Renewable energy generation,agricultural value added and globalization in relation to environmental degradation in the five most populous countries in Asia

Abstract

This study aims to examine the impact of globalization, renewable energy generation, and agricultural value addition on the ecological footprint (EF) and carbon emissions (CO₂) of selected five most populous countries in Asia during the period 1975–2020. The Westerlund cointegration test supports long-term cointegration relationships among the considered variables in selected countries. The long-term elasticity results of the mean group, augmented mean group, and common correlated effects mean group estimators clearly show that agricultural value added and globalization make significant contributions to the ecological footprint and carbon emissions of the five most populous countries in Asia. However, renewable energy generation significantly reduces the ecological footprint and carbon emissions. Moreover, the impact of gross domestic product (GDP) on ecological footprint and carbon emissions is significantly positive while the impact of GDP² on ecological footprint and carbon emissions is significantly negative, thus validating the inverted U-shaped Environmental Kuznets Curve hypothesis for specific Asian densely populated countries. The results of the causality test indicated a two-way causality between renewable energy generation and ecological footprint, supporting the feedback hypothesis. There is also a two-way causal relationship between agricultural value added and ecological footprint. Overall, these empirical results show that renewable energy plays a prominent role in combating environmental pollution in selected densely populated Asian countries. Thus, these countries should achieve their sustainable development goals by cultivating existing renewable energy generation policies. Moreover, specific densely populated countries in Asia should encourage clean energy production and consumption in the agricultural sector, and increased investment in renewable energy can improve environmental quality and agricultural production.

Keywords

Renewable energy generation agriculture value added globalization ecological footprint

Introduction

The resources required to meet the needs of the present generation without destroying the resources of the next generation are called sustainable development. The goal of sustainable development is also to protect the natural environment for future generations while meeting critical needs.¹ The sustainable development revolution gained popularity after the Brundtland report, “Our Common Future: The World Commission on Environment and Development (WCED). The report highlights that the main obstacles to sustainable development are population growth and the overuse of energy and natural resources. As the commission emphasized, sustainability requires not only economic growth but also energy intensity and fewer resources.² With the aim of contributing to the sustainable development debate, this study focuses on the role of renewable energy generation, agriculture, and globalization on environmental pollution in the five most populous Asian countries (China, India, Indonesia, Pakistan, and Bangladesh). The process of globalization in developing countries is accompanied by rapid upgrading and differentiation of production and consumption. A major source of environmental pollution and health hazards is the fossil fuels used to produce commodities. Society is turning to solar and wind power as sources of renewable energy that are free in nature and do not pollute the air. Besides, it is more critical that food production and supply systems meet social needs, but agricultural activities degrade environmental quality. Thus, the current study seeks answers to the following questions: Does renewable energy generation contribute to improved environmental quality, and are agriculture and globalization the main drivers of environmental degradation in densely populated countries? Alleviating the environmental problems in densely populated countries in Asia is the main purpose of this study, which can be achieved by answering the above questions.

The latest forecast for the world population is 8 billion in 2022, projected to reach 8.5 billion by 2030, 9.7 billion in 2050, and 10.4 billion in 2100. The never-ending trend of rapid population growth poses a challenge to sustainable development.³ Addressing environmental challenges is even more critical to achieving the targets set in the 17 sustainable development goals (SDGs). In this regard, the halting of environmental hazards such as global warming, air pollution, deforestation, desertification and water pollution, as well as the sustainable consumption of natural resources, embody SDGs 12, 13, 14, and 15.⁴ The goal of eliminating the effects of global warming and climate change is sustainable and equitable and must be achieved.^5,6 The extent of recorded global warming over the past five years as determined by the world's highest temperatures. Global carbon dioxide emissions account for about 76% of total greenhouse gas emissions, reaching a peak of 36.3 billion tons in 2021.⁷ Emissions of carbon dioxide, methane, and nitrogen dioxide increase to 495.4 ppm, 1991ppb, and 370.4 ppb in 2021, an increase of 152%, 281%, and 148%, respectively, compared with the preindustrial era.⁸ Global warming and climate change are significantly affected by other environmental pollutants besides carbon dioxide emissions.⁹ In this context, Wackernagel and Rees¹⁰ proposed a cumulative environmental metric, the ecological footprint (EF), for the simultaneous analysis of soil, water, and air pollution. In doing so, it captures overall environmental dynamics by summing six subcomponents (forest footprint, fisheries, rangeland, cropland, carbon, and building land) to measure human gravitational pull on the environment in global hectares. The ecological footprint reflects human pressure on natural resources and services are on the demand side, while the regenerative capacity of natural resources is a reflection of the bio capacity of the supply side to meet human needs. While linking EF to biocapacity, it is possible to infer whether there is a minimum environment for sustainable development in a geographical area.^11,12 Environmental degradation caused by increased world regenerative capacity since 1970 due to ecological footprint negatively impacting human well-being and human health on earth.¹³ Global warming and climate change are environmental problems caused by carbon dioxide emissions and ecological footprint, adversely affecting human health and the national economy. Thus, it is imperative to identify the factors that lead to environmental degradation. The key factors affecting environmental degradation and sustainable development are renewable energy, globalization and agriculture. Globalization can increase production levels and provide environmental technology, therefore, globalization can be considered as one of the main determinants of environmental pollution. Higher CO₂ emissions and ecological footprints are directly affected by production and consumption activities. Environmental conditions may worsen as production technologies remain unchanged in the process of globalization.¹⁴ Conversely, if economic globalization provides environmentally friendly technologies, it can improve environmental quality as foreign direct investment and trade volumes increase.¹⁵

Agriculture in developing economies is identified as a key factor for sustainable development.^16,17 As an important tool within the context of the SDGs, agriculture can eradicate absolute poverty. Thus, increasing agricultural productivity, ensuring food supply security and providing better nutrition require sustainable consumption, production, and distribution chains.¹⁸ The 2030 target can be achieved through a global transformation of agricultural systems and food that considers climate change.¹⁹ Agriculture sustains economies by creating jobs for people, providing food, supplying raw materials, and increasing competition.²⁰ Hunting, fishing and forestry are agricultural activities in developing countries that play a vital role in wealth creation.²¹ However, higher water, land and carbon footprints may be caused by agriculture. Agriculture such as livestock and crop irrigation uses 70% of the world's water.²² Additionally, the primary production stages of raising livestock, tilling the soil and using agricultural inputs add to greenhouse gas emissions and carbon footprints. Agrifood production increases land footprint over time.²³ Agricultural vegetation based on fossil fuel inputs and outdated technologies exacerbates environmental quality and hinders the achievement of SDGs. Agricultural productivity accounts for about 20% of global carbon dioxide emissions.²⁴ Increases in agricultural carbon dioxide emissions may worsen global environmental quality, so a sharp decline in agricultural emissions is critical for sustainable development.²⁵ The agricultural sector is the third largest contributor to climate change, after industry (22%) and energy (36%).²⁶ Clearing land, burning soil and crops, and growing rice are all agricultural activities that use fossil fuels, leading directly to higher emission levels.^27,28 Using renewable energy can offer farmers the advantage of compensating for the economic and environmental losses that fossil fuels inflict on agricultural soil and environmental quality.¹² Renewable energy can be used in a variety of decisions to support agricultural activities such as soil improvement, drying of produce, cooling, irrigation and heating.²⁹ Additionally, environmental concerns can be mitigated through green investments in agriculture. More investment in the agricultural sector needs to address resource depletion, forest degradation and groundwater pollution.³⁰

The world is focusing on a sustainable development agenda that requires the use of renewable and clean energy because fossil fuels contribute to global warming.^5,31,32 A technically feasible and economically effective strategy to reduce greenhouse gas emissions is to use renewable energy.³³ Renewable energy can reduce dependence on fossil fuels, thereby ensuring energy security and preventing air pollution.^34,35 In addition to improving air quality, human health and job creation, renewable energy can help reduce environmental and climate impacts and energy costs.³⁶ Moreover, the use of domestic renewable energy sources, such as solar energy, geothermal energy, biomass energy, wind energy, and hydropower, are all domestic renewable energy sources that can be used to reduce costs associated with energy imports.³² Thus, countries for the aforementioned environmental reasons need to switch to renewable energy sources. Renewables are more likely to generate more electricity than coal by 2025, and by 2050, 50% of global electricity production will rely on renewables.³⁷

Five developing countries (China, India, Pakistan, Indonesia, and Bangladesh) with 45% of the world's population develop agricultural activities to meet social needs. Developing countries in Asia are the countries with the highest agricultural production because these high-level communities have a higher demand for agricultural products. Table 1 lists the information on the top eight producers of agricultural products in the world from 2019 to 2021. The top two countries are China and India, and Indonesia ranks fourth. As can be seen, agricultural production increased in China, Indonesia, and India, while it decreased slightly in the United States, Turkey, and the Russian Federation. Agricultural value added in selected developing Asia (China, India, Indonesia, Pakistan, and Bangladesh) is equivalent to 49.6% of world agricultural production by 2021.

Table 1.

The world's top eight agricultural producers.

Countries	2019		2020		2021
	AVA*	% of GDP	AVA*	% of GDP	AVA*	% of GDP
China	1030.3	34.3	1095.3	36.6	1173	39.9
India	395.5	20.3	431.6	22.1	444.1	24.6
United States	203.2	11.8	227.8	13.0	188.6	11.3
Indonesia	129.2	7.3	136.9	8.4	139.2	8.9
Nigeria	111.3	4.6	116.1	5.6	119.5	5.9
Brazil	85.7	2.6	88.1	2.9	88.5	3.6
Turkey	61.8	2.1	67.9	2.4	65.8	2.1
Russian Federation	55.1	1.8	57.6	2.1	56	1.9
World	3067.7		3084.4		3096.7

Note: AVA indicates agriculture value added (billion US$).

Sources: World Development Indicators, World Bank.

