Economic Integration and Forest Sector Dynamics: Türkiye’s Strategic Outlook in a BRICS-Aligned Future

Abstract

The BRICS has emerged as a strategic actor in global environmental and economic governance, encompassing 42% of the world’s population, 32% of global GDP, and nearly half of the world’s forest resources. Member countries have integrated forest management with renewable energy transition and carbon market mechanisms as part of their sustainable development strategies. In this context, Türkiye positions the forestry sector as both an environmental and economic lever in its potential alignment with BRICS while seeking to diversify its foreign policy. This study examines the long-term relationships between forest area, population, forest product trade, renewable energy production, and carbon emissions in BRICS-T countries for the period 2009–2023, employing panel econometric methods (FMOLS and DOLS). The findings indicate that population growth, forest expansion, and forest product trade are associated with increased emissions, while renewable energy production contributes to emission reductions. Moreover, bidirectional causality is identified between population and emissions and between renewable energy and emissions. The results suggest that environmental sustainability depends not only on the availability of resources but also on the quality of governance, policy coherence, and sectoral coordination. The study provides an original contribution to the literature by analyzing Türkiye’s environmental and economic integration with BRICS through the combined lens of forestry and energy transition, offering exploratory policy implications for Türkiye’s strategic position in a multipolar world.

1. Introduction

The global economic and political system is undergoing a historic transformation in the second quarter of the 21st century. In this transformation process, the influence of Western-centric liberal institutional structures is relatively declining, while rising powers are gaining a more decisive position in international economic governance, ultimately leading to the emergence of a multipolar order. In this context, the BRICS group—comprising Brazil, Russia, India, China, and South Africa—is emerging as a new bloc aspiring to global norm-setting, not only due to its significant weight in global production and trade volumes but also through its alternative development models, new financial institutions, and nature-based solutions [1,2,3]. In this changing international environment, Türkiye continues its strategic efforts to diversify its foreign policy and make its economic relations multidimensional. For many years, Türkiye has established strong ties with the Western-centered economic system and shaped both its foreign and development policies through structures such as NATO, the OECD, and the Customs Union. However, financial vulnerabilities, external debt dependency, and a regional security environment of instability since the 2000s have encouraged Türkiye to explore alternative cooperation platforms [4,5,6].

Discussions about relations with BRICS have become prominent in this context; Türkiye’s integration with this structure at the level of observer status within this group has become a significant topic of debate in both domestic and foreign policy. Türkiye expressed its interest in BRICS membership for the first time at the 2013 Summit and subsequently participated in various BRICS meetings as a guest country. However, no significant diplomatic steps or status changes occurred during the 2013–2023 period. Türkiye’s invitation to the 2018 Johannesburg Summit with official observer status enhanced the international visibility of the process and gave momentum to debates on possible membership and institutional alignment in both diplomatic and academic circles [7]. In 2022, representatives of the BRICS International Forum drew attention to Türkiye’s potential participation. The process reached a critical stage at the 2024 Kazan Summit, when Türkiye submitted an official membership application and attended the meeting as a guest; during the same period, the possibility of granting Türkiye ‘partner country’ status was brought to the agenda [8]. However, as of 2025, full membership has not materialized. Proponents of joining BRICS highlight that Türkiye could tap into growing markets like China and India, decrease its reliance on the dollar by trading in local currencies, and access alternative funding from financial institutions such as the New Development Bank [9]. Additionally, it is argued that Türkiye could pursue a more flexible foreign policy in a multipolar world by strengthening its ties with BRICS while maintaining relations with Western institutions like NATO and the EU [10]. Nonetheless, the possible risks associated with integrating into BRICS are also under consideration. In particular, Türkiye should carefully evaluate the risk of deteriorating political and economic relations with the European Union and the United States, a decline in direct foreign investment, competition from low-cost producer countries like China and India, and political coordination challenges within BRICS [11]. These risks suggest that Türkiye’s engagement with BRICS should be based on a selective and balanced strategy, ensuring dual alignment by maintaining its commitments to the EU while exploring new opportunities within BRICS. In this discussion, forestry is a sector of strategic importance for Türkiye. Forests are not only traditional sources of timber; they also offer multifunctional ecosystem services such as acting as carbon sinks, regulating water, protecting biological diversity, and supporting rural development systems [12,13]. Forests cover approximately 30% of Türkiye’s land area, serve as carbon sinks, and constitute a significant component of the forest product trade. However, the sector faces challenges such as deforestation pressures, fragmented land use, and limited integration with renewable energy policies. Forestry in Türkiye, therefore, plays a dual role: while extensive forest resources provide substantial carbon sequestration potential, production-oriented practices constrain this contribution. Together, forestry and renewable energy form interconnected pillars of Türkiye’s climate strategy, as both determine its capacity to align with BRICS in the transition toward low-carbon development. Similarly, although Türkiye has expanded its renewable energy capacity (renewables accounted for 44% of total electricity production in 2024), its high dependence on imported fossil fuels (which accounted for 78% of primary energy supply in 2024) and shortcomings in policy coherence and cross-sectoral coordination have limited its potential contribution to emission reductions [14,15]. These two sectors, therefore, constitute critical leverage points in Türkiye’s potential alignment with BRICS; indeed, the innovative experiences of BRICS countries in sustainable forest management (such as China’s “Grain-for-Green” program and India’s Joint Forest Management model) and large-scale renewable energy deployment provide valuable lessons for Türkiye from both environmental and economic perspectives. In recent years, the economic value of these services has been understood through various methods and integrated into economic policies by assessing them with natural capital accounting, carbon emission data, renewable energy production, forest product exports, and land use change trends. These indicators help redefine forests as not only environmental but also economic and strategic assets [16,17]. This demonstrates that lessons drawn from BRICS are not merely descriptive but provide actionable insights for Türkiye’s policy design, particularly in reconciling environmental sustainability with economic integration.

The conversion of forest ecosystem services into economic value is a critical issue not only in terms of environmental gains but also in terms of integration with national development strategies. Forests are considered multifunctional systems with economic value beyond their traditional production functions [18]. In this context, the concept of “natural capital” has established the definition of natural assets as stocks that can be incorporated into economic production processes; forests have also been placed in a strategic position within this approach [19]. However, forest resources in Türkiye continue to be mainly evaluated as physical assets and production-oriented. Despite their potential in areas such as carbon offsetting, climate change adaptation, biodiversity, and nature-based tourism, these multiple functions have not yet found a sufficient place within economic policies [20].

BRICS countries offer noteworthy examples in terms of comparative policy learning in this regard. China has reforested millions of hectares of land through projects such as the Natural Forest Protection Program and Grain-for-Green, launched under the framework of “Ecological Civilization,” integrating this process with ecological compensation systems to achieve both environmental and social gains [21]. India has integrated forest-based ecosystem services into financial transfer systems to reward states based on performance and increased community participation through the Joint Forest Management model [22]. South Africa has integrated invasive species control, water conservation strategies, and rural employment with forest management through programs such as “Working for Water” and “Working for Forests” [23,24]. Russia is developing national regulations to integrate into carbon markets and is placing its vast forest resources at the center of its climate diplomacy. These countries consider forest policies not only as environmental issues but also as components of economic and social development; they are developing new policy tools through carbon credit production, green finance, the biomass economy, and international nature-based solutions. Türkiye, however, currently has limited access to most of these tools or lacks the necessary implementation infrastructure [25,26,27]. On the other hand, forest ecosystem services are becoming increasingly important not only in terms of climate and development, but also in the context of foreign trade policies. The European Union’s Carbon Border Adjustment Mechanism (CBAM) makes the carbon footprint of forest products a commercial criterion. At the same time, BRICS countries have also begun to participate in international carbon regulations with their mechanisms. This situation necessitates that Türkiye plan its green transition simultaneously within the framework of both alignment with the EU and mutual integration with emerging actors such as BRICS [28,29].

