The Impact of Economic Growth on the Ecological Environment and Renewable Energy Production: Evidence from Azerbaijan and Hungary

Abstract

This article reflects on the necessity of employing renewable energy sources in the modern era to mitigate the negative environmental impact caused by traditional energy sources and address environmental pollution. Through research conducted in Azerbaijan and Hungary, it analyses the influence of economic growth on the ecological environment and renewable energy production. Due to limitations in the general dataset, the study considers the period of 1997–2022 for CO₂ emissions causing environmental pollution, 2007–2022 for renewable energy production in Azerbaijan, and 2000–2021 for the same in Hungary. Information regarding wind and solar energy in Azerbaijan has been available since 2013. Temporal sequences have been utilised in the research, employing Augmented Dickey–Fuller and Phillips–Perron (PP) unit root tests to examine the stationarity of the time series. An Autoregressive Distributed Lag (ARDL) model has been constructed, and the credibility of the model has been verified using Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegrating Regression (CCR) models. The findings reveal that in Azerbaijan, the long-term impact of economic growth on hydro-energy has been negative, while dependence on biomass and waste has been insignificant but positive. The influence on wind and solar energy production has also been negative and insignificant, akin to hydro-energy production. However, energy supply from renewable sources has been positively affected by the aggregate indicator of economic growth, albeit insignificantly. The impact of economic growth on carbon dioxide has been significant in two magnitudes, whereas in other cases, it has been insignificant but positive. In Hungary, economic growth has positively affected renewable energy production. However, the impact on carbon dioxide has been negative, meaning that this indicator has decreased as economic growth has increased. The study concludes that the impact of economic growth on indicators of both countries has been more effective in Hungary, which can be attributed to economic development.

1. Introduction

The rapid technological advancement across all sectors and domains worldwide has brought about positive economic outcomes alongside disturbing the natural balance of the environment. Industrial development has begun to alarmingly affect the environment and climate change. Since the aftermath of World War II, starting from the 1960s, the world, especially European countries and the United States, has encountered ecological issues that necessitate resolution. The objectives of sustainability, emerging from the sustainable development concept advocated by world politicians and economists in those years, began to materialise toward the end of the 20th century and in the early years of the 21st century. Following the Rio+20 negotiations in 2012, governments decided on the green economy’s crucial role as a sustainable development tool. However, the term “green economy” was first used in a report titled “Green Economy Plan” by the United Kingdom government in 1989. Consequently, significant strides have been taken in this field in recent years (Zhilkina et al. 2020; Mittal et al. 2024).

All countries are transitioning to a climate-neutral economy to mitigate climate change, environmental pollution, and their economic impact. The scale of the problem varies depending on a country’s natural resources and industrial development. Countries heavily reliant on fossil fuels and carbon-intensive sectors will face significant ecological, social, and economic repercussions from these changes (United Nations Department of Economic and Social Affairs 2020).

However, the “green economy” serves as a new form of the economy, unifying all countries toward a common goal. Nevertheless, transitioning to this economic form requires different approaches depending on the national context of various states (Kirushin 2014). Azerbaijan actively engages in these processes, having declared 2024 as the “Year of Solidarity for a Green World” and planning to host the 29th session of the Conference of the Parties to the United Nations Framework Convention on Climate Change (COP-29) in 2024 (https://unfccc.int/cop29, access on 23 March 2024.). It should be noted that Azerbaijan ratified the United Nations Framework Convention on Climate Change in 1995 and acceded to the Kyoto Protocol in 2000 after its adoption in 1997 (Alqudah et al. 2024; Ashfaq et al. 2024).

The European Union has agreed to implement climate policies to achieve climate neutrality by 2050 with all member states. The strategy encompasses support for modelling greenhouse gas emissions; the construction of “smart cities” in urban areas; support for land and forest management; preparation of solutions for combating and protecting against abnormal heat, drought, and desertification; financial issues within the European Union emissions trading system; increasing the use of renewable energy sources; support for decarbonising energy supply systems; and addressing transportation-related issues, such as reducing carbon emissions and promoting water transportation and the development of high-speed railways (Holm et al. 2024).

Hungary is currently in the development stage of its green energy sector. Environmental legislation and government support policies create favourable conditions for the sector’s development. The country has opportunities for solar, wind, and biomass energy utilisation. Solar energy has seen an increase in recent years in Hungary, and this trend is expected to continue in the forecasted period. The Hungarian government is implementing measures to reduce greenhouse gas emissions from fossil fuels by up to 40% by 2030 within the European Union climate and energy policy framework, focusing on promoting wind energy adoption and usage. On the other hand, the issue of obtaining biomass energy, which constitutes the main source of recovered energy resources resulting from agricultural activities, is also relevant here (Szeberényi et al. 2024; Hungary 2022).

The National Clean Development Strategy 2020–2050 document in Hungary envisages various activities in various areas within the framework of current technological development to reduce environmental pollution and achieve zero-level emissions of greenhouse gases into the atmosphere. These activities include improving energy efficiency in all sectors of the national economy and creating a circular economy, electrification across all sectors of the economy based on renewable energy sources, reducing CO₂ emissions into the atmosphere through the application of CCUS (Carbon Capture, Utilization and Storage) technology in high-emission industrial sectors, expanding the use of hydrogen technology, continuous use of bioenergy within certain limitations, application of modern and innovative methods in agriculture, stimulating economic and financial forest management, increasing scientific research, and implementing relevant educational programs in an innovative direction (Babber and Mittal 2023).

For this purpose, in 2022, Hungary became the first European country to import green electricity from Azerbaijan via fibre-optic cables, and currently, both countries continue their cooperation toward rapid transitions to green economic policies. The strategic cooperation agreement signed on 17 December 2022 in Bucharest envisages the construction of a Black Sea submarine cable from Azerbaijan and Georgia to support electricity supply to Hungary and Romania. The agreement covers cooperation in various specific directions, such as the trade and transportation of recovered energy and green hydrogen, government and investor collaboration for developing projects related to recovered energy and green hydrogen, and the development of infrastructure for the transit of electricity generated from recovered energy sources (RFE/RL’s Romanian Service 2022). All of this highlights the investigation of the transformation of the two countries into a green economy.

The article explores the impact of the United Nations Development Programme (UNDP) on the transformation of economic growth into a green economy, including hydroelectricity production, biomass and waste, energy supply from recovered sources, and carbon dioxide (CO₂) emissions due to environmental pollution, during separate research periods from 1997 to 2022.

Environmental issues are not a recent problem. As early as the 1960s, through to the 1990s, this concern began to alarm ecologists, economists, sociologists, and policymakers on an international level. Initially, it was discussed in the context of sustainable development. However, in the 21st century, with rapid growth of the global economy, the expansion of economic globalisation, and new labour divisions, this issue has escalated. It has highlighted the necessity for renewable energy production and a transition to a green economy for humanity.

Like other resource-rich countries, Azerbaijan will eventually face resource depletion and more severe environmental challenges. Therefore, to mitigate these future difficulties, it is crucial to accelerate the use of renewable energy sources and the transition to a green economy. On the other hand, Hungary must deepen its reforms in this direction to strengthen its economic position within the European Union and bring its ecological environment up to global standards.

