Search

Since General Secretary Xi Jinping pledged the "30·60" targets to the world, the importance of pollution reduction and carbon reduction has become increasingly prominent. The previous focus on the pollution reduction effects of emission trading systems, while overlooking their carbon reduction impacts, as well as the limitations of research methods and scope, is no longer suitable for the needs of the new era. Therefore, this paper constructs a CGE model to study the synergistic effects, simulating the synergistic emission reduction effects of sulfur dioxide emission trading policies, and comes to the following conclusions: After the implementation of the sulfur dioxide emission trading mechanism, under different sulfur price scenarios, the synergistic reduction amounts of SO 2 and CO 2 vary greatly. As the sulfur price increases, the reduction amount becomes larger. In the five sulfur price scenarios set in this paper, the maximum reduction of SO 2 can reach about 111,400 tons, and the maximum for CO 2 is about 399 million tons. The CO 2 reduction rate is approximately 1.0767 times that of SO 2. This indicates that the sulfur dioxide emission trading policy is a very effective path for synergistic pollution and carbon reduction, with good reduction effects. In addition, sulfur dioxide emission trading will reduce the consumption of fossil energy, decrease industry output, raise product prices, cause GDP loss, and lead to reduced social welfare to varying degrees. [ABSTRACT FROM AUTHOR]

The OECD community consumes excessive amounts of raw materials and materials, which leads to pollution and increases the material footprint. The material footprint is directly linked to the Sustainable Development Goals (SDGs)-8 and 12. The main reason for the material footprint is the usage of fossil fuels, and the dependence of OECD countries on fossil fuels continues to this day. By expanding renewable energy utilization due to rising energy security risk, a community can take steps to improve ecological quality and reduce its material footprint. In this context, the paper focuses to examine the impact of energy security, green innovation, economic policy stringency and income on the fossil fuel material footprint for 24 OECD countries over the period 1995–2018. To this end, the paper uses the Augmented Mean Group (AMG) method and novel Half Panel Jackknife (HPJ) causality and estimation approach. The study adds value to existing knowledge by empirically testing the determinants of fossil fuel material footprint for OECD countries for the first time using the novel HPJ approach. The results of the AMG show that increasing energy security risk index reduces the material footprint, while green innovation and economic policy stringency have no impact on environmental degradation. The environmental Kuznets curve (EKC) in the form of an inverted U and an inverted N are valid for OECD countries. The HPJ approach also illustrates that there is a causality from green innovation and energy security risk to material footprint, and innovation and risk are support footprint reduction. Based on these results, the study recommends that OECD policymakers pay attention to ensuring energy security to reduce the material footprint by expanding renewable resources and allocate more economic resources to green innovations with higher returns, considering the validity of the EKC. [Display omitted] • Applies second-generation panel data methods. • Investigates the fossil fuel material footprint for 24 OECD countries. • The EKC hypothesis holds for the fossil fuel material footprint of OECD countries. • Green innovation and energy security risk reduce fossil fuel material footprint. • Increasing risks and technological development contribute to SDGs 8 and 12. [ABSTRACT FROM AUTHOR]

This study aims to elucidate the profound implications of industrial transformation and upgrading in mitigating dependence on fossil fuels, which holds paramount significance in shaping energy transition strategies—an aspect that is currently missing in existing studies. Leveraging data spanning from 2000 to 2020, encompassing fossil energy consumption and economic development, this paper developed a second-order classification model as a new measure of energy elasticity. Comparing regional variations in fossil energy resilience, an in-depth analysis was conducted on four representative provinces to understand the evolution of their fossil energy consumption structures over time. Correlation analysis was employed to reveal the impact of industrial structures on the reduction of fossil energy dependence. The results indicate distinct spatial variations in the energy transition effect, with the southwest inland region retaining a pronounced reliance on fossil energy sources. However, approximately 80% of the provinces have made substantial progress in reducing fossil energy dependence from 2000 to 2020. Notably, the financial sector and the accommodation and catering industry contribute positively to mitigating fossil energy dependence, while the industrial sector exhibits the opposite effect. Urban agglomeration planning attributes the uneven regional energy transition to the delineation of urban industrial functions. Suggestions for narrowing the energy transition gap include measuring the sustainability of the industrial chain from a life-cycle perspective, avoiding the singular functional division of cities, and developing systematic policies. In the catering industry, adopting alternative cooking energy and implementing the conversion and recycling of catering waste can help prevent risks. This study underscores the positive role of the accommodation and catering industry in the energy transition, along with its potential risks. The study also makes efforts toward advancing the application of the widely used energy elasticity assessment method. [Display omitted] • A second-order classification model is developed as a widely used energy elasticity assessment method. • 80% of China's provinces have successfully reduced their reliance on fossil fuels. • The financial sector and the accommodation and catering sector play pivotal roles in diminishing reliance on fossil fuels. • In the forthcoming years, the catering industry has the potential to engender a reliance on fossil fuels. [ABSTRACT FROM AUTHOR]

