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

Diverse opinions exist in the time series analysis of energy and related indices, difference in methodology, sample size, and time variation. This paper will make a conscious effort to converge the divergent outlooks. To accomplish this essential task, five energy indices consisting of energy consumption (EC), gross domestic product (GDP), carbon dioxide emission (CDE), the human development index (HDI), and oil price (OP) were selected. Two analytical methods were adopted, namely logarithmic and normalized techniques, which are designed to complement each other in drawing unfalsified statistical inference concerning the causality between the energy indices. The methods were subjected to four statistical tests and analyses: the augmented Dickey-Fuller, cointegration, pairwise Granger causality, and vector error correction model (VECM). Irrespective of prevailing challenges, both logarithmic and normalized techniques unanimously filtered out causalities. This consisted of neural flow between oil price and energy consumption, gross domestic product and carbon dioxide emission, and energy consumption and the human development index, unidirectional flow between energy consumption and the human development index, oil price and energy consumption, gross domestic product and carbon dioxide emission, and the human development index and oil price, whereas a normalized technique established bidirectional flow between gross domestic product and the human development index, and the human development index and oil price. Pertinently, the research suggests appropriate policies that will generate sustainable development in all the causal directions. Assiduously, the overwhelming agreement between both techniques at the 0.05 level is recommended for further validation with more modern econometric tests.

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.

Aquaculture is a major source of protein in Sub-Saharan Africa (SSA), a region experiencing rapid population growth, changing lifestyles and preferences, and increased health awareness. However, the industry is still underdeveloped and is of a subsistence nature. Climate change has impacted aquaculture production (AQUAP) in SSA because of greenhouse gas (GHG) emissions. However, AQUAP activities also results in GHG emissions. In SSA, the causal effect of GHG emissions and AQUAP has not yet been empirically established and quantified. The objective of the study was to determine the relationship between GHG emissions and AQUAP in SSA. The parsimonious vector autoregressive (VAR) model was used in the study, with annual time series data of Gross Domestic Product (GDP), meat production (MP), GHG emissions, and AQUAP from 1970 to 2020. The findings demonstrate that AQUAP in SSA was suppressed until 2006 when it suddenly increased. Western and Central Africa have dominated AQUAP in SSA. GHG emissions were dropping sporadically until 1991 when they began to rise gradually. In both the long and short run, GHG emissions had a negative influence on AQUAP, while AQUAP had an asymmetric impact on GHG emissions. AQUAP impacts GDP positively in both the long and short run, and GHG emissions had an asymmetric impact on GDP. In conclusion, GHG emissions negatively affect AQUAP. In addition, AQUAP reduced GHG emissions in the short run but however increased it in the long run. This indicates the infancy of the sector in SSA, the initial phase of the Environmental Kuznets Curves (EKC). Furthermore, GDP is positively affected by both GHG emissions and AQUAP. This also cements the initial stages of the EKC, with economic development also powered by GHG emissions, with also the positive contribution of AQUAP to economic growth. Overall, the study concludes of initial economic, and aquaculture sectoral development powered by GHG emissions. However, this is also leading to increased emissions. The study recommends upscaling AQUAP in SSA given its infancy, huge economic potential, sustainability and low GHG emission potential but should be grounded on environmentally sustainable practices.

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.

In this paper, we have explored the relationship between three key indicators of the health of any economy, namely, human development index (HDI), foreign direct investment (FDI) and gross domestic product (GDP). Three alternative models were assessed in the study to investigate the dynamics of the cause and effect relationship between the three variables. Thirty countries were selected for the purpose of the study on the basis of a positive change in their HDI ranks from 2012 to 2017. Further, the countries were categorized into countries with very high, high, medium and low HDI ranks. Annual FDI, HDI and GDP data for the period from 1990 through 2016 were used to investigate the nature of the relationship among the selected variables. The proposed models testing the outcome-explanatory variable relationship between FDI, HDI, and GDP were tested using cointegrating regression with panel dynamic least square model (DOLS) and panel fully modified least square model (FMOLS). The findings of the study show that HDI and FDI are the statistically significant variables that positively impact the changes in GDP. Further, the impact of HDI is of larger magnitude than FDI. However, GDP and FDI were not found to exert any statistically significant impact on HDI for the period under the study. Similarly, HDI and GDP were also not found to exert any significant impact on FDI. Before applying DOLS and FMOLS, stationarity was confirmed using five panel unit root tests, namely, LLC, IPS, Fisher-type tests using ADF and PP and Hadri. Thereafter, Pedroni residual cointegration test and Kao residual cointegration test were used to confirm the existence of cointegration among the variables, which is a necessary condition for applying DOLS and FMOLS. The study has key implications for the policymakers in search of models and plans to boost GDPs of their respective countries. Since HDI exerts more than proportionate positive impact on GDP, governments can focus on improving the three components of HDI, namely, life expectancy, adult literacy and education enrolment to promote economic growth. Further, since FDI exerts a positive impact on GDP, policymakers can strategize to make more friendly policies to attract foreign investors.

