Breaking the CO₂ Gridlock: Can Renewables Lead the Way for the OECD?

Abstract

The use of low-carbon energy in power grids is essential for minimizing negative effects on the environment. Energy consumption causes environmental damage to the OECD’s economy. This study aims to investigate the effect of energy consumption, population, and GDP on CO₂ emissions using panel data from 17 OECD countries over the period 2000–2023. We use regression approaches, such as partial least squares and principal components, to study the effects of GDP, urban and total population, oil and nuclear use, renewable energy, and industrialization on CO₂ emissions. The regression process in this study reduces the data to a two-dimensional representation using a stochastic model and estimation techniques. The findings of this empirical investigation indicate that the United States, Canada, France, Germany, Italy, Korea, Mexico, and the United Kingdom exhibit higher levels of primary energy consumption in comparison to value-added sectors, renewable–geothermal energy, and nuclear energy. We determined the effects of CO₂ emissions, GDP, and energy consumption by considering these as the most significant elements. This has made it possible to reduce CO₂ emissions by focusing one’s attention and energy on the development of novel technologies, the use of renewable energy sources, and the execution of strategic plans. Attracting increasing attention are technological shifts that deliver enormous quantities of clean energy to combat climate change. Findings from this study can help environmentalists and policymakers better understand the role of structural change and energy consumption processes in the globalization process.

1. Introduction

In order to prevent global warming, it has been demonstrated that CO₂ emissions must be eliminated. To accomplish this objective and establish carbon-free zones (with energy usage taxes and carbon taxes), it is most effective to establish a carbon price [1]. As a result of climate change, the environment behaves differently, and new weather patterns emerge [2]. According to the National Oceanic and Atmospheric Administration (NOAA), the expansion of industry, energy consumption, and GDP is a direct result of climate change. The IEA’s latest analysis of CO₂ emissions in 2022 reveals that emissions are still on an unsustainable growth trajectory, despite the fact that the increase in 2022 was significantly lower than the extraordinary increase of over 6% in 2021. Consequently, the world must take more robust actions to expedite the clean energy transition and ensure that it meets its energy and climate objectives. CO₂ emissions from the world’s energy sector increased by 0.9% in 2022, or 321 million tons, to an all-time high of more than 36.8 billion tons, the report finds [3,4]. As the demand for energy continues to expand rapidly and internationally, it is vital to assess and estimate the consumption of global energy in order to make informed decisions regarding energy production, distribution, and sustainability. Individual nations can contribute to an international effort to improve environmental changes [5,6]. Energy demand has been rising continuously as a result of factors like population growth, economic expansion, and technological innovation. Additionally, there are a variety of strategies for coping with climate change, including altering the technical ways that energy is consumed and cutting emissions [7]. According to the Organization for Economic Co-operation and Development (OECD), the rise in CO₂ emissions can be traced back in large part to two factors: cement manufacturing and the usage of fossil fuels. The energy consumption practices of the OECD’s member countries are one of the most significant concerns they encounter. The environment is adversely affected by the erratic growth and sizable populations of nations. The literature has extensively examined the relationship between the use of renewable energy and economic expansion in OECD nations. The OECD was responsible for 54% of energy-related emissions in 1990, and it is anticipated that this figure will decrease to 32% by 2040. The expansion or contraction of critical variables in relation to total carbon output is illustrated in Figure 1.

The OECD economies are of the highest complexity in the world, and their consumption of primary energy and hydrocarbons significantly contributes to global warming [8]. Greater energy consumption may be necessary to support the global economy’s growing complexities [9]. The United States used 19.4 million barrels of oil, or 94.65 exajoules of primary energy, in 2019 [10]. From 2005 to 2015, the United States and Japan were identified as the world’s two highest emitters of carbon dioxide (CO₂). Global energy-related CO₂ emissions are projected to increase by 0.6% each year between 2023 and 2050, per the International Energy Outlook 2019 (IEO2019). The findings of this investigation indicate that Slovenia had the lowest CO₂ emissions, while the United States had the highest. There has been a substantial increase in PEC (or precise energy consumption) over the past decade. Nevertheless, the following nations are anticipated to experience substantial growth: Mexico, Spain, Sweden, the Republic of Korea, the United Kingdom, Canada, France, Germany, Italy, and Canada [11].

The purpose of this study is to assess the combined influence of 17 OECD nations’ industrialization, oil and nuclear use, GDP, urban and total population, renewable energy use, and environmental degradation on CO₂ emissions [12,13,14]. The stochastic model for regression technique is employed to investigate the individual impact of the urban and overall population in order to convert the data into a two-dimensional format [5]. The technological advancements in the energy, nuclear and hydrocarbon consumption, renewable energy, and industrial sectors are also analyzed separately for each nation in this model. Furthermore, technical revaluation in the renewables sector is one of the most effective strategies for long-term sustainable development. In an effort to foster green growth, countries worldwide are attempting to transform their industrial and economic structures [15,16].

Figure 2 shows the GDP per capita in OECD countries in comparison to the population size of those countries. In spite of a growing population, the GDP per person in the United States climbed by 9.49% during 2014. The US population expanded by 3.11% between 2010 and 2014, to 327.8 million [17]. As a consequence, we have observed an increase in energy consumption and the concomitant changes in lifestyle that are associated with urbanization [18]. The OECD’s attempts to improve commercial procedures will lead to an increase in CO₂ emissions. In the past and present, CO₂ emissions regulation has been within the purview of the Organization for Economic Co-operation and Development (OECD) [19]. Between 1950 and 2016, only roughly 5–10% of global CO₂ emissions were produced, whereas the rest were produced before 1950. The Structural Decomposition Analysis (SDA) model was used to analyze the shifts in Spain’s CO₂ emissions, to mitigate the climate change rebound effect [20].

The correlation between GDP growth, environmental regulations, and CO₂ emissions was also investigated by employing the “Stochastic Influence by Regression on Population, Affluence, and Technology” (STIRPAT) model [21,22]. There was a 3% drop in OECD CO₂ emissions for every 1% increase in environmental tax revenue [23,24,25]. The number of cycles and the magnitude of the growth rate cycle were shown to be related to the reduction in CO₂ emissions in recycled parts [26,27]. The relationship between GDP, population growth, CO₂ emissions, and the technological revolution is a fundamental aspect of the OECD energy consumption study, and we have underscored a number of interconnected topics. There is an increase in energy consumption in the hydrocarbon, renewable–geothermal, nuclear, and value-added industries, among other sectors [28]. In addition, the growth of economies in countries that produce an unfair number of global CO₂ emissions is no longer the single most important driver. Investments in superior infrastructure and state-of-the-art technologies have propelled the economies of these regions to new heights [29]. The use of renewable geothermal energy has a significant impact on CO₂ emissions in the industrial sectors, proving that the introduction of new technologies can reduce environmental impacts. Last, the enjoyment and passion associated with driving are closely correlated with how much oil and energy it consumes.

In contrast to earlier studies that merely identified factors impacting energy efficiency, this one offers a thorough analysis of the topic. It has been shown that energy efficiency is affected by urbanization, technical progress, industrialization, and international trade [30]. Over the past few decades, global industrialization and population growth have contributed significantly to an increase in energy demand. As a result, more fossil fuels are being burned than ever before, which threatens both economic growth and environmental stability [31,32]. The OECD has emphasized the necessity of a sustainable growth model that prioritizes energy conservation and renewable energy sources to mitigate the adverse effects of fossil fuels, thereby addressing these challenges [33]. Thus, there is a strong and consistent connection between issues like finite fossil fuel sustainability, environmental concerns, acid rain, global warming, overcrowding, economic growth, and rising demand, and the issues associated with energy use in OECD countries [34]. The rising number of people all around the world means that more power will be needed [18,35,36]. By applying the STIRPAT model [37] of partial least squares regression, this study adds something new to the existing literature by examining whether or not the OECD countries’ excessive energy use stunts their economic development [38,39,40]. The significance of the impact analysis in gauging the effect of energy use on the OECD is demonstrated by the findings of this study. This study’s findings demonstrate that the authors’ strategy was correct and that their substantial efforts to develop a methodology and collect a dataset were valuable [30,41]. In addition, the modern world’s two most pressing problems are energy consumption and environmental sustainability [42,43]. As the global energy demand continues to increase, it is imperative that we closely monitor the environmental impacts of energy production, consumption, and distribution processes in order to guarantee a sustainable future [44]. It is critical to strike a balance between economic development and environmental safeguarding if sustainable development is to be achieved [45,46]. Consider this in light of the EU’s strategy for the sustainable development of energy sources and the associated goals for all member states. Pollutants in the environment, energy usage, and GDP growth have all received more focus in recent decades.

