Methane Emissions in the ESG Framework at the World Level

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The following issues in require consideration to improve the overall quality of this manuscript.

 

Issue 1: “Methane, a potent greenhouse gas (GHG) with a significantly higher global warming potential than carbon dioxide (CO₂), plays a critical role in environmental sustainability efforts.” Please correct the logical problem in the sentence.

 

Issue 2: Please enlarge the font in Figures to ensure its clarity.

 

Issue 3: What does “**” represent in the picture? Please explain.

 

Issue 4: Is there any research basis and experimental verification for the establishment of calculation formulas in the article?

 

Issue 5: Please further refine and streamline the content of the conclusion

Author Response

Point to Point Answers to Reviewer 1

 

The following issues in require consideration to improve the overall quality of this manuscript.

 

Issue 1: “Methane, a potent greenhouse gas (GHG) with a significantly higher global warming potential than carbon dioxide (CO₂), plays a critical role in environmental sustainability efforts.” Please correct the logical problem in the sentence.

Answer 1.  The sentence has been amended to read as follows: “Emissions of methane, a potent greenhouse gas (GHG) with a significantly higher global warming potential than carbon dioxide (CO2), are a very critical factor in the fight against climate change."

Issue 2: Please enlarge the font in Figures to ensure its clarity.

Answer 2: All figures have been redone to increase the readability of the text within the flow chart boxes as well as by simplifying them.

Issue 3: What does “**” represent in the picture? Please explain.

The symbol * represents p-value. Specifically * P-value < 0.10, ** P-value < 0.05, *** P-value < 0.01.

Issue 4: Is there any research basis and experimental verification for the establishment of calculation formulas in the article?

The theoretical background that takes into account the relationship between methane emissions and each of the variables analyzed is indicated both in the citations reported in Table 1, and in the citations that are reported within the subparagraphs of Section 4 entitled “Econometric Results” relating to the commentary and interpretation of the econometric results obtained.

Issue 5: Please further refine and streamline the content of the conclusion

Answer 5. The conclusions were summarized as follows:

Methane emission analysis via the ESG framework is demonstrating how tricky the prob-lem of this GHG will be. With econometric models for data of 193 countries for 2011-2020, one seeks correlations among methane emissions and their main components in the ESG index. Although ESG performance generally keeps improving all over the world, methane emissions are seen to continue growing especially in the agriculture and extraction of fos-sil fuels sectors, which proved measures taken to be inefficient so far. Critical in this re-gard are the environmental factors that this study identifies, wherein consumption of re-newable energy is related to low emissions. However, countries that are big importers of energy might shift their environmental burden to the exporting countries and underline global inequalities in methane management. Countries with more agricultural land have lower emissions on account of sustainable practices; however, further optimization is needed. Also, very relevant is governance: it contributes much to more effective methane management. This points out the role of international cooperation and the capacities of developing countries in achieving methane reduction. The driving socio-economic factors for emissions are some of the contributors to methane emissions. As health care is more developed and thus more industrial, which would relate to higher CH4 emissions, eco-nomic disparity will then be related to increased emissions due to methane-intense industries. Countries which are less industrialized are reflected in child mortality and generally have lower emissions but may tend to increase as they undergo development, unless proactive policy implementation takes place. Hence, mitigation strategies for me-thane have to be addressed and customized according to the stage of development at the nation-al level of socioeconomic structuring. Energy transition, changing of energy sources to-wards renewables in developed countries, would then become a priority against developed support for agriculture and reinforcing government capacity in devel-oping ones. Process principles underlying responsibilities and support mechanisms should be based on equity: more developed countries should provide full technology transfer, financial as-sistance, and capacity-building in developing countries in light of historical emissions.  Mitigation of methane emissions will have to be placed in a wider ESG frame to avoid disproportionate impacts on the most vulnerable, especially within agriculture-dependent developing countries. The policies should contain financial sup-port and alternatives for the affected communities. What is required is for international cooperation to enhance the international framework for action, such as the Global Me-thane Pledge, supported by a financial and technology commitment to con-tribute to the global harmonization of regulations in the field of emissions reduction. Mitigation of me-thane needs complex technological innovation, good governance, policy equity, and in-ternational collaboration to align the environmental aspiration with economic growth and social welfare within the ESG paradigm.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The abstract needs to be expanded to follow the standard structure:
        A brief introduction to the topic under investigation.
        Explanation of why this topic is significant in the field.
        Identification of the research gap being addressed.
        Statement of the research question(s) or aim(s).
        Indication of the research methods and approach used.
        A concise summary of the key findings or message of the study.

The econometric models used in the study (Panel Data with Random Effects, Fixed Effects, Pooled OLS, and WLS) should be described in detail. This includes their mathematical structure and relevant citations supporting their application in this context.
The introduction should be more focused on the specific topic of the study—methane emissions in the ESG framework—rather than being too broad. A clear outline of the research context and relevance is needed.

The form of citations and article structure should be thoroughly checked to ensure compliance with the journal's guidelines for authors.
Table 2 needs to be self-explanatory. It should include sufficient information or footnotes to allow readers to understand its content without referring to the main text.
The analysis includes 193 countries, but the rationale behind their selection is unclear. The authors should specify why these countries were chosen and describe their main characteristics.
The authors have used three separate equations for Environmental, Social, and Governance variables. It would be valuable to explain why these were not combined into a single equation to identify the primary causes of methane emissions more holistically.
The rationale behind the choice of variables should be provided. This includes their characteristics, units, range, and relevance to the study.
Methane emissions are measured at the national level. Would it not be more insightful to analyze methane emissions per capita for each country to account for population differences?
Some information in the Results and Discussion sections appears to be repetitive. These sections should be reviewed to ensure conciseness and avoid redundancy.
The explanation of abbreviations on page 30 seems unnecessary and could be removed unless deemed crucial for comprehension.

Author Response

Point to Point Answers to Reviewer 2

Q1. The abstract needs to be expanded to follow the standard structure:
A brief introduction to the topic under investigation. Explanation of why this topic is significant in the field. Identification of the research gap being addressed. Statement of the research question(s) or aim(s). Indication of the research methods and approach used. A concise summary of the key findings or message of the study.

A1. We have re-written the article as follows:

Methane is a strong green gas that has higher GWP. Methane emissions, therefore, form one of the critical focuses within climate change mitigation policy. Indeed, the present study represents a very novel analysis of methane emission within the ESG framework by using the data across 193 countries within the period of 2011-2020. Methane reduction on account of ESG delivers prompt climate benefits and thereby preserves the core environment, social and governance objectives. Inspite of its importance, the role of methane remains thinly explored within ESG metrics. This study analyzes how factors like renewable energy use, effective governance, and socio-economic settings influence the emission rate of the study subject, as many previous ESG studies are deficient in considering methane. By using econometric modeling, this research identifies that increasing methane emissions remain unabated with the improvement of ESG performances around the world, particularly within key agricultural and fossil fuel-based industrial sectors. Renewable energy cuts emissions, but energy importation simply transfers the burdens to exporting nations. It therefore involves effective governance and targeted international cooperation, as socio-economic elements act differently in different developed and developing countries to drive various emission sources. These findings strongly call for balanced, targeted strategies to integrate actions of mitigation into ESG goals related to methane abatement.

Specifically in the following table we show how each element of the proposed structure is matched in the renewed abstract.

Section	Content
A brief introduction to the topic under investigation	Methane is a strong green gas that has higher GWP. Methane emissions, therefore, form one of the critical focuses within climate change mitigation policy. Indeed, the present study represents a very novel analysis of methane emission within the ESG framework by using the data across 193 countries within the period of 2011-2020.
Explanation of why this topic is significant in the field	Methane reduction on account of ESG delivers prompt climate benefits and thereby preserves the core environment, social, and governance objectives. Inspite of its importance, the role of methane remains thinly explored within ESG metrics.
Identification of the research gap being addressed	Many previous ESG studies are deficient in considering methane. This study analyzes how factors like renewable energy use, effective governance, and socio-economic settings influence the emission rate of the study subject.
Statement of the research question(s) or aim(s)	The research aims to analyze how factors like renewable energy use, effective governance, and socio-economic settings influence the emission rate of methane within the ESG framework.
Indication of the research methods and approach used	By using econometric modeling, this research identifies that increasing methane emissions remain unabated with the improvement of ESG performances around the world, particularly within key agricultural and fossil fuel-based industrial sectors.
A concise summary of the key findings or message of the study	Renewable energy cuts emissions, but energy importation simply transfers the burdens to exporting nations. It therefore involves effective governance and targeted international cooperation, as socio-economic elements act differently in different developed and developing countries to drive various emission sources. These findings strongly call for balanced, targeted strategies to integrate actions of mitigation into ESG goals related to methane abatement.

 

Q2. The econometric models used in the study (Panel Data with Random Effects, Fixed Effects, Pooled OLS, and WLS) should be described in detail. This includes their mathematical structure and relevant citations supporting their application in this context.

A2. We have added the following part:

Methodologies. To analyze methane emissions within the ESG framework, four econometric models are applied: Random Effects (RE), Fixed Effects (FE), Pooled Ordinary Least Squares (OLS), and Weighted Least Squares (WLS), specifically:

• Panel Data with Random Effects (RE). The Random Effects (RE) model assumes that individual-specific effects (unobserved heterogeneity) are randomly distributed and uncorrelated with the independent variables. This allows the model to estimate both time-invariant and time-varying variables, making it suitable for large datasets with repeated observations across entities. The mathematical structure is as follows: where = dependent variable (methane emissions) for entity i at time t; = vector of independent variables (e.g. renewable energy use, governance indicators); = coefficients to be estimated; =random individual effect; =error term. The RE model is appropriate for assessing the impact of variables that vary over time while accounting for unobserved individual effects across countries [128; 129; 130].
• Panel Data with Fixed Effects (FE). The Fixed Effects (FE) model assumes that individual-specific effects are constant over time, allowing for the control of unobserved variables that may differ across entities but remain invariant over time. The mathematical structure is as follows:  where   is the entity specific fixed effect (constant across time). The FE model is ideal for analysing the effect of time-varying variables on methane emissions while controlling for country specific characteristics that do not change over time, such as geography [131; 132; 133].
• Pooled Ordinary Least Squares (OLS). The Pooled OLS model treats the dataset as simple cross-sectional regression, ignoring the panel structure. It assumes that there is no unobserved heterogeneity between entities. The mathematical structure is as follows: . While simple, this model is less robust in capturing the individual-specific effects or temporal dynamics. It serves as a baseline with other models [134; 135; 136].
• Weighted Least Squares (WLS). The WLS model addresses heteroscedasticity by assigning weights to observations, ensuring that observations with lower variability receive higher importance in the regression. The mathematical structure is as follows: where = weights assigned to each observation, inversely proportional to variance. WLS is particularly useful in this study due to varying levels of data reliability across countries and years, ensuring unbiased and efficient estimates [137; 138; 139]. 

