Prioritising sustainability: how economic growth, energy use, forest area, and globalization impact on greenhouse gas emissions and load capacity in Poland?

ABSTRACT

The main purpose of this paper is to investigate the environmental repercussions of a booming economy, fossil fuel, renewable energy, globalisation, technological innovation, and forest cover in Poland from 1990 to 2018. This study used greenhouse gas (GHG) emissions and load capacity factor as proxies of ecological damage to provide a comprehensive assessment of the surrounding ecosystem in Poland to help implement future policies to reduce emissions and achieve environmental sustainability. Besides, Poland is the only European Union (EU) country lacking a climate neutrality objective and policy instruments to mitigate ecological concerns. The Autoregressive Distributed Lag (ARDL) bounds model and the Dynamic Ordinary Least Squares (DOLS) procedure evaluated yearly data from 1990 to 2018. The empirical outcomes of the DOLS point out that economic growth and fossil fuel energy use enhance GHG emissions despite reducing the load capacity factor in Poland. Instead, renewable power, globalisation, technological innovation, and forest cover might help to reduce GHG emissions and enhance the load capacity factor in Poland. The concluding thoughts from this study suggest the implementation of policies that support raising additional funding for eco-friendly technology studies while promoting renewable energy use and enhancing forest carbon sinks.

KEYWORDS:

• Environmental degradation
• renewable energy
• emission reduction
• Poland
• environmental sustainability

Highlights

• Economic growth and fossil fuel use in Poland increase greenhouse gas emissions and decrease load capacity factor.

• Renewable energy, globalisation, technological innovation, and forest area all have the opposite effect.

• The study used data from 1990 to 2018 and multiple estimation methods to ensure reliable results.

• Poland, lacking climate neutrality goals, can benefit from policies promoting these beneficial factors.

• The research emphasises a comprehensive approach for Poland to achieve environmental sustainability.

1. Introduction

When the Paris Agreement entered into force in 2016, nations pledged to reduce their GHG emissions by 2050 to keep a decline in the world's temperature of no more than 1.5 degrees Celsius (Raihan et al. 2022). Consequently, the EU has initiated research into the measures required to significantly reduce emissions by 2030 and attain carbon neutrality by 2050. The European Union has promised to switch to cleaner energy sources, which will help the world meet its climate change goals and make clean energy accessible to all (Cifuentes-Faura 2022; Pattak et al. 2023). To ensure that this promise is kept, the EU has set mandatory climate and energy goals for 2030, including a 40% drop in carbon output, a 32% boost in energy savings, and a 32% elevate in the portion of clean power in EU usage of power (Kulovesi and Oberthür 2020; Zimon 2019; Zimon and Zimon 2020). However, Poland is one of the EU nations with the highest emission levels. In 2019 Poland emitted 353 million metric tons of GHGs, accounting for 0.73 percent of global emissions (World Bank 2023). The country accounts for 10.5% of all GHG emissions in the EU. The carbon intensity of the Polish economy exceeds the EU average by 172% and is the second greatest emitter among EU Member States. In 2019, Poland's average GHG emissions per capita were 9.4 tons of CO₂ equivalent, exceeding the EU average of 7.6 tons per capita (World Bank 2023). Between 2005 and 2019, Polish emissions per capita decreased by only 2.3%, compared to a reduction of 22% in the EU. While energy sector emissions decreased by 17% between 2005 and 2019, the emissions from the transportation sector increased by 84%, reaching a 17% share in 2019 in Poland (Zimon et al. 2022). Poland is the only EU member state that has refused to attain carbon neutrality by 2050, citing a need for additional time and funds to conclude the transition to zero emissions (WWF 2020). reported that Poland needs to reduce emissions by implementing net-zero energy standards in four critical areas (buildings; transportation; electricity; agriculture, forestry, and land use); with promising outcomes.

In comparison to other Euro Zone countries, Poland's economy is expanding at a rapid rate (Addai and Kirikkaleli 2023). The Polish economy grew between 2005 and 2019, with an annual growth fluctuating between 1% and 7%, while emissions remained relatively stable (World Bank 2023). The financial crisis of 2009 had no significant effect on Poland's economy or greenhouse gas emissions (Addai and Kirikkaleli 2023). Thus, governments in both emerging and rich nations must prioritise achieving acceptable economic growth. Additionally, expanding the economy while simultaneously aiming to improve the ecology and minimise adverse impacts is one of the essential actions’ governments must take to accomplish a healthy environment (Raihan and Tuspekova 2022). With robust economic growth and relatively stable GHG emissions, there has been a relative decoupling between economic performance and environmental impact that must be strengthened if Poland is to meet its national emission goals and help meet EU-level objectives. Being a part of the European Union (EU) Poland is currently attracting more foreign investments. For example, the increase of Poland’s globalisation index from 40 points in 1970 to 81 points in 2020 makes it a rapidly growing market increasingly integrated into the global economy. The Polish economy's contemporary industrial and coal, oil, and natural gas are crucial for the functioning of the service economy, which is widening rapidly. Poland is one of nine nations responsible for 90 percent of the world's coal production, but Polish coal is expensive and coal mining is unprofitable (Zimon et al. 2023). Poland has the greatest hard coal reserves and lignite deposits in the European Union. Coal is commonly called ‘black gold’ in Poland, contributing to the nation's energy independence (Sadowska et al. 2023). Consequently, the energy industry sector relies significantly on coal. In 2020, 41% of Poland's total energy supply and 69% of Poland's electricity were derived from coal. In addition to coal, Poland has extensive natural gas reserves, which generate electricity and heat (Gołaś 2023). Thus, natural gas and hydrocarbon consumption increased in Poland between 2005 and 2019. The country heavily emphasises fossil fuels instead of renewable energy sources (Zimon 2019). It is difficult to understand this type of policy, especially in the case of unprofitable mines that produce expensive coal for the Polish energy sector. The transport sector consumes the most fossil fuel in Poland, followed by industry, accommodation, and heat production (Addai and Kirikkaleli 2023). Energy is critical for a flourishing economy, but it may also be the leading cause of environmental degradation. Several experts argue that fossil fuels are responsible for the negative environmental effects (Addai et al. 2023; Voumik, Mimi, and Raihan 2023). Coal is the major source of air pollution in Poland (Olczak et al. 2021; Zimon et al. 2023). This will probably not change in the next few decades, especially now that there have been Covid crises in recent years, and the crisis caused by the war in Ukraine. In 2019, coal accounted for 38% of total greenhouse gas emissions and 59% of energy-related CO₂ emissions in Poland (World Bank 2023). Therefore, many issues could be alleviated by shifting away from fossil fuels in favour of cleaner energy. These include lowering greenhouse gas emissions, diminishing dependence on fossil power, and boosting energy security. The portion of renewable power in Poland nearly doubled between 2005 and 2019, reaching 12.2%, although it was still below the country's 2020 goal of 15%. The use of biofuels more than doubled to 9.4% of Poland's total energy supply by 2020, while wind and solar energy account for only 1.7%. By 2030, Poland's energy policy aims for at least a 23 percent share of green power. To be an EU member, Poland confronts an urgent need to develop environmentally, technologically, and economically viable new solutions for the energy sector (Raihan et al. 2022). Technological progress is crucial to slowing global warming (Pattack et al.2023 ). Change management and mitigation strategies for ecological damage will be more productive and successful with the help of new technologies. By nurturing technological innovation, nations can construct resilient infrastructure while promoting inclusive and sustainable industrialisation. Green employment generation, less loss of biodiversity, and improved ecological compatibility are all outcomes of technological innovations that will facilitate the shift from fossil fuels to energy savings (Kirikkaleli et al. 2021). In addition, globalisation could improve implementation strategies and be a great step forward for the international movement towards eco-friendly progress. Recent discussions have centred on how ecological preservation and financial expansion are interrelated through globalisation. The rapid growth of the worldwide economy could result in a boost in the quantity of energy that is consumed and the release of carbon output. This is especially true in this age of globalisation while emerging and developing economies are working towards enhancing the national economic foundation of their countries through incorporating business, knowledge transfer, and finance (Xia et al. 2022). Nevertheless, the ‘'land use, land use change, and forestry (LULUCF)'’ sector has consistently served as a carbon sink, reducing Poland's net GHG emissions in 2019 to 358 metric tons of CO₂ equivalent. Poland has approximately 9.4 million hectares of forest, which accounts for 31% of its total land area. The Polish State Forests Institution initiated the ‘'Carbon Forests'’ project in 2017, seeking to increase forest carbon stocks while preserving biological diversity and water retention capabilities. Numerous studies have investigated the ramifications of socioeconomic and environmental factors on CO₂ (Begum, Raihan, and Said 2020; Khan et al. 2020; Salehi et al. 2022; Voumik, Mimi, and Raihan 2023). In contrast, Akinsola et al. (2022) pointed out that carbon dioxide emissions, which account for a significant portion of greenhouse gas emissions, were insufficient for considering and assessing worldwide ecological damage. Nonetheless, several researchers have asserted that ecological footprint measures ecological damage more precisely (Khan et al. 2020; Kihombo et al. 2021). However, ecological footprint accounting requires ecological footprints and biocapacity. Much research has examined how different elements influence ecological footprint (Danish, Ulucak, and Khan 2020; Rafique et al. 2022; Siche et al. 2010) but the supply side (biocapacity) has been neglected. Consequently, it is necessary to design the most suitable and accurate measuring ecological compatibility. Also, Siche et al. (2010) recommended the load capacity factor. In addition, Fareed et al. (2021) pointed out that the load capacity factor demonstrated a nation's ability to maintain its population's modern lifestyles. Figure 1 presents the annual trends of biocapacity, ecological footprint, and load capacity factor in Poland. Due to the deterioration of the ecology in Poland, biocapacity is decreasing, and the ecological footprint is growing, resulting in a decline in load capacity. When carrying capacity drops below 1, environmental sustainability is threatened. As a direct result, the critical mass required for sustainability equals one. The premise of this argument demonstrates that the load capacity ratio is a more all-encompassing assessment compared to emission levels and ecological footprints. However, using metrics such as carbon emissions and ecological footprint, several studies have exhaustively analyzed environmental issues in different countries worldwide. A comprehensive ecological impact study is essential to mitigate environmental issues and achieve the SDGs. Consequently, this study employed the GHG emissions and load capacity factor as proxies of ecological damage that provides a comprehensive evaluation scale for assessing the surrounding ecosystem in the context of Poland, which would help to implement future government action to achieve national goals aimed at lowering greenhouse gas emissions and achieving a healthy environment.