Our objective with this study is to compare two economies managed under systems of planned economies, with one being a net exporter of oil (Azerbaijan) and the other a net importer (Hungary). Another reason is that Azerbaijan is categorised among developing countries at a medium level, while Hungary is considered a medium-level developed country. Moreover, it is noteworthy that Hungary became the first European country to receive green electricity from Azerbaijan in December 2022. We anticipate that future data availability will enable a more comprehensive evaluation. This comparative analysis contributes significantly to understanding how different economic management systems impact environmental sustainability and renewable energy development. By exploring these dynamics between Azerbaijan and Hungary, our study adds to the global discourse on sustainable development, energy security, and international cooperation in renewable energy.

The remainder of this paper is structured as follows: Literature Review, Methodology, Data, Discussion and Results, Conclusions and Practical Implications, Limitations of the Study, and Further Research Directions. Each section elaborates on key aspects, from theoretical frameworks and data sources to empirical findings and policy suggestions.

2. Literature Review

Several scientific studies were reviewed during the research, and their findings were considered for the development of this article. The relationship between economic growth and recovered energy consumption was examined by Chen et al. (2020) and Namahoro et al. (2021), while further investigation into CO₂ emissions was conducted by İnal et al. (2022), Azam et al. (2016), and Wang et al. (2016) in various national contexts. Studies by Sharma et al. (2021) and Le et al. (2023) highlighted the possibilities of using hydroelectricity to reduce greenhouse gas emissions and mitigate environmental pollution, along with current conditions and recommendations for the future.

CO₂ emissions in the environment threaten both the global population and ecosystems. Ameyaw and Yao (2018) investigated the impact of CO₂ emissions on five West African countries from 2007 to 2014, revealing a unidirectional causal relationship between these two variables. Using these results, they forecasted CO₂ emissions based on future consumption levels and provided sustainable policy recommendations for West African countries and Africa.

Research on the relationship between agriculture, economic growth, and recovered energy in G-20 countries revealed a long-term relationship among variables during the 1990–2014 period. Economic growth significantly increases CO₂ emissions in developing G-20 countries, while using recovered energy reduces environmental pollution. Qiao et al. (2019) found that the regulatory bodies of G-20 countries should aim for sustainable development of agriculture to reduce pollution and incentivise the consumption of recovered energy sources in developing countries.

Mardani et al. (2019) extensively examined the systematic relationship between carbon dioxide emissions and economic growth throughout 1995–2017, analysing 175 articles. Their research demonstrated that economic growth contributes to increased CO₂ emissions, with a bidirectional relationship in which changes in economic growth stimulate corresponding changes in carbon dioxide emissions. They recommended implementing measures to limit factors influencing economic growth to reduce CO₂ emissions.

Yan et al. (2022) investigated the relationship between economic growth and environmental pollution in China using the nonlinear MS(M)-VAR(p) model. The results showed significant inertia between economic growth and SO₂ emissions, with a negative short-term correlation between economic growth and CO₂ emissions in a moderate-growth regime. In contrast, high- or low-growth regimes exhibited a long-term negative correlation between economic growth and CO₂ emissions. Overall, economic growth influences the increase in pollution, with the correlation between GDP and CO₂ emissions linked to regional status and the entire system.

To achieve economic growth, energy must be maintained in a functional state in the global economy, affecting environmental quality. Greenhouse gases and CO₂ emissions degrade the environment. Ekonomou and Halkos (2023) determined that sustained economic growth could limit CO₂ emissions and increase the share of recovered energy in the energy consumption structure.

The impact of economic growth on motorisation and environmental pollution was investigated by Ziyazov and Pyzhev (2023) through research in 56 major Russian cities from 2013–2018. They analysed vehicle emissions to determine nonlinear dependence, noting that emissions initially increase with rapid motorisation and economic growth but then decrease, likely due to modern ecological standards and higher-quality fuel in affluent cities.

The rapid transition to a carbon-neutral economy will lead to economic consequences depending on countries’ models and policies. The European Commission’s 2020 plan anticipates minor changes in the GDP in the Eurozone and EU countries by 2030, ranging from −0.7% to +0.55% (Pisany-Ferry 2021).

Cader et al. (2021) investigated the correlation between the GDP, population, recovered energy consumption, CO₂ emissions, and their impact on hydrogen utilisation across nine countries. Their study from 2008 to 2018 determined the statistical significance of these indicators as leading indicators of the hydrogen economy.

Le et al. (2024) highlighted the numerous advantages of hydrogen energy in their article “The Future of Energy,” addressing issues related to production, infrastructure, costs, and safety. They emphasised the importance of public awareness, new storage systems, improved infrastructure, and technology processing to enhance hydropower utilisation.

Addis and Cheng (2023) explored the relationship between the GDP, recovered energy consumption, and CO₂ emissions for BRICS and OECD countries from 1995 to 2021. Their long-term assessments using FMOL and DOLS showed significant variables, with a 1% GDP increase leading to a 0.399% rise in recovered energy consumption, while CO₂ emission increase led to a 1.369% decrease in recovered energy consumption.

These scientific studies highlight the importance of research in this field today. We apply econometric modelling based on the same indicators for Azerbaijan and Hungary to understand the situation in these countries, respectively. Furthermore, Zakaria Zoundi (2017) studied the short- and long-term effects of renewable energy on CO₂ emissions in 25 African countries from 1980 to 2012, revealing that CO₂ emissions occur due to increases in per capita income. Similarly, Sarkodie and Strezov (2018) found that increasing the share of renewable energy in Australia’s energy portfolio reduces CO₂ emissions, whereas increasing non-renewable energy sources exacerbates emissions.

Sriyana (2019) demonstrated that energy consumption and renewable energy use impact economic growth in Indonesia, showing asymmetric effects over short- and long-term periods. Balsalobre-Lorente et al. (2019) identified an inverse relationship between renewable energy use and ecological changes in MINT countries, while Dogan and Inglesi-Lotz (2020) confirmed the EKC hypothesis in European countries, highlighting the role of economic structure.

Balsalobre-Lorente et al. (2022) validated the EKC hypothesis in PIIGS countries, finding that renewable energy negatively impacts CO₂ emissions, while international investments increase environmental degradation. Obiora et al. (2022) analysed economic growth and environmental sustainability in 44 countries, revealing that, in developed countries, private sector credit reduces CO₂ emissions, while in developing countries, it increases emissions. These studies underscore the complex interplay between economic growth, energy consumption, and environmental sustainability, emphasising the need for tailored policy interventions (See

Table 1. Summary of empirical studies.