The substantial separation between the two groups in the subset of components used to generate the 408 scores in this research can be inferred if the T2-range value in the PLS model for 16 groups is greater than the critical restrictions. Consequently, the model of this group classification is derived from other group clustering [47]. This suggests that the economies of the Netherlands, Spain, and Sweden have experienced growth as a result of the enhanced infrastructure and technology and that economic development in countries with significant CO₂ emissions is no longer a significant factor. Furthermore, the results of this study also support the utilization of principal component analysis and principal axis rotation to reduce the extent of the consumption and expansion dimensions. This implies that the OECD may execute strategies for energy conservation and CO₂ emissions reduction without concern for their adverse economic repercussions [46,48]. The research results indicate that the CO₂ emissions generated by energy use are in turn generated by building, transportation, and technological advancement. In conclusion, the results of the study indicate that the primary concerns of contemporary city planners are technological advancement, ecological sustainability, and economic viability. The rate of urbanization is no longer a significant factor in the production of CO₂ emissions. By meticulously selecting the most pertinent variables, we can construct a more informative and parsimonious model that offers a more comprehensive understanding of the factors that influence CO₂ emissions [48]. The process of feature selection entails the identification of the primary predictors that account for the majority of the variance in the objective variable. This procedure can be accomplished using regularization methods, hypothesis testing, correlation analysis, or feature importance techniques [49]. Ultimately, a more robust and generalizable analysis can be achieved by simplifying the model, improving its interpretability, and reducing the risk of overfitting by concentrating on these significant predictors [50].

The objective of this study is to evaluate the contribution of energy consumption, high-value-added sectors, and population to the CO₂ emissions of OECD nations from 2000 to 2023. The primary inquiry of the investigation is whether renewable energy can act as a catalyst for the 17 nations participating in the OECD to overcome the CO₂ impasse. The analysis method also considers the blockade of CO₂ emissions by GDP and other energy sources. Other energy sources and the GDP are also taken into account by this analysis technique. The selection of the OECD was driven by two key factors. Firstly, the OECD is confronted with a serious environmental disaster and tremendous economic growth. Second, by using the OECD as a case study, advanced economies will be alerted and guided regarding the excessive energy consumption of their enterprises. In addition, it contributes significantly to the existing body of literature in a number of important ways. The purpose of this study is to assess the combined influence of 17 OECD nations’ industrialization, oil and nuclear use, GDP, urban and total population, renewable energy use, and environmental degradation on CO₂ emission. The impact of the urban and overall population on the environment is also analyzed, as measured by the addition of CO₂ emissions using the stochastic model for regression technique. The incorporation of these new components facilitates a more comprehensive evaluation of the long-term viability of OECD countries in 16 clusters that have higher technology adoption rates, as well as the development of more precise policy recommendations for each cluster of countries.

Consequently, we contend that the motivation and originality of this study are derived from these justifications and objectives. Second, the IPAT study of how CO₂ emissions evolve as a function of GDP, technology, and population served as the foundation for this research on the urban and total population, GDP growth, CO₂ emissions, and energy consumption in high-carbon-emitting nations. In terms of technology, it is critical to draw attention to the industry, renewable energy use, and energy consumption of 17 OECD nations. The results of this study will provide emergent economies with substantial benchmarks to pursue when combined with the OECD’s global influence. Third, this study employs stochastic (STIRPAT) and two-dimensional reduction testing techniques, thus boosting its usefulness and validating its robustness. The relevance of the clusters formed by high-carbon-intensity countries has also been diminished as a result of the clustering of the greatest CO₂-emitting countries. Fourth, the PLS model estimates the relative weight of each explanatory variable by calculating the variable importance in a project (VIP). Finally, this study establishes the foundation for future research on energy consumption and assists decision-makers in understanding the issue of energy and climate change. People will gain a more comprehensive understanding of the impact of energy use, population growth, and economic development on CO₂ emissions in OECD countries as a result of the study’s findings.

The remaining sections of the paper are structured as follows: Section 2 presents a literature review. In Section 3, we describe the research’s methodology and data sources. The results are summarized in Section 4. Section 5 contains the discussion, while Section 6 contains the study’s results and recommendations.

2. Literature Review

Previous studies’ findings were helpful enough to influence OECD policy shifts in areas such as oil and nuclear energy consumption, high-value manufacturing, value-added industries, renewable power, and CO₂ emissions [51,52,53]. Similarly, a trade agreement [54] was used to verify the effects of reducing emissions on the economy, value-added industries, and climate change [55]. The utilization of renewable and nuclear energy, as well as technological advancements, has been demonstrated to decrease CO₂ emissions, whereas the use of fossil fuels and economic expansion have been demonstrated to increase them. The results of the study revealed a previously unrecognized consequence of trade: a decrease in greenhouse gas emissions that can be attributed to energy consumption from 1980 to 2019 [56,57]. Energy consumption has indirect environmental effects on climate change, and the transportation industry has experienced substantial development over the past several decades, resulting in consistent annual growth. Consequently, the issue of transportation-related energy use and emissions has been raised [27,57,58,59]. Despite its importance, research on how growing industries and commercial activity affect greenhouse gas emissions is lacking, especially in the OECD. The emphasizes the developing world’s contribution to the global economy and offers advice to climate and trade policymakers in the major emitter countries on ways to decrease the rate of economic expansion, industry, and trade-related CO₂ emissions [22,60,61,62]. On the other hand, increased economic activity in this economic bloc increases CO₂ emissions due to consumer expenditure, which has a cumulative effect on environmental degradation [63,64]. This report calls for increased investments in green technology and the implementation of legislation to reduce CO₂ emissions in the energy, industrial, and commercial sectors [64,65]. Thus, CO₂ emissions will be strongly impacted by policies directed toward globalization, economic growth, energy consumption, and the usage of renewable energy [66,67]. In addition, rapid infrastructural and industrial expansion to help provide excellent services to the populace has resulted in severe environmental dilapidation [68]. The additional expenses associated with environmental deterioration necessitate the introduction of environmental protection measures [69].

2.1. Energy Consumption and CO₂ Emissions

Important implications for energy consumption and environmental policies are drawn from this study, which examines the OECD in light of the connections between economic growth, the use of renewable energy, industries and trade, and CO₂ emissions [13,69]. When it comes to factors contributing to excessive pollution in recent years, there has been an increased emphasis on the interconnectedness of energy transitions, energy consumption, and environmental sustainability. The countries of the OECD are technologically advanced and have strong environmental policies [70,71], which gives them a high environmental risk absorption capacity; nonetheless, the United States is still battling to regulate and curb continuous emissions. Where there is an increasing demand for environmentally sustainable energy usage, the significance of this research has increased [33,72]. The level of energy consumption is expected to rise by 2050 as a result of changes in the structure of the industrial sector, economic growth, and the living standards of the population. Consequently, the primary objective of this research is to examine the interconnections among OECD members [18]. CO₂ emissions are a major contributor to environmental degradation and pose the greatest threat to global environmental health [69]. Supporting OECD policymakers in their decision-making and analysis of population, GDP, and technology in energy use is a key contribution of this study. The stochastic model was applied to the 17 most influential OECD countries, utilizing statistical analysis and dimension-reduction techniques [1,2,73]. The high energy consumption in OECD countries, caused by industrialization, transportation, and domestic activities, has long made them a leading source of global CO₂ emissions [74]. Renewable energy, energy efficiency, and carbon pricing are just a few of the ways that numerous OECD nations have been working to lessen their impact on the environment in recent years [75,76,77,78,79]. The fact that OECD nations are still major contributors to world CO₂ emissions shows how urgent the need is to decarbonize and move toward a low-carbon future, even with current initiatives [80,81].

Every sector of the global economy and society is under threat from the destructive consequences of climatic changes and global warming [82]. Natural resource consumption and ecological footprints continue to rise, at rates that are far higher than population expansion, making them the key factors following ecological degradation [74]. Some studies stress the minor or even growing effects of CO₂ emissions and their repercussions, but others show no correlation between the two [83] and have found that across 19 developed and developing nations, renewable energy does not aid in the decrease of emissions in the short term. There were short-term reports of a link between CO₂ emissions and the use of renewable energy [84]. Nevertheless, there was a limited amount of prior research conducted to investigate the direction of causality between the two. The utilization of renewable energy sources did not exhibit any correlation with CO₂ emissions [85]. Furthermore, at the present time, the EKC (Environmental Kuznets Curve) hypothesis is often employed to represent the connection between economic growth and CO₂ emissions. The positive and considerable effect of technology in reducing CO₂ emissions is supported by the validity of the EKC hypothesis, and the results of this study will provide policymakers in these nations with substantial guidance for achieving carbon neutrality [86]. Moreover, various studies have highlighted economic expansion as a crucial factor in CO₂ emissions. To reach carbon neutrality, the highest-polluting nations must decouple their economic growth from the lavish consumption of fossils by an immediate or phase-by-phase conversion to renewables [87]. As the economy grows and natural resources interact, it is possible that the already murky definition of EKC may become even more so [63]. For starters, the OECD (2010) describes the pattern of parallel shifts in economic growth and industrial pollution emissions [63,88,89].