Basically, in application, the choice among the different panel data models depends on some basic underlying assumptions regarding unobserved heterogeneity and the kind of available data. The RE model captures both time-invariant and time-varying features when individual effects are thought to be uncorrelated with the explanatory variables. On the other hand, we consider the FE model suitable for controlling the effects of time-invariant heterogeneity at individual levels, which are unobserved, when we want to get robust estimates specifically for the time-varying variables. While the pooled OLS model is a simple baseline, it does not take into consideration either individual heterogeneity or temporal dynamics and is therefore less robust. The WLS model controls finally for heteroscedasticity by weighting observations in order to ensure efficiency and unbiased estimates. Together, all these models form an overall toolkit of panel data analysis, allowing nuanced insight into methane emissions and the determinants associated with different entities across time.

Q3. The introduction should be more focused on the specific topic of the study—methane emissions in the ESG framework—rather than being too broad. A clear outline of the research context and relevance is needed.

A3 The introduction has been re-written with a focus on the relationship between methane emission and ESG emissions:

Methane is an extremely powerful greenhouse gas with much higher global warming potential compared to CO₂ and has been considered one of the key priorities in mitigating climate change. Although methane stays in the atmosphere for a relatively short period, its large heat-trapping capacity makes reduction of emissions very important to achieve near-term climate benefits. Holistic coverage of environmental impacts due to methane, its social consequence, and the role of governance for effective mitigation befits the ESG framework of addressing methane emissions. Major sources of methane, such as agriculture, extraction of fossil fuel, waste management, and coal mining, are deeply entwined with economic life and the fabric of society. This underlines the need for strategies that balance sustainability with economic growth and food security. The current study considers methane emissions as a significant sustainability metric and uses panel data from the World Bank ESG database, covering 193 countries from 2011 to 2020, in order to explore the complex relationships between methane emissions and the three pillars of ESG. By decomposing the drivers of methane into environmental, social, and governance dimensions, through Random Effects, Fixed Effects, Pooled OLS, and Weighted Least Squares econometric models, this paper therefore gives complete insight into the drivers of methane and their wide effects on global sustainability.

This becomes even more evident from the ESG environmental dimension perspective, underlining the fundamental driving elements of land use, energy systems, and the methods of agriculture. In particular, agriculture by itself is responsible for the lion's share of global methane emission through the processes of enteric fermentation in livestock and rice cultivation, respectively. Some key sustainable activities in great ways reducing the above emissions while not compromising productivity include precise farming, optimized livestock feeding, and the use of AWD in rice cultivation. Transitioning into renewable energy is by all means necessary; methane leakages during the extraction, transportation, and distribution of fossil fuel alone contribute so much to methane in the atmosphere. In the broader view, this transition into sources of cleaner energy reduces reliance on fossil fuel and decreases overall emission. Besides, deforestation and land-use change release methane stored in biomass and soil; hence, methods concerning methane emissions would encourage policy regulations for the preservation and rehabilitation of forests. Finally, to complete it all, methane emission is strongly connected with agriculture, use of energy, and land management. The environmental pillar of ESG should include mitigating strategies like sustainable agriculture, renewable energy adoption, and conservation of forests.

In light of the effect it could have on public health and socioeconomic structures and labor in areas that aren't that well advanced, the social aspect of ESG should take into account methane emission concerns. Methane forms tropospheric ozone, an air pollutant, the cause of respiratory illness, cardiovascular diseases, and, finally, preterm mortality in poor income, overpopulated places. Accordingly, reduced emission of methane can produce huge public health benefits: decreasing burdens from diseases and improved life quality, particularly at the vulnerable point of societies. The complex interlinkage of methane emission, economic development, and inequality speaks to a complex relationship in which both are cause and symptom. Thus, the countries that have high levels of economic growth are highly industrialized and, therefore, emit high levels of methane as a result of high energy and agricultural production. However, the developing countries have a tendency to have relatively low incomes and, with the infectious diseases, are normally at low economic activity and hence emit lower amounts of methane. This dichotomy puts in rather sharp relief what perhaps could be one of the challenging balances of economic progress by nations and the environmental sustainability of the planet, and how this decrease in emission might be executed in a fashion that does not exacerbate global inequalities.

Methane is an extremely potent GHG with very high global warming potential; therefore, controlling methane emissions represents one of the most important challenges for global sustainability. Though difficult to detach from key sectors of the economy, such as agriculture, energy production, and waste management, they are considered crucial for solving environmental, social, and governance challenges. In the environmental dimension, the most relevant sources of methane emissions include agriculture, extraction of fossil fuels, and deforestation. Transitioning into renewable energy, sustainable agriculture, and conservation of forests would also go a long way in reduction of emissions for protection of the environment. From a social point of view, management of methane has strong implications for the health and socioeconomic systems of a population. The tropospheric ozone formed by methane leads to respiratory diseases and cardiovascular diseases that seriously affect vulnerable groups of people with low incomes. Improving methane emission can avoid severe outcomes such as improved health equality and sustained economic growth. On the other hand, methane-emitting activities, including agriculture and energy, provide vital jobs to the economies of developing countries. The call will be, therefore, in balancing economic stability with emissions reductions and job security.

Governance underlines that strong policy, technological development, and international cooperation are some of the key elements which would help manage methane emissions. Those countries with an appropriate regulatory framework and institution would thus be better equipped to carry out a strategy relating to the capture technology for methane, reduction in the leakage in fossil fuel systems, and R&D. Certain technological innovations-irrigation for methane storage systems and optimizing livestock feed-offer pragmatic solutions to cut emissions. In all, methane management under the ESG framework evokes a fine nexus between environmental health, public welfare, and governance capacity. Panel data evidence from the ESG database of the World Bank, drawn across 193 countries and a decade-long period spanning 2011–2020, forms the analytical base of this study in an effort to shed critical light on the drivers of methane emissions in widely varying global contexts. This calls for policies, innovations at the sectoral levels, and internationally coordinated strategies with specificity toward effective emissions reductions. By placing methane emissions as the most focal ESG metric, this study has further underlined their linkage with climate goals, economic sustainable growth, and social equity while also providing an avenue toward a balanced and resilient future.

Methane is a potent greenhouse gas with a much higher warming potential than CO₂; methane emissions are considered one of the most pressing challenges to mitigate climate change. Being relatively short-lived in the atmosphere, the notable heat-trapping capacity of methane makes its reduction an effective solution for near-term climate benefits. Its primary sources are so congenitally embedded in the economic and social systems of agriculture, extraction of fossil fuel, waste management, and coal mining that a trade-off between environmental sustainability and economic growth, on one hand, needs to be balanced with the food security of a region. This study positions methane as a critical indicator of sustainability and uses World Bank ESG data for 193 countries between 2011 and 2020 to understand its drivers along environmental, social, and governance dimensions using econometric models. The environmental pillar highlights emissions of methane related to agriculture, fossil fuel, and deforestation. Large sums of livestock, rice cultivation, methane leakage from fossil fuel all add up as major contributors in this regard; deforestation worsens these gases by releasing stored methane. Their controls would involve shifting toward renewable energy sources, switching over to sustainable farming, and the preservation of forests to be able to cut down the growing emissions.

From a social perspective, methane emissions directly affect public health since it is one of the tropospheric precursors to ozone, a pollutant that triggers respiratory and cardiovascular illness in low-income parts of the world with very high populations. These health burdens, quality of life, and inequalities can be reduced by reducing the amount of methane emitted, especially for those most at risk. While the basic sources are agriculture and energy production, these are fundamental to employment in developing economies, and solutions need to balance control of emissions with job security and economic stability. Governance has become instrumental in methane management through policy, technology innovation, and global cooperation. This would provide for the installation of methane capture technologies, reduction of leakage levels, and further research development of practical options available, including additives and renewable energy technologies where feasible, for countries that can afford the developed systems of regulation. International cooperation in reduction targets is important through cooperation and sharing experiences for the pursuit of global targets for reduction through efforts such as the Global Methane Pledge. By placing methane management in an ESG framework, this work underlines the interconnected roles that environmental systems, public health, and governance play in emissions reduction. This report also calls for targeted policy action, technological innovation, and international approaches as superior ways to ensure the world economy is sustainable and equitably developed, using comprehensive data and robust models.

Q4. The form of citations and article structure should be thoroughly checked to ensure compliance with the journal's guidelines for authors.

A4. The form of citations i.e. the IEEE format and the structure are coherent with the journal’s guidelines.

Q5. Table 2 needs to be self-explanatory. It should include sufficient information or footnotes to allow readers to understand its content without referring to the main text.

A5. The following notes are added

Notes: The presents the results of four panel data estimation models—Fixed Effects, Random Effects, Weighted Least Squares (WLS), and Pooled Ordinary Least Squares (OLS)—to analyze the relationship between methane emissions (METHANE) and a set of environmental predictors. The table includes the coefficients, standard errors, and t-ratios for each model, with significance levels marked by * (P < 0.10), ** (P < 0.05), and *** (P < 0.01).* P-value < 0.10, ** P-value < 0.05, *** P-value < 0.01. Results of the Table unmask some of the key contributors to methane emissions in this environmental component, and NFD, CO₂E and energy intensity, as also FPI, which con-tributes highly positive features to methane emission, ultimately proving that unsustainable land utilization, carbon emissions, inefficiency in using energy has increased the level of me-thane. On the other hand, agricultural land, AL, energy imports (EIMP), renewable energy consumption, and its relation to methane emissions are of negative relations; this just means that with good agricultural practices, with less reliance on energy whose importation will reduce supply lines for their production due to their exhaustion, but, on the contrary, renewables help to decrease emissions of it.

Q6. The analysis includes 193 countries, but the rationale behind their selection is unclear. The authors should specify why these countries were chosen and describe their main characteristics.

A6. The following propositions has been added in the section 3 “Data and Methodologies”:

Statistical sample size. Data for this study have been drawn from a wide range of 193 countries in order to ensure that the analysis of methane emissions within an ESG framework is really global. For such big numbers, many stages of development would thereby fall in place, setting up a rich dataset that could eventually explore several associations between methane emissions and key ESG indicators. Global coverage would therefore reveal trends, inequalities, and links among major economic, ecological, and political systems. The sample is representative because it includes countries that are in various stages of development-from developed to developing economies. In this regard, it allows comparing methane drivers in economies that have various degrees of industrialization, agricultural intensity, and effective governance. Usually, countries in the developed world feature high methane emissions due to intensive industrial agriculture and heavy fossil fuel activities, whereas developing nations normally have lower methane emissions, though tending to increase as their economies grow. These 193 countries also represent methane-emitting sectors, including agriculture, fossil fuel extraction, waste management, and forestry. Variations in this dominance across regions signal substantial differences in methane sources and a need for tailored methods in mitigation. The dataset will cover countries with diverse geographies and environmental conditions that will also reflect land use patterns, energy systems, and use of natural resources. As an example, tropical countries that are involved in massive deforestation processes have high methane emissions, whereas some other countries whose land is being used for agricultural purposes try to manage those emissions by using certain sustainable methods. There is also a variation in governance capacities across the sample countries. While strong governance structures are implemented to carry out effective strategies for emissions mitigation, including methane capture technologies and the adoption of renewable energy, countries that have relatively weak systems are unable to develop the proper regulatory frameworks or institutional strength for effective emissions control. For example, with such a wide range of countries over the period 2011–2020, it captures global trends, insights into cross-border effects of energy imports, renewable energy adoption, and forest depletion. Diversity in economic, social, and governance systems across these wide-ranging countries allows for a robust analytical approach toward methane emission drivers, with targeted policies taken by region in a bid to balance sustainability with economic growth and global equity.

