Exploring the influence of sustainable development factors on the environmental quality in OECD economies Open Access

The Sustainable Development Goals (SDGs) promote sustainable development by addressing economic, social and environmental issues (Dai et al., 2024; ÖQUIST et al., 2009). Government policies and practices must consistently implement local, national, regional and global SDGs (Wei et al., 2023). Local and regional governments are crucial to SDG economic, social and environmental goals (Ullah et al., 2023). The US-established SDGs encourage fairness, prosperity and environmental stewardship for global sustainability and conservation (Ullah et al., 2023). Under SDGs, governments should address global concerns beyond demographics and boundaries (Udeagha and Muchapondwa, 2023).

World leaders seek innovative solutions to economic stagnation, poverty, famine, malnutrition, illness, environmental degradation and globalization (Uddin et al., 2023). UN Agenda 21 promoted sustainability. Global warming is serious (Pata et al., 2023). The SDGs focus on hunger, malnutrition, poverty, health and the local environment (Fang et al., 2023). Environmental degradation, vulnerability and adaptability are critical areas of concern that must be addressed (Caglar and Yavuz, 2023). Development efforts can reduce emissions (Caglar and Askin, 2023), and alternative strategies may influence climate and sustainability. Moreover, environmental degradation can impede economic growth and aggravate climate change challenges (Bashir et al., 2023). Hence, addressing a research gap in SDGs, this study uses a wide range of indicators – including renewable energy consumption, water resources, forest area, electricity access and food production – along with a particular political, economic and environmental (PNG) analysis to assess the effect of SDGs on environmental degradation in OECD countries. This study is unique in two ways: it is the first to empirically test the relationship between SDGs and environmental degradation, quantifying a country’s level of sustainability and revealing why signing environmental treaties might not efficiently address climate change issues. The results highlight the need for enforcing treaty requirements on member countries and suggest having a single global authority that regulates a country’s compliance with treaties rather than multiple existing organizations.

Solar, wind, biomass and hydropower can meet growing energy requirements without damaging the environment or economy (Ali et al., 2023). Renewables reduce or increase emissions (Ullah et al., 2023; Zhu, 2022). Low-carbon economies promote renewable energy worldwide. The International Energy Agency Renewables Report 2018 predicts that 12.4% of worldwide energy will be renewable by 2023, up from 8% in 2018 (Zhao et al., 2022). This forecast analyzed global climate change knowledge and fossil fuel rejection (Xu et al., 2022). Recent decades have seen OECD countries use renewable energy (Udemba and Tosun, 2022). Sustainable energy and climate change awareness drive this trend (Sadiq et al., 2022).

In recent decades, a meat-heavy diet and population growth have boosted global food consumption (Musah et al., 2022). Farmers use insecticides and fertilizers to feed humanity (Mpofu, 2022). Soil degradation, CO₂ emissions, water pollution and biodiversity loss impact the environment (Liu et al., 2022). Food security requires organic cultivation and chemical reduction (Khan et al., 2022). Naturally occurring pesticides and fertilizers reduce pollution in organic farming. Organic farming increases water, soil and biodiversity without pesticides. Changing to organic farming needs customer education and product demand. Promote organic and sustainable farming (Khan and Rehan, 2022). Figure 1 Shows the food production, forest area and CO₂ emissions of OECD countries.

Similarly, the relationship between greenhouse gas emissions, precipitation, agricultural output and forest area is crucial globally and in Cameroon (Jiang et al., 2022; Cui et al., 2022). CO₂ emissions cause global warming, impacting forests and food production systems (Awad, 2022). Rainfall is essential for tree and crop survival, and water remains a valuable human resource. The absence or disappearance of forests can have significant environmental effects, including increased CO₂ levels (Artyukhov et al., 2022). Agriculture is responsible for 96% of deforestation in Africa, Asia and Latin America, including Cameroon. Poverty is a significant barrier to sustainable development in emerging nations and globally (Ahmad et al., 2022). Addressing poverty requires prioritizing opportunities, such as providing modern energy sources like electricity (Breuer et al., 2019). Energy poverty is defined as the state where the availability and accessibility of energy resources have not been fully realized (Allen et al., 2018). Water is another essential aspect that impacts environmental quality (Müller et al., 2015). Excessive extraction of water resources leads to climatic shock, whereas appropriate withdrawal maintains ecological equilibrium (Xu et al., 2022). Ineffective land and water management affects the natural environment by depleting water supplies, contaminating water systems and increasing soil sterility and erosion (Dai et al., 2024). Emerging countries face water shortages, contributing to global water contamination and worsening environmental conditions (Pata et al., 2023).

