Methane Emissions in the ESG Framework at the World Level

Abstract

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. In spite 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 socioeconomic 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 internationational cooperation, as socioeconomic 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.

1. Introduction

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 the 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. In addition, deforestation and land use changes 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 are not 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 the reduction of emissions for protection of the environment. From a social point of view, the 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 with 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 that 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, form 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 level, 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 the 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, and 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.

Originality of the research concerning the actual scientific debate. Reference [1] 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 at 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 socioeconomic structure, along with governance effectiveness and the level of renewable energy take-up, drives methane emissions from both developed and developing economies, 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 reporting accountability through adding spatial granularity to global methane inventories—in particular, from fossil fuel exploitation—reliance on self-reported data underlines a governance gap, given that weak institutional capacity is often associated with underreporting. Our work furthers this discussion by quantifying the role of governance effectiveness, including legal frameworks and institutional performance, in methane emission mitigation. Our analysis of governance indicators highlights that this study identifies the key underlying roles of institutional accountability and international cooperation in making sure of accurate methane reporting and, therefore, emissions reductions around the globe. Ref. [2] 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 productivity. This paper, therefore, explores the role of renewable energy consumptions as a mitigating instrument within the ESG circle that highlights how energy transition could accommodate methane emissions reduction at limited costs of balanced economic growth and thus propose a solution for more than a single-country case. While Ref. [3] juxtaposed methane reduction against long-term CO₂ mitigation, underlining the need for resource prioritization within the Environmental pillar of ESG, their analysis was theoretical in nature. 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 [4,5], 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 discussion 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 importance of international cooperation and specific financial support for methane reduction in susceptible economies. Referring to the SDGs, Ref. [6] 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 a subtle approach by integrating socioeconomic 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 Ref. [7] called for standardized global policies on methane, the geopolitical and economic hurdles were 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. Ref. [8] focused on technological solutions for methane mitigation but ignored the issues of affordability and accessibility for low-income regions. Our research bridges that gap in an innovative method by analyzing how environmental policy, socioeconomic 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 and feasible in various economic contexts.

The article continues as follows: Section 2 presents the literature review, Section 3 describes the data and methodology, Section 4 contains the results, Section 5 shows the implications of the findings, and Section 6 concludes the paper. The Appendix A contains the composition of clusters.

2. Literature Review

2.1. Methane Emissions and Environmental Mitigation

Methane emissions, a significant contributor to climate change, require urgent mitigation through technological, policy, and governance strategies. Ref. [9] stressed the need for strong governance and policy reforms in oil-producing nations like Nigeria to address methane leaks. Ref. [10] advocated for market-based solutions like carbon pricing and methane-specific taxes, emphasizing international cooperation to support developing nations. Ref. [11] underscored advanced monitoring technologies for real-time data and regulatory compliance, while Ref. [12] called for standardized Measurement, Reporting, and Verification (MRV) frameworks to ensure credible methane reductions. Together, these studies promoted integrated strategies for global methane emission reduction.

Ref. [13] proposed atmospheric methane removal as an innovative strategy to complement traditional mitigation efforts for near-term climate impact. Ref. [14] stressed collaboration between industry, government, and civil society in developing effective European Union (EU) methane regulations that balance environmental and economic goals. Ref. [8] advocated for international cooperation and ambitious policies, highlighting technologies like leak detection and capture systems. Ref. [15] analyzed historical methane emissions data, emphasizing sustained mitigation in Organization for Economic Cooperation and Development (OECD) countries. Ref. [16] focused on methane’s environmental and health impacts, particularly ground-level ozone, urging comprehensive policies. Ref. [17] highlighted the need for improved fossil fuel emissions reporting. Refs. [18,19] provided strategies for reducing livestock and agricultural methane emissions, stressing policy support.

Ref. [20] explored the political complexities of methane emissions from livestock, emphasizing how geopolitical power dynamics shape methane reduction policies between developed and developing nations. Ref. [21] provided a roadmap for achieving global methane pledge targets, highlighting the need for technological advancements, regulatory reforms, and international cooperation. Ref. [22] focused on methane emissions from municipal wastewater systems, advocating for methane capture technologies to reduce emissions and generate energy, expanding mitigation efforts to urban infrastructure. Ref. [23] analyzed global methane trends, noting emissions stabilization in developed nations but increases in developing ones due to industrialization, calling for tailored strategies. Ref. [24] highlighted that methane mitigation’s benefits outweigh its costs, stressing immediate action to reduce emissions and slow global warming.

A synthesis of the relationships between methane emission and environmental mitigation is shown in Figure 1.

