Sustainable Transport Between Reality and Legislative Provisions—The Source for the Climate Neutrality of the European Union

Abstract

Environmental protection and climate change are the most debated issues, both in economic research and in EU legislative regulations. This work is also focused on these issues and aims to analyze the evolution of GHG emissions from transport, the main contributor to total emissions. The novelty of this research aligns with the EU climate policy established within the European Green Deal’s “Fit for 55” package, aiming for a 55% reduction in net greenhouse gas emissions by 2030 (compared to 1990 levels), and the broader objective of climate neutrality by 2050 as outlined in the European Climate Law. The study’s originality lies in the selection of variables that can influence the reduction of emissions and the anticipation of future developments using the Prophet model, for the period 2024–2050. The estimates obtained allowed the construction of clusters to identify EU member states that will record reductions in GHG emissions in the analyzed period and will be able to represent models for other economies. The results obtained are beneficial for decision-makers at European and national levels and highlight the need for urgent action to reduce emissions and limit the negative effects of climate change.

1. Introduction

Climate change and environmental degradation represent an existential threat at European and global levels. The EU combats climate change and environmental degradation through ambitious policies and close cooperation with international partners. In response to resource use and sustainability challenges, the European Union has embarked on an ambitious green transformation agenda. The European Commission’s European Green Deal [1] outlines a vision for a modern, competitive, and sustainable EU economy. This plan is further bolstered by the European Climate Law [2], legislated by the European Parliament. The Climate Law strengthens the EU’s commitment to greenhouse gas (GHG) reduction, mandating a minimum 55% reduction by 2030 compared to 1990. The ultimate goal is to achieve climate neutrality by 2050, which means net zero GHG emissions across the EU. This aspiration will be achieved through a combination of emissions reduction strategies, investments in green technologies, and environmental protection efforts [2].

The European Parliament responded further to these challenges and legislated the objective set out in the European Green Deal through the European Climate Law [2]. The legislation enhances the EU’s climate goals, setting a new target for a minimum 55% reduction in greenhouse gas (GHG) emissions by 2030 relative to 1990. Furthermore, it aims for climate neutrality by 2050. This signifies achieving net-zero greenhouse gas emissions across all EU member states, primarily through emission reductions, investments in green technologies, and preserving natural ecosystems [3]. The European Commission has also implemented its latest climate and energy package—the so-called Fit for 55– to increase its ambition for climate change mitigation. The package includes an interconnected set of measures in energy, transport, taxation, and climate policies.

These EU climate concerns are considered in other policy areas (e.g., transport and energy) and promote low-carbon technologies and adaptation measures [2]. Transport has a significant economic, social, and environmental impact on society [4]. Thus, transport decisively influences both the quality of life and sustainability issues [5]. The transport sector contributes around 5% to EU GDP and provides jobs for over 10 million people in Europe, essential for European businesses and global supply chains [6]. The European Environment Agency (EEA) reports that the transport sector is a major contributor to EU greenhouse gas (GHG) emissions. In 2022, these emissions were 25.9% higher compared to 1990 levels [7]. Among all transport modes, road transport emerges as the primary source of GHG emissions, accounting for 73.2% of total transport emissions in 2022 and 21.1% of all EU GHG emissions, according to the EEA. While emissions have notably declined in other sectors, transport emissions have shown an upward trend in recent years.

To address this, a crucial shift towards more efficient and less fossil fuel-dependent passenger and freight transport within the EU is imperative. In alignment with this objective, the Council has endorsed conclusions on the Commission’s Strategy for Sustainable and Smart Mobility (2021), aiming to foster a green, smart, and resilient mobility system across the EU [8].

Decarbonizing the transport sector is essential to achieving the EU’s climate goals. To contribute to this ambitious goal, the transport sector needs to undergo a radical transformation that will require a 90% reduction in greenhouse gas emissions (compared to 1990 levels) by 2050 [9]. The European Union has undertaken efforts to transition towards a more sustainable transport system, prioritizing the provision of more affordable and environmentally friendly options for goods and passenger transportation. Despite these efforts, air pollution and greenhouse gas (GHG) emissions continue to pose significant challenges within the sector.

Sustainable transport takes a multifaceted approach, encompassing considerations such as climate impact, air quality, security, traffic safety, and human health. This paper focuses on the most complex aspect of sustainable transport policy: reducing greenhouse gas emissions from the transport sector.

The European Green Deal acknowledges the critical role of renewable energy in facilitating the transition to a clean energy future. Domestically sourced renewable energy presents a cost-effective alternative to fossil fuels while diminishing dependence on external energy providers. To attain climate neutrality by 2050, the EU must substantially increase its utilization of renewable energy sources.

The Renewable Energy Directive (2018/2001/EU) [10] initially set a 32% target for renewable energy in total energy consumption by 2030. However, the amending Directive (EU/2023/2413) [11] raised this target to at least 42.5% by 2030. Since the inception of the first Renewable Energy Directive (2009/28/EC) [12], the EU has witnessed substantial growth in renewable energy sources, with their share in energy consumption rising from 12.5% in 2010 to 23% in 2022 [13].

Despite these policy successes, the EU continues to face challenges in fully harnessing renewable energy’s potential to achieve climate neutrality. Significant efforts are still needed, both from researchers and policymakers.

The continuous development of the transport sector and the increase in greenhouse gas emissions from this sector attract decision-makers’ attention in the field of sustainable transport. Therefore, it is essential to understand the factors determining pollution from the transport sector. The purpose of this study is to analyze the impact of economic growth, urbanization, and renewable energy consumption on greenhouse gas emissions from the transport sector.

The contribution of the research to the theoretical concern begins with selecting relevant variables for the transition to a decarbonized economy by 2050. Thus, the variables used in carrying out the scientific approach and covering the components of sustainability are GDP, the share of the urban population in the total population, greenhouse gas emissions from transport (CO₂ equivalent), final energy consumption in transport (oil equivalent), and the share of renewable energy in transport. The five indicators were selected from the Eurostat database for 2012–2022, at the EU level, and used to make forecasts, using the Prophet model, for 2024–2050. Prophet is a forecasting tool that models time series with trends and seasonality. It is often chosen for its ease of use and ability to handle various data patterns automatically. For an in-depth analysis, using the estimates obtained, we created homogeneous clusters at the level of the 27 EU member states. Thus, we identified groups of countries with similar trajectories of CO₂ emissions, reflecting differences in economic structure, energy policies, and level of development. For this cluster-level grouping, we used the SPSS v26 program, with the Hierarchical Clusters function.

Although previous research provides clear evidence of the link between GDP and greenhouse gas emissions, the contribution of transport to total greenhouse gas emissions is essential for EU member states, which aim to balance economic growth with environmental sustainability. The EU transition to a decarbonized economy is a complex process that requires a more analytical approach, capable of capturing sustainable development and reducing greenhouse gas emissions. Unlike previous studies, which focused on a limited number of variables, this research considers variables from the economic, social, and environmental spheres to cover the entire area of sustainability and obtain an estimate, as accurate as possible, of greenhouse gas emissions from the transport sector. The scientific relevance of this study represents the originality of the work, as follows: the careful selection of variables with influence on the reduction of greenhouse gas emissions from the transport sector, the main contributor to their total level; the extended scope of these variables at the level of sustainable development; the use of the Prophet model, a remarkable tool for time series forecasting, in estimating GHG emissions for EU Member States to identify countries that can achieve the objectives set by the European Climate Law; and hierarchical cluster analysis, which allows grouping a set of observations into homogeneous groups, called clusters, based on their similarity in terms of GHG emissions evolution.

