An overview of greenhouse gases emissions in Hungary

Mohammed, Safwan; Gill, Abid Rashid; Alsafadi, Karam; Hijazi, Omar; Yadav, Krishna Kumar; Hasan, Mohd Abul; Khan, Afzal Husain; Islam, Saiful; Cabral-Pinto, Marina M.S.; Harsányi, Endre

doi:10.1016/j.jclepro.2021.127865

Cited by 46 publications

(29 citation statements)

References 91 publications

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“…However, future steps will employ the environmental Kuznets curve (EKC) for exploring the relationship between economic growth and environmental degradation. Previous studies also reveal that, in the long run, economic growth could lead to the reduction in atmospheric pollution [2,82].…”

Section: Discussionmentioning

confidence: 97%

“…Rapid population growth and the concomitant increase in anthropogenic activities have resulted in climate change-induced challenges, and pose major threats to the sustainability of natural resources and the stability of the Earth's biosphere, especially in the recent past [1]. These challenges are leading to uncontrolled accumulation of greenhouses gases in the Earth's atmosphere [2]. The global concentration of greenhouse gas emissions (GHGs) has been accelerating particularly rapidly since the beginning of the industrial era because of various anthropogenic activities [3]; for instance, although the concentration of CO 2 in the 1760s was 280 ppm, the current estimate is 410 ppm, and is expected to reach 590 ppm by the end of 2100 [2].…”

Section: Introductionmentioning

confidence: 99%

“…These challenges are leading to uncontrolled accumulation of greenhouses gases in the Earth's atmosphere [2]. The global concentration of greenhouse gas emissions (GHGs) has been accelerating particularly rapidly since the beginning of the industrial era because of various anthropogenic activities [3]; for instance, although the concentration of CO 2 in the 1760s was 280 ppm, the current estimate is 410 ppm, and is expected to reach 590 ppm by the end of 2100 [2]. The global tracking of greenhouse gas emissions provides a framework for assessing the contribution of individual countries to the climate change challenge.…”

Section: Introductionmentioning

confidence: 99%

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GHGs Emission from the Agricultural Sector within EU-28: A Multivariate Analysis Approach

et al. 2021

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Climate mitigation and adaptation planning (CMAP) has recently been implemented across the EU-28 to reduce GHG emissions (CO2, CH4, N2O). Thus, the aim of this study was to provide an overview of GHG emissions from the agricultural sector in the EU-28 from 1990 to 2019, and cluster the EU-28 countries regarding their total GHG emissions. The results emphasize the positive impact of CMAP through a negative trend of the total GHG emissions (−2653.01 thousand tons/year, p < 0.05). Despite the positive and not significant trend of the total CO2 emissions, both CH4 and N2O exhibited a negative and significant trend. At the country scale, Italy, the United Kingdom, and the Netherlands showed the highest reduction in total GHG emissions, by −282.61thousand tons/year (p < 0.05), −266.40 thousand tons/year (p < 0.05), and −262.91 thousand tons/year (p < 0.05), respectively. The output of the multivariate analysis approach indicates changes in the pattern of GHG emissions between 1990 and 2019, where CO2 emissions decreased in the case of Poland and Czechia. The output of this study highlights the positive impact of CMAP, adopted by EU countries, in minimizing GHG emissions. Despite some fluctuations in CO2 emissions, strategies for attaining carbon neutrality in the agricultural sector, across the European Union, should be pursued.

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Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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GHGs Emission from the Agricultural Sector within EU-28: A Multivariate Analysis Approach

et al. 2021

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“…Its rapid economic development has been accompanied by increasingly prominent ecological and environmental problems in the Yangtze River Basin, such as soil erosion, floods, and ecological imbalances along the river. Due to the increasingly prominent environmental resource problems of the Yangtze River, the protection of its natural ecology has become ineffective, restricting the growth rate of the Yangtze River Economic Belt [15]. Figure 1 shows a sketch of the regional locations of provinces and cities in the Yangtze River Economic Belt.…”