Figure 1. Poland's ecological footprint, biocapacity, and load capacity factor.

Considering the preceding, this research aims to apply a variety of econometric techniques to the question of how the development of the Polish economy, the use of fossil fuels, the adoption of renewable power sources, the spread of globalisation and new technologies, and growth in the nation's forest cover have impacted the country's GHG emissions and its load capacity factor between 1990 and 2018. Based on the scientific questions, the attainable objectives of this study are set as below:

1. To empirically analyze the impact of GDP per capita on the environment.

2. To investigate the effect of fossil fuels on the environment.

3. To examine the influence of renewable energy use on the environment.

4. To investigate the impact of globalisation on the environment.

5. To explore the effect of technological innovation on the environment.

6. To assess the influence of forest cover on the environment.

This study adds multiple substantial new insights to the previous research. First, this research addresses the knowledge unavailability of relevant prior literature by generating novel insight into the association between the load capacity factor and its influencing parameters. This research adds to the growing knowledge about the complex associations between the economy, globalisation, as well as ecology. Secondly, in the case of Poland, the previous studies (Zimon et al. 2023) investigated the associations between carbon emissions and factors like economic development and energy use. This research is the first to use greenhouse gas emissions as a surrogate for environmental deterioration in Poland. Thirdly, this research is also the first endeavour to use the load capacity factor as an aspect of ecological degradation in Poland. Supply and demand-side approaches to ecological problems are considered. Discussions concerning environmentally responsible practices are taken to a higher level attributable to this indication.

Regarding the load capacity factor to assess ecological deterioration, an inadequate study has been conducted into the ramifications of globalisation, technological advancement, and forest cover. The present study filled a void in the literature by examining the influence of globalisation, technological innovation, and forest cover on load capacity factors in an emerging economy. Fifth, the analysis drew from the latest available data set for Poland that was obtainable, covering the period from 1990 to 2018.

The period covering the years 2019–2023 was not used for the research since these years were the time of the crisis caused by the Covid-19 pandemic and the war in Ukraine. During this time, crisis management was introduced. Various sometimes unusual and wrong decisions were made. At that time, there was an energy crisis during which coal prices reached record values. These were extraordinary situations. The period of the Covid crisis and the war in Ukraine will be analyzed in separate studies in the future.

Three-unit root tests (ADF, DF-GLS, and P–P) were used to investigate the integrated order of the dataset, and the ARDL bounds testing method was applied to examine whether the variables were integrated. Sixthly, FMOLS and CCR were implemented to ensure the estimates of the coefficient values in the DOLS model. The utilisation of DOLS, FMOLS, and CCR in this research paper exemplifies a meticulous and comprehensive approach to analyzing the intricate relationships between economic growth, energy utilisation, technological innovation, and environmental impacts in Poland. DOLS, by incorporating lagged variables, adeptly addresses endogeneity concerns, ensuring that estimates are robust and reflective of true relationships. FMOLS, on the other hand, stands out for its ability to rectify potential omitted variable bias, an invaluable feature when studying a multifaceted system with numerous influencing factors. Lastly, the application of CCR underscores the paper's commitment to robust cointegration analysis, providing a solid foundation for understanding the long-term dynamics among the variables. These methodologies collectively bolster the credibility and reliability of the research findings, making them exceptionally well-suited for a study of this nature and depth. Finally, the study's results offer policymakers complete information and evidence-based insights for designing efficient carbon-free economic solutions, promoting renewable power use, financing technological innovation, enhancing forest carbon sinks, and improving global cooperation to achieve environmental sustainability, which would ensure an increase in load capacity factor and ecological sustainability in Poland. It additionally helps strengthen measures for adapting to and this research may provide useful information for other developing countries to implement more effective strategies for coping with the impacts of warming climates for attaining sustainable growth.

The remaining portion of the paper is set up as follows. In the second part of the paper, the current literature studies pertinent to the topic at hand are analyzed. Section 3 of this study describes the investigation data, theoretical framework, empirical model creation, and econometric estimate methodologies in depth. The article's fourth part presents the results of an empirical model evaluation. The fifth part of the paper contains an overview and contrast of the outcomes of the current study with additional pertinent research in the available research. This paper analyses conclusions, evaluates their significance, makes recommendations for further study based on what is known now, and acknowledges the study's shortcomings.

2. Literature review

More and more studies are exploring the fundamental interactions between economic growth, energy use, and ecological mortification.

Table

Authors	Year	Country	Variables	Method	Findings
Pata and Samour (2023)	1990–2018	27 OECD countries	Renewable energy, load capacity factor	Method of Moments Quantile Regression	Favourable association between the use of renewable power and the load capacity factor.
Huang, Villanthenkodath, and Haseeb (2023)	1975–2021	India	Load capacity factor, energy use	Autoregressive Distributed Lag model (ARDL)	Load capacity factors were lowered by energy use while eco-friendly technologies reduced the load capacity factor
Raihan, Pavel, et al. (2023)	1971–2018	Mexico	Economic growth, financial globalisation, urbanisation, fossil fuel, renewable energy, and load capacity factor	ARDL	Mexico's load capacity factor experienced a decline in environmental quality due to economic growth, increased fossil fuel usage, and urbanisation. Conversely, the adoption of renewable energy sources and the impact of financial globalisation had positive effects on the load capacity factor, both in the short and long term.
Wenlong et al. (2023)	1995–2018	Eleven Asian countries	Energy efficiency, technological innovation, and CO2	Cross-Sectionally Augmented Auto-regressive Distributed Lag (CS-ARDL)	Energy savings and technological breakthroughs improved the ecology
Alola, Özkan, and Usman (2023)	1965–2018	India	Fossil fuel, renewable power, and load capacity factor	ARDL method	The productivity of fossil fuels and the utilisation of sources of clean energy improve the ecology by raising the load capacity factor.
Zimon et al. (2023)	1972–2021	South Korea	Fossil fuel, renewable energy, nuclear energy, CO2 emission	ARDL method	The increasing GDP and population in South Korea may result in elevated CO2 emissions, but implementing a strategy that transitions to renewable energy can effectively reduce these emissions in the country.
Voumik, Rahman, and Akter (2022)	1990–2021	European Union	GDP, energy use, energy intensity, research, and development, and CO2 emission	CS-ARDL	As income rises, pollution tends to decrease initially, reaching a threshold where it starts to rise again. The long-term reduction of pollution is achieved through the adoption of renewable energy and investment in research and development (R&D). However, it is noteworthy that an increase in energy intensity contributes to higher CO2 emissions.
Pata and Isik (2021)	1981–2017	China	Economic expansion, energy usage, and load capacity factor	ARDL method	China's load capacity factor was decreasing due to increased economic expansion and energy intensity
Pata and Balsalobre-Lorente (2022)	1965–2017	Turkey	economic development, energy consumption, and load capacity factor	Auto-regressive Distributed Lag (ARDL)	Economic development and energy consumption negatively influence load capacity.
Fareed et al. (2021)	1965–2014	Indonesia	Load capacity factor, renewable energy, income, non-renewable energy, CO2 emission	Fourier quantile causality test	The load capacity factor was higher when renewable energy was used, indicating better ecological compatibility and it was lower when income and nonrenewable energy use were higher.
Pata (2021)	1992–2016	Japan and United States	Economic prosperity, load capacity factor, renewable energy	ARDL	A negative correlation between economic prosperity and load capacity factor while a positive link between renewable power and load capacity factor in Japan and the United States
Zhao et al. (2023)	1990–2018	BRICS-T	Renewable power, stock market capitalisation, load capacity factor	CS-ARDL	Renewable power increased environmental efficiency and stock market reduced environmental quality
Guloglu, Caglar, and Pata (2023)	1980–2018	26 OECD countries	Renewable power, load capacity factor	Quantile common correlated effects mean group (QMG) estimator	Renewable power enhanced the load capacity factor
Agila et al. (2022)	1970–2018	South Korea	Economic growth, energy use, trade globalisation, load capacity	Quantile-On-Quantile Regression	The consequences of economic growth, energy use, and trade globalisation mitigated the load capacity factor.
Awosusi et al. (2022)	1980–2017	South Africa	Economic growth, non-renewable energy, load capacity factor, globalisation	ARDL procedure	A detrimental consequence of economic growth and nonrenewable energy on the load capacity factor while a positive effect of globalisation on the load capacity factor
Dhingra (2023)	1991–2020	Brazil, Russia, India, and South Africa	Economic expansion, globalisation, and greenhouse gas emission	ARDL	Economic expansion and globalisation harmed greenhouse gas emissions
Raihan (2023c)	1990–2020	Colombia	Economic prosperity, GHG emission, renewable power, technological innovation, globalisation, and forest cover	DOLS, FMOLS, and CCR	A sharp improvement in economic prosperity was related to improved GHG emissions, whereas an increase in renewable power, technological innovation, globalisation, and forest cover helped lower Colombia's GHG emissions.
Mishra, Sarangi, and Mishra (2020)	1971–2014	India	Economic expansion, energy consumption, GHG emission, forest cover	ARDL	Economic expansion and energy consumption contributed to higher GHG emissions; forest cover had the opposite effect.
Addai et al. (2023)	1990 Q1 to 2019Q4	Poland	Green power, CO2 emission, power consumption, economic expansion	Fourier-based ARDL procedure	Use of green power helped reduce CO2 emissions, whereas power consumption and economic expansion were expected to push emissions upward.
Radmehr, Henneberry, and Shayanmehr (2021)	1995–2014	Poland	Economic prosperity, non-refillable power, CO2 emission	Generalised spatial two-stage least squares (GS2SLS) method	Economic prosperity and non-refillable power boosted CO2 emissions, while refillable power helped cut Poland's carbon output
Zimon (2019)	2018 and 2019	Poland	Cleaner power, GHG emission	Individual quarter	Cleaner power mitigated total GHG emissions in Poland

