The Nexus Between CO2 Emission, Economic Growth, Trade Openness: Evidences From Middle-Income Trap Countries

Galvan, Lisette Paola Cervantes; Bhatti, Uzair Aslam; Campo, Carlos Carmona; Trujillo, Ricardo Antonio Simancas

doi:10.3389/fenvs.2022.938776

Cited by 28 publications

(18 citation statements)

References 52 publications

(51 reference statements)

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“…Climate change is a global environmental problem. The continuous increase of carbon dioxide emissions in various countries poses a threat to life systems [1][2]. As the largest developing country and carbon emitter in the world, China needs to achieve rapid emission reduction while promoting development, and the task is arduous.…”

Section: Introductionmentioning

confidence: 99%

Temporal-Spatial Characteristics and Driving Factors of Total Factor Carbon Productivity in the Yangtze River Economic Belt

Zhang¹

2023

Pol. J. Environ. Stud.

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Under the policy background of carbon neutralization and carbon peaking, how to promote total factor carbon productivity (TFCP) has become an important part of promoting green and low-carbon development of the Yangtze River Economic Belt. This paper uses SBM model to measure the TFCP of the Yangtze River Economic Belt, and uses empirical analysis methods such as Dagum Gini coefficient and geographical detector to measure the spatio-temporal evolution characteristics and driving factors of the TFCP of the Yangtze River Economic Belt. The research finds that: First, the development trend of TFCP in the Yangtze River Economic Belt shows a downward trend no matter from the overall regional or subregional level. There are differences in the number and spatial agglomeration patterns of different levels of cities. Second, the overall difference of TFCP in the Yangtze River Economic Belt is gradually decreasing, and the regional difference has become the main factor of spatial difference. The dispersion of TFCP has decreased, and the regional gap has gradually narrowed. TFCP is characterized by non -normal distribution, with local agglomeration development trend, and its focus gradually shifted to the southwest. Third, technological innovation, infrastructure construction and economic growth are the key drivers of the Yangtze River Economic Belt TFCP. The dominant driving factors of TFCP are different in different regions.

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Section: Introductionmentioning

confidence: 99%

Temporal-Spatial Characteristics and Driving Factors of Total Factor Carbon Productivity in the Yangtze River Economic Belt

Zhang¹

2023

Pol. J. Environ. Stud.

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“…3a). Carbonate ions required in travertine deposition come from large amounts of noncondensable CO 2 transported from lithospheric mantle magmatic, metamorphic and geothermal systems to regional aquifers through fissure axes [26][27][28][29][30]. Meanwhile, hydrothermal waters feed into extensional fissure axes and deposit successive layers of carbonate as fissure travertine [8].…”

Section: The Sedimentary Model Of Travertine and Mors Are Similarmentioning

confidence: 99%

Travertine and Mid-Ocean Ridges Are Related Analogues Regarding Geographical Location and Sedimentary Model

Yan¹,

Dong²,

Ye³

2022

Pol. J. Environ. Stud.

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The rise of the mantle asthenosphere and tectonic activity are linked to travertine and mid-ocean ridges, although their relationship has not been clarified. To investigate the connection between travertine and mid-ocean ridges, we gathered information on the geographic distribution of travertine from around the world, plotted it, and linearized it. Making a map of the locations of linearized travertine, mid-ocean ridges, and 2016-2020 (M>5) earthquakes (which may or may not constitute seismic belts) and assessing its global distribution. The sedimentary models of travertine and mid-ocean ridges, on the other hand, were drawn based on previous studies, comparing and analyzing the similarities and differences between the sedimentary models of travertine and mid-ocean ridges. The results indicate that: 1) travertine and mid-ocean ridges are both primarily distributed in the seismic belts, 2) their deposition is closely related to the mantle asthenosphere, and 3) travertine occurs on land but mid-ocean ridges are in the ocean, which is a significant difference. This study examines the relationship between travertine and mid-ocean ridges are related analogs regarding geographical location and sedimentary model and suggests that travertine, like mid-ocean ridges, may be a driving force for plate drift. Researchers may gain a better understanding of the otherwise difficult-to-study mid-ocean ridge system by analyzing similarities between travertine and mid-ocean ridges.

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“…Therefore, efficient picking machines are a current research focus and an area of importance in orchard intelligence. Automated picking can increase the income of fruit farmers and promote economic development ( Galvan et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

YOLO-P: An efficient method for pear fast detection in complex orchard picking environment

Sun

Wang²,

Jing³

2023

Front. Plant Sci.

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IntroductionFruit detection is one of the key functions of an automatic picking robot, but fruit detection accuracy is seriously decreased when fruits are against a disordered background and in the shade of other objects, as is commmon in a complex orchard environment.MethodsHere, an effective mode based on YOLOv5, namely YOLO-P, was proposed to detect pears quickly and accurately. Shuffle block was used to replace the Conv, Batch Norm, SiLU (CBS) structure of the second and third stages in the YOLOv5 backbone, while the inverted shuffle block was designed to replace the fourth stage’s CBS structure. The new backbone could extract features of pears from a long distance more efficiently. A convolutional block attention module (CBAM) was inserted into the reconstructed backbone to improve the robot’s ability to capture pears’ key features. Hard-Swish was used to replace the activation functions in other CBS structures in the whole YOLOv5 network. A weighted confidence loss function was designed to enhance the detection effect of small targets.ResultAt last, model comparison experiments, ablation experiments, and daytime and nighttime pear detection experiments were carried out. In the model comparison experiments, the detection effect of YOLO-P was better than other lightweight networks. The results showed that the module’s average precision (AP) was 97.6%, which was 1.8% higher than the precision of the original YOLOv5s. The model volume had been compressed by 39.4%, from 13.7MB to only 8.3MB. Ablation experiments verified the effectiveness of the proposed method. In the daytime and nighttime pear detection experiments, an embedded industrial computer was used to test the performance of YOLO-P against backgrounds of different complexities and when fruits are in different degrees of shade.DiscussionThe results showed that YOLO-P achieved the highest F1 score (96.1%) and frames per second (FPS) (32 FPS). It was sufficient for the picking robot to quickly and accurately detect pears in orchards. The proposed method can quickly and accurately detect pears in unstructured environments. YOLO-P provides support for automated pear picking and can be a reference for other types of fruit detection in similar environments.

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The Nexus Between CO2 Emission, Economic Growth, Trade Openness: Evidences From Middle-Income Trap Countries

Cited by 28 publications

References 52 publications

Temporal-Spatial Characteristics and Driving Factors of Total Factor Carbon Productivity in the Yangtze River Economic Belt

Temporal-Spatial Characteristics and Driving Factors of Total Factor Carbon Productivity in the Yangtze River Economic Belt

Travertine and Mid-Ocean Ridges Are Related Analogues Regarding Geographical Location and Sedimentary Model

YOLO-P: An efficient method for pear fast detection in complex orchard picking environment

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