Section: Yangtze River Economic Beltmentioning

confidence: 99%

The Prediction of Carbon Emission Information in Yangtze River Economic Zone by Deep Learning

Huafang

Cheng

2021

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This study aimed to respond to the national “carbon peak” mid-and long-term policy plan, comprehensively promote energy conservation and emission reduction, and accurately manage and predict carbon emissions. Firstly, the proposed method analyzes the Yangtze River Economic Belt as well as its “carbon peak” and carbon emissions. Secondly, a support vector regression (SVR) machine prediction model is proposed for the carbon emission information prediction of the Yangtze River Economic Zone. This experiment uses a long short-term memory neural network (LSTM) to train the model and realize the experiment’s prediction of carbon emissions. Finally, this study obtained the fitting results of the prediction model and the training model, as well as the prediction results of the prediction model. Information indicators such as the scale of industry investment, labor efficiency output, and carbon emission intensity that affect carbon emissions in the “Yangtze River Economic Belt” basin can be used to accurately predict the carbon emissions information under this model. Therefore, the experiment shows that the SVR model for solving complex nonlinear problems can achieve a relatively excellent prediction effect under the training of LSTM. The deep learning model adopted herein realized the accurate prediction of carbon emission information in the Yangtze River Economic Zone and expanded the application space of deep learning. It provides a reference for the model in related fields of carbon emission information prediction, which has certain reference significance.

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“…Over the last few centuries, the burning of fossil fuels and widespread deforestation have resulted in increased atmospheric greenhouse gas (GHG) concentrations, which have led to significant climate shifts across the planet [2]. Since 1750, the concentrations of GHGs such as methane (CH 4 ), carbon dioxide (CO 2 ), and nitrous oxide (N 2 O) have risen by 156%, 47%, and 23%, respectively [1,3]. Due to anthropogenic activities, the average temperature rose by 0.99 • C, in the first two decades of the 21st century (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020), and according to Monthly CMI [35] Crop Moisture Index Mean temperature and precipitation Weekly CSDI [36] Crop Specific Drought Index Evapotranspiration Seasonal RI [37] National Rainfall Index Precipitation Annually and every century RDI [38] Reclamation Drought Index Level of river water, snowfall, stream flows, reservoirs level and temperature…”

Section: Introductionmentioning

confidence: 99%

Impact of Agricultural Drought on Sunflower Production across Hungary

et al. 2021

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In the last few decades, agricultural drought (Ag.D) has seriously affected crop production and food security worldwide. In Hungary, little research has been carried out to assess the impacts of climate change, particularly regarding droughts and crop production, and especially on regional scales. Thus, the main aim of this study was to evaluate the impact of agricultural drought on sunflower production across Hungary. Drought data for the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) were collected from the CARBATCLIM database (1961–2010), whereas sunflower production was collected from the Hungarian national statistical center (KSH) on regional and national scales. To address the impact of Ag.D on sunflower production, the sequence of standardized yield residuals (SSYR) and yield losses YlossAD was applied. Additionally, sunflower resilience to Ag.D (SRAg.D) was assessed on a regional scale. The results showed that Ag.D is more severe in the western regions of Hungary, with a significantly positive trend. Interestingly, drought events were more frequent between 1990 and 2010. Moreover, the lowest SSYR values were reported as −3.20 in the Hajdu-Bihar region (2010). In this sense, during the sunflower growing cycle, the relationship between SSYR and Ag.D revealed that the highest correlations were recorded in the central and western regions of Hungary. However, 75% of the regions showed that the plantation of sunflower is not resilient to drought where SRAg.Dx < 1. To cope with climate change in Hungary, an urgent mitigation plan should be implemented.

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An overview of greenhouse gases emissions in Hungary

Cited by 46 publications

References 91 publications

GHGs Emission from the Agricultural Sector within EU-28: A Multivariate Analysis Approach

GHGs Emission from the Agricultural Sector within EU-28: A Multivariate Analysis Approach

The Prediction of Carbon Emission Information in Yangtze River Economic Zone by Deep Learning

Impact of Agricultural Drought on Sunflower Production across Hungary

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