Carbon dioxide emissions,output, and energy consumption categories in Algeria

This study examines the relation between CO₂ emissions, income, non-renewable, and renewable energy consumption in Algeria during the period extending from 1980 to 2011. Our work gives particular attention to the validity of environmental Kuznets curve (EKC) hypothesis. The autoregressive distributed lag (ARDL) with break point method outcome demonstrates the positive effect of non-renewable type of energy on CO₂ emissions consumption. On the contrary, the results reveal an insignificant effect of renewable energy on environment improvement. Moreover, the results accept the existence of EKC hypothesis but the highest gross domestic product value in logarithm scale of our data is inferior to the estimated turning point. Consequently, policy-makers in Algeria should expand the ratio of renewable energy and should decrease the quota of non-renewable energy consumption.     

Economic complexity,ICT, biomass energy consumption,and environmental degradation: evidence from Iran

Economic complexity, biomass energy consumption, and information communication technology (ICT) have diverse impacts on energy consumption and carbon dioxide (CO₂) emissions. Nevertheless, analysis of these variable effects is not addressed in the previous literature; the antiqueness of this article is stuffing this gap. This study assessed the relationship between gross domestic product (GDP) per capita, biomass consumption, economic complexity index (ECI), ICT, and CO₂ emissions in Iran in 1994–2018. The autoregressive distributed lag (ARDL) model and the quantile regression (QR) econometric technique were used to investigate the factors affecting CO₂ emissions in the tails of the conditional distribution. The share of each influential factor was predicted through the variance decomposition analysis (VD) for the next 10 years. The empirical results showed a long-run relationship between the variables. So, the variables of biomass consumption, ECI, and ICT improve the quality of the environment in Iran by reducing CO₂ emissions, and the per capita GDP variable increases CO₂ emissions. Results suggest no evidence indicating the presence of environmental Kuznets curve (EKC); however, QR demonstrated the existence of EKCs in the lower quantiles of the conditional distribution. The ECI will have the most share to change the CO₂ emissions in the future. The income threshold should be determined at the turning point of the EKC to increase economic development. Moreover, investing in increasing biomass consumption is vital. Policymakers also need to consider strict added value for the export of products.

     

Roadmap for climate alliance economies to vision 2030: retrospect and lessons

The United Nations Climate Conference 25, held in December 2019, reached a significant agreement against implementing the Paris agreement come 2020. Bound by the contract, 189 countries who are party to the deal agreed to constrain worldwide temperature to ascend to 1.5° Celsius. To this end, the present study attempts to investigate the readiness of selected countries in the European Union to implement the agreement, which will better the quality of the global environment. In line with this, this study appraises the connection between economic growth, renewable and non-renewable energy consumption, on emissions in 11 countries in the European Union from 1990 to 2016. The study utilises the Pooled Mean Group-Auto Regressive Distributed Lag (PMG-ARDL) model estimator and Dumitrescu and Hurlin Panel Causality analysis to analyse the long-run and short-run impact and direction of causality among these factors, respectively. The long-run study's empirical results show a U-shaped Environmental Kuznets Curve (EKC) and a negative connection between renewable energy use and emissions in the EU-11 countries. In the short-run, non-renewable energy use worsens CO₂ emissions while renewable energy use leads to a fall in emissions. Similarly, causality tests show a feedback mechanism between emissions and renewable energy use and between non-renewable energy and renewable use. Also, there is unidirectional causality from income to CO₂ emissions, non-renewable energy use to CO₂ emissions. The investigation recommends an expanded proportion of renewable energy sources in the EU countries’ energy mix to cut down on emissions.

