0.000)	(0.000)	(0.000)
Urbanization Level	0.004^**	0.007^**	0.004^***	0.003^**
	(0.002)	(0.003)	(0.001)	(0.001)
Educational level	0.146^***	0.117^*	0.169^***	0.147^***
	(0.040)	(0.062)	(0.036)	(0.029)
Year fixed effects	Yes	Yes	Yes	Yes
Province fixed effects	Yes	Yes	Yes	Yes
_cons	-0.287^**	-0.427^**	-0.180	-0.157^*
	(0.124)	(0.193)	(0.113)	(0.089)
N	163	163	163	163
adj. R²	0.833	0.846	0.859	0.870

Standard errors in parentheses ^* p < 0.1, ^** p < 0.05, ^*** p < 0.01

15 Data Quality and Sources

Comment: While the authors mention that data comes from the Statistical Yearbook and official bulletins, it would be beneficial to include a brief discussion of any known biases or data reliability issues. For example, local-level statistics in certain regions can be incomplete or delayed. Indicate if any interpolation or smoothing was necessary.

Response 15: We thank the reviewers for their comments. The data we used are official data, which are relatively more accurate in terms of quality. At the same time, we also affirm that the interpolation method has been used to calculate a few missing values to ensure the accuracy of the data.

Below is an overview of the revised data statistics:

2.1 Data sources

This paper takes 11 district cities in Jiangxi Province as the research object, based on the principles of geographic proximity, similarity of resource endowment and continuity of administrative divisions, divides them into four major regions: North of Jiangxi (Nanchang, Jiujiang), Northeast of Jiangxi (Shangrao, Jingdezhen, Yingtan), West of Jiangxi (Yichun, Pingxiang, Xinyu), and Central-South of Jiangxi (of Ganzhou, Ji'an, Fuzhou), corresponding to the main functional area positioning of the collaborative belt of the city clusters in the middle reaches of the Yangtze River, the node area of the Shanghai-Kunming Economic Corridor, the demonstration area of the transformation of the Xiang-Gan border, and the revitalization belt of the former Central Soviet Union. It corresponds to the positioning of the main functional zones such as the collaborative belt of city cluster in the middle reaches of Yangtze River, the node area of Shanghai-Kunming Economic Corridor, the demonstration area of transformation of Xiang-Gan Border and the revitalization belt of the former Central Soviet Region. The study period is 2008–2022. China Statistical Yearbook, Jiangxi Statistical Yearbook, and the statistical bulletin of national economic and social development of each prefectural-level city are the primary sources of the indicator data used in the study, which primarily consists of ACE, AEG, and its influencing factors. Some of the residual values were supplemented using interpolation. In this study, the linear interpolation algorithm was utilized in the data preprocessing stage to interpolate the missing values to ensure the accuracy and reliability of the data.

16 Definition of “Industrial Structure”

Comment: The variable “industrial structure” is measured as the ratio of “gross value of agricultural output” to “gross value of agriculture, forestry, livestock, and fishery output.” This might be somewhat confusing, because both the numerator and denominator are agricultural in scope. Clarify the logic behind using that ratio, or consider labeling it differently, as it might not fully capture “industrial structure optimization.”

Response 16: Thank you for the reviewer's suggestion, this is really our oversight, what we want to express is the industrial structure of agriculture, not the industrial structure of one, two, three industries. Therefore we have rechecked the manuscript and corrected it in the article. Thanks again to the reviewing experts for their suggestions.

Table 3. Selection and Explanation of Indicator Variables of Factors Influencing the Coupling Coordination Degree of ACE and AEG in Jiangxi Province.

variable	norm	Description of indicators	unit (of measure)
explained variable	degree of coupling coordination	ACE and AEG Coupling Harmonization Degree	-
explanatory variable	government input	Expenditure on agriculture, forestry, and water/general expenditure budget of local finances	%
	educational level	Ratio of effective irrigated area to sown crop area	%
	Agricultural industrial structure	Gross value of agricultural output/gross value of agricultural, forestry, livestock, and fishery output	%
	energy use	Rural electricity consumption	kW·h
	living standards	Per capita disposable income of rural residents	Yuan
	urbanization level (of a city or town)	Urban residents as a proportion of total population	%

17 Interpretation of the Negative Impact of Rural Living Standards

Comment: The Tobit regression suggests that rising rural living standards impede the coupling coordination. That is an interesting result that diverges from some prior literature. Add more in-depth discussion or plausible mechanisms—e.g., changes in consumption habits that increase emissions? Or do higher rural incomes shift land usage? This is a notable finding that warrants more careful interpretation.

Response 16: We have reformulated the Tobit model by incorporating temporal and provincial fixed effects. The empirical findings demonstrate that the impact on rural living standards is statistically insignificant. Following a battery of robustness checks, these results remain consistent with those obtained from the baseline regression analysis. Additionally, we have elaborated on the following explanations within the manuscript to provide further clarity.

The coupling effect of living standards on agricultural carbon emission efficiency and economic development in Jiangxi Province is insignificant. Based on prior research, enhancements in living standards can alter farmers' consumption patterns, leading to increased energy consumption and carbon emissions. However, within the agricultural sector, agricultural carbon emission efficiency is predominantly influenced by agricultural technology and the structure of agricultural production. Technological advancements or optimizations in the agricultural production structure can notably reduce agricultural carbon emissions. Although improvements in living standards may foster progress in agricultural technology and the transformation of agricultural production methods, thereby influencing agricultural carbon emissions, this impact is indirect and subject to various constraints. Changes in living standards do not directly affect the coupling effect between agricultural carbon emission efficiency and economic development.

18-19 Calculating Regional Gini Coefficients \Choice of Decomposition Method

Comment: Dagum Gini coefficient decomposition is employed to analyze sources of disparity. Provide a quick example or short table to illustrate how the decomposed Gini is interpreted. This will help readers who are less familiar with Dagum’s approach.

Comment: The authors reference that Dagum’s Gini is superior to other decomposition methods (e.g., Theil index, mean log deviation) for addressing negative contributions and overlap. A short paragraph summarizing these advantages would be helpful so that readers understand why this approach is especially relevant to measuring coordination disparities.

Response 18-19: Thanks to the reviewer's suggestion, we have presented the Gini coefficient in our methodology for the 2.4Gini Gini coefficient, describing the within-group and between-group differences and comparing it with other models to illustrate the applicability of the model. For the reader's convenience.

Below is a demonstration of the modified Gini coefficient methodology:

2.4 Dagum Gini coefficient decomposition method

The Dagum Gini coefficient decomposition is a statistical method that decomposes overall income inequality into within-group differences, between-group differences and hyper variance density. Among them, the within-group disparity reflects the differences within the same group, the between-group disparity reflects the average differences between different groups, and the hyper variance density reflects the differences in the overlapping parts of the groups. Compared with Theil's index, which was mostly used in earlier studies[47], Dagum's Gini coefficient decomposition method overcomes the limitations of the subgroup overlap problem by introducing an index of the degree of net difference between subgroups[48], and is able to analyze the differences in the intra-region, inter-region, and hyper variance densities of the YEB in a clearer way. In order to examine the spatial variations in the coupled coordination degree of ACE and AEG, as well as its causes, this research uses the Dagum Gini coefficient decomposition approach.