The selected top agricultural-producing countries are also leaders in renewable energy generation. Between 2009 and 2018, installed renewable energy capacity in developing Asia nearly tripled from 349.1GW to 1023.5GW. This growth now accounts for around 68% of the region's total installed capacity, largely led by China. China is not only the leading renewable energy producer in Asia, but also the world's leading producer of renewable energy, generating twice as much electricity as the world's second-largest country, the United States. Hydropower is the largest source of renewable energy, and nearly half of China's renewable energy comes from hydropower.³⁸ India has the fourth most attractive renewable energy market in the world, ranking fourth in terms of installed renewable energy capacity, fifth in solar power and fourth in wind power in 2020.³⁹ With incredible potential for solar and wind power generation, Pakistan needs only 0.071% of the country's current electricity needs to be powered by solar photovoltaics (solar PV). Promoting renewable energy could help Pakistan save up to $5 billion over the next 20 years by lowering electricity costs, reducing carbon emissions and achieving greater energy security.⁴⁰ Mobilizing investment in renewable energy and energy efficiency through reforms could make Indonesia a world leader in clean energy, according to the latest report of the OECD.⁴¹ The OECD report further demonstrates that Indonesia has substantial untapped financing and investment potential in renewable energy and energy efficiency, areas critical to supporting a sustainable recovery from the COVID-19 crisis and accelerating the country's green energy transition. Bangladesh achieved economic growth and mitigated climate change during 2019-2021 with the help of USAID's Scaling Up Renewable Energy (SURE) program.⁴²

Figure 1 shows that India, China, Pakistan, Bangladesh, and Indonesia have a slight decline in renewable energy generation as a percentage of total energy generation over the period 2000–2015. However, renewable energy generation in these specific countries start to grow slightly again in 2021.

Figure 1.

Renewable energy as a percentage of total energy generation in developing Asia from 2000 to 2021. Source: WDI, world Bank.

Therefore, in view of environmental degradation, air pollution in particular will worsen with the increase of fossil fuels. Countries such as China, India, Pakistan, Indonesia, and Bangladesh must diversify their energy mix. These countries are considered the world's largest consumers of fossil fuels. India, China, Pakistan, Bangladesh, and Indonesia must replace fossil fuels with renewable energy sources such as solar, wind, and biomass to reduce pressure on the environment.

Based on the above information flows, this study aims to examine the impact of globalization, renewable energy consumption, and agricultural value addition on the EF and carbon emissions (CO₂) of selected five most populous countries in Asia. Wen et al.⁴³ examined the impact of globalization, renewable energy consumption, and economic growth on CO₂ emission for selected South Asian economies. Anwar et al.⁴⁴ selected 15 Asian economies to empirically reveal the impact of urbanization, renewable energy consumption, financial development, agriculture, and economic growth on CO₂ emissions. Similarly, Sharma, Sinha, and Kautish⁴⁵ selected eight developing countries in South and Southeast Asia to explore the effects of per capita income, renewable energy, life expectancy, and population density on ecological footprint. Wang et al.⁴⁶ investigated the impacts of globalization (GL), renewable energy (RE), and value-added agriculture (AG) on CO₂ emissions in South Asian countries. Contrasting the above studies with the current study, this is the first attempt to explore the impact of globalization, renewable energy consumption, and agricultural value added on the ecological footprint and carbon emissions of the five most populous countries in Asia. The synchronized effects of globalization, renewable energy consumption, and agricultural value addition on the ecological footprint, the use of the ecological footprint as a proxy for environmental damage, and the proposed sample data and panel selection have not been examined in other studies. Furthermore, unlike the traditional methods previously used in panel studies, this study determines to use the CS-ARDL method, which is robust to cross-sectional dependence (CSD), heterogeneity, and endogeneity.

Literature review

Various studies over the past three decades have obviously identified the determinants of environmental pollution. Groundbreaking studies by Grossman and Krueger⁴⁷ and Shafik and Bandyopadhyay⁴⁸ concluded that there is an inverted U-shaped relationship between pollution indicators and economic development. Panayotou⁴⁹ delineated the nonlinear relationship between economic growth and environmental pollution while testing the Environmental Kuznets Curve (EKC) hypothesis. Abbasi et al.⁵⁰ explored the inverted N-type EKC hypothesis, reflecting the fact that renewable energy sources mitigate pollution rather than nonrenewable energy sources exacerbate pollution. Validation of the EKC hypothesis is still controversial, such as, few studies have found the EKC to be valid,^51–54 while others have found the EKC hypothesis to be invalid.^55–58 In addition to the level of economic development, various studies have linked environmental pollution to nonrenewable energy consumption,^59–62 trade openness,^53,54,63,64 urbanization,^65–67 foreign direct investment,^51,68,69 regulatory quality,^70–72 economic complexity,^73–75 and globalization.^76–78

Given its importance, many studies have revealed the impact of renewable energy on environmental pollution. These studies, mainly focused on the consumption side, determined that renewable energy consumption has adverse impacts on environmental degradation.^79–84 Furthermore, the environmental impacts (air, water pollution, and soil) of renewable energy generation have only been explored with very limited research. Among them, Ibrahim et al.⁸⁵ revealed that renewable energy generation in Algeria, Egypt, Morocco, Nigeria, and South Africa could reduce environmental degradation over the period 1990–2019. Koengkan et al.⁸⁶ investigated the reduction of air pollution by renewable energy in 15 countries in the Latin America and Caribbean region from 1990 to 2017 using panel quantile regression. Zeng, Bao, and McFarland.⁸⁷ collected data from 282 Chinese cities using the spatial difference in differences model and found that renewable energy generation reduced air pollution in China. Alsaleh and Abdul-Rahim⁸⁸ investigated the impact of renewable energy generation on water quality in EU participating countries using a panel fully corrected ordinary least squares method over the period 1990–2019. The results of the analysis show that increasing the amount of renewable energy can effectively exacerbate water quality degradation in the EU-27 region.

Moreover, recent studies have employed multiple methodologies and data samples and selected different regions to investigate the impact of agriculture on environmental degradation. Abbasi et al. ⁸⁹ used a panel nonlinear autoregressive distributed lag (NARDL) model to reveal the impact of agricultural activities on carbon emissions in the world's top 22 forest-covered countries during the period 1980–2019. Results show that agricultural activities have gradual and statistically significant long-term effects on CO₂ emissions in selected regions. Abbasi et al.⁸⁹ pointed out that positive shocks to agricultural value added and forest area have significant adverse effects on environmental degradation while negative shocks have significant long-term positive effects on CO₂ emissions in the specific 22 most forested countries. Usman et al.⁹⁰ also explored the detrimental contribution of agricultural value added to environmental quality in South Asian countries using FMOLS techniques during 1995–2017. Likewise, Adekoya et al.⁹¹ concluded that agricultural activities reduce environmental quality in resource-rich African countries. Muoneke et al.⁹² used FMOLS and dynamic ordinary least squares (DOLS) methods and found that the ecological footprint increased as agricultural development in the Philippines accelerated. In contrast, Raihan and Tuspekova⁹³ used a DOLS method for the period 1996–2020 and found that a 1% increase in agricultural productivity and forest area could lead to a reduction in CO₂ emissions of 0.34% and 2.59%, respectively in Kazakhstan. Anwar et al.⁴⁴ published the impact of renewable energy and agriculture on carbon dioxide emissions in 15 Asian economies during the period 1990 and 2014. The results of the analysis show that renewable energy significantly reduces CO₂ emissions, while agriculture contributes only marginally to the reduction in CO₂ emissions in selected countries. Likewise, Raihan and Tuspekova⁹⁴ used DOLS to identify that a 1% increase in agricultural development and renewable energy use in Malaysia would reduce CO₂ emissions by 3.86% and 0.10%, respectively, over the period of 1996–2020. Raihan and Tuspekova⁹⁵ again reveal that using the DOLS approach, agricultural value addition increases Brazil's CO₂ emissions, leading to environmental degradation, while increasing renewable energy use and forest area helps reduce Brazil's CO₂ emissions.

Many recent studies have shown that globalization is another important driver of accelerated environmental degradation. Using wavelet techniques, Adebayo and Kirikkaleli⁹⁶ found that globalization increased Japan's CO₂ emissions while renewable energy use decreased Japan's CO₂ emissions during the period from the first quarter of 1990 to the fourth quarter of 2015. Another study by Aladejare⁹⁷ shows that globalization using augmented mean groups (AMGs) reduces environmental degradation in the five wealthiest African economies during the period 1990–2019. Jahanger⁷⁶ empirically finds that economic, social, and overall globalization reduce environmental quality by applying the generalized method of moments in 78 developing economies during the period of 1990–2016. Farooq et al.⁷⁷ also analyzed that globalization helped improve environmental degradation in 180 countries over the period of 1980–2016. In contrast, Usman et al.⁹⁸ explores the adverse effects of renewable energy consumption and globalization on the ecological footprint of financially resource-rich countries from 1990 to 2018. Similarly, another study by Usman et al.⁹⁹ used the AMG strategy and found that renewable energy consumption exacerbated environmental degradation, while globalization contributed to environmental degradation in eight Arctic countries over the period 1990–2017. Rehman et al.¹⁰⁰ also concluded that globalization has a significant positive effect on CO₂ emissions, while renewable energy consumption has no effect on global CO₂ emissions, using an NARDL technique from 1985 to 2020. Similarly, Sun et al.¹⁰¹ employed the method of moment quantile regression (MMQR) to conclude that renewable energy consumption significantly reduces carbon emissions while globalization leads to an increase in carbon emissions in the top 10 polluting countries.

As noted from the above literature few studies have analyzed the impacts of renewable energy generation, globalization and agriculture value addition on the EF simultaneously. Furthermore, few studies have used EF as a proxy for environmental degradation while exploring the links between renewable energy generation, agricultural development, globalization, and EF. There is also a lack of research in the literature analyzing the causal links between renewable energy, agriculture, globalization, and environmental degradation.