2.2. Populations, Thriving Economies, and CO₂ Emissions

Population and economic growth are inextricably linked to CO₂ emissions. A correlation of one to one has been seen between population growth and CO₂ emissions, with a 1% rise in population resulting in a 1% increase in CO₂ emissions [18,60,90]. This correlation demonstrates that the demand for energy and resources increases as the human population and economic activities expand, resulting in increased CO₂ emissions [33,38,90,91]. In addition, the rapid urbanization in the OECD has moved energy demand from manufacturing to consumption. The assessment of energy inputs is the most critical component of the entire production cost in the contemporary economy. It may be economically problematic for a country if its energy supply is insufficient to support its expanding middle class and increasing industrial output [71]. This change in energy consumption has resulted in a notable but controllable change in lifestyle and energy consumption [92]. Previous studies suggested that in the evolution of CO₂ emissions, construction and infrastructure changes, including both lifestyle changes and urbanization, have overtaken efficiency expansion [19,75,79,82]. While both economic and environmental sustainability are important, it is believed that the two are at odds with one another, with rapid economic expansion putting the environment at risk in the near term [60]. According to the findings of this study, energy consumption is a significant aspect of urbanization, GDP, and industrialization. Numerous approaches have been utilized in prior studies to provide greater insight into the foundations of CO₂ emissions and their possible challenges [93]. In addition, sustainable development can be furthered when the proceeds from natural resource extraction are invested in new technologies and economic diversification [94]. The influence of the double-edged sword on the mitigation of CO₂ emissions can only be supervised to a limited extent, even though ecological security and technological advancement are not the only metrics of importance [82,95]. Historically, OECD countries have observed a robust correlation between population growth, economic prosperity, and the rise in CO₂ emissions [96]. As these countries have undergone industrialization and advancement, their energy consumption has increased, resulting in elevated emissions. Nevertheless, recent endeavors to disentangle economic expansion from emissions, by means of allocating resources to renewable energy, enhancing energy efficiency, and promoting sustainable consumption, have demonstrated advancement [76]. Although attempts have been made, OECD countries continue to contribute a substantial amount to worldwide CO₂ emissions, underscoring the persistent difficulty of achieving a harmonious equilibrium between economic development and environmental preservation [73,97,98].

According to the available literature, the STIRPAT model can be used to examine how nations’ per capita incomes relate to their carbon footprints [99,100]. Nevertheless, there is a dearth of research that utilizes the STIRPAT model to evaluate the composition of sectors in relation to their utilization of primary energy, hydrocarbons, and nuclear power. The authors have endeavored to address the knowledge deficit by introducing new metrics in this paper. The literature review concludes with the presentation of contradictory findings regarding the influence of energy consumption on CO₂ emissions in OECD countries. Specifically, the mixed results are obtained by Ref. [94] in energy utilization; by Ref. [60]; and by Ref. [86] in terms of technology and renewable energy. The varied results can be attributed to the diverse selection of empirical growth measures, country samples, and econometric methodologies. The STIRPAT model and two-dimensional reduction testing techniques are employed in this study to introduce a novel strategy, thereby improving its applicability, validity, and resilience. At present, we do not assert that STIRPAT is preferable [75]. It is our objective to illustrate the necessity of a distinct approach in order to enhance comprehension of the impact of energy consumption, population expansion, and economic development on CO₂ emissions in OECD countries. We demonstrate that the panel data of 17 OECD countries are effectively suited to a novel methodology.

3. Empirical Approach

3.1. Method and Data

The dataset has passed manual inspection and has been determined to be normal. The dataset was created using the BP Statistic review reports and available sources from the World Bank (WB) (Appendix A). From 2000 to 2023, this paper’s 24-year time span, the panel data present the indicators of investigation, and this annual data source is supplied in numerous acronyms (

Table 1. Description of indicators.

Variables	Code		IPAT	Description
CO2 emissions	CO2 emissions		I	CO2 emissions manufactured via the combustion of coal, gas, and oil.
Urban population growth (annual%)	UPG	P1	P	The proportion of the total population that lives in urban regions as a percentage.
Population total (POP/1 million)	POP	P2		Using this definition of population and some expected values for the middle of the year, we can calculate the total population.
GDP (constant 2010 US$)	GDP		A	The GDP is calculated using the prices paid by purchasers, and it represents the total gross value added by all residents.
Primary energy consumption	PEC	T1	T	The overall energy consumption of a nation is typically referred to as the “gross inland energy consumption” of that nation.
Oil consumption (tons)	OIC	T2		The process of burning things in various fields results in the release of energy.
Nuclear energy consumption (Mtoe)	NEC	T3		Energy consumption.
Renewable–geothermal, biomass, and others	RGB	T4		Geothermal and biomass.
Industry (including construction) value added (% of GDP)	ICG	T5		Value-added construction spending is a proportion of total economic output. The construction, mining, energy, water, and gas industries are the ones that most significantly contribute to economic value.

Sources: The data for PEC, OIC, NEC, and RGB were collected from the BP Statistics Review of World Energy, 2 June 2019 (http://www.bp.com/statisticalreview), while the data for UPG, POP, GDP, and ICG were collected from the World Bank database, 7 September 2022 (https://datatopics.worldbank.org/world-development-indicators/).

). The Statistical Review of World Energy was used to collect the PEC, OIC, NEC, and RGB datasets. The global bank sources are used to obtain the datasets for UPG, POP, GDP, and ICG. According to the BP Statistical Review, the estimated results demonstrate a change in CO₂ emissions (17 countries in 24 years) (Appendix B). We have chosen 17 countries on the basis of CO₂ emissions, population, economic growth, energy consumption, oil consumption, renewable energy, and industrial development, where individual countries first fall into 16 groups; second, we have highlighted the distribution of countries on the basis of G7/G8 membership, and on the basis of highest development in industrialization sectors, we classify the membership of the countries. Furthermore, in order to accomplish this, we employ stochastic and two-dimensional reduction approaches to examine the 17 most populous OECD nations. In this study, the regression procedure utilizes a stochastic model and estimating approaches to condense the data into a two-dimensional representation. We have applied the stochastic (STIRPAT) model to analyze several indicators, such as economic growth (measured by GDP and population), CO₂ emissions, value-added industries, and energy consumption (including primary energy, oil, renewable–geothermal, and nuclear). This strategy is inspired by the accounting calculation known as the Impact, Population, Affluence, and Technology (IPAT) formula. According to IPAT, environmental damages are proportional to a nation’s wealth, standard of living, and technological development. In accordance with the STIRPAT model, variables and IPAT terms have been selected and assigned. OECD nations measure and account for CO₂ emissions and energy usage in accordance with Table 1.

The environment (I), population (P), prosperity (GDP), and technology (T) are broken out as follows, with subcategories for energy consumption (PEC, OIC, NEC), renewable energy (RGB), and industries (ICG), and subcategories under ICG for elements T1, T2, T3, T4, and T5. This study’s findings are consistent with the stated objective, suggesting that the revolutionary shift in technology surrounding energy consumption, renewable energy sources, and industry has an indisputable impact on the economy, the population, and the natural world.

The primary contribution of this model is a review of the most recent scientific literature, with a particular emphasis on environmental cross-sectional and panel data regression models. The conclusion is that the scientific literature frequently utilizes ridge regression and partial least squares to analyze the influence of the driving force. In contrast, refs. [101,102] recommended these methodologies for STIRPAT models when the objective is prediction and the estimated parameters are linear. The flawed results of much research can be traced back to the neglect of important methodological details. Although refs. [82,103] discouraged the utilization of PLS and ridge estimations when the objective is prediction and the estimated parameters are not understood as causal effects, these techniques are frequently employed to evaluate the impact of environmental driving factors. Multivariate analysis is primarily concerned with dimension reduction. PLS and PCA are implemented to achieve dimension reduction. The cluster analysis (CA) dataset was analyzed in this study using a PLS tree, as PLS regression modeling was independently fitted within each cluster. In total, 17 distinct nationalities were represented [104]. The correlation between variables does not change with the total number of observations (408 in total) (

Table 2. Clustering of OECD.

