A dynamic analysis on the load capacity curve of forests in Türkiye

One of the problems directly related to environmental pollution and climate change is deforestation. Forests worldwide are currently facing several threats due to rapidly advancing climate change (Bärmann et al., 2025). In addition, forests have undergone major human-induced changes and today 75% of the world's forests have been modified by humans (FAO, 2020; Astigarraga et al., 2025). Activities such as energy production, urbanization and expansion of agricultural areas, which accelerated with the industrial revolution, have not only increased atmospheric pollution, but also caused the rapid destruction of forested areas.

This process has further exacerbated climate change by leading to a decrease in carbon sinks. Forests contribute to maintaining the global climate balance by reducing greenhouse gas emissions thanks to their carbon storage capacity (FAO, 2020). They also provide multifaceted ecosystem services such as erosion control by regulating the water cycle, maintaining soil fertility and supporting biodiversity (MEA, 2005). Lambin and Meyfroidt (2011) state that economic globalization has accelerated deforestation processes and made environmental sustainability efforts more complex.

Deforestation, land use changes and biodiversity loss have increased pressures on climate change. Foley et al. (2011) state that land use changes increase forest loss, and this leads to biodiversity loss. Hansen et al. (2013) show that rapid deforestation in tropical regions is caused by human activities and that this affects global climate systems. Deforestation triggers climate instability and soil erosion as well as the reduction of carbon sink capacities (MEA, 2005; Bonan, 2008).

In recent years, there has been an increase in the number of studies showing that the carrying capacity of forests under increasing pressure from economic activities and tourism is under threat (Sun et al., 2020; Kocak and Cavusoglu, 2024). In particular, the impact of tourism on forest areas contributes to economic growth on the one hand and increases environmental pressure on the other. Buckley (2012) emphasizes the impacts of sustainable tourism on forest ecosystems, while Gössling and Hall (2006) elaborate on the contribution of tourism to global environmental changes. Furthermore, Lindberg et al. (1996) discuss the pressures of ecotourism on forest areas, while Eagles et al. (2002) emphasize the importance of sustainable tourism management in protected areas.

In this context, the protection of Türkiye's forest assets and natural resources is of particular importance in terms of the environmental impacts of tourism. According to the Republic of Türkiye Ministry of Agriculture and Forestry General Directorate of Forestry report, approximately 29.8% of Türkiye's total surface area consists of forest areas and this ratio has increased in recent years. From 20.2 million hectares in 1973, the forest area reached 23.1 million hectares by 2023. This increase has been realized thanks to intensive afforestation efforts and forest protection policies. However, natural disasters also pose a serious threat to forest ecosystems. For example, 139,503 hectares of forest areas were damaged in fires in 2021. However, thanks to the measures taken in 2023, the areas affected by fires were significantly reduced (GDF, 2025). Following this structural framework, Figure 1 illustrates the evolution of the forest footprint (FF) over the period 1974–2022. The figure reveals a fluctuating but overall increasing trend, particularly becoming more pronounced after the early 2000s. While relatively lower and stable levels are observed during the 1980s and early 1990s, a notable increase emerges in the subsequent period. This indicates that although forest areas have expanded through afforestation policies, the demand pressure on forest resources has also increased over time.

The conservation and sustainable management of forests in Türkiye is also closely linked to economic growth and the development of the tourism sector. The impact of tourism activities on forest ecosystems and balancing this impact with environmental sustainability is a critical problem for developing countries. Especially in developing countries with rich ecosystems such as Türkiye, ensuring the balance between economic growth and the protection of natural resources is of great importance in terms of sustainable development goals. Türkiye's economy has recorded significant growth in recent years, displaying a remarkable performance on a global scale. By 2023, according to the International Monetary Fund (IMF), Türkiye's Gross Domestic Product (GDP) reached $1.024 trillion, making it the 17th largest economy in the world. One of the important driving forces of this economic growth has been the tourism sector. According to 2024 data, Türkiye's tourism sector exhibited a strong growth. Compared to the previous year, tourism revenue increased by 8.3% to USD 61.1 billion, while the number of visitors increased by 9% to 62.2 million people (TurkStat, 2025). As of 2023, Türkiye ranked 5th in the world in terms of the number of tourists and 7th in terms of tourism revenues. The tourism sector plays a critical role with its direct and indirect contributions to the national economy. The sector directly and indirectly creates over 2 million jobs in Türkiye and contributes USD 60 billion in GDP. It also played an important role in improving the foreign trade balance by making a net contribution of USD 41.6 billion to the current account deficit of USD 45 billion in 2023 (TTYD, 2025).

