Search

The pulp-paper industry is one of the main industrial sectors that produce massive amounts of residual sludge, constituting an enormous environmental burden for the industries. Traditional sludge management practices, such as landfilling and incineration, are restricted due to mounting environmental pressures, complex regulatory frameworks, land availability, high costs, and public opinion. Valorization of pulp-paper industry sludge (PPS) to produce high-value products is a promising substitute for traditional sludge management practices, promoting their reuse and recycling. Valorization of PPIS for biorefinery beneficiation includes biomethane, biohydrogen, bioethanol, biobutanol, and biodiesel production for renewable energy generation. Additionally, the various thermo-chemical technologies can be utilized to synthesize bio-oil, hydrochar, biochar, adsorbent, and activated carbon, signifying potential for value-added generation. Moreover, PPIS can be recycled as a byproduct by incorporating it into nanocomposites, cardboard, and construction materials development. This paper aims to deliver a comprehensive overview of PPIS management approaches and thermo-chemical technologies utilized for the development of platform chemicals in industry. Substitute uses of PPIS, such as making building materials, developing supercapacitors, and making cardboard, are also discussed. In addition, this article deeply discusses recent developments in biotechnologies for valorizing PPIS to yield an array of valuable products, such as biofuels, lactic acids, cellulose, nanocellulose, and so on. This review serves as a roadmap for future research endeavors in the effective handling of PPIS. [Display omitted] • Valorization of pulp-paper industry sludge is a sustainable option for getting waste to wealth. • Pre-treatment of pulp-paper industry sludge helps in yielding platform chemicals in the biorefinery scenario. • Pulp-paper industry sludge is a sustainable feedstock for renewable energy production. • Biochars derived from pulp-paper industry sludge exhibited excellent adsorption performance for metals removal. [ABSTRACT FROM AUTHOR]

Within the literature on energy and environmental economics, it is generally acknowledged that renewable energy can improve environmental quality; however, certain papers suggest that an optimal level of the usage of renewable energy sources may exist. Consequently, the utilization of renewable energy sources can result in environmental degradation up to a certain threshold. Then, environmental quality can be enhanced through the continued application of renewables. This indicates that the link between renewable energy and environmental devastation is inverted U-shaped. This paper presents empirical evidence concerning this possible association between renewable energy and environmental destruction in Türkiye, a country where fossil energy predominates in the energy mix. Additionally, the paper investigates the environmental influences of natural resource rents and schooling. This study utilizes annual data from 1971 to 2020 and implements time series methodologies that rely on the Fourier approximation. The paper thus accounts for an undetermined quantity of structural breaks. The results suggest that an inverted U-shaped link occurs between renewable energy and environmental destruction, signifying renewable energy initially contributes to a diminution in environmental quality before subsequently improving it. Additionally, environmental quality is positively associated with natural resource rents and negatively associated with schooling, according to the findings. Furthermore, the findings reveal that schooling worsens the combined effect of renewable energy on environmental degradation. These conclusions are discussed in the paper. [Display omitted] • A nonlinear pattern exists between renewable energy and the environment. • Natural resource rents decrease environmental destruction. • Schooling increases environmental destruction. [ABSTRACT FROM AUTHOR]

The decarbonization of the energy sector has been a subject of research and of political discussions for several decades, gaining significant attention in the last years. It is commonly acknowledged that the most obvious way to achieve decarbonization is the use of renewable energy sources. Within the context of the energy sector decarbonization, many mainly industrialized countries recently started developing national plans to establish a hydrogen-based economy in the very near future. The plans for green hydrogen initially try to (a) target sectors that are difficult to decarbonize and (b) address issues related to the storage and transportation of CO 2 -free energy. To achieve almost complete decarbonization, electric power must be generated exclusively from renewable sources. In so-called Power-to-X (PtX) technologies, green hydrogen is generated from electricity and subsequently converted to another energy carrier which can be further stored, transported and used. In PtX, X stands, for example, for liquid hydrogen, methanol or ammonia. The challenges associated with decarbonization include those associated with (a) the expansion of renewable energies (e.g., high capital demand, political and social issues), (b) the production, transportation, and storage of hydrogen and the energy carriers denoted by X in PtX (e.g., high cost and low overall efficiency), and (c) the expected significant increase in the demand for electrical energy. The paper discusses whether and under which conditions the current national and international hydrogen plans of many industrialized countries could lead to a maximization of decarbonization in the world. It concludes that, in general, as long as the conditions for generating large excess amounts of green electricity are not met, the quick establishment of a hydrogen economy could not only be very expensive, but also counterproductive to the worldwide decarbonization efforts. • The paper addresses the decarbonization of the energy sector. • Use of hydrogen in the very near future does not promote decarbonization. • Power-to-X technologies are inefficient and very costly. • Currently we must focus on the expansion of green electricity. [ABSTRACT FROM AUTHOR]