20 Consistency between Figures and Tables

Comment: Ensure the data in the text (e.g., Gini values or coordination degree results) precisely match the numerical values in the tables and figures. Any rounding or averaging differences should be explained, or the same rounding rules used consistently throughout.

Response 20: Thanks to the reviewer's suggestion, we have made changes to the data in the article to ensure that the data in the text accurately matches the values in the tables and charts.

The following are some of the modifications:

Table 4. Mean value of ACE in Jiangxi Province, 2008-2022.

Year	Average Efficiency Value	Year	Average Efficiency Value	Year	Average Efficiency Value
2008	0.172	2013	0.256	2018	0.422
2009	0.178	2014	0.275	2019	0.472
2010	0.193	2015	0.324	2020	0.529
2011	0.22	2016	0.362	2021	0.579
2012	0.241	2017	0.382	2022	0.624

Minor and Editorial Comments

21 Title Specificity

Comment: The current title highlights “Research on the Relationship and Coordination of Agricultural Carbon Emission Efficiency and Agricultural Economic Growth—Using Jiangxi Province as an Example.” Consider slightly shortening it or making it more direct, e.g., “Coupling Agricultural Carbon Emission Efficiency and Economic Growth: Evidence from Jiangxi Province, China.”

Response 21: Thank you for the reviewer's suggestion, we very much agree with you and after discussion we have decided to correct the title to “Coupling Agricultural Carbon Emission Efficiency and Economic Growth: Evidence from Jiangxi Province, China”.

22 Keywords

Comment: The keywords list overlaps somewhat (e.g., “Agricultural Carbon Efficiency” vs. “Low-Carbon Agricultural Transformation”). Merge or refine to boost discoverability. Possibly add “Data Envelopment Analysis (DEA)” or “SBM” if those are central to your methodology.

Response 22: Thanks to the reviewer's suggestion, we have revised and improved the content of the keywords and deleted the inappropriate keywords.

The following are the revised keywords:

Keywords: Agricultural Carbon Efficiency; Agricultural Economic Growth; Coupling Coordination; Regional Disparities

23 Abstract Clarity

Comment: The abstract is reasonably comprehensive but could more explicitly mention the key numerical findings—e.g., the final coupling coordination degree, the largest factor behind its improvement, or the leading city. This will make the abstract’s results more memorable.

Response 23: We thank the reviewers for their suggestions, and we have improved the content of the abstract based on the revision of the paper in the hope that it will be more appealing to the readers' interest.

The following is the revised abstract:

Exploring the law and evolution mechanism of coupling and coordination between agricultural carbon emission efficiency (ACE) and agricultural economic growth (AEG) can provide a reference basis for agricultural low-carbon transformation. This study takes 11 cities in Jiangxi Province as the research object, measures the level of ACE based on the panel data from 2008 to 2022, and analyzes the development and influencing factors of the coupling and coordination between ACE and AEG by using the coupling coordination degree model, the Dagum Gini coefficient decomposition method, and the Tobit regression model. The results reveal that: (1) The overall ACE in Jiangxi Province has displayed a significant upward trend, with the average efficiency value increasing from 0.172 to 0.624, reflecting an average annual growth rate of 72.43%. Nonetheless, there remains clear regional heterogeneity, characterized by lower efficiencies in central and southern Jiangxi compared to the higher efficiencies found in northern and western Jiangxi. (2) Despite gradual improvements in regional coordination, the central and southern Jiangxi regions still lag northern and western Jiangxi in terms of the linked coordination between ACE and AEG, symptoms of which had been previously misaligned. (3) The results of Dagum's Gini coefficient decomposition show that inter-regional disparities are the main source of overall disparities, with a contribution of 37.43%, which is higher than the synergistic effect of intra-regional disparities and hyper-variable densities, corroborating the core contradiction of uneven development across regions. (4) The Tobit model reveals that government investment, industrial structure optimization, urbanization, and educational attainment exert a significant positive influence on promoting coupling coordination. To establish a scientific basis for achieving a low-carbon agricultural transformation and equitable AEG in Jiangxi Province, this research recommends bolstering regional cooperation, fostering innovations in agricultural science and technology, optimizing the industrial structure, and enhancing farmers' awareness of low-carbon practices. This study expands the theoretical system of agricultural low-carbon transition in terms of research methods and scales to provide a scientific basis for agricultural provinces to realize agricultural low-carbon transition and balanced economic development.

24 Abbreviations

Comment: The text uses several acronyms (ACE, AEG, AC, etc.). Make sure each acronym is fully spelled out and defined at first mention in both the abstract and main text. Also, avoid introducing acronyms not used repeatedly, to keep the manuscript concise.

Response 24: Thank you to the reviewers for their comments, we checked the content of the article to ensure that each abbreviation used was fully spelled and defined when first mentioned in the abstract and in the body of the article to keep the manuscript concise.

25 Reference Formatting

Comment: The references (e.g., [1], [2], [3]) appear consistent, but check that each cited work in the text matches a complete entry in the reference list (and vice versa). Also ensure correct formatting for the journal’s style (e.g., MDPI’s Sustainability typically requires certain referencing formats).

Response 25: Thank you for the reviewer's suggestion, we have checked the formatting of the references after revising the paper to ensure that it meets the mdpi requirements for publication, thank you again.

The following are some of the modifications:

26 Citation Updates

Comment: Some references to “Tan, 2024; Huang, 2024” in the partial text appear to be placeholders or references to future works. Verify that these references are correct, properly published, and correspond to real sources. Otherwise, remove or replace them.

Response 26: Thanks to your suggestion, we removed inappropriate citations and added some novel literature to support the article's point. Thanks again to the reviewers.

27 Section Headings

Comment: The standard structure (Introduction, Literature Review, Methodology, Results, Discussion, Conclusions) is present. However, some overlap occurs between the Literature Review and Introduction. Merge or reorganize to avoid redundancy.

Response 27: Thanks to the reviewer's comments, we have integrated the introduction and literature review content to comply with the publication requirements of thesis journals.

28-29 Figure Quality、Table Formatting

Comment: While the partial text shows some references to figures (e.g., Figure 1, Figure 2), confirm that the resolution, labeling, and titles on each figure meet publication standards. Each figure should be self-explanatory with a descriptive caption. For the main methodological or results tables, ensure the columns are adequately labeled. For example, clarify column headings for Gini decomposition, coupling coordination classification, etc. Also, confirm that each table has a short “Table X. Title” format above it.

Response 28-29: Thanks to the suggestions of the reviewers, we have checked the pictures and tables in the paper, modified the inappropriate descriptions, and replaced the pictures with clearer and more accurate ones, and we hope that we can meet the requirements for publication, thank you.