2.2. Socioeconomic Aspects and Impacts of Methane Emissions

The increasing relevance of ESG metrics underscores the vital connection between sustainability and economic development. Various studies explore how methane emissions, governance, and economic policies intersect with ESG goals, emphasizing the need for strong regulatory frameworks to balance growth with environmental and social priorities. Ref. [25] highlighted how integrating ESG in Gulf Cooperation Council (GCC) countries can help resource-dependent economies transition toward sustainability, attracting foreign investment and supporting long-term growth. Ref. [26] showed that ESG ratings guide investment decisions, linking financial returns to sustainability. Ref. [27] advocated including methane’s social cost in economic decision-making, urging policymakers to internalize these external costs in industries to align with ESG sustainability objectives.

Ref. [28] highlighted the crucial role of institutions in regulating methane emissions, particularly in the natural gas sector. He emphasized the need for strong governance, international cooperation, and best practices to align methane reduction efforts with global climate goals. Ref. [29] presented a N-shaped Environmental Kuznets Curve (EKC) model, suggesting that methane emissions initially rise with economic growth, decrease, and may rise again at advanced stages, urging for balanced policies. Ref. [30] examined methane emissions in Central African countries, showing that environmental degradation can impede long-term development and calling for its integration into economic planning. Ref. [31] used a methane-adjusted Dynamic Integrated Climate–Economy (DICE) model to project the long-term costs of inaction.

Ref. [32] emphasized the importance of financial mechanisms, such as public–private partnerships, to fund methane capture technologies in the oil and gas sector, promoting innovation through strategic investments. Ref. [33] critiqued US natural gas emissions certification, calling for stricter enforcement and transparency to improve regulatory frameworks. Ref. [34] argued that methane reduction strategies must consider their impact on vulnerable populations, promoting equitable global policies. Ref. [35] linked methane emissions to negative public health outcomes, urging policies that address both emissions and health. Ref. [36] suggested the EU leverage its collective purchasing power to enforce stricter methane standards globally (see Figure 2).

2.3. Technologies for Monitoring and Regulation of Methane Emissions

Reducing methane emissions is crucial for sustainability due to methane’s strong greenhouse effect. Recent studies have highlighted the importance of advanced monitoring technologies, strong policy frameworks, and improved industry practices in high-emission sectors like oil, gas, and marine fuel. Ref. [5] emphasized real-time methane detection tools, such as ground-based sensors, airborne systems, and satellite technologies, but stress the need for globally harmonized emissions reporting for transparency. Ref. [37] showcased the cost-effectiveness of satellite and airborne remote sensing in oil and gas, emphasizing collaboration between regulators and industry to maximize emissions reductions. Ref. [38] addressed methane slip during liquefied natural gas (LNG) combustion in the marine fuel sector, advocating for stricter regulations, better monitoring, and technological innovations to minimize emissions.

Ref. [39] reviewed methane emissions from the global oil and gas industry, highlighting research gaps, particularly in the long-term effectiveness of reduction technologies and the socioeconomic impacts of regulations. The study emphasizes the need for interdisciplinary research, especially in developing countries. Ref. [40] demonstrated the transformative role of satellite technology in detecting methane leaks and improving emissions reporting in remote areas, enhancing transparency and accountability. They argued that satellite-based monitoring can standardize global reporting, which is crucial for international climate goals. Ref. [41] provided a cost–benefit analysis of methane abatement technologies in natural gas production, highlighting that the long-term environmental and health benefits far outweigh the initial costs, urging policymakers to invest in methane reduction efforts.

Ref. [42] advocated using Audit 4.0 technologies, especially satellite imagery, to improve transparency in methane emissions reporting and enhance ESG assurance. Satellite data provide real-time, verifiable emissions information, helping companies meet regulatory and investor expectations. Ref. [43] stressed the importance of precise methane data through advanced monitoring technologies for shaping effective mitigation policies. Ref. [44] called for a demand-side approach to methane management, targeting the entire supply chain to reduce fuel consumption. Ref. [45] highlighted the transformative role of satellite technology in improving emissions reporting transparency. Ref. [46] emphasized the need for standardized reporting in Canada’s oil and gas sector. Ref. [47] prioritized methane mitigation, advocating for international cooperation and stricter regulations. Ref. [48] introduced a model for tracking flare emissions. Ref. [49] called for flexible, industry-specific mitigation strategies, while Refs. [50,51] explored the growing role of ESG management and digital technologies in methane reporting and sustainability (see Figure 3).

3. Data and Methodologies

Data. The World Bank’s ESG dataset [52] has been used for the empirical estimates.

Table 1. Variables used for the estimation of the econometric model.