In the context of carbon neutrality target, renewable energy sources have been transforming from "supplementary energy" to "main energy", which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the "resource-environment-demand" comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry. • This study examines the impact of high-dimensional features variables on urban solar energy development. • Over 70% of solar PV installation capacity is concentrated in Northern and Northwest parts of China. • Water resources, solar radiation, population density have significant impact on solar PV distribution. • Solar PV distribution in China should shift from resource orientation to the comprehensive orientation. [ABSTRACT FROM AUTHOR]

This paper presents an overview of the scholarly works employing the life cycle assessment (LCA) approach to evaluate the environmental impact of construction and demolition waste (CDW) fine fractions derived from concrete elements throughout their life cycle. Unlike conventional studies, this work addresses the challenge of reducing the carbon footprint associated with CDW-based building materials, emphasizing environmental impact mitigation. The study highlights that approximately 30% of CDW is landfilled, 50% is recycled, and 20% is used as fill material, underscoring the potential for increasing recycling rates through improved processing techniques and management practices. In the reviewed studies, most research has been conducted in Europe, Asia, the USA, and China. The primary and secondary data sources for the life cycle inventory (LCI) vary depending on the study region and locality. By exploring innovative practices and critical stages in CDW fine fractions utilization for concrete components, the study aims to contribute to greener construction practices and sustainable resource management. The distinctive aspect of this research lies in its comprehensive review of CDW-based aggregates, binders, and alternative cementitious materials, highlighting the significance of sustainable energy resources and transportation strategies in enhancing the sustainability of CDW-derived concrete. Key findings highlight the necessity of sustainable energy for pretreatment and optimized transportation strategies, including route planning and vehicle selection, to produce greener CDW fine fraction-based building materials. Additionally, the study suggests key steps and parameters required for defining the system boundary and preparing the inventory for conducting an LCA of building materials based on CDW fine fractions. Through a detailed analysis of environmental burdens at each production stage, this study seeks to promote the adoption of greener concrete solutions worldwide. The use of CDW in concrete production promotes environmental sustainability and greener concrete regardless of the region. • LCA examine lifecycle impacts of CDW fine fractions in concrete. • LCI and system boundary designed to assess concrete's environmental impacts. • Transport and pretreatment raise emissions by 10%–30%. • Waste treatment necessitates 100% sustainable energy sources. [ABSTRACT FROM AUTHOR]

Countries have become increasingly concerned about the impact of their activities and their alignment with sustainable development goals. Consequently, it is necessary to examine their performance efficiency in a unified manner, accounting for economic, environmental, and social variables. A country's performance efficiency is defined as the ratio of observed output to frontier output, given the country's productive resources. In this paper, we use the frontier technique of Data envelopment analysis (DEA) to estimate the frontier output. The objective of this study is to assess the performance efficiency of 111 worldwide countries and to examine whether gender inequality, the share of renewable energy consumption, the government control of corruption and government expenditure on education could explain differences in performance. We employed the non-parametric DEA model using three inputs (capital, labor, and primary energy consumption) and one output (GDP). We also considered an additional output (CO 2 emissions); however, the results were identical to the one output model because CO 2 emissions and primary energy consumption are highly correlated (0.96). We find that there is a negative impact of gender inequality, a positive impact of the share of renewable energy consumption and government control of corruption on performance efficiency. Our results also indicate that reducing CO 2 emissions required transitioning towards renewable energy and using the energy efficiently. However, we did not find a positive relationship between government expenditures on education and country efficiency. • A comprehensive analysis at the country level between efficiency and sustainability. • Positive association between countries' efficiency and renewable energy use. • Positive association between countries' efficiency and control of corruption. • Negative association between countries' efficiency and gender inequality. • Interdisciplinary study that tackles the triple bottom lines of sustainability. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multi-RREH (Remote Renewable Energy Hub) based optimization framework. This framework allows a valorization of CO 2 using carbon capture technologies. This valorization is grounded on the idea that CO 2 gathered from the atmosphere or post combustion can be combined with hydrogen to produce synthetic methane. The hydrogen is obtained from water electrolysis using renewable energy. Such renewable energy is generated in RREH, which are locations where RE is cheap and abundant (e.g., solar PV in the Sahara Desert, or wind in Greenland). We instantiate our framework on a case study focusing on Belgium and 2 RREH, and we conduct a techno-economic analysis under uncertainty. This analysis highlights, among others, the interest in capturing CO 2 via Post Combustion Carbon Capture (PCCC) rather than only through Direct Air Capture (DAC) for methane synthesis in RREH. By doing so, a notable reduction of 10% is observed in the total cost of the system under our reference scenario. In addition, we use our framework to derive a carbon price threshold above which carbon capture technologies may start playing a pivotal role in the decarbonation process of our industries. • We demonstrate PCCC technology potential for reducing RREH's CH4 production cost. • We derive a carbon price threshold for crucial industrial decarbonization. • An uncertainty analysis has been carried out on the reference scenario. [ABSTRACT FROM AUTHOR]

