Energy, Urbanisation and Carbon Footprint: Evidence from Western Balkan Countries

Abstract

The role of carbon emissions in the worsening of global warming and other climate change implications has been well recognised. This study empirically investigates the effect of economic growth, urbanisation, and energy consumption on carbon emissions using panel cointegration tests and pooled mean group autoregressive distributed lag (PMG-ARDL) techniques. The research is based on panel data from Western Balkan countries spanning 2001 to 2022. Urbanisation is incorporated into the model to determine its significance in the dynamic relationship among economic growth, energy consumption, and carbon emissions. The inclusion of urbanisation in the Western Balkans context is particularly novel because of its acceleration in this region. The findings suggest that energy consumption, economic growth, and urbanisation significantly affect environmental quality in the long run. In contrast, it has been demonstrated that only economic growth significantly impacts the environment in the short run. Subsequent investigations have revealed that environmental distortion is a long-term consequence of energy consumption, urbanisation, and economic expansion in the examined nations. These countries must prioritise enhancing energy efficiency, urban planning, and pollution mitigation measures while ensuring that economic growth remains unhindered.

1. Introduction

Carbon dioxide emission has experienced significant growth, making it the most rapidly expanding source of greenhouse gases in recent decades. The number of nations declaring commitments to attain net zero emissions in the forthcoming decades is steadily increasing. According to the findings of the Intergovernmental Panel on Climate Change in their special report on the implications of global warming of 1.5 °C, it is projected that there will likely be a rise in global warming of around 1.5 °C between the time frame of 2030 to 2052, assuming the current trajectory persists [1]. However, the commitments made by countries thus far are insufficient to achieve the necessary reduction in global energy-related carbon dioxide emissions to reach net zero by 2050. Despite the notable expansion of clean energy under current policy frameworks, global emissions would persist at levels that would substantially raise global average temperatures by around 2.4 °C this century [2]. This exceeds the critical threshold established in the Paris Agreement [3]. Consequently, these commitments do not provide the world with a reasonable probability of limiting the increase in global temperature to 1.5 °C.

Adequate modelling causality between environmental growth variables provides a valuable framework for analysing key issues facing economic policy in contemporary conditions, including sustainability and global warming and determining the level and fluctuations of growth. The monitoring and examination of these potential connections and relationships will be studied using the example of Western Balkan (WB) countries (Albania, Bosnia and Herzegovina, Montenegro, North Macedonia, and Serbia). The WB countries are experiencing notable structural transformations due to economic growth, urbanisation, and energy consumption, significantly influencing increased carbon emissions. Expanding economic activities and infrastructure projects rely heavily on fossil fuels, given the region’s dependence on coal as a primary energy source. While critical for improving living standards, this growth intensifies environmental challenges due to insufficient renewable energy integration. Urbanisation is another key driver of emissions in the WB countries. As urban centres expand, demand for housing, transportation, and services grows, often leading to energy-intensive developments. Rapid urbanisation, inadequate public transit, and outdated infrastructure contribute to higher per capita energy consumption and pollution. For example, Sarajevo, the capital of Bosnia and Herzegovina, is frequently ranked among the most polluted cities globally. The city’s geographic location in a valley and heavy reliance on coal for heating and energy contribute to severe air pollution, particularly during winter. Industrial emissions, outdated vehicles, and urban congestion exacerbate the problem, posing serious health risks to residents. Energy consumption patterns in the WB region exacerbate carbon emissions due to inefficient energy use and reliance on coal-based power plants. Efforts to diversify energy sources have been slow, while energy-intensive industries such as mining and metallurgy dominate the economic landscape, further increasing emissions. Complexity and the previously highlighted importance are key motivations for this study.

The paper examines the impact of economic growth, urbanisation, and energy consumption on carbon emissions. WB countries are at the centre of attention, and the research period is limited to 2001–2022. To date, no research has been conducted to investigate the possible effects of economic growth, energy consumption, and urbanisation on carbon emissions in the WB countries. This study demonstrates the complex interaction of variables within a panel data framework. Paper contribution is twofold. First, the subject of the analysis is the WB transition. European countries should follow the changes in the area of environmental protection, and their achievements will form future potential for sustainable development. Secondly, the paper will analyse countries methodologically by applying specific techniques such as PMG-ARDL. Additionally, we addressed the role of urbanisation in the environmental growth nexus.

The empirical analysis of the relationship between energy consumption and CO₂, as well as the examination of the association between economic growth and harmful emissions, have been extensively conducted in research studies over the past few decades (to provide a limited sample) [4,5,6,7], frequently employing relevant variables such as urbanisation [8,9,10]. Urbanisation is a sociocultural occurrence characterised by the migration of individuals from rural regions to urban areas. This study aims to enhance the existing knowledge framework by extending the application of panel data analysis to a selected sample of countries. Examining the interplay between economic growth, urbanisation, energy consumption, and CO₂ emissions is a subject of ongoing research curiosity among scholars globally [11,12,13,14,15]. These studies encompass many countries and periods, yielding diverse outcomes.

