The impact of agricultural intensification on carbon dioxide emissions and energy consumption: A comparative study of developing and developed nations

Energy consumption has become a necessity in today’s world, and economies in developing nations cannot thrive without it. Countries with less developed economies face the same challenges of achieving sustained economic growth as those with more advanced economies. Herein, we examine the environmental Kuznets curve (EKC) hypothesis by looking at the interplay between GDP growth, energy use, agricultural output, and the effects of carbon dioxide (CO₂) emissions. From 1991 to 2016, we used panel and quantile regression analyses to compare emissions in nine developing countries with those in 13 developed countries. There is the beginning of a reverse U-shaped relationship between agricultural energy use and greenhouse gas emissions. As a result, the verified EKC hypothesis paves the way for a watershed moment in the progress of industrialized nations’ economies. The estimated results of agriculture have a favorable impact on CO₂ emissions by 15.16 percent but a negative influence of 2.92 percent on CO₂ emissions from using liquid fuels, leading to more severe environmental deterioration. Additionally, in developing countries, feed cropping, deforestation, biomass burning, and deep soil and cropping all have detrimental consequences on the ecosystem. There is a negative correlation between CO₂ emissions and economic growth in developing countries and their energy consumption. Although the EKC hypothesis for CO₂ emissions was rejected at lower quantiles, it was validated for Qatar, Canada, China, and other high-emitting economies according to the empirical estimation of quantile regression. The findings of this study have important policy implications for reducing carbon dioxide emissions, suggesting that policymakers account for the stage of economic growth currently being experienced when formulating measures to cut energy use and protect the environment. Possible solutions to mitigate environmental degradation include enactment of policies to reduce energy consumption.

Introduction

Both in developing and developed nations, the link between economic growth and CO₂ emissions remains debatable and doubtful. Economies can use this correlation to develop policies for managing energy consumption and advancing their secure energy resources (Gnangoin et al., 2022; Shahbaz et al., 2022; Tutak and Brodny, 2022; Xia et al., 2022). This study measures countries’ economic growth based on their agricultural sector, cereal yield, renewable energy, manufacturing, and commerce. Quantile regression techniques with panel data have not been studied in this field, despite the fact that a number of recent studies have performed in-depth analyses of the relationship between economic growth and CO₂ emissions (Liu H. et al., 2022; Raghutla et al., 2022). The current environmental Kuznets curve (EKC) theory suggests that economic growth is associated with a decline in environmental effectiveness (Bradley, 2021; Liu Z. et al., 2022). Consequently, there is less concern for the profound exhaustion of oil or magnesium and more concern for air quality, global warming, and the divine emanations of mechanical and industrial production.

Greenhouse gas emissions are a problem in many countries, and modern agriculture is a major contributor. In many developing countries, agriculture is now the leading source of CO₂ emissions. In particular, unsustainable agricultural practices—such as bush burning, deforestation, and the burning of biomass fuel—account for 21% of the world’s greenhouse gas emissions and are responsible for the oxidation of organic compounds in soil. Soil organic compounds from cultivated land can mitigate environmental CO₂ emissions from agricultural production and modification. In this light, both developing and developed nations need to investigate the connection between rising agricultural output and increasing carbon dioxide emissions (Han et al., 2022). Quantile regression and the EKC hypothesis examined how economic growth impacts agricultural output and energy consumption. However, agriculture is essential for several other reasons, including non-oil exports, food security, and foreign exchange. It plays an important part in a growing economy, as evidenced by its share of total energy consumption as measured by gross national product per capita and its presence in the manufacturing and commercial sectors (Koondhar et al., 2021; Pakrooh et al., 2021). By expanding and improving its spending on sustainable development, the country can lessen its carbon footprint and make its infrastructure more resilient to climate change (Kocak and Alnour, 2022). Several concepts related to CO₂ emissions have been investigated using the environmental Kuznets curve (EKC). The EKC, which promotes rapid economic expansion at the price of environmental sustainability, is not the best growth path for developing countries (Balsalobre-Lorente et al., 2021; Beyene, 2022). The U-shaped environmental Kuznets curve (EKC) shows that environmental degradation is proportional to income, rising as income rises due to agricultural output and falling once income is no longer a constraint (Liu and Lai, 2021), but if trade policies and economic growth are coordinated, the energy sector will benefit. Keeping the economy growing at its current rate is not conducive to protecting the environment (Leal and Marques, 2020). A large amount of production in emerging economies travels to fulfill consumption in wealthy countries, contributing to the renowned carbon leakage problem and the embedded carbon emissions in exports not mentioned in the production-based emission accounting.

Agriculture accounted for 16.5 billion tons of the world’s total agri-food production in 2019, making it a substantial contributor to greenhouse gas emissions and carbon dioxide emissions in developed economies due to its reliance on fossil fuels for its energy demands (UN, 2021). Although agricultural output fell by 2% a decade ago, GHG emissions from farms increased to 582 million metric tons in 2017. Nonetheless, farmers help with CO₂ emission confiscation efforts through reforestation, wetland restoration, grassland preservation, and eliminating greenhouse gases (Aguilera et al., 2021). The most important takeaway from this research was identifying issues and CO₂ emissions associated with using liquid fuels in developing and developed nations’ agricultural, industrial, and commercial sectors. In addition, the existing hypothesis and economic factors in the 22 countries influence the income-induced EKC emissions from agriculture. This study hypothesized that agriculture production in developing countries would be more integrated than in developed countries. In addition, the liquid consumption of developing (Brazil, Argentina, Mexico, and Malaysia) countries is growing due to agricultural and industrial production, while the intensity level of CO₂ emissions in developed (the Netherlands, Germany, and Canada) and developing (Russia, Qatar, and Malaysia) countries is higher. Agricultural goods’ production and consumption trends in developing and developed countries are compared, as are the inputs required to produce these goods. Agricultural output was evaluated across a wide range of cereals, including wheat, rice, maize, and more. From a global perspective, nine developing countries and 13 developed countries are analyzed for their CO₂ emissions and liquid fuel consumption in the agriculture sector, with maize being the most intensively studied crop and grain being the least.

