Abstract Sustainable development is a globally significant concept with various definitions. Problems stemming from climate change, in particular, have made sustainable development goals a necessity for global economies. The sustainable development index is calculated by considering environmental, economic, and social dimensions. This study examines the effects of economic growth, financial development, digitalization, and renewable energy consumption on the sustainable development index in G7 countries for the period 2000–2022. Using advanced panel data techniques that account for cross-sectional dependence and heterogeneity, the study employed the Nazlioglu et al. (2023) PANIC Fourier unit root test and the Westerlund and Edgerton (2008) cointegration test. After determining the cointegration relationship, long-term panel cointegration coefficients were determined using the Augmented Mean Group (AMG) and Regularized Common Correlated Error (rCCE) estimators. The findings showed that economic growth decreased the sustainable development index, while renewable energy consumption and financial development increased it. The effect of the digitalization variable on the sustainable development index was not statistically significant. These findings highlight that sustainable development in G7 countries requires not only continued support for renewable energy and green finance systems, but also a critical reassessment of growth strategies. Policymakers should strengthen environmental governance frameworks to ensure that economic and technological progress translates into measurable improvements in sustainable development outcomes.

Bu çalışmada Türk Devletleri Teşkilatı’na üye ülkelerde deprem riskinin ekonomik ve finansal göstergeler üzerindeki etkileri araştırılmaktadır. Afet yönetimi perspektifinden ele alınan çalışmada 2000-2024 dönemi incelenmiştir. Çalışmada deprem riskinin ekonomik büyüme, enflasyon, sermaye birikimi, doğrudan yabancı yatırımlar ve para arzı üzerindeki nedensel ilişkileri analiz edilmektedir. Ekonometrik analizlerde yatay kesit bağımlılığı dikkate alınarak heterojen panel nedensellik testlerinden yararlanılmıştır. Bu kapsamda panel SUR, panel VAR, panel LA-VAR ve PANICCA ile panel LA-VAR nedensellik testlerinden faydalanılmıştır. Ampirik bulgular, deprem riskinin iktisadi ve finansal kırılganlıkları artırarak ilgili ülkelerde afetlere karşı hazırlık düzeyi ve zarar azaltma kapasitesi üzerinde önemli etkiler oluşturduğunu belirtmektedir. Elde edilen sonuçlar, deprem riski özelinde afet ve risk yönetimi politikalarının ekonomik ve finansal göstergelerle bütünleşik olarak ele alınmasının gerekliliğini ortaya koymaktadır.

This study investigates the factors influencing agricultural energy effectiveness in the Netherlands during the period 1990-2022. The explanatory variables include agricultural raw materials imports, agricultural land, agriculture, forestry, and fishing value added per worker, agriculture, forestry, and fishing natural gas use, and agriculture, forestry, and fishing oil use. Advanced time-series techniques incorporating Fourier functions were employed. Unit root testing was conducted using standard ADF and Fourier ADF tests, while cointegration analysis utilized the Fourier Engle-Granger test. Long- and short-term coefficients were estimated through Fourier-augmented FMOLS and CCR, with DOLS serving as a robustness check. The findings reveal that agricultural raw materials imports, agricultural land, and value added per worker positively influence agricultural energy effectiveness. Conversely, natural gas and oil use in agriculture, forestry, and fishing negatively impact energy effectiveness. These results highlight the importance of transitioning toward renewable energy sources and adopting energy-efficient technologies in agriculture to enhance sustainability. It is also important that policymakers develop programs such as tax exemptions, subsidies, and interest-free loans that would motivate agricultural enterprises to embrace and adapt to green energy systems. Additionally, the formulation of energy efficiency standards for agricultural irrigation systems, as well as the establishment of an institutional framework for monitoring the standards, is seen as vital.

This study aims to analyze the effects of domestic, foreign, and green technologies on industrialization in the Turkish economy during the globalization period, and to identify the key determinants of industrialization. The study covers data from 1990 to 2021; patent applications represent domestic technology, foreign direct investments represent foreign technology, renewable energy technologies represent green technology, and industrial value added is used as an indicator of industrialization. The Fourier Bootstrap Autoregressive Distributed Lag (ARDL) method was applied in the analysis. The findings show that domestic technologies have the strongest and statistically significant positive effect on industrialization, green technologies play a supportive role in industrialization, and the effect of foreign technologies remains relatively limited. According to the ARDL results, a 1 % increase in domestic technologies increases industrialization by approximately 0.27 %, while 1 % increases in green and foreign technologies increase industrialization by approximately 0.03 % and 0.01 %, respectively. The validity and reliability of the results were tested using Fully Modified Least Squares (FMOLS) and Dynamic Least Squares (DOLS) methods, and the findings were consistent with the ARDL results. These findings reveal that the sustainability of industrialization in Turkey largely depends on domestic and green technology investments. The study emphasizes the importance of technology-based industrial policies and offers strategic recommendations to policymakers to strengthen domestic R&D capacity and promote environmentally friendly technologies in order to support long-term sustainable economic growth.