In recent decades, there has been a notable phenomenon of fast urbanisation, characterised by a significant increase in the global urban population [16,17,18,19]. Urbanisation is associated with increased income levels, improved infrastructure, and enhanced access to health and education services [20,21,22]. However, it should be noted that the adverse consequences of environmental contamination accompany the provision of these amenities. Urbanisation is a multifaceted phenomenon characterised by social and economic change in a given area. The phenomenon under consideration involves the migration of individuals from rural regions to towns and cities and the deliberate and organised transition from a rural economic structure to one centred around industrial and service activities. In the entire WB, each country experienced an increased and relatively stable urbanisation trend during the observation period, as shown in Figure 1. Figure 1 depicts urbanisation trends.

Although the relatively stable economic growth and urbanisation of the WB countries can have positive aspects for the population, such as higher per capita income and low unemployment, it is not clear what the impact of this phenomenon is in terms of environmental sustainability. The extensive use of fossil fuel-based energy plays a significant role within WB countries [23]. The increase in output levels reflects the substantial reliance in Balkan economies on energy-intensive sectors. This region’s primary contributors to atmospheric pollutants are coal power stations and air pollution from towns and cities [24]. However, these economies’ current energy consumption pattern seriously impacts the environment, resulting in negative externalities. The countries in the WB region are increasing their contribution to global CO₂ emissions. This is particularly evident in the cases of Serbia and Bosnia and Herzegovina, as seen in Figure 2.

Conversely, the limited increase in the economic growth rate has led to a corresponding increase in population movement from rural regions. Therefore, due to aspiring for improved living conditions, metropolitan areas in these economies have witnessed a steady rise in the urban population. Hence, addressing the issue of environmental degradation in these nations necessitates the development of a comprehensive policy framework that encompasses these implications. Drawing inspiration from the preceding discourse, the economic development trajectory in this region is exerting ecological strain through the creation of airborne contaminants. Air pollution is the cause of up to 20% of premature mortality in 19 cities in the WB countries, according to the United Nations Environment Programme [25]. Therefore, the increase in carbon dioxide emissions in WB economies can be attributed to extensive energy consumption, urbanisation, and the growth in economic activity.

The subsequent sections of the paper are organised as follows: the next part provides materials and methods relevant to examining the relationships among the variables under investigation. This section elucidates the employed models and approaches derived from the provided data. The third section analyses the research findings, the fourth gives some discussion points, and the final section includes the concluding remarks.

2. Materials and Methods

Researchers have extensively investigated the relationship between urbanisation and carbon dioxide emissions [26,27]. Urbanisation often leads to increased energy consumption due to industrialisation, transportation, and residential needs, which can elevate CO₂ emissions. Nevertheless, the nexus continues to be regarded as a challenging topic within the scientific community. The empirical research conducted across various areas and periods has shown diverse findings, including a positive relationship, a negative connection, and no conclusive link. Numerous studies (to name but a few) have tested the relationship between urbanisation, energy consumption, economic growth, and environmental pollution.

Yuan et al. [28] examined the connection between urbanisation and additional carbon emissions from a particular industry. The impact of urbanisation on energy-associated carbon emissions was also investigated by [29]. Ref. [30] a cross-country panel data analysis was conducted to investigate the association between urbanisation and cumulative carbon emissions. Ref. [31] examined the effects of urbanisation, gross domestic product (GDP) per capita, use of capital per capita, and trade openness on carbon emissions per capita. The study by [32] examined the effects of urbanisation, GDP per capita, and the manufacturing sector on carbon emissions. Ref. [33] conducted a study to explore the economic factors, both in the short and long term, that influence urbanisation of scale, energy consumption, and carbon emissions. Ref. [34] also explored the relationship between urbanisation, population, age distribution, GDP per capita, and energy consumption on total carbon emissions.

Ref. [35] examined a sample of 73 countries from 1971 to 2010 in their investigation. Their findings indicate that urbanisation has a restraining effect on carbon emissions in upper-middle-income nations. In contrast, it promotes carbon emissions in low-income, lower-middle-income, and high-income countries. Ref. [36] conducted an empirical investigation encompassing 99 nations from 1975 to 2005. Their findings indicate that urbanisation positively links carbon emissions across countries of varying income levels, with middle-income countries exhibiting quite a strong association. In the research they conducted, ref. [37] examined the ASEAN countries during a period ranging from 1980 to 2009. They argue that a 1% rise in urban population corresponds to a 0.2% rise in carbon emissions. Ref. [38] surveyed a sample of 28 provinces in China during a period spanning from 1996 to 2012. Their findings suggest urbanisation can contribute to elevated carbon dioxide levels, water contamination, and pollution of waste particles.