30 Precision in Numerical Results

Comment: Results for ACE or coupling degrees are reported to three decimal places in some places and four decimal places in others. Decide on a consistent style (e.g., three decimals throughout) to increase readability.

Response 30: Thanks to the reviewer's comments, we have refined the precision of the numerical results of the article to 3 decimals to improve the readability of the content. Thanks again to the reviewing experts.

31 Policy Context

Comment: The text sometimes references government initiatives like “Zero Growth of Fertilizers” or “River Chief System.” Cite official policy documents or local government guidelines when referencing them, so that readers can see the official basis for your statements.

Response 31: Thank you for the reviewer's comments, we have cited or cited the source when revising the high school description of the government initiative so that readers are aware of the official basis for your statement.

Below are some of the revisions:

Figure 2 demonstrates the average value of ACE in each district and city in Jiangxi Province. It is not difficult to find that most of the ACE values of the 11 district cities in Jiangxi Province are below 0.5 from 2008 to 2022, which is in a non-efficient state. Among them, the highest average value is that of Jingdezhen, with an efficiency value of 0.565, which may be attributed to its active exploration in the green development of agriculture. Jingdezhen vigorously promotes green agriculture and a recycling economy, on the one hand, through the implementation of the "Zero Growth of Fertilizers" action[51] to reduce carbon emissions from agricultural inputs, and on the other hand, focuses on the innovation of agricultural science and technology and promotes intelligent greenhouse bases.

[51] Ministry of Agriculture. Notice on Issuing the Action Plan for Zero Growth in Fertilizer Use by 2020 and the Action Plan for Zero Growth in Pesticide Use by 2020. Ministry of Agriculture 2015-02-17.

32 Stylistic Consistency

Comment: In some parts of the partial text, the word “carbon emission efficiency” and “agricultural carbon emission efficiency” are used interchangeably. Similarly, the terms “coordination degree” or “coupling coordination degree” are also used. Standardize these terms to reduce confusion.

33 English Grammar and Syntax

Comment: Overall, the writing is understandable, but there are occasional grammar slips (e.g., use of “dissonance” vs. “disorder,” inconsistent singular vs. plural references). A careful read-through or light language editing would help ensure clarity.

Response 32-33: We thank the reviewers for their comments. After revising the paper, we checked the language and grammar of the article and invited English professional scholars to review the article again to ensure the accuracy of the content expression of the paper.

34 Pagination or Section Numbering

Comment: MDPI journals often require a particular referencing style for sections and subsections. Verify that the headings (1., 2., 2.1., etc.) conform to the required style, and make sure references to tables and figures match their final numbering in the revised version.

Response 34: Thanks to the reviewer's comments, we have revised and improved the citation format of references after revising the paper to meet the journal's publication requirements.

35 Clarity on Unit of Measurement

Comment: For variables like “fertilizer usage,” “pesticide usage,” and “rural electricity consumption,” ensure the units are unambiguously stated in the text and in relevant tables. Sometimes you mention “tons,” “kW·h,” or “thousand hectares,” but ensure it is consistent.

Response 35: We thank the reviewers for their comments and we have checked the indicator units in the article to ensure they are correct.

36 Completeness of Conclusion

Comment: The conclusion currently lists four main findings. Consider adding a short reflection on how policymakers could use these findings to guide targeted intervention in carbon reduction or how local governments might implement your recommendations.

Response 36: Thank you to the reviewers for their comments. We couldn't agree with you more, and we have added a paragraph summary after the summary section and to set the stage for the presentation of the response recommendations below. Below is the new paragraph that has been added:

37 Limitations Section

Comment: The manuscript would benefit from a short “Limitations and Future Research” subsection after Discussion or Conclusion. Mention issues like data availability, potential unobserved variables, or the risk of model misspecification, and propose ways future research could address them.

Response 37: Thanks to the reviewers for their comments. We have improved and supplemented the research deficiencies and future outlook links, and the following revisions have been made:

4.3.Research Shortcomings and Future Prospects

Although this study has achieved some results in analyzing the coupled and coordinated development of ACE and AEG in Jiangxi Province, there are still some shortcomings. However, there are still some shortcomings, and future research can be further improved and expanded through the following aspects: First, the scope of the study is limited. This study only focuses on Jiangxi Province, and lacks comparisons with other provinces in the country and the Yangtze River Economic Belt. Future studies can broaden the study area and analyze Jiangxi Province in comparison with other agricultural provinces to better grasp the inter-regional variability and its potential impact on the coupling and coordination of ACE and AEG. Second, data limitations. The study data only cover the prefecture-level city level, making it difficult to capture the refined characteristics of county-level units. Meanwhile, key environmental variables such as water footprint and biodiversity index were not included, which may underestimate the comprehensive pressure on agroecosystems. Future research can expand data sources by integrating multi-source data and further incorporating more comprehensive environmental indicators, such as water use and biodiversity index, to assess more comprehensively the coupled and coordinated relationship between ACE and AEG. Third, methodological limitations. the Dagum Gini coefficient decomposition does not consider interregional spatial spillover effects and may underestimate the impact of cross-regional interactions. Future research can introduce spatial econometric methods, combining spatial autocorrelation analysis and geographic information system technology, so as to more accurately assess the coupled and coordinated relationship between ACE and AEG.

38 Expanding on Policy Recommendations

Comment: The manuscript closes with “recommendations,” but they are fairly generic (e.g., “strengthen regional cooperation,” “innovate in agricultural technology,” etc.). Provide more specific or practical measures, such as exact types of technologies, or how local policy might reduce carbon emissions effectively without hurting farmers’ incomes.

Response 38: Thanks to the reviewer's comments, after we revised the paper, we proposed development suggestions for different regions of Jiangxi Province based on the results presented. The following is the revised response proposal display:

39 Comparative Statements

Comment: The text makes comparative statements, e.g., “ACE in north and west Jiangxi is higher than in central and southern Jiangxi.” It would help to quantify these statements (e.g., “X% higher”) and provide references to relevant figures or tables each time a comparison is stated.

Response 39: Thanks to the suggestions of the reviewers, we have revised and improved the content of the article to support the results and ideas through data.

The following is a partial presentation of the revised content:

40 Terminology for Agricultural Inputs

Comment: The term “agricultural inputs” is used broadly (fertilizers, pesticides, films, etc.). Consider discussing any dynamic changes in the composition of these inputs (for instance, whether new biological or organic fertilizers are being adopted) to explain changes in ACE over the study period.

Response 40: Thank you for the reviewer's suggestion, due to the limited nature of our data collection, we have not been able to further investigate the dynamics of input materials in our study for the time being. We will look into this further in future studies, thank you for your suggestion.

41 Role of Technology Adoption

Comment: The manuscript briefly suggests that adopting more advanced agricultural technology can cut carbon emissions while boosting yields. However, no indicator of technology adoption (e.g., mechanization rates, R&D investment) is included in the models. At least mention this limitation in the Discussion.