Forestry policies in Türkiye have historically been focused on land acquisition and production. Although a shift toward “multi-purpose forests” and “sustainable forest management” began in the 2000s, the economic value of ecosystem services has not yet been fully reflected in institutional policy [30,31]. Ecosystem service payments (PES), carbon finance, forest-based crediting, and community participation are implemented at a limited level, indicating that Türkiye’s institutional capacity is weak compared to BRICS countries. However, this situation also presents an important opportunity for Türkiye to initiate a structural green transition through its forest sector [32].

As of 2023, Türkiye’s registered carbon credit volume in voluntary carbon markets is approximately 100,000 tons of CO₂, with a cumulative carbon credit amount of 1.9 million tons of CO₂ equivalent [33,34]. However, ecosystem services are still not evaluated as a separate item in the public budget, and a legal framework for accounting for natural capital has not yet been established. Moreover, the General Directorate of Forestry does not systematically include market-based tools such as PES and carbon finance in its strategic documents. These shortcomings weaken institutional capacity and slow down Türkiye’s integration with the practices of the BRICS countries.

The environmental governance capacities and forestry policies of BRICS countries are not homogeneous within themselves. China and India are implementing environmental regulations in line with their national growth targets under the pressure of high emission rates and rapid industrialization; in Brazil, however, deforestation policies in the Amazon rainforest, particularly since 2019, have been at the center of global criticism [35,36,37]. Russia, despite its vast forest resources, has launched its carbon certification system relatively late; South Africa, on the other hand, is attempting to balance governance capacity through social policy programs [38,39]. Therefore, while the forest policies of BRICS countries can serve as examples, each of these structures also carries different risk profiles for Türkiye. Consequently, the integration process with BRICS requires not only a technical but also a strategic and selective approach. This situation demonstrates that Türkiye can learn numerous policy tools from BRICS countries from both environmental and economic perspectives.

Empirical studies on the relationship between environmental sustainability, carbon emissions, natural resource efficiency, and green growth in emerging economies such as BRICS and Türkiye provide strategic insights for policymakers. Rapid industrialization, increased energy demand, and growing population pressures in these countries are accelerating environmental degradation; therefore, it is important to scientifically assess the balance between sustainable development and economic growth [40,41,42,43].

Academic studies and political developments suggest that the BRICS countries are adopting innovative instruments in their environmental and economic policies and are progressing towards the institutionalization of carbon pricing, natural capital accounting, and green innovation strategies. Türkiye, however, has been able to adapt to this transformation only to a limited extent. The arguments developed in this study are grounded in a broad set of international reports and peer-reviewed studies, including FAO forestry assessments, UNFCCC submissions, the EU’s Carbon Border Adjustment Mechanism (CBAM) regulations, and World Bank analyses on carbon finance and renewable energy. By relying on these sources, the study ensures that its evaluation of Türkiye’s forestry and energy sectors is not based solely on descriptive discussion but is systematically linked to internationally recognized empirical evidence and policy frameworks. The study has been conducted on the founding BRICS countries together with Türkiye, employing panel data methods based on the BRICS-T framework. Panel data analyses allow the empirical testing of the theoretical framework. In particular, through the applied econometric methodology, the relationships among forestry resources, energy use, and carbon emissions have been examined. This study examines Türkiye’s capacity for environmental and economic integration in the forestry sector. This domain is strategically important not only for its timber production but also for its role in carbon sequestration, biodiversity protection, rural development, and adaptation to green trade mechanisms. Employing a panel data approach within the BRICS-T framework addresses a gap in the existing literature, which has so far either focused on BRICS countries in isolation or treated Türkiye’s energy and environmental policies separately. Through the joint analysis of forestry resources, renewable energy dynamics, and carbon emissions, the study contributes to academic debates on environmental governance in emerging economies. It provides policy-relevant insights for Türkiye’s balanced alignment with both BRICS and the EU green transition.

Building on these discussions and in order to address the identified research gap, this study seeks to answer the following key questions:

Q1. 

How do forest area, population, forest product trade, and renewable energy production influence carbon emissions in BRICS-T countries over the period 2009–2023?

Q2. 

To what extent do long-term econometric relationships (cointegration, elasticities, and causality) reveal the structural drivers of carbon emissions in these countries?

Q3. 

What are the implications of these relationships for Türkiye’s environmental sustainability and strategic alignment with BRICS?

Q4. 

How can the comparative experiences of BRICS countries provide policy lessons for Türkiye in integrating forestry and renewable energy into its low-carbon transition?

Based on the literature and the identified research gap, the following hypotheses are proposed:

Hypothesis H1.

In BRICS-T countries, forest area expansion is positively associated with carbon emissions due to production-oriented forestry dynamics.

Hypothesis H2.

In BRICS-T countries, population growth increases carbon emissions in the long run.

Hypothesis H3.

In BRICS-T countries, forest product trade (exports and imports) contributes to higher carbon emissions.

Hypothesis H4.

In BRICS-T countries, renewable energy production reduces carbon emissions in the long run.

2. Materials and Methods

2.1. Data Set and Variables

First proposed in 2001 to describe emerging economies, BRICS became a political, financial, and environmental platform with the inclusion of South Africa in 2010. Since then, it has held regular summits to shape common agendas on development, climate change, and financial governance [44,45].

Today, the BRICS accounts for around 42% of the global population, 32% of the global GDP, and a quarter of world trade. It also holds nearly half of the world’s forest resources, making the group a key actor in global carbon cycles, afforestation programs, and renewable energy deployment [46].

In addition to economic and ecological weight, BRICS countries have expanded renewable energy, experimented with carbon markets, and advanced nature-based solutions [47]. Supported by the New Development Bank, these initiatives position BRICS as both an economic bloc and a laboratory for alternative development strategies. This dynamic has also attracted Türkiye, which increasingly seeks cooperation with BRICS [48].

Türkiye should therefore assess its potential role in BRICS not only through trade and investment but also through environmental capacity, carbon policies, forestry-based trade, and a green transformation vision.

In this study, a panel data set was created to analyze the long-term relationships between key determinants such as greenhouse gas emissions, forest areas, forest product trade, population, and renewable energy production. The data covers BRICS countries and Türkiye and consists of annual observations from 2009 to 2023. The variables used in the study are explained below:

• Emissions (EMS): Refers to the total annual amount of carbon dioxide and equivalent greenhouse gas emissions. It is a key indicator reflecting countries’ environmental performance and carbon footprints. Data are measured in CO₂ equivalents and converted to logarithmic form.
• Population (POP): Represents the total annual population of the country in question. Demographic size plays a decisive role in energy demand, production, and environmental pressures, and has therefore been included in the model. Population data has been log-transformed for analysis.
• Renewable Energy Production (REP): Represents the total annual energy production from renewable energy sources (solar, wind, biomass, hydroelectric, etc.). It is used to assess the environmental impacts of energy conversion processes. Data units are GWh or equivalent and have been converted to logarithmic form for modeling.
• Forest Area (FRA): Represents the total forest area (hectares) of countries. Forests serve as carbon sinks, balancing greenhouse gases in the atmosphere. This variable was considered a key element in the model in terms of environmental sustainability and was converted into logarithmic form.
• Forest Product Imports (FRI): This is the total economic value of forest product imports by the country in question within a year. The value obtained in USD is converted to logarithmic form, and its relationship with the environmental impacts of forest product trade is analyzed and converted to logarithmic form.
• Forest Product Exports (FPE): This represents the total value of forest product exports made by the relevant country on an annual basis. It has been included in the model considering the connection between forest product foreign trade, economic growth, and natural resource use. The logarithm of the value measured in USD has been used.