Ref. [39] analysed the period from 1960 to 2010 in the United States and proposed that urbanisation has a positive relationship with carbon emissions. In their study conducted between 1995 and 2010, ref. [40] examined the relationship between urbanisation and its effects on energy consumption and carbon emissions in China. Their findings suggest that urbanisation results in an increase in both energy consumption and carbon emissions. However, the study reveals that the influence of urbanisation on energy consumption is more significant than its impact on carbon emissions, especially throughout the eastern region of China. Ref. [41] examined China’s megacities from 1990 to 2010 and proposed that carbon emissions are encouraged by factors such as expansion of the economy, urbanisation, and modernisation. In their study, ref. [42] utilised fixed and random effects models to analyse a sample of 80 nations between 1983 and 2005. Their findings revealed the adverse impact of urbanisation on the environment. The study found that a 1% increase in urban population is associated with a 0.95% increase in CO₂ emissions released into the earth’s atmosphere.

Ref. [43] conducted a study in which he analysed a dynamic panel on a sample of 69 countries. The study revealed a statistically significant relationship between urbanisation, GDP per capita, energy consumption, and environmental pollution. Urbanisation hurts CO₂ emissions in a global panel of high-, middle-, and low-income countries. Ref. [44] carried out research in which they looked at 19 emerging countries and made the case that urbanisation reduces CO₂ emissions. The study by [45] investigated urbanisation’s influence on Singapore’s carbon dioxide emissions from 1970 to 2015. The primary discovery indicates a statistically significant and negative effect of urbanisation on carbon emissions in Singapore. This implies that the process of urban growth in Singapore does not hinder the enhancement of environmental quality. Their results also indicate that economic expansion has a detrimental effect on environmental quality due to its direct influence on increased carbon emissions. Based on the defined goal of the research study and the literature review, the following hypotheses should be derived:

H1: 

There is a long-run relationship between energy consumption, carbon emissions, economic growth and urbanisation;

H2: 

In the long run, there is a statistically significant positive effect of energy consumption, economic growth, and urbanisation on carbon emissions;

H3: 

In the short run, economic growth has a statistically significant positive effect on carbon emissions.

From a theoretical point of view, energy consumption is a critical driver of economic growth, as it powers industries, transportation, and urban infrastructure. However, excessive reliance on non-renewable energy sources can lead to environmental degradation. Furthermore, urbanisation often leads to increased energy demand, which results in higher carbon emissions if met by fossil fuels. Urban areas are hotspots for emissions due to concentrated industrial activities and transportation. Urbanisation drives energy consumption by developing infrastructure, housing, and transportation systems. Efficient urban planning and renewable energy integration can mitigate the environmental impact. This part also provides an overview of the methodologies employed and the formulated equations and hypotheses underpinning the subsequent analysis of the results. The sample utilised in this study was obtained from the five WB countries, specifically Albania, Serbia, North Macedonia, Bosnia and Herzegovina, and Montenegro, spanning the yearly period from 2001 to 2022. This study aims to evaluate the interconnection of energy consumption, urbanisation, economic growth, and carbon dioxide emissions based on the model:

$$CO2=f(GDP, ENC, URB)$$

(1)

The log-linear model is utilised to determine the long-term relationship between variables in a model. This model’s error term is denoted as ${\epsilon }_{it}$, where i represents the cross-section and t represents the period.

$${LnCO2}_{it}={\alpha }_o+{\alpha }_1{LnGDP}_{it}+{\alpha }_2{LnENC}_{it}+{\alpha }_3{LnURB}_{it}+{\epsilon }_{it}$$

(2)

Table 1. Variables description.

Abbreviation	Variable	Description	Sources (2023)
CO2	Carbon emissions	CO2 Emissions (metric tons per capita)	WDI
GDP	Economic Growth	GDP per capita (current US$)
URB	Urbanisation	Urban population (% of the total population)
ENC	Energy consumption	Total energy consumption in quadrillion Btu	EIA

Notes: WDI denotes the World Development Indicators [46], the World Bank, and EIA denotes the U.S Energy Information Administration [47].

depicts the measurement and origin of the parameters. The present research examines an equation that assesses the relationship between environmental degradation and economic progress, explicitly focusing on the carbon footprint.

The autoregressive distributed lag (ARDL) model, specifically the pooled mean group (PMG) approach and panel cointegration methods called fully modified least squares (FMOLS) and parametric dynamic OLS (DOLS) are used to look at these connections. Previous methods were devised to enhance ordinary least squares (OLS) to address autocorrelation. The FMOLS method can also address the issue of cross-sectional heterogeneity. Subsequently, both approaches are utilised to verify the robustness of the results obtained from the pooled mean group estimators. The current study employs both techniques, as they have the potential to provide a more accurate assessment of the probable connections between variables. Additionally, initial assessments of panel unit roots (using Im-Pesaran-Shin (IPS), cross-sectional (C)IPS, and cross-sectional augmented Dickey-Fuller (CADF)), the multicollinearity test variance inflation factor (VIF), cross-section dependence tests (CD), and the Johansen Fisher and Pedroni cointegration test were conducted to examine the characteristics of the variables under investigation. The Holt method was used to forecast the last missing values in the time series of carbon emissions and energy consumption [48].

