Controlling air pollution by lowering methane emissions, conserving natural resources, and slowing urbanization in a panel of selected Asian economies.

The destruction of the earth's ecosystems is the most pressing issue globally. Carbon emissions account for nearly half of global air pollution. Methane is the primary source of ground-level ozone and a significant source of greenhouse gases (GHGs), with greater warming potential than carbon dioxide emissions. The study examines the impact of the different methane emissions (released by agriculture, energy, and industrial sectors), urbanization, natural resource depletion, and livestock production on carbon emissions in the panel of selected Asian countries for the period of 1971 to 2020. The results show that energy associated methane emissions, livestock production, natural resource depletion, and urbanization are the main detrimental factors of environmental degradation across countries. The causality estimates show the unidirectional relationship running from livestock production and agriculture methane emissions to carbon emissions, from total methane emissions and carbon emissions to urbanization and from urbanization to energy methane emissions and livestock production. The forecasting estimates suggest that total methane emissions, natural resource depletion, and urbanization will likely increase carbon emissions over the next ten years. The study concludes that the energy sector should adopt renewable energy sources in its production process to minimize carbon emissions. Urbanization and excessive resource exploitation must be curtailed to attain carbon neutrality.

1. Introduction

Climate change undoubtedly has negative repercussions for individuals in less developed countries since their chances of survival in the event of a natural catastrophe, or climate change are likely to be lower than in affluent ones [1, 2]. As climate change is unavoidable and must occur, the challenge is to exist in unpredictable circumstances while avoiding the greatest amount of harm [3, 4]. Recent climatic catastrophes have directly impacted underprivileged people and nations [5, 6]. Carbon dioxide and methane are gases found in the environment. Naturally, these gases are in a balanced state. Due to industrialization, these gases became unbalanced, transforming them into poisonous gases that deteriorated the state of the atmosphere and are now driving climate change through global warming [7, 8]. Global warming has concentrated the heat in the atmosphere, damaged the ozone layer, and is making the temperature rise, which causes water vapors and heavy rain, which causes rising sea levels and flooding [9, 10].

Carbon emissions account for more than half of all greenhouse gases produced when anything is burned in the open environment. At the same time, fossil fuels and industry are responsible for 89% of global CO2 emissions [11]. Carbon dioxide concentrations grew to 407.380.1 parts per million (ppm) in 2018 from 277 parts per million (ppm) in 1750. Despite the COVID-19 pandemic, carbon dioxide levels in the atmosphere reached a global record high of 412.5 parts per million (ppm), the sixth most significant rise in the past 63 years [12]. Carbon dioxide emissions have a devastating effect on the quality of the atmosphere. The COP26 climate conference begins in Glasgow on October 31st, 2021, providing global leaders with an opportunity to deliberate and commit to climate action. Nonetheless, coal, oil, and gas production and use will be subsidized by $5.9 trillion in 2020, and this figure is predicted to increase to $6.4 trillion by 2025 [13, 14]. All nations have pledged to maintain a temperature to limit global warming to 1.5 degrees since 2 degrees is a death sentence. At COP 26, almost $20 billion in money will be made available to help developing countries move away from coal and toward renewable energy [15]. Glasgow Climate Pact (COP26) introduced strong "building blocks" to progress the Paris Agreement through projects that may put the world on a low-carbon path. The Glasgow Pact requests triple adaptation and resilience spending. Not enough for poorer countries, but it would enhance financing for saving lives and livelihoods, which account for 25% of climate funds. One hundred three countries, including 15 major emitters, joined the Global Methane Pledge to cut methane emissions by 30% by 2030. Methane, a potent greenhouse gas, accounts for a third of current warming from human activities [16].

Climate change adaptation is critical for policy planning and effect evaluation. Adaptation is mainly determined by its features, especially its vulnerabilities and sensitivities. There is variation in the adaptation process that varies by location owing to differences in the features such as timing, type, and impact of adaptation processes and forms that may be characterized by a variety of characteristics such as timing, purposefulness, and effect [17-19]. Adaptation is not only about people; it also encompasses a series of activities undertaken by the group and society in order to accomplish their goals. Furthermore, since this collection of actions may be undertaken individually or collectively, governments can formulate any policy to address such crises and limit the loss of lives and property to their inhabitants due to the expansion of the social network [20, 21]. Climate change has two distinct approaches: adaptation and vulnerability reduction. Adaptation refers to our response to a calamity after it has impacted society and individual lives. What efforts have been made at the individual or societal level to mitigate the effects of climate change and minimize the associated losses? This strategy aims to mitigate the losses caused by climate change on a case-by-case basis. It entails risk mitigation, while vulnerability mitigation refers to the measures to lessen our vulnerability to climate change. By incorporating adaptation into the development process, we lower our risk of being impacted by climate change [22, 23]. Planned adaptation refers to the actions taken to mitigate the risks associated with climate change and capitalize on the benefits of global climate change [24].

It is suggested that human behavior and its associated set of actions in response to climate change are not prescribed. Different characteristics of adaptive behavior are determined, including the climate-sensitive domain, prediction, and kind. Diverse adaptability demonstrates no universally accepted standard for planning, assessing, and implementing preventive measures. Additionally, adaptation is characterized by the climatic context since it is contingent on the social, environmental, political, and climate factors in the studied region, all of which vary over time [25]. Due to practical restrictions, we cannot escape climate change’s influence on adaptation. As a result, adaptation cannot mitigate climate change [26].

South Asia is home to one-fourth of the world’s population, and this growth in population, poverty, and food insecurity makes South Asia particularly sensitive to climate change. This rising air temperature has resulted in climate extremism [27, 28]. South Asia supplies essential ecosystem services to its 1.7 billion people. However, climate change and land-use change endanger biodiversity and exacerbate climate change, with dire repercussions for humanity if we do not act against climate change and carbon emissions [29]. Urbanization is responsible for 0.51°C of daily warming, accounting for 7.41 percent of the near-term temperature increase [30]. Livestock is also a significant cause of deforestation each year. To feed the millions of animals, we need them to graze and create food for them. 70% to 75% of deforestation occurs due to livestock grazing and soy farming. Around 90 billion animals are transported, farmed, and slaughtered each year, accounting for an estimated 37% of greenhouse gas emissions [31]. Methane contributes 80 times more to global warming than carbon emissions, and its exposure results in the death of one million people each year. It is the primary source of greenhouse gases, hazardous chemicals, and air pollutants [32]. Economic growth always results in increased consumption and the depletion of natural resources in nations with abundant resources. There is an inverse U-shaped link between natural resource depletion and economic growth per capita. In comparison, the increased per capita income results in increased health spending and fatalities [33]. Pakistan is very susceptible to climate change due to its high exposure and inadequate adaptation ability. Being an agricultural-based economy, Pakistan would suffer the greatest losses in the agriculture sector due to climate change. According to data obtained in Punjab, 58% of farmers adjust their agricultural practices in response to climate change by switching to other crop kinds. The findings show that farmers use adaptation techniques based on their education, land area, weather forecasts, and family size [34].