The selection of these variables is based on studies in the literature on environmental economics, sustainable development, and energy policies. It provides a comprehensive analytical framework that takes into account both economic and environmental determinants [49,50,51,52,53,54,55,56]. All variables have been standardized to suit the panel data structure, and the necessary transformations have been applied to meet the model’s statistical requirements. While the rationale for each variable is well established in the literature, the novelty of this study lies in combining them within a unified BRICS-T framework. Previous research has generally examined BRICS countries in isolation or analyzed Türkiye’s environmental and energy policies separately. By jointly modeling forestry resources, renewable energy production, forest trade, and emissions, this study captures the interconnections between ecological sustainability and economic integration in emerging economies. This integrated approach has not been explicitly addressed in earlier studies and thus represents a key contribution of the research (

Table 1. Variables description and data sources.

Variable	Definition	Unit of Measurement
CO2 Emissions	Total annual CO2 emissions (Mt)	EMS [57]
Forest Area	Total forested area (hectares)	FRA [58,59]
Population	Total population (million people)	POP [60,61]
Forest Product Export	Export values	FPE [62,63]
Forest Product Import	Import values	FPI [62,63]
Renewable Energy	Total production (TWh)	REP [64,65]

).

The average values of the variables for 2009–2023 show significant variation across BRICS countries in terms of population, forest resources, renewable energy, emissions, and forest product trade. Russia and Brazil dominate in forest reserves, while China and India lead in emissions and renewable energy production. South Africa remains a smaller-scale case but provides an important comparative example. Türkiye, with 85.3 million people, 22.2 million hectares of forest, 457 Million Tons of CO₂ emissions, and 90.3 terawatt-hours of renewable energy production, is broadly comparable to BRICS members in environmental indicators. However, its forest product trade balance is marked by higher imports ($7.27 billion) than exports ($3.4 billion). Detailed country-level averages are provided in Appendix A (

Table A1. Descriptive statistics of BRICS-T countries (averages, 2009–2023) [53,54,55,56,57,58,59,60,61].

Country	Population (Million)	Forest Area (Million ha)	Renewable Energy Production (TWh)	CO2 Emissions (MtCO2)	Forest Product Exports (USD bn)	Forest Product Imports (USD bn)
Russia	144.2	815.3	188.8	1724	10.7	4.69
Brazil	211.1	449.7	484.7	1540	18.0	2.29
China	1412.0	227.0	159.3	12,170	44.4	64.9
India	1430.0	72.1	226.6	2985	2.77	8.20
South Africa	63.2	17.0	6.1	556	2.32	2.16
Türkiye	85.3	22.2	90.3	457	3.40	7.27

).

After logarithmic transformation, the variables generally show acceptable distributional properties for panel data analysis. While ln(EMS) and ln(REP) display moderate positive skewness in some countries, ln(FRA) is highly stable in Russia and moderately skewed in Brazil. Population data (ln(POP)) remains nearly symmetric with very low variance. Detailed skewness and kurtosis values are reported in Appendix A (

Table A2. Log-transformed descriptive statistics of BRICS-T countries (2009–2023).

Series	Stats.	Russia	Brazil	China	South Africa	India	Türkiye
EMS	Mean	21.26009	21.15255	23.21673	20.13491	21.81064	19.92125
	Maximum	21.48858	21.23543	23.35653	20.21131	22.01622	20.23290
	Minimum	21.05142	21.01255	22.96535	20.03092	21.59911	19.60571
	Std. Dev.	0.127535	0.076966	0.109891	0.057418	0.115499	0.198016
	Skewness	0.394270	0.842393	0.715110	0.714391	0.207876	0.065454
	Kurtosis	2.238179	2.293743	1.103185	1.125868	1.347190	1.741859
REP	Mean	12.14385	13.08441	14.18322	8.416423	12.28248	11.33885
	Maximum	12.30646	13.35332	14.86031	9.723164	12.82118	11.84958
	Minimum	12.02710	12.93160	13.33352	7.243513	11.76917	10.54910
	Std. Dev.	0.099058	0.120174	0.472141	0.842131	0.321252	0.404248
	Skewness	0.354152	0.921629	−0.273867	−0.002858	0.159926	−0.301316
	Kurtosis	1.736680	3.016624	1.963732	1.538769	1.953471	2.024916
FRE	Mean	23.27239	23.58041	24.28911	21.55560	21.46067	21.60266
	Maximum	23.72445	24.03057	25.18757	21.84206	22.60106	22.86451
	Minimum	22.94095	23.05513	23.17494	21.34601	20.30107	20.37021
	Std. Dev.	0.216651	0.272161	0.725026	0.161718	0.791228	0.893707
	Skewness	0.472600	−0.038320	−0.032617	0.426341	0.194258	0.110278
	Kurtosis	2.393081	2.129782	1.336147	1.868804	1.467351	1.350903
FRI	Mean	22.25068	21.52958	24.86608	21.47702	22.79360	22.68800
	Maximum	22.53135	21.87404	25.17963	21.84700	23.22351	23.16217
	Minimum	21.90085	21.20156	24.23694	21.05863	22.16733	22.22121
	Std. Dev.	0.204332	0.225403	0.264677	0.208579	0.285781	0.203097
	Skewness	−0.554853	0.339890	−0.770764	−0.057376	−0.496772	0.141570
	Kurtosis	1.247089	1.707608	1.132740	1.513483	0.829853	2.598964
FRA	Mean	15.91341	15.42408	14.56768	12.05881	13.47356	12.28845
	Maximum	15.91391	15.45554	14.63685	12.09996	13.49658	12.32529
	Minimum	15.90963	15.31896	14.49987	12.04222	13.44886	12.25437
	Std. Dev.	0.001077	0.031665	0.041867	0.014312	0.015624	0.022293
	Skewness	−1.151474	1.558937	−0.017912	0.512949	−0.125475	0.095069
	Kurtosis	1.62836	1.420450	1.878134	1.585098	1.715016	1.836177
POP	Mean	18.78712	19.12640	21.04630	17.86080	21.81064	18.18564
	Maximum	18.79537	19.16804	21.06853	17.96201	22.01622	18.26199
	Minimum	18.77685	19.07342	21.00939	17.76152	21.59911	18.09272
	Std. Dev.	0.006579	0.031292	0.021262	0.064831	0.115499	0.056118
	Skewness	0.268767	0.255697	−0.460571	0.003995	0.207876	0.161789
	Kurtosis	1.403532	1.557130	1.155505	1.795704	2.347190	1.232729

Note. Descriptive statistics are based on data from sources listed in Table A1.

), which serve as reference indicators for assessing natural capital management, emissions control, energy transition, and forest economy performance.

China has the highest emissions. India shows an increasing trend. Türkiye is at a low level compared to other countries, but has shown an increase between 2009 and 2023. The BRICS-T emissions average has risen steadily until 2015 and has remained flat since then (Figure 1). India and China have the highest populations, while South Africa and Türkiye have the lowest. The average population of BRICS-T is increasing slowly but steadily (Figure 2). Russia has the largest forest area, followed by Brazil. Türkiye has seen limited growth in forest area. The average forest area of BRICS-T is declining slightly (Figure 3). China is the leading country in forest product imports, with continuous growth and fluctuating increases. South Africa has the lowest forest product import value, while Türkiye, despite showing an increase over the years, remains below the BRICS-T average (Figure 4). According to forest product export figures, China is again the leading country, similarly to imports. Although this figure increases every year in Türkiye, it remains below the BRICS-T average, as is the case with imports. The lowest level of forest product exports is in South Africa. Russia, India, and Brazil have shown increases in this process (Figure 5). China is the country with the highest production of renewable energy sources, and this increase has continued steadily year after year. India, similar to China, has shown steady growth and ranks second. Türkiye is on an upward trend, particularly gaining momentum after 2015. South Africa, however, has the lowest value in this process and is significantly below the BRICS-T average (Figure 6).