The novelty of the study:

The novelty of this study consists of using the Prophet model to forecast GHG emissions from transport activities at the level of EU Member States, taking into account a wide range of socio-economic and environmental factors (economic, social, environmental, and technological). Unlike previous studies, which have focused on a smaller number of variables, our approach allows for a deeper understanding of the dynamics of emissions.

Another novel aspect of this study refers to the forecast period, which includes an extended forecast interval, covering the time interval 2024–2050, an interval mentioned in the European Commission report as a reference. Other studies that forecast the values of GHG emissions in transportation include using the gray prediction model, a multivariate linear regression analysis (MRA) combined with a double exponential smoothing model (DES), or an ARIMA-based approach [14,15,16]. Other forecasting methods have limitations, making the Prophet model a strong choice for this study. For example, the gray prediction model works best with small datasets and short-term predictions, while our research needs to look at long-term trends. In addition, methods like MRA with DES and ARIMA have trouble with the complex seasonal changes and non-linear patterns we see in transport emissions. ARIMA models also assume that data stay consistent, which is not always true for emissions. The Prophet model, however, is built to handle these complex patterns and long-term forecasts, making it better for analyzing the changing nature of transport emissions.

The present paper offers a new vision of the correlation between sustainable economic growth and reducing GHG emissions from transport. This study provides concrete evidence to decision-makers in European countries, drawing attention to the impact of greenhouse gases from the European transport sector on sustainable development.

The paper is structured as follows: introduction; review of the specialized literature; description of the methodology used in the analysis; presentation of the obtained results; discussion of these results compared to the results of previous studies; and conclusions and limitations.

2. Theoretical Background and Research Questions

Among the components of sustainability (economic, social, and environmental), the environmental component is the most intensely debated and concerns aspects regarding the protection of the natural environment, more careful use of natural resources, and reducing the carbon footprint. Sustainable development is the basic idea around which today’s society develops [17]. Reducing atmospheric emissions plays a significant role in supporting sustainable development. Greenhouse gas (GHG) emissions are the main contributor to climate change, and air pollutants threaten human health [18].

The EU has set ambitious goals to combat climate change and become a carbon-neutral region, playing a global leadership role in promoting sustainable practices [19,20].

The 2030 Agenda (UN, 2015), together with the Paris Agreement on Climate Change (UN, 2016) [21], represents the roadmap for a better world, the global framework for international cooperation on sustainable development, and its economic, social and environmental dimensions [22].

The 17 SDGs contain precise targets for sustainable development of society, contributing to poverty reduction, food security, education, and pollution reduction. Green energy and climate change are at the heart of the 2030 Agenda (SDGs 7 and 13). In general, the SDGs aim to improve the population’s health by reducing greenhouse gas emissions, using green energy in transport and through a sustainable transport industry. The presence of transport activity in most of the SDGs reveals their importance for the economic development of society and adverse effects, such as CO₂ emissions, air pollution, traffic congestion for sustainable development, etc. However, the current state of the transport industry is considered unsustainable due to the large-scale use of fossil fuels.

The 2024 edition of the “Sustainable development in the European Union” [23] report presents an assessment of the progress made by the European Union towards achieving the 17 sustainable development goals, analyzing the performance of the EU Member States over the past 5 years, highlighting both achievements and areas for improvement. The report mainly highlights the progress at the EU level in increasing the share of renewable energy sources and indirectly reducing GHG emissions.

Along with the development of the economy, other problems related to increasingly congested traffic, fuel consumption, and GHG emissions have emerged, all of which contribute to environmental degradation [24].

As a result of the increasing use of cars and the burning of fossil fuels that generate air pollution, many substances harmful to life and the environment have been released into the atmosphere [25].

Transport activities are a significant source of pollution, producing a large amount of CO₂—nearly a quarter of global energy-related emissions—and contributing to harmful air pollution. Establishing a more sustainable transport system will be essential in combating climate change and its associated impacts.

The transport sector is an issue that is intensely debated among researchers, with its decarbonization being seen as an increasingly important challenge, given the increasing need for mobility, its significant impact on economic development, and the associated negative environmental consequences [26]. The gap between the importance of transport system development and its negative consequences is growing [27], requiring solutions to limit environmental degradation. According to the European Environment Agency, the transport sector is one of the largest polluters and producers of GHG emissions. It is necessary to activate the EU forces to transform the transport sector from tradition (with the most significant negative impact on the environment) to ecological development.

The transport industry is currently facing two major challenges: climate change and the increase in demand for passenger and freight mobility. Economic growth and the continuous urbanization process of the global population determine a significant increase in the demand for transport services [28]. This has resulted in increased greenhouse gas emissions, contributing substantially to climate change. Freight and passenger transport determine economic development, but primary energy consumption also has a negative impact on the environment [29]. Therefore, transport plays a significant role in stimulating economic development [30], a major contributor to GHG emissions [31]. Transport represents the second most important source of GHG emissions that cause global warming [4]. Therefore, transforming the transport sector into low-carbon transport options is imperative for sustainable development [32]. Economic growth determines the increase in demand for transport services and urbanization, influencing long-term environmental conservation [33]. The need to develop urban planning strategies that ensure efficient public transport to counteract the harmful effects of the urbanization process is highlighted [34].

The paper “Road Transport and Its Impact on Air Pollution during the COVID-19 Pandemic” investigates the effects of the COVID-19 pandemic on the level of pollution, demonstrating a positive effect of the lockdown period on it. It demonstrates that the limitation of human activities had the effect of restricting transport activity and implicitly limiting emissions of pollutants [35].

The transition to future sustainable urban areas can only be ensured by reducing car use, reducing traffic congestion, reducing GHG emissions, and reducing the stress attributed to transport [36].

Some studies emphasize the importance of identifying solutions to limit GHG emissions while maintaining economic growth [37]. Others, however, promote the need to identify sustainable solutions for freight transport, such as promoting efficient logistics and developing sustainable infrastructure to limit the effects of urbanization and economic growth on the environment [38]. Zhu et al. [39] draw attention to the identification of urgent and clear measures to ensure the reduction of GHG emissions, such as the promotion of public transport, sustainable urban planning, or electric vehicles.

The study “Synergistic Effect of Atmospheric Boundary Layer and Regional Transport on Aggravating Air Pollution in the Taiwan-Hu Basin: A Case Study” shows that transport combined with meteorological conditions can exacerbate the level of pollution [40]. The study aims to express the need for cooperation as well as the existence of effective management strategies to improve the effects of air pollution in the region. The study “Examining the determinants of CO₂ emissions caused by the transport sector: Empirical evidence from 12 European countries” investigates the factors that can influence CO₂ emissions from the transport sector at the level of 12 EU member states. The research analyzes the influence of the relationship established between CO₂ emissions and economic, social, and technological factors to draw attention to the triggering factors in the emission of CO₂ from the transport sector. The authors of this study consider investments in energy-efficient vehicles as well as the implementation of effective environmental policies as an effective formula to reduce the impact on the climate [41].

The role of international trade and logistics in generating CO₂ emissions is essential, so that studies demonstrate how the development of global trade has contributed significantly to the increase in emissions generated by transport, especially in the maritime and air freight sectors. The study “Assessment of International Trade-Related Transport CO₂ Emissions—A Logistics Responsibility Perspective” emphasizes the importance of logistics activity in reducing these emissions by implementing efficient transport practices and technological developments, ensuring sustainable supply chain management. The paper emphasizes the need to adopt eco-friendly solutions that consider the reduction of the carbon footprint of international trade [42].

