Aquaculture production, GHG emission and economic growth in Sub-Sahara Africa

The study ascertained the relationship between aquaculture production and greenhouse gas (GHG) emissions in South Africa. The study used the Autoregressive Distributed Lag—Error Correction Model (ARDL-VECM) with time series data between 1990 and 2020. The results showed that the mean annual aquaculture production, GHG emissions, and Gross Domestic Product (GDP) in the period were 5200 tonnes, 412 tonnes, and US$447 billion, respectively. There was a long-run relationship between GHG emissions and GDP. In the short run, GHG emissions had a positive relationship with GDP and a negative relationship with beef production. Furthermore, there was a bi-directional relationship between aquaculture production and GHG emissions. In addition, beef production and GDP had a bi-directional relationship. Beef production also had a positive relationship with aquaculture production. The study concludes that aquaculture production is affected and tends to affect GHG emissions. Aquaculture legislation should consider GHG emissions in South Africa and promote sustainable production techniques.

A city’s economic structure and energy mix would change when the city is developed to accommodate more residents, visitors, and activities. This paper reviews Macao’s economic growth, energy use, and greenhouse gases (GHG) emission from 1985 to 2020. Specifically, Macao’s gross domestic product (GDP), energy use, and GHG emission have surged after the gaming industry was liberalized in 2002. The official data show that Macao’s GDP was MOP 11 billion in 1985, increased by four-fold to MOP 54 billion in 2000, and then surged rapidly to MOP 445 billion in 2019. Additionally, Macao’s total energy use increased from 8,840[Formula: see text]TJ in 1985 to 48,330[Formula: see text]TJ in 2019 while Macao’s GHG emission increased from 0.70[Formula: see text]Mt of CO2-equivalent in 1985 to 6.13[Formula: see text]Mt of CO2-equivalent in 2019. Macao’s GHG emission from all local sources per capita and GDP per capita exhibit an inverted U-shaped relationship, showing an environmental Kuznets curve. Due to the negative impact of COVID-19 pandemic, Macao’s GDP dropped by 56% to MOP 194 billion while its total energy use and GHG emission dropped by 33% and 17% to 32,198[Formula: see text]TJ and 5.06[Formula: see text]Mt of CO2-equivalent, respectively, in 2020.

Greenhouse gas (GHG) emissions have become a critical environmental issue with significant implications for global climate change. Understanding the factors that influence GHG emissions is essential for developing effective mitigation strategies. This study focuses on Mexico, a country that has experienced substantial economic and social changes over the past two decades. The primary objective was to analyze the impact of various economic and social variables on GHG emissions in Mexico using correlation and Vector Autoregression (VAR) analysis. The variables under consideration included Gross Domestic Product (GDP), energy consumption, population, per capita income, income inequality (measured by the Gini coefficient), and educational levels. Results showed that GDP, energy consumption, and population are positively correlated with GHG emissions and negatively correlated with income inequality. The Granger causality analysis showed that GDP and per capita income are strong predictors of GHG emissions; in contrast, income inequality and educational levels do not exhibit direct causative impacts on emissions. Finally, it was found that higher educational levels may contribute to lower GHG emissions. With this evidence, climate policies in Mexico can be formulated by addressing key areas, and policymakers can design strategies that effectively manage and reduce GHG emissions, aligning with sustainable development goals and mitigating the adverse effects of climate change.