Ecological deterioration was measured differently depending on the period, country, method, and indicator employed; the paper found inconsistent results from the reviewed literature. The previous studies provided critical background information for the growing ecological concerns faced by developing nations like Poland. Yet, no study has been done on the impact of environmental factors on either GHG emissions or the load capacity factor in Poland. While some econometric research has explored the effects of forest cover on GHG emission reduction, there is no empirical research exploring the ramifications of forest cover on the load capacity factor. In addition to economic expansion and energy consumption, technological innovation and globalisation may devastate the ocean, plants, animals, and other forms of wildlife. Put simply, evaluating the impact of technological progress, globalisation, and forest cover on the potential of the available resources is necessary. To address this gap, this research implemented the load capacity factor, which considers both biocapacity and ecological impact. Through efforts, the paper hopes to give globalisation a fresh perspective–environment relationship, technological innovation–environment nexus, and forest cover-environment association and improve the extent of human understanding already out there by examining the impact of technological progress, globalisation, and forest cover on the load capacity factor. Consequently, this research considers these attributes important for Poland's long-term prosperity.

3. Methodology

3.1. Data

This study investigated the effect of GDP, clean power, globalisation, technological progress, and forest land on GHG emissions in Poland from 1990 to 2018.

Since statistics on greenhouse gas emissions, GDP, renewable energy, and forest land were not available before 1990, a period starting in 1990 was selected; similarly, a period ending in 2018 was not selected due to a lack of data on load capacity factors through 2018. LGHG reflects the logarithmic form of total greenhouse gas emission as measured in Kt of CO₂ equivalent indicating that this metric is used to express the total impact of various greenhouse gases in terms of the equivalent amount of carbon dioxide (CO₂). LLCF indicates the logarithmic form of load capacity factor measured in global hectares per person, indicating how much land and resources are required to support the lifestyle and activities of an individual in terms of global hectares. LY denoted the logarithmic form of economic growth measured in constant 2015US$, refers to a measure of the economic output per capita adjusted for inflation to the constant prices of the year 2015, using the United States dollar (US$) as the currency. LFOS means the logarithmic form of fossil fuel consumption measured in % of total energy consumption meaning the percentage of total final energy consumption that comes from fossil fuels. LREN represents the logarithmic form of renewable energy consumption measured percentage of total final energy indicates the proportion of a country or region's total energy consumption that comes from renewable sources. LGL denotes the logarithmic form of globalisation measured as a globalisation index. LTI denotes the logarithmic form of technological innovation measured as the total number of patent applications (resident and non-resident) measuring the total number of patent applications filed with a country's or region's patent office. LFC denotes the logarithmic form of forest cover measured percentage of land area indicating the proportion of a country or region's total land area that is covered by forests.

The GFN was the primary source of the load capacity factor data. LCF is a measure of environmental sustainability that compares biocapacity (the capacity of ecosystems to create biological materials and absorb waste) to the ecological footprint (the demand of human activities on ecosystems). Calculating LCF is done as follows: $\mathrm{Load}\mathrm{capacity}\mathrm{factor}=\mathrm{Biocapacity}/\mathrm{Ecological}\mathrm{footprint}$LCF values lower than 1 indicate that the human demand on ecosystems exceeds the capacity of ecosystems to meet that demand, resulting in environmental degradation. LCF values greater than 1 indicate that the human demand on ecosystems is within the capacity of ecosystems to meet that demand, resulting in environmental sustainability.

The globalisation statistics came from the KOF database in Switzerland. Economic prosperity, fossil power, renewable energy utilisation, technological innovations, globalisation, and forest area were the explanatory factors in this study, with GHG emissions and load capacity factor as the dependent variables. It's also important to remember that the enthusiasm of businesses and industries for exploring uncharted technological territory is a key indicator of technical innovation, whose metrics like patent production can measure. Thus, the global sum of patent applications stands for technical development. A logarithmic transformation is performed on them to confirm that the variables have a normal distribution. An examination for outliers was conducted, and no significant outliers were identified that could unduly influence the regression results. Table 1 lists each variable, its logarithm, measurements, and the investigators that compiled the results.

Table 1. Detailed description of the variables.

Variables	Description	Logarithmic forms	Measurement units	Sources
GHG	Total GHG emissions	LGHG	Kt of CO2 equivalent	WDI (2023)
LCF	Load capacity factor	LLCF	Global hectares per person	GFN (2023)
Y	Economic growth	LY	GDP per capita (constant 2015 US$)	WDI (2023)
FF	Fossil fuel consumption	LFOS	% of total final energy	WDI (2023)
RE	Renewable energy consumption	LREN	% of total final energy	WDI (2023)
GL	Globalisation	LGL	Globalisation index	KOF (2023)
TI	Technological Innovation	LTI	Total number of patent applications (resident and non-resident)	WDI (2023)
FC	Forest cover	LFC	Percentage of land area	WDI (2023)

3.2. Theoretical framework and empirical models

The Polish economy has been expanding steadily. Fossil power, which makes up around 88% of the energy mix (World Bank 2023), is a key factor in this development. Despite energy being the lifeblood of any economy, its use is a leading cause of pollution and ecological damage. The country’s energy mix needs to be adjusted to reduce environmental impacts. To make these adjustments, however, the manufacturing and industrial sectors will need to lessen their reliance on fossil fuel energy sources by increasing their use of cutting-edge green energy technology. Alternatives to fossil fuels had been lacking until recently when renewable energy sources emerged as competitive. One major factor in the current spike in the popularity of renewable energy sources is their ubiquitous availability, unlike fossil fuels which contribute to climate change. Making the switch to clean electricity can reduce energy costs, enhance human health, and air quality, and create new jobs (Paramati, Shahzad, and Doğan 2022). To keep the lights on and cut down on pricey imports, clean power sources like solar, wind, geothermal, and biomass can be employed locally.

Meanwhile, the country must ensure that technological innovation continues to advance to address environmental damage. However, technological progress is essential to drive economic expansion by boosting factor productivity and ensuring energy efficiency. Furthermore, technological development necessitates the economic association of the world. The transfer of environmentally friendly technologies from advanced to underdeveloped countries is a major benefit of globalisation for the planet. Since Poland is a developing economy, it stands to gain both economically and environmentally through closer ties to the EU and the world. Additionally, woods are both an emission source and a carbon sink, two crucial roles in the climate system. Forests engage in the storage of carbon when they absorb carbon dioxide from the air and store it in their plant life and soil. The importance of forests in absorbing atmospheric carbon and improving ecological accuracy improved despite the projections of rising temperatures and warming weather circumstances, which state that temperatures will likely climb by 1.5 degrees Celsius above the historical average between 2030 and 2052.

Following the preceding discussion, it is apparent that, this study developed the following economic functions adopting the mechanism of the Cobb–Douglas production function (Cobb and Douglas 1928) at time t by considering the dependent variables GHG emissions and load capacity factor for two different models: (1) $\mathrm{Model}1:\mathrm{GH}{\mathrm{G}}_{\mathrm{t}}=\mathrm{f}({\mathrm{Y}}_{\mathrm{t}}\mathrm{FO}{\mathrm{S}}_{\mathrm{t}}\mathrm{RE}{\mathrm{N}}_{\mathrm{t}}\mathrm{G}{\mathrm{L}}_{\mathrm{t}}\mathrm{T}{\mathrm{I}}_{\mathrm{t}}\mathrm{F}{\mathrm{C}}_{\mathrm{t}})$(1) (2) $\mathrm{Model}2:\mathrm{LC}{\mathrm{F}}_{\mathrm{t}}=\mathrm{f}({\mathrm{Y}}_{\mathrm{t}}\mathrm{FO}{\mathrm{S}}_{\mathrm{t}}\mathrm{RE}{\mathrm{N}}_{\mathrm{t}}\mathrm{G}{\mathrm{L}}_{\mathrm{t}}\mathrm{T}{\mathrm{I}}_{\mathrm{t}}\mathrm{F}{\mathrm{C}}_{\mathrm{t}})$(2) The empirical models are presented as follows: (3) $\mathrm{Model}1:\mathrm{GH}{\mathrm{G}}_{\mathrm{t}}={\tau }_0+{\tau }_1{\mathrm{Y}}_{\mathrm{t}}+{\tau }_2\mathrm{FO}{\mathrm{S}}_{\mathrm{t}}+{\tau }_3\mathrm{RE}{\mathrm{N}}_{\mathrm{t}}+{\tau }_4\mathrm{G}{\mathrm{L}}_{\mathrm{t}}+{\tau }_5\mathrm{T}{\mathrm{I}}_{\mathrm{t}}+{\tau }_6\mathrm{F}{\mathrm{C}}_{\mathrm{t}}+{\epsilon }_{\mathrm{t}}$(3) (4) $\mathrm{Model}2:\mathrm{LC}{\mathrm{F}}_{\mathrm{t}}={\tau }_0+{\tau }_1{\mathrm{Y}}_{\mathrm{t}}+{\tau }_2\mathrm{FO}{\mathrm{S}}_{\mathrm{t}}+{\tau }_3\mathrm{RE}{\mathrm{N}}_{\mathrm{t}}+{\tau }_4\mathrm{G}{\mathrm{L}}_{\mathrm{t}}+{\tau }_5\mathrm{T}{\mathrm{I}}_{\mathrm{t}}+{\tau }_6\mathrm{F}{\mathrm{C}}_{\mathrm{t}}+{\epsilon }_{\mathrm{t}}$(4) Where τ₀ and ${\epsilon }_{\mathrm{t}}$ are the intercept and the error term respectively. In addition, τ₁, τ₂, τ_3, τ₄, τ_5, and τ₆ characterise the coefficients.