Study	Time Period	Country	Method(s)	Result
Zakaria Zoundi (2017)	1980–2012	25 African countries	Panel Cointegration Analysis (PCA)	Positive impact of EG on CO2 emission.
Sarkodie and Strezov (2018)	1974–2013	Australia	OLS, DOLS, Canonical Cointegrating Regression Estimators (CCRE)	Impact of energy production (EP) on the environment.
Jaka Sriyana (2019)	1990–2107	Indonesia	Autoregressive Distributed Lag (ARDL)	Link between economic growth (EG) and energy.
Daniel Balsalobre-Lorente et al. (2019)	1990–2013	MINT countries	Fully Modified Ordinary Least Squares (FMOLS), DOLS	Negative connection between renewable energy use (REU) and the ecological footprint (EF).
Eyup Dogan and Inglesi-Lotz (2020)	1980–2014	Euoropean countries	Environmental Kuznets Curve (EKC) Hypothesis	Impact of economic growth (EG) on EKC.
Daniel Balsalobre-Lorente et al. (2022)	1990–2019	PIIGS countries	DOLS	Negative impact of impact of RES on CO2 emission.
Sandra Chukwudumebi Obiora et al. (2022)	1990–2017	44 developed and developing economies	Least-Squares Estimation (LSE)	Link between GDP and carbon emissions (CE).
(Chen et al. 2020)	1990–2015	China	Panel Data Analysis	Examined relationship between economic growth and recovered energy consumption.
(Namahoro et al. 2021)	1990–2018	Lower-, middle-, and upper-middle-income groups	ARDL, Granger Causality Test	Examined relationship between economic growth and recovered energy consumption.
(İnal et al. 2022)	1980–2019	Turkey	ARDL, VECM	Investigated CO2 emissions in various national contexts.
(Azam et al. 2016)	1971–2012	21 Asian countries	Dynamic Ordinary Least Squares (DOLS)	Investigated CO2 emissions in various national contexts.
(Wang et al. 2016)	1985–2014	BRICS countries	Panel Data Analysis	Investigated CO2 emissions in various national contexts.
(Sharma et al. 2021)	1980–2018	India	ARDL	Highlighted possibilities of using hydroelectricity to reduce greenhouse gas emissions and mitigate environmental pollution.
(Le et al. 2023)	1990–2020	Southeast Asian countries	Panel Data Analysis	Highlighted possibilities of using hydroelectricity to reduce greenhouse gas emissions and mitigate environmental pollution.
(Ameyaw and Yao 2018)	2007–2014	Five West African countries	Granger Causality Test	Unidirectional causal relationship between CO2 emissions and consumption levels; provided sustainable policy recommendations.
(Qiao et al. 2019)	1990–2014	G-20 countries	Panel Data Analysis	Found long-term relationship between agriculture, economic growth, and recovered energy; recommended sustainable development policies.
(Mardani et al. 2019)	1995–2017	Global	Meta-analysis of 175 articles	Demonstrated bidirectional relationship between economic growth and CO2 emissions.
(Yan et al. 2022)	2000–2018	China	Nonlinear MS(M)-VAR(p) Model	Showed significant inertia between economic growth and SO2 emissions; identified long-term negative correlation between economic growth and CO2 emissions.
(Ekonomou and Halkos 2023)	1990–2019	European Union countries	Dynamic Panel Data Analysis	Determined that sustained economic growth could limit CO2 emissions and increase the share of recovered energy.
(Ziyazov and Pyzhev 2023)	2013–2018	56 major Russian cities	Spatial Econometric Analysis	Analyzed vehicle emissions; identified initial increase in emissions followed by a decrease due to modern ecological standards.
(Cader et al. 2021)	2008–2018	Nine countries	Spearman’s correlation and linear regression	Determined the statistical significance of selected indicators for hydrogen economy.
(Addis and Cheng 2023)	1995–2021	BRICS and OECD countries	FMOLS and DOLS	Showed significant variables; GDP increase led to rise in recovered energy consumption, while CO2 emissions decrease led to reduced recovered energy consumption.

Legend: LSE—Least-Squares Estimation, ARDL—Autoregressive Distributed Lag, OLS—Ordinary Least Squares, DOLS—Dynamic Ordinary Least Squares, CCRE—Canonical Cointegrating Regression Estimators, PCA—Panel Cointegration Analysis, FMOLS—Fully Modified Least Squares. GDP—Gross Domestic Product, CE—Carbon Emission, EG—Economic Growth, EKC—Environmental Kuznets Curve, EP—Energy Production, CO₂—Carbon Dioxide, PIIGS countries—Portugal, Ireland, Italy, Greece and Spain, RES—Renewable Energy Source, REU—Renewable Energy Using, EF—Ecological Footprint.

,

Table 2. Data and Internet resources.

Variables	Research Year		Source
In Azerbaijan
Gross domestic product—dollar	2007–2022	mln	(State Statistical Committee of the Republic of Azerbaijan n.d.)
Hydro-energy production	2007–2022	thousand TOE	(State Statistical Committee of the Republic of Azerbaijan n.d.)
Biomass and fuels	2007–2022	thousand TOE	(State Statistical Committee of the Republic of Azerbaijan n.d.)
Total energy supply from recovered sources	2007–2022	thousand TOE	(State Statistical Committee of the Republic of Azerbaijan n.d.)
Wind electricity generation	2007–2022	thousand TOE	(State Statistical Committee of the Republic of Azerbaijan n.d.)
Solar electricity generation	2007–2022	thousand TOE	(State Statistical Committee of the Republic of Azerbaijan n.d.)
Carbon dioxide (CO2), (total), million tons of emissions	1997–2022	mln ton	(Energy Institute n.d.)
In Hungary
Gross domestic product—dollar	2000–2021	mln	(Hungarian Central Statistical Office n.d.)
Hydro-energy production	2000–2021	PJ	(Hungarian Central Statistical Office n.d.)
Biomass and fuels	2000–2021	PJ	(Hungarian Central Statistical Office n.d.)
Total energy supply from recovered sources	2000–2021	PJ	(Hungarian Central Statistical Office n.d.)
Wind electricity	2000–2021	PJ	(Hungarian Central Statistical Office n.d.)
Solar electricity generation	2000–2021	PJ	(Hungarian Central Statistical Office n.d.)
Carbon dioxide (CO2) (total), million tons of emissions	1997–2022	mln ton	(Energy Institute n.d.)

Tonne of oil equivalent, toe. 1 TOE = 11.63 MVt-s, PJ—Petajoules. 1PJ = 238,902,957,618.615 Kgcal. Kgcal-Kilogram calories.

,

Table 3. Renewable energy sources in Azerbaijan.

	Hydro-Energy	Biofuels and Waste (PJ)	Wind Electricity Energy (PJ)	Solar Electricity Energy (PJ)	Total Energy Supply from Renewables	Gross Domestic Product—Dollar
2007	203.3	88.0	-	-	291.3	33,050.3
2008	192.0	85.5	-	-	277.5	48,852.5
2009	198.5	73.3	0.2	-	272.0	44,297.0
2010	296.4	90.2	-	-	386.6	52,909.3
2011	230.1	96.6	-	-	326.7	65,951.6
2012	156.6	98.4	-	-	255.0	69,683.9
2013	128.1	157.1	0.1	0.1	285.4	74,164.4
2014	111.8	158.9	0.2	0.2	271.1	75,234.7
2015	140.8	160.7	0.4	0.4	302.3	52,996.8
2016	168.5	100.8	2.0	3.0	274.3	37,862.8
2017	150.2	102.4	1.9	3.2	257.7	40,867.9
2018	152.0	110.7	7.1	3.4	273.2	47,112.9
2019	134.6	115.9	9.1	3.8	263.4	48,174.2
2020	92.0	108.4	8.3	4.0	212.7	42,693
2021	109.8	102.6	7.9	4.8	225.1	54,825.4
2022	137.2	96.1	7.2	5.2	245.9	78,807.5

,

Table 4. Carbon dioxide (CO₂) emissions (total) in Azerbaijan and Hungary.