The Load Capacity Curve (LCC) hypothesis, one of the important theoretical approaches of environmental economics, states that there is a U-shaped relationship between economic growth and the load capacity factor (the ratio of biocapacity to footprint) of the environment. The aim of this study is to investigate the validity of the LCC hypothesis for forests in Türkiye over the period 1974–2022. This study contributes to the existing literature in three areas. Firstly, we observed that this is the first study to examine the LCC hypothesis for forests for Türkiye. Although the LCC hypothesis has been investigated for Türkiye, this study differs from the existing literature in terms of its specific focus. Secondly, the forest LCC hypothesis is analyzed by including tourism and energy consumption variables in the analysis. Despite the importance of the issue, there are very few studies on the environmental impact of tourism in Türkiye. Thirdly, the non-linear relationship between FLC and economic growth is tested by using the Kalman filter technique, which is a time-varying parameter (TVP) method. In this study, the environmental quality of forests is analyzed dynamically during the sample period.

The LCC hypothesis provides an important analytical framework for environmental sustainability. Hakkak et al. (2023) confirmed the validity of the LCC hypothesis in Russia and identified a U-shaped relationship between economic growth and the LCF. Yang et al. (2023) found that the LCC hypothesis is valid in BRICS countries. Hossain et al. (2024) confirmed the LCC by finding a U-shaped relationship between income and load capacity factor in the Nordic Region. Javed et al. (2024) showed that green investments and environmental policies in G-7 countries support the LCC hypothesis by increasing freight capacity. Shahbaz et al. (2024), on the other hand, showed that the LCC hypothesis is valid in the analysis conducted with the A-ARDL method in the Netherlands. In Türkiye, Daştan (2024) found that renewable energy use and human capital confirmed the LCC hypothesis in the short and long run by increasing ecological welfare.

Some studies have revealed that the LCC hypothesis is not valid under certain conditions. Pata and Tanrıöver (2023) found that financial development decreases environmental quality in the most popular tourism destinations, but tourism does not support the LCC hypothesis by increasing environmental quality. Aydın and Değirmenci (2024) examined the impact of clean energy, green innovation and technological diffusion in EU countries and found that LCC is not generally valid, but it is supported in countries such as Denmark, France, Portugal and Spain. In the analysis conducted by Shahzad et al. (2024) using data from G20 countries for the period 2000–2020, it was found that the LCC hypothesis is not valid. The study conducted by Tiwari et al. (2024) on India does not support the validity of the LCC hypothesis.

In the case of Türkiye, Pata and Balsalobre-Lorente (2022) found that tourism, energy consumption and economic growth reduce the load capacity factor in the long run and that the LCC hypothesis is not valid. These findings show that the LCC varies depending on country groups and the characteristics of economic structures and may not be sufficient to explain environmental sustainability in all cases. Altıntaş et al. (2024) validate the LCC hypothesis in both in the long- and the short-run in Malaysia. Their analysis also reveals that banking sector development positively affects LCC while renewable energy consumption has a negative impact on environmental sustainability.

In some studies, the validity of the LCC hypothesis varies according to country, sector and environmental indicators. Bekun et al. (2024) stated that LCC is not valid in MINT economies, but renewable energy increases environmental sustainability. Feng et al. (2024) found a U-shaped relationship between economic growth and load capacity factor in E‐7 countries and showed that growth negatively affects environmental quality in the first stage but improves it after a certain threshold. In addition, it is determined that the use of renewable energy improves environmental quality, but the environmental impacts of technological innovations are negative. The study by Altıntaş et al. (2025) examines the LCC hypothesis using data from the Middle East, North Africa and Türkiye (MENAT) region for the period 1984–2018. The analysis reveals an inverted U-shaped relationship in Egypt and Iraq, whereas a U-shaped relationship is observed in Morocco. These findings indicate that the LCC hypothesis is valid for Morocco, while it is not supported in the cases of Egypt and Iraq.