Q7. The authors have used three separate equations for Environmental, Social, and Governance variables. It would be valuable to explain why these were not combined into a single equation to identify the primary causes of methane emissions more holistically.

A7. The following propositions has been added in the third section “Data and Methodologies”:

Breaking down the ESG model into three equations. There are advantages of three separate equations, all to analyze the relationships existing between methane emissions and ESG. Decomposing the analysis into three components provides a closer look at the direct influence of each ESG pillar on methane emissions separately. The environmental equation in turn isolates the variables on agricultural and renewable energy consumption and efficient energy practice. This overview looks into the direct environmental strategy taken vis-à-vis methane levels. By the same vein, the social equation displays concern for all subjects that involve socioeconomic conditions or the general health of the population and dynamics of working relations which will be linked indirectly to methane emission. In addition, institutional effectiveness, investment in R and D, and a binding regulatory framework have been emphasized by the governance equation to bring down the level of emission. The chosen specification also enabled the researchers to dichotomize ESG components into separate categories, since each one of the three pillars influence methane emissions through different channels. Also, it will not be easy to explain such kinds of relationships even when integrating all the variables into a single equation-either due to the problem of overlapping effects or because some of them might have variables that hold the problem of multicollinearity. For instance, some of the governance-related issues mask the variables that would show a direct influence of environmental policies, while socio-economic factors blur on the other side the environmental impact analysis. The presence of three equations allows for the application of specific econometric models for each pillar, such as Fixed Effects, Random Effects, and Weighted Least Squares when applying. Analysis at the sectoral level better controls for unobserved heterogeneity and sector-specific dynamics. This is an important reason, rightly pointed out by this study, because each component interacts with methane emissions through different channels and thus requires independent treatment if robust and reliable conclusions are to be derived.

 

Q8. The rationale behind the choice of variables should be provided. This includes their characteristics, units, range, and relevance to the study.

A8. The following parts have been added in the section 3 “Data and Methodologies”:

The variables were chosen on the basis of data provided by the World Bank within the ESG-Environmental, Social and Governance database. Specifically, the metric characteristics of the analyzed variables are indicated in the following table:

Variable	Mean	Median	Minimum	Maximum	Std. Dev.	C.V.	Skewness	Ex. kurtosis	5% Perc.	95% Perc.	IQ range
METHANE	14.517	0.81156	0.00000	27.700	28.231	19.447	52.737	33.483	0.00000	55.828	10.607
AL	0.67621	0.00000	0.00000	24.381	23.772	35.154	57.085	39.428	0.00000	44.133	0.087963
EIMP	30.572	31.102	0.00000	82.996	24.721	0.80861	0.23903	-11.598	0.00000	72.006	44.180
REC	32.710	13.797	-16.800	33.373	47.443	14.504	25.130	82.881	0.00000	14.479	46.178
NFD	-63.588	0.00000	-1058.1	99.200	79.874	12.561	-53.508	39.454	-104.58	73.194	0.00000
CO2E	37.660	0.00000	-122.88	574.79	24.451	64.926	19.319	420.87	0.00000	10.154	45.423
INTENSITY	76.605	97.280	0.00000	157.47	42.051	0.54893	-11.767	-0.39229	0.00000	110.47	21.515
FPI	25.208	13.066	0.00000	97.031	28.608	11.349	0.99387	-0.30793	0.00000	84.693	41.205
CD	40.705	0.00000	-0.23000	66.470	11.996	29.472	35.313	12.006	0.00000	33.155	0.00000
HB	14.452	0.00000	0.00000	16.460	23.521	16.276	21.097	52.668	0.00000	66.045	24.225
IL20	21.709	0.00000	0.00000	10.500	33.087	15.241	10.524	-0.55088	0.00000	88.000	52.000
LFPR	56.380	66.870	0.00000	92.170	27.265	0.48359	-12.942	0.26899	0.00000	83.498	23.780
MR5	0.11267	15.700	-27013.	153.20	870.85	7729.1	-30.953	957.41	0.00000	97.670	37.400
PSSS	35.623	24.602	0.00000	100.00	36.399	10.218	0.49289	-13.079	0.00000	98.074	70.931
P65	87.101	52.140	-0.40846	214.09	15.733	18.063	10.824	135.10	0.00000	19.759	92.438
PO	27.482	25.100	0.00000	88.500	26.595	0.96775	0.27419	-14.627	0.00000	65.600	55.300
UT	70.469	56.150	0.00000	31.380	58.620	0.83185	12.526	14.743	0.00000	19.224	66.300
GE	0.14773	-0.15046	-24.751	28.200	26.665	18.050	87.700	86.714	-15.556	17.611	12.681
RAND	0.52954	0.00000	-17.162	44.477	27.352	51.653	13.724	202.83	0.00000	22.002	0.34966
STRENGHT	53.072	20.000	0.00000	11315.	699.42	13.179	15.128	229.61	0.00000	10.000	60.000
VA	-0.012676	0.0020447	-22.592	15.470	10.928	86.206	22.711	29.211	-16.898	14.167	16.271

 

The means for variables like methane, AL, and FPI are higher by a big margin in relation to the medians, and this is at positive skews, hence extreme values pulled it upwards. On the contrary, LFPR, MR5, and STRENGTH show variables for which extreme values have really impacted the general distribution. These differences in the measures of central tendency show asymmetry in the data. Some of the variables, from the range, like MR5 and STRENGTH, have huge ranges; MR5, for instance, ranges from -27013 to 153.2. This shows that either there are extreme outliers or huge variability among the observations. Other variables such as AL, HB, and CD have a small spread that reflects a tighter spread of the data. The same can be made out from the standard deviation which is highest in MR5 and STRENGTH at 870.85 and 699.42 respectively indicating that both are high on variability. For instance, in variables GE and P65 standard deviations are low. The C.V gives the relative variability of each variable. High values are constituted by MR5, with a C.V of 7729.1, and AL with 35.154, showing extremely dispersed values with respect to their mean. In contrast, other variables involve EIMP and LFPR showing a pretty low coefficient of variation; that could point out quite stable and consistent data dispersion. Skewness conveys more information with regards to symmetry. For example, for the variables METHANE and GE, the skews are positive, standing at 52.737 and 87.700, respectively, an indication of these sets of data having a tail toward the higher values; as such, they are considered right-skewed. The set of variables such as those of LFPR and MR5 were negatively skewed with -12.942 and - 30.953, indicating the opposite-that here, the clumping has been to the high-value side as the tail faces the low-value side. Again, it is reflected in the excess kurtosis. Very keen peaks may be seen, with extreme outliers for some variables such as MR5 at 957.41 and that of CO2E at 420.87. Other variables include FPI and PO, which have almost normal to moderately flat distributions. The investigation into the 5th and 95th percentiles gives good insight into the presence of outliers and dissemination. Percentiles have values of the 5th percentile for MR5 that corresponds to 0.000 while the 95th percentile is 97.670. It means that high inclusion of data despite their existence at higher values despite existence or great occurrence of outliers. These exist a high variance between such created percentiles for variables GE and RAND. For example, 70.931 is extremely high. The IQ range for PSSS is indicative that the middle 50% of the data points are highly spread out. In contrast, other variables like CD and AL have very low IQ range, indicating that most of their values are closely packed together. At the extreme ends, the MR5 and STRENGTH are highly dispersed; this could be due to some outliers or a characteristic of the variables that may require further investigation. Examples of positively-skewed variables with most of the measurements huddled around zero are METHANE and AL. INTENSITY and LFPR are relatively stable in their averages but are negatively skewed, with most of their data concentrated towards the top of their respective ranges. High kurtosis and variability in CO2E and PSSS, respectively, show that these series have long tails with extreme values. The data thus reflect a mix of compact distributions, skewed patterns, and significant outliers.

Q9.  Methane emissions are measured at the national level. Would it not be more insightful to analyze methane emissions per capita for each country to account for population differences?

A9. The following part has been introduced in the section “Data and Methodologies”.

Aggregating emissions to a national scale, rather than per capita, may be a better proxy of the overall emissions and drivers of a country. The methane emissions on a national scale represent the absolute environmental burden that a country imposes because it is relevant to an international climate mitigation policy at a country level. Again, it puts into the spotlight those large emitters, such as countries either with significant agricultural and/or fossil fuel-producing sectors that are disproportionally important to global methane levels. In addition, national data capture the contribution of some of the most important economic sectors, such as agriculture, energy production, and waste management, which is very different in each country. Per capita conceals the scale of emissions that are related to national economic activities. Policymakers also use national-level data to design effective mitigation strategies targeting major sources of emissions. Although per-capita figures can point to inequalities, either in efficiency or equity in emissions, they do not present absolute values of the emissions that have to be reduced with the aim of being compatible with climate targets. National emissions data also reveal something about global inequalities and responsibilities: high-income countries that are intensive in their industrial and energy sectors are higher emitters in absolute terms, while per capita emission rates may distort perceptions in countries with a smaller but industriously dense population. This bigger perspective makes sure the responsibility of reductions falls in line with the real scale of environmental impact caused by each country. Lastly, methane mitigation strategies call for focused interventions at the level of countries-investing in renewable energies, enhancing agricultural practices, and methane capture. National-scale analyses also enable the formulation of country-specific policies, aligned with broader global climate initiatives such as the Global Methane Pledge. Altogether, the national scale analysis of methane emissions provides a very clear picture of the total contribution to the environment every country makes, how the activities are responsible in an economic context, and also the mitigation potential of these emissions. This is especially important when efficiently pursuing reduction targets related to methane in order to take into account the contribution each nation will play while dealing with the global warming factor.

 

Q10.  Some information in the Results and Discussion sections appears to be repetitive. These sections should be reviewed to ensure conciseness and avoid redundancy.

A10. The following parts have been reduced and rewritten in a more concise manner reducing the text by 1.200 words approximately:

The relationship between agricultural land and methane emissions. The results show that the percentage of agricultural land as a fraction of total land area and methane emissions are inversely related. Any increase in the proportion of agricultural land necessarily leads to a reduction in methane emissions. Several factors can be cited for this inverse relation-ship. For instance, prompt regions with a higher share of agricultural land to apply land management practices with minimal methane emission through direct methods or using methane-reducing technologies. Countries with more land could also implement policies to reduce GHG emissions within the agriculture sector. However, this might vary by the form of agriculture practiced, the intensity of land use, and regional climatic conditions. Further, the scale and nature of agriculture crop-based or livestock-intensive systems can also be reasons for determining the scale of emissions (see Figure 4). Thus, though there is a negative trend, its internal dynamics are complex and do need further investigation if nuances across different regions are to be understood [81], [82], [83].

The relationship between renewable energy consumption and methane emissions. Fully 60% of methane emissions come from human activities, predominantly the extraction, produc-tion, and transportation of fossil fuels such as natural gas, oil, and coal. Increased renew-able energy in a country's energy mix replaces demand for fossil fuels and reduces me-thane emissions. Unlike fossil fuels, renewable energy technologies such as solar, wind, and hydropower produce energy with no methane emissions during operation and without the environmental hazards of combustion or fugitive methane leaks. Once in-stalled, renewable systems have very limited or zero operational emissions; thus, they are an essential solution to mitigate both the direct and indirect methane emissions that come with fossil fuel infrastructure. Furthermore, the shift towards renewables reduces the need for natural gas-a so-called "bridge fuel"-and, by extension, cuts methane emissions, de-spite its relatively low carbon dioxide emissions compared with coal (See Figure 6). As re-newable energy deployment goes up, methane emissions from energy production dra-matically go down, showcasing the environmental dividend from investment in renewa-ble infrastructure and its critical role that it will play in lowering global greenhouse gas emissions [84], [87], [88].

