Can biomass energy curtail environmental pollution? A quantum model approach to Germany

Spatial effect of biomass energy consumption on carbon emissions reduction: the role of globalization

As globalization proceeds, increasing biomass energy consumption is an important pathway to replace fossil fuels for tackling climate change by reducing emissions. This study explores the spatial spillover effect in biomass energy carbon reduction, which is frequently ignored when investigating environmental factors. It uncovers whether globalization and its dimensions can strengthen the spatial effect of biomass energy carbon reduction. Besides, we reveal whether biomass energy consumption can promote CO2 emissions reduction while ensuring economic progress. Results show that (1) owing to the spillover effect, biomass energy consumption plays a significant role in direct and indirect enhancing carbon emissions reduction, with their feedback effects of - 0.003 or 3.3% of the direct effect. (2) Increasing overall, social and political globalization enhances biomass energy consumption's carbon reduction effect. (3) In countries with higher economic development, overall, economic and political globalization has a better promotion in the spatial effect of biomass energy carbon reduction. (4) Developing biomass energy can support the environment quality while enhancing economic growth.

Investigation on the co-combustion characteristics of multiple biomass and coal under O2/CO2 condition and the interaction between different biomass

The co-combustion of coal and biomass in O₂/CO₂ conditions is a promising technology for CO₂ capture and waste disposal. Little attention has been paid to the interaction between different biomass in co-combustion process, which is of great significance to the study of the co-combustion mechanism. The co-combustion behavior of coal and multiple biomass under isothermal conditions was characterized by thermogravimetric method, and the interaction between different biomass was investigated from the perspective of thermogravimetric and proximate analysis. It found that biomass blending could remarkably improve the combustion performance of coal. Compared to the theoretical prediction, the interaction between coal and biomass showed remarkably promoting effects when the coal was blended with different biomass. While the interaction between different biomass was weak. Moreover, the influence of proximate analysis on combustion characteristic parameters was studied by establishing the linear relationship between combustion characteristic parameters and proximate analysis. The effects of proximate analysis on characteristic time/S were divided into five categories, and it were mainly controlled by the interaction both between coal with biomass and between different biomass.

Economic complexity and CO2 emissions in OECD countries: sector-wise Environmental Kuznets Curve hypothesis

This study examines the Environmental Kuznets Curve hypothesis by estimating the relationship between economic structure and economic complexity with the environmental pollution in OECD countries during 1971-2016. In that respect, this research investigates how various economic sectors affect environmental pollution differently. The results confirm the Environmental Kuznets Curve hypothesis, which implies the influential role of economic structure and complexity in socio-economic developmental phases. In addition, the results show that most of the OECD countries are on the left side of the curve, implying positive connection between economic complexity and CO₂ emissions. In contrast, only 3 OECD countries (Japan, Switzerland, and Germany) are close to the turning point, indicating that their patterns are sustainable for socio-economic development. The sectoral economic results affirm the most pollutant structure of the service sector, compared with the other economic sectors. Hence, new projects should attach great attention to their environmental impacts, specifically in the service sector planning. Regarding the complexity analysis, policymakers are advised to embrace knowledge-intensive restructuring of economic sectors.

The integrated impact of GDP growth, industrialization, energy use, and urbanization on CO2 emissions in developing countries: Evidence from the panel ARDL approach