In general, the validity of the LCC hypothesis varies according to country groups and economic structure and has limited explanatory power in some regions. These findings show that LCC alone is not sufficient in environmental sustainability analyses and should be considered together with different factors.

Although the tourism sector is recognized as an important driver of economic growth, it also causes environmental problems such as depletion of natural resources, deforestation and increased carbon emissions. Tourism may influence forest load capacity (FLC) through multiple and potentially opposing mechanisms. On the one hand, the expansion of tourism activities can exert significant pressure on forest ecosystems through land-use change, infrastructure development and increased demand for natural resources. In particular, the conversion of forest lands into tourism facilities, transportation networks and recreational areas may lead to habitat fragmentation and ecosystem degradation (Gössling, 2002; Davenport and Davenport, 2006). In addition, tourism-related activities increase energy and water consumption, waste generation and environmental stress, which may intensify the FF and reduce the regenerative capacity of forest ecosystems (Gössling et al., 2012). On the other hand, tourism can also contribute positively to environmental sustainability when managed effectively. Revenues generated from tourism can support conservation financing, protected area management and ecotourism practices, thereby enhancing the preservation of forest ecosystem services (Eagles et al., 2002; Balmford et al., 2009). Therefore, the relationship between tourism and FLC is theoretically ambiguous, as tourism may either increase environmental pressure or promote conservation depending on its scale, structure and management.

In this context, the LCC hypothesis provides an important theoretical framework for assessing the environmental impacts of tourism. In the literature, there has been a recent increase in the number of studies analyzing the environmental impacts of tourism activities in different methods and contexts. Akadiri et al. (2019), one of the researchers focusing on the effects of tourism activities on the environment, stated that tourism increases carbon emissions in the context of globalization and energy consumption and that the ecological footprint expands in intensive tourism regions. Similarly, Gao et al. (2021), focusing on the Mediterranean region, revealed that tourism activities increase CO₂ emissions through energy consumption and cause regional ecological degradation. Mikayilov et al. (2019) examined the long-term impacts of tourism on ecological footprint in the context of Azerbaijan. The research findings showed that although tourism activities increase the ecological footprint, urbanization and management quality do not play a significant role in environmental impacts. In line with these results, it is determined that the EKC hypothesis is not valid in Azerbaijan. In another study, Lee and Chen (2021) examined the impact of tourism revenues on ecological footprint over the period 1992–2016 with a panel data analysis covering 123 countries. In the study, different ecological footprint components (forest area, grasslands, carbon emissions, agricultural land, fishing grounds) were considered and it was determined that tourism revenues increase the ecological footprint. It was also found that the EKC hypothesis is valid in upper-middle income countries.

For the United Arab Emirates, Majumdar and Paris (2022) investigated the environmental impact of tourism and found that the EKC hypothesis is valid. The study highlighted that tourism activities increase carbon emissions through energy intensive sectors (transport, hotels, shopping malls). Similarly, Porto et al. (2023), in their study of 69 countries, found that tourism activities increase environmental pressure by increasing carbon emissions. The study found that the tourism-induced EKC hypothesis is valid in the Americas and Asia-Oceania regions, but not in Europe. Rahman et al. (2022), in a panel cointegration and CS-ARDL model analysis of the top ten tourist destinations, found that tourism confirms the EKC hypothesis by increasing carbon emissions in the long run.