PurposeEnvironmental sustainability, which is among the most important goals of sustainable development, is a globally debated issue. In this study, China, which is among the major environmental polluters on a global scale, has been examined in depth. The high growth rates and high energy consumption of the Chinese economy provide an important research framework. In this context, the validity of the Environmental Kuznets Curve (EKC) and Renewable Energy Kuznets Curve (RKC) hypotheses has been investigated. The main purpose of the study is whether environmental degradation or renewable energy increase occurs first with increasing income in China.Design/methodology/approachTwo empirical analyzes were conducted for the period 1990-2022 for China with the help of CO2 emissions, renewable energy, income, trade openness and urbanization variables. In the study where Augmented ARDL analysis was used, short-term causal relationships were measured with the Fourier Toda-Yamamoto causality test.FindingsAccording to the results obtained by calculating the turning points for the EKC and RKC hypotheses in two different models, the RKC turning point occurs before the EKC. These results provide important evidence that income growth in China can increase environmental quality before environmental degradation. These results, especially for China, a heavily polluting country, provide important policy insights for global economies.Originality/valueThe RKC hypothesis, which argues the opposite of the inverted U hypothesis put forward by the EKC hypothesis, is quite new. In addition, to the best of the authors' knowledge, there is no study on China where the EKC and RKC hypotheses are used together and considered within the framework of robustness testing. The main motivation in the study is the question of whether environmental degradation or renewable energy increase occurs first with increasing income in China. The answer to this question, which is thought to contribute to the literature, reveals the validity of the EKC and RKC hypotheses and the environmental consequences of income.

ABSTRACT This study examines the determinants of environmental efficiency in Germany between 1990 and 2022, focusing on labor productivity, green R&D productivity, fossil fuel inefficiency, and energy efficiency R&D expenditures. Using the Inverse Load Capacity Factor per unit of GDP as an indicator of environmental efficiency, the analysis utilizes time series techniques, including ADF and Fourier ADF tests, which confirm the first‐difference stationarity of all variables. Cointegration relationships are identified using Fourier Engle‐Granger and Fourier ADL tests, while FMOLS and CCR estimators assess the magnitude and direction of effects, with DOLS results validating robustness. Key findings reveal that increased labor efficiency enhances environmental performance by reducing resource intensity and optimizing productivity. Similarly, green R&D efficiency improves environmental outcomes by driving renewable energy adoption and innovation. However, fossil energy inefficiency negatively impacts environmental efficiency, highlighting the misalignment in energy policies and the persistent reliance on carbon‐intensive energy sources. Energy R&D expenditures also contribute significantly to environmental efficiency by fostering technological advancements in energy optimization. These findings suggest that Germany's sustainable development goals and growth strategies should be revised to take into account environmental targets, global warming and the prevention of environmental pollution. By integrating advanced econometric approaches and exploring under‐researched variables, this study provides critical insights into Germany's environmental efficiency and its broader implications for sustainable growth.

This study pursues two main objectives. The first is to explain the relationships between natural resource rents, energy transformation, economic growth, and environmental degradation in the Turkish economy. The second is to assess the contribution of boron mining, in which Türkiye is a clear world leader, to environmental sustainability. This study utilizes the autoregressive distributed lag (ARDL) bounds test with structural breaks using data from 1970 to 2021. The short-term results demonstrate that an inverted-U relationship exists between economic growth and environmental degradation. Moreover, the findings indicate that natural resource rents exert a positive effect on environmental degradation, whereas renewable energy consumption contributes to its mitigation. Conversely, fossil fuel consumption is associated with heightened environmental deterioration. Similar findings are obtained in the long term, confirming the validity of the Inverse Load Capacity Curve hypothesis. This result also confirms the environmental dimension of the natural resource curse hypothesis. The dynamic ordinary least square (DOLS) estimator was employed to verify the robustness of the long-run results. Finally, the study utilized Toda-Yamamoto causality analysis. These results emphasize Türkiye's need to channel its resource revenues toward renewable and low-carbon energy investments rather than consumption-driven growth. It is crucial to develop green innovation and integrate sustainability goals into energy planning. In this context, boron, one of Türkiye's most strategic natural resources, is considered to significantly contribute to sustainable energy technologies and the country's transition to a low-carbon, resource-efficient, and environmentally friendly economy.