42 Organization of the Discussion

Comment: The Discussion section repeatedly references “some scholars” or “previous studies” without systematically comparing your results to theirs. Try adding a paragraph specifically contrasting your main findings with those from the literature review to highlight novel insights or resolve discrepancies.

Response 41-42: Thanks to the suggestions of the reviewers, we have further revised and improved the discussion section of the paper content on the basis of revising the paper, comparing this paper with the previous studies to illustrate the reliability, authenticity and innovation of this study.

The following is the content of the revised discussion section:

43 Highlighting Non-Linear Effects

Comment: The text mentions that some studies found an inverted U-shaped (EKC-type) relationship for carbon and economic growth. Your results do not explicitly test for non-linearity. If feasible, you could test that or at least address whether your data might fit such patterns.

Response 43: Thanks to the reviewer's comments, we did mention the inverted U-shape relationship between agricultural carbon emissions and economic development in the literature, but this was not specifically analyzed in this study because of the different research focus in carbon emission efficiency. We will focus on analyzing this issue in future studies. Thank you again for providing us with research ideas.

44 Robustness to Data Outliers

Comment: Some city-level data points (e.g., very high or low fertilizer usage in a certain year) could skew the efficiency estimates. Discuss whether any data smoothing or outlier diagnosis was carried out prior to the DEA. If not, mention that as a possible limitation.

Response 44: We thank the reviewers for their comments, and we have rechecked the data sources to ensure the accuracy of the data used.

45 Implications Beyond Agriculture

Comment: The link between agricultural emissions and broader environmental impacts (like water pollution, biodiversity loss) is hinted at but not detailed. You could briefly address whether lower carbon emissions also bring other co-benefits, such as improved soil quality, to show broader relevance.

Response 45: Thanks to the reviewer's comments, we have elaborated on this in the Discussion section after revising the paper to show the role of green production in improving soil quality and ecology.

Below is the revised research discussion (in part):

3.5.Discussion

46 Use of Sub-Indices

Comment: When presenting the super-efficiency SBM approach, consider including an explanation or formula in an appendix to show exactly how slack-based measures of undesired output are incorporated. This will improve reproducibility.

Response 46: Thank you to the reviewers for their comments, which are briefly described in our methodology section describing carbon efficiency in agriculture.

2.2 Agricultural carbon efficiency

The expression is:

&nbsp (1)

t. ;

&nbsp (2)

Table 1. Selection and Explanation of ACE Variables in Jiangxi Province

Level 1 title	standard	variable	unit (of measure)
Input metrics	labor input	People working in agriculture	ten thousand people
	land input	Crop sown area	thousand hectares
	water input	Effective irrigated area	thousand hectares
	Fertilizer inputs	Fertilizer usage	tones
	Pesticide inputs	Pesticide usage	tones
	Agricultural film inputs	Agricultural plastic film use	tones
Expected outputs	Gross agricultural output	Gross agricultural output	trillion yuan
Non-expected outputs	Agricultural carbon emissions	Carbon emissions from agricultural inputs	tones

47 Section on Theoretical Framework

Comment: The manuscript jumps quickly from a general background to methodology. If possible, include a short conceptual diagram or theoretical framework that visually represents how inputs, outputs, and coupling coordination interact. This helps readers follow the logic flow.

Response 47: Thanks to the reviewers' comments, we have created a flowchart based on the content of the article to make it easier for readers to read and understand the main content of the article. Below is the flowchart:

Figure 1 Research roadmap

48 Policy Context for Urbanization

Comment: The results find that urbanization positively affects coupling coordination. Delve into how changes in land use policy, urban expansion, or the migration of the rural workforce might be contributing. That can further clarify the mechanism behind your regression finding.

Response 41-42: We have made the following modifications in the text:

Fourth, the level of urbanization is significant at the 1 per cent level, and the coefficient of the variable is positive. The deepening of the urbanization process promotes the implementation of the land transfer policy and promotes the transformation of agriculture to large-scale and intensive operation. At the same time, the acceleration of urbanization absorbs excess rural labor on the one hand and promotes agricultural modernization and low-carbon development through scientific and technological innovation on the other. In addition, urbanization drives the extension of the agricultural industry chain and the formation of industrial clusters, reduces production and transaction costs, and improves the efficiency of resource utilization, which further promotes the positive coupling of the ACE and AEG.

49 Data Availability Statement

Comment: Journals often require a statement about data sharing or data availability. Indicate if the municipal data is publicly available (e.g., from statistical yearbooks), or if it can be obtained from the authors upon reasonable request.

Response 49: Thanks to the reviewers' comments, we have filled in the data availability affirmation later in the text. Here is the content:

Data Availability Statement: The data supporting this study's findings are available on request from the corresponding author, upon reasonable request.

50 Potential for Future Extensions

Comment: In your concluding section, outline how future research could incorporate more comprehensive environmental indicators (e.g., water usage, biodiversity indices) or extend the analysis to examine cross-provincial interactions. This demonstrates the broader potential impact and fosters follow-up studies.

Response 50: Thank you for the reviewer's suggestion, we have revised and improved the future outlook based on your suggestion and the actual situation of the paper.

The following is the revised future outlook:

4.3.Research Shortcomings and Future Prospects

Question : Comments on the Quality of English Language

Grammar and Syntax:Some sentences would benefit from more concise phrasing. Occasional grammar slips (e.g., subject–verb agreement and singular/plural forms) appear in a few paragraphs.
Word Choice and Terminology:Key terms (e.g., “carbon emission efficiency,” “agricultural inputs,” “coordination degree”) should be used consistently throughout the text. Where possible, use simpler or more direct vocabulary to avoid ambiguity (replace “dissonance” with “imbalance” or “misalignment” if it fits better in context).
Overall Flow and Readability:A brief, thorough proofreading or professional language editing pass could unify writing style across sections, making the paper read more cohesively. Check that each paragraph opens with a clear topic statement and transitions naturally to the next.

Answer: Dear reviewer, Thank you for your suggestion of compounding stereotypes. On the basis of revising the paper, we have checked the whole article, standardized the use of key terms, and used simple words to avoid ambiguity. In addition, we invited scholars specializing in English to check the article and revise the inappropriate use of words and phrases. Thank you again for your professional review, so that we can continuously improve the quality of the paper.

Wen Li （corresponding author）

Lecturer, School of Humanities and Public Administration, Jiangxi Agricultural University

E-mail: liwen13870963721@163.com

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Agriculture is a significant contributor to climate change and one of the sectors of the economy most affected by climate change. Low-carbon agricultural systems have become a global consensus to support the agricultural sector in addressing climate change by reducing agricultural carbon emissions. In this study, by using the panel data from 2008 to 2022, the focus is placed on 11 prefecture-level cities in Jiangxi Province, and the study adopts the super-efficiency SBM-DEA method that takes non-expected outputs into account to evaluate the efficiency of agricultural carbon emissions. Additionally, this research establishes a model to evaluate the coupled coordination between agricultural carbon efficiency (ACE) and agricultural economic growth (AEG), aiming to uncover the interaction mechanisms. Although this study has significant implications for promoting agricultural scientific and technological innovation, optimizing the industrial structure, and enhancing farmers’ low-carbon awareness, and its research findings are quite interesting, there are still some errors in the manuscript that need to be corrected before it can be accepted.