Methodology

The main objective of this study is to reveal the impact of renewable energy generation, agricultural value addition, and globalization on the ecological footprint and CO₂ emissions of the five most populous countries in Asia. Yurtkuran¹⁰² used renewable energy production, globalization, and agriculture as independent variables in the model, and carbon dioxide emissions as dependent variables, revealing the impact of renewable energy production, globalization, and agriculture on carbon dioxide emissions. Ozturk and Acaravci¹⁰³ used per capita carbon emissions as the dependent variable in model development, and considered per capita energy consumption, per capita globalization, per capita real income, and the square of per capita real income as independent variables. This model obviously embodies the impact of per capita energy consumption, per capita globalization, per capita real income, and the square of per capita real income on per capita carbon emissions. Following the models of Yurtkuran¹⁰² and Ozturk and Acaravci¹⁰³ above, the following econometric models are derived in log-linear form.

lnE F_{it} = β_{0} + β_{1} lnRE G_{it} + β_{2} lnKO F_{it} + β_{3} lnAGRit + β_{4} lnGD P_{it} + β_{5} lnGD P_{it}^{2} + μ_{it}

(1)

lnC O_{2 it} = β_{0} + β_{1} lnRE G_{it} + β_{2} lnKO F_{it} + β_{3} lnAG R_{it} + β_{4} lnGD P_{it} + β_{5} lnGD P_{it}^{2} + ε_{it}

(2)

In the above equations, i denotes the country, β₁, β₂, β₃, β₄, and β₅ symbolize estimated coefficients, t signifies time, ln is the natural logarithm, μ and ε indicate error terms. EF and CO₂ are included as dependent variables in the above two models while exploring the impact of agriculture, globalization and renewable energy on environmental degradation. CO₂ represents carbon dioxide emissions, EF signifies ecological footprint, REG denotes renewable energy generation, AGR indicates agricultural value added, gross domestic product (GDP) is a gross domestic product, while GDP² is the square of gross domestic product and KOF shows the globalization index. The most commonly used dependent variable, the aforementioned CO₂ emissions, is used to assess environmental degradation,¹⁰⁴ which is closely related to sustainability issues.¹⁰⁵ However, water, soil and other environmental pollution cannot be reflected by CO₂ emissions. Sustainable development depends primarily on addressing issues such as deforestation, desertification, and water pollution. EF is responsible for providing us with more comprehensive environmental pressure information within the scope of sustainable development. EF based on many resource stocks is a more useful measure for environmental quality assessment and policy modeling.¹⁰⁶ Both CO₂ emissions and EF in this study were used as dependent variables for comparison.

Variable measurements, descriptions, and data sources are highlighted in the Appendix below. Renewable energy consumption data in million tons of oil equivalent (Mtoe) obtained from OECD.¹⁰⁷ EF data in global hectares received from global footprint network.¹⁰⁸ Data for the KOF Globalization Index, which measures the political, economic, and social dimensions of globalization, is available from the KOF Swiss Economic Institute.¹⁰⁹ Data on agricultural value added as a percentage of GDP, carbon dioxide emissions (Mtoe), and GDP (constant 2015 U.S. dollars) are available from the World Bank's public website world development indicators (WDI).

CSD and slope heterogeneity tests

Testing the CSD of panels is critical as panels may have significant CSD across countries due to rising trends in interdependence. To this end, the current study sets out to examine cross-sectional correlation tests, which address the problem of panel data estimation and ensure that empirical estimators are unbiased, consistent, and valid. Originally, Pesaran scaled LM, Pesaran CSD, Breusch-Pagan LM, and bias-corrected scaled LM were four different CSD tests developed by Pesaran,¹¹⁰ Pesaran, Ullah, and Yamagata,¹¹¹ Breusch and Pagan,¹¹² Baltagi, Feng, and Kao¹¹³ with the expression of the following equations 3 and 4.

CSD = \sqrt{\frac{2 T}{N (N - 1)}} (\sum_{i = 1}^{N - 1} \sum_{K = i + 1}^{N} \hat{β} i k) \sim N (0, 1) I, k

(3)

$CSD = (1, 2, 3, \dots . .45, \dots \dots . N)$

M = \sqrt{\frac{2 T}{N (N - 1)}} (\sum_{i = 1}^{N - 1} \sum_{K = i + 1}^{N} \hat{β} i k) [\frac{(T - K) {\hat{β}}_{i k}^{2} - (T - K) {\hat{β}}_{i k}^{2}}{V a r (T - K) {\hat{β}}_{i k}^{2}}]

(4)

where the symbols N and T reflect the panel size (cross section) and sample size (period), respectively. It turns out that the presence of CSDs in the dataset will put more emphasis on applying second-generation panel data methods. This will demonstrate more appropriate steady-state results by using second-generation panel stationary tests.

Panel unit root tests

Augmented Dickey Fuller, Phillip Perron, Lin and Chu, Levin, Hadri, Breitung, Im, Pesaran, and Shin are first-generation panel stationarity tests that fail to account for CSDs in longitudinal datasets. Thus, the cross-sectional Im, Pesaran and Shin (CIPS) test developed by Pesaran¹¹⁴ and the cross-sectional augmented Dickey-Fuller (CADF) test are the second generation panel unit root tests applied in this study to reduce the concern. These panel unit root tests perform better and are more robust because of their asymptotic assumptions and do not require (N⁓∞). Accurate information on the order of sequence integration can be generated using CADF and CIPS tests. The CADF panel unit root test is described in equation (5).

Δ z_{it} = α_{i} + β_{i} z_{it - 1} + κ_{i} z_{t - 1} + Ψ Δ z_{t} + μ_{it}

(5)

The following equation (6) can be obtained by inserting the lag term (t-1) in equation (5).

Δ z_{it} = α_{i} + β_{i} z_{it - 1} + κ_{i} z_{t - 1} + \sum_{j = 0}^{k} Ψ_{_{i j}} Δ {\bar{z}}_{i t - 1} + \sum_{j = 0}^{k} λ_{i j} Δ z_{i t - 1} + υ_{i t}

(6)

where

{\bar{z}}_{i t - 1}

and

Δ z_{i t - 1}

represent the lag level of each cross section and the mean value of the first-order difference operator, respectively. The second-generation CIPS test is also shown in equation (7).

CIPS = N^{- 1} \sum_{i = 1}^{N} λ_{i} (N, T)

(7)

The coefficient

λ_{i} (N, T)

represents the CADF test statistic, so the following equation (8) can be obtained by replacing

λ_{i} (N, T)

with CADF.

CIPS = N^{- 1} \sum_{i = 1}^{N} λ_{i} CADF

(8)

Westerlund cointegration test

After testing the order of integration of selected variables, it is more critical to examine long-term associations between series. Thus, for this purpose, Westerlund¹¹⁵ developed an error correction model (ECM)-based cointegration test that takes into account the CSD and slope heterogeneity issues, without delving into previous studies analyzing the integration order of series topic. Informatively, this test tolerates differences in the robustness of the regressors. Thus, the test can be used in very general cases, and moreover, the method tends to be arbitrary bootstrapping, allowing multiple repetitions of the long-term cointegration test. The method is mainly based on mean-group tests (Gt and Ga) and panel tests (Pt and Pa), which are combined into four test statistics. The alternative hypothesis (H1) mean-group test statistic indicates that there is at least one long-run association between the variables and the presence of panel cointegration in the long run, as assumed by the alternative hypothesis (H1) of the panel test.^116,117 Equation (9) clearly highlights Westerlund's long-run cointegration test.

Z_{it} = \overset{'}{α_{i}} d_{i} + β_{i} (X_{i, t - 1} - λ_{i} {\overset{'}{Y_{i}}}_{, t - 1}) + \sum_{J = 1}^{q} β_{i j} Z_{i, t - j} + \sum_{J = 0}^{q} Ψ_{i j} X_{i, t - j} + ε_{i, t}

(9)

where β_i denotes the speed of adjustment to correct the system to long-run equilibrium after a short-run shock. However, the four different test statistics Gt, Ga, Pt, and Pa are based on the error-correction Westerlund cointegration test expressed in the following equations (10) and (11).

G_{t} = \frac{1}{N} \sum_{i = 1}^{N} \frac{Ψ_{i}}{S . E . ({\hat{Ψ}}_{i})}

(10)

G_{a} = \frac{1}{N} \sum_{i = 1}^{N} \frac{Tcy Ψ_{i}}{1 Ψ_{i} (1)}

(11)

Like the panel cointegration test statistic, Pt and Pa are expressed by equations (12) and (13).

P_{t} = \frac{{\hat{Ψ}}_{i}}{S . E . ({\hat{Ψ}}_{i})}

(12)

P_{a} = Tcy {\hat{Ψ}}_{i}

(13)

Long-run elasticity estimation

Long-term parameters are expected to be estimated after confirming long-term associations between series. Eberhardt and Bond¹¹⁸ proposed mean group (MG), common correlated effects mean group (CCEMG), and AMG estimators that can be used for long-term estimation in this study. These estimators can be used to reveal the impact of relevant explanatory variables on environmental degradation. Furthermore, the AMG estimator proposes a two-stage constraint, mainly the first stage of the AMG estimator, highlighted in Equation (14).

Y_{i t} = κ_{i} + β_{i} X_{i t} + λ_{i} f_{t} + \sum_{t = 2}^{T} c y ρ_{i} D_{t} + μ_{i t}

(14)

whereas the second stage of the AMG estimator is expressed in Equation (15).

{\hat{β}}_{A M G} = N^{- 1} \sum_{t = 2}^{T} c y {\hat{β}}_{i}

(15)

where κ_i is the intercept,

Y_{i t}

and

X_{i t}

identify observed values, ft denotes unobserved factors shared by individuals in terms of heterogeneity, Δ shows the first-order difference,

{\hat{β}}_{A M G}

is the mean group estimate of AMG,

μ_{i t}

is the random error term. The CCEMG method with cross-section dependence can estimate reliable and robust parameters. This approach allows the use of heterogeneous slope parameters across segments through familiarity with country-specific mean elasticities. The CCEMG estimator expression is highlighted in equation (16).

X_{i t} = ρ_{1 i} + α_{i} Y_{i t} + β_{i} κ_{i t} + μ_{i t}

(16)

Where

α_{i}

is the individual cross-sectional slope,

β_{i}

denotes an unobserved common static property combined with

α_{i}

heterogeneity, and the

μ_{i t}

term denotes a random error factor.

An enhanced version of the mean cross-section with all the above variables is set forth in equation (17).