The importance of hybrid technology in reducing CO₂ emissions is also emphasized in other studies. Hybrid technology aims to develop hybrid vehicles that combine traditional internal combustion engines with electric motors in order to reduce CO₂ emissions. The proposed technology considers the technical feasibility, economic viability, and environmental benefits of heavy hybrid trucks and buses [43].

European countries have become aware of this environmental impact and have taken the initiative to combat climate change in the transport industry.

Thus, they have significantly modified their legal systems, focusing on improving pollution control measures (taxes/emission limits) and encouraging companies to adopt environmentally friendly products to combat climate change and global warming [28].

Furthermore, adopting environmentally friendly and sustainable solutions is essential for effectively reducing greenhouse gas emissions in the transport sector. Given the objectives of the transition to sustainable mobility, specialists in the field have proposed a series of actions aimed at urban sustainability, even proposing the expansion of research on pollution sources from transport [44]. Therefore, these measures can facilitate a profound transition towards a more sustainable future in the transport sector and contribute substantially to global efforts to mitigate the impact of climate change. Developed countries tend to have higher GDPs and lower emissions per unit of economic output due to cleaner technologies and more efficient infrastructure [45,46,47].

Previous studies [48,49,50] show that environmental technologies and renewable energy sources play an essential role in reducing GHG emissions in the transport sector by offering cleaner and more sustainable alternatives to fossil fuel-based transport. Environmental technologies and renewable energy solutions can improve energy efficiency in transport [51].

The transition to a sustainable transport system has materialized in strategies and regulations, the main objective of which was to reduce primary energy consumption and GHG emissions through technological improvements. In this regard, a series of directives have been legislated at the EU level, such as Renewable Energy Directives (Directive 2009/28/EC [52] and Directive 2018/2001EC [53]), Energy Efficiency Directives (Directive 2012/27/EC [54] and Directive 2018/2002/EC [55]), and the Clean Vehicles Directive (Directive 2009/33/EC [56]). An emissions trading system has also been implemented—the European Emissions Trading System (EU ETS)—through which companies in sectors with intensive CO₂ emissions can purchase emissions certificates, thus contributing to reducing emissions [57].

The latest legislative package, “Ready for 55 in 2030”, includes a series of proposals to promote renewable energies, improve building energy efficiency, increase carbon absorption by forests and other ecosystems, and reduce emissions from road, air, and maritime transport.

All these legislative proposals aim to improve the quality of life by reducing air pollution, transitioning to a green economy, and combating climate change by reducing GHG emissions, thus avoiding serious phenomena affecting the global population.

Increasing the share of renewable energy and, therefore, its contribution to the decarbonization of transport is vital in the context of sustainable development and the climate neutrality of the Green Deal.

Increasing the share of green energy (to at least 42.5% in 2030, according to the EU/2023/2413 Renewable Energy Directive) [11] will help decouple economic growth from fossil fuel consumption. The use of renewable energy will contribute to reducing GHG emissions and thus achieving climate neutrality by 2050. Renewable energy sources, such as wind, solar, and hydroelectric power, serve as a clean and sustainable energy source [58,59,60]. To this end, investments are needed in the exploitation of these natural resources available at the level of each EU Member State, as well as the adaptation of consumption infrastructure. The integration of renewable energy in the transport sector allows for the reduction of dependence on fossil fuels, the strengthening of energy security, and the creation of jobs in the renewable energy sector [61].

In analyzing the drivers of transport-related GHG emissions within the EU, our selection of GDP as a key indicator is grounded in established theoretical frameworks, namely the IPAT model and the Environmental Kuznets Curve (EKC) hypothesis. The IPAT model, which posits that environmental impact is a product of Population, Affluence (GDP), and Technology, underscores GDP’s role as a proxy for affluence, directly influencing transport emissions through increased consumption patterns and demand for mobility. As EU member states experience economic growth, reflected in rising GDP, we anticipate a corresponding increase in transport activity and associated emissions. Furthermore, the EKC hypothesis suggests a potential non-linear relationship between economic development and environmental degradation, indicating that while initial GDP growth may lead to heightened transport emissions, subsequent stages could witness a decoupling effect through technological advancements and policy interventions. Therefore, by incorporating GDP as a core variable, we aim to capture both the direct and indirect impacts of economic growth on transport emissions, providing a robust foundation for our Prophet model forecasts and cluster analysis within the context of the EU’s climate neutrality goals [62,63,64].

A multidisciplinary approach is essential for sustainable transport, requiring the integration of traffic engineering with urban planning and other related fields. The article “New Perspectives and Challenges in Traffic and Transportation Engineering Supporting Energy Saving in Smart Cities” highlights the importance of optimizing urban traffic to promote public transport and reduce emissions. These measures aim to create sustainable cities with low GHG emissions from transport activity [65].

Based on the literature reviewed, we can state that urgent decarbonization of transport is essential for achieving EU climate goals. Current research highlights the complex connection between economic growth, transport activity, and negative environmental impact, underlining the significance of relevant indicators in achieving this goal, such as renewable energy, innovation, and efficient and well-formulated environmental policies. Transport activity generates greenhouse gas emissions, thus increasingly demonstrating the need for a transition to sustainable mobility. The recent studies we have reviewed in our paper express a clear picture of the main factors influencing transport emissions, drawing attention to the need to harmonize economic development with environmental protection measures. This analysis of existing research in the field represents the starting point for investigating the relationships between significant indicators and enunciating research hypotheses, thus contributing to achieving climate neutrality at the level of EU Member States.

Research Hypotheses:

Hypothesis 1.

There is a positive, direct, and strong relationship between GDP and GHG emissions, as well as an inverse and strong negative relationship between the level of renewable energy consumption and GDP, GHG emissions, and energy consumption.

Hypothesis 2.

The variables energy consumption and GDP have the most significant positive influence on GHG, acting on the output layer and thus influencing the level of GHG emissions.

Hypothesis 3.

GHG levels from transport in the European Union countries will continue to increase over the next 10 years, despite substantial efforts to reduce them, due to economic growth in developing countries and continued dependence on fossil fuels.

Hypothesis 4.

Central and Eastern European countries with economies in transition will experience a faster increase in GHG emissions by 2030 than Western European countries due to accelerated industrialization and increased energy consumption.

Hypothesis 5.

Nordic countries with a strong renewable energy economy and ambitious climate policies will experience a significant decrease in GHG emissions from transport by 2030 compared to the EU average.

Hypothesis 6.

Southern EU countries with an economy dependent on tourism and agriculture will implement more ambitious emission reduction policies, leading to a faster decrease.

Hypothesis 7.

None of the EU member states will achieve climate neutrality regarding GHG emissions from the transport sector in 2050.

The scientific objective of our research is to investigate the links between the five indicators relevant for reducing GHG emissions and achieving EU climate neutrality in 2050.

3. Research Methodology—Materials and Methods

The research design implies a process of six steps as shown in Figure 1.

3.1. Data Selection

To achieve the research objective, but also to overcome the gap observed in the specialized literature, we identified the factors that can influence the level of GHG emissions from the transport sector, the largest contributor to the total level of GHG emissions.

Thus, the indicators used, which also cover the components of sustainability, are GDP, the share of the urban population in the total population, greenhouse gas emissions from transport (CO₂ equivalent), final energy consumption in transport (oil equivalent), and the share of renewable energy in transport (

Table 1. Indicators used in research.