Furthermore, the time series data has been smoothed by taking a logarithm. Unlike simple linear models, log-linear models provide evidence-based findings that perform. The ‘augmented multivariate production functions for the linear log model’ can be found in the following table: (5) $\mathrm{Model}1:\mathrm{LGH}{\mathrm{G}}_{\mathrm{t}}={\tau }_0+{\tau }_1\mathrm{L}{\mathrm{Y}}_{\mathrm{t}}+{\tau }_2\mathrm{LFO}{\mathrm{S}}_{\mathrm{t}}+{\tau }_3\mathrm{LRE}{\mathrm{N}}_{\mathrm{t}}+{\tau }_4\mathrm{LG}{\mathrm{L}}_{\mathrm{t}}+{\tau }_5\mathrm{LT}{\mathrm{I}}_{\mathrm{t}}+{\tau }_6\mathrm{LF}{\mathrm{C}}_{\mathrm{t}}+{\epsilon }_{\mathrm{t}}$(5) (6) $\mathrm{Model}2:\mathrm{LLC}{\mathrm{F}}_{\mathrm{t}}={\tau }_0+{\tau }_1\mathrm{L}{\mathrm{Y}}_{\mathrm{t}}+{\tau }_2\mathrm{LFO}{\mathrm{S}}_{\mathrm{t}}+{\tau }_3\mathrm{LRE}{\mathrm{N}}_{\mathrm{t}}+{\tau }_4\mathrm{LG}{\mathrm{L}}_{\mathrm{t}}+{\tau }_5\mathrm{LT}{\mathrm{I}}_{\mathrm{t}}+{\tau }_6\mathrm{LF}{\mathrm{C}}_{\mathrm{t}}+{\epsilon }_{\mathrm{t}}$(6) The following Figure 2 depicts an overview of the estimation procedures.

Figure 2. The methodological framework of the study.

Econometric analyses were performed using Stata and Eviews software to ensure robustness of the results. Both packages offer advanced features for time series data analysis and estimation of dynamic models, including the chosen FMOLS, DOLS, and CCR techniques.

3.3. Unit root tests for data stationarity

Avoiding errors in the regression requires conducting unit root tests. This test checks to verify if the regression variables are stationary, and if they are, it estimates the necessary equation using the stationary processes. Empirical literature generally believes that the integration sequence needs to be established before cointegration techniques can be used (Cobb and Douglas 1928). Therefore, the stationarity of the given variables is tested using the unit root test in this article. When the probability distribution of a variable's mean variance and co-variance changes over time the variable is non-stationary. This research used three different tests to figure out the existence of an autoregressive unit root: the ‘Augmented’ Dickey-Fuller (ADF) test proposed by Dickey and Fuller (1979), the Dickey-Fuller generalised least squares (DF-GLS) test proposed by Elliott, Rothenberg, and Stock (1992), and the Phillips-Perron (P–P) test proposed by Phillips and Perron (1988).

3.4. Assessing cointegration using ARDL bounds

To ascertain whether the variables were stable over time, the paper used Pesaran, Shin, and Smith's (2001) ARDL bounds analysis method of cointegration. This technique can be used if it is found that the parameters are steady at both the I (1) and I (0) levels (Konstantakopoulou 2019). To realistically describe data emergence, a model for empirically testing ARDL bounds uses many lags inside a structure for modelling from broad to precise scope. To test for cointegration using the ARDL framework, the F-statistic can be computed using different values of the ideal lag for each variable. If the ARDL F-statistic is above a certain value, then the hypothesis is supported; it demonstrates that the variables are cointegrated. When the ARDL F-statistic value falls below the critical value, then the variables are not cointegrated. Equations (7) and (8) are econometric models used to develop the ARDL bound testing strategy. (7) $\begin{array}{rl}\mathrm{Model}1:\mathrm{\Delta LGH}{\mathrm{G}}_{\mathrm{t}}& ={\tau }_0+{\tau }_1\mathrm{LGH}{\mathrm{G}}_{\mathrm{t}-1}+{\tau }_2\mathrm{L}{\mathrm{Y}}_{\mathrm{t}-1}+{\tau }_3\mathrm{LFO}{\mathrm{S}}_{\mathrm{t}-1}+{\tau }_4\mathrm{LRE}{\mathrm{N}}_{\mathrm{t}-1}+{\tau }_5\mathrm{LG}{\mathrm{L}}_{\mathrm{t}-1}\\ & +{\tau }_6\mathrm{T}{\mathrm{I}}_{\mathrm{t}-1}+{\tau }_7\mathrm{LF}{\mathrm{C}}_{\mathrm{t}-1}+\sum _{\mathrm{i}=1}^{\mathrm{q}}{\gamma }_1\mathrm{\Delta LGH}{\mathrm{G}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathrm{q}}{\gamma }_2\mathrm{\Delta L}{\mathrm{Y}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathrm{q}}{\gamma }_3\mathrm{\Delta LFO}{\mathrm{S}}_{\mathrm{t}-\mathrm{i}}\\ & +\sum _{\mathrm{i}=1}^{\mathrm{q}}{\gamma }_4\mathrm{\Delta LRE}{\mathrm{N}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathrm{q}}{\gamma }_5\mathrm{\Delta LG}{\mathrm{L}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathrm{q}}{\gamma }_6\mathrm{\Delta LT}{\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathrm{q}}{\gamma }_7\mathrm{\Delta LF}{\mathrm{C}}_{\mathrm{t}-\mathrm{i}}+{\epsilon }_{\mathrm{t}}\end{array}$(7) (8) $\begin{array}{rl}\mathrm{Model}2:\mathrm{\Delta LLC}{\mathrm{F}}_{\mathrm{t}}& ={\tau }_0+{\tau }_1\mathrm{LLC}{\mathrm{F}}_{\mathrm{t}-1}+{\tau }_2\mathrm{L}{\mathrm{Y}}_{\mathrm{t}-1}+{\tau }_3\mathrm{LFO}{\mathrm{S}}_{\mathrm{t}-1}+{\tau }_4\mathrm{LRE}{\mathrm{N}}_{\mathrm{t}-1}+{\tau }_5\mathrm{LG}{\mathrm{L}}_{\mathrm{t}-1}\\ & +{\tau }_6\mathrm{T}{\mathrm{I}}_{\mathrm{t}-1}+{\tau }_7\mathrm{LF}{\mathrm{C}}_{\mathrm{t}-1}+\sum _{\mathrm{i}=1}^{\mathrm{q}}{\gamma }_1\mathrm{\Delta LLC}{\mathrm{F}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathrm{q}}{\gamma }_2\mathrm{\Delta L}{\mathrm{Y}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathrm{q}}{\gamma }_3\mathrm{\Delta LFO}{\mathrm{S}}_{\mathrm{t}-\mathrm{i}}\\ & +\sum _{\mathrm{i}=1}^{\mathrm{q}}{\gamma }_4\mathrm{\Delta LRE}{\mathrm{N}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathrm{q}}{\gamma }_5\mathrm{\Delta LG}{\mathrm{L}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathrm{q}}{\gamma }_6\mathrm{\Delta LT}{\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathrm{q}}{\gamma }_7\mathrm{\Delta LF}{\mathrm{C}}_{\mathrm{t}-\mathrm{i}}+{\epsilon }_{\mathrm{t}}\end{array}$(8) Where Δ denotes the first difference operator, and q represents the optimum lag length.

3.5. DOLS cointegration regression

This research used DOLS purposely to examine time-based data (Stock and Watson 1993). To eliminate serial correlation in the covariance matrix of errors used to calculate standard deviations, the DOLS test considers both the timing of differences and their eventual leads and delays through explanatory parameters (Raihan, Pavel, et al., 2023). To demonstrate orthogonalization, just add each error term's initial and final terms. Normal asymptotic distributions are produced by the DOLS method's standard deviation estimates, giving them a credible choice for p-value analysis. The dependent variable is estimated using DOLS by considering the illustrated components’ levels, leads, and lags, making it suitable for combining cointegrated outlines with parts that associate in a different sequence. DOLS estimation's key strength is that it takes for the mixed order combining of different elements within the cointegrated form. Parameters I (0) with a constant term and I (1) with leads (p) and delays (-p) of the initial discrepancy were also included in the regression. Once it was established that the variables were cointegrated, the long-run coefficient could be approximated using the DOLS process (Eq. (7) and Eq. (8)).

3.6. Robustness check with FMOLS and CCR techniques

To guarantee the accuracy of the DOLS assessment, the methodologies of CCR and FMOLS were implemented for this investigation. Phillips and Hansen (1990) created the FMOLS procedure to incorporate the best possible co-integration estimations. Using the FMOLS approach, a form of least squares, it is possible to account for the serial impact of correlation due to cointegration and dependence on exogenous factors. The FMOLS methodology applies a semi-parametric technique for the estimation of long-run parameters. The application of this methodology yields reliable parameters even when working with a limited sample size, effectively addressing issues such as endogeneity, serial correlation, omitted variable bias, and measurement errors. Additionally, it accommodates the presence of heterogeneity in the long-run parameters. The FMOLS method is employed to estimate a solitary cointegrating relationship that encompasses a combination of integrated order of I (1) variables. The focus of this approach is on the conversion of both data and parameters. The FMOLS method addresses the inference issues seen in classic cointegration techniques, resulting in valid estimated t-statistics for the long-run estimates.