The relationship between net forest depletion and methane emissions. This positive rela-tionship between net forest depletion and methane emissions is linked to the linkages among deforestation, land use changes, and methane-emitting activities. This is a case whereby forest depletion, very much induced by the expansion of agriculture, logging, and land conversion for energy production, further worsens methane emission by destroying the forests' capacity to act as a carbon sink in modulating methane cycles. After being cut and cleared, forests release stored carbon and methane through the decaying process, in-cluding within ecosystems where a tropical nature combines with that of peatlands to provide particularly favorable environmental outgassing of methane. Deforestation also contributes indirectly through land use conversion for very intensive methane-related ac-tivities such as livestock and rice. The livestock sector is one of the major sources of me-thane emission, especially from cattle, due to enteric fermentation. Rice paddies, newly deforested areas release methane due to anaerobic decomposition of organic matter in water-logged soils. Organic decay of cleared biomass further enhances methane emis-sions. In all, net forest depletion increases methane emission due to a reduction in natural methane sequestration, the extension of agricultural lands, and the release of methane by the decay of organic matter (See Figure 7). This essentially points to the severe environ-mental impacts brought about by deforestation: it increases carbon emissions and hastens methane release, each of these acting to further worsen the current global climate crisis [89], [90], [82].

The relationship between CO₂ emissions and methane emissions. This positive correlation between CO₂ and methane is linked to the same origin of their emissions: through fossil fuel combustion, industrial activity, and agriculture. Both gases contribute to global warming, whose sources come from the same sectors or sectors that partly coincide: mainly from extraction and burning of fossil fuels. In such combustion, there is produc-tion of CO₂ emissions. Regarding methane gas, this takes place with leakages during ex-traction, processing, and transport-particularly with so-called fugitive emissions. All sorts of industrial activities, mining, for example, coal or refined oil, emit huge quantities of both gases. From coal mining, methane is the gas released from the coals themselves, and even energy utilization in extraction processes involves the generation of much CO2. Ag-riculture, especially related activities about livestock and rice, two very common agricul-tural elements today in vast productions. An animal's enteric digestion process and an-aerobic disintegration liberate methane gas to the atmosphere. The CO₂ emissions in this sector are given off through the use of fossil fuel in machinery and land-use changes. In sum, the positive correlation in the CO₂-methane emissions is defined by sources across fossil fuel use, industrial processes, and agricultural activities (See Figure 8). Both would rise in relation to economic activity, with increased energy needs worsening the impact on the climate crisis [91], [92], [93].

The relationship between energy intensity and methane emissions. Energy intensity is the amount of energy needed to produce one unit of economic output; the higher the value, the lower the energy efficiency. Inefficient use of energy is closely linked to higher methane emissions, since an energy-intensive economy relies mostly on fossil fuels, which include coal, oil, and natural gas. Fuels in these categories emit methane during extraction, pro-cessing, and transport in the form of fugitive emissions, in particular from poorly main-tained or outdated infrastructure. Key industries within high energy-intensity economies that account for methane emission include manufacturing, mining, and the production of energy. As such, coal mining involves the release of methane gas trapped in coal seams during its extraction. Oil and gas operations cause large releases of methane coupled with carbon dioxide. Agriculture is also an economic activity in the energy-intensive economy, and most practices utilize fossil fuels, furthering methane emission through livestock and rice cultivation and thus bolstering the established relationship between inefficiency in energy use and methane emission. It can also be said that a higher energy intensity itself symbolizes reliance on inefficient methane-intensive energy systems and energy-intense industries, inflating both energy use and methane emissions (See Figure 9). This is in-creased by outdated infrastructure coupled with dependence on fossil fuel sources, while there is an implied better efficiency in energy utilization that is expected to eventually de-crease the level of CH4 emissions. [94], [95].

Food production index and methane emissions. Activities accompanying increased food production, mainly in agriculture, explain the positive correlation between the FPI and methane emissions, which are methane-intensive. Greater volumes of food production, expressed through an increased FPI, are directly proportional to increased methane emis-sion from livestock farming, rice cultivation, and decomposition of organic waste. Live-stock is the main contributor because some farm animals, such as cows, sheep, and goats, by enteric fermentation-a process in digestion wherein methane is released into the at-mosphere as a byproduct-produce methane. In plain words, as global demand for meat and dairy rises, especially in developing nations, so does the volume of livestock and, thereby, methane emissions. The case is similar with rice cultivation, as it too has been reported to be a serious source of methane, arising from the anaerobic decomposition of organic matter in flooded rice paddies. Organic matter in low oxygen conditions of flooded rice fields decomposes; this increases methane emission by scaling up the food production to meet increasing demand. In addition to this, intensified agriculture increased fertilizer use that indirectly feeds methane production because of increasing organic waste due to manure from animals. As this organic matter decomposes, it either produces me-thane during storage or application in the fields; hence, relating higher food production with rising emissions. In all, the positive relationship between the Food Production Index and methane emissions results from the increase in livestock farming, rice cultivation, and organic waste management (Figure 10). While the world's food production is in-creased through intensified methods of production to meet global demand, the said agricultural activities responsible for methane emission further increase and consequently heighten environmental impacts [96], [97], [98].

The relationship between the cause of death and methane emissions. This inverse relation-ship is a function of the variation in the level of economic development and profile of pub-lic health across countries, given that those countries that have high mortality rates from infectious diseases, maternal conditions, prenatal conditions, and nutritional conditions are less industrialized, thus with lower economic activities contributing to low methane emissions. Agricultural, energy, and industrial sectors cannot be compared in the level and mechanization to those of industrialized economies. Instead, traditional, small-scale farming is more common, with generally lower densities of livestock and little rice culti-vation - activities which are major methane sources in advanced agriculture. Other factors contributing to low methane emissions by countries of the region include a limited indus-trial infrastructure and dependence on hard fossil fuels, which preclude large-scale ex-traction and other energy-intensive activities. Overall, most of the economies where infec-tious diseases and/or maternal or nutritional conditions are predominant, the focus is on meeting vital needs rather than basic agriculture and energy production, and as such, are not that developed (See Figure 11). Obviously, it sets up a negative relation between such health indicators of a nation and methane emission, because the methane production-intensive activities associated with it are not there in the first place of industrialization [99], [100], [101].

The positive relationship between labor force participation and methane emissions. It can also be seen in a positive relationship in methane emission through increased economic activ-ity and industrial development and agricultural practices. A higher LFPR reflects a strong workforce composition in the methane-intensive sectors related to agriculture, energy production, and manufacturing. In agriculture, labor-intensive processes like livestock and paddy cultivation have contributed much to CH4 emissions. Livestock increases the methane generation through enteric fermentation produced in ruminant animals, while rice paddies emit methane under anaerobic waterlogged conditions; agricultural production increases with an increase in workforce, thereby increasing the intensity of these very sources of emission. Besides, high labor supports industrial activities and energy production and, in particular, the fossil fuel sectors such as natural gas extraction and coal mining, where methane leakages occur. Urbanization and resultant economic growth accompanying a large labor force further drive the emissions due to increased waste generation in the form of methane emanating from poorly managed landfill sites (See Figure 12). A higher labor force participation rate only heightens the activities contributing to agricultural, industrial, and energy-related sources of methane emission, thus reinforcing the positive relationship between workforce engagement and methane output [102], [103], [104].

The relationship between mortality rate and methane emissions. It means that the inverse relationship between mortality rates and methane emissions is closely associated with the level of economic development and industrialization of a country. The high mortality rates typical in low-income or developing countries reflect poor health outcomes with less healthcare infrastructure and lower socio-economic development. These usually have lower methane emissions, as most of them have subsistence agriculture and no, or very little, industry. In such an economy, small-scale livestock farming- normally less than a few animals per farm-means there is less production of methane by enteric fermentation. Rice cultivation is also not very expansive, let alone mechanized; furthermore, rice pad-dies do produce a lot of CH4 emissions. The underdeveloped energy industries in such a region result in the limited extraction and use of fossil fuels, hence a contributory factor towards low emission compared to industrialized nations, as these are substantial sources of CH4 emissions. At low mortalities, the very reverse in seen; this is essentially a setting dominated by health care and economically stable advanced countries, agricultur-al production, of course, energy, and much greater production of methane. Intensified livestock sectors within those countries with more mechanical rice crops, extracting fuels, include fossil fuel, which all lead to greater production of methane (See Figure 13). This would, on the other hand, reflect the opposite relationship of mortality rates with me-thane-in other words, it would reflect a positive correlation between economic develop-ment, health status, and larger magnitude of activities leading to the production of me-thane by the more developed economy. [105], [106], [107].

The relationship between population ages 65 and above and methane emissions. The so-cio-economic and demographic factors explain the positive relationship between an age-ing population and methane emissions: In developed countries, aged population triggers an increased demand for health services and facilities, which are energy-intensive, gener-ating organic wastes that decompose in landfills, emitting methane. Additionally, the in-crease in meat and dairy products by all previous generations contributes to these dietary habits, creating quite a demand for livestock farming--one of the leading avenues for me-thane production via either enteric fermentation or manure management. Besides, other urbanization and housing, associated with aging populations are smaller households or retirement communities that result in increased energy consumption per capita, often supplied by natural gas. For colder climates, the result is a further increase in methane emissions caused by the heating needs. Waste management systems face other challenges too: an aging population also generates great volumes of both medical and organic waste decomposing in landfills. The support for elderly populations, in most economies, binds investment that could be made towards green technologies and modern agricultural prac-tices, thereby reinforcing reliance on methane-intensive systems (Figure 14). To sum up, the aging of the population is related to higher emissions of methane due to the rise in en-ergy and healthcare demand, livestock farming, problems in waste management, and a reduction in environmental investment [85], [108], [109].

The relationship between the prevalence of overweight and methane emissions. In fact, this is in consonance with the same reality that dietary patterns and methods of agriculture, in addition to the processes of economic development, do take place. Among all countries considered, higher in percentage would relate to a high number of overweight adults and signify a diet mostly based on meat and dairy products feeding into high levels of me-thane emission. Livestock farming has been reported for contribution towards methane especially by the rearing of ruminant animals, namely cattle through processes of enteric fermentation and manure management. The higher meat consumption in economically developed or rapidly growing countries is associated with more use of intensive agricul-ture to sustain high productivity, thereby serving to further increase methane release. In addition to the production of livestock, higher overweight prevalence is related to larger per capita income and energy consumption, leading to methane generation from fossil fuel use, waste decomposition, and infrastructure for food production. It mostly consists of processed and packaged food items in their diet, belonging to those kinds of populations, which again lengthens methane production by means of production, transportation, and waste. It means, in other words, that overweight in adults is a condition positively related to methane production, since a diet represents the consumption of food and, by extension, economic development promoting the production of goods linked with intensive agricul-ture and livestock raising, hence high energy use (Figure 15). These various facts, put to-gether, explain health consequences with associated impact on the environment [110], [111], [112].