	Azerbaijan		Hungary
	Gross Domestic Product—Dollar	Carbon Dioxide (CO2) Emissions (Total). Million Tons	Gross Domestic Product (GDP)—Dollars	Carbon Dioxide (CO2) Total Million Tons of Emissions
1997	3960.7	27.9	47,306.9	57.3
1998	4446.4	27.9	48,695.8	57.9
1999	4583.7	27.3	49,039.3	58.8
2000	5272.8	28.6	47,203.3	55.5
2001	5707.7	26.3	53,740.8	56.8
2002	6235.9	25.9	67,571.9	55.3
2003	7276.0	28.0	85,235.2	58.0
2004	8680.4	30.8	104,180.2	57.3
2005	13,238.7	33.5	113,164.9	57.3
2006	20,983.0	33.3	115,650.9	57.0
2007	33,050.3	30.7	140,031.0	55.0
2008	48,852.5	30.2	158,614.3	53.8
2009	44,297.0	25.7	131,122.2	48.2
2010	52,909.3	24.6	132,044.6	48.7
2011	65,951.6	29.1	142,023.5	48.7
2012	69,683.9	30.2	128,662.3	44.6
2013	74,164.4	30.7	135,681.3	42.0
2014	75,234.7	31.4	141,083.4	41.3
2015	52,996.8	33.9	125,117.1	43.8
2016	37,862.8	33.4	128,647.9	44.7
2017	40,867.9	32.4	143,197.4	46.9
2018	47,112.9	33.8	160,542.9	47.3
2019	48,174.2	34.4	164,026.1	47.1
2020	42,693.0	35.2	157,261.1	44.7
2021	54,825.4	37.6	182,000.5	46.1
2022	78,807.5	37.5	177,088.3	42.9

and

Table 5. Renewable energy sources in Hungary.

	Hydro-Energy	Biofuels and Waste (PJ)	Wind Electricity (PJ)	Solar Electricity (PJ)	Total Energy Supply from Renewables (PJ)	GDP Mln Dollar
2000	0.6	29.3	0	0	34.7	47,203.3
2001	0.7	30.6	0	0.1	36.4	53,740.8
2002	0.7	31.2	0	0.1	36.7	67,571.9
2003	0.6	32.7	0	0.1	38	85,235.2
2004	0.7	34.3	0	0.1	39.7	104,180.2
2005	0.7	64.5	0	0.1	70.7	113,164.9
2006	0.7	64.4	0.2	0.1	72	115,650.9
2007	0.8	69.6	0.4	0.1	77.6	140,031
2008	0.8	68.9	0.7	0.2	84.2	158,614.3
2009	0.8	91.1	1.2	0.2	106.9	131,122.2
2010	0.7	98.2	1.9	0.2	114.7	132,044.6
2011	0.8	101.6	2.3	0.3	119.6	142,023.5
2012	0.8	106.9	2.8	0.4	131.3	128,662.3
2013	0.8	112.3	2.6	0.5	138.8	135,681.3
2014	1.1	98.9	2.4	0.6	124.7	141,083.4
2015	0.8	105.2	2.5	1	136	125,117.1
2016	0.9	100.6	2.5	1.3	134	128,647.9
2017	0.8	99	2.7	1.7	133.4	143,197.4
2018	0.8	89.3	2.2	2.8	125.3	160,542.9
2019	0.8	85.9	2.6	5.9	127.2	164,026.1
2020	0.9	85.2	2.4	9.5	129.1	157,261.1
2021	0.8	91.8	2.4	14.3	142.6	182,000.5

and Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 10 and Figure 11).

3. Methodology

Collection of Data

In our research, the following indicators were utilised by both countries. Refer to Table 2.

The constraints posed by limited data availability regarding green economy indicators present significant challenges for research endeavours. Chief among these is the fact that while Hungary has seen substantial advancements in wind and solar energy production, Azerbaijan’s efforts in this realm began only in 2007. However, both countries exhibit relatively lower indicators, particularly Azerbaijan. Consequently, research focusing on hydro-energy production, which dominates recovered energy production, has been relatively straightforward.

Due to data constraints, the research periods span from 2007 to 2022 for Azerbaijan and from 2000 to 2021 for Hungary (See Table 1). Notably, data on wind and solar electricity in Azerbaijan are available only from 2013 onward. An examination of the overall landscape in Azerbaijan reveals a stable trend in hydro-energy production, with the highest indicator (TOE) recorded at 296.4 in 2010 and the lowest indicators observed in 2014 (111.8), 2020 (92.0), and 2021 (109.8) (See Table 2 and Table 4). However, there appears to be some variability in energy production from biomass and waste despite an overall increasing trend. While the highest indicators were observed in 2013–2015, there was a notable decrease in 2016. (See Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 10 and Figure 11).

Conversely, there has been a noticeable upward trend in wind and solar electricity production, albeit with relatively modest magnitudes. Nevertheless, the total recovered energy supply from renewable sources maintains a balanced stability overall. It is important to acknowledge that such indicators introduce some limitations in model establishment and dependency determination.

In Azerbaijan, carbon dioxide (CO₂) emissions exhibit a consistent upward trajectory over the years, closely tied to GDP growth. Naturally, this relationship is intertwined with the country’s overall economic development. Notably, during the studied period from 1997 to 2022, while CO₂ emissions increased in Azerbaijan, they consistently decreased annually in Hungary (See Table 3).

In Azerbaijan, the full operation of oil contracts primarily drove the surge in the GDP from 2005 to 2008, the rapid increase in oil exports, and the relatively dramatic rise in oil prices compared to previous periods. However, expecting a proportional increase in other sectors was unrealistic, particularly in the recovered energy sector we researched. The sharp decline in the price of a barrel of oil from $140 to $38 in 2008–2009, resulting in a decrease in oil revenues, contributed to both the decline in the GDP of the oil sector and the overall GDP of the country. Subsequent stabilisation in global oil prices also stabilised GDP growth.

Due to Azerbaijan’s national currency being dependent on oil, there were two devaluations against the dollar in February and December 2015. The exchange rate plummeted from 0.78 AZN to 1.05 AZN per dollar and later to 1.52 AZN, currently hovering around 1.70 AZN. While GDP volume increased from 2016 to 2019, there was a contraction in 2020, and currently, there is a high growth rate depending on prices.