Produced and consumed agricultural goods in developing and developed countries are compared in terms of output and input patterns. It was a gauge of grain production in the agricultural sector overall. Carbon dioxide emissions are measured in terms of agricultural output in both developing and developed countries (see Figure 1). There was a noticeable decrease in the expected results across all commodities, with the cereal output growth rate being the most noticeable. CO₂ emissions associated with farming and grain harvesting are shown in Figure 2. Using the estimated values for agriculture (AGR) and cereal (CY), we first calculated the sum of ARG and CY for whole years, and then we divided that number by the total number of developing and developed countries’ sum of ARG and CY values. This allowed us to determine the actual percentage of individual countries in Figure 2 and Figure 3, and it allowed us to examine the highest influence of explanatory variables with percentages. The highest combined AGR and CY growth rate is %. China has the highest cereal yield (in kilograms per hectare) among developing countries due to its high value-added agriculture, forestry, and fishing. In addition, 45 percent of agricultural output is the root of 23 percent of C-LF. Furthermore, Turkey has the highest AGR production at 6%, including 2% each of C-LF and C-EM (Yu et al., 2020; Yang et al., 2021b; Le, 2022).

FIGURE 1

FIGURE 1. Agriculture productions. Source: http://www.fao.org/3/x9447e07.htm#Notep.

FIGURE 2

FIGURE 2. Carbon emissions with agriculture and cereal production.

FIGURE 3

FIGURE 3. CEM of developing and developed countries.

There has been conflicting evidence from previous research on the EKC’s effect on agricultural output. No clear distinction can be made between the CO₂ emissions emitted by agriculture production and the emissions stimulated by the various methods of cultivation practice and new mechanical strategies because not enough research has been carried out to identify and examine the impact of environmental degradation on agriculture, industries, and trade (Xu D. et al., 2022; Gaies et al., 2022; Jiang et al., 2022). The EKC development method was refined on the back of a wealth of clean data, an abundance of serious resources, and a massive natural windfall; as a result, it is a model that other developing countries should emulate (Wang et al., 2021a; Chen et al., 2022). The peak energy of intensity theory states that energy rises during industrialization, reaches a peak, and then declines, as borne out by CO₂ emissions and the EKC. According to the Sun, a carbon emission EKC peak occurred due to abnormal economic and development growth only in developing countries (Mardani et al., 2019; Yang et al., 2021a; Yirong, 2022).

A portion of the literature investigates the relationship between the EKC and national income and greater environmental quality and their effects on developed and developing nations (Balado-Naves et al., 2018; Balsalobre-Lorente et al., 2021). The EKC starting point revealed an inverted U-shaped relationship between per capita income and energy intensity in developing countries, and the error correction model identified CO₂ emissions (Rashid Gill et al., 2018; Ben Cheikh et al., 2021). By looking at the data and using the existing EKC hypothesis, it was found that developed countries have less inequality than developing countries (Farooq et al., 2022). The evidence of the inverted U hypothesis, concerning the relationship between economic growth and inequality, contradicts the hypothesis. It indicates that income inequality was greater in developing nations than in developed nations, but after a certain point, economic growth will reduce environmental pressure (Akram et al., 2020; Liu and Lai, 2021; Tenaw and Beyene, 2021). We examine how agricultural output affects the EKC, GDP growth, CO₂ emissions, and energy consumption. CEM (C-EM and C-LF) is depicted in Figure 3; as a developing country, the highest carbon emission is recorded in Qatar, which accounts for 28% of C-EM and 15% of C-LF, while a developed country such as Canada accounts for 9% of C-EM and 10% of C-LF (Xu G. et al., 2019). The lowest C-EM and C-LF are recorded in Nigeria and Turkey in developing and developed countries (Billig et al., 2019; Hao et al., 2020; Shabani et al., 2021). Due to CO₂ spillage issues and the need to enclose CO₂ emissions, a sizable portion of the developing world’s economic output is met by the generation sector (Amin et al., 2020; Jiang et al., 2021). In this study, we looked at 22 different nations and disentangled the growth in CO₂ emissions from the expansion of agricultural production and the use of machinery in the field. This implication will aid in shifting the focus of governments and private organizations in developing and developed nations toward effective measures to curb carbon dioxide (CO₂) emissions (Lee et al., 2023).

Earlier research has shown a growing interest in the intersection of agricultural products, industrial output, trade, and energy efficiency for both the developed and developing worlds. Three distinct categories may be drawn from the literature on economic growth as affected by agricultural output, commercial activity, industrial output, and ecological factors. The first set of studies examined how economic expansion affects environmental quality using the well-established EKC hypothesis of an inverse U-shaped link between economic expansion and environmental degradation. At first (Rashid Gill et al., 2018), we considered the “grow now and clean later” idea while discussing the significance of the EKC hypothesis to the global environmental crisis. Governments should create a unique policy for renewable energy by taxing fossil fuels and subsidizing green alternatives to reduce pollution levels. Growing first and cleaning up later was an idea explored by Rashid Gill et al. (2018) as a solution to the world’s environmental problems. Since energy is the single most influential factor in determining environmental impact, policymakers should provide incentives for renewable energy by heavily taxing fossil fuels (Leal and Marques, 2020; Munir et al., 2020; Wang and Yan, 2022). Expenditures on manufacturing and overall economic growth were the subjects of the second body of work (Odedokun, 1999). The monitoring sector’s impact on economic growth was discussed in this literature (Hamilton and Kelly, 2017; Du et al., 2022; Duan et al., 2022). Lastly, the third group of researchers examined how agricultural practices and carbon dioxide emissions interact (Li and Zheng, 2021; Deng et al., 2022; Wang, 2022). Most of the research looked at how CO2 emissions affected things such as agricultural output, GDP growth, and industrialization; however, very few looked at how developing and developed countries affected CO2 emissions.