Consequently, there is a pressing need for empirical research on the water availability–environment connection. The study’s theoretical framework is designed to provide comprehensive information on sustainable development and environmental degradation in OECD countries. Therefore, this research aims to examine the environmental effects of OECD countries and learn more about their diplomatic strategies to promote sustainability through an in-depth PNG analysis. The primary objectives of this study are to examine the influence of five SDG parameters on carbon dioxide emissions and provide insight into their environmental impact. By examining these parameters, the study attempts to formulate policy recommendations based on empirical evidence to assist policymakers in defining appropriate strategies to reduce CO₂ emissions.

The present research is organized into the following series: Section 1 discusses the study’s background; Section 2 includes a literature review; Section 3 elaborates on the data and methodology used; and Section 4 gives a detailed explanation of the results. Finally, Section 5 looks at the policy implications of the results.

Renewable energy consumption allows us to decrease greenhouse gas emissions by reducing the use of depleted fossil fuels and moving toward nondepletable sources (Zang et al., 2023). However, opposing climate change and promoting sustainability depends on this approach. Renewable energy is gaining global interest due to its ability to counteract environmental degradation and reduce carbon dioxide emissions (Zhuo and Qamruzzaman, 2022). This research analyzes different studies that examine the association between renewable energy consumption and its effects on CO₂ emissions within OECD countries. Dagar et al. (2022) examined the effect of renewable energy consumption (REC) on CO₂ emissions. Nonlinear methods such as wavelet coherence, Granger causality-in-quantiles and quantile-on-quadrate regression are used on monthly data from 1989 to 2021. The outcomes revealed that REC has substantial impact on CO₂ emissions.

Similarly, Turedi and Turedi (2021) explored the association between REC, industrial output and total reserve and environmental deterioration in 38 OECD nations from 1995 to 2019, as shown in research. Degradation increases with financial development, industrial output and total reserve and decreases with renewable energy usage and natural resources. Implications for policy change are provided in the study to enhance OECD nations’ environmental quality. Altinoz and Dogan (2021) analyzed the connection between RE and CO₂ emissions in key nations between 2000 and 2015. The study’s findings demonstrate that a one percentage point increase in RE usage is associated with a 0.5 percentage point decrease in CO₂ emissions. According to Eyuboglu and Uzar (2020), growing greenhouse gas emissions were a significant cause of environmental damage on a worldwide scale. Agriculture’s contribution to emissions increased and renewable energy’s role as a catalytic factor was highlighted in a study of seven fortunate nations from 1995 to 2014. According to the findings of panel vector error correction model, variables were long-term causes, whereas economic growth was short-term. Based on the above empirical evidence, the developed hypothesis of the study is:

Food production entails cultivating, harvesting and processing crops and livestock for human consumption (Sharif and Khan, 2024). It involves various activities like farming, livestock management and aquaculture (Ashraf et al., 2024). Using agricultural inputs like fertilizers and pesticides is vital in the global food system. However, environmental impacts include land use change, water resource depletion and greenhouse gas emissions (Raza et al., 2021). Food production significantly contributes to global greenhouse gas emissions, with deforestation, livestock methane emissions and fertilizer use being major drivers (Ching et al., 2021). This relationship, however, is critical to the development of sustainable agricultural systems (Aleksandrowicz et al., 2019). By developing innovative approaches and adopting different farming styles, the world could reduce its damaging impact on the environment and create a healthy future for our planet (Amri, 2017).

According to Ahmad et al. (2017), the study examine the impact of forest area on the environmental performance in Pakistan. The outcomes of the study revealed that the FA has substantial impact on environmental performance. The study measured environmental performance by CO₂ emissions. Similarly, another research by Abas et al. (2017) studied that the forest has substantial impact on ecological performance in the country. The findings indicated that index quality (IQ) mitigates pollution, whereas FP and EC exacerbate it. High intelligence lessens the negative effects of FP and EC on the environment. Improving energy and agricultural efficiency while decreasing CO₂ emissions requires objective domestic IQ. These results inform public policy decisions to curb carbon emissions and boost agricultural output without negatively impacting environmental quality.

Al-Mulali et al. (2013) used three estimators and a panel of 53 nations to determine the effect environmental deterioration has had on agricultural output from 1996 to 2017. The results demonstrated that greenhouse gas emissions are bad for agriculture, but investments in infrastructure and scientific research are beneficial. Food output falls when labor costs rise. Research and development and food production are shown to have a causal effect in both directions, as are CO₂ emissions and food production. Based on the above empirical evidence, the developed hypothesis of the study is:

Environmental pollution includes air, water and soil pollution and the loss of ecosystems and species (Liu et al., 2023). It greatly contributes to environmental degradation, with the planet’s 2.5% freshwater being drinkable to only 30% (Kousar et al., 2020). Water in flowing and standing forms is fundamental to all life forms on this earth. It transports important chemical elements and compounds in the physical landscape through processes such as erosion and deposition (Ali et al., 2023).