Variable	Data Sets	Measures	References
Air emissions account by NACE Rev. 2 activity (transportation and storage)—carbon dioxide	EU states air emissions accounts by NACE Rev. 2 activity (transportation and storage)—Air pollutants and greenhouse gases:Greenhouse gases—carbon dioxide, nitrous oxide (CO2, N2O in CO2 equivalent)	Tonne	Eurostat [66]
Gross Domestic Product (GDP)	EU states GDP 2009–2021	Current prices, million Euro	Eurostat [67]
Share of renewable energy in gross final energy consumption by sector—Renewable energy sources in transport	EU states—Share of renewable energy in gross final energy consumption by sector—Renewable energy sources in transport	Percentage	Eurostat [68]
Urban population	EU states—Urban population	Percentage	Data Worldbank [69]
Final energy consumption in transport by type of fuel—of oil equivalent	EU states—Final energy consumption in transport by type of fuel	Thousand tons of oil equivalent	Eurostat [70]

Source: own design based on Eurostat and Data Worldbank.

). These indicators were selected from the Eurostat database and are used in the policies of the European Commission in Sustainable Development Goals (SDGs). We extracted data for these indicators from the Eurostat database, for the period 2012–2022, and using the Prophet model, we estimated GHG emissions from the transport sector for the time interval 2024–2050.

• Economic Factors—GDP

In general, a higher GDP correlates with a higher level of energy consumption as well as with greater demand in the transport sector, inevitably leading to higher CO₂ emissions. Of course, this relationship can be determined by energy efficiency or transport policies in different countries, but economic development still remains the main element that generates the increase in GHG emissions.

Technological Factors—Share of renewable energy in gross final energy consumption by sector (renewable energy sources in transport):

The impact of this technological factor is obvious in terms of the level of GHG emissions, so that a higher rate of renewable energy in the transport sector will impact GHG emissions in the opposite direction, resulting in a decrease in their level. Thus, renewable energy sources such as solar, wind, or hydroelectric power generate electricity with significantly lower carbon footprints compared to fossil fuels. An example of this is the widespread adoption of electric vehicles powered by these renewable energy sources.

• Social Factors—the share of the urban population in the total population

The degree of urbanization of the population leads directly to a greater dependence on personal transport, contributing to a higher level of GHG emissions from transport. Cities tend to have a car-centric infrastructure, contributing to increased GHG emissions.

• Environmental Factors—Final energy consumption in transport by type of fuel (oil equivalent)

The type of fuel used in the transport sector significantly impacts GHG emissions. Fossil fuels release significant amounts of CO₂, unlike greener alternatives such as electricity or hydrogen, which have smaller carbon footprints.

3.2. Using Pearson Correlation

To investigate hypothesis number 1, we used the SPSS program, with Pearson correlation. Pearson correlation is a statistical measure that tells us how strongly and in what direction two continuous variables are related. In other words, it shows us whether and how variations in one variable are associated with variations in the other. The strength of the correlation is indicated by the absolute value of the correlation coefficient (r). The closer the value of r is to 1 (either positive or negative), the stronger the relationship between the two variables.

SPSS (Statistical Package for the Social Sciences) uses the standard formula for calculating the Pearson correlation coefficient [64].

$$\mathrm{r}=\sum \left[\right(\mathrm{X}\mathrm{i}-\overline{\mathrm{X}}\left)\right(\mathrm{Y}\mathrm{i}-\overline{\mathrm{Y}}\left)\right]/\surd [\sum (\mathrm{X}\mathrm{i}-\overline{\mathrm{X}}{)}^2 \times \sum (\mathrm{Y}\mathrm{i}-\overline{\mathrm{Y}}{)}^2]$$

where

• r: Pearson correlation coefficient
• Xi, Yi: Individual values of the two variables
• $\overline{\mathrm{X}}, \overline{\mathrm{Y}}$: Means of the two variables
• ∑: Sum

3.3. To Investigate Hypothesis Number 2, We Used the SPSS Program, with the Neural Networks Function

An artificial neural network (ANN) is a computational model inspired by the structure and functioning of the human brain. These networks comprise interconnected nodes, called artificial neurons, that process information and learn from data. A particular type of ANN is the multilayer perceptron (MLP), which can solve complex problems by using multiple layers of neurons.

An MLP is made up of the following [71]:

Input layer: Receives input data and passes it to the next layer.

Hidden layers: Perform complex calculations on the data and extract relevant features.

Output layer: Produces the final result of the network.

Operating process:

Feedforward: Input data are multiplied and summed by synaptic weights. The result is passed through an activation function to introduce nonlinearity. This process is repeated for each layer until the final output is obtained.

Backpropagation: The error between the desired output and the obtained one is calculated. This error is propagated back through the network, and the synaptic weights are adjusted to minimize the error.

3.4. To Verify the Research Hypotheses with Numbers Between 3 and 7, We Used the Prophet Forecasting Model and the SPSS Program’s Hierarchical Clusters Function

3.4.1. The Prophet Model: A Detailed Analysis

Prophet is a powerful forecasting tool developed by the Core Data Science team at Facebook in 2017, standing out from other forecasting programs through its flexibility, accuracy, and ease of use. This statistical time series forecasting model is particularly effective in handling data with strong seasonal patterns, making it a popular method in various industries, especially in the e-commerce sector.

At its core, Prophet decomposes a time series into several components:

• Trend Component (g(t)): Captures the general direction of the time series, whether it is increasing, decreasing, or remaining relatively constant.
• Seasonal Component (s(t)): Models periodic patterns such as daily, weekly, monthly, or annual cycles.
• Holiday Component (h(t)): Takes into account the impact of holidays on the time series.
• Error Component (e(t)): Represents the residual noise or unexplained variation.

The model can be represented mathematically as [72,73,74,75]:

y(t) = g(t) + s(t) + h(t) + e(t)

where

• y(t): Observed value of the time series at time t.
• g(t): Trend component at time t.
• s(t): Seasonal component at time t.
• h(t): Holiday component at time t.
• e(t): Error term at time t.

Prophet’s Key Benefits:

• Flexibility: Easily handles various time series patterns, including linear and nonlinear trends; the program handles seasonality and holiday effects.
• Accuracy: Provides accurate forecasts, even for complex time series data.
• Scalability: Efficiently handles large data sets and complex models.
• By leveraging these strengths, Prophet has become a valuable tool for businesses and researchers alike, enabling them to make data-driven decisions and optimize their strategies based on precise forecasts.

3.4.2. Using the Hierarchical Clusters Function in SPSS

The Hierarchical Clusters function in SPSS implements algorithms that build a hierarchical tree, called a dendrogram, representing the gradual merging of observations into larger and larger clusters.

The average linkage method in this article presents the distance between two clusters as the arithmetic mean of all distances between pairs of observations, one in each cluster.

Average linkage clustering is a hierarchical method of grouping data, where the distance between two clusters is calculated as the arithmetic mean of all distances between pairs of points, one in each cluster. This means that each point in a cluster contributes to the total distance between the two clusters.

Formula:

If we have two clusters, C1 and C2, the average distance between them, d(C1, C2), is calculated as follows:

d(C1, C2) = (1/(|C1| × |C2|)) × Σ(dij)

where:

• |C1| and |C2| represent the number of points in each cluster.
• dij represents the Euclidean distance (or other distance metric) between point i in C1 and point j in C2.
• ∑ represents the sum of all distances dij.

4. Results and Discussions

4.1. Validation of Hypothesis Number 1

First of all, we used the Pearson correlation to highlight the extent to which the selected indicators correlate with the GHG emissions variable, and whether this correlation is strong, thus using the Pearson correlation.