This study aims to comprehensively address the impacts of trade, foreign direct investment, migration, and human development on environmental sustainability. The study examines the Turkish economy, a developing market economy, for the period 1990–2022. Per capita ecological footprint is used as an indicator of environmental sustainability. Within the framework of the Environmental Kuznets (EKC) Curve approach, the study aims to reveal the short-term and long-term relationships between indicators of economic and social globalization and environmental degradation. Fourier-based time series methods that consider structural breaks are used in the study. The long-term relationship between variables is analyzed using the Fourier-Engle-Granger cointegration test. Short-term and long-term coefficients are estimated using the FMOLS method. The CCR estimator is used to test the robustness of the obtained results. The Environmental Kuznets Curve approach is adopted to examine the nonlinear relationship between trade and environmental degradation. The analysis results show the existence of a long-term cointegration relationship between the variables. FMOLS findings revealed an inverted U-shaped relationship between trade and ecological footprint in both the short and long term. This result confirms the Environmental Kuznets Curve hypothesis in Türkiye. In the long term, foreign direct investment and human development have significant impacts on environmental sustainability. In the short term, migration was identified as a significant factor in terms of environmental policies. CCR results support the FMOLS findings. This study is one of the limited number of studies that address trade, investment, migration, and human development in Türkiye within the same framework. The use of ecological footprint as a comprehensive environmental indicator and the consideration of structural breaks using Fourier-based methods provide a methodological contribution to the literature. The findings offer important implications for policymakers in ensuring environmental sustainability and achieving sustainable development goals.

This study examines the determinants of economic growth in the 27 countries with the highest GDP during the period 2008–2020. The analysis employs new-generation panel data methods that account for cross-country common shocks, spillover effects, and structural differences. In this context, the relationship between economic growth and artificial intelligence readiness, renewable energy use, the level of industrialisation, and the unemployment rate has been tested. The findings indicate that technological capacity and advancements in artificial intelligence support growth through the channels of productivity, innovation, and total factor productivity. The use of renewable energy, meanwhile, contributes to growth by reducing production costs, strengthening energy security and encouraging environmentally sustainable investments. Whilst industrialisation emerges as one of the key drivers of growth through economies of scale and increased value added, unemployment constrains growth due to idle labour and weak domestic demand. The results indicate that R&D, digital transformation, renewable energy infrastructure, industrial policies and inclusive employment strategies must be strengthened in tandem to ensure long-term stability.

This study aims to examine the key factors determining climate vulnerability in India's energy transition process, within the framework of its goal to achieve net zero emissions by 2070. In this context, the effects of renewable energy use, income inequality, economic growth, globalization, and technological progress on India's climate vulnerability are analyzed. Annual data specific to India for the period 1995-2023 are used in the study. To account for nonlinear relationships and potential structural breaks, fractional frequency Fourier ADF unit root tests, a fractional frequency Fourier ARDL model, and the Fourier bootstrap Toda-Yamamoto causality approach were applied in the empirical analysis. Furthermore, FMOLS and CCR estimators with Fourier functions were used to test the robustness of the findings. The empirical findings yield consistent results in both the short and long term. The results show an inverted U-shaped relationship between economic growth and climate vulnerability, and thus the Environmental Kuznets Curve hypothesis is valid in the case of India. Furthermore, it has been found that the use of renewable energy and technological progress reduce climate vulnerability, while globalization increases it. The impact of income inequality appears to manifest not directly, but rather through structural channels such as growth dynamics and institutional capacity. These findings indicate that for India to achieve its net-zero target, its energy transition policies should not focus solely on emission reduction, but should also be supported by holistic policy designs that strengthen climate resilience and promote social inclusion.