Groups	Total Observations (408)	Country Codes	Countries (17)
Group 1	24	USA	United States
Group 2	168	CAN, FRA, DEU, ITA, KOR, MEX, ESP, SWE, GBR	Canada, France, Germany, Italy, Rep. of Korea, Mexico, Spain, Sweden, United Kingdom
Group 3	23	CAN, KOR, ESP	Canada, Rep. of Korea, Spain
Group 4	28	NLD, ESP, SWE	Netherlands, Spain, Sweden
Group 5	9	NLD	Netherland
Group 6	15	BEL	Belgium
Group 7	12	SWE	Sweden
Group 8	34	FIN, SVN, CHE	Finland, Slovenia, Switzerland
Group 9	9	BEL	Belgium
Group 10	16	FIN, HUN	Finland, Hungary
Group 11	13	HUN	Hungary
Group 12	10
Group 13	14	SVK	Slovak Republic
Group 14	10
Group 15	15	SVN	Slovenia
Group 16	8

Sources: Sartorius Stedim Data Analytics (PLS tree) X and Y scaling of the study dataset was used for the group-based selection, and Table 2’s first score (t) was then used to spill the data. The OECD countries have been divided into 16 categories using a PLS tree.

). Variables within one dataset group are more analogous to those inside another group. New ideas for improving data analysis were generated by using data mining of CDA in clusters.

The PLS tree structure shown in Figure 3 classifies the 17 OECD countries into 16 categories. Table 2 shows the PLS tree generated by the cluster analysis, and X and Y perform the scaling analysis. We analyzed a total of 41.17% of the total population in the group 2 observations. This listing is ranked by the total area of the countries, with a scale of zero to one. Subsequently, the PLS tree is divided into two parts (groups 1–8 and groups 9–16) according to its size, and the two halves are distributed among 17 distinct nations. PLS model cross-validation eliminates superfluous nodes in the tree structure (Figure 3) and clusters component numbers (Table 2). We examine the X and Y matrices using the PLS method, which requires that they be scaled and centered before a PLS tree can be generated from them. Further, the cross-validations determine the total number of components for each cluster PLS model, capping off the tree. Cross-validation enables us to determine the number of components that should be included in each cluster PLS model and to trim the tree branches to that length.

Utilizing a PLS tree, the subsequent tasks may be executed: After sorting, a cluster in a cluster PLS model is divided in half along the initial X-score (t1). The best position of the split is determined by the sum of (a) the improvement in the variance of the X-score (t1), (b) the improvement in the variance of Y, and (c) a penalty function that promotes a balanced split with approximately equal numbers of observations in each branch. Afterward, X and Y are subjected to a PLS analysis. Subsequently, the Y-data are split in half using the initial X-score (t1) as the dividing point (X and Y are sorted along t1). It is determined that t1 is the time at which the point is searched that divides X and Y into equal portions, 1 and 2 [105].

3.2. CO₂ Emissions Estimation

The IPCC (Intergovernmental Panel on Climate Change) asserts that its methodology for calculating CO₂ emissions offers a distinctive principle and procedure for calculating GHG emissions. During its 43rd meeting, the group examined potential solutions to the 1.50-degree Celsius rise in global temperature from preindustrial levels and the subsequent greenhouse gas emissions. Furthermore, it provides preventative measures to address poverty and advance sustainable development in the context of climate change. The following equation is used to calculate CO₂ emissions from a growth and development perspective when the CO₂ emissions produced during the growth and development process over a certain time period are unknown.

$$COE={\sum }_{I=1}^{8}CO{E}_i={\sum }_{I=1}^8{E}_i\left(CV\right)\left(CEF\right)\left(COF\right)44/12$$

(1)

Equation (1) represents energy consumption by the OECD from 2000 to 2023. Common Value refers to the average calorie content of a food. While COF is the coefficient of CO₂ emissions factor and CEF stands for CO₂ factor, 44/12 displays the molecular weights of CO₂ emissions. Carbon has a molecular weight of around 12 g/mol, while CO₂ emissions have a molecular weight of about 44 g/mol [106,107].

3.3. The STIRPAT Model

While experts believe that people have altered the global ecosystem, there are still ways to determine which countries produce the most CO₂ emissions and the extent of their impact. While the previous research looked at the effects of OECD countries’ CO₂ emissions on the environment, it did not take into account technical considerations such as PEC, OIC, NEC, RGB, and ICG with T1, T2, T3, T4, and T5 indicators [80,100,108]. We use advanced analytic techniques, including stochastic (STIRPAT) modeling, IPAT identification, IPABT, and IPACT, to assess the historical impact of environmental stress. The approach for assessing performance was introduced and developed in 2002. The evaluation model identifies three primary environmental influences: population (P), wealth (A), and technology (T).

I = PAT

(2)

In Equation (2), we see an elementary conceptual framework between the variables. When behavior is included, and only then, does the IPABT model determine relative impact. The re-conceptualized model IPACT breaks down T into consumption per unit of GDP, which displays the total CO₂ emission (I), which is the product of population and GDP (C). However, neither model can evaluate the relative importance of elements [109]. The nonlinear STIRPAT model utilizes a number of parameters to calculate environmental degradation and quantify unit elasticity [102,109,110]. It also facilitates the empirical analysis. The IPAT breakdown is shown in Equation (3), where I represents CO₂ emissions, P represents the total population (UPG and POP), A represents the economic level (GDP), and T represents energy consumption (T1, T2, T3, T4, and T5) (Table 1).

$$I_t=\beta P_t^a{A}_t^b{T}_t^c{\mu }_t$$

(3)

For statistical analysis, we converted the model to its logarithmic form:

$$ln{I}_t=\beta +aln{P}_t+bln{A}_t+cln{T}_t+{\mu }_t$$

(4)

Equation (4) demonstrates that $\beta$ is a constant term, t represents the period,$\mu$ is an error term, and $a,b,$ and $c$ represent the elastic coefficient of independent variables. The equation illustrates a unit change in environmental effect, which leads to a unit change in population, wealth, and technology. Regardless of the model used, such as IPAT (traditional) or STIRPAT, P, A, and T can be broken down given the following scenario:

$$ln{COE}_t=\beta +aln{POP}_t+bln{GDP}_t+cln{PEC}_{it}+dln{OIC}_{2t}+eln{NEC}_{3t}+fln{RGB}_{4t}+glnICG+hlnUPG+{\mu }_t$$

(5)

Equation (5) represents the logarithmic version of the variables, where I represents CO₂ emissions, P represents urban (UPS) and total population (POP) with the symbols P1 and P2, A represents economic level (GDP), and T represents technology (PEC, OIC, NEC, RGB, and ICG), with the T further subdivided into T1, T2, T3, T4, and T5 (Table 1). In addition, the STIRPAT model is frequently employed in this study for environmental research and policy analysis. It enables the quantitative analysis of causal pathways connecting economic, social, and environmental outcomes. It is imperative to establish the theoretical framework prior to commencing work on the empirical schema, as this is the process by which we ascertain the variables to incorporate into our models. The OECD nations fall into the category of high technology; their rapid economic expansion is spurred on by constant energy use. With the passage of time, several countries have switched to using renewable energy, often known as green energy, instead of fossil fuels. We use this framework to investigate the causes of environmental degradation due to rising energy consumption, including but not limited to population growth, economic development, urbanization, and technological progress [21]. The model’s adaptability to speculating on the effect of energy consumption and how different elements contribute to environmental pollution is enabled by the model’s flexibility in estimating variable coefficients and altering impact factors [111].

3.4. Correlation Matrix

We refer to tests of correlation or connection between the disposable variables. Each pair of variables’ correlation coefficient is displayed in

Table 3. Correlation matrix.

Probability	CO2	POP	GDP	PEC	OIC	NEC	RGB	ICG	UPG
CO2	1.000
POP	0.932 ***	1.000
GDP	0.963 ***	0.950 ***	1.000
PEC	0.997 ***	0.934 ***	0.970 ***	1.000
OIC	0.996 ***	0.943 ***	0.968 ***	0.997 ***	1.000
NEC	0.874 ***	0.844 ***	0.895 ***	0.898 ***	0.890 ***	1.000
RGB	0.903 ***	0.866 ***	0.923 ***	0.905 ***	0.895 ***	0.766 ***	1.000
ICG	−0.219 ***	−0.136 ***	−0.282 ***	−0.238 ***	−0.205 ***	−0.335 ***	−0.272 ***	1.000
UPG	0.169 ***	0.252 ***	0.135 ***	0.172 ***	0.193 ***	0.119 ***	0.111 ***	−0.074 *	1.000

Note: The significance levels for the variables listed in Table 1 are as follows: *** designates 1%, * specifies 10%.