X_{i t} = ρ_{1 i} + α_{i} Y_{i t} + Ψ_{i} {\bar{y}}_{i t} + λ_{i} {\bar{z}}_{i t} + + β_{i} κ_{i t} + μ_{i t}

(17)

Granger causality test

The Granger causality test of Dumitrescu and Hurlin¹¹⁹ can be used after long-term estimated parameters to reveal bidirectional causality between variables of interest. This method is not subject to any restriction of T > N, and is very flexible and applicable. This test is suitable for heterogeneous and unbalanced panels. This method provides robust conclusions even for small samples and CSD, as recognized by Monte Carlo simulations.¹²⁰ The Dumitrescu–Hurlin causality test equation can be expressed as:

y_{i, t} = \emptyset_{i} + \sum_{j = 1}^{p} \partial_{i}^{j} y_{i, t - j} + \sum_{j = 1}^{p} λ_{i}^{j} z_{i, t - j}

(10)

Results and interpretation

First, to obtain robust estimation results, cross-sectional dependencies in panel datasets can be detected. Pesaran Scaled LM, Pesaran CSD, bias-corrected scaled LM, and BreuschPagan LM are four different CSD tests highlighted in Table 2, providing strong evidence for CSD in the panel data set for the five most populous Asian countries. Therefore, the results clearly show that specific countries in the panel are strongly correlated. The advent of cross-sectional dependency testing suggests that second-generation techniques will yield reliable, robust, efficient, and consistent results.

Table 2.

Results of cross-sectional dependence tests.

	Breusch-pagan LM		Pesaran scaled LM		Biased-corrected scaled LM			Pesaran CSD
Variables	Statistics	Probability	Statistics	probability	Statistics	Probability	Statistics	Probability
lnEF	240.81***	.002	27.71***	.000	43.85***	.001	21.67**	.021
lnCO₂	310.83***	.004	87.28***	.000	57.83***	.000	14.37***	.002
lnREG	158.36***	.000	76.38***	.000	26.38***	.002	26.67***	.000
lnKOF	411.87***	.003	61.72***	.001	35.73***	.000	24.17***	.005
lnAGR	417.32**	.021	15.28**	.036	26.27***	.000	31.24***	.000
lnGDP	573.28***	.000	98.21***	.000	27.36***	.000	25.16***	.000

Note: *, **, and *** indicate statistical significance levels of 10%, 5%, and 1%, respectively.

The slope heterogeneity results show that both models in Table 3 suffer from heterogeneity issues, which clearly shows that traditional cointegration techniques and unit root tests may provide biased results. The model test statistic is statistically significant, thus, in this case, the most appropriate method adopted in this study is the second-generation unit root test of CADF and CIPS, and the results are summarized in Table 4. The results of the CADF and CIPS tests show that the model variables are stationary at 1(1) and 1(0), which allows the use of the Westerlund cointegration test.

Table 3.

Pesaran and Yamagata slope heterogeneity test results.

Models	Test	Value	Probability
Model-1	$\hat{Δ}$	24.27***	.000
	${\tilde{Δ}}_{a d j u s t e d}$	31.13***	.004
Model-2	$\hat{Δ}$	63.93***	.000
	${\tilde{Δ}}_{a d j u s t e d}$	63.57***	.000

Note: *** indicates statistical significance levels of 1%.

Table 4.

Panel unit root tests.

	CADF		CIPS
Variables	Level	First difference	Level	Difference
lnEF	−1.48	−3.64***	−1.53	−4.74***
lnCO₂	−1.03	−2.73***	−2.82	−5.85***
lnREG	−1.58*	−3.31***	1.74**	−3.53***
lnKOF	−1.43	−4.63***	−2.61	−4.59***
lnAGR	−2.41	−3.21***	−1.15	−3.03***
lnGDP	−1.57**	−3.63***	−2.98*	−5.63***

Note: *, **, and *** indicate statistical significance levels of 10%, 5%, and 1%, respectively.

The descriptive statistics of the variables are shown in Table 5, reflecting that US$1682.18 billion is the average GDP of specific countries, illustrating the large variation in the standard deviation during the period 1975–2020. The average EF was recorded at 15.19 global hectares, with a variation of 7.19 global hectares, and the average CO₂ emissions were 13.29 million tons, with a variation of nearly 6.97 million tons. Average renewable energy, average agricultural value-added, and average globalization were 4.86 Mtoe, 15.82% and 14.21%, respectively. The skewness statistics are positive, reflecting that all variable data are positively skewed. The kurtosis results show that only GDP is stretched and peaked due to its high statistics. In addition, the Jarque-Bera test obviously shows that none of the variables are significant, reflecting the normal distribution of the variable data.

Table 5.

Panel descriptive statistics.

Variables	Average	SD	Max	Min	Kurtosis	Skewness	Jarqua-Bera	Probability
lnGDP	1682.18	881.97	2246.03	688.59	1.28	0.15	1.42	0.77
InREG	4.86	0.91	6.13	1.53	1.69	0.61	1.62	0.81
InCO₂	13.29	6.97	29.14	13.87	1.25	0.18	1.17	0.54
InEF	15.19	7.19	31.27	8.18	1.59	0.71	1.47	0.61
InAGR	15.82	5.29	31.93	8.37	1.26	0.35	1.36	0.29
InKOF	14.21	4.21	37.96	7.19	1.63	0.47	1.03	0.53

Multicollinearity issues have been checked using the correlation coefficients and variance inflation factors (VIF) for each variable shown in Table 6. The correlation matrix results show that renewable energy generation and agricultural added value are positively correlated with ecological footprint, while globalization and GDP are negatively connected with ecological footprint. The results for the VIF indicate that there is no multicollinearity in the series, as all values of VIF are below 5.

Table 6.

Correlation matrix and VIF tests.

	lnEF	lnREG	lnAGR	lnKOF	lnGDP	VIF
lnEF	1
lnREG	0.782	1				3.91
lnAGR	0.874	0.832	1			4.15
lnKOF	−0.419	−0.972	0.639	1		3.93
lnGDP	−0.316	−0.608	0.703	0.982	1	3.53

Next, the Westerlund (2007) cointegration test is applied based on the current study using partial integral regressors, which is widely used in the energy-environment literature. This test compromises an exclusive feature of accommodating stationary regressors in the model. The ability to accommodate partially stationary regressors in the model is a unique feature of this test. Thus, for the current study, this test is more appropriate. The results of the Westerlund test shown in Table 7 illustrate the acceptance of the alternative hypothesis implying cointegration in the panel data for the five most populous countries in Asia.

Table 7.

Westerlund (2007) long-term cointegration test.

Westerlund¹²¹ cointegration test	Gt	Ga	Pt	Pa
Model-EF
Statistics	−6.82***	−4.24**	−8.15***	−13.23***
Z-values	−1.32	−0.38	−1.28	−0.84
p-values	0.00	0.02	0.00	0.00
Robust p-values	0.00	0.03	0.00	0.00
Model-CO₂
Statistics	−5.09***	−6.95***	−7.98**	−10.57***
Z-values	−2.85	−0.86	−1.93	−0.94
p-values	0.00	0.00	0.03	0.00
Robust p-values	0.00	0.00	0.02	0.00

Note: *** indicates statistical significance levels of 1%.

The derived long-term elasticity results are shown in Table 8. According to the results of the AMG strategy, agricultural value addition contributes significantly to the ecological footprint and carbon emissions of the five most populous countries in Asia. For every 1% increase in agricultural added value, the EF will increase significantly by 0.304%, and the carbon emissions will increase significantly by 0.471%. This finding is consistent with the studies by Abbasi et al. (2021), Usman et al.,⁹⁰ Adekoya et al.,⁹¹ and Muoneke et al.⁹² However, this result contradicts the studies of Raihan and Tuspekova,⁹³ Anwar et al.,⁴⁴ Raihan and Tuspekova,⁹⁴ and Raihan and Tuspekova.⁹⁵ A 1% increase in renewable energy generation can significantly reduce the ecological footprint by 0.426% and carbon emissions by 0.309%. This result is in good agreement with the studies by Ramzan et al.,³⁴ Chien et al.,⁷⁹ Abbasi, Kirikkaleli, and Altuntaş,¹²² Omri et al.,⁸⁰ Usman, Alola, and Sarkodie,⁸¹ Ahmad et al.,⁸² Suki et al.,⁸³ and Kartal.⁸⁴ For every 1% increase in globalization, the ecological footprint can be significantly increased by 0.318%, and the carbon emissions can be significantly increased by 0.528%. This finding is in good agreement with studies by Jahanger,⁷⁶ Farooq et al.,⁷⁷ Usman et al.,⁹⁹ and Adebayo and Kirikkaleli,⁹⁶ also contradicted by Usman et al.⁹⁸ The impact of GDP on ecological footprint and carbon emissions is significantly positive, while the impact of GDP² on ecological footprint and carbon emissions is significantly negative, thus validating the EKC hypothesis for specific Asian populous countries. This result is consistent with the studies by Liu et al.,⁶⁵ Luo et al.,⁵² Liu et al.,⁵³ and Ali et al.⁵⁴ However, this result contradicts studies by Zeraibi et al.,⁵⁵ Murshed, Haseeb, and Alam,⁵⁶ Dai et al.,⁵⁷ and Jahanger et al.⁵⁸

Table 8.

Long-term elasticities results.

	MG		AMG		CCEMG
Variables	Coeff	p-value	Coeff	p-value	Coeff	p-value
InEF = f(InAGR, InREC, InKOF, InGDP, InGDP²)
InAGR	0.716***	.005	0.304***	.071	0.791***	.000
InREG	−0.689***	−.000	−0.426***	−.026	−0.205***	−.004
InKOF	0.694***	.006	0.318***	.000	0.122**	.047
InGDP	0.382**	.032	0.428***	.007	0.194***	.002
InGDP²	−0.296***	−.000	−0.412**	−.027	−0.278***	−.001
Constant	23.821***	.000	−13.538***	.004	6.061***	.002
RMSE	0.036		0.072		0.094
InCO₂ = f(InAGR, InREC, InKOF, InGDP, InGDP²)
InAGR	0.287***	.003	0.471***	.003	0.396***	.002
InREG	−0.392***	−.000	−0.309***	−.009	−0.484***	.000
InKOF	0.371**	.041	0.528**	.005	0.379**	.028
lnGDP	0.286***	.000	0.328***	.002	0.835***	.000
InGDP²	−0.287***	−.004	−0.662***	.002	−0.384**	−.029
Constant	21.604***	.000	11.094***	.002	25.388***	.000
RMSE	0.042		0.0619		0.024

Note: ***, **, and * indicate statistical significance at 1% level, 5% level, and 10% level respectively, where inside in the parentheses are probability values.