ÖQUIST et al. (2009) examined South Asian nations from 1988 to 2018 to see how their usage of renewable energy, water resources and environmental deterioration are related. It concluded that foriegn direct investment (FDI) contributes to environmental deterioration, whereas investments in renewable energy and water resources mitigate the problem. Renewable energy, water availability and environmental deterioration are all strengthened by good governance. Tiemeyer et al. (2024) found that terrestrial ecosystems were responsible for 32% of human CO₂ emissions during the previous six decades. Land–atmosphere carbon fluxes in the tropics are primarily responsible for interannual changes in the atmospheric CO₂ growth rate (CGR). There was a clear correlation between water supply and CGR.

Hamed et al. (2024) analyzed the temperature response of CO₂ generation in 23 surface soil samples from boreal forests and peatland ecosystems before and after freezing. It indicates that water constraints caused by soil water freezing are the primary cause of variability in temperature responses at subfreezing temperatures. The quality of the soil’s organic matter and the amount of plant cover largely determine how much liquid is not frozen. Response coefficients for CO₂ temperatures that consider water scarcity are consistent with those forecast by thermodynamic theory. To comprehend low-temperature soil processes and to accurately anticipate carbon balances in northern terrestrial ecosystems, it is essential to differentiate between temperature response and water availability. Based on the above empirical evidence, the developed hypothesis of the study is:

A forest area is an area in which trees and greenery prevail (Guo et al., 2024). Deforestation and forest fragmentation, primarily due to agricultural growth, have resulted in biodiversity loss (Zhang et al., 2023). Over the past 10 years (2000–2010), 7 million hectares of tropical forest declined, with commercial and subsistence agriculture responsible for 73% of the total. The estimated US$45bn loss of forest capital is due to deforestation (Yang et al., 2023).

Raihan et al. (2023) analyzed how many factors, including using renewable energy, agricultural output and forest area, affect Pakistan’s contribution to global greenhouse gas emissions. The Autoregressive Distributed Lag model was applied to data collected between 1990 and 2014 to examine long- and short-term trends. Long-term effects on carbon dioxide emissions are shown to be harmful for using renewable energy and forest resources while favorable for agricultural productivity. Short-term impacts from agriculture decline while those from renewable energy use and forest area usage remain relatively stable. Planting forests may be a more effective way to reduce CO₂ emissions. Raihan (2023) examined the connection between forest area, CO₂ emissions, rainfall and arable production in Cameroon. It used time series data from 1961 to 2000 regression and correlation analysis to determine the most significant variables. Results showed that reduced forest area increases CO₂ emissions while reduced arable production decreases. The study concluded that CO₂ and forest areas have the most significant interactions.

According to Korkiakoski et al. (2023), forests play a crucial role in controlling climate change and extreme events. However, 21st-century economic development, including resource exploitation, urbanization and deforestation, negatively impacts natural forest habitats. A study in China found that carbon emissions declined across 30 provinces by expanding forest investment and management activities rather than increasing forest land without proper management. Based on the above empirical evidence, the developed hypothesis of the study is:

Electricity access is essential to households, companies and communities to power lighting, heating, cooking and appliances (Li et al., 2023). Energy security, economic development and environmental sustainability are essential reasons (Sadiq et al., 2024). Energy consumption, environmental impact and reduction of greenhouse gas emissions depend on access to electricity (Voumik et al., 2023). Gyamerah and Gil-Alana (2023) argued that increasing access to electricity in developing countries is critical but may be at odds with attempts to reduce greenhouse gas emissions. Three to four percentage of India’s total emission increases over the past three decades may be attributed to increased family access to power. Over 650 million people in India consume power, contributing 11%–25% of the country’s rising CO₂ emissions. However, preventing future carbon lock-in necessitates lowering carbon intensity, which was crucial for sustainable development.