L19-43: The abstract should focus more on the novel aspects of the research. Highlight what makes your study unique.
L47-169: The Introduction and Literature Review sections should be combined into one Introduction section. Additionally, this part is too complicated and should be further streamlined.
L76-92: These two paragraphs need to be further refined.
L134-147: There is a lack of coherence between the research question and the rest of the introduction. Ensure that all parts of the introduction build up to and support the research question.
L172-175: Jiangxi Province is divided into four regions, and the basis and criteria for the division should be explained.
L200-202: This manuscript puts forward the view that the agricultural input indicators should include three elements: labor input, land input, and agricultural input. The theoretical basis for selecting these three indicators should be elaborated in detail.
L213-215: The author has drawn on Tan’s research findings and used the per capita agricultural value added as an indicator of AEG. Has the author considered whether applying the research methods from different research regions in this study will have an adverse impact on the results?
L170-307: The data presented in the study appear to be reliable, but there is a lack of detail in the methods section regarding how the data were collected and analyzed.
L239-240: The expressions in the “Rating Levels” part of Table 2 still need to be carefully considered.
L359-360: Due to space limitations, this paper selected 2008, 2012, 2018 and 2022 for visualization (Figure 2). This sentence should be restated.
L416-418: From the current situation, the coupling coordination of each district city in Jiangxi Province has reached excellent coordination and above, and the overall situation is much better than that in 2008. Such a conclusion cannot be drawn from the current data.
L539-600: This part is too lengthy. It is recommended to be refined.
L602-638: The Discussion section should address any unexpected findings or outliers. Analyze and explain these phenomena to enhance the credibility of the study.
L703-717: In section “5.3.Research Shortcomings and Future Prospects”, the limitations of the study are not adequately addressed. Be honest about the weaknesses of your research and suggest ways to overcome them in future work.
L709-711: There is a clerical error in this sentence where “AEG” appears twice. One of them should be “ACE” and the other should be “AEG”. Please correct it. There are some minor language errors throughout the manuscript. The authors should carefully proofread the manuscript to correct these errors and improve the overall quality of the writing.

Comments on the Quality of English Language

The English could be improved to more clearly express the research.

Author Response

Response to Reviewer 3

Dear reviewers,

Thank you very much for your professional review comments, which are greatly appreciated by our team. We have revised and improved the content of the article according to your proposed revisions and hope to improve the quality of the paper.

Below is the description of the revisions and responses:

Question 1: L19-43: The abstract should focus more on the novel aspects of the research. Highlight what makes your study unique.

Answers 1：Thank you for the editor's suggestions; we have revised the content of the paper and improved the abstract, hoping to enhance the quality of the paper.

Here is the revised abstract content:

Question 2: L47-169: The Introduction and Literature Review sections should be combined into one Introduction section. Additionally, this part is too complicated and should be further streamlined.

Question 3: L76-92: These two paragraphs need to be further refined.

Question 4: L134-147: There is a lack of coherence between the research question and the rest of the introduction. Ensure that all parts of the introduction build up to and support the research question.

Answers 2-4:Thanks to the comments of the reviewers, we have revised and improved the introduction and literature review of this paper by combining them into an introductory section based on careful examination of the manuscript and reading of other papers. The main modifications are, firstly, streamlining the literature review, secondly, explaining the study area, thirdly, clarifying the research objectives and potential innovations, and fourthly, producing a technology roadmap to give readers a clear understanding of the content of the article.

The following is the content of the revised introduction:

Introduction

1.1 Literature review

1.2 Research Problems

Jiangxi Province is an important ecological barrier and agricultural province in China, with rich natural resources and ecological advantages, and its agricultural structure and carbon emissions also have significant uniqueness, which provides an ideal research sample for analyzing the mechanism of low-carbon transition in agriculture. The rice sowing area in Jiangxi Province ranks among the top three in the country, and methane emissions from rice cultivation account for more than 50% of the total agricultural greenhouse gases^[25] , while compared with Hunan Province, which is also a major rice-producing area, Jiangxi Province has a higher fertilizer application per unit of production, resulting in lower carbon emission efficiency^[32] , and this “high-carbon lock” phenomenon is closely related to the crude production model. This “high carbon lock-in” phenomenon is closely related to the rough production mode. Despite the implementation of the “river chief system”^[33] and the ecological compensation mechanism^[34], the shrinking of wetland area^[35] and agricultural surface pollution^[36] still constrain the green development of Jiangxi Province^[37]. In addition, there are significant differences in the agricultural structures of the 11 district cities in the province, leading to prominent spatial heterogeneity in ACE^[38]. Therefore, clarifying the synergistic law of ACE and AEG in Jiangxi Province is both an urgent need for regional low-carbon development and an important supplement to improve small- and medium-scale studies.

1.3 Research objectives

Figure 1 Research roadmap

Question 5: L172-175: Jiangxi Province is divided into four regions, and the basis and criteria for the division should be explained.

Answer 5: Thanks to the suggestions of the reviewers, we have divided Jiangxi Province into Northeast Gan, North Gan, West Gan, and Central-South Gan, mainly based on the geographic features and agricultural development of the province.

The revised original text is shown below:

2.1 Data sources

Question 6: L200-202: This manuscript puts forward the view that the agricultural input indicators should include three elements: labor input, land input, and agricultural input. The theoretical basis for selecting these three indicators should be elaborated in detail.

Answer 6:Thanks to the reviewers' comments, we have improved the basis for the selection of indicators for super-efficient SBM in the revised manuscript, and the following is the revised formulation:

2.2 Agricultural carbon efficiency

Based on the economic growth theory and drawing on Wang's study[42], this study, combined with the characteristics of agricultural development in Jiangxi Province, utilizes the super-efficient SBM model to construct an ACE evaluation system covering production inputs, desired outputs, and non-desired outputs. The construction of the specific index system contains three dimensions: (1) labor, land, water resources, fertilizers, pesticides and other core elements are selected at the production input end, reflecting the characteristics of the allocation of agricultural production factors; (2) the desired output characterizes the economic efficiency in terms of the total agricultural output value; and (3) the non-desired output adopts the agricultural carbon emission to characterize the environmental load. The specific indicator system is shown in Table 1.