Before conducting panel unit root analysis on the research variables, assessing the presence of cross-sectional dependence between the dependent and explanatory factors is imperative. As [49,50] demonstrated, specific tests are the primary strategy for predicting cross-sectional dependence. Nevertheless, it is essential to note that the validity of these tests is more significant if the number of cross-sections (N) exceeds the chosen time scale (T) in the study being examined [51,52]. The data analysis in the study indicates that T is more significant than N. This observation suggests that the previous tests are insufficient for predicting cross-sectional dependence among variables. Hence, the Lagrange multiplier test has been employed, as proposed by [53]. To address this concern, the theoretical formulation of the LM test might be expressed in this manner:

$${LM}_{BP}=T{\sum }_{i=1}^{N-1}\hspace{1em}{\sum }_{j=i+1}^N{\rho }_{ji}^2$$

(3)

The LM statistic follows an asymptotic distribution under the null hypothesis with N(N − 1)/2 degrees of freedom, where ρ_ji is the sample of the residual correlation. Ref. [54] utilised empirical research to examine stationarity patterns in panel data sets. This methodology adheres to the Dickey-Fuller technique and combines data from both time series and cross-sections, resulting in a reduced period requirement to achieve statistical power for the IPS test. In the analysis, the second-generation unit root tests that [55] introduced consider the presence of cross-sectional dependence. The underlying formula for the CADF panel unit root can be stated as follows:

$${\Delta y}_{it}={\alpha }_i+b_i{y}_{i.t-1}+c_i{\overline{y}}_{t-1}+d_i{\overline{y}}_t+{\epsilon }_{it}$$

(4)

An equation can represent the mathematical expression for the t-statistic of the CADF test:

$$(t_t(N,T)={\displaystyle \frac{{\Delta y}_i{\overline{M}}_w{y}_{i-1}}{{\widehat{\delta }}_i{\left(y_i{\overline{M}}_w{y}_{i-1}\right)}^{\frac{1}{2}}}}$$

(5)

The aforementioned generalised form has a more particular situation, expressed in Equation (6). The cross-sectionally augmented Dickey-Fuller statistic for the i-th cross-section unit, denoted as t_i (N, T), is calculated as the t-ratio of the coefficient of y_i.t−1 in the CADF regression as stated by Equation (4).

$$CIPS(N, T)=\overline{t}=N^{-1}{\sum }_{i=1}^N{t}_i(N, T)$$

(6)

If the variables exhibit a unit root, examining the presence of cointegration among the variables becomes crucial to establishing a long-term relationship. Panel cointegration tests were conducted to explore the cointegrating relationship among the variables in the model. The study employed the tests proposed by [56,57] to investigate the presence of cointegration in panel data. The Pedroni test, as well as the benchmark test of Johansen Fisher panel cointegration, were employed for this purpose. Cointegration tests on panel data exhibit greater statistical power and validity than tests on individual sections. According to [58], these tests can be applied even when the period is limited and the sample size is small.

Pedroni’s technique allows for the presence of consistent effects and varied time trends across different sections. Pedroni presents an inquiry in which seven distinct statistics are employed to assess the null hypothesis that no cointegration vector exists between variables. These statistics are divided into two separate groups. The initial set of tests is commonly referred to as intra-group tests, which include the following: (1) panel v-statistic, (2) panel rho-statistic, (3) panel PP- PP-statistics, and (4) panel ADF-statistic. The second set of test statistics consists of the following inter-group measures: (1) the rho-statistic group, (2) the group PP-statistic, and (3) the group ADF-statistic. Five statistics are classified as parametric, while the remaining two are categorised as nonparametric. The null hypothesis, which posits the absence of cointegration, can be refuted when a minimum of four out of the seven tests yield statistically significant results. The interference components derived from Equation (7) are utilised for conducting the test. The null hypothesis posits that γ_i equals 1, while the alternative hypothesis suggests that γ_i is less than 1.

$${\Delta Y}_{i,t}={\sum }_{j=1}^m{\beta j,i X}_{j, i,t}+{\alpha }_i+{\delta }_{i}t+{\epsilon }_{it}$$

(7)

$${\epsilon }_{i,t}={\gamma i\epsilon }_{i,t-1}+{\nu }_{it}$$

(8)

The PMG method by [59] adapts the autoregressive distributed lag (ARDL) model for analysing time series data inside panel data sets. This adaptation enables the intercepts, short-run equilibrium relationship coefficients, and cointegrating components to vary across the different cross-sectional segments. The PMG paradigm can be represented as follows:

$${\Delta Y}_{it}={\sum }_{j=o}^{m-1}{\Omega i,t \Delta X}_{i,t-j}+{\sum }_{j=1}^{s-1}{\alpha }_{i, j}{\Delta Y}_{i,t-j}+{\beta ecm}_{i,t}+{\epsilon }_{i,t}$$

(9)

In this model, the difference operator is Δ, the dependent variable is Y, and the independent variables are denoted by X. The adjustment coefficient β is employed to account for any necessary adjustments. The error correction term ecm_i,t represents the correction for individual countries i at time t, and ${\epsilon }_{it}$ represent the error term. Ref. [59] elaborates on the log-likelihood function and the long-run and adjustment coefficients.