The relationship between unemployment and methane emissions. The inverse relation be-tween the unemployment rate and methane is justified because the labor force and eco-nomic activities are highly linked. For example, with a low unemployment rate, economic activities in agriculture, manufacturing, and production of energy run on higher scales. These sectors-primarily intensive livestock farming, rice cultivation, and extraction of fos-sil fuels-are significant sources of methane emission. On the other hand, the higher the unemployment rate, the less economic activity occurs. This means that agricultural and industrial operations are reduced in scale, energy demand is lower, and less fossil fuel is extracted, thereby lowering methane emissions (See figure 16). Put simply, as employment increases, the activities that drive methane emissions expand, and high unemployment suppresses those activities, thus yielding lower emissions. [113], [114], [115].

The relationship between government effectiveness and methane emissions. The inverse rela-tionship between government effectiveness and methane emissions comes because effec-tive governments are able not only to design but also to put into place and enforce policies that cut the level of emissions. Countries with high government effectiveness-that is, with strong public services, competent institutions, and credible policymaking-are much better positioned to regulate activities that produce methane, such as agriculture, energy pro-duction, and waste management. Effective governments invest in monitoring technologies to find and fix methane leaks, incentivize adoption of cleaner energy alternatives such as renewables, and foster practices for mitigating methane from agriculture. They would en-sure adequate waste management systems with minimal leaking of methane from land-fills. In addition, under such governance, full commitment to international agreements on climate would see facilitation of funding toward the mitigation initiatives, clear systems for monitoring compliance toward reduction targets for methane, for instance. Contrari-wise, low government effectiveness mostly results in a lack of resource development, in-frastructure, and regulation control mechanisms that help in reeling in the emissions (Figure 17). The bottom line, therefore, is that higher government effectiveness allows sys-tematic and successful implementation of methane mitigating strategies, hence resulting in lower overall emissions [116], [117], [118].

The relationship between R&D expenditure and methane emissions. The positive relation-ship between R&D expenditure and methane emission arises because economic growth and industrialization usually associate with high investment in innovation. Countries accounting for high R&D spending actually have developed economies that have huge energy, agricultural, and industrial sectors contributing a lot to methane emissions. Ener-gy sector: R&D investment in the energy sector may prolong the extraction and use of fos-sil fuel, especially natural gas, which has considerable leaks in CH4 during extraction, processing, and transport. While technologies increase efficiency, at the same time, effi-ciency gains are often offset, as this extends the operations of methane-emitting sources of fossil fuel. R&D in agriculture generally strives to enhance food production by means of intensified livestock farming and rice cultivation. These, however, have a high methane emission due to enteric fermentation in cattle and anaerobic conditions in rice paddies. Industries receiving R&D funding are among the biggest contributors to methane emis-sions through energy-intensive procedures of manufacturing and waste produced. Hence, R&D expenditure promotes not only economic and technological development but also acts as a counterbalancing force on methane emissions when complemented by mitiga-tion strategies (Figure 18). This would create a balance between innovation and sustaina-ble means of reduction in methane output [119], [120], [121].

The relationship between the strength of the legal rights index and methane emissions. The positive relationship between the Strength of Legal Rights Index and methane emissions reflects the link between strong legal frameworks, economic development, and me-thane-intensive activities. Countries with stronger legal protections, which support finan-cial markets and economic growth, tend to engage in large-scale industrial, energy, and agricultural activities—major sources of methane emissions. A robust legal system facili-tates investments in sectors like fossil fuel extraction and processing, particularly natural gas and oil, where methane leaks occur during extraction, transportation, and refining. Additionally, these countries often have advanced, mechanized agricultural systems, in-cluding intensive livestock farming and modern rice cultivation, both significant contrib-utors to methane emissions. Urbanization and infrastructure development in these economies further drive methane emissions, particularly from waste management sys-tems like landfills. In summary, while strong legal rights promote economic stability and development, they are also associated with higher methane emissions due to the expan-sion of energy, agricultural, and industrial activities, highlighting the environmental challenges of economic growth [122], [123], [124].

The relationship between Voice and Accountability and methane emissions. The positive re-lationship between Voice and Accountability and methane might be related to the fact that higher economic development and industrialization are often associated with countries with strong democratic governance and civil liberties. The more open and participatory the government, the more it is likely to promote economic growth, technological ad-vancement, and infrastructure development, which are causes of increased methane emissions. Responsive governance in these countries leads to the growth of such me-thane-emitting sectors as large-scale livestock farming and rice cultivation. Livestock farming involves methane production through enteric fermentation, while rice paddies emit methane because of anaerobic decomposition. Besides this, the energy sector often involves natural gas; though cleaner than coal, this results in methane leakage during ex-traction, processing, and distribution. The high rate of urbanization in highly accountable countries significantly amplifies methane emissions, caused mainly by organic waste de-composition in landfills (Figure 20). In general, while voice and accountability stimulate economic and social development, they are associated with increasing methane emissions resulting from industrial, agricultural, and urban activities [125], [126], [127].

Q11. The explanation of abbreviations on page 30 seems unnecessary and could be removed unless deemed crucial for comprehension.

A.11 Table with abbreviation has been deleted.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Authors,

After a careful review of the manuscript, it is evident that the current structure, particularly in the introductory sections, could be significantly improved to enhance the clarity and impact of your work. Specifically, the way the literature is presented should be more directly aligned with the need for your study and its contribution to the field. If instead to separating the literature review into its own section, we suggest integrating the discussion of relevant studies into the introductory framework. This will allow you to clearly articulate how existing research leads to the necessity of your manuscript and to position your work as a meaningful contribution to the ongoing discourse. By doing so, the introduction will naturally build toward your research objectives, establishing a logical progression and stronger rationale for your study. While your work presents potentially valuable ideas, there are significant issues that need to be addressed to improve its quality and alignment with the standards of a scientific article. This analysis throughout the manuscript is overly general and suggestive, lacking the depth and rigor necessary for robust scientific discourse. The discussions, such as those related to the relationship between agricultural land and methane emissions, and the analysis of model variables, rely heavily on speculative arguments. These ideas are presented without clear evidence, detailed explanations, or methodological support to substantiate the claims. My fundamental concern lies in the lack of clarity regarding the origin and methodology used to obtain the data upon which the results are based. The absence of transparency in describing the data sources and the processes for integrating variables raises significant questions about the reliability and reproducibility of the findings. This gap not only weakens the scientific validity of the study but also creates uncertainty about the robustness of the conclusions drawn.

Additionally, the manuscript does not follow the structure expected of a scientific article. Essential sections, such as a clear and detailed methodology, are either missing or insufficiently developed. For example, there is little explanation of how variables are defined, measured, or integrated into the analysis. Furthermore, the conclusions appear to be speculative rather than grounded in robust results, and the limitations of the findings are not adequately addressed. It is also crucial to note that the manuscript does not adhere to the journal's guidelines for author. I have to strongly recommend revisiting these guidelines to ensure that the structure, content, and presentation of the article meet the expected standards. This includes providing a more rigorous methodology, detailed results, and a discussion that integrates findings within the context of existing literature.

Irecommend addressing these issues by providing detailed explanations of the data collection process, ensuring the methodology is explicit and transparent, and grounding the results in clear, evidence-based analysis. It is also essential to avoid speculative interpretations and instead focus on presenting conclusions that are fully supported by the data.

I encourage you to revise your manuscript thoroughly to align with these expectations and improve its scientific contribution.

Comments for author File: Comments.pdf

Author Response

Point to Point Answers to Reviewer 3

 

Q1. After a careful review of the manuscript, it is evident that the current structure, particularly in the introductory sections, could be significantly improved to enhance the clarity and impact of your work.

A1. The introduction has been modified by more specifically relating methane emissions to the ESG model.

Methane is an extremely powerful greenhouse gas with much higher global warming potential compared to CO₂ and has been considered one of the key priorities in mitigating climate change. Although methane stays in the atmosphere for a relatively short period, its large heat-trapping capacity makes reduction of emissions very important to achieve near-term climate benefits. Holistic coverage of environmental impacts due to methane, its social consequence, and the role of governance for effective mitigation befits the ESG framework of addressing methane emissions. Major sources of methane, such as agricul-ture, extraction of fossil fuel, waste management, and coal mining, are deeply entwined with economic life and the fabric of society. This underlines the need for strategies that balance sustainability with economic growth and food security. The current study con-siders methane emissions as a significant sustainability metric and uses panel data from the World Bank ESG database, covering 193 countries from 2011 to 2020, in order to ex-plore the complex relationships between methane emissions and the three pillars of ESG. By decomposing the drivers of methane into environmental, social, and governance di-mensions, through Random Effects, Fixed Effects, Pooled OLS, and Weighted Least Squares econometric models, this paper therefore gives complete insight into the drivers of methane and their wide effects on global sustainability. This becomes even more evident from the ESG environmental dimension perspective, underlining the fundamental driving elements of land use, energy systems, and the methods of agriculture. In particular, agriculture by itself is responsible for the lion's share of global methane emission through the processes of enteric fermentation in livestock and rice cultivation, respectively. Some key sustainable activities in great ways reducing the above emissions while not compromising productivity include precise farming, opti-mized livestock feeding, and the use of AWD in rice cultivation. Transitioning into re-newable energy is by all means necessary; methane leakages during the extraction, trans-portation, and distribution of fossil fuel alone contribute so much to methane in the at-mosphere. In the broader view, this transition into sources of cleaner energy reduces reli-ance on fossil fuel and decreases overall emission. Besides, deforestation and land-use change release methane stored in biomass and soil; hence, methods concerning methane emissions would encourage policy regulations for the preservation and rehabilitation of forests. Finally, to complete it all, methane emission is strongly connected with agricul-ture, use of energy, and land management. The environmental pillar of ESG should in-clude mitigating strategies like sustainable agriculture, renewable energy adoption, and conservation of forests.

In light of the effect it could have on public health and socioeconomic structures and labor in areas that aren't that well advanced, the social aspect of ESG should take into ac-count methane emission concerns. Methane forms tropospheric ozone, an air pollutant, the cause of respiratory illness, cardiovascular diseases, and, finally, preterm mortality in poor income, overpopulated places. Accordingly, reduced emission of methane can pro-duce huge public health benefits: decreasing burdens from diseases and improved life quality, particularly at the vulnerable point of societies. The complex interlinkage of me-thane emission, economic development, and inequality speaks to a complex relationship in which both are cause and symptom. Thus, the countries that have high levels of economic growth are highly industrialized and, therefore, emit high levels of methane as a result of high energy and agricultural production. However, the developing countries have a tendency to have relatively low incomes and, with the infectious diseases, are normally at low economic activity and hence emit lower amounts of methane. This dichotomy puts in rather sharp relief what perhaps could be one of the challenging balances of economic progress by nations and the environmental sustainability of the planet, and how this de-crease in emission might be executed in a fashion that does not exacerbate global inequal-ities.