The Pearson correlation is a statistical measure that shows us how strongly and in what direction two continuous variables are related to each other. Specifically, it tells us whether when one variable increases, the other tends to increase as well (positive correlation) or decrease (negative correlation) or whether there is no obvious relationship between them (

Table 2. Pearson Correlations with SPSS for Renewable energy GDP, GHG, Energy Consumption, Urban Population.

Correlations
		Renewable Energy	GDP	GHG	Energy Consumption	Urban Population
Renewable energy	Pearson Correlation	1	−0.157	−0.135	−0.181	−0.124
	Sig. (2-tailed)		0.435	0.502	0.365	0.537
	N	27	27	27	27	27
GDP	Pearson Correlation	−0.157	1	0.939 **	0.972 **	0.171
	Sig. (2-tailed)	0.435		0.000	0.000	0.395
	N	27	27	27	27	27
GHG	Pearson Correlation	−0.135	0.939 **	1	0.906 **	0.222
	Sig. (2-tailed)	0.502	0.000		0.000	0.267
	N	27	27	27	27	27
Energy Consumption	Pearson Correlation	−0.181	0.972 **	0.906 **	1	0.118
	Sig. (2-tailed)	0.365	0.000	0.000		0.557
	N	27	27	27	27	27
Urban Population	Pearson Correlation	−0.124	0.171	0.222	0.118	1
	Sig. (2-tailed)	0.537	0.395	0.267	0.557
	N	27	27	27	27	27

** Correlation is significant at the 0.01 level (two-tailed). Source: own design based on Eurostat and Databank using SPSS.

).

• Hypothesis Number 1

There is a positive, direct, and powerful relationship between GDP and GHG emissions, as well as a negative and strong inverse relationship between the level of renewable energy consumption and GDP, GHG, and energy consumption.

From the Pearson correlation analysis run with the help of the SPSS program, it emerges that a series of relationships are established between the analyzed variables, thus demonstrating the degree of influence between them.

After running the program, a direct, positive, and very strong relationship is identified between GDP and GHG emissions, demonstrating the fact that a strong economy is at the same time an economy that generates a significant level of pollution. At the same time, a very strong and positive relationship is identified between GDP and the level of energy consumption, so that a possible restriction of activities involving energy consumption will directly impact the economic level. The urban population directly and positively influences he levels of both GDP and GHG emissions without having a defining impact.

Even if there is no direct and strong relationship between these two indicators, the urban population can indirectly influence emissions through other factors included in the model. Urban areas tend to concentrate on industrial activities, which are often significant sources of emissions.

Regarding energy consumption, we can state that the urban population generally has a higher energy consumption per capita than the rural population, which can influence total emissions. At the level of transport activity, urbanization is associated with the increase in public and private transport, which contributes to the increase in GHG emissions.

The renewable energy consumption indicator generates an inverse, negative relationship with all indicators, influencing in the opposite direction the levels of both GDP and gas emissions and especially energy consumption. However, although the impact is visible and impacts all indicators in the opposite direction, the influence is not very strong. Considering this aspect, hypothesis number 1 is partially validated, given that a very strong relationship is not established between these variables. This signals that in order to have a significant effect, a much higher level of investment in renewable energy is needed, investments that at this moment, although influencing GHG emissions, are not sufficient to trigger a significant decrease in them.

4.2. Validation of Hypothesis Number 2

To investigate hypothesis number 2, an artificial neural network (ANN) model, specifically a multilayer perceptron (MLP), was used to analyze and model a certain data set (Figure 2). MLPs are a type of feedforward neural network that consists of several layers of interconnected neurons. Each neuron processes the input signals, applies an activation function, and transmits the result to the next layer.

The analysis results, presented in the

Table 3. Model summary.

Model Summary
Training	Sum of Squares Error	2.587
	Relative Error	0.323
	Stopping Rule Used	1 consecutive step(s) with no decrease in error a
	Training Time	0:00:00.00
Testing	Sum of Squares Error	0.107
	Relative Error	0.035
Dependent Variable: GHG

^a Error computations are based on the testing sample. Source: own design based on Eurostat and Databank using SPSS.

provided, indicate the MLP model’s performance on the training and testing sets. The specific interpretation of the results depends on the context of the problem and the chosen performance metrics.

In the testing phase, the sum of squares error (SSE) level: The recorded value indicates a small error on the test set, suggesting that the model generalizes well to unknown data (0.107).

We have a value of 0.035 for the relative error, which means that on average, the model predictions are about 3.5% different from the actual values in the test set. Thus, we can conclude that the model performs well, especially on the test set.

• Hypothesis Number 2

The variables energy consumption and GDP have the most significant positive influence on GHG emissions, acting on the output layer and thus influencing the level of GHG emissions in the transport sector (

Table 4. Parameter Estimates.

Parameter Estimates
Predictor		Predicted
		Hidden Layer 1	Output Layer
		H(1:1)	GHG
Input Layer	(Bias)	−0.515
	Renewable energy	0.002
	GDP	0.583
	Energy Consumption	0.650
	Urban Population	0.089
Hidden Layer 1	(Bias)		0.485
	H(1:1)		1.348

Source: own design based on Eurostat and Databank using SPSS.

).

After running the SPSS program’s Neural Networks function, it is observed that the variables energy consumption and GDP have the greatest positive influence on GHG emissions: the weights associated with energy consumption and GDP are the largest and positive. This means that an increase in consumption or GDP has a significant impact on the rise in GHG emissions.

The indicators renewable energy and urban population have a smaller influence: The weights associated with renewable energy and urban population are relatively small. This suggests that these variables have a lower impact on GHG emissions compared to consumption and GDP.

The hidden layer (with the H(1:1) neuron) adds complexity to the model, allowing the network to learn more complex relationships between variables. The high connection ratio between H(1:1) and the output layer indicates that this neuron plays an important role in determining the final value of GHG.

The model suggests that GHG emissions are closely related to the level of consumption and economic growth (measured by GDP). In contrast, variables such as renewable energy and urban population have a minor impact on GHG emissions, at least in the context of the data run.

4.3. Investigating Research Hypotheses with Numbers Between 3 and 7

To investigate the research hypotheses with numbers between 3 and 7, we forecasted GHG emissions from the transport sector for the period 2024–2050 at the level of EU member states, using the Prophet program. Subsequently, we used the hierarchical clusters function in SPSS in order to group countries into homogeneous clusters considering the level of GHG emissions for the year 2030, the reference year for the European Climate Law.

4.3.1. Estimates of GHG Emissions from the Transport Sector Using the Prophet Model

After entering the data and running the information, we obtained the forecasted data for the time period 2024–2050. The reference years analyzed were 2030 and 2050, according to the latest FIT for 55% legislative package, which aims to reduce GHG emissions by at least 55% by 2030, as well as a goal of their total elimination by 2050 to achieve EU climate neutrality. Thus, of the 27 countries analyzed, a decreasing trend in GHG emissions is observed for 14 countries, with an increasing trend for 12 countries and a hybrid situation in the case of France, which, although it records a reduction in GHG emissions in 2030, subsequently records an increase until 2050. Although we are talking about a decreasing trend for 14 countries, the level of GHG emissions from transport remains high, both for 2030 and for 2050. None of the 27 countries analyzed record the complete cancellation of GHG emissions from the transport sector by 2050, demonstrating that reality is still far from the European Commission directives (

Table 5. Estimates of GHG emissions from the transport sector using the Prophet model.