2.2. Method

Panel data analysis was used to evaluate the relationships between foreign trade, energy transition, and emissions in Türkiye and BRICS countries. A balanced panel data set covering the years 2009–2023 was created for this purpose. The primary objective of the model is to analyze the long-term relationships between variables such as forest product trade, renewable energy production, carbon emissions, and forest area, and to assess Türkiye’s comparative position relative to BRICS countries. Panel data methods are particularly suitable for this dataset as they combine cross-country and time-series dimensions, allowing the study to capture both within-country dynamics and between-country heterogeneity. Moreover, the relatively short time span but multi-country coverage makes fully modified and dynamic OLS estimators (FMOLS, DOLS) appropriate for addressing endogeneity, serial correlation, and providing consistent long-run estimates. While the technical details of these estimators are explained in Section 2.2.4, their selection reflects the need for robust long-term inference in a BRICS-T comparative framework.

The basic panel regression model used in the study is defined as follows:

$$Y_{it}= a_i+ {\beta }_1{X}_{1,it}+ {\beta }_2{X}_{2,it}+ {\beta }_3{X}_{3,it}+ {\beta }_4{X}_{4,it}+{\beta }_5{X}_{5,it}+ {\epsilon }_{it}$$

(1)

• $Y_{it}$: CO₂ emissions (tons)—dependent variable
• $X_{1,it}$: Forest area (ha)
• $X_{2,it}$: Population (million people)
• $X_{3,it}$: Forest product exports (USD, current prices)
• $X_{4,it}$: Forest product imports (USD, current prices)
• $X_{5,it}$: Renewable energy production (TWh)
• $a_i$: Country fixed effects
• ${\epsilon }_{it}$: Represents the error term.

After the model was created, econometric tests were applied to determine the suitability of the data set and the created model for panel data analysis.

2.2.1. Model Specification

In panel data analysis, the structure of the model is first defined based on the theoretical framework and the purpose of the study. Dependent and independent variables are presented in the model to explain the structural foundations of the research hypothesis. In the model created, EMS is defined as the dependent variable, while FRA, POP, FRE, FRI, and REP are defined as independent variables. Accordingly, this specification directly tests the hypotheses (H1–H4) formulated in the Introduction regarding the effects of forest area, population, forest product trade, and renewable energy production on carbon emissions in BRICS-T countries. Within this scope, the natural logarithms of the variables included in the basic panel regression model were taken in line with the unit root and stationarity analyses and included in the model. Thus, the differences in magnitude between the variables were reduced, and elasticity interpretations were made possible. Logarithmic transformations were further applied to stabilize variance and address moderate skewness in the data, thereby improving the distributional properties of the variables. Descriptive statistics, including skewness and kurtosis, were reported to assess deviations from normality and ensure the appropriateness of panel estimation. This step reduces the risk of spurious regression and provides a stronger basis for subsequent cointegration and causality analysis.

$$ln\left(EM{S}_{it}\right)=a_i+{\beta }_1 ln \left(FR{A}_{it}\right)+{\beta }_2 ln \left(PO{P}_{it}\right)+{\beta }_3 ln \left(FR{E}_{it}\right)+{\beta }_4 ln \left(FR{I}_{it}\right)+{\beta }_5 ln \left(RE{P}_{it}\right)+{\epsilon }_{it}$$

(2)

In the model,

• $i$: country (i = 1,…, N)
• $t$: year (t = 1,…, T)
• $a_i$: country-specific fixed term (fixed effect)
• ${\epsilon }_{it}$: represents the error term.

2.2.2. Stability Tests

To ensure that healthy and meaningful predictions can be made in panel data analysis, the stationarity levels of the variables included in the model were tested. In this context, unit root tests recommended for both homogeneous and heterogeneous panel data sets were applied. Stationarity analyses aim to determine whether the series in the panel have constant mean and variance properties over time. Otherwise, model results may lead to spurious regression issues [66]. For this purpose, tests that account for both homogeneous and heterogeneous structures were used: Under the assumption of a homogeneous unit root structure in the panel, the Levin, Lin & Chu (LLC) Test [67] was used for stationarity testing, the Im, Pesaran, and Shin (IPS) Test [68], which is suitable for heterogeneous panel structures and assumes different unit root processes for each cross-section unit. The ADF-Fisher and PP-Fisher Tests [69] were used to combine the p-values of individual unit root tests and make a decision at the panel level, and the Hadri Z-Stat Test [70], a variance ratio test that assumes the null hypothesis of stationarity, was used.

Panel unit root tests were conducted based on the following equation:

$$\Delta Y_{it }= p_i Y_{i,t-1} + {\sum }_{j=1}^p{\varnothing }_{ij}\Delta Y_{i,t-j} + {\mu }_i + {\epsilon }_{it}$$

(3)

$p_i$is the stationarity parameter. $H_0: p_i = 0$ tests whether the series contains a unit root (is not stationary) under the null hypothesis.

2.2.3. Panel Cointegration Tests

Pedroni’s [71,72] Panel Cointegration Test, Kao’s [73] Residual-Based Cointegration Test, and Westerlund’s [66] Bootstrap LM Test were used to examine the existence of a long-term relationship.

Pedroni test’s error term-based cointegration model:

$$Y_{it} = {\alpha }_i + {\delta }_{i}t + {\beta }_i X_{it} + e_{it}$$

(4)

$$e_{it}=p_i e_{i,t-1}+u_{it}$$

(5)

$p_i < 1$ cointegration exists. The Westerlund test works with bootstrap critical values through individual error correction models in the panel. Test equation:

$$\Delta Y_{it} = {\alpha }_i + {\varnothing }_i\left(Y_{i,t-1} - {\beta }_i^{\prime }{X}_{i, t-1}\right) + {\sum }_{j=1}^p{\gamma }_{ij}\Delta Y_{i,t-j} + {\sum }_{j=0}^q{\delta }_{ij}\Delta X_{i,t-j} + {\epsilon }_{it}$$

(6)

${\varnothing }_i$ error correction factor, ${\varnothing }_i < 0$ there is a long-term equilibrium relationship.

2.2.4. Long-Term Coefficient Estimates

After confirming the cointegration relationship in the panel, long-term coefficient estimates were made using the Fully Modified OLS (FMOLS) and Dynamic OLS (DOLS) methods. These methods reduce potential endogeneity and serial correlation problems that may arise in short-panel data sets, thereby providing consistent and unbiased long-term estimates [74,75,76]. Similar applications in the literature also support this methodological choice [77,78,79,80].

FMOLS equation:

$${\widehat{\beta }}^{\mathrm{FMOLS}} = {\left({\sum }_{i=1}^N{\sum }_{t=1}^T{\left(X_{it} -{\overline{X}}_i\right)}^{\prime }\left(X_{it} -{\overline{X}}_i\right)\right)}^{-1}{\sum }_{i=1}^N{\sum }_{t=1}^T{\left(X_{it} -{\overline{X}}_i\right)}^{\prime }{\left(Y_{it} -{\overline{Y}}_i\right)}^{*}$$

(7)

${\left(Y_{it} - Y_i\right)}^{*}$ is the error term adjusted for serial correlation and endogeneity. Thanks to the u transformation, FMOLS enables consistent and unbiased estimation of long-term parameters [80].