The UN’s “Sustainable Energy to All” (SE4All) effort intends to eliminate the worldwide power access deficit by 2030, as reported by the researcher Dimnwobi et al. (2023). The Paul Scherer Institute and the World Energy Council examined two future scenarios to determine the money necessary to accomplish this goal. The regions of the world with the highest percentages of people living without access to electricity at the time of the study’s writing include developing Asia, Latin America and sub-Saharan Africa. The results suggested that substantial expenditures in power production infrastructure are necessary to provide universal electricity access, with little effect on primary energy consumption and carbon dioxide emissions. According to Cheng et al. (2023), access to electricity has been linked to improved economic and social conditions in rural parts of developing nations. The research examines the electrified regions in the Indian state of Assam. Multiple regression analysis and an energy-economic model were used to make projections about the standard of living and the number of people who could read and write. According to Asiedu et al. (2023), carbon intensity targets for nations’ electrical sectors were an effective way for them to minimize energy-related CO₂ emissions. According to Freudenberg’s findings, a significant reduction in emissions was possible by decreasing the power of the worst polluters in the energy sector. To examine how CO₂ emissions and intensities vary among energy sectors in different nations, the study used a global database of power plants. It discovered that the dirtiest 5% of power plants account for a disproportionate share of overall emissions in their respective industries. Based on the above empirical evidence, the developed hypothesis of the study is:

The study framework was created after comprehensively examining the pertinent academic literature. The present study examines the relationship between environmental degradation, as the dependent variable, and several independent factors, including water resources, forest area, access to electricity, renewable energy consumption and food production. The entire framework of the investigation is depicted in Figure 2.

In this study, the impact of sustainable development factors on CO₂ emissions in the OECD will be determined by using panel data sets from 1991 to 2020 of CO₂ emissions, water resources, forest area, access to electricity, renewable energy consumption and food production with the World Economic Indicators of the World Bank analyzed by STATA software. The results of the analysis will contribute to a better understanding of the relationship between sustainable development factors and environmental degradation in OECD countries over the specified period.

Sustainable development factors may significantly affect environmental degradation (with regard to carbon dioxide emissions). Reducing carbon dioxide emissions through sustainable development can determine the willingness of governments, companies and individuals. Carbon emissions will be reduced due to increased compliance with the policy from industries and families. Due to the increased rate of economic activities that may result from sustainable development, energy usage may be enhanced. If environmental policy is ambiguous, CO₂ emissions may increase. Due to policy uncertainty, carbon-intensive FDI firms abandon industrialized nations for developing nations with less stringent environmental regulations (Mrabet, 2023). On the contrary, environmental legislation can respond to public concerns about environmental quality and mitigate market failures that lead to pollution during periods of rapid economic growth (Kaleem Ullah and Shabir, 2023). Limits on total energy consumption and incentives for energy efficiency are essential for pollution control. The empirical model for reducing CO₂ emissions comprises water resources, forest area, access to electricity, renewable energy consumption and food production. The variables used in the model are describes in Table 1.

Long-term stability (pace of adjustment) is separate from error fluctuation. The long-run coefficients must be consistent across nations. This strategy is ideal because it is more efficient and compatible with long-term cooperation. Second, the study has the Mean Group (MG) estimate. According to Pesaran et al. (2004), it uses less stringent methodologies to estimate parameter diversity. Furthermore, it can calculate different coefficients for each nation. Lag length selection using the Schwarz Bayesian Criterion or the Akaike Information Criterion (Maichum et al., 2016) is critical for both the MG and PMG estimators. The MG estimator could be more efficient in the presence of homogeneity, yet it consistently produces long-run mean estimates. Pooled estimators are efficient and consistent in the presence of long-run homogeneity. The Dynamics Fixed Effect is the third estimator. This estimator is equivalent to the PMG estimator. To ensure uniformity across all long-run panels, the cointegration vector coefficient may be limited. In addition, each of the three estimators (PMG-ARDL, MG and fully modified ordinary least squares [FMOLS]) may show both the short- and long-term impacts of each variable. According to Pesaran and Smith (1998), regardless of whether the integration order is I (0) or I (1), these techniques provide more consistent long-run coefficients. This method combines cross-sectional and time series data when the time series (T) exceeds N. To account for the effects of heterogeneity in the estimation, this method reweights the data and uses the long-run covariance of cross-sectional estimates. Due to the importance of this method, this study uses the Group-FMOLS technique to forecast the second and long-run relationships between the variables. The final FMOLS tally is presented in Table 4 under group. The effect of sustainable developmental factors on carbon dioxide was tested using the PMG-ARDL model developed by Pesaran et al. (2008). Table 4 shows the final Group-FMOLS tally. The impact of sustainable developmental factors on carbon dioxide was examined using the PMG-ARDL model established by Pesaran et al. (2008). Long-run coefficients cannot vary between countries due to the pooled mean group, although short-run coefficients can be greatly different. Moreover, with the help of the MG, the coefficients set can change and differ by the short-term and long-term fluctuations. According to Ashraf et al. (2024), the PMG estimator has higher long-term homogeneity than the MG estimator.