In the recent literature, Magazzino et al. (2023) tested the environmental impact of tourism in post-Brexit European Union (EU) countries. In the analysis for the period 1998–2019, it was determined that tourism reduces carbon emissions in the long run, but economic growth increases emissions. Şeker and Avşar (2023) examined the relationship between tourism and ecological footprint with ARDL bounds test using data for the period 1975–2018 in Türkiye. The study found an N-shaped relationship between the number of international tourists and the ecological footprint in the short and long run, suggesting that the classical inverted-U-shaped EKC hypothesis is not valid for Türkiye. Similarly, Pata and Tanrıöver (2023) tested the LCC hypothesis for the period 1990–2022 in Türkiye. The study searched the relationships between tourism, financial development and environmental quality using ARDL bounds test and Fourier ARDL method. The findings revealed that tourism activities have an increasing effect on environmental quality, but financial development has a negative impact on environmental sustainability. Voumik et al. (2023) determined that tourism plays an important role in reducing carbon emissions in 10 tourism destinations in the Middle East. They emphasized that the use of ecotourism and renewable energy with the AMG method increases environmental sustainability. Shahzad et al. (2024) identified significant effects of tourism mobility on the load capacity factor in their study on G20 countries. Mahadevan and Suardi (2024) examined the tourism-induced EKC hypothesis using a panel fixed effects model covering 117 countries. The study indicated that tourism has an inverted U-shaped relationship with carbon emissions and ecological footprint in developed countries, whereas there is a U-shaped relationship in developing countries. Tiwari et al. (2024) found that tourism mobility in India contributes to economic growth while simultaneously leading to environmental degradation. Finally, Ngo et al. (2024) investigated the impact of aviation-based tourism on carbon emissions in the case of New Zealand. The study revealed that in the short run, tourism growth reduces air transport carbon emissions, but in the long run, carbon emissions increase with economic growth. The long-term results show that the EKC hypothesis is valid and that there is an improvement in environmental quality. Overall, the findings reveal that the environmental impacts of tourism vary in different contexts.

Furthermore, Radulescu et al. (2024) examine the dynamic effects of resource productivity and green technologies on renewable energy consumption in 28 middle-income countries over the period 2006–2022 using MMQR, QR and causality approaches. The findings indicate that green technological innovation significantly increases renewable energy use, while resource productivity promotes renewable energy consumption particularly at higher quantiles by decoupling economic growth from natural resource use. Aydin et al. (2024) tested the EKC hypothesis for Finland over the period 1970–2018 using the FF and found that economic growth initially increases environmental degradation but reduces it after a certain threshold. Additionally, biomass energy consumption and forest product trade were found to have no significant long-run effects. Cutçu and Radulescu (2025) analyze the determinants of the cropland footprint for the top 10 agricultural countries over the period 1991–2021 using the MMQR approach. The findings indicate that arable land has a positive and significant effect on the cropland footprint, particularly at medium and high levels, while GDP per capita increases environmental pressure across almost all quantiles. Additionally, urbanization, temperature and agricultural employment are found to have no statistically significant impact.

This study aims to analyze the validity of the LCC hypothesis for forest area in Türkiye. For this purpose, we use the Bound test, ARDL (Autoregressive Distributed Lag) model and Kalman filter technique covering the period from 1974 to 2022. The study also investigates the effects of tourism income and energy consumption on FLC. The estimated model of this study is presented in Equation 1.

FLC refers to forest sustainability measured as FB divided by FF. If the value of FLC is above 1, it indicates sustainability, whereas a value below 1 indicates non-sustainability (Ayad et al., 2024). L, Y, Y², TI and EC represent natural logarithm, real GDP per capita (constant 2015 USD), the square of real GDP per capita, tourism income (% of GDP) and primary energy consumption. α₀ is the constant term, and β₁, β₂, β₃ and β₄ are the long-term coefficients. The FLC curve suggests the U-shaped relationship between FLC and economic growth, and this relationship is confirmed if β₁ < 0, and β₂ > 0.

FLC (FB/FF) is obtained from the Global Footprint Network (GFN, 2025) database. Real GDP per capita is derived from the World Bank database (2025). Tourism income as percentage of GDP is extracted from Turkish Statistical Institute (2025) and World Bank database (2025). Finally, energy consumption is obtained from the British Petroleum (BP, 2025) Statistical Review of World Energy. Table 1 presents the definitions, measurement units and data sources of the variables used in the empirical analysis, while Table 2 reports the summary statistics of these variables.

Every environmental asset has a dual nature. On the demand side, the FF summarizes the demand calculated based on a country's annual consumption of timber, pulp, timber products and fuelwood. On the other hand, the supply side is represented by FB and refers to the ability to replenish what people demand from forested surfaces. FLC is used to express the environmental sustainability of forest areas and is obtained by dividing FB by FF. FLC encompasses the supply and demand sides of forests, thereby providing substantial evidence on deforestation and FB simultaneously (GFN, 2025; Ayad et al., 2024). Figure 1 shows the FF, FB and FLC (FB/FF) per capita in Türkiye from 1974 to 2022.