Economic growth stands out as one of the basic macroeconomic goals of national economies. There are many studies on which factors affect economic growth. The difference between this study and other studies is that it investigates the economic growth relationship in depth using high-tech product exports, foreign direct investments, labour and capital variables. This study uses high-tech exports, foreign direct investments, labour, capital and economic growth variables for the Turkish economy for the period 1990-2023 are used. As an empirical method, robustness tests were performed using methods with various strength characteristics. In this context, the traditional ADF and PP unit root tests and the Fourier ADF unit root test were applied. The cointegration methodology is based on the conventional Shin (1994) cointegration test and the Fourier Shin cointegration test proposed by Tsong et al. (2016). The FMOLS, DOLS, and CCR techniques were used as cointegration estimators. According to the empirical findings, it is concluded that increases in the exports of high-tech products, foreign direct investments, labour and capital increase economic growth. The fact that all the estimators with different power properties yield similar findings guarantees the robustness of the results. Finally, Hacker and Hatemi-J (2006) and Hatemi-J (2012) tests were applied to determine the bootstrap symmetric and asymmetric causality relationships between the variables. These results also supportthe long-run co-integration estimator results. Accordingly, there is a symmetric and asymmetric causality relationship between the exports of high-tech products, foreign direct investment, labour and capital variables and economic growth.

Sustainable human development cannot be explained solely by economic growth indicators. It is increasingly accepted that institutional quality, resource efficiency, and socio-economic transformation processes are also decisive factors in development outcomes. In the current literature, these elements are addressed in a limited way or only separately. This study examines the determinants of sustainable human development in 40 economies, both developed and developing, during the period 1996–2022, focusing on quality of governance, resource efficiency, and socio-economic dynamics. Using the Human Development Index (HDI) as a multidimensional measure of progress, the analysis explores how democratization, rule of law, corruption control, globalization, natural resource rents, urbanization, fossil fuel dependence, renewable energy adoption, and unemployment interact to shape development outcomes. Employing the Common Correlated Effects Mean Group (CCEMG) and Augmented Mean Group (AMG) estimators, the study addresses potential cross-sectional dependence and multicollinearity within the panel. The findings suggest that governance quality, globalization, urbanization, and balanced energy use (both renewable and fossil-based) have significant positive impacts on sustainable human development. These results highlight the importance of institutional strength and resource management in achieving multiple Sustainable Development Goals (SDGs), particularly those related to inclusive growth, clean energy, and strong institutions.

This study explores the impact of value-added agriculture, environmental taxes, and financial development on environmental quality in the Turkish economy during the period 1994-2022. Additionally, the agricultural area, which can also affect environmental quality, is added as a control variable in the research model. Longterm relationships are examined using traditional Shin cointegration and Fourier Shin cointegration methods, and long-term coefficients are examined through the extended least squares method with Fourier functions. Dynamic Least Squares method extended with Fourier functions is applied as a robustness test, followed by the Fourier Bootstrap Toda-Yamamoto causality analysis. The findings indicate that value-added agriculture, environmental taxes, and increases in the agricultural area enhance environmental quality. Developments in the financial structure, on the other hand, have a detrimental effect on environmental quality. Short-term causality test findings indicate that there is a bidirectional causal association between value-added agriculture, environmental taxes, agricultural area, and environmental quality. However, there is a unidirectional causality from financial development to environmental quality.

Environmental sustainability is a topic of global debate. Environmental degradation, a frequently discussed topic within the context of sustainable development goals, has been frequently discussed in the context of economic growth and energy consumption. However, the impact of the logistics sector on environmental pollution has received relatively limited discussion. This study examines the impact of logistics activities, trade openness, foreign direct investment, and economic growth on environmental degradation. This research, conducted on Germany, a leading country in the logistics field, is expected to fill a gap in the relevant literature. In this study, the stationarity level of the variables logistics, trade openness, foreign direct investment, economic growth, and environmental degradation for the period 1980-2024 was determined using traditional and Fourier-based unit root and stationarity tests. Based on the results, the long-term relationship between the variables was investigated using the A-ARDL cointegration method proposed by Sam et al. (2019). FMOLS and CCR estimation were used as long-term estimators. The DOLS method was used as a robustness test. The A-ARDL cointegration test findings in the study revealed a long-run relationship between the variables logistics, trade openness, foreign direct investment, economic growth, and environmental degradation. In addition to the FMOLS and CCR tests used in coefficient estimation, the DOLS robustness test results are consistent. The results indicate that logistics activities increase environmental pollution. Conversely, increasing trade openness reduces environmental degradation. Furthermore, increased economic growth improves environmental quality. Given that Germany has a strong presence among developed countries, particularly in logistics activities and export-led growth, the findings of this study provide important insights for policy makers.