The motivation of the study is to assess the damaging role of methane emissions as widely discussed in the recent COP26 platform. The production of methane, which contributes to global warming and is 80 times more potent than carbon emission, is a potent greenhouse gas. Reducing coal, oil, and gas emissions via methane abatement is efficient in saving costs. The Global Methane Pledge may assist in better comprehending agricultural and waste-related emissions and steps to mitigate them [35]. The Pledge establishes a limit of 1.5 degrees Celsius for methane emissions. Over one hundred countries have reduced methane emissions by thirty percent by 2030 [36]. A new initiative aims to stop methane from escaping from wells, pipelines, and other fossil fuel infrastructure sources. Both animals and landfills are major contributors to the production of methane. Since the beginning of the industrial revolution, carbon dioxide emissions have led to an increase in temperature of 1.1 degrees Celsius. Methane emissions have been on the rise since 2007, contributing thirty percent to the planet’s overall warming. Methane reduction is beneficial to carbon reduction initiatives. If pledges are turned into policies, achieving such reductions within the next eight years is possible [37, 38].

Based on the stated discussion, the following research questions need to be answered: First, do agriculture and industrial methane emissions adversely affect environmental quality? Methane emissions are mainly considered a driver of climate change, and agriculture is the predominant source of spreading it. Along with methane, toxic compounds are emitted during oil and gas production. Hence, there is a dire need to improve agricultural and industrial production processes to reduce methane and carbon emissions through green production. Second, has livestock production become the primary source of methane emissions? The demand for animal protein is exacerbated as the world’s population grows, contributing to livestock emissions. Hence, it is time to rethink agricultural practices and livestock production, which mainly enforce plant-rich diets as an alternative protein source to mitigate livestock emissions. Third, to what extent are energy-released methane emissions toxic for the environment? It is evident that non-renewable energy sources adversely affect the environmental sustainability agenda. Hence, switching from non-renewable fuels to renewable fuels is essential to achieve energy efficiency and carbon neutrality agendas worldwide. Finally, does natural resource depletion and higher urbanization increase carbon emissions? The overexploitation of natural resources and greater rural-urban migration are global challenges that economies face, which lead to increased climatic vulnerability. The efficient use of natural resources and green urbanization policies would be helpful to move forward towards sustainable development. The stated research questions led to the following research objectives for the study:

To find out how methane emissions from agriculture and industry affect the quality of the environment in a panel of Asian countries.

To analyze energy-related methane emissions as part of the environmental sustainability agenda.

To observe the impact of livestock emissions on air quality levels, and

To assess the impact of natural resource depletion and urbanization on environmental quality across countries.

Cointegration, Granger causality tests, and forecasting tools should be used to check the stated goals in a group of selected Asian economies that have been chosen.

2. Literature review

The literature is limited in explaining the effects of different sources of methane emissions on air quality levels. Emissions released by the energy sector, agriculture, and industrial sectors are damaging environmental quality. Earlier literature used different economic and environmental factors to assess the deteriorating environmental quality damaged by human-based activities. Climatic vulnerability further escalated through increased GHG emissions, which threatened the world. It should reduce the average global temperature to less than 1.5 degrees Celsius. For instance, a detailed literature review is presented here, i.e., Schipper [39] argues that the literature on linking development and adaptation to climate change does not provide sufficient guidance for policymakers. Hence raises numerous conceptual and practical issues, including the lack of information on whether achieving sustainable development is distinct from achieving adaptation. Inadequate cooperation between adaptive policy and science is another impediment. Existing adaptation efforts have emphasized responding to climate change rather than mitigating susceptibility to climate change. Therefore, development must play a role in addressing the underlying causes of vulnerability. In a case study conducted in Bangladesh, Brouwer et al. [40] examined the link between environmental risk, vulnerability, and poverty in a case study. Due to Bangladesh’s poverty and vulnerability to flooding, they conducted a household survey in southeast Bangladesh. On the bank of the River Meghna, which was living without flood protection, 700 flood plains were examined, and they were questioned about the flood damage and issues they were experiencing. The conclusion reveals that people with low incomes have a greater risk of flooding than those with higher incomes, implying that uneven asset distribution and income inequality signal a household’s proclivity for high risk and sensitivity to climate change. Patt and Schröter [41] suggest that effective adaptation is impossible without stakeholder participation. The author contends that if the person or society impacted by climate change and policymakers cannot agree on a policy to address the effects of climate change, and the beneficiary rejects the policy and does not adopt the policies offered by policymakers, the policy would eventually fail. A case study in Africa was undertaken in a workshop and a questionnaire survey; the outcome indicated that farmers and policymakers had divergent perceptions of the flood, which might jeopardize the policy. The research concluded that just informing individuals about the flood danger is ineffective. We must engage beneficiaries and educate them about climate change and its repercussions. Nielsen and Reenberg [42] argued that human adaptation to climate change is a complex process that varies by place and is influenced by economic and technical growth variables. Culture presents a significant obstacle to adopting tactics such as working on development projects, including women in economic activities, gardening, and labor in Northern Burkina Faso. Truelove et al. [43] found that floods and droughts are the two most significant variables affecting the farming community. Research conducted in Sri Lanka on farmers indicated that descriptive social norms strongly impacted the farmers’ goals. Drought risk perceptions are associated with an individual’s intentions to change any action, but village identity influences collective behavior. The findings are beneficial to concerned authorities, which may aid farmers in adapting ways to make the most use of scarce water resources.

Qayyum et al. [44] explored the link between financial development, renewable energy consumption, technical innovation, and carbon dioxide emissions in India from 1980 to 2019. The study’s findings indicate that while financial development positively correlates with carbon emissions, renewable energy, and technical innovations negatively correlate with carbon emissions. Urbanization contributes significantly to carbon emissions, resulting in a decline in the quality of the atmosphere. Thus, the government must design a strategy to encourage technological innovation to achieve financial growth while also improving the quality of the environment. Alola and Kirikkaleli [45] examine the effect of carbon emissions from fossil fuel combustion on the world average temperature using yearly data from 1851 to 2017. The data indicated that global carbon dioxide emissions increase the world’s average temperature due to the diverse periods in which carbon emissions are emitted worldwide. The study demonstrates the critical nature of the global surface temperature as the primary topic of discussion in international forums convened to address global warming concerns. Adebayo et al. [46] examine the impact of financial growth and globalization on coal use and environmental quality in South Africa from 1980 to 2017. When coal consumption and financial development increase by 1%, environmental deterioration is raised by 1.077 percent and 0.973 percent, respectively. Financial changes must be made if increasing renewable energy consumption and mitigating environmental damage are feasible. Mehmood et al. [47] examine the influence of tourism and globalization on carbon emissions in South Asian nations between 1996 and 2016, utilizing yearly data from 1996 to 2016. Globalization is how economies become more linked, and increased results in technical improvement and cleaner energy. Tourism’s influence on carbon dioxide emissions is decreasing due to globalization, which improves the quality of the atmosphere in South Asian countries. South Asian economies rely on fossil fuels for growth; these emerging nations must transition to cleaner energy sources to improve the quality of the environment. Qayyum et al. [48] study the influence of urbanization and the informal sector on South Asian nations’ ecological footprints. South Asian nations are rapidly urbanizing, posing a danger to the quality of their environment. The informal economy and urbanization are wreaking havoc on the environment. Rural populations must be educated enough to support autonomous local governments with solid governance to alleviate pressure from metropolitan areas, contributing to environmental deterioration. Usman et al. [49] examine the influence of agricultural output, economic expansion, and renewable and non-renewable energy sources on environmental deterioration. The findings indicate that agriculture production, economic growth, and renewable and non-renewable energy negatively impact the environment’s quality, as these variables act as a hazard to the environment’s quality. However, renewable energy has the potential to improve the environment’s quality. Consistent findings show that policymakers in South Asian countries should focus on achieving their long-term development objectives. Li et al. [50] found an inverse U-shaped link between carbon emissions and urbanization in China. The government should collaborate with neighboring nations on both sides of the Yangtze River delta to minimize carbon emissions. The geographical component, carbon intensity, GDP per capita, and population contribute significantly to CO2 emissions reduction. This discovery will aid in developing low-carbon urbanization. According to Hashmi et al. [51], urbanization and population agglomeration support ecological modernization. While this urban agglomeration improves the environment’s quality over time, excessive concentration degrades the city’s environmental quality and efficiency. Additionally, economic growth and energy intensity increase CO2 emissions, while trade openness has a negative effect on CO2 emissions in Asia. Anwar et al. [52] concluded that attaining sustainable development objectives remains a difficulty for Asian countries since they trail behind other economies in this regard. The primary cause is environmental deterioration. According to the findings, urbanization and economic expansion raised carbon emissions, while renewable energy reduced them, and agriculture has a negligible effect on reducing carbon emissions. Policymakers should prioritize policies aligned with the SDGs to accomplish the goals. Zhang et al. [53] stated that rapid industrialization in China has led to urbanization and increased carbon emissions. Economic growth, population growth, and new residential construction contribute to CO2 emissions. The impact decreases as one moves from urban to less urbanized regions. The inflation rate is higher in urbanized areas than in other regions, and the urbanization ratio is negatively correlated with carbon emissions. Hence, the need for smart socio-economic and environmental policies would help move towards a carbon neutrality agenda.