. Consistent with hypothesized results, PEC, CO₂ emissions, and OIC all displayed a high degree of association. UPG and RGB, however, have very little in common. First we look at strong positive correlations: CO₂ and POP: A strong positive correlation implies that there is a direct relationship between population growth and CO₂ emissions, meaning that as the population expands, so does the amount of CO₂ emissions released into the atmosphere. The variables CO₂, GDP, PEC, OIC, and NEC demonstrate significant positive relationships, indicating that higher levels of CO₂ emissions are associated with economic growth, total energy consumption (both overall and per person), and non-energy consumption. RGB and CO₂: Although the link is not very significant, there is a positive association between renewable energy production and CO₂ emissions. This implies that while renewable energy can help decrease emissions, it may not be adequate as a standalone solution. Next, we turn to negative correlations. A negative correlation between the ICG and CO₂ emissions levels suggests that increased institutional corruption is linked to decreased CO₂ emissions [31]. This can be ascribed to issues such as ineffective allocation of resources, lax environmental restrictions, and diminished economic growth [112]. There was little to no association observed in the past week. The relationship between urban population growth (UPG) and CO₂ emissions shows a modest connection, indicating that UPG has a very minor influence on CO₂ emissions compared to other factors. In summary, the correlation matrix demonstrates that economic growth, energy consumption, and population expansion are all influential factors contributing to CO₂ emissions in the countries under investigation. Nevertheless, the impact of institutional corruption is intriguing, as it indicates a negative relationship where higher degrees of corruption may result in fewer emissions [32]. This could be attributed to the harmful consequences it has on economic growth and development. We also show the results of the variance influence factor (VIF). The results from the VIF have been inflated to show the level of collinearity with the other regressors. Collinearity’s effect on the variance of a regressor’s coefficient estimate was also studied. As a result, VIF was displayed as 1/(1 − R²), where R² is the regression coefficient (repressor). The computed VIF values reflect the degree of multicollinearity present among the variables. In the presence of multicollinearity among the explanatory variables, the VIF result is more significant than 10. The use of primary energy in the United States, Canada, France, Germany, Italy, Korea, Mexico, and the United Kingdom is greater than that of the oil, nuclear, renewable (geothermal), and value-added industries.

The results of VIF are shown in

Table 4. Ordinary least square.

Variable	Unstandardized Coefficient	Std. Error	Standardize Coefficient	Elasticity of Means	t-Statistic	VIF
CO2	122.011	23.681	-	0.197	5.152 ***	-
POP	0.511	0.177	0.029	0.045	2.893 ***	17.036
GDP	−3.738	0.583	−0.102	−0.125	−6.406 ***	41.561
PEC	1.658	0.095	0.668	0.719	17.412 ***	242.433
OIC	3.048	0.242	0.484	0.533	12.620 ***	242.544
NEC	−2.686	0.182	−0.099	−0.124	−14.792 ***	7.370
RGB	0.520	0.522	0.008	0.009	0.996 *	9.406
ICG	−4.498	0.822	−0.018	−0.190	−5.475 ***	1.754
UPG	−49.060	6.027	−0.024	−0.064	−8.140 ***	1.412

Sources: Computation by authors. The significance levels for the variables listed in Table 1 are as follows: *** designates 1%, * specifies 10%.

. NEC, RGB, ICG, and UPG each have a population of fewer than 10. This shows that they are not multicollinear. The observation stands alone in comparison to the other factors. RGB did not pass the t-test. This suggests that the ordinary least square (OLS) method may not produce completely reliable findings.

3.5. PLS Regression

When the PLS method was used, it was found that there were several correlations between the explanatory factors. All of the initial X- and Y-score variables are incorporated into the final model, and the PLS is used to identify the link between subsequent scores. Differentiating PLS from OLS is accomplished through the use of synthetic variables identified as components. Components t1 and u1 of the PLS technique are derived from X and Y data, respectively. These variables are independent of one another and provide a substantial amount of additional information about the initial variables. We determined the most efficient approaches for a thorough examination of the explanatory variable. We used PLS analysis, which resulted in the regression of X and Y to the constants t1 and u1. The coefficient results are calculated using the scaled and centered coefficient and the rotated coefficient of the PLS method. The centered coefficient, where X is a coordinate system indicating the scale and the starting point of the calculation, is based on the impact of the independent variables. It is a scalar multiplicator of a scalar Y. When the X block is filled with spectral information, the pure spectral profile of the Y variables can be written in terms of the rotational coefficient. It is important to note that spectral observations represent only a small part of the calibration of the rotation coefficient. Aside from the assumption that Y contributes to the shift in X, the VIP coefficient also identifies the explanatory variable; X’s significance shifts for each in the projection model. Because of this, the importance attached to each X variable in fitting the Y and X variables that were previously unaccounted for is directly proportional to the weight assigned to that X variable. Assuming the same effect on X from a change in Y, we propose the following.

4. Results

The PLS model is used to show that the data fit the model well. This research was deciphered using the same technique as the t1 vs. t2 graphic, by comparing the values of u1 and u2 in Y. Both the t1/t2 and u1/u2 scatter plots are reflected in the T2 oval plot. The T2 map displays all 16 of the possible groups, providing the ability to distribute the sample over t1 and t2. It is a two-dimensional scoring plot, where the maximum value for the Y variables is calculated based on the X data.

4.1. Scatter Plot

Using the partial least squares method, the relationship between t1 and t2 is depicted as a scatter plot in Figure 4. If a data point lies outside the T2 oval plot, we call it an outlier and draw an ellipse to represent it. The first group showed an exceptional grasp of the data’s outliers. The T2 oval is useful for conceptualizing the remaining groups, though. Consequently, group 1 (the United States) was found to be an outlier using the t1 vs. t2 estimation (green stars). As a result, CO₂ emissions inside the OECD expanded.

4.2. Principal Component Analysis

The OECD and the scatter plot are first filtered using groups in the PCA model. Principal component analysis (PCA) is a statistical method for reducing the dimensionality of a dataset while maintaining the majority of its variability [113]. In this research, principal component analysis (PCA) is utilized as a statistical method to simplify a dataset without losing any of its key characteristics [114]. In PCA, the original variables are linearly transformed into a collection of primary components that are independent of one another and account for the majority of the variance in the data. This method is most effective when applied to high-dimensional datasets, as the underlying structure may be challenging to understand and visualize [73]. PCA streamlines data analysis without losing any information by reducing the number of variables in the dataset. The T2 summarizes the overall score values for all 16 groups as positive numbers larger than or equal to zero, based on the group distribution. T2 = 0 characterizes the circumstance where the group’s observation is centered on the model (where all group values are averaged out). The 16-group data are transformed using SIMCA-16 into the multivariate T2 distribution, or F-distribution. Assuming that all 17 countries use the same grouping strategy, we can calculate the value below which 95% of the data will fit into the model as the lower bound of the confidence interval for T2 Figure 5. Hotelling’s T2 range at the 0.05 level of significance displays the distance from the origin of the model plane (score space). The T2 range was determined for components 1, 3, and 4. Therefore, the significance levels for T2 Hotelling fall within the range of 0.05 to 0.01. In order to be considered at the 0.05 level on this graph, a value must exceed the yellow area, while a value must exceed the red area to be considered at the 0.01 level. Consequently, the value observed in group 1 can be regarded as an outlier. If the T2-range value for 16 groups exceeds the critical restrictions, it is possible to infer the substantial separation between the two groups in the subset of components used to generate the 408 scores. Therefore, it is possible that this group is utilizing a model that was developed by other groups, as the model encompasses a very unique set of responsibilities. This suggests that the Netherlands, Spain, and Sweden have experienced economic growth as a result of their enhanced infrastructure and technology, and that economic development in countries with significant CO₂ emissions is no longer a significant factor.

The principal component analysis of the estimated eigenvalues and eigenvectors can also be performed in EViews. We analyze nine indices, totaling 408 observations, from 2000 to 2023. The dispersion matrix and supporting data for a total of nine indicators (PC 1–9) are calculated using principal component analysis. Dissimilarity and fraction of total variance were summed up by the eigenvalues. However, the main components of the correlation matrices reveal that the combined scale variance of the indicators is 9. The length or size of an eigenvector is determined by its eigenvalue, which is a coefficient applied to the vector. Therefore, principal component analysis is a technique that calculates a covariance matrix to evaluate the degree to which each variable is correlated with all others [115,116,117]. By employing eigenvectors as indicators, it is possible to determine the direction of this study’s data. Based on the data in

Table 5. Eigenvalues by PCA.