Can we live within environmental limits and still reduce poverty? Degrowth or decoupling?

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.

Lithuania, Latvia and Estonia successfully implemented Kyoto protocol commitments in the period from 2008 to 2012. Moreover, targets of the Europe 2020 strategy, in which countries committed to reduce the greenhouse gas emissions of 1990 by 20% until 2020 are also achievable for Lithuania, Latvia and Estonia. It is forecasted that the reduction of GHG emissions in 2020 in the Baltic States will be much higher than EU average target. Baltic States have achieved significant reduction of GHG emissions during 1990-2015, especially in energy sector which is the major sources of GHG emissions in Baltic States. During the period 1990?2013, Lithuania?s gross domestic product (GDP) per capita increased by 56.8 per cent, while GHG emissions per GDP and GHG emissions per capita decreased by 66.7 and 47.8 per cent, respectively. The major reason for the decrease in per capita emissions are the structural changes in the energy sector. At the same period, Latvia?s population decreased by 24.4 per cent, GDP per capita increased by 64.0 per cent, while GHG emissions per GDP and GHG emissions per capita decreased by 66.4 and 44.8 per cent, respectively. Latvia?s economy grew rapidly in the period 2000?2007, with a GDP increase of 82.0 per cent. Economic growth rates and climatic conditions have been the most important drivers for GHG emissions trends in Latvia. Estonia?s gross domestic product (GDP) per capita increased by 85.1 per cent, while GHG emissions per GDP and GHG emissions per capita decreased by 65.1 and 35.3 per cent, respectively. Such significant GHG emission reduction in Estonia was driven by restructuring of the economy and efficiency improvement in the energy industry and energy demand sectors. There is a significant decoupling of emissions from economic growth in all three countries however countries have very different energy supply balances and implemented various climate change mitigation policies.

This chapter discusses the viability of gross domestic product (GDP) per capita in purchasing power parity as an indicator of economic development and well-being and estimates the factors which diminish its ability to represent the level of life. Firstly, we theoretically outline the factors that might be undermining GDP per capita’s ability to serve as a measurement of well-being and debate other development indicators. Subsequently, we confront GDP per capita with the most well-known development indicator – Human Development Index (HDI) – and calculate the deviations between those two indicators. To empirically evaluate the potential limitations of GDP in measuring development, we regress the development-GDP gaps on an array of economic, social and political variables employing a broad panel dataset and modified fixed effects estimators. The results reveal that factors such as income inequality and level of economic freedom cause negative gap between development and GDP; the size of shadow economy has positive impact on deviation of HDI from GDP levels, while certain sociocultural factors such as higher fertility rates and alcohol consumption have negative effect on the dependent variable.Key wordsEconomic developmentGDP per capitaHuman development index

This paper investigates the long-run relationship between Canada’s total greenhouse gas emissions (as an indicator of environmental quality) and economic development captured by gross domestic product (GDP) and GDP-alternative measures (which are argued to be more representative of the wider-scale economic progress, Rani & Mandal, 2020). The three GDP-alternative measures assessed were gross national disposable income (GNDI), human development index (HDI), and index of economic freedom (IEF). Time series properties of per capita greenhouse gas emissions (GHGpc) were evaluated. Augmented Dickey Fuller stationarity test was performed for GHGpc, after which, Johansen tests were performed to evaluate cointegration between GHGpc and the economic growth measures. Error correction models were run to evaluate the long-run behavior of GHGpc with per capita GDP and GNDI (GDPpc and GNDIpc, respectively), HDI, and IEF. GHGpc was found to be cointegrated with both GDPpc and all the GDP-alternative indicators. The paper contributes to the existing literature by demonstrating that Canada’s per capita GHG emission has a long-run relationship with both GDP and GDP-alternative indicators. This study represents the first assessment in the body of knowledge of the relationship between Canada’s national-level total GHG emissions and GDP-alternative measures. Keywords: Greenhouse gas, Stationarity, Cointegration, Error correction, GDP-alternatives.