This study also investigated long-run cointegration coefficients using the FMOLS and DOLS methods. The FMOLS approach was initially developed by [60] and later adapted for panel data by [61]. The FMOLS method addresses the issue of second-order bias effects by considering the interdependencies among the error terms of the variables’ equations. The FMOLS estimator effectively addresses diagnostic problems commonly encountered with conventional estimators. The DOLS method is a parametric procedure proposed by [62]. The DOLS estimator incorporates the inclusion of lagged and differenced explanatory variables in the cointegration model of regression to manage the endogenous input impact effectively. FMOLS and DOLS methods are suitable for analysing cross-sectional dependent and heterogeneous panels. This can ensure an appropriate strategy in terms of the research objective.

3. Results

Table 2. Descriptive statistics of variables.

Variable	Carbon Dioxide Emissions (CO2)	Energy Consumption (ENC)	Economic Growth (GDP)	Urbanisation (URB)
Mean	4.230	0.235	5057.393	55.387
Median	4.078	0.116	5003.073	56.434
Max.	7.704	0.700	9893.516	68.164
Min.	1.056	0.038	1281.660	42.435
Std. Dev.	1.830	0.229	1995.158	6.856
Skewness	0.009	1.271	0.110	−0.171
Kurtosis	1.902	2.945	2.522	2.185
VIF	/	1.41	1.02	1.38

concisely overviews CO₂, GDP, ENC, and URB variables. The greatest mean value for CO₂ (4.230) falls from 1.056 to 7.704. Similarly, the GDP (5057.393) ranges from 1281.660 to 9893.516. The ENC (0.235) varies between 0.038 and 0.700, while the URB (55.387) runs from 42.435 to 68.164. The variables’ standard deviations suggest significant dispersion from their respective means. The parameters of ENC, GDP, and CO₂ are skewed positively. The URB exhibits a negative skewness. Table 2 also presents the corresponding variables’ variance inflation factor (VIF). Since all variance inflation factor values are below the threshold of five (5), this suggests no evidence of multicollinearity in the created model. The stack cross-sections of multiple graphs are illustrated in Figure 3, where 1, 2, 3, 4, and 5 denote specifically Albania, Bosnia and Herzegovina, North Macedonia, Serbia, and Montenegro. Accordingly, further investigation may proceed.

Refs. [49,50,53,55] techniques are employed to evaluate the existence of cross-sectional dependence among the variables under investigation. The results obtained from the analysis of

Table 3. Cross-sectional dependence tests.

Test	Statistic	p-Value
Breusch-Pagan LM	65.050 ***	0.000
Pesaran scaled LM	11.191 ***	0.000
Pesaran CD	2.413 **	0.015
Friedman	13.753 ***	0.008

***, ** show 1 and 5% significance.

provide compelling evidence to reject the null hypothesis, which asserts the absence of cross-sectional dependency. This rejection is observed at a significance level of 1% for collective variables.

The results presented in Table 3 confirmed the presence of cross-sectional dependence among variables. Two second-generation non-stationarity panel unit root tests were applied to overcome this obstacle.

To mitigate potential issues related to spurious regression, a unit root test is employed to assess the stationary nature of the variables. The IPS unit root test results estimate that two study variables have no unit roots at their level. Hence, we employed the CIPS and CADF panel unit root tests formulated by [55].

Table 4. Stationarity test of the variables.

Variable	IPS	CIPS	CADF
	Statistic	Statistic	Statistic
Ln CO2	−0.674	−2.752 ***	−1.893
LnGDP	−3.302 ***	−2.055	−1.644
LnURB	3.167	−1.513	−2.290
LnENC	−1.892 **	−2.107	−1.743
∆ Ln CO2	−5.976 ***	−5.588 ***	−3.731 ***
∆ LnGDP	−3.492 ***	−4.404 ***	−2.363 *
∆ LnURB	−16.988 ***	−2.115	−2.777 **
∆ LnENC	−6.553 ***	−4.743 ***	−3.174 ***

All variables are in natural logarithms. ***, **, * represent significance levels of 1, 5 and 10%, respectively, ∆ in the first order of integration. Deterministic component: constant: Lag length AIC: 1.

displays the examination of stationarity for the variables utilised in the model within this study, taking into account the CIPS and CADF tests. The CADF test is more suitable for a small sample as it considers individual heterogeneity, giving more reliable results. It is observed that the variables exhibit stationarity at the first difference. The results of the panel unit root tests show that the variables LnCO₂, LnGDP, LnURB, and LnENC have a unit root process at the level, as indicated by the CADF test statistics. However, all these variables demonstrate stationarity at the first difference, with statistical significance ranging from 1% to 10%. The results of the cointegration test run on the Pedroni panel indicate that most of the variables, precisely six out of eleven, do not support the null hypothesis (

Table 5. Pedroni Cointegration test.