Macrocategory	Variable	Acronym	Description
	Methane Emissions (kt of CO2 equivalent)	METHANE	Anthropogenic methane emissions refer to those CH4 releases to the atmosphere resulting from human activities including agriculture, landfills, and fossil fuel systems. Emissions are expressed in kt CO2 equivalent, using a 28–34 times higher global warming effect of methane compared to CO2 over 100 years [3,53].
E-Environment	Agricultural land (% of land area)	AL	Agricultural land includes land area used for arable land, permanent crops, and permanent pastures. Arable land comprises land for temporary crops and pastures, while permanent crops include land for cocoa, coffee, etc., that do not require replanting. It is measured as a percentage of total land area [54,55].
	Energy imports, net (% of energy use)	EIMP	It is the difference between energy production and its consumption in an economy, with net energy imports accounting for it. This would indicate whether or not the country is a net importer or exporter regarding energy. Oil equivalents are the units of measurements used. A negative value means it is a net exporter of energy [56,57].
	Renewable energy consumption (% of total final energy consumption)	REC	Renewable energy consumption refers to the percentage of total final energy use that is accounted for by renewable resources such as wind, solar, hydro, geothermal, and biomass. The generally accepted measure for renewable energy is a proportion of the total energy consumption, usually expressed in percentage [58,59]
	Adjusted savings: net forest depletion (% of GNI)	NFD	Net forest depletion is the loss of forest resources when the roundwood is harvested at an excess of natural growth, hence an indicator of poor forest management. It is measured as a percentage of Gross National Income, indicating how much value has been lost economically due to over-harvesting [60,61].
	CO2 emissions (metric tons per capita)	CO2E	CO2 emissions are particularly produced by the combustion of fossil fuels and cement production, including those using solid, liquid, and gaseous fuels, and gas flaring. Commonly, these emissions are measured as metric tons per capita to track environmental impact and develop strategies for decreasing greenhouse gas emissions [62,63]
	Energy intensity level of primary energy (MJ/$2017 PPP GDP)	INTENSITY	Primary energy Energy intensity is the amount of energy used in producing one unit of economic output, adjusted for PPP. The usual measurement is megajoules per dollar of GDP, that is in MJ/$2017 PPP GDP [64,65].
	Food Production Index (2014–2016 = 100)	FPI	Food Production Index measures the output of crops that are edible and contribute to human nutrition, omitting non-nutritive crops such as coffee and tea. Consequently, the unit of measurement adopted is a relative index, with the base period 2014–2016 set at 100 [66,67].
S-Social	Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total)	CD	Cause of death refers to the percentage share in the total deaths in all age groups by causes that include communicable diseases, maternal health conditions, congenital anomalies, and nutritional disorders. It is measured in respect to total deaths within the population in percentage form [68,69].
	Labor force participation rate, total (% of total population ages 15–64) (modeled ILO estimate)	LFPR	Labor force participation rate refers to the percent of the population aged 15–64 which is economically active: either employed or unemployed. It is given as a percentage of the total population of working age [70,71]
	Mortality rate, under-5 (per 1000 live births)	MR5	Under-five mortality rate refers to the probability that a newborn baby will die before reaching age five; it reflects the key factors of the quality of healthcare, disease prevention, nutrition, and living conditions. It is expressed as the number of deaths per 1000 live births in a given year [72,73].
	Population ages 65 and above (% of total population)	P65	The population aged 65 and older as a percentage of the total population represents the proportion of people in this age group relative to the entire population. By convention, this indicator is expressed as a percent and is calculated based on the de facto population, which comprehends all persons present on the territory regardless of their legal status or citizenship [74,75].
	Prevalence of overweight (% of adults)	PO	Adult obesity prevalence is the percentage of people ages 18 and over with a Body Mass Index of equal to or greater than 30 kg/m2, which is indicative of obesity. For any given height, a calculation of weight in kilograms divided by height in meters squared determines BMI (kg/m2) [76,77].
	Unemployment, total (% of total labor force) (modeled ILO estimate)	UT	Unemployment represents the percentage of the labor force that is jobless, actively seeking, and available for work. It is measured as a percentage of the total labor force [78,79].
G-Governance	Government Effectiveness: Estimate	GE	Government effectiveness captures the quality of public services, the quality and independence of the civil service, and the credibility of government commitment to policies and plans. It is a score calculated from a standard normal distribution ranging from approximately −2.5 to 2.5. Higher scores indicate better government performance [80].
	Research and development expenditure (% of GDP)	RAND	Gross domestic expenditure on R&D refers to the share of a nation’s GDP that goes to research and experimental development, operating costs, and capital spending in business enterprises, government, higher education, and private non-profit organizations. This is measured as a percentage of GDP and reflects the investment in scientific and technological innovation for the nation [81,82].
	Strength of legal rights index (0 = weak to 12 = strong)	STRENGHT	The Strength of Legal Rights Index provides a rating with respect to the laws of collateral and bankruptcy that enable borrowers and lenders to make a shift towards easier credit accessibility. Higher scores indicate that the legal environment is more conducive to secured forms of lending. It is measured from 0 to 12 [83].
	Voice and Accountability: Estimate	VA	Voice and Accountability measures the extent to which a country’s citizens are able to participate in the political process, exercise freedom of expression, associate freely, and have an independent media. Scores for this dimension normally follow the normal distribution between −2.5 and 2.5, where higher values represent stronger democratic institutions and greater participation of the public [84,85].

shows the variables used.