This research investigates the relationship between greenhouse gas (GHG) emissions and energy production and consumption patterns in 122 countries over the period from 1990 to 2020. Particular attention is paid to the role of different types of energy, including renewable, hydro and nuclear energy, as well as oil, gas and coal, in shaping GHG emissions. Unlike previous studies, the paper analyzes the full range of energy resources and focuses on different quantiles of data using the quantile-quantile regression (QQR) method. The results of this approach are compared with traditional quantile regression (QR) to assess its validity. Among the findings of this study are: (1) different parts of the energy mix have a heterogeneous impact on GHG emissions in different quantiles; (2) energy consumption patterns play a more significant role in shaping GHG emissions than production patterns; (3) increasing production and consumption of fossil fuels does not necessarily increase GHG emissions; (4) similarly, the development of renewable energy sources is not always accompanied by a reduction in GHG emissions. Our general conclusion is that existing climate policy requires significant diversification to not be focused only on renewable energy, which impact on GHG emissions is not as homogeneous as it usually considered, and which cannot solve the problem of raising emissions solely. Instead of this, it could be more rational to focus on the searching the ways of hydrocarbon industries sustainable development and support them in this journey, especially in the field of Scope 2 and Scope 3 operations management. • Impact of energy consumption and production on GHG emissions were analyzed for 122 countries in 1990–2020. • Quantile and Quantile-on-Quantile regression were used to catch the specifics of energy consumption and production patterns. • Two temporal breakpoints (1999/2000 and 2009/2010) were introduced to catch shifts in variables dependencies. • The influence of fossil fuels on GHG emissions is not homogeneous as it usually considered, the same is fare for renewables. • A promising option to reduce GHG emissions is carbon capture, utilization and storage (CCUS). [ABSTRACT FROM AUTHOR]

The fossil energy and the carbon market are closely connected, but few articles have applied the Granger causality method on quantiles to explore the bi-directional causality between carbon prices and fossil fuels. Most articles use conditional mean regression models to test linear Granger causality, but are unable to assess tail causality or nonlinear causality. In order to accomplish the objective of studying the interaction of carbon and fossil energy markets at different quartiles, we use the method of Granger causality at quartiles, which allows us to assess median and tail causality and to gain insights into how carbon and traditional fossil energy markets interact under different market conditions. This paper analyzes the causal relationships between carbon prices and coal, oil, and gas from June 1, 2015 to October 31, 2022 across all quantiles. We find no indication of Granger-causality from carbon to oil and gas at the middle quantile. However, at the lower and upper quantiles, we discover the causal relationship between changes in carbon prices and traditional energy prices. The asymmetric two-way causal relationship between the carbon market and the fossil energy market has prompted the government to carry out differentiated management, as well as to improve energy efficiency. • We study the bi-causality between coal, oil, gas markets and carbon market. • We test for Granger-causality for each quantile of the distribution. • The negative correlation between carbon prices and fossil energy prices under mean reversion analysis is unreliable. • The impact of carbon taxes on oil prices varies widely across different quantiles. • We make demands on governments to develop energy policies. [ABSTRACT FROM AUTHOR]

The reduction of CO 2 by moving from fossil to renewable energy sources is currently high on the agenda of many governments. Simultaneously these governments are also forcing the reduction of energy consumption. The primary focus of these agendas is on mobility, building, and industrial sectors. For the latter, energy-efficient shop floors and machining processes assist the reduction of energy consumption. Previous research has focused on energy-efficient machining strategies during machining processes. However, an energy-efficient start-up of these machines or their spindle axis start-up has been neglected until now. This paper focuses on this neglected issue by comparing the energy-efficiency, production time, and cost-efficiency of the CNC (computer numeric control) machine by varying the power input at the spindle axis. This is done by analysing the high-frequency data (500 H z) of the machine from machining operations that is retrieved via the edge device. Concepts of data analytics and especially EDA (exploratory data analytics) were used to interactively visualize the inter-dependencies and develop results. It is shown that optimized reduction of spindle power input value leads to both: peak power smoothing from 20kW to 10kW and lowering of overall energy consumption by approximately 1.4%. Moreover, the costs and production time are marginally affected (0.518% and 0.523% respectively) by this optimized reduction of spindle power input value. Thus, this paper highlights a novel method from data acquisition to process improvement towards energy-efficient and sustainable machining. [Display omitted] • Energy-efficient machining via innovative technique of data acquisition and analysis. • Sustainable peak smoothing for spindle operations. • Utilization of high-frequency machine data for enhancement of process. [ABSTRACT FROM AUTHOR]