To the best of our information, we found no previous research that used panel data to examine the effects of the EKC hypothesis on agricultural output across 22 countries (nine developing and 13 developed). In light of those, as mentioned earlier, this study contributes to our understanding of the prior study by critiquing a subset of the EKC hypothesis that proposes an inverted U-shaped relationship between national income (while controlling for agriculture production effects) and environmental quality in developing and developed countries (Ridzuan et al., 2020; Jiang et al., 2021). In light of the EKC hypothesis, this research seeks to identify important implications for economic and environmental mitigation policies by analyzing the connections between economic growth, agriculture, cereal yield, renewable energy consumption, industries and trade, and CO₂ emissions in both developing and developed nations. Additionally, this study uses two distinct technical models to determine the EKC effects, one of which (total CO₂ emissions, C-EM) is an endogenous variable and the other (CO₂ emission, C-LF) is by liquid sources.

The contribution of this study to the field of literature is significant. Adopting the panel and quantile regression models is the main advantage of these investigations as a robust answer to the problems of agricultural goods, industrial production, industries and trade, and energy efficiency. Second, most of these studies emphasize the smart grid system and renewable energy, while fossil energy is the main fuel in economic sectors. There has been a lack of focus on agriculture products, industries, and trade with energy consumption and CO₂ emissions as well as adjusting prices by using panel quantile regression models by percentile. Third, the present study is similar to the previous research in that it uses a panel quantile regression approach to identify a correlation between energy consumption and economic expansion. Fourth, there are fewer gaps in the literature than in other studies. While it has already been mentioned that transportation accounts for a significant portion of total CO₂ emissions, we also considered agricultural activities an important source. In addition, the current study is distinct because of its emphasis on energy consumption, its use of a more expansive time frame for precise analysis, and its evaluation of the disparity between CO₂ emissions from developing and developed countries’ energy consumption. A panel quantile regression method is recommended for CO₂ emissions from different countries with non-additive fixed effects (FE) and random effects (RE). The study confirms agriculture productivity through the use of policy frameworks that are based on panel estimation (cross-sectional dependence) techniques and focus on keeping and altering the CLAD environment in countries. This study’s panel quantile regression is novel and essential for foreseeing the development of the agricultural energy market in both developing and developed nations. Fossil energy consumption and economic growth, particularly in the agricultural sector, may be explored further in this study. Furthermore, it aids policymakers in understanding the energy and climate challenges they face. It organizes the structure of this study as follows: Introduction provides a summary of the relevant literature; Introduction demonstrates the procedure for collecting data; Introduction provides an overview of the study’s findings; and Introduction draws conclusions and makes recommendations.

Data and methodology

Data specification

In estimations, panel data for 22 developing and developed countries for the period 1991–2010 are used. Data availability also affected the length of the extracted database’s data. Due to a shortage of data, we only examined nine developing and 13 developed countries over the 25-year period. The data definition and source are depicted in Table 1; twenty-five countries’ varying degrees of social and economic development are mapped out by the human development index (HDI). The higher the HDI, the more advanced a country is, and developed nations have an HDI of 0.8 or higher in terms of education, life expectancy, and standard of living. As a result of data availability, this investigation contacted 22 national economic datasets (Boyle, 2022). Analyzing environmental pollution (C-EM and C-LF), energy consumption (REN), and economic growth (GNI, GNI², IDC, and TRD) across nine developing (Argentina, Brazil, China, Mexico, Malaysia, Nigeria, the Philippines, Qatar, and the Russian Federation) and thirteen developed (Australia, Canada, Chile, Germany, France, Greece, Israel, Italy, the Netherlands, Norway, Spain, Sweden, and Switzerland) countries at varying income levels and within the context of the existing hypothesis of the EKC. To provide a visual inspection of the variables, the relevant line graphs of the panel variables are presented (Appendix A).

TABLE 1

TABLE 1. Panel variables.

The response variables are measured in metric tons per capita (C-EM) is equivalent to metric tons per capita (C-LF), but C-LF also considers liquid consumption in various economies. C-EM regressors have been selected and analyzed based on previous research with similar aims (Kong and Khan, 2019; Chen X. et al., 2020; Khan, 2021; Usman and Makhdum, 2021; Adekoya et al., 2022; Shah et al., 2022). Consistent with previous findings, the factors of C-EM-retained explanatory indicators are reliable. Furthermore, concluding that there is a link between CO2 emissions and economic growth that has been the subject of numerous research studies, but the results have been contradictory because both developing and developed countries’ economic growth is examined when aspects such as agriculture, trade, industry, and renewable energy are considered. (Nwaka et al., 2020; Beyene, 2022; Farooq et al., 2022; Rehman et al., 2022). On the other hand, the explanatory indicators such as AGR, CY, REN, GNI, GNI², IDC, and TRD can be studied in detail in panels A and B. Table 1. Each residual in the set has its descriptive statistics calculated. According to Table 2, the experimental indicators of AGR, CY, REN, GNI, GNI², IDC, and TRD are inconsistent with the normal distribution hypothesis, as determined by the Jarque–Bera statistics. Moreover, given that the experimental indicators have been log-transformed, the exponents’ regression coefficient can be understood intuitively. The log-transformed version of the data is altered to make it more roughly normal. Explanatory variables of the CEM allow for greater specificity, and the change in descriptive value over time can be tracked. Across both developing and developed countries, all CEM predictors are used, with C-LF having the most impact on the average ARG value. Covariance is used to examine the relationship between countries. The explanatory factors are split into two panels (A and B) that each focus on one of the two primary regressors (C-EM and C-LF). Based on the data in Table 3, both C-EM and AGR have negative covariance, with -0.124, and C-LF and ARG have negative covariance, with -3.377. These data suggest that both of these associations are negative. Similarly, we find positive coefficients in the covariance between C-EM and C-LF and CY, GNI, GNI², IDC, and TRD.

TABLE 2

TABLE 2. Summary of descriptive statistics.

TABLE 3

TABLE 3. Covariance analysis.

Methodology

In the first step, we calculated the descriptive statistics, covariance, and unit root test of overall developing and developed countries, and then in the second step, we separately computed C-EM and C-LF in two panels. Next, FMOLS, RE, FE, and quantile regression (QR) (QR (25), QR (50), and QR (75)) for both developing and developed countries overall were calculated (Table 4). We worked on the computation for both groups so that the C-EM and C-LF results would be more illustrative of developing and developed countries, respectively (Table 5). The description of the model is shown as follows:

TABLE 4

TABLE 4. FMOLS, random effect (RE), fixed effect (FE), and quantile regression across countries.