Figure 2 shows that the FB has decreased continuously over the period. This indicates that the capacity of the ecosystem to regenerate what people demand from forest surfaces is gradually decreasing and deforestation is increasing. In addition, FF has generally been on an upward trend over the last 2 decades, although it did show small decreases at the beginning. Biocapacity reserve (FB-FF) decreased from 0.84 at baseline to 0.23 in 2022. Even though biocapacity deficit implying that FF is more than FB was not observed during this period, the gap between FB and FF was gradually closing. Finally, FLC decreased from 4.36 in 1974 to 1.77 in 2022, meaning a 59.4% reduction in FLC over the period. Accordingly, Figure 1 reveals that the environmental sustainability of forests in Türkiye is gradually decreasing.

In the empirical analysis, we explore the cointegration analysis by using the Bound test developed by Pesaran et al. (2001). The bound test allows the application of cointegration analysis regardless of the degree of stationarity of the variables (Narayan and Narayan, 2005). Equation 2 (The equation to estimate the Bound test) presents the Unrestricted Error Correction Model (UECM) for the application of the Bound test in this study.

In Eq. (2) “m” and “t” specify the lag and trend variable, respectively. The null hypothesis for the Bound test is that there is no cointegration relationship between the variables. Therefore, the null hypothesis of the Bound test for this study is established as Ho: α₇ = α₈ = α₉ = α_{1 0} = α₁₁. The null hypothesis is tested by comparing the calculated F-statistic with the critical values of Pesaran et al. (2001). If the estimated F-statistic is greater than the upper critical value, the null hypothesis is rejected; if the estimated F-statistic is less than the lower bound of the critical values, the null hypothesis cannot be rejected (Pesaran et al., 2001; Narayan and Narayan, 2005).

Following the cointegration analysis, the ARDL model is applied to detect the long and short-run static relationships between the series. ARDL allows simultaneous analysis of short- and long-term effects of independent variables on the dependent variable (Şeker et al., 2015). The ARDL model specifications for this study are presented in Equation (3) (The equation to estimate the long-run ARDL model) and Equation (4) (The equation to estimate the short-run ARDL model).

ECT is the error correction term that indicates the speed of the adjustment process and is expected to be negative and statistically significant.

We lastly employ the Kalman filter methodology to examine the validity of the FLC Curve hypothesis dynamically. The study also explores the dynamic impacts of the control variables on forest sustainability. The Kalman filter is an algorithm for correctly computing the state of time-varying systems with noisy and incomplete observations. This method is used to obtain the best estimate of dynamical systems with observed data, and to continuously update the estimated data with new observations to obtain the most accurate state (Harvey, 1990). The recursive implementation of the Kalman filter can be used in real time, which is an attractive feature of this technique. After estimating the new state at time (t), the Kalman filter algorithm adds a correction term. This new 'corrected' state acts as an initial condition in the next stage (t + 1). In this way, the estimation of the state variables uses all the information available up to that point, not just the information from the stage prior to the estimation. The Kalman filter permits the identification of the hidden (unmeasurable) state of a dynamic linear system. It works even when the system is subject to additive white noise (Urrea and Agramonte, 2021).

A linear state space representation of the dynamic system is presented in.

Equation (5) (The Kalman filter equation).

where αt is the mx1 vector of unobserved state variables; ct, Zt, dt and Tt are appropriate vectors and matrices and εt and νt are vectors of mean zero and Gaussian disturbances. As expressed in Equation (5), it is assumed that the unobserved state vector, αt, changes over time as a first-order vector autoregression (Bulut, 2017). The Kalman filter specification used in this study is presented in Equation (6) (The equation to estimate the Kalman filter).

where α_1,t, α_2,t, α_3,t and α_4,t are the TVP estimates associated with the elasticities of FLC.

The Bound test assumes that the variables are I(0) or I(1) and the procedure does not work in the presence of I(2) variables (Frimpong and Oteng-Abayie, 2006). Thus, the study first analyzes the stationary properties of the variables by using Augmented Dickey-Fuller (ADF), Phillips Perron (PP) and Ng-Perron tests. Table 3 presents the results of the unit root tests.