This study used a standard group mean estimate for correlated effects, which has been improved upon and is consistent with recent research (Al-Mulali, 2011). The current study used a long-run estimating method based on a panel of FMOLS models. FMOLS is an effective panel econometric tool for estimating heterogeneity (Kaleem Ullah and Shabir, 2023). Moreover, OECD countries are more ecologically diplomatic but emit more carbon dioxide. Table 4 depicts the findings for OECD countries, giving radically different outcomes for countries. The findings emphasize the significance of expanding water resources, forest area, access to electricity and rapid increase in renewable energy consumption and food production as significant predictors of lower carbon emissions in OECD countries’ economies. The current study used the following empirical models: equation (1) shows the basic model, equation 3 shows FMOLS, equation 4 shows CCE-MG and equation 5 shows the PMG-ARDL estimation Model:

Table 2 demonstrates the findings of descriptive analysis, whereas Figure 3 show the trend the series. The descriptive analysis contains statistics related to several variables, including LNED, LNWR, LNFA, LNAE, LNREC and LNFP. The mean indicates the central tendency of distribution with values ranging from 1.96 to 4.53. The median presents the middle value, indicating the data set’s central value when ordered. The values range lies between 0.218 and 3.243, which expands on the fact of higher variability. The average LNFA value is 6.00, and the standard deviation is slight, which may suggest that the values in our sample fall outside of the range of 1.0–3.0. The values for LNAE are somewhat limited, suggesting that distribution is also relatively stable. LN REC has a variability of −0.821 to 4.416, respectively, for LNREC. LNFP varies from 3.958 to 4.927. From this, the variability within the data set is determined by the standard deviation, with higher values implying more considerable variability. The auxiliary analysis of the data reveals that the mean value of LNAE is the least volatile, meaning it has the highest stability in its distribution. Thus, LNWR has the highest variability; the observed values vary significantly. The result shows that LNED has a moderate coefficient of variation and LNREC, whereas LNFP has a relatively small SD, though it is slightly skewed to the right.

Table 3 highlights correlation coefficients for LNED, LNWR, LNFA, LNAE, LNREC and LNFP variables. The Pearson correlation coefficients reveal the strength and the direction of a linear relationship between each two variables. The correlations are signed, meaning that the sign indicates whether the correlation is positive or negative. A positive correlation means that an increase in one variable results in an increase in another. A negative correlation means that an increase in one lead to a decline in the other. LNED and LNREC have coefficients of +0.577, which suggests that LNAE has a weak positive relationship with LNED in that there is a tendency to increase as LNED rises. LNWR and LNREC have a fragile positive association, and the relationship shows a sleepy tendency of LNREC to rise as LNWR rises. Thus, the study get shallow positive relations between LNFA and LNFP, indicating that if LNFA increases slightly, LNFP is also likely to increase. LNAE and LNREC have coefficients of significance 0.53, indicating that as LNAE rises LNFP also tends to rise. The results imply that LREC and LNAE have negative and relatively small, inversely related correlations. The Pearson coefficient tests show that LNAE has a moderate positive correlation with LNREC, which can be assumed to have a linear relationship with these variables. All other coefficients are less than 0.50, implying that the variables in this data set do not possess linear solid interdependencies with each other.

Table 4 displays the results of a cross-country, cross-sectional dependencies (CD) test using the methodology of Pesaran et al. (2004), which commences the empirical section. The data provides evidence against rejecting the null hypothesis of cross-sectional independence and alludes to cross-country dependencies. This illustrates that a disturbance in one country indirectly impacts the other countries in the study (Pesaran et al., 2008).

The CD test outcomes serve as the foundation for the subsequent phase, which involves assessing the degree of series stationarity. To prevent inconsistencies, it is imperative to acknowledge that all research processes are independent. The CIPS uses the second-generation panel unit root test to confirm international correlations. The test results for unit roots conducted on the CIPS panel are presented in Table 5. The CIPS analysis reveals that the variables are not stationary at the level but are at the first difference. This signifies time series data integration of the first order. This means that series data are first order integrated.

The cointegration test established by Westerlund in 2007 is used for this objective. The results indicate that Ga (−9.741) and Gt (−8.961) demonstrate that all test statistics with p-values of 0.000 reject the null hypothesis for the group mean tests to a substantial extent. This implies that each cross section has a cointegration, implying that every group (say, individual country or any other unit) has long-run equilibrium. Pt (−9.49) and Pa (−11.60). The test statistics estimated with a p-value of 0.000 underscores the cointegration tests across the whole panel. This means that the relationship between variables is long-term for the combined data set and not subject to variable fluctuation. All four statistics (Ga, Gt, Pt and Pa) are above the critical values; thus, the study establish substantial evidence of cointegration in the given panel data. This suggests that long-term stability exists, even if short-term inputs vary from the long-term trend’s mean. The results of the cointegration test are demonstrated in Table 6.