     

The effects of economic growth and innovation on CO2 emissions in different regions

Economic growth and economic energy consumption have received greater attention due to its contribution to global CO₂ emissions in recent decades. The literature on CO₂ emissions and innovation for regional differences is very scanty as there is not enough study that considered different regions in a single analysis. We adopt a holistic approach by incorporating different regions so as to assess how innovation contributes to emission reduction. The study, therefore, examined the effects of innovation and economic growth on CO₂ emissions for 18 developed and developing countries over the period of 1990 to 2016. The study used panel technique capable of dealing with cross-section dependence effects: panel cross-sectional augmented Dickey-Fuller (CADF) unit root to determine the order of integration, Westerlund cointegration tests confirmed that the variables are co-integrated. We employed panel fully modified ordinary least square (FMOLS) and panel dynamic ordinary least square (DOLS) to estimate the long-run relationship. The results show that energy consumption increases CO₂ emissions at all panel levels. However, innovation reduces CO₂ emissions in G6 while it increases emissions in the MENA and the BRICS countries. Environmental Kuznets curve (EKC) hypothesis is valid for the BRICS. The pollution haven hypothesis (PHH) and pollution halo effect were confirmed at different panel levels. Based on the findings different policy recommendations are proposed.

     

Conditional effect of governance quality on the finance-environment nexus in a multivariate EKC framework: evidence from the method of moments-quantile regression with fixed-effects models

This paper investigates the mitigating effect of governance quality on the finance-environment nexus in a multivariate EKC framework in 123 selected countries during the 1990–2017 period. We mainly employ the method of moments-quantile regression (MM-QR) with the fixed-effects model, among others. First, the MM-QR estimator reveals that financial development reduces environmental quality more significantly in countries with initially higher levels (the 75^th and 90^th quantiles) of CO₂ emissions than in other countries (the 25^th and 10^th quantiles). Second, the attenuating effect of governance quality on the finance-environment nexus is more remarkable in nations with low initial levels (the 25^th and 10^th quantiles) of CO₂ emissions. Third, we find that the marginal positive effect of financial development on CO₂ emissions is smaller under a good regulatory framework than under corruption control and the rule of law, especially in the top emitters (the 75^th and 90^th quantiles). Fourth, unlike oil, which has a considerable negative impact on the environmental quality of the major emitters, renewable energy usage reduces CO₂ emissions in countries in all quantiles, primarily in the lowest quantiles. Fifth, the findings also show that urbanization dramatically worsens environmental quality in all economies, particularly those in the lowest quantiles. Finally, we confirm that the EKC hypothesis holds in all countries across different quantiles. The study’s final section discusses policy implications for sustainable development in all countries.

     

Effects of urbanization and nonrenewable energy on carbon emission in Africa

This study investigates the impact of urbanization and nonrenewable energy consumption on carbon emissions. The context of the analysis is 54 African Union countries from 1996 to 2019. For estimation, we use panel quantile regression (PQR) and fully modified ordinary least squares (FMOLS). Our regression results demonstrate that there is a positive correlation between urbanization and CO₂ emission. Further, our empirical results confirmed that nonrenewable energy consumption increases environmental pollution in African Union countries. The outcomes demonstrate the EKC hypothesis because at the initial stage of development, when economic growth increases, environmental pollution increases; after a threshold point, environmental pollution decreases as economic growth increases. It can find an inverted U-shaped relationship between economic growth and CO₂ emission. The findings also show that urbanization should be planned; otherwise, it can lead to environmental degradation in the long run. Africa continent takes strict action and builds a blueprint for efficient and effective energy production and consumption. The only solution to achieve green growth in Africa is to shift from fossil fuel energy supply to renewable energy supply.

     

Influencing factors of carbon emissions and their trends in China and India: a machine learning method