The CCR procedure, developed by Park (1992), involves changing only data that remains constant throughout time in a cointegration model. After evaluating the data this way, no changes will be made to the cointegrating association in the cointegration model. Because of the CCR modification, the zero-frequency component of the error term minimised dependence on the regressors in a cointegrating model. Asymptotically, the CCR technique generates effective and parameter-free estimators and chi-square tests. Moreover, the CCR is a regression model that can be used for both single equation regression and multivariate regression without any need for change, hence ensuring the preservation of efficiency. Therefore, using Eq. (7) and Eq. (8), the FMOLS and CCR methods utilised to conduct the DOLS conclusions are subjected to a robustness test.

4. Results

4.1. Descriptive statistics

Before performing the actual analysis (using methods like unit root, cointegration, and other analysis methods), it is necessary to determine the dataset's descriptive properties, particularly its normality. Table 2 demonstrates the dataset's descriptive characteristics across 29 years. The findings show that the variable means fall within the normal range, proving that the dataset lacks outliers. So far, results from calculating the standard deviation indicate that the factors studied reveal an acceptable volatility range. Moreover, all the applied parameters’ predicted skewness is between +1 and −1. The data on GHG emissions, technological innovation, and forest land are positively skewed, while the load capacity factor, economic prosperity, fossil power, renewable power, and globalisation are negatively skewed. A kurtosis value of less than three was also observed in all observed series, indicating that nature is platykurtic. Therefore, the Jarque-Bera test and the probability values for each observed series are consistent with the hypothesis that they have a normal distribution.

Table 2. Descriptive statistics of the variables.

Variables	LGHG	LLCF	LY	LFOS	LREN	LGL	LTI	LFC
Mean	12.837	−0.8125	9.0203	4.5452	1.9641	4.2475	8.4378	3.3993
Median	12.825	−0.7994	9.0193	4.5600	1.9865	4.3175	8.3885	3.3987
Maximum	12.949	−0.7352	9.5756	4.5855	2.4740	4.3945	8.9542	3.4306
Minimum	12.753	−0.9261	8.4629	4.4848	0.7250	3.9318	7.9205	3.3673
Std. Dev.	0.0621	0.0536	0.3439	0.0311	0.4577	0.1415	0.3037	0.0208
Skewness	0.5494	−0.3246	−0.1154	−0.7339	−0.7401	−0.7477	0.0346	0.0227
Kurtosis	2.0058	2.0524	1.7710	2.2258	2.6845	2.4419	1.9988	1.6033
Jarque-Bera	2.6534	1.5943	1.8893	3.3274	3.1241	3.2765	1.2171	2.3596
Probability	0.2654	0.4506	0.3888	0.1894	0.1502	0.1942	0.5441	0.3074

4.2. Findings of unit root tests

Testing for the unit root is essential to establish whether the variables are stationary, and descriptive statistics should be used to confirm normality before cointegration is applied. This is a crucial stage since it determines whether the applied variable is stationary and helps researchers choose the most appropriate test to use going forward. In this study, the paper employed the usage of the ‘ADF, DF-GLS, and P–P unit root’ techniques. Table 3 demonstrates that all relevant parameters remain unchanged at the initial difference. LRE and LGL were found stationary in the levels and first difference under the ADF and P–P test. Hence, cointegration with the ARDL and the DOLS estimator are strong contenders for these data.

Table 3. The results of unit root tests.

Logarithmic form of the variables	ADF		DF-GLS		P-P
	Log levels	Log first difference	Log levels	Log first difference	Log levels	Log first difference
LGHG	−2.212	−4.154***	−1.312	−4.235***	−1.984	−4.163***
LLCF	−2.575	−4.956***	−2.377	−5.045***	−2.652	−4.941***
LY	−0.416	−6.912***	−0.616	−4.567***	0.500	−6.573***
LFOS	−1.179	−3.595 ***	−0.420	−3.369***	0.399	−5.433***
LREN	−3.616***	−4.990***	−1.574	−4.357***	−2.976**	−4.990***
LGL	−4.206***	−3.395**	−0.474	−3.476***	−5.798***	−3.427**
LTI	−1.977	−4.587***	−1.964	−4.438***	−2.123	−4.577***
LFC	−1.580	−3.887***	−1.537	−3.997***	−0.377	−4.057***

*** and ** signify significance at the 1% and 5% levels, respectively.

4.3. ARDL bounds analysis outcomes

After validating the stationarity characteristics of the dataset, this research proceeded to predict the variable's cointegration by using the ARDL bounds testing approach. This investigation selected an acceptable lag time (2) to measure the F-statistic created on the lowest Akaike Information Criterion (AIC) values to perform the ARDL bounds test for cointegration assessment. The ARDL bound analysis findings for examining the hypothesis of cointegration between the variables are shown in Table 4. The existence of a long-term interaction between the variables may be inferred if the predicted value of the F-test is bigger than the values of both thresholds. The projected F-statistic value for Model 1 (4.897753) and Model 2 (4.506229) showed evidence of a long-run connection between dependent and independent variables, are more than the 10%, 5%, 2.5%, and 1% of the upper limit in the I (0) and I (1).

Table 4. ARDL bounds test results.

	F-bounds test		Null hypothesis: No degrees of relationship
	Test statistic	Estimate	Significance	I(0)	I(1)
Model 1: GHG emissions	F-statistic	4.897753	At 10%	1.99	2.94
	K	6	At 5%	2.27	3.28
			At 2.5%	2.55	3.61
			At 1%	2.88	3.99
Model 2: Load capacity factor	F-statistic	4.506229	At 10%	1.99	2.94
	K	6	At 5%	2.27	3.28
			At 2.5%	2.55	3.61
			At 1%	2.88	3.99

4.4. Results of the DOLS estimation

Table 5 displays the estimated DOLS values. The estimated long-run coefficients of economic growth, a 1% rise in Poland's economic prosperity, lead to a 1.44% enhancement in the country's greenhouse gas emissions and a 0.47 percentage point decrease in the load capacity factor. Long-term GHG emissions would grow by 2.76%, and the load capacity factor would fall by 1.10% if an increase energy demand from fossil fuels by just 1%. Both Zimon et al. (2022), Umair and Yousuf (2023), and Yi et al. (2023) confirmed that using fossil fuels boosted ecological damage. However, the coefficients of renewable power, enhancing renewable power use by 1% (Table 5), are linked to decreasing greenhouse gas emissions by 0.06% and increasing the load capacity factor by 0.04%. Renewable energy has been shown to drastically lessen the GHG emission, as reported by Candra et al. (2023) and Quang, Milani, and Zahra (2023). The research also indicated that a 1% boost in globalisation would reduce GHG emissions by 0.05% and increase the load capacity factor by 0.04%. It was proven by Sheraz et al. (2021) that GHG emissions fell because of globalisation. According to DOLS projections, Poland's GHG emissions would fall by 0.08%, and the country's load capacity factor would improve by 0.12% with every 1% increase in technical innovation. Finally, the long-run forest area coefficients showed that a 1% increase in forest cover would cut Poland's total GHG emissions by 1.11% while boosting the load capacity factor by 0.72%.

Table 5. The outcomes of the DOLS estimation.

Variables	Model 1: Dependent variable LGHG			Model 2: Dependent variable LLCF
	Coefficient	t-Statistic	p-value	Coefficient	t-Statistic	p-value
LY	1.4418***	4.6914	0.0001	−0.4715***	−3.9543	0.0005
LFOS	2.7608***	3.4189	0.0003	−1.1026***	−3.8927	0.0007
LREN	−0.0598**	−3.1284	0.0231	0.0444**	3.0679	0.0195
LGL	−0.0522**	−3.1839	0.0247	0.0439**	2.9914	0.0122
LTI	−0.0843***	−4.3745	0.0002	0.1189***	5.7615	0.0000
LFC	−1.1104***	−4.1896	0.0000	0.7155***	4.7971	0.0002
C	35.637	1.6827	0.1109	38.714	1.6667	0.1468
R^2	0.9854			0.9823
Adjusted R^2	0.9767			0.9691

*** and ** signify significance at the 1% and 5% levels, respectively.

According to this study's empirical findings, Poland's efforts to alleviate emissions and ensure ecological longevity are bolstered by the rising adoption of green power across the country, a more globally integrated environment, technological advancements, and better forest management. The predicted coefficients have the same mental and numerical interpretations in both contexts. With an R² of 0.9854 for the GHG model and 0.9823 for the LCF model, as well as an adjusted R² of 0.9767 for the GHG model and 0.9691 for the LCF model, it appears that the developed regression model provides a good match to the data. The explanatory factors have the potential to explain nearly all of the observed variance seen in the variables being evaluated.

4.5. The results of the robustness check

This analysis tested the credibility of FMOLS and CCR procedures. Tables 6 and 7 display the FMOLS and CCR regression coefficients for the models, respectively. The favourable benefits of economic expansion and the use of fossil fuels on GHG emission, as well as the unfavourable repercussions of booming economies and the use of fossil fuels on the load capacity factor in Poland, are accurately predicted by DOLS, which is supported by the conclusions of FMOLS and CCR. The FMOLS estimates that a 1% boost in economic prosperity and fossil power will boost 1.43% in greenhouse gas emissions and 2.59% in the load capacity factor. The CCR calculation also showed that a 1% enhancement in GDP expansion and fossil power increases GHG emissions by 1.49% factor by 0.56% and 1.19%.

Table 6. The results of the FMOLS estimation.