Methane emissions globally or per capita. The aggregate national methane emissions provide a more meaningful indicator of the contribution of the country to the environmental impact and its role in global methane levels rather than per capita emissions. National emissions present an absolute environmental burden that is relevant for international climate policy and point to large emitters with intensive agricultural, fossil fuel production, or industrial activities. Contributions from such key economic sectors—agriculture, energy, and waste management—can then be represented with this approach to involve more countries in a more realistic development or mitigation strategy. While per capita data can be informative to discern inequality, it obscures the scale of emissions linked to national economies and the absolute reductions needed to reach climate targets. National data put global inequalities into sharper focus: high-income industrial countries are the major absolute emitters in most cases where their per capita rates are more moderate. From a national point of view, policymakers will be able to create targeted interventions in the investment of renewable energy, better agricultural practices, and deployment of methane capture technologies. It harmonizes national policies with international efforts such as the Global Methane Pledge and ensures accountability toward the actual environmental impact. This perspective is important for the effectiveness and equitability of reductions crucial in tackling methane’s position in global warming.

Characteristics of the 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. 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
STRENGTH	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 the variables of methane, AL, and FPI are far above their medians. This is indicative that extreme values are responsible for positive skewness in the data distribution for such variables. Also, there exist asymmetrical distributions among the LFPR, MR5, and Strength variables; the implication in these kinds of data distributions involves outliers. The range of MR5 is, for example, from −27,013 to 153.2, thereby showing that this series contains an extreme variability or outliers, whereby, in the rest of the variables, such as AL, HR, and CD, the lower value of range—hence, their respective distributions—are close. Standard deviations also point out the variability, of which the highest values are taken by MR5 and STRENGTH at 870.85 and 699.42, respectively. This was also confirmed by coefficients of variation: the C.V. for MR5 is 7729.1 and AL is 35.154, exhibiting extreme dispersion, while, for other variables such as EIMP and LFPR, it is extremely low, hence showing stability in its values. Skewness varies: Methane and GE show positive skews, meaning their tails tend toward higher values, while, in the case of LFPR and MR5, the reverse happens—they are negatively skewed—meaning their tails are towards the lower values. Kurtosis has reflected extreme outliers for MR5 and CO2E, while FPI and PO are normally or flat distributed. Massive differences between the 5th and 95th percentiles of MR5 and GE reflect the occurrence of outliers. Also, IQR has given an indication that the middle 50% data are spread out in PSSS while compact in CD and AL. Again, the distribution of variables in CO2E and PSSS is highly leptokurtic and with extreme values. This suggests that a mixture of compact distributions, as well as skewed patterns, together with considerable outliers, do exist in the data.

Sample size. It aims at investigating methane emissions from an ESG perspective based on extensive data deriving from a total of 193 countries, while, at the same time, the present study seeks to have global coverage on the associations among methane emissions and significant ESG metrics. The sample contains both developed and developing countries, from developed, mature economies characterized by intensive agriculture and fossil fuel use and accordingly high levels of emission to developing ones still at a fairly low stage of development of growth on account of rapid recent economic progress on the increase. It includes the following methane-emitting fields: agriculture, fuel extraction, waste management, land use, land use change, and forestry underline regional disparities into sources of methane and reduction requirements. The largest emitters are those with tropical deforestation, while, in some regions, emissions are more controlled in a highly sustainable manner by way of improved farming practices. Governance capacities vary a great deal, and stronger systems are able to implement mitigation strategies through capture and renewable energy use, while weaker systems lack the capacity to apply their rules in regulating the emissions. The dataset includes trends from 2011 to 2020 and thus provides global trends and cross-border effects due to imports of energy, adoption of renewables, and land use impacts. Representation on such a panel allows a wide breadth of analyses across methane drivers and regional policy analyses that balance sustainability and economic growth with equity.

Breaking down the ESG model into three equations. Having three different equations to analyze methane emissions and ESG relationships allows each one of the ESG pillars to be focused on separately. The Environmental equation represents agricultural practices, renewable energy, and efficiency, while socioeconomic factors and health are embraced by the Social equation. The Governance equation emphasizes institutional effectiveness, R&D, and regulations. This separation is a must to avoid overlapping and indirect effects that mask the multicollinearity of other direct impacts. Second, this might contribute to taking full advantage of specific econometric models, such as Fixed Effects, for instance, which control for the sector-specific dynamics at work and thereby provide robust results by looking at different pathways via which each pillar affects the emission.