The following are recent studies that mainly used methane emissions as a source of climatic vulnerability and GHG emissions. Sohoo et al. [54] argued that a green developmental agenda could be achieved by reducing waste disposal, as waste disposal sites mainly increase methane emissions, which adversely affect the sustainable production agenda. Tarazkar et al. [55] collected data from a panel of 11 OPEC countries from 1995 to 2012 to assess the main detrimental factors that increase methane emissions. The results found that energy consumption and crop and livestock production are the main ecologically damaging factors that increase methane emissions across countries. The need to switch conventional energy sources to renewable energy sources and plant-rich protein diets would be helpful to reduce methane emissions. Pata [56] considers a case study of BRIC countries and collects data from 1971–to 2006 to assess the main determinants of carbon emissions. The results show that, in general, globalization is the factor causing higher air pollution levels, while renewable energy sources improve environmental quality and reduce carbon intensity across countries. Green energy sources should be included in the national energy grid to achieve a zero-carbon agenda. Chojnacka et al. [57] conclude that sustainable agricultural farming can tackle environmental issues and move towards green livestock farming. Wang [58] collected data from the Chinese economy from 1985 through 2019 to assess the main contributing factors to agricultural growth. The results show that agricultural exports, energy consumption, and environmental stressors are the crucial factors that influence agricultural productivity. Technological up-gradation in agricultural practices and green energy sources are pivotal for improving agricultural value-added in a country. Im et al. [59] found that manure methane emissions increase by around 1.5 to 2 times due to increased automatic temperatures. Methane emissions can be reduced by cooling that offsets global GHG emissions. Based on the cited literature, the following hypotheses have been proposed:

.

.

The overexploitation of natural resources and massive urbanization jeopardize environmental quality. For instance, several studies discussed the stated nexus in different economic settings with various factors. Hung et al. [60] argued that carbon emissions increase through globalization, financial development, and overexploitation of natural resources in Vietnam. The study emphasized the need to balance economic and environmental resources efficiently in production to achieve a zero-emissions target. Chopra et al. [61] consider a case study of ten selected ASEAN countries where different factors cause sustainable agriculture value-added. The results show that the higher the carbon intensity level, deforestation, and overexploitation of natural resources, the more substantially agricultural productivity declines across countries. On the other hand, green energy sources in agricultural production and farming practices help increase agricultural yield, which remains a step towards sustainable farming. Chen et al. [62] evaluated the green developmental agenda using different economic and environmental factors in BRICS nations between 1990 and 2019. They discovered that ongoing economic expansion, rising energy consumption, overcrowding, and urbanization contributed significantly to increased carbon emissions. The study emphasized the need to include green energy sources in national energy grids to sustain economic growth and manage urbanization issues across countries. Shaheen et al. [63] collected data from high-income countries from 1976 to 2019 to assess the relationships between inward FDI, ICTS, R&D expenditures, and economic growth and their resulting impact on carbon emissions. The results confirmed the N-shaped relationship between economic growth and carbon emissions, with the latter controlling the other factors. The pollution haven hypothesis, energy-associated emissions, and technology embodied emissions are confirmed with different statistical techniques. The results emphasized the need to move towards cleaner production technologies, which amalgamate renewable energy sources, climate financing, and innovation to sustain environmental resources. Sadiqa et al. [64] concluded that high-income countries required carbon pricing instruments to limit emissions. Furthermore, they require more investment in renewable energy sources, financial development, and sustainable trade policies to improve environmental quality. Based on the cited studies, the following hypotheses need to be tested:

.

Wang et al. [30] argued that in urban agglomerations, various demographic trends have moulded the distinctive character of places, raising questions about the link between urbanism and ecological dangers. Cross-scale research on the effects of unbalanced urban sprawl will help us better grasp the relationship between rural-urban migration and societal reasons. Addai et al. [65] suggested that natural resource exploitation and unregulated demand are unhealthy in sovereign countries and trading blocs. Policy measures, including improving existing structures involving extractive industries, water contamination, soil erosion, and compliance along the development route, are pivotal for sustainable development. Mata et al. [66] concluded that the development of sound legislation governing Thailand’s energy and industrial sectors will benefit the country. The regulation of companies and institutions in the industrial sector might help reduce CO2 emissions, and draconian fines might dissuade those who break the law. Hydroelectricity, wave energy, and wind power is renewable energy sources that sustain the ecology of Thailand for better shaping resource planning. Liu et al. [67] analyzed the vulnerability of rapid urbanization in China’s economy, causing a greater pressure on environmental resources, leading to exacerbate PM2.5 emissions. Policies to improve environmental quality required stringent ecological policies that help to move forward towards COP26 policy guidelines. Xu et al. [68] concluded that a complete ecological evaluation is needed to alleviate environmental concerns and achieve the SDGs. Despite public comprehension of climate change and its mitigation, worldwide emissions and other environmental contaminants have not declined appreciably. Policymakers should establish measures to open the economy to international investment. Aslan et al. [69] suggested that Turkish economy must prepare for income-driven shifts in consumption and insulate new and existing buildings and decrease automotive carbon emissions. Most vital is to assure energy efficiency, establish renewable energy sources, and reduce fossil fuel use, which helps move the country towards green development.