Developing economies are an important engine of world economic growth. However, ensuring the quality of environmental assets is maintained amid rapid economic change remains a major challenge for most developing countries. Using the Panel Autoregressive Distributed Lag (ARDL) approach and the heterogeneous causality test, this study analyzes the combined effects of energy usage, industrialization, gross domestic product (GDP) growth, and urbanization on CO₂ emissions for 23 developing countries across the 1995 to 2018 period. From our analysis, the long-run results reveal that a 1% increase in energy use, economic growth, industrialization, and urbanization increases CO₂ emissions by 0.23%, 0.17%, 0.54%, and 2.32%, respectively. Moreover, our model's short- to long-term equilibriums are adjusted at a yearly rate of 0.19%. Finally, to verify the panel ARDL long-run results, robustness tests were carried out using the Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) approaches. Our results confirm that in the case of developing countries, CO₂ emissions are primarily influenced by GDP growth, energy use, industrialization, and urbanization. Furthermore, the panel causality analysis identified a bidirectional causal relationship between energy use, GDP growth, urbanization, industrialization, and CO₂ emissions. While these results can play an instrumental role in formulating CO₂ emission policies among our selected countries, our research can also assist policy makers and stakeholders in other developing economies implement important policy initiatives. These include, tax incentives and infrastructural developments that nurture environmentally friendly industrialization, deploy low-carbon technologies, promote sustainable forms of urbanization and urban planning, while also facilitating increases in both the investment in and adoption of renewable energy platforms. The establishment of such a comprehensive policy agenda can help emerging economies achieve strong and environmentally sustainable GDP growth over the long-term.

Convergence behaviours of energy series and GDP nexus hypothesis: A non-parametric Bayesian application

With the EU Green Deal initiatives, European members seek to launch the first climate neutral continent by 2050. This paper assesses the stochastic convergence of per capita energy consumption series for an illustrative sample of 15 EU countries with memberships prior to the 2004 enlargement, using data spanning the 1970–2018 period. Results from the confidence interval subsampling (asymmetric and equal-tailed) highlight that 11 out of the 15 EU series exhibit a long-run memory behaviour, while a diverging pattern was observed for the UK, Germany, Portugal, and Italy. Finally, per capita energy use series persist but fail to reveal one of the above dynamics for Ireland and Spain. Also, findings from the non-parametric Bayesian application (ANOVA/linear Dependent Dirichlet Process (DDP) mixture model) show how economic growth operates as a converging energy consumption-enabler over the long-run, from which the EU membership cannot be excluded. In particular, we highlight how the nature of energy-GDP hypotheses vary with the stochastic properties of energy use (converging behaviour with temporary shocks, converging pattern with permanent shocks, and diverging dynamic). Finally, our conclusions overcome the well-established development stage argument as we claim that countries with similar energy convergence patterns may need to adopt similar energy policies.

The dynamic impacts of environmental-health and MDR-TB diseases and their influence on environmental sustainability at Chinese hospitals

The purpose of this study is to identify at what extent multidrug-resistant tuberculosis (MDR-TB) diseases effect on environmental health issues in selected provinces of Chinese hospitals. In survival analysis approach, this study employs the Cox proportional hazard model (CPM) to incorporate the duration of event, probability of occurrence of an event, and the issue of right censoring. An advantage of using CPM is that one does not need to specify the distribution of baseline hazard H₀ (t) as it considers a common value for all units in population. The results indicate that male and travel expenditures have negative association with the duration of cure. Furthermore, the medical expenditures and the spatial characteristic of time expenditure have positive association with the duration of cure of MDR-TB patients. The inconsistent behavior of males in taking medicines as compared to females and males is also more prone to tuberculosis (TB).

A comparative study of carbon tax and fuel tax based on panel spatial econometric model

The balance between economic development and environmental governance has always been the focus of attention, and this has become a key issue facing in China. In recent years, the means of improving the environment through taxation are common, and it is more in line with China's national conditions. Carbon tax and fuel tax are considered to be effective environmental supervision measures, and the implementation of this policy is bound to have a critical impact on the advance of economic level. However, the implementation effects of these two mechanisms may be different, and they may also have various effects on regional development. Therefore, based on the panel data of China's 29 provinces from 2008 to 2018, we adopt the spatial autocorrelation method to explore the relationship between the economic levels of various areas. Then, establishing the panel spatial econometric model of economic growth and carbon tax, economic growth and fuel tax respectively to compare the implementation effects of the two tax policies. It turns out that there is a positive correlation between the economic growth of 29 provinces in China. And whether choosing to levy carbon tax or fuel tax, they all have their own advantages and disadvantages. Finally, according to the results of empirical analysis results, some relevant policy suggestions are put forward.