Variables	Model 1: Dependent variable LGHG			Model 2: Dependent variable LLCF
	Coefficient	t-Statistic	p-value	Coefficient	t-Statistic	p-value
LY	1.4348***	4.6002	0.0000	−0.5949***	−3.9532	0.0006
LFOS	2.5935***	4.1396	0.0002	−1.2003***	−3.9221	0.0009
LREN	−0.0559**	−2.9363	0.0348	0.0437**	2.7074	0.0271
LGL	−0.0503**	−2.9366	0.0367	0.0426**	2.6362	0.0378
LTI	−0.1004***	−4.8697	0.0001	0.1356***	5.6634	0.0000
LFC	−1.0593***	−4.1396	0.0003	0.7239***	3.9106	0.0004
C	33.695	1.4072	0.1254	41.057	1.6289	0.1606
R^2	0.9837			0.9826
Adjusted R^2	0.9721			0.9697

*** and ** signify significance at the 1% and 5% levels, respectively.

Table 7. The results of the CCR estimation.

Variables	Model 1: Dependent variable LGHG			Model 2: Dependent variable LLCF
	Coefficient	t-Statistic	p-value	Coefficient	t-Statistic	p-value
LY	1.4893***	4.1023	0.0004	−0.5646***	−3.5879	0.0007
LFOS	2.8864***	3.7037	0.0009	−1.1961***	−3.8193	0.0008
LREN	−0.0567**	−3.1749	0.0258	0.0456**	3.1252	0.0136
LGL	−0.0502**	−3.1029	0.0269	0.0438**	3.0567	0.0174
LTI	−0.0993***	−4.2717	0.0003	0.1377***	5.1113	0.0000
LFC	−1.1226***	−4.3223	0.0002	0.7218***	4.1627	0.0002
C	35.678	1.3463	0.1926	42.295	1.754	0.1675
R^2	0.9787			0.9802
Adjusted R^2	0.9691			0.9689

*** and ** signify significance at the 1% and 5% levels, respectively.

Renewable power, globalisation, technological innovations, and forest area all had negative and statistically significant coefficients in the GHG emissions model, confirmed by the FMOLS and CCR tests. FMOLS predicts that a 0.06%, 0.05%, 0.1%, and 1.06% reduction in Poland's GHG emissions might result from a 1% increase in the country's usages of renewable power, globalisation, technical progress, and forest cover, respectively. Results from the CCR also showed that a 1% worsening of renewable power, globalisation, technical innovation, and forest area might lead to a 0.06%, 0.05%, 0.1%, and 1.12% reduction in GHG emissions in Poland. Positive and statistically significant coefficients were also found for globalisation, technical innovation, forest area, and renewable power in the load capacity factor model by the FMOLS and CCR tests. According to FMOLS's estimation, a rise of just one percent in Poland's clean energy, globalisation, technological breakthroughs, and forest area would enhance the country's load capacity by 0.04%, 0.04%, 0.14%, and 0.72%, respectively. A 1% increase in renewables, globalisation, technological breakthroughs, or forests would raise the load capacity factor by 0.05%, 0.04%, 0.14%, or 0.72%, respectively, according to the conclusions of the CCR estimation.

The findings from the FMOLS and CCR procedure indicate that the upsurge of GHG emissions and the lessening of load capacity factor in Poland are associated with the country’s economic prosperity and increased utilisation of fossil fuel power sources, whereas decreasing emission level and a boost in the load capacity factor are the conclusions of Poland's renewable power, technological breakthroughs, forest area, and globalisation. The FMOLS and CCR conclusions are consistent with the DOLS conclusions. Since the FMOLS and the CCR estimations have high R² and modified R² values, the factors can explain almost 97% of the variation in the dependent variables.

4.6. Causality test

Table 8 provides the results of the Granger Causality Test, which examines causal relationships between the dependent variable, GHG emissions, and the independent variables. The test evaluates whether past values of the independent variables provide information about the current value of GHG emissions. The findings indicate several important causal relationships. Specifically, Economic Growth (LY) exhibits unidirectional causality, suggesting that past values of economic growth significantly influence current GHG emissions. Additionally, Fossil Fuel Consumption (LFOS) shows unidirectional causality, indicating that past levels of fossil fuel consumption impact current GHG emissions. Similarly, Technological Innovation (LTI), and Forest Cover (LFC) show the unidirectional causality with GHG emission. On the other hand, variables such as Renewable Energy Consumption (LREN) and Globalization (LGL) do not exhibit statistically significant causal relationships with GHG emissions. These results shed light on the specific drivers influencing GHG emissions in Poland, providing valuable insights for policymakers aiming to implement targeted environmental sustainability measures. Overall, the Granger Causality Test underscores the nuanced interplay between economic factors and environmental outcomes, highlighting areas where interventions may be most effective.

Table 8. Granger causality test results (Dependent variable is GHG).

Null Hypothesis:	Obs	F-Statistic	Prob.
LY does not Granger Cause LGHG	27	0.63760	0.5380
LGHG does not Granger Cause LY	3.80684	0.0380
LFOS does not Granger Cause LGHG	27	3.20155	0.0490
LGHG does not Granger Cause LFOS	0.00599	0.9940
LREN does not Granger Cause LGHG	27	0.49864	0.6141
LGHG does not Granger Cause LREN	0.34725	0.7104
LGL does not Granger Cause LGHG	27	0.69690	0.5088
LGHG does not Granger Cause LGL	2.83853	0.0800
LTI does not Granger Cause LGHG	27	0.48914	0.6197
LGHG does not Granger Cause LTI	0.21049	0.8118
LFC does not Granger Cause LGHG	27	0.26485	0.7697
LGHG does not Granger Cause LFC	3.78434	0.0387

Table 9 presents the results of the Granger Causality Test with the Load Capacity Factor (LLCF) as the dependent variable. This test assesses causal relationships between LCF and the independent variables. The findings reveal insightful patterns. Load capacity factors exhibit statistically significant causal relationships with Economic Growth (LY) and Fossil Fuel Consumption (LFOS), indicating unidirectional influence between these variables. Similarly, Technological Innovation (LTI), and Globalization (LGL) show the unidirectional causality with GHG emission. On the other hand, variables such as Renewable Energy Consumption (LREN) and Technological Innovation (LTI) do not exhibit statistically significant causal relationships with LLCF. Similarly, Renewable Energy Consumption (LREN), and Forest Cover (LFC) also show no significant causal connections with LLCF. These results suggest that, in the context of Poland, the Load Capacity Factor may operate independently from the examined economic and environmental variables. This nuanced understanding of causal relationships provides valuable guidance for policymakers seeking to enhance load capacity factor sustainability without being solely dependent on traditional economic and environmental factors.

Table 9. Granger causality test results (Dependent variable is LCF).

Null Hypothesis:	Obs	F-Statistic	Prob.
LY does not Granger Cause LLCF	27	0.12459	0.8835
LLCF does not Granger Cause LY	2.70785	0.0644
LFOS does not Granger Cause LLCF	27	0.11190	0.8946
LLCF does not Granger Cause LFOS	2.82908	0.0541
LREN does not Granger Cause LLCF	27	0.18315	0.8339
LLCF does not Granger Cause LREN	1.39857	0.2681
LGL does not Granger Cause LLCF	27	0.49594	0.6156
LLCF does not Granger Cause LGL	2.46825	0.0720
LTI does not Granger Cause LLCF	27	2.65281	0.0657
LLCF does not Granger Cause LTI	0.08089	0.9226
LFC does not Granger Cause LLCF	27	0.08062	0.9228
LLCF does not Granger Cause LFC	0.44916	0.6439

4.7. Diagnostic inspection

Multiple diagnostic procedures were used in this investigation to determine the accuracy of model 1 and model 2 estimates. The outcomes of a test for serial correlation that was carried out with the help of the Breusch-Godfrey Langrage Multiplier (LM) are presented in Table 10. The findings indicate that a sequential relationship does not exist. Heteroscedasticity was tested using the Breusch-Pagan-Godfrey test, and it was found that the data did not contain heteroscedastic problem. The Normality test was employed to look at how typical the series was. The p-value and Jarque-Bera statistic both pointed to a normally distributed residual. The outcome of the Ramsey RESET test suggests that the model specifications are valid. In addition, the research tested the model's resistance to recursive modifications using the cumulative sum (CUSUM) and cumulative sum of squares (CUSUMSQ) procedures. The charts of the CUSUM and CUSUMQ statistics at the 5% significance can be seen in Figure 3. The confidence intervals are highlighted in red, but the residual values are signified in blue in this example. If the confidence intervals for the analyzed residuals produce values consistent with the estimates, then the model is considered stable at the 5% significance level.

Figure 3. The plots of CUSUM and CUSUMQ tests.

Table 10. The results of diagnostic tests.

Diagnostic tests	Model 1: GHG		Model 2: LCF		Decision
	Coefficient	p-value	Coefficient	p-value
Jarque-Bera test	0.333274	0.8486	0.601828	0.7401	Residuals are normally distributed
Lagrange Multiplier test	2.762685	0.1763	2.354415	0.1195	No serial correlation exits
Breusch-Pagan-Godfrey test	0.468136	0.8244	1.632993	0.1853	No heteroscedasticity exists
Ramsey RESET test	0.941211	0.3898	0.269397	0.7901	The model is specified correctly.