Based on the cited literature, the study filled the existing literature gap(s) by using different sources of methane emissions in the pollution damage function, including energy methane emissions, agriculture methane emissions, and industrial methane emissions. The earlier studies were mainly limited to single sources of methane emissions in environmental modeling, which remains the thrust to add more sources of methane emissions for sound policy inferences [70-72]. Further, the study used livestock production index as an additional factor causing more methane emissions. The stated factor is mainly related to agricultural methane emissions and is considered crucial to increase GHG emissions. A few studies included livestock emissions coupled with different sources of methane emissions in environmental pollution modeling [73-75]. Finally, the study used natural resource depletion and urbanization as control variables, which directly linked it to the increased emissions released into the atmosphere due to over-exploitation of natural resources and poor urban planning. A few studies added the stated factors in emissions modeling and methane emissions across countries [76-78]. Based on the novelty of using the stated factors, the study evaluated all the factors in a panel of selected Asian economies mainly affected by rising methane emissions, natural resource depletion, urbanization, and carbon emissions. The study looked at how environmental modeling factors could be used across countries to support a green and clean goal.

3. Data sources and methodological framework

The study used carbon emissions (denoted by CO2) as a response variable, whereas agricultural methane emissions, industrial methane emissions, energy methane emissions, and livestock emissions were used as regressors. Further, the study used two controlled variables: natural resource depletion and urbanization, as both of these factors also caused environmental damage. The study collected data from a panel of six Asian economies, namely Bangladesh, India, Pakistan, Nepal, Afghanistan, and Sri Lanka, while excluding the Maldives and Bhutan economies due to the non-availability of the data for the concerned variables. The data was collected from 1971 through 2020 from the World Development Indicators, published by the World Bank [79]. Table 1 shows the list of variables and their measurement.

Table 1

List of variables.

Variable	Symbol	Measurement	Definition
Carbon emissions	CO2	Kiloton (kt)	When fossil fuels are burnt and cement is made, emissions are generated. Coal, oil, and gas are all sources of carbon dioxide, as is gas flaring.
Agriculture methane emission	AGM	% of total	Cattle, animal feces, rice farming, agricultural residue burning, and habitat burning emit methane.
Methane emission	TME	Kt of CO2 equivalent	Human-caused methane emissions include those from agricultural and industrial methane generation.
Methane emission in energy sector	EME	Thousand metric tons of CO2 equivalent	The manufacturing, refining, transportation, exhaust fumes, and biofuels emit methane.
Livestock production	LVS	Livestock production index (2014–2016 = 100)	The livestock production index includes all meat, milk, and dairy products.
Natural resource depletion	NRD	% of GNI	Natural resource depletion is the sum of forest, energy, and mineral losses.
Urbanization	URB	% of urban population	The fraction of a country’s urban population lives in its greatest metropolitan district.

The study’s goal is to look at the state of the environment in a sample of South Asian economies. Most countries are still growing up and making progress, but these countries are always going to become more industrialized, which hurts the environment in the long run. In December 2021, the COP meeting gave $20 billion in grants to developing countries to help them keep the environment clean. In 2014, 55.3 MT of methane emissions were produced by China. That accounts for 10.4 percent of the country’s total CO2 emissions and is only surpassed by CO2 emissions (81.6 percent). Methane is a less familiar gas than carbon dioxide, and methane emissions may be controlled to decrease global warming. According to the US-China Joint Glasgow Declaration, China will implement policies, technology, regulations, and a means for detecting, measuring, accounting for, and verifying emissions to reduce methane emissions to the lowest possible levels. Second, these nations have a large workforce since South Asia accounts for over 25% of the world’s population. Thus, an excessive population results in more activity, which results in increased carbon emissions. Climate extremes in storms and floods affect over 600 million absolute poor people. More than half of the world’s poor people rely on industries that are especially vulnerable to climate change, like farming, wood harvesting, and traditional fish fishing, for most of their daily needs. These industries are especially vulnerable to climate change. The study looked at the Asian economies and found that they had many environmental problems.

3.1. Theoretical structure

A conceptualization of the link between environmental quality and technological innovation has been developed due to the Ehrlich-Holdren/Commoner debate in the early 1970s. This equation is known as the IPAT equation. IPAT illustrates that population (P), affluence (A), and technology (T) all have an impact on environmental quality. When Ehrlich and Holden [80] showed the link between environmental quality and the population, they attempted to disprove the primary assumption of the time, which was that the population is a minimal contribution to environmental damage. According to this theory, the population is the primary underlying factor contributing to environmental contamination.

IPAT serves as the theoretical framework for our investigation. The study assessed environmental quality by using different methane emissions, natural resource depletion, and urbanization in the IPAT formulation. The more urbanization occurs, the more people will live in the city, resulting in a congested population. Methane emission is concerned with the production of methane, which damages the country’s ecological affluence. Hence, the study substituted affluence with the methane emissions factors. Usually, the pollutants emitted from these factors have the most significant influence on the ecosystem. For example, the amount of harm caused by methane emissions, urbanization, livestock, and natural resource depletion all significantly impacts the environment. The contribution of each of these three IPAT variables is compared arithmetically in this study. Population (urbanization), affluence (methane and cattle), and technological advancement (natural resource extraction) are all factors to consider in IPAT formulation. Urbanization combined with methane emissions from animals and the loss of natural resources equals a high amount of pollution.

3.2. Framework for econometric analysis

When it comes to panel data analysis, the panel cross-sectional dependency test needs to be checked to assess the right choice of cointegration tests to apply. Next, we should use the second generation unit root test. The study checked the following four CSD tests, i.e.,

Breusch-Pagan LM

Pesaran scaled LM

Bias-corrected scaled LM, and

Pesaran CD

The significant probability values would confirm that the cross-sections have some dependency due to the structural shocks of the given period. The second generation of panel unit root tests is intended to overcome the weakness of cross-sectional dependency present in the first generation of panel unit root tests. In this respect, all of the tests, except Bai and Ng [81], assume that the data has a unit root. The heterogeneity assumption serves as the foundation for the second-generation tests. The null hypothesis in the panel root test is tested by the majority of the panel unit root tests. Formulating an alternative hypothesis is a significant problem depending on the tested hypothesis. First-generation tests are based on the idea that cross-section units are independent of one another throughout time. However, the second generation of panel unit root testing is less restrictive, allowing cross-sectional dependency tests to be performed. Cross-sectional dependency was a drawback of the first generation of panel unit root tests, and the second generation of panel unit root tests tries to address this issue. In this respect, all of the tests, except Bai and Ng [81], assume that the data has a unit root. The heterogeneity assumption serves as the foundation for the second-generation tests. Compared to the first generation of tests produced by Levin and Lin [82], the second generation tests are less restricted and powerful [83] The first-generation tests have a disadvantage in that they do not account for variability in the autoregressive coefficient. However, by assuming heterogeneity between units in a dynamic panel framework, the tests suggested by IPS enabled the solution of Levin and Lin’s serial autocorrelation issue. The study used Pesaran’s CDAF test for unit root analysis.

Specific drawbacks of the first generation test are addressed by the second generation of the panel root test, which is an improvement over the first generation. Except for the Bai and Ng [81] test, all other tests in the second generation assume that the data has a unit root. When looking at the panel, there is the heterogeneity, and the structure is autoregressive (AR). The cross-sectional dependency in the data is not robust when using the frequently used panel cointegration tests to examine the data. We employ the cointegration test developed by Westerlund [84] to determine resilience in the presence of cross-section dependence. When cointegration is absent, a second-generation panel cointegration test determines whether or not there is error correction over the whole panel in the absence of cointegration. Further, the study used Pesaran-Yamagata [85] slope homogeneity test for further confirmationof CSD among panel of countries.