A critical review on metal-based catalysts used in the pyrolysis of lignocellulosic biomass materials

This review discusses the technical aspects of improving the efficiency of the pyrolysis of lignocellulosic materials to increase the yield of the main products, which are bio-oil, biochar, and syngas. The latest aspects of catalyst development in the biomass pyrolysis process are presented focusing on the various catalyst structures, the physical and chemical performance of the catalysts, and the mode of the catalytic reaction. In bio-oil upgrading, atmospheric catalytic cracking is shown to be more economical than catalytic hydrotreating. Catalysts help in the upgrading process by facilitating several reaction pathways such as polymerization, aromatization, and alkyl condensation. However, the grade of bio-oil must be similar to that of diesel fuel. Hence, the properties of the pyrolysis liquid such as viscosity, kinematic viscosity, density, and boiling point are important and have been highlighted. Switching between types of catalysts has a significant influence on the final product yields and exhibits different levels of durability. Various catalysts have been shown to enhance gas yield at the expense of the yields of bio-oil and biochar that shift the overall purpose of pyrolysis. Therefore, the catalytic activity as a function of temperature, pressure, and catalyst biomass ratio is discussed in detail. These operational parameters are crucial because they determine the overall yield as well as the ratio of the oil, char, and gas products. Although significant progress has been made in catalytic pyrolysis, the economic feasibility of the process and the catalyst cost remain the major obstacles. This review concludes that the catalytic process would be feasible when the fuel selling price is reduced to less than US $ 4 per gallon of gasoline-equivalent, and when the selectivity of catalysts is further enhanced.

Assessing a Fossil Fuels Externality with a New Neural Networks and Image Optimization Algorithm: The Case of Atmospheric Pollutants as Cofounders to COVID-19 Lethality

This paper demonstrates how the combustion of fossil fuels for transport purpose might cause health implications. Based on an original case study [i.e. the Hubei province in China, the epicentre of the coronavirus disease-2019 (COVID-19) pandemic], we collected data on atmospheric pollutants (PM_2.5, PM₁₀ and CO₂) and economic growth (GDP), along with daily series on COVID-19 indicators (cases, resuscitations and deaths). Then, we adopted an innovative Machine Learning approach, applying a new image Neural Networks model to investigate the causal relationships among economic, atmospheric and COVID-19 indicators. Empirical findings emphasise that any change in economic activity is found to substantially affect the dynamic levels of PM_2.5, PM₁₀ and CO₂ which, in turn, generates significant variations in the spread of the COVID-19 epidemic and its associated lethality. As a robustness check, the conduction of an optimisation algorithm further corroborates previous results.

The trilemma of innovation, logistics performance, and environmental quality in 25 topmost logistics countries: A quantile regression evidence

While the deployment of technological innovation was able to avert a devastating global supply chain fallout arising from the impact of ravaging COronaVIrus Disease 19 (COVID-19) pandemic, little is known about potential environmental cost of such achievement. The aim of this paper is to identify the determinants of logistics performance and investigate its empirical linkages with economic and environmental indicators. We built a macro-level dataset for the top 25 ranked logistics countries from 2007 to 2018, conducting a set of panel data tests on cross-sectional dependence, stationarity and cointegration, to provide preliminary insights. Empirical estimates from Fully Modified Ordinary Least Squares (FMOLS), Generalized Method of Moments (GMM), and Quantile Regression (QR) model suggest that technological innovation, Human Development Index (HDI), urbanization, and trade openness significantly boost logistic performance, whereas employment and Gross Fixed Capital Formation (GFCF) fail to respond in such a desirable path. In turn, an increase in the Logistic Performance Index (LPI) is found to worsen economic growth. Finally, LPI exhibits a large positive effect on carbon emissions, which is congruent with a strand of the literature highlighting that the modern supply chain is far from being decarbonized. Thus, this evidence further suggest that more global efforts should be geared to attain a sustainable logistics.