Country	2025	2030	2035	2040	2045	2050
Austria	7392.92	8259.07	9226.09	8597.44	9604.79	10,730.05
Belgium	4961.78	3797.16	2913.16	1872.67	1429.30	1093.65
Bulgaria	7738.76	8692.62	9760.92	10,213.59	11,475.87	12,890.30
Croatia	1144.70	941.26	773.72	580.94	477.73	392.72
Cyprus	259.65	215.65	179.07	159.46	132.38	109.86
Czechia	9966.01	11,091.78	12,342.64	12,911.58	14,372.19	15,995.63
Denmark	36,592.07	32,270.54	28,450.53	22,497.02	19,845.58	17,501.76
Estonia	910.98	549.97	331.75	159.36	95.91	57.55
Finland	6415.76	4836.21	3645.61	2545.20	1918.32	1445.86
France	59,232.11	65,809.14	72,700.98	59,846.37	66,865.56	74,290.19
Germany	57,720.84	44,810.13	34,772.82	21,376.56	16,600.69	12,887.37
Greece	20,310.47	20,609.88	20,970.84	18,400.71	18,620.27	18,894.77
Hungary	6380.24	7060.78	7812.29	6772.98	7496.41	8295.99
Ireland	12,749.32	13,053.05	13,362.90	10,208.10	10,451.23	10,700.21
Italy	40,779.52	46,141.67	52,143.54	44,327.98	50,215.19	56,818.01
Latvia	1781.91	1376.89	1063.82	639.99	494.36	381.83
Lithuania	8626.49	9588.78	10,661.17	13,775.68	15,309.03	17,016.68
Luxembourg	4388.78	4002.81	3665.31	3005.69	2730.37	2490.22
Malta	287.94	228.40	181.11	106.16	84.07	66.54
Netherlands	18,857.18	15,894.20	13,424.74	8614.45	7245.06	6106.56
Poland	13,767.49	18,878.11	25,880.42	21,110.00	28,947.36	39,692.49
Portugal	7131.72	7157.62	7182.57	5460.54	5480.70	5500.60
Romania	5944.07	5617.89	5308.13	4803.11	4540.73	4291.54
Slovakia	1488.18	834.46	467.70	220.03	123.00	68.57
Slovenia	1126.29	1338.56	1590.62	1546.18	1837.60	2183.80
Spain	38,147.10	39,142.98	40,159.26	35,596.94	36,529.72	37,483.38
Sweden	5409.02	4319.62	3449.31	2425.88	1937.26	1546.96

Source: own design based on Eurostat and Databank using the Prophet model.

).

A Temporary Decline, Followed by a Rapid Recovery

Following the data recorded in the Eurostat database, it is observed that in 2020, at the beginning of the COVID-19 pandemic, there was a sudden and significant decrease in global CO₂ emissions from the transport sector, the leading cause being the reduction in economic activities, especially in the industrial and transport sectors. However, with the relaxation of restrictions and the implicit relaunch of economies, GHG emissions from transport recorded rapid increases, exceeding the pre-pandemic level. Transport emissions sharply declined in 2020 due to pandemic lockdowns, then began rebounding in 2021–2022 as restrictions eased. In this scenario, the focus remains on electric vehicles and improved public transport for sustained reductions [76].

The recovery was extremely rapid, with economic activities returning more strongly than ever, wanting to compensate for the pause period. Production activity intensified, consumerism reached huge proportions, and tourism made a spectacular comeback, as if in an attempt to recover from the pandemic period.

To revive the economy, governments provided a series of economic incentives to support economic recovery, which directly led to the increase in emissions. To stimulate economic recovery, governments implemented diverse initiatives that, while aiming to mitigate the energy crisis, contributed to emission increases. These included solidarity funds, like those addressing rising energy prices [77], providing immediate financial relief that often supported existing energy consumption patterns. Another initiative presents a dual approach to energy, where investments in renewable energy are coupled with a temporary, or in some cases prolonged, reliance on fossil fuels to ensure energy security amidst supply chain disruptions [78], effectively slowing the transition to a low-carbon economy.

At the same time, the intense concern regarding the recovery of world economies has put the level of investment in renewable energies in the background, so that the pandemic has delayed and even reduced the investments initially envisaged for specific renewable energy projects.

Considering the data recorded by the Eurostat database, the pandemic has offered a real opportunity to accelerate the transition to a greener economy characterized by reduced CO₂ emissions, but this opportunity has not been adequately assessed. As such, governments must implement measures and policies that promote sustainable economic growth that enhances the sectors of the economy without generating additional environmental problems.

4.3.2. Analysis Clusters

In part 4 of our analysis, we grouped EU member states into clusters, based on estimates of GHG emissions from the transport sector obtained using the Prophet model, for the year 2030, the year in which, according to the European Climate Law, they should have decreased by 55%.

Thus, depending on the evolution of GHG emissions from transport, in 2030, compared to 2012, two clusters and seven subclusters were identified (Figure 3).

• CLUSTER Number 1—Countries that record a reduction in GHG emissions from transport

This cluster, obtained for 2030 compared to 2012, is characterized by significant reductions in GHG emissions, expressing the commitment of the countries within this cluster to environmental policies (

Table 6. CLUSTER No. 1—Countries that record a reduction in GHG emissions from transport.

Subclusters	Country	2012	2030	Percentage Change (%)	Region in Europe
Subcluster 1A	Estonia	2620.75	549.97	−79.01	Northern Europe
	Slovakia	3515.00	834.46	−76.26	Central and Eastern Europe
Subcluster 2A	Cyprus	548.20	215.65	−60.66	Southern Europe
	Finland	10,602.02	4836.21	−54.38	Northern Europe
Subcluster 3A	Germany	89,054.37	44,810.13	−49.68	Western Europe
	Belgium	7477.20	3797.16	−49.21	Western Europe
	Latvia	2569.45	1376.89	−46.41	Northern Europe
	Malta	407.34	228.40	−43.92	Southern Europe
	Netherlands	28,232.16	15,894.20	−43.70	Western Europe
	Sweden	7481.73	4319.62	−42.26	Northern Europe
Subcluster 4A	Croatia	1259.61	941.26	−25.27	Southern Europe
	Denmark	42,113.57	32,270.54	−23.37	Northern Europe
	Portugal	8243.28	7157.62	−13.17	Southern Europe
	France	70,354.22	65,809.14	−6.46	Western Europe

Source: own design based on Eurostat and Databank using SPSS.

). It is noteworthy that within this cluster, we mainly find Nordic countries such as Estonia, Finland, Sweden, and Denmark. This is due to massive investments in renewable energies, as well as ambitious energy policies. As for Western Europe, we find in this cluster countries such as Germany, France, and the Netherlands, which, although they have industrialized economies, are in clusters with emission reduction rates due to ambitious climate policies, investments in clean technologies, and a diversified energy mix. This cluster includes four subclusters, respectively, 1A, 2A, 3A, and 4A, built according to the rate of decrease in GHG emissions from transport for the year 2030 compared to 2012.

• Subcluster 1A—Countries that record a reduction in GHG emissions (76–79% decrease)

Among these countries with reductions of GHG emissions between 76–79%, Estonia and Slovakia are included. These countries have recorded the largest reductions in GHG emissions by 2030 compared to 2012, suggesting an advanced decarbonization process and a rapid transition to a low-carbon economy. Estonia has a significant share of renewable energy, while Slovakia has focused on reducing its dependence on coal.

• Subcluster 2A—Countries that record a reduction in GHG emissions (54–60% decrease)

Among the countries with greenhouse gas emission reductions between 54–60%, we find Cyprus and Finland. These countries have recorded significant reductions in GHG emissions, suggesting an advanced decarbonization process. Cyprus has a tourism-based economy with significant potential for developing renewable energies.