Table 7 represents results from three different econometric methods: Three types of estimators are used, all derived from the mean group estimator, which includes the MG, PMG-ARDL for both short and long-run effects, and the FMOLS. These methods are often applied in panel data econometrics to quantify independent variables’ permanent and transient impacts on a dependent variable. The coefficients depict the direction and strength of various independent constructs with the dependent construct. Positive coefficients (e.g. for LNAE and LNFP in most tests) indicate a direct positive significant impact on environmental degradation (CO₂ emissions). In contrast, negative coefficients (e.g. for LNWR and LNREC in most tests) show a direct negative impact on environmental degradation (CO₂ emissions). Therefore, CCE-MG results highlighted that LNWR −2.709*** negative and 1% significance level; this indicates that LNWR has a negative direct relationship with the LNED; thus, the higher the LNWR, the lower the LNED (Zang et al., 2023; Zhuo and Qamruzzaman, 2022). Furthermore, LNFA (−1.298, n0): Nonsignificant results, which means there is no strong rationale for a particular effect in this model (Ashraf et al., 2024; Raza et al., 2021; Sharif and Khan, 2024). After that, LNAE 3.147*** was positive and significant at 1%, which means that there is a direct significant relationship between LNAE and LNED (Liu et al., 2023; Tiemeyer et al., 2024). LNREC −2.147** significantly negatively influences LNED, which means that the higher the exposure to green concepts and renewable energy sources, the lower the CO₂ emission (Guo et al., 2024; Korkiakoski et al., 2023). Likewise, LNFP 3.458*** has a positive and significant influence on LNED; this means that food production has a positive effect and a highly significant degree on environmental degradation (CO₂ emissions) (Cheng et al., 2023; Dimnwobi et al., 2023).

Moreover, the findings of PMG-ARDL short-run estimation highlighted that LNWR −2.533** the estimate is negative and statistically significant in the short-run influence on LNED; the CO₂ emission reduces as water resources increase (Dagar et al., 2022; Kartal et al., 2024). LNFA −1.996** negative and significant; it indicates moderate negative short-run impact on LNED (Aleksandrowicz et al., 2019; Ching et al., 2021). Whereas LNAE (2.209) ** has a positive and significant short-run influence on LNED, the availability of electricity is the cause of increasing CO₂ emission (Ali Warsame and Hassan Abdi, 2023; Hamed et al., 2024). LNREC (−2.719) *** has a negative and significant short influence on LNED. Renewable energy is the substitute for fossil oil-generated energy (Raihan, 2023; Raihan et al., 2023; Raihan and Tuspekova, 2023). The LNFP (1.969) ** has a positive and significant short-run influence on LNED; food production is the cause of enhancing CO₂ emissions (Gyamerah and Gil-Alana, 2023; Kocak, 2023). In addition, the long-run PMG-ARDL model estimation results highlighted that LNWR (−3.187*) ** long-run was negative and significant at a 1% level of influence on LNED (Altinoz and Dogan, 2021; Turedi and Turedi, 2021). LNFA (−1.981) ** Negative and significant but its effect moderately influenced LNED (Ahmad et al., 2017; Amri, 2017). LNAE (3.961) *** has a positive, significant and long-run influence on LNED (Majeed and Luni, 2019; Majeed and Mazhar, 2019). After that, the LNREC (−3.196) *** indicates a negative and significant long-run influence on LNED (Kousar et al., 2020). In addition, LNFP (2.749*):** has a positive and statistically significant long-term influence on LNED (Li et al., 2023; Naeem, 2023).

Results of FMOLS indicate that LNWR (−2.894) *** also has a negative and statistical influence on LNED at a significant 1% level, same as the long run PMG-ARDL analysis (Eyuboglu and Uzar, 2020). LNFA (−1.91)* has a negative with weak implications, meaning that the LNFA has a weak negative and long-run influence on LNED (Abas et al., 2017; Al-Mulali et al., 2013). LNAE (2.941) *** has a positive and significant long-run influence on LNED (Azam, 2016; ÖQUIST et al., 2009; McCarthy and Smyth, 2009). Therefore, LNREC (−1.942) ** has a negative but significant 10% influence on LNED (Selvanathan et al., 2023; Yang et al., 2023; Zhang et al., 2023). Finally, LNFP (2.591) ** positively and significantly affected LNED (Sadiq et al., 2024; Voumik et al., 2023). The findings indicate that LNWR and LNREC have a negative relationship with the dependent variable, especially in the long run, but LNAE and LNFP are positive and statistically significant. Such a constructive, consistent pattern shows that LNAE and LNFP apply to positively influencing long-term outcomes. However, LNWR and LNREC need to be managed so as not to lead to adverse future effects. The presence of several models provides reliability by focusing on the short- and long-term patterns of the dynamic nature of the relationships under analysis.