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

• Panel Data with Random Effects (REs). The Random Effects (REs) 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: $y_{it}=\beta X_{it}+{\alpha }_i+{ϵ}_{it}$, where $y_{it}$= dependent variable (methane emissions) for entity i at time t; $X_{it}$= vector of independent variables (e.g., renewable energy use and governance indicators); $\beta$= coefficients to be estimated; ${\alpha }_i$= random individual effect; ${ϵ}_{it}$= 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 [86,87,88].
• Panel Data with Fixed Effects (FEs). The Fixed Effects (FEs) 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: $y_{it}=\beta X_{it}+{\mu }_i+{ ϵ}_{it}$, where ${\mu }_i$ is the entity specific fixed effect (constant across time). The FE model is ideal for analyzing the effect of time-varying variables on methane emissions while controlling for country specific characteristics that do not change over time, such as geography [86,89,90].
• 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: $y_{it}=\beta X_{it}+{ϵ}_{it}$. While simple, this model is less robust in capturing the individual-specific effects or temporal dynamics. It serves as a baseline with other models [91,92,93].
• 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: $Minimize\sum _{i=1}^n{w}_i{\left(y_i-\beta X_i\right)}^2$, where $w_i$ = 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 [94,95,96].

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 obtain 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 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.

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

The proposed methodology begins through the setting of variables by categorizing them into three pillars, say ESG. Methane emission, a share of agricultural land area, the use of 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 the mortality rate, participation in the labor force, the percentage of the elderly population, focusing on socioeconomic 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 the ESG data points with minimal risk 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 the formulation of mitigating strategies is properly and equitably defined.

4. Econometric Results

4.1. Methane Emissions and the E (Environmental Component) Within the ESG Model

Regarding the relationship between methane emissions and the Environmental component of the ESG model, the following equation is estimated:

$$\begin{array}{ll}Methan{e}_{it}=& {\alpha }_1+{\beta }_1{\left(NFD\right)}_{it}+{\beta }_2{\left(AL\right)}_{it}+{\beta }_3{\left(C{O}_{2}E\right)}_{it}\\ & +{\beta }_4{\left(EIMP\right)}_{it}+{\beta }_5{\left(INTENSITY\right)}_{it}+{\beta }_6{\left(FPI\right)}_{it}\\ & +{\beta }_7{\left(REC\right)}_{it}\end{array}$$

(1)

where i = 193 and t = [2011; 2020].

The econometric results are shown in

Table 3. Panel data estimates for the Environmental component.

Model		Constant	NFD	AL	CO2E	EIMP	Intensity	FPI	REC
Fixed Effects	Coefficient	0.062	0.190 ***	−0.008 ***	0.146 ***	−0.001 ***	0.014 ***	0.015 ***	−0.007 ***
	Std. Error	0.057	0.027	0.002	0.014	0.000	0.001	0.001	0.002
	t-ratio	1.090	6.989	−2.965	10.07	−3.745	10.28	8.914	−2.734
Random Effects	Coefficient	0.062	0.203 ***	−0.009 ***	0.141 ***	−0.001 ***	0.0151 ***	0.015 ***	−0.008 ***
	Std. Error	0.174	0.025	0.002	0.013	0.000	0.001	0.001	0.002
	t-ratio	0.3589	7.850	−3.364	10.23	−4.037	10.95	9.672	−3.321
Weighted Least Squares	Coefficient	0.018	0.121 ***	−0.0119 ***	0.086 ***	−0.002 ***	0.023 ***	0.017 ***	−0.010 ***
	Std. Error	0.014	0.009	0.000	0.004	0.000	0.002	0.000	0.000
	t-ratio	1.277	12.22	−25.83	21.01	−8.764	11.83	49.69	−23.55
Pooled OLS	Coefficient	0.105	0.244 ***	−0.014 ***	0.097 ***	−0.004 ***	0.034 ***	0.020 ***	−0.018 ***
	Std. Error	0.112	0.025	0.002	0.014	0.000	0.002	0.002	0.002
	t-ratio	0.9339	9.414	−5.004	6.779	−6.124	14.47	10.28	−7.000

Notes: This 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 < 0.01). The results of the table unmask some of the key contributors to methane emissions in this environmental component and NFD, CO₂E, and energy intensity, and also FPI, which contributes highly positive features to methane emissions, ultimately proving that unsustainable land utilization, carbon emissions, and inefficiency in using energy have increased the level of methane. On the other hand, agricultural land, AL, energy imports (EIMP), renewable energy consumption, and its relation to methane emissions have negative relations; this just means that good agricultural practices include less reliance on energy which importation will reduce supply lines for their production due to their exhaustion, and renewables help to decrease emissions of it.