Utilization of Biomass to Ash: An Overview of the Potential Resources for Alternative Energy

Climate change and the potential depletion of fossil fuels have increased international demand for alternative and renewable energy sources. In terms of the energy sector, for example, most of the South-East Asian countries (SACs) have a large number of biomass sources due to their vast forest resources and agriculture-based economies. Thus, the critical review was aimed at highlighting the overview of biomass energy in South-East Asia as a dynamically developing region, in order to obtain economic and environmental benefits from the existing sources of biomass in the world. The current review analyzed the sources of biomass, as well as their energy potential, use, and management, based on reports from different countries, published studies, and scientific articles. In SAC, the main sources of biomass were found to be coconut residues, oil palm residues, sugar cane residues, rice straw, rice husks, wood waste, and firewood. The combined annual biomass potentials in the forestry and agricultural sectors in South-East Asia were approximately over 500 million tons per year and more than 8 gigajoule of total energy potentials. The study identified the challenges and barriers to using biomass in these countries to achieve sustainable use of biomass sources and recommended sustainable approaches to using biomass energy by comparing traditional uses of biomass. Smart grid technologies have ways for solutions for better electric power production and efficient ways for distribution and transmission of electricity. Smart grids require less space and can be more easily installed when compared to traditional grids because of their versatilities. Upcoming challenges include technology optimization for the following uses of biomass energy: direct combustion of woody biomass; pyrolysis and gasification of biomass; anaerobic digestion of organic waste to produce biogas; landfill gas production direct incineration of organic waste. The barriers in this technology are emissions of carbon and nitrogen oxides, unpleasant odors, as well as the uncontrolled harvesting of biomass, which can harm nature.

Bioenergy consumption, carbon emissions, and agricultural bioeconomic growth: A systematic approach to carbon neutrality in China

China is the world's largest fossil fuel consumer and carbon emitter country. In September 2020, China pledged to reduce carbon emissions, and achieve carbon neutrality by 2060. Therefore, this study aimed to contribute to the literature and show the pictorial nexus of bioenergy and fossil fuel consumption, carbon emission, and agricultural bioeconomic growth, a new pathway towards carbon neutrality. For this study, time-series data from 1971 to 2019 were used to analyze the autoregressive distributed lag (ARDL) bound testing and novel dynamic autoregressive distributed lag (DYNARDL) simulation models. Initially, the unit root tests results showed that all variables were stationarity at the level and first difference. The presence of cointegration between selected variables was confirmed by the results from ARDL bound test. In addition, the results of long-run and short-run nexus show an increase in bioenergy consumption that caused an increase in agricultural bioeconomic growth both in the long and short-run nexus. A decrease in fossil fuel consumption was shown to result in increased agricultural bioeconomic growth with respect to both long- and short-term effects. Furthermore, the results of the novel dynamic ARDL simulation model demonstrated that a 10% positive shock from bioenergy consumption caused an increase in agricultural bioeconomic growth, while at the same time, a 10% negative shock in bioenergy consumption led to a decrease. A 10% negative shock from fossil fuels caused an increase in agricultural bioeconomic growth, whereas a 10% positive shock from fossil fuels led to a decrease. Therefore, this study suggests that China needs to switch from fossil fuel and other non-renewable energy consumption to sources of bioenergy and other renewable energy consumption to achieve carbon neutrality by 2060.

Revisiting the dynamic interactions between economic growth and environmental pollution in Italy: evidence from a gradient descent algorithm

Although the literature on the relationship between economic growth and CO₂ emissions is extensive, the use of machine learning (ML) tools remains seminal. In this paper, we assess this nexus for Italy using innovative algorithms, with yearly data for the 1960-2017 period. We develop three distinct models: the batch gradient descent (BGD), the stochastic gradient descent (SGD), and the multilayer perceptron (MLP). Despite the phase of low Italian economic growth, results reveal that CO₂ emissions increased in the predicting model. Compared to the observed statistical data, the algorithm shows a correlation between low growth and higher CO₂ increase, which contradicts the main strand of literature. Based on this outcome, adequate policy recommendations are provided.