In Hungary, GDP growth steadily increased until 2008, which aligned with global economic development and growth trends. However, the financial economic crisis of 2008 and 2009 led to a decline in the GDP. From then until 2018, there was instability. In other words, over a span of 10 years, the GDP was lower than in 2008. Although Hungary experienced its highest GDP indicator in 2021, the COVID-19 pandemic and subsequent Russia-Ukraine conflict negatively affected the GDP, as in other countries.

4. Hypothesis Development

The purpose of the research and the model has led to the formulation of the following general hypothesis:

H1. 

The GDP growth rate increase leads to an increase in the total energy supply from hydro-energy, biomass, fuels, wind electricity, solar electricity, and recovered sources, as well as carbon dioxide (${CO}_2$) emissions. This hypothesis aims to demonstrate the relationship between GDP growth and the use of different energy sources and (${CO}_2$) emissions. A relevant model has been developed to determine the impact of GDP growth on the total energy supply from hydro-energy, biomass, fuels, wind electricity, solar electricity, and recovered sources, as well as carbon dioxide (${CO}_2$) emissions. This model utilises statistical and econometric methods to describe the relationships between variables and assess the influence of these variables on each other.

Following the purpose and model of the research, another general hypothesis has been formulated:

H2. 

The increase in the gross domestic product (GDP) leads to an increase in the total energy supply from hydro energy, biomass, fossil fuels, wind electricity, solar electricity, and recovered sources, as well as carbon dioxide emissions. To determine the impact of GDP growth on the total energy supply from hydro-energy, biomass, fossil fuels, wind and solar electricity, and recovered sources, as well as its effect on carbon dioxide (${CO}_2$) emissions, an appropriate model has been constructed.

$$y=f\left(x\right)$$

(1)

Later, the model was transformed into a linear regression model.

$$ln{y}_t={\beta }_0+{\beta }_{1}ln{x}_t+{\epsilon }_t$$

(2)

$y$	=	Hydro-energy, the energy obtained from biomass, fuels, wind electricity, solar electricity, the total energy supply from recovered sources, and the sum of carbon dioxide (${\mathrm{C}\mathrm{O}}_2$) emissions.
$x$	=	GDP

The econometric method of time series analysis has been employed in the research. Initially, certain time series were logarithmised in the study. Subsequently, unit root tests were utilised to examine the stationarity of the time series. For this purpose, the Augmented Dickey–Fuller (ADF, Dickey and Fuller 1981) and Phillips–Perron (PP, Phillips and Perron 1988) unit root tests were employed. The literature on unit root tests is primarily attributed to Fuller (1976) and Dickey and Fuller (1979, 1981).

Following the stationarity test, the Autoregressive Distributed Lag Model (ARDL) was utilised to explore the relationship and directionality between the time series. In the next stage, to strengthen and support the reliability of ARDL cointegration test results, long-term estimators were employed, including Fully Modified OLS (FMOLS), (Phillips and Hansen 1990), Dynamic OLS (DOLS, Stock and Watson 1993), and Canonical Cointegration Regression (CCR, Park 1992) models.

Cointegration Analysis (ARDL)

Various cointegration tests are employed in time series-related research to verify long-term relationships between variables. Among these, the Engle and Granger (1987), Johansen (1988), and Johansen and Juselius (1990) tests are the most widely used in research. However, these tests require all variables to be stationary at the first level, meaning that all variables must be integrated at the I(1) level. By contrast, the ARDL approach developed by Pesaran et al. (2001) has recently been widely used in cointegration analyses. The ARDL approach allows for the stationarity level of the time series to be either I(0) or I(1), and it can also be applied to small time series samples. Therefore, preference has been given to the ARDL model in this research. A model was constructed to determine the impact of hydro-energy, the energy obtained from biomass and fuels, wind electricity, solar electricity, the total energy supply from recovered sources, and carbon dioxide (CO₂) emissions on the total energy supply and carbon dioxide emissions.

$${∆\mathrm{y}}_{\mathrm{t}}={\mathsf{\alpha }}_0+\sum _{i=1}^k{\alpha }_{1i}∆y_{t-i}+\sum _{i=0}^k{\alpha }_{2i}∆x_{t-i}+{\alpha }_{3y_{t-1}}+{{\alpha }_{4}x}_{t-1}+{\mathsf{\epsilon }}_t$$

(3)

Here, $∆$ denotes the first difference operator for variables, ${\mathsf{\epsilon }}_t$ represents the error term, and $k$ indicates the optimal lag length. In addition, ${\alpha }_1$ and ${\alpha }_2$ coefficients signify short-term relationships between variables, while ${\alpha }_3$ and ${\alpha }_4$ coefficients denote long-term dynamic relationships between variables.

The $ARDL$ method relies on $F$ and $Wald$ statistics. The obtained F value from the model is compared with the lower and upper critical values calculated by Pesaran et al. (2001) and Narayan (2005), depending on the number of observations. Since the number of observations in our study is relatively small, both the critical values calculated by Narayan (2005) and Pesaran et al. (2001) have been utilised. If the calculated $F$ statistic is smaller than the lower critical value, the null hypothesis that there is no cointegration relationship between the time series is accepted. When the $F$ statistic exceeds the upper critical value, the alternative hypothesis indicating the presence of a cointegration relationship between the time series is accepted. The result is inconclusive if the F statistic falls between the lower and upper critical values. Subsequently, $ARDL$ models were constructed to determine short-term and long-term relationships between the time series.

FMOLS, DOLS, and CCR Cointegration Methods

FMOLS, DOLS, and CCR cointegration methods were employed to reinforce and support the reliability of the ARDL model results.

5. Results and Discussion

Results of Unit Root Test

Before analysing any cointegration relationship between variables, the stationarity of the time series was examined, and the results of the stationarity tests are provided in

Table 6. The results of the root test.

Azerbaijan
	Augmented Dickey–Fuller	Phillips–Perron
	First Differencing	First Differencing
LOQ CO2	−3.882525 ***	−3.771070 ***
LOQ HYDRO-ENERGY	−4.560992 ***	−2.859598 ***
LOQ BIOFUELS AND WASTE	−3.468851**	−3.468851 **
LOQ TOTAL ENERGY SUPPLY FROM RES	−4.038109 **	−7.595262 ***
LOQ WIND ELECTRICITY ENERGY	−2.872086 *	−2.872086 *
LOQ SOLAR ELECTRICITY ENERGY	−3.525318 **	−3.525388 **
LOQ GDP	−1.905792 *	−2.755785 ***
Hungary
LOQ CO2	−4.596385 ***	−4.593892 ***
LOQ HYDRO-ENERGY	−8.495749 ***	−11.52776 ***
LOQ BIOFUELS AND WASTE	−4.389952 ***	−4.388061 ***
LOQ TOTAL ENERGY SUPPLY FROM RES	−4.452605 ***	−4.452525 ***
LOQ WIND ELECTRICITY ENERGY	−3.772652 **	−3.706250 **
LOQ SOLAR ELECTRICITY ENERGY	I (2)	−1.683359 *
LOQ GDP	−2.055306 **	−2.612753 **

Notes: $ADF$, $PP,$ and $KPSS$ denote the Augmented Dickey–Fuller, Phillips–Perron tests, and Kwiatkowski–Phillips–Schmidt–Shin, respectively. Maximum lag order is set to two, and optimal lag order (k) is selected based on the Schwarz criterion in the tests; *, **, and *** denote rejection of the null hypotheses at the 10%, 5%, and 1% significance levels, respectively. The critical values for the tests are taken from.