The null hypothesis of the ADF and PP tests is that the series include unit roots. The results of both the ADF and PP tests show that the t-statistics calculated for all the series are greater than the critical values in their level forms and that they become stationary after differencing. The null hypothesis for the Ng-Perron test is that the series are non-stationary according to the MZa and MZt tests, while the null hypothesis for the MSB and MPT tests is that the series are stationary. The estimated t-statistics for all series are less than the critical values in level forms according to the MZa and MZt tests, and greater than the critical values in level forms according to the MSB and MPT tests. In conclusion, the results of the ADF, PP and Ng-Perron tests used in this study indicate that all series are integrated of order 1 (I(1)). After confirming that none of the variables are integrated in two and beyond, the study uses the Bound test for cointegration analysis. Table 4 demonstrates the results of the Bound test.

According to Table 4, the null hypothesis that there is no cointegration relationship between the series is rejected since the F-statistic is greater than the upper bound of the critical values. Accordingly, we detect the cointegration relationship between FLC and the explanatory variables.

After establishing the existence of a cointegration relationship among the series, the study analyzes both long-run and short-run relationships using the ARDL model. Table 5 presents the results of the estimated ARDL (1,1,0,2,0) model. Optimal lengths are determined via the Schwarz criterion (SIC).

The diagnostic results in Table 5 show that the estimated ARDL model does not contain problems of serial correlation, heteroskedasticity, misspecification and normality. In addition, the estimated coefficients in the model are found to be stable, shown by the results of CUSUM and CUSUM-squared tests in Figure 3.

The long-run ARDL model results show that all estimated coefficients are statistically significant. The signs of LY and LY² are found to be negative and positive, respectively. The negative sign of the income and the positive sign of the square of the income imply the presence of the U-shaped relationship between FLC and economic growth. This result means that FLC deteriorates in the initial stages of income; however, after income reaches a turning point, it begins to improve forest sustainability. Accordingly, this study corroborates the LCC hypothesis for forest area in Türkiye. This result is consistent with the findings of Ayad et al. (2024) for validation in forest LCC for Brazil, as well as with the conclusions of Yang et al. (2023), Hossain et al. (2024), Shahbaz et al. (2024) and Daştan (2024) that confirm the LCC hypothesis. Furthermore, the findings reveal that tourism has a positive effect on environmental quality in forest areas, a 1% rise in tourism income increases the FLC by nearly 0.50% in the long-run. The positive impact of tourism on the environment is consistent with Pata and Tanrıöver (2023), Magazzino et al. (2023), Voumik et al. (2023) and Ngo et al. (2024). On the other hand, energy consumption has a detrimental impact on FLC, as a 1% increase in energy consumption leads to a decrease in environmental quality of forests by 0.66% in the long-run.

Variables with zero lag length in the ARDL model enter the short-run equation only in first-difference form (ΔXt) in the error correction representation. The short-run ARDL model results show that all the estimated short-run coefficients are statistically significant. In line with the long-run ARDL model results, LY and LY² are found to be negative and positive, respectively. This finding validates the forests’ LCC hypothesis in the short-run. In the short-run, tourism improves the FLC while energy consumption has a negative impact on environmental sustainability of forests. The error correction term, ΔECT, is found to be negative and statistically significant. The estimated coefficient of the error correction term is −0.60, indicating that 60% of the disequilibrium caused by the previous year's shock is eliminated within the current year. This suggests that the adjustment process is working at a high speed.

In the empirical analysis, we finally applied the Kalman filter approach to test the LCC hypothesis for forests dynamically in Türkiye. The dynamic analysis also includes the control variables used in the study. The results of the Kalman filter estimation show that all the coefficients of the variables are statistically significant, in line with the results obtained from the ARDL model, except for energy consumption. The signs of the estimated coefficients of LY, LY² and LTI in equation (6) are negative, positive and positive, respectively.

Figure 4 demonstrates the dynamic impact of income, the square of income and tourism on FLC, respectively. As seen from Figure 4, income has a negative effect on the environmental quality of forests, and the square of income increases the FLC in the sample period of the study. Hence, the Kalman filter results support the ARDL model results and show that the LCC hypothesis is valid for forests in Türkiye. The results of the dynamic effect of the income on the load capacity of forests in Figure 3 indicate that the negative effect of income on the environmental quality of forests generally decreased until 2005, although it fluctuated. This negative impact significantly weakened between 1990 and 2005. In this period, the change in the estimated TVP magnitude was found to be 1.81. However, the change in the magnitude of TVP over the last ten years was found to be −0.14, meaning that the negative impact of economic growth on the carrying capacity of forests has increased over the last decade.