Table 1. Selection and Explanation of ACE Variables in Jiangxi Province

Level 1 title	standard	variable	unit (of measure)
Input metrics	labor input	People working in agriculture	ten thousand people
	land input	Crop sown area	thousand hectares
	water input	Effective irrigated area	thousand hectares
	Fertilizer inputs	Fertilizer usage	tones
	Pesticide inputs	Pesticide usage	tones
	Agricultural film inputs	Agricultural plastic film use	tones
Expected outputs	Gross agricultural output	Gross agricultural output	trillion yuan
Non-expected outputs	Agricultural carbon emissions	Carbon emissions from agricultural inputs	tones

Question 7: L213-215: The author has drawn on Tan’s research findings and used the per capita agricultural value added as an indicator of AEG. Has the author considered whether applying the research methods from different research regions in this study will have an adverse impact on the results?

Answer 7: Thanks to the reviewer's comments, we noticed the problem and checked some literature. At the beginning of the study, some scholars were using agricultural value added as an indicator of agricultural economic growth, but the method did not take into account the problem of regional development differences, so we drew on the relevant literature while consulting our supervisors and adopted agricultural value added per capita as an indicator of agricultural economic growth.

Question 8: L170-307: The data presented in the study appear to be reliable, but there is a lack of detail in the methods section regarding how the data were collected and analyzed.

Answer 8: Thanks to the suggestions of the reviewers, we have reorganized the methodology section to elaborate on data sources and processing and explain the research methodology for the convenience of the readers.

The following are some of the changes in 2. Methodology and Data Source:

Methodology and Data Source

2.1 Data sources

2.2 Agricultural carbon efficiency

Based on the economic growth theory and drawing on Wang's study[42], this study, combined with the characteristics of agricultural development in Jiangxi Province, utilizes the super-efficient SBM model to construct an ACE evaluation system covering production inputs, desired outputs, and non-desired outputs. The construction of the specific index system contains three dimensions: (1) labor, land, water resources, fertilizers, pesticides and other core elements are selected at the production input end, reflecting the characteristics of the allocation of agricultural production factors; (2) the desired output characterizes the economic efficiency in terms of the total agricultural output value; and (3) the non-desired output adopts the agricultural carbon emission to characterize the environmental load. The specific indicator system is shown in Table 1.

Table 1. Selection and Explanation of ACE Variables in Jiangxi Province

Level 1 title	standard	variable	unit (of measure)
Input metrics	labor input	People working in agriculture	ten thousand people
	land input	Crop sown area	thousand hectares
	water input	Effective irrigated area	thousand hectares
	Fertilizer inputs	Fertilizer usage	tones
	Pesticide inputs	Pesticide usage	tones
	Agricultural film inputs	Agricultural plastic film use	tones
Expected outputs	Gross agricultural output	Gross agricultural output	trillion yuan
Non-expected outputs	Agricultural carbon emissions	Carbon emissions from agricultural inputs	tones

Question 9: L239-240: The expressions in the “Rating Levels” part of Table 2 still need to be carefully considered.

Question 10: L359-360: Due to space limitations, this paper selected 2008, 2012, 2018 and 2022 for visualization (Figure 2). This sentence should be restated.

Answer 9-10: Thanks to the reviewer's comments, we have revised the content of the article and rechecked the textual presentation to minimize grammatical problems and unclear presentation, thanks again to the reviewer.

The following are some of the revisions:

To analyze the evolution of their coupling coordination, the degree of coupling coordination is simultaneously separated into the Five levels listed below (Table 2).

Table 2. Classification of coupling coordination degree.

Harmonization of development degree ranges	Rating Levels
[0～0.2)	Severe disorder
[0.2~0.4)	Moderate disorder
[0.4~0.6)	Elementary dissonance
[0.6~0.8)	Moderate coordination
[0.8~1.0)	Quality coordination

For brevity, this study visualizes key years (2008, 2012, 2018, 2022) in Figure 2. From the perspective of regional spatial distribution, there are obvious differences in carbon emission efficiency among the cities in Jiangxi Province, and the overall distribution is characterized by a gradual decrease from west to east and from south to north. The West Jiangxi city cluster has promoted the low-carbon development of agriculture due to the support of agricultural hi-tech industry and precise agricultural inputs. Meanwhile, North and Northeast Jiangxi, relying on superior natural resource endowment, have less demand for agricultural production factors and relatively higher ACE in agricultural production. In contrast, central and southern Jiangxi are mainly mountainous and hilly, with barren and scattered land, which is not conducive to large-scale mechanized farming, relying more on traditional manpower and animal power, and with a greater demand for agricultural materials, thus leading to a lower ACE. In terms of absolute value analysis, nine cities in Jiangxi province have achieved high or high levels of agricultural carbon emission efficiency in 2022, accounting for about 81.818% of the total number of cities in the province. This data indicates that the overall agricultural carbon emission efficiency in the region has significantly improved, and the practice of low-carbon agricultural production has achieved positive results.

Question 11: L416-418: From the current situation, the coupling coordination of each district city in Jiangxi Province has reached excellent coordination and above, and the overall situation is much better than that in 2008. Such a conclusion cannot be drawn from the current data.

Answer 11: Thanks to the reviewer's comments, we have re-checked the following article content, and there does exist a presentation of the results that is not supported by data. Therefore, we have improved the content of 3.2 Coupling Coordination Analysis by using the data and the causes of the emergence of the results to argue the result situation.

The following are some of the modifications:

Question 12: L539-600: This part is too lengthy. It is recommended to be refined.

Answer 12: Thank you to the reviewers for their suggestions. Based on the revisions to the paper, we have rewritten the results presentation in Section 3.4, using concise language and detailed data to display the results.

Here is the revised content of Section 3.4:

Table 7 displays the findings of this study, which use the Tobit model to investigate the external factors influencing the linked and coordinated development of ACE and AEG in Jiangxi Province. It is easily observed that the ACE is significantly impacted positively by government inputs, industrial structure, energy use, and the degree of urbanization; conversely, the ACE is significantly impacted negatively by the standard of living of the rural population. The relationship between agroecological efficiency and educational attainment is unaffected.

First, government input is significant at the 1% level with positive variable coefficients. This indicates that the government's expansion of investment in the agricultural sector contributes to the optimization and upgrading of the agricultural industrial structure and improves the level of coupling coordination. At the same time, the government establishes an ecological compensation mechanism to improve the agricultural production environment, which provides support for the sustainable development of agriculture and promotes the benign interaction between ACE and AEG.

Second, the industrial structure is significant at the 5% level, and the variable coefficients are positive. The government has realized the positive interaction between the two systems through the promotion of green agriculture and the circular economy model and the implementation of the “zero-growth of chemical fertilizers and pesticides” action. In addition, Jiangxi Province, as one of the first demonstration zones of ecological civilization, has extended the agricultural industry chain through the development of high-value-added agricultural industries such as specialty agriculture and high-efficiency agriculture, which further promotes the coupling and coordination between the ACE and AEG.