Methane is an extremely potent GHG with very high global warming potential; therefore, controlling methane emissions represents one of the most important challenges for global sustainability. Though difficult to detach from key sectors of the economy, such as agriculture, energy production, and waste management, they are considered crucial for solving environmental, social, and governance challenges. In the environmental dimen-sion, the most relevant sources of methane emissions include agriculture, extraction of fossil fuels, and deforestation. Transitioning into renewable energy, sustainable agricul-ture, and conservation of forests would also go a long way in reduction of emissions for protection of the environment. From a social point of view, management of methane has strong implications for the health and socioeconomic systems of a population. The tropo-spheric ozone formed by methane leads to respiratory diseases and cardiovascular dis-eases that seriously affect vulnerable groups of people with low incomes. Improving me-thane emission can avoid severe outcomes such as improved health equality and sus-tained economic growth. On the other hand, methane-emitting activities, including agri-culture and energy, provide vital jobs to the economies of developing countries. The call will be, therefore, in balancing economic stability with emissions reductions and job secu-rity.

Governance underlines that strong policy, technological development, and interna-tional cooperation are some of the key elements which would help manage methane emissions. Those countries with an appropriate regulatory framework and institution would thus be better equipped to carry out a strategy relating to the capture technology for methane, reduction in the leakage in fossil fuel systems, and R&D. Certain technological innovations-irrigation for methane storage systems and optimizing livestock feed-offer pragmatic solutions to cut emissions. In all, methane management under the ESG frame-work evokes a fine nexus between environmental health, public welfare, and governance capacity. Panel data evidence from the ESG database of the World Bank, drawn across 193 countries and a decade-long period spanning 2011–2020, forms the analytical base of this study in an effort to shed critical light on the drivers of methane emissions in widely var-ying global contexts. This calls for policies, innovations at the sectoral levels, and interna-tionally coordinated strategies with specificity toward effective emissions reductions. By placing methane emissions as the most focal ESG metric, this study has further under-lined their linkage with climate goals, economic sustainable growth, and social equity while also providing an avenue toward a balanced and resilient future.

Methane is a potent greenhouse gas with a much higher warming potential than CO₂; methane emissions are considered one of the most pressing challenges to mitigate climate change. Being relatively short-lived in the atmosphere, the notable heat-trapping capacity of methane makes its reduction an effective solution for near-term climate benefits. Its primary sources are so congenitally embedded in the economic and social systems of agriculture, extraction of fossil fuel, waste management, and coal mining that a trade-off between environmental sustainability and economic growth, on one hand, needs to be balanced with the food security of a region. This study positions methane as a critical in-dicator of sustainability and uses World Bank ESG data for 193 countries between 2011 and 2020 to understand its drivers along environmental, social, and governance dimensions using econometric models. The environmental pillar highlights emissions of me-thane related to agriculture, fossil fuel, and deforestation. Large sums of livestock, rice cul-tivation, methane leakage from fossil fuel all add up as major contributors in this regard; deforestation worsens these gases by releasing stored methane. Their controls would involve shifting toward renewable energy sources, switching over to sustainable farming, and the preservation of forests to be able to cut down the growing emissions.

From a social perspective, methane emissions directly affect public health since it is one of the tropospheric precursors to ozone, a pollutant that triggers respiratory and cardiovascular illness in low-income parts of the world with very high populations. These health burdens, quality of life, and inequalities can be reduced by reducing the amount of methane emitted, especially for those most at risk. While the basic sources are agriculture and energy production, these are fundamental to employment in developing economies, and solutions need to balance control of emissions with job security and economic stabil-ity. Governance has become instrumental in methane management through policy, tech-nology innovation, and global cooperation. This would provide for the installation of me-thane capture technologies, reduction of leakage levels, and further research development of practical options available, including additives and renewable energy technologies where feasible, for countries that can afford the developed systems of regulation. Interna-tional cooperation in reduction targets is important through cooperation and sharing ex-periences for the pursuit of global targets for reduction through efforts such as the Global Methane Pledge. By placing methane management in an ESG framework, this work un-derlines the interconnected roles that environmental systems, public health, and govern-ance play in emissions reduction. This report also calls for targeted policy action, techno-logical innovation, and international approaches as superior ways to ensure the world economy is sustainable and equitably developed, using comprehensive data and robust models.

Q2. Specifically, the way the literature is presented should be more directly aligned with the need for your study and its contribution to the field. If instead to separating the literature review into its own section, we suggest integrating the discussion of relevant studies into the introductory framework. This will allow you to clearly articulate how existing research leads to the necessity of your manuscript and to position your work as a meaningful contribution to the ongoing discourse. By doing so, the introduction will naturally build toward your research objectives, establishing a logical progression and stronger rationale for your study.

A2. The following part has been added in the introduction showing the innovativeness and originality of the research in respect to the scientific debate critically discussed:

Originality of the research concerning the actual scientific debate. Reference [113] is a basic global methane budget and gives the overall picture of methane emissions and their major sources. Their study fits within the Environmental (E) pillar of ESG, which still remains mainly oriented to global trends without consideration of regional and sectoral variations that are important for local ESG policies. Where its aggregation of means might prove most critical, our research features an econometric analysis based on a sample size of 193 countries over the period from 2011 to 2020. We set up the way in which the so-cio-economic structure, along with governance effectiveness and the level of renewable energy take-up, drives methane emissions from both developed and developing econo-mies, serving as a significant departure point. By taking account of these differences, our findings allow for actionable insights from local regional ESG strategies so far missing due to poor localization of sustainability challenges. While this enhances methane re-porting accountability through adding spatial granularity to global methane inventories, in particular from fossil fuel exploitation, reliance on self-reported data underlines a gov-ernance gap given that weak institutional capacity is often associated with underreport-ing. Our work furthers this discussion by quantifying the role of governance effectiveness, including legal frameworks and institutional performance, in methane emission mitiga-tion. Our analysis of governance indicators highlights that this study identifies the key underlying roles of institutional accountability and international cooperation in making sure accurate methane reporting and, therefore, emissions reductions around the globe. [141] focuses on sectoral drivers for methane emissions in China. The dual dominance of fossil fuel and food systems is striking therein. While they discuss the trade-offs between economic development and cares for the environment, their study fails to highlight how technological innovation might reduce emissions without ultimately affecting productiv-ity. This paper, therefore, explores the role of renewable energy consumptions as a miti-gating instrument within the ESG circle that highlights how energy transition could ac-commodate methane emissions reduction at limited costs of balanced economic growth and thus propose a solution for more than a single-country case. While [48] juxtapose methane reduction against long-term CO2 mitigation, underlining the need for resource prioritization within the Environmental pillar of ESG, their analysis is theoretical in na-ture. We provide empirical evidence to demonstrate that methane emissions, although short-lived, need urgent governance and technological solutions integrated across ESG pillars if global sustainability goals are to be met without undermining CO2 strategies. While methane monitoring technologies are needed, as stated by [142] and [30], poor countries cannot afford these due to their financial and infrastructural constraints, which creates an equity issue within the Social dimension of ESG. Our work develops this dis-cussion further, with the highlighting that methane mitigation burdens are distributed inequitably, given that in many cases, the energy import by developed countries displaces methane-intensive activities to the exporting nations. This is an indication of the im-portance of international cooperation and specific financial support for methane reduc-tion in susceptible economies. Referring to the SDGs, [143] aligns methane mitigation with the Sustainable Development Goals, underlining the effects of agricultural emissions on food security and poverty reduction. However, they downplay the economic trade-offs faced by developing countries reliant on agricultural expansion. Our study brings in a subtle approach by integrating socio-economic indicators-labor force engagement and food production indices-into the ESG framework, which reflects the complex interaction of methane emission, economic livelihoods, and governance capacity. The subtle approach allows for more balanced sector-specific policies that meet both environmental and social priorities. While [144] call for standardized global policies on methane, geopolitical and economic hurdles are underestimated. Our research contributes to this debate by showing how the effectiveness of governance and institutional capacity directly influences methane emissions and provides evidence-based foundations for crafting policies that take into account economic inequalities while advancing global accountability. [7] are focused on technological solutions for methane mitigation but ignore the issues of affordability and accessibility for low-income regions. Our research bridges that gap in an innovative method by analyzing how environmental policy, socio-economic factors, and governance systems interact toward methane emissions. This makes for an integrated ESG approach that will ensure methane reduction strategies can be technologically viable and equitable, feasible in various economic contexts.

 

 

 

Q3. While your work presents potentially valuable ideas, there are significant issues that need to be addressed to improve its quality and alignment with the standards of a scientific article. This analysis throughout the manuscript is overly general and suggestive, lacking the depth and rigor necessary for robust scientific discourse. The discussions, such as those related to the relationship between agricultural land and methane emissions, and the analysis of model variables, rely heavily on speculative arguments. These ideas are presented without clear evidence, detailed explanations, or methodological support to substantiate the claims. My fundamental concern lies in the lack of clarity regarding the origin and methodology used to obtain the data upon which the results are based. The absence of transparency in describing the data sources and the processes for integrating variables raises significant questions about the reliability and reproducibility of the findings. This gap not only weakens the scientific validity of the study but also creates uncertainty about the robustness of the conclusions drawn.

A3. The section “Data and Methodologies” has been increased to give greater representation of the characteristics of the data, of the chosen sampling, and of the variables used from the metric point of view with indication of the basic statistics. In this way it is possible to obtain a complete and clear vision of the essential elements of the analyzed variables.

Methane emissions globally or per capita. Aggregating emissions to a national scale, rather than per capita, may be a better proxy of the overall emissions and drivers of a country. The methane emissions on a national scale represent the absolute environmental burden that a country imposes because it is relevant to an international climate mitigation policy at a country level. Again, it puts into the spotlight those large emitters, such as countries either with significant agricultural and/or fossil fuel-producing sectors that are disproportionally important to global methane levels. In addition, national data capture the contribution of some of the most important economic sectors, such as agriculture, energy production, and waste management, which is very different in each country. Per capita conceals the scale of emissions that are related to national economic activities. Policymakers also use national-level data to design effective mitigation strategies targeting major sources of emissions. Although per-capita figures can point to inequalities, either in efficiency or equity in emissions, they do not present absolute values of the emissions that have to be reduced with the aim of being compatible with climate targets. National emissions data also reveal something about global inequalities and responsibilities: high-income countries that are intensive in their industrial and energy sectors are higher emitters in absolute terms, while per capita emission rates may distort perceptions in countries with a smaller but industriously dense population. This bigger perspective makes sure the responsibility of reductions falls in line with the real scale of environmental impact caused by each country. Lastly, methane mitigation strategies call for focused interventions at the level of countries-investing in renewable energies, enhancing agricultural practices, and methane capture. National-scale analyses also enable the formulation of country-specific policies, aligned with broader global climate initiatives such as the Global Methane Pledge. Altogether, the national scale analysis of methane emissions provides a very clear picture of the total contribution to the environment every country makes, how the activities are responsible in an economic context, and also the mitigation potential of these emissions. This is especially important when efficiently pursuing reduction targets related to methane in order to take into account the contribution each nation will play while dealing with the global warming factor.

Characteristics of data. The variables were chosen on the basis of data provided by the World Bank within the ESG-Environmental, Social and Governance database. Specifically, the metric characteristics of the analyzed variables are indicated in the following Table 2.