In this study, fully modified OLS and dynamic OLS are employed because they are more trustworthy in estimating the long-run connection in the panel data. Kao and Chiang [86] demonstrated that the OLS and FMOLS estimators have negligible sample biases. However, the DOLS estimator seems to outperform the other estimation methods. The DOLS may solve the problems of endogeneity bias and serial correlation. The following regression equation is used for empirical testing, i.e.,

Where, CO2 shows carbon missions, TME shows total methane emissions, EME shows energy methane emissions, AGM shows agricultural methane emissions, LVS shows livestock production, NRD shows natural resource depletion, URB shows urbanization, ln shows natural logarithm, ‘i’ shows cross-sections, and ‘t’ shows time period.

The expected results of the equation are as follows:

The higher the total methane emissions, the greater the increase in carbon intensity.

The higher the energy associated emissions, the larger the share of carbon emissions in an atmosphere.

The higher the agriculture methane emissions, the greater the carbon emissions.

The greater the livestock production, the larger the carbon intensity.

The greater the natural resource depletion, the larger the carbon emissions, and

The greater the urbanization, the larger the carbon emissions intensity.

Based on the expectations, the study calculated the FMOLS and DOLS estimator parameter estimates and evaluated the results for possible policy implications. Furthermore, the study employed the Dumitrescu Hurlin panel causality test for the possible causal inferences, i.e.,

Carbon emissions Granger causes methane emissions, livestock production, natural resource depletion, and urbanization, which refer to unidirectional causality between the stated variables.

Methane emissions, livestock production, natural resource depletion, and urbanization Granger causes carbon emissions, referring to reverse causality between the variables.

A bidirectional causality relationship can exist between the candidate variables, and

No causal relationships were established between the variables, although highly correlated.

The ordinary Granger causality test is performed under the panel vector autoregressive framework, mentioned in Eq (2), i.e.,

Eq (3) is simplified by VAR(2) model for multivariate system, i.e.,

Based on the stated equations, the possible causal outcomes can be checked and are likely to get any one of them verified with it. Finally, the study employed Impulse Response Function (IRF) and Variance Decomposition Analysis (VDA) to forecast the variables’ relationships over the next ten years. The IRF test suggests that the regressors positively or negatively impact the outcome variable over the time horizon. On the other hand, VDA suggests that innovation shocks influence outcome variables by their regressors to have a greater magnitude of influence on the regressand over time. Eq (4) shows the VDA decomposition for ready reference, i.e.,

Eq (5) shows the mean square error term between the regressors, i.e., Where, MSE shows mean square error.

4. Result and discussion

Table 2 shows the descriptive statistics of the variables. The average value of carbon emissions is 185060.2 kilotons, with a maximum value of 2434510 kilotons and a minimum value of 198.018 kilotons. The average value of agriculture methane emissions, total methane emissions, and energy-associated methane emissions is 72.424% of total emissions, 129855.1 kilotons of carbon equivalent, and 18268.73 thousand metric tonnes of carbon equivalent. The livestock production index value is around 63.436 on average. Natural resource depletion and urbanization have a maximum value of 3.991% of GDP and 59.2475% of the total population, with a minimum value of 0.029% and 5.490%, respectively. The average resource depletion and urbanization values are 0.797% and 24.845%, respectively. Except for agriculture methane emissions, all other variables have a positively skewed distribution. On the other hand, carbon emissions have a higher kurtosis value that shows the peak of the distribution, followed by natural resource depletion, agriculture methane emissions, energy methane emissions, and total methane emissions. The overall statistics give the trend behavior of the variables that would be helpful to move forward to estimate correlation analysis between the variables.

Table 2

Descriptive statistics.

Methods	CO2	AGM	TME	EME	LVS	NRD	URB
Mean	185060.2	72.424	129855.1	18268.73	63.436	0.797	24.845
Maximum	2434520	88.559	666510	104550	117.490	3.991	59.247
Minimum	198.018	26.395	7415.090	519.178	17.900	0.029	5.490
Std. Dev.	457559.1	13.975	194732.9	29054.61	26.528	0.605	13.297
Skewness	3.359	-1.532	1.798	1.779	0.325	1.497	0.563
Kurtosis	14.190	4.801	4.643	4.735	2.133	6.863	2.910

Note: CO2 shows carbon emissions, AGM shows agriculture released methane emissions, TME shows total methane emissions, EME shows energy released methane emissions, LVS shows livestock production, NRD shows natural resource depletion, and URB shows urbanization.

Table 3 shows that methane emissions, including agricultural methane, industrial methane, and energy-related methane emissions, are positively correlated with carbon emissions, with correlation coefficients of r = 0.112, r = 0.865, and r = 9.851, respectively. Additionally, both livestock production and natural resource depletion are positively connected with carbon emissions, indicating that both variables contribute to carbon emissions in a panel of selected Asian countries. The negative correlation between urbanization and carbon emissions suggests that widespread urbanization does not increase carbon emissions. However, this finding should be more critically evaluated in the regression estimations to suggest appropriate policy implications. Depletion of natural resources is positively connected with agricultural, industrial, and energy-related emissions, but urbanization increases livestock output across nations. The correlation matrix depicts the relationships between variables that need a more thorough examination of the regression apparatus’s claimed findings.

Table 3

Panel correlation matrix.

Variables	CO2	AGM	TME	EME	LVS	NRD	URB
CO2	1
AGM	0.112 (0.051)	1
TME	0.865 (0.000)	0.178 (0.001)	1
EME	0.851 (0.000)	0.010 (0.849)	0.937 (0.000)	1
LVS	0.193 (0.000)	-0.334 (0.000)	-0.048 (0.406)	0.092 (0.111)	1
NRD	0.389 (0.000)	0.426 (0.000)	0.509 (0.000)	0.441 (0.000)	-0.153 (0.007)	1
URB	-0.514 (0.000)	-0.136 (0.017)	-0.641 (0.000)	-0.538 (0.000)	0.224 (0.000)	-0.309 (0.000)	1

Note: CO2 shows carbon emissions, AGM shows agriculture released methane emissions, TME shows total methane emissions, EME shows energy released methane emissions, LVS shows livestock production, NRD shows natural resource depletion, and URB shows urbanization. Small bracket shows probability value.

Table 4 shows the cross-sectional dependence (CSD) test before using the panel cointegration technique. By using all four CSD tests, it is evident that there exists a cross-sectional dependence. The Breusch-Pagan LM test, Pesaran scaled LM, and Bias-corrected scaled LM tests significantly reported all the stated variables at a 1% confidence level, which infers that the CSD exists among the countries. On the other hand, except for agricultural methane emissions and urbanization, the remaining variables are significant at 1% in the Pesaran CD test. Hence, overall, the study shows that the selected Asian countries have a cross-sectional dependence between them. Thus, the research used second-generation panel cointegration tests to get credible parameter estimates.

Table 4

Cross-sectional dependence test estimates.