The TVP results in Figure 4 show that the positive effect of tourism on the load capacity of forests increased particularly between 1980 and 1996 and decreased between 1996 and 2002. In addition, this effect has generally been seen to be steady over the last two decades, although it has shown small fluctuations. On the other hand, the change in the magnitude of the calculated TVP was found to be –0.012 in the last decade, implying that the positive impact of tourism on FLC has begun to weaken.

Forests play a critical role in maintaining ecosystem sustainability, conserving biodiversity and regulating the global carbon cycle. In this context, FLC, which reflects the balance between forest biocapacity (FB) and FF, serves as an important indicator for evaluating environmental sustainability. This study investigates the relationship between FLC and economic growth in Türkiye over the period 1974–2022 within the framework of the LCC hypothesis. In the empirical analysis, the ARDL Bound testing approach is employed to identify the long-run relationship among the variables, while short-run and long-run effects are estimated using the ARDL model. Furthermore, to capture the time-varying nature of this relationship, the Kalman filter method is applied to analyze the dynamic evolution of the coefficients over time. In this respect, the study contributes to the literature by testing the forest-based LCC hypothesis for Türkiye and by incorporating a dynamic analytical approach.

The findings reveal a U-shaped relationship between economic growth and FLC in Türkiye. Accordingly, in the early stages of economic growth, pressure on forest ecosystems increases, whereas environmental improvement is observed after a certain income threshold is reached. This suggests that, in the initial phase, economic growth exerts a negative impact on forests through channels such as intensive natural resource use, energy-intensive production structures and land-use pressures. However, as income levels rise, factors such as stronger environmental regulations, technological progress and increased environmental awareness contribute positively to forest sustainability. Nevertheless, the TVP results indicate that this improvement is neither linear nor guaranteed over time. In particular, the adverse impact of economic growth on FLC has shown a tendency to increase again in recent years. This finding highlights that environmental improvement cannot be left solely to the process of economic growth and requires active policy interventions. Therefore, while the LCC hypothesis is validated, it does not imply that environmental sustainability will automatically be achieved without appropriate policy measures.

The results regarding control variables also provide important insights. The positive impact of tourism income on FLC suggests that tourism, when properly managed, can support environmental sustainability. However, the dynamic analysis indicates that this positive effect has weakened in recent years. This implies that the environmental impact of tourism depends largely on its structure and management, and that uncontrolled expansion of tourism activities may exert increasing pressure on forest ecosystems.

The negative effect of energy consumption on FLC indicates that economic growth in Türkiye is still characterized by an energy-intensive structure with significant environmental externalities. A fossil fuel-based energy mix contributes to environmental degradation through increased carbon emissions and pressure on natural resources, thereby indirectly affecting forest ecosystems. This finding underscores that not only the level but also the composition of energy consumption is crucial for environmental sustainability.

Based on these findings, several policy implications can be derived. First, to mitigate the adverse effects of economic growth on forest ecosystems, land-use planning should be strengthened, and activities such as mining, industrial expansion and agricultural encroachment that threaten forest areas should be more strictly regulated. In addition, policies that promote energy efficiency and environmentally friendly production processes should be effectively implemented.

Second, sustainable tourism policies should be restructured to reduce the pressure of tourism activities on forest ecosystems. In this regard, carrying capacity-based planning should be adopted, particularly in ecologically sensitive forest areas, while ecotourism practices should be encouraged. Moreover, stricter environmental standards should be introduced for tourism enterprises and the environmental impacts of tourism activities should be regularly monitored and controlled.

Finally, to reduce the negative impact of energy consumption on forest ecosystems, the energy transition process should be accelerated. Reducing the share of fossil fuels, increasing the use of renewable energy sources and promoting energy efficiency-enhancing technologies are of critical importance. In particular, stricter environmental regulations in energy-intensive sectors will contribute indirectly to the protection of forest ecosystems.

In conclusion, this study demonstrates that the relationship between economic growth and FLC in Türkiye is nonlinear and time-varying. This finding indicates that environmental sustainability cannot be ensured solely through economic growth, and that coordinated policies in energy, tourism and natural resource management are essential for maintaining forest sustainability.