Third, the coupling effect of living standards on agricultural carbon emission efficiency and economic development in Jiangxi Province is insignificant. Based on prior research, enhancements in living standards can alter farmers' consumption patterns, leading to increased energy consumption and carbon emissions. However, within the agricultural sector, agricultural carbon emission efficiency is predominantly influenced by agricultural technology and the structure of agricultural production. Technological advancements or optimizations in the agricultural production structure can notably reduce agricultural carbon emissions. Although improvements in living standards may foster progress in agricultural technology and the transformation of agricultural production methods, thereby influencing agricultural carbon emissions, this impact is indirect and subject to various constraints. Changes in living standards do not directly affect the coupling effect between agricultural carbon emission efficiency and economic development.

Fourth, the coupling effect of energy utilization on agricultural carbon emission efficiency and economic development in Jiangxi Province is found to be insignificant, which deviates from the findings of previous studies. Energy utilization intensity emerges as the primary factor influencing carbon emissions in the region, with reductions in intensity or enhancements in energy utilization efficiency playing a pivotal role in determining emission levels. However, this study reveals that the impact of energy utilization is not as pronounced. Potential reasons for this phenomenon are elaborated as follows: Firstly, agricultural production constitutes an organic integration of natural and social reproductive processes, inevitably subject to dual constraints imposed by natural and societal factors. Furthermore, the agricultural production methods in Jiangxi Province are relatively entrenched, thereby limiting the potential of energy utilization to significantly reduce agricultural carbon emissions. Secondly, the proportion of clean energy adoption in Jiangxi Province remains comparatively low, particularly within the agricultural sector. The absence of widespread new energy utilization and the constraints on technological promotion severely impede the capacity of energy utilization to mitigate agricultural carbon emissions. Thirdly, the coupled development of energy utilization, agricultural carbon emission efficiency, and economic growth in Jiangxi Province is more pronounced in the industrial and service sectors. In contrast, the agricultural sector exhibits a relatively backward mode of energy utilization, resulting in an insignificant effect on the aforementioned coupling relationship.

Sixth, the level of education exhibits a significant positive coefficient at the 1% significance level. An enhancement in educational attainment directly bolsters agricultural carbon emission efficiency and spurs economic development, fostering a coupled and coordinated advancement between the two domains. Firstly, an elevated level of education serves as a catalyst for technological innovation and dissemination. It enhances agricultural production efficiency and optimizes agricultural planting structures, thereby promoting green and sustainable agricultural practices. Secondly, the augmentation of educational levels facilitates the accumulation of human capital in rural regions. This, in turn, drives the industrial upgrading of the agricultural sector and sustains economic development in rural areas. Consequently, the level of education plays a pivotal role in promoting the coupled development of agricultural carbon emission efficiency and economic growth in Jiangxi Province.

Question 13: L602-638: The Discussion section should address any unexpected findings or outliers. Analyze and explain these phenomena to enhance the credibility of the study.

Answer 11: Thank you to the reviewers for their suggestions. Based on a thorough review of numerous references, we have rewritten the discussion section to enhance the content and quality of the article.

The revised discussion content for section 3.5 is as follows:

Question 14: L703-717: In section “5.3.Research Shortcomings and Future Prospects”, the limitations of the study are not adequately addressed. Be honest about the weaknesses of your research and suggest ways to overcome them in future work.

Answer 14: Thanks to the reviewer's comments, we have honestly pointed out the shortcomings of the study in the revised paper in the research shortcomings and future outlook content, and hope to improve it in future research.

The following is the revised 4.3 Research Deficiencies and Future Prospects Content:

4.3.Research Shortcomings and Future Prospects

Question 15: L709-711: There is a clerical error in this sentence where “AEG” appears twice. One of them should be “ACE” and the other should be “AEG”. Please correct it. There are some minor language errors throughout the manuscript. The authors should carefully proofread the manuscript to correct these errors and improve the overall quality of the writing.

Answer 15: Thanks to the reviewers' comments, we have revised the article's content and rechecked the textual presentation to minimise grammatical problems and unclear presentation. Thanks again to the reviewers.

The following are some of the revisions:

Second, data limitations. The study data only cover the prefecture-level city level, making it difficult to capture the refined characteristics of county-level units. Meanwhile, key environmental variables such as water footprint and biodiversity index were not included, which may underestimate the comprehensive pressure on agroecosystems. Future research can expand data sources by integrating multi-source data and further incorporating more comprehensive environmental indicators, such as water use and biodiversity index, to assess more comprehensively the coupled and coordinated relationship between ACE and AEG.

Question 16: The English could be improved to more clearly express the research.

Answer 16: Thanks to the advice of the reviewers, we have invited scholars specializing in English to double-check the structure and grammar of the article after revising the paper and corrected the errors, hoping that the revision will improve the quality of the paper and clearly convey the research content to the readers.

Wen Li （corresponding author）

Lecturer, School of Humanities and Public Administration, Jiangxi Agricultural University

E-mail: liwen13870963721@163.com

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

1. Overall Evaluation

The revised manuscript has improved significantly in terms of structure, clarity, methodological justification, and academic rigor. The authors have addressed most of the concerns raised in the initial round of review, and the enhancements are evident throughout the introduction, methodology, and discussion sections.

2. Strengths of the Revised Version

Clarified Research Gap & Novelty: The introduction now includes a clear statement of novelty, emphasizing the spatially disaggregated study across Jiangxi's cities and the use of the Dagum Gini coefficient, which adds methodological depth and regional relevance.
Improved Structure: The objectives are now presented as bullet points and supported by a visual "research roadmap," enhancing reader accessibility.
Expanded Literature Integration: The literature review is better linked to the study's framework and methodology, although minor further alignment is still recommended.
Methodological Justification: The rationale for using the super-efficiency SBM-DEA model, the Tobit model, and the Gini decomposition method is clearly articulated and technically sound.
Policy and Practical Relevance: The discussion section now references relevant provincial and national agricultural transformation policies, which help ground the findings in a real-world context.
Robustness Checks Added: Inclusion of quantile regression and outlier analysis strengthens confidence in the empirical findings.
Endogeneity Discussion: The use of lagged variables and fixed-effects modeling is a solid approach to mitigating potential endogeneity concerns.

3. Constructive Recommendations for Further Enhancement

(A) Language and Presentation:

The manuscript would benefit from another round of professional English editing. Certain sentences are awkward or repetitive, and improved grammar would significantly enhance clarity and professionalism.
Example: Phrases like "this high-carbon lock-in phenomenon is closely related to the rough production mode" could be rephrased as "this high-carbon lock-in is attributed to inefficient traditional production practices."

(B) Literature Review:

While the literature review is much improved, consider explicitly contrasting your findings with 2–3 key empirical studies from similar regions or methodologies (e.g., other provinces or studies using SBM/DEA).
Add recent references (2023–2024) if available, particularly those dealing with regional decarbonization strategies in agriculture.

(C) Visualization:

Figures such as the spatial ACE maps and trends in coupling coordination degree could be more effectively labeled and discussed. Include clearer legends, titles, and explanations in the captions.

(D) Minor Technical Suggestions:

Clarify whether the efficiency score range of [0–1] is strict (e.g., can exceed 1 in super-efficiency models?).
Recheck the model formulation numbering (equations are a bit jumbled in formatting).
When discussing policy implications, consider integrating a subsection on replicability for other provinces, making the work more generalizable.