Tests	Statistic	p-Value	Weighted Statistic	p-Value
Panel v-Statistic	−0.664	0.746	−0.819	0.793
Panel rho-Statistic	−0.717	0.236	−0.838	0.200
Panel PP-Statistic	−3.131 ***	0.000	−3.537 ***	0.000
Panel ADF-Statistic	−2.177 **	0.014	−2.571 ***	0.005
Group rho-Statistic	0.339	0.632
Group PP-Statistic	−2.781 ***	0.002
Group ADF-Statistic	−2.083 **	0.018

Asterisks (*** and **) denote the statistical rejection level of the null hypothesis, which states that there is no cointegration at a significance level of 1% and 5%, respectively.

).

Despite the existing knowledge of prior cointegration investigations conducted using the Pedroni approach, an additional cointegration test was performed on the research sample during the data analysis process. The analysis is backed up by a sufficient quantity of panel data, which indicates the presence of an established connection among the variables under investigation, as confirmed by the Johansen Fisher panel cointegration test developed by [57]. This statistical test is referred to as the error correction panel cointegration test.

Table 6. Johansen Fisher Panel Cointegration test.

Hypothesised No. of CE(s)	Fisher Stat. (from Trace Test)	p-Value	Fisher Stat. (from Max-Eigen Test)	p-Value
None	96.50 ***	0.000	58.55 ***	0.000
At most 1	49.41 ***	0.000	25.38 ***	0.004
At most 2	35.85 ***	0.000	21.19 **	0.019
At most 3	32.93 ***	0.000	32.93 ***	0.000

Asterisk signs (** and ***) denote the significance level (5% and 1%).

presents the results of the panel cointegration test that was undertaken. Furthermore, it is essential to note that each variable is intricately linked to carbon emissions in the context of long-run equilibrium. The Johansen-Fisher panel cointegration test indicates that the analysed variables are linked in the long term. The results obtained from the Johansen Fisher and Pedroni tests reveal a potentially significant relationship between carbon emissions, energy consumption, urbanisation, and economic growth in the long-term equilibrium. This observation implies that a long-term equilibrium relationship exists among the abovementioned variables.

Table 7. Long- and short-run estimates for WB countries. Dependent Variable: LnCO₂. Selected model: ARDL (1, 1, 1, 1).

Long-Run Analysis
Variable	Coefficient	Std. Error	t-Statistic	p-Value
LnENC	0.423 ***	0.100	4.195	0.000
LnGDP	0.079 *	0.045	1.731	0.087
LnURB	3.188 ***	0.541	5.888	0.000
Short-Run Analysis
ECT(−1)	−0.597 ***	0.195	−3.060	0.003
∆ LnENC	0.298	0.199	1.497	0.138
∆ LnGDP	0.169 ***	0.037	4.475	0.000
∆ LnURB	11.258	19.681	0.572	0.568
Constant	−6.403 ***	2.352	−2.721	0.007

*** indicates statistical significance at the 1% level, whereas * indicates statistical significance at the 10% level.

presents the outcomes of the ARDL calculation for WB countries in both the short and long run. The findings suggest that, in the long term, energy consumption, economic growth, and urbanisation will significantly influence environmental degradation in these nations—a 1% increase in energy consumption results in long-term development of CO₂ emissions of 0.423%.

The lag of the error correction term (ECT(−1)) signifies the rate at which CO₂ emissions adjust to their long-term equilibrium after experiencing a shock. The coefficient of −0.597 exhibits a negative sign and demonstrates statistical significance at the 1% level. The analysis of Table 7 reveals that GDP significantly and positively influences CO₂ emissions in the short run.

The short-run results for the specified country are demonstrated in

Table 8. Estimated cross-section short-run values based on PMG-ARDL method, dependant variable LnCO₂.