				Cumulative Value	Cumulative Proportion
Number	Value	Difference	Proportion
1	6.931	5.565	0.770	6.931	0.770
2	1.366	0.931	0.152	8.297	0.922
3	0.435	0.284	0.048	8.733	0.970
4	0.152	0.086	0.017	8.884	0.987
5	0.065	0.032	0.007	8.950	0.994
6	0.034	0.019	0.004	8.983	0.998
7	0.014	0.013	0.002	8.998	1.000
8	0.001	0.001	0.000	8.999	1.000
9	0.001	---	0.000	9.000	1.000

Note: Eigenvalues (sum = 9, average = 1).

, we can see that the first PCA (eigenvalue) explains 77.02% of the overall variance (6.931/9.00 = 0.7702), whereas the second PC explains just 15.18% of the whole variance (1.365/9.00 = 0.1518). Over 92% of the overall variation could be accounted for by the first two factors. The findings also back up the use of principal component analysis and principal axis rotation to shrink the size of the consumption and expansion dimensions. This means that the OECD may implement energy conservation and CO₂ emissions reduction strategies without worrying about their negative consequences on the economy. The findings suggest that the CO₂ emissions produced by building, transportation, and technological advancement are in turn generated by energy use.

The cumulative ratio between screen size and eigenvalue is graphically represented in Figure 6. We can observe the dramatic decrease in eigenvalues from the first to the second screen in the upper section of the graph. A horizontal line is used to represent the median eigenvalue. We examined the correlation matrices’ eigenvalues in screen plot (scree plot) and eigenvalue cumulative proportion. Our analysis displays the cumulative proportion of indicators that fall below the mean line, as this line represents the minimum of the overall variation. The first two parameters are expected to be responsible for approximately 92% of the variation. As an alternative approach to approximating the size of an eigenvalue, we suggest the diagonal reference line. It is also possible that the slope of the reference line, which is proportional to the slope of the cumulative proportion, is the cause. As opposed to the initial segments, the latter segments exhibit excessively high eigenvalues.

Table 6. Factor analysis eigenvectors (loadings).

Variable	PC 1	PC 2	PC 3	PC 4	PC 5	PC 6	PC 7	PC 8	PC 9
CO2	0.373	(0.116)	0.018	(0.235)	0.201	0.367	(0.011)	0.178	0.765
POP	0.370	0.057	(0.032)	(0.010)	(0.842)	0.099	(0.366)	0.072	(0.021)
GDP	0.375	(0.066)	0.072	0.037	(0.234)	(0.334)	0.822	0.074	0.034
PEC	0.375	(0.103)	0.009	(0.121)	0.262	0.287	(0.012)	0.588	(0.580)
OIC	0.376	(0.084)	(0.004)	(0.137)	0.106	0.378	0.092	(0.774)	(0.266)
NEC	0.354	(0.087)	0.094	0.874	0.204	(0.089)	(0.194)	(0.038)	0.076
RGB	0.370	(0.066)	0.083	(0.378)	0.231	(0.709)	(0.377)	(0.102)	(0.011)
ICG	0.078	0.754	0.644	(0.029)	0.063	0.073	0.016	0.004	0.000
UPG	0.184	0.617	(0.750)	0.040	0.126	(0.054)	0.042	0.006	0.026

shows that eigenvectors PC1 and PC9 of the coefficient can be combined linearly. A linear combination is roughly identical to PC 1. This may be the PC 2 factor with negative (CO₂ emissions, GDP, PEC, OIC, NEC, and RGB) as well as positive (NEC, OIC, and RGB) loadings (POP, ICG, and UPG). Indicator-specific components appear to be grouped by this loading.

4.3. Linear Relationship

The t3/u3 scatter plot displays the groups’ nonlinearity. Scatter plots normally display t3 vs. u3; however, this can be altered. Groups with the strongest linear relationship between t3 and u3 have their scores shown in Figure 7. Moreover, t3 and u3 can be utilized to identify the components of X and Y. Since the United States has such a significant effect on OECD emissions, this straight-hulled vessel is part of group 1.

4.4. Observed and Predicted Analysis

By contrasting the observed and predicted variables, the PLS model’s expected and actual value of CO₂ emissions could be seen. There is a linear relationship between the Y variable (cost estimates) and the number of nations in a given group [118]. Distributing groups’ replies along or close to the slope yields a desirable result. The predicted values are extremely similar to the observed values [119]. The R² value of 0.998 shows that the research fitting model is optimal, suggesting a good linear connection between the observed and predicted values (0.996).

4.5. Effects of Variables on Projection (VIP)

By arranging the X variables used for explanation and the Y variables used to display the results according to the VIP, the relative weight of each explanatory variable may be calculated. A VIP score greater than 1 indicates that this predictor is the most important one for depicting Y. The explanatory factors may be statistically significant in explaining the Y variable when the VIP value is greater than 1. Consequently, the explanatory variables’ statistical significance in the estimated model is not statistically significant if the VIP value is less than 0.5. Figure 8 illustrates the jackknife confidence interval in conjunction with the appropriate independent variables (PEC, OIC, GDP, POP, RGB, and NEC). Uncertainty bars are included in the graph that illustrates the plots to indicate this. If the statistical values exceed 1 and the VIP is less than 0.5, they are the most critical for comprehending Y.

No explanatory factors are relevant when thinking about ICG and UPG. As the weighted sum of squares of the PLS weight, w*, VIP in PLS explains the Y variance across all dimensions. It exemplifies the model’s usefulness for making accurate predictions about Y and X. Considering the correlation terms of all responses, the VIP value is then automatically derived from all extorted components.

Pearson’s correlation analysis of the PLS data is shown in

Table 7. Results of centered and rotated coefficient PLS method.

Variable	Scale and Centered Coefficient			Rotated Coefficient
	t1	t1 and t2	t1, t2, and t3	t1	t1 and t2	t1, t2, and t3
CO2 emissions	0.507	0.507	0.507	0.507	0.507	0.507
POP	0.165	0.160	(0.038)	0.975	0.857	(0.064)
GDP	0.169	0.162	0.019	0.999	0.871	0.032
PEC	0.175	0.213	0.539	1.038	1.144	0.915
OIC	0.175	0.215	0.531	1.038	1.150	0.902
NEC	0.155	0.130	(0.110)	0.919	0.697	(0.187)
RGB	0.158	0.154	0.048	0.933	0.827	0.082
ICG	(0.042)	0.048	0.006	(0.249)	0.257	0.011
UPG	0.035	(0.008)	0.006	0.206	(0.043)	0.011
R2Y (cum)	0.961	0.972	0.996	0.961	0.972	0.996
Q2 (cum)	0.961	0.972	0.995	0.961	0.972	0.995

Sources: Computation by authors. Note: The variable’s definition is stated in Table 1.

. The R²Y (cum) shows the combined explanatory power of the extracted key components relative to the original Y variable. Unless otherwise specified, Q² (cum) refers to the Q² obtained via repeated cross-validation. Over-estimations for R²Y (cum) and Q² (cum) show significant and effective relationship between variables. Component t1 of the PEC represents the centered coefficient and the rotated coefficient of the scale’s analysis of the variables. The calculated values for the centered coefficient and rotated coefficient are 0.17 and 1.03, respectively. Extortion and compression can also be applied to the PEC center coefficient result at times t1 and t2. The new value of the coefficient for this variable is 0.213. When t1, t2, and t3 are removed from the equation, the coefficient shifts to 0.539. The PEC rotated coefficient study yielded t1 = 1.144 and t2 = 0.915 as the results. The PEC’s centered coefficient and rotated coefficient values suggest that changing primary energy consumption by 1% would cause changes of 0.175% to 0.5395% and 1.0386% to 0.9159% in CO₂ emissions. The PEC showed notable flexibility in terms of both the centered coefficient and the rotated coefficient. When looking at the two major components, t1 and t2 centered coefficient, the elasticities for POP, GDP, OIC, NEC, and RGB were 0.160, 0.162, 0.215, 0.130, and 0.154, respectively. Both the VIP and the regression coefficient values are highly consistent. Thus, POP, GDP, PEC, OIC, NEC, and RGB are all potential influences on CO₂ emissions. Small ICG and UPG coefficients, such as 0.1, may not have a significant impact on GHG emissions. Although the OECD countries share numerous similarities with developed economies, they exhibit differences in energy consumption and CO₂ emissions. Economic structure, energy balance, population density, and climate are all significant factors that contribute to these disparities. Energy consumption and emissions are typically higher in countries that have a high population density, rely heavily on fossil fuels, and have energy-intensive industries. Examples of OECD countries with relatively high energy consumption and CO₂ emissions include the United States, Canada, Japan, Germany, and the United Kingdom. Nevertheless, it is crucial to acknowledge that these rankings may vacillate over time as a result of factors such as economic growth, technological advancements, and policy changes.

