Irfan Kadioglu

The European Union (EU) Carbon Border Adjustment Mechanism (CBAM) prices the embedded emissions of imported fertilisers, while nitrogen fertilisers are among the most volatile farm inputs in supplier countries. This study analyses both exposures for Türkiye in two complementary rankings. Stage 1 ranks 21 non-EU suppliers of six CBAM-covered fertilisers from an EU importer’s sourcing perspective; Stage 2 ranks 16 Turkish input groups on descriptive price-risk indicators for January 2020–July 2026 and on base-year index weights. Rankings use the Relational Membership Value Calculation (RMVC), its weighted extension, the Maji–Roy choice value, three compensatory multi-criteria methods and a consensus ranking. Under a 79 €/tCO₂e reference price, Türkiye’s modelled 2030 charge equals 32.6% of export value (supplier mean 22.9%), with ammonia, urea and calcium ammonium nitrate carrying 93.9% of it; on the carbon criteria alone Türkiye ranks 18th of 21. Its overall rank (1st to 17th) is driven by market attributes and falls to 9th to 18th when carbon criteria receive half of the weight. Nitrogen fertilisers rank first or second under all twelve full-sample combinations, but their lead weakens after 2023 and under block-bootstrap resampling. RMVC resolves ties, yet with ties handled at fixed cardinality it identifies top alternatives no better than Maji–Roy.

This book chapter aims to comprehensively examine the application of circular economy principles in agricultural production, the contributions of digital transformation to these processes, and, particularly, the potential of artificial intelligence applications within the framework of circular economy in agriculture. Throughout the chapter, a theoretical foundation will be established, the current literature will be discussed, and practice-oriented examples will be provided to develop a strategic perspective for the resilient and sustainable agricultural systems of the future.

Agriculture is under increasing environmental, economic, and social pressure due to climate change, resource depletion and food insecurity, requiring new approaches to production systems. This review explores how circular economy (CE) principles, digital transformation (DT), and artificial intelligence (AI)can be used together to achieve sustainable agricultural development. Using a systematic literature review guided by the PRISMA method, peer-reviewed studies published between 2016 and 2025 were analysed from the Web of Science and Scopus databases. The inclusion criteria targeted papers that addressed at least one sustainability dimension and focused on CE, DT, or AI applications in agriculture. The results show three interlinked themes. First, CE practices such as composting, biogas production, water reuse, and valorisation of agricultural waste reduce environmental impacts while creating new economic value streams. Second, DT tools, including Internet of Things (IoT) sensors, blockchain, and data analytics, enhance resource efficiency, supply chain transparency, and decision-making precision. Third, AI applications in crop monitoring, yield forecasting, and disease detection offer substantial productivity gains while minimizing inputs and emissions. However, adoption is hindered by high investment costs, limited digital literacy, regulatory gaps, and uneven infrastructure, particularly among smallholders. The review highlights the need for integrated policy frameworks that align investments in CE and DT, expand rural broadband networks, provide targeted training, and incentivize sustainable practices. A conceptual synthesis is proposed that links technological enablers with environmental, economic, and social outcomes. Addressing these barriers through coordinated multi-stakeholder strategies could accelerate the transition to resilient, environmentally friendly, and inclusive agricultural systems.Keywords: artificial intelligence, climate-friendly agriculture, digital agriculture, green transition, precision agriculture, sustainable farming.

This study critically examines the financial and trade determinants of the green transition in Turkey by employing Toda-Yamamoto causality analysis. Using time-series data for 1990–2022, it analyzes how bank credit volume, foreign direct investment (FDI), and trade openness affect renewable energy production and carbon emissions. The results confirm that bank credit significantly promotes renewable energy production but also increases carbon emissions by supporting carbon-intensive sectors. FDI shows a weak positive impact on renewable energy but a strong positive impact on carbon emissions, which is consistent with the Pollution Haven Hypothesis. Trade openness supports renewable energy development through technology transfer, but also raises emissions via increased production and logistics. The causality results confirm complex bidirectional relationships between the variables, highlighting the need for integrated financial, trade, and environmental policies. Despite certain data and scope limitations, this study provides robust policy insights that emphasize the qualitative improvement of green finance, sector-focused incentives for clean investments, and sustainable trade practices. Future research should explore micro-level data and sectoral specifics to deepen our understanding of green transition mechanisms in emerging economies. This study contributes to global literature by offering empirical evidence and policy reflections relevant to developing countries that aim to align growth with sustainability.