.

Upon reviewing the findings presented in Table 5 and Table 6, it is evident that based on the outcomes of both unit root tests, the time series exhibit first-order differences, implying a state of I(1) stationarity.

Results of the ARDL Cointegration Test:

The initial requirement of the ARDL model is to determine the appropriate lag length. Accordingly, models ARDL (1,0) and ARDL (1,1) were specified for the variables based on the data metrics (See Table 6,

Table 7. Estimated primary ARDL model.

Azerbaijan
Variable	Coefficient
	Model 1	Model 2	Model 3	Model 4
LOQ CO2(-1)	0.821850 ***
LOQ HYDRO-ENERGY(-1)		0.690766 **
LOQ BIOFUELS AND WASTE (-1)			0.663881 **
LOQ TOTAL ENERGY SUPPLY FROM RES (-1)				0.444902
LOQ WIND ELECTRICITY ENERGY(-1)					0.937784 ***
LOQ SOLAR ELECTRICITY ENERGY (-1)						1.012065 ***
LOQ GDP	0.012081	−0.136230	0.395661 *	0.068545	−2.012677	−1.096365
C	0.526930	3.022296	−2.735703	2.360808	22.57139	12.27496

Note: ***, **, and * indicate rejection of the null hypotheses at the 1%, 5%, and 10% significance levels, respectively.

,

Table 8. Results of the bound tests.

Azerbaijan
Estimated Model		Bound Test
		$\mathit{F}$-St.
Model 1		0.793905			No Cointegration
Model 2		0.983893			No Cointegration
Model 3		2.989625			No Cointegration
Model 4		1.606775			No Cointegration
Model 5		1.029601			No Cointegration
Model 6		2.195205			No Cointegration
Critical Values			10%	5%	2.5%	1%
Bounds	Lower I(0)	n = 1000 1	3.02	3.62	4.18	4.94
		n = 30 2	3.303	4.09		6.027
	Upper I(1)	n = 1000 1	3.51	4.16	4.89	5.58
		n = 30 2	3.797	4.663		6.76

Notes: ***, **, and * indicate rejection of the null hypotheses at the 1%, 5%, and 10% significance levels, respectively. ¹ (Pesaran et al. 2001). ² (Narayan 2005).

,

Table 9. Conditional error correction regression and short-run coefficients.

Azerbaijan
Variable	Coefficient
	Model 1	Model 2	Model 3
Panel A: Conditional error correction regression
LOQ CO2 (-1)	−0.178150
LOQ HYDRO-ENERGY (-1)		−0.309239
LOQ BIOFUELS AND WASTE (-1)			−0.336119
LOQ TOTAL ENERGY SUPPLY FROM RES (-1)				−0.555098 *
LOQ WIND ELECTRICITY ENERGY (-1)					−0.062216
LOQ SOLAR ELECTRICITY ENERGY (-1)						0.012065
LOQ GDP	0.012081	−0.136230	0.395661 *	0.068545	−2.012677	−1.096365
C	0.526930	3.022296	−2.735703	2.360808	22.57139	12.27096
Panel B: Short-run estimation Azerbaijan
CointEq(−1)	−0.178150	−0.309239	−0.336119 **	−0.555099 *	−0.062216	0.012065

Note: ***, **, and * indicate rejection of the null hypotheses at the 1%, 5%, and 10% significance levels, respectively.

and

Table 10. Coefficients of long-range models.

Azerbaijan
	OLS	ARDL	FMOLS	DOLS	CCR	VECM
LOQ CO2
LOQ GDP	0.052532 **	0.067810	0.058319	0.052033	0.057680 *	0.068765
$\mathrm{C}$	2.870095 ***	2.789397 ***	2.830757 ***	2.867057 ***	2.837836 ***	2.718662 **
LOQ HYDRO-ENERGY
LOQ GDP	−0.224788	−0.440522	−0.298152	−0.267558	−0.322539	1.510631
$\mathrm{C}$	7.492293	9.773502	8.253822	7.923190	8.513890	11.25556
LOQ BIOFUELS AND WASTE
LOQ GDP	0.363068	1.177145	0.396518	0.502881	0.457171 *	0.398719
$\mathrm{C}$	0.720867	−8.139081	0.082305	−0.726078	−0.206386	0.259806
LOQ TOTAL ENERGY SUPPLY ROM RES
LOQ GDP	0.023583	0.123983	0.003063	0.033982	0.009891	−0.313598
$\mathrm{C}$	5.355729 **	4.252948	5.578270 *	5.259698	5.557152 *	9.024880
LOQ WIND ELECTRICITY ENERGY
LOQ GDP	−1.025950	−32.35000	−2.053602	−4.776887	−1.990386	72.07927
$\mathrm{C}$	13.92563	362.7918	25.05375	53.76405	25.30296	−781.0338
LOQ SOLAR ELECTRICITY ENERGY
LOQ GDP	−0.850330	77.95734	−0.859283	−3.105715	−0.789020	4.843910
$\mathrm{C}$	10.99813	−872.8142	11.16180	35.58485	10.39388	−52.03031

Note: ***, **, and * indicate rejection of the null hypotheses at the 1%, 5%, and 10% significance levels, respectively.

). Table 7 and

Table 11. Estimated primary ARDL model.

Hungary
Variable	Coefficient
	Model 1	Model 2	Model 3	Model 4
LOQ CO2	0.767137 ***
LOQ HYDRO-ENERGY (-1)		0.121153
LOQ BIOFUELS AND WASTE (-1)			0.671740 ***
LOQ TOTAL ENERGY SUPPLY FROM RES (-1)				0.770218 ***
LOQ WIND ELECTRICITY ENERGY (-1)					0.846279 ***
LOQ SOLAR ELECTRICITY ENERGY (-1)						1.553856 ***
LOQ GDP	0.181377 *	0.155270	−40.52799 *	−39.98998	−1.038303	0.150385
LOQ GDP (-1)	−0.231256 **		52.03007 **	53.23761 **	1.328825 *
C	1.470295 *	−1.129888	−126.9464	−193.7087	−3.080301	−1.750556

Note: ***, **, and * indicate rejection of the null hypotheses at the 1%, 5%, and 10% significance levels, respectively.

present the cointegration test results for variables in Azerbaijan and Hungary, respectively, under the ARDL model. Upon scrutiny of the table results, it is observed that for variables in Azerbaijan, the F-statistic is smaller than the critical upper value at the 5% significance level, as determined by both Pesaran and Narayan. Conversely, for variables in Hungary, the F-statistic exceeds the critical value. This indicates the absence of cointegration among variables in Azerbaijan and the presence of cointegration among variables in Hungary.

Long and Short-Term Effects in the ARDL Model:

Once the cointegration relationship among variables is determined, the coefficients of long- and short-term effects among these variables are identified. The results of the coefficients for long- and short-term effects are presented in Table 8, Table 9,

Table 12. Results of the bound tests.