.

The relationship between agricultural land and methane emissions. The results show that the percentage of agricultural land as a fraction of the 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 relationship. 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 5). 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 [97,98,99].

The relationship between energy imports and methane. A regression by the authors shows that, when examining domestic energy imports and balancing its relations with domestic methane emission, most oil-exporting countries have very low fractions of domestic methane emissions from these fuels, since, often, their extraction, processing, and transportation already take place in lands abroad, hence simply shifting a share of local emissions over onto importing ones. In other words, this reduction is at home but misleading; it hides, in this way, the reality that this is at a global, or higher, scale level. In this respect, such leakage is believed to be sufficiently high during the production and transport of these fuels in countries exporting them to feed the present worldwide increases in methane to sustain this. As a result, this might create the appearance of improved environmental performance in energy-importing nations while the global climate crisis is deteriorating. This would mean that there would be less incentive for investment domestically in renewable energy infrastructure, a path furthering methane-intensive fuels such as natural gas, oil, and coal (Figure 6). This would indeed mean that, while net imports of energy give the impression of reduced local emissions, they actually worsen methane buildup globally and degrade shared climate and environmental outcomes [100,101,102].

The relationship between renewable energy consumption and methane emissions. Fully 60% of methane emissions come from human activities, predominantly the extraction, production, and transportation of fossil fuels such as natural gas, oil, and coal. Increased renewable energy in a country’s energy mix replaces the demand for fossil fuels and reduces methane 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 installed, 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, despite its relatively low carbon dioxide emissions compared to coal (see Figure 7). As renewable energy deployment goes up, methane emissions from energy production dramatically go down, showcasing the environmental dividend from investment in renewable infrastructure and the critical role that it will play in lowering global greenhouse gas emissions [103,104].

The relationship between net forest depletion and methane emissions. This positive relationship 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, including 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 activities such as livestock and rice. The livestock sector is one of the major sources of methane emission, especially from cattle, due to enteric fermentation. Rice paddies, newly deforested areas, release methane due to the anaerobic decomposition of organic matter in water-logged soils. Organic decay of cleared biomass further enhances methane emissions. 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 8). This essentially points to the severe environmental 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 [98,105,106].

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, which sources come from the same sectors or sectors that partly coincide, mainly from the extraction and burning of fossil fuels. In such combustion, there is the production of CO₂ emissions. Regarding methane gas, this takes place with leakages during extraction, 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 considerable CO₂. Agriculture is important, especially related activities about livestock and rice, two very common agricultural elements today in vast productions. An animal’s enteric digestion process and anaerobic disintegration liberate methane gas into 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 9). Both will rise in relation to economic activity, with increased energy needs worsening the impact on the climate crisis [107,108,109].

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, processing, and transport in the form of fugitive emissions—in particular, from poorly maintained 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 10). This is increased 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 decrease the level of CH₄ emissions [110,111].

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 emissions from livestock farming, rice cultivation, and the decomposition of organic waste. Livestock 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 atmosphere as a byproduct—produce methane. In plain words, as the 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 methane 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 11). While the world’s food production is increased through intensified methods of production to meet global demand, the said agricultural activities responsible for methane emission further increase and consequently heighten environmental impacts [112,113,114].

4.2. Methane Emissions and the S (Social Component) Within the ESG Model

Regarding the relationship between methane emissions and the Social component of the ESG model, the following equation is estimated:

$$\begin{array}{ll}Methan{e}_{it}=& {\alpha }_1+{\beta }_1{\left(CD\right)}_{it}+{\beta }_2{\left(LFPR\right)}_{it}+{\beta }_3{\left(MR5\right)}_{it}\\ & +{\beta }_4{\left(P65\right)}_{it}+{\beta }_5{\left(PO\right)}_{it}+{\beta }_6{\left(UT\right)}_{it}\end{array}$$

(2)

where i = 193 and t = [2011; 2020].

The econometric results are shown in

Table 4. Panel data estimates for the Social component.