Next, the Dumitrescu–Hurlin causality method was applied to examine the causality among the variables we were interested in for the specific Asian densely populated countries, and the results are shown in Table 9. Renewable energy generation and ecological footprint have bidirectional causality, thus supporting the feedback hypothesis. There is also a two-way causal relationship between agricultural value added and ecological footprint. Moreover, there are one-way causal relationships from carbon emissions to globalization, from agricultural added value to GDP, and from renewable energy power generation to carbon emissions and GDP. There is also a unidirectional causality from globalization to ecological footprint, from GDP to ecological footprint, carbon emissions and agricultural added value.

Table 9.

Dumitrescu–Hurlin panel causality test results.

Variables	lnEF	lnCO₂	lnAGR	lnREG	lnKOF	lnGDP
InEF	———-	2.348	3.413**	4.365***	4.052	3.821
		(0.362)	(0.012)	(0.003)	(0.023)	(0.241)
InCO₂	2.241	———	3.051	4.491	2.163***	2.178
	(0.214)		(0.154)	(0.215)	(0.002)	(0.153)
InAGR	2.469**	3.235***	——-	2.045	4.316	3.038***
	(0.021)	(0.002)		(0.234)	(0.154)	(0.002)
lnREG	3.428***	1.087***	3.807	——	4.302	5.325**
	(0.002)	(0.000)	(0.325)		(0.105)	(0.026)
InKOF	2.541***	4.234	3.245	3.245	——-	4.345
	(0.004)	(0.327)	(0.254)	(0.251)		(0.264)
LnGDP	3.301***	2.054**	2.408***	5.324	2.008	———
	(0.000)	(0.014)	(0.000)	(0.265)	(0.322)

Note: ***, ** and * indicate statistical significance at 1% level, 5% level, and 10% level respectively, where inside in the parentheses are probability values.

Discussion

This study aims to be inquisitive about a vital theme in sustainable development research based on the upgrading of renewable energy production systems to mitigate carbon dioxide and GHG emissions. Countries such as China, India, Pakistan, Indonesia, and Bangladesh were chosen because these countries are considered to be the largest consumers of fossil fuels in the world. These countries must replace fossil fuels with renewable energy sources such as solar, wind, and biomass to reduce pressure on the environment.

The results are consistent with the international literature, provocative statements of scholars and experts, and the performance of various countries. Agricultural value addition contributes significantly to the ecological footprint and carbon emissions of the five most populous countries in Asia. This finding is consistent with the studies by Abbasi et al. (2021), Usman et al.,⁹⁰ Adekoya et al.,⁹¹ and Muoneke et al.⁹² However, this result contradicts the studies of Raihan and Tuspekova,⁹³ Anwar et al.,⁴⁴ Raihan and Tuspekova,⁹⁴ and Raihan and Tuspekova.⁹⁵ The result reflects that agricultural vegetation based on fossil fuel inputs and outdated technologies exacerbates environmental quality and hinders achievement of SDGs.

An increase in renewable energy generation can significantly reduce ecological footprint and carbon emissions. This result is in good agreement with the studies by Ramzan et al.,³⁴ Chien et al.,⁷⁹ Abbasi, Kirikkaleli, and Altuntaş,¹²² Omri et al.,⁸⁰ Usman, Alola, and Sarkodie,⁸¹ Ahmad et al.,⁸² Suki et al.,⁸³ and Kartal.⁸⁴ A technically feasible and economically effective strategy to reduce environmental pollution is to use renewable energy. Renewable energy can reduce dependence on fossil fuels, thereby ensuring energy security and preventing air pollution. Moreover, the use of renewable energy sources, such as solar energy, geothermal energy, biomass energy, wind energy, and hydropower, are all renewable energy sources that can be used to reduce costs associated with energy imports.

The rise of globalization can significantly increase ecological footprints and carbon emissions. This finding is in good agreement with studies by Jahanger,⁷⁶ Farooq et al.,⁷⁷ Usman et al.,⁹⁹ and Adebayo and Kirikkaleli,⁹⁶ also contradicted by Usman et al.⁹⁸ Higher CO₂ emissions and ecological footprints are directly affected by production and consumption activities. Environmental conditions may worsen as production technologies remain unchanged in the process of globalization. Conversely, if economic globalization provides environmentally friendly technologies, it can improve environmental quality as foreign direct investment and trade volumes increase.

The impact of GDP on ecological footprint and carbon emissions is significantly positive, while the impact of GDP² on ecological footprint and carbon emissions is significantly negative, thus validating the EKC hypothesis for specific Asian populous countries. This result is consistent with the studies by Liu et al.,⁶⁵ Luo et al.,⁵² Liu et al.,⁵³ and Ali et al.⁵⁴ However, this result contradicts studies by Zeraibi et al.,⁵⁵ Murshed, Haseeb, and Alam,⁵⁶ Dai et al.,⁵⁷ and Jahanger et al.⁵⁸ Moreover, renewable energy generation and EF have bidirectional causality, thus supporting the feedback hypothesis. There is also a two-way causal relationship between agricultural value added and ecological footprint.

Conclusion and policy recommendation

Deforestation, water pollution and global warming are environmental issues that threaten the sustainable production of natural resources and consumption by future generations. Several international conferences have been held on mitigating environmental problems and achieving SDGs. However, despite pledges to reduce emissions under the Kyoto Protocol and the Paris Conference, the ecological footprint and CO₂ emissions of developing countries are still rising. Thus, innovative research is needed to detect factors affecting environmental pollution and implement further policy actions. In this regard, this study examines the impact of agricultural value added, renewable energy, and globalization on CO₂ emissions and ecological footprints in selected Asian densely populated countries. In performing this task, the Westerlund cointegration test, the MG, AMG, and CCEMG estimators for the long-run coefficient elasticity, and the Dumtrescu–Hurlin causality test are applied.

Westerlund's cointegration test shows that agricultural value added, renewable energy generation, globalization, GDP, and pollution indicators have long-term cointegration relationships. The long-term resilience results clearly show that agricultural value added and globalization make significant contributions to the ecological footprint and carbon emissions of the five most populous countries in Asia. However, renewable energy generation significantly reduces the ecological footprint and carbon emissions. Moreover, the impact of GDP on ecological footprint and carbon emissions is significantly positive, while the impact of GDP² on ecological footprint and carbon emissions is significantly negative, thus validating the EKC hypothesis for specific Asian populous countries. The results of the causality test indicated a two-way causality between renewable energy generation and ecological footprint, supporting the feedback hypothesis. There is also a two-way causal relationship between agricultural value added and ecological footprint. Moreover, there are one-way causal relationships from carbon emissions to globalization, from agricultural added value to GDP, and from renewable energy power generation to carbon emissions and GDP. There is also a uni-directional causality from globalization to ecological footprint, from GDP to ecological footprint, carbon emissions and agricultural added value.

Based on the above empirical findings, this study provides some key policy implications. For the densely populated countries in Asia, renewable energy generation is the best strategy to fight environmental pollution. The bidirectional causality of the relationship between agricultural added value and environmental pollution shows that agriculture is the main determinant of environmental pollution, and environmental problems affect agricultural activities. Certain densely populated countries in Asia (China, India, Bangladesh, Pakistan, and Indonesia) should encourage clean energy production and consumption in the agricultural sector, and increased investment in renewable energy can improve environmental quality and agricultural production. In densely populated countries, rational use of fertilizers and pesticides, as well as environmentally friendly and advanced technologies can help reduce carbon dioxide emissions. Carbon dioxide emissions can also be reduced as agricultural areas are used more efficiently, producing more crops with less soil. In addition, governments with large populations must promote modern farming techniques, such as tunnel farming and organic farming, to help reduce pressure on the environment. Moreover, farmers must be aware of environmental issues, encouraging the use of animal fertilizers, preventing forest encroachment, providing clean inputs in farming activities and rewarding low-carbon-intensive agricultural production will help specific Asian countries to achieve SDGs. Sustainable development policies must be revised on the basis of the conflict between globalization and environmental pollution. In addition, sustainable development policies in densely populated countries in Asia are not conducive to the environment, as these regions rely heavily on fossil fuel energy consumption. These countries can overcome environmental degradation through regional, economic, and social cooperation.

As a future research direction, the impact of renewable energy, globalization and agriculture on environmental pollution could be examined by considering the subcomponents of globalization and ecological footprint. The study could also be expanded to cover more populous countries outside of Asia. Thus, more comprehensive information and a higher level of knowledge can be obtained on the impact of agriculture and renewable energy on environmental pollution.

Footnotes

Availability of data and material

The data that support the findings of this study are openly available on the World Development Indicator page published by World Bank⁴⁰ at https://databank.worldbank.org/source/world-development-indicators. Infrastructure investment data are available online on the OECD page published by OECD¹⁰⁷ retrieved from .

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Arshad Ali

Appendix: Description and measurement of variables and data sources.

Variables	Description	Measurment	Sources
GDP	Gross domestic product	Constant 2015 US$	WDI³⁹
AGR	Agriculture value added	Percentage of GDP	WDI³⁹
REC	Renewable energy consumption	Million tons of oil equivalent (Mtoe)	¹⁰⁷
CO₂	Carbon dioxide emission	Million metric tons (Mmt),	WDI³⁹
KOF	Globalization	Globalization index	¹⁰⁹
EF	Ecological footprint	Global hectares	¹⁰⁸

References

Slocum

Dimitrov

Webb

. The impact of neoliberalism on higher education tourism programs: meeting the 2030 sustainable development goals with the next generation. Tourism Manage Perspect 2019; 30: 33–42. doi:https://doi.org/10.1016/j.tmp.2019.01.004

Burton

. Report on reports: our common future: the world commission on environment and development. Environ: Sci Policy Sustainable Dev 1987; 29: 25–29.

