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The purpose of this paper is to give a good overview of the relationship between industrial growth and industrial pollution in Turkey. The question is to what extent dirty industries have been affected by the regulations on the control of environmental degradation.

The approach for this study uses all regulations which serve for protecting human and its environment from danger arising from dirty industries in Turkey. After presenting brief explanations on green industry, next sessions explain and compare the situations of the Turkish dirty industries and its relationship with related regulations in the European Union (EU).

The authors offer three solutions. First, clean consumption should be stimulated in Turkish society. Second, Turkish Government should conduct more joint projects with the EU. Third, EU funds should be directed to cleaner production technologies to subsidize dirty industries during the negotiation process.

Green industry can be assessed as a steep road to build a sustainable future. For a long time, the unsustainability of current forms of industrial production has been discussed in Turkey. As a solution some argue that if governments support, industries can finance their own transformation more rapidly. However, these arguments do not mean that industries voluntarily accept these changes.

The purpose of this paper is to analyze the relationship between liberalization and greenhouse gases (GHGs) emissions in Central and Eastern European countries (CEECs). After their memberships, most of the CEECs have already committed to reducing their GHGs emissions. Although emissions have decreased on average, there is a substantial heterogeneity among the countries. Within the liberalization and integration efforts, increasingly huge amount of foreign direct investment (FDI) has flown to the region. Therefore, the question is whether or not this increase in foreign investment to CEECs is related to the polluting industries. The coincidence of increased FDI and GHGs emission has led us to study the relationship between them.

The paper exploited cross-sectional and time series variation of the data.

The paper found that the polluting FDI is positively associated with GHGs emissions in CEECs.

Few previous studies have taken into account FDI and environmental performance together, so the analysis represents a notable contribution to the pollution haven literature.

Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV).

Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV.

In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%.

An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.

This paper aims to design an AI-based drone that can facilitate the complicated and time-intensive control process for detecting healthy and defective solar panels. Today, the use of solar panels is becoming widespread, and control problems are increasing. Physical control of the solar panels is critical in obtaining electrical power. Controlling solar panel power plants and rooftop panel applications installed in large areas can be difficult and time-consuming. Therefore, this paper designs a system that aims to panel detection.

This paper designed a low-cost AI-based unmanned aerial vehicle to reduce the difficulty of the control process. Convolutional neural network based AI models were developed to classify solar panels as damaged, dusty and normal. Two approaches to the solar panel detection model were adopted: Approach 1 and Approach 2.

The training was conducted with YOLOv5, YOLOv6 and YOLOv8 models in Approach 1. The best F1 score was 81% at 150 epochs with YOLOv5m. In total, 87% and 89% of the best F1 score and mAP values were obtained with the YOLOv5s model at 100 epochs in Approach 2 as a proposed method. The best models at Approaches 1 and 2 were used with a developed AI-based drone in the real-time test application.

The AI-based low-cost solar panel detection drone was developed with an original data set of 1,100 images. A detailed comparative analysis of YOLOv5, YOLOv6 and YOLOv8 models regarding performance metrics was realized. Gaussian, salt-pepper noise addition and wavelet transform noise removal preprocessing techniques were applied to the created data set under the proposed method. The proposed method demonstrated expressive and remarkable performance in panel detection applications.

This paper investigates income convergence using different convergence concepts and methodologies for 72 countries over the period between 1960 and 2010.

This study applies beta (β), sigma (s), stochastic and club convergence approaches. For β-convergence analysis, it derives the cross-country growth regressions of the Solow growth model under the basic and augmented Cobb–Douglass (CD) production functions and estimates them using cross-section and panel data estimators. While it employs both the widely used coefficient of variation and recently developed weak s-convergence approaches for s-convergence, it applies three different unit root tests for stochastic convergence. To test club convergence, it estimates the log-t regression.

The results reveal that (1) there exists conditional β-convergence, meaning that poorer countries grow faster than richer countries; (2) income per worker is not (weakly) s-converging, and cross-sectional variation does not tend to fall over the years; (3) stochastic convergence is not found and (4) countries in the sample do not converge to the unique equilibrium, and there exist five distinctive convergence clubs.

The results clearly show that heavily relying on one of the convergence techniques might lead researchers to obtain misleading results regarding the existence of convergence. Therefore, to draw reliable inferences, the results should be checked using different convergence concepts and methodologies.

Contrary to the previous literature, which is generally restricted to testing the existence of absolute and conditional β-convergence between countries, to the best of the author’s knowledge, this is the first study to consider and compare all originally and recently developed fundamental concepts of convergence altogether. Besides, it uses the Penn World Table (PWT) 9.1 and extends the period to 2010. From this point of view, this study is believed to provide the most up-to-date empirical evidence.