Author Response

Response Letter

Dear reviewer:

We sincerely thank you for your meticulous review and constructive comments on this paper! Under your guidance, we have made substantial progress on our paper. We have completed the following substantial revisions based on your suggestions:

Question 1: Language and Presentation: The manuscript would benefit from another round of professional English editing. Certain sentences are awkward or repetitive, and improved grammar would significantly enhance clarity and professionalism.

Answer 1:Thank you for your suggestion, we have revised the content of the article according to your proposed changes, based on the revision of the article, the grammar and other issues have been completely revised, to delete redundant expressions, and we hope to make readers easy to read this article.

Question 2: Literature Review: While the literature review is much improved, consider explicitly contrasting your findings with 2–3 key empirical studies from similar regions or methodologies (e.g., other provinces or studies using SBM/DEA).Add recent references (2023–2024) if available, particularly those dealing with regional decarbonization strategies in agriculture.

Answer 2: Thank you for your suggestion, we have added four literatures in the literature review, from carbon emission to carbon emission efficiency, and from regional provinces to national comparisons, which provide theoretical support for this study and enrich the literature sources.

The following is a display of the additions:

By decomposing the drivers of ACE, Cui et al. revealed that the spatial and temporal evolution of agricultural carbon emissions is characterized by a gradient of "high in the east, low in the west, and fast in the north and slow in the south", which provides a theoretical basis for the analysis of regional heterogeneity[27]. On this basis, Qian et al. introduced a dynamic DEA model to measure provincial efficiency, and found that there are also significant spatial differences in the efficiency of agricultural carbon emissions in Chinese provinces. It is worth noting that existing studies began to pay attention to the synergistic relationship between carbon emissions and economic development[28]; Ma et al's case study for the Yellow River Basin showed that the inter-provincial coupling coordination degree presents presents different coupling coordination states[29]; and Tian et al. further expanded the research perspective to the dimension of new urbanization, which contributes to the research ideas of this study[3]. Meanwhile, some studies found that the spatial distribution of carbon emission efficiency gradually shifted from decentralization to agglomeration, and its relationship with the agricultural economy also transitioned from weak decoupling to negative decoupling[30]. In addition, the utilization of renewable energy and the promotion of low-carbon technologies can not only alleviate the inhibitory effect of carbon emissions on the agricultural economy[31-32], but also promote sustainable development by enhancing the competitiveness of agricultural exports[33]. Thus, it is necessary to improve low-carbon production technologies, increase renewable energy consumption, and improve agricultural production and ecological conditions in order to realize the goal of sustainable development in the agricultural sector[34].

[27] Cui, X.; Wang, Y.; Zhang, G. Measuring and Analyzing the Temporal and Spatial Evolution of Agricultural Ecological Efficiency for Low-Carbon Development Based on the SBM-ESDA Model. Issues Agric. Econ. 2022, 9, 47–61.

[28] Qian, L.; Sun, F.; Song, J. Measuring Agricultural Carbon Emission Efficiency and Spatial Convergence: A Dynamic Analysis Based on Chinese Provincial Panel Data. J. Jianghan Univ. (Soc. Sci. Ed.) 2024, 41, 104–115.

[29] Ma, Z. Research on the Spatiotemporal Coupling Relationship between Agricultural Carbon Emissions and Economic Growth in the Yellow River Basin. Chin. J. Agric. Resour. Reg. Plan. 2024, 45, 15–26.

[3] Tian, Y.; Lin, Z. Coupling Coordination between Agricultural Carbon Emission Efficiency and Economic Growth at Provincial Level in China. China Population, Resources and Environment 2022, 32 (4), 13-22.

Question 3: Visualization: Figures such as the spatial ACE maps and trends in coupling coordination degree could be more effectively labeled and discussed. Include clearer legends, titles, and explanations in the captions.

Answer 3: Thank you for your suggestion. We have checked and revised each chart, adding legends and titles to the images so that readers can quickly understand what the charts are showing. Below are some of the revised images:

Figure 4. Coupling coordination degree of 11 cities in Jiangxi Province.

Question 4: Minor Technical Suggestions: Clarify whether the efficiency score range of [0–1] is strict (e.g., can exceed 1 in super-efficiency models?).

Answer 4: Thank you for your comments, we have rechecked the presentation of the Super-SBM model. We have presented the model while comparing it with other models to show that the model breaks through previous limitations and is suitable for this study.

The following is the revised content:

2.2 Agricultural carbon efficiency

ACE is a comprehensive indicator of agricultural carbon emissions and agricultural economic growth^[⁴²^], and its measurement needs to consider the dual attributes of desired outputs (economic value) and non-desired outputs (environmental costs). Traditional DEA models may have limitations in dealing with non-desired outputs, making it difficult to effectively identify inefficiencies due to non-desired outputs. And although the GML index can capture the dynamic characteristics of productivity, it is unable to decompose the components of efficiency over a specific time period^[4³^]. In contrast, the Super-SBM model can effectively solve the problems of neglecting slack variables in the radial model and the inability to rank the efficiency values by restricting them to the [0,1] interval. This model is closer to practical applications and thus has been widely used in the assessment of carbon emission efficiency^[4⁴^].

Question 5: Recheck the model formulation numbering (equations are a bit jumbled in formatting).

Answer 5: Thank you for your comments, we rechecked the formatting of the formulas and standardized them after revising the paper.

Question 6: When discussing policy implications, consider integrating a subsection on replicability for other provinces, making the work more generalizable.

Answer 6: Thanks to your comments, we have added a paragraph to the summary of the policy recommendations that explains the relevance of this study's recommendations for other provinces. The following is the added content:

In conclusion, the policy recommendations proposed in this study are not only applicable to Jiangxi Province, but also have the potential to be replicated in other provinces. Different provinces can formulate low-carbon agricultural development strategies suitable for their regions based on their own resource endowments, economic development levels and agricultural characteristics. For example, similar cross-regional cooperation mechanisms can be established among provinces in the Yangtze River Economic Belt to promote appropriate agricultural technologies and management models and optimize the structure of agricultural industries such as rice cultivation. In addition, Shanghai, Jiangsu, and Zhejiang can establish technology transfer platforms and demonstration zones, which can promote the exchange and application of agricultural technologies and improve the efficiency of agricultural production and ecological benefits. Therefore, the policy recommendations of this study are not only of guiding significance for Jiangxi Province, but also provide replicable models for other provinces, which can help promote the development of low-carbon agriculture nationwide.

All changes have been highlighted in the revised draft. An itemized response document is attached for your review. The quality of this study has been significantly enhanced by your professional guidance, and we will cooperate fully if further clarification is needed.

Thank you again for your valuable time in refining this thesis!

Wen Li

25.3.2025

Author Response File: Author Response.docx

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Coupling Agricultural Carbon Emission Efficiency and Economic Growth: Evidence from Jiangxi Province, China

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