TABLE 5

TABLE 5. FMOLS, random effect (RE), fixed effect (FE), and quantile regression of developing and developed countries.

According to the Environmental Kuznets Curve (EKC) model,

$C_{it}=f\left(AG{R}_{it},C{Y}_{it},RE{N}_{it},GN{I}_{it},{GNI}_{it}^{2}ID{C}_{it},TR{D}_{it}\right).(1)$

For the two dependent indicators (C-EM and C-LF), we use the notation $C_{it}$ for carbon dioxide emissions. AGR’s calculation of agricultural value added includes forestry, fishing, hunting, livestock farming, and crop farming. In CY, we see the harvest of cereal crops such as wheat, maize, and rice expressed as a percentage of the total acreage. Use of eco-friendly power sources is measured in REN. ${GNI}^2$ indicates the country’s total annual income divided by its midyear population; this metric captures the way in which income has a non-linear effect on environmental quality. The International Standard Industrial Classification (ISIC) of All Economic Activities is reflected in IDC’s value-added industry classifications. TRD refers to the proportion of GDP that is generated through trade (exports and imports).

$CE{M}_{it}={\mathrm{\beta }}_o+{\mathrm{\beta }}_{1}AG{R}_{it}+{\mathrm{\beta }}_{2}C{Y}_{it}+{\mathrm{\beta }}_{3}RE{N}_{it}+{\mathrm{\beta }}_{4}GN{I}_{it}+{{\mathrm{\beta }}_{5}GNI}_{it}^2+{\mathrm{\beta }}_{6}ID{C}_{it}+{\mathrm{\beta }}_{7}TR{D}_{it}{+{\theta }_i+\mathrm{\mu }}_{it}.(2)$

The estimators suggest that ${\beta }_4$ and ${\beta }_5$ will have positive and negative values, respectively. The coefficients of βs in Eq. 2 capture the effect of covariates on the response indicators ($CE{M}_{it})$, where ${\theta }_i$ and ${\mu }_{it}$ are the fixed effects of the state and s is the stochastic error term with a mean of zero and a variance of one. Unbiased distributed coefficient estimates were calculated using panel data estimated with random effects (RE), fixed effects (FE), and fully modified ordinary least squares (FMOLS) estimators. Constraints require a model with impact in both (cross section and period) dimensions, implying fixed and random effects from the cross section. The relationship between environmental quality and output follows an EKC and an inverted U-shape.

Panel quantile regression

This model is predicated on examining the conditional means of analysis (C-EM and C-LF) indicators across countries, particularly emphasizing the condition determinants of CO₂ emissions. A linear link exists between the repressors in panels A and B, thanks to the increasing approach of this model to quantiles. Quantile regression (QR) was utilized to generate objective estimates in the presence of probable outliers. Since environmental deterioration is more severe in developing nations, the QR technique (first, second, and third quantiles) captures CEM elements that are extremely important to both of these economic sectors. More income and secure economics from expansion are more conscientious, and this effect grows with the size of the economy. As a result, there might be variations in how industrialized and emerging economies react to environmental degradation. Agriculture is vital to the environment’s health in many regions and plays a big role in developing economies. This research departs from the standard QR in favor of a panel data framework characterized by non-additive fixed effects and the notion of an inseparable disturbance. Accordingly, the repressors can interact with the fixed effects of both developing and developed countries in this research specification. As a result, the following equation is the quantile specification for the panel:

$Q_{\mathrm{\tau }}\left(CE{M}_{it}\right)=AG{R}_{it}{\alpha }_{\tau }\left({\mathrm{\mu }}_{it}\right)+Y_{it}{\mathrm{\beta }}_{\mathrm{\tau }}\left({\mathrm{\mu }}_{it}\right).(3)$

Eq 3 revealed $Q_{\tau }$ to be the conditional quantile of CEM such that $\tau =\left(0.25,0.50,\mathrm{a}\mathrm{n}\mathrm{d}0.75\right).$ Consequently, country-specific heterogeneous effects ${\mu }_{it}=f\left({Ø}_i,{\partial }_{it}\right)$ are inextricable from the indicators in the panel, and AGR is as stated, whereas Y captures additional co-variables such as AGR, CY, REN, GNI, GNI², IDC, and TR.

Quantile decomposition

Given that previous research (Koenker and Bassett, 1978; Koenker, 2004; Siriopoulos and Pomonis, 2009; Nwaka et al., 2020) on regression analysis suggests that the ordinary least square (OLS) regression method’s predictions do not qualify as effective predictions when their assumption is not met, this study uses the quantile regression technique to further investigate the conditional determinants of CEM in developing and developed countries (JW, 2000). It is important to note that if the variance has a heterogeneous structure, the OLS analysis may not provide an accurate estimate of the B vector. Quantile regression models, which consider the heterogeneity structure and quantile structure of the data, are one alternative regression model we need in this situation (Katchova, 2013; John and Nduka, 2009). Due to the fact that no assumptions are made about the distribution of the error term it predicts (John OO, 2009; A K., 2013), quantitative regression model estimates are more robust and versatile than those from the OLS model (Belaïd et al., 2020; Raghutla et al., 2022; Sun et al., 2022). The conditional mean (anticipated mean value) of the response of the dependent variable to the independent variable is used to make predictions in the OLS technique. Quantile regression (Ike et al., 2020; Sun et al., 2022), on the other hand, seeks to estimate not only the median but also the 25th, 75th, and 90th quantiles of the response variable, among others (Xu and Lin, 2018; Iddrisu and Alagidede, 2021).