Greenhouse Gases and Human Well-Being: China in a Global Perspective

Purpose - The purpose of this study was to investigate the relationship between Korea agricultural productions and Greenhouse Gas (GHG) emissions based on Environmental Kuznets Curve (EKC) hypothesis. Design/methodology/approach This study utilized time series data of economic growth, greenhouse gas, agricultural productions, trade dependency, and energy usages. In order to econometric procedure of EKC hypothesis, this study utilized unit root test and cointegration test to check staionarity of each variable and also adopted Vector Error Correction Model (VECM) and Ordinary Least Square (OLS) to analyze the short and long run relationships. Findings In the short run, greenhouse gas emissions resulting from economic growth show an inverse U-shape relationship, and an increase in agricultural production and energy consumption led to increase in greenhouse gas emission. In the long run, total GHG emissions and CO2 emissions show an N-shaped relationship with economic growth, and an increase in agricultural production has resulted in a decrease in total GHG and CO2 emissions. However, methane (CH4) and nitrous oxide (N2O) emissions showed an inverse U-shape relationship with economic growth, which indicated the environment and production process of agricultural production. Research implications or Originality Korea agricultural production has different effects on the GHG emission sources, and in particular, methane (CH4) and nitrous oxide (N2O) emissions show to increase as the agricultural production expansions, so policy or technological development in related sector is required. Especially, in the context of the 2030 GHG reduction road-map, if GHG-related reduction technologies or policies are spread, national GHG emission reduction targets can be achieved and this is possible to predict the decline in production in the sector and damage to the related industries.

Current status of greenhouse gas emissions from aquaculture in China

The greenhouse gas (GHG) emissions of the global ceramic production is estimated at more than 400 Mt CO2/year, which have increased steadily from economic growth. Among ceramic industries, ceramic tableware industry (CTI) is a highly energy-intensive and high GHG emissions industry. Thailand was the fourth highest ranking ceramic tableware exporting country in the world. However, information on GHG emission from this industry was limited. This research aimed to investigate the carbon dioxide(CO2) intensity of CTI in Thailand and the annual projections of GHG emission during 2017–2050 with different GDP growths. Then, the energy saving potentials and GHG mitigation measures with their GHG abatement cost for small and large-scale CTI were proposed. The results indicated that the average CO2 intensity of Thailand CTI was 1.75 kg CO2e/kg of product. The projections for GHG emissions of ceramic tableware production with gross domestic production (GDP) growth rates of 1.5, 3.5 (BAU), and 5.5%, reached their maximum emissions at 220,500 t CO2 in 2029, 2022, and 2020, respectively. Under a BAU scenario, ceramic tableware production in 2022 would emit GHG at a rate approximately 1.37 times greater compared to the emissions in 2016. The maximum GHG reduction (100% implementation) was 48,902 t CO2e, accounting for 22% of GHG emissions in 2030. The average mitigation cost was 6.64 USD/t CO2e reduction. This study provided a guideline for the assessment of CO2 intensity and the technical information for long-term GHG emission projection in CTI which could be applied in worldwide.

Poland ranks among the leading European countries in terms of greenhouse gas (GHG) emissions. Many European countries have higher emissions per capita than the EU average. This research aimed to quantify the complex relationships between the consumption variables of the main fossil fuels, accounting for economic indicators such as population and gross domestic product (GDP) in relation to GHG emissions. This research attempted to find similarities in the group of 16 analyzed European countries. The hypothesis of an inverted U-shaped environmental Kuznets curve (EKC) was tested. The resulting multiple regression models showed similarities in one group of countries, namely Poland, Germany, the Czech Republic, Austria and Slovakia, in which most of the variables related to the consumption of fossil fuels, including HC and BC simultaneously, are statistically significant. The HC variable is also significant in Denmark, Estonia, the Netherlands, Finland and Bulgaria, and BC is also significant in Lithuania, Greece and Belgium. Moreover, results from Ireland, the Netherlands, and Belgium indicate a negative impact of population on GHG emissions, and in the case of Germany, the hypothesis of an environmental Kuznets curve can be accepted.