United Nations. Peace, dignity and equality on a healthy planet. https://www.un.org/en/global- issues/population. 2022a.

United Nations. Sustainably manage forests, combat desertification, halt and reverse land degradation, halt biodiversity loss. https://www.un.org/sustainabledevelopment/biodiversity/. 2022b.

Olabi

Obaideen

Elsaid

, et al. Assessment of the pre-combustion carbon capture contribution into sustainable development goals SDGs using novel indicators. Renewable Sustainable Energy Rev 2022; 153: 111710. doi:https://doi.org/10.1016/j.rser.2021.111710

Zhao

Chen

, et al. Challenges toward carbon neutrality in China: strategies and countermeasures. Resour Conserv Recycl 2022; 176: 105959. doi:https://doi.org/10.1016/j.resconrec.2021.105959

USEAPA. Sources of Greenhouse Gas Emissions. https://www.epa.gov/ghgemissions/sources- greenhouse-gas-emissions. 2022.

World Metrological Organization. More bad news for the planet: greenhouse gas levels hit new highs. https://public.wmo.int/en/media/press-release/more-bad-news-planet-greenhouse-gas-levels-hit-new- highs. 2022.

Samour

Moyo

Tursoy

. Renewable energy, banking sector development, and carbon dioxide emissions nexus: a path toward sustainable development in South Africa. Renewable Energy 2022; 193: 1032–1040. doi:https://doi.org/10.1016/j.renene.2022.05.013

10.

Wackernagel

Rees

. Our ecological footprint: reducing human impact on the earth (Vol. 9). New society publishers, 1998.

11.

Sharif

Baris-Tuzemen

Uzuner

, et al. Revisiting the role of renewable and non-renewable energy consumption on Turkey’s ecological footprint: evidence from Quantile ARDL approach. Sustainable Cities Soc 2020; 57: 102138. doi:https://doi.org/10.1016/j.scs.2020.102138

12.

Pata

. Linking renewable energy, globalization, agriculture, CO₂ emissions and ecological footprint in BRIC countries: a sustainability perspective. Renewable Energy 2021; 173: 197–208. doi:https://doi.org/10.1016/j.renene.2021.03.125

13.

Dogan

Majeed

Luni

. Revisiting the nexus of ecological footprint, unemployment, and renewable and non-renewable energy for South Asian economies: evidence from novel research methods. Renewable Energy 2022. doi:https://doi.org/10.1016/j.renene.2022.05.165

14.

Agnoletti

Santoro

. Agricultural heritage systems and agrobiodiversity. Biodivers Conserv 2022: 1–11.

15.

Shabir

Ali

Hashmi

, et al. Heterogeneous effects of economic policy uncertainty and foreign direct investment on environmental quality: cross-country evidence. Environ Sci Pollut Res 2022; 29: 2737–2752. doi:https://doi.org/10.1007/s11356-021-15715-3

16.

Abbasi

Nadeem

, et al. Agricultural and Manufacturing Sector Determinants of Electricity Consumption, Price, and Real GDP from Pakistan. ttps://doi.org/10.5281/zenodo.3604943. 2020a.

17.

Clune

. Conceptualising policy for sustainable agriculture development. Aust J Public Adm 2021; 80: 493–509. doi:https://doi.org/10.1111/1467-8500.12436

18.

Ruben

Cavatassi

Lipper

, et al. Towards food systems transformation—five paradigm shifts for healthy, inclusive and sustainable food systems. Food Secur 2021; 13: 1423–1430. doi:https://doi.org/10.1007/s12571-021-01221-4

19.

Clark

Domingo

Colgan

, et al. Global food system emissions could preclude achieving the 1.5 and 2 C climate change targets. Science 2020; 370: 705–708. doi:https://doi.org/10.1126/science.aba7357

20.

Kumpanalaisatit

Setthapun

Sintuya

, et al. Current status of agrivoltaic systems and their benefits to energy, food, environment, economy, and society. Sustainable Prod Consumption 2022; 33: 952–963. doi:https://doi.org/10.1016/j.spc.2022.08.013

21.

Hernandez Marentes

Venturi

Scaramuzzi

, et al. Traditional forest-related knowledge and agrobiodiversity preservation: the case of the chagras in the indigenous reserve of Monochoa (Colombia). Biodivers Conserv 2022; 31: 2243–2258. doi:https://doi.org/10.1007/s10531-021-02263-y

22.

Singh

Sobti

Jain

, et al. Lora based intelligent soil and weather condition monitoring with internet of things for precision agriculture in smart cities. IET Commun 2022a; 16: 604–618. doi:https://doi.org/10.1049/cmu2.12352

23.

Musa

SFPD

Basir

. Smart farming: towards a sustainable agri-food system. British Food J 2021; 123: 3085–3099. https://doi.org/10.1108/BFJ-03-2021-

24.

Jiang

Khattak

Rahman

. Measuring the simultaneous effects of electricity consumption and production on carbon dioxide emissions (CO₂e) in China: new evidence from an EKC-based assessment. Energy 2021; 229: 120616. doi:https://doi.org/10.1016/j.energy.2021.120616

25.

Adams

Boateng

Acheampong

. Transport energy consumption and environmental quality: does urbanization matter? Sci Total Environ 2020; 744: 140617. doi:https://doi.org/10.1016/j.scitotenv.2020.140617

26.

USEAPA. Global Greenhouse Gas Emissions Data. https://www.epa.gov/ghgemissions/global- greenhouse-gas-emissions-data. 2022.

27.

Rather

Wani

Mumtaz

, et al. Bioenergy: a foundation to environmental sustainability in a changing global climate scenario. J King Saud Univ Sci 2022; 34: 101734. doi:https://doi.org/10.1016/j.jksus.2021.101734

28.

Singh

Abbhishek

Kuttippurath

, et al. Decadal variations in CO₂ during agricultural seasons in India and role of management as sustainable approach. Environ Technol Innov 2022b; 27: 102498. doi:https://doi.org/10.1016/j.eti.2022.102498

29.

Engler

Krarti

. Review of energy efficiency in controlled environment agriculture. Renewable Sustainable Energy Rev 2021; 141: 110786. doi:https://doi.org/10.1016/j.rser.2021.110786

30.

Wassie

. Natural resource degradation tendencies in Ethiopia: a review. Environ Syst Res 2020; 9: 1–29. https://doi.org/10.1186/s40068-020-00194-1

31.

Abbasi

Jiao

Shahbaz

, et al. Asymmetric impact of renewable and non-renewable energy on economic growth in Pakistan: new evidence from a nonlinear analysis. Energy Explor Exploit 2020b; 38: 1946–1967. doi:https://doi.org/10.1177/0144598720946496

32.

Zhao

Patwary

Qayyum

, et al. The determinants of renewable energy sources for the fueling of green and sustainable economy. Energy 2022; 238: 122029. doi:https://doi.org/10.1016/j.energy.2021.122029

33.

Mostafaeipour

Bidokhti

Fakhrzad

, et al. A new model for the use of renewable electricity to reduce carbon dioxide emissions. Energy 2022; 238: 121602. doi:https://doi.org/10.1016/j.energy.2021.121602

34.

Ramzan

Abbasi

Iqbal

, et al. What's at stake? The empirical importance of government revenue and debt and renewable energy for environmental neutrality in the US economy. Renewable Energy 2023; 205: 475–489. doi:https://doi.org/10.1016/j.renene.2023.01.071

35.

Abbas

Alharthi

, et al. Integration of renewable hydrogen in light-duty vehicle: nexus between energy security and low carbon emission resources. Int J Hydrogen Energy 2020; 45: 27958–27968. doi:https://doi.org/10.1016/j.ijhydene.2020.06.177

36.

Agostini

Colauzzi

Amaducci

. Innovative agrivoltaic systems to produce sustainable energy: an economic and environmental assessment. Appl Energy 2021; 281: 116102. doi:https://doi.org/10.1016/j.apenergy.2020.116102

37.

SUSTAINABLE ENERGY. Renewables to overtake coal and become world’s biggest source of electricity generation by 2025, IEA says. https://www.cnbc.com/2022/12/06/renewables-to-be-main-source-of-electricity-generation-by-2025-iea.html. 2022.

38.

Asian Developing Bank. FINANCING CLEAN ENERGY IN DEVELOPING ASIA, https://www.adb.org/sites/default/files/publication/706641/financing-clean-energy-developing-asia.pdf. 2021.

39.

World Bank. Green Energy Can Accelerate Sustainable Growth Across South Asia. https://blogs.worldbank.org/endpovertyinsouthasia/green-energy-can-accelerate-sustainable-growth- across-south-asia. 2022.

40.

World Bank. Expanding Renewable Energy in Pakistan’s Electricity Mix. https://www.worldbank.org/en/news/feature/2020/11/09/a-renewable-energy-future-for-pakistans-power-system. 2020.

41.

OECD. Improving investment conditions could make Indonesia a world leader in clean energy, says OECD. https://www.oecd.org/newsroom/improving-investment-conditions-could-make-indonesia-a- world-leader-in-clean-energy-says-oecd.htm. 2021.

42.

USAID. Developing Renewable Energy in Bangladesh. https://www.usaid.gov/energy/re-bangladesh. 2022.

43.

Wen

Mughal

Zhao

, et al. Does globalization matter for environmental degradation? Nexus among energy consumption, economic growth, and carbon dioxide emission. Energy Policy 2021; 153: 112230. doi:https://doi.org/10.1016/j.enpol.2021.112230

44.

Anwar

Sinha

Sharif

, et al. The nexus between urbanization, renewable energy consumption, financial development, and CO₂ emissions: evidence from selected Asian countries. Environ Dev Sustainability 2022; 24: 6556–6576. doi:https://doi.org/10.1007/s10668-021-01716-2

45.

Sharma

Sinha

Kautish

. Does renewable energy consumption reduce ecological footprint? Evidence from eight developing countries of Asia. J Cleaner Prod 2021; 285: 124867. doi:https://doi.org/10.1016/j.jclepro.2020.124867

46.