	Random Effects			Pooled OLS			Fixed Effects			Weighted Least Squares
Variable	Coefficient	Std. Error	z	Coefficient	Std. Error	t-Ratio	Coefficient	Std. Error	t-Ratio	Coefficient	Std. Error	t-Ratio
Constant	−0.162	0.233	−0.696	0.284 *	0.149	1.901	4.477 ***	0.629	7.114	0.147 ***	0.025	5.882
CD	−0.017 ***	0.002	−6.662	−0.009 *	0.004	−1.852	−0.016 ***	0.002	−6.439	−0.008 ***	0.000	−11.89
LFPR	0.018 ***	0.001	11.66	0.008 ***	0.002	4.073	0.016 ***	0.001	9.456	0.009 ***	0.000	23.63
MR5	−0.0001 ***	3.861	−3.516	−0.0002 ***	7.227	−3.563	−0.0001 ***	3.79	−2.882	−0.000 ***	0.000	−3.126
P65	0.053 ***	0.009	5.512	0.065 ***	0.004	16.23	−0.407 ***	0.058	−6.910	0.015 ***	0.003	4.998
PO	0.013 ***	0.001	10.16	0.014 ***	0.002	6.562	0.007 ***	0.001	5.120	0.009 ***	0.000	20.01
UT	−0.031 *	0.0166	−1.906	−0.037 ***	0.009	−3.817	−0.075 ***	0.021	−3.505	−0.012 ***	0.001	−6.854

Notes: * p-value < 0.10 and *** p-value < 0.01.

.

The relationship between the cause of death and methane emissions. This inverse relationship is a function of the variation in the level of economic development and profile of public 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. The agricultural, energy, and industrial sectors cannot be compared by 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 cultivation, activities that are major methane sources in advanced agriculture. Other factors contributing to low methane emissions by countries of the region include a limited industrial infrastructure and dependence on hard fossil fuels, which preclude large-scale extraction and other energy-intensive activities. Overall, in most of the economies where infectious 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 12). 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 in industrialization [115,116,117].

The positive relationship between labor force participation and methane emissions. A positive relationship can also be seen in methane emission through increased economic activity 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 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 the workforce, thereby increasing the intensity of these very sources of emission. In addition, 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 the 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 13). 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 [118,119,120].

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 socioeconomic 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 paddies 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 is seen; this is essentially a setting dominated by healthcare and economically stable advanced countries, agricultural production, energy, and a 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 a greater production of methane (see Figure 14). This would, on the other hand, reflect the opposite relationship of mortality rates with methane; in other words, it would reflect a positive correlation between economic development, health status, and larger magnitude of activities leading to the production of methane by a more developed economy [121,122,123].

The relationship between populations aged 65 and above and methane emissions. The socioeconomic and demographic factors explain the positive relationship between an aging population and methane emissions: In developed countries, an aged population triggers an increased demand for health services and facilities, which are energy-intensive, generating organic wastes that decompose in landfills, emitting methane. Additionally, the increase 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 methane production via either enteric fermentation or manure management. In addition, 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 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 investments that could be made towards green technologies and modern agricultural practices, thereby reinforcing reliance on methane-intensive systems (Figure 15). To sum up, the aging of the population is related to higher emissions of methane due to the rise in energy and healthcare demand, livestock farming, problems in waste management, and a reduction in environmental investment [83,124,125].

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 the countries considered, an increase in percentage relates to a high number of overweight adults and signifies a diet mostly based on meat and dairy products, feeding into high levels of methane emission. Livestock farming has been reported for its contribution of 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 agriculture to sustain high productivity, thereby serving to further increase methane release. In addition to the production of livestock, a 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 agriculture and livestock raising and hence high energy use (Figure 16). These various facts, put together, explain health consequences with the associated impact on the environment [126,127,128].

The relationship between unemployment and methane emissions. The inverse relation between the unemployment rate and methane is justified, because the labor force and economic activities are highly linked. For example, with a low unemployment rate, economic activities in agriculture, manufacturing, and the production of energy run on higher scales. These sectors—primarily intensive livestock farming, rice cultivation, and extraction of fossil 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, the energy demand is lower, and less fossil fuel is extracted, thereby lowering methane emissions (see Figure 17). Put simply, as employment increases, the activities that drive methane emissions expand, and high unemployment suppresses those activities, thus yielding lower emissions [1,129,130].

4.3. Methane Emissions and the G (Governance Component) Within the ESG Model

Regarding the relationship between methane emissions and the Governance component of the ESG model, the following equation is estimated:

$$Methane={\alpha }_1+{\beta }_1{\left(GE\right)}_{it}+{\beta }_2{\left(RAND\right)}_{it}+{\beta }_3{\left(STRENGHT\right)}_{it}+{\beta }_4{\left(VA\right)}_{it}$$

(3)

where i = 193 and t = [2011;2020].

Table 5. Panel data estimates for the Governance component.