Greenhouse gas (GHG) emissions must be precisely estimated in order to predict climate change and achieve environmental sustainability in a country. GHG emissions are estimated using empirical models, but this is difficult since it requires a wide variety of data and specific national or regional parameters. In contrast, artificial intelligence (AI)-based methods for estimating GHG emissions are gaining popularity. While progress is evident in this field abroad, the application of an AI model to predict greenhouse gas emissions in Saudi Arabia is in its early stages. This study applied decision trees (DT) and their ensembles to model national GHG emissions. Three AI models, namely bagged decision tree, boosted decision tree, and gradient boosted decision tree, were investigated. Results of the DT models were compared with the feed forward neural network model. In this study, population, energy consumption, gross domestic product (GDP), urbanization, per capita income (PCI), foreign direct investment (FDI), and GHG emission information from 1970 to 2021 were used to construct a suitable dataset to train and validate the model. The developed model was used to predict Saudi Arabia’s national GHG emissions up to the year 2040. The results indicated that the bagged decision tree has the highest coefficient of determination (R2) performance on the testing dataset, with a value of 0.90. The same method also has the lowest root mean square error (0.84 GtCO2e) and mean absolute percentage error (0.29 GtCO2e), suggesting that it exhibited the best performance. The model predicted that GHG emissions in 2040 will range between 852 and 867 million tons of CO2 equivalent. In addition, Shapley analysis showed that the importance of input parameters can be ranked as urbanization rate, GDP, PCI, energy consumption, population, and FDI. The findings of this study will aid decision makers in understanding the complex relationships between the numerous drivers and the significance of diverse socioeconomic factors in defining national GHG inventories. The findings will enhance the tracking of national GHG emissions and facilitate the concentration of appropriate activities to mitigate climate change.

Unveiling the past, shaping the future: Analyzing three centuries of data to explore China's trajectory towards carbon neutrality

In this study, a comparative analysis was presented to detect the quota of urban and rural areas from total greenhouse gas (GHG) emissions in 26 selected countries of the Middle East and Central Asia (MECA) during 1994–2014. For this purpose, 18 independent variables such as land area, population characteristics, energy use and consumption, gross domestic product (GDP), CO2 emissions, etc., were considered in addition to one dependent variable of total GHG emissions. Statistical modeling to investigate GHG emissions was constructed comprising the quantitative procedures of the correlation test and clustering analysis, which can be considered as the fundamental basis of each econometric analysis. The GHG emissions from the urban (rural) sector of total countries in 2014 were obtained as 3313.4 (1135.6) Mt of CO2 equivalents, which is about 74.5% (25.5%) of the total GHG emissions (4449.1 Mt of CO2 equivalents) in the MECA region. The correlation test between GHG emissions and urban indicators revealed the significant records (R from 0.745 to 0.981) compared with rural indicators (R from 0.337 to 0.890). Based on the clustering analysis of the countries, Cluster A, comprised of three countries of Iran, Saudi Arabia, and Turkey, was categorized as countries with very high contributing to the total GHG emissions in the MECA region (~ 43.3%). The quotas of emissions from urban and rural sectors in the Cluster A were estimated as 83.1% and 16.9% from the total GHG emissions in 2014 (1921.3 Mt of CO2), while the same quotas were predicted as 73.1% and 26.9% from the total GHG emissions in 2030 (1921.3 Mt of CO2). This study carried out comprehensive research on the GHG emissions from the urban and rural areas in a crucial region of the world, which is faced with the rising growth of population, urbanization, globalization, high-energy use, and fuel consumption.

Over the last decades, all countries have pursued an ambitious climate policy, thus showing a growing concern about climate change, global warming, greenhouse gas (GHG) emissions, or environmental taxes. Water, air, and soil pollution caused by gas emissions directly affect human health, but also the economies of states. As people’s ability to adapt to novel changes becomes increasingly difficult, globally, they are constantly trying to reduce their greenhouse gas emissions in a variety of ways. Environmental taxes, in general, and energy taxes, in particular, are considered effective tools, being recommended by specialists, among other instruments used in environmental policy. The aim of this research is to assess, empirically, the influence of environmental taxes levels on greenhouse gas emissions in 28 European countries, with a time span between 1995 and 2019. Regarding the empirical research, the proposed methods are related to Autoregressive Distributed Lag (ARDL) models in panel data and also at country level. At panel level, we used the estimation of non-stationary heterogeneous panels and also the dynamic common-correlated effects model with heterogeneous coefficients over cross-sectional units and time periods. The results obtained show that the increase in environmental taxes leads, in most countries, to a decrease in greenhouse gas emissions. To test the robustness of our results, we have included supplementary economic and social control variables in the model, such as gross domestic product (GDP), population density, exports, or imports. Overall, our paper focuses on the role of environmental policy decisions on greenhouse gas emissions, the results of the study showing, in most cases, an inverse impact of the taxation level on the reduction of gas emissions.