This study analyzes Turkey’s development toward a green economy between 1990 and 2022 within the framework of certain green economic indicators. The data consist of secondary data from the official databases of the World Bank and the Turkish Statistical Institute (TURKSTAT). In the study, the total amount of carbon emissions was chosen as an indicator of green growth, while gross domestic product per capita (GDP) represents economic growth, domestic loans granted by banks to the private sector (as a percentage of GDP) and foreign direct investment represent financial development, and electricity generation represents pollution. To determine whether the variables are cointegrated and to determine the direction and strength of the relationship between the variables, the ARDL bounds test and the FMOLS and DOLS long-run estimators were used. Finally, Toda Yamamoto (TY)–Granger tests were performed to determine causality. The long-term relationship between the variables was confirmed by the results of the ARDL bounds test. The error correction coefficient (CointEq(−1)) was estimated to be statistically significant and negative (−0.757) when the short-term analysis was performed. This result shows that the short-term imbalances will be corrected in less than a year, and the system will approach the long-term equilibrium. In the long-term analysis of the model, all variables selected to explain the dependent variable were found to have a statistically significant impact on the dependent variable. The GDP per capita variable, the indicator of economic growth, has a negative effect on the dependent variable, while the other independent variables have a positive effect. The results of the causality analysis indicate that the dependent variable carbon emissions (CO₂) has a unidirectional causality relationship with domestic credit provided to the private sector by banks (DC), which represents financial development, and with total electricity production (EP), which serves as an indicator of pollutants.

Turkey is attempting to achieve carbon neutrality targets by implementing environmentally friendly techniques. This study aims to assess the short- and long-term relationships between Turkey’s annual carbon emissions and economic growth, as well as annual electricity generation from renewable fossil fuels. Data for this study were obtained from the World Bank and the official website of the Turkish Statistical Institute (TURKSTAT) from 1986 to 2020. The Autoregressive Distributed Lag Bound Test (ARDL) technique was used to estimate variable cointegration. The cointegration test shows a long-run relationship between the selected variables. A 1% increase in the GDP, COAL, and PTRLGZ variables increases CO₂ emissions by 0.65%, 33%, and 7%, respectively. The RE variable was found to have a negative effect on CO₂ emissions. The effect of the RE variable on CO₂ emissions is −0.10. This result indicates that a 1% increase in the RE variable reduces CO₂ emissions by 0.10%. Looking at the short-term data, the error correction coefficient (CointEq (−1)) was found to be negative (−0.832) and significant. Based on the error correction variable, it is expected that the short-run deviations from equilibrium will be corrected and that a long-run equilibrium will be reached in less than one year. The long-run results also suggest that the factors used in the model have a statistically significant effect on the dependent variable. While this effect is negative for annual electricity production from renewable energy sources, it is positive for annual electricity production from fossil fuels and gross domestic product (GDP).

This study aims to evaluate the long-run and causality relationships between the annual grain production (kg per hectare) in Turkey, fertilizer used in agriculture, the number of tractors, agricultural greenhouse gas emissions, and grain production area from 1988 to 2018. The study’s data for the years 1988–2018 were taken from the World Bank and Turkish Statistical Institute (Turkstat) databases. The autoregressive distributed lag bounds (ARDL) test was applied to estimate the cointegration between the variables. The cointegration test results confirmed a long-run relationship between the variables. The short-run estimation revealed that the error correction coefficient was negative and statistically significant. The result obtained for the error correction term estimated that the deviations from the short-run equilibrium would be corrected, and the system would converge to the long-run equilibrium within 1.05 years. Further, the long-run estimation showed that all variables included in the model had a statistically significant effect on the dependent variable. While this relationship was negative for grain production amount and carbon emission, it was positive for fertilizer use and the number of tractors. The grain areas estimated as the dependent variable in the ARDL model were in a feedback relationship with the current production and number of tractors variables, while the fertilizer and carbon emission variables were in a unidirectional causality relationship towards the grain production area. There is a negative relationship between grain production (kg per hectare) and grain production areas (hectares). A 1% increase in grain production leads to a decrease of approximately 0.30% in grain production areas. Agricultural greenhouse gas emissions, another variable that stands out with its negative impact in ARDL long-run estimation results, indicate that product groups produced as an alternative to grain have a higher emission-generating power. The other long-run estimation results reveal that the tractor variable positively affects grain production areas.