Dear Editor, The human development index (HDI) is a composite index calculated on the basis of three socioeconomic indicators that reflect three major dimensions of human development: longevity, educational attainment and standard of living to sufficiently capture the multidimensionality of human development. Longevity is measured by life expectancy at birth (LEB); educational attainment is measured by a weighted average of the adult literacy rate (ALR) and the combined gross educational enrolment ratios (GER). An adjusted gross domestic product (GDP) per capita, converted into US dollars on the basis of the purchasing power parity exchange rate (PPP USD), is used as a measure of a decent standard of living. For the components of the HDI, except of the GDP per capita, individual indices are calculated according to the general linear transformation: Index = actual value - fixed min imumvalue / fixed max imumvalue - fixed min mumvalue To construct the income index, the following nonlinear transformation is applied on GDP per capita, taking into account diminishing returns of higher incomes (utility adjustment): Income Index = log (actual GDP for capita) - log (min imum GDP for Capita) / log ( fixed max imum value) - ( fixed min imum value) The fixed minimum and maximum values of indicators are 25 and 85 years for LEB, 0% and 100% for ALR and GER and 100 and 40000 US$ for GDP.[1][2][3][4][5] Data envelopment analysis (DEA) is the leading technique for measuring the relative efficiency of decision making units on the basis of multiple inputs and outputs. The efficiency of a unit is defined as the weighted sum of its outputs divided by a weighted sum of its inputs and it is measured on a bounded ratio scale. The weights for inputs and outputs are estimated by a linear program in the best advantage for each unit so as to maximize its relative efficiency. Basically, DEA provides a categorical classification of the units into efficient and inefficient ones by assuming either constant returns to scale (introduced by Charnes, Cooper and Rhodes named CCR model) or variable returns to scale (introduced by Banker, Charnes and Cooper named BCC model) for the inputs and outputs.[6][7][8][9] In this paper, we have been considered the assessment of Kerman Province towns technical efficiency in accessing HDI via DEA. For this objective, in the descriptive study, we applied CCR and BCC models for assessing Kerman town’s technical efficiency in accessing HDI by using DEP2 software. Each town’s HDI was considered as output and the number of physicians for 1000 people (a proxy for life expectancy), educational staff rates (a proxy for educational attainment) and the employment rate of over 10 years working workers (a proxy for GDP) was considered as inputs for calculating the efficiency. All findings of study have been summarized in Table 1. Table 1 HDI and Efficiency scores with BCC and CCR output oriented models in the years 2000 and 2007 Based on the findings of study we concluded HDI score of all towns of Kerman Province have been improved in the year 2007 relative to year 2000. All towns were in the average range of HDI (0/5 to 0.8).[2] We can conclude that middle range scores of some towns including Bardsir, Ravar and Kahnouj resulted from input shortage but other towns scores showed some inefficiencies that can improve by more efficient use of inputs.

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.

Background: In recent decades, the human development index has become one of the most practical indicators for measuring the level of development in countries. There are several factors that affect the human development index, including health expenditures that increase human development along with manpower and physical capital. The purpose of this study was to investigate the effect of health costs on human development index for the period 2005-18. Methods: The present study examined the effects of health on human development index in different countries. The study population includes 187 countries with 3 types of divisions (continental, oil and income distribution). The data used by the World Bank were collected and analyzed using data panel regression or composite data. Results: Findings from estimation of models indicated that health expenditures in continents of Europe, South America, Africa and Oceania had a positive and significant effect on human development index. But in Asia, the effect of health expenditure was negative and significant. The effect of per capita Gross Domestic Product (GDP) on the continents of Asia, North and South America, Africa and Oceania had been positive and significant, but it was positive and insignificant in continental Europe. In oil-rich countries, the effect of educational expenditures, GDP per capita on human development index was positive and significant, but the effect of health expenditures was positive and insignificant. Also, the effect of mortality index on human development index had been negative and significant. Similar results had been obtained for non-oil countries, albeit with different coefficients. The percentage of health expenditures, compared with GDP, had a direct and significant relationship with the human development index in countries with relatively equal and relatively unequal income distribution; considering the fact that this value for the group of countries with completely unequal income distribution suggested an insignificant value in the model. The percentage of educational expenditures showed a direct and significant relationship on human development in all income groups, and GDP per capita for all groups with different income distributions had a significant and direct effect on the human development index. Also, the under-5 mortality rate in all groups with different income distributions had an inverse and significant relationship with the human development index. Conclusion: Results showed a significant effect of health expenditures on improvement of the health status and development of the studied countries except oil countries, North America and countries with unequal income distribution. Furthermore, increasing the cost of health care is an important step in achieving countries' development goals. Therefore, it is necessary for managers and policy makers of the health system to consider the efficiency in allocating health expenditures to different sectors.

One of variable to calculate the Human Development Index (HDI) is a decent standard of living derived from the Gross Domestic Product (GDP) figure. Therefore, the increase in HDI could be influenced by GDP. Furthermore, as a Muslim-majority country and adopting a dual banking system, Indonesia's GDP is certainly also influenced by the Islāmic financial sector, such as zakat and Islāmic bank financing. In this case, zakat plays role as a wealth distribution instrument. Then Islamic bank financing could be a funding capital for the community and the country. So, this study analyzes the direct and indirect relationship between the variables of zakat, Islamic bank financing and GDP to the HDI. The method used is path analysis which aims to look at the hypothesis of causal relationships. The results obtained are the direct relationship of Islamic bank financing and GDP significantly positive effect on HDI. Zakat and Islamic bank financing variables have a positive effect on GDP. The greatest coefficient value between direct relationships is the coefficient of Islamic bank financing on GDP, which is equal to 81.7%. But the direct relationship of zakat to the HDI cannot be known. While the indirect relationship shows that there is an influence between the variables of zakat on the HDI through GDP and there is an influence between the variables of Islamic bank financing on the HDI through GDP.

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.