This study also examines the causal effect of water resources (WR), forest area (FA), access to electricity (AE), renewable energy consumption (REC) and food production (FP) on CO₂ emission (CDE) in OECD countries. Table 8 shows the outcomes of the causality test. The findings showed a one-way causal link between variables. The present study discovered evidence of a unidirectional causal relationship between WR and ED, indicating that FA changes will impact ED (CO₂ emission). Furthermore, the study discovered that AE could predict ED (CO₂ emission) in OECD countries, indicating a unidirectional causal link between the two variables. The research discovered a unidirectional causal link between REC and ED (CO₂ emission), implying that changes to REC will have far-reaching effects on carbon dioxide. Furthermore, the study discovered evidence of a unidirectional causal link between FP and Ed. (CO₂ emission), indicating that FP changes will impact ED (CO₂ emission). Finally, evidence indicates a one-way chain of events. OECD policymakers can primarily rely on these data to shape policy decisions.

The findings of the current study highlight the significant negative influence of water resources on environmental degradation, particularly in reducing CO₂ emissions in the OECD countries. The management of water involves processing, treatment, desalination pumping and transportation, which, when affected inefficiently or in excess, results in high CO₂ emission. Water used in agriculture, especially in areas with high-intensity farming, can also lead to environmental pollution. Water resources that are overexploited yield loss of ecosystems that otherwise capture carbon, thus interfering with natural carbon sinks. Another factor that makes a mega contribution to CO₂ emission is the high use of water in the urban and industrial sectors. There exists considerable ineffective utilization of water resource management standards; this is evident even with efficient regulatory measures in place. Water management practices that emphasize the impacts of climate change can only be addressed by adopting sustainable water management. This study confirms the findings of a previous study (Altinoz and Dogan, 2021; Dagar et al., 2022; Kartal et al., 2024; Turedi and Turedi, 2021; Zang et al., 2023; Zhuo and Qamruzzaman, 2022).

Second, forests are usually credited with the capability of limiting environmental deterioration due to absorbing CO₂ emissions. However, variables such as deforestation, forest degradation, intensive forest management, afforestation with non-native species and urbanization complicate the relationship between forest areas and CO₂ emissions in OECD countries. Flooding also liberates carbon from tree trunks, roots and ground, whereas degradation minimizes the volume of carbon that could be stocked in the forests. Intensive forest management practices may bring temporary CO₂ emissions up, whereas afforestation of nonindigenous trees generally has lower biodiversity and adaptability to stress conditions. In the same way, the loss of forest area due to urbanization decreases the amount of CO₂ sequestered and generally increases emissions. Only through practicing sustainable forest management, afforestation with native species and policies against deforestation can some of these potential detriments be avoided. This study confirms the findings of a previous study (Aleksandrowicz et al., 2019; Ashraf et al., 2024; Ching et al., 2021; Raza et al., 2021; Sharif and Khan, 2024).

Furthermore, the availability of electricity power is important for the development and enhancement of welfare, which can lead to environmental harm and an increase in CO₂ emissions in OECD countries. The use of fossil fuels, enhanced energy needs, the development of industries, the population of cities and emergent societal trends have occasioned this. Equality of access to electricity invariably leads to high-output industrial economies and consequently higher emissions unless the use of green technologies is embraced or emission control policies are stringent. The shift to electric-based systems, such as electric vehicles or heat pumps, boosts CO₂ emissions in OECD countries, where the use of fossils produces electricity. Despite the improved electricity access to the population, its negative effects on the environment need attention through the immediate shift of the populace to renewable energy sources and the implementation of sound energy efficiency policy, green infrastructure and other low-carbon essential technologies for improved environmental stewardship. This study confirms the findings of a previous study (Ali Warsame and Hassan Abdi, 2023; Hamed et al., 2024; Kousar et al., 2020; Liu et al., 2023; Tiemeyer et al., 2024).