China and India are the largest coal consumers and the most populated countries in the world. With industrial and population growth, the need for energy has increased, which has inevitably led to an increase in carbon dioxide (CO₂) emissions because both countries depend on fossil fuel consumption. This paper investigates the impact of energy consumption, financial development (FD), gross domestic product (GDP), population, and renewable energy on CO₂ emissions. The study applies the long short-term memory (LSTM) method, a novel machine learning (ML) approach, to examine which influencing driver has the greatest and smallest impact on CO₂ emissions; correspondingly, this study builds a model for CO₂ emission reduction. Data collected between 1990 and 2014 were analyzed, and the results indicated that energy consumption had the greatest effect and renewable energy had the smallest impact on CO₂ emissions in both countries. Subsequently, we increased the renewable energy coefficient by one and decreased the energy consumption coefficient by one while keeping all other factors constant, and the results predicted with the LSTM model confirmed the significant reduction in CO₂ emissions. Finally, this study forecasted a CO₂ emission trend, with a slowdown predicted in China by 2022; however, CO₂ emission’s reduction is not possible in India until 2023. These results suggest that shifting from nonrenewable to renewable sources and lowering coal consumption can reduce CO₂ emissions without harming economic development.

     

Do renewable energies contribute to enhancing environmental quality in Eastern Africa?

East Africa has enormous renewable energy potential, but only a small portion of it has been exploited, and little is known on its role in improving environmental quality. Thus, this study empirically examines the impact of renewable energy on the environment using ecological footprint (EF; positive indicator) and CO₂ emissions (negative indicator) as proxy indicators for environmental quality in a panel of ten East African countries from 1990 to 2015. These indicators were chosen due to their potential impact in the environment. The work used the pooled mean group (PMG) as the main panel estimator to determine the impact while controlling non-renewable energy consumption, GDP per capita, and foreign direct investment (FDI). PMG has been used as it forces the long-run coefficients to be equal across all panel groups. The findings show that in the long run, there is a significant negative relationship between CO₂ emissions and renewable energy consumption, as well as a significant positive relationship (with a low impact) between EF and renewable energy consumption, suggesting that renewable energy use enhances the area’s environmental quality. Also, results indicate that non-renewable energy use degrades environmental quality in both metrics, whereas GDP degrades environmental quality through CO₂ emissions and improves environmental quality through EF. This requires East African countries to focus a higher emphasis on accessible renewable energy sources to achieve quick and sustainable economic growth and minimize environmental effects. To accomplish this, strategic policies and legislation, as well as the promotion of green technology, are required.

     

A global prospective of environmental degradations: economy and finance

Finance plays a crucial role in a fast-growing economy that can lead to environmental degradation. The present study utilizes balanced panel data of 105 countries for the time span 1980–2016 to investigate empirical linkage among environmental degradations: economy and finance. It also unfolds the nonlinear impact of economy and finance on environmental degradation. Existing literature on environmental issues mainly focuses on individual case studies uncovering particular regions, but the comprehensive analysis is not available. To fill this gap, panels were classified into five divisions: global, regional, income-based, OECD-based, and carbon emission. The cross-sectional dependence test is applied to identify the degree of cross-sectional dependence among concerned 16 divisions. The second-generation panel models (CADF and Westerlund cointegration, DOLS, and DH heterogenous causality) are employed on a sample set to compute to unit root, cointegration, and long-run and short-run dynamics among concerned variables, respectively. The findings infer the inverted EKC and U-shaped EKC in 10 and 3 out of 16 divisions with respect to environmental degradation—economy nexus, respectively, while 8 and 2 out of 16 divisions indicate the inverted EKC and U-shaped EKC, respectively, in terms of environmental degradation—finance nexus. In 12 out of 16 divisions, the energy consumption uplifts the CO₂ emissions. The DH causality affirmed a bidirectional causality among economy, finance, and energy consumption, respectively.

     

Does agriculture-induced environmental Kuznets curve exist in developing countries?

Even though the economic growth and the increased agricultural production bring welfare increment, they could also load significant environmental costs to society. This study aims to investigate the existence of agriculture-induced EKC hypothesis in 47 developing countries during 1976–2017 by using dynamic panel data estimators. According to the long-run findings of the DOLS and the FMOLS estimators, there is an inverted U-shaped relationship that exists between income and the CO₂ emissions, while agricultural production has a negative impact on environmental quality. In addition, the PMG estimator is also employed for robustness check. Likewise the DOLS and the FMOLS findings, the long-run results of the PMG estimator also support the existence of agriculture-induced EKC for developing countries. The empirical findings of this study provide stimulus results of policy makers to re-consider their current production infrastructure of agricultural sector to achieve sustainable development.