5. Discussion

Based on the VIP analysis and the explanatory factors’ coefficients (centered coefficient and rotated coefficient), it appears that POP, GDP, PEC, OIC, NEC, and RGB are likely major contributors in determining CO₂ emissions (Y). ICG and UPG have weak coefficients, which means that they have a negligible effect on the efficiency coefficient for CO₂ emissions. The following observations are made in light of the anticipated analysis of this study: Although numerous recent studies have shown that urbanization increases energy consumption, some have argued that it can reduce energy consumption by promoting the efficient use of public infrastructure [120]. It is less clear which types of energy are likely to be affected by urbanization. The recent trend toward using more of this form of energy, especially for power generation in large cities, raises the question of whether urbanization may enhance the use of renewable energy [121]. Therefore, policymakers must investigate the effects of urbanization on disaggregated energy use in terms of renewable and non-renewable sources [122]. OECD’s Outlook modeling suggests that if poor nations were to stop subsidizing fossil fuels, global energy-related greenhouse gas emissions might be reduced by 6%. This study demonstrates that for every 1% movement in CO₂ emissions, there is a corresponding 0.0127 shift in POP (0.0165–0.038), with a correlation of 0.932. An increase in PEC, OIC, and NEC affects CO₂ emissions and pushes further energy consumption, as seen above, illustrating a relationship between energy use and economic growth. Long-term efforts boosting energy efficiency and moving focus to sources like solar, wind, and biomass as well as nuclear power may, thus, have an influence [123]. The data imply that energy consumption is the dominant driver of CO₂ emissions; however, construction, transportation, and technical progress are all contributors. As a result, effective energy use policies and shifting to renewable energy, biomass, and nuclear could have long-term effects [124].

A multifaceted approach is required to surmount the obstacles to the implementation of renewable energy policies in OECD countries. Strategic lobbying endeavors, public education, and robust political leadership are necessary to address political resistance. Financial incentives, technological advancements, and cost–benefit analyses can be implemented to alleviate economic challenges [125]. Investing in grid infrastructure, energy storage, and domestic supply chains can be used to surmount technological obstacles. Ultimately, regulatory and policy obstacles can be mitigated by facilitating international co-operation, assuring policy coherence, and streamlining permitting processes [126]. Policymakers can establish a more conducive environment for the widespread implementation of renewable energy in OECD countries by addressing these challenges. Furthermore, we will investigate potential obstacles to the implementation of these policies and propose strategies for surmounting them [37]. This may involve addressing challenges such as political resistance, economic expenditures, and technological obstacles [127]. We aspire to offer policymakers practical guidance in their efforts to reduce CO₂ emissions in their respective countries by providing a more detailed analysis of policy implications [127,128].

Several studies indicate that the factors that determine CO₂ emissions vary from country to country [129], and the EIA report from 2014 indicates that the United States, Canada, and France consumed 18,964, 2289, and 4,464,000 barrels per day, respectively. These results validate this study. The OECD discovered that GDP was the most relevant factor in CO₂ emissions intensity relative to population [130]. Based on the results, the PEC (0.175–0.539) centered coefficient is significantly affected by even a 1% shift in CO₂ emissions (Table 7). CO₂ emissions and PEC (0.997) are also shown to have a significant relationship with each other, as are PEC and GDP, as seen in Table 3 (0.970). In addition, the IPCC projects a global temperature rise of 2 degrees Celsius above pre-industrial levels by the end of the 21st century. Economies are already benefiting from the usage of nuclear power because it helps them lower their carbon footprints. This source accounts for over 20% of the electricity produced in the OECD. Since the commercialization of nuclear electricity generation, nuclear power plants have replaced coal-fired units, resulting in the avoidance of approximately 20% of the power sector’s total emissions during the period. This equates to an estimated total savings of approximately 60 Gt CO₂-eq. The preceding estimates show that for every 1% shift in CO₂ emissions, there is a 0.045% shift in NEC. We also found a connection between NEC and CO₂ emissions as well as GDP and NEC (0.87). Renewable, hydro, biomass, and geothermal energy account for about 41% of total global consumption. The OECD’s primary energy supply (PES) has decreased by 0.02% over the past decade (0.124–0.104) [1,2]. The significance of considering the relationship between renewable and non-renewable energy consumption and CO₂ emissions is further underscored by the discovery that a 1% increase in CO₂ emissions leads to a 0.11% change in RGB (0.158–0.048), with a 0.903 correlation between these two variables, as seen in Table 3.

As defined by the OECD, the “industrial sector” encompasses several different fields. The manufacturing output of the OECD has grown in recent years. These favorable results, which include improved output level and GDP, can be attributed to the innovation and technological wind that has swept these countries. Relationships between CO₂ emissions and industrial structure are weak (ICG). The correlation between CO₂ emissions and ICG is 0.219, as indicated by the centered coefficient value. A 1% change in CO₂ emissions results in a 0.04% change in ICG. In conclusion, the VIP forecast indicates that the relationships between ICG, NEC, and RGB in urbanization are at best tenuous. It would suggest that the OECD’s national policy objective prioritizes the enhancement of the urban sector. Globalization and the global competition for investment necessitate substantial structural changes in the economy [131]. Overall, this study’s results show that technological progress, ecological sustainability, and economic viability are at the forefront of today’s city planners’ minds. The correlation between UPG and ICG has been found to be negative. A change of 0.029% (from 0.035 to 0.006) in UPG’s centered coefficient translates to a 1% change in CO₂ emissions. Therefore, the rate of urbanization is no longer a primary factor in CO₂ emissions production. In addition, the OECD countries, which are generally regarded as developed economies, encounter distinctive obstacles in the areas of economic development, environmental sustainability, and social well-being. Their development trajectories are complicated by the pressures of globalization, income inequality, and aging populations [128,131]. Furthermore, these nations are confronted with environmental challenges, including pollution, resource depletion, and climate change. In the 21st century, sustainable development necessitates the implementation of energy efficiency measures, the transition to renewable energy, and strong governance to resolve these challenges [132]. Specifically, the OECD countries have experienced substantial technological advancements in renewable energy, which have facilitated a transition away from fossil fuels. Wind power has expanded through both onshore and offshore installations, while solar photovoltaic technology has become more efficient and affordable. Pumped storage capabilities and enhanced efficacy have been advantageous to hydropower. Advancements in drilling technology and direct-use applications have broadened the scope of geothermal energy [133]. The development of second-generation biofuels and biogas production has facilitated the evolution of bioenergy. The share of renewable energy in OECD countries has increased significantly as a result of these technological shifts, which have facilitated the transition to a more sustainable energy future.

6. Conclusions and Recommendations

Rising CO₂ emissions from the development sector call for a fresh organizational structure and strict regulations to rein in pollution through the use of cutting-edge technologies. The authors use STIRPAT and the two-dimensional reduction method to examine the relationship between growing economies, CO₂ emissions, and energy use from 2000 to 2023. In relation to the potential constraints of relying exclusively on the World Bank and the BP Statistical Review of World Energy, despite the fact that these sources provide valuable information, the comprehensiveness of this study analysis could be enhanced by the inclusion of additional datasets. We acknowledge that the inclusion of national energy reports, environmental performance indexes, and other international databases would offer a more granular comprehension of the factors that influence CO₂ emissions. Regrettably, we were unable to incorporate these supplementary sources into this particular study as a result of data availability limitations and schedule constraints. However, we recognize the importance of expanding the data sources of this analysis in order to facilitate future research. We will prioritize the integration of these supplementary datasets into future research to improve the generalizability and robustness of the findings of this study. The potential heterogeneity within OECD countries is well-received. We also recognize the importance of undertaking a more thorough investigation into the regional disparities in energy consumption and emissions, despite the fact that the initial analysis provided a comprehensive perspective. The objective of this study is to divide OECD countries into distinct regional categories based on similarities in policy, economics, or geography, in order to conduct a more detailed analysis in future research. This approach will allow us to identify potential outliers, identify specific regional trends, and develop a more sophisticated understanding of the factors that influence CO₂ emissions within the OECD.

The results of the two-dimensional reduction demonstrate that the development sector consumes a disproportionate amount of energy. Primary energy sources, such as solar and wind, are more efficient than oil. Also, the manufacturing sector benefits more from geothermal energy and other renewable sources than it does from nuclear power. These findings indicate that in the contemporary industrialized economy, change is no longer the most significant factor; rather, it is more dramatic than other variables. The consumption of oil is highly comparable to that of primary energy. The renewable-geothermal technical indicator generates a considerable return on investment (ROI) from research development and manufacturing, owing mostly to recent technological developments. This demonstrates how OECD countries have reduced their emissions as a result of technological advancements. The absence of a correlation between economic growth and CO₂ emissions supports the current belief that OECD nations have already achieved a certain level of energy efficiency gains and are therefore content to pursue pro-growth policies without a great deal of worry about emissions [134]. Consequently, it is advised that the OECD nations continue to implement their current measures to mitigate any potential environmental damage resulting from the rapid expansion of development, such as the reduction of CO₂ emissions through post-combustion capture. [135,136].