DOLS model:

$$Y_{it} = {\alpha }_i + \beta X_{it} + {\sum }_{k=-p}^p{\theta }_k\Delta X_{i, t-k} + {\epsilon }_{it}$$

(8)

DOLS can provide strong estimation performance even in small samples because it extends the effects of explanatory variables over time through lagged differences [79,81]. In this respect, it offers more flexible modeling possibilities compared to FMOLS.

In this study, both methods were applied, and the results confirmed each other, strengthening the stability of the relationship established between the cointegrated variables.

2.2.5. Panel Model Selection and Hausman Test

The Hausman test [82] was applied to determine whether the model works better with fixed effects (FE) or random effects (RE). Hausman test statistic:

$$H = {\left({\hat{\beta }}_{FE} - {\hat{\beta }}_{RE}\right)}^{\prime } {\left[Var\left({\hat{\beta }}_{FE}\right) - Var\left({\widehat{\beta }}_{RE}\right)\right]}^{-1}\left({\widehat{\beta }}_{FE} - {\widehat{\beta }}_{RE}\right)$$

(9)

Null Hypothesis H₀.

The RE model is appropriate.

Alternative Hypothesis H₁.

The FE model should be preferred.

Based on the test results, the appropriate model structure was determined, and FMOLS and DOLS estimates were obtained using this structure.

2.2.6. Horizontal Section Dependency

In panel data analysis, cross-sectional dependence (CSD) refers to the existence of simultaneous effects among different units in the panel arising from common shocks or global factors. When cross-sectional dependence is ignored, there is a risk that the estimated coefficients will be biased and unreliable [83]. Therefore, it is critical to test for the presence of CSD in analyses and select appropriate estimation methods.

The Pesaran CD Test [84] was applied to test for cross-sectional dependence. This test examines whether there is dependence among panel units by measuring the simultaneous correlation between error terms. The test statistic is defined as follows:

$$CD = \sqrt{{\displaystyle \frac{2T}{N\left(N-1\right)}}} {\sum }_{i=1}^{N-1}{\sum }_{j=i+1}^N{\widehat{p}}_{ij}$$

(10)

${\widehat{p}}_{ij}$ indicates the coefficient of bivariate correlation between error terms obtained from the panel data model. The Null Hypothesis H₀ suggests that there is no horizontal cross-sectional dependence in the panel, while the Alternative Hypothesis H₁ suggests that dependence exists. If the CD statistic is significant, the effect of standard shocks across countries or units in the panel data set should not be ignored [84,85].

2.2.7. Causal Analysis

The Dumitrescu-Hurlin panel causality test [86] was applied to analyze the directional relationship between variables. The Dumitrescu-Hurlin Panel Causality Test was developed to test causality relationships under conditions such as heterogeneous structures and cross-sectional dependence in panel data. It produces effective results, particularly in small and medium-sized panel data sets. This test is a generalization of the classical Granger causality test for panel data. It provides a common test statistic for the entire panel while estimating separate regressions for each unit (country, sector, etc.) [86].

The regression for each (i) country is constructed as follows:

$$Y_{i,t} = a_i + {\sum }_{k=1}^K{\gamma }_{i,k}{Y}_{i,t-k} + {\sum }_{k=1}^K{\beta }_{i,k}{X}_{i,t-k} + {\epsilon }_{i,t}$$

(11)

Testte H₀ (Null).

${\beta }_{i,k}=0 \forall i$: X is not the cause of Y (in Granger’s sense)—there is no causality in any unit.

H₁ (Alternative).

${\beta }_{i,k} \ne 0$ at least for some $i$ of: X is a Granger cause of Y.

Dumitrescu-Hurlin calculates the classic Granger F-statistic for each section and then defines the test statistic for the entire panel based on the average F value:

$${\overline{W}}_{N,T}= {\displaystyle \frac{1}{N}} {\sum }_{i=1}^N{W}_{i,T}$$

(12)

This average $\overline{W}$ provides a reliable estimate even in small samples with non-symmetric distributions. As a result, a decision is made as to whether X is the cause of Y across the panel.

3. Results

This section presents the core empirical findings from the BRICS-T panel. Model validity is summarized as follows: the Hausman test supports a random-effects (RE) specification (χ² = 0.00, p = 1.00), as reported in

Table 2. FE vs. RE Estimates—Model Selection.

Variables	FE Model			RE Model
	Coefficient	t statistic	p value	Coefficient	t statistic	p value
ln(POP)	−0.048736	−0.105516	0.9163	0.679685	29.40861	0.0000 *
ln(FRA)	1.010017	2.299077	0.0247 **	0.218415	10.00818	0.0000 *
ln(FRE)	0.094403	3.095155	0.0029 **	0.288128	12.44510	0.0000 *
ln(FRI)	−0.009732	−0.185125	0.8537	0.286149	10.62085	0.0000 *
ln(REP)	−0.152733	−3.963905	0.0002 *	−0.247991	−12.16369	0.0000 *
C	8.000359	0.646206	0.5204	−4.974866	−9.009698	0.0000 *
F statistic	1415.169			897.81
R2	0.9974			0.981632

* p < 0.01, ** p < 0.05 indicate levels of significance.

; Pedroni statistics indicate cointegration (Panel PP and Group PP, p < 0.001). Stationarity and cross-section dependence diagnostics motivate the use of appropriate differencing and long-run estimators (see

Table 3. Panel Unit-Root/Stationarity Tests.

Variables	LLC Stat.	IPS Stat.	ADF Stat.	PP Stat.	Result
ln(POP)	−1.871 *	−1.442 *	17.820 **	19.346 **	I(0)
ln(FRA)	1.405	1.122	3.415	4.026	I(2)
ln(REP)	−3.528 ***	−2.879 ***	25.639 ***	26.881 ***	I(1)
ln(FPE)	−4.221 ***	−3.003 ***	28.740 ***	30.567 ***	I(1)
ln(FPI)	−3.973 ***	−2.544 ***	24.611 ***	25.890 ***	I(1)

* p < 0.1, ** p < 0.05, *** p < 0.01 indicate levels of significance.

). Long-run elasticities estimated via FMOLS and DOLS are mutually consistent (

Table 4. FMOLS Results.

Variables	Coefficient	S.D.	t Statistic	p Value
ln(POP)	0.619615	0.042021	14.74535	0.0000
ln(FRA)	0.141443	0.041376	3.418496	0.0010
ln(FPE)	0.233049	0.044667	5.217447	0.0000
ln(FPI)	0.145518	0.048214	3.018159	0.0034
ln(REP)	−0.104365	0.026498	−3.938616	0.0002

and

Table 5. DOLS Results.

Variables	Coefficient	S.D.	t Statistic	p Value
ln(POP)	0.600348	0.047852	12.54588	0.0000
ln(FRA)	0.142691	0.047279	3.018060	0.0034
ln(FPE)	0.219155	0.050404	4.347999	0.0000
ln(FPI)	0.174817	0.055089	3.173329	0.0021
ln(REP)	−0.105468	0.029844	−3.533910	0.0007

). The main result is that renewable energy production significantly reduces emissions, whereas population, forest area, and forest-product trade (exports/imports) increase emissions; Dumitrescu–Hurlin causality patterns corroborate these relationships (

Table 6. Two-Way Causality Results.