The Paris Agreement, a landmark international treaty signed in 2016 to limit global warming to 2 ° C , has urged researchers to explore various strategies for achieving its ambitious goals. While Renewable Energy (RE) innovation holds promise, it alone may not be sufficient as critical deadlines approach. This field of research presents numerous challenges, foremost among them being the costliness of materials involved. However, emerging advancements in Machine Learning (ML) technologies provide a glimmer of hope; these sophisticated algorithms can accurately predict the output of energy systems without relying on physical resources and instead leverage available data from diverse energy platforms that have emerged over recent decades. The primary objective of this paper is to comprehensively explore various ML techniques and algorithms in the context of Renewable Energy Systems (RES). The investigation will address several vital inquiries, including identifying and evaluating existing RE technologies, assessing their potential for further advancement, and thoroughly analyzing the challenges and limitations associated with their deployment and testing. Furthermore, this research examines how ML can effectively overcome these obstacles by enhancing RES performance. By identifying future research opportunities and outlining potential directions for improvement, this work seeks to contribute to developing environmentally sustainable energy systems. • Discussed the advantages and limitations of current renewable energy systems. • Reviewed the different machine learning algorithms and their applications in renewable energy systems. • Highlighted existing renewable energy datasets and feature selection methods. • Discussed the use of machine learning and renewable energy in the hydrogen value chain. • Provided future directions for the application of machine learning in the renewable energy sector. [ABSTRACT FROM AUTHOR]

The world is currently facing urgent climate and environmental issues, such as global warming, ecosystem collapse, and energy shortages. In this context, this study selected data from 2000 to 2021 and employed the Method of Moment Quantile Regression (MMQR) to thoroughly investigate the impact of renewable energy consumption, economic complexity, and geopolitical risks on the ecological footprint of the Group of Twenty (G20) countries. The results indicate that in countries with lower quantiles, renewable energy consumption significantly reduces the ecological footprint, whereas its effect is not prominent in countries with higher quantiles. Economic complexity has a negative impact on the ecological footprint, and this impact becomes stronger as the quantile of the ecological footprint rises. Additionally, economic complexity moderates the effect of renewable energy on the ecological footprint. Geopolitical risks facilitate the growth of the ecological footprint. Likewise, robustness tests such as DOLS, FMOLS, and quantile regression confirm these estimates in the same framework. This study has conducted a profound analysis of global environmental issues, offering innovative perspectives and recommendations for achieving goals related to sustainable energy utilization, mitigating climate change, and improving the ecological environment. The findings of this research will guide policymakers in G20 countries to adopt more effective environmental protection measures, thereby contributing to the construction of a sustainable future. • This paper investigates the factors influencing the ecological footprint of G20 countries. • The study utilized a novel Method of Moment Quantile Regression to explore nexus among the study variables. • Renewable energy consumption has no significant effect on the ecological footprint in higher quantiles. • Economic complexity moderates the effect of renewable energy on the ecological footprint. • Geopolitical risks exhibit a unidirectional causality with the ecological footprint. [ABSTRACT FROM AUTHOR]

Rapid developments in environmental infrastructure have contributed to significant improvements in green total factor productivity, but further investigation is required to provide a detailed assessment to understand the policy mechanisms involved. This paper analyzes environmental progress in China through MMQR, CCEMG, and AMG as empirical strategies for 30 provinces in China. Our empirical results reveal that energy optimization through renewable energy is the most effective channel to improve green total factor productivity, though it is not the only available option. Since environmental regulations, infrastructure development, and green technology innovation also directly impact energy efficiency, adopting these within policy channels will positively impact environmental sustainability. Our empirical approach helps suggest novel environmental policy suggestions. In particular, policymakers must introduce structural changes within energy developments to foster renewable energy. Furthermore, China must increase environmental spending to upgrade its energy infrastructure further and solve ecological issues. These insights offer valuable policy guidance for decision-makers in China and globally, aiming to foster economic and environmental sustainability and achieve zero-carbon transition goals. • The current study analyses the role of green factor productivity in zero carbon transition. • We use 30 chinese provincial datasets from 2005 to 2020. • Energy transition is the most effective channel to improve green total factor productivity. [ABSTRACT FROM AUTHOR]