Tests	CO2	AGM	TME	EME	NRD	URB	LVS
Breusch-Pagan LM	594.571 (0.000)	284.870 (0.000)	279.843 (0.000)	330.852 (0.000)	163.920 (0.000)	220.877 (0.000)	642.188 (0.000)
Pesaran scaled LM	105.814 (0.000)	49.271 (0.000)	48.353 (0.000)	57.666 (0.000)	27.188 (0.000)	37.587 (0.000)	114.508 (0.000)
Bias-corrected scaled LM	105.756 (0.000)	49.210 (0.000)	48.292 (0.000)	57.605 (0.000)	27.127 (0.000)	37.526 (0.000)	114.447 (0.000)
Pesaran CD	24.189 (0.000)	1.677 (0.093)	5.133 (0.000)	12.494 (0.000)	8.146 (0.000)	-0.745 (0.456)	25.253 (0.000)

Note: CO2 shows carbon emissions, AGM shows agriculture released methane emissions, TME shows total methane emissions, EME shows energy released methane emissions, LVS shows livestock production, NRD shows natural resource depletion, and URB shows urbanization. Small bracket shows probability value.

Table 5 shows the Pesaran and Yamagata [85] panel homogeneity test and found that delta and adjusted delta value fall in the critical region of 1% significance level. Hence, we reject the homogeneity among the cross-sections while accepting the alternative hypothesis of heterogeneity among the sample countries. Hence, second generationcointegration test is vital for obtaining parameter estimates.

Table 5

Pesaran and Yamagata slope homogeneity test estimates.

Delta	Statistics	Prob. value
Δ	14.212	0.000
Δadjusted	15.507	0.000

Table 6 shows the second generation unit root test estimates. We checked for significance at both the level and first difference of all the stated variables and discovered that they are all significant at first difference. Thus, we can safely conclude that the order of integrating the variables is one, i.e., I(1) variables. Carbon emissions, natural resource depletion, and urbanization are all significant at a 10% confidence level, which is not included in the analysis since the acceptable significance level for parameter estimations are set at a 5% confidence interval. The unit root estimates are obtained for the variables at various lags operators.

Table 6

Pesaran’s CADF unit root test estimates.

Variables	Level	First Difference	Decision
CO2	-2.346 (0.070)	-4.027 (0.000)	I(1) variable
AGM	-1.015 (0.977)	-5.007 (0.000)	I(1) variable
TME	-0.856 (0.992)	-5.023 (0.000)	I(1) variable
EME	-1.786 (0.494)	-2.692 (0.009)	I(1) variable
LVS	-1.048 (0.972)	-5.108 (0.000)	I(1) variable
NRD	-2.373 (0.061)	-6.190 (0.000)	I(1) variable
URB	-2.189 (0.663)	-2.433 (0.044)	I(1) variable

Note: CO2 shows carbon emissions, AGM shows agriculture released methane emissions, TME shows total methane emissions, EME shows energy released methane emissions, LVS shows livestock production, NRD shows natural resource depletion, and URB shows urbanization. Small bracket shows probability value.

After confirmation of the variable’s order of integration, the study used the Westerlund cointegration test and Johansen Fisher panel cointegration test and presented the estimates in Table 7.

Table 7

Westerlund and Johansen Fisher panel cointegration test estimates.

Westerlund Cointegration Test Estimates
Statistics	Value		Z-value	P-value
Gt	-3.140		-0.776	0.219
Ga	-15.073		0.549	0.709
Pt	-10.434		-3.830	0.000
Pa	-15.854		-0.690	0.245
Johansen Fisher Panel Cointegration Test
Hypothesized	Fisher Statistics*		Fisher Statistics*
No. of CE(s)	(from trace test)	Prob.	(from max-eigen test)	Prob.
None	145.1	0.0000	87.76	0.0000
At most 1	74.15	0.0000	36.85	0.0002
At most 2	42.27	0.0000	25.62	0.0121
At most 3	22.64	0.0310	11.78	0.4637
At most 4	16.49	0.1696	9.185	0.6870
At most 5	15.32	0.2242	13.73	0.3186
At most 6	16.72	0.1604	16.72	0.1604

Note

* indicates the number of cointegrating equations.

The results rejected the null hypothesis of no cointegration relationship between the candidate variables and accepted the alternative hypothesis, as panel statistics, i.e., Pt value is significant at 1% confidence interval. Hence, we safely conclude that the model has a long-term relationship with the variables. Further, Johansen Fisher cointegration test confirmed four cointegrating equations in the trace test and three cointegrating equations in the maximum Eigen test, hence, the results safely conclude the cointegration relationship exists between the variables. The study estimated panel FMOLS estimator in Table 8 for ready reference.

Table 8

Panel FMOLS estimates.

Dependent Variable: CO2
Variable	Coefficient	Std. Error	t-Statistic	Prob.
AGM	-0.012144	0.230279	-0.052736	0.9580
TME	-1.019415	0.169609	-6.010387	0.0000
EME	0.840404	0.104439	8.046855	0.0000
LVS	1.471288	0.086402	17.02845	0.0000
NRD	0.148111	0.061384	2.412869	0.0165
URB	0.987763	0.410484	2.406335	0.0168
Statistical Tests
R2	0.980609	Mean dependent var		9.806555
Adjusted R2	0.979853	S.D. dependent var		2.248119

Note: CO2 shows carbon emissions, AGM shows agriculture released methane emissions, TME shows total methane emissions, EME shows energy released methane emissions, LVS shows livestock production, NRD shows natural resource depletion, and URB shows urbanization. The variables are in natural logarithm.

The findings indicate that energy-related emissions, livestock production, natural resource depletion, and urbanization all have a significant role in driving up carbon emissions in a panel of selected Asian nations. The livestock production index has the greatest influence on carbon emissions, with an estimated elasticity of 1.471 percent, followed by urbanization, which has a nearly one-to-one relationship with carbon emissions. Additionally, a 1% increase in energy-related methane emissions and natural resource depletion increases carbon emissions by 0.840 percent and 0.148 percent, respectively. Total methane emissions that account for a greater proportion of industrial methane emissions reduce carbon emissions due to more efficient industrial production processes that emit less methane and improved environmental quality through the use of cleaner production technologies. Table 9 summarizes the panel DOLS estimates.

Table 9

Panel dynamic OLS estimates.

Dependent Variable: CO2
Variable	Coefficient	Std. Error	t-Statistic	Prob.
AGM	0.136461	0.176896	0.771421	0.4416
TME	-1.161085	0.131782	-8.810659	0.0000
EME	1.021163	0.088960	11.47895	0.0000
LVS	1.487932	0.118386	12.56843	0.0000
NRD	0.134268	0.050708	2.647876	0.0089
URB	2.247474	0.399012	5.632599	0.0000
Statistical Tests
R2	0.995325	Mean dependent var		9.805529
Adjusted R2	0.991890	S.D. dependent var		2.241747

Note: CO2 shows carbon emissions, AGM shows agriculture released methane emissions, TME shows total methane emissions, EME shows energy released methane emissions, LVS shows livestock production, NRD shows natural resource depletion, and URB shows urbanization. The variables are in natural logarithm.

The DOLS findings are comparable to the FMOLS estimations but of different magnitude. Energy-related methane emissions, livestock production, urbanization, and resource depletion contribute significantly to carbon emissions that impair the natural environment. The most significant impact has been on environmental quality via increased carbon emissions, livestock production index, energy methane emissions, and natural resource depletion. Increases of 1% in the following components result in increases of 2.247 percent, 1.487 percent, 1.021 percent, and 0.134 percent in carbon emissions, respectively. On the other hand, total methane emissions significantly reduce carbon emissions as a result of the Asian economies’ adoption of several greening policies, including the use of renewable energy in manufacturing processes [87], cleaner manufacturing technologies [88, 89], and climate financing [90]. The diagnostic statistics in Table 10 reveal that the model has not multicollinearity since each regressors’ variance inflation factor (VIF) value is less than the threshold value of 10. The findings indicate that the residual is normally distributed and that the suggested model does not exhibit autocorrelation. As a result, the regression coefficients are both reliable and efficient.