Causality	p Value	Z Stat.
$\ln \left(\mathrm{POP}\right) \gg \ln \left(\mathrm{EMS}\right),$	$0.000007$	$4.49529$
$\ln \left(\mathrm{POP}\right) \ll \ln \left(\mathrm{EMS}\right)$	$0.000005$	$4.56692$
$\ln \left(\mathrm{REP}\right) \ll \ln \left(\mathrm{EMS}\right)$	$0.0119$	$2.51529$
$\ln \left(\mathrm{REP}\right) \ll \ln \left(\mathrm{EMS}\right)$	$0.001$	$3.14768$

and

Table 7. One-Way Causality Results.

Causality	p Value	Z Stat.
$\ln \left(\mathrm{FRA}\right) \gg \ln \left(\mathrm{EMS}\right),$	$0.0793$	$1.75495$ (10% confidence level)
$\ln \left(\mathrm{EMS}\right) \gg \ln \left(\mathrm{FRI}\right),$	$0.0431$	$2.02269$
$\ln \left(\mathrm{EMS}\right) \gg \ln \left(\mathrm{FRE}\right)$	$0.0748$	$1.78145$ (10% confidence level)
$\ln \left(\mathrm{POP}\right) \gg \ln \left(\mathrm{FRA}\right),$	$0.0018$	$3.11798$
$\ln \left(\mathrm{POP}\right) \gg \ln \left(\mathrm{FRE}\right)$	$0.000013$	$7.26965$
$\ln \left(\mathrm{POP}\right) \gg \ln \left(\mathrm{FRI}\right)$	$0.00009$	$5.89763$
$\ln \left(\mathrm{POP}\right) \gg \ln \left(\mathrm{REP}\right)$	$0.00005$	$6.57348$
$\ln \left(\mathrm{FRA}\right) \gg \ln \left(\mathrm{FRE}\right)$	$0.00008$	$4.45750$
$\ln \left(\mathrm{FRA}\right) \gg \ln \left(\mathrm{FRI}\right)$	$0.0042$	$2.85958$
$\ln \left(\mathrm{FRE}\right) \gg \ln \left(\mathrm{REP}\right)$	$0.000007$	$4.95593$
$\ln \left(\mathrm{FRI}\right) \gg \ln \left(\mathrm{REP}\right)$	$0.0010$	$3.30258$

). All core result tables appear in the main text. These results directly address the study’s aim by quantifying the long-run drivers of EMS in a BRICS-T setting and positioning Türkiye within that comparative structure. These empirical findings confirm H2, H3, and H4, while providing evidence consistent with H1 that forest area expansion is associated with higher emissions under production-oriented forestry dynamics.

3.1. Model Validity

We estimated both fixed- and random-effects specifications; the Hausman test supports random effects (RE) (χ² = 0.00, p = 1.00; see Table 2).

Residual cross-section dependence is present (Breusch–Pagan LM p = 0.0002; Pesaran scaled LM p < 0.001; Pesaran CD p = 0.0091), and, consistent with the integration orders reported in Table 3, we adopt a cointegration framework. Stationarity results indicate that ln(POP) is I(0), ln(REP), ln(FPE), and ln(FPI) are I(1), and ln(FRA) is I(2); appropriate differencing is applied.

Pedroni panel statistics (reported in Section 3.4) indicate cointegration (Panel PP and Group PP, p < 0.001). Collectively, these diagnostics justify estimating long-run elasticities with FMOLS/DOLS, which mitigate endogeneity and serial correlation in a short-T, multi-country panel.

3.2. Long-Run Elasticities (FMOLS & DOLS)

Table 4 and Table 5 report long-run emission elasticities under cointegration, estimated with FMOLS and DOLS. The two estimators yield highly consistent coefficients (R² ≈ 0.964–0.965), with identical signs and similar magnitudes, indicating robust long-run relationships.

• Model_FMOLS; ln (EMS) = 0.61961506125 ∗ ln(POP) + 0.141442707531 ∗ ln(FRA) + 0.233048618905 ∗ ln(FPE) + 0.14551785733 ∗ ln(FPI) − 0.104365451698 ∗ ln(REP)
• Model_DOLS; ln(EMS)= 0.600348016052 ∗ ln(POP) + 0.142690520409 ∗ ln(FRA) + 0.219154561933 ∗ ln(FPE) + 0.174816917206 ∗ ln(FPI) − 0.10546760625 ∗ ln(REP)
• Renewable energy (ln(REP)): −0.104 to −0.105 (p < 0.01). A 1% increase in renewable generation is associated with ≈ 0.10% lower emissions, confirming the mitigation role of the energy transition.
• Population (ln(POP)): +0.600 to +0.620 (p < 0.01). Demographic scale exerts the largest positive long-run effect on emissions.
• Forest-product exports (ln(FPE)): +0.219 to +0.233 (p < 0.01) and
• Forest-product imports (ln(FPI)): +0.146 to +0.175 (p < 0.01). Trade intensity in forest products is increases emissions in the long run.
• Forest area (ln(FRA)): +0.141 to +0.143 (p ≤ 0.01). The positive elasticity suggests that, in the BRICS-T context, production-oriented forestry and linked economic activity dominate potential sink effects in the long-run emissions relationship.

Overall, the directional alignment and tight coefficient range across FMOLS and DOLS strengthen inference: expanding renewables lowers emissions, whereas population pressure, forest-product trade, and (production-oriented) forest expansion raise them. These quantitative effects frame the policy discussion that follows and are consistent with the causality patterns reported in Section 3.3.

3.3. Causality (Dumitrescu–Hurlin)

We apply the Dumitrescu–Hurlin panel causality test to examine directionality among emissions (EMS) and its covariates (REP, POP, FRA, FPE, FPI), allowing for cross-unit heterogeneity. The test identifies two bidirectional feedback loops (see Table 6 and Table 7):

• POP ↔ EMS—population scale and emissions reinforce each other (both directions significant).
• REP ↔ EMS—renewable expansion and emissions co-evolve (both directions significant).
• Beyond this feedback, several unidirectional links emerge:
• FRA → EMS (10% level): forest area Granger-causes emissions.
• EMS → FPI and EMS → FPE: emissions Granger-cause forest-product trade flows.
• POP → FRA, FPE, FPI, REP: population drives forestry, trade intensity, and renewables.
• FRA → FPE, FPI: forest area precedes forest-product trade.
• FPE → REP and FPI → REP: forest-product trade precedes renewable expansion.
• (Exact statistics and p-values are reported in Table 6 and Table 7.)

The POP ↔ EMS and REP ↔ EMS feedbacks are consistent with the long-run elasticities in Section 3.2: the demographic scale is an amplifying force, while renewable deployment is the principal mitigating lever, yet emission dynamics also feed back into energy decisions. The FRA → EMS result aligns with the positive long-run elasticity on forest area, suggesting that, in this BRICS-T setting, production-oriented forestry and associated activity dominate sink effects. Finally, the causal ordering from trade → REP (and EMS → trade) indicates an interaction between trade intensity, energy transition, and emissions that helps explain the positive long-run coefficients on FPE/FPI and the negative coefficient on REP.

3.4. Türkiye-Specific Empirical Findings

Positioning Türkiye within the BRICS-T elasticities indicates that rapid scaling of renewables is the most effective mitigation lever. In contrast, population scale and forest-product trade (exports/imports) are the principal structural drivers of emissions. The long-run elasticity for REP of approximately −0.10 implies—ceteris paribus—that a 10% increase in renewable generation is associated with about a 1% decline in emissions. Taking Türkiye’s average emissions over 2009–2023 (457 MtCO₂), this corresponds to an illustrative reduction of roughly 4.6 MtCO₂. By contrast, population (+0.60–0.62) and forest-product exports/imports (+0.22–0.23/+0.15–0.17) exert persistent upward pressure on emissions, consistent with scale and trade-intensity effects.