Hungary
Estimated Model		Bound Test
		$\mathit{F}$-St.
Model 1		4.837683 **			Cointegration
Model 2		5.231783 **			Cointegration
Model 3		6.673097 ***			Cointegration
Model 4		8.717506 ***			Cointegration
Model 5		4.453890 **			Cointegration
Model 6		89.14865 ***			Cointegration
Critical Values			10%	5%	2.5%	1%
Bounds	Lower I(0)	n = 1000 1	3.02	3.62	4.18	4.94
		n = 30 2	3.303	4.09		6.027
	Upper I(1)	n = 1000 1	3.51	4.16	4.89	5.58
		n = 30 2	3.797	4.663		6.76

Notes: ***, **, and * indicate rejection of the null hypotheses at the 1%, 5%, and 10% significance levels, respectively. ¹ (Pesaran et al. 2001). ² (Narayan 2005).

,

Table 13. Conditional error correction regression and short-run coefficients.

Hungary
Variable	Coefficient
	Model 1	Model 2	Model 3	Model 5	Model 5	Model 6
Panel A: Conditional error correction regression
LOQ CO2(-1)	−0.232863 *
LOQ HYDRO-ENERGY(-1)		−0.878847 ***
LOQ BIOFUELS AND WASTE (-1)			−0.328260 ***
LOQ TOTAL ENERGY SUPPLY FROM RES (-1)				−0.230782 **
LOQ WIND ELECTRICITY ENERGY(-1)					−0.153721 *
LOQ SOLAR ELECTRICITY ENERGY(-1)						0.553856 ***
LOQ GDP	−0.049880 *	0.155270	13.50208	19.24763 *	0.292521	0.150385
LOQ GDP (-1)	0.181377 *		−40.52799 *	−33.98998	−1.038303
C	1.470295 **	−1.129888	−126.9464	193.7087	−3.080301	−1.750556
Panel B: Short-run estimation
D LOQ GDP	0.181377 **		−40.52799 **	−33.98998	−1.038303 *
CointEq(−1)	−0.232863 ***	−0.878847 ***	−0.328260 ***	−0.230782 ***	−0.153721 *	0.553856 ***

Note: ***, ** and * indicate rejection of the null hypotheses at the 1%, 5% and 10% significance levels, respectively.

and

Table 14. Coefficients of long-range models.

Hungary
	OLS	ARDL	FMOLS	DOLS	CCR	VECM
LOG CARBON DIOXIDE
LOG GDP	−0.188186 ***	−0.210203 **	−0.188186 ***	−0.183380 ***	−0.196282 ***
$\mathrm{C}$	6.100866 ***	6.313991 ***	6.100866 ***	6.023163 ***	6.172096 ***
LOG HYDRO-ENERGY
LOG GDP	0.183506 **	0.176675 *	0.156210 *	0.152515 *	0.170915 **	0.833057 **
$\mathrm{C}$	−1.367256 *	−1.285649	−1.033738	−1.014652	−1.253852	9.008429
LOG BIOMASS AND WASTE
LOG GDP	62.68918 ***	41.13223 *	60.08558 **	53.06181 **	65.00297 ***	12.75398 ***
$\mathrm{C}$	−655.7932 ***	−386.7248	−619.6282 **	−552.8223 **	−665.0929 ***	70.86159
LOG TOTAL ENERGY SUPPLY FROM RENEWABLE SOURCES
LOG GDP	90.75125 ***	78.31176 **	93.70198 ***	86.35330 ***	95.94493 ***	64.98063 ***
$\mathrm{C}$	−962.7640 ***	−788.1319 *	−991.3273 ***	−904.2629 ***	−1016.797 ***	−662.2736 *
LOG WIND ELECTRICITY ENERGY
LOG GDP	2.201651 ***	1.915908	2.311309 *	2.151072 *	2.397913 **	−0.769856
$\mathrm{C}$	−24.75384 ***	−19.77806	−25.36531 *	−23.50389	−26.36852 **	10.59759 *
LOG SOLAR ELECTRICITY ENERGY
LOG GDP	4.562528 *	−0.271522	6.600642	6.567220	5.678365 *	5.308052 ***
$\mathrm{C}$	−52.52336 *	3.160668	−75.65573	−75.48696	−65.73911	−62.13553

Note: ***, **, and * indicate rejection of the null hypotheses at the 1%, 5%, and 10% significance levels, respectively.

.

In both Azerbaijan and Hungary, negative and positive relationships are evident between the GDP and the dependent variables, including hydro-energy, energy obtained from biomass and waste, wind electricity, solar electricity, total energy supply from renewable sources, and carbon dioxide (CO₂) emissions. However, while Azerbaijan did not meet the 5% significance level, Hungary did. Short-term coefficients are also provided alongside long-term coefficients in the models. The results are consistent with the long-term coefficients.

The analysis reveals that economic growth has had a predominantly negative long-term impact on hydro-energy production in Azerbaijan. The coefficients obtained were consistently negative and statistically insignificant, indicating that despite economic expansion, hydro-energy development has not seen substantial improvement. This trend is largely attributed to Azerbaijan’s rapid economic growth fueled by its significant oil and gas reserves. The emphasis on fossil fuels has diverted attention and resources away from developing hydro-energy infrastructure, resulting in minimal progress in this renewable sector. Similarly, the relationship between economic growth and biomass and waste energy production in Azerbaijan showed a positive yet statistically insignificant correlation. This suggests a potential influence of economic growth on these renewable sources, although current data do not reflect significant contributions. Further investment and development in biomass and waste energy technologies could potentially enhance their role in Azerbaijan’s energy mix.

The study also observed negative and statistically insignificant effects of economic growth on wind and solar energy production in Azerbaijan, largely due to limited data availability until recent years. Nonetheless, there has been a noticeable upward trend in both wind and solar energy production, indicating a gradual shift toward these renewable sources. This positive trajectory underscores the potential for significant growth in renewable energy sectors if adequately supported by policy and investment. Overall, while the impact of economic growth on the total renewable energy supply in Azerbaijan was statistically insignificant, there is a discernible positive trend. This suggests a slow but promising integration of renewable energy sources into Azerbaijan’s energy portfolio, contingent upon sustained policy interventions and technological advancements.

In contrast to Azerbaijan, Hungary has experienced a significantly positive impact of economic growth on renewable energy production. This outcome highlights Hungary’s effective policy framework and regulatory environment, which is aligned with EU standards and directives on renewable energy. The substantial and positive correlation between economic growth and renewable energy production underscores Hungary’s success in leveraging economic expansion to enhance its renewable energy capacities. Furthermore, economic growth in Hungary has shown a negative impact on carbon dioxide (CO₂) emissions, indicating a successful decoupling of economic growth from environmental degradation. This achievement reflects Hungary’s proactive measures in energy efficiency and transitioning to cleaner energy sources, in compliance with EU environmental policies.