The stochastic and intermittent features of wind power as well as the high percentage of wind power grid-connected significantly increase the additional operating costs of the power system. It is difficult to accurately calculate the impact of complex fluctuations in wind power on additional operating costs. To solve the above problems, a power system operating cost model adapted to various wind power fluctuation processes is established. Firstly, based on a two-layer clustering strategy, different types of wind power fluctuations are obtained. Then, a production simulation model of the power system with renewable energy is established. The production simulation model costs include thermal plant operating costs, energy storage system operating costs, positive reserve costs and negative reserve costs. With the optimization objective of minimizing the total operating cost of the power system, realistic and representative system operating parameters and cost samples are obtained for various wind power fluctuations and different wind power grid-connected scenarios. Finally, a data-driven approach based on a deep neural network algorithm is proposed to achieve precise mapping between wind energy fluctuations and the operating costs of power systems and thermal power units, and the operating costs of the power system during the four seasons with different types of wind power fluctuations can be precisely analyzed. The results demonstrate that the method proposed in this paper has high simulation accuracy for the overall simulation operating cost of the power system and the operating cost of thermal power plants. The simulation errors are 4%–18% and 3%–13%, respectively, which verified the effectiveness of the method. • Two-layer clustering strategy with variable time windows identifies wind power fluctuation types. • A simulation model of power system operating costs is established based on a data-driven approach. • Power system operating costs simulation accuracy varies with seasons and wind power fluctuations types. [ABSTRACT FROM AUTHOR]

Renewable energy (RE) plays a crucial role in global energy transformation, and a thorough study of the potential impact of RE on regional carbon emissions is of great significance. This is particularly relevant to China, which needs to clarify its path to carbon reduction. Using the sample data of 30 provinces in China from 2000 to 2021, this paper uses the Granger causality test to verify the causal relationship between carbon emission intensity (CEI) and other factors. It builds a mediation effect model on this basis to explore the direct impact effect and indirect transmission path of renewable energy utilization (REU) on CEI. The results show that REU has a one-way causal relationship with CEI. REU can directly and indirectly reduce CEI by improving social wealth and changing the direction of energy investment. In addition, REU indirectly increases CEI through the transmission paths of investment in the energy industry - social affluence and industrial level-social affluence. The CEI is indirectly reduced through the conduction paths of (social affluence-Urbanization rate), (Investment in the energy industry-Urbanization rate), (Industrial level-Urbanization rate), and (Industrial level-Investment in the energy industry). These conclusions will assist policymakers in exploring targeted pathways for low-carbon power development, providing a reference for strategic and sustainable carbon reduction policies. [Display omitted] • Enrich the view of renewable energy's indirect impact on carbon emissions. • Renewable energy's carbon emission reductions go beyond replacing fossil fuels. • Constructed a mediating effect model between renewable energy and carbon emissions. • Renewable energy can promote or inhibit carbon emissions through various pathways. • Research helps develop more effective renewable energy sustainability strategies. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the responsiveness of renewable energy production (REP) to fluctuations in geopolitical risks, oil prices and economic policy uncertainty (EPU). It applies a cross-quantilogram framework to examine monthly data of the US economy for the period of 1986–2022. The findings illustrate the asymmetric effect of historical geopolitical risk (GPRH) on REP under long memory. The findings also hold after different subcategories of GPRH, including geopolitical threats and geopolitical acts, are considered. A positive shock in GPRH has the most decisive positive impact on REP when the policies are driven by both energy security and environmental commitments. A positive shock in GPRH can negatively impact REP when policies are driven by energy security causes only. EPU exerts strong negative effects on REP in bearish and bullish states of the market under medium and long memory across different measures of EPU. Dynamic connectedness analysis applying TVP-VAR method between pairwise variables indicates that net REP is a volatility receiver to the changes in GPRH, its subcomponents, oil prices and different measures of EPU. [Display omitted] • Application of cross-quantilogram approach to US monthly data during 1986–2022. • Responsiveness of renewable energy production to historical geopolitical risk. • A positive shock in GPRH has asymmetric effects on REP in long memory. • This nexus is sensitive to the motivation of energy policies in US. [ABSTRACT FROM AUTHOR]