Wang

Mehmood

Agyekum

, et al. Associating renewable energy, globalization, agriculture, and ecological footprints: implications for sustainable environment in South Asian countries. Int J Environ Res Public Health 2022; 19: 10162. doi:https://doi.org/10.3390/ijerph191610162

47.

Grossman

Krueger

. Environmental impacts of a North American free trade agreement. National Bureau of Economic Research Working Paper. No. 3194, Cambridge. 1991.

48.

Shafik

Bandyopadhyay

. Economic growth and environmental quality: time-series and cross- country evidence (Vol. 904). World Bank Publications, 1992.

49.

Panayotou

. Empirical tests and policy analysis of environmental degradation at different stages of economic development, International Labour Organization Working paper no. 292778. 1993.

50.

Abbasi

Awan

Bandyopadhyay

, et al. Investigating the inverted N-shape EKC in the presence of renewable and nuclear energy in a global sample. Clean Technol Environ Policy 2023; 25: 1179–1194. doi:https://doi.org/10.1007/s10098-022-02436-5

51.

Liu

Ali

Chen

, et al. The effect of transport infrastructure (road, rail, and air) investments on economic growth and environmental pollution and testing the validity of EKC in China, India, Japan, and Russia. Environ Sci Pollut Res 2022a: 1–15.

52.

Luo

Guo

Ali

, et al. A dynamic analysis of the impact of FDI, on economic growth and carbon emission, evidence from China, India and Singapore. Environ Sci Pollut Res 2022; 29: 82256–82270. doi:https://doi.org/10.1007/s11356-022-21546-7

53.

Liu

Sadiq

Ali

, et al. Does tourism development, energy consumption, trade openness and economic growth matters for ecological footprint: testing the environmental Kuznets curve and pollution haven hypothesis for Pakistan. Energy 2022b; 245: 123208. doi:https://doi.org/10.1016/j.energy.2022.123208

54.

Ali

Radulescu

Lorente

, et al. An analysis of the impact of clean and non- clean energy consumption on economic growth and carbon emission: evidence from PIMC countries. Environ Sci Pollut Res 2022a: 1–14.

55.

Zeraibi

Ahmed

Shehzad

, et al. Revisiting the EKC hypothesis by assessing the complementarities between fiscal, monetary, and environmental development policies in China. Environ Sci Pollut Res 2022; 29: 23545–23560. doi:https://doi.org/10.1007/s11356-021-17288-7

56.

Murshed

Haseeb

Alam

. The environmental Kuznets curve hypothesis for carbon and ecological footprints in South Asia: the role of renewable energy. GeoJournal 2022; 87: 2345–2372. doi:https://doi.org/10.1007/s10708-020-10370-6

57.

Dai

Zhang

Cheng

, et al. Whether ecological measures have influenced the environmental Kuznets curve (EKC)? An analysis using land footprint in the Weihe River Basin, China. Ecol Indic 2022; 139: 108891. doi:https://doi.org/10.1016/j.ecolind.2022.108891

58.

Jahanger

Hossain

, et al. Going away or going green in NAFTA nations? Linking natural resources, energy utilization, and environmental sustainability through the lens of the EKC hypothesis. Resour Policy 2022: 103091. doi:https://doi.org/10.1016/j.resourpol.2022.103091

59.

Islam

Irfan

Shahbaz

, et al. Renewable and non-renewable energy consumption in Bangladesh: the relative influencing profiles of economic factors, urbanization, physical infrastructure and institutional quality. Renewable Energy 2022; 184: 1130–1149. doi:https://doi.org/10.1016/j.renene.2021.12.020

60.

Ali

Guo

Kartal

, et al. The impact of renewable and non-renewable energy consumption on carbon emission intensity in China: fresh evidence from novel dynamic ARDL simulations. J Environ Manag 2022b; 320: 115782. doi:https://doi.org/10.1016/j.jenvman.2022.115782

61.

Shang

Han

Gozgor

, et al. The impact of climate policy uncertainty on renewable and non-renewable energy demand in the United States. Renewable Energy 2022; 197: 654–667. doi:https://doi.org/10.1016/j.renene.2022.07.159

62.

Djellouli

Abdelli

Elheddad

, et al. The effects of non-renewable energy, renewable energy, economic growth, and foreign direct investment on the sustainability of African countries. Renewable Energy 2022; 183: 676–686. doi:https://doi.org/10.1016/j.renene.2021.10.066

63.

Wang

Rehman

Fahad

. The dynamic influence of renewable energy, trade openness, and industrialization on the sustainable environment in G-7 economies. Renewable Energy 2022; 198: 484–491. doi:https://doi.org/10.1016/j.renene.2022.08.067

64.

Chen

Zhang

Wang

. Trade openness, economic growth, and energy intensity in China. Technol Forecast Soc Change 2022; 179: 121608. doi:https://doi.org/10.1016/j.techfore.2022.121608

65.

Liu

Cui

Zhang

. Exploring the causal relationship between urbanization and air pollution: evidence from China. Sustainable Cities Soc 2022; 80: 103783. doi:https://doi.org/10.1016/j.scs.2022.103783

66.

Chatti

Majeed

. Information communication technology (ICT), smart urbanization, and environmental quality: evidence from a panel of developing and developed economies. J Cleaner Prod 2022; 366: 132925. doi:https://doi.org/10.1016/j.jclepro.2022.132925

67.

Wang

. Does urbanization redefine the environmental Kuznets curve? An empirical analysis of 134 countries. Sustainable Cities Soc 2022; 76: 103382. doi:https://doi.org/10.1016/j.scs.2021.103382

68.

Wang

Sun

. The impact of foreign direct investment on environmental pollution in China: corruption matters. Int J Environ Res Public Health 2020; 17: 6477. doi:https://doi.org/10.3390/ijerph17186477

69.

Kim

Seok

. The impact of foreign direct investment on CO₂ emissions considering economic development: evidence from South Korea. J Int Trade Econ Dev 2022: 1–16.

70.

Mesagan

Olunkwa

. Heterogeneous analysis of energy consumption, financial development, and pollution in Africa: the relevance of regulatory quality. Util Policy 2022; 74: 101328. doi:https://doi.org/10.1016/j.jup.2021.101328

71.

Huang

Chen

Wei

, et al. The impacts of FDI inflows on carbon emissions: economic development and regulatory quality as moderators. Front Energy Res 2022; 9: 938. doi:https://doi.org/10.3389/fenrg.2021.820596

72.

Wang

Zhang

. Effects of environmental regulation on CO₂ emissions: an empirical analysis of 282 cities in China. Sustainable Prod Consumption 2022; 29: 259–272. doi:https://doi.org/10.1016/j.spc.2021.10.016

73.

You

Zhang

Lee

. The dynamic impact of economic growth and economic complexity on CO₂ emissions: an advanced panel data estimation. Econ Anal Policy 2022; 73: 112–128. doi:https://doi.org/10.1016/j.eap.2021.11.004

74.

Khezri

Heshmati

Khodaei

. Environmental implications of economic complexity and its role in determining how renewable energies affect CO₂ emissions. Appl Energy 2022; 306: 117948. doi:https://doi.org/10.1016/j.apenergy.2021.117948

75.

Adebayo

Altuntaş

Goyibnazarov

, et al. Dynamic effect of disintegrated energy consumption and economic complexity on environmental degradation in top economic complexity economies. Energy Rep 2022; 8: 12832–12842. doi:https://doi.org/10.1016/j.egyr.2022.09.161

76.

Jahanger

. Impact of globalization on CO₂ emissions based on EKC hypothesis in developing world: the moderating role of human capital. Environ Sci Pollut Res 2022; 29: 20731–20751. doi:https://doi.org/10.1007/s11356-021-17062-9

77.

Farooq

Ozturk

Majeed

, et al. Globalization and CO₂ emissions in the presence of EKC: a global panel data analysis. Gondwana Res 2022; 106: 367–378. doi:https://doi.org/10.1016/j.gr.2022.02.002

78.

Nan

Huo

You

, et al.

Globalization spatial spillover effects and carbon emissions: what is the role of economic complexity?

Energy Econ 2022; 112: 106184. doi:https://doi.org/10.1016/j.eneco.2022.106184

79.

Chien

Ajaz

Andlib

, et al. The role of technology innovation, renewable energy and globalization in reducing environmental degradation in Pakistan: a step towards sustainable environment. Renewable Energy 2021; 177: 308–317. doi:https://doi.org/10.1016/j.renene.2021.05.101

80.

Omri

Slimani

, et al.

Environmental degradation and life satisfaction: do governance and renewable energy matter?

Technol Forecast Soc Change 2022; 175: 121375. doi:https://doi.org/10.1016/j.techfore.2021.121375

81.

Usman

Alola

Sarkodie

. Assessment of the role of renewable energy consumption and trade policy on environmental degradation using innovation accounting: evidence from the US. Renewable Energy 2020; 150: 266–277. doi:https://doi.org/10.1016/j.renene.2019.12.151

82.

Ahmad

Draz

Ozturk

, et al. Looking for asymmetries and nonlinearities: the nexus between renewable energy and environmental degradation in the Northwestern provinces of China. J Cleaner Prod 2020; 266: 121714. doi:https://doi.org/10.1016/j.jclepro.2020.121714

83.

Suki

Sharif

, et al. The role of technology innovation and renewable energy in reducing environmental degradation in Malaysia: a step towards sustainable environment. Renewable Energy 2022; 182: 245–253. doi:https://doi.org/10.1016/j.renene.2021.10.007

84.

Kartal

. The role of consumption of energy, fossil sources, nuclear energy, and renewable energy on environmental degradation in top-five carbon producing countries. Renewable Energy 2022; 184: 871–880. doi:https://doi.org/10.1016/j.renene.2021.12.022

85.

Ibrahim

Ajide

Usman

, et al.

Heterogeneous effects of renewable energy and structural change on environmental pollution in Africa: do natural resources and environmental technologies reduce pressure on the environment?