5. Discussion

The study's findings reveal that GDP growth positively impacts greenhouse gas (GHG) emissions and has a detrimental influence on the load capacity factor in Poland. These results suggest that GDP growth, while beneficial for economic development, is associated with detrimental environmental and biodiversity effects. The findings are consistent with previous studies that reported a positive relationship between economic growth and GHG emission. For example, Mishra, Sarangi, and Mishra (2020) in India; Raihan (2023a) in South Korea; Raihan (2023b) and Zimon et al. (2023) in Colombia; Usman et al. (2023) in Mercosur countries; and Dhingra (2023) in BRICS. In addition, the present study’s finding is supported by several studies that revealed the negative association between economic growth and LCF. For instance, Pata and Isik (2021) in China; Fareed et al. (2021) in Indonesia; Pata and Balsalobre-Lorente (2022) in Turkey; Shang et al. (2022) in ASEAN countries; Xu et al. (2022) in Brazil; Akadiri et al. (2022) in India; and Khan et al. (2023) in the G7 and E7 countries. GDP growth is often driven by increased consumption and production activities, leading to higher energy consumption and greater utilisation of fossil fuels. As economic output expands, industries and households require more energy to meet their growing needs, resulting in increased emissions of GHGs. Industries heavily reliant on fossil fuels for energy generation, such as manufacturing, transportation, and power generation, contribute significantly to GHG emissions. The composition of an economy's industrial structure is key in determining the level of GHG emissions. The countries with a higher share of energy-intensive and carbon-intensive industries tend to have higher emission levels. As GDP grows, there is often a concentration of more emissions-intensive industries, leading to a higher carbon footprint. In the case of Poland, industries such as mining, steel production, and coal-fired power generation have traditionally played significant roles in the economy.

The study's conclusions stated that fossil fuel usage had a positive association with greenhouse gas (GHG) emissions and had a detrimental impact on the load capacity factor in Poland. These results emphasise the adverse consequences of fossil fuel consumption on ecology and biodiversity. The finding is consistent with the previous studies of Dahmani, Mabrouki, and Ragni (2021) and Raihan (2023a) reported a positive relationship between fossil fuel energy use and GHG emission in Tunisia and South Korea, respectively. In addition, Fareed et al. (2021). Awosusi et al. (2022), and Khan et al. (2023) revealed the negative association between fossil fuel energy use and load capacity factor in Indonesia, South Africa, and the G7 countries, respectively. Furthermore, Radmehr, Henneberry, and Shayanmehr (2021) found that fossil fuel energy uses increased CO₂ emissions in Poland. About 88% of Poland’s energy bundle is comprised of fossil fuels, with coal accounting for the largest share which are responsible for industrial and production activities in the economy. The construction of new, even modern, coal-fired power plants only exacerbates the problem of greenhouse gas emissions and increases the long-term arrears in the modernisation of Poland's energy sector, burdening future generations with the responsibility of addressing these issues. However, Poland's energy policy seeks to reduce emissions through investments in renewables, natural gas, and nuclear energy, as well as by increasing the energy efficiency and promoting energy productivity. Since 2016, Poland has substantially diversified its gas supply and made substantial investments in renewable energy technologies and offshore wind projects. By investing heavily in LNG terminals and pipeline links to other EU neighbouring countries such as Norway, Poland has effectively reduced its gas imports from 90% in 2010 to 55% in 2020 (Zimon et al., 2023). Historically, coal, oil, and natural gas – all examples of fossil fuels – have been the most popular for various sectors, such as power generation, transportation, and industrial processes. However, burning fossil fuels results in the emission of enormous quantities of GHG into the air. These gases include carbon dioxide (CO₂) and methane (CH₄). These emissions are a part of what causes the greenhouse effect, leading to changing climate patterns and deteriorating ecosystems. Furthermore, the extraction, transportation, and combustion of fossil fuels can cause air pollution, soil contamination, and water pollution, posing risks to ecosystems and biodiversity. Consequently, these findings underscore the urgent need to transition to cleaner and cleaner power sources to alleviate the harmful impacts of fossil fuel usage on the environment and biodiversity.

Findings from the research demonstrated that renewable power had an unfavourable influence on greenhouse gas (GHG) emissions and positively impacted the load capacity factor in Poland. The findings are consistent with previous studies. For instance, Khan, Ali, and Ashfaq (2018), Dahmani, Mabrouki, and Ragni (2021), Raihan (2023a), and Usman et al. (2023) reported a negative relationship between renewable energy use and GHG emission in Pakistan, Tunisia, South Korea, Colombia, and Mercosur countries, respectively. In addition, several studies revealed the positive association between renewable energy use and load capacity factor. For example, Fareed et al. (2021) in Indonesia; Pata (2021) in Japan and the United States; Awosusi et al. (2022) in South Africa; Shang et al. (2022) in ASEAN countries; Xu et al. (2022) in Brazil; Alola, Özkan, and Usman (2023) in India; Zhao et al. (2023) in BRICS-T nations; Khan et al. (2023) in the G7 and E7 countries; Pata and Samour (2023) and Guloglu, Caglar, and Pata (2023) in the OECD countries. This conclusion brings to light the advantages of using sustainable energy sources on the environment and biodiversity. Renewable power sources, such as solar, wind, hydro, and biomass, offer cleaner and more sustainable alternatives to fossil fuels. Unlike fossil fuels, renewable energy generation does not release significant amounts of GHGs during operation. This reduction in emissions contributes to mitigating climate change and minimising environmental damage. Moreover, alternative energy production using non-conventional means promotes the diversification of the energy mix, reducing dependence on fossil fuels and enhancing energy security. Additionally, renewable energy projects often have lower ecological footprints than conventional energy infrastructure, preserving natural habitats and biodiversity. These findings underscore the importance of prioritising and accelerating the transition towards renewable power sources to attain ecological compatibility and safeguard biodiversity in Poland.

The study found that while globalisation boosted Poland's load capacity factor, it affected the country's GHG emissions. These results suggest that globalisation can contribute to positive environmental outcomes and biodiversity preservation. Globalisation entails increased international trade, economic integration, and the exchange of goods and services. This integration fosters the diffusion of cleaner technologies and environmental practices as countries adopt and implement international environmental standards. Furthermore, globalisation can transfer knowledge and expertise, promoting sustainable development strategies and innovation in environmental technologies. Moreover, globalisation facilitates the sharing of best practices, collaboration in environmental initiatives, and the implementation of international agreements, such as the Paris Agreement. These efforts collectively contribute to reducing GHG emissions and promoting sustainable development. Consequently, these findings highlight the potential benefits of globalisation in fostering the conservation of natural resources and preservation of animal and plant life in Poland and emphasise the importance of international cooperation in addressing global environmental challenges. The finding is consistent with Dhingra (2023) who reported a negative relationship between globalisation and GHG in BRICS. In addition, Akadiri et al. (2022), Awosusi et al. (2022), and Xu et al. (2022) revealed the positive association between globalisation and load capacity factor in the case of India, South Africa, and Brazil, respectively. The present study’s finding indicates that foreign direct investment (FDI) from multinational corporations facilitates the transmission of greener technologies in Poland. The transfer of technologies includes green technologies such as pollution reduction technologies and renewable energy technologies, as well as enhanced energy efficiency technologies that reduce the demand for conventional energy sources. Also, Poland is not a technological innovation powerhouse, therefore transfers of cutting-edge energy-efficient technology and investment from the rest of the globe help to reduce energy use and improve resource efficiency. There are resources available to support the transition because Poland is the largest recipient of the EU Just Transition Fund and will also receive recovery monies. The EU's 2021–2027 budget, which contains large reconstruction funds, as well as increased ETS earnings are predicted to bring the country a total of €219.5 billion in various EU funds. Additionally, extra money from the upcoming budget might be available by 2030 (NEP 2022). Poland needs to demonstrate to the other EU leaders that it is prepared to contribute to economic modernisation and the response to the climate emergency (Alpopi et al. 2019; Nica et al. 2023; Popescu et al. 2018; Sultana et al. 2023; WWF 2020). By utilising the benefits of its integration with the EU and the OECD, Poland might be able to enhance its potential for reducing pollution and improving the quality of environment.

The research also reveals that technological innovation has a negative influence on greenhouse gas (GHG) emissions and conclusions that benefit Poland's load capacity factor. The result is consistent with previous studies. For instance, Dahmani, Mabrouki, and Ragni (2021), Raihan (2023b), Wenlong et al. (2023) reported a negative relationship between renewable energy use and GHG emission in Tunisia, South Korea, Mercosur countries, and Asian economies, respectively. In addition, Awosusi et al. (2022), Rahman et al. (2023), Nica et al. (2023), Popescu et al. (2018), and Huang, Villanthenkodath, and Haseeb (2023) revealed the positive association between renewable energy use and load capacity factor in South Africa and India, respectively. These results underscore the beneficial effects of technological advancements on the environment and biodiversity. Technological innovation contributes significantly to developing cleaner and more efficient technologies, promoting sustainable practices across various sectors. For instance, advancements in clean power, for example, solar panels and wind turbines, have contributed to the shift towards cleaner energy sources. Additionally, improvements in energy efficiency have reduced overall energy consumption, leading to lower GHG emissions. Moreover, technological innovation enables the development of environmentally friendly production processes, waste management systems, and transportation solutions, further reducing the ecological footprint. These findings emphasise the potential of technological innovation in driving sustainable development, mitigating climate change, and preserving biodiversity in Poland.

The paper's findings indicate that forest area has a detrimental ramification on greenhouse gas (GHG) emissions and a positive association with the load capacity factor in Poland. The study finding is supported by Khan, Ali, and Ashfaq (2018), Mishra, Sarangi, and Mishra (2020), and Raihan, Rashid, et al. (2023) who established a negative relationship between forest cover and GHG emissions in Pakistan, India, Bangladesh, and Colombia, respectively. Since the land use, land-use change and forestry (LULUCF) sector is the second-largest source of global emissions, forest degradation has been identified as a factor in environmental destruction. The conclusions above demonstrate the vital role of forest areas in promoting environmental benefits and biodiversity conservation. Forests soak up excess CO₂ from the air, absorbing and storing substantial amounts of CO₂ through photosynthesis. By sequestering carbon, forests help mitigate GHG emissions and combat climate change. Additionally, forests assist the load capacity factor by regulating the local climate, preventing soil erosion, and preserving water resources. They provide habitats for numerous plant and animal species, supporting biodiversity and ecological balance. Forest conservation and sustainable management practices are essential for maximising their positive environmental impact. Protecting and expanding forest areas can reduce GHG emissions and foster biodiversity conservation in Poland, highlighting the significance of incorporating forest preservation strategies into environmental policies and initiatives.