The construction sector contributes to 8% of carbon dioxide emissions, most of them caused by clinker production: in recent years, the efforts to reduce environmental impacts are growing. This paper investigates the environmental performances of clinker production and the factors driving emissions. For this purpose, the environmental impacts of 41 different grey clinkers produced in Italy from 2016 to 2021 certified by a third party according to the European standard EN 15804 have been assessed. Three input datasets (i.e., energy consumption, raw materials, and transport) have been investigated to identify the most significant variables that affected the environmental impact categories in the period 2016–2020: a stepwise regression allowed the identification of the optimal predictors to be included in the multiple regression models. The most significant variables are pet coke, fossil fuels, natural raw materials, and lorry transport. The identified models have been validated having regard to 2021 data. Therefore, for each impact category, multiple positive and negative dependencies which bear an effect on the environmental results have been quantified. The output provides proven quantitative results that can support cement producers to develop low-impacting clinker recipes. [ABSTRACT FROM AUTHOR]

All economies need energy for growth, particularly emerging ones with continued demand for fossil fuels that move them to unconventional energy sources such as heavy oil. However, the presence of large quantities of heteroatoms -sulfur, nitrogen, oxygen, and metals – in heavy oil is a major contributor to environmental pollution during the combustion phase. This requires the crude and residue to be upgraded and fractionated within tight specifications. Hot compressed water (HCW), at or beyond the critical point (374 °C, 22.1 MPa), has emerged as a highly promising environmentally benign and efficient method for heavy oil upgrading to replace conventional toxic solvents. The synergistic effect of HCW with energy generated from renewable sources is a new avenue into a cleaner carbon energy system for fuel production with the benefits of reduced energy consumption and lower carbon dioxide emission. This paper provides a comprehensive review in multi-scale which underlines the recent evolution and the challenges in this field. In addition, the critical factors and emerging gaps influencing the heavy oil upgrading in HCW are appended. Eventually, a set of suggestions for integrating renewable energy technologies with heavy oil upgrading in HCW and future perspective has been reported. [Display omitted] • The synergistic effect between HCW and heavy oil as a cleaner carbon conversion system is reviewed. • A comprehensive view in multi-scale which underlines the recent evolution and the challenges in this field. • Critical factors and emerging gaps influencing the heavy oil upgrading in HCW are appended. • A logical integration of renewable technologies with carbon-based energy conversion system in HCW is suggested. • Possible research suggestions and directions on this field have been proposed. [ABSTRACT FROM AUTHOR]

Biofuel manufacturing from renewable biomass through a smart manufacturing system is an important alternative to fossil fuels, which helps to decrease dependability on conventional fuel and decrease carbon emanations. By utilizing efficient labors, smart machines, and minimized energy utilization, the conventional biofuel manufacturing framework can be converted into a smart sustainable manufacturing framework. The existing conventional fuel demand can be replaced by biofuel. This study efforts to make pure biofuel with less amount of carbon emanations and energy utilization through a smart multi-type biofuel manufacturing framework, where the demand of the biofuel is selling price dependent. The different energy costs including air handling cost, lighting cost are calculated in this work-in-process inventory. The carbon emissions cost is included in every stage of this model. A variable demand has introduced for the maximization of profit. To reduce energy consumption and carbon emissions, a two-stage inspection cost with a variable manufacturing rate is taken to make the manufacturing process flexible such that the amount of impure biofuel is minimized. Although a random manufacturing rate is applied through a smart manufacturing system, still impure biofuel is manufactured. The impure biofuel is remanufactured again through refining just after the well-planned manufacturing ends. In this model, the multi-delivery technique is used such that a fixed amount of biofuel of n segments of the pure biofuel is transported to market places at predetermined intervals at the time of delivery frame. The classical optimization technique is used for continuous and differentiable variables and the mixed integer programming technique is utilized for discrete variables to maximize the total profit globally. To validate the model's usefulness, four numerical observations are studied. It is shown that by utilizing the said procedure, the percentage of impure biofuel can be decreased truly through the minimized energy consumption. The graphical representation and the sensitivity experiment show the impact of every parameter with the whole cost of the research paper. Numerical results assist to get the maximum profit and the optimum selling price of biofuel globally in a sustainable smart multi-type biofuel manufacturing system. [Display omitted] [ABSTRACT FROM AUTHOR]