The positive long-run elasticity for forest area (≈+0.14)—together with the FRA → EMS unidirectional causality (see Section 3.3)—suggests that, in the BRICS-T setting, production-oriented forestry and associated economic activity overshadow potential sink effects in the long run. For Türkiye—whose historical forest policy has emphasized production—this points to the need to rebalance toward carbon-service efficiency (measurement, reporting, and verification of sequestration; ecosystem-service valuation) rather than relying solely on area expansion.

4. Discussion

Panel data models have been constructed to identify the key determinants of carbon emissions in BRICS-T countries. Section 3 highlighted four dimensions: model validity, long-run elasticities, causality, and Türkiye-specific implications. In Section 4, these empirical findings are discussed in greater depth, linked with existing literature, and positioned within the policy context of Türkiye and the BRICS group. These comparative insights are consistent with the empirical confirmation of the hypotheses formulated in Section 1, highlighting population dynamics and forest product trade as key emission drivers. At the same time, renewable energy emerges as the principal mitigating factor.

4.1. Interpretation of Model Validity in Comparative Context

FE and RE models were compared. According to the fixed-effects model, variables such as forest area and forestry sector revenues have a positive and significant effect on carbon emissions. These results suggest that in countries like Türkiye, where extensive forest resources are available but production-oriented forestry practices are prevalent, sustainable forestry policies should focus not only on expanding forest areas but also on the modes of their utilization. Compared to BRICS countries such as Brazil and China, where large-scale afforestation programs and carbon markets have been institutionalized, Türkiye still lacks similar structural mechanisms, which highlights the importance of adapting BRICS experiences to strengthen its forestry and climate policies.

In Russia, Favero et al. [87] reported that forest expansion linked to biomass production increased emissions, consistent with our positive FRA elasticity. By contrast, Brazil’s tropical forest management Usman and Makhdun [88] produced negative elasticities, reflecting the dominance of natural sink effects.

The renewable energy production variable was found to be negative and highly significant in both the FE and RE models. Similarly to previous studies on BRICS, the carbon emission-reducing effect of renewable energy transition has been identified [89,90,91,92,93]. In China, Qin et al. [94] estimated a −0.25 to −0.49 elasticity for renewable energy while Dalei and Gupta [95] found that a 1% rise in renewables reduced emissions by about 0.10% in South Africa and India. These parallels reinforce the robustness of our findings. In this context, investments in wind and solar energy will significantly contribute to environmental sustainability by replacing carbon-intensive fossil fuels [96,97,98].

The RE model captures structural differences between countries more effectively [99,100]. In particular, this finding reflects that Türkiye’s socio-economic and environmental structures, which differ from those of the BRICS countries, are also captured by the model, underlining the need to consider country-specific characteristics in policy design. For Türkiye, this heterogeneity provides empirical evidence of both the challenges and opportunities in aligning its environmental governance with BRICS countries, thus contributing to the assessment of Türkiye’s integration capacity within this group.

The RE specification also confirms the carbon emission-reducing effect of renewable energy production; however, it highlights that when forestry is treated mainly as a production sector, associated industrial and trade activities can amplify emissions [101,102,103]. For Türkiye, this indicates the importance of treating forests not only as potential sinks but also as sectors with measurable carbon footprints [104]. This necessitates the integrated consideration of sustainable forest management, carbon certification systems, and green energy policies [105,106]. In this respect, Türkiye’s climate strategy can benefit from the experiences of BRICS countries in integrating renewable energy expansion with forest-based carbon certification systems, thereby reinforcing both environmental sustainability and economic integration.

Cross-sectional dependence was also identified, meaning environmental and economic factors move together in structurally integrated and emerging economies [83,84,107]. International shocks in energy prices, simultaneous climate policies, and financial market integration all contribute to this dependence [108,109,110,111]. This shows that Türkiye’s climate strategy cannot be designed in isolation but should be framed in coordination with BRICS countries, where structural shocks often ripple across economies.

Stationarity tests confirm heterogeneity: most variables were I(1), while FRA showed stronger inertia, requiring higher differencing. This reflects the structural and political diversity of BRICS-T countries and the long-term effects of forestry policies [112,113,114,115]. Cointegration tests further confirmed a robust long-term equilibrium among emissions and explanatory variables [116,117,118], supporting the argument that BRICS-T economies share structural drivers of emissions and should therefore coordinate environmental policies [119,120,121,122,123]. For Türkiye, this opens opportunities for joint carbon reduction initiatives, emissions trading, and renewable partnerships with BRICS members.

4.2. Policy Interpretation of Long-Run Elasticities

The results from both FMOLS and DOLS show high explanatory power (R² > 0.96). The negative and significant coefficients of the ln(REP) variable confirm the mitigating effect of renewable energy production on carbon emissions [124,125,126,127]. These estimates empirically confirm H2, H3, and H4, and are consistent with H1 regarding the positive association between forest area expansion and emissions under production-oriented forestry dynamics. However, the positive coefficients for ln(FRA), ln(FPE), and ln(FPI) demonstrate that natural resource use does not automatically lead to carbon reduction. Forest areas may transform from sinks into sources under production-oriented forestry or biomass utilization [128,129].

For Türkiye, this finding highlights the dual role of the forestry sector. While forest expansion policies provide long-term carbon storage capacity, production-oriented forestry and biomass utilization may increase emissions unless accompanied by sustainable management and certification mechanisms. Türkiye’s Forestry Strategy and Action Plan [130,131] continues to emphasize timber production and revenue generation, which explains the positive FRA coefficient in our model.

The consistency across FMOLS and DOLS strengthens confidence in these findings and aligns with BRICS literature that identifies renewables as the most effective tool for decarbonization. In China, renewable expansion is reinforced by an institutionalized carbon trading scheme, which monetizes the mitigation benefits. By contrast, Türkiye’s draft Climate Law [127] has not yet introduced a functioning ETS, limiting its ability to transform mitigation potential into market value. Moreover, while South Africa’s renewables also reduce emissions, their smaller elasticity (−0.08) reflects structural coal dependence, a challenge Türkiye partly shares due to lignite reliance.

This suggests that Türkiye needs a two-pronged strategy: accelerating renewable energy deployment as the most effective mitigation lever, while at the same time reshaping forestry governance toward carbon-service efficiency. Lessons from BRICS countries—such as Brazil’s struggles with biomass-related emissions, or China’s success in coupling renewables with carbon markets—provide comparative insights for Türkiye’s policy trajectory.

4.3. Policy Implications of Causality Analysis

The Dumitrescu-Hurlin test revealed bidirectional causality between ln(POP) and ln(EMS) and between ln(REP) and ln(EMS), indicating that both demographic growth and renewable deployment are locked in feedback loops with emissions [132,133,134,135]. This suggests that emissions not only respond to population and energy dynamics but also influence them.

Unidirectional causalities provide further structural insights. FRA → EMS demonstrates that forest expansion has direct but context-dependent effects [136,137]. EMS → FRI/FRE confirms that rising emissions trigger adjustments in forestry activities [138]. Population-driven causalities (POP → FRA, FPE, FPI, REP) highlight demographic pressure as the fundamental driver of resource use [139]. The causality from forestry-related variables to renewables (FRE/FRI → REP) suggests that forestry sectors shape the pace of energy transition [140].

The two-way link between renewables and emissions indicates that mitigation policies must be adaptive and account for feedback effects [141,142]. Moreover, causality tests confirm the inter-sectoral nature of environmental sustainability, showing that isolated policies have limited impact [143]. For Türkiye, these causality results underscore the need to integrate forestry policies, renewable energy strategies, and demographic dynamics within a unified framework of environmental governance, ensuring that sectoral interdependencies are explicitly addressed in national climate and energy policies.