 

Table 2. Characteristics of the variables

Variable	Mean	Median	Minimum	Maximum	Std. Dev.	C.V.	Skewness	Ex. kurtosis	5% Perc.	95% Perc.	IQ range
METHANE	14.517	0.81156	0.00000	27.700	28.231	19.447	52.737	33.483	0.00000	55.828	10.607
AL	0.67621	0.00000	0.00000	24.381	23.772	35.154	57.085	39.428	0.00000	44.133	0.087963
EIMP	30.572	31.102	0.00000	82.996	24.721	0.80861	0.23903	-11.598	0.00000	72.006	44.180
REC	32.710	13.797	-16.800	33.373	47.443	14.504	25.130	82.881	0.00000	14.479	46.178
NFD	-63.588	0.00000	-1058.1	99.200	79.874	12.561	-53.508	39.454	-104.58	73.194	0.00000
CO2E	37.660	0.00000	-122.88	574.79	24.451	64.926	19.319	420.87	0.00000	10.154	45.423
INTENSITY	76.605	97.280	0.00000	157.47	42.051	0.54893	-11.767	-0.39229	0.00000	110.47	21.515
FPI	25.208	13.066	0.00000	97.031	28.608	11.349	0.99387	-0.30793	0.00000	84.693	41.205
CD	40.705	0.00000	-0.23000	66.470	11.996	29.472	35.313	12.006	0.00000	33.155	0.00000
HB	14.452	0.00000	0.00000	16.460	23.521	16.276	21.097	52.668	0.00000	66.045	24.225
IL20	21.709	0.00000	0.00000	10.500	33.087	15.241	10.524	-0.55088	0.00000	88.000	52.000
LFPR	56.380	66.870	0.00000	92.170	27.265	0.48359	-12.942	0.26899	0.00000	83.498	23.780
MR5	0.11267	15.700	-27013.	153.20	870.85	7729.1	-30.953	957.41	0.00000	97.670	37.400
PSSS	35.623	24.602	0.00000	100.00	36.399	10.218	0.49289	-13.079	0.00000	98.074	70.931
P65	87.101	52.140	-0.40846	214.09	15.733	18.063	10.824	135.10	0.00000	19.759	92.438
PO	27.482	25.100	0.00000	88.500	26.595	0.96775	0.27419	-14.627	0.00000	65.600	55.300
UT	70.469	56.150	0.00000	31.380	58.620	0.83185	12.526	14.743	0.00000	19.224	66.300
GE	0.14773	-0.15046	-24.751	28.200	26.665	18.050	87.700	86.714	-15.556	17.611	12.681
RAND	0.52954	0.00000	-17.162	44.477	27.352	51.653	13.724	202.83	0.00000	22.002	0.34966
STRENGHT	53.072	20.000	0.00000	11315.	699.42	13.179	15.128	229.61	0.00000	10.000	60.000
VA	-0.012676	0.0020447	-22.592	15.470	10.928	86.206	22.711	29.211	-16.898	14.167	16.271

 

The means for variables like methane, AL, and FPI are higher by a big margin in relation to the medians, and this is at positive skews, hence extreme values pulled it upwards. On the contrary, LFPR, MR5, and STRENGTH show variables for which extreme values have really impacted the general distribution. These differences in the measures of central tendency show asymmetry in the data. Some of the variables, from the range, like MR5 and STRENGTH, have huge ranges; MR5, for instance, ranges from -27013 to 153.2. This shows that either there are extreme outliers or huge variability among the observations. Other variables such as AL, HB, and CD have a small spread that reflects a tighter spread of the data. The same can be made out from the standard deviation which is highest in MR5 and STRENGTH at 870.85 and 699.42 respectively indicating that both are high on variability. For instance, in variables GE and P65 standard deviations are low. The C.V gives the relative variability of each variable. High values are constituted by MR5, with a C.V of 7729.1, and AL with 35.154, showing extremely dispersed values with respect to their mean. In contrast, other variables involve EIMP and LFPR showing a pretty low coefficient of variation; that could point out quite stable and consistent data dispersion. Skewness conveys more information with regards to symmetry. For example, for the variables METHANE and GE, the skews are positive, standing at 52.737 and 87.700, respectively, an indication of these sets of data having a tail toward the higher values; as such, they are considered right-skewed. The set of variables such as those of LFPR and MR5 were negatively skewed with -12.942 and - 30.953, indicating the opposite-that here, the clumping has been to the high-value side as the tail faces the low-value side. Again, it is reflected in the excess kurtosis. Very keen peaks may be seen, with extreme outliers for some variables such as MR5 at 957.41 and that of CO2E at 420.87. Other variables include FPI and PO, which have almost normal to moderately flat distributions. The investigation into the 5th and 95th percentiles gives good insight into the presence of outliers and dissemination. Percentiles have values of the 5th percentile for MR5 that corresponds to 0.000 while the 95th percentile is 97.670. It means that high inclusion of data despite their existence at higher values despite existence or great occurrence of outliers. These exist a high variance between such created percentiles for variables GE and RAND. For example, 70.931 is extremely high. The IQ range for PSSS is indicative that the middle 50% of the data points are highly spread out. In contrast, other variables like CD and AL have very low IQ range, indicating that most of their values are closely packed together. At the extreme ends, the MR5 and STRENGTH are highly dispersed; this could be due to some outliers or a characteristic of the variables that may require further investigation. Examples of positively-skewed variables with most of the measurements huddled around zero are METHANE and AL. INTENSITY and LFPR are relatively stable in their averages but are negatively skewed, with most of their data concentrated towards the top of their respective ranges. High kurtosis and variability in CO2E and PSSS, respectively, show that these series have long tails with extreme values. The data thus reflect a mix of compact distributions, skewed patterns, and significant outliers.

Sample size. Data for this study have been drawn from a wide range of 193 countries in order to ensure that the analysis of methane emissions within an ESG framework is really global. For such big numbers, many stages of development would thereby fall in place, setting up a rich dataset that could eventually explore several associations between methane emissions and key ESG indicators. Global coverage would therefore reveal trends, inequalities, and links among major economic, ecological, and political systems. The sample is representative because it includes countries that are in various stages of development-from developed to developing economies. In this regard, it allows comparing methane drivers in economies that have various degrees of industrialization, agricultural intensity, and effective governance. Usually, countries in the developed world feature high methane emissions due to intensive industrial agriculture and heavy fossil fuel activities, whereas developing nations normally have lower methane emissions, though tending to increase as their economies grow. These 193 countries also represent methane-emitting sectors, including agriculture, fossil fuel extraction, waste management, and forestry. Variations in this dominance across regions signal substantial differences in methane sources and a need for tailored methods in mitigation. The dataset will cover countries with diverse geographies and environmental conditions that will also reflect land use patterns, energy systems, and use of natural resources. As an example, tropical countries that are involved in massive deforestation processes have high methane emissions, whereas some other countries whose land is being used for agricultural purposes try to manage those emissions by using certain sustainable methods. There is also a variation in governance capacities across the sample countries. While strong governance structures are implemented to carry out effective strategies for emissions mitigation, including methane capture technologies and the adoption of renewable energy, countries that have relatively weak systems are unable to develop the proper regulatory frameworks or institutional strength for effective emissions control. For example, with such a wide range of countries over the period 2011–2020, it captures global trends, insights into cross-border effects of energy imports, renewable energy adoption, and forest depletion. Diversity in economic, social, and governance systems across these wide-ranging countries allows for a robust analytical approach toward methane emission drivers, with targeted policies taken by region in a bid to balance sustainability with economic growth and global equity.

Breaking down the ESG model into three equations. There are advantages of three separate equations, all to analyze the relationships existing between methane emissions and ESG. Decomposing the analysis into three components provides a closer look at the direct influence of each ESG pillar on methane emissions separately. The environmental equation in turn isolates the variables on agricultural and renewable energy consumption and efficient energy practice. This overview looks into the direct environmental strategy taken vis-à-vis methane levels. By the same vein, the social equation displays concern for all subjects that involve socioeconomic conditions or the general health of the population and dynamics of working relations which will be linked indirectly to methane emission. In addition, institutional effectiveness, investment in R and D, and a binding regulatory framework have been emphasized by the governance equation to bring down the level of emission. The chosen specification also enabled the researchers to dichotomize ESG components into separate categories, since each one of the three pillars influence methane emissions through different channels. Also, it will not be easy to explain such kinds of relationships even when integrating all the variables into a single equation-either due to the problem of overlapping effects or because some of them might have variables that hold the problem of multicollinearity. For instance, some of the governance-related issues mask the variables that would show a direct influence of environmental policies, while socio-economic factors blur on the other side the environmental impact analysis. The presence of three equations allows for the application of specific econometric models for each pillar, such as Fixed Effects, Random Effects, and Weighted Least Squares when applying. Analysis at the sectoral level better controls for unobserved heterogeneity and sector-specific dynamics. This is an important reason, rightly pointed out by this study, because each component interacts with methane emissions through different channels and thus requires independent treatment if robust and reliable conclusions are to be derived.

Econometric analysis. To analyze methane emissions within the ESG framework, four econometric models are applied: Random Effects (RE), Fixed Effects (FE), Pooled Ordinary Least Squares (OLS), and Weighted Least Squares (WLS), specifically:

• Panel Data with Random Effects (RE). The Random Effects (RE) model assumes that individual-specific effects (unobserved heterogeneity) are randomly distributed and uncorrelated with the independent variables. This allows the model to estimate both time-invariant and time-varying variables, making it suitable for large datasets with repeated observations across entities. The mathematical structure is as follows: where = dependent variable (methane emissions) for entity i at time t; = vector of independent variables (e.g. renewable energy use, governance indicators); = coefficients to be estimated; =random individual effect; =error term. The RE model is appropriate for assessing the impact of variables that vary over time while accounting for unobserved individual effects across countries [128; 129; 130].
• Panel Data with Fixed Effects (FE). The Fixed Effects (FE) model assumes that individual-specific effects are constant over time, allowing for the control of unobserved variables that may differ across entities but remain invariant over time. The mathematical structure is as follows:  where   is the entity specific fixed effect (constant across time). The FE model is ideal for analysing the effect of time-varying variables on methane emissions while controlling for country specific characteristics that do not change over time, such as geography [131; 132; 133].
• Pooled Ordinary Least Squares (OLS). The Pooled OLS model treats the dataset as simple cross-sectional regression, ignoring the panel structure. It assumes that there is no unobserved heterogeneity between entities. The mathematical structure is as follows: . While simple, this model is less robust in capturing the individual-specific effects or temporal dynamics. It serves as a baseline with other models [134; 135; 136].
• Weighted Least Squares (WLS). The WLS model addresses heteroscedasticity by assigning weights to observations, ensuring that observations with lower variability receive higher importance in the regression. The mathematical structure is as follows: where = weights assigned to each observation, inversely proportional to variance. WLS is particularly useful in this study due to varying levels of data reliability across countries and years, ensuring unbiased and efficient estimates [137; 138; 139]. 

Basically, in application, the choice among the different panel data models depends on some basic underlying assumptions regarding unobserved heterogeneity and the kind of available data. The RE model captures both time-invariant and time-varying features when individual effects are thought to be uncorrelated with the explanatory variables. On the other hand, we consider the FE model suitable for controlling the effects of time-invariant heterogeneity at individual levels, which are unobserved, when we want to get robust estimates specifically for the time-varying variables. While the pooled OLS model is a simple baseline, it does not take into consideration either individual heterogeneity or temporal dynamics and is therefore less robust. The WLS model controls finally for heteroscedasticity by weighting observations in order to ensure efficiency and unbiased estimates. Together, all these models form an overall toolkit of panel data analysis, allowing nuanced insight into methane emissions and the determinants associated with different entities across time.