     

The influence of biomass energy consumption on CO<Subscript>2</Subscript> emissions: a wavelet coherence approach

In terms of today, one may argue, throughout observations from energy literature papers, that (i) one of the main contributors of the global warming is carbon dioxide emissions, (ii) the fossil fuel energy usage greatly contributes to the carbon dioxide emissions, and (iii) the simulations from energy models attract the attention of policy makers to renewable energy as alternative energy source to mitigate the carbon dioxide emissions. Although there appears to be intensive renewable energy works in the related literature regarding renewables’ efficiency/impact on environmental quality, a researcher might still need to follow further studies to review the significance of renewables in the environment since (i) the existing seminal papers employ time series models and/or panel data models or some other statistical observation to detect the role of renewables in the environment and (ii) existing papers consider mostly aggregated renewable energy source rather than examining the major component(s) of aggregated renewables. This paper attempted to examine clearly the impact of biomass on carbon dioxide emissions in detail through time series and frequency analyses. Hence, the paper follows wavelet coherence analyses. The data covers the US monthly observations ranging from 1984:1 to 2015 for the variables of total energy carbon dioxide emissions, biomass energy consumption, coal consumption, petroleum consumption, and natural gas consumption. The paper thus, throughout wavelet coherence and wavelet partial coherence analyses, observes frequency properties as well as time series properties of relevant variables to reveal the possible significant influence of biomass usage on the emissions in the USA in both the short-term and the long-term cycles. The paper also reveals, finally, that the biomass consumption mitigates CO₂ emissions in the long run cycles after the year 2005 in the USA.     

The impact of environment degrading factors and remittances on health expenditure: an asymmetric ARDL and dynamic simulated ARDL approach

This paper investigates the impact of CO₂ emissions, air pollution (PM2.5) exposure, foreign remittances, energy consumption, renewable energy consumption, trade openness, and gross domestic product per capita on health expenditure in a panel of the 27 highest emitting countries from 2000 to 2019. Focusing on objectives, panel ARDL, and dynamic simulated ARDL models are used to examine the short-run and long-run impact of the variables on health expenditure. An asymmetric or nonlinear ARDL model is used to test the asymmetric effect of CO₂ emissions, air pollution exposure, and foreign remittance inflows on health expenditure. The results show that environment-degrading factors, remittances, and GDP per capita significantly impact health expenditure. There is an asymmetric effect of remittances, CO₂ emissions, and air pollution (PM2.5) exposure on health expenditure. Based on the results, the study suggests policymakers should make policies regarding environment-degrading elements as these factors cause huge increases in health spending in a country. Consumption of renewable energy helps reduce health expenditure as it does not cause environmental degradation, irrespective of other forms of energy, and it is suggested that policies relating to foreign remittance inflows should be encouraged and made efficient.

     

Does the level of renewable energy matter in the effect of economic growth on environmental pollution? New evidence from PSTR analysis

This study investigates the relationship between environmental pollution and economic growth in the context of renewable energy in OECD countries using the panel smooth transition regression (PSTR) model for 1995–2018. The study finds the value of the threshold variable, calculated as the share of renewable energy use in total energy consumption, to be 7.825%. In this context, economic growth affects the environment negatively and increases environmental pollution when the share of renewable energy use in energy consumption is below the threshold. When this share is above the threshold, it reduces environmental pollution by affecting the environment positively. In addition, the findings reveal a non-linear inverted U-shaped relationship between the environment and economic growth in the context of renewable energy, and the Environmental Kuznets Curve (EKC) hypothesis is valid. Therefore, the widespread use of renewable energy is a solution to reducing environmental pollution. Accordingly, it is very important for policymakers to both highlight and encourage renewable energy use. Furthermore, countries need to both invest in this area and focus on R&D to increase renewable energy production.