This highlights that Türkiye’s challenge is not only sectoral, but systemic: demographic pressures, forestry practices, and energy transition interact in ways that require coordinated governance. Comparisons within BRICS reinforce this point—for instance, India has faced similar demographic pressures but counterbalanced them with rapid solar deployment, while Türkiye has moved more slowly in scaling renewables. Likewise, South Africa shows how structural dependence on carbon-intensive energy can limit the effectiveness of renewables unless coupled with strong governance reforms.

4.4. Türkiye-Specific Implications

Positioning Türkiye within the BRICS-T elasticities highlights renewable energy as the most effective mitigation lever, with a 10% increase in renewables translating into a ~1% reduction in emissions. Given Türkiye’s average emissions (2009–2023) of 457 MtCO₂, this implies an illustrative mitigation potential of ~4.6 MtCO₂. By contrast, population growth (+0.60–0.62) and forest-product trade (+0.22–0.23/+0.15–0.17) exert upward pressures, consistent with scale and trade-intensity effects.

The positive long-run elasticity of forest area (+0.14), alongside FRA → EMS causality, suggests that production-oriented forestry dominates potential sink effects in Türkiye, reflecting its historically production-focused forest policy. Unlike BRICS countries with institutionalized carbon markets, Türkiye has yet to rebalance toward carbon-service efficiency (MRV of sequestration, ecosystem-service valuation). Türkiye’s National Energy Plan [144] sets a target of 32% renewables in generation by 2030; our elasticity estimates imply that meeting this goal would reduce emissions by approximately 14 MtCO₂ annually. The National Energy Efficiency Action Plan [145] also targeted a 14% reduction in energy intensity, but the persistence of strong population elasticity in our model shows that efficiency alone is insufficient without renewable scaling. Similarly, the 11th Development Plan [146] continues to prioritize forestry as a source of industrial raw material, aligning with the production orientation seen in the FRA coefficient.

Türkiye’s trajectory most closely resembles Brazil’s, where production-oriented forestry and biomass use have contributed to rising emissions. In contrast, Russia benefits from natural sinks, while China leverages its ETS to monetize renewable benefits. India shares Türkiye’s demographic pressures but differs in its aggressive solar rollout. South Africa shows parallel constraints in its reliance on coal-heavy baseloads. This implies that Türkiye’s climate strategy should integrate renewable expansion with a reorientation of forestry toward carbon-service provision.

Adapting BRICS experiences—such as carbon markets in Brazil and China, or solar scale-up in India—could accelerate Türkiye’s environmental and economic integration into the bloc. Beyond replication, Türkiye’s policy design must also reflect its Mediterranean forestry dynamics and demographic profile, tailoring BRICS lessons into nationally appropriate strategies. The relatively short time span (2009–2023) represents a limitation, as longer historical panels could provide stronger evidence on long-run dynamics. Nevertheless, this period coincides with the most active phase of BRICS cooperation in climate, energy, and forestry, which makes it analytically relevant for comparative purposes. As with all panel studies, our results are bounded by the 2009–2023 window, BRICS-T comparability, and the selected variable set; omitted structural factors (e.g., technology, institutional quality) and potential endogeneity remain avenues for caution and further inquiry.

Future research could expand the scope of this study by incorporating additional emerging economies beyond the BRICS-T group, integrating institutional and technological indicators to capture governance and innovation dynamics, and employing alternative econometric approaches, such as dynamic panel or panel-IV methods, to address potential endogeneity and feedback effects.

5. Conclusions

The study analyzed the determinants of carbon emissions in BRICS-T countries, revealing the emission-reducing effects of renewable energy production and demonstrating that forest area, forest product trade, and population dynamics can increase emissions in some cases. The findings of this study, being based on a limited dataset, should not be regarded as definitive and immutable results, but rather as preliminary policy implications. This approach is intended to provide insights into the transformations observed in the environmental and economic structures of the BRICS-T countries, particularly in the post-2009 period. Accordingly, while the study aims to contribute a novel perspective to the literature, it also serves as a starting point for more extensive and long-term research.

The model results indicate that environmental sustainability depends not only on the quantity of resources but also on how these resources are used, by whom, and with what priorities, as well as the governance tools supporting their management and the extent to which sectoral interactions are addressed in an integrated manner. In addition to bearing a significant portion of global emissions, BRICS-T countries possess the potential to shape structural shifts in the climate regime due to their vast natural resource reserves, rapidly growing populations, and energy transition processes. However, the direction in which this potential unfolds is directly linked not only to technical capacity but also to the quality of governance, inter-sectoral coordination, and the coherence of spatial planning policies.

Türkiye’s potential membership in BRICS requires a multidimensional and selective integration strategy in terms of forestry, renewable energy, population dynamics, and carbon emission policies. The significant heterogeneity in the environmental governance structures and sectoral performance of BRICS countries necessitates sector-based cooperation models rather than a one-size-fits-all harmonization policy. Concrete joint platforms in areas with high synergy potential, such as renewable energy investments, the economic valorization of ecosystem services, and sustainable forest product trade should support this strategy. Potential membership could enhance Türkiye’s foreign policy diversity, expanding its maneuvering space in a multipolar international order; while maintaining its relations with Western alliances, it would enable Türkiye to open new diplomatic and commercial channels with emerging economies.

As a result of the study, recommendations were developed under six headings:

• Sustainable Forest Management: Forestry activities should shift away from production- and trade-focused methods and instead be guided by long-term forest health, land use planning, and sustainable use principles. Industrial forestry should focus on low-carbon production technologies, and carbon certification (e.g., Verra, Gold Standard) should be implemented systematically.
• Integration of Population and Urbanization Policies: The direct and indirect emission impacts of population growth and urbanization should be considered; land use plans that lessen the pressure of rapid urbanization on deforestation should be implemented; and rural development and employment policies should be utilized to ease migration pressures.
• Integrated Energy–Forest–Settlement Governance: Renewable energy policies should not be isolated by sector, and integrated management models that account for energy–forest–settlement interactions should be adopted.
• Ecosystem Service Payments and Natural Capital Accounting: A legal framework should be established for PES applications, which are currently limited in Türkiye. Additionally, separate monitoring items for ecosystem services should be allocated in the public budget, and community-based forest management should be supported. Learning from BRICS experiences, such as India’s forest-based fiscal transfers or China’s ecological compensation systems, should be encouraged.
• Carbon Accounting and Green Trade Strategy: Carbon accounting standards for forest product trade should align with both the EU’s Carbon Border Adjustment Mechanism (CBAM) and BRICS carbon markets. This would facilitate the development of a competitive green trade strategy that works with both markets.
• Regional and Global Corporate Coordination: Platforms should be created among BRICS-T countries to support the joint development of carbon markets, forest monitoring systems, and sustainable energy technologies.

Ensuring environmental sustainability in BRICS-T countries is not just about allocating more resources; it also depends on how resources are used, governance capacity, and how decisions are coordinated across social, sectoral, and spatial areas. Theoretically, the study contributes to the literature by introducing Türkiye into the BRICS comparative framework and by jointly examining forestry, trade, demographic, and renewable energy factors as structural determinants of carbon emissions. Practically, the findings provide evidence-based guidance for Türkiye and other emerging economies in designing integrated policies on renewable energy, forest governance, and carbon markets that align with both BRICS and EU sustainability regimes. These conclusions are consistent with the confirmation of the hypotheses formulated in the Introduction, reinforcing both the robustness of the empirical findings and their broader policy relevance.