• Subcluster 3A—Countries that record a reduction in GHG emissions (decrease 42–49%)

Among the countries with greenhouse gas emission reductions between 42–49%, we find Malta, Netherlands, Sweden, Belgium, Germany, and Latvia. Countries in this cluster have recorded significant reductions in GHG emissions, indicating a strong commitment to ambitious climate policies and investment in renewable energy, with Germany being considered a leader in renewable energy and an economy with a strong industrial sector. Many of these countries have implemented favorable fiscal policies for the purchase of electric vehicles, such as tax breaks, subsidies, or bonuses.

• Subcluster 4A—Countries that record a reduction in GHG emissions (decrease −6% to −25%)

Among the countries with greenhouse gas emission reductions between 6–25%, we may find Croatia, Denmark, France, and Portugal. Countries in this cluster have managed to significantly reduce GHG emissions, indicating investment in renewable energy. For example, Denmark is a world leader in wind energy, is recognized for its massive investment in bicycle and train infrastructure, as well as the large-scale use of biofuels in maritime transport, and promotes a low-carbon economy. At the same time, in France, we find important investments in renewable energy. In Croatia, the level of GHG emissions from transport can be influenced by seasonal tourism, which generates a variable demand for transport, but investments in public transport in urban areas and the promotion of sustainable tourism have had a positive effect. At the same time, Portugal has invested in charging infrastructure for electric vehicles, promoted public transport, and implemented car-sharing schemes. The country also benefits from an abundance of solar energy, which facilitates the transition to electric transport.

• CLUSTER Number 2—Countries registering an increase in GHG emissions from transport

This cluster is characterized by the presence of countries whose rate of GHG emissions is increasing (

Table 7. Cluster no. 2—Countries registering an increase in GHG emissions from transport.

Subclustere	Country	2012	2030	Percentage Change (%)	Region in Europe
Subcluster 1B	Ireland	12,577.77	13,053.05	3.77	Western Europe
	Italy	42,712.44	46,141.67	8.02	Southern Europe
	Spain	35,648.51	39,142.98	9.80	Southern Europe
	Romania	5027.23	5617.89	11.74	Central and Eastern Europe
Subcluster 2B	Poland	15,944.10	18,878.11	18.40	Central and Eastern Europe
	Austria	6591.28	8259.07	25.30	Central Europe
	Luxembourg	3193.08	4002.81	25.35	Western Europe
	Hungary	5477.78	7060.78	28.89	Central and Eastern Europe
	Greece	15,312.32	20,609.88	34.59	Central and Eastern Europe
	Bulgaria	6287.09	8692.62	38.26	Central and Eastern Europe
Subcluster 3B	Czechia	7463.32	11,091.78	48.61	Central and Eastern Europe
	Slovenia	882.52	1338.56	51.67	Central and Eastern Europe

Source: own design based on Eurostat and Databank using SPSS.

). This cluster is mainly dominated by the countries of Central and Eastern Europe. Many countries in this region are in the cluster with significant emission growth rates (Czech Republic, Slovenia, Romania, Poland, Austria, Luxembourg, Hungary, Greece, Bulgaria). This is partly due to the region’s industrial history, which is based on fossil fuels, and the ongoing economic transition process. Within this cluster, only two countries are part of Southern Europe (Italy and Spain), but although they record growth rates of GHG emissions, these are lower compared to those of the countries in the same cluster. The same situation is present in Ireland. Although they are included in the second cluster, characterized by the percentage increase in GHG emissions, they are found in the first subcluster, which creates the transition between cluster number 1 and cluster number 2, having low growth rates. Cluster number 2 is formed by three subclusters, namely 1B, 2B, and 3B.

• Subcluster S1B—Countries registering an increase in GHG emissions (3–11% increase)

The countries in this cluster (Italy, Spain, Romania, Ireland) have experienced a moderate increase in GHG emissions, indicating a balance between economic development and efforts to reduce emissions. For example, in Italy, we find a diverse energy mix but with a significant share of fossil fuels and a strong automotive industry, which also emphasizes the existence of electric car factories, while in Romania, we find an economy in transition, with an energy sector undergoing modernization and a precarious infrastructure in terms of bike paths, public transport, or charging stations for electric cars.

• Subcluster S2B—Countries registering an increase in GHG emissions (over 18% increase)

The countries in this subcluster (Austria, Bulgaria, Greece, Hungary, Luxembourg, Poland, Hungary) have experienced a significant increase in GHG emissions from transport, suggesting a relatively slow rate of decarbonization. For example, Bulgaria is still characterized by its dependence on fossil fuels, being a country whose industrialization process is ongoing. At the same time, in Hungary, the energy sector is dominated by the use of fossil fuels. At the same time, there is insufficient investment in public transport, and infrastructure for bicycles and pedestrians can discourage the use of these more sustainable modes of transport.

• Subcluster S3B—Countries registering an increase in GHG emissions (48–51% increase)

These countries (the Czech Republic and Slovenia) have recorded a significant increase in GHG emissions, suggesting an urgent need to accelerate the transition to a low-carbon economy. An aging car fleet, with a high share of diesel vehicles, can contribute to increasing emissions, and the expansion of urban areas and increased urban mobility can generate additional pressure on the transport system.

Validation of Research Hypotheses:

H3—GHG levels from transport in the European Union countries will continue to increase in the coming years, despite substantial efforts to reduce them, due to economic growth in developing countries and continued dependence on fossil fuels.

Research hypothesis number 3 is partially validated, given that in the case of 13 countries, the trend is one of growth, and for 14 countries, out of the 27 analyzed, the trend is a decrease in these emissions. Thus, if the evolution of the variables that influence GHG emissions from transport is not improved, EU member states will certainly face a negative evolution in GHG emissions from this sector of activity, given the natural evolution of developing countries, which will register increases for all economic activities and which will generate consistent GHG emissions.

H4—Central and Eastern European countries with economies in transition will experience a faster increase in GHG emissions by 2030 than Western European countries due to accelerated industrialization and increased energy consumption.

Hypothesis 4 is partially validated. Eight Central and Eastern European countries experience increasing rates of GHG emissions from transport, in contrast to Western European countries, which are predominantly in the category of countries with decreasing rates. Slovakia is the only country located in Eastern Europe that experiences an astonishing decreasing rate.

The results obtained highlight the importance of analyzing the situation of each country in detail, as there is significant variability in the evolution of GHG emissions. The case of Slovakia demonstrates that it is possible to achieve significant emission reductions even in the context of economic and social challenges.

H5—The Nordic countries, with a strong renewable energy economy and ambitious climate policies, will see a significant decrease in GHG emissions from transport in 2030, compared to the EU average.

Hypothesis number 5 is fully validated. All Nordic countries in the EU see a significant decrease in GHG emissions, which contributes significantly to the achievement of the objectives set at the European Commission level.

The Nordic countries have invested substantially in developing renewable energy sources, such as hydropower, wind, and solar energy. These investments have allowed for a diversification of the energy mix and a reduction in dependence on fossil fuels. At the same time, the Nordic countries have a long tradition of regional cooperation in the field of energy and the environment, which has facilitated the exchange of good practices and accelerated the transition to a low-carbon economy.

H6—Southern EU countries, with an economy dependent on tourism and agriculture, will implement more ambitious policies to reduce emissions, which will lead to a faster decrease in them.

Hypothesis number 6 is partially validated. Thus, three countries recorded a decrease in these emissions, respectively Malta, Croatia, and Portugal, while Italy and Spain continue to record increases at this level. Although the general trend aims for improvement also with regard to the two countries mentioned above, we cannot yet speak of sustainable economies in all southern European countries.