	Weighted Least Squares			Random Effects			Pooled OLS			Fixed Effects
Variable	Coefficient	Std. Error	t-Ratio	Coefficient	Std. Error	z	Coefficient	Std. Error	t-Ratio	Coefficient	Std. Error	t-Ratio
Costant	−0.0007 ***	9.841	−7.624	0.008 ***	0.012	0.7176	−0.009 ***	0.001	−4.843	0.008 ***	0.0002	40.08
GE	−0.002 ***	0.0002	−9.498	−0.021 ***	0.0002	−87.28	−0.0367 ***	0.001	−32.30	−0.021 ***	0.0002	−86.74
RAND	0.001 ***	0.0002	5.618	−0.002 ***	0.0003	−6.978	0.020 ***	0.002	9.497	−0.002 ***	0.0003	−6.977
STRENGHT	5.425 ***	7.436	7.296	6.917 ***	1.673	41.34	0.0002 ***	9.121	23.96	6.89 ***	1.682	41.00
VA	0.0008 ***	0.0001	6.709	0.008 ***	0.0004	20.32	0.014 ***	0.001	7.239	0.008 ***	0.0004	20.21

Notes: *** p-value < 0.01.

provides the econometric results.

The relationship between government effectiveness and methane emissions. The inverse relationship between government effectiveness and methane emissions comes because effective 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 production, and waste management. Effective governments invest in monitoring technologies to find and fix methane leaks, incentivize the adoption of cleaner energy alternatives such as renewables, and foster practices for mitigating methane from agriculture. They would ensure adequate waste management systems with minimal leaking of methane from landfills. In addition, under such governance, full commitment to international agreements on climate would see the facilitation of funding toward the mitigation initiatives—clear systems for monitoring compliance toward the reduction targets for methane, for instance. Contrariwise, low government effectiveness mostly results in a lack of resource development, infrastructure, and regulation control mechanisms that help in reeling in the emissions (Figure 18). The bottom line, therefore, is that higher government effectiveness allows the systematic and successful implementation of methane mitigating strategies, hence resulting in lower overall emissions [131,132,133].

The relationship between R&D expenditure and methane emissions. The positive relationship 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. Energy sector: R&D investment in the energy sector may prolong the extraction and use of fossil fuel, especially natural gas, which has considerable leaks in CH₄ during extraction, processing, and transport. While technologies increase efficiency, at the same time, efficiency 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 emissions 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 mitigation strategies (Figure 19). This would create a balance between innovation and sustainable means of reduction in methane output [134,135,136].

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 methane-intensive activities. Countries with stronger legal protections, which support financial markets and economic growth, tend to engage in large-scale industrial, energy, and agricultural activities, major sources of methane emissions. A robust legal system facilitates 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, including intensive livestock farming and modern rice cultivation, both significant contributors to methane emissions. Urbanization and infrastructure development in these economies further drive methane emissions, particularly from waste management systems like landfills (see Figure 20). In summary, while strong legal rights promote economic stability and development, they are also associated with higher methane emissions due to the expansion of energy, agricultural, and industrial activities, highlighting the environmental challenges of economic growth [137,138,139].

The relationship between Voice and Accountability and methane emissions. The positive relationship 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 advancement, and infrastructure development, which are causes of increased methane emissions. Responsive governance in these countries leads to the growth of such methane-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 extraction, processing, and distribution. The high rate of urbanization in highly accountable countries significantly amplifies methane emissions, caused mainly by organic waste decomposition in landfills (Figure 21). 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 [140,141,142].

5. Implications of Findings

From the standpoint of ESG, it is 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, urge organizations and governments to 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, bringing it lower 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].

Social considerations in the ESG framework relate to accruing public health benefits of methane abatement. Methane is a precursor to ground-level ozone, among the 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 socioeconomic 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 [21,27,42].

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-capturing 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 a 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 socioeconomic 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. In addition, 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 [3,27].

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

6. Conclusions

Methane emission analysis via the ESG framework demonstrates the trickiness of this GHG problem. 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 the ESG performance generally keeps improving all over the world, methane emissions are seen to continue growing, especially in the agriculture and extraction of fossil fuel sectors, which proves the measures taken to be inefficient so far. Critical in this regard are the environmental factors that this study identifies, wherein the consumption of renewable 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 socioeconomic factors for emissions are some of the contributors to methane emissions. As healthcare is more developed and thus more industrial, which would relate to higher CH₄ emissions, economic disparity will then be related to increased emissions due to methane-intense industries. Countries that 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 methane have to be addressed and customized according to the stage of development at the national level of socioeconomic structuring. Energy transition, changing of energy sources to renewables in developed countries, would then become a priority against developed support for agriculture and reinforcing government capacity in developing ones. Process principles underlying the responsibilities and support mechanisms should be based on equity: more developed countries should provide full technology transfer, financial assistance, and capacity-building in developing countries in light of historical emissions. The 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 support and alternatives for the affected communities. What is required is for international cooperation to enhance the international framework for action, such as the Global Methane Pledge, supported by a financial and technology commitment to contribute to the global harmonization of regulations in the field of emissions reduction. The mitigation of methane needs complex technological innovation, good governance, policy equity, and international collaboration to align the environmental aspiration with economic growth and social welfare within the ESG paradigm.