Table 10

Multicollinearity, normality, and autocorrelation test estimates.

Variable		Coefficient		Uncentered
		Variance		VIF
AGM		0.031		1.611
TME		0.017		3.391
EME		0.007		6.205
LVS		0.014		1.528
NRD		0.002		1.567
URB		0.159		2.130
VAR Residual Serial Correlation LM Tests
Lags	LM-Stat		Prob
2	43.860		0.6810
3	49.210		0.4647
Normality Test
Jarque-Bera	4.6147		0.099

Note: CO2 shows carbon emissions, AGM shows agriculture released methane emissions, TME shows total methane emissions, EME shows energy released methane emissions, LVS shows livestock production, NRD shows natural resource depletion, and URB shows urbanization. The variables are in natural logarithm.

After evaluating the regression estimates, Table 11 shows the Granger causality estimates and found the unidirectional causality is running from livestock production and agriculture methane emissions to carbon emissions supported the notion of ‘agricultural methane led carbon emissions.’ Further, total methane and carbon emissions Granger cause urbanization, whereas urbanization Granger causes energy methane emissions and livestock production. The result implies that the greater pressure of rural-urban migration on environmental and resource factors tends to degrade by increasing energy-associated methane release and livestock methane emissions. On the one hand, the bidirectional causality is running between energy ethane emissions and carbon emissions. On the other hand, the feedback relationship was found between natural resource depletion and livestock production. Finally, urbanization has a two-way linkage with natural resource depletion across countries. The results infer that energy-associated methane and carbon emissions move together and complement each other. On the other hand, natural resource depletion causes livestock emissions, while livestock production causes greater resources depletion. Similarly, urbanization and natural resource depletion affect each other and cause more environmental damages across countries.

Table 11

Dumitrescu Hurlin panel causality test estimates.

Null Hypothesis: ҂ Alternative Hypothesis: ↔ or →	W-Stat.	Zbar-Stat.	Prob.
AGM → CO2	4.34890	2.50381	0.0123
CO2 ҂ AGM	0.93865	-1.28887	0.1974
EME ↔ CO2	4.25258	2.39668	0.0165
CO2 ↔ EME	3.97241	2.08509	0.0371
LVS → CO2	4.99812	3.22582	0.0013
CO2 ҂ LVS	2.51212	0.46105	0.6448
URB ҂ CO2	2.32955	0.25800	0.7964
CO2 → URB	5.54858	3.83801	0.0001
URB ҂ TME	2.04863	-0.05442	0.9566
TME → URB	4.99163	3.21861	0.0013
URB → EME	4.08396	2.20915	0.0272
EME ҂ URB	1.65278	-0.49465	0.6208
NRD ↔ LVS	3.71989	1.80425	0.0712
LVS ↔ NRD	5.20433	3.45516	0.0005
URB → LVS	3.78144	1.87271	0.0611
LVS ҂ URB	3.42629	1.47773	0.1395
URB ↔ NRD	4.68778	2.88069	0.0040
NRD ↔ URB	4.19086	2.32804	0.0199

Note: CO2 shows carbon emissions, AGM shows agriculture released methane emissions, TME shows total methane emissions, EME shows energy released methane emissions, LVS shows livestock production, NRD shows natural resource depletion, and URB shows urbanization.

Table 12 shows the IRF estimates and suggests that total methane emissions, natural resource depletion, and urbanization would be the critical factors likely to increase carbon emissions over time. On the other hand, agriculture methane emissions and livestock production are likely to decrease carbon emissions for the next ten years. Energy-associated methane emissions are likely to positively impact environmental quality in the next eight years while deteriorating after 2030.

Table 12

IRF estimates of CO2 emissions.

Period	CO2	AGM	TME	EME	LVS	NRD	URB
1	15046.15	0	0	0	0	0	0
2	17323.84	-55.032	1158.318	-1446.226	-183.953	1148.042	203.689
3	18226.06	-405.232	1661.989	-1622.974	-154.005	2471.951	394.671
4	18871.92	-757.228	2176.059	-1485.889	-200.734	3449.595	539.294
5	19428.11	-1074.781	2670.701	-1219.646	-278.810	4215.696	639.101
6	19945.43	-1373.000	3156.773	-889.922	-372.842	4824.839	700.902
7	20440.76	-1664.408	3639.463	-519.375	-476.549	5317.345	731.563
8	20921.61	-1957.294	4123.176	-117.999	-586.630	5721.693	737.107
9	21392.08	-2257.278	4611.363	308.726	-700.845	6058.838	722.624
10	21854.77	-2568.357	5106.710	757.258	-817.463	6344.411	692.393

Note: CO2 shows carbon emissions, AGM shows agriculture released methane emissions, TME shows total methane emissions, EME shows energy released methane emissions, LVS shows livestock production, NRD shows natural resource depletion, and URB shows urbanization.

Table 13 shows the VDA estimates and suggests that carbon emissions would likely have more than 90% innovation shocks over time. Natural resource depletion would likely increase carbon emissions with a percentage change of 4.813% for the next ten years. Total methane emissions would be another critical factor influencing carbon emission with a variance error shock of 2.512%. The livestock production index would likely have the least impact on carbon emissions with a variance of 0.049% over time.

Table 13

VDA estimates.

Period	S.E.	CO2	AGM	TME	EME	LVS	NRD	URB
1	15046.15	100	0	0	0	0	0	0
2	23050.63	99.091	0.001	0.252	0.393	0.006	0.248	0.007
3	29586.68	98.094	0.019	0.468	0.539	0.006	0.848	0.022
4	35373.27	97.088	0.059	0.706	0.554	0.007	1.544	0.039
5	40703.24	96.109	0.114	0.964	0.508	0.010	2.239	0.054
6	45728.82	95.169	0.180	1.240	0.440	0.015	2.887	0.066
7	50539.81	94.270	0.256	1.534	0.371	0.021	3.470	0.075
8	55194.72	93.408	0.340	1.844	0.311	0.029	3.984	0.080
9	59734.88	92.573	0.433	2.170	0.268	0.038	4.430	0.083
10	64191.39	91.757	0.535	2.512	0.246	0.049	4.813	0.084

Note: CO2 shows carbon emissions, AGM shows agriculture released methane emissions, TME shows total methane emissions, EME shows energy released methane emissions, LVS shows livestock production, NRD shows natural resource depletion, and URB shows urbanization.

There is a greater need to control overexploitation of natural resources, which hampers the environmental sustainability agenda across Asian economies. Further, it is imperative to reduce methane emissions from agriculture, the industrial, and the energy sector that adversely affect the resource market and escalate carbon emissions. Finally, reducing massive rural-urban migration to conserve economic and environmental resources is crucial for moving toward a green economy.