Italy and Spain are two of the largest economies in southern Europe, and they continue to record increases in GHG emissions from transport. These countries face difficulties in implementing effective policies to reduce emissions, possibly due to their dependence on certain industrial sectors. In general, the economies of southern European countries are not sustainable. Although there is progress in some areas, a deeper transformation of transport models is needed to significantly reduce the environmental impact.

H7—Hypothesis 7, stating that no EU Member State will achieve climate neutrality in transport GHG emissions by 2050, is directly supported by empirical data from Table 5, which presents Prophet program forecasts of transport sector emissions for all 27 member states. This analysis, using the Prophet model to project future trends, unequivocally validates the hypothesis by demonstrating that the complete elimination of transport emissions by 2050 is not anticipated under current trajectories. Consequently, the statistically backed predictions indicate that without significant, drastic changes, the EU is unlikely to meet its climate neutrality goals for the transport sector because the estimated GHG emissions from the transport sector for 2050 do not reach zero.

5. Conclusions and Future Developments

The scientific contribution of this work is highly relevant and particularly complex due to the fact that greenhouse gas emissions represent the main objective of the Green Deal and the European Climate Law, which aims to reduce them by 55% in 2030 and achieve climate neutrality in 2050. The results of the study can provide solutions to national and European decision-makers to establish more effective legislative measures to reduce GHG emissions for the protection of the climate and, implicitly, the natural environment.

The EU has developed ambitious legislative measures to limit the level of these emissions, the most recent instrument being the EU Emissions Trading System, which currently addresses the energy and industrial sectors and, beginning in 2027, will include road traffic and buildings.

Consequently, all Member States must actively engage in climate action that reduces or eliminates the negative effects of GHG emissions.

The topic of this research was a great challenge for us because previous studies have been limited to a small number of variables influencing GHG emissions in the transport sector, and the forecasts have been made over short time periods. Currently, there are no studies that address these variables at the level of EU Member States for the period 2024–2050. The method used to estimate these results, namely the Prophet model, allowed us to model the five variables that influence the level of emissions. The Prophet model is not yet a widely used model in economic research, but it is a remarkable tool for time series forecasting. Thus, we have made estimates of GHG emissions from transport, the most significant contributor to the total level of emissions, starting from a set of five relevant indicators, with the help of which we have created clusters to identify the Member States that have the greatest potential to reduce emissions.

The results obtained in this research by using GHG emissions from the transport sector in correlation with GDP, urban population, primary energy consumption, and the share of renewable energy at the level of EU states contribute to finding solutions to solve this problem in the current context of sustainable development and the objective of the Green Deal.

The analysis shows that there are states that record high levels of reduction for GHG emissions from transport in 2030, such as Estonia (−79%) and Slovakia (−76.26%), as compared to 2012, but also states with highly industrialized economies, such as Portugal (−13.17%) and France (6.46%), which are far from this performance. However, smaller economies can also contribute to reducing GHG emissions through national measures to encourage the reduction of fossil fuel consumption and an increase in the share of green energy.

Given the European Commission’s commitments to reduce GHG emissions, the research results provide valuable insight into the links between the indicators taken into account as an important step in achieving EU climate neutrality.

Following the study, we identified strong correlations between the level of GDP, energy consumption, and the evolution of GHG emissions from the transport sector, demonstrating that, especially in developing countries, economic growth exerts significant pressure on the environment. Another important aspect is the crucial role of renewable energy confirmed by the inverse correlation between this indicator and GHG emissions from transport, but the magnitude of this impact depends on the level of investment. In order to achieve significant emission reductions, it is necessary to intensify these investments. Through cluster analysis, significant differences were identified between EU Member States, depending on the level of economic development, energy structure, and implemented climate policies. For example, the Nordic countries (Estonia, −79%; Finland, 54.38%, Latvia, 46.41%) with ambitious climate policies have achieved the best results in terms of reducing GHG emissions, while central and Eastern European countries register important increases in GHG emissions levels from transport (Slovenia, +51.67%, Czechia, +48.31%, or Bulgaria, +38.26%)

In this regard, the present study aims to outline a series of recommendations for EU member states to reduce GHG emissions from transport, as follows:

Practical Implications: EU member states should prioritize investments in renewable energy to reduce dependence on fossil fuels, as well as massive investments in transport infrastructure by developing public transport, cycle paths, and charging stations for electric cars. At the same time, governments should implement ambitious climate policies such as setting stricter standards on GHG emissions but also provide support for innovation, financing research, and development of new and efficient technologies to reduce emissions. Another aspect that can be considered international cooperation is the existence of global climate agreements. At the same time, a transfer of technologies between EU member states can be facilitated, which facilitates the transfer of technologies from Nordic countries to developing countries to help them adopt more sustainable development models. The last aspect, but probably the most important and sometimes even neglected, refers to behavioral changes by promoting a culture of sustainability and environmentally responsible behavior and by encouraging the use of public transport, bicycles, and other low-emission modes of transport.

As such, we believe that this change must be driven and accelerated as urgently as possible, considering the effect of the increasingly devastating meteorological changes on the environment, and these changes must be developed on four levels, respectively:

-

Energy transition;

-

Implementation of specific climate policies for the transport sector;

-

International cooperation;

-

Call for behavioral changes among populations with the support of education systems.

In conclusion, the study emphasizes the importance of rapid and coordinated action at a global level to address this major challenge.

Limitations and further research: Although the research provided valuable information on the level of GHG emission estimates from transport, the main limitation is the different databases used to make the forecast. Thus, the Eurostat database, the European Environment Agency, and EDGAR, due to different time series, publish different data sets, with differences between them, which can easily modify the final results. However, all these estimates made for the period 2024–2050 confirm reality and are far from the climate objectives established by the EU.

Not only should GHG emissions from transport be analyzed, but other activities that have significant shares in total emissions should be analyzed as well, such as industry, agriculture, and residential and commercial activities. However, these could represent future research directions, along with environmental protection spending or other efficient spending aimed at reducing emissions.

Another limitation of this study is the fact that the model does not take into account certain unpredictable external events, such as pandemics or military conflicts. However, our study provides a solid basis for future research that integrates such events.

Another significant limitation of this study is the absence of detailed technical information, such as modal choice in transport, the impact of non-mobility, the evolution of propulsion systems, and the sustainability of the electric vehicle market. Although we included the indicator of renewable energy in our research, hoping to capture technological progress relevant to reducing greenhouse gas emissions from transport, we recognize that future analysis of these specific aspects could provide valuable insight. Thus, we believe a detailed exploration of these technical factors is an important direction for further research.

Critique of FIT for 55

The Fit for 55 legislative package, with its ambitious goal of drastically reducing GHG emissions, is at the center of a heated global debate. While the need to combat climate change is undeniable, the implementation of such radical measures raises fundamental questions about their impact on the economy.

The link between economic growth, measured by GDP, and greenhouse gas emissions is a well-documented phenomenon, as can be seen both through the Pearson correlation and through the strong neural connections identified at the level of the two indicators. Industrialization, transportation, energy production, and other major economic activities are significant sources of emissions. Thus, drastically reducing them could slow down or even reverse economic growth, at least in the short term. Therefore, a decrease in GHG emissions will necessarily lead to a decrease in living standards.

However, as we have shown in the study, the link between GDP and emissions is not necessarily linear or irreversible. Technological innovations, energy efficiency, and the development of circular economic models can contribute to decoupling economic growth from carbon emissions. In other words, it is possible to achieve sustainable economic growth without compromising climate objectives, but this requires important, significant investments for the effect to be positive and lasting.