     

Do transportation taxes promote pro-environmental behaviour? An empirical investigation

The transportation sector is a crucial driver of energy intensity and environmental degradation. Therefore, we aim to explore the nexus of transportation taxes, energy intensity, and CO₂ emissions for the BICS economies. The econometric approaches, CS-ARDL and PMG-ARDL, have been employed to compute the estimates. The long-run estimates of the green transportation tax variable are negatively significant in both energy intensity and CO₂ emissions models irrespective of the estimation technique. These findings imply that green transportation taxes help reduce energy intensity and CO₂ emissions in BICS economies. Conversely, in the short-run, the effects of transportation taxes on energy intensity and CO₂ emissions are mixed and inconclusive. Hence, transportation taxes are necessary to keep the polluters under control not only from the transport sector but also serve as a deterrent for other sectors as well.

     

The role of innovations and renewable energy consumption in reducing environmental degradation in OECD countries: an investigation for Innovation Claudia Curve

Rising economic growth in recent ages is the primary concern of most of the countries to enhance the living standard, but the ever-increasing production of economic activities consumes a lot of energy, which leads to a sharp increase in carbon dioxide emissions. Innovation may be a remedy that can help improve energy efficiency, obtain renewable energy, and promote economic growth, thereby protecting the quality of the environment. Therefore, this paper examines the role of innovation and renewable energy consumption in CO2 reduction in OECD countries from 2004 to 2019. By using the two-step system generalized of moment estimator, the results show that economic growth and innovation significantly increase carbon emissions, however the innovation Claudia Curve (ICC) is verified, and the environmental Kuznets curve does not exist. Foreign direct investment has a negative impact on carbon emissions, thus verifying the Pollution Hao hypothesis, whereas renewable energy also improves environmental quality, but the interaction between innovation and renewable energy consumption still increases carbon emissions. Financial development, industrialization, trade, and energy consumption have also been found to be harmful factors of environmental quality. Our findings have considerable policy implications for OECD countries on the improvement of innovation indicators and investment in renewable energy sources to rise environmental quality.

     

Estimates of carbon dioxide emissions based on incomplete condition information: a case study of liquefied natural gas in China

Recent calculations of carbon dioxide (CO₂) emissions have faced challenges because data consist of only partial information, which is called “incomplete information.” According to the emission factor method, energy consumption and CO₂ emission factors with incomplete information may lead to unmatched multiplication between themselves, which affects accuracy and increases uncertainties in emission results. To address a specific case of incomplete information that has not been fully explored, we studied the effects of incomplete condition information on the estimates of CO₂ emissions from liquefied natural gas (LNG) in China. Based on Chinese LNG sampling data, we obtained the specific-country CO₂ emission factor for LNG in China and calculated the corresponding CO₂ emissions. By applying hypothesis testing, regression analysis, variance analysis, or Monte Carlo (MC) simulations, the effects of incomplete information on the uncertainty of CO₂ emission calculations in three cases were analyzed. The results indicate that calorific values have more than a 9.8% impact on CO₂ emission factors and CO₂ emissions with incomplete sample information. Regarding incomplete statistical information, the impact of statistical temperature on CO₂ emissions exceeds 5.5%. Regarding incomplete sample and statistical information, sample and statistical temperatures can individually increase estimate biases by more than 5.2%. Significantly, the impacts of sample temperature and statistical temperature may offset each other. Therefore, the incomplete condition information is quite important and cannot be ignored in the estimation of CO₂ emissions from LNG and international fair comparison.

     

A new perspective to environmental degradation: the linkages between higher education and CO2 emissions

Although there have been many studies, focusing on basic determinants of carbon dioxide emissions (CO₂), the effect of higher education, which has the potential to be an important determinant of CO₂, has been neglected. The paper aims to display the potential mechanisms between higher education and CO₂ and expand the environmental economics literature. In the paper, the trade-off between higher education and CO₂ is tested by autoregressive distributed lag (ARDL) during the period 1983–2017 in Turkey. Results denote that cointegration exists and an increase in higher education negatively affects CO₂. We also find that economic growth and energy consumption positively affect CO₂ both in the long run and short run. Vector error correction model (VECM) reveals that higher education, economic growth, energy consumption, trade openness, and exchange rates are the causes of CO₂ in the long run. Also, energy consumption and economic growth are the causes of CO₂ in the short run. Therefore, higher education can be used to overcome environmental problems.