The sequence of economic variables is not assumed to be normally distributed in the quantile regression first proposed by Koenker and Bassett (1978) and refined by Koenker and Hallock (2001). Models for the selected quantities in the conditional distribution of the dependent variable are established via quantile regression (Xu X. et al., 2019; Akram et al., 2021; Cheng et al., 2021). Herein, quantile regression approaches are used in both panels A and B to compare and contrast the perceptions of various income groups in developing and developed countries with respect to environmental degradation (as measured by AGR, CY, REN, GNI, GNI², IDC, and TRD). At last, Eq 4 depicts the panel quantile regression model.

${Q_{\tau }(CEM}_{it}^{LIG})-{Q_{\tau }(CEM}_{it}^{HIG})={{[Q}_{\tau }(CEM}_{it}^{cHIG})-{Q_{\tau }(CEM}_{it}^{HIG})]+{{[Q}_{\tau }(CEM}_{it}^{LIG})-{Q_{\tau }(CEM}_{it}^{cLIG})].(4)$

In Eq 4, t represents the year, and i stands for either a developing nation or a developed nation. The regressors, ${\mathit{C}\mathit{E}\mathit{M}}_{\mathit{i}\mathit{t}}^{\mathit{L}\mathit{I}\mathit{G}}$ and ${\mathit{C}\mathit{E}\mathit{M}}_{\mathit{i}\mathit{t}}^{\mathit{H}\mathit{I}\mathit{G}}$, reflect the carbon emissions of low- and high-income nations, respectively; the regressor of CEM also displays the distributions of counterfactuals for LIG and HIG. Since agriculture is the LIG in these developing countries, a comparison of environmental quality and economic features is necessary. Regression by quantiles is captured by ${\mathit{Q}}_{\mathit{\tau }}$ (first, second, and third quantiles). Consistent with earlier research, various economic and agricultural factors cause varying degrees of income capture in developing and developed countries.

Findings and results

Panel cointegration test

Unit root tests and cointegration tests, which take cross-sectional dependence into account, must be run before estimations can be performed to determine whether or not the variables are cointegrated and to determine whether the variables follow an I (0), I (1), or higher order integration process. In light of this, we then conduct panel stationary and cointegration tests while considering cross-sectional dependency. Over time, statistical evidence leads to the conclusion that the variables are I (1) and cointegrated, indicating that the variables have a long-run relationship. Estimations can be made using Fully Modified Ordinary Least Squares (FMOLS), fixed effects (FE), random effects (RE), and quantile regressions based on the outcomes of unit root and cointegration tests (QR). More instruments can be included with the first-difference assumption in the explanatory variables, making the method more effective and efficient. CEM’s effect on various explanatory variables is to be examined under the directive (Wu et al., 2018; Hung et al., 2022; Mirziyoyeva and Salahodjaev, 2022). When conducting a test for a unit root on an estimate of a residual, it is important to account for differences in methodology by location and over time. Developed and developing nations have contributed to contemporary analyses examining long-run cointegration relationships. Individual intercept, trend, and the absence of cointegration are all statistically rejected by the Padroni panel test in panel ADF. Unit root tests for single countries in both the developed and developing nations yield the same statistically mean value for the individual autoregressive coefficient (Iranmanesh and Jalaee, 2021; Xu L. et al., 2022).

Whether the panel data processes are stationary or not specifies in the selection of the regression model; if the panel data process is non-stationary, the conformist OLS estimation method would lead and solve a spurious problem in regression. Therefore, it is essential to examine each variable and indicate the stationary level via the unit root test (Liddle, 2012). There are four sorts of determined panel unit root tests that guarantee the accuracy, of results. Thus, according to first-generation tests such as common root-Levin, Lin, and Chu (LLC), Breitung, individual Im, Pesaran, and Shin (IPS), augmented Dickey–Fuller (ADF), and individual root-Fisher-PP and Hadri have been computed individually from all explanatory variables (Kao C, 1995; Pedroni, 2001; Apergis and Payne, 2009). The Padroni panel test by a statistical technique (Kao) was investigated, where the panel’s cointegration was used with ADF, PP, and rho statistics, and the test results showed the dynamic residual value. A study of the Padroni panel test for cointegration is presented for CEM in developing and developed countries. Calculating an autoregressive coefficient for the estimated residual and running a unit root test are possible when we account for variability across nations and over time, as in the dimensional approach to statistics.

After that, we calculated the cointegration test using the individual unit root tests of Pedroni and Kao based on the Engle–Granger method and Fisher (combined Johansen) with a lag of one period and a residual to adjust for errors. Testing for cointegration using the Padroni panel is investigated (Appendix B). Fisher’s panel cointegration test is another dynamical approach that has been used for Johansen’s causal method (Appendix C). Together, the p values of the Johansen trace statistics and the eigenvalues are used to reject the null hypothesis of no cointegration in this panel cointegration test (Ai et al., 2021; Koç and Gülmez, 2021). Furthermore, developed and developing nations have contributed to the modern series analysis of long-run cointegration relationships. Non-stationary indicators increased the likelihood that panel A and B models contained false data estimation of cointegration regression using fully modified ordinary least squares (FMOLS), with an emphasis on the maximum likelihood approach (Choi, 2001; Wang et al., 2021b). Due to the linear trend specification by trend variable assumption, a non-stationary estimate necessitates a triangular system of equations. The trend coefficient in each cross section was analyzed separately for developing and developed countries.

Panel regression

Panels A and B of Tables 4 and 5 display the FMOLS regression and the results for each dependent indicator (C-EM and C-LF, respectively) under both fixed and random influences. The effect of FE and RE regressions is analyzed and compared using standard error, and the results are stable against the occurrence of cross-sectional influences (Bui et al., 2021; Dong et al., 2022). For the FE estimator, we utilize fully modified OLS to control for random intercepts. The non-parametric method of analyzing panel data for properties of heterogeneous serial correlation is enhanced by this method. Both the FE and the RE demonstrate the FE’s usefulness in relation to the other model. As a result, the FMOLS will be the basis for the analysis as estimated and reported by the FE. The middle of the output is where you can access the estimated panel coefficients (A and B). The expected cointegrating vector (CV) for C-EM and AGR, CY, REN, GNI, GNI², IDC, and TRD is defined by the coefficients on these variables. C-EM and C-LF had their standard errors and t-statistics (p values) tested. First, instead of using the FMOLS transformation, we analyze all of the fit statistics with the raw data. After removing the deterministic components from the experimental indicator and the regressor, the non-stationary model performs standard FMOLS on the pooled sample. According to default (homogenous variance) and a sandwich (heterogeneous variance) in the coefficient covariance matrix with d. f., the long-run covariance is calculated for pooled FMOLS estimation. The indicators in both panels A and B are statistically significant, and their results are not affected by the estimators used.