Abstract. This paper summarizes currently available data on greenhouse gas (GHG) emissions from African natural ecosystems and agricultural lands. The available data are used to synthesize current understanding of the drivers of change in GHG emissions, outline the knowledge gaps, and suggest future directions and strategies for GHG emission research. GHG emission data were collected from 75 studies conducted in 22 countries (n = 244) in sub-Saharan Africa (SSA). Carbon dioxide (CO2) emissions were by far the largest contributor to GHG emissions and global warming potential (GWP) in SSA natural terrestrial systems. CO2 emissions ranged from 3.3 to 57.0 Mg CO2 ha−1 yr−1, methane (CH4) emissions ranged from −4.8 to 3.5 kg ha−1 yr−1 (−0.16 to 0.12 Mg CO2 equivalent (eq.) ha−1 yr−1), and nitrous oxide (N2O) emissions ranged from −0.1 to 13.7 kg ha−1 yr−1 (−0.03 to 4.1 Mg CO2 eq. ha−1 yr−1). Soil physical and chemical properties, rewetting, vegetation type, forest management, and land-use changes were all found to be important factors affecting soil GHG emissions from natural terrestrial systems. In aquatic systems, CO2 was the largest contributor to total GHG emissions, ranging from 5.7 to 232.0 Mg CO2 ha−1 yr−1, followed by −26.3 to 2741.9 kg CH4 ha−1 yr−1 (−0.89 to 93.2 Mg CO2 eq. ha−1 yr−1) and 0.2 to 3.5 kg N2O ha−1 yr−1 (0.06 to 1.0 Mg CO2 eq. ha−1 yr−1). Rates of all GHG emissions from aquatic systems were affected by type, location, hydrological characteristics, and water quality. In croplands, soil GHG emissions were also dominated by CO2, ranging from 1.7 to 141.2 Mg CO2 ha−1 yr−1, with −1.3 to 66.7 kg CH4 ha−1 yr−1 (−0.04 to 2.3 Mg CO2 eq. ha−1 yr−1) and 0.05 to 112.0 kg N2O ha−1 yr−1 (0.015 to 33.4 Mg CO2 eq. ha−1 yr−1). N2O emission factors (EFs) ranged from 0.01 to 4.1 %. Incorporation of crop residues or manure with inorganic fertilizers invariably resulted in significant changes in GHG emissions, but results were inconsistent as the magnitude and direction of changes were differed by gas. Soil GHG emissions from vegetable gardens ranged from 73.3 to 132.0 Mg CO2 ha−1 yr−1 and 53.4 to 177.6 kg N2O ha−1 yr−1 (15.9 to 52.9 Mg CO2 eq. ha−1 yr−1) and N2O EFs ranged from 3 to 4 %. Soil CO2 and N2O emissions from agroforestry were 38.6 Mg CO2 ha−1 yr−1 and 0.2 to 26.7 kg N2O ha−1 yr−1 (0.06 to 8.0 Mg CO2 eq. ha−1 yr−1), respectively. Improving fallow with nitrogen (N)-fixing trees led to increased CO2 and N2O emissions compared to conventional croplands. The type and quality of plant residue in the fallow is an important control on how CO2 and N2O emissions are affected. Throughout agricultural lands, N2O emissions slowly increased with N inputs below 150 kg N ha−1 yr−1 and increased exponentially with N application rates up to 300 kg N ha−1 yr−1. The lowest yield-scaled N2O emissions were reported with N application rates ranging between 100 and 150 kg N ha−1. Overall, total CO2 eq. emissions from SSA natural ecosystems and agricultural lands were 56.9 ± 12.7 × 109 Mg CO2 eq. yr−1 with natural ecosystems and agricultural lands contributing 76.3 and 23.7 %, respectively. Additional GHG emission measurements are urgently required to reduce uncertainty on annual GHG emissions from the different land uses and identify major control factors and mitigation options for low-emission development. A common strategy for addressing this data gap may include identifying priorities for data acquisition, utilizing appropriate technologies, and involving international networks and collaboration.

GHG emissions from electricity consumption: A case study of Hong Kong from 2002 to 2015 and trends to 2030

A hybrid input–output multi-objective model to assess economic–energy–environment trade-offs in Brazil