Country	Variable	Coefficient	t-Statistic	p-Value
Albania	ECT(−1)	−1.159 ***	−50.661	0.000
	∆ LnENC	−0.357 ***	−37.209	0.000
	∆ LnGDP	0.275 ***	23.225	0.000
	∆ LnURB	83.757	0.247	0.820
	Constant	−14.029	−1.591	0.209
Bosnia and Herzegovina	ECT(−1)	−0.140 ***	−11.766	0.001
	∆ LnENC	0.194 ***	9.226	0.002
	∆ LnGDP	0.241 ***	14.748	0.000
	∆ LnURB	−25.976	−0.039	0.970
	Constant	−1.104	−1.000	0.390
North Macedonia	ECT(−1)	−0.268 ***	−7.931	0.004
	∆ LnENC	0.596 **	4.243	0.024
	∆ LnGDP	0.076 **	3.106	0.053
	∆ LnURB	−7.768	−0.124	0.909
	Constant	−2.685	−0.818	0.473
Serbia	ECT(−1)	−0.480 ***	−18.399	0.000
	∆ LnENC	0.813 ***	10.265	0.002
	∆ LnGDP	0.137 ***	23.070	0.000
	∆ LnURB	20.616	0.112	0.917
	Constant	−4.899	−1.495	0.231
Montenegro	ECT(−1)	−0.937 ***	−15.349	0.000
	∆ LnENC	0.246 **	3.303	0.045
	∆ LnGDP	0.118 **	5.179	0.014
	∆ LnURB	−14.337	−0.296	0.786
	Constant	−9.300	−0.966	0.405

Note: ***, ** show the significance at 1%, 5%, respectively.

, where it can be observed that GDP is increasing CO₂ emissions in Albania, Bosnia and Herzegovina, North Macedonia, and Serbia. The short-term increase in carbon dioxide emissions in North Macedonia, Serbia, Montenegro, and Bosnia and Herzegovina can be attributed to rising energy consumption levels. Energy consumption will decrease CO₂ emissions in Albania in the short term. In the short run, urbanisation does not exhibit a statistically significant impact. The error correction term exhibits a negative and statistically significant relationship across every country’s analyses. The coefficients associated with this term indicate that deviations from the long-run equilibrium are rectified at a pretty rapid pace in the case of Albania and Montenegro. Table 8 shows divergent short-run impacts (energy consumption reduces CO₂ in Albania but increases it elsewhere). Country-specific policies, industrial structures, or energy mixes should explain these discrepancies.

The coefficients associated with the natural logarithm of urbanisation (lnURB), the natural logarithm of GDP (lnGDP), and the natural logarithm of energy consumption (lnENC) exhibit positive values and demonstrate statistical significance at the 5 and 1% level in WB nations, as shown in

Table 9. Estimation of long-run coefficients.

	FMOLS			DOLS Lags 1 (2)
Variables	Coefficient	t-Statistic	p-Value	Coefficient	t-Statistic	p-Value
LnENC	0.613 ***	7.289	0.000	0.665 ** (0.833 ***)	1.972 (4.32)	0.055 0.00
LnGDP	0.186 ***	5.595	0.000	0.172 ** (0.194 **)	2.095 1.997	0.042 0.049
LnURB	5.252 **	2.128	0.035	2.554 ** 2.835 ***	2.307 3.05	0.026 0.001

Notes: ***, ** represent 1 and 5% significance levels, respectively. (2) We checked the robustness by applying different lag length.

. This kind of result confirms the robustness of the previous ARDL PMG model. According to [63], the FMOLS method can address the challenges posed by potential distortions arising from the cointegration process, heterogeneity, and serial correlation. We also include sensitivity analysis (alternative lag structures) to validate model stability. A marginal increase of one per cent in energy consumption, economic growth, and urbanisation would result in a corresponding rise in carbon emissions of 0.613, 0.186, and 5.252 per cent, respectively. The empirical model has also been evaluated using another approach to assess the strength and reliability of the study results. DOLS results give a similar interpretation at the 5% significance level. This suggests that the long-run coefficients calculated by the FMOLS approach are robust.

4. Discussion

Before doing panel cointegration analysis on the study variables, evaluating the existence of cross-sectional dependence between the dependent and explanatory components is crucial. According to [64], it is essential to incorporate cross-sectional dependence in the error term when utilising panel data models to avoid biased inferences and potentially misleading conclusions. The phenomenon is postulated to originate from latent variables and shared factors that contribute to the residual term. Per the information above, the study utilises cross-sectional dependence tests as a residual diagnostic analysis to investigate the cross-sectional independence of the disturbances inside the model.

It has been observed that the first-generation panel unit root tests such as [65,66,67] have not produced satisfactory outcomes after the identification of cross-section dependence [68]. Consequently, traditional panel unit root tests that were created based on the premise of error independence are not entirely reliable. According to [69], the first-generation panel unit root tests are primarily utilised in scenarios characterised by a lack of cross-sectional dependence in the data. The accuracy of first-generation unit root testing results is compromised when the data displays cross-sectional dependence.

Several cointegration approaches have been used to examine cointegrating relationships among variables. The basis of this investigation relies on the outcomes of a panel unit root test. The cointegration test, as devised by [70], assists in enhancing our understanding of these properties. This analysis provides an expanded sense of the attributes of residual-based tests used to evaluate the null hypothesis of no cointegration for dynamic panels. It takes into consideration the potential variation in the long-run gradient and short-term changing coefficients among the individual members of the panel. The Pedroni analysis incorporates the within-dimension pooled tests, and the group mean tests run across dimensions. An independent intercept supports each test. The null hypothesis assumes the absence of cointegration. This test is frequently used to assess cointegration [71,72,73].