The results estimated using C-EM and C-LF for both equations in panels A and B reveal some latent pragmatic dynamics. The results of the EKC hypothesis are valid in 22 countries (nine developing and 13 developed countries). However, C-LF is more prevalent in developing nations and is utilized in all robust results. The GNI has a positive correlation with the coefficient of the covariance matrix in panel A, which is 5.801. In panel B, however, the GNI results revealed a positive correlation of 0.617. A 1% change in AGR is associated with a -4.409% increase in environmental degradation, indicating that AGR has a positive and significant influence on C-LF. In developing nations, environmental issues are influenced by agricultural activity; likewise, deforestation for feed crops, burning of biomass, and deep soil agriculture (Abdurrahman et al., 2020; Hao et al., 2020; Dong et al., 2022). The AGR indicator shows a negative association, as shown by the countervailing impact of the C-EM measure of environmental degradation. That means a change of just one percent in AGR can result in a 15.16 percent drop in total emissions of carbon dioxide. The results indicate that it does not imply a reduction in overall carbon emissions in developing and developed nations but rather a reduction in mechanization-induced emissions from agriculture (Rabnawaz, 2021).

Agriculture exports may also imply that the industrial sector (IDC) in developing and developed economies is fuel-intensive (Shahzad et al., 2020). Across developing and developed economies, an inverted U-shape is observed. Nonetheless, this may imply that developing economies focus on economic growth rather than ecological quality in the short-term and long-term effects of sustainable environmental techniques. Adopting renewable energy (REN) mitigates environmental degradation using both CEM and industrial revolution indicators. However, the high consumption in developing countries degrades environmental safeguards by increasing the C-LF. In both panels, the TRD openness decreased. Also, emerging nations (Brazil, Mexico, and Russia) have high trade values, whereas developed nations (Spain and Canada) have low IDC. C-EM fell by -7.631 percent and C-LF by -6.089 percent for every percentage point change in trade openness. These outcomes revealed the economic structure and nature of international trade in developing and developed economies (Jiao et al., 2020; Kicińska and Wikar, 2021; Zhou et al., 2022).

Despite the fit that Durbin–Watson FE (0.616) and RE coefficient restriction (CR)/the Wald test estimated output results with a CV of (1,-1) and coefficient restrictions (CR) with “C (1) = 0,” t-statistics (p value) estimates suggest that we do not accept the null hypothesis of the panels A and B that the coefficient value of the cointegrating regressor is equal to 1 under linear restriction. The estimated result of the t-statistics (p value) is around 0.000, indicating that we rejected the null hypothesis of the panels (A and B) that the coefficient value of the cointegrating regressor is equal to 1 with linear restriction. At lag 6, d. f. = 6, and the residual diagnostic value of Akaike AC’s (-2 (l/T)+2k/T) is estimated (reporting the correlogram Q-statistics). Panels A and B both have p values below 0.05 (at 0.034 for the TRD and 0.006), indicating that the residuals are random noise and permitting us to reject the null hypothesis; this holds true for lags 1 through 6. Fixed effects (FE) and random effects (RE) models were used to estimate the pool equation. Regressors (AGR, CY, REN, GNI, IDC, and TRD) and only cross-sectional identifiers (for the FE) are used to analyze a regressing model (panels A and B) in both the FE and RE. We investigate the coefficient of covariance for the default and white cross sections in accordance with Tables 4 and 5. Additionally, the default and white cross sections are used to evaluate the coefficient standard errors (CSEs) and robust coefficient covariance (RCC). The cross section, period, and idiosyncratic error components of S.D. and rho in FE and RE are used to measure the effect specification (ES). The fraction of regressor variation is shown by the intra-class correlation or rho.

Quantile regression

When it comes to estimating the median response, the least absolute deviation (LAD) estimator that best fits the bill is the quantile regression (QR) estimator. Compared to conditional mean analysis, conditional distribution analysis is a more in-depth method. The first, second, and third quantiles are displayed in panels A and B. The regressors affect the model’s 25th, 50th, and 75th percentile response indicators. By using quantile regressions, this study not only looks at the effects of emissions across different groups but also highlights the most significant period. Hence, the model describes the 25th percentile (0.25 quantile), 50th percentile (0.50 quantile), and 75th percentile (0.75 quantile) in terms of carbon emissions. A strong distributional assumption is not necessitated by the QR method. The focus here is on a robust method (RM) for creating such connections. Panels A and B contain the results of the explanatory indicators, and their findings are discussed here. In Tables 4 and 5, we also show the 25th, 50th, and 75th percentile values for the diverse factors contributing to environmental degradation. This variation in quantiles between developing and developed countries’ determinants of environmental degradation is reflected in the conditional determinant estimates of panels A and B’s equations. The effect of AGR on C-EM and C-LF is highly variable and statistically significant at the QR (25), QR (50), and QR (75) quantiles in the total equations presented in panels A and B.