5. Discussions

The provided estimations revealed significant findings:

The energy sector is primarily responsible for increasing methane emissions. Energy-related methane emissions have harmed the environmental sustainability agenda, which must be decreased by increasing green energy sources and reducing emissions from coal mining, biofuel burning, and oil and gas systems [91, 92].

Methane emissions are a significant source of GHG emissions that contribute to global warming. Agriculture, industry, energy, and waste contribute significantly to methane emissions. Agriculture accounts for most methane emissions; hence, it is critical to look ahead and enhance agricultural methods and animal production to reduce global carbon emissions [93].

Municipal solid waste management and the elimination of open dumping are critical for reducing methane emissions [94]. Methane emissions from oil and gas systems are unknown due to their high cost and must be estimated using sustainable measurement instruments [95].

Excessive exploitation of natural resources exhausted scarce resources, resulting in harm to the natural environment [96, 97]. Additionally, its inefficient usage in manufacturing corroborates the resource curse hypothesis [98, 99]. The time has come to conserve natural resources and use efficient technologies to minimize their waste in order to recover [100, 101], and

Increased rural-urban migration strained the country’s economic and natural resources, depleting them significantly and resulting in increased health damages [102, 103]. Additionally, urbanization results in increased environmental and resource degradation, which must be mitigated by green city development projects [104-106].

Cheng et al. [107] suggested that methane emissions from coal have lowered the quality of the Chinese environment, and the country needs ways to reduce the effect of methane on the environment through a sustainable policy mix. Similarly, Hussain and Rehman [108] analyze the relationship between livestock and carbon emissions in Pakistan, concluding that livestock has a long-term adverse effect on carbon emissions. Wang et al. [109] examine the effect of urbanization on carbon emissions in 30 different Chinese regions. The study’s findings imply that urbanization contributes to rising carbon emissions. The enhancement of the quality of urban infrastructure may result in an improvement of the environment’s quality and a reduction of negative environmental externalities across provinces. According to Ulucak and Khan [110], natural resource rent and renewable energy are positively related to the environment and contribute to the quality of the ecosystem. As a result, both of these factors are critical for sustainable growth.

According to the discussion, carbon emissions should be lowered via reducing energy-related methane emissions, conserving natural resources, managing livestock production, and halting significant rural-urban movement between nations.

6. Conclusion and policy implication

The increasing methane emissions from energy activities, agricultural practices, and industrial processes increase the threat of GHG emissions and global warming. It is pivotal to mitigate it through sustainable energy sources. The study aims to examine the effects of methane emissions (released by energy, agriculture, and industrial activities), livestock production, natural resource depletion, and urbanization on carbon emissions in a panel of 6 selected Asian economies by using data from 1971 to 2020. The study used second-generation panel cointegration tests, including FMOLS and DOLS. The results show that energy methane emissions, livestock production, natural resource depletion, and massive urbanization damaged the natural flora of the economies and exacerbated carbon emissions. According to the Granger causality estimates, livestock production and agricultural methane emissions impacted carbon emissions.Methane emissions and carbon emissions influence urbanization. However, urbanization also influences methane emissions and livestock production. The feedback relationship is found between energy methane emissions and carbon emissions, between natural resource depletion and livestock production, and between urbanization and natural resource depletion. The forecasting estimates suggested that total methane emissions, urbanization, and natural resource depletion would likely influence carbon emissions for the next ten-year period. Based on the stated results, the following policy implications have been proposed for the Asian economies:

Methane emissions increased globally due to high energy demand, contributing to global warming and jeopardizing sustainability concerns. Anthropogenic sources of its emissions include agriculture, electricity, and waste. Growing demand for natural gas has increased the need for monitoring and measuring methane emissions. At the limit of an average global temperature of fewer than 1.5 degrees Celsius, economies reduce oil and gas production, agriculture, and landfills that cause methane emissions. Renewable energy produces more clean power than natural gas, which helps minimize methane emissions. Further, food resource efficiency and healthier choices, including less meat and dairy, have been urged to cut agricultural emissions. Effective central and provincial GHG reduction plans may be challenging to execute without appropriate methane emissions estimates.

Methane is virtually always produced as a byproduct of digestion in animal production for human consumption and export. Methane emissions from cattle anaerobic fermentation and waste management are expected to skyrocket, accounting for around two-fifths of human-induced GHG emissions. Livestock methane emissions cause more significant health damage and environmental pollution than any other pollutants that need to be regularly monitored for changes in cattle feed intake, carbohydrate type, feed processing, and dietary lipids. Efficient monitoring helps to lessen environmental challenges.

Climate change affects livestock. The industry’s contribution to worldwide anthropogenic GHG emissions may be cut by one-third.Seaweed reduces methane emissions from cows. Adding seaweed to cow feed requires multiple stages and regulatory approval. The industry requires mitigation, institutions, and governance for sustainable development.Farmers and ranchers may earn money by lowering cow emissions. Climate scientists must measure, monitor, and verify methane emissions from livestock. Such laws might let farmers earn carbon offset credits worldwide.Changes in animal husbandry techniques and consumption habits are urgently needed to reduce GHGs from the farm animal industry.

The overexploitation of natural resources leads to greater resource waste and healthcare damage in increasing carbon emissions. Technology-oriented extractive industries play an essential role in efficiently extracting natural resources and conserving them for future generations. Authorities should thus offer the infrastructure and technology to companies engaged in resource extraction, which will ultimately increase economic growth while minimizing environmental pollution. The environmental sustainability agenda is affiliated with the conservation of natural resources. Hence, its price should be well considered while devising green resource policies.

Massive rural-urban migration significantly deteriorates the natural environment and exhausts economic and environmental resources, sabotaging the green urban infrastructure. Urban development cannot be attained without rural sector development. Hence, innovative city development, efficient use of economic and ecological resources, promoting SMEs in the rural developmental sector, subsidized economic policies, and technology-driven urban planning are vital to sustaining economic and environmental resources. Governments should work to enhance the structure of rural regions and the infrastructure of rural areas so that people may continue to live in their home country for education and employment rather than relocating to urban areas.

Fuel efficiency, heat and power, alternative resources, and recycling, may minimize industrial greenhouse gas emissions. Adopt a public infrastructure design initiative to encourage renewable energy and resilience. It may be done using subsidies, financing, rewards, and public-private renewable energy collaborations. These technologies have domestic and export potential. Lowering HFC emissions will reduce future temperature spikes due to their high emission rates and short atmospheric life. Many industrial operations have no low-emission alternative and need carbon capture and storage for cleaner production.

Reducing methane emissions from the petroleum sector is critical for tackling climate change and natural gas’s role in the energy transition. Around one-third of human-caused methane emissions come from agriculture. Preventing post-harvest burning, changing animal feed to reduce methane, and draining rice paddies are reduction measures to tackle climate change. Governments design and execute policies and legislation to reduce emissions, and

Sustainable transition options include efficiency gains, performance and development at the output levels, fossil energy replacement by alternative energy sources and carbon-free nuclear energy infrastructure. Governmental efforts are needed to make the global power transition technically and economically viable and profitable.

The stated policy proposals contribute to carbon neutrality by lowering energy-related methane emissions, agricultural methane emissions, livestock methane emissions, and industrial methane generation. Reducing overexploitation of natural resources and conserving economic and biological resources improves ecological standards. Urbanization should be slowed down by significant changes in rural health care and jobs near them, encouraging people to stay in the rural area and not move to the cities.