In addition, renewable energy consumption has a negative impact on environmental degradation. Therefore, the consumption of renewable energy sources in OECD countries decreases CO₂ emissions and environmental deterioration. However, it may pose uncertainty or potential negative social/economic repercussions. Measures such as strategic planning on the sustainable supply of resources and a proper assessment of the effects that renewable energy systems would have on ecosystems could reduce the positive effects and promote the negative effects of renewable energy. This study confirms the findings of a previous study (Guo et al., 2024; Korkiakoski et al., 2023; Raihan, 2023; Raihan et al., 2023; Raihan and Tuspekova, 2023; Selvanathan et al., 2023; Yang et al., 2023; Zhang et al., 2023). This study also confirmed that food production in OECD countries remained a major cause of environmental degradation, particularly CO₂ emissions. Recent practices depend on many nonrenewable resources, including power, water, fossil fuels, chemical fertilizers and pesticides, water, meat production, processing, packaging, water, waste generation and disposal and agricultural soil. Some of the practices include regenerative agriculture, a shift to reduced meat consumption, efficient food waste management and sourcing foods locally, which are very critical in mitigating these effects. This study confirms the findings of a previous study (Cheng et al., 2023; Dimnwobi et al., 2023; Gyamerah and Gil-Alana, 2023; Kocak, 2023; Li et al., 2023; Naeem, 2023; Sadiq et al., 2024; Voumik et al., 2023).

This study’s primary objective is to investigate how sustainable development (including water resources, forest area, access to electricity, consumption of renewable energy and food production) influences environmental degradation (CO₂ emissions). Environmental protection is an essential tool in the fight against environmental degradation. It functions as a channel for global cooperation on environmental issues, preserving the existence of future generations. International collaboration through diplomacy is critical for restoring the health of Earth’s ecosystems and establishing a more sustainable and peaceful planet. This study contributes to our comprehension of the role of sustainable development in reducing CO₂ emissions by providing a fresh perspective on sustainable development from the perspective of OECD nations. To achieve this, the authors of this paper use panel data econometric methodologies with data spanning 1991–2020. PMG-ARDL and FMOLS findings are highly congruent with MG-CCE findings. However, access to electricity and food production has more significant coefficients than environmental degradation. Water resources negatively and statistically significantly impact environmental degradation. Second, forest areas have a statistically significant negative effect on environmental degradation. In addition, electricity access negatively and statistically significantly impacts environmental degradation. Access to electricity positively affects environmental degradation. Furthermore, the consumption of renewable energy has a very negative impact on environmental degradation. Food production harms the environment in a large negative way.

For climate change mitigation, the research emphasizes the need for robust environmental policies for OECD countries. That helps enforce these regulations in positive ways when it comes to reducing carbon emissions. The result is that it also shows the importance of renewable energy consumption, promoting electricity access and developing sustainable food production as an alternative to environmental degradation and reduction of CO₂ emissions. The research also highlights the need for international cooperation and a high degree of collaboration among OECD member states to fight the problem of climate change successfully. Environmentally, it becomes more efficient and effective as the sharing of best practices, know-how and resources becomes the norm. The study also underlines the significance of public knowledge and education in support of sustainable habits and dynamic support of people’s active involvement in action against climate change.

Reducing CO₂ emissions can enhance environmental performance. Therefore, the study emphasized that OECD governments should prioritize the development of sound policies for countering climate change in OECD countries. Global cooperation requires international collaboration and diplomacy. To reduce CO₂ emissions and promote sustainable development, manage water resources, expand the forest area, reduce the consumption of renewable energy and expand access to electricity. Addressing policy uncertainty and providing clear policies can prevent carbon-intensive firms from relocating to less stringent regulations. Investing in research and development of new technologies can speed up the transition of economic growth to a low-carbon economy. In addition, raising public awareness and education on the need for the adoption and utilization of sustainable practices would lead to the adoption of environmentally friendly choices in people’s everyday affairs.

Research on sustainable development and degradation is limited by its focus on OECD countries and its use of panel data econometric methodologies. The study’s time frame from 1991 to 2020 may not capture recent environmental policy and practice developments. Aggregated data may not capture nuances and variations in sustainable development and degradation at the country or regional level. The study does not explicitly address the influence of socioeconomic factors, technological advancements or cultural differences on the relationship between sustainable development and degradation. Future research should explore the evolving landscape of sustainable development in OECD countries, examining innovative policy implementations, multistakeholder collaborations, geopolitical factors, education, public engagement, circular economy practices, postpandemic recovery plans, natural capital accounting, climate adaptation strategies, environmental justice considerations and global governance structures. Additional research should explore the role of sustainable development on environmental degradation in other countries or continents like Asia, South Asian association for regional cooperation, etc.

The authors extend their heartfelt thanks to the editorial board and the anonymous reviewers for their valuable suggestions.

Funding: This study was supported by King Saud University, Riyadh, Saudi Arabia, Project number (RSPD 2025R932).

Data availability: The data sets used and analyzed during the current study are available from the corresponding author upon reasonable request.

Ethics approval and consent to participate: This is an observational study. The authors confirmed that no ethical approval is required.

Consent for publication: Not applicable.

Competing interests: The authors declare no competing interests.