The estimation results from the panel FMOLS, FE, RE, and quantile regression (QR) analyses are presented in Table 4. The estimated AGR in terms of C-EM from FMOLS, FE, RE, QR (25), QR (50), and QR (75), respectively, is 5.686, 15.166, 6.745, 0.740, 2.216, and 4.863. All of them are significant at the 1% level. However, the effects of AGR on C-LF as determined by FMOLS, FE, RE, QR (25), QR (50), and QR (75) are, respectively, -0.158, -2.926, -1.637, -1.521, 5.063, and -5.993. According to a hypothetical expectation, the AGR has a negative impact on C-LF, and all coefficients are determined to be significant at the 1% level (Bilgili et al., 2021). As a result, utilizing the QR (25), QR (50), and QR (75) of real income in terms of C-EM and C-LF, AGR is linked to environmental degradation. We calculated expected outcomes for CY, REN, GNI, GNI², IDC, and TRD in terms of C-EM and C-LF in the same manner. According to a hypothetical scenario, CY, REN, GNI, and TRD have a detrimental impact on C-EM and C-LF. Herein, Table 5 also showed that CY, REN, GNI, and TRD negatively impacted C-EM, whereas AGR and GNI² negatively impacted C-LF in developing nations. In the case of developed nations, the anticipated outcomes of AGR, CY, and REN reveal a detrimental impact on both C-EM and C-LF (Awan et al., 2022). As per the EKC, it worked particularly well at QR (50) and QR (75) but only moderately well at QR (50), proving its validity. Figure 3 indicates that both Canada and Qatar are among the top carbon emitters among both developed and developing nations. Nigeria, the Philippines, Brazil, Chile, and Argentina all had results that supported the EKC theory.

Furthermore, Canada, Qatar, Russia, and the Netherlands are among the top QR (50) countries in terms of per capita carbon emissions, and the effect of real income in these developing and developed economies is consistent with the inverted U-shaped pattern Kuznets curve (EKC) hypothesis (Mehmood Mirza et al., 2022). The quantile results for C-LF consumption show that despite the greatest influence and having a significant pollution abatement effect, all indicators, with the exception of CY and IDC in QR (50), where the cereal yield and industry association have a little low effect, are presented in Figure 3. This includes countries such as France, Italy, Norway, and Germany. As quantile-based mean estimators, AGR, REN, and TRD all show nearly identical influences. The CEM intensity level is shown in Tables 4 and 5, in panels A and B, per the prevailing EKC hypothesis. From 1991 through 2016, the developing and developed countries changed over time. The largest AGR was anticipated to occur in China in 2016, with C-EM and C-LF levels forecast to be -0.3 and -0.6, respectively. Because China is a developing nation, it has modified its agricultural technology and decreased its intensity. With the exception of China, all other developing countries (including Qatar, Malaysia, Nigeria, and the Russian Federation) exhibit a high intensity level in AGR, CY, TRD, and especially IDC. Similar to Qatar, 2003 saw the highest intensity, estimated at 7.7, while 2016 saw the lowest, at 0.3, for C-LF. The highest C-EM value was also recorded in 2002. However, the developed countries have a significantly lower level of intensity in AGR, TRD, CY, and REN than developing nations such as Turkey, Sweden, and Norway.

Discussion

The quantile decomposition analysis for CEM emissions reveals that unidentified factors outside the research model contribute to the carbon emission gap between LIG and HIG due to different developing and developed economic structures and environmental policy frameworks. Nonetheless, C-LF and C-EM are disproportionately high compared to observed economic factors and agriculture. Table 4 shows the results of an FMOLS, FE, RE, and cross-country quantile regression (QR (25), QR (50), and QR (75)) analysis for all 22 countries, including nine developing and 13 developed nations, with panels A and B providing separate elaborations on the existence of the EKC hypothesis in terms of CEM. Panel A shows a positive effect for AGR, GNI, and IDC, while panel B shows a favorable effect for GNI, GNI², and IDC. Based on the projected results, a one percent shift in an explanatory indicator seems to have a far larger impact on C-EM than on C-LF. Results for nine developing nations and 13 developed countries are shown in Table 5. Estimates imply that developing countries have a greater degree of similarity in their growth plans and C-EM shifts (Farooq et al., 2022). Assumptions used in the EKC model of CEM are valid for low-income nations, but their predicted outcomes fall short of the achievements of high-income countries. Further explanation of the empirical investigation of environmental degradation caused by agricultural output, economic growth, trade openness, renewable energy, industry, and trade implications is as follows.

Research by the United Nations Food and Agriculture Organization (FAO) shows that emissions from forestry, agriculture, and fisheries have increased in the last 50 years and are projected to increase by 30% by 2050. As developing and developed countries increase their energy demands, CO₂ emissions continue to rise and have reached a record high for the fourth consecutive year (Rapier, 2020). Consider that Asia is home to only 4.3 billion people or 60% of the world’s population. Table 5 reveals that the current EKC hypothesis predicts that AGR and GNI² have the greatest impact on attitudes in panel A, whereas the least optimistic estimates are seen in panel B, which represents emerging countries. In addition, the CEM emissions per capita in developing countries are currently greater than double those in developed nations due to the 1 percent population growth in these regions. Concerning AGR, Brazil and Malaysia for panel A and China and Qatar for panel B had the highest CO₂ emissions intensity. To be more ecologically sound, CEM must strike a balance between mechanization and inappropriate cultivation practices that call for greater additives and accuracy (Chen J. et al., 2020; Munir et al., 2020). The EKC-high effect of AGR output is positive for all QR (25), QR (50), and QR (75) in China, Qatar, and Malaysia in panels A and B. High intensity is shown in Turkey, Germany, and Greece (all developed countries) in panel A and France, Italy, and Turkey (all developed countries) in panel B. Low- and high-income rural and urban farmers are still needed, especially in developing and developed nations. This inference may explain why agriculture has a range of impacts on CO₂ emissions.

It is predicted, however, that developing nations like those that rely heavily on agricultural exports and primary sector exports would have the same growth and development in agriculture and trade as developed countries (Li et al., 2020; Zubair et al., 2020). In addition, the coefficients for various economic activities reveal that the effects of agriculture and international trade are supported by more robust statistical evidence than those involving any particular economic sector (Nasir et al., 2021). Table 5 shows that the REN, GNI, and GNI² of developing nations such as Argentina, Mexico, China, and Nigeria and developed countries such as Germany, France, Italy, and the Netherlands, have a significant impact on the EKC hypothesis for C-EM. Comparing the developed nations such as Germany, Italy, and Sweden to the developing nations such as Brazil, Qatar, and Nigeria, C-LF reveals the existence of the EKC hypothesis in all six countries. By comparing the emission rates of developing and developed countries, we find that the EKC hypothesis for C-EM and C-LF holds true for higher emissions, with the EKC hypothesis verified for Qatar, Italy, and China (Xu and Lin, 2020). Moreover, it shows that environmental policy formulation has occurred in both high and low economies, and the heterogeneity of gaseous and solid fuel use between countries can substantially impact emissions (Wada et al., 2021).