Renewable Energy 2022; 200: 244–256. doi:https://doi.org/10.1016/j.renene.2022.09.134

86.

Koengkan

Fuinhas

Kazemzadeh

, et al. The impact of renewable energy policies on deaths from outdoor and indoor air pollution: empirical evidence from Latin American and Caribbean countries. Energy 2022; 245: 123209. doi:https://doi.org/10.1016/j.energy.2022.123209

87.

Zeng

Bao

McFarland

. Clean energy substitution: the effect of transitioning from coal to gas on air pollution. Energy Econ 2022; 107: 105816. doi:https://doi.org/10.1016/j.eneco.2022.105816

88.

Alsaleh

Abdul-Rahim

. Moving toward sustainable environment: the effects of hydropower industry on water quality in EU economies. Energy Environ 2022; 33: 1304–1325. doi:https://doi.org/10.1177/0958305X211039452

89.

Abbasi

Adedoyin

Radulescu

, et al. The role of forest and agriculture towards environmental fortification: designing a sustainable policy framework for top forested countries. Environ Dev Sustainability 2022; 24: 8639–8666. doi:https://doi.org/10.1007/s10668-021-01803-4

90.

Usman

Anwar

Yaseen

, et al. Unveiling the dynamic relationship between agriculture value addition, energy utilization, tourism and environmental degradation in South Asia. J Public Affairs 2022a; 22: e2712. doi:https://doi.org/10.1002/pa.2712

91.

Adekoya

Ajayi

Suhrab

, et al. How critical are resource rents, agriculture, growth, and renewable energy to environmental degradation in the resource-rich African countries? The role of institutional quality. Energy Policy 2022; 164: 112888. doi:https://doi.org/10.1016/j.enpol.2022.112888

92.

Muoneke

Okere

Nwaeze

. Agriculture, globalization, and ecological footprint: the role of agriculture beyond the tipping point in the Philippines. Environ Sci Pollut Res 2022: 1–25.

93.

Raihan

Tuspekova

. Dynamic impacts of economic growth, energy use, urbanization, agricultural productivity, and forested area on carbon emissions: new insights from Kazakhstan. World Dev Sustainability 2022a; 1: 100019. doi:https://doi.org/10.1016/j.wds.2022.100019

94.

Raihan

Tuspekova

. Toward a sustainable environment: nexus between economic growth, renewable energy use, forested area, and carbon emissions in Malaysia. Resour Conserv Recycl Adv 2022b; 15: 200096. doi:https://doi.org/10.1016/j.rcradv.2022.200096

95.

Raihan

Tuspekova

. Dynamic impacts of economic growth, energy use, urbanization, tourism, agricultural value-added, and forested area on carbon dioxide emissions in Brazil. J Environ Stud Sci 2022c; 12: 794–814. doi:https://doi.org/10.1007/s13412-022-00782-w

96.

Adebayo

Kirikkaleli

. Impact of renewable energy consumption, globalization, and technological innovation on environmental degradation in Japan: application of wavelet tools. Environ Dev Sustainability 2021; 23: 16057–16082. doi:https://doi.org/10.1007/s10668-021-01322-2

97.

Aladejare

. Natural resource rents, globalisation and environmental degradation: new insight from 5 richest African economies. Resources Policy 2022; 78: 102909. doi:https://doi.org/10.1016/j.resourpol.2022.102909

98.

Usman

Balsalobre-Lorente

Jahanger

, et al.

Pollution concern during globalization mode in financially resource-rich countries: do financial development, natural resources, and renewable energy consumption matter?

Renewable Energy 2022b; 183: 90–102. doi:https://doi.org/10.1016/j.renene.2021.10.067

99.

Usman

Jahanger

Makhdum

MSA

, et al. How do financial development, energy consumption, natural resources, and globalization affect Arctic countries’ economic growth and environmental quality? An advanced panel data simulation. Energy 2022c; 241: 122515. doi:https://doi.org/10.1016/j.energy.2021.122515

100.

Rehman

Alam

Ozturk

, et al. Globalization and renewable energy use: how are they contributing to upsurge the CO₂ emissions? A global perspective. Environ Sci Pollut Res 2022: 1–14.

101.

Sun

Anwar

Razzaq

, et al. Asymmetric role of renewable energy, green innovation, and globalization in deriving environmental sustainability: evidence from top-10 polluted countries. Renewable Energy 2022; 185: 280–290. doi:https://doi.org/10.1016/j.renene.2021.12.038

102.

Yurtkuran

. The effect of agriculture, renewable energy production, and globalization on CO₂ emissions in Turkey: a bootstrap ARDL approach. Renewable Energy 2021; 171: 1236–1245. doi:https://doi.org/10.1016/j.renene.2021.03.009

103.

Ozturk

Acaravci

. The long-run and causal analysis of energy, growth, openness and financial development on carbon emissions in Turkey. Energy Econ 2013; 36: 262–267. doi:https://doi.org/10.1016/j.eneco.2012.08.025

104.

Syed

Bouri

. Impact of economic policy uncertainty on CO₂ emissions in the US: evidence from bootstrap ARDL approach. J Public Affairs 2022; 22: e2595. doi:https://doi.org/10.1002/pa.2595

105.

Salahodjaev

Isaeva

. Post-Soviet states and CO₂ emissions: the role of foreign direct investment. Post-Communist Econ 2021: 1–22. https://doi.org/10.1080/14631377.2021.1965360

106.

Ulucak

Bilgili

. A reinvestigation of EKC model by ecological footprint measurement for high, middle and low income countries. J Cleaner Prod 2018; 188: 144–157. doi:https://doi.org/10.1016/j.jclepro.2018.03.191

107.

OECD. Renewable energy. https://data.oecd.org/energy/renewable-energy.htm. 2020.

108.

Global Footprint Network. TOTAL ECOLOGICAL FOOTPRINT OF COUNTRY'S POPULATION (in global hectares). https://data.footprintnetwork.org/#/. 2020.

109.

KOF Swiss Economic institute. KOF Globalisation Index. https://kof.ethz.ch/en/forecasts-and- indicators/indicators/kof-globalisation-index.html. 2022.

110.

Pesaran

. General diagnostic tests for cross-sectional dependence in panels. University of Cambridge, Cambridge Working Papers in Economics, 435. https://doi.org/10.1007/s00181-020-01875-7. 2004.

111.

Pesaran

Ullah

Yamagata

. A bias-adjusted LM test of error cross-section independence. Econom J 2008; 11: 105–127. doi:https://doi.org/10.1111/j.1368-423X.2007.00227.x

112.

Breusch

Pagan

. The Lagrange multiplier test and its applications to model specification in econometrics. Rev Econ Stud 1980; 47: 239–253. https://doi.org/10.2307/2297111

113.

Baltagi

Feng

Kao

. A Lagrange multiplier test for cross-sectional dependence in a fixed effects panel data model. J Econom 2012; 170: 164–177. doi:https://doi.org/10.1016/j.jeconom.2012.04.004

114.

Pesaran

. A simple panel unit root test in the presence of cross-section dependence. J App Econom 2007; 22: 265–312. doi:https://doi.org/10.1002/jae.951

115.

Westerlund

. Testing for error correction in panel data. Oxf Bull Econ Stat 2007; 69: 709–748.

116.

Badulescu

Simut

Mester

, et al. Do economic growth and environment quality contribute to tourism development in EU countries? A panel data analysis. Technol Econ Dev Econ 2021; 27: 1509–1538. doi:https://doi.org/10.3846/tede.2021.15781

117.

Zhang

Zhao

Cai

, et al. Influence of renewable energy infrastructure, Chinese outward FDI, and technical efficiency on ecological sustainability in belt and road node economies. Renewable Energy 2023.

118.

Eberhardt

Bond

. Cross-section dependence in nonstationary panel models: a novel estimator. MPRA Paper. University Library of Munich, Germany, 17692. https://mpra.ub.uni- muenchen.de/id/eprint/17692. 2009.

119.

Dumitrescu

Hurlin

. Testing for granger non-causality in heterogeneous panels. Econ Model 2012; 29: 1450–1460. doi:https://doi.org/10.1016/j.econmod.2012.02.014

120.

Pesaran

. General diagnostic tests for cross-sectional dependence in panels. Empir Econ 2021; 60: 13–50. doi:https://doi.org/10.1007/s00181-020-01875-7

121.

Westerlund

. Panel cointegration tests of the fisher effect. J Appl Econom 2008; 23: 193–233. https://doi.org/10.1002/jae.967

122.

Abbasi

Kirikkaleli

Altuntaş

. Carbon dioxide intensity of GDP and environmental degradation in an emerging country. Environ Sci Pollut Res 2022; 29: 84451–84459. doi:https://doi.org/10.1007/s11356-022-21679-9

123.

Eberhardt

. Estimating panel time-series models with heterogeneous slopes. Stata J 2012; 12: 61–71. doi:https://doi.org/10.1177/1536867X1201200105

124.

IPCC. Climate Change 2022, Mitigation of Climate Change, Summary for Policymakers. https://www.ipcc.ch/report/ar6/wg3/downloads/report/IPCC_AR6_WGIII_SPM.pdf. 2022.

125.

Khan

SAR

Quddoos

Akhtar

, et al. Re- investigating the nexuses of renewable energy, natural resources and transport services: a roadmap towards sustainable development. Environ Sci Pollut Res 2022; 29: 13564–13579. doi:https://doi.org/10.1007/s11356-021-16702-4

126.

Pesaran

Yamagata

. Testing slope homogeneity in large panels. J Econom 2008; 142: 50–93. https://doi.org/10.1016/j.jeconom.2007.05.010

127.

World Economic Forum. These will be the world's most populous countries by 2030. https://www.weforum.org/agenda/2022/08/world-population-countries-india-china-2030/. 2022.

128.

World Population Review. Ecological Footprint by Country 2022. https://worldpopulationreview.com/country-rankings/ecological-footprint-by-country. 2022.

129.

Zhao

Sinha

Inuwa

, et al. Does structural transformation in economy impact inequality in renewable energy productivity? Implications for sustainable development. Renewable Energy 2022b; 189: 853–864. doi:https://doi.org/10.1016/j.renene.2022.03.050