 

Q4. Additionally, the manuscript does not follow the structure expected of a scientific article. Essential sections, such as a clear and detailed methodology, are either missing or insufficiently developed. For example, there is little explanation of how variables are defined, measured, or integrated into the analysis. Furthermore, the conclusions appear to be speculative rather than grounded in robust results, and the limitations of the findings are not adequately addressed. It is also crucial to note that the manuscript does not adhere to the journal's guidelines for author. I have to strongly recommend revisiting these guidelines to ensure that the structure, content, and presentation of the article meet the expected standards. This includes providing a more rigorous methodology, detailed results, and a discussion that integrates findings within the context of existing literature.I recommend addressing these issues by providing detailed explanations of the data collection process, ensuring the methodology is explicit and transparent, and grounding the results in clear, evidence-based analysis. It is also essential to avoid speculative interpretations and instead focus on presenting conclusions that are fully supported by the data.

4. The section “Data and Methodology” has been added with new insights.

 

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The paper has been revised according to the suggestions. Something happened with the references; they are not in order. It would be worth mentioning the countries included in the analysis. The paper is very long. Perhaps it should be restructured instead of adding more sections?

Author Response

Point to point answer to Reviewer 2

 

The paper has been revised according to the suggestions.

Q1. Something happened with the references; they are not in order.

A1. The articles have been correctly renumbered through the application of the IEEE references method.

Q2. It would be worth mentioning the countries included in the analysis.

A2. We have added an Appendix after References to show the analyzed countries.

 

Appendix

The following 193 countries have been analyzed: Afghanistan, Albania, Algeria, Andorra, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, The Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei Darussalam, Bulgaria, Burkina Faso, Burundi, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Dem. Rep. Congo, Rep. Congo, Costa Rica, Cote d'Ivoire, Croatia, Cuba, Cyprus, Czech Republic, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt (Arab Rep.), El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, The Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary,  Iceland, India, Indonesia, Iran (Islamic Rep.), Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Korea (Dem. People's Rep.), Korea (Rep.), Kuwait, Kyrgyz Republic, Lao PDR, Latvia, Lebanon, Lesotho, Liberia, Libya, Liechtenstein, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia (Fed. Sts.), Moldova, Monaco, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russian Federation, Rwanda, Samoa, San Marino, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovak Republic, Slovenia, Solomon Islands, Somalia, South Africa, South Sudan, Spain, Sri Lanka, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Sudan, Suriname, Sweden, Switzerland, Syrian Arab Republic, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Tuvalu, Uganda, Ukraine, United Arab Emirates, United Kingdom, United States, Uruguay, Uzbekistan, Vanuatu, Venezuela (RB), Vietnam, Yemen (Rep.), Zambia, Zimbabwe.

 

Q3. The paper is very long. Perhaps it should be restructured instead of adding more sections?

A3. We have reduced the length of many subsections within the "Data and Methodology" section.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Authors,

I would like to first thank you for the opportunity to review the manuscript titled "Methane Emissions in the ESG Framework at the World Level."

After reviewing the revised version of your manuscript, I am pleased to inform you that many of the major issues previously identified have been successfully addressed. The necessary corrections have been made, significantly improving the clarity of the methodology and the overall structure of the paper. These revisions have strengthened the robustness of your analysis and made the results more coherent and aligned with the manuscript's objectives.

However, a few minor points remain that could still benefit from slight adjustments for further refinement. These issues do not affect the overall quality of the manuscript or its suitability for publication. Addressing these would simply enhance the clarity and completeness of the study.

 

For example:

 

Methodological Details: While the methodology is clearer, a few additional clarifications regarding specific analytical steps or assumptions would help further bolster the study’s robustness.

 

Consistency in Terminology: There are occasional inconsistencies in the use of terminology across sections that might confuse readers. A final proofreading to ensure uniformity would be beneficial.

 

Implications of Findings: Although the discussion has improved, a more focused explanation of the practical implications of the results in the context of the ESG framework would add value.

 

I believe that with the corrections made, the manuscript now meets the standards required for publication. Thank you once again for the opportunity to review your work, and I look forward to seeing the final version in the esteemed journal.

Best regards,

Author Response

Point to Point Answers to Reviewer 3

 

I would like to first thank you for the opportunity to review the manuscript titled "Methane Emissions in the ESG Framework at the World Level."

After reviewing the revised version of your manuscript, I am pleased to inform you that many of the major issues previously identified have been successfully addressed. The necessary corrections have been made, significantly improving the clarity of the methodology and the overall structure of the paper. These revisions have strengthened the robustness of your analysis and made the results more coherent and aligned with the manuscript's objectives.

However, a few minor points remain that could still benefit from slight adjustments for further refinement. These issues do not affect the overall quality of the manuscript or its suitability for publication. Addressing these would simply enhance the clarity and completeness of the study.

 

For example:

Q1. Methodological Details: While the methodology is clearer, a few additional clarifications regarding specific analytical steps or assumptions would help further bolster the study’s robustness.

A1. Within the "Data and Methodology" section, a section entitled "Synthesis" has been added with a graphical analysis of the proposed research design.

Synthesis. Following is the glimpse of methodology followed in the context of methane emissions within an ESG framework. The Figure 4 is divided into three parts: setting of variables, econometric analysis, and results, which show a logical flow of the process involved in the research process.

.

Figure 4. The three phases of the proposed research. A: setting the variables. B: econometric analysis. C: Focus on the analysis of the results.

 

The proposed methodology begins through the setting of variables by categorizing into three pillars, say ESG. Methane emission, a share of agricultural land area, the use of finally renewable energy, and CO2 emission are the variables describing ecological impact in every country and represent an environmental pillar. Further, a social pillar will be represented by such an indicator as mortality rate, participation in the labor force, the percentage of the elderly population, focusing on socio-economic and demographical conditions. Governance pillar: Government effectiveness, spending on R&D, and rule of law are the institutional or political capabilities concerning the economy of emissions. The second part is the Econometric Analysis, meaning the application of the econometric model to the data. This extended model introduces random effects for both time-invariant and time-varying variables, with the fixed effect that controls for unobserved heterogeneity at the individual level varying. While the Pooled OLS model is only a rather simple baseline model, WLS does take into account the possibility of heteroscedasticity; weighting of the observations resolves this issue. The fact that such diversity within the methodological approach can provide deep analysis of ESG data points with minimal risks of the effects of overlap or, in fact, multicollinearity, is promising.  The last section shows the results. The three major sub-outcomes are: the reassessment of sectoral performance related to methane emissions, capturing regional disparities and governance capacitates; in detail, links between methane emission measures and ESG metrics for driving insights into targeted policies. This structured approach would be important in the full comprehension of methane emissions, due to their complex interaction with environmental, social, and governance variables, in such a way that formulation of mitigating strategies is properly and equitably defined.

Q2. Consistency in Terminology: There are occasional inconsistencies in the use of terminology across sections that might confuse readers. A final proofreading to ensure uniformity would be beneficial.

A2. A final review was made by harmonizing the terminology in order to ensure better understanding.

Q3. Implications of Findings: Although the discussion has improved, a more focused explanation of the practical implications of the results in the context of the ESG framework would add value.

A3. The following section entitled “Implications of finding” has been added.

5. Implications of findings

From the standpoint of ESG, it's long-term methane that has become essential for making a practical effect; indeed, methane meets all those parameters relating to climate change and sustainability. The approach will, at last bring organizations and governments to urge and take appropriate urgent climate actions by incorporating methane management in ESG strategies. Indeed, since methane is filled with enormous possibilities of being used as one of the causes for global warming, thereby bringing it low would finally create one main realization of a short-term set of ecological goals. This would include renewable energy, sustainable agriculture, and actions to prevent deforestation. Transitioning into renewable energy sources, therefore, will not only reduce methane emission but also lessen the dependence on fossil fuels and hence methane leakages in the energy systems. These have to be taken as an act of collective responsibility since energy import and exports often displace the burden geographically without alleviating it globally [143], [144], [145].

Social considerations in the ESG framework relate to accruing public health benefits of methane abatement. Methane is a precursor to ground-level ozone among major causes of respiratory and cardiovascular diseases. This will go a long way toward improvement in air quality, thus being beneficial to the most vulnerable among the population, particularly those from low-income regions. In addition to this, strategies aimed at reducing methane must be authorized so as to achieve socio-economic equity. This is particularly true in developing countries where methane-emitting sectors form the very basic livelihood activities of the people, including agriculture and energy production. It will also create new job opportunities in renewable energy and other advanced agricultural techniques, thereby cushioning the probable economic distortions [42], [27], [21].

From the ESG perspective, good governance is an exquisite player in methane management; good governance will make a difference in the implementation of numerous policies and regulations that will reduce methane emissions. It means better monitoring, reporting, and verification to pursue accountability. Indeed, technology innovation, including the development and deployment of methane capture technologies, plays an important role in effective abatement. This can be further stimulated with financing and development by a governmental framework in the direction of more sustainable technologies. Good governance enhances governance in ways to make specific elements, such as transparency and accountability, work across borders in ways important to pursue goals like the Global Methane Pledge. The integration of methane management into the general ESG framework cements its position within the ambit of attaining sustainability-oriented goals. Under the environmental pillar, methane reduction represents such direct support that might reduce climate change and degradation. From the social standpoint, reduction in methane emissions would be about health equity and vulnerable populations' protection. Governance makes sure these environmental objectives along with social ones are pursued responsibly in an adequate manner. Indeed, it will be achieved through targeted policy, taking into consideration regional context and varieties of socio-economic and environmental challenges facing different countries. It also involves worldwide cooperation in the provision of finance and technology to developing countries. This is something that will ensure that these countries are able to transition towards greener futures. Besides, the contribution of the private sector will only scale it up, given that companies that integrate methane reduction within their respective ESG initiatives can make much greater changes if an enabling incentive and regulatory environment is provided [3], [146], [27].

Moreover, such prioritization of methane management within the ESG will be able to give a balanced approach wherein environmental, social, and governance challenges will be sought out toward resilience and a sustainable future in stakeholders.

 

 

I believe that with the corrections made, the manuscript now meets the standards required for publication. Thank you once again for the opportunity to review your work, and I look forward to seeing the final version in the esteemed journal.

 

Author Response File: Author Response.pdf

Round 3

Reviewer 2 Report

Comments and Suggestions for Authors

I have no further suggestions.

Reviewer 3 Report

Comments and Suggestions for Authors  

Dear Authors,

Thank you for submitting the revised version of your manuscript titled "Methane Emissions in the ESG Framework at the World Level." After reviewing the updated manuscript, I am pleased to see that you have effectively addressed the primary concerns from the initial review.

The methodology section has been significantly clarified, and the manuscript now presents a more logical and coherent structure. Additionally, the figures have been improved, providing a clearer visual representation of the data that enhances the overall understanding of your results.

With these revisions, the manuscript now meets the journal’s publication standards, and I recommend its acceptance.

Thank you for your hard work in refining the manuscript, and I believe it will be a valuable contribution to the field.

Best regards,