The EKC hypothesis for C-EM is profoundly affected by the IDC and TRD practices of both developing nations such as Argentina, Mexico, Brazil, and Malaysia and industrialized nations such as Greece, Turkey, and Spain. C-LF shows that the EKC hypothesis holds true in all five countries when comparing developed nations such as Germany, Greece, and Turkey to developing nations such as Qatar and Malaysia. Estimated results of quantile regression have a distinct non-standard and favorable pattern in both panels, even though renewable energy is consistently negative in developing and developed nations. Long-term economic growth in developing countries positively correlates with renewable energy, while renewable energy costs negatively correlate with C-EM and C-LF. As a result, renewable energy interacts with carbon emissions from the use of liquid fuel to boost economic growth and social progress in both developing and developed nations. In developing countries, it is more consistent because it detects a negative dynamic in CO₂ emissions and holds up well across different means of estimation (Omri and Belaïd, 2021). In last, it is possible that developing economies’ industrial sectors are weaker than those of developed economies according to these estimates. When looking at QR (25), the effect of C-LF becomes more significant (p < 1), and this indicates that developing countries’ energy consumption rises along with technological advancement (Hu et al., 2021). The industrial revolution has been crucial to the expansion of the global economy. It has a positive and statistically significant impact on both panels. C-LF impacts economies, as shown by the negative effect of trade in QR (25)–QR (75) in developing countries.

Conclusion and recommendation

Impacts on agriculture (AGR), cereal yield (CY), renewable energy consumption (REN), gross national product (GNP), industrial output (IDC), and international trade (TRD) are analyzed for both developing and developed nations. Using sinusoidal data from 22 to nine developing and 13 developed countries, it examines environmental degradation from two CEM (C-EM and C-LF) sources from 1991 to 2016. This research used panel quantile decomposition methods with FMOLS to examine the gap in CO₂ emissions between low (LIG) and high (HIG) economies and the relationship between AGR and economic factors at varying income and CO₂ emitter levels. As shown in the estimated results, a wide range of factors contribute to environmental quality across both developing and developed countries, from those with high to those with low levels of CO₂ emissions. These recommendations are presented as an expected result of the aforementioned. To begin, the developed (France, Italy, and Spain) and selected (China, Brazil, and Nigeria) countries must eliminate crude agricultural practices caused by land fragmentation. In addition, the increased production of agricultural goods and the widespread practice of burning brush have debilitating ecological consequences. More farming needs to be carried out in developing nations, both in terms of cultivating techniques and applying new sustainable methods.

Second, the government in developing countries should be given the authority and the means to implement strategies to prevent and reduce the risk of CO₂ emissions in urban areas. In addition, the ongoing evolutions of eco-friendly practices that safeguard soil, cut greenhouse gas emissions, and lessen the energy use. Third, the Agricare conservative agriculture approach, based on strip cropping and no-till farming, needs to be introduced to developing countries along with improved precision agriculture techniques. In addition, expanding populations and booming economies are major contributors to rising atmospheric carbon dioxide levels. In this scenario, the effects of widespread technological adoption on carbon emission reduction are outweighed by population and economic growth. In this study, we find that the developed world is cutting back on carbon emissions, thanks to the countervailing effects of technology and nuclear power plants on their use of oil and other energy sources, while the impact of alternative energy sources on the consumption patterns of developing nations is significant but in flux. Human activity cannot be discounted as a significant contributor to global warming’s onset at an earlier stage in the industrial revolution and economic development. Nonrenewable fossil fuels are the primary source of carbon emissions; depending on their abundance characteristics, oil, gas, coal, and nuclear sources all contribute differently to the temperature rise in industrialized nations.

More importantly, both the private and public sectors are committed to fostering positive change in agriculture via natural methods and advancing more sustainable economic models. In fourth place, it is important to promote the adaptation of renewable energy innovations in underdeveloped nations and the use of energy-saving practices and all-natural strategies for preventing and reversing environmental degradation. It would make the switch to a cleaner industrial economy in developing countries possible. To solve the fundamental issues of low industrial capacity, food shortages, and unsustainable manufacturing practices, agricultural and industrial products in both developing and developed countries should be stable. Furthermore, 22 countries, both developed and developing, may gain social stability and employment opportunities as a result of modernized technologies. As a final goal of our research, we hope to encourage developed countries to take environmental protection measures by using environmental performance as a test case for the EKC hypothesis in future studies.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author.

Author contributions

RK: Conceptualization, Writing—review & editing, Methodology, Writing—original draft, Formal analysis, Data curation, Software, and Supervision, ON: Funding acquisition, review, WZ: Investigation, RB: Funding acquisition, Validation, IA: Funding acquisition, review, MA: Funding acquisition, Validation.

Acknowledgments

The author responds to insightful advice and is extremely appreciative of the instructors for all the encouragement and guidance they have given. The educational opportunities provided by the Fujian University of Technology are gratefully acknowledged. National Social Science Foundation of China (22BGL007), Fujian Zhi-lian-yun Supply Chain Technology and Economy Integration Service Platform from the Fujian Association for Science and Technology, the Fujian-Kenya Silk Road Cloud Joint R&D Center (2021D021) from Fujian Provincial Department of Science and Technology, the Fujian Social Sciences Federation Planning Project (FJ2021Z006), and General program of Fujian Natural Science Foundation (2022J01941). He is grateful to his loved ones for their support as he pursues his goal of earning a doctorate. He cannot ignore the value of the disabled student who, in the name of success, is willing to sacrifice anything, including their feelings, in order to succeed. He knows that love is the one that holds him up and gives him the energy to keep going.

Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Appendix A: All data are obtained from the World Bank Development Indicator (2020). https://datatopics.worldbank.org/world-development-indicators/

Appendix B: Padroni (Engle–Granger based) test.

Appendix C: Johansen–Fisher panel cointegration test.