6. Conclusions and policy implications

6.1. Conclusions

Due to the accelerated development of its economy, Poland is a developing state in the EU zone with significant environmental issues. A broader and more comprehensive ecological assessment is required for environmental challenges to be mitigated and the SDGs to be attained. Consequently, this paper investigated the influence of economic expansion, fossil fuels, renewable power, globalisation, technological innovation, and forest cover on GHG emissions and load capacity from 1990 to 2018, with Poland as its focal point. Economic progress and fossil power are detrimental to GHG emissions and load capacity factors, highlighting their adverse environmental impact. As the economy grows and reliance on fossil fuels increases, GHG emissions rise, posing significant challenges to environmental sustainability. These findings underscore the urgent need for Poland to pursue sustainable development strategies that isolate rising prosperity from deteriorating ecosystems and transition towards cleaner and more sustainable energy sources. In contrast, renewable power, globalisation, technological breakthroughs, and forest areas have emerged as beneficial environmental factors. The study demonstrates their negative impacts on GHG emissions, indicating that increased adoption of renewable energy sources, globalisation practices that promote sustainable trade and knowledge sharing, technological innovation for green solutions, and forest conservation efforts can contribute to reducing Poland's carbon footprint and enhancing environmental sustainability. These findings provide important insights for policymakers in Poland. Emphasising renewable energy deployment, supporting technological innovation, and promoting sustainable trade practices can help reduce GHG emissions, enhance energy efficiency, and foster environmental resilience. Additionally, preserving and expanding forest areas and sustainable forest management practices can contribute to carbon sequestration, biodiversity conservation, and improved load capacity factors. In conclusion, this research highlights the relevance of adopting an extensive and integrated procedure to address the environmental challenges of economic growth, fossil fuel consumption, and climate change. By prioritising renewable energy, sustainable trade, technological innovation, and forest conservation, Poland can move towards a more sustainable and resilient future while mitigating GHG emissions and enhancing load capacity factors for the environment.

6.2. Policy implications

Several key policy recommendations are essential considering the harmful contribution of GDP growth on greenhouse gas (GHG) emissions and load capacity factors. Firstly, governments should prioritise implementing sustainable development strategies that separate the two, economic expansion and ecological decline. This can be attained by promoting green technologies, renewable power sources, and energy-efficient practices. Secondly, establishing and enforcing stringent environmental regulations and standards can help mitigate GHG emissions associated with economic activities. Thirdly, improvements in clean technologies, along with other types of innovation, can help facilitate a transition to a low-carbon, ecologically conscious society. Additionally, adopting pricing methods like carbon pricing and emission systems for trade can provide incentives to alleviate their carbon output and promote environmentally friendly practices. Finally, promoting public awareness and education on the importance of ecologically sound production and consumption habits can lead to behavioural changes and a more sustainable approach to economic growth. These policy recommendations aim to achieve an equilibrium between economic prosperity and ecological sustainability, ensuring a more resilient and environmentally responsible future.

Given the adverse effects of fossil power on GHG emissions and load capacity factors, it is crucial to implement effective policies to address this issue. First and foremost, governments should prioritise development and implementation of comprehensive energy transition strategies. One way to accomplish this is by providing financial incentives for the widespread use of green power. Second, phasing out fossil fuel subsidies and redirecting those funds toward renewable energy projects can accelerate the replacement of fossil power. Third, implementing methods of putting a price on carbon output, such as carbon taxes and emission trading systems, can internalise the costs of GHG emissions and incentivize the shift towards low-carbon alternatives. Fourth, devoting resources to developing new technologies of clean technologies and supporting innovation in renewable energy can drive technological advancements and reduce reliance on fossil fuels. Finally, promoting energy efficiency measures and encouraging sustainable consumption patterns can further reduce energy demand and fossil fuel dependency. These policy recommendations aim to mitigate the detrimental effect of fossil fuel consumption on the ecology, reduce GHG emissions, and enhance the resilience of load capacity factors.

Considering the positive impacts of globalisation on GHG emissions reduction and load capacity factors, it is important to leverage its potential through effective policy measures. Firstly, governments should prioritise promoting international cooperation and collaboration on environmental issues. This includes fostering partnerships and agreements that encourage sharing best practices, technologies, and knowledge related to environmental conservation and sustainability. Secondly, implementing and enforcing international environmental standards and regulations can ensure that globalisation processes adhere to environmentally responsible practices. Thirdly, facilitating the transfer of green technologies and promoting sustainable trade practices can help minimise the environmental footprint associated with global supply chains. Fourthly, investing in sustainable infrastructure development and improving transportation efficiency can contribute to reducing GHG emissions associated with globalisation activities. Additionally, promoting responsible consumption patterns and raising awareness about the environmental consequences of globalisation has the potential to inspire people and businesses to make environmentally conscious choices. These policy recommendations aim to harness the potential of globalisation to foster sustainable development, reduce GHG emissions, and enhance load capacity factors for a more environmentally resilient world.

Recognising the beneficial impacts of technological breakthroughs on GHG emissions reduction and load capacity factors, fostering an environment conducive to innovation through effective policy measures is crucial. Firstly, investing in R&D should be a top priority for government (R&D) programmes focused on clean technologies and sustainable solutions. This includes allocating adequate funding and providing incentives for businesses and research institutions to engage in R&D activities related to environmental conservation and climate change mitigation. Secondly, supportive policies such as tax credits, grants, and intellectual property protections can incentivize private sector participation in technological innovation for sustainability. Thirdly, promoting collaboration between academia, industry, and government agencies can foster knowledge sharing, interdisciplinary research, and the commercialisation of green technologies. Fourthly, creating innovation-friendly regulatory frameworks that streamline the approval process and reduce barriers to entry can accelerate the adoption and deployment of environmentally beneficial technologies. Investing in digitalisation and smart technologies can enhance energy efficiency, optimise resource utilisation, and contribute to more sustainable production and consumption patterns. These policy recommendations aim to unleash the transformative potential of technological innovation, driving sustainable development, reducing GHG emissions, and improving load capacity factors for a more environmentally resilient future.

Given the positive impacts of forest-covering areas on greenhouse gas (GHG) emissions reduction and load capacity factors, it is crucial to establish policy priorities that foster forest conservation and expansion. Firstly, governments should establish and enforce robust forest protection measures to prevent deforestation and illegal logging. This includes implementing strict regulations, monitoring systems, and penalties for illegal activities. Secondly, promoting afforestation and reforestation programmes can help increase forest covering areas and enhance carbon sequestration. Governments should incentivize and support landowners, communities, and organisations engaged in tree-planting initiatives. Thirdly, implementing sustainable forest management practices that ensure the preservation of biodiversity, ecosystem services, and the sustainable use of forest resources is crucial. Fourthly, fostering partnerships between governments, NGOs, and local communities can encourage community-based forest management and enhance forest conservation efforts. Additionally, integrating forests into minimising temperature rise strategies, such as through mechanisms like REDD (Reducing Emissions from Deforestation and Forest Degradation), offers monetary incentives for forest conservation. These policy recommendations aim to protect existing forest areas, restore degraded lands, and expand forest-covering areas, leading to increased carbon sequestration, improved load capacity factors, and overall environmental resilience.

6.3. Limitations and future researches

The study relies on the availability and accuracy of data compiled from several places, such as WDI and GFN. The limitations and potential biases in the data sources may affect the findings’ robustness. The study employs an observational approach, which limits the ability to establish causal relationships between the variables. The presence of endogeneity, where variables reciprocally influence each other, may lead to biased estimates and affect the interpretation of the results. Moreover, an extension of this work could be the application of nonlinear and asymmetric ARDL models. The outcomes of this paper are established in the context of Poland and may not be directly applicable to other countries or regions. Poland's unique socio-economic, political, and environmental characteristics might limit the generalizability of the results. Future research in this field could explore several areas to enhance further understanding of the dynamic influences on greenhouse gas emissions and load capacity factors. Firstly, it would be valuable to investigate the role of policy interventions and their effectiveness in reducing emissions and improving load capacity factors. Assessing the ramifications of specific renewable power policies, carbon pricing mechanisms, and energy efficiency regulations could provide insights into the most effective strategies for achieving sustainability goals. Secondly, conducting regional or cross-country comparative studies would allow for a broader analysis of the factors influencing emissions and load capacity factors, considering variations in economic, social, and environmental contexts. Additionally, exploring the interactions between different sectors, such as transportation, industry, and agriculture, and their contribution to emissions and load capacity factors could offer a deeper insight into the system's workings. Lastly, incorporating climate change adaptation measures into the analysis, including the resilience of infrastructure and ecosystems, could further enhance our understanding of the long-term sustainability of energy systems. By addressing these areas, future research can contribute to more targeted and effective policies for mitigating climate change impacts and promoting sustainable energy practices.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Appendix A.

Abbreviations

Table

Abbreviation	Details
AIC	Akaike information criteria
ARDL	Autoregressive distributed lag model
ADF	Augmented Dickey–Fuller
CO2	Carbon dioxide emission
CCR	Canonical Cointegrating Regression
CUSUM	Cumulative sum
CUSUMSQ	Cumulative sum square
FMOLS	Fully Modified Ordinary Least Squares
FOS	Fossil fuel
GDP	Gross Domestic Product
GHG
GE	General Electric Company
IPCC	Intergovernmental Panel on Climate Change
IPAT	Environmental impact (I) is the product of three factors: population (P), affluence (A), and technology (T)
LCF	Load capacity factor
LPOP	Log of population
LREN	Log of renewable energy
LFOS	Log of fossil fuel
LREN	Log of Renewable energy consumption
LGL	Log of Globalisation
REN	Renewable energy
STIRPAT	Stochastic impacts by regression on population, affluence, and technology