Comparative Analysis. The comparative analysis between Azerbaijan and Hungary underscores the influence of differing economic contexts and policy frameworks on renewable energy outcomes. Hungary’s membership in the EU and adherence to stringent environmental regulations have facilitated more robust progress in renewable energy production and emissions reduction compared to Azerbaijan, which faces challenges associated with its oil and gas-dependent economy. The findings underscore the critical need for Azerbaijan to diversify its energy mix away from fossil fuels and toward renewable sources. Policy recommendations include enhancing investment frameworks for renewable energy infrastructure, promoting technological innovation, and fostering partnerships between the public and private sectors. Comprehensive data collection and analysis mechanisms are essential to monitor progress effectively and inform evidence-based policy decisions.

6. Conclusions and Practical Implications

The results suggest that, in Azerbaijan, the long-term impact of economic growth on hydro-energy has been negative. This is evidenced by the obtained long-term coefficients being negative and statistically insignificant. The reason for this lies in the rapid pace of economic growth in Azerbaijan during the research period. Although the dependence on biomass and waste was statistically insignificant, it was positive. The effects on wind and solar energy production were similarly negative and statistically insignificant, mirroring the processes observed in hydro-energy production.

Additionally, until 2013, data indicated zero production of wind and solar energy, which has influenced the outcome. However, data from the tables revealed an increasing trend in the production of both energy sources over the years. While the overall impact of economic growth on the total energy supply from renewable sources was statistically insignificant in Azerbaijan, it was positive. The impact of economic growth on carbon dioxide emissions was significant in both magnitudes, albeit positive in one instance and statistically insignificant in another.

In Hungary, the impact of economic growth on renewable energy production has been significant and positive. However, the impact on carbon dioxide emissions has been negative, meaning that as economic growth increases, this indicator decreases, as evidenced by the data in the table. The influence of economic growth on the mentioned indicators has been more effective in Hungary than in Azerbaijan, which is likely attributable to differences in economic development. The relationship between the GDP, the green economy, and environmental issues has become increasingly important. The continuity of the environment is crucial for human existence. Therefore, it is imperative to determine the degree of dependence of hydro-energy, energy obtained from biomass and waste, wind electricity, solar electricity, total energy supply from renewable sources, and carbon dioxide (CO₂) emissions on the GDP, the causal factor, and take appropriate measures in this area.

As with any research, this study has limitations. The available data on some variables necessary to clearly demonstrate the impact of the GDP on the aforementioned indicators were not extensive or long-term enough. Investigating the impact of the GDP on wind and solar electricity production in Azerbaijan was particularly challenging. Both republics, especially Azerbaijan, are expected to continue their efforts to align their economies with green economic parameters in the future.

One significant implication of this research is the need for Azerbaijan to develop more robust policies and frameworks to support the growth of renewable energy sectors. This includes investing in infrastructure, providing incentives for renewable energy projects, and fostering research and development in green technologies. The positive trend observed in renewable energy production over recent years suggests that, with the right support, Azerbaijan can significantly increase its share of renewable energy in the total energy supply. For Hungary, the findings reinforce the effectiveness of current policies aimed at reducing carbon dioxide emissions and promoting renewable energy. Hungary’s approach can serve as a model for other countries, including Azerbaijan, demonstrating the potential benefits of aligning economic growth with environmental sustainability.

In an era where the entire world faces ecological challenges, both Azerbaijan and Hungary are not exempt. The urgency of transitioning to renewable energy will increasingly resonate in Azerbaijan. Therefore, based on the modest significance of studies like ours, Azerbaijan can assess the importance of transitioning to a green economy. Meanwhile, Hungary, as a member of the European Union, continues to make strides in this field. Furthermore, we believe that Azerbaijan and Hungary urgently need to expand scientific publications and activities in this field. Given Azerbaijan’s dominance in oil and gas, shifting focus toward renewable energy could serve as a catalyst for increasing scientific awareness and community-based approaches. This could significantly enhance public perception and foster a more sustainable energy future.

7. Limitations of the Study

We understand that the regional scope of our study, focusing specifically on Azerbaijan and Hungary, may appear restrictive at first glance. However, we would like to emphasise that our choice of these two countries was deliberate and aimed at providing in-depth insights into the unique economic and environmental dynamics within distinct geographical contexts. By focusing on Azerbaijan and Hungary, we were able to explore how economic growth influences ecological sustainability and renewable energy development in regions with different socio-economic backgrounds and environmental policies. Moreover, while our study’s primary empirical evidence is drawn from Azerbaijan and Hungary, the theoretical framework and methodological approach we present hold broader relevance and applicability. The findings contribute to the existing literature by highlighting generalisable patterns and mechanisms that could be observed in other regions facing similar challenges or opportunities in balancing economic development with environmental conservation and renewable energy adoption.

Moreover, although we were faced with a constraint imposed by the limited availability of data, particularly concerning the relatively short observation period for renewable energy production in Azerbaijan, the data coverage primarily spans recent years, reflecting the nascent stage of renewable energy development in the region. Importantly, our study represents one of the initial comparative analyses between Azerbaijan and Hungary in this evolving field.

Despite these limitations, our research provides a significant contribution by shedding light on the early stages of renewable energy adoption and its impact on the ecological environment in Azerbaijan and Hungary. This exploration is timely and critical, especially as Azerbaijan prepares to host COP29 in November 2024. By highlighting these findings, we aim to contribute to broader discussions on green energy adoption, environmental sustainability, and policy formulation in the context of global climate agendas.

These limitations explain the choice of employing traditional econometric methods such as ARDL, FMOLS, DOLS, and CCR, which lies in their simplicity, particularly due to the short time span of the study. While more advanced methods exist, their application might not yield sufficiently robust results given the short duration of the research. Similarly, the choice of conventional ADF, PP, and KPSS unit root tests is also constrained by the short time series.

Also, this study does not encompass all possible variables of economic growth and its impact on the ecological environment and renewable energy production. Specifically, there is no information available on environmental taxes and related technologies concerning environmental protection. Such a tax is not currently implemented in Azerbaijan. Furthermore, proposals are being prepared for the introduction of environmental taxes in Azerbaijan. This could be an incentive for further research.

8. Future Research Directions

Future research should focus on expanding the dataset to include longer time periods and more comprehensive variables. This would allow for a more detailed analysis of the long-term trends and impacts of economic growth on renewable energy production and environmental sustainability. Additionally, exploring the role of government policies, international cooperation, taxation, and technological innovations in shaping the renewable energy landscape in both countries would provide valuable insight.

In the research conducted on both countries, particularly Azerbaijan, the use of renewable energy sources and the transition to a green economy are relatively new. Consequently, the scarcity of data and studies in this area, compared to other countries with more established renewable energy usage and green economies, has posed certain limitations. This has affected the adequacy of the formulation of policy recommendations. Nonetheless, the obtained results hold significant theoretical and practical relevance. Moreover, to fully evaluate the effectiveness of the green economy in such studies, it is recommended to increase the number of both theoretical and practical research endeavours. Support from both governmental and private sectors is crucial in this regard. Unlike some other fields, positive impacts from research, especially in the social sciences, require a considerable amount of time to be fully realised.