     

The nexus between remittances,natural resources,technological innovation,economic growth,and environmental sustainability in Pakistan

Globally, the issues about sustainable development are on the increase. Moreover, these issues are rising every day in Pakistan, as remittances are increasing, technology innovation is ambiguous, natural resources are degraded, and economic expansion might pose serious challenges to the environment. Thus, this research looks at how remittances, natural resources, technological innovation, and economic growth affect carbon dioxide (CO₂₎ emissions in Pakistan by controlling energy consumption and urbanization from 1990 to 2019. The Bayer and Hanck test of combined cointegration discloses a cointegration between remittances, natural resources, technological innovations, economic growth, and CO₂ emissions. Moreover, the autoregressive distributive lag model (ARDL) proposes a significant positive association between remittances and CO₂ emissions in the long run, indicating that the increase in remittances distresses the environmental performance of Pakistan. Our study confirms that natural resources decrease CO₂ emissions while technological advancement, economic progress, energy use, and urbanization increase CO₂ emissions. In addition, the results of robustness checks by employing fully modified ordinary least squares and dynamic ordinary least squares are parallel to the conclusions of ARDL estimations. Furthermore, the frequency causality test results show that remittances, natural resources, technological innovation, economic growth, energy use, and urbanization cause CO₂ emissions at different frequencies. Therefore, to achieve the sustainable development goals, appropriate policy repercussions can be developed toward advanced and environmentally sustainable sources of energy.

     

A Life-Cycle Inventory Model of Municipal Solid Waste Combustion

ABSTRACT

Evaluation of alternate strategies for municipal solid waste (MSW) management requires models to calculate environmental emissions as a function of both waste quantity and composition. A methodology to calculate waste component-specific emissions associated with MSW combustion is presented here. The methodology considers emissions at a combustion facility as well as those avoided at an electrical energy facility because of energy recovered from waste combustion. Emission factors, in units of kg pollutant per metric ton MSW entering the combustion facility, are calculated for CO₂-biomass, CO₂-fossil, SO_x , HCl, NO_x , dioxins/furans, PM, CO, and 11 metals. Water emissions associated with electrical energy offsets are also considered. Reductions in environmental emissions for a 500-metric-ton-per-day combustion facility that recovers energy are calculated.     

Use of Energy Scenarios in Addressing the CO2 Question

A reference scenario for CO₂ emissions was developed using a model of world energy supply and demand. In the reference scenario, world GNP and world energy demand increase at average rates of 2.1 percent per year and 1.5 percent per year, respectively during the period 1975-2100. The corresponding annual CO₂ emissions rise to a maximum of 16 gigatons of carbon around 2050 and then decline as a result of a transition to nonfossil fuel energy systems. A modified scenario for high CO₂ emissions was obtained by assuming an abundant supply of low cost coal, thus eliminating the transition. A low case was developed in which the low cost of alternative energy (i.e., solar, nuclear) induces an earlier shift away from fossil fuels.

Annual emissions of the three scenarios were used as input to a global carbon cycle model and the CO₂ buildup in the atmosphere during the period 1980-2100 was determined by the model. All three scenarios showed continuous rises in atmospheric CO₂ concentration. The reference scenario reached 775 ppm by 2100. The high CO₂ case resulted in concentrations of over 1040 ppm, and for the low case the 2100 concentration was just under 700 ppm. If the climate theory is correct, even 700 ppm is sufficient to give significant climate warming, but by experiencing the change gradually over a century, adaptation may not be painful. An early transition to nonfossil fuel supplies makes the problem less severe but does not eliminate it.