This study analyzes the LCF convergence of the world's ten largest polluters-Brazil, the United States, Germany, South Korea, India, China, Indonesia, Japan, Mexico, and France-using data for the period 1961-2024. The research goes beyond the static framework and the heterogeneous findings of the linear, structural-break, and nonlinear unit root tests. As an analytical contribution, it applies the Nahar-Inder (2002) methodology to the LCF literature for the first time. This approach models convergence towards the leading country (Brazil) as a dynamic polynomial function of time, thereby relaxing the restrictive assumption of stationarity. With the contribution of Bentzen and Tung (2021), changes in the speed of convergence (acceleration or deceleration) are also captured through second-derivative analysis. The findings stand in sharp contrast to those obtained from unit root tests: the Nahar-Inder methodology identifies strong convergence for seven countries (the United States, India, Japan, Indonesia, Germany, France, and South Korea), while China and Mexico exhibit divergence. This result indicates that economies appearing to be divergent under traditional tests (e.g., the U.S., Germany, France) are, in fact, dynamically converging. The results emphasise the necessity of differentiated policy trajectories for converging and diverging countries.

Environmental degradation has become a global issue. CO2 emissions lie at the heart of this issue. Therefore, the determinants of CO2 emissions are becoming important. This study focuses on the relationship between CO2 emissions and environmental technological innovation, environmental taxes and income for the Turkish economy. Traditional unit root tests and Fourier ADF unit root tests were used in the light of data for the period 1994-2022. Augmented ARDL method was used to determine the long-term relationship. FMOLS, DOLS and CCR were used as long-term estimators. Empirical findings revealed that environmental technical innovations, environmental taxes and increases in income increased CO2 emissions. These results showed that technological innovations and environmental measures were not successful in reducing CO2 emissions. The findings provide important insights into environmental and technology policy implementations for Türkiye, which is among the developing countries.

In this study, the validity of fiscal sustainability is investigated by using the variables of interest paid on public debt, primary surplus, public revenue, public expenditure and public debt in the economy of T & uuml;rkiye for the period 1980-2023. In empirical analysis, in addition to traditional unit root tests, nonlinear unit root tests and Fourier-based nonlinear unit root tests are used. On the other hand, for financial sustainability, in addition to traditional and nonlinear cointegration tests, current approaches based on Fourier functions proposed by Yaz & imath;c & imath; (2024) are used. Empirical findings show that current empirical methods reveal fiscal sustainability more strongly in the economy of T & uuml;rkiye.

China's rise in the maritime sector is notable on a global scale. China, which attaches great importance to developments in the blue economy as a national goal, is a world leader in aquaculture production. Developments in the blue economy are crucial for economic growth and the sustainability of international trade. Determining China's fishing grounds footprint, a subcomponent of its ecological footprint, is of critical importance for China, which has a significant trade volume in aquaculture production. In this context, the determinants of the fishing area footprint are being investigated in the Chinese economy. The relationship between the fishing grounds footprint and fishing production, trade globalization, foreign direct investment, and economic growth is investigated for the period 1980-2022. The A-ARDL method is chosen as the empirical method in the study, and cointegration estimators with various power characteristics are utilized. In this context, the FMOLS and CCr estimators are used. The DOLS estimator is used for robustness testing. According to the empirical results, the FMOLS, CCR, and DOLS (robustness) estimation results are consistent with each other. The results indicate that while increases in fishery production and trade globalization have contributed to a rise in the fishing footprint, economic growth appears to have had a mitigating effect, potentially due to increased efficiency, technological advancements, or sectoral shifts in the Chinese economy. These results offer important insights into development models that support sustainable development goals in China's marine sector.

This study addresses a critical gap in the literature by analyzing the environmental impact of renewable energy R&D effectiveness alongside other energy-related R&D investments, which has been largely neglected. Focusing on the UK from 1990 to 2022, the research examines the role of R&D in renewable energy, fossil fuels, nuclear energy, and GDP in driving the Ecological Footprint (EF). While previous studies on the Environmental Kuznets Curve (EKC) have rarely incorporated energy-related R&D, this study emphasizes that increasing renewable energy use, relative to R&D effectiveness, is key to understanding its environmental benefits. Using advanced econometric techniques such as Fourier Engle-Granger and Fourier ADL cointegration tests, FMOLS, CCR, and DOLS estimators, the findings reveal that enhanced renewable energy R&D significantly reduces EF, while fossil fuel and nuclear energy R&D investments also contribute to environmental mitigation. Additionally, GDP shows a long-term negative relationship with EF, aligning with the EKC hypothesis, though its short-term effect on environmental quality is limited. By highlighting the underexplored role of energy R&D, this study provides critical insights for policymakers seeking to balance economic growth with environmental sustainability.