Policymakers around the world are increasingly concerned about energy consumption and its effects on the environment. The need for developing policy recommendations and proactive solutions to achieve sustainable energy use is especially pressing in the context of OECD countries. An advisable course of action is to allocate greater resources towards the implementation of sustainable energy regulations, which have the potential to significantly reduce pollution and greenhouse gas emissions [5,125]. This study’s empirical findings offer valuable insights for further investigation into the relationship between the variables in question [113]. Policymakers can use them as a theoretical basis and a helpful guide to formulating strategies in light of the examined linkages. In order to safeguard the environment and promote economic growth, we propose the following recommendations. Initially, it is essential to establish a unified energy transition strategy that prioritizes the organized generation of hydroelectric power in OECD countries [137], the organized generation of solar energy, and the enhancement of the interconnection network. Furthermore, the industrialized nations must collaborate, upgrade, and enhance investments in energy infrastructures. The issue at hand is finding a solution to the problem of power outages, which have a detrimental impact on both the productivity and the quality of life of the population [138]. Furthermore, this study advocates for the establishment of a conducive ecosystem and effective oversight of the utilization of natural resources in a manner that ensures long-term viability and growth. These efforts are intended to establish a stable socioeconomic geopolitical environment. This research can be effectively incorporated into a sustainable environment, where well-managed natural resources will play a significant role in promoting sustainable development and the welfare of populations. At this stage of analysis, we believe that addressing this area is highly helpful in terms of initiating more productive lines of study [57]. Future studies should take into account additional variables that could impact environmental degradation, such as institutional indicators including political stability, government effectiveness, and democracy. This investigation is limited to the examination of both developed and developing regions. Hence, forthcoming studies can consider all OECD nations [13]. Moreover, the utilization of renewable energy sources such as solar, wind, and hydropower can contribute to achieving this objective. The adoption of energy-efficient technologies has the potential to improve both greenhouse gas emissions and energy productivity [139,140]. Smart grid systems and energy-efficient buildings that make use of natural light are two examples of technology that can reduce energy use without negatively impacting the environment. In addition, officials may consider offering incentives for the purchase of hybrid and electric vehicles to encourage sustainable transportation and cut down on CO₂ [141]. A transition toward circular economy models for energy generation and consumption is another policy route that OECD nations could investigate. A low-cost approach to emissions reduction is carbon pricing, also known as the emissions trading scheme. Alternative energy sources, including solar and wind, necessitate increased investment. In the OECD countries, the rate of energy consumption has increased as a result of factors such as industrialization, population growth, and economic expansion, while the rate of CO₂ emissions has decreased as a result of factors such as current technology. The data support this assertion. Thus, the energy industry’s policymakers should prioritize precision. These recommendations are consistent with the preceding statistical study.

First, population increases, GDP expansion, oil use, renewable geothermal energy, and conventional energy sources all have major impacts on global CO₂ emissions. When it comes to the OECD, PEC stands head and shoulders above all other groups. Given this, governments should keep growth restrained and work to replace the country’s energy infrastructure with low-carbon options. To regulate the nation’s way of life, there must be a low-carbon energy strategy, alternative energy sources, and low-carbon consumption rules. To meet the demand for development and industrialization and to establish a carbon-free zone, we must place special emphasis on alternative, clean energy sources. Secondly, municipalities in high-CO₂-emission zones can benefit from energy efficiency programs to help them achieve their energy reduction objectives. Adopting energy-efficient production processes and initiatives could be one approach to enhance productivity while simultaneously reducing energy consumption. Third, the two primary driving indicators of economic development and population growth both contribute to the increase in a country’s CO₂ emissions. Industrialized countries frequently generate lower CO₂ emissions than developing countries due to their superior technology and considerably larger economies. The findings of this investigation indicate that industrialized nations have successfully mitigated their CO₂ emissions by employing technological solutions, including nuclear power facilities, to compete with primary energy sources and oil. In order to meet domestic demand, OECD countries have cut energy consumption and shifted to cleaner fuels. Fourth, the two most crucial indicators, population and GDP growth, drive a country’s increasing CO₂ emissions. Using alternative energy production methods could help developed countries cut their CO₂ emissions.

Developed countries are reducing their carbon dioxide emissions by shifting away from using oil and other major energy sources and increasing their usage of nuclear power and technology. In order to meet domestic demand, OECD member governments have regulated and transferred alternative fuels, which have lower emissions. The fifth point is that most human efforts to promote industrialization and economic progress have negative effects on the environment, and therefore it is hard to ignore the fact that people have greatly contributed to the acceleration of global warming. The combustion of fossil fuels, which are not replenishable, releases carbon dioxide into the air whenever electricity is produced. Furthermore, the amount of fuel used in production might be significantly decreased if the OECD adopts energy utilization. The use of any of these energy sources results in less pollution. We also propose environmental tax policies to curb actions that lead to higher CO₂ emissions. The OECD needs to adopt green technology. Economic considerations and other acts by the former may cause both long-term and short-term damage to the environment (with emissions from others). Sixth, a quantifiable energy efficiency objective and the promotion of increased energy efficiency through means such as a demand-side management program and renewable portfolio standards in NLD, ESP, and SWE are mandated by energy efficiency resource standards. Seventh, increases in the usage of alternative-fuel cars (biofuel-driven, hydrogen-powered, and electric) have been attempted as a means to decrease pollution and greenhouse gas emissions, but progress has been slow. Extensive evidence and expert opinion both point to CO₂ emissions as a substantial contributor to climate change, even though they may not be the primary cause. The OECD may provide money for developments in carbon-free technology that aim to cut emissions and build a carbon-free planet. Eight, research and professional opinions alike agree that this is a crucial consideration. In addition, it is probable that CO₂ emissions are not the only factor in global warming. As demonstrated by the findings of this research (GDP), it appears that an expanding economy and an increase in energy consumption are linked. Due to the fact that CO₂ emissions are produced by people during the process of constructing an economy, policymakers have a heavy responsibility to fortify the economy while simultaneously reducing CO₂ emissions.

However, this study does have certain restrictions. First, due to data limitations, the study did not include all OECD countries. Second, the study spans a long period of time (from 2000 to 2023), hence the findings may not be applicable to similar scenarios in the future. The investigated connections may still be valid even though the data are outdated. Many nations are implementing policies to reduce energy consumption as public awareness of this issue rises. The OECD has implemented initiatives and legislation to reduce energy use in its member countries. Although the OECD countries have made efforts to reduce energy consumption and adopt ecologically friendly policies and approaches, such as utilizing renewable energy sources, their contribution to global energy consumption remains relatively insignificant. According to reports, the OECD countries comprise only 44% of the global energy consumption. Although OECD countries are increasing their use of renewable energy at a faster pace compared to non-OECD countries, this alone may not suffice to mitigate the adverse environmental impacts of energy consumption. We offer a variety of resources to help readers learn more about this topic. For academic purposes, we recommend integrating more variables (such as oil industries, fossil fuel energy usage, degree of enforcement, cost of access to renewable energy sources, etc.) to see if these additional variables further modify the analyzed correlations. Potential areas for future research include the examination of the relevance of the findings to more recent events. If implemented, such research would determine the extent to which the energy consumption of OECD countries contributes to global CO₂ emissions. Despite the fact that this research provides valuable insights into the correlation between energy consumption, industrialization, and CO₂ emissions in OECD countries, it is not without its weaknesses. Firstly, the reliance on data from the BP Statistical Review of World Energy and the World Bank, while dependable, may not completely capture the complexities of energy use and emissions across different regions. Secondly, the study’s emphasis on OECD countries restricts the generalizability of the findings to non-OECD nations, which may have distinct energy consumption patterns and policy contexts. Third, the analysis neglects to adequately consider non-energy-related factors, such as land use changes, deforestation, and agricultural practices, which also significantly contribute to CO₂ emissions. Lastly, the potential consequences of behavioral and societal changes are not as exhaustively examined, despite the fact that technological advancements are prioritized as a method of reducing emissions. Future research should endeavor to integrate a broader array of data sources, conduct a more detailed regional and comparative analysis, and maintain a balance between technological solutions and behavioral changes in order to achieve a more comprehensive understanding of the factors that influence CO₂ emissions.