Results from Table 7 suggest that using non-renewable energy sources might contribute to accelerating greenhouse effects. The findings of [74,75,76] provide empirical evidence that corroborates these findings. A change of 1% in GDP leads to an increase of 0.079% in CO₂ emissions. Furthermore, an increase in urbanisation by 1% leads to a rise in CO₂ emissions by 3.188% in the long run. Urbanisation should affect emissions through various channels, such as transportation and infrastructure.

The findings concerning the impact of economic growth indicate that the short-run economic expansion in the WB comes at the expense of environmental quality, as evidenced by the detrimental effects of rising CO₂ emissions.

A notable upsurge in carbon emissions is anticipated within WB nations. Furthermore, it can be observed that economic growth has a lesser influence on the carbon emissions of the WB countries based on the long-run coefficient estimates. This is due mainly to the relatively lower productivity levels and higher energy consumption in the WB countries. The empirical results presented in Table 9 demonstrate that the effects of the model parameters are mainly consistent for both methodologies and their significance levels. This can ensure the complementarity of methods for the investigation. The findings demonstrate the reliability of the long-term coefficients computed using the FMOLS and DOLS methodologies. The study’s analytical contribution is positioned in that direction.

The WB countries face significant environmental challenges due to economic growth, urbanisation, and heavy reliance on coal-based energy, contributing to rising carbon emissions and pollution. These issues severely affect public health, ecosystems, and regional economic development. Environmental degradation hampers agricultural productivity and biodiversity, while unsustainable energy practices and urban expansion hinder progress toward EU climate targets, potentially delaying regional integration.

The WB countries should prioritise energy efficiency through carbon pricing and subsidies for renewables, modernise infrastructure, and transition toward renewable energy [77]. Regional cooperation and integration efforts can support these transitions through financial aid, technical expertise, and policy alignment with global climate goals. Balancing economic growth with sustainable practices is essential for reducing carbon emissions while fostering long-term regional development. Therefore, among the recommendations are energy transition fostering the transition to cleaner energy, promotion of energy-efficient technologies in industries and heating systems, upgrading transportation infrastructure to reduce emissions, strengthening and enforcement of stricter environmental regulations for industries and urban planning to minimise emissions, raising public awareness through education of citizens about pollution’s health effects and encourage sustainability practices, including the air quality monitoring network to provide accurate data for policymakers. Sustainable practices can mitigate carbon emissions, improve air quality, and foster economic resilience, ensuring a healthier and more sustainable development for the WB countries.

5. Conclusions

The current research conducted a panel data analysis to examine the relationship among energy consumption, economic growth, urbanisation, and carbon dioxide emissions in nations within the WB region during the time frame spanning from 2001 to 2022. It can be observed that there is a significant long-term association between energy consumption, urbanisation, and carbon dioxide emissions (H1 has been confirmed). This implies that using energy sources such as oil and coal, coupled with unregulated urbanisation, ultimately plays a role in intensifying greenhouse effects. The analysis reveals that the variables exhibit a considerable statistical significance at a 1% threshold (H2 has been confirmed). It is also perceived that there is a positive association between per capita income (measured by LnGDP) and carbon dioxide emissions in the short and long run (H3 has been confirmed). It means a direct and undeniable relationship between economic growth and climate change. As urban populations continue to grow, there is a mutually beneficial increase in overall income levels. However, improving living standards is not commensurate, as these countries’ other indicators, such as income inequality, are rising.

There is also an increase in emissions in both urban centres and their surrounding periphery areas. Balkan countries currently lag behind developed nations in renewable energy generation technologies. Adopting and disseminating these technologies throughout these nations could mitigate the negative environmental impacts of their current energy consumption trajectory. To improve the energy policy framework, officials must implement specific steps. The officials of these nations must facilitate the efficient dissemination of clean energy solutions in this region. The starting point could be implementing import substitution measures on oil and coal to reduce the overall use of fossil fuels to a minimum.

Furthermore, there is also the establishment of more precise parameters for public property rights aimed at curbing the excessive depletion of natural resources. These few measures might be effective, as regarded as policy considerations. Based on the observation above, it should be suggested that future research attempts incorporate other indicators of environmental pollution. The existing body of literature regarding the factors influencing carbon emissions indicates that variables involving industrialisation, international trade, financial development, agriculture production, and trade liberalisation are considered to exhibit significant impacts on carbon emissions [78]. The variables in question have not been included in the elaborated model. In further investigations, more control factors may be taken into account. Limitations of the study are also related to data availability and potential endogeneity problems. Additionally, urban population (% of total)” as the sole proxy for urbanisation overlooks qualitative aspects (e.g., infrastructure density, energy-efficient urban planning). Conducting such research would contribute to refining policy measures